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WikiLeaks Document Release http://wikileaks.org/wiki/CRS-RL31985

February 2, 2009

Congressional Research Service

Report RL31985

Weak Dollar, Strong Dollar Causes and Consequences Craig K. Elwell, Government and Finance Division

July 10, 2008

Abstract. This report provides background information on the forces that most likely determine the path of the dollar exchange rate. It also considers recent events in international markets for goods and assets as well as suggest what implications these forces carry for the state of the U.S. economy and economic policy.

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�������� ��� ���� ��� Prepared for Members and Committees of Congress

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After a long and large appreciation, in early 2002, the dollar peaked and steadily weakened in value relative to other major currencies through 2004. In 2005 and through most of 2006, the dollar was essentially steady. At the end of 2006, however, depreciation resumed and it has continued in 2007. A weaker dollar will be good news for exporters and those who compete with imports, while consumers of imports will be correspondingly unhappy. Yet it is important to recognize that a falling dollar is symptomatic of the ebb and flow of international capital in and out of the American economy. Those flows will have important implications for domestic interest rates and activities sensitive to credit conditions, such as housing and business investment.

The exchange rates movement will be strongly influenced by the effect of changes in interest rates on the flow of financial capital between countries. Also consider how the expected movement of future exchange rates influences investors now. Inflation, safe-haven and speculative effects, and the size of the trade balance can also be important. The central role of relative interest rates in generating international capital flows and exchange rate movements makes it important to understand the forces that move interest rates. This points toward an understanding of the demand for and supply of loanable funds. The economy’s pattern of saving and investment will exert a strong force on interest rates. For the United States, a structural tendency for domestic savings to fall short of domestic investment leads to significantly higher interest rates when economic activity picks up speed. Government policy can also affect interest rates and the exchange rate. Large government budget deficits will tend to push up interest rates and the exchange rate. Budget surpluses have the opposite effect. Tight monetary policy tends to raise interest rates and the exchange rate. A stimulative monetary policy has the opposite effect.

As the significance of a weakening dollar is contemplated, it is important to consider the effect of the outflow of foreign capital that causes that weakening on domestic investment and overall economic welfare. In the 1980s, macroeconomic policy had a substantial effect on the level of interest rates and the path of the dollar. Tight monetary policy and large budget deficits pushed interest rates and the dollar upward through 1985 and a reversal of those policies pushed interest rates and the dollar down over the last half of the decade. In the 1990s, a steady rise of the dollar from mid-decade on was primarily the consequence of an investment boom in the United States that kept rates of return high and attracted large inflows of foreign capital. In both of these periods, upward pressure on the dollar was intensified by a persistently low U.S. saving rate and relatively weak economic performance abroad. The depreciation of the dollar between 2002 and 2004 was likely the consequence of slower U.S. growth and a move toward a more diversified portfolio by foreign investors. However, in 2005 the dollar strengthened again as foreign investor demand for dollars was rejuvenated. Since then, weakening demand for dollar assets by foreign investors has put the dollar on a downward path and important forces seem poised to continue to put downward pressure on the currency. This report will be updated as events warrant.

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Introduction ..................................................................................................................................... 1

What Determines the Dollar’s Exchange Rate ................................................................................ 2 Demand, Supply, and the Dollar Exchange Rate ...................................................................... 3 The Importance of Trade in Assets............................................................................................ 4

Expected Rate of Return and Asset Flows .......................................................................... 4 Diversification, Safe-Havens, and Official Purchases ........................................................ 6

Fundamental Factors Determining the Level of Interest Rates ....................................................... 7

Capital Inflows, an Appreciating Dollar, and a Rising Trade Deficit.............................................. 9

The Ups and Downs of the Dollar: 1980 to 2007.......................................................................... 10 The 1980s .................................................................................................................................11 The 1990s ................................................................................................................................ 12 The 2000s ................................................................................................................................ 12 Instability and the Prospect of a Dollar Crash......................................................................... 13 Where Will the Dollar Go ....................................................................................................... 15

Economic Policy and the Ups and Downs of the Dollar ............................................................... 16

Conclusion..................................................................................................................................... 18

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Figure 1. Real Trade -Weighted Dollar Exchange Rate .................................................................. 1

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Author Contact Information .......................................................................................................... 19

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From 1994 to early 2002, the real (inflation adjusted) trade-weighted dollar exchange rate (see Figure 1) appreciated nearly 30%.1 This appreciation occurred even as the U.S. trade deficit and foreign debt climbed steadily higher. From 2002 to the present, the dollar, for the most part, steadily depreciated, falling about 24%. From early 2002 through 2006, the dollar’s fall was moderately paced at about 2.0% to 5.0% annually. Recently, however, the slide has accelerated, falling nearly 10% between June of 2007 and June of 2008.

Figure 1. Real Trade -Weighted Dollar Exchange Rate

1 9 8 5 1 9 9 0 1 9 9 5 2 0 0 0 2 0 0 5

7 0

8 0

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Source: Board of Governors of The Federal Reserve System

The dollar’s fall from 2002 through early 2008 has not been uniform against individual currencies, however. For example, it fell 44% against the euro, 36% against the Canadian dollar, 21% against the yen, and 17% against the yuan. These differing amounts of depreciation are in part a reflection of how willing these countries have been to let their currencies fluctuate against the dollar. The euro is free-floating, the yen has been moderately managed (mostly before 2005), and the yuan is actively managed (rigidly fixed before 2005 and less rigidly fixed since 2005). But it also reflects the structure of the international trade in assets, with larger effects occurring on the exchange rates of countries with a high volume of asset trade with the United States. The significance of the international trade in assets for exchange rates will be a central concern of this report.

The strong dollar in the 1994-2002 period was certainly a benefit to U.S. consumers because the rising exchange rate substantially lowered the price of foreign goods relative to the price of

1 The trade-weighted exchange rate index used is the price-adjusted broad dollar index reported monthly by the Board of Governors of the Federal Reserve System. The real or inflation-adjusted exchange rate is the relevant measure for gauging effects on exports and imports. A trade-weighted exchange rate index is a composite of a selected group of currencies, each dollar’s value weighed by the share of the associated country’s exports or imports in U.S. trade. The broad index cited here is constructed and maintained by the Federal Reserve. The broad index includes 26 currencies— the seven in the major currencies index plus that of 19 more important trading partners. Among the 19 are the currencies of China, Mexico, Korea, Singapore, and India. The 26 countries account for about 90% of United States trade, and, therefore, the broad index is a good measure of changes in the competitiveness of U. S. goods on world markets.

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competing domestic products. However, the strengthening dollar was a growing impediment to the sales of U.S. exporting and import competing industries because the price of their products increased relative to those of foreign competitors. This also meant that as the dollar rose so did the U.S. trade deficit. Conversely, a weakening dollar would be celebrated by U.S. exporters and lamented by foreign exporters and domestic consumers. Further, a sustained dollar depreciation could be expected to slow and then reverse the steady rise of the U.S. trade deficit. Also, a depreciating dollar tends to improve the U.S. net debt position by raising the value U.S. foreign assets. But a falling dollar also tends to raise the dollar price of commodities such as oil, metals, and food.

The dollar, of course, is not just moving on its own. Appreciation and depreciation of the dollar are most often a reflection of the ebb and flow of international capital in and out of the United States as it is propelled by fundamental economic forces in the United States and abroad. Moreover, these asset market events will have strong effects on economic activity in the United States, activity seemingly unrelated to the dollars international exchange value. Because asset market transactions most often occur at a higher volume and at greater speed than do transactions in goods (i.e., imports and exports), most economists would argue that it is events in international asset markets that “call the tune the dollar dances to,” and exports and imports of goods respond accordingly.

This means that the net size and direction of these asset flows dictate the state of a country’s trade balance. A country receiving a net inflow of capital will have an appreciating exchange rate and run an equal sized trade deficit. In contrast, a country generating a net outflow of capital will have a depreciating currency and run an equal sized trade surplus. The exchange rate moves to equilibrate the inflow with the outflow of goods and assets. This also suggests that because the ups and downs of the dollar are driven by asset flows in and out of the economy, these dollar movements will be associated with impacts on domestic credit markets, affecting domestic interest rates and, in turn, interest sensitive spending such as housing, consumer durables, and business investment. Thus, while a rising dollar may be bad news for the tradeable goods sector, it is likely good news for interest rate sensitive sectors and vice versa for a falling dollar.

The importance of U.S. international economic transactions to a healthy economy is well recognized by Congress, which in recent years has closely monitored many dimensions of U.S. trade performance. The dollar exchange rate, cross border financial flows, and the trade deficit are known to be important to the functioning of the U.S. economy and for the implementation of sound economic policy. These factors are also germane to an understanding the recent issue of exchange rate manipulation by China and Japan. The determination of the dollar’s exchange rate is, therefore, an ongoing area of congressional concern. This report provides background information on the forces that most likely determine the path of the dollar exchange rate. The report also considers recent events in international markets for goods and assets as well as suggest what implications these forces carry for the state of the U.S. economy and for economic policy.

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The exchange value of the dollar is determined by the interplay of the demand for and supply of dollars in global foreign exchange markets. Prior to 1973, in the so-called fixed exchange rate era, the dollar’s value was fixed at a rate established by international agreement, and the U.S. and foreign governments were actively maintaining that fixed rate. This was accomplished by monetary policy changing the level of domestic interest rates relative to foreign interest rates so

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as to induce the buying or selling of dollar assets necessary to keep the exchange rate at the mandated fixed rate. The fixed rate exchange rate regime grew increasingly untenable in part because of the growing size and mobility of capital flows between countries. In the early 1970s, the United States and many other nations changed by default to a “flexible exchange rate” system. That system continues today.2

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With flexible exchange rates and wide-spread abandonment of capital controls the dollar is largely free to move up or down as market forces dictate. In most circumstances the government plays little or no direct day to day role in determining the dollar’s value relative to other currencies. The government can certainly use macroeconomic policy to affect the market forces that determine the exchange rate, but instances where the primary policy goal is the exchange rate are relatively rare. The exchange rate is almost always subordinate to the goal of domestic economic stabilization.3 But the exchange rate will certainly move as a collateral consequence of pursuing other economic goals. On occasion, governments will intervene directly in the foreign exchange market, buying or selling particular currencies to induce some adjustment of the exchange rate, but such interventions are also infrequent and, when used, their impact on the exchange rate is often problematic unless the intervention is supported by changes in macroeconomic policy.

In this framework it is reasonable to infer that any observed weakening or depreciation of the dollar is most likely the result of a reduced demand for dollars in the foreign exchange market, an increased supply of dollars in that market, or some combination of both forces. Similarly, an appreciating, or strong dollar, is the consequence of an increase in the demand for dollars, or a decreased supply of dollars, or both in the foreign exchange markets. And most often these changing market forces are the result of actions by private market participants rather than government policy.

The demand for dollars for use in international exchange is a derived demand, driven by foreigner demand for U.S. goods and assets, which of course are denominated in dollars and can only be purchased with dollars. Therefore, to purchase U.S. goods or assets, a foreign buyer must first exchange their home currency for dollars. Transactions in the foreign exchange market do not involve the transfer of large parcels of paper currency between countries. These exchanges are most often speedily achieved by the shifting of electronic balances between commercial banks or foreign exchange dealers. With the purchase of a U.S. good or asset there has also been an increase in the demand for dollars and an increase in the supply of foreign currency in the foreign exchange market. Other factors unchanged, these actions repeated on a larger scale would tend to increase the exchange value of the dollar relative to foreign currency. That is, the dollar will

2 For a discussion of the collapse of the fixed exchange rate regime, often called the Bretton Woods System, see Barry Eichengreen, Globalizing Capital (Princeton, New Jersey: Princeton University Press, 1996), pp. 93-124. Currently about half of IMF member countries allow their currencies to float. That floating is sometimes not completely free because governments, attempting mitigate adverse effects on their currency from the foreign exchange markets, do from time to time buy or sell foreign exchange on the open market in an effort to influence the value of their currencies exchange rate. 3 Many would argue that the great virtue of floating over fixed exchange rates is that in that regime the monetary authority, free from the need to use monetary policy to maintain the fixed rate, can make domestic stabilization its primary focus.

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appreciate, meaning that each dollar can be exchanged for a greater amount of foreign currency, and as a result command a greater volume of foreign goods or assets.

Similarly, when Americans buy foreign goods or assets they initiate a similar process; however, it will have the opposite effect on the dollar’s exchange value. Exchanging dollars for a foreign currency represents an increase in the demand for foreign currency and an increase in the supply of dollars on the foreign exchange market. This type of transaction repeated on a larger scale would tend to depreciate the exchange value of the dollar relative to foreign currencies, causing each dollar to exchange for less of the foreign currency, and as a result to command a smaller volume of foreign goods or assets.

The salient point is that the relative strength or weakness of the dollar will depend on the relative strength or weakness of the demand of foreigners for dollar denominated goods and assets in comparison to the strength of U.S. demand for foreign goods or assets.

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A closer look at the dynamics of world trade today shows that the volume and speed of international asset transactions far exceed that of goods transactions.4 It is estimated that the daily global turnover on foreign exchange markets is near $2 trillion, with the dollar accounting for 90% of that. This compares with annual U.S. export sales of only $1.3 trillion. In addition, a very large share of asset transactions can be done electronically and therefore move far more rapidly than do transactions for goods, which will most often require a much slower physical transfer. This size and speed means that at any point in time it is most likely that the relative demand for assets here and abroad will be the dominant force in the foreign exchange market, transmitting the essential energy that drives movement in the exchange rate for the dollar and other widely traded currencies.

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What determines the size and direction of cross-border asset flows? One can expect that the demand for assets (e.g., bank accounts, stocks, bonds, and real property) by foreigners will be strongly influenced by the expected rate of return on those assets. The level of nominal interest rates can be used as a fairly reliable first approximation of the rate of return on assets that can be earned in a particular country. Therefore, differences in the level of interest rates between economies are likely to animate and direct international capital flows, as investors seek the highest rate of return. When interest rates in the United States are significantly higher than interest rates abroad, the demand for U.S. assets will, other factors unchanged, strengthen the demand for those assets, increase the demand for the dollars needed to buy U.S. assets, and appreciate the value of the dollar relative to foreign currencies. In contrast, if interest rates in the United States are on average lower than interest rates abroad, the demand for foreign assets will likely strengthen and the demand for U.S. assets will likely weaken. This will cause the demand for foreign currencies needed to purchase foreign assets to strengthen and the demand for the dollar will weaken, leading to a depreciation of the dollar relative to foreign currencies.

4 For a discussion of the tremendous growth of cross-border asset transactions, see CRS Report RL30514, Global Capital Market Integration: Implications for U.S. Economic Performance, by Craig K. Elwell; and CRS Report RL32462, Foreign Investment in U.S. Securities, by James K. Jackson.

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Yet differences in nominal interest rates may not be all an investor needs to know to guide his/her decision. Also consider that the return actually realized from an investment is paid out over some future period. This means that the realized value of that future payment can be altered by changes in other economic variables. Therefore, investor expectations of those future events will influence the investors “expected pay off” and, in turn, the relative attractiveness of an asset. Two economic variables of particular relevance to this decision are the expected change in the exchange rate itself over the term of the investment and the expected rate of inflation.

Expectations about the future path of the exchange rate itself will figure prominently in the investor’s calculation of what she will actually earn from an investment denominated in another currency. Even a high nominal return would not be attractive if one expects the denominating currency to depreciate at a similar or greater rate and erase all economic gain. On the other hand, if the exchange rate is expected to appreciate the realized gain would be greater than what the nominal interest rate alone would indicate and the asset looks more attractive.

The influence of exchange rate expectations can significantly complicate the task of judging how exchange rates will move, as we can only imperfectly assess what informs those expectations and the strength of their effect. It is also possible for exchange rate expectations to introduce some degree of volatility into the exchange rate system, as “speculation” by some investors on the future path of the exchange rate can push the currency, up or down, as speculative actions feed on each other and generate “herd like” behavior. In these situations exchange rate expectations become a sort of self-fulfilling prophecy that works to exaggerate the path the currency is already set upon, pushing the currency well beyond what more basic fundamentals alone would dictate.

But this is going to be a bounded process. For at some point this speculative motive will also likely work to counter the ongoing trend, as the risk versus reward calculus causes a growing number of traders to doubt the likelihood of the dollar moving further on its current path and to come to believe that depreciation is the more probable event. As might be expected, such speculative behavior often makes it difficult to accurately predict the magnitude and duration of exchange rate movements, particularly in the short run.

The impact of expected inflation on investor decisions is more indirect. To a foreign investor, the U.S. rate of inflation would have little direct effect on the expected rate of return from a dollar- denominated asset. The critical uncertainty for the foreign investor is the path of the exchange rate, which will determine how any given dollar return will translate into his/her own currency. However, relative inflation rates among nations can be a predictor of where and how much the exchange rate will move in the future and, therefore, potentially relevant to the foreign investor’s assessment of the expected return. If the United States has a lower inflation rate than that of a trading partner, the dollar can be expected to appreciate relative to that currency by an amount necessary to maintain parity in real purchasing power. If the United States has the higher rate of inflation, then the dollar would tend to depreciate so as to maintain real purchasing power. In other words, inflation differences will change the nominal exchange rate but not the real exchange rate.

Another reason inflation may influence the demand for assets is that trends in the level of prices can be a telling indicator of how well or poorly an economy is managed and whether the investment climate will change for better or worse. Economies with accelerating inflation are more likely to be ones that are poorly managed, with poor investment prospects; while economies with stable or decelerating inflation may be seen as better managed and likely a more attractive destination for investment. The aggressive and successful U.S. dis-inflation policy in the early

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1980s may have contributed to the dollar’s sharp appreciation in this period. In recent years, inflation in the United States has been consistently low and the current stance of the Fed gives no indication that this pattern will change, making this factor of diminished importance for judging recent and prospective movements of the dollar exchange rate. Changes in inflation trends in other countries will still be a factor, however.

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While relative levels of interest rates between countries and expected return are likely to be a strong and prevalent force directing capital flows among economies, other factors will also influence these flows at certain times. For instance, the size of the stock of assets in a particular currency in investor portfolios can cause a change in investor preferences. Prudent investment practice counsels that one’s portfolio should have an appropriate degree of diversification, across asset types, including the currency in which they are denominated. Diversification spreads risk across a wider spectrum of assets and reduces over exposure to any one asset. Therefore, even though dollar assets may still offer a high relative return, if the accumulation has been large, at some point foreign investors, considering both risk and reward, will decide that their portfolio’s share of dollar denominated assets is large enough. To improve the diversity of their portfolios, investors will slow or halt their purchase of such assets. Given that well over $8 trillion in U.S. assets are now in foreign investor portfolios, diversification may be an increasingly important factor governing the behavior of international investors toward dollar assets.

There is also likely to be a significant safe-haven effect behind some capital flows. This is really just another manifestation of the balancing of risk and reward by foreign investors. Some investors may be willing to give up a significant amount of return if an economy offers them a particularly low risk repository for their funds. In recent decades the United States, with a long history of stable government, steady economic growth, and large and efficient financial markets can be expected to draw foreign capital for this reason. The size of the safe-haven effect is not easy to determine, but the disproportionate share of essentially no risk U.S. Treasury securities in the asset holdings of foreigners suggests the magnitude of safe-haven motivated flows is probably substantial and must exert a bias toward capital inflows and upward pressure on the dollar.

Governments through their central bank also often purchase international assets for reasons apart from rate of return. From 2002 to 2007, the IMF reports that official holdings of foreign exchange reserves world-wide increased from about $2 trillion to nearly $6.4 trillion. The dollar’s status as the dominant international reserve currency has resulted in a large portion of the accumulation being held in dollar denominated assets. For the $4 trillion of official holdings whose currency composition in known, nearly $2.6 trillion is in dollar assets.5 In addition, the U.S. Treasury reports that through 2007, $1.3 trillion or 26% of the $5 trillion outstanding marketable Treasury securities were being held as foreign official reserves.6

Official purchases can serve two objectives. One, the accumulation of a reserve of foreign exchange denominated in readily exchangeable currencies such as the dollar to afford international liquidity for coping with periodic currency crises arising out of often volatile private capital flows. This is most often a device used by developing economies that periodically need to finance short-run balance of payments deficits and can not fully depend on international capital 5 IMF, Currency Composition of Official Foreign Exchange Reserves, March 31, 2008. 6 U.S. Department of the Treasury, Treasury Bulletin, (Washington: April 2008), p. 56.

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markets for such finance. In the wake of the Asian financial crisis of the late-1990s, many emerging economies around the globe have over the last few years built up large stocks of foreign exchange reserves, in large part denominated in dollars.

Two, official purchases are used to counter the impact of capital flows that would otherwise lead to unwanted changes in the countries exchange rate. This is a common practice for many east Asian economies who buy and sell foreign assets to influence their currencies exchange rate relative to the dollar and other major currencies to maintain the price attractiveness of their exports. In recent years, China and Japan have both been highly visible practitioners of international asset accumulation to stabilize their exchange rates relative to the dollar. In 2007, Japan held foreign exchange reserves valued at about $900 billion, an increase of $500 billion since 2002. Similarly, in 2007, China held foreign exchange reserves valued at more than $1 trillion, an increase of nearly $750 billion since 2002. India, Korea, Taiwan, and Russia also amassed sizable amounts of foreign exchange in this period. In contrast, the United States in this time period held foreign exchange reserves of less than $200 billion on average, with annual increments of only $1 billion to $10 billion. It is estimated that 30% to 40% of the worldwide increase in foreign exchange reserves since 2000 are of dollar assets.7

Given the importance of expectations in decision making and the speed with which many asset transactions can occur, exchange rates can be volatile and predicting the magnitude and duration of short-run exchange rate movement with precision is a very elusive goal. But broad, long-term trends can most often be explained by assessing the fundamental macroeconomic forces that affect the relative level of interest rates and the expected rate of return between the United States and the other major economies.8

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Changes in the level of interest rates are usually central to understanding movement of the dollar’s exchange rate. So what factors are likely to move interest rates up or down? Again, the level of interest rates is largely a market driven phenomenon governed by the demand for and supply of loanable funds.

7 See CRS Report RS21951, Financing the U.S. Trade Deficit: Role of Foreign Governments, by Marc Labonte and Gail E. Makinen. 8 The issue of exchange rate volatility has been the focus of much discussion among economists. Contrary to expectation, exchange rates have been much more volatile since the demise of the Bretton Woods system. There are two principal explanations. There is an inherent tendency for “overshooting” of equilibrium in these markets or exchange markets are subject to large scale “destabilizing speculation.” For the creators of the Bretton Woods system the deleterious effects of destabilizing speculation were thought to be substantial and an important reason for not allowing exchange rates to float. In recent years, the locus of opinion has shifted more toward the destabilizing speculation explanation as evidence of investor irrationality has accumulated. The effect of volatility on the prices and volumes of goods in world trade seems to have been small, however. The enhanced ability to hedge exchange rate risk in modern markets may explain this small effect. It is expected that economies with large trade sectors, such as those in Europe, will find volatile exchange rates more disruptive than will economies with relatively small trade sectors, such as the United States. Yet, whatever costs exchange rate volatility does cause must be balanced against the considerable benefits of liberalized international capital flows.

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On the demand side of the loanable funds market we look for changes in the forces that commonly influence the use of credit. A strong, briskly growing economy with rapidly expanding investment expenditure can be expected to have a rising demand for loanable funds and exert upward pressure on interest rates. In contrast, economic weakness and attenuated investment opportunities would tend to exert downward pressure on interest rates. In addition to the vigor of the private economy, the demand for loanable funds and the level of interest rates can be influenced by the balance of the government budget. Government budget deficits mean that the public sector must borrow to fully fund its expenditures. Such borrowing is a demand for loanable funds and can certainly influence the level of interest rates in the market. Any movement toward larger budget deficits tends to exert upward pressure on interest rates and movement toward smaller deficits would have the opposite effect. Of course, these outcomes will be tempered by the economy’s position in the business cycle. In or just after a recession when the demand for loanable funds is weak, these elevating effects on interest rates would be nil, but would become increasingly manifest as an economic expansion matures.

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Of primary importance on the supply side of the market for loanable funds is the nation’s rate of saving. That flow represents the portion of current income that the economy has diverted from spending on current consumption and provides a supply of loanable funds, available to finance current investment expenditures. For any given level of demand for loanable funds, one can expect that a higher rate of saving would likely lead to a lower level of interest rates than would a lower rate of saving. Domestic saving can be augmented by an inflow of foreign saving, which is precisely what the capital inflows are. But that inflow will be primarily a response to pressures and incentives initially generated by the relative size of domestic saving and investment. And, of course that response will move the exchange rate.

One of the more significant macroeconomic characteristics of the U.S. economy to emerge over the past 25 years is the economy’s low and declining domestic saving rate. That rate has fallen from about 20% of GDP in the 1970s to nearly 13% today.9 For comparison with other advanced economies, the saving rate for Canada is 24%, for the euro area it is 21%, and for Japan it is 27%. A persistently low saving rate in the United States creates a significant structural bias toward relatively high interest rates during periods when economic activity and, in turn, the demand for loanable funds is on the rise. In these periods, it is expected that the dollar exchange rate will likely rise as an increased flow of foreign capital is attracted by those relatively high interest rates.

Government can also influence interest rates from the supply side of the loanable funds market. On the fiscal policy side, whereas budget deficits are an absorber of saving, budget surpluses are government saving that augments the economy’s supply of loanable funds. Therefore, any move toward larger budget surpluses (or smaller deficits) will exert downward pressure on interest rates, while smaller surpluses (or larger deficits) tend to increase interest rates. Monetary policy can influence the level of interest rates through its governing of the financial intermediation activities of the banking system. A large share of the nation’s saving is channeled to borrowers by 9 See CRS Report RL30873, Saving in the United States: How Has It Changed and Why Is It Important?, by Brian W. Cashell and Gail E. Makinen.

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banks. By altering the reserve position of banks, the monetary authority can alter the level of loanable funds they will have available for extending credit and thereby the level of short-term interest rates. A restrictive monetary policy tends to raise interest rates, while a expansionary monetary policy tends to lower interest rates. Also, monetary policy, less encumbered by administrative and political constraints, is in practice a more flexible tool than is fiscal policy and will be used more often to implement macroeconomic policy, particularly in the short run.

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This policy involves the Federal Reserve buying or selling foreign exchange in an attempt to influence the exchange rate. (This intervention will most often be a sterilized intervention that alters the currency composition of the Fed’s balance sheet but does not change the size of the monetary base, neutralizing any associated impact on the money supply.) To strengthen the dollar, the Fed would attempt to boost the demand for dollars by selling some portion of its foreign exchange reserves in exchange for dollars. (Sterilization in this case would require the Fed to also purchase a like value of domestic securities to offset the negative effect on the monetary base of its selling of foreign exchange reserves.) The problem with intervention is that the scale of the Fed’s foreign exchange holdings will be small relative to the size of global foreign exchange markets which have a daily turnover of more than $3 trillion. Facing markets of this scale, currency intervention by the Fed would likely be insufficient to counter a strong market trend away from dollar assets.

A coordinated intervention by the Fed and other central banks has a greater chance of success because it can increase the scale of the intervention. Since 1985 there have been five coordinated interventions: the Plaza Accord of 1985 to weaken the dollar, the Louvre Accord of 1987 to stop the dollar’s fall, joint actions with Japan in 1995 and 1998 to stabilize the yen/dollar exchange rate, and G-7 action in 2000 to support the newly introduced euro. All but the Louvre Accord do correspond with turning points for the targeted currencies. However, these interventions were most often accompanied by a change in monetary policy that was consistent with moving the currencies in the desired direction. Many economists argue that coordinated intervention in these circumstances played the useful role of a signaling device helping overcome private investors’ uncertainty about the future direction of monetary policy and the direction the central banks want the currency to move. But absent an accompanying change in monetary policy it is unlikely that even coordinated intervention would be successful at altering the exchange rate’s path if it were being strongly propelled by private capital flows.

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Also, as cross-border asset flows move the exchange rate, it has an impact on trade in goods. An appreciating dollar makes U.S. exports more expensive to foreign buyers and imports less expensive to domestic buyers. With net inflows of foreign capital and a rising exchange rate the trade balance will move toward deficit as export sales weaken and import sales strengthen. The size of the deficit in goods trade will generally be equal to the size of the net inflow of foreign capital, with the dollar’s exchange rate working as the equilibration mechanism.

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This sequence makes sense if you consider that a net inflow of foreign capital to the United States represents a net transfer of purchasing power from foreign economies to the United States. However, that purchasing power is denominated in a foreign currency and can be used only to purchase foreign goods. Of course, this process works in the opposite direction for countries that have a net capital outflow. They will experience a depreciating currency and a surplus in goods trade commensurate with the size of the capital inflow. A net capital inflow means a country has sold more assets to foreigners than it has purchased from foreigners or is running a surplus in its asset account. By the same reasoning, a net capital outflow will represent a deficit in its asset account. Thus, across both goods and assets transactions trade is always balanced, a surplus in asset trade must balance a deficit in goods trade, and vice versa.

As expected, those whose economic activities are sensitive to credit market conditions and the level of interest rates will find the forces causing the appreciating dollar to be favorable to their economic well-being. Similarly, those who export or who must compete with imports will find these circumstances unfavorable to their economic well-being. It is often argued that the trade deficits that accompany a strong dollar also tend to increase the prospect of the nation implementing protectionist policies. Such policies do not change the forces causing the net inflow of capital and, therefore, will not change the trade deficit, but ultimately will impose costs on the economy that exceed any benefits gained.

As with most economic events, there are benefits gained from capital inflows, but at some cost. The strong dollar and its attendant capital inflows was a valuable support to domestic investment activity in the 1990s. Higher investment will boost economic growth and improve economic well- being. Without the capital inflow, U.S. investment would have been lower and the future benefits to our living standard reduced. Some of those benefits flow to foreigners who own U.S. assets, but the economy is better off than it would be without the capital inflow. The salient point is that the strength or weakness of the dollar is not necessarily a positive or a negative event, but rather a manifestation of an underlying economic process that helps some, hurts others, but on balance may often bring a net benefit to the overall economy.10 As with many other things, economic virtue need not occur at the extremes of no capital inflows and no capital outflows, but at some intermediate point were the benefit and cost of international capital flows are equal. However, judging this “golden mean” is difficult.

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It is revealing to examine the general path of the dollar since the 1980s in the framework outlined above. In both the 1980s and the 1990s, the dollar soared to record highs but for different reasons. It will also be revealing to see what caused the dollar to fall.11

10 For a fuller discussion of trade deficits, see CRS Report RL31032, The U.S. Trade Deficit: Causes, Consequences, and Cures, by Craig K. Elwell. 11 The discussion in this section follows that found in Paul Krugman and Maurice Obstfeld, International Economics: Theory and Policy (New York, NY: Harper-Collins, 1998), pp. 577-586.

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During the 1980s, the dollar exchange rate followed a path of sustained and substantial appreciation followed by sustained and substantial depreciation. The dollar actually began its ascent in 1979 in response to a sharp tightening of monetary policy, which pushed up domestic interest rates. The Fed’s goal at this time was not dollar appreciation, but to rein in the double digit inflation afflicting the economy. Nevertheless, as the markets came to appreciate the Fed’s resolution in fighting inflation and the likely dual prospect of steadily rising interest rates and decelerating inflation, the United States became an attractive destination for foreign investment.

The long recession from 1981 to 1983 did not do much to abate the dollar’s rise. But the new Reagan Administration’s fiscal policy would give a sharp upward push to the dollar as the economic recovery commenced in 1983. Sizable tax cuts along with large increases in defense spending generated large federal budget deficits. That federal borrowing increased the demand for a shrinking pool of domestic saving and added to the upward push on interest rates. Capital inflows increased and the dollar climbed higher. It is also likely that once the dollar’s rise appeared relatively steady, a strong round of speculative buying of dollar assets exacerbated the appreciation of the exchange rate. The dollar peaked in 1985, about 50% above its level in 1979.

The next half of the decade would see depreciation of the dollar that was nearly as large. What caused the change? One factor, difficult to isolate precisely, was a turn in the speculative belief that the dollar would continue to rise. At this point, a sufficient number of investors came to believe that the dollar was far above a sustainable level and was now more likely to depreciate than appreciate. Of far more importance to the process of depreciation, however, was a change in economic policy. Investor expectations were given reinforcement by sizable currency interventions by the U.S. and other major economies aimed at weakening the dollar. Whatever the actual effectiveness at changing the exchange rate, these interventions could be taken by international investors as a strong signal as to where the government wanted the dollar to go and that more fundamental changes in macroeconomic policy would support that desire. The Fed moved toward a more stimulative monetary policy that pushed interest rates down. Fiscal policy also slowly began to change toward a lower interest rate track, cutting the size of budget deficits over the last half of the decade.

The depreciation of the dollar during 1986, 1987, and 1988 was precipitous, falling to about 40% of its peak value in 1985 and below its 1979 level. In fact, the concern among policy makers here and abroad was that the dollar would fall too far and needed to be stabilized. Particularly, in 1986 and 1987, the United States and other governments made active use of intervention policies in an attempt to halt the dollar’s slide. How effective these policies were is unclear, but for this or other reasons the dollar did enter a period of relative stability. This was interrupted in late 1987, when the Fed moved aggressively to counter the depressing effects of that year’s stock market crash. Reserves were pumped into the financial system and interest rates fell and with them so did the dollar in 1988. For the remainder of the decade the dollar would not experience any sharp movements, remaining relatively weak.

On balance, the decade showed us that strong dollar trends were not haphazard, but were broadly predictable responses to changes in economic fundamentals that influence the expected rate of return on dollar denominated assets. Moreover, in this period those changes were largely induced by changes in macroeconomic policy. However, the structural fact of the low U.S. saving rate clearly influenced the economic events in this period.

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The 1990s began in economic weakness. The pace of economic growth decelerated sharply in 1990 and the economy fell into recession in 1991. In response to the weakening economy, monetary policy turned to a more stimulative stance and the federal budget deficit grew as economic weakness automatically increased government spending and dampened tax receipts. Interest rates in the United States fell. In contrast, economic activity abroad was moving relatively briskly. In this environment, the demand for dollar assets ebbed and the dollar exchange rate fell, depreciating about 15% between 1989 and 1992. In 1992, an economic recovery got underway in the United States, but abroad economic conditions weakened substantially. This change in relative economic performance was enough to induce a moderate appreciation of the dollar, but it remained well below the values of the 1980s.

By mid-decade, however, the pace of economic growth in the United States accelerated greatly. What lay behind this change to faster growth was a sharp increase in the pace of investment spending by business and a marked acceleration in productivity growth. The confluence of strong consumer demand, deregulation, trade liberalization, and a rush to more fully integrate computers and information technology into the production process propelled investment spending up at a record pace. Expenditures on new plant and equipment went from about 13% of GDP in 1993 to average over 20% of GDP for the remainder of the decade. But even with the move of the federal budget towards surplus, the flow of domestic saving could not keep pace with investment and interest rates edged up. Couple this bourgeoning saving-investment gap with a falling rate of inflation, and juxtapose the exuberant economic conditions in the United States with very weak economies abroad, and the United States became a very attractive destination for foreign investors. A quickly rising foreign demand for dollar denominated assets would push the dollar steadily higher, rising over 30% from 1995 through 2001. With the strongly appreciating dollar, the trade deficit increased to a record high.

This time the dollar’s sharp ascent was driven by the private sector. Economic policy moved in conflicting directions, probably making its net impact on the dollar a minor one. The government’s move toward budget surpluses certainly added to national saving and likely muted the dollar’s rise, but this was unlikely the immediate goal of this policy change. In contrast, the Fed implemented a steadily more restrictive monetary policy that increased interest rates and this may have added to the dollars upward momentum. Again, the Fed’s primary goal was to slow a very fast moving economy and head off any re-acceleration of inflation. A rising dollar’s pushing down of import prices was supportive of this anti-inflation goal and made the Fed’s task easier, but the Fed was not the principal force behind that appreciation.

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A rising dollar and the growing net inflow of borrowing that pushes it is unlikely to be sustainable indefinitely. Borrower and lender alike may find good reasons to reduce the size of the capital inflow. For the lender, rising risk and the imperative of adequate portfolio diversification can prompt a diminished willingness to acquire dollar denominated assets. For the borrower, a rising burden of debt service (current and prospective) may curb the desire to borrow. And, of course, if the capital inflow is not checked by changes in private market decisions, it can be changed by macroeconomic policy.

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Perhaps more fundamentally, it is important to consider that given the magnitude of dollar assets that have accumulated abroad, foreign investors would be ready to seek a greater degree of diversity in their portfolios and are now moving out of dollar assets. Our knowledge of foreign investor portfolios is limited, but a 2003 survey by The Economist magazine shows that American assets make up 53% of the typical foreign investor’s equity portfolio and 44% of the typical bond portfolio. As recently as the mid-1990s, these percentages where only about 30%. It has also been estimated that the average investor since 2001 has allocated about 80% of his increased wealth to dollar assets.12 Considering that historically investors have shown a marked preference for home assets, rarely letting the foreign share in their portfolios rise above 30%, then one might reasonably conclude that the holdings of U.S. assets had so greatly reduced portfolio diversity that the saturation point had been reached. In any event, total net purchases of U.S. assets by private foreign investors fell from $460 billion in 2002 to $186 billion in 2004.

The effect of this swing in private foreign investor behavior on the dollar, however, has been muted but not offset by the counter effect of large foreign official purchases of dollar assets. In the same time period, net official purchases of dollar assets increased from $111 billion to $399 billion.

In 2005, however, the dollar changed course and slowly but steadily appreciated in value, up 7% in the major currencies index and about 2.3% in the broad index. The appreciation was much more sizable against individual currencies, up about 14% against the yen and 11% against the euro, but appreciated little or not at all against the currencies of China and several other Asian economies that maintain their currencies at a fixed rate to the dollar. This appreciation occurred even as the U.S. trade deficit and foreign debt climbed to record levels. This appreciation was rooted in a sharp bounce-back of the demand for dollar denominated assets by foreign purchasers. The net inflow of foreign funds jumped from $186 billion in 2004 to a record $585 billion in 2005. The motivating forces included continued strong U.S. economic growth relative to the rest of the world, further Fed induced increases in domestic interest rates, and rising profits of oil- exporting countries in need of a safe and liquid means of wealth storage. Also, the demand for dollar reserves by foreign central banks, although down from that of 2004, remained strong in 2005.

Through mid-2006, the dollar was steady, responding to the halt of short-term interest rate increases by the Fed and to a moderation of petro-dollar inflows. But, since then, the dollar has depreciated about 12% and is now down about 24% from its high in early 2002. This weakening appears to have been caused by some slackening in private investment flows, most likely responding not only to a desire for diversification but also to (1) slower economic growth in the United States, (2) interest rate reductions by the Fed, and (3) faster economic growth in the rest of the world. Additionally, if investors generally expect the dollar to depreciate further, the expected home currency yield on dollar assets is reduced further, exerting downward pressure on the currency.

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When the dollar begins to fall, particularly after a sharp appreciation, concerns are raised about whether the process of depreciation could soon devolve into an outright crash, wreaking

12 The Economist, September 18, 2003.

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devastation on the wider economy. The critical issue is not the dollar per se but the underlying macroeconomic forces that are propelling it. Again the critical force in this regard is the flow of international capital into and out of the U.S. economy.

The dollar crash scenario is as follows: We are in a situation where there is widespread agreement that the dollar needs to depreciate substantially and there is a strong consensus in the financial markets that the dollar will fall rather than rise. This raises the prospect of a run on the dollar that leads to a rapid and large depreciation of the dollar that goes far beyond what is needed for the desired economic adjustment. The fear in some minds is that the move out of dollars could become a stampede if investors try to flee from dollar assets on a large scale. To shed dollar assets one needs to find a buyer, but this occurs only through a tremendous bidding down of the price of the now less desirable dollar assets. This leads not only to a sharply falling exchange rate, but also to sharply rising interest rates in U.S. financial markets as lower asset prices translates into higher effective interest rates. Thus, two sharp negative impulses are transmitted. One, a sharply falling dollar will likely mean a sharply rising euro and yen, and lead to severe decreases in the export sales these counties are very dependent on. Two, sharply rising interest rates in the United States will dampen spending in interest sensitive sectors as well as reveal any lurking weaknesses in financial markets.

There are, of course, positive impulses associated with a falling dollar: Increased export sales in the United States and stimulus to interest sensitive sectors abroad. In the dollar crash scenario, however, the negative impulses have a more immediate effect and are not sufficiently offset soon enough to prevent recession in the United States, Europe, and Japan.

A disorderly adjustment is possible, but not inevitable.13 For one thing, the tendency for interest rates to rise in this circumstance works to brake the process, as higher yields assuage uneasy investors. But there is no guarantee that interest rates still would not rise to a dangerously disruptive level. There are, however, other reasons why a dollar crash is unlikely. First, why run from the dollar assets if there are no better alternatives? The U.S. economy is still the most productive and innovative economy in the world, producing more than a quarter of world output and an even greater share of quality marketable assets. U.S. assets typically offer higher returns on average then those of Europe or Japan and that return accrues more reliably then higher yielding assets of emerging economies. Therefore, a reasonable case can be made that it is unlikely that the rest of the world would easily absorb the net inflow of $700 billion to $800 billion of world saving into the U.S. market, suggesting that, despite some prudent investor reshuffling of their portfolios, the demand for dollar assets is likely to remain very strong, assuring that dollar depreciation will likely be orderly.

Second, a substantial portion of the foreign investment in the United States is typically long-term investment (e.g., direct investment in plant and equipment, long maturity bonds, and stocks), which tends to be far more stable than short-term portfolio investment flows because it is based on expectations of long-run return that are less sensitive to adverse short-run changes in economic conditions and, thereby, highly panic resistant.

Third, as discussed above, China and other emerging economies seem to be strongly tied to an economic development program propelled by export sales, particularly to the American market. To maintain the competitive position of their currencies in this market, they will continue to

13 See CRS Report RL33186, Is the U.S. Current Account Deficit Sustainable?, coordinated by Marc Labonte.

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absorb large stocks of dollar assets, maintaining upward pressure on the dollar. Also, a growing share of Japanese household saving has become more internationally mobile and likely to be looking for investment alternatives to typically low yielding domestic Japanese assets.

Fourth, the pool of world saving is likely growing, with substantial new inflows from China, India, and the oil-exporting countries. Dollar assets will likely be an attractive lure for a large share of this new saving. This new demand for dollar assets will, therefore, tend to offset some of the downward pressure on the dollar exchange rate caused by diversification out of dollar assets by other foreign investors.

Fifth, the dollar is the world economy’s reserve currency of choice. The large size and stability of the dollar asset markets along with the ongoing needs of international investors for liquidity and a store of value undergirds the strong persistent international demand for dollar assets. However, a depreciating dollar over a substantial time period could undermine the dollar’s reserve currency status.

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Predicting the path of the dollar is always difficult. Economic fundamentals predict that the dollar’s near-term path will broadly reflect the resolution by international investors of an ongoing balancing of risk and return. Nevertheless, the weight of economic fundamentals on the dollar can be easily countered in the short-run by sudden shifts in investor sentiment that are imperfectly understood and difficult to anticipate. Adding to the difficulty at this time are the large dollar asset holding by foreign central banks that are likely to respond to factors other than calculations of expected return. What this section of the report will lay out is the probable disposition of forces that will have the potential to influence the two key investor motives for holding dollar assets: the incentive to earn a high rate of return, and the need to diversify to minimize the risk of capital losses from holding too many assets in any particular currency. Considering this array of potential forces will at least give some overall sense of how the relative probabilities for appreciation versus depreciation stack up.

In 2007, economic growth in the United States slowed relative to that of other advanced economies and this relatively slower growth is expected to persist in 2008 and 2009. This economic performance differential has probably led to a reduction of the expected return on dollar assets. In addition, the Fed in mid-2007 began to lower short-term interest rates, falling from 5.25% to 2.0% by mid-2008. However, it appears that the Fed has now ceased lowering interest rates in the face of rising concerns about inflation. These changes suggest that the foreign investors, with an already strong need to diversify away from dollar assets, will turn more to alternatives with more attractive expected rates of return.

In addition, most experts argue that the U.S. current account deficit is too large to be sustainable and that the real dollar’s exchange rate might have to fall by 20% to 40% beyond the depreciation that has already occurred to shrink the trade deficit from its current level of 5.3% of GDP to a sustainable level of about 3% of GDP. Dollar depreciation of that magnitude would further erode the expected home currency yield of dollar assets, diminishing their attractiveness and increasing the attractiveness of assets denominated in appreciating currencies.

Whether the central banks of countries that actively use foreign exchange reserves to fix or stabilize their currencies relative to the dollar will continue to amass dollar reserves on the large scale seen in recent years probably hinges on the direction of the several market forces just

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discussed and on whether China remains committed to maintaining its fixed parity with the dollar. It may be that China sees the fixed parity as a critical anchor that contributes to the economic stability needed to attract long-term foreign investment, and sustain the rapid pace of economic growth needed to continue the still formidable task of absorbing China’s huge labor force into the industrial sector. If it does, it will accumulate dollar assets as necessary to counter downward pressure on the dollar relative to the yuan. In general central banks are likely to have longer investment horizons than private investors and be less sensitive to near-term rate of return differences between assets in different currencies.

What is difficult to assess is the extent to which liquidity needs, distinct from that of currency stabilization, will influence the holding of dollar reserves by the Central Bank of China. While China’s current reserves are large, it is also true that China is under considerable international political pressure to open up its financial markets and make the yuan a flexible, convertible currency. A huge stock of foreign exchange reserves may be seen as necessary to make the passage through this potentially very stormy transition.

It also seems unlikely that the Bank of Japan, the foreign holder with the second largest stock of dollar assets, would now undertake a large sell-off those assets. If Japan’s central bank were to dump a large share of its dollar assets on the market, the yen would appreciate, eroding the competitiveness of Japanese products in the large U.S. market. After nearly a decade of stagnation, Japan is unlikely to risk derailing its current economic expansion by inducing such a negative shock to its economy.

In a world awash in dollar assets, many offering only a modest rate-of-return advantage over alternatives in other hard currencies, and with the looming prospect that at some point a large deprecation of the dollar will be necessary to correct the United State’s huge current account imbalance, prudent foreign investors might try to get ahead of impending earnings and capital losses on their dollar investments that a large dollar depreciation would cause, and diversify out of dollar assets.

That this sell-off of dollar assets has not occurred so far may speak to the stabilizing effect of rising official holdings and to the significant liquidity advantage offered by the broad and deep U.S. financial markets. But it may also indicate a significant degree of investor myopia and the risk of an all too abrupt clearing of vision down the road. Diversification of assets, however, can occur without a selling of dollar assets. Investors can merely shift the composition of additions to their portfolios toward nondollar assets. Also, foreign investors holding a high concentration of U.S. Treasury securities can manage risk by accumulating other types of dollar assets such as agency bonds or high-grade corporate bonds that pay a higher yield but offer only a small increase in risk.

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The macroeconomic tools of monetary and fiscal policy have the potential to strongly influence the value of the dollar exchange rate. In practice, however, these strong policy instruments only rarely take the dollar as their primary concern. The goals of rapid and stable economic growth, high employment, and low inflation are usually the principal targets of macroeconomic policy. The dollar will likely be influenced by such policy actions, and its movement might well support

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achieving broader macroeconomic goals; but a particular level for the exchange rate is unlikely to be an explicit policy goal, and it would be misguided to describe such indirect exchange rate effects as evidence of an explicit “strong” or “weak” dollar policy.

A major benefit of moving from fixed to floating exchange rates is that it frees the monetary authority from having to move interest rates to maintain the exchange rate at a fixed value, and allows it to focus monetary policy on domestic stabilization. Discretionary fiscal policy, to the extent that it can be used, will exert its effect on the exchange rate through the budget balance. Whether that balance is a surplus or deficit will be driven by forces largely unconcerned with the exchange rate.

If the dollar looked as if it were crashing and sharp increases of interest rates were threatened, a quick policy response would be called for, and would most likely be by the Fed. Such circumstances could place the Fed in a difficult spot. Stabilizing the exchange rate would dictate raising interest rates, but that would intensify the pressures faced by domestic interest-sensitive sectors. Insulating domestic economic activity would dictate lowering interest rates, but that would intensify the dollar’s depreciation. Most often, domestic stabilization goals can be expected to take precedent.

The policy task would be easier if fiscal policy could also be used and easier still if other countries pursued complementary adjustment policies. (Remember, if the dollar is falling, other currencies must be rising, and that may not be desired, particularly if those other countries are more dependent on exports to sustain economic activity.) A crashing dollar could be a difficult policy problem. But, as discussed above, such a crash seems to be a remote possibility.

The dollar may not crash. Nevertheless, most economists argue that the dollar needs to make a further sizable, but orderly, downward correction. The correction is needed to give relief to domestic producers of tradable goods and to stem the growth of U.S. net external indebtedness. How much additional correction will be needed to achieve these goals is open to debate. Certainly, erasing the trade deficit would require a larger depreciation of the dollar than only reducing the deficit to a sustainable size. The dollar’s path is highly dependent on decisions in international capital markets, made by lenders and borrowers alike. Capital markets are capable of carrying out an orderly adjustment, and such a market initiated adjustment may now be underway. But economic policy can also influence that adjustment.

The pertinent issue for economic policy is the character of the market forces that are propelling capital flows. The direction and magnitude of prospective movement of the dollar’s exchange value will be substantially intertwined with the U.S. economy’s use of sizable inflows of foreign financial capital to partially finance the economy’s domestic investment spending.

Healthy levels of investment spending undergird long-term prosperity, and it is probably worth monitoring how well this important activity is proceeding. Because investment spending in the United States will likely rise with continued economic expansion, and because the level of domestic saving will likely continue to be smaller than what is needed to finance that investment, the demand for foreign capital will also grow. This will be a persistent force inclining the dollar toward appreciation. “Relatively strong” is an ambiguous term: what is being suggested is that the dollar in this environment may hover well above the level consistent with balanced trade. Whether this points to some further depreciation from recent highs or a renewal of appreciation is difficult to judge.

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For economic policy to prudently counter United States reliance on foreign capital and push and hold the dollar at a far lower value, would most likely require an increase in the rate of national saving. How to achieve a larger flow of domestic saving is problematic. Because the government’s most direct link to the level of national saving—the state of balance of the federal budget—is widely projected to be incurring deficits for the next several years, fiscal policy is assuming a posture that tends to appreciate the dollar. The path of monetary policy is certainly more flexible and the needs of a slowly recovering economy make it more likely that the Fed will follow a generally stimulative path in the near-term. This would perhaps be mildly supportive of depreciation of the dollar. But there is no strong reason to expect monetary policy to exert such strong downward pressure on the dollar that it would overcome even relatively moderate forces pushing to appreciate the dollar, such as rising investment spending, larger budget deficits, and economic weakness abroad.

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A “weak” dollar is not necessarily bad and a “strong” dollar is not necessarily good. An accurate evaluation will depend on what has made the dollar weak or strong. The exchange rate is most often a symptom of movements of capital between countries. As such, it is these flows, and the forces behind them, that are likely to shape our final opinion about what is good or bad economic performance.

A strong dollar that is the result of large capital inflows used to support budget deficits and consumption, as in the 1980s, may be viewed differently than a strong dollar that is the result of capital inflows that finance a higher level of investment spending as in the 1990s. The latter, because it will likely lead to a smaller decrement to our future living standard, seems superior. Similarly, a dollar that weakens in response to a shift to a higher level of domestic saving may be viewed differently than a weakening that is the result of investors moving away from a poorly run economy with few good investment opportunities. The former, because it will mean that more of the benefit of future growth will accrue to U.S. citizens, seems superior.

The depreciation of the dollar from 2002 through 2004 was most likely due to a prudent response of investors to concurrent events in the U.S. economy, many of them likely transitory, however. The modest rise of the dollar in 2005 is most likely the consequence of increased demand for dollars due to the current and prospective strong performance of the U.S. economy, Fed interest rate increases, and rising petroleum earnings. Yet, the path of the dollar exchange rate remains very problematic. The very large accumulation of dollar assets in foreign investment portfolios likely indicates a continuing need for diversification away from dollar assets. Also, it is difficult to predict if foreign central banks will continue their high volume official purchases of dollar assets, but an abrupt change is unlikely. Further, petroleum earnings are likely to remain large and a sizable proportion will flow into liquid U.S. asset markets. Under the most plausible scenario, the U.S. economy will continue to use a sizable inflow of foreign capital to help finance its domestic investment and a seeming glut of foreign saving shows no sign of ebbing. This suggests that the dollar may continue to depreciate in 2008 and perhaps beyond that. A crash of the dollar remains a significant risk, but an orderly adjustment seems more likely.

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Craig K. Elwell Specialist in Macroeconomic Policy [email protected], 7-7757

weber_content-analysis.pdf

WorldEnergyOutlook-2007.pdf

© OECD/IEA - 2007

IEEJ: December 2007

World Energy Outlook 2007: China and India Insights

Singapore, 9 November 2007

William C. Ramsay Deputy Executive Director

International Energy Agency

© OECD/IEA - 2007

IEEJ: December 2007

Approach

Co-operation with China’s NDRC & ERI, India’s TERI

Workshops / meetings in Beijing, Delhi Chinese and Indian experts joined the IEA More than 50 Chinese and Indian peer reviewers

Scenario approach Reference Scenario Alternative Policy Scenario & 450 Stabilisation Case High Growth Scenario (China/India)

Full global update of projections (all scenarios) Analysis of the impact of China & India on global economy, energy markets & environment

© OECD/IEA - 2007

IEEJ: December 2007

Reference Scenario

© OECD/IEA - 2007

IEEJ: December 2007

The Emerging Giants of World Energy

China & India will contribute more than 40% of the increase in global energy demand to 2030 on current trends

0%

20%

40%

60%

80%

100%

Total energy

Coal Oil Nuclear Hydro Power sector investments

Rest of the world India China

Increase in Primary Energy Demand & Investment Between 2005 & 2030 as Share of World Total

© OECD/IEA - 2007

IEEJ: December 2007

Global Oil Supply Prospects to 2015

Oil supply/demand balance is set to remain tight In total, 37.5 mb/d of gross capacity additions needed in 2006-2015

13.6 mb/d to meet demand & rest to replace decline in existing fields

OPEC & non-OPEC producers have announced plans to add 25 mb/d through to 2015 Thus, a further 12.5 mb/d of gross capacity would need to be added or demand growth curbed Otherwise, a supply crunch cannot be ruled out

© OECD/IEA - 2007

IEEJ: December 2007

New Light-Duty Vehicle Sales in China

China’s oil imports reach 13 mb/d in 2030 as car ownership jumps to 140 per 1 000 people from 20 today

Overtake US sales

Overtake Japan sales

2 4 6 8

10 12 14 16 18

1995 2000 2005 2010 2015 2020 2025 2030

m illi

on

0

© OECD/IEA - 2007

IEEJ: December 2007

China & India Coal Imports

China recently became a net coal importer like India, with both putting increasing pressure on international coal markets

-50

0

50

100

150

200

250

300

350

2005 2015 2030

M tc

e

-5%

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5%

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20%

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35%China net trade India net trade India's and China's net trade as share of interregional trade (right axis)

© OECD/IEA - 2007

IEEJ: December 2007

China & India in Global CO2 Emissions

Around 60% of the global increase in emissions in 2005-2030 comes from China & India

Cumulative Energy-Related CO2 Emissions

0 100 200 300 400 500

United States

European Union

Japan

China

India

billion tonnes

1900-2005 2005-2030

© OECD/IEA - 2007

IEEJ: December 2007

World’s Top Five CO2 Emitters

33.331.851.1India

51.251.341.2Japan

42.041.831.5Russia

111.418.625.1China

26.926.415.8US

rankGtrankGtrankGt

203020152005

China becomes the largest emitter in 2007 & India the 3rd largest by 2015

© OECD/IEA - 2007

IEEJ: December 2007

CO2 Emissions from Coal-Fired Power Stations built prior to 2015 in China & India

0

1 000

2 000

3 000

4 000

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6 000

2006 2015 2030 2045 2060 2075

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of C

O 2

Existing power plants Power plants built in 2005-2015

Capacity additions in the next decade will lock-in technology & largely determine emissions through 2050 & beyond

© OECD/IEA - 2007

IEEJ: December 2007

Cumulative Investment in Energy- Supply Infrastructure, 2006-2030

Just over half of all investment needs to 2030 of $22 trillion are in developing countries, 17% in China & another 5% in India alone

0 1 000 2 000 3 000 4 000 5 000

OECD North America China

OECD Europe Russia

Middle East Latin America

Africa Rest of developing Asia

India OECD Pacific

Inter regional transport

billion dollars (2006)

Coal Oil Gas Electricity

0 1 000 2 000 3 000 4 000 5 000

-

billion dollars (2006)

Coal Oil Gas Electricity

© OECD/IEA - 2007

IEEJ: December 2007

Alternative Policy Scenario

© OECD/IEA - 2007

IEEJ: December 2007

Increase in Net Oil Imports, 2006-2030

New policies reduce global oil demand by 14 mb/d by 2030, cutting sharply the need for imports

-2

0

2

4

6

8

10

OECD North America

OECD Europe

OECD Pacific

China India Other Asia

m b/

d Reference Scenario Alternative Policy Scenario

© OECD/IEA - 2007

IEEJ: December 2007

Global Energy-Related CO2 Emissions

Global emissions will increase by 57% in the Reference Scenario, but they level off in the Alternative Policy Scenario

10

15

20

25

30

35

40

45

50

1980 1990 2000 2010 2020 2030

bi llio

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nn es

(G t) Reference Scenario 42 Gt

Alternative Policy Scenario

34 Gt

19%

27 Gt

© OECD/IEA - 2007

IEEJ: December 2007

Effectiveness of Policies to Promote Energy Efficiency in China

Tougher efficiency standards for air conditioners & refrigerators alone would save the need to build a Three Gorges Dam by 2020

Electricity Savings from More Efficient Air Conditioners & Refrigerators in the Alternative Policy Scenario

0

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TW h

pe r y

ea r

Air conditioner savings Refrigerator savings

Three Gorges Dam (85 TWh)

© OECD/IEA - 2007

IEEJ: December 2007

India’s Local Pollution

New policies reduce substantially emissions of SO2 and NOx – largely from coal-fired power plants, cars & trucks

Alternative Policy Scenario

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3

6

9

12

15

18

1990 1995 2000 2005 2010 2015 2020 2025 2030

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Reference Scenario

SO2

NOx

© OECD/IEA - 2007

IEEJ: December 2007

CO2 Emissions - 450 Stabilisation Case

By 2030, emissions are reduced to some 23 Gt, a reduction of 19 Gt compared with the Reference Scenario

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2005 2010 2015 2020 2025 2030

G to

f C O 2

CCS in industry CCS in power generation Nuclear Renewables Switching from coal to gas End Use electricity efficiency

End Use fuel efficiency

Reference Scenario

450 Stabilisation Case27 Gt

42 Gt

23 Gt

Energy-Related CO2 Emissions

© OECD/IEA - 2007

IEEJ: December 2007

High Growth Scenario

© OECD/IEA - 2007

IEEJ: December 2007

China & India Oil Demand

Faster economic growth in China & India would have major implications for energy security & climate

Additional demand in High Growth Scenario

India in Reference Scenario China in Reference Scenario

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2006 2015 2030

m b/

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© OECD/IEA - 2007

IEEJ: December 2007

Summary & Conclusions

© OECD/IEA - 2007

IEEJ: December 2007

Conclusions

Global energy system is on an increasingly unsustainable path China and India are transforming the global energy system by their sheer size Challenge for all countries is to achieve transition to a more secure, lower carbon energy system New policies now under consideration would make a major contribution Next 10 years are critical

The pace of capacity additions will be most rapid Technology will be “locked-in” for decades Growing tightness in oil & gas markets

Challenge is global so solutions must be global

Contact: [email protected]

zhang&Wildemuth.pdf

1

Qualitative Analysis of Content by

Yan Zhang and Barbara M. Wildemuth

If there were only one truth, you couldn’t paint a hundred canvases on the same theme.

--Pablo Picasso, 1966

Introduction As one of today’s most extensively employed analytical tools, content analysis

has been used fruitfully in a wide variety of research applications in information and library science (ILS) (Allen & Reser, 1990). Similar to other fields, content analysis has been primarily used in ILS as a quantitative research method until recent decades. Many current studies use qualitative content analysis, which addresses some of the weaknesses of the quantitative approach.

Qualitative content analysis has been defined as: • “a research method for the subjective interpretation of the content of text data

through the systematic classification process of coding and identifying themes or patterns” (Hsieh & Shannon, 2005, p.1278),

• “an approach of empirical, methodological controlled analysis of texts within their context of communication, following content analytic rules and step by step models, without rash quantification” (Mayring, 2000, p.2), and

• “any qualitative data reduction and sense-making effort that takes a volume of qualitative material and attempts to identify core consistencies and meanings” (Patton, 2002, p.453).

These three definitions illustrate that qualitative content analysis emphasizes an integrated view of speech/texts and their specific contexts. Qualitative content analysis goes beyond merely counting words or extracting objective content from texts to examine meanings, themes and patterns that may be manifest or latent in a particular text. It allows researchers to understand social reality in a subjective but scientific manner.

Comparing qualitative content analysis with its rather familiar quantitative counterpart can enhance our understanding of the method. First, the research areas from which they developed are different. Quantitative content analysis (discussed in the previous chapter) is used widely in mass communication as a way to count manifest textual elements, an aspect of this method that is often criticized for missing syntactical and semantic information embedded in the text (Weber, 1990). By contrast, qualitative content analysis was developed primarily in anthropology, qualitative sociology, and psychology, in order to explore the meanings underlying physical messages. Second, quantitative content analysis is deductive, intended to test hypotheses or address questions generated from theories or previous empirical research. By contrast, qualitative content analysis is mainly inductive, grounding the examination of topics and themes, as well as the inferences drawn from them, in the data. In some cases, qualitative content

2

analysis attempts to generate theory. Third, the data sampling techniques required by the two approaches are different. Quantitative content analysis requires that the data are selected using random sampling or other probabilistic approaches, so as to ensure the validity of statistical inference. By contrast, samples for qualitative content analysis usually consist of purposively selected texts which can inform the research questions being investigated. Last but not the least, the products of the two approaches are different. The quantitative approach produces numbers that can be manipulated with various statistical methods. By contrast, the qualitative approach usually produces descriptions or typologies, along with expressions from subjects reflecting how they view the social world. By this means, the perspectives of the producers of the text can be better understood by the investigator as well as the readers of the study’s results (Berg, 2001). Qualitative content analysis pays attention to unique themes that illustrate the range of the meanings of the phenomenon rather than the statistical significance of the occurrence of particular texts or concepts.

In real research work, the two approaches are not mutually exclusive and can be used in combination. As suggested by Smith, “qualitative analysis deals with the forms and antecedent-consequent patterns of form, while quantitative analysis deals with duration and frequency of form”(Smith, 1975, p.218). Weber (1990) also pointed out that the best content-analytic studies use both qualitative and quantitative operations.

Inductive vs. Deductive Qualitative content analysis involves a process designed to condense raw data into

categories or themes based on valid inference and interpretation. This process uses inductive reasoning, by which themes and categories emerge from the data through the researcher’s careful examination and constant comparison. But qualitative content analysis does not need to exclude deductive reasoning (Patton, 2002). Generating concepts or variables from theory or previous studies is also very useful for qualitative research, especially at the inception of data analysis (Berg, 2001).

Hsieh and Shannon (2005) discussed three approaches to qualitative content analysis, based on the degree of involvement of inductive reasoning. The first is conventional qualitative content analysis, in which coding categories are derived directly and inductively from the raw data. This is the approach used for grounded theory development. The second approach is directed content analysis, in which initial coding starts with a theory or relevant research findings. Then, during data analysis, the researchers immerse themselves in the data and allow themes to emerge from the data. The purpose of this approach usually is to validate or extend a conceptual framework or theory. The third approach is summative content analysis, which starts with the counting of words or manifest content, then extends the analysis to include latent meanings and themes. This approach seems quantitative in the early stages, but its goal is to explore the usage of the words/indicators in an inductive manner.

The Process of Qualitative Content Analysis The process of qualitative content analysis often begins during the early stages of

data collection. This early involvement in the analysis phase will help you move back and forth between concept development and data collection, and may help direct your

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subsequent data collection toward sources that are more useful for addressing the research questions (Miles & Huberman, 1994). To support valid and reliable inferences, qualitative content analysis involves a set of systematic and transparent procedures for processing data. Some of the steps overlap with the traditional quantitative content analysis procedures (Tesch, 1990), while others are unique to this method. Depending on the goals of your study, your content analysis may be more flexible or more standardized, but generally it can be divided into the following steps, beginning with preparing the data and proceeding through writing up the findings in a report.

Step 1: Prepare the Data Qualitative content analysis can be used to analyze various types of data, but

generally the data need to be transformed into written text before analysis can start. If the data come from existing texts, the choice of the content must be justified by what you want to know (Patton, 2002). In ILS studies, qualitative content analysis is most often used to analyze interview transcripts in order to reveal or model people’s information related behaviors and thoughts. When transcribing interviews, the following questions arise: (1) should all the questions of the interviewer or only the main questions from the interview guide be transcribed; (2) should the verbalizations be transcribed literally or only in a summary; and (3) should observations during the interview (e.g., sounds, pauses, and other audible behaviors) be transcribed or not (Schilling, 2006)? Your answers to these questions should be based on your research questions. While a complete transcript may be the most useful, the additional value it provides may not justify the additional time required to create it.

Step 2: Define the Unit of Analysis The unit of analysis refers to the basic unit of text to be classified during content

analysis. Messages have to be unitized before they can be coded, and differences in the unit definition can affect coding decisions as well as the comparability of outcomes with other similar studies (De Wever et al., 2006). Therefore, defining the coding unit is one of your most fundamental and important decisions (Weber, 1990).

Qualitative content analysis usually uses individual themes as the unit for analysis, rather than the physical linguistic units (e.g., word, sentence, or paragraph) most often used in quantitative content analysis. An instance of a theme might be expressed in a single word, a phrase, a sentence, a paragraph, or an entire document. When using theme as the coding unit, you are primarily looking for the expressions of an idea (Minichiello et al., 1990). Thus, you might assign a code to a text chunk of any size, as long as that chunk represents a single theme or issue of relevance to your research question(s).

Step 3: Develop Categories and a Coding Scheme Categories and a coding scheme can be derived from three sources: the data,

previous related studies, and theories. Coding schemes can be developed both inductively and deductively. In studies where no theories are available, you must generate categories inductively from the data. Inductive content analysis is particularly appropriate for studies that intend to develop theory, rather than those that intend to describe a particular phenomenon or verify an existing theory. When developing categories inductively from

4

raw data, you are encouraged to use the constant comparative method (Glaser & Strauss, 1967), since it is not only able to stimulate original insights, but is also able to make differences between categories apparent. The essence of the constant comparative method is (1) the systematic comparison of each text assigned to a category with each of those already assigned to that category, in order to fully understand the theoretical properties of the category; and (2) integrating categories and their properties through the development of interpretive memos.

For some studies, you will have a preliminary model or theory on which to base your inquiry. You can generate an initial list of coding categories from the model or theory, and you may modify the model or theory within the course of the analysis as new categories emerge inductively (Miles & Huberman, 1994). The adoption of coding schemes developed in previous studies has the advantage of supporting the accumulation and comparison of research findings across multiple studies.

In quantitative content analysis, categories need to be mutually exclusive because confounded variables would violate the assumptions of some statistical procedures (Weber, 1990). However, in reality, assigning a particular text to a single category can be very difficult. Qualitative content analysis allows you to assign a unit of text to more than one category simultaneously (Tesch, 1990). Even so, the categories in your coding scheme should be defined in a way that they are internally as homogeneous as possible and externally as heterogeneous as possible (Lincoln & Guba, 1985).

To ensure the consistency of coding, especially when multiple coders are involved, you should develop a coding manual, which usually consists of category names, definitions or rules for assigning codes, and examples (Weber, 1990). Some coding manuals have an additional field for taking notes as coding proceeds. Using the constant comparative method, your coding manual will evolve throughout the process of data analysis, and will be augmented with interpretive memos.

Step 4: Test Your Coding Scheme on a Sample of Text If you are using a fairly standardized process in your analysis, you’ll want to

develop and validate your coding scheme early in the process. The best test of the clarity and consistency of your category definitions is to code a sample of your data. After the sample is coded, the coding consistency needs to be checked, in most cases through an assessment of inter-coder agreement. If the level of consistency is low, the coding rules must be revised. Doubts and problems concerning the definitions of categories, coding rules, or categorization of specific cases need to be discussed and resolved within your research team (Schilling, 2006). Coding sample text, checking coding consistency, and revising coding rules is an iterative process and should continue until sufficient coding consistency is achieved (Weber, 1990).

Step 5: Code All the Text

When sufficient consistency has been achieved, the coding rules can be applied to the entire corpus of text. During the coding process, you will need to check the coding repeatedly, to prevent “drifting into an idiosyncratic sense of what the codes mean” (Schilling, 2006). Because coding will proceed while new data continue to be collected, it’s possible (even quite likely) that new themes and concepts will emerge and will need to be added to the coding manual.

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Step 6: Assess Your Coding Consistency After coding the entire data set, you need to recheck the consistency of your

coding. It is not safe to assume that, if a sample was coded in a consistent and reliable manner, the coding of the whole corpus of text is also consistent. Human coders are subject to fatigue and are likely to make more mistakes as the coding proceeds. New codes may have been added since the original consistency check. Also, the coders’ understanding of the categories and coding rules may change subtly over the time, which may lead to greater inconsistency (Miles & Huberman, 1994; Weber, 1990). For all these reasons, you need to recheck your coding consistency.

Step 7: Draw Conclusions from the Coded Data This step involves making sense of the themes or categories identified, and their

properties. At this stage, you will make inferences and present your reconstructions of meanings derived from the data. Your activities may involve exploring the properties and dimensions of categories, identifying relationships between categories, uncovering patterns, and testing categories against the full range of data (Bradley, 1993). This is a critical step in the analysis process, and its success will rely almost wholly on your reasoning abilities.

Step 8: Report Your Methods and Findings For the study to be replicable, you need to monitor and report your analytical

procedures and processes as completely and truthfully as possible (Patton, 2002). In the case of qualitative content analysis, you need to report your decisions and practices concerning the coding process, as well as the methods you used to establish the trustworthiness of your study (discussed below).

Qualitative content analysis does not produce counts and statistical significance; instead, it uncovers patterns, themes, and categories important to a social reality. Presenting research findings from qualitative content analysis is challenging. Although it is a common practice to use typical quotations to justify conclusions (Schilling, 2006), you also may want to incorporate other options for data display, including matrices, graphs, charts, and conceptual networks (Miles & Huberman, 1994). The form and extent of reporting will finally depend on the specific research goals (Patton, 2002).

When presenting qualitative content analysis results, you should strive for a balance between description and interpretation. Description gives your readers background and context and thus needs to be rich and thick (Denzin, 1989). Qualitative research is fundamentally interpretive, and interpretation represents your personal and theoretical understanding of the phenomenon under study. An interesting and readable report “provides sufficient description to allow the reader to understand the basis for an interpretation, and sufficient interpretation to allow the reader to understand the description” (Patton, 2002, p.503-504).

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Computer Support for Qualitative Content Analysis Qualitative content analysis is usually supported by computer programs, such as

NVivo1 or ATLAS.ti.2 The programs vary in their complexity and sophistication, but their common purpose is to assist researchers in organizing, managing, and coding qualitative data in a more efficient manner. The basic functions that are supported by such programs include text editing, note and memo taking, coding, text retrieval, and node/category manipulation. More and more qualitative data analysis software incorporates a visual presentation module that allows researchers to see the relationships between categories more vividly. Some programs even record a coding history to allow researchers to keep track of the evolution of their interpretations. Any time you will be working with more than a few interviews or are working with a team of researchers, you should use this type of software to support your efforts.

Trustworthiness Validity, reliability, and objectivity are criteria used to evaluate the quality of

research in the conventional positivist research paradigm. As an interpretive method, qualitative content analysis differs from the positivist tradition in its fundamental assumptions, research purposes, and inference processes, thus making the conventional criteria unsuitable for judging its research results (Bradley, 1993). Recognizing this gap, Lincoln and Guba (1985) proposed four criteria for evaluating interpretive research work: credibility, transferability, dependability, and confirmability.

Credibility refers to the “adequate representation of the constructions of the social world under study” (Bradley, 1993, p.436). Lincoln and Guba (1985) recommended a set of activities that would help improve the credibility of your research results: prolonged engagement in the field, persistent observation, triangulation, negative case analysis, checking interpretations against raw data, peer debriefing, and member checking. To improve the credibility of qualitative content analysis, researchers not only need to design data collection strategies that are able to adequately solicit the representations, but also to design transparent processes for coding and drawing conclusions from the raw data. Coders’ knowledge and experience have significant impact on the credibility of research results. It is necessary to provide coders precise coding definitions and clear coding procedures. It is also helpful to prepare coders through a comprehensive training program (Weber, 1990).

Transferability refers to the extent to which the researcher’s working hypothesis can be applied to another context. It is not the researcher’s task to provide an index of transferability; rather, he or she is responsible for providing data sets and descriptions that are rich enough so that other researchers are able to make judgments about the findings’ transferability to different settings or contexts.

Dependability refers to “the coherence of the internal process and the way the researcher accounts for changing conditions in the phenomena” (Bradley, 1993, p.437). Confirmability refers to “the extent to which the characteristics of the data, as posited by the researcher, can be confirmed by others who read or review the research results” (Bradley, 1993, p.437). The major technique for establishing dependability and 1 http://www.qsrinternational.com/products_nvivo.aspx. 2 http://www.atlasti.com/.

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confirmability is through audits of the research processes and findings. Dependability is determined by checking the consistency of the study processes, and confirmability is determined by checking the internal coherence of the research product, namely, the data, the findings, the interpretations, and the recommendations. The materials that could be used in these audits include raw data, field notes, theoretical notes and memos, coding manuals, process notes, and so on. The audit process has five stages: preentry, determinations of auditability, formal agreement, determination of trustworthiness (dependability and confirmability), and closure. A detailed list of activities and tasks at each stage can be found in Appendix B in Lincoln and Guba (1985).

Examples Two examples of qualitative content analysis will be discussed here. The first

example study (Schamber, 2000) was intended to identify and define the criteria that weather professionals use to evaluate particular information resources. Interview data were analyzed inductively. In the second example, Foster (2004) investigated the information behaviors of interdisciplinary researchers. Based on semi-structured interview data, he developed a model of these researchers’ information seeking and use. These two studies are typical of ILS research that incorporates qualitative content analysis.

Example 1: Criteria for Making Relevance Judgments Schamber (2000) conducted an exploratory inquiry into the criteria that

occupational users of weather information employ to make relevance judgments on weather information sources and presentation formats. To get first-hand accounts from users, she used the time-line interview method to collect data from 30 subjects: 10 each in construction, electric power utilities, and aviation. These participants were highly motivated and had very specific needs for weather information. In accordance with a naturalistic approach, the interview responses were to be interpreted in a way that did not compromise the original meaning expressed by the study participant. Inductive content analysis was chosen for its power to make such faithful inferences.

The interviews were audio taped and transcribed. The transcripts served as the primary sources of data for content analysis. Because the purpose of the study was to identify and describe criteria used by people to make relevance judgments, Schamber defined a coding unit as “a word or group of words that could be coded under one criterion category” (Schamber, 2000, p.739). Responses to each interview were unitized before they were coded.

As Schamber pointed out, content analysis functions both as a secondary observational tool for identifying variables in text and an analytical tool for categorization. Content analysis was incorporated in this study at the pretest stage of developing the interview guide as a basis for the coding scheme, as well as assessing the effectiveness of particular interview items. The formal process of developing the coding scheme began shortly after the first few interviews. The whole process was an iteration of coding a sample of data, testing inter-coder agreement, and revising the coding scheme. Whenever the percentage of agreement did not reach an acceptable level, the coding scheme was revised (Schamber, 1991). The author reported that, “based on data from the first few respondents, the scheme was significantly revised eight times and tested by 14

8

coders until inter-coder agreement reached acceptable levels” (Schamber, 2000, p.738). The 14 coders were not involved in the coding at the same time; rather, they were spread across three rounds of revision.

The analysis process was inductive and took a grounded theory approach. The author did not derive variables/categories from existing theories or previous related studies, and she had no intention of verifying existing theories; rather, she immersed herself in the interview transcripts and let the categories emerge on their own. Some categories in the coding scheme were straightforward and could be easily identified based on manifest content, while others were harder to identify because they were partially based on the latent content of the texts. The categories were expected to be mutually exclusive (distinct from each other) and exhaustive. The iterative coding process resulted in a coding scheme with eight main categories.

Credibility evaluates the validity of a researcher’s reconstruction of a social reality. In this study, Schamber carefully designed and controlled the data collection and data analysis procedures to ensure the credibility of the research results. First, the time- line interview technique solicited respondents’ own accounts of the relevance judgments they made on weather information in their real working environments instead of in artificial experimental settings. Second, non-intrusive inductive content analysis was used to identify the themes emerging from the interview transcripts. The criteria were defined in respondents’ own language as it appeared in the interviews. Furthermore, a peer debriefing process was involved in the coding development process, which ensures the credibility of the research by reducing the bias of a single researcher. As reported by Schamber (1991), “a group of up to seven people, mostly graduate students including the researcher, met weekly for most of a semester and discussed possible criterion categories based on transcripts from four respondents” (p.84-85). The credibility of the research findings also was verified by the fact that most criteria were mentioned by more than one respondent and in more than one scenario. Theory saturation was achieved as mentions of criteria became increasingly redundant.

Schamber did not claim transferability of the research results explicitly, but the transferability of the study was made possible by detailed documentation of the data processing in a Codebook. The first part of the Codebook explained procedures for handling all types of data (including quantitative). In the second part, the coding scheme was listed; it included: identification numbers, category names, detailed category definitions, coding rules, and examples. This detailed documentation of the data handling and the coding scheme makes it easier for future researchers to judge the transferability of the criteria to other user populations or other situational contexts. The transferability of the identified criteria also was supported by the fact that the criteria identified in this study were also widely documented in previous research works.

The dependability of the research findings in this study was established by the transparent coding process and inter-coder verification. The inherent ambiguity of word meanings, category definitions, and coding procedures threaten the coherence and consistency of coding practices, hence negatively affecting the credibility of the findings. To make sure that the distinctions between categories were clear to the coders, the Codebook defined them. To ensure coding consistency, every coder used the same version of the scheme to code the raw interview data. Both the training and the experience of the coder are necessary for reliable coding (Neuendorf, 2002). In this study,

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the coders were graduate students who had been involved the revision of the coding scheme and, thus, were experienced at using the scheme (Schamber, 1991). The final coding scheme was tested for inter-coder reliability with a first-time coder based on simple percent agreement: the number of agreements between two independent coders divided by the number of possible agreements. As noted in the previous chapter, more sophisticated methods for assessing inter-coder agreement are available. If you’re using a standardized coding scheme, refer to that discussion.

As suggested by Lincoln and Guba (1985), confirmability is primarily established through a comfirmability audit, which Schamber did not conduct. However, the significant overlap of the criteria identified in this study with those identified in other studies indicates that the research findings have been confirmed by other researchers. Meanwhile, the detailed documentation of data handling also provides means for comfirmability checking.

When reporting the trustworthiness of the research results, instead of using the terms, “credibility,” “transferability,” “dependability,” and “confirmability,” Schamber used terms generally associated with positivist studies: “internal validity,” “external validity,” “reliability,” and “generalizability.” It is worth pointing out that there is no universal agreement on the terminology used when assessing the quality of a qualitative inquiry. However, we recommend that the four criteria proposed by Lincoln and Guba (1985) be used to evaluate the trustworthiness of research work conducted within an interpretive paradigm.

Descriptive statistics, such as frequency of criteria occurrence, were reported in the study. However, the purpose of the study was to describe the range of the criteria employed to decide the degree of relevance of weather information in particular occupations. Thus, the main finding was a list of criteria, along with their definitions, keywords, and examples. Quotations excerpted from interview transcripts were used to further describe the identified criteria, as well as to illustrate the situational contexts in which the criteria were applied.

Example 2: Information Seeking in an Interdisciplinary Context Foster (2004) examined the information seeking behaviors of scholars working in

interdisciplinary contexts. His goal was threefold: (1) to identify the activities, strategies, contexts, and behaviors of interdisciplinary information seekers; (2) to understand the relationships between behaviors and context; and (3) to represent the information seeking behavior of interdisciplinary researchers in an empirically grounded model. This study is a naturalist inquiry, using semi-structured interviews to collect direct accounts of information seeking experiences from 45 interdisciplinary researchers. The respondents were selected through purposive sampling, along with snowball sampling. To “enhance contextual richness and minimize fragmentation” (Foster, 2004, p.230), all participants were interviewed in their normal working places.

In light of the exploratory nature of the study, the grounded theory approach guided the data analysis. Foster did not have any specific expectations for the data before the analysis started. Rather, he expected that concepts and themes related to interdisciplinary information seeking would emerge from the texts through inductive content analysis and the constant comparative method.

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Coding took place in multiple stages, over time. The initial coding process was an open coding process. The author closely read and annotated each interview transcript. During this process, the texts were unitized and concepts were highlighted and labeled. Based on this initial analysis, Foster identified three stages of information seeking in interdisciplinary contexts – initial, middle, and final – along with activities involved in each stage. Subsequent coding took place in the manner of constantly comparing the current transcript with previous ones to allow the emergence of categories and their properties. As the coding proceeded, additional themes and activities emerged – not covered by the initially-identified three-stage model. Further analysis of emergent concepts and themes and their relationships to each other resulted in a two-dimensional model of information seeking behaviors in the interdisciplinary context. One dimension delineates three nonlinear core processes of information seeking activities: opening, orientation, and consolidation. The other dimension consists of three levels of contextual interaction: cognitive approach, internal context, and external context.

The ATLAS.ti software was used to support the coding process. It allows the researcher to code the data, retrieve text based on keywords, rename or merge existing codes without perturbing the rest of the codes, and generate visualizatios of emergent codes and their relationships to one another. ATLAS.ti also maintains automatic logs of coding changes, which makes it possible to keep track of the evolution of the analysis.

As reported by Foster, coding consistency in this study was addressed by including three iterations of coding conducted over a period of one year. However, the author did not report on the three rounds of coding in detail. For example, he did not say how many coders were involved in the coding, how the coders were trained, how the coding rules were defined, and what strategies were used to ensure transparent coding. If all three rounds of coding were done by Foster alone, there was no assessment of coding consistency. While this is a common practice in qualitative research, it weakens the author’s argument for the dependability of the study.

The issue of trustworthiness of the study was discussed in terms of the criteria suggested by Lincoln and Guba (1985): credibility, dependability, transferability, and confirmability. Credibility was established mainly through member checking and peer debriefing. Member checking was used in four ways at various stages of data collection and data analysis: (1) at the pilot stage, the interviewer discussed the interview questions with participants at the end of each interview; (2) during formal interviews, the interviewer fed ideas back to participants to refine, rephrase, and interpret; (3) in an informal post-interview session, each participant was given the chance to discuss the findings; and (4) an additional session was conducted with a sample of five participants willing to provide feedback on the transcripts of their own interview as well as evaluate the research findings. Peer debriefing was used in the study to “confirm interpretations and coding decisions including the development of categories” (Foster, 2004, p.231). No further details about who conducted the debriefing or how it was conducted were reported in the paper.

The transferability of the present study was ensured by “rich description and reporting of the research process” (Foster, 2004, p.230). Future researchers can make transferability judgments based on the detailed description provided by Foster. The issues of dependability and confirmability were addressed through the author’s “research notes, which recorded decisions, queries, working out, and the development results” (Foster,

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2004, p.230). By referring to these materials, Foster could audit his own inferences and interpretations, and other interested researchers could review the research findings.

The content analysis findings were reported by describing each component in the model of information seeking behaviors in interdisciplinary contexts that emerged from this study. Diagrams and tables were used to facilitate the description. A few quotations from participants were provided to reinforce the author’s abstraction of three processes of interdisciplinary information seeking: opening, orientation, and consolidation. Finally, Foster discussed the implications of the new model for the exploration of information behaviors in general.

Conclusion Qualitative content analysis is a valuable alternative to more traditional

quantitative content analysis, when the researcher is working in an interpretive paradigm. The goal is to identify important themes or categories within a body of content, and to provide a rich description of the social reality created by those themes/categories as they are lived out in a particular setting. Through careful data preparation, coding, and interpretation, the results of qualitative content analysis can support the development of new theories and models, as well as validating existing theories and providing thick descriptions of particular settings or phenomena.

Cited Works Allen, B., & Reser, D. (1990). Content analysis in library and information science

research. Library & Information Science Research, 12(3), 251-260. Berg, B.L. (2001). Qualitative Research Methods for the Social Sciences. Boston: Allyn

and Bacon. Bradley, J. (1993). Methodological issues and practices in qualitative research. Library

Quarterly, 63(4), 431-449. De Wever, B., Schellens, T., Valcke, M., & Van Keer, H. (2006). Content analysis

schemes to analyze transcripts of online asynchronous discussion groups: A review. Computer & Education, 46, 6-28.

Denzin, N.K. (1989). Interpretive Interactionism. Newbury Park, CA: Sage. Foster, A. (2004). A nonlinear model of information-seeking behavior. Journal of the

American Society for Information Science & Technology, 55(3), 228-237. Glaser, B.G., & Strauss, A.L. (1967). The Discovery of Grounded Theory: Strategies for

Qualitative Research. New York: Aldine. Hsieh, H.-F., & Shannon, S.E. (2005). Three approaches to qualitative content analysis.

Qualitative Health Research, 15(9), 1277-1288. Lincoln, Y.S., & Guba, E.G. (1985). Naturalistic Inquiry. Beverly Hills, CA: Sage

Publications. Mayring, P. (2000). Qualitative content analysis. Forum: Qualitative Social Research,

1(2). Retrieved July 28, 2008, from http://217.160.35.246/fqs-texte/2-00/2- 00mayring-e.pdf.

Miles, M., & Huberman, A.M. (1994). Qualitative Data Analysis. Thousand Oaks, CA: Sage Publications.

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Minichiello, V., Aroni, R., Timewell, E., & Alexander, L. (1990). In-Depth Interviewing: Researching People. Hong Kong: Longman Cheshire.

Neuendorf, K.A. (2002). The Content Analysis Guidebook. Thousand Oaks, CA: Sage Publications.

Patton, M.Q. (2002). Qualitative Research and Evaluation Methods. Thousand Oaks, CA: Sage.

Picasso, P. (1966). Quoted in Hélène Parmelin, “Truth,” In Picasso Says. London: Allen & Unwin (trans. 1969).

Schamber, L. (2000). Time-line interviews and inductive content analysis: Their effectivenss for exploring cognitive behaviors. Journal of the American Society for Information Science, 51(8), 734-744.

Schamber, L. (1991). Users’ Criteria for Evaluation in Multimedia Information Seeking and Use Situations. Ph.D. dissertation, Syracuse University.

Schilling, J. (2006). On the pragmatics of qualitative assessment: Designing the process for content analysis. European Journal of Psychological Assessment, 22(1), 28-37.

Smith, H.W. (1975). Strategies of Social Research: The Methodological Imagination. Englewood Cliffs, NJ: Prentice-Hall.

Tesch, R. (1990). Qualitative Research: Analysis Types & Software Tools. Bristol, PA: Falmer Press.

Weber, R.P. (1990). Basic Content Analysis. Newbury Park, CA: Sage Publications.

Hess_Theuvsen_discourse.pdf

Conceptualizing Path Dependence through Discourse Analysis:

The Case of Persistent Agricultural Policies

Hess, Sebastian; Kleinschmit, Daniela; Theuvsen, Ludwig; von Cramon-Taubadel, Stephan

Department for Agricultural Economics and Rural Development

Georg-August-University Göttingen

Corresponding author:

Sebastian Hess

Georg-August-University Göttingen

Department for Agricultural Economics and Rural Development

Platz der Göttinger Sieben 5

37073 Göttingen

email: [email protected]

Fax: 0551/39-4621

Tel.: 0551/39-4046

1

Conceptualizing Path Dependence through Discourse Analysis:

The Case of Persistent Agricultural Policies

Abstract

This paper introduces discourse analysis as a theoretical concept and an empirical methodology that may enable the endogenization of path creation and path breaking changes within conventional models of political path dependencies. Discourse analysis implies that specific elements within the political discourse heavily influence and predetermine the policy creation path and, therefore, must be taken into account when political path creation is analysed. Discourses themselves, however, are far too complex to be quantified. Instead, this paper proposes to trace individual story lines over time that may represent important elements of a specific discourse. Therefore, a brief analysis of the discourse underlying the restriction of seasonal farm workers from central and eastern European countries in Germany is presented in order to illustrate how dominant speakers and their story lines have been and currently are interacting to shape this policy. Keywords: Agricultural Policy, Path Dependencies, Discourse Analysis, Seasonal Farm Workers

1. Introduction

This paper proposes discourse analysis as a new concept to be integrated into the

framework of path dependence in order to reconstruct self-reinforcing feedback effects within

politics. We argue that discourse analysis presents a potentially fruitful theoretical model that

can be applied to empirical analysis.

Path dependencies within politics are marked by self-reinforcing feedback effects that alter

the costs of switching from one policy regime to another (for instance, Kay 2005). As a result

of such re-affirmative dynamic processes, politics and institutions (North 1990) may get

locked into situations that become, once in place, difficult to change. The Common

Agricultural Policy (CAP) of the EU has frequently been cited and analyzed as an almost

‘classical’ example in this regard (Ackrill and Kay 2006).

In economics as well as in political science, as Pierson (2000) points out in his

comprehensive comparison of the application of the path dependence framework in

economics and politics, the concept of path dependence corrects the ubiquity of claims about

efficient or functional elements in politics (Pierson 2004). Pierson (2000) further notes that

the political phenomena surrounding path dependencies are associated with far more

complexity and, due to a lack of easily measureable indicators such as prices and (cash-based)

costs, are far more difficult to analyze than cases of purely economic path dependencies (for

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instance, the frequently cited examples such as QWERTY keyboards, VHS videos etc. in the

field of economics of technology). Therefore, in the literature related to political science,

Pierson (2000; 2004) constitutes a rich body of analyses that identifies path dependencies and

explains why these dependencies exist within politics, yet without convincing and

theoretically deeply rooted explanations of the reasons why certain – potentially inefficient –

policies were introduced in the first place. In the economics literature, Dixit and Romer

(2006) survey the theoretical and empirical work that investigates whether and why

(inefficient) economic policies exist. The authors conclude with regard to the economic

literature that there are myriads of models that in most cases explain very specific, sometimes

even artificial circumstances under which certain (inefficient) policies exist and persist. Yet,

these models largely rely upon stylized and ad hoc assumptions about agents’ behaviour and

the constraints these agents face. Thus, up to now economic models do not allow for general

empirical or theoretical predictions of the conditions under which specific policies will

typically be introduced and are likely to persist. In other words, path dependency is

introduced and treated as largely exogenous in economic models, instead of being at the

center of a model’s focus, as the concept of path dependency would suggest.

Therefore, in economics as well as in political science the process of path dependence to

date largely constitutes a research field with a just emerging and still incomplete theoretical

framework (Garud and Karnøe 2001; Schreyögg, Sydow and Koch 2003). In addition, no

empirical methods have so far been widely used that would allow general predictions of the

causes and circumstances under which specific policies are introduced and the way they have

been introduced in reality, implying that especially the process of political path creation is not

well understood yet.

This paper, proposes discourse analysis as a new concept that should be integrated into the

framework of path dependence in order to reconstruct self-reinforcing feedback effects in

politics. Since empirical results are not available yet, discourse analysis is also introduced as a

methodological approach to empirically explore the processes of path creation and path

dependence1. We argue that discourse analysis may enable the use of qualitative as well as

quantitative methods to test hypotheses about key influential factors in the process of political

path creation.

Against this background, section 2 turns to agricultural polices which have for long been

described as especially inefficient. At the same time, many agricultural policies show

especially stubborn persistence over time and different political environments. This paper

1 The paper is the result of the first phase of the project “Agricultural policy between path dependence and path creation” financed by the DFG. This project started in July 2007, and later phases will focus on empirical applications.

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focusses on the example of a special agricultural policy in Germany, the regulation of

seasonal farm workers from central and eastern European countries (CEEC). We summarize

what established theoretical and empirical evidence can say about this policy, and why the

concept of path dependency is in this context promises to fill certain gaps in conventional

analysis. Section 3 introduces the concept of discourse analysis in connection with path

dependencies and outlines a methodological framework that describes how the explanatory

power of this concept could be empirically tested. Section 4 presents preliminary insights

derived from this perspective that are discussed in section 5, and section 6 concludes.

2. What Explains the Existence and Persistence of Inefficient Agricultural Policies? The

Case of Seasonal Farm Workers

In (agricultural) economics, rent-seeking behaviour (Krueger 1974) and the associated

activities of lobby groups often provide convincing explanations for the existence of

protectionist policies, which in turn have, in many instances, especially distortive effects

(Alston, Norton and Pardey 1995). Lobby groups aim at the redistribution of income in their

own favour and accordingly lobby actively within politics. Assuming utility maximizing

behaviour, the cost of the lobbying effort will be equal to or less than the volume of the actual

rent involved (Krueger 1974). In this context, agricultural policies have been analyzed by

economists as well as political scientists for a long time (for instance, Kay 2003), and may be

considered a classical example of redistributive policies that benefit the various farm lobby

groups involved (for instance, Tangermann 1976; Koester and von Cramon-Taubadel 1992;

Alston and James 2002).

From the rational choice perspective, politicians can be viewed as aiming to provide best

policies given various political constraints (for instance, pressure arising from the activities of

lobby groups, see the literature cited in Dixit and Romer 2006). Alternatively, politicians and

political institutions themselves can be seen as rent seekers (Olsen 1965: “stationary and

roving bandits”) with selfish preferences who are trying to maximize their own benefits rather

than being motivated by the best possible provision of public goods. In this context,

economists seem to be split with regard to the question whether the election process leads to a

selection of the “best” politicians in the long run, or whether elections constitute an institution

that introduces increasing returns and path dependencies into policy making (Pierson 2000).

Dixit and Romer (2006) provide a survey of alternative economic explanations for the

existence of inefficient economic polices, with many recent approaches establishing links

between institutional theories and game theory. However, the analysis of distortive market

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policies that are common in, for instance, agriculture describes the incidence of policy (Alston

and James 2002) typically as a failure to provide socially optimal outcomes due to some

redistribution of income in favour of certain lobby groups (Alston, Norton and Pardey 1995).

These redistributive policies typically create economic rents. Once an economic rent has

been created and is assigned to a group of beneficiaries, it can be argued that policy makers

may already have induced political path dependency since this rent creates a large potential

for self-reinforcement due to the fact that beneficiaries will be unwilling to give up their

privileges again (Krueger 1974). In other words, the assignment of rents to a group of

beneficiaries constitutes a self-reinforcing momentum (Pierson 2000) that will make the

existence of this rent in the future more likely than it has been in the past since it will strongly

motivate (and can fund) rent-seeking behaviour by the beneficiaries (Alston, Norton, Pardey

1995).

However, a closer look at different definitions of path dependency on the one hand, and

individual agricultural policy measures on the other, does not always clearly suggest that what

is observed in reality necessarily fulfils anything more than the broadest criteria of ‘path

dependency’ (e.g., not more than the general argument that ‘history matters’, Ackrill and Kay

2006). This is especially true if broad aggregates of various policies, such as the Common

Agricultural Policy of the EU, are investigated (Kay 2003). Therefore, a closer look at more

specific policy fields provides better opportunities for analyzing processes of path creation

and path dependence in the political sphere in more detail.

An example of a very specific, highly protective and very persistent agricultural policy in

Germany is the regulation of seasonal farm workers from central and eastern European

countries (CEEC) who work each year in German agriculture. Although it can be traced back

to the late 19th century, this policy does not seem to benefit farmers nor workers and is, at the

same time, a perennial source of tensions between lobbyists and politicians (Hess 2004). In de

facto, if not de jure, violation of the EU’s common market, Germany and Austria continue

restrict the employment of workers from new EU member states in agriculture and

neighbouring economic sectors. Germany and Austria are the only countries in the EU that

still apply this restrictive policy.

Under the current regulation for seasonal farm workers from CEEC in Germany, farmers

have to apply formally for a certain number of workers several months ahead of the harvest

season. Farmers have to prove that they really need these workers on their farms and that they

were unable to fill vacant positions with German unemployed persons. In addition, German

wages have to be paid under these seasonal contracts, and the workers’ housing conditions

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and working hours have to meet German standards. In general, farmers are currently granted

only 80% of the workers they have requested. Hence, in theory they are obliged to hire at least

20% of their seasonal workforce on the German labour market. In practice, however, German

workers are not able or willing to do the work in question. Therefore,, a 20% input restriction

is imposed on labour-intensive agricultural products in Germany, or, in other words, an input

quota equal to 80% of total seasonal farm labour demand is in place. German farmers are,

independent of the size of their farms, all equally restricted by this 20% cut of their labour

demand. If rent seeking were the key motivation for the existence of this policy, at least one

of the interest groups involved should clearly benefit in monetary terms. The following

analysis shows that this is in fact not the case.

Input quotas typically limit the competitive market output of a farm product (Alston,

Norten and Pardey 1995). They also reduce the factor price equalisation that would otherwise

take place as high wages for farm labour in Germany attract low-priced workers from CEECs.

This will, ceteris paribus, increase the price of labour as well as of the corresponding output

product(s). Seasonal farm workers in Germany (both Germans and those within-quota

workers from CEECs) clearly benefit through higher wages, while consumers of labour

intensive agricultural products clearly lose as a result of higher prices. The impact on farm

enterprises that produce the seasonal fruit and vegetable products is ambiguous; as both

output and input prices increase.

The political influence of seasonal workers from CEEC in Germany can be assumed to be

low. Furthermore, workers in CEEC who do not get in-quota positions in Germany lose as a

result of the policy. Hence, it is unlikely that this interest group has had an impact on the

introduction and persistence of this policy. Consumers typically have little voice in

agricultural market policy (price and trade policy measures) in the EU and specifically

Germany, being more concerned with questions of food quality (e.g. pesticide residues in

fruits and vegetables) than food prices. It turns out that farmers’ organizations are the

strongest political opponents of seasonal farm worker regulations in Germany and lobby very

actively against this policy. This indicates that of the two effects outlined above (increasing

output and increasing input prices), the latter dominates and that farmers would be better off

without the quota system.

German farm workers represented by the German labour union (“Industriegewerkschaft

Bauen Agrar Umwelt”, IG BAU) may fear incoming competitors who drive down wages.

Therefore, the union might have a strong incentive to lobby against seasonal farm workers

from CEEC. However, since Germans are typically not willing to take seasonal jobs, there is

6

no direct competition and, hence, German wages for year-round employees in agriculture will

not be affected by the wages paid for seasonal farm hands. Thus, no direct rent seeking effort

by German labour unions is likely to be the driving force behind the politically induced

reduction of farm labour migration. On the contrary, from the union’s perspective the CEEC

workers can be considered safeguards against societal pressure on union members to accept

low-paid, arduous seasonal jobs in agriculture.

In theory the quota on migrant farm labour from CEECs creates jobs for unemployed

Germans in the amount of 20% of total seasonal farm labour demand. It would be reasonable

to expect this group to have a vital interest in even more restrictive labour market protection

and to be the real beneficiary of the rent that is generated by this policy. Instead, experience

shows that the German labour administration initially had difficulties finding Germans who

were willing and able to take on this work. Only after special training programs and additional

monetary rewards were issued by the labour administration, were a few positions filled by

Germans. German farmers have frequently blamed policy makers for the resulting labour

shortage. The lack of motivation for unemployed Germans to apply for unoccupied jobs in

agriculture indicates that rent-seeking by this group is not a convincing explanation for the

persistence of an inefficient agricultural policy.

Land owners are also frequently identified as the ultimate beneficiaries of protective

agricultural policies. Although this is likely an important interest group with regard to the

market protection of crops that are especially land intensive, less than 5% of total farm land is

cultivated with seasonal, labour intensive crops in Germany (although these crops account for

about 50% of total sales from crops in Germany). Therefore, there are much more attractive

policy arenas for land owners to invest in lobby activities, for instance the emerging

extremely land intensive production of bio energies.

Taking into account all the arguments discussed before, it is obviously hard to identify any

specific interest group that clearly benefits in monetary terms from the existing policy that

reduces farm labour employment. Nevertheless, the policy persists, a fact that obviously

requires an alternative explanation.

Excursus: The History of Seasonal Farm Worker Policies in Germany

The history of Polish2 seasonal farm workers in German agriculture started more than 100

years ago under very similar circumstances as today.

2 Until 2006, more than 85% of seasonal farm workers in Germany came from Poland.

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During the 19th and early 20th century, large parts of the rural population in Northeast

Germany left for the newly established “boom areas” especially in the western part of the

country in order to find jobs in growing industries (“Landflucht”). The influx of Poles from

territories occupied by Austria and Russia was welcomed by farmers in the Northeast as

replacement farm hands, but it was not welcomed by the Prussian government which feared a

political destabilization due to the growing minority of foreigners permanently settling on

Prussian territory. In 1885, about 40,000 Polish farm workers and their families were expelled

from Prussia because of this fear (Herbert 2001).

At the same time farmers adopted labour intensive crops and, therefore, increased political

pressure to re-open the border. Simultaneously, massive irregular employment evolved. As a

compromise, the government introduced seasonal work permits for Poles around the year

1890. Workers were allowed to stay on German farms in the summer and fall, but had to

return home for the winter. This system of seasonal work permits was retained with minor

changes until 1914, and was accompanied by some 20% of irregular employment according to

a contemporary estimate (Herbert 2001).

After the end of WWI, the Poles were sent home again within a few months because the

administration intended to fill vacant positions with returning soldiers. However, many of

these soldiers had been employed in industry prior to the war and were not willing to take jobs

in agriculture. At the same time, existing working and housing conditions were regarded to be

“unacceptable” for Germans, and the new socialist government grudgingly allowed some

50,000 Poles to work seasonally in East German agriculture, giving in to farmers’ pressure

(Herbert 2001). The early 1920s mark the introduction of a political compromise concerning

seasonal labour that is still valid today: employment is strictly limited to the agricultural

sector and only allowed if no Germans are available for the jobs. At the same time attempts

were made to prevent farmers from paying foreigners less than the official wage for Germans

– an aim that can still be found nowadays in the official regulations for employment of

seasonal farm workers (Bundesanstalt 2002).

The late 1920s, however, mark a period when legal seasonal employment of foreigners

came to a halt due to very high domestic unemployment and a conservative shift in

government. By 1936, the employment rate had been improved to a level at which farm

workers again had become scarce and the Nazi administration again allowed a quota of

10,000 workers from Poland for the agricultural sector. This quota rose to 90,000 legal farm

workers by 1939, with significant irregular employment occurring due to an unemployment

rate in Poland of some 40% (Herbert 2001).During WWII, vacant positions in German

8

agriculture were mostly filled by forced labour such as prisoners of war or civilians from

occupied countries.

In the former West Germany, the fact that networks between Poles and German farmers had

already existed prior to 1989 or even 1945 seems to have played at least a minor role for the

establishment of new calculative farm labour networks in Germany. Obviously, there had

already been Poles working in West German agriculture and other industry sectors prior to

1989/90. These labourers had been staying legally as tourists (visa on request) in Germany

and were working mostly irregularly (“moonlightning”) (Cyrus 1993), but some were also

part of legal projects (project-tied workers) (Hönekopp 1997). However, in the case of

seasonal farm work there were no legal programs prior to 1991.

Since April 8, 1991, Polish citizens have been allowed to enter and stay in Germany for up

to three month without having to apply for a visa. However, work is strictly prohibited for

individuals without permits. When this system was introduced in 1991, about 100,000

requests by name were immediately submitted, which shows that informal networks must

have been established long before the seasonal work contracts were officially introduced

(Velling 1995).

Seasonal contracts for workers from CEE countries are limited to 3 months. In the late

1990s the labour administration tried to regulate and limit the employment of seasonal

workers from CEE countries with various restrictions that had to be removed partially only

few years later: By 1997, the total employment period of seasonal workers had been limited to

six months per year, but farmers could choose to spread these six months over the entire year

(Gerdes 2000). In 1997 a minimum employment of 30 hours per week and six hours a day

was introduced. Finally, in 1998, an attempt was made to limit the total number of seasonal

workers to some 180,000, and allow each farm no more than 85% of the seasonal workers that

it had in 1996. Farms that had started to plant labour intensive crops in 1997 and hence had no

seasonal workers employed in 1996, were exempted from this limitation. The imposition of a

quota of 180,000 contracts turned out to be insufficient to meet the needs of German

agriculture. During the years 2000 to 2003, a farm would usually get the requested number of

workers subject to the limitation that only 85% of these workers could be from CEECs; the

remaining positions had to be filled with Germans (Abrecht 2002; Bundesanstalt 2002).

In 2005, a new coalition of conservative and social democratic parties in Germany started

again a joint effort to restrict the number of farm workers by establishing the current legal

framework which limits the amount of seasonal workers per farm to 80% of the farm’s total

farm labour demand. This regulation had to be relaxed later since in regions with high

9

demand for seasonal farm labour and low domestic unemployment rate (south west Germany)

virtually no German farm workers could be found.

According to German law, farmers shall not receive any financial gain by employing CEEC

workers at wages that are below the German level (Cyrus 1993; Bundesanstalt 2002). Hence,

farmers are obliged to pay legal tariff wages that are negotiated between the farmers’

representatives and the German labour union for construction, agriculture and “environment”,

which includes gardening and landscaping. However, legal wages in these sectors are low and

enforcement is difficult. On the other hand, farmers have to provide housing etc. for seasonal

workers (Bundesanstalt 2002).

It is clear that farmers in Germany would have hired more CEEC workers for decades if

they had been allowed to. CEEC workers would likely have filled vacant positions and

unemployed Germans overall do not show much effort to apply for farm jobs and, thus, do not

regard CEEC workers as competitors for domestic jobs. The monetary rents involved in this

policy do not clearly benefit any German interest group, and the only beneficiaries due to

higher wages have little or no opportunity to lobby in Germany because they are not German

citizens and have no legal electoral vote. Clearly, a convincing economic explanation for the

existence and persistence of this policy is missing and, therefore, alternative approaches must

be considered.

The attempt of the German administration to regulate and limit seasonal farm worker

policies has exhibited similar patterns for more than a century and across political systems as

varied as monarchy, dictatorship and two democracies. Nevertheless, it would appear that this

policy could be changed at any time without damaging the interests of any lobby group.

Hence, one might argue that path dependencies should not exist. However, the German

administration has not only frequently returned to the concept of regulated seasonal farm

labour migration, but it also defends this political approach even today, when most other

European countries have already completely freed their labour markets to workers from the

new EU member states. The German administration (and policy makers) obviously seems to

be locked into a situation, where restrictive migration policies are still considered desirable

and unavoidable, although they have to be defended against the protest of almost all the

interest groups that are directly involved.

10

3. Explaining Path Dependence of Political Processes by Discourse Analysis

The example of the restriction of seasonal farm workers in Germany shows that economic

criteria such as rents created by a policy do not provide a comprehensive explanation of path

dependent political decisions. The basic assumption used in economic as well as in social

science theories3 that the capacity of actors to design and implement optimal solutions in the

sense of efficiency to the problems that confront them is not compatible with many aspects of

social and political behaviour and realities (Pierson 2004). There have to be different sources

of positive feedback processes in the political system. Therefore, for theoretically more

substantiated and empirically sound explanations of the reasons for path dependent processes,

the framework needs to be enriched by an alternative theoretical concept. This concept needs

to include reality constituting and designing factors such as discourses in policy analysis

(Nullmeier 2001). Discourse analysis is a rich theoretical concept which offers the

opportunity to understand social and political behaviour in a specific direction ex post and

which can also be applied to empirical analyses. The results of these analyses may in future

also help to identify path dependence ex ante.

3.1 The Concept of Discourse Analysis with Regard to Path Dependence

In a methodological sense, policy discourse analysis is a controlled analysis of politically

relevant (mass) texts with the goal of finding basic and extensive coherent areas which can be

regarded as the organising core of politics (Nullmeier 2001).

Different concepts of discourses are used in policy analysis (Keller and Viehöver 2006;

Kerchner 2006). This paper focuses on the Foucaultian perspective of discourse practices.

Foucault expects discourses to actively construct society along various dimensions and

hypothesizes interdependencies between the discursive practises of a society and its

institutions. Such practices, understood as texts, always draw upon and transform other

contemporary and historically prior texts. Any given type of discursive practice, thus, is

generated out of combinations of other analyses of collective knowledge orders and discursive

practices. Therefore, a discourse is a bounded “positive” field of statement accumulation

implying at the same time that other possible statements, questions, perspectives and

difficulties etc. are excluded. These exclusions can be consolidated by institutions (Link and

Link-Herr 1990). In this meaning discourses have a formative or constitutive power that

structures basic definitions and meanings that are later on taken for granted. The historical

aspect of discourses is important in forming the identities of subjects and objects (Howarth

3 The rational choice approach is often used in social sciences and features similar assumptions as in economic theories.

11

2002). In Foucault’s opinion, a discourse is a form of power by dominating the field in which

it was formed, fixing specific meanings to be employed in interpreting the social situation

(Fischer 2003). Therefore, discourse analysis aims at analyzing political and politically

relevant texts with the goal of finding the organizing cores of politics (Nullmeier 2001).

Linking the concept of discourse analysis with the framework of path dependence leads to

the assumption that discourses and their constitutive character can be seen as explanations for

self-reinforcing processes in politics because political discourses are always built on

historically prior texts so that the past strongly determines future political actions. The taken-

for-granted nature of definitions and meanings structured by discourses creates (psychological

and institutional) switching costs for policy makers and administrations.

Critiques may argue that this definition of discourse would never allow a change in politics,

something that is obviously in conflict with political reality. At this point and with regard to

policy changes, a specific character of discourses has to be discussed. As Fischer (2003)

argues, a most fundamental question discourse analysts have to ask is whether the used story

line of an issue contributes to a fixed ‘homogenized’ problem or whether it leads to the

opening up or ‘heterogenization’ of established discourses. In this sense the function of story

lines is to condense large amounts of factual information intermixed with normative

assumptions and value orientations that assign meaning to them (Fischer 2003). They are the

main mechanism for creating and maintaining the discursive order. Other authors define them

as “collective symbols” which are “cultural stereotypes”, used and established collectively

(Drews, Gerhard and Link 1985: 265). The two decisive characteristics of story lines are the

determination of rational but also emotional knowledge based on a simplifying nature. Linked

to this knowledge is a related logic and option of action. These characteristics are highlighted

especially in conflicting discourses when collective symbols can dramatize the interpretation

of a situation and at the same time produce the necessity to normalise this perceived situation.

Opening routines is facilitated by exogenous crises or shocks. At such points, events

especially highlighted by media and politics can have great impact on the direction and

quality of discourses (Jäger and Jäger 2007).

Since the breaking of existing paths requires external shocks in order to allow the necessary

mindful deviation (Garud and Karnøe 2001a), the mechanism of discourses and the

requirement for changing policy are concepts that can provide rich explanations for path

dependency, the breaking of paths and the creation of new ones well.

12

3.2 Discursive Policy Analysis: Methodological Opportunities

Existing rules and conventions constituting the social order are routinely reproduced and

reconfirmed in actual speech situations. It is not trivial to break up these routines (Hajer

1995). Discursive policy analysis conceptualizing path dependence has the task to uncover the

defining claims of a particular position. Therefore, it is necessary to examine the structure,

style and historical context of the arguments to understand why some modes of argumentation

serve effectively and justify specific actions, while others do not (Hajer 1995). Against this

background, the most promising starting point for applying discourse analysis as a concept of

path dependence is an analysis on the micro level. From a Foucaultian perspective, the goal

here is to uncover the ways discourses embedded in institutional practices function to

reproduce the existing power relationship (Fischer 2003). The discourse analysis can

contribute to the understanding of what the relevant utterances mean. This requires

approaching the institutional setting as an “argumentative field” in which statements are

made.

The concept of discourse analysis combines power and communication on a theoretical

meta-level. Therefore, the question is raised how the concept can be applied empirically.

Since discourse analysis has recently become very popular in qualitative analyses in political

science, empirical studies with a Foucaultian background predominate. However, there are

also quantitative analyses that reconstruct existing discourses. A first analysis with regard to

path dependence combined qualitative and quantitative analyses in order to mirror the

synchronical dimension of the BSE crisis as well as the diachronical dimension of agricultural

policy (Feindt, Kleinschmit and Theuvsen 2005).

Discourses take place at different levels: media, politics, science, literature, administration

etc. Identifying and explaining positive feedback processes in politics requires, of course, the

analysis of the political as well as the media discourse. While the first is a sign of political

behaviour, the latter provides a master forum including virtually everyone. It is “the major site

of political contest because all of the players in the policy process assume its pervasive

influence” (Marx Ferree et al. 2002, 10).

With regard to path dependence processes in politics, two main categories may help to

explain positive feedback processes as well as path breaking or path creation: actors and story

lines. Those who speak in the discourse represent the interests of collective actors, for

instance government, political parties, NGOs, labour unions etc. This position is very

powerful, especially in the media. These speakers have the chance to give their interpretative

pattern of a problem and, thus, actively shape the discourse, and they can be connected to the

13

used story lines. Thus, considering the diachronic dimension of the discourse, the prevalence

of certain speakers and story lines can be interpreted as path dependence, i.e. the supremacy

of a predominant mental model or frame of reference. On the other hand, considerable

changes in the composition of the speakers’ ensemble or the emergence of new ideas

underlying new story lines are indications of path breaking and path creation processes.

Analysing texts is a very important but not the only element of discourse analysis. It is

necessary to reflect the context behind the text, because the text is only a sign of discursive

activities. Therefore, a comprehensive discourse analysis also has to consider the policy

arrangement, such as the affected institutions, actors and their relationships with regard to

networks and power.

Figure 1 illustrates the role of texts and speeches (symbolized by asterisks “*”) within

various story lines, that in turn are all relevant for a specific discourse. The discourse leads

political decision makers to take certain actions and to implement certain policies. This

bundle of political actions results from a discourse’s stage at a certain point in time as well as

in previous stages and is represented by the black line in figure 1. If a discourse is clearly

dominant and supersedes alternative discourses, policies will be strongly influenced by the

taken-for-granted interpretations represented by this discourse. As a consequence, path

dependences can emerge. But the solid line also shows that path dependencies in politics do

not necessarily imply that a given set of policies remains completely unchanged. If the

relative strength of a certain story line is significantly altered by external shocks, for instance

an economic crisis, existing paths may break up and new paths may be created through the

imposition of a different mix of political actions.

14

Figure 1: Discourses, Story Lines and Path Dependency in Politics. Source: Own based on Schreyögg, Sydow and Koch (2003).

No. of events, restrictiveness

of policy measure

Note: The solid line represents the changing path of a specific policy. "*" represents individual events

as they appear in (mass) media texts. Once a political path has been created, minor adjustments are still possible though the path remains in principle until new small events enable path break and new path creation.

Story line 1 * *

* * *

*

* * * *

* * * *

* * * * * *

* * * * *

* * *

** * *

Story line 2 * *

* * * * * * * * * *

Story line 3 * *

* * *

* * *

* * * *

* * * ** ***

D iscourses

Story line 4 * *

* * *

* * *

* * * *

* * * *

* * *

*

* * *

*

* * * * * *

* * * * *

*

Time (t): 1 2 3 4 5 6 7 … t

Path creation Path dependency

Path break/

path creation

New path dependency

3.3 Illustrative Example: Restricting Seasonal Farm Workers: Expected Results of

Discursive Analysis

As mentioned above, no results of empirical analyses are available so far. But, nevertheless,

at this point of the research project the restrictive policies against seasonal farm workers will

be used as an illustrative example to visualize what results can be expected from a discourse

analysis.

With regard to the argument that historical aspects of discourses are important in forming

the identities of subjects and objects (Howarth 2002), the example of the restriction of

seasonal farm workers should be analysed in a diachronical dimension.

One of the first texts on the restriction of seasonal farm workers is part of a letter written by

Chancellor Bismarck in February 1885 In which he claimed that even if agriculture were the

most important economic sector of society, it is the lesser evil that this sector would lack

manpower than that the state and the future would have to suffer (after Herbert 2001, 17).

This statement is part of a discourse on the political level. It reveals the story line of the idea

of nationality which is threatened by foreigners. The idea of nationality in this regard is

strongly combined with the aspect that work is a value in itself. Therefore, foreign workers

are seen as a danger that takes jobs that are needed by domestic workers.

15

The discourse of unemployment is one of the prevailing discourses in Germany. It includes

strong emotions which are often used in (conflicting) political as well as media discourses.

This dominant story line is backed up by a second one. In this second story line it is claimed

that unemployed people who receive money from the state without working for it are

defrauding the social security system. The implication is that unemployed people should be

forced to do work on the farms instead of seasonal farm workers: “Obvious refusal of work

should directly lead to a reduction of benefits.” (Süddeutsche Zeitung, August 10, 2006).

Despite their very different basis, both story lines support the restriction of seasonal farm

workers.

A third conflicting story line which can be currently identified is the suffering sector of

agriculture. A prevailing and often used argument of agricultural actors is that harvesting is

not manageable due to a lack of manpower: “German farmers need help from East Europe”

(AFP, April 13, 2007) is a typical dictum.

Recently the former two story lines have dominated the discourse on seasonal farm

workers. Following the definition of Foucault, the discourse practices are not independent of

the social situation. Institutions resulting from the prior historical discourse on seasonal farm

workers, such as the government bureau that administers the quota system, take (direct or

indirect) part in the discourse with the goal of preserving the status quo. But other powerful

actors are also taking part in this discourse, for example the German federal ministry of

agriculture and the labour union of agriculture (IG BAU). In this regard the discourse is

already a result of power, but it also constitutes power. Discourse analyses can reconstruct the

speech situation and, therefore, reveal the power relations in the discourse. Analysing the

discourse of the restriction of seasonal farm workers will make it possible to identify

dominant speakers and their story lines. Thus, the discourses can provide an explanation for

path dependent processes, in this case in agricultural policy.

5. Conclusions

Conventional models from economic as well as political sciences explain path dependency

of policies by looking back and identifying exogenous elements that cause, shape, and

constitute a specific path dependency. A typical explanation refers to the rents generated by a

specific policy, and the rent-seeking behaviour of stakeholders who benefiting from this

policy. This paper has introduced discourse analysis as a theoretical concept and an empirical

methodology that may enable the endogenization of path creation, path dependencies and path

breaking in the field of policy analysis.

16

Discourse analysis highlights that specific elements in a political discourse heavily

influence and predetermine the policy creation path and, therefore, must be taken into account

when political path dependencies are analysed. Discourses are too complex to be quantified in

their entirety. Nevertheless, if story lines of (mass) media and political as well as scientific

texts are considered as representatives of the underlying discourses within a society at a

certain point in time, and if these story lines can be framed and coded in a meaningful way,

then the absolute and relative intensity of certain story lines over time can be quantified and

assessed empirically. Political path creation can be modelled as a process that is driven by

discourses after they have been approximated in this manner.

As an illustrative example how discourse analysis can be linked with the concept of path

dependence in politics, this paper has chosen the policies applied by the German

administration in order to restrict the employment of seasonal farm workers from CEEC in

Germany. The existence of this farm labour policy cannot readily be explained by economic

activities that would benefit one or the other interest group in monetary terms. Instead, the

general discourse of unemployment in Germany, and various story lines within this discourse,

explain much better the underlying forces that have shaped policy vis-à-vis seasonal farm

labour over time. A quantification of these story lines remains to be done and will enable

empirical assessments of the question how changes within this discourse over time have

changed the policy path. In combination with conventional policy analysis, this method

appears to be a promising complement that will allow to explain why some policies turn out

to be path dependent and how they become path dependent, and whether the discourses

around certain policies can be approximated reasonably well through the qualitative and/or

quantitative reconstruction of the speaking actors and corresponding story lines.

References

Ackrill, R., Kay, A. (2006). Historical-institutionalist Perspectives on the Development of the EU Budget System. Journal of European Public Policy. 13:1, January, pp. 113-133. AFP, April 13th, 2007. Bauernverband: Deutsche Bauern auf osteuropäische Hilfe angewiesen. Alston, J., James, J. (2002). The Incidence of Agricultural Policy. In: Gardener, B., Rausser, G. (eds.): Handbook of Agricultural Economics. Volume 2B Agricultural and Food Policy. Amsterdam, Elsevier: pp. 1689-1749. Alston, J., Norton, G., Pardey, P. (1995). Science under Scarcity. Ithaca, Cornell University Press. Bundesanstalt für Arbeit (2002). Merkblatt für Arbeitgeber zur Vermittlung und Beschäftigung ausländischer Saisonarbeitnehmer und Schaustellergehilfen und Hinweise zum

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Ausfüllen der Einstellungszusage/des Arbeitsvertrages (December 2002). Bonn, Zentralstelle für Arbeitsvermittlung. Cyrus, N. (1993). German Gates of Entry and Polish Migration – The Making of a Recent System of Labour Circulation. www.polskarada.de/psc.htm, download 15.12.2000. Dixit, A., Romer, T. (2006). Political Explanations of Inefficient Economic Policies – An Overview of Some Theoretical and Empirical Literature. Prepared for Presentation at IIPF conference “Public Finance: Fifty Years of the Second Best – and Beyond,” in Paphos, Cyprus. Drews, A., Gerhard, U., Link, J. (1985): Moderne Kollektivsymbolik. Eine diskurstheoretisch orientierte Einführung mit Auswahlbibliographie. In: Internationales Archiv für Sozialge- schichte der deutschen Literatur, 1. Sonderheft Forschungsreferate, pp. 256-375. Feindt, P., Kleinschmit, D., Theuvsen, L. (2005): Path Dependence and Creation in Agricultural Policy: Using Media Analysis for Exploring the Emergence of a New Dominant Political Paradigm. Paper accepted for presentation at the 21st EGOS Colloquium, Berlin, 30.6. - 2.7. 2005. Fischer, F. (2003): Reframing Public Policy: Discursive Politics and Deliberative Practices. Oxford University Press. Oxford.

Garud, R., Karnøe, P. (Eds.) (2001): Path Creation as a Process of Mindful Deviation. Mahwah, NJ – London.

Garud, R., Karnøe, P. (2001b): Path Creation as a Process of Mindful Deviation. In: Garud, R., P. Karnøe (Eds.): Path Dependence and Creation. Mahwah, NJ – London, pp. 1-38. Gerdes, G. (2000). Bedeutung der Arbeitskräftewanderung aus Mittel- und Osteuropa für den deutschen Agrarsektor. Kiel, Christian-Albrechts-Universität, Agrar- und Ernährungswissen- schaftliche Fakultät: 267. Hajer, M. (1995): The Politics of Environmental Discourse. Oxford Univ. Press. Oxford. Herbert, U. (2001). Geschichte der Ausländerpolitik in Deutschland – Saisonarbeiter, Zwangsarbeiter, Gastarbeiter, Flüchtlinge. Munich, Beck. Howarth, D. (2000): Discourse. Open University Press. Buckingham. Hönekopp, E. (1997). Labour Migration to Germany from Central and Eastern Europe – Old and New Trends. IAB Labor Market Topics (23). Hess, S. (2004). Die Beschäftigung mittel-und osteuropäischer Saisonarbeitskräfte in der deutschen Landwirtschaft, Berichte über Landwirtschaft, Band 82(4), December 2004, pp. 602-627. Jäger, M., Jäger, S. (2007): Deutungskämpfe. Theorie und Praxis kritischer Diskursanalyse. VS Publisher. Wiesbaden.

Kerchner, B. (2006): Diskursanalyse in der Politikwissenschaft. Ein Forschungsüberblick. In: Kerchner, B., Schneider, S. (Eds.): Foucault: Diskursanalyse der Politik: Eine Einführung. – VS-Verlag. Wiesbaden. pp. 33-67. Koester, U., von Cramon-Taubadel, S. (1992). Agrarreform ohne Ende? Wirtschaftsdienst, Vol. 72, pp. 355-361. Marx Ferree, M, Gamson, W., Gerhards, J., Rucht, D. (2002): Shaping Abortion Discourse. Democracy and the Public Sphere in Germany and the United States. Cambridge Univ. Press. Cambridge.

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Nullmeier, F. (2001): Politikwissenschaften auf dem Weg zur Diskursanalyse? In: Keller, R., Hirseland, A., Schneider, W. (Eds.): Handbuch Sozialwissenschaftliche Diskursanalyse. Theorien und Methoden. Bd. 1. Leske + Budrich, Opladen, pp. 285-311. Kay, A. (2003). Path dependency and the CAP. Journal of European Public Policy, Vol. 10(3), pp. 405-420. Kay, A. (2005): A Critique of the Use of Path Dependency in Policy Studies, In: Public Administration Vol. 83, No 3, pp. 553-571.

Keller, R., Viehöver, W. (2006): Diskursanalyse. In: Behnke, J.; Gschwend, T, Schindler, D. (Eds): Methoden der Politikwissenschaft. Neuere qualitative und quantitative Analyseverfah- ren. Nomos: Baden-Baden, pp. 103-111.

Kerchner, B. (2006): Diskursanalyse in der Politikwissenschaft. Ein Forschungsüberblick. In: Kerchner, B., Schneider, S. (Eds.): Foucault: Diskursanalyse der Politik: Eine Einführung. – VS-Verlag. Wiesbaden, pp. 33-67. Krueger, A. (1974). The political economy of the rent-seeking society. The American Economic Review, Vol. 64(3), pp. 291-303. Link, J., Link-Herr, U. (1990): Diskurs- Interdiskurs und Literaturanalyse. In: LiLi 77, pp. 88- 99. Olsen, M. (1965). The Logic of Collective Action. Cambridge, MA. Harvard University Press. Pierson, P. (2000). Increasing Returns, Path Dependence, and the Study of Politics. American Political Science Review, Vol. 94(2), pp. 251-267. Pierson, P. (2004): Politics in Time. History, Institutions, and Social Analysis. Princeton Univ. Press. Princeton/Oxford.

Schreyögg, G., Sydow, J., Koch, J. (2003). Organisatorische Pfade – Von der Pfadabhängig- keit zur Pfadkreation? In: Schreyögg, G. and J. Sydow (eds.): Strategische Pfade und Prozes- se. Wiesbaden, Gabler, pp. 257-294. Süddeutsche Zeitung, Issue of August 10th, 2006. Erntearbeit ist oft ein Knochenjob, p.21 Tangermann, S. (1976). Entwicklung von Produktion, Faktoreinsatz und Wertschöpfung in der deutschen Landwirtschaft seit 1950/51. In: Agrarwirtschaft 25, pp. 154-164. Velling, J. (1995). Immigration und Arbeitsmarkt: Eine empirische Analyse für die Bundes- republik Deutschland. Baden-Baden, Nomos.

Hess_Theuvsen_discourse-1-.pdf

Conceptualizing Path Dependence through Discourse Analysis:

The Case of Persistent Agricultural Policies

Hess, Sebastian; Kleinschmit, Daniela; Theuvsen, Ludwig; von Cramon-Taubadel, Stephan

Department for Agricultural Economics and Rural Development

Georg-August-University Göttingen

Corresponding author:

Sebastian Hess

Georg-August-University Göttingen

Department for Agricultural Economics and Rural Development

Platz der Göttinger Sieben 5

37073 Göttingen

email: [email protected]

Fax: 0551/39-4621

Tel.: 0551/39-4046

1

Conceptualizing Path Dependence through Discourse Analysis:

The Case of Persistent Agricultural Policies

Abstract

This paper introduces discourse analysis as a theoretical concept and an empirical methodology that may enable the endogenization of path creation and path breaking changes within conventional models of political path dependencies. Discourse analysis implies that specific elements within the political discourse heavily influence and predetermine the policy creation path and, therefore, must be taken into account when political path creation is analysed. Discourses themselves, however, are far too complex to be quantified. Instead, this paper proposes to trace individual story lines over time that may represent important elements of a specific discourse. Therefore, a brief analysis of the discourse underlying the restriction of seasonal farm workers from central and eastern European countries in Germany is presented in order to illustrate how dominant speakers and their story lines have been and currently are interacting to shape this policy. Keywords: Agricultural Policy, Path Dependencies, Discourse Analysis, Seasonal Farm Workers

1. Introduction

This paper proposes discourse analysis as a new concept to be integrated into the

framework of path dependence in order to reconstruct self-reinforcing feedback effects within

politics. We argue that discourse analysis presents a potentially fruitful theoretical model that

can be applied to empirical analysis.

Path dependencies within politics are marked by self-reinforcing feedback effects that alter

the costs of switching from one policy regime to another (for instance, Kay 2005). As a result

of such re-affirmative dynamic processes, politics and institutions (North 1990) may get

locked into situations that become, once in place, difficult to change. The Common

Agricultural Policy (CAP) of the EU has frequently been cited and analyzed as an almost

‘classical’ example in this regard (Ackrill and Kay 2006).

In economics as well as in political science, as Pierson (2000) points out in his

comprehensive comparison of the application of the path dependence framework in

economics and politics, the concept of path dependence corrects the ubiquity of claims about

efficient or functional elements in politics (Pierson 2004). Pierson (2000) further notes that

the political phenomena surrounding path dependencies are associated with far more

complexity and, due to a lack of easily measureable indicators such as prices and (cash-based)

costs, are far more difficult to analyze than cases of purely economic path dependencies (for

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instance, the frequently cited examples such as QWERTY keyboards, VHS videos etc. in the

field of economics of technology). Therefore, in the literature related to political science,

Pierson (2000; 2004) constitutes a rich body of analyses that identifies path dependencies and

explains why these dependencies exist within politics, yet without convincing and

theoretically deeply rooted explanations of the reasons why certain – potentially inefficient –

policies were introduced in the first place. In the economics literature, Dixit and Romer

(2006) survey the theoretical and empirical work that investigates whether and why

(inefficient) economic policies exist. The authors conclude with regard to the economic

literature that there are myriads of models that in most cases explain very specific, sometimes

even artificial circumstances under which certain (inefficient) policies exist and persist. Yet,

these models largely rely upon stylized and ad hoc assumptions about agents’ behaviour and

the constraints these agents face. Thus, up to now economic models do not allow for general

empirical or theoretical predictions of the conditions under which specific policies will

typically be introduced and are likely to persist. In other words, path dependency is

introduced and treated as largely exogenous in economic models, instead of being at the

center of a model’s focus, as the concept of path dependency would suggest.

Therefore, in economics as well as in political science the process of path dependence to

date largely constitutes a research field with a just emerging and still incomplete theoretical

framework (Garud and Karnøe 2001; Schreyögg, Sydow and Koch 2003). In addition, no

empirical methods have so far been widely used that would allow general predictions of the

causes and circumstances under which specific policies are introduced and the way they have

been introduced in reality, implying that especially the process of political path creation is not

well understood yet.

This paper, proposes discourse analysis as a new concept that should be integrated into the

framework of path dependence in order to reconstruct self-reinforcing feedback effects in

politics. Since empirical results are not available yet, discourse analysis is also introduced as a

methodological approach to empirically explore the processes of path creation and path

dependence1. We argue that discourse analysis may enable the use of qualitative as well as

quantitative methods to test hypotheses about key influential factors in the process of political

path creation.

Against this background, section 2 turns to agricultural polices which have for long been

described as especially inefficient. At the same time, many agricultural policies show

especially stubborn persistence over time and different political environments. This paper

1 The paper is the result of the first phase of the project “Agricultural policy between path dependence and path creation” financed by the DFG. This project started in July 2007, and later phases will focus on empirical applications.

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focusses on the example of a special agricultural policy in Germany, the regulation of

seasonal farm workers from central and eastern European countries (CEEC). We summarize

what established theoretical and empirical evidence can say about this policy, and why the

concept of path dependency is in this context promises to fill certain gaps in conventional

analysis. Section 3 introduces the concept of discourse analysis in connection with path

dependencies and outlines a methodological framework that describes how the explanatory

power of this concept could be empirically tested. Section 4 presents preliminary insights

derived from this perspective that are discussed in section 5, and section 6 concludes.

2. What Explains the Existence and Persistence of Inefficient Agricultural Policies? The

Case of Seasonal Farm Workers

In (agricultural) economics, rent-seeking behaviour (Krueger 1974) and the associated

activities of lobby groups often provide convincing explanations for the existence of

protectionist policies, which in turn have, in many instances, especially distortive effects

(Alston, Norton and Pardey 1995). Lobby groups aim at the redistribution of income in their

own favour and accordingly lobby actively within politics. Assuming utility maximizing

behaviour, the cost of the lobbying effort will be equal to or less than the volume of the actual

rent involved (Krueger 1974). In this context, agricultural policies have been analyzed by

economists as well as political scientists for a long time (for instance, Kay 2003), and may be

considered a classical example of redistributive policies that benefit the various farm lobby

groups involved (for instance, Tangermann 1976; Koester and von Cramon-Taubadel 1992;

Alston and James 2002).

From the rational choice perspective, politicians can be viewed as aiming to provide best

policies given various political constraints (for instance, pressure arising from the activities of

lobby groups, see the literature cited in Dixit and Romer 2006). Alternatively, politicians and

political institutions themselves can be seen as rent seekers (Olsen 1965: “stationary and

roving bandits”) with selfish preferences who are trying to maximize their own benefits rather

than being motivated by the best possible provision of public goods. In this context,

economists seem to be split with regard to the question whether the election process leads to a

selection of the “best” politicians in the long run, or whether elections constitute an institution

that introduces increasing returns and path dependencies into policy making (Pierson 2000).

Dixit and Romer (2006) provide a survey of alternative economic explanations for the

existence of inefficient economic polices, with many recent approaches establishing links

between institutional theories and game theory. However, the analysis of distortive market

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policies that are common in, for instance, agriculture describes the incidence of policy (Alston

and James 2002) typically as a failure to provide socially optimal outcomes due to some

redistribution of income in favour of certain lobby groups (Alston, Norton and Pardey 1995).

These redistributive policies typically create economic rents. Once an economic rent has

been created and is assigned to a group of beneficiaries, it can be argued that policy makers

may already have induced political path dependency since this rent creates a large potential

for self-reinforcement due to the fact that beneficiaries will be unwilling to give up their

privileges again (Krueger 1974). In other words, the assignment of rents to a group of

beneficiaries constitutes a self-reinforcing momentum (Pierson 2000) that will make the

existence of this rent in the future more likely than it has been in the past since it will strongly

motivate (and can fund) rent-seeking behaviour by the beneficiaries (Alston, Norton, Pardey

1995).

However, a closer look at different definitions of path dependency on the one hand, and

individual agricultural policy measures on the other, does not always clearly suggest that what

is observed in reality necessarily fulfils anything more than the broadest criteria of ‘path

dependency’ (e.g., not more than the general argument that ‘history matters’, Ackrill and Kay

2006). This is especially true if broad aggregates of various policies, such as the Common

Agricultural Policy of the EU, are investigated (Kay 2003). Therefore, a closer look at more

specific policy fields provides better opportunities for analyzing processes of path creation

and path dependence in the political sphere in more detail.

An example of a very specific, highly protective and very persistent agricultural policy in

Germany is the regulation of seasonal farm workers from central and eastern European

countries (CEEC) who work each year in German agriculture. Although it can be traced back

to the late 19th century, this policy does not seem to benefit farmers nor workers and is, at the

same time, a perennial source of tensions between lobbyists and politicians (Hess 2004). In de

facto, if not de jure, violation of the EU’s common market, Germany and Austria continue

restrict the employment of workers from new EU member states in agriculture and

neighbouring economic sectors. Germany and Austria are the only countries in the EU that

still apply this restrictive policy.

Under the current regulation for seasonal farm workers from CEEC in Germany, farmers

have to apply formally for a certain number of workers several months ahead of the harvest

season. Farmers have to prove that they really need these workers on their farms and that they

were unable to fill vacant positions with German unemployed persons. In addition, German

wages have to be paid under these seasonal contracts, and the workers’ housing conditions

5

and working hours have to meet German standards. In general, farmers are currently granted

only 80% of the workers they have requested. Hence, in theory they are obliged to hire at least

20% of their seasonal workforce on the German labour market. In practice, however, German

workers are not able or willing to do the work in question. Therefore,, a 20% input restriction

is imposed on labour-intensive agricultural products in Germany, or, in other words, an input

quota equal to 80% of total seasonal farm labour demand is in place. German farmers are,

independent of the size of their farms, all equally restricted by this 20% cut of their labour

demand. If rent seeking were the key motivation for the existence of this policy, at least one

of the interest groups involved should clearly benefit in monetary terms. The following

analysis shows that this is in fact not the case.

Input quotas typically limit the competitive market output of a farm product (Alston,

Norten and Pardey 1995). They also reduce the factor price equalisation that would otherwise

take place as high wages for farm labour in Germany attract low-priced workers from CEECs.

This will, ceteris paribus, increase the price of labour as well as of the corresponding output

product(s). Seasonal farm workers in Germany (both Germans and those within-quota

workers from CEECs) clearly benefit through higher wages, while consumers of labour

intensive agricultural products clearly lose as a result of higher prices. The impact on farm

enterprises that produce the seasonal fruit and vegetable products is ambiguous; as both

output and input prices increase.

The political influence of seasonal workers from CEEC in Germany can be assumed to be

low. Furthermore, workers in CEEC who do not get in-quota positions in Germany lose as a

result of the policy. Hence, it is unlikely that this interest group has had an impact on the

introduction and persistence of this policy. Consumers typically have little voice in

agricultural market policy (price and trade policy measures) in the EU and specifically

Germany, being more concerned with questions of food quality (e.g. pesticide residues in

fruits and vegetables) than food prices. It turns out that farmers’ organizations are the

strongest political opponents of seasonal farm worker regulations in Germany and lobby very

actively against this policy. This indicates that of the two effects outlined above (increasing

output and increasing input prices), the latter dominates and that farmers would be better off

without the quota system.

German farm workers represented by the German labour union (“Industriegewerkschaft

Bauen Agrar Umwelt”, IG BAU) may fear incoming competitors who drive down wages.

Therefore, the union might have a strong incentive to lobby against seasonal farm workers

from CEEC. However, since Germans are typically not willing to take seasonal jobs, there is

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no direct competition and, hence, German wages for year-round employees in agriculture will

not be affected by the wages paid for seasonal farm hands. Thus, no direct rent seeking effort

by German labour unions is likely to be the driving force behind the politically induced

reduction of farm labour migration. On the contrary, from the union’s perspective the CEEC

workers can be considered safeguards against societal pressure on union members to accept

low-paid, arduous seasonal jobs in agriculture.

In theory the quota on migrant farm labour from CEECs creates jobs for unemployed

Germans in the amount of 20% of total seasonal farm labour demand. It would be reasonable

to expect this group to have a vital interest in even more restrictive labour market protection

and to be the real beneficiary of the rent that is generated by this policy. Instead, experience

shows that the German labour administration initially had difficulties finding Germans who

were willing and able to take on this work. Only after special training programs and additional

monetary rewards were issued by the labour administration, were a few positions filled by

Germans. German farmers have frequently blamed policy makers for the resulting labour

shortage. The lack of motivation for unemployed Germans to apply for unoccupied jobs in

agriculture indicates that rent-seeking by this group is not a convincing explanation for the

persistence of an inefficient agricultural policy.

Land owners are also frequently identified as the ultimate beneficiaries of protective

agricultural policies. Although this is likely an important interest group with regard to the

market protection of crops that are especially land intensive, less than 5% of total farm land is

cultivated with seasonal, labour intensive crops in Germany (although these crops account for

about 50% of total sales from crops in Germany). Therefore, there are much more attractive

policy arenas for land owners to invest in lobby activities, for instance the emerging

extremely land intensive production of bio energies.

Taking into account all the arguments discussed before, it is obviously hard to identify any

specific interest group that clearly benefits in monetary terms from the existing policy that

reduces farm labour employment. Nevertheless, the policy persists, a fact that obviously

requires an alternative explanation.

Excursus: The History of Seasonal Farm Worker Policies in Germany

The history of Polish2 seasonal farm workers in German agriculture started more than 100

years ago under very similar circumstances as today.

2 Until 2006, more than 85% of seasonal farm workers in Germany came from Poland.

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During the 19th and early 20th century, large parts of the rural population in Northeast

Germany left for the newly established “boom areas” especially in the western part of the

country in order to find jobs in growing industries (“Landflucht”). The influx of Poles from

territories occupied by Austria and Russia was welcomed by farmers in the Northeast as

replacement farm hands, but it was not welcomed by the Prussian government which feared a

political destabilization due to the growing minority of foreigners permanently settling on

Prussian territory. In 1885, about 40,000 Polish farm workers and their families were expelled

from Prussia because of this fear (Herbert 2001).

At the same time farmers adopted labour intensive crops and, therefore, increased political

pressure to re-open the border. Simultaneously, massive irregular employment evolved. As a

compromise, the government introduced seasonal work permits for Poles around the year

1890. Workers were allowed to stay on German farms in the summer and fall, but had to

return home for the winter. This system of seasonal work permits was retained with minor

changes until 1914, and was accompanied by some 20% of irregular employment according to

a contemporary estimate (Herbert 2001).

After the end of WWI, the Poles were sent home again within a few months because the

administration intended to fill vacant positions with returning soldiers. However, many of

these soldiers had been employed in industry prior to the war and were not willing to take jobs

in agriculture. At the same time, existing working and housing conditions were regarded to be

“unacceptable” for Germans, and the new socialist government grudgingly allowed some

50,000 Poles to work seasonally in East German agriculture, giving in to farmers’ pressure

(Herbert 2001). The early 1920s mark the introduction of a political compromise concerning

seasonal labour that is still valid today: employment is strictly limited to the agricultural

sector and only allowed if no Germans are available for the jobs. At the same time attempts

were made to prevent farmers from paying foreigners less than the official wage for Germans

– an aim that can still be found nowadays in the official regulations for employment of

seasonal farm workers (Bundesanstalt 2002).

The late 1920s, however, mark a period when legal seasonal employment of foreigners

came to a halt due to very high domestic unemployment and a conservative shift in

government. By 1936, the employment rate had been improved to a level at which farm

workers again had become scarce and the Nazi administration again allowed a quota of

10,000 workers from Poland for the agricultural sector. This quota rose to 90,000 legal farm

workers by 1939, with significant irregular employment occurring due to an unemployment

rate in Poland of some 40% (Herbert 2001).During WWII, vacant positions in German

8

agriculture were mostly filled by forced labour such as prisoners of war or civilians from

occupied countries.

In the former West Germany, the fact that networks between Poles and German farmers had

already existed prior to 1989 or even 1945 seems to have played at least a minor role for the

establishment of new calculative farm labour networks in Germany. Obviously, there had

already been Poles working in West German agriculture and other industry sectors prior to

1989/90. These labourers had been staying legally as tourists (visa on request) in Germany

and were working mostly irregularly (“moonlightning”) (Cyrus 1993), but some were also

part of legal projects (project-tied workers) (Hönekopp 1997). However, in the case of

seasonal farm work there were no legal programs prior to 1991.

Since April 8, 1991, Polish citizens have been allowed to enter and stay in Germany for up

to three month without having to apply for a visa. However, work is strictly prohibited for

individuals without permits. When this system was introduced in 1991, about 100,000

requests by name were immediately submitted, which shows that informal networks must

have been established long before the seasonal work contracts were officially introduced

(Velling 1995).

Seasonal contracts for workers from CEE countries are limited to 3 months. In the late

1990s the labour administration tried to regulate and limit the employment of seasonal

workers from CEE countries with various restrictions that had to be removed partially only

few years later: By 1997, the total employment period of seasonal workers had been limited to

six months per year, but farmers could choose to spread these six months over the entire year

(Gerdes 2000). In 1997 a minimum employment of 30 hours per week and six hours a day

was introduced. Finally, in 1998, an attempt was made to limit the total number of seasonal

workers to some 180,000, and allow each farm no more than 85% of the seasonal workers that

it had in 1996. Farms that had started to plant labour intensive crops in 1997 and hence had no

seasonal workers employed in 1996, were exempted from this limitation. The imposition of a

quota of 180,000 contracts turned out to be insufficient to meet the needs of German

agriculture. During the years 2000 to 2003, a farm would usually get the requested number of

workers subject to the limitation that only 85% of these workers could be from CEECs; the

remaining positions had to be filled with Germans (Abrecht 2002; Bundesanstalt 2002).

In 2005, a new coalition of conservative and social democratic parties in Germany started

again a joint effort to restrict the number of farm workers by establishing the current legal

framework which limits the amount of seasonal workers per farm to 80% of the farm’s total

farm labour demand. This regulation had to be relaxed later since in regions with high

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demand for seasonal farm labour and low domestic unemployment rate (south west Germany)

virtually no German farm workers could be found.

According to German law, farmers shall not receive any financial gain by employing CEEC

workers at wages that are below the German level (Cyrus 1993; Bundesanstalt 2002). Hence,

farmers are obliged to pay legal tariff wages that are negotiated between the farmers’

representatives and the German labour union for construction, agriculture and “environment”,

which includes gardening and landscaping. However, legal wages in these sectors are low and

enforcement is difficult. On the other hand, farmers have to provide housing etc. for seasonal

workers (Bundesanstalt 2002).

It is clear that farmers in Germany would have hired more CEEC workers for decades if

they had been allowed to. CEEC workers would likely have filled vacant positions and

unemployed Germans overall do not show much effort to apply for farm jobs and, thus, do not

regard CEEC workers as competitors for domestic jobs. The monetary rents involved in this

policy do not clearly benefit any German interest group, and the only beneficiaries due to

higher wages have little or no opportunity to lobby in Germany because they are not German

citizens and have no legal electoral vote. Clearly, a convincing economic explanation for the

existence and persistence of this policy is missing and, therefore, alternative approaches must

be considered.

The attempt of the German administration to regulate and limit seasonal farm worker

policies has exhibited similar patterns for more than a century and across political systems as

varied as monarchy, dictatorship and two democracies. Nevertheless, it would appear that this

policy could be changed at any time without damaging the interests of any lobby group.

Hence, one might argue that path dependencies should not exist. However, the German

administration has not only frequently returned to the concept of regulated seasonal farm

labour migration, but it also defends this political approach even today, when most other

European countries have already completely freed their labour markets to workers from the

new EU member states. The German administration (and policy makers) obviously seems to

be locked into a situation, where restrictive migration policies are still considered desirable

and unavoidable, although they have to be defended against the protest of almost all the

interest groups that are directly involved.

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3. Explaining Path Dependence of Political Processes by Discourse Analysis

The example of the restriction of seasonal farm workers in Germany shows that economic

criteria such as rents created by a policy do not provide a comprehensive explanation of path

dependent political decisions. The basic assumption used in economic as well as in social

science theories3 that the capacity of actors to design and implement optimal solutions in the

sense of efficiency to the problems that confront them is not compatible with many aspects of

social and political behaviour and realities (Pierson 2004). There have to be different sources

of positive feedback processes in the political system. Therefore, for theoretically more

substantiated and empirically sound explanations of the reasons for path dependent processes,

the framework needs to be enriched by an alternative theoretical concept. This concept needs

to include reality constituting and designing factors such as discourses in policy analysis

(Nullmeier 2001). Discourse analysis is a rich theoretical concept which offers the

opportunity to understand social and political behaviour in a specific direction ex post and

which can also be applied to empirical analyses. The results of these analyses may in future

also help to identify path dependence ex ante.

3.1 The Concept of Discourse Analysis with Regard to Path Dependence

In a methodological sense, policy discourse analysis is a controlled analysis of politically

relevant (mass) texts with the goal of finding basic and extensive coherent areas which can be

regarded as the organising core of politics (Nullmeier 2001).

Different concepts of discourses are used in policy analysis (Keller and Viehöver 2006;

Kerchner 2006). This paper focuses on the Foucaultian perspective of discourse practices.

Foucault expects discourses to actively construct society along various dimensions and

hypothesizes interdependencies between the discursive practises of a society and its

institutions. Such practices, understood as texts, always draw upon and transform other

contemporary and historically prior texts. Any given type of discursive practice, thus, is

generated out of combinations of other analyses of collective knowledge orders and discursive

practices. Therefore, a discourse is a bounded “positive” field of statement accumulation

implying at the same time that other possible statements, questions, perspectives and

difficulties etc. are excluded. These exclusions can be consolidated by institutions (Link and

Link-Herr 1990). In this meaning discourses have a formative or constitutive power that

structures basic definitions and meanings that are later on taken for granted. The historical

aspect of discourses is important in forming the identities of subjects and objects (Howarth

3 The rational choice approach is often used in social sciences and features similar assumptions as in economic theories.

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2002). In Foucault’s opinion, a discourse is a form of power by dominating the field in which

it was formed, fixing specific meanings to be employed in interpreting the social situation

(Fischer 2003). Therefore, discourse analysis aims at analyzing political and politically

relevant texts with the goal of finding the organizing cores of politics (Nullmeier 2001).

Linking the concept of discourse analysis with the framework of path dependence leads to

the assumption that discourses and their constitutive character can be seen as explanations for

self-reinforcing processes in politics because political discourses are always built on

historically prior texts so that the past strongly determines future political actions. The taken-

for-granted nature of definitions and meanings structured by discourses creates (psychological

and institutional) switching costs for policy makers and administrations.

Critiques may argue that this definition of discourse would never allow a change in politics,

something that is obviously in conflict with political reality. At this point and with regard to

policy changes, a specific character of discourses has to be discussed. As Fischer (2003)

argues, a most fundamental question discourse analysts have to ask is whether the used story

line of an issue contributes to a fixed ‘homogenized’ problem or whether it leads to the

opening up or ‘heterogenization’ of established discourses. In this sense the function of story

lines is to condense large amounts of factual information intermixed with normative

assumptions and value orientations that assign meaning to them (Fischer 2003). They are the

main mechanism for creating and maintaining the discursive order. Other authors define them

as “collective symbols” which are “cultural stereotypes”, used and established collectively

(Drews, Gerhard and Link 1985: 265). The two decisive characteristics of story lines are the

determination of rational but also emotional knowledge based on a simplifying nature. Linked

to this knowledge is a related logic and option of action. These characteristics are highlighted

especially in conflicting discourses when collective symbols can dramatize the interpretation

of a situation and at the same time produce the necessity to normalise this perceived situation.

Opening routines is facilitated by exogenous crises or shocks. At such points, events

especially highlighted by media and politics can have great impact on the direction and

quality of discourses (Jäger and Jäger 2007).

Since the breaking of existing paths requires external shocks in order to allow the necessary

mindful deviation (Garud and Karnøe 2001a), the mechanism of discourses and the

requirement for changing policy are concepts that can provide rich explanations for path

dependency, the breaking of paths and the creation of new ones well.

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3.2 Discursive Policy Analysis: Methodological Opportunities

Existing rules and conventions constituting the social order are routinely reproduced and

reconfirmed in actual speech situations. It is not trivial to break up these routines (Hajer

1995). Discursive policy analysis conceptualizing path dependence has the task to uncover the

defining claims of a particular position. Therefore, it is necessary to examine the structure,

style and historical context of the arguments to understand why some modes of argumentation

serve effectively and justify specific actions, while others do not (Hajer 1995). Against this

background, the most promising starting point for applying discourse analysis as a concept of

path dependence is an analysis on the micro level. From a Foucaultian perspective, the goal

here is to uncover the ways discourses embedded in institutional practices function to

reproduce the existing power relationship (Fischer 2003). The discourse analysis can

contribute to the understanding of what the relevant utterances mean. This requires

approaching the institutional setting as an “argumentative field” in which statements are

made.

The concept of discourse analysis combines power and communication on a theoretical

meta-level. Therefore, the question is raised how the concept can be applied empirically.

Since discourse analysis has recently become very popular in qualitative analyses in political

science, empirical studies with a Foucaultian background predominate. However, there are

also quantitative analyses that reconstruct existing discourses. A first analysis with regard to

path dependence combined qualitative and quantitative analyses in order to mirror the

synchronical dimension of the BSE crisis as well as the diachronical dimension of agricultural

policy (Feindt, Kleinschmit and Theuvsen 2005).

Discourses take place at different levels: media, politics, science, literature, administration

etc. Identifying and explaining positive feedback processes in politics requires, of course, the

analysis of the political as well as the media discourse. While the first is a sign of political

behaviour, the latter provides a master forum including virtually everyone. It is “the major site

of political contest because all of the players in the policy process assume its pervasive

influence” (Marx Ferree et al. 2002, 10).

With regard to path dependence processes in politics, two main categories may help to

explain positive feedback processes as well as path breaking or path creation: actors and story

lines. Those who speak in the discourse represent the interests of collective actors, for

instance government, political parties, NGOs, labour unions etc. This position is very

powerful, especially in the media. These speakers have the chance to give their interpretative

pattern of a problem and, thus, actively shape the discourse, and they can be connected to the

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used story lines. Thus, considering the diachronic dimension of the discourse, the prevalence

of certain speakers and story lines can be interpreted as path dependence, i.e. the supremacy

of a predominant mental model or frame of reference. On the other hand, considerable

changes in the composition of the speakers’ ensemble or the emergence of new ideas

underlying new story lines are indications of path breaking and path creation processes.

Analysing texts is a very important but not the only element of discourse analysis. It is

necessary to reflect the context behind the text, because the text is only a sign of discursive

activities. Therefore, a comprehensive discourse analysis also has to consider the policy

arrangement, such as the affected institutions, actors and their relationships with regard to

networks and power.

Figure 1 illustrates the role of texts and speeches (symbolized by asterisks “*”) within

various story lines, that in turn are all relevant for a specific discourse. The discourse leads

political decision makers to take certain actions and to implement certain policies. This

bundle of political actions results from a discourse’s stage at a certain point in time as well as

in previous stages and is represented by the black line in figure 1. If a discourse is clearly

dominant and supersedes alternative discourses, policies will be strongly influenced by the

taken-for-granted interpretations represented by this discourse. As a consequence, path

dependences can emerge. But the solid line also shows that path dependencies in politics do

not necessarily imply that a given set of policies remains completely unchanged. If the

relative strength of a certain story line is significantly altered by external shocks, for instance

an economic crisis, existing paths may break up and new paths may be created through the

imposition of a different mix of political actions.

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Figure 1: Discourses, Story Lines and Path Dependency in Politics. Source: Own based on Schreyögg, Sydow and Koch (2003).

No. of events, restrictiveness

of policy measure

Note: The solid line represents the changing path of a specific policy. "*" represents individual events

as they appear in (mass) media texts. Once a political path has been created, minor adjustments are still possible though the path remains in principle until new small events enable path break and new path creation.

Story line 1 * *

* * *

*

* * * *

* * * *

* * * * * *

* * * * *

* * *

** * *

Story line 2 * *

* * * * * * * * * *

Story line 3 * *

* * *

* * *

* * * *

* * * ** ***

D iscourses

Story line 4 * *

* * *

* * *

* * * *

* * * *

* * *

*

* * *

*

* * * * * *

* * * * *

*

Time (t): 1 2 3 4 5 6 7 … t

Path creation Path dependency

Path break/

path creation

New path dependency

3.3 Illustrative Example: Restricting Seasonal Farm Workers: Expected Results of

Discursive Analysis

As mentioned above, no results of empirical analyses are available so far. But, nevertheless,

at this point of the research project the restrictive policies against seasonal farm workers will

be used as an illustrative example to visualize what results can be expected from a discourse

analysis.

With regard to the argument that historical aspects of discourses are important in forming

the identities of subjects and objects (Howarth 2002), the example of the restriction of

seasonal farm workers should be analysed in a diachronical dimension.

One of the first texts on the restriction of seasonal farm workers is part of a letter written by

Chancellor Bismarck in February 1885 In which he claimed that even if agriculture were the

most important economic sector of society, it is the lesser evil that this sector would lack

manpower than that the state and the future would have to suffer (after Herbert 2001, 17).

This statement is part of a discourse on the political level. It reveals the story line of the idea

of nationality which is threatened by foreigners. The idea of nationality in this regard is

strongly combined with the aspect that work is a value in itself. Therefore, foreign workers

are seen as a danger that takes jobs that are needed by domestic workers.

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The discourse of unemployment is one of the prevailing discourses in Germany. It includes

strong emotions which are often used in (conflicting) political as well as media discourses.

This dominant story line is backed up by a second one. In this second story line it is claimed

that unemployed people who receive money from the state without working for it are

defrauding the social security system. The implication is that unemployed people should be

forced to do work on the farms instead of seasonal farm workers: “Obvious refusal of work

should directly lead to a reduction of benefits.” (Süddeutsche Zeitung, August 10, 2006).

Despite their very different basis, both story lines support the restriction of seasonal farm

workers.

A third conflicting story line which can be currently identified is the suffering sector of

agriculture. A prevailing and often used argument of agricultural actors is that harvesting is

not manageable due to a lack of manpower: “German farmers need help from East Europe”

(AFP, April 13, 2007) is a typical dictum.

Recently the former two story lines have dominated the discourse on seasonal farm

workers. Following the definition of Foucault, the discourse practices are not independent of

the social situation. Institutions resulting from the prior historical discourse on seasonal farm

workers, such as the government bureau that administers the quota system, take (direct or

indirect) part in the discourse with the goal of preserving the status quo. But other powerful

actors are also taking part in this discourse, for example the German federal ministry of

agriculture and the labour union of agriculture (IG BAU). In this regard the discourse is

already a result of power, but it also constitutes power. Discourse analyses can reconstruct the

speech situation and, therefore, reveal the power relations in the discourse. Analysing the

discourse of the restriction of seasonal farm workers will make it possible to identify

dominant speakers and their story lines. Thus, the discourses can provide an explanation for

path dependent processes, in this case in agricultural policy.

5. Conclusions

Conventional models from economic as well as political sciences explain path dependency

of policies by looking back and identifying exogenous elements that cause, shape, and

constitute a specific path dependency. A typical explanation refers to the rents generated by a

specific policy, and the rent-seeking behaviour of stakeholders who benefiting from this

policy. This paper has introduced discourse analysis as a theoretical concept and an empirical

methodology that may enable the endogenization of path creation, path dependencies and path

breaking in the field of policy analysis.

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Discourse analysis highlights that specific elements in a political discourse heavily

influence and predetermine the policy creation path and, therefore, must be taken into account

when political path dependencies are analysed. Discourses are too complex to be quantified in

their entirety. Nevertheless, if story lines of (mass) media and political as well as scientific

texts are considered as representatives of the underlying discourses within a society at a

certain point in time, and if these story lines can be framed and coded in a meaningful way,

then the absolute and relative intensity of certain story lines over time can be quantified and

assessed empirically. Political path creation can be modelled as a process that is driven by

discourses after they have been approximated in this manner.

As an illustrative example how discourse analysis can be linked with the concept of path

dependence in politics, this paper has chosen the policies applied by the German

administration in order to restrict the employment of seasonal farm workers from CEEC in

Germany. The existence of this farm labour policy cannot readily be explained by economic

activities that would benefit one or the other interest group in monetary terms. Instead, the

general discourse of unemployment in Germany, and various story lines within this discourse,

explain much better the underlying forces that have shaped policy vis-à-vis seasonal farm

labour over time. A quantification of these story lines remains to be done and will enable

empirical assessments of the question how changes within this discourse over time have

changed the policy path. In combination with conventional policy analysis, this method

appears to be a promising complement that will allow to explain why some policies turn out

to be path dependent and how they become path dependent, and whether the discourses

around certain policies can be approximated reasonably well through the qualitative and/or

quantitative reconstruction of the speaking actors and corresponding story lines.

References

Ackrill, R., Kay, A. (2006). Historical-institutionalist Perspectives on the Development of the EU Budget System. Journal of European Public Policy. 13:1, January, pp. 113-133. AFP, April 13th, 2007. Bauernverband: Deutsche Bauern auf osteuropäische Hilfe angewiesen. Alston, J., James, J. (2002). The Incidence of Agricultural Policy. In: Gardener, B., Rausser, G. (eds.): Handbook of Agricultural Economics. Volume 2B Agricultural and Food Policy. Amsterdam, Elsevier: pp. 1689-1749. Alston, J., Norton, G., Pardey, P. (1995). Science under Scarcity. Ithaca, Cornell University Press. Bundesanstalt für Arbeit (2002). Merkblatt für Arbeitgeber zur Vermittlung und Beschäftigung ausländischer Saisonarbeitnehmer und Schaustellergehilfen und Hinweise zum

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Ausfüllen der Einstellungszusage/des Arbeitsvertrages (December 2002). Bonn, Zentralstelle für Arbeitsvermittlung. Cyrus, N. (1993). German Gates of Entry and Polish Migration – The Making of a Recent System of Labour Circulation. www.polskarada.de/psc.htm, download 15.12.2000. Dixit, A., Romer, T. (2006). Political Explanations of Inefficient Economic Policies – An Overview of Some Theoretical and Empirical Literature. Prepared for Presentation at IIPF conference “Public Finance: Fifty Years of the Second Best – and Beyond,” in Paphos, Cyprus. Drews, A., Gerhard, U., Link, J. (1985): Moderne Kollektivsymbolik. Eine diskurstheoretisch orientierte Einführung mit Auswahlbibliographie. In: Internationales Archiv für Sozialge- schichte der deutschen Literatur, 1. Sonderheft Forschungsreferate, pp. 256-375. Feindt, P., Kleinschmit, D., Theuvsen, L. (2005): Path Dependence and Creation in Agricultural Policy: Using Media Analysis for Exploring the Emergence of a New Dominant Political Paradigm. Paper accepted for presentation at the 21st EGOS Colloquium, Berlin, 30.6. - 2.7. 2005. Fischer, F. (2003): Reframing Public Policy: Discursive Politics and Deliberative Practices. Oxford University Press. Oxford.

Garud, R., Karnøe, P. (Eds.) (2001): Path Creation as a Process of Mindful Deviation. Mahwah, NJ – London.

Garud, R., Karnøe, P. (2001b): Path Creation as a Process of Mindful Deviation. In: Garud, R., P. Karnøe (Eds.): Path Dependence and Creation. Mahwah, NJ – London, pp. 1-38. Gerdes, G. (2000). Bedeutung der Arbeitskräftewanderung aus Mittel- und Osteuropa für den deutschen Agrarsektor. Kiel, Christian-Albrechts-Universität, Agrar- und Ernährungswissen- schaftliche Fakultät: 267. Hajer, M. (1995): The Politics of Environmental Discourse. Oxford Univ. Press. Oxford. Herbert, U. (2001). Geschichte der Ausländerpolitik in Deutschland – Saisonarbeiter, Zwangsarbeiter, Gastarbeiter, Flüchtlinge. Munich, Beck. Howarth, D. (2000): Discourse. Open University Press. Buckingham. Hönekopp, E. (1997). Labour Migration to Germany from Central and Eastern Europe – Old and New Trends. IAB Labor Market Topics (23). Hess, S. (2004). Die Beschäftigung mittel-und osteuropäischer Saisonarbeitskräfte in der deutschen Landwirtschaft, Berichte über Landwirtschaft, Band 82(4), December 2004, pp. 602-627. Jäger, M., Jäger, S. (2007): Deutungskämpfe. Theorie und Praxis kritischer Diskursanalyse. VS Publisher. Wiesbaden.

Kerchner, B. (2006): Diskursanalyse in der Politikwissenschaft. Ein Forschungsüberblick. In: Kerchner, B., Schneider, S. (Eds.): Foucault: Diskursanalyse der Politik: Eine Einführung. – VS-Verlag. Wiesbaden. pp. 33-67. Koester, U., von Cramon-Taubadel, S. (1992). Agrarreform ohne Ende? Wirtschaftsdienst, Vol. 72, pp. 355-361. Marx Ferree, M, Gamson, W., Gerhards, J., Rucht, D. (2002): Shaping Abortion Discourse. Democracy and the Public Sphere in Germany and the United States. Cambridge Univ. Press. Cambridge.

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Nullmeier, F. (2001): Politikwissenschaften auf dem Weg zur Diskursanalyse? In: Keller, R., Hirseland, A., Schneider, W. (Eds.): Handbuch Sozialwissenschaftliche Diskursanalyse. Theorien und Methoden. Bd. 1. Leske + Budrich, Opladen, pp. 285-311. Kay, A. (2003). Path dependency and the CAP. Journal of European Public Policy, Vol. 10(3), pp. 405-420. Kay, A. (2005): A Critique of the Use of Path Dependency in Policy Studies, In: Public Administration Vol. 83, No 3, pp. 553-571.

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Kerchner, B. (2006): Diskursanalyse in der Politikwissenschaft. Ein Forschungsüberblick. In: Kerchner, B., Schneider, S. (Eds.): Foucault: Diskursanalyse der Politik: Eine Einführung. – VS-Verlag. Wiesbaden, pp. 33-67. Krueger, A. (1974). The political economy of the rent-seeking society. The American Economic Review, Vol. 64(3), pp. 291-303. Link, J., Link-Herr, U. (1990): Diskurs- Interdiskurs und Literaturanalyse. In: LiLi 77, pp. 88- 99. Olsen, M. (1965). The Logic of Collective Action. Cambridge, MA. Harvard University Press. Pierson, P. (2000). Increasing Returns, Path Dependence, and the Study of Politics. American Political Science Review, Vol. 94(2), pp. 251-267. Pierson, P. (2004): Politics in Time. History, Institutions, and Social Analysis. Princeton Univ. Press. Princeton/Oxford.

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Hoyos, Poverty Effects of Higher Food Prices.pdf

Policy ReseaRch WoRking PaPeR 4887

Poverty Effects of Higher Food Prices

A Global Perspective

Rafael E. De Hoyos Denis Medvedev

The World Bank Development Economics Development Prospects Group March 2009

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Produced by the Research Support Team

Abstract

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Policy ReseaRch WoRking PaPeR 4887

The spike in food prices between 2005 and the first half of 2008 has highlighted the vulnerabilities of poor consumers to higher prices of agricultural goods and generated calls for massive policy action. This paper provides a formal assessment of the direct and indirect impacts of higher prices on global poverty using a representative sample of 63 to 93 percent of the population of the developing world. To assess the direct effects, the paper uses domestic food consumer price data between January 2005 and December 2007—when the relative price of food rose by an average of 5.6 percent —to find that the implied increase in the extreme poverty

This paper—a product of the Development Prospects Group, Development Economics—is part of a larger effort in the department to monitor the poverty and income distribution impacts of global economic trends and policies. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dmedvedev@ worldbank.org.

headcount at the global level is 1.7 percentage points, with significant regional variation. To take the second- order effects into account, the paper links household survey data with a global general equilibrium model, finding that a 5.5 percent increase in agricultural prices (due to rising demand for first-generation biofuels) could raise global poverty in 2010 by 0.6 percentage points at the extreme poverty line and 0.9 percentage points at the moderate poverty line. Poverty increases at the regional level vary substantially, with nearly all of the increase in extreme poverty occurring in South Asia and Sub- Saharan Africa.

Poverty Effects of Higher Food Prices:

A Global Perspective

Rafael E. De Hoyos and Denis Medvedev

 Chief of Advisors to the Under-Secretary of Ministry of Education, Mexico, and Economist, Development Prospects Group, World Bank. The views expressed here are those of the authors and should not be attributed to the World Bank, its Executive Directors, or the countries they represent. For their comments we are grateful to Ataman Aksoy, Maurizio Bussolo, Andrew Burns, Nora Lustig, Will Martin, Hans Timmer, Dominique van der Mensbrugghe, and seminar participants at a conference on food prices and poverty organized at the World Bank. Rebecca Lessem and Li Li provided excellent research assistance. The usual caveat applies. Address for correspondence: J9-144, The World Bank Group, 1818 H Street, NW, Washington, DC 20433; [email protected].

1 Introduction The rapid rise in food prices between 2005 and the first half of 2008 has raised numerous concerns about potential negative welfare impacts of a world with higher food prices, particularly among poor households and those with incomes just above the poverty line.1 At the same time, to date there have been few formal assessments of the likely impacts of higher food prices on global poverty, and none using a large sample of developing countries. This paper aims to bridge the existing knowledge gap by providing a set of estimates of the likely impacts of higher food prices on poverty and income distribution at the global level using a unique set of household survey data. The economic effects of changes in relative prices have been a well-researched subject including contributions by Deaton (1989), Ravallion (1990), and Ravallion and van de Walle (1991) among others. According to this literature, changes in food prices can affect poverty and inequality through consumption and income channels (see Figure 1). On the consumer side, as food prices increase, the monetary cost of achieving a fixed consumption basket increases hence reducing consumer’s welfare. However, for the segment of the population whose income depends --directly or indirectly-- on agricultural markets, i.e. self-employed farmers, wage workers in the agricultural sector, and rural land owners, the rise in food prices represents an increase in their monetary income. For each household, the net welfare effect of an increase in food prices will depend on the combination of a loss in purchasing power (consumption effect) and a gain in monetary income (income effect). Clearly, for those households whose income has no linkages with the agricultural markets, for instance urban dwellers, the net welfare effect of an increase in food prices will be entirely determined by the negative consumption effect. For households whose incomes are closely related to the performance of agricultural markets and for which food consumption represents a small proportion of their total budget, higher food prices would be welfare-improving. Therefore, the first-order, or direct, welfare effects of shifts in food prices will be determined by the household’s net position on food supply or demand. In the medium run, once quantities produced are adjusted to reflect the new set of prices in the economy, wages and/or employment in the agricultural sectors will increase to attract the necessary factors of production to increase output --this is what it is known as the second-order, or indirect, income effect (see Figure 1).2 The approach depicted in Figure 1 was undertaken in a recent study by Ivanic and Martin (2008). Using detailed household-level information, the authors find that the proportion of the population living below the poverty line has increased as a result of higher food prices in eight of the nine countries included in their study. In a related study, Friedman and Levinsohn (2002) identify the urban poor as the most vulnerable group during a period of food inflation. Ravallion (1990) develops and tests a methodology to assess the

1 Between July 2008 and February 2009, international agricultural prices (in nominal terms) have come down by 32 percent, but are still 45 percent above their January 2005 levels. 2 Arguably, there is also a “second-order effect” taking place in the consumption side, that is, given the new set of prices, the consumer can chose a different consumption basket. This effect is ignored in the present analysis based on the high degree of correlation among food prices and the little scope that the poor have for food consumption substitution.

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poverty effects of changes in food prices taking into account the induced wage responses caused by price changes. The author finds that, even including induced wage responses in the analysis, rural poverty in Bangladesh tends to increase as a result of an increase in the relative price of food staples. A recent study by Aksoy and Isik-Dikmelik (2008) challenges the idea that higher food prices unambiguously deteriorate the income of the poor. Using household survey data from nine low-income countries, the authors find that net food sellers are disproportionately represented among the poor, hence suggesting that an increase in food prices can transfer income from richer to poorer households. As one can see, the country-specific and global net poverty effect of higher food prices remains an empirical question to be addressed.

Figure 1 Relationship between International Food Prices and Household Welfare

The paper is organized in the following way. A conceptual framework linking international food prices with household real incomes is briefly delineated in Section 2. Based on the importance of price transmission for poverty impacts (see top part of Figure 1), Section 3 shows the recent changes in domestic food price indices for developing countries and compares them to the evolution of the international food price index.

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Sections 4 and 5 describe the methodology and present the estimates of direct and indirect poverty impacts, respectively. Section 4 develops two simulations: the first one, particularly relevant for urban areas where the income effects tend to be small or non- existing, takes into account the consumption effect only, while the second simulation combines income and consumption effects imputing a household-specific share of agricultural income in rural areas. Section 5 adds the second-order impacts of higher food prices on poverty to the analysis by linking the household survey data with a global general equilibrium model in a macro-micro simulation framework. Scenarios in this section link higher food prices to the recent and expected (2004-2010) trends in the production of biofuels and allow the households (at the macro level) to re-optimize their consumption and labor supply choices. Section 6 offers concluding remarks.

2 Food Prices and Poverty: Conceptual Links An increase in international food prices will redistribute resources domestically as long as the pass-through or link between international and domestic food prices is different from zero (Macro Level in Figure 1). Assuming a positive pass-through effect, the increase in international food prices will be followed by an increase in domestic food prices enhancing a redistribution of resources from non-agricultural to the agricultural sector of the economy. According to Bussolo, De Hoyos and Medvedev (2009), almost 45 percent of the population in the world lives in a household where the main income-generating activity of the household head takes place in the agricultural sector. The authors show that a large share of this agriculture-dependent group, close to 32 percent, is poor and that these so-called “agricultural households” contribute disproportionately to global poverty: three of every four poor people belong to this group (see Table 1). So redistributing resources from agricultural to non-agricultural households --as an outcome of higher food prices-- could help reduce global poverty and inequality via higher incomes for farmers. However, household purchasing power will also deteriorate as a result of the increase in prices, making the link between agricultural trade liberalization and global household welfare a complex one. Higher food prices will enhance a redistribution of real income between net food producers and net food consumers of agricultural products, with the welfare of the former improving at the expense of the latter (see Micro Level in Figure 1).3 Finally, factor prices will also change following the change in prices of final products therefore changing the real incomes of households that are not directly involved in agricultural production (see Meso Level in Figure 1).

Table 1: Poverty is higher among agricultural households even if their incomes are less unequal

Gini (%)

Pop Shares

(%)

Average Monthly Income

(US$ of 1993, PPP) 1-Dollar Poverty

Incidence (%) Poverty Share

(%) Agriculture 44.9 44.8 65.4 31.7 75.9 Non-Agri. 62.8 55.2 328.9 8.1 24.0 World 67.0 1 210.8 18.7 1

Source: Bussolo, De Hoyos and Medvedev (2009)

3 A household is defined as a net producer (consumer) of agricultural products when the monetary income it derives from merchandising these products is greater (smaller) than the amount spent on them.

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Ultimately, the short- to medium-term poverty effects of higher international food prices will be determined by: (1) the degree of pass-through; (2) the incidence and severity of poverty among net food producers versus net food consumers; and (3) the extent to which higher food prices translate into higher income for farmers (in the form of profits and wages). The degree of pass-through will be, in turn, determined by domestic market conditions such as: government intervention in the form of subsidies or price controls, infrastructure and market access, the degree of domestic competition and trade barriers among others. Net food production/consumption patterns are determined by the importance of the agricultural sector as an income source of the poor and the proportion of total household budget allocated to food consumption. Finally, the relationship between higher food prices and farmer incomes is a function of the heterogeneity in domestic price transmission among large versus small farmers, and the ability of rural factor (labor) markets to adjust to changes in prices of final products.

3 International vs. Domestic Food Prices Between January 2005 and December 2007, the international food price index increased 74 percent.4 Is this a good indicator of the reduction in purchasing power suffered by consumers in developing countries? The international food CPI reflects changes in the international food prices weighted by commodity-specific global trade volumes. In a world where as little as 7 percent of total food consumption is being traded internationally, the international and domestic food CPIs are only marginally related. Consumption patterns can be quite different between countries with the importance of internationally traded commodities in domestic food CPIs varying across countries. The relevant price changes for welfare analysis are the domestic food CPIs which, although they have shown a rapid increase between 2005 and 2008, have a growth rate that is far from being as large as the increase shown by the international food CPI.

Figure 2: Distribution of Cumulative Increases in Nominal Food Prices (LCU, Jan 2005 – Dec 2007)

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0 20 40 60 80 100 Percentage Change in Price

Cumulative Increase in International Food Prices = 74 %

4 Using figures from The World Bank (DECPG).

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Figure 2 shows the domestic increase in food CPI for 76 developing countries between January 2005 and December 2007 and compares it with the increase in the international food CPI.5 In all but three countries, the domestic food price index increased less than the international food prices (74 percent). Differences between the domestic and international food price indices could be explained by differences in the consumption basket with domestic food baskets containing non-traded food items. International and domestic food CPIs can also differ due to: (i) a weak price transmission in internationally traded food commodities (Baffes and Gardner, 2003), (ii) imperfect domestic markets characterized by lack of competition (Levinsohn, 1996) and poor infrastructure, and (iii) government intervention in the form of subsidies and price controls, and other market distortions. The food CPIs in Figure 2 are expressed in local currency units (LCU) and are therefore influenced by local inflation rates. To account for local inflation rates, Figure 3 reports the change in domestic food CPI relative to the change in non-food CPI between January 2005 and December 2007 and compares these indices with the change in international food CPI relative to the manufacturing unit value (MUV) index.6 In 18 of the 76 developing countries included in our sample the non-food price index increased at a faster rate than the change in food prices, in other words, non-food items became relatively more expensive. This is not surprising given the large price increases observed in an important non-food item such as fuels. For the great majority of the developing countries analyzed (58 out of 76) food items became more expensive in terms of non- food items. On average, relative food prices increased 5.6 percent far below the 31 percent increase registered by the international food CPI relative to the MUV.

Figure 3: Distribution of Cumulative Increase in Relative Food Prices (LCU, Jan 2005 – Dec 2007)

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P e rc

e n ta

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-20 0 20 40 Percentage Change in Relative Price

Cumulative Increase in International Food Prices = 31 %

As we mentioned before, there are several reasons why domestic and international prices can differ; nevertheless, this section shows that focusing on the international food CPI to

5 The domestic food CPIs are collected by ILO (http://laborsta.ilo.org/) directly from the national statistical offices (or central banks). The international food CPI is constructed by the research department at the World Bank (http://go.worldbank.org/MD63QUPAF1). 6 The MUV index comes from the World Bank (http://go.worldbank.org/VDQ5AA3VP0)

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make inferences about the welfare effects of domestic price changes could be misleading. Not only the international food CPI can divert from the average domestic food CPI but also price changes across countries show a high level of heterogeneity. Therefore domestic price indices should be use to infer the ex-post welfare effects of price changes. Changes in domestic nominal prices are more relevant for short-term welfare evaluation since we assume that prices of all non-food items remains constant. On the other hand, relative prices are more appropriate for a medium- to long-run evaluation of the welfare effects of higher food prices. The following section shows the possible poverty effects brought about by the changes in domestic food prices discussed in this section.

4 Direct Poverty Effects of Higher Food Prices

4.1 Methodology

Let us define the monetary income of household “h”, , as the sum of incomes from

profits from agricultural activities, , and incomes deriving from all other sources,

. These monetary income components are assumed to be a function of the vector of

prices in the economy, , hence . The purchasing power of

household “h”, , is defined by the ratio of it money income divided by a household-

specific price index capturing the household’s consumption patters in terms of food and non-food expenditure:

hY

)(P

A hY

hY

NA hY

P )(P NAh A

h YY  r

hY

(1) nf

h f

h

NA h

A h

h

hr h

PP

YY

P

Y Y

*)1(

)()(

  

 PP

where fP and nfP are food and non-food price indices and h is the proportion of household’s “h” budget spent on food. Equation (1) captures the dual effect of a price increase depicted in Figure 1, i.e. the possible higher monetary income on the one hand, and the loss in purchasing power on the other. The changes in real incomes brought about

by a change in relative prices of food versus non-food,  

p dt

Pnff 

Pd , can be

approximated by the following linear expression: (2) pYpYY hh

A h

r h

  Equation (2) states that, in the short term and for sufficiently small changes in , profits

from farming activities, , will increase in the same proportion as the changes in

relative prices and the loss in purchasing power will be proportional to the amount of the total household budget spent on food,

p A

hY

hhY . Therefore, in the short term, the proportional change in real income with respect the base period can be written as follows:

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(3) p Y

Y hh

h

r h  

)(  

where h is the share of total household income that is accrue to profits from farming activities. Hence, in the short term, higher food prices will benefit net producers of agricultural goods )( hh   and hurt net consumers of agricultural products )( hh   . Equations (2) and (3) assume that production and consumption patterns remain constant after the change in prices (Deaton, 1989) and therefore these results should be complemented with a medium- to long-term analysis.

4.2 Simulation Results

The simulations presented here make use of the Global Income Distribution Dynamics (GIDD) dataset that has been recently developed at the World Bank. The GIDD dataset consists of 73 detailed household surveys for low and middle-income countries, 21 of which include information on food expenditure by household.7 Together, this dataset covers 63 percent of the population in the developing world--the major missing country being China. The majority of the surveys (54) use per capita consumption as the welfare indicator, while the remaining surveys--all but one for countries in Latin America-- include only per capita income as a measure of household welfare. The welfare measures are expressed in 2005 PPP prices for consistency with the $1.25 and $2.5 a day poverty lines recently developed in Chen and Ravallion (2008).8 All the ex-ante poverty simulations presented in this section capture the ceteris-paribus effects of changes in relative food prices observed between January 2005 and December 2007 (see Figure 3). The results presented here differ from Ivanic and Martin’s (2008) estimates in several ways: (1) the country coverage is substantially different, (2) while Ivanic and Martin’s (2008) focus on the poverty effects of changes in 7 food items, we assess the poverty of changes in prices of the total food basket, (3) Ivanic and Martin’s (2008) use the changes in international prices of their 7 food items as the price shock whereas we use the domestic change in the food CPI relative to the non-food CPI.

4.2.1 Loss in Urban Household Purchasing Power

As it is clear from equation (3), the share of total household budget that is spent on food,

h , is an important element determining the deterioration in purchasing power originated from an increase in food prices. For some countries, this information is readily available from household surveys, however, in several cases one has to estimate or impute this value. In 21 out of the total 73 countries included in the GIDD’s sample, household-level information on total food expenditure was available. Using the information for these 21

7 See Table 9 in Annex II for a complete country list. A complete description of the dataset is available at http://www.worldbank.org/gidd 8 Most of the household surveys in the GIDD are for years between 2000 and 2005. When the GIDD dataset did not include the newest household survey available from the World Bank’s PovCal, the GIDD’s survey mean income (or consumption) was modified so that the extreme poverty headcount matched the latest information available from PovCal.

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relatively large countries, a developing countries’ Engel curve was estimated which was then used to impute the values of food shares in all other countries, h̂

pYh 

; the

methodological details if this procedure are explained in De Hoyos and Lessem (2008), which echoes the techniques developed in Cranfield, Preckel, Eales and Hertel (2002). For urban dwellers, where, most likely, the quantities of food produced are close to zero, the welfare effects of higher food prices will be largely determined by the loss in purchasing power. To capture the small income effects in urban areas, we assume that

in equation (2) is zero for all households in this strata, therefore Y . The

results of the simulation focusing on the loss in purchasing power in urban areas can be seen as an instructive way of summarizing the following country-specific information: i) domestic changes in food prices, ii) the initial incidence and severity of poverty in urban areas, iii) the proportion of the total budget spent on food among poor urban households.

A hY h

r h  ̂

Table 2 shows the urban poverty impacts of the negative consumption effects brought about by the increase in the relative price of food using a poverty line of $1.25 per day in 2005. Given the large number of results, Table 2 shows regional weighted average poverty effects, however country-specific impacts can be requested from the authors. According to Table 2, the extreme poverty headcount in urban areas increased by 2.86 percentage points as a result of the rise in food prices observed between January 2005 and December 2007. Additionally, the average gap between the poor’s income and the poverty line grew 0.51 percentage points. This deterioration in the poverty indices translates into an additional 68 million individuals below the poverty line and an increase of [20.6] percent in the monetary cost of alleviating total urban poverty under perfect targeting conditions.9 To understand better the relationship between food prices and urban poverty Table 2 presents the elements that determine the increase in urban poverty: (1) the relative change in domestic food prices faced by urban households; (2) the proportion of the total budget that poor urban households allocate to food; and (3) the initial incidence and intensity of poverty among urban dwellers. As it was discussed in Section 2, the magnitude of the food price increase faced by households is, in all regions, significantly lower than the changes registered by the international food price index. The weighted average increase in relative food CPI for urban areas in the developing world is 4.10 percent with food prices increasing at slower rates in Latin America and the Caribbean (LAC) and Eastern Europe and Central Asia (ECA) and quite the opposite in East Asia and the Pacific (EAP) and the Middle East and North Africa (MENA). Notice that, on average, food prices decreased with respect non- food prices in ECA, as it was mention earlier, this could be the result of higher energy prices in this region. LAC and ECA are regions where the expected poverty effects are mild given that poor households in Latin America spend a relatively low proportion of their total budget on food and because the initial poverty rates in these two regions are rather low. On the other hand, poverty indicators in other regions show a considerable

9 Using the change in the poverty deficit as the cost measurement, Dessus, Herrera, and de Hoyos (2008) show that, on average, 90 percent of the additional cost of alleviating urban poverty can be attributable to the reduction of real income of households classified as poor before the price increase.

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deterioration as a result of higher food prices. With an increase in the headcount ratio of 6.34 percentage points, East Asia is, by far, the region experiencing the largest increase in poverty; this region by itself saw an increase of 51 million individuals in urban areas below the extreme poverty line. This massive increase in the number of poor is explained by the importance of food items in poor urban households and a large increase in food prices. Middle East and North Africa also experienced a relatively large increase in urban poverty due to a sharp increase in the relative prices of food in this region (12.54 percent).

Table 2: Urban Poverty Effects of the Changes in Relative Food Prices (Jan. 2005 – Dec. 2007)

Initial (circa 2005)

Change

Region

Shock to Food Prices

(%)

̂ among the Poor (%)

0P 1P 0P 1P

Number of Poor

(Million) East Asia 13.81 67.46 13.28 2.69 6.34 1.86 51.08 Eastern Europe -0.49 56.87 1.31 0.22 0.04 0.01 0.12 Latin America 1.64 40.36 3.73 1.39 0.12 0.02 0.51 Middle East 12.54 57.03 2.71 0.48 2.49 0.72 4.36 South Asia 4.84 61.86 32.27 8.07 1.89 0.66 8.16 Sub-Saharan Africa 4.91 52.75 34.09 12.97 1.65 0.75 4.57 Developing World 4.10 58.76 15.17 4.29 2.86 0.89 68.80

* Notes: (1) The regional changes in food prices are weighted averages of the cumulative increase in domestic food CPIs relative to non-food CPI observed between January 2005 and December 2007; (2) the poverty line is set at $1.25 (2005, PPP) per day; (3) the share of food consumption to total consumption among the poor is computed as described in De Hoyos and Lessem (2008); (4) to get the increase in number of poor the regional change in headcount was applied to all countries in the region; (5) East Asia does not include China and the Middle East includes only Jordan, Morocco and Yemen. These results should be taken with caution as they represent an upper bound of the real poverty impact. In the medium-to long-run, urban households would change their consumption patterns towards less expensive food baskets; additionally, some of the general equilibrium effects of higher incomes in the agricultural sector will eventually benefit urban areas. These effects will be explored in more detail in section 5.

4.2.2 Poverty Effects in Rural Areas

As we already mentioned, the adverse poverty effects of higher food prices documented in the previous section could be compensated by an increase in farmers’ income. Since the incidence of poverty among agricultural households --the beneficiaries of higher food prices-- is higher than among non-agricultural households (see Table 1), a net poverty reduction as a result of a rise in food prices is not an implausible outcome (Aksoy and Isik-Dikmelik, 2008). The GIDD dataset classifies each household as “rural” and “urban” according to the official domestic classification. This classification of rural household agglomerates into a

10

single group: large land owners, self-sufficient farmers, agricultural wage earners, and households that indeed do not derive income from agricultural activities. Additionally, the GIDD dataset identifies a welfare aggregate (income or consumption) only at the household level. This posses a serious challenge since, as oppose to the information on food shares, h , we do not have information on the level and distribution of the proportion of total household income that is accrue to agricultural self-employment activities h . Both h and h vary across households but, as oppose to h there is no economic theory that we can use to estimate a relationship between h and other observable characteristics like household per capita income. In order to get plausible values of h we rely on the information from the Rural Income Generating Activities (RIGA) project. RIGA is a FAO-World Bank funded project that uses LSMS household surveys to disentangle the sources of rural income with the purpose of understanding the relationship between the various income generating activities.10 Taking the reported share of self-employed agricultural income at the household level for 19 countries located in 5 of the 6 World Bank developing regions, we estimate a simple polynomial relationship between the share of income that is attributable to self-employment agricultural incomes, h , and per capital household income (or consumption), , and regional fixed effects: hy

(4) hh

hhhhh

SASLAC

ECAEAPyy

*49.0*44.0

*30.0*38.0*0002.0*54.076.0ˆ 2

 

692,930N ; 5.02 R

This simple specification is enough to give a rather good fit of the data with an R2 of 0.5. According to the observed data, controlling for income differences, the share of self- employed income in rural areas is highest in Sub-Saharan Africa and much lower in Latin America and South Asia. The results of this simple specification are used to impute the share of self-employed agricultural income in all rural households taking into account their per-capita household income (or consumption) and regional location. Figure 4 shows the difference between the observed and imputed agricultural self- employed income share for each percentile of per capita consumption in rural areas. The share labeled “all countries” shows that the average share in the poorest households in rural areas is close to 80 percent while this falls to 15 percent for households in upper percentiles. Figure 3 also shows the prediction power of the model by comparing the observed shares, h , versus the fitted values, h̂ , for two rather different countries, Nigeria and Panama. The country-specific fitted values in Figure 3 are based on two separate regressions that excluded Nigeria and Panama, respectively. Overall, the

10 For more details on the LSMS household surveys see http://www.worldbank.org/LSMS/. For a complete description of the RIGA project including publication of the first results see Carletto et. al. (2007) and visit: http://www.fao.org/es/ESA/riga/index_en.htm

11

imputed share was not substantially different from the observed one, with the average absolute difference between observed and imputed shares in Panama and Nigeria being around 7 percentage points. In the short-run, incomes of self-employed farmers will increase in proportion to the increase in prices of their produce. The lack of household-level information on rural income sources, implies that, as a result of higher food prices, all rural households experience an increase in nominal income equal to pYhh ̂ . Therefore, as long as hh  ˆˆ  , household “h” will experience a reduction in real income as a result of higher food prices. For the same increase in price, given the higher value of h̂ estimated by specification (4), rural households in Sub-Saharan Africa experience a higher increase in nominal income compared with rural households in Latin America.

Figure 4: Observed and Imputed Share of Agricultural SE Income

0 2

0 4

0 6

0 8

0 1

0 0

S e

lf- e

m p

lo ym

e n

t A

g ri cu

ltu ra

l S h

a re

, %

0 20 40 60 80 100 Percentiles of Per-Capita Consumption

(1) Using data from RIGA; (2) the percentiles are country-specific

Nigeria

All Countries

Panama

The rural poverty effects of a simulation accounting for the consumption and income effects assuming hh  ˆ are presented in Table 3. Despite the fact that we are allowing for positive income effects in the relatively poorer rural areas, indicators in all regions show deterioration in terms of the incidence and depth of poverty. Notice that, although the initial poverty headcount is much higher in rural areas, the increase in this poverty indicator is smaller than in urban areas capturing the offsetting income effects of higher food prices taking place in rural households. For each region except for Latin America, the change in the rural poverty headcount ratio is smaller than the change taking place in urban areas. At the global level, the headcount ratio in rural areas increases by 2.06 percentage points representing an additional 87.19 million individuals falling below the poverty line. The rural poverty deficit, i.e. the resources needed to alleviate extreme

12

poverty in rural areas, jumps by 6 percent after the change in relative prices--much lower than 21 percent increase taking place in urban areas. Given the importance of self-employed agricultural incomes for rural households in Sub- Saharan Africa, higher food prices are not translated into a significantly higher poverty rate in this region. Despite the relatively mild increase in the incidence of poverty in rural South Asia an extra 19.5 million individuals fall short the extreme poverty line after the price shock. As in urban areas, the deterioration of rural poverty indicators is more acute in East Asia with this region accounting for 62 million out of the total 87 million new poor.

Table 3: Rural Poverty Effects of the Changes in Relative Food Prices (Jan. 2005 – Dec. 2007)

Initial (circa 2005)

Change

Region

Shock to Food Prices

(%)

Food Share Among the Poor (% of

total Y) 0P 1P 0P 1P

Number of Poor

(Million) East Asia 12.37 71.48 31.98 7.41 5.71 2.05 62.48 Eastern Europe -0.21 63.09 3.01 0.54 0.04 0.01 0.06 Latin America 6.85 45.29 18.75 8.16 0.37 0.21 0.45 Middle East 25.89 62.40 15.41 3.53 2.35 0.87 3.12 South Asia 5.00 65.64 43.31 10.38 1.83 0.64 19.53 Sub-Saharan Africa 9.65 67.63 54.88 22.79 0.31 0.17 1.54 Developing World 6.67 66.08 38.06 10.87 2.06 0.66 87.19

* Notes: (1) The regional changes in food prices are weighted averages of the cumulative increase in domestic food CPIs relative to non-food CPI observed between January 2005 and December 2007; (2) the poverty line is set at $1.25 (2005, PPP) per day; (3) the share of food consumption to total consumption among the poor is computed as described in De Hoyos and Lessem (2008); (4) to get the increase in number of poor the regional change in headcount was applied to all countries in the region; (5) East Asia does not include China and the Middle East includes only Jordan, Morocco and Yemen.

4.2.3 Total Poverty Effects

Overall, the number of individuals living on less than $1.25 a day, 2005 PPP increased by 155 million as a result of the cumulative increase in the relative price of food observed between January 2005 and December 2007 (see Table 4). Notice that this result contrasts with the 105 million reported in Ivanic and Martin (2008). There are several reasons behind this difference: (i) the present paper uses data for 73 developing countries as opposed to 9, (ii) the estimates of Ivanic and Martin (2008) are based on nominal price changes for 7 commodities whereas our study takes the cumulative change in food CPI relative to non-food CPI as the price shock, (iii) the income/consumption household aggregates are expressed in 2005 PPP and the newly developed $1.25 and $2.5 poverty lines are used to measure the initial poverty indices (see Chen and Ravallion, 2008), and (iv) Ivanic and Martin (2008) total poverty estimates are valid for low-income countries covering a total population of 2.3 billion whereas our estimates are for all the developing world covering a population equal to 5.4 billion. Given all these differences, the

13

discrepancy of 50 million between the number of new poor presented in this study and the number of new poor estimated in Ivanic and Martin (2008) is indeed a small one.

Table 4: Total Poverty Effects of the Changes in Relative Food Prices (Jan. 2005 – Dec. 2007)

Initial (circa 2005)

Change

Region

Shock to Food Prices

(%)

Food Share Among the Poor (% of

total Y) 0P 1P 0P 1P

Number of Poor

(Million) East Asia 12.98 70.65 24.77 5.59 5.98 1.97 113.53 Eastern Europe -0.39 60.42 1.94 0.34 0.04 0.01 0.18 Latin America 3.09 44.10 7.97 3.23 0.19 0.07 1.08 Middle East 19.79 61.70 9.61 2.14 2.41 0.80 7.44 South Asia 4.96 64.90 40.60 9.81 1.84 0.65 27.65 Sub-Saharan Africa 8.14 64.35 48.32 19.69 0.74 0.36 5.76 Developing World 5.60 64.51 28.72 8.18 2.38 0.75 155.63

* Notes: (1) The regional changes in food prices are weighted averages of the cumulative increase in domestic food CPIs relative to non-food CPI observed between January 2005 and December 2007; (2) the poverty line is set at $1.25 (2005, PPP) per day; (3) the share of food consumption to total consumption among the poor is computed as described in De Hoyos and Lessem (2008); (4) to get the increase in number of poor the regional change in headcount was applied to all countries in the region; (5) East Asia does not include China and the Middle East includes only Jordan, Morocco and Yemen. The results presented in Table 4 hide important heterogeneities across countries. Figure 5 shows the changes in poverty headcount and gap for each of the countries in our sample. The changes in food prices have different impacts in different countries with the net poverty effect --in terms of poverty headcount and gap-- being close to zero (less than a fifth of a percentage point) for 60 percent of the countries included in our sample. In around half of the developing countries analyzed, higher food prices raise the headcount ratio by at least 0.2 percentage points; Indonesia, Yemen, Ethiopia, Pakistan, and Bangladesh are the countries with the highest adverse poverty effects with increases in the headcount ratio of more than 3.5 percentage points. By contrast, in 7 developing countries the change in relative prices reduces the incidence of poverty by at least 2 percentage points. In 5 of these 7 countries, the reduction in poverty is attributable to a reduction in relative food prices (Dominican Republic, Sri Lanka, Madagascar, Benin, and Moldova). Nevertheless, in Kenya and Mali the reduction in poverty in rural areas is large enough to compensate for the poverty increase observed in the cities and pull down the national poverty headcount by 0.42 and 0.75 percentage points, respectively.

14

Figure 5: Changes in the Poverty Headcount and Gap due to the Increase in Food Prices

Notes: (1) the poverty line is set at $1.25 (2005, PPP) per day; (2) using data from the GIDD.

5 Incorporating Indirect Poverty Effects of Higher Food Prices Although international agricultural prices have retreated substantially from their peak in July 2008, they remain more than 45 percent above their January 2005 level. While this is clearly not convincing evidence of a reversal in the long-term trend of declining agricultural prices, there are several reasons why the scope for additional declines may be limited: slower progress in development of new technologies, limited take-up of existing advanced techniques due to infrastructure and institutional constraints, sooner- or larger- than-expected damages from climate change, or large and growing additional demand for agricultural output from biofuels. In fact, the latter has played a major role in the 2005- 2008 spike in food prices, according to Mitchell (2008) and World Bank (2009, Chapter 2). This section explores the implications of the continued high demand for first- generation biofuels through 2010, satisfied through increased production of corn, sugar cane, and wheat for ethanol, and oil seeds for biodiesel. This is done by linking a recursive-dynamic global computable equilibrium (CGE) model with the GIDD micro- simulation model. The CGE model contrasts a baseline scenario, in which the demand for biofuels (as a share of total demand for a specific crop) remains at 2004 levels, with a biofuels scenario in which demand follows its historical path through 2007 and is projected through 2010 using current mandates and production trends.

5.1 Methodology

The general equilibrium model used in this paper is the World Bank's Environmental Impacts and Sustainability Applied General Equilibrium model (ENVISAGE). The detailed description is available in van der Mensbrugghe (2008), while the next two paragraphs summarize its most relevant features. Production is modeled with a series of nested CES functions that allow for different degrees of substitutability across inputs, which include intermediate inputs, energy, skilled and unskilled labor, different capital

15

vintages, land, and natural resources. The latter are sector-specific, while land has limited transformation across agricultural uses. New capital vintages and skilled labor are freely mobile across sectors, while the mobility of old vintages is limited. Unskilled workers are freely mobile within farm and non-farm activities, but the movement from farm to non- farm employment is limited with a Harris-Todaro migration function. Consumer demand is modeled with a nesting of Cobb-Douglas and constant-differences-in-elasticity (CDE) utility functions. International trade is specified with nested CES and CET functions which allow for limited substitution between domestically produced goods and imports or exports (the Armington assumption). The model contains an integrated climate module which links CO2 emissions to changes in global temperature with feedbacks to agricultural productivity (following the approach of Nordhaus and Boyer, 2000, and Nordhaus, 2007, and calibrated with estimates in Cline, 2007). The current version of the model is based on the GTAP database with a 2004 base year, which has been aggregated to 26 country/regions and 22 sectors (Table 8). The model is solved forward, in recursive fashion, until 2010, with labor force and population growth rates lined up to the UN’s medium variant population forecast. TFP growth in agriculture is set at 2.5 percent per annum with no differentiation across sectors or regions, based on estimates in Martin and Mitra (1999). Labor-augmenting productivity growth in the other sectors is endogenized to achieve the World Bank's forecasted growth of real GDP. The macro closure has government expenditures as a share of GDP fixed at 2004 levels, while a demographically-driven savings function determines the allocation of private expenditures between consumer demand and domestic investment. The manufactured export price index of the high-income countries is the numéraire. The distributional analysis is carried out with the World Bank’s GIDD model, which generalizes the existing CGE-microsimulation methodologies—e.g., Bourguignon, Bussolo, and Pereira da Silva (2008), Chen and Ravallion (2003), and Bussolo, Lay, and van der Mensbrugghe (2006)—at the global level and is described in detail in Bussolo, De Hoyos, and Medvedev (2008a).11 The conceptual framework of the model is depicted in Figure 6. The expected changes in population structure by age (upper left part of Figure 6) are exogenous, meaning that fertility decisions and mortality rates are determined outside the model. The change in shares of the population by education groups incorporates the expected demographic changes (linking arrow from top left box to top right box in Figure 6). Next, new sets of population shares by age and education subgroups are computed and household sampling weights are re-scaled according to the demographic and educational changes above (larger box in the middle of Figure 6). The impact of changes in the demographic structure on labor supply (by skill level) is incorporated into the CGE model, which then provides a set of link variables for the micro-simulation: (a) change in the allocation of workers across sectors in the economy, (b) change in returns to labor by skill and occupation, (c) change in the relative price of food and non-food consumption baskets, and (d) differentiation in per capita income/consumption growth rates across countries. The final distribution is obtained by applying the changes in these link variables to the re-weighted household survey (bottom link in Figure 6). 11 The detailed description of the methodology can also be found at http://www.worldbank.org/gidd

16

The data for the exercise is a combination of the 73 household surveys described earlier in section 4.2 and more aggregate data on income groups (usually vintiles) for 25 high income and 22 developing countries. The final sample covers more than 90 percent of the world’s population (see Table 9 in Annex II for country coverage).

Figure 6: GIDD methodological framework

Population Projection by Age Groups ( Exogenous )

Education Projection (Semi- Exogenous )

Household Survey (new sampling weights by age and education)

CGE (Growth, New Wages, New Prices, Sectoral Reallocation)

Simulated Distribution

5.2 Simulation Results

In the baseline scenario, prices of agricultural products continue to rise modestly from their 2004 levels, with the total increase reaching nearly 5 percent above the OECD industrial exports price index (MUV) by 2010. This gradual rise in prices is driven partially by lower crop yields due to climate change, partially by a re-orientation of the food consumption basket in developing countries to meats and more processed foods, which raise the demand for feed grains and are thus less ‘efficient’ in meeting caloric intake requirements, and partially by the lack of investment in agriculture due to years of declining prices. However, this rise in agricultural prices is fully offset by a decline in the price of processed food—where large productivity gains are realized in fast-growing developing countries—such that the price of the agriculture and food bundle (at the global level) remains nearly constant throughout the model horizon.

17

When rising demand for biofuels is introduced into the model, agricultural producers dramatically accelerate the output of biofuel crops by shifting resources away from other agricultural activities. This is illustrated in Figure 7, which shows the contribution of each agricultural activity in the model to the total increase in agricultural output. The production increases vary substantially by country and type of grain (Table 5), with the largest gains realized in countries with relatively more abundant land, higher initial demand (e.g., the legislative mandates adopted in the US and the EU), and the existing penetration of biofuel technologies (e.g., Brazil is more competitive in sugar-base ethanol than other producers). At the same time, the supply expansion is limited by the amount of additional land that may be brought under cultivation—which we assume is limited in the six-year horizon of the model—as well as the additional labor that may be attracted to the agricultural sector, which is limited by the large and persistent wage gaps between rural and urban incomes in the developing world.12 Therefore, output of other agricultural goods—such as rice, other crops, and livestock—declines relative to baseline as farmers find it more profitable to focus on biofuels. Given that many biofuels crops use land intensively, the returns to land rise substantially, ranging from above 40 percent in Brazil to just under 4 percent in Japan. The returns to unskilled labor rise substantially less: for developing countries as a whole, unskilled wages increase by 11 percent while land prices go up by 16 percent.

Figure 7 Impact of biofuels on global agricultural production

-1

0

1

2

3

4

5

6

7

2005 2006 2007 2008 2009 2010

P e

rc e

n t

d if

fe re

n c

e i n

r e

a l o

u tp

u t

re la

ti v

e t

o b

a s

e li n

e

Rice Wheat Corn Oil seeds

Sugar cane Other crops Livestock Agr iculture

Source: Simulations with World Bank’s ENVISAGE model.

12 In other words, although higher prices of agriculture contribute to a faster closing of rural-urban wage gaps in developing countries (relative to the baseline scenario) and reduce the incentive to migrate at the margin, an average agricultural worker still finds it advantageous to move to an urban area where earnings tend to be much higher. This labor market rigidity limits the supply response in developing countries.

18

Table 5 Biofuels impact on output prices and volume of select crops

(percent change in 2010 relative to non-biofuels scenario)

Output price Output volumes

Other cereal grains

Oil seeds Wheat

Sugar cane and

beet Agri-

culture

Other cereal grains

Oil seeds Wheat

Sugar cane and

beet Agri-

culture

United States 7.2 9.7 3.2 3.6 4.2 52.6 62.2 3.2 -0.3 13.0

Canada 4.7 5.9 2.9 3.4 3.1 61.6 65.9 11.2 4.3 17.3

Japan 2.7 2.6 1.0 0.2 0.3 28.4 23.9 10.1 0.3 1.3

Rest of high income 5.6 7.2 2.3 1.0 2.1 42.1 24.8 14.2 1.0 4.5

EU 27 and EFTA 5.2 3.4 1.7 0.6 1.4 51.6 42.6 12.3 0.8 6.9

China 7.6 6.6 2.8 2.5 3.1 40.5 25.9 5.8 -1.0 1.2

Indonesia 24.9 21.4 9.6 12.6 32.8 27.6 -5.3 1.1

Rest of developing East Asia 14.1 11.2 3.8 4.1 4.8 39.4 20.4 -4.4 -0.8 0.6

India 29.8 31.1 15.1 19.0 20.4 42.5 45.7 5.9 -3.2 4.9

Rest of South Asia 8.3 7.6 3.2 2.5 2.6 32.9 27.7 7.0 0.1 0.8

Russia 8.0 8.0 3.9 2.4 3.8 46.2 47.1 10.8 -1.1 7.1

Rest of Europe and Central Asia 7.9 8.9 4.9 4.5 5.2 48.6 49.3 5.8 -1.4 2.5

MENA Energy exporters 3.2 4.2 2.8 2.3 3.2 36.3 41.1 5.2 0.0 2.4

Rest of MENA 6.8 7.6 5.0 5.2 5.3 30.6 35.6 -0.5 -1.5 1.7

Argentina 17.8 18.7 12.6 13.2 16.3 35.9 37.6 -16.3 -16.1 9.0

Chile 6.5 3.8 3.5 4.5 55.6 8.1 0.3 4.5

Brazil 13.2 14.4 8.6 12.7 12.0 41.1 123.4 -12.7 48.5 22.2

Colombia 7.1 8.5 3.6 3.8 4.0 24.6 35.8 -1.1 -0.5 1.8

Mexico 12.1 4.9 3.9 7.0 7.1 26.8 33.7 -3.6 -2.7 1.5

Peru 14.6 16.7 7.7 7.7 8.6 29.5 39.1 -5.1 -1.1 0.8

Venezuela, R.B. 9.4 8.9 4.5 5.8 31.0 36.3 -5.7 2.8

Bolivia and Ecuador 8.1 13.8 3.8 4.8 6.1 35.6 57.1 -4.0 -1.4 2.7

Paraguay and Uruguay 18.6 19.2 9.7 14.1 13.5 35.1 47.0 -11.1 -8.2 4.8

Central America 8.5 10.2 3.1 4.7 5.0 32.8 40.2 -1.8 -1.2 2.0

Caribbean 10.0 7.9 3.8 4.8 5.4 29.8 36.9 -2.5 -1.9 1.9

Sub Saharan Africa 11.3 13.5 6.3 6.0 9.2 41.4 52.4 -13.0 -2.1 3.6

High income countries 6.3 7.4 2.3 1.3 2.2 52.2 56.2 9.5 0.6 8.6

East Asia and Pacific 10.8 11.4 2.9 4.3 4.0 39.1 26.0 5.4 -1.6 1.1

South Asia 29.2 30.5 14.0 16.5 16.2 42.2 45.3 6.0 -2.7 3.9

Europe and Central Asia 8.0 8.6 4.5 4.2 4.7 47.3 48.7 7.3 -1.3 4.1

Middle East and North Africa 4.4 5.4 3.7 4.1 3.7 33.9 38.7 2.6 -0.9 2.2

Sub Saharan Africa 11.3 13.5 6.3 6.0 9.2 41.4 52.4 -13.0 -2.1 3.6

Latin America and the Caribbean 12.4 15.6 8.4 8.7 9.2 32.0 85.1 -7.3 17.2 9.2

Developing countries 11.9 19.4 7.8 11.0 7.5 38.8 56.3 4.1 3.1 3.8

World total 9.6 15.2 5.6 8.9 5.5 45.2 56.3 6.8 2.5 6.0

The increase in factor incomes is offset by a rise in consumer prices. The world price of agricultural goods increases by 10 percent relative to the base year (2004) and by 5.6 percent relative to the baseline price in 2010, while the price of agriculture and processed food rises by 2.2 percent. The incidence of the price increases is heavily biased towards the poorer regions of the world (Figure 8). This is not particularly surprising, since the two poorest regions—South Asia and Sub-Saharan Africa—do not produce large amounts of biofuels but consume large amounts of grains. As a result of this vulnerability, combined with limited producer gains in these regions, South Asia and Sub-Saharan Africa experience the largest welfare losses (in percentage terms) in the biofuels scenario (Table 6).

19

Figure 8 Impact of biofuels on consumer prices

0 2 4 6 8 10 12 14 16 18

Middle East and North Africa

Europe and Central Asia

East Asia and Pacific

Latin America and the Caribbean

Sub Saharan Africa

South Asia

Developing countries

High income countries

World total

Percent difference in CPI relative to baseline

Agriculture and food Agriculture

Source: Simulations with World Bank’s ENVISAGE model.

As a result of these price shocks, the extreme and moderate poverty headcounts in developing countries increase by 0.6 and 0.9 percentage points, respectively (Table 7).13 This increase is determined entirely by South Asia, where an additional 32.5 million people slip into extreme poverty due to higher food prices brought about by increased production of biofuels. South Asia followed by Sub-Saharan Africa, where extreme poverty rises by 1.8 million. On the other hand, the number of poor is reduced significantly in Latin America, where higher farm incomes contribute to an exit of 2.3 million people out of extreme poverty. Overall, extreme poverty rises by 32 million people; while a large number, this is only one-fifth of the near-term increase in the number of poor shown in the previous section. At the higher (moderate) poverty line, an additional 15 million people slip into poverty due to higher prices of agriculture and food commodities. The regional incidence of moderate poverty changes is very different from changes in extreme poverty, with the differences determined by sources of income and density around each poverty line. In the case of East Asia, extreme poverty hardly changes because the 2.5 million persons increase in urban poverty is nearly offset by a compensating reduction in rural poverty. On the other hand, moderate poverty in East Asia rises by 29 million people (more than 60 percent of the total poverty increase) because there are many more urban households in the vicinity of the higher poverty line. In South Asia, where both farm and non-farm households experience welfare losses due to higher food prices, the density of the 13 This paper uses the new World Bank poverty line of $1.25 (2005 PPP) per day, and, in accordance with earlier practice, defines the moderate poverty line as twice the extreme poverty line ($2.50 per day, 2005 PPP). The poverty estimates presented in this paper do not line up to the official World Bank poverty estimates published in World Development Indicators or in Chen and Ravallion (2008) due to differences in country coverage. The extreme poverty statistics in this paper are fully consistent with Chen and Ravallion (2008) at the country level, and are reasonably close at the global and regional level.

20

population around the moderate poverty line is substantially less than the density around the extreme poverty line. As a result, fewer additional households slip into moderate poverty than into extreme poverty; this is particularly true of households who earn their primary income from farming.

Table 6 Biofuels impact on consumer prices and real income

(percent change in 2010 relative to non-biofuels scenario)

Consumer price index Real income % change $2004 million

Agri-

culture Processed

food Agriculture

and food All goods

and services Households National Households National

United States 3.4 1.0 1.3 0.1 0.0 -0.3 -3,919 -43,864

Canada 2.8 1.0 1.3 0.2 -0.1 -0.5 -631 -6,206

Japan 0.6 0.3 0.3 -0.1 -0.1 -0.1 -1,730 -2,779

Rest of high income 2.6 0.7 1.0 0.0 -0.1 -0.1 -1,134 -3,268

EU 27 and EFTA 1.6 0.3 0.5 0.1 -0.1 -0.3 -11,553 -41,170

China 2.9 1.4 2.3 0.7 -1.1 -1.1 -9,933 -30,231

Indonesia 10.3 4.4 6.1 1.2 -1.4 -3.0 -2,905 -10,075

Rest of developing East Asia 4.7 1.5 2.2 0.4 -0.3 -0.6 -919 -3,099

India 19.8 5.2 13.5 5.7 -3.9 -5.5 -21,512 -54,105

Rest of South Asia 2.6 1.2 1.9 0.6 -0.5 -0.7 -1,026 -1,821

Russia 3.0 1.5 2.0 0.5 -0.5 -1.2 -2,002 -10,305

Rest of Europe and Central Asia 4.9 1.4 2.9 0.7 -0.5 -1.2 -2,027 -9,199

MENA Energy exporters 3.3 1.3 2.1 0.4 -0.4 -0.5 -2,477 -7,938

Rest of MENA 5.0 1.9 3.2 0.9 -0.8 -1.9 -1,065 -4,035

Argentina 12.9 6.5 7.1 -0.5 -0.7 -4.8 -839 -10,080

Chile 5.8 1.4 1.8 0.1 -0.1 -0.4 -55 -424

Brazil 11.0 4.7 5.8 0.0 -1.3 -5.1 -5,068 -37,377

Colombia 3.9 1.7 2.2 0.3 -0.2 -0.5 -125 -657

Mexico 6.2 2.0 3.6 -0.1 -0.4 -1.1 -2,155 -9,148

Peru 8.1 2.0 3.8 0.8 -0.6 -1.2 -382 -1,175

Venezuela, R.B. 5.2 1.6 2.5 0.3 -0.4 -0.7 -386 -1,258

Bolivia and Ecuador 5.6 2.1 2.9 0.7 -0.1 -1.2 -35 -615

Paraguay and Uruguay 10.7 4.8 5.9 0.8 -1.0 -6.3 -200 -1,744

Central America 5.1 1.6 2.4 0.6 -0.3 -1.2 -235 -1,415

Caribbean 4.9 1.7 2.6 0.0 -0.4 -0.7 -746 -2,147

Sub Saharan Africa 9.0 1.9 4.9 1.8 -1.4 -2.5 -6,455 -19,170

High income countries 1.9 0.5 0.7 0.1 -0.1 -0.3 -18,967 -97,287

East Asia and Pacific 3.5 1.9 2.7 0.7 -1.0 -1.2 -13,757 -43,405

South Asia 16.1 4.1 10.6 4.4 -3.0 -4.4 -22,538 -55,926

Europe and Central Asia 4.2 1.4 2.5 0.6 -0.5 -1.2 -4,028 -19,504

Middle East and North Africa 3.7 1.4 2.3 0.4 -0.4 -0.7 -3,543 -11,973

Sub Saharan Africa 9.0 1.9 4.9 1.8 -1.4 -2.5 -6,455 -19,170

Latin America and the Caribbean 7.2 3.1 4.1 0.0 -0.6 -2.4 -10,227 -66,040

Developing countries 7.6 2.4 4.7 1.0 -1.0 -1.8 -60,548 -216,018

World total 5.6 1.0 2.2 0.2 -0.3 -0.6 -79,516 -313,305

The previous discussion alluded several times to the critical importance of the farm/non- farm distinction to the poverty outcomes. Compared with the baseline, in which the urban wage premium of unskilled workers in developing countries reduces by 8 percent between 2004 and 2010, the same wage premium is reduced by 24 percent in the biofuels scenario. On the other hand, these income gains are offset by the increase in the cost of

21

consumption basket of farmers, who spend a larger portion of their income on food than the richer urban consumers. As a result, the extreme poverty headcount in agriculture remains virtually unchanged between the biofuels scenario and baseline, while the headcount for households with a primary income source from non-agriculture activities rises by 1.3 percentage points. Therefore, nearly all of the poverty increase at the global level is accounted for by the rise in urban poverty, although this statement does not hold at the regional level (Figure 9).

Table 7 Biofuels impact on poverty

Poverty headcount Number of poor

Circa 2005

Baseline, 2010

Biofuels, 2010

Circa 2005 Baseline,

2010 Biofuels,

2010 US$1.25 (PPP) per day poverty line East Asia and Pacific 16.96 7.42 7.42 307,152,633 137,376,331 137,441,961 Eastern Europe and Central Asia 5.38 3.07 3.04 20,747,445 11,748,843 11,656,271 Latin America and Caribbean 8.13 5.93 5.48 39,872,727 30,501,838 28,203,873 Middle East and North Africa 2.88 1.14 1.13 5,889,996 2,522,362 2,483,783 South Asia 39.32 26.51 28.57 566,604,647 400,893,876 433,458,721 Sub-Saharan Africa 49.70 37.30 37.52 268,110,910 215,159,468 216,962,042 Developing countries 24.80 15.78 16.38 1,208,378,358 798,202,718 830,206,651 US$2.50 (PPP) per day poverty line East Asia and Pacific 51.72 36.15 37.71 936,465,080 669,278,004 698,355,547 Eastern Europe and Central Asia 24.23 16.03 16.13 93,394,142 61,314,686 61,852,500 Latin America and Caribbean 21.45 16.84 15.78 105,239,042 86,580,829 81,254,933 Middle East and North Africa 29.72 18.54 18.77 60,874,303 40,913,398 41,440,242 South Asia 85.81 80.04 81.06 1,236,590,090 1,210,566,763 1,229,975,339 Sub-Saharan Africa 80.46 71.90 72.27 434,028,868 414,785,230 417,889,848 Developing countries 58.84 49.10 49.95 2,866,591,525 2,483,438,909 2,530,768,410 Source: Authors' simulations with the GIDD and ENVISAGE models

Figure 9 Decomposition of poverty impact of biofuels

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

East As ia and Pacific

Easte rn Europe a nd Central Asia

Latin Ame rica

and Ca ribbea n

Middle East and North

Afric a

South Asia Sub- Saharan

Africa

Developing countries

C o

n tr

ib u

ti o n

t o

t o

ta l

c h

a n

g e

i n t

h e

e x tr

e m

e p

o v e

rt y h

e a d c o

u n

t ra

ti o

Source: Simulations w ith World Bank’s GIDD model.

Households with primary income source in non-agriculture

Households with primary income source in agriculture

22

6 Conclusions The spike in food prices between 2005 and the first half of 2008 has highlighted the vulnerabilities of poor consumers to higher prices of agricultural goods and has generated calls for massive policy action. This paper has provided a formal assessment of the first- and second-order implications of higher prices for global poverty using a representative sample of 63 to 93 percent of the population of the developing world. Using data on changes in the domestic food CPI over the period covering January 2005 and December 2007--when food prices increased by an average of 5.6 percent in real terms--the paper finds that the implied increase in the extreme poverty headcount at the global level is 1.7 percentage points. This estimate takes into account both the increase in the cost of each household’s food consumption basket and the rise in incomes of households that derive at least some of their earnings from the production of agricultural goods. The global number hides a significant amount of regional variation, with poverty in Eastern Europe and Central Asia and Latin America remaining roughly unchanged, while the headcount ratios in East Asia and the Middle East and North Africa increase by more than almost 6 and 2.4 percentage points, respectively. Although agricultural prices have declined from their mid-2008 highs, there are some indications that the long-term downward trend in the prices of agricultural commodities may be coming to an end, and thus the recent food crisis may be just a 'preview' of a world with higher food prices. By linking the household survey data with a general equilibrium model, the paper finds that a 5.5 percent increase in agricultural prices due to rising demand for first-generation biofuels could raise global poverty in 2010 by 0.6 percentage points at the extreme poverty line and 0.9 percentage points at the moderate poverty line. Poverty increases at the regional level vary substantially, with nearly all of the increase in extreme poverty occurring in South Asia and Sub-Saharan Africa. Although farmers benefit from higher output prices, they also tend to consume more food than the richer urban dwellers, which results in the agricultural poverty headcount remaining unchanged while the non-agriculture poverty headcounts increases by 1.3 percentage points. The results in this paper suggest that the poverty consequences of higher food prices are substantial, but that the implied total poverty elasticity of high prices (taking indirect effects into account) is much lower than the first-order, or direct, elasticity. Still, millions of consumers could fall into extreme poverty due to higher food prices, and millions more already under the poverty line are likely to experience a further deterioration in their living standards. The paper's results are dependent on a number of assumptions and estimated relationships--including food consumption shares in a number of countries, the share of self-employed income of agricultural households, structural features of the general equilibrium model, and the link between variables of the micro-simulation--and therefore should not be interpreted as the effect of higher food prices on poverty. The results nonetheless provide an important contribution to the discourse by identifying the relevant transmission channels, establishing the orders of magnitude, and exposing the regional and country variation concealed in the aggregate numbers.

23

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26

Annex I

Table 8 ENVISAGE dimensions

Regions Sectors United States MENA Energy exporters Paddy rice Other mining Canada Rest of MENA Wheat Processed food Japan Brazil Other cereal grains Refined oil Rest of high income Mexico Oil seeds Chemicals etc. Western Europe Colombia Sugar cane and beet Energy int. manu. China Peru Other crops Other manufacturing Indonesia Venezuela, R.B. Livestock Electricity Rest of Dev. East Asia Argentina Forestry Gas distribution India Chile Coal Construction Rest of South Asia Bolivia and Ecuador Crude oil Transport services Russia Paraguay & Uruguay Natural gas Other services Rest of ECA Central America Sub Saharan Africa Caribbean

27

Annex II Table 9: Country composition of the GIDD dataset

Region Covered population Actual population Covered Population (%)

World 5,498,162 6,076,509 90.48 East Asia and Pacific 1,733,358 1,817,232 95.38 Eastern Europe and Central Asia 460,385 471,549 97.63 High Income Countries 764,285 974,612 78.42 Latin America 500,199 515,069 97.11 Middle East and North Africa 190,397 276,447 68.87 South Asia 1,332,800 1,358,294 98.12 Sub-Saharan Africa 516,737 663,305 77.90

Economy Covered population Actual population Data used

East Asia and Pacific 1,733,358 1,805,691 China 1,260,000 1,260,000 grouped Indonesia 212,000 212,000 individual Vietnam 80,400 80,400 individual Philippines 71,600 71,600 individual Thailand 61,700 61,700 individual Malaysia 23,300 23,300 grouped Cambodia 11,900 11,900 individual Lao PDR 4,927 4,927 individual Papua New Guinea 5,133 5,133 grouped Mongolia 2,398 2,398 grouped Myanmar 47,700 Korea, Dem. Rep. 21,900 Fiji 811 Timor-Leste 784 Solomon Islands 419 Vanuatu 191 Samoa 177 Micronesia, Fed. Sts. 107 Tonga 100 Kiribati 91 Marshall Islands 53 Eastern Europe and Central Asia 460,385 471,549 Russian Federation 136,000 146,000 individual Turkey 69,600 67,400 individual Ukraine 47,600 49,200 individual Poland 38,300 38,500 individual Uzbekistan 25,100 24,700 individual Romania 21,800 22,400 individual Kazakhstan 15,000 14,900 individual Serbia and Montenegro 10,600 8,137 grouped Czech Republic 10,300 10,300 grouped Hungary 9,876 10,200 individual Belarus 9,994 10,000 individual Azerbaijan 8,199 8,049 individual Bulgaria 7,906 8,060 individual Tajikistan 6,376 6,159 individual Slovak Republic 5,393 5,389 grouped

28

Georgia 4,514 4,720 individual Kyrgyz Republic 5,008 4,915 individual Turkmenistan 4,644 4,502 grouped Croatia 4,446 4,503 grouped Moldova 4,259 4,275 individual Lithuania 3,477 3,500 individual Armenia 3,065 3,082 individual Albania 3,139 3,062 individual Latvia 2,383 2,372 grouped Estonia 1,363 1,370 individual Macedonia, FYR 2,044 2,010 individual Bosnia and Herzegovina 3,847 High Income Countries 764,285 974,612 United States 282,000 282,000 grouped Germany 82,200 82,200 grouped France 58,900 58,900 grouped United Kingdom 58,800 59,700 grouped Italy 57,700 56,900 grouped Korea, Rep. 47,000 47,000 grouped Spain 40,500 40,300 grouped Canada 30,800 30,800 grouped Netherlands 15,900 15,900 grouped Greece 10,900 10,900 grouped Belgium 10,300 10,300 grouped Portugal 10,100 10,200 grouped Sweden 8,875 8,869 grouped Austria 8,011 8,012 grouped Hong Kong, China 6,669 6,665 grouped Israel 6,282 6,289 grouped Denmark 5,338 5,337 grouped Finland 5,177 5,176 grouped Norway 4,492 4,491 grouped Singapore 4,020 4,018 grouped New Zealand 3,864 3,858 grouped Ireland 3,815 3,805 grouped Slovenia 1,986 1,989 grouped Luxembourg 441 438 grouped Netherlands Antilles 215 176 grouped Japan 127,000 Taiwan, China 22,200 Saudi Arabia 20,700 Australia 19,200 Switzerland 7,184 Puerto Rico 3,816 United Arab Emirates 3,247 Kuwait 2,190 Cyprus 694 Bahrain 672 Qatar 606 Macao, China 444 Malta 390 Brunei Darussalam 333 Bahamas, The 301 Iceland 281

29

French Polynesia 236 New Caledonia 213 Guam 155 Channel Islands 147 Virgin Islands (U.S.) 109 Antigua and Barbuda 76 Isle of Man 76 Bermuda 62 Greenland 56 Latin America 500,199 515,069 Brazil 172,000 174,000 individual Mexico 98,000 98,000 individual Colombia 41,600 42,100 individual Argentina 37,300 36,900 individual Peru 26,800 26,000 individual Venezuela, RB 24,300 24,300 individual Chile 15,200 15,400 individual Ecuador 12,000 12,300 individual Guatemala 11,800 11,200 individual Bolivia 8,514 8,317 individual Dominican Republic 7,950 8,265 individual Haiti 8,146 7,939 individual Honduras 6,281 6,424 individual El Salvador 6,409 6,280 individual Paraguay 5,386 5,346 individual Nicaragua 5,186 4,920 individual Costa Rica 3,805 3,929 individual Uruguay 3,332 3,342 individual Panama 2,849 2,950 individual Jamaica 2,607 2,589 individual Guyana 733 744 individual Cuba 11,100 Trinidad and Tobago 1,285 Suriname 434 Barbados 266 Belize 250 St. Lucia 156 St. Vincent and the Grenadines 116 Grenada 101 Dominica 71 St. Kitts and Nevis 44 Middle East and North Africa 190,397 276,447 Egypt, Arab Rep. 67,300 67,300 grouped Iran, Islamic Rep. 63,700 63,700 grouped Morocco 27,800 27,800 individual Yemen, Rep. 16,500 17,900 individual Tunisia 9,565 9,564 grouped Jordan 5,532 4,857 individual Algeria 30,500 Iraq 23,200 Syrian Arab Republic 16,800 Libya 5,306 Lebanon 3,398 West Bank and Gaza 2,966

30

Oman 2,442 Djibouti 715 South Asia 1,332,800 1,358,294 India 1,020,000 1,020,000 individual Pakistan 142,000 138,000 individual Bangladesh 131,000 129,000 individual Nepal 20,800 24,400 individual Sri Lanka 19,000 19,400 individual Afghanistan 26,600 Bhutan 604 Maldives 290 Sub-Saharan Africa 516,737 663,305 Nigeria 137,000 118,000 individual Ethiopia 64,300 64,300 individual South Africa 43,900 44,000 individual Tanzania 34,500 34,800 individual Kenya 28,100 30,700 individual Uganda 24,600 24,300 individual Ghana 19,300 19,900 individual Côte d'Ivoire 16,500 16,700 individual Madagascar 16,000 16,200 individual Cameroon 15,500 14,900 individual Zimbabwe 12,600 12,600 grouped Zambia 12,600 10,700 grouped Niger 11,800 11,800 grouped Mali 11,100 11,600 individual Burkina Faso 10,800 11,300 individual Malawi 10,300 11,500 grouped Rwanda 8,024 8,025 grouped Guinea 7,929 8,434 individual Senegal 7,914 10,300 individual Benin 6,718 7,197 individual Burundi 6,563 6,486 individual Sierra Leone 4,509 4,509 grouped Mauritania 2,668 2,645 individual Lesotho 1,743 1,788 grouped Gambia, The 1,217 1,316 individual Comoros 554 540 grouped Congo, Dem. Rep. 50,100 Sudan 32,900 Mozambique 17,900 Angola 13,800 Chad 8,216 Somalia 7,012 Togo 5,364 Central African Republic 3,777 Eritrea 3,557 Congo, Rep. 3,438 Liberia 3,065 Namibia 1,894 Botswana 1,754 Guinea-Bissau 1,366 Gabon 1,272 Mauritius 1,187

31

32

Swaziland 1,045 Cape Verde 451 Equatorial Guinea 449 São Tomé and Principe 140 Seychelles 81

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2005 15: 1277Qual Health Res Hsiu-Fang Hsieh and Sarah E. Shannon

Three Approaches to Qualitative Content Analysis

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10.1177/1049732305276687QUALITATIVE HEALTH RESEARCH / November 2005Hsieh, Shannon / PROBLEMS WITH INTERVIEWS

Three Approaches to Qualitative Content Analysis

Hsiu-Fang Hsieh Sarah E. Shannon

Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conven- tional, directed, or summative. All three approaches are used to interpret meaning from the content of text data and, hence, adhere to the naturalistic paradigm. The major differences among the approaches are coding schemes, origins of codes, and threats to trustworthiness. In conventional content analysis, coding categories are derived directly from the text data. With a directed approach, analysis starts with a theory or relevant research findings as guid- ance for initial codes. A summative content analysis involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context. The authors delineate analytic procedures specific to each approach and techniques addressing trustworthiness with hypothetical examples drawn from the area of end-of-life care.

Keywords: content analysis; qualitative research; research methodology; end-of-life care

Content analysis is a research method that has come into wide use in healthstudies in recent years. A search of content analysis as a subject heading term in the Cumulative Index to Nursing and Allied Health Literature produced more than 4,000 articles published between 1991 and 2002. The number of studies reporting the use of content analysis grew from only 97 in 1991 to 332 in 1997 and 601 in 2002.

Researchers regard content analysis as a flexible method for analyzing text data (Cavanagh, 1997). Content analysis describes a family of analytic approaches rang- ing from impressionistic, intuitive, interpretive analyses to systematic, strict textual analyses (Rosengren, 1981). The specific type of content analysis approach chosen by a researcher varies with the theoretical and substantive interests of the researcher and the problem being studied (Weber, 1990). Although this flexibility has made content analysis useful for a variety of researchers, the lack of a firm definition and procedures has potentially limited the application of content analysis (Tesch, 1990).

The differentiation of content analysis is usually limited to classifying it as pri- marily a qualitative versus quantitative research method. A more thorough analysis of the ways in which qualitative content analysis can be used would potentially illu- minate key issues for researchers to consider in the design of studies purporting to

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AUTHORS’ NOTE: We wish to express our gratitude to Drs. Pamela L. Jordan, Carol J. Leppa, and J. Randall Curtis for their feedback and support in writing this article.

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use content analysis and the analytic procedures employed in such studies, thus avoiding a muddling of methods (Morse, 1991).

Our purpose in this article is to present the breadth of approaches categorized as qualitative content analysis. We have identified three distinct approaches: con- ventional, directed, and summative. All three approaches are used to interpret text data from a predominately naturalistic paradigm. We begin with a brief review of the history and definitions of content analysis. We then illustrate the three different approaches to qualitative content analysis with hypothetical studies to explicate the issues of study design and analytical procedures for each approach.

BACKGROUND ON THE DEVELOPMENT OF CONTENT ANALYSIS

Content analysis has a long history in research, dating back to the 18th century in Scandinavia (Rosengren, 1981). In the United States, content analysis was first used as an analytic technique at the beginning of the 20th century (Barcus, 1959). Initially, researchers used content analysis as either a qualitative or quantitative method in their studies (Berelson, 1952). Later, content analysis was used primarily as a quan- titative research method, with text data coded into explicit categories and then described using statistics. This approach is sometimes referred to as quantitative analysis of qualitative data (Morgan, 1993) and is not our primary focus in this arti- cle. More recently, the potential of content analysis as a method of qualitative analy- sis for health researchers has been recognized, leading to its increased application and popularity (Nandy & Sarvela, 1997).

Qualitative content analysis is one of numerous research methods used to ana- lyze text data. Other methods include ethnography, grounded theory, phenomenol- ogy, and historical research. Research using qualitative content analysis focuses on the characteristics of language as communication with attention to the content or contextual meaning of the text (Budd, Thorp, & Donohew, 1967; Lindkvist, 1981; McTavish & Pirro, 1990; Tesch, 1990). Text data might be in verbal, print, or elec- tronic form and might have been obtained from narrative responses, open-ended survey questions, interviews, focus groups, observations, or print media such as articles, books, or manuals (Kondracki & Wellman, 2002). Qualitative content anal- ysis goes beyond merely counting words to examining language intensely for the purpose of classifying large amounts of text into an efficient number of categories that represent similar meanings (Weber, 1990). These categories can represent either explicit communication or inferred communication. The goal of content analysis is “to provide knowledge and understanding of the phenomenon under study” (Downe-Wamboldt, 1992, p. 314). In this article, qualitative content analysis is defined as a research method for the subjective interpretation of the content of text data through the systematic classification process of coding and identifying themes or patterns.

To illustrate the possible applications of content analysis, we constructed hypo- thetical studies drawn from the area of end-of-life (EOL) research. Content analysis has been a popular analytic method in studies related to EOL care, an area of increasing emphasis as demonstrated by its inclusion as one of the five research themes supported by the National Institutes of Health, National Institute of Nurs-

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ing Research (NINR) for 2003 (“Enhancing the End-of-Life Experience for Patients and Their Families,” NINR, 2003).

CONVENTIONAL CONTENT ANALYSIS

Researcher X used a conventional approach to content analysis in her study (Table 1). Conventional content analysis is generally used with a study design whose aim is to describe a phenomenon, in this case the emotional reactions of hos- pice patients. This type of design is usually appropriate when existing theory or research literature on a phenomenon is limited. Researchers avoid using precon- ceived categories (Kondracki & Wellman, 2002), instead allowing the categories and names for categories to flow from the data. Researchers immerse themselves in the data to allow new insights to emerge (Kondracki & Wellman, 2002), also described as inductive category development (Mayring, 2000). Many qualitative methods share this initial approach to study design and analysis.

If data are collected primarily through interviews, open-ended questions will be used. Probes also tend to be open-ended or specific to the participant’s comments rather than to a preexisting theory, such as “Can you tell me more about that?” Data analysis starts with reading all data repeatedly to achieve immersion and obtain a sense of the whole (Tesch, 1990) as one would read a novel. Then, data are read word by word to derive codes (Miles & Huberman, 1994; Morgan, 1993; Morse & Field, 1995) by first highlighting the exact words from the text that appear to capture key thoughts or concepts. Next, the researcher approaches the text by making notes of his or her first impressions, thoughts, and initial analysis. As this process continues, labels for codes emerge that are reflective of more than one key thought. These often come directly from the text and are then become the initial coding scheme. Codes then are sorted into categories based on how different codes are related and linked. These emergent categories are used to organize and group codes into meaningful clusters (Coffey & Atkinson, 1996; Patton, 2002). Ideally, the numbers of clusters are between 10 and 15 to keep clusters broad enough to sort a large number of codes (Morse & Field, 1995).

Depending on the relationships between subcategories, researchers can com- bine or organize this larger number of subcategories into a smaller number of cate- gories. A tree diagram can be developed to help in organizing these categories into a hierarchical structure (Morse & Field, 1995). Next, definitions for each category, subcategory, and code are developed. To prepare for reporting the findings, ex- emplars for each code and category are identified from the data. Depending on the purpose of the study, researchers might decide to identify the relationship between categories and subcategories further based on their concurrence, antecedents, or consequences (Morse & Field, 1995).

With a conventional approach to content analysis, relevant theories or other research findings are addressed in the discussion section of the study. In Researcher X’s study, she might compare and contrast her findings to Kübler-Ross’s (1969) the- ory. The discussion would include a summary of how the findings from her study contribute to knowledge in the area of interest and suggestions for practice, teach- ing, and future research.

The advantage of the conventional approach to content analysis is gaining direct information from study participants without imposing preconceived catego-

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ries or theoretical perspectives. Researcher X’s study depicts a research question appropriate for this approach. Knowledge generated from her content analysis is based on participants’ unique perspectives and grounded in the actual data. Her sampling technique was designed to maximize diversity of emotional reactions, and the analysis techniques were structured to capture that complexity.

One challenge of this type of analysis is failing to develop a complete under- standing of the context, thus failing to identify key categories. This can result in findings that do not accurately represent the data. Lincoln and Guba (1985) de- scribed this as credibility within the naturalistic paradigm of trustworthiness or internal validity within a paradigm of reliability and validity. Credibility can be established through activities such as peer debriefing, prolonged engagement, per- sistent observation, triangulation, negative case analysis, referential adequacy, and member checks (Lincoln & Guba, 1985; Manning, 1997).

Another challenge of the conventional approach to content analysis is that it can easily be confused with other qualitative methods such as grounded theory

1280 QUALITATIVE HEALTH RESEARCH / November 2005

TABLE 1: Hypothetical Research Study Using a Conventional Approach to Content Analysis— Researcher X’s Study

Little is known about the emotional reactions of terminally ill patients who are receiving hospice care, possibly because of their reluctance to discuss death issues (Wilson & Fletcher, 2002). Some patients might feel relieved to have active therapy end, whereas others might feel afraid or even abandoned. Researcher X wanted to learn more about the emotional experiences of hospice patients to be able to address their needs more effectively. Because there was no existing theory to serve as a framework for her study, her research question was “What are the emotional reactions of terminally ill patients who are receiving hospice care?”

Based on her clinical experience, Researcher X suspected that the emotional reactions of patients who were new to hospice care differed from those who had been in hospice care for a longer period. She also suspected that those receiving home hospice care had different experiences from those receiving in-patient hospice care. Researcher X therefore decided to use a stratified sampling tech- nique to ensure heterogeneity of the sample. The target sample size was 10 home hospice patients and 10 inpatient hospice patients, with 5 from each group being recruited within 48 hours of enroll- ment into hospice and 5 recruited 7 to 10 days following enrollment. In addition, the sample would include both men and women and both older and middle-aged people.

Prior to recruitment and data collection, the research procedures were approved for use with human subjects. Informed consent was obtained from all participants. Researcher X collected data through individual interviews using open-ended questions such as “What has it been like to be in hospice care?” followed by specific probes. All interviews were audiotape-recorded and tran- scribed verbatim.

Researcher X used content analysis to analyze the data. She began by reading each transcript from beginning to end, as one would read a novel. Then, she read each transcript carefully, highlighting text that appeared to describe an emotional reaction and writing in the margin of the text a keyword or phrase that seemed to capture the emotional reaction, using the participant’s words. As she worked through the transcript, she attempted to limit these developing codes as much as possible. After open coding of three to four transcripts, Researcher X decided on preliminary codes. She then coded the remaining transcripts (and recoded the original ones) using these codes and adding new codes when she encountered data that did not fit into an existing code.

Once all transcripts had been coded, Researcher X examined all data within a particular code. Some codes were combined during this process, whereas others were split into subcategories. Finally, she examined the final codes to organize them into a hierarchical structure if possible.

In the findings, the emotional responses of hospice patients were described using the identified codes and hierarchical structure. In discussion of the findings, the results from this content analysis were compared and contrasted with Kübler-Ross’s (1969) model to highlight similarities and differences.

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method (GTM) or phenomenology. These methods share a similar initial analytical approach but go beyond content analysis to develop theory or a nuanced under- standing of the lived experience. The conventional approach to content analysis is limited in both theory development and description of the lived experience, be- cause both sampling and analysis procedures make the theoretical relationship between concepts difficult to infer from findings. At most, the result of a conven- tional content analysis is concept development or model building (Lindkvist, 1981). For example, Researcher X might find that patients who are new to hospice care express worry about how their social obligations will be met (such as finding care for a pet), whereas patients who have been in hospice for long periods might express more anticipatory grief. Researcher X might compare her findings to those of Kübler-Ross (1969) and conclude that an additional emotional reaction to enter- ing hospice care is the process of “tying up loose ends,” which she might define as making both financial and social arrangements.

DIRECTED CONTENT ANALYSIS

Sometimes, existing theory or prior research exists about a phenomenon that is incomplete or would benefit from further description. The qualitative researcher might choose to use a directed approach to content analysis, as Researcher Y did (Table 2). Potter and Levine-Donnerstein (1999) might categorize this as a deductive use of theory based on their distinctions on the role of theory. However the key ten- ets of the naturalistic paradigm form the foundation of Researcher Y’s general approach to the study design and analysis. The goal of a directed approach to con- tent analysis is to validate or extend conceptually a theoretical framework or theory. Existing theory or research can help focus the research question. It can provide pre- dictions about the variables of interest or about the relationships among variables, thus helping to determine the initial coding scheme or relationships between codes. This has been referred to as deductive category application (Mayring, 2000).

Content analysis using a directed approach is guided by a more structured pro- cess than in a conventional approach (Hickey & Kipping, 1996). Using existing the- ory or prior research, researchers begin by identifying key concepts or variables as initial coding categories (Potter & Levine-Donnerstein, 1999). Next, operational definitions for each category are determined using the theory. In Researcher Y’s study, Kübler-Ross’s (1969) five stages of grief served as an initial framework to identify emotional stages of terminally ill patients.

If data are collected primarily through interviews, an open-ended question might be used, followed by targeted questions about the predetermined categories. After an open-ended question, Researcher Y used probes specifically to explore par- ticipants’ experiences of denial, anger, bargaining, depression, and acceptance. Coding can begin with one of two strategies, depending on the research question. If the goal of the research is to identify and categorize all instances of a particular phe- nomenon, such as emotional reactions, then it might be helpful to read the transcript and highlight all text that on first impression appears to represent an emotional reaction. The next step in analysis would be to code all highlighted passages using the predetermined codes. Any text that could not be categorized with the initial coding scheme would be given a new code.

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The second strategy that can be used in directed content analysis is to begin cod- ing immediately with the predetermined codes. Data that cannot be coded are iden- tified and analyzed later to determine if they represent a new category or a subcate- gory of an existing code. The choice of which of these approaches to use depends on the data and the researcher’s goals. If the researcher wants to be sure to capture all possible occurrences of a phenomenon, such as an emotional reaction, highlighting identified text without coding might increase trustworthiness. If the researcher feels confident that initial coding will not bias the identification of relevant text, then coding can begin immediately. Depending on the type and breadth of a cate- gory, researchers might need to identify subcategories with subsequent analysis. For example, Researcher Y might decide to separate anger into subcategories de- pending on whom the anger was directed toward.

The findings from a directed content analysis offer supporting and nonsup- porting evidence for a theory. This evidence can be presented by showing codes with exemplars and by offering descriptive evidence. Because the study design and analysis are unlikely to result in coded data that can be compared meaningfully using statistical tests of difference, the use of rank order comparisons of frequency

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TABLE 2: Hypothetical Research Study Using a Directed Approach to Content Analysis— Researcher Y’s Study

Despite their wide acceptance and popularity, Kübler-Ross’s (1969) five stages of grief (denial, anger, bargaining, depression, and acceptance) have not been confirmed through other research. In taking care of terminally ill patients, Researcher Y wondered how well Kübler-Ross’s theory described his patients’ experiences with imminent death. His research question was “How well does Kübler-Ross’s model describe the emotional passages or journeys of patients who have been diag- nosed with a terminal illness?”

Researcher Y designed a sampling plan to maximize the chance of recruiting participants at differ- ent stages. All participants were diagnosed with a terminal illness, but one third were recruited while receiving “last chance” forms of curative therapy, one third after they refused further curative ther- apy but were not enrolled in hospice care, and one third who were contemplating (or had recently made) the decision to enter hospice care. In addition, the sample was recruited for gender balance and diagnostic diversity, specifically both oncology and non-oncology diagnoses. The target sample size was 18 to 21 participants. Interviews were conducted with individuals using open-ended questions, such as “What has your emotional journey been since being diagnosed with this illness?” Specific probes were developed based on Kübler-Ross’s model, such as Have you felt angry since your diagnosis? After institutional review board approval, informed consent from all participants was obtained. All interviews were audiotape-recorded and transcribed verbatim.

Researcher Y developed operational definitions of the five emotional responses (anger, bargaining, etc.) identified in Kübler-Ross’s model. He then reviewed all transcripts carefully, highlighting all text that appeared to describe an emotional response. All highlighted text was coded using the predeter- mined categories wherever possible. Text that could not be coded into one of these categories was coded with another label that captured the essence of the emotion. After coding, Researcher Y exam- ined the data for each category to determine whether subcategories were needed for a category (e.g., anger toward self, anger toward doctors, anger toward spiritual being). Data that could not be coded into one of the five categories derived from the theory were reexamined to describe different emotional reactions. Finally, Researcher Y compared the extent to which the data were supportive of Kübler-Ross’s theory versus how much represented different emotional responses. The report of study findings described the incidence of codes representing the emotional stages suggested by Kübler-Ross with those that represented different emotional responses by comparing the rank order of all codes. In the discussion section, Researcher Y summarized how the study validated Kübler- Ross’s model and what new perspectives were added.

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of codes can be used (Curtis et al., 2001). Researcher Y might choose to describe his study findings by reporting the incidence of codes that represented the five main categories derived from Kübler-Ross (1969) and the incidence of newly identified emotional reactions. He also could descriptively report the percent of supporting versus nonsupporting codes for each participant and for the total sample.

The theory or prior research used will guide the discussion of findings. Newly identified categories either offer a contradictory view of the phenomenon or might further refine, extend, and enrich the theory. In Researcher Y’s study, the discussion might focus on the extent to which participants’ emotional journeys paralleled Kübler-Ross’s (1969) model and the newly identified emotional reactions or stages that were experienced by participants in the study.

The main strength of a directed approach to content analysis is that existing theory can be supported and extended. In addition, as research in an area grows, a directed approach makes explicit the reality that researchers are unlikely to be working from the naive perspective that is often viewed as the hallmark of natural- istic designs.

The directed approach does present challenges to the naturalistic paradigm. Using theory has some inherent limitations in that researchers approach the data with an informed but, nonetheless, strong bias. Hence, researchers might be more likely to find evidence that is supportive rather than nonsupportive of a theory. Sec- ond, in answering the probe questions, some participants might get cues to answer in a certain way or agree with the questions to please researchers. In Researcher Y’s study, some patients might agree with the suggested emotional stages even though they did not experience the emotion. Third, an overemphasis on the theory can blind researchers to contextual aspects of the phenomenon. In Researcher Y’s study, the emphasis on Kübler-Ross’s (1969) stages of emotional response to loss might have clouded his ability to recognize contextual features that influence emotions. For example, the cross-sectional design of the study might have overemphasized current emotional reactions. These limitations are related to neutrality or confirm- ability of trustworthiness as the parallel concept to objectivity (Lincoln & Guba, 1985). To achieve neutral or unbiased results, an audit trail and audit process can be used. In Researcher Y’s study, the vague terminology used in Kübler-Ross’s de- scription of the model would be a challenge for the researcher in creating useful operational definitions. Having an auditor review and examine these definitions before the study could greatly increase the accuracy of predetermined categories.

SUMMATIVE CONTENT ANALYSIS

Typically, a study using a summative approach to qualitative content analysis starts with identifying and quantifying certain words or content in text with the purpose of understanding the contextual use of the words or content (Table 3). This quantifi- cation is an attempt not to infer meaning but, rather, to explore usage. Analyzing for the appearance of a particular word or content in textual material is referred to as manifest content analysis (Potter & Levine-Donnerstein, 1999). If the analysis stopped at this point, the analysis would be quantitative, focusing on counting the frequency of specific words or content (Kondracki & Wellman, 2002). A summative approach to qualitative content analysis goes beyond mere word counts to include latent content analysis. Latent content analysis refers to the process of interpretation

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of content (Holsti, 1969). In this analysis, the focus is on discovering underlying meanings of the words or the content (Babbie, 1992; Catanzaro, 1988; Morse & Field, 1995). In Researcher Z’s study, the initial part of the analysis technique, to count the frequency of death, die, and dying is more accurately viewed as a quantitative approach. However, Researcher Z went on to identify alternative terms for death and to examine the contexts within which direct versus euphemistic terms were used. Hence, Researcher Z used a summative approach to qualitative content analysis.

Researchers report using content analysis from this approach in studies that analyze manuscript types in a particular journal or specific content in textbooks. Examples include studies examining content related to EOL care in medical text- books (Rabow, Hardie, Fair, & McPhee, 2000), EOL care in critical care nursing textbooks (Kirchhoff, Beckstrand, & Anumandla, 2003), palliative care in nurs- ing textbooks (Ferrell, Virani, Grant, & Juarez, 2000), death and bereavement in nursing textbooks (Ferrell, Virani, Grant, & Borneman, 1999), and spirituality in

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TABLE 3: Hypothetical Research Study Using a Summative Approach to Content Analysis— Researcher Z’s Study

Talking about death has virtually been banished from our language (Callahan, 1995). Use of the terms die, dying, and death remain taboo in U.S. society in favor of euphemisms such as passing, going to a better place, and so on. A failure to use explicit terms might hinder the effectiveness of communi- cation between physicians and patients (Levy, 2001; Vincent, 1997). Recognizing this problem, Researcher Z wanted to know how often health care providers, patients, or family members used explicit terms versus euphemisms. Under what circumstances are these explicit terms used? Her research question was How are the terms die, dying, and death used in clinician-patient communica- tion when discussing hospice care, and what alternative terms are used?

Researcher Z designed a sampling plan to maximize the diversity of the sample around demographic characteristics of both the clinician and the patient/family. Patient characteristics included gender, age, diagnosis, and ethnic background. Clinician characteristics included gender, discipline, and area of specialization. Two types of communication events with patients who had received a terminal diagnosis were sampled. One was discharge teaching for hospitalized patients who were being transferred to home hospice, inpatient hospice, or skilled nursing facilities for end- of-life (EOL) care. The other communication event was clinician-patient/family conferences in out- or inpatient settings to plan EOL care. Fifty separate communication events were sampled for 50 differ- ent clinicians and patient/family pairs. The research proposal was approved by institutional review boards before data collection. Informed consent was obtained from each participant. All clinician- patient conversations were audiotape-recorded and transcribed verbatim.

Data analysis started with computer-assisted searches for occurrences of the terms die, death, and dying in the transcripts. Word frequency counts for each of the three death-related terms in a transcript were calculated and compared to the total length of the communication event. Researcher Z also coded the identity of the speaker, such as physician, nurse, patient, or family member. Frequency counts by type of speaker were calculated and compared to the total number of terms coded.

Next, Researcher Z tried to identify alternative terms or expressions used instead of death, die, or dying. Occurrences of these terms were counted both as a total number and for each alternative term. Frequencies of euphemisms versus direct terms were compared for type of speaker, demographic characteristics of clinician, and demographic characteristics of patient within each communication event and across the total sample.

The major study findings described the occurrences of the three explicit terms used in clinician- patient communication as compared to euphemistic terms. Comparisons across type of speaker and characteristics of clinicians and patients were made. The discussion of this study focused on exploring possible explanations for differences in the use of explicit versus euphemistic terms when discussing EOL care for different groups and in different situations.

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nursing textbooks (McEwen, 2004). These researchers started with counting the pages that covered specific topics followed by descriptions and interpretations of the content, including evaluating the quality of the content. Others have compared the results of a content analysis with other data collected within the same research project, such as comparing preferences for various types of television programming with socioeconomic indicators of participants (Krippendorff, 1980).

In a summative approach to qualitative content analysis, data analysis begins with searches for occurrences of the identified words by hand or by computer. Word frequency counts for each identified term are calculated, with source or speaker also identified. Researcher Z wanted to know the frequency of words that were used to refer to death but also to understand the underlying contexts for the use of explicit versus euphemistic terms. He or she illuminated the context of euphemistic versus explicit terms by reporting how their usage differed by variables such as the speaker (patient versus clinician), the clinician’s specialization, and the age of the patient. Counting is used to identify patterns in the data and to contextualize the codes (Morgan, 1993). It allows for interpretation of the context associated with the use of the word or phrase. Researchers try to explore word usage or discover the range of meanings that a word can have in normal use.

A summative approach to qualitative content analysis has certain advantages. It is an unobtrusive and nonreactive way to study the phenomenon of interest (Babbie, 1992). It can provide basic insights into how words are actually used. How- ever, the findings from this approach are limited by their inattention to the broader meanings present in the data. As evidence of trustworthiness, this type of study relies on credibility. A mechanism to demonstrate credibility or internal consistency is to show that the textual evidence is consistent with the interpretation (Weber, 1990). For Researcher Z’s study, validation by content experts on what terms are used to replace the death terms would be essential. Alternatively, researchers can check with their participants as to their intended meaning through the process of member check (Lincoln & Guba, 1985).

SUMMARY OF KEY ASPECTS

All approaches to qualitative content analysis require a similar analytical process of seven classic steps, including formulating the research questions to be answered, selecting the sample to be analyzed, defining the categories to be applied, outlining the coding process and the coder training, implementing the coding process, deter- mining trustworthiness, and analyzing the results of the coding process (Kaid, 1989). We have outlined how this process differs depending on the specific content analysis approach used. The success of a content analysis depends greatly on the coding process. The basic coding process in content analysis is to organize large quantities of text into much fewer content categories (Weber, 1990). Categories are patterns or themes that are directly expressed in the text or are derived from them through analysis. Then, relationships among categories are identified. In the coding process, researchers using content analysis create or develop a coding scheme to guide coders to make decisions in the analysis of content. A coding scheme is a translation device that organizes data into categories (Poole & Folger, 1981). A cod- ing scheme includes the process and rules of data analysis that are systematic, logi-

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cal, and scientific. The development of a good coding scheme is central to trust- worthiness in research using content analysis (Folger, Hewes, & Poole, 1984).

Key differences among conventional, directed, and summative approaches to content analysis center on how initial codes are developed. In a conventional con- tent analysis, categories are derived from data during data analysis. The researcher is usually able to gain a richer understanding of a phenomenon with this approach. With a directed content analysis, the researcher uses existing theory or prior re- search to develop the initial coding scheme prior to beginning to analyze the data (Kyngas & Vanhanen, 1999). As analysis proceeds, additional codes are developed, and the initial coding scheme is revised and refined. Researchers employing a directed approach can efficiently extend or refine existing theory. The summative approach to content analysis is fundamentally different from the prior two ap- proaches. Rather than analyzing the data as a whole, the text is often approached as single words or in relation to particular content. An analysis of the patterns leads to an interpretation of the contextual meaning of specific terms or content (Table 4).

CONCLUSIONS

Different research purposes require different research designs and analysis tech- niques (Knafl & Howard, 1984). The question of whether a study needs to use a con- ventional, directed, or summative approach to content analysis can be answered by matching the specific research purpose and the state of science in the area of interest with the appropriate analysis technique.

It is important for health researchers to delineate the specific approach to con- tent analysis they are going to use in their studies before beginning data analysis. Creating and adhering to an analytic procedure or a coding scheme will increase trustworthiness or validity of the study. Careful description of the type of approach to content analysis used can provide a universal language for health researchers and strengthen the method’s scientific base. Examples used in this article were drawn from the area of research on end of life, but the content analysis techniques described could be used in a broad range of studies. Content analysis offers re- searchers a flexible, pragmatic method for developing and extending knowledge of the human experience of health and illness.

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TABLE 4: Major Coding Differences Among Three Approaches to Content Analysis

Timing of Defining Source of Type of Content Analysis Study Starts With Codes or Keywords Codes or Keywords

Conventional content analysis

Observation Codes are defined dur- ing data analysis

Codes are derived from data

Directed content analysis

Theory Codes are defined be- fore and during data analysis

Codes are derived from theory or relevant research findings

Summative content analysis

Keywords Keywords are identified before and during data analysis

Keywords are derived from interest of re- searchers or review of literature

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Nurse Specialist, 16, 298-305.

Hsiu-Fang Hsieh, Ph.D., is an assistant professor at Fooyin University, Kaohsiung Hsien, Taiwan.

Sarah E. Shannon, Ph.D., is an associate professor at the University of Washington, Seattle.

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HURTON.pdf

World Development. Vol. 16, No. 7. pp. 8-17-856, 1988. Printed in Great Britain.

0305-750x/88 S3.00 + 0.00 0 1988 Pergamon Press plc

The Social Costs of Higher Food Prices:

Some Cross-Country Evidence

SUE HORTON University of Toronto

TOM KERR Food and Agriculture Organization, Rome

and

DIMITRIS DIAKOSAVVAS* Food and Agriculture Organization, Rome

Summary. - This paper uses cross-country data from 34 developing countries to examine the correlation between the real cereal price paid by consumers, aggregate calorie intake, and the infant mortality rate. The results su,, eoest that higher cereal prices across countries are associated with higher infant mortality (the elasticity is between 0.2 and 0.6). However this correlation does not exist using time series data for individual countries. The results require further investigation: nevertheless they suggest that although the short-run costs of higher farm prices may not immcdi- ately be evident, there may be important long-run social costs.

1. INTRODUCTION

A large literature exists on the adverse effects of depressing farm prices in developing coun- tries. Several studies have used cross-country data to measure such effects. Peterson (1979) estimates that for a group of 27 developing coun- tries, more favorable farm prices could have led to agricultural output being 40-60% higher than it was, and to an additional 3% per annum increase in national income. Scandizzo (1984) likewise finds that price elasticities of supply are significant, although somewhat lower than Peter- son’s estimates (the latter estimates have been criticized as excessively high). Organizations such as the World Bank (1981) have stressed the importance of price policy in affecting the success of agricultural projects.

There are, however, costs of raising farm prices, in the form of higher consumer prices. The price of food is particularly important in the poorer countries, where the lowest income groups may spend 80% or more of their income on food. In several countries the response to increases in food prices has been in the form of riots. In many developing countries food sub- sidies are used because of concern for the effects of high prices on consumers.

Several macro models have tried to quantify the trade-offs in individual countries. when farm and consumer prices change. Some examples of these are Quizon and Binswangcr (1984). de Janvry and Subbarao (1984), McCarthy and Taylor (1980) and de Janvry and Sadoulct (1985). Other studies have quantified the macroecon- omic costs of food subsidies, for example Scobie (1983) and Cavallo and Mundlak (1982). The results tend to vary by country and by the type of accompanying policy assumptions, as well as to be rather sensitive to how the model is specified.

The present paper therefore uses some data which have only recently become available to do a cross-country study of the effects of increased consumer prices on consumers. It examines the correlation between the real cereal price, average calorie intake and the infant mortality rate. This may not necessarily indicate the social costs for any single country which raises its food price. It

*The authors would like to thank the FAO for permis- sion to use the price data. Thanks also to Lance Taylor, Albert Berry. Angelo Melino, Harold Alderman and two anonymous referees for helpful comments on an earlier draft. Christina Salvioni and Margaret Sander- son provided very helpful research assistance.

817

8-a WORLD DEVELOPMENT

does, however, provide an estimate of social costs, to compare against those estimates of social losses due to lowsr farm prices, also ob- tained from cross-country data.

The paper is organized as follows: the follow- ing section discusses previous literature. and the model used. Section 3 describes the data, Sec- tion 4 presents the results and Section 5 the conclusions.

2. LITERATURE SURVEY AND MODEL

Although there are certain theoretical prob- lems, cross-country evidence on the effects of food prices is potentially of great interest. Argu- ably cross-country data can provide information on long-run adjustments whereas time series data for a single country capture more transitory responses. There is also a greater range of price variatiqn than typically observed in cross-section within a single country, at the same time with a wider variation in income than observed with time-series data within a single country. A study of the effect on consumers of food prices would also be complementary to the number of studies using cross-country evidence on supply response to relative prices (surveyed in Scandizzo. 1984).

Such a study could also be useful given the ambiguous predictions of macro models on the effects of changes in the price of food. In such models, cheap food policies can have very differ- ent impacts on income distribution between urban and rural sectors, on the real incomes and food consumption of poor groups, and on agri- cultural output and growth, depending how the model is specified. Cross-country empirical evi- dence may help shed some light on the issue.

The methodology here is to try to estimate a type of aggregated demand function, relating food intake to prices and incomes, using cross- country data. There are some theoretical issues raised by this approach. Although demand equa- tions and systems have frequently been applied to aggregated data (e.g. time series national accounts data), the theoretical constraints de- rived from theory need not hold when data are aggregated (Lluch, Powell and Williams (1977) cite Theil(l954) and Green (1964) on this issue). However it is frequently assumed that the con- straints do hold (Barten, 1974) and that what is being estimated is the behavior of a “representa- tive consumer.”

The theoretical problems become more for- midable when estimation across countries is considered. In tl& case, the notion of a “repre- sentative consumer” becomes rather more sus- pect. Moreover, it becomes necessary to take

account of different patterns of income distribu- tion in different countries. Suppose the demand for a commodity has a non-linear relation with income. For example in the case of food, the response to income is expected to be greatest at low incomes. Then countries with more equal income distribution will tend to have higher aver- age food consumption than countries with more unequal income distribution, holding constant incomes and food prices. However it is very difficult to get comparable data on income distri- bution for different countries. Another problem is that many rural households are both producers and consumers of food, and their net response to the food price is theoretically indeterminate.

Previous cross-country studies on consumer re- sponse to food prices have been scarce, largely because of lack of comparable data on food prices across countries. Lluch, Powell and Williams (1977) analyze demand patterns within 17 countries, and compare results across coun- tries. World Bank (1986) analyzes the effects of income and food prices across countries for 1975, using the data collected for the International Comparison Project (ICP) study (Kravis et nl., 1982). The present study uses data constructed from Food and Agriculture Organization (FAO) sources, which were amassed for a large study of agricultural prices (FAO, 1984). These latter data include food prices for up to five cereals for 34 countries for the years 1966 to 1981, and enable more comprehensive analysis than in pre- vious studies.

The prices are recorded in domestic currency for a number of different years. In order to per- form the cross-country analysis, prices and in- comes have to be converted to some common unit. Two different approaches are used here. One is to use the ICP data, which provide both a purchasing power parity (PPP) exchange rate and annual expenditure deflators for different expenditure categories (Kravis, Heston and Summers (1982) and Summers and Heston (1984) augmented by corresponding data for 1981 made available by the ICP project). Using the PPP exchange rates and the expenditure de- flators, prices and incomes for different countries for different years can be reduced to a common unit, namely ICP units of 1975. Food prices are deflated by the ICP consumption expenditure de- flator, and GDP by the ICP GDP deflator.

Since the ICP data involve a number of assumptions, a second approach used here is to use nominal exchange rates, and consumer price indices and GDP deflators for individual coun- tries (IMF, various years). With the latter data it is possible to convert prices and incomes of a number of countries for a single year to a com-

SOCIAL COSTS OF HIGHER FOOD PRICES 849

mon unit (US dollars of the same year), using the region. (This is equivalent to setting 6; = c, = 0 exchange rate. It is likewise possible to convert for all i > 1). This restriction was rejected (using prices and incomes of a single country for a num- a Chow test): i.e. the effects of GDP and food ber of years to a common unit (domestic currency prices differ amongst regions. This is what would units at 1975 prices), using the consumer price be expected since different regions have rather index (CPI) to deflate food prices, and the GDP different levels of GDP, and also exhibit differ- deflator to deflate GDP. ent patterns of income distribution.

Thus the second (non-ICP) approach allows for estimates of cross-country demand functions for individual years, and time series demand functions for individual countries, whilst the first (ICP) approach allows for estimates of demand functions using pooled cross-country and time series data.

Several functional forms were tested, for ex- ample using a quadratic rather than a log func- tion for per capita GDP. The present formulation was chosen on the grounds of satisfactory fit, as well as parsimony of regression coefficients.

The following model is estimated: 3. DATA

C_ALORIES IMR > = a, + b, LOGGDP + c,CERPRC

+ ;: a,D, + : b,D,LOGGDP i=2 i=2

n + Z c,D,CERPRC + dTIME + u

i=2

where CALORIES = calories per capita available per day (source: FAO, various years)

IMR = infant mortality rate (source: World Bank World Development Report, various years)

LOGGDP = real per capita GDP, natural logs

CERPRC = real cereal price

Di = dummy for country or region: unity for country or region i, zero otherwise

TIME = linear time trend: zero for 1966,X in 1981

ll = disturbance term with the usual properties

For the ICP approach, LOGGDP and CERPRC are in 1975 ICP units. For the non-ICP approach with the cross-country data, LOGGDP and CERPRC are in US dollars of the particular year concerned (and the TIME variable is dropped since only one year is involved). For the non-ICP approach and the individual country time series data, LOGGDP and CERPRC are in 1975 prices (and ai, bi and ci are equal to zero ex- cept for i= 1, since only one country is involved).

One restriction was tested, namely that the coefficients on LOGGDP and CERPRC are the same for all regions, or for countries within a

Before presenting the results, it is important to discuss the variables and their limitations. The two dependent variables used are average calorie intake, and the infant mortality rate. Both have their drawbacks. Average calorie intake is not ideal, since calories come from sources other than cereals (although 60% or more may come from cereals for poor households in the lowest in- come countries). Nor is a country average highly sensitive to calorie intake of the poorest groups, for whom policymakers may have particular con- cern.

Calorie intake per capita is obtained from FAO food balance sheets. Since some of the components of the balance sheets are known imprecisely (particularly wastage, change in stocks, and subsistence production), there are unknown errors in the measure of calorie intake. Calorie intake is also a country-wide average and may not be highly correlated with calorie intake of the poor, who may be of particular interest to policymakers. However, data on calorie intake of particular groups are available for very few coun- tries. Even if interpolated from income distribu- tion data, these latter data are still not readily available across countries.

An alternative dependent variable is therefore used, namely the infant mortality rate. Various studies have suggested that infant mortality is correlated to birth weight and to infant nutri- tional status, especially at the lower end of the birth weight and nutritional status distribution. Hence the recorded infant mortality rate may be sensitive to food intake of the poorer households. However it must be appreciated that many other factors such as access to health care affect mortal- ity rates. Also, the data reported by the World Bank are collected by health-care institutions and not from a population survey, and may therefore not be very representative of the IMR of poor households, and may be subject to measurement error.

850 WORLD DEVELOPMENT

Both calories and the IMR have been used as dependent variables in other cross-country studies (some of those on IMR are surveyed in.Cochrane, O’Hara and Leslie, 1980). These other studies have not in general included the food price, with the exception of the World Bank (1986). In the latter study the cross-country elas- ticity of calorie intake was found to range from 0.08 to 0.1-l. It is, however, worth mentioning an interesting study by Isenman (1980) who did time series estimates for Sri Lanka in the 1970s in which he concluded that a 10% increase in food prices was associated with a 1.5% increase in death rates.

The GDP data are standard. For the ICP approach they were converted using the ICP total expenditure deflators (Summers and Heston, 1984) to constant 1975 comparable units. For the non-ICP cross-section approach they were con- verted to US dollars of a single year using market exchange rates. For the non-ICP time series approach they were converted to domestic cur- rency units at 1975 prices using the GDP de- flator (IMF, various years).

Derivation of the price variable was rather complicated. Internationally comparable price data are very expensive to collect. The variable here was constructed from series of open market and controlled retail prices of five cereals (rice, maize, wheat, millet and sorghum) which make up most of cereal consumption in the 34 countries considered. These series had been collected by FAO, from statistics for individual countries. The prices were then converted to ICP units of 1975, using the ICP consumer expenditure defla- tor for the ICP approach. For the non-ICP cross- section approach they were converted to US dollars of a particular year using exchange rates. For the non-ICP time series approach they were deflated to domestic currency units at 1975 prices using the consumer price index.

The end result represents the price of “cheap calories” for each country. It is likely that food intake of the poor is quite sensitive to this price. Analysis of one household survey in Gujarat in India for example (Horton, 1982) suggested that sample households obtained 6O-70% of their calories from cereals. A number of assumptions were required to obtain the price data, and these are described in the Appendix.

Since the ICP price data themselves may be of some interest, these are presented in Appendix Table Al. These show that year to year varia- tions in real cereal prices averaged across coun- tries have been relatively modest. There appears however to have been a break around 1973-74, corresponding to a period when traded prices of foodgrains increased rapidly, and a sharp jump

between 1980 and 1981 which is less easy to ex- plain. The variation across countries is quite striking however. The highest price country (based on an average for those years for which data are available) has a real cereal price over four times that of the lowest price country. There are also quite pronounced regional differences, with the Near East having the lowest prices, and Latin America having the highest.

4. RESULTS

Four sets of results are presented. First there are the ICP approach worldwide data with sepa- rate region slope and intercept dummies (Table 1). Second, in Table 1 are the non-ICP approach cross-section data for a single year (1971), also with separate region slope and intercept dummies. Non-ICP cross-section estimates were made for a number of years: the signs of the coef- ficients were very similar for all years, although the significance and actual magnitude varied somewhat (results available from authors).

Table 2 presents a third set of results, namely estimates using the ICP-adjusted data within the four regions, with individual country slope and intercept dummies. This is similar to running in- dividual country time series regressions, however the time trend and error structure is assumed the same for countries within a region. These results can be compared with those in Table 3, which are time series regressions for individual countries, using non-ICP adjusted data. In Table 3 esti- mates are missing for a number of countries in Africa, due to missing data on either the CPI or the GDP deflator (or both).

Appendix Table A2 presents the full regres- sion results, from which Table 1 was obtained. (Regression results underlying Tables 2 and 3 are not presented, and are available from the authors on request.),

The results overall suggest that there is some evidence that higher cereal prices are associated with higher infant mortality using cross-section data, but there is little evidence of such a relation using time-series data for individual countries.

In Table 1 GDP has the expected effect on (increasing) calorie intake and (decreasing) infant mortality in all 16 cases (four regions, two dependent variables and two alternate approaches).

For calories, the cereal prices variable has the predicted sign in three of the eight cases (two out of four using ICP data, one out of four for non- ICP data). For infant mortality the cereal price sign is as predicted in six out of eight cases, and significant in five. Latin America is the exception

SOCIAL COSTS OF HIGHER FOOD PRICES 851

Table 1. Elasticities calculated using worldwide data

Independent variable

Dependent variable ICP converted data Exchange rate converted data. 1971

Calories Mortality Calories Mortality

GDP Latin America Asia Africa Near East

Cereal price Latin America Asia Africa Near East

0.268”’ -1.011*** 0.225”’ -0.700” 0.166*** -0.733 0.133 -0.839 0.112”’ -0.213** 0.100* -0.271 0.161” -0.247** 0.158 -0.359

-0.027 -0.420”’ 0.074 -0.699 0.107*** 0.180*** 0.096 0.515

-0.072 0.331”’ -0.089 0.560** 0.004 0.325*** 0.207 0.637’

***denotes coefficient significant at 1% level, 1 tail test **denotes coefficient significant at 5% level, 1 tail test

*denotes coefficient significant at 10% level, 1 tail test

Regression coefficients are given in Table Al, with t statistics in brackets. Coefficients and elasticities are given to three decimal places.

both times, and in one case for Latin America the sign is wrong and significant. Both ICP and non- ICP data yield broadly similar results both as to sign and magnitude of the coefficients. However the non-ICP coefficients are less frequently signi- ficant due to the much smaller sample size. (The ICP results are pooled across 16 years, whereas the non-ICP results can only be obtained for individual years.)

It is noteworthy that the four regions exhibit rather different income and price elasticities. This might be attributed to different levels of GDP and to different patterns of income dis- tribution. Latin America has the highest levels of GDP and the best developed targeted food inter- vention mechanisms (school lunch, maternal and child feeding and food distribution programs for example). Hence perhaps it is not surprising that no adverse effect of overall food prices is noted. Africa has the lowest levels of GDP (on a region basis), and the least well developed food inter- vention programs. In Africa higher food prices are seen consistently to be associated with lower calorie intake. Africa also has the largest adverse response of mortality to cereal prices using the ICP-adjusted data, and the second largest ad- verse response using the non-ICP-adjusted data. It is also notable that Africa is the region for which higher farm prices (and hence higher consumer food prices) are being most consis- tently advocated by international agencies and external observers.

The within-country results are, however, less in accordance with the predictions of theory.

Table 2 contains the ICP results within regions, with individual country slope and intercept dum- mies, which (as stated earlier) is almost equiva- lent to individual country time series regressions, and Table 3 contains the non-ICP results for indi- vidual country time series regressions. The sign on GDP is correct in a majority of cases (26 out of 34 for calories using ICP data, 21 out of 34 for mortality using ICP data, 16 out of 25 for calories using non-ICP adjusted data, and 13 out of 25 for mortality using non-ICP adjusted data). However there are cases where the sign on GDP is incorrect and significant. The sign on price has the wrong sign as many times as the expected sign, and there are equal numbers of cases with the wrong sign and significant, as with the_ expected sign and significant.

One possible explanation is that measurement errors particularly for the dependent variables (calories and infant mortality) are much more serious when compared to the (relatively small) annual fluctuations within a country, than when compared to the larger differences which exist between countries.

Another possibility is that simultaneity prob- lems become more serious when using time series data for individual countries. Good harvests in predominantly agriculture-based economies mean higher GDP as well as a higher calorie availability as measured by food balance sheets. One way to deal with such simultaneity would be to build simultaneous demand and supply esti- mates for individual countries, to disentangle causality, linking farm and food prices explicitly.

852 WORLD DEVELOPMENT

Table 2. Elasticities calculated from within-region regressions. ICP-converted data

Country

Elasticity of: Calories Calories hiortality Mortality

with GDP with price with GDP with price

Latin America Brazil Colombia Costa Rica Dominican Republic Ecuador Jamaica Mexico Peru

Asia Bangladesh India Indonesia Korea Pakistan Philippines Sri Lanka Thailand

Africa Cameroon Ethiopia Ghana Ivory Coast Kenya Malawi Mali Niger Nigeria Senegal Sierra Leone Tanzania Zambia

Near East

Egypt Morocco Sudan Syria Turkey

-0.070’“’ 0.246*** 0.080’“’

-0.048”’ -0.009***

0.283”’ -0.100”’ -0.755***

0.000 0.061 0.5748’ -0.061 0.302’** -0.023 0.273”’ 0.073 0.282’ 0.087 0.308 0.260’ 0.500”’ -0.039 0.329** 0.030

0.579 0.926*‘* 0.150** o.os7** 0.286 0.054”

-0.001*** 0.347* 0.012** 0.083” 0.173”

-0.006** 0.044***

0.179”

0.219 -0.047

0.044 0.055

5. CONCLUSIONS

Thus the results suggest that real cereal prices may be important in explaining inter-country differences in infant mortality rates when using worldwide data. The elasticities are large and usually significant (between 0.2 and 0.6 in ab- solute size). The response of calorie intake is more mixed, but is most adverse for Africa. Elasticities for aggregate calorie intake are smal- ler than those for mortality. This suggests that

-0.009*** -0.090” -0.003*

0.068**’ 0.003”’ 0.073*** 0.103*-

-0.284***

0.108 0.074

-0.187”’ -0.00s

0.113 0.017

-0.165” 0.169 0.082

-0.034 -0.059

0.165 0.061

-0.109

-0.155 0.030’

-0.056 0.011”

-0.008” O.OOS***

-0.355 0.103”

-0.072’ 1.177”’ 0.163”*

-0.528**

-0.001 -0.210”’ -0.124*** -0.003 -0.053 -0.110 -0.175*

0.041

0.021 -0.385*** -0.003*** -0.014***

0.077”’ 0.018”’ 0.004*** 0.002*** 0.002***

-0.086*‘* -0.043*** -0.027*** -0.022***

-0.017 0.041** 0.117 0.042

-0.000 -0.004 -0.087 0.050

0.081** -0.002**

0.015” -0.014***

0.296 -0.075”

0.020’ 0.225***

-0.004*** -0.140”

-0.025 0.047***

-0.011 -0.062

0.006 0.004

-0.035 -0.317***

-0.017** 0.078*** 0.020*** 0.004*** 0.077

-0.007” -0.006”’ -0.030*** -0.003*** -0.009***

0.026” * 0.000*** 0.014

there indeed may be large responses of food in- take of the poor to food price changes, which are not picked up by average calorie intake data, but which affect calorie intake of the poor and hence mortality rates.

It is also not surprising that there is a substan- tial difference between regions in the price effect, given that regions differ quite substantially in patterns of income distribution, and in the pro- portion of households which are both producers and consumers. The least adverse effects are

SOCIAL COSTS OF HIGHER FOOD PRICES 853

Table 3. Elasticities calculated from time-series regressions, local currency

Country

Elasticity of: Calories Calories Mortality Mortality

with GDP with price with GDP with price

Latin America Brazil Colombia Costa Rica Dominican Republic Ecuador ’ Jamaica Mexico Peru

Asia Bangladesh India Indonesia Korea Pakistan Philippines Sri Lanka Thailand

Africa Cameroon Ethiopia Ghana Ivory Coast Kenya Mali Malawi Niger Nigeria Senegal Sierra Leone Tanzania Zambia

Near East Egypt Morocco

Syria Turkey

-0.341’ -0.211

0.023 0.423 0.205 0.316”* 0.072 0.125’ -0.047 0.015 0.173 -0.032 -0.304*** -0.030

0.732 -0.292 -0.031 -0.001 0.676 -0.068 -0.120 0.021 1.097’ -0.076 0.194 -0.049

-0.226’;’ -0.121”’ -0.440 -0.286 -0.060” 0.028 0.015” 0.013

0.361 0.003 -0.195” -0.054 1.471** -0.144*** -0.056 -0.006 0.183 0.072 0.486’: 0.364’

da -0.883”

n/a n/a n/a n/a

-0.120 n/a

0.119 n/a n/a

0.174 0.202

-0.097 -0.143

n/a -0.019

0.092’:

0.014 0.141 0.001 -0.070** 0.023 -0.020

0.023 -1.380*** -0.342 -0.013 -0.098”’ 0.010 -0.062 0.008 -0.004’

0.015 1.914*** O.UO*

o.%***

n/a 0.224**

n/a n/a n/a n/a

0.013 n/a

0.006 n/a

n/a

n/a 0.034

-0.046*’ n/a n/a n/a n/a

0.039 n/a

-0.009 n/a

0.093 -0.001

n/a 0.002

n/a nla n/a n/a

0.000 n/a

0.000 n/a n/a

-0.006 0.003’

-0.213 -o.ooo

n/a

-0.011 0.009 -0.011 0.006’

n/a n/a 0.228” -0.145 0.067

-0.006 0.082’ -0.012

found in Latin America, where GDP is highest and targeted interventions are the most preva- lent. The most adverse effects are observed in Africa. The latter is particularly worrying due to the present emphasis on raising farm prices in Africa, with little attempt to protect vulnerable groups.

The findings also suggest that results obtained from cross-section analysis at one period in time, such as World Bank (1986), without the inclusion of even region dummies, may be rather mislead-

ing, There are quite definite region and country effects, in which region and country intercept and slope dummies prove significant.

Real cereal prices do not, however, appear to be associated with year-to-year changes in calorie intake and mortality within countries. This find- ing merits further investigation, perhaps incor- porating simultaneous aggregate demand and supply equations. The lack of year-to-year im- pact does not imply that policymakers can ig- nore the adverse effects of raising farm prices

8.5-l WORLD DEVELOPMENT

on poor consumers. Rather it suggests that intake for the poor, and measures of income although the short-run response is not noticeable distribution, would be useful. Also, joint supply- (based on the time series results), in the long run demand estimates are to be encouraged. Never- there may be a response (based on the cross- theless the results do suggest that policymakers country results). considering raising farm prices, might at the same

Given the deficiencies of the data, these results time consider measures designed to mitigate the should be held as indicative and a basis for future impact of higher food prices on the poorest research, rather than definitive. They suggest groups. that further work with better measures of food

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Barten, A. P., ‘Complete systems of demand equa- tions: Some thoughts about aggregation and func- tional form,” Recherches Economiques de Louvain. Vol. 40 (197-q, pp. l-18.

Cavallo, D., and Y. Mundlak, “Agriculture and eco- nomic growth in an open economy: The case of Argentina,” lnrernarional Food Policy Research Instirute, Research Report No. 36 (Washington, DC: International Food Policy Research Institute, 1982).

Cochrane, S. H.. D. J. O’Hara, and J. Leslie, “The effects of education on health,” World Bunk Staff Working Paper No. JO5 (Washington. DC: World Bank, 1980).

de Janvry, A., and E. Sadoulet. “Agricultural price policy in general equilibrium frameworks: A com- parative analysis.” Division of Agriculture and Natural Resources. Working Paper No. 312 Mimeo (Berkeley: University of California, 1985).

de Janvry, A., and K. Subbarao. ‘*Agricultural price policy and income distribution in India,” Division of Agricultural Sciences, Working Paper No. 274 (Berkeley: University of California, 1984).

FAO. Technical Conversion Factors for Agricultural Commodities (Rome: Food and Agriculture Organi- zation of the UN, 1972).

FAO, “Agricultural price policies,” Committee on Agriculture Eighth Session. COAG/85/8 (Rome: FAO, 1984). -

FAO, Food Balance Sheers (Rome: FAO, various years).

Green, H. A. J., Aggregarion in Economic Analysis (Princeton: Princeton University Press, 1964).

Horton, S., “Labor use, nutrition and household behavior: Results from Western India,” Un- published doctoral dissertation (Cambridge, MA: Harvard University, 1982).

International Monetary Fund, Infernational Financial Sfafistics Yearbook (Washington, DC: IMF, various years).

Isenman, P. J., “Basic needs: The case of Sri Lanka,” World Development, Vol. 8 (1980), pp. 237-258.

Kravis. I. B.. A. Heston. and R. Summers. WorLd Pro- duct and Income: International Comparisons of Real Gross Producr(Baltimore: Johns Hopkins University Press, 1982).

Lluch, C.. A. A. Powell and R. A. Williams, Pafterns in Household Demand and Saving (New York: Oxford University Press, 1977).

McCarthy. F. D., and L. Taylor, “Macro food policy planning: A general equilibrium model for Pakistan,” Review of Economics and Statistics, Vol. 62 (1980). pp. 107-121.

Peterson, W. L., “International farm prices and the social cost of cheap food policies.” American Journal of Agriculiural Economics. Vol. 61 (1979). pp. 11-21.

Quizon, J. B., and H. P. Binswanger, “Income dis- tribution in India: The impact of policies and growth in the agricultural sector,” World Bank Agriculfure and Rural Developmenr Department, Discussion Paper No. ARU 21 (Washington DC: World Bank, 1984).

Scandizzo. P. L., “Aggregate supply response: empiri- cal evidence on key issues.” Mimeo (Rome: FAO. draft, 198-l).

Scobie, G. M.. “Food subsidies in Egypt: Their impact on foreign exchange and trade.” Inrernational Food Policy Research Insritute Research Report No. 40 (Washington, DC: International Food Policy Re- search Institute, 1983).

Summers. R., and A. Heston. ‘-Improved inter- national comparis ns of real product and its com- position, 1950-8 ,” 4 Review of Income and Wealth, Vol. 30 (198-t), pp. 207-262.

Theil, H., Linear Aggregation of Economic Relations (Amsterdam: North Holland, 1954).

US HEW er al., Food Consumprion fable for IJse in East Asia. US Dent. of Health, Education and Welfare, and Nutriiion Program. CDC, and Food and Agriculture Organization of he UN/(Washing- / ton, DC: US HEW, 1972). (Also other,regions avail- able.)

World Bank, Accelerared Development in Sub-Saharan Africa: An Agenda for Action (Washington, DC: World Bank, 1981).

World Bank, “Poverty and hunger - Issues and options for food security in developing countries,” World Bank Policy Study (Washington, DC: World Bank, 1986).

World Bank, World Developmem Reporr (Washington, DC: World Bank, various years).

APPENDIX: CALCULATION OF CEREAL PRICE

Data were obtained for retail prices of the five major grains, for 34 countries for 16 years. The data were not

SOCIAL COSTS OF HIGHER FOOD PRICES 855

always complete, in that not all price series were avail- price was available for each country would have been a able for each country for each grain for each year. lengthy task.

The first step was to convert the prices for grain and flour to a common unit (flour). The milling ratios assumed were 0.75 (wheat). 0.90 (maize) and 0.90 (millet and sorghum). based on FAO (1972).

The next step was to choose whether open market or controlled prices were used. The former were used where possible, and a dummy variable for type of price used did not turn out to be significant in any of the regressions (results available from authors on request). The prices were then all converted to 1975 ICP units using the ICP consumer expenditure deflator (Summers and Heston, 1984).

It was assumed that one kg of flour from wheat, millet and sorghum. one kg of rice and one kg of maize meal or flour have approximately the same caloric value based on US HEW er al. (1972). Whilst this is not

The third step was to calculate a weighted average price of “cheap calories’. for each country. Data on the share of each cereal in total cereal calories were obtained for each country from FAO (various years). These were then used to weight the prices of individual cereals. In general it was not possible to find prices of all cereals for each country. An effort was made to find prices accounting for over 60% of cereal calories (in 20 of the 34 cases over 80% were accounted for). For ex- ample for Costa Rica the shares of rice. maize and wheat are approximately 10%. 30% and 30%, respec- tively. The wheat series was not available, and an index was calculated with weights 0.40/(0.40 + 0.30) for rice and 0.30/(0.40 + 0.30) for maize. Data on calorie share are available for each year, and the calorie weights used are therefore those of the year corresponding to the price data.

, I exactly correct, it is reasonably accurate. Ascertaining caloric values corresponding to the exact item for which One major omission is that the index does not

Table Al. Summary dara on real cereal price (I975 ICP units per lonne): Mean cereal price by country. year and region

Country Mean Sample size Year Mean Sample size

Bangladesh Brazil Cameroon Colombia Costa Rica Dominican Republic Ecuador

Egypt Ethiooia Ghana India Indonesia Ivory Coast Jamaica Kenya Korea Malawi Mali Mexico Morocco Niger Nigeria Pakistan Peru Philippines Senegal Sierra Leone Sri Lanka Sudan Syria Tanzania Thailand Turkey

505.20 704.31 613.30 963.94 568.88 868.57 752.22 261.30 568.81 887.26 549.15 638.52 716.27 666.81 314.57

1036.39 357.59 543.60 359.66 542.37 533.38 779.27 443.37 519.35 576.13 622.74 925.97 483.82 467.98 229.97 509.39 568.63 657.92 205.29

12 7

1; 12 10

8 10 16 6

10 12 16 15 14 13 15 7

13 7

12 6

10 12 10 11 14 10 10 14 9

11 13 8

1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981

Region

584.49 31 579.18 31 570.35 27 584.15 26 592.82 23 714.41 21

Mean Sample size

Latin America Asia Africa Near East

658.79 88 613.38 88

Overall

572.96 139 423.37 54

581.17 369

537.16 5 506.08 8 507.76 11 574.63 14 578.27 23 515.12 26 546.11 30 577.02 31 583.57 31 611.50 31

856 WORLD DEVELOPMENT

include non-cereal staples, such as cassava or potatoes. cheap calorie sources, in particular sugars and fats. This is of particular importance for a few countries such Whilst the former could be incorporated (with some as Ghana. However non-cereal staples are rarely considerable effort), the latter posed much more traded or handled by government agencies, and there- serious problems due to non-comparability of the items fore price data are very hard to obtain. The index could sold in different countries. likewise be improved by adding in the prices for other

Table A2. Regression resuh, worldwide

Independent variable

Dependent variable ICP-converted data Exchange rate-converted data, 1971

Calories Mortality Calories Mortality

CERPRC

Log GDP

Time

Intercept

Asia X CERPRC

Africa X CERPRC

Near East x CERPRC

Asia x Log GDP

Africa X' Log GDP

Near East x Log GDP

Intercept X Asia

Intercept X Africa

Intercept X Near East

Adjusted R Squared

F Statistic

Degrees of freedom

-0.101 -0.043 (0.928) (2.787)“*

643.497 -67.179 (6.851)*** (5.061)***

0.685 0.953 (0.276) (2.712)‘**

-2282.015 599.113 (3.102)*** (5.764)***

0.485 0.073 (2.969)*** (3.047)“’

-0.177 0.132 (1.402) (7.384)***

0.128 0.141 (0.787) (6.112)“’

-282.397 -0.844 (2.780)‘** (0.059)

-401.355 (4.117)***

-221.596 (2.094)**

35.001 (2.541)**

38.241 (2.558)**

-80.135 (0.741)

-291.656 (2.735)***

-314.929 (2.739)*‘*

1922.663 (2.512)**

3116.329 (4.130)“’

1979.450 (2.432)**’

0.710 0.780

71.921”’ 103.353***

12,335 12,347

-0.694 -0.491 (0.430) (0.127)

541.417 -97.818 (2.455)‘: (2.107)”

- -

-898.815 (0.576)

-0.440 (0.183)

0.083 (0.046)

4.304 (1.641)

-68.807 (0.251)

- 340.873 (1.363)

- 157.207 (0.512)

922.027 (0.542)

2250.871 (1.369)

783.277 (0.411)

783.586 (2.727)”

1.054 (2.383)**

0.881 (0.625)”

I.035 (2.141)’

5.124 (0.101)

54.99-l (0.258)

52.262 (0.924)

-313.5-Q (l.cQl)

-474.221 (1.565)

-473.980 (1.350)

0.591

3.ss5**

11,23

0.690

5.US”’

11.23

IEA2007.pdf

WORLD ENERGY

OUTLOOK 2OO7

China and India Insights

INTERNATIONAL ENERGY AGENCY

Executive Summary

WORLD ENERGY

OUTLOOK 2OO7 China

and India Insights

World leaders have pledged to act to change the energy future. Some new policies are in place. But the trends in energy demand, imports, coal use and greenhouse gas emissions to 2030 in this year’s World Energy Outlook are even worse than projected in WEO 2006.

China and India are the emerging giants of the world economy. Their unprecedented pace of economic development will require ever more energy, but it will transform living standards for billions. There can be no question of asking them selectively to curb growth so as to solve problems which are global.

So how is the transition to be achieved to a more secure, lower-carbon energy system?

WEO 2007 provides the answers. With extensive statistics, projections in three scenarios, analysis and advice, it shows China, India and the rest of the world why we need to co-operate to change the energy future and how to do it.

WORLD ENERGY

OUTLOOK 2OO7

China and India Insights

INTERNATIONAL ENERGY AGENCY

Executive Summary

INTERNATIONAL ENERGY AGENCY

The International Energy Agency (IEA) is an autonomous body which was established in November 1974 within the framework of the Organisation for Economic Co-operation and Development (OECD) to implement an inter national energy programme.

It carries out a comprehensive programme of energy co-operation among twenty-six of the OECD thirty member countries. The basic aims of the IEA are:

To maintain and improve systems for coping with oil supply disruptions.

To promote rational energy policies in a global context through co-operative relations with non-member countries, industry and inter national organisations.

To operate a permanent information system on the international oil market.

To improve the world’s energy supply and demand structure by developing alternative energy sources and increasing the effi ciency of energy use.

To promote international collaboration on energy technology.

To assist in the integration of environmental and energy policies.

The IEA member countries are: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Republic of Korea, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, United Kingdom and United States. The Slovak Republic and Poland are likely to become member countries in 2007/2008. The European Commission also participates in the work of the IEA.

ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT

The OECD is a unique forum where the governments of thirty democracies work together to address the economic, social and environmental challenges of globalisation. The OECD is also at the forefront of efforts to understand and to help governments respond to new developments and concerns, such as corporate governance, the information economy and the challenges of an ageing population. The Organisation provides a setting where governments can compare policy experiences, seek answers to common problems, identify good practice and work to co-ordinate domestic and international policies.

The OECD member countries are: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Republic of Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Spain, Sweden, Switzerland, Turkey, United Kingdom and United States. The European Commission takes part in the work of the OECD.

© OECD/IEA, 2007 International Energy Agency (IEA),

Head of Communication and Information Offi ce, 9 rue de la Fédération, 75739 Paris Cedex 15, France.

Please note that this publication is subject to specifi c restrictions that limit its use and distribution.

The terms and conditions are available online at http://www.iea.org/Textbase/about/copyright.asp

3Executive Summary

EXECUTIVE SUMMARY

China and India are the emerging giants of the world economy and international energy markets. Energy developments in China and India are transforming the global energy system by dint of their sheer size and their growing weight in international fossil-fuel trade. Similarly, both countries are increasingly exposed to changes in world energy markets. The staggering pace of Chinese and Indian economic growth in the past few years, outstripping that of all other major countries, has pushed up sharply their energy needs, a growing share of which has to be imported. The momentum of economic development looks set to keep their energy demand growing strongly. As they become richer, the citizens of China and India are using more energy to run their offices and factories, and buying more electrical appliances and cars. These developments are contributing to a big improvement in their quality of life, a legitimate aspiration that needs to be accommodated and supported by the rest of the world.

The consequences for China, India, the OECD and the rest of the world of unfettered growth in global energy demand are, however, alarming. If governments around the world stick with current policies – the underlying premise of our Reference Scenario – the world’s energy needs would be well over 50% higher in 2030 than today. China and India together account for 45% of the increase in demand in this scenario. Globally, fossil fuels continue to dominate the fuel mix. These trends lead to continued growth in energy-related emissions of carbon-dioxide (CO2) and to increased reliance of consuming countries on imports of oil and gas – much of them from the Middle East and Russia. Both developments would heighten concerns about climate change and energy security.

The challenge for all countries is to put in motion a transition to a more secure, lower-carbon energy system, without undermining economic and social development. Nowhere will this challenge be tougher, or of greater importance to the rest of the world, than in China and India. Vigorous, immediate and collective policy action by all governments is essential to move the world onto a more sustainable energy path. There has so far been more talk than action in most countries. Were all the policies that governments around the world are considering today to be implemented, as we assume in an Alternative Policy Scenario, the world’s energy demand and related emissions would be reduced substantially. Measures to improve energy efficiency stand out as the cheapest and fastest way to curb demand and emissions growth in the near term. But even in this scenario, CO2 emissions are still one-quarter

4 World Energy Outlook 2007

above current levels in 2030. To achieve a much bigger reduction in emissions would require immediate policy action and technological transformation on an unprecedented scale.

Both the Reference and Alternative Policy Scenario projections are based on what some might consider conservative assumptions about economic growth in the two giants. They envisage a progressive and marked slow-down in the rate of growth of output over the projection period. In a High Growth Scenario, which assumes that China’s and India’s economies grow on average 1.5 percentage points per year faster than in the Reference Scenario (though more slowly than of late), energy demand is 21% higher in 2030 in China and India combined. The global increase in energy demand amounts to 6%, making it all the more urgent for governments around the world to implement policies, such as those taken into account in the Alternative Policy Scenario, to curb the growth in fossil-energy demand and related emissions.

The World Faces a Fossil Energy Future to 2030

The world’s primary energy needs in the Reference Scenario are projected to grow by 55% between 2005 and 2030, at an average annual rate of 1.8% per year. Demand reaches 17.7 billion tonnes of oil equivalent, compared with 11.4 billion toe in 2005. Fossil fuels remain the dominant source of primary energy, accounting for 84% of the overall increase in demand between 2005 and 2030. Oil remains the single largest fuel, though its share in global demand falls from 35% to 32%. Oil demand reaches 116 million barrels per day in 2030 – 32 mb/d, or 37%, up on 2006. In line with the spectacular growth of the past few years, coal sees the biggest increase in demand in absolute terms, jumping by 73% between 2005 and 2030 and pushing its share of total energy demand up from 25% to 28%. Most of the increase in coal use arises in China and India. The share of natural gas increases more modestly, from 21% to 22%. Electricity use doubles, its share of final energy consumption rising from 17% to 22%. Some $22 trillion of investment in supply infrastructure is needed to meet projected global demand. Mobilising all this investment will be challenging.

Developing countries, whose economies and populations are growing fastest, contribute 74% of the increase in global primary energy use in this scenario. China and India alone account for 45% of this increase. OECD countries account for one-fifth and the transition economies the remaining 6%. In aggregate, developing countries make up 47% of the global energy market in 2015 and more than half in 2030, compared with only 41% today. The developing countries’ share of global demand expands for all primary

5

energy sources, except non-hydro renewables. About half of the increase in global demand goes to power generation and one-fifth to meeting transport needs – mostly in the form of petroleum-based fuels.

World oil resources are judged to be sufficient to meet the projected growth in demand to 2030, with output becoming more concentrated in OPEC countries – on the assumption that the necessary investment is forthcoming. Their collective output of conventional crude oil, natural gas liquids and non-conventional oil (mainly gas-to-liquids) is projected to climb from 36 mb/d in 2006 to 46 mb/d in 2015 and 61 mb/d in 2030 in the Reference Scenario. As a result, OPEC’s share of world oil supply jumps from 42% now to 52% by the end of the projection period. Non-OPEC production rises only slowly to 2030, with most of the increase coming from non- conventional sources – mainly Canadian oil sands – as conventional output levels off at around 47 mb/d by the middle of the 2010s. These projections are based on the assumption that the average IEA crude oil import price falls back from recent highs of over $75 per barrel to around $60 (in year-2006 dollars) by 2015 and then recovers slowly, reaching $62 (or $108 in nominal terms) by 2030. Although new oil-production capacity additions from greenfield projects are expected to increase over the next five years, it is very uncertain whether they will be sufficient to compensate for the decline in output at existing fields and keep pace with the projected increase in demand. A supply-side crunch in the period to 2015, involving an abrupt escalation in oil prices, cannot be ruled out.

The resurgence of coal, driven primarily by booming power-sector demand in China and India, is a marked departure from past WEOs. Higher oil and gas prices are making coal more competitive as a fuel for baseload generation. China and India, which already account for 45% of world coal use, drive over four-fifths of the increase to 2030 in the Reference Scenario. In the OECD, coal use grows only very slowly, with most of the increase coming from the United States. In all regions, the outlook for coal use depends largely on relative fuel prices, government policies on fuel diversification, climate change and air pollution, and developments in clean coal technology in power generation. The widespread deployment of more efficient power-generation technology is expected to cut the amount of coal needed to generate a kWh of electricity, but boost the attraction of coal over other fuels, thereby leading to higher demand.

In the Alternative Policy Scenario, global primary energy demand grows by 1.3% per year over 2005-2030 – 0.5 percentage points less than in the Reference Scenario. Global oil demand is 14 mb/d lower in 2030 – equal to the entire current output of the United States, Canada and Mexico combined. Coal use falls most in absolute and percentage terms. Energy-related CO2 emissions stabilise in the 2020s and, in 2030, are 19% lower than in the

Executive Summary

6 World Energy Outlook 2007

Reference Scenario. In the High Growth Scenario, faster economic growth in China and India, absent any policy changes, boosts their energy demand. The stimulus to demand provided by stronger economic growth more than offsets the dampening effect of the higher international energy prices that accompany stronger demand. Worldwide, the increase in primary energy demand amounts to 6% in 2030, compared with the Reference Scenario. Demand is higher in some regions and lower in others.

China’s Share of World Energy Demand will Continue to Expand That China’s energy needs will continue to grow to fuel its economic development is scarcely in doubt. However, the rate of increase and how those needs are met are far from certain, as they depend on just how quickly the economy expands and on the economic and energy-policy landscape worldwide. In the Reference Scenario, China’s primary energy demand is projected to more than double from 1 742 million toe in 2005 to 3 819 Mtoe in 2030 – an average annual rate of growth of 3.2%. China, with four times as many people, overtakes the United States to become the world’s largest energy consumer soon after 2010. In 2005, US demand was more than one- third larger. In the period to 2015, China’s demand grows by 5.1% per year, driven mainly by a continuing boom in heavy industry. In the longer term, demand slows, as the economy matures, the structure of output shifts towards less energy-intensive activities and more energy-efficient technologies are introduced. Oil demand for transport almost quadruples between 2005 and 2030, contributing more than two-thirds of the overall increase in Chinese oil demand. The vehicle fleet expands seven-fold, reaching almost 270 million. New vehicle sales in China exceed those of the United States by around 2015. Fuel economy regulations, adopted in 2006, nonetheless temper oil-demand growth. Rising incomes underpin strong growth in housing, the use of electric appliances and space heating and cooling. Increased fossil-fuel use pushes up emissions of CO2 and local air pollutants, especially in the early years of the projection period: SO2 emissions, for example, rise from 26 million tonnes in 2005 to 30 Mt by 2030.

China’s energy resources – especially coal – are extensive, but will not meet all the growth in its energy needs. More than 90% of Chinese coal resources are located in inland provinces, but the biggest increase in demand is expected to occur in the coastal region. This adds to the pressure on internal coal transport and makes imports into coastal provinces more competitive. China became a net coal importer in the first half of 2007. In the Reference Scenario, net imports reach 3% of its demand and 7% of global coal trade in 2030. Conventional oil production in China is set to peak at 3.9 mb/d early

7

in the next decade and then start to decline. Consequently, China’s oil imports jump from 3.5 mb/d in 2006 to 13.1 mb/d in 2030, while the share of imports in demand rises from 50% to 80%. Natural gas imports also increase quickly, as production growth lags demand over the projection period. China needs to add more than 1 300 GW to its electricity-generating capacity, more than the total current installed capacity in the United States. Coal remains the dominant fuel in power generation. Projected cumulative investment in China’s energy-supply infrastructure amounts to $3.7 trillion (in year-2006 dollars) over the period 2006-2030, three-quarters of which goes to the power sector.

China is already making major efforts to address the causes and consequences of burgeoning energy use, but even stronger measures will be needed. China is seeking ways to enhance its energy-policy, regulatory and institutional framework to meet current and future challenges. In the Alternative Policy Scenario, a set of policies the government is currently considering would cut China’s primary energy use in 2030 by about 15% relative to the Reference Scenario. Energy- related emissions of CO2 and local pollutants fall even more. Energy demand, nonetheless, increases by almost 90% between 2005 and 2030 in the Alternative Policy Scenario. Energy-efficiency improvements along the entire energy chain and fuel switching account for 60% of the energy saved. For example, policies that lead to more fuel-efficient vehicles produce big savings in consumption of oil-based fuels. Structural change in the economy accounts for all the other energy savings. Demand for coal and oil is reduced substantially. In contrast, demand for other fuels – natural gas, nuclear and renewables – increases. In this scenario, the government’s goal of lowering energy intensity – the amount of energy consumed per unit of GDP – by 20% between 2005 and 2010 is achieved soon after. The majority of the measures analysed have very short payback periods. In addition, each dollar invested in more efficient electrical appliances saves $3.50 of investment on the supply side. And China’s efforts to improve the efficiency of vehicles and electrical appliances contribute to improved efficiency in the rest of the world, as the country is a net exporter of these products. Such policies would be all the more critical were China’s economy to grow more quickly than assumed in the Reference and Alternative Policy Scenarios. China’s primary energy demand is 23% higher in 2030, and coal use alone 21% higher, in the High Growth Scenario than in the Reference Scenario.

India’s Energy Use is Similarly Poised for Rapid Growth Rapid economic expansion will also continue to drive up India’s energy demand, boosting the country’s share of global energy consumption. In the Reference Scenario, primary energy demand in India more than doubles by 2030, growing on average by 3.6% per year. Coal remains India’s

Executive Summary

8 World Energy Outlook 2007

most important fuel, its use nearly tripling between 2005 and 2030. Power generation accounts for much of the increase in primary energy demand, given surging electricity demand in industry and in residential and commercial buildings, with most new generating capacity fuelled by coal. Among end-use sectors, transport energy demand sees the fastest rate of growth as the vehicle stock expands rapidly with rising economic activity and household incomes. Residential demand grows much more slowly, largely as a result of switching from traditional biomass, which is used very inefficiently, to modern fuels. The number of Indians relying on biomass for cooking and heating drops from 668 million in 2005 to around 470 million in 2030, while the share of the population with access to electricity rises from 62% to 96%.

Much of India’s incremental energy needs to 2030 will have to be imported. It is certain that India will continue to rely on imported coal for reasons of quality in the steel sector and for economic reasons at power plants located a long way from mines but close to ports. In the Reference Scenario, hard coal imports are projected to rise almost seven-fold, their share of total Indian coal demand rising from 12% in 2005 to 28% in 2030. Net oil imports also grow steadily, to 6 mb/d in 2030, as proven reserves of indigenous oil are small. Before 2025, India overtakes Japan to become the world’s third-largest net importer of oil, after the United States and China. Yet India’s importance as a major exporter of refined oil products will also grow, assuming the necessary investments are forthcoming. Although recent discoveries are expected to boost gas production, it is projected to peak between 2020 and 2030, and then fall back. A growing share of India’s gas needs is, therefore, met by imports, entirely in the form of liquefied natural gas. Power-generation capacity, most of it coal-fired, more than triples between 2005 and 2030. Gross capacity additions exceed 400 GW – equal to today’s combined capacity of Japan, Korea and Australia. To meet demand in the Reference Scenario, India needs to invest about $1.25 trillion in energy infrastructure – three-quarters in the power sector – in 2006-2030. Attracting electricity investment in a timely manner – a huge challenge for India – will be crucial for sustaining economic growth.

Stronger policies that the Indian government is now considering could yield large energy savings. In the Alternative Policy Scenario, India’s primary energy demand is 17% lower than in the Reference Scenario in 2030. Coal savings – mainly in power generation – are the greatest in both absolute and percentage terms, thanks to lower electricity-demand growth, higher power- generation efficiency and fuel-switching in the power sector and in industry. As a result, coal imports in 2030 are little more than half their Reference Scenario level. Oil imports are 1.1 mb/d lower in 2030 than in the Reference Scenario, but oil-import dependence remains high at 90%. Lower fossil-fuel use results in a 27% reduction in CO2 emissions in 2030, most of which stems from energy-efficiency improvements on the demand and supply sides. Lower energy demand in the power and transport sectors also reduces emissions of

9

local pollutants: SO2 emissions fall by 27% and NOx emissions by 23% in 2030, compared with the Reference Scenario. The picture is markedly different in the High Growth Scenario. Primary demand is 16% higher than in the Reference Scenario, with coal and oil accounting for most of the difference. Faster economic growth accelerates the alleviation of energy poverty, but results in much higher energy imports, local pollution and CO2 emissions.

The World Benefits Economically from Growth in China and India Rapid economic development in China and India will inevitably push up global energy demand, but it will also bring major economic benefits to the rest of the world. Economic expansion in China and India is generating opportunities for other countries to export to them, while increasing other countries’ access to a wider range of competitively priced imported products and services. But growing exports from China and India also increase competitive pressures on other countries, leading to structural adjustments, particularly in countries with competing export industries. Rising commodity needs risk driving up international prices for commodities, including energy – especially if supply-side investment is constrained.

Commodity exporters would gain most from even faster economic expansion in China and India than assumed in the Reference Scenario. In the High Growth Scenario, the Middle East, Russia and other energy-exporting countries see a significant net increase in their gross domestic product in 2030, compared with the Reference Scenario. GDP growth in other developing Asian countries, the United States, the European Union and OECD Pacific slows marginally, mainly because of higher commodity import costs. Assuming there are no policy changes in major countries, the average IEA crude oil import price rises to $87 per barrel (in year-2006 dollars) in 2030 – 40% higher than in the Reference Scenario. Overall, world GDP grows by 4.3% per year on average, compared with 3.6% in the Reference Scenario.

Structural changes in China’s and India’s economies will affect their trade with the rest of the world, including their need to import energy. Light industry and services are expected to play a more important role in driving economic development in both countries in the longer term. The economic policies of all countries will be crucial to sustaining the pace of global economic growth and redressing current imbalances. Rising protectionism could radically change the positive global impact of economic growth in China and India. By contrast, faster implementation of energy and environmental policies to save energy and reduce emissions worldwide, such as those included in the Alternative Policy Scenario, would boost significantly the net global benefits,

Executive Summary

10 World Energy Outlook 2007

by reducing pressures on international commodity markets and lowering fuel-import bills for all. More rapid economic development worldwide may also pave the way for faster development and deployment of emerging, clean energy technologies, such as second-generation biofuels and CO2 capture and storage, given the right policy environment.

But Threats to the World’s Energy Security Must be Tackled Rising global energy demand poses a real and growing threat to the world’s energy security. Oil and gas demand and the reliance of all consuming countries on oil and gas imports increase in all three scenarios presented in this Outlook. In the Reference Scenario, China’s and India’s combined oil imports surge, from 5.4 mb/d in 2006 to 19.1 mb/d in 2030 – more than the combined imports of Japan and the United States today. Ensuring reliable and affordable supply will be a formidable challenge. Inter-regional oil and gas trade grows rapidly over the projection period, with a widening of the gap between indigenous output and demand in every consuming region. The volume of oil trade expands from 41 mb/d in 2006 to 51 mb/d in 2015 and 65 mb/d in 2030. The Middle East, the transition economies, Africa and Latin America export more oil. All other regions – including China and India – have to import more oil. As refining capacity for export increases, a growing share of trade in oil is expected to be in the form of refined products, notably from refineries in the Middle East and India.

The consuming countries’ growing reliance on oil and gas imports from a small number of producing countries threatens to exacerbate short-term energy-security risks. Increasing import dependence in any country does not necessarily mean less secure energy supplies, any more than self-sufficiency guarantees uninterrupted supply. Indeed, increased trade could bring mutual economic benefits to all concerned. Yet it could carry a risk of heightened short-term energy insecurity for all consuming countries, as geographic supply diversity is reduced and reliance grows on vulnerable supply routes. Much of the additional oil imports are likely to come from the Middle East, the scene of most past supply disruptions, and will transit vulnerable maritime routes to both eastern and western markets. The potential impact on international oil prices of a supply interruption is also likely to increase: oil demand is becoming less sensitive to changes in price as the share of transport demand – which is price-inelastic, relative to other energy services – in overall oil consumption rises worldwide.

Longer-term risks to energy security are also set to grow. With stronger global energy demand, all regions would be faced with higher energy prices in the medium to long term in the absence of concomitant increases in supply-side investment or stronger policy action to curb demand growth in all

11

countries. The increasing concentration of the world’s remaining oil reserves in a small group of countries – notably Middle Eastern members of OPEC and Russia – will increase their market dominance and may put at risk the required rate of investment in production capacity. OPEC’s global market share increases in all scenarios – most of all in the Reference and High Growth Scenarios. The greater the increase in the call on oil and gas from these regions, the more likely it will be that they will seek to extract a higher rent from their exports and to impose higher prices in the longer term by deferring investment and constraining production. Higher prices would be especially burdensome for developing countries still seeking to protect their consumers through subsidies.

China’s and India’s growing participation in international trade heightens the importance of their contribution to collective efforts to enhance global energy security. How China and India respond to the rising threats to their energy security will also affect the rest of the world. Both countries are already taking action. The more effective their policies are to avert or handle a supply emergency, the more other consuming countries – including most IEA members – stand to benefit, and vice-versa. In addition, many policies to enhance energy security also directly support policies to address the environmental damage from energy production and use. Diversification of the energy mix, of the sources of imported oil and gas, and of supply routes, together with better emergency preparedness, especially through the establishment of emergency stockpiles and co-ordinated response mechanisms, will be necessary to safeguard their energy security. China and India are increasingly aware that overseas acquisitions of oil assets will do little to help protect them from the effects of supply emergencies. China’s and India’s oil security – like that of all consuming countries – is increasingly dependent on a well-functioning international oil market.

Unchecked Growth in Fossil Fuel Use will Hasten Climate Change Rising CO2 and other greenhouse-gas concentrations in the atmosphere, resulting largely from fossil-energy combustion, are contributing to higher global temperatures and to changes in climate. Growing fossil-fuel use will continue to drive up global energy-related CO2 emissions over the projection period. In the Reference Scenario, emissions jump by 57% between 2005 and 2030. The United States, China, Russia and India contribute two-thirds of this increase. China is by far the biggest contributor to incremental emissions, overtaking the United States as the world’s biggest emitter in 2007. India becomes the third-largest emitter by around 2015. However, China’s per-capita emissions in 2030 are only 40% of those of the United States and

Executive Summary

12 World Energy Outlook 2007

about two-thirds those of the OECD as a whole in the Reference Scenario. In India, they remain far lower than those of the OECD, even though they grow faster than in almost any other region.

Urgent action is needed if greenhouse-gas concentrations are to be stabilised at a level that would prevent dangerous interference with the climate system. The Alternative Policy Scenario shows that measures currently being considered by governments around the world could lead to a stabilisation of global emissions in the mid-2020s and cut their level in 2030 by 19% relative to the Reference Scenario. OECD emissions peak and begin to decline after 2015. Yet global emissions would still be 27% higher than in 2005. Assuming continued emissions reductions after 2030, the Alternative Policy Scenario projections are consistent with stabilisation of long-term CO2-equivalent concentration in the atmosphere at about 550 parts per million. According to the best estimates of the Intergovernmental Panel on Climate Change, this concentration would correspond to an increase in average temperature of around 3°C above pre-industrial levels. In order to limit the average increase in global temperatures to a maximum of 2.4°C, the smallest increase in any of the IPCC scenarios, the concentration of greenhouse gases in the atmosphere would need to be stabilised at around 450 ppm. To achieve this, CO2 emissions would need to peak by 2015 at the latest and to fall between 50% and 85% below 2000 levels by 2050. We estimate that this would require energy-related CO2 emissions to be cut to around 23 Gt in 2030 – 19 Gt less than in the Reference Scenario and 11 Gt less than in the Alternative Policy Scenario. In a “450 Stabilisation Case”, which describes a notional pathway to achieving this outcome, global emissions peak in 2012 at around 30 Gt. Emissions savings come from improved efficiency in fossil- fuel use in industry, buildings and transport, switching to nuclear power and renewables, and the widespread deployment of CO2 capture and storage (CCS) in power generation and industry. Exceptionally quick and vigorous policy action by all countries, and unprecedented technological advances, entailing substantial costs, would be needed to make this case a reality.

Government action must focus on curbing the rapid growth in CO2 emissions from coal-fired power stations – the primary cause of the surge in global emissions in the last few years. Energy efficiency and conservation will need to play a central role in curbing soaring electricity demand and reducing inputs to generation. Nuclear power and renewables can also make a major contribution to lowering emissions. Clean coal technology, notably CCS, is one of the most promising routes for mitigating emissions in the longer term – especially in China, India and the United States, where coal use is growing fastest. CCS could reconcile continued coal burning with the need to cut emissions in the longer term – if the technology can be demonstrated on a large scale and if adequate incentives to invest are put in place.

13Executive Summary

Collective Action is Needed to Address Global Energy Challenges The emergence of China and India as major players in global energy markets makes it all the more important that all countries take decisive and urgent action to curb runaway energy demand. The primary scarcity facing the planet is not of natural resources nor money, but time. Investment now being made in energy-supply infrastructure will lock in technology for decades, especially in power generation. The next ten years will be crucial, as the pace of expansion in energy-supply infrastructure is expected to be particularly rapid. China’s and India’s energy challenges are the world’s energy challenges, which call for collective responses. No major energy consumer can be confident of secure supply if supplies to others are at risk. And there can be no effective long-term solution to the threat of climate change unless all major energy consumers contribute. The adoption and full implementation of policies by IEA countries to address their energy-security and climate-change concerns are essential, but far from sufficient.

Many of the policies available to alleviate energy insecurity can also help to mitigate local pollution and climate change, and vice-versa. As the Alternative Policy Scenario demonstrates, in many cases, those policies bring economic benefits too, by lowering energy costs – a “triple-win” outcome. An integrated approach to policy formulation is, therefore, essential. The right mix of policies to address both energy-security and climate concerns depends on the balance of costs and benefits, which vary among countries. We do not have the luxury of ruling out any of the options for moving the global energy system onto a more sustainable path. The most cost-effective approach will involve market-based instruments, including those that place an explicit financial value on CO2 emissions. Regulatory measures, such as standards and mandates, will also be needed, together with government support for long- term research, development and demonstration of new technologies. In China and India, the urgent need to tackle local air pollution will undoubtedly continue to provide the primary rationale for further efforts to stem the growth in greenhouse-gas emissions.

There are large potential gains to IEA countries, on the one hand, and to China and India, on the other, from enhanced policy co-operation. IEA countries have long recognised the advantages of co-operation with China and India, reflected in a steady broadening of the range of co-operative activities through the IEA and other multilateral and bilateral agreements. These activities need to be stepped up, with China and India establishing a deeper relationship with the Agency. IEA co-operation with China and India on enhancing oil-emergency preparedness and on developing cleaner and more efficient technologies, especially for coal, remains a priority. Collaboration

14 World Energy Outlook 2007

between IEA countries and developing countries, including China and India, is already accelerating deployment of new technologies – a development that will yield big dividends in the longer term. Mechanisms need to be enhanced to facilitate and encourage the financing of such technologies in China, India and other developing countries. Given the scale of the energy challenge facing the world, a substantial increase is called for in public and private funding for energy technology research, development and demonstration, which remains well below levels reached in the early 1980s. The financial burden of supporting research efforts will continue to fall largely on IEA countries.

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IPFR-2008-appendixler cok oneml?.pdf

GLOBAL FOOD CRISeS Monitoring and Assessing Impact to Inform Policy Responses Todd Benson, Nicholas Minot, John Pender, Miguel Robles, Joachim von Braun

RepORt F O O D p O L I C y

ABOut IFpRI

the International Food policy Research Institute (IFpRI) was established in 1975. IFpRI is one

of 15 agricultural research centers that receives its principal funding from governments, private

foundations, and international and regional organizations, most of which are members of the

Consultative Group on International Agricultural Research.

FInAnCIAL COntRIButORS AnD pARtneRS

IFpRI’s research, capacity strengthening, and communications work is made possible by its finan-

cial contributors and partners. IFpRI receives its principal funding from governments, private

foundations, and international and regional organizations, most of which are members of the

Consultative Group on International Agricultural Research (CGIAR). IFpRI gratefully acknowl-

edges the generous unrestricted funding from Australia, Canada, China, Finland, France,

Germany, India, Ireland, Italy, Japan, netherlands, norway, South Africa, Sweden, Switzerland,

united Kingdom, united States, and World Bank.

Cover design by Shirong Gao.

Global Food Crises Monitoring and Assessing Impact

to Inform Policy Responses

International Food Policy Research Institute Washington, D.C.

September 2008

Todd Benson, Nicholas Minot, John Pender, Miguel Robles, Joachim von Braun

Copyright © 2008 International Food Policy Research Institute.All rights reserved. Sections of this report may be reproduced for noncommercial and not-for-profit purposes without the express written permission of but with acknowledgment to the International Food Policy Research Institute.

ISBN 10-digit: 0-89629-533-8 ISBN 13-digit: 978-0-89629-533-9

DOI: 10.2499/0896295338

Contents Executive Summary vi

Acknowledgments viii

Introduction 1

Conceptual Framework for Understanding the Impact of a Food Crisis 2

Monitoring and Assessing the Impact of a Food Crisis 4

Monitoring and Assessing Current and Future Policy Responses 14

An Implementation Plan for Action on Monitoring and Impact Assessment 27

Conclusion 31

Appendix 1—Methods for Measuring the Impact of Food Crises 32

Appendix 2—Government Policy Responses to the Current Global Food Crisis 35

Notes 38

References 39

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Tables 1. Basic information needed to assess the exposure of countries and

households to the effects of rising global food prices 9

2. Data to assess impact of global food crises on countries, households, and individuals 12

3. Analytical methods to assess impact of global food crises on countries, households, and individuals 13

4. Potential policy responses to food crises 15

5. Potential national policy responses to food crises: Favorable and unfavorable effects and conditioning factors 16

6. Methods for monitoring and assessing impacts of selected price-oriented policies on domestic food prices 21

7. Methods for monitoring and assessing impacts of selected supply-oriented policies on domestic food prices 23

8. Methods for monitoring and assessing impacts of selected income-oriented policies on domestic food prices 26

9. Main objectives and activities of the implementation plan for providing information and decision-support tools to respond to global food crises 28

A.1. Government policy responses to the food crisis and the symptoms of political actions triggered by the crisis, 2006–August 2008 35

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Figures 1. Linking policy and policy analysis in addressing the effects of the global

food crisis at the national level and below: A conceptual framework 3

2. Conceptual framework for understanding the welfare impact of food crises 5

3. Conceptual framework for the policy information portal 29

A.1. Impact of releasing food from a food reserve 34

Executive Summary Strong upward trends and increased variability in globalfood prices over the past two years have led to concern that hunger and poverty will increase across the world.At the same time, rising food prices provide an incentive and opportunity for many developing countries to strengthen the contribution their farmers make to national economic growth and poverty reduction. Policymakers and opinion leaders in developing countries, however, often lack sufficient information to gauge the likely effects of global food crises on their country and to identify, design, and implement policy actions that can best avoid risks and take advantage of opportunities.The deficiencies in information and analysis can lead to over- and underreactions, resulting in policy and market failures. Experiences across countries in 2007 and 2008 show ample evidence of such outcomes.

This report seeks to support national decisionmakers, as well as their international development partners, in acquiring information and applying methods for under- standing the likely effects of a global food crisis on their country and acting to alleviate the risks and exploit the opportunities brought about by such crises. It describes data and methods and suggests how to facilitate their collection and use.The report then outlines the design and implemen- tation of an open Internet-based portal for sharing reliable, appropriate information and decision-support tools for national policymakers so they can respond quickly to changes in world food markets in an informed manner.

National decisionmakers and policy analysts must understand the degree to which their country and population groups within it are exposed to the negative effects of rising food prices or could exploit new oppor- tunities offered by the higher prices.This requires information on

• global market developments;

• the characteristics of the country with regard to international trade in food;

• the trends in local wages, agricultural prices, and fuel prices;

• the composition of income and expenditure among different population groups in the country; and

• the responses of producers, consumers, and the government to rising food prices.

The actual effects of the food crisis at the national level depend on

• the net trade position (exporter or importer) in agricultural commodities relative to the size of the economy;

• the degree to which changes in global prices are transmitted to local markets;

• the sensitivity of government revenue and expendi- tures to rising food prices; and

• the political and fiscal capacity of the government to respond to the crisis.

Moreover, the effects of a crisis will differ among com- munities and from household to household, depending on

• the net sales (or net purchases) of food relative to household income;

• the level of income and assets, which influence food security and vulnerability to shocks; and

• the existence and effectiveness of government programs and policies to protect vulnerable households.

Within households, members are likely to be affected by a crisis to varying degrees, with the nutritionally vulnerable members—women of childbearing age and young children—most at risk.

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This report distinguishes the basic information needed to assess the broad implications of a global food crisis for a country from the more advanced information and analyses that are needed to design and implement specific responses to such crises. In considering the vulnerability of countries, households, and individuals to a global food crisis, the report points to information that national decisionmakers can use to assess the degree to which their country as a whole is likely to be affected by rising global food prices in one way or another and to determine which population groups are likely to see a change in their well-being.The most important sources of such data include nationally representative household surveys, food price series from important commodity marketplaces in a country, and trade statistics.Where such data are missing for a country, it is necessary to rely on qualitative or indicative, rather than representative, data to make the needed assessments in the short run.To undertake relatively thorough assessments of the impact of a global food crisis on a country and its citizens, however, and to determine the best course of action to follow in response, detailed data are required.

The analytical capacity required at the national level to respond to a global food crisis will vary. Some powerful initial analyses to gauge the probable impact of a crisis can be conducted without much specialized skill using relatively basic data sets. Detailed studies of the second-round and economywide effects of a global food crisis on a country, however, call for more compre- hensive data, sophisticated analytical tools, and specialized skills.

A wealth of information on the world food situation and its shifts is available, but not everywhere, quickly, or at the needed level of disaggregation. In some contexts, even when information is available, access to it is not assured for all stakeholders. Frequently, government

leaders and their analysts, civil society, and business actors are not sufficiently informed for sound decision- making. In response to these information challenges, the report outlines a global initiative by which networks of partners and individual experts would provide reliable, appropriate information and decision-support tools for national policymakers.This plan includes the creation of an Internet-based information portal to serve as a reliable information- and decision-support tool to strengthen the ability of policymakers to respond quickly to dynamic developments in world food markets. In today’s Internet world, many useful websites and portals exist, including important ones operated by FAO, the World Bank, the CGIAR, and others.The portal designed here will not duplicate them but add specific value.The portal will become a reliable information and decision- support tool to strengthen the ability of policymakers in the developing world to respond quickly to dynamic developments in the world food system, especially crises. It will include policy analysis tools that users can employ directly and detailed country-by-country data and other food policy information, assembled from a wide array of sources. Because the portal will be designed in an open Wikipedia-type fashion, access to the portal both to obtain and to add information and tools will be open to the wider public as an international public good.

The adequacy of the response to a global food crisis depends to a large degree on the policy- and program- related reactions of national-level policymakers around the globe.This report provides insight on the information and analytical tools that national-level decisionmakers need to assess the risks and opportunities posed to their country and its citizens by a global food crisis, to determine how they might respond to those risks and opportunities, and to identify ways to monitor the impact of the food crisis and the effects of policy responses.

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Acknowledgments The authors are grateful to those who provided guidance, feedback, background materials, or other supportat various stages in the preparation of this report. In particular, we would like to thank Luz Marina Alvaré, Stephan Dohrn, Shenggen Fan, Ashok Gulati, Derek Headey, Rajul Pandya-Lorch, Mark Rosegrant, Marie Ruel, Maximo Torero,Teunis van Rheenen, and Klaus von Grebmer. We also thank Heidi Fritschel for her editorial help in finalizing the document.

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The current world food crisis has revealed serious deficiencies in the information available forguiding policy responses at global and national levels.This report seeks to support national governments, as well as international development partners that assist country-level actions, with the

information and tools they require to assess the impact of the food crisis and to design and

implement policy responses to it.

Introduction

Strong upward trends in global food prices over the past two years have led to widespread concern that hunger and poverty will increase sharply across the world. At the same time, rising food prices provide a strong incentive and opportunity for many developing countries to strengthen the contribution their farmers, and the agricultural sector as a whole, make to national economic growth and poverty reduction. Although a coordinated response is urgently needed at interna- tional and regional levels, national governments in particular face the challenge of responding to their people’s immediate increased food and nutritional needs while stimulating the agricultural sector to increase the food supply.The adequacy of the global response to the global food crisis depends to a large degree on the policy- and program-related reactions of national-level policymakers around the globe.

Policymakers in developing countries often do not, however, have sufficient information to gauge the likely effects of the global food crisis on their country and to implement appropriate policy actions. For example, the

imposition of domestic food price controls in many countries in reaction to the current crisis can be expected to limit farmers’ incentives to increase the production of food crops in subsequent cropping seasons. It is clear that many national leaders require tools to assess the impact of global food crises on their country, on its economy, and on vulnerable population groups, as well as to design and implement national policies and programs to address the risks and opportu- nities presented by such crises.

Since the implications of high and volatile food prices differ widely across countries and across groups within each country, policy responses must be adapted to country-specific needs and conditions. Although policy responses are likely to be country-specific, however, a relatively consistent set of information and analytical tools is required to guide policymaking in countries affected by global food crises.This report describes these data and methods and suggests how to coordinate their collection and use.

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The process starts at the upper left with the interaction of factors governing global demand for and supply of food. The factors most commonly highlighted as contributing to the current global food crisis include increased costs of food production, processing, and marketing linked to sharply higher oil prices; the use of food crops for biofuel production in the United States and Europe; growing meat consumption that stimulates increased demand for animal feed; poor harvests in certain major agricultural regions; and consistent underinvestment in agriculture over past decades resulting in agricultural production that lags behind population growth or broader economic growth. Several other factors must also be noted, including a weak U.S. dollar on foreign exchange markets, disincentives to agricultural production and trade stemming from protec- tionist or distortionary trade policies, and speculative behavior by both governments and commercial agents (see Abbott, Hurt, and Tyner 2008; FAO 2008; von Braun et al. 2008). Debate continues about the relative importance of each factor at the global level, but the net effect has been sharply higher world food prices.

The global food crisis has effects at a range of scales—national, household, and individual—and across a range of sectors of the economy.The impact of higher global prices on each country and its citizens depends, however, on local conditioning factors. For example, the degree to which global price changes are transmitted to the national economy depends on a country’s structure of imports and exports, transportation costs, and trade policy. Similarly, the degree to which higher local food prices affect household welfare depends on the importance of net food purchases relative to the size of the household budget. (These conditioning factors are

examined in more detail later in this report.) It is worth noting, however, the double-ended arrow running between effects and conditioning factors in Figure 1—the effects of the high food prices have feedback effects on the conditioning factors.

Feedback effects are the final element in tracing the effects of a global food crisis at the national level and below. These feedback effects are shown in the diagram by the looped arrows running between effects and feedback effects, and from feedback effects to global supply and demand factors. The initial effects of rising global prices will themselves lead to a cascade of secondary effects that may reinforce or mitigate the initial effects.Although these feedback effects are strongest within a country, as shown by the looped arrows, the impact of some of them will contribute to further changes in global food prices, particu- larly through the regional and international impact of a country’s trade and agricultural policies.

The pathways for policy analysis and policy action in response to higher global food prices are diagrammed in the lower half of Figure 1. For a national government to design and implement effective policy in response to a global food crisis, its leaders and policy analysts must understand the degree to which the country and population groups within it are exposed to risks or opportunities presented by the higher prices.This under- standing requires information on the characteristics—the conditioning factors—of the country and the population groups that determine how they are likely to be affected by the global food crisis, information on global demand for and supply of food, and information on the effects of rising global food prices, both initially and in the second- round feedback processes.

Figure 1 provides a framework for understanding the context for this report. The key elements intracing the effects of a global food crisis at the national level and below are presented in the top half of the figure, and the pathways for policy analysis and policy action are diagrammed in the lower

half. This figure is necessarily simplified; several of the elements are described in further detail later in

the report.

Conceptual Framework for Understanding the Impact of a Food Crisis

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Figure 1—Linking policy and policy analysis in addressing the effects of the global food crisis at the national level and below: A conceptual framework

Feedback Effects

World Food Prices

Effects

Conditioning Factors

• National—balance of trade, fiscal balance, political ramifications, commodity markets, labor markets

• Household—income, expenditure • Individual—nutrition, health, school

attendance

• National policy, trade market structure, infrastructure, etc.

• Household characteristics • Intrahousehold factors, gender

Monitoring and Policy Analysis

• Data requirements • Analytical tools

Policy Response

Global Supply Factors

Global Demand Factors

With appropriate data and the application of appropriate methods for policy analysis and monitoring, political leaders and policy analysts within government will have the evidence they need to design effective policies and programs to respond to the effects (and feedback

effects) of a global food crisis on the country and its citizens. Moreover, although the impact of the policies implemented will be strongest within a country, some policies will also contribute to further changes in demand and supply factors determining global food prices.

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Effects of a food crisis Figure 2 is a conceptual diagram of how changes in global food prices affect the national economy, the operations of government, and commodity and labor markets.These adjustments result in some immediate short-term effects on the incomes of households, the nature of which will depend on key household charac- teristics. Finally, changes in the economic condition of the household will lead to changes in the well-being of individuals within affected households.

Effects at the national level Ultimately, the welfare of communities and individuals is of the greatest interest, but changes at the national level may be indicators of current or future impacts at these levels. Five elements contribute to the aggregate national impact—changes in local commodity markets, local labor markets, fiscal balance, terms of trade, and political activity.

Local commodity markets. Higher world commodity prices generally increase the price of food, fuel, and fertilizer in developing-country markets, but the degree to which the price increase is transmitted depends on the commodity, the location of the country relative to global market centers, and the country’s trade policies. Almost all of the global price increase is likely to be transmitted to local markets when the commodity is internationally traded, when local and international commodities are close substitutes, when the country’s trade policy is relatively open, and when there are good transportation links with international markets. In many countries, wheat, maize, and to a lesser degree rice are tradable, so their domestic price generally reflects international prices.

For commodities that are not widely traded inter- nationally, however, such as cassava, sweet potato, and, in some countries, rice, the impact of world prices on local

prices is likely to be muted, as local prices follow local supply and demand conditions.Yet even the local prices of nontradable staples can be expected to rise in response to a food crisis as consumers seeking lower- priced substitutes shift their staple food consumption to these nontradables. In addition, to the extent that farmers shift into production of tradable crops in response to their higher prices, the supply of nontrad- ables may tighten, contributing to price increases for those commodities.

A rise in fuel prices increases the cost of trans- portation, which has a disproportionate effect on agricultural commodities because of their low value–bulk ratio. Increased transportation costs further raise the delivered cost of imported food and reduce the local prices obtained for export crops.To the extent that the fuel price hikes are transmitted to local markets, they also increase the cost of domestic agricultural marketing.

Local labor markets. Higher prices for commodities, particularly food, will put upward pressure on wage rates. In the face of higher food prices, employees will seek to renegotiate the wages they receive from employers in order to reestablish their previous purchasing power.Typically, however, wages are “sticky” and upward adjustments lag behind price increases. Because labor is often an important source of income for the urban poor, the rural landless, and small part-time farmers, it is important to monitor changes in wage rates, particularly for unskilled labor, to assess the impact of a global food crisis.

Fiscal balance. Global food crises can affect government revenue and expenditure in several ways. First, changes in the volume and value of trade due to a food crisis will influence tariff revenue, an important source of revenue for many developing countries. Second, changes in the price of food, fuel, and fertilizer will affect government spending on subsidies, particularly if the after-subsidy price, rather than the size of the

Monitoring and Assessing the Impact of a Food Crisis

To assess the impact of global food price changes on countries, households, and individuals, thissection considers two elements of the broad conceptual framework presented in Figure 1—the effects and the conditioning factors—in more detail and then examines the data and the methods

required to monitor and to assess the likely effects of a global food crisis.

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Conditioning Factors Monitoring and Analysis

Global-to-national price transmission • Transport and transaction costs • Market efficiency (competition, regulation) • Increasing returns to scale of trade flows • Degree of substitutability for commodity • Exchange rate dynamics • Trade and domestic marketing controls

(tariffs, quotas, etc.)

National level • Diversity of food staples consumed

(particularly nontradables) • Net trade position in food • National balance of payment status (scope

to increase import bill) • Food as proportion of average total

household consumption • Dependence of population on market for access

to food • Impact on general price inflation • Degree of political pressure on government • Labor market dynamics (scope for wages to track

food price movements)

Household level • Net food-buying or net food-selling household? • Household welfare level • Level of vulnerability to food insecurity due to

nonprice factors • Scope for increasing household income • Increased production of food • Expanding household livelihood strategies

Individual level • Intrahousehold resource distribution • Nutritional vulnerability (demographic) • Children under two • Childbearing-age women • Access to social services

Individuals: changes in human capital factors

Short-term impact on welfare and real income of different types of households:

• Urban rich: negative but small proportional effect • Urban poor: negative and large proportional effect • Rural wage laborers: negative and large effect • Rural farmers, net sellers: positive effect • Rural farmers, net buyers: negative effect

Figure 2—Conceptual framework for understanding the welfare impact of food crises

Agricultural and Food Markets

Rising prices in world markets for agricultural commodities (and oil)

External balance: exchange rate impact through terms of trade effect

Local labor markets: rise in wages relative to food prices

Local commodity markets for nontradable goods: substitution effects

Fiscal balance: changes in tariff revenue, subsidy and other program costs

Local commodity markets for tradable goods especially staples: price trends, production and consumption effects

• Monitor world commodity prices, particularly staple grains and fuel oil

• Monitor local staple food prices, terms of trade, and fiscal impact

• Analyze transmission of world price rises to local markets

• Measure effect of higher fuel prices on marketing margins

• Monitor unskilled wage rates

• Monitor prices of nontradable staples

• Analyze substitution in production and consumption

• Analyze impact on different types of households using partial and general equilibrium modeling methods

• Capture both immediate and second-round effects

• Monitor child and adult nutritional indicators

• Monitor school attendance, use of health services

subsidy, is fixed—a situation that can result in a rapid ballooning of subsidy costs.Third, government spending on social assistance programs will be affected to the extent that a food crisis causes more (or fewer) people to participate in the program or increases the cost per beneficiary, as would be the case if program benefits are defined in terms of a quantity of goods, for example.

Any adverse fiscal impact of a food crisis will eventually be transmitted to households in the form of higher taxes or reduced provision of goods and services, although the costs may be passed on in future years or to future generations in the form of debt.

External balance. Global food crises also can affect exports, imports, and the market for foreign currency through changes in the terms of trade between importers and exporters. For countries that are net importers of food and fuel, higher world prices result in a decline in the terms of trade. If the exchange rate floats, the increased demand for foreign currency results in depreciation of the country’s currency. If the exchange rate is fixed, the result will be a “shortage” of foreign

currency and the possible emergence of a parallel market for foreign currency. In either case, the impact is eventually transmitted to households in the form of higher relative prices for households purchasing tradable goods (imported and exportable goods), but higher returns for households selling tradable goods (exports and import-substitute goods).

Political activity. In many countries, the recent increase in food and fuel prices has led to street demonstrations and even riots.These events are not necessarily a good indicator of the size of the adverse impact of a food crisis because political activity is also affected by the degree of political mobilization in urban areas, the government’s tolerance of dissent, and other factors.These political effects are likely, however, to influence the government’s reaction to the food crisis, to define the range of possible policy responses it might undertake, and to condition how effective its actions might be to safeguard the welfare of the most vulnerable households and individuals.

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Effects at the household level The broadest measure of the impact of a food crisis on household welfare is the change in the real value of per capita consumption expenditure, although asset ownership and nonmonetary measures (such as nutrition or incidence of crime) may be useful as well. As diagrammed in Figure 2, household welfare is directly influenced by the price of food and other commodities the household buys and sells, as well as prevailing wage rates. Similarly, the fiscal balance of government affects households through changes in taxes and in the provision of government services from which households might benefit.The external balance affects households indirectly by influencing the exchange rate, which in turn affects the relative price of tradable and nontradable goods that households face in the market.

The impact of a food crisis will vary across different types of households in a country. Net food-selling households are likely to benefit from rising food prices. These generally better-off farming households will see an increase in income that will more than compensate for the rise in the price of any foods they purchase. Net food-buying households, however, which generally make up the majority of the population in most developing countries, are likely to be adversely affected by the global food crisis.Their purchasing power will be eroded by higher food prices, potentially resulting in a shift to cheaper sources of calories, a reduction in nonfood spending, the sale of assets, or a combination of these responses. For urban poor and landless rural households, significant reductions in welfare can be expected due to rising food prices, at least initially. Although wage rates for workers in these groups will adjust upward, these wage increases usually occur with a time lag and fail to keep up with food price increases.

Similarly, the household-level effects of higher fuel prices, which are an integral element of the current global food crisis, depend on the importance of trans- portation, fuel, and fertilizer in household expenditure patterns, as well as having an indirect impact through the higher cost of transportation of goods that they purchase. For poor households in developing countries, the impact of higher fuel prices is usually much smaller than the impact of higher food prices.

Given this variation in impact across households, it is misleading to talk about the “average impact” of a global food crisis.To fully understand the effects of a crisis, it is important to evaluate the impact of a particular crisis on different types of households within a country.

Effects at the individual level

The welfare impact of a food crisis may differ across members of the same household, a fact that is not taken into account in the preceding household-level analysis. Many studies have demonstrated that resources are generally not distributed equally to all household members, with women and girls often being disadvantaged, although the degree varies across countries and regions and by household characteristics (Quisumbing 2003).

In considering the effects of a global food crisis at the individual level and how governments might respond, the focus should be principally on the degree to which past and future investments in the human capital of individuals can be safeguarded so that individuals can attain their full social and economic potential and contribute creatively to their own and the country’s economic well-being. Educational attainment and health and nutritional status are key factors to consider, particularly because households may cope with the negative impacts of a food crisis by disinvesting in the human capital of individuals in the households, particularly the young.They may, for example, withdraw children from school to reduce costs or to generate income from their labor, reduce expenditures on preventative health care, and change the household diet away from protein- and micronutrient-rich foods (meat and vegetables) to less expensive staples.Through such pathways, the negative impact of a global food crisis on vulnerable households may extend into the next generation.

Factors influencing vulnerability to a food crisis As already noted, the effects of higher global food prices depend on a range of conditioning factors that operate at different levels.These factors determine the vulnera- bility of countries, communities, households, and individuals to adverse impacts due to rising global food prices. A closer examination of these factors will provide national leaders with a better understanding of the type and size of impact that a global food crisis is likely to have on their country and its citizens.

On the left side of Figure 2, four sets of conditioning factors are highlighted—those governing the degree to which global food prices are transmitted to national food markets, those that determine national-level impacts, those that determine the degree to which households might benefit from or be adversely affected by higher food prices, and, finally, those that influence how individuals within those households are likely to be affected.

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The impact of a global food crisis is transmitted to the level of the individual in a certain sequence. National-level effects are dependent upon the degree to which global prices are transmitted to national markets. If a country has poor trading links to global market centers because it is landlocked or has inadequate transportation links or because it has adopted strongly protectionist policies, the global food crisis is likely to have only a limited direct impact on the country. Several other factors that affect the transmission of prices are noted in Figure 2 (Rapsomanikis, Hallam, and Conforti 2006). Characteristics of the food economy of the country—whether it is a net importer or exporter of food, the importance of subsistence food production for the population, and the degree to which food dominates the consumption baskets of households in the country—will determine the actual national effects of the rise in global food prices. Broader economic and trade considerations are also important, such as the size of the food import bill relative to total exports, the fiscal impact of higher prices, and the ability of the country to implement social protection programs. Similarly, the responsiveness of labor markets to changes in commodity prices will be an important factor in determining both national-level and household-level effects of a food crisis.

As noted, households vary in the degree to which they are exposed to the impact of rising food prices and in the nature of that impact. Some households will see an improvement in their welfare, others a decline, with some slipping into poverty in consequence (Ivanic and Martin 2008).The position of households as consumers or producers with regard to food markets was already highlighted as a key characteristic in determining the initial impact of higher food prices in local markets.The general welfare level of a household is also important— the wealthier a household is, the more resilient it will be to such economic shocks. Similarly, those households that can exploit additional income sources or expand existing sources will be better able to cope with the negative aspects of rising food prices and potentially benefit from them.

Finally, although the vulnerability of their household will be the most important determinant of individuals’ vulnerability to risks from a global food crisis, the vulnerability of members of a household will vary. Where continued access to adequate food and basic social services is jeopardized, the nutritional well-being of household members is placed at risk.The most severe and enduring effects of any resulting malnutrition are felt by women of childbearing age and their young

children, both in the womb and in the early years of life when physical and mental development occurs rapidly. The degree to which household resources are directed to the needs of these individuals will determine how significant and persistent the effects of a global food crisis will be for them. Similarly, the provision of basic public social services or community assistance to the most vulnerable will mitigate some of the adverse individual effects of a food crisis.

Most national leaders seek to act so that any positive impacts of a global food crisis are enhanced and sustained and any adverse effects that constrain the development ambitions of the country or its people are avoided or reduced. Moreover, the entire social and economic framework within which the effects of a global food crisis play out at the national level is dynamic. Any public policy responses or any adjustments made by private firms, households, and individuals in the face of changing economic conditions due to rising global food prices will alter the conditioning factors that determined the nature of the initial impact of these rising prices. Consequently, feedback or second-round effects will foster additional adjustments. Many of these second-round effects may operate in a manner opposite to the initial effects experienced. Relatively sophisticated policy analysis will be required to identify these second- round effects and adequately address the threats and opportunities facing a country and its citizens.

In considering the vulnerability of countries, households, and individuals to global food crises, national leaders can use a relatively small set of information to assess the degree to which their country as a whole is likely to be affected by rising global food prices in one way or another and to determine which groups in the population of the country are thus likely to see a change in their well-being.Table 1 provides a list of key national- and household-level indicators to use for this purpose.

The most important sources of these data for a country include nationally representative household surveys with information on consumption, expenditure, and income; food price series from important commodity marketplaces in a country; and trade statistics. Most countries have such data, but where they are missing, it will be necessary to rely on qualitative or indicative, rather than representative, data to make this assessment. For national leaders focusing on their own country’s vulnerability to a global food crisis, qualitative assessments of the factors noted in Table 1 should be sufficient to provide a reasonable indication of the degree to which the country is exposed. In such a

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decisionmaking context, the value of the list of indicators presented in Table 1 lies more in identifying what sort of factors to consider than in their precise levels. Undertaking a quantitative assessment of these factors, however, particularly if cross-country or within- country trend analyses are needed, requires professional analysis to compute some of the indicators listed.

Finally, for investigating what policy responses to global food crises are most appropriate for particular types of countries or households, the conditioning factors at the national and household level listed in Table 1 provide an initial set of criteria upon which to construct typologies of country, household, or country- by-household types.With this relatively restricted set of categorical criteria, such groups can be defined simply through the construction of matrices based on them.

Data and analyses to assess impact of a food crisis The preceding section discussed the factors that make some countries and households more vulnerable than others to the effects of global food crises.This section considers what sort of information and analysis are needed to measure the effects of such food crises, including both monitoring and impact analysis. Monitoring refers to the regular measurement of indicators in order to understand historical trends, regardless of causes. In contrast, impact analysis tries to identify the effect of one causal factor, such as the increase in world food and fuel prices, excluding the influence of other factors, such as changes in local weather or agricultural policy.

Measuring national effects Understanding the five elements that contribute to the overall national impact of a global food crisis—changes in local commodity markets, local labor markets, fiscal balance, external balance, and political activity—calls for certain data and analytical requirements.

Local commodity markets. The basic data requirements for monitoring and analyzing the effects of global food price changes on local food prices include

• monthly or weekly prices in key markets for staple foods, fertilizer, fuel, and other commodities that are important to poor households;

• estimates of the cost of shipping imported commodities from international markets and shipping export commodities to their final destination; and

• the consumer price index.

These data can be used to plot the movement of

nominal and real local prices in different markets, as well as import and export parity prices for each commodity. A rough measure of the degree of price transmission is the ratio of the percentage change in local prices to the percentage change in world prices over a specific time period—an elasticity of global price transmission for the country.

A more advanced analysis of the effects of global price changes on local commodity markets would examine the degree of price transmission using time- series econometrics. If data permit, it would be useful to include subnational estimates of production of the most recent harvest of the food crops in question.The analysis could be done for different commodities and different markets within the country in order to assess the relative importance of local supply shocks and global price changes in determining local prices (see Delgado, Minot, and Tiongco 2005).

A broad set of qualitative, somewhat more contextual information would usefully inform any quanti- tative analysis on the interactions of global and local commodity markets.This information would include the trade policy orientation of government, the quality of a country’s market infrastructure—transport, communi- cation, contract enforcement—and a profile of food consumption patterns across households and regions.

Local labor markets. Basic data requirements to consider how local labor markets respond to global food crises include monthly data on unskilled wage rates by gender and the rate of unemployment in the formal sector.Where possible, these data should be disaggregated by region and between types of labor (such as agricultural or construction). A qualitative understanding of local labor markets would be required to judge their flexibility in the face of rising food prices. Key market characteristics of interest would include the relative size of the formal and informal labor markets, its sectoral composition, and the range of labor market regulations, including those governing minimum wages and employment contracts. In addition to being useful in themselves, these data are also an important input in the analysis of household-level impact, as discussed later.

Fiscal balance. A basic analysis of the impact of rising global food prices on the fiscal condition of a national government would examine each source of revenue or type of expenditure separately and estimate the change in revenue or expenditure that would result from a given increase in food and fuel prices. For example, what is the change in revenue associated with an ad valorem tariff on imported cereals, assuming actual changes in prices but no change in trade volume or tariff rate?

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Table 1—Basic information needed to assess the exposure of countries and households to the effects of rising global food prices

INFORMATION INDICATOR NOTES

National

Degree of integration into global food trade

The extent of relative global food basket price transmission for a country

Alternative measures: qualitative indicators of conditioning factors for price transmission from Figure 2 (transport and transaction costs, trade regulation, exposure to terms-of-trade effects, etc.)

Diversity of staples consumed nationally

Proportion of all calories consumed from staple foods that comes from the staple food providing most calories

• Provides indication of scope for substitution in staples away from those globally traded

• Possibly restrict focus to staple consumption patterns of poor households

• Alternative measure: proportion of all calories consumed that come from globally traded staple foods

Net food trade position Ratio of net staple food imports to GDP • Net exporting countries gain from higher prices, net importers lose, and countries that do not trade staple foods are relatively unaffected

• Alternative measure: Ratio of net staple food imports to total value of exports

Variability in national food production

Annual variability in estimates of total sta- ple food production over past 10 years

• Poor production in one cropping season will result in height- ened exposure to global food crisis.

• Alternative measure: seasonal rainfall variability

Trade balance National balance of payment status (strong surplus, in balance, or strong deficit)

Exposure to global fuel price shocks an element of this: degree of self-sufficiency in petroleum and other energy consumption

Importance of food consump- tion to all household con- sumption

Food consumption as a proportion of the value of all household consumption

• Alternative measure: proportion of food in basket of items used for national consumer price index

• Indicates inflationary impact of higher food prices; also the political pressure that government may experience from rising food prices.

Private supply response to higher food prices

Elasticity of supply in response to price changes for principal domestically pro- duced staple foods

Alternative measure: agricultural population as a proportion of total population

Household

Net food buyers or net food sellers

Net sales (sales minus purchases) of trad- able food as a proportion of household income

In countries where staples are tradable, net buying households lose, net sellers gain, and autarkic households are relatively unaf- fected. In countries where staples are not traded, little direct effect on households

Agricultural households Share of income from agriculture • Scope for self-provisioning in face of rising food price • Opportunity to respond to higher prices with increased

production

Options for household response to rising food prices

Poverty level Poor are constrained in terms of assets and capital, resulting in a more restricted suite of household coping strategies to draw upon

Food consumption as a proportion of value of all household consumption

Scope for reducing nonfood expenditures

Vulnerability to food insecu- rity in absence of food price shocks

Households already vulnerable : • conflict affected • recent agricultural shocks • certain demographic types (elderly or

single-parent households) • already reliant on food assistance

before food price increase

Rising food prices will exacerbate current vulnerability, particu- larly for those vulnerable households that depend on the market for much of their access to food

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More advanced analysis of the impact of these price increases on a nation’s expenditures, revenues, and debt would simulate the changes in revenue and expenditure as part of a larger system of equations, thus incorpo- rating interactions between markets and between government programs.This analysis may be a multimarket model describing the main agricultural commodities or a computable general equilibrium (CGE) model describing the economy as a whole. A CGE model can trace the effect of an adverse fiscal impact to households based on assumptions about how the government responds to the fiscal deficit through higher taxes, lower expenditures, increased debt, or some combination of the three. Similarly, such a model can be used to assess changes in demand for targeted public social services as a consequence of changes in the well-being of households due to a global food crisis.

External balance. A basic measure of the effect of world prices on the external balance of a country is the terms of trade effect, defined as the change in the value of export revenue minus the change in the cost of imports (based on the actual price changes but no change in the volume of trade), expressed as a percentage of gross domestic product (GDP) (more detail on the computation of this effect is provided in Appendix 1).This analysis would use annual or, preferably, monthly data on the volume of food and fuel imports and exports to calculate changes in the terms of trade on an annual or monthly basis. Expressing the change in the terms of trade as a percentage of GDP provides an idea of the size of the shock relative to the size of the economy, as well as allowing cross-country comparisons. It also can be decomposed to measure the proportion of the impact caused by each commodity (such as wheat, maize, or oil). The likely effect on the exchange rate may be approxi- mated using previously estimated elasticities of excess demand for foreign currency.

A more advanced analysis of these effects would use an economic model, ideally a CGE model. Such a model would generate estimates of the impact of the food and fuel price increases on the terms of trade and the equilibrium exchange rate. An appropriately designed CGE model would also be able to simulate the impact of the depreciation on different types of households.

Political activity. The data collection and analytical methods for assessing the political impact of rising global food prices are principally qualitative. Nevertheless, it is important to take these effects into account in under- standing the potential range of policy responses that can be considered when evaluating alternative policies to respond to the effects of a food crisis.

Measuring household effects

The largest and most direct effect of a food crisis on household welfare is likely to be through the prices of agricultural commodities and, possibly, fuel and fertilizer. Thus, most analyses of the impact of a global food crisis at the household level should focus on prices and how changes in prices of various sorts influence household welfare.

In the short run, before a household responds to changing local prices, the impact of price changes on welfare can be estimated using the changes in the price of goods and services and the composition of income and expenditure of the household (see Appendix 1).

An extension of this analysis uses price elasticities of supply and demand to simulate the response of households to the price changes, thus yielding the change in welfare after the household responds to the price changes.This approach corresponds to a simulation of the welfare impact in the medium or long term. The welfare impact in the longer term is generally more positive (or less negative) than the short-run impact owing to second-round effects. As such, the short-run impact generally serves as a lower limit of the possible long-term impact of changing local food prices on household welfare (see Appendix 1).

Both the short-term and the medium- to longer- term expressions of welfare change for a household in the face of rising food prices can also be calculated for a set of representative households or, preferably, for every household in a nationally representative household survey. In the latter case, by estimating the change in income associated with the price change, researchers can estimate the resulting changes in poverty and inequality for the nation as a whole.

The basic data requirements for understanding the household welfare impact of price changes include

• information on the price changes of important goods and services;

• information on the composition of household expenditure; and

• information on the composition of household income.

At a minimum, this information should be available for one or more “typical” types of households, with an emphasis on different types of poor households. It is, however, much more useful to have this information for all of the households in a nationally representative household income and expenditure survey.This type of data set would permit the simulation of the impact of price changes on all households in the sample, and then

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aggregate the effects simulated to any desired categories of households (such as by region, by income category, by occupation, and by net position in staple crops).This approach has been used in various studies of the distribu- tional effects of hypothetical or historical changes in food prices, including studies by Deaton (1989) for Thailand, Budd (1993) for Côte d’Ivoire, Barrett and Dorosh (1996) for Madagascar, Minot (1998) for Rwanda, and Ivanic and Martin (2008) for various countries.

The analyses described use exogenous price changes, either historical prices or assumed price changes. A more advanced analysis is to develop a model in which prices are endogenous, being a function of change in world prices, trade policy, technology, weather, transportation costs, or other factors.This type of analysis generates estimates of the detailed distribu- tional impact of selected policies and external shocks. The model may be a partial equilibrium model, such as an agricultural sector model with or without spatial equilibrium built into it (Minot and Goletti 1998). Alternatively, it may be a general equilibrium model that includes all sectors and makes income fully endogenous. Such models require more skills, resources, and data than simpler approaches, but they present the most comprehensive approach to analyzing the impact of policies on households.

Measuring individual effects The basic data requirements for monitoring the impact of a food crisis at the individual level include a number of administratively collected variables:

• school enrollment by age and gender, collected by primary and secondary schools;

• attendance at health clinics by age and gender, collected by clinics; and

• nutritional status by age and gender, collected by clinics or from nutritional surveys.

These indicators may be poor measures of the underlying variables. For example, school enrollment figures do not indicate the share of each age group in school and may be misleading indicators of trends if there is migration. Similarly, clinic-based nutrition figures are not necessarily representative of the general population.

A preferable but more costly and time-consuming approach to monitoring these individual-level welfare indicators is with household surveys that collect detailed individual-level data. Such surveys can also measure the quantities of food consumption by each member of the household, leading to individual-level estimates of nutritional intake.

An analysis of the impact of a food crisis on individuals would be based on the analysis of the impact of the crisis on household-level welfare (already discussed) and an estimated relationship between household welfare and the individual-level outcomes of interest (such as nutrition or school attendance).This relationship would be estimated using econometric methods and would take into account other factors such as household composition, parental education, access to services, and measures of the role that women play in household decisions.

Summary of data and methods Table 2 summarizes the data that would be needed to undertake a relatively thorough assessment of the impact of a global food crisis on a country and its citizens.The data listed here represent something of an ideal set. Moreover, the more detailed the data in terms of variables, frequency (weekly or monthly, rather than annual), and spatial resolution (regional or district level, rather than national), the better. Important insights could be gained, however, with a more basic set of information: a nationally representative household budget survey, relatively detailed commodity price series, information on the food trade patterns of the country, and the consumer price index.

The ease with which the basic data sets for a country can be assembled will vary considerably between countries, depending on the strength of their statistical systems and the degree to which quantitative policy analysis is used in decisionmaking.Where data are missing for a country, the final column in Table 2 notes additional sources of data or alternative practical types of information that may be used.

Similarly,Table 3 summarizes the most important analyses to which the data listed in Table 2 could be applied. One can categorize the methods into subsets corresponding to basic, intermediate, and more advanced analyses.The basic analyses are those that can be done without strong quantitative skills or specialized software; the intermediate analysis requires more skills but no econometric analysis or modeling; and the more advanced techniques are considerably more demanding in terms of quantitative skills (econometrics and modeling), data, and specialized software.

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Table 2—Data to assess impact of global food crises on countries, households, and individuals

INFORMATION PURPOSE RESPONSIBLE ALTERNATIVES

Basic data sets

Household consumption and expenditure survey (nationally repre- sentative)

• Composition of household expenditures • Composition of household income • Food group consumption patterns, by population

group; importance of imported staple foods • Proportion of population that are net food buyers

or net food sellers • Household poverty status • Specification of sectoral or CGE models for

advanced policy analysis

• National statistical office (NSO)

• Survey analysts at academic and research institutes, if NSO does not do analysis

• Household surveys from NGOs and researchers that are indicative but likely not nationally representative

• Estimates from neighboring countries with similar household livelihood and welfare patterns; international databases (UN agencies, ERS/USDA)

Price series for food, agricultural inputs, and fuel from key national and international marketplaces

Determine national market integration into global trade

• NSO • Ministry of Trade • International com-

modity price data- bases (e.g., IMF, FAO)

• Primary price data for CPI calcula- tion from NSO

• Ministry of Agriculture • Private sector commodity whole-

salers

Import and export data (commodities, amounts, value)

Importance of trade for food security and economic growth—require data on both formal and informal trade

Customs Department • NSO (data on informal trade) • Food security agency (monitor all

trade in food)

CPI Deflate nominal to real prices or relate to wage rate changes

NSO International financial databases (e.g., IMF)

Additional data sets

Agricultural production estimates

• Net trade position in food • Variability in national food production

Ministry of Agriculture • FAO national food balance sheets • National food import and export

data (formal and informal)

Elasticities of sup- ply and demand in response to price changes

• Own and cross-price elasticities of supply (can farm- ers shift to higher-priced commodities?)

• Own and cross-price elasticities of demand (can consumers substitute away from higher-priced trad- able staples to local nontraded alternatives?)

• National planning authority, Ministry of Finance

• Analysts at academic and research institutes

Global elasticity datasets (e.g., FAPRI, ERS/USDA); elasticities for neighbor- ing countries with similar agricultural production, trade, and food consump- tion patterns

Nutritional surveys, vulnerability assessments

• Pre-crisis vulnerability to nutrition insecurity • Impact of global food crisis on child and maternal

nutritional status

• NSO • Ministry of Health • Food security moni-

toring agencies

Estimates from neighboring countries with similar household livelihood and welfare patterns (see DHS database, UNICEF,WHO, FEWSNET)

Use of social services

Monitoring the impact of crisis on use of social serv- ices that promote learning, health, and good nutrition

Administrative data from Ministries of Education and Health

Household survey data with informa- tion on all members’ education, health, and nutrition status

Wage rates; labor market structure

Determine scope for response in labor markets to effects of global food crisis

NSO (household, labor, and enterprise surveys)

Ministry of Labor (information on labor regulations)

• Import and export transac- tion costs

• Regulations on trade in food

• Identifying barriers to trade • Export and import parity price computations; com-

parative advantage assessment

Ministry of Trade • More comprehensive data on costs from private sector importers and exporters

• “Doing Business” dataset of the World Bank

Fiscal position of government

• Sources of revenue, nature of expenditures, and how each might change with effects of global food crisis

• Specification of sectoral or CGE models for advanced policy analysis

Ministry of Finance Sectoral working groups (education, health, agriculture, etc.) for details on program design and expenditures

External balance • National balance of payment status • Assess terms-of-trade effects

• Ministry of Finance • Ministry of Trade

International macroeconomic data- bases (e.g., IMF)

Note: CGE = computable general equilibrium; CPI = consumer price index; DHS = Demographic and Health Survey; ERS/USDA = Economic Research Service/U.S. Department of Agriculture; FAO = Food and Agriculture Organization of the United Nations; FAPRI = Food and Agricultural Policy Research Institute; FEWSNET = Famine Early Warning System; IMF = International Monetary Fund; NSO = National Statistical Office; UNICEF = United Nations Children’s Fund;WHO = World Health Organization.

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Table 3—Analytical methods to assess impact of global food crises on countries, households, and individuals

METHOD PURPOSE ANALYTICAL SKILLSREQUIRED

Basic analyses

Monitoring of real food prices (world and local) and wages

To understand the potential and actual magnitude of the price shocks households face and their ability to cope with them

Basic

Cross-tabulations of household survey data

To develop profiles of population groups identified by likely impact of global food crisis (by region, poverty status, livelihood, net-seller/net- buyer status, etc.)

Basic to moderate

Investigations of food group con- sumption patterns with house- hold survey data

To permit evaluation of the impact of rising food prices on the compo- sition of the diets of various population groups in the country

Basic to moderate

Profile of a country’s trade in food

Significance of trade for the food security of a country and for that of its trading partners

Basic

Intermediate analyses

Price transmission from global to national markets—basic correla- tion analysis

To assess exposure of national consumers to shifts in world prices of traded commodities

Moderate

Analysis of terms of trade effects To determine the effect of world price changes on the value of net exports as a percentage of GDP and the impact on real household income

Moderate

Analysis of household-level effects

To understand the impact of world price changes on different types of households based on the composition of their expenditure and income (with or without demand and supply response)

Moderate

Advanced analyses

Demand and supply estimation To estimate the effect of changes in prices, income, and other factors on demand for food commodities; to estimate the effect of price and other factors on agricultural supply response

Moderate to advanced

Partial-equilibrium, sector-specific models

To simulate the impact of policies and global price changes on the agri- cultural sector and (potentially) poverty and distributional effects

Moderate to advanced

Computable general equilibrium (economywide) models

To simulate the impact of global price changes and the policies adopted in response on disaggregated economic growth and poverty; to gain insights on fiscal implications of the crisis and the government’s responses to it

Advanced

Price transmission from global to local markets (time-series econo- metric analysis)

To better understand barriers to trade in food and other commodities for a country

Moderate to advanced

Note: Basic analytical skills should be available in government institutions with planning and budgeting responsibilities. Moderate skills can be expected to be found in stronger ministerial planning departments. Advanced skills are likely to be found only in research universities or other research institutes (local and international).

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National governments have responded to the current world food crisis in several ways. Responsesintended to reduce the prices faced by domestic consumers in the short term, such as reductions in tariffs or consumption taxes, food price controls, actions against speculators and

profiteers, consumer subsidies, export bans or taxes, government food imports, and release of food

reserve stocks, have been quite common. Less common are interventions to increase domestic food

production by triggering a supply response to the crisis, such as through providing input subsidies or

support prices for farmers. A third category of responses has sought to increase food availability for

or the income of vulnerable or politically powerful groups through social protection programs such as

food rations, food or cash for work, and cash transfer programs. Appendix 2 lists policy responses by

different governments to the current food crisis through mid-2008, as well as a listing of political

agitation triggered in part by the crisis.

Monitoring and Assessing Current and Future Policy Responses

Table 4 illustrates these policy responses, as well as others that could be used in the medium and longer term.The table classifies the interventions according to the types of interventions mentioned—that is, those intended to affect consumer prices fairly directly, those intended to increase food production (with indirect impacts on food prices), and those intended to increase food availability for or the income of target groups. It also classifies interventions according to whether they are likely to have an impact in the immediate or short term, in the medium term (one to three years), or in the longer term.

One point evident in Table 4 is that the set of possible policy responses expands as the time frame for impacts is increased. In the short term, policymakers can little do to change domestic food production if farmers have already made their planting and input use decisions for the upcoming harvest. Policy responses are limited to changes in tariffs, taxes, consumer subsidies, food price controls, export restrictions, government food imports, or the release of public reserve stocks. In the medium term, the scope of action widens— governments can implement price stabilization policies based on the use of reserves, tariffs, or subsidies; promote food production using subsidies, producer price supports, or provision of agricultural support services; and extend social protection programs. In the

longer term, larger sustained impacts can be achieved through broader investments for economic development and poverty reduction.

Given the breadth of possible policy responses to a food crisis in the longer term, the challenge of monitoring and assessing the impacts of such responses is great and hardly distinguishable from the need to monitor and evaluate policies and programs to promote economic development and poverty reduction in general.This report focuses mainly on methods of monitoring and assessing the short-term responses to a global food crisis and their impacts, although it also provides some discussion of the need for and approaches to assessing medium- and longer-term responses and impacts.

Types of impacts expected from policy responses Although the purpose of this report is not to present an analysis of the impacts of actual or potential policy responses to the current global food crisis, it is useful to consider the types of impacts that can be expected from policy responses in order to help guide decisions about what information should be monitored and what analytical methods should be used to assess impacts. Table 5 provides a set of hypotheses about favorable and unfavorable impacts that may result from various

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Table 4—Potential policy responses to food crises

TYPE OF INTERVENTION

TIME FRAME

SHORT TERM (< 1 YEAR) MEDIUM TERM (1–3 YEARS) LONG TERM (> 3 YEARS)

Reduce food prices for consumers (price-oriented policies)

• Reduce tariffs/taxes on food • Adopt food price controls/take

action against profiteers • Adopt consumer subsidies • Adopt food export bans or taxes • Pursue government food imports • Release food reserve stocks

Same options as short term plus: • Establish food reserves and

release policy • Establish variable tariffs or

variable export subsidies/taxes • Pursue options to increase

domestic food production (see below)

Same options as medium term plus: • Invest in marketing infrastructure,

institutions, and information • Invest in increased food production

capacity (see below)

Increase food production (supply-oriented policies)

Limited short-term options • Adopt input subsidies • Adopt producer price supports

and subsidies • Expand agricultural credit • Strengthen agricultural extension

Same options as medium term plus: • Pursue agricultural R&D • Invest in productive infrastructure

and assets (e.g., irrigation, mecha- nization)

• Improve natural resource manage- ment

• Improve property rights and resource tenure systems

Increase food avail- ability for or income of target groups (income-oriented policies)

• Increase support through existing social protection programs

• Increase public sector wages • Increase food aid programs

Same options as short term plus • Establish new social protection

programs or expand/improve existing ones

Same options as medium term and those for increasing food production plus • Invest in other development and

antipoverty programs (e.g., education, promote rural nonfarm enterprises)

policy responses and about conditioning factors influencing these impacts. As already noted, most of the short-term policy responses aim to reduce consumer food prices, which is a favorable effect from the standpoint of net food buyers. Policies and programs to promote increased food production also have beneficial impacts on net food buyers to the extent that they result in reduced domestic food prices.They also can benefit food producers by reducing their costs of production (such as through input subsidies) or increasing producer prices (such as through price support and producer subsidies), although the net impact on producers depends on the relative strength of these effects compared with the downward pressure on producer prices caused by increased production. Targeted food aid or income-oriented interventions likely have favorable impacts on the direct beneficiaries, and these may have beneficial spillover impacts on other households or individuals such as by increasing demand for goods and services provided by households as a result of the increased incomes of beneficiary households.

All of these policy responses have costs and potentially unfavorable impacts as well. All price-oriented interventions, to the extent they are successful in reducing food prices, will reduce the incomes of net food sellers and the incentive for producers to respond by increasing production. Reducing tariffs or consumption taxes, increasing consumer or producer subsidies, or increasing social protection programs will have direct budgetary costs, potentially increasing government deficits, credit shortages (if budget deficits are financed by borrowing), or inflationary pressures (if budget deficits are financed through monetary expansion).The benefits of interventions may not be well targeted to poorer and more vulnerable households, especially interventions focused on affecting market prices, leading to potentially high costs relative to the improvement in food security achieved. Direct subsidies to producers or consumers or social protection programs have more potential for targeting, although targeting may not always be politically acceptable and may involve high administrative costs. Efforts to control prices and speculative behavior may lead to black markets, and thus be ineffective, and may

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Table 5—Potential national policy responses to food crises: Favorable and unfavorable effects and conditioning factors

POLICY RESPONSE FAVORABLE EFFECTS UNFAVORABLE EFFECTS CONDITIONING FACTORS

Reduced tariffs or other taxes on food

• Lower domestic food prices • Increased domestic consumption and

welfare of net food buyers • Contribution to liberalized agricultural

trade

• Lower government revenue • Lower income of net food sellers • Reduced food production in longer

term • Benefits accrued by wealthier

(as well as poorer) net food consumers

• Reduced food available on world markets

• Initial level of tariffs and taxes • Availability of offsetting sources

of government revenue • Difficulties for low-income

countries with lack of other revenue sources

Export restric- tions (including taxes and minimum export prices)

• Lower domestic food prices • Increased consumption and welfare of

net food buyers • Increased government revenue

(if export taxes are used)

• Lower income of net food sellers • Reduced incentives for food

production • Benefits accrued by wealthier

(as well as poorer) net food consumers

• Reduced food available on world markets

• Ability to enforce restrictions • Difficulty of applying export

taxes for countries with limited administrative capacity

Release of food from stocks

• Lower domestic food prices (quickly but temporarily)

• Increased consumption and welfare of net food buyers

• Increased government revenue if government stocks are sold

• Reduced cost of storing food

• Lower income of net food sellers • Reduced incentives for production

response • Possible undermining of private

storage activity if public stocks are used

• Benefits accrued by wealthier (as well as poorer) net food buyers

• Availability of food stocks • Expectations about future prices

(useful only for countries that have accumulated stocks)

• Release of food stocks will not affect the price of a tradable commodity

Price controls on food

• Lower domestic food prices (if price controls can be enforced)

• Increased consumption and improved welfare of net food buyers

• Lower income of net food sellers • Reduced incentives for supply

response • Market disequilibria leading to

quantity rationing • Rent-seeking behavior, black

markets • Deadweight efficiency losses • Benefits accrued by wealthier (as

well as poorer) net food consumers, if quantity restrictions can be overcome

• Ability to enforce price controls and quantity restrictions (difficult to do this effectively in most countries)

• Political attractiveness of such measures

Consumer food subsidies

• Lower consumer prices (if effective)

• Increased consumption and welfare of food consumers

• Increased producer prices and production incentives for nontradable foods if supported by budget expenditures

• Increased income for producers

• High budgetary burden • Benefits accrued by wealthier (as

well as poorer) food consumers and producers, if subsidies are not targeted

• Political and administrative difficulties of targeting

• Benefits leak to food exporters and nontargeted consumers

• Political feasibility of targeted versus general subsidies

• Budgetary capacity • Administrative costs and

feasibility of implementing targeted subsidies (difficult for low- income countries)

Actions against or appeals to profi- teers, speculators

• Possibly lowered food prices (if effective) with benefits to net food consumers

• Possible undermining of private markets

• Reduced food storage and marketing by private agents, leading to more volatile food prices or larger food marketing margins

• Lower prices for farmers, reducing production and marketing

• Political attractiveness of finding scapegoats in marketing system

• Ability to distinguish “profi- teering” from reasonable speculation and trading activities and to hold the guilty accountable (very difficult to do this effectively in most countries)

Cash transfer programs- conditional cash transfer (CCTs) and means-based transfers

• Possible targeting of poor and vulnerable

• Less costly than food aid, general food subsidies, or tariff/tax cuts

• Increased food consumption and improved welfare of recipients

• Promotion of use of health and education services and facilitation of long-term human capital investment

• Difficulty of establishing effective programs quickly, especially CCTs

• Possible political unpopularity of targeting

• Potential for leakages of targeted programs

• High administrative and possibly budget costs

• Prior existence of system • Administrative capacity to target

and distribute transfers (difficult for most low-income countries)

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POLICY RESPONSE FAVORABLE EFFECTS UNFAVORABLE EFFECTS CONDITIONING FACTORS

Food vouchers or food stamps

• Similar favorable effects as for cash transfer programs

• Also may serve as a transition from in- kind to cash transfers

• Similar unfavorable effects as for cash transfer programs

• More costly to administer than cash transfers, but cheaper than in-kind transfers

• Prior existence of system • Administrative capacity (difficult

for low-income countries)

Food- or cash-for- work and public works schemes

• Potential self-targeting to poor individ- uals with capacity to work

• Contributions to valuable infrastruc- ture and other public investments

• Maintenance of incomes of vulnerable populations in face of shocks (espe- cially labor demand shocks)

• Adjustability of wage payments in response to food price shocks

• Possible exclusion of vulnerable people facing labor constraints (e.g., HIV/AIDS affected, women)

• Potential negative impacts on other uses of labor, including agriculture

• Possible poor-quality public investments

• High administrative demands in general; higher with food-for-work

• Lack of complementary nonlabor inputs may undermine effectiveness

• Potential for aid dependency

• Administrative capacity to imple- ment and assure quality

• Access to source of food aid or funds (usually donor funds)

• Availability of surplus labor at certain times of the year (useful option for many low-income countries, especially for those with access to donor assistance)

Increased public sector wages

• Rapid response to impacts on a politically important group

• Support for political stability • Spillover benefits to others through

increased demand for goods and services by public sector employees

• Increased wages

• Failure to target those most vulnerable to food price increases

• High budgetary costs • Possible contribution to public deficit

and an inflationary spiral

• Size and political clout of the civil service

• Budgetary capacity and risk of inflation

Minimum support prices for farmers

• Stimulation of production response (if effective)

• Increased income of net food sellers • Increased price stability for net food

buyers and sellers

• Possible excess supply and stocks • High budgetary costs • Difficulty of enforcing minimum

prices • Possible disproportionate accrual of

benefits by wealthier net food sellers • Costs to net food buyers • Contribution to instability in interna-

tional markets if based on variable tariffs or export subsidies (not if based on stocking policies)

• Difficulty of eliminating once established

• Political strength of net food sell- ers for particular commodities

• Dependence on food imports (easier to implement a variable tariff than stocks-based approach)

• Fiscal and administrative capacity (especially for stocks-based approach)

Subsidies to farm- ers, e.g., input vouchers

• Stimulation of production response (if effective)

• Increased income of farmers • Reduced prices of nontradable foods,

leading to benefits to food consumers

• High budgetary costs if not well targeted

• Political and administrative difficulty of targeting

• Potential for leakages of benefits to advantaged groups

• Difficulty of eliminating once established

• Potential negative impacts on private market development (which can be addressed through “smart” subsidies)

• Possible inefficiently excessive use of some inputs

• Political strength of farmers and input suppliers

• Fiscal and administrative capacity to implement “smart” and targeted subsidies

Building of food reserves

• Stimulation of production in near term (while stocks accumulate) and buffering of future price instability, which benefits domestic food producers and consumers and interna- tional markets

• Assurance of reliable supply for exporting countries

• Possible price increases in near term • Cost of establishing and maintaining • Limited impacts on prices except for

nontradable (or trade-prevented) commodities and large players in international trade

• Undermining of private stockholding by government stocks

• Size of country’s net supply or demand in international market

• Tradability of the commodity • Fiscal and administrative capacity

(not likely the most effective intervention for small low- income food importers)

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contribute to corrupt practices by regulators.To the extent that price controls are effective, they can cause shortages that must be addressed by other rationing mechanisms, leading to inefficient and possibly inequitable allocation of commodities. Leakages and spillover effects of interventions may also undermine their effectiveness. For example, export bans may lead to increased contraband food exports, while changes in public food reserve stocks may be offset by induced changes in private stockholdings.Trade interventions such as export bans on staple foods also can precipitate protec- tionist reactions by other nations, undermining the food security of trade-dependent countries as the interna- tional market for food becomes increasingly volatile.

Many political, administrative, and economic condi- tioning factors can influence the feasibility and impacts of these policy interventions. For example, the ability to use tariff or tax reductions to offset food price increases depends on the initial level of these tariffs or taxes and on the political will and fiscal capacity of the government to offset or forgo the revenues that would have been collected. Budgetary constraints may also limit the use of subsidies, social protection programs, or public sector wage increases.The use of export restrictions depends on the government’s capacity to enforce such restric- tions. International treaty obligations under the World Trade Organization (WTO) or other trade agreements may also limit national governments’ ability to use trade measures to buffer food price changes.The capacity of the government to enforce price controls or regulations on speculators will determine the effectiveness of such measures, while the political context may promote or inhibit their use.The ability to expand the use of social protection programs will depend upon prior experience with such measures and the administrative capacity of the government to implement targeted approaches.

These conditioning factors imply that low-income countries dependent on food and oil imports may be limited in their ability to use most of these potential responses effectively. Budgetary constraints are likely to limit the use of large untargeted subsidy programs, reductions in tariffs and taxes, or public sector wage increases, whereas administrative capacity constraints will often limit the ability to target social protection programs. Some social protection programs, such as food-for-work or cash-for-work, tend to be self-targeted and thus more readily usable than more administratively complex approaches, such as conditional cash transfer programs. Low-income food-importing countries that are large exporters of oil or other commodities whose prices have also increased will have more budgetary

capacity to use subsidies, tariff and tax reductions, or social protection programs to buffer the impacts of food price increases, although they are still likely to face many administrative capacity constraints. Higher-income countries tend to have more budgetary and adminis- trative capacities to implement a range of these options.

Monitoring and assessing the impacts of policy responses It is important to clearly distinguish the concepts of monitoring and impact assessment. Monitoring involves collecting data on selected indicators and observing how those indicators change over time.The purpose of monitoring may be diagnostic or prescriptive. For example, changes in food security vulnerability indicators may be used to diagnose a serious problem occurring for some population in some location, whereas such indicators combined with indicators of the conditioning factors affecting responses and outcomes (such as indicators of prior investment and coverage of social protection programs) may help to prescribe promising policy responses.

Monitoring by itself does not tell policymakers what impacts a given policy or program is expected to have (ex ante assessment), is having (assessment during implementation), or has had (ex post assessment).To assess impacts, one must define the counterfactual or baseline situation against which impacts are to be assessed and use analytical methods to measure the difference in outcomes between the situation with the policy or program being evaluated and the counter- factual situation. Conceptually, the counterfactual situation should be the situation that is expected to occur (in an ex ante assessment) or that would have occurred (in an assessment during implementation or ex post) without the intervention. One of the main diffi- culties in impact assessment work is that the counterfactual situation is not observed (nor is the factual situation—the situation with the policy— observed in ex ante assessments).To address this problem, some assumptions and models, whether explicit or implicit, are necessary. For ex ante assessments, predictive models are needed to predict what will happen with the intervention versus without the intervention. Such models could be as simple as assuming that the quantities of the commodities of interest produced and consumed would be the same in the future as in the recent past and that the only difference between the counterfactual and factual scenarios is in the prices of these commodities (for

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example, commodity prices might be assumed to be reduced by 25 percent due to a tariff reduction). Or they could be complex multimarket or general equilibrium models that seek to assess how changes in the market for one commodity affect other commodities and factors of production.

Assessing impacts during or after an intervention offers the advantage that the factual situation is observable.The counterfactual situation, however, is not. Some common ways of addressing this problem are to

• assume that the situation observed before the policy or program is what would have occurred without intervention (before-after comparison);

• assume that the outcomes for some comparator group not directly affected by the policy or program represent the outcomes that would have occurred for the affected population without the program (with-without comparison);

• assume that the changes in outcomes for a comparator group represent the changes in outcomes that would have occurred for the affected population without the program (double- difference comparison); or

• use a model to predict what would have happened without the intervention.

The first approach (before-after comparison) is problematic if other factors besides the policy or program being assessed that affect the outcomes of interest also are changing over time. In this case, the before situation may be a very poor proxy for what would have occurred without the policy or program. For example, food prices may be changing over time as a result of many supply- and demand-related factors, so attributing a change in food prices as due solely to a policy change can be problematic.

The second approach (with-without comparison) is problematic if the comparison group is different from the affected group in ways that affect the outcomes of interest.This problem can be addressed by (1) randomly assigning groups or individuals to “treatment” versus “control” groups using an experimental design, which assures that the with and without groups are statisti- cally similar in all observable and unobservable characteristics; (2) selecting the comparison groups by matching members between the groups on relevant observable characteristics; or (3) using econometric approaches to correct for selection bias. Because it ensures that treatment and control groups are similar in both observable and unobservable characteristics, random assignment is seen as the “gold standard”

approach in impact evaluations of targeted programs (Heckman et al. 1998).This approach is not always feasible, politically acceptable, or appropriate to the nature of the intervention, however. Demand-driven development programs, for example, do not easily lend themselves to the use of supply-driven random assignment.

Moreover, all of these with-without comparison approaches assume that the comparison group is unaffected by the policy or program being assessed. For interventions that affect food prices throughout a country, it is difficult to find counterfactual households in the same country who are unaffected. Even for targeted subsidies or social protection programs, spillover effects of such programs to nonparticipants may affect outcomes for potential comparison groups as well. Such effects are easier to avoid for small pilot programs or small targeted changes in such programs than for changes occurring on a large nationwide scale.

The third approach (double-difference) combines the strengths of the before-after and with-without comparisons, since it nets out the effects of common factors affecting both with and without groups (like the effects of common changes in prices affecting both groups) and fixed factors that may cause differences in outcomes between the groups (like differences in their abilities).This method is also subject to the shortcoming that the comparison group may be indirectly affected by the policy or program, however, and to any measurement error problems in comparing differences (Ravallion 2005).

The fourth approach (using a model to predict the counterfactual) can overcome the problem of not being able to identify a suitable comparison group unaffected by the intervention.This approach is thus particularly useful for assessing the impacts of policies that affect food prices and allows a similar approach to be used as for assessing impacts ex ante.The validity of the assessment will depend on the validity of the model and the assumptions on which it is based, however, many of which may not be readily testable.

The preceding discussion points out that no method of impact assessment is free of assumptions or potential problems.The best method to use will depend on the type of policy or program being assessed, the time frame and outcomes of interest, the data available for the assessment, the ability to build on prior assessments and models, and the ability of key stake- holders to use and comprehend the method used. In the next three subsections, this report suggests some methods of monitoring and assessing impacts of the

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three different types of policy interventions—price- oriented, supply-oriented, and income-oriented—with these considerations in mind.The methods discussed range from the simple to the complex, depending on the nature of responses and impacts considered. Simple methods require more restrictive assumptions concerning the responses of and impacts on producers, consumers, and others in the economy but are easier to implement.

Price-oriented policies and interventions

Assessing the impacts of policies that affect households and individuals primarily through their impacts on food prices—changes in tariffs, taxes, or subsidies; price or export controls; grain reserve policies; and so on—can be done in two steps. First, one estimates the impacts of these policies on domestic food prices and other national-level outcomes such as fiscal and external balances. Second, the impacts of these price changes on households and individuals can be assessed using the methods discussed in earlier for monitoring and assessing the impacts of food price increases. Because the methods and data needs for the second part of this assessment were discussed earlier, this section focuses only on the first part of the assessment.

A distinction can be made between policies that affect food prices fairly directly, such as changes in tariffs, and policies that affect prices by affecting the quantity of food available in the market, such as grain reserve policies. In the former case, a first approxi- mation of the impact on prices is given by the policy itself. For example, a reduction in the tariff rate of $10 per ton for an imported commodity can be expected to reduce the domestic price of that commodity by $10 per ton if domestic markets are well integrated with the international market (perfect price transmission). If this assumption holds, there is no need for any monitoring or assessment to determine the resulting domestic price change. Because of transaction costs, market or government imperfections, and other factors, however, the actual changes in domestic prices of the commodity in particular locations may be different (probably smaller) than the change in the tariff rate. Hence it is useful to monitor what happens to prices in different locations and assess the extent to which the policy change or other factors contributed to such changes.

In the case of interventions affecting the supply of food in the market, such as release of public grain reserve stocks, a different approach is needed.The impacts of these quantitative supply shifts on prices must

be estimated.This estimation can be done using a simple single-commodity partial equilibrium model, assuming that only one commodity is affected, or using a more complex multimarket model, assuming that prices of other commodities may also be affected. Other indirect effects of the policy, such as the effects of releasing public stocks on private stockholding behavior, may also need to be taken into account to draw reliable conclusions about the impacts of the policy.

Table 6 summarizes some approaches to monitoring and assessing the impacts of a change in the import tariff on an imported food commodity and of a change in public food stocks. In the interest of brevity, the table does not describe specific approaches for all of price-oriented interventions mentioned earlier. Rather, it presents these two examples as illustrations of the approaches and issues involved in assessing impacts of, first, an intervention with a direct price impact and, second, an intervention with an indirect impact on prices by changing the available supply.

Reduction in import tariff. As noted, with perfect price transmission to domestic markets, there is no need for monitoring or analysis to know the impact of a change in tariff on the price of the affected commodity. The case of imperfect price transmission is thus considered here, along with methods for monitoring and assessing impacts on other commodities.

To assess the potential short run impact of a tariff reduction ex ante, considering imperfect price trans- mission, one multiplies the percentage import price change due to the tariff reduction by the elasticity of price transmission. For example, if a tariff reduction implies a 10 percent reduction in the import price of rice and the elasticity of price transmission for rice in the country is 0.5, then the predicted impact on the domestic rice price is a 5 percent reduction.This calculation assumes that the elasticity of price trans- mission can be estimated from available data or from values in the literature and that the elasticity estimated is valid for a change in the tariff. Estimates of trans- mission elasticities can be found in the literature (for example,Valdés and Foster 2008). It is advisable, however, to use sensitivity analysis with a range of values from the literature estimated for countries having similar economic and policy environments. If suitable values are not available from the literature, the price transmission elasticity can be computed. (More detail on computing the short-term effects of a reduction in tariffs on food commodities is provided in Appendix 1.)

Changing the tariff for one commodity may affect the domestic demand, supply, and prices of other

Table 6—Methods for monitoring and assessing impacts of selected price-oriented policies on domestic food prices

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POLICY NATURE OFIMPACTS ASSESSMENT APPROACH

KEY ASSUMPTIONS

DATA AND PARAMETERS

NEEDED

SOURCES OF DATA AND PARAMETERS

Changes in tariffs on food commodities

Direct short-run tariff impact with full transmission

No assessment necessary; price changes in domestic market fully reflect tariff change

• Change in domestic price = change in tariff (100% price transmission)

• No effects on other commodities

Level of tariffs before and after change

National trade policies

Direct short-run tariff impact with partial transmission

Ex ante: • estimate % domestic

price change using % import price change times elasticity of price transmission

Ex post: • Same method • Before-after compari-

son of changes in import price to change in domestic prices

• Econometric time series analysis

• Transmission of change in tariff to domestic markets incomplete due to imperfect market integration, transac- tion costs, contra- band imports

• Transmission elastic- ity stable; estimate from past prices rep- resents elasticity for tariff change

• No effects on other commodities

Same as above, plus • Import price level,

before and after • Domestic price level,

before and after, in different locations

• Quantity imported, before and after (to compute revenue impli- cations)

• Time series data on prices in domestic and import markets and on transaction costs affecting price margins

Same as above, plus • Commodity price

information system • Agricultural trade sta-

tistics • Media reports, key

informants on political and natural shocks

• Trader surveys on transaction costs and barriers

• Key informants on contraband trade

Indirect medium-run tariff impacts on other commodities

Multimarket model Changes in tariffs for particular commodities induce changes in prices of other commodities owing to cross-price demand and supply effects

Same as above, plus • Quantities of supply and

demand of all relevant commodities (possibly by regions)

• Direct and cross-elastic- ities of supply and demand of all commodities

Same as above, plus • Supply and use data

for relevant commodities from Ministry of Agriculture or National Statistical Office

• Elasticities estimated from data or literature on similar contexts in this or other countries

Use of public grain reserve stocks

Direct short-run effect of increased supply on selected commodity

Single-commodity par- tial equilibrium model (supply shift leading to change in equilibrium price); estimation using elasticities of demand and import supply

• No effect of release on private stock- holding

• No effect on pro- duction

• No effect on other commodities

• Quantity of stock released

• Total supply • Domestic price • Price elasticity of

demand • Price elasticity of

import supply (not commonly available)

• Supply and use data for relevant commodi- ties

• Elasticities estimated from data or literature on similar contexts in this or other coun- tries

• Public stocks data from the national food reserve agency

• Estimates of elasticity of import supply from trade and price data

Indirect short-run effect of increased supply

Single-commodity partial equilibrium model with supply of private storage included

• Private stockholding demand affected by prices

• No effect on production

• No effect on other commodities

Same as above, plus • Quantity of private

stocks of commodity • Elasticity of private

stockholding demand with respect to price

Same as above, plus • Private stocks esti-

mated from household and trader surveys

• Elasticity of private stockholding demand computed using price and private stocks data

Indirect medium-term effect of increased supply and reduced prices

Multimarket model including supply and demand of substitute commodities

• Private stockholding demand affected by prices

• Demand for and production of substitute or complementary commodities affected

Same as above, plus • Supply, use, and stocks

of substitute commodities

• Supply, demand, and stockholding elasticities for commodity and substitutes

Same as above

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commodities. For example, a reduction in the tariff on imported rice may reduce the demand for other staple commodities, reducing their prices in the near term.The effects on supply may be mixed. Since the price of rice and other staples may fall, their production may also fall in the medium term, although substitution between crops in farmers’ supply decisions could lead to expanded area of alternative crops. A multimarket supply and demand model could be used to assess these cross-commodity effects. Implementing such models would require data on the supply, use, and prices of other commodities that are substitutes or complements to the directly affected commodity, as well as information on the elasticities of supply and demand of these commodities (including cross-price elasticities). The supply and use data are likely to be readily available (though not always reliable) in the national statistics of the Ministry of Agriculture or from international organi- zations such as the Food and Agriculture Organization of the United Nations (FAO). Estimated elasticities of supply and demand may be available for the country from the literature, although typically it is difficult to find estimates of cross-price elasticities. If sufficient data on production, use, and prices are available for the selected commodities, these parameters could be estimated econometrically. Otherwise, it may be necessary to use values based on values for similar countries and commodities in the literature, combined with expert judgment and sensitivity analysis of the results.

Such a multimarket modeling approach could be used either ex ante or ex post to assess the impacts of a tariff change. If used ex post, some of the predicted impacts could be tested against actual data and used to improve the model and estimated impacts. For example, the predicted impacts on prices of substitute commodities could be compared with actual price changes, and the deviations could be used to adjust the model parameters to obtain better predictions. Implementing this approach would be a fairly intensive research endeavor and not something that is likely to be readily implementable by government agencies in most developing countries.

Food reserve policies. As in the case of a tariff change, if the domestic market is fully integrated with the international market for an imported food commodity and the country is a small player in the inter- national market, then the effect of domestic food reserve policies are perfectly predictable and no monitoring or assessment analysis is needed. In this case, release or purchase of domestic stocks will have no impact on the

world or domestic price of the commodity, since the change in domestic supply will have an insignificant impact on the world market price, and the domestic price will not change relative to the world market price because the market is fully integrated. Release of public stocks in this case can only reduce the quantity of imports but cannot help reduce domestic food prices. Hence, it makes sense to consider buffer stock policies only in countries where the domestic market is not fully integrated with the world market, perhaps because of trade or transportation barriers.

The short-run impact of releasing food stocks from a reserve can be estimated using a simple partial equilibrium supply and demand model. The only data required to do so are data on the total short-run supply of the commodity, the amount of reserve stocks that will be released, and estimates of the elasticities of demand and supply for the commodity.A similar computational approach can also be used to estimate the price response for the medium- and longer-run cases in which a production response is possible.This calculation can be made by expanding the supply elasticity to be the sum of both the elasticity of import supply and the elasticity of production with respect to price (for more detail, see Appendix 1.)

Finally, this estimation method can also be used to gauge the price impacts of public reserve policies, taking into account the responses of private stockholding to prices. A simple formulation would treat demand for private stocks as just another component of demand for the commodity, this demand being typically larger when prices are lower.The elasticity of private stock demand would be added to the elasticity of consumption demand to determine the total elasticity of demand. Since private stockholding demand is likely to increase the total elasticity of demand, the price impact of releasing public stocks is likely to be less when such private responses are taken into account.

Food reserve stock policies can have impacts on the prices of other food commodities similar to the way import tariff changes do.As in that case, a multimarket model could be used to assess impacts on substitute and complementary commodities.The data and parameter requirements to implement these models would be similar to those needed to assess the impact of tariff changes across food commodities.As discussed for the tariff case, these models could be used to predict impacts ex ante or ex post. In the ex post case, the predictions of the models could be compared with observed changes in prices and quantities produced, consumed, and imported,

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Table 7—Methods for monitoring and assessing impacts of selected supply-oriented policies on domestic food prices

POLICY NATURE OFIMPACTS ASSESSMENT APPROACH

KEY ASSUMPTIONS

DATA AND PARAMETERS

NEEDED

SOURCES OF DATA AND PARAMETERS

General (nongroup-specific) policies

Subsidies on inputs— fertilizers, seeds, credit

Direct impact on input prices and costs of food production; effects on other related markets (e.g., labor market)

By level of disaggregation

National average farmer

Average farmer captures effects at national level

National-level data: input prices, imports, exports, national production capacity, etc.

• National Statistical Office

• Ministry of Agriculture

• National accounts • Trade information

Several types of farmers Selected types of farmers capture most of the heterogeneity in the country

Same as above, plus Data for construction of farmer types

Same as above, plus Household survey and agricultural census

Individual farmers Aggregation of effects for individual farmers adds up to national effects

Same as above, plus Data at household and farm level

Same as above, plus

By level of complexity

Food supply response at farmer level

No other effects are present other than farmer supply response

Supply elasticity with respect to input prices

Same as above, plus Estimations of elastic- ities from previous studies

Food supply response and (equilibrium) effects on other markets

Model selected accurately represents macroeconomic linkages

Same as above, plus Parameters for macro- economic linkages across markets

Same as above, plus Social accounting matrices from previous studies

General agricultural research and development

Specific to programs (e.g., improved varieties, soil and water management)

Likely same as analysis of subsidies, reflecting different levels of disag- gregation and complexity

Assumptions will be based • on assessment

approach • on scenarios ana-

lyzed (e.g., high, medium, low yields)

• on impact pathway examined

Indicators that are specific to the agricultural outcomes expected

Ongoing agricultural research and develop- ment programs

Group-specific (targeted) policies

Examples include input voucher programs, micro-lending schemes, and small-scale irrigation programs

Direct impact on benefi- ciaries to achieve a food supply response; out- of-program spillover effects

• Randomized design • nonrandomized

designs: • Before/after • Matching. • Double

differences • Instrumental

variables • Discontinuity

regressions • If general equilibrium

effects are expected, similar approaches as above for assessing general (nongroup- specific) policies

• Attrition from group of beneficiaries or control group will not affect valid comparisons across groups

• In nonrandom designs, the control group provides accurate information on what would have happened to benefi- ciaries if they had not participated in the program being evaluated

• Outcome indicator: agricultural production

• Input indicator (or treatment indicator)— examples include dummy variable for participation, amount received on vouchers, and size of loan

• Other factors that affect outcome indicator: land size and quality, labor, equipment, human capital

• Baseline and after- program surveys of beneficiaries and control group

• If nonrandomized design, control group can be selected from analysis of a represen- tative household survey

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and the discrepancies used to improve the specification of the model.

Supply-oriented policies and interventions

Higher food prices can potentially benefit farmers and induce more food production and hence trigger a positive chain-reaction effect in local and regional economies.This desirable outcome may require time, however, or may not happen at all if farmers face constraints in gaining access to productive inputs and resources.Typically small farmers are the ones who cannot quickly take advantage of better market opportunities or increase their production because of borrowing constraints or limited access to inputs.Therefore policy actions can help accelerate supply responses by relaxing the key constraints faced by small- scale farmers.

Policy interventions and programs aimed at supporting increased agricultural production include subsidies to farmers for key inputs like fertilizer or improved seeds, agricultural extension and credit programs, investment in small-scale irrigation, and support for agricultural research and technology development. Subsidies for fertilizer and seeds and programs for extension and credit can have impact in the medium run, whereas investments in small-scale irrigation and agricultural research are expected to have longer-term impacts. Moreover, to monitor and evaluate the impact of these programs, it is important to differ- entiate between targeted policies that apply to specific groups of producers and those that apply to all farmers, such as a general subsidy on fertilizers.

As a summary of the following discussion,Table 7 sketches some approaches to monitoring and assessing the impacts of supply-oriented policies, both general— input subsidies and agricultural research and development—and targeted.

General supply-oriented policies. These policies can be directly monitored by observing selected indicators over time and across groups. In the case of general subsidies for fertilizers or seeds, indicators of the input price paid by farmers after the subsidy will be relevant. Because the subsidy is intended to reduce the actual price farmers pay for the input, it is important to confirm this is actually happening.Time-series data on fertilizer prices can be collected at the household level or from commercial suppliers. In some cases or regions, most of the subsidy may be captured by intermediaries instead of reaching farmers.Therefore input prices must be collected nationally to make sure the program is effectively implemented in all regions.

Policies oriented to increase agricultural research and development can be monitored by auditing how additional funding is spent. Indicators such as the ratio of researchers’ salaries to total salaries can give an indication of excessive leakage of resources to adminis- trative activities. Depending on the nature of the specific research and development programs, outcome and coverage indicators should be selected, such as the number of farmers adopting new technologies or seeds and the number of publications.

To evaluate the impact of these policies, different methods can be applied.The final output of an impact assessment exercise, however, should be the answer to the following question:What is the food supply response due to the policy or program being implemented? When policies are designed to lower input prices, methods similar to those used to assess the impact of rising food prices can be used. Instead of assessing the varied impacts of increasing food prices, researchers would assess the impact of decreasing prices, say, for fertilizers or credit, on food production and food prices.

Such impact analyses can use different levels of disaggregation: national, assuming a single representative farmer; by farmer group, assuming several fixed types of farmers; or for as many different farmers as are available in a representative survey. On another dimension, the complexity of the analysis can be divided into two levels: the food supply response at the level of the farmer, and the food supply response and equilibrium effects on other markets at the level of the economy.

With regard to data needs, in the case of subsidies to fertilizers, a key parameter to estimate is the supply elasticity with respect to the price of fertilizers—in other words, an estimate of how much food production increases with decreases in the price of fertilizers.The same concept applies for the price of other inputs, including for the interest rate or access to credit. For some farmers changes in input prices might imply a discrete jump in the way they produce—for example, shifting from not using fertilizers or improved seeds at all to making intensive use of them.This supply elasticity with respect to the price of inputs can be estimated or drawn from the literature for a nationally representative farmer, for several types of farmers, or for a whole set of farmers with different characteristics.

In evaluating the impact of agricultural research programs, the methods and indicators must be tailored to the nature of the specific research programs or interventions being evaluated. For example, research programs aimed at increasing yields can be evaluated

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using several alternative scenarios, such as high-, medium-, and low-yield increments. In terms of the level of disaggregation, researchers can assume that yield achievements are the same for all farmers or that they vary across different type of farmers or regions. No matter what method is used, however, a good practice is to establish the impact pathway—for example, from research to improved seeds to adoption to higher yields and then to increased agricultural production and food availability.

Targeted supply-oriented policies. Programs like input vouchers, microlending schemes, and small-scale irrigation programs, among others, are usually targeted to specific groups of farmers within a country. Similarly, targeted crop insurance schemes may facilitate increased risk taking by farmers and foster an improved supply response.To monitor these types of programs, researchers must identify a set of program-specific indicators and collect data such that the following questions can be answered: How is the project being implemented? How is the project operating in the field? How is the program progressing relative to targets? For example, monitoring input voucher programs will require data on the number of beneficiaries relative to the target population, average time to deliver a voucher, number of regions covered by the program, timing and capacity to use the available program funds, and quality and quantity of data collection for ex post impact evaluation, among other information.

For impact assessment the focus must be on the expected final outcomes, including higher food production by the targeted farmers. As long as the target groups and participants are clearly identified, the impact assessment can be directly applied to them.The key objective is to know how much the food production of participant farmers increased owing to the intervention.To answer this question, the impact evaluator needs to estimate what the output of these farmers would have been had they not participated in the program. As discussed in considerable detail at the start of this section, a range of different methods can be applied to gain such an understanding.

Two special considerations must be mentioned when assessing the impact of targeted policies and programs. First, ex ante evaluations of expected impacts can be made using evaluations of existing programs or similar programs implemented in the past or in other countries.To minimize errors in extrapolating results from other programs, a good practice is to extrapolate from programs where participants were, on average,

similar to the participants in the targeted program one wants to evaluate. Second, when the policy response implies the scaling up of existing programs, then an assessment of impacts outside the program may be relevant. As an example, imagine that a successful microlending program allows farmers to improve their farming equipment in such a way that they can save on their labor demands. If this program reaches a large scale of operation relative to a given region, it is likely that some effects of the program will operate through the labor market by changing the aggregate regional labor demand, affecting equilibrium wages in the region. In this case, researchers should combine the impact evaluation methods proposed here for targeted programs with those for the general policies and programs discussed earlier.

Income-oriented policies and interventions

By income policies, we group together those policies that are intended to compensate the most vulnerable groups for their real income loss and erosion in their access to food due to higher food prices.These policies include cash and conditional cash transfer programs, food vouchers or food stamps, food or cash for work and public work schemes.Table 8 outlines some approaches to monitoring and assessing the impacts of such income-oriented policies and programs. Essentially, the same considerations as for assessing targeted supply- oriented policies and programs hold for the assessment of the effects of most income-oriented policies.

As already mentioned, when monitoring programs with well-defined target groups and participants, researchers must define a set of process indicators to help answer a set of central questions. How is the program being implemented? How is the program operating in the field? How is the program progressing relative to targets? Hence it is important to choose indicators that will meaningful measure progress toward objectives.With a monitoring system in place built around such indicators, the information collected can be used to adjust program implementation to better attain program targets. Indicators such as the number of participants, the number of those participants who should not have qualified for the program, the time since identification of participants until delivery of actual program benefits, and the share of expenditure on administrative processes out of total program costs would be relevant.

For impact assessment, a range of different methods can be used. An extensive literature describes

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the design of impact evaluations, and the accumulated experience on evaluating income-oriented social programs across the developing world is large. Several key points on best practices for the impact evaluation of social programs are mentioned here. First, such programs should be evaluated based on an assessment of how different the situation of participants (and sometimes nonparticipants) is relative to what their situation would have been had the program not been implemented.To answer this question, one needs to evaluate the meaning of “situation” in this context. Hence, one crucial element is to identify outcome indicators to define the situation of the participants.These indicators could be measures of food expenditure, income, nutritional levels, or calorie intake, among others.

Second, identifying a control group from whom evaluators can acquire information on the counter-

factual situation the participants would have experienced had the program not been implemented becomes central.The challenges of correctly identifying a control group for comparison were discussed in detail earlier in this section.

Finally, the data needed for impact evaluations will depend on the evaluation design. Data should be collected on outcome indicators and on variables that help explain or condition those outcome indicators.This information must be collected from both the benefici- aries and the selected control group and for at least two points in time, before and after the program, to enable the use of a double-difference approach. If possible, it is ideal to collect data on multiple points in time after the program has been implemented to help determine the duration of any attributed impact to the program.

Table 8—Methods for monitoring and assessing impacts of selected income-oriented policies on domestic food prices

POLICY NATURE OFIMPACTS ASSESSMENT APPROACH

KEY ASSUMPTIONS

DATA AND PARAMETERS

NEEDED

SOURCES OF DATA AND

PARAMETERS

Examples include cash transfers, conditional cash transfers, food vouchers or food stamps, food- or cash- for-work schemes, and other public work schemes

Direct impact on beneficiaries: • More real

income • Greater, more

diverse food consumption

• Improved nutritional levels

• Higher calorie intake

Also, out-of- program spillover effects may be important

• Randomized design • Nonrandomized

designs: • Before/after • Matching • Double

differences • Instrumental

variables • Discontinuity

regressions • If general equilibrium

effects expected, similar approaches as for assessing general supply-oriented inter- ventions can be used

• Attrition from group of beneficiaries or control group will not affect valid comparisons across groups

• In nonrandom designs, the control group provides accurate information on what would have happened to benefici- aries if they had not participated in the program being evaluated

• Outcome indicators are program specific:

• Real income • Food

consumption and nutrition levels

• Calorie intake • Input indicators (or

treatment indicators): • Dummy variables

for participation • Amount of cash

transferred • Amount of food

transferred • Other factors that

affect outcomes: • Demographic

variables • Education levels • Other human

capital factors • Environmental

factors • Social service

access

• Baseline and after- program surveys of beneficiaries and control group

• If nonrandomized design, control group can be selected from analysis of represen- tative household survey

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Access to comprehensive and detailed information on a timely basis is vital to influence and inform policy responses to the current and future global food crises. Although abundant data are available on food issues, relevant information is often outdated, spotty in coverage, and insuffi-

ciently disaggregated to local levels. Further, much of the information is collected in an

uncoordinated fashion by different international and regional organizations. In some contexts, even

when information is available, the principles of freedom of access to information about the vital

issues related to food security are not always followed and public, civil society, and corporate actors

are not sufficiently informed for sound decisionmaking in their domains. It would also be unrealistic

to assume that all the needs for information collection, policy analyses, and policy and program

monitoring are met by appropriate human capacity in most developing countries affected by food

crises. Therefore, coordinated action is needed not only to get the data and to conduct timely

analysis so that it can be shared with decisionmakers, but also to identify mechanisms for obtaining

advice and for cross-country learning and capacity strengthening. Many actors are already engaged in

such efforts, including multilateral and bilateral agencies. Yet knowledge about where to get advice,

for instance, on the implementation of sound context-specific food production investments, on trade

policy measures that do not backfire, or on the design of targeted food and income transfer

programs or effective nutrition interventions is still often out of reach for developing countries.

Learning from the experiences of other countries, based on sound research, can often help, but

mechanisms for doing so are lacking.

An Implementation Plan for Action on Monitoring and Impact Assessment

This section of the report sketches out the main elements of a global initiative to develop the means to provide reliable, appropriate information and decision- support tools for national policymakers so that they can respond quickly to changes in world food markets.1 The implementation plan’s objectives and components are outlined in Table 9.

An important element of the initiative is to set up an Internet-based open access policy information portal to provide comprehensive and detailed information on food crisis and related developments, including formal and informal responses, country by country. In today’s Internet world, many useful websites and portals exist, including important ones operated by FAO, the World Bank, the CGIAR, and others.The portal will not

duplicate them but add specific value.The portal should become a reliable information- and decision-support tool to strengthen the ability of policymakers in the developing world to respond quickly to dynamic devel- opments in world food system, especially crises.The portal will also facilitate monitoring of actual donor- supported investments (and pledges) that address the current food crisis at the country level. Salient elements of the portal are as follows:

• Capacity-strengthening toolbox. In the initial stages of the development of the information portal, existing tested tools for analysis will be brought together in a capacity-strengthening toolbox.These tools will include key questions to ask, decision-support for specific well-defined

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problems, and comparisons between countries and issues. As the information portal is expanded and strengthened, new or adapted tools will be added, including many of those described earlier in this report.This toolbox will allow portal users to learn how to use different tools of analysis and employ those techniques with country-specific data collected and presented elsewhere on the portal.

• Country-by-country food policy information. Vital food statistics and trends will be made available through the portal or, where sources already exist, through links.These datasets will include statistics on food prices, production, consumption, stocks, markets, and trade, as well as poverty and food security information, at global, regional, and national levels. Both historical and current data will be presented, as well as projections of some current trends. Also, data on the policy and program measures taken by various countries in responding to the current food crisis will be provided.

• Food policy in the news. This element of the portal will contain latest news reports on the food situation across countries, including on food-related protests and other manifestations of the current food crisis, and on policy and program actions under consideration. ‘In the news’ here means not only the formal media, but also the fast-evolving informal media such as blogs. This part of the initiative is significant because in a crisis open communication is crucial for maintaining trust and for sound decisionmaking by all actors.

• Key players. The portal will provide technical links to the public information bases of major national organi- zations in each country and of global institutions addressing the effects of food crises at global, regional and national levels. Such institutions include the FAO, the World Food Programme (WFP), the International Fund for Agricultural Development (IFAD), the United Nations Children’s Fund (UNICEF), the World Health Organization (WHO), other UN organizations, the World Bank, regional development banks, the Consultative Group on International Agricultural Research (CGIAR), nongovernmental organizations (NGOs), and the private sector.

• Research findings. This section of the portal will contain key publications by organizations working in this field to allow users to quickly retrieve the latest knowledge resources for assessing and responding to the effects of food crises.

• User forums. Users of the portal will be able to submit specific questions, suggestions, and comments. The forum will not only provide help in using the portal, but also serve as a platform to discuss global food price issues and connect with other users and experts.The forum will be monitored and moderated by technical and content experts.

A conceptual flow chart of the information portal is presented in Figure 3.An initial needs assessment, including interviews with the primary target audiences and users, will be undertaken to fine-tune the structure of the portal and the information provided.The portal will

Table 9—Main objectives and activities of the implementation plan for providing information and decision-support tools to respond to global food crises

OBJECTIVES ACTIVITIES

1. Information strengthening and monitoring • Development of an Internet-based portal • Development of a capacity-strengthening toolbox • Facilitation of urgent advisory action

2. Advisory services for policy actions • Assessment of impacts of high and volatile food prices in countries

• Identification of risks and vulnerabilities

3. Closing of important specific information gaps that limit appropriate food crisis responses

• Specific studies designed to strengthen actions and imple- mentation in countries, such as on women and food crisis, supply response, moving from emergency to social protec- tion, and others to be identified as the program is built

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Project partners • Africa • Asia • Latin America

Users • Senior policymakers • Researchers • NGOs and civil society

organizations • International agencies • Private sector

Coordinator

Sources Users Research data Experts Early warning systems Open databases Project Þndings

WORLD FOOD SITUATION PORTAL

Capacity- strengthening toolbox

News

Collaborative spaces

Statistics/policies

Country data

Figure 3—Conceptual framework for the policy information portal

also be designed to allow contributions from all users. Users will be able to suggest changes to any part of the site, and the portal will have built-in feedback mechanisms, such as user satisfaction surveys, to ensure that the data provided serve the needs of the portal’s users. In addition, Web statistics will be used to evaluate the performance of the portal to determine which sections and types of information need to be further developed.Working together with partners, the portal’s managers will adjust it to respond to national and regional needs.

All information on the site will be searchable, and the site will abide by standards for low-bandwidth environments to be accessible in areas where Internet connectivity is a problem. Information provided will be translated into various languages (first French and Spanish, and later Chinese and Arabic), and offline versions in the form of CD-ROMs will be made available.

The portal aims to assemble information from a wide array of sources. In addition to information and data on the Internet, the portal will contain information assembled directly from a wide range of national (such as national statistical agencies and relevant ministries),

regional (such as regional economic commissions and organizations), and global sources. In addition, it will provide organizations working on food and agricultural policy issues with the opportunity to join this initiative and to integrate their expertise and information into the site, given its open access format. In this regard, the portal will be designed in an open Wikipedia-type fashion. Access to the portal both to obtain and to add information and tools will be open as an international public good to the wider public, including civil society, policymakers, and the private sector.

The portal will be rolled out with information on a number of countries in three regions—Sub-Saharan Africa, Asia, and Latin America and Caribbean—selected based on criteria such as high share and number of undernourished people and diversity in size, economic and social conditions, and geographic location. Once established, the portal will quickly embrace a larger set of countries, including about 45 countries where most of the world’s food-insecure people live. Over time it will be broadened to serve as a self-monitoring device for other countries.

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Key partners and sources of policy advice and support for this initiative include policymakers, policy advisors, and food, nutrition, and agricultural researchers well connected to national and regional food and agricultural policy processes.Their networks and linkages to policymakers will be important, not only for providing information to policymakers, but also for indicating where there might be knowledge gaps that would require extra information and capacity strength- ening. Collaboration between partners will facilitate the exchange of relevant information, strengthen capacity, and support mutual learning. Moreover, policymakers’ needs, related to the policy processes in which they are engaged, will guide the nature and expansion of the advisory services, based on sound assessments of policy options and actions delivered through consultative processes that will be an element of the initiative.

An international steering committee composed of policymakers and advisors will guide this initiative from the beginning.This committee will help ensure that the implementation plan is well embedded in partner organ- izations and adds value without duplication.

A step-by-step approach is envisioned to build this initiative into a sustainable international public good. There will be three distinctive phases to this initiative:

1. the build-up phase during which functions, processes, and organizational designs will be tested and established;

2. the maintenance phase during which the initiated functions and designs are optimized; and

3. the “auto-pilot” phase during which authority, accountability, and responsibility will be handled by the user community with minimal coordination.

As a contribution to the monitoring and assessment of national policy responses to world food crises and to provide support for obtaining high-quality information and knowledge management for appropriate policy responses, the International Food Policy Research Institute (IFPRI), in collaboration with national, regional, and international partners, proposes to begin implementation of this action plan by December 2008.

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Conclusion

It is hoped that the description in this document of the information sets and analytical tools needed to guide policy responses to global food crises will contribute to a further fleshing out of this and related frameworks for action on the current global food crisis at international and continental levels in coming months. Ultimately,

however, it is in the use of the information detailed here—in the collection of basic data and its use in analysis—at national and more local levels that will lead to long-term resilience to the effects of rising and variable global food prices and contribute to sustained global food security.

Decisionmakers who serve leaders of national governments need information and analytical toolsin order to assess the risks and opportunities that their country and its citizens face from the current and future global food crises, to determine how they might respond to those risks and oppor-

tunities, and to monitor both the impact of a food crisis and the effects of governments’ policy

responses.Although the implications of a global food crisis differ across countries and population

groups, there are relatively well-defined sets of information and analyses that governments can employ

to manage such crises in their respective countries.As such, economies of scale can be captured at the

international level through joint action to collect data on food crises and on their national-, household-,

and individual-level effects; to build capacity in the analysis needed to guide policy formulation and pro-

gram design; and to evaluate the effectiveness of those policy responses. Similarly, there are a relatively

small number of types of policy responses that governments might take in the face of these crises.

Here too there is scope for international action—the lessons learned from effective and failed policy

responses by national leaders can be shared to aid countries considering similar policies.The proposed

global initiative to provide reliable, appropriate information and decision-support tools to enable

national policymakers to respond quickly to changes in world food prices sketched out in the preceding

section was formulated in recognition of the gains that can be realized through joint action across

countries and institutions to address the crisis.

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APPENDIX 1

Methods for Measuring the Impact of Food Crises

This appendix provides additional detail on how to implement some of the basic analyses described in this report. In particular, it explains the equations for calculating the terms-of-trade effect of changes in world prices, the short- term and the long-term welfare effects of changes in prices, the effect on prices of a reduction in the tariff for a food commodity, and the impact of releasing food stocks onto the market on food prices.

Terms-of-trade effects. One way to measure the terms-of-trade effect is to calculate the change in the value of net exports due to changing world price (assuming that the country maintains the same volume of imports and exports) as a proportion of the size of the economy.This effect can be calculated as follows:

terms-of-trade effect = [∑xi (∆pi /pi ) -∑ mi (∆pi /pi )]/GDP, (1)

where xi is the value of export commodity i, ∆pi /pi is the proportional change in the world price of export i, mi is the value of import commodity i, ∆pi /pi is the change in the world price of import i, and GDP is the gross domestic product of the country. Equation (1) can be applied to individual commodities (such as maize and wheat) or to broad categories (such as agricultural commodities). As a simple example, if a country has agricultural exports of US$0.1 billion, agricultural imports of US$1 billion, and a GDP of US$10 billion, the terms of trade effect of a 50 percent increase in agricultural prices would be (0.1 x 0.50 – 1.0 x 0.50)/10 = -0.45/10 = -4.5 percent.Thus, the loss due to terms of trade effects is about 4.5 percent of GDP.

Short-term welfare effects of higher food prices. The proportional change in welfare in the short run (before the household responds to the new prices) can be expressed as follows:

short-run ∆y/y = ∑fi (∆pi /pi ) - ∑si (∆pi /pi ) = ∑(fi -si ) (∆pi /pi ), (2)

where ∆y/y is the proportional change in household welfare (usually expressed in terms of the value of household consumption), fi is the share of income from the sale of commodity i, ∆pi /pi is the proportional change in the price of commodity i, and si is the share of expenditure going to the purchase of commodity i.This implies that (fi -si ) is the net sales of commodity i divided by household income or expenditure. Deaton (1989) calls this the net benefit ratio and notes that it can be considered the short-term elasticity of welfare with respect to the price of i.The impact of changes in wage rates can be incorporated into this framework by assuming that one of the “commodities” is labor and its “price” is the wage rate.

Medium-term welfare effects of higher food prices. In the medium term, the welfare impact of price changes must take into account the response of the household to the new prices, but it can be calculated by an extension of the preceding equation:

long-run ∆y/y = ∑fi (∆pi /pi ) + ∑ 0.5 fi εsi (∆pi /pi ) 2 - ∑si (∆pi /pi ) - ∑0.5 si εDi (∆pi /pi )

2, (3)

where εsi is the elasticity of supply of commodity i and εDi is the price elasticity of demand for commodity i.Thus, the only additional information needed to estimate the medium-run impact is estimates of the price elasticity of demand and the supply elasticity.This expression can be easily estimated with information about the composition of demand, source of income, and the elasticities. Equation (3) does not, however, take into account shifts in spending patterns due to changes in income, cross-price effects in supply and demand, changes in the wage rate, changes in the exchange rate, or other general equilibrium effects.

Both the short-term and the medium-term expressions of welfare change can be calculated for a set of repre- sentative households or, preferably, for every household in a survey. In the latter case, by estimating the change in income associated with the price change, one can estimate the resulting changes in the incidence of poverty and other measures of poverty and inequality.

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Short-term impact of a tariff reduction. To assess ex ante the likely short-term impact of a reduction in tariffs for food commodities, considering imperfect price transmission from global to national markets, one multiplies the percentage import price change due to the change in tariff by the elasticity of price transmission:

∆Pd /Pd = ε(Pd, Pi ) x ∆Pi /Pi (4) where ∆Pd /Pd and ∆Pi /Pi are the proportional change in the domestic and import price, respectively, and ε(Pd, Pi ) is the elasticity of price transmission.

This calculation assumes that the elasticity of price transmission can be estimated from available data or from values in the literature and that the elasticity estimated is valid for a change in the tariff. If suitable values are not available from the literature, the price transmission elasticity can be estimated. A simple method for estimating the elasticity is to calculate the ratio between percentage changes in domestic food prices and import prices over some specified time period in the past (see, for example, Dawe 2008).2 More complex methods use econometric analysis of time-series price data (for example, Syrovátka and Lechanová 2005).The first method is simpler to use and requires less data but produces only a point estimate for the elasticity, which could be biased because of the selection of unrepresentative time periods for the comparison or effects of other factors affecting the relative changes of import and domestic prices. Econometric methods can help address these problems. Nonetheless, neither the simple two-point method nor econometric methods may produce a valid estimate of the elasticity that would apply to a change in tariff, because the elasticity evident in the historical price data may have been affected by other factors besides changing tariffs that do not apply in the present period. Such problems will be difficult to overcome in an ex ante assessment. An ex post assessment, however, could be used to check the validity of ex ante predictions, producing estimates of the elasticity of price transmission specifically in response to a change in tariff.

Short-run impact of releasing food stocks. The short-run impact of releasing food stocks from a reserve can be estimated using a simple partial equilibrium supply and demand model, as illustrated in Figure A.1.3 In the figure, release of the quantity R from the reserve increases short-run supply from S to S + R, causing the market equilibrium price to fall from P(S) to P(S + R). Assuming that the demand and supply functions have constant price elasticities (or assuming small changes), the proportional change in price resulting from the change in supply is given by

∆P/P = (∆Q/Q) /(εD - εS) = R/ [S x (εD - εS))], (5) where ∆P/P is the proportional change in the domestic price of the commodity resulting from the increase in supply, ∆Q/Q is the proportional increase in supply, and εD and εS are the price elasticities of demand and supply for the commodity, respectively.4 For example, if the price elasticity of demand is -0.2, the price elasticity of supply is 0.3, and releasing the reserve stock increases supply by 10 percent, then the predicted impact on the price is -20 percent.

To use this approach, the only requirements are data on the total short-run supply of the commodity, the amount of reserve stocks that will be released, and estimates of the elasticities of demand and supply for the commodity. Relevant estimates of the elasticity of demand may be available in the literature or could be estimated econometrically.The elasticity of supply in this case includes the responsiveness of the supply of imports to domestic price changes and is related to the elasticity of price transmission (see endnote 3).5 This elasticity is not commonly estimated, so it may be difficult to find appropriate values in the literature. It could be estimated econometrically using data on imports of the commodity and domestic prices, controlling for levels of production and import prices.6

It is important that any elasticity estimates used are subjected to sensitivity analysis. A similar computational approach to Equation (5) can be used to estimate the price response for the medium-

and longer-run cases in which a production response is possible. In this case, the supply elasticity will be the sum of the elasticity of import supply and the elasticity of production with respect to price. Estimated elasticities of

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production with respect to price are commonly found in the literature or could be estimated econometrically using appropriate data.

Finally, equation (5) can also be used to estimate the price impacts of public reserve policies, taking into account the responses of private stockholding to prices. A simple formulation would treat demand for private stocks as just another component of demand for the commodity, this demand being typically larger when prices are lower.The elasticity of private stock demand would be added to the elasticity of consumption demand to determine the total elasticity of demand. Since private stockholding demand is likely to increase the total elasticity of demand, the price impact of releasing public stocks is likely to be less when such private responses are taken into account. Estimates of the elasticity of private stockholding are less common than estimates of consumer demand, as this elasticity requires data on private stockholding levels, which may not be very reliable. Hence, addressing this issue may require collection of new data on private stocks in many countries. Data from household production and consumption surveys (which often include food stock levels) and trader surveys could be used for this purpose.

Figure A.1—Impact of releasing food from a food reserve

• National—balance of trade, fiscal balance, political ramifications, commodity markets, labor markets

• Household—income, expenditure • Individual—nutrition, health, school

attendance

Quantity

Price

P(S + R)

P(S)

S + RS

D

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APPENDIX 2

Government Policy Responses to the Current Global Food Crisis

Table A.1—Government policy responses to the food crisis and the symptoms of political actions triggered by the crisis, 2006–August 2008

COUNTRY

GOVERNMENT RESPONSES PROTESTS

Trade restriction

Trade liberalization

Consumer subsidy

Social protection

Increase supply Violent Nonviolent

Afghanistan X X X X

Algeria X X

Angola X

Argentina X X X X X

Armenia X

Austria X

Azerbaijan X

Bahrain X X X X

Bangladesh X X X X X X

Belgium X

Benin X X X

Bolivia X X X X X

Brazil X X X

Burkina Faso X X X X X

Burundi X

Cambodia X X X X

Cameroon X X X X

China X X X X X

Comoros X

Congo, Rep. X X

Côte d'Ivoire X X X X

Cuba X

Dominican Republic X

Ecuador X X X

Egypt X X X X X

El Salvador X X

Ethiopia X X X X X

The Gambia X

Germany X

Ghana X X

Guatemala X X X

Guinea X X

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Table A.1—Government policy responses to the food crisis and the symptoms of political actions triggered by the crisis, 2006–August 2008

COUNTRY

GOVERNMENT RESPONSES PROTESTS

Trade restriction

Trade liberalization

Consumer subsidy

Social protection

Increase supply Violent Nonviolent

Guinea-Bissau X

Haiti X X X X

Honduras X X X

India X X X X X X

Indonesia X X X X

Iran X X

Italy X

Japan X

Jordan X X X X

Kazakhstan X X X X

Kenya X X X

Kuwait X

Lebanon X X

Liberia X X X

Madagascar X X X

Malawi X X

Malaysia X X X

Mali X X X X

Mauritania X

Mexico X X X X

Mongolia X X X X

Morocco X X X X

Mozambique X

Namibia X X

Nepal X X

Netherlands X

Nicaragua X X X

Niger X X X X X

Nigeria X X X

North Korea X

Oman X X X

Pakistan X X X X X X

Panama X X

Paraguay X X

Peru X X X X X

Philippines X X X

Qatar X

Russia X X X X X

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Table A.1—Government policy responses to the food crisis and the symptoms of political actions triggered by the crisis, 2006–August 2008

COUNTRY

GOVERNMENT RESPONSES PROTESTS

Trade restriction

Trade liberalization

Consumer subsidy

Social protection

Increase supply Violent Nonviolent

Rwanda X

Saint Lucia X

Saudi Arabia X X X X

Senegal X X X X X

Sierra Leone X X X X X

Singapore X

Somalia X X X

South Africa X X X

South Korea X X X

Sri Lanka X X

Sudan X

Suriname X

Switzerland X

Syria X

Tajikistan X X X

Tanzania X X X

Thailand X X X X X

Timor-Leste X

Togo X X

Trinidad and Tobago X X

Tunisia X X X

Turkey X X X

Turkmenistan X

Uganda X X

United Kingdom X

Ukraine X X

United Arab Emirates X X X

Uruguay X X

Uzbekistan X X X

Venezuela X X X X

Vietnam X X X

Yemen X X X X

Zambia X X

Zimbabwe X X

Sources: Government responses: International Monetary Fund (IMF), FAO, and news reports, 2007–08; food-related protests: news reports, 2007–08.

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Notes

1. Teunis van Rheenen, Klaus von Grebmer, and Rajul Pandya-Lorch contributed to the design of this plan.

2. This method uses equation (4) in a modified form: ε(Pd, Pi ) = (∆Pd /Pd)/(∆Pi /Pi).

3. Although domestic production is assumed to be fixed in the short run, the supply curve in Figure A.1 is upward sloping (rather than a perfectly inelastic vertical line) because the supply of the imported commodity responds positively to increases in prices.This assumes that the elasticity of price transmission from the world to the domestic market is positive but less than one. If the elasticity of price transmission is one (perfect transmission), the supply curve (including imports) will be an infinitely elastic horizontal line and the release of stocks would have no effect on the domestic price. If the elasticity of price transmission were zero, imports would not respond at all to domestic prices and the short-run price elasticity of supply would be zero. In this case, the change in prices would be determined by the elasticity of demand. In the medium or long run, the supply is more price elastic because production can respond to price changes in the longer term. Hence, the price impacts of releasing reserves or other shifts in supply or demand are likely to be smaller in the long run than in the short run.

4. The second equality follows because ∆Q/Q = R/S.

5. In the short-run case, production is fixed and the elasticity of supply is equal to the price elasticity of imports. In the medium or longer run, the elasticity of supply equals the sum of the elasticity of production and elasticity of imports.

6. This elasticity could be estimated using an equation of the form: ln(Importst) = b0 + b1ln(Pdt) + b2ln(Pit) + b3ln(Productiont) + ut . The estimated value of b1would be the estimated price elasticity of import supply, controlling for production and import price level.

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Minot, N., and F. Goletti. 1998. Export liberalization and household welfare:The case of rice in Viet Nam. American Journal of Agricultural Economics 80 (4): 738–749.

Quisumbing, A. R., ed. 2003. Household decisions, gender, and development: A synthesis of recent research.Washington, DC: International Food Policy Research Institute.

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Rapsomanikis, G., D. Hallam, and P. Conforti. 2006. Market integration and price transmission in selected food and cash crop markets of developing countries: Review and application. In Commodity market review, 2003–2004. Rome: Food and Agriculture Organization of the United Nations.

Ravallion, M. 2005. Evaluating anti-poverty programs. Policy Research Working Paper No. 3625.Washington, DC: Development Research Group,World Bank. Available at http://ssrn.com/abstract=922915.

Syrovátka, P., and I. Lechanová. 2005. Price transmission and estimations of price elasticity of secondary demand functions: Application on commodity market for food grains. Agricultural Economics – Czech 51 (7): 293–303.

Valdés, A., and W. Foster. 2008. Do high world commodity prices hurt or help family farms? A simulation case study for Chile. Presentation at the FAO Expert Workshop on Policies for the Effective Management of Sustained Food Price Increases, July, Rome.

von Braun, J., with A. Ahmed, K. Asenso-Okyere, S. Fan, A. Gulati, J. Hoddinott, R. Pandya-Lorch, M.W. Rosegrant, M. Ruel, M.Torero,T. van Rheenen, and K. von Grebmer. 2008. High food prices:The what, who, and how of proposed policy action.Washington, DC: IFPRI.

World Bank. 2008. Double jeopardy: Responding to high food and fuel prices. G8 Hokkaido-Toyako Summit, July 2. Accessed online at http://www.worldbank.org/html/extdr/foodprices/.

Todd Benson is senior research fellow in the Development Strategy and Governance Division of IFPRI. Nicholas Minot is senior research fellow in the Markets,Trade, and Institutions Division of IFPRI. John Pender is senior research fellow in the Environment and Production Technology Division of IFPRI. Miguel Robles is postdoctoral fellow in the Markets,Trade, and Institutions Division of IFPRI. Joachim von Braun is the director general of IFPRI.

InteRnAtIOnAL FOOD pOLICy ReSeARCh InStItute 2033 K Street, NW Washington, DC 20006-1002 USA Telephone: +1-202-862-5600 Skype: ifprihomeoffice Fax: +1-202-467-4439

Email: [email protected]

www.ifpri.org

Irwin, et.al.2007.pdf

THE PERFORMANCE OF CHICAGO BOARD OF TRADE CORN, SOYBEAN, AND WHEAT FUTURES CONTRACTS AFTER RECENT CHANGES IN

SPECULATIVE LIMITS

by

Scott H. Irwin, Philip Garcia, and Darrel L. Good *

*Scott H. Irwin is the Laurence J. Norton Professor of Agricultural Marketing, Philip Garcia is the T.A. Hieronymus Distinguished Chair in Futures Markets, and Darrel L. Good is a Professor in the Department of Agricultural and Consumer Economics at the University of Illinois at Urbana-Champaign. The authors thank Nicole Aulerich, Tracy Brandenberger, Fabio Mattos, and Robert Merrin for their assistance in collecting the data for this study. Corresponding author: Scott Irwin, Department of Agricultural and Resource Economics, 344 Mumford Hall, 1301 W. Gregory Dr., University of Illinois at Urbana-Champaign, Urbana, IL 61801, voice: 217-333-6087, fax: 217-333- 5538, email: [email protected].

May 2007

THE PERFORMANCE OF CHICAGO BOARD OF TRADE CORN, SOYBEAN, AND WHEAT FUTURES CONTRACTS AFTER RECENT CHANGES IN

SPECULATIVE LIMITS

ABSTRACT

Three attributes of futures contract behavior important for market performance—liquidity, volatility, and convergence—are investigated before and after the 2005 increase in speculative position limits for corn, soybean, and wheat contracts at the Chicago Board of Trade. The analysis of liquidity and market depth reveals a sharp increase in open interest for corn, soybeans and wheat beginning in late 2005. The increase in position limits likely accommodated the increase in speculative interest in corn, soybean and wheat futures, but some of the increase would have occurred without the increase as new market participants received hedge exemptions. The analysis of price volatility revealed no large change in measures of volatility after the change in speculative limits. For corn and soybeans, the picture that unfolds relative to convergence patterns is one of weakness, but not failure. For wheat, the picture that unfolds relative to convergence patterns is not only one of weakness, but failure to accomplish one of the fundamental tasks of a futures market. The persistence and growing magnitude of the delivery location basis in wheat suggests a problem with the contract specifications.

THE PERFORMANCE OF CHICAGO BOARD OF TRADE CORN, SOYBEAN, AND WHEAT FUTURES CONTRACTS AFTER RECENT CHANGES IN

SPECULATIVE LIMITS INTRODUCTION

Questions about the performance of the Chicago Board of Trade (CBOT) corn, soybean, and

wheat contracts have arisen on several occasions. Concerns about hedging effectiveness, liquidity in

cash and futures markets, and convergence of cash and futures prices have resulted in a number of

contract performance studies that led to rule changes by the CBOT (e.g., Peck and Williams, 1991,

pp. 2-3). In 2005, the CBOT and the Commodities Futures Trading Commission (CFTC) approved

revisions to Regulation 425.01 to increase the CBOT’s “Single Month” and “All Months”

speculative position limits for its corn and mini-size corn, wheat and mini-size wheat, soybean and

mini-size soybean, soybean oil, soybean meal, and oat contracts. Those changes occurred in two

phases, with the first phase effective on June 10, 2005 and the final phase effective December 10,

2005 (Table I). Spot month limits for these contracts were not changed. Since the change, market

participants have expressed concern that this added speculative activity has caused futures prices to

be artificially inflated and volatile, contributing to weak and erratic basis levels from late 2005

through the first half of 2006 and to a lack of convergence of cash and futures prices during delivery

in the first half of 2006 (e.g., Roberts, 2006). Weak basis and lack of convergence are antithetical to

the core risk transfer function of futures markets, and consequently, adversely influence their

usefulness to market participants.

We investigate three attributes of futures contract behavior—liquidity, volatility, and

convergence—that are of importance for market performance (Hieronymus, 1971; Peck and

Williams, 1991). Descriptive and statistical comparisons of these attributes for corn, soybeans, and

wheat contracts before and after the 2005 change in speculative position limits are made to ascertain

2

changes in behavior. The period before the 2005 speculative position limit changes starts with the

first contracts impacted by the CBOT’s 2001 change in storage charges for corn and soybeans.

The analysis and presentation have several components. First, to analyze liquidity and

market depth, the volume and structure of open interest before and after the recent change in

speculative position limits is examined. Data from the CFTC’s Commitment of Traders Report are

used for the analysis. Second, to analyze volatility, daily nearby futures settlement prices before and

after the recent changes in speculative position limits are examined. Measures of volatility are

constructed across all contracts by commodity and across contracts of the same maturity for each

commodity to account for seasonality. Third, to analyze convergence, publicly available cash price

data collected and reported by the USDA’s Agricultural Marketing Service are used to compare the

pattern of convergence by commodity and market before and after the change in position limits.

Finally, we present conclusions and suggest measures to improve market performance.

LIQUIDITY AND MARKET DEPTH

Liquidity of futures markets generally refers to the magnitude of outstanding, or unfilled,

contracts. A liquid market is one that has enough contracts outstanding to allow large transactions

without a substantial change in price. Hieronymus (1971, p. 297) provides a succinct statement in

this regard, “A liquid, speculatively active futures market is a useful and effective tool that can be

and is used extensively by people who have risk problems.” A common measure of liquidity and

market depth is open interest. The magnitude of open interest should be sufficient to effectively

provide the risk-carrying capacity needed in a given market.

The procedure used here is to compare the magnitude and the structure of open interest

before and after the change in speculative position limits. The source of data is the weekly CFTC

Commitments of Traders (COT) Reports.1 The structure of open interest refers to the percentage

held by traders in the commercial, non-commercial, and non-reporting categories.

3

Total open interest for all contracts, total open interest by reporting category, and percent of

open interest by reporting categories are shown for corn, soybeans, and wheat in Figures 1, 2, and 3.

The dashed vertical line identifies the date of the Phase I increases in position limits (June 10, 2005)

which were effective beginning with the July 2005 contracts. For corn, total open interest was

generally flat from September 2001 through about January 2004, spiked with the high prices that

existed during the spring of 2004, and remained at the higher level through late 2005. Beginning in

January 2006, open interest trended sharply higher, but appeared to reach a plateau in the summer of

2006. Open interest peaked at about 1.4 million contracts, more than three times the pre-2004 level.

The magnitude of open interest among reporting commercial and non-commercial traders followed

roughly the same pattern, although the percentage of open interest held by commercials (non-

commercials) declined (increased) from the summer of 2005 through the summer of 2006. The

portion of open interest held by non-reporting traders declined during that period.

For soybeans, total open interest trended higher from the spring of 2002 through the spring of

2006, with seasonal declines occurring in the fall of 2004 and 2005. Some decline was also noted in

the summer of 2006. An increase in open interest occurred for both categories of reporting traders,

with the percentage of open interest held by non-commercials increasing from the summer of 2005.

Open interest peaked in the spring of 2006 at about 390,000 contracts, up from 150,000 to 200,000

contracts in the early period.

The most dramatic increase in open interest occurred in wheat, moving from about 100,000

contracts in the earlier period to a peak near 550,000 in the spring of 2006. The structure of the open

interest followed a similar pattern to that of corn and soybeans, with reporting non-commercial

traders accounting for a larger percentage of open interest from the spring of 2005 forward.

The increase of open interest in corn, soybeans, and wheat since the summer of 2005 and

particularly since January 2006 likely reflects the widely-publicized increase in commodity futures

trading by hedge funds and so-called long-only index funds (O’Hara, 2006; Acworth, 2006). At face

4

value, the structure of open interest indicates that reporting non-commercials have accounted for a

larger portion of open interest following the increase in speculative position limits. Because some

fund traders who might traditionally be viewed as speculative traders can receive hedge exemptions,

the reports of open interest by category of trader may actually understate the activity of traders

considered non-commercials. One piece of evidence in this regard is provided by CFTC reports on

bank participation in futures markets, available starting in September 2004 on a monthly basis.

While it cannot be stated with certainty, the banks tracked in these reports are thought to: 1) have

hedge exemptions and therefore their positions are counted in the commercial totals and 2) the

positions are largely related to over-the-counter-instruments offered by or financed through the

banks. Figure 4 presents the net long open interest of banks from September 2004 through

September 2006 for corn, soybeans and wheat. Growth in open interest during that period was

especially large in corn and wheat. Open interest in corn grew from about 30,000 contracts to nearly

150,000. Bank open interest as a percentage of total open interest reached 20 percent for wheat and

12 percent for corn in March 2006.

Starting with January 2006, the CFTC began reporting open interest for commodity index

traders of which the bank positions are a subset (Figure 5). In the corn and soybean markets, index

fund participation increased steadily in terms of the number of contracts, and registered about 28

percent of the open interest in both markets. In the wheat market, index activity increased steadily in

contract numbers to May 2006 and then declined. Index fund participation ranged from 50 to 35% of

open interest and appeared to stabilize at nearly 40% at the end of the period.

Due to the limited history of public data available from the CFTC, it is difficult to draw

definitive conclusions about the structure of open interest. However, it is clear that activity in these

markets increased significantly in 2006, apparently led by non-traditional market participants. The

increases in speculative limits initiated in 2005 appear to have accommodated the increased interest

of non-traditional traders in late 2005 and particularly in 2006, contributing significantly to liquidity

5

and market depth. The change in speculative limits by the CBOT accommodated the participation of

non-traditional traders in these markets and contributed to the significant rise in open interest. To the

degree that the new trading activity consists of long-only index funds, a larger percentage of the trade

and open interest is likely held by traders who may be more price insensitive to individual

commodity conditions than are traditional market participants.

VOLATILITY

Volatility of futures prices is a measure of the emergence of new and unexpected market

information. Hieronymus (1971, p.297) notes that, “If all things were foreseeable and their effects on

prices perfectly discounted, the results would be unchanging prices at equilibrium levels and

relationships.” Some level of price volatility is desirable and necessary to attract trading activity, but

extreme volatility can also discourage participation by some sectors of the market.

To examine volatility, the pattern and magnitude of return volatility is calculated prior to and

after the change in speculative limits using the daily change in returns—percentage change in nearby

settlement prices from September 4, 2001 through August 31, 2006, for corn, soybeans and wheat

(Figure 6). The average absolute daily return is calculated for the periods prior to and following the

implementation of Phase I in 2005. For corn, the range of daily returns after the change was not

outside the experience of the period prior to the change, but the average absolute return was slightly

higher in the post-change period. For soybeans and wheat, the range of daily returns after the change

in speculative limits was within the experience of the period prior to the change, while the average

absolute return was lower in the post-change period.

Similar patterns of volatility (but not presented) were also encountered when measuring

volatility in terms of the monthly standard deviations of daily nearby futures returns and daily

standard deviation of nearby futures returns calculated by contract month (i.e., when the contract is

nearby). For example, the magnitude of monthly standard deviations of returns in the post-change

6

period was within the range of the pre-change period except for one month for both corn and wheat.

The average standard deviation in the post-change period was slightly higher for corn and lower for

soybeans and wheat than during the pre-change period.

With limited observations available for the period following the change in speculative limits

in 2005, conclusions about the impact on volatility are tentative. Additional observations will be

required across varying scenarios of supply, demand, and price level, to have full confidence in the

conclusions. However, there is little to suggest that the change in speculative limits has had a

meaningful impact on price volatility.

CONVERGENCE

Convergence generally refers to the pattern of cash and futures prices tending to come

together, that is, basis approaching zero, at the delivery market as the futures contract expires. In

theory, arbitrage in the cash and futures market should force the prices to converge. If futures were

above the cash price, the cash commodity would presumably be bought, futures sold, and delivery

made. If the cash price exceeded futures, users could buy futures and stand for delivery. Problems

with convergence emerged following the change to Illinois River delivery system for corn and

soybeans, and were the motivation for changes to contract specifications in 2001. Convergence

issues for corn, soybeans and wheat emerged again in the last half of 2005 and particularly in 2006.

A brief conceptual discussion about convergence is useful at this point. Hranaiova and

Tomek, 2002, p.784) note that, “Assuming a frictionless market and a futures contract with no

implicit options, the basis for the par commodity will converge to zero at expiration. Thus, a firm

hedging the par asset is exposed to no basis risk and earns the exact convergence: no forecast of

convergence is needed. Delivery options specified in futures contracts and costs of arbitrage result in

imperfect convergence: in practice, there is basis risk.” Peck and Williams (1991, pp. 99-100)

observe that, “Lack of convergence of a particular basis in any particular month need not indicate a

7

problem with the contract but the natural workings of a contract with more than one delivery point.”

The existence delivery options and costs of arbitrage means that convergence should be thought of as

some range of basis, not necessarily a zero basis.

For each commodity and delivery location over November 2001 – September 2006 the

pattern of convergence was plotted. Each graph presents the 5-year basis pattern for a specific

contract at a particular location, using available cash price data from the USDA’s Agricultural

Marketing Service. For corn, cash prices were available for Chicago, Illinois River North of Peoria,

and Illinois River South of Peoria. For soybeans, cash prices were available for the same three

delivery locations and for the delivery market at St. Louis. For wheat, cash prices were available for

Chicago and Toledo, but not for the delivery market at St. Louis.2 The first observation each year for

November soybean and December corn contract basis analysis is the first business day after October

1st and the last observation is for the last day of trading. For all other contracts, the first observation

is the day after the preceding contract expires and the last observation for each contract is the day of

expiration. For wheat, this process means that early basis observations for the July contract each

year may reflect old crop cash prices rather than prices of the crop being harvested. These prices

were included to provide more observations and to acknowledge that the wheat harvest does not

begin uniformly across time and space. Finally, since the total number of plots is rather large, we

present only three representative sets of plots here (July corn, March soybeans, and September

wheat).

Analysis of the basis plots shows that convergence did not occur in the corn market in July

2005 (Figure 7) and was clearly prevented for the September 2005 contract due to the disruptive

effects of hurricane Katrina. Convergence was not an issue for corn in December 2005. Lack of

convergence is apparent for the March, May, and July 2006 contracts at the Illinois River locations

and the March, July, and September 2006 contracts at Chicago. Following lack of convergence early

in the year at the Illinois River locations, corn basis at expiration of the September 2006 approached

8

zero. The picture that unfolds for corn is one of basis weakness since mid-2005, but not an overall

failure of convergence. For each delivery month from March 2006 through September 2006,

convergence or near convergence was observed for at least one delivery location.

For soybeans, issues with convergence began with the January 2006 contract and continued

in March (Figure 8), except for the St. Louis market. Lack of convergence at the Illinois River

existed in May, July, August and to some degree in September of 2006. Non-convergence was

observed in Chicago for the July and September contracts. For St. Louis, lack of convergence was

observed for the August 2006 contract. The same picture unfolds for soybeans as for corn, with a

general weakness in basis, but not a failure of convergence at all locations for every contract. Only

the July 2006 contract experienced very poor convergence at all locations.

For wheat, convergence failed beginning with the July 2005 contract and persisted through

September 2006 (Figure 9). The magnitude of non-convergence was large and increasing, reaching

90 cents per bushel under the expiring September 2006 contract in Toledo. In addition, the

magnitude of non-convergence was greater and the duration of the weakness was longer for wheat

than for corn and soybeans.3

Using regression analysis, convergence also is examined by assessing the ability of basis

immediately after expiration of the previous contract to predict the change in basis to the first day of

the delivery month and to expiration. This approach was originally proposed by Working (1953) and

has been used in several previous studies of the performance of commodity futures markets to assess

the degree of convergence (Peck and Williams, 1991; Williams, 2001; Hranaiova and Tomek, 2002).

For the present study, the dependent variable in the regressions is the change in the delivery location

basis from the day after the preceding contract expires (except new crop corn and soybean contracts,

which start on the first trading day of October) to the first day of delivery or the last day of delivery.

The independent variable is the delivery location basis on the day after the preceding contract expires

(except new crop corn and soybean contracts, which start on the first trading day of October).

9

Hranaiova and Tomek (2002) note that hedgers are likely most interested in the relationship for the

first day of delivery since hedges held past this date would normally be rolled to the next contract.

Hence, we focus the discussion on regression results for the first day of delivery.

Figure 10 provides examples of the data and the estimated relationship for the first day of the

delivery month for the corn contract (excluding September 2005 due to the effects of hurricane

Katrina) at the Illinois River Peoria North, the soybean contract at the Illinois River Peoria South,

and the wheat contract at Toledo. Table II provides a more complete set of estimated relationships

by commodity and delivery point for basis changes through the first day of the expiration month.

Interpretation of the estimated relationships is facilitated by recalling that basis is in cents per bushel

and considering the corn contract in Figure 10. The slope of the initial basis variable for corn is

-0.83, implying a less than one-to-one relationship between the size of the initial basis and its change

to the first day of delivery. The intercept at this par delivery point does not differ appreciably from

zero, indicating the absence of significant transaction costs. The R2 is 0.80; therefore, initial basis

explains or predicts 80 percent of the basis change.

Inspection of the plots for corn and soybeans in Figure 10 shows that data points since July

2005 (open triangles) generally fall to the left of data points before July 2005 (filled diamonds),

consistent with the weak basis and convergence problems discussed earlier. In addition, all F-

statistic tests for corn and soybeans reject the null hypothesis that the intercept is zero and the slope

is negative one, which would occur in the case of optimal forecasts and zero transaction costs. While

there is evident weakness in corn and soybean convergence since July 2005, it is important to point

out that overall convergence performance since July 2002 is nonetheless reasonably strong. More

specifically, the initial basis for corn and soybeans over the entire sample provides relatively accurate

forecasts of the basis change to the first day of the expiration month, with R2s ranging between 0.64

in Chicago for corn and 0.80 for both the Illinois River Peoria North for corn and Chicago for

10

soybeans. These compare favorably with R2s reported in previous studies of convergence in

commodity futures markets.4

In contrast to the results for corn and soybeans, regression results for wheat offer very little

evidence of predictive ability at either the Chicago or Toledo delivery location. The R2s are low,

slope coefficients do not differ from zero, and the (0,-1) null hypothesis is soundly rejected. The

poor performance is dramatically illustrated in the lower left plot in Figure 10, where the estimated

slope is actually slightly positive and R2 is a miniscule 0.09. The most generous interpretation of

convergence performance is provided in the lower right plot. Excluding the December 2005 through

September 2006 contracts results in a downward sloping regression line but R2 is still only 0.51. Of

course, there is no obvious justification for excluding these observations and this only serves to

highlight the dismal convergence performance in wheat since December 2005.

Results from the final expiration-day analysis (not presented) do not differ dramatically for

corn from those presented in Table II, but for soybeans they show a general tendency for R2s to

increase marginally and for slope coefficients to approach -1.0. For wheat, the predictive ability

continues to be very poor, with R2 at the Toledo market declining to 0.02.

Corn and Soybean Convergence Factors

For corn and soybeans, three factors are related to the weak basis and convergence problems

since mid-2005. These are: 1) sharply higher barge rates, 2) high futures valuations and 3) a large

carry in the futures market that influenced delivery and load-out decisions. From September 2001

through August 2004, nearby barge rates on the Illinois River varied from 110 percent to 325 percent

of tariff (or base rate). The disruption caused by hurricane Katrina in late August 2005 resulted in a

sharp rise in nearby barge rates, peaking at about 800 percent of tariff in mid-October 2005. Barge

rates per bushel for corn (calculated as the product of $0.135 and the percent of tariff) and soybeans

(calculated as the product of $0.144 and the percent of tariff) from Peoria to the Gulf were also

11

generated. Rates increased from the $0.20 to $0.30 per bushel range prior to the fall of 2004, to the

$0.70 to $0.80 per bushel range in early September 2006. Higher barge rates alone, however, do not

necessarily lead to weak basis levels at delivery locations on the Illinois River. Basis at those

locations is also a function of basis at the Mississippi Gulf and the freight cost to the port which can

be approximated by a basis value of corn and soybeans loaded on a barge at Peoria.

Using one daily observation per week, the difference between the Gulf basis and the freight

cost to Peoria, Illinois was calculated to approximate the basis value of corn and soybeans loaded on

a barge at Peoria. For corn, the difference between the Gulf basis and the barge rate to Peoria was

generally negative during the time that the March, May and July 2006 contracts were the nearby

contracts (Figure 11). For example, on July 6, 2006, the Gulf price of corn was $0.4625 per bushel

above July futures and the barge rate to Peoria was calculated at $0.6075 per bushel, suggesting that

the value of a loaded barge at Peoria was $0.145 per bushel under July futures. Negative values

persisted until near maturity of the September 2006 contract. For soybeans, the difference between

the Gulf basis and transportation costs was generally negative when the March, May, July August

and September 2006 contracts were nearby. Some convergence toward zero, however, was observed

in September 2006. The basis and barge rate data indicate that while the Gulf basis strengthened as

transportation costs increased, the strengthening did not completely offset the higher costs,

contributing to a lack of convergence at Illinois River delivery markets.

On the surface, the failure of the Gulf basis to consistently offset higher transportation costs

tends to support the second factor identified above, that futures prices were supported above

fundamental value during this period. During much of this period, futures prices reflected higher

crop values than could be explained by historic relationships between year-ending stock-to-use ratios

and average farm prices (e.g., Good, 2006) and than forecast by the USDA’s World Agricultural

Outlook Board.5 For example, in January 2006, a model developed in the Marketing and Outlook

Program at the University of Illinois between historic stocks-to-use ratios and average farm price

12

suggested that the average farm price of corn from February 2006 through August 2006 should be

near $1.85 per bushel. The USDA was forecasting a price near that same level. The futures market

forecast an average price of $2.05 per bushel for that same period. Some have argued that the wave

of fund interest in commodities, particularly the long-only index funds, contributed to a “bubble” or

“risk premium” in corn and soybean futures prices at times over the past couple of years (e.g.,

Morrison, 2004; Evans, 2005). This influx of trading was at least in part accommodated by the

increase in speculative limits. To the extent that new entrants obtained hedge exemptions, however,

much of the increase may have occurred without the change in limits. For corn, an expansion of

ethanol plants could also have justified higher values particularly at more distant contracts. It is

noteworthy that the decline in futures prices from mid-August through mid-September 2006 brought

values back in line with fundamental value. For example, on September 12, 2006, the USDA’s

forecast for the 2006-07 marketing year average price centered on $2.35 per bushel, the stocks-to-use

model projected an average of $2.36 per bushel, and the futures market forecast an average of $2.40

per bushel. For soybeans those projections were $5.40, $5.49 and $5.41 per bushel, respectively.

More normal convergence was observed with the September 2006 contracts of corn and soybeans.

A third development during late 2005 through August 2006 was a general increase in the

magnitude of the carry in both the corn and soybean markets. The magnitude of the spread in cents

per bushel per month from the second nearest to maturity contract to the nearest to maturity contracts

for corn and soybeans from September 2001 through August 2006 tended to increase. For example,

spreads in corn futures increased from around $0.04 per bushel per month in early 2005 to near $0.06

per month in 2006 (Figure 12). A similar pattern unfolded for soybeans. The size of the carry is

important because it influences the decision to make and take delivery. Consider a merchant regular

for delivery, where the delivery decision depends on available alternatives for current sales and

alternatives for continued storage in order to sell later. The large carry relative to the cost of storage

in 2006 provided such a merchant incentive to hold for later delivery. It also provided incentive for

13

takers of delivery to hold the delivery instrument and sell deferred futures to earn the carry rather

than to immediately load out. While corn deliveries were large in May and July and soybean

deliveries were large in May, July and August (Figure 13), they were apparently not sufficient to

force convergence. The larger carry in the corn and soybean markets in the past year likely resulted

from large crop inventories, increased commercial long hedging in deferred contracts by exporters

and ethanol producers, and the large increase in speculative interest in owning corn and soybean

futures. Open interest data suggest that there was significant interest in owning deferred contracts.

Spreads, however, narrowed from mid- August 2006 into late September 2006 as futures prices also

declined, a period when deliveries increased sharply and much better convergence was observed at

the Illinois River.

Wheat Convergence Factors

For wheat, the factors contributing to lack of convergence generally center on three issues: (1)

futures prices for soft red winter (SRW) wheat that exceeded fundamental value of that class of

wheat, (2) a large carry in the futures market, and (3) insufficient deliveries. A change in the

vomitoxin specifications for delivery satisfaction implemented with the September 2006 and

subsequent contract months also had an apparent influence on the July–September 2006 spread. The

change in specification reduced the level of vomitoxin that a taker of wheat can request at load out

from five parts per million to four parts per million. That change was announced in January 2005

and should have allowed adequate time for issuers and takers of warehouse receipts to make the

necessary adjustments, but the July-September 2006 spread indicates otherwise. In any case, the

change should not have been a factor in March and May 2006 deliveries and convergence issues.

In the case of wheat futures values, wheat prices started moving higher in early 2006 amid

concerns about conditions of the US hard red winter (HRW) wheat crop. Prices were further

supported by confirmation of a small HRW crop, stress on the spring wheat crop, and poor

14

conditions of the southern hemisphere crops. Prospects for significant tightening of U.S. and world

stocks unfolded in 2006. Within this environment, however, the U.S. SRW wheat crop was large, at

390 million bushels. Even though the CBOT contract is effectively for SRW, the price of that

contract was driven higher by the overall increase in wheat prices and the preference of many market

participants to trade in the more liquid Chicago market rather than at other exchanges. Futures prices

of SRW wheat were higher than could be supported by fundamentals of supply and demand, and

therefore, higher than could be supported by the cash market. As an illustration, the USDA’s

September 2006 projection for the year-ending stocks-to-use ratio for the 2006-07 marketing year

was 28.4% for soft red winter wheat. The average price of December 2006, March 2007, and May

2007 futures at the CBOT on October 3, 2006 was $4.52 per bushel. A year earlier the USDA’s

projection of the year-ending stocks-to-use ratio was 28.5% and the average price of the December

2005, March 2006 and May 2006 futures at the CBOT was only $3.57 per bushel. As a result, an

extremely weak basis persisted at most markets, including delivery locations.

The role of the large carry in the wheat market was similar to that described for corn and

soybeans. The monthly carry increased from about $0.05 per bushel in late 2005 to more than $0.06

in 2006, with a spike to about $0.15 associated with the carry from July to September 2006.

Even in a generally very weak basis environment, the magnitude of the basis at delivery

markets from July 2005 through September 2006 was surprisingly large. For example, the basis at

Toledo on the last day of delivery for the May, July, and September 2006 contracts was -$0.42, -

$0.58, and -$0.90 per bushel, respectively. The persistence and growing magnitude of the delivery

basis suggests a problem with the delivery process. Further evidence is provided by the delivery data

shown in Figure 13. Deliveries in wheat during 2006 were quite small compared to corn and

soybeans. Perhaps even more surprising in light of the magnitude of arbitrage opportunities is the

fact that wheat deliveries for the July and September 2006 contracts were smaller than year earlier

levels. The limited extent of cash and futures price expiration arbitrage activity during 2006

15

indicates the presence of a constraint or bottleneck in the delivery system for CBOT wheat. Several

possibilities exist, including a lack of available storage space for deliverable stocks, operations of the

warehouse certificate system used for wheat (corn and soybeans use a shipping certificate system), or

delivery locations out of the normal trade flows for soft red winter wheat.

CONCLUSIONS

In 2005, the Chicago Board of Trade (CBOT) and the Commodities Futures Trading

Commission (CFTC) approved revisions to Regulation 425.01 to increase the CBOT’s “Single

Month” and “All Months” speculative position limits for its corn and mini-size corn, wheat and mini-

size wheat, soybean and mini-size soybean, soybean oil, soybean meal and oat contracts. Since the

change, market participants have expressed concern that this added speculative activity has caused

futures prices to be artificially inflated and volatile, contributing to weak and erratic basis levels from

late 2005 through the first half of 2006 and to a lack of convergence of cash and futures prices during

delivery in the first half of 2006. Three attributes of futures contract behavior importance for market

performance—liquidity, volatility, and convergence—are investigated in this study. Descriptive and

statistical comparisons of these attributes for corn, soybeans, and wheat contracts before and after the

2005 change in speculative position limits are made to ascertain changes in behavior.

The analysis of liquidity and market depth reveals a sharp increase in open interest for corn,

soybeans, and wheat beginning in late 2005 and particularly in 2006. A larger percent of the open

interest was held by non-commercials in the period after the increase in speculative trading limits.

That increase likely accommodated the increase in speculative interest in trading corn, soybean and

wheat futures, but some of the increase would have occurred without the increase as new market

participants received hedge exemptions. With the ambiguity about trader classification in the CFTC

Commitment of Traders Report and only the recent availability of the CFTC Commodity Index

Trader Report, it is difficult to draw definitive conclusions about the structure of the open interest.

16

To the degree that the new trading activity consists of long-only index funds, a larger percentage of

the trade and open interest is likely held by traders who may be relatively price insensitive to

individual commodity conditions than traditional market participants. Domanski and Heath (2007)

describe this process as the ‘financialization” of commodity markets, which they argue can

fundamentally alter price dynamics in these markets. A particular concern is that the huge inflow of

commodities investment has raised prices, at least temporarily, to higher levels than can be justified

by economic fundamentals.

The analysis of price volatility revealed no large change in measures of volatility after the

change in speculative limits. A relatively small number of observations are available since the

change was made, but there is little to suggest that the change in speculative limits has had a

meaningful overall impact on price volatility to date.

The analysis of convergence revealed differences in the degree of convergence before and

after the changes in speculative limits. Non-convergence was observed in some delivery markets for

corn and soybeans beginning as early as July 2005, but non-convergence was most prominent in

March, May, and July 2006. A return to more normal convergence was observed in September 2006,

particularly for corn at Illinois River locations. The difference in convergence before and after July

2005 was likely only partially related to the change in speculative limits. Other factors that impacted

the delivery process included higher futures values, higher barge rates, and a large carry. Despite the

observed weakness, it is important to point out that overall convergence performance since July 2002

was reasonably strong, in the sense that corn and soybean basis before delivery provided relatively

accurate forecasts of the basis change to expiration.

Non-convergence in the wheat market was also observed for the Chicago and Toledo markets

after the change in speculative limits. The difference in convergence before and after July 2005 was

likely only partially related to the change in speculative limits. Inflated values of Chicago futures

associated with the small supply of wheat in classes other than soft red winter wheat and a large carry

17

in the futures market likely contributed to the period of weak basis and poor convergence. However,

unlike corn and soybeans, basis levels during delivery remained extremely weak for an extended

period of time and became weaker over time. Furthermore, wheat basis before delivery over the

entire sample provided very little evidence of predictive ability at either the Chicago or Toledo

delivery locations.

For corn and soybeans, the picture that unfolds relative to recent convergence patterns is one

of weakness, but not failure. While large deliveries are traditionally thought to be evidence of

market failure, large deliveries of soybeans, and particularly, corn in 2006 were likely in reaction to a

unique situation relative to futures prices, spreads, and high barge rates. The delivery process

ultimately worked to provide more normal convergence behavior. Convergence issues should be

carefully monitored, but current performance does not point to the need for major adjustments in

contract provisions. Consideration of an increase in the CBOT storage rate that would make storage

alternatives less attractive is likely warranted. Such an increase might be a fixed rate, a seasonally

adjusted rate, or a rate adjustable to the market rate.

For wheat, the picture that unfolds relative to recent convergence patterns is not only one of

weakness, but failure to accomplish one of the fundamental tasks of a futures market. The

persistence and growing magnitude of the delivery location basis suggests a problem with the

contract specifications. More specifically, the limited extent of cash and futures price expiration

arbitrage activity during 2006 indicates the presence of a constraint or bottleneck in the delivery

system for CBOT wheat. This prolonged period of weak basis suggests that the contract is not

providing an effective hedging mechanism, may not be providing proper signals to wheat producers

and consumers, and may be reducing the effectiveness of crop revenue insurance products based on

CBOT wheat futures prices.

It is important to recognize that concerns about the performance of the CBOT wheat futures

contract are not a recent phenomenon. Gray and Peck (1981) review concerns about delivery

18

specifications of the wheat contract that stretch all the way back to the 1920s. The fundamental

problem is that changes in wheat production patterns, transportation logistics, and trade flows have

left the contract with an increasingly narrow flow of stocks to draw upon in the delivery process.

Given this history, it is not surprising that the CBOT wheat contract now appears to reflect world

conditions for generic ‘wheat” rather than soft red winter wheat market conditions. This has a

marked influence on domestic basis patterns during periods of diverging market conditions across

wheat classes. While the extent of trading suggests that the CBOT contract must be providing price

discovery services and cross-hedging opportunities for some market participants, a key question that

emerges is whether the benefits from these services outweigh the inevitable convergence problems

associated with such an imprecise definition of price.

The CBOT recently approved several changes in the delivery specifications of the wheat

contract.6 Assuming approval by the CFTC, the changes will include: i) moving the delivery

instrument from a warehouse receipt to a shipping certificate; ii) increasing the official storage rate

for wheat from 15/100s of one cent per bushel per day (approximately 4.5 cents per bushel per

month) to 16.5/100s of one cent per bushel per day (approximately 5 cents per bushel per month), iii)

changing several items related to rail-load out at Chicago and Toledo delivery locations; and iv)

lowering the vomitoxin limit for par delivery from 4 parts per million to 3 parts per million. While

these changes are likely to be helpful, they do not address the underlying fundamental problem with

the contract, i.e., the narrow base of the contract. More detailed research is warranted to investigate

the need for and development of a new contract that more precisely reflects world supply and

demand conditions for wheat.

19

BIBLIOGRAPHY Acworth, W. (2006). “Going long on commodities.” Futures Industry, May/June, 24-28. Domanski, D., and A. Heath. (2007). “Financial investors and commodity markets.” BIS Quarterly

Review, March, 53-67. Evans, M. (2006). “Hot money switches into commodities.” Industry Week, April 11.

[http://forums.industryweek.com/showthread.php?t=145] Good, D. (2006). Grain Price Outlook. Department of Agricultural and Consumer Economics,

University of Illinois at Urbana-Champaign, January, April, and July. [www.farmdoc.uiuc.edu/marketing/outlook_grain.html]

Gray, R.W., and A.E. Peck. (1981). “The Chicago wheat futures market: recent problems in

historical perspective.” Food Research Institute Studies, 18, 89-115. Hranaiova, J., and W.G. Tomek. (2002). “Role of delivery options in basis convergence.” The

Journal of Futures Markets, 22, 783-809. Hieronymus, T.A. (1971). Economics of futures trading for commercial and personal profit. New

York: Commodity Research Bureau, Inc. Morrison, K. (2004). “Commodities lure funds investors.” Financial Times, December 28.

[http://www.ft.com, accessed October 19, 2006] O’Hara, N. (2006). “Mutual funds tap into commodities.” Futures Industry, May/June, 19-22. Peck, A. E., and J.C. Williams. (1991). An evaluation of the performance of the Chicago Board of

Trade wheat, corn, and soybean futures contracts during delivery periods from 1964-65 through 1988-89. Report to the National Grain and Feed Association.

Roberts, M. (2006). “What’s up with wheat?” Grain Marketing Outlook Monthly Update, July 18.

[http://aede.osu.edu/people/roberts.628/extension/newsletter/n06.pdf] Williams, J.C. (2001). “Commodity futures and options.” In B.L. Gardner and G.C. Rausser (Eds.),

Handbook of agricultural economics, volume 1b: marketing, distribution and consumers (pp. 745-816). Amsterdam: Elsevier Science B.V.

Working, H. (1953). “Hedging reconsidered.” Journal of Farm Economics, 35, 544-561.

20

ENDNOTES 1 COT reports can be found at:

http://www.cftc.gov/cftc/cftccotreports.htm?from=home&page=cotcontent.

2 The cash price for corn is No. 2 yellow, the same as par delivery on futures contracts. It should be

noted that the cash price is for No. 1 yellow soybeans, as opposed to the par delivery grade of No. 2

yellow soybeans. The cash price is for No. 2 soft red winter wheat, the same as the par delivery

grade for the wheat futures contracts.

3 While not included in the original analysis for this paper, convergence patterns for corn, soybean,

and wheat contracts expiring between November 2006 and May 2007 generally were similar to the

patterns discussed in the text for contracts expiring between July 2005 and September 2006. For

corn, convergence or near convergence was observed for at least one delivery location in December

2006, March 2007, and May 2007. For soybeans, significant convergence issues were not observed

for the November 2006 and January 2007 contracts. Some weakness was evident for the March and

May 2007 contracts. For wheat, non-convergence at expiration continued to be a problem for the

December 2006, March 2007, and May 2007 contracts. As an example, the cash price at Toledo was

49 cents per bushel under the May 2007 CBOT wheat futures price on the last day of delivery.

4 Working (1953) studied Kansas City wheat futures over 1922-1952 and reported an R2 of 0.70.

Williams (2001) reports an R2 of 0.62 in a study of New York coffee futures over 1993-1997.

Hranaiova and Tomek (2002) find that R2 ranges between 0.65 and 0.79 for Chicago corn futures

over 1989-1997.

5 World Agricultural Outlook Board forecasts can be found at:

http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1194.

6 See http://www.cbot.com/cbot/pub/cont_detail/0,3206,1032+47942,00.html for complete details

regarding proposed changes to the CBOT wheat contract.

CBOT Contract Old Phase I Phase II Old Phase I Phase II Phase I Phase II Corn 5,500 9,500 13,500 9,000 15,500 22,000 July 2005 March 2006 Soybeans 3,500 5,000 6,500 5,500 7,750 10,000 July 2005 January 2006 Wheat 3,000 4,000 5,000 4,000 5,250 6,500 July 2006 March 2006

Table I. 2005 Changes in CFTC Speculative Position Limits for CBOT Corn, Soybean, and Wheat Futures Contracts

Note: The first phase was effective on June 10, 2005 and the final phase was effective on December 10, 2005.

Single Month Limit All Months Limit First Futures Month

21

Commodity/ Delivery Location Intercept Slope R2 DW F-Statistic

Corn Chicago 1.73 -0.62 0.64 2.10 9.21

(1.33) (-6.25)

Illinois River Peoria North -1.58 -0.83 0.80 1.54 5.93 (-0.81) (-9.32)

Illinois River Peoria South -0.08 -0.77 0.75 1.85 5.89 (-0.05) (-8.15)

Soybeans Chicago -2.30 -0.70 0.80 1.58 14.06

(-1.79) (-11.49)

Illinois River Peoria North -7.93 -0.78 0.72 1.53 15.75 (-4.02) (-9.15)

Illinois River Peoria South -6.88 -0.83 0.76 1.65 13.06 (-3.90) (-10.25)

St. Louis 4.82 -0.96 0.73 1.72 3.47 (2.34) (-9.54)

Wheat Chicago 6.21 -0.09 0.02 1.76 28.98

(2.32) (-0.67)

Toledo 6.78 0.12 0.09 1.60 145.34 (3.60) (1.51)

Regression Estimates

Table II. Basis Predictability Regressions for CBOT Corn, Soybean, and Wheat Delivery Locations, November 2001 - September 2006 Contracts

Notes: The dependent variable in the regressions is the change in the delivery location basis from the day after the preceding contract expires (except new crop corn and soybean contracts, which start on the first trading day of October) to the first day of delivery. The independent variable is the delivery location basis on the day after the preceding contract expires (except new crop corn and soybean contracts, which start on the first trading day of October). N is 24 for corn, one less than the total sample size because the September 2005 observation is deleted from these regressions. N is 35 for soybeans and 25 for wheat. DW is the Durbin-Watson statistic. The F-statistic tests the joint null hypothesis that the intercept equals zero and the slope equals negative one. Two stars indicate statistical significance at the one-percent level and one star at the five-percent level.

****

** ****

** ** **

*** *

* **

****

** **

** *

** **

22

Notes: The dashed vertical line separates the period before and after speculative position limits were changed. The source is CFTC Commitments of Traders Reports (http://www.cftc.gov/cftccotreports.htm).

Figure 1. Open Interest for CBOT Corn Futures Contracts, Weekly for All Contracts, September 4, 2001 - August 29, 2006.

CBOT Corn: Reporting Categories

0

10

20

30

40

50

60

70

Se p-0

1 De

c-0 1

M ar-

02 Ju

n- 02

Se p-0

2 De

c-0 2

M ar-

03 Ju

n- 03

Se p-0

3 De

c-0 3

M ar-

04 Ju

n- 04

Se p-0

4 De

c-0 4

M ar-

05 Ju

n- 05

Se p-0

5 De

c-0 5

M ar-

06 Ju

n- 06

Month-Year

% o

f O pe

n In

te re

st

Commercial

Non-Commercial

Non-Reporting

CBOT Corn: Total Open Interest

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

Se p-0

1 De

c-0 1

M ar-

02 Ju

n- 02

Se p-0

2 De

c-0 2

M ar-

03 Ju

n- 03

Se p-0

3 De

c-0 3

M ar-

04 Ju

n- 04

Se p-0

4 De

c-0 4

M ar-

05 Ju

n- 05

Se p-0

5 De

c-0 5

M ar-

06 Ju

n- 06

Month-Year

O pe

n In

te re

st (#

o f c

on tr

ac ts

) CBOT Corn: Reporting Categories

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

Se p-0

1 De

c-0 1

M ar-

02 Ju

n- 02

Se p-0

2 De

c-0 2

M ar-

03 Ju

n- 03

Se p-0

3 De

c-0 3

M ar-

04 Ju

n- 04

Se p-0

4 De

c-0 4

M ar-

05 Ju

n- 05

Se p-0

5 De

c-0 5

M ar-

06 Ju

n- 06

Month-Year

O pe

n In

te re

st (#

o f c

on tr

ac ts

)

Commercial

Non-Commercial

Non-Reporting

23

Notes: The dashed vertical line separates the period before and after speculative position limits were changed. The source is CFTC Commitments of Traders Reports (http://www.cftc.gov/cftccotreports.htm)

Figure 2. Open Interest for CBOT Soybean Futures Contracts, Weekly for All Contracts, September 4, 2001 - August 29, 2006.

CBOT Soybeans: Reporting Categories

0

10

20

30

40

50

60

Se p-0

1 De

c-0 1

M ar-

02 Ju

n- 02

Se p-0

2 De

c-0 2

M ar-

03 Ju

n- 03

Se p-0

3 De

c-0 3

M ar-

04 Ju

n- 04

Se p-0

4 De

c-0 4

M ar-

05 Ju

n- 05

Se p-0

5 De

c-0 5

M ar-

06 Ju

n- 06

Month-Year

% o

f O pe

n In

te re

st

Commercial

Non-Commercial

Non-Reporting

CBOT Soybeans: Total Open Interest

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

Se p-0

1 De

c-0 1

M ar-

02 Ju

n- 02

Se p-0

2 De

c-0 2

M ar-

03 Ju

n- 03

Se p-0

3 De

c-0 3

M ar-

04 Ju

n- 04

Se p-0

4 De

c-0 4

M ar-

05 Ju

n- 05

Se p-0

5 De

c-0 5

M ar-

06 Ju

n- 06

Month-Year

O pe

n In

te re

st (#

o f c

on tr

ac ts

)

CBOT Soybeans: Reporting Categories

0

25,000

50,000

75,000

100,000

125,000

150,000

175,000

200,000

Se p-0

1 De

c-0 1

M ar-

02 Ju

n- 02

Se p-0

2 De

c-0 2

M ar-

03 Ju

n- 03

Se p-0

3 De

c-0 3

M ar-

04 Ju

n- 04

Se p-0

4 De

c-0 4

M ar-

05 Ju

n- 05

Se p-0

5 De

c-0 5

M ar-

06 Ju

n- 06

Month-Year

O pe

n In

te re

st (#

o f c

on tr

ac ts

)

Commercial

Non-Commercial

Non-Reporting

24

Notes: The dashed vertical line separates the period before and after speculative position limits were changed. The source is CFTC Commitments of Traders Reports (http://www.cftc.gov/cftccotreports.htm)

Figure 3. Open Interest for CBOT Wheat Futures Contracts, Weekly for All Contracts, September 4, 2001 - August 29, 2006.

CBOT Wheat: Reporting Categories

0

10

20

30

40

50

60

Se p-0

1 De

c-0 1

M ar-

02 Ju

n- 02

Se p-0

2 De

c-0 2

M ar-

03 Ju

n- 03

Se p-0

3 De

c-0 3

M ar-

04 Ju

n- 04

Se p-0

4 De

c-0 4

M ar-

05 Ju

n- 05

Se p-0

5 De

c-0 5

M ar-

06 Ju

n- 06

Month-Year

% o

f O pe

n In

te re

st

Commercial

Non-Commercial

Non-Reporting

CBOT Wheat: Total Open Interest

0

100,000

200,000

300,000

400,000

500,000

600,000

Se p-0

1 De

c-0 1

M ar-

02 Ju

n- 02

Se p-0

2 De

c-0 2

M ar-

03 Ju

n- 03

Se p-0

3 De

c-0 3

M ar-

04 Ju

n- 04

Se p-0

4 De

c-0 4

M ar-

05 Ju

n- 05

Se p-0

5 De

c-0 5

M ar-

06 Ju

n- 06

Month-Year

O pe

n In

te re

st (#

o f c

on tr

ac ts

) CBOT Wheat: Reporting Categories

0

50,000

100,000

150,000

200,000

250,000

300,000

Se p-0

1 De

c-0 1

M ar-

02 Ju

n- 02

Se p-0

2 De

c-0 2

M ar-

03 Ju

n- 03

Se p-0

3 De

c-0 3

M ar-

04 Ju

n- 04

Se p-0

4 De

c-0 4

M ar-

05 Ju

n- 05

Se p-0

5 De

c-0 5

M ar-

06 Ju

n- 06

Month-Year

O pe

n In

te re

st (#

o f c

on tr

ac ts

)

Commercial

Non-Commercial

Non-Reporting

25

Notes: The dashed vertical line separates the period before and after speculative position limits were changed. The source is CFTC Bank Participation in Futures and Options Reports (http://cftc.gov/dea/bank/deabank.htm).

Figure 4. Bank Open Interest in CBOT Corn, Soybean and Wheat Futures Contracts, Monthly for All Contracts, September 2001 - September 2006.

CBOT Corn

0

25,000

50,000

75,000

100,000

125,000

150,000

175,000

Se p-0

1 De

c-0 1

M ar-

02 Ju

n- 02

Se p-0

2 De

c-0 2

M ar-

03 Ju

n- 03

Se p-0

3 De

c-0 3

M ar-

04 Ju

n- 04

Se p-0

4 De

c-0 4

M ar-

05 Ju

n- 05

Se p-0

5 De

c-0 5

M ar-

06 Ju

n- 06

Se p-0

6

Month-Year

N et

L on

g O

pe n

In te

re st

(# o

f c on

tr ac

ts )

CBOT Soybeans

0

25,000

50,000

75,000

100,000

125,000

150,000

175,000

Se p-0

1 De

c-0 1

M ar-

02 Ju

n- 02

Se p-0

2 De

c-0 2

M ar-

03 Ju

n- 03

Se p-0

3 De

c-0 3

M ar-

04 Ju

n- 04

Se p-0

4 De

c-0 4

M ar-

05 Ju

n- 05

Se p-0

5 De

c-0 5

M ar-

06 Ju

n- 06

Se p-0

6

Month-Year

N et

L on

g O

pe n

In te

re st

(# o

f c on

tr ac

ts )

CBOT Wheat

0

25,000

50,000

75,000

100,000

125,000

150,000

175,000

Se p-0

1 De

c-0 1

M ar-

02 Ju

n- 02

Se p-0

2 De

c-0 2

M ar-

03 Ju

n- 03

Se p-0

3 De

c-0 3

M ar-

04 Ju

n- 04

Se p-0

4 De

c-0 4

M ar-

05 Ju

n- 05

Se p-0

5 De

c-0 5

M ar-

06 Ju

n- 06

Se p-0

6

Month-Year

N et

L on

g O

pe n

In te

re st

(# o

f c on

tr ac

ts )

26

Notes: The source is CFTC Historical Commitments of Ttraders Commodity Index Trader Supplement Reports (http://www.cftc.gov/dea/history/deahist-cit.htm).

Figure 5. Commodity Index Trader Open Interest in CBOT Corn, Soybean and Wheat Futures and Options Contracts, Weekly for All Contracts, January 3, 2006 - September 5, 2006.

CBOT Soybeans

80,000

85,000

90,000

95,000

100,000

105,000

110,000

115,000

120,000

125,000

130,000

1/3/2006 2/7/2006 3/14/2006 4/18/2006 5/23/2006 6/27/2006 8/1/2006 9/5/2006

Week in 2006

# co

nt ra

ct s

20

22

24

26

28

30

32

% o

f t ot

al o

pe n

in te

re st

# of contracts

% of open interest

CBOT Wheat

160,000

170,000

180,000

190,000

200,000

210,000

220,000

230,000

1/3/2006 2/7/2006 3/14/2006 4/18/2006 5/23/2006 6/27/2006 8/1/2006 9/5/2006

Week in 2006

# co

nt ra

ct s

30

35

40

45

50

55

% o

f t ot

al o

pe n

in te

re st

# of contracts

% of open interest

CBOT Corn

200,000

250,000

300,000

350,000

400,000

450,000

500,000

1/3/2006 2/7/2006 3/14/2006 4/18/2006 5/23/2006 6/27/2006 8/1/2006 9/5/2006

Week in 2006

# co

nt ra

ct s

24

25

26

27

28

29

30

% o

f t ot

al o

pe n

in te

re st

# of contracts

% of open interest

27

Figure 6. Nearby CBOT Futures Returns, Daily, September 4, 2001 - August 31, 2006

Notes: The dashed vertical line separates the period before and after speculative position limits were changed. Daily returns are computed as ln[p(t)/p(t-1)]*100, where p(t) is the settlement price for the nearby futures contract. The futures price source is Commodity Systems Inc. (http://www.csidata.com/).

CBOT Corn

-8

-6

-4

-2

0

2

4

6

8

Au g-0

6 Ju

n-0 6

M ar-

06 No

v-0 5

Au g-0

5 M

ay -05

Fe b-0

5 No

v-0 4

Au g-0

4 M

ay -04

Fe b-0

4 No

v-0 3

Au g-0

3 M

ay -03

Fe b-0

3 No

v-0 2

Au g-0

2 M

ay -02

Fe b-0

2 Oc

t-0 1

Date

D ai

ly R

et ur

ns (%

)

Avg. Absolute Return = 1.3%

Avg. Absolute Return = 1.0%

CBOT Soybeans

-8

-6

-4

-2

0

2

4

6

8

Au g-0

6 Ju

n-0 6

M ar-

06 No

v-0 5

Au g-0

5 M

ay -05

Fe b-0

5 No

v-0 4

Au g-0

4 M

ay -04

Fe b-0

4 No

v-0 3

Au g-0

3 M

ay -03

Fe b-0

3 No

v-0 2

Au g-0

2 M

ay -02

Fe b-0

2 Oc

t-0 1

Date

D ai

ly R

et ur

ns (%

)

Avg. Absolute Return = 1.1%

Avg. Absolute Return = 1.2%

CBOT Wheat

-8

-6

-4

-2

0

2

4

6

8

Au g-0

6 Ju

n-0 6

M ar-

06 No

v-0 5

Au g-0

5 M

ay -05

Fe b-0

5 No

v-0 4

Au g-0

4 M

ay -04

Fe b-0

4 No

v-0 3

Au g-0

3 M

ay -03

Fe b-0

3 No

v-0 2

Au g-0

2 M

ay -02

Fe b-0

2 Oc

t-0 1

Date

D ai

ly R

et ur

ns (%

)

Avg. Absolute Return = 1.2%

Avg. Absolute Return = 1.4%

28

Figure 7. Basis at Delivery Points for CBOT Corn Futures Contracts, July 2002 - 2006

Notes: Basis is plotted daily and computed as cash minus futures. The first observation for each contract year is the day after the preceding contract expires, around the 15th of the month. The last observation for each contract year is the expiration day for the given contract, again around the 15th of the month. The dashed vertical line separates the period before and after speculative position limits were changed. The cash price source is Agricultural Marketing Service (http://marketnews.usda.gov/portal/lg/) and the futures price source is Commodity Systems Inc. (http://www.csidata.com/).

July CBOT Corn Basis (Chicago)

-40

-30

-20

-10

0

10

20

30

40

50

60

Contract Year

B as

is (c

en ts

/b u.

)

2002 2003 2004 2005 2006

July CBOT Corn Basis (IL River Peoria North)

-40

-30

-20

-10

0

10

20

30

40

50

60

Contract Year

B as

is (c

en ts

/b u.

)

2002 2003 2004 2005 2006

July CBOT Corn Basis (IL River Peoria South)

-40

-30

-20

-10

0

10

20

30

40

50

60

Contract Year

B as

is (c

en ts

/b u.

)

2002 2003 2004 2005 2006

29

Figure 8. Basis at Delivery Points for CBOT Soybean Futures Contracts, March 2002 - 2006

Notes: Basis is plotted daily and computed as cash minus futures. The first observation for each contract year is the day after the preceding contract expires, around the 15th of the month. The last observation for each contract year is the expiration day for the given contract, again around the 15th of the month. The dashed vertical line separates the period before and after speculative position limits were changed. The cash price source is Agricultural Marketing Service (http://marketnews.usda.gov/portal/lg/) and the futures price source is Commodity Systems Inc. (http://www.csidata.com/).

March CBOT Soybean Basis (Chicago)

-30

-20

-10

0

10

20

30

40

50

Contract Year

B as

is (c

en ts

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)

2002 2003 2004 2005 2006

March CBOT Soybean Basis (IL River Peoria North)

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March CBOT Soybean Basis (St. Louis MS River)

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50

Contract Year

B as

is (c

en ts

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2002 2003 2004 2005 2006

30

Figure 9. Basis at Delivery Points for CBOT Wheat Futures Contracts, September 2002 - 2006

Notes: Basis is plotted daily and computed as cash minus futures. The first observation for each contract year is the day after the preceding contract expires, around the 15th of the month. The last observation for each contract year is the expiration day for the given contract, again around the 15th of the month. The dashed vertical line separates the period before and after speculative position limits were changed. The cash price source is Agricultural Marketing Service (http://marketnews.usda.gov/portal/lg/) and the futures price source is Commodity Systems Inc. (http://www.csidata.com/).

September CBOT Wheat Basis (Chicago)

-110

-100

-90

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-70

-60

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-30

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10

Contract Year

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2002 2003 2004 2005 2006

September CBOT Wheat Basis (Toledo)

-110

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-10

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Contract Year

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is (c

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/b u.

)

2002 2003 2004 2005 2006

31

Figure 10. Examples of Basis Predictability at Delivery Points for CBOT Corn, Soybean, and Wheat Futures Contracts, July 2002 - 2006

Notes: Basis is computed as cash minus futures. Initial basis is computed for the day after the preceding contract expires, around the 15th of the month (except new crop corn and soybean contracts, which start on the first trading day of October). Triangles indicate observations that occur after the change in speculative position limits

CBOT Soybeans (IL River Peoria South)

y = -6.88 - 0.83x R2 = 0.76

-140

-120

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-80 -60 -40 -20 0 20 40 60 80

Initial Basis (cents/bu.)

B as

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F ir

st D

ay o

f D el

iv er

y (c

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/b u.

)

CBOT Wheat (Toledo)

y = 6.79+ 0.12x R2 = 0.09

-30

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40

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20

Initial Basis (cents/bu.)

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CBOT Wheat (Toledo)

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F ir

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ay o

f D el

iv er

y (c

en ts

/b u.

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y = -0.43 - 0.68x R2 = 0.51

Mar 2006

Dec 2005

May 2006

Jul 2006

Sep 2006

CBOT Corn (IL River Peoria North)

-30

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y (c

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Sep 2005

y = -1.58 - 0.83x R2 = 0.80

32

Figure 11. Basis at the Gulf Minus Barge Rate for December, March, and May CBOT Corn Futures Contracts, 2001 - 2006

Notes: The series is plotted weekly and computed as Gulf cash minus futures minus barge rates. Barge rates refer to shipping between Peoria, Illinois and the Mississipi Gulf. The first observation for each December contract year is the first trading day in October. The first observation for each March and May contract year is the week after the preceding contract expires, around the 15th of the month. The last observation for all contract years is the expiration week for the given contract, again around the 15th of the month. The dashed vertical line separates the period before and after speculative position limits were changed. The cash price source is Agricultural Marketing Service (http://marketnews.usda.gov/portal/lg/) and the futures price source is Commodity Systems Inc. (http://www.csidata.com/). Barge rates (cents/bu.) were computed as 0.135 times the quoted percentage of tariff for corn.

December CBOT Corn Gulf Basis minus Barge Rate

-60

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2001 2002 2003 2004 2005

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2002 2003 2004 2005 2006

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2002 2003 2004 2005 2006

33

Notes: Spreads are computed daily as the second nearest to maturity contract minus the nearest to maturity contract. Each day's spread is adjusted to a monthly basis by dividing the observed spread by the number of months between the two contracts used in the computation. Spreads are computed through the expiration date of the nearest to maturity contract. The dashed vertical line separates the period before and after speculative position limits were changed. The futures price source is Commodity Systems Inc. (http://www.csidata.com/).

Figure 12. Spread Between the Two Nearest to Expiration CBOT Corn Futures Contacts, September 4, 2001 - August 31, 2006

-8

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-4

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Date

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34

Note: Source of delivery data is the CBOT Issues and Stops Year to Date Report (http://cbot.com/cbot/pub/page/0,3181,1216,00.html)

Figure 13. Total Volume of Deliveries for CBOT Futures Contracts, 2004 - 2006

CBOT Corn

0

5,000

10,000

15,000

20,000

25,000

30,000

Mar May Jul Sep Dec

Contract Month

D el

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2004 2005 2006

CBOT Wheat

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2004 2005 2006

35

IVANICMARTINZAMAN.pdf

World Development Vol. 40, No. 11, pp. 2302–2317, 2012 � 2012 World Bank. Published by Elsevier Ltd. All rights reserved

0305-750X/$ - see front matter

www.elsevier.com/locate/worlddev http://dx.doi.org/10.1016/j.worlddev.2012.03.024

Estimating the Short-Run Poverty Impacts of the 2010–11

Surge in Food Prices

MAROS IVANIC, WILL MARTIN The World Bank, Washington, DC, USA

and

HASSAN ZAMAN *

Bangladesh Bank, Dhaka, Bangladesh

Summary. — Global food prices increased sharply between June and December 2010. This paper assesses the impact of price changes for 38 agricultural commodities on poverty using detailed data on patterns of production and consumption in 28 countries. We find con- siderable heterogeneity in the impacts, but estimate that poverty rose by 44 million people, with 68 million people falling into poverty and 24 million people raised out of poverty at the extreme poverty line of $1.25 per day. This corresponds to an average poverty increase of 1.1% points in low income countries and 0.7% points in middle income countries. � 2012 World Bank. Published by Elsevier Ltd. All rights reserved.

Key words — food prices, poverty, global, welfare, trade, sensitivity

* We gratefully acknowledge valuable comments provided on this body of

work by Jose Cuesta, Gabriela Inchauste, Jos Verbeek, Sailesh Tiwari, and

1. INTRODUCTION

Global food prices rose rapidly between June 2010 and early 2011, taking the World Bank Food Price Index above its peak of the 2008 “food price crisis” by early March. Like the 2008 price spike (Ivanic & Martin, 2008), the direct impact of this rise in food prices seems likely to have been a substantial in- crease in poverty. The purpose of this paper is to provide esti- mate of the severity of the impact of this price surge on poverty as a guide for policy responses.

A key difference between the 2008 food crisis and the more recent price surge was that the latter was much more broadly- based across food groups, which implies a potentially very different impact on poverty relative to the 2008 episode. In particular, commodities such as rice saw more moderate price increases in 2010, while prices of items such as edible oils in- creased considerably more than in 2008. Another potentially important difference between the two episodes is in the extent to which changes in world prices of key staples were transmit- ted into domestic markets—an issue on which we have much better information than in 2008 (World Bank, 2011a)

The poverty and nutritional implications of even short-lived price spikes can be serious as poor people spend very large fractions of their incomes on food, and hence are likely to experience large declines in their real income levels, and poor producers do not have time to increase their output in re- sponse to the price change. Even short-run price spikes both reduce calorie intake and compromise dietary diversity (Skoufias, Tiwari, & Zaman, 2011), and may have adverse long-run con- sequences especially when infants are affected (Alderman, Hoogeveen, & Rossi, 2006).

Developments in agricultural commodity markets have been analyzed in detail in many studies, including Abbott, Hurt, and Tyner (2011) and World Bank (2011b). World food prices rose particularly sharply between June 2010 and the end of that year. A confluence of weather shocks in large food producing countries, followed in some cases by export restrictions,

2302

curtailed supply and contributed to world wheat prices dou- bling between June 2010 and year end. Global maize prices rose 64% in the 6 months after June 2010, driven by a range of factors including: a series of downward revisions of crop forecasts; the positive relationship between maize and wheat prices; and the use of maize for biofuels, which both increased demand and had contributed to lowering stock levels in previ- ous years. Global rice prices increased at a slower rate than other grains. The export price for Thai rice (5% broken grade) increased by 8% between October 2010 and January 2011, and by 17% between June 2010 and December 2010. On this occa- sion, price rises extended beyond staple grains. Sugar prices rose by 76% between June and December 2010 due to supply shortfalls from Brazil, the largest exporter, and weather shocks in Australia. Similarly, edible oil prices were up on account of a number of weather-related shocks. Prices of soybean and palm oil were up by 64% between June and December 2010. In- creases in Chinese import demand—including for stockbuild- ing—put upward pressure on soybean prices (Abbott et al., 2011).

The extent to which these global food price increases affect poverty depends on a number of factors. These include the rate at which global prices are passed through to local prices, the distribution of net sellers and net buyers of food staples, the specific commodities for which prices increase, the ability of consumers to substitute into other less expensive food items, the coping strategies available to households, and policy responses by governments. The evidence from the 2008 food price spike suggests that in most countries poverty will in- crease when domestic food prices rise substantially, even in rural areas, because both rural and urban poor are typically net consumers of food (Dessus, Herrera, & de Hoyos, 2008;

four anonymous reviewers. Final revision accepted: March 27, 2012.

ESTIMATING THE SHORT-RUN POVERTY IMPACTS OF THE 2010–11 SURGE IN FOOD PRICES 2303

Ivanic & Martin, 2008; Wodon & Zaman, 2010). There are wide variations in the magnitudes of these impacts, which sug- gest that a careful analysis of the poverty impact of this more recent price spike is needed in order to inform policy re- sponses.

There is a fundamental difference in the methodology that we use and that used in many recent studies of changes in pov- erty and/or food security, such as Headey (2011), Verpoorten, Arora, and Swinnen, (2012) and Mason, Jayne, Chapoto, and Donovan (2011). We seek to estimate the partial, ceteris pari- bus, effect of food price changes on poverty in a particular per- iod. Many of the other studies focus on the total effect of changes in all of the variables affecting poverty or food secu- rity over a particular period. While we believe that both types of study can produce valuable insights, we agree with Swinnen (2011) that it is very important to distinguish between the two types of result when drawing policy conclusions.

Estimates of the ceteris paribus impacts of food price changes on poverty seem likely to be of interest and impor- tance to policy makers responsible for policies affecting food prices. Having such estimates is particularly important since it is not possible to predict even the direction of the impact on poverty from theory, without recourse to data on sources of income and patterns of expenditure at the household level. This causes the estimated impacts on different countries to dif- fer considerably (Wodon et al., 2008). The partial impact of food price changes on poverty may be important even if it is opposite in sign to the overall change in poverty over a partic- ular period. If, as estimated by Headey (2011, p. 34), the par- tial impact of food price rises between 2005–06 and 2007–08 was an increase of 128 million globally in the number of food-insecure people, it seems appropriate for policy makers to be concerned about food-price impacts, even if high overall income growth over the same period did lower the total num- ber of food-insecure people.

Like Headey and Fan (2010) we are conscious that energy prices also rose sharply in 2010, as in 2007–08, and that these price increases may have contributed to the changes in food prices both through supply-side and demand-side effects (e.g., the increased incentive to use food to produce biofuels). In this analysis we focus specifically on the impact of changes in food prices on poverty. This is partly because food prices are likely to have the largest direct impact on poverty given the large shares of food in the expenditures of the poor, and because of the importance of agricultural income for many poor households. It is also because food prices can be influ- enced by a range of factors, such as agricultural trade policies, stockholding policies (Gouel & Jean, 2012), and policies on re- search, development, and extension that are quite separate from the factors affecting energy prices. We recognize, how- ever, that rises in fuel prices may, especially when all linkages are considered, as in Arndt, Benfica, Maximiano, Nucifora, and Thurlow (2008), have major impacts on poverty.

There are several reasons why global prices are only par- tially transmitted to domestic prices. One set of insulating fac- tors arises from differences in remoteness, infrastructure quality, and transportation costs that governments cannot readily change in the short term. Another source of differences in price transmission is trade policies, with many countries seeking to insulate themselves from increases in world prices by counteracting variations in trade measures. To the extent that trade measures reduce the increases in domestic prices rel- ative to world prices, they might be expected to reduce the poverty impacts of the price changes. However, this need not be the case when multiple countries use these measures. While insulation policies may appear to individual countries

to be effective, they can fundamentally only redistribute price volatility, rather than reduce it. This is most clear in the case where all countries attempt to insulate to the same degree. As Martin and Anderson (2012) point out, if all countries at- tempt to insulate through trade policies to the same degree, the policy of insulation is completely ineffective—domestic prices are exactly as volatile as they would have been in the absence of insulation. All that has been achieved is to destabilize inter- national transfers of income by intensifying the volatility of world prices.

In reality, however, the degree of insulation differs between countries, with some countries achieving a substantial degree of stabilization and others insulating to a much more limited degree (Martin & Anderson, 2012). In this situation, the world price is increased to an extent that depends on a weighted average of protection rates, thus reducing the effectiveness of price insulation in all countries from its apparent level, and providing no insulation for countries using the weighted-aver- age level of insulation.

For the purposes of the current paper, where we observe a set of changes in world prices and a set of changes in domestic prices, the key thing for assessing the impact on poverty is to use the best available information on the changes in the domes- tic prices that most directly affect consumers and producers. In this paper, we avoid the need to specify elasticities of price transmission from world to domestic prices of the type esti- mated by Minot (2010) and Dawe (2008) by using, where avail- able from the FAO’s GIEWS database, direct observations on changes in domestic prices. While these prices are available only for a subset of commodities, the products included tend to be the most important staple commodities for each country. These prices are typically market prices excluding any taxes paid by purchasers or levied on producers. We assume that these changes reflect the changes in producer and consumer prices. While countries frequently change their border mea- sures in response to changes in world prices, it seems much less common for countries to change domestic taxes such as the va- lue-added tax in response to changes in world prices.

As Deaton and Laroque (1992) have observed, prices of storable commodities are characterized by long periods in the doldrums, punctuated by intense but short-lived price spikes. These price spikes for food commodities are particu- larly important for poverty because the poorest people spend as much as three-quarters of their incomes on food; because even poor farmers in low-income countries are typically net buyers of food; and because the short-lived nature of the spikes provides little opportunity for households to soften the blow by increasing their output of food or augmenting their incomes. In this study, we focus on a particularly sharp increase in prices, taking into account the fact that households have only limited opportunities to adjust to such a rapid in- crease in prices.

In the next section of the paper we discuss the methodology used for the analysis. We then turn to the key features of the data used and a discussion of the results for our 28 sample countries. Following this, we consider our estimates of the glo- bal poverty impacts. After this, we present robustness checks. Finally, we offer some conclusions.

2. METHODOLOGY

In this study, we assess the poverty impact of the food price increase between June and December 2010 in 28 low- and middle-income countries. We do so by gathering detailed information on individual households’ food production and

2304 WORLD DEVELOPMENT

consumption levels for 38 agricultural and food commodities, and using a model which assesses the impacts of these com- modity price changes on household welfare. The methodology is an extension of that used by Ivanic and Martin (2008) to as- sess the impact of the food price spike in 2008 on 9 developing countries. Aside from the larger number of countries in this study, another key improvement over the earlier work is that for key consumption commodities in most countries we are able to use information on local price changes to assess the im- pact of higher prices. This provides a better approximation of the welfare impact as for these key items we do not have to use estimated pass-through rates between global and local prices, though we do use pass-through rates for the food items for which we do not have local price data.

To analyze the ceteris paribus short-run poverty impacts of changes in prices of key agricultural commodities, we use a set of observed and estimated changes in domestic agricultural and food prices in a sample of developing countries and calcu- late their implications for individual households’ costs of liv- ing and agricultural net incomes. Based on the simulated changes in individual households’ welfare relative to the $1.25 extreme poverty line (Ravallion, Chen, & Sangraula, 2009), we determine the changes in the poverty headcount and poverty gap for each country in our sample. As the final step, we calculate population-weighted average poverty changes for the low- and middle-income countries included in our sample—which represent 40% of the population of low and middle income countries—and extrapolate these to other countries in order to estimate the global poverty impact of the recent changes in food prices.

In our household welfare calculations, we closely follow the methodology described in Ivanic and Martin (2008) with two important modifications. Essentially, this methodology in- volves estimating the impact of price changes on each house- hold’s real income by multiplying the price change experienced by the household by the quantity of the good pro- duced and by the negative of the quantity consumed by that household (see Deaton, 1989 for a justification of this ap- proach). The first modification involves allowing households to substitute away from commodities whose prices rise. We do this by introducing a Constant Difference of Elasticities (CDE) demand system parameters (Hanoch, 1975). Because the CDE demand system is semi-flexible, it was possible for Liu, Surry, Dimaranan, & Hertel, 1997 to estimate its param- eters for 112 countries and regions to provide empirically- based estimates of the second-order impacts of price changes on households’ real incomes through changes in the volume of each good consumed. The actual demand elasticities used in the analysis are based on a combination of these parameters and actual expenditure shares at the household level. In re- lated work, we used panel data for Pakistan, Uganda, and Vietnam—some of the few countries for which panel data are currently available—to compare the actual demand adjust- ments with those predicted by the CDE system. The results of this analysis suggested that substitution during the 2008 food price crisis may have been somewhat greater than implied by our CDE parameter estimates but that this made only a small difference to the results for the estimated poverty impacts (Ivanic & Martin, 2012).

A second modification in our core simulation is to omit modeling the effects of commodity price changes on wage rates on the grounds that commodity price changes appear to take some time to affect wage rates for unskilled labor in develop- ing countries (Ravallion, 1990). As in Ivanic and Martin (2008)—but with more justification given the speed of the 2010 price increase—we ignore the potential second-order

effects of price changes on incomes through increases in output of products whose prices have risen. However, in light of the findings in Lasco, Myers, and Bernsten (2008) of a short-term impact of rice prices on rural wages in the Philippines, we do consider impacts on wage rates in a supplementary simulation. We do this by using a single-country, percentage-change ver- sion of the GTAP model for each country and calculating the Stolper–Samuelson elasticities for the unskilled wage rate with respect to the price of each commodity. The results for all food prices are positive but vary greatly across regions: the largest effects are found in Africa where a uniform food price rise of 1% raises wages by 0.42%. For other regions, the effect lies between 0.08 and 0.16% points.

Because of our focus in this paper on a specific episode, with markedly different changes in the prices of key commodities across countries, our core results do not provide any insights into the vulnerability of households to changes in particular product prices. As an aid to understanding the aggregate im- pacts, however, we include the impacts of a standard 10% in- crease in the prices of key agricultural commodities in Appendix Table 10. These impacts reflect the effects of price changes on both the cost of expenditure and the revenues from production at the household level, and hence provide an indi- cation of the number of households vulnerable to changes in the prices of each good.

Our focus is on the impact of food price changes on the real income of each household. Households have many coping strategies that may allow them to reduce the impact of these income shocks on key outcomes such as their levels of calorie consumption. As noted by Alem and Söderbom (2012), inter- temporal smoothing approaches, such as temporarily in- creased reliance on remittances, borrowing, sale of assets, or cutting back on investments such as enrollment of children in school may be particularly important in protecting con- sumption levels when an income shock has been experienced. While some of these smoothing adjustments may reduce the adverse impacts of the income shock on the household’s long term economic opportunities, others—such as withdrawing children from school—may have seriously adverse economic impacts.

Our measures of the poverty headcount and the poverty gap index follow the definitions of Foster, Greer, & Thornbecke, 1984. Hence, our poverty headcount reflects the number of people whose daily expenditure is below the defined poverty- line income while the poverty gap measures the average expen- diture shortfall of the poor as a share of the poverty-line in- come for the entire population.

3. DATA

In this study, we use household survey information on household food production, sales, and consumption of 38 food and agricultural commodities for 28 developing countries (Appendix Table 7). This is an increase from twenty commod- ities and ten country-periods in the 2008 study. The agricul- tural commodities identified in the surveys include not only basic staples (e.g., wheat, maize, and rice), but also various types of animal products (e.g., poultry, eggs, pork, beef) and a number of commodities that are important to the poor in a range of developing countries (e.g., sorghum, groundnuts, soybeans). The full list of agricultural commodities included is presented in Appendix Table 8. The breadth of the product coverage is important given that the food price increases on this occasion included far more than the staple grains that were the main focus of the 2008 food price crisis.

Table 1. Changes in global prices of key agricultural commodities: June– December 2010

Change in price,%

Tobacco 89 Sugar 76 Sorghum 69 Wheat 68 Maize 64 Palm and soybean oil (proxy for oilseeds) 64 Cotton 55 Soybeans 34 Groundnut oil (proxy for groundnuts and oils) 31 Barley 30 Rice 21 Beef 17 Coffee, tea, cocoa 13 Bananas 6 Poultry �2 Fish �13 Oranges (proxy for fruits) �40

Source: World Bank Development Prospects Group.

ESTIMATING THE SHORT-RUN POVERTY IMPACTS OF THE 2010–11 SURGE IN FOOD PRICES 2305

The countries in our sample are drawn from all developing regions. While the coverage is influenced heavily by the avail- ability of surveys which include detailed information on the in- come sources as well as the expenditure patterns of the poor, we sought to improve the coverage of our country sample in the countries that contain the largest numbers of poor people: for example, in terms of population our sample represents 98% in the South Asia region, 32% in Sub-Saharan Africa, 41.8% in middle income countries, and 34.5% in low-income countries.

Table 2. Changes in domestic prices of key stap

Country Commodity Change in price, %

Armenia Wheat 11 Potatoes 82

Bangladesh Rice 19 Wheat 45

Cambodia Rice 0 Ecuador Rice 0

Wheat 0 Vegetables 0 Maize �2

Guatemala Maize �1 Rice 0 Vegetables 2

India Rice 5 Wheat 4 Sugar 8

Indonesia Rice 20 Wheat 1

Malawi Maize 2 Rice �16 Cassava �16 Maize

Vegetables 67 38 Moldova Maize �2

Wheat 5 Mongolia Wheat 34

Rice 11 Mutton �38

Source: FAO GIEWS database.

Where available, instead of the observed global prices pre- sented in Table 1, we used country-level data on actual changes in domestic food prices from Food Price Watch (World Bank, 2011a), presented in Table 2, and based on esti- mates in the FAO GIEWS database. Comparison of Tables 1 and 2 suggests that the transmission of global price increases to domestic prices has been high in many countries. For instance, between June 2010 and December 2010, the 68% increase in the international price of wheat was associated with large price increases in Bangladesh (45%), Tajikistan (37%), Sri Lanka (31%), and Pakistan (16%). The domestic price of rice rose broadly in line with the 21% increase in global prices in Indonesia (20%), Bangladesh (19%), and Pakistan (19%) during this 6 month period. Several countries have intervened to temper this pass-through. In Algeria, taxes and import du- ties on sugar and edible oil were sharply reduced in January 2011 due to double-digit price rises. In Indonesia, the govern- ment reduced taxes on sugar and increased subsidies to local cooking oil producers.

In cases where no domestic price data were available, we used import shares reported in version 7 of the GTAP data- base (Hertel, 1997) to link global prices (Table 1) with domes- tic consumer prices. This approach is consistent with imported goods being imperfect substitutes for domestically-produced goods and the changes in domestic prices being a weighted average of the prices of imported and domestic goods.

4. RESULTS

In Table 3, we first present the estimated effects of price changes on the standard international extreme poverty head- count of people living below $1.25 per day. The first column

le food commodities: June–December 2010

Country Commodity Change in price, %

Nicaragua Maize 0 Rice �1 Vegetables 90

Niger Millet �27 Nigeria Sorghum �13

Millet 2 Pakistan Rice 19

Wheat 16 Panama Rice 0

Wheat 11 Peru Rice 6

Wheat �1 Maize �3

Rwanda Vegetables 7 Maize 19

Sri Lanka Rice 12 Wheat 31

Tajikistan Wheat 37 Potatoes �20

Uganda

Vietnam Rice 46 Zambia Maize �4

Table 3. Poverty headcount changes, in percentage points measured at poverty line of $1.25 per day

Initial poverty rate Out of poverty Into poverty Net change Net change w/wage impacts

Albaniaa 0.8 0.00 0.50 0.50 0.39 Armeniaa 10.6 �0.04 0.67 0.63 0.43 Bangladesha,b 50.5 �0.49 2.08 1.59 1.49 Belizea 33.4 0.00 1.15 1.15 0.96 Cambodiaa,b 40.2 �0.01 0.05 0.03 �0.15 Côte d’Ivoirea 23.3 �0.67 0.67 0.00 �0.25 Ecuador 15.8 �0.01 0.05 0.04 0.01 Guatemala 12.6 0.00 1.50 1.50 1.41 India 39.6 �0.41 1.19 0.77 0.69 Indonesia 7.5 �0.24 0.57 0.33 0.34 Moldovaa 8.1 0.00 0.32 0.32 0.28 Mongoliaa 22.4 �0.69 1.37 0.68 0.68 Malawib 73.9 0.00 1.03 1.03 1.04 Nigera,b 65.9 �0.31 0.40 0.09 0.07 Nigeriaa 64.4 �0.29 1.06 0.76 0.70 Nicaragua 45.1 �1.59 2.09 0.50 0.24 Nepala,b 55.1 �0.06 0.21 0.15 0.15 Pakistana 22.6 0.00 1.92 1.92 1.75 Panamaa 9.4 �0.07 0.11 0.05 0.05 Peru 7.9 0.00 0.12 0.12 0.09 Rwandaa,b 76.6 �0.03 0.22 0.18 0.18 Sri Lankaa 14.0 �0.05 1.49 1.44 1.29 Tajikistanb 21.5 �0.05 3.68 3.62 3.18 Timor-Lestea 52.9 0.00 0.12 0.12 0.12 Ugandab 51.5 �0.77 1.92 1.15 0.76 Vietnam 21.4 �2.92 1.68 �1.24 �1.34 Yemena 17.5 �0.01 0.81 0.79 0.78 Zambiab 61.8 0.00 0.27 0.27 0.43

a Net-food importer (GTAP database, 2007). b Low-income country.

Figure 1. Changes in the poverty headcount at $1.25 per day, percentage points.

2306 WORLD DEVELOPMENT

of the table shows the initial poverty headcount. The second shows the gross movement of people out of poverty in percent- age points. The third gives the gross movement into poverty, while the fourth shows the net change in the poverty rate. The final column shows the net change in the poverty rate allowing for the impacts of commodity price changes on wage rates for unskilled labor. The results for the net change in the poverty headcount are also presented in rank order by country in Figure 1. To help interpret these measures, we present the estimated price changes by commodity and the contributions

of each commodity to the overall change in poverty in Appen- dix Tables 9–13.

As expected, there is enormous variation in the poverty im- pacts between countries. Part of this is driven by the differ- ences in the initial poverty headcount and the distribution of incomes close to the poverty line. Another factor relates to the difference in the extent to which changes in international food prices are passed through into the country. A third differ- ence arises from the structure of the economy. If for instance, many poor people are net sellers of food, then an increase in

Table 4. Poverty gap and squared poverty gap changes, in percentage points measured at a poverty line of $1.25 per day

Change in poverty gap, %

Change in squared poverty gap, %

Albania 0.08 0.02 Armenia 0.25 0.14 Bangladesh 1.28 0.82 Belize 0.47 0.32 Cambodia 0.01 0.00 Côte d’Ivoire 0.06 0.03 Ecuador 0.04 0.02 Guatemala 0.33 0.11 India 0.53 0.25 Indonesia 0.10 0.04 Moldova 0.10 0.04 Mongolia 0.38 0.25 Malawi 0.74 0.49 Niger 1.16 13.50 Nigeria 0.63 0.46 Nicaragua 0.24 0.16 Nepal 0.15 0.11 Pakistan 0.50 0.18 Panama 0.01 0.00 Peru 0.04 0.02 Rwanda 0.36 0.38 Sri Lanka 0.46 0.17 Tajikistan 0.98 0.41 Timor-Leste 0.05 0.03 Uganda 0.99 0.74 Vietnam �0.19 �0.02 Yemen 0.21 0.09 Zambia 0.20 0.14

ESTIMATING THE SHORT-RUN POVERTY IMPACTS OF THE 2010–11 SURGE IN FOOD PRICES 2307

food prices is likely to lower the poverty rate. If, on the other hand, more poor people are net buyers of food, then it is likely that higher food prices will increase the poverty headcount.

We find it useful to consider the countries in terms of the net impact of food prices on poverty, and hence follow the order- ing in Figure 1. Higher food prices lead to increased poverty in all countries except Vietnam which is a striking exception, as it was in Ivanic and Martin (2008) and numerous earlier studies (Vu & Glewwe, 2009). The food price increases in this country translate into a reduction in poverty, despite substantial in- creases in poor consumers’ costs of living, because many poor households are net sellers of commodities whose prices have risen most significantly. For the next nine countries in the graph (Côte d’Ivoire, Cambodia, Ecuador, Panama, Niger, Peru, Timor Leste, Nepal, and Rwanda), the increases in the headcount poverty rate are very small—less than 0.20. In some cases, such as Côte d’Ivoire, the zero net change in poverty re- flects significant churning around the poverty line, with 0.67% of net buyer households falling into poverty mainly because of higher rice prices and 0.67% of households escaping poverty, mainly due to the benefits of higher prices of cash crops such as cotton, coffee, tea, and cocoa (Appendix Table 10). A quite different dichotomy in poverty impacts is observed in Niger where rising prices of sugar and wheat put some people into poverty, while declining prices of other grains raises a similar number of people out of poverty. Poverty rates in Cambodia, Peru, Timor Leste, and Rwanda remain mainly unchanged be- cause most of the price changes were quite modest, with the exception of those commodities which are not major consump- tion or production items of the poor (wheat in Cambodia, Ti- mor Leste, and Rwanda, soybeans and cotton in Peru, and maize in Rwanda). In Ecuador, Panama, and Nepal all agri- cultural commodity prices appear to be insulated from the glo- bal price changes, resulting in very small increases in poverty in these countries as well.

The next five countries in the graph—Zambia, Moldova, Indonesia, Albania, and Nicaragua—have changes in the pov- erty headcount between 0.2 and 0.5% points. In Zambia the relatively small change in domestic price of maize, which is an important consumption item, and in Moldova a large in- crease in the price of sugar, which is a relatively important consumption commodity too, both result in small increases in poverty. By contrast, in Indonesia local prices of key items such as rice rose by around 20% between June and December 2010, and the overall impact of only 0.33% points is the result of a gross movement into poverty of 0.57% points offset by movement of 0.24% of the population out of poverty.

We estimate that another set of countries—Armenia, India, Mongolia, Nigeria, and Yemen—had increases in headcount poverty of between 0.5% and 1.0% points. In Armenia, the 0.67% points among net food buyers had very little offsetting poverty reduction among net sellers since most of the poverty impacts come from the significantly higher prices of potatoes, sugar and oils and fats, little of which are produced and sold by the poor. There is a similar pattern in Yemen where the in- crease in prices was driven by higher sugar, rice, and wheat prices. By contrast, in India, Mongolia, and Nigeria, there were sizeable flows in each direction. For instance the 0.68% point net increase in Mongolia reflects 1.37% of the popula- tion entering poverty mainly due to the higher prices of wheat and sugar, while 0.69% leave poverty as a result of lower price of meat. By contrast, in Malawi, domestic food prices—partic- ularly of fruits, rice, and cassava—fell, however most of the poverty impacts were felt through higher prices of wheat, su- gar and oils and fats.

Seven countries in our sample are estimated to experience poverty headcount increases of above one percentage point due to the surge in food prices since June 2010. In Belize, the negative impact of higher wheat prices is a major factor be- hind the 1.15% increase in poverty. In Uganda, the price of vegetables rose by 38.1% which, along with the higher prices of sugar and oils and fats, and maize have contributed to driv- ing almost 2% of the population into poverty through adverse impacts on net buyers, while nearly 0.7% of the population is raised out of poverty through benefits to net sellers of maize. In Sri Lanka, the rise in poverty among net buyers is 1.49%, due to the rising prices of rice, sugar, and wheat, and only a minuscule 0.05% of the population is raised out of poverty by higher prices for products that they sell.

In Bangladesh, rice and wheat prices rose by 19% and 45%, respectively between June and December 2010. While benefits to net sellers of higher prices reduced poverty by almost 0.5%, 2% of the population was thrown into poverty by the adverse impacts on net rice buyers. Similarly, in Pakistan, poverty is estimated to have been increased by almost 2% points largely due to double digit increases in wheat prices, and partly rice and fats and oils prices. Tajikistan was the country with the largest estimated overall increase in poverty in our sample of 28 countries. The price of wheat, which constitutes 54% of cal- orie consumption (World Bank, 2011a) rose by 37%. We esti- mate that this, along with price changes of sugar and oils and fats, led to a net increase in poverty of 3.6%.

The increase in the number of poor, discussed above, is only one measure of the impact of higher food prices on poverty. Earlier work (Dessus et al., 2008) has shown that the existing poor are likely to be made worse off during such crises—a fact better measured by the impact of higher food prices on the

2308 WORLD DEVELOPMENT

poverty gap or severity measures. In most cases, the change in the poverty gap (given in Table 4) is smaller than the change in the poverty headcount. However, there are several cases in which changes in the headcount and the poverty gap measures give quite different interpretations. The most striking such case is Niger, where a very small increase (0.09% points) in the headcount is associated with an increase of 1.16% points in the poverty gap and of 13.5% points in the poverty gap squared. In Rwanda, the poverty gap increases more than the headcount, but by a much smaller multiplier.

5. GLOBAL POVERTY ESTIMATES

We take advantage of the size of our country sample which represents about forty percent of the developing countries by population and use it to extrapolate the changes observed in the sample to all developing countries. To account for possible differences between low- and middle-income countries, we cal- culate population-weighted poverty headcount changes sepa- rately for the two groups of countries included in our sample and apply them to their full populations. The results of this global extrapolation are shown in Table 5. They indi- cate that the average poverty change is 1.1% points in low-in- come countries and 0.7% points among middle income countries. Applying the average changes to the total popula- tions of the groups, we estimate that the recent increase in food prices raised poverty by 9.5 million people among low-in-

Figure 2. Distribution of movements across the poverty line

Table 5. Global extrapolation of

Population-wtd poverty change, share of population

in percentage points

Population, in millions

Escaping poverty

Entering poverty

Total change

Low income countries �0.4 1.5 1.1 828 Middle income countries �0.4 1.2 0.7 4,758 All developing countries �0.4a 1.2a 0.8a 5,586

a These average values are not used in the calculations of the poverty headcou

come countries and 34.1 million among middle-income coun- tries, for a total poverty increase of 43.7 million.

In addition to showing the net changes in poverty, Table 5 decomposes them further into those people who escape pov- erty as a result of the food price changes and those who are made poor in this process. These numbers are very illuminat- ing because they suggest that most of the observed difference between the poverty impacts of low- and middle-income coun- tries lies in the greater importance of net-food consumers near the poverty line in low-income countries, which results in a much greater number of people in this group being pushed into poverty as a result of higher food prices. On the other hand, the role of net-sellers of food is largely similar in both groups. Extrapolating these sample averages, we estimate that 67.7 million of people became poor as a result of the recent changes in food prices while 24.0 million people were removed from poverty as a result of the same price changes.

The size of the observed poverty change raises an important question of the depth of the poverty impact—if only the peo- ple initially right at the poverty line were reduced to poverty by the price shock then the change in the poverty headcount may not be a reliable indicator of the overall impact on the poor. In order to address this question, we plot the distribu- tion of households whose poverty status was affected by the price shock in Figure 2. Based on the original per capita household income, we show the distribution of those house- holds who either moved above the poverty line if they were originally below the poverty line or vice versa. Even though the figure confirms that most of the movements across the

by initial income level relative to the poverty line = 1.

changes in poverty headcount

Global extrapolation, in millions

People escaping poverty

People entering poverty

Combined impact

Combined impact with wages

�3.1 12.6 9.5 8.6 �21.0 55.1 34.1 30.3 �24.0 67.7 43.7 38.9

nt changes.

ESTIMATING THE SHORT-RUN POVERTY IMPACTS OF THE 2010–11 SURGE IN FOOD PRICES 2309

poverty line were concentrated near the poverty line, many households whose initial incomes were more than several per- centage points above the poverty line were also drawn into poverty as a result of the price shock.

6. ROBUSTNESS CHECKS

Our set of robustness checks aims to assess the responsive- ness of our results to changes in the underlying data and our assumptions. As the first robustness check, we replicate our analysis with each country removed from the sample one at a time in order to evaluate the responsiveness of our conclu- sions to any single country’s results. Our second check in- volves the omission of country-level domestic price information in order to assess the role of the assumed pass- through parameters in our results. Finally, we verify the pov- erty impacts for a wider range of poverty lines in order to see whether the conclusions of our study are sensitive to differing definitions of poverty.

To analyze the sensitivity of our results to individual coun- tries’ results, we repeat our analysis 28 times, each time omit- ting one country from our sample. Our plot of the distribution of the obtained global poverty changes (Figure 3) shows that our result of an increase of 43.7 million people is largely insen- sitive to the omission of any single country included in our

35 40

0 .0

0 0

.0 5

0 .1

0 0

.1 5

0 .2

0 0

.2 5

0 .3

0

Global change in pover

D en

si ty

Figure 3. Kernel-smoothed distribution of povert

Table 6. Global poverty estimat

Poverty line, PPP USD/person/day

Net change in global poverty, millions of people

1.00 46.5 1.13 47.0 1.19 44.4 1.25 43.7 1.31 42.1 1.38 41.5 2.00 26.9

sample. The two countries that are found to impact our results most are Vietnam, whose omission from the sample would raise the estimate to 47.9 million people, and Pakistan, whose exclusion would lower the final count to 38.2 million people.

As a second robustness check, we calculate global poverty implications of higher food prices without using country-spe- cific information on domestic price changes for the commodi- ties for which this is available. Redoing our calculations using only the global price changes scaled by the domestic import shares to capture the impact on consumer prices, we arrive at a lower estimate of poverty change of 30.9 million people.

As an additional check on our results, we calculate the responsiveness of our estimates to poverty lines higher or low- er than the internationally standard $1.25 per day measure for extreme poverty. We repeat our calculations for four addi- tional poverty lines in the range of the standard definition of extreme poverty. Our results (Table 6) show that while the esti- mates of global poverty vary depending on the poverty line chosen—the poverty estimates decline as the poverty line rises—the results are not vastly different within the range from $1 to $1.38. They do, however, decline considerably when we move to a poverty rate of $2 per day, around which there ap- pears to be less vulnerability to changes in food prices than there is in the range around $1.25 per day.

The observed negative relationship between the poverty im- pacts and the poverty line appears to arise primarily from the

45 50

ty (millions of people)

y impacts with individual countries removed.

es for different poverty lines

Gross reductions in poverty, millions of people

Gross increases in poverty, millions of people

�19.2 65.7 �20.9 67.9 �23.4 67.8 �24.0 67.7 �25.2 67.3 �20.9 67.9 �23.1 50.0

2310 WORLD DEVELOPMENT

declining share of expenditure on food as incomes rise. While the shares of net food sales rises with income, this appears to be a less important determinant of the overall result than the decline in the share of food in total consumption. These find- ings are reflected in the last two columns of Table 6 which show little relationship between gross reductions in poverty and poverty line, and a much stronger negative relationship between the poverty line and gross increases in poverty.

As a final check of the robustness of our results with regard to a wider range of poverty lines, in Figure 4 we present a marginal poverty impact chart for the sample of 28 countries, in which we plot the change in poverty for poverty rates spanning the interval from 0% to 100% of the population. Because for each poverty rate in this interval we observe a poverty increase as a result of the observed food price in- creases, it appears that the recent food price shock was neg- ative for the poor at any conceivable poverty definition. The downward slope of the line suggests that the marginal poverty impacts were consistently greater at more rigorous

C ha

ng e

in p

ov er

ty fo

llo w

in g

th e

si m

ul at

ed p

ric e

s

ho ck

,% p

oi nt

s

Initial popu

Figure 4. Change in poverty inci

Figure 5. World Bank Food Price Index and

poverty lines that classify increasingly smaller shares of the population as poor.

7. COMPARISON WITH THE IMPACTS OF THE 2008 FOOD PRICE CRISIS

The recent surge in food prices, which has raised the World Bank Food Price Index above its 2008 peak, raises an impor- tant question of how the poverty impacts of this surge might relate to those of the earlier food crisis in 2008. The poverty impacts of the 2008 food crisis were extensively analyzed by Ivanic and Martin (2008) who, using the price change from 2005–08, found its average impact to be an increase in extreme poverty of about 105 million. Because this estimate is much greater than the estimate of this study which predicts an in- crease of extreme poverty by 44 million people as a result of the June–December 2010 food price crisis, in this section we explain the differences between these two estimates by

lation in poverty

dence by initial poverty rate.

price index weighted by poverty impacts.

ESTIMATING THE SHORT-RUN POVERTY IMPACTS OF THE 2010–11 SURGE IN FOOD PRICES 2311

analyzing the differences in magnitudes of the price changes as well as their composition.

Global food prices reached very similar historical highs dur- ing the period of January 2005–March 2008 considered in Ivanic and Martin (2008), and the more recent period of June 2010–December 2010 analyzed here, but the relative changes during these periods were quite different: the increase in food prices over the 3 years leading up 2008 represented a consider- ably greater relative price change from the historically low food prices observed prior to 2005 while the food price in- crease in 2010 occurred when the overall food prices were al- ready at double their historical levels (Figure 5). Using the available values of the World Bank’s Food Price Index during these periods, we estimate that the 2008 food price increase raised average global food prices by 118%, while the food price increase of 2010 raised food prices by 37%.

Most of the difference between the poverty changes re- ported in Ivanic and Martin (2008) and in this study appear to be due largely to the differing scale of the global price shocks over these two time spans. However, the poverty change of 44 million for 2010 is somewhat higher than would have been predicted by applying the 2008 ratio between the percentage increase in poverty and the observed change in food prices. In order to examine the possible positive or neg- ative poverty bias of the more recent food price increase rel- ative to the price shock observed in 2008, in Figure 5 we present an alternative food price index weighted by individual commodities’ marginal impacts on global poverty. Examina- tion of the figure shows that even though these two food price indices appear strongly correlated, sometimes the rate of change in the food price index differs greatly from the associ- ated poverty rate change depending on how the prices of the food items important to the poor are affected—for example between November 2008 and June 2010 global food prices rose noticeably while leaving global poverty unchanged. Be- cause of the heterogeneity of individual food price changes, the increase in poverty, corresponding to the 46% increase in the poverty-weighted food price index, between June 2010 and December 2010 was much steeper than the 37% increase in overall food prices.

8. CONCLUDING REMARKS

This study concludes that the sudden food price surge in the second half of 2010 is likely to have led to an increase in the number of poor globally though with significant differences

across countries. These differences are partly related to the wide variation in the extent of transmission of global prices to local prices. We show that in countries where these sharp price increases were matched by commensurate increases in lo- cal prices (e.g., Tajikistan and Pakistan) there were significant increases in poverty. Net sellers of food benefit from the higher prices but they are typically medium and large farmers, except in Vietnam where a significant share of the rural poor are net producers of rice. On balance, the adverse welfare impact on the net consumers outweighs the benefits to net producers resulting in an increase in the number of poor and in the depth of poverty. A second factor behind these variations is that the prices of some cash crops have increased, thereby moving some poor farmers out of poverty e.g., cotton producers in Côte D’Ivoire. Moreover the results show that those who are already poor were disproportionately affected by the in- crease in prices as the share of food in their consumption bas- ket is higher than for the nonpoor.

These results do not take into account supply response by producers or impacts of commodity prices on wage rates. Ear- lier studies which have taken these into account suggest that these effects only partially compensate for the adverse welfare impact described in this paper. Over the longer term, wages and incomes will adjust but it is the impact of sudden spikes which can have serious long-term consequences especially for infants and pregnant women.

The policy implications relate to the importance of cushion- ing poor households from sharp food price spikes. There are various aspects to this which go beyond the poverty impacts assessed in this paper, but also relate to the adverse nutritional impacts discussed in the introduction. First, countries can lim- it their exposure to global commodity price fluctuations by entering into forward contracts and other market-based hedg- ing mechanisms. Second, the impact of local price volatility can be mitigated if households have access to safety net pro- grams. Third, investments in domestic agricultural productiv- ity, where it makes environmental sense, can increase domestic food supply. Fourth, strengthening the management of food stocks may be able to help smooth domestic price volatility. Fifth, nutritional interventions targeting infants and pregnant mothers and the fortification of food grains can mitigate the detrimental impact of sharp increases in food prices on nutri- tional outcomes. Finally, these shocks underscore the impor- tance of broad-based economic growth that raises incomes, thereby reducing the vulnerability of households to sudden changes in food prices.

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APPENDIX A

See Appendix Tables 7–13.

ed in the study

Year Population, millions

Number of households

Number of people

Poverty rate, %

2005 3.2 1,671 4,814 0.8 2005 3.3 6,815 28,502 10.6 2000 150 7,440 38,518 40.2 2009 0.3 1,546 6,794 33.5 2003 13.4 14,984 74,719 50.5 2002 21.6 10,798 57,906 23.3 2006 14.3 13,581 55,666 15.8 2006 14.4 13,686 68,739 12.6

2002–04 1193.6 301,085 1,499,010 43.8 2007 230.0 12,999 69,624 7.5 2004 15.7 11,280 52,707 73.9 2009 3.6 5,532 15,066 8.1 2002 2.8 3308 14789 22.4 2002 28.6 5,071 28,099 55.1

ida 2005 5.8 6,619 36,642 45.1 enages 2007 15.2 4,000 28,683 65.9

2003 158.3 19,121 92,501 64.4 2005 171.7 15,453 79,354 22.6 2003 3.4 6362 26,434 9.4 2007 29.5 22,201 95,466 7.9 2005 10.4 6,900 34,785 76.6 2007 20.4 4,633 20,290 14.0 2007 7.1 4,644 29,412 21.5 2000 1.2 1,800 9,113 52.9 2005 31.8 7,425 42,220 51.5 2004 86.9 9,188 40,438 21.4 2006 22.5 13,136 98,941 17.5 2002 13.3 4,166 23,074 61.9

– 2,272.3 535,444 2,672,306 38.8

Table 8. Agricultural commodities considered in the study

Rice Cattle Wheat Other animal products Other grains Eggs Maize Fowl Sorghum Swine Cassava Raw milk Plantains and bananas Wool Potatoes Forestry Other vegetables Fish Other fruits Other cattle meat Other oil seeds Beef Peanuts Other meat Soybeans Poultry Sugar cane/beets Pork Plant based fibers Oils and fats Other crops Dairy Coffee, tea, cocoa Sugar Tobacco Other food Other bovine animals Tobacco and beverages (consumers)

Table 9. Estimated and observed domestic price changes for key agricultural commodities, percent

Sugar cane, beets

Beef Other bovine meat

Cattle Fish Maize Other grains

Coffee, tea,

cocoa

Cotton Rice Sugar Fruits Potatoes Vegetables Oils and fats

Wheat

Albania 1.5 1.3 0 0.5 �5.3 29.9 6.3 0.4 54.8 17.0 75.8 �7.0 0 0 26.0 33.3 Armenia 0.2 2.2 0 0 0 7.0 0.3 2.5 2.5 14.2 19.8 �3.7 82.3 0 23.3 11.1 Bangladesh 0 16.1 0 0 0 46.8 14.6 0.4 19.7 18.9 17.0 �7.3 0 0 16.8 45.0 Belize 0 0.1 0 0 0 4.4 0.3 0.1 3.6 0 0 �0.4 0 0 1.5 64.2 Cambodia 0.1 0.4 0 0.1 �0.2 4.6 0.2 0.2 10.7 0 5.6 �5.1 0 0 13.8 67.2 Cote D’Ivoire 0 1.1 0 0.1 �0.1 0.4 0.2 8.3 46.3 7.5 75.0 �0.7 0 0 15.7 66.9 Ecuador 0 0.1 0 0 �0.2 �2.2 2.6 0.7 17.2 0 2.0 �5.1 0 0 8.6 0 Guatemala 0 0.9 0 0.2 �0.3 �0.9 1.0 1.1 49.7 �0.1 0.8 �2.4 0 1.7 11.2 65.5 India 0 0.1 0 7.5 �0.1 0 0 0.3 4.5 4.5 7.7 �3.7 0 0 13.3 3.7 Indonesia 0 3.9 0 2.0 �0.1 11.5 2.6 0.4 5.6 19.5 18.9 �2.7 0 0 1.8 0.6 Malawi 50.6 0.5 0 9.6 0 2.1 0.5 1.6 5.7 �15.8 37.3 �17.8 0 0 25.2 67.9 Moldova 0 2.6 0 0 �1.2 �2.0 7.6 13.0 46.1 1.5 37.6 �0.1 0 0 7.9 5.3 Mongolia 0.1 4.1 �37.5 6.8 �0.6 56.0 26.3 12.5 54.5 11.3 28.9 �3.5 0 0 7.4 34.4 Nepal 0 3.7 0 0.8 0 2.5 2.5 0.9 0.1 0.4 8.7 �7.1 0 0 10.9 9.4 Nicaragua 18.7 1.9 0 0 �0.4 0.2 0.5 5.2 0.3 �1.2 0.3 �5.2 0 89.6 27.1 68.0 Niger 0 0.1 0 0 0 0.1 �27.3 6.8 20.6 1.2 68.1 �0.1 0 0 3.2 59.3 Nigeria 0.2 2.1 0 0 0 0 2.0 12.1 41.0 14.7 76.0 0 0 0 15.6 67.8 Pakistan 12.5 1.3 0 0 0 60.4 27.5 2.3 54.5 18.5 0.5 �1.2 0 0 10.1 16.2 Panama 0 0 0 3.7 0 0.1 0 0.3 7.1 0 0.6 �15.3 0 0 0.3 10.8 Peru 0 0.4 0 0.2 �0.1 �2.9 1.9 0.2 34.4 5.9 5.1 �2.0 0 0 9.8 �0.8 Rwanda 0 0 0 0 0 19.0 0 0 0.1 0.5 3.7 �0.1 0 6.8 0.7 25.5 Sri Lanka 0 0.2 0 0.1 �0.3 48.3 14.7 0 50.4 11.8 68.6 �2.3 0 0 23.2 31.4 Tajikistan 1.4 0.3 0 0 �0.7 0.8 0.3 11.7 0.1 6.5 75.8 �0.2 �20.2 0 25.6 37.1 Timor Leste 0 0.2 0 0.1 0 0.1 0 0 2.7 0 0.3 �0.3 0 0 4.8 67.7 Uganda 15.7 0 0 0 0 66.7 0.1 12.5 38.6 6.2 16.9 �0.5 0 38.1 30.8 67.8 Vietnam 0 0 0 0.1 0 45.0 7.9 0.4 55.0 45.9 3.4 �3.3 0 0 24.4 67.9 Yemen 0 0.3 0 0 �0.1 2.1 0.1 0.2 0.1 15.2 10.8 �0.4 0 0 4.6 3.0 Zambia 0 0.1 0 0.1 0 �3.5 1.3 0.3 0.9 5.2 1.8 �17.6 0 0 11.4 7.0

Source: authors’ calculations.

ESTIMATING THE SHORT-RUN POVERTY IMPACTS OF THE 2010–11 SURGE IN FOOD PRICES 2313

Table 10. Poverty impacts of 10% changes in prices for key commodities, percentage points

Sugar cane, beets

Other bovine meat

Cattle Maize Other grains

Sorghum Coffee, tea, cocoa

Other oil seeds

Soybeans Cotton Rice Sugar Fruits Potatoes Vegetables Oils, fats Wheat

Albania 0 0 �0.10 0 0 0 0 0 0 0 0 0 �0.10 0 �0.02 0.08 0 Armenia 0 0 �0.02 0 0 0 0.06 0 0 0 0.03 0.03 �0.04 0.03 0.04 0.05 0.08 Bangladesh �0.03 0.01 �0.01 0 0 0 0.05 0 0 �0.01 0.67 0.05 0.07 0 0.25 0.14 0 Belize 0 0 �0.25 0 0 0 0.03 0 0 0 0.24 0 0 0.18 0.29 0.03 0.29 Cambodia �0.01 0 �0.18 �0.04 0 0 �0.14 �0.05 �0.05 0 �1.37 0 �0.08 0 �0.12 0 0 Côte D’Ivoire 0 0 0 0.08 0 0 �0.37 0 0 �0.18 0.42 0.02 �0.06 0.03 0.17 0.02 0.03 Ecuador 0 0 �0.02 �0.11 0 0 �0.06 0 0 0 0.11 0.04 �0.01 0.03 0.09 0.06 0.01 Guatemala 0 0 �0.01 0.07 0.07 0 0.02 0 0 0 0.03 0.19 0 0.03 0.16 0.05 0.26 India �0.06 0.06 0 �0.02 0.01 0 0.14 �0.06 �0.02 �0.05 0.45 0.16 0.02 0.05 0.49 0.33 0.10 Indonesia 0 0 �0.01 0 0 0 0.05 0 0 0 0.04 0.07 0.04 0 0.08 0.07 0 Malawi 0 0.01 �0.01 0.16 0 0 0.01 0 0 �0.01 0.02 0.12 0 0 0.16 0.09 0.07 Moldova 0 0 0 0 0.01 0 0.02 0 0 0 0.07 0.09 0.05 0.10 0.14 0.15 0.18 Mongolia 0 0.57 �0.14 0 0.05 0 0.10 0 0 0 0.16 0.16 0.05 0.09 0.03 0 0.43 Nepal �0.05 0.11 �0.11 0.02 0 0 0.02 �0.05 0.04 0 0.16 0.08 0.01 0 0.04 0.10 �0.03 Nicaragua 0 0.02 0 �0.14 0 0 �0.10 0 0 0 0.09 0.03 0 0.01 �0.08 0.04 0 Niger �0.01 0.02 �0.05 0.06 0.02 0 0.05 0 0 0 0.22 0.04 �0.02 0.01 0 0.09 0 Nigeria �0.02 0.01 �0.04 �0.01 �0.07 0 �0.03 0.01 0 �0.05 0.17 0 0.13 0 0.04 0.16 0.08 Pakistan 0 0.02 �0.03 0 0.01 0 0.10 0 0 0 0.17 0.33 0.06 0.11 0.37 0.30 0.71 Panama 0 0 0.03 �0.02 0 0 0.05 0 0 0 0.20 0.13 0 0 0.06 0.09 0.06 Peru 0 0.01 �0.05 �0.03 0 0 0 0 0.01 0 0.07 0.03 �0.03 �0.01 0 0.04 0.10 Rwanda 0 0 �0.05 0.03 0 �0.02 �0.02 0 0 0 0.01 0.03 0.01 0.06 0.11 0.04 0.02 Sri Lanka 0 0 0 0 0 0 0.05 0.13 0 0 0.40 0.20 0.27 0.11 0.26 0.11 0.12 Tajikistan 0 0 0.01 0 0 0 0.03 0 0 �0.02 0.11 0.14 0.18 0.10 0.21 0.37 0.21 Timor Leste 0 0 �0.26 0.24 0 0 0.18 0.19 0 0 0.92 0.26 �0.05 0 0.54 0.24 0 Uganda �0.02 0.02 �0.04 0 0 �0.03 �0.04 0.01 0 �0.03 0.01 0.13 �0.13 0 0.16 0.03 0 Vietnam �0.07 0 �0.33 �0.15 0 0 �0.07 �0.01 0.05 0 �0.29 0.12 �0.13 �0.01 �0.01 0.12 0.01 Yemen 0 0.04 0 0.01 �0.01 0 0.05 0 0 0 0.16 0.20 0.03 0.06 0.29 0.07 0.69 Zambia 0 0 �0.04 0.12 0 0 0 0 0 0 0.05 0.17 0 0 0.18 0.17 0.09

Source: authors’ calculations.

2 3

1 4

W O

R L

D D

E V

E L

O P

M E

N T

Table 11. Gross reductions in the poverty headcount for key commodities, percentage points

Sugar cane, beet

Beef Other bovine meat

Cattle Fish Maize Other grains Coffee, tea, cocoa

Cotton Rice Sugar Fruits Potatoes Vegetables Oils and fats

Wheat Total

Albania 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Armenia 0 0 0 0 0 0 0 0 0 0 0 0 �0.03 0 0 �0.02 �0.04 Bangladesh 0 �0.01 0 0 0 0 0 0 �0.03 �0.52 0 �0.04 0 0 0 �0.11 �0.49 Belize 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cambodia 0 0 0 0 0 �0.01 0 0 0 0 0 0 0 0 0 0 �0.01 Côte D’Ivoire 0 0 0 0 0 0 0 �0.27 �0.5 �0.01 0 �0.01 0 0 0 0 �0.67 Ecuador 0 0 0 0 �0.01 0 0 �0.01 0 0 0 �0.01 0 0 0 0 �0.01 Guatemala 0 0 0 0 0 �0.03 0 �0.01 0 0 0 0 0 �0.02 0 0 0 India 0 0 0 �0.01 0 0 0 0 �0.02 �0.23 0 �0.02 0 0 0 �0.12 �0.41 Indonesia 0 0 0 0 0 0 0 0 0 �0.25 0 �0.01 0 0 0 0 �0.24 Malawi 0 0 0 �0.01 0 �0.01 0 0 �0.01 �0.07 0 �0.01 0 0 0 0 0 Moldova 0 0 0 0 0 �0.01 0 0 0 0 0 0 0 0 0 0 �0.69 Mongolia 0 �0.1 �2.18 �0.14 0 0 0 0 0 0 0 0 0 0 0 0 0 Nepal 0 0 0 �0.02 0 0 0 0 0 0 0 �0.03 0 0 �0.02 �0.05 �0.06 Nicaragua 0 0 0 0 0 �0.02 0 �0.05 0 �0.02 0 0 0 �1.65 0 0 �1.59 Niger 0 0 0 0 0 0 v0.42 0 0 0 0 0 0 0 0 0 �0.31 Nigeria 0 0 0 0 0 0 �0.01 �0.06 �0.09 �0.16 0 0 0 0 0 0 �0.29 Pakistan 0 0 0 0 0 0 0 0 0 0 0 �0.03 0 0 0 0 0 Panama 0 0 0 �0.03 0 0 0 0 0 0 0 �0.03 0 0 0 0 �0.07 Peru 0 0 0 0 0 0 0 0 0 0 0 �0.01 0 0 0 �0.01 0 Rwanda 0 0 0 0 0 �0.02 0 0 0 0 0 0 0 �0.04 0 0 �0.03 Sri Lanka 0 0 0 0 0 0 0 0 0 �0.11 0 �0.05 0 0 0 0 �0.05 Tajikistan 0 0 0 0 0 0 0 0 0 �0.02 0 0 �0.37 0 0 �0.02 �0.05 Timor Leste 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Uganda �0.04 0 0 0 0 �0.74 0 �0.13 �0.13 �0.05 0 0 0 �0.2 0 0 �0.77 Vietnam 0 0 0 0 0 �0.7 0 0 �0.03 �2.14 0 �0.01 0 0 0 0 �2.92 Yemen 0 0 0 0 0 �0.01 0 0 0 �0.01 0 0 0 0 0 0 �0.01 Zambia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Source: authors’ calculations.

E S

T IM

A T

IN G

T H

E S

H O

R T

-R U

N P

O V

E R

T Y

IM P

A C

T S

O F

T H

E 2

0 1

0 –

1 1

S U

R G

E IN

F O

O D

P R

IC E

S 2

3 1

5

Table 12. Gross increases in poverty headcount for key commodities, percentage points

Sugar cane, beets

Beef Other bovine meat

Cattle Fish Maize Other grains Coffee, tea, cocoa

Cotton Rice Sugar Fruits Potatoes Vegetables Oils and fats

Wheat Total

Albania 0 0 0 0 0 0 0 0 0 0 0.08 0 0 0 0.35 0.06 0.5 Armenia 0 0.01 0 0 0 0 0 0.03 0 0 0.11 0.03 0.17 0 0.25 0.08 0.67 Bangladesh 0 0.13 0 0 0 0 0 0 0 1.8 0.05 �0.02 0 0 0.24 0.09 2.08 Belize 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.15 1.15 Cambodia 0 0 0 0 0 0 0 0 0 0 0 0.05 0 0 0 0 0.05 Côte D’Ivoire 0 0.02 0 0 0 0 0 0 0 0.36 0.19 0.04 0 0 0.02 0.16 0.67 Ecuador 0 0 0 0 0 0.01 0 0 0 0 0 0.02 0 0 0.04 0 0.05 Guatemala 0 0 0 0 0 0.03 0.01 0 0 0 0.02 0 0 0.07 0.02 1.4 1.5 India 0 0 0 0.02 0 0 0 0 0 0.37 0.15 0.01 0 0 0.47 0.19 1.19 Indonesia 0 0.01 0 0 0 0 0 0 0 0.47 0.08 0.02 0 0 0.02 0 0.57 Malawi 0 0.01 0 0 0 0.05 0 0 0 0.01 0.32 0 0 0 0.25 0.53 1.03 Moldova 0 0 0 0 0 0 0.01 0.03 0 0.03 0.16 0 0 0 0.11 0.04 1.37 Mongolia 0 0.05 0.82 0.09 0 0 0.09 0.05 0 0.16 0.22 0 0 0 0 1.55 0.32 Nepal 0 0.04 0 0.04 0 0 0 0 0 0.02 0.04 0.02 0 0 0.07 0.04 0.21 Nicaragua 0 0.02 0 0 0 0 0 0.03 0 0.01 0.01 0.01 0 1.89 0.28 0 2.09 Niger 0 0 0 0 0 0 0.12 0 0 0.07 0.15 0 0 0 0.01 0.15 0.4 Nigeria 0 0.03 0 0 0 0 0.01 0.01 0 0.39 0 0 0 0 0.21 0.43 1.06 Pakistan 0 0.02 0 0 0 0.02 0.02 0.04 0 0.24 0.02 0.03 0 0 0.31 1.29 1.92 Panama 0 0 0 0.01 0 0 0 0.06 0 0 0 0.03 0 0 0 0 0.11 Peru 0 0 0 0 0 0 �0.01 0 0 0.03 0.02 0.03 0 0 0.04 0.01 0.12 Rwanda 0 0 0 0 0 0.06 0 0 0 0 0 0 0 0.14 0 0.04 0.22 Sri Lanka 0 0 0 0 0 0 0 0 0 0.53 0.63 �0.02 0 0 0.1 0.32 1.49 Tajikistan 0 0.02 0 0 0 0 0 0.03 0 0.09 1.69 0 �0.07 0 1.17 1.11 3.68 Timor Leste 0 0 0 0 0 0.05 0 0 0 0 0 0 0 0 0.07 0 0.12 Uganda 0 0 0 0 0 0.89 0 0.05 0.02 0.05 0.23 0 0 0.79 0.25 0.15 1.92 Vietnam 0 0 0 0 0 �0.06 0 0 �0.07 1.86 0.02 0.06 0 0 0.13 0.04 1.68 Yemen 0 0.01 0 0 0 0.01 0 0 0 0.27 0.25 0.01 0 0 0.07 0.2 0.81 Zambia 0 0 0 0 0 0 0 0 0 0.03 0.02 0 0 0 0.16 0.07 0.27

Source: authors’ calculations.

2 3

1 6

W O

R L

D D

E V

E L

O P

M E

N T

Table 13. Net change in the poverty headcount for key commodities, percentage points

Sugar cane, beets Beef Other bovine meat

Cattle Fish Maize Other grains Coffee, tea, cocoa

Cotton Rice Sugar Fruits Potatoes Vegetables Oils and fats Wheat Total

Albania 0 0 0 0 0 0 0 0 0 0 0.08 0 0 0 0.35 0.06 0.5 Armenia 0 0.01 0 0 0 0 0 0.03 0 0 0.11 0.03 0.14 0 0.25 0.06 0.63 Bangladesh 0 0.12 0 0 0 0 0 0 �0.03 1.28 0.05 �0.06 0 0 0.24 �0.02 1.59 Belize 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.15 1.15 Cambodia 0 0 0 0 0 �0.01 0 0 0 0 0 0.05 0 0 0 0 0.03 Côte D’Ivoire 0 0.02 0 0 0 0 0 �0.27 �0.50 0.35 0.19 0.03 0 0 0.02 0.16 0 Ecuador 0 0 0 0 �0.01 0.01 0 �0.01 0 0 0 0.01 0 0 0.04 0 0.04 Guatemala 0 0 0 0 0 0 0.01 �0.01 0 0 0.02 0 0 0.05 0.02 1.40 1.5 India 0 0 0 0.01 0 0 0 0 �0.02 0.14 0.15 �0.01 0 0 0.47 0.07 0.77 Indonesia 0 0.01 0 0 0 0 0 0 0 0.22 0.08 0.01 0 0 0.02 0 0.33 Malawi 0 0.01 0 �0.01 0 0.04 0 0 �0.01 �0.06 0.32 �0.01 0 0 0.25 0.53 1.03 Moldova 0 0 0 0 0 �0.01 0.01 0.03 0 0.03 0.16 0 0 0 0.11 0.04 0.68 Mongolia 0 �0.05 �1.36 �0.05 0 0 0.09 0.05 0 0.16 0.22 0 0 0 0 1.55 0.32 Nepal 0 0.04 0 0.02 0 0 0 0 0 0.02 0.04 �0.01 0 0 0.05 �0.01 0.15 Nicaragua 0 0.02 0 0 0 �0.02 0 �0.02 0 �0.01 0.01 0.01 0 0.24 0.28 0 0.5 Niger 0 0 0 0 0 0 �0.30 0 0 0.07 0.15 0 0 0 0.01 0.15 0.09 Nigeria 0 0.03 0 0 0 0 0 �0.05 �0.09 0.23 0 0 0 0 0.21 0.43 0.76 Pakistan 0 0.02 0 0 0 0.02 0.02 0.04 0 0.24 0.02 0 0 0 0.31 1.29 1.92 Panama 0 0 0 �0.02 0 0 0 0.06 0 0 0 0 0 0 0 0 0.05 Peru 0 0 0 0 0 0 �0.01 0 0 0.03 0.02 0.02 0 0 0.04 0 0.12 Rwanda 0 0 0 0 0 0.04 0 0 0 0 0 0 0 0.10 0 0.04 0.18 Sri Lanka 0 0 0 0 0 0 0 0 0 0.42 0.63 �0.07 0 0 0.10 0.32 1.44 Tajikistan 0 0.02 0 0 0 0 0 0.03 0 0.07 1.69 0 �0.44 0 1.17 1.09 3.62 Timor Leste 0 0 0 0 0 0.05 0 0 0 0 0 0 0 0 0.07 0 0.12 Uganda �0.04 0 0 0 0 0.15 0 �0.08 �0.11 0 0.23 0 0 0.59 0.25 0.15 1.15 Vietnam 0 0 0 0 0 �0.76 0 0 �0.10 �0.28 0.02 0.05 0 0 0.13 0.04 �1.24 Yemen 0 0.01 0 0 0 0 0 0 0 0.26 0.25 0.01 0 0 0.07 0.20 0.79 Zambia 0 0 0 0 0 0 0 0 0 0.03 0.02 0 0 0 0.16 0.07 0.27

Source: authors’ calculations.

E S

T IM

A T

IN G

T H

E S

H O

R T

-R U

N P

O V

E R

T Y

IM P

A C

T S

O F

T H

E 2

0 1

0 –

1 1

S U

R G

E IN

F O

O D

P R

IC E

S 2

3 1

7

  • Estimating the Short-Run Poverty Impacts of the 2010–11 Surge in Food Prices
    • 1 Introduction
    • 2 Methodology
    • 3 Data
    • 4 Results
    • 5 Global poverty estimates
    • 6 Robustness checks
    • 7 Comparison with the impacts of the 2008 food price crisis
    • 8 Concluding remarks
    • References
    • Appendix A

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EMPIRICAL METHODS FOR BIOETHICS: A PRIMER

ADVANCES IN BIOETHICS

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ADVANCES IN BIOETHICS VOLUME 11

EMPIRICAL METHODS FOR BIOETHICS:

A PRIMER

EDITED BY

LIVA JACOBY Associate Professor of Medicine, Office of the Vice Dean for Academic Affairs and The Alden March Bioethics Institute,

Albany Medical College, Albany, NY, USA

LAURA A. SIMINOFF Professor and Chair, Department of Social and Behavioral

Health, School of Medicine, Virginia Commonwealth University, VA, USA

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CONTENTS

LIST OF CONTRIBUTORS ix

INTRODUCTION Liva Jacoby and Laura A. Siminoff 1

SECTION I: PERSPECTIVES ON EMPIRICAL BIOETHICS

THE ROLE OF EMPIRICAL DATA IN BIOETHICS: A PHILOSOPHER’S VIEW

Wayne Shelton 13

THE SIGNIFICANCE OF EMPIRICAL BIOETHICS FOR MEDICAL PRACTICE: A PHYSICIAN’S PERSPECTIVE

Joel Frader 21

SECTION II: QUALITATIVE METHODS

QUALITATIVE CONTENT ANALYSIS Jane Forman and Laura Damschroder 39

ETHICAL DESIGN AND CONDUCT OF FOCUS GROUPS IN BIOETHICS RESEARCH

Christian M. Simon and Maghboeba Mosavel 63

CONTEXTUALIZING ETHICAL DILEMMAS: ETHNOGRAPHY FOR BIOETHICS

Elisa J. Gordon and Betty Wolder Levin 83 vii

SEMI-STRUCTURED INTERVIEWS IN BIOETHICS RESEARCH

Pamela Sankar and Nora L. Jones 117

SECTION III: QUANTITATIVE METHODS

SURVEY RESEARCH IN BIOETHICS G. Caleb Alexander and Matthew K. Wynia 139

HYPOTHETICAL VIGNETTES IN EMPIRICAL BIOETHICS RESEARCH

Connie M. Ulrich and Sarah J. Ratcliffe 161

DELIBERATIVE PROCEDURES IN BIOETHICS Susan Dorr Goold, Laura Damschroder and Nancy Baum

183

INTERVENTION RESEARCH IN BIOETHICS Marion E. Broome 203

SUBJECT INDEX 219

CONTENTSviii

LIST OF CONTRIBUTORS

G. Caleb Alexander Section of General Internal Medicine, Department of Medicine, MacLean Center for Clinical Medical Ethics, The University of Chicago, Chicago, IL, USA

Nancy Baum University of Michigan, School of Public Health, Ann Arbor, MI, USA

Marion E. Broome Indiana University, School of Nursing, Indianapolis, IN, USA

Laura Damschroder Ann Arbor VA HSR&D Center of Excellence, Ann Arbor, MI, USA

Jane Forman Ann Arbor VA HSR&D Center of Excellence, Ann Arbor, MI, USA

Joel Frader Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

Susan Dorr Goold Internal Medicine and Health Management and Policy, University of Michigan, Ann Arbor, MI, USA

Elisa J. Gordon Alden March Bioethics Institute, Albany Medical College, Albany, NY, USA

Nora L. Jones Center for Bioethics, University of Pennsylvania, Philadelphia, PA, USA

Maghboeba Mosavel Center for Reducing Health Disparities, MetroHealth Medical Center, Case Western Reserve University, Cleveland, OH, USA

Sarah J. Ratcliffe University of Pennsylvania, Department of Biostatistics, School of Medicine, Philadelphia, PA, USA

ix

Pamela Sankar Center for Bioethics, University of Pennsylvania, Philadelphia, PA, USA

Wayne Shelton Program on Ethics and Health Outcomes, Alden March Bioethics Institute, Albany Medical College, Albany, NY, USA

Christian M. Simon Department of Bioethics, School of Medicine, Case Western Reserve University, Cleveland, OH, USA

Connie M. Ulrich University of Pennsylvania School of Nursing, Philadelphia, PA, USA

Betty Wolder Levin Department of Health and Nutrition Sciences, Brooklyn College/Brooklyn City University of New York, Brooklyn, NY, USA

Matthew K. Wynia The Institute for Ethics, American Medical Association, Chicago, IL, USA

LIST OF CONTRIBUTORSx

INTRODUCTION

Liva Jacoby and Laura A. Siminoff

In recent years, concerns over how to use the results of scientific advances, changing expectations of how medical decisions are made, and questions about the implications of demographic changes have raised ethical challenges regarding allocation of resources, justice, and patient autonomy. Bioethics – no longer the singular purview of moral philosophy – is now accepted as a legitimate field in the academic health sciences and is helping to guide policy and clinical decision-making. To achieve its full potential, it must seamlessly integrate the methods of the humanities, social sciences and medical sciences.

This volume is intended to open a window to how empirically based social research helps illuminate and answer ethical questions in health care. Its primary aim is to examine the nature, scope and benefits of the relationship between empirical social science research and bioethics. Through a thorough examination of key research methods in sociology, anthropology and psychology and their applications, the book explores the study of bioethical phenomena and its impact on clinical and policy decision making, on scholarship and on the advancement of theory. The many and varied illustrations of research investigations presented in this book, allow readers to learn how different methodological approaches can address a wide range of ethical questions on both micro- and macro levels. In this vision of bioethics, fundamental questions are formulated using the tools of the social sciences, and then systematically studied with the thoughtful and methodical application of empirical methods. In this way, bioethics achieves the widest

Empirical Methods for Bioethics: A Primer

Advances in Bioethics, Volume 11, 1–10

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ISSN: 1479-3709/doi:10.1016/S1479-3709(07)11013-X

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lens allowing it to become a translational, as well as theoretical, area of inquiry.

The book provides a primer and a guide to those who are interested in learning how to collect and analyze empirical data that informs matters of bioethical concern. It provides readers an in-depth understanding about a range of qualitative and quantitative research methodologies and is designed to convey the breadth, depth and richness of such work. This book demonstrates how synergy between the social sciences and moral philosophy can integrate into a new vision of bioethics. It is our hope that this approach will expand not only our understanding of the complex bioethical environment surrounding the provision of health care, but also will encourage continued collaboration across disciplines relevant to bioethics.

This book builds on over 20 years of work in which individual researchers have attempted to shine an empirical spotlight onto bioethics. In 1984, Fox and Swazey (1984) characterized American bioethics as devoid of recognition of the social and cultural forces influencing ethical phenomena and being ‘‘sealed into itself’’ (p. 359). Five years later, Fox (1989) produced an eloquent analysis of the relationship between bioethics and the social sciences characterizing it as ‘‘y tentative, distant and susceptible to strain’’ (p. 237). In her analysis, she described how each field contributed to the tension – bioethics largely due to its focus on individualism and equating the social sciences with a quantitative and non-humanistic perspective, and the social sciences due to their limited interest in studying values and beliefs and favoring structural and organizational variables which, she contended, reduced their understanding of the importance of ethical and moral values in society. Her conclusion was that the ethos of both fields, with resultant ‘‘blind spots,’’ constituted barriers to collaboration and synergy.

Since this bleak picture was articulated two decades ago, the relationship between the two fields has evolved to the point where bioethics is a multidisciplinary field of study (as opposed to a singular discipline), where moral philosophy, the medical sciences, the humanities and the social sciences intersect. This evolution of bioethics into a truly multidisciplinary field makes the present book not only possible but also necessary. In a sense, the book represents a culmination of the coming together of these areas of inquiry. Thus, in order to situate it appropriately, we present a brief overview of some of the literature that has helped move this development forward over the past two decades.

Leading the path are Social Science Perspectives on Medical Ethics, edited by George Weisz (1990) and Bioethics and Society: Constructing the Ethical Enterprise, edited by DeVries and Subedi (1998), The SUPPORT study

LIVA JACOBY AND LAURA A. SIMINOFF2

(The SUPPORT Principal Investigators, 1995), and several articles by sociologists, philosophers/ethicists and physician/ethicists. George Weisz, in presenting the rationale for the book, describes three key areas where he believed social scientists could make important contributions to medical ethics: (1) provide data, (2) place ethical problems in their social contexts and (3) facilitate critical self-reflection on the part of medical ethicists. Of note is that although Weisz chronicles the important contributions of the social sciences to what he then termed ‘‘medical’’ ethics, he did not see the social sciences as an integral part of the field. In one of the chapters, and perhaps one of the most forceful deliberations on the topic at hand, Hoffmaster (1990) – a philosopher-ethicist – introduces the notion of ethical contextualism whose aim is to ‘‘explain the practice of morality’’ (p. 250). In order to understand and explain morality and the nature of ethical dilemmas, he posits that the focus has to be on practice and on the social and historical contexts in which these dilemmas are located. In the same volume, Fox expounds on the limited attention given by bioethicists to reciprocity, interconnectedness and community when analyzing ethical phenomena. Despite the often pessimistic perspectives presented on the differences in disciplinary approaches, Weisz concludes the volume with the hope that the exchange of ideas and the ‘‘stretching of disciplinary boundaries’’ (p. 15) will continue.

In 1992, Hoffmaster (1992) continued his vigorous arguments for incorporating contextualism into bioethical analysis by deliberating on the question ‘‘Can Ethnography Save the Life of Medical Ethics?’’ Presenting ethnography as a method to better understand the structural forces and individual particularities that influence values, behaviors and decisions in medical settings, he posits that moral theory must be tested in practice in order for theoretical development to occur. To justify his position, Hoffmaster uses several highly acclaimed ethnographic studies on decision making in neonatal intensive care units and genetic counseling.

One of the most comprehensive empirical studies in bioethics so far has been the SUPPORT study, published in 1995. With two physicians as its principal investigators, the study employed quantitative as well as qualitative methods to examine a range of end of life care issues, both descriptively and experimentally, and produced a vast amount of data showing not only troubling results but engendering new questions, debates and more significant research. Despite criticism from many quarters concerning the study’s methods and aspects of the intervention, this investigation was evidence of how bioethics and the social sciences informed each other and it constituted a vital example of the testing of ethical theories

Introduction 3

in practice. In fact, it demonstrated that thinking of bioethics simply as a branch of moral philosophy that could be ‘‘aided’’ by social scientific inquiry, was largely an outdated approach to the field.

In the first chapter of the volume Bioethics and Society: Constructing the Ethical Enterprise (1998), DeVries and Conrad call attention to the ‘‘bad habits’’ of analytic bioethics that have resulted in its asocial nature and practical irrelevance (1998). They also point out the shortcoming among social scientists of separating data from norms, arguing that, ‘‘a richly rendered social science y must be normative’’ (p.6). Introducing the notion of ‘‘social bioethics,’’ they further posit that without empirical underpinnings, princip- alism and analytic bioethics will never lead to workable solutions to moral problems. A recurring theme in this volume is a critique of how the weight given to autonomy and individualism in American bioethics limits bioethicists’ recognition of social and cultural contexts and respect for pluralism. In one chapter, bioethics is criticized for protecting the status quo through its lack of attention to justice and structural factors in the health care system.

In 2000, The Hastings Report moved the discourse about the relationship between the social sciences and medical ethics forward by publishing an issue entitled ‘‘What Can the Social Scientist Contribute to Medical Ethics?’’ In one of the articles, sociologist Zussman (2000) recognizes how medical ethicists have become more inclined to incorporate empirical data in their analyses and that social scientists studying medical ethics have shown more openness to the normative implications of their research. Importantly, he states that the classic ‘‘ought-is’’ distinction and other differences between the fields denote complementarity rather than incompatibility. He concludes by calling for a combination of empirical methods and an applied ethics model as an approach to pursuing scholarly work immersed in practice. In the same issue of the Hastings Report, Lindemann Nelson – a philosopher – states that the social sciences can and should help bioethics by enriching an understanding of prevailing ethical values and how these ‘‘come to be installed or resisted in patterns of practice’’ (2000; p. 15). Using the SUPPORT study as an illustration of his arguments, he recognizes the need to give attention to how structural and institutional factors impact human behaviors and practice patterns.

Bioethics in Social Context, edited by Hoffmaster, and Methods in Medical Ethics, edited by Sugarman and Sulmasy were published in 2001. Both constitute important work in this genre, and reflect what many of the scholars reviewed above have called for. The first volume consists of essays on qualitative research emphasizing the context within which ethical decisions take place. The second volume describes a wide range of empirical approaches

LIVA JACOBY AND LAURA A. SIMINOFF4

to studying bioethical questions – from religion and theology, history and legal methods to ethnography, survey research, experimental methods and economics. Setting the stage for their book with a discussion on the relationship between descriptive and normative research in medical ethics, Sugarman and Sulmasy posit that empirical research ‘‘can raise questions about the universalizability of normative claims’’ and ‘‘can identify areas of disagreement that are ripe for ethical inquiry’’ (p. 15). Claiming that ‘‘good ethics depends upon good facts’’ (p. 11), one of their premises is that good moral reasoning needs both moral and factual elements. Their conclusion is that descriptive and normative inquiries are mutually supportive. Including a number of methods outside as well as within the social sciences, the book sheds light on the wide range of disciplines that have contributed to the study of bioethical phenomena during the past couple of decades.

In the present book, we advance the field of empirical bioethical inquiry another step by focusing on empirical methods in bioethics and on their practical applications to investigating a wide spectrum of bioethical problems. One thing that is noteworthy, when compared to much of the work preceding this volume, is that we use the term ‘‘bioethics’’ rather than ‘‘medical ethics.’’ We believe the term ‘‘bioethics’’ denotes a broader meaning related to the study of the ethical, moral and social implications of the practice of medicine in all its aspects along with the social and ethical problems generated by new biotechnology and biomedical advances.

We have included eight basic research methodologies – and asked the authors to describe how they have employed ‘‘their’’ particular method to examine matters of bioethical concern. These range from informed consent, human subjects research, end of life care, decision making regarding organ donation, to the tension between privacy rights and the facilitation of medical research to community standards concerning health care spending priorities. Before the eight chapters on methodology, are two chapters that provide two different and, in many ways, complementary perspectives on contemporary empirical bioethics – one by a practicing physician/ethicist and the other by a clinical ethicist/philosopher. Their discussions of how empirical research has contributed to their areas of expertise, demonstrate how such research brings together moral philosophy, clinical practice and clinical ethics and serves as a valuable framework for the remainder of the book.

The methodology chapters are organized under the headings of methods that are generally classified as ‘‘qualitative’’ research methods and those that are ‘‘quantitative.’’ The particular methods were chosen that have been demonstrated to have a practical application in the study of bioethics, including those that are used frequently and have proven to yield valuable

Introduction 5

results. The qualitative chapters include: Content analysis, Focus groups, Ethnography and Semi-structured interviews. Chapters that focus on quantitative methods are: Survey Research, Hypothetical Vignettes, Deliberative Procedures and Intervention Studies.

The section on qualitative methods begins with a chapter on content analysis by Forman and Damschroder. As the first chapter in this section, it introduces the reader to a method that constitutes the basis for much of analysis of qualitative research data and, as such, frames the following three chapters. The authors provide a detailed description of how qualitative content analysis can be used to analyze textual data of various kinds and is aimed at generating detail and depth. Their focus is on the examination of data gathered through open-ended interviews. By using specific examples, they illustrate how content analysis provides comprehensive descriptions of phenomena; illuminates processes; captures beliefs, motivations and experiences of individuals; and explains the meaning that individuals attach to their experiences. The chapter provides ample information on the many steps inherent in content analysis, from the framing of the research question, deciding on the unit(s) of analysis, to the various and specific forms of engaging with the data, and performing the actual analysis.

In the second chapter, Simon and Mosavel discuss focus groups as a useful method to stimulate discussion and gather data on multifaceted and complex bioethical issues. The use of focus groups in bioethical inquiry has increased in recent years, and drawing on their own research experiences in South Africa, the authors explore and highlight some of the uses of this method as an investigatory tool. Referring to focus groups as a method that is comparatively cost effective and easy to implement, Simon and Mosavel present the reader with practical information on the processes and procedures of designing and conducting focus groups in an ethical, culturally appropriate and scientifically rigorous way. They go on to present a novel and unique form of analysis of focus group data, referred to as ‘‘workshop-based summarizing and interpretation’’ and describe how this approach was used with members of communities in South Africa as part of their research. Finally, they provide insights into ways of disseminating findings from focus group research.

The chapter by Gordon and Levin illuminates how ethnography, as one of the most prominent empirical methods in early bioethics research, has and still does contribute significantly to our understanding of bioethical phenomena. In this chapter, Gordon and Levin start by giving a brief overview of seminal ethnographic work in the field, followed by a detailed description of participant observation as ‘‘the heart’’ of ethnography. Using examples from research of their own and that of others conducted in a

LIVA JACOBY AND LAURA A. SIMINOFF6

variety of health care settings, they continue by outlining the steps involved in preparing and implementing a participant observation study in the field. Their accounts give the reader valuable insights into the unique role of the participant observer, the significance of good note-taking, and common challenges encountered by ethnographers. The authors offer helpful ideas on precautions that researchers can take in order to maximize the rigor of their research and to generate valid and meaningful data. The section on the elements of data interpretation and analysis connects with Forman’s and Damschroder’s chapter on content analysis, providing the reader with a comprehensive guide to the collection and analysis of qualitative data. The authors end by reviewing ethical considerations in conducting ethnographic research as well as the strengths and weaknesses of such research.

The final chapter on qualitative methods describes semi-structured interviews. Along with surveys, interviews have long constituted one of the basic methods in social science research. In this chapter, Sankar and Jones begin by presenting the advantages of semi-structured interviews character- ized by the combination of closed-ended questions with open-ended queries making possible both comparisons across subjects and the in-depth exploration of data. Comparing to quantitative research, the authors contend that the main strength of semi-structured interviews is in the richness of the data they generate and that the method is particularly useful in exploratory research. Paying a good deal of attention to considerations in designing an interview guide, Sankar and Jones discuss the importance of pilot testing, and using an example from their study on medical confidentiality, address ways of maximizing the validity of interview questions and steps involved in finalizing questions and queries. Important segments of the chapter are the discussion of sampling and the actual conducting of semi-structured interviews.

Focusing on audiotaping and the digital recording of interviews, Sankar and Jones provide a valuable complement to Gordon’s and Levin’s discussion of note taking in ethnography. Similarly, the review of coding procedures and the particular approach referred to as ‘‘multi level consensus coding,’’ add to the perspectives offered by Foreman and Damschroder in their chapter on content analysis.

The first chapter in the book’s section on quantitative methods presents the basics of survey research. As Alexander and Wynia point out, surveys have been the bedrock of much of the research conducted by social scientists, stating that, few researchers who conduct empirical research in bioethics do not use some survey research techniques. Alexander and Wynia further observe that surveys about ethically important topics have made important contributions to bioethics. However, it is not a simple task to

Introduction 7

conduct a good survey. The authors make clear that in order to obtain meaningful information from a survey, the researcher needs to pay careful attention to the development of the survey’s design, including formulating the research question, how to draw the sample, developing the questionnaire and how the data will be managed and analyzed. The authors contend that using rigor throughout this process can be the difference between an important study that makes fundamental contributions and one that is irrelevant to ethical analysis, health policy or clinical practice. The chapter provides a primer to the reader in how to balance rigor with feasibility at all stages of survey development, fielding, analysis and presentation and helps the reader plan, develop and conduct a survey.

A related technique to survey research is the use of hypothetical vignettes. This technique is especially relevant to bioethics research where it can be difficult to directly observe certain ethical problems because of the intensely personal nature of the questions of interest (removal of ventilator support from a patient), the rarity of the occurrence (requests for organ donation in the hospital), or a sensitive question that may reside at the edge or over the edge of what is legally permitted (assisted suicide and euthanasia). As Ulrich and Ratcliffe point out, hypothetical vignettes provide a less personal and, therefore, less threatening presentation of such issues to research participants. The chapter provides an overview of hypothetical vignettes with examples of how this method has been used to examine and analyze critical ethical problems. It reviews ways to evaluate the reliability, validity, strengths and limitations of studies using vignettes. The chapter will take the reader through what constitutes a vignette, how to develop a vignette about a bioethics-relevant problem, how to evaluate the psycho- metric properties of vignettes, the determination of sample size and sampling considerations and examples of published vignettes used in empirical bioethics research.

The chapter by Goold, Damschroder and Baum will introduce many readers to a methodology unfamiliar to them – deliberative procedures. This methodology is based on theories of deliberative democracy with the idea of providing community members with a ‘‘voice’’ in community-wide decisions, for instance about health care spending priorities or research regulation. Deliberative procedures offer an opportunity for individuals to assess their own needs and preferences in light of the needs and desires of others. In bioethics research, deliberations involve individuals in a community decision-making process about bioethical issues with policy implications and may provide acceptance and legitimacy to a given issue within a population.

LIVA JACOBY AND LAURA A. SIMINOFF8

The authors describe how deliberative procedures entail gathering non- professional members of the public to discuss, deliberate and learn about a particular topic with the intention of forming a policy recommendation or casting an informed ‘‘vote.’’ For researchers involved in exploring bioethical issues, deliberative procedures can be a valuable tool for gathering information about public views, preferences and values. This chapter focuses on de novo deliberative procedures used for research purposes, or combined policy and research purposes, where sampling issues, and research aims are known and planned up front. The chapter offers a review of methodological considerations unique to, or particularly important for, deliberative methods include sampling, specifically substantive representa- tion, what to measure and when, the use of group dialog in the data collection process, and the role that deliberative procedures can play in educating the public and informing policy.

The final chapter deals with intervention research. As Broome makes clear, the use of intervention designs, while a relatively recent phenomenon in bioethical inquiry, has a distinct and important role to play in advancing the field of bioethics. Its importance will grow as more empirical bioethics research provides data that not only informs policy and/or practice, but asks questions about what policies or practices work best. Although many ethical questions of interest are not appropriate for intervention research, the author contends that some questions can only be answered using experimental or quasi-experimental designs. The chapter provides the reader with a review of the application of experimental methods to bioethics research including randomized controlled trials and quasi-experimental designs ranging from the more rigorous two-group repeated measures or pre-test/post-test designs to the one-group post-test-only design. Strengths of each design, including the threats to internal and external validity, are presented. As Broome stresses, not all bioethical phenomena are appro- priate for study using experimental or quasi-experimental designs. Examples of intervention research related to informed consent are provided.

With this book, we hope to create enthusiasm for empirical research that will continue to bring synergy between disciplines representing bioethics and, in so doing, further enhance our understanding of bioethical phenomena.

REFERENCES

Devries, R., & Conrad, P. (1998). Why bioethics needs sociology. In: R. DeVries & J. Subedi

(Eds), Bioethics and society: Constructing the ethical enterprise. Englewood Cliffs, NJ:

Prentice Hall.

Introduction 9

DeVries, R., & Subedi, J. (Eds). (1998). Bioethics and society: Constructing the ethical enterprise.

Englewood Cliffs, NJ: Prentice Hall.

Fox, R. (1989). The sociology of bioethics. In: R. Fox (Ed.), The sociology of medicine.

Englewood Cliffs, NJ: Prentice Hall.

Fox, R., & Swazey, J. (1984). Medical morality is not bioethics – medical ethics in China and

the United States. Perspectives in Biology and Medicine, 27(3), 337–360.

Hoffmaster, B. (1990). Morality and the social sciences. In: G. Weisz (Ed.), Social science

perspectives on medical ethics. Boston: Kluwer Academic Publishers.

Hoffmaster, B. (1992). Can ethnography save the life of medical ethics? Social Science and

Medicine, 35(12), 1421–1431.

Hoffmaster, B. (Ed.) (2001). Bioethics in social context. Philadelphia, PA: Temple University

Press.

Lindemann Nelson, J. (2000). Moral teachings from unexpected quarters – lessons for bioethics

from the social science and managed care. Hastings Center Report, 30(1), 12–21.

Sugarman, J., & Sulmasy, D. (2001). Methods in medical ethics. Washington, DC: Georgetown

University Press.

The SUPPORT Principal Investigators. (1995). The SUPPORT study. A controlled clinical trial

to improve care for seriously ill hospitalized patients. Journal of the American Medical

Association, 274, 1591–1598.

Weisz, G. (Ed.) (1990). Social science perspectives on medical ethics. Boston: Kluwer Academic

Publishers.

Zussman, R. (2000). The contributions of sociology to medical ethics. Hastings Center Report,

30(1), 7–11.

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SECTION I:

PERSPECTIVES ON EMPIRICAL

BIOETHICS

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THE ROLE OF EMPIRICAL DATA IN

BIOETHICS: A PHILOSOPHER’S

VIEW

Wayne Shelton

THE CRISIS OF TRADITIONAL ETHICAL THEORY

How many textbooks or introductory articles in bioethics begin with a section on ethical theory? Of the many that do, the relevance of basic theories of utilitarianism, deontology, virtue ethics, feminist ethics, casuistry and so on, is assumed. These theories are also considered in light of the well- accepted principles of medical ethics: (1) respect for patient autonomy, (2) beneficence, (3) non-maleficence and (4) justice. Those of us trained in philosophy find these sections on theory terse summations of complex philosophical views. Physicians and nurses, and others not trained in philosophy, sometimes struggle to get their gist, and end up with an ability to make a basic analysis and formulate arguments about ethical problems from each of these perspectives, and to write and discuss the issues that arise with fellow ethicists. But how essential are these theoretical perspectives to the real work of clinical ethics consultants? It is important that we do not forget just how applied and practical that work is.

Regardless of one’s background perspective coming into bioethics, particularly clinical ethics, if he or she wishes to become a clinical ethics consultant and work in the field applied clinical ethics, it is essential to

Empirical Methods for Bioethics: A Primer

Advances in Bioethics, Volume 11, 13–20

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ISSN: 1479-3709/doi:10.1016/S1479-3709(07)11001-3

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grapple first hand with value conflicts in real life situations. Applied clinical ethics is the study of range of value-laden, ethical conflicts and dilemmas that arise in the clinical setting, and especially in the physician–patient relationship. The ethics consultant, a specialist in applied clinical ethics, combines both core clinical skills necessary to provide support and help people embroiled in value conflicts, as well as to employ advanced analytical skills and knowledge in offering a considered analysis of the opposing value positions in order to make recommendations consistent with the rights and obligations of those involved. Some ethics consultants are also expected to communicate directly with patients and families to facilitate an ethically acceptable outcome. For those ethics consultants directly involved in clinical value conflicts, both in terms of their practical resolution in individual situations and in terms of their import for academic reflection, what is the role of ethical theory in the work of applied ethics? And more specifically, can traditional ethical theory serve as a normative basis on which we judge one moral option better than another? These are indeed legitimate theoretical ethical questions. But, it is doubtful that our typical educational training prepares ethics consultants to answer them.

It is unfortunate that philosophical ethical theories still have an uncertain and, I would say, awkward fit into the practical realm of applied ethics. As someone who has taught a number of graduate courses in clinical ethics, the justification I use for providing a brief introduction to ethical theory, after the nature of real clinical value conflicts have been established, is to stimulate the student’s imagination in analyzing and seeing all the possible ways of viewing and justifying a case ethically. But again, there is the continuing sense of not knowing quite how to use ethical theory in bioethics in relation to problem solving, particularly in knowing the normative force of ethical theory. The purpose of this brief chapter is to provide an innovative alternative that was proposed by John Dewey in the early part of last century. I will argue that the emergence of an applied ethical field like bioethics provides the occasion to reconstruct our understanding of the relation of theory and practice, and creates a whole new appreciation of, and need for, empirical bioethics. From this perspective, ethical theory can be viewed in a different, and more constructive, light.

Dewey’s critique of western philosophy and ethical theory was the springboard for a new understanding of ethics. Dewey believed that western philosophy since Plato was largely based on false dualisms that created a bogus dichotomy between some ultimate, rational understanding of truth versus what can be known from ordinary experience. It is because of such dichotomies that the ‘‘Is-Ought’’ problem has appeared so impenetrable, i.e.

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because reason and experience are assumed to be disconnected, empirical knowledge is of little or no help in deriving moral obligations. (Those interested in Dewey’s critique, please see The Quest for Certainty: A Study of the Relation of Knowledge and Action (1960)). Dewey was quite convinced, as are many of his followers, that western philosophy has failed to reach final understandings of rational truth, and that we are wasting our time to continue to search for them. He contended that there has never been and will never be final agreement about such quests. It is for this reason that Dewey believed philosophy, including ethics, is in need of reconstruction – a whole new pragmatic understanding that grounds ethical inquiry in human experience. In light of this critique of western philosophy, the problem of knowing just how to use traditional ethical theory can be fully appreciated, and why the pragmatic turn in bioethics marks a new beginning for philosophy and ethics.

PRAGMATIC ETHICS

Pragmatic ethics begins with the reality of lived experience of human beings who are connected biologically, socially and politically within a natural environment. Thus, the moral life is connected to the conditions that best foster human flourishing and reduce suffering. ‘‘Right’’ and ‘‘good’’ are the ends of moral inquiry, not assumptions grounded in normative religion or philosophical theory. Many have taken this approach as a step toward moral relativism and crisis of value. But pragmatists believe the turn toward naturalism is the occasion to fully grasp the vital role of human beings in shaping their own fate, and promoting a better society. Human beings’ highest aspirations – justice, peace and alleviation of suffering – lie in the enhancement of human intelligence and forms of inquiry that allows humans to better understand how to craft a better future for everyone. Questions of moral conflict, thus, become more problems of strategy for humans to resolve using principles that guide expedient resolution within a social circumstance or context. According to this perspective, since ethical reflection begins and ends in experience and not some pre-established, a priori patterns of reasoning, the tension between ‘‘is’’ versus ‘‘ought’’ begins to subside.

Because ethicists no longer work only in rarified academic settings but more and more function as applied ethicists alongside practitioners in clinical settings, there is a need to provide working solutions to medical ethical problems. Not many applied ethicists I know are looking for

The Role of Empirical Data in Bioethics 15

ultimate answers; rather, they seek solutions that help people in conflict to make decisions and to cope better in their environments. Ethicists must themselves know the experiential landscape of a given situation in order to provide meaningful and helpful moral advice to practitioners. But in order to have viable interpretations of such situations ethicists need to be able to use imagination to creatively propose viable strategies for amelioration of the human condition – both at the micro and macro level. This requires knowledge and understanding of the empirical circumstances in which value conflicts arise. In order to generate empirical knowledge the field of bioethics must rely on the same empirical methodological basis as the social sciences. This is consistent with Dewey’s hope for philosophy: that it would be put to work along side the other sciences for the betterment of society.

EMPIRICAL BIOETHICS

In its reconstructed role, ethical theory is no longer isolated from experience in preexisting forms as it appears in traditional philosophical ethics, but is connected to experience and informed by it. The goals of ‘‘right’’ action is not to make a determination of a final moral duty, but to make provisional statements that must be continually monitored and, like scientific claims, revised when warranted by further empirical knowledge. The philosophical quest of seeking final rational answers is replaced by a commitment to improve at least certain aspects of human life. For the clinical ethics consultant, it is to improve the conditions for people with conflicting values and/or preferences in particular clinical settings.

Thus, empirical knowledge, and therefore, empirical bioethics are essential to the field of bioethics, and all ethical inquiries that seek solutions to value conflicts. The rise of empirical knowledge in bioethics also allows us to generate new knowledge about the empirical landscape of clinical ethical conflicts. This knowledge leads to greater insight into the associative and causal elements that generate ethical conflicts in the first place. With more empirical knowledge, more strategic mastery of the course of clinical events is possible. This can occur by providing more effective ethics consultations in individual cases. But empirical knowledge can also become a basis for developing broader, preemptive strategies for dealing with ethical conflicts. It is a sign of a maturing field, more aligned with other fields based in scientific methodology that many applied ethicists, health care practitioners, policy makers and others are becoming more focused on quality of care improvement by reducing the incidence of ethical conflicts. This is done by

WAYNE SHELTON16

testing, through scientific empirical studies, the efficacy of new strategies that focus on improved management of the contributing elements of ethical conflict. Most ethics consultants quickly realize that it is much more efficient to prevent major ethical conflicts from arising than to have to grapple with them in their often final and intractable manifestation as ethical dilemmas. This requires the ethicist to become proficient in empirical studies of clinical decision making and outcomes and related issues that affect patient and family care. A major empirical study in the Surgical ICU at an academic health care institution, led by a philosopher/ethicist, is an example.

AN ILLUSTRATION

The study grew out of extensive experience providing ethics consultations in the ICU setting. Based on recent ICU data accumulated from an internal study, we learned that up to 60% of patients where ethics consultations were done, died. In these cases, families typically go through extremely stressful and, sometimes, gut-wrenching experiences of decision making for their loved ones. Families in this setting are routinely required to make decisions about whether or not to continue life-sustaining treatments, and how best to follow the expressed wishes of the patient in situations where the patient lacks capacity. In the course of providing ethics consultations, it is often necessary to have extensive conversations with the family of an incapaci- tated patient allowing them to share their intimate knowledge of the patient’s values and how those values apply to medical decision making. Many times a consensus emerges about the right course of action, but unfortunately, sometimes there is deep disagreement between family members, and between family and care providers. At those times, the ethics consultant is there to help mediate what is often a value-laden conflict, which has reached an impasse. Attitudes and dispositions among those involved can become hardened and people’s positions entrenched. Conflicts that drag on frequently lead to a lack of clarity in defining goals of care and patients may stay in the ICU for an extended length of time, using costly resources. In situations where conflicts persist, it is possible that patients are receiving care that is not medically indicated. In many instances, the ethics consultant can serve as an outside mediator, clarifying facts and values, and help the parties in conflict reach mutually acceptable outcomes. But from the ethics consultant’s point of view the time of entry into the situation is late, and improvements in care are made, one case at a time. Commonly, much has happened prior to the point of the ethics consultation that impacts

The Role of Empirical Data in Bioethics 17

the conflict, and often the key factor is how the information flow and communication has occurred between the health care team and the family. Over the years I have often drawn from my experience as an ethics consultant dealing with ethical conflicts in the ICU for teaching purposes to illustrate the ways how care providers can interact with families so as to prevent conflicts from arising in the first place. Based on these observations and insights, my hunch grew into a well-formulated research question about how a highly tailored plan for focused family support might reduce ethical conflicts, as well as increase family satisfaction and reduce length of stay for patients most at risk for extended length of stay. Thus, the inspiration for a study!!

As a philosopher trained in clinical ethics consultation, and also in social work and health care policy, my interests expanded to considering how one might do an empirical study of my, at that point, hunch. After an exhaustive literature review, numerous discussions with many clinicians and researcher, several pilot studies, two related publications (Gruenberg et al., 2006; Rose & Shelton, 2006) and extensive planning and proposal writing, funding was received to begin the ‘‘ICU study’’. The study is designed to test the hypothesis that a focused, multidisciplinary model of family support, including the combined resources of ethics consultation, pastoral care, social work and

palliative care, all led and coordinated by a nurse practitioner will lead to (1)

increased family satisfaction with care, (2) decreased unnecessary and

unwanted care, and (3) reduced cost and resource utilization. The intervention will use a nurse practitioner to gather information about

the family from the non-medical support services. Working directly with the physicians, he or she will ensure that this information in used meaningfully and robustly in the medical decision making and goal-setting process. Thus, the nurse practitioner will be the crucial link between the physicians in charge of directing medical care for the patient and the family. Thus, the intervention will resolve one of the most pervasive and well-known problems of hospital case: no one taking responsibility for the patient and family, with one central line of communication to manage the flow of medical and other essential information.

This perennial problem regarding the flow of information to families and patients in hospitals was identified by Michael Balint as ‘‘collusion of anonymity’’ in his book The Doctor, His Patient and the Illness first published in 1957. Since that time, the problem has grown much more complicated with the rise of highly complex, specialized medical fields, especially in the context of large teaching hospitals. It is common in such hospital settings for families to come into contact with numerous physicians

WAYNE SHELTON18

from many specialty services, each with their own medical perspective and sometimes at variance with one another in terms of the prognosis and goals of care for the patient. To say this situation can be confusing for a stressed family distraught over the illness of their loved one is an understatement. Such confusion can also fuel misunderstandings, leading to confusion and strong emotions of anger and resentment. Most experienced ethics consultants know, at least anecdotally from their own experiences, that such situations are a breeding ground for ethical conflicts and dilemmas, and for dissatisfaction with care among families.

The goal of the study intervention is to preclude clinical ethics dilemmas of these kinds, and to engage in what is sometimes referred to as ‘‘preemptive ethics’’ by preventing the problems from occurring. Instead of dealing with ethical problems one at a time, from one crisis to another, the study will provide the benefit of collecting empirical data that can illuminate and address underlying root causes of ethical conflicts at the bedside. Our premise is that by providing focused support to stressed families, they will gain an enhanced ability to make decisions with ease and understanding, according to their values and preferences and those of the patient. The emphasis of the study is on improving the quality of care for seriously ill patients, and testing whether such an improvement reduces cost of care.

Is this a clinical ethics study? I would say, yes! Most importantly, it shows how an emerging field like empirical bioethics is connected to other key areas of health care research, such as quality improvement, resource utilization and outcomes studies. Therefore, one significant point is that as bioethical inquiry evolves into empirical investigations it clearly becomes more multidisciplinary and gains more standing as an important area of health care research.

CONCLUSIONS

In light of this beginning trend toward empirical bioethics, where does this leave ethical theory, and what is its role in applied ethics? Applied ethicists who have taken the pragmatic turn and are now exploring empirical bioethics in terms of outcomes research, see the tradition of western ethical theory as a body of literature with no special moral authority. This is not to say ethical theory is not interesting or even important, but clearly the perspective of the naı̈ve graduate student looking for foundational moral authority is gone. Instead, its use is more as a set of tools – they are handy to

The Role of Empirical Data in Bioethics 19

have at one’s disposal. They provide ways of asking relevant questions, structuring arguments and formulating alternative resolutions to pressing problems. To the extent theory is now used for the empirically oriented, applied philosopher, they represent structured ways of conceiving alter- native moral perspectives that stem from direct experience in the empirical details of ethical problem solving. The theories stem from the imagination and provide the ethical visions for forging a better state of affairs with respect to some human value problems.

Perhaps with more bioethical empirical research there will emerge a new type of fully articulated theory, as Dewey was hoping, that is better suited for practical problem solving. This also means much less of a focus, if any, on the questions that have flowed out of modern philosophy, based on dualisms that force us to separate theory and practice, mind and body, and fact and value. So at this point of a new beginning for applied philosophy, a field we call empirical bioethics, it is a good thing that philosophers are getting their hands dirty in the real world of experience and grounding their theoretical approaches toward improving the human condition.

REFERENCES

Balint, M. (2000). The doctor, the patient and his illness (2nd ed.). Amsterdam, The Netherlands:

Churchill Livingstone.

Gruenberg, D., Shelton, W., Rose, S., Rutter, A., Socaris, S., & McGee, G. (2006). Factors

influencing length of stay in the intensive care unit. American Journal of Critical Care,

15(5), 502–509.

Dewey, J. (1960). The quest for certainty. New York: Capricorn Books.

Rose, S., & Shelton, W. (2006). The role of social work in the ICU: Reducing family distress

and facilitating end-of-life decision-making. Journal of Social Work in End-of- Life and

Palliative Care, 2(2), 3–23.

WAYNE SHELTON20

THE SIGNIFICANCE OF EMPIRICAL

BIOETHICS FOR MEDICAL

PRACTICE: A PHYSICIAN’S

PERSPECTIVE

Joel Frader

INTRODUCTION

While some of us enjoy engaging in many forms of bioethical activity, including philosophical analysis and debate, clinical ethics consultation, and empirical research, only the latter matters much to the practicing physician. Practically minded, most doctors have little concern with fine moral distinctions when faced with a patient’s request for assistance in dying or a pharmaceutical company’s offer to attend a product ‘‘consultation’’ session at a first class resort in addition to an attractive fee for participation. Physicians want to know what facts might bear on ethical questions they confront, how ethical conflicts that have an impact on patient care can be understood and resolved, and whether research reveals consistently clear, helpful findings. The following discussion offers some examples of how empirical research related to bioethical issues has provided evidence and guides for physicians at both individual-patient care and policy levels, and further reviews areas that warrant continued research attention.

Empirical Methods for Bioethics: A Primer

Advances in Bioethics, Volume 11, 21–35

Copyright r 2008 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1479-3709/doi:10.1016/S1479-3709(07)11002-5

21

AREAS OF RELATIVELY CLEAR EVIDENCE

Informed Consent

During the second-half of the twentieth century in the United States, medicine experienced enormous change. Scientific and technological advances made medical interventions vastly more effective and health care became a major economic engine. In accord with the political and cultural changes emphasizing the rights of individuals, ethical and legal thinking about the relationship between professional providers and researchers on the one hand and patients and subjects on the other hand, shifted from beneficent paternalism to consumerist autonomy. The doctrine of informed consent became the judicial and ethical cornerstone of decisions about medical treatment and/or research participation. Public policy, fashioned to prevent recurrences of Nazi medical atrocities, the kind of researcher arrogance documented by Beecher (1966), Barber, Lally, Makarushka, and Sullivan (1973) and Gray (1975), and medical over-treatment noted by the President’s Commission for the Study of Ethical Problems in Medicine and Biomedical and Behavioral Research (1983) and Weir (1989) focused on individual consent by the patient, subject, or surrogate authorization.

Unfortunately, research about the realities of informed consent shows that practice fails to live up to ethical theory and legal doctrine. In the context of medical treatment, evidence indicates that differences in knowledge, social status, and emotional states of practitioners and patients/family members undermine the value of information about the risks, benefits, and alternatives to proposed plans of care (Lidz et al., 1983; Appelbaum, Appelbaum, & Grisso, 1998; Siminoff & Drotar, 2004). Research also reveals that physicians often lack adequate training for and motivation to communicate clearly with patients and surrogates (Angelos, DaRosa, & Sherman, 2002; Sherman, McGaghie, Unti, & Thomas, 2005). Similar problems plague the implementation of the doctrine of infor- med consent in research. Potential subjects frequently suffer from the ‘‘therapeutic misconception’’ believing that proposed clinical research is designed to provide them with the best known therapy, despite randomiza- tion schemes ensuring distribution of subjects into treatment arms of competing value (Lidz & Appelbaum, 2002). In recent studies on research participation among children with cancer, Kodish, et al. (2004) have dramatically demonstrated that parents commonly do not understand that their children could receive treatment without enrolling in research nor do they typically comprehend that treatment assignment proceeds by chance,

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rather than the doctor’s deliberative decision about which regimen would work best for their child. Finally, especially in the research context, studies have repeatedly shown that the obsessive, legalistic focus on written consent forms generally fails to produce truly informed consent (Baker & Taub, 1983; Ogloff & Otto, 1991; Waggoner & Mayo, 1995; Lawson & Adamson, 1995; Agre & Rapkin, 2003). Consent forms, despite all the attention lavished on them by investigators, research coordinators, institutional review board (IRB) members and staffs, commonly contain language far too complex and technical for the general population to comprehend. What seems astonishing, in the face of years and volumes of research about the failure of efforts to effectuate meaningful informed consent, is the continued reliance on incomprehensible consent forms, and the lack of adequate preparation of clinicians (Wu & Pearlman, 1988; Sherman et al., 2005) or research team members for communicating clearly to patients and their loved ones about illness, treatment, research, and about the maintenance of ethical and legal notions of the consent process. The disconnection between ethical theory and application suggests one of two things: the theory needs to change to better reflect social practicality and/or the need for clinicians and investigators to become more creative and diligent in the way they convey information and assess understanding.

Some recent developments, largely beyond the scope of this brief review, provide some hope that a much better job can be done with the process of informed consent, though not without substantial investment of time and resources. Researchers starting from the perspective of appropriate use of health care resources, particularly those noting wide regional variations in practice in the United States (Wennberg, Fisher, & Skinner, 2004), have become interested in standardized presentation of information to patients and surrogates. A recent review by O’Connor, Llewellyn-Thomas, and Flood (2004) concluded that use of various patient decision aids, such as interactive computer programs, up-to-date multimedia presentations, tools to help consumers identify and actualize their health-related values, and forms of personal coaching by health care professionals or experienced peers can improve the quality of decisions and reduce the use of invasive interventions without a decline in health outcomes. We will need additional efforts to assess whether such mechanisms enhance the informed consent process and if they have practical and cost effective use in everyday health care settings. This is particularly the case in situations involving emotionally charged decisions, such as testing for genetic susceptibility to disease, forgoing life support, or the choice among alternative therapies for cancer.

The Significance of Empirical Bioethics for Medical Practice 23

Advance Directives

With the dramatic increase in intensive care units (ICUs) in American hospitals during medical and technical advances in the past 50 years, patients and families became concerned with over-treatment leading to the ‘‘right to die’’ movement. A series of legal cases related to family members’ requests to discontinue unwanted prolongation of life for their loved ones led to the notion that patients could avoid undesired life-sustaining interventions through preparation of documents designed to communicate treatment preferences once they lost capacity to interact effectively with health care providers. The first case to receive national attention in the U.S., involved a young woman named Karen Ann Quinlan who sustained severe brain injury. After prolonged treatment, including mechanical ventilation, her parents requested discontinuation of life support, but the patient’s doctors and hospital administrators, fearing legal sanctions, refused. The New Jersey courts in this case (In re Quinlan, 1976) and another subsequent seminal case involving Claire Conroy (In re Conroy, 1985), an elderly woman with several chronic conditions and no longer able to speak for herself, helped established criteria doctors and surrogates might use to justify forgoing life-sustaining treatment, recognizing the importance of evidence of what the patient him- or herself would want under the circumstances. This became a matter of Supreme Court consideration in the Nancy Cruzan case (Cruzan v. Director, 1990), which came to center on the issue of the quality of evidence necessary to establish the patient’s wishes.

As a result of such cases, institutional policies and, eventually, federal and state laws, began to recognize ‘‘advance directives’’ as a valid means by which patients and surrogates could communicate their wishes in the face of lost decision-making capacity. One type of document, the ‘‘instructional’’ directive, in which the individual attempts to project what treatments to employ or avoid if she/he can no longer express him- or herself, was designed to provide the means for limiting – or, in some cases, continuing – medical interventions, especially in situations where treatment might involve marginal benefits.

Research has shown that instructional directives – whether oral or written – lead to little change in what happens to patients. In the SUPPORT study, research nurses with the responsibility for ascertaining and communicating treatment preferences of patients to physicians in ICUs, failed to affect physicians’ decisions when compared to decisions among matched control

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patients without enhanced communication intervention (SUPPORT Princi- pal Investigators, 1995). Various other studies have documented that even when one can find the properly executed document, no matter what form a prescriptive written instrument takes (i.e., using check boxes, blanks that patients fill in with their own words, prepared descriptions of therapies to avoid or use, etc.), clinicians frequently feel that their patients’ circum- stances insufficiently match what the document says or anticipates. Thus, as suggested in an Institute of Medicine (IOM, 1997) study, Approaching Death: Improving Care at the End of Life and in a review by Lo and Steinbrook (2004), instructional directives may confuse, rather than clarify, end of life treatment decisions. Based on current research, and in the wake of the Schiavo case (Quill, 2005), ‘‘proxy’’ directives, or a combination of surrogate appointment, clarifying who should make decisions for the incapacitated patient, and instructions, rather than prescriptive documents alone, are needed.

Health Care Disparities

One of the tenets of modern bioethics involves the importance of social justice. With regard to medical care, most ethicists believe that everyone should have access to a ‘‘decent minimum’’ level of care, presumably including at least that which primary care physicians can deliver. In research settings, most agree and federal regulations require that the benefits and burdens of research and the advantages of access to research studies should be distributed equitably across social classes, ethnic groups, males and females, young and old, and other subgroups. Unfortunately, empirical research suggests that our health care and research systems have not lived up to these ideals.

Politicians like to proclaim the glories of the health care ‘‘system’’ in the U.S. Studies show that some miraculous things do occur, as long as patients or family members have the means to ensure payment for desired services. For example, kidney transplants for those with end stage renal disease occur in substantially greater proportion among white, middle-class patients than among those who are poor and African-American (Eggers, 1995, Epstein et al., 2000; Churak, 2005). This holds true even though kidney failure affects a significantly larger percentage of the African-American population (Martins, Tareen, & Norris, 2002). When eliminating the variable of direct payment, such as within the Veterans Administration system, research

The Significance of Empirical Bioethics for Medical Practice 25

shows that African-American patients with equivalent medical conditions receive less aggressive care for serious heart disease than do Caucasians, i.e., fewer referrals for cardiac catheterization and/or coronary artery bypass surgery (Whittle, Conigliaro, Good, & Lofgren, 1993; Peterson, Wright, Daley, & Thibault, 1994). Recent findings have indicated similar results when comparing male cardiac patients with females, with the latter receiving less intervention (Maynard, Every, Martin, Kudenchuk, & Weaver, 1997; Fang & Alderman, 2006). The lack of access of poor patients to primary care has meant greater, more expensive, and less efficient and effective care (often in emergency departments), leading to delayed or inadequate maintenance and preventive services, and thus more frequent and/or more severe exacerbations of chronic conditions such as asthma, arthritis, and diabetes (Forrest & Starfield, 1998; Stevens, Seid, Mistry, & Halfon, 2006).

Similar patterns can be seen in the research arena. For example, as only a small proportion of children require extensive medical intervention, drug manufacturers often decline to include children in clinical studies on new medications (Yoon, Davis, El-Essawi, & Cabana, 2006). As a result, those treating young patients lack systematic knowledge of proper dosing and differences in toxicities for immature human bodies. In the same vein, fear of liability, rather than patient benefit, seems to have affected pharmaceu- tical companies’ decisions not to study the effects of new drugs in women who are pregnant or at risk of becoming pregnant, even though these women may have or may acquire the same conditions in need of treatment as women who cannot bear children. Those favoring the inclusion of known-to-be or possibly pregnant women in clinical studies view their exclusion as discriminatory and feel that the women, not corporate legal counsel, can and should balance potential harms to themselves or their fetuses compared to the benefits that a clinical trial may offer (McCullough, Coverdale, & Chervenak, 2005). Only additional research focused on this issue can begin to clarify both the likelihood of harm and the adequacy of decision making in the face of possible or actual pregnancy.

Further research aimed at ameliorating injustice in the worlds of clinical care and clinical research seems imperative. As noted, while we know African-Americans receive disproportionately fewer kidney transplants than whites with end stage renal disease, it is poorly understood how much a lack of timely access to primary care, sub-specialty (nephrology) care, transplant center evaluation, or other factors contribute to the low transplantation rate. Without such knowledge, one cannot recommend ethically optimal interventions that can address the inequities.

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AREAS OF CONFLICTING OR UNCLEAR EVIDENCE

Assessment of Decision-making Capacity

As reliance on the doctrine of informed consent depends on the ability of patients or subjects to understand and make use of information about proposed clinical or research interventions, many researchers and clinicians have sought accurate, reproducible methods to determine the adequacy of a patient’s or subject’s decision-making capacity. Putting aside efforts to decide if persons in the criminal justice system have sufficient capacity to stand trial, the search for an assessment instrument or process to adequately assess decisional capacity has had mixed success. In part, the problem seems to stem from the different questions researchers or clinicians believe ought to be asked. For example, does the individual have sufficient capacity to consent to medical care, to participate in research, to return to independent living, to make financial decisions, etc.? According to one review (Tunzi, 2001), the well known MacArthur Competence Assessment Tool (Grisso & Appelbaum, 1998) applies best to persons with known psychiatric or neurological disorders while others, such as the Capacity Assessment Tool (Carney, Neugroshi, Morrison, Marin, & Siu, 2001) or the Aid to Capacity Evaluation (Etchells et al., 1999) work in a general patient population. As Baergen (2002) and Breden and Vollman (2004) comment, the instruments may over- or under-estimate patients’ understanding of their situation, focus mostly on performance of cognitive tasks, inadequately assess the importance of a person’s values and feelings, and minimize the complexity of decision making in actual medical situations, fraught, as they often are, with several levels of uncertainty.

In one particular population, that of minors, confusion and conflict reign regarding decision-making capacity. The work of ethnographers and others, most notably Bluebond-Langner (1978), show that the experience of chronic illness brings knowledge and decision-making ability well beyond one’s years for many children with serious medical conditions. On the other hand, focusing on the need for broad and effective public policy, social psychologists and lawyers (Scott, Reppucci, & Woolard, 1995) rely on research findings that adolescents typically and excessively (1) attend to the short-term consequences of decisions and actions; (2) tolerate risks; and (3) bow to pressure from others, especially adults in authority or peers. From this perspective, one should delay adolescent decisional authority as long as possible, hoping for the onset of maturity. It seems that empirical research

The Significance of Empirical Bioethics for Medical Practice 27

has failed to provide information as to how to determine medical decision- making capacity among adolescents under particular circumstances. Additional research could clarify how to balance an adolescent’s experience and maturity against population-based concerns about psychological and social development. When might an individual teenager, for example one who has lived with severe cystic fibrosis for years, despite relatively young age, say 14 years, have sufficient judgment to decline another round of mechanical ventilation in the ICU, with or without support from her parents? Targeted clinical studies could provide valuable practical data for agonizing ethical decisions regarding such thorny issues.

Protection of Human Subjects of Research

Since the mid-1970s with the introduction of federal regulations governing how institutions must review and oversee research involving human subjects, distressed and disgruntled investigators have often wondered about the extent to which bureaucratic processes actually protect subjects from research-related risk(s). As noted above, the common failure to produce intelligible consent forms suggests the current regulatory structure might not be effective. On the other hand, in the face of enormous increases in biomedical and behavioral research with human subjects since World War II, commentators, e.g. Emanuel (2005) and Fost (2005), point to the apparent low frequency of coercive abuses of human subjects in the era of oversight by IRBs. From this perspective, the rare dramatic and tragic deaths of research subjects prove the rule that the system works well, despite federal rebuke to IRBs at institutions where the deaths occurred.

Only a few studies have systematically assessed the effectiveness of IRBs. One early study that used sham protocols submitted to IRBs around the U.S. found considerable differences in the quality and level of detail reviews, not to mention willingness to approve or reject questionably acceptable studies (Goldman & Katz, 1982). Several more recent publica- tions regarding differing kinds of research (critical care, genetics, health services, and adult surgery) have indicated continuing variability in IRB procedures and responses to federal regulations aimed at protecting human subjects (Silverman, Hull, & Sugarman, 2001; McWilliams et al., 2003; Dziak et al., 2005). At least one ongoing study (Lidz, 2006) should begin to shed needed light on how IRBs routinely do their work.

With regard to research including children, a classic vulnerable group, one might expect greater concern for subject protection and therefore greater

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consistency in the application of rules and regulations. However, some studies do not bear out that expectation. A review of published human subjects research with children led to a survey of authors whose articles failed to indicate whether their research had had IRB review and/or used required informed consent procedures (Weil, Nelson, & Ross, 2002). The survey found significant misclassification by IRBs of studies felt to be ‘‘exempt’’ from IRB review. Another survey of IRB chairs who frequently assessed studies involving children noted wide variations in the definitions the IRBs used for interventions constitution ‘‘minimal’’ or ‘‘minor increase over minimal’’ risk (Shah, Whittle, Wilfond, Gensler, & Wendler, 2004). A recent study showed large differences between IRBs in how they required investigators to respond to federal regulations regarding child assent to participation in research (Whittle, Shah, Wilfond, Gensler, & Wendler, 2004), while a review of IRB websites revealed some ‘‘incorrect advice’’ to investigators about meeting regulatory requirements concerning children (Wolf, Zandecki, & Lo, 2005). These studies begin to point to the areas where interventional research designed to determine how best to help IRB committee and staff members understand ethical considerations and regulations for pediatric research would be useful.

There have also been studies of IRB members that suggest potential problems for human subjects protections in general. Campbell et al. (2003) found that of the nearly 3000 US medical school faculty members responding to their survey, 11% had served on IRBs. Almost half of these individuals (47%) consulted for industry, raising concerns about conflicts of interest in judging research protocols. Van Luijn, Musschenga, Keus, Robinson, and Aaronson (2002) found that ‘‘a substantial minority’’ of the 53 IRB members they interviewed in the Netherlands about reviews of phase II clinical oncology trials ‘‘felt less than fully competent at evaluating’’ key aspects of the research protocols.

Several US federal government bodies have attempted to produce overviews of the adequacy of human subjects protections in the last several years. In 2000, The Office of the Inspector General of the Department of Health and Human Services (2002) issued a report entitled ‘‘Protecting Human Research Subjects: Status of Recommendations.’’ Citing an earlier report from 1998 from the same office warning of problems in the system, the follow-up report indicated that few of its previously recommended reforms had been put in place. They noted little progress in ‘‘continuing protections’’ of research subjects beyond initial IRB reviews, inadequate educational requirements for investigators or IRB members on protecting research subjects generally and preventing or minimizing conflicts of interest, especially.

The Significance of Empirical Bioethics for Medical Practice 29

In 2001, the National Bioethics Advisory Commission (NBAC, 2001), appointed by President Clinton, issued its report Ethical and Policy Issues in Research Involving Human Participants. The introduction, entitled ‘‘The need for change,’’ identified challenges faced by the research oversight system, highlighting the enormous workload faced by IRBs at research- intensive institutions and the high financial stakes involved. NBAC noted inadequate protections for potential subjects from vulnerable populations, inconsistency and rigidity in federal regulations, weaknesses in enforcement mechanisms available to agencies overseeing human subjects research, and inadequate resources for IRBs (administratively) and for IRB members in terms of time and education about research ethics.

In 2001 and 2002, the Institute of Medicine (IOM, 2001, 2002) published two volumes concerned with protection of human subjects. These reports point to various problems in participant protection, including the fact that federal regulations do not necessarily apply to non-federally funded research, depending on arrangements at the institution where the research takes place. Even if that issue were resolved, the IOM studies acknowledge a host of additional problems, some of them well-documented, others simply feared or hypothesized, such as the extent to which potential subjects are exposed to ‘‘coercive’’ efforts to secure their enrollment in research (Emanuel, 2005).

In summary, sufficient and clear data are lacking about the adequacy of protections of human subjects to assist physician-investigators considering referrals of patients to clinical studies and subjects considering participation in research. Some would claim that the combination of federal regulations, local IRB oversight, investigator education, and public good-will provide at least adequate protection for human subjects of biomedical and behavioral research in the US and Western Europe. (The controversies about research in the developing world fall outside the scope of this review.) Alternatively, some believe that the host of demonstrated and feared inadequacies in the system, especially when one considers the financial stakes involved, suggest widespread disregard of subject protection. The skeptics point to the few well-publicized deaths of research subjects in the last decade and suggest that we have only learned about the tip of the proverbially iceberg that is a poorly regulated and possibly corrupt system. We would all benefit from much more detailed studies of actual IRB function, including field observations of research team members as they interact with potential and actual subjects, and studies that monitor or audit compliance of institutions with existing rules for the conduct of human subjects research. For example, the latter research might attempt to generate generalizable results about

JOEL FRADER30

subject understanding of risks and benefits, the completeness of research records, including properly executed consent forms, and the appropriate notification of subjects when new information becomes available that might affect their willingness to continue in a study.

IMPLICATIONS FOR MEDICAL PRACTICE

Empirical research in the area of bioethics has helped clarify ‘‘best practices’’ in many areas. There is compelling evidence that the theory and practice of informed consent have failed to live up to expectations in both clinical and research arenas. Likewise, advance directives have provided insufficient guidelines to most patients and many clinicians for treatment decisions when individuals lose decision-making capacity. Reliance on directives available to clinicians has proved frustrating because the instructions frequently do not cover all possible circumstances that patients, surrogates, and clinicians may face. As a result, health care attorneys, hospital administrators, clinicians, and ethicists now tend to recommend that patients both clearly designate a proxy decision maker and engage in detailed discussions with the appointed surrogate regarding the values that should guide decisions when he/she no longer has decision-making capacity. Empirical studies have clearly shown that ethnic, economic, and gender disparities persist in health care and clinical research despite increases in civil liberties and social justice in the last century. Unfortunately, studies have not yet pointed to ways to reduce the inequities. Clinicians need to maintain a high level of awareness about the potential influences of their unconscious biases on treatment and research recommendations, emergency decision making, and interactions with patients and family members. Institutions may need to develop systems to monitor for inequitable patterns of care and, if discovered, system-wide methods to correct imbalances. Of course, to the extent that patterns reflect larger social problems regarding risk for disease and disability and inadequate health care insurance coverage and payment schemes, providers may face serious financial problems if they undertake to redress inequality on their own. Empirical evidence in other areas of bioethics still remains scant or presents an unclear picture. For example, with regard to the ability to assess the adequacy of patient or (potential) research subject decisional capacity, research results are somewhat mixed. Similarly, physician-investigators trying to decide whether sufficient protections exist for patients who might become research subjects – not least those who are part of vulnerable populations – will find a

The Significance of Empirical Bioethics for Medical Practice 31

confusing array of reassurance, scandal, and troublesome study results. Other bioethical issues that need research attention in efforts to optimize ethical standards and quality in medical care and research include a better understanding of the potential benefits and problems of clinical ethics consultation, the consequences – intended and unintended – of universal health care insurance and rationing schemes, and methods to reduce the administrative burdens on investigators and institutions of ethics reviews of human, animal, and embryonic stem cell research.

In conclusion, the above review and discussion indicate that there is fertile ground for more work at the intersection of bioethics and empirical research that can guide practitioners, investigators, patients, and their families in the quest to continue to advance medical practice and research consistent with ethical principles.

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The Significance of Empirical Bioethics for Medical Practice 35

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SECTION II:

QUALITATIVE METHODS

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QUALITATIVE CONTENT

ANALYSIS

Jane Forman and Laura Damschroder

INTRODUCTION

Content analysis is a family of systematic, rule-guided techniques used to analyze the informational contents of textual data (Mayring, 2000). It is used frequently in nursing research, and is rapidly becoming more prominent in the medical and bioethics literature. There are several types of content analysis including quantitative and qualitative methods all sharing the central feature of systematically categorizing textual data in order to make sense of it (Miles & Huberman, 1994). They differ, however, in the ways they generate categories and apply them to the data, and how they analyze the resulting data. In this chapter, we describe a type of qualitative content analysis in which categories are largely derived from the data, applied to the data through close reading, and analyzed solely qualitatively. The generation and application of categories that we describe can also be used in studies that include quantitative analysis.

QUANTITATIVE VERSUS QUALITATIVE

CONTENT ANALYSIS

In quantitative content analysis, data are categorized using predetermined categories that are generated from a source other than the data to be

Empirical Methods for Bioethics: A Primer

Advances in Bioethics, Volume 11, 39–62

Copyright r 2008 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1479-3709/doi:10.1016/S1479-3709(07)11003-7

39

analyzed, applied automatically through an algorithmic search process (rather than through reading the data), and analyzed solely quantitatively (Morgan, 1993). The categorized data become largely decontextualized. For example, a researcher who wants to compare usage by physicians, patients, and family members of words such as die, dying, or death versus euphemisms such as pass away or demise, would make a list of words, use a computer to search for them in relevant documents (e.g., audio-recordings of oncology outpatient visits), and compare usage in each group using statistical measures (Hsieh & Shannon, 2005).

In qualitative content analysis, data are categorized using categories that are generated, at least in part, inductively (i.e., derived from the data), and in most cases applied to the data through close reading (Morgan, 1993). There is disagreement in the literature on the precise definition of qualitative content analysis; these differences are about how the data is analyzed once it has been sorted into categories. For some authors, qualitative content analysis always entails counting words or categories (or analyzing them statistically if there is sufficient sample size) to detect patterns in the data, then analyzing those patterns to understand what they mean (Morgan, 1993; Sandelowski, 2000). For example, in one study, researchers used qualitative data from semi-structured interviews to identify, count, and compare respondents who personalized the task they were asked to do versus those who did not (Damschroder, Roberts, Goldstein, Miklosovic, & Ubel, 2005). This study derived categorical data from the qualitative data in order to quantitatively analyze differences in the types of responses people gave to the different types of elicitations. Qualitative content analysis is defined more broadly by some researchers to also include techniques in which the data are analyzed solely qualitatively, without the use of counting or stati- stical techniques (Hsieh & Shannon, 2005; Mayring, 2000; Patton, 2002).

USES OF QUALITATIVE CONTENT ANALYSIS

Qualitative content analysis is one of many qualitative methods used to analyze textual data. It is a generic form of data analysis in that it is comprised of an atheoretical set of techniques which can be used in any qualitative inquiry in which the informational content of the data is relevant. Qualitative content analysis stands in contrast to methods that, rather than focusing on the informational content of the data, bring to bear theoretical perspectives. For example, narrative analysis uses a hermeneu- tical perspective that emphasizes interpretation and context, and focuses

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‘‘on the tellings [of stories] themselves and the devices individuals use to make meaning in stories’’ (Sandelowski, 1991, p. 162). There are some methods of qualitative inquiry (e.g., ethnography, grounded theory, and some types of phenomenology) that, though they bring a theoretical perspective to qualitative inquiry, use content analysis as a data analysis technique. Grounded theory, which has been used extensively in nursing research, uses a specific form of content analysis whose goal is to generate theory that is grounded in the data. Because the term ‘‘grounded theory’’ is often used generically to describe the technique of inductive analysis, it is often confused with a form of inquiry called qualitative description (Sandelowski, 2000) or pragmatism (Patton, 2002). The goal of these approaches to data analysis is to answer questions of practice and policy in everyday terms, rather than to generate theory. What we describe in this chapter is a generic form of content analysis commonly used in the health sciences to answer practical questions. The validity of an atheoretical approach is a source of controversy among qualitative researchers, but is widely accepted by researchers in the health sciences.

As compared to quantitative inquiry, the goal of all qualitative inquiry is to understand a phenomenon, rather than to make generalizations from the study sample to the population based on statistical inference. Examples include providing a comprehensive description of a phenomenon; under- standing processes (e.g., decision-making, delivery of health care services); capturing the views, motivations, and experiences of participants; and explaining the meaning they make of those experiences. When used as part of a study or series of studies using a combination of qualitative and quantitative methods, qualitative methods can be employed to explain the quantitative results and/or to generate items for a closed-ended survey.

Qualitative content analysis examines data that is the product of open- ended data collection techniques aimed at detail and depth, rather than measurement. For example, a closed-ended survey can be used to measure the level of trust patients have in their physicians. As an alternative, open- ended interviews in which participant responses are not constrained by closed-ended categories can be used in order to explore the topic of trust more deeply. While a closed-ended survey may provide an assessment of patient trust, it fails to provide any information on the process through which patients come to trust or distrust their physicians, and what trust means to them.

Empirical bioethics studies take advantage of the open-ended nature of qualitative research to, for example, examine and challenge bioethical assumptions, inform clinical practice, policy-making or theory, or describe

Qualitative Content Analysis 41

and evaluate ethics-related processes or programs. For example, in a study to understand how housebound elderly patients think about and approach future illness and the end of life, interviewees ‘‘described a world view that does not easily accommodate advance care planning’’ (Carrese, Mullaney, Faden, & Finucane, 2002, p. 127). In another study, female patients talked about their views on medical confidentiality to inform clinical practice around confidentiality protections (Jenkins, Merz, & Sankar, 2005). Findings showed that some patients ‘‘might have expectations not met by current practice nor anticipated by doctors’’ (p. 499). As a first step toward developing benchmarks of clinical ethics practices, another study described and compared the structure, activity, and resources of clinical ethics services at several institutions (Godkin et al., 2005). Results indicated a high degree of variability across services and that increasing visibility was a challenge within organizations.

DOING QUALITATIVE CONTENT ANALYSIS

In the rest of this chapter, we will discuss the choice of qualitative content analysis as one of many decisions in designing a study. We will review the processes and procedures involved in this type of analysis, including data management, memoing developing a coding scheme, coding the data, using coding categories to facilitate further analysis, and interpretation. We will also discuss the use of software developed to aid qualitative content analysis.

STUDY DESIGN

As we discussed, qualitative content analysis is one of many techniques for performing analysis of textual data. Although it is beyond the scope of this chapter to discuss study design in any detail, we will briefly illustrate the types of design decisions required for qualitative studies and their effect on data analysis.

As with all empirical research, study design starts and flows from the research question(s). Thoughtfully and deliberately matching data sources, sampling strategy, data collection methods, and data analysis techniques matched to the research questions is fundamental to the quality and success of any study. After formulating the research question(s), Mason suggests that the researcher ask the following questions when designing a qualitative

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study: ‘‘What data sources and methods of data generation are potentially available or appropriate? What can these methods and sources feasibly describe or explain? How or on what basis does the researcher think they could do this? Which elements of the background (literature, theory, research) do they relate to?’’ (Mason, 2002, p. 27) Examples of data sources are individuals and groups (e.g., persons with kidney failure, family members), sites (e.g., ICU, primary care clinic, governmental agency), naturally occurring interactions (e.g., primary care visit, congressional hearing), documents and records (e.g., policies and procedures, medical records, legislation), correspondence, diaries, and audio-visual materials. Data collection techniques include individual interviews, focus groups, observations, and sampling of written text(s).

Once it is clear that a qualitative approach is appropriate, a number of related issues need to be addressed. First, an exploration of what is already known about the topic of study is necessary. The more that is known about the topic, the more structured or deductive the data collection and analysis are likely to be. This is because previous empirical and theoretical work will provide a conceptual framework consisting of concepts and models that direct data collection and analysis (Marshall & Rossman, 2006).

The study’s research question will point to a particular unit or units of analysis. Units of analysis can be individual people, groups, programs, organizations, communities, etc. The unit of analysis is the object about which the researcher wants to say something at the end of the study. There may be more than one unit of analysis in a study (Patton, 2002). For example, in a multi-site study of communication in ICUs, ICUs, health provider groups (e.g., physicians and nurses) and individuals (e.g., individual nurse points of view) could all be units of analysis.

Having identified an appropriate unit of analysis, the researcher must decide how best to sample it. Sampling in qualitative studies is what is called purposeful. In purposeful sampling, the goal is to understand a phenomenon, rather than to enable generalizations from study samples to populations. In-depth study of a particular phenomenon involves an intensive look at a relatively small sample, rather than a surface look at a large sample. ‘‘Information-rich’’ cases are selected for in-depth study to provide the information needed to answer research questions. It is important to choose those cases that will be of most use analytically (Patton, 2002; Sandelowski, 1995b).

Finally, the study’s conceptual framework, unit(s) of analysis, sampling, and data collection technique(s) will affect the data analysis performed: the

Qualitative Content Analysis 43

conceptual framework will influence the categories used to code the data, as well as how deductive or inductive the analysis will be; the unit(s) of analysis will determine the entities around which the analysis is organized; the sampling strategy may create subgroups that can be compared; and the data collection technique selected will produce data of varying degrees of depth. On this last point, the more in-depth the data (e.g., a few extended in-depth interviews), the more challenging and time-intensive it is to analyze. Also, because more in-depth data provides more information about what participants mean by their statements, and may indicate causal linkages emerging from the data, it provides more evidence to support a higher inference analysis than data that focuses on breadth (e.g., a set of focus groups). By higher inference, we mean that the researcher can interpret the data at a higher level of abstraction. For example, in a focus group study, participants may name the quality of communication with various members of the health care team as a barrier to appropriate care in the ICU. Data collected through in-depth interviews may contain enough evidence to characterize types of communication barriers with more abstract concepts, such as manifest versus latent communication.

It is helpful to consider several additional points when designing a study. First, having several people analyzing the data demands a more structured approach. Second, resource constraints (e.g., time, money, personnel) force trade-offs between the richness of the data, the amount of data collected, and the quality of analysis. The researcher must make all these decisions must be made with the purpose of the study in mind by considering the resources available and the optimal way to expend the resources to obtain the study’s goals.

Finally, there is only so much that can be learned from hearing, reading, and thinking about content analysis. In our experience, knowing about qualitative methods is not the same as having experience using them. We suggest that when embarking on a qualitative study, the novice seek a mentor to learn how to use methods and techniques effectively. The most effective learning comes from being actively engaged in a project. Qualitative research requires a somewhat more hands-on approach than quantitative techniques.

DATA MANAGEMENT

As in any study, before starting data collection, it is important to develop a system for labeling the data (e.g., participant, site, etc.). Qualitative data can

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take on more complex and varied forms than quantitative data that depend on its sources, for example, interviews, direct observation, complicated textual sources, and video-recordings. Therefore, the data sources must be clearly labeled in preparation for coding and electronic entry. For instance, audio-recordings of interviews should be labeled with the participant id number, the date, and the interviewer’s initials. After an interview, it is important to make a copy of the audio-recording so that there is a backup in case the original is lost or destroyed. In most qualitative content analyses, the next step is to transcribe the recording so that it appears as a written text. However, it is essential to recognize that any transcription of spoken words will be incomplete. Qualitative content analysts are interested in the informational content of the data. Therefore, the transcription focuses on a verbatim representation of the words on the audio-recording and may also include some indicators of emotion (e.g., exclamation point, notation of laughter). In contrast, transcription for analysis of discourse has highly detailed specifications that may include, for example, representation of interruptions, pauses, intonation, and simultaneous talk.

It is important to use clear rules for how recordings are to be transcribed, whether the transcription is done by the researcher or delegated to a transcriptionist. Having these rules avoids unnecessary work later on, and makes it easier to read and analyze multiple transcripts. Table 1 shows an example of rules that can be included. This is not meant to be an exhaustive list; items and rules for any specific project will vary.

Table 1. Example Rules for Transcription.

The finished product should be in word (no tables) – Version x; with the following specifications:

Times new roman 12 point font

Create a separate file for each tape. The filename must include the tape, and site identifier (e.g.

TapeB-SiteX.doc) and insert in the first line of the file)

Insert page numbers in footer

Header 1: Person ID; place on a separate line from the text

1.5uu margins

Note interruptions and inaudible conversation by inserting [INTERRUPTION],

[INAUDIBLE] in the text. (Only indicate [INAUDIBLE] when words cannot be

distinguished because of the recording)

Indicate significant deviations from normal conversational tone as [LAUGHTER], [ANGRY],

[LOUD] after text

Insert tape counter position periodically through the document

If you are unsure of a word, put a [?] after the word(s)

Qualitative Content Analysis 45

After a transcript is completed, it will need to be compared to the recording to make sure it is accurate. It is best if the interviewer can verify the accuracy of the transcript because the interviewer is more likely to recall information that may be difficult for the transcriptionist to hear due to inaudible speech. If the interviewer is also the transcriptionist, additional editorial comments about the interpretation of what occurred during the interview at specific times can be notated. If editorial comments are added, it is vital to clearly label them as such so that they are not mistaken for raw data.

The final step in preparing the transcript for analysis is stripping it of information that identifies participants, such as names and places. It is important to have clear rules for how identifying information will be replaced. The aim is to include enough information about a particular word being replaced so that the informational contents are not lost.

DATA IMMERSION, REDUCTION, AND

INTERPRETATION

Content analysis requires considerably more than just reading to see what’s there.

(Patton, 2002, p. 5)

In qualitative content analysis, data collection and analysis should occur concurrently. One danger in qualitative research is the collection of large amounts of data with no clear way to manage or analyze it. To maximize the chances of success, one should ‘‘engage with’’ the data early on by starting to develop a coding scheme. By examining the data as it is collected, the researcher will become familiar with its informational content, and may identify new topics to be explored and develop analytic hunches and connections that can be tested as analysis progresses. These insights also inform subsequent data collection. For example, a question might be added to the interview guide to explore a new topic with a subsequent interviewee. Although this chapter presents content analysis as a series of sequential steps, it is important to note that it is an inherently iterative process.

One useful approach is to divide content analysis into three phases: immersion, reduction, and interpretation (Coffey & Atkinson, 1996; Miles & Huberman, 1994; Sandelowski, 1995a). Through each of these phases, the goal is to create new knowledge from raw, unordered data. Content analysis requires both looking at each case (e.g., participant, site, etc.) as a whole

JANE FORMAN AND LAURA DAMSCHRODER46

and breaking up and reorganizing the data to examine individual cases systematically, and compare and contrast data across cases.

Immersion: Engagement with the Data

During immersion, the researcher engages with the data and obtains a sense of the whole before rearranging it into discrete units for analysis. There are several ways to accomplish this. First, the researcher can write what is called a ‘‘comment sheet’’ immediately after the data collection activity to record first impressions, new topics to be added in future data collection, comparisons to data collected previously (if this is not the first data collection activity), and analytic hunches. Second, the audio-recording is listened to. Third, when transcripts are available they can be read several times. The last process that can be employed is to write thoughts that are triggered while listening to and/or reading the data. This form of ‘‘free association’’ is often associated with meaningful insights that can be tested later on. While the science of qualitative content analysis – the process of developing and implementing a systematic approach to data analysis – is vital, much of the art of content analysis takes place when the analyst makes connections that occur once the data are considered holistically.

An essential part of the immersion phase is referred to as ‘‘memoing.’’ Memos are documents written by the researcher as he/she proceeds through the inspection of the data and can contain just about anything that will help make sense of it. Memos serve as a way to get the researcher engaged in the data by recording early thoughts and hunches. They also serve to initiate the data analysis by identifying and sharpening categories and themes (core topics or meanings) that begin to emerge. This diminishes the potential for losing ideas and thoughts in the process. Throughout the analysis, memos can describe themes and the connections among them that are developed through interactive inspection of the data. Memos serve as an audit trail of the researchers’ analytic processes and add credibility to the final analysis and conclusions. Memos can be coded along with the raw text data (e.g., transcripts) so that their contents appear in the reorganized data along with the raw data. The researcher can then read the raw data in a particular category along with the relevant category descriptions and analytic hunches recorded in memos during the immersion, prelimi- nary coding, and code development phases of analysis. We describe the process of developing a coding scheme and coding the data in the next section.

Qualitative Content Analysis 47

Reduction: Developing a Consistent Approach to the Data

One of the most paralyzing moments in conducting qualitative research is beginning

analysis, when researchers must first look at their data in order to see what they should

look for in their data. (Sandelowski, 1995a, p. 371)

The reduction phase is when the researcher develops a systematic approach to the data. It constitutes the heart of the content analysis process and supplies rigor to the process. The goals of the reduction phase are to: (1) reduce the amount of raw data to that which is relevant to answering the research question(s); (2) break the data (both transcripts and memos) into more manageable themes and thematic segments; and (3) reorganize the data into categories in a way that addresses the research question(s).

Codes: What are They and Where do They Come from?

Codes provide the classification system for the analysis of qualitative data. Codes can represent topics, concepts, or categories of events, processes, attitudes or beliefs that represent human activity, and thought. Codes are used by the researcher to reorganize data in a way that facilitates interpretation and enables the researcher to organize and retrieve data by categories that are analytically useful to the study, thereby aiding interpretation. The thoughtful and deliberative development of codes provide rigor to the analytic process. Codes create a means by which to exhaustively identify and retrieve data out of a data set as well as enable the researcher to see a picture of the data that is not easily discernable in transcript form. As Coffey and Atkinson state, ‘‘attaching codes to data and generating concepts have important functions in enabling us rigorously to review what our data are saying’’ (Coffey & Atkinson, 1996, p. 27).

Codes can be either deductive or inductive. Deductive codes exist a priori and are identified or constructed from theoretical frameworks, relevant empirical work, research questions, data collection categories (e.g., interview questions or observation categories), or the unit of analysis (e.g., gender, rural versus urban, etc.). Inductive codes come from the data itself: analytical insights that emerge during immersion in the data and during what is called ‘‘preliminary coding’’ (see below). Although there are studies that use codes developed either deductively or inductively, content analysts most often employ a combination of both approaches. This means using a priori deductive codes as a way to ‘‘get into’’ the data and an inductive approach to identify new codes and to refine or even eliminate a priori codes.

JANE FORMAN AND LAURA DAMSCHRODER48

Developing the Coding Scheme

We now turn to a description of how to develop a coding scheme and codebook. Coding the data allows the researcher to rearrange the data into analytically meaningful categories. Code definitions must be mutually exclusive; that is, they must have definitions that do not overlap in meaning. When coding categories are created it is important to consider how the coded data will look once retrieved, and how the rearranged data facilitate addressing the research question(s) during the next phase of the analysis.

Codebook development is an iterative process, and begins with what is called ‘‘preliminary coding.’’ This consists of reading through the text, highlighting or underlining passages that may be potentially important and relevant to the research questions, and writing notes in the margins. As noted above, because code development most often is based on inductive and deductive reasoning, it often starts with deductively developed codes but remains open to new topics suggested by the data (inductive codes).

To illustrate preliminary coding and subsequent codebook development (and, later in the chapter, the application of codes and data interpretation), we will use a short interview extract from the following hypothetical empirical bioethics study.

1 The study explores the development of

interpersonal trust in physicians from the point of view of patients with kidney disease who are at risk of losing enough function to need dialysis or a kidney transplant. Using semi-structured interviews, it seeks to understand which physician behaviors and qualities are important in the formation and maintenance of patient trust. This study conceptualizes trust as a phenomenon that arises from the patient’s experience of illness. Phenom- enologists have shown that illness disrupts patients’ previously taken-for granted ways of being in the world (Zaner, 1991). Patients’ physical, emotional, and existential need arise from these disruptions and attendant feelings of distress. Trust is a central moral issue in the physician–patient relationship because patients, in their vulnerable state, need to trust that physicians use their expertise and power in their patients’ best interest.

Table 2 shows an extract from an interview with a patient who has kidney disease and who is concerned about the progression of her disease. Based on the literature, which shows that physicians who more freely share complete information with patients engender more trust (Keating, Gandhi, Orav, Bates, & Ayanian, 2004), the researcher would begin with a deductive code, for example ‘‘clinical information.’’ The researcher would read the transcript, looking for statements related to ‘‘clinical information,’’ and highlight the text starting at line 4 in the transcript, in which the participant

Qualitative Content Analysis 49

says, ‘‘y eventually, I’ll need to make a decision about what kind of treatment I want y the thing I like most about him is that he always explains things really well.’’ The researcher would also make a notation in the margin about the concept ‘‘explaining,’’ and about how the patient may use the information, namely to make a treatment decision. After reading through several transcripts, the researcher may find descriptions of different uses of clinical, information and a deductive code called ‘‘clinical information’’ may evolve to ‘‘uses of clinical information.’’

The researcher will also want to use inductive reasoning to develop new codes, specifically in vivo codes, which reflect the way informants make sense

Table 2. Extract of Transcript from an Interview with a Patient with Kidney Disease Shortly after Visiting her Nephrologist.

I: How has your kidney disease been lately?

P: Well, Dr. [name of nephrologist] said that I’m pretty stable, but that eventually I’ll need to

make a decision about what kind of treatment I want. You know, the thing I like most about

him is that he always explains things really well. Like what my labs are compared to last time.

And he tells me in regular language, so I can understand. Not like some doctors, who, um,

well, you might as well be a number, for all they care. Like one doctor I went to, he barely

looked at me, much less answered my questions. Dr. [name of nephrologist] is just the

opposite. When he comes into the room, he looks you in the eye.

I: When you say that Dr. [name of nephrologist] explains things really well, what do you mean?

P: Well, you know, it’s scary having this disease. Like when I get a pain in my back, I’m

thinking that my kidney is deteriorating or there’s too much poison in my blood or I have an

infection or something. That actually happened today. I told Dr. [name of nephrologist] I was

having these pains, and he examined me and looked at my lab tests and said that it was

actually muscle pain; it didn’t have anything to do with my kidneys, just normal stuff. He said

that if I had an infection, I’d have a fever and a real different kind of pain, described the

difference. He also said that my kidneys getting worse wouldn’t cause pain like that. So I feel

relieved. I mean I thought last week maybe I should come in and have him take a look at me,

but I wasn’t sure, so I didn’t. So I just spent the week worrying. You know, I’m afraid I’m

going to have to start dialysis or get put on the transplant list.

I: What has Dr. [name of nephrologist] told you about that?

P: Well, he was real clear about what I should expect. This sure isn’t going to get better, but I

could stay the same for a while, or I could get worse more quickly. So he doesn’t know

exactly what’s going to happen, but that’s ok, as long as he’s honest about it. It just helps to

know. If I do get bad enough, I’ll have to start dialysis, and decide what to do about a

transplant. He told me stuff about how that works, getting on the list and what I’ll need to

consider. That puts my mind at ease; I know I won’t be hit with all this stuff that I don’t

know about when the time comes

JANE FORMAN AND LAURA DAMSCHRODER50

of their world. For example, the text beginning at line 6, ‘‘y he tells me in regular language, so I can understand,’’ can be highlighted with a note about the patient’s desire for information from the physician in ‘‘regular language.’’ In the course of codebook development this statement may be used to support a code developed inductively, called ‘‘physician commu- nication of clinical information.’’ Second, a preliminary code called, ‘‘relief from worry’’ might be created based on lines 18–26 in the transcript and other passages in this transcript and other transcripts. After reading through several transcripts, the code may evolve into a name that defines the concept more broadly, and frames it in terms of use of information, such as ‘‘reassurance.’’

A codebook must be developed to organize codes and to help ensure they are used reliably. A codebook is especially important for projects using multiple coders. Table 3 shows an extract of the codebook from our hypothetical study, and contains a partial list of codes, definitions, and example quotes for each code. The example shows the fundamental elements that a codebook should contain: (1) name of the element; (2) an abbreviated label for that code (e.g., [REASSURE]); (3) the node type; (4) a description of the code that includes a clear definition, often with inclusion and exclusion criteria; and (5) example quotes that further illustrate the correct use of that code, along with a notation of the transcript and line numbers where the quote is located in the data set. The node type refers to the hierarchical position of that code in the coding framework. For example, uses of information [INFOUSES], a parent node, is a high-level category (code) that has four different types of uses: (1) what to expect on progression of the disease; (2) making a decision; (3) reassurance; and (4) monitoring symptoms. Fig. 1 shows how these codes relate to one another and help visualize how ‘‘parent’’ nodes relate to ‘‘child’’ nodes (see p. 43).

When working with a team, the code development process will proceed differently than for a solitary researcher. Team coding has its perils, but those are far outweighed by the benefits of having multiple perspectives to establish content validity and the ability to establish and test coding reliability. Multiple coders use much of the same procedures previously outlined in the chapter but their preliminary coding is done independently. The researchers come together to share their impressions of the data. It is important that all team members who might be involved in coding or later stages of the analysis (e.g., interpretation, writing manuscripts) be involved in these early meetings. The goal of this early development is not to review pages of transcripts but rather to engage in high-quality conceptualization through an iterative, negotiated process. Usually, to produce a revised

Qualitative Content Analysis 51

Table 3. Example Codebook to Guide Data Coding.

Code Node

Type

Description Example

Uses of clinical

information

(INFOUSES)

Parent Discussion of the uses of information by the

patient. EXCLUDE sub-codes

WHAT to expect on

progression of the

disease (EXPECT)

Child Information about progression of the disease

and what the patient can expect as it

progresses, or the need for such information.

Also, INCLUDE statements about the lack

of information about disease progression and

feeling in the dark about what to expect

‘‘y he was real clear about what I should

expect. This sure isn’t going to get better, but

I could stay the same for a while, or I could

get worse more quickly. So he doesn’t know

exactly what’s going to happen, but that’s ok,

as long as he’s honest about it.’’ [30–33, 2035]

Making a decision

(DECISION)

Child Information that is useful to the patient to

make decisions about medical treatment of

the disease, or need for such information

‘‘eventually I’ll need to make a decision about

what kind of treatment I want’’ [4, 2035]

Reassurance

(REASSURE)

Child Information to help relieve worry or fear, or the

need for such information. Also, INCLUDE

statements related to communication that

causes worry or anxiety

‘‘He also said that my kidneys getting worse

wouldn’t cause pain like that. So I feel

relieved.’’ [22–23, 2035]

Monitoring

symptoms

(MONITOR)

Child Information to help monitor symptoms, or the

need for such information

‘‘Like when I get a pain in my back, I’m

thinking that my kidney is deteriorating or

there’s too much poison in my blood or I

have an infection or something .... He said

that if I had an infection, I’d have a fever and

a real different kind of pain, described the

difference.’’ [15–22, 2035]

Md communication of

clinical information

(INFOCOMMMD)

Parent Descriptions of and/or judgments about the

ability of the physician to communicate

information in a way the patient can

understand or apply

‘‘he tells me in regular language, so I can

understand.’’ [6–7, 2035]

‘‘he barely looked at me, much less answered

my questions.’’ [8, 2035]

J A N E F O R M A N

A N D

L A U R A

D A M S C H R O D E R

5 2

(or initial) list of codes, the team will first review a few pages of one transcript. The team will then enter into an iterative process in which analysts apply the revised codes to a portion of the data and then meet again to add or delete codes, and further refine code definitions. After each meeting, decisions and definitions must be documented as codes are proposed, refined and finalized. This makes the process both transparent and systematic, thereby increasing the rigor of the analysis. The codebook is a good place to track changes over time by dating revisions made to code definitions.

Mason (2002) provides guidance as to the number of codes and precision of definitions to use when creating codes. Codes allow the researchers to index the data so that they can easily find salient text segments that relate to particular topics or concepts in the next stage of analysis. Discovering, even after coding, that it is difficult to find meaning in the data because the reorganized text is not focused sufficiently on analytically useful topics or concepts is an indication that the codes have been defined too broadly. Codes can also be defined too narrowly. When this occurs coders will have difficulty discerning which of the two closely defined codes should be applied. Too narrow coding can also obscure the ability to see larger patterns and themes. Study goals, resources and the amount of time available will dictate the depth and breadth of coding done and the extent to which multiple, independent coders can be used.

The framework for codes refers to the way the codes are arranged in relation to each other to form a conceptual map. It must be carefully designed in a way that best fits the data and that meets the goals of the study. Although each study must be approached individually, the development of 20–40 codes is the norm. As we saw in the example above, it is helpful conceptually to create coding ‘‘trees’’ in which there is a primary or parent code, with all related sub-codes or child codes listed under the parent (e.g., uses of clinical information and its children). Codes should parsimoniously categorize text and yet thoroughly cover the richness of information contained in that text. The framework chosen can make the

INFOUSES

EXPECT Child Nodes DECISION REASSURE MONITOR

Parent Node

Fig. 1. Diagram Showing Relationship Between Types of Coding Nodes.

Qualitative Content Analysis 53

difference between juggling hundreds of unrelated codes versus a fraction of that number of codes organized conceptually; the difference between creating a coding quagmire and providing a launching point for the next phase of analysis.

Coding Agreement

When code definitions have become substantially stable, and prior to applying the codes to the entire data set, coding agreement must be established. Agreement is when two or more coders who code text data independently, using the same codebook, can consistently apply the same codes to the same text segments. Although differences in how codes are applied are almost guaranteed to occur, regardless of how detailed codebook definitions may be, a sound conceptualization process, along with a well-constructed codebook with well-defined codes will help guide all coders to apply codes consistently. These constitute key methods in ensuring rigor in content analysis. When working in teams, the codebook is especially important for facilitating agreement because several different people may code different portions of the data. When using these methods it is common for solo researchers to assess agreement by having a second coder code a portion of the data and compare the results.

The issue of coding agreement exposes a basic tension between the positivist view that bias introduced by human involvement in research must be minimized to increase the validity of research results, and the constructivist view that validity is derived from community consensus, through the social process of negotiation (Lincoln & Guba, 2003; Sandelowski & Barroso, 2003). A tenet of qualitative research is that the researcher is the primary instrument of the research, and brings with her particular experiences, assumptions, and points of view that will affect interpretation of the data (Mason, 2002). Also, some coders may be more familiar with study aims or the data set than others. Thus, multiple coders mean multiple research instruments. Those with a positivist orientation label this as bias and aim to minimize it, while those with a constructivist orientation see it as an inherent feature of the interpretive process.

These differing orientations lead to two basic approaches as to how the agreement process should be structured in order to increase the validity of study findings. The first is measuring inter-coder agreement: using quantitative measures of agreement of the coding of two or more independent coders to establish coding reliability. Agreement is measured toward the end of coding scheme development; when it reaches a particular level, the codes are deemed reliable, and coding of the whole data set

JANE FORMAN AND LAURA DAMSCHRODER54

proceeds. There are many ways to quantitatively measure agreement (Lombard, Snyder-Duch, & Bracken, 2002) and some qualitative research- ers, following a positivist philosophy, believe that using quantitative measures are essential to establish reliability, especially when working in teams (Krippendorff, 2004; Neuendorf, 2002).

The second basic approach to the agreement process is using a consensus process in which two or more coders independently code the data, compare their coding, and discuss and resolve discrepancies when they arise, rather than measuring them. Qualitative researchers who follow a constructivist philosophy do not believe that quantitative measures of reliability are appropriate in content analysis, largely because of their view that unanimity among coders often leads to over-simplification that compromises validity, and that reflexivity and reason-giving are more important aspects of an agreement process than achieving a pre-specified level of agreement independently (Harris, Pryor, & Adams, 2006; Sandelowski & Barroso, 2003). Mason (2002) defines reflexivity as ‘‘thinking critically about what you are doing and why, confronting your own assumptions, and recognizing the extent to which your thoughts, actions and decisions shape how you research and what you see’’ (p. 5). A negotiated agreement process happens when coders meet to discuss the rationale they used to apply particular codes to the data. Through discussion, team members are able to explain their perspectives and justifications, how and why it differs from other team members’ perspectives, and reach consensus on how the data ultimately should be coded. It is important to understand the strengths and weaknesses of each approach and develop a process that best fits the study. The preferred approach will depend on study aims, the coding process used, the type of codes that are being applied (e.g., low versus high inference), the richness of the text being analyzed, the degree of interpretation required for the final product, and the targeted venue for publication and dis- semination of the study results.

Coding and Reorganizing the Data

After a codebook is developed, and the codes can be used reliably or a consensus process is established, the codes can be applied to all the text in the data set. Once accomplished, the text is rearranged into code reports, which list all of the text to which each particular code has been applied.

When applying a code to a segment of text, the coder must be sure to include text that will provide sufficient context so that its meaning can be discerned. For example, the entire section spanning lines 15–26 in Table 2 could be coded as REASSURANCE to provide full context for how the

Qualitative Content Analysis 55

physician was able to ‘‘put [her] mind at ease.’’ To understand why this is necessary, imagine if only lines 22–23 were coded, ‘‘He also said that my kidneys getting worse wouldn’t cause pain like that. So I feel relieved.’’ When the text segment is read in a report that contains all of the text in the data set coded with REASSURANCE, instead of read in the context of a transcript, the reader loses information as to what led the patient to worry, including the connection to her experience of illness as ‘‘scary’’ because it could result in the need for dialysis or a kidney transplant.

Even after the codebook can be used reliably or a consensus process has been established, there may be changes in code definitions. New codes may be added as existing codes are applied to new text and as conceptualization progresses. It is a challenge to manage the tension between the desire for a predictable, sequential, and efficient process and allowing the process to be guided by intuitions, concepts, and theories arising from the data. Especially for large data sets, however, there is a point when the codebook, including code definitions, should be considered final, unless it is deemed critical to add a new code. Depending on resources, it may be possible to recode smaller data sets, say less than 20 transcripts, when new code definitions and codes arise.

Interpretation

Data to be interpreted include the code reports and memos that can contain anything from interpretive notes to preliminary conclusions, as mentioned earlier. These products need to be further analyzed, interpreted, and synthesized in order to formulate results. This phase of the analysis involves using the codes to help re-assemble data in ways that promote a coherent and revised understanding or explanation of it. Through this process the researcher can identify patterns, test preliminary conclusions, attach significance to particular results, and place them within an analytic framework (Sandelowski, 1995a). There is no clear line between data analysis and interpretation; ordering and interpretation of data occurs throughout the analysis process. However, by the interpretation phase, the groundwork has been laid to produce a finished product that communicates what the data mean. There are many ways to go about interpreting data, but almost all will include re-organizing it, writing descriptive and interpretive summaries, displaying key results, and drawing and verifying conclusions (Miles & Huberman, 1994).

To reorganize data in a way that facilitates interpretation, the researcher chooses to produce particular code reports, and organize these reports by

JANE FORMAN AND LAURA DAMSCHRODER56

cases, i.e., subsets of the data that represent the unit(s) of analysis, for example, site or health provider type. (If, as in our example study, the unit of analysis is the individual, each participant counts as a case.) Code reports can represent all of the data in the data set coded with a single code (e.g., REASSURANCE), or a combination of codes (e.g., INFOCOMMMD and DECISION). Code reports and how they are summarized are determined by the research questions, what has been learned from the data analysis to date, and the specifics of what the researcher wants to examine. Code reports can enable case-by-case analysis or can help the researcher delve more deeply into a particular topic. The process is dynamic and iterative and guided by what is learned from the data, so a number of code reports may be produced.

After choosing and organizing the code reports, the researcher writes descriptive and interpretive summaries of the data contained in each report. The structure of these code report summaries will depend on the project, but usually includes the main points obtained from reading the report, quotations selected to provide evidence for those points, and an interpretive narrative at the code and/or case level. As discussed earlier, it is vital to draw a distinction between the raw data and the interpretation of the data. Summaries for each case should be grouped together so that each can be examined before making cross-case comparisons.

Data displays (e.g., matrices, models, charts, networks) can be helpful for exploring a single case, but are particularly helpful in looking across cases. Miles and Huberman (1994) define a display as ‘‘an organized, compressed assembly of information that permits conclusion drawing and action’’ (p.11). Seeing the data in a compressed form, organized in a systematic way, makes it easier to recognize patterns. It facilitates comparisons, which are important in drawing conclusions from the data. For example, a matrix with categories found to be analytically meaningful arrayed horizontally across the top and cases arrayed vertically can be created. These categories can be codes that were used to break up the data and/or themes which reflect a higher level of interpretive understanding that are developed as interpreta- tion progresses. Each cell in the matrix is filled in with text, numbers, or ordinal group (e.g., text excerpts; main points; 1, 2, 3; high, medium, low) that summarize the characteristics of that category in each case. Fig. 2 is an extract from a data display matrix from our example study (an actual display would include more participants). It assumes that the researcher identified a new theme early in the interpretive phase: ‘‘Attributing physician motives,’’ defined as what motivations the participant attributes to their physician to account for the way the physician communicates with

Qualitative Content Analysis 57

them. The researcher creates a data display that arrays attributed motivations along with a brief summary of the contents of each of the codes listed in the example codebook. The researcher would then look at the display to discern patterns in the data and draw preliminary conclusions. Data displays are powerful tools and often are used throughout the interpretation process.

Drawing Conclusions

Drawing and verifying conclusions involve developing preliminary conclu- sions and testing them by going back into the data. The researcher may develop the conclusion that one of the ways patients develop trust in their physicians is when physicians explain clinical information in a way that they can understand and that these behaviors denote physician competence to the patient. It is important to look for alternative themes and conclusions that

ID INFOCOMM MD

EXPECT DECISION REASSURE MONITOR

101 “doesn’t know what he’s doing”

“doesn’t think I’d understand”

Didn’t answer my questions

Wants more info from MD on potential progression to dialysis

Not mentioned

Worry re: acute symptoms (pain): Does it indicate disease progression?

MD did not address satisfactorily when asked.

Wants to know whether should call the physician when she has pain or fever; are kidneys infected?.

102 “a brilliant physician”

“cares about me”

Can understand what MD says (no jargon)

Gives detailed info

Describes what MD told her about expected treatment progression; was detailed and included uncertainty

Needs to make decision about a transplant; MD gave useful info, including rationale for each treatment option

Was worried re: fatique. MD told her it was typical and why it was occurring.

Not mentioned

Attribution of MD motivation

Fig. 2. Data Display Matrix.

JANE FORMAN AND LAURA DAMSCHRODER58

may ‘‘fit’’ the data better throughout the interpretation process and not settle on premature analytic closure.

Conclusion verification is derived from going back into the data to find evidence that supports or refutes a particular conclusion. The result of verification can be finding that the conclusion holds in most cases, or is refuted, or that an alternative or refined conclusion is supported. If it does hold in most cases, it is not enough to report the theme and show supporting evidence for it. The researcher also must examine ‘‘negative cases’’ – cases for which the conclusion does not hold. In the example, patients for whom information is not useful and who instead judge physician competence and develop trust based on non-verbal and social cues is one such instance. In the final product, discussion of these ‘‘negative’’ cases as they relate to the conclusions adds credibility to findings by showing that the researcher searched for what made most sense rather than simply using data to support one conclusion (Patton, 2002). What about them or their situation is different and what does that say about the phenomenon under study? Finally, the act of writing a report or manuscript presenting study findings refines and clarifies the interpretation and should not be minimized as an important step in the interpretive process.

Using Software

Qualitative researchers are increasingly using software to manage data and facilitate data analysis and interpretation. Commonly used software includes ATLAS.ti (http://www.atlasti.com/index.php), MaxQDA (http:// www.maxqda.com/), and NVivo (www.qsrinternational.com). It should be emphasized that software is a tool to help manage, retrieve, and connect data, but cannot perform data analysis. Too often, researchers unfamiliar with qualitative coding invest in a software purchase in the mistaken belief that the software will produce and code the data. Correctly used, and with the appropriate data set, such programs can enhance the efforts of the qualitative researcher.

Nearly any kind of data source (e.g., text, pictures, video clips) can be imported into the software and coded or linked using tools within that software. Some researchers will use software primarily to enter codes and rearrange their data into coding reports. Others will use it more comprehensively as they work through code development, coding, creating code reports, interpretation, and final manuscript writing.

Qualitative Content Analysis 59

The software allows researchers to link source documents with notes, memos, summaries and even theoretical models. For example, notes in the margin of a transcript can be created within NVivo by adding ‘‘annotations,’’ memos and summaries of code reports can be created and coded or linked to other documents in the data set. These kinds of software programs are especially helpful when working in teams because they facilitate sharing annotations, code reports, and summaries. For example, after the interview shown in Table 2 is coded, code reports can be generated to include text related to ‘‘uses of clinical information,’’ grouped by each of the sub-codes (progress, monitor, reassure, and decision). The software also allows one to do special queries, such as reporting all text coded with a designated union or intersection of codes. Analyses can be performed on subsets of transcripts (e.g., patient groups, sites) so that a variety of focused comparisons can be made.

SUMMARY

In this chapter, we have defined qualitative content analysis and discussed the choice of this method in qualitative research in the field of bioethics. As compared to quantitative inquiry, the major goal of qualitative inquiry is to understand a phenomenon, rather than to make generalizations from study samples to populations based on statistical inference. Qualitative content analysis is one of the many ways to analyze textual data, and focuses on reducing it into manageable segments through application of inductive and/or deductive codes, and reorganizing data to allow for the drawing and verification of conclusions (Miles & Huberman, 1994). The product of this process is an interpretation of the meaning of the data in a particular context. Qualitative content analysis that can be used by itself or in combination with other empirical methods can be employed to examine textual data derived from several sources and constitutes a versatile strategy to explore and understand complex bioethical phenomena.

NOTE

1. The data and analysis presented here are based on an unpublished project on ‘‘The Physician-patient Relationship in Function-threatening Illness’’ funded by the Niarchos Foundation, on which Dr. Forman was a co-investigator with Dr. Daniel Finkelstein and Dr. Ruth Faden.

JANE FORMAN AND LAURA DAMSCHRODER60

ACKNOWLEDGMENT

The authors wish to thank Dr. Holly A. Taylor for her insightful comments on this chapter.

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ETHICAL DESIGN AND

CONDUCT OF FOCUS GROUPS IN

BIOETHICS RESEARCH

Christian M. Simon and Maghboeba Mosavel

ABSTRACT

Focus groups can provide a rich and meaningful context in which to

explore diverse bioethics topics. They are particularly useful for

describing people’s experiences of and/or attitudes toward specific ethical

conundrums, but can also be used to identify ethics training needs among

medical professionals, evaluate ethics programs and consent processes,

and stimulate patient advocacy. This chapter discusses these and other

applications of focus group methodology. It examines how to ethically

and practically plan and recruit for, conduct, and analyze the results of

focus groups. The place of focus groups among other qualitative research

methods is also discussed.

INTRODUCTION

Focus groups are a versatile and useful tool for bioethical inquiry. Successful focus groups shed light on the diversity of views, opinions, and experiences of individuals and groups. Their group-based, participatory

Empirical Methods for Bioethics: A Primer

Advances in Bioethics, Volume 11, 63–81

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All rights of reproduction in any form reserved

ISSN: 1479-3709/doi:10.1016/S1479-3709(07)11005-0

63

nature is ideal for stimulating discussion of the kinds of multifaceted and contentious issues that bioethicists wrestle with daily. Apart from their role as a source of information on people’s perceptions of topical issues, focus groups can also be used to determine the ethics-related needs of health care institutions and professionals, to evaluate the effectiveness of interventions, and to generate rapport and trust among research subjects and commu- nities. Fontana and Frey (MacDougall & Fudge, 2001) have summed up the advantages of focus groups as, ‘‘being inexpensive, data rich, flexible, stimulating to respondents, recall aiding and cumulative and elaborative’’ (p. 118).

This chapter explores some of the multiple characteristics and uses of focus groups with the purpose of highlighting their potential as a useful investigatory tool in bioethics. We consider a number of issues that we anticipate will be of special interest to bioethics researchers. These issues include the question of how one designs and conducts focus groups in an ethical, culturally appropriate, and scientifically rigorous way, and how researchers can use focus groups to stimulate critical reflection and generate new knowledge on key ethical and social issues. We draw on our experiences conducting focus groups in South Africa to illustrate both the challenges and rewards of using this methodology. Further information about using focus groups in empirical research can be found in a variety of sources, including our own work (Mosavel, Simon, Stade, & Buchbinder, 2005) and in several general guides to focus group methodology (Krueger & Casey, 2000; Morgan, 1997).

BACKGROUND

A focus group typically is composed of one or two moderators and six to ten individuals who may or may not share a common interest in the issue or topic under investigation. Focus group research originated in the 1930s among social scientists seeking a more open-ended and non-directive alternative to the one-on-one interview (Krueger & Casey, 2000). They later became popular in the marketing world as a tool for establishing consumer preferences for or opinions about different products, brands, and services. This commercial use of focus groups made some social scientists mistrustful of the methodology; however, focus groups are widely used and reported on today in the literature of the social sciences and other disciplines.

Focus groups are especially popular among researchers of health and patient care issues, in part because they are comparatively cost effective,

CHRISTIAN M. SIMON AND MAGHBOEBA MOSAVEL64

easy to implement, and less intimidating to some patients or individuals than interviews, questionnaires, or other methods of inquiry. In bioethics, focus groups have been used in a variety of ways, including exploration of public perceptions of the continued influence of the Tuskegee experiments on the disinclination of some groups from participating in biomedical research (Bates & Harris, 2004); how to improve end-of-life care (Ekblad, Marttila, & Emilsson, 2000; McGraw, Dobihal, Baggish, & Bradley, 2002); genetic testing and its medical, social, cultural, and other implications (Bates, 2005; Catz et al., 2005); environmental justice and environmental health issues (Savoie et al., 2005); and the appropriateness and effectiveness of medical informed consent procedures (Barata, Gucciardi, Ahmad, & Stewart, 2005). They have also been used as a tool for evaluating the effectiveness of medical ethics education and training initiatives (Goldie, Schwartz, & Morrison, 2000). In community-based health research, focus groups have been used to explore community health needs and concerns, build rapport and trust, and to empower community members to work toward constructive change (Mosavel et al., 2005; Clements-Nolle & Bachrach, 2003). Other uses of focus groups are possible and likely to emerge in the future.

THE FOCUS GROUP PROCESS

Some initial considerations: Empirical researchers often face the question of when it is appropriate to use focus groups, as opposed to other empirical methods such as individual interviews or surveys, in order to explore a particular issue, problem, or phenomenon. In making this decision, the researcher will want to bear in mind several factors. Focus group research typically involves far fewer research subjects than interview or survey research, where sample sizes tend to be significantly larger. This may make focus group research less costly and time consuming to conduct than individual interviews or surveys. However, the generally small sample sizes in focus group research also mean that it is harder to generalize findings to the larger group, community, or population from which the focus group participants were sampled.

For these reasons, among others, focus groups are often used to obtain preliminary or formative data that can be used to gain an initial impression of participant opinions and attitudes, and to inform the development of individual interviews, surveys, vignettes, or other instruments to be administered at a later date and with larger samples.

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Focus groups are often used in combination with methods such as individual interviews, surveys, or other methods of inquiry. Findings from focus groups can help improve the design of a larger study, including the content, language, and sequence of items, and other essential elements of an interview or survey instrument. An example of this approach is provided by Fernandez et al. who conducted focus groups as a first step in determining the kinds of interview questions to ask pregnant women with regard to the ethical and other issues surrounding the collection, testing, and banking of cord blood stem cells (Fernandez, Gordon, Hof, Taweel, & Baylis, 2003). However, focus groups need not always be used as a first step in empirical exploration. In some cases, researchers have reversed the order of methods, for example, by using surveys to identify salient issues that would benefit from further, in-depth, exploration through focus group discussions (Weston et al., 2005).

Selecting Participants for Focus Groups

The proper sampling of research participants is essential for all types of empirical research. Different sampling techniques can be used in focus group research (MacDougall & Fudge, 2001); however, a primary objective of all these techniques is to bring together individuals who are generally representative of the larger group, community, or population that the research is interested in. One relatively simple way to achieve this representation is through intentional or purposive sampling, that is, by purposefully selecting specific individuals representative of the age, gender, racial and ethnic characteristics, professional training and skills, and other characteristics evident in the group or community of interest. This sampling approach has the advantage of being flexible and can evolve as the study develops (MacDougall & Fudge, 2001, p. 120). Researchers can draw on informal networks of colleagues, community organizations, advocacy groups, or other sources to help identify potential participants to invite to the focus groups.

A second approach to focus group sampling is to randomly select potential subjects. Random sampling typically has more scientific cachet than other approaches do; however, it does present unique challenges. For example, a focus group study aimed at better understanding how pediatric oncologists view the merits and problems of assent with children is unlikely to result in diverse and rich focus groups if participants simply have been randomly selected from, say, a pediatric hospital’s directory of oncologists.

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The resulting sample is likely to be overwhelmingly English-speaking, male, and Caucasian. This level of homogeneity may be perfectly acceptable if the research question at hand is limited to exploring the attitudes and perceptions of individuals who share only these characteristics. However, if the attitudes of individuals of different genders and linguistic and ethnic backgrounds are also of interest to the research, stratified random sampling needs to be employed. In this case, the researcher aiming to explore the issue of assent will need to sample a cross section of pediatric oncologists, sorted into separate lists according to gender, ethnicity, among other possible characteristics. Thus, in addition to male Caucasian oncologists, the researcher may want to invite a number of female and minority oncologists to join his or her focus groups. Of course, if there happens to be only one or two female and/or minority oncologists at the institution the researcher has selected for study, stratified random sampling will not be possible. In the event that there is no diversity of gender or racial background among the oncologists at the institution, the researcher may want to convene a focus group at a second, more demographically diverse institution, or at a number of institutions. Alternatively, the sample can be broadened beyond physicians to include nurses or nurse practitioners, residents, and other oncology staff. However, the decision to take this step would depend on the particular research question at hand.

A stratified random sample is therefore one way in which focus group research can be made more representative and rigorous. A variety of sources have discussed sampling procedures for focus groups in more detail. Interested readers are referred to, among other sources, MacDougall and Fudge (2001) on the purposive approach to sampling and Krueger and Casey (2000) on random sampling.

Preparing for Focus Groups

Focus groups typically involve a series of questions that the moderator poses in order to generate discussion and get feedback on a particular topic. Depending on the length of the focus group (typically between 60 and 90 min), between 8 and 10 core questions are usually posed. Focus group questions need to be carefully developed so that they address the research question(s) at hand, are relevant, comprehensible and interesting to participants, and can be covered in the time allotted. They also need to be appropriately sequenced, for example, by asking questions of a more complex or controversial nature after participants have had a chance to

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develop confidence and rapport among themselves, and with the moderator. Questions also need to flow logically and be guided by participants’ particular responses and by the moderator’s comments as facilitator.

Researchers may need to consider the sensitive and complex nature of many of their bioethics-related research topics when deciding on the questions they want to pose in a focus group. Inquiring about people’s views on stem cell or genetics research, informed consent, end-of-life care, and other similar topics has the potential to intimidate focus group participants due to being politically and morally loaded. Similarly, questions that require conceptual or technical knowledge may be beyond participants’ level of knowledge or experience. One way of avoiding the dreaded silences that questions about such issues can introduce into a focus group is to first consult with group or community leaders on how best to approach the issue at hand. In fact, this community consulting process should be considered for a range of reasons, including to facilitate access to potential focus group participants, to develop linguistically and culturally appropriate questions, and to enrich the analysis of focus group data (see description later). By consulting with key stakeholders, researchers can quickly establish what kinds of topics and questions will likely be viewed as acceptable and engaging, or which ones ought better be avoided. Different levels of community or group engagement can be sought. For example, a researcher may want simply to submit a list of focus group questions to selected community members or leaders to gain their feedback on how comprehen- sible, engaging, and appropriate the questions are, or, he or she may involve the community from the beginning in the formulation of the research questions so that they are reflective of the interests and concerns of the wider community in which the research is taking place. Cost, logistics, and other considerations will likely determine which of these options the researcher can feasibly take. Regardless, focus groups are far more likely to attract a good turnout, lively discussion, and rich data if preceded by efforts to engage the target group or community in developing and validating the questions to be asked.

Informed Consent for Focus Groups

The consent process for potential participants of focus groups should be responsive to all the elements of good informed consent, plus a number of additional considerations. The group-based nature of focus groups makes it harder to ensure confidentiality when compared to individual interviews or

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surveys. Focus group participants may disclose personal and sensitive information about themselves to the moderators, the researchers, and other focus group participants. Some researchers attempt to discourage this kind of disclosure by asking participants to comment generally on the topic and not to share personal information about themselves. This may be partly effective, however, the interactive and intimate nature of focus groups can get the better of participants and prompt them to share sensitive personal information before the moderator can intervene and stop them. While the researcher can take steps to keep focus group recordings and transcripts containing personal information confidential, he or she has little to no control over whether or not the information will be more widely shared by group participants once the focus group is over.

Researchers can take a number of steps to help reduce, if not eliminate, concerns about the confidentiality and privacy of information that is shared in focus groups. One such step is to ask participants to use only their first names while engaged in focus group discussions. This will help protect participants’ identities if they are not already known to one another. Another step is to balance the need for diversity in focus groups against the need for confidentiality and respect among their participants, which may mean not including in the same focus group individuals who may be antagonistic toward one another on ideological, religious, or other grounds. However, such focus groups are unlikely to stimulate constructive discussion or yield important data. Finally, focus group moderators can help dispel some anxiety about confidentiality. This should not involve a formal review of the confidentiality statement that would have been included in the consent document for the research study, but a brief verbal reminder that what is said during the focus group must remain in the group. The usual practice is to provide this reminder before the focus group discussion begins and once again when it ends

Selecting and Training Moderators

Selecting and training effective moderators are critical steps in the successful conduct of focus groups. A poorly selected or trained focus group moderator will not be able to promote optimal interaction among participants, keep their discussion from straying, and ask pertinent follow-up questions. Many focus groups use two moderators. This has the advantage of allowing one moderator to concentrate fully on introducing the topic, asking questions, and stimulating discussion while the other keeps

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track of time, operates the audio recorder, and helps in asking follow-up questions. Obviously, if two moderators are used, they need to clearly understand their own as well as one another’s roles and responsibilities, and spend time training together.

Training is particularly critical for first-time moderators, and should focus, among other things, on the development of appropriate responses to classic focus group problems. For example, training could involve a few participant-actors who respond to the moderator’s questions in any number of predetermined and realistic ways. In actual focus group encounters, for example, it is not unusual for some participants to gravitate toward dominating discussions, while others grow increasingly passive. Training sessions that simulate this dynamic can be used to help moderators identify and negotiate this potential problem. Actors and moderators can debrief afterwards to discuss what sorts of moderator-initiated interventions work and do not work in the effort to address issues of passivity and dominance, among others.

It is also important to consider how well moderators are matched to the focus group participants they will be interacting with. Moderators who are matched to focus group participants in terms of age, gender, social and ethnic background, dress code, and so forth will engender greater rapport and openness. It may be appropriate for the researcher him- or herself to facilitate the focus groups, for example, if the focus groups include researcher colleagues who may expect a certain level of sophistication from their interactions with their moderator. On the other hand, some focus group participants may feel intimidated if the researcher serves as moderator. This may be the case particularly if the focus groups are led by the researcher–moderator is older or more experienced than most of the participants. These and other potential advantages and drawbacks should be carefully considered before the researcher decides whether or not to serve as a moderator.

Where to Conduct Focus Groups and How

Focus groups, particularly if they are addressing sensitive or controversial issues, will need to be conducted in as private and comfortable and accessible facilities as possible. Such settings may be difficult to secure given that many researchers may want or need to conduct their focus groups in hospitals, clinics, medical schools, and other health-related facilities. Space constraints, noise, unplanned interruptions, and other typical features of

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many medical and clinical settings need to be negotiated as a result. Furthermore, if participants are patients and/or family members, they may not feel comfortable openly sharing their opinions in a medical or clinical setting, even if their health care providers are not immediately present. Health care facilities may also be hard for participants to access in terms of location, parking, or finding the room where the focus group is being held. Alternative locations for focus groups might include local libraries, community centers, or other public facilities that can offer quiet and comfortable environments.

The focus groups themselves should be flexible, exploratory, and not overly controlled. At the same time, too little structure or direction in a focus group can result in a lack of focus, confusion, argument, and, ultimately, highly disconnected data. Hence, focus group researchers have used a variety of strategies in an effort to balance the need for flexibility and informality against the need for direction and focus. These strategies include the use of question-and-answer formats, discussion guides, vignettes, ‘‘show cards,’’ video or audiotape, and Internet. For example, in a study designed to identify the key issues associated with the use of human-genes in other organisms, the Bioethics Council in New Zealand used show cards depicting various scientific claims associated with gene research to stimulate discussion on the topic (retrieved October 6, 2005 from http://www.bioethics.org.nz/ publications/human-genes). The well-established ‘‘case study’’ in bioethics also potentially lends itself well to stimulating focus group discussion on any number of ethics topics.

These and other techniques can be used in combination with a question- asking approach. A well-developed series of questions has the capacity to generate lively discussion and valuable feedback on the topic of interest to the researcher. Many sources caution against the temptation to ask focus group participants too many questions; typically, participants in an hour- long focus group should not be asked to consider more than five core or primary questions.

Moderators should promote mutual respect among focus group participants by, for example, stating at strategic moments throughout the focus group that, ‘‘there are no right or wrong answers.’’ Focus groups dealing with sensitive topics can also be introduced through an ‘‘icebreaker question.’’ Here, again, prior consultation with community or group members can be helpful. For example, as part of our focus group research on a key social justice issue in South Africa, namely access to women’s health care resources, we asked community members to suggest an appropriate way of starting off the focus groups. Because some of our

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groups included youth, the suggestion was made that each participant should provide their first name and the name of a country whose first letter matched that of their name. This simple strategy helped to get the focus groups off on a lighthearted note, without asking participants to grapple with too difficult a topic or to divulge anything too intimate about themselves.

Monitoring the Focus Group Process

Monitoring focus group processes is essential to the quality and success of a focus group study. There are many different ways in which researchers can accomplish this; here, we mention two possible steps: (1) the use of debriefing reports that are put together by moderators after each focus group, and (2) ongoing review of the focus group audiotapes and/or transcripts by research staff, including the principal investigator (PI). Debriefing reports are completed by the moderator usually within 24 h of a focus group discussion and summarize the group dynamics, the quality of responses to questions, the main themes, any peculiarities in the group that may have affected its responses, and suggestions for conducting future groups. Data for debriefing could also be based on observations made or notes taken by moderators of participants’ nonverbal behaviors and of their own responses. Researchers can also meet and verbally debrief with moderators immediately following a focus group. However, a written debriefing report is useful as a record that can later help in the evaluation and analysis phase of the research process (see description later). The second step, reviewing the audiotapes or transcripts from a focus group, allows researchers to evaluate for themselves the quality of interaction and response in a given focus group. Both these steps help ensure not just that quality data are being collected, but that the focus group experience is mutually rewarding for both the researcher and the participants.

DATA ANALYSIS

Preliminary Analysis

The data analysis process for focus groups is largely driven by the research question or aims of the research. Data analysis can begin immediately after

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the first group ends or once all focus groups have been conducted. It is a good idea to start informally analyzing the data from the outset of the study so that the moderator and researcher have the opportunity to identify any challenges in the process or with the content. Materials for this informal analysis can include the audiotapes or transcripts (if already available) of the discussions and any debriefing notes that the moderator(s) may have taken about nonverbal and other behaviors among participants. Preliminary data analysis can also be used to identify issues or themes that the researcher may want to take up in subsequent focus groups. However, this strategy can be problematic if the study design or its anticipated outcomes depend on consistency in the kinds of questions being asked from one focus group to the next.

Full Analysis

Analysis of focus group data can be and often is quite complex, especially given that the researcher may need to process large amounts of narrative. Despite the qualitative nature of the data, its analysis must be systematic, verifiable, and context driven. The analysis process should be guided by the aims, overall philosophy, and anticipated outcomes of the research. Often the main outcome will take the form of a report of the pertinent issues discussed in the focus groups. As noted above, in other cases, the researcher may use the focus group data as part of formative research which will inform a larger, more representative study.

Researchers have found that engaging multiple participants in the data analysis process can greatly facilitate rich analysis and interpretation of focus group data. For example, the authors employed and trained South African community members as well as US-based research assistants to help in analyzing their focus group data. This approach helped address the researchers’ concern that their data might be distorted if analyzed through the social and cultural lens of only South African or American research assistants. In this approach, the South African research assistants brought their intimate knowledge of the local community and its wider social and cultural context to the data, while the US. research assistants added a useful critical distance, along with their prior training and experience in data analysis. By involving South African community members in the analysis phase, the researchers were also being consistent with their participatory philosophy, which emphasized the need for active community involvement in all phase of their research.

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Below, we describe one method that is increasingly used in focus group data analysis: workshop-based summarizing and interpretation of data. However, it should be noted that focus group researchers have described many different kinds of methods for analyzing data. Different software programs also exist to facilitate qualitative data analysis, including focus group data. These programs offer ways of innovatively and rapidly sorting and organizing qualitative data, moving between data sets, and streamlining their analysis. Online tutorials can help users learn how to use these programs; however, the upfront time and effort required to master these programs are still significant. The choice of analytic method may be affected by many factors, including the size of the focus group study. Data analysis for large studies involving 10 or more focus groups, for example, may best be conducted using a computer program rather than through a workshop- based or manual cut and paste method (Krueger & Casey, 2000). The research objectives and anticipated outcomes should also play a key role in deciding what method to use for data analysis.

Workshop-Based Summaries and Interpretation

One practical and useful way to analyze focus group data is to begin by reviewing the audiotapes and transcripts, and then creating summaries of participants’ responses to each question asked. Since the summaries contain only a synopsis of what was said, the original audiotapes and transcripts may need to be repeatedly consulted to place the summaries back into their contexts. Research assistants can help with this process by writing summaries and offering, in a separate section, their initial interpretations of what was said and not said in the focus group, and what appeared to be the most compelling theme or themes for each question asked. Having these summaries and interpretations independently generated by at least two people will allow them to be placed side by side for comparison and validation.

Data analysis workshops can be used to review and verify summaries and interpretations. Often, what to include or leave out of a summary or interpretation will need to be decided through a process of discussion and negotiation among the researchers and research assistants. This process can be difficult, in part because people can be overzealous in their efforts to highlight certain themes in the data or to interpret the information in one way or another. Nonetheless, this workshop-based negotiation of the data and its interpretation may be one of the most effective ways of minimizing

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the intrusion of individual bias into the data analysis and interpretation phase of the research.

In our South African work, we held workshops both in the US and South Africa, aimed at analyzing and interpreting our focus group findings. Research Associates (RAs) in both countries were trained to create comprehensive, substantive response summaries for each focus group question. Each summary had three components: a quantitative list of responses, showing how many times a particular behavior, issue, or theme was mentioned or talked about over the course of the focus group; a narrative synopsis of the themes and issues that emerged from participants’ responses; and, the RAs’ personal interpretations of the responses to each focus group question. An example of each of these components is provided below:

Example of analyzed focus group data Focus group question: What do young people in this community do for

fun?

1. Quantitative list of responses:

Listen to music – 8 Singing – 1 Go out to malls – 1 Watch movies – 2 Washing and cleaning my place at home – 1 Hanging out with friends – 2

2. Qualitative summary

The majority of focus group participants (8 girls) reported that they liked to listen to music, particularly R&B. Other types of music the participants reported liking are hip-hop, gospel, Kwaito, jazz, and Indian music. The participants reported a wide variety of activities they do for fun. The girls reported that friends’ houses, the library, taverns, game shops, malls, and the ice rink are places where they spend time with their friends. Game shops are places where alcohol is not served and children go to play games. One participant explained that taverns are places where grown people drink alcohol: ‘‘Sorry, there’s a difference between a game shop and the taverns, they don’t put games in the taverns, grown people go there, and

at the game shops, children go there’’ (p. 3). On the other hand, several girls said that young girls, including some of their friends, from their school go to taverns.

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A few participants reported that crime makes it difficult for youth to have fun. When asked, one participant said that crime means: ‘‘Some people will rape you, take you away from your home, and kill you,’’ (p. 4).

3. Interpretations

Interpretation (RA 1)

These girls implicitly communicate the importance of friends and the peer group, particularly in relation to how they spend their time. The prevalence of sexual violence and other types of violence in the daily lives of these girls is evident. The fact that they feel they have nowhere safe to go indicates a dangerous environment that must be navigated. These girls have identified a few safe places for themselves, however, such as the library, though from their laughter it seems that may not necessarily be a fun place to spend time.

Interpretation (RA 2)

While participants list a wide variety of fun activities that they do in their spare time, they also depict their community as a dangerous place where girls must face potential violence at many public places, even the supermarket. It seems that a lot of bad activity centers at the taverns, which several attribute to the alcohol there. The girls portray their town with a matter-of-fact attitude and seem to speak optimistically, despite all the crime.

These summaries were created using a workshop approach, both by RAs based in the United States and in South Africa. Prior to each workshop, each member of the research team read the full transcripts for each particular focus group. One RA prepared a detailed, three-part summary for each question, consisting of the list of categories, narrative synopsis, and subjective interpretations. We used the same process both in the US and in South Africa, with slight variations due to the local context and resources.

Process in United States: Prior to each workshop, the research team, consisting of the two principal investigators (PIs) and two RAs, read the full transcripts for each particular focus group. One RA would prepare a detailed, three-part summary for each question. For the first two focus groups we analyzed, each of the two RAs summarized the same transcript and then merged their summaries. It was then determined that the summaries were similar enough that this verification process was unnecessary. In the workshops, we read the summaries of each focus group question for accuracy, and discussed modifications. In general, the

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summaries did not require substantive changes. Nonetheless, the subjective interpretation portions of each summary were especially illuminating in that they often revealed the contextual biases of the RAs. Interpretations were discussed, but not corrected. After we reached agreement on the accuracy of the summaries for all focus groups, we analyzed the narratives for common themes across all groups.

Process in South Africa: Similar to the US data analysis procedure, two RAs were assigned to each transcript. The summaries were written in English. To establish coder reliability, and to replicate the role of the study investigators in the US as much as possible, a senior RA was assigned to verify the accuracy and completeness of each summary. In addition, the PIs received and delivered regular feedback via telephone and email to the analysis team in South Africa.

This analytic approach had several distinctive advantages. It allowed for the creation of a manageable and dynamic data set, consisting of short summaries of otherwise long and potentially unwieldy transcripts; a combination of qualitative and quantitative data; and, interpretations reflective of the social and cultural context in which they were done. It was also a creative and lively way to analyze focus group data, and, in South Africa, a way of keeping the community members whom we hired to help us with the research involved and interested in a phase of research that can be all too easily desegregated from the community setting. One potential drawback of this approach is that key information can be lost as a result of generating short narrative summaries of the much longer transcripts. We addressed this limitation by moving back and forth between the summaries and the original transcripts in order to contextualize quotations, to identify what may have been said both before and after a summarized segment, and to retain the original tone or texture of the focus group discussion. It is possible and in some cases more appropriate to use other methods to analyze focus group data. It is also possible to construct only one or two components of our approach, for example, only the quantitative listings or only the narrative summaries. However, using them in combination will make for a richer, more multifaceted analysis of focus group data.

DATA DISSEMINATION

After the analysis, the nature and the scope of data dissemination is largely determined by the purpose of the focus groups and the overarching

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philosophy guiding the research. In cases where the researcher was contracted to do the research, the goal in this phase is usually to provide the client with a report. Reports have different formats including narrative, report memo, top-line, and bulleted reports (Krueger & Casey, 2000). The narrative report is the lengthier document, which is framed by the main focus group questions or the issues that has emerged from the data. This report usually includes recommendations for the client. The report memo is typically geared toward focus group participants and its purpose is to assure the focus group participants that they were heard. It commonly focuses on progress that has been made since the groups met or includes future goals that will further address the concerns of participants. Similarly, the top-line report is a much more concise report which includes a combination of bulleted points and narrative about the focus group. In fact, this report may be somewhat similar to the debriefing report in that it is usually presented to the client within a day or two after the group. The top-line report is usually prepared without careful data analysis but is based on a more immediate evaluation of the focus group. This report is a standard in market research but it may have value to the researchers attempting to disseminate findings about sensitive or controversial issues.

When focus groups are conducted for academic research purposes, their findings are usually shared with an academic audience through journals, other publications, or at conference proceedings. However, other audiences can also be reached with focus group findings. In fact, because focus group research often marks the researcher’s first entrée into a community, it provides a unique opportunity to help build support and trust through data sharing and dissemination. Researchers may find it particularly useful to share data with individuals, groups, or communities who participated in the research in order to stimulate constructive discussion of sensitive or controversial issues. Sharing data in this way can boost trust, support, and accountability between researchers and research participants.

The format and scope of data dissemination needs to be determined by the research question. In their South African study, the authors returned to the community and met with various local stakeholders to share their findings (Mosavel et al., 2005). One of their goals in doing this was to demonstrate accountability to their initial contacts and to the community. The researchers used a variety of reporting methods including written reports, structured and unstructured conversational reports, as well as informal and formal briefing sessions. They met with representatives of local government, with school principals, and with potentially interested health care agencies and provided them with an executive summary of the focus

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group findings. They also discussed the findings in informal briefings with community teachers, clinic staff, and library personnel who had daily contact with young people in the community. Other members of the community were invited to informal presentations at which the results of the focus groups were presented for commentary and discussion.

In sharing data with research participants or communities, the focus group researcher should anticipate that some people might ask, ‘‘how is this information relevant to us?’’ or ‘‘why are you telling us this?’’ It is also important for the presenter to emphasize that the focus group does not generate generalizable data. These and other factors may lead audiences to justifiably question the relevance or significance of focus group data. Krueger and Casey call this the, ‘‘ho-hum syndrome,’’ which tends to accompany the presentation of focus group data that has not been appropriately or clearly presented (Krueger & Casey, 2000). Researchers need to anticipate this reaction and provide clear information that indicates to the audience the relevance of the data and how it might affect their lives or the life of their communities.

CONCLUSION

Focus groups can provide a rich and meaningful context for exploring many different kinds of bioethics issues. They are an excellent tool for formative exploration of sensitive or controversial topics and for partnership building with research participants and communities. The strengths of focus group methodology lie in its participatory and interactive nature. By generating interaction and discussion, focus groups can explore issues to a degree not possible in interviews, surveys, or other empirical tools. The tradeoff for this depth and richness lies in the limited generalizability of focus group findings, the large amount of qualitative data that focus groups generate, and the challenge of analyzing these data. Researchers interested in using focus groups in their studies need to evaluate these strengths and limitations against their research goals, available resources, and other factors.

We conclude this chapter with a note on possible future innovations in focus group research. Researchers have recently started exploring the benefits of conducting ethics-focused and other kinds of focus group research utilizing the Internet or World Wide Web. The benefits of this technology include the ability to ‘‘bring together’’ participants who may live or work far apart, even in different countries. Online focus group research may also provide certain social groups with an appreciated sense of

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anonymity, social distance, and safety. One possible drawback of internet- based focus groups is that they may be able to recruit only individuals who have access to and are able to use online computers. Economically disadvantaged, elderly, and other individuals who typically have limited access to the Internet therefore may be excluded from important research. Adequate informed consent may also be difficult to obtain over the Internet, and discussions may be hard to keep private if they are conducted, recorded, and/or stored online. Unless they are televised in some way, online focus groups may also lack the visual and proximal intimacy that enriches face-to-face interaction. These and other potential advantages and draw- backs still need to be fully explored before the usefulness of conducting online bioethics focus group research can be determined.

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CONTEXTUALIZING ETHICAL

DILEMMAS: ETHNOGRAPHY

FOR BIOETHICS

Elisa J. Gordon and Betty Wolder Levin

ABSTRACT

Ethnography is a qualitative, naturalistic research method derived from the

anthropological tradition. Ethnography uses participant observation sup-

plemented by other research methods to gain holistic understandings of

cultural groups’ beliefs and behaviors. Ethnography contributes to bioethics

by: (1) locating bioethical dilemmas in their social, political, economic,

and ideological contexts; (2) explicating the beliefs and behaviors of

involved individuals; (3) making tacit knowledge explicit; (4) highlighting

differences between ideal norms and actual behaviors; (5) identifying

previously unrecognized phenomena; and (6) generating new questions for

research. More comparative and longitudinal ethnographic research can

contribute to better understanding of and responses to bioethical dilemmas.

INTRODUCTION

Ethnography aims to understand the meanings that individuals attach to situations or events under study and the myriad of factors that affect beliefs

Empirical Methods for Bioethics: A Primer

Advances in Bioethics, Volume 11, 83–116

Copyright r 2008 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1479-3709/doi:10.1016/S1479-3709(07)11004-9

83

and behavior. Because of this, ethnography is well suited to the study of bioethics. Bioethical issues and dilemmas are morally charged, laden with meaning, and unfold through social interaction. Ethnographic research is therefore ideal for opening the door to the world of meanings attributed to health-related events and moral decisions, and for understanding the broader socioeconomic and political factors shaping how cultures and cultural members frame, interpret, and respond to such phenomena.

Ethnography was one of the first methods used to conduct empirical research on bioethical issues (Fox, 1959; Glaser & Strauss, 1965). Ethnographic studies relating to bioethics can be categorized into 3 groups. The first group is specifically about the work of bioethics itself, such as research on the role and functioning of Institutional Review Boards (IRBs) or hospital ethics committees (chapters in Weisz, 1990; DeVries & Subedi, 1998; Hoffmaster, 2001). The second group aims to elucidate bioethical issues with a focus on how bioethical dilemmas and conflicts develop and/or are addressed (see Guillemin & Holmstrum, 1986; Levin, 1986; Anspach, 1993; Zussman, 1992; DeVries, Bosk, Orfali, & Turner, 2007). The third set of ethnographic studies is not framed primarily as research in bioethics, but is relevant to the field, such as Fox’s and Swazey’s (1978, 1992) classic studies of dialysis and organ transplantation and Bosk’s (1979) examination of the socialization of surgeons in the context of surgical mistakes. Other seminal studies in this genre include Bluebond-Langer (1978) on children with leukemia; Estroff (1981) on people living with mental impairments; Ginsburg (1989) on the abortion debates in an American community; Rapp (1999) and Bosk (1992) on prenatal genetic testing; and Farmer (1999) on the social context of AIDS and other infectious diseases.

Bioethicists may see ethnography as a good source of illustrative cases or dramatic stories gathered simply through observation. But ethnography involves more than just observation – it relies on understanding the social processes underlying phenomena that are observed, building on prior research and analytic methods developed by social scientists, and on the systematic analysis of data that goes beyond simple description.

The art of ethnography relies on the skills, knowledge, and sensitivity of the researcher. Interpretation based on good ethnography may provide all the information one needs in many circumstances. Or, it may be only the starting point, raising questions to be further investigated with the use of other methods such as a survey instrument that can more systematically collect information from larger numbers of respondents than can be observed through ethnography.

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In this chapter, we introduce the qualitative method of ethnography, provide practical guidance on how to conduct ethnographic research, particularly as it relates to bioethical issues, and in doing so, highlight the value of ethnography to bioethics. Although some ethnographic research has been undertaken in the area of public health ethics and related topics (Brugge & Cole, 2003; Marshall & Rotimi, 2001; Lamphere, 2005), most ethnographic research cited in the field of bioethics has been clinically based. Thus, we primarily discuss ethnographic research done in clinical settings and will include examples drawn from the authors’ own experiences conducting ethnographic research on decision-making within neonatal intensive care units (NICUs), and on kidney transplantation, to offer insights into this research endeavor.

Defining Ethnography and Culture

There are many ways to define ethnography, grounded in different theoretical schools of thought about social research. However, all agree that ethnography has the following characteristics: it is a qualitative, naturalistic research method that derives from the anthropological tradition. An ethnographer typically goes into the field with a research question developed to build on existing social theory and/or previous substantive research with the aim of better understanding a cultural group or groups. The ethnographer aims to explicate the behaviors as well as the meaning of those behaviors of the people observed within a holistic context. In other words, ethnographers aim to describe a culture – whether it be the culture of the Navajo, intensive care, or people involved in organ donation and transplantation – by examining the worldviews, beliefs, values, and behaviors among its members. Often ethnography seeks to explore the historical, social, economic, political, and/or ideological factors which may account for cultural phenomena.

The work is inductive, rather than deductive. It does not test a pre- established, fixed hypothesis and does not collect data only using previously defined variables. Instead, concepts and variables emerge through the ethnographic process. As we detail below, the prime method of ethnography is participant observation. This entails immersion in the field situation, establishing rapport with individuals, and gaining knowledge through first-hand observation of social behaviors. This is complemented by direct interaction with people in the field, and participation in their activities. Ethnographers may also use other techniques such as semi-structured

Contextualizing Ethical Dilemmas: Ethnography for Bioethics 85

interviews, surveys, focus groups, and do textual analysis of relevant documents. Ethnography is not only the process just described, but also a product – a written account or ethnography – derived from the process (Roper & Shapira, 2000).

The concept of culture is essential to ethnography. Some fundamental points to understand about culture are: (1) culture is shared among a group of people (i.e., members of a nation, religion, profession, or institution); (2) culture entails patterns of behavior, values, beliefs, language, thoughts, customs, rituals, morals, and material objects made by people; (3) culture provides a framework for interpreting and modeling social behavior; (4) culture is learned through social interaction; (5) structural factors determine social positions which affect people’s worldviews and behaviors; (6) culture interacts with gender, class, ethnicity/race, age, (dis)ability, and other social characteristics; (7) culture is fundamental to a person’s self- identity; and (8) cultures change over time in response to changes in social, political, economic, and physical environments.

There are many definitions of culture, and ethnographers vary in the approaches they use to describe it. Here, we present two definitions – the first is a classic definition by Tylor (1958[1871]) that provides a broad sense of culture as ‘‘y that complex whole which includes knowledge, belief, art, morals, law, custom, and any other capabilities and habits acquired by man as a member of society’’ (p. 1). A second and often-quoted conception of culture is provided by Geertz who stated: ‘‘man is an animal suspended in webs of significance he himself has spun y I take culture to be those webs, and the analysis of it to be y not an experimental science in search of law but an interpretive one in search of meaning’’ (Geertz, 1973, p. 5). According to this definition, the meaning of all actions and things are socially constructed and shared. Culture is comprised of the symbols and meanings attached to actions and other phenomena that help people communicate, interpret, and understand their world.

Both definitions can be helpful for analyzing the culture of medicine, and specifically the culture of bioethics. Bioethics is situated at the confluence of complex legal and moral systems. Given their moral content, bioethical issues are laden with multiple meanings, and the actions agents take to resolve ethical problems or dilemmas are symbolically charged. For example, many clinicians perceive withdrawing life-sustaining therapy as different from not initiating life-sustaining therapy. So, by custom, they try to avoid withdrawing therapy when their goals can be met by not initiating a new therapy. However, for most bioethicists, these practices are concep- tually and ethically synonymous.

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Most ethnographies conducted in the area of bioethics have been done in complex, pluralistic societies where biomedicine is the dominant profes- sional medical system. In such societies, there may be significant cultural variations between members from different backgrounds, social statuses, and other groups (e.g., health professionals, patients, ethnic/religious groups, nationalities, genders, and age groups). People from different groups often vary in worldview and behaviors. When people interact they may assume that all the social ‘‘players’’ in a given situation share the same assumptions, knowledge, and beliefs that they hold, even when this is not the case. Moreover, people may believe their way of seeing things is the most valid or only way of interpreting reality. Conversely, people may assume that other groups hold different beliefs from their own, even when they do not. These assumptions can be a source of confusion, and contribute to bioethical dilemmas and value conflicts. Ethnography is an excellent method for examining cultural assumptions and studying their effects on behaviors, social interactions, and decisions in the health care environment.

Objectives of Ethnography

Researchers conduct ethnography to accomplish one or more of the following objectives: (1) understanding a phenomenon from the ‘‘native’s’’ or ‘‘participant’s’’ (i.e., member of the culture’s) point of view; (2) describing a given culture by making culturally embedded norms or tacit assumptions shared by members of a cultural group explicit; (3) discerning differences between ideal and actual behavior; (4) explaining behavior, social structure, interactions within and/or between groups, or the effects of economic, institutional, global, or ecological factors; (5) examining social processes in-depth; and (6) revealing unanticipated findings that can generate new research questions. Each of these objectives is described below.

(1) Understanding human behaviors from the ‘‘insider’s’’ point of view. To describe and analyze the insider’s point of view, ethnographers distinguish between ‘‘emic’’ and ‘‘etic.’’ Emic refers to words and concepts that the people who are observed in a given setting use themselves (and are therefore significant in their culture), while etic refers to the abstracted concepts that scholars or researchers use for analysis. This distinction is illustrated well in a study of terms used to describe infections: whereas the emic ‘‘folk’’ term ‘‘flu’’ was often used by lay people, the emic term ‘‘viral syndrome’’ was used by physicians

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documenting patient’s problems (McCombie, 1987). However, neither term was adequate for epidemiologists conducting enteric disease surveillance. Epidemiological investigations and disease control required the use of etic or analytic terms to more precisely categorize diseases by the specific causal agent (McCombie, 1987).

(2) Revealing culturally embedded norms or tacit assumptions shared among members of a cultural group. Because people understand their physical and cultural worlds through the lens of their cultural understandings, their values, beliefs, norms, and facts are usually assumed to be naturally given or taken for granted and are sometimes subconscious. Accordingly, members often cannot explain or even articulate many aspects of their culture. As Jenkins and Karno (1992) state, ‘‘In everyday life, culture is something people come to take for granted—their way of feeling, thinking and being in the world – the unselfconscious medium of experience, interpretation, and action’’ (p. 10). Traditionally, ethnographers have conducted research on cultural

groups to which they do not belong. However, conducting ethnographic research about one’s own culture, which is common in current bioethics research, can be very difficult. Ethnographers may find it easier to understand the perspectives of people from their own culture, but more difficult to identify subconscious assumptions. The ability to identify tacit assumptions depends on not having been socialized as a member of the group under examination, and/or by using techniques designed to reveal such assumptions.

For example, in a 1977 study of decision-making in the NICU, the researcher initially knew little of the culture of biomedicine or bioethics and was naı̈ve about the distinction between CPAP (a device supplying continuous positive airway pressure to keep lungs inflated) and a respirator. She was confused when a nurse told her that an infant on CPAP might not be put back on a respirator if his respiratory condition deteriorated because they believed he probably had a cerebral bleed and therefore would have a poor quality of life (Levin, 1985, 1986). The researcher knew that decisions were sometimes made not to treat a baby on the basis of the future quality of life. However, when she asked what difference there would be between keeping him on CPAP or putting him on the respirator, the clinicians just described the technical differences in the two technologies. Through participant observation of the care of infants in the unit, talking to doctors, nurses, social workers, and parents, attending rounds, reading charts, and reading the medical and bioethics literature, the ethnographer realized that clinicians made emic

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distinctions between treatments according to the level of ‘‘aggressiveness’’ as well as between ‘‘withholding’’ and ‘‘withdrawing’’ treatment. Since taking into consideration these aspects of the treatment seemed ‘‘natural’’ to them, they could not articulate clearly the reasons for their decision.

(3) Discerning differences between ideal behavior and what actually happens. For example, physicians may say that it is important for patients or family members to be involved in important decisions about care. Yet in many cases, informed consent has become a ritual where clinicians follow the letter, but not the spirit, of the law in the process of obtaining consent. Physicians frequently influence patients’ treatment decisions in subtle and direct ways, through body language or by emphasizing the risks of one treatment and the benefits of another treatment to obtain the decision physicians prefer (Zussman, 1992).

(4) Viewing phenomena in their economic, political, social, ideological, and historical contexts. In the NICU study mentioned above, the ethno- grapher endeavored to understand the clinicians’ views and decisions that were made in the context of the history of the care of newborns, the development of life support technology and of the care of people with disabilities, as well as those who were critically and terminally ill. In the middle of the study, when the ‘‘Baby Doe Controversy’’ over the treatment of ‘‘handicapped newborns’’ occurred (Caplan & Cohen, 1987), the investigation was expanded to include examination of the ways NICU physicians’ understanding of the regulations and the controversy affected decisions about care (Levin, 1985, 1988). Examining how political, economic and social forces impact cultural

understandings, cultural values, and social structures, and how these in turn shape both behavior and the discourse surrounding an issue highlights the cultural construction and ‘‘situatedness’’ of given phenomena. This kind of information is valuable because it demon- strates that matters of bioethical concern do not have to be framed in only one way, and it illustrates alternative routes to construing issues or addressing them. For example, Margaret Lock’s (2002) work on views of death and organ transplantation in Japan is an excellent illustration of the ways a non-Western cultural system leads to different ethical perspectives than a Western culture even when the biotechnology is similar in both cultures.

(5) Examining social processes and social phenomena in greater depth. Immersion in the research setting enables researchers to understand the subtleties and nuances of phenomena under study and how components of phenomena are related. For example, one can conduct a series of case

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studies to examine the experiences, attitudes, and behaviors of patients, family members, and health care professionals encountering ethical dilemmas. Such research aims to elucidate how social interactions and power dynamics in the clinical setting affect the resolution of bioethical problems. Additionally, immersion helps ethnographers to become sensitive to the political implications of what they observe as well as how they represent cultural perspectives in their reports (Clifford & Marcus, 1986).

(6) Uncovering factors and processes unanticipated at the beginning of the research process. Quite often, serendipitous events lead to new and revealing observations and insights. Accordingly, ethnography can be helpful in generating new analytic frameworks, research questions, and hypotheses that can be tested using other research methods. For example, during the course of a study of disparities in gaining access to kidney transplantation (Gordon, 2001b), emerging evidence indicated that patients faced difficulties with maintaining the transplant. This concern led to development of a new research question concerning long- term graft survival.

The Application of Ethnography to Bioethics

In the ethnographic study of bioethical issues, three main, albeit overlapping, sets of problems are generally examined. First is the examination of everyday ethics in clinical settings. However, as Powers (2001) states, ‘‘The challenge of recognizing everyday ethical issues lies in their ordinariness’’ (p. 339). In other words, it may be difficult to problematize or consider as cultural those practices and beliefs that members of the group being studied treat as ‘‘normal’’ or ‘‘natural.’’ Ironically, it is precisely when cultural members construe issues as ‘‘normal’’ or ‘‘natural’’ that ethnographers can identify a phenomenon with important cultural dimensions. Second, much research focuses on examining whether bioethical principles and assumptions derived from philosophy are actually applied in reality, and if not, why not. For example, a study by Drought and Koenig (2002) examining the principle of respect for autonomy and the concept of patient ‘‘choice’’ in decision-making of dying patients, their families, and their health care providers, found that patients did not perceive that they had choices when discussing treatment options, contrary to bioethics scholars’ expectations that patient autonomy would be respected. A study by Dill (1995) also illustrates a challenge to assumptions about the principle of respect for autonomy in hospital discharge planning decisions for older adults.

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Third, ethnographic research in bioethics aims to elucidate how cultural context(s) shape ethical reasoning. Researchers taking this approach seek to highlight Western assumptions that pervade bioethics, and/or investigate ethical reasoning in other cultures for a comparative approach. For example, Jecker and Berg (1992) examined how the face-to-face dynamics of living in a small, rural American setting shaped the way scarce medical resources were allocated on an individual patient level in a primary care setting. This con- trasts with philosophical expectations about justice as a blinded, impersonal process. A related approach seeks to examine the diversity of experiences within a culture or among subgroups regarding particular bioethical pheno- mena. For example, numerous studies have shown that African Americans prefer more aggressive life-sustaining treatments compared to European Americans (Blackhall et al., 1999). Other research suggests that Koreans and Mexican Americans appear to favor physician disclosure of grave diagnoses or terminal prognoses to family members instead of to the patient in order to protect the patient and enable familial decision-making rather than patient autonomy (Blackhall Murphy, Frank, Michel, & Azen, 1995; Orona, Koenig, & Davis, 1994; Hern, Koenig, Moore, & Marshall, 1998).

ETHNOGRAPHIC DATA COLLECTION TECHNIQUES

Ethnography is best learned through experience, reading ethnographies, engaging in discussions with people who have done them, and doing smaller or pilot studies, rather than through purely didactic training. Accordingly, ethnography is a difficult research method to teach. We emphasize that ethnography is more than just observing a situation. One must know what to look for, how to make observations through the lens of the complex concept of culture, and how to interpret and analyze data in light of the social, historical, and cultural contexts in which data are collected. Ethnographers generally use multiple data sources and data collection techniques to obtain rich and overlapping data. Their primary source of data is participant observation. They may also use other techniques including interviews and case studies. These techniques are discussed below along with the skills necessary to use each one.

Participant Observation

Participant observation is the heart of ethnography; it is a strategy enabling the ethnographer to ‘‘listen to and observe people in their natural

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environments’’ (Spradley, 1979, p. 32). Participant observation allows for the examination of several dimensions of a social situation simultaneously – the physical, behavioral, verbal, nonverbal, and interactional – in the context of the broader social and physical environment. Doing so ‘‘give[s] the researcher a grasp of the way things are organized and prioritized, how people relate to one another, and the ways in which social and physical boundaries are defined’’ (Schensul & LeCompte, 1999, p. 91). A strength of participant observation is that researchers are the ‘‘instrument of both data collection and analysis through [their] own experience’’ (Bernard, 1988, p. 152).

Ethnographic techniques vary depending on the extent to which ethno- graphers identify themselves as insiders or outsiders; how involved the people who are observed are in the data collection effort, and the kinds of activities that researchers engage in as part of fieldwork (Atkinson & Hammersley, 1994). Although the technique is commonly referred to as participant observation, not all ethnographers are actual ‘‘participants.’’ For example, in bioethics research, ethnographers are often participant observers and join providers during rounds and team meetings, or share lunchtime conversations with staff. However, they do not express their opinions about bioethical or other issues discussed. In some settings, however, the ethnographer may be only an observer. Examples of this are attending a committee meeting and listening to and observing interactions of its members. Ethnographers who are studying problems in bioethics may find it enlightening to ‘‘observe’’ phenomena outside the clinical setting such as advocacy groups or representations of issues in the media. For example, members of Not Dead Yet, which advocates for disability rights, constitute a valuable source for understanding non-clinical perspectives on end of life practices (see, for example, http://www.notdeadyet.org/docs/about.html). All data collection approaches can be used simultaneously. In one study examining access to transplantation, the ethnographer observed transplant team meetings; the formal interactions between transplant coordinators and transplant candidates and their families; clinical encounters between nephrologists and dialysis patients; shadowed transplant surgeons on their medical rounds; and observed monthly social support group meetings run by the transplant center (Gordon & Sehgal, 2000; Gordon, 2000).

Skills Necessary for Effective Participant Observation

Ethnography, specifically the strategy of participant observation, requires that researchers develop many skills, including self-reflexivity, having a good memory, attending to details, flexibility, interpersonal skills, the ability to exert discretion, building rapport, and appreciating cultural differences.

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Self-reflexivity is critical to an investigator’s success in using her or him- self as a research tool. Self-reflexivity can be defined as working to assess one’s own biases and their potential influence on perceptions of phenomena (Frank, 1997; Ahern, 1999). (For an excellent example of how a medical anthropologist engaged in self-reflexivity to understand her informant’s disability experience, see Gelya Frank’s (2000) monograph, Venus on Wheels).

An important skill required in ethnography – whether observing or interviewing – is having a good memory. During unstructured interviews, it is essential not only to have the next question ready at hand, but also several possible leads for additional questions or comments in mind, based on earlier parts of the conversation. In addition, it can be useful to keep a list of important points to cover during an unstructured interview. Because the interviewer cannot always take notes or audiotape interviews, useful stra- tegies are: writing everything down immediately after making observations; avoiding speaking to people about the observations before writing them down; recalling things chronologically as they were witnessed; and drawing a map of the physical space in which events occurred (Bernard, 1988, p. 157).

Developing ‘‘explicit awareness’’ of details of ordinary life is another skill (Spradley, 1979, p. 55). People go about life aware, but not attending to many details, e.g., what people are wearing, music playing in public places, the process involved in deciding which products to buy at supermarkets, etc. (Bernard, 1988). In fact, this occurs because many aspects of cultural life are tacit. Since one definition of culture refers to the knowledge necessary for a person to get by in his/her culture, generating information on such ordinary details provides insight into the daily lives of members in a given culture. Attending to details helps to keep biases in check and often leads to insights about assumed realities. For example, ethnographers in medical settings can gain important knowledge by noticing which cases are talked about the longest during rounds or noticing who regularly attends or skips rounds or meetings.

The researcher must exert a fair degree of flexibility when conducting ethnographic research. For example, flexibility to alter the course of inquiry if needed is a hallmark of the ethnographic approach. Drought and Koenig (2002) exercised flexibility as the data emerging during their data collection indicated that the AIDS and cancer patients in their study did not conform to the categories anticipated by the normative assumptions in bioethics regarding the existence of discrete decision points at the end of life.

Conducting ethnography requires that the researcher have interpersonal skills and exercise discretion. Building rapport with people is necessary to

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establish trust and open the door to communication. Ethnographers need to speak to people of diverse backgrounds to understand how power dynamics and social status shape attitudes and behaviors. In the context of bioethics research in the clinical setting, this may entail going on medical rounds daily, shadowing clinicians, talking to providers and staff, sitting at the computer station with them, drinking coffee together, staying at the unit on night shift hours, and participating in social events on the unit. The ethnographer’s goal is to ‘‘fade’’ into the social fabric of the group under observations with as little impact as possible on the phenomena under study.

Participant observers must also engage key informants to help guide them about the culture under study and provide insider information about aspects of a culture. One needs at least one or two key informants in each setting who have necessary competence to provide in-depth information about a particular domain of culture. Researchers can ask key informants about how things work, factual information, or about things that are typically perceived by members of the culture to be essential to understand their culture. For example, to understand treatment decisions about critically ill neonates, one might ask, for what kinds of conditions do NICU babies tend to have an ethics consultation requested (Orfali & Gordon, 2004). However, informants are not selected for their representativeness and they may not be able to report accurately about opinions or attitudes that vary in the culture (Bernard, 1988). Key informants can also be extraordinarily helpful in guiding observations, enlisting the involvement of others, and partaking in ad hoc interviews. In the study of dialysis patients’ choices about trans- plantation, one key informant was an administrative secretary who provided behind-the-scenes information about the structure and functioning of the transplant center and background of the health care professionals working there (Gordon, 2000).

It is important to be mindful of how one selects key informants in terms of the political alignments among people in the particular social context. One should avoid aligning with key informants who are too marginal to the group under study so that one does not lose access to people and information. Ethnographers must also be careful that alignment with a key informant does not alienate other members of the group who are important to the study. Informants may emerge through establishing friendships based on trust or luck. The best informants tend to be articulate people who are somewhat cynical about their own culture and, even though they are insiders, feel somewhat marginal to it (Bernard, 1988).

Ethnography requires an ability to adopt a culturally relative perspective when doing research. By this we mean endeavoring to understand and

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respect the views of others and events within cultural, social, and historical contexts rather than making judgments about what is observed based on one’s own cultural perspective. Ethnographers must be able to appreciate that people are ‘‘rational’’ and systematic in their thinking (Horton, 1967), and that the beliefs and values underlying people’s thought processes and behaviors may differ from their own. As a result, when confronted with the same choices, people may vary in the kinds of conclusions they reach. Using a culturally relative perspective when doing research does not require ethnographers to accept or adopt a different value system than their own, but to strive to be as nonjudgmental and open-minded as possible, in order to understand, and explicate alternative worldviews when collecting and interpreting data.

Interviews

Interviews constitute another major technique for collecting ethnographic data. There are three main types of interviews: unstructured, semi- structured, and structured, of which unstructured interviews are the most commonly used. Unstructured and semi-structured interviews can occur spontaneously in the course of participant observation research, or researchers may seek out people to ask them about specific issues. Informal and unstructured interviews are ideally conducted in the midst of participant observation when ethnographers can get immediate input on the meaning of events as they occur, especially those that are unexpected. The ethnographer may have a general idea about the topic they want to learn about or they may let the respondent drive the course of the interview. This can allow respondents to raise issues unanticipated by the researcher.

When conducting semi-structured interviews, researchers prepare a written interview schedule with a set of questions or discussion points. Whereas the bulk of unstructured interviewing is open-ended, semi- structured and structured interviews commonly include both open- and closed-ended questions (for a detailed discussion on this method, see chapter on Semi-Structured Interviews).

Case Studies

Case studies are another ethnographic technique that entails ‘‘examin[ing] most or all aspects of a particular distinctly bounded unit or case (or series of

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cases)’’ (Crabtree & Miller, 1999, p. 5; Stake, 1994). Cases can be individual patients, sets of interactions, programs, institutions, nations, etc. The goal of this data collection method is to describe cases in great detail and context, which may generate hypotheses or explain relationships of cause and effect (Aita & McIlvain, 1999). Cases may be selected based on whether they are representative of a phenomenon, setting, or demographics, and whether they ‘‘offer an opportunity to learn’’ (Stake, 1994, p. 243). Alternatively, one may choose atypical cases to explore the limits of what is the norm, and to set limits to generalizability (Stake, 1994, p. 245).

Rapid Assessment Process

An adaptation of ethnography has been developed called ‘‘rapid ethno- graphy’’ or ‘‘rapid assessment process’’ (RAP) (Scrimshaw & Hurtado, 1988; Bloor, 2001; Hahn, 1999). RAP is used as a faster approach to data collection in various applied settings, including public health. RAP was developed by the World Health Organization (WHO) to accommodate shorter time frames for conducting research and tends to be more problem- oriented than traditional ethnography. Generally, the RAP data collection period lasts from 3 days to 6 weeks, depending on time, resources, and previous data collected; and typically RAP uses small sample sizes (Trotter & Schensul, 1998). The RAP approach commonly uses several observers, a narrower research focus, and multiple collaborators and emphasizes the use of a number of methods including direct observation, informal conversa- tion, and key informant interviews.

Other Methods in Ethnography

Ethnographers can draw upon a wealth of additional data sources, such as administrative records kept at hospitals or public records regarding morbidity and mortality in a population. Other relevant data sources constitute media reports, newspaper clippings, television programs, and other forms of popular culture. The Internet offers the opportunity to engage in different kinds of observations. For example, online support groups, such as a listserv that provides a venue for dialysis patients to exchange views and give each other advice, served as a useful counterpart to observations of the in-person support group, Transplant Recipients International Organization, which was observed by one of the authors.

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Ethnographers may also draw upon results of surveys previously conducted by other researchers as Long (2002) did for her work on euthanasia in Japan to understand how Japanese people construed the issue.

Ethnographers also collect quantitative data (Bernard, 1988). For example, one can collect data on the number of cases with certain characteristics, the number of days in the hospital for people with different medical or demographic characteristics, ask individuals to list the number of people they could call on to care for them in their homes after leaving the hospital, etc. Although ethnographers conducting statistical tests should not assume that the quantitative data represent a random sample from a defined population, they can use descriptive statistics to identify rates and correlations using such data in combination with qualitative data.

DEVELOPING ETHNOGRAPHIC RESEARCH

Conducting ethnographic research requires considerable preparation in advance. Key preparatory steps include: (1) conceptualizing a research question, (2) establishing a research plan, (3) obtaining permission to gain access to the research site, and (4) determining the unit(s) of analysis and sampling frame(s). Depending on the nature of the study, it may involve additional elements.

1. Conceptualizing a research question. The first step in ethnographic research is conceptualizing a research question. An important characteristic of ethnographic research is that it does not usually aim to test an a priori hypothesis and is not geared toward achieving generalizability, characteristic of statistical hypothesis testing. Rather, as discussed above, the ethnographer is seeking to gain an in-depth understanding of a problem, group, or social setting within its broader context. Conceptualizing a research question requires sufficient familiarity with a topic or human group to find a gap in the scholarly literature. Accordingly, ethnographers develop a research topic derived from social theory and/or from substantive gaps in knowledge. A topic may not have been investigated at all, or phenomena may have been studied with a different theoretical framework, methodological approach, or in a different group or setting or timeframe. As with all research, a comprehensive review of the existing literature is essential in this phase.

The central research question is generally broad and subsumes multiple other questions. For example, the investigation of social and cultural factors shaping treatment decision-making regarding renal transplanta- tion (Gordon, 2001a,b) included inquiries into the doctor–patient

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communication, transplant evaluation, and patients’ decisions about donation (Gordon & Sehgal, 2000; Gordon, 2001a). In addition, researchers must consider the potential significance of their proposed research, i.e., the extent to which the knowledge gained will: (a) advance theory or methodology in bioethics and/or in the social sciences, i.e., help to re-conceptualize the doctor–patient relationship or bioethical principles, (b) change clinical practice, (c) inform health interventions, or (d) inform health policy.

2. Establishing a research plan. An important step after conceptualizing a research question is devising a preliminary plan for undertaking the research. This can be done by a comprehensive review of the existing substantive and methodological literature and by identifying and, if possible, preparing the methodological technique(s) to use during fieldwork.

The next step entails distilling the research design logistics: identifying who or what situations to observe, which people to interview, when to do these steps, and how. Although ethnographers usually enter the field with a fairly open plan to develop a holistic view of the situation, ethnographers may draft interview guides, have experts in the field to review them, or conduct pilot studies with specific methods before initiating the main phase of research. Collecting background data i.e., population statistics, medical data, or administrative information, is important for gaining an appreciation for the broader context. However, ethnographers cannot plan all of their fieldwork in advance. Serendipitous occasions and unanticipated informal conversa- tions occur during participant observation, which may lead to the most important insights – this constitutes the heart of ethnography.

3. Obtaining permission to gain access to the research site. The next step entails gaining access to the field site. Essential components of this process illustrated below are based upon experiences by one of the authors in conducting research with kidney transplant and dialysis patients (Gordon 2001a,b). In this study, the investigator planned to conduct the research in dialysis units. This necessitated obtaining permission from the nephrologist directing all the dialysis centers that served as observation settings. The next group from whom the ethno- grapher needed permission was the individual nephrologists who ran each dialysis center, followed by the other health care providers who staffed the dialysis centers and provided the ethnographer with direct entrée to the dialysis patients. Finally, the IRB approval was required and consent forms were needed in order to speak with patients.

Negotiating access with chairs of clinical units or with the IRB can also be challenging given that many chairs and IRB members lack knowledge about

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qualitative research and often perceive it as a ‘‘fishing expedition’’ or ‘‘not really science’’ (Koenig, Black, & Crawley, 2003). Nonetheless, anticipating in advance potential critiques of social science and qualitative methods can better enable ethnographers to address them when they arise. For example, it is common for biomedical practitioners to comment that social science research – particularly qualitative research – is ‘‘subjective’’ rather than ‘‘objective.’’ By highlighting the strengths of qualitative research in terms of validity rather than generalizability, ethnographers can advance the understanding of their methods, foster acceptance and support for their research, facilitate IRB approval, and improve communication between ethnographer and study subjects (Anspach & Mizrachi, 2006; Koenig et al., 2003; Cassell, 1998, 2000; Lederman, 2007).

Gaining access to the research site is an informal as well as formal process. In addition to the formal permission from health care institutions and IRBs, it is crucial to obtain ‘‘buy-in’’ from relevant administrative and/or clinical staff and other individuals who can effectively facilitate or hinder data collection. Such informal gatekeepers can help the ethnographer gain access to electronic data, unfolding situations, potential research partici- pants, and answer questions, all of which can help ethnographers to realize their research goals.

Moreover, gaining access to the research site is not a one-time matter but rather a process that must be negotiated repeatedly. The researcher often needs to re-introduce him/herself and re-explain his or her role to staff in order to ensure that the research itself and the researcher’s presence are understood and accepted. This is especially true in clinical settings with high staff turnover and multiple shifts. Staff may feel that ethnographers are interfering with their work space, time, and patients. To minimize this perception, ethnographers must make clear to staff that they are in the setting to learn about the people there, that they will keep information confidential, and will not spread gossip. The ethnographer can let cultural members know that his/her process of learning entails asking basic questions, the answers to which might be perceived as obvious to cultural members but help the ethnographer uncover tacit and fundamental assumptions. After they have been in the field for a long time, the ethnographer must be careful to not be seen as a regular member of the group under study as this will diminish their effectiveness (Bosk, 2001).

Finally, ethnographers may encounter challenges gaining access to research sites because (a) respondents/subjects hold powerful positions and (b) ethical issues are sensitive in nature. Nevertheless, ‘‘studying-up’’ or studying the powerful – such as health care professionals, administrators, or

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ethicists – is important because they affect the well-being of many other members of society (Nader, 1972; Sieber, 1989, p. 1). The powerful are typically difficult to study. Anthropologist Laura Nader explains just why this is the case: ‘‘The powerful are out of reach on a number of different planes: they don’t want to be studied; it is dangerous to study the powerful; they are busy people; they are not all in one place, and so on’’ (Nader, 1972, p. 302). Nader provides further insights into the concerns held by the powerful about being studied: ‘‘Telling it like it is may be perceived as muckraking by the subjects of study y or by fellow professionals who feel more comfortable if data is [sic] presented in social science jargon which protect the work from common consumption’’ (Nader, 1972, p. 305). Indeed, the effort by one of the co-authors to examine how ethics consultants discuss cases during ethics committee meetings was thwarted by some of the committee leaders and members because of fears about the uses of data to be obtained. Finally, the sensitive nature of many medical ethical issues requires a great deal of tact, strong communication skills, discretion, and often empathy on the part of the ethnographer.

4. Determining the units of analysis and sampling frame. Because ethnographic research operates at a number of different levels of inquiry at once, it is essential to consider the particular unit(s) of analysis before implementing a study. The choice of unit(s) is driven by the study’s aims and focus and may be at the micro level of patients or clinicians, or can be broader and include an entire medical floor or hospital or a society’s response to an ethical issue. In the study of ethics consultations, the unit of analysis was the aggregate of the patient, family, and health care professionals treating one patient (Kelly, Marshall, Sanders, Raffin, & Koenig, 1997). This study examined the interactions between individuals within these small groups and with ethics consultants to obtain a rich understanding of how consultants influence decision-making and of each party’s perspectives about the value of ethics consultations.

Determining the unit of analysis is related to the issue of sampling. In ethnographic research, sampling is not geared toward statistical general- ization. Instead, sampling is performed to enable ethnographers to study a sufficient scope and depth of processes and interactions to gain under- standing of situations in all their complexity. An in-depth appreciation often comes at the expense of generalizability. Data collection ideally proceeds until saturation has been achieved. Saturation is the point where no new insights, themes, or patterns are being generated. However, the ability to reach saturation may depend on the unit(s) of analysis. A tradeoff often exists between the number of situations observed and the richness of data

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collected (Morse, 2000). To enhance validity of a study’s findings and obtain a better sense of the range of phenomena under study, ethnographers may make visits to other comparable programs, medical units, or institutions, albeit with less time than in the primary site.

Informed Consent

Ethnographic research presents challenges regarding informed consent not usually encountered in other ethnographic or bioethical research. Tradition- ally, much of what anthropologists observe has been ‘‘public’’ behavior or has occurred in settings which they have been invited to attend as participants. Often, an invitation may not even be required after anthropologists have immersed themselves and become accepted by the group or community under study. IRBs consider much ethnographic research to be exempt or subject to expedited review; oral consent is often considered adequate.Yet in clinical settings, because of the sensitive nature of the situations observed, written informed consent is expected from patients, family members, clinicians, and others who participate in formal interviews or whose cases or behavior are studied in depth. However, it may not be practical or even possible to obtain prospective informed consent from everyone who will be observed during an ethnographic study (e.g., from the dozens of people who will pass through a unit or clinic on a given day, and who are not the main focus of research). Although some IRBs will exempt ethnographers from obtaining written consent, most require verbal informed consent for observations and interviews that states the purpose of the research, guarantees confidentiality, and enables people who would be studied the choice not to participate or to withdraw from the study at any time. Of note is also the fact that the act of requesting consent to be an observer of group events (e.g., clinical meetings and consultations) sets the researcher off from the group being studied which may undermine efforts to establish rapport (Miller, 2001).

The American Anthropological Association (AAA) (2004) has published a statement on Ethnography and Institutional Review Boards ([URL: http://www.aaanet.org/stmts/irb.htm] accessed 6-28-07). Briefly, the AAAs position is that anthropological research often falls under expedited and exempt review, yet investigators may have difficulty getting the IRB to use such mechanisms. Some IRB members are unaware of how conducting ethnographic research diverges from the biomedical model leading to difficulties for ethnographers in obtaining IRB approval for verbal consent. The AAA points out that the common rule does allow for waivers of written

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consent, according to certain criteria. The tension between the requirements of good ethnography and the strictures of modern informed consent regulations has been further intensified by the Health Information Portability and Accountability Act (HIPAA) of 2003 that aims to protect identifying information and prevent its transmission outside of an institution. Local variations of how informed consent for ethnography is handled mean that there are few clear guidelines for ethnographers seeking to do research in clinical settings.

Other Groundwork

Throughout the preparation process ethnographers need to be involved in other groundwork. For instance, researchers may need to learn a foreign language, in this context, that of medicine, bioethics, and the local clinic. Whereas much technical medical language can be learned in advance, learning how it is used on an everyday basis occurs through observation after entering the field. It is essential for the ethnographer to become familiar with the vocabulary, jargon, and lore of those under study; study subjects will likely teach ethnographers if they indicate a willingness to learn (Bernard, 1988). For this purpose attending rounds and case conferences can be invaluable.

Other groundwork involves learning the lay of the land, which is essential for understanding the social and symbolic meanings associated with physical spaces. For example, for the NICU study it was helpful to know that babies in the beds on one side of the unit were in the most critical condition. This awareness helped make sense of their treatment regimens and clarified that movement from room to room denoted improvement and preparation for discharge. Early stages of ethnographic research may also entail a macro-level form of site survey (Anderson & McFarlane, 2004), where one assesses the broader environmental setting in which the study takes place, i.e., the neighborhood.

DATA MANAGEMENT AND ANALYSIS

Recording Data

Ethnographic research generates large quantities of data such as field notes, responses to interviews and surveys, photographs and video-recordings, and copies of documents.

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Field notes are the main method of recording data. These start as hand- written notes which are then entered into a word processing program and/or database. Fieldnotes record what ethnographers observe about the unfolding events, the environment or context, and/or the people in it, and information provided by participants (Dewalt, Dewalt, & Wayland, 1998; Pelto & Pelto, 1978). Much has been written on how to write good field notes (Emerson, Fretz, & Shaw, 1995). Some brief, general guidelines are as follows: First, and most importantly, ethnographers must write down notes promptly, and not rely on memory. Without the note, there is no data point (Dewalt et al., 1998; Bernard, 1988; Pelto & Pelto, 1978). Field notes should be written as close to the time that the data are collected as possible to prevent memory loss. Notes should be taken throughout the day or course of observation, and not only at the end of the day. Second, it is important that ethnographers exercise discretion about note-taking while undertaking participant observation. Not infrequently, observations of emotionally laden interactions common in sensitive situations may preclude taking notes as an event unfolds. Study participants and others in the field may perceive the researcher’s note-taking as intrusive. Note-taking may also disrupt the natural flow of events. When taking notes is not appropriate, writing brief quotes, key words, or short descriptions immediately after observing events, interactions, and discus- sions is important to optimize proper recall and documentation.

Third, ethnographers should record at a low level of abstraction. This means that researchers should note concrete details regarding the actions, interactions, communication, and nonverbal communication that they ob- served. Furthermore, they need to document the specific observations that led to their impression about a cultural process or pattern, such as emotional events transpiring during the observation. For example, researchers studying communication between health professionals and patients would benefit by noting nonverbal expressions, the physical proxemics, and relative positions between social actors, such as facial expressions or crying, in order to later interpret and make sense of these expressions in light of cultural meanings and expectations in the clinical setting. Otherwise, taking note that a patient expressed ‘‘agreement’’ (an abstraction based on one’s own cultural lens) may be grossly inaccurate since, for example, for many Asian patients, nodding is a form of respect rather than agreement (McLaughlin & Braun, 1998).

Fourth, writing field notes does not necessarily mean writing down every bit of minutia observed, but should be directed toward a certain focus, depending on one’s theoretical orientation and study aim (Pelto & Pelto, 1978). For instance, noting the color and length of physicians’ jackets/coats may be important for a study of the symbolic power of physicians and

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interactions based on their status, but would be moot if the focus is on language used during doctor–patient communication during telephone consults. Further, it is common for researchers to jot down key phrases heard, rather than noting entire sentences word for word, to stay abreast of observations without getting bogged down with the logistics of recording. At the end of a day of data collection, ethnographers should fill in the details of their field notes and write fuller notes by reviewing their jottings, reflecting on the events observed. Notes should also record steps in the development of the ethnographer’s analysis. If an ethnographer is collaborating with others, then debriefing with mentors or colleagues can trigger memories of observations and should also be recorded.

Fifth, it is important for ethnographers to recognize that their notes are not objective representations of events, but rather constructions infused with their own interpretation and analysis (Dewalt et al., 1998). Accordingly, there is a debate within anthropology about the use of one or more sets of field notes to record: (a) log of field activities, (b) observations and information, (c) analytic interpretations, and (d) personal perceptions and experiences to aid in self-reflexivity. Some anthropologists recommend that ethnographers keep a separate journal or diary to record personal experiences in line with the notion of self-reflexivity. One way to keep check on biases in field notes is to write reflexively, staying as fully attuned as possible and documenting how the events observed make the ethnographer feel and respond. However, others contend that such experiences are themselves data which reveal much about the difference between the culture observed and the ethnographer’s own cultural framework. Thus, such notes yield a form of cross-cultural comparison that should be kept with notes on observations of the setting.

In addition to field notes, ethnographers may collect data from interviews and surveys which may or may not be audio-recorded. Even if they are audio-recorded, it is important to take notes during interviews as a backup in case recordings fail, and to write notes after the interview to provide greater context with details. In many clinical settings, health care professionals have expressed great reluctance to have their interactions and meetings tape recorded, likely owing to the American cultural context of litigation as well as to confidentiality considerations.

The Management of Data

The collection of extensive data requires that researchers manage them systematically. This means establishing proper storage and retrieval systems

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(Huberman & Miles, 1994). Most data can be entered into computer databases. Software for storing and analyzing qualitative data are readily available e.g., QSR NUD*IST, THE ETHNOGRAPH, ASKSAM, NVIVO, and ATLAS-TI. Some of these programs even include statistical capacities. Additional ways to manage and protect data entail using a physical filing system, dubbing audiotapes and transcribing tape recordings, making photocopies of all documents, and backing up electronic data on a regular basis. An important facet of data management involves the development of a codebook ensuring that data are managed in a systematic manner. This fosters clarity and enhances consistency in data collection, interpretation, and coding procedures. Codebook development, data collection, coding, and analysis are not linear but inform each other and are refined over the course of research. (For more information about these aspects of qualitative data management, see chapters on Content Analysis and Semi-Structured Interviews).

THE ANALYSIS AND INTERPRETATION OF DATA

Social scientists have developed many methods of inductive analysis. Excellent sourcebooks on how to conduct qualitative data analysis are available (Miles & Huberman, 1994; Strauss, 1987), and some of these techniques are discussed further in other chapters of this book. Here, we briefly define some analytic approaches that are commonly used in or suited to ethnographic research in bioethics: grounded social theory, componential analysis, and content analysis.

Grounded Theory

Iterative procedures have always been the norm in the practice of ethnography. While ethnographers are in the field they begin to interpret their data. Subsequent data collection is guided by earlier observations and endeavors to develop more nuanced understandings of the phenomena under study. Efforts are taken to reject or elaborate earlier interpretations and develop better explanations. In the 1960s, sociologists Glaser and Strauss (1967) formally developed a rationale for this approach and showed how such interpretations grounded in data could be used to develop theory. They labeled their approach ‘‘Grounded Theory’’ and called for rigorous attention to the process used by researchers to move between data and

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analysis. In particular, they advocated the use of a technique they called the ‘‘constant comparative method’’ in which the ethnographer would use later observations to verify or modify previously suggested theories (Strauss & Corbin, 1994). According to Strauss and Corbin, an important feature that distinguishes grounded theory methodology from other more descriptive inductive approaches is that sampling, questioning, and coding are all explicitly theoretically informed and chosen with the intention of explaining the relationships between concepts and patterns of social behavior. They wrote:

‘‘In doing our analyses, we conceptualize and classify events, acts and outcomes. The

categories that emerge, along with their relationships, are the foundations for our

developing theory. This abstracting, reducing, and relating is what makes the difference

between theoretical and descriptive coding (or theory building and doing description)’’

(Strauss & Corbin, 1998, p. 66).

It should be made clear that grounded theory describes a broader approach for handling the analysis of data, but does not provide guidance on specific cognitive elements to focus on for analysis, as does componential analysis, described below.

Componential Analysis

Although componential analysis has not been employed much in bioethics research, it could be especially useful. Componential analysis, which is also referred to as semantic analysis, entails two goals: (1) describing how members of a cultural group categorize a given meaningful, culturally valid, behavioral issue from an emic perspective and (2) delineating the cognitive processes, components of meaning, or criteria that cultural members use to distinguish between cultural categories (Bernard, 1988; Pelto & Pelto, 1978). This process entails charting out, classifying, and contrasting semantic networks of emic constructs among group members to understand how they view an issue under study. Anthropologist Edward Sapir hypothesized that language is a reflection of a cultural group’s conception of the world, which is often tacitly embedded in how people classify the things in their world (Mandelbaum, 1949). Accordingly, identifying how people classify, for example, kinds of personhood, the meanings attached to the concepts of person, and the rationale for the classification system, may yield insight into the moral underpinnings for decisions made about medical treatment.

Research in bioethics has used techniques derived from sociolinguistics. These entail the study of discourse as it naturally occurs in terms of its content and structure. For example, bioethics researchers have inquired into

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the multiple meanings of constructs like ‘‘fairness’’ in allocation of primary health care services (Jecker & Berg, 1992) and of ‘‘do-not-resuscitate orders’’ for different social and cultural groups (Muller, 1992).

Content Analysis

A commonly used technique for analyzing data is content analysis. Content analysis is a process of analyzing data by systematically searching for themes and repetitions emergent from the data (Luborsky, 1994; Huberman & Miles, 1994). The first step is to prepare data for analysis – whether that means pulling out descriptions of cases, organizing hand-written and word- processed field notes systematically, transcribing audiotapes verbatim, or coding data for use with a computer program. Content analysis is an iterative process whereby themes are developed by grouping coded segments into schema of larger domains, followed by a review of the categorization schema for appropriate thematic fit, and adjusting and reviewing the schema again until the researcher(s) reach consensus. Traditionally, ethnographers coded data by hand, e.g., by highlighting or marking portions of handwritten or typed text, or by sorting index cards. Although many researchers continue to code by hand, there are now many qualitative data analysis software programs available, as noted above. (For a detailed discussion, see the chapter on Content Analysis in this volume).

Validity Checking

Ethnographers use numerous techniques to enhance the internal validity of their research, that is, ‘‘the extent to which it gives the correct answer’’ (Kirk & Miller, 1986, p. 19; Patton, 1999). To increase the validity of the data, ethnographers use multiple techniques including thick description, triangulation, member checking, using paradigm cases, and maintaining a detailed paper trail. These will be discussed further below.

Thick description entails depicting phenomena in rich detail (Geertz, 1973). Geertz likened culture to ‘‘an assemblage of texts’’ (1973, p. 448) with socially shared and generated meanings that can be interpreted. The ethnographer’s analytic goal is to uncover and understand the meanings woven throughout his/her field notes. Detailed accounts in field notes help to increase the likelihood that interpretations remain data-driven and are truly inductive accounts of the cultural group or other phenomena under study.

Triangulation involves the use of several kinds of methods or sources of information to obtain overlapping data which helps to support reliability and

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validity of findings. There are different kinds of triangulation: (1) investigator triangulation which involves several researchers collecting data; (2) theory triangulation which entails using multiple perspectives to interpret data; (3) methodological triangulation which uses multiple data collection methods and strategies; and (4) interdisciplinary triangulation involving the inclusion of other disciplines to inform the research process (Crano, 1981).

Member checking involves asking informants to evaluate aspects of the researcher’s analysis to find out if interpretations are analytically sound. It can take several forms such as asking informants how and why cases are categorized in a certain way, or asking informants to review a written description of an aspect of their culture. For example, after having observed meetings during which transplant professionals made decisions about placing patients on the waiting list, I used member checking by having a transplant surgeon review a draft of my manuscript for accuracy, correct technical details, and to obtain clarification (Gordon, 2000).

Looking for ‘‘paradigm’’ cases generally involves researchers identifying ‘‘typical’’ or representative cases to illustrate situations in which certain norms apply. Ethnographers may also select anomalous or contradictory cases which do not conform to the dominant pattern. Study of these exceptional cases can help the ethnographer to differentiate between those conditions in which the cultural or ethical norms apply while simultaneously help to explain limits of application; this may reveal the ‘‘rules for breaking the rules.’’ In a study of perceptions about truth-telling by members of different ethnic groups, Blackhall, Frank, and Murphy (2001) deliberately used this technique by seeking further interviews with at least two respondents whose responses to an initial survey were atypical in order to obtain insight into the diversity within groups.

Finally, maintaining a detailed and accurate paper trail is another useful form of validity checking (Pelto & Pelto, 1978). This can be fostered by ensuring that dates and decision points are written on all data materials to provide a chronological account of the development of interpretations while data are being collected.

ETHICAL CONSIDERATIONS

Conducting ethnographic research raises several ethical concerns and challenges. Earlier in this chapter, we discussed informed consent and the lack of familiarity IRBs frequently have regarding ethnographic methods. Detailed accounts of the problems and proposed solutions to working with

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IRBs regarding ethnography can be found in Gordon (2003), Marshall (2003), and Koenig et al. (2003).

Other ethical challenges in ethnographic research pertain to ‘‘studying up,’’ studying one’s colleagues, confidentiality, and the questions about whether to intervene in ethically troublesome situations. Confidentiality is a major concern for ethnographers who frequently live or work intimately with those they are studying. Informants may see their roles and lives described in detail or in unanticipated ways that they feel can harm their reputations (Bosk, 2001). To protect confidentiality, most ethnographers try to conceal or mask the exact location of the work and identities of the participants. Part of the problem with good ethnography is that it ‘‘penetrate[s] deeply enough into the social world being described,’’ and ‘‘make[s] the latent manifest’’ (Bosk, 2001, p. 209). Fieldworkers can thereby make subjects uncomfortable (Bosk, 2001). Moreover, informants may feel betrayed when ethnographers leave the field after they have developed friendships or paid concerted attention to them over time. Respect for study participants and their culture can be demonstrated by sharing results with them and/or having them review interim findings during the study.

Care must be taken so that individuals in the group being studied cannot be identified in written reports. One way to circumvent this problem is to write composite cases that incorporate aspects from multiple cases into one. Additionally, ethnographers commonly use pseudonyms for participants and anonymize the research site. It is important to convey to participants that all data will be kept confidential and that their names will not be used in publications or presentations.

Ethnographers in their research can become privy to many kinds of behaviors and discussions, including illicit and unethical behavior. This raises the question of whether the ethnographer should intervene. Intervening could help those in need. At the same time, it can potentially jeopardize the quality and reliability of data, as well as impact relationships with key informants and those providing access to the research setting. Anthropologists have examined this issue as part of the ethical standards for professional practice ([URL: http://www.aaanet.org/stmts/irb.htm] accessed 2-28-07).

SUMMARY

Ethnography is a qualitative research method based predominantly on participant observation; this is usually supplemented by the use of interviews, surveys, and document analysis. The concept of culture is

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central – the ethnographer seeks to articulate how the members of the culture(s) being studied themselves understand phenomena (emic perspec- tive), and to analyze (etic perspective) the complex set of historical and contemporary forces that shape beliefs and behavior. Ethnography is best learned through experience, reading ethnographies, engaging in discussions with people who have done them, and doing smaller or pilot studies, rather than through purely didactic training. Accordingly, ethnography is a difficult research method to teach. We emphasize that ethnography is more than just observing a situation. One must know what to look for, how to make observations through the lens of the complex concept of culture, and how to interpret and analyze data in light of the social, historical, and cultural contexts in which data are collected. In the following, we discuss a number of strengths and weaknesses of ethnography.

Strengths

Ethnography offers much strength as a research method in bioethics. Foremost, ethnography enables researchers to contextualize bioethical issues in their broader social, historic, economic, political, ideological, and cultural contexts. Ethnography provides insight by uncovering elements of a culture that other methods cannot provide because of the tacit nature of culture. It also provides unique insights because it focuses on natural behaviors occurring in specific settings. It collects data on both what people say they do and what the ethnographer observes them doing. Ethnography does not depend on a rigid research plan; therefore, it can detect unanticipated phenomena. The sensitivity ethnography brings to examining subtlety and meaning renders it ‘‘an ideal vehicle for examining normative language or decision making’’ (DeVries & Conrad, 1998, p. 248). Ethnography is valuable for showing ‘‘how flexible values are, how the same values are used to justify a wide range of seemingly incompatible behaviors’’ (Bosk, 2001, p. 200). In addition, ethnography enables researchers to understand the complexity of a phenomenon. Since one of the hallmarks of ethnography is its flexibility, investigators are able to follow up leads that arise unexpectedly during the research, adjust techniques, and modify research questions as unforeseen information emerges to more thoroughly investigate a topic (Drought & Koenig, 2002; Briggs, 1970). Moreover, ethnography lends itself to informing other research methods. Specifically, it can help generate hypotheses that can be tested using other qualitative or quantitative methods, and interpret the findings of such research.

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Weaknesses

A major weakness of ethnography is its time consuming nature. Most studies last at least one year, frequently longer. Thus, ethnography is expensive and resource intensive. Another weakness that critics are quick to point out is that it is subjective in data collection and analysis. By using the ethnographer as a primary tool for data collection, personal biases may enter unchecked into the data collection and analysis processes. Another weakness is the limited ability to generalize study findings.

Other weaknesses pertain to the process of conducting ethnography itself. Ethnography relies, to a great extent, on luck. It is unknown what kinds of cases or events will emerge over the course of the research period, and ethnographers are essentially dependent on the luck of the draw. Moreover, levels of cooperation by members of the group under study with the researcher may vary unexpectedly. Research in clinical settings may also be affected by turnover in medical and administrative staff each month and by changes in nursing staff requiring re-negotiation throughout a study.

ACKNOWLEDGMENTS

This work was supported by grant DK063953 from the National Institute of Diabetes and Digestive and Kidney Diseases (EJG). We thank Becky Codner and Elizabeth Schilling for their research assistance, and Laura Siminoff Ph.D. and Liva Jacoby, Ph.D. for their helpful suggestions with this manuscript.

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SEMI-STRUCTURED INTERVIEWS

IN BIOETHICS RESEARCH

Pamela Sankar and Nora L. Jones

ABSTRACT

In this chapter, we present semi-structured interviewing as an adaptable

method useful in bioethics research to gather data for issues of concern to

researchers in the field. We discuss the theory and practice behind

developing the interview guide, the logistics of managing a semi-structured

interview-based research project, developing and applying a codebook,

and data analysis. Throughout the chapter we use examples from

empirical bioethics literature.

INTRODUCTION

Interviewing provides an adaptable and reliable means to gather the kind of data needed to conduct empirical bioethics research. There are many kinds of interviews to choose among, and a primary feature that distinguishes them is the degree of standardization imposed on the exchange between interviewer and respondent. Semi-structured interviews, as their name suggests, integrate structured and unstructured exchanges. They rely on a fixed set of questions but ask respondents to answer in their own words, and they allow the interviewer to prompt for a more detailed answer or for

Empirical Methods for Bioethics: A Primer

Advances in Bioethics, Volume 11, 117–136

Copyright r 2008 by Elsevier Ltd.

All rights of reproduction in any form reserved

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clarification. Such interviews are more structured than in-depth or ethnographic interviews that explore topics by following and probing a respondent’s largely self-directed account. But they are less structured than typical surveys that administer the same questions to all subjects and confine responses to a fixed set of choices. Semi-structured interviews combine the advantage of closed-ended questions, which allow for comparison across subjects, and the advantage of open-ended queries, which facilitate exploring individual stories in-depth. Similarly, semi-structured interview data is more flexible than data generated from strictly quantitative studies. Analysis of semi-structured interview data can run the spectrum from the statistical to the descriptive and in-depth. This combination of types of analyses contributes to the strength and wide-ranging applicability of semi- structured interviewing.

Researchers can use semi-structured interviews for many types of inquiry, including confirming existing research results or opening up new fields of inquiry, but the method is particularly well suited to exploratory research or to very focused theory-testing. This is the case because the effort entailed in conducting good semi-structured interviews and then analyzing the considerable data that they produce work against the large sample sizes required to examine certain kinds of population-based questions character- istic of epidemiological and some sociological studies. While semi-structured interview results can be analyzed quantitatively, this is not their primary strength. Their main contribution lies in the richness of the data they provide, a strength that is sacrificed when data are reduced to numerical values.

In bioethics, semi-structured interviews have been used effectively to examine several topics, including genetic testing, end of life care, medical confidentiality, and informed consent. Genetic testing studies demonstrate the suitability of semi-structured interviews to explore new areas of ethical concern. For example, studies relying on semi-structured interviews have been instrumental in elucidating the challenge of successfully educating patients about the complexities of BRCA1/2 testing (Press, Yasui, Reynolds, Durfy, & Burke, 2001), examining the special circumstances of some patient groups, such as men who test positive for the gene (Hallowell et al., 2005; Liede et al., 2000), and studying how families communicate about test results (Claes et al., 2003; Forrest et al., 2003). Research on end of life care demonstrates the advantages of semi-structured interviews when examining sensitive topics such as how patients and families would like clinicians to answer questions about physician-assisted suicide (PAS) (Back et al., 2002) or what factors influence a terminally ill patient’s wish to hasten

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death (Kelly et al., 2002). Another example of exploring sensitive topics with semi-structured interviews is the authors’ study of patients’ deliberations to tell or not tell their physicians about medical problems they consider sensitive. Using semi-structured interviews, we found that patients withhold information about a wider range of topics than practitioners might expect (Sankar & Jones, 2005). Semi-structured interviews also have been productively used to study situations distinguished by complex or confusing communication (Featherstone & Donovan, 1998; Stevens & Ahmedzai, 2004; Pang, 1994).

THE INTERVIEW PROCESS

Developing the Interview Guide

Semi-structured interviews rely on an interview guide that includes questions, possible prompts, and notes to the interviewer about how to handle certain responses. Constructing the interview guide derives from the study’s research question(s) and begins with reviewing the literature on a chosen topic to see how others have approached similar inquiries. Working back and forth between the previous research – what has been done – and the draft interview guide – what could be done – helps to clarify the aims and hypotheses of a study and brings into focus possible questions to include in the interview guide. The guide should be designed to balance the need to collect roughly similar kinds of information from all subjects while capturing each subject’s unique perspective on that information. Novices to interview guide development might consider reading existing interview guides available from colleagues or those posted online associated with published articles (see Box 1 for an excerpt of the interview guide we developed for our study on medical confidentiality). Several excellent articles also offer suggestions on how to formulate interview questions (Britten, 1995; Fowler, 1992; Mishler, 1986a, 1986b; Sanchez, 1992; Weiss, 1994). Here we offer some pointers for crafting and asking semi-structured interview questions, as well as suggestions for helping subjects answer those questions (Box 2). Reading draft questions aloud or trying them on colleagues can be useful. Very often what makes sense to the author eludes the listener.

The order of questions in an interview guide merits careful consideration. Topics lend themselves to different schemes, such as chronological, or by degrees of complexity or intrusiveness. The interview guide for our medical

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confidentiality study, for example, started with questions about how subjects thought medical confidentiality operated in a clinic setting, and then asked them how they thought it should operate. Only after 30 or 40 min of exchange did we ask respondents to recount their own personal experiences related to medical confidentiality (Jenkins, Merz, & Sankar, 2005; Sankar & Jones, 2005).

Once a rough draft of the interview guide is complete, it is essential to try it out, start to finish, with a colleague to see if the questions make sense and flow sensibly from one to the next. Subsequent to this, it is advisable to revise the guide and try it on members of the study population while taping these pilot interviews, and if possible take notes while conducting them. Asking subjects to explain if and why a question seems unclear is important. Transcribing these interviews is not necessary, although if working in a large research team, transcribing allows everyone to review the results and facilitates revising the guide. The goal is to create an interview guide that is comprehensible to respondents and that evokes responses which answer the questions the research asks. While this goal might seem self-evident, achieving it can be far more difficult than anticipated. Absent careful review of pilot interview transcripts, the insight that the interview failed to ask the

Box 1. The Interview Guide: A Brief Example.

7. DEFINE ‘‘CONF’’: If you had to explain to a friend what ‘‘confidentiality’’ means, what would you tell her it means? 7a. So then, what part of the information that you talk about with your doctor or nurse is ‘‘use respondent’s wording, e.g., ‘just between a person and her doctor’’’ 7b. Does this confidential information go into your medical record?

[SKIP 8 & 9 IF ALREADY ANSWERED IN 7] 8. RELEASE?: Do you think there are any situations where your doctor or nurse would decide NOT to keep confidential information use respondent’s wording, e.g., ‘just between a person and her doctor’ – an give out or release your confidential medical information?

a. NO b. YES 9. Could you give me examples of this kind of situation?

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Box 2. Asking Questions: Some Pointers.

� Tense and Specificity. The verb tense of a question influences its likely responses � Questions asked in the present tense, for example, ‘‘What happens when you call the doctor?’’ tend to elicit generalized accounts. � Conversely, questions phrased in the past tense, such as ‘‘What happened the last time you called the doctor?’’ will tend to elicit specific actions and experiences.

� Phrasing � Avoid phrasing that allows for yes/no answers. Questions beginning with what or how are better at eliciting fuller answers than those starting with did, as in ‘‘Did you ask the doctor any questions?’’ � Ask only one question at a time. If an interviewer asks, ‘‘What did you do after your diagnosis and how did you feel about that,’’ respondents can get caught up in answering the second part of the question and be unable or unwilling to go back and provide a complete answer to the first part. � Often the best follow-up question consists of repeating back the last phrase of the subject’s previous statement, as in, ‘‘y you called her after dinner y’’ when the subject has stated, ‘‘I couldn’t reach the doctor all day so I had to call her after dinner.’’ This response indicates that you are listening and encourages the subject to continue talking without directing him or her toward any particular response.

� Helping respondents answer your questions � Extending: Can you tell me more about how you met her? � Filling in detail: Can you walk me through that? So were talking to him after the phone call? � Making indications explicit: when subjects nod or sigh, try to confirm verbally what they indicated. ‘‘So would you say that was good news?’’ or ‘‘Tell me what you’re thinking when you sigh like that.’’ Or, most directly, ‘‘Can you say what you’re thinking for the tape recorder.’’

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‘‘right’’ question may not emerge until all the data are collected and the opportunity to revise the interview guide long past. Reviewing the interview guide through extensive pilot testing should also mitigate the need to revise questions during the data collection phase of a project, a common remedy for poorly phrased questions that complicates data analysis and reduces internal validity.

Formulating, testing, reviewing, and revising questions so that they successfully communicate to the subject what interests the researcher is roughly equivalent to the steps that quantitative researchers go through in scale development when they ‘‘validate’’ an instrument. Validity refers generally to the relationship between measurements (such as scales or questions) and the phenomenon they claim to capture or measure. The closer that relationship is the more ‘‘valid’’ a measure.

There are several types of validity, including internal, external, and construct validity. For a useful review of these concepts written for qualitative researchers, see Maxwell (1996). A major difference between the import of these ideas for qualitative and quantitative research is the degree of specificity or formalism in assessing validity. The latter is standard in quantitative research. The logical problems posed by validity concerns, however, are equally salient in qualitative research. For example, in question development for semi-structured interviews, construct validity focuses attention on assessing whether the question asked is the question answered. In our medical confidentiality study for example, we struggled with devising a question to determine how subjects understood medical confidentiality. Asking them directly, ‘‘What does medical confidentiality mean?’’ seemed to suggest a test question with a right/wrong answer. Subjects interpreted the question more like ‘‘What is the definition of medical confidentiality?’’ and responded with their approximation of a dictionary definition. The question they answered was not the question we meant to ask. We went through several iterations, including versions that added ‘‘to you’’ at the end of the original question, and another that asked subjects what it meant if a doctor or nurse told them they would keep something confidential (which elicited responses about the doctor or nurse’s character, such as ‘‘It would mean they cared about me’’). Finally we arrived at the solution of asking the subject to tell us how she would explain to a friend what medical confidentiality meant. These questions elicited answers such as, ‘‘I would tell her that it means keeping a secret. That whatever I tell a doctor goes to no one else.’’ These were the kind of responses we were seeking, that is, what patients think it means in a medical setting when a health care practitioner characterizes an exchange as ‘‘confidential.’’ Formulating, testing, and

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reformulating questions brought the query we posed and the phenomenon we were trying to measure closer and closer into alignment.

Piloting the interview guide will require finalizing most of the bureaucratic and practical steps that actual interviews require. This includes completing all necessary human subjects research review, and deciding who will be inter- viewed, how they will be contacted, and where the interviews will be conducted. While such complete preparation for piloting might seem onerous and unnecessary, every moment spent testing the interview guide and refining recruitment and other logistics before the study’s full implementation will pay off by revealing obstacles that eventually might have delayed the project.

Sampling: Choosing Whom to Interview

The research question will determine the population to be sampled for interviews. How to approach the population and solicit its members for participation needs to be worked out carefully in advance based on familiarity with the targeted group and discussion with some of its members. The best designed study can founder on unanticipated obstacles in subject recruitment. If the target group is large and loosely defined, such as women who have had mammograms, the issues are quite different than if research calls for talking with a small or more isolated group, such as people who have undergone genetic testing for hearing loss. Abundance might pose a sampling problem in the former – which women who have had mammograms are of interest? Old, young, insured, uninsured, rural, or urban? Scarcity presents a challenge in the latter. How does one find people who have had genetic testing, who are few in number, geographically dispersed and, if themselves deaf or hard of hearing, who might not be reachable by methods typically used by the hearing research community?

A method often relied on in qualitative research is snowball sampling, in which potential participants are identified by asking initial subjects to suggest names of other people who might be interested in participating in the study. The resulting sample is likely to consist of people who know one another, which also means it might represent a fairly narrowly defined group. Inferences from such a study cannot be generalized much beyond the subjects interviewed or individuals deemed highly similar to them. While this might be acceptable for studies examining very specific issues confined to a well-defined group, it is less effective for more broadly relevant issues. Another common strategy for identifying potential participants is to use

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clinic staff or other gatekeepers to estimate the flow of people through a recruitment site. Relying solely on this information can be a mistake. Overestimating is common, as is failure to recall seasonal fluctuations. Researchers would do well to review any sort of available records that might document actual numbers of individuals or groups pertinent to the research.

Estimating how many and what kinds subjects to enroll requires knowing what kind of inferences you plan to make from findings. The way one estimates and selects a sample will directly influence what inferences one can make from the data, and these sampling decisions will have to be explained in any publication that results from one’s research. There are several excellent articles on sampling for semi-structured interviews (Creswell, 1994; Curtis, Gesler, Smith, & Washburn, 2000; Marshall, 1996; Morse, 1991; Schensul, Schensul, & LeCompte, 1999). In general, sampling should control as much as possible for subject bias. Biased sampling occurs when researchers assume that the perspective of one group of potential respondents is representative of a more inclusive group, for example, using race or ethnicity as proxy for a wider range of socioeconomic and sociodemographic variables. A study focused on minority perspectives on genetic testing that only sampled northern urban African Americans would suffer from such sample bias, if it purported to speak for a general US minority population that is more diverse than the group sampled.

Finally, in studies focused on the beliefs, attitudes, and behaviors of a narrowly defined group of people, such as oncology clinic patients, or about a specific topic, such as attitudes about hospice care, the question of the exact number of subjects may be left open until after a few rounds of preliminary coding. Some researchers decide to keep recruiting subjects until a point of theoretical saturation, meaning that new interviews begin to be redundant and are not adding new substantive issues (Eliott & Olver, 2003).

Conducting the Interview

The setting for the interview should be comfortable and foster an open exchange, which often, but not necessarily, means a private room. Regardless of where the interview is conducted, it is important to make sure the space is available for the entire time needed to conduct the interview. Completing the interview in a timely manner shows respect to the participant and minimizes scheduling problems.

Regardless of what is explained when arranging to meet the respondent, he/she needs to be re-oriented of the study objectives. It is important to

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explain what in his/her background makes him or her able to contribute to the research. Another essential strategy is to briefly review the aim(s) of the study, topics of the interview and the order in which they will appear, and to underscore that unlike other interviews or surveys that ask for simple yes or no answers, this type of interview emphasizes learning what respondents consider important about the research topic and relies on them to provide answers in their own words.

Semi-structured interviews allow a high degree of flexibility not only to the respondent but to the interviewer as well. A question mistakenly asked so that it can be answered with ‘‘yes’’ or ‘‘no’’ when the objective was to obtain a detailed response, can simply be re-stated. Having gone to the trouble to start the interview, respondents usually share the researcher’s interest in successfully completing it.

Technicalities of Data Collection and Transcription

There are two primary reasons why audiotaping or digitally recording interviews is preferable to note taking: memory and foresight. As semi- structured interviews can be very lengthy, it is highly unlikely that the interviewer can remember the full story behind the abbreviated notes jotted down during an interesting story early in the interview. Also, as research progresses, new patterns in the interviews emerge and new questions can be asked about the data. At the time of the interview, these new issues will not catch the interviewer’s attention and will not be documented, or not be documented fully enough to permit subsequent detailed analysis.

To assure high quality recordings and to avoid contributing to the overflowing stock of ‘‘interviews that got away’’ stories, one should obtain reliable equipment, bring several extra batteries and cassettes, and test the recorder before each interview. After obtaining informed consent, it is useful to establish the respondent’s permission to record the interview and have the respondent and the interviewer both speak while the recorder is in the exact position it will be in during the interview. It is also important to play back this recording and check for levels and intelligibility. It is not advisable to use the ‘‘voice activation’’ option as words or whole sentences can be cut- off. A good idea is to glance at the recorder every 10 min or so to make sure it is still functioning properly. Moreover, it is advisable to use one tape for each interview and not taping the next interview on the same cassette, as it is too easy to mix up the sides and tape over the previous interview. Cassettes can be reused after transcribing a set of interviews, so this practice need not

Semi-Structured Interviews in Bioethics Research 125

be wasteful. More and more, researchers in the field are switching to digital recording to eliminate tapes altogether, but it is important to practice a few times with any new technology before relying on it in an interview.

Finally, it is essential to carefully label interview cassettes with an appropriate identification code and arrange for transcription. With respect to transcription, every hour of talk takes upwards of 3–4 h to transcribe. The task is tedious and best left to a professional transcriptionist if the project can afford one, although in smaller studies transcribing interviews can provide a good way to review data. Whether analysis will rely on a software program or pen and paper coding, it will proceed more smoothly with established transcription guidelines to help assure consistency across transcripts and to facilitate a review of the documents for errors or to remove any identifying personal information.

Creating a data preparation chart to track the progress of individual interviews helps to organize interview tasks. It is important to keep any information that might identify a subject out of a data preparation chart and to create instead a code that links the data from this chart to other data sets containing subject contact information. A data preparation chart might contain fields that track an interview from its occurrence (including the date, time, place, and initials of the person who conducted an interview) to its completion as a fully prepared document ready for analysis (including the date the cassette was sent and received back for transcription, checks on transcript accuracy, reviews to remove personally identifying information, formatting text for database importing, and finally, the cassette’s erasure).

CODING AND DATA ANALYSIS

Researchers opt for semi-structured interviews because they provide more and more nuanced data than close-ended questions. However, the amount and kind of data that such interviews can produce will also greatly increase data management and analysis tasks. Conducting fewer interviews might cut down on these burdens, but analyzing any amount of unstructured text inevitably increases the work required in the post-data collection phase of research.

Much analysis of semi-structured interview data is the same irrespective of the number of interviews conducted or the means of analysis (note cards or qualitative data analysis software programs). However, here we assume that study data will be entered into a qualitative data analysis program and that somewhere between 30 and 100 interviews have been conducted. This

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range is somewhat arbitrary, but reflects our experience that studies with fewer than 30 subjects are less likely to require the team coding process described here, and those that exceed 100 are likely to benefit from primarily quantitative analysis, which we address only in passing. There are examples of studies with larger sample sizes using semi-structured interviews (Kass et al., 2004; Mechanic & Meyer, 2000), but these studies are atypical, either in their lengthy duration (extending over many years) or in their reliance on a high proportion of close-ended questions, which minimizes coding burden.

Coding is the process of mapping interview transcripts so that patterns in the data can be identified, retrieved, and analyzed. In other words, coding provides a means to gain access to interview passages or data, rather than becoming the data itself. Unlike coding survey responses for quantitative analysis, which requires reducing responses to numeric values, the goal of coding semi-structured interview transcripts is to index the data to facilitate its retrieval, while retaining the context in which data was originally identified.

Researchers should investigate different qualitative software packages before beginning the codebook development process, as each program has different systems for coding. Common programs worth exploring include N6 (2002), ATLAS.ti (Muhr, 2004) and HyperResearch (2003). See the ‘‘Computer Assisted Qualitative Data Analysis (CAQDAS)’’ website for software comparisons and tips for choosing a qualitative software program (www.caqdas.soc.surrey.ac.uk/).

Codes

Codes classify and represent relevant passages in the interview. Devising codes that accomplish these tasks requires identifying the central themes in the research, developing concepts to represent them, and assessing how these concepts relate to each other and to overall research questions that motivate the study. Developing and defining codes thus constitutes the first stage in data analysis, which means that developing the coding manual requires careful attention. Coding itself consists of reading transcripts and deciding how and where to apply codes. Ideally the coding manual contains codes that are sufficiently well suited to the data and well enough defined that the process of coding, while unlikely to be automatic, does not require continually rethinking the meaning or limits of codes. There are several types of codes, which can be thought of as roughly paralleling the stages of coding, or at a minimum, the stages of developing a coding manual.

Semi-Structured Interviews in Bioethics Research 127

Coding Manual

The first stage of developing a coding manual consists of listing codes that cover information that the interview explicitly sought. For example, in our study on confidentiality, we asked women how they would explain medical confidentiality to a friend. All of the passages containing answers to this question were assigned the code ‘‘DEF CONFI,’’ which stood for ‘‘definition of confidentiality.’’ Many codes of this type need to be divided into sub-codes. Sometimes sub-codes are logically suggested by the original code, as one might expect a code such as ‘‘family member’’ to have the following sub-codes: ‘‘spouse,’’ ‘‘child,’’ and ‘‘parent.’’ Other times sub- codes are needed to represent distinctions that emerge from the data.

Subdividing codes based on emergent data moves the focus from codes that are implied in the interview guide to codes that capture themes that the interviews have elicited. Thematic coding identifies recurrent ideas that are implicit in the data. Some of these codes might be envisioned in advance of the interviews. More typically, however, thematic coding is held out as the means for capturing what one learns or discovers when examining the interviews together as a completed set. In our confidentiality study, repeated transcript readings and examinations of the primary code ‘‘DEF CONFI’’ revealed that respondents often ground their definitions either in the personal relationships between patient and practitioner or in bureaucratic procedures required to safely store and accurately transmit sensitive information. Two thematic sub-codes of ‘‘DEF CONFI,’’ ‘‘personal’’ and ‘‘bureaucratic,’’ captured these recurrent ideas (Jenkins et al., 2005). Also, further analysis determined that these themes connected with many other features emblematic of different ways of understanding and using medical confidentiality.

A similar staged coding process was used by researchers studying patients’ interactions with their physicians regarding PAS (Back et al., 2002). The first level of primary codes included ‘‘interactions with health care providers,’’ ‘‘reasons for pursuing PAS,’’ and ‘‘planning for death.’’ Further examina- tion of the text coded to the primary code ‘‘interactions with health care providers’’ revealed three distinctive themes, which became three individual sub-codes: ‘‘explicit PAS discussion,’’ ‘‘clinician willingness to discuss dying,’’ and ‘‘clinician empathy.’’ Through this process, researchers were able to see what their subjects valued in their clinician’s responses, or what they wanted but did not get.

The provisional coding manual provides draft definitions of the codes, which are tested by applying them to a set of completed interviews.

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The experience of trying to fit draft codes to actual interviews clarifies which codes effectively capture responses and which do not, which might with revision, and what new codes should be tested. This process is analogous to the work required to develop interview questions that accurately express the researcher’s interests in that the task is one of proposing, matching, and testing representation to concepts. Testing draft codes also helps to identify example passages from the interviews that researchers can incorporate in the coding manual to demonstrate a code’s definition or limits.

After a test-round of coding, a revised coding manual is generated and applied to a second set of interviews that preferably includes some from the prior coding round as well as new ones. The second round continues largely in the manner of the first. Codes are evaluated and revised, and their definitions revised in parallel. This process continues until a codebook emerges that contains a set of codes judged to have sufficient range to effectively represent the material of interest to researchers and sufficient clarity to be consistently applied by coders (see Box 3 for an excerpted section of the codebook used in our confidentiality study).

Multi-Level Consensus Coding (MLCC)

With studies based on 30 or more interviews, coding typically requires, and nearly always benefits from, more than one coder. We have developed an approach to coding large semi-structured interview data sets called multi- level consensus coding (MLCC) (Jenkins et al., 2005). MLCC meets the challenge of analyzing a large number of open-ended qualitative interview transcripts by generating a high degree of inter-coder reliability and comparability among interviews, while maintaining the singular nature of interviewee’s experiences. This coding model depends on the use of a code and retrieval software program, in this case, N6, that aids in the analysis of qualitative data through flexible coding index systems and searching protocols that identify thematic patterns, essential in the mapping of qualitative knowledge.

MLCC differs from other coding strategies described in most standard methods texts in two ways. First, it makes explicit the connections between training to code, coding, and analysis by fore-fronting the vital connection between the codes, the coders, and the process of coding. The codes do not exist separately from the coders in the sense that assigning a code (that is, coding) requires coders to have a shared understanding of the relationship between code and text. In turn, having to reach consensus about a code

Semi-Structured Interviews in Bioethics Research 129

reproduces and, overtime, changes that understanding. The change in understanding should not be taken to mean the original relationship between the code and its definition and examples was flawed (although certainly that is sometimes the case). Rather, the change in understanding refers to evolving understanding of the phenomenon to which the code was meant to apply and to the capacity of the code to represent it. Second, MLCC formalizes and makes a virtue of the need for several stages of coding. While similar to the types of coding described in grounded theory methods (open, axial, and so on), the levels or stages laid out in MLCC are

Box 3. Sample Codebook.

This codebook example with numbering follows the system used in Nud � ist v4 (non-numerical unstructured data indexing searching and

theorizing) (see Box 1 to compare the codebook with the interview guide).

(7) Q7 – Def confi

(7 1) Between 2 people/personal (7 2) Need to know (7 3) Continuity of care model/bureaucratic model (7 4) Other (7 5) Don’t know (7 6) NA (8) Q8-release?

(8 1) Yes (8 2) No (9) Q9 – Desc Release

(9 1) Happens – w/consent (9 2) Happens – but not supposed to (9 3) Happens – continuity of care model (9 4) Happens – infectious disease (9 5) Happens – emergency (9 6) Happens – research (9 7) Happens – other (9 8) Happens – doesn’t specify (9 9) Don’t know (9 10) NA

PAMELA SANKAR AND NORA L. JONES130

less tied to a specific interpretive framework, and are intended more to serve a broader range of projects. Further, it highlights the steps required to code semi-structured interview data as a particular method because as much as the basic steps described here differ little from those that appear in many qualitative methods textbooks, a review of articles claiming to follow qualitative methods suggests that many studies omit or abbreviate the process in ways that seriously undermine the validity of the data and the legitimacy of claims that accepted qualitative data analysis produced it.

MLCC responds to concerns about inter-coder reliability and compar- ability between interviews through extensive training of coders and through consensus coding, or group decision-making. At the beginning of training coders jointly code two to three transcripts with PI supervision. When the coders have gone through a sufficient number of transcripts so that they are familiar with the content of the interviews and the coding rules, they then move onto ‘‘round-robin’’ coding. A round-robin coding session is a group of four coders (at least one PI among them) who each code three interviews, each interview with a different partner. The discussion session for round- robin coding is an occasion to test individual perceptions of the meaning of coding categories and rules, and an occasion where irregular coding is brought into conformity with the rules. The round-robin training sessions are similar to consensus sessions that coders will participate in during regular coding. Coders then code several more interviews individually and in pairs, mirroring the regular coding method.

Following training the coding teams are divided into pairs, and two coders independently read and code each level of each interview. Coding pairs are rotated so that each coder codes at least a subset of interviews with every other coder, although often certain pairs seem to work better together if only because of scheduling issues. Rotating members of the pair helps to prevent ‘‘coding drift,’’ which occurs over the course of a long coding effort when one or more coders starts to apply codes in an increasingly idiosyncratic manner. Occasional general data checks of all coded interviews can also help catch the wayward evolution of codes. Alternatively, the wayward codes these checks identify sometimes prove to be better than the approved ones. If this happens, the new code can either be added to the coding manual or substituted for an existing code. In either case, changing the coding manual should be done only after careful consideration, as it requires reviewing and amending all completed coding to bring it in line with the new codes.

After each member of the coding pair has coded an interview, they meet to compare individual coding choices and come to agreement on the final

Semi-Structured Interviews in Bioethics Research 131

coding for the interview. Printing line and page numbers on the interview transcripts facilitates such crosschecking. Disagreements are discussed in these pairs, and any that the pair is unable to resolve are brought to a weekly consensus meeting, which all pairs of coders, as well as the project manager, attend. Data that cannot be coded after being discussed in these consensus meetings are omitted from analysis.

Using the MLCC method, interviews are coded in three levels or stages. The objective of first-level coding is to generate standard, comparable answers to basic questions asked during each interview. The objective of second-level coding is to generate a series of vignettes characterizing the central experiences, ideas, and issues informing each interviewee’s percep- tions. When examined together through N6, first- and second-level coding blend a structured and comparative analytic lens with a nuanced representation of each research participant. Third-level coding uses the first two layers of coding to theorize about the themes that emerge from the interviews.

While requiring considerable organizational effort, the method has several advantages, foremost among them creating a high level of consistency, or validity, across interviews. This process also creates a collective memory of how codes have been applied, a regular forum that catches problem codes earlier than might be the case if coders interact less frequently, and a handful of people intimately acquainted with the data and interested in proposing ideas for its analysis.

Analysis

Once coding has been completed, the codes need to be entered into the qualitative data analysis computer program. This can even be done during the preliminary coding phase if the program will be used for codebook development.

Most software programs allow for online coding, but pair coding and consensus meetings require that paper versions of coded documents be available. Our typical strategy is to start on paper and then enter coding into the program only after if has been finalized. Each program has different logistics for entering the codebook and the coding interviews, but in general, what these programs do is create a ‘‘filing cabinet’’ that stores the interviews and the coding. This filing cabinet organizes material in a myriad of ways – by interview, by the codes, or by demographic information. In response to different search commands the program retrieves any combination of

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‘‘files,’’ or sections of coded text. Some programs can also use the existence of particular coding to create matrices that can be exported to various statistical software packages.

It is the automated data retrieval function of these programs that so benefits qualitative analysis by allowing researchers to retrieve all data coded to a particular code and to examine relationships between different segments of coded data. For example, a search of our medical confidenti- ality interviews for all passages coded to ‘‘sexuality concerns’’ might return primarily responses from young interviewees. A second search on these passages for the context in which these concerns were expressed might suggest they are most common among college age women attending a university health service. These data are useful and could be reported as a quantitative statement, such as: ‘‘Three fourths of women who reported having hesitated to discuss sexuality concerns with health care practitioners were college students between the ages of 18 and 22 who used the college health service for their care.’’ However, ending there would miss the point of semi-structured interview data analysis. The software has simply located the respondents and passages where sexuality was characterized as a sensitive topic.

Complete analysis entails going back to the transcripts and examining the selected passages in the context of the interview to discern whether the comments relate to one of the thematic codes, or whether they suggest ideas for additional analyses. Given the initial finding that younger women in university health service settings had a high number of ‘‘sexuality concerns,’’ one might, for example, query the status or gender of the health care practitioner seen by these women and what women expressed as their concerns in this setting (if these factors have been coded), and how those concerns related to other themes in the interview. As patterns start to emerge linking concerns, setting, age, and gender, hypotheses about the relationships between age and gender relations in health care might be generated that can be tested on broader segments of the sample or on the whole sample. The point, however, is always to reach back into the interview, at least to the coded passages if not to lengthier exchanges, with the goal of situating patterns in the context of the respondent’s story as a whole. These forays into the data also provide the opportunity to collect exemplary quotes that can be used to illustrate the resulting presentation or article.

Before beginning analysis, it is important to devise an overall plan that includes steps needed to examine a set of relationships between thematic codes. Researchers can and typically do deviate from such a plan. Still,

Semi-Structured Interviews in Bioethics Research 133

having a written version of a strategy helps to highlight when one has departed from it, which encourages examining the reasons for doing so and keeping track of why old ideas were discarded and new ones proposed. Also, most qualitative data analysis programs lack an easy or effective way to keep track of the order in which analyses were conducted so it is the responsibility of the analyst to do so.

Summary

Empirical research is increasingly gaining an equal footing with philoso- phical analysis in bioethics inquiry. Among the myriad of methods brought to bear in this work, semi-structured interviewing is a reliable and flexible means to gather data. The semi-structured interview allows for compa- rison across subjects as well as the freedom to explore what distinguishes them.

Semi-structured interviews are useful for examining the complex moral issues that bioethics confronts. The give and take of this method allows the interviewer to follow the subject’s lead, within the parameters of the study. The volume of the data acquired through semi-structured interviews entails, however, a trade off, usually in sample size. Given the demands of the method, including its time-consuming coding, and the difficulty of adapting a single interview guide to widely variant populations, the generalizability of findings will always be somewhat limited. Additionally, it can be difficult for interviewers to repeatedly hear lengthy personal stories that are distressing, such as those related by women about a breast cancer diagnosis or confidentiality breaches.

Research findings from studies relying on semi-structured interviews can be considered problematic for policymakers because results are not easily generalizable, given the uniqueness of the real-world situations from which the data are drawn (Koenig, Back, & Crawley, 2003). Similarly, for clinicians looking for strategies to improve practice, the absence of easily digested bullet points in many qualitative studies may lead readers to ignore such research. However, these limitations are offset by the distinct strengths of this method, including detailed, in-depth, and unanticipated responses. Such data can often help identify novel factors or explain complex relationships, which can contribute substantially to the understanding of complex ethical issues, policy assessment, decision-making, and develop- ment of interest in bioethics.

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SECTION III:

QUANTITATIVE METHODS

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SURVEY RESEARCH IN BIOETHICS

G. Caleb Alexander and Matthew K. Wynia

ABSTRACT

Surveys about ethically important topics, when successfully conducted

and analyzed, can offer important contributions to bioethics and, more

broadly, to health policy and clinical care. But there is a dynamic

interplay between the quantitative nature of surveys and the normative

theories that survey data challenge and inform. Careful attention to the

development of an appropriate research question and survey design can be

the difference between an important study that makes fundamental

contributions and one that is perceived as irrelevant to ethical analysis,

health policy, or clinical practice. This chapter presents ways to enhance

the rigor and relevance of surveys in bioethics through careful planning

and attentiveness in survey development, fielding, and analysis and

presentation of data.

INTRODUCTION

Surveys are a common method in empirical bioethics. They cannot say what is right or wrong, but they can reflect, within bounds, what people are actually doing or thinking. They can also provide information concerning whether consensus exists about a given issue. As a result, they can both inform ethical analysis and be useful in policy making and clinical practice.

Empirical Methods for Bioethics: A Primer

Advances in Bioethics, Volume 11, 139–160

Copyright r 2008 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1479-3709/doi:10.1016/S1479-3709(07)11007-4

139

Consider three examples of surveys examining ethically important topics. The first, a survey of healthy volunteers and firefighters regarding their attitudes towards quantity and quality of life, indicates that approximately one-fifth would choose radiation instead of surgery to manage laryngeal cancer in an effort to preserve voice, even though doing so would lead to a lower likelihood of survival (McNeil, Weichselbaum, & Pauker, 1981). The second, a survey of family members, demonstrates that their ability to accurately predict a loved one’s preference for life-sustaining treatment is quite limited (Seckler, Meier, Mulvihill, & Paris, 1991). The third, a survey of physicians, suggests that nearly two-fifths of respondents report having used one of three tactics to deceive insurance companies in order to help their patients obtain coverage for restricted services (Wynia, Cummins, VanGeest, & Wilson, 2000).

These examples illustrate the diversity of ethically important topics that can be explored by surveys, as well as the various purposes they can serve (Table 1). The first survey, examining the tension that may exist between treatments that impact both quality and length of life, demonstrates that length of life is not always paramount, and some people are willing to make trade-offs in ‘‘cure-free survival’’ for improved quality of life such as preservation of voice. Many surveys of ethically important topics are of this type; they are useful in the process of developing, supporting or refuting normative empirical claims (e.g. the claim that most people value quantity over quality of life).

The second survey provides important evidence of limitations in the ability of surrogates to predict what family members without decision- making capacity would want in situations of life-threatening illness. This has profound and direct clinical implications: physicians should strive to ensure that patients’ wishes are known, work with family members to distinguish between their own wishes and those of the patient whose best interests they are trying to serve, and be cautious of the reliability of ‘‘substituted judgment’’ to truly reflect patient’s wishes. Many other noteworthy surveys examine similarly important topics in clinical ethics, such as those exploring the accuracy of physician’s prognoses for their terminally ill patients (Christakis & Lamont, 2000).

The final example examines insurance company deception. It demon- strates regulatory failures or the inoperability of common normative assumptions that physicians should not deceive on behalf of their patients, whether to protect a third party (Novack et al., 1989) or to secure reimbursement for services (Wynia et al., 2000). Such findings may lead to policy changes to make the adjudication of appeals to managed care

G. CALEB ALEXANDER AND MATTHEW K. WYNIA140

Table 1. Select Examples of Surveys Examining Ethically Important Topics.

Group Objectives Sample Main Findings

Novack et al. (1989) To assess physicians’ attitudes

toward the use of deception in

medicine

211 practicing physicians Most physicians were willing to

misrepresent a screening test as

diagnostic test to get insurance

payment, 1/3 indicated that they

would give incomplete or misleading

information to patient’s family if a

mistake caused patient’s death

Wynia et al. (2000) To examine physicians attitudes

toward and frequency of

manipulation of reimbursement

rules to obtain insurance

coverage for services

720 practicing physicians Thirty-nine percent of physicians used

one of the three techniques to

manipulate reimbursement rules

during the previous year

Cohen, Fihn, Boyko,

Jonsen, and Wood

(1994)

To assess physicians’ attitudes

toward physician-assisted suicide

and euthanasia

938 physicians in one

state

Forty-eight percent of physicians

thought euthanasia is never ethically

justified and 39% believed physician-

assisted suicide is never ethically

justified. Fifty-four percent believed

euthanasia and 53% believed

physician-assisted suicide should be

legal in some situations

Patients or family

Seckler et al. (1991) To determine accuracy of family

members’ and physicians’

predictions of patients’ wishes to

be resuscitated

70 patients and their

family members and

physicians

Family members had 88% and 68%

agreement rate with patients on each

scenario while physicians had only

72% and 59% agreement rate with

patients on each scenario, respectively

S u

rv e y

R e se

a rc

h in

B io

e th

ic s

1 4 1

Table 1. (Continued )

Group Objectives Sample Main Findings

Ganzini et al. (1998) To determine attitudes toward

assisted suicide among patients

diagnosed with amyotrophic

lateral sclerosis (ALS) and their

care givers

100 patients with ALS

and 91 family care

givers

Fifty-six percent of patients would

consider assisted suicide and 73% of

caregivers and patients have the same

attitude toward assisted suicide

General public

McNeil et al. (1981) To explore preference for

laryngectomy (high survival rate,

high loss of speech) versus

radiation (low survival rate, low

loss of speech) for throat cancer

37 healthy volunteers, 12

firefighters, 25 middle

and upper

management

executives

Twenty percent of volunteers would

choose radiation to preserve quality

of life over quantity

Multiple groups

Ubel et al. (1996) To determine whether individuals

make cost-effective decisions

about medical care given budget

constraints

568 prospective jurors,

74 medical ethicists, 73

decision-making

experts

A little over half of the jurors and

medical ethicists made non-cost-

effective medical decisions when the

non-cost-effective choices offered

more equity among patients

Bachman et al. (1996) To assess physician and citizen

attitudes toward physician-

assisted suicide

1119 physicians and 998

members of the

general public

Most physicians and members of the

public preferred legalizing physician-

assisted suicide over banning it

Degner and Sloan

(1992)

To determine how involved cancer

patients and members of the

general public would want to be

in their treatment decisions

436 newly diagnosed

cancer patients and

482 members of the

public

Most patients wanted physicians to

make treatment decisions on their

behalf; most members of the public

wanted to decide on their own

treatment if they developed cancer

G . C A L E B A L E X A N D E R

A N D

M A T T H E W

K . W Y N IA

1 4 2

organizations as transparent and fair as possible and, equally important, these conclusions tell us something about how physicians view the equity of the current health insurance system.

Despite the many purposes of surveys in bioethics, there are important limitations to the data they produce, including the authenticity of the responses and how far they can be generalized. In addition, the number of people doing X does not necessarily mean that X is desirable or undesirable. Heterogeneity of an attitude or behavior is almost always present, warranting consideration at the outset of how this will affect data analysis, conclusions, and presentation of an empirical inquiry (Lantos, 2004).

FINDING A QUESTION

A successful survey begins with identifying a good research question (Table 2). Identifying such a question is not trivial. The literature is vast and certain areas of inquiry in ethics, such as end-of-life care, have been extensively explored. However, the tremendous effort to examine ethical

Table 2. Select Examples of Ethically Important Topics Suited for Survey-Based Analysis.

Domain Examples of Survey Topics

Principles of medical

ethics

Truth-telling Cancer patient end-of-life care

Justice Resource allocation; duty to treat

Non-malfeasance Physician involvement in capital

punishment

Ethically concerning

behaviors

Deception Physician support for deception of

third-party payers

Poor quality care Non-adherence to appropriate

guidelines

Unprofessional behavior Sexual relations with patients

Clinically charged areas Prognostication Accuracy of physicians’ prognoses

Euthanasia Endorsement of euthanasia to treat

suffering

Surrogate judgment Predictive fidelity of surrogate

judgments

Informed consent Adequacy of process of informed

consent

Trade-offs Quantity versus quality

of life

Willingness to forgo length to

enhance quality of life

Advocacy versus honesty Endorsement of insurance

company deception

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dimensions of end-of-life care speaks to the perceived importance of the topic, and an investigator with a strong interest in a well-explored area should not be dissuaded simply because others have examined the topic already. Big problems benefit from numerous approaches, and none are solved by one study alone. On the other hand, the best research questions are not only those that are feasible, but also novel. Whether or not a topic has previously been studied, any survey topic should also be relevant, either contributing to the development and understanding of normative theories or to more directly inform health policy or clinical care. Steps to identifying a good research question include a comprehensive review of the literature and existing theories, findings from qualitative studies, and discussion with content experts. Furthermore, the researcher must maintain a careful balance of creative intellectual meandering, considering multiple questions, and focusing on a specific topic. The importance of working from a theoretical framework and hypothesis varies somewhat depending upon the type of question being examined; detailed conceptual models and hypotheses are especially crucial for studies examining predictors or determinants of behaviors or attitudes. On the other hand, simple descriptive analyses of the frequency of an important outcome may demand a less well-developed theoretical framework to make the results interesting and useful. The question to ask, as in any research, is: ‘‘So what?’’ Who might care, and why, if you find what you expect to find, or if you do not? What ethical, policy, and/or clinical implications would follow from having answers to the questions you hope to pose?

STUDY DESIGN

There are a host of issues to consider in survey design and analysis, many of which this chapter will explore only briefly. There are several excellent references for further reading on general survey design, conduct, and analysis (Aday, 1996; Dillman, 2000; Fink, 1995). Here, we will focus on a few aspects of survey research that are most likely to arise during surveys on ethical issues.

Who is to be Surveyed?

Most bioethics surveys are of patients, family members, clinicians, others involved in health care (chaplains, social workers, and so on), or the general

G. CALEB ALEXANDER AND MATTHEW K. WYNIA144

public. The population selected should be driven by the research question and can inform survey development. For example, health professionals may have high literacy levels and the capacity to respond to complex survey designs due to their familiarity with standardized testing. On the other hand, health professionals also have a low threshold of tolerance for any time- consuming activity, suggesting that short surveys will be better received than longer ones. Certain populations of patients might have lower literacy or might not speak English – obviously, both would have a profound effect on survey design and administration. Although methodologically more complex, there are times when comparing or contrasting two different populations, rather than examining a single group, may be advantageous. Such an effort allows a broader and more rigorous approach to describing and reaching conclusions about attitudes or behaviors and may highlight important similarities and differences between groups of decision-makers, such as patients and families (Ganzini et al., 1998) or patients and clinicians (Alexander, Casalino, & Meltzer, 2003).

Sampling

Once a target group to be surveyed has been selected, it is important to consider how the sample within this group will be selected; this is called ‘‘sampling’’ or ‘‘sample design.’’ The degree to which those who receive the survey reflect the larger group from which they are drawn will affect the generalizability of survey findings, and hence the survey’s relevance and usefulness. At the same time however, important subgroups might be overlooked if special care is not taken in the survey’s sample design. Ethical issues might be rare, or might be most relevant to certain subsets of survey recipients (e.g. those recently testing positive for a genetic screening test).

There are two main types of sampling designs, probability and non- probability designs. The most common probability sampling design is a simple random sample, in which the probability of a sub-group’s selection into the survey sample is proportional to the frequency of that sub-group within the universe from which the sample is derived. In some cases, more sophisticated probability sampling methods may be applied to enhance the rigor of the survey protocol. For example, a stratified random sample allows for sampling of different sub-groups or strata within the sample universe, which is particularly helpful when different strata of interest are not equally represented within the sample universe. For instance, a survey of patients in the intensive care unit (ICU) might be designed to over-sample (i.e., include

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more than would be chosen by chance) patients who are younger than 55 years, so that it can provide more information about this particular population, which is relatively uncommon in the ICU. Although the use of stratified probability sampling may improve efficiency and allow for greater representativeness and accuracy for selected populations, it introduces increased expense and complexity into the project and also depends upon a priori information about the populations (or strata) of interest. For analysis and reporting, most surveys using probability designs will benefit from developing sample weights. How to develop weights is beyond the scope of this chapter, but suffice it to say that weighting data allows one to report the results as if the whole sample had been drawn at random and was exactly representative of the universe of potential respondents. Weights allow for this by accounting for stratified sampling designs, as well as by taking into account potential sources of bias among survey respondents, such as differential response rates among different subsets of participants (Korn & Graubard, 1999).

There are many types of non-probability sample designs. For example, convenience samples comprise populations that are surveyed because of ease and availability, such as a sample derived of patients and family members passing through a hospital cafeteria. Purposive samples and quota samples consist of subjects surveyed based on pre-specified criteria; in the latter case subjects are sampled until certain quota are fulfilled, such as a certain number of subjects of a given age range or race. Snowball samples use survey participants to recruit other potential participants, such as by asking physicians in retainer medical practices to name other physicians in similar type practices that might be contacted to participate in the survey (Alexander, Kurlander, & Wynia, 2005).

Non-probability survey designs are neither inferior nor superior to probability designs overall; rather, each design has strengths and limitations and should be chosen based on the research question, available resources, and the degree to which external validity of the findings is important. External validity refers to how generalizable the data are to a larger (external) population. In some cases, survey findings only need to apply to a limited population of special interest. For example, a survey of physicians who practice in pediatric ICU’s was conducted to assess whether family presence during resuscitation attempts was more or less acceptable to them, and desired by patient families (Kuzin, et al., 2007). Whether these physicians’ views were representative of the general pediatrician population was not especially important, since most pediatricians outside the ICU have very little experience with acute resuscitations (Tibbals & Kinney, 2006).

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On the other hand, samples that are too small or poorly reflective of the population from which they were drawn should not be used to derive conclusions about the broader universe of subjects. For example, researchers studying the coping skills of parents of children hospitalized for chronic diseases might err in generalizing the results of their study to parents of children hospitalized for acute illness; these two groups of parents may differ in important ways with respect to the research question at hand. Or a study of family physicians’ views regarding home childbirth might be inappropriately generalized to obstetricians and general practitioners, but again, important differences may exist among these specialties with regard to the topic of interest.

Survey Mode

Most surveys are administered in-person or else conducted by U.S. mail, telephone, or increasingly, the Internet. Each survey modality has strengths and limitations (Table 3). A particular survey mode (e.g. telephone) should be selected with care, since it may influence both the response rate and patterns of responses. In addition, some populations may be more or less responsive to certain types of surveys. For example, those with low literacy will be less likely to respond to a written survey, and telephone surveys only reach people owning telephones. Recent trends in ownership of cellular versus landline phones also should be taken into account, such as the younger ages of people who own a cell phone but not a home phone (Tuckel & O’Neill, 2005).

Survey Design

Good survey questions are simple, clear, and often very easy to answer. However, survey development is a time-consuming task, and formulating specific survey questions to be ‘‘just right’’ may be quite difficult. Nevertheless, the time and effort required to create a good survey are well spent. Poor wording and design of surveys do not just frustrate respondents, it also leads to difficulties in item interpretation, higher rates of non- response and, as a result, more difficulty getting the results analyzed, published, and used.

Survey questions (often referred to as ‘‘items’’) consist of three parts: (1) the introduction to the question, (2) the question itself (question stem),

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Table 3. Pros and Cons of Different Survey Modes.

Mode Commonly

Targeted

Respondents

Benefits Limitations

Phone General public o Inexpensive

o Random-digit dial

allows broad sampling

of general public

o Avoids problems of

illiteracy

o Rapid data entry with

Computer Assisted

Telephone

Interviewing (CATI)

software

o High non-response

rates

o Misses those without

phones

o Prevents use of visual

aids as well as visual

cues from respondent

In-person o Patients

o General public

o Allows for sampling of

patients or family

members in clinical

setting or public space

o Offers use of visual

aids and visual cues

from respondents

o Allows means of

assisting with survey

completion

o Very expensive

o Use in clinical settings

over-samples frequent

users of care

o Use in public settings

generally based on

convenience sample of

respondents who may

differ from non-

respondents

U.S. Mail Physicians o Common method to

reach physicians

o Well-established

protocols and mailing

lists

o Some ability to track

and compare

respondents with non-

respondents

o Expensive

o Use of survey waves

complicates using

anonymous design

Internet o Physicians

o Internet users

o Very inexpensive

o Automatically entry of

data while collected

o Fast data collection

o Limited ability to

generalize findings to

non-Internet users

o Email address lists

often contain wrong

addresses

o High non-response

rates

G. CALEB ALEXANDER AND MATTHEW K. WYNIA148

and (3) the responses to the question (response frame). This structure is helpful to understand, both for conceptual clarity in communicating with others, as well as during the process of survey design.

The introduction to a question or set of questions is crucial because it orients the respondent as to what is expected. Framing the issue is often critically important in ethics surveys (see section on Precautions with Sensitive Topics). For example, respondents can be put at ease through reassurance (e.g. ‘‘there is no right or wrong answer’’) or by acknowledging the challenge of the question (e.g. ‘‘please balance both the patients’ quality and length of life’’).

Survey questions are either forced-choice or, less frequently, open-ended. The benefits of an open-ended question, where a respondent writes in a response (or provides the verbal equivalent during an interview), are that it is less leading and elicits a broader range of responses than a forced-choice question. Drawbacks are that the coding of such questions is technically complicated due to illegible (on self-administered surveys) or long-winded responses, and is conceptually complicated due to responses that are unclear in meaning or intent (regardless of survey mode). In addition, response rates tend to be lower for open-ended questions.

A good survey question uses simple words, presents a simple idea, and uses as few words as possible to do so. Each survey question should contain a single idea and ask a single question. So-called ‘‘double-barrelled’’ questions, where two questions are asked at once, are generally to be avoided because answers to these questions are very difficult to interpret (for example, ‘‘How often have you been sued or been afraid you were going to be sued?’’). The challenge with question design is to navigate certain tensions that are inherent in this process. For example, sometimes more words are needed to precisely explain a question or complicated idea, yet more words may make a survey item more cumbersome to read and understand. There is a large literature devoted to the psychology of survey response, and readers interested in advanced study in this area should refer to one of several excellent references (Aday, 1996; Fink, 1995; Tourangeau, Rips, & Rasinski, 2000).

Finally, the response options available for answering a survey question are important to consider. Response frames can be simple (e.g. yes/no or agree/disagree) or more complex (e.g. excellent, very good, good, fair, or poor) and sometimes it is necessary to create unique response frames that are specific to certain questions. The response frame of an item is crucial to making the question easy for the respondent to answer accurately. It should be developed based on an iterative process of piloting and pre-testing to

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develop a response frame that is comprehensive, comprehensible, and, in the case of best choice answers, mutually exclusive.

In addition to giving careful attention to design of the introductions, question stems, and response frames, care should be given to other survey design factors that may influence data quality. These include the survey aesthetics, framing (e.g. survey title), item grouping, and item order. For example, although selecting the title of a survey may seem a mundane or unimportant task, even this can make a considerable difference in response rates. Consider creating a title that is engaging and that will draw the recipient in (e.g. Managed care: What do physicians think?), but avoid titles that might alienate recipients or that suggest a bias in the research (e.g. Are doctors aware of widespread inequities in American health care?). Similarly, it is important to consider the ordering of items. Generally, it is advisable to avoid placing overly sensitive or conceptually difficult items early in the survey or first in a series of questions, use open-ended prior to forced-choice questions if they are about similar topics, and try to group items that have the same response frame together so as to maximize the flow and readability of the survey. In addition, it is helpful to number items within each response frame to maximize the accuracy and ease of data entry.

SURVEY PLANNING

A considerable amount of work on survey analysis, sampling, and projections of costs should be conducted prior to fielding the survey instrument. This helps to minimize the collection of data that inevitably are not used, and the inadvertent omission of data that would have been useful. Questions to consider in planning the survey include: What is (are) the main outcome(s) of interest? Will simple descriptive statistics of the frequency of an attitude or behavior suffice, or is the goal to conduct more detailed analyses of associations between different variables? If the latter, what is the dependent variable and what are the independent (predictor) variables of interest? What potential variables may confound the associations of interest? There is considerable value to developing ‘‘mock-up’’ tables of the expected results or lists of potential correlates prior to fielding the survey. This can help to locate gaps in the survey and allow careful consideration for how responses will be analyzed.

The costs of surveys should also be considered during planning. Methods can be tailored to better fit the available budget, for example, by modifying the survey mode, sample size, financial incentives, or rigor of survey

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development and statistical analyses. Many surveys of ethically important topics have been conducted on small budgets. However, if the budget allows, there are many organizations, often university-affiliated, that can be contracted with to develop, test, or administer surveys. Similarly, firms exist that will provide survey support for telephone-based or Internet samples.

Ethics surveys demand special attention to response rates. Since they are often about sensitive topics, ethics surveys may suffer from poor response rates, and there may be important differences between respondents and non- respondents (i.e., non-response bias, see below). These issues can stimulate questions about a survey’s relevance. General efforts to improve response rates are of several types: (1) financial or non-financial incentives; (2) endorsements from opinion leaders/people of influence; (3) minimizing the burden of the survey; (4) personalizing the survey through efforts such as hand-written notes, adhesive stamps, or human signature; and (5) persuasion about the importance of the topic and the respondents’ views. This last point can be accomplished, for example, through a particular way of framing the survey in the cover letter and through its title, or through the use of special delivery methods (e.g. Federal Express for mailed survey). Of these five methods, the use of financial incentives has been studied most extensively. In general, studies of financial incentives suggest that the marginal benefit of larger financial incentives may be relatively small compared with the impact of smaller incentives (VanGeest, Wynia, Cummins, & Wilson, 2001). In addition, financial incentives, when used, are more effective when a small amount is offered upfront to everyone, rather than the use of a lottery or incentives upon survey completion (Dillman, 2000).

SURVEY DEVELOPMENT AND PRE-TESTING

After developing a general research question and theoretical framework, efforts should turn to identifying specific conceptual domains and factors to be explored within these domains. Qualitative data are often helpful to inform this process, and there are various ways to gather such data, including key informant interviews, focus groups, and ethnography (see chapters on qualitative methods). Such efforts are often invaluable in helping to identify important areas of inquiry, and may provide sufficient material for analyses that can take place in parallel with the quantitative

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focus of the survey. Finally, most surveys need to be piloted and pre-tested to enhance and ensure brevity, clarity, and measurement accuracy.

Accuracy consists of both the validity and reliability of the survey instrument. Validity is the degree to which a survey actually measures the phenomenon it purports to measure. In this regard, it is important to note that ethics surveys often attempt to measure phenomena (or ‘‘constructs’’) that are complex and on which clear consensus may not exist. For instance, ethics surveys may explore the meaning of ‘‘consent’’ or the importance of ‘‘privacy’’ or ‘‘fairness’’ in health care. When assessing such challenging and obscure constructs, particular attention must be given to the validity of the survey to ensure that it measures what it claims to measure. There are three main types of validity relevant to survey research: face validity, content validity, and construct validity.

Face validity refers to whether the survey domains and items appear reasonable at face value. A common way to assess face validity is to share the instrument with relevant parties, such as patients, family members, and clinicians, and ask if they agree that the items measure what you intend them to measure.

Content validity refers to how well the items examined represent the important content of the domains of interest. To assess content validity one must ask, does the survey address all the facets of the issue in question or, on the other hand, does it include aspects that are unrelated to the issue? For example, in the development of their survey instruments for assessing privacy in hospitals, researchers asked a group of experts to evaluate the survey items by rating each item on a 1–10 scale where (1) represented items necessary to the issue and (10) represented items extraneous to the issue. The experts were also asked for ideas on any areas that might have been missed. As this example shows, ensuring adequate content validity is usually achieved by the researchers’ comprehensive knowledge of the subject matter the survey is examining and is supported by careful review of the survey domains and items with experts in the field of inquiry. As ‘‘soft’’ as simple reliance on experts may seem, content validity is crucial to a good survey on an ethically important topic. Criticisms of ethics surveys are frequently focused on items that the survey team did not ask, or items that while the team asserts are related to the central question, there is not a strong outside consensus from experts that this is so.

Finally, construct validity refers to the degree to which one can safely make inferences from a survey to broader theoretical constructs operationalized in the survey. Construct validity is very difficult to prove in many ethics surveys, because the constructs in question are often quite complex. How

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does one prove that constructs such as ‘‘informed consent,’’ ‘‘concern for privacy,’’ ‘‘fear,’’ or a sense of ‘‘disrespect’’ or ‘‘mistrust’’ are accurately measured? Construct validity is assessed by determining that the measure in use is related in expected ways to other known measures of the construct in question. For instance, the results of a survey that purports to measure ‘‘pain’’ would be expected to correlate with other measures that are known to be associated with pain, such as sweating, rapid pulse, and asking for pain medication. The challenge in ethics surveys is often to determine, a priori, what are the expected correlates of the relevant constructs.

In addition to being valid, surveys should be reliable as well: they should get the same results each time they are used in the same circumstances. The reliability of a survey can be assessed through repeated administrations to one individual (intra-observer, or test-retest reliability) and by assessments of a given event or practice across multiple individuals (inter-observer reliability) (also see Chapter on The Use of Vignettes). Statistical tests of reliability are well described in most basic biostatistics or clinical epidemiology textbooks.

SURVEY FIELDING AND DATA ENTRY

In any ethics survey, especially one using new items, there is considerable benefit in examining early responses as they come in. For instance, it may be helpful to perform some analyses on the first wave of survey responses. This may allow for the identification of potentially serious systematic flaws, such as if respondents are unintentionally skipping questions printed on the back of a page.

As data are collected, several methods can be used to ensure systematic initiation of data entry and analysis. Errors in data entry are almost impossible to avoid, but the rigor of data entry may be enhanced by double entry or randomly checking a subset of respondents for the frequency of incorrectly entered data, or both. Questions about how to code unclear responses, such as when a response is somewhat illegible or does not clearly fit the response frames offered, are inevitable. The researcher should anticipate these and work to treat them in a systematic and fair way that does not introduce unnecessary bias into the measurements. Data entry can be enhanced by the use of limited data fields and specialized software that may simplify tasks such as providing easier database management of complicated survey skip patterns. Data entry can also be simplified by minimizing the number of steps between survey query and response in the

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case of telephone or in-person surveys through the use of Computer Assisted Telephone Interviewing (CATI) software. Even with these efforts, data cleaning is important to ensure that the data are of high quality; that is, data for given variables are within expected ranges (e.g. a survey item with a response frame from 1–5 should not have any 8 s entered as responses) and the distributions of data make sense (e.g. an item where all respondents answered the same would raise suspicions of a data entry error).

BIASES AND RESPONDENT BURDEN

Respondent bias and burden are two of the most important considerations to guide survey development. Bias refers to any systematic tendency to over- or underestimate whatever is being measured. There are numerous types of bias that are important to consider.

Socially desirable response bias, sometimes referred to as ‘‘yeah-saying,’’ is a special threat to the rigor of ethics surveys. It can be addressed in several ways, including sensitively wording questions, carefully designing the survey framing, paying attention to item ordering, ensuring protection of confidentiality (or, rarely, even ensuring anonymity), and by using statistical methods that help to adjust for the likelihood of this bias.

Recall bias may be present when asking about past events, and can be minimized by carefully framing the time period in question, such as by limiting the length of the retrospective period (e.g. ‘‘In the last week y’’) or by using discrete events that are more likely to be accurately recalled (e.g. ‘‘At the last Grand Rounds you attended, ...’’).

Non-response bias refers to bias introduced by systematic differences between respondents and non-respondents. That is, those who return the survey may be different in some relevant way from those who do not. This type of bias is a perennial challenge to survey development, fielding, and interpretation, and it is especially relevant to ethics surveys, which often touch on very sensitive or controversial topics. For instance, survey recipients who are especially affected by the survey topic (e.g. malpractice) may be more, or less, likely to respond. In addition, the association between respondent burden and poor response rates should not be underestimated. Although low response rates do not mean that non-response bias is present, longer and more cumbersome surveys are less likely to be completed, and lower response rates, all other things being equal, will raise concerns of possible non-response bias. In addition to seeking to improve response rates, there are four common methods used to address non-response bias.

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First, one can compare respondents with non-respondents on all known variables (the absence of differences suggests that non-response bias is less likely). Second, one can look for ‘‘response-wave bias’’ in surveys conducted by Internet or U.S. mail by exploring whether there is any association between the length of time until survey response and the primary outcome(s) of interest. Response-wave bias is based on an assumption that respondents who took a long time to respond to the survey, such as those responding to a third survey wave, somewhat resemble non-respondents in that they were less motivated to respond than their counterparts. The absence of any association between length till response and the primary outcome(s) of interest suggest that non-response bias is less likely. Third, the active pursuit of a subset of non-respondents may be helpful to ascertain the response frequencies to one or two key questions among this group. For example, in a survey of physicians’ support for capital punishment, researchers might be concerned of a significant non-response bias among the 45% of subjects who were non-respondents. To examine for this bias, the researchers might select a random 10% sample of non-respondents and call them by phone with one short question from the survey to find out whether their global beliefs about capital punishment are similar to survey respondents. Finally, advanced statistical methods can be used, such as weighting of survey responses, in an effort to account for non-response bias. Other biases are important to consider depending upon the unique circumstances of the project (e.g. interviewer bias, which introduces variation in survey response based on the characteristics of the interviewer administering the survey), but are less ubiquitous threats to survey validity than those discussed above.

PRECAUTIONS WITH SENSITIVE TOPICS

Many ethics surveys examine the prevalence of specific behaviors. One-way to identify the frequency of any behavior, sensitive or not, is to directly question the respondent (e.g. ‘‘In the last week, how often did you y?’’). However, the benefit of being able to report direct prevalence must be balanced with an acknowledgment that such reports are especially prone to socially desirable response bias and therefore may over- or underestimate the actual frequency of the behavior in question. Positive behaviors are likely to be over-reported, while negative behaviors are likely to be under- reported. In the case of negative behaviors, direct questions can also alienate survey recipients, because direct questions about negative behaviors are often perceived as leading (e.g. ‘‘How often, if ever, do you kick your

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dog?’’). As a result, direct questions may be most useful for non-sensitive topics or under circumstances in which modest misestimation of prevalence might not reduce the likely ethical, policy, or clinical impact of the survey results. For instance, even if only a few respondents directly report a very concerning behavior it might be worth investigation (e.g. admission of illicit drug use among physicians).

An alternative to direct questions is to use indirect, or third party, questions, which can allow respondents to discuss sensitive topics without having to personally admit to socially undesirable or stigmatized behavior. For example, instead of asking individual patients whether they have ever faked an illness to skip work, inquire as to whether they know of any colleagues that have done so. The trade-off is that such questions do not allow for direct assessment of the frequency of the behavior and hence, these questions too might not produce good estimates of prevalence. For instance, perhaps only one employee has skipped work on a medical excuse to go fishing, but many employees completing the survey know about this situation – in this case, the frequency of this behavior might be overestimated.

There are several methods to help maximize respondent honesty and comfort when completing both direct and indirect questions about sensitive topics. First, the way that each question is framed is crucial, and calming stems that acknowledge the legitimacy of different or controversial viewpoints and actions are helpful to allow respondents to answer honestly (e.g. ‘‘Patients often find health care frightening and stressful, and they handle this stress in many ways y’’ or ‘‘There are no right answers to the following questions y’’). Question order can be used to one’s advantage as well – generally, questions that are more sensitive should be introduced later in the survey while more sensitive responses are better earlier in the response frame. The risk of socially desirable response bias in response to direct questions can also be diminished with any interventions that help to protect respondents’ confidentiality or anonymity.

Another option, useful for both sensitive and non-sensitive topics, is hypothetical vignettes. For a detailed discussion of this method, please see chapter on hypothetical vignettes.

DATA ANALYSIS

After data entry and cleaning, examination of univariate (single item) distributions is helpful. A blank survey instrument can be used as a template upon which to write these distributions. In some, but not all, cases, bivariate

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and multivariate distributions may be of interest, in order to see how the primary measure(s) of interest, such as patient’s preference for end-of-life care, may be associated with other variables of interest, such as illness chronicity, hospice availability, and the specialty of the treating clinician. Multivariate regression analyses and other advanced statistical analytic techniques are possible; however, they require additional skills that may be beyond most bioethicists and they may not be relevant to the research question at hand. While it is sometimes critical to evaluate associations while holding other factors constant, as multivariate regression allows one to do, in many important ethics-related surveys, simple descriptive statistics are sufficient to examine the question of interest (e.g. what proportion of surgeons would override a patient’s ‘‘Do Not Resuscitate’’ order in the immediate post-operative period?). In this regard, it is important to return to the initial survey question or hypothesis and the conceptual model that one is using to frame the question. Where advanced statistical methods are required, it may be helpful to collaborate with statisticians and others with advanced training in health services research. Despite the relative ease with which statistical programs can generate multivariate analyses, creating appropriate models, using appropriate tests, and interpreting the results of these models demands special expertise.

CONCLUSIONS

Surveys have great promise as a method to inform bioethics, clinical practice, and health policy. To achieve this promise, the researcher must balance rigor with feasibility at all stages of survey development, fielding, analysis, and presentation. Table 4 provides a case study of an empiric ethics survey and illustrates examples of some key elements of survey design, administration, and analysis. Identifying a good research question is crucial, yet the difficulty of this stage of survey research may be easily overlooked. When conducting ethics surveys, it is particularly important to guard against constant threats to survey validity, such as unclear wording of survey questions or biased responses to questions, because the issues under study are often complex, conceptually inchoate, and/or sensitive or controversial. Finally, in bioethics the relationship between the quantitative data gathered by surveys and the qualitative nature of normative theories is a dynamic one, making survey interpretation a challenge. Nevertheless, a well-constructed, carefully analyzed survey in ethics can have a meaningful impact on policy and practice. Surveys are well suited for examining areas of

Survey Research in Bioethics 157

Table 4. Case Study: An Example of a Survey in Bioethics (Alexander & Wynia, 2003).

Aspect of Survey Methods

Goal of survey � To explore physicians’ bioterrorism preparedness, willingness to

treat patients despite personal risk, and beliefs in a professional

duty to treat during epidemics

Finding a question � Question motivated by: (1) significant topical interest in duty to

treat since September 11th, 2001, (2) prior debates regarding duty

to treat during outset of HIV epidemic, (3) long and varied

historical tradition of physicians’ response to epidemics, and (4)

salient ethical dilemmas regarding balance of physician self-

interest with beneficence

Who is to be

surveyed?

� Decision to survey patient-care physicians

Sampling � Simple random sample taken from universe of all practicing

patient-care physicians in the United States; use of representative

sample allowed for inferences to be made regarding broader

physician population

Survey mode � Survey conducted by US mail given impracticality and costs of in-

person or phone surveys, and given generally low response rates to

Internet surveys and the absence of reliable email addresses for

physicians

Survey design � Single page survey to maximize response rate; most response

frames similar to reduce respondent burden � Survey title emphasized importance of respondents’ experiences;

survey layout facilitated completion in less than 5 min

Survey planning � Decision that main outcomes of analysis would be simple

descriptive statistics; awareness a priori that causal direction of

any associations would be unclear � Length and rigor of pretesting balanced with need for timely data

collection given potential shifting interest in the topic among

policy makers, providers, and the general public � Efforts to obtain maximal response rates included minimizing

burden by limiting survey to one sheet of paper, using persuasion

that important topic, and using a $2 financial incentive

Development and

pretesting

� Face validity maximized through piloting and pretesting with

practicing clinicians � Content validity maximized by expert review of survey by

clinicians involved in disaster response planning � Construct validity maximized by examining expected correlations

between items on training and preparedness

Fielding and data

entry

� Analysis of early survey respondents allowed us to observe an

association between survey response time and duty to treat; it was

unclear if this was due to response-wave bias or temporal trends

G. CALEB ALEXANDER AND MATTHEW K. WYNIA158

health care that raise vexing ethical issues for patients, clinicians, and policy- makers alike.

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about out-of-pocket costs. Journal of the American Medical Association, 290, 953–958.

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Table 4. (Continued )

Aspect of Survey Methods

� A random sample of 100 additional physicians selected to receive

survey; analyses of these new respondents suggested that temporal

trends were present

Bias and respondent

burden

� Socially desirable response bias a significant threat, so survey

included language reassuring subjects that survey was strictly

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respondents, looking for associations between response wave and

main outcomes of interest

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analyses; multivariate models based on logistic regression, which

allowed for a more simple presentation of results

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maximize validity of responses

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Cambridge University Press: New York, NY.

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setting of budget constraints: Is it equitable? New England Journal of Medicine, 334,

1174–1177.

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monetary incentives on the return rate of a national mail survey of physicians. Medical

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G. CALEB ALEXANDER AND MATTHEW K. WYNIA160

HYPOTHETICAL VIGNETTES IN

EMPIRICAL BIOETHICS RESEARCH

Connie M. Ulrich and Sarah J. Ratcliffe

ABSTRACT

Hypothetical vignettes have been used as a research method in the social

sciences for many years and are useful for examining and understanding

ethical problems in clinical practice, research, and policy. This chapter

provides an overview of the value of vignettes in empirical bioethics

research, discusses how to develop and utilize vignettes when considering

ethics-related research questions, and reviews strategies for evaluating

psychometric properties. We provide examples of vignettes and how they

have been used in bioethics research, and examine their relevance to

advancing bioethics. The chapter concludes with the general strengths and

limitations of hypothetical vignettes and how these should be considered.

INTRODUCTION

The Significance and Value of Vignettes in Empirical Bioethics Research

The value and significance of empirical bioethics research lies in its ability to advance our knowledge and understanding of a variety of ethical issues to promote opportunities for dialog among clinicians, researchers,

Empirical Methods for Bioethics: A Primer

Advances in Bioethics, Volume 11, 161–181

Copyright r 2008 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1479-3709/doi:10.1016/S1479-3709(07)11008-6

161

policy-makers, and members of other disciplines. Additionally, this type of research generates new lines of descriptive and normative inquiry.

Hypothetical vignettes represent one type of empirical approaches to examining bioethical issues. This method can be used to better understand attitudes, beliefs, and behaviors related to bioethical considerations in clinical practice, health service delivery and financing, and health policy (Finch 1987; Flaskerud, 1979; Gould, 1996; Hughes & Huby, 2002; Veloski, Tai, Evans, & Nash, 2005). Issues range from the macro level, such as access to care, costs of care, and allocation of resources to micro level concerns of bedside rationing, provider–patient relationships, and beginning of life and end-of-life care problems. Given the potentially sensitive nature of many bioethic problems, hypothetical vignettes provide a less personal and, therefore, less threatening presentation of issues to research participants. Furthermore, some bioethics-related events are relatively rare and vignettes provide a mechanism to explore the attitudes and extrapolated behaviors concerning such events using larger numbers of participants than would be otherwise possible.

This chapter will give an overview of hypothetical vignettes in research with examples of how this method has been used to examine and analyze critical ethical problems. We will also review ways to evaluate the reliability, validity, strengths, and limitations of studies using vignettes.

WHAT IS A VIGNETTE?

Vignettes have been used in social science research since the 1950s (Gould, 1996; Schoenberg & Ravdal, 2000) and have been described as ‘‘short stories about hypothetical characters in specified circumstances, to whose situation the interviewee is invited to respond’’ (Finch, 1987, p. 105). Stories are designed to represent an issue of importance that simulates real life and require a focused response from participants. Depending on the research question of a study, vignettes are appropriate for both qualitative and quantitative methodological designs and studies using mixed methods. They can be used in isolation or as adjuncts to other data collection methods (Hughes & Huby, 2002), for example, self-administered survey question- naires, focus groups, and face-to-face semi-structured interviews. Vignettes can be presented in a variety of ways such as verbal administration, written surveys, audiotape, videotape, and computers. The latter allows for a flexible approach to reaching groups across various settings.

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Unlike attitudinal scales that ask direct questions about values and beliefs, vignettes offer an approach that assesses individuals’ attitudes or values in a contextualized scenario or situation (Alexander & Becker, 1978; Finch, 1987; Flaskerud, 1979; Gould, 1996; Hughes, 1998; Hughes & Huby, 2002; Schoenberg & Ravdal, 2000; Veloski et al., 2005). Various disciplines have used vignettes in health care and social science research among nurses, physicians, and in the general population to examine and ascertain attitudes, values, behaviors, and norms (Alexander, Werner, Fagerlin, & Ubel, 2003; Asai, Akiguchi, Miura, Tanabe, & Fukuhara, 1999; Barter & Renold, 2000; Christakis & Asch, 1995; Denk, Fletcher, & Reigel, 1997; Emanuel, Fairclough, Daniels, & Clarridge, 1996; Gump, Baker, & Roll, 2000; Kodadek & Feeg, 2002; McAlpine, Kristjanson, & Poroch, 1997; Nolan & Smith, 1995; Rahman, 1996; Wolfe, Fairclough, Clarridge, Daniels, & Emanuel, 1999). The interpretation of standardized vignettes by different groups of people within the same study can also be examined and compared (Barter & Renold, 1999).

Most studies that use vignettes rely on the constant-variable vignette method (CVVM) where identical scenarios are presented to respondents with multiple forced-choice questionnaires or rating scales (Cavanagh & Fritzsche, 1985; Wason, Polonsky, & Hymans, 2002). For example, in a survey using vignettes developed by Christakis and Asch (1995) to measure physician characteristics associated with decisions to withdraw life support, respondents were asked to rate on a 5-point Likert scale how likely they would be to withdraw life support from a hypothetical character presented in the scenarios (Box 1).

In another study to assess whether informed consent should be required for biological samples derived clinically and/or from research, Wendler and Emanuel (2002) surveyed two cohorts of subjects via telephone using three vignettes. Instead of a Likert response set, however, participants were simply asked to respond by using the following categories: ‘‘yes,’’ ‘‘no,’’ ‘‘don’t know,’’ and ‘‘it depends.’’

Different versions of the same vignette can also be constructed in a single study using a factorial design. In this design, researchers can test multiple hypotheses by systematically manipulating two or more independent variables (for example, age and gender) within the vignette and randomly allocate subjects to the vignette as a means to evaluate both main effects, of each independent variable on the outcome variable, and interaction effects of two or more independent variables on the outcome variable (Polit & Hungler, 1999).

Hypothetical Vignettes in Empirical Bioethics Research 163

Qualitative researchers use vignettes to explore meanings of particular issues by asking participants to discuss the situation presented in their own words in response to open-ended questions. For example, to better understand the ethical awareness among first year medical, dental, and nursing students, Nolan and Smith (1995) asked students to respond to different vignettes that contained ethical dilemmas. In response to the vignettes, subjects were asked: ‘‘What course of action would you suggest, giving reasons?’’ Other authors (Kodadek & Feeg, 2002) have used similar open-ended questions based on vignettes to explore how parents approach end-of-life decisions for terminally ill infants by asking respondents (1) ‘‘What is your first reaction as you begin to think about this problem?’’; (2) ‘‘What specific questions will you ask the physician when you have a chance to discuss this problem?’’; or (3) ‘‘Name at least 5 aspects of the problem that you will consider in making your decision.’’ Open-ended response sets allow for in-depth probing and identification of issues deemed salient and emerging themes. Grady et al. (2006) presented four different hypothetical scenarios about financial disclosure to active research participants in NIH intramural sponsored protocols. Subjects were asked to openly discuss their views on each scenario, whether or what they would want to know about the financial interests of investigators, and how knowledge of financial disclosures would influence their research participation. Lastly,

Box 1. Example of a Vignette Used to Assess Physicians’ Perceptions on Withdrawal of Life Support.

EL is a 66-year-old patient of yours with a 15-year history of severe chronic pulmonary disease. One week ago, he was admitted to the ICU with pneumonia, hypotension, and respiratory failure. He required antibiotics, intravenous vasopressors, and mechanical ventilation to survive. He has now lapsed into a coma and shows no signs of clinical improvement. Consultant pulmonologists assert that his lung function is such that he will never be independent of the ventilator. After his most recent hospitalization, the patient had clearly

expressed to his family and to you that he would never want to live by artificial means. In view of these wishes and his poor prognosis, the family asks you to withdraw life support. You are deciding whether to stop the intravenous vasopressors or the mechanical ventilation. (Christakis & Asch, 1995)

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Berney et al. (2005) developed two clinical vignettes from semi-structured interviews related to allocation of scarce resources and followed by discussions of the vignettes in focus groups with general practitioners. Participants were asked, through open-ended queries, to describe the ethical issues they perceived when presented with the vignettes. Usually, both qualitative and quantitative research ask respondents to rank, rate, or sort particular aspects of vignettes into categories, or to choose what they think the hypothetical characters should or ought to do in the presented scenario(s) (Martin, 2004).

HOW TO DEVELOP A VIGNETTE

The complexity of many bioethical problems often necessitates constructing vignettes to meet a study’s particular purpose. If possible, however, using established valid and reliable vignettes is always preferable. Vignettes can be constructed from the literature as well as from other sources, and must appear realistic, relevant, and be easily understood (Hughes, 1998; Wason et al., 2002). Several approaches to the development and evaluation of vignettes can be used as described below (Box 2).

1. Focus groups Focus groups provide an opportunity to gather information for vignettes

from a population of interest related to the specific bioethical issue being

Box 2. How Can Vignettes be Constructed and Presented in Bioethics Research?

Constructed: � Previous research findings/literature review � Real life experiences with clinical and/or research cases � Focus groups � Cognitive interviewing

Presented: � Narrative story � Computer based, music videos � Comic book style; flip book; cards; surveys; audio tapes (Hughes, 1998)

Hypothetical Vignettes in Empirical Bioethics Research 165

studied, especially if limited information on the topic exists (Krueger & Casey, 2000). Focus groups generally range from 6 to 8 individuals who participate in an interview for a specified time period with the object of obtaining ‘‘high-quality data in a social context where people can consider their own views in the context of the views of others’’ (Patton, 2002, p. 386).

Using focus groups to develop vignettes allows for the identification of the test variables (i.e., age, gender, level of education), how to structure and present content of interest in a vignette, and the number of vignettes needed. Schigelone and Fitzgerald (2004) convened a focus group of experts in geriatrics, nursing, and social science to identify key variables for geriatric vignettes to assess the treatment of older and younger patients by first year medical students. Based on the responses, the authors drafted initial versions of vignettes that examined age of patients in relation to levels of aggressive treatment (for more information, please see chapter on focus groups).

Focus groups help to:

� A priori refine the objectives of the research utilizing vignettes. � Clarify and provide in-depth understanding of how subjects think and interpret the topic of interest. � Design and construct vignettes for a larger quantitative study.

2. Cognitive Interviewing Cognitive interviewing (CI) is an important technique used to evaluate

survey questionnaires and/or identify difficulties in vignettes. CI is used to ‘‘understand the thought processes used to answer survey questions and to use this knowledge to find better ways of constructing, formulating, and asking survey questions’’ (Forsyth & Lessler, 1991). Because vignettes often are presented in written surveys, CI explores respondents’ abilities to interpret vignettes presented within a survey format, assesses whether the wording of questionnaires accurately conveys the objective of the vignette(s) and subjects’ techniques for retrieving information from memory, as well as their judgment formation on the material presented (Willis, 1994, 2006). CI is usually undertaken ‘‘between initial drafting of the survey ques- tionnaire and administration in the field’’ (Willis, 2006, p. 6). Subjects are often asked to ‘‘think aloud,’’ that is, to verbalize their thoughts as they respond to each vignette (self administered or interviewer administered). One researcher usually conducts the interview while a second researcher will observe the interview, tape record, take notes, and transcribe responses. By asking respondents if the proposed vignettes are measuring what they claim

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to measure (content validity), an important means of pre-survey evaluation can be established (Polit & Hungler, 1999).

Example of ‘‘Think Aloud’’ exercise and verbal probes: ‘‘While we are going through the questionnaire, I’m going to ask you to think aloud so that I can understand if there are problems with the questionnaire. By ‘‘think aloud’’ I mean repeating all the questions aloud and telling me what you are thinking as you hear the questions and as you pick the answers.’’ Verbal probing techniques can be used as an alternative to ‘‘think aloud exercises’’ allowing the interviewer to immediately ask follow-up questions based on the subject’s response to the vignette. Probes can be both spontaneous and scripted (prepared prior to the interview and used by all interviewers) to test subjects’ comprehension of questionnaire items, clarity, interpretation, and intent of responses and recall. Examples of verbal probes are given below (Grady et al., 2006; Willis, 2006).

� General probe:

o ‘‘Do you have any questions or want anything clarified about this study?’’ o ‘‘What are your thoughts about this study?’’ o ‘‘How did you arrive at that answer?’’ � Paraphrasing probe:

o ‘‘Can you repeat the question in your own words?’’ � Recall probe:

o ‘‘What do you need to remember or think about in order to answer this question?’’

� Comprehension probe:

o ‘‘What does the term ‘‘research subject’’ mean to you?’’ � Confidence judgment:

o ‘‘How sure are you that your health insurance covers your medications?’’ � Specific probe:

o ‘‘Would you consider individuals who consent to participate in this study vulnerable in any way?’’ ‘‘Vulnerable to what?’’ ‘‘Why?’’

3. Field Pre-testing The purpose of a pretest is to simulate the actual data collection

procedures that will be used in a study and to identify problem areas associated with the vignettes prior to administration with a sample (Fowler, 2002; Platek, Pierre-Pierre, & Stevens, 1985; Presser et al., 2004). Pre-testing is carried out with a small convenience sample of subjects, similar to the characteristics of the population planned for in the actual study. It is important to assess if the vignettes and related questions are consistently understood and believable so that researchers can improve on any reported

Hypothetical Vignettes in Empirical Bioethics Research 167

practical problem (s). Confusing and complex wording, misunderstandings, or misreading of vignettes can lead to missing data that ultimately biases sample estimates, underestimates correlation coefficients, and decreases statistical power (Platek et al., 1985; Kalton & Kasprzyk, 1986; Ulrich & Karlawish, 2006). Problems may also be related to length of completion time and burden or simply typographical errors. Therefore, evaluating response distributions through pre-testing can help the researcher to revise questions. For example, if a vignette with open-ended questions is planned, the pre-test can identify redundant participant responses and help to narrow the range of answers needed to respond. As a result, the researcher might deem fixed response categories as being more appropriate (Fowler, 1995). Revising and refining the instrument based on pre-testing improves the data collection procedures and data quality.

Areas to assess in pre-testing:

� Completion time: How long is the questionnaire? Are all items completed? If items were skipped, why? � Clarity of wording: Are any items or terms used in the vignettes and questionnaire confusing to respondents? Are questions unidimensional? � Complication of the instructions: Are the instructions easy to follow, understandable, and comprehensive? Does the questionnaire flow well; does the question order appear logical? � Is the questionnaire user- friendly? Are the vignettes easy to understand? � Is the information in the vignettes accurate and unambiguous? � Are important terms in the vignette defined? � Do respondents perceive any of the items as too sensitive to answer? � What was the response rate?

Illustrations of Pre-Survey Testing

Siminoff, Burant, and Younger (2004) conducted 12 focus groups of different ethnicities, including Hispanics, Muslims, and African Americans to guide their research on public attitudes surrounding death and organ procurement. The focus groups were the basis for the development of vignettes that presented varying patient conditions (i.e., brain death, severe neurological damage, and persistent vegetative state). In doing so, these groups provided clarification of the ethical terms in lay language with diverse perspectives, addressed concerns not readily apparent in the literature, and provided input into the final random digit dial version of a survey that included vignettes and was to be conducted with citizens in

CONNIE M. ULRICH AND SARAH J. RATCLIFFE168

Ohio. Following the focus groups, a pre-test of the survey was randomly administered to 51 individuals to further assess the questionnaire and vignettes for clarity, completion time, and reliability of respondents’ classification of when death occurs. Curbow, Fogarty, McDonnell, Chill, and Scott (2006) developed eight video vignettes to measure the effects of three physician-related experimental characteristics (physician enthusiasm, physician affiliation, and type of patient–physician relationship) on clinical trial knowledge and acceptance, and beliefs, attitudes, information processing, and video knowledge. To evaluate the video vignettes, pre- testing was conducted with eight focus groups of former breast cancer patients and patients without cancer. Alterations to the vignettes were made based on participants’ responses.

EVALUATING PSYCHOMETRIC PROPERTIES

OF VIGNETTES

Before administering vignettes to a sample and analyzing the results, it is important that the vignettes are valid and reliable. Only reliable and valid vignettes should be used to describe phenomena or test hypotheses of interest in a study. A comprehensive review of reliability and validity issues can be found in Litwin (1995) and Streiner and Norman (2003).

Validity of Vignettes

Internal validity is important for quantitative and qualitative measures and expresses the extent to which an instrument adequately reflects the concept(s) under study. Because vignettes in bioethics research are often constructed solely for the particular topic under study, validity and reliability are essential to achieve meaningful analyses and interpretation of data. In other words, empirical bioethics researchers often have fewer pre-existing instruments to draw from and need to develop measurement tools de novo (Ulrich & Karlawish, 2006). Thus, vignettes must be internally consistent. Two issues related to the internal validity of vignettes are important: (1) the extent to which the vignette(s) adequately depict the phenomenon of interest and (2) the degree to which each question in response to the vignette(s) measures the same phenomenon (Flaskerud, 1979; Gould, 1996).

Hypothetical Vignettes in Empirical Bioethics Research 169

Content Validity

Content validity, constituting one type of internal validity, addresses the degree to which an instrument represents the domain of content being measured and is a function of how it was developed and/or constructed (Waltz, Strickland, & Lenz, 1991). Ways to assess content validity of vignettes include using a panel of experts, focus group interviews, and/or CI techniques. Using a panel of experts, at least two (to a maximum of 10) experts in the field are asked to quantify or judge the relevance of vignettes and corresponding questions based on the following criteria: (1) does the vignette adequately reflect the domain of interest?; (2) is the vignette plausible and easily understood?; (3) are the corresponding questions representative of the vignette’s content?; and (4) do the objectives that guided the construction of the vignette correspond with its content and its response set? (Lanza & Cariaeo, 1992; Lynn, 1986; Waltz et al., 1991). Relevancy is generally rated on a four-point scale, from totally irrelevant (1) to extremely relevant (4). A formal content validity index (CVI) can be calculated based on the proportion of experts who rate a vignette 3 or 4. CVI hence indicates the extent of agreement by expert raters on the relevancy of the vignettes. Generally, an index of 0.80 or higher represents good content validity. Haynes, Richard, and Kubany (1995) provide a thorough guide to assessing content validity (Table 1).

Illustration of Content Validity

To compare adolescent and parental willingness to participate in minimal risk and above-minimal risk pediatric asthma research protocols, Brody, Annett, Scherer, Perryman, and Cofrin (2005) asked an expert panel of ethicists and pediatric pulmonary investigators to review 40 pediatric asthma protocol consent forms and choose those protocols that represented minimal risk and above-minimal risk. The researchers then

Table 1. Types of Reliability Important for Quantitative Vignettes.

Test-retest: Repeated measurements of the vignettes can determine the stability of the measure’s

performance at two distinct time periods with the same group of subjects (generally within

two weeks time). A Pearson’s product moment correlation coefficient is calculated; a

coefficient closer to 1.00 generally represents stability of the measure. (Non-parametric

measures of association are used for nominal and/or ordinal data).

Internal Consistency: A measure of an instrument’s reliability is how consistently the items in

the scale measure the designated attribute. Cronbach’s alpha is the most widely used measure

of reliability and an alpha of 0.70 is considered acceptable.

Source: Waltz, Strickland, and Lenz (1991).

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developed standardized vignettes using key information from each of the selected protocols.

Reliability of Vignettes

A reliable instrument is one that is consistent, dependable, and stable on repeated measurements. Thus, it is free of measurement error (Waltz et al., 1991). Test-retest, inter- and intra-rater, and internal consistency reliability are three different types of reliability that can be reported for quantitative instruments. Test-retest reliability measures how stable study results are over time. Thus, if vignettes are given to respondents on two occasions using a time interval in which conditions for subjects have not changed, vignette results should be comparable. For continuous or ordinal responses (e.g. ratings or rankings), Pearson or Spearman correlation is often used to measure reliability. The kappa coefficient (Cohen, 1960) or a weighted kappa (Cohen, 1968) can be used to measure the agreement between dichotomous response categories.

Intra-rater reliability is similar to test-retest but measures one rater’s variation as a result of multiple exposures to the same stimulus. Inter-rater reliability refers to the stability of responses when rated by multiple raters. When multiple experts rate the same vignette, the intra-class correlation coefficient (ICC) is often used to measure the reliability.

Internal consistency refers to the homogeneity of the items used to measure an underlying trait or attribute via a scale. For a scale to be internally consistent, items in the scale should be moderately correlated with each other and with the total scale score. One option is to use Cronbach’s alpha (Cronbach, 1951) to assess the homogeneity of the scale, with a value of 0.70 generally being considered acceptable. Qualitative vignettes can also be measured for rigor and reliability by addressing the following questions (Lincoln & Guba, 1985):

� How credible are the vignettes? � Are the findings transferable? How applicable are the findings to other areas of inquiry? � Are the findings dependable? Was an audit trail or process of verification used to clarify each step of the research process?

Illustration of Reliability

Gump et al. (2000) developed vignettes depicting six ethical dilemmas (two justice-oriented situations, two care-oriented situations, and two mixed orientations), each with 8 response items to test a measure of moral

Hypothetical Vignettes in Empirical Bioethics Research 171

justification skills in college students. Eight expert judges determined the degree of representation of the justice and care constructs by the vignettes and their corresponding items. Test-retest and internal consistency reliability scores were established for each subscale.

ADDITIONAL EXAMPLES OF

PUBLISHED VIGNETTES USED IN EMPIRICAL

BIOETHICS RESEARCH

In bioethics research, vignettes have been used to address several issues at the end-of-life, such as withdrawing life support, euthanasia, physician assisted suicide, neonatal ethics, and other sensitive topics (Alexander et al., 2003; Asai et al., 1999; Christakis & Asch, 1995; Emanuel et al., 1996; Freeman, Rathore, Weinfurt, Schulman, & Sulmasy, 1999; Kodadek & Feeg, 2002; McAlpine et al., 1997; Wolfe et al., 1999). Using a sophisticated factorial vignette design, Denk et al. (1997) conducted a computer assisted telephone interview to assess Americans’ attitudes about treatment decisions in end-of-life cases. To avoid a limited content domain and maturation bias, several vignettes were randomly presented to participants. Attitudes were solicited on continuation or termination of costly medical care of critically ill patients. Manipulated variables in vignettes included patients’ age, contribution to medical condition, quality of life, type of insurance, and patients’ right to decide about treatment.

Several authors have used vignettes in self-administered mailed ques- tionnaires. Mazor et al. (2005) surveyed 115 primary care preceptors who were attending a faculty development conference to examine the factors that influence their responses to medical errors. The researchers developed two medical error vignettes and randomly varied nine trainee-related factors, including gender, trainee status, error history, and trainee response to error. In another study, Alexander et al. (2003) developed two clinical vignettes to study public support for physician deception of insurance companies by surveying 700 prospective jurors in Philadelphia. The vignettes depicted clinical situations in which a 55 year old individual (gender was changed for each vignette) with a known condition required further invasive and/or noninvasive procedures, determined by their physician, for which the insurance company would not pay. Respondents were asked to either accept or appeal the restriction or misrepresent the patient’s condition to receive desired service.

CONNIE M. ULRICH AND SARAH J. RATCLIFFE172

Similarly, Freeman et al. (1999) developed six clinical vignettes to study perceptions of physician deception in a cross-sectional random sample of internists using a self-administered mailed questionnaire. The vignettes varied in terms of clinical severity and risks ranging from a life-threatening illness to the need for a psychiatric referral to a patient in need of cosmetic surgery (rhinoplasty). Based on the vignettes, respondents were asked to indicate whether a colleague should deceive third party payers and how they, their colleagues, and society would judge such behavior. Although these clinical vignettes were less threatening to respondents than if presented in interviews, they are limited in capturing actual misrepresentation and/or deception in clinical practice.

A few authors have used vignettes within a multi-method framework. Arguing that the use of vignettes with both qualitative and quantitative methods is a powerful tool, Rahman (1996) used long and complex case vignettes with both open-ended and fixed choice responses to understand coping and conflict in caring relationships of elderly individuals. Using an innovative internet survey design, Kim et al. (2005) used qualitative and quantitative approaches to understand the views of clinical researchers on the science and ethics of sham surgery in novel gene transfer interventions for Parkinson Disease patients. Researchers were asked to quantitatively estimate a number of issues, as well as to provide open commentary on their responses (Kim et al., 2005).

METHODOLOGICAL CONSIDERATIONS

Sample Size Estimates for Studies using Vignettes

The required number of respondents (sample size) for a quantitative study using vignettes varies depending on the study aims and design. Factors that influence the design include the research questions; the type of measurements (e.g. dichotomous forced choice, Likert scale) for respon- dents to rate, rank, or sort vignettes; the number of vignettes given to each respondent; and the number of respondent and situational characteristic effects to be examined. Once these factors have been determined, sample size estimates can be calculated based upon the statistical analysis planned.

For example, in the simplest case, suppose a study is designed to examine only the effect of respondents’ race (Caucasian and African American) on organ donation. Each respondent would be given a single vignette and asked to rate, on a Likert scale, how willing they are for their organs to be

Hypothetical Vignettes in Empirical Bioethics Research 173

donated in the particular situation. The differences in the ratings by race would then be analyzed using a Student’s t-test, assuming normally distributed responses. Sample size calculations are based on a meaningful minimum difference (or effect size=difference/standard deviation s) between the mean response rates in the two groups, that is, the smallest difference that will be found to be statistically significant in the analyses (Table 2).

When multiple vignettes are given to each respondent, there is an inherent correlation between responses from the same individual. That is, the response to one vignette is assumed to be related to or affected by the responses given to the other vignettes. When only two vignettes are given to each respondent, analyses and, hence, sample size calculations, can be based on the differences between each individual’s responses. However, as the number of vignettes increases, the within-respondent variance needs to be explicitly taken into account. This is particularly important as the within- respondent variance can potentially be large, and may ‘‘swamp’’ the situational effects of interest. Larger sample sizes are needed in order to detect any situational effects. For example, to test the hypothesis of no differences between two groups or four situations when four vignettes are given to each respondent and comparisons are to be made between two respondent groups, 16 respondents per group may be required when the within-respondent variation is 2, but 60 respondents per group would be required if the within-respondent variation was doubled. For the interested reader, sample size tables can be found in Rochon (1991).

Factorial Designs

When characteristics in vignettes are manipulated, a factorial design is often employed. If responders are given every possible vignette, the study would be considered a full factorial design. When only a couple of situational characteristics are being studied, a full factorial design may be burden- some to the respondents. For example, if only the gender (male/female) and race (Caucasian/African American) of the hypothetical person in the

Table 2. Number of Subject per Group for Two-sided t-test with a=0.05 and Power of at least 80%, Assuming Equal Group Sizes.

Difference in means 0.10s 0.20s 0.30s 0.50s 0.75s 1.00s Number of subjects needed per group 1571 394 176 64 29 17

CONNIE M. ULRICH AND SARAH J. RATCLIFFE174

vignette were changed, each subject is given 2�2=4 vignettes to respond to. However, when there are a number of characteristics to be changed with multiple levels or categories, the number of vignettes can become excessive. For example, changing 5 characteristics with 2 options for each would result in 2

5 =32 vignettes being given to each respondent. In these cases, a

fractional factorial design would be appropriate. In fractional factorial designs, each respondent is given a fraction of all

the possible situational combinations being studied. While this design does not affect the sample size, it does limit the hypotheses that can be tested. Generally, a study is designed so that main effects of characteristics can be estimated (e.g. differences between races) but higher order interactions cannot (e.g. age�race�gender), as they are assumed to be zero. If the effect of one situational change does vary with the level of another change and is not taken into account in the design, effect estimates may become biased or confounded. Thus, the fraction to be used is based upon the hypotheses or inferences that are most important in the study as well as hypothesized or known relationships between the situational character- istics. For example, Battaglia, Ash, Prout, and Freund (2006) used a fractional factorial design to explore primary care providers’ willingness to recommend breast cancer chemoprevention trials to at-risk women. Five different dichotomous characteristics, including age, race, socioeconomic status, co-morbidity, and mobility, were manipulated in clinical vignettes to assess physician decision-making. Using all five characteristics would yield 32 possible vignettes in a complete factorial design (2

5 ). To reduce this

number, a balanced fraction approach of half of all possible vignette combinations were used and participants were asked to respond to one of the 16 versions of the vignette.

Hypothesis Testing

In quantitative hypothesis-testing studies, and when each respondent is only given one vignette, standard statistical methods such as t-tests, analysis of variance (ANOVA), and nonparametric tests can be used to analyze the data. In studies when multiple vignettes are considered by each respondent, statistical methods need to account for the inherent correlation between measurements from the same respondent, as noted above. The data are considered balanced if every pair of situational characteristics is presented to respondents an equal number of times, and complete if there is no missing data. For balanced and complete data, repeated measures ANOVA or

Hypothetical Vignettes in Empirical Bioethics Research 175

analysis of covariance (ANCOVA) can test the hypothesis of interest. Issues of multiple comparisons need to be addressed before using any of these models. If the data are not balanced or complete, more advanced statistical methods can be used, such as linear mixed effect models (Laird & Ware, 1982) or generalized estimating equations (GEE) (Zeger & Liang, 1986). These methods also allow for testing complex correlation structures that may emerge in some studies.

VALIDITY OF CONCLUSIONS

External validity, or the extent to which generalizations can be made from the study sample to the population, is limited in studies employing hypothetical vignettes since vignettes may not reflect the clinical nuances of real-life situations. Therefore, caution must be used in interpreting predictive relationships between what participants report ‘‘ought to be done’’ in constructed vignettes and actual behaviors. However, vignettes provide a means for understanding attitudes, opinions, and beliefs about moral dilemmas. Rahman (1996) notes that findings will be more general- izable when a vignette is closer to real-life situations.

STRENGTHS AND LIMITATIONS OF

HYPOTHETICAL VIGNETTES

Vignettes in bioethics research pose many practical advantages. They are economical; gather large amounts of data at a single time; provide a means of assessing attitudes, beliefs, and practices on sensitive subject areas; are less personal and threatening than other methods; and avoid observer influences (Alexander & Becker, 1978; Finch, 1987; Flaskerud, 1979; Gould, 1996; Hughes, 1998; Hughes & Huby, 2002; Rahman, 1996; Schoenberg & Ravdal, 2000; Wilson & While, 1998) (see Table 3).

Vignettes have been criticized, however, for their limited applicability to ‘‘real life.’’ Hughes (1998) argues that the characters, social context, and situation of vignettes must be presented in an authentic, relevant, and meaningful way for participants. Vignettes are also subject to measurement error, which may lead to ‘‘satisficing.’’ Stolte (1994) described satisficing ‘‘as a tendency for participants to process the vignettes less carefully than under

real conditions’’ (p. 727). This may occur because of difficulties completing

CONNIE M. ULRICH AND SARAH J. RATCLIFFE176

the interpretation of and/or response to a vignette, or because of insufficient motivation of participants to perform these tasks. In turn, participants’ responses may be biased or incomplete by simply choosing the first presented response option that seems reasonable, acquiescing to common assertions, randomly choosing among the offered responses, failing to differentiate responses on a particular measure, or reporting a ‘‘don’t know’’ answer (Krosnick, 1991). Attention to contextual factors, such as interview setting, participant compensation, instrument attributes, and mode of administration may help to control satisficing (Stolte, 1994).

CONCLUSION

Although the use of vignettes in bioethics research has largely focused on end-of-life care issues, this type of data collection method provides a

Table 3. Strengths and Limitations of using Hypothetical Vignettes in Bioethics Research.

Strengths Limitations

� Flexible method, varying in length and style,

of gathering sensitive information from

participants; depersonalizes information and

provides a distancing effect � Easily adaptable for both quantitative and

qualitative research; used individually or in

focus groups and modified to ‘‘fit’’ the

researcher’s population of interest and

topical foci � Complementary adjunct to other types of

data collection methods (i.e. semi-structured

interviews, observational data) or

appropriately used in isolation � Systematic manipulation of specific

characteristics in vignettes (e.g. age or

gender) can be done to assess changes in

attitudes/judgments � Cost effective and economical in terms of

surveying a population sample � Does not require respondents’ in-depth

understanding of the subject matter � Potential to reduce socially desirable answers

� A lengthy vignette with complex wording

may lead to misinterpretation or

misunderstanding with resulting

measurement error, especially in

individuals with learning disabilities or

cognitive impairments � Limited external validity cannot

generalize findings of beliefs/perceptions

or self-reported actions/behaviors from

hypothetical scenarios to actual actions/

behaviors � Potential for psychological distress based

on the presentation, sensitive nature, and

context of the scenario and its

interpretation/importance to

participants’ life experiences � Potential for unreliable measurement(s) � Potential for satisficing: ‘‘a tendency for

subjects to process vignette information

less carefully and effectively than they

would under ideal or real conditions’’

(Stolte, 1994).

Hypothetical Vignettes in Empirical Bioethics Research 177

practical and economical means to understanding complex, challenging, and burgeoning ethical concerns. Vignettes can be constructed from a variety of sources and can be presented in several formats. The method is flexible in allowing the researcher to manipulate experimental variables of interest for a sophisticated analytic design. It can be incorporated in mailed questionnaires (paper-and-pencil method or web-based approach) or be administered face-to-face. Caution must be given in generalizing research findings based on the use of vignettes since responses regarding hypothetical behaviors are not necessarily indicative of actual behaviors. Overall, however, reliable and internally valid vignettes provide an important means to empirically advance our understanding of bioethics in key arenas such as clinical practice, research, and policy.

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DELIBERATIVE PROCEDURES

IN BIOETHICS

Susan Dorr Goold, Laura Damschroder and

Nancy Baum

ABSTRACT

Deliberative procedures can be useful when researchers need (a)

an informed opinion that is difficult to obtain using other methods,

(b) individual opinions that will benefit from group discussion and

insight, and/or (c) group judgments because the issue at hand affects

groups, communities, or citizens qua citizens. Deliberations generally

gather non-professional members of the public to discuss, deliberate, and

learn about a topic, often forming a policy recommendation or casting an

informed vote. Researchers can collect data on these recommendations,

and/or individuals’ preexisting or post hoc knowledge or opinions. This

chapter presents examples of deliberative methods and how they

may inform bioethical perspectives and reviews methodological issues

deserving special attention.

In the face of scarcity, deliberation can help those who do not get what they want or even

what they need come to accept legitimacy of a collective decision.

–Gutmann & Thompson, 1997

Empirical Methods for Bioethics: A Primer

Advances in Bioethics, Volume 11, 183–201

Copyright r 2008 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1479-3709/doi:10.1016/S1479-3709(07)11010-4

183

INTRODUCTION

Bioethical issues are, by definition, morally challenging. Topics in bioethics of interest to empirical researchers frequently carry enormous policy relevance, and would benefit from informed, reflective public input. Unfortunately, such topics as stem cell research, ‘‘rationing,’’ cloning, and organ transplantation tend to be technically and conceptually complex, intimidating and sometimes even frightening, making public input difficult. Deliberative procedures, based on deliberative democratic theory, may be an appropriate choice when either: (a) an informed opinion is needed but difficult to obtain; (b) individual opinions will benefit from group discussion and insight; and/or (c) group judgments are relevant, usually because the issue affects groups, communities, or citizens. The development of health policy (including many bioethics issues) can benefit from several of these characteristics, and potentially from deliberative public input as well.

Theories of deliberative democracy, despite important differences, share an emphasis on political decision making that relies on a process in which political actors listen to each other with openness and respect, provide reasons and justifications for their opinion, and remain open to changing their point of view through a process of discourse and deliberation. Deliberation has been justified by appeals to develop a more informed public (Fishkin, 1995), create decisional legitimacy (Cohen, 1997), and/or claim that participants in deliberations and their constituents have consented to informed decisions (Fleck, 1994). Just as traditional bioethical principles have been invoked to ensure that individual patients have a voice in their own medical decision making, so might deliberation provide community members with a ‘‘voice’’ in community- wide decisions, for instance about health spending priorities (Goold & Baum, 2006) or research regulation. Deliberative procedures offer an opportunity for individuals to assess their own needs and preferences in light of the needs and desires of others. Morally complex decisions may enjoy public legitimacy if they are the result of such fair and public processes. Beyond legitimacy, individuals involved in a community decision-making process about bioethical issues with policy implications may be more likely to accept even intensely difficult decisions if they feel they have had an opportunity to fully understand and consider the issues and to contribute to the final resolution.

WHAT ARE DELIBERATIVE PROCEDURES?

In general, deliberative procedures call for gathering non-professional (non-elite and lay) members of the public to discuss, deliberate, and learn

SUSAN DORR GOOLD ET AL.184

about a particular topic with the intention of forming a policy recommen- dation or casting an informed vote. Some deliberative procedures aim primarily to inform policy. Occasionally, deliberative procedures include both research and policy aims. We will focus this chapter on de novo deliberative procedures used for research purposes, or combined policy and research purposes, where research aims are known and planned up front.

For researchers, deliberative procedures can be a valuable tool for gathering information about public views, preferences, and values, and provide some advantages over other methods. Although it is common practice to conduct public opinion polls or nationally representative surveys to gauge public views on particular policy questions, opinions measured in this way can be unstable, subject to manipulation, poorly informed, or individuals may not have formed opinions (Bartels, 2003). This may especially be the case when issues are morally and/or technically complex. For example, a public survey about a rare genetic test is likely to encounter ignorance about genetics, genetic testing, and how a particular condition may impact health. Respondents may refuse to answer, respond despite their lack of knowledge, or respond based on flawed information. Also, using a survey, one may encounter a great deal of influence from the way the questions are framed. Furthermore, surveys often fail to capture the ‘‘public’’ aspect of public input, since the information collected is aggregated individual opinion. Deliberative methods prompt a discussion about what we should do as a political community; participants in deliberation are encouraged to reconsider their opinions in light of the interests of others. Like national polls, deliberative efforts can engage a representative (though smaller) sample from a constituency. Unlike national polls, deliberative procedures emphasize reasons and rationales for and against an issue or policy as a natural part of the deliberative process.

Individual and group interviews, while providing opportunities for reflection, generally do not aim to inform participants. Individual interviews would be poorly suited to discovering what people think as a community (rather than as individuals). ‘‘Town hall meetings’’ have been used to gather public input on policies, although they can also include time spent informing the audience. However, they may suffer from heavily biased attendance and often are structured more for political than research objectives.

OPERATIONALIZING DELIBERATIVE PROCEDURES

In this section, we emphasize methodological issues unique to, or particularly important for, deliberative methods. Similar issues and concerns can be

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found in other methods, for instance the role of group dynamics that arises in focus group research. We emphasize a limited number of methodological questions, and recommend that the reader turn to the many other excellent chapters in this volume and other resources related to specific portions of empirical research (e.g., survey question wording). To illustrate some of the issues, we provide two examples of deliberative projects.

Representation, Recruitment and Sampling

Representation, recruitment, and sampling are key aspects of research using deliberative procedures. Randomized selection methods can be used to recruit participants into a deliberative study, however, since deliberation nearly always involves gathering participants into groups, assembling them inherently introduces bias, which has both research and policy implications. In deliberative projects, even more so than, for instance, focus group projects, equal participation and equal opportunity for participation on the part of citizens is vital. A sample that omits important voices of those affected by the issue at hand, or a deliberative group that stifles certain points of view because of a dominant point of view or experience, an imbalance in perceived power, or stigma or sensitivity, undermines the legitimacy of the process and can lead to dissatisfaction, distrust, and/or recommendations that are not truly reflective of the public. There is some evidence that heterogeneous groups deliberate more effectively than homogenous groups (Schulz-Hardt, Jochims, & Frey, 2002). However, it may be important to have homogenous groups when some participants might not otherwise speak freely about sensitive issues, for example, mental illness or racism.

Substantive representation presents an alternative to random (propor- tional) sampling, and entails selecting those most affected by a policy (Goold, 1996). It resembles the practice of convening ‘‘stakeholders,’’ although distinguishes those most affected from those with the greatest interests at stake. For example, medical researchers have an interest in policies for human subjects research but the policies disproportionately affect the human subjects themselves. In addition, the opinions of researchers and laypersons on the topic are likely to be quite different, researchers often already have a forum for input on the topic, and, finally, researchers’ expertise could stifle laypersons’ comfort and active participation in deliberations. In this example, one can justify the disproportionate inclusion of laypersons in a deliberative project about policies for human subjects research, and should ensure that researchers (if they are involved in the project) are in

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groups separate from laypersons. Importantly, community-based research (of which deliberative methods represent one type) includes input from the community at the beginning of the project to guide priorities and needs; this input can identify groups that would be most important to include and to identify types of groups where homogeneity can be especially advantageous.

Early community involvement in the research process can help with recruitment of participants and add legitimacy to recruitment methods. Most projects with policy-making implications should include a component of public advertising in recruitment to ensure that important voices are identified and heard and that the project adheres to standards of openness. Careful screening of volunteers, including questions about motivation for participation, can help minimize the potential for bias.

Methods and Structure of Deliberation

Deliberative sessions can last as long as 4 days (Rawlins, 2005; Lenaghan, 1999), a weekend (Fishkin, 1995), or just a single day (Damschroder et al., 2007; Ackerman & Fishkin, 2002). A deliberative project may also consist of a series of discussions over time (Goold, Biddle, Klipp, Hall, & Danis, 2005). Sometimes a large group (several hundred individuals) breaks into smaller groups and then reconvenes.

Deliberative methods typically include: educating and informing, discus- sion and deliberation, and describing and/or measuring group (and often individual) views. Balanced education is one approach commonly used to ensure that the information provided to participants meets their needs and enhances credibility. In one arrangement, experts who represent a variety of perspectives on the topic present information from their respective perspectives, and then respond to questions from participants. The opportunity for participants to construct their own questions helps guard against undue influence by the research team in what or how information is provided. The use of ‘‘competing experts’’ allows participants, like a jury in a court case, to judge for themselves the credibility of particular experts based on responses to questions raised by participants. However this type of approach can present a problem when an issue is of an adversarial nature. Participants may take sides rather than listen to experts and fellow deliberators with an open mind. It can also be frustrating if participants sense there are no ‘‘right answers.’’ Alternatively, printed or other materials rather than (or in addition to) experts can be provided to participants.

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Discussion and deliberation should be led by professional facilitators, trained specifically for the deliberation at hand, whenever feasible. Trained facilitators avoid influencing participants (‘‘leading’’) and ensure that participation in the deliberation is as equally distributed as possible. Even more important in deliberations than in focus group research, dominant personalities need to be diffused and the opinions and perspectives of quieter participants actively sought. A round robin method, nominal group technique, or other approaches should ensure that all participants have an opportunity to speak. Equally important, participants must feel comfortable speaking; besides group composition, facilitator characteristics (men leading a group of women discussing sex, for instance), may be important considerations.

The structure of deliberative methods ranges from highly unstructured protocols, starting with an open-ended general question about a topic, to highly structured sequences of tasks. The structure of deliberations can profoundly influence the credibility and legitimacy of the method for public input on policy as well as the rigor of the research. Less structure in the outline for deliberations is advantageous because participants have more freedom to frame or emphasize issues from their own perspective. It is possible, however, that participants will not stay on task or obtain needed information, resulting in an unfocused discussion of less important aspects of the issue at hand. Maintaining the appropriate balance between greater structure and more openness depends on the topic, time available, resources, and other factors. Deliberative groups need enough structure to remain on task and cover important information domains, but participants also need enough flexibility to raise questions and process responses.

WHAT TO MEASURE AND WHEN?

Research aims determine what data to collect, as well as how and when to collect it. The following includes examples of data that might be collected in a deliberative project:

1. Individual participant viewpoints ex ante relevant to the topic 2. Relevant characteristics or experiences (e.g., participation in research or

out-of-pocket health spending) 3. Political engagement, self-efficacy, judgment of social capital (before

and/or after deliberation) 4. Individual participant viewpoints ex post relevant to the topic

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5. Views on the deliberative process (e.g., others’ sincerity, chance to present views, group decision)

6. Group dialog and/or behavior 7. Group decisions or recommendations (or lack thereof) 8. Impact on policy

Typically, data collected in deliberative projects include a combination of survey responses, group dialog, and group recommendations, decisions, and statements. Researchers may want to know if individuals change with respect to knowledge or opinion as a result of the deliberative process, and so propose to measure a given variable before and after deliberation. Data collected about individuals, besides the usual demographic information, can include pre-deliberation opinions, knowledge of the topic, and measures of characteristics that are likely to influence views. For example, if you are measuring opinions about mental health parity, personal or family experience with mental illness would be a relevant variable to include. Other data that is often valuable to collect, particularly after group deliberation, includes judgments and views of the group process or the group’s final decision. In the project described below, for instance, we used measures of perceived fair processes and fair outcomes (Goold et al., 2005).

Group dialog can be audiotaped and, if needed, transcribed. Observation of group dialog and group behavior can include structured or open-ended options to document the group dynamic or process. For example, observation can document the distribution of participation in conversation, decision-making style, dominance of particular individuals in the discussion, judgments about the group’s cooperative or adversarial decision-making processes, and the like. Group dialog lends itself to a number of analytic possibilities. One can analyze dialog for the reasons, rationalizations, arguments, or experiences used to justify points of view. One can analyze the quality of reasoning, the persuasiveness of arguments, or characteristics of group dialog that influenced individual or group viewpoints.

EXAMPLES OF STUDIES USING

DELIBERATIVE METHODS

Veterans, Privacy, Trust, and Research

The Federal Privacy Rule was implemented in the United States in 2003, as part of the Health Insurance Portability and Accountability Act of 1996

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(HIPAA), with the hope that it would address growing concerns people had about how personal medical information was being used in contexts outside of medical treatment. However, the Rule has affected research in unanticipated ways. Researchers generally can only access medical records if they have permission from each individual patient or they obtain a waiver of this consent requirement from an oversight board (an Institutional Review Board (IRB) or a privacy board). For researchers who need to review thousands of medical records going back a long period of time, obtaining individual authorization is difficult if not impossible (US HHS (United States Department of Health and Human Services), 2005). Requiring patient permission for each study can add significant monetary costs and result in selection biases that threaten the validity of findings (Armstrong et al., 2005; Ingelfinger & Drazen, 2004; Tu et al., 2004).

Study Aim

In the study described below, the investigators wanted to learn what patients thought about researchers’ access to medical records and what influenced those opinions.

Representation and Sampling

A sample of 217 patients from 4 Veteran Affairs (VA) health care facilities deliberated in small groups at each of 4 locations. They had the opportunity to question experts and inform themselves about privacy issues related to medical records research and patient privacy. Participants were recruited from a randomized sample of patients (stratified by age, race, and visit frequency) from four geographically diverse VA facilities to participate in baseline and follow-up phone surveys. Ensuring balanced numbers of older and heavier users of the health care system was important in order to gain insight into whether these patients were more sensitive about researchers using their medical records or, conversely, whether they were more supportive of the need for research compared to those who have a lower burden of illness. Additionally, the sampling approach used in this study sought to ensure that the voices of people with racial minority status, particularly African Americans (since they represent the vast majority of non-white patients for the four sites) were adequately represented. In past studies, African-American patients have expressed a reluctance to participate in clinical research (Corbie-Smith, Thomas, Williams, & Moody-Ayers, 1999; Shavers, Lynch, & Burmeister, 2002) and have exhibited lower levels of trust in researchers than white patients (Corbie-Smith, Thomas, & St George, 2002).

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Deliberation Procedure and Data Collection

Participants who completed a baseline survey were invited to an all-day deliberative session. Each participant was randomly assigned to a group of 4–6 individuals and each deliberative session included 9 or 10 of these smaller deliberative groups. A non-facilitated deliberative process was chosen to minimize researcher bias and to encourage a fresh approach to the complex issues at hand. Small groups used a detailed, written protocol starting with a review of background information about medical records, minimal risk research, and the HIPAA Privacy Rule. Participants were asked to imagine that they were acting as an advisory committee for a ‘‘research review board y [that] judges whether a research study will pose minimal risk and whether the study will adequately protect private information.’’ The protocol and background information explained that researchers cannot use personally identifiable medical records in a research study unless the IRB agrees that three waiver criteria have been met. Small group deliberations were interspersed with larger, plenary sessions led by presentations from experts in medical records research and privacy advocacy. Participants had the opportunity to pose questions to the experts and hear the answers as a plenary group.

Baseline and follow-up surveys (including some completed on-site the day of the session) elicited the level of trust in various health care entities, attitudes about privacy, prior knowledge about research and privacy, and general demographic information. Analysis followed a mixed-methods approach, combining qualitative data from deliberations with quantitative data from baseline surveys.

Results

Baseline opinions of participants confirmed that many aspects of the topic for deliberation were unknown to participants; 75% did not know that sometimes researchers could access their medical records without their explicit consent and 39% had never heard of the HIPAA Privacy Rule. The issue was value-laden on the heels of much publicity surrounding implementation of the Rule; 75% of participants said they were very or somewhat concerned about privacy but 89% also said that conducting medical records research in the VA was critically or very important.

When asked whether they were satisfied with provisions of the HIPAA Privacy Rule, 66% wanted a procedure in place that would cede more control to patients over who sees their medical records. However, no consensus was reached as to how much control should be in the hands of patients. After deliberation, nearly everyone (96%) said they would be

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willing to share their medical records with VA researchers conducting a study about a serious medical condition.

Participants’ trust in VA researchers was the most powerful determinant of the degree of control they recommended for patients who overuses their medical records. Mechanic and Schlesinger’s (1996) work on trust inspired a framework to describe trust between patients and a medical research enterprise: (1) Are medical records kept confidential? (2) Does the research being conducted demonstrate high priority on patient welfare? (3) Are researchers held accountable and responsible for protecting privacy? (4) Are systems to protect medical records sufficiently secure? (5) Do researchers fully disclose the research being conducted and how medical records are used to conduct that research? (Mechanic, 1998). Participants reported the need to see and understand how their records are kept private, that violators will be punished consistently and relatively severely (e.g., job loss, fines), and assurance that computerized systems are truly secure. Participants expressed the need to see that the institution’s research actually benefits patients and is not subject to conflicts of interest. They wanted transparency in the research process in terms of what research is being done and how their medical information may have contributed to new findings. Further analyses will explore reasons for the apparent contradiction that participants are willing to share their own information, yet united in their call for more control over how their medical records are used.

Deliberation changed opinions in that the overwhelming majority of participants who said they would be willing to share their medical records with VA researchers actually increased compared to baseline willingness (89%). When asked how important it was for researchers to obtain permission for each and every research study before deliberation, 74% said it was critically or very important to do so at the time of the baseline survey, whereas immediately and 4–6 weeks after deliberation, only 48% and 50%, respectively, held this position.

Participants were satisfied with the deliberation process. When asked anonymously, 98% of participants thought the deliberation process was fair, 94% felt others in their group listened to what they had to say, and 89% said they would make the same recommendation as the one made by their group as a whole (through consensus or voting). One participant summarized his experience by saying, ‘‘... with more exposure and thought, my decisions are more in line with my moral values.’’

In this example, a single, one-day deliberative session generated informed, and possibly more public-spirited, points of view about an important, complex, and morally challenging health policy issue.

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Health Spending Priorities

How to allocate and prioritize limited resources fairly and openly is perhaps the most pressing moral and practical concern in the health care arena today; certainly the issue is of importance to nearly all citizens. Setting health care priorities for the use of scarce public resources requires attention to both economics and justice; justice, in turn, is enhanced by the participation of those most affected by the decisions. According to this participatory conception, rationing decisions should incorporate the preferences and values of those affected (Eddy, 1990; Menzel, 1990; Fleck, 1992, 1994; Goold, 1996; Emanuel, 1997; Daniels & Sabin, 1998). Engaging and involving citizens in health care priority setting, however, confronts obstacles. Because health concerns lack salience for most healthy citizens, decisions tend to be influenced heavily by health care experts or those, like the disabled or senior citizens, with specific interests (Kapiriri & Norheim, 2002; Goold, 1996; Jacobs, 1996). Discussions about health services and financing can be complex, making citizens frustrated or intimidated and reliant on others to decide for them, trusting that the services they need will be provided to them in the event of illness (Goold & Klipp, 2002). Talking about future health care needs requires that people think about illness and death; the emotionally laden tension when money appears to be pitted against health in rationing decisions has been a thorny problem for proponents of the ‘‘citizen involvement in rationing’’ model (Ham & Coulter, 2001).

Besides the complexity and value-laden nature of the problem, health care trade-offs involve pooled (often public) resources. Individual health and health care priorities must be balanced by the current or future needs of others in a community. The need for interpersonal trade-offs, and the balancing of individual with social or group needs requires either procedural justice (i.e., fair processes for decision making) or distributive justice (i.e., fair distribution of benefits and burdens), or, ideally, both. The topic of health care spending priorities lends itself well to deliberative procedures.

Accordingly, Drs. Goold and Danis designed CHAT ‘‘Choosing Healthplans All Together,’’ a simulation exercise based on deliberative democratic procedures in which laypersons in groups design health insurance benefit packages for themselves and for their communities. Participants make trade-offs between competing needs for limited resources. To test the choices they have made as individuals and as a group, participants encounter

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hypothetical ‘‘health events’’ that illustrate the consequences of their priorities, which inform subsequent group deliberations.

Study Aims

The project described below (for full details see Goold et al., 2005) aimed to describe public priorities for health benefits and evaluate the CHAT exercise as a deliberative procedure. We assessed aspects of feasibility, structure (including the content of the exercise), process, and outcomes.

Representation and Sampling

Participants were North Carolina residents without health care expertise, recruited from ambulatory care and community settings. Groups were homogeneous with regard to type of health insurance coverage (Medicare, Medicaid, private, uninsured) and heterogeneous with regard to other characteristics (gender, age, race). Low-income participants were over- sampled to assess whether the exercise was accessible and acceptable since typically, these groups are less well represented in policy decisions.

Deliberation Procedure and Data Collection

The CHAT simulation exercise is a highly structured, iterative process, led by a trained facilitator using a script, and progresses from individual to group decision making. Data collection included pre-exercise questionnaires about demographics, health and health insurance status, health services utilization, out-of pocket costs, and the importance of health insurance features (Mechanic, Ettel, & Davis, 1990). Post-exercise questionnaires rated participant enjoyment of CHAT, understanding, ease of use, and informativeness (Danis, Biddle, Henderson, Garrett, & DeVellis, 1997; Biddle, DeVellis, Henderson, Fasick, & Danis, 1998; Goold et al., 2005). Other items asked participants to rate their affective response to the exercise, perceptions of the group process, outcome of decision making, informational adequacy, and range of available choices. Half the group discussions were tape-recorded to analyze the values, justifications, and reasons expressed by participants during deliberation.

Results

Five hundred sixty-two individuals took part in 50 sessions of CHAT. Transcripts of group deliberations were analyzed to understand the reasoning, values, and justifications participants emphasized during

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deliberation. Over 60 themes were identified and organized into four major categories: (1) Insurance as Protection against Loss or Harm; (2) Preferences for the Process of Care; (3) Economics or Efficiency; and (4) Equity or Fairness.

Insurance as Protection against Loss or Harm described the greatest proportion of the dialog (55% of coded text) and had the largest number of sub-themes. Dialog was coded under this theme when participants justified coverage selections on the basis of planning for future health care needs and avoiding future harms or losses through adequate insurance coverage. For example, participants discussed the likelihood that they or others would suffer health related losses/harms in the future:

I know in our group we had chosen medium coverage because depending on [heredity] and

your age, you may have a lot more dental problems and it becomes certainly more

important as you age. So that would be my choice.

Participants also frequently discussed the types (financial, physical, social, and emotional) and the magnitude of the harms or losses that might be avoided, either because care would be prohibitively expensive without coverage, because not having coverage could lead to serious harm, or because coverage was essential for large numbers of people.

The second major category, Economics or Efficiency, represented participants’ concerns about costs and resources (20% of coded text). Such concerns included the use/abuse of insurance, the perceived value of care, and issues of supply and demand of health care services. Participants were concerned with waste in the health care system, and recognized the potential for increased use related to insurance coverage – the concept of ‘‘moral hazard’’:

Looking in the long run, if you, specialty just staying at the basic is kind of like a gate

keeper. If you’re thinking of group coverage, some people may, I don’t, but some people

may jump to a specialist quicker than they need to if they have the free choice of going

there.

Dialog coded in the third theme, Preferences for the Process of Care, included concerns for quality of care, the ability to choose physicians and treatment facilities, system ‘‘hassles’’ such as the wait time for appointments and, in some instances, one’s own beliefs or attitudes about health care services. This example illustrates the value placed on timely access:

This is very important, I think y. I mean, just the thought of waiting four weeks for a

routine appointment. What do they consider routine?

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The fourth major theme, Equity or Fairness, (8% of coded dialog) included discussions of justice, including dialog about equality, personal responsibility, and social responsibility, especially a responsibility to care for the worse off:

y because there are people of course that just simply can’t afford $50 a day. It would be an

extreme hardship on them y.

Several policy and research projects have used CHAT to involve citizens in health benefit design. Evaluation data from the project reported above and several other CHAT projects shows that participants, including low- income and poorly educated participants (Goold et al., 2005):

1. Find CHAT understandable, informative, and easy to do 2. Judge the fairness of the group process and decision favorably 3. Would be willing to abide by the decisions made by their groups 4. Gain an understanding of the reality of limited resources and trade-offs

between competing needs 5. Alter the choices they make for themselves and their families after the

exercise, for instance by more frequently choosing coverage for mental health services.

As is true for many deliberative methods, however, much more critical and rigorous evaluations of processes and outcomes are needed.

DATA MANAGEMENT, ANALYSIS, AND

INTERPRETATION: SPECIAL ISSUES

Since deliberative procedures always involve gathering individuals into large or small groups for discussion, any data analysis using individuals as the unit of analysis needs to adjust for clustering effects. Owing to the fact that membership in a particular deliberative group discussion and other events will vary from group to group, individual responses on any post- deliberation measures may be affected. Another important issue for deliberative procedures is the need, for many researchers, to compare individual responses before and after deliberations. Since merely measuring something once can work as an intervention (a well documented effect in the educational research literature), it is better to either (a) use a control group that does not participate in deliberations, or (b) ensure the sample size is large enough to randomize a portion of participants to complete the measures only after group deliberations, and analyze the impact of

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completing the measure prior to deliberations on post-deliberation responses to the same measure. A related concern inherent in deliberative methods relates to the effect of the group on the individual. Do individuals tend to change their point of view depending on the group’s overall point of view? What about particular events (knowledge statements, e.g.,) that occur in some groups but not others? The recognition or mention of a particular issue (e.g., discrimination) could sway whole groups and many individuals to a different opinion. Finally, while group dialog is a valuable and rich source of information, it can be a challenge to manage. Transcription can be error-prone and should be checked in the usual manner. Transcribers who have not been present at the group discussion are unlikely to be able to recognize the gender of speakers, much less consistently identify individual speakers.

Policy makers and other users of data from deliberative procedures often raise concerns about the representativeness of non-random sampling, inevitable whenever recruitment takes place into groups. When this occurs, it is important to acknowledge the limitations of non-random sampling but compare that limitation with the limitations of other methods, including comparisons with proportional representative sampling for surveys where generalization may be easier but the responses may be less informed, less insightful, more vulnerable to framing, and have other profound limitations. It is also important to talk about alternatives to proportional representative sampling, such as substantive sampling or, often more familiar to policy makers, the inclusion of important stakeholders. Compare, for example, a randomly selected public opinion survey related to high-risk brain surgery for Parkinson’s Disease to information gained from deliberative groups that purposefully includes family members of those with Parkinson’s, those with early Parkinson’s Disease, or those otherwise at high risk of having the condition.

A variety of analytic techniques may be used in deliberative studies. Analyzing individual responses to survey items, before and/or after deliberations, will typically include descriptive statistics, but also should, when possible, include analyses of the responses of important subgroups. For example, in a project about health care resource allocation, subgroup analyses might include low-income or chronically ill participants. The specific approach to analysis of group dialog will be guided by the study’s specific research aims and how much is known about public views on the issue. For example, although group discussions about resource allocation might include many valuable insights, research might focus on dialog about the relative importance of mental health services. Some analysis will be

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straightforward (e.g., labeling comments about job discrimination in a discussion of privacy), while some may require more interpretation (e.g., expectations of beneficence as an element of trust in medical researchers).

APPLICATIONS AND IMPLICATIONS

OF THE METHOD

Deliberative procedures have been designed for public input into policy making, and hence have special applications for that arena. For example, where policies and/or regulations are relatively silent about surrogate decision making for research participation, deliberative methods can help learn how the public feels about degrees of risk, informed consent, and other issues. As such, these methods can be useful for the examination of the public’s views on bioethical issues that are often complex and benefit from public reflection, discourse and understanding.

SUMMARY

In this chapter, we have tried to review briefly the use and application of deliberative procedures to empirical research questions in bioethics, illustrated with two projects. Like many other methods, the use of deliberative procedures is varied and consequently the strengths, weak- nesses, and implications of research results vary as well. Statistical (proportional) generalization is not a strength of deliberative procedures since convening groups will eliminate the ability to consider a sample truly random. However, deliberative procedures can gain public opinion that is informed, reflective, and more focused on the common good than on individual interests.

Deliberative procedures hold a great deal of promise for research on relevant bioethical policy questions. However, like any other method, there is a need for conceptual and empirical research to better define when deliberative procedures are most appropriate, describe the impact of particular methodological choices, and improve our ability to draw conclusions. It may be tempting to regard deliberative procedures as simply the means to a better end – the end being ‘‘better’’ decisions and outcomes. Proponents of this outcome-oriented view evaluate only the products of deliberations (Abelson, Forest et al., 2003; Rowe & Frewer, 2000).

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Evaluating only outcomes, however, misses the normative argument that ‘‘good’’ deliberative democratic processes can be valued in and of themselves, and that the procedures can be justifiably criticized if they fail to meet normative procedural standards, for example, fair representation or transparency.

Research on deliberative methods should examine, in particular, the impact of choices of sampling methods, group composition (relatively heterogeneous or homogeneous), and deliberations’ structure. Researchers using deliberative procedures should be encouraged to include these and other sorts of ‘‘methods’’ questions, as survey researchers have included research aims that address issues of framing, question ordering, and the like. Recently, a number of scholars (Abelson, Eyles et al., 2003; Fishkin & Luskin, 2005; Neblo, 2005; Steenberger, Bächtigerb, Spörndlib, & Steinerab, 2003) have begun to examine and evaluate deliberative procedures, and a few (e.g., Fishkin) have used deliberative procedures directly to answer research as well as policy questions. Researchers should use and interpret the results of deliberations carefully until more is known about the influence of particular aspects of the method.

There are sound theoretical and philosophical reasons for involving the public directly in policy decisions, including policy decisions in bioethics. Deliberative methods present a promising research approach to addressing morally, technically, and politically challenging policy questions, with the additional advantage that research and policy aims can, at times, be fruitfully combined.

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INTERVENTION RESEARCH

IN BIOETHICS

Marion E. Broome

ABSTRACT

This chapter discusses the role of intervention research in bioethical

inquiry. Although many ethical questions of interest are not appropriate

for intervention research, some questions can only be answered using

experimental or quasi-experimental designs. The critical characteristics

of intervention research are identified and strengths of this method are

described. Threats to internal validity and external validity are discussed

and applied to a case example in bioethical research. Several recent

intervention studies that were federally funded in the area of informed

consent are discussed, and recommendations for future intervention

research are presented.

INTRODUCTION

Empirical research in bioethics has been defined as ‘‘the application of research methods in the social sciences (i.e., anthropology, epidemiology, psychology, and sociology) to the direct examination of issues in medical ethics’’ (Sugarman & Sulmasy, 2001, p. 20). Empirical research methods

Empirical Methods for Bioethics: A Primer

Advances in Bioethics, Volume 11, 203–217

Copyright r 2008 by Elsevier Ltd.

All rights of reproduction in any form reserved

ISSN: 1479-3709/doi:10.1016/S1479-3709(07)11009-8

203

have been successfully applied to several areas of bioethical inquiry, such as informed consent, education of health professionals in ethical reasoning, assessment of patient–provider communication preferences, end-of-life decision making, and assessment of the effectiveness of various interven- tions.

The purpose of this chapter is to discuss intervention studies applied to the study of bioethical phenomena. The chapter will review topic areas to which intervention research has been successfully applied, describe critical design elements of such research, discuss the strengths and challenges of this approach, and provide an in-depth analysis from a specific intervention study on informed consent. Finally, recommendations will be made for future research in bioethics that may benefit from intervention methods.

INTERVENTION STUDIES IN BIOETHICS

The use of empirical methods in the study of bioethical inquiry has increased over the past two decades. Sugarman, Faden & Weinstein (2001) conducted an analysis of empirical studies posted in BIOETHICSLINE during the decade of the 1980s. At that time 3.4 percent or 663 of the postings were reports of empirical research. In a subsequent analysis of the reports of empirical studies in bioethics in MEDLINE in 1999 (which by then had subsumed the BIOETHICSLINE database), the number of postings had doubled from 0.6 percent of all MEDLINE postings in 1980–1984 to 1.2 in 1995–1999 (Sugarman, 2004). The most frequently studied topic, regardless of type of empirical approach, was informed consent with physician–patient relationship and ethics education among the top 30 of 50 topics overall. Since 2000, studies have reported on the effectiveness of different types of materials (written, tailored, videotapes, etc.) in increasing individuals’ understanding of a health condition they have or are at risk for (Skinner et al., 2002; Rimer, et al., 2002). Sugarman identified eight types of empirical research ranging form ‘‘purely descriptive studies’’ to ‘‘case reports.’’ Only one type is based on interventional research which Sugarman calls ‘‘demonstration projects.’’ This multitude of types of empirical approaches is very appropriate in the field of bioethics as, the overwhelming majority of the topics in bioethics would not lend themselves to intervention studies, or manipulation of the independent variables, such as medical care at end-of- life, confidentiality, or euthanasia (Sugarman & Sulmasy, 2001).

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EXPERIMENTAL DESIGNS

Many descriptive studies in bioethics have examined issues surrounding clinical trials, including therapeutic misconception, informed consent, placebo groups, randomization, physician–scientist conflict, and the provi- sion of drugs for individuals who are addicted (Friedman, Furberg, & DeMets, 1998; Sugarman, 2004). Such studies are designed to build a descriptive body of knowledge generating hypotheses that can be tested in intervention studies (Sugarman, 2004) and by now some topics have been sufficiently well described to suggest that interventions be developed and tested. The purpose of a well-designed clinical trial is to prospectively compare the efficacy of an intervention in one-group of human participants to the effects in another group of individuals to which no treatment was intentionally administered. The application of experimental designs, such as the randomized controlled trial (RCT), has been limited in bioethical inquiry for several reasons, including difficulties applying the stringent requirements for random selection, as well as difficulties with assignment, blinding, and achieving a sufficient sample size (Friedman et al., 1998). Therefore, instead of using the RCT, much of intervention research conducted in bioethics often follows the precepts of quasi-experimental designs, in which randomization is limited to random assignment to conditions and control groups are referred to as comparison groups (Rossi, Freeman, & Lipsey, 1999). Quasi- experimental designs range from the more rigorous two-group repeated measures or pre-/post-test designs to the one-group post-test-only design. The latter provides the least amount of control over extraneous factors. The nature of these designs allows for varying degrees of control for group differences in pre-existing characteristics (e.g., education, age, etc.) and events that may occur during the study and that may influence outcomes (Cozby, 2007). The degree of control is determined in part by the topic under study, and as noted above, there are topics in bioethics where not all conditions can be met for a purely experimental design. In quasi-experimental designs, the degree to which findings are generalizable (which depends on controls such as random selection and assignment), and the degree to which cause and effect conclusions (which depends on manipulation of the independent variable and use of control groups) can be drawn will be limited.

Essential Attributes of Experimental Designs

Experimental designs use a variety of procedures to distribute equally pre- existing differences among participants across conditions (i.e., experimental

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and control), in order to maximize the similarity of circumstances under which participants receive an intervention and study groups are observed. These procedures can be categorized into three primary components: (1) randomization of control and experimental groups to the intervention (random assignment), (2) manipulation and systematization of the inter- vention, and (3) exposing the control group to contextual experiences as similar as possible as the experimental group during the study (Shadish, Cook, & Campbell, 2002).

Randomization to intervention and control groups is essential in order for the investigator to assure that any differences that individuals bring to the study, such as previous experiences and personal or socio-demographic characteristics that could interact with the intervention, will be equally distributed across groups and thus not influence the outcome (Shadish et al., 2002; Lipsey, 1990). Manipulation of the independent variable, constituting an intervention in the experimental group only, is an essential attribute of experimental studies (Lipsey, 1990) and in this respect differs from field studies in which naturally occurring phenomena that affect a group are observed as the study unfolds. For instance, in a hypothetical observational field study of informed consent in a research trial, the investigator would observe conversations between the researcher and study participants under different conditions to draw conclusions about how factors may influence participants’ understanding about risk level. In this study the investigator does not manipulate an intervention. In an experimental intervention study, the investigator would randomly assign researchers to different scripts, use vignettes with varied characteristics, or manipulate other variables in the experimental group in order to ascertain how different types of information delivery affect a participant’s understanding of risk.

Another important aspect of intervention studies is standardization of the intervention. This means that the investigator must develop and adhere to a protocol so that all participants in the experimental group are exposed to the intervention in the same way and for the same amount of time (Cozby, 2007). This will enable the investigator to interpret results with more confidence related to the effect of the intervention. Additionally, others can then replicate the study by following the same protocol.

Finally, the researcher must make efforts to ensure that external events or experiences that may have a bearing on the study outcome are not significantly different for participants in experimental and control conditions.

The RCT is the most rigorous and best-controlled experimental design. Adhering to proscribed guidelines (CONSORT, 2004), this design always employs a control group, randomization, often random selection, and an

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intervention that follows a strict, standardized protocol. The RCT is well established, highly reliable, and valid, allowing for the use of multivariate statistical procedures to make causal inferences about the effect of an intervention on an outcome. It requires as much systematic control over extraneous variables as is feasible. When this is not possible, the extent to which an effect can be attributed to the intervention is less certain.

Strengths of the Experimental Design

The overall aim of an experimental design is to control as many threats to internal and external validity as is possible, with RTC being the design that provides the most rigorous application of control. When a researcher examines the relationships between two or more variables he/she must be concerned about minimizing threats to internal validity and external validity. Internal validity is defined as the extent to which the effects detected are a true reflection of reality rather than being the result of extraneous variables (Burns & Grove, 1997, p. 230). That is, the researcher wants to be assured that the relationship between variables of interest is not influenced by unmeasured variables. External validity refers to the extent to which study findings are considered generalizable to other persons, settings or time (Shadish et al., 2002). The significance of a study is judged, at least in part, by whether the findings can be applied to individuals and groups separate from those in the sample studied (Burns & Grove, 1997).

THREATS TO INTERNAL VALIDITY

There are 12 commonly acknowledged threats to internal validity, or the ability to be confident that a proposed causal relationship reflects known, rather than unknown or unmeasured variables. These threats are: history; maturation; testing; instrumentation; statistical regression; selection; mor- tality; ambiguity about direction of a causal relationship; interactions with selection; diffusion or imitation of intervention; compensatory equalization of treatments; and compensatory rivalry by or resentful demoralization of respondents receiving less desirable treatments (Cook & Campbell, 1979; Shadish et al., 2002). Of these, history, selection, testing, instrumentation, and diffusion of intervention are especially relevant in bioethical interven- tion studies. Each of these threats will be illustrated by an intervention study whose purpose was to examine the effectiveness of education about research

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integrity delivered to graduate students with the use of different teaching techniques. Outcome variables were knowledge and attitudes about ethical approaches to research and scientific misconduct. Content was delivered via standard in-person classes compared to self-paced, interactive web-based modules. Groups receiving these interventions were compared to a group of students not taking the course (see Table 1).

History

The threat of history refers to the possibility that participants in the experimental and the control group have different experiences while under observation in a study and that such experiences (extraneous variables) influence the outcomes differently in the groups. In the case study described in Table 1, the psychology students at one of the universities were all mandated to take a research ethics workshop provided by an official from the Office of Research Integrity at NIH. That is, the experience of taking a mandated research ethics course had the potential of affecting attitudes and knowledge about research ethics in that university but not in the other. This meant that the control group at one of the sites was no longer in the control condition after being exposed to the mandated class.

Table 1. Case Example: Illustration of Threats to Internal and External Validity.

Purpose: The purpose of this study was to examine the effectiveness of education about research

integrity delivered using different teaching techniques. The outcome variables included

knowledge and attitudes about ethical approaches to research and scientific misconduct. The

content was delivered via standard in-person classes compared to self-paced, interactive

modules via web-based platform and both were compared to a group of students not taking

the course.

Methods: Ninety-six Ph.D. students in psychology from two different universities were

randomly selected from a group of 200 volunteers to participate in the study. These 96 were

then randomly assigned to one of two interventions or one control group. The first

intervention group consists of a series of 4 on-line modules to be completed over a four-week

period, the second intervention consists of six in class two hour sessions and the control

group did not receive any instruction. The Scientific Misconduct Questionnaire-Revised

(Broome, Pryor, Habermann, Pulley, & Kincaid, 2005) was used to assess knowledge,

attitudes, and experiences with scientific misconduct after completion of the intervention and

one year later for all three groups. All participants were surveyed using the SMQ-R pre-

intervention, eight weeks and one year after the start of the study.

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Selection

The threat of selection refers to bias that occurs when recruiting and assigning study subjects does not give every potential participant the same opportunity to become enrolled in the study (random selection) or the same opportunity to be randomly assigned to the treatment or control condition (random assignment). In the case example, the investigators advertised the study widely to all psychology graduate students on both campuses. As one would expect, only those who were interested in participating volunteered, and those who volunteer to participate in studies may represent a specific subset of the population. The investigators addressed this bias by randomly selecting 96 of the 200 students who volunteered. Random selection involves selecting study subjects by chance (e.g., using a table of random numbers) to represent the population from which they are chosen (Shadish et al., 2002) and gives each volunteer an equal chance of being chosen. The investigators then randomly assigned each individual to one of the three-groups: one which completed four on-line modules over a four week period; another which took part in six didactic two-hour sessions; and a control group that received no instruction. Thus, the threat of selection bias was minimized.

Testing

Testing is a threat that occurs when participants are asked to respond to the same measure on several occasions and, as a result, may remember some of the specific items. In the case study, after the first administration, some of the students may have become sensitized to the items on the instrument used to evaluate knowledge and attitudes (Broome et al., 2005) and remembered how they answered the questions when being administered the same instrument second time. Hence, any change in responses may not have been explained by being exposed to the intervention. Some investigators handle this problem by using alternative but parallel forms of a measure, so that the questionnaire tests the same concepts but uses different wording for the items. Others measure both control and intervention groups pre- and post with the same instrument so that patterns of change can be statistically tested to control for initial differences between groups. That is, investigators can test the assumption that due to random assignment, one would expect no differences in pretest scores and the potential for testing problems would be the same for both groups. Thus, differences in outcome would be attributed to the intervention.

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Instrumentation

This threat is related to a change in the method of measurement from pre- to post intervention. An example from the case study is the use of a quantitative survey to measure the students’ knowledge about research integrity before the intervention and the use of open-ended interviews assessing this knowledge after the intervention. Any change in knowledge (either via a score on the survey or a coded analysis of open-ended responses) cannot, with a reasonable level of certainty, be attributed to the intervention, but could likely be affected by the manner in which knowledge was measured at the two points in time.

Another problem related to instrumentation can occur as a result of the response option format. For instance, when intervals on a scale are narrower on the ends than in the middle (e.g., extremely positive, very positive, positive, somewhat positive, somewhat negative, negative, very negative, extremely negative), responses on a second administration of an instrument may often tend to cluster around the middle rather than reflecting the full-range of options (Cook & Campbell, 1979). This may be due to individuals becoming frustrated at attempting to differentiate between the outer options of ‘‘very’’ and ‘‘extremely,’’ rendering the measure less a reflection of subjects’ actual responses and more a result produced by the format of the instrument itself.

Diffusion of the Intervention

When an intervention study is designed to assess the acquisition of knowledge or skills, and when individuals in the intervention and control groups can interact about an intervention (e.g., discuss it outside the study), the control group may gain access to information that may reduce differences between the two-groups on outcome measures. In the case example, this threat is especially salient as psychology graduate students often take the same classes or participate in similar activities within a university and may thus discuss with each other particulars of the educational intervention. A method to control for this is to randomize the intervention at the university level (across settings) rather than at the individual level (within a given setting).

THREATS TO EXTERNAL VALIDITY

Threats to external validity can limit the ability of the investigator to generalize the results of a study beyond the current sample, which, in turn,

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limits the usefulness of the study’s findings. The three major threats fall into the following categories: (1) interaction of selection and treatment; (2) interaction of setting and treatment; and (3) interaction of history and treatment. In the first situation, study participants possess a specific characteristic as a group that interacts with the intervention in such a way that change in the outcome is not generalizable to other groups of individuals. For example, a significant effect of an intervention to increase medical students’ skills in clinical decision making in morally ambivalent situations may not be generalizable to a group of nursing assistants. In this case, the significant difference in educational level between groups is such that it interacts with the intervention to produce different outcomes. In the second and third situations (i.e., setting and history) contextual events (e.g., pay raises, new institutional leadership climate, additional bioethics workshops) that may occur during an intervention study are not replicable in a subsequent study or in other settings and will therefore restrict generalizability of findings.

In summary, it is important that an investigator select a research design to test an intervention that will control for as many threats to internal and external validity as possible. Some threats (e.g., selection, testing, and history) can be controlled for by using randomization. Other threats (diffusion of treatment and instrumentation) can be planned for by randomizing across sites rather than within one site and using the same instrument for all assessments. Choosing well-established measures that have been tested in other studies and which have demonstrated adequate reliability and validity are crucial to obtaining credible responses. Maintaining strict protocols regarding instrumentation and data collection help to ensure that data are collected under as similar conditions as possible and that any differences in responses between groups is due to the intervention. Plans that include adequate time and rigor in cleaning and managing data and applying statistical tests best fit for the type of data collected will decrease threats to both types of validity and enhance the reliability and credibility of the findings (Cook & Campbell, 1977).

ADVANTAGES OF INTERVENTION RESEARCH

IN BIOETHICAL INQUIRY

Many questions asked by bioethicists can be answered by any one of the many non-experimental designs available to researchers. However, there are

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several important questions that require the investigator to systematically and rigorously compare, in a controlled context, the effects of one or more treatments on selected outcomes. The experimental or quasi-experimental designs used to test the effectiveness of an intervention are most useful when assessing changes in knowledge, understanding, attitudes, or behaviors related to some aspect of ethical phenomena (Danis, Hanson, & Garrett, 2001). Examples of some questions that can only be answered by intervention designs are included in Table 2.

There are at least five distinct advantages to intervention research in bioethical inquiry: (1) the ability to examine whether a certain action (intervention) can be safely used with a selected group of individuals (efficacy) under relatively controlled conditions; (2) the ability to examine how useful an intervention is when used in the real world with a variety of people (effectiveness); (3) the ability to use multiple measurement methods (e.g., behavioral observation and questionnaires) to assess the impact of an intervention; (4) the possibility to maximize confidence in relationships found between variables (i.e., cause and effect); and (5) the ability to reach greater acceptance of findings by the larger scientific research community.

CHALLENGES IN INTERVENTIONAL RESEARCH

IN BIOETHICAL INQUIRY

Not all bioethical phenomena are appropriate for study using experimental or quasi-experimental designs. In general, these include naturally occurring phenomena that cannot be manipulated, the presence of conditions to which

Table 2. Selected Research Questions for Intervention Research.

1. Does the timing of information (72 h, 24 h, and immediately before an elective procedure)

about a research study influence the understanding of the purpose of the study, the risks and

benefits, and the refusal rate of participation?

2. Are nurses who are assigned to work with patient actors who are labeled as hospice patients

more likely to discuss organ donation during a clinic visit than those assigned to patient

actors who have cancer but are not enrolled in hospice?

3. Are tailored instructional materials for individuals at-risk for genetic conditions more

effective than standardized materials in increasing knowledge and satisfaction?

4. Do school-age children who are shown a DVD depicting a child engaged in a study

demonstrate greater comprehension and a more positive affect in regards to study

participation?

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one cannot randomly assign individuals, and circumstances that cannot be scripted or protocolized. For instance, one cannot manipulate or randomize individuals to different conditions at the end-of-life to study how they cope. Another challenge is the ethics of studying ethical issues. For example, in some situations the imposition of a research study, no matter how well intended, can burden participants during difficult or stressful times (Sachs et al., 2003). One example of this is to study how parents make decisions about whether or not to enroll their child in an end-of-life research study soon after the death of the child. Therefore, it is critical to carefully evaluate the study’s potential for benefit (in relation to risk or harm) and for expanding knowledge prior to approval or implementation.

Another challenge in intervention research is the ability to recruit an adequate number of participants so that statistically significant differences between groups can be detected. Given the nature of bioethical phenomena, it can be difficult to attract a large enough sample that meets requirements for rigorous statistical analyses. Multi-site studies that facilitate obtaining large samples can address this challenge.

ILLUSTRATIONS OF INTERVENTION

RESEARCH – APPROACHES AND FINDINGS

In 1998, the National Institutes of Health funded 18 studies on the topic of informed consent. The purpose of this initiative was to produce (1) new and improved methods for the informed consent process; (2) methods that would address the challenges in obtaining consent from vulnerable populations; and (3) data to inform public policy (Sachs et al., 2003). The studies tested interventions designed to improve two different but related areas: (1) knowledge among potential research participants, and (2) decision-making abilities in vulnerable individuals (Agre et al., 2003). Six studies were RCTs and one used a quasi-experimental design where patients were not randomized to groups. The studies had in common the testing of a variety of media interventions such as videotapes, decision aids, and computer software to convey information to potential research subjects. A selection of the seven funded projects that were intervention studies will be reviewed below to illustrate the range of questions, designs, and analyses used in such research.

One of the studies was conducted with patients and families in a hospital waiting room who were going to make a decision about participating in a

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clinical trial (Agre et al., 2003). Subjects were randomly assigned to one of the four modalities: standard verbal consent, a video, a computer program, or a booklet. A brief quiz was used to measure knowledge as the outcome. Findings revealed that the more complex the research protocol described to the potential participant, the higher the knowledge score. It also showed that the better-educated patients had higher scores, while more distressed individuals and those who were minorities scored lower. There were no primary effects for the four media suggesting none were superior to the others. However, the video was more effective for those deciding about complex protocols and for minority participants, while the booklet was more effective for those in poor health.

In a second study on informed consent also focusing on knowledge outcomes, Campbell, Goldman, Boccio, and Skinner (2004) conducted a simulated recruitment for two pediatric studies, one high risk (e.g., insertion of device in patients awaiting heart transplant) and one low risk (e.g., longitudinal assessment of low birth weight infants), with parents of children enrolled in Head Start. Four different interventions were tested: (1) a standard consent form; (2) a consent form with enlarged type and more white space; (3) a videotape; and (4) a PowerPoint presentation. None of the four methods of conveying information was superior. However, parents were significantly less likely to enroll their child in a high-risk protocol regardless of the nature of the method tested.

In one of the other studies (an Early Phase Research Trials – EPRT – with oncology patients), researchers first conducted a descriptive study in which interactions of patients and their physicians were audiotaped when the study was described and participation was offered (Agre et al., 2003). Based on this, an intervention was designed to increase the patients’ understanding of EPRT consisting of a 20 minute, self-paced, touch screen computer-based educational program. Patients were randomly assigned to the intervention or control group, with the latter receiving a pamphlet that explained the EPRT. Results showed that the intervention had minimal impact on agreement to participate, with equal numbers in both groups deciding to join the trial, although patients in the intervention group were more likely to say the intervention changed the way they made their decision.

Mintz, Wirshing and colleagues (Agre et al., 2003) developed two videotapes preparing potential participants to consider enrollment in a medical study or in a psychiatric study. In addition to content and information, the intervention videotape encouraged individuals to be active participants during the informed consent process. The control video presented historical information and federal regulations about informed

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consent. Participants for both studies were divided into intervention and control conditions. Knowledge about consent processes among participants in both the medical and psychiatric studies improved as a result of the intervention video compared to those viewing the control tape.

In another study, Merz and Sankar recruited participants from a General Clinical Research Center (GCRC) to test the effectiveness of a standard consent form compared to a series of vignettes (Agre et al., 2003) on participants’ knowledge. At post-test, participants in both groups demon- strated relatively good comprehension and no significant difference emerged between control and intervention groups on knowledge.

Overall, these studies show that the medium used to deliver the message did not consistently make a difference in the outcome variables of interest. What several of the studies did show were some important subgroup differences as results of the interventions. The strength of these efforts is that for the first time several studies with similar designs and interventions (albeit different populations) could be compared and some preliminary conclusions made that validate previous thinking about informed consent. These include assumptions that younger age, higher education, higher literacy, and stronger medical knowledge influence outcomes in positive directions. This suggests that investigators must give more attention to how consents are presented, the characteristics of subjects, and how comprehension is evaluated (Agre et al., 2003).

Limitations of these informed consent studies include a lack of diversity in the samples, use of patient surrogates, participants who were well educated, and the use of complex designs with multiple variables. The majority of the subjects were white and well educated, reflecting the ethnic and socio-demographic settings in which most clinical trials are undertaken and, thus, restricting the generalizability of findings. The use of patient vignettes as a method to inform potential research subjects, while not unusual given the sensitive nature of many of the clinical trials (e.g., blood donation for DNA banking), also limits the generalizability of results to researchers, patients, and families (for a detailed discussion of the use of vignettes, see chapter on hypothetical vignettes). The testing of multiple variables in some of the studies reviewed, although realistic, also presents a challenge related to how external variables or the particular medium used may have influenced outcomes. Yet, findings from these studies provide preliminary evidence on which to build future research toward expanding our knowledge and offering ways to maximize the protection of human subjects through optimizing their comprehension and informed decision making related to consent to participate in research investigations.

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FUTURE RESEARCH

The use of intervention designs, while a relatively recent phenomenon in bioethical inquiry, has a distinct and important role to play in advancing the field of bioethics. It is especially important to examine ethical practices that have widespread implications for patients, families, and health care professionals. This is particularly the case with practices that have been proposed by policy makers, such as advanced directives, organ donation, and do not resuscitate orders. Although not all, or even most, bioethical questions are appropriate to study using empirically driven intervention designs, some are and, in fact, some questions must be addressed using intervention models. Without a systematic, controlled approach to examining the effectiveness of interventions designed to change and test various outcomes, we will never know which actions work and which do not. Clinicians depend on rigorously designed intervention studies that provide evidence for the establishment of guidelines for conflict resolution and decision making in the delivery of quality health care. Furthermore, findings generated by intervention studies in bioethics makes available data for policy makers to formulate policies, fund programs, and enact legislation that may assist clinicians and ethicists to resolve value conflicts and other ethical problems. Ultimately, this will improve the lives of patients and families who experience suffering, not only from their illnesses, but also from vexing questions related to these illnesses.

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Burns, N., & Grove, S. (1997). The practice of nursing research: Conduct, critique and utilization.

Philadelphia, PA: W.B. Saunders Co.

Campbell, F., Goldman, B., Boccio, M., & Skinner, M. (2004). The effect of format

modifications and reading comprehension on recall of informed consent information by

low-income parents: A comparison of print, video, and computer-based presentations.

Patient Education and Counseling, 53, 205–216.

CONSORT. (2004). http://www.consort-statement.org/. Last accessed on November 28, 2005.

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Cook, T., & Campbell, D. (1977). Quasi-experimentation: Design and analysis issues for field

setting. Boston: Houghlin-Mifflin Co.

Cook, T., & Campbell, D. (1979). Quasi-experimentation: Design and analysis issues for field

setting. Boston: Houghlin-Mifflin Company.

Cozby, P. C. (2007). Methods in behavioral research (9th ed.). New York: McGraw-Hill.

Danis, M., Hanson, L. C., & Garrett, J. M. (2001). Experimental methods. In: J. Sugarman &

D. L. Silmas (Eds), Methods in medical ethics (pp. 207–226). Washington, DC:

Georgetown University Press.

Friedman, L., Furberg, C., & DeMets, D. (1998). Fundamentals of clinical trials. New York:

Springer.

Lipsey, M. W. (1990). Design sensitivity: Statistical power for experimental research. Newbury

Park, CA: Sage.

Rimer, R. K., Halibi, S., Skinner, C. S., Lipkus, I. M., Strigo, T. S., Kaplan, E. B., & Samsa,

G. P. (2002). Effects of mammography decision-making intervention at 12 & 24 months.

American Journal of Preventive Medicine, 22, 247–257.

Rossi, P., Freeman, H., & Lipsey, M. (1999). Evaluation: A systematic approach (pp. 309–340)

Thousand Oaks, CA: Sage Publication.

Sachs, G., Houghman, G., Sugarman, J., Agre, P., Broome, M., Geller, G., Kass, N., Kodish,

E., Mintz, J., Roberts, L., Sankar, P., Siminoff, L., Sorenson, J., & Weiss, A. (2003).

Conducting empirical research on informed consent: Challenges and questions (Suppl.).

IRB: Ethics and Human Research, 25(5), 4–10.

Shadish, W. R., Cook, T., & Campbell, D. (2002). Eperimental and quasi-experimental designs

for generalized causal inference. Boston: Houghlin-Mifflin.

Skinner, C. S., Schildkraut, J. M., Berry, D., Calingaert, B., Marcom, P. K., Sugarman, J.,

Winer, E. P., Iglehart, J. D, Futreal, P. A., & Rimer, B. K. (2002). Pre-counseling

education materials for BRCA testing: Does tailoring make a difference? Genetic

Testing, 6(2), 93–105.

Sugarman, J. (2004). The future of empirical research in bioethics. Journal of Law, Medicine and

Ethics, 32, 226–231.

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In: J. Sugarman & D. P. Sulmasy (Eds), Methods in medical ethics (pp. 19–28).

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Sugarman, J., & Sulmasy, D. P. (2001). Methods in medical ethics (p. 20). Washington, DC:

Georgetown University Press.

Intervention Research in Bioethics 217

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SUBJECT INDEX

Advance directives 24, 31

applied clinical ethics 14, 15, 16, 19

ATLAS.ti 127

audio-recordings 45, 47, 125

automated data retrieval 133

Bias 54, 75, 154, 186, 191

Categories 47, 48

clarity of wording 168

clinical ethics 14

clinical ethics consultation 32

closed-ended survey 41

clustering effects 196

codebook 49, 51

code development 59

code reports 55, 56, 57

codes 48, 53, 127

coding agreement 54

coding drift 131

coding manual 128

coding reliability 51, 54

coding scheme 49

cognitive interviewing 166

community-based research 187

community decision-making 184

community involvement 68, 73

computer assisted telephone

interviewing 154

conceptual map 53

confidentiality 42, 68, 156

conflict of interest 29

consensus coding 131

consensus process 55

CONSORT 206

constant-variable vignette

method 163

construct validity 122, 152

content validity 51, 152, 170

control groups 205, 206

convenience samples 146

cost of surveys 150

Data immersion 47

data preparation chart 126

debriefing notes 73

decision-making capacity 27, 31

decisional capacity 31

deductive codes 48, 49

descriptive studies 205

diffusion of intervention 207

digitally recording interviews 125

distributive justice 193

doctrine of informed consent 22, 27

Ethical theories 13, 14, 15, 16, 19, 23

ethics consultants 13, 14, 16, 17,

18, 19

ethics consultations 16, 17, 18

experimental designs 205

exploratory research 118

external validity 176, 203, 207

extraneous variables 207

Face validity 152

factorial design 174

financial incentives 150, 151

focus group questions 67

219

focused theory-testing 118

follow-up surveys 191

forced-choice responses 149

forgoing treatment 24

formative data 65

factorial designs 175

Generalizability 79, 211

goals of care 19

grounded theory 41

Health care disparities 25

health care priorities 184, 193

HIPAA Privacy Rule 191

human subjects research 186

hypothesis-testing 175

Inductive codes 48

inductive reasoning 50

informed consent 31, 68, 125, 198, 213,

214, 215

Institute of Medicine 25, 30

instrumentation 207

inter-coder reliability 129

internal consistency 169, 171

intervention studies 204

interview guide 46, 119

intra-rater reliability 171

IRBs 28, 29, 30

Justice 15, 193

Karen Ann Quinlan 24

key informant interviews 151

Labeling 44

legitimacy 184, 186, 188

life-sustaining interventions 24

Likert scale, 173

literacy levels 145

Memoing 47

memos 56, 60

multi-level consensus coding (MLCC)

129

Nancy Cruzan 24

National Bioethics Advisory

Commission 30

node type 51

nominal group technique 188

non-probability sampling 145

non-random sampling 197

non-response bias 154

Online coding 132

open-ended interviews 41

open-ended queries 118, 168

ordering of survey items 150

Pilot testing 120, 122

positivist view 54

pragmatic ethics 15

pre-/post-test designs 205

preliminary coding 49

pretest 167

privacy of information 69

procedural justice 193

protecting human subjects 28, 30

provisional coding manual 128

proxy directives, 25, 31

public opinion 198

public opinion polls 185

purposeful sampling 43, 146

Qualitative content analysis 40

qualitative data analysis 132

quantitative content analysis 39

quasi-experimental designs 205, 212,

213

question order 156

quota samples 146

Randomization 186, 190, 205, 206, 213

rates of non-response 147

recall bias 154

SUBJECT INDEX220

recruitment of subjects 123, 186

reliability 152, 169, 171

representative 185, 186, 194

research questions 18, 43, 57, 151

respondent burden 154

response frames 149

response rates 151

response summaries 75

response-wave bias 155

right to die 24

round robin method 188

Sampling 43, 66, 123, 124, 145, 146,

150, 173, 186, 190, 194, 199

selection bias 207

self-administered surveys 149

semi-structured interviews 40, 117

simple random sample 145

simulation exercise 193, 194

snowball sampling 123, 146

social justice 25, 31

socially desirable response bias, 154,

156

stratified random sampling 67, 145

sub-codes 128

substantive representation 186

SUPPORT study 24

survey development 145

survey fielding 153

survey question wording 186

survey responses 189

Team coding 51

test-retest reliability 171

textual data 39

thematic coding 128

themes 47, 48, 128

theoretical saturation 124

therapeutic misconception 22

town hall meetings 185

transcription 45, 47, 120, 126

Units of analysis 43, 196

univariate (single item) distributions

156

Validity 54, 122, 152, 169

value conflicts 14

vulnerable populations 28, 30

Subject Index 221

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Leibtag-corn.pdf

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©Jupiterimages Corporation (gas pump and corn field); PhotoDisc (grocery bags)

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Corn Prices Near Record

High, But What About

Food Costs?

Record U.S. trade driven by economic growth in developing countries and favorable exchange rates, combined with tight global grain supplies, resulted in record or near-record prices for corn, soybeans, and other food and feed grains in 2007. For corn, these fac- tors, along with increased demand for ethanol, helped push prices from under $2 per bushel in 2005 to $3.40 per bushel in 2007. By the end of the 2006/07 crop year, over 2 billion bushels of corn (19 percent of the harvested crop) were used to produce ethanol, a 30- percent increase from the previous year. Higher corn prices motivated farmers to increase corn acreage at the expense of other crops, such as soybeans and cot- ton, raising their prices as well.

� Higher corn prices increase animal feed and ingredient costs for farmers and food manufacturers, but pass through to retail prices at a rate less than 10 percent of the corn price change.

� Given that foods using corn as an ingredient make up less than a third of retail food spending, overall retail food prices would rise less than 1 percentage point per year above the normal rate of food price inflation when corn prices increase by 50 percent.

� Even this increase may be partially tempered by changes to corn use in food production.

Ephraim Leibtag [email protected]

What effect do these higher commod- ity costs have on retail food prices? In gen- eral, retail food prices are much less volatile than farm-level prices and tend to rise by a fraction of the change in farm prices. The magnitude of response depends on both the retailing costs beyond the raw food ingredients and the nature of competition in retail food mar- kets. Ethanol’s impact on retail food prices depends on how long the increased demand for corn drives up farm corn prices and the extent to which higher corn prices are passed through to retail. ERS research has traced the effect of higher corn prices on U.S. retail food prices by analyzing data on price trends and price response of corn-dependent foods to cost changes.

Retail Competition Moderates Food Price Inflation

Retail food prices adjust as the cost of inputs into retail food production change and the competitive environment in a given market evolves. Strong competition among three to five retail store chains in most U.S. markets has had a moderating effect on food price inflation. Overall, retail food prices have been relatively sta- ble over the past 20 years, with prices increasing an average of 3.0 percent per year from 1987 through 2007, just below the overall rate of inflation. The main exception occurred when sharply higher farm prices increased retail prices 5.8 per- cent in 1989 and 1990. Since then, food price inflation has averaged just 2.5 per- cent per year.

Retail prices are a function of both consumer demand and the interaction between food manufacturers, distributors, and retailers, with each group having some pricing power in the supply chain. Ultimately, though, the retailer has a more complicated pricing decision since it is selling a wider variety of products to a more diverse consumer clientele than

most manufacturers or distributors. The challenge for the food retailer is to deter- mine how best to distribute the costs of providing both food and services to con- sumers across a wide range of products. This pricing challenge removes some of the direct connection between the costs of a given product and the retail price charged.

When there are cost shocks in the food production system due to changes in the commodity or farm product market, most retailers respond by passing on a fraction of their higher costs to con- sumers. Among factors affecting this pass- through rate is the level of processing and value-added services that take place between the farmgate and the grocery store aisle. Products that require more pro-

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Retail food prices increase 3 percent per year, on average

Annual percent change

Source: ERS calculations using Bureau of Labor Statistics’ Consumer Price Index data.

1987 89 91 93 95 97 99 01 03 05 07 0

1

2

3

4

5

6

All-items CPI

Food CPI

Corn, wheat, and soybean prices at or near record highs in 2007

Dollars per bushel

Source: USDA, National Agricultural Statistics Service, Agricultural Price Series, 1976-2007.

Soybeans

Wheat

Corn

1976 78 80 82 84 86 88 90 92 94 96 98 2000 02 04 06 0

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2

3

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cessing and packaging are usually less directly linked to changes in farm prices, while the price of less processed foods more closely follows the changes in farm prices. For example, changes in farm prices for eggs, fresh fruit, and fresh veg- etables show up in more volatile retail prices for these less processed foods. The price volatility of pork and beef is also above the average for all foods. The other food categories average between 2.3 and 3.4 percent in price change per year.

Higher Farm Corn Prices, Slightly Higher Food Prices

Field corn is the predominant corn type grown in the U.S., and it is primarily used for animal feed. Currently, less than 10 percent of the U.S. field corn crop is used for direct domestic human consump- tion in corn-based foods such as corn meal, corn starch, and corn flakes, while the remainder is used for animal feed, exports, ethanol production, seed, and industrial uses. Sweet corn, both white and yellow, is usually consumed as imma- ture whole-kernel corn by humans and also as an ingredient in other corn-based foods, but makes up only about 1 percent of total U.S. corn production.

Since U.S. ethanol production uses field corn, the most direct impact of increased ethanol production should be on field corn prices and on the price of food products based on field corn. However, even for those products heavily based on field corn, the effect of rising corn prices is dampened by other market factors. For example, an 18-ounce box of corn flakes contains about 12.9 ounces of milled field corn. When field corn is priced at $2.28 per bushel (the 20-year average), the actual value of corn repre- sented in the box of corn flakes is about 3.3 cents (1 bushel = 56 pounds). (The remainder is packaging, processing, adver- tising, transportation, and other costs.) At $3.40 per bushel, the average price in 2007, the value is about 4.9 cents. The 49- percent increase in corn prices would be expected to raise the price of a box of corn flakes by about 1.6 cents, or 0.5 percent, assuming no other cost increases.

In 1985, Coca-Cola shifted from sugar to corn syrup in most of its U.S.-produced soda, and many other beverage makers fol- lowed suit (see “High-Fructose Corn Syrup Usage May Be Leveling Off” on page 4 in this issue). Currently, about 4.1 percent of U.S.-produced corn is made into high- fructose corn syrup. A 2-liter bottle of soda contains about 15 ounces of corn in the form of high-fructose corn syrup. At $3.40 per bushel, the actual value of corn repre- sented is 5.7 cents, compared with 3.8 cents when corn is priced at $2.28 per bushel. Assuming no other cost increases, the higher corn price in 2007 would be expected to raise soda prices by 1.9 cents per 2-liter bottle, or 1 percent. These are notable changes in terms of price meas- urement and inflation, but relatively minor changes in the average household food budget.

Eggs, fruit, and vegetables have the most volatile prices

Average absolute percent change in prices, 1987-2007

Source: ERS calculations using Bureau of Labor Statistics’ Consumer Price Index data.

Nonalcoholic beverages

Sugar and sweets

Fats and oils

Poultry

Processed fruit & vegetables

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0

All food

Eggs

Fresh fruit

Fresh vegetables

Pork

Beef and veal

Cereals and bakery

Dairy

Fish and seafood

Corbis

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Impacts Extend to Meats Through Higher Feed Costs

Given that livestock feed rations tra- ditionally contain a large amount of corn, a bigger impact would be expected in meat and poultry prices due to higher feed costs than in other food products. Currently, about 55 percent of corn produced in the U.S. is used as animal feed for livestock and poultry. However, estimating the actu- al corn used as feed to produce retail meat is a complicated calculation. Livestock pro- ducers have many options when deciding how much corn to include in a feed ration. For example, at one extreme, grass-fed cat- tle consume no corn, while other cattle may have a diet consisting primarily of corn. For hog and poultry producers, ration variations may be less extreme, but can still vary quite a bit. To estimate the impact of higher corn prices on retail meat

prices, it is necessary to make a series of assumptions about feeding practices and grain conversion rates from animal to final retail meat products. To avoid downplay- ing potential impacts, this analysis uses upper-bound conversion estimates of 7 pounds of corn to produce 1 pound of beef, 6.5 pounds of corn to produce 1 pound of pork, and 2.6 pounds of corn to produce 1 pound of chicken.

Using these ratios and data from the Bureau of Labor Statistics, a simple pass- through model provides estimates of the expected increase in meat prices given the higher corn prices. The logic of this model is illustrated by an example using chicken prices. Over the past 20 years, the average price of a bushel of corn in the U.S. has been $2.28, implying that a pound of chicken at the retail level uses 8 cents worth of corn, or about 4 percent of the

$2.05 average retail price for chicken breasts. Using the average price of corn for 2007 ($3.40 per bushel) and assuming pro- ducers do not change their animal-feeding practices, retail chicken prices would rise 5.2 cents, or 2.5 percent. Using the same corn data, retail beef prices would go up 14 cents per pound, or 8.7 percent, while pork prices would rise 13 cents per pound, or 4.1 percent.

These estimates for meat, poultry, and corn-related foods, however, assume that the magnitude of the corn price change does not affect the rate at which cost increases are passed through to retail prices. It could be the case that corn price fluctuations have little impact on retail food prices until corn prices rise high enough for a long enough time to elicit a large price adjustment by food producers and notably higher retail food prices.

On the other hand, these estimates may be overstating the effect of corn price increases on retail food prices since they do not account for potential substitution by producers from more expensive to less costly inputs. Such substitution would dampen the effect of higher corn prices on retail meat prices. Even assuming the upper-bound effects outlined above, the impact of rising corn commodity prices on overall food prices is limited. Given that less than a third of retail food contains corn as a major ingredient, these rising prices for corn-related products would raise overall U.S. retail food prices less than 1 percentage point per year above the normal rate of inflation.

While higher commodity costs may have a relatively modest impact on U.S. retail food prices, there may be a greater effect on retail food prices in low-income developing countries. As a relatively low- priced food, grains have historically accounted for a larger share of the diet in less developed countries. Even with incomes rising, consumers in such coun- tries consume a less processed diet than is

Creatas

typical in the U.S. and other industrialized countries, so food prices are more closely tied to swings in both domestic and global commodity prices (see “Rising Food Prices Intensify Food Insecurity in Developing Countries” on page 16 in this issue).

Markets Adjust and Prices Stabilize

Continuing elevated prices for corn will depend on the extent to which corn remains the most efficient feedstock for ethanol production and ethanol remains a viable source of alternative energy. Both of these conditions may change over time as other crops and biomass are used to pro- duce ethanol and other alternative energy sources develop.

Even if these conditions do not change in the near term, market adjust- ments may dampen longrun impacts. In 1996, when field corn prices reached an all-time high of $3.55 per bushel due to drought-related tighter supplies in the U.S. and strong demand for corn from China

and other parts of Asia, the effect on food prices was short lived. At that time, retail prices rose for some foods, including pork and poultry, but these effects did not extend beyond the middle of 1997. For the most part, food markets adjusted to the higher corn prices and corn producers increased supply, bringing down price.

Food producers, manufacturers, and retailers may also adjust to the changing market conditions by adopting more effi- cient production methods and improved technologies to counter higher costs. For example, soft drink manufacturers may consider substituting sugar for corn syrup as a sweetener if corn prices remain high, while livestock and poultry producers may develop alternative feed rations that minimize corn needed for animal feed. Adjustments by producers, manufactur- ers, and retailers, along with continued strong retail competition, imply that U.S. retail food prices will remain relatively

stable.

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“U.S. Ethanol Expansion Driving Changes Throughout the Agricultural Sector,” by Paul C. Westcott, in Amber Waves, Vol. 5, No. 4, USDA, Economic Research Service, September 2007, avail- able at: www.ers.usda.gov/amberwaves/ september07/features/ethanol.htm

“The Future of Biofuels: A Global Perspective,” by William Coyle, in Amber Waves, Vol. 5, No. 5, USDA, Economic Research Service, November 2007, avail- able at: www.ers.usda.gov/amberwaves/ november07/features/biofuels.htm

ERS Briefing Room on Food CPI, Prices, and Expenditures, www.ers.usda. gov/briefing/cpifoodandexpenditures/

You may also be interested in . . .

For more information . . .

USDA

Macnamara.pdf

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Media content analysis: Its uses; benefits and best practice methodology

Jim Macnamara University of Technology Sydney

The ‘power’ of media Mass media are believed to cause violence, sexual promiscuity and contribute to discrimination against women. Media advertising is used to sell products and services. News in leading media has been shown to significantly affect stock prices; lead to corporate collapses; cause falls in sales of products; result in the resignation of senior office-holders – even bring down Presidents. Further information on the effects of mass media is provided in Macnamara (2003), Mass Media Effects: A Review of 50 Years of Media Effects Research. Sociologists have been interested in mass media content since the early 20th century, starting with Max Weber who saw media content as a means of monitoring the ‘cultural temperature’ of society (Hansen, Cottle, Negrine & Newbold, 1998, p. 92). Media content analysis – an overview Media content analysis is a specialized sub-set of content analysis, a well-established research methodology. Neuendorf (2002) describes content analysis as “the primary message- centred methodology” (p. 9) and cites studies such as Riffe and Freitag (1997) and Yale and Gilly (1988) which “reported that in the field of mass communication research, content analysis has been the fastest-growing technique over the past 20 years or so” (Neuendorf, 2002, p.1). Riffe and Freitag (1997) found that the number of content analyses published in Journalism & Mass Communication Quarterly increased from 6.3% of all articles in 1971 to 34.8% in 1995 – nearly a six-fold increase. Fowler (as cited in Neuendorf (2002) reported that by the mid-1980s over 84% of masters level research methods courses in journalism in the US included content analysis (p. 27) Content analysis is used to study a broad range of ‘texts’ from transcripts of interviews and discussions in clinical and social research to the narrative and form of films, TV programs and the editorial and advertising content of newspapers and magazines. Media content analysis was introduced as a systematic method to study mass media by Harold Lasswell (1927), initially to study propaganda. Media content analysis became increasingly popular as a research methodology during the 1920s and 1930s for investigating the rapidly expanding communication content of movies. In the 1950s, media content analysis proliferated as a research methodology in mass communication studies and social sciences with the arrival of television. Media content analysis has been a primary research method for studying portrayals of violence, racism and women in television programming as well as in films. Lasswell, Lerner and Pool (1952) said: “… content analysis operates on the view that verbal behaviour is a form of human behaviour, that the flow of symbols is a part of the flow of

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events, and that the communication process is an aspect of the historical process … content analysis is a technique which aims at describing, with optimum objectivity, precision, and generality, what is said on a given subject in a given place at a given time (p. 34). Lasswell’s better known statement which succinctly encapsulates what media content analysis is about, published in 1948, (as cited in Shoemaker and Reese, 1996), describes it as:

Who says what through which channel to whom with what effect (p.12).

A widely used definition of content analysis which illustrates the early focus on quantitative analysis was provided by Berelson (1952) who described it as a “research technique for the objective, systematic and quantitative description of the manifest content of communication (p. 18). While it remains oft-quoted, this definition has been found wanting in several respects. First, the word “objective” is disputed by researchers including Berger and Luckman (1966) in their classic text, The Social Construction of Reality, in which they point out that even the most scientific methods of social research cannot produce totally objective results. Specifically in relation to media content, they point out that media texts are open to varied interpretations and, as such, analysis of them cannot be objective. Also, some criticize the definition as restrictive, pointing out that latent as well as manifest content can be analysed. But, mostly, the early approach to content analysis was criticized because of its focus on basic quantitative elements and an inherent assumption that quantitative factors indicated likely social impact. Other definitions of content analysis include:  “Content analysis is any research technique for making inferences by systematically and

objectively identifying specified characteristics within text” (Stone, Dunphy, Smith & Ogilvie, 1996, with credit given to Holsti, p. 5);

 In more contemporary times, Weber (1990) says: “Content analysis is a research method

that uses a set of procedures to make valid inferences from text” (p. 9);  Berger (1991) says: “Content analysis … is a research technique that is based on

measuring the amount of something (violence, negative portrayals of women, or whatever) in a representative sampling of some mass-mediated popular form of art” (p. 25);

 Neuman (1997) lists content analysis as a key non-reactive research methodology (i.e.

non-intrusive) and describes it as: “A technique for gathering and analysing the content of text. The ‘content’ refers to words, meanings, pictures, symbols, ideas, themes, or any message that can be communicated. The ‘text’ is anything written, visual, or spoken that serves as a medium for communication” (pp. 272–273);

 Kimberley Neuendorf (2002) is one of the most prominent contemporary researchers

using, teaching (at Cleveland State University) and writing about media content analysis. She provides this definition: “Content analysis is a summarizing, quantitative analysis of messages that relies on the scientific method … and is not limited as to the types of variables that may be measured or the context in which the messages are created or presented”. Noteworthy about Neuendorf’s definition is that she argues that media content analysis is quantitative research, not qualitative, and she strongly advocates use of

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scientific methods “including attention to objectivity-intersubjectivity, a priori design, reliability, validity, generalisability, replicability, and hypothesis testing” (p. 10). Neuendorf argues that qualitative analysis of texts is more appropriately described and categorized as rhetorical analysis, narrative analysis, discourse analysis, structuralist or semiotic analysis, interpretative analysis or critical analysis (pp. 5-7). However, she acknowledges that “with only minor adjustment, many are appropriate for use in content analysis as well”. In The Content Analysis Guidebook, Neuendorf discusses an “integrative” model of content analysis and notes that a range of methodologies can be used for text analysis, even though she maintains a narrow definition of content analysis (p. 41);

 Shoemaker and Reese (1996) are other prominent authors on media content analysis.

They do not fully support Neuendorf’s strict interpretation of content analysis as quantitative research only. Shoemaker and Reese categorize content analysis into two traditions – the behaviourist tradition and the humanist tradition. The behaviourist approach to content analysis is primarily concerned with the effects that content produces and this approach is the one pursued by social scientists. Whereas the behaviourist approach looks forwards from media content to try to identify future effects, the humanist approach looks backwards from media content to try to identify what it says about society and the culture producing it. Humanist scholars draw on psychoanalysis and cultural anthropology to analyse how media content such as film and television drama reveal ‘truths’ about a society – what Shoemaker and Reese term “the media’s symbolic environment” (pp. 31–32). This dual view of the media also helps explain the age-old debate over whether mass media create public opinion, attitudes and perceptions (effects) or reflect existing attitudes, perceptions and culture. Most researchers agree that, with limitations, mass media do both. Shoemaker and Reese say that social scientists taking a behaviourist approach to content analysis rely mostly on quantitative content analysis, while humanist approaches to media content tend towards qualitative analysis. They also note that social scientists may use both types of research as discussed in the following.

Berelson (1952) suggested five main purposes of content analysis as follows:  To describe substance characteristics of message content;  To describe form characteristics of message content;  To make inferences to producers of content;  To make inferences to audiences of content;  To predict the effects of content on audiences. Carney (as cited in Neunendorf, 2002) broadly agreed with this view summarizing the three main uses of content analysis as (a) descriptive; (b) hypothesis testing and (c) facilitating inference (p. 52). Neuendorf (2002) points out that inferences cannot be made as to producers’ intent or audiences’ interpretation from content analysis alone, arguing that an integrated approach is required involving use of content analysis with other research such as audience studies. However, Neuendorf supports Carney’s view of media content analysis as useful for “facilitating” inference even though it cannot directly prove it and, further, Neuendorf adds that content analysis has some predictive capabilities as well as other specialist uses. Neuendorf concludes that there are four main approaches to and roles of content analysis:  Descriptive;  Inferential;

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 Psychometric; and  Predictive (p. 53). While psychometric refers to specialized medical and psychoanalytic uses of content analysis for interpreting the text of patient interviews or statements, the three other approaches are highly relevant to a range of applications. The first and most basic role, descriptive, provides insights into the messages and images in discourse and popular culture represented in mass media. The inferential and predictive roles of content analysis, even though they are ‘facilitating’ rather than conclusive, allow researchers to go further and explore what media content says about a society and the potential effects mass media representations may have on audiences. However, the reliability of media content analysis for description of mediated discourses, and particularly for drawing inferences or making predictions concerning likely effects of these mediated discourses, depends on the methodology employed. Key methodological decisions and considerations in media content analysis are discussed in the following. Quantitative v qualitative content analysis Shoemaker and Reese (1996) note that media content is characterized by a wide range of phenomena including the medium, production techniques, messages, sources quoted or referred to, and context, and they say that the task of content analysis is “to impose some sort of order on these phenomena in order to grasp their meaning.” They continue: “Part of this ordering process consists of singling out the key features that we think are important and to which we want to pay attention. Researchers approach content in different ways, using different conceptual and methodological tools” (p. 31). Quantitative content analysis collects data about media content such as topics or issues, volume of mentions, ‘messages’ determined by key words in context (KWIC), circulation of the media (audience reach) and frequency. Quantitative content analysis also should consider media form (eg. visual media such as television use more sophisticated semiotic systems than printed text and, thus, are generally regarded as having greater impact). Neuendorf (2002) says: “What’s important is that both content and form characteristics ought to be considered in every content analysis conducted. Form characteristics are often extremely important mediators of the content elements” (p. 24). While Neuendorf argues that media content analysis is quantitative only, Shoemaker and Reese’s categorization of content analysis into humanist and behaviourist traditions indicates that content analysis can be undertaken using both approaches. They say: “Behavioural content analysis is not always or necessarily conducted using quantitative or numerical techniques, but the two tend to go together. Similarly, humanistic content study naturally gravitates towards qualitative analysis.” Shoemaker and Reese further note: “Reducing large amounts of text to quantitative data … does not provide a complete picture of meaning and contextual codes, since texts may contain many other forms of emphasis besides sheer repetition” (p. 32). Researchers who advocate analysing latent as well as manifest content as a way of understanding meanings of texts integrate qualitative and quantitative message analysis. Media researchers Newbold et al. (2002) note: “The problem [with quantitative content analysis] is the extent to which the quantitative indicators are interpreted as intensity of meaning, social impact and the like. There is no simple relationship between media texts and

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their impact, and it would be too simplistic to base decisions in this regard on mere figures obtained from a statistical content analysis” (p. 80). In simple terms, it is not valid to assume that quantitative factors such as size and frequency of media messages equate to impact. Nor is it valid to assume that these quantitative factors are the only or even the main determinants of media impact. Neuman (1997), in a widely used text on social research methodology, comments on the quantitative-qualitative dichotomy in content analysis: “In content analysis, a researcher uses objective and systematic counting and recording procedures to produce a quantitative description of the symbolic content in a text” but he adds “there are qualitative or interpretative versions of content analysis”. Neuman notes: “Qualitative content analysis is not highly respected by most positivist researchers. Nonetheless, feminist researchers and others adopting more critical and interpretative approaches favour it” (p. 273).

Newbold et al. (2002) note that quantitative content analysis “has not been able to capture the context within which a media text becomes meaningful” (p. 84) and advocate attention to qualitative approaches as well. Proponents of qualitative text analysis point out factors that have a major bearing on audience interpretation and likely effects, include:  Prevailing perceptions of media credibility (e.g. a report in a specialist scientific or

medical journal which will have greater credibility than a report on the same subject in popular press);

 Context (e.g. a health article published or broadcast during a disease outbreak will be

read differently than at other times);  Audience characteristics such as age, sex, race, ethnicity, education levels and socio-

economic position which will all affect ‘readings’ of media content. Qualitative content analysis examines the relationship between the text and its likely audience meaning, recognizing that media texts are polysemic – i.e. open to multiple different meanings to different readers – and tries to determine the likely meaning of texts to audiences. It pays attention to audience, media and contextual factors – not simply the text. Accordingly, qualitative content analysis relies heavily on researcher ‘readings’ and interpretation of media texts. This intensive and time-consuming focus is one of the reasons that much qualitative content analysis has involved small samples of media content and been criticized by some researchers as unscientific and unreliable. In summary, quantitative content analysis can conform to the scientific method and produce reliable findings. Qualitative content analysis is difficult and maybe impossible to do with scientific reliability. But qualitative analysis of texts is necessary to understand their deeper meanings and likely interpretations by audiences – surely the ultimate goal of analysing media content. So a combination of the two seems to be the ideal approach. Within mass media and communication studies, most media researchers do not draw the sharp definitional distinctions that Neuendorf does between text, content and discourse analysis. Media researchers and academics such as Newbold et al. (2002), Gauntlett (2002) and Curran (2002) refer to quantitative and qualitative content analysis and most view the fields as complementary and part of a continuum of analysing texts to try to determine their likely meanings to and impact on audiences.

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Hansen et al. (1998) comment: “… rather than emphasizing its alleged incompatibility with other more qualitative approaches (such as semiotics, structuralist analysis, discourse analysis) we wish to stress … that content analysis is and should be enriched by the theoretical framework offered by other more qualitative approaches, while bringing to these a methodological rigour, prescriptions for use, and systematicity rarely found in many of the more qualitative approaches” (p. 91). Shoemaker and Reese’s (1996) categorization of a humanist approach which studies media content as a reflection of society and culture, and a behaviourist approach which analyses media content with a view to its likely effects, is also useful in understanding how media content analysis should be conducted. Any research exploring media content for both what influence it may have on and for how it might reflect society – i.e. employing both behaviourist and humanist traditions – should use a combination of quantitative and qualitative content analysis. It can be concluded from Hansen et al. (1998), Shoemaker and Reese (1996) and others cited, that a combination of quantitative and qualitative content analysis offers the best of both worlds and, further, that a combination of quantitative and qualitative content analysis methodologies is necessary to fully understand the meanings and possible impacts of media texts. It is important to note that some researchers reject altogether the view that the meanings of texts can be accessed through analysis of the texts (Newbold, et al., 2002, p. 16). Certainly, researchers using content analysis need to be cautious in making predictions of likely audience effects, as already noted. However, while audience research remains a primary approach to gain direct insights into audience perceptions, it too faces methodological problems. Respondents forget where they received information from (e.g. many respondents in interviews and group discussions say “someone told me” when, in fact, they received the information through mass media). Others lie – perhaps not intentionally, but often people do not want to admit that they read some ‘trash’ magazine or watched daytime television. Furthermore, respondents talking directly to a researcher sometimes say what they think the researcher wants to hear, referred to as ‘response generation’ (interviews and ethnographic research methods are affected by researcher intrusion). Audience studies also have their own problematic issues with sample, question construction and interpretation of responses. Media content analysis is a non-intrusive research method that allows examination of a wide range of data over an extensive period to identify popular discourses and their likely meanings. Another benefit of content analysis is that it can be conducted frequently (eg. every month), whereas audience research such as large-scale surveys are, because of their cost and time taken, restricted to once per year or every few years. Shoemaker and Reese (1996) propose that “media content and media effects [i.e. audience] research can be combined to help our understanding of the role that the mass media play in society” and also to understand societal attitudes (p. 256). Human v computer coding Media content analysis increasingly uses computer programs. Computer software is applied at two levels: 1. For storing, analysing and reporting research data such as coding and notations by

researchers (including constructing tables, charts and graphs); and

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2. In some cases, for automatic scanning of texts and identification and coding of words and phrases. This stage can lead to automation of the entire process of coding and analysis, or provide partial automation with a combination of computer scanning and coding along with human notations manually entered into the program.

In the first level, texts are read and coded by humans (usually trained researchers) and computer software programs are used as tools to assist in the analysis in the same way they are used to analyse the results of surveys and other research. Programs commonly used at this level are databases for data storage; SPSS for statistical analysis; Excel for tabulation of data and calculations such as pivot tables; and Excel or graphics programs for generation of charts. Also, a range of specialist commercial media content analysis systems are used for storing, analysing and reporting media analysis data such as CARMA® (Computer Aided Research and Media Analysis), Delahaye (now part of AB Observer/Bacon’s Information Services), Echo Research, IMPACT™ and Millward Brown Précis. Most are database programs with customized data entry screens and fields created for the specialized needs of media content analysis. Many of these proprietary programs have specialized features such as inbuilt media databases providing circulation and audience statistics and sometimes demographic data which enriches and speeds up media content analysis. At the second level, computer software automatically conducts either all or a large part of content analysis including scanning texts using Optical Character Recognition (OCR) technology and matching words and phrases in texts with ‘dictionaries’ of key words and phrases previously set up in the software program. Some programs do all coding automatically, while others allow the researcher to enter notations and comments and tag or link these to relevant articles. Software programs such as General Inquirer developed at Harvard University in the 1960s; NUD*IST; NVIVO; TextSmart by SPSS; INTEXT; TextAnalyst; TEXTPACK 7.0, CATPAC, DICTION 5.0, DIMAP and VBPro perform a variety of content analysis functions. Mayring (2003) also cites experience using two German software programs for qualitative text analysis, MAXqda (MAX Qualitative Data Analysis for Windows) and ATLASti. A number of social researchers claim that computers are not relevant to media content analysis, suggesting that it must be done manually by detailed human study (Newbold et al., 2002, p. 84). This claim, per se, is Luddite, or more likely confuses the two levels of computerization in media content analysis. Few would argue that using a computer database, spreadsheet, or a specialized program to store and analyse data entered by researchers is inconsistent with the scientific method. It is most likely that use of computers enhances accuracy of analysis. However, Neuendorf (2002) says that “the notion of the completely ‘automatic’ content analysis via computer is a chimera … The human contribution to content analysis is still paramount” (p. 40). Most content analysts agree with this viewpoint based on professional experience. Automated (fully computerized) content analysis makes mostly arbitrary associations between words and phrases. While neurolinguistic software programming and Artificial Intelligence (AI) systems in which computers are purported to ‘learn’ to interpret the way humans do are developing, such programs remain unreliable for subtle and sophisticated interpretational work and their analysis is simplistic. Neuman (1997, p. 275) gives the example of the word ‘red’ and how it can be used with multiple nuances that are not visible to a computer:

“I read a book with a red cover that is real red herring. Unfortunately, its publisher drowned in red ink because the editor couldn’t deal with the red tape that occurs when a book is red hot. The

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book has a story about a red fire truck that stops at red lights only after the leaves turn red. There is also a group of Reds who carry red flags to the little red schoolhouse. They are opposed by red- blooded rednecks who eat red meat and honour the red, white and blue …”

Machine coding of the above text would be very unlikely to identify the range of meanings of the word ‘red’. A further disadvantage of automated computer coding is that it results in what Neuendorf (2002) terms “black box measurement”. Most software programs do not reveal the details of their measures or how they construct their scales and indexes. The researcher enters text into “a veritable black box from which output emerges” (p. 129). This is inconsistent with the scientific method of research which requires that full information is disclosed on how results were obtained. Also, it limits replicability as other researchers cannot conduct similar studies unless they use the same software program and, even then, key functions and calculations are hidden within the ‘black box’. When content analysis is conducted across multiple languages and cultures, such as for global or non-western media studies, the problems of machine coding become even more marked, as most automated coding systems work with English language text only and computer translations are unreliable except for the most rudimentary applications. Furthermore, and perhaps most important of all, computers cannot consider the context of content; they only view the text which can result in narrow incomplete interpretations. However, computers can clearly support quantitative and qualitative content analysis by serving as a repository for coding data and provide powerful tools for analyzing and reporting research. When human coding is used, the software employed for data storage and analysis is not materially significant to the research, provided a reliable program is used. Methodology is more important, as is the training of the coders who need to conduct the analysis in accordance with strict criteria. Quantitative content analysis methodology Quantitative media content analysis should be conducted in accordance with ‘the scientific method’, Neuendorf (2002) argues, involving the following elements.  Objectivity/intersubjectivity

A major goal of any scientific investigation must be to provide a description or explanation of a phenomenon in a way that avoids or minimizes the biases of the investigator and, while true objectivity may not be possible, it should strive for consistency and what scholars term intersubjectivity (Babbie, 1986, p. 27; Lindlof, 1995 as cited in Neuendorf, 2002, p. 11). Objectivity, or intersubjectivity, is maximised by several techniques, most notably selection of a representative sample. (See ‘Media content sample’)

 A priori design

Media content analyses often fail the test of objectivity/intersubjectivity because researchers construct the list of issues and messages being studied as they go, adding issues and messages as they find them in articles, arguing that they need to begin media content analysis before they can accurately identify the issues and messages contained in the content.

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A deductive scientific approach to research design requires that “all decisions on variables, their measurement, and coding rules must be made before the observation begins” (Neuendorf, 2002, p. 11). An inductive approach which measures variables after they have been observed leads to major biases and invalidity in a study. In effect, it allows issues, topics and messages to be added to the list of those tracked at the whim of the researcher, and those added during a study may have been present from the outset but not observed, leading to inaccuracies in data. Kuhn’s (1970) observation in his seminal work on paradigms that the scientific requirement for deduction to be based on past research, theories and bodies of evidence is self-limiting and does not foster innovation is noted. Equally, the view of some media researchers that it is difficult to identify the variables for study (issues and messages in media content analysis) before they begin analysis of media content has some basis. However, this apparent dichotomy can be overcome. Exploratory work can and should be done before a final coding scheme is established for content analysis to identify the issues and messages appropriate for study. Neuendorf (2002) says: “Much as a survey researcher will use focus groups or in-depth interviewing (qualitative techniques) to inform his or her questionnaire construction, so may the content analyst use in-depth, often contemplative and incisive observations from the literature of critical scholars.” Furthermore, Neuendorf suggests that media content analysts can “immerse himself or herself in the world of the message pool” by conducting “a qualitative scrutiny of a representative subset of the content to be examined” – i.e. conduct preliminary reading of texts within the field (pp. 102–103). Thus, a grounded theory approach, as explained by Glaser and Strauss (1967) and Strauss and Corbin (1990), can be applied to identify issues and messages appropriate for analysis through preliminary reading of existing research literature in the field and reading of a sub-sample of the media content to be studied. In media content analysis, a priori design is operationalised in a Coding System. A key component of a Coding System is a comprehensive written Code Book or Coding List. This contains the list of variables (units of analysis) to be researched and provides researchers involved in the project with a consistent framework for conducting the research. Content analysis should involve examination of multiple variables (i.e. multivariate analysis) – not be a simplistic rating of a single variable such as positive, negative or neutral which is univariate and tells us little about the likely meaning and effects of a text. The primary units of content analysis (variables) are messages expressed as words or phrases – e.g. ‘violent’, ‘leader’, ‘funding should be increased’, etc. The Coding List should establish all the messages (both positive and negative) that are relevant. In addition, the Coding List may establish certain categories of issues or topics, and may further identify names of certain sources (individuals or organizations) to be analysed in association with issues or messages. All positive messages identified for analysis should be equally matched with their corresponding negative form, and vice versa, to ensure balance. For instance, if ‘boys in schools are aggressive and violent’ is analysed, the oppositional positive message ‘boys in schools are not aggressive or violent or are passive and non-violent’ should equally be analysed in the research. Failure to apply equal vigour to analysing oppositional messages can seriously distort and invalidate a study.

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As well as the specific subject-orientated issues and messages to be analysed as part of a study, a content analysis coding system should also allow coding of other key variables that determine the likely impact of a text. In specialist content analysis software programs, these variables are often built in as standard ‘fields’. If not, they should be established in the Coding List. Typical variables identified by researchers as important and required for Best Practice content analysis include:  Media weighting or categorization to allow high circulation, high rating or highly

influential media to be scored higher than small, less important media;  Prominence to record impact factors such as page number or order in an electronic

media bulletin and use of photos or visuals;  Positioning such as headline mentions, first paragraph mentions, prominent mentions,

or passing mentions and ‘share of voice’ in articles;  Size of articles or length of radio and TV segments;  Sources quoted including the balance of supportive and opposing sources cited in the

texts and their position/credibility (e.g. an official government authority or known expert is likely to be more credible than a little known unqualified source).

Samples of coding lists and coding forms are published on the Cleveland State University, Ohio Web site as an adjunct to The Content Analysis Guidebook authored by Kimberley Neuendorf (2002) and can be downloaded free of charge from (http://academic/csuohio.edu/kneuendorf/content/hcoding/patcball/html).

 Intercoder reliability A rigorous ‘scientific’ approach to media content analysis to gain maximum reliability requires that two or more coders are used – at least for a sample of content (called the reliability sub-sample). Even when a primary researcher conducts most of the research, a reliability sub-sample coded by a second or third coder is important to ensure that, in the words of Tinsley and Weiss (1975), “obtained ratings are not the idiosyncratic results of one rater’s subjective judgement” (p. 359).

Neuendorf (2002) says: “There is growing acknowledgement in the research literature that the establishment of intercoder reliability is essential, a necessary criterion for valid and useful research when human coding is employed.” Neuendorf adds: “This has followed a period during which many researchers were less than rigorous in their reliability assessment” (p. 142). Reporting on an analysis of 486 content analysis studies published in Journalism and Mass Communication Quarterly from 1971 through 1995, Riffe and Freitag (1997) found that only 56% of these reported intercoder reliability and that most failed to report reliability variable by variable, which is recommended. Even as recently as 2001, a study of 200 content analyses by Lombard, Synder-Duch and Bracken (2003, 2004) found that only 69% discussed intercoder reliability and only 41% reported reliability for specific variables. A number of statistical formulae have been developed for measuring intercoder reliability. Researchers propose that coding between coder pairs and multiple coders should be compared at two levels: (a) agreement and (b) co-variation (Neuendorf, 2002, p. 144). Agreement is a simple comparison of the level of agreement between the coders’ scores and ratings. Co-variation assesses whether, when scores do vary as they no doubt will in human coding, they go up and down together – i.e. whether there is consistency or a high level of variance. Bartko and Carpenter (1976) note that in clinical and other psychological research, researchers report co-variation and not simple agreement, while

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in communication and business research simple agreement only is reported. Neuendorf, citing Tinsley and Weiss (1975), concludes: “The best situation, of course, would be one in which coded scores are shown to have both high agreement and high co-variation” (Neuendorf, 2002, p. 144). In terms of specific formulae to use, Lombard et al. (2003) report: [T]here are few standard and accessible guidelines available regarding the appropriate procedures to use to assess and report intercoder reliability, or software tools to calculate it. As a result, it seems likely that there is little consistency in how this critical element of content analysis is assessed and reported in published mass communication studies. Following a review of relevant concepts, indices, and tools, a content analysis of 200 studies utilizing content analysis published in the communication literature between 1994 and 1998 is used to characterize practices in the field. The results demonstrate that mass communication researchers often fail to assess (or at least report) intercoder reliability and often rely on percent agreement, an overly liberal index. Based on the review and these results, concrete guidelines are offered regarding procedures for assessment and reporting of this important aspect of content analysis. Lombard et al. (2004) note that there are “literally dozens” of different measures or indices of intercoder reliability. Popping (1988) reported 39 different “agreement indices”. However, Lombard et al., Neuendorf (2002) and a number of other researchers agree that the following indices are the most reliable and important:  Per cent agreement (basic assessment);  Scott’s pi ();  Cohen’s kappa ();  Spearman’s rho;  Pearson’s correlation coefficient (r);  Krippendorf’s apha; and  Lin’s concordance correlation coefficient (rc). According to professional and academic content analysts, “… the reliability sub-sample should probably never be smaller than 50 and should rarely need to be larger than about 300” (Neuendorf, 2002, p. 159). ‘Blind coding’ should be conducted by coders of the intercoder reliability sub-sample (i.e. neither coder should see coding of the others prior to completion of the assessment) to minimize what researchers term ‘demand characteristic’ – a tendency of participants in a study to try to provide what the primary researcher wants or to skew results to meet a desired goal. Intercoder reliability should ideally be assessed for each of the variables studied – in the case of content analysis, for all messages and issues analysed. Thus, in analyses with a wide range of issues and messages, intercoder reliability assessment is a time-consuming and challenging process. The relatively complex formulae for calculating these reliability indices are provided in Neuendorf (2002). Manual calculation requires familiarity with statistics and considerable time – no doubt the reason that most content analyses do little more than assess percent agreement, if that, as reported by Riffe and Freitag (1997 and Lombard et al. (2003). However, a number of software programs help calculate intercoder reliability assessment, including statistics programs such as SPSS which can assess Cohen’s kappa () and

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Simstat from Provalis Research which can calculate a number of intercoder reliability statistics. Also, specialist software programs have been and are being developed for this purpose including Popping’s AGREE (1984) and Krippendorf’s Alpha 3.12a, although the latter is a beta (test) program and not available widely (Lombard et al., 2004). A US company, SkyMeg Software, in consultation with academics from Cleveland State University, has developed PRAM (Program for Reliability Assessment of Multiple Coders) which can calculate reliability statistics for each of the most recommended indices. PRAM is still in development and an academic version alpha release 0.4.4 available as at January 2004 was found to contain some minor ‘bugs’ and ‘clunky’ features. However, release notes on the program state that all coefficients have been tested and verified by Neuendorf’s students at Cleveland State University. The program, which analyses coding data exported to Microsoft Excel® spreadsheets, provides reliability statistics for each variable assessed on a scale of 0 – 1 where one is 100% agreement or co-variation (SkyMeg Software, 2003). Neuendorf (2002) notes in relation to coder reliability that “most basic textbooks on research methods in the social sciences do not offer a specific criterion or cut-off figure and those that do report a criterion vary somewhat in their recommendations” (p. 143). However, Neuendorf cites Ellis (1994) as offering a “widely accepted rule of thumb”. Ellis states that correlation coefficients exceeding 0.75 to 0.80 indicate high reliability (p. 91). In relation to specific statistics, Frey, Botan and Kreps (2000) declare 70% agreement (0.70) is considered reliable. Popping (1988) suggests 0.80 or greater is required for Cohen’s kappa which he cites as the optimal (ie. strictest) measure, while Banerjee, Capozzoli, McSweeney and Sinha (1999) propose that a 0.75 score for Cohen’s kappa indicates excellent agreement beyond chance. Riffe, Lacy and Fico (1998), without specifying the type of reliability coefficient, recommend high standards and report that content analysis studies typically report reliability in the 0.80 to 0.90 range. As Neuendorf (2002) notes, it it is clear from a review of work on reliability of content analysis that reliability coefficients of 0.80 or greater are acceptable to all and 0.75 is acceptable in most situations. Furthermore, Neuendorf notes that the ‘beyond chance’ statistics such as Scott’s pi and Cohen’s kappa are afforded a more liberal criterion. A further principle of sound research is that agreement and co-variation rates between coders, along with details of the intercoder reliability sample, are reported in the research (Snyder-Duch, et al, 2001; Neuendorf, 2002). Such data should be appended to a content analysis report, along with the Code Book/Coding list and details of methodology used. Strategies to maximize agreement and co-variation and, if necessary, address low agreement or high variation between coders are: 1. Pre-coding training to familiarize all coders with variables such as issues and

messages for analysis and guidelines for classifications and coding; 2. Pilot coding (doing a test first); 3. Review of the Code Book/List and re-briefing to ensure descriptions and instructions

are clear; 4. Retraining if required.

 Validity

Validity of content analysis is achieved through thoroughly understanding the research objectives, preliminary reading of a sub-set of relevant content (what Neuendorf calls

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‘immersion in the message pool’), and careful selection of the sample of media content to be analysed. (See ‘Media content sample’)

 Generalizabilty

Generalizability refers to the extent to which research findings can be applied to and taken as a measure of the target population generally (in the case of content analysis, the target population is the total mass media message pool). Generalisability is largely determined by selection of a representative and sufficiently large sample, as well as the overall thoroughness of the methodology. (See ‘Media content sample’)

 Replicability Replicability, the ability and degree of difficulty or otherwise for other researchers to replicate the research to confirm or challenge the results, is a key criterion for all scientific research. Replicability is determined by full disclosure of information on methodology and procedures. In the case of content analysis, this should include the Code Book/Coding List; coding guidelines and instructions to coders; method of coding used in the case of human coding; details of any software programs used; and all data supporting conclusions. As Neuman (1997) notes, a researcher undertaking content analysis “carefully designs and documents procedures for coding to make replication possible” (p. 274).

Media content sample Sampling for media content analysis comprises three steps, Newbold et al. (2002) propose: 1. Selection of media forms (i.e. newspapers, magazines, radio, TV, film) and genre (news,

current affairs, drama, soap opera, documentary, and so on); 2. Selection of issues or dates (the period); 3. Sampling of relevant content from within those media (pp. 80–81). The simplest form of selecting content for analysis is a census – i.e. selection of all units in the sampling frame. This provides the greatest possible representation. However, a census may not be possible in some cases – e.g. where a large volume of media coverage has to be analysed such as a study over many months or years. In such cases, a sample of media content may be selected. Sampling needs to be conducted in an objective way, ensuring reliability is maintained. Typical methods of sampling for media content analysis include:  Systematic random (selecting every nth unit from the total population of articles or

advertisements/commercials for study);  Purposive such as selecting all articles from key media (and not from less important

media. This is valid provided there is some basis for the criteria applied);  Quota such as selecting a proportion of articles from each of several regions or areas

(either geographic, demographic, psychographic, or subject category);  Stratified composite samples constructed by randomly selecting units for analysis (articles

or ads) from certain days or weeks over a period.

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Riffe, Lacy and Fico (1998), Riffe, Lacy and Drager (1996) and Riffe, Lacy, Nagovan and Burkum (1996) have identified the most accurate sampling methods for analysing weekday TV news and media publications over a period and report that stratification by month or week provides the optimum result. However, often a purposive method focusing on the most relevant media is appropriate. Editorial or advertising media content can be collected in a number of ways including:  Reading and manually ‘clipping’ relevant items from newspapers and magazines and

taping electronic media broadcasts;  Subscribing to a media monitoring service;  Downloading items from online media sites. However, it should be noted that online

editions often do not contain all printed and broadcast content – e.g. special supplements and sections may not be available online;

 Online news services such as Factiva, Lexis-Nexis and Dow Jones. It should be noted

similarly that these services often provide a narrow sample of media content, usually from major newspapers only.

Two methods are used for recording coding: (a) electronic into a computer system and (b) ‘paper coding’. In modern computerized content analysis systems, the Coding List is usually contained in software menus or screens and coding data may be entered directly into a computer system. However, many coders still prefer ‘paper coding’ (i.e. writing coding on to the articles or transcripts or recording coding on a coding form attached to the text.) ‘Paper coding’ data is later entered into a computer system for analysis. During coding, issues and messages are identified by either, or a combination of (a) word- matching (i.e. an exact match), and (b) presence of acceptable synonyms or similar phrases. Acceptable synonyms or similar phrases should be identified in guidelines provided to coders attached to or as part of the Coding List. For example, if ‘participatory’ is a message for analysis, acceptable synonyms could be ‘joins in activities’, ‘works with others’, ‘takes part’ and ‘engages’. The more comprehensive the Coding List and guidelines to coders, the more reliable the analysis will be. Coding guidelines should be strictly followed. Reading and coding for content analysis is a time-intensive process and produces a veritable ‘data mountain’. However, ‘coding’ allows key data about media articles and programs rather than the full text to be entered into a computer database, providing data reduction and, when a scientific method has been employed, quantitative analysis can be carried out using computer-aided statistical and reporting tools.

Qualitative content analysis Qualitative content analysis can, to some extent, be incorporated within or conducted simultaneously with quantitative content analysis. For instance, positive and negative words and phrases can be analysed to identify the tone of text. Also, analysts can record notations during coding in relation to contextual factors. However, in many cases, in-depth analysis of selected content using qualitative research methods is required to fully understand the potential meanings (manifest and latent) for audiences and likely effects of texts.

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The precise methodology best used for qualitative message or text analysis is poorly defined. McKee (2004) notes that “we have a very odd lacuna at the heart of cultural studies of the media. Textual analysis is the central methodology, and yet we do not have a straightforward published guide as to what it is and how we do it”. He explains this as partly:

… the ambivalence of cultural studies practitioners towards disciplinarity and institutionalization [which] lead (sic) to an odd interpretation of our axioms that knowledge is power, that discourses define reality and that there is no such thing as ‘objective’ knowledge. We know that every methodology is partial, producing particular kinds of information. Linked with an anti-displinarian trend, this seems to have led us to refuse to think seriously about our own methodologies. Instead, we tend towards a kind of ‘transgressive’ methodological approach, where we do whatever takes our fancy.

McKee adds: “we insist that the specificity of any methodology must be investigated to reveal the limits to the kinds of knowledge it can produce, and yet our own central methodology is woefully under investigated, and still largely intuitive”. Despite this lack of specific guidelines for qualitative text analysis, research procedures for qualitative text and message analysis are informed by the work of Denzin and Lincoln (1994); Hijams (1996); Mayring (2000; 2003); Patton (1990; 2002); Robson (1993); and Silverman (1993) and these can be drawn on to frame a study with reasonable levels of reliability and validity. Qualitative message analysis methods applicable to analysis of media content include text analysis, narrative analysis, rhetorical analysis, discourse analysis, interpretative analysis and semiotic analysis, as well as some of the techniques used in literary studies such as critical analysis, according to Hijams (1996). Within the broad hermeneutic tradition concerned with text analysis, there are two main strands particularly relevant to qualitative content analysis. The first, narratology, focuses on the narrative or story-telling within a text with emphasis on meaning that may be produced by its structure and choice of words. The second draws on semiotics and focuses attention on signs and sign systems in texts and how readers might interpret (decode) those signs (Newbold et al., 2002, p. 84). Semiotics utilizes a number of different approaches, description of which is outside the scope of this paper other than a broad summary of their essential elements. Two main streams of semiotics, sometimes referred to as semiology and semiotics, have evolved from the work of Swiss linguist Ferdinand de Saussure and American Charles Sanders Peirce respectively. While quantitative content analysis has its complexities and requires considerable statistical rigor to comply with the requirements of scientific research, as outlined earlier in this chapter, the coding task in quantitative analysis is predominantly “one of clerical recording”, according to Potter and Levine-Donnerstein (1999, p. 265). In comparison, they note “objectivity is a much tougher criterion to achieve with latent than with manifest variables” as studied in qualitative content analysis. Newbold et al. (2002) warn:

The logic of deconstructing latent meanings, and privileging them over the more obvious ‘manifest’ ones, is questionable, for the audience may not see this latest dimension; the analysis may be longer than the text. The task is time-consuming, and often tells us what we already know in a language we don’t understand (p. 249).

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Newbold et al. go further in warning of the inherent challenges in semiology, the tradition of semiotics based on theories developed by de Saussure, in the following terms:

The scientific validity of semiology is questionable – in comparison with traditional positivistic science, at least – for it is not replicable (it is impossible to repeat with exactly the same results). It is not easy to show that semiology examines the subject it sets out to study … (p. 249).

However, like others, Newbold et al. acknowledge that there are advantages of using semiology as a tool. “It exposes the ideological, latent meaning behind the surface of texts, allowing us to grasp the power relations within them” (p. 249). The essential concepts of semiotics and semiology are that words and images are signs that ‘stand for’ or ‘signify’ something else beyond their obvious manifest meaning and relate to one another to form codes or code systems – collectives of signs that produce certain meanings (Newbold et al., 2002, p. 87; Selby & Cowdery, 1995, p. 47). Early semiotics took a structuralist approach, seeing the meaning of signs as largely fixed and interpreted according to a system, whereas later post-structuralist influenced semiotics theory saw signs as interpreted by audiences – often differently to the intentions of the author and differently between audiences. Jensen (1995) brought together what he terms an integrated social semiotics theory of mass communication which draws on structuralist semiotic research as well as more modern post- structuralist theories of active audience participation in interpretation of mediated meanings. In other words, elements of both de Saussure influenced semiology and Peirce influenced semiotics can be applied and each has something to offer to a comprehensive study of mass media representations. Newbold et al. (2002) observe: “So in studying media texts … we can use these ideas as they can provide a way of assessing the meaning production in a text” (p. 87). They cite Van Zoonen (1994) who explains that semiotic analysis of a media text can begin by identifying the signs in the text and their dominant characteristics. Then, citing Selby and Cowdery (1995), they say “these signs can be analyzed as a result of selection and combination” (Newbold et al., 2002, p. 87). Images such as photographs and icons are key signs in media texts. For instance, a photograph of a man holding a baby suggests fatherhood, family commitment and, depending on how it is composed, gentleness and caring. For instance, a photograph may contains several signs such as the man cradling the baby’s head in his hand and or gazing at the baby with a kind and caring expression (signifying love and protection), or holding the baby with outstretched arms away from his body and peering quizzically at the infant (signifying confusion and aversion). Road signs and international symbols such as $ representing dollar or money, © for copyright and  for ‘No’ (as in No Entry or No Smoking) are examples of icons and symbols that signify meanings beyond themselves. Similarly, audiences routinely interpret the sign + as denoting the mathematical function of addition and  as multiplication, while the slightly different  is symbolic of Christianity or the Christian Church. In terms of language, Campbell and Pennebaker (2003) and others identify pronouns as key signifiers of meaning in texts and a focus of qualitative text analysis. Campbell and Pennebaker investigated the relationship between linguistic style and physical health using latent semantic analysis to analyse writing samples provided by students and prison inmates. Campbell and Pennebaker reported that change in the frequency with which participants used

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pronouns (e.g. I, me, he, she) is the linguistic feature that best predicts improvement in physical health. Their data showed that flexibility in pronoun use is associated with improved physical health (pp. 60–65). Over-use of personal pronouns such as I, me and my can also indicate self-centredness and egotism.

Other key text elements commonly studied in qualitative content analysis are:  Adjectives used in descriptions (positive and negative) which give strong indications of a

speaker’s and writer’s attitude (e.g. it was ‘disgusting’);  Metaphors and similes used (e.g. labelling a car a ‘lemon’ or a person a ‘rat’);  Whether verbs are active or passive voice;  Viewpoint of the narrator (i.e. first person, second person, third person);  Tonal qualities such as aggressiveness, sarcasm, flippancy, emotional language;  Binaries established in texts and how these are positioned and used;  Visual imagery in text; and  Context factors such as the position and credibility of spokespersons or sources quoted

which affects meaning taken from the text (e.g. if one message is presented by a high profile expert it will generally outweigh a non-expert opinion).

Mayring (2000) developed a number of procedures for qualitative text analysis, among which he says two are central: inductive category development and deductive category application. Inductive analysis involves working from specific observations of categories and patterns (eg. issues or messages) to a broad theory or conclusion. Deductive analysis involves working from a broad theory or general position to specific observations to confirm or disprove the former (Trochim, 2002). After inductively determining categories, Mayring (2003) says “the qualitative step of analysis consists in a methodological controlled assignment of the category to the passage of the text”. Mayring’s procedures bring some systematic approach to qualitative text analysis. In essence, his method involves a priori design of the categories – they should not be created as the analyst goes along – and, importantly, this method requires matching of a category to a passage of text; not matching of the text to a category. By starting with pre-determined categories, which by their nature are specific, this increases the systematicity of qualitative analysis. Intercoder reliability assessment also should be used with qualitative analysis to assist reliability and validity, Mayring (2003) recommends, although he notes that more flexible measures need to be applied. His studies maximized reliability and validity by using “only trained members of the project team” and he reduced the standard of coder agreement stating that Cohen’s kappa () of 0.7 would be sufficient. Mayring (2003) also notes that several computer programs have been developed for qualitative analysis, but he stresses that these are to “support (not to replace) steps of text interpretation”. He reported experience using MAXqda (MAX Qualitative Data Analysis for Windows) (dressing&pehl GbR & Verbi GmbH, 2004). Sample for qualitative analysis Sampling for qualitative analysis is not required to meet the statistically valid formulae of quantitative analysis. Nevertheless, sampling for in-depth qualitative study should not be simply drawn at the researcher’s whim, and even random methods may not yield useful data as the purpose of qualitative research is to investigate certain issues or themes in detail. Random or even representative methods of sampling may not capture the issues or themes

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which are the subject of qualitative analysis. Miles and Huberman (1994) argue that sampling strategies for qualitative research should be driven by a conceptual question, not by concern for “representativeness” (p. 29). They suggest instead, three techniques which can be used together to yield rich results in qualitative analysis: 1. Selecting apparently typical/representative examples; 2. Selecting negative/disconfirming examples; and 3. Selecting exceptional or discrepant examples (p. 34). By choosing a combination of typical, disconfirming and exceptional examples for study, qualitative analysis can explore the boundaries of the data field and identify the range of views including discordant ones and extremes in various directions, as well as the typical. While quantitative research has the benefit of yielding empirical data that is generalisable and representative with a high probability, it reduces research findings to the average or median position on key questions. Qualitative analysis using the sampling approach identified by Miles and Huberman allows exploration of discourse at various points within the range. An overview of the processes of content analysis by Kimberley Neuendorf (2002) is provided in Figure 1. Commercial Media Analysis Commercially, media content analysis has a number of uses and offers significant benefits to companies, organizations, government agencies and political parties – particularly those that receive wide media coverage. In practical terms, organizations receiving a small amount of publicity can review media coverage using personal observation. But, when multinational companies and large organizations receive hundreds or even thousands of mentions in mass media, often in a number of countries and in multiple languages, simple observation cannot provide reliable understanding of likely outcomes and effects. Media content analysis is increasingly used commercially because of the two key roles of the mass media. Mass media – the world’s most powerful communication channel While media effects theory is a complex and ongoing field of research, many research studies show that mass media have significant impact and effects on public awareness, perceptions and sometimes behaviour such as buying decisions and voting. CEOs, marketers, advertisers and PR professionals know that mass media are important influences affecting brands, reputation, corporate image and the success of marketing and communication campaigns. It is because of this influence that mass media are used for advertising products and services. Editorial media content also influences readers, viewers and listeners – sometimes even more than advertising. But unlike advertising, editorial is highly variable in content and format. It may be critical, promote competitors, or raise issues impacting an organization. And mass media are also increasingly global. Reports from far corners of the world can impact a share price, a brand or reputation. So understanding the content of editorial mass media is increasingly important for organizations involved in public communication.

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Media Content Analysis Flowchart

Continued over:

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Figure 1. A flowchart for the typical process of content analysis research (Neuendorf, 2002).

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Mass media – one of the world’s largest databases As well as influencing public opinion, mass media reflect opinion and perceptions through reporting what other people, companies and organizations are saying and doing. Furthermore, the media report issues and trends, often ‘breaking’ news and setting or framing the agenda of public debate. (See Macnamara, 2003 for more information on effects of mass media.) Media analysis therefore provides two types of research: 1. Evaluation to measure effectiveness of an organization’s communication (PR) to and

through the media including audience reach, messages communicated, ‘share of voice’ and benchmarking its profile against competitors or in its sector;

2. Strategic insights and intelligence through issues tracking (environmental scanning), competitor analysis and trend identification.

Figure 2 provides an overview of the four roles and uses of media content analysis within these two areas – i.e. for formative (strategic planning) research and for evaluation.

Figure 2. The four roles of media content analysis

Client

Issues

Sources

PublicMEDIAClient

Issues

Sources

PublicMEDIA

1. Evaluate effectiveness of your media relations & PR (what you are getting into the media)

3. Gain strategic insight & intelligence into issues & trends reported in the media

2. Gain strategic insight & intelligence on other sources including competitors reported in the media

4. Evaluate messages reaching target audiences, ‘share of voice’, etc to determine likely impact on public opinion

MEDIA

Media Content Analysis

© Jim R. Macnamara, 1999

Client

Issues

Sources

PublicMEDIAClient

Issues

Sources

PublicMEDIA

1. Evaluate effectiveness of your media relations & PR (what you are getting into the media)

3. Gain strategic insight & intelligence into issues & trends reported in the media

2. Gain strategic insight & intelligence on other sources including competitors reported in the media

4. Evaluate messages reaching target audiences, ‘share of voice’, etc to determine likely impact on public opinion

MEDIA

Media Content Analysis

© Jim R. Macnamara, 1999

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California: Sage Publications. Dressing&pehl GbR & Verbi GmbH. (2004). MAXqda. [Computer Software]. Retrieved July 19,

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assessment and reporting of intercoder reliability. Human Communication Research, 29, 469–472. Lombard, M., Snyder-Duch, J., & Bracken, C. C. (2004). Practical resources for assessing and

reporting intercoder reliability in content analysis research projects. Retrieved April 28, 2004, from www.temple.edu/mmc/reliability

Macnamara, J. (2003). Mass media effects: a review of 50 years of media effects research. Retrieved July 30, 2004, from http://www.archipelagopress.com/jimmacnamara

Mayring, P. (2000). Qualitative inhaltsanalyse. Grundlagen und Techniken (7th ed.). Weinheim: Psychologie Verlags Union. (Original work published 1983)

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McKee, A. (2004). A beginner’s guide to textual analysis. Retrieved April 13, 2004, from http://www/enhancetv.com.au/articles/article3_1.htm

Miles, M., & Huberman, M. (1994). Qualitative data analysis. California: Sage. Neuendorf, K. (2002). The Content Analysis Guidebook, Thousand Oaks, CA: Sage Publications. Neuman, W. (1997). Social research methods: qualitative and quantitative approaches. Needham

Heights, MA: Allyn & Bacon. Newbold, C., Boyd-Barrett, O., & Van Den Bulck, H. (2002). The media book. London: Arnold

(Hodder Headline). Patton, M. (1990). Qualitative evaluation and research methods. (2nd ed.). Newbury Park: Sage

Publications. Patton, M. (2002). Qualitative evaluation and research methods. (3rd ed.). Newbury Park: Sage

Publications.

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Riffe, D., Lacy, S., & Drager, M. (1996). Sample size in content analysis of weekly news magazines. Journalism and Mass Communication Quarterly, 73, 635–644.

Riffe, D., Lacy, S., Nagovan, J., & Burkum, L. (1996). The effectiveness of simple and stratified random sampling in broadcast news content analysis. Journalism and Mass Communication Quarterly, 73, 159–168.

Robson, C. (1993). Real world research: a resource for social scientists and practitioner-researchers. Oxford: Blackwell.

Selby, K., & Cowdery, R. (1995). How to study television. Basingstoke: Macmillan. Shoemaker, P. & Reese, S. (1996). Mediating the message: theories of influences on mass media

content. White Plains, NY: Longman. Silverman, D. (1993). Interpreting qualitative data: methods for analysing talk, text and interaction.

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Van Zoonen, L. (1994). Feminist media studies. London: Sage. Weber, R. (1990). Basic content analysis (2nd ed.). Newbury Park, CA: Sage. Yale, L., & Gilly, M. (1988). Trends in advertising research: a look at the content of marketing-

orientated journals from 1976 to 1985. Journal of Advertising, 17(1), 12–22. Published Reference: Macnamara, J. (2005). Media content analysis: Its uses, benefits and Best Practice

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* Jim Macnamara PhD, MA, FPRIA, FAMI, CPM, FAMEC is Professor of Public Communication at the

University of Technology Sydney and Director of the Australian Centre of Public Communication, positions he took up in 2007 after a 30-year professional career spanning journalism, public relations, advertising, and media research. He is the author of 12 books including ‘The 21st Century Media (R)evolution: Emergent Communication Practices’ published by Peter Lang, New York in 2010.

Magdoff-World-Food-Crisis.pdf

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The World Food Crisis Sources and Solutions

F R E D M A G D O F F

An acute food crisis has struck the world in 2008. This is on top of a longer-term crisis of agriculture and food that has already left billions hungry and malnourished. In order to understand the full, dire implica- tions of what is happening today it is necessary to look at the interaction between these short-term and long-term crises. Both crises arise primar- ily from the for-profit production of food, fiber, and now biofuels, and the rift between food and people that this inevitably generates.

‘Routine’ Hunger before the Current Crisis

Of the more than 6 billion people living in the world today, the United Nations estimates that close to 1 billion suffer from chronic hun- ger. But this number, which is only a crude estimate, leaves out those suffering from vitamin and nutrient deficiencies and other forms of mal- nutrition. The total number of food insecure people who are malnour- ished or lacking critical nutrients is probably closer to 3 billion—about half of humanity. The severity of this situation is made clear by the United Nations estimate of over a year ago that approximately 18,000 children die daily as a direct or indirect consequence of malnutrition (Associated Press, February 18, 2007).

Lack of production is rarely the reason that people are hungry. This can be seen most clearly in the United States, where despite the produc- tion of more food than the population needs, hunger remains a signifi- cant problem. According to the U.S. Department of Agriculture, in 2006 over 35 million people lived in food-insecure households, including 13 million children. Due to a lack of food adults living in over 12 million households could not eat balanced meals and in over 7 million families someone had smaller portions or skipped meals. In close to 5 million families, children did not get enough to eat at some point during the year.

Fred Magdoff is professor emeritus of plant and soil science at the University of Vermont in Burlington and a director of the Monthly Review Foundation.

R E V I E W O F T H E M O N T H

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In poor countries too, it is not unusual for large supplies of wasted and misallocated food to exist in the midst of widespread and persistent hunger. A few years ago a New York Times article had a story with the fol- lowing headline “Poor in India Starve as Surplus Wheat Rots” (December 2, 2002). As a Wall Street Journal headline put it in 2004 “Want Amid Plenty, An Indian Paradox: Bumper Harvests and Rising Hunger” (June 25, 2004).

No ‘Right to Food’

Hunger and malnutrition generally are symptoms of a larger underly- ing problem—poverty in an economic system that recognizes, as Rachel Carson put it, no other gods but those of profit and production. Food is treated in almost all of the world’s countries as just another commodity, like clothes, automobiles, pencils, books, diamond jewelry, and so on. People are not considered to have a right to purchase any particular commodity, and no distinction is made in this respect between necessi- ties and luxuries. Those who are rich can afford to purchase anything they want while the poor are often not able to procure even their basic needs. Under capitalist relations people have no right to an adequate diet, shelter, and medical attention. As with other commodities, people without what economists call “effective demand” cannot buy sufficient nutritious food. Of course, lack of “effective demand” in this case means that the poor don’t have enough money to buy the food they need.

Humans have a “biological demand” for food—we all need food, just as we need water and air, to continue to live. It is a systematic fact of capitalist society that many are excluded from fully meeting this bio- logical need. It’s true that some wealthy countries, especially those in Europe, do help feed the poor, but the very way capitalism functions inherently creates a lower strata of society that frequently lacks the ba- sics for human existence. In the United States there are a variety of government initiatives—such as food stamps and school lunch pro- grams—aimed at feeding the poor. Yet, the funding for these programs does not come close to meeting the needs of the poor, and various charities fight an uphill battle trying to make up the difference.

In this era relatively few people actually die from starvation, aside from the severe hunger induced by wars and dislocations. Most instead become chronically malnourished and then are plagued by a variety of diseases that shorten their lives or make them more miserable. The scourge of malnutrition impedes children’s mental and physical devel- opment, harming them for the rest of their lives.

R E V I E W O F T H E M O N T H 3

The Acute and Growing Crisis: The Great Hunger of 2008

At this moment in history there are, in addition to the “routine” hun- ger discussed above, two separate global food crises occurring simulta- neously. The severe and acute crisis, about two years old, is becoming worse day by day and it is this one that we’ll discuss first. The severity of the current crisis cannot be overstated. It has rapidly increased the number of people around the globe that are malnourished. Although statistics of increased hunger during the past year are not yet available, it is clear that many will die prematurely or be harmed in other ways. As usual, it will be the young, the old, and the infirm that will suffer the worst effects of the Great Hunger of 2008. The rapid and simultaneous rise in the world prices for all the basic food crops—corn (maize), wheat, soybeans, rice, and cooking oils—along with many other crops is having a devastating effect on an increasing portion of humanity.

The increases in the world market prices over the past few years have been nothing short of astounding. The prices of the sixty agricultural commodities traded on the world market increased 37 percent last year and 14 percent in 2006 (New York Times, January 19, 2008). Corn prices began their rise in the early fall of 2006 and within months had soared by some 70 percent. Wheat and soybean prices also skyrocketed during this time and are now at record levels. The prices for cooking oils (main- ly made from soybeans and oil palm)—an essential foodstuff in many poor countries—have rocketed up as well. Rice prices have also risen over 100 percent in the last year (“High Rice Cost Creating Fears of Asia Unrest,” New York Times, March 29, 2008).

The reasons for these soaring food prices are fairly clear. First, there are a number of issues related directly or indirectly to the increase in petroleum prices. In the United States, Europe, and many other coun- tries this has brought a new emphasis on growing crops that can be used for fuel—called biofuels (or agrofuels). Thus, producing corn to make ethanol or soybean and palm oil to make diesel fuel is in direct competi- tion with the use of these crops for food. Last year over 20 percent of the entire corn crop in the United States was used to produce ethanol—a process that does not yield much additional energy over that which goes into producing it. (It is estimated that over the next decade about one- third of the U.S. corn crop will be devoted to ethanol production [Bloomberg, February 21, 2008].) Additionally, many of the inputs for large-scale commercial agricultural production are based on petroleum and natural gas—from building and running tractors and harvesting equipment to producing fertilizers and pesticides and drying crops for

4 M O N T H L Y R E V I E W / M A Y 2 0 0 8

storage. The price of nitrogen fertilizer, the most commonly used fertil- izer worldwide, is directly tied to the price of energy because it takes so much energy to produce.

A second cause of the increase in prices of corn and soybeans and soy cooking oil is that the increasing demand for meat among the middle class in Latin America and Asia, especially China. The use of maize and soy to feed cattle, pigs, and poultry has risen sharply to satisfy this de- mand. The world’s total meat supply was 71 million tons in 1961. In 2007, it was estimated to be 284 million tons. Per capita consumption has more than doubled over that period. In the developing world, it rose twice as fast, doubling in the last twenty years alone. (New York Times, January 27, 2008.) Feeding grain to more and more animals is putting growing pressure on grain stores. Feeding grain to produce meat is a very inefficient way of providing people with either calories or protein. It is especially wasteful for animals such as cows—with digestive sys- tems that can derive energy from cellulose—because they can obtain all of their nutrition from pastures and will grow well without grain, al- though more slowly. Cows are not efficient converters of corn or soy to meat—to yield a pound of meat, cows require eight pounds of corn; pigs, five; and chickens, three (Baron’s, March 4, 2008).

A third reason for the big jump in world food prices is that a few key countries that were self-sufficient—that is, did not import foods, al- though plenty of people suffered from hunger—are now importing large quantities of food. As a farm analyst in New Delhi says “When countries like India start importing food, then the world prices zoom....If India and China are both turning into bigger importers, shifting from food self-sufficiency as recently we have seen in India, then the global prices are definitely going to rise still further, which will mean the era of cheaper food has now definitely gone away” (VOA News, February 21, 2008). Part of the reason for the pressure on rice prices is the loss of farmland to other uses such as various development projects—some 7 million acres in China and 700,000 acres in Vietnam. In addition, rice yields per acre in Asia have reached a plateau. There has been no per acre increase for ten years and yield increases are not expected in the near future (Rice Today, January–March 2008).

Some of the reasons for the recent price increases for wheat and rice are related to weather. The drought in Australia, a major wheat export- ing country, and low yields in a few other exporters has greatly affected wheat prices. A 2007 cyclone in Bangladesh destroyed approximately 600 million dollars worth of its rice crop, leading to rice price increases

R E V I E W O F T H E M O N T H 5

of about 70 percent (The Daily Star [Bangladesh], February 11, 2008). The drought last year in northcentral China combined with the unusual cold and snow during the winter will probably lead the government to greater food purchases on the international markets, keeping the pres- sure on prices.

Speculation in the futures market and hoarding at the local level are certainly playing a part in this crisis situation to make food more expen- sive. As the U.S. financial crisis deepened and spread in the winter of 2008, speculators started putting more money into food and metals to take advantage of what is being called the “commodities super cycle.” (The dollar’s decline relative to other currencies stimulates “invest- ment” in tangible commodities.) While it would be a mistake to see these aspects, however despicable and inhumane, as the cause of the crisis, they certainly add to the misery by taking advantage of tight mar- kets. It is certainly possible that the commodity bubble will burst, bring- ing down food prices a bit. However, speculation and local hoarding will continue to put an upward pressure on food prices. Transnational corporations that process agricultural products, manufacture various foods, and sell food to the public are, of course, all doing exceptionally well. Corporate profits usually do well in a time of shortages and price increases.

Although not a cause for the increase in prices of other foods, the higher prices for ocean fish have created an added burden for the poor and near poor. Overfishing of many ocean species is removing this im- portant protein source from the diet of a large percentage of the world’s population.

The response to the crisis has come in the form of demonstrations and riots as well as changes in government policies. Over the past few months there have been protests and riots over the increasing cost of food in many countries, including Pakistan, Guinea, Mauritania, Morocco, Mexico, Senegal, Uzbekistan, and Yemen. China has instituted price controls for basic foods and Russia has frozen the price of milk, bread, eggs, and cooking oil for six months. Egypt, India, and Vietnam have banned or placed strict control on the export of rice so that their own people will have sufficient food. Egypt, the world’s largest wheat importer, has expanded the number of people eligible to receive food aid by over 10 million. Many countries have lowered protectionist tariffs to try to lessen the blow of dramatically higher prices of imported foods. Countries heavily dependent on food imports such as the Philippines, the world’s largest importer of rice, are scrambling to make deals to

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obtain the needed imports. But these various stop-gap efforts have mainly marginal effects on the problem. Almost all people are forced into a lower standard of living as those in the middle class become in- creasingly careful about the foods they purchase, the near poor drop into poverty, and the formerly poor become truly destitute and suffer greatly. The effects have been felt around the world in all classes of so- ciety except the truly wealthy. As Josette Sheeran, the head of the UN’s World Food Program, said in February, “This is the new face of hun- ger....There is food on shelves but people are priced out of the market. There is vulnerability in urban areas we have not seen before. There are food riots in countries where we have not seen them before” (The Guardian, Feb. 26, 2008).

Although Haiti has been a very poor country for years—80 percent of the people try to subsist on less than what two dollars a day can pur- chase in the United States—the recent situation has brought it to new depths of desperation. Two cups of rice, which cost thirty cents a year ago, now cost sixty cents. The description of an Associated Press article from earlier this year (January 29, 2008) is most poignant in its details:

It was lunchtime in one of Haiti’s worst slums, and Charlene Dumas was eating mud. With food prices rising, Haiti’s poorest can’t afford even a daily plate of rice, and some take desperate measures to fill their bellies. Charlene, 16 with a 1-month-old son, has come to rely on a traditional Haitian remedy for hunger pangs: cookies made of dried yellow dirt from the country’s central plateau.

The “cookies” also contain some vegetable shortening and salt. Toward the end of the article is the following:

Marie Noel, 40, sells the cookies in a market to provide for her seven children. Her family also eats them.

“I’m hoping one day I’ll have enough food to eat, so I can stop eating these,” she said. “I know it’s not good for me.”

Many countries in Africa and Asia have been severely impacted by the crisis with hunger spreading widely—but all nations are affected to one extent or another. In the United States—where over the past year the price of eggs increased 38 percent, milk by 30 percent, lettuce by 16 per- cent, and whole wheat bread by 12 percent—many people are starting to purchase less costly products. “Higher Food Prices Start to Pinch Consumers” is the way the Wall Street Journal put it in a headline (January 3, 2008).

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It should be noted here that while wheat prices are at record levels and prices of wheat products in the United States will certainly go higher, the cost of the wheat in a loaf of bread is only small part of the retail price. When wheat prices double, as they have, the price of a loaf of bread may increase by 10 percent, perhaps from $3 to $3.30. However, the effect of a doubling of prices for corn, wheat, soybeans, and rice is devastating for poor people in the third world who primarily purchase raw commodities.

With food pantries and soup kitchens stretched to the breaking point, the U.S. poor are experiencing deepening suffering. In general, the poor in the United States tend to first pay their rent, heat, gas (for a car to get to work), and electricity bills. That leaves food as one of the few “flexible” items in their budgets. In the central part of my home state of Vermont, over the last year the use of food shelves (i.e., aid from local, charitable food assistance programs that give groceries directly to the needy) has increased 133 percent among all users and 180 percent among the working poor! (Hal Cohen, with the Central Vermont Community Action Council, personal communication February 20, 2008.)

The economic recession is beginning to be felt in many parts of the United States, adding to the rise in requests for help from the various government food assistance programs (“As Jobs Vanish and Prices Rise, Food Stamp Use Nears Record,” New York Times, March 31, 2008). But, frequently people using the inadequately funded government programs tend to run out of food toward the end of the month, resulting in a huge increase in demand at food shelves and soup kitchens at that time. And as the need for food has increased, food donations have actually de- clined—with a large drop in federal donations (with high prices there are fewer “surplus” commodities from farm programs, so $58 million in food was given to food shelves last year versus $242 million five years before).

Supermarkets have found ways to make money from damaged or dated goods they previously donated to charities. In Connecticut, there has been a surge in demand for food while supply is not keeping up. A food pantry in Stamford is supplying food to four hundred families, double the number of a year ago. According to the food pantry’s direc- tor, “I have had to turn people away....There were times I went home and wanted to cry” (New York Times, December 23, 2007). A professor at Cornell University who studies food-assistance programs in the United States has summarized the situation: “There is a nascent crisis build- ing....Demand for food-bank assistance is climbing rapidly when the

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resources are falling in dramatic terms because the dollars just don’t go as far” (Wall Street Journal, March 20, 2008).

The Long-Term Food Crisis

As critical as the short-term food crisis is—demanding immediate world notice as well as attention within every country—the long-term, structural crisis is even more important. The latter has existed for de- cades and contributes to, and is reinforced by, today’s acute food crisis. Indeed, it is this underlying structural crisis of agriculture and food in third world societies which constitutes the real reason that the immedi- ate food crisis is so severe and so difficult to surmount within the sys- tem.

There has been a huge migration of people out of the countryside to the cities of the third world. They leave the countryside because they lack access to land. Often their land has been stolen as a result of the inroads of agribusiness, while they are also forced from the land by low prices they have historically received for their products and threats against campesino lives. They move to cities seeking a better life but what they find is a very hard existence—life in slums with extremely high unemployment and underemployment. Most will try to scrape by in the “informal” economy by buying things and then selling them in small quantities. Of the half of humanity that lives in cities (3 billion), some 1 billion, or one-third of city dwellers, live in slums. The chairman of a district in Lagos, Nigeria described it as follows: “We have a massive growth in population with a stagnant or shrinking economy. Picture this city ten, twenty years from now. This is not the urban poor—this is the new urban destitute.” A long New Yorker article on Lagos ended on a note of extreme pessimism: “The really disturbing thing about Lagos’ pickers and vendors is that their lives have essentially nothing to do with ours. They scavenge an existence beyond the margins of macroeconomics. They are, in the harsh terms of globalization, superfluous” (November 13, 2006).

One of the major factors pushing this mass and continuing migration to the cities—in addition to being landless or forced off land—is the difficulty to make a living as a small farmer. This has been made espe- cially difficult, as countries have implemented the “neoliberal” policies recommended or mandated by the IMF, the World Bank, and even some of the western NGOs working in the poor countries of the third world. The neoliberal ideology holds that the so-called free market should be allowed to work its magic. Through the benign sanctions of the “invis-

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ible hand,” it is said, the economy will function most efficiently and will be highly productive. But in order for the market to be “free” govern- ments must stop interfering.

With regard to agriculture, governments should stop subsidizing farmers to purchase fertilizers, stop being involved in the storage and transportation of food, and just let farmers and food alone. This ap- proach also holds that governments should stop subsidizing food for poor people and then the newly unbridled market will take care of it all. This mentality was evident as the Haitian food crisis started to develop late in 2007. According to the Haitian Minister of Commerce and Industry, “We cannot intervene and fix prices because we have to com- ply with free market regulations” (Reuters, December 9, 2007). This was the same response that colonial Britain adopted in response to the Irish potato famine as well as to the famines in India in the late 1800s. But to a certain extent this way of thinking is now internalized in the thinking of many leaders in the “independent” countries of the periphery.

This ideology, of course, has no basis in reality—the so-called free market is not necessarily efficient at all. It is also absolutely unable to act as a mechanism to end poverty and hunger. We should always keep in mind that this ideology represents the exact opposite of what the core capitalist countries have historically done and what they are actually doing today. For example, the U.S. national government has supported farmers in many ways for over a century. This has occurred through government programs for research and extension, taking land from Indians and giving it to farmers of European origin, subsidizing farmers directly through a variety of programs including low-cost loans, and stimulating the export of crops. It should also be noted that the United States, Europe, and Japan all developed their industrial economies un- der protectionist policies plus a variety of programs of direct assistance to industry.

The effects of the governments of the third world stopping their sup- port of small farmers and consumers has meant that the life for the poor in those countries has become more difficult. As an independent report commissioned by World Bank put it: “In most reforming countries, the private sector did not step in to fill the vacuum when the public sector withdrew” (New York Times, October 15, 2007). For example, many African governments under pressure from the neoliberal economic policies pro- moted by the World Bank, the IMF, and the rich countries of the center of the system stopped subsidizing the use of fertilizers on crops. Although it is true that imported fertilizers are very expensive, African

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soils are generally of very low fertility and crop yields are low when you use neither synthetic nor organic fertilizers. As yields fell after govern- ments were no longer assisting the purchase of fertilizers and helping in other ways, more farmers found that they could not survive and mi- grated to the city slums. Jeffrey Sachs—a partially recovered free-trade shock doctor—has had some second thoughts. According to Sachs, “The whole thing was based on the idea that if you take away the government for the poorest of the poor that somehow these markets will solve the problems....But markets can’t step in and won’t step in when people have nothing. And if you take away help, you leave them to die” (New York Times, October 15, 2007).

Last year one country in Africa, Malawi, decided to reverse course and go against all the recommendations they had received. The govern- ment reintroduced subsidies for fertilizers and seeds. Farmers used more fertilizers, the yields increased, and the country’s food situation improved greatly (New York Times, December 2, 2007). In fact, they were able to export some food to Zimbabwe—although there are those in Malawi, who consider that to have lowered their own supplies too far.

Another problem occurs as capitalist farmers in some of the poor countries of the periphery enter into world markets. While subsistence farmers usually sell only a small portion of their crops, using most for family consumption, capitalist farmers are those that market all or a large portion of what they produce. They frequently expand production and take over the land of small farmers, with or without compensation, and use fewer people than previously to work a given piece of land be- cause of mechanized production techniques. In Brazil, the “Soybean King” controls well over a quarter of a million acres (100,000 hectares) and uses huge tractors and harvesting equipment for working the land. In China corrupt village and city officials frequently sell “common land” to developers without adequate compensation to the farmers—some- times there is no compensation at all.

Thus, the harsh conditions for farmers caused by a number of factors, made worse by the implementing of free-market ideology, have created a continuing stream of people leaving the countryside and going to live in cities that do not have jobs for them. And those now living in slums and without access to land to grow their own food are at the mercy of the world price for food.

One of the reasons for the growing consolidation of land holdings and forcing out of subsistence farmers is the penetration of multina- tional agricultural corporations into the countries of the periphery. From

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selling seeds, fertilizers, and pesticides to processing raw agricultural products to exporting or selling them through new, large supermarkets, agribusiness multinationals are having a devastating effect on small farmers. With the collapse of extension systems for helping farmers save seeds and with the disbanding of government seed companies the way was paved for multinational seed companies to make major inroads.

The giant transnational corporations such as Cargill and Monsanto now reach into most of the third world—selling seeds, fertilizers, pesti- cides, and feeds while buying and processing raw agricultural products. In the process they assist larger farms to become “more efficient” —to grow over larger land areas. The main advantage of genetically modified organism (GMO) seeds is that they help to simplify the process of farm- ing and allow large acreages to be under the management of a single entity—a large farmer or corporation—squeezing out small farmers.

The negative effects of the penetration of large supermarket chains are being felt as well. As a 2004 headline in the New York Times put it “Supermarket Giants Crush Central American Farmers” (December 28, 2004). Large supermarkets would rather deal with a few farmers grow- ing on a large scale than with many small farmers. And the opening of large supermarkets does away with the traditional markets used by small farmers.

The Prolonged Crisis Is Intensifying

It seems logical that with higher food prices, farmers should be better off and produce more to satisfy the “demand” indicated by the market. To a certain extent that is true—especially for farmers that can take ad- vantage of all the physical and monetary advantages of large-scale pro- duction. Yet, the input costs for just about everything used in agricul- tural production have also increased, thus profit gains for farmers are not as large as might be expected. This is a particularly difficult problem for farmers raising animals fed on increasingly expensive grains.

In addition, things are not necessarily going well for small and sub- sistence farmers. Many are stuck in debt so deep that it’s hard for them to get back on their feet. An estimated 25,000 Indian farmers committed suicide last year because they could see no other way out of their pre- dicament. (The Indian government has proposed a budget that includes loan wavers for small farmers that have borrowed through banks. However, if it actually goes into effect, the millions that have borrowed from local usurers will not benefit.) The consolidation of land holdings and the removal of small farmers and landless workers from the land has

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been exacerbated by the exceptional crop price increases over the last few years.

Rising crop prices cause the price of farmland to increase—especial- ly of large fields that can be worked by large-scale machinery. This is happening in the United States and in certain countries of the periph- ery. For example, Global Ag Investments, a company based in Texas, owns and operates 34,000 acres of Brazilian farmland. At one of its farms, a single field of soybeans covers 1,600 acres—that’s two and a half square miles! A New Zealand company has purchased approxi- mately 100,000 acres in Uruguay and has hired managers to operate dairy farms established on their land.

Private equity firms are purchasing farmland in the United States (Associated Press, May 7, 2007) as well as abroad. A U.S. company is cooperating with Brazilian and Japanese partners to purchase 385 square miles in Brazil, approximately a quarter of a million acres! This is also happening with South American capital taking the lead—a Brazilian investment fund, Investimento em Participacoe, is buying a minority stake in a an Argentine soybean producer that owns close to 400,000 acres in Uruguay and Argentina.

Rising crop prices have also led to an acceleration of deforestation in the Amazon basin—1,250 square miles (about the size of Rhode Island) in the last five months of 2007—as capitalist farmers hunger for more land (BBC, January 24, 2008). In addition, huge areas of farmland have been taken for development—some of dubious use, such as build- ing suburban style housing and golf courses for the wealthy.

In China during 2000 to 2005, there was an average annual loss of 2.6 million acres as farmland is used for development. The country is fast approaching the self-defined minimum amount of arable farmland that it should have—approximately 290 million acres (120 million hectares)—and the amount of farmland will most likely continue to fall. As part of an effort to gain access to foreign agricultural produc- tion, a Chinese company has made an agreement to lease close to 2.5 million acres of land in the Philippines to grow rice, corn, and sugar— setting off a huge protest in the Philippines that has temporarily stalled the project (Bloomberg, February 21, 2008). As one farmer put it: “The [Philippine] government and the Chinese call it a partnership, but it only means the Chinese will be our landlords and we will be the slaves.’’

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Ending World Hunger

Ending world hunger is conceptually quite simple. However, actually putting it into practice is far from simple. First, the access to a healthy and varied diet needs to be recognized for the basic human right that it clear- ly is. Governments must commit to ending hunger among their people and they must take forceful action to carry out this commitment. In many countries, even at this time, there is sufficient food produced to feed the entire population at a high level of nutrition. This is, of course, most evi- dent in the United States, where so much food is produced. It is nothing less than a crime that so many of the poor in the United States are hungry, malnourished, or don’t know where their next meal will come from (which itself takes a psychological toll) when there is actually plenty of food.

In the short run, the emergency situation of increasingly severe hunger and malnutrition needs be addressed with all resources at a country’s disposal. Although mass bulk distribution of grains or powdered milk can play a role, countries might consider the Venezuelan innovation of setting up feeding houses in all poor neighborhoods. When the people believe that the government is really trying to help them, and they are empowered to find or assist in a solution to their own problems, a burst of enthusiasm and volunteerism results. For example, although the food in Venezuela’s feeding program is supplied by the government, the meals for poor chil- dren, the elderly, and the infirm are prepared in, and distributed from, peoples’ homes using considerable amounts of volunteer labor. In addi- tion, Venezuela has developed a network of stores that sell basic food- stuffs at significant discounts over prices charged in private markets.

Brazil started a program in 2003 that is aimed at alleviating the condi- tions of the poorest people. Approximately one-quarter of Brazil’s popula- tion receive direct payments from the national government under the Bolsa Família (Family Fund) antipoverty program. Under this program a family with a per capita daily income below approximately $2 per person per day receives a benefit of up to $53 per month per person (The Economist, February 7, 2008). This infusion of cash is dependent on the family’s chil- dren attending school and participating in the national vaccination pro- gram. This program is certainly having a positive effect on peoples’ lives and nutrition. It is, however, a system that does not have the same effect as Venezuela’s programs, which mobilize people to work together for their own and their community’s benefit.

Urban gardens have been used successfully in Cuba as well as other countries to supply city dwellers with food as well as sources of income.

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These should be strongly promoted—with creative use of available space in urban settings.

Agriculture must become one of the top priorities for the third world. Even the World Bank is beginning to stress the importance of govern- ments assisting agriculture in their countries. As Dr. Ngozi Okonjo- Iweala, managing director of the World Bank, has stated,

Today the attention of the world’s policy makers is focused on the sub- prime woes, and the financial crises. But the real crisis is that of hunger and malnutrition...this is the real problem that should grab the world’s attention. We know that 75 percent of the world’s poor people are rural and most of them depend on agriculture for their livelihoods. Agriculture is today, more than ever, a fundamental instrument for fighting hunger, malnutrition, and for supporting sustainable development and poverty reduction. (All-Africa Global Media, February 19, 2008)

Almost every country in the world has the soil, water, and climate resources to grow enough food so that all their people can eat a healthy diet. In addition, the knowledge and crop varieties already exist in most countries so that if farmers are given adequate assistance they will be able to grow reasonably high yields of crops.

Although enhanced agricultural production is essential, much of the emphasis in the past has been on production of export crops. While this may help a country’s balance of payments, export oriented agriculture does not ensure sufficient food for everyone nor does it promote a healthy rural environment. In addition to basic commodities such as soybeans, export-oriented agriculture also leads naturally to the produc- tion of high-value luxury crops demanded by export markets (luxuries from the standpoint of the basic food needs of a poor third world coun- try), rather than the low-value subsistence crops needed to meet the needs of the domestic population. Production of sufficient amounts of the right kinds of food within each country’s borders—by small farmers working in cooperatives or on their own and using sustainable tech- niques—is the best way to achieve the goal of “food security.” In this way the population may be insulated, at least partially, from the price fluctuations on the world market. This, of course, also means not taking land out of food production to produce crops for the biofuel markets.

One of the ways to do this and at the same time help with the prob- lem of so many people crowded into urban slums—the people most susceptible to food price increases—is to provide land through meaning- ful agrarian reforms. But land itself is not enough. Beginning or return-

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ing farmers need technical and financial support in order to produce food. Additionally, social support systems, such as cooperatives and community councils, need to be developed to help promote camaraderie and to solidify the new communities that are developed. Perhaps each community needs to be “seeded” with a sprinkling of devoted activists. Also, housing, electricity, water, and wastewater need to be available to make it attractive for people living in the cities to move to the country- side. Another way to encourage people to move to the country to be- come farmers is to appeal to patriotism and instill the idea that they are real pioneers, establishing a new food system to help their countries gain food self-sufficiency, i.e., independence from transnational agribusiness corporations and provision of healthy food for all the nation’s people. These pioneering farmers need to be viewed by themselves, the rest of the society, and their government as critical to the future of their coun- tries and the well-being of the population. They must be treated with the great respect that they deserve.

Conclusion

Food is a human right and governments have a responsibility to see that their people are well fed. In addition, there are known ways to end hunger—including emergency measures to combat the current critical situation, urban gardens, agrarian reforms that include a whole support system for farmers, and sustainable agriculture techniques that enhance the environment. The present availability of food to people reflects very unequal economic and political power relationships within and between countries. A sustainable and secure food system requires a different and much more equitable relationship among people. The more the poor and farmers themselves are included in all aspects of the effort to gain food security, and the more they are energized in the process, the greater will be the chance of attaining lasting food security. As President Hugo Chávez of Venezuela, a country that has done so much to deal with pov- erty and hunger, has put it,

Yes, it is important to end poverty, to end misery, but the most important thing is to offer power to the poor so that they can fight for themselves.

MAXWEL_SLATER_food_policy_DPR_03.pdf

Development Policy Review, 2003, 21 (5-6): 531-553

 Overseas Development Institute, 2003.

Published by Blackwell Publishing, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

Food Policy Old and New

Simon Maxwell and Rachel Slater∗

The character of the food system and the nature of food policy are both changing, as urbanisation, technical change and the industrialisation of the food system transform the way food is produced, marketed and consumed in developing countries. This overview presents an evaluation framework and explores new policy options. Some issues feature more prominently in richer, more urbanised, more industrialised developing countries, but the new food policy agenda is relevant in all countries – and it is in the poorest countries where challenges are set to emerge most rapidly. The agenda is more one of ‘food policy’ than ‘food security’: developing countries need both, but particularly a greater engagement with the new food policy.

Remember ‘food policy’? It is what some of us used to do before we discovered ‘food security’. The very term ‘food policy’ induces nostalgia for the 1970s and early 1980s: the first meetings of the World Food Council (following the World Food Conference in 1974), the establishment of the International Food Policy Research Institute (in 1975), and of the journal Food Policy (1976), the World Bank Staff Working Paper by Clay and others (1981), the path-breaking book by Timmer, Falcon and Pearson (1981).1

The emphasis on food policy in developing countries was necessary. It was not just that the world food crisis of 1972-4 had triggered new interest in the availability of and access to food, especially at global and national levels. It was also that policy-makers had begun to appreciate the interdependence between supply- and demand-side issues, and the value of applying especially economic analysis to the links. Thus, Timmer and his colleagues dealt separately with the production, marketing and consumption of food, but then in a more holistic manner with what they termed ‘macro food policy’. Those concerned with nutrition had already become familiar with integrated planning (Joy, 1973). Timmer, Falcon and Pearson reminded us that

where the food system is headed, of course, is the key question. Developing an intuitive understanding of the critical pressures on the system at any particular time is the artistic part of analysis, but having a framework of how issues are connected is the starting point for the craft (1981: 262) ... [However] no country has put the pieces together. (ibid: 269)

∗ Respectively Director and Research Officer, Overseas Development Institute, London. Our thanks to the contributors, and to David Sunderland for assistance. Responsibility is ours.

1. By contrast, the well-known reader edited by J. Price Gittinger and others came somewhat later (Gittinger et al., 1987). For a chronology of food-related initiatives, particularly relating to Africa, see Maxwell (2001a: 22-3).

532 Simon Maxwell and Rachel Slater

It was not long before the ‘putting together’ began, stimulated, for example, by the European Union’s 1981 Plan of Action to Combat Hunger in the World and by its pilot programme of food strategies in Kenya, Zambia, Rwanda and Mali.

The ‘food policy’ discourse was short-lived, however. Amartya Sen (1981) is usually credited with shifting the discourse towards entitlement and access. In fact, similar ideas, perhaps less elegantly expressed, could be found in the nutrition literature (for example, Berg, 1973; Levinson, 1974; Kielman et al., 1977), and, indeed, in the contributions in food policy to the debate about safety nets (see for example, Timmer et al., 1981: 269ff). Whatever the source, the primary concern of the international discourse did shift quite rapidly, from food supply to food demand. Entitlement, vulnerability and risk became the new watchwords: this was the emergent language of food security.

The idea of ‘food security’ has predominated since the early 1980s.2 From Sen, it was a short step to Reutlinger (1985), to Reutlinger and van Holst Pellekaan (World Bank, 1986), and eventually to Drèze and Sen (1989). Donors developed an enthusiasm for national food security planning (Maxwell, 1990), partly as a ‘proxy for poverty planning’ during the darkest years of structural adjustment (Hindle, 1990). The International Conference on Nutrition (1992), the World Food Summit (1996) and WFS-five years later (2002) cemented the consensus. A reduction in under-nutrition even made it into the Millennium Development Goals.3 The core concept of food security evolved over time, but was commonly taken to include both supply and access, also safety, and, in some cases, cultural suitability (Box 1). These ideas were also reflected in the debate about the right to food (for example, Eide, 1996).

Meanwhile, however, other issues began to infiltrate. They included a concern for

the commercialisation and industrialisation of food systems, a stronger focus on the institutional actors in food trade, including supermarkets (see Reardon and Berdegué, 2002; Weatherspoon and Reardon, 2003), warnings about the environmental consequences of new technologies (including salinisation, pesticides, and the risk of

2. For a history, see Maxwell (1996, 2001a). 3. See www.undp.org/mdg/goalsandindicators.html

Box 1: Definitions of food security ‘A basket of food, nutritionally adequate, culturally acceptable, procured in keeping with human dignity and enduring over time’ (Oshaug, 1985).

‘Access by all people at all times to enough food for an active, healthy life’ (World Bank, 1986).

‘A country and people are food secure when their food system operates efficiently in such a way as to remove the fear that there will not be enough to eat’ (Maxwell, 1988).

‘Food security exists when all people, at all times, have physical and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life’ (FAO, 1996).

Food Policy Old and New 533

mono-cropping, as well as more recent worries about GMOs), and issues to do with health, including problems of food safety and the growth of nutrition-related illnesses, especially heart disease and diabetes. Often, these issues were picked up outside the mainstream (Lappé and Collins, 1977; Tudge, 1977; Bernstein et al., 1990; Hewitt de Alcantara, 1993; Tansey and Worsley, 1995), or mainly in developed countries (Leather, 1996; Riches, 1997; Dowler et al., 2001; Dowler and Jones Finer, 2002; Schlosser, 2001; Nestle, 2002). Perhaps, to those primarily concerned with famine and severe under-nutrition in the very poorest countries, they seemed superfluous.

Not so. The core message of this volume is that what we term the ‘new food policy’ cannot be ignored, even by the poorest countries.4 The world food system, described only a few years ago, by Gaull and Goldberg (1993), as ‘emerging’, is no longer quite the chrysalis it once was. The pace of change is accelerating.5 The challenges are daunting. They are immediate. And they need to be on the agenda of policy-makers throughout the developing world. A preoccupation with food security is no longer sufficient. It is necessary to rediscover food policy.

In the pages that follow, we explore why this should be so. With the aid of our contributors, we track the changes, ask why they matter, and begin to map what might be done. We conclude that developing countries need both ‘food security’ and ‘food policy’ – including a more vigorous engagement than has so far been the case with the new agenda. What are the issues? The changing character of the food system, and the changing nature of food policy, are summarised in schematic form in Table 1.6 Few countries, if any, will conform exactly to the ‘old’ or ‘new’ characterisations: most are in between, but are also moving along a continuum from old to new. The changes have many causes. The articles in this volume identify drivers in many sectors: urbanisation, technical change, income growth, lifestyle changes, mass media and advertising, and changes in relative prices. There are three main sets of issues: (i) the character of the food system; (ii) the effects on the human population; and (iii) the actors and agendas of food policy. The food system The collected papers offer a vivid picture of a global food system undergoing transformation – as Lang observes, ‘a revolution in the nature of the food supply chain ... characterised by unprecedented changes in how food is produced, distributed, consumed and controlled’. We should not judge the transformation, at least not yet; but we should certainly observe.

The transformation has many features. In Table 1, we point particularly to the industrialisation and globalisation of the food system. The food system can no longer be

4. Critics will say that we should have reached this conclusion long ago. Probably. Indeed, many of the

contributors to this volume did so. 5. As Popkin (in this volume) demonstrates, for example, with reference to the nutrition transition. 6. An earlier version of this table appeared in EC Courier No. 197, March-April 2003.

534 Simon Maxwell and Rachel Slater

Table 1: Food policy old and new

Food policy ‘old’ Food policy ‘new’

1 Population Mostly rural Mostly urban

2 Rural jobs Mostly agricultural Mostly non-agricultural

3 Employment in the food sector

Mostly in food production and primary marketing

Mostly in food manufacturing and retail

4 Actors in food marketing

Grain traders Food companies

5 Supply chains Short – small number of food miles

Long – large number of food miles

6 Typical food preparation

Mostly food cooked at home High proportion of pre-prepared meals, food eaten out

7 Typical food Basic staples, unbranded Processed food, branded products More animal products in the diet

8 Packaging Low High

9 Purchased food bought in

Local stalls or shops, open markets

Supermarkets

10 Food safety issues Pesticide poisoning of field workers Toxins associated with poor storage

Pesticide residues in food Adulteration Bio-safety issues in processed foods (salmonella, listeriosis)

11 Nutrition problems

Under-nutrition Chronic dietary diseases (obesity, heart disease, diabetes)

12 Nutrient issues Micronutrients Fat Sugar

13 Food-insecure ‘Peasants’ Urban and rural poor

14 Main sources of national food shocks

Poor rainfall and other production shocks

International price and other trade problems

15 Main sources of household food shocks

Poor rainfall and other production shocks

Income shocks causing food poverty

16 Remedies for household food shortage

Safety nets, food-based relief Social protection, income transfers

17 Fora for food policy

Ministries of agriculture, relief/rehabilitation, health

Ministries of trade and industry, consumer affairs

Food activist groups, NGOs

18 Focus of food policy

Agricultural technology, parastatal reform, supplementary feeding, food for work

Competition and rent-seeking in the value chain, industrial structure in the retail sector, futures markets, waste management, advertising, health education, food safety

19 Key international institutions

FAO, WFP, UNICEF, WHO, CGIAR

FAO, UNIDO, ILO, WHO, WTO

Food Policy Old and New 535

understood simply as a way of moving basic commodities from farm to (often local) plate. Today, food is increasingly produced by commercial growers, feeding long and sophisticated supply chains which market often processed and branded products to mainly urban consumers. Many people work in the food industry, but few of them are farmers or farm workers: in developed countries, as few as one in ten (Tansey and Worsley, 1995).

The papers document the transformation, in both developed and developing countries. Lang, in particular, lists thirteen changes, ranging from how food is grown and animals reared, to the mass marketing of food brands and the concentration of power in food manufacturing and marketing. The top ten food manufacturers in the world, he tells us, have a combined turnover of around $225 billion; the top thirty retailers a combined turnover of $930 billion. Concentration, he believes

is strongly linked to power, and the concentration of power over the food system is now remarkable, whether one looks nationally, regionally or globally. A web of contractual relationships turns the farmer into a contractor, providing the labour and often some capital, but never owning the product as it moves through the supply chain.

Lang’s description of the food chain will be familiar to those who have tackled the

ideas in Fast Food Nation (Schlosser, 2001). They find an echo here in the articles by Gibbon and Deshingkar et al., who describe the operation of horticultural value chains in Africa and India respectively. What Gibbon describes as ‘the central reference point’ for work in this area is the study by Dolan and Humphrey (2001) on horticulture in Kenya, but there is now much other research on the growth of contractual arrangements between supermarkets and growers, often through intermediary ‘category managers’ and specialised importers.7

Supermarkets play a key role, and not just as purchasers of exotic products for export to the North. Pioneering work by Reardon and others, some of it published in this journal, documents the growing importance of supermarkets in developing countries: in Latin America, for example, supermarkets controlled 50-60% of food marketing in 2000 (Reardon and Berdegué, 2002: 371; Reardon et al., 2002, 2003). The share is smaller in Africa, but is growing: in South Africa, supermarkets control 55% of food retailing (Weatherspoon and Reardon, 2003). The same pattern is found in India: Deshingkar and her colleagues describe the growth of the FoodWorld chain, and the future plans of large business houses like Tata. Many supermarket chains in developing countries are now multinational: for example, the South African chain, Shoprite, has 64 outlets in 13 countries outside South Africa itself (Weatherspoon and Reardon, 2003). Wherever supermarkets enter the market, the supply chain is greatly changed, driven by issues like quality standards and traceability, as well as by the need to deliver large quantities to tight schedules.

Supermarkets are inevitably involved in the business of ‘selling’ food, part of what Dowler describes here as a ‘dominant policy framework for food [favouring] consumer and individual choice rather than public health and citizenship’. There has been much debate about the proliferation of new food products and the role of advertising: Marion Nestle’s recent book, for example, reports that 11,037 new food products were brought

7. For example, see the articles in Gereffi and Kaplinsky (2001).

536 Simon Maxwell and Rachel Slater

to market in the US in 1998 (Nestle, 2002). Advertising plays a big part in shaping food preferences, as Dowler and Lang both observe.

We should note that the food system is changing, even for those who do not shop in supermarkets. Urbanisation has a lot to do with this. As Haddad observes

The urban environment is ... marked by a greater physical distance between places of work and of residence, and by smaller household sizes. In this environment, where time is scarcer, at least for those gainfully employed, and where the fixed costs of food preparation are higher in smaller families, more food tends to be purchased outside the home, even for poor households.

The data support this conclusion. Haddad cites data showing that rich and poor

households acquire significant shares of calories outside the home, often in the form of ‘street foods’, with the share often being higher for the poor. Thus, Dan Maxwell established in Accra that the poorest quintile acquired 31% of calorie intake away from home; Tinker has similar findings in Bangladesh and the Philippines (both cited in Haddad).

Finally, it is important to note that globalisation and changing food preferences, especially the growing demand for livestock products, have a large impact on food trade. De Haen and his colleagues make this point: they note that the main growth in production in developing countries will be of livestock products, oilseeds and livestock feed; nevertheless, the current agricultural trade surpluses of developing countries will shrink and turn into substantial deficits. This will have political as well as economic repercussions (Brown and Kane, 1994). Diet and social impacts People are not unaffected by the changes in the food system. Many are very directly affected by changes on the production side: Gibbon, Deshingkar et al. and Page and Slater all discuss the impact on small producers, who generally face a much more difficult trading environment as a result of higher standards and the scale, quality, traceability and timeliness requirements of commercial supply chains. Retailers are also affected: in Argentina, 64,198 small shops went out of business from 1984 to 1993; in Chile, 5240 small shops closed from 1991 to 1995 (Reardon and Berdegué, 2002: 374). At the same time, some benefit: street foods can provide a good source of employment, especially for women, and can be useful for the poor who lack the facilities to cook (FAO, 2002a). Similarly, freeing up women’s reproductive labour in the home enables them to spend more time on remunerative activities.

Large numbers are affected by changes in diet associated with higher income, changing lifestyles and the pressures of living with a market-driven retail sector. Popkin has famously described this as the ‘nutrition transition’, and it is a major theme of the papers here. Popkin’s thesis is that

Modern societies seem to be converging on a diet high in saturated fats, sugar and refined foods and low in fibre – often termed the ‘Western diet’ – and on lifestyles characterised by lower levels of activity. These changes are reflected in nutritional outcomes, such as changes in average stature, body composition and morbidity.

Food Policy Old and New 537

Popkin’s own article provides a definitive account of dietary shifts and resultant health problems. The key changes are increases in the consumption of edible oil, caloric sweeteners (mainly sugar), and animal source foods. In China, for example, overall per capita consumption of cereals fell by about a fifth during the 1990s, with a particularly marked fall in consumption of coarse grains like millet and sorghum. Meanwhile, the consumption of animal products rose sharply, among the poor as well as the rich (though more for the rich). And the share of energy from fat, mainly vegetable oil, rose by nearly 50%.

These changes are occurring throughout the world, and at progressively lower levels of income. They have serious health implications, for the poor as well as the rich. Popkin assembles data on obesity, diabetes and heart disease, all of which are increasing rapidly in developing countries. He shows that overweight in countries as diverse as Mexico, Egypt and South Africa is equal to or greater than in the US, and points out that the rate of increase in Asia, North Africa and Latin America is two to five times greater than in the US. Obesity is frequently a marker of poverty and is associated with a poor quality diet. The health costs are substantial. The cost of diet-related non-communicable diseases will soon equal or exceed the costs of under-nutrition in developing countries: by 2025 in the cases of China and India.

Other papers provide corroborating evidence. Lang reviews the health costs of changes in diet, and makes the important point that the costs are leading insurance industries and Finance Ministries to take an unaccustomed interest in issues like obesity. Dowler makes similar points. She cites data suggesting that the UK National Health Service could save £30 billion a year by 2022 if ‘the population ate better, was less obese, smoked less, and took more physical activity’.

Dowler extends the argument by emphasising the social costs of the new food economy. Writing about the UK, she focuses particularly on the social exclusion associated with not being able to buy the foods that are advertised and available in supermarkets, particularly for families with children:

For those who live on tight budgets, there is continual anxiety over whether or not their children can or will exhibit the sophistication required to resist the persuasiveness of advertisements, and the need to ensure that their children are not victimised because they do not eat the latest ‘fashionable’ food.

Finally, it is important to note the issue of food safety. This is not a ‘new’ issue in

itself, and there have always been problems with adulteration and food quality. However, new problems arise in the rapidly growing cities of developing countries: in Ghana, Tomlins and his colleagues found that street-food vendors had limited access to clean potable water, that 69% of them handled food with their bare hands, and that only about 41% washed their hands before or after handling food (Tomlins et al., 2001).

More generally, there are many food safety problems associated with the industrialised food system. As the FAO argues, the

public generally perceives agricultural residues, pesticides and veterinary drugs as the major sources of health risks, but they are not. In Europe, for example, they account for just 0.5 per cent of food-borne illnesses. More common, and possibly increasing in frequency, is contamination by bacteria, protozoa, parasites, viruses and fungi or their toxins, introduced during food handling. (FAO, 2002b)

538 Simon Maxwell and Rachel Slater

In industrialised countries, up to 30% of people suffer from food-borne illnesses every year (see Lang, this volume). The incidence of food-borne disease may be 300 to 350 times higher than the number of reported cases worldwide. An estimated 70% of the approximately 1.5 million annual cases of diarrhoea in the world are caused by biological contamination in foods (FAO, 2002b).

As Lang notes, referring, inter alia, to mad cow disease, concerns over food safety have become an important driver of reform of food policy. Food policy The new global food system requires a new food policy, and there is progress towards this, albeit uneven. Many of the papers in this volume document new initiatives, ranging from community nutrition projects to international initiatives on issues like obesity. However, there are also issues in the wider food economy.

Much attention has been focused on trade policy, as a factor shaping livelihoods as well as access to food. Stevens is our guide here. He points out that ‘patterns of agricultural trade are changing so fast that the effects are likely to be powerful in the medium term’. The priorities are counter-intuitive, however, because a complex pattern of trade policy rents plays out differently for different products. Writing about Africa, Stevens distinguishes between traditional products (such as beverages) that are exported to a relatively undifferentiated liberal world market, other traditional exports (such as beef and sugar) that are exported to heavily protected markets, and non-traditional products (like horticulture). Paradoxically, he concludes that

Africa’s greatest gains from exporting to Europe have been in the products that appear at first glance to be the most heavily protected and to receive the least generous preferences.

Beef and sugar are prime examples. Stevens foresees serious threats ahead for

Africa, not least in the area of standards: more rigorous safety requirements, new areas of health concern, and new forms of monitoring. This is also a theme taken up by de Haen et al., including with respect to the Codex Alimentarius Commission, the joint FAO/WHO body concerned with food safety.

International regulation plays an important part in other areas, also. Millstone and van Zwanenberg explore biosafety issues, analysing the extent to which developing countries can find room for manoeuvre within the rules of the World Trade Organization and the Cartagena Protocol on Biosafety. They examine two cases in detail, the beef hormones dispute between the US and the European Union, and the parallel dispute about rBST, a hormone which increases milk yields. They are cautious about the role of science, but do conclude that there is scope for the exercise of discretion by developing countries. The Codex Alimentarius again has a role to play.8

International regulation matters because the risks to food security, whether climatic, environmental, political or economic, are more easily transmitted between countries in a more globalised food system. Lang writes eloquently of a food system in which ‘slack

8. For decisions taken on this topic in July 2003, see www.fao.org/english/newsroom/news/2003/20363-

en.html

Food Policy Old and New 539

(has) been so cleverly taken out of the system that if something (goes) wrong , it (does) so catastrophically’. The risks are no longer local, nor principally climatic.

New actors are then drawn in. Historically, food policy has been the preserve of Ministries of Agriculture, with a supporting role played by Ministries of Health and, in some countries, departments dealing with drought relief and rehabilitation. Increasingly, however, food policy is becoming the concern of Ministries of Trade and Industry, Ministries of the Environment, and competition authorities. It is notable, for example, that the EU, and many of its Member States, have created independent Food Standards Agencies, and that competition authorities have taken an interest in food retailing (Competition Commission, 2001). The same is true internationally: as de Haen and his colleagues document for FAO, the new food policy is driving change in the organisation’s work programme. Do the changes matter? ‘Do the changes matter?’ is an evaluation question, and this points to the need for an evaluation framework. However, the construction of a framework is not straightforward.

We might start with the general issues used in evaluation, deriving from the logical framework approach to project and programme planning (Figure1): sustainability, relevance, impact, effectiveness and efficiency (Norwegian Ministry of Foreign Affairs, 1993). But what do these words mean – in the context of food policy – and are there other factors to take into account?

Figure 1: An evaluation model for analysing

development assistance

Source: Norwegian Ministry of Foreign Affairs (1993).

Sustainability The long-term viability of the project

Relevance The direction and usefulness of the project

Impact Other effects of the project

Effectiveness Achievement of objectives

Efficiency Achievement of results

Inputs Outputs Purpose Goal

Goal Hierarchy

E va

lu at

io n

c om

p on

en ts

540 Simon Maxwell and Rachel Slater

Efficiency is a good place to start, since this has precise economic content: in terms of production function, technical efficiency describes a position in which output is maximised for a given level of inputs, and allocative efficiency describes a position in which the output mix correctly reflects prices.9 The term ‘economic efficiency’ is sometimes used to describe a situation in which both technical and allocative efficiency have been achieved. Note that efficiency can be assessed from the point of view of private actors, using market prices, or from the point of view of society as a whole, correcting for price distortions and externalities (for example, environmental costs). As one of us observed in 1991, expanding on the definition that a food system should be ‘efficient’ (as well as equitable), this means that

all stages in the food chain, from production to final consumption, should be efficient in a social-welfare sense. Production policies should take account of dynamic comparative advantage; marketing margins should provide no more than normal profits in the long term; and consumer prices should reflect real scarcity values. (Maxwell, 1991: 16)

Beyond efficiency, the evaluation framework points to impact and sustainability.

The impact of the food system is perhaps best approached in terms of welfare, and here there are valuable lessons to be learnt from the literature on poverty. This is no longer thought of in terms of income alone, but has many other dimensions. Again, Amartya Sen has been very influential, through his work on human capability and human development (ODI, 2001).

For example, the livelihoods perspective, much favoured by aid agencies working on rural development (Hussein, 2002), features income as an objective, but also reduced vulnerability, more sustainable use of the natural resource base, and stronger ‘voice’. More generally, the poverty framework adopted by aid agencies, in a set of guidelines agreed in 2001 by the Development Assistance Committee of the OECD, identifies thirteen facets of poverty, grouped into five clusters: economic, human, socio-cultural, political, and protective. In this model, reproduced in Figure 2, gender and environment are cross-cutting issues.

Equity is not specifically mentioned in the DAC model, but of course is frequently discussed in the context of poverty reduction, for both instrumental and intrinsic reasons (Killick, 2002; McKay, 2002; Naschold, 2002). It is particularly relevant to remember Townsend’s definition of poverty as

the lack of the resources to obtain the types of diet, participate in the activities and have the living conditions and amenities which are customary, or at least widely encouraged and/or approved, in the societies to which they belong. (Townsend, 1979:21)

9 See Ellis, 1993: 67ff for a succinct definition of these terms.

Food Policy Old and New 541

Figure 2: Interactive dimensions of poverty and well-being

Source: DAC (2001).

Dowler is one who has written extensively on the interpretation of this definition

for the understanding of food poverty in the UK, as an element of social exclusion (for example, Dowler, 1998, and in this volume). The definition of food security by one of us, reproduced in Box 1, which refers to the subjective nature of food poverty, is also relevant: the shift from objective to more subjective indicators of food shortage has been identified as a major shift in thinking about food security (Maxwell, 1996), and has become a recurrent analytic theme (Radimer et al., 1992; Gordon et al., 2000; Bickel et al., 2000). Dowler reminds us that

in the general public’s mind, food is more than a bundle of nutrients: it represents an expression of who a person is, where they belong, and what they are worth, and is a focus for social exchange.

Sustainability is the other issue present in the initial framework, and has been a

long-standing feature of the debate on food and agriculture, at least since the publication of Silent Spring in 1962 (Carson, 1962). Concerns have multiplied around Green Revolution technologies and the environmental cost of ‘food miles’ (Tansey and Worsley, 1995). In a recent review, Pretty and Hine cite the environmental costs of British agriculture at £2.3 billion p.a., or £208/hectare (Pretty and Hine, 2000).

Finally, it is worth referring back to the other definitions of food security in Box 1, to remind ourselves of the importance particularly of food safety – certainly a dominant issue in recent discussion about food policy in the North (Millstone and van Zwanenberg, 2002; Draper and Green, 2002; Lang, Millstone and van Zwanenberg, and de Haen et al., all in this volume).

Can all these different themes be integrated? As Barling et al. (2002) have suggested, ‘joined up food policy’ is certainly needed, and can be thought of using what they describe as an ‘ecological public health model’. In this connection, they refer approvingly to a WHO-Europe initiative on ‘Better Health through Safe Food and Good Nutrition’, which links food safety, nutrition and sustainability (WHO-Europe, 2000).

PROTECTIVE Security

Vulnerability

POLITICAL Rights

Influence Freedom

ECONOMIC Consumption

Income Assets

HUMAN Health

Education Nutrition

SOCIO- CULTURAL

Status Dignity

Gender

Environment

542 Simon Maxwell and Rachel Slater

This looks plausible, but misses some of the efficiency (and equity) arguments advanced earlier, and also the democratisation aspects, which, as it happens, those same authors strongly advocate (see, for example, Lang in this volume).

It looks, then, as though we need a new list of evaluation criteria. This is attempted in Table 2, which draws together the points from the previous discussion. There are no fewer than 19 criteria against which a food system can be judged.

Table 2: Criteria for a food system

A food system can be judged by whether it:

• is technically efficient in social prices;

• is allocatively efficient in social prices;

• leads to increased consumption by the poor;

• leads to increased asset- holding by the poor;

• is good for health;

• is good for nutrition; • supports higher standards

of education; • enables people to have

status; • enables people to have

dignity; • enables people to have

rights; • enables people to have

influence; • underpins freedom;

• offers security; • reduces vulnerability; • is good for

environmental sustainability;

• promotes gender equality;

• promotes equality in general;

• promotes social inclusion.

The very number of criteria, and their diverse character, immediately illustrate a

challenge of aggregation in evaluating food systems, whether globally or locally. Some of the criteria are economic and financial, so that it might be possible to hope for a quantitative summary, using money as a numéraire. Others, however, are qualititative, and some are subjective. No single cost-benefit analysis is likely to be possible, even with heroic assumptions about valuation, weighting and time preference. As an alternative, the way forward may be to use multiple-criteria tables, as has been done before in evaluating food policy interventions (Huddleston, 1990; Maxwell, 1990).

Sadly, we do not feel strong enough at this point to evaluate the world food system – or even any local part of it – using a formal, multiple criteria approach. We are not that ambitious. Instead, we note that most of the topics identified in Table 2 are dealt with in one way or another in the contributions to this volume. We can identify seven major themes.

First, it is important not to be dismissive of technical and organisational changes which increase the productivity and efficiency of the food system. The many actors in the world food system, including farmers, have been astonishingly successful in increasing the supply and diversity of food, whilst simultaneously reducing prices. Lang is correct to talk of a ‘cornucopia’, at least in aggregate terms, for which we owe much to the kinds of innovations he lists (from the Chorleywood process for baking bread to the use of satellite tracking of lorries delivering food to supermarket distribution centres). Innovations shift the production function outwards and help improve both technical and allocative efficiency. They have included the Green Revolution, which, despite much criticism, turned out to be good for poor people (Mellor, 1976; Lipton with Longhurst, 1989), and they have the capacity to deliver much more, including the

Food Policy Old and New 543

hoped-for ‘Doubly Green Revolution’ (Conway, 1997). This is no time to be Luddite about technical change.

Second, however, and at the same time, there do need to be significant concerns about both the technical and allocative efficiency of the food system, when the costs and benefits are expressed in social prices, and when all externalities are taken into account. Market failure is ever present (Haddad in this volume), and there is at least circumstantial evidence of oligopoly, monopsony and rent-seeking in the food system. Lang’s analysis of concentration in input supply and marketing does not prove uncompetitiveness, but it certainly, as he observes, raises questions about power along the global supply chain, and about the scope for regulation by single states.10 This, of course, is a major theme of value global chain analysis, of which there has been a good deal especially in the horticulture sector (Dolan and Humphrey, 2001; Gereffi and Kaplinsky, 2001). It is also a theme of Stevens’ work on trade policy rents across a range of commodities: there are many costs associated with the current policy stance, and not all of them are reflected in budget allocations.

Third, health externalities need to feature in the social analysis, if not in the market calculation. The figures cited for the health costs of poor diet are remarkable, as Popkin, Lang and Dowler emphasise, among others.

Fourth, environmental externalities also need to feature, both on and off the farm. Pretty and Hines’ estimates of the environmental cost of British agriculture, cited earlier, provide a powerful reminder. Water is another focus of concern, as de Haen et al. demonstrate.11

Fifth, the income distribution effects of changes in the food system need to be kept under review. In the wider literature, for example about the Green Revolution, or about agricultural growth more widely, the consensus is that increases in output tend to benefit the poor, because they are small farmers themselves, or work on farms, or buy food the price of which is falling (Lipton with Longhurst, 1989; Irz et al., 2001). In the papers in this volume, the focus is more on the difficulties faced by the poor: as producers (Gibbon, Page and Slater, Deshingkar et al., de Haen et al.); as traders (especially poor African countries – Stevens); and as consumers (Dowler, Haddad, de Haen et al.).

Sixth, policy-making and regulation are problematic. This is partly a familiar problem of how to deal with a cross-cutting issue (Lang, Dowler, Haddad), but it arises particularly in relation to ‘new’ topics like biotechnology (Millstone and van Zwanenberg, de Haen et al.), and to other issues that cut across national borders (Lang, Stevens). Self-regulation by the food industry will certainly be insufficient.

Finally, the process of improving policy is also problematic. Public pressure for change is beginning to mount (Haddad, Lang), but there is a limit to piecemeal adaptation (Clay).

10. An enquiry into the competitiveness of the UK supermarket sector, conducted by the Competition

Commission in 2001, found that the industry ‘is currently broadly competitive and that, overall, excessive prices are not being charged, nor excessive profits earned’.

11. See also ODI (2002).

544 Simon Maxwell and Rachel Slater

What might be done? There is a process answer to the question of what needs to be done about food policy, and an answer about substance. The mainstream answer to the process question is easy, and is the same as in the 1970s: prepare a food strategy. We have, however, learned a good deal since the 1970s, about how to prepare food and nutrition strategies – in particular, about how to avoid over-loading such strategies with analysis, designing excessively complex organisational structures, and planning in such detail as to make implementation impossible (Field, 1987; Berg, 1987; Maxwell, 1997, 2001b). The main lessons are summarised in Box 2, emphasising a process approach of learning by doing and constant iteration between planning and practice. Clay (in this volume) effectively provides a case study of the method in action, illustrating the gradual adaptation to changing circumstances of the World Food Programme. He emphasises, however, that adaptation has limits: at a certain point, it is necessary to grapple with the fundamental reformulation of what policy is about, and with the reconstruction of institutions. In the case of the WFP, he argues, this point has now been reached.

The currently most popular form of strategy planning is for poverty reduction,

through the mechanism of Poverty Reduction Strategy Papers. These have much to learn from past experience in the food and nutrition sectors (Maxwell, 1998a), but have also contributed new insights, especially about the value of participation and the importance of political processes (Booth, 2003). As Booth reminds us, ‘politics matter’.

This theme is again taken up in the papers in this volume. Lang, for example, identifies public pressure as one of the main drivers of policy change in the food arena,

Box 2: Lessons of food security planning

• On planning: o set clear, short-term goals and work towards them; focus on the task; o train the team to work together, with training in communication, conflict

resolution and multi-disciplinary skills; o build team cohesion, through collaborative fieldwork, participative

leadership; o stay close to the customer, build in participation.

• On implementation: o build in a bias to action; start small and grow; o take risks and innovate; embrace error; o downgrade overt integration – integrated planning but independent

implementation.

• On evaluation and public relations: o constant iteration between planning, execution and evaluation; be flexible; o monitor progress; be publicly accountable for targets; o raise the profile of the topic; raise consciousness.

Source: Maxwell (2001b: 315).

Food Policy Old and New 545

reflecting concerns about health, but also about the state of the planet. Food activism has an honourable history and is growing fast.12

Haddad explores in more detail the ‘triggers’ for public action. Drawing on the work of Kersh and Morone, and taking the issue of obesity as an example, he identifies seven triggers, including social disapproval, mass movements, and interest-group action. Only three of the seven triggers have been tripped so far. Haddad concludes that

such constructs help us to remember that evidence is only one ingredient in the formulation and implementation of public health policy.

This is especially true because the evidence itself is often unreliable: science does

not provide the certainty that policy-makers might hope for. Millstone and van Zwanenberg provide evidence on this point, using the case of genetic modification. They describe the state of scientific knowledge as ‘rudimentary’, and the scientific debate as ‘fractious’, and conclude that

the assumption (that science might settle … regulatory disputes) is seriously undermined by the fact that our scientific understanding of the risks that GM crops and seeds might pose is chronically uncertain, incomplete and contested.

What, then, can be done? The papers are actually rich in prescription, ranging from

ideas well outside the narrow remit of food policy (for example, Popkin’s thoughts on urban design and the connectivity of streets, designed to encourage higher levels of physical activity), to those which are very precisely about food (for example, Haddad’s ideas about how to increase the price, and thereby reduce the attraction, of unhealthy diet options). The proposals made in the papers relate to both the public and the private sectors, to international as well as national policy, and to all aspects of the production, marketing and demand for food. Table 3 summarises some of the policy ideas contained in the various papers.

There are various ways of classifying the proposals, various entry points for more detailed analysis. For example, Lang identifies a key choice between regulation and self-regulation:

An important duality has emerged. On the one side, we find a state system of regulations, on the other a system of self-regulation, largely driven by the major forces in supply chain management, the food retailers in particular.

Examples of self-regulation are found in the area of standards, for example in

horticulture (Page and Slater). However, Lang is sceptical about the potential of self- regulation to deliver a food system that meets the multiple criteria listed in the previous section. This is largely, he argues, because of the interconnectedness of food policy.

Haddad takes a different route, focusing on public intervention, and distinguishing interventions on the demand and supply sides. His supply-side list includes technology,

12. See, for example, the Food Commission in the UK (www.foodcomm.org.uk), the NGO consortium which

works together in the UK Food Group (www.ukfg.org.uk), and the food sovereignty movement (www.forumfoodsovereignty.org and www.peoplesfoodsovereignty.org).

546 Simon Maxwell and Rachel Slater

Table 3: Policies for a new food policy: an initial list

• Learn how to increase consumption of fruit and vegetables and high fibre products (Popkin)

• Modify the physical environment to enhance physical activity (Popkin) • More investment in technology to deliver high-productivity, low-cost vegetables and fruits

and low-fat livestock products to poorer consumers (Haddad) • Eliminate price incentives on growing high-fat foods and relax quantity restrictions on

growing healthier foods (Haddad) • Evaluate food trade policy from a health perspective (Haddad) • Impose tougher standards on the fat content of food away from home and in schools

(Haddad) • Reduce malnutrition in utero (Haddad) • Increase the relative price of unhealthy choices (Haddad) • Clearer information about product contents (Haddad) • Better awareness about consequences of poor diet (Haddad, de Haen et al.) • Promote healthy eating and dietary change (Dowler, de Haen et al.) • Local food projects (Dowler) • Set state benefits at realistic levels (Dowler) • Trade regulation at EU level (Gibbon) • Regulation by exporting countries (Gibbon) • Regulation of markets within developed countries (Gibbon) • New production and marketing arrangements at local level that support small and marginal

farmers(Deshingkar et al.) • Direct foreign investment enabling small producers to keep in touch with tastes and

standards in foreign markets (Page and Slater) • Large direct private buyers providing partial access to production and technology

advantages via technical advice and training (Page and Slater) • Initiatives by developing country producers where there is no external private or public

sector intervention (Page and Slater) • Alternative trading companies offer inputs into production and organisation (Page and

Slater) • Establish export promotion agencies as the first point of contact for new exporters (Page

and Slater) • Establish import promotion agencies to encourage trade from developing to developed

countries (Page and Slater) • Use aid programmes to analyse the poverty reduction effects from trade and developed

policies that maximise these effects (Page and Slater) • Target technical research towards new export opportunities (Page and Slater) • Encourage agencies promoting small production not just for export but also for local

markets (Page and Slater, Deshingkar et al.) • Better scientific risk assessment (Millstone and van Zwanenberg) • Regional co-operation on biosafety (Millstone and van Zwanenberg) • Better food security analysis of trade policy (Stevens) • Better understanding of standards (Stevens) • Better advocacy and monitoring (de Haen et al.) • Promoting sustainable intensification (de Haen et al.) • Strengthen Codex Alimentarius (de Haen et al.) • Rethink the role of international food aid (Clay)

Food Policy Old and New 547

prices, standards, and a variety of nutrition interventions. His demand-side list again includes pricing, but also adds labelling and information/education. There are some intriguing ideas here. For example, in the US, a policy-induced increase in meat prices is shown to have some positive effects on diet, such as a reduction in fat and cholesterol intake, but also some negative effects, such as a reduction in iron and calcium. By contrast, an increase in the price of edible oil has much more generally favourable effects. Haddad does note, however, that ‘in a developing country context, edible oil is often used to increase the energy density of infant diets’ – and that the policy may therefore not be transferable.

Other papers explore particular aspects of policy. Thus, Gibbon reviews the potential of three types of public regulation designed to help small producers. Deshingkar et al. identify three forms of collaboration by farmers that can help small producers. Page and Slater assess nine ways in which the obstacles to market access by small producers can be overcome.

Some of these policies are more promising than others. Gibbon is probably the least sanguine. He concludes from his review of market regulation options that WTO rules, EU competition regulations, and structural adjustment practice all militate against intervention:

unfortunately, at least from the viewpoint of small-scale producers, regulation in all the forms mentioned has become difficult to maintain and virtually impossible to (re-)introduce.

Others are more optimistic. The village-level interventions identified by

Deshingkar and her colleagues, for example labour-water exchange arrangements and group leasing of land, have sprung up of their own accord in response to market opportunities for the sale of exotic vegetable crops, like asparagus and baby sweetcorn. The interventions identified by Page and Slater are mostly at a national level. They include sub-contracting by the private sector, farmers’ organisations, fair trade arrangements, and trade promotion agencies. There are many examples of success.

An important stream of recommendations concerns the international level. Thus, Stevens takes a characteristically careful and pragmatic look at current trade issues, and identifies key threats to developing countries, especially in Africa, from the erosion of preferences, higher import costs, and changes in standards. These are not, it needs to be emphasised, the places where most observers focus their attention. Stevens argues that debate about liberalisation of Northern agriculture is

largely irrelevant as a practical policy concern, since we are not about to see anything resembling liberal trade in OECD agriculture, despite the much-heralded ‘reforms’ to the Common Agricultural Policy (CAP) and the on-going agricultural reforms in the WTO Doha Round.

In a similar vein, Clay examines in detail the evolution of the global food aid

regime, and argues strongly that a fundamental review is needed. He wonders whether a crisis is needed to trigger change – and whether such a crisis has now arrived, because of the increasing dependence of the WFP on US food aid. Lang would probably agree

548 Simon Maxwell and Rachel Slater

with the general thesis: his description of changes in UK food policy corresponds to a crisis-driven model, including health crises. Conclusion The papers in this volume make powerful points about the scale and speed of changes in the global food system. We have argued that these changes matter, and that new policies and new policy processes are required to deal with them. But are the issues of equal salience everywhere? Is this really a rich or middle-income country problem? One of us has written about ‘comparisons, convergence and connections’ between developed and developing countries (Maxwell, 1998b). Do we have connections and convergence, or merely interesting comparisons?

There are certainly issues which feature more prominently in richer, more urbanised or more industrialised developing countries. For example, Millstone and van Zwanenberg make a distinction between the bulk of developing countries and those few, like Argentina, China and Cuba, which have deliberately set out to foster a domestic industry dealing with GM crops and food. Similarly, the urbanisation issues will, for now, feature much more strongly in countries which are already highly urbanised than in those which are not: the salt and fat content of street foods is likely to matter much more in Zambia, say (39.8% urbanised in 2001), than in Ethiopia (15.9% urbanised in 2001) (UNDP, 2003).

However, there are also grounds for arguing that the new food policy agenda is relevant in some degree to all countries, and to a high degree in very many. This is for four reasons.

First, all countries engage in food trade, as both importers and exporters. All countries therefore need to be aware of the way in which global value chains are evolving, to review negotiating strategies, and to consider the regulatory environment and institutional structure within which trade takes place. Stevens, Page and Slater, Gibbon, and de Haen et al. are all eloquent on this point. There is much in the wider literature, for example on the WTO, to back them up (Morrissey, 2002); also on the opportunities for improved negotiation by developing countries (Page, 2003).

Second, the domestic food systems of the developing world are evolving rapidly. The best evidence on this comes from the extensive work on supermarkets by Reardon and his colleagues, in Latin America and Africa. Even in India, a laggard in this transformation, as Deshingkar et al. show, the supply chain is beginning to undergo the kinds of revolutionary changes seen elsewhere.

Third, the diet-related changes in nutrition and health are pervasive, and become visible at progressively lower levels of per capita GDP. Popkin’s data are particularly persuasive here: in 1962, countries reliant on fat for 20% of energy had an average per capita income of US$1475; by 1990, the income figure had halved, to only US$750 per capita. At the same time, overweight and obesity are increasing rapidly in the poorest countries, and the rate of change is higher in poorer countries than in rich ones. In general, Popkin tells us, obesity is associated with poverty, both between countries, and, importantly, within countries.

Fourth, the capacity to make food policy is probably weakest in just those poorest countries where the new challenges are emerging most rapidly. As various papers in this volume make clear, food policy-making is difficult because of the number of sectors

Food Policy Old and New 549

involved. It is also expensive. Millstone and van Zwanenberg cite the budget of the UK’s new Food Standards Agency as £115 million in 2001-2, covering a staff of nearly 600 people, and the budget of the new European Food Safety Authority as over £25m. in its first year, with an initial staff of 250. For developing countries, they point out, the scarcity of expertise and financial resources is likely to be a ‘significant constraint’. Of course, these numbers are trivial compared to the cost of getting policy wrong: the recent foot and mouth crisis in the UK is estimated to have cost around £6 billion (Anderson, 2002).

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mayring,2000.pdf

Volume 1, No. 2 – June 2000

Qualitative Content Analysis

Philipp Mayring

Abstract: The article describes an approach of systematic, rule guided qualitative text analysis, which tries to preserve some methodological strengths of quantitative content analysis and widen them to a concept of qualitative procedure.

First the development of content analysis is delineated and the basic principles are explained (units of analysis, step models, working with categories, validity and reliability). Then the central procedures of qualitative content analysis, inductive development of categories and deductive application of categories, are worked out. The possibilities of computer programs in supporting those qualitative steps of analysis are shown and the possibilities and limits of the approach are discussed.

Keywords: qualitative content analysis, content analysis, category, induction, intercoder-reliability

1. Introduction 2. History of Content Analysis 3. Basic Ideas of Content Analysis 4. Procedures of Qualitative Content Analysis 4.1 Inductive category development 4.2 Deductive category application 5. Computer Programs for Support of Qualitative Content Analysis 6. Examples of Projects Working with Qualitative Content Analysis 7. Discussion References Author Citation

1. Introduction

The qualitative content analysis (MAYRING 1983; 7th edition 2000), as it is presented here, consists in a bundle of techniques for systematic text analysis which we developed ca. 20 years ago in a longitudinal study about psycho-social consequences of unemployment (ULICH, HAUSSER, MAYRING et al. 1985). Conducting about 600 open-ended interviews we received more than 20.000 pages of transcripts which had to be analyzed in a qualitative oriented way. [1]

The main idea of the procedure of analysis is thereby, to preserve the advantages of quantitative content analysis as developed within communication science and to transfer and further develop them to qualitative-interpretative steps of analysis. [2]

Further information to quantitative content analysis are available via internet at http://www.gsu.edu/~wwwcom/content.html, http://www.zuma- mannheim.de/research/en/methods/textanalysis/ or http://www.aber.ac.uk/media/Sections/textan01.html. [3]

The object of (qualitative) content analysis can be all sort of recorded communication (transcripts of interviews, discourses, protocols of observations, video tapes, documents ...). Content analysis analyzes not only the manifest content of the material—as its name may suggest. BECKER & LISSMANN (1973) have differentiated levels of content: themes and main ideas of the text as primary content; context information as latent content. The analysis of formal aspects of the material belongs to its aims as well. As outlined below content analysis embeds the text into a model of communication within which it defines the aims of analysis. This is expressed by KRIPPENDORFF, who defines "content analysis as the use of replicable and valid method for making specific inferences from text to other states or properties of its source" (KRIPPENDORFF 1969, p.103). [4]

Qualitative content analysis defines itself within this framework as an approach of empirical, methodological controlled analysis of texts within their context of communication, following content analytical rules and step by step models, without rash quantification. [5]

2. History of Content Analysis

We can distinguish different phases in the historical background of content analysis (cf. MERTEN 1983; KRIPPENDORFF 1980; MAYRING 1994a):

• Precursors: We find different approaches to analysis and comparison of texts in hermeneutic contexts (e.g. bible interpretations) early newspaper analysis, graphological procedures, up to the dream analysis by Sigmund FREUD.

• Communication theoretical foundation: The basis of quantitative content analysis had been laid by Paul F. LAZARSFELD and Harold D. LASSWELL in USA during he 20ies and 30ies of 20th century. The first textbook about this method had been published (BERELSON 1952).

• Interdisciplinary broadening and differentiation: In the sixties of 20th century the methodological approach found its way into linguistics, psychology (cf. RUST 1983), sociology, history, arts etc. The procedures had been refined (fitting into different models of communication; analysis of non-verbal aspects, contingency analysis, computer applications) (cf. POOL 1959; GERBNER,HOLSTI, KRIPPENDORFF, PAISLEY & STONE 1969).

• Phase of qualitative critics: Since the middle of 20th century objections were raised against a superficial analysis without respecting latent contents and contexts, working with simplifying and distorting quantification (KRACAUER 1952). In the following qualitative approaches to content analysis had been developed (RITSERT 1972; MOSTYN 1985; WITTKOWSKI 1994; ALTHEIDE 1996). [6]

3. Basic Ideas of Content Analysis

If we say, qualitative content analysis wants to preserve the advantages of quantitative content analysis for a more qualitative text interpretation, so what are those advantages? I want to emphasize four points:

• Fitting the material into a model of communication: It should be determined on what part of the communication inferences shall be made, to aspects of the

communicator (his experiences, opinions feelings), to the situation of text production, to the socio-cultural background, to the text itself or to the effect of the message.

• Rules of analysis: The material is to be analyzed step by step, following rules of procedure, devising the material into content analytical units.

• Categories in the center of analysis: The aspects of text interpretation, following the research questions, are putted into categories, which were carefully founded and revised within the process of analysis (feedback loops).

• Criteria of reliability and validity: The procedure has the pretension to be inter- subjectively comprehensible, to compare he results with other studies in the sense of triangulation and to carry out checks for reliability. For estimating the inter-coder reliability we use in qualitative content analysis (in contrary to quantitative content analysis) only trained members of the project team and we reduce the standard of coder agreement (COHENS Kappa over .7 would be sufficient). [7]

4. Procedures of Qualitative Content Analysis

The above listed components of quantitative content analysis will be preserved to be the fundament for a qualitative oriented procedure of text interpretation. We developed a number of procedures of qualitative content analysis (cf. MAYRING 2000) amongst which two approaches are central: inductive category development and deductive category application. [8]

4.1 Inductive category development

Classical quantitative content analysis has few answers to the question from where the categories come, how the system of categories is developed: "How categories are defined ... is an art. Little is written about it." (KRIPPENDORF 1980, p.76). [9]

But within the framework of qualitative approaches it would be of central interest, to develop the aspects of interpretation, the categories, as near as possible to the material, to formulate them in terms of the material. For that scope qualitative content analysis has developed procedures of inductive category development, which are oriented to the reductive processes formulated within the psychology of text processing (cf. BALLSTAEDT, MANDL, SCHNOTZ & TERGAN 1981; van DIJK 1980). [10]

Fig. 1: Step model of inductive category development (MAYRING 2000) [11]

The specific steps cannot be explained largely within this short overview. The main idea of the procedure is, to formulate a criterion of definition, derived from theoretical background and research question, which determines the aspects of the textual material taken into account. Following this criterion the material is worked through and categories are tentative and step by step deduced. Within a feedback loop those categories are revised, eventually reduced to main categories and checked in respect to their reliability. If the research question suggests quantitative aspects (e.g. frequencies of coded categories) can be analyzed. [12]

4.2 Deductive category application

Deductive category application works with prior formulated, theoretical derived aspects of analysis, bringing them in connection with the text. The qualitative step of analysis consists in a methodological controlled assignment of the category to a passage of text. Even if several procedures of text analysis are processing that step, it is poorly described. Here the step model within qualitative content analysis: [13]

Fig.2: Step model of deductive category application (MAYRING 2000) [14]

Then main idea here is to give explicit definitions, examples and coding rules for each deductive category, determining exactly under what circumstances a text passage can be coded with a category. Those category definitions are putted together within a coding agenda. [15]

Category Definition Examples Coding Rules

C1: high self confidence

High subjective conviction to have successfully coped with the situational demands, which means

- to be clear about the demands and their coping possibilities,

- to have a positive, hopeful feeling in handling the situation,

- to be sure to have coped with the demands on ones own efforts.

"Of course there had been some little problems, but we solved them all, either I myself or the student gave in, depends who made a mistake. Everyone can make mistakes." (17, 23)

"Sure there had been problems, but in the end we had a fine relationship. We got it all together." (27, 33)

All three aspects of thew definition have to point to "high" self confidence no aspect only "middle"

Otherwise C2: middle self confidence

C2: middle self confidence

Only partly or fluctuating conviction to have successfully coped with the situational demands

"Quite often I found it hard to maneuver through the problems, but finally I made it." (13, 45)

"Time by time everything got better , but I couldn’t tell if it was me or the circumstances." (77, 20)

If not all aspects of definition point to "High" or "low"

K3: low self concept

Conviction to have badly coped with the situational demands, which means

- not to know what the situation exactly demands,

- to have a negative, pessimistic feeling in handling the situation,

- to be sure that ones own efforts had no effect on improving the situation.

"that stroke my self confidence; I thought I’m a nothing – or even less than that." (5, 34)

All three aspects of definition point to low self confidence,

no fluctuations recognizable

Fig.3: Example for a coding agenda [16]

Category definitions, prototypical text passages, and rules for distinguishing different categories were formulated in respect to theory and material, are completed step by step, and are revised with the process of analysis. [17]

5. Computer Programs for Support of Qualitative Content Analysis

Especially within the last years several computer programs had been developed within the framework of qualitative analysis to support (not to replace) steps of text

interpretation (cf. HUBER 1992; WEITZMAN & MILES 1995; MAYRING 1996; FIELDING & LEE 1998). The computer plays here a triple role:

• He works as assistant, supporting and making easier the steps of text analysis on screen (working through the material, underlining, writing marginal notes, defining category definitions and coding rules, recording comments on the material ...). He offers helpful tools handling the text (searching, jumping to different passages, collecting and editing passages ...).

• He works as documentation center, recording all steps of analysis of all interpreters, making the analysis comprehensible and replicable (e.g. to trace back in the material causes of non-reliabilities between two coders).

• He offers links to quantitative analysis (often already implemented within the program), e.g. to compare frequencies of categories, without the dangers of errors in data transfer by hand to another computer program. [18]

Working with qualitative content analysis two computer programs had especially proved it’s worth, ATLAS/ti and winMAX, which both are available in free demo- versions (http://www.atlasti.de and http://www.winmax.de). [19]

6. Examples of Projects Working with Qualitative Content Analysis

To demonstrate the possibilities of qualitative content analysis we want to give some short examples of research projects working with the above explicated procedures:

• Sandro VICINI (1993) has conducted 14 open-ended in-depth interviews with educational advisors about concrete case-studies from their advisory service with the aim to reconstruct their theory of mind of advice. He used summarizing qualitative content analysis leading to eight main categories. The results were, that advice praxis had become therapy-oriented, that there are totally different concepts of advice, and that the advisors react highly professional. [20]

• Christa GERWIN (1993) made a diary study with 21 middle school teachers about their daily hassles and uplifts and analyzed the transcripts with summarizing qualitative content analysis. She could demonstrate, that being a teacher means severe stresses, from everyday problems with the copy machine to treating students with behavior disorders. [21]

• Klaus BECK and Gerhard VOWE (1995) have analyzed 25 media products (newspapers, journals, radio transmissions) concerning new multimedia approaches. With a combination of inductive and deductive qualitative content analysis they found patterns of argumentation like: euphoria about multimedia; economic optimism; political critic; apocalyptic predictions. [22]

• Claudia DOLDE and Klaus GOETZ (1995) have conducted 5 open-ended interviews with adult students in a on-job computer education studio. Working with inductive and deductive qualitative content analysis they analyzed their learning activities and learning strategies. The main advantage of the learning concept in the course seemed to be flexibility in time, as main disadvantage appeared heterogeneity of course members. [23]

• Joachim BAUER et al. (1998) analyzed the biographies of 21 Alzheimer disease patients to find out common patterns and to compare them with 11 vascular dement

patients of the same age. The biographical interviews had been worked through with qualitative content analysis and led to typical biographical patterns (e.g. over- protecting social network) of the Alzheimer patients. [24]

• In our own team we just finished a study on unemployment of teachers in the Eastern parts of Germany (MAYRING, KOENIG, BIRK & HURST 2000). The material had been open-ended interviews and open-ended biographical questionnaire of 50 unemployed teachers, asking for their psycho-social stresses and coping behavior. The results had been compared with a former study on teacher unemployment in West Germany of our team. Inductive and deductive computer-assisted content analysis pointed out that the doubled crisis situation of the persons (unemployment and German unification) causes specific stresses and new chances for adaptation. [25]

7. Discussion

With the qualitative content analysis we wanted to describe procedures of systematic text analysis, which try to preserve the strengths of content analysis in communication science (theory reference, step models, model of communication, category leaded, criteria of validity and reliability) to develop qualitative procedures (inductive category development, summarizing, context analysis, deductive category application) which are methodological controlled. Those procedures allow a connection to quantitative steps of analysis if it seems meaningful for the analyst. [26]

The procedures of qualitative content analysis seem less appropriate,

• if the research question is highly open-ended, explorative, variable and working with categories would be a restriction, or

• if a more holistic, not step-by-step ongoing of analysis is planned. [27]

On the other hand qualitative content analysis can be combined with other qualitative procedures. The research question and the characteristics of the material should have the priority in the decision about adapted methods. So it would be in my opinion better to discuss questions about methods in respect to specific content areas (cf. coping of illness MAYRING 1994b; emotion research SCHMITT & MAYRING 2000) and then to compare different methodological approaches (quantitative approaches as well). [28]

References

Altheide, D.L. (1996). Qualitative media analysis. Qualitative Research Methods Vol. 38. Thousand Oaks: Sage.

Ballstaedt, S.-P., Mandl, H., Schnotz, W. & Tergan, S.-O. (1981). Texte verstehen, Texte gestalten. München: Urban & Schwarzenberg.

Bauer, J., Qualmann, J., Stadtmüller, G., Bauer, H. (1998). Lebenslaufuntersuchungen bei Alzheimer- Patienten: Qualitative Inhaltsanalyse prämorbider Entwicklungsprozesse. In Kruse, A. (Ed.), Psychosoziale Gerontologie. Band 2: Intervention (pp.251-274). Göttingen: Hogrefe.

Beck, K. & Vowe, G. (1995). Multimedia aus der Sicht der Medien. Argumentationsmuster und Sichtweisen in der medialen Konstruktion. Rundfunk und Fernsehen, 43, 549-563.

Becker, J. & Lißmann, H.-J. (1973). Inhaltsanalyse - Kritik einer sozialwissenschaftlichen Methode. Arbeitspapiere zur politischen Soziologie 5. München: Olzog.

Berelson, B. (1952). Content analysis in communication research. Glencoe, Ill.: Free Press.

Dijk van, T.A. (1980). Macrostructures. Hillsdale, N.J.: Erlbaum.

Dolde, C. & Götz, K. (1995). Subjektive Theorien zu Lernformen in der betrieblichen DV-Qualifizierung. Unterrichtswissenschaft, 23, 264-287.

Fielding, N.G. & Lee, R.M. (1998). Computer analysis and qualitative research. London: Sage.

Gerbner, G., Holsti, O.R., Krippendorff, K., Paisley, W.J.& Stone, Ph.J. (Eds.) (1969). The analysis of communication content. New York: Wiley.

Gerwin, C. (1994). Streß in der Schule - Belastungswahrnehmung von Lehrerinnen und Lehrern. Zeitschrift für Pädagogische Psychologie, 8, 41-53.

Huber, G.L. (Ed.) (1992). Qualitative Analyse. Computereinsatz in der Sozialforschung. München: Oldenbourg Verlag.

Kracauer, S. (1952). The challenge of qualitative content analysis. Public Opinion Quarterly, 16, 631- 642.

Krippendorff, K. (1969). Models of messages: three prototypes. In G. Gerbner, O.R. Holsti, K. Krippendorff, G.J. Paisly & Ph.J. Stone (Eds.), The analysis of communication content. New York: Wiley.

Krippendorff, K. (1980). Content analysis. An Introduction to its Methodology. Beverly Hills: Sage.

Mayring, Ph. (1994a). Qualitative Inhaltsanalyse. In A. Böhm, A. Mengel & T. Muhr (Eds.), Texte verstehen: Konzepte, Methoden, Werkzeuge (pp.159-176). Konstanz: Universitätsverlag.

Mayring, Ph. (1994b). Qualitative Ansätze in der Krankheitsbewältigungsforschung. In E. Heim & M. Perrez, (Eds.), Krankheitsverarbeitung. Jahrbuch der Medizinischen Psychologie 10 (pp.38-48). Göttingen: Hogrefe.

Mayring, Ph. (1996). Einführung in die qualitativer Sozialforschung. Eine Anleitung zu qualitativem Denken (3

rd edition). Weinheim: Psychologie Verlags Union.

Mayring, Ph. (2000). Qualitative Inhaltsanalyse. Grundlagen und Techniken (7 th edition, first edition

1983). Weinheim: Deutscher Studien Verlag.

Mayring, Ph., König, J., Birk, N. & Hurst, A. (2000). Opfer der Einheit. Eine Studie zur Lehrerarbeitslosigkeit in den neuen Bundesländern. Opladen; Leske & Budrich.

Merten, K. (1983). Inhaltsanalyse. Einführung in Theorie, Methode und Praxis. Opladen: Westdeutscher Verlag.

Mostyn, B. (1985). The content analysis of qualitative research data: A dynamic approach. In M. Brenner, J. Brown & D. Cauter (Eds.), The research interview (pp.115-145). London: Academic Press.

Pool, J.d.S. (1959). Trends in content analysis. Urbana: University of Illinois Press.

Ritsert, J. (1972). Inhaltsanalyse und Ideologiekritik. Ein Versuch über kritische Sozialforschung. Frankfurt: Athenäum.

Rust, H. (1983). Inhaltsanalyse. Die Praxis der indirekten Interaktionsforschung in Psychologie und Psychotherapie. München: Urban & Schwarzenberg.

Schmitt, A. & Mayring, Ph. (2000, in press). Qualitativ orientierte Methoden. In J.H. Otto, H.A. Euler & H. Mandl. (Eds.), Handbuch Emotionspsychologie. Weinheim: Psychologie Verlags Union.

Ulich, D., Haußer, K., Mayring, Ph., Strehmel, P., Kandler, M. & Degenhardt, B. (1985). Psychologie der Krisenbewältigung. Eine Längsschnittuntersuchung mit arbeitslosen Lehrern. Weinheim: Beltz.

Vicini, S. (1993). Subjektive Beratungstheorien. Bernische ErziehungsberaterInnen reflektieren ihre Praxis. Bern: Lang.

Weitzmann, E.A. & Miles, M.B. (1995). Computer Programs for Qualitativ Data Analysis. Newbury Park: Sage.

Wittkowski, J. (1994). Das Interview in der Psychologie. Interviewtechnik und Codierung von Interviewmaterial. Opladen: Westdeutscher Verlag.

Author

Philipp MAYRING

Citation

Please cite this article as follows (and include paragraph numbers if necessary):

Mayring, Philipp (2000, June). Qualitative Content Analysis [28 paragraphs]. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research [On-line Journal], 1(2). Available at: http://qualitative-research.net/fqs/fqs-e/2-00inhalt-e.htm [Date of access: Month Day, Year]

Copyright © 2000 FQS http://qualitative-research.net/fqs

Revised: April 2001

Mayring.pdf

Volume 1, No. 2 – June 2000

Qualitative Content Analysis

Philipp Mayring

Abstract: The article describes an approach of systematic, rule guided qualitative text analysis, which tries to preserve some methodological strengths of quantitative content analysis and widen them to a concept of qualitative procedure.

First the development of content analysis is delineated and the basic principles are explained (units of analysis, step models, working with categories, validity and reliability). Then the central procedures of qualitative content analysis, inductive development of categories and deductive application of categories, are worked out. The possibilities of computer programs in supporting those qualitative steps of analysis are shown and the possibilities and limits of the approach are discussed.

Keywords: qualitative content analysis, content analysis, category, induction, intercoder-reliability

1. Introduction 2. History of Content Analysis 3. Basic Ideas of Content Analysis 4. Procedures of Qualitative Content Analysis 4.1 Inductive category development 4.2 Deductive category application 5. Computer Programs for Support of Qualitative Content Analysis 6. Examples of Projects Working with Qualitative Content Analysis 7. Discussion References Author Citation

1. Introduction

The qualitative content analysis (MAYRING 1983; 7th edition 2000), as it is presented here, consists in a bundle of techniques for systematic text analysis which we developed ca. 20 years ago in a longitudinal study about psycho-social consequences of unemployment (ULICH, HAUSSER, MAYRING et al. 1985). Conducting about 600 open-ended interviews we received more than 20.000 pages of transcripts which had to be analyzed in a qualitative oriented way. [1]

The main idea of the procedure of analysis is thereby, to preserve the advantages of quantitative content analysis as developed within communication science and to transfer and further develop them to qualitative-interpretative steps of analysis. [2]

Further information to quantitative content analysis are available via internet at http://www.gsu.edu/~wwwcom/content.html, http://www.zuma- mannheim.de/research/en/methods/textanalysis/ or http://www.aber.ac.uk/media/Sections/textan01.html. [3]

The object of (qualitative) content analysis can be all sort of recorded communication (transcripts of interviews, discourses, protocols of observations, video tapes, documents ...). Content analysis analyzes not only the manifest content of the material—as its name may suggest. BECKER & LISSMANN (1973) have differentiated levels of content: themes and main ideas of the text as primary content; context information as latent content. The analysis of formal aspects of the material belongs to its aims as well. As outlined below content analysis embeds the text into a model of communication within which it defines the aims of analysis. This is expressed by KRIPPENDORFF, who defines "content analysis as the use of replicable and valid method for making specific inferences from text to other states or properties of its source" (KRIPPENDORFF 1969, p.103). [4]

Qualitative content analysis defines itself within this framework as an approach of empirical, methodological controlled analysis of texts within their context of communication, following content analytical rules and step by step models, without rash quantification. [5]

2. History of Content Analysis

We can distinguish different phases in the historical background of content analysis (cf. MERTEN 1983; KRIPPENDORFF 1980; MAYRING 1994a):

• Precursors: We find different approaches to analysis and comparison of texts in hermeneutic contexts (e.g. bible interpretations) early newspaper analysis, graphological procedures, up to the dream analysis by Sigmund FREUD.

• Communication theoretical foundation: The basis of quantitative content analysis had been laid by Paul F. LAZARSFELD and Harold D. LASSWELL in USA during he 20ies and 30ies of 20th century. The first textbook about this method had been published (BERELSON 1952).

• Interdisciplinary broadening and differentiation: In the sixties of 20th century the methodological approach found its way into linguistics, psychology (cf. RUST 1983), sociology, history, arts etc. The procedures had been refined (fitting into different models of communication; analysis of non-verbal aspects, contingency analysis, computer applications) (cf. POOL 1959; GERBNER,HOLSTI, KRIPPENDORFF, PAISLEY & STONE 1969).

• Phase of qualitative critics: Since the middle of 20th century objections were raised against a superficial analysis without respecting latent contents and contexts, working with simplifying and distorting quantification (KRACAUER 1952). In the following qualitative approaches to content analysis had been developed (RITSERT 1972; MOSTYN 1985; WITTKOWSKI 1994; ALTHEIDE 1996). [6]

3. Basic Ideas of Content Analysis

If we say, qualitative content analysis wants to preserve the advantages of quantitative content analysis for a more qualitative text interpretation, so what are those advantages? I want to emphasize four points:

• Fitting the material into a model of communication: It should be determined on what part of the communication inferences shall be made, to aspects of the

communicator (his experiences, opinions feelings), to the situation of text production, to the socio-cultural background, to the text itself or to the effect of the message.

• Rules of analysis: The material is to be analyzed step by step, following rules of procedure, devising the material into content analytical units.

• Categories in the center of analysis: The aspects of text interpretation, following the research questions, are putted into categories, which were carefully founded and revised within the process of analysis (feedback loops).

• Criteria of reliability and validity: The procedure has the pretension to be inter- subjectively comprehensible, to compare he results with other studies in the sense of triangulation and to carry out checks for reliability. For estimating the inter-coder reliability we use in qualitative content analysis (in contrary to quantitative content analysis) only trained members of the project team and we reduce the standard of coder agreement (COHENS Kappa over .7 would be sufficient). [7]

4. Procedures of Qualitative Content Analysis

The above listed components of quantitative content analysis will be preserved to be the fundament for a qualitative oriented procedure of text interpretation. We developed a number of procedures of qualitative content analysis (cf. MAYRING 2000) amongst which two approaches are central: inductive category development and deductive category application. [8]

4.1 Inductive category development

Classical quantitative content analysis has few answers to the question from where the categories come, how the system of categories is developed: "How categories are defined ... is an art. Little is written about it." (KRIPPENDORF 1980, p.76). [9]

But within the framework of qualitative approaches it would be of central interest, to develop the aspects of interpretation, the categories, as near as possible to the material, to formulate them in terms of the material. For that scope qualitative content analysis has developed procedures of inductive category development, which are oriented to the reductive processes formulated within the psychology of text processing (cf. BALLSTAEDT, MANDL, SCHNOTZ & TERGAN 1981; van DIJK 1980). [10]

Fig. 1: Step model of inductive category development (MAYRING 2000) [11]

The specific steps cannot be explained largely within this short overview. The main idea of the procedure is, to formulate a criterion of definition, derived from theoretical background and research question, which determines the aspects of the textual material taken into account. Following this criterion the material is worked through and categories are tentative and step by step deduced. Within a feedback loop those categories are revised, eventually reduced to main categories and checked in respect to their reliability. If the research question suggests quantitative aspects (e.g. frequencies of coded categories) can be analyzed. [12]

4.2 Deductive category application

Deductive category application works with prior formulated, theoretical derived aspects of analysis, bringing them in connection with the text. The qualitative step of analysis consists in a methodological controlled assignment of the category to a passage of text. Even if several procedures of text analysis are processing that step, it is poorly described. Here the step model within qualitative content analysis: [13]

Fig.2: Step model of deductive category application (MAYRING 2000) [14]

Then main idea here is to give explicit definitions, examples and coding rules for each deductive category, determining exactly under what circumstances a text passage can be coded with a category. Those category definitions are putted together within a coding agenda. [15]

Category Definition Examples Coding Rules

C1: high self confidence

High subjective conviction to have successfully coped with the situational demands, which means

- to be clear about the demands and their coping possibilities,

- to have a positive, hopeful feeling in handling the situation,

- to be sure to have coped with the demands on ones own efforts.

"Of course there had been some little problems, but we solved them all, either I myself or the student gave in, depends who made a mistake. Everyone can make mistakes." (17, 23)

"Sure there had been problems, but in the end we had a fine relationship. We got it all together." (27, 33)

All three aspects of thew definition have to point to "high" self confidence no aspect only "middle"

Otherwise C2: middle self confidence

C2: middle self confidence

Only partly or fluctuating conviction to have successfully coped with the situational demands

"Quite often I found it hard to maneuver through the problems, but finally I made it." (13, 45)

"Time by time everything got better , but I couldn’t tell if it was me or the circumstances." (77, 20)

If not all aspects of definition point to "High" or "low"

K3: low self concept

Conviction to have badly coped with the situational demands, which means

- not to know what the situation exactly demands,

- to have a negative, pessimistic feeling in handling the situation,

- to be sure that ones own efforts had no effect on improving the situation.

"that stroke my self confidence; I thought I’m a nothing – or even less than that." (5, 34)

All three aspects of definition point to low self confidence,

no fluctuations recognizable

Fig.3: Example for a coding agenda [16]

Category definitions, prototypical text passages, and rules for distinguishing different categories were formulated in respect to theory and material, are completed step by step, and are revised with the process of analysis. [17]

5. Computer Programs for Support of Qualitative Content Analysis

Especially within the last years several computer programs had been developed within the framework of qualitative analysis to support (not to replace) steps of text

interpretation (cf. HUBER 1992; WEITZMAN & MILES 1995; MAYRING 1996; FIELDING & LEE 1998). The computer plays here a triple role:

• He works as assistant, supporting and making easier the steps of text analysis on screen (working through the material, underlining, writing marginal notes, defining category definitions and coding rules, recording comments on the material ...). He offers helpful tools handling the text (searching, jumping to different passages, collecting and editing passages ...).

• He works as documentation center, recording all steps of analysis of all interpreters, making the analysis comprehensible and replicable (e.g. to trace back in the material causes of non-reliabilities between two coders).

• He offers links to quantitative analysis (often already implemented within the program), e.g. to compare frequencies of categories, without the dangers of errors in data transfer by hand to another computer program. [18]

Working with qualitative content analysis two computer programs had especially proved it’s worth, ATLAS/ti and winMAX, which both are available in free demo- versions (http://www.atlasti.de and http://www.winmax.de). [19]

6. Examples of Projects Working with Qualitative Content Analysis

To demonstrate the possibilities of qualitative content analysis we want to give some short examples of research projects working with the above explicated procedures:

• Sandro VICINI (1993) has conducted 14 open-ended in-depth interviews with educational advisors about concrete case-studies from their advisory service with the aim to reconstruct their theory of mind of advice. He used summarizing qualitative content analysis leading to eight main categories. The results were, that advice praxis had become therapy-oriented, that there are totally different concepts of advice, and that the advisors react highly professional. [20]

• Christa GERWIN (1993) made a diary study with 21 middle school teachers about their daily hassles and uplifts and analyzed the transcripts with summarizing qualitative content analysis. She could demonstrate, that being a teacher means severe stresses, from everyday problems with the copy machine to treating students with behavior disorders. [21]

• Klaus BECK and Gerhard VOWE (1995) have analyzed 25 media products (newspapers, journals, radio transmissions) concerning new multimedia approaches. With a combination of inductive and deductive qualitative content analysis they found patterns of argumentation like: euphoria about multimedia; economic optimism; political critic; apocalyptic predictions. [22]

• Claudia DOLDE and Klaus GOETZ (1995) have conducted 5 open-ended interviews with adult students in a on-job computer education studio. Working with inductive and deductive qualitative content analysis they analyzed their learning activities and learning strategies. The main advantage of the learning concept in the course seemed to be flexibility in time, as main disadvantage appeared heterogeneity of course members. [23]

• Joachim BAUER et al. (1998) analyzed the biographies of 21 Alzheimer disease patients to find out common patterns and to compare them with 11 vascular dement

patients of the same age. The biographical interviews had been worked through with qualitative content analysis and led to typical biographical patterns (e.g. over- protecting social network) of the Alzheimer patients. [24]

• In our own team we just finished a study on unemployment of teachers in the Eastern parts of Germany (MAYRING, KOENIG, BIRK & HURST 2000). The material had been open-ended interviews and open-ended biographical questionnaire of 50 unemployed teachers, asking for their psycho-social stresses and coping behavior. The results had been compared with a former study on teacher unemployment in West Germany of our team. Inductive and deductive computer-assisted content analysis pointed out that the doubled crisis situation of the persons (unemployment and German unification) causes specific stresses and new chances for adaptation. [25]

7. Discussion

With the qualitative content analysis we wanted to describe procedures of systematic text analysis, which try to preserve the strengths of content analysis in communication science (theory reference, step models, model of communication, category leaded, criteria of validity and reliability) to develop qualitative procedures (inductive category development, summarizing, context analysis, deductive category application) which are methodological controlled. Those procedures allow a connection to quantitative steps of analysis if it seems meaningful for the analyst. [26]

The procedures of qualitative content analysis seem less appropriate,

• if the research question is highly open-ended, explorative, variable and working with categories would be a restriction, or

• if a more holistic, not step-by-step ongoing of analysis is planned. [27]

On the other hand qualitative content analysis can be combined with other qualitative procedures. The research question and the characteristics of the material should have the priority in the decision about adapted methods. So it would be in my opinion better to discuss questions about methods in respect to specific content areas (cf. coping of illness MAYRING 1994b; emotion research SCHMITT & MAYRING 2000) and then to compare different methodological approaches (quantitative approaches as well). [28]

References

Altheide, D.L. (1996). Qualitative media analysis. Qualitative Research Methods Vol. 38. Thousand Oaks: Sage.

Ballstaedt, S.-P., Mandl, H., Schnotz, W. & Tergan, S.-O. (1981). Texte verstehen, Texte gestalten. München: Urban & Schwarzenberg.

Bauer, J., Qualmann, J., Stadtmüller, G., Bauer, H. (1998). Lebenslaufuntersuchungen bei Alzheimer- Patienten: Qualitative Inhaltsanalyse prämorbider Entwicklungsprozesse. In Kruse, A. (Ed.), Psychosoziale Gerontologie. Band 2: Intervention (pp.251-274). Göttingen: Hogrefe.

Beck, K. & Vowe, G. (1995). Multimedia aus der Sicht der Medien. Argumentationsmuster und Sichtweisen in der medialen Konstruktion. Rundfunk und Fernsehen, 43, 549-563.

Becker, J. & Lißmann, H.-J. (1973). Inhaltsanalyse - Kritik einer sozialwissenschaftlichen Methode. Arbeitspapiere zur politischen Soziologie 5. München: Olzog.

Berelson, B. (1952). Content analysis in communication research. Glencoe, Ill.: Free Press.

Dijk van, T.A. (1980). Macrostructures. Hillsdale, N.J.: Erlbaum.

Dolde, C. & Götz, K. (1995). Subjektive Theorien zu Lernformen in der betrieblichen DV-Qualifizierung. Unterrichtswissenschaft, 23, 264-287.

Fielding, N.G. & Lee, R.M. (1998). Computer analysis and qualitative research. London: Sage.

Gerbner, G., Holsti, O.R., Krippendorff, K., Paisley, W.J.& Stone, Ph.J. (Eds.) (1969). The analysis of communication content. New York: Wiley.

Gerwin, C. (1994). Streß in der Schule - Belastungswahrnehmung von Lehrerinnen und Lehrern. Zeitschrift für Pädagogische Psychologie, 8, 41-53.

Huber, G.L. (Ed.) (1992). Qualitative Analyse. Computereinsatz in der Sozialforschung. München: Oldenbourg Verlag.

Kracauer, S. (1952). The challenge of qualitative content analysis. Public Opinion Quarterly, 16, 631- 642.

Krippendorff, K. (1969). Models of messages: three prototypes. In G. Gerbner, O.R. Holsti, K. Krippendorff, G.J. Paisly & Ph.J. Stone (Eds.), The analysis of communication content. New York: Wiley.

Krippendorff, K. (1980). Content analysis. An Introduction to its Methodology. Beverly Hills: Sage.

Mayring, Ph. (1994a). Qualitative Inhaltsanalyse. In A. Böhm, A. Mengel & T. Muhr (Eds.), Texte verstehen: Konzepte, Methoden, Werkzeuge (pp.159-176). Konstanz: Universitätsverlag.

Mayring, Ph. (1994b). Qualitative Ansätze in der Krankheitsbewältigungsforschung. In E. Heim & M. Perrez, (Eds.), Krankheitsverarbeitung. Jahrbuch der Medizinischen Psychologie 10 (pp.38-48). Göttingen: Hogrefe.

Mayring, Ph. (1996). Einführung in die qualitativer Sozialforschung. Eine Anleitung zu qualitativem Denken (3

rd edition). Weinheim: Psychologie Verlags Union.

Mayring, Ph. (2000). Qualitative Inhaltsanalyse. Grundlagen und Techniken (7 th edition, first edition

1983). Weinheim: Deutscher Studien Verlag.

Mayring, Ph., König, J., Birk, N. & Hurst, A. (2000). Opfer der Einheit. Eine Studie zur Lehrerarbeitslosigkeit in den neuen Bundesländern. Opladen; Leske & Budrich.

Merten, K. (1983). Inhaltsanalyse. Einführung in Theorie, Methode und Praxis. Opladen: Westdeutscher Verlag.

Mostyn, B. (1985). The content analysis of qualitative research data: A dynamic approach. In M. Brenner, J. Brown & D. Cauter (Eds.), The research interview (pp.115-145). London: Academic Press.

Pool, J.d.S. (1959). Trends in content analysis. Urbana: University of Illinois Press.

Ritsert, J. (1972). Inhaltsanalyse und Ideologiekritik. Ein Versuch über kritische Sozialforschung. Frankfurt: Athenäum.

Rust, H. (1983). Inhaltsanalyse. Die Praxis der indirekten Interaktionsforschung in Psychologie und Psychotherapie. München: Urban & Schwarzenberg.

Schmitt, A. & Mayring, Ph. (2000, in press). Qualitativ orientierte Methoden. In J.H. Otto, H.A. Euler & H. Mandl. (Eds.), Handbuch Emotionspsychologie. Weinheim: Psychologie Verlags Union.

Ulich, D., Haußer, K., Mayring, Ph., Strehmel, P., Kandler, M. & Degenhardt, B. (1985). Psychologie der Krisenbewältigung. Eine Längsschnittuntersuchung mit arbeitslosen Lehrern. Weinheim: Beltz.

Vicini, S. (1993). Subjektive Beratungstheorien. Bernische ErziehungsberaterInnen reflektieren ihre Praxis. Bern: Lang.

Weitzmann, E.A. & Miles, M.B. (1995). Computer Programs for Qualitativ Data Analysis. Newbury Park: Sage.

Wittkowski, J. (1994). Das Interview in der Psychologie. Interviewtechnik und Codierung von Interviewmaterial. Opladen: Westdeutscher Verlag.

Author

Philipp MAYRING

Citation

Please cite this article as follows (and include paragraph numbers if necessary):

Mayring, Philipp (2000, June). Qualitative Content Analysis [28 paragraphs]. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research [On-line Journal], 1(2). Available at: http://qualitative-research.net/fqs/fqs-e/2-00inhalt-e.htm [Date of access: Month Day, Year]

Copyright © 2000 FQS http://qualitative-research.net/fqs

Revised: April 2001

MCCLUSKEY_Swinnen_media_11.pdf

©2011 EuropEan MolEcular Biology organization EMBo reports 1

outlookoutlook

i n 1996, at the height of the scandal about mad cow disease in the uK, a guest on oprah Winfrey’s talk show claimed that

meat produced in the uSa could cause bovine spongiform encephalopathy (BSE). “that just stopped me cold from eating another burger,” Winfrey responded. later, beef farmers from texas sued Winfrey’s show, claiming that it was partly respon- sible for the steep decline in beef prices in the uSa during the following months, even though the country did not have a single case of BSE. this episode demonstrates not only the power of the media and its influ- ence on the public, but also how easily the public is swayed, particularly by fear, even in the absence of information.

nevertheless, more information is not necessarily a panacea for disinformation. Households in developed countries have greater access to information than ever before—through television, newspapers, journals, radio and the internet—yet the public remains, ironically, poorly informed. this is most evident when consumption of a food dramatically declines after media reports about contamination or harm, or when European consumers vehemently oppose genetically modified food, despite accumulating scientific evidence that these products do not harm the environment and are safe for human consumption.

there are various understandable causes of public reactions to food scares or food-

health stories in the media, but the media itself sets the stage for the public’s response by choosing which information to present and, perhaps more importantly, how to present it. Extensive media coverage affects consumer perceptions of products and risks and, consequently, can influence demand for services and products.

t he function of the media is not to fos- ter the public good or to reassure the public that they are safe. Most tele-

vision stations and newspapers are now privately owned—many of them by one of a few huge companies. the media therefore has its own financial and other interests, and needs to please both shareholders and audi- ences by providing the kind of information and analysis that mass audiences expect. Similarly, other sources of information— such as agriculture and biotechnology companies, universities and farmers—have equally powerful incentives that could bias the information they are willing to share and the conclusions they seek to draw. in the uSa, news coverage has always been largely commercial in this way, whereas in Europe, private companies have only become the dominant source of informa- tion during the past two decades. Moreover, the structure of the media market itself has changed with the growth of 24-hour news and the internet—notably in terms of blogs, social media and the ability to distribute videos online.

one criticism that is often levelled at the media is that it sensationalizes news and is biased against positive news stories. instead, the media seems to focus on negative news stories and shun careful and balanced analysis of an issue, favouring ‘sound bites’ and simplistic conclusions. commercial news reporting tends to focus on events,

such as a sudden food-safety problem or an organized event accompanying the launch of a new product or policy.

the overall concern is that the increas- ing commercialization of the media has led to a ‘dumbing down’ of the news; that is, lower-quality journalism and less coverage of complex issues, driven by competitive pressures that have forced media companies to cut back on reporting and editorial staff in areas that do not attract many readers or viewers (alterman, 2008; zaller, 1999). the emergence of the 24-hour news cycle might even have further weakened journalistic standards; modern news reports have been found to contain an increasing number of factual errors (pew, 2004).

these concerns have caused many European governments to continue their subsidized public broadcasting, in order to maintain the overall quality and reliability of news and information. However, if sub- sidized public media cover the high-quality news market, it might further decrease the quality of coverage offered by commercial companies (canoy & nahuis, 2005). this

The media and food-risk perceptions Science & Society series on Food and Science

Jill McCluskey & Johan Swinnen

There are various understandable causes of public reactions to food scares or food-health stories in the media, but the media itself sets the stage for the public’s response…

The function of the media is not to foster the public good or to reassure the public that they are safe

ssssss Science & Society Series on Food and Science

This article is part of the EMBO reports Science & Society series on 'food and science' to highlight the role of natural and social sciences in understanding our relationship with food. We hope that the series serves a delightful menu of interesting articles for our readers.

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argument is supported by studies of the uS media market, which show that the regional expansion of so-called ‘quality’ news- papers such as The New York Times and The Washington Post has led to a reduc- tion in the quality of local and regional newspapers (george & Waldfogel, 2006).

all of this is particularly relevant in the context of food, as most consumers prima- rily receive information about food and bio- technology through the popular press and television (Hoban & Kendall, 1993; Marks et al, 2003). Extensive media coverage of an event can contribute to a heightened perception of risk and amplify its conse- quences. Food scares are prime examples of this effect: they are typically accompa- nied by a flood of media coverage and lead to a decline in demand for the product in question, often concomitant with a level of panic that scientists would argue is not appropriate, given the real risks.

a ccordingly, social scientists and psy- chologists have conducted research into how information shapes

and determines perceived risks of food. generally, most consumers are “rationally ignorant” (Mccluskey & Swinnen, 2004); they rationally choose not to fully inform themselves about an issue. in other words, although consumers have access to huge amounts of information, they choose to be less than fully informed. there are three explanations for this attitude. First, if it costs money to access the news and doing so only provides limited benefits, it is rational not to purchase the information. Second, although reducing the price of news will make information more accessible, acquir- ing and processing it takes time, energy and attention. consequently, consumers reach a threshold at which the cost of processing the information is larger than the benefit. the third reason has to do with the information source: ideological bias or distrust of a news source might cause consumers not to inform themselves fully.

the decision about how much informa- tion is enough also depends on consumers’ ex ante (previous) risk perceptions. in one of the first surveys of consumer perceptions of

…although consumers have access to huge amounts of information, they choose to be less than fully informed

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health risks in food, van ravenswaay (1990) concluded that most consumers acknowl- edge the existence of risks, but perceive them to be small. although the public adjust their risk perceptions in the light of new information, they are only willing to pay modest amounts for information that would reduce perceived food risks. one explana- tion is that the cost of risk avoidance is low because consumers can stop purchasing a specific food if they learn that it poses a higher risk than they thought.

in fact, ex ante beliefs tend to have a stronger influence on risk perceptions than news or other types of information. For example, many consumers think that organ- ically produced products—which carry a higher risk of mycotoxins—are safer than more-intensively farmed crops, ir respective of information about management activities (loureiro et al, 2001). generally, consum- ers perceive natural risks as being easier to manage because they seem to be less threatening than technological risks.

i n general, risk perception varies between consumers, owing to many factors. gender and education are consistent

demographic predictors of food-risk per- ceptions. non-demographic predictors include the nature of the perceived threat, trust in regulatory authorities, the source of the information and the way in which it is distributed, and health and environmental concerns (Ellis & tucker, 2009). For exam- ple, consumers of organic foods perceive greater risks from pesticide residues than other consumers.

Both social and individual factors can amplify or dampen perceptions of risk (Flynn et al, 1998; Koné & Mullet, 1994), and the media is an important mechanism in this process. Slovic (1987) suggests that risk perception is influenced by two factors: dread and unknown risks. Dreaded risks are those deemed to be uncontrollable, invol- untary and affect many people with poten- tially catastrophic consequences. unknown risks are new, uncertain and unobservable, or might have delayed effects. Food scares are often rated highly as dreaded risks, but because they are understood they receive lower ratings as unknown risks. By contrast,

new food technologies, such as genetically modified foods, are rated highly as unknown risks. thus, differences in consumer knowl- edge might influence risk perceptions; most scientists tend not to think that genetically modified foods are risky.

previous beliefs also have an important role in the selection and processing of infor- mation provided by the media. poortinga & pidgeon (2004) studied the perception of genetically modified food in the uK and found a strong confirmatory bias—selecting information that agrees with your previ- ous beliefs; those with positive or negative beliefs interpret the same events as being in line with their attitude. Frewer et al (1997) also found the the initial attitude to genetic engineering is the most important determi- nant of how people assess new information about it. these attitudes remain stable, even if persuasive arguments against them are provided. in fact, initial attitudes also affect perception of the quality of information; respondents with a negative view are likely to perceive positive information about the technology as less accurate and more biased than people with positive views.

t he nature of the information also matters. in general, consumers give more weight to negative than posi-

tive information. this is ironic because one often-heard complaint about the media is that news coverage is too negative. this tendency is actually driven by demand (Mccluskey & Swinnen, 2004), as the value of information is higher for consumers if it concerns an issue with a negative effect on welfare. the rationale is that consumers can use negative information to make decisions in order to avoid losses. as media compa- nies care about profits, they will inevitably offer more negative stories.

Siegrist & cvetkovich (2001) conducted psychological experiments to assess this bias towards negative information in regard to health risks in food. they found that peo- ple place greater trust in results that indi- cate a health risk, and that confidence in the results increases with a higher indica- tion of risk. the authors suggest three pos- sible explanations: diagnosticity—negative information is more diagnostic than positive information, and might therefore be given greater weight; loss aversion—for most peo- ple it is important to avoid losses; and credi- bility—negative information might be more credible than positive information because positive information can be regarded as

self-serving, whereas negative information often seems to lack this quality. However, critics of these studies warn against confus- ing negativity bias and confirmatory bias in explaining how information shapes citi- zens’ perceptions. yet, after controlling for confirmatory bias, negativity bias still has a role: negative items have more impact than positive ones.

the source of information is also impor- tant for shaping risk perception, as distrust of the institution providing the informa- tion increases the perception of risk (renn, 2005). there is some debate about the importance of source credibility. Some stud- ies find that source credibility has a key role in determining the impact of a message on public opinion, while others find that source credibility seems to have a limited effect and is less important than initial attitudes. Kumkale et al (2010) show in a meta-anal- ysis that the credibility of the source matters mostly for attitude-formation conditions, whereas its impact in attitude-change con- ditions is lower. conversely, recent stud- ies show that internet users pay little or no attention to source credibility when they seek health information.

Many people, in fact, anticipate that information from the media might be biased and take this into account when evaluating it. However, several behavioural studies conclude that even when viewers know that media sources are biased, they do not sufficiently discount the information to account for this bias. Exposure to media can thus systematically alter or reinforce beliefs and consumer behaviour. in con- clusion, the impact of bias in media report- ing on consumer attitudes is bidirectional and complex. consumer bias in personal preferences and beliefs affect the media’s reporting strategies to convince these con- sumers to buy their media products. Similar complex interactions occur between the media and politicians and between the media and business.

although the media’s effects on public perception are complex, their impact can be significant. curtis et al (2008) argue that differences in the structure of the media

…consumers give more weight to negative than positive information

…competition in the commercial media intensifies the scale of the scare, as well as bringing it to a fast—and often premature—conclusion

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between countries might have important implications for food-risk perceptions. the negative attitude towards genetically modi- fied foods that is typical of consumers in rich countries is in contrast to attitudes in poorer countries, where studies have found that consumer attitudes towards genetically modified foods are not as negative, and in many cases even positive. the authors claim that this might be partly explained by dif- ferences in the organization of the media. in poorer countries, information is more expensive and scarce and people often have less time to read and acquire informa- tion, which leads to an overall lower level of information. Moreover, government con- trol of the media in poorer countries tends to be more extensive and might lead to more-positive coverage of biotechnology, if the government has a positive attitude.

a n important issue is the dynamics of the media market—that is, not only whether, but when to publish news.

the structure of the mass media encour- ages fast, concentrated coverage. as col- lecting information requires time, effort and other costs, publishing a story on the basis of incomplete information risks biasing reports, which might hurt the reputation of the media outlet, and thereby future profits. However, covering a story early on might yield market share and profits if an outlet can be the first to provide information on a new issue. consumers also face a trade-off. they might be willing to take the risk of get- ting biased information, as long as they get whatever information is available. in other words, any news is better than no news.

these issues are particularly important in food scares. a case in point is the 1989 alar controversy in the uSa. alar was the trade name for daminozide, a plant growth- regulator used to stimulate the growth, appearance and ripening of fruits, prima- rily apples. in February 1989, the uS news programme 60 Minutes covered the natural resources Defence council’s report, which said that alar poses a cancer risk to children. Most uS media organizations followed suit. as a result, supermarkets took apples off their shelves and schools removed apples

from their cafeterias. uS apple growers lost millions of dollars in revenues and announced a voluntary ban on alar, which became effective in the autumn of 1989. in hindsight, analysts argue that the media con- fused a long-term cumulative effect with an imminent danger, resulting in unnecessary panic and financial losses (negin, 1996).

BSE, commonly known as mad cow dis- ease, is another example of this. in March 1996, the uK government announced that mad cow disease was the likely cause of death for ten people. in april 1996, cover- age of BSE on the oprah Winfrey show in the uSa was followed by a steep decline in beef prices in the following month, even though there were no BSE-infected cattle in the uSa.

t abloid newspapers and the popular press typically worry less about their reputation in terms of quality, and more

about being the first to publish or broadcast a story. the elite press worries more about quality. However, there is an interesting dynamic component: once one media com- pany reports a story—no matter how biased their coverage is—if can initiate a chain reaction. if the issue is important enough, competitive forces will cause elite press organizations to follow suit, even before they are able to verify the story. the first story becomes the basis of their reporting.

there are two reasons for this dynamic. First, competition and consumer choice force the media to pay attention to an issue, otherwise consumers ask why their preferred media source is not covering the story and will go elsewhere. the second reason is that by commenting on a story that was launched by another media com- pany, more-reputable media outlets are covered if things go wrong—that is, when the primary information turns out to be biased. they can hide behind the fact that they were not the first to cover it, and only reflected on a story launched by someone else. the first factor minimizes the imme- diate losses from waiting too long, and the second limits future negative effects on reputation. these dynamics are summa- rized by the following quote, “Even appar- ently responsible papers [...] contribute to building up [food] scares. When the scare has run its course, they will argue against it. But when the scare dynamic is up and run- ning, [the quality press] will join with the throng and become more tabloid than the tabloids” (north, 2000).

although competition for audiences leads to an intensification of media attention in the early reporting of a story, it also induces a rapid decline in attention afterwards. the popular press is often first to report on a cri- sis and more intense in its initial coverage, but quickly loses interest. thus, competi- tion in the commercial media intensifies the scale of the scare, as well as bringing it to a fast—and often premature—conclusion.

there is also evidence that early claims, even when they are false, are reported more extensively than later corrections. Swinnen et al (2005) examined the media response to two food-safety crises: the 1999 dioxin crisis, and the 2001 foot and mouth disease outbreak. comparing tabloids and the elite press, they found that overall coverage was almost the same, but that tabloids initially responded more quickly and intensely and also lost interest more quickly. they also found that initial errors in the news were not properly corrected when new facts emerged and initial interest had waned.

t he short-term impacts of food-safety information on consumer demand can be significant. one example

is BSE, which had a negative effect on the consumer demand for beef, the severity of which was increased by the media. Verbeke & Ward (2001) found considerable mis- perception of the problem by consumers, a lack of knowledge about the relevant sci- ence and biased perception of the scien- tific criteria relevant to the safety of meat. television coverage of meat safety had a negative effect on the demand for red meat after the BSE outbreak (Verbeke et al, 2000), and younger people were most susceptible to negative media coverage.

However, in the long run, consumption and sales typically recover if the problems are addressed (Henneberry et al, 1999; piggott & Marsh, 2004), although the effects on policy can be lasting. in 1993, after an Escherichia coli outbreak at the Jack in the Box restaurant chain, 144 people were hos- pitalized and three died. the restaurant chain almost went out of business in the wake of the event, but after two years, sales had

…the media has great power to lead policy-makers, especially when there is uncertainty or limited information

Even if the commercial media provide simple and clear messages, consumers might realize that reality is more complex

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recovered to pre-scare levels (Entine, 1999). By contrast, the legislative repercussions on burger restaurant chains have persisted.

t he most-significant long-term effect of mass-media reporting is its impact on public policy. By invoking strong

responses in their audiences through con- centrated, emotionally charged coverage, media outlets put pressure on govern ments to react to situations, effective ly setting the agenda on a certain issue; this is sometimes called the ‘cnn factor’ (Hawkins, 2002). Similarly, an absence of media cover- age of even important events or problems lowers their priority in legis lative agendas. robinson (2001) suggests that the media has great power to lead policy- makers, especially when there is un certainty or lim- ited information. For example, in the wake of the media frenzy surrounding the Jack in the Box E. coli outbreak, uS president Bill clinton called congressional hearings about the safety of the food supply. the uS Food and Drug administration raised the recom- mended internal temperature of cooked burgers to 155 ° fahrenheit (68 °c). it is now almost impossible to order a burger cooked less than ‘medium’ in uS restaurants.

another interesting example is the use of the precautionary principle in regulation in the Eu and the uSa. the precautionary principle is now used as a major regula- tory tool in food safety issues in the Eu, in particular to regulate genetically modi- fied foods. However, it was used more in the uSa from the 1960s to the mid-1980s (Vogel, 2003). Several European food scares in the 1990s, heavily publicized in the mass media, changed this. it pushed politicians to introduce a series of new regulations and it caused consumers to be more concerned about food safety. although ex post stud- ies showed that several of these food-safety problems were exaggerated, the massive press cover age induced strong political reactions, leading to regulations and shifts in consumer preferences that are having long -lasting effects on perceptions of food risk and the regulation of the food system in Europe (Swinnen & Vandemoortele, 2010).

t he examples considered above and the power of the media to influ- ence an ignorant public—willfully

or otherwise—have important implications for risk communication, education and management. First, because initial beliefs are important—affecting not only overall

risk perceptions, but also the way in which consumers process new information—it is important to enhance consumer under- standing of risk through education and by providing early information. this should cre- ate a realistic framework within which people can assess risks. pre-emptive risk communi- cation and the establishment of institutions that are responsive to problems can mitigate negative, long-term consequences on public policy or consumer preferences.

Second, businesses, scientists and governments should be prepared to pro- vide accurate, open and understandable information when crises occur. the media will report on the issues regardless and will draw on whichever ‘expert’ they can find if companies, scientists and govern- ments are not ready to put events and facts into perspective.

third, the growth of the internet as a source of information and a communication tool not only imposes challenges, but also provides important opportunities. it ena- bles direct communication with the public to provide information without depending on the mass media as brokers. Hence, even if the media do not report—or do so with a lack of nuance—companies, scientists and governments can communicate correct and nuanced information through the internet.

Fourth, it is generally considered that successful risk management in regard to food safety critically depends on commu- nication. yet communication about food risk is difficult because the science is com- plex, uncertain and ambiguous. Even if the commercial media provide simple and clear messages, consumers might realize that reality is more complex. For exam- ple, Frewer et al (1997) have found that an admission of scientific uncertainty, which seems to reflect honesty, has a positive effect on the efficiency of communication. risk communication should aim to enable citizens to make their own judgements, without trying to convince them that a cer- tain risk is (in)tolerable. in order to be suc- cessful, communication should integrate documentation, information, dialogue and participation, and these four elements

should be tailored towards meeting the three challenges of complexity, uncertainty and ambiguity (renn, 2005 [Au:OK?]).

Finally, there seem to be cultural varia- tions in the impact of the media and risk- communication strategies and in how food risks are perceived. Van Dijk et al (2007) found variation in the impact of communication strategies, even among western European countries: the com- munication of uncertainty has a positive impact in germany, whereas the same information has a negative impact in the uK and norway. Hence, effective risk- communication strategies depend on the culture in which the scientist, company or government is operating.

Food scares are serious issues that have a significant impact in terms of con- sumer behaviour, economics and politics. nevertheless, it would be wrong to blame the media for disproportionate public responses to such stories, although their influence is important and sometimes detri- mental to public understanding. Scientists, businesses, interest groups and politicians can also influence public perception, in particular by using the internet to circum- vent the mass media as the main source of information. as such, it is important for all parties to work together to become better at communicating with the public and provid- ing education. in this way, the public should enjoy a heightened baseline of knowledge that will allow them to assess critically the sensationalist reports that appear in the media, and perhaps reduce the demand for such reporting in the first place.

acKnoWlEDgEMEntS We thank t. Vandemoortele and a. guariso for their comments and research assistance.

conFlict oF intErESt the authors declare that they have no conflict of interest. [Au:OK?]

rEFErEncES alterman E (2008) out of print: the death and life

of the american newspaper. The New Yorker, pp 48–59, 31 Mar

canoy M, nahuis r (2005) Public Service Broadcasting and the Quality of News on Commercial Channels Substitutes or Complements? cpB netherlands Bureau for Economic policy analysis and utrecht School of Economics [Au: please provide place of publication]

curtis Kr, Mccluskey JJ, Swinnen J (2008) Differences in global risk perceptions of biotechnology and the political economy of the media. Int J Global Environmental Issues 8: 77–89

Scientists, businesses, interest groups and politicians can also influence public perception, in particular by using the internet to circumvent the mass media

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Ellis JD, tucker M (2009) Factors influencing consumer perception of food hazards. CAB Rev: Persp Agr Vet Sci Nutr Nat Resour 4: 1–8

Entine J (1999) How ‘Jack’ turned crisis into opportunity. Business Digest. http:// www.jonentine.com/ethical_corporation/ jack_crisis.htm

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Hawkins V (2002) the other side of the cnn factor: the media and con flict. Journalism Stud 3: 225–240

Henneberry Sr, piewthongngam K, Qiang H (1999) consumer food safety concerns and fresh produce consumption. J Agr Resour Econ 24: 98–113

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negin E (1996) the alar ‘scare’ was for real. Columbia Journalism Rev [AU please provide issue number]: 13–15

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pew (2004) How Journalists See Journalists. Washington, Dc, uSa: pew research center for the people and the press. http://people-press. org/reports/pdf/214.pdf

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renn o (2005) risk perception and communication: lessons for the food and food packaging industry. Food Addit Contam 22: 1061–1071

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Jill McCluskey is Professor of Economics at Washington State University in Pullman, Washington, USA. Johan Swinnen is Professor of Economics at the Katholieke Universiteit Leuven, Belgium. E‑mail: [email protected]

Received and accepted 26 May 2011; published online XX Month 2011

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2011; doi:10.1038/embor.2011.118

MediaBias.docx

Bias

Media bias can take various forms, and there is no generally accepted definition. Anand, Di Tella, and Galetovic (2007: 637) write that “[t]he phenomenon of bias in the media appears to be quite different than, say, a statistician's notion of bias—because bias lies in the eyes of the beholder (consumer).” Others define bias as the “absence of balance resulting in one side of a story receiving unwarranted attention” (Baron 2006: 4) or in other words, “sins of omission—cases where a journalist chose facts or stories that only one side of the…spectrum is likely to mention” (Groseclose and Milyo 2005: 1205). In terms of political bias, Sutter (2001) defines media bias in terms of the media outlet's position on the political spectrum relative to the views of the median voter. Gentzkow and Shapiro (2007: 3) develop a “slant” index, which measures “differences in news content that…would tend to increase a reader's support for one side of the political spectrum.”

Many empirical studies analyze political bias in the media. A survey by the American Society of Newspaper Editors (ASNE) in 1999 revealed that 78 per cent of the public believed there was bias in news reporting. Groseclose and Milyo (2005) measure media bias by computing an index for various media outlets based on a comparison of the number of times the media outlet cites various think tanks and other policy groups with the citation counts of members of US Congress. They find that there is diversity among US media outlets with substantial liberal bias in news reporting. In contrast, d'Alessio and Allen (2000) do not find significant bias. Hamilton (2004) examines bias based on data from Pew Center surveys and finds that both self‐identified liberals and self‐identified conservatives see media biases towards the opposing side. It seems that bias is perceived relative to one's ideology. Personal assessments of bias may be affected by one's personal ideology (Vallone, Ross, and Lepper 1985; Gentzkow and Shapiro 2004).

Studies have identified several possible theoretical explanations for the existence of bias. Bias can be induced by supply and/or demand factors. It can be due to ideology or partisan politics, where owners, editors, or journalists present stories that support particular world views. It can also result from falsehoods or from information hidden or distorted by sources or journalists eager for a scoop or under pressure to attract attention, or due to consumer preferences. The most obvious source of bias is preferences from the owners, editors, or journalists who may affect the news coverage (Bovitz, Druckman, and Lupia 2002). This bias is most evident in mass media owned by the state, such as in totalitarian countries (such as China and North Korea) and many

developing countries where the state continues to control mass media. In those countries the media is used by the government to disseminate the political communication of the ruling parties and to control information which may threaten their legitimacy or their hold on power. One source of evidence is a measure of freedom of the press. Freedom House (Sussmann and Karlekar 2002) assigns an index of freedom of the press and rates each country with one of the three designations: “free,” “partly free,” and “not free.” By these rankings, all the developed countries of Western Europe and North America have a “free press,” while the press situation in the less developed countries is more mixed and certainly less free on average. In particular, in countries such as China and Colombia, the press is considered not free (Table 28.1).

However, also in less rigorously controlled media regimes, such bias can be important. In many European countries, until recently, much of the nonstate‐ owned printed media and television stations were owned or closely related to political parties; and the different media expressed the preferences of their parties. Similarly, public television organizations were often influenced by the parties in government. An interesting illustration is Italy, where the main leader of the right‐wing political parties, Silvio Berlusconi, owns much of the commercial TV stations and control of the public TV stations—and

their political news coverage—switches when left or right wing parties take over government. Governments can also put strong pressure on them not to

publish stories. Gentzkow and Shapiro (2008b) discuss several examples, including the political and legal pressure used by the US government on

CBS not to broadcast the Abu Graib photographs during the Iraq War or on the New York Times not to publish the Pentagon Papers during the

Vietnam War.

Owners of commercial media may wish to impose their personal preferences on their media reporting. In doing so they may face a trade‐off between

political objectives (i.e., using the media to express the owners' ideological bias) and commercial objectives (McCluskey and Swinnen 2004;

Mulainathan and Shleifer 2005). Commercial objectives may be affected, first, by potential consumers' distaste for bias, or the negative utility they get

from consuming media products which differ from their personal political preferences. Second, as discussed earlier, commercial media's profitability

depends not only on consumers, but to a large extent also on its advertising revenues. Bagdikian (1992) writes, “As mass advertising grew, the liberal

and radical ideas—in editorials, in selection of news, and in investigative initiatives—became a problem. If a paper wished to attract maximum

advertising, its explicit politics might create a disadvantage” (129–30; reprinted in Gabszewicz, Laussel, and Sonnac 2001). Gabszewicz, Laussel,

and Sonnac (2001) show that the media's incentives to appeal to a larger audience and hence be more attractive to advertisers may induce editors to

moderate the political messages they display to their readers.

The importance of attracting large numbers of readers or viewers may in itself also lead to bias. One mechanism is explained by Strömberg (2004a) who argues that media coverage is biased towards large groups as the media is more likely to cover issues that are of interest to them. This bias can result from the need to attract as much readers as possible or from economies of scale in the media. Kuzyk and McCluskey (2006) provide empirical support for Strömberg's theoretical model with content analysis of media coverage of the US–Canadian lumber trade dispute. The coverage of the trade dispute was largely negative, which coincides with the interests of the vast majority of readers.

Other empirical studies find that there is a bias towards “negative coverage” in mass media in a variety of policy and public interest areas, such as trade policy and globalization (Swinnen and Francken 2006) and food safety (Swinnen, McCluskey, and Francken 2005). Marks, Kalaitzandonakes, and Konduru (2006) find that reporting on globalization was positive early on but switched to more negative in recent years. McCluskey and Swinnen (2007) explain that negative news coverage is likely to dominate positive news stories because of demand side effects. Their argument is based on the premise that consumers use the information from positive media stories to take advantage of opportunities from positive shocks and use the information from negative stories to avoid negative shocks. If utility is concave, the marginal loss in utility from not consuming the first bad news story is greater than the marginal gain in utility from consuming the first positive news story. As a result, consumers will choose to consume more negative stories than positive stories.

Spatial models of firm location provide a consumer‐driven rationale for bias based on product differentiation. Mullainathan and Shleifer (2005) argue

that readers or viewers have a preference for news that is consistent with their initial beliefs, and that media organizations have therefore an incentive

to bias their reporting towards confirming their readers' or viewers' initial beliefs. When readers are heterogeneous in their beliefs, accuracy increases

due to cross‐checking of facts across newspapers. This is a “wisdom of crowds” argument (Surowiecki 2004) that aggregation of signals reduces

noise. Anand, Di Tella, and Galetovic (2007) assume that facts are not always verifiable, and that consumers have heterogeneous ideologies. They

find that when facts are verifiable, there is no bias. However, when a news item comprises information that is mostly non‐verifiable, then consumers

may care both about opinion and editorials, and the media firm's report will contain both these aspects. The diversity of opinion and editorials results in a differentiated products market. A dynamic version of this type of argument can be made when media organizations attempt to obtain a reputation for

accuracy induces bias in reporting. Gentzkow and Shapiro (2006) consider the Bayesian consumer who is uncertain about the quality of an

information source. The consumer infers the source is of higher quality when its report conforms to the consumer's prior expectations. Consequently,

media first slant their reports toward the prior beliefs of their customers in order to build a reputation for quality.

Others have focused on other aspects of the supply side of the media market to explain bias. Baron (2006) explains that bias may be related to the

availability of potential journalists who are willing to work for lower wages in positions in which they can advance their careers or demonstrate influence

by exercising the discretion granted by new organizations. Dyck and Zingales (2002) and Baron (2005) focus on the relationship between journalists

and their sources of information as the reason for media bias. Sources may release partial information that supports their preferences, or journalists

may use partial information to reward sources for providing information. Baron (2005) models how the competition between information sources affects

the news report. Private information may be held by two sources with opposing views. The sources have incentives to reveal only information that

supports their own views, and it is costly for the media to obtain additional information from independent sources. Dyck and Zingales (2002) argue that

to induce a source to reveal information, the journal provides a positive spin to stories to reward the source for providing the information.

An interesting empirical study on these issues is by Gentzkow and Shapiro (2007). They use data from a large set of US media and come to the

conclusion that “newspapers' actual slant is neither to the right nor to the left of the profit maximizing level on average.” While their results are consistent

with Groseclose and Milyo's (2005) findings that the average newspaper's language is similar to that of a left‐of‐center member of Congress, they

estimate that the profit maximizing average slant is also left‐of‐center on average. They conclude that the slant (or bias) in newspapers is strongly

related to the political distribution of their potential readers, much more so than to the political preferences of their owners or the journalists.

Competition may play an important role in media organizations' trade‐off between ideology and profits. Some researchers, such as Baron (2006),

show that bias can persist in the face of competition. Gentzkow and Shapiro (2006, 2008a) argue that competition reduces supply side induced bias

because it increases the likelihood that erroneous reports will be exposed ex post, but argue that the impact of competition on demand side induced

bias is less clear. Mullainathan and Shleifer (2005) in their two‐firm location model show that price competition results in greater product differentiation

—e.g., more “slanting” of news. When advertising revenues are included in the product differentiation models of media, minimum differentiation can

result (Gal‐Or and Dukes 2003; Gabszewicz, Laussel, and Sonnac 2004; Barros et al. 2004).

mitchell_2008a note on rising food prices.pdf

POLICY RESEARCH WORKING PAPER 4682

A Note on Rising Food Prices

Donald Mitchell

The World Bank Development Prospects Group

JULY 2008

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POLICY RESEARCH WORKING PAPER 4682

Abstract The rapid rise in food prices has been a burden on the poor in developing countries, who spend roughly half of their household incomes on food. This paper examines the factors behind the rapid increase in internationally traded food prices since 2002 and estimates the contribution of various factors such as the increased production of biofuels from food grains and oilseeds, the weak dollar, and the increase in food production costs due to higher energy prices. It concludes that the most important factor was the large increase in biofuels production in the U.S. and the EU. Without these increases, global wheat and maize stocks would not have declined appreciably, oilseed prices would not have tripled, and price increases due to other factors, such as droughts, would have been more moderate. Recent export bans and speculative activities would probably not have occurred because they were largely responses to rising prices. While it is difficult to compare the results of this study with those of other studies due to differences in methodologies, time periods and prices considered, many other studies have also recognized biofuels production as a major driver of food prices. The contribution of biofuels to the rise in food prices raises an important policy issue, since much of the increase was due to EU and U.S. government policies that provided incentives to biofuels production, and biofuels policies which subsidize production need to be reconsidered in light of their impact on food prices.

_____________________________________

This paper is a product of the Development Prospects Group. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at [email protected].

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Produced by the Research Support Team

A Note on Rising Food Prices1

Donald Mitchell2

I. Introduction Internationally traded food commodities prices have increased sharply since 2002 and especially since late-2006, and prices of major staples, such as grains and oilseeds,3 have doubled in just the past two years. Rising prices have caused food riots in several countries and led to policy actions such as the banning of grain and other food exports by a number of countries and tariff reductions on imported foods in others. The policy actions reflect the concern of governments about the impact of food price increases on the poor in developing countries who, on average, spend half of their household incomes on food. This paper examines how internationally traded food commodities prices (maize, wheat, rice, soybeans, etc.) have changed, and analyzes the factors contributing to these increases. In particular, it looks at the contribution of biofuels production to food price increases. In this paper biofuels refer to ethanol and biodiesel.4 II. The rise in global food prices The IMF’s index of internationally traded food commodities prices5 increased 130 percent from January 2002 to June 2008 and 56 percent from January 2007 to June 2008 (Figure 1). Prior to that, food commodities prices had been relatively stable after reaching lows in 2000 and 2001 following the Asia financial crisis. The low levels of global grain stocks had been identified as a cause for concern in a number of fora6 and the risk of higher food prices was highlighted in a recent World Bank publication7 and online.8

1 The views expressed in this paper are those of the author and should not be attributed to the World Bank or its Executive Directors. 2 Lead Economist, Development Prospects Group (DECPG), World Bank, Washington. Comments should be sent to [email protected]. Thanks are expressed to Hans Timmer, Ron Steenblik, Harry de Gorter, and Masami Kojima for useful comments. Any remaining errors or omissions are the sole responsibility of the author. 3 Oilseeds are crops with high oil content such as soybeans, rapeseed, sunflower, flax and cottonseed. 4 Ethanol is produced from sugar crops, such as sugar cane or beets, or starchy crops such as maize. Biodiesel is produced from vegetable oils or animal fats. 5 A nominal dollar index of food commodity prices using global export value weighs. 6 “Are we facing a food price spike”, session at Rural Week 2004, Mitchell and Le Vallee (2005) Food Price Variability in Global Markets. 7 Global Development Finance 2007, May 2007, 8 Mitchell, Donald “A coming spike in grain prices?” Focus Topic, April 2007.

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Figure 1. Food prices

(Nominal $ Index, Jan 2000=100, world export value weights)

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Jan-00 Jan-02 Jan-04 Jan-06 Jan-08

Source: DECPG

The increase in food commodities prices was led by grains (Figure 2) which began sustained price increases in 2005 despite a record global crop in the 2004/05 crop year9 that was 10.2 percent larger than the average of the three previous years and a near- record crop in 2005/06 that was still 8.9 percent larger. Global stocks of grain increased in 2004/05 but declined in 2005/06 as demand increased more than production. From January 2005 until June 2008, maize prices almost tripled, wheat prices increased 127 percent and rice prices increased 170 percent. The increase in grain prices was followed by increases in fats & oils prices in mid-2006, and that also followed a record 2004/05 global oilseed crop that was 13 percent larger than in the previous year and an even larger crop in 2005/06. Fats & oils prices have shown similar increases to grains, with palm oil prices up 200 percent from January 2005 until June 2008, soybean oil prices up 192 percent, and other vegetable oils prices increasing by similar amounts. Other foods prices (sugar, citrus, bananas, shrimp and meats) increased 48 percent from January 2005 to

Figure 2. Food price sub-indices (Nominal $ Index, 2000=100, world export weights)

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300

350

Jan-00 Jan-02 Jan-04 Jan-06 Jan-08

Fats & Oils Grains Other

Source: DECPG

June 2008.

9 Crop years begin with harvest and continue until the next harvest.

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III. Recent estimates of the contribution of biofuels production to food prices Estimates of the contribution of biofuels production to food price increases are difficult, if not impossible to compare. Estimates can differ widely due to different time periods considered, different prices (export, import, wholesale, retail) considered, and different coverage of food products. Moreover, the analyses depend on the currency in which prices are expressed, and whether the price increases are inflation adjusted (real) or not (nominal). Different methodologies will likely yield different results. General equilibrium model exercises generate long-term price impacts of specific shocks. They take into account interactions with other markets, but do not capture short-term price dynamics that are significantly more pronounced. Detailed studies of specific crops may include the short-term dynamics, but often exclude the impact on other markets. Methodologies may also differ to the extent they consider shocks to be independent. For example, speculation may be seen as an independent driver, or may be attributed to a change in fundamentals that would not have otherwise occurred. Despite all the differences in approach, many studies recognize biofuels production as a major driver of food prices. The USDA’s chief economist in testimony before the Joint Economic Committee of Congress on May 1, attributed much of the increase in farm prices of maize and soybeans to biofuels production (Glauber, May 1, 2008). The IMF estimated that the increased demand for biofuels accounted for 70 percent of the increase in maize prices and 40 percent of the increase in soybean prices (Lipsky, May 8, 2008). Collins (2008) used a mathematical simulation to estimate that about 60 percent of the increase in maize prices from 2006 to 2008 may have been due to the increase in maize used in ethanol. Rosegrant, et al. (2008), using a general equilibrium model, calculated the long-term impact on weighted cereal prices of the acceleration in biofuel production from 2000 to 2007 to be 30 percent in real terms. Maize prices were estimated to have increased 39 percent in real terms, wheat prices increased 22 percent and rice prices increased 21 percent. During this period, the U.S. CPI increased by 20.4 percent, which would imply nominal prices increases of 47, 26, and 25, respectively, for maize, wheat and rice prices. This is the same order of magnitude as was calculated with the World Bank’s linkages model (van der Mensbrugghe 2006). Differences in the estimates of the impact of biofuels on the price index of all food depend largely on how broadly the food basket is defined and what is assumed about the interaction between prices of maize and vegetable oils (directly influenced by demand for biofuels) to prices of other crops such as rice through substitution on the supply or demand side. For example, the Council of Economic Advisors (Lazear, May 14, 2008) estimated that retail food prices increased only about 3 percent over the past 12 months due to ethanol production, in part because they only considered the impact of maize prices, directly and indirectly, on retail prices. Many other potential drivers of the escalating food prices are mentioned in discussions, but there are few quantitative estimates of their impacts. For example, a recent USDA report (Trostle, May 2008) attributed the increase in world market prices for major food commodities such as grains and vegetable oils to many factors including biofuels as well as other factors including the declining dollar, rising energy prices, increasing agricultural costs of production, growing foreign exchange holdings by major

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food-importing countries, and recent policies by some exporting countries to mitigate their own food-price inflation. The methodology used in this paper is ad hoc as it does not use structural models to calculate the driving factors. Instead, the paper tries to identify a few key factors that have contributed to the increase in food commodities prices and identify other indirect impacts that were the result of scarcity in agricultural markets that was caused by the key drivers. This is an ad hoc approach, but it has the advantage that indirect, difficult-to- quantify, and short-term impacts can be explored in detail. The analysis focuses on the increase in individual food crop prices such as maize, wheat, rice oilseeds, and on the index of food commodities prices since 2002. These prices reflect export prices of food commodities, not retail prices or import prices of developing countries, which would be influenced more by freight rates, exchange rates and domestic inflation. The analysis is not forward looking and does not consider how supply would respond to high commodity prices and moderate price increases over time. IV. Estimates of factors contributing to the rise in food commodities prices There are a number of factors that have contributed to the rise in food prices. Among these are the increase in energy prices and the related increases in prices of fertilizer and chemicals, which are either produced from energy or are heavy users of energy in their production process. This has increased the cost of production, which ultimately gets reflected in higher food prices. Higher energy prices have also increased the cost of transportation, and increased the incentive to produce biofuels and encouraged policy support for biofuels production. The increase in biofuels production has not only increased demand for food commodities, but also led to large land use changes which reduced supplies of wheat and crops that compete with food commodities used for biofuels. Drought in Australia in 2006 and 2007 and poor crops in Europe in 2007 added to the grain and oilseed price increases, and rapid import demand increases for oilseeds by China to feed its growing livestock and poultry industry contributed to oilseed price increases. Other factors, including the decline of the dollar, and the increased investment in commodities by institutional investors to hedge against inflation and diversify portfolios may have also contributed to the price increases. The remainder of this section will examine these factors. High energy prices have contributed about 15-20 percent to higher U.S. food commodities production and transport costs. Production costs per acre for U.S. corn10, soybeans and wheat increased 32.3, 25.6 and 31.4 percent, respectively, from 2002 to 2007, according to the USDA’s cost-of-production surveys (USDA 2008b) and forecasts (Table 1). However, yield increases during this period reduced the per bushel cost increases to 17.0, 24.1 and 6.7 percent, respectively. The contribution of the energy- intensive components of production costs—fertilizer, chemicals, fuel, lubricants and electricity—were 13.4 percent for corn, 6.7 percent for soybeans and 9.4 percent for wheat per bushel. The production-weighted average increase in the cost of production due to these energy-intensive inputs for these crops was 11.5 percent between 2002 and 10 Corn and maize are used interchangeably in this paper.

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2007. In addition to the increase in production costs, transport costs also increased due to higher fuel costs and the margin between domestic and export prices reflect this cost (Table 2). However, these margins also include handling and other charges, such as insurance, which increase with crop prices. The margin for corn between central Illinois cash and the Gulf Ports barge increased from $0.36 to $0.72 per bushel for an increase of 15.5 percent, while the margin between Kansas City and the Gulf Ports wheat increased only $1 per metric ton. An export weighted average of these prices suggests that transport costs could have added as much as 10.2 percent to the export prices of corn and wheat. Comparable data was not available for soybeans. Thus, the combined increase in production costs and transport costs for the major U.S. food commodities—corn, soybeans and wheat—was at most 21.7 percent, and this amount likely overstates the increase, because transport costs are not estimated separately. It therefore seems reasonable to conclude that higher energy and related costs increased export prices of major U.S. food commodities by about 15-20 percent between 2002 and 2007. Table 1. Cost of production for corn, soybeans and wheat, 2002 vs. 2007 (dollars per acre)

Corn Soybeans Wheat 2002 2007** 2002 2007** 2002 2007**

Operating costs:

Seed 31.84 48.93 25.45 38.27 6.65 9.51

Fertilizer 42.51 93.96 6.79 13.94 17.71 33.33

Chemicals 26.11 24.67 17.12 14.79 7.13 9.23

Custom operations 10.79 10.93 6.16 7.25 5.67 6.93

Fuel* 18.93 30.98 6.98 16.98 8.67 19.20

Repairs 13.91 14.86 9.76 11.93 10.15 12.78

Other 0.22 0.12 0.63 0.15 0.61 0.34

Interest 1.17 5.16 0.61 2.37 0.48 2.14

Total Operating 145.48 229.61 73.5 105.68 57.07 93.46

Allocated overhead:

Hired labor 3.06 2.22 1.84 2.15 2.53 2.52

Unpaid labor 25.74 23.86 15.59 17.02 16.72 21.97

Capital recovery 55.26 69.99 43.30 54.00 48.97 53.86

Land 87.44 95.44 80.74 92.72 39.19 42.93

Taxes & ins. 5.42 7.39 5.66 6.93 3.90 7.24

Overhead 11.91 13.83 11.37 12.90 7.25 8.78

Total Allocated Overhead 188.83 212.73 158.5 185.72 118.56 137.3

Total Costs ($per Acre) 334.31 442.34 232 291.4 175.63 230.76

Yields 134 151.5 40 40.5 27.9 34.4

Total Cost ($/bu) 2.49 2.92 5.80 7.20 6.29 6.71

Source: USDA Cost of Production Surveys and Forecasts, July 2008. *Fuels include lubricants and electricity. ** is USDA’s forecast.

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Table 2. Margins between major producing areas and the U.S. Gulf Ports.

Corn prices $/bu. Wheat prices $/metric ton Crop year Central Illinois Gulf Port Margin Kansas

City HRW

Gulf Port HRW

Margin

2002 2.34 2.70 0.36 155 160 5.00 2003 2.52 2.94 0.42 148 156 8.00 2004 1.93 2.48 0.55 147 151 4.00 2005 2.00 2.69 0.69 164 168 4.00 2006 3.33 3.94 0.61 198 204 6.00 2007 4.43 5.16 0.72 335 341 6.00

Increase 2002-07 (percent) 15.53 0.65

Source: USDA Feed Grains and Wheat Yearbook Tables, July 2008. Increased biofuel production has increased the demand for food commodities. The use of maize for ethanol grew especially rapidly from 2004 to 2007 and used 70 percent of the increase in global maize production (Figure 3). In contrast, feed use of maize, which accounts for 65 percent of global maize use, grew by only 1.5 percent per year from 2004 to 2007 while ethanol use grew by 36 percent per year. The share of global feed use of total use declined in response to maize price rises from 69 to 64 percent from 2004 to 2007, and from 70 to 67 percent when the feed by-products from biofuel production are included in feed use.11 The United States is the largest producer of ethanol from maize and is expected to use about 81 million tons for ethanol in the 2007/08 crop year. Canada, China and the European Union used roughly an additional 5 million tons of maize for ethanol in 2007 (USDA 2008a), bringing the total use of maize for ethanol to 86 million tons, which was about 11 percent of global maize production. The large use of maize for ethanol in the U.S. has important global implications, because the U.S. accounts for about one-third of global maize production and two-thirds of global exports and used 25 percent of its production for ethanol in 2007/08. About 7 percent of global vegetable oil supplies were used for biodiesel production in 2007 and about one-third of the increase in consumption from 2004 to 2007 was due to biodiesel.12 The largest biodiesel producers were the European Union, the United States, Argentina, Australia, and Brazil, with a combined use of vegetable oils for biodiesel of about 8.6 million tons in 2007 compared with global vegetable oils production of 132 million tons according to the USDA (2008f). From 2004 to 2007, global consumption of vegetable oils for all uses increased by 20.8 million tons, with food use accounting for 80 percent of total use and 60 percent of the increase. Industrial uses of vegetable oils (which include biodiesel) grew by 15 percent per annum from 2004

11 Biofuels production from maize uses only the starch in the maize kernel and 30 percent of the maize kernel remains as by-product called distillers dried grains with soluabales (DDGS) which is a high-protein livestock feed. 12 Data on biodiesel are incomplete and do not allow a precise estimate.

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to 2007, compared with 4.2 percent per annum for food use. The share of industrial use of total use rose from 14.4 percent in 2004 to 18.7 percent in 2007 (Figure 4). Imports of vegetable oils by the EU and U.S. have increased substantially, with the EU-27 increasing imports from 4.4 to 6.9 million tons from 2000 to 2007 (Figure 5) and the U.S. increasing imports from 1.7 to 2.9 million tons. The large imports coincided with the increase in biodiesel production in the EU-27 from .45 billion gallons in 2004 to 1.9 billion gallons in 2007 and from .03 billion gallons in the U.S. in 2004 to an estimated .44 billion gallons in 2007.

Figure 3. Global maize use

0%

20%

40%

60%

80%

100%

2000 2002 2004 2006

Biofuel Use Other Uses Feed Use

Source: DECPG calculations based on USDA data.

Figure 4. Global vegetable oils use

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80%

100%

2000 2001 2002 2003 2004 2005 2006 2007

Industrial Other Food

Source: DECPG calculations based on USDA data.

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Figure 5. EU oilseeds imports (Index 2000=100)

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160

180

2000 2001 2002 2003 2004 2005 2006 2007

Meal Oil Oilseeds

Source: DECPG calculations based on USDA data.

Brazilian ethanol production from sugar cane has not contributed appreciably to the recent increase in food commodities prices, because Brazilian sugar cane production has increased rapidly and sugar exports have nearly tripled since 2000. Brazil uses approximately half of its sugar cane to produce ethanol for domestic consumption and exports and the other half to produce sugar. The increase in cane production has been large enough to allow sugar production to increase from 17.1 million tons in 2000 to 32.1 million tons in 2007 and exports to increase from 7.7 million tons to 20.6 million tons. Brazil’s share of global sugar exports increased from 20 percent in 2000 to 40 percent in 2007, and that was sufficient to keep sugar price increases small except for 2005 and early 2006 when Brazil and Thailand had poor crops due to drought. The increases in biofuels production in the EU, U.S. and most other biofuel- producing countries have been driven by subsidies and mandates. The U.S. has a tax credit available to blenders of ethanol of $0.51 per gallon and an import tariff of $0.54 per gallon, as well as a biodiesel blenders tax credit $1.00 per gallon. The U.S. mandated 7.5 billion gallons of renewable fuels by 2012 in its 2005 legislation and raised the mandate to 15 billion gallons of ethanol from conventional sources (maize) by 2022 and 1.0 billion gallons of biodiesel by 2012 in energy legislation passed in late-2007. The new U.S. mandates will require ethanol production to more than double and biodiesel production to triple if they are met from domestic production. The EU has a specific tariff of €0.192/liter of ethanol (€0.727 or about $1.10 per gallon) and an ad valorem duty of 6.5 percent on biodiesel. EU member states are permitted to exempt or reduce excise taxes on biofuels, and several EU member states have introduced mandatory blending

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requirements. Individual member states have also provided generous excise tax concessions without limit, and Germany for example, has provided tax exemptions of €0.4704/ ($0.64) per liter of biodiesel and €0.6545 ($0.88) per liter of ethanol prior to new legislation in 2006 (Kojima, Mitchell and Ward, 2007; Global Subsidies Initiative 2008). These strong incentives and mandates encouraged the rapid expansion of biofuels in both the EU and U.S. The EU began to rapidly expand biodiesel production after the EU directive on biofuels (2003/03/EC) entered into effect in October 2001 stipulating that national measures must be taken by EU countries aimed at replacing 5.75 percent of all transport fossil fuels with biofuels by 2010. This led to an increase in biodiesel production from 0.28 billion gallons in 2001 to 1.78 billion gallons in 2007 (FAPRI 2008). Rapeseed was the primary feedstock, followed by soybean oil and sunflower oil. The combined use of vegetable oils for biodiesel was 6.1 million tons in 2007 compared with about 1.0 million tons in 2001. The U.S. expanded its biodiesel production following legislation passed in 2004 which took effect in January 2005, providing an excise tax credit of US$1.00 per gallon of biodiesel made from agricultural products. This contributed to an increase in biodiesel production in the U.S. from 0.03 billion gallons in 2005 to .44 billion gallons in 2007 and used 3.0 million tons of soybean oil and 0.3 million tons of other fats and oils. These two policies encouraged the rapid expansion of oilseeds production for biodiesel and contributed to the surge in vegetable oils prices, with annual average soybean oil prices rising from $354/ton in 2001 to $881 per ton in 2007. Monthly soybean oil prices rose to $1,522/ton in June 2008. Since oilseeds are close substitutes and prices highly correlated, this led to similar increases in other oilseeds prices. Land use changes due to expanded biofuel’s feedstock production have been large and have led to reduced production of other crops. The U.S. expanded maize area 23 percent in 2007 in response to high maize prices and rapid demand growth for maize for ethanol production. This expansion resulted in a 16 percent decline in soybean area (Figure 6) which reduced soybean production and contributed to a 75 percent rise in soybean prices between April 2007 and April 2008.

Figure 6. U.S. Maize and Soybean Area (million hectares planted)

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25

30

35

40

2000 2001 2002 2003 2004 2005 2006 2007

Maize Soybeans

Source: DECPG calculations based on USDA data.

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While maize displaced soybeans in the U.S., other oilseeds displaced wheat in the EU and other wheat exporting countries. The expansion of biodiesel production in the EU diverted land from wheat and slowed the increase in wheat production which would have otherwise kept wheat stocks higher. In response to the increased demand and rising prices for oilseeds, land planted to oilseeds increased, especially rapeseed and to a lesser extent sunflower. The increase was primarily in the countries that are also major wheat exporters such as Argentina, Canada, the EU, Russia and Ukraine. Oilseeds and wheat are grown under similar climatic conditions and in similar areas and most of the expansion of rapeseed and sunflower displaced wheat or was on land that could have grown wheat. The 8 largest wheat exporting countries13 expanded area in rapeseed and sunflower by 36 percent (8.4 million hectares) between 2001 and 2007 while wheat area fell by 1.0 percent (Figure 7). The wheat production potential of this land was 26 million tons in 2007 based on average wheat yields in each country, and the cumulative wheat production potential of that land totaled 92 million tons from 2002 to 2007. To illustrate the impact of this land shift on wheat stocks, Figure 8 shows the simulated wheat stocks compared to actual wheat stocks if the land planted to rapeseed and sunflower had been planted to wheat and if wheat stocks had increased by the same amounts. The simulation shows that wheat stocks would have been almost as large in 2007 as in 2001 rather than lower by almost half. Figure 9 shows the relationship between wheat stocks and prices.

Figure 7. Wheat and Oilseeds Area (Index 2001=100)

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2001 2002 2003 2004 2005 2006 2007 Wheat Rapeseed and Sunflower

Source: DECPG

13 Eight countries and groups accounted for 90 percent of global wheat exports during 2005-07. These countries and their shares were: U.S. 25.4%. Canada 15.3%, EU-27 11.9%, Russian Federation 9.8%, Australia 9.3%, Argentina 8.8%, Kazakhstan 6.0% and Ukraine 3.2%.

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Figure 8. Wheat Stocks, Actual & Simulated

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2001 2002 2003 2004 2005 2006 2007

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Figure 9. Wheat Prices vs. Stocks (Index 2000=100)

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2000 2001 2002 2003 2004 2005 2006 2007

Prices World Stocks

Source: DECPG

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Export bans and restrictions fueled the price increases by restricting access to supplies. A number of countries have imposed export restrictions or bans on grain exports to contain domestic price increases. These include Argentina, India, Kazakhstan, Pakistan, Ukraine, Russia and Vietnam. The impact of these bans or restrictions is illustrated in Figure 10 which shows Thailand’s rice export price in the weeks prior to and after India banned rice exports on October 9, 2007. According to the USDA (USDA 2007) and the International Grains Council (2007), there were no other important market developments at that time that could account for the subsequent rice price increases. The USDA had projected India to export 4.1 million tons in the month prior to the ban and that was revised to 3.4 million tons in the month following the ban. The ban on exports led to a steady increase in prices over the following weeks. While it is probably not correct to say that all of the price increases were due to the ban, it likely focused attention on the market fundamentals and the rise in wheat prices and caused market participants to reconsider their imports and exports.

Figure 10. Impact of India's ban on rice exports (Thai rice export prices, $/ton)

300

320

340

360

380

400

07 /17

/20 07

08 /07

/20 07

08 /28

/20 07

09 /18

/20 07

10 /09

/20 07

10 /30

/20 07

11 /20

/20 07

12 /11

/20 07

01 /01

/20 08

Source: International Grains Council data.

Rice is not used for biofuels, but the increase in prices of other commodities contributed to the rapid rise in rice prices. Rice prices almost tripled from January to April 2008 despite little change in production or stocks. This increase was mostly in response to the surge in wheat prices in 2007 (up 88 percent from January to December) which raised concerns about the adequacy of global grain supplies and encouraged several countries to ban rice exports to protect consumers from international price increases, and caused others to increase imports. Weather-related production shortfalls have been identified as a major factor underpinning world cereals prices, especially in Australia, U.S., EU, Canada, Russia and Ukraine (OECD-FAO 2007). The back-to-back droughts in Australia in 2006 and

13

2007 reduced grain exports by an average of 9.2 million tons per year compared with 2005, and poor crops in the EU and Ukraine reduced their exports by an additional 10 million tons in 2007. However, these declines were more than offset by large crops in Argentina, Kazakhstan, Russia and the U.S. Total grain exports from these countries in 2007 increased by about 22 million tons compared with 2006. Global grain production did decline by 1.3 percent in 2006 but it then increased 4.7 percent in 2007. Thus the production shortfall in grains would not, by itself, have been a major contributor to the increase in grain prices. But when combined with large increases in biofuels production, land use changes, and stock declines it undoubtedly contributed to higher prices. The production shortfall was most significant in wheat, where global production declined 4.5 percent in 2006 and then increased only 2 percent in 2007. Global oilseed production rose 5.4 percent in 2006/07 and declined 3.4 percent in 2007/08. Rapid income growth in developing countries has not led to large increases in global grain consumption and was not a major factor responsible for the large grain price increases. However, it has contributed to increased oilseed demand and higher oilseed prices as China increased soybean imports for its livestock and poultry industry. Both China and India have been net grain exporters since 2000, although exports have declined as consumption has increased. Global consumption of wheat and rice grew by only 0.8 and 1.0 percent per annum, respectively, from 2000 to 2007 while maize consumption grew by 2.1 percent (excluding the demand for biofuels in the U.S.) as shown in Figure 11. This was slower than demand growth during 1995-2000 when wheat, rice and maize consumption increased by 1.4, 1.4 and 2.6 percent per annum, respectively.

Figure 11. Global Grain Consumption (Index, 2000=100)

90

95

100

105

110

115

120

125

130

2000 2001 2002 2003 2004 2005 2006 2007

Maize Maize (Ex US biofuels) Wheat Rice (milled)

Source: DECPG calculations based on USDA data.

14

Other factors, such as the decline of the dollar contributed to food commodity price increases. The U.S. dollar depreciated about 35 percent against the euro from January 2002 to June 2008, and the depreciation of the dollar has been shown to increase dollar commodity prices with an elasticity between 0.5 and 1.0 (Gilbert 1989, Baffes 1997). However the dollar depreciated much less against most Asian currencies and a trade- weighted real exchange rate for U.S. bulk agricultural exports computed by the USDA (USDA 2008h) depreciated only 26 percent during that period. The elasticity should be less than 1.0, because the exchange rate does not pass-through completely in many countries due to policies (Shane and Liefert 2007). A comparison of the real trade- weighted exchange rate and the index of food prices (Figure 12) shows a general correspondence between dollar depreciation and food price increases. If the elasticity is taken as the mid-point of the range from 0.5 to 1.0, the increase in food prices due to the decline of the dollar would have been about 20 percent (26% x 0.75) between January 2002 and June 2008.

Figure 12. Food Prices vs. Exchange Rate

0

50

100

150

200

250

Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08

Food Real Exchange Rate

Source: DECPG calculations based on USDA data.

Speculative and investor activity has also increased and could have contributed to food price increases. A reflection of this increased activity was the quadrupling of the number of wheat futures contacts traded on the Chicago Board of Trade from 2002 to 2006 as shown in Figure 13. However, the increase in futures contracts does not coincide closely with the increase in wheat prices, which raises doubts about the impact on prices. The impact on prices is hard to quantify and most studies do not find that such activity changes prices from the levels which would have prevailed without such activity (Gilbert 2007), however, they may change the rate of adjustment to a new equilibrium when fundamental factors change.

15

Figure 13. Wheat open interest & prices (000 contracts and $/ton)

0

100

200

300

400

500

600

Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 0

100

200

300

400

500

600 Open Interest Prices

Source: DECPG

V. Summary and Conclusions The increase in internationally traded food prices from January 2002 to June 2008 was caused by a confluence of factors, but the most important was the large increase in biofuels production from grains and oilseeds in the U.S. and EU. Without these increases, global wheat and maize stocks would not have declined appreciably and price increases due to other factors would have been moderate. Land use changes in wheat exporting countries in response to increased plantings of oilseeds for biodiesel production limited expansion of wheat production that could have otherwise prevented the large declines in global wheat stocks and the resulting rise in wheat prices. The rapid rise in oilseed prices was caused mostly by demand for biodiesel production in response to incentives provided by policy changes in the EU beginning in 2001 and in the U.S. beginning in 2004. The large increase in rice prices was largely a response to the increase in wheat prices rather than to changes in rice production or stocks, and was thus indirectly related to the increase in biofuels. Recent export bans on grains and speculative activity would probably not have occurred without the large price increases due to biofuels production because they were largely responses to rising prices. Higher energy and fertilizer prices would have still increased crop production costs by about 15-20 percentage points in the U.S. and lesser amounts in countries with less intensive production practices. The back- to-back droughts in Australia would not have had a large impact because they only reduced global grain exports by about 4 percent and other exporters would normally have been able to offset this loss. The decline of the dollar has contributed about 20 percentage points to the rise in dollar food prices. Thus, the combination of higher energy prices and related increases in fertilizer prices and transport costs, and dollar weakness caused food prices to rise by about 35-40

16

percentage points from January 2002 until June 2008. These factors explain 25-30 percent of the total price increase, and most of the remaining 70-75 percent increase in food commodities prices was due to biofuels and the related consequences of low grain stocks, large land use shifts, speculative activity and export bans. It is difficult, if not impossible, to compare these estimates with estimates from other studies because of different methodologies, widely different time periods considered, different prices compared, and different food products examined, however most other studies have also recognized biofuels production as a major factor driving food prices. The increase in grain consumption in developing countries has been moderate and did not lead to large price increases. Growth in global grain consumption (excluding biofuels) was only 1.7 percent per annum from 2000 to 2007, while yields grew by 1.3 percent and area grew by 0.4 percent, which would have kept global demand and supply roughly in balance. This was slower than growth during 1995-2000 when wheat, rice and maize consumption increased by 1.4, 1.4 and 2.6 percent per annum, respectively. The large increases in biofuels production in the U.S. and EU were supported by subsidies, mandates, and tariffs on imports. Without these policies, biofuels production would have been lower and food commodity price increases would have been smaller. Biofuels production from sugar cane in Brazil is lower-cost than biofuels production in the U.S. or EU and has not raised sugar prices significantly because sugar cane production has grown fast enough to meet both the demand for sugar and ethanol. Removing tariffs on ethanol imports in the U.S. and EU would allow more efficient producers such as Brazil and other developing countries, including many African countries, to produce ethanol profitably for export to meet the mandates in the U.S. and EU. Biofuels policies which subsidize production need to be reconsidered in light of their impact on food prices.

17

References ABARE, “Australian commodities,“ March 2008. http://www.abare.gov.au/publications_html/ac/ac_08/ac_march08.pdf Baffes, John. “Explaining Stationary Variable with Non-stationary Regressors.” Applied Economics Letters, 4(1997):69-75. Collins, Keith, “The Role of Biofuels and Other Factors in Increasing Farm and Food Prices: A Review of Recent Development with a Focus on Feed Grain Markets and Market Prospects,” June 19, 2008. FAO, “Crop Prospects and Food Situation,” February 2008. http://www.fao.org/giews/english/cpfs/index.htm FAO, “Growing demand on agriculture and rising prices of commodities,” February 2008. http://www.ifad.org/events/gc/31/roundtable/food.pdf FAPRI, US Baseline Briefing Book, March 2008. http://www.fapri.missouri.edu/outreach/publications/2008/FAPRI_MU_Report_03_08.pd FAPRI, “The Energy Independence Act of 2007: Preliminary Evaluations of Selected Provisions,” January 2008. http://www.fapri.missouri.edu/outreach/publications/2008/FAPRI_MU_Report_01_08.pdf Glauber, Joseph, USDA Chief Economist, in testimony before the Joint Economic committee of Congress on May 1, 2008. Gilbert, Christopher, “How Should Governments React to High Food Prices?” manuscript, July 2008. Gilbert, Christopher, “Commodity Speculation and Commodity Investmetns,” manuscript, October 2007. Gilbert, Christopher. “The Impact of Exchange Rate Changes and Developing Country Debt on Commodity Prices.” Economic Journal, 99(1989):773-784. Global Subsidies Initiative, International Institute for Sustainable Development, Geneva, Switzerland, 2008. http://www.globalsubsidies.org/en/research/biofuel-subsidies. IFPRI, von Braun, J. “The World Food Situation: New Driving Forces and Required Actions,” IFPRI, December 2007. http://www.ifpri.org/pubs/fpr/pr18.pdf International Grains Council, “Grain Market Indicators,” October 2007. Lazear, Edward, “White House Disputes Role of Biofuels in Food Prices,” Associated Press, May 15, 2008.

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Lipsky, John, First Deputy Managing Director, IMF, Commodity Prices and Global Inflation, Remarks At the Council on Foreign Relations, New York City, May 8, 2008. Mitchell, Donald, “The Case for Higher Grain Prices,” April 2007. OECD-FAO, “Agricultural Outlook 2007-2016,” 2007. http://www.oecd.org/dataoecd/6/10/38893266.pdf Rosegrant, Mark W., Tingju Zhu, Siwa Msangi, Timothy Sulser, “The Impact of Biofuel Production on World Cereal Prices, International Food Policy Research Institute, Washington, D.C., unpublished paper quoted with permission July 2008. Shane, Mathew and William Liefert, “Weaker Dollar Strengthens U.S. Agriculrue,” Amber Waves, February 2007. Steenblick, Ron, personal communication, July 2008. U.S. Bureau of Labor Statistics, Consumer Prices, July 2008. USDA, GAIN, “Thailand Grain and Feed Report Intervention Policy Overhauled,” November 2006. http://www.fas.usda.gov/gainfiles/200611/146249481.pdf USDA, 2007. “Grain: World Markets and Trade,” October 2007. U. S. Department of Agriculture (USDA), 2008a. “Agricultural Projections to 2017,” February 2008. http://www.ers.usda.gov/Publications/OCE081/OCE20081.pdf –——. 2008b. Cost-of-Production Forecasts, Data Sets. http://www.ers.usda.gov/Data/CostsAndReturns/ –——. 2008c. GAIN report, “Grain and feed annual report,” February 2008. http://www.fas.usda.gov/gainfiles/200802/146293727.pdf –——. 2008d. “Grain and Oilseeds Outlook, February 22, 2008”, Agricultural Outlook Forum, February 22, 2008. http://www.usda.gov/oce/forum/2008Speeches/Commodity/GrainsandOilseeds.pdf –——. 2008e. “Grain: World Markets and Trade,” March 2008. http://www.fas.usda.gov/grain/circular/2008/03-08/graintoc.asp –——. 2008f. PS&D online database, March 2008. –——. 2008g. “Oilseeds: World Markets and Trade,” March 2008. http://www.fas.usda.gov/oilseeds/circular/Current.asp

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–——. 2008h. “Real Monthly Commodity Exchange Rates,” Macroeconomic Briefing Room, July 2008. van der Mensbrugghe, Dominique (2006), “Linkage Technical Reference Document,” The World Bank, Washington, DC. World Bank, Global Development Finance 2007. http://www.worldbank.org/prospects

obwona&chirwa2008.pdf

1

Impact of Asian Drivers on SSA agriculture

and food security: Issues and challenges

Revised Draft

Marios Obwona

Economic Policy Research Centre (EPRC)

Makerere University Campus

Kampala, Uganda

and

Ephraim Chirwa

University of Malawi, Chancellor College

Zomba, Malawi

2

1. Background

Africa is the only region in the world where poverty and hunger are on the increase.

About 49 percent of the people in Africa lived below poverty in 2000, an increase from

47.7 percent in 1990 (Resnick, 2004). On current projections, Africa will be the only

continent that is unlikely to meet the international community’s targets to reduce poverty,

hunger and disease - the Millennium Development Goals (MDGs) by 2015. The World

Bank (2004) estimated that, on current trends, Sub-Saharan Africa (SSA) will meet the

MDGs in 2147, more than a century off target. Resnick (2004) notes that meeting the

MDG targets requires growth rates of 7 percent per annum, but only 10 African countries

have achieved a growth rate of at least 5 percent per annum. GDP growth averages about

3 percent per year and the population has continued to grow at 2.7 percent per year. It is

estimated that about a third of the African population is undernourished, representing

about double the number of undernourished in the late 1960s (FAO, 2005).

Agriculture is by far the single most important economic activity in SSA and it remains

key to achieving the poverty targets of the MDGs in Africa. Several studies have

emphasized the importance of the agriculture sector in SSA (NEPAD, 2003; Haggblade et

al., 2004; FAO, 2005). It is estimated that nearly 80 percent of the population in SSA

lives in rural areas and 70 percent of this rural population are dependent on food

production through farming or livestock keeping for most of their livelihood. Small-scale

farming provides most of the food produced in Africa, as well as employment for 60

percent of working people. According to NEPAD (2003) for most countries in SSA,

agriculture contributes an average of 30-60 percent of GDP and about 30 percent of the

value of exports.

Except for countries with sizable populations of European descent – such as South Africa,

Zimbabwe and Kenya – agriculture has been largely confined to subsistence farming and

has been considerably dependent on rain and an inefficient system of shifting cultivation,

in which land is temporarily cultivated with simple implements (such as hand-hoes) until

its fertility decreases and then abandoned for a time to allow the soil to regenerate. In

addition, over most of African arable land generally has been allocated through a complex

system of communal tenure and ownership rather than through individually acquired title,

3

and peasant farmers have had rights to use relatively small and scattered holdings. This

system of land ownership has tended to keep the intensity of agricultural production low

and has inhibited the rate at which capital has been mobilized for modernizing

production. The productivity in agriculture has been declining due to several factors

including lack of access to capital, soil degradation, poor access to markets and new

technologies, low investments in agricultural research, training and extension services.

Africa has also experienced more than its share of the impacts of climatic changes

including extreme weather patterns in form of more frequent and prolonged droughts,

floods and crop pests all of which have adversely affected agricultural production. There

is also the problem of HIV/AIDS that is reducing life expectancy and productive capacity

of farming households in SSA (Haggblade et al., 2004). Eicher (2003) also notes that in

some countries donor support towards agricultural development in SSA has been

declining.

Farmers in SSA are struggling to adapt to these crises but support is declining. Whilst

total aid of SSA remained stable during the 1990s, the proportion allocated to agriculture

declined year by year. But even more worrying are the global trade rules that have been

forced onto African governments whose own structures are not strong enough to protest

these unfair and detrimental policies. The Uruguay Round of trade agreements, which

began in 1994, are generally held as the turning point in global agricultural policy, and

are called the Agreements on Agriculture.

Trade liberalization and tariff barriers have been just some of the areas that have been

detrimental to African farmers. Structural adjustment policies and trade conditions have

resulted in the collapse of agricultural support institutions, the elimination of subsidies

and reduction in tariffs for most African countries. At the same time, highly subsidized

European and American farmers undermine the African farmers in both domestic and

export markets – leaving African farmers unable to compete in the global market.

Furthermore, these subsidized goods lead to overproduction, which then results in

lowered prices.

The growing importance of China and India in the global economy has generated a lot of

academic research interest on their likely impact on African economies and poverty

4

reduction (among others Kaplinsky et al., 2006; Kaplinsky and Morris, 2006; Jenkins and

Edwards, 2005; Chen et al., 2005). Both China and India (‘Asian Drivers’ or ADs) are

recording higher growth rates in their economies and are increasingly engaging with

African countries in various ways. These relationships are bound to have complementary

and competitive effects on African growth and poverty reduction.

An important sector in Africa that could be affected by the growth of Asia Drivers is the

agriculture sector. This paper, therefore, provides the framework for assessing the impact

of the Asian Drivers on Sub-Saharan Africa agriculture. In particular, we review the

performance of the agriculture sector in SSA and the challenges it faces to uplift the

growth and poverty reduction potentials of African economies. We identify key issues of

concern to SSA agriculture arising out of the emergence of Ads. We also identify key

policy research questions, methods and approaches for investigating the impact of ADs

as well as suggesting the selection criteria of countries to be included as case studies.

2. Sub-Saharan Africa (SSA) Agriculture and food security:

Performance, Issues and Challenges

2.1 Agricultural Production

The performance of the agricultural sector in SSA has been disappointing, and since the

1960s agricultural output has been declining. According to the U.S. Department of

Agriculture (USDA, 1987), Sub-Saharan Africa is the only region in the world where per

capita food production has not stopped declining over the past three decades. The

disappointing performance of the agricultural sector in SSA has manifested in increased

food insecurity and declining export earnings from agricultural produce. Others have

noted that Africa is unable to feed its population and this situation has persisted over

many years (NEPAD, 2003). Diouf (1989), for instance, notes that while agricultural

production grew at an average rate of 2.4 percent per annum, population growth increased

at 2.6 percent per annum between 1965 and 1973. Haggblade et al. (2004) notes that over

the past 40 years or so, agriculture production has increased at a rate of 2.5 percent per

year in Africa compared to 2.9 percent in Latin America and 3.5 percent in developing

Asia. The index of per capita agricultural production shows a declining trend for African

countries in contrast to the increasing trends in Latin America and East Asia.

5

The declining agricultural sector performance has led to increased food insecurity in SSA.

Other studies show that the number of chronically under-nourished people increased from

168 million in 1990-92 to 194 million in 1997-99 (NEPAD, 2003). The performance of

agricultural GDP shows that growth rates improved since 1995 but were not sustained

due to variations in weather, conflict, diseases and insect as well as lack of good

technology.

In the last four decades in Africa, less than 40 percent of the gains in cereal production

came from increased yields. The rest was from expansion of the land devoted to arable

agriculture (Runge et al., 2003). In future, Africa must depend more on yield gains than

land expansion to achieve food security. In the past two decades, cereal yield growth in

Sub-Saharan Africa was virtually stagnant, whereas it grew by about 2.3 percent per year

in West Asia/North Africa (Rose Grant et al., 2001). The growth was however better for

roots and tubers, fruits and vegetables, some cash crops and fisheries. Furthermore, there

was better performance in 1961-1974 although Kenya and Zimbabwe failed to maintain

maize revolution in 1960s and 1970s. There is still no sign of green revolution with

respect to sorghum, rice and wheat.

Oil crop production was less than 2 percent in 1961-2004. However, South Africa

performed better. SSA was negatively affected by the cheap import and monetized food

aid. The pulses had a better performance except between 1975-1984 and the best

performance was recorded in West Africa. The beans are both cash and food crops. The

main obstacle in this sub-sector was limited support from research and extension.

With respect to tubers and roots, SSA’s global share is insignificant and increased from

40 percent in the 1970s to over 50 percent in the 2000s. Their production fluctuated but

annual growth is better than other food crops with cassava production increasing rapidly,

hence ensuring food security but its contribution to the economy and income remains

insignificant.

With respect to fruits and vegetable, output grew by 2.2 percent p.a. between 1961and

2004. Exports increased at faster rate (5.3 percent). Important non-traditional exports item

have performed well because of the favorable growing conditions and better marketing

6

opportunity. Kenya, South Africa, Zambia and Zimbabwe were among the successful

cases.

In terms of agricultural trade, statistics show that the share in world agricultural exports

declined from 8 percent in early 1960s to 2 percent in the early 2000 and SSA has fallen

from a net food exporter to a net food importer (Haggblade et al., 2004). Based on

agricultural trade balance, there is an increasing dependence on agricultural imports, with

imported food replacing traditional food. Agricultural imports are growing at faster rates

than agricultural exports.

There are a number of constraints that are responsible for the depressed food production

performance in Africa. These include: declining productivity, poor mechanization, and

weak research base, lack of incentives to producers, poor infrastructure and poor access to

markets. For example, fertilizer use on food crops is about 5 kg per hectare, compared

with an average of 30 kg per hectare for export crops (FAO, 1988). Agricultural research

capability is inadequate and has often been confined to research stations with little or no

on-farm experimentation or to cash crops. There is dearth of skilled researchers as the

brain-drain resulting from unsatisfactory work and social conditions prevail at home.

Studies show that SSA agriculture has been experiencing declining productivity. Grain

yield per hectare in SSA has remained constant compared to substantial increases in

South and Southeast Asia (Otsuka, 2006). Generally, per hectare yield has barely

increased since 1960 for the case of Africa. Of the total 1.6 per cent increase in food

production, yield increased only by 0.1 percent. This means that the overall food

production growth in Africa has been achieved mostly through expansion of area under

cultivation. In other words, there has not been a significant technological change in

African agriculture. Others have attributed the decline in productivity to increased soil

degradation, underutilization of water resources, the low input use of fertilizers, limited

use of improved soil-fertility management practices and weak support services -research,

extension and finance- (Diagana, 2003; FAO, 2005). Otsuka (2006) notes that although

some of the farmers have adopted high-yielding varieties, the impact on the crop yield is

limited because of lack of application of fertilizers to food crops.

7

In addition to the increasing poverty partially engendered by the deteriorating food

security, Africa's economic crisis has also been characterized by the disintegration of the

productive and infrastructural facilities. Apart from the decline of food and agriculture,

most African industries including agro-industries have also been increasingly operating

much below their installed capacities and genuine, cottage agro-industries have been non-

existent.

The physical infrastructure which was built during the aftermath of independence has, to

a very large extent, deteriorated due to poor maintenance and lack of renovation while

social services and welfare, especially education, public health and sanitation, housing,

etc., have rapidly deteriorated and continue to decay.

Agricultural research and development (R&D) investments are one of the most crucial

determinants of agricultural productivity growth, besides basic education. Investments in

research to develop risk-reducing and productivity-enhancing technology are of critical

importance. Others have argued that SSA has underinvested in research and development.

NEPAD (2003) notes that spending on agricultural research in Africa has stagnated at

US$200 million per year in the 1980s and 1990s compared to substantial increases in

Asia and Pacific countries. According to projections by Runge et al. (2003), trend

investments in rural roads, irrigation, clean water, education and agricultural research also

would have to increase by about 80 percent to achieve these outcomes. Such rates of

increase occurred in Asia during the Green Revolution. Essentially, the decline in the real

price of food - facilitated by crop yield growth from increased investments in agricultural

research, infrastructure and environmental protection - drives increased access to food,

with consequent reductions in under nutrition and especially child malnutrition. Science

and technology can directly contribute to food security through improved crops and

cropping practices, labor-saving technologies, better communications, and improved

quality of food processing, packaging and marketing.

2.2 Market access

Market access also remains a critical problem for SSA agriculture. This is because, most of the tariff peaks are in agriculture, including processed products, and most post- Uruguay Round tariffs1 have escalated between raw and semi-finished as well as between

1 Data on tariffs are from the WTO Integrated Data Base (MFN Applied Tariffs).

8

semi-finished and finished products, with a greater impact on more advanced stages of processing2. Coffee beans and final processed coffee, for example, are subject to tariffs of 7.3 per cent and 12.1 per cent respectively in the EU, 0.1 percent and 10.1 percent in the United States, and 6 per cent and 18.8 percent in Japan. In the case of cocoa, tariffs at the raw, intermediate and final stages are 0.5 per cent, 9.7 percent and 30.6 per cent respectively in the EU; and 0 per cent, 0.2 per cent and 15.3 percent in the United States. Japan accords tariff-free treatment to raw cocoa beans, but cocoa products exported at the intermediate stage are subject to a 7 per cent tariff, while final cocoa products are levied at 21.7 per cent.

The African Growth and Opportunities Act (AGOA) which was introduced in 2000 and Everything But Arms (EBA) in 2001 by the United States and the EU respectively have raised prospects for African countries as regards the issue of market access. However, an analysis of EBA in 2001 revealed little use of the scheme, owing in part to the fact that the beneficiaries continued utilizing Lomé protocols, which arguably have less restrictive rules of origin than the former (Brenton, 2003).

An assessment of AGOA has revealed that the additional benefits represent a modest expansion over the preferential treatment that SSA countries already enjoyed under the generalized system of preferences (GSP) (UNCTAD, 2003b: 2). On the other hand, other economists contend that, if it were not for the restrictive rules of origin governing market access under AGOA, its medium-term benefits would have been five times greater (Mattoo et al., 2002).

In SSA per capita exports of goods and services vary enormously, from an estimated $9

in Mozambique to $3743 for the Seychelles, with median value of $98. Service exports,

especially earnings from tourism in a number of countries, play an important and

increasing role. Merchandise exports have fluctuated in countries with the highest rate of

growth in export earnings in the late 1970s and early 1980s. However, the commodity

exporters experienced much higher degrees of fluctuations in exports earnings than

mineral and oil exporters. Overall, for African countries there has been a general decline

in world exports in all categories of resource –based exports: as percentage of total world

2 Granting of preferences does not mitigate tariff escalation. Residual protection in developed-country markets, after accounting for preferences, is typically in the more processed products, as is discussed below. On the other hand, while preferential market access might lead to a general reduction in both national and international tariff peaks, in some cases national peaks may actually rise taking preferences into account, as a lower overall average would be used as a reference point (see WTO, 2003: 9).

9

exports, SSA’s share dropped from 0.8 percent in 1970 to about 0.3 percent in 1995.

African countries have thus seen a steady decline in their market share. To reverse this

trend and assure a proper integration into the world trading system they need to identify

and correct constraints to export activities.

The constraints for most SSA is the share of primary exports as a proportion of total

merchandise remaining high, although some countries have experienced significant

improvements in export diversification, not only do most SSA countries depend on

primary commodity exports, their export earnings are also highly concentrated in a few

primary products: some countries rely on as few as three commodities for as much as 90

percent of their total merchandise exports. Countries with very high export concentration

are essentially mineral and oil exporters.

Furthermore, the current trend of price incentives favors the capital intensive production

of hard commodities and disfavors labor intensive production of manufactures.

Accordingly, there has been a substantial decline in the prices of key export commodities

by the Sub-Saharan African countries leading to deterioration in the net present value

(NPV) of debt to exports ratios. It is therefore envisaged that such a trend poses critical

challenges to poverty alleviation and income distribution strategies.

NEPAD (2003) identifies several challenges facing agricultural development in SSA

including:

• low internal effective demand due to poverty;

• poor and un-remunerative external markets (with declining and unstable world

commodity prices and severe competition from the subsidized farm products of

industrial countries);

• vagaries of climate and consequent risk that deters investment;

• limited access to technology and low human capacity to adopt new skills;

• low levels of past investments in rural infrastructure (such as roads, markets,

storage, rural electrification, etc.) essential for reducing transaction costs in

farming and thereby increasing its competitiveness in serving production,

processing and trade; and

• Institutional weaknesses for service provision to the entire agricultural chain from

farm to market.

10

A cross-country study by FAO in (2002) estimated that more equal access to land and

increased tenure security would result in more rapid growth in GDP and reduce

prevalence of undernourishment. Tenure security can be achieved by respecting

decentralized customary tenure and does not call for centralized top-down land tenure and

titling reforms. Land tenure security also provides the safety required for productivity-

enhancing and longer-run technology investments to be made.

The near stagnant economies in parts of Africa are to a large extent a reflection of

stagnant agriculture. Lower unit costs in production, resulting from productivity

increases, would lead to lower consumer prices for food and higher farm incomes, which,

in turn would promote economic growth through lower wage costs, higher investments,

and increasing consumer demand outside agriculture. Smallholder-led economic growth

could lead to dramatic improvements in food security and nutrition.

Projections to 2020 from the International Food Policy Research Institute (IFPRI)

indicate that, as a consequence of poor growth in incomes, poverty is expected to remain

pervasive in Sub-Saharan Africa (Pinstrup-Andersen et al., 1999). Food availability

should increase marginally but remain at the unacceptably low average of 2,276 calories

per day (compared to 2,633 for South Asia; 3,008 for Latin America and the Caribbean

and 2,902 for the world). The situation in many countries in Sub-Saharan Africa will

continue to cause concern, with per capita food consumption reaching only marginally

acceptable levels. The FAO predicts that of the 17 countries below the recommended

2,200 kilocalories per person per day in 2015, 12 will be in Sub-Saharan Africa (FAO,

2000).

2.3 Value addition chain

Evidence on commodity prices and commodity dependent countries reveals a mismatch

between prices paid by final consumers and those received by producers, because of

higher profits at later stages of the value chain. The stage in the value chain where

concentration is largest tends to acquire a large share of the profits, with a smaller share

of the final price going to the other stages. The underlying cause of this is oligopolistic

markets in which intermediaries largely appropriate the benefits of productivity

improvements. A case in point is where despite the fact that business in several

commodities (such as coffee and tea) has been booming in recent years in the markets of

consuming developed countries, this has only been reflected in higher prices for final

11

(processed) products, not in the prices received by producers in developing countries

especially SSA.

While African producers have incurred income losses, traders and firms in the higher

steps of the value chain have been reaping significant benefits. According to the

International Coffee Organization (ICO), for example, in the early 1990s, earnings by

coffee-producing countries (exports f.o.b.) were some $10–12 billion, while the value of

retail sales was about $30 billion. Currently, the value of retail sales is $70 billion, while

producers receive only $5.5 billion. World market prices for coffee have declined from

about 120 US cents/pound in the 1980s to around 55 US cents, reaching their lowest

levels in real terms in 2002 (Osorio, 2002). With an estimated 125 million people in the

developing world dependent on coffee production for their livelihoods, the impact of such

a price decline has been devastating in terms of social dislocation, including social

exclusion and poverty.

A value chain analysis of the coffee market reveals that, since 1985, a growing share of

total incomes in the chain has accrued to economic agents in the importing countries (see

Figure 1). The asymmetrical character of power in the coffee value chain explains the

unequal distribution of total incomes. “In the producer countries, farming is highly

fragmented and the destruction of marketing boards further reduces the capacity of

farmers to raise their share of value chain rents. At the importing end of the chain, there

are three major residues of power – importers, roasters and retailers. They compete with

each other for a share of value rents, but combine to ensure that few of these return to the

farmer or producer country intermediaries or governments” (Fitter & Kaplinsky, 2001:

16).

Figure 1. The build-up of retail coffee prices (circa mid 1990s)

12

F ar

m F

ac to

ry E

xp or

te r

Im po

rt a

ge nt

s F

ac to

ry R

et ai

l

P ro

d u

ci n

g C

ou n

tr y

C on

su m

in g

C ou

n tr

y

Fresh cherry

Dry process: dry cherry

Wet process: washed parchment

Unwashed green bean

Washed green beans

Beans for export

Export duty

Beans cleared for market

Freight and insurance

Import duty

Dealer

Processing company

Coffee house

Instant coffee Roasted ground coffee

Shop retail for home market

Commercial and catering

Coffee bar

Factory door costs: 343

Wholesale costs: 214

CIF: 180

FOB: 170

Factory door costs: 136

Farm gate costs: 45/91

Retail costs: 440

* Costs variable but very high. Include: overheads, advertising, other products ( i.e., milk), and the ‘experience’ of the coffee bar. (see breakdown of the price of a cup of coffee)

The Coffee Value Chain US cents/lb (1994)

B ar

Cappuccino costs.*

% retail value added

10/21

4

7

20/9

22

29

8

Source: Kaplinsky (2003)

13

From Figure 1, we observe that a striking feature of the coffee value chain has been the trend of inter-country income retention over the past four decades. The trend shows that the share of returns accruing to growers has remained consistently stable, while that of the producing countries has fallen just as the share of consuming countries has increased. This trend partly reflects the “unintended consequence” of the abolition of various coffee marketing boards as a consequence of structural adjustment policies. Although intended to provide the grower with a larger share of coffee incomes (by abolishing the “rents” accruing to the cooperative and marketing board agents), instead it transferred this income to parties in the rich countries. The damage was not only one of incomes but it also led to the destruction of the extension support required to maintain grower quality and education. As Gibbon (2001) shows, this can be directly traced to the decline in the quality of coffee and cotton grown in east and southern Africa.

Accordingly, it is more crucial for economies in Sub-Saharan Africa to shift from the

‘raw materials producer paradigm’ which have been their pre-occupation into the

production of value added globalised products which are competitive in price and world

class in quality. In this case, ‘world class’ takes the form of a retail-store-ready product

for the North American (through the African Growth and Opportunity Act - AGOA) and

EU (Everything But Arms – EBA – initiative) markets, Japan and, increasingly,

Arab/Middle East ‘for sale’ shelf display. As such, export revenue can be raised not by

increasing quantity exported but instead by raising price through greater value addition.

Also crucial to breaking Africa’s poverty trap, Sachs believes, is an agricultural

revolution similar to that of the Green Revolution in India which helped double, triple,

and even quadruple agricultural yields and generate income that now has India on a path

towards sustained economic growth. High yield variety seeds created in the developed

world were introduced through the 1960s and 70s in rural India and Southeast Asia and

were responsible for historic gains in agricultural output. Sachs believes India to be a

useful comparison because “places like India and others that were in extreme poverty

only two generations ago have been able to develop out of extreme poverty substantially

on their own resources, but I emphasize substantially and not entirely, because aid played

a huge role in spurring this development.” Aid, in this case, came in the form of new

seeds, fertilizer, and irrigation.

However, these high yield seeds were adopted at very low rates in Africa. “There has

been only a 20 percent uptake of high yield varieties in Africa, but an 80 percent uptake

in South and East Asia. African maize growers are planting open-pollinated varieties that

14

are not improved. They’re using their own seeds for the next season. So the difference is

you have farmers who don’t use fertilizer, who don’t use irrigation, who don’t use

improved seed varieties in these semi-arid environments and you put the package together

and you get 1 ton per hectare and they’re starving. And population in the rural areas is

doubling every 23 years. And the environment is being degraded because when you farm

a crop and don’t fertilize it, don’t put nutrients back, you’re depriving the soil of

nutrients. In traditional African agriculture when populations were lower, they used to

slash and burn. But once you reach a population of about 70 per square kilometer in the

rural areas, the fallowing system which takes 20 years to naturally restore the soil

nutrients can no longer work. So what happens is that the situation is getting worse, not

just staying steady”. Sachs is describing a Malthusian crisis in rural Africa – where

populations have run ahead of their ability to feed themselves, causing their hunger and

poverty to worsen. Because these high yield seeds require irrigation and fertilizer

Africans either cannot access or cannot afford, Sachs believes aid should be targeted

towards distributing inputs to help increase agricultural productivity.

3 What Can We Learn from the Asian Drivers?

3.1 Food self-sufficiency policy

The Chinese food market is undergoing rapid change. On the demand side, these

transformations include rising consumer incomes, urbanization, demographic changes

(including an ageing population and increasing female labour participation rates) and the

modernization of retailing. These trends will shift food demand towards meat, fish and

horticultural products. On the supply side, there are also significant structural

transformations and sources of uncertainty. These include, above all, the food self-

sufficiency policies of the Chinese government, and also the changing balance of food

production and exports, which increased production and export of high-value and value-

added products, including meat, fish and horticultural products.

For over a decade, analysts have been arguing that China’s comparative advantage lies in

shifting agricultural production from grains, particularly wheat, and towards products

which are labour-intensive, can be produced on marginal lands, and which require a

higher level of processing (Lu 1998; Felloni et al. 2003, among many others). These

include meat, fish and horticultural products whose exports have been increasing. In

15

contrast, there have been increasing concerns about the sustainability of grain production

both on environmental grounds (land degradation and water shortages) and economic

grounds. International bodies such as the World Bank and OECD have frequently argued

in favour of China increasing its grain imports.

In spite of all these, the Chinese government has remained committed to a policy of the

food self-sufficiency, which in practical terms means that domestic production of grain

should meet at least 95% of domestic demand (Felloni et al. 2003). Since the mid-1990s,

this policy has been pursued through a variety of policy measures, and its importance is

frequently restated by government sources. This policy effectively limits agricultural

trade, and as a result, the direct impact of China’s trade in food products on SSA remains

very limited. China’s overall level of agricultural trade is relatively low. Exports of

products for which it has a comparative advantage have tended to increase, while self-

sufficiency policies limit imports of products for which China does not have a

comparative advantage.

3.2 Food Production

The link between increased farm productivity and rural poverty reduction is compelling.

East Asia's relatively successful fight against poverty and chronic malnutrition over the

past several decades has been based from the start on productivity increases in

agriculture. Farm production among the developing countries of East Asia has grown at

an average annual average rate of 3.9 percent over the past two decades, well above the

average annual population growth rate for this region of 1.4 percent (Paarlberg, 2001).

Much of this farm production growth came from successful adoption of new farming

technologies, led particularly by the introduction of new high yielding seed varieties

(especially for rice) accompanied by large new investments in irrigation, marketing

infrastructure, and increased fertilizer use. New technologies and intensive input use

were not the only key factors of success. After 1978, market-oriented policy reforms also

played a significant role in stimulating farm productivity for the Chinese economy. Yet

even in China 60 percent of the nation's significantly increased rice yields between 1975-

1990 came from new technologies, including hybrid seeds developed by China's own

scientists.

16

The success of China’s agriculture with these new technologies has been remarkable:

total grain production in China increased from a level of just 305 million tons in 1978 to

annual average levels above 500 million tons by 1999. This farm productivity boom

stimulated growth throughout China’s economy which in turn reduced poverty.

Higher farm productivity in East Asia and South Asia have helped bring the growth of

hunger into check in those regions, but in Africa the prevalence of hunger remains high,

and the actual incidence of hunger remains on the rise. Unfortunately, most governments

in Africa are not investing enough in this agriculture. In fact, African governments invest

less than 5 percent of their annual budget in agricultural development, even though 75

percent of their citizens - and an even larger share of their poorest citizens - still depends

on farming.

The situation has been made worse by international public sector investments in

agricultural development in poor countries which have recently registered a steady

decline. For example, annual World Bank lending for agriculture and rural development

has fallen by 47 percent over the past dozen years, from $6 billion in 1986 to just $3.2

billion in 19983. Annual foreign aid by governments to agriculture in poor countries has

also fallen, by 57 percent between 1988 and 1996 (from $9.24 billion down to just $4.0

billion). The U.S. Agency for International Development (USAID) has unfortunately

been a leader in this trend away from supporting poor farmers abroad. USAID spending

for agriculture has fallen from $594 million as recently as 1992 down to $310 million in

2001.

Within the urban market segment in China, incomes vary greatly. An emerging middle

class of relatively high-income consumers based largely in Beijing, Shanghai,

Guangzhou, Shenzhen and other wealthy coastal cities. Other urban residents including

many residents of inland cities, the unemployed and growing numbers of migrants from

rural areas and retirees, have much lower incomes. High income urban residents consume

more of most foods on a per capita basis, especially milk, fruits, beer, poultry, meat, fish,

egg and vegetables.

3 Ibid.

17

Income growth may affect both the quantity and the mix of foods demanded in China.

Demand analysis indicated that both rural and urban residents in China increased their

purchases of all major food items as their incomes grew while holding prices constant.

Income elasticities estimated by Chen illustrate how the response to income varies across

food items. Price elasticities of demand indicate that China’s consumers are sensitive to

food prices, suggesting that realignments of prices could have important effects on foods

demand.

As China has continued to develop and per capita incomes of its consumers have risen,

dietary patterns have shifted away from staple grains and starches towards animal

proteins and fish. Based on survey data collected by China’s National Bureau of

Statistics, per capita meat and egg consumption by urban residents increased an average

of 1.5 percent annually from 1985 to 1999. Continued income growth and urbanization

will further expand meat consumption.

China’s dramatic increase in animal protein consumption would not have been possible

without a rapid expansion of its domestic livestock industry. Since 1985, China’s pork

output has increased markedly, reaching over 40 mmt (4.7 times the level in the US) in

2000. China’s beef sector has grown from an inconsequential output level in the 1980s to

the third largest in the world. Likewise, China has moved into second place behind the

US in total output of poultry. Overall per capita meat consumption in China however is

still lower than in the US.

The Chinese and Indians demand for food products exhibits some caveats also. The most

rapid phase of growth in Chinese demand for food is over. First the population growth in

China is slowing and is expected to be a third of what has been in the past decades.

Second, the gap between China and developed countries with respect to daily calorie

intake is being bridged. Over the next three decades, Chinese per capita food

consumption is therefore expected to grow at a quarter of the rate seen in the past three

decades. The Chinese demand for food will continue to rise while this increase will go

hand in hand with structural changes in food consumption patterns. The out come should

be for instance a growing demand for meat. Increased demand for edible oils and sugar,

products form aquaculture, fresh fruits and vegetables are also expected. As a result,

18

opportunities in the field of agro-business and related exports to China might be opening

up in a near future.

In India, on the other hand, average food energy intake per person is 2500kcal and its

population is set to grow at an average of over one percent a year over the next three

decades.

3.3 Export orientation

In spite of having 20% of the world’s population and 7% of the world’s land, China has

been, up to now, a net food exporter. The direct impact of this trade on SSA seems to be

limited. The main markets for Chinese food exports are Japan, the US and Europe, and

the richer Asian economies such as Hong Kong and Taiwan. There are some specific

exceptions to this rule, such as Chinese garlic exports which have taken significant

market shares in countries such as Vietnam and Thailand. One of the ‘China effects’ is

that because of its size, exports which are not significant for total Chinese trade may have

significance for importing countries.

Similarly, SSA countries are not the main suppliers of Chinese food imports. As is well-

known, China is a mjor global importer of soya beans and animal feed more generally,

but these are imported mostly from Latin Americas. Along with grains and feed, China

also imports large quantities of oilseeds, fats and oils; it is the largest export destination

for the palm oil industry of Malaysia and also a major destination for Indonesia. But

while various authors have argued that China’s rapid urbanization and industralization

will turn it into a net food importer, Lu (1998) argues that China can remain competitive

in high-value agricultural production.

The indirect effects on SSA of China’s food trade are harder to gauge. For example,

China targeted the Japanese market in the 1990s, moving its market shares of fresh and

frozen vegetables from the 5-6% range in the early 1990s to the 35-37% range in the late

1990s (Huang 2004). However, the main losers were not other Asian countries, but rather

the US.

Based on the Asian drivers as markets for SSA exports, China and India’s growing

demand for commodities has not only resulted in higher commodity prices and in a

19

subsequent improvement of most African countries’ terms of trade, but also brought

about a redirection of African exports towards Asian markets away from OECD markets,

yet the increase in SSA’ trade with China and India has been essentially driven by the

exports of agricultural/raw materials.

The Asian drivers’ do impact the macroeconomic determinants of the price of raw

material because India’s contribution to global output is impressive, i.e. 20.6 percent and

7.1 percent respectively in 2004 based on IMF, World Economic Outlook (2005). Each

year since 2001, their combined contribution to global output growth has been around 30

percent. China’s contribution has been consistently higher than that of India by almost

three times. Moreover, this contribution has helped to hold global output growth above

the 4 percent threshold which is critical for improving the terms of trade for

primary/agricultural producers. The sustainable high level of growth in both energy and

metal use since 2000 has sparked China’s (to lesser extent India) demand for

commodities on a global scale.

Regardless of the currency regimes, any sustained growth differential to China and her

main trading partners will imply trend appreciation of the real effective exchange rate.

This will raise China’s purchasing power, while it will negatively affect her export

competitiveness. Africa’s primary commodity exporters would be likely to benefit from

real effective appreciation. Recent CGE simulations at Deutsche Bank (2005a)

emphasized that the substitution effect of currency appreciation as China’s demand for

commodities would shift away from domestic suppliers to cheaper African supplies.

Many African economies are prominently linked to the world economy as important

producers of raw materials and soft commodities. China’s and India’s emergence over the

last decade as a key net importers of commodities means that global commodity markets

are likely to be the main channels through which the impact of China and India’s

ascendancy has been (and will be) felt on the African continent. This evidenced by the

correlation with the growth of its major commodity exports to China and India.

Africa is linked to the Asian drivers’ demand for primary commodities via the Asian

impact on the world prices of primary commodities that count in Africa’s export mix and

20

the magnitude and speed at which African exports direct exports towards the Asian giants

so as to satisfy their demand.

3.4 Agro Processing and Value Additions

With respect to cotton, China is not the only importer world over, but also a large scale

producer. Sub-Saharan Africa countries that produce cotton have indeed been confronted

with record low prices since 2004, due to a world record harvest in 2004. It is expected

that China will account for 40 percent of the prospective fall in world production in

2005/2006, hence contributing to the expected rebound of cotton prices.

Beijing’s motives are clear, China’s growing industries demand new energy and raw

material suppliers; its exporters want markets; its diplomats require support in

international organizations; and its propaganda still seeks support from allies to advance

Chinese interests and when necessary, to counter United States.

In 2004, Chinese exports to Ethiopia made up over 93 percent of the two nations’ bilateral

trade, and in the first half of 2005, Chinese purchases from Djibouti, Eritrea, Somalia

were negligible, an imbalance that could alienate the Latin American states. In an attempt

to ease the lopsided trade relationship, this year Beijing scrapped tariffs on 190

commodities from 25 African nations (Eisenman et al., 2006).

As Chinese investment in the continent has grown, some 80,000 migrant workers from

China have moved to Africa, creating a new Chinese diaspora that is unlikely to return

home. In some cases, this diaspora, along with imports of cheap Chinese goods, has

sparked anger in Africa. Many African business people believe that Chinese goods are

unfairly undercutting them, and fear the diaspora is remitting nearly all of its money back

to China rather than reinvesting it into local economies. These are the kinds of concerns

that once led to anger against Indian populations on the continent (Eisenman et al, 2006).

The Food and Agricultural Organization (FAO), lists 47 developing countries, 24 of them

in Africa, which depend on agricultural exports. Many countries have set out to diversify

their production but with less success. The relatively poor performance of Africa’s

exports can be attributed to a combination of a number of factors, including external

barriers facing Africa’s exports (market access issues), Africa’s own trade barriers,

21

supply-side constraints, including inadequate trade related infrastructure and institutional

capacities.

These factors have led to a heavy concentration of African exports in a few markets and

products, principally agricultural products and minerals (Figure.1), which are growing

relatively slowly in export markets, as opposed to dynamic products that have high global

demand and high productivity potential. Even so, the share of food and agricultural

products for the Sub-Saharan Africa (SSA) declined from around 50 percent during the

1960s to around 21 percent by the turn of the new millennium. The Hirschman

concentration index4, which measures the relative importance of individual products in a

country’s exports, was around 0.49 for SSA countries compared with 0.15 for middle

income Asian countries and 0.11 for the OECD countries during the mid-nineties5. This

clearly shows lack of diversification of African exports.

Source: ADB, 2006.

Exports from African countries to China particularly have been predominantly of

extractive products, minerals, petroleum and timber. They are not likely to have had a

significant positive impact on the poor. However, some types of exports which do offer

potential for pro-poor impacts include fruit and cotton. Competition from China within

the African countries is less of a challenge to the countries of the region than to those of

South and South-east Asia which have specialised in exporting labour-intensive

manufactures, particularly textiles and garments. The major exception to this is Lesotho, 4 The higher the Hirschman concentration index, the less diversified are a country’s exports, and vice versa. 5 Ng, F. and Yeats, A. (1996), ‘Open Economies Work Better!: Did Africa’s Protectionist Policies Cause its Marginalization in World Trade?’ World Bank Research Working Paper, No. 1636.

Figure 1: Composition of Exports, 2003

0

10

20

30

40

50

60

70

80

90

EAP SSA LAC

%

Agric food fuel Manf Ores Other

22

which over the last few years developed a significant garment industry which is

threatened by the ending of the MFA (Multi-Fibre Agreement).

Angola, Cameroon, Democratic Republic of Congo and Sudan are significant exporters of

crude petroleum to meet China’s demand for energy. Cameroon and Congo, along with

Mozambique and Tanzania are exporters of wood, while Ghana, Namibia and Zambia

supply non-ferrous base metals, which are important raw materials for China’s booming

industrial sector. In all cases the exports involve very limited processing within the

African countries. The only other product to feature significantly in China’s imports from

Africa is cotton from Cameroon, Sudan and Tanzania. This has been to supply the

demand for cotton from the rapidly growing Chinese textile industry which it has not

been possible to meet domestically because of the decline in the area planted to cotton as

farmers switch to more profitable crops.

Exports from most African countries to China are predominantly extractive in nature, as

reflected in the dominant share of minerals and petroleum and forestry in Chinese imports

from the countries which have become important exporters (Table.1). Labour-intensive

agricultural and manufactured goods do not feature significantly for any of the African

countries. In terms of potential impact on poverty, the major effect of such extractive

products is likely to be via the government revenue channel since employment creation is

usually limited.

3.5 Global governance

China is becoming an important actor in multiple arenas of global and regional

governance. This presents new challenges as it disrupts patterns of global governance

established in the past few decades. These have been based around the dominance of

Western countries, as expressed in the G7 club of industrialized countries. But, the global

order is rapidly changing. By 2025-2030 at the latest, China and India, will join the US

and possibly Europe to create four substantial poles of power in arenas of global

governance. The way these countries interact will largely determine whether and how

these problems are dealt with and what role SSA will be able to play in world politics and

world economy.

23

For a long time China displayed reticence in global institutions, only making active

interventions on issues it considered directly relevant to its security interests: notably

disarmament and the status of Taiwan. However, China’s willingness to criticize US

policy in Iraq in 2004 and its increasingly active search for partnerships in Asia and SSA

in pursuit of access to resources mark a new-found assertiveness.

The size and rapid growth of China and India, together with its increasing assertiveness,

represent a challenge to the established order. Global governance has increased in

complexity and scope in recent years. Up to the 1980s, global governance was largely

confined to economic issues – GATT, WTO, the IMF, the World Bank- and the UN. The

United States took the lead, followed by the European powers. Now more issues are

subject to global negotiations (for example, climate change, intellectual property rights,

humanitarian interventions, etc.). At the same time, the results of these negotiations have

much greater impacts on developed and developing countries alike. For example, WTO

agreements create obligations in areas such as subsidies and competition policy. The

rapidly expanding area of food safety standards has clear and direct consequences both

for access to developed country markets and domestic food production. International

financial agreements have consequences both for capital flows and for the regulation of

domestic financial systems. Policy conditionalities link aid, loans and domestic policies.

The impact of the global on the national has been extended further through use of

conditionality in bilateral or plurilateral trade agreements – AGOA, EU Economic

Partnership Agreements and US bilateral treaties.

Developing countries have had limited effectiveness and participation in both multilateral

negotiations and global standards setting bodies. Even in trade negotiations, where

developing countries have had more than two decades to develop capabilities, the clearest

impacts on framing agendas and staking out positions occurred as recently as the

negotiations for the Doha Round.

The voice of developing countries in global governance is increasing led by countries

such as India, China, South Africa and Brazil. They often articulate the views held by

broad groups of developing countries and their size and political sophistications make

them less subject to coercion by the industrialized countries.

24

4. Asian Drivers’ Policies towards SSA Agriculture

4.1 Chinese Policies towards African Agriculture

According to the Chinese Government (2006), China intends to promote agricultural

cooperation and exchanges with African nations at various levels, through multiple

channels and in various forms. These channels and forms include land development,

agricultural plantation, breeding technologies, food security, agricultural machinery and

the processing of agricultural and side-line products. China also intends to intensify

cooperation in agricultural technology, organize training courses of practical agricultural

technologies, and carry out experimental and demonstrative agricultural knowledge

projects in Africa and speed up the formulation of China-Africa Agricultural Cooperation

Program.

China is potentially an important global trader of agricultural commodities, a role that

will become more pronounced following the country’s world trade organization

accession. At various times during the 1990s, China imported as much as 17 percent of

the world’s traded wheat, 25 percent of its fertilizers and 28 percent of its Soya bean oil,

while exporting as much as 10 percent of the world traded corn. China’s role in

agricultural trade has been modest and the country has run small annual agricultural trade

surpluses in recent years.

China still maintains many barriers to agricultural trade, but it has liberalized trade

considerably in a sector in which protection is high in many other countries. A shift

towards freer trade may provide added side benefits from scale economies and increased

domestic competition. With an abundant rural labour force relative to its land base. China

has a comparative advantage in labour intensive agricultural products, such as fruits and

vegetables, and manufactured agricultural products.

China’s policy emphasis on grain self sufficiency may have impeded the shift towards

comparative advantage in trade. In the 1990s, as China’s lack of comparative advantage

in grain production became more apparent, the target for domestic grain self sufficiency

was lowered to 95 percent of total grain consumption needs. During the 1990s, China

initiated a number of policy and institutional reforms to improve market efficiency,

25

including consolidating exchange rates, eliminating most government-determined prices

among other policies.

What we need to know is

(i) Will China increase imports of grain and other bulk commodities in

accordance with its comparative advantage and if it can’t then what

opportunities does SSA have?

(ii) Will increased competition in trade improve domestic efficiency and

transparency and reduce farm processor price spreads?

(iii) Will non tariff barriers remain important after WTO accession?

Other observers noted the “profound impact”, both good and bad, that China’s demand is

having on African economies. The procurement process is not always open; the footing of

both sides is not always even. All over Africa you will see Chinese construction firms

building railroads, highways, telecoms, enormous dams, even presidential palaces.

The sheer power of a big actor like China raises four challenges that, while negative,

could be turned into opportunities for growth and stability on all sides.

• Chinese infrastructure investment often mean importing thousands of Chinese

workers to the competitive disadvantage of Africa’s struggling domestic firms

• There is no such thing as avoiding mixing politics and business, especially if

China engaged in arms transfer to unstable countries, which has ramifications for

the region as a whole

• The new partnership for African development (NEPAD) adheres to values of

transparency, accountability and good governance, but China doesn’t always

operate on these principles

• China’s interest is resource-intensive, half are in extractive industries that pose

higher risks in labour, safety and the environment

Therein lies the greatest long-term opportunity in trade: capacity through collaboration,

concluded Donald Kaberuka, President, African development bank (ADB), Abidjan.

“Some welcome the emergence of China because it is a major donor and is less intrusive

in domestic politics. But we can learn from them how to organize our trade policy, to

26

move from low to middle income status, to educate our children in skills and areas that

pay off in just a couple of years”.

4.2 Indian Policies towards African Agriculture

In India, on the other hand, even though overall economic growth was high, it is clear that

slower growth in agriculture was the major reason behind the slower poverty reduction.

Prompted by macroeconomic imbalances, India's reforms began with macroeconomic and

non-agricultural policy changes. The reforms led to impressive rates of economic growth

in the 1990s, but since reforms were largely focused on the non-agricultural sectors, they

had limited impact on poverty reduction. Agricultural policy changes occurred only at

later stages, and even then were only partial. Therefore, the evidence suggests that

successful agriculture-led reforms reduce poverty faster.

SSA could learn from the experience of India and seek to encourage agricultural growth

in the future while at the same time avoiding the large inefficient subsidies provided to its

agricultural sector. This issue is of increasing relevance given the recent introduction of

the direct transfer program to farmers and the emphasis placed by many scholars and

government officials on increasing government support to agriculture and rural areas.

Over the next 15 years, there is probably a greater possibility of India developing a

distinct foreign policy with political interests toward Africa. India has the relative

advantage of geography and of speaking English. India also has a strategic interest in the

Indian Ocean. Indeed, it is more likely that India will have a foreign policy toward the

Indian Ocean littoral than to Africa in general, as evidenced by the development of the

IBSA (India, Brazil, and South Africa) forum. For example, official statistics show that in

2005, trade between India and South Africa stood at $1.9 billion. Indian exports to South

Africa include vehicles, rice medicinecs, cotton, leather goods, machinery and hand-made

carpets – while South Africa exports a range of goods and commodities such as

chemicals, gold, iron, steel, fertiliser and precious stones to India.

27

5. Impact of Asian Drivers on SSA agriculture

The impacts of ADs on SSA agriculture can either be categorized as competitive or

complementary. We can also distinguish effects as direct or indirect on African

agriculture.

The issue of the impact of Asian Drivers on African economies is a recent phenomenon in

the literature compared to considerable work on this issue in Latin America (Chen et al.,

2005). The literature on the impact of the ADs on developing economies has mostly

focused on trade and particularly trade in mineral and oil resources and manufactured

products. Jenkins and Edwards (2005) develop a framework for analysing the impact of

the ADs growth on poverty reduction in African economies, focusing mainly on trade

issues and foreign direct investments. Similarly, Kaplinsky and McCormick (2006) focus

on trade related issues and the foreign direct investment channel of the ADs’ influence on

developing countries while Kaplinsky and Morris (2006) focus on the implications of

quota removal on trade in the clothing and textile sector. Chen et al. (2005) focus on the

global macroeconomics, implications on raw commodity markets, trade links and

policies, foreign direct investments and governance issues. Kaplinsky et al. (2006) find

that China has predominantly imported limited number of commodities, especially oil and

hard commodities, from a limited number of countries.

There is therefore a considerable research gap in understanding the channels through

which the ADs engage in SSA agriculture and the resultant impact on growth and poverty

reduction in SSA. The existing literature on the impact of ADs on developing countries

identify several channels including trade flows, foreign direct investments and technology

transfer, aid flows, general and governance channels (Chen et al., 2005; Kaplinsky et al.,

2006; Jenkins and Edwards, 2005). Within these channels the impact of ADs can be direct

or indirect complementary but also competitive with different implications on welfare and

growth of developing countries.

There are several pathways which need to be investigated on the impact of ADs on SSA

agriculture. Figure 1 presents a simplified framework of analysing the impact of ADs on

the agriculture sector in SSA. We identify four particular areas through which ADs can

28

affect SSA agriculture. These pathways include aid flows, agricultural development

cooperation, trade flows and foreign direct investment flows. The agricultural

development cooperation channel is critical in delivering growth and poverty reduction

effects through productivity improvements, as a large proportion of people in SSA derive

their livelihoods from agriculture.

5.1 Financial flows Channel

Not much is known about ADs’ aid to SSA. However, Kaplinsky et al. (2006) note for

instance that most Chinese aid to SSA is directed towards broader strategic objectives of

developing links with resource rich countries in SSA. Aid to SSA has been in the form of

assistance to key infrastructure investments, limited debt-relief, training of Africans in

various fields, technical assistance in education, health and agriculture, tariff exemptions

and through peace-keeping forces in SSA (Kaplinsky et al., 2006). Aid from developed

countries to SSA agriculture has recently been declining (Eicher, 2003) and increased aid

from the ADs to SSA may help to somehow reverse such a trend. In this case aid is likely

to have direct complementary effects on SSA agriculture.

29

Figure 1 Framework for Analysing Impact of Asian Drivers on Sub-Saharan Africa Agriculture

Technical Assistance

Technology Transfer

Research and Development

Agricultural Development Cooperation

FDI Flows

Exports to ADs

Agriculture Activities

Agro- processing

Competition in Third Markets exports

Competition in Domestic

Markets

Output Prices

Exports to ROW

Productivity and Production

Consumer Welfare

Agricultural Growth

Poverty Reduction (FS, LS, EC)

Infrastructure Development

Aid Flows Trade Flows

Competition in Markets

30

With respect to SSA agriculture we envisage two ways in which aid flows can affect

agricultural development as presented in Figure 1. First, through assistance directed to

agricultural-productivity enhancing infrastructure investments such as roads and transport

networks, education and health facilities. For example, assistance provided to the TAZARA

railway linking Zambia and Tanzania (Kaplinsky et al., 2006) facilitates the flow of goods

including agricultural products. CCS (2006b) notes that the TAZARA railway currently

handles exports and imports between Tanzania, Zambia, Malawi, Congo, the Great Lakes

region, South Africa and Zimbabwe. Secondly, aid through technical assistance in agriculture

can directly contribute to agricultural development through increased productivity.

There are several examples of the ADs involvement in SSA agriculture through technical

assistance. According to CCS (July, 2006) the Sierra Leone and Chinese governments signed

an agreement to support agricultural development through the fielding of technical assistance

and expert services by China for the identification, designing and implementation of the

special programme for food security including water control, sustainable intensification of

crop production systems and the diversification of production. In Malawi, the Chinese have

been involved for a long time in irrigation rice farming through demonstration schemes,

working with the local communities on how to improve rice production. Different from the

technical assistance from the developed countries that concentrates at policy level in the

Ministry of Agriculture, the Chinese technical assistance in agriculture is provided at the

community level through the demonstration rice schemes. Although, there is no documented

evidence on the impact of these demonstration schemes, such activities have the potential to

raise agricultural productivity in SSA agriculture.

5.2 Agricultural Development Cooperation Channel

The development cooperation between SSA and ADs has the potential to enhance the

production capabilities in SSA agriculture through improvements in the productivity. There

are two pathways through which the agricultural development cooperation with ADs is likely

to benefit SSA agriculture: transfer of technology and research and development (see Figure

1). These pathways are likely to have direct and complementary effects on SSA agriculture;

particularly given the failure of the Green Revolution is the past. The direct effects may be

inform of cheaper technologies and low cost production systems. First, the transfer of

agricultural technologies from ADs to SSA agriculture can be in form of farming systems,

31

agricultural inputs and machines, and introduction of high yielding varieties. In Malawi,

some farmers use composite manure, and more recently smallholder farmers have been

introduced to what is known as ‘Chinese’ manure. Secondly, research and development

activities can be inform of development of new crop varieties and new farming technologies.

These activities are particularly important in improving productivity in SSA agriculture that

has been declining. Improvements in agricultural productivity have the potential to enable

SSA countries achieve critical thresholds and scales to compete effectively in international

markets. In turn increased participation in international trade can provide further incentives to

improve productivity. Improvements in productivity will be critical in promoting pro-poor

growth in SSA agriculture with implications for food security, livelihood security and

employment creation. Elsewhere, it has been shown that pro-poor agricultural growth has

occurred in situations in which average farm productivity have increased (Ravallion and Datt,

2002). Similarly, East Asia’s relative success in the fight against poverty and chronic

malnutrition over the past several decades have been attributed to productivity increases in

agriculture. Farm production among the developing countries of East Asia has grown at an

average rate of 3.9 percent over the past two decades, well above the annual population

growth rates for this region of 1.4 percent (Paarlberg, 2001).

The success of the Green Revolution in ADs provides potential avenues from which SSA

agriculture can benefit through ADs cooperation in agriculture. FAO (2005) notes that

increase in agricultural production and productivity in India has been brought about by

expanding cultivated area, developing irrigation facilities, promoting the use of improved

high yielding varieties and of better crop husbandry techniques developed by agricultural

research, improved water management, and plant protection. The Indian experience also

reveal the importance of improving coordination and management of education, research and

extension; ensuring public distribution of subsidised inputs and supporting agricultural prices

through a system of administered prices (Dorward et al., 2004).

Much of the farm production growth in East Asian countries came from successful adoption

of new farming technologies, led particularly by the introduction of new high yielding seed

varieties (especially for rice) accompanied by large new investments in irrigation, marketing

infrastructure, and increased fertilizer use. New technologies and added input use were not

the only key factors. After 1978, market-oriented policy reforms also played a significant role

in stimulating farm productivity for the Chinese economy. Yet even in China 60 percent of

32

the significantly increased rice yields between 1975 and 1990 came from new technologies,

including hybrid seeds developed by China's own scientists. The success of China’s

agriculture with these new technologies has been remarkable: total grain production in China

increased from a level of just 305 million tons in 1978 to annual average levels above 500

million tons by 1999. This farm productivity boom stimulated growth throughout China’s

economy which in turn reduced poverty.

China, for instance, has signed a number of agreements with SSA governments focusing on

the development of the agricultural sector including cooperation in technology transfer and

research and development. Examples include the Agricultural Development Project in

Nigeria focusing on cooperation in agricultural machinery, seedlings and technical experts

and transfer of Chinese technology in rice, fruits and vegetable production; supply of high-

tech farming equipment in support of the agrarian reform programme in Zimbabwe (CCS,

2005b); and the agreement bolster Kenya coffee exports through value addition (CCS,

2005a).

5.3 Trade Flows Channel

According to the FAO (2005) the share of SSA agricultural exports in world agricultural

exports has been declining while that of East and South Asian economies has been

increasing. Agriculture still plays a dominant role in the ADs economies. Lu (2006) note that

China is one of the top producers in agricultural products particularly grains (rice, wheat,

soybeans and corn), cotton, peanuts, canola seeds, fruits, vegetables, tobacco, meat, poultry,

eggs and aquatic products in which it ranks first in the world. Although agricultural

production has increased over the years in ADs, the ADs are also significant importers of

agricultural products. FAO (2005) notes that although agricultural development occurred in

India, it is not sufficient to eradicate hunger; India has the highest number of undernourished

people. Similarly, China, in spite progress in agricultural development, it is a net importer of

food and imports of sugar, cotton and vegetable oil have also been on the increase recently

(Lu, 2006). Trinh et al. (2006) estimate that China’s commodity hunger of commodities such

as agricultural products (mainly meat, soy and wood) will continue in the next 15 years.

The economic growth in the ADs has the potential to stimulate increased trade in agricultural

exports from SSA through the increased demand in food and non-food agricultural products.

33

However, SSA will have to compete with countries that are major exporters of agricultural

products to ADs with comparative advantage such as United State of America, Brazil,

Argentina, Malaysia, Australia, Thailand and Russia (Lu, 2006).6 The growth of ADs has

implications for SSA agriculture through the trade flows. There are two pathways in which

ADs impact on SSA agricultural trade: through increased exports to ADs and through

competition between ADs and SSA products in third and domestic markets. Thus, the impact

of trade can be both complementary and competitive.

The growing incomes in the ADs have generated increased demand for agricultural products

in which domestic supply in ADs can not meet domestic demand. Most studies show that the

growth in the ADs is creating demand for some commodities from SSA. Lu (2006) notes that

the increase in urbanisation and increases in incomes in China have lead to the demand for

improved diets and new and different foods. Income growth may affect both the quantity and

the mix of foods demanded in China. Demand analysis indicated that both rural and urban

residents in China increase their purchases of all major food items as their incomes grow

while holding prices constant. Price elasticities of demand indicate that China’s consumers

are sensitive to food prices, suggesting that realignments of prices could have important

effects on foods demand.

As China has continued to develop and per capita incomes of its consumers have risen,

dietary patterns have shifted away from staple grains and starches towards animal proteins

and fish. Based on survey data collected by China’s National Bureau of Statistics, per capita

meat and egg consumption by urban residents increased an average of 1.5 percent annually

from 1985 to 1999. Continued income growth and urbanization will further expand meat

consumption.

China’s dramatic increase in animal protein consumption would not have been possible

without a rapid expansion of its domestic livestock industry. Since 1985, China’s pork output

has increased markedly, reaching over 40 million metric tonnes (4.7 times the level in the

US) in 2000. China’s beef sector has grown from an inconsequential output level in the 1980s

to the third largest in the world. Likewise, China has moved into second place behind the US

6 The only SSA country that appear among the top 25 exporting countries of agricultural products to China is Gabon, ranked 24th in terms of volume of agricultural exports (Lu, 2006).

34

in total output of poultry. Overall per capita meat consumption in China however is still

lower than in the US.

The ADs’ demand for food products also exhibits some caveats. The most rapid phase of

growth in Chinese demand for food is over. First, the population growth in China is slowing

and is expected to be third of what has been in the past decades. Second, the gap between

China and developed countries with respect to daily calorie intake is being bridged. Over the

next three decades, Chinese per capita food consumption is therefore expected to grow at a

quarter of the rate seen in the past three decades. The Chinese demand for food will continue

to rise while this increase will go hand in hand with structural changes in food consumption

patterns. The out come should be for instance a growing demand for meat. Increased demand

for edible oils and sugar, products from aquaculture, fresh fruits and vegetables are also

expected. As a result, opportunities in the field of agro-business and related exports to China

might be opening up in a near future. In India, on the other hand, average food energy intake

per person is 2500 kcal and its population is set to grow at an average of over 1 percent per

year over the next three decades.

Whilst there is currently greater demand for hard commodities and oil from SSA, the

continued growth and rising incomes in ADs is likely to generate demands for different types

of agricultural products for which SSA may have comparative advantage. In some SSA

countries the growth of exports to ADs has increased both in terms of the share of exports to

ADs in SSA export and the growth rate of in exports (Jenkins and Edwards, 2005; Chen et

al., 2005). Nonetheless, there are variations across countries in the increases in exports to

ADs. For instance, on the top end exports to ADs as a proportion of total country exports

accounted for 43.9 percent in Sudan and 17.3 percent in Somalia and on the lower end only

accounted for 0.1 percent in Botswana and 0.4 percent in Uganda (Jenkins and Edwards,

2005). In terms of growth of exports as a proportion SSA exports to ADs between 1998 and

2003, Sudan registered 209.3 percent growth while Sierra Leone registered 85 percent

growth. Kaplinsky et al. (2006) also find that between 1990 and 2004, SSA exports to ADs

increased from 1.8 percent of SSA exports to ADs in 1990 to 10.5 percent in 2004.

Existing studies, however, show that most of the exports to ADs, particularly to China are

hard commodities and oil. Nonetheless, there is also evidence that trade relations between

SSA and ADs have extended to traditional and non-traditional SSA agricultural exports. For

35

example, Chen et al. (2005) and Kaplinsky et al. (2006) single out the increased demand by

ADs for cotton from SSA. Both China and India are not only net importers of cotton, but net

imports of cotton are also increasing. The increase in the demand for cotton may also lead to

improvements in international markets. Such increased demand for cotton offers hope to SSA

cotton farmers that have found it difficult to break into major markets such as USA due to

subsidies given to cotton farmers in USA. Increased demand for SSA cotton, may offer

opportunities for countries such as Malawi that are trying to revive cotton production. The

increased demand in SSA cotton by the ADs is likely to generate pro-poor agricultural

growth, since cotton is mainly grown by smallholder farmers in SSA.

Agricultural products exported to ADs by some SSA countries include cassava (Nigeria),

fruits and nuts (Mozambique, Ghana, Tanzania, Nigeria), cotton (Sudan, Ghana, Cameroon),

cocoa (Ghana), oil seeds (Congo), edible vegetables (Ethiopia, Ghana, Tanzania) (Jenkins

and Edwards, 2005; Chen et al., 2005). In some cases, the ADs have opened opportunities for

SSA countries to export to ADs non-traditional exports and to expand their traditional

exports. Some of the agricultural products that are less tradable such as cassava for instance

are becoming more tradable. For instance, CCS (2006a) notes that many Chinese firms have

been placing orders for Nigerian cassava above what Nigeria could supply. Such demand for

non-traditional agricultural commodities grown mainly by smallholder farmers in SSA is

likely to have enormous potential for agricultural growth in SSA agriculture and poverty

reduction. The major challenge for SSA agriculture, however, is how to gain the competitive

edge in agricultural exports to ADs markets where agricultural trade is currently dominated

by developed countries, East Asian economies and Latin American economies. This will

require productivity improvements in SSA agriculture. Hence, there is therefore a possibility

that competition for ADs’ markets may provide incentives for productivity improvements in

SSA agriculture, hence generating further impetus to pro-poor agricultural growth in SSA.

There are, however, questions about market access to ADs for SSA agricultural products.

China is undertaking economic reforms and with its strategic positioning in the global

political and economic spheres it is opening up its market to countries in SSA. For instance,

in 2005, China announced a zero-tariff treatment of particular products from all of the 39

least developed countries that have diplomatic ties with China (CCS, 2005b). Kaplinsky et al.

(2006) notes tariff exemptions on 190 products including food from 25 countries in SSA.

However, what is not known is whether there are other non-tariff barriers that may restrict the

36

export penetration of SSA countries into the Chinese market. The benefits of China’s and

India’s rising global demand for African commodities are nevertheless, attenuated by the

volatility of demand of the Asian giants, partly due to cyclical variations but importantly also

to arbitrage between home production and imports. The rising raw material demand from

China and in India is not necessarily an unfettered blessing for Africa.

The second pathway through which trade stimulated by ADs impact on SSA agriculture is

indirectly through competition in third markets and directly through competition in SSA

domestic markets in form of competing imported products from ADs. These have both

complementary and competitive effects on agricultural growth and poverty reduction in SSA.

The effects on competition from ADs in third markets and domestic markets in agricultural

products will depend on the similarity of commodities traded by ADs and SSA in these

markets. Competition in third markets may have indirect impact on SSA agriculture through

lower global prices, leading to declining agricultural incomes. It is, however, also possible

that such competition in third markets can provide incentives for productivity enhancement in

SSA agriculture. Competition in domestic markets through imports from ADs may have

indirect and competitive effects by displacing domestic products and complementary effects

through productivity enhancement effects of competition. Both competition in third markets

and domestic markets have output and price effects. Jenkins and Edwards (2005) assert that a

higher export similarity index between ADs and SSA is likely to have negative effects on

SSA exports and domestic production and may lead to depression of prices. This argument is

particularly reinforced by the fact that ADs has made significant strides in productivity gains

that have emerged from the success of the Green Revolution. However, such negative effects

may be moderated in the long-term by the productivity gains in SSA agriculture that may

result from ADs/SSA agricultural development cooperation in specific crops.

Many SSA economies are prominently linked to the world economy as important producers

of raw materials and soft commodities. The ADs’ emergence over the last decade a key net

importers of commodities means that global commodity markets are likely to be the main

channels through which the impact of ADs’ ascendancy has been (and will be) felt on the

African continent. This evidenced by the by the correlation with the growth of its major

commodity exports to China and India. SSA is linked to the ADs’ demand for primary

commodities via the Asian impact on the world prices of primary commodities that count in

37

Africa’s export mix and the magnitude and speed at which African exports direct exports

towards the Asian giants so as to satisfy their demand.

There is evidence from disaggregated data that shows the potential impacts. Jenkins and

Edwards (2005), for instance, find that 64 percent of exports from Malawi compete with

China in third markets with 52.7 percent being agricultural exports. Similarly, 54.4 percent of

exports from Namibia, 26 percent of which are agricultural exports compete with Chinese

products in third markets. This presents a potential threat to SSA agriculture, and the

challenge is how such SSA countries maintain or increase their exports to third markets with

such competition.

Competition in the in domestic markets of SSA economies from agricultural imports from

ADs may also have implications for SSA agricultural development. This is particularly the

case in situations in which ADs’ agricultural imports are cheaper in SSA domestic markets

compared to domestically-produced agricultural products. There is growing anecdotal

evidence already on the impact of ADs imports of manufactured products on SSA domestic

industries. Thus, if ADs have comparative advantage in the production of such products, this

is likely to displace domestic products in domestic markets. This may in turn lead to

reduction in the domestic production and agricultural growth with consequences on income

distribution. Jenkins and Edwards (2005) argue that if imports from ADs displace local

production in agricultural products which employ large numbers of unskilled worker, there

may be negative effects on the poor. This is also true if displaced products are mainly

produced by smallholder resource-poor farmer. In addition, increased imports from ADs in

agricultural products that compete with locally produced products, may lead to depressed

prices which will offer further disincentives to local farmers.

Nonetheless, from a welfare point of view, the overall effects will depend on the extent to

which the negative effects on domestic production outweigh the positive effects on depressed

consumer prices. This is also particularly the case where imports from ADs compete with

imports from third countries in SSA domestic markets (Jenkins and Edwards, 2005). Kennan

and Stevens (2005) carried out a preliminary review of the impact of imports from China to

African consumer welfare and local industries’ competitiveness. With respect to the many

sectors of exports, households are set to gain as consumers of Chinese final goods and local

producers as users of Chinese imported semi final goods.

38

Kennan and Stevens (2005) further offer a typology of African winners and looser. Countries

that are “winners” are those for which the number of sectors recording trade gains associated

with lower costs of imports or higher prices for exports exceeds the number of sectors

undergoing losses due to increased Chinese competition on third markets or higher prices for

imports attributable to higher Chinese demand for a given product. Those countries are

Angola, Nigeria, Sudan, Tanzania and to a lesser extent Benin, Burkina, Cameroon, DRC,

Ghana, mail, Mauritania, Mauritius, Niger, Senegal, RSA, Togo, Uganda and Zimbabwe. The

impact is neutral for Chad, Ethiopia, Kenya, Madagascar, Mozambique and Zambia and

negative for Malawi only. However, they do not reckon the adverse effects of cheap Chinese

imports on local producers either, with its potential trail of lay-offs and subsequent revenue

losses from local households. Cheap Chinese imports are merely regarded as a source of

welfare gain associated with lower costs of imports. Therefore, and as acknowledged by the

authors, they provide a very preliminary and partial assessment of China’s impact on African

countries.

5.4 Investment Flows Channel

With growth in ADs, there is increasing flow of direct foreign investments from ADs to other

developing countries including countries in SSA. The extent of such flows of investments is

difficult to measure, as some of such investments are in form of small scale trading

investments (Kaplinsky et al., 2006). Nonetheless, available studies point to increasing

investment flows from the ADs to SSA (Jenkins and Edwards, 2005). Literature also suggest

that most of the investments from ADs into SSA are in energy and resource sectors and small

scale trading activities (Jenkins and Edwards, 2005; Kaplinsky et al., 2006; Chen et al.,

2005).

There are two ways in which FDI from ADs can affect agricultural development in SSA.

First, investments in agricultural activities can directly lead to development of commercial

agriculture thereby leading into agricultural growth. If such agricultural investments are

labour intensive, agricultural growth resulting from direct investments may also lead to

employment creation for unskilled workers, with implications on poverty reduction.

Secondly, FDI in agro-processing manufacturing activities may create backward linkages

with the agricultural sector which may lead to the growth of the agricultural sector. The FDI

39

in agricultural and agro-processing activities may also lead to consumer welfare through

lower prices for agricultural products (as supply increases) and due to the availability of high

value processed food products.

However, FDI from ADs may also have negative effects especially with respect to

environment, work environments and conditions of service and product standards. The

negative environmental and social effects of FDI from ADs have been of concern in the

literature (Jenkins and Edwards, 2005; Kaplinsky et al., 2006). For instance, in Malawi, the

authorities have had to close two Chinese oil processing companies due to poor hygiene and

workers have complained of poor working conditions and low wages (The Daily Nation, 13

September 2006).

The evidence on the extent of foreign direct investments in SSA agriculture and in agro-

processing manufacturing activities, is however scanty. Kaplinsky et al. (2006) note that

foreign direct investments in agriculture only accounted for 7.1 percent of FDI of the top 20

countries in SSA that received Chinese FDI between 1979 and 2001. Other studies show that

ADs are increasingly investing in agro-processing activities with linkages to local agricultural

sectors such as in Nigeria (textiles) and Ghana (food and beverages) (Jenkins and Edwards,

2005), and oil processing industries in Malawi (The Daily Nation, 13 September 2006).

6. Research Issues and Method of analysis

6.1 Research Questions

The framework for analysing the impact of the ADs on SSA agriculture points to several

unknown areas through which ADs interact with SSA countries and how such interactions

impact on SSA agriculture. As noted earlier, there exist studies on the impact of ADs on

developing countries and countries in SSA, but no study has specifically focused on the

impact on SSA agriculture. In most SSA economies, agriculture plays a dominant role and

the poverty reduction efforts in many development strategies of SSA, look at the agricultural

sector as the one with the highest potential for achieving pro-poor economic growth. The

relations between ADs and SSA countries are opening up areas of opportunities and threat for

agricultural development in SSA. The broader research question to ask in country case

40

studies is: how do ADs interact with SSA agriculture and what are the implications of such

interactions on agricultural development in SSA?

The main challenges for SSA agriculture are how to increase production to achieve scale and

threshold for exports and to meet the continued flow of materials to support agro-processing

industries; the low adoption of appropriate technologies and extensive methods of farming;

the weak supply response and the underutilization of preferential markets; and the challenge

of providing appropriate support for agricultural development in SSA. The framework for

analysing the impact of ADs on SSA agriculture developed above, reveal that ADs may help

in addressing some of these challenges. The framework developed above reveal complexity

of various interactions that occur through several channels through with ADs engage with

SSA countries. Nonetheless, as the framework shows, the ultimate output of the assessment

of the impact of ADs on SSA agriculture is to identify impact indicators and how these have

changed or are likely to change with SSA’s increasing engagement with ADs. More

particularly, we need to know the impact on addressing the main challenges of agricultural

development in SSA and the potential for poverty reduction.

The country case studies should therefore attempt to provide answers to the following

research questions:

1. In what ways do the Asian Drivers engage in SSA agriculture? What motivates

the ADs in various channels through which they interact with SSA countries in

the agricultural sector?

The country case studies should bring out the economic, political and social

factors that facilitate or hinder ADs engagement in SSA agriculture.

2. What are the modalities of aid from ADs to SSA agriculture? How different are

such modalities from the major donors that support the agricultural sector in a

particular country?

The country case studies should consider the forms of aid (tied or untied, grants or

soft loan, technical assistance) that comes from the ADs directed at the

agricultural sector directly or indirectly. It will also be important for case studies

41

to political economy of aid, the motives, the conditionalities of aid and the relative

importance of Chinese aid in the country’s total aid over time. It will be important

to undertake a comparative analysis of aid modalities between China and India

and between the ADs and a selected sample of major donors that support

agricultural development in the country. The direct and indirect impacts of aid

have to be identified. The specific questions under this theme are:

a) Is there foreign aid from ADs directed at the agricultural sector in the country?

In what form is such aid?

b) What types of assets are being created with aid from ADs that have

implications for agricultural development?

c) Is aid from ADs to SSA agriculture any different from that of other donors in

SSA agriculture?

d) At what level do ADs and SSA countries interact with respect to technical

assistance? Is it at central government or policy level, local government and

local communities?

e) Are the modalities in technical assistance any different from other donors in

agriculture?

f) What is the impact of such technical assistance on agricultural productivity?

3. In what ways have ADs influenced SSA agriculture? How do ADs interact with

various stakeholders in agricultural development cooperation?

The country case studies should investigate the various ways in which ADs

interact with SSA countries in agricultural development. Some of the SSA

countries have specifically signed agreements with ADs on agricultural

development cooperation. The country case studies need to analyse these

agreements and document the progress that has been made. The framework for

analysing the impact of ADs in SSA agriculture identifies two broad areas of

agricultural development cooperation: transfer of technologies and research and

development. In some countries, the ADs have demonstration fields and it will be

important to undertake comparative case studies of farmers that have adopted

ADs’ technologies and those from other countries. The following are the specific

research questions:

42

a) Has the country entered in a formal agreement of cooperation in agricultural

activities? What stakeholders were involved in such agreements?

b) What are the types of technologies (inputs, seeds, equipment and machinery,

and farming systems) that are coming from the ADs?

c) What are the scales and conditionalities of access to agricultural technologies,

the scale of the technologies?

d) To what extent do the technologies from the ADs serve the smallholder farmer

needs in SSA?

e) Are there specific crops that are being promoted and what are the motives

behind targeting such crops?

f) What is the nature of research and development cooperation? What crops are

being promoted and whether new crops are being introduced to farmers?

g) To what extent are technologies from ADs adopted and how they are

introduced to the farmers?

h) How do activities in agricultural development cooperation impact on

productivity and agricultural growth and poverty?

4. What is the impact of ADs on SSA in agricultural trade?

The impact of ADs on SSA with respect to trade has been studied with a general

context. The research issues here should focus on agricultural trade and this is the

area in which the impacts are likely to be complimentary, competitive, dynamic,

direct and indirect. The direct and complementary impact of ADs in agricultural

trade will emerge from exports from SSA. The main question is what is the impact

of increased trade relations and increased demand of commodities by ADs on SSA

pro-poor growth, employment and poverty? Country studies should investigate the

direct effects and complementary nature of agricultural trade. The following are

specific questions:

a) What is the composition and trend of agricultural trade between ADs and

SSA? Has increased trade promoted non-traditional exports?

b) Has increased trade with ADs provided incentives for productivity

improvements in SSA agriculture?

43

c) What are the factors that are important in gaining access to ADs’ markets? To

what extent do SSA countries negotiate with ADs on market access? To what

extent do SPS standards are impeding trade in ADs?

d) Are ADs’ markets better markets for SSA agricultural products and what are

the prospects of growth in agricultural exports to ADs?

The other channel through which trade in agricultural products can affect SSA

agriculture is through competition in third markets. This will involve

identification of the destination of SSA agricultural exports and determining the

extent to which such exports compete with exports from ADs in those economies.

In such products and markets, there will be need to assess the trend in exports and

determine the extent to which such trends can be explained by the impact of

competition from ADs. The specific questions include:

a) What agricultural products from ADs compete with SSA agricultural exports

in third markets?

b) To what extent do competing exports from ADs displacing SSA agricultural

exports?

c) What have been the trends in international prices of such products?

d) How are various categories farmers in SSA affected by the competition with

ADs in third markets?

e) Has increased competition in trade improve domestic efficiency and improve

farmers’ returns from agricultural activities?

Country case studies should investigate the extent to which imports of agricultural

products from ADs are competitive or complementary in the domestic markets.

Specific questions include:

a) Are ADs dumping agricultural products in SSA countries?

b) How competitive are agricultural imports from ADs?

c) What are the welfare implications of increased imports of agricultural products

from the ADs?

d) To what extent are various groups of domestic producers affected or likely to

be affected by the import competition from ADs?

44

5. What is the nature and impact of foreign direct investments from ADs in SSA

agriculture?

The ADs are increasing engaged in foreign direct investment in SSA in various

forms. It will be important for country case studies to document the extent of

foreign direct investment in agricultural activities and agro-processing activities.

FDI can have both direct and indirect effects. Direct effects may result from direct

investments in farming activities while indirect effects may result from

investments in agro-processing activities which may generate additional demand

for agricultural products. Specific questions include:

a) What is the trend in FDI from ADs into the agricultural or/and agro-processing

sector?

b) What are the types of entrepreneurs from ADs that are involved in FDI in the

agricultural sector (private, state or multinationals)?

c) To what extent do investors from ADs engage in joint ventures with SSA

entrepreneurs?

d) Are these investments vertically integrated in the value chain?

e) To what extent are agro-processing investments utilizing domestically

produced agricultural products?

f) What are the environmental and social consequences of such investments? Are

there any mitigation measures in place?

g) How does FDI in agriculture and agro-processing feed into agricultural

growth?

6.2 Selection of Country Case Studies

The selection of country case studies can be based on the specific crops demanded by ADs or

indirectly affected by ADs through competition in third markets, the importance of

agriculture and the recent growth of the agricultural sector. However, selecting country case

studies on the basis of traded agricultural commodities is a bit problematic. First, SSA

agriculture is diverse in the number of crops grown and exported to ADs and the rest of the

world. Very few crops are known to be exported to ADs. Secondly, the channels through

which ADs affect SSA are many and selecting countries based on commodities exported to

45

ADs may mask the need to look at some of the channels. Thirdly, there is very little

information on the agricultural produce imported by ADs from SSA. The available evidence

is scanty and would point to the following commodities: cassava (Nigeria), fruits and nuts

(Mozambique, Ghana, Tanzania, Nigeria), cotton (Sudan, Ghana, Cameroon), cocoa (Ghana),

oil seeds (Congo), edible vegetables (Ethiopia, Ghana, Tanzania).

6.2.1 According to importance of agriculture in the economy

The selection of countries should be guided by share of agriculture GDP, the growth rate in

agricultural GDP and the growth in agricultural exports which the ADs are also competinig in

producing and exporting to the same markets. We also look at the imports by ADs from SSA.

In the first country selection criterion, the primary factor in the selection is the contribution of

the agricultural sector to gross domestic product. The agricultural sector should contribute

more than 20 percent to GDP. The country case studies should include countries that have

recently achieved high growth rates and low growth rates in agricultural GDP. It will be

important to study the role of the ADs countries with varying growth rates. The third variable

in the selection of countries in the study is the growth agricultural exports. Here too, it is

important to study low achievers and high achievers.

FOA (2005) provides a good summary of the performance of SSA countries in agriculture

based on these three indicators. On the basis of the primary indicator, the agriculture in the

following countries contributed more than 20 percent between 2000 and 2004: Tanzania,

Cameroon, Ethiopia, Malawi, Uganda and Kenya. Using the latest figures on agricultural

growth and growth in agricultural characteristics, these countries are presented in the matrix

in Table 1. Kenya and Ethiopia had low growth both in agricultural GDP and agricultural

exports. Malawi and Uganda had high growth rates in agricultural GDP but low growth rates

in agricultural exports. Finally, Cameroon and Tanzania had high growth rates in both

agricultural GDP and agricultural exports.

46

Table 1 Matrix of Potential Country Case Studies

Indicator L o w G r o w t h i n Agricultural GDP (less than 3 percent)

H i g h G r o w t h i n Agricultural GDP (more than 3 percent)

L o w G r o w t h i n Agricultural Exports (less than 2 percent)

Kenya Ethiopia

Malawi Uganda

H i g h G r o w t h i n Agricultural Exports (more than 2 percent)

Cameroon Tanzania

6.2.2 According to SSA exports to China

Based on the trade statistics criterion, we shall target which exports from SSA are competing

with the exports from ADs. A good example is the cutflower products where the Chinese are

heavily investing in to become the world leader in the production. We also look at the

imports by ADs from SSA. For example, both China and India are not only net importers of

cotton, but net imports of cotton are also increasing. For example, Sudan, Ghana and

Cameroon export cotton to ADs. This has been to supply the demand for cotton from the

rapidly growing Chinese textile industry which it has not been possible to meet domestically

because of the decline in the area planted to cotton as farmers switch to more profitable

crops.

The increase in the demand for cotton may also lead to improvements in international

markets. Such increased demand for cotton offers hope to SSA cotton farmers that have

found it difficult to break into major markets such as USA due to subsidies given to cotton

farmers in USA. Increased demand for SSA cotton, may offer opportunities for countries

such as Malawi that are trying to revive cotton production. The increased demand in SSA

cotton by the ADs is likely to generate pro-poor agricultural growth, since cotton is mainly

grown by smallholder farmers in SSA.

Some other agricultural products exported to ADs by some SSA countries include cassava

(Nigeria), fruits and nuts (Mozambique, Ghana, Tanzania, Nigeria), cocoa (Ghana), oil seeds

(Congo), edible vegetables (Ethiopia, Ghana, Tanzania) (Jenkins and Edwards, 2006; Chen et

47

al., 2005). In some cases, the ADs have opened opportunities for SSA countries to export to

ADs non-traditional exports and to expand their traditional exports. Some of the agricultural

products that are less tradable such as cassava for instance are becoming more tradable.

Cameroon and Congo, along with Mozambique and Tanzania are exporters of wood to China.

China is a major global importer of soya beans and animal feed more generally, but these are

imported mostly from Latin Americas. Along with grains and feed, China also imports large

quantities of oilseeds, fats and oils; it is the largest export destination for the palm oil industry

of Malaysia and also a major destination for Indonesia.

6.3 Fields of Expertise

The framework for analysing the impact of ADs on SSA agriculture proposed above has

revealed that the engagement of ADs with SSA countries occur within a political economy.

Hence, the study of such interactions requires diversity in the Country Research Team

compositions. The framework raises issues relating to the processes of interactions and the

political economy of ADs and SSA relations in agriculture. Such issues can be able handled

by expertise in Political Science. There are also issues that relate to agricultural production

systems requiring the expertise of an agricultural economist and trade-related issues requiring

the expertise of an international economist. The country study teams should have the

following expertise:

a) Agricultural Economics

b) International Economics

c) Political Science

6.4 Approaches to Analysing Impact of ADs on SSA Agriculture

6.4.1 Methods of Analysis

The focus on ADs is quite new and there is not much evidence on how ADs interact with

SSA and the impact of such interactions in agriculture. The various channels through which

ADs interaction with SSA countries is likely to affect agriculture require a diverse

methodology. The analysis can range from descriptive to more quantitative modelling

48

approaches. It will therefore be important for country case studies to use both qualitative and

quantitative research methods. The case studies should focus on the following areas:

a) Description of the nature of interactions between ADs and SSA agriculture in the

country, including the various channels of interactions and the motives that drive

such relationships, the pros and cons of such relationships;

b) Analysis of trends in key economic indicators that would indicate the extent of the

economic relationship between ADs and SSA countries in agriculture. These

indicators should include amount of ADs aid of various forms towards agriculture,

agricultural trade (imports and exports) and foreign direct investments, and should

be analysed comparatively with aid or investment flows from other donor

partners.

c) Analysis of various channels on agricultural production and productivity at

national and household level, and the extent to which such improvements can be

attributed to the ADs effects. This should include identification of commodities

affected and trends in productivity and production.

d) Analysis of the trade effects including identification of exports and imports to and

from ADs and the competing products in third markets and domestic markets and

analysing their effects on SSA agriculture.

e) In all cases, analysis of the dynamic issues – what if the international environment

and bilateral AD and SSA countries relations change what are the likely

implications for SSA agriculture.

6.4.1.1 Descriptive and Qualitative Analysis

The approach here should be more qualitative based on key informants interviews with key

stakeholders such as AD investors and experts in SSA agriculture, policy makers, non-

governmental organisations that interact with AD investors and experts, and local farmers.

Such qualitative work will enable us better understand the ways in which the ADs engage

SSA in the agricultural sector, the modalities of aid, motives behind the aid, associated

benefits and disadvantages of such aid to recipient countries, their interactions with policy

makers and the levels at which the ADs interact with SSA agriculture ministries. Such

interactions involve process issues and political economy issues that can be best studied using

qualitative approaches. Such descriptive analysis should analyse macroeconomic indicators

that show the extent of ADs engagement with the case study country agriculture over the

49

most recent five years similar to existing studies on impact of ADs on SSA trade and

investment (Jenkins and Edwards, 2005; Kaplinsky et al., 2006).

6.4.1.2 Micro Modelling

There is also need to undertake micro level economic modelling especially in understanding

the impact of the ADs in agriculture through the agricultural development channel at

household level. This will require modelling household responses and behaviour in

agricultural production. However, such modelling may be more demanding in terms of the

primary data requirements. Small sample studies may be justified as exploratory studies. The

major issues to investigate at household level include the impact of technical know-how,

research and development and technology transfer on the performance of the agricultural

sector. The approach would be to interview farmers that have been exposed to ADs

interventions and a control group that has not been exposed to such interventions. Micro

studies may therefore analyse the improvements in productivity, technical efficiency,

production growth and food security.

6.4.2 Stakeholder Interactions

The rise of the ADs and their increased relationships with the SSA countries has implications

for different stakeholders. It is therefore important for researchers to engage with policy

makers and other stakeholders of throughout the research activity. These stakeholders should

include the Ministry of Agriculture, Ministry of Finance, Ministry of Foreign Affairs,

Investment Promotion Agencies, Parliamentary Committee on agriculture, Non-govermentak

Organisations, Farmer Organisations at national and sub-regional level. It will also be

important to interact with other donor partners, particularly those that support the agricultural

sector in the country. Some of these stakeholders will prove to be vital key informants for the

country case study.

6.4 Method of Analysis

Both quantitative and qualitative approaches will be applied. Based on secondary data

sources, the trend analyses of production and price levels of agricultural products (exports as

well imports) will reveal the impact of ADs entrance in the world market. Of great concern

50

will be similar exports from ADs as well as SSA to the same markets and imports by ADs

from SSA region.

The qualitative analyses will be based on selected case studies in a given country. A

structured questionnaire will be administered to the target firms/farmers to obtain the relevant

information on the perceived effects of ADs in their operations (eg economies of scales),

profit margins and export potentials to ADs.

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55

ANNEX

1. Country choice scenarios through dependency on agriculture

(a) Countries that are heavily dependent on agricultural GDP

Table A1: Share of Agriculture in Total GDP, 2000 – 2004 (%) Less than 20 percent 20-39.99 percent 40 percent and above Botswana, Seychelles, South Africa, Mauritius, Gabon, Angola, Congo, Namibia, Swaziland, Eritrea, Zambia, Cape Verde, Zimbabwe, Lesotho, Senegal, Equatorial Guinea

(16 Countries)

Guinea, Mauritania, Madagascar, Kenya, Côte d'Ivoire, Sao Tome and Principe, Mozambique, Gambia, Burkina Faso, Malawi, Chad, Nigeria, Benin, Ghana, Uganda, Niger, Mali.

(17 Countries)

Togo, Tanzania, Sudan, Cameroon, Ethiopia, Rwanda, Sierra Leone, Burundi, Central African Republic, Comoros, Guinea-Bissau, Dem. Rep. of Congo,

(13 Countries)

Source: FAO (2005)

(b) Countries that have achieved low or high agricultural growth

Table A2: Performance of agriculture: growth rate of agricultural GDP, 1995 - 2004 (%) >5.00 percent 3.00-5 percent 1.00-3 percent <1.00 percent Rwanda, Sudan, Angola, Malawi, Equatorial Guinea, Cape Verde, Gambia, Cameroon, Mozambique, Comoros, Benin (11 Countries)

Central African Republic, Guinea, Nigeria, Ghana, Uganda, Tanzania, Mauritania, Mali, Sao Tome and Principe, Côte d'Ivoire, Niger, Swaziland, Lesotho Burkina Faso (14 Countries)

Seychelles, Togo, Zambia, Djibouti, Chad, Ethiopia, Madagascar, Zimbabwe, South Africa, Senegal, Congo, Rep. of, Namibia, Burundi, Kenya, Guinea-Bissau, Mauritius, Gabon (17 countries)

Sierra Leone, Congo, Dem Rep. of, Botswana, Eritrea (4 countries)

Source: FAO (2005)

© Countries that have experienced decline or growth in agricultural exports.

Table A3: Growth of agricultural export in SSA countries, 1995 - 2004 More than 3 Percent

From 3 to 2 percent

From 1 to 2 Percent

less 1.00 percent

Gambia, Nigeria, Côt ed'Ivoire, Niger, Togo , Mozambique, Tanza nia, Senegal, Guinea- Bissau, Rwanda, Con go, Republic of (11 countries)

Benin, Cameroon, Burkina Faso, Ghana, Swaziland (5)

Madagascar, Seychelles, Comoros, Congo, Dem Rep, Kenya, Namibia. Zimbabwe, Angola, Ethiopia, Uganda, Sudan, Guinea, Central African Rep (13)

Botswana, Gabon, Sao Tome and Principe, Malawi, Chad, Mali, Mauritius, Burundi , Lesotho, Mauritania, Sierra Leone, Djibouti (12)

Source: FAO (2005)

56

2. Country choice scenarios through trade – SSA exports to ADs

Countries could be chosen according the exports of agricultural commodities that China is also producing or planning to engage in. For example the cut flower sub-sector. China’s drive to dominate the world cut flower export market will be at the expense of leading developing countries producers such as Kenya, Ethiopia and Uganda. In the Yunnan province where development of the flower industry is taking place, 12-lane highways are already under construction, and the local government is offering interest-free loans for greenhouse construction . Refrigerated trucks are being offered free or at big discounts to farm groups so that flowers do not wilt in transit.

At the equivalent of a $25 a month, improverished rural Chinese are also reportedly providing a more competitive labout force than in more politically open countries like Kenya where labour and human rights activism has pushed average salaries closer to around $100. depending on the time of year, Chinese roses cost as little as half the price of roses in iother developing countries, excluding air fright.

Agricultural products exported to ADs by some SSA countries include cassava (Nigeria), fruits and nuts (Mozambique, Ghana, Tanzania, Nigeria), cotton (Sudan, Ghana, Cameroon), cocoa (Ghana), oil seeds (Congo), edible vegetables (Ethiopia, Ghana, Tanzania) (Jenkins and Edwards, 2006; Chen et al., 2005)

oce20081_1_USDA,2008_A.pdf

United States Department of Agriculture Office of the Chief Economist World Agricultural Outlook Board Long-term Projections Report OCE-2008-1 February 2008

USDA Agricultural Projections to 2017 Interagency Agricultural Projections Committee

World Agricultural Outlook Board, Chair Economic Research Service Farm Service Agency Foreign Agricultural Service Agricultural Marketing Service Office of the Chief Economist Office of Budget and Program Analysis Risk Management Agency Natural Resources Conservation Service Cooperative State Research, Education, and Extension

Service

USDA Long-term Projections

Order Additional Copies of this Report Online: Visit www.ntis.gov. By Phone: Dial 1-800-999-6779. Toll free in the United States and Canada.

Or call 1-703-605-6220. Ask for USDA Agricultural Projections to 2017 (OCE-2008-1).

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USDA Long-term Projections

February 2008

i

USDA Agricultural Projections to 2017. Office of the Chief Economist, World Agricultural Outlook Board, U.S. Department of Agriculture. Prepared by the Interagency Agricultural Projections Committee. Long-term Projections Report OCE-2008-1, 104 pp.

Abstract

This report provides projections for the agricultural sector through 2017. Projections cover agricultural commodities, agricultural trade, and aggregate indicators of the sector, such as farm income and food prices. The projections are based on specific assumptions regarding macroeconomic conditions, policy, weather, and international developments. The report assumes that there are no shocks due to abnormal weather, further outbreaks of plant or animal diseases, or other factors affecting global supply and demand. The Farm Security and Rural Investment Act of 2002, the Energy Policy Act of 2005, and the Agricultural Reconciliation Act of 2005 are assumed to remain in effect through the projections period. The projections are one representative scenario for the agricultural sector for the next decade. As such, the report provides a point of departure for discussion of alternative farm sector outcomes that could result under different assumptions. The projections in this report were prepared in October through December 2007, reflecting a composite of model results and judgment-based analyses.

Longrun developments for global agriculture reflect continued high crude oil prices as well as strong demand for biofuels, particularly in the United States and the European Union (EU). U.S. agricultural projections reflect large increases in corn-based ethanol production, which affects production, use, and prices of farm commodities throughout the sector. Expansion of biodiesel use in the EU raises demand for vegetable oils in global markets. Additionally, steady domestic and international economic growth in the projections supports gains in consumption, trade, and prices. Although export competition is projected to continue, global economic growth, particularly in developing countries, provides a foundation for gains in world trade and U.S. agricultural exports. Combined with increases in domestic demand, particularly related to growth in ethanol production, the results are generally higher market prices. As a result, overall net farm income remains strong and reaches record levels in the latter part of the projections. Higher energy-related costs and agricultural commodity prices push U.S. retail food prices up more than general inflation in the near term, but then food prices increase less than the general inflation rate over the remainder of the projections period.

Keywords: Projections, crops, livestock, biofuel, ethanol, trade, farm income, food prices.

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on the basis of race, color, national origin, age, disability, and, where applicable, sex, marital status, familial status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because all or a part of an individual's income is derived from any public assistance program. (Not all prohibited bases apply to all programs.) Persons with disabilities who require alternative means for communication of program information (Braille, large print, audiotape, etc.) should contact USDA's TARGET Center at (202) 720-2600 (voice and TDD). To file a complaint of discrimination write to USDA, Director, Office of Civil Rights, 1400 Independence Avenue, S.W., Washington, D.C. 20250-9410 or call (800) 795-3272 (voice) or (202) 720-6382 (TDD). USDA is an equal opportunity provider and employer.

ii USDA Long-term Projections

Contents Page

A Note to Users of USDA Long-term Projections ........................................................................ iii

Long-term Projections on the Internet ........................................................................................... iv

Contacts for Long-term Projections............................................................................................... iv

Acknowledgments.......................................................................................................................... iv

Introduction......................................................................................................................................1

Overview of Assumptions and Results ............................................................................................2

Macroeconomic Assumptions..........................................................................................................9

Crops ..............................................................................................................................................20

Livestock........................................................................................................................................47

U.S. Agricultural Sector Aggregate Indicators: Farm Income, Food Prices and Expenditures, and U.S. Trade Value ...............................57

Agricultural Trade..........................................................................................................................65

List of Tables ...............................................................................................................................104

Features in this Report Page

U.S. Financial Market Effects on the Macroeconomic Outlook .............................................. 10

Strong Ethanol Expansion Projected ........................................................................................ 22

Energy Independence and Security Act of 2007 ...................................................................... 23

Global Demand for Biofuel Feedstocks ................................................................................... 67

China’s Cotton Supply and Demand Estimates and the Residual Component ........................ 88

USDA Long-term Projections iii

A Note to Users of USDA Long-term Projections USDA’s long-term agricultural projections presented in this report are a Departmental consensus on a longrun scenario for the agricultural sector. These projections provide a starting point for discussion of alternative outcomes for the sector. The scenario presented in this report is not a USDA forecast about the future. Instead, it is a conditional, longrun scenario about what would be expected to happen under a continuation of current farm legislation and specific assumptions about external conditions. The report uses as a starting point the short-term projections from the November 2007 World Agricultural Supply and Demand Estimates report. Critical long-term assumptions are made for U.S. and international macroeconomic conditions, U.S. and foreign agricultural and trade policies, and growth rates of agricultural productivity in the United States and abroad. Normal weather is assumed. Also, the report assumes no further outbreaks of animal or plant diseases. Changes in assumptions for any of these items can significantly affect the projections, and actual conditions that emerge will alter the outcomes. The projections in this report assume that biofuel blending tax credits and the 54-cent-per-gallon tariff on imported ethanol used as fuel are extended beyond their currently legislated expiration dates. This is in contrast to President’s Budget baseline that assumes those tax credits and the tariff are not extended. The projections analysis was conducted by interagency committees in USDA and reflects a composite of model results and judgment-based analyses. The Economic Research Service has the lead role in preparing the Departmental report. The projections and the report were reviewed and cleared by the Interagency Agricultural Projections Committee, chaired by the World Agricultural Outlook Board. USDA participants in the projections analysis and review include the World Agricultural Outlook Board; the Economic Research Service; the Farm Service Agency; the Foreign Agricultural Service; the Agricultural Marketing Service; the Office of the Chief Economist; the Office of Budget and Program Analysis; the Risk Management Agency; the Natural Resources Conservation Service; and the Cooperative State Research, Education, and Extension Service.

iv USDA Long-term Projections, February 2008

Long-term Projections on the Internet The Economic Research Service of USDA has a briefing room for long-term projections at:

http://www.ers.usda.gov/briefing/projections/ Also, data from the new USDA long-term projections are available electronically at:

http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1192

Contacts for Long-term Projections

Questions regarding these projections may be directed to: Paul Westcott, Economic Research Service, Room 5188, 1800 M Street, N.W., Washington,

D.C. 20036-5831, phone: (202) 694-5335, e-mail: [email protected] Ronald Trostle, Economic Research Service, Room 5190, 1800 M Street, N.W., Washington,

D.C. 20036-5831, phone: (202) 694-5280, e-mail: [email protected] C. Edwin Young, Economic Research Service, Room 5191, 1800 M Street, N.W., Washington,

D.C. 20036-5831, phone: (202) 694-5336, e-mail: [email protected] David Stallings, World Agricultural Outlook Board, MS 3812, 1400 Independence Ave., S.W.,

Washington, D.C. 20250-3812, phone: (202) 720-5715, e-mail: [email protected]

Acknowledgments

The report coordinators, on behalf of the Interagency Agricultural Projections Committee, thank the many analysts in different agencies of USDA for their contributions to the long-term projections analysis and to the preparation and review of this report.

USDA Long-term Projections, February 2008 1

USDA Agricultural Projections to 2017

Interagency Agricultural Projections Committee

Introduction

This report provides longrun projections for the agricultural sector through 2017. Major forces and uncertainties affecting future agricultural markets are discussed, such as prospects for long-term global economic growth and population trends. Projections cover production and consumption for agricultural commodities, global agricultural trade and U.S. exports, commodity prices, and aggregate indicators of the sector, such as farm income and food prices. The projections are a conditional scenario with no shocks and are based on specific assumptions regarding the macroeconomy, agricultural and trade policies, the weather, and international developments. The report assumes that the Farm Security and Rural Investment Act of 2002 (the 2002 Farm Act), the Energy Policy Act of 2005, and the Agricultural Reconciliation Act of 2005 remain in effect through the projection period. Projections do not reflect the Energy Independence and Security Act of 2007. The projections are not intended to be a Departmental forecast of what the future will be, but instead are a description of what would be expected to happen under a continuation of current farm legislation, with very specific external circumstances. Thus, the projections provide a neutral backdrop, reference scenario that provides a point of departure for discussion of alternative farm sector outcomes that could result under different domestic or international assumptions. The projections in this report were prepared in October through December 2007 and reflect a composite of model results and judgment-based analyses. Normal weather is assumed. Also, the projections assume no further outbreaks of plant or animal diseases. Short-term projections used as a starting point in this report are from the November 2007 World Agricultural Supply and Demand Estimates report.

Long-term Projections and the President’s Budget Baseline Projections in this report assume that biofuel blending tax credits and the 54-cent-per-gallon tariff on imported ethanol used as fuel are extended beyond their currently legislated expiration dates. This is in contrast to President’s Budget baseline that assumes those tax credits and the tariff are not extended.

2 USDA Long-term Projections, February 2008

Overview of Assumptions and Results Key assumptions underlying the projections include:

Economic growth

• World economic growth is projected to increase at a 3.5-percent average annual rate between 2008 and 2017, after averaging 2.9 percent annually in 2001-07. U.S. gross domestic product (GDP) increases from the 2007 slowdown toward a sustainable rate of about 3 percent over the longer term. Strong economic growth in developing countries, particularly important for growth in global food demand, is projected at 5.8 percent annually for 2008-17.

Population

• Growth in global population is assumed to continue to slow to an average of about 1.1 percent per year over the projection period compared with an annual rate of 1.7 percent in the 1980s. Although slowing, population growth rates in most developing countries remain above those in the rest of the world. As a consequence, the share of world population accounted for by developing countries increases to nearly 84 percent by 2017, up from 79 percent in the 1980s.

The value of the U.S. dollar

• The U.S. dollar continues to depreciate through 2011, with a drop in value of about 14 percent from 2002. Over the rest of the projection period, the dollar is assumed to show a small appreciation. Strong economic growth in the United States relative to the European Union (EU) and Japan will mitigate continuing pressure for the euro to appreciate relative to the dollar and will offset much of the trade-driven appreciation of the yen. In addition, capital continues to move into the United States to benefit from well-functioning and diverse financial markets.

Oil prices

• Large increases in oil prices over the past several years reflected strong demand for crude oil resulting from world economic recovery and rapid manufacturing growth in China and India. Following a continuation of increases through 2009, crude oil prices are expected to drop modestly in 2010 through 2013 as new crude supplies help offset the rise in demand from Asia. After 2013, oil prices are projected to rise slightly faster than the general inflation rate.

• Underlying these longer term price increases, world oil demand is expected to rise due to strong global economic growth, particularly in highly energy-dependent economies in Asia. Factors expected to constrain longer run oil price increases include new oil discoveries, new technologies for finding and extracting oil, the ability to switch to non-oil energy sources, the ability to increase energy efficiency by substituting nonenergy inputs for energy, and continued expansion and improvement in renewable energy.

USDA Long-term Projections, February 2008 3

U.S. agricultural policy • The 2002 Farm Act, as amended, and the Agricultural Reconciliation Act of 2005 are

assumed to continue through the projection period. • Area enrolled in the Conservation Reserve Program (CRP) is assumed to decline through

2009 as high prices encourage the return of some land to production when CRP contracts expire. CRP acreage is then assumed to gradually rise toward its legislated maximum of 39.2 million acres, reaching 37 million acres by the end of the projections.

U.S. biofuels

• The projections in this report were completed prior to enactment of the Energy Independence and Security Act of 2007. Thus, provisions of that legislation are not reflected in these projections, which are based on the Energy Policy Act of 2005.

• The projections also assume that the tax credits available to blenders of biofuels (ethanol

and biodiesel) and the 54-cent-per-gallon tariff on imported ethanol used as fuel remain in effect. Combined with the Energy Policy Act of 2005, State programs, high oil prices, and other factors, returns for ethanol production provide economic incentives for a continued expansion in the production capacity of the ethanol industry over the next several years. As a result, over 12 billion gallons of ethanol are assumed to be produced by 2010. Although more moderate growth is projected in subsequent years, over 14 billion gallons of ethanol are produced annually by the end of the projection period. Corn starch is expected to remain the primary feedstock for ethanol projection during the projection period. Cellulosic-based production of renewable fuels is assumed to meet the minimum specified in the Energy Policy Act of 2005 of 250 million gallons in 2013 and subsequent years. Biodiesel production is assumed to increase to near 600 million gallons by 2013.

Cattle and beef trade

• The projections assume a gradual rebuilding of U.S. beef exports to Japan and South Korea. Due to recent changes in U.S. regulations, the projections assume Canadian cattle and beef from cattle over 30 months of age can be exported to the United States under the conditions that they are age-verifiable and born after March 1, 1999.

International policy

• Trade projections assume that countries comply with existing bilateral and multilateral

agreements affecting agriculture and agricultural trade. The report incorporates effects of trade agreements and domestic policy reforms in place in November 2007.

• Domestic agricultural and trade policies in individual foreign countries are assumed to

continue to evolve along their current path, based on the consensus judgment of USDA’s regional and commodity analysts. In particular, economic and trade reforms underway in many developing countries are assumed to continue.

4 USDA Long-term Projections, February 2008

International biofuels • The production of biofuels is experiencing rapid growth in a number of countries. The

projections assume that the most significant increases in foreign biofuel production over the next decade will be in the EU, Brazil, Argentina, and Canada. In particular, the projections assume that the EU biofuel target of 5.75 percent of total transportation fuel use by 2010 is only partially met by that date, and is still not fully reached by 2017. Nonetheless, growth in biodiesel demand in the EU is a key factor underlying gains in global vegetable oils and oilseeds demand.

Key results in the projections include: Steady domestic and international economic growth in the projection period supports gains in consumption, trade, and prices of agricultural products. Additionally, the projections reflect continued high crude oil prices and increased demand for biofuels, particularly in the United States and the EU. U.S. aggregate indicators

• Although net farm income initially declines from high levels of 2007 and 2008, it is projected to remain historically strong throughout the projection period, and reach record levels beyond 2011. Growth in export demand contributes to increases in agricultural commodity prices and gains in farm cash receipts. Increases in corn-based ethanol production also provide a major impetus for this strong income projection. Higher commodity prices lower government payments for price-dependent program benefits, although annual CRP payments increase. With lower government payments, the agriculture sector relies increasingly on the market for its income. Cash receipts represent more than 90 percent of gross cash income in the projections, up from about 85 percent in 2005.

• The value of U.S. agricultural exports rises in the projections as steady global economic growth and stronger world trade lead to gains for U.S. agricultural export volumes and higher commodity prices. The lower value U.S. dollar is also an important factor underlying recent export gains and the projected growth. Additionally, higher commodity prices due to expansion of global biofuel demand contribute to the projected gains in export values. Increases in U.S. consumer income and demand for a large variety of foods underlie continued strong growth in U.S. agricultural imports.

• For most of the projections period, consumer food prices increase less than the general inflation rate. However, adjustments in retail prices due to higher energy and agricultural commodity prices lead to food price increases somewhat larger than general inflation in 2008 and 2009. Relatively large price increases are expected in 2008 for fats and oils and for cereals and bakery products, reflecting higher prices for vegetable oils and wheat. Consumer prices for red meats, poultry, and eggs exceed the general inflation rate in 2009 as the livestock sector adjusts to higher feed costs. Consumer expenditures for food away from home continue to grow in importance and account for more than half of overall food spending during most of the projection period.

USDA Long-term Projections, February 2008 5

U.S. agricultural commodities

• Strong expansion of corn-based ethanol production in the United States affects virtually every aspect of the field crops sector, ranging from domestic demand and exports to prices and the allocation of acreage among crops. A higher portion of overall plantings is allocated to corn. Higher feed costs also affect the livestock sector, mitigated somewhat by the increased availability of distillers grains.

• Ethanol production in the United States continues its strong expansion through 2009/10,

with slower growth in subsequent years. By the end of the projections, ethanol production exceeds 14 billion gallons per year, using almost 5 billion bushels of corn. The projected large increase in ethanol production reflects the Energy Policy Act of 2005, State programs, ongoing ethanol plant construction, and economic incentives provided by continued high oil prices. Feed use of corn declines in the initial years of the projections and then rises only moderately as increased feeding of distillers grains helps meet livestock feed demand, particularly for beef cattle.

• Growth in the food use of wheat is projected to match the rate of population increase. Feed

use of wheat rebounds from the low levels of 2006/07 and 2007/08 as higher corn prices encourage increases in wheat feeding. Wheat feeding then levels off as wheat prices relative to corn stabilize.

• Soybean acreage falls in the projections after 2008 due to more favorable returns to corn

production. Longrun growth in domestic soybean crush is mostly driven by increasing demand for domestic soybean meal for livestock feed. Some gains in crush also reflect increasing domestic soybean oil demand for biodiesel production.

• Moderate expansion of domestic food use of rice is projected. Although growth is

somewhat faster than population growth, it is well below the rates of growth in the 1980s and 1990s when per capita use rose rapidly. Imports of rice account for a growing share of domestic use in the projections.

• Mill use of upland cotton in the United States falls in the projections as U.S imports of

apparel continue to increase, reducing domestic apparel production and lowering the apparel industry’s demand for fabric and yarn produced in the United States.

• Duties and quantitative restraints on sugar and high fructose corn syrup (HFCS) trade

between the United States and Mexico ended on January 1, 2008. This results in increased use of HFCS by Mexico’s beverage industry and, consequently, larger sugar exports from Mexico to the United States.

• The production value of U.S. horticultural crops is projected to grow by more than

3 percent annually over the next decade, with consumption of horticultural products continuing to rise. Imports play an important role in domestic supply during the winter and, increasingly, during other times of the year, providing U.S. consumers with a larger variety of horticultural products.

6 USDA Long-term Projections, February 2008

• Production of all meats slows or declines in the first half of the projections, largely

reflecting higher feed costs and lower producer returns as more corn is used in the production of ethanol. After those production adjustments, strong domestic demand and some strengthening in meat exports result in higher prices and higher returns, providing economic incentives for expansion in the sector.

• Per capita meat consumption declines through 2012-14 as the livestock sector lowers

overall production and retail prices rise. Meat consumption per person then rises again at the end of the projections period. Rising incomes facilitate gains in consumer spending on meat. Nonetheless, overall meat expenditures represent a declining proportion of disposable income.

• Strong domestic and international demand for dairy products contributed to high U.S dairy

prices in 2007. Despite higher feed costs, strong farm-level milk prices are projected to encourage further increases in milk cow numbers through 2009. Combined with an upward trend in output per cow, the results are relatively strong gains in milk production in 2008 and 2009 and decreases in milk prices. Smaller production gains are projected on average over the rest of the projection period because milk cow numbers decline after 2009. Milk prices rise after 2009.

Agricultural trade

• World consumption of many grain, oilseed, and meat commodities has exceeded world production in the past several years, reducing global stocks. As a result, global stocks-to- use ratios have dropped sharply and prices have risen. Tight market conditions are projected to persist for many commodities over most of the coming decade, keeping agricultural commodity prices high.

• Broad-based global economic growth provides a foundation for robust gains in world demand for agricultural products. Economic growth in developing countries is especially important because food consumption and feed use are particularly responsive to income growth in those countries, with movement away from staple foods and increased diversification of diets. Rapid expansion of ethanol and biodiesel production in some countries also adds to global agricultural demand growth.

• Population growth rates are slowing in most countries but rates in developing countries remain nearly double those of developed countries. Many developing countries are also projected to achieve rapid economic growth rates.

• The United States will remain competitive in global agricultural markets, although trade competition will continue to be strong. Expanding production in a number of countries, including Brazil, Argentina, Canada, Ukraine, and Russia, provides competition to U.S. exports for some agricultural commodities. The lower-valued U.S. dollar assumed in the first half of the projection period boosts U.S. agricultural competitiveness and export growth. Even as the U.S. dollar strengthens later in the projection period, export gains continue to contribute to gains in cash receipts for U.S. farmers.

USDA Long-term Projections, February 2008 7

• Continuing growth in the livestock sectors of developing countries in Asia, Latin America, North Africa, and the Middle East accounts for most of the growth in world coarse grain imports projected during the next decade. The United States is the major corn exporter in the world. However, with increasing use of corn for U.S. ethanol production, U.S. corn exports show very little growth through 2012/13. In response, corn production and exports are assumed to increase for Argentina, Ukraine, Republic of South Africa, and Brazil. China is also assumed to increase corn production, which changes its net corn trade by slowing the decline in its exports and the increase in its imports. Nonetheless, China is projected to become a net importer of corn in the longer run, reflecting declining stocks of grain and increasing demand for feed for its growing livestock sector.

• Vegetable oil prices rise in response to rapidly increasing demand for food use in low- and middle-income countries. Vegetable oil prices also rise relative to prices for oilseeds and protein meals because of expanding biodiesel production in a number of countries. Brazil’s rapidly increasing soybean area enables it to gain a larger share of world soybean and soybean meal exports, despite increasing domestic feed use. Argentina is the leading exporter of soybean meal and soybean oil, reflecting the country’s large and growing crush capacity, its small domestic market for soybean products, and an export tax structure that favors exports of soybean products and biodiesel rather than soybeans. The former Soviet Union, Eastern Europe, and Southeast Asia increase rapeseed and palm oil production for use as biodiesel feedstocks.

• The United States, Australia, the EU, Canada, and Argentina have historically been the primary exporters of wheat, although exports from the Black Sea region have grown in the past 10 years. Over the next decade, Russia and Ukraine are projected to have a growing importance in world wheat trade, reflecting low costs of production and continued investments in their agricultural sectors. However, high year-to-year volatility in these countries’ production and trade can be expected due to typical weather-related variation in yields.

• Cotton consumption and textile production are projected to increase in countries where labor and other costs are low, such as China, India, and Pakistan. China is the largest importer of cotton in the world. Although China’s cotton imports are expected to grow more slowly than the rapid gains since 2001, these increases account for most of the gains in global cotton trade in the projections. The United States continues as the world’s leading cotton exporter, reflecting its large production capacity and its reduced domestic mill use of cotton as apparel imports continue to grow.

• Long-grain varieties of rice account for around three-fourths of global rice trade and are expected to account for the bulk of trade growth over the next decade. Indonesia, the Philippines, and Bangladesh become the three largest rice-importing countries and account for about 30 percent of the increase in global rice trade over the next decade. Sub-Saharan Africa, a large importing region, accounts for more than a fourth of the increase in trade, with the Middle East also contributing to rice trade gains. Thailand, Vietnam, the United States, India, and Pakistan remain the world’s largest rice-exporting countries.

• U.S. meat exports benefit from strong foreign economic growth. However, even with U.S. beef exports to Japan and South Korea assumed to gradually rebuild, total U.S. beef exports do not return to levels of 2000-03 until late in the projection period.

8 USDA Long-term Projections, February 2008

• Pacific Rim nations and Mexico are key markets for long-term growth of U.S. pork exports. Higher income countries of East Asia increase pork imports as their domestic hog sectors are constrained by environmental concerns. Mexican pork imports rise rapidly, driven by increases in income and population. Brazil is constrained in its pork trade by the presence of foot-and-mouth disease, but continues to be a major pork exporter to markets such as Russia, Argentina, and Asian markets other than Japan and South Korea.

• Brazil remains a leading poultry exporter as low production costs allow the Brazilian poultry

sector to remain competitive in global trade. Poultry exports from countries affected by avian influenza, such as Thailand and China, are expected to be mostly fully cooked products destined for higher income markets.

USDA Long-term Projections, February 2008 9

Macroeconomic Assumptions

Macroeconomic assumptions underlying USDA’s long-term projections reflect steady growth at near-average historical rates over most of the projection period. Most of the world will be moving toward longrun sustainable economic growth, with trend rates in 2009 and beyond. Overall, world economic growth is projected to increase at a 3.5-percent average rate between 2008 and 2017, after averaging below 3 percent annually between 2001 and 2007. The projections have moderating growth in developed countries and accelerating growth in developing and former Soviet Union countries. High crude oil prices and the U.S. subprime mortgage problems are assumed to have a moderate impact on the U.S. economy into 2008, holding growth to 2.5 percent (see box, U.S. Financial Market Effects on the Macroeconomic Outlook, page 10). U.S. gross domestic product (GDP) growth then moves back toward a sustainable rate of about 3 percent. The U.S. share of global GDP declines to 28 percent from 30 percent in 2005-07. Continued strong growth in China, India, and the rest of Asia make this region an increasingly important part of the global economy, with Asia overall rising to more than a 30-percent share by the end of the projection period.

Improved global economic performance and continuing, although slowing, population growth is expected to boost food demand in the projections. Increased global purchasing power and population growth, competing against demand for biofuels and other domestic uses, are important factors shaping the projections for U.S. agricultural exports and the strong outlook for commodity prices. Supporting the outlook for U.S. agricultural exports is also the cumulative effect of a depreciating U.S. dollar.

Even with the U.S. and world economies projected to move toward sustainable longrun growth, global inflation rates are projected to remain relatively low through 2017, averaging about 3 percent. Some inflationary pressures have resulted because of energy price increases and the movement towards full employment and full capacity utilization. In response, the U.S. Federal Reserve Board and central banks in other countries are assumed to continue policies to constrain inflation.

-1

0

1

2

3

4

5

1990 1995 2000 2005 2010 2015

U.S. and world gross domestic product (GDP) growth

Percent

World

United States

10 USDA Long-term Projections, February 2008

U.S. Financial Market Effects on the Macroeconomic Outlook

Problems in the U.S. subprime mortgage market starting in 2007 resulted in a decline in housing prices and a rise in foreclosure and delinquency rates, slowing the U.S. housing market. These problems resulted from a substantial underpricing of risk in home-mortgage lending. Although effects spilled over into nonhousing related financial markets, as well, the U.S. subprime mortgage market problems are assumed to have only a moderate effect on overall U.S. economic growth in 2008. Projected U.S. GDP growth for 2008 of 2.5 percent is somewhat lower than the longer term projected level of about 3 percent.

Reasons for an expected containment of the effects of the mortgage market problems reflect strength of the banking system; aggressive actions by the Federal Reserve Board, housing market regulators and financial intermediaries, and the Federal Government (led by the U.S. Treasury Department); and sound bank and corporate balance sheets.

• The Federal Reserve Board lowered the federal funds rate and discount rate, which reduces the cost of borrowing from other banks and from the Federal Reserve. As a consequence, the banking system is able to provide liquidity for mortgages and other loans.

• Actions of housing market regulators and the U.S. Treasury Department are encouraging renegotiation of terms of troubled mortgages to make them more favorable to borrowers. This will reduce foreclosures and minimize the effect that foreclosures have on real estate prices.

• Financial intermediaries, such as banks, are taking losses on their balance sheets in a timely manner. Given the overall strength of the capital positions in the banking system, banks are expected to make such adjustments without undue loss in the ability to expand loans in the future.

• The corporate sector also has strong balance sheets. This provides ample ability to raise capital through the banking system, equity markets, or direct issuance of corporate bonds or commercial paper. As a result, there is generally plentiful credit.

• Riskier segments of the credit market are experiencing higher interest rates as differences in yields between Baa-rated (medium quality) bonds and Aaa-rated (highest quality) bonds better represent differences in underlying risk. Prior to the subprime mortgage market problems, the differentials were very small by historical standards. As the bond markets raise risk premiums, corporations are deterred from engaging in excessively risky projects. Although this slows investment expansion, it makes business expansion more sustainable.

As housing prices fall, downward pressure on consumer spending is offset by growth in employment and wages, and continued strength in equity markets. As housing construction has slowed, skilled tradesmen are being re-employed in home improvement and commercial construction. Realtors, mortgage brokers, and bank employees have lost jobs, but the strong U.S. economy is creating enough new jobs to offset those losses, in aggregate. Additionally, a weak dollar and strong foreign economic growth support a very strong export sector, also providing new jobs. Overall, these factors mitigate the net impact of the financial situation on the U.S. economy.

USDA Long-term Projections, February 2008 11

-2

-1

0

1

2

3

4

5

6

1990 1995 2000 2005 2010 2015

GDP growth for developed countries, European Union-27, and Japan

Percent

Developed countries

JapanEuropean Union-27

Developed economies are projected to grow at rates similar to those of the 1990s, averaging around 2.5 percent in 2008-17. Economic growth rates for the EU and Japan increase from recent gains, but remain around 2 percent per year in the projection period. As a consequence, both the EU and Japan account for smaller shares of global GDP.

• Enlargement of the European Union (EU) to include more countries of Central and Eastern Europe creates additional trade and investment opportunities within the expanded EU. The EU economy, however, does not grow as rapidly as the U.S. economy because of lingering EU structural rigidities, particularly rigid labor laws and a very expensive social security system. Political difficulties also constrain the benefits of economic integration, particularly with continued restrictions on labor mobility between EU countries and a very cumbersome EU decisionmaking process. Unemployment rates decline from double-digit rates, however, indicating some progress in increasing employment flexibility.

• Japan continues to face constraints to economic growth, largely the result of long-term structural rigidities, a difficult political process of economic reform, and a rapidly aging population. Japan’s labor market liberalization partly offsets these constraints, aiding productivity growth. Japan’s increasing integration with the other economies of Asia, especially China, further mitigates the growth constraints in the Japanese economy. The projections assume sustained economic growth in Japan at 2 percent a year, with the country’s share of world GDP declining to below 12 percent by 2017, down from almost 18 percent in 1991. While Japan’s projected growth remains relatively low compared with most other countries, it represents a major improvement from its growth in the 1990s.

12 USDA Long-term Projections, February 2008

-15

-12

-9

-6

-3

0

3

6

9

1990 1995 2000 2005 2010 2015

GDP growth for developing economies and the former Soviet Union

Percent Developing Asia

Former Soviet Union

Latin America

Africa

Economic growth in developing countries is projected to average 5.8 percent annually during 2008-17. Developing countries will play an increasingly important role in global growth in food demand and will become a more important destination for U.S. farm exports. Relatively high income growth, along with large responsiveness of consumption and imports of food and feed to income growth in these countries, underlies this result. As incomes rise in developing countries, consumers generally diversify their diets, moving away from staple foods to include more meat, dairy products, fruits and vegetables, and processed foods (including vegetable oils). These consumption shifts increase import demand for feedstuffs and high-value food products.

• Long-term growth of 4 percent is projected for Latin America. An overall improvement in macroeconomic policies should attract foreign capital inflows, particularly foreign direct investment, and sustain growth.

• Projected growth for Southeast Asia exceeds 5.1 percent for the next decade while growth in developing countries of East Asia exceeds 7 percent. Although large, these projected growth rates are below the very strong average economic growth in these regions in 1971-2007.

• China’s economic growth has been consistently the strongest in Asia, exceeding 10 percent between 2003 and 2006. While some moderation is expected, China’s growth is expected to average above 8 percent over the next decade.

• India’s projected average economic growth of almost 8 percent a year puts it in the top tier of high-growth countries. Nonetheless, India is still a low-income country, with real 2000-based per capita income of $664 in 2007. Continued strong income growth is expected to bring India’s real per capita income to more than $1,200 by 2017 and is expected to move a significant number of people out of poverty.

• High oil prices assumed in the projections modestly constrain Asia from even higher economic growth since its manufacturing sector is far more dependent on energy for GDP growth than more developed economies.

• Economic growth in the countries of the former Soviet Union (FSU) is projected to average 5.5 percent annually for the next decade. Russia, Ukraine, and other FSU countries benefit greatly from their shift to more market-oriented economies. Russia and other energy-rich FSU countries also benefit from high oil prices.

USDA Long-term Projections, February 2008 13

0

1

2

3

1981-90 1991-2000 2001-07 2008-17

Population growth continues to slow

Percent

Source: Population projections, U.S. Department of Commerce, U.S. Census Bureau.

World

United States Former

Soviet Union

Africa

Middle East

Asia

Latin America

Developing countries

A continued slowing of population growth around the world is an important factor limiting increases in food and agricultural demand over the next decade. World population growth declines from an annual rate of 1.7 percent in the 1980s to an average of about 1.1 percent per year for the projection period.

• Developed and FSU countries have very low projected rates of population growth, at 0.4 percent and 0.1 percent, respectively. The projected annual average population growth rate for the United States is the highest among developed countries, at 0.9 percent, in part reflecting large immigration. Population growth rates in developing economies decline by more than 40 percent between the 1980s and the end of the projection period, but remain above those in developed countries and the FSU. As a result, the share of world population accounted for by developing countries increases to 84 percent by 2017.

• China and India together account for more than one-third of the world’s population. China’s population growth rate slows from 1.5 percent per year in 1981-90 to 0.6 percent in 2008-17. The population growth rate in India, the world’s second most populous nation, is projected to decline from 2.1 percent to 1.5 percent per year between the same periods. The differential in population growth narrows the gap between India’s and China’s populations.

• Brazil’s population growth rate falls from 2.1 percent per year in 1981-90 to 0.8 percent annually in 2008-17. Sub-Saharan Africa’s population growth rate declines from 2.9 percent to 2.2 percent per year between the same periods, leaving this impoverished region with the highest population growth rate in the world.

• There are a number of countries with declining populations. Most of these are mature economies such as Japan and countries in Western Europe, Central Europe, and the FSU. However, several countries in Sub-Saharan Africa have declining populations resulting from the AIDS epidemic, including the Republic of South Africa, Botswana, Lesotho, and Swaziland.

14 USDA Long-term Projections, February 2008

60

70

80

90

100

110

120

130

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

U.S. agricultural trade-weighted dollar projected to stabilize 1/ Index values, 2000=100

1/ Real U.S. agricultural trade-weighted dollar exchange rate, using U.S. agricultural export weights, based on 192 countries.

The U.S. dollar continues to depreciate through 2011, with moderate strengthening over the rest of the projection period. The lower-value dollar is a facilitating factor in the growth in U.S. exports in the projections. Combined with strong global economic growth, particularly in developing countries, the result is strong growth in the demand for U.S. farm exports.

• Strong GDP growth in the United States relative to the EU and Japan will mitigate continuing pressure for the euro to appreciate relative to the U.S. dollar and offsets much of the trade-driven appreciation of the yen.

• China initiated a process for appreciating its currency in 2005 after a long period of maintaining a fixed nominal exchange rate and an undervalued currency. The projections assume that China allows its real exchange rate to continue to appreciate at modest rates. The appreciation of China’s currency also leads to appreciation of other Asian currencies.

• Capital continues to move into the United States to benefit from well-functioning and diverse financial markets, although these inflows are projected to continue to decline. The overall U.S. trade deficit and the capital account surplus fall over the projections period.

• Among agricultural products, U.S. exports of bulk commodities and horticultural products tend to be the most sensitive to swings in the U.S. dollar’s value, because they face more global trade competition.

USDA Long-term Projections, February 2008 15

0

10

20

30

40

50

60

70

80

90

100

1990 1995 2000 2005 2010 2015

Crude oil prices

Dollars per barrel

Refiner acquisition cost, crude oil imports

Refiner acquisition cost, adjusted for inflation

Crude oil prices rose sharply from late 2002 through 2007, largely reflecting increased crude oil demand due to a robust world economic recovery and rapid manufacturing growth in China and India. In 2008 through 2009, crude oil prices are expected to continue to rise modestly. Between 2010 and 2013, oil prices are expected to decline slightly. This is followed by a period when they rise slightly faster than inflation, reflecting rising world oil demand, due to strong global economic growth, particularly in highly energy-dependent economies in Asia. By the end of the projection period, the refiner acquisition cost for crude oil imports is projected at over $85 per barrel. Partly offsetting those effects, factors expected to constrain longrun increases in oil prices include:

• The ability to switch to non-oil energy sources, such as coal and natural gas, especially in industrial uses and electric power generation;

• Increasing energy efficiency due to the substitution of nonenergy inputs (such as

microchip-driven equipment) for energy as well as improved energy-use technology; • Continued expansion and improvement in renewable energy, such as wind and water

power, thermal energy, solar power, and biofuels;

• Continued extraction of fossil fuels from unconventional sources such as oil shale and tar sands; and

• New oil discoveries, along with new technologies for finding and extracting oil.

16 USDA Long-term Projections, February 2008

0

50

100

150

200

250

300

1990 1992 1994 1996 1998 2000 2002 2004 2006

Crude oil, natural gas, and nitrogen-based fertilizer prices move together

Nitrogen fertilizer

Crude oil

Source: Producer Price Indexes, U.S. Department of Labor, Bureau of Labor Statistics.

Natural gas

Producer Price Indexes, 1992=100

Oil prices have historically affected prices of natural gas and nitrogen-based fertilizer. However, the links between the oil and natural gas markets have weakened significantly due to dramatic growth in the demand for natural gas and deregulation throughout the natural gas supply and demand system. At the same time, fertilizer imports have become more important in domestic supply. Prices for natural gas and nitrogen-based fertilizer have become somewhat more volatile than prices for oil, largely because natural gas is less transportable and, as a result, its supply is more inelastic. Nevertheless, over a longer period of time, oil and natural gas prices are expected to move more closely together as the United States and other natural gas importers develop the capacity to import more liquefied natural gas.

USDA Long-term Projections, February 2008 17

Table 1. U.S. macroeconomic assumptions Item 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

GDP, billion dollars Nominal 13,195 13,839 14,554 15,336 16,143 16,993 17,888 18,830 19,822 20,845 21,922 23,054 Real 2000 chained dollars 11,319 11,546 11,834 12,166 12,531 12,907 13,294 13,693 14,103 14,512 14,933 15,366 percent change 2.9 2.0 2.5 2.8 3.0 3.0 3.0 3.0 3.0 2.9 2.9 2.9

Disposable personal income Nominal (billions) 9,629 10,139 10,677 11,253 11,850 12,478 13,139 13,835 14,569 15,341 16,154 17,010 percent change 5.9 5.3 5.3 5.4 5.3 5.3 5.3 5.3 5.3 5.3 5.3 5.3 Nominal per capita, dollars 32,115 33,500 34,949 36,500 38,087 39,748 41,485 43,302 45,203 47,192 49,273 51,449 percent change 4.9 4.3 4.3 4.4 4.3 4.4 4.4 4.4 4.4 4.4 4.4 4.4 Real (billion 2000 chained) 8,397 8,607 8,839 9,096 9,378 9,668 9,968 10,277 10,596 10,924 11,263 11,612 percent change 3.1 2.5 2.7 2.9 3.1 3.1 3.1 3.1 3.1 3.1 3.1 3.1 Real per capita, 2000 dollars 28,005 28,437 28,935 29,629 30,142 30,799 31,473 32,165 32,876 33,605 34,354 35,122 percent change 2.1 1.5 1.8 2.4 1.7 2.2 2.2 2.2 2.2 2.2 2.2 2.2

Consumer spending Real (billion 2000 chained) 8,044 8,269 8,484 8,722 8,975 9,235 9,503 9,778 10,062 10,344 10,633 10,931 percent change 3.1 2.8 2.6 2.8 2.9 2.9 2.9 2.9 2.9 2.8 2.8 2.8

Inflation measures GDP price index, chained 116.6 119.9 123.0 126.1 128.8 131.7 134.6 137.5 140.5 143.6 146.8 150.0 percent change 3.2 2.8 2.6 2.5 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 CPI-U, 1982-84=100 201.6 207.2 213.0 218.8 224.3 229.9 235.6 241.5 247.6 253.7 260.1 266.6 percent change 3.2 2.8 2.8 2.7 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 PPI, finished goods 1982=100 160.3 165.9 170.7 174.5 176.9 179.4 181.9 184.5 187.0 189.7 192.3 195.0 percent change 2.9 3.5 2.9 2.2 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 PPI, crude goods 1982=100 184.8 204.4 219.1 225.7 227.9 230.2 232.5 234.8 237.2 239.6 242.0 244.4 percent change 1.4 10.6 7.2 3.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Crude oil price, $/barrel EIA refiner acq. cost, imports 59.0 67.0 74.0 79.0 77.6 77.0 76.4 75.4 77.8 80.3 82.9 85.6 percent change 20.6 13.5 10.4 6.8 -1.8 -0.8 -0.8 -1.3 3.2 3.2 3.2 3.2 Real 2000 chained dollars 50.6 55.9 60.1 62.7 60.2 58.5 56.8 54.8 55.4 55.9 56.5 57.0 percent change 16.9 10.4 7.6 4.2 -3.9 -2.9 -2.9 -3.4 1.0 1.0 1.0 1.0

Labor compensation per hour nonfarm business, 1992=100 168.5 175.7 183.7 192.8 199.4 206.2 213.2 220.4 227.9 235.7 243.7 252.0 percent change 3.8 4.3 4.5 5.0 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4

Interest rates, percent 3-month Treasury bills 4.7 4.6 4.2 4.8 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.6 3-month commercial paper 5.0 5.1 4.8 5.8 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 Bank prime rate 8.0 8.0 7.5 7.7 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 Ten-year Treasury bonds 4.8 4.7 4.5 5.5 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 Moody's Aaa bond yield index 5.6 5.5 5.4 6.0 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5

Civilian unemployment rate, percent 4.6 4.7 4.8 4.4 4.8 4.8 4.8 4.8 4.8 4.8 4.8 4.8 Nonfarm payroll emp., millions 136.2 137.7 139.2 140.9 142.3 143.7 145.2 146.6 148.1 149.6 151.0 152.6 percent change 1.9 1.1 1.1 1.2 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Total population, millions 299.8 302.7 305.5 308.3 311.1 313.9 316.7 319.5 322.3 325.1 327.8 330.6 percent change 1.0 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.8 Domestic macroeconomic assumptions were completed in October 2007.

18 USDA Long-term Projections, February 2008

Table 2. Global real GDP growth assumptions

2006 2007 2008 2009 2010 2011 1991-2000 2001-2007 2008-2017

Percent 2000

dollars Percent change

World 100.0 5,884 3. 8 3.4 3.5 3.5 3.5 3.5 2.8 2.9 3.5 less United States 69.9 4,334 4. 2 4.0 3.9 3.7 3.7 3.7 2.6 3.2 3.8

North A merica 32.3 37,099 2. 9 2.0 2.5 2.8 3.0 3.0 3.3 2.4 2.9 Canada 2.2 25,899 3. 2 2.5 2.7 2.8 2.8 2.8 2.9 2.9 2.8 United States 30.1 38,340 2. 0 2.5 2.8 3.0 3.0 3.0 3.0 2.9 2.9

Latin America 6.6 4,578 5. 3 4.5 4.3 4.2 4.1 4.0 3.3 3.2 4.0 Mexico 1.8 6,329 3. 9 3.4 3.6 3.7 3.7 3.7 3.5 2.5 3.7 Caribbean & Central A merica 0.6 3,077 3. 8 3.5 3.8 4.0 4.0 3.9 4.0 3.1 3.8 South America 4.2 4,394 5. 7 5.2 4.7 4.4 4.3 4.1 3.1 3.5 4.2

Argentina 0.9 9,006 7. 0 6.8 6.0 5.3 5.0 4.8 4.4 3.6 5.0 Brazil 2.0 4,189 3. 5 4.5 4.0 4.0 3.8 3.6 2.6 3.0 3.7 Other 1.3 3,413 7. 0 5.8 4.8 4.5 4.4 4.3 3.2 4.2 4.4

E urope 26.2 19,208 2. 8 2.7 2.5 2.3 2.2 2.2 2.1 2.0 2.2 European Union-27 24.8 19,643 2. 8 2.7 2.5 2.3 2.2 2.2 2.2 2.0 2.3 Other Europe 1.4 13,698 2. 8 2.1 2.2 2.2 2.0 1.9 1.8 1.9 2.0

Former Soviet Union 1.4 2,019 7. 0 7.2 6.5 5.9 5.7 5.6 -4.1 6.9 5.6 Russia 1.0 2,819 6. 7 6.8 6.1 5.5 5.4 5.4 -3.6 6.3 5.5 Ukraine 0.1 1,066 3. 0 6.1 6.8 6.2 5.5 4.5 -7.7 6.8 4.9 Other 0.3 1,255 10. 0 9.1 7.8 7.3 6.9 6.5 -3.6 9.1 6.1

A sia and Oceania 28.5 3,038 5. 0 4.9 4.9 4.6 4.6 4.6 3.2 4.0 4.7 East Asia 22.6 5,705 4. 7 4.7 4.6 4.3 4.2 4.3 2.9 3.7 4.5

China 5.6 1,755 10. 7 10.8 10.2 8.4 8.2 8.2 10.5 9.9 8.4 Hong Kong 0.6 32,747 6. 4 6.0 5.6 5.5 5.3 5.0 4.5 4.8 5.1 Japan 13.6 40,803 3. 0 2.3 2.0 2.0 2.0 2.0 1.2 1.8 2.0 Korea 1.8 14,322 5. 0 4.5 5.4 5.4 5.2 4.7 6.2 4.6 4.8 Taiwan 1.0 17,668 4. 7 4.0 4.7 4.7 4.7 4.7 6.5 3.6 4.7

Southeast Asia 2.1 1,457 5. 6 5.5 5.3 5.3 5.2 5.1 5.2 4.9 5.1 Indonesia 0.6 993 5. 5 6.1 5.8 5.7 5.6 5.6 4.4 5.1 5.6 Malaysia 0.3 5,036 5. 4 5.6 5.0 5.0 5.0 5.0 7.2 4.8 5.0 Philippines 0.3 1,139 5. 4 5.8 5.2 5.0 4.8 4.7 3.1 4.6 4.8 Thailand 0.4 2,633 5. 0 4.1 5.0 5.3 5.2 5.1 4.6 4.9 4.9 Vietnam 0.1 607 8. 0 7.2 7.1 7.3 7.2 7.1 7.4 7.5 6.9

South Asia 2.3 621 8. 7 8.2 7.7 7.4 7.3 7.3 5.2 6.9 7.2 Bangladesh 0.2 442 6. 5 5.9 5.7 5.6 5.3 5.3 4.8 5.6 5.3 India 1.8 664 9. 4 8.9 8.2 8.0 7.9 7.8 5.5 7.3 7.7 Pakistan 0.3 623 6. 6 6.0 5.9 5.0 5.0 5.0 4.0 5.3 5.1

Oceania 1.5 16,231 3. 0 3.0 3.2 3.4 3.4 3.3 3.5 3.2 3.3 Australia 1.3 23,683 3. 1 3.1 3.2 3.4 3.4 3.4 3.6 3.2 3.4 New Zealand 0.2 15,892 2. 4 2.6 2.8 3.1 3.0 2.7 2.9 3.3 2.8

Other Asia and Oceania 0.5 1,107 6. 1 5.1 4.8 4.6 4.4 4.2 6.1 4.3 4.2

Middle East 2.9 4,317 5. 1 5.0 5.2 5.0 4.8 4.7 4.1 4.2 4.7 Iran 0.4 2,230 4. 5 4.6 4.5 4.4 4.4 4.4 4.0 5.4 4.2 Iraq 0.1 2,143 3. 0 6.0 9.2 7.5 6.1 5.6 9.5 2.4 5.5 Saudi A rabia 0.6 9,096 4. 3 5.1 5.3 5.2 5.1 5.1 2.6 4.2 5.0 Turkey 0.7 3,867 6. 1 5.3 5.4 5.0 5.0 5.0 3.6 4.9 5.0 Other 1.0 5,650 5. 5 4.8 4.8 4.6 4.5 4.3 4.9 4.4 4.2

A frica 2.0 864 6. 1 5.8 5.4 5.2 5.1 5.0 3.0 4.7 5.0 North Africa 0.8 2,064 6. 5 6.0 5.5 5.2 5.0 4.8 3.9 4.8 5.0

Algeria 0.2 2,291 6. 4 6.4 6.4 6.0 6.0 5.7 1.7 5.2 5.2 Egypt 0.3 1,745 6. 8 6.6 5.8 5.2 4.8 4.8 4.5 4.6 4.9 Morocco 0.1 1,333 6. 7 5.2 4.7 4.5 4.4 3.2 8.4 5.3 5.0 Tunisia 0.1 2,629 5. 5 4.5 4.1 4.0 4.0 4.0 2.4 4.4 4.0

Sub-Saharan Africa 1.2 609 5. 8 5.7 5.4 5.3 5.2 5.1 2.4 4.7 5.0 Republic of South Africa 0.4 3,931 4. 7 4.5 4.6 4.9 5.2 5.2 1.8 3.8 5.2 Other Sub-Saharan Africa 0.7 408 6. 5 6.4 5.8 5.5 5.2 5.1 2.8 5.2 4.9

AverageShare of world GDP 2005-2007Region/country

International macroeconomic assumptions were completed in October 2007.

Per capita income,

2007

USDA Long-term Projections, February 2008 19

Table 3. P opulation growth assumptions

2006 2007 2008 2009 2010 2011 1991-2000 2001-2007 2008-2017 Millions Percent change

World1 6,605 1.2 1.2 1.2 1.2 1.2 1.1 1.4 1.2 1.1 less United States 6,304 1.2 1.2 1.2 1.2 1.2 1.2 1.4 1.2 1.1

North A merica 335 0.9 0.9 0.9 0.9 0.9 0.9 1.2 0.9 0.9 Canada 33 0.9 0.9 0.9 0.9 0.8 0.8 1.2 0.9 0.8 United States 301 0.9 0.9 0.9 0.9 0.9 0.9 1.2 0.9 0.9

Latin America 569 1.2 1.2 1.2 1.2 1.1 1.1 1.6 1.3 1.1 Mexico 109 1.2 1.2 1.2 1.1 1.1 1.1 1.6 1.2 1.1 Caribbean & Central A merica 80 1.5 1.5 1.5 1.5 1.5 1.4 1.7 1.5 1.4 South America 380 1.2 1.2 1.1 1.1 1.1 1.0 1.6 1.3 1.0

Argentina 40 1.0 1.0 0.9 0.9 0.9 0.8 1.3 1.0 0.8 Brazil 190 1.1 1.0 1.0 1.0 0.9 0.9 1.5 1.1 0.9 Other 150 1.4 1.4 1.3 1.3 1.3 1.3 1.9 1.5 1.2

E urope 526 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.0 European Union-27 488 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.0 Other Europe 39 0.4 0.3 0.3 0.2 0.2 0.2 0.4 0.5 0.2

Former Soviet Union 278 -0.1 -0.1 0.0 0.0 0.0 0.0 0.0 -0.2 0.0 Russia 141 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.1 -0.5 -0.5 Ukraine 46 -0.7 -0.7 -0.7 -0.6 -0.6 -0.6 -0.5 -0.8 -0.6 Other 90 0.9 0.9 0.9 1.0 1.0 1.0 0.5 0.8 1.0

A sia and Oceania 3,698 1.2 1.2 1.2 1.1 1.1 1.1 1.4 1.2 1.1 East Asia 1,555 0.5 0.5 0.6 0.6 0.6 0.6 0.9 0.6 0.6

China 1,322 0.6 0.6 0.6 0.6 0.7 0.7 1.0 0.6 0.6 Hong Kong 7 0.6 0.6 0.5 0.5 0.5 0.5 1.6 0.7 0.4 Japan 127 0.0 0.0 0.0 -0.1 -0.1 -0.1 0.3 0.1 -0.2 Korea 49 0.4 0.4 0.4 0.4 0.3 0.3 1.0 0.5 0.3 Taiwan 23 0.6 0.6 0.6 0.6 0.5 0.5 0.9 0.6 0.5

Southeast Asia 574 1.3 1.2 1.2 1.2 1.2 1.1 1.7 1.3 1.1 Indonesia 235 1.3 1.2 1.2 1.2 1.1 1.1 1.6 1.3 1.1 Malaysia 25 1.8 1.8 1.8 1.7 1.7 1.7 2.2 1.9 1.7 Philippines 91 1.8 1.8 1.8 1.7 1.7 1.7 2.2 1.9 1.6 Thailand 65 0.7 0.7 0.7 0.6 0.6 0.6 1.1 0.7 0.5 Vietnam 85 1.0 1.0 1.0 1.0 1.0 1.0 1.6 1.1 1.0

South Asia 1,534 1.8 1.8 1.7 1.7 1.7 1.6 1.9 1.8 1.6 Bangladesh 150 2.1 2.1 2.1 2.0 2.0 1.9 1.7 2.1 1.9 India 1,130 1.7 1.6 1.6 1.6 1.5 1.5 1.8 1.7 1.5 Pakistan 169 2.1 2.1 2.0 2.0 1.9 1.9 2.5 2.1 1.8

Oceania 35 1.2 1.2 1.2 1.2 1.1 1.1 1.5 1.3 1.1 Australia 20 0.9 0.8 0.8 0.8 0.8 0.8 1.2 0.9 0.7 New Zealand 4 1.0 1.0 0.9 0.9 0.9 0.8 1.3 1.1 0.8

Other Asia and Oceania 194 1.8 1.6 1.6 1.5 1.5 1.5 2.1 1.9 1.5

Middle East 264 1.6 1.6 1.7 1.7 1.7 1.6 2.0 1.7 1.6 Iran 65 0.4 0.6 0.7 0.8 0.9 1.0 1.1 0.5 1.0 Iraq 27 2.7 2.7 2.6 2.6 2.5 2.5 2.3 2.8 2.4 Saudi A rabia 28 2.3 2.2 2.0 1.9 1.8 1.7 3.7 2.5 1.6 Turkey 71 1.1 1.1 1.0 1.0 1.0 0.9 1.6 1.2 0.9 Other 72 2.7 2.6 2.6 2.6 2.5 2.5 3.0 2.7 2.5

A frica 935 2.2 2.2 2.2 2.1 2.1 2.1 2.5 2.2 2.1 North Africa 164 1.6 1.6 1.5 1.5 1.5 1.5 2.1 1.7 1.4

Algeria 33 1.2 1.2 1.2 1.2 1.2 1.2 1.9 1.3 1.2 Egypt 80 1.8 1.7 1.7 1.7 1.6 1.6 2.2 1.9 1.5 Morocco 34 1.6 1.6 1.5 1.5 1.5 1.4 2.0 1.6 1.4 Tunisia 10 1.0 1.0 1.0 1.0 1.0 1.0 1.5 1.0 0.9

Sub-Saharan Africa 772 2.3 2.3 2.3 2.3 2.3 2.3 2.6 2.4 2.2 Republic of South Africa 44 -0.4 -0.4 -0.5 -0.5 -0.5 -0.5 1.4 0.0 -0.5 Other Sub-Saharan Africa 728 2.5 2.5 2.5 2.4 2.4 2.4 2.7 2.5 2.4

Average P opulation

in 2007Region/country

1/ Totals for the world and world less United States include countries not otherwise listed in the table. Source: U.S. Department of Commerce, B ureau of the Census and U.S. Department of Agriculture, Economic Research Service. The population assum ptions were completed in A ugust 2007.

20 USDA Long-term Projections, February 2008

Crops

Strong expansion of corn-based ethanol production in the projections affects virtually every aspect of the field crops sector, ranging from domestic demand and exports to prices and the allocation of acreage among crops. Additionally, steady U.S. and global economic growth assumed in the projections provides a favorable setting for other uses of field crops, which, following the initially large ethanol expansion, supports longer run increases in consumption and trade and keeps prices at historically high levels.

Although tempered somewhat by higher feed prices, global livestock production rises in the projections in response to growing incomes and demand for meats, which supports gains in world consumption and trade for feed grains. Following a moderate depreciation of the U.S. dollar in the first several years of the projections, the dollar (U.S. agricultural export-weighted basis) is then projected to appreciate. The strengthening U.S. dollar, combined with trade competition from Brazil, Argentina, and the Black Sea region, constrains U.S. exports for some crops. Additionally, strong domestic use of corn due to increased ethanol production and the shift of land to corn from soybeans limit U.S. exports in the early years of the projections, particularly for corn and soybeans.

Assumptions for field crops reflect provisions of the Farm Security and Rural Investment Act of 2002 (2002 Farm Act), which is assumed to continue through the projection period. However, with high prices projected, benefits from price-sensitive programs are reduced. For example, marketing loan benefits and counter-cyclical payments for feed grains are minimal, even accounting for stochastic factors. High prices also lead to a reduction in area enrolled in the Conservation Reserve Program (CRP) through 2011. The CRP is then assumed to expand toward its 39.2 million acre maximum, reaching 37 million acres in 2017. CRP rental rates will increase as farmers’ bids for participation in the program rise to reflect higher crop prices.

Projected plantings for the eight major field crops in the United States increase from about 246.5 million acres in 2007 to over 252 million in 2008 as the market responds to current high prices prompted by strong demand and lower global supplies of oilseeds and wheat. Although plantings for these eight crops then fall, they level off near 244 million acres during most of the projection period, as continued high prices and producer net returns hold land in production.

225

250

275

300

1980 1985 1990 1995 2000 2005 2010 2015

U.S. planted area: Eight major crops 1/

Million acres

1/ The eight major crops are corn, sorghum, barley, oats, wheat, rice, upland cotton, and soybeans.

USDA Long-term Projections, February 2008 21

40

50

60

70

80

90

100

1980 1985 1990 1995 2000 2005 2010 2015

U.S. planted area: Corn, wheat, and soybeans

Million acres

Corn

Wheat

Soybeans

Plantings of different crops are influenced by expected net returns. Net returns are determined by market prices, yields, and production costs, with returns augmented by marketing loan benefits when prices are low.

• Corn, wheat, and soybeans account for about 88 percent of acreage for the eight major field crops over the projection period. In 2008, there is some shift in the cropping mix toward wheat and soybeans and away from corn due to short-term global supply reductions for those crops. However, longer term shifts move acreage back to corn, reflecting the growth in domestic corn-based ethanol production that raises corn prices and producer returns.

• Following a decline in 2008, corn acreage increases and remains above 90 million acres over

the remainder of the projections as the expansion in ethanol production increases corn demand, prices, and net returns.

• Soybean plantings decline to less than 70 million acres after 2008 reflecting more favorable

returns to corn production.

• Wheat plantings rise sharply in 2008 in response to high prices resulting from tight global supplies. Wheat acreage falls back to about 56 million acres in the longer run due to competition from other crops.

22 USDA Long-term Projections, February 2008

Strong Ethanol Expansion Projected

Ethanol production in the United States has increased rapidly over the past several years, from less than 3 billion gallons in 2003 to over 6 billion gallons in 2007. Expansion in the industry is projected to continue, particularly over the next few years, exceeding 12 billion gallons by 2010. Although more moderate growth is projected in subsequent years, over 14 billion gallons of ethanol are produced annually by the end of the projection period. These projections assume the tax credit available to blenders of ethanol and the 54-cent-per-gallon tariff on imported ethanol used as fuel remain in effect. Provisions of the Energy Independence and Security Act of 2007 are not reflected in this report since the projections were completed prior to enactment of that legislation (see box, Energy Independence and Security Act of 2007, pages 23-24).

Most ethanol production in the United States uses corn as the feedstock. The large ongoing expansion results in almost a third of the corn crop used to produce ethanol by 2009/10, remaining near that share in subsequent years. Nonetheless, even by 2017, ethanol production (by volume) represents only about 8.5 percent of annual gasoline use in the United States.

Market adjustments to the increased demand for corn to produce ethanol extend well beyond the corn sector. Movements in relative prices trigger supply and demand adjustments for other crops. Higher feed costs affect the livestock sector, slowing increases in or reducing production of all meats over the next several years.

0

1

2

3

4

5

1990 1995 2000 2005 2010 2015

U.S. corn: Use for et hanol product ion

Billion bus hels

0

2

4

6

8

10

12

14

16

1990 1995 2000 2005 2010 2015

Ethanol FSI less ethanol 1/ Exports Feed & residual

U.S. corn use

Billion bushels

1/ Food, seed, and industrial less ethanol.

USDA Long-term Projections, February 2008 23

Energy Independence and Security Act of 2007

The Energy Independence and Security Act of 2007 was enacted on December 19, 2007, after projections in this report were completed. Although the projections do not reflect the new energy act, major features of the legislation that relate to the Renewable Fuel Standard are illustrated in the following charts. Also, general qualitative effects are highlighted below.

The first chart shows the new Renewable Fuel Standard (RFS) from the 2007 Energy Act. The overall standard calls for total renewable fuel “sold or introduced into commerce in the United States” to reach 36 billion gallons by 2022. Within this standard, ethanol derived from corn starch is to reach 15 billion gallons. The remainder is to consist of “advanced biofuel” with specific volumes designated for cellulosic biofuel and biomass-based diesel.

The second and third charts compare the corn-based ethanol and biodiesel projections in this report with those designated in the RFS of the 2007 Energy Act. Ethanol derived from corn starch in the RFS reaches 15 billion gallons in 2015, about 2 billion gallons higher than projected for 2015 in this report. With the RFS for ethanol derived from corn starch holding at that level beyond 2015, the gap between it and the 2008 long-term projections narrows to about 1.5 billion gallons by 2018. The RFS for biomass-based diesel reaches 1 billion gallons in 2012 and “shall not be less than” that amount in later years. This compares with soybean-oil based biodiesel production of about 600 million gallons in the 2008 long-term projections.

Although a complete quantitative analysis of the effects of the 2007 Energy Act’s RFS for ethanol derived from corn starch and biomass-based diesel is not presented here, general qualitative effects would include:

• Increased demand for corn and soybean oil raises prices for those commodities. Soybean prices would be higher as well.

• Higher commodity prices raise overall acreage planted to crops, with a greater combined share of the total going to corn and soybeans. Acreage planted to competing crops, such as cotton and wheat, would be expected to be lower, raising their prices.

• With a greater share of output going to biofuels, higher crop prices would lower other uses of crops, including exports and domestic feed use of feed grains. In contrast, soybean meal would be more plentiful as increased soybean crush for biodiesel production would raise soybean meal production as well.

• Higher feed prices would lead to further adjustments in the livestock sector than those presented and discussed in the Livestock chapter of this report.

--Continued

24 USDA Long-term Projections, February 2008

Energy Independence and Security Act of 2007 (Continued)

0

5

10

15

20

25

30

35

2008 2010 2012 2014 2016 2018 2020 2022

Billion gallons

Renewable Fuel Standard, 2007 Energy Act

Calendar year

Total RFS

RFS, ethanol derived from corn starch

8

9

10

11

12

13

14

15

16

2008 2010 2012 2014 2016 2018 2020 2022

Billion gallons

Ethanol derived from corn starch

Renewable Fuel Standard, 2007 Energy Act

Production, 2008 USDA long-term projections

Calendar year

0.000

0.250

0.500

0.750

1.000

2008 2010 2012 2014 2016 2018 2020 2022

Billion gallons

Biodiesel

Calendar year

RFS, 2007 Energy Act (through 2012)

RFS, “not less than” 1.0 billion gallons, 2013-2022

Production, 2008 USDA long-term projections

USDA Long-term Projections, February 2008 25

0

2

4

6

8

10

12

14

1990 1995 2000 2005 2010 2015

U.S. corn: Domestic use, ethanol, and exports

Billion bushels

Total domestic use

Exports

Ethanol

Domestic corn use grows throughout the projection period, primarily reflecting increases in corn used in the production of ethanol. Global economic growth underlies increases in U.S. corn exports after 2011/12.

• Large increases are projected in corn used for ethanol production over the next several years. Relatively high prices for crude oil contribute to favorable returns for ethanol production, which combine with government programs to provide economic incentives for a continuation of the ongoing expansion in ethanol production capacity.

• Feed and residual use of corn declines in the initial years and then rises only moderately as increased feeding of distillers grains, a coproduct of dry mill ethanol production, helps meet livestock feed demand.

• Gains in food and industrial uses of corn (other than for ethanol production) are projected to be smaller than increases in population. Consumer dietary concerns and other changes in tastes and preferences limit increases in the combined use of corn for high fructose corn syrup, glucose, and dextrose to about half the rate of population gain.

• U.S. corn exports fall over the next several years as global corn trade declines from the record 2007/08 level and as more corn is used domestically in the production of ethanol. After growth in ethanol production in the United States slows, U.S. corn exports rise in response to stronger global demand for feed grains to support growth in meat production.

• Additionally, U.S. corn exports to Mexico are boosted because of the elimination of tariffs on corn imports from the United States. This shifts some U.S. exports to corn from sorghum and corn products, which already had tariff-free status.

• Strong ethanol demand in the projections pushes U.S. corn stocks lower than current levels.

26 USDA Long-term Projections, February 2008

0.0

0.5

1.0

1.5

1990 1995 2000 2005 2010 2015

U.S. wheat: Domestic use and exports

Billion bushels

Domestic use

Exports

Overall demand in the U.S. wheat sector grows very slowly through the projection period.

• Domestic demand for wheat reflects a relatively mature market. Food use of wheat is projected to show moderate gains, generally in line with population increases.

• Feed use of wheat, a lower-value use of the crop, rises in the initial years of the projections

from the levels of recent years as higher corn prices encourage increases in wheat feeding. As price relationships between wheat and corn stabilize, wheat feeding levels off after 2010/11.

• U.S. wheat exports are steady over the projections period as competition continues from the

European Union (EU), Canada, Argentina, Australia, and the Black Sea region. In particular, wheat prices are projected at levels high enough that the EU can export wheat without subsidies, thus permitting higher EU exports. Consequently, the U.S. market share declines through the projections to under 20 percent by 2017/18. Market shares for Australia, Argentina, the EU, and the Black Sea region increase, while the market share for Canada continues to decline.

• Wheat stocks rebound from low 2007/08 levels as higher prices encourage additional

acreage and production. Then in the later years of the projections, stocks decline as wheat acreage falls.

USDA Long-term Projections, February 2008 27

0.0

0.5

1.0

1.5

2.0

2.5

1990 1995 2000 2005 2010 2015

U.S. soybeans: Domestic use and exports

Billion bushels

Domestic use

Exports

Domestic use of soybeans continues to rise slowly. U.S. soybean exports fall, however, as acreage declines and as more soybeans are processed domestically.

• Longrun growth in domestic soybean crush is mostly driven by increasing demand for domestic soybean meal for livestock feed. Some gains in crush also reflect increases in domestic soybean oil demand for biodiesel production through 2013/14. Increases in export demand for soybean oil and soybean meal also add to crush demand.

• U.S. soybean exports fall below 900 million bushels as competition from Brazil strengthens

and U.S. acreage shifts to corn to support ethanol production. Consequently, the U.S. market share of global soybean trade declines from 35 percent in 2007/08 to about 21 percent at the end of the projections.

• Although U.S. exports of soybean oil and soybean meal increase modestly, the United

States loses market share in global trade of these products against the strengthening competition from South American producers.

• Following a decline in 2007/08 from historically high stocks, a rebound in soybean acreage

in 2008 keeps stocks from falling further. After 2008, shifts in acreage to corn from soybeans keep soybean stocks from rebuilding and the stocks-to-use ratio declines.

28 USDA Long-term Projections, February 2008

0

1

2

3

4

5

6

7

8

9

10

1990 1995 2000 2005 2010 2015

U.S. farm-level prices: Corn, wheat, and soybeans

Dollars per bushel

Corn

Wheat

Soybeans

Projected farm-level prices for corn, wheat, and soybeans reflect, in part, movements in U.S. stocks-to-use ratios.

• Corn prices continue to rise through 2009/10 as increases in ethanol production strengthen

corn demand. As ethanol expansion slows, stocks rebuild somewhat and corn prices decline. Then in the longer run, corn stocks-to-use ratios fall slowly as gains in corn used for ethanol production and moderate export growth outpace increases in production (resulting from generally higher acreage and gains in yields). Consequently, corn prices resume moderate growth and remain historically high.

• With competition from corn keeping soybean acreage lower, stocks are held relatively

constant, the stocks-to-use ratio falls, and soybean prices remain high throughout the projections.

• Wheat prices decline from current levels in the early years of the projections as higher

production facilitates the rebuilding of stocks. As wheat acreage declines in the latter years of the projections, stocks decline and push wheat prices up.

USDA Long-term Projections, February 2008 29

0

20

40

60

80

100

120

140

160

1990 1995 2000 2005 2010 2015

U.S. rice: Domestic use and exports

Million hundredweight

Domestic use

Exports

Continued expansion in domestic food use of rice is projected over the next decade. U.S. rice exports show moderate increases.

• Domestic use of rice is projected to grow somewhat faster than population growth, although well below the rates in the 1980s and 1990s when per capita use rose rapidly. Imports of aromatic varieties of rice from Asia account for a growing share of domestic use in the projections.

• U.S. rice exports are projected to increase at a moderate pace after 2008/09, as the U.S.

price difference over Asian competitors falls, increasing U.S. competitiveness in global rice markets. Exports of rough rice to Latin America are expected to continue increasing, and account for most of the U.S. export expansion.

• Stocks of rice initially fall, but then gradually increase after 2008/09 as rice acreage rises.

• Global rice prices are projected to increase 2.5 to 3 percent per year, exceeding $10.50 per

hundredweight (rough basis) at the end of the projection period. These price increases largely reflect a tightening global stocks situation due to slow yield growth and little ability to expand area in most producing countries. This effect is partially offset by declining global per capita disappearance, largely due to dietary shifts away from staple foods in Asia as incomes rise.

• U.S. rice prices rise through the projection period, reaching about $12.50 per

hundredweight by 2017. The U.S. price difference over Asian competitors declines, but still remains relatively high at $2.00 at the end of the projection period.

30 USDA Long-term Projections, February 2008

0

2

4

6

8

10

12

14

16

18

20

1990 1995 2000 2005 2010 2015

U.S. upland cotton: Domestic mill use and exports

Million bales

Domestic mill use

Exports

U.S. mill use of upland cotton declines in the projections while upland cotton exports rise after 2009/10.

• At the end of the projection period, domestic mill use is projected at less than 40 percent of its 1997/98 level. Textile and apparel import quotas that had been established under the Multifiber Arrangement (MFA) were eliminated at the start of calendar year 2005. As a result of this and other factors, apparel imports by the United States increase through the projections, reducing domestic apparel production and lowering the apparel industry’s demand for fabric and yarn produced in the United States. Some increase in U.S. yarn and fabric exports is projected due to trade liberalization, but the net effect is for declining domestic mill use.

• U.S. upland cotton exports decline in 2009/10 from levels in the previous 2 years that were

facilitated by high stock levels. Exports then grow moderately, accounting for about 80 percent of U.S. cotton production throughout much of the projection period.

• Growth in the textile industry in China slows from the rapid expansion of recent years,

reducing growth in China’s cotton imports. As a result, world cotton consumption and trade slow as well. With global trade growth slowing, gains in U.S. cotton exports after 2009/10 keep the U.S. cotton trade share at about one-third, down from 41 percent in 2003/04 and 2004/05.

• Cotton stocks decline in the first several years of the projections as some acreage shifts to

corn. Beyond 2009/10, cotton acreage increases and stocks rebuild through the end of the projections.

USDA Long-term Projections, February 2008 31

0

2

4

6

8

10

12

1990 1995 2000 2005 2010 2015

Fiscal year

U.S. sugar: Domestic production, use, and imports

Million short tons

Domestic use

Imports

Production

The U.S. sugar price support program includes the loan rate program and marketing allotments as set out in the 2002 Farm Act. Sugar projections for the United States also are strongly interrelated with projections for Mexico. Starting January 1, 2008, there are no duties or quantitative restraints on sugar or high fructose corn syrup (HFCS) trade between the United States and Mexico, in compliance with the North American Free Trade Agreement (NAFTA).

• Use of HFCS by Mexico’s beverage industry is projected to increase beyond current levels, implying a higher exportable surplus of sugar in Mexico. Returns in Mexico from exporting sugar to the United States are projected to be higher than either delivering sugar to domestic food manufacturers for use in sugar-containing-product exports or exporting sugar to other countries at world prices. Over the period from fiscal year (FY) 2009 through 2018, annual U.S. sugar imports from Mexico are projected to average 1.568 million short tons, raw value (STRV), about 15 percent of human consumption of sugar in the United States.

• U.S. sugar imports are projected to exceed the trigger (1.532 million STRV) for suspension of marketing allotments in all years of the projections. U.S. sugar prices are driven down to the minimum level to avoid forfeiture to the Commodity Credit Corporation (CCC). It is assumed that the USDA uses all available measures to reduce CCC program costs. In spite of flat sugar prices, historical growth trends in productivity measures underlying domestic U.S. sugar production projections (sugar per acre, and beet and cane yields) are assumed to continue.

• Long term sugar projections assume that the raw sugar tariff-rate quota (TRQ) is established each year at 1.231 million STRV, the World Trade Organization (WTO) minimum access level. The refined sugar TRQ is established each year at 94,251 STRV. The refined TRQ includes 71,826 STRV of specialty (mostly organic) sugar. Sugar imported under the Dominican and Central American Free Trade Agreement is projected at 121,761 STRV in FY 2009 and increases by 2,237 STRV each year. The yearly raw sugar TRQ shortfall is assumed to equal 70,000 STRV.

• Overall sweetener consumption in the United States is assumed to grow at about the same rate as population. Imports of sugar-containing product are expected to grow faster than population, so per capita consumption of domestically delivered sugar decreases slightly during the projections period.

32 USDA Long-term Projections, February 2008

0

10

20

30

40

50

60

70

80

1990 1995 2000 2005 2010 2015

Nursery and greenhouse

Fruit and nuts

Vegetables and melons

Billion dollars

Value of U.S. horticultural production

The total farmgate production value of U.S. horticultural crops for 2007 was $55 billion, with about a third contributed by each of vegetables, fruits and nuts, and nursery and greenhouse crops. The total production value grows by 3.2 percent annually over the next decade, reaching $73.7 billion in 2017.

• U.S. imports of horticultural products (fruit and nuts, vegetables, greenhouse and nursery products, essential oils, beer, and wine) are projected to continue outpacing exports, with net imports expected to increase about $12 billion from 2007 to 2017. The appreciation of the U.S. dollar after 2011 is an important factor affecting trade, slowing export demand for U.S. horticultural products and raising U.S. import demand.

• U.S. horticultural imports are expected to grow by about 4 percent annually through 2017.

Imports play an important role in domestic supply during the winter and, increasingly, during other times of the year. Reduced trade barriers offer U.S. consumers increased variety, with freer trade also enhancing global competition.

• The EU is the top source of U.S. horticultural imports, accounting for $9 billion out of a

total $32.4 billion in 2007. Mexico is the second biggest source of U.S. horticultural imports ($7.4 billion in 2007) followed by Canada ($3.5 billion). Chile and Brazil are also large sources of horticultural product imports by the United States. Key import commodities include potatoes, tomatoes, bananas, grapes, frozen concentrated orange juice, apple juice, melons, tree nuts (especially cashews), wine, beer, and essential oils.

• U.S. horticultural exports are expected to grow by 3 percent a year through 2017, with the major export markets including Canada, the EU, Mexico, Japan, and Southeast Asia. Exports of almonds, other tree nuts, and noncitrus fruits will lead export growth of fruit and nuts. Exports of fresh vegetables will be stronger than processed vegetables. Exports of wine and essential oils are also expected to increase.

USDA Long-term Projections, February 2008 33

Table 5. Conservation Reserve Program acreage assumptions 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Million acres

Corn 6.2 6.3 6.0 5.9 5.7 5.7 5.7 5.8 5.9 6.1 6.3 6.4 Sorghum 0.9 1.0 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 1.0 1.0 Barley 0.9 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.9 0.9 Oats 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 W heat 8.6 8.8 8.3 8.2 8.0 7.9 8.0 8.0 8.2 8.5 8.8 8.9 Upland cotton 1.6 1.6 1.5 1.5 1.4 1.4 1.4 1.4 1.5 1.5 1.6 1.6 Soybeans 5.7 5.8 5.5 5.4 5.3 5.2 5.2 5.3 5.4 5.6 5.8 5.8

Subtotal 24.3 24.8 23.5 23.1 22.5 22.2 22.4 22.5 23.1 23.9 24.7 25.0

Other 11.7 12.0 11.3 11.1 10.9 10.7 10.8 10.9 11.2 11.5 11.9 12.0 Total 36.0 36.8 34.8 34.2 33.4 32.9 33.2 33.4 34.3 35.4 36.6 37.0

Crop allocation

Table 4. S um mary policy variables for major field crops, 2006-2017 Direct payment

rate Marketing assistance

loan rate Target price

Dollars 1

Corn 0.28 1.95 2.63 Sorghum 0.35 1.95 2.57 Barley 0.24 1.85 2.24 Oats 0.024 1.33 1.44 Wheat 0.52 2.75 3.92 Rice 2.35 6.50 10.50 Upland cotton 0.0667 0.52 0.724 Soybeans 0.44 5.00 5.80 1/ Units are dollars per bushel except for upland cotton (per pound) and rice (per hundredweight).

34 USDA Long-term Projections, February 2008

Table 6. Planted and harvested acreage for major field crops, long-term projections 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Million acres Planted acreage, eight major crops

Corn 78.3 93.6 88.0 91.0 93.0 92.0 91.0 91.0 91.5 91.5 91.5 92.0 Sorghum 6.5 7.7 7.0 6.5 6.0 6.0 5.9 5.9 5.8 5.8 5.7 5.7 Barley 3.5 4.0 4.5 4.0 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 Oats 4.2 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 W heat 57.3 60.4 65.0 60.0 58.5 57.5 56.5 56.5 56.0 56.0 55.5 55.5 Rice 2.8 2.7 2.8 2.9 3.0 3.0 3.1 3.1 3.1 3.2 3.2 3.2 Upland cotton 14.9 10.6 10.5 11.2 11.5 11.7 11.8 11.9 12.0 12.1 12.2 12.3 Soybeans 75.5 63.7 71.0 69.5 69.0 68.5 68.5 68.5 68.0 68.0 68.0 68.0

Total 243.0 246.5 252.6 248.9 248.3 246.0 244.1 244.2 243.7 243.9 243.4 244.0

Harvested acreage, eight major crops

Corn 70.6 86.1 80.6 83.6 85.6 84.6 83.6 83.6 84.1 84.1 84.1 84.6 Sorghum 4.9 6.7 6.0 5.5 5.1 5.1 5.0 5.0 4.9 4.9 4.9 4.9 Barley 3.0 3.5 3.9 3.5 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 Oats 1.6 1.5 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 W heat 46.8 51.0 55.3 51.0 49.7 48.9 48.0 48.0 47.6 47.6 47.2 47.2 Rice 2.8 2.7 2.8 2.9 2.9 3.0 3.0 3.1 3.1 3.1 3.2 3.2 Upland cotton 12.4 10.3 9.7 10.3 10.6 10.8 10.9 10.9 11.0 11.1 11.2 11.3 Soybeans 74.6 62.8 70.1 68.6 68.1 67.6 67.6 67.6 67.1 67.1 67.1 67.1

Total 216.7 224.6 230.0 227.0 226.6 224.6 222.7 222.8 222.4 222.5 222.3 222.9

USDA Long-term Projections, February 2008 35

Table 7. S elected supply, use, and price variables for major field crops, long-term projections 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Yields1

Corn 149.1 153.0 155.3 157.3 159.3 161.3 163.3 165.3 167.3 169.3 171.3 173.3 S orghum 56.2 76.8 66.1 66.5 67.0 67.4 67.9 68.3 68.8 69.2 69.7 70.1 B arley 61.1 60.4 65.0 65.6 66.2 66.8 67.4 68.0 68.6 69.2 69.8 70.4 Oats 59.8 60.9 63.1 63.5 63.9 64.3 64.7 65.1 65.5 65.9 66.3 66.7 W heat 38.7 40.5 42.5 42.8 43.1 43.4 43.7 44.0 44.3 44.6 44.9 45.2 Rice 6,868 7,247 7,222 7,284 7,351 7,419 7,481 7,543 7,608 7, 666 7,725 7,784 Upland cotton 806 845 860 875 885 895 905 915 925 935 945 955 S oybeans 42.7 41.3 42.1 42.6 43.0 43.5 43.9 44.4 44.8 45.3 45.7 46.2

Production2

Corn 10,535 13,168 12,515 13,150 13,635 13,645 13,650 13,820 14,070 14, 240 14,405 14,660 S orghum 278 515 395 365 340 345 340 340 335 340 340 345 B arley 180 212 255 230 200 200 200 205 205 210 210 210 Oats 94 92 100 100 100 105 105 105 105 105 105 105 W heat 1,812 2,067 2,350 2,185 2,140 2,120 2,100 2,110 2,110 2, 125 2,120 2,135 Rice 193.7 197.9 201.0 210.0 215.5 221.2 226.8 232.4 236.3 240.0 243.8 247.6 Upland cotton 20,823 18,050 17,400 18,800 19,500 20,100 20,600 20,800 21,200 21, 600 22,100 22,500 S oybeans 3,188 2,594 2,950 2,920 2,930 2,935 2,970 3,000 3,005 3, 035 3,065 3,095

Exports2

Corn 2,125 2,350 2,150 2,150 2,125 2,125 2,150 2,200 2,250 2, 325 2,400 2,475 S orghum 157 275 150 150 150 155 160 165 170 175 180 185 B arley 20 50 25 25 25 25 25 25 25 25 25 25 Oats 3 2 3 3 3 3 3 3 3 3 3 3 W heat 909 1,150 950 950 950 950 950 950 950 950 950 950 Rice 91.3 107.0 98.0 104.0 108.0 112.0 117.0 121.0 124.0 127.0 129.5 132.0 Upland cotton 12,338 15,400 16,000 14,800 15,100 15,400 15,800 16,200 16,800 17, 400 18,000 18,500 S oybeans 1,118 975 905 865 850 825 820 825 815 820 825 825 S oybean meal 8,850 8,300 8,700 8,850 8,950 9,050 9,100 9,100 9,100 9, 100 9,100 9,100

Ending stocks2

Corn 1,304 1,897 1,327 1,202 1,402 1,502 1,447 1,377 1,372 1, 327 1,262 1,237 S orghum 32 57 52 52 52 52 52 52 52 52 52 52 B arley 69 51 86 91 90 89 88 92 90 93 91 89 Oats 51 45 47 49 51 53 55 57 54 51 48 45 W heat 456 312 606 703 742 749 732 716 696 683 661 645 Rice 39.3 27.1 25.9 26.4 27.3 28.5 29.1 30.0 30.6 30.5 30.5 30.5 Upland cotton 9,368 7,519 4,469 4,069 4,119 4,519 5,069 5,469 5,719 5, 819 5,869 5,869 S oybeans 573 210 219 210 202 193 199 204 204 203 201 204

Prices 3

Corn 3.04 3.50 3.75 3.80 3.60 3.50 3.50 3.55 3.55 3.55 3.60 3.60 S orghum 3.29 3.30 3.50 3.55 3.35 3.25 3.25 3.30 3.30 3.30 3.35 3.35 B arley 2.85 3.85 4.30 4.25 4.00 3.85 3.80 3.85 3.85 3.85 3.90 3.90 Oats 1.87 2.40 2.45 2.45 2.30 2.25 2.25 2.25 2.25 2.25 2.30 2.30 W heat 4.26 6.10 5.50 5.00 4.65 4.50 4.50 4.50 4.55 4.55 4.60 4.65 Rice 9.74 11.00 11.15 11.30 11.46 11.58 11.71 11.84 11.98 12.12 12.32 12.53 S oybeans 6.43 9.00 8.85 8.90 8.75 8.80 8.80 8.80 8.85 8.90 8.95 9.00 S oybean oil 0.310 0.395 0.385 0.385 0.383 0.383 0.383 0.383 0.385 0. 385 0.385 0.385 S oybean meal 205.4 250.0 240.0 242.5 237.0 238.0 238.0 238.5 238.5 240.0 241.5 243.0

1/ Bushels per acre except for upland cotton and rice (pounds per acre). 2/ Million bushels except for upland cotton (thousand bales), rice (million hundredweight), and soybean meal (thousand tons). 3/ Dollars per bushel except for soybean oil (per pound), rice (per hundredweight), and soybean meal (per ton).

36 USDA Long-term Projections, February 2008

Table 8. U.S. corn long-t erm projections Item 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Area (million acres):

Planted acres 78.3 93.6 88.0 91.0 93.0 92.0 91.0 91.0 91.5 91.5 91.5 92.0 Harvested acres 70.6 86.1 80.6 83.6 85.6 84.6 83.6 83.6 84.1 84.1 84.1 84.6

Yields (bushels per acre):

Yield/harvested acre 149.1 153.0 155.3 157.3 159.3 161.3 163.3 165.3 167.3 169.3 171.3 173.3

Supply and use (million bushels):

Beginning stocks 1,967 1,304 1,897 1,327 1,202 1,402 1,502 1,447 1,377 1,372 1,327 1,262 Production 10,535 13,168 12,515 13,150 13,635 13,645 13,650 13,820 14,070 14,240 14,405 14,660 Imports 12 15 15 15 15 15 15 15 15 15 15 15 Supply 12,514 14,487 14,427 14,492 14,852 15,062 15,167 15,282 15,462 15,627 15,747 15,937

Feed & residual 5,598 5,650 5,450 5,425 5,525 5,550 5,600 5,650 5,700 5,750 5,775 5,825 Food, seed, & industrial 3,488 4,590 5,500 5,715 5,800 5,885 5,970 6,055 6,140 6,225 6,310 6,400 Ethanol for fuel 2,117 3,200 4,100 4,300 4,375 4,450 4,525 4,600 4,675 4,750 4,825 4,900 Dom estic use 9,086 10,240 10,950 11,140 11,325 11,435 11,570 11,705 11,840 11,975 12,085 12,225 Exports 2,125 2,350 2,150 2,150 2,125 2,125 2,150 2,200 2,250 2,325 2,400 2,475 Total use 11,210 12,590 13,100 13,290 13,450 13,560 13,720 13,905 14,090 14,300 14,485 14,700

Ending stocks 1,304 1,897 1,327 1,202 1,402 1,502 1,447 1,377 1,372 1,327 1,262 1,237 Stocks/use ratio, percent 11.6 15.1 10.1 9.0 10.4 11.1 10.5 9.9 9.7 9.3 8.7 8.4

Prices (dollars per bushel):

Farm price 3.04 3.50 3.75 3.80 3.60 3.50 3.50 3.55 3.55 3.55 3.60 3.60 Loan rate 1.95 1.95 1.95 1.95 1.95 1.95 1.95 1.95 1.95 1.95 1.95 1.95

Variable costs of production (dollars):

Per acre 203 227 237 244 248 251 255 257 261 264 268 271 Per bushel 1.36 1.48 1.53 1.55 1.56 1.56 1.56 1.56 1.56 1.56 1.56 1.57

Returns over variable costs (dollars per acre):

Net returns 250 309 345 354 326 313 317 329 333 337 349 352 Note: Marketing year beginning S eptember 1 for corn.

USDA Long-term Projections, February 2008 37

Table 9. U.S. sorghum long-term projections Item 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Area (million acres):

Planted acres 6.5 7.7 7.0 6.5 6.0 6.0 5.9 5.9 5.8 5.8 5.7 5.7 Harvested acres 4.9 6.7 6.0 5.5 5.1 5.1 5.0 5.0 4.9 4.9 4.9 4.9

Yields (bushels per acre):

Yield/harvested acre 56.2 76.8 66.1 66.5 67.0 67.4 67.9 68.3 68.8 69.2 69.7 70.1

Supply and use (million bushels):

Beginning stocks 66 32 57 52 52 52 52 52 52 52 52 52 Production 278 515 395 365 340 345 340 340 335 340 340 345 Imports 0 0 0 0 0 0 0 0 0 0 0 0 Supply 343 547 452 417 392 397 392 392 387 392 392 397

Feed & residual 109 180 190 150 120 120 110 105 95 95 90 90 Food, seed, & industrial 45 35 60 65 70 70 70 70 70 70 70 70 Dom estic 154 215 250 215 190 190 180 175 165 165 160 160 Exports 157 275 150 150 150 155 160 165 170 175 180 185 Total use 311 490 400 365 340 345 340 340 335 340 340 345

Ending stocks 32 57 52 52 52 52 52 52 52 52 52 52 Stocks/use ratio, percent 10.3 11.6 13.0 14.2 15.3 15.1 15.3 15.3 15.5 15.3 15.3 15.1

Prices (dollars per bushel):

Farm price 3.29 3.30 3.50 3.55 3.35 3.25 3.25 3.30 3.30 3.30 3.35 3.35 Loan rate 1.95 1.95 1.95 1.95 1.95 1.95 1.95 1.95 1.95 1.95 1.95 1.95

Variable costs of production (dollars):

Per acre 117 126 132 136 139 141 143 145 147 150 152 155 Per bushel 2.07 1.64 2.00 2.05 2.07 2.09 2.11 2.12 2.14 2.17 2.18 2.21

Returns over variable costs (dollars per acre):

Net returns 68 127 99 100 86 78 78 80 80 78 81 80 Note: Marketing year beginning S eptember 1 for sorghum .

38 USDA Long-term Projections, February 2008

Table 10. U.S. barley long-t erm projections Item 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Area (million acres):

Planted acres 3.5 4.0 4.5 4.0 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 Harvested acres 3.0 3.5 3.9 3.5 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0

Yields (bushels per acre):

Yield/harvested acre 61.1 60.4 65.0 65.6 66.2 66.8 67.4 68.0 68.6 69.2 69.8 70.4

Supply and use (million bushels):

Beginning stocks 108 69 51 86 91 90 89 88 92 90 93 91 Production 180 212 255 230 200 200 200 205 205 210 210 210 Imports 12 20 25 25 25 25 25 25 25 25 25 25 Supply 300 301 331 341 316 315 314 318 322 325 328 326

Feed & residual 56 50 65 70 45 45 45 45 50 50 55 55 Food, seed, & industrial 156 150 155 155 156 156 156 156 157 157 157 157 Dom estic 211 200 220 225 201 201 201 201 207 207 212 212 Exports 20 50 25 25 25 25 25 25 25 25 25 25 Total use 231 250 245 250 226 226 226 226 232 232 237 237

Ending stocks 69 51 86 91 90 89 88 92 90 93 91 89 Stocks/use ratio, percent 29.9 20.4 35.1 36.4 39.8 39.4 38.9 40.7 38.8 40.1 38.4 37.6

Prices (dollars per bushel):

Farm price 2.85 3.85 4.30 4.25 4.00 3.85 3.80 3.85 3.85 3.85 3.90 3.90 Loan rate 1.85 1.85 1.85 1.85 1.85 1.85 1.85 1.85 1.85 1.85 1.85 1.85

Variable costs of production (dollars):

Per acre 100 109 114 117 119 121 123 124 126 128 130 132 Per bushel 1.63 1.80 1.75 1.79 1.80 1.81 1.82 1.82 1.84 1.85 1.86 1.87

Returns over variable costs (dollars per acre):

Net returns 74 124 166 162 146 136 134 138 138 139 142 143 Note: Marketing year beginning June 1 for barley.

USDA Long-term Projections, February 2008 39

Table 11. U.S. oats long-term projections Item 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Area (million acres):

Planted acres 4.2 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 Harvested acres 1.6 1.5 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6

Yields (bushels per acre):

Yield/harvested acre 59.8 60.9 63.1 63.5 63.9 64.3 64.7 65.1 65.5 65.9 66.3 66.7

Supply and use (million bushels):

Beginning stocks 53 51 45 47 49 51 53 55 57 54 51 48 Production 94 92 100 100 100 105 105 105 105 105 105 105 Imports 106 110 100 100 100 100 100 100 100 100 100 100 Supply 252 252 245 247 249 256 258 260 262 259 256 253

Feed & residual 125 130 120 120 120 125 125 125 130 130 130 130 Food, seed, & industrial 74 75 75 75 75 75 75 75 75 75 75 75 Dom estic 199 205 195 195 195 200 200 200 205 205 205 205 Exports 3 2 3 3 3 3 3 3 3 3 3 3 Total use 202 207 198 198 198 203 203 203 208 208 208 208

Ending stocks 51 45 47 49 51 53 55 57 54 51 48 45 Stocks/use ratio, percent 25.2 21.7 23.7 24.7 25.8 26.1 27.1 28.1 26.0 24.5 23.1 21.6

Prices (dollars per bushel):

Farm price 1.87 2.40 2.45 2.45 2.30 2.25 2.25 2.25 2.25 2.25 2.30 2.30 Loan rate 1.33 1.33 1.33 1.33 1.33 1.33 1.33 1.33 1.33 1.33 1.33 1.33

Variable costs of production (dollars):

Per acre 95 105 110 113 115 117 118 120 121 123 125 127 Per bushel 1.59 1.72 1.74 1.78 1.80 1.81 1.83 1.84 1.85 1.87 1.89 1.90

Returns over variable costs (dollars per acre):

Net returns 17 42 45 43 32 28 27 27 26 25 27 26 Note: Marketing year beginning June 1 for oats.

40 USDA Long-term Projections, February 2008

Table 12. U.S. wheat long-term projections Item 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Area (million acres):

Planted acres 57.3 60.4 65.0 60.0 58.5 57.5 56.5 56.5 56.0 56.0 55.5 55.5 Harvested acres 46.8 51.0 55.3 51.0 49.7 48.9 48.0 48.0 47.6 47.6 47.2 47.2

Yields (bushels per acre):

Yield/harvested acre 38.7 40.5 42.5 42.8 43.1 43.4 43.7 44.0 44.3 44.6 44.9 45.2

Supply and use (million bushels):

Beginning stocks 571 456 312 606 703 742 749 732 716 696 683 661 Production 1,812 2,067 2,350 2,185 2,140 2,120 2,100 2,110 2,110 2,125 2,120 2,135 Imports 122 90 100 100 105 105 110 110 115 115 120 120 Supply 2,505 2,613 2,762 2,891 2,948 2,967 2,959 2,952 2,941 2,936 2,923 2,916

Food 934 940 950 959 968 977 986 995 1,004 1,013 1,022 1,031 Seed 81 86 81 79 78 76 76 76 76 75 75 75 Feed & residual 125 125 175 200 210 215 215 215 215 215 215 215 Domestic 1,140 1,151 1,206 1,238 1,256 1,268 1,277 1,286 1,295 1,303 1,312 1,321 Exports 909 1,150 950 950 950 950 950 950 950 950 950 950 Total use 2,049 2,301 2,156 2,188 2,206 2,218 2,227 2,236 2,245 2,253 2,262 2,271

Ending stocks 456 312 606 703 742 749 732 716 696 683 661 645 Stocks/us e ratio, percent 22.3 13.6 28.1 32.1 33.6 33.8 32.9 32.0 31.0 30.3 29.2 28.4

Prices (dollars per bushel):

Farm price 4.26 6.10 5.50 5.00 4.65 4.50 4.50 4.50 4.55 4.55 4.60 4.65 Loan rate 2.75 2.75 2.75 2.75 2.75 2.75 2.75 2.75 2.75 2.75 2.75 2.75

Variable costs of production (dollars):

Per acre 86 94 98 101 103 105 106 107 109 111 112 114 Per bushel 2.21 2.32 2.31 2.37 2.39 2.41 2.43 2.44 2.46 2.48 2.51 2.52

Returns over variable costs (dollars per acre):

Net returns 79 153 135 113 97 91 91 91 92 92 94 96 Note: Marketing year beginning June 1 for wheat.

USDA Long-term Projections, February 2008 41

Table 13. U.S. soybean and products long-term projec tions Item 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Soybeans Area (million acres): Planted 75.5 63.7 71.0 69.5 69.0 68.5 68.5 68.5 68.0 68.0 68.0 68.0 Harves ted 74.6 62.8 70.1 68.6 68.1 67.6 67.6 67.6 67.1 67.1 67.1 67.1 Yield/harvested acre (bushels) 42.7 41.3 42.1 42.6 43.0 43.5 43.9 44.4 44.8 45.3 45.7 46.2 Supply (million bushels) Beginning stocks, September 1 449 573 210 219 210 202 193 199 204 204 203 201 Production 3,188 2,594 2,950 2,920 2,930 2,935 2,970 3,000 3,005 3,035 3,065 3,095 Imports 9 6 6 4 4 4 4 4 4 4 4 4 Total supply 3,647 3,173 3,166 3,143 3,144 3,141 3,167 3,203 3,213 3,243 3,272 3,300 Disposition (million bushels) Crush 1,806 1,825 1,865 1,895 1,920 1,950 1,975 2,000 2,020 2,045 2,070 2,095 Seed and residual 149 163 177 173 172 173 174 174 174 175 176 177 Exports 1,118 975 905 865 850 825 820 825 815 820 825 825 Total disposition 3,074 2,963 2,947 2,933 2,942 2,948 2,969 2,999 3,009 3,040 3,071 3,097 Carryover stocks, August 31 Total ending stocks 573 210 219 210 202 193 199 204 204 203 201 204 Stocks/us e ratio, percent 18.6 7.1 7.4 7.2 6.9 6.5 6.7 6.8 6.8 6.7 6.5 6.6 Prices (dollars per bushel) Loan rate 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 Soybean price, farm 6.43 9.00 8.85 8.90 8.75 8.80 8.80 8.80 8.85 8.90 8.95 9.00 Variable costs of production (dollars): Per acre 97 105 109 113 114 115 116 117 119 120 122 123 Per bushel 2.27 2.55 2.60 2.64 2.65 2.65 2.65 2.65 2.65 2.65 2.66 2.66 Returns over variable costs (dollars per acre): Net returns 178 266 263 267 262 268 270 273 278 283 287 293

Soybean oil (million pounds)

Beginning s tocks, O ctober 1 3,010 2,912 2,017 1,882 1,967 1,967 1,987 1,947 1,872 1,782 1,757 1,772 Production 20,484 20,715 21,215 21,575 21,880 22,240 22,545 22,850 23,100 23,405 23,710 24,020 Imports 40 40 50 60 70 80 90 100 110 120 130 140 Total supply 23,533 23,667 23,282 23,517 23,917 24,287 24,622 24,897 25,082 25,307 25,597 25,932 Domestic disappearance 18,721 20,100 20,150 20,300 20,550 20,775 21,100 21,400 21,650 21,900 22,150 22,400 For methyl ester1 2,794 4,200 4,200 4,200 4,250 4,250 4,350 4,400 4,400 4,400 4,400 4,400 Exports 1,900 1,550 1,250 1,250 1,400 1,525 1,575 1,625 1,650 1,650 1,675 1,700 Total demand 20,621 21,650 21,400 21,550 21,950 22,300 22,675 23,025 23,300 23,550 23,825 24,100 Ending stoc ks, September 30 2,912 2,017 1,882 1,967 1,967 1,987 1,947 1,872 1,782 1,757 1,772 1,832 Soybean oil price (dollars per lb) 0.310 0.395 0.385 0.385 0.383 0.383 0.383 0.383 0.385 0.385 0.385 0.385

Soybean meal (thousand short tons)

Beginning s tocks, O ctober 1 314 351 300 300 300 300 300 300 300 300 300 300 Production 43,021 43,384 44,385 45,085 45,735 46,385 46,985 47,560 48,135 48,710 49,310 49,910 Imports 155 165 165 165 165 165 165 165 165 165 165 165 Total supply 43,489 43,900 44,850 45,550 46,200 46,850 47,450 48,025 48,600 49,175 49,775 50,375 Domestic disappearance 34,288 35,300 35,850 36,400 36,950 37,500 38,050 38,625 39,200 39,775 40,375 40,975 Exports 8,850 8,300 8,700 8,850 8,950 9,050 9,100 9,100 9,100 9,100 9,100 9,100 Total demand 43,138 43,600 44,550 45,250 45,900 46,550 47,150 47,725 48,300 48,875 49,475 50,075 Ending stoc ks, September 30 351 300 300 300 300 300 300 300 300 300 300 300 Soybean meal price (dollars per ton) 205.44 250.00 240.00 242.50 237.00 238.00 238.00 238.50 238.50 240.00 241.50 243.00

Crushing yields (pounds per bushel) Soybean oil 11.34 11.35 11.38 11.39 11.40 11.41 11.42 11.43 11.44 11.45 11.46 11.47 Soybean meal 47.64 47.54 47.60 47.60 47.60 47.60 47.60 47.60 47.60 47.60 47.60 47.60 Crush margin (dollars per bushel) 1.98 1.43 1.24 1.25 1.25 1.23 1.23 1.25 1.23 1.22 1.21 1.20

Note: Marketing year beginning September 1 for soybeans; O ctober 1 for soybean oil and meal. 1/ Soybean oil used for methyl ester for production of biodiesel, history from the U.S. Department of Commerce.

42 USDA Long-term Projections, February 2008

Table 14. U.S. rice long-term projections, rough basis Item 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Area (thousand acres):

Planted 2,838 2,748 2,800 2,900 2,950 3,000 3,050 3,100 3,125 3,150 3,175 3,200 Harvested 2,821 2,731 2,783 2,883 2,932 2,982 3,032 3,081 3,106 3,131 3,156 3,181

Yields (pounds per acre):

Yield/harvested acre 6,868 7,247 7,222 7,284 7,351 7,419 7,481 7,543 7,608 7,666 7,725 7,784

Supply and use (million cwt):

Beginning stocks 43.0 39.3 27.1 25.9 26.4 27.3 28.5 29.1 30.0 30.6 30.5 30.5 Production 193.7 197.9 201.0 210.0 215.5 221.2 226.8 232.4 236.3 240.0 243.8 247.6 Imports 20.6 21.5 22.0 22.7 23.3 24.0 24.8 25.5 26.3 27.1 27.9 28.7 Total supply 257.3 258.7 250.1 258.5 265.3 272.5 280.1 287.0 292.6 297.6 302.2 306.8

Domestic us e and residual 126.7 124.7 126.2 128.1 130.0 132.0 134.0 136.0 138.0 140.1 142.2 144.3 Exports 91.3 107.0 98.0 104.0 108.0 112.0 117.0 121.0 124.0 127.0 129.5 132.0 Total use 218.0 231.7 224.2 232.1 238.0 244.0 251.0 257.0 262.0 267.1 271.7 276.3

Ending stocks (million cwt.) 39.3 27.1 25.9 26.4 27.3 28.5 29.1 30.0 30.6 30.5 30.5 30.5 Stocks/us e ratio, percent 18.0 11.7 11.5 11.4 11.5 11.7 11.6 11.7 11.7 11.4 11.2 11.0

Milling rate, percent 71.0 70.5 70.5 70.5 70.5 70.5 70.5 70.5 70.5 70.5 70.5 70.5

Prices (dollars per cwt.):

W orld price 7.31 8.10 8.35 8.60 8.86 9.08 9.31 9.54 9.78 10.02 10.27 10.53 Average market price 9.74 11.00 11.15 11.30 11.46 11.58 11.71 11.84 11.98 12.12 12.32 12.53 Loan rate 6.50 6.50 6.50 6.50 6.50 6.50 6.50 6.50 6.50 6.50 6.50 6.50

Variable costs of production (dollars):

Per acre 437 470 492 508 516 523 530 536 544 552 560 568 Per cwt. 6.36 6.49 6.81 6.97 7.01 7.05 7.08 7.11 7.15 7.20 7.25 7.30

Returns over variable costs (dollars per acre):

Net returns 232 327 313 315 327 336 346 357 367 377 391 407 Note: Marketing year beginning August 1 for rice.

USDA Long-term Projections, February 2008 43

Table 15. U.S. upland cotton long-term projections Item 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Area (million acres):

Planted acres 14.9 10.6 10.5 11.2 11.5 11.7 11.8 11.9 12.0 12.1 12.2 12.3 Harvested acres 12.4 10.3 9.7 10.3 10.6 10.8 10.9 10.9 11.0 11.1 11.2 11.3

Yields (pounds per acre):

Yield/harvested acre 806 845 860 875 885 895 905 915 925 935 945 955

Supply and use (thousand bales):

Beginning stocks 5,981 9,368 7,519 4,469 4,069 4,119 4,519 5,069 5,469 5,719 5,819 5,869 Production 20,823 18,050 17,400 18,800 19,500 20,100 20,600 20,800 21,200 21,600 22,100 22,500 Imports 10 10 10 10 10 10 10 10 10 10 10 10 Supply 26,814 27,428 24,929 23,279 23,579 24,229 25,129 25,879 26,679 27,329 27,929 28,379

Domestic us e 4,907 4,560 4,450 4,400 4,350 4,300 4,250 4,200 4,150 4,100 4,050 4,000 Exports 12,338 15,400 16,000 14,800 15,100 15,400 15,800 16,200 16,800 17,400 18,000 18,500 Total use 17,245 19,960 20,450 19,200 19,450 19,700 20,050 20,400 20,950 21,500 22,050 22,500

Ending stocks 9,368 7,519 4,469 4,069 4,119 4,519 5,069 5,469 5,719 5,819 5,869 5,869 Stocks/us e ratio, percent 54.3 37.7 21.9 21.2 21.2 22.9 25.3 26.8 27.3 27.1 26.6 26.1

Prices (dollars per pound):

Farm price1 0.465 --- --- --- --- --- --- --- --- --- --- --- Loan rate 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52

Variable costs of production (dollars):

Per acre 366 412 429 444 450 456 462 468 474 481 488 494 Per pound 0.45 0.49 0.50 0.51 0.51 0.51 0.51 0.51 0.51 0.51 0.52 0.52

Returns over variable costs (dollars per acre):

Net returns 157 148 248 265 258 252 258 269 270 262 263 264 Note: Marketing year beginning August 1 for upland cotton. 1/ USDA is prohibited from publishing cotton price projections.

44 USDA Long-term Projections, February 2008

Table 16. U.S. sugar long-term projections 1/ Item Units 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Sugarbeets Planted area 1,000 acres 1,367 1,263 1,230 1,180 1,172 1,148 1,135 1,137 1,140 1,143 1,144 1,147 Harvested area 1,000 acres 1,304 1,241 1,203 1,155 1,147 1,124 1,111 1,114 1,117 1,119 1,121 1,123 Yield Tons/acre 26.1 25.4 26.3 26.6 26.8 27.1 27.3 27.5 27.7 27.9 28.1 28.3 Production Mil. s. tons 34.1 31.6 31.6 30.7 30.7 30.4 30.3 30.6 30.9 31.2 31.5 31.8

Sugarcane Harvested area 1,000 acres 847 833 835 820 750 752 753 754 755 756 757 758 Yield Tons/acre 33.0 34.7 34.2 34.3 34.5 34.6 34.7 34.8 34.9 35.0 35.1 35.2 Production Mil. s. tons 28.0 28.9 28.6 28.1 25.9 26.0 26.1 26.2 26.3 26.4 26.5 26.6

Supply: Beginning stocks 1,000 s. tons 1,698 1,787 1,849 1,850 1,846 1,850 1,846 1,845 1,843 1,841 1,839 1,838 Production 1,000 s. tons 8,434 8,451 8,466 8,311 8,071 8,074 8,109 8,206 8,302 8,399 8,491 8,591 Beet sugar 1,000 s. tons 5,002 4,791 4,811 4,688 4,712 4,679 4,683 4,747 4,811 4,875 4,934 5,000 Cane sugar 1,000 s. tons 3,432 3,659 3,655 3,622 3,359 3,394 3,426 3,458 3,491 3,524 3,557 3,591 Total imports 1,000 s. tons 2,080 2,194 2,614 2,945 3,177 3,486 3,475 3,530 3,585 3,638 3,693 3,748

TRQ imports 1,000 s. tons 1,624 1,339 1,377 1,380 1,382 1,385 1,390 1,392 1,395 1,397 1,402 1,405 Total supply 1,000 s. tons 12,211 12,431 12,928 13,105 13,095 13,410 13,430 13,580 13,730 13,878 14,023 14,176

Use: Exports 1,000 s. tons 422 250 250 250 250 250 250 250 250 250 250 250 Domestic deliveries 1,000 s. tons 10,124 10,300 10,394 10,440 10,531 10,567 10,630 10,693 10,756 10,818 10,883 10,945 Miscellaneous 1,000 s. tons -122 0 0 0 0 0 0 0 0 0 0 0 Total use 1,000 s. tons 10,424 10,550 10,644 10,690 10,781 10,817 10,880 10,943 11,006 11,068 11,133 11,195

CCC Dispositions 1,000 s. tons -- 33 435 569 464 746 706 794 883 970 1,052 1,146 Ending stocks 1,000 s. tons 1,787 1,849 1,850 1,846 1,850 1,846 1,845 1,843 1,841 1,839 1,838 1,836

Raw sugar price: New York (No. 14) Cents/lb. 20.79 20.67 20.68 20.68 20.67 20.66 20.66 20.66 20.66 20.66 20.66 20.66

Raw sugar loan rate Cents/lb. 18.00 18.00 18.00 18.00 18.00 18.00 18.00 18.00 18.00 18.00 18.00 18.00 Beet sugar loan rate Cents/lb. 22.90 22.90 22.90 22.90 22.90 22.90 22.90 22.90 22.90 22.90 22.90 22.90 Grower prices:

Sugarbeets Dol./ton 38.73 35.13 34.66 34.29 34.55 34.80 35.05 35.32 35.59 35.86 36.13 36.39 Sugarcane Dol./ton 28.46 28.84 29.16 29.29 29.57 29.69 29.81 29.94 30.06 30.18 30.30 30.42

1/ Fiscal years, October 1 through September 30.

USDA Long-term Projections, February 2008 45

Table 17. Horticultural crops long-term projections: Production, values, and prices, calendar years I tem Unit 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Production, farm value: Fruit and nuts Citrus $ Mil. 2,738 2,440 2,819 2,575 2,495 2,571 2,661 2,755 2,866 2,982 3, 087 3,196 Noncitrus1 $ Mil. 11,341 11,492 11,893 12,307 12,736 13,180 13,640 14,115 14,607 15,117 15, 644 16,189 Tree nuts $ Mil. 3,454 3,540 3,647 3,802 3,963 4,131 4,307 4,489 4,680 4,879 5, 086 5,301 Total fruit and nuts $ Mil. 17,534 17,472 18,359 18,684 19,195 19,882 20,608 21,360 22,154 22,977 23, 817 24,687

Vegetables Fresh market2 $ Mil. 10,379 11,481 11,174 11,584 12,009 12,450 12,907 13,381 13,872 14,381 14, 909 15,457 Processing3 $ Mil. 2,088 2,296 2,282 2,355 2,431 2,509 2,590 2,673 2,760 2,848 2, 940 3,035

Potatoes4 $ Mil. 3,226 3,307 3,417 3,530 3,647 3,768 3,894 4,023 4,156 4,294 4, 437 4,584 Other5 $ Mil. 2,659 2,733 2,810 2,889 2,970 3,053 3,138 3,226 3,316 3,409 3, 505 3,603 Total vegetables $ Mil. 18,351 19,818 19,683 20,358 21,057 21,780 22,529 23,303 24,104 24,933 25, 791 26,679

Nursery and greenhouse6 $ Mil. 16,892 17,230 17,574 17,996 18,428 18,870 19,323 19,787 20,262 20,748 21, 246 21,756

Total, horticultural crops7 $ Mil. 53,254 55,006 56,110 57,542 59,192 61,054 62,991 64,990 67,070 69,219 71, 424 73,702

Production, farm weight: Fruit and nuts Citrus Mil. lbs. 23,490 20,528 24,960 25,334 25,841 26,358 27,017 27,692 28,246 28,811 29, 243 29,682 Noncitrus1 Mil. lbs. 40,378 40,436 40,746 41,058 41,372 41,689 42,008 42,330 42,654 42,981 43, 310 43,642 Tree nuts Mil. lbs. 3,186 3,628 3,664 3,745 3,827 3,911 3,997 4,085 4,175 4,267 4, 361 4,457 Total fruit and nuts Mil. lbs. 67,054 64,592 69,370 70,137 71,040 71,958 73,022 74,107 75,075 76,059 76, 914 77,780

Vegetables and melons Fresh market2 Mil. lbs. 42,738 43,000 44,052 44,555 45,063 45,578 46,098 46,624 47,157 47,696 48, 241 48,793 Processing3 Mil. lbs. 38,915 42,800 40,500 40,865 41,232 41,603 41,978 42,356 42,737 43,121 43, 510 43,901 Potatoes4 Mil. lbs. 44,135 44,797 45,155 45,517 45,881 46,248 46,618 46,991 47,367 47,746 48, 128 48,513 Other5 Mil. lbs. 8,000 8,120 8,242 8,365 8,491 8,618 8,748 8,879 9,012 9,147 9, 284 9,424 Total vegetables Mil. lbs. 133,788 138,717 137,950 139,301 140,667 142,047 143,441 144,849 146,272 147,710 149, 162 150,630

Total, produce and nuts 7 Mil. lbs. 201,095 203,564 207,576 209,696 211,966 214,265 216,725 219,219 221,611 224,033 226, 342 228,677

Producer price indexes8

Fruit and nuts Citrus 2000=100 160.3 163.4 155.3 139.7 132.8 134.1 135.4 136.8 139.5 142.3 145.2 148.1 Noncitrus 2000=100 144.5 146.2 150.2 154.2 158.4 162.7 167.1 171.6 176.2 181.0 185.9 190.9 Tree nuts 2000=100 157.4 141.7 144.5 147.4 150.3 153.3 156.4 159.5 162.7 166.0 169.3 172.7 Total fruit and nuts 2000=100 168.0 173.8 170.1 171.2 173.6 177.6 181.4 185.2 189.6 194.1 199.0 204.0

Vegetables Fresh market 2000=100 111.5 122.5 116.4 119.3 122.3 125.4 128.5 131.7 135.0 138.4 141.8 145.4 Processing 2000=100 101.8 101.8 106.9 109.4 111.9 114.4 117.1 119.8 122.5 125.3 128.2 131.2 Potatoes 2000=100 144.9 146.4 150.0 153.8 157.6 161.6 165.6 169.7 174.0 178.3 182.8 187.4 Total vegetables 2000=100 121.4 126.4 126.3 129.3 132.5 135.7 139.0 142.4 145.8 149.4 153.0 156.7

Total produce and nuts 2000=100 138.9 142.6 142.6 144.9 147.7 151.2 154.8 158.4 162.2 166.2 170.3 174.5 1/ Includes melons; excludes olives. 2/ Includes sweet potatoes and fresh-market mushrooms; excludes melons. 3/ Includes pulses (dry edible beans, peas, and lentils), processing mushrooms, and olives. 4/ Includes seed, feed, own farm use, or unutilized potatoes. 5/ Specialty and minor vegetables; farm weight is from California (California Department of Food and Agriculture). 6/ Includes floral crops and greenhouse vegetables, such as tomatoes, cucumbers, and colored peppers. Data source is USDA, Economic Research Service. 7/ Includes honey, maple syrup, hops, peppermint and spearmint oils, and Hawai ian tropical crops. 8/ Computed from unit values of production, or production value divided into production volume. Data source: USDA, National Agricultural Statistics S ervice

46 USDA Long-term Projections, February 2008

Table 18. Horticultural crops long-term projections: Exports and imports, fiscal years I tem Unit 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Exports Fruit and nuts Fresh fruits $ Mil. 2,842 3,005 3,077 3,147 3,218 3,291 3,366 3,444 3,523 3,605 3, 689 3,775 Citrus $ Mil. 673 668 670 672 674 676 678 680 683 685 687 689 Noncitrus $ Mil. 2,169 2,337 2,407 2,474 2,544 2,615 2,688 2,763 2,841 2,920 3, 002 3,086 Processed fruits $ Mil. 1,739 2,013 2,054 2,095 2,137 2,179 2,223 2,267 2,313 2,359 2, 406 2,454 Fruit juices $ Mil. 893 1,020 1,045 1,071 1,098 1,126 1,154 1,183 1,212 1,242 1, 274 1,305 Tree nuts $ Mil. 2,926 2,938 3,026 3,117 3,210 3,306 3,406 3,508 3,613 3,721 3, 833 3,948 Total fruit and nuts $ Mil. 7,507 7,956 8,157 8,358 8,565 8,777 8,995 9,219 9,449 9,685 9, 928 10,177

Vegetables Fresh $ Mil. 1,629 1,771 1,824 1,877 1,932 1,988 2,045 2,105 2,166 2,228 2, 293 2,360 Processed1 $ Mil. 2,185 2,389 2,444 2,495 2,548 2,601 2,656 2,711 2,768 2,827 2, 886 2,946 Frozen $ Mil. 656 771 789 805 822 839 857 875 893 912 931 951 Total vegetables $ Mil. 3,814 4,160 4,268 4,372 4,479 4,589 4,701 4,816 4,934 5,055 5, 179 5,306

Other horticulture Nursery and greenhouse $ Mil. 310 355 373 381 390 399 408 417 427 437 447 457 Essential oils $ Mil. 1,041 1,141 1,199 1,236 1,274 1,314 1,354 1,396 1,439 1,484 1, 530 1,578 Wine $ Mil. 787 905 996 1,035 1,077 1,120 1,165 1,211 1,260 1,310 1, 363 1,417 Beer $ Mil. 210 231 245 246 248 249 250 251 253 254 255 256

Other2 $ Mil. 3,006 3,162 3,320 3,446 3,577 3,713 3,854 4,001 4,153 4,310 4, 474 4,644

Total horticulture $ Mil. 16,675 17,911 18,557 19,075 19,610 20,160 20,727 21,312 21,915 22,536 23, 176 23,836 Fresh3 $ Mil. 4,471 4,776 4,902 5,024 5,150 5,279 5,412 5,548 5,689 5,833 5, 982 6,134 Processed3 $ Mil. 3,924 4,402 4,498 4,590 4,684 4,780 4,879 4,979 5,081 5,186 5, 292 5,401

Export share of production4 Percent 31 33 33 33 33 33 33 33 33 33 32 32

Imports Fruit and nuts Fresh fruits $ Mil. 4,687 5,406 6,035 6,342 6,601 6,805 7,016 7,233 7,457 7,689 7, 928 8,175 Citrus $ Mil. 398 499 539 571 600 630 661 694 729 765 804 844 Noncitrus $ Mil. 4,289 4,907 5,496 5,771 6,001 6,175 6,355 6,539 6,728 6,924 7, 124 7,331 Processed fruits $ Mil. 2,601 3,418 4,034 4,276 4,404 4,510 4,618 4,729 4,842 4,958 5, 077 5,199 Fruit juices $ Mil. 1,056 1,618 1,973 2,171 2,279 2,339 2,399 2,462 2,526 2,591 2, 659 2,728 Tree nuts $ Mil. 1,071 1,079 1,241 1,315 1,381 1,431 1,482 1,536 1,591 1,648 1, 707 1,769 Total fruit and nuts $ Mil. 8,360 9,903 11,309 11,933 12,386 12,746 13,116 13,497 13,890 14,296 14, 713 15,143

Vegetables Fresh $ Mil. 3,979 4,165 4,415 4,636 4,844 5,062 5,290 5,528 5,777 6,037 6, 309 6,592 Processed1 $ Mil. 2,754 3,149 3,401 3,605 3,785 3,929 4,087 4,250 4,420 4,597 4, 781 4,972 Frozen $ Mil. 1,072 1,202 1,298 1,376 1,445 1,500 1,560 1,622 1,687 1,755 1, 825 1,898 Total vegetables $ Mil. 6,733 7,314 7,816 8,241 8,630 8,992 9,377 9,778 10,197 10,634 11, 089 11,564

Other horticulture Nursery and greenhouse $ Mil. 1,424 1,531 1,607 1,672 1,730 1,787 1,846 1,907 1,970 2,035 2, 102 2,172 Essential oils $ Mil. 2,469 2,427 2,499 2,574 2,646 2,721 2,797 2,875 2,956 3,038 3, 123 3,211 Wine $ Mil. 4,043 4,544 4,817 5,058 5,290 5,534 5,788 6,055 6,333 6,624 6, 929 7,248 Beer $ Mil. 3,376 3,686 3,981 4,220 4,431 4,586 4,747 4,913 5,085 5,263 5, 447 5,638

Other2 $ Mil. 2,738 2,986 3,195 3,386 3,573 3,751 3,939 4,136 4,342 4,560 4, 788 5,027

Total horticulture $ Mil. 29,142 32,391 35,225 37,084 38,687 40,116 41,609 43,161 44,774 46,450 48, 192 50,003 Fresh3 $ Mil. 8,666 9,571 10,450 10,978 11,446 11,868 12,306 12,761 13,234 13,726 14, 237 14,767 Processed3 $ Mil. 5,356 6,568 7,435 7,881 8,189 8,439 8,704 8,979 9,262 9,555 9, 858 10,171

Import share of consumption Percent 44 47 48 49 49 50 50 50 50 50 50 50 1/ Includes dry edible beans, peas, lentils, and pot atoes. 2/ Includes hops, ginseng, sauces, condiments, food preparations, yeast, starches, etc. 3/ Includes fruits and vegetables only. 4/ Percent shares are based on values. Exports are free alongside ship (FAS) value at U.S. port of exportation. Imports are customs value at U.S. port of entry. Data source: U.S. Department of Commerce, Bureau of the Census.

USDA Long-term Projections, February 2008 47

Livestock Projections for the livestock sector include production adjustments in response to high grain and soybean meal prices resulting from the expansion of corn-based ethanol production. Returns to U.S. meat and poultry production fall below levels in recent years, slowing increases in or reducing production of all meats over the next several years. Once the sector adjusts, lower overall production combined with strong domestic demand and some strengthening in meat exports result in higher prices and higher returns, providing economic incentives for expansion in the sector and a resumption in meat production gains.

15

20

25

30

35

40

1990 1995 2000 2005 2010 2015

U.S. red meat and poultry production

Billion pounds

Beef

Pork

Broilers

Production of all meats slows or declines in the first half of the projection period, reflecting higher feed costs as more corn is used in ethanol production. Distillers grains, a coproduct of ethanol production, can be used in livestock rations, partially substituting for corn and sometimes for soybean meal. However, distillers grains can more easily be used by ruminants (such as cattle) compared to monogastric animals (such as hogs and chickens). Beef cattle feedlots located close to ethanol plants are best situated to benefit from a steady supply of distillers grains, also reflecting the ability of those animals to use the wet form of distillers grains. Meanwhile, distillers grains are less suitable in poultry and hog rations.

• Higher grain prices as well as effects of drought in recent years hold down cattle inventories, pushing U.S. beef production down in 2008-10. Production then rises in the remainder of the projection period as returns improve and herds are rebuilt. The cattle inventory remains in a range of 96-99 million head throughout the projections. Rising slaughter weights contribute to the moderate expansion of beef production beyond 2010. Higher costs of feedlot gain will result in stocker cattle remaining on pasture to heavier weights before entering feedlots.

• Pork production declines in 2009-11 in response to higher feed prices and then grows for the remainder of the projections as higher hog prices improve returns. Production coordination and market integration between the United States and Canada continue in the hog sector. Canada is the major supplier of live swine imported by the United States. Imported feeder pigs from Canada are finished and processed in the United States, where both finishing and processing costs are lower.

• Poultry production slows in 2009-13 while adjusting to higher feed costs, but begins to rise towards the end of the projections period. During the period, rising exports account for a larger share of total production.

48 USDA Long-term Projections, February 2008

30

40

50

60

70

80

90

100

1985 1990 1995 2000 2005 2010 2015

U.S. per capita meat consumption

Retail weight, pounds per capita

Beef

Broilers

Pork

Livestock sector production adjustments to higher feed costs as well as gains in meat and poultry exports result in higher consumer prices and lower per capita consumption. Annual per capita consumption of red meats and poultry falls from 222 pounds in 2006 to a low of 214 pounds in 2012-14. Per capita consumption of red meats and poultry then resumes growth to almost 217 pounds in 2017.

• Per capita beef consumption declines through the projection period, reflecting production adjustments in the industry to higher feed costs. U.S. beef exports rise through the projection period, further limiting domestic per capita beef consumption. A gradual rebuilding of U.S. beef exports to Japan and South Korea is assumed.

• Strong demand for consistent, high-quality beef continues in the domestic hotel and

restaurant market, and increasingly in the retail market. Demand for U.S. beef in export markets is also primarily for high-quality beef.

• Higher feed costs lead to reductions in pork production, which combine with rising pork

exports to push per capita pork consumption down through 2012. A gradual rebound in per capita pork consumption occurs over the remainder of the projection period as production gains strengthen.

• Due partly to higher feed conversion rates, poultry prices remain lower than red meat

prices. However, as returns are squeezed, slower production growth and higher exports result in per capita consumption declines in 2010-12. Following these adjustments, production strengthens and per capita consumption slowly grows toward the end of the projection period.

USDA Long-term Projections, February 2008 49

30

40

50

60

70

80

90

100

1990 1995 2000 2005 2010 2015

Nominal U.S. livestock prices

Dollars per hundredweight

Beef cattle: Choice steers, Nebraska

Broilers: 12-city market price

Hogs: National base

Livestock prices rise through most of the projection period reflecting production adjustments in response to higher feed costs.

0.0

0.5

1.0

1.5

2.0

2.5

1990 1995 2000 2005 2010 2015

Poultry Pork Beef

U.S. spending on meat

Percent of income

Rising incomes facilitate gains in consumer spending on meat. Nonetheless, overall meat expenditures represent a declining proportion of disposable income, continuing a long-term trend.

50 USDA Long-term Projections, February 2008

0

2

4

6

8

10

12

14

1990 1995 2000 2005 2010 2015

Beef

Pork

Poultry

U.S. meat exports

Billion pounds

The domestic market remains the dominant source of overall meat demand, but exports account for a growing share of U.S. meat use. Despite higher prices, U.S. meat exports rise throughout the projection period, supported by global economic growth and a continued weak U.S. dollar. Beef • U.S. beef exports primarily reflect demand for high-quality fed beef, with most U.S. beef exports

typically going to Mexico, Canada, and markets in Pacific Rim nations. A gradual recovery of U.S. beef exports is assumed in the Japanese and South Korean export markets lost following the first U.S. case of bovine spongiform encephalopathy (BSE) in December 2003.

• U.S. imports of processing beef from Australia and New Zealand increase in the projections. With more demand in East Asian markets being met by the United States, exports from Australia and New Zealand are reduced, resulting in more of their product being shipped to the United States. The United States is a net beef importer by volume throughout the projection period as the recovery of high-quality fed beef exports does not reach levels of 2000-03 until the last several years of the projections.

Pork • Pacific Rim nations and Mexico remain key markets for long-term growth of U.S. pork exports. Brazil

is also a major pork exporter. However, no changes in the set of countries recognizing Brazil as free of foot-and-mouth disease (FMD) are assumed, thus limiting Brazilian pork producers’ ability to compete in some markets. Consequently, Brazil’s pork exports expand to markets such as Russia, Argentina, and Asian markets other than Japan and South Korea.

• Despite higher feed costs, increased efficiency in U.S. pork production enhances the competitiveness of U.S. pork products. Nonetheless, longer term gains in U.S. pork exports will be determined by costs of production and environmental regulations relative to competitors. Such costs tend to be lower in countries which are developing integrated pork industries, such as Brazil.

• The value of the U.S. dollar relative to currencies of other pork exporting countries is expected to enhance U.S. pork export volumes, particularly in the early years of the projection period.

Poultry • U.S. broiler exports rise through the projection period, although at a slower pace than in earlier years.

Major U.S. export markets include China, Russia, and Mexico. Gains in these markets reflect economic growth and increasing consumer demand. Demand for poultry also remains strong due to its lower cost relative to beef and pork. U.S. producers will continue to face strong competition from other major exporters, particularly Brazil. For most of the projection period, exports from avian influenza-affected countries are expected to be limited to fully cooked products.

USDA Long-term Projections, February 2008 51

8

9

10

11

1985 1990 1995 2000 2005 2010 2015 10

15

20

25

U.S. dairy herd and milk production per cow

Million cows

Milk cows

Output per cow

1,000 pounds per cow

In 2007, U.S. prices for farm-level milk and for dairy products, such as cheese, nonfat dry milk, and dry whey, were high relative to historic levels due in part to the international dairy situation. Relatively high prices are expected to extend into 2008. As incomes in developing countries have grown, so has the global demand for dairy products. World milk supplies have been tight, however, due in part to reform of the Common Agricultural Policy (CAP) in the European Union and drought conditions in Australia. The U.S. dairy industry has become a major commercial exporter of nonfat dry milk, dry whey products, and cheese. Although U.S. milk production grew significantly in 2007, the growth was limited by high feed costs relative to historic levels and tight supplies of dairy heifers.

• Despite higher feed costs, strong farm-level milk prices are projected to encourage further increases in milk cow numbers through 2009. Combined with an upward trend in output per cow, the results are relatively strong gains in milk production in 2008 and 2009 and decreases in milk prices. Smaller production gains are projected on average over the rest of the projection period because milk cow numbers decline after 2009.

• Milk output per cow is projected to increase, although some slowing in these gains occurs in 2009 and 2010 in response to higher feed costs. Nonetheless, further development of large, specialized operations in most regions will contribute to a continuation of gains in output per cow.

• Milk cow numbers are expected to decline after 2009, although reductions are moderate as increasing specialization of dairy farms over time slows exit rates from milk production compared with past decades.

• Commercial use of dairy products increases slightly faster than the growth in population. Cheese demand benefits from greater consumption of prepared foods and increased away-from- home eating. However, per capita consumption of fluid milk is expected to continue to decline slowly.

• Farm-level milk prices decrease in 2009 from recent high levels as milk production gains are relatively strong. Milk prices then rise through the rest of the projections, but increases are projected to be less than the general inflation rate. Efficiency gains in production accommodate moderately higher overall per capita consumption at declining real prices.

52 USDA Long-term Projections, February 2008

Table 19. Per capita meat consumption, retail weight Item 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

P ounds

Total beef 65.7 65.0 63.7 62.0 61.2 61.7 61.5 61.1 60.4 60.3 60.3 60.1 Total veal 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.4 0.4 0.4 Total pork 49.3 50.5 51.1 49.4 48.8 47.8 47.6 47.8 48.1 48.3 48.5 48.8 Lamb and mutton 1.1 1.1 1.1 1.1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.9 Total red meat 116.6 117.1 116.4 113.0 111.5 111.0 110.7 110.4 110.0 110.0 110.2 110.3

Broilers 87.1 85.4 86.8 87.0 86.4 85.9 85.6 85.7 86.2 86.8 87.4 88.1 Other chicken 1.2 1.1 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 Turkeys 16.9 17.3 17.1 17.3 17.1 17.0 16.9 16.8 16.8 16.9 17.0 17.2 Total poultry 105.1 103.9 105.1 105.5 104.7 104.1 103.7 103.7 104.2 104.9 105.6 106.5

Red meat & poultry 221.7 221.0 221.5 218.5 216.2 215.1 214.3 214.1 214.2 214.8 215.8 216.8

Table 20. Consumer expenditures for m eats Item 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Beef, dollars per person 260.81 270.42 265.54 279.05 289.75 293.82 295.86 297.64 302.72 306.66 309.10 311.33 Percent of income 0.80 0.79 0.74 0.75 0.74 0.72 0.69 0.67 0.65 0.63 0.61 0.58 Percent of meat expenditures 47.01 47.05 45.85 46.17 46.81 46.84 46.67 46.42 46.44 46.47 46.41 46.21

Pork, dollars per person 138.54 145.00 149.28 152.49 154.72 155.72 157.81 160.13 162.54 164.41 166.12 168.06 Percent of income 0.43 0.42 0.42 0.41 0.40 0.38 0.37 0.36 0.35 0.34 0.33 0.32 Percent of meat expenditures 24.97 25.23 25.78 25.23 25.00 24.83 24.89 24.97 24.93 24.91 24.94 24.94

Broilers, dollars per person 136.78 139.45 145.06 153.46 154.92 158.31 160.99 164.17 167.42 169.51 171.18 174.38 Percent of income 0.42 0.41 0.41 0.41 0.40 0.39 0.38 0.37 0.36 0.35 0.34 0.33 Percent of meat expenditures 24.65 24.26 25.05 25.39 25.03 25.24 25.40 25.60 25.68 25.69 25.70 25.88

Turkeys, dollars per person 18.72 19.92 19.21 19.44 19.59 19.39 19.28 19.24 19.21 19.37 19.57 20.03 Percent of income 0.06 0.06 0.05 0.05 0.05 0.05 0.05 0.04 0.04 0.04 0.04 0.04 Percent of meat expenditures 3.37 3.47 3.32 3.22 3.17 3.09 3.04 3.00 2.95 2.93 2.94 2.97

Total meat, dollars per person 554.85 574.80 579.08 604.44 618.98 627.24 633.93 641.18 651.90 659.95 665.98 673.79 Percent of income 1.71 1.68 1.62 1.62 1.59 1.54 1.49 1.44 1.40 1.35 1.31 1.26

USDA Long-term Projections, February 2008 53

Table 21. Beef long-term projections Item Units 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Beginning stocks Mil. lbs. 571 630 575 600 600 600 600 600 600 600 600 600 Comm ercial production Mil. lbs. 26,153 26,135 26,000 25,565 25,515 26,017 26,273 26, 365 26,373 26,586 26,893 27,132 Change from previous year Percent 6.0 -0.1 -0.5 -1.7 -0.2 2.0 1.0 0.4 0.0 0.8 1.2 0.9

Farm production Mil. lbs. 105 105 105 105 105 105 105 105 105 105 105 105 Total production Mil. lbs. 26,258 26,240 26,105 25,670 25,620 26,122 26,378 26, 470 26,478 26,691 26,998 27,237 Imports Mil. lbs. 3,085 3,244 3,420 3,466 3,513 3,560 3,607 3, 656 3,705 3,754 3,804 3,855 Total supply Mil. lbs. 29,914 30,114 30,100 29,736 29,733 30,282 30,585 30, 726 30,783 31,045 31,402 31,692

Exports Mil. lbs. 1,145 1,432 1,710 1,824 1,927 2,032 2,138 2, 245 2,354 2,464 2,575 2,696

Ending stocks Mil. lbs. 630 575 600 600 600 600 600 600 600 600 600 600

Total consumption Mil. lbs. 28,139 28,107 27,790 27,312 27,206 27,650 27,847 27, 881 27,829 27,981 28,227 28,396 Per capita, carcass weight Pounds 93.8 92.9 91.0 88.6 87.4 88.1 87.9 87.3 86.3 86.1 86.1 85.9 Per capita, retail weight Pounds 65.7 65.0 63.7 62.0 61.2 61.7 61.5 61.1 60.4 60.3 60.3 60.1 Change from previous year Percent 0.4 -1.0 -2.0 -2.6 -1.3 0.7 -0.2 -0.8 -1.0 -0.3 0.0 -0.2

Prices:

Beef cattle, farm $/cwt 87.09 90.16 89.07 91.22 92.77 92.78 92.20 92.50 94.53 95.04 94.66 94.57 Calves, farm $/cwt 133.42 125.03 123.56 127.77 121.91 123.84 123.50 123.74 126.78 127.44 126.35 125.30 Choice steers, Nebraska $/cwt 85.41 91.61 90.50 92.68 94.26 94.27 93.68 93.99 96.05 96.57 96.18 96.09 Deflated price $/cwt 42.37 44.21 42.49 42.36 42.02 41.01 39.76 38.92 38.79 38.06 36.98 36.04 Yearling steers, Oklahoma City $/cwt 107.18 108.21 106.75 110.39 105.32 106.99 106.70 106.91 109.53 110.10 109.16 108.26 Deflated price $/cwt 53.16 52.22 50.12 50.45 46.96 46.54 45.29 44.27 44.24 43.40 41.97 40.61 Retail: Beef and veal 1982-84=100 202.1 211.1 216.0 230.5 236.1 237.7 239.8 243.1 249.8 253.9 255.8 258.3 Retail: Other meats 1982-84=100 180.7 184.8 186.0 191.0 194.8 198.5 201.7 204.7 207.6 210.5 213.3 216.1 ERS retail beef $/lb. 3.97 4.16 4.17 4.50 4.73 4.77 4.81 4.87 5.01 5.09 5.13 5.18

Costs and returns, cow-calf enterprise:

Variable expenses $/cow 257.78 285.28 283.52 287.26 289.23 290.35 293.96 298.63 303.03 307.25 308.75 312.67 Fixed expenses $/cow 130.49 135.54 139.89 142.63 145.03 147.28 149.48 151.65 153.95 156.21 156.21 158.54 Total cash expenses $/cow 388.27 420.82 423.42 429.89 434.25 437.64 443.45 450.28 456.98 463.46 464.96 471.21 Returns above cash costs $/cow 140.98 123.25 119.09 140.63 118.55 130.96 132.14 135.27 152.50 158.69 161.50 159.61

Cattle inventory 1,000 head 96,702 97,003 96,900 96,800 97,132 97,472 97,532 97, 443 97,469 97,814 98,130 98,503 Beef cow inventory 1,000 head 32,994 32,894 32,780 32,910 33,175 33,603 33,685 33, 935 34,203 34,486 34,712 34,974 Total cow inventory 1,000 head 42,123 42,003 41,859 41,987 42,208 42,593 42,645 42, 868 43,100 43,340 43,519 43,725

54 USDA Long-term Projections, February 2008

Table 22. Pork long-term projections Item Units 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Beginning stocks Mil. lbs. 494 514 560 565 565 565 565 565 565 565 565 565 Comm ercial production Mil. lbs. 21,055 21,754 22,265 21,884 21,866 21,737 21,904 22,230 22,623 23,004 23,372 23,756 Change from previous year Percent 1.8 3.3 2.3 -1.7 -0.1 -0.6 0.8 1.5 1.8 1.7 1.6 1.6

Farm production Mil. lbs. 20 20 20 20 20 20 20 20 20 20 20 20 Total production Mil. lbs. 21,075 21,774 22,285 21,904 21,886 21,757 21,924 22,250 22,643 23,024 23,392 23,776 Imports Mil. lbs. 990 1,005 1,025 1,045 1,065 1,085 1,105 1,125 1,150 1,175 1,200 1,225 Total supply Mil. lbs. 22,559 23,293 23,870 23,514 23,516 23,407 23,594 23,940 24,358 24,764 25,157 25,566

Exports Mil. lbs. 2,995 3,027 3,180 3,304 3,403 3,493 3,582 3,684 3,805 3,956 4,090 4,205

Ending stocks Mil. lbs. 514 560 565 565 565 565 565 565 565 565 565 565

Total consumption Mil. lbs. 19,050 19,706 20,125 19,645 19,548 19,349 19,447 19,691 19,988 20,243 20,502 20,796 Per capita, carcass weight Pounds 63.5 65.1 65.9 63.7 62.8 61.6 61.4 61.6 62.0 62.3 62.5 62.9 Per capita, retail weight Pounds 49.3 50.5 51.1 49.4 48.8 47.8 47.6 47.8 48.1 48.3 48.5 48.8 Change from previous year Percent -1.3 2.5 1.2 -3.3 -1.4 -1.9 -0.4 0.4 0.6 0.4 0.4 0.6

Prices: Hogs, farm $/cwt 46.25 46.17 44.46 46.82 49.24 51.89 53.36 54.04 54.42 54.77 55.10 55.37 National base, live equivalent $/cwt 47.26 46.98 45.25 47.65 50.11 52.81 54.31 55.00 55.38 55.74 56.07 56.35 Deflated price $/cwt 23.76 22.06 21.32 21.78 22.34 22.97 23.05 22.78 22.37 21.97 21.56 21.14 Retail: Pork 1982-84=100 177.3 180.9 185.0 195.4 201.1 206.3 209.8 212.1 214.0 215.5 216.9 218.1 ERS retail pork $/lb. 2.81 2.87 2.92 3.08 3.17 3.26 3.31 3.35 3.38 3.40 3.42 3.44

Costs and returns, farrow to finish:

Variable expenses $/cwt 33.54 40.48 45.63 46.20 47.04 46.04 45.94 46.42 47.22 47.72 47.80 48.61 Fixed expenses $/cwt 7.72 7.81 5.23 5.73 5.62 5.49 5.44 5.45 5.48 5.51 5.39 5.40 Total cash expenses $/cwt 41.25 48.28 50.86 51.93 52.66 51.53 51.38 51.87 52.70 53.23 53.19 54.01 Returns above cash costs $/cwt 8.89 1.49 -5.61 -4.28 -2.55 1.28 2.92 3.14 2.68 2.50 2.89 2.34

Hog inventory, December 1, previous year 1,000 head 61,449 62,489 64,400 65,200 65,148 64,786 65,257 66,172 67,276 68,346 69,378 70,457

Table 23. Young chicken long-term projections Item Units 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Beginning stocks Mil. lbs. 924 745 675 750 750 750 750 750 750 750 750 750 Federally inspected slaught er Mil. lbs. 35,752 35,825 36,850 37,144 37,279 37,465 37,727 38,129 38,699 39,281 39,883 40,503 Change from previous year Percent 1.1 0.2 2.9 0.8 0.4 0.5 0.7 1.1 1.5 1.5 1.5 1.6

P roduction Mil. lbs. 35,369 35,442 36,456 36,772 36,906 37,090 37,350 37,748 38,312 38,888 39,484 40,098 Total supply Mil. lbs. 36,340 36,251 37,191 37,582 37,716 37,900 38,160 38,558 39,122 39,698 40,294 40,908

Change from previous year Percent 1.7 -0.2 2.6 1.1 0.4 0.5 0.7 1.0 1.5 1.5 1.5 1.5

E xports Mil. lbs. 5,205 5,468 5,565 5,600 5,664 5,762 5,842 5,932 6,017 6,106 6,196 6,265

E nding stocks Mil. lbs. 745 675 750 750 750 750 750 750 750 750 750 750

Consumption Mil. lbs. 30,390 30,108 30,876 31,232 31,302 31,388 31,568 31,876 32,355 32,842 33,348 33,893 Per capita, carcass weight Pounds 101.4 99.5 101.1 101.3 100.6 100.0 99.7 99.8 100.4 101.0 101.7 102.5 Per capita, retail weight Pounds 87.1 85.4 86.8 87.0 86.4 85.9 85.6 85.7 86.2 86.8 87.4 88.1 Change from previous year Percent 1.7 -1.9 1.6 0.2 -0.7 -0.6 -0.3 0.1 0.6 0.6 0.7 0.8

Prices:

Broilers, farm Cent s/lb. 38.6 39.4 39.0 38.0 38.6 39.6 40.3 41.2 42.0 42.9 43.7 44.8 12-city market price Cent s/lb. 64.4 76.1 75.3 73.4 74.5 76.4 77.8 79.6 81.1 82.8 84.4 86.5 Deflated wholesale price Cent s/lb. 31.9 36.7 35.4 33.5 33.2 33.2 33.0 33.0 32.7 32.6 32.4 32.5 Change from previous year Percent -12.0 14.9 -3.5 -5.4 -0.9 0.0 -0.6 -0.2 -0.7 -0.3 -0.6 0.1 Composite retail broiler price Cent s/lb. 157.1 163.2 167.1 176.4 179.3 184.3 188.0 191.6 194.1 195.3 195.9 198.0

Costs and returns:

Total costs Cent s/lb. 66.94 70.84 74.74 76.50 77.50 76.78 77.22 78.02 79.08 79.93 80.91 82.59 Net returns Cent s/lb. -2.54 5.26 0.56 -3.15 -3.00 -0.38 0.56 1.58 1.98 2.90 3.48 3.95

USDA Long-term Projections, February 2008 55

Table 25. Egg long-term projections Item Units 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Beginning stocks Mil. doz. 16 13 13 12 12 12 12 12 12 12 12 12 Production Mil. doz. 7,572 7,533 7,625 7,656 7,694 7,748 7,825 7,911 7,998 8,086 8,175 8,265 Change from previous year Percent 0.8 -0.5 1.2 0.4 0.5 0.7 1.0 1.1 1.1 1.1 1.1 1.1

Imports Mil. doz. 11 14 14 14 14 14 14 14 14 14 14 14 Total supply Mil. doz. 7,599 7,559 7,652 7,682 7,720 7,774 7,851 7,937 8,024 8,112 8,201 8,291

Change from previous year Percent 0.8 -0.5 1.2 0.4 0.5 0.7 1.0 1.1 1.1 1.1 1.1 1.1

Hatching use Mil. doz. 994 1,017 1,030 1,041 1,045 1,048 1,053 1,060 1,070 1,082 1,094 1,106 Exports Mil. doz. 202 242 230 233 236 239 242 245 248 251 254 257

E nding stocks Mil. doz. 13 13 12 12 12 12 12 12 12 12 12 12

Consumption Mil. doz. 6,390 6,287 6,380 6,396 6,427 6,475 6,544 6,620 6,694 6,767 6,841 6,916 Per capita Number 255.8 249.3 250.6 248.9 247.9 247.5 248.0 248.6 249.2 249.8 250.4 251.0 Change from previous year Percent 0.2 -2.5 0.5 -0.7 -0.4 -0.2 0.2 0.3 0.2 0.2 0.2 0.2

Prices:

Eggs, farm Cents/doz. 57.2 87.6 75.6 82.8 86.1 87.7 88.6 89.4 90.2 91.0 91.8 92.7 New York, Grade A large Cents/doz. 71.8 109.1 93.0 101.0 105.0 107.0 108.0 109.0 110.0 111.0 112.0 113.0 Deflated wholesale prices Cents/doz. 35.6 52.7 43.7 46.2 46.8 46.5 45.8 45.1 44.4 43.8 43.1 42.4 Retail, Grade A, large Cents/doz. 131 161 162 172 179 182 184 185 187 189 190 192 Retail: Eggs 1982-84=100 151.2 195.3 189.0 198.0 205.8 210.7 213.7 216.7 219.7 222.7 225.7 228.7

Costs and returns:

Total costs Cents/doz. 71.85 86.71 97.76 100.59 102.38 100.23 100.03 101.05 102.77 103.84 104.00 105.75 Net returns Cents/doz. -0.05 22.39 -4.76 0.41 2.62 6.77 7.97 7.95 7.23 7.16 8.00 7.25

Table 24. Turkey long-term projections Item Units 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Beginning stocks Mil. lbs. 206 218 245 275 275 275 275 275 275 275 275 275 Federally inspected slaught er Mil. lbs. 5,686 5,892 5,940 5,940 5,956 5,973 5,988 6,024 6,077 6,156 6,257 6,389 Change from previous year Percent 3.3 3.6 0.8 0.0 0.3 0.3 0.2 0.6 0.9 1.3 1.6 2.1

P roduction Mil. lbs. 5,612 5,815 5,862 5,863 5,879 5,896 5,910 5,946 5,998 6,076 6,176 6,306 Total supply Mil. lbs. 5,830 6,043 6,119 6,150 6,166 6,183 6,197 6,233 6,285 6,363 6,463 6,593

Change from previous year Percent 1.8 3.7 1.3 0.5 0.3 0.3 0.2 0.6 0.8 1.2 1.6 2.0

E xports Mil. lbs. 547 554 605 549 557 567 575 584 592 601 610 619

E nding stocks Mil. lbs. 218 245 275 275 275 275 275 275 275 275 275 275

Consumption Mil. lbs. 5,065 5,244 5,239 5,326 5,334 5,341 5,347 5,374 5,419 5,487 5,578 5,699 Per capita Pounds 16.9 17.3 17.1 17.3 17.1 17.0 16.9 16.8 16.8 16.9 17.0 17.2 Change from previous year Percent 1.3 2.6 -1.0 0.7 -0.8 -0.8 -0.8 -0.4 0.0 0.4 0.8 1.3

Prices:

Turkey, farm Cents/lb. 48.6 45.4 43.0 42.6 44.5 45.5 46.8 48.2 49.4 51.0 52.6 54.7 Hen turkey (wholesale) E ast Cents/lb. 77.0 82.4 78.0 77.3 80.8 82.6 85.0 87.4 89.7 92.6 95.4 99.2 Deflated hen turkey Cents/lb. 38.9 35.9 35.5 35.3 36.0 35.9 36.1 36.2 36.2 36.5 36.7 37.2 Retail frozen turkey Cents/lb. 110.8 115.0 112.0 112.6 114.3 114.0 114.2 114.4 114.3 114.7 115.0 116.2 Retail: Poultry 1982-84=100 182.0 191.4 194.5 203.0 206.3 210.8 214.3 217.6 219.9 221.2 221.8 224.2

Costs and returns:

Total costs Cents/lb. 62.40 68.48 72.63 73.87 74.90 74.36 74.62 75.27 76.15 76.79 77.48 77.40 Net returns Cents/lb. 14.60 13.92 5.37 3.42 5.88 8.26 10.36 12.16 13.58 15.79 17.94 21.79

56 USDA Long-term Projections, February 2008

Table 26. Dairy long-t erm projections Item Units 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Milk production and marketings: Number of cows 1,000 9,112 9,148 9,215 9,230 9,195 9, 160 9,130 9,105 9,075 9,040 9,015 8,985 Milk per cow Pounds 19,951 20,260 20,625 20,835 21,070 21, 335 21,670 21,895 22,185 22,480 22,845 23,090 Milk production Bil. lbs. 181.8 185.3 190.1 192.3 193.7 195.4 197.8 199.4 201.3 203.2 205.9 207.5 Farm use Bil. lbs. 1.1 1.1 1.1 1.0 1.0 1.0 1.0 1.0 0.9 0.9 0.9 0.9 Marketings Bil. lbs. 180.7 184.2 188.9 191.3 192.7 194.4 196.8 198.4 200.4 202.3 205.0 206.6

Supply and use, milkfat basis:

Beginning com mercial stocks Bil. lbs. 8.0 9.5 9.9 9.3 9.1 9.0 8.9 8.8 8.7 8.7 8.7 8.6 Marketings Bil. lbs. 180.7 184.2 188.9 191.3 192.7 194.4 196.8 198.4 200.4 202.3 205.0 206.6 Imports Bil. lbs. 5.0 4.8 4.7 4.9 5.0 5.2 5.3 5.4 5.5 5.6 5.7 5.8 Com mercial supply Bil. lbs. 193.6 198.5 203.6 205.5 206.8 208.6 211.0 212.6 214.6 216.6 219.4 221.0

Comm ercial use Bil. lbs. 184.1 188.6 194.3 196.4 197.8 199.7 202.2 203.9 205.9 207.9 210.8 212.4 Ending commercial stocks Bil. lbs. 9.5 9.9 9.3 9.1 9.0 8.9 8.8 8.7 8.7 8.7 8.6 8.6 CCC net removals Bil. lbs. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Supply and use, skim solids basis:

Beginning com mercial stocks Bil. lbs. 9.0 9.1 9.3 9.2 9.4 9.5 9.5 9.6 9.7 9.8 9.9 10.0 Marketings Bil. lbs. 180.7 184.2 188.9 191.3 192.7 194.4 196.8 198.4 200.4 202.3 205.0 206.6 Imports Bil. lbs. 4.8 4.4 4.4 5.2 5.3 5.4 5.6 5.7 5.9 6.0 6.1 6.2 Com mercial supply Bil. lbs. 194.4 197.7 202.7 205.7 207.4 209.3 211.9 213.7 216.0 218.1 221.0 222.8

Comm ercial use Bil. lbs. 184.5 188.4 193.5 196.3 197.9 199.8 202.3 204.0 206.2 208.2 211.0 212.7 Ending commercial stocks Bil. lbs. 9.1 9.3 9.2 9.4 9.5 9.5 9.6 9.7 9.8 9.9 10.0 10.1 CCC net removals Bil. lbs. 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Prices: All milk $/cwt 12.90 19.00 18.15 17.05 17.30 17.60 17.85 18.10 18.35 18.65 18.95 19.25 Retail, all dairy products 1982-84=100 181.4 194.8 200.0 199.5 203.5 208.0 212.5 217.0 221.5 226.0 231.0 236.0

USDA Long-term Projections, February 2008 57

U.S. Agricultural Sector Aggregate Indicators Farm Income, Food Prices and Expenditures, and U.S. Trade Value

Steady domestic and international economic growth supports gains in consumption, trade, and prices. In addition, large increases in corn-based ethanol production affect production, use, and prices of farm commodities throughout the sector. These factors combine to result in higher market prices and increasing cash receipts. Rising production expenses and lower government payments offset some of the gains in cash receipts and other sources of farm income, although net farm income remains strong and reaches record levels in the second half of the projections. Similarly, U.S. agricultural export values are robust through the projections and set new records beyond 2012. On average, consumer food prices are projected to rise more slowly than the general rate of inflation over the next decade, although continuing adjustments due to higher energy- related costs and agricultural commodity prices push food prices up faster in some years.

0

10

20

30

40

50

60

70

80

90

100

1985 1990 1995 2000 2005 2010 2015

U.S. net farm income

Billion dollars

Strong domestic use and export demand push U.S. net farm income from a projected record of about $90 billion in 2008 to almost $100 billion by 2017.

• Expansion of corn-based ethanol production is a major factor underlying large increases in cash receipts in 2006-08. Continuing demand strength in domestic and international markets result in further gains in cash receipts through the projections. Lower government payments, due to higher commodity prices, and rising farm production expenses offset some of the gains in cash receipts, although net farm income remains historically high.

58 USDA Long-term Projections, February 2008

0

5

10

15

20

25

1985 1990 1995 2000 2005 2010 2015

Direct government payments

Total direct government payments

Billion dollars

Direct government payments to farmers are projected to fall from $13 billion in 2008 to an average of less than $10 billion annually in 2009 to 2017, largely due to higher commodity prices and correspondingly lower price-dependent program benefits.

• Strong domestic and international demand for crops, including corn for ethanol production, causes projected market prices for crops to be at levels that result in very low government payments for price-sensitive income-support programs. For example, even with stochastic considerations included here to capture potential variation in farm program benefits due to shocks to production yields, payments for marketing loan benefits and counter-cyclical payments for feed grains are minimal, totaling less than $500 million over calendar years 2008-17 for the projections scenario in this report.

• In contrast, with higher crop prices, use of land for production is more valuable, so rental

rates for land in the Conservation Reserve Program (CRP) rise and push overall annual CRP payments to close to $3 billion toward the end of the projections. As a result, fixed direct payments under the 2002 Farm Act and conservation payments account for a larger share of total direct government payments in the farm income accounts, over 80 percent in 2015-17.

• With lower government payments, the agriculture sector relies on the market for more of its

income, and the share of income provided by government payments falls. Government payments, which represented more than 8 percent of gross cash income in 2005, account for less than 3 percent during most of the projection period. Conversely, cash receipts plus farm-related income rises to over 97 percent of gross cash income.

USDA Long-term Projections, February 2008 59

0

50

100

150

200

250

300

1985 1990 1995 2000 2005 2010 2015

Other Manufactured Farm-origin

U.S. farm production expenses

Billion dollars

Total production expenses increase at less than the general inflation rate from 2009-17, following the adjustments to higher energy costs that started in 2004. These expenses are divided into three categories in the chart above: farm-origin (seed, feed, and feeder livestock), manufactured (fuel, fertilizer, pesticides, and electricity), and other (labor, interest, net rent to nonoperator landlords, and other expenses).

• The largest percentage increase is for “other” expenses, reflecting increases in labor expenses, interest costs, and net rent to nonoperator landlords. Labor expenses rise as sector output increases and wage rates rise. Projected increases in interest costs reflect higher interest rates, as well as increased debt facilitated by higher income. Increases for net rent reflect higher cash receipts and profitability as well as larger sector output.

• Projected manufactured-input expenses reflect high oil prices and larger crop production.

After increases in 2004-08 that were mostly due to rising oil prices, these expenses increase moderately through 2013 during a period of relatively stable oil prices. Then as oil prices rise faster than the general inflation rate over the remainder of the projection period, production expenses for manufactured inputs rise more rapidly.

• Farm-origin expenses rise less than the general inflation rate. Feed expenses, which have

risen sharply in recent years with higher corn prices, are relatively flat in the projections period as corn prices stabilize. Seed expenses increase only moderately as acreage declines from 2008 highs and then stabilizes. Expenses for purchased livestock also increase moderately as the livestock sector continues to adjust to higher feed costs.

• Cash operating margins remain relatively stable over the projections period at about

73 percent as cash receipts and gross cash incomes rise at close to the same pace as cash expenses.

60 USDA Long-term Projections, February 2008

0

2

4

6

1985 1990 1995 2000 2005 2010 2015

U.S. food inflation

Percent change

Consumer Price Index (CPI), all items

Food CPI

Adjustments in retail prices due to higher energy and agricultural commodity prices are projected to continue for the next 2 years and lead to food price increases somewhat larger than general inflation in 2008 and 2009. For the rest of the projections period, however, retail food prices increase less than the general inflation rate.

• Relatively large price increases are expected in 2008 for fats and oils and for cereals and bakery products, reflecting higher prices for vegetable oils and wheat. Consumer prices for red meats, poultry, and eggs exceed the general inflation rate in 2009 as the livestock sector adjusts to higher feed costs due to the expansion in corn-based ethanol production. Smaller increases in meat prices are then projected as production growth resumes.

• Prices for food away from home reflect the overall inflation rate as well as some effect of price movements for retail meat and poultry. Income growth supports continuing gains in prices for food consumption away from home, although competition in the fast-food and foodservice industries tend to moderate these price increases.

0

200

400

600

800

1990 1995 2000 2005 2010 2015

U.S. food expenditures

Food at home

Food away from home

Billion dollars

• Expenditures for meals prepared away from home account for a growing share of food spending, reaching about 52 percent of total food expenditures by 2017.

USDA Long-term Projections, February 2008 61

0

20

40

60

80

100

1985 1990 1995 2000 2005 2010 2015

High value Bulk

U.S. agricultural export value: Bulk and high value 1/

1/ Bulk commodities include wheat, rice, feed grains, soybeans, cotton, and tobacco. High-value products include semi-processed and processed grains and oilseeds, animals and animal products, horticultural products, and sugar and tropical products.

Billion dollars

The value of U.S. agricultural exports rises in the projections due to increases in both export volumes and prices. Strong domestic economic growth and consumer demand boost agricultural imports.

• The value of U.S. agricultural exports is projected to grow from $82 billion in fiscal year 2007 to more than $103 billion in 2017. The lower value U.S. dollar is an important factor underlying recent export gains and the projected growth. In addition, steady world economic growth, particularly in developing countries, provides a foundation for gains in trade and U.S. agricultural exports. Higher commodity prices due to expansion of global biofuel demand also contribute to the gains in export values.

• Recent increases in bulk commodity prices have strengthened bulk export values, pushing

the share of exports accounted for by high-value products (HVP) down through fiscal year 2008. In the longer run, however, HVP export values grow in importance again, representing about 65 percent of the value of U.S. exports by the end of the projection period. Much of the growth in HVP exports is for animal products and horticultural products.

• U.S. agricultural import values rise to almost $105 billion in 2017, boosted by gains in

consumer income and demand for a large variety of foods. Strong growth in horticultural imports is assumed to continue, contributing over half of the overall agricultural import increase.

• Overall, the U.S. agricultural trade surplus is projected at $15.5 billion in fiscal year 2008,

largely due to recent gains in bulk commodity prices and bulk export values. The agricultural trade balance declines from this level over the projections period, as bulk exports remain relatively flat while imports continue steady gains.

62 USDA Long-term Projections, February 2008

Table 27. Farm receipt s, expenses, and income, long-term projections 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

B illion dollars Cash receipts: Crops 120.0 143.2 161.5 162.1 163. 2 164.1 166.0 169.3 172.9 176.4 180.1 184.2 Livestock and products 119.3 141.0 138.3 137.6 139. 1 142.0 143.8 145.6 148.4 150.8 152.9 154.9 All commodities 239.3 284.2 299.8 299.8 302. 3 306.1 309.9 314.9 321.3 327.2 333.0 339.1 Farm-related income 17.5 18.8 19.3 19.8 20. 3 20.7 21.2 21.7 22.2 22.7 23.2 23.7 Government payments 15.8 11.7 13.1 9.7 9. 7 9.8 10.0 10.2 10.3 9.6 9.7 9.7 Gross cash income 272.5 314.6 332.2 329.3 332. 2 336.6 341.1 346.7 353.8 359.5 366.0 372.5

Cash expenses 204.7 227.2 238.1 241.4 243. 8 245.2 247.5 251.8 256.4 261.2 265.7 270.9 Net cash income 67.9 87.4 94.1 87.9 88. 4 91.5 93.5 95.0 97.4 98.3 100.3 101.7

Value of inventory change -1.6 5.8 0.4 1.5 1. 6 0.6 0.5 0.8 0.9 1.2 1.1 1.4 Noncash income 20.5 23.8 24.7 25.8 26. 3 26.8 27.4 27.9 28.4 29.0 29.5 30.1 Gross farm income 291.5 344.2 357.3 356.5 360. 1 364.1 369.0 375.4 383.2 389.7 396.6 404.0

Noncash expenses 18.2 18.3 18.7 19.5 19. 8 20.1 20.3 20.5 20.7 20.9 21.1 21.3 Operator dwelling expenses 9.6 10.1 11.2 11.4 11. 5 11.6 11.8 11.9 12.1 12.2 12.3 12.5 Total production expenses 232.5 255.5 268.1 272.3 275. 1 276.9 279.6 284.2 289.2 294.3 299.1 304.7 Net f arm income 59.0 88.6 89.2 84.2 85. 0 87.2 89.4 91.2 94.0 95.3 97.5 99.4

USDA Long-term Projections, February 2008 63

Table 28. Consumer food price indexes and food expenditures, long-term projections CPI category 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Consumer price indexes: All food 195.2 202.9 209.4 215.5 220.8 225.7 230.6 235.8 241.5 246.8 252.2 257.7

Food away from home 199.4 206.7 212.9 219.7 225.4 231.0 236.5 242.4 248.7 254.7 260.8 267.1

Food at home 193.1 201.2 207.9 213.7 218.7 223.3 227.9 232.7 238.0 242.9 247.9 253.0

Meats 188.8 195.0 198.9 210.3 215.6 218.6 221.4 224.3 228.8 231.9 233.9 236.1 Beef and veal 202.1 211.1 216.0 230.5 236.1 237.7 239.8 243.1 249.8 253.9 255.8 258.3 Pork 177.3 180.9 185.0 195.4 201.1 206.3 209.8 212.1 214.0 215.5 216.9 218.1 Other meats 180.7 184.8 186.0 191.0 194.8 198.5 201.7 204.7 207.6 210.5 213.3 216.1 Poultry 182.0 191.4 194.5 203.0 206.3 210.8 214.3 217.6 219.9 221.2 221.8 224.2 Fish and seafood 209.5 219.1 226.8 233.6 240.6 247.8 255.2 262.9 270.8 278.9 287.3 295.9 Eggs 151.2 195.3 189.0 198.0 205.8 210.7 213.7 216.7 219.7 222.7 225.7 228.7 Dairy products 181.4 194.8 200.0 199.5 203.5 208.0 212.5 217.0 221.5 226.0 231.0 236.0 Fats and oils 168.0 172.9 182.4 186.6 191.0 195.6 200.3 205.1 210.0 214.9 220.1 225.4 Fruits and veget ables 252.9 262.6 271.5 276.6 282.5 289.2 296.0 303.0 310.4 318.0 325.8 333.8 Sugar and sweets 171.5 176.8 181.2 185.3 188.6 192.5 196.3 200.2 204.3 208.3 212.5 216.7 Cereals and bakery products 212.8 222.1 235.4 239.7 243.9 248.6 254.0 259.9 266.1 272.3 278.8 285.5 Nonalcoholic beverages 147.4 153.4 159.5 165.1 170.1 174.4 178.8 183.3 187.9 192.6 197.4 202.3 Other foods 185.0 188.2 193.9 199.2 204.0 208.7 213.5 218.4 223.5 228.6 233.9 239.3

Food expenditures: Billion dollars

All food 1,082.5 1,137.7 1,190.2 1,241. 9 1,290.7 1,339.2 1,388.6 1, 440.9 1,497.0 1,553.0 1,610.5 1,671.1 Food at home 553.4 583.6 608.3 629. 8 650.4 670.2 690.3 711.2 733.8 756.1 778.6 802.4 Food away from home 529.1 554.1 581.9 612. 1 640.3 669.0 698.3 729.7 763.2 796.9 831.9 868.7

1982-84=100

Table 29. Changes in consumer food prices, long-term projections CPI category 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

All food 2.4 4.0 3.2 2.9 2.5 2.2 2.2 2.3 2.4 2.2 2.2 2.2

Food away from home 3.1 3.6 3.0 3.2 2.6 2.5 2.4 2.5 2.6 2.4 2.4 2.4

Food at home 1.7 4.2 3.3 2.8 2.3 2.1 2.1 2.1 2.3 2.1 2.1 2.1

Meats 0.7 3.3 2.0 5.7 2.5 1.4 1.3 1.3 2.0 1.4 0.9 0.9 Beef and veal 0.8 4.4 2.3 6.7 2.4 0.7 0.9 1.4 2.8 1.6 0.7 1.0 Pork -0.2 2.0 2.3 5.6 2.9 2.6 1.7 1.1 0.9 0.7 0.6 0.6 Other meats 1.8 2.3 0.6 2.7 2.0 1.9 1.6 1.5 1.4 1.4 1.3 1.3 Poultry -1.8 5.1 1.6 4.4 1.6 2.2 1.7 1.5 1.1 0.6 0.3 1.1 Fish and seafood 4.7 4.6 3.5 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 Eggs 4.9 29.2 -3.2 4.8 3.9 2.4 1.4 1.4 1.4 1.4 1.3 1.3 Dairy products -0.5 7.4 2.7 -0.2 2.0 2.2 2.2 2.1 2.1 2.0 2.2 2.2 Fats and oils 0.2 2.9 5.5 2.3 2.4 2.4 2.4 2.4 2.4 2.3 2.4 2.4 Fruits and vegetables 4.8 3.8 3.4 1.9 2.1 2.4 2.4 2.4 2.4 2.4 2.5 2.5 Sugar and sweets 3.8 3.1 2.5 2.3 1.8 2.1 2.0 2.0 2.0 2.0 2.0 2.0 Cereals and bakery product s 1.8 4.4 6.0 1.8 1.8 1.9 2.2 2.3 2.4 2.3 2.4 2.4 Nonalcoholic beverages 2.1 4.1 4.0 3.5 3.0 2.5 2.5 2.5 2.5 2.5 2.5 2.5 Other foods 1.4 1.8 3.0 2.7 2.4 2.3 2.3 2.3 2.3 2.3 2.3 2.3

Percent

64 USDA Long-term Projections, February 2008

Table 30. Summ ary of U.S. agricultural trade long-term projections, fiscal years 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billion dollars Agricultural exports (value): Livestock, dairy, and poultry 13.4 16.3 17.1 16.9 17.6 18.4 19.1 19.8 20.7 21.5 22.3 23.2 Livestock, poultry, and products 11.6 13.9 14.5 14.7 15.4 16.1 16.8 17.5 18.3 19.1 19.8 20.7 Dairy products 1.8 2.5 2.6 2.2 2.2 2.3 2.3 2.3 2.4 2.4 2.5 2.5 Grain and feeds 18.3 24.2 27.5 24.9 24.9 24.0 23.8 24.2 24.9 25.4 26.2 27.1 Coarse grains 6.8 9.6 11.7 11.0 11.2 10.5 10.2 10.3 10.7 11.0 11.3 11.9 Oilseeds and products 10.6 13.7 16.3 14.8 14.9 14.5 14.6 14.6 14.8 14.9 15.1 15.3 Soybeans and products 8.2 11.0 13.3 11.8 11.9 11.4 11.5 11.5 11.5 11.5 11.6 11.7 Horticultural products 16.7 17.9 18.6 19.1 19.6 20.2 20.7 21.3 21.9 22.5 23.2 23.8 Fruits and vegetables , fresh 4.5 4.8 4.9 5.0 5.1 5.3 5.4 5.5 5.7 5.8 6.0 6.1 Fruits and vegetables , processed 3.9 4.4 4.5 4.6 4.7 4.8 4.9 5.0 5.1 5.2 5.3 5.4 Cotton and linters 4.7 4.3 5.8 6.6 6.4 6.5 6.6 6.9 6.9 7.2 7.3 7.6 Other exports 5.0 5.6 5.8 5.4 5.5 5.7 5.8 6.0 6.1 6.2 6.3 6.5

Total agricultural exports 68.6 81.9 91.0 87.8 88.9 89.2 90.7 92.8 95.3 97.7 100.3 103.4

Bulk commodity exports 24.5 31.4 38.0 35.7 34.6 33.4 32.9 33.2 33.9 34.4 35.2 36.3 High-value product exports 44.1 50.6 53.0 52.1 54.3 55.8 57.8 59.6 61.4 63.3 65.1 67.1 High-value product share 64.4% 61.7% 58.2% 59.3% 61.1% 62.6% 63.7% 64.2% 64.5% 64.8% 64.9% 64.9%

Million metric tons Agricultural exports (volume):

Bulk commodity exports 120.7 124.7 130.8 116.9 115.2 114.4 114.2 115.2 117.0 118.4 121.4 124.3

Billion dollars

Livestock, dairy, and poultry 11.5 12.0 12.3 13.2 13.6 14.0 14.3 14.7 15.2 15.7 16.0 16.5 Livestock and meats 8.5 8.9 9.2 9.9 10.2 10.5 10.8 11.0 11.4 11.7 12.0 12.3 Dairy products 2.6 2.7 2.7 2.8 2.9 3.0 3.0 3.1 3.2 3.3 3.4 3.5 Grain and feeds 4.9 6.0 6.6 6.9 7.1 7.3 7.6 7.9 8.2 8.6 9.0 9.4 Grain products 3.4 3.9 4.2 4.5 4.7 4.9 5.2 5.5 5.7 6.0 6.3 6.6 Oilseeds and products 3.5 4.0 4.8 5.1 5.3 5.5 5.7 5.9 6.1 6.3 6.5 6.8 Vegetable oils 2.4 2.8 3.5 3.7 3.9 4.0 4.1 4.3 4.4 4.6 4.8 4.9 Horticultural products 29.1 32.4 35.2 37.1 38.7 40.1 41.6 43.2 44.8 46.4 48.2 50.0 Fruits and vegetables , fresh 8.7 9.6 10.4 11.0 11.4 11.9 12.3 12.8 13.2 13.7 14.2 14.8 Fruits and vegetables , processed 5.4 6.6 7.4 7.7 7.8 7.9 8.0 8.1 8.2 8.4 8.5 8.6 Wine and beer 7.4 8.2 8.8 9.3 9.7 10.1 10.5 11.0 11.4 11.9 12.4 12.9 Sugar and tropical products 13.6 14.1 15.0 15.6 16.2 16.7 17.2 17.8 18.4 19.0 19.6 20.3 Sugar and related products 3.3 2.8 3.1 3.2 3.4 3.5 3.6 3.7 3.8 3.9 4.0 4.1 Cocoa, coffee, and products 5.8 6.2 6.5 6.8 7.0 7.2 7.5 7.7 8.0 8.2 8.5 8.8 Other imports 1.4 1.5 1.6 1.5 1.5 1.6 1.7 1.6 1.7 1.7 1.7 1.6

Total agricultural imports 64.0 70.0 75.5 79.4 82.4 85.2 88.1 91.1 94.4 97.7 101.0 104.6

Net agricultural trade balance 4.6 11.9 15.5 8.3 6.5 4.0 2.6 1.7 0.9 0.0 -0.7 -1.2

Sources: U.S. Department of Agriculture and Bureau of Census, U.S. Department of Commerce. Notes: The projections were completed in November 2007 based on policy decisions and other information known at that time. For updates of the nearby year forecasts, see USDA's Outlook for U.S. Agricultural Trade report, published in February, May, August, and November. Other exports includes tobacco, seeds, sugar and tropical products, and beverages and preparations. Bulk commodity exports covers wheat, rice, feed grains, soybeans, cotton, and tobac co. High-value product (HVP) exports is calculated as total exports less the bulk commodities. HVP's inc lude semiprocessed and processed grains and oilseeds, animals and animal products, horticultural products, and sugar and tropical products. Other imports include cotton, tobacco, and planting seeds.

Agricultural imports (value):

USDA Long-term Projections, February 2008 65

Agricultural Trade

World consumption of many grain, oilseed, and meats has exceeded production in the past several years. As a result, global stocks have dropped sharply—to record lows in some cases—and prices have risen. Tight market conditions are projected to persist for many commodities over most of the coming decade, keeping agricultural commodity prices high.

Robust global economic growth provides a foundation for gains in world demand for agricultural products. Rapid expansion of ethanol and biodiesel production in some countries also adds to the growth in global agricultural demand.

The growing economies of developing countries are the main source of growth in world agricultural demand and trade. Food consumption and feed use are particularly responsive to income growth in those countries, with movement away from staple foods and increased diversification of diets. The import demand of developing countries is further reinforced by population growth rates that remain nearly twice that of developed countries.

International trade in animal products, however, remains heavily dependent on demand from developed countries and from the market access achieved under existing trade agreements. Strong policy support for domestically produced meat is expected to motivate growth in feed grain imports, especially in regions where limited land availability or agroclimatic conditions preclude expanding domestic crop production, such as North Africa, the Middle East, and East and Southeast Asia.

Traditional exporters of a wide range of agricultural commodities, such as Argentina, Australia, Canada, the European Union (EU-27), and the United States, remain important in the coming decade. But countries that are making significant investments in their agricultural sectors, including Brazil, Russia, Ukraine, and Kazakhstan, are expected to have an increasing presence in export markets for basic agricultural commodities.

World agricultural production rises in response to high prices and technology enhancements. However, limited ability to expand planted area in many countries and higher input costs, particularly for energy intensive inputs such as fuel and fertilizer, constrain production growth and raise uncertainties about future supply response.

General International Assumptions

Trade projections to 2017 are founded on assumptions concerning trends in foreign area, yields, and use and on the assumption that countries comply with existing bilateral and multilateral agreements affecting agriculture and agricultural trade. The projections incorporate the effects of trade agreements and domestic policy reforms in place or signed by November 2007. Domestic agricultural and trade policies in individual foreign countries are assumed to continue to evolve along their current paths, based on the consensus judgment of USDA’s regional and commodity analysts. In particular, economic and trade reforms underway in many developing countries are assumed to continue. Similarly, the development and use of technology and changes in consumer preferences are assumed to continue evolving based on past performance and analysts’ judgments regarding future developments.

66 USDA Long-term Projections, February 2008

50

75

100

125

150

175

200

1990 1995 2000 2005 2010 2015

Global trade: Wheat, coarse grains, and soybeans and soybean products

Million metric tons

Soybeans and soybean products 1/

Coarse grains

1/ Soybeans and soybean meal in soybean-equivalent units.

Wheat

Global trade in soybeans and soybean products has risen rapidly since the early 1990s, and has surpassed not only wheat—the traditional leader in agricultural commodity trade—but also total coarse grains (corn, barley, sorghum, rye, oats, millet, and mixed grains). Continued strong growth in global demand for vegetable oil and protein meal, particularly in China, is expected to maintain soybean and soybean-product trade well above wheat and coarse grains trade throughout the next decade.

• Wheat, coarse grains, and oilseeds (including soybeans) compete with each other and with other crops for limited cropland. Higher prices for vegetable oils, partially the result of increased demand for biodiesel, are bringing previously uncropped land in Brazil and Indonesia into soybean and palm oil production.

• In the projections, the growth in total area planted to all crops rises less than a half-percent per year in most countries. Area expansion occurs more rapidly in countries with a reserve of available land and policies that enable farmers to respond to higher projected world prices. Such countries include Brazil, Argentina, other South American countries, some Eastern European countries, and Ukraine. About two-thirds of the growth in global production is derived from rising yields. The growth rate in crop yields has slowed somewhat during the last several decades and is projected to continue to do so.

• The impact of slowing growth in total crop production is partially offset by slowing growth in world population. Nonetheless, population is a significant factor driving overall growth in demand for agricultural products. Additionally, rising per capita income in many countries generates growth in demand for vegetable oils, livestock products, and horticultural products.

• In the coming decade, overall gains in global grain trade come from a broad range of countries, particularly from countries in Africa and the Middle East. Also, China exports less grain and imports more.

USDA Long-term Projections, February 2008 67

Global Demand for Biofuel Feedstocks Investments in biofuel production capacity are occurring in many countries. Although the main feedstocks used are corn and sugarcane for ethanol and rapeseed and soybean oils for biodiesel, other feedstocks are also being used, such as barley, wheat, rye, wine, and cassava for ethanol production and a variety of other vegetable oils, recycled oils, and fats from the food industry for biodiesel. Assumptions Used for the USDA Projections Biofuels production and the demand for biofuels feedstocks are projected to continue growing in a number of countries. The projections are based on a combination of historical biofuel production data, USDA interpretation of statements by foreign governments about their plans for biofuel development, and other information about potential investments in biofuel production capacity. Country Assumptions EU: The EU has a “target” to obtain 5.75 percent of transportation fuel from biofuels by 2010. Additionally, EU policy has provided a per-acre subsidy for the production of energy crops, although the subsidy level has been reduced recently. Individual member states also offer tax credits on biofuels. The projections assume that about two-thirds of the EU target is met by 2010 and that, with increasing total fuel use, the 2010 target is still not quite reached by 2017. The projections further assume that biodiesel accounts for two-thirds of total biofuels and ethanol accounts for the other third. Rapeseed oil is the feedstock for nearly all EU biodiesel production. In the EU, area planted to rapeseed and oilseed crushing capacity are both projected to increase sharply. In addition, the EU increases rapeseed oil imports from Russia and Ukraine. It also imports some palm oil from Southeast Asia, as well as some biodiesel (processed from palm oil) from Southeast Asia. Some biodiesel is also imported from the United States. Since the 5.75 percent “target” was set, the EU has established a “mandate” that biofuels account for 10 percent of transportation fuel use by 2020. USDA’s assumptions imply progress toward this mandate would be behind schedule throughout the projections period. Brazil: Sugarcane is the feedstock for nearly all of Brazil’s ethanol production. In southern Brazil, some land has already been shifted from grain and oilseeds production to sugarcane. The projections assume this trend continues, but at a slower pace. As a result of implementation of a domestic fuel mandate in 2008, biodiesel production is assumed to increase sharply during the next several years before leveling off. Much of the new capacity will be in the soybean production areas in the Central-West region of the country, which will reduce petroleum-based diesel fuel that has to be trucked to the interior. Canada: Canadian biodiesel production is projected to more than double between 2007 and 2017. Most of the increased production will be from rapeseed produced and processed in the Prairie Provinces. Ethanol production is projected to continue expanding rapidly also. The amount of corn and wheat used for feedstocks is expected to rise sharply during the next half decade.

--Continued

68 USDA Long-term Projections, February 2008

Global Demand for Biofuel Feedstocks (Continued) Argentina: The production of biodiesel in Argentina is assumed to more than double between 2007 and 2017. Argentina has a system of differential export taxes that has lower tax rates for biofuels exports than the tax rate on exports of feedstocks such as corn or soybean oil. In turn, the export tax on soybean oil is lower than the tax on soybean exports. For biodiesel, this provides an incentive for further investments in Argentina’s already large crushing industry. Argentina is projected to import some soybeans from other South American countries to keep its crushing facilities running at near full capacity. Other Europe and the former Soviet Union: This region is assumed to respond to the EU’s expanding demand for biodiesel by rapidly increasing rapeseed production. In Russia and Ukraine, rapeseed production more than triples in the projections. Much of the production gains are destined for export to the EU, either as rapeseed oil or as rapeseed for crushing in the EU. China: In 2007, approximately 3.5 million tons of corn were used to produce fuel ethanol in China. About five times as much corn was used to produce ethanol for industrial and beverage uses. Because of its food security policy, the government is trying to slow the growth in overall corn-based ethanol production, attempting to focus on the use of nongrain feedstocks such as sweet potatoes and cassava. Consequently, increases in corn-based ethanol production for industrial and beverage uses are expected to slow and no further growth is projected for corn-based fuel ethanol. Malaysia and Indonesia: Growing worldwide demand for vegetable oils for human consumption and biodiesel production stimulates further expansion of the area planted to oil palm. The projections assume moderate growth in Malaysian and Indonesian palm oil production and exports for biodiesel use.

USDA Long-term Projections, February 2008 69

0

25

50

75

100

125

150

1990 1995 2000 2005 2010 2015

Other 1/

Africa & M. East

China

NAFTA

Latin America

FSU & OE 2/

European Union 3/

East Asia

Global wheat imports

Million metric tons

1/ Predominantly South and Southeast Asia. 2/ Former Soviet Union and other Europe; prior to 1999, includes Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia. 3/ EU-27 excludes intra-trade after 2002, EU-15 intra-trade before 2003, Slovenia before 1992.

Growth in wheat imports is concentrated in those developing countries where robust growth in income and population underpins increases in demand. Important growth markets include Sub- Saharan Africa, Egypt, Pakistan, Algeria, Indonesia, the Philippines, and Brazil. World wheat trade (including flour) expands by more than 26 million tons (23 percent) between 2008 and 2017 to 137 million tons.

• Egypt maintains its position as the world’s largest importing country, as imports climb slowly to nearly 9 million tons. Imports by Brazil, another large importer, are projected to exceed 8 million tons. Brazil’s climate generally does not favor wheat, and in some key wheat-producing states, winter corn is expected to have better returns than wheat.

• Imports by developing countries in Sub-Saharan Africa, North Africa, and the Middle East

rise nearly 12 million tons and account for 45 percent of the total increase in world wheat trade. In most developing countries, little change in per capita wheat consumption is expected but imports expand modestly because of population growth and limited potential to expand production.

• Changing consumption patterns will boost wheat imports by some major importing

countries. In Indonesia, strong economic growth and diversification of diets are projected to increase per capita wheat consumption. Mexican consumers are projected to continue substituting wheat for corn in their diets as incomes rise.

• Lower wheat-to-corn price ratios during most of the projection period enable wheat to

compete effectively with corn for feed use in a number of countries. Europe is expected to continue to account for the largest share of global wheat feeding.

• China has been a small net exporter of wheat in recent years, but production constraints

cause it to become a small net importer toward the end of the projections.

70 USDA Long-term Projections, February 2008

0

25

50

75

100

125

150

1990 1995 2000 2005 2010 2015

Other

FSU & OE 1/

European Union 2/

Australia

Argentina

Canada

United States

Global wheat exports

Million metric tons

1/ Former Soviet Union and other Europe; prior to 1999, includes Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia. 2/ EU-27 excludes intra-trade after 2002, EU-15 intra-trade before 2003, Slovenia before 1992.

The top five wheat-exporting nations (the United States, Australia, the EU, Argentina, and Canada) account for 70 percent of world trade in 2008-17. This is down from 89 percent in 1997/98, mostly due to increased exports from the Black Sea area. U.S. wheat exports are projected to account for less than 19 percent of global wheat trade at the end of the projection period, down from 25 percent in the past 5 years. The global stocks-to-use ratio has declined sharply during the last half decade to the lowest level on record. Despite a significant rebound in global production, low stocks and relatively high prices are projected to persist for most of the next decade.

• Shares of the world wheat market held by Canada and the United States decline slightly, while shares increase for the EU, Ukraine, Russia, Australia, and Argentina.

• In Canada, increased demand for vegetable oils, especially rapeseed oil for biodiesel production, and increasing demand for barley are expected to reduce wheat area, and limit any growth in wheat exports.

• Ukraine, Russia, and Kazakhstan have become significant wheat exporters in recent years. Low costs of production and new investment in their agricultural sectors have enabled their combined world market share to climb to about 20 percent in the last 2 years. Exports from Ukraine and Russia are projected to continue gaining market share, more than offsetting a slight decline in the share held by Kazakhstan. However, because of the region’s highly variable weather and yields, year-to-year volatility in production and trade can be expected. Also, continued real appreciation of these countries’ currencies, caused mainly by strong foreign exchange earnings and domestic inflation, could moderate the rise in exports.

• Wheat exports by Turkey and other smaller exporters change little or trend slowly downward during the projection period. Although India has exported some wheat in recent years, exports are expected to be minimal and imports to increase as domestic demand outpaces production and stocks remain relatively low.

USDA Long-term Projections, February 2008 71

0

20

40

60

80

100

120

140

1990 1995 2000 2005 2010 2015

Other 1/ Corn Barley Sorghum

Global coarse grain trade, by type

Million metric tons

1/ Rye, oats, millet, and mixed grains.

Growth in coarse grain trade is strongly linked to expansion of livestock production in regions unable to meet their own feed needs. Key growth markets include China, Mexico, North Africa, the Middle East, and Southeast Asia. Japan and South Korea are large but mature import markets for coarse grains.

• Corn is the dominant feed grain traded in international markets. Corn accounts for an average of 79 percent of all coarse grain trade through the projection period, followed by barley (15 percent) and sorghum (4 percent).

• Commercialization of livestock feeding has been a driving force behind the growing

dominance of corn in international feed grain markets. Hogs and ruminants, such as cattle and sheep, are capable of digesting a broad range of feedstuffs, making demand relatively price-sensitive across alternate feed sources. However, as pork and poultry production become increasingly commercialized, higher quality feeds are used, boosting the demand for corn and soybean meal.

• Mexico’s composition of coarse grain imports is expected to change during the early part

of the projection period. Under the North American Free Trade Agreement (NAFTA), Mexico’s over-quota tariff on U.S. and Canadian corn ended on January 1, 2008. Consequently, Mexico’s grain imports shift more to corn rather than sorghum. Also, after 2008/09, Mexico’s imports of kibbled and cracked corn (processed corn that has already been tariff free) are projected to be increasingly replaced by whole-grain corn. Mexico’s corn imports continue to rise through the rest of the projections, while sorghum imports resume growth after 2009/10.

72 USDA Long-term Projections, February 2008

World coarse grain trade expands nearly 21 million tons (18 percent) from 2008 to 2017. About two-thirds of global coarse grain production is used as animal feed. Industrial uses, such as starch, ethanol, and malt production, are smaller but growing. Food use of coarse grains, concentrated in parts of Latin America, Africa, and Asia, is projected to continue declining.

• World prices for grains have risen during the last several years as global stocks of grain declined sharply. Although the higher prices are projected to stimulate grain production, neither stocks-to-use ratios nor prices return to levels common during the last 3 decades.

• Steady longrun growth in the livestock sectors of developing countries in Asia, Latin America, North Africa, and the Middle East is projected to account for most of the growth in world coarse grain imports during the next decade.

• Mexico’s corn imports are projected to rise from 8.8 million tons in 2006/07 to 15 million tons in 2017. Imports will be stimulated by rising poultry production and the elimination of Mexico’s over-quota tariff on U.S. and Canadian corn on January 1, 2008. Some corn imports will substitute for imports of kibbled corn and sorghum, which already had tariff-free status.

• North Africa and the Middle East experience continued growth in import demand for grain and protein meals through 2017 as rising populations and increasing incomes sustain strong demand growth for domestically-produced animal products. In Egypt, government policy has shifted toward allowing more poultry meat imports. Still, poultry production is projected to rise, boosting corn imports more than 1 million tons.

• In Japan, South Korea, and Taiwan, environmental regulations constrain meat production, which results in increasing meat imports and no growth in coarse grain imports.

• The EU’s corn and sorghum imports decline in 2008 as production returns to normal levels, but in subsequent years corn imports from Other Europe, particularly Serbia, are expected to increase.

• Countries in Southeast Asia raise corn imports more than 1.5 million tons (30 percent) during the projection period as their increased demand for livestock products exceeds their capacity to grow more feed grains.

0

30

60

90

120

150

1990 1995 2000 2005 2010 2015

Other

EU-27 1/

Africa & ME

China & HK

Mexico

Latin America

FSU & OE 2/

East Asia

Global coarse grain imports

Million metric tons

1/ EU-27 excludes intra-trade after 2002, EU-15 intra-trade before 2003, Slovenia before 1992. 2/ Former Soviet Union and other Europe; prior to 1999, includes Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia.

USDA Long-term Projections, February 2008 73

0

20

40

60

80

100

1990 1995 2000 2005 2010 2015

Other 1/ Argentina Other Europe China United States

Global corn exports

Million metric tons

1/ Republic of South Africa, Brazil, EU, former Soviet Union, and others.

The United States dominates world trade in coarse grains, particularly corn. However, increasing use of corn for U.S. ethanol production and reduced world trade are assumed to limit U.S. export growth early in the projection period. During the next half decade, some countries respond to higher world prices by increasing corn production and exports—most notably Argentina and Ukraine. Still, U.S. corn exports are projected to resume growth during the middle part of the projection period after the ramp-up in domestic ethanol production slows. The U.S. share of world corn trade stays close to 60 percent as few countries have the capability to respond to rising international demand for corn.

• Argentina, with a small domestic market, remains the world’s second-largest corn exporter. Argentina’s corn planted area gradually increases in response to higher prices. Corn exports rise steadily by 27 percent to more than 21 million tons. Argentina and other South American countries increase corn exports to Chile to support its expanding pork exports to South Korea.

• Corn exports from some countries of the former Soviet Union, primarily Ukraine, double to 7 million tons by 2017. Favorable resource endowments, increasing economic openness, and greater investment in their agricultural sectors stimulate corn production, and combined with increasing meat imports, leave a corn surplus available for export.

• Brazil’s corn exports are at record high levels during the early years of the projections in response to higher corn prices relative to soybean prices. In the last several years, Brazil has targeted the EU’s demand for non-genetically modified grain. This ability is assumed to diminish as Brazil legalizes planting genetically modified varieties of corn and the EU reduces imports. Also, strong growth in domestic demand from its livestock and poultry sectors and the profitability of growing soybeans limits corn exports.

• China’s corn exports decline in the projections, reflecting strengthening domestic demand driven by its expanding livestock and industrial sectors.

74 USDA Long-term Projections, February 2008

0

2

4

6

8

10

12

14

16

1990 1995 2000 2005 2010 2015

China: Corn imports and exports

Million metric tons

Exports

Imports

Although Chinese corn production is projected to increase, China becomes a net corn importer midway through the projection period as demand for livestock feed overtakes China’s internal supplies of corn. Due to regional supply and demand differences, China continues to export corn throughout the projection period, although in declining amounts (see note below).

• Corn is the favored crop in northeast China. Proximity to Asian markets, especially South Korea, provides a nearby source of demand, while various government measures— including waivers of certain transportation construction taxes—keep corn exports competitively priced in international markets. High ocean-freight rates raise the delivered cost of U.S. corn to Asian markets, another factor that keeps Chinese corn competitive. Shipments of corn from northeast China to the country’s southern markets are limited by China’s high internal transportation costs.

• As China’s corn consumption continues to grow, the country is projected to increase

imports and reduce exports, and to eventually become a net corn importer by the middle of the projection period. Livestock feeding continues to increase as income growth raises meat demand. Industrial use of corn, especially for starch, is also expected to grow robustly in China, but direct human consumption declines.

Note: Projections do not reflect China’s December 2007 policy changes that reduce incentives to export grains and grain products. The first policy change eliminated an export subsidy (refunds of value-added taxes on exports of various grain and grain products). A second policy change imposed export taxes on shipments of a similar set of grain and grain products.

USDA Long-term Projections, February 2008 75

Global barley trade expands 3.6 million tons (22 percent) during the projection period. Rising demand for both malting and feed barley underpin the increased trade.

• Feed barley imports by North African and Middle Eastern countries grow steadily over the next decade. In the mid-1990s, corn overtook barley as the principal coarse grain imported by these countries, due mainly to rising poultry production. This pattern is expected to continue through the projection period. However, the North Africa and Middle East region is expected to remain the world’s largest barley importing area.

• Saudi Arabia—the world’s foremost barley importer—accounts for over 35 percent of

world barley trade through the coming decade. Saudi Arabia’s barley imports are used primarily as feed for camels, goats, and sheep.

• International demand for malting barley is boosted by strong growth in beer demand in

many developing countries, notably China—the world’s largest malting barley importer. China’s beer demand is rising steadily due to growth in incomes and population. Expansion in China’s brewing capacity is being aided by foreign investment. China’s breweries also use rice and other grains to produce alcoholic beverages. Australia and Canada are China’s main sources of malting barley imports.

0

5

10

15

20

1990 1995 2000 2005 2010 2015

Other

Other N. Africa & M. East

S Arabia

China

FSU & OE 1/

Latin America 2/

Japan

USA

Global barley imports

Million metric tons

1/ Former Soviet Union and other Europe; prior to 1999, includes Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia. 2/ Includes Mexico.

76 USDA Long-term Projections, February 2008

0

5

10

15

20

1990 1995 2000 2005 2010 2015

European Union 1/

FSU 2/

Australia

Canada

Other

Global barley exports

Million metric tons

1/ EU-27 excludes intra-trade after 2002, EU-15 intra-trade before 2003, Slovenia before 1992. 2/ Former Soviet Union.

Historically, global barley exports have originated primarily from the EU, Australia, and Canada. However, Ukraine and, to a lesser extent, Russia have emerged as important competitors in international feed barley markets and remain so throughout the projection period.

• Barley production is expected to increase in the EU as a result of Common Agricultural Policy (CAP) reform. The abolition of EU intervention for rye, combined with high barley prices, will stimulate the allocation of more area to barley production. EU exports to non- EU countries are projected to climb nearly 50 percent to 4.7 million tons over the projection period (24 percent of world trade), as projected prices are high enough that the EU is able to export barley without subsidies.

• The FSU remains a major barley exporter throughout the coming decade as exports surpass

8 million tons. Together, the FSU and EU account for nearly 65 percent of world barley exports by 2017.

• Malting barley is a different quality than feed barley and commands a substantial price

premium over feed barley. This premium is expected to influence planting decisions in Canada and Australia and, in both countries, malting barley’s share of total barley area rises during the projection period. However, some of Canada’s total barley area shifts to canola because of stronger prices due to the demand for biodiesel feedstocks, and total barley exports trend downward during the coming decade.

USDA Long-term Projections, February 2008 77

0

2

4

6

8

10

1990 1995 2000 2005 2010 2015

Other

Mexico

Japan

Global sorghum imports

Million metric tons

World sorghum trade, which averaged nearly 6.5 million tons during the last decade, declines to below 5 million tons in the middle of the projection period before rising slightly through the remainder of the coming decade. This trade is driven almost entirely by U.S. exports to Mexico and Japan.

• Mexico is the world’s leading sorghum importer in most years. Some sorghum imports are expected to be replaced by corn imports because, under NAFTA, Mexico’s over-quota tariff on U.S. and Canadian corn ended on January 1, 2008. Mexico’s sorghum imports are projected to increase slightly in the later years, but remain below 2.7 million tons. Even at this reduced import level, Mexico accounts for more than 45 percent of world sorghum imports.

• The EU normally imports small quantities of sorghum, but became the world’s largest importer in 2007/08. As the EU’s corn production declined in 2006 and again in 2007, the region increased imports of nongenetically modified corn, generally from Brazil. However, as exportable world supplies of nongenetically modified corn became more limited and corn prices jumped, the EU began to import sorghum as an alternative. EU corn production is assumed to return to normal levels in 2008 and its 2008/09 imports of both corn and sorghum are projected to recede.

• Japan imports a fairly constant volume of sorghum (1.3 million tons) throughout the period to maintain diversity and stability in its feed grain supplies.

• The United States is the largest exporter of sorghum, accounting for more than 80 percent of world trade in recent years. During most of the projection period, the U.S. share remains in the 80-83 percent range even though some of its sorghum exports to Mexico shift to corn.

• The primary sorghum markets for Argentina, the world’s second largest exporter, are Japan, Chile, and Europe. In Argentina, prices and profitability are expected to favor planting soybeans and corn, so sorghum exports remain relatively flat during the projection period.

• Brazil has begun to export small quantities of sorghum and the volume is projected to rise during the projection period. In the Central-West region of Brazil, sorghum is increasingly planted during the dry season between crops of soybeans or cotton.

78 USDA Long-term Projections, February 2008

0

20

40

60

80

100

1990 1995 2000 2005 2010 2015 0

2

4

6

8

10

12

14

16

18

Global exports: Soybeans, soybean meal, and soybean oil

Soybeans and soybean meal, million metric tons

Soybeans

Soybean meal

Soybean oil

Soybean oil, million metric tons

Strong income and population growth in developing countries generate increasing demand for vegetable oils for food consumption and for protein meals used in livestock production. Additional demand is generated by the use of vegetable oils in biodiesel production in some countries. As a result, world trade in soybeans and soybean oil each grow at an average annual rate of 3.3 percent through the projection period, compared with 3.1 percent for soybean meal.

• Prices for vegetable oils rise due to increasing consumer demand in developing countries and the expansion of biodiesel production. As more of the value of oilseeds derives from the oil content relative to the protein meal content, vegetable oil prices rise in comparison to prices for oilseeds and protein meals.

• Many countries with limited opportunity to expand oilseed production continue investment in oilseed crushing capacity, such as China and some countries in North Africa, the Middle East, and South Asia. As a result, import demand for soybeans and rapeseed grows rapidly. However, strong competition in international protein meal markets is expected to shift some of the import demand from oilseeds to cheaper meals. The competitive pressure of new oilseed crushing capacity is expected to result in some inefficient crushers going out of business.

• China’s expansion of domestic crushing capacity instead of importing protein meal and vegetable oil significantly influences the composition of world trade by raising global import demand for soybeans and other oilseeds rather than for oilseed products.

• Brazil’s rapidly increasing soybean area enables it to gain a larger share of world soybean and soybean meal exports, despite increasing domestic feed use. Its share of world exports of soybeans plus the soybean equivalent of soybean meal rises from about 30-35 percent in recent years to 43 percent by 2017.

• The expansion in Argentine soybean area slows as incentives to grow corn and sunflower seed improve and the conversion of pasture land to crop land slows.

• The EU set-aside rate is assumed to be zero during the projections. Except in 2008, most land previously set aside will be planted to rapeseed destined for biodiesel production.

USDA Long-term Projections, February 2008 79

0

20

40

60

80

100

1990 1995 2000 2005 2010 2015

Other China & Hong Kong N. Africa & M. East Latin America 1/ East Asia European Union 2/

Global soybean imports

Million metric tons

1/ Includes Mexico. 2/ EU-27 excludes intra-trade after 2002, EU-15 intra-trade before 2003, Slovenia before 1992.

World soybean trade is projected to rise rapidly, climbing more than 27 million metric tons (35 percent) during the next decade.

• The EU was the world’s leading importer of soybeans until 2002. However, increases in grain and rapeseed meal feeding and rising imports of soybean meal have resulted in declining soybean imports since then.

• China will face policy decisions regarding tradeoffs in producing or importing corn and

soybeans. The projections assume that Chinese policies will support maintaining domestic corn production and importing soybeans. Thus, China accounts for 80 percent of the world’s 27-million-ton growth in soybean imports over the next 10 years. Significant investments in oilseed crushing infrastructure by China drive strong gains in soybean imports as China seeks to capture the value added from processing oilseeds into protein meal and vegetable oil. The use of vegetable oils for biofuels production is assumed to have a negligible impact on China’s total vegetable oil use.

• East Asia’s trade outlook is dominated by a continuing shift from importing feedstuffs to

importing meat and other livestock products. As a result, this region’s import demand for protein meal and oilseeds does not rise during the coming decade despite rising meat consumption.

• As Argentina seeks to operate its expanding crushing facilities at full capacity, it is

projected to import 4 million tons of soybeans from Brazil, Paraguay, Uruguay, and Bolivia by the end of the period.

80 USDA Long-term Projections, February 2008

0

20

40

60

80

100

1990 1995 2000 2005 2010 2015

Other

Other South America

Brazil

Argentina

United States

Global soybean exports

Million metric tons

The three leading soybean exporters—the United States, Brazil, and Argentina—have accounted for more than 90 percent of world trade in recent years. Their market share is projected to decline to slightly less than 90 percent as exports rise from minor exporting countries, such as Uruguay, Paraguay, and Bolivia.

• With continuing area gains, Brazil maintains its position as the world’s leading exporter of soybeans and soybean products. Combating soybean rust disease increases production costs. However, because of increased domestic demand for soybean meal for feed and soybean oil for human consumption and biodiesel production, soybeans remain more profitable than other crops in most areas of Brazil. It is assumed that some land in southern Brazil will shift from oilseed to corn production during the middle of the projection period in response to higher corn prices and more limited competition from U.S. corn exports. Still, with expanded soybean plantings in the Cerrado regions, the growth rate for Brazil’s soybean planted area is projected to average nearly 3.5 percent a year, reaching about 31 million hectares by 2017. Soybean exports are projected to almost double.

• Argentina’s export tax rates are higher for soybeans than for soybean products. This favors domestic crushing of whole seeds and exporting the products. Also, Argentina is projected to divert some land from soybeans to corn. As a result, Argentina’s soybean exports remain around 8 to 9 million tons.

• Other South American countries, principally Uruguay, Paraguay, and Bolivia, expand exports 40 percent to more than 9 million tons. Four million tons are destined for the crushing industry in Argentina.

• Russia and Ukraine respond to higher international market prices for oilseeds by increasing production of rapeseed and soybeans. Although rapeseed production will be most affected, soybean exports are projected to increase somewhat.

• In the United States, reduced soybean acreage and increased domestic crush limit exportable supplies, but their competitiveness is aided by depreciation of the U.S. dollar.

USDA Long-term Projections, February 2008 81

World trade in soybean meal grows briskly during the projections, rising more than 18 million tons (over 30 percent) by 2017. Continuing growth in the demand for livestock products and limited capability to increase oilseed production boost demand for soybean meal by a number of countries with rising middle-income populations. Lower import prices of soybean meal relative to soybeans and grains provide incentives for countries to import soybean meal for inclusion at a higher rate in livestock feed rations.

• The EU remains the world’s largest destination for soybean meal throughout the projection

period, despite increased domestic feeding of grains. Growth in soybean meal imports is expected to continue even though there will be more rapeseed meal available as a result of the biofuels expansion. Also, an increase in the dairy production quota increases soybean meal feeding.

• The regions of Southeast Asia, Latin America, and North Africa and the Middle East all

become larger importers of soybean meal as the demand for livestock feed boosts import demand in a number of countries.

• Mexico’s strong growth in demand for protein feed and vegetable oils is projected to

continue. The crushing industry in Mexico is also expected to continue expansion. This will boost soybean imports, but soybean meal imports from the United States are also expected to grow rapidly.

0

10

20

30

40

50

60

70

80

1990 1995 2000 2005 2010 2015

Other European Union 1/ Southeast Asia L. America 2/ N. Africa & Middle East FSU & OE 3/ East Asia

Global soybean meal imports

Million metric tons

1/ EU-27 excludes intra-trade after 2002, EU-15 intra-trade before 2003, Slovenia before 1992. 2/ Includes Mexico. 3/ Former Soviet Union and other Europe; prior to 1999, includes Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia.

82 USDA Long-term Projections, February 2008

0

10

20

30

40

50

60

70

80

1990 1995 2000 2005 2010 2015

Other

Argentina

Brazil

United States

Global soybean meal exports

Million metric tons

Argentina, Brazil, and the United States remain the three major exporters in international protein meal markets. Together they account for around 90 percent of total world soybean meal trade during the next 10 years. Argentina, the world’s largest soybean meal exporter, increases its share of the world market from around 45 percent in recent years to more than 55 percent after 2008/09. Brazil’s share of world exports remains in the 20-25 percent range while the shares held by the United States and other exporters fall. • Argentina imposes higher export taxes on soybeans than on soybean products. This has

provided an incentive for the country to develop a large oilseed crushing capacity. Argentina maintains high utilization of its growing crushing capacity by importing soybeans from Brazil and other South American countries.

• In Brazil, strong growth in domestic meal consumption due to rapid expansion of the

poultry and pork sectors limits increases in soybean meal exports. Also, domestic soybean crushing capacity is not expected to grow as fast as soybean production because Brazil’s differential export tax structure favors exporting soybeans rather than soybean meal or soybean oil.

• U.S. soybean meal exports hold steady at around 8 million tons throughout the projections,

but the U.S. share of world trade declines steadily from more than 14 percent in recent years to less than 11 percent by 2017.

• The EU continues to be a small but steady exporter of soybean meal to Russia and other

East European countries. India remains an exporter, although export volume declines as domestic use, especially for poultry feed, rapidly expands.

USDA Long-term Projections, February 2008 83

0

2

4

6

8

10

12

14

16

1990 1995 2000 2005 2010 2015

Rest of world EU, FSU, & OE 1/ India China Other Asia 2/ N. Africa & M. East Latin America 3/

Global soybean oil imports

Million metric tons

1/ European Union, former Soviet Union, and other Europe. 2/ Asia excluding India and China. 3/ Includes Mexico.

World demand for soybean oil imports climbs 3.8 million metric tons (33 percent) in the projections, bolstered by rising food use and increased demand for use in biofuel production. China and India are the world’s two largest soybean oil importers—importing primarily for food use. In recent years, their combined imports have been around 3.5 million tons, nearly 40 percent of the world total.

• Import demand for soybean oil rises in nearly all countries and regions. Income and population growth in North Africa, the Middle East, and Latin America (particularly Central America and the Caribbean) drive rapid gains in soybean oil imports. Although rising international prices for soybean oil will temper consumption, especially in developing countries, imports by the North Africa and the Middle East region are projected to be exceeded only by those of China.

• India is one of the world’s largest soybean oil importers. Factors that contribute to continued growth in imports include burgeoning domestic demand for vegetable oils and limited capacity for domestic production of oilseeds. Low yields, associated with erratic rainfed growing conditions and low input use, inhibit growth of oilseed production in India. Lower Indian tariffs on soybean oil (held down by World Trade Organization (WTO) tariff-binding commitments) compared with tariffs for other vegetable oils support continued large imports of soybean oil.

• China experiences a growing demand for vegetable oils. However, land-use competition from other crops constrains area planted to oilseed crops. Even with strong increases in soybean imports for crush, domestic demand outpaces domestic vegetable oil production and fuels a moderate expansion in soybean oil imports.

• The EU imports more soybean oil to replace some of the rapeseed oil that is used in the production of biodiesel.

84 USDA Long-term Projections, February 2008

0

2

4

6

8

10

12

14

16

1990 1995 2000 2005 2010 2015

Other

Argentina Brazil

European Union 1/ United States

Global soybean oil exports

Million metric tons

1/ EU-27 excludes intra-trade after 2002, EU-15 intra-trade before 2003, Slovenia before 1992.

Argentina’s and Brazil’s combined share of world soybean oil exports rises from less than 80 percent in recent years to more than 85 percent by the end of the projections. • Argentina is the leading exporter of soybean oil, reflecting the country’s large crushing

capacity, its small domestic market for soybean oil, and an export tax structure that favors exports of soybean products rather than soybeans. Increases in soybean crush and soybean oil exports are supported by gains in Argentine soybean production due to extensive double- cropping, further adjustments to crop-pasture rotations, and the addition of marginal lands in the northwest part of the country. Argentina also increases soybean imports from other South American countries in order to more fully utilize its crushing capacity. Growth in Argentina’s biodiesel production capacity, with incentives from a lower export tax for biodiesel than for soybean oil, may constrain growth in soybean oil exports in the future.

• Brazil’s expansion of soybean production into new areas of cultivation enables it to increase

both its volume of soybean oil exports and its share of world trade. • The United States remains the world’s next largest soybean oil exporter. U.S. soybean oil

exports are initially constrained by increased use for biodiesel production but expand moderately after 2010/11 as output gains exceed growth in domestic consumption. However, the U.S. share of world trade is projected to be well below the average of recent years.

• In the EU, exportable supplies of vegetable oils are limited by the growth in biodiesel

production.

USDA Long-term Projections, February 2008 85

0

5

10

15

20

25

30

35

40

1990 1995 2000 2005 2010 2015

Other Other Asia Indonesia N. Africa & M. East Sub-Saharan Africa EU, FSU, & OE 1/ Latin America 2/

Global rice imports

Million metric tons

1/ European Union, former Soviet Union, and other Europe. 2/ Includes Mexico.

Global rice trade is projected to grow 2.2 percent per year from 2008 to 2017. By 2017, global rice trade exceeds 37 million tons, nearly 24 percent above the record set in 2005. The main factors driving the expansion in global trade are a steady growth in demand—largely due to population growth in developing countries—and the inability to significantly boost production in key importing nations.

• Long-grain varieties account for around three-fourths of global rice trade and are expected to account for the bulk of trade growth over the next decade. Medium- and short-grain rice account for 10-12 percent of global trade, with Northeast Asia the largest market. Aromatic rice, primarily basmati and jasmine, makes up most of the rest of global rice trade.

• Indonesia, the Philippines, and Bangladesh become the three largest rice-importing countries by the end of the projection period. By 2017, each country is projected to import 1.9 million tons or more. These three countries have limited ability to expand production and are expected to account for nearly 30 percent of the increase in global rice imports over the next decade.

• In Sub-Saharan Africa and the Middle East, strong demand growth is driven by rapidly expanding populations. Production growth is limited by climate in the Middle East and by infrastructure deficiencies in Sub-Saharan Africa. Sub-Saharan Africa accounts for 27 percent of the increase in world rice trade between 2008 and 2017. Iraq and Saudi Arabia account for most of the increase in imports by the Middle East.

• The Central American and Caribbean region is projected to expand imports over the next decade, increasing about 0.5 million tons to 2.2 million by 2017. Population growth and rising per capita incomes boost rice consumption and raise this region’s imports.

• The EU will remain a major market for rice, although import growth will be modest. Consumption growth will be driven by a larger immigrant population. North American imports will also expand over the next decade, with both total and per capita consumption rising. Imports by the former Soviet Union are projected to decline as result of strong production growth and stagnant demand.

86 USDA Long-term Projections, February 2008

0

5

10

15

20

25

30

35

40

1990 1995 2000 2005 2010 2015

Other India China Thailand Pakistan Vietnam South America United States

Global rice exports

Million metric tons

Asia remains the largest rice-exporting region throughout the projection period.

• Thailand and Vietnam, the world’s largest rice-exporting countries, account for half of all rice exports and nearly three-fourths of the growth in world exports in the coming decade. Thailand’s exports increase 3.6 million tons to more than 13 million by 2017. Both area and yield are projected to increase in Thailand. Vietnam’s export expansion is smaller, from 5.3 to 6.5 million tons, as the area planted to rice is not expected to expand. Per capita consumption declines for both exporters.

• India is currently the third-largest rice exporter. India has been a major exporter since the mid-1990s, although export levels have been rather volatile, primarily due to fluctuating production and stock levels. Exports are projected to decline about 300,000 tons to 3.3 million as consumption growth outpaces production. Inability to expand area is the main production constraint. India’s export volume is surpassed by the United States in 2011 and by Pakistan in 2012.

• The United States is currently the fourth-largest rice-exporting country and is expected to increase exports from 3.4 million tons in 2007 to 4.2 million by 2017. Modest area expansion, continued yield growth, and slow growth in domestic consumption result in larger exportable supplies. By 2011, the United States is expected to become the third- largest exporting country. The Western Hemisphere remains the top market for U.S. rice.

• Pakistan is currently the world’s fifth-leading rice exporter and exports are projected to slightly increase over the next decade to 3.5 million tons by 2017. Pakistan has boosted rice area and production in the past few years. However, Pakistan can expand rice area only a little beyond its current record level, and its agricultural sector is confronting a growing water shortage and a decaying infrastructure, limiting production and export gains.

• China, the sixth-largest rice exporting country, is projected to raise exports by 0.8 million tons to more than 2.4 million tons by 2017. The increase in exports is primarily due to a long-term decline in stocks. Little change in production or total disappearance is expected. Higher yields are projected to offset declining area. Reductions in per capita consumption, a result of continued diet diversification resulting from higher incomes, are expected to offset population growth.

USDA Long-term Projections, February 2008 87

0

10

20

30

40

50

60

1990 1995 2000 2005 2010 2015

Other China Latin America 1/ Southeast Asia 2/ South Asia 3/ EU, FSU, & OE 4/ East Asia

Global cotton imports

Million bales

1/ Includes Mexico. 2/ Malaysia, Indonesia, Philippines, Thailand, and Vietnam. 3/ Bangladesh, India, and Pakistan. 4/ European Union, former Soviet Union, and other Europe.

With global cotton consumption growing dramatically, international trade has become increasingly important in world cotton markets. During the last decade, world consumption climbed at a 4.7-percent growth rate while world trade rose 6.5 percent a year. Not only has textile trade liberalization helped boost world cotton demand through increased efficiency, but geographic shifts in mill use of cotton have increased the role of trade in meeting the global textile industry’s need for cotton. Trade’s importance has rebounded in recent years as the textile sectors in China and, to a lesser extent, Pakistan have grown substantially faster than domestic cotton production.

• The textile industries in China, India, and Pakistan are the major beneficiaries of textile trade liberalization through the elimination of Multifiber Arrangement (MFA) quotas.

• China has been importing record amounts of cotton as its textile industry’s growth rapidly accelerated with a booming economy and WTO accession. Both its textile industry and its cotton imports are expected to grow more slowly than the rapid increases since 2001. However, during the next decade, the increase in cotton imports by China is projected to more than offset the decline in imports by other countries, and China accounts for almost half of world imports by 2017.

• Pakistan has emerged as a major importer in recent years and is projected to be the world’s third largest importer during the next 10 years.

• In recent years, Turkey’s textile industry has benefited from favorable trade access to the EU, its major market for textile and apparel exports. However, the end of the MFA quotas gives lower cost competitors more favorable access to EU markets. Turkey’s cotton imports are projected to rise slowly over the next 10 years, but not enough to keep its share of world trade from falling slightly.

• The EU, Japan, Taiwan, and South Korea all steadily reduce their cotton imports as textile trade reforms and/or higher wages in these countries drive textile production to countries with lower wages and other costs.

88 USDA Long-term Projections, February 2008

China’s Cotton Supply and Demand Estimates and the Residual Component China and its role in the world economy have been transformed beyond recognition in the last decade. Nevertheless, a textbook on cotton marketing from the 1920s highlights a problem that remains all too familiar to this day: “The size of the Chinese cotton crop has always been a puzzle, owing to the lack of reliable statistics.” W. Hustace Hubbard, Cotton and the Cotton Market, D. Appleton and Company, New York, 1923. Now, 85 years after this observation, the world continues to face uncertainties in how to interpret information on China’s cotton. The data available to the world at large have developed severe inconsistencies in recent years. As a result, there have been some official revisions to the cotton production estimates produced by China’s National Bureau of Statistics. However, a large inconsistency remains, and USDA publishes supply-and-demand estimates for cotton in China that include an unexplained residual. The inconsistency is a shortfall between net imports and estimated excess demand (consumption minus production). Since 1999, China’s net cotton imports have been smaller than excess demand every year. Ordinarily, a gap between net imports and excess demand would be made up by drawing down stocks from earlier years. China’s gap has persisted for so long, and at such a high level, that it has become clear that previous estimates of stocks were not sufficient to account for this shortfall. Therefore, either the estimates of cotton production from China’s government were too low, or the widely accepted estimates of cotton consumption in China were too high. China’s international demand for cotton has historically been both volatile and crucial in global price determination. Given this, and the ambiguity of the historical data for supply and demand, USDA has traditionally sought to link its historical data estimates to official data from China. In the interest of transparency, USDA has sought to minimize its deviations from this official data even though this necessitates the inclusion of an unexplained residual. Therefore, production and consumption for China in these projections start from China’s official historical data. Although the residual has grown significantly in recent years, the absence of good information to explain the residual makes its future movements uncertain. Thus, in the projections, the unexplained residual is held constant throughout the coming decade. This methodology simplifies an understanding of the projections. Further, it allows readers who feel they have insights about future trends in the factors causing the residual to easily adjust the USDA forecasts for alternative assumptions. Hopefully, in the future, improved data and a better understanding of China’s cotton sector will prevail so there will no longer be a need for either ad hoc adjustments or unexplained residuals. (For further information see: MacDonald, Stephen, China’s Cotton Supply and Demand: Issues and Impact on the World Market, Outlook Report No. CWS-07I-01, U.S. Department of Agriculture, Economic Research Service, November 2007.)

USDA Long-term Projections, February 2008 89

0

10

20

30

40

50

60

1990 1995 2000 2005 2010 2015

Other South America United States Sub-Saharan Africa Australia India Former Soviet Union

Global cotton exports

Million bales

Globalization is expected to continue to move raw cotton production to countries with favorable resource endowments and technology. Traditional producers with large land bases suitable for cotton production continue to benefit from post-MFA trade patterns. Such producer/exporter regions include the United States, Sub-Saharan Africa, and Brazil. The importance of technology has been highlighted by the impact of India’s rapid adoption of genetically modified cotton, nearly all Bacillus thuringiensis (Bt) cotton.

• The United States continues as the world’s leading cotton exporter throughout the projections. Exports climb 16 percent to more than 19 million bales by 2017/18.

• The Central Asian countries of the former Soviet Union have been the principal U.S. competitors since the early 1990s. However, government policies in Central Asia promoting investment in textiles have resulted, to some extent, in exports of textile products rather than exports of raw cotton. Furthermore, the region’s cotton production is expected to grow only slowly.

• Sub-Saharan Africa’s exports rose rapidly during the last decade in large part due to economic reforms. West Africa’s 1994 currency devaluation led to nearly a decade of growth within the region’s monetary union. As West Africa’s production gains began to lag at the end of the 1990s, several southern African countries began increasing their cotton production, aided by reforms such as eliminating marketing board monopolies. Continued increases in output are expected as these economies develop and Bt cotton is adopted by the region’s producers. The region’s exports are projected to rise more than 50 percent during the next 10 years.

• Improved cotton yields in India, in part due to the adoption of Bt cotton, have raised India’s output in recent years. Rapid yield growth is projected to continue as the area planted to Bt cotton expands rapidly. The increase in cotton output is expected to enable India to increase domestic textile production as well as significantly raise cotton exports. By 2009/10, India’s cotton exports are projected to surpass those of Central Asia, making India the principal competitor to the United States in world markets.

90 USDA Long-term Projections, February 2008

0

2

4

6

8

10

1990 1995 2000 2005 2010 2015

Meat exports 1/

Million metric tons

1/ Major exporters.

Beef and veal

Pork

Poultry

Growth rates of exports from major exporters of beef, pork, and poultry meat average 1.1, 2.1, and 1.9 percent a year, respectively, between 2008 and 2017. During this period, exports rise 0.8 million tons for beef, 1.0 million for pork, and 1.2 million for poultry. Rising per capita incomes combined with population growth in a number of countries are the driving forces behind the projected growth in global meat demand.

• Bovine spongiform encephalopathy (BSE) in Canada and the United States affected Canada’s beef and live cattle exports to the United States in recent years. After falling in 2006 and 2007, Canadian beef exports are expected to recover once again in 2008, rising to a level just below their 2002 record. Additionally, recent changes in U.S. regulations, the projections assume Canadian cattle and beef from cattle over 30 months of age can be exported to the United States under the conditions that they are age-verifiable and born after March 1, 1999.

• EU beef exports remain well below the annual WTO limit on subsidized exports (817,000 tons) as a stronger euro limits their competitiveness in international markets and policy changes lower both beef production and the need to remove beef from the domestic market.

• Argentine beef exports rose sharply in 2004 and 2005. However, export taxes on beef and changes in other policies have made Argentina’s exports less competitive. Beef production and exports are projected to decline and remain below their 2004 level.

• The projections assume no changes in the set of countries recognizing Brazil as free of foot- and-mouth disease (FMD), thus limiting Brazilian pork’s ability to compete in some markets. However, exports from Brazil’s expanding pork sector are expected to be competitive in price- sensitive markets such as Russia and Asian countries other than Japan and South Korea.

• Canada is projected to remain the world’s third largest pork exporter. • During the coming decade, Brazil is expected to continue to be the largest exporter of poultry

products, bolstered by low production costs and competitive export prices. • U.S poultry meat exports are projected to increase, due in part to a weak dollar that increases

the competitiveness of U.S. exports. • Poultry exports from countries affected by avian influenza, such as Thailand and China, are

expected to be mostly fully cooked products.

USDA Long-term Projections, February 2008 91

Beef imports by major importers expand about 1.3 million tons (23 percent) between 2008 and 2017. Traditionally, developed countries were the primary importers of beef. However, Brazil has become a large exporter of lower quality beef that is imported by lower income countries. The projections assume gradual recovery of U.S. and Canadian exports to Japan and South Korea.

• Higher income countries, such as Japan and South Korea, increase beef imports, reflecting domestic cattle sectors that are constrained by land availability. These imports are primarily of grain-fed beef. U.S. beef exports to these countries are projected to rebuild over the next 10 years. Also, there continues to be a strong presence of Australia and New Zealand in these East Asian markets.

• U.S. beef imports, primarily of grass-fed lean beef from Australia and New Zealand for use

in ground beef and processed products, rise slightly through the period. Even with decreases in exports due to weather and land availability in the early part of the projection period, strong Asian imports of beef from Australia and New Zealand enable these countries to maintain significant levels of exports over the projection period.

• Robust import growth of higher quality beef from the United States is projected for

Mexico. • The projections assume that Russia’s tariff-rate quota (TRQ) for beef, first imposed in

2003, remains in effect until 2009. In the longer run, the growth in Russia’s beef imports resumes as rising consumer demand outpaces gains in domestic production. Russia remains a large market for EU and Brazilian beef exports.

0

1

2

3

4

5

6

7

1990 1995 2000 2005 2010 2015

Russia East Asia N. Africa & M. East European Union 2/ Canada & Mexico United States

Beef imports 1/

Million metric tons

1/ Selected importers. 2/ EU-27 excludes intra-trade after 2002, EU-15 intra-trade before 2003, Slovenia before 1992.

92 USDA Long-term Projections, February 2008

0

1

2

3

4

5

1990 1995 2000 2005 2010 2015

Mexico

East Asia

Russia

China & Hong Kong

United States

Pork imports 1/

Million metric tons

1/ Selected importers.

The major pork importers are projected to increase trade by nearly 1 million tons (24 percent) between 2008 and 2017. • Mexican pork imports increase more than 150,000 tons (38 percent) between 2008 and

2017, making Mexico one of the fastest growing pork importers. Increases in income and population are the primary drivers of Mexico’s increasing demand for pork.

• Higher income parts of East Asia, such as Japan, Hong Kong, and South Korea, increase

pork imports as their domestic hog sectors are constrained by environmental concerns. In South Korea and Japan, BSE-related concerns regarding beef also boost pork demand.

• As with beef, the projections assume the TRQ that Russia imposed for pork in 2003

remains in effect until 2009. Although the TRQ initially lowered pork imports, Russia remains a major destination for competitively priced pork exports from the EU and Brazil as demand growth continues to exceed Russian pork production gains. By 2017, Russia is projected to import over 200,000 tons more pork than in 2008, a larger increase than any other country.

• In China, increasing incomes boost per capita pork consumption and raise imports in the

projections. However, China’s pork production and exports also continue to rise.

USDA Long-term Projections, February 2008 93

0

1

2

3

4

5

6

1990 1995 2000 2005 2010 2015

Russia Other N Afr. & M. East European Union 2/ East Asia China & Hong Kong Saudi Arabia Mexico

Poultry imports 1/

Million metric tons

1/ Selected importers. 2/ EU-27 excludes intra-trade after 2002, EU-15 intra-trade before 2003, Slovenia before 1992.

Poultry meat imports by major importers are projected to increase by about 1.0 million tons (18 percent) from 2008 to 2017.

• Russia is expected to remain the world’s largest poultry importer. Despite slow population growth, rising consumer income increases demand for poultry products. However, increased consumption is expected to be met by rising domestic production, so imports are not expected to change much during the coming decade.

• In Mexico, economic growth raises poultry consumption and imports. Domestic poultry production continues to increase, but lags rising consumer demand.

• China’s rising consumption of poultry meat is met by expanding domestic production, while the country’s poultry imports and exports each grow by more than 25 percent.

• East Asia, a major importing region, is projected to import 20 percent more poultry meat in 2017 than in 2008. Most of the increase is imported by South Korea.

• Because of avian influenza, some major poultry-exporting countries such as Thailand and China have shifted most of their exports to fully cooked products. Due to their higher costs, these cooked poultry products will be marketed to developed or high-income countries in Asia, Europe, and the Middle East.

• Poultry imports by Saudi Arabia and the Other North Africa and the Middle East region are projected to grow throughout the projection period. Animal disease issues in a number of countries is expected to slow growth in domestic production and increase demand for imports.

• Rising consumer incomes increase poultry demand and imports in a number of Central America and Caribbean countries. Poultry products remain relatively less expensive than beef or pork, further stimulating demand. Together with Mexico, these countries form one of the largest markets for poultry imports.

94 USDA Long-term Projections, February 2008

Table 31. Coarse grains trade long-term projections 2006/07 2007/08 2008/ 09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Importers Former Soviet Union1 0.9 0.6 0.7 0.8 0.8 0.8 0.8 0.9 0.9 1.0 1.0 1.1 Other E urope 0.8 0.6 0.6 0.6 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 European Union2 8.1 13.8 3.7 3.8 3.9 4.0 4.1 4.2 4.3 4.4 4.5 4.6 North Africa & Middle East 29.8 28.7 30.5 31.4 32.0 33.0 33.9 34.9 35.7 36.6 37.5 38.5 Sub-Saharan A frica3 2.8 1.4 1.7 1.8 1.8 1.8 1.8 1.9 1.9 1.9 2.0 2.0 Japan 19.6 19.1 19.1 19.1 19.1 19.1 19.1 19.1 19.1 19.1 19.0 19.0 South Korea 8.8 8.9 8.8 8.8 8.8 8.8 8.9 8.9 8.9 8.8 8.8 8.8 Taiwan 4.6 4.5 4.6 4.6 4.6 4.6 4.6 4.6 4.6 4.6 4.6 4.6 China 1.2 1.5 1.8 2.2 2.7 3.4 4.0 4.4 4.5 4.7 5.4 5.7 Other A sia & Oceania 5.0 5.2 5.3 5.1 5.1 5.2 5.3 5.6 5.9 6.2 6.6 6.9 Mexico 11.0 12.3 14.0 15.1 15.3 15.6 15.9 16.3 16.7 17.0 17.5 17.8 Central America & Caribbean 4.9 5.0 5.0 5.2 5.3 5.5 5.6 5.8 6.0 6.2 6.4 6.6 Brazil 1.3 1.0 0.9 0.8 0.7 0.8 0.8 0.9 0.9 1.0 1.1 1.2 Other S outh Am erica 8.4 8.2 8.3 8.4 8.4 8.5 8.5 8.6 8.7 8.8 8.8 8.9 Other f oreign4 5.1 4.4 5.0 4.6 4.5 4.5 4.4 4.4 4.5 4.5 4.5 4.5

United States 2.5 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8

Total trade 114.7 118.0 112.8 115.1 116.5 118.8 121.2 123.8 125.9 128.1 130.8 133.4

Exporters European Union2 4.7 5.1 3.9 3.8 4.2 4.4 4.7 5.0 5.2 5.3 5.4 5.5 China 5.4 1.5 1.4 1.2 1.3 1.2 1.1 1.0 1.0 1.0 1.0 1.0 Argentina 17.2 17.6 17.8 19.2 19.9 20.8 21.5 22.0 22.2 22.1 22.4 22.4 Australia 2.1 2.2 4.4 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.6 Canada 3.6 4.6 4.4 4.3 4.1 4.0 3.9 3.9 3.8 3.7 3.6 3.5 Republic of South Africa 0.5 1.0 1.7 1.7 1.5 1.8 1.8 1.8 1.8 1.8 1.8 1.9 Other E urope 1.1 0.3 1.1 1.2 1.5 1.7 1.8 1.8 1.9 1.9 1.9 2.0 Former Soviet Union1 8.4 5.4 8.8 9.9 10.8 11.7 12.6 13.2 13.8 14.2 14.7 15.3 Other f oreign 13.1 12.4 10.3 10.3 10.3 10.2 10.1 10.0 9.8 9.6 9.4 9.2

United States 58.4 67.8 59.0 59.0 58.4 58.5 59.3 60.7 62.1 64.1 66.1 68.2

Percent

U. S. trade share 51.0 57.5 52.3 51.3 50.1 49.2 48.9 49.0 49.3 50.0 50.5 51.1

1/ Covers FSU-12, includes intra-FSU trade. 2/ Covers E U-27, excludes intra-E U trade. 3/ Includes Republic of South Africa. 4/ Includes unaccounted. The projections were completed in November 2007.

Imports, million metric tons

Exports, million metric tons

USDA Long-term Projections, February 2008 95

Table 32. Corn trade long-term projections 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Importers European Union1 7.1 9.5 3.5 3.6 3.7 3.8 3.9 4.0 4.1 4.2 4.3 4.4 Former Soviet Union2 0.5 0.3 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.6 Egypt 4.8 4.5 4.8 4.9 5.0 5.1 5.3 5.4 5.6 5.8 5.9 6.0 Algeria 2.4 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.1 3.2 3.3 Morocco 1.6 1.5 1.5 1.6 1.6 1.6 1.6 1.7 1.7 1.7 1.8 1.8 Iran 3.0 2.7 2.9 3.0 3.0 3.1 3.2 3.2 3.3 3.4 3.5 3.6 Saudi Arabia 1.3 1.4 1.4 1.4 1.5 1.5 1.5 1.6 1.7 1.7 1.8 1.8 Turkey 1.0 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.5 Other N. Africa & Middle East 5.1 6.0 6.2 6.3 6.3 6.5 6.6 6.8 6.9 7.1 7.2 7.4 Japan 16.7 16.3 16.3 16.3 16.3 16.3 16.3 16.3 16.3 16.3 16.2 16.2 South Korea 8.7 8.8 8.8 8.8 8.7 8.7 8.7 8.7 8.7 8.7 8.6 8.6 Taiwan 4.4 4.3 4.4 4.4 4.4 4.4 4.4 4.4 4.4 4.4 4.4 4.4 China 0.0 0.1 0.3 0.6 0.9 1.4 1.9 2.4 2.5 2.7 3.3 3.6 Indonesia 1.2 1.0 1.0 1.0 1.0 1.0 1.0 1.1 1.1 1.2 1.3 1.4 Malaysia 2.6 2.7 2.7 2.8 2.8 2.9 3.0 3.0 3.1 3.2 3.3 3.4 Other A sia & Oceania 1.2 1.5 1.6 1.4 1.3 1.3 1.4 1.5 1.7 1.8 2.0 2.2 Canada 2.1 2.3 2.2 1.8 1.7 1.6 1.6 1.6 1.6 1.6 1.6 1.6 Mexico 8.8 10.2 12.0 13.1 13.4 13.6 13.9 14.0 14.4 14.5 14.8 15.0 Central America & Caribbean 4.9 5.0 5.0 5.2 5.3 5.5 5.6 5.8 6.0 6.2 6.3 6.5 Brazil 0.9 0.8 0.7 0.5 0.5 0.5 0.6 0.6 0.7 0.7 0.8 0.8 Other S outh Am erica 7.9 7.7 7.8 7.9 7.9 8.0 8.0 8.1 8.1 8.1 8.2 8.3 Sub-Saharan A frica3 2.3 0.9 1.2 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.4 1.4 Other f oreign4 2.8 1.8 1.8 1.8 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9

United States 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

Total trade 91.6 92.0 89.3 91.0 91.9 93.5 95.3 97.4 99.1 100.9 103.1 105.1

Exporters European Union1 0.8 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 China 5.3 1.5 1.3 1.2 1.2 1.1 1.1 1.0 1.0 0.9 0.9 0.9 Argentina 15.8 16.0 17.0 18.3 19.1 20.1 20.8 21.2 21.4 21.3 21.6 21.6 Brazil 9.7 8.0 7.0 6.8 6.6 6.4 6.2 6.0 5.7 5.4 5.1 4.8 Republic of South Africa 0.5 1.0 1.7 1.7 1.5 1.7 1.7 1.8 1.7 1.8 1.8 1.9 Other E urope 1.1 0.3 1.0 1.2 1.4 1.6 1.7 1.7 1.8 1.8 1.9 1.9 Former Soviet Union2 1.1 1.6 3.2 3.8 4.4 4.9 5.4 5.9 6.4 6.6 6.8 7.0 Other f oreign 3.4 3.6 3.1 3.2 3.3 3.4 3.5 3.6 3.6 3.7 3.8 3.9

United States 54.0 59.7 54.6 54.6 54.0 54.0 54.6 55.9 57.2 59.1 61.0 62.9

P ercent

U. S. trade share 58.9 64.9 61.1 60.0 58.8 57.7 57.3 57.4 57.7 58.6 59.1 59.8 1/ Covers E U-27, excludes intra-E U trade. 2/ Covers FSU-12, includes intra-FSU trade. 3/ Includes Republic of South Africa. 4/ Includes unaccounted. The projections were completed in November 2007.

Import s, million metric tons

Exports, million metric t ons

96 USDA Long-term Projections, February 2008

Table 33. Sorghum trade long-term projections 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Importers Japan 1.3 1.4 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.2 1.2 1.2 Mexico 2.1 2.0 1.8 1.9 1.8 1.9 2.0 2.1 2.2 2.4 2.5 2.6 North Africa & Middle East 0.0 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.3 South America 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Sub-Saharan A frica1 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.5 Other2 1.4 4.2 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9

Total trade 5.3 8.2 4.7 4.8 4.7 4.8 4.9 5.1 5.2 5.4 5.5 5.7

Exporters Argentina 1.0 1.1 0.4 0.5 0.3 0.3 0.3 0.4 0.3 0.4 0.4 0.4 Australia 0.1 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 Other f oreign 0.3 0.0 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.3

United States 4.0 7.0 3.8 3.8 3.8 3.9 4.1 4.2 4.3 4.4 4.6 4.7 P ercent

U. S. trade share 74.7 84.9 80.9 79.8 81.7 81.8 83.2 82.3 82.7 82.4 82.8 82.8

1/ Includes the Republic of South Africa. 2/ EU-27 and the rest of the world. Excludes intra-E U trade. Includes unaccounted. The projections were completed in November 2007.

Import s, million metric tons

Exports, million metric t ons

Table 34. Barley trade long-term projections 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/ 17 2017/18

Importers

Former Soviet Union1 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0. 3 0.3 0.3 0.3 0.3 Japan 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1. 4 1.4 1.4 1.4 1.4 South Korea 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0. 1 0.1 0.1 0.1 0.1 Taiwan 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0. 1 0.1 0.1 0.1 0.1 China 1.1 1.4 1.5 1.6 1.8 1.9 2.0 2. 0 2.0 2.0 2.1 2.1

European Union2 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0. 2 0.2 0.2 0.2 0.2 Latin America3 0.8 0.7 0.7 0.7 0.7 0.7 0.7 0. 7 0.8 0.8 0.8 0.8 Algeria 0.2 0.1 0.2 0.2 0.2 0.2 0.2 0. 1 0.1 0.1 0.1 0.1 Saudi Arabia 6.5 5.8 6.5 6.7 6.9 7.1 7.3 7. 4 7.5 7.5 7.6 7.7 Morocco 0.4 0.9 0.8 0.6 0.6 0.6 0.6 0. 6 0.6 0.6 0.7 0.7 Tunisia 0.6 0.5 0.5 0.5 0.5 0.5 0.6 0. 6 0.6 0.6 0.6 0.6 Republic of South Africa 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0. 1 0.1 0.1 0.1 0.1 Iran 0.6 0.6 0.7 1.0 1.2 1.3 1.3 1. 4 1.4 1.5 1.5 1.6 Other N. Africa & M. East 2.1 2.0 2.1 2.2 2.2 2.3 2.4 2. 5 2.5 2.6 2.7 2.8

Other f oreign4 0.2 0.7 0.6 0.6 0.6 0.6 0.6 0. 6 0.6 0.6 0.7 0.7

United States 0.3 0.4 0.5 0.5 0.5 0.5 0.5 0. 5 0.5 0.5 0.5 0.5

Total trade 15.0 15.1 16.2 16.7 17.4 18.0 18.5 18. 7 18.9 19.2 19.5 19.8

Exporters European Union2 3.5 4.5 3.2 3.1 3.5 3.7 4.0 4. 2 4.4 4.5 4.6 4.7 Australia 2.0 2.0 4.0 4.0 4.0 4.0 4.0 4. 0 4.0 4.0 4.0 4.0 Canada 1.2 2.4 2.3 2.2 2.1 2.0 1.9 1. 8 1.7 1.6 1.5 1.4 Russia 1.5 1.6 1.6 1.6 1.6 1.6 1.6 1. 6 1.6 1.6 1.6 1.6 Ukraine 5.1 1.0 2.7 3.3 3.5 3.8 3.9 3. 9 3.9 4.0 4.2 4.4

Other Former Soviet Union5 0.6 1.0 1.2 1.2 1.2 1.4 1.6 1. 7 1.8 1.9 2.0 2.1 Turkey 0.3 0.0 0.1 0.3 0.4 0.4 0.4 0. 4 0.4 0.4 0.4 0.4 Other f oreign 0.3 1.5 0.5 0.5 0.6 0.6 0.6 0. 6 0.6 0.6 0.6 0.6

United States 0.4 1.1 0.5 0.5 0.5 0.5 0.5 0. 5 0.5 0.5 0.5 0.5

P ercent

U. S. trade share 2.9 7.2 3.4 3.3 3.1 3.0 2.9 2. 9 2.9 2.8 2.8 2.8

1/ Covers FSU-12, includes intra-FSU trade. 2/ Covers E U-27, excludes intra-E U trade. 3/ Includes Mexico. 4/ Includes unaccounted. 5/ Covers FSU-12 except Russia and Ukraine, includes intra-FSU trade. The projections were completed in November 2007.

Exports, million metric tons

Imports, million metric tons

USDA Long-term Projections, February 2008 97

Table 35. W heat trade long-term projections 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Importers Algeria 4.9 4.4 5.0 5.2 5.4 5.6 5.8 6.0 6.2 6.4 6.5 6.7 Egypt 7.3 6.8 6.9 7.3 7.9 8.1 8.3 8.5 8.5 8.6 8.7 8.8 Morocco 1.8 4.0 3.0 3.2 3.4 3.5 3.5 3.6 3.7 3.7 3.8 3.8 Iran 1.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 Iraq 3.0 3.0 3.8 4.0 4.0 4.1 4.2 4.3 4.4 4.5 4.6 4.7 Tunisia 1.4 1.3 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.6 1.6 Other N. Africa & Middle East 9.2 8.0 8.4 8.6 8.9 9.1 9.3 9.6 9.8 10.0 10.2 10.4 Sub-Saharan A frica1 11.4 10.8 12.9 13.2 13.5 13.9 14.3 14.6 15.0 15.4 15.8 16.2 Mexico 3.6 3.6 3.6 3.6 3.7 3.8 3.9 4.0 4.0 4.1 4.2 4.2 Central America & Caribbean 3.3 3.2 3.4 3.4 3.4 3.5 3.5 3.5 3.6 3.6 3.6 3.7 Brazil 7.8 7.0 7.2 7.2 7.3 7.5 7.6 7.7 7.8 7.9 8.1 8.2 Other S outh Am erica 6.2 6.0 6.3 6.3 6.4 6.5 6.5 6.6 6.6 6.7 6.8 6.8 European Union2 5.1 6.5 5.5 5.5 5.6 5.7 5.7 5.8 5.8 5.9 5.9 6.0 Other E urope 1.5 1.2 1.8 1.7 1.7 1.7 1.6 1.6 1.5 1.5 1.4 1.3 Former Soviet Union3 5.9 5.1 5.2 5.3 5.2 5.3 5.3 5.4 5.4 5.4 5.5 5.5 Japan 5.7 5.5 5.5 5.5 5.5 5.5 5.4 5.4 5.4 5.4 5.3 5.3 South Korea 3.4 3.0 3.6 3.7 3.8 3.9 4.0 4.1 4.2 4.3 4.4 4.4 Philippines 2.7 2.5 2.7 2.8 2.9 2.9 3.0 3.0 3.1 3.2 3.2 3.3 Indonesia 5.6 5.3 5.7 5.9 6.0 6.2 6.4 6.6 6.8 7.0 7.2 7.4 China 0.4 0.2 0.6 0.8 0.9 1.0 1.2 1.4 1.7 1.9 2.1 2.3 Bangladesh 1.8 1.6 1.7 1.7 1.7 1.7 1.7 1.8 1.8 1.8 1.8 1.8 Malaysia 1.2 1.2 1.3 1.3 1.3 1.4 1.4 1.4 1.4 1.4 1.4 1.4 Thailand 1.1 1.2 1.3 1.3 1.4 1.4 1.4 1.5 1.5 1.6 1.6 1.7 Vietnam 1.3 1.2 1.3 1.3 1.4 1.5 1.6 1.6 1.7 1.8 1.9 1.9 Pakistan 0.1 0.5 0.2 0.4 0.6 0.8 1.0 1.1 1.3 1.5 1.8 2.0 Other A sia & Oceania 11.7 6.6 7.0 7.3 7.7 8.2 8.6 8.8 9.1 9.4 9.6 9.9 Other f oreign4 -0.7 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 3.2

United States 3.3 2.4 2.7 2.9 3.0 3.0 3.1 3.1 3.3 3.3 3.4 3.4

Total trade 111.2 105.1 111.2 114.0 117.4 120.5 123.6 126.2 128.8 131.5 134.2 137.2

Exporters European Union2 13.9 9.5 12.0 13.5 15.0 16.6 17.5 17.9 18.4 19.0 19.6 20.4 Canada 19.6 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0 14.0 Australia 9.0 9.0 15.5 16.0 16.5 17.0 17.6 18.2 18.8 19.4 20.0 20.6 Argentina 10.5 10.5 11.1 11.1 11.6 11.7 12.2 12.8 13.4 13.9 14.5 15.0 Russia 10.8 12.0 12.2 12.7 13.2 13.7 14.2 14.7 15.2 15.7 16.3 17.0 Ukraine 3.4 1.5 4.1 4.5 5.1 5.7 6.2 6.8 7.2 7.6 8.1 8.5 Other Former Soviet Union5 8.2 8.1 7.0 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 Other E urope 0.6 0.3 0.3 0.3 0.2 0.3 0.4 0.5 0.5 0.6 0.6 0.6 India 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 China 2.8 3.0 2.8 2.7 2.4 2.3 2.1 2.0 1.8 1.7 1.6 1.5 Turkey 2.0 1.5 1.6 1.6 1.6 1.6 1.6 1.5 1.5 1.5 1.5 1.5 Other f oreign 5.6 4.4 4.6 4.6 4.6 4.5 4.5 4.4 4.4 4.4 4.3 4.3

United States 24.7 31.3 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9

Percent

U. S. trade share 22.2 29.8 23.3 22.7 22.0 21.4 20.9 20.5 20.1 19.7 19.3 18.8 1/ Includes Republic of South Africa. 2/ Covers E U-27, excludes intra-E U trade. 3/ Covers FSU-12, includes intra-FSU trade. 4/ Includes unaccounted which can be negative. 5/ Covers FSU-12 except Russia and Ukraine, includes intra-FSU trade. The projections were completed in November 2007.

Imports, million metric tons

Exports, million metric tons

98 USDA Long-term Projections, February 2008

Table 36. Soybean trade long-term projections 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Importers European Union1 15.4 15.8 15.5 15.4 15. 2 15.0 14.8 14.6 14.4 14.3 14.1 13.9 Japan 4.1 4.2 4.1 4.1 4. 1 4.1 4.1 4.1 4.1 4.2 4.2 4.2 South Korea 1.3 1.2 1.3 1.3 1. 3 1.3 1.3 1.3 1.3 1.3 1.3 1.2 Taiwan 2.4 2.5 2.5 2.5 2. 5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 Mexico 3.9 4.0 4.1 4.2 4. 3 4.4 4.5 4.7 4.8 4.9 5.0 5.1 Former Soviet Union2 0.0 0.0 0.0 0.1 0. 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Other E urope 0.5 0.5 0.5 0.5 0. 5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 China 28.7 33.5 36.2 38.8 41. 8 44.7 47.0 49.3 51.5 53.8 56.0 58.3 Malaysia 0.5 0.7 0.6 0.7 0. 7 0.7 0.7 0.7 0.8 0.8 0.8 0.8 Indonesia 1.5 1.6 1.6 1.7 1. 7 1.8 1.8 1.9 1.9 2.0 2.0 2.0 Other 12.4 11.4 12.1 12.5 13. 1 13.7 14.3 14.9 15.5 16.1 16.7 17.3

Total imports 70.7 75.2 78.5 81.6 85. 2 88.6 91.6 94.5 97.3 100.3 103.1 106.0

Exporters Argentina 8.7 10.2 9.1 8.5 7. 7 8.2 8.2 8.2 8.3 8.5 8.6 8.7 Brazil 23.5 30.7 36.1 40.7 45. 0 48.3 51.0 53.4 56.1 58.4 60.6 62.9 Other S outh Am erica 5.4 5.8 6.4 6.7 7. 0 7.3 7.6 7.9 8.2 8.5 8.8 9.1 China 0.4 0.3 0.3 0.3 0. 3 0.3 0.3 0.2 0.2 0.2 0.2 0.2 Other f oreign 2.2 1.7 2.0 2.0 2. 1 2.1 2.2 2.3 2.3 2.4 2.5 2.6

United States 30.4 26.5 24.6 23.5 23. 1 22.5 22.3 22.5 22.2 22.3 22.5 22.5

Total exports 70.7 75.2 78.5 81.6 85. 2 88.6 91.6 94.5 97.3 100.3 103.1 106.0

P ercent

U. S. trade share 43.0 35.3 31.4 28.8 27. 2 25.3 24.4 23.8 22.8 22.3 21.8 21.2

1/ Covers E U-27, excludes intra-E U trade. 2/ Covers FSU-12, includes intra-FSU trade. The projections were completed in November 2007.

Import s, million metric tons

Exports, million metric t ons

USDA Long-term Projections, February 2008 99

Table 38. Soybean oil trade long-term projections

2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Importers China 2.4 2.6 2.7 2.8 2.8 2.8 2.9 3.0 3.1 3.2 3.2 3.3 India 1.5 1.5 1.6 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.4 2.5 Other A sia 1.2 1.2 1.3 1.3 1.3 1.4 1.4 1.4 1.5 1.5 1.5 1.6 Latin America 1.5 1.5 1.6 1.6 1.7 1.7 1.8 1.8 1.9 1.9 1.9 1.9 North Africa & Middle East 1.8 2.0 2.1 2.1 2.2 2.3 2.3 2.4 2.4 2.5 2.5 2.5

European Union1 0.9 1.1 1.1 1.2 1.3 1.4 1.5 1.5 1.6 1.7 1.7 1.8 Former Soviet Union & Other Europe2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Other 0.8 1.0 1.0 1.0 1.0 1.0 1.0 1.1 1.1 1.1 1.1 1.1

Total imports 10.1 11.0 11.5 11.8 12.2 12.6 13.1 13.5 13.9 14.4 14.4 14.8

Exporters Argentina 6.0 6.6 6.9 7.2 7.6 7.8 8.0 8.3 8.5 8.7 8.7 8.9 Brazil 2.5 2.3 2.9 2.8 2.8 3.0 3.2 3.4 3.7 3.9 3.9 4.2

European Union1 0.2 0.2 0.2 0.3 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.1 Other f oreign 1.0 1.0 0.9 0.9 0.9 1.0 1.0 1.0 1.0 1.0 1.0 1.0

United States 0.9 0.7 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.7 0.7 0.8

Total exports 10.5 10.7 11.5 11.8 12.2 12.6 13.1 13.5 13.9 14.4 14.4 14.8

Percent

U. S. trade share 8.2 6.5 4.9 4.8 5.2 5.5 5.5 5.5 5.4 5.2 5.2 5.1 1/ Covers E U-27, excludes intra-E U trade. 2/ Includes intra-FSU trade. The projections were completed in November 2007.

Imports, million metric tons

Exports, million metric tons

Table 37. Soybean meal trade long-term projections 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Importers European Union1 22.9 24.4 24.0 24.8 25. 6 26.4 27.2 27.9 28.7 29.5 30.3 31.1 Former Soviet Union2 0.9 1.1 1.4 1.3 1. 4 1.5 1.6 1.7 1.8 1.9 2.1 2.2 Other E urope 0.6 0.6 0.7 0.6 0. 6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 Canada 1.4 1.5 1.5 1.5 1. 5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 Japan 1.7 1.7 1.7 1.7 1. 8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 Southeast Asia 8.4 9.0 9.4 9.8 10. 2 10.6 11.0 11.5 11.9 12.3 12.8 13.2 Latin America 7.4 7.8 8.2 8.6 8. 9 9.3 9.7 10.1 10.4 10.8 11.2 11.6 North Africa & Middle East 4.3 4.6 4.8 5.0 5. 3 5.5 5.7 5.9 6.2 6.4 6.6 6.9 Other 4.8 5.7 6.0 5.9 6. 1 6.2 6.4 6.6 6.8 7.0 7.2 7.4

Total imports 52.4 56.4 57.7 59.3 61. 3 63.3 65.4 67.6 69.7 71.9 74.2 76.4

Exporters Argentina 25.6 29.5 30.2 33.1 35. 0 35.9 37.0 38.2 39.2 40.2 41.1 42.0 Brazil 12.7 12.0 12.3 11.0 11. 1 12.1 13.1 14.1 15.2 16.5 17.8 19.1 Other S outh Am erica 2.0 2.0 2.0 2.1 2. 1 2.2 2.2 2.3 2.3 2.3 2.4 2.4 China 0.9 0.7 0.6 0.6 0. 6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 India 3.5 3.5 3.5 3.4 3. 3 3.3 3.2 3.1 3.1 3.0 3.0 2.9 European Union1 0.6 0.7 0.6 0.6 0. 6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 Other f oreign 0.5 0.5 0.5 0.5 0. 5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

United States 8.0 7.5 7.9 8.0 8. 1 8.2 8.3 8.3 8.3 8.3 8.3 8.3

Total exports 53.7 56.4 57.7 59.3 61. 3 63.3 65.4 67.6 69.7 71.9 74.2 76.4

P ercent U. S. trade share 14.9 13.4 13.7 13.5 13. 2 13.0 12.6 12.2 11.8 11.5 11.1 10.8 1/ Covers E U-27, excludes intra-E U trade. 2/ Covers FSU-12, includes intra-FSU trade. The projections were completed in November 2007.

Import s, million metric tons

Exports, million metric t ons

100 USDA Long-term Projections, February 2008

Table 39. Rice trade long-term projections 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Importers Canada 0.35 0.37 0.37 0.38 0.38 0.39 0.39 0.40 0.41 0.41 0.42 0.42 Mexico 0.60 0.63 0.64 0.65 0.67 0.68 0.70 0.72 0.74 0.76 0.78 0.80 Central America/Caribbean 1.74 1.67 1.77 1.82 1.85 1.91 1.96 2.01 2.05 2.10 2.14 2.19 Brazil 0.85 0.85 0.80 0.77 0.76 0.75 0.74 0.70 0.71 0.66 0.61 0.55 Other S outh Am erica 0.46 0.47 0.49 0.47 0.44 0.46 0.47 0.46 0.46 0.47 0.47 0.46

European Union1 1.10 1.10 1.13 1.15 1.18 1.21 1.25 1.27 1.29 1.32 1.35 1.37 Former Soviet Union2 0.36 0.44 0.41 0.41 0.43 0.42 0.42 0.42 0.41 0.41 0.40 0.40 Other E urope 0.13 0.12 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.13 0.13 0.13 Bangladesh 0.77 0.80 1.00 1.00 1.15 1.25 1.27 1.37 1.48 1.62 1.78 1.92 China 0.60 0.70 0.70 0.72 0.73 0.74 0.75 0.76 0.78 0.79 0.81 0.82 Japan 0.65 0.70 0.68 0.68 0.68 0.68 0.68 0.68 0.68 0.68 0.68 0.68 South Korea 0.27 0.27 0.29 0.31 0.33 0.35 0.37 0.39 0.41 0.41 0.41 0.41 Indonesia 1.90 1.60 1.70 1.70 1.75 1.80 1.90 2.00 2.10 2.20 2.30 2.40 Malaysia 0.90 0.80 0.80 0.81 0.82 0.83 0.84 0.85 0.87 0.88 0.89 0.91 Philippines 1.80 1.80 1.90 1.92 1.95 1.97 2.00 2.02 2.05 2.08 2.13 2.20 Other A sia & Oceania 2.46 2.64 2.70 2.68 2.67 2.70 2.72 2.74 2.77 2.82 2.87 2.92 Iraq 0.70 1.10 1.10 1.16 1.20 1.25 1.29 1.33 1.37 1.41 1.45 1.49 Iran 1.20 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 Saudi Arabia 1.45 0.96 1.18 1.28 1.30 1.32 1.35 1.37 1.39 1.41 1.43 1.45 Other N. Africa & M. East 1.62 1.49 1.59 1.66 1.70 1.75 1.80 1.84 1.88 1.93 1.98 2.03

Sub-Saharan A frica3 6.82 6.70 6.90 7.22 7.45 7.61 7.80 8.00 8.18 8.35 8.53 8.70 Republic of South Africa 0.96 0.90 0.90 0.94 0.94 0.95 0.97 0.98 0.99 1.00 1.01 1.03

Other f oreign4 0.33 2.03 1.90 1.94 1.99 2.04 2.08 2.16 2.20 2.25 2.26 2.28

United States 0.64 0.68 0.70 0.72 0.74 0.76 0.79 0.81 0.84 0.86 0.89 0.91

Total imports 28.65 29.69 30.66 31.42 32.13 32.85 33.57 34.30 35.08 35.85 36.61 37.35

Exporters Australia 0.20 0.02 0.02 0.03 0.03 0.03 0.03 0.04 0.04 0.04 0.04 0.04 Argentina 0.45 0.45 0.46 0.47 0.48 0.49 0.52 0.55 0.58 0.62 0.66 0.70 Other S outh Am erica 1.14 1.31 1.32 1.32 1.32 1.31 1.30 1.30 1.29 1.29 1.31 1.34

European Union1 0.15 0.15 0.15 0.16 0.16 0.16 0.16 0.16 0.17 0.17 0.17 0.17 China 1.30 1.60 1.70 1.80 1.90 2.00 2.10 2.20 2.30 2.35 2.40 2.45 India 4.20 3.40 3.60 3.50 3.45 3.42 3.40 3.37 3.35 3.32 3.30 3.28 Pakistan 3.00 3.20 3.43 3.37 3.40 3.40 3.42 3.43 3.45 3.47 3.50 3.50 Thailand 8.50 9.00 9.52 10.10 10.50 10.88 11.20 11.60 12.00 12.40 12.80 13.20 Vietnam 4.60 5.00 5.28 5.36 5.50 5.65 5.77 5.90 6.05 6.20 6.35 6.50 Egypt 1.00 1.10 1.03 0.97 0.91 0.87 0.83 0.81 0.80 0.79 0.79 0.79 Other f oreign 1.17 1.04 1.01 1.02 1.03 1.05 1.08 1.08 1.09 1.13 1.15 1.17

United States 2.94 3.42 3.13 3.33 3.45 3.58 3.74 3.87 3.97 4.06 4.14 4.22

Total exports 28.65 29.69 30.66 31.42 32.13 32.85 33.57 34.30 35.08 35.85 36.61 37.35

P ercent

U. S. trade share 10.3 11.5 10.2 10.6 10. 7 10.9 11.1 11.3 11.3 11.3 11.3 11.3 1/ Covers E U-27, excludes intra-E U trade. 2/ Covers FSU-12, includes intra-FSU trade. 3/ Excludes Republic of South Africa 4/ Includes unaccounted. The projections were completed in November 2007.

Import s, million metric tons

Exports, million metric t ons

USDA Long-term Projections, February 2008 101

Table 40. All cotton trade long-term projections 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18

Importers

European Union1 2.1 2.0 1.9 1.9 1.9 1.9 1.8 1.7 1.6 1.6 1. 6 1.5

Former Soviet Union2 1.8 1.8 1.7 1.6 1.6 1.6 1.5 1.5 1.5 1.5 1. 5 1.5 Indonesia 2.2 2.3 2.2 2.2 2.3 2.3 2.3 2.4 2.4 2.4 2. 4 2.5 Thailand 2.0 1.9 1.9 1.9 2.0 2.0 2.0 2.0 2.0 2.0 2. 0 2.1 India 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0. 4 0.4 Brazil 0.5 0.4 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0. 3 0.3 Other E urope 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0. 1 0.1 Other A sia & Oceania 4.0 4.1 4.2 4.4 4.7 5.0 5.1 5.4 5.7 6.0 6. 3 6.6 Pakistan 2.3 3.0 3.1 3.2 3.3 3.4 3.4 3.5 3.6 3.6 3. 7 3.8 Japan 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.5 0.5 0. 5 0.5 South Korea 1.1 1.0 1.0 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0. 9 0.9 China 10.6 14.5 17.6 19.0 20.1 21.8 23.0 24.0 24.8 25.9 26. 8 27.8 Taiwan 1.2 1.2 1.1 1.1 1.1 1.2 1.2 1.2 1.2 1.1 1. 1 1.1 Turkey 4.0 3.8 4.1 4.2 4.3 4.3 4.3 4.4 4.4 4.5 4. 6 4.6 Mexico 1.4 1.5 1.5 1.5 1.5 1.4 1.5 1.5 1.4 1.4 1. 4 1.4 Other 3.0 2.9 2.9 3.0 3.0 3.0 3.0 3.0 3.0 3.1 3. 1 3.1

Total imports 37.3 41.4 44.6 46.4 48.0 50.1 51.4 52.8 53.9 55.3 56. 7 58.1

Exporters

Former Soviet Union2 6.9 6.9 6.7 6.8 6.8 7.0 7.1 7.1 7.2 7.3 7. 4 7.5 Australia 2.1 1.5 1.5 1.9 2.4 2.8 3.0 3.2 3.2 3.2 3. 2 3.3 Argentina 0.0 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0. 2 0.2 Pakistan 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0. 2 0.2 India 5.1 5.0 6.7 7.0 7.2 7.5 7.7 7.9 7.9 7.9 8. 0 8.0 Egypt 0.5 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0. 8 0.9 Brazil 1.3 2.8 3.8 5.0 5.3 5.8 6.0 6.1 6.2 6.4 6. 7 6.9 Other Latin America 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0. 4 0.4 Sub-Saharan A frica3 5.3 4.7 4.8 5.4 5.5 5.8 6.0 6.3 6.6 6.9 7. 2 7.5 Other f oreign 2.6 2.6 2.7 3.0 3.1 3.1 3.2 3.3 3.3 3.4 3. 4 3.5

United States 13.0 16.2 16.6 15.4 15.7 16.1 16.5 16.9 17.5 18.1 18. 7 19.2

Total exports 37.6 41.1 44.2 45.9 47.5 49.6 50.9 52.3 53.4 54.8 56. 2 57.6

Percent

U. S. trade share 34.6 39.4 37.6 33.6 33.1 32.4 32.3 32.3 32.7 33.0 33. 2 33.3 1/ Covers E U-27, excludes intra-E U trade. 2/ Covers FSU-12, includes intra-FSU trade. 3/ Includes Republic of South Africa. The projections were completed in November 2007.

Imports, million bales

Exports, million bales

102 USDA Long-term Projections, February 2008

Table 42. Pork trade long-term projections 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Importers Japan 1,154 1,200 1,210 1,220 1,230 1,240 1,250 1,260 1,270 1,280 1,290 1,300 China 90 130 150 168 183 206 223 231 236 244 256 266 Hong Kong 277 293 300 307 315 323 332 343 354 366 379 391 South Korea 410 450 475 495 510 520 531 542 553 564 575 587 Russia 835 855 875 897 919 942 966 990 1,015 1,040 1,066 1,093 Mexico 446 435 410 398 400 412 428 450 477 506 536 566 Canada 145 160 165 175 185 195 205 215 225 235 245 255

United States 449 456 465 474 483 492 501 511 522 533 545 556

Major importers 3,806 3,979 4,050 4,132 4,225 4,329 4,435 4,541 4,652 4,768 4,891 5,013

Exporters Brazil 639 715 775 785 764 803 840 860 891 911 932 961 Canada 1,081 1,040 1,010 990 980 978 980 995 1,012 1,032 1,055 1,078 Mexico 66 70 80 83 85 88 91 94 97 100 103 106 European Union1 1,283 1,270 1,147 1,148 1,190 1,203 1,225 1,243 1,253 1,264 1,281 1,300 China 595 440 465 479 499 513 526 539 547 555 566 576

United States 1,359 1,373 1,442 1,500 1,545 1,586 1,626 1,672 1,727 1,796 1,857 1,909

Major exporters 5,023 4,908 4,919 4,985 5,063 5,171 5,287 5,403 5,527 5,657 5,793 5,929

1/ Covers E U-27, excludes intra-E U trade. The projections were completed in November 2007.

Imports, thousand met ric tons, carcass weight

Exports, thousand metric tons, carcass weight

Table 41. Beef trade long-term projections 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Importers Japan 678 715 725 783 811 822 830 836 839 843 849 851 South Korea 298 315 320 356 366 373 383 394 399 409 416 430 Taiwan 104 105 105 112 116 119 122 125 127 130 132 135 Philippines 136 160 167 179 183 190 194 199 207 216 222 229

European Union1 717 725 750 764 757 740 732 725 718 716 712 709 Russia 939 1,050 1,100 1,219 1,232 1,252 1,268 1,290 1,320 1,348 1,374 1,398 Other E urope 30 30 30 31 32 36 37 39 39 39 39 39 Egypt 291 250 255 272 280 287 295 302 309 316 324 332 Mexico 383 400 410 471 499 534 562 602 641 681 724 765 Canada 180 225 255 278 283 286 292 298 305 311 320 332

United States 1,399 1,471 1,551 1,572 1,594 1,615 1,636 1,658 1,680 1,703 1,725 1,748

Major importers 5,155 5,446 5,669 6,038 6,151 6,254 6,352 6,468 6,584 6,712 6,837 6,969

Exporters Australia 1,430 1,450 1,380 1,295 1,304 1,315 1,325 1,327 1,332 1,335 1,336 1,339 New Zealand 530 515 530 504 501 504 504 508 510 512 515 517 Other A sia 767 824 902 851 873 892 912 932 951 965 980 995

European Union1 216 175 175 164 165 172 174 175 173 176 177 180 Argentina 552 525 535 480 449 408 375 367 372 386 405 417 Brazil 2,084 2,400 2,650 2,688 2,749 2,812 2,865 2,913 2,946 2,980 3,013 3,051 Canada 477 480 550 555 562 561 561 567 576 587 598 608

United States 519 650 776 828 874 922 970 1,018 1,068 1,118 1,168 1,223

Major exporters 6,575 7,019 7,498 7,365 7,476 7,585 7,687 7,807 7,927 8,059 8,191 8,330 1/ Covers E U-27, excludes intra-E U trade. The projections were completed in November 2007.

Imports, thousand met ric tons, carcass weight

Exports, thousand metric tons, carcass weight

USDA Long-term Projections, February 2008 103

Table 43. Poultry trade long-term projections1

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Importers Russia 1,285 1,245 1,235 1,241 1,248 1,255 1,259 1,262 1,264 1,264 1,265 1,265

European Union2 717 655 655 658 662 665 668 672 675 678 682 685 Other E urope 5 5 5 7 6 5 5 5 5 5 6 6 Canada 122 150 162 164 166 168 170 172 175 177 179 182 Mexico 619 612 632 654 677 729 758 791 834 884 930 979 Central America/Caribbean 307 310 327 326 334 338 342 347 351 359 368 376 Japan 716 675 680 683 692 692 697 695 700 703 704 704 Hong Kong 243 233 245 250 253 256 259 262 265 269 272 275 China 343 513 560 577 605 626 642 657 677 690 702 724 South Korea 76 61 70 79 92 105 118 131 147 162 178 195 Saudi Arabia 423 440 450 476 492 505 521 537 553 569 585 601 Other N. Africa & M. East 293 397 550 471 508 534 563 592 621 661 702 745

Major importers 5,149 5,296 5,571 5,586 5,734 5,879 6,002 6,123 6,267 6,421 6,571 6,737

Exporters

European Union2 820 810 805 709 714 710 691 668 640 640 644 652 Brazil 2,658 3,068 3,279 3,300 3,388 3,462 3,538 3,610 3,703 3,813 3,914 4,044 China 322 353 390 432 442 451 461 474 495 509 522 531 Thailand 261 315 320 350 352 360 374 384 396 401 406 412

United States 2,609 2,732 2,799 2,789 2,822 2,871 2,911 2,956 2,998 3,042 3,087 3,123

Major exporters 6,670 7,278 7,593 7,580 7,719 7,854 7,974 8,091 8,232 8,405 8,574 8,762 1/ Broilers and turkeys only. 2/ Covers E U-27, excludes intra-E U trade. The projections were completed in November 2007.

Imports, thousand metric tons, ready to cook

Exports, thousand metric tons, ready to cook

104 USDA Long-term Projections, February 2008

List of Tables Page

Table 1. U.S. macroeconomic assumptions........................................................................................... 17 Table 2. Global real GDP growth assumptions ..................................................................................... 18 Table 3. Population growth assumptions............................................................................................... 19 Table 4. Summary policy variables for major field crops, 2005-2016 .................................................. 33 Table 5. Conservation Reserve Program acreage assumptions ............................................................. 33 Table 6. Planted and harvested acreage for major field crops, long-term projections .......................... 34 Table 7. Selected supply, use, and price variables for major field crops, long-term projections .......... 35 Table 8. U.S. corn long-term projections .............................................................................................. 36 Table 9. U.S. sorghum long-term projections ....................................................................................... 37 Table 10. U.S. barley long-term projections ........................................................................................... 38 Table 11. U.S. oats long-term projections ............................................................................................... 39 Table 12. U.S. wheat long-term projections ............................................................................................ 40 Table 13. U.S. soybean and products long-term projections ................................................................... 41 Table 14. U.S. rice long-term projections, rough basis ........................................................................... 42 Table 15. U.S. upland cotton long-term projections................................................................................ 43 Table 16. U.S. sugar long-term projections............................................................................................. 44 Table 17. Horticultural crops long-term projections: Production, values, and prices, calendar years .... 45 Table 18. Horticultural crops long-term projections: Exports and imports, fiscal years......................... 46 Table 19. Per capita meat consumption, retail weight............................................................................. 52 Table 20. Consumer expenditures for meats ........................................................................................... 52 Table 21. Beef long-term projections ...................................................................................................... 53 Table 22. Pork long-term projections ...................................................................................................... 54 Table 23. Young chicken long-term projections ..................................................................................... 54 Table 24. Turkey long-term projections .................................................................................................. 55 Table 25. Egg long-term projections ....................................................................................................... 55 Table 26. Dairy long-term projections .................................................................................................... 56 Table 27. Farm receipts, expenses, and income, long-term projections.................................................. 62 Table 28. Consumer food price indexes and food expenditures, long-term projections ......................... 63 Table 29. Changes in consumer food prices, long-term projections ....................................................... 63 Table 30. Summary of U.S. agricultural trade long-term projections, fiscal years ................................. 64 Table 31. Coarse grains trade long-term projections............................................................................... 94 Table 32. Corn trade long-term projections............................................................................................. 95 Table 33. Sorghum trade long-term projections ...................................................................................... 96 Table 34. Barley trade long-term projections .......................................................................................... 96 Table 35. Wheat trade long-term projections .......................................................................................... 97 Table 36. Soybean trade long-term projections....................................................................................... 98 Table 37. Soybean meal trade long-term projections .............................................................................. 99 Table 38. Soybean oil trade long-term projections.................................................................................. 99 Table 39. Rice trade long-term projections ........................................................................................... 100 Table 40. All cotton trade long-term projections .................................................................................. 101 Table 41. Beef trade long-term projections ........................................................................................... 102 Table 42. Pork trade long-term projections ........................................................................................... 102 Table 43. Poultry trade long-term projections....................................................................................... 103

oecd.pdf

Rising Food Prices

CAUSES AND CONSEQUENCES

RISING FOOD PRICES: CAUSES AND CONSEQUENCES — © OECD 2008 2

EXECUTIVE SUMMARY

 Recent steep price increases of major crops (cereals, oilseeds) were triggered by a combination of production remaining somewhat below trend and strong growth of demand.

 A low and declining level of stocks has added to the price rise, as has probably a significant increase in investments in agricultural derivative markets.

 The OECD-FAO Agricultural Outlook expects prices to come down again, but not to their historical levels. On average over the coming ten year period, prices in real terms of cereals, rice

and oilseeds are projected to be 10% to 35% higher than in the past decade.

 The acute price hike adds to inflationary pressures in developed countries. Poor consumers in developing countries, and food importing developing countries overall, will have to spend an

even higher share of their limited income on food.

 In the short term, humanitarian aid is required, where appropriate in the form of cash or vouchers so as to strengthen, rather than undermine domestic markets in recipient countries.

 In the medium term, there is a need to foster growth and development in poor countries, to improve the purchasing power of food buyers. Agricultural trade policies require further reform

in order to ensure an effective supply response. Investments in productivity growth, particularly

in less developed countries, should also strengthen the supply side of global agriculture. On the

demand side, policies that encourage increased production and use of biofuels from agricultural

commodity feed stocks warrant review.

Introduction

World prices of wheat, coarse grains, rice and oilseed crops all nearly doubled between the 2005 and

2007 marketing years and continued rising in early 2008. These increases in agricultural commodity prices

have been a significant factor driving up the cost of food and have led to a fuller awareness and a

justifiably heightened concern about problems of food security and hunger, especially for developing

countries.

The causes of this price spike are complex and due to a combination of mutually reinforcing factors,

including droughts in key grain-producing regions, low stocks for cereals and oilseeds, increased feedstock

use in the production of biofuels, rapidly rising oil prices and a continuing devaluation of the US dollar, the

currency in which indicator prices for these commodities are typically quoted. This turmoil in commodity

markets has occurred against the backdrop of an unsettled global economy, which in turn appears to have

contributed to a substantial increase in speculative interest in agricultural futures markets.

Tight market conditions for essential agricultural commodities pose policy challenges for national

governments as well as for international organisations. In order to take the right policy decisions, we need

to understand what caused the current price spike, what the implications may be for prices and price

volatility in the future, and how various countries and members of society may be affected. This note aims

to improve this understanding and thereby to contribute to sound policy formulation.

RISING FOOD PRICES: CAUSES AND CONSEQUENCES — © OECD 2008 3

Are food price hikes unprecedented?

The commodity price developments witnessed recently are certainly unusual when viewed from the

perspective of the last decade or so, but less so when seen in a longer historical context. Figure 1 shows the

evolution – in nominal and in real terms – of annual average world prices of wheat, coarse grains, rice and

oilseeds from 1971 to 2007 with projections from 2008 to 2017. While spot prices for April-May 2008 are

not shown, for these commodities price levels greatly exceeded the expected annual average for 2008. Two

points are clear: first, agricultural commodity markets are notoriously volatile; second, the current price

spike is neither the only nor even the most significant one to occur in the last forty years.

Figure 1. Food commodity price trends 1971 – 2007, with projections to 2017

0

100

200

300

400

500

600

700

1 9

7 1

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U S D / t

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Wheat

0

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300

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500

1 9

7 1

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Nominal Real

Coarse Grains

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U S D / t

Nominal Real

Oilseeds

0

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400

600

800

1000

1200

1400

1600

1 9

7 1

1 9

7 3

1 9

7 5

1 9

7 7

1 9

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8 1

1 9

8 3

1 9

8 5

1 9

8 7

1 9

8 9

1 9

9 1

1 9

9 3

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9 5

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2 0

0 7

2 0

0 9

2 0

1 1

2 0

1 3

2 0

1 5

2 0

1 7

U S D / t

Nominal Real

Rice

Note: Real prices deflated by USA GDP deflator 2007 = 1.

Source: OECD-FAO Agricultural Outlook, 2008-2017

RISING FOOD PRICES: CAUSES AND CONSEQUENCES — © OECD 2008 4

It is also important to recall that prices for meat and poultry have seen no or modest increases during

this same period. There have been substantial increases in the prices of dairy products, at least in part due

to recent policy developments, though more recently prices have again declined somewhat. As the

international debate has focused on the implications of increases in prices for crop markets this note

focuses primarily on prices for cereals and oilseeds.

How can the current price spike be explained?

Prices rise when markets get tight. Between 2005 and 2007 there were coinciding spells of

unfavourable weather in major producing regions in the world, pushing crop yields in those areas below

long term average levels. World cereal output in 2007 was just 3% larger than in 2005 while there was a

decline in overall oilseeds output; nevertheless, vegetable oil production rose by 7% due to rapid growth in

palm oil output.

At the same time, there was strong demand growth. Demand for wheat and coarse grains grew almost

twice as much as did production, and demand for vegetable oil increased two percentage points more than

output. More than half of the increase in use of both coarse grains and vegetable oil was due to higher use

in the biofuels industry. Use of cereals and vegetable oil for food also continued to grow, as did cereal use

for feed. This food and feed demand growth came primarily from countries outside the OECD area and

accounted for the remaining nearly 50% of the total increase in demand.

The production shortfall, relative to trend, would in itself have been enough to send prices higher,

although under ‗normal‘ conditions stocks would have buffered the market and dampened the price rise.

But stocks were already low and they kept declining in 2006 and 2007 because of bad weather and low

yields in major exporting countries. Supply shortfalls, the absence of a sufficient buffer, the continued

increase in food and feed use, and the high growth in relatively price-insensitive demand for biofuels all

coincided to make the price increases exceptional. More recently, there has also been a significant increase

in investments in agricultural derivative markets from non-traditional sources, whether for portfolio

diversification or speculation. It is likely that this has contributed to the rise in short term futures prices and

is an additional factor in the current spike in spot market prices.

Some of the factors behind the current price hike are transitory while others may be more permanent.

Making that distinction is an important ingredient in projecting market developments over the coming ten

year period, as is done in the OECD-FAO Agricultural Outlook. This differentiation is also important in

designing good policy to deal with the implications of the price increases.

What factors will shape future prices and price volatility?

The recent negative yield shocks in key agricultural commodity producing regions that have

contributed to price increases should be viewed as temporary. Barring any underlying climate change or

water constraints that could lead to permanent reductions in yield, normal higher output can be expected in

the very short term.

Macroeconomic conditions that favour economic growth, increases in purchasing power, and stronger

demand for agricultural commodities are expected to continue, at least for many non-OECD economies.

This is a permanent factor in future price determination, but not a new one: strong GDP growth in

developing countries has been a feature of commodity markets for many years. Thus, this factor should

slow the decline in real prices in the future, but not lift average prices to permanently higher levels.

RISING FOOD PRICES: CAUSES AND CONSEQUENCES — © OECD 2008 5

The oil price, and energy prices more generally, is a critically important contributing factor to the

increase in production costs for agricultural commodities and food and ultimately in the market prices for

these goods. Price projections discussed here reflect the widely held belief that the oil price increases are

permanent, lifting future prices to higher average levels.

Feedstock demand for biofuel production is expected to increase further, albeit at a slower rate than in

the past three years, and under current policy settings appears to represent a permanent factor in price

formation. Unlike strong income growth in developing countries, this is a new source of demand which is

seen as one of the factors lifting prices to higher average levels in the future.

Stocks of wheat, coarse grains and vegetable oil have fallen to low levels relative to use, reducing the

buffer against shocks in supply and demand. Stocks are not expected to be fully replenished over the

coming ten years, implying that tight markets may be a permanent factor in the period to 2017. This should

not lead to permanently higher prices, but provides the background for more price volatility in the future.

The surge of investment in futures commodity markets from non-traditional sources may have short

term price effects. But relative to the ten year outlook period these may prove temporary, given adjustment

in markets and participants‘ behaviour: funds can move rapidly in and out of commodity markets as profit

opportunities dictate. Given their size, this may well be a new and permanent element in future price

volatility.

A more general point concerning price volatility relates to the ‗thinness‘ of markets, or the share of

imports and exports relative to the size of global consumption or production. When markets are thinner and

prices in domestic markets do not follow those in international trade because of insulating policies or

market imperfections, world market prices must change more to accommodate an external shock to traded

quantities, all else equal. Such market characteristics are expected to remain a permanent feature in the

volatility of prices.

Finally, the nature and composition of demand are factors that may increase the future variability in

world prices. First, industrial demand for grains and oilseeds and in particular policy-driven demand for

biofuels production is generally considered less responsive to prices than traditional food and feed demand.

Second, food demand becomes less responsive to price changes as incomes rise and the commodity share

in the food bill falls. Such changes are permanent factors that may lead to greater volatility in future world

prices.

What are current expectations for future prices?

The current outlook for crop production in 2008 is generally positive. The April forecast for global

cereal production suggests an increase of over 3% from 2007, with the bulk of the growth in wheat due to a

substantial recovery in production in major exporting countries. Rice harvests in major producing countries

are just beginning. Wheat production in major exporting countries is expected to be above 2005 levels. The

increased supply should bring some relief to the market in the course of 2008, but with use forecast to

again exceed supply and stocks forecast to be further drawn down, the overall market situation is likely to

remain tight throughout the year.

Over the 2008-2017 period covered by the OECD-FAO Agricultural Outlook, a strong combination of

supply response and continued growth in demand is expected to keep prices above historical levels, but

well below the peaks experienced today.

RISING FOOD PRICES: CAUSES AND CONSEQUENCES — © OECD 2008 6

Looking ahead to 2017, the average level of wheat and coarse grain prices is expected to remain

higher than in 2005, but well below levels in 2007-2008. World wheat and coarse grain areas are expected

to increase somewhat and yields are expected to grow along historical trends. Oilseeds prices are expected

to remain strong, though slightly lower than today. Current high prices are expected to bring about a supply

response that results in more land allocated to this sector and good yield growth. In addition, palm oil

production is expected to increase 40%. Rice production is expected to grow modestly with continued

productivity growth offsetting a small decline in the area planted.

Demand for cereals for use as feed stocks in biofuel production is projected, under current policies, to

almost double between 2007 and 2017, but the largest part of future growth in total use is explained by

rising food and feed demand, particularly in countries outside the OECD area that are experiencing strong

economic growth. Little rice is used for feed and almost none in biofuel production. Demand for rice,

almost all for food use, is expected to increase by less than 1% per year and is dominated entirely by

growth in developing countries. Biofuel use of vegetable oils is forecast to account for more than a third of

the expected growth in vegetable oil use from 2005 to 2017, and other uses are also expected to grow

substantially. Income growth drives much of this expansion in demand, with countries outside the OECD

area increasing their consumption of vegetable oils by 50%.

Based on these market developments, the Agricultural Outlook forecasts relatively tight markets to

continue, with prices down from current peaks but remaining higher on average than prices experienced

over the past decade, as shown in Figure 2. On average over the coming ten year period, nominal prices for

cereals, rice and oilseeds are expected to be 35% to be 60% higher than on average in the past ten years.

Prices in real terms are projected to be 10% to 35% higher than in the past decade. Productivity gains and

increasing competition in trade from countries outside the OECD area will eventually overtake stronger

demand. As that happens, prices will resume their decline in real terms, though more gradually than in the

past (see Figure 1).

Figure 2. Nominal prices fall but stay above average levels of the past

0

100

200

300

400

500

600

700

800

900

Wheat Maize Rice Oilseeds

U S $ / to n n e

1998-2007 highest 2007-2008 2008-2017 2017

Which factors might change these price projections?

The recent spikes in food commodity prices surprised most economic forecasters, reminding us of the

inherent vulnerability of projections to unanticipated developments. The price projections discussed here

assume normal weather, unchanged policies, and stable economic performance. Looking at alternatives to

these assumptions provides additional insights regarding the factors influencing future prices. Key results

of four alternative scenarios are illustrated in Figure 3 and summarised below:

RISING FOOD PRICES: CAUSES AND CONSEQUENCES — © OECD 2008 7

 If biofuel production is assumed to remain at 2007 levels, rather than doubling over the next 10 years as expected, the projected prices for coarse grains would be 12% lower and vegetable oil

15% lower in 2017 than currently expected.

 If it is also assumed that oil prices stay at their 2007 level over the next decade, projected prices for wheat and maize fall by a further 10% and for vegetable oil by a further 7%.

 If the rates of growth in GDP in Brazil, China, India, Indonesia and South Africa are then reduced to half the rate assumed in the Outlook – with exchange rates and inflation also changed

consistently – projected wheat and coarse grains prices would fall by a modest 1 to 2% more but

vegetable oil prices by a further 10%.

 Finally, if it is also assumed that cereals and oilseeds yields are 5% higher than expected, projected wheat and maize prices would be a further 6 and 8% lower, respectively, but there is

little further change in vegetable oil prices.

All these assumptions tend in the same direction, to lower prices, and taken together would lead to

prices for wheat, coarse grains and vegetable oils that are 20 to 35% lower in 2017 than what is now

projected. It is unlikely that these factors would all combine in the configuration described here, but it

serves to illustrate the relative significance of some of the factors that will determine future price levels. Of

course, alternative scenarios that could push prices back up could also be envisaged – an equivalent

production shortfall in a major exporting country again this year, for example, would be expected to lead to

prices that remain near current levels.

Figure 3. Sensitivity of projected world prices to changes in four key assumptions

Percentage difference from baseline values, 2017

-40%

-35%

-30%

-25%

-20%

-15%

-10%

-5%

0%

Wheat Maize Vegetable oil

Scenario 1: Biofuel production constant at 2007 level

Scenario 2: Scenario 1 + Oil price constant at 2007 level (72$)

Scenario 3: Scenario 2 + Lower income growth in EE5 countries (half annual growth rate)

Scenario 4: Scenario 3 + Yield for wheat, oilseeds and coarse grains 5 % higher than over the projection period

RISING FOOD PRICES: CAUSES AND CONSEQUENCES — © OECD 2008 8

What are the impacts of high food prices?

The impact of high food prices on developing countries depends on the interplay of various factors. In

general, commercial producers of these commodities will benefit directly from higher prices, as will in

many cases the people they employ (assuming, of course, that governments do not prevent higher prices on

world markets from being transmitted to domestic markets). Livestock producers, on the other hand, are

squeezed by both higher feed and energy costs and relatively flat prices. For farm households producing

mainly for their own consumption or for local markets insulated from price fluctuations on national and

international markets, the impacts will be mitigated. But for the urban poor and the major food importing

developing countries, the impacts will be strongly negative as an even higher share of their limited income

will be required for food. Each 10% increase in the prices of all cereals (including rice) adds nearly USD

4.5 billion to the aggregate cereals import bill of those developing countries that are net importers of

cereals.

The impact of high agricultural commodity prices on developed countries is relatively modest, overall.

The agricultural commodity price component of final food product prices is relatively small (often 35% or

less), as is the proportion of disposable income spent on food (10-15% for most OECD countries). Of

course these averages mask much more significant impacts on lower income consumers who spend a larger

share of their expenditure on food. In addition, and to the extent that high prices persist and hence do not

reduce the future rate of inflation, indirect economic impacts might also be important.

What are appropriate policy responses?

In the short term, humanitarian aid is required. Before recent price increases, hundreds of millions of

people were going hungry because they could not afford food. With higher prices, the numbers of people

suffering from extreme hunger has increased even further – the 1st UN MDG has become an even greater

challenge. Immediate and substantial aid is required, where appropriate in the form of cash or vouchers so

as to strengthen, rather than undermine domestic markets in the recipient countries.

In the medium term, there is a real need to improve the purchasing power of poor food buyers so they

can acquire enough food even at the higher prices, relative to past averages, that are expected to prevail in

the future. Fundamentally that requires to foster growth and development in poor countries. In some of the

poorest countries, investment in agriculture, including in agricultural research, extension and education,

may be the best way to cut poverty and stimulate economic activity. In other situations, investment in

agriculture may also be helpful, but there may equally be a need to diversify the structure of the economy.

In many cases, investments in improving the overall environment in which agriculture operates may be

most appropriate – improving basic governance systems, macroeconomic policy, infrastructure,

technology, education, health, etc. In other words, a tailored approach is needed, one that builds upon the

capacity and potential of individual countries, rather than a generalized rush to develop agriculture.

Agricultural trade policies require further reform. Trade restricting policies – whether they restrict

exports or imports – have undesirable and often unintended impacts, especially in the medium and long

term. Subsidies that distort markets are equally unhelpful. Export taxes and embargos may in the short

term provide some relief to domestic consumers, though such measures do not distinguish between low

and high income consumers, and they also impose a burden on domestic producers and limit their supply

response. Export restrictions contribute to global commodity market uncertainty and drive international

market prices further up. On the import side, ―protecting‖ domestic producers of agricultural commodities

by providing high price support and border protection restricts growth opportunities for producers abroad

and imposes a burden on domestic consumers. A swift and ambitious conclusion of the Doha Round of

WTO negotiations could make an important contribution to exploiting the potential of markets to balance

global supply and demand.

RISING FOOD PRICES: CAUSES AND CONSEQUENCES — © OECD 2008 9

It is also instructive to look closely at the causes of recent price increases. On the supply side, the link

between production and yield shortfalls and climate change might be further explored. Investments in

R&D, technology transfer and extension services, particularly in less developed economies, could do much

to increase productivity and output. The use of genetic modification (GMOs) also offers potential that

could be further exploited, to improve productivity, to enhance the attributes of crops destined for either

food or non-food uses, and to enhance the resilience of crops against stress such as drought. On the

demand side, policies that encourage increased production and use of biofuels warrant a close review.

OECD/IEA analysis to date suggests that the energy security, environmental, and economic benefits of

biofuels production based on (first generation) agricultural commodity feed stocks are modest and are

unlikely to be delivered by current policies. Alternative approaches (for example, that encourage reduced

energy demand and GHG emissions, provide for freer trade in biofuels, and accelerate introduction of

‗second generation‘ production technologies that do not rely upon current commodity feed stocks) offer

potentially greater benefits without the unintended impact on food prices.

High agriculture commodity prices also have an impact on close substitutes, such as fish, and could

contribute to even further pressure on already depleted fish stocks, as well as to increased demand for fish

from aquaculture. Policies that ensure the sustainable and responsible use of ocean resources have a key

role to play, both within national boundaries and on the high seas; concerted action to control illegal

fishing is needed. Options to improve the business environment for private investment in aquaculture

might also be explored.

The current hike in food prices is an issue of a truly global nature. It has complex causes and impacts,

and requires a complex response at the international level. Current developments on global food markets

are having dramatic implications for food security among poor people. At the same time, speculative

factors and inward looking policy actions contribute to the nervousness and volatility of markets. What is

needed now is an objective, effective and coherent global response to avoid making a difficult situation

worse.

Now, more than ever, it is important to counter growing calls for trade protectionism. Closing markets

to either imports or exports will have exactly the wrong result. More secure global food supplies will only

come from competitive producers around the world being provided the freedom to respond to current

market opportunities. Continuation or introduction of policies that create distortions and that undermine the

appropriate market responses should be avoided. The OECD will continue to monitor commodity market

developments and government initiatives over the coming months, and report periodically on its findings.

OECD-FAO2007-2016.pdf

ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS

OECD-FAO Agricultural Outlook

2007-2016

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Also available in French under the title:

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FOREWORD

Foreword

This is the third occasion that the Agricultural Outlook report has been prepared jointly by the Organisation for Economic Co-operation and Development (OECD) and the Food and Agriculture

Organization (FAO) of the United Nations. The report draws on the commodity, policy and country

expertise of both Organisations in providing a medium-term assessment of future prospects in the

major world agricultural commodity markets. The report is published annually, as part of a continuing

effort to promote informed discussion of emerging market and policy issues. This edition of the

Agricultural Outlook offers an assessment of agricultural markets covering cereals, oilseeds, sugar,

meats, milk and dairy products over the period 2007 to 2016. It takes account of the enlargement of the

European Union, from twenty-five to twenty-seven member states and for the fist time includes

explicitly assumptions on biofuel production. The market assessments are based on a set of projections

that are conditional on specific assumptions regarding macroeconomic conditions, agricultural and

trade policies and production technologies; it also assumes average weather conditions. Using the

underlying assumptions, the Agricultural Outlook presents a plausible scenario for the evolution of

agricultural markets over the next decade and provides a yardstick or benchmark for the analysis of

agricultural market outcomes that would result from alternative assumptions.

This year’s projections are set against a backdrop of a steady global economic growth over the

medium term, slowing population growth, continuing low inflation, and markets that globally are

responding to the challenge of a rapidly changing biofuel industry. Global economic growth is

propelled mainly by fast growing economies of large developing countries. In particular, the emerging

economies of China, India, Brazil and Russia are key to global and agricultural market developments.

Over the projection period, the countries in the non-OECD region are expected to continue to

experience a much stronger increase in consumption of agricultural products than countries in the

OECD area. This trend is driven by population and, above all, income growth – underpinned by rural

migration to higher income urban areas. The strong growth in demand in many developing and

emerging economies is also expected to spur expansion in imports and provide the impetus to the

development of domestic production capacity. But exports are growing strongly in a number of

developing countries as well. As a result, OECD countries as a group are projected to lose production

and export shares in many commodities to non-OECD countries. Growth in the use of agricultural

commodities as feedstock to a rapidly increasing biofuel industry is one of the main drivers in the

outlook and one of the reasons for international commodity prices to attain a significantly higher

plateau over the outlook period than has been reported in the previous reports. However, new

production technologies, changes in biofuel policies, or unexpected price changes in crude oil and

feedstock prices could significantly alter market developments in the future.

The projections and assessments provided in this report are the result of close co-operation

between the OECD and FAO Secretariats and national experts in member countries, and thus reflect

the combined knowledge and expertise of this wide group of participants. As a result of FAO

participation in the Outlook, the country coverage of the projections has been considerably extended

to a larger number of developing countries and developing country regions. A jointly developed

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 3

FOREWORD

modelling system, based on the OECD’s Aglink and FAO’s Cosimo models, facilitated the assurance

of consistency in the projections. The fully documented outlook database, including historical data

and projections, is available through the OECD-FAO joint Internet site www.agri-outlook.org.

Within the OECD, this publication is prepared by the Trade and Agriculture Directorate, while at

FAO, the Trade and Markets Division was responsible for the report.

Acknowledgements. This Agricultural Outlook was prepared by the following staff members of the OECD and FAO Secretariats:

At the OECD, the team of economic and market analysts of the OECD Trade and Agriculture Directorate that contributed to this report consisted of Loek BOONEKAMP (team leader), David DOWEY, Céline GINER, Garry SMITH, Pavel VAVRA (baseline co-ordinator) and Martin VON LAMPE.

R e s e a rch a n d s t a t i s t i c a l a s s i s t a n c e w e re p ro v i d e d by D av i d D OW EY, Armelle ELASRI, Alexis FOURNIER and Claude NENERT. Secretarial services and co-ordination in report preparation was provided by Christine CAMERON. Technical assistance in the preparation of the Outlook database was provided by Eric ESPINASSE and Frano ILICIC. Many other colleagues in the OECD Secretariat and member country delegations furnished useful comments on earlier drafts of the report.

At FAO, the team of economists and commodity officers from the Commodities and Trade Division contributing to this edition consisted of Abdolreza ABBASSIAN, El Mamoun AMROUK, Concepcion CALPE, Kaison CHANG, Merritt CLUFF, Piero CONFORTI, Cheng FANG, David HALLAM (team leader), Holger MATTHEY (baseline co-ordinator), Jennifer NYBERG, Adam PRAKASH, Grégoire TALLARD, Peter THOENES, Koji YANAGISHIMA and Carola FABI from the Statistics Division. AliArslan GURKAN and Alexander SARRIS initiated support for FAO’s Cosimo modelling project.

Research assistance and database preparation was provided by Claudio CERQUILINI, Daniela CITTI, Berardina FORZINETTI, John HEINE, Massimo IAFRATE, Marco MILO and Barbara SENFTER. Secretarial services were provided by Rita ASHTON and Silvia RIPANI.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 20074

TABLE OF CONTENTS

Table of Contents

Acronyms and Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Outlook in Brief . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Chapter 1. Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

The main underlying assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Assumptions related to evolving biofuel production . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Main trends in commodity markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

A short review of historical patterns in trade flows for agricultural products . . . . . 37

Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Annex A. Statistical Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Annex B. Trade Annex Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Annex C. Glossary of Terms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

List of boxes

1.1. Partial stochastic analysis: Variability around deterministic projections . . . . . . . 33

List of tables

1.1. Where population and income is projected to grow . . . . . . . . . . . . . . . . . . . . . . . . . 14

1.2. Consumption and production annual (least squares) growth rates, 2007-16 . . . . 22

1.3. Consumption and production of OECD countries as a share of world total . . . . . 23

1.4. Total merchandise and agriculture exports 1985-2004

(with and without intra-EU trade) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

1.5. Leading agro-food exporting countries (average 1985-89 and 2000-04) . . . . . . . . . 39

A.1. Economic assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

A.2. World prices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

A.3. World trade projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

A.4. Main policy assumptions for cereal markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

A.5. World cereal projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

A.6. Main policy assumptions for oilseed markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

A.7. World oilseed projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

A.8. Main policy assumptions for meat markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

A.9. World meat projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

A.10. Main policy assumptions for dairy markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

A.11. World dairy projections (butter and cheese). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

A.12. World dairy projections (powders and casein) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

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TABLE OF CONTENTS

A.13. Main policy assumptions for sugar markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

A.14. World sugar projections (in raw sugar equivalent) . . . . . . . . . . . . . . . . . . . . . . . . . . 71

B.1. Concordance of product groupings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

B.2. Top 20 exporters and importers of bulk products (excludes intra-EU) . . . . . . . . . . 74

B.3. Top 20 exporters and importers of horticultural products

(excludes intra-EU) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

B.4. Top 20 exporters and importers of semi-processed products

(excludes intra-EU) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

B.5. Top 20 exporters and importers of processed products (excludes intra-EU) . . . . . 77

List of figures

1.1. Trends in output growth in selected countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.2. Expansion of US ethanol production and corresponding use of maize . . . . . . . . . 18

1.3. Ethanol and bio-diesel use in the EU to increase – based on wheat,

rapeseed and imports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

1.4. Canadian ethanol and bio-diesel production to expand,

using growing cereal quantities in particular. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

1.5. Expanding Chinese ethanol industry to increase maize use for biofuels . . . . . . . 20

1.6. Continued growth in Brazil cane-based ethanol production . . . . . . . . . . . . . . . . . . 21

1.7. Outlook for world crop prices to 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

1.8. Outlook for world livestock product prices to 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . 29

1.9. The range of world oilseed yields in the stochastic simulations. . . . . . . . . . . . . . . 33

1.10. Evolution range of the world oilseed price (expressed in real terms)

in the stochastic simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

1.11. Outcomes of stochastic simulations versus deterministic baseline in 2016:

Relation between world oilseed price (expressed in real terms)

and world oilseed yields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

1.12. Outcomes of stochastic simulations versus deterministic baseline in 2016:

Relation between world maize price (expressed in real terms)

and world coarse grains yields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

1.13. Outcomes of stochastic simulations versus deterministic baseline in 2016:

Relation between world oilseed price (expressed in real terms)

and world coarse grains yields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

1.14. Outcomes of stochastic simulations versus deterministic baseline in 2016:

Relation between world oilseed and maize prices

(both expressed in real terms) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

1.15. Agriculture export share (excludes intra-EU trade) by income group

(1985-2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

1.16. Share of agriculture exports (excludes intra-EU trade) by stage (1985-2004) . . . . . 41

1.17. Exports of bulk and horticultural products by various groups of countries

(1985-2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

1.18. Exports of semi processed and processed products by various groups

of countries, 1985-2004 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

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ACRONYMS AND ABBREVIATIONS

Acronyms and Abbreviations

Acronyms and abbreviations AAFC Agriculture and agri-food Canada ACP African, Caribbean and Pacific countries AMAD Agricultural Market Access Database AI Avian influenza BRIC Brazil, Russia, India, China BSE Bovine Spongiform Encephalopathy CAFTA Central American Free Trade Agreement CAP Common Agricultural Policy (EU) CCC Commodity Credit Corporation CET Common External Tariff CIS Commonwealth of Independent States CPI Consumer Price Index CRP Conservation Reserve Program of the United States CMO Common Market Organisation for sugar (EU) CO2 Carbon dioxide Cts/lb Cents per pound cwe Carcass weight equivalent DBES Date-based Export Scheme DDA Doha Development Agenda DDG Dried Distiller’s Grains dw Dressed weight EBA Everything But Arms Initiative (EU) ECOWAP West Africa Regional Agricultural Policy ECOWAS Economic Community of West African States EPAs Economic Partnership Agreements (between EU and ACP countries) ERS Economic Research Service of the US Department for Agriculture est. Estimate EU European Union EU15 Fifteen member states of the European Union EU10 Ten new member states of the European Union from May 2004 EU27 Twenty seven member states of the European Union (including Bulgaria

and Romania from 2007)

FAO Food and Agriculture Organisation of the United Nations FMD Foot and Mouth Disease FOB Free on board (export price) FSRI ACT Farm Security and Rural Investment Act (US) of 2002 FTA Free Trade Agreement GDP Gross Domestic Product

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 7

ACRONYMS AND ABBREVIATIONS

G10 Group of 10 countries (see Glossary) G20 Group of 20 developing countries (see Glossary) GDPD Gross Domestic Product Deflator GM Genetically modified HFCS High Fructose Corn Syrup HS Harmonised Commodity Description and Coding System kt Thousand tonnes LAC Latin America and the Caribbean LDCs Least Developed Countries LICONSA Leche Industralizada lw Live weight MERCOSUR Common Market of the South MFN Most Favoured Nation Mha Million hectares MPS Market Price Support Mt Million tonnes MTBE Methyl Tertiary Butyl Ether NAFTA North American Free Trade Agreement OECD Organisation for Economic Co-operation and Development OIE World Organisation for Animal Health PCE Private Consumption Expenditure PROCAMPO Mexican Farmers Direct Support Programme PPP Purchasing Power Parity PSE Producer Support Estimate pw Product weight rse Raw sugar equivalent rtc Ready to cook RFS Renewable Fuels Standard in the US, which as part of the Energy Policy Act

of 2005 adjusts fuel standards in favour of ethanol and other biofuels

and sets increased mandated biofuel consumption quantities

rwt Retail weight SEAC Spongiform Encephalopathy Advisory Committee SFP Single Farm Payment SMP Skim milk powder SPS Sanitary and Phytosanitary measures STRV Short Tons Raw Value t Tonnes t/ha Tonnes/hectare TRQ Tariff rate quota UK United Kingdom UN The United Nations URAA Uruguay Round Agreement on Agriculture UNCTAD United Nations Conference on Trade and Development US United States of America USDA United States Department of Agriculture VAT Value added tax vCJD New Creutzfeld-Jakob-Disease

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 20078

ACRONYMS AND ABBREVIATIONS

WAEMU West African Economic and Monetary Union WMP Whole milk powder WTO World Trade Organisation

Symbols AUD Dollars (Australia) ARS Pesos (Argentina) bn Billion BRL Real (Brazil) CAD Dollars (Canada) CNY Yuan (China) EUR Euro (Europe) gal Gallons ha Hectare hl Hectolitre INR Indian rupees KRW Korean won lb Pound Mn Million MXN Mexican pesos NZD Dollars (New Zealand) p.a. Per annum RUB Ruble (Russia) THB Thai baht USD Dollars (United States) ZAR South African rand

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OUTLOOK IN BRIEF

Outlook in Brief

● Currently strong world market prices for many agricultural commodities in international trade are, in large measure, due to factors of a temporary nature, such as drought related supply shortfalls, and low stocks. But, structural changes such as increased feedstock demand for biofuel production, and the reduction of surpluses due to past policy reforms, may keep prices above historic equilibrium levels during the next 10 years.

● Higher commodity prices are a particular concern for net food importing developing countries as well as the poor in urban populations, and will evoke on-going debate on the “food versus fuel” issue. Furthermore, while higher biofuel feedstock prices support incomes of producers of these products, they imply higher costs and lower incomes for producers that use the same feedstock in the form of animal feed.

● The expectation that world market prices have attained a higher plateau may facilitate further policy reform away from price support. This would reduce the need for border protection and would provide flexibility for tariff reductions.

● Growing use of cereals, sugar, oilseeds and vegetable oils to satisfy the needs of a rapidly increasing biofuel industry, is one of the main drivers in the outlook. Over the outlook period, substantial amounts of maize in the US, wheat and rapeseed in the EU and sugar in Brazil will be used for ethanol and bio-diesel production. This is underpinning crop prices and, indirectly through higher feed costs, the prices for livestock products as well.

● Given that in most temperate zone countries ethanol and bio-diesel production are not economically viable without support, a different combination of production technologies, biofuel policies and crude oil prices than is assumed in this Outlook could to lead to lower prices than are projected in this Outlook.

● The assumed strong growth in demand in many developing and emerging economies will spur expansion in imports as well as provide the impetus to the development of domestic production capacity. As a result, OECD countries as a group are projected to lose production and export shares in many commodities to non-OECD countries over the outlook period.

● Measured by global imports, world trade is projected to grow for all commodities reviewed in this report, without exception. By 2016, and compared to the average for 2001-05, trade expansion remains modest for SMP (7%), is situated at 13% to 17% for coarse grains and wheat respectively, but grows by between over 50% for beef, pigmeat and WMP and by close to 70% for vegetable oils.

● Imports grow more strongly in developing countries than in OECD countries for all products except vegetable oils. And for all products except wheat and coarse grains, these growing markets are increasingly satisfied through larger exports from other developing countries. Agricultural world markets are thus characterised by growing south-south trade, raising the competition for exporting countries within the OECD.

● The growing presence on export markets of Argentina and Brazil is staggering. While Brazil’s growth is mostly concentrated in sugar, oilseeds and meats, Argentina’s export performance also covers cereals and many dairy products. Other growing exporters in the developing and transition economies include Russia and the Ukraine for coarse grains, Viet Nam and Thailand for rice, Indonesia and Thailand for vegetable oils, and Thailand, Malaysia, India and China for poultry.

● Import growth is much more widely spread across countries. However, China’s dominance of oilseeds and oilseed products trade is striking. By 2016, China will have become the world’s largest importer of oilseed meals and it will have further consolidated its leading position in imports of oils and oilseeds. For the latter product, its share in global imports will have risen to almost 50%.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200710

OECD-FAO Agricultural Outlook 2007-2016

© OECD/FAO 2007

Chapter 1

Overview

11

1. OVERVIEW

Introduction The Agricultural Outlook is a collaborative effort of the OECD in Paris and the Food and

Agriculture Organisation (FAO) of the United Nations in Rome. Its main purpose is to produce an

updated annual 10-year assessment of global commodity markets that includes analysis of

recent developments and emerging issues, bringing together the commodity, policy and

country expertise of both Organisations. The projections for production, consumption,

stocks, trade and prices described and analysed in this report cover the years 2007 to 2016.

The projections are presented in the Statistical Annex, and can be viewed in more detail at

the website www.agr-outlook.org. They reflect many specific assumptions concerning key

external factors such as macroeconomic performance, agricultural and trade policies, and

trends in technologies as well as consumer preferences. The projections do not take account

of weather shocks and related impacts on crop yields and livestock production, nor are

changes considered to agricultural and trade policies – anticipated or otherwise – that have

yet to be adopted by legislation or international agreements. Such deviations from these

assumptions constitute some of the important uncertainties in the Outlook, the potential

impacts of which are also assessed in this report.

The main underlying assumptions

Global economic growth may be the strongest in decades

Brightened prospects prevail in the macroeconomic climate for this year’s Outlook.

Global economic growth has remained vigorous through 2006. Demand continues to be

strong in OECD countries with output growth in the OECD area remaining robust and near-

term prospects optimistic, in particular in OECD member countries in Europe, Australia

and Asia. GDP growth for the OECD area increased to 3.2% in 2006 and is expected to

remain buoyant at close to 2.5% throughout the outlook horizon. In per capita terms,

economic growth is anticipated to be the strongest in recent times, due to, among other

factors, the spread of technology and globalization of markets as well as an income

dividend due to declining population growth.1

The recent downturn of activity in the United States is not expected to last beyond the

short-term, and thereafter growth is assumed to remain solid. Conversely, short-term

prospects are bright for Canada, the US’s main trading partner, given the stable economic

climate in this country as well as expanding trade reinforced by high commodity prices. In

the European Union (EU), confidence prevails now that solid growth seems to finally have

taken root, even though output is assumed to moderate over the outlook period. The

recovery is also established in Japan, but with weakening potential over the longer term

coming chiefly from its ageing workforce. In the short term, interest rates are expected to

notch upwards in both of these latter countries while the euro and yen continue to

appreciate against the dollar, diminishing the prospects for EU agricultural exports but

boosting import demand in Japan. Activity has surged back in Mexico with GDP growth

rates beyond 2009 expected to exceed 4%, and the dynamic economies of Korea and Turkey

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1. OVERVIEW

continue to steam ahead. In the near-term, a rebound is also expected in Australia, which,

if it eventually spreads to New Zealand, will bring renewed optimism in this latter country

as well after several years of declining performance.

Because of their growth potential, the large emerging economies of China, India, Brazil

and Russia are key drivers of global economic growth. Moreover, the relative significance

and growth potential of their agricultural sectors mean that they play an expanding role in

world trade of agricultural commodities. Higher responsiveness of food demand to income

growth imply that income gains in Russia and the high growth developing countries will

translate directly into increased consumption, in particular for high value-added food

items such as meat and dairy products.

With rising investment, surging demand and expanding trade prospects, output growth

is expected to remain strong in China and India over the outlook period, providing the

dynamic behind activity throughout much of Asia. Export demand, in particular for

agricultural commodities, is essential to continued GDP growth in the main South American

economies. Exports should spur a return to solid growth in Brazil which is expected to

remain strong thereafter at near 4%. In Argentina, however, the rapid growth of the past few

years should slow somewhat. Likewise, economic growth in Russia, as in other CIS countries,

should dampen slightly amid concerns over fiscal discipline, but growth rates in both

countries are assumed to remain higher than in most OECD countries. Even though

economic growth in the BRIC countries is expected to remain high by OECD standards, the

assumed growth rates are nevertheless lower than they were in the recent past.

Population and income growth assumptions constitute the principal elements of the

global economic outlook in that they are the key drivers in demand developments, but

also because with globalisation, differences in regional growth prospects increasingly

determine both the future landscape of the world agricultural markets and global trade

patterns. While recent fluctuations have some impact on short term economic growth

expectations, over the longer term, projected growth rates are based on broad assumptions

Figure 1.1. Trends in output growth in selected countries Annual growth in real GDP, percentage change from previous period

Source: OECD Economic Outlook, No. 80 (December 2006), World Bank Global Economic Prospects 2007 (November 2006).

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OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 13

1. OVERVIEW

about the trends of such diverse underlying factors as fertility, ageing, urbanisation, land

use and production technology, not to mention the structure and evolution of labour and

capital markets. In general, these factors change slowly over time, and in any case they are

not specifically taken into account in the present projections.

Growth in developing countries should increase potential for south-south agricultural trade

As illustrated in Table 1.1, income growth is closely related to population growth. The

regions where income growth is the highest, like Africa, Asia and Latin America, are also

those where population growth is the highest, at rates close to or exceeding 4% on average

over the next decade. Countries in these regions often have a comparative advantage in the

production of labour-intensive agricultural commodities such as fruits and vegetables due

to a substantial supply of low-cost labour and relatively limited resources of arable land.

Nevertheless, available crop land in these countries is usually utilised for year-round

cultivation of products such as sugar and rice or other staples. As shown later in

this section in the review of historical patterns of agricultural trade flows, exports of

semi-processed and processed agricultural and horticultural product have been much

larger in lower middle-income countries than they have been in low-income countries.2 For

higher value agricultural commodities such as meat and dairy products, demand is more

responsive to the rising incomes in emerging economies than it is in the mature markets

of OECD countries. In high growth developing countries this will continue to lead growth in

imports not only of processed products, but also of bulk agricultural commodities destined

for budding domestic processing industries.

Much of the uncertainty in constructing a global economic outlook comes from

projecting the nominal elements such as price indices and exchange rates. It is more

difficult to gauge the long-term dynamics of these variables which are influenced by a wide

variety of economic and political factors, particularly when in some countries their recent

trends have been unstable. Interest rate differentials, unprecedented global liquidity in

financial markets and high volatility commodity prices, in particular oil and energy prices,

contribute to the inherent uncertainties related to making assumptions for a ten year

outlook horizon.

Table 1.1. Where population and income is projected to grow Population in 2006, million. Average annual growth over 10 year period and income share, percentage

Population Income

1997-2006 2007-2016 2006

million 1997-2006 2007-2016

2006 income share

World 1.23 1.08 6 530 2.86 3.05 100

Africa 2.20 2.04 923 4.21 4.32 1.8

Latin America and Caribbean 1.40 1.17 564 2.27 3.79 5.9

North America 1.02 0.86 332 2.81 2.62 32.3

Europe 0.29 0.06 527 2.20 2.13 27.6

Asia 1.15 0.98 4 150 3.55 4.02 30.3

Oceania 1.36 1.08 33 3.33 2.72 2.0

Note: Income is measured by GDP at USD 2000 market prices. Average annual growth is the least-squares growth rate (see glossary). Source: UN World Population Prospects (2004 Revision), World Bank Global Economic Prospects 2007 (November 2006).

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200714

1. OVERVIEW

Inflation is assumed to remain low in OECD countries, despite high commodity prices

Inflation expectations remain low in most developed countries, as governments are

assumed to enforce low inflation targets through the use of appropriate monetary policies.

Throughout the OECD, consumer prices have shown substantial resilience over recent

years to oil price movements despite being subjected to upward pressure from strong

commodity price increases. Nevertheless, in most OECD countries consumer price

inflation is anticipated to remain below 3%, and in many is closer to 2% in the medium

term. For the OECD as a whole, inflation was contained at 2.4% in 2006; it is assumed to fall

and to remain below 2% by 2010. In the recent past, monetary policy responses in major

OECD countries have been swift as inflation measures neared the upper thresholds of

established targets. Although several years of sustained tightening in the United States

have ended, interest rates in the euro area and Britain have risen over the past year and

seem to have contained price pressures. Even in Japan, positive but low inflation at the end

of 2006 has led to the Bank of Japan to abandon its five-year long zero interest rate policy.

The observed effectiveness of these measures in developed OECD economies has led to

longer-term expectations that prices will remain under control in these countries.

Food price inflation is an increasing concern in emerging economies

Conversely, in many rapidly growing developing countries, inflation has become more

and more of a concern over the past year. Whereas large increases in the prices of

non-agricultural commodities have widely been attributed to the strong demand and

accelerating growth in these emerging economies, more and more, price pressure is being

felt in markets through increased demand for food products. This pressure can be either

direct, through growing demand and changes in consumption patterns as incomes rise, or

indirect as alternative uses of food crops, such as inputs for biofuels, have led to higher

domestic prices. As energy prices have subsided over the past year, food price inflation has

been increasingly accused of driving higher headline inflation. In India, inflation rates

above 6% have led to both fears of an overheating economy and concern that surging

demand for wheat will continue to exceed supply. In Argentina, where beef consumption per

capita is the highest in the world, beef exports were temporarily banned in an attempt to

lower domestic beef prices and help cut economy-wide inflation levels. Mexico too, despite

moderate inflation expectations, has experienced dramatic increases in maize flour prices.

World oil prices remain high relative to historical levels

The world benchmark Brent crude oil price assumption underlying this year’s

Agricultural Outlook is based on the assumption for the (real) average price of OECD crude oil

imports of the International Energy Agency’s 2006 World Energy Outlook. The nominal Brent

price is assumed to decline over the medium term to about USD 55 by 2012, rising again

slowly thereafter to finish just over USD 60 by the outlook horizon. This price path is

significantly higher than in last year’s outlook reflecting the sustained tightness of oil

markets. Price pressure has been maintained as geopolitical tensions combine with

processing capacity constraints to keep global supply from the major oil producers below

demand. With the easing of this tightness, the world price should decline. However, in the

longer term beyond 2012, rising marginal production costs of non-OPEC producers may tend

to impart market power to a small number of dominant, Middle East OPEC members whose

collective investment and production policies are generally expected to push prices higher.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 15

1. OVERVIEW

Increasing global focus on the exchange rates of high growth developing economies

The depreciation of the US dollar against several major currencies, including the euro,

Japanese yen, the Chinese yuan and the Brazilian real that began in 2006, is not expected

to persist beyond the near term. While a stronger euro may dampen the euro area’s export

prospects the weaker dollar is not expected to substantially impact Brazil’s and China’s

booming export markets. The renewed strength of the yen will improve the import position

of Japan, a major importer of US agro-food products. Likewise, the continuing appreciation

of the Korean won throughout the outlook period, in the context of strong domestic growth

and rising incomes, would help drive an expansion in Korean agricultural imports.

With the expansion of global trade opportunities, there is an increasing importance

placed on the exchange rates of developing countries vis-à-vis the US dollar because of their

prime influence on global terms of trade and external imbalances. Of particular interest is

the Chinese yuan, which has appreciated by almost 5% since the adoption of a more flexible

management system in July 2005 and is expected to appreciate further over the outlook

period. In strong growth countries like Argentina, Brazil, India, Mexico and Russia, export

markets are expanding solidly. Yet over the longer term to 2016, projected inflation rates are

higher than in the United States, amid strong demand growth, in particular for imports. This

constitutes a depreciating influence on the exchange rate vis-à-vis the dollar.

Domestic support and trade policies affect agricultural markets

Agricultural and trade policies play an important role in both domestic and

international agricultural markets, directly affecting the levels of production and

consumption of agricultural commodities and food products. More and more, agricultural

policies are directed towards achieving specific objectives (e.g. environmental performance

or biofuel development) and beneficiaries (e.g. specific groups of farmers) within broader

goals with respect to national, regional or global concerns (e.g. domestic and trade policy

reform, income inequality, food quality and safety, global warming, etc.). At the same time,

non-agricultural policies, such as energy, environment and rural development policies,

have a growing impact on the agri-food sector. Policies influence the composition and

levels of both production and consumption, thereby creating (or sometimes correcting)

market distortions and influencing prices.

No conjecture as to the future outcome of negotiations for the completion of the Doha

Development Agenda is incorporated in the Outlook projections and consequently, it is

assumed that trade policies as agreed in the Uruguay Round Agreement on Agriculture (URAA)

will hold for the entire period to 2016. As noted later in this chapter in the review of trade

flows, despite the URAA, trade in agricultural products continues to be dominated by a

relatively small number of countries. Trade flows are increasingly influenced by policies

that have been negotiated as part of regional trade agreements such as the North American

Free Trade Agreement (NAFTA), the Everything But Arms (EBA) initiative of the European

Union and the Mercosur agreement between Argentina, Brazil, Paraguay and Uruguay. The

policy assumptions of the Outlook take into account the provisions of these agreements, in

addition to existing bilateral preferential trade provisions covering specific agricultural

commodities. Regional or bilateral trade agreements have not always been explicitly taken

into account in the underlying modeling system but allowance for such agreements has

been made where they are expected to have an impact on growth in trade. This is the case

for both the Central American Free Trade Agreement (CAFTA) and the Australia-US FTA, which

is expected to have a substantial impact on Pacific region beef trade.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200716

1. OVERVIEW

This Outlook makes no anticipation of changes to agricultural policies which may be

part of forthcoming farm legislation in the United States. Although current legislation is

slated for expiry in 2007, the programmes and provisions of the Farm Security and Rural

Investment Act (FSRI) of 2002 are assumed to continue for the entire Outlook period and

moreover, no changes are anticipated in crop loan rates which are extended at constant

levels through to 2016. The requirements of the Renewable Fuels Standard (the Energy Policy

Act of 2003, modified 2005) have been taken into account, as discussed later in this section

under the assumptions related to biofuel production. The main policy elements of the EU

Common Agricultural Policy Reform of 2003, as described in previous editions of the Outlook,

are assumed to remain unchanged. For other countries, established support measures and

policy programmes (such as PROCAMPO in Mexico) are implemented as legislated. Where

well-defined termination dates exist, they are factored into the projections; otherwise

payments, provisions and other policy measures are assumed to continue through 2016.

For sugar, projections take into account the EU sugar reform implemented as of

1 July 2006, which includes a progressive cut in price support of 36% over four years and the

reduction of EU sugar subsidised exports from the current level of 7.6 Mt to the agreed

URAA limit of 1.4 Mt. The provisions also include a progressive reduction of duties followed

by unrestricted sugar exports to the EU from LDC countries under the EBA Initiative

from 2009. Another important development which has been taken into account in the

sugar projections is the resolution of a long standing sweetener dispute between the US

and Mexico under NAFTA which has resulted in an elimination of both the consumption

tax on Mexican beverages manufactured with HFCS and, from 2008, of export restrictions

and duties which should spur exports of Mexican sugar to the US.

Assumptions related to evolving biofuel production World markets for cereals, sugar and, increasingly, oilseeds and palm oil, are strongly

influenced by developments in biofuels. Production of renewable energy, in general, and

biofuels in particular, has risen rapidly to the top of the policy agendas in many countries

and has become a major issue for markets. There are numerous motives behind political

support for biofuels and the composition and priorities of objectives differ across countries.

Most of the objectives can be grouped within three broad categories. First, concerns about

future energy supplies; in particular expectations of finite availability of crude oil and

increasing reliance on oil imports from countries considered as less reliable suppliers;

second, environmental concerns – most notably the increased emissions of carbon dioxide

(CO2) as one of the main causes for climate change; and finally, the development of new

markets for agricultural produce and hence increased revenues for farmers.

This Outlook does not analyse the developments in the biofuels sector, but treats

biofuel production through implicit and exogenous assumptions in a number of countries.

In particular these include the US, the EU, Canada and China, while ethanol production in

Brazil is an explicit part of the sugar baseline.

US

The US is assumed to substantially increase its ethanol production, which

predominantly is based on domestic maize. Ethanol output and corresponding maize use

is assumed to grow by almost 50% in 2007, and while growth rates are assumed to decline

thereafter, US ethanol production is still assumed to double between 2006 and 2016

(Figure 1.2). This expansion would exceed the requirements stated in the Renewable Fuel

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 17

1. OVERVIEW

Standard (RFS) by far. In consequence, maize use for fuel production, which has doubled

from 2003, would increase from some 55 Mt or one-fifth of maize production in 2006 to

110 Mt or 32% at the end of the projection period.

Bio-diesel production, in contrast, is assumed to remain relatively limited in the US,

due to lower profitability caused by high feedstock costs. Soya oil use for bio-diesel

production is expected to reach 2 Mt in 2007 and to further increase to 2.3 Mt in 2011, with

no growth assumed for the remaining projection years.

EU

Biofuel production and use in the EU was historically for bio-diesel based on oilseeds,

mostly rapeseed. Increasingly it is assumed that ethanol, made mostly from wheat and

maize, will become important on EU markets. Despite growth in total biofuel use by some

170% between 2006 and 2010, however, it is assumed that the share of biofuels in total

transport fuel consumption will not exceed 3.3% in energy terms, rather than the 5.75%

target envisaged by the EU Biofuels Directive. Further growth is, however, expected

throughout the projection period (Figure 1.3).

Despite some increased imports of biofuels, this growth in biofuel markets translates

into strongly increased demand for feedstock products. Use of wheat in particular is set to

increase twelvefold and to reach some 18 million tonnes by 2016. Growth in the use of

oilseeds (largely rapeseed) and maize is less dramatic, but would still reach 21 Mt and

5.2 Mt by 2016, respectively.

Canada

Compared to both the US and the EU, biofuel production in Canada (a country with large

fossil-based energy resources) is small in absolute terms. In 2006, ethanol production

doubled and bio-diesel production commenced. In addition to this, the Canadian

government announced its intention to regulate biofuel by mandating a 5% ethanol blend in

gasoline by 2010 and a 2% bio-diesel blend in on-road diesel and heating-oil by 2012. In this

report projections it is assumed that these mandates are met. In compliance with the 5%

target, ethanol production, based to a larger extent on maize and to a smaller part on wheat,

Figure 1.2. Expansion of US ethanol production and corresponding use of maize

Source: ERS.

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OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200718

1. OVERVIEW

is assumed to grow by another 150% in 2007 to reach almost 1.9 billion litres in 2009,

compared to 550 million litres in 2006. Little growth, following the increased gasoline use, is

assumed for the remainder of the projection period. Bio-diesel production is assumed to see

an even stronger growth in relative terms, though at much lower levels. Standing at

70 million litres in 2006, bio-diesel production is assumed to reach 600 million litres by 2012,

with little growth thereafter (Figure 1.4).

About half the growth in bio-diesel production is expected to be derived from oilseed

oils; the remainder should be made from yellow grease and tallow. The assumed growth in

ethanol production would consume significant quantities of maize and wheat. Maize use

for ethanol is assumed to increase from 1 Mt or 4% of domestic production in 2006 to

Figure 1.3. Ethanol and bio-diesel use in the EU to increase – based on wheat, rapeseed and imports

Note: Ethanol and bio-diesel data before 2006 refer to production, from 2006 to 2016 to consumption.

Source: EU Commission, OECD Secretariat.

Figure 1.4. Canadian ethanol and bio-diesel production to expand, using growing cereal quantities in particular

Source: AAFC.

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OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 19

1. OVERVIEW

almost 3.4 Mt or more than 13% in 2008 before growing at a slow pace only for the rest of

the projection period. Wheat use will remain less important, but with an increase to close

to 1.5 Mt from 2009, ethanol production is still assumed to consume some 5.5% of domestic

production by 2016.

China

Fuel ethanol production in China is assumed to grow steadily and to reach some

3.8 billion litres by 2016, up from 1.5 billion litres in 2006. Most of the fuel ethanol is

expected to be based on maize, even though other feedstocks are being used or their use is

currently under exploration. Maize use for fuel ethanol should exceed 9 Mt in 2016,

compared to 3.5 Mt in 2006 (Figure 1.5).

Brazil

In contrast to the other countries, ethanol production in Brazil is not based on an

assumption, but explicitly projected. Ethanol production in Brazil is expected to continue

its growth at increased rates, and to reach some 44 billion litres by 2016, 145% more than

what was produced in 2006. As ethanol yields per tonne of sugar are expected to increase,

sugar cane used in ethanol production would grow less in relative terms, but would still

grow by 120% over the 10 years projected (Figure 1.6) and would represent some 60% of

total sugar cane output, up from less than 50% today.

Main trends in commodity markets Compared with previous editions of the Agricultural Outlook, developments in bioenergy

policy, technology, and feedstock production have become even more important factors in

future outcomes for commodity markets. While the run-up in commodity prices in 2006 is

only partially due to increased demand for bioenergy feedstocks, this Outlook presents

projections that show some considerable changes in price projections from past reports.

Agricultural markets have been reacting to higher energy prices since 2000 in that

commodity production costs have increased. But increased demand for agricultural products

Figure 1.5. Expanding Chinese ethanol industry to increase maize use for biofuels

Source: ERS.

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OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200720

1. OVERVIEW

in the form of bioenergy feedstocks, largely from sugar, maize, vegetable oils and wheat,

constitute an important change from previous market situations. While the emergence of

these prospects has been noted in past editions of this report, it is now a major point of

discussion and analysis worldwide. What remains to be seen is whether bioenergy

constitutes a lasting structural change for agricultural markets, and a change which is

revealed by a higher plateau for real prices. Another question is whether there will be

increased uncertainty and more price variability with higher dependence on developments

in the energy market, including the policies that affect them.

Globalisation and the rising importance of key emerging economies are having diverse

effects on world agricultural markets. The assumed strong growth in demand will initially

spur expansion in import demand of processed products as well as agricultural raw

materials. Subsequently, growing demand provides the impetus to the development of

domestic production capacity, especially given the unprecedented level of global liquidity

and the acceleration of foreign direct investment flows towards emerging markets. For

example, investment in processing capacity is expected to be particularly strong in India

and China, and it is a shared priority of many governments in high growth developing

countries to capture a larger share of the added value in domestically consumed

agricultural products. Trade patterns are also changing. In the context of growing global

markets, larger export shares are not only gained by displacing competitors, but more

importantly by growing faster than others. Against this background, OECD countries as a

whole are projected to lose export shares in many commodities to non-OECD countries

over the outlook period.

These developments taken together lead to the projection of lower production and

consumption growth prospects in the OECD region than in the developing and former

transition countries for all of the 15 agricultural commodities listed in Table 1.2, but wheat.

The largest growth differentials occur in the high value added products such as beef,

pigmeat, butter and SMP, but also sugar. They affect production and consumption equally.

The bulk of the global production growth for these products, and most of the consumption

growth as well, will originate in developing countries and transition economies.

Figure 1.6. Continued growth in Brazil cane-based ethanol production

Source: OECD and FAO Secretariats.

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OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 21

1. OVERVIEW

As a result, Table 1.3 shows that developing and transition countries will take a growing share of total world production and consumption over the outlook period – and that the share of OECD countries is consequently declining – for the majority of products. The exceptions are for wheat and coarse grains, where the OECD’s share in global production is increasing. OECD shares in milk powders are much larger for production than they are for consumption and the production shares decline initially before stabilising. The largest losses in shares over the outlook period are for butter and milk, but also for meat products, especially beef. These products have much larger growth potential in developing countries, in particular in the largest amongst them such as Brazil, China and India, than in the mature markets of the OECD. While the OECD’s coarse grains production share is increasing and the consumption share is stable, that for feed use is declining, reflecting the growing importance of biofuel use in OECD countries. Production and consumption shares are decreasing only slightly for cheese, for which OECD countries remain dominant market players.

Cereal markets recover from production shortfalls while biofuel use of maize increases

Under the assumption of a return to normal yields, and the incentive of currently higher

prices, global cereal production is projected to recover from the shortfalls experienced in the

past year. The unprecedented demand for maize coming from rapidly growing biofuel

production in the United States is in the process of transforming the coarse grain market.

The impact of these changes on cereal markets may gradually ease over the years, but that

will much depend on the evolution of renewable fuel policies and further development of the

biofuel industry, particularly from a technological perspective. Driven by current low stocks

and high prices there will be a shift towards more area planted in cereals, either from

reallocation of land from other crops in the main OECD producers (Australia, Canada and the

US), from land taken out of set aside (EU) or out of CRP reserves (US) or from cultivation

of new land in many developing countries, particularly in South and Latin America.

Table 1.2. Consumption and production annual (least squares) growth rates, 2007-16

Production Consumption

% %

Total OECD Non-OECD Total OECD Non-OECD

Wheat 0.7 1.0 0.5 0.8 0.9 0.8

Rice 0.9 0.1 1.0 0.9 0.1 1.0

Coarse grains 1.2 1.2 1.3 1.2 0.9 1.5

Coarse grains used for feed 1.0 0.5 1.5 1.0 0.5 1.5

Oilseeds 2.1 1.3 2.6 1.9 1.3 2.2

Oilseed meal 2.1 1.4 2.5 2.1 0.9 3.2

Beef 1.5 0.2 2.4 1.5 0.2 2.4

Pig meat 1.7 0.4 2.3 1.7 0.5 2.2

Poultry meat 1.9 1.0 2.6 1.9 1.1 2.4

Milk 1.8 0.7 2.8 . . . . . .

Butter 2.2 –0.2 3.6 2.3 0.0 3.4

Cheese 1.3 1.1 2.1 1.3 1.1 2.0

Skim milk powder 1.0 0.6 2.1 1.1 1.0 1.3

Whole milk powder 2.4 2.2 2.6 2.5 1.0 2.8

Vegetable oils 2.5 1.6 2.8 2.5 2.4 2.6

Sugar 1.8 0.3 2.2 1.8 0.5 2.2

Source: OECD and FAO Secretariats.

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1. OVERVIEW

Nevertheless, beyond the initial years of the outlook, much of the growth in output is

expected to stem from area productivity gains as world prices decline from current highs.

The bulk of wheat and coarse grain production will continue to be concentrated with the

largest producers, the EU, China and the United States, along with India for wheat,

dominating over half of total world output. By 2016 global production will reach 673 million

tonnes of wheat and 1.2 billion tonnes of coarse grains.

Exports have been substantially reduced in recent years in several important

countries, in particular because of severe drought in Australia, but also because of poor

harvests in the EU and the United States. But global cereal trade is projected to rebound

and grow at close to 1.5% annually over the outlook period. The EU is expected to surpass

Canada and Australia as the second largest wheat exporter after the United States.

However, the recuperation of traditional export sources will be supplemented by export

expansion in Russia, the Ukraine and Argentina and in Brazil for coarse grains, while

Chinese exports of both cereals are expected to diminish.

Developing countries cereal imports set to grow

Significant import demand for wheat will continue to develop in India, and will grow

further in Brazil and Egypt as well as in an increasing number of developing countries.

Although the Outlook projects expanding exports from the CIS countries and Argentina,

most of the growth in import demand will be satisfied through larger shipments from

OECD countries. Rising per capita incomes and developing food markets are behind the

swelling demand that has outpaced domestic production capacity. More generally growth

in per capita food consumption of wheat is expected to remain modest in most countries.

Despite the prospects of increased biofuel use of maize, which will be largely grown

domestically, demand growth for coarse grains in world markets will be predominantly

driven by increased feed demand from thriving livestock industries in emerging economies

Table 1.3. Consumption and production of OECD countries as a share of world total

Production Consumption

% %

2006 2011 2016 2006 2011 2016

Wheat 39.6 43.0 43.3 33.6 34.3 34.2

Rice 5.0 4.9 4.7 5.2 5.1 4.8

Coarse grains 50.8 52.6 52.5 50.2 50.9 50.0

Coarse grains used for feed . . . . . . 54.7 53.0 51.8

Oilseeds 42.1 38.5 37.7 39.4 38.4 36.9

Oilseed meal 40.0 38.6 37.0 53.6 49.6 46.8

Beef 41.1 37.7 36.3 41.5 38.6 37.1

Pig meat 34.9 32.5 30.2 33.6 31.4 29.5

Poultry meat 45.5 43.1 41.8 43.8 41.2 40.2

Milk 46.6 44.0 41.6 . . . . . .

Butter 41.3 36.1 32.4 35.8 31.3 28.3

Cheese 78.4 77.6 76.9 76.0 75.7 74.9

Skim milk powder 76.7 73.0 73.7 54.6 54.0 54.1

Whole milk powder 46.1 43.6 43.8 19.5 17.7 16.7

Vegetable oils 26.0 25.4 23.8 35.4 35.8 35.2

Sugar 24.0 22.4 21.0 26.9 24.7 23.3

Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 23

1. OVERVIEW

such as China, India and Argentina. Import growth in China will augment its position as a

major coarse grain importer. While the quantities of coarse grains destined for dominant

importers such as Japan, Korea, Mexico and Saudi Arabia remain broadly stable throughout

the outlook, a rising share will be headed for key importers such as China, Egypt and the

Islamic Republic of Iran as well as Colombia and Chile.

Rice production set to expand

More than cereals, rice is an essential crop for many developing countries because its

cultivation is particularly suited to their climate and arable land characteristics, and

consequently, rice has been a staple food in their traditional diet. While growth in wheat

and coarse grain consumption is linked to increases in per capita incomes, growth in rice

consumption remains tied to underlying population growth, with per capita consumption

expected to rise only slightly over the outlook period, mostly because of growth in Africa.

Nevertheless, rice production is set to expand, in part because of policies in many

developing countries to promote rice cultivation as a means of supporting farmer incomes

and limiting rural emigration, as well as both national and regional efforts to encourage

food self-sufficiency, especially in Sub-Saharan Africa. Still, the largest production gains

will come from the major rice producers, such as India, Indonesia, Thailand and Viet Nam.

Rice stocks throughout the world have declined dramatically from their high levels of

the past decade and there has been a significant increase in global rice trade. At the same

time rice export prices have risen, with particularly sharp escalations in recent years. The

trend in trade expansion is expected to persist, with prices climbing even higher in the

short-term before beginning a gradual decline. Underlying this expansion is the higher

import dependency projected for Asian producers such as China and Indonesia, along with

growing demand in Turkey and in Middle East countries like Saudi Arabia. In addition,

changes to trade policy in some OECD countries, like scaled back import duties in the EU

and an enlarged quota in Korea, will also spur imports. In terms of exports, despite recent

contractions, steady growth in the longer term will continue to be driven by the small

number of dominant market players in Asia, principally Thailand, but also Viet Nam and

India, with only moderate export growth expected in the United States.

Global oilseed production and oilseed meal exports to expand

Biofuels are also strong drivers of oilseeds markets both directly through demand for

oilseed oils in the bio-diesel production process and indirectly through the impact of the

relative prices of oilseeds and maize which affect the competition for arable land between

these crops, particularly in the US. Furthermore, because of rapidly rising maize prices

relative to those for oilseeds, there is an increasing demand for oil meals to replace maize

in livestock feed rations as a source for energy. In the current context of high cereal prices,

oilseed meals are cheaper than coarse grain sourced feed – but this relative cost advantage

may be short lived as maize-based ethanol production develops, feed will become available

from low-cost distiller by-products, creating new sources of competition for oilseed derived

protein meals, particularly in the United States. OECD oilseed production will remain

broadly stable with most of the changes taking place through crop reallocation and a

geographical redistribution of production.

Oilseed production in Brazil and Argentina will intensify as arable land is diverted from

pasture to oilseed crops. With Brazilian production growing by 3.9% per year on average over

the outlook period, it will overtake the United States by 2009 as the world’s largest oilseed

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1. OVERVIEW

exporter. Argentina will cultivate its position as a regional hub for oilseed crushing with

differential export tax enticements and investment in processing capacity contributing to

promote the domestic crushing industry. This will lead to a 33% rise in protein meal exports

as well as higher exports of both meal and oil to satisfy growing import demand in China.

By 2016, China will have become the world’s largest importer of oilseed meals and it will have

further consolidated its leading position in imports of oils and oilseeds. For the latter

product, its share in global imports will have risen to almost 50%.

Increasing world livestock production will continue to drive the consumption of

oilseed-derived protein meal, with most of the growth taking place in developing

countries. Oilseed meal consumption in the non-OECD region will swell by over 55% with

over two-thirds of the growth attributed to Brazil and China alone because of expanding

livestock production. While the EU should continue to hold its position as the largest

importer of oilseed meals, its import dependency will diminish as a growing proportion of

the region’s protein meal consumption comes from domestically produced and crushed

oilseeds, in particular rapeseed meal. The nurturing of bio-diesel production capacity will

stimulate oilseed oil demand in the EU which, when combined with the growing demand

for oilseed and palm oil for food use, will almost double EU imports of vegetable oils over

the outlook period. Despite strong investment led growth in China’s domestic oilseed oil

production capacity, expanding demand for food oils will continue to spur imports in this

country as well as in India.

Largely driven by income growth, vegetable oils, both from oilseed crops and from

palm, will remain the fastest growing commodity in terms of consumption covered in this

Outlook. Within this overall context, growth rates of the developing countries almost double

those of developed countries. Over time, increased vegetable oil consumption has made a

large contribution to increased calorie consumption. Use of vegetable oils for bioenergy

purposes is expected to grow strongly, and may alter trade patterns and the consumption

mix in diets in some countries/regions depending on policies in place. This may be

particularly the case in the EU where bioenergy use of vegetable oils has been mostly

oriented to the use of rapeseed oil.

A closer link between sugar and ethanol

Brazil is the world’s leading sugar and ethanol producer and currently accounts for

around 40% of world sugar trade. Demand for sugarcane-based ethanol by domestic

motorists and for export is expected to continue to rise at a rapid rate and to account for a

larger share of Brazil’s sugar cane crop. However, these developments are not expected to

unduly constrain the amount of cane available for sugar production and sugar exports

projected to rise strongly and to exert a moderating influence on world price prospects over

the coming decade. Further production and trade growth is also expected in other leading

sugar exporting countries, such as Australia and Thailand. Following reform of its sugar

regime, the EU is expected to reduce production in a context of rising imports and tight

controls on subsidized exports and may eventually challenge the Russian Federation for its

role as the leading sugar importer. Mexican sugar exports to the US should increase when

duties and restrictions are eliminated under NAFTA in 2008, although rising consumption

is expected to reduce its exportable surplus. Countries in Asia are expected to experience

the fastest growth in sugar consumption, with China, Indonesia, Korea and Japan

remaining significant sugar importers.

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1. OVERVIEW

Developing countries increasingly dominant in the meat outlook

The global outlook for meat is increasingly characterised by rising production and

consumption trends of developing countries and a more stable and mature path of

development for markets of OECD countries. Still, animal disease outbreaks in recent years

have affected established trade patterns for meat products, led to short-term perturbations

to supply and demand in major trading countries and an increased market share of

disease-free exporting countries. In response to these outbreaks, consumption decisions in

OECD countries will be to a greater extent driven by quality assurances such as traceability,

meat-packing requirements and processing controls which reinforce an underlying

preference for premium quality meats. While per capita consumption in high income

countries is expected to increase only marginally over the outlook period, rising incomes

and the ensuing diversification of diets will lead to a shift towards significantly higher

meat consumption in developing countries, representing more than 80% of expected world

growth. Much of this expansion will take place in Asia and the Pacific region, and will

reflect in particular the rise in consumption of pigmeat.

Over the outlook period, world meat production is expected to grow by 1.7% per year,

mostly because of expanding markets in Brazil, China and India. As a result, the production

share of major OECD producers will continue to fall, despite expectations of renewed growth

in the United States. With trade recovering from the effects animal disease outbreaks, a

small number of major exporters, namely Brazil, the US, Canada, Argentina and Australia,

will remain dominant in world markets with export growth particularly strong in South

America. By 2016, net exports of Brazil are expected to surpass those of the four others

combined to take a 28% share of total world meat exports. Beef trade is continuing to recover

between the US and Canada ensuring that the United States remains the world’s largest

meat importer at the end of the outlook period followed by Japan and Russia.

The burgeoning economies and strong income growth in Korea, Saudi Arabia, Mexico

and the Philippines will contribute to a considerable rise in meat imports in these

countries, increasing their importance in regional markets. Import dependency in meat

products is likewise expected to grow in many other dynamic developing countries as

nascent demand surpasses the domestic capacity for meat production throughout the

duration of the outlook period.

Growing importance of developing countries in dairy supply and demand

One of the most prominent trends in the Agricultural Outlook is the increasing

importance of developing countries in the supply and demand for dairy products. Milk

production gains over the outlook period will be overwhelmingly driven by output growth

in non-OECD countries. Expansion in India, the largest individual producing country in the

world, where surging demand growth will stimulate a strong increase in milk and butter

production, will be especially marked. Driven by substantial yield gains, strong growth in

milk production is also expected in China. This contrasts the moderate growth in the OECD

area where milk production mainly increases due to gains in Oceania and the United States

and is chiefly constrained by domestic production controls in many other countries.

The escalation of world dairy prices of recent years may now be regarded as symptoms

of broader structural changes. First, urbanisation and higher incomes have shifted diets in

emerging economies towards higher consumption of not only butter and cheese, but also

to increasingly more versatile milk powders. These trends have been encouraged by

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200726

1. OVERVIEW

growth in dairy marketing as retailing channels develop and through government

programs in some countries. Second, with technological advances and wider global

investment there is a shift towards higher value-added processing of dairy products. In

developing countries this includes improvements in storage and processing capacity which

allows the production of more fresh dairy products, but also improved processing of WMP.

In the mature markets of developed countries, value-added innovation means increased

convenience and a wider variety of products, in particular cheeses and flavoured fresh

dairy products, which cater to specific consumer tastes. Lastly, but indeed not least

importantly, with dairy market reform, intervention stocks have broadly ceased to be

systematically unloaded onto world markets while at the same time, subsidised exports

have diminished significantly. Both of these distortionary policy practices, which

traditionally had the effect of holding down international dairy prices, are thus likely to be

much less prevalent over the outlook period than in previous years.

Dairy exports continue to be dominated by OECD countries

Nevertheless, trade in world dairy markets will continue to be dominated by the

traditional OECD exporters of Australia, New Zealand and the EU, with growth expected for all

products except butter. Trade remains regional, with for example, intra-EU trade larger than all

remaining global trade put together. Still, non-OECD countries gain export share in butter and

SMP, filling the place left by declining EU exports in light of diminished intervention stocks.

Argentina’s surging milk production is behind its emergence as an up-and-coming WMP and

cheese exporter, while exports of these products from the EU should remain roughly stable.

Rising exports of all dairy products are expected from New Zealand. Russia, Japan and the US

will continue to be key cheese importers while more and more milk powders are destined for

milk reconstitution in developing countries, most notably in the Middle East and North Africa

but also in Mexico. China’s strong increase in consumption of dairy products will be largely met

by a sharp growth in domestic production with only a marginal growth in imports, in particular

of whole milk powder.

High world prices for most products at the beginning of the outlook period

Actual world prices rose much more strongly in 2006 than earlier anticipated for

cereals and dairy products, and to a lesser extent also for oilseeds, but weakened markedly

for sugar. Are these unexpected price developments the result of systemic changes in

commodity markets, leading to longer term price strength? Or are they the result of short

term factors, such as weather-related production shocks, with prices in the longer term

returning to their historical equilibrium levels?

In looking at the price developments that have taken place in 2006, a number of

factors have been identified as contributing to the observed price changes for the

agricultural products covered by the Agricultural Outlook.

● For cereals, weather-related shortfalls in production have occurred in a number of

producing countries and regions such as the US, the EU, Canada, Russia, Ukraine and

most notably in Australia, where production fell by more than 50%. In a global context of

low global cereal stocks in recent years, these lower supplies have been a strong factor

underpinning world prices.

● Reduced global stocks and production were confronted with stronger than expected

demand for cereals for biofuel production, notably in North America and Europe. This

additional demand compounded the already tight supply situation and contributed to

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 27

1. OVERVIEW

further strengthening of world cereal prices. It is noteworthy, however, that the

combined cereal supply shortfall in North America, Europe and Australia in 2006 of over

60 Mt was nearly four times larger than the 17 Mt increase in cereal use for ethanol in

these countries.

● Growing cereal use for ethanol lead to a reduction in planted acreage to oilseeds,

particularly in the US, in favour of maize. Increasing cereal prices relative to those for

oilseeds caused this land reallocation. As a knock-on effect, oilseed prices then also

increased as a result of tightening supplies and this price strength was enhanced by

rising demand for meals as a cereal feed substitute and increasing demand for vegetable

oils for bio-diesel production.

● World sugar prices surged in late 2005 and early 2006 to reach 25-year highs under the

pressure of tight global supplies and growing linkages between international sugar and

oil prices, but then fell back again later in the year. Sugar prices remained below earlier

expectations for 2006-07, reflecting abundant supplies, higher stocks and an emerging

global surplus. Sugar reform in the EU and the retraction of large white sugar supplies

from the international market contributed to a widening white sugar premium in 2006.

● Continuing solid demand for dairy products in combination with rising feed costs and

reduced overall supplies, most notably in the European Union and Australia, accounted

for most of the price increase for these products, particularly for milk powders. Policy

reforms in the EU are behind the reduction in EU dairy surpluses and the drop in

subsidised exports. This may constitute a more permanent element of price strength in

world dairy markets.

● World meat prices stayed in line with earlier expectations for 2006. Abundant supplies

and the demand-reducing impacts of Avian influenza continued to exert downward

pressure on prices for pigmeat and poultry. A number of factors, including FMD in Brazil,

drought induced slaughter in Australia, and export taxes in Argentina, offset each other

to keep beef prices leveled. Lamb prices, however, fell more strongly than earlier

expected for 2006 due to drought-induced slaughter in Australia.

World market prices in the medium term remain above previous projections

The foregoing would suggest that much of the observed variation between actual and

projected prices in 2006 can be explained largely by short-term production shocks and

resulting supply/demand imbalances. But longer-term influences may also be at work,

even though they may have been masked by the more traditional market fundamentals.

For instance, policy reform leading to lower use of export subsidies may have lifted prices

for dairy products and sugar. And maize prices in the US have undoubtedly been supported

by increased biofuel production. There is obviously growing interest in many countries in

the development of renewable energy supplies based on the use of agricultural feedstocks.

This link is well established in the case of the US and Brazil, and is emerging as an

important additional dimension to global demand for cereals, oilseeds and sugar products

over the projection period.

In a context of generally lower global stocks in recent years, this additional demand is

expected to underpin prices and to lead to price levels for field crops that are, on average,

higher than in past projections. Nevertheless, cereal, oilseed and sugar prices are expected

to fall below current or recent peak levels. Higher average crop prices and associated feed

costs, in turn, lead to higher livestock product prices over the outlook period as well.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200728

1. OVERVIEW

There are a number of uncertainties in relation to biofuel markets and how important

they will prove to be in underpinning prices in agricultural markets in the future. These

uncertainties include the nature of agricultural and trade policies that will be implemented

to nurture biofuel production from domestic agricultural crops, the pace of technological

progress in developing viable “second generation” biofuel production plants that utilise

cellulosic feedstocks rather than food and feed crops, and the future price of oil. A different

combination of these factors than is anticipated in this Outlook could lead to lower prices

than are now projected.

Cereal prices lose some of their current strength

Trends in nominal world indicator prices for the different commodities are shown, first

for crop commodities in Figure 1.7, and then for livestock products in Figure 1.8. World cereal

prices have been driven higher as the weather-related production shortfalls of the past year

and dwindling global stocks have tightened supply on world markets. They should decline

towards the end of the outlook horizon, but should stay substantially higher than prices

Figure 1.7. Outlook for world crop prices to 2016 Index of nominal prices, 1996 = 1

Source: OECD and FAO Secretariats.

Figure 1.8. Outlook for world livestock product prices to 2016 Index of nominal prices, 1996 = 1

Source: OECD and FAO Secretariats.

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OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 29

1. OVERVIEW

observed over the past decade because of expanding food demand in developing countries as

well as budding demand for maize in ethanol production. Very similar prospects are seen in

rice markets, with expanding global food demand as incomes and populations grow pushing

international prices to their highest levels in a decade, before falling back gradually.

Price strength in the oilseed sector dominated by vegetable oils

Oilseeds and oilseed meals prices will continue to rise through 2007 partly as a result

of the run-up in cereal prices that have made oilseed protein meals to become a more a

cost-competitive animal feed. In subsequent years however prices will gradually fall back

as supply and demand adjust. For the sugar market, world indicator prices had swelled to

quarter-century highs during the 2005/06 marketing year, almost doubling in the space of

two years. However, their subsequent decline in 2006/07 as sugar balances moved into

surplus has been equally dramatic, particularly for raw sugar which fell 27%. Sugar prices

will remain under pressure throughout the outlook period, with the white sugar margin

remaining substantial, particularly in the first years, as high quality EU white sugar is

pulled from world markets under reforms to the EU sugar regime.

Meat prices stay above recent averages

A return to normal market conditions for meat products has brought about

diminishing world prices. For beef, this trend will continue for most of the projection

period, with prices moderately strengthening again during the outer years. Pigmeat prices

rally in the first years of the outlook to 2009, but thereafter remain stable. A similar trend

prevails for poultry prices, although they are expected to continue to rise for a longer

period before stabilising, reflecting growing demand in North and Latin America and in

Europe. World prices of dairy products, which had escalated strongly in 2006 and 2007, will

remain at these elevated levels throughout the outlook, partly reflecting the structural

changes that reforms have brought about on world markets.

Uncertainties Weather-related production shocks, future policy developments, animal diseases

outbreaks and unstable macroeconomic performance are among the main uncertainties

affecting the prospects for world agricultural markets over the medium term. The effects

of recent drought in Australia attest to this degree to which such shocks may impact

markets – wheat and coarse grain production fell by more than half in 2006, and in a

context of global cereal production shortfalls, contributed to rising world prices. While

economic growth seems to be firming up in Europe and Japan, recent years have shown

that optimism about future output growth has sometimes been premature and does not

necessarily mean that growth in demand, imports or exports will be forthcoming. Past

experience has shown that it is very difficult to predict the future level of world oil prices,

or even to correctly guess the direction in which they will move. Yet, the outlook

projections are dependent upon a world oil price assumption – one that is felt to be the

most consistent assumption available for the 10-year projection horizon.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200730

1. OVERVIEW

Trade and domestic support policy

The future of international trade policy is a key uncertainty in this outlook. If and when

the Doha Development Agenda of multinational trade negotiations come to a conclusion, the

agreement will result in generally lower barriers to trade in agricultural products and

diminished levels of domestic support for agricultural production. The overall outcome

would be less distortion to world markets, leading to a better distribution of production

according to comparative advantages, implying increased trade in agricultural commodities

and generally higher world prices; but there may be downward price pressure from increased

competition in some specific markets where protection has traditionally shielded producers

from declining world prices. While the effects of regional trade agreements, such as CAFTA,

have been implicitly incorporated in the outlook projections, it is difficult to accurately gauge

the response in the diverse range of agricultural sectors to increased liberalisation,

particularly during implementation periods. Similarly, there is a general trend toward more

bilateral agreements, which may both reinforce existing trade patterns as well as creating

new and unanticipated trade channels.

A forthcoming United States’ farm bill may have significant implications given the

relative importance of US agricultural output and its dominant position in world markets.

As any new policies will be likely implemented as soon as 2008, only the second year of the

current outlook projections, any substantial changes to domestic support payments and

crop loan rates would have consequential impacts on the present projections, which are

based on policy assumptions according to the 2003 FSRI Act.

The future developments in the biofuel industry – in particular in terms of policy and

technological developments – are unclear, and this implies uncertainty for agricultural

markets, especially those for cereals, oilseeds and sugar crops. Earlier in this section an

overview of biofuel assumptions were presented which set the foundations for the current

outlook. However, public support measures are necessary in a majority of countries (and in

almost all OECD countries) for biofuel production to be profitable. The form and substance

of these biofuel policies can have significant implications for biofuel production but also

for cereal, oilseed and sugar use, for feed prices and subsequently for livestock numbers

and meat and dairy production. Moreover, most biofuel policies are new and it is not clear

which measures are most effective in achieving the mix of objectives such as lower fossil

fuel dependence or less greenhouse gas emissions, not to mention domestic support for

farmers. It is natural to assume that these measures may be adjusted in unpredictable

ways over the coming decade as biofuel production unfolds. In addition, even if this Outlook

assumes crude oil prices in a range from USD 55 to USD 60, it is not excluded that lower

prices may prevail, impacting on the profitability of ethanol/bio-diesel production and

demand and prices for feedstocks.

Animal disease impacts

As previously stressed, the current outlook has been produced within the context of

“normal” conditions for the meat sector, which is to say an absence of animal disease

outbreaks and no explicit accounting of animal disease restrictions on production, trade or

consumption. At the same time, the projections anticipate a recovery from trade

disruptions resulting from recent disease outbreaks. These recent occurrences include

reduced beef trade in North America due to BSE, export restrictions on beef and pigmeat

following FMD in Argentina and Brazil and the effects of Avian influenza in Asia and

Europe. Any renewed occurrences would likely reduce the speed of recovery.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 31

1. OVERVIEW

Since the magnitude and extent of potential epizootics is by nature unknown, the

evolution of global meat markets could be dramatically different from the baseline

anticipated in the outlook if either fresh outbreaks of known diseases occur or if a new

epizootic of an unfamiliar disease strikes. Nevertheless, substantial international efforts

have been made to limit the impacts of new outbreaks. On the supply side, these include

the regionalisation of export embargoes, more stringent animal health and inspection

regulations as well as implementation of vaccination policies. On the demand side,

consumers have been reassured by measures to ensure early detection of infection,

information on potential health risks, improved production control standards and efforts

to ensure meat traceability. The implications of animal disease occurrences have been

investigated in recent joint OECD-FAO work on animal disease scenarios.

Strong growth in emerging economies

The projections have been produced under the assumption that the strong growth in

countries such as China, India and Brazil will persist, in turn spurring broader growth in

Asia and South America. All three countries have a growing presence in agricultural

markets, albeit India is less of a trader than the other two. However, the robust growth in

these countries is a relatively recent and unprecedented phenomenon, therefore it is

difficult to foresee the consequences of expansion being plagued by what are commonly

referred to as downside risks.

Inflation is one of these risks. There has been increasing speculation that the economy

in India is overheating and that with demand outpacing supply, imports cannot keep up.

Additionally, price pressure on commodities and food products is compounded by the lack

of consolidation in markets. In Brazil, with historical bouts of high inflation, there are risks

that strong export growth will, as in Argentina, drive domestic prices higher. While China

does not currently have significant inflation worries – indeed its projected inflation rate is

lower than that of the United States – there may be some risk inherent to the future path

of the Yuan-US dollar exchange rate. The assumption in this report, in the aim of

consistency, implies constant exchange rates in real terms from 2008 and thus, because of

the differential in inflation rate vis-à-vis the United States, there is an appreciation of the

Yuan in the medium term, before depreciation over the outer years of the outlook period.

The Yuan is currently under a flexible, but managed system, yet it is widely anticipated

that given the current size of Chinese dollar reserves, the Yuan might appreciate, perhaps

over the entire period of the outlook. If this were the case, then Chinese agricultural

imports may be even larger than projected in the outlook, and simultaneously, exports may

be diminished. Lastly, past government policies in favour of self-sufficiency in both China

and India have impeded the flow of imports of some agricultural commodities. While

decisions in such a direction are not anticipated, further policies of this type would have an

impact on the outlook for agricultural trade.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200732

1. OVERVIEW

Box 1.1. Partial stochastic analysis: Variability around deterministic projections

The projections presented in this Outlook are deterministic in the sense that they correspond to a particular market environment that is conditioned by specific assumptions on exogenous variables. However, there are uncertainties concerning that environment, notably with respect to key assumptions with regard to weather and macroeconomic conditions. Varying these assumptions would directly affect the outlook accordingly: the question is by how much and what would be the implications for the projections. If assumptions for these variables were to be at least partly defined by a range of possible values, then projection outcomes can be assessed for the many resulting different situations. The process is then partially stochastic rather than deterministic, in the sense that the range of assumptions defines a range of projection outcomes. Thus, a set of more robust projections can be generated where uncertainty can at least be described by a range around the specific deterministic baseline.

The analysis presented in this box is carried out with the use of the Aglink-Cosimo model that has been applied in the generation of the baseline projections. Details on the process of doing partial stochastic analysis are given in the methodology section of the Outlook. To carry out the stochastic experiments, the model is calibrated to the final set of baseline projections and is then simulated 500 times under different values for yields (to allow for weather variability) and for GDP and inflation (to allow for variability in key macroeconomic variables). These simulations provide a set of 500 different outcomes for all projection variables, in particular for the evolution of world market prices, which is assessed below.

Figure 1.9 illustrates the process of undertaking a stochastic analysis. It presents the evolution of world oilseed yields in the 500 stochastic simulations by three lines: The average value of the 500 stochastic simulations for the focus variable, the 10% percentile value, i.e. the value below which 10% of the simulations can be found and the 90% percentile, i.e. the value below which 90% of the simulations can be found. These three lines give an overview of the projected distribution of world oilseed yields for each year in the projection period. The world oilseed yield is an aggregate measure. It summarises the yield information from all producing countries and as such is a production weighted aggregate of the different yield simulations in producing countries. The figure underlines the fact that historical deviations from trend for world oilseed yields have been globally, and at least historically, relatively modest.

Figure 1.9. The range of world oilseed yields in the stochastic simulations

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OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 33

1. OVERVIEW

Box 1.1. Partial stochastic analysis: Variability around deterministic projections (cont.)

Price impacts with partial stochastic simulations

Figure 1.10 presents the evolution of world oilseed prices expressed in real terms when deterministic assumptions on yields and macroeconomic variables are replaced by a range determined through stochastic simulations. The particular interest of Figure 1.10 is to see the combined effect of the different simulation assumptions on the world price of a given commodity. One first point to underline is that the evolution of the average of world oilseed prices expressed in real terms over the stochastic simulation is different from the evolution of the deterministic baseline. In 2016, the world oilseed price expressed in real terms in the deterministic baseline is 8% lower than the average of stochastic simulations. This is due to interactions between the different variables that are being shocked in the stochastic analysis in comparison to the benchmark scenario and to the non linearity of the Aglink-Cosimo model. Another interesting point regarding the distribution of outcomes of stochastic simulations for world oilseed prices is that there is a diversity of outcomes around the average of the stochastic simulation. At the end of the projection period, half of the stochastic outcomes are within a range of –20% to +15 % around the average stochastic outcome whereas the complete range of outcomes is much wider.

Drivers for world oilseed prices expressed in real terms

What drives the uncertainty in world oilseed price projections (expressed in real terms) that have been illustrated in Figure 1.10? Obviously many variables as well as interactions between variables influence the evolution of world commodity prices, but the focus here is on the relation between world yields and world price levels only.

To answer the question, a simple comparison is presented in the next four graphs. They show the respective projected distributions in 2016 of four variables: World oilseed yields, world coarse grains yields, the world maize price and the world oilseed price, both expressed in real terms. The deterministic baseline is also shown in the different figures.

Figure 1.10. Evolution range of the world oilseed price (expressed in real terms) in the stochastic simulations

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OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200734

1. OVERVIEW

Box 1.1. Partial stochastic analysis: Variability around deterministic projections (cont.)

Figure 1.11 shows that the relationship between world oilseed prices and yields is not obvious. The distribution of prices expressed in real terms and yields is fairly strongly concentrated with relatively few outliers. The deterministic baseline outcomes are within that part of the stochastic distribution that is most heavily concentrated.

The relationship between world coarse grains yields and the world maize price is presented in Figure 1.12. The negative correlation between yields and prices seems to be more obvious and stable than in the case of oilseeds. Again the deterministic baseline projection is in the most concentrated part of the cloud of points.

Figure 1.11. Outcomes of stochastic simulations versus deterministic baseline in 2016: Relation between world oilseed price (expressed in real terms)

and world oilseed yields

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Figure 1.12. Outcomes of stochastic simulations versus deterministic baseline in 2016: Relation between world maize price (expressed in real terms)

and world coarse grains yields

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OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 35

1. OVERVIEW

Box 1.1. Partial stochastic analysis: Variability around deterministic projections (cont.)

Figures 1.13 and 1.14 illustrate both the same point: World oilseed prices expressed in real terms are directly influenced by world coarse grain markets. If coarse grain yields are low then world maize prices tend to be high, and this in turn tends to push world oilseed prices higher too.

Figure 1.13. Outcomes of stochastic simulations versus deterministic baseline in 2016: Relation between world oilseed price (expressed in real terms)

and world coarse grains yields

Figure 1.14. Outcomes of stochastic simulations versus deterministic baseline in 2016: Relation between world oilseed and maize prices

(both expressed in real terms)

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OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200736

1. OVERVIEW

A short review of historical patterns in trade flows for agricultural products The Agricultural Outlook provides an assessment of the evolution of agricultural

markets and trade over the next 10 years, assuming constant policies and “normal”

weather conditions. As the focus of the Outlook is on selected temperate-zone products,

occasionally it is useful to review the trade developments of the entire agriculture and food

sectors in the recent past to place current and future developments in perspective. This

section reviews agricultural and food trade over the twenty-year period from 1985 to 2004

and puts the spotlight on agriculture as defined at the WTO, i.e. including the whole gamut

of produce from farm gate to dinner plate. In order to simplify the presentation,

the commodity composition of agricultural trade has been segregated into four broad

sub-sectors following the classification in Regmi et al. (2005). These categories are: 1) bulk

commodities such as wheat or coffee; 2) horticultural commodities such as bananas

or cut flowers; 3) semi-processed commodities such as live animals or vegetable oils;

and 4) processed products, i.e. goods that require extensive transformation prior to

consumption such as chocolates, beverages, and fresh or chilled meats. This classification

is primarily based upon the relative dependence of production upon land and climatic

conditions. While products in the first two categories depend disproportionately on land

availability, geography, and climatic conditions, those in Categories 3 and 4 are less

dependant upon those factors and in principle, can be produced almost anywhere.3 A

complete listing of the products and the concordance with the trade data is given in

Table B.1. As the period that is reviewed ends before the enlargement of the EU to

27 member states, references to aggregate EU data in this section covers members prior

to 2004, that is, EU15 only.

Evolution in total agricultural and merchandise trade

During the twenty-year period 1985 to 2004, world agricultural exports (excluding

intra-EU trade) increased more than threefold from USD 123 billion to USD 393 billion4

resulting in an annual compound growth rate averaging 6.3% a year (Table 1.4). Over the

same time period however, total world merchandise exports expanded at an even faster

Box 1.1. Partial stochastic analysis: Variability around deterministic projections (cont.)

Conclusion

Partial stochastic analysis has only a partial coverage of uncertainties; this analysis focuses on exogenous uncertainties linked to climate and macroeconomic evolution. There are several other sources of uncertainty in the benchmark projections. In particular, there is an empirical uncertainty on the estimation of the parameters used in the model jointly developed by the OECD and the FAO and an endogenous uncertainty on the functioning of agricultural markets. Despite these limitations, the information that partial stochastic analysis is of interest for better assessing the evolution of agricultural commodity markets would be possible from the analysis of a deterministic baseline. This box has described how a partial stochastic analysis has been undertaken with the 2007 Agricultural Outlook projections. A number of conclusions emerge from this analysis using the Aglink-Cosimo model: First, the deterministic baseline projections differ slightly from the averages of stochastic simulations. Second, stochastic projections of world crop prices (expressed in real terms) are relatively highly concentrated around the average. And finally, the analysis underlines strong price correlations across commodities.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 37

1. OVERVIEW

rate, increasing more than fivefold from USD 1.1 trillion to USD 6.1 trillion, revealing an

average compound growth rate of 9.6% a year. Given different growth rates in total

merchandise exports and agricultural exports, the share of agricultural exports to total

merchandise fell from almost 12% of the total in 1985 to about 7% of total merchandise

exports in 2004 (Table 1.4).

The value of agriculture and total merchandise exports increased over the time period

examined because countries exported more products and because more countries became

engaged in trade (globalization). Between 1985 and 2004, the number of reporting countries

or economic regions (all referred to as countries) increased from 88 to 130, with the number

of reporting countries reaching 164 in 2000. Of this number, only 74 countries are

considered consistent traders, defined as countries with at least 18 years of reported

exports during the sample. These countries increased their merchandise exports more

than fivefold during this period growing from USD 1 trillion in 1985 (96% of total

merchandise exports) to USD 5.6 trillion (these figures and all figures in the rest of the

section exclude intra-EU trade) in 2004 (92% of total). Agricultural exports by this group of

countries grew from USD 119 billion USD 362 billion, representing 96% and 92% of total

agricultural exports in 1985 and 2004 respectively.

This information suggests that exports are relatively concentrated; although globalisation

has led to more countries participating in trade, they play a relatively minor role. Which

countries are the major world exporters, how has this changed over time, and what share of

agricultural exports do they control? In the 1985 to 1989 period, the US was the largest

Table 1.4. Total merchandise and agriculture exports 1985-2004 (with and without intra-EU trade)

Data exclude intra-EU Data include intra-EU trade

Total agricultural exports

Total merchandise exports

Agriculture share of total

Total agricultural exports

Total merchandise exports

Agriculture share of total No. of countries

reporting Billion USD Billion USD Per cent Billion USD Billion USD Per cent

1985 123 1 071 11.5 175 1 477 11.9 88

1986 126 1 137 11.1 194 1 656 11.7 98

1987 134 1 335 10.1 218 1 980 11.0 95

1988 156 1 590 9.8 248 2 307 10.8 96

1989 179 1 858 9.6 274 2 628 10.4 102

1990 189 2 105 9.0 300 3 037 9.9 105

1991 190 2 208 8.6 308 3 137 9.8 103

1992 212 2 093 10.1 341 3 081 11.1 106

1993 212 2 573 8.2 327 3 411 9.6 111

1994 245 2 928 8.4 372 3 908 9.5 118

1995 290 3 464 8.4 438 4 661 9.4 134

1996 313 3 741 8.4 463 4 968 9.3 139

1997 316 3 899 8.1 456 5 124 8.9 146

1998 295 3 832 7.7 435 5 106 8.5 144

1999 277 4 006 6.9 416 5 301 7.9 152

2000 284 4 683 6.1 411 5 955 6.9 164

2001 292 4 425 6.6 423 5 719 7.4 161

2002 300 4 459 6.7 443 5 788 7.6 153

2003 352 5 166 6.8 527 6 742 7.8 149

2004 393 6 140 6.4 594 8 032 7.4 131

Growth rate 6.29 9.63 6.63 9.32

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200738

1. OVERVIEW

agricultural exporter with an average of USD 34.3 billion in exports (about 23% of total),

followed by the EU15 with almost USD 30 billion (20% of total). Australia, with an average of USD 9.7 billion was the third largest exporter followed by the Canada and Brazil. These OECD countries exported, on average, some 54% of the world total in that period. Table 1.5 shows the

remaining top exporters and indicates that eight of the leading exporting countries are not

OECD countries and that the leading agricultural exporting countries exported on average

about 80% of the world total during this time. Among the members of the EU15, France,

Germany, the Netherlands and the United Kingdom are among the top 10 exporting countries.

Twenty years later, the leading exporting countries remained basically the same,

except that Colombia and Hong Kong (China) were replaced by Indonesia and Spain, and even though the value of exports more than doubled, the market share of the leading

exporters fell as other countries expanded their exports. The share of the leading countries

listed in Table 1.5 fell to 75% of the total. In addition, individual ranking also changed. The

EU15 jumped ahead of the US to become the largest exporter while Brazil replaced Australia as the third largest exporter with an average market share of 5.5% a year. Although most of the leading exporters are OECD countries, developing countries

increased their market share and the top exporting developing countries increased their

share of trade slightly to 21% of the total.

A more comprehensive representation of the relative dominance of OECD countries in

world agricultural trade is shown in Figure 1.15 below. The figure breaks out world exports

based on countries grouped by income and the 30 OECD countries.5 Based on this level of

Table 1.5. Leading agro-food exporting countries (average 1985-89 and 2000-04)

Data exclude intra-EU trade Average 1985-89

Data exclude intra-EU trade Average 2000-04

Economy USD billion Share (%) Economy USD billion Share (%)

(1) United States 34.34 22.80 (1) EU15 61.78 18.68

(2) EU15 29.86 19.83 of which:

of which: France 11.08 3.35

France 6.99 4.64 Netherlands 9.34 2.82

Netherlands 4.33 2.87 Germany 9.20 2.78

United Kingdom 3.95 2.63 United Kingdom 6.66 2.01

Germanya 3.93 2.61 Italy 6.59 1.99

Italy 2.54 1.69 Denmark 4.56 1.38

Denmark 2.49 1.65 Spain 3.92 1.19

(3) Australia 9.65 6.41 (2) United States 60.18 18.19

(4) Canada 7.38 4.90 (3) Brazil 18.18 5.49

(5) Brazil 6.61 4.39 (4) Canada 17.72 5.36

(6) China 6.12 4.06 (5) Australia 16.16 4.89

(7) New Zealand 4.40 2.92 (6) China 14.00 4.23

(8) Argentina 4.22 2.80 (7) Argentina 12.54 3.79

(9) Thailand 3.50 2.32 (8) Mexico 8.36 2.53

(10) Malaysia 2.76 1.83 (9) New Zealand 8.05 2.43

(11) Colombia 2.57 1.71 (10) Malaysia 7.45 2.25

(12) Mexico 2.47 1.64 (11) Thailand 7.38 2.23

(13) Turkey 2.35 1.56 (12) India 5.80 1.75

(14) Hong Kong (China) 2.31 1.53 (13) Indonesia 5.27 1.59

(15) India 2.28 1.51 (14) Turkey 4.15 1.25

Total of above 120.81 80.22 Total above 247.02 74.67

a) Excludes data for the German Democratic Republic.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 39

1. OVERVIEW

aggregation, the share of agricultural exports of OECD countries peaked in 1987-88 at

almost 70% of exports but fell from this high level to around 60% in the latter years

(Figure 1.15).6 The share of high income non-OECD countries (not shown in figure) also

declined somewhat from around 4% in 1985 to 3% in 2004 and that of low income countries

from around 6% in 1985 to around 4% in 2004. The declining share from OECD and high-

income countries has been captured by the middle income countries. The upper-middle-

income countries increased their share from around 8% in 1985 to around 11% in 2004,

while lower middle income countries increased their share from 19% to 23% of the total

during this time.

Shifting the focus to the G207 group of developing countries – countries with particularly strong views on agricultural trade in the Doha negotiations – the data reveals

that total merchandise exports by this group increased almost 13 times to USD 1.3 trillion,

representing 21% of world’s total in 2004. The average growth rate of 14% per year

considerably outpaced that of all exporting countries. Total agricultural exports by the

G20 on the other hand increased only fourfold to USD 111 billion in 2004 or 28% of the

world total. Reflecting the different growth rates of agricultural and merchandise exports,

the export sector of this group of countries exhibited traits similar to all countries, namely,

the share of agricultural goods to total merchandise exports declined. During the 20 years

from 1985, the value of agricultural exports in total exports dropped by 19 percentage

points to 9% in 2004.

Evolution in the exports of the four agricultural sub-sectors

Within an overall growing agricultural export trade over the 20-year period, the value

of exports in each of the four sub-sectors, bulk, horticultural, semi-processed and

processed, also expanded, but at very different rates of growth. While exports of bulk

commodities increased at an annual growth rate of 2.6% a year, the growth in exports of

horticultural products was much faster at 8.6% a year. Nevertheless, the share of these two

broad groups of commodities – both heavily dependant upon land and climatic conditions

– in the value of total agricultural exports fell from 45% to 30% from 1985 to 2004

(Figure 1.16).

Figure 1.15. Agriculture export share (excludes intra-EU trade) by income group (1985-2004)

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OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200740

1. OVERVIEW

Within the group of goods that are less dependent on climatic conditions, exports of

semi-processed products grew at 5.9% a year to more than USD 97 billion in 2004, with a

little changed share in total agricultural exports. On the other hand, exports of highly

processed products increased fivefold from USD 35 billion in 1985 to USD 177 billion

in 2004, raising their share in total agricultural exports from 28% to 42%. The average

annual growth rate of these products, 8.9% a year, is comparable to the annual average

growth rate of total merchandise exports.

OECD countries are the largest exporters of bulk commodities but their share of the total declined during the 20-year period from 61% in 1985 to 54% in 2004 (Figure 1.17). Most of this

was captured by lower-middle-income countries whose share in total bulk product exports

more than doubled during the period to 28%. Bulk exports by low- and upper-middle-income

countries are of lesser importance but nevertheless exhibited much stronger growth than

the OECD countries.

Figure 1.16. Share of agriculture exports (excludes intra-EU trade) by stage (1985-2004)

Figure 1.17. Exports of bulk and horticultural products by various groups of countries (1985-2004)

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OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 41

1. OVERVIEW

Looking at individual countries (EU15 counting as one), the US, Canada and the EU15 are the top three exporters of bulk commodities with an annual average export value of

USD 17.2 billion, USD 3.8 billion and USD 3.2 billion respectively during the 1985 to 1989

period, representing more than half of average world exports during those years (Table B.2).

Even though many countries export bulk products, trade is concentrated and the top

20 exporters captured on average more than 91% of world total. But, over time, the

concentration of the top 20 exporting countries declined and stood at 86% in 2000 to 2004

period. Within this overall trend, the relevance of OECD countries is declining and by 2004

there were only five OECD countries among the leading 20 exporters of bulk commodities.

Thus, unlike the exports of all agricultural products where the OECD countries dominate,

exports of bulk commodities that depend more on climatic conditions, and land

availability has shifted more toward developing countries.

Production of horticultural commodities is also relatively location specific, i.e. relatively

more dependent on land and climatic conditions. As already stated, trade in this sector has

been much more dynamic than trade in bulk products. While the OECD dominates horticultural exports with a total of USD 24 billion in 2004, the strongest growth was exhibited

by the upper-middle-income countries, with an growth rate of 10.8% a year to USD 6 billion

in 2004 (Figure 1.18). As a group the G20 exhibited a high growth rate (9.6% per year, not shown in the graph), followed by the OECD countries (9.4%), the lower-middle-income countries (8%) and the low-income countries with an average annual growth of horticulture exports of 5.8%.

As for bulk commodities, the leading horticultural exporting country is the US with an average of USD 2.2 billion a year during the 1985 to 1989 period and USD 6.2 billion a year

for the 2000 to 2004 period representing 16% of the world’s total of these products during

each of these periods (Table B.3). The rank ordering of the leading horticultural product

exporters has changed over time, but overall and in contrast to trade in bulk commodities,

the importance of OECD countries in horticultural products trade increased with its share

of total horticultural exports growing from 46% in 1985 to 54% in 2004.

The third agricultural sub-sector, semi processed products includes products that are

less dependant on climatic conditions with key inputs into their production process that

Figure 1.18. Exports of semi processed and processed products by various groups of countries, 1985-2004

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OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200742

1. OVERVIEW

are importable. This group of products as mentioned above is the second largest exported

sub-sector. As a group, OECD countries increased their exports in this segment by 4.9% a year to USD 48.1 billion in 2004 (Figure 1.18). Nevertheless, their share in the world total

fell by 10 percentage points to an average of about 50% as that of upper-middle-income

developing countries increased by 6 percentage points to 14% of the total in 2000-04

reflecting an average growth rate of 8.2% a year. Strong export growth of 7.3% per year to

USD 30 billion in 2004 was also exhibited by members of the G20 (not shown in the figure).

Exports of semi-processed products by the least developed countries (not shown in the

figure) increased from USD 166 million to USD 693 million in 2003. But with slower growth

than that of other developing countries, their share in world total exports hardly changed.

The EU15 and the US are the world’s largest exporters of semi-processed products, with respective shares of total world trade in 2004 of 17% and 16%. On average, the EU15 exported

some USD 13.4 billion a year during 2000-04 and the US just above USD 13 billion (Table B.4).

The final group of products considered here those with the highest level of

transformation or processing prior to consumption. Production of this group of products is

not very location specific, is very little concerned with climatic conditions, most of the

required inputs can be sourced from practically anywhere and other considerations loom

more important in firms decisions as to where to locate. This group of products has the

largest share of agricultural exports and has the highest growth rate. OECD exports of processed products have grown by more than 8% per year since 1985 to USD 120.4 billion

in 2004 (Figure 1.18). But, although from a much lower base, exports in this segment by

upper-middle-income and lower-middle-income countries grew at double digit rates, averaging respectively 13.6% and 10.7% per year, reaching respectively USD 14.1 billion and

USD 34 billion in 2004.

OECD countries dominate trade in this segment: The six leading processed product

exporters are all members of the OECD; the number of OECD countries in the top 20

increased to 15 by 2004; and on average these countries exported almost USD 87 billion a

year or 60% of the total (Table B.5). Nevertheless, reflecting the very high growth rates in

processed product exports by developing countries, these countries are increasing their

share in total world trade. For instance, processed products became the most important

export segment for the G20 countries, overtaking exports of bulk or semi-processed products. Their share of total world exports increased from 15% to 23% since 1985. Other

developing countries (except the least developed countries) also demonstrated impressive

growth rates in exporting products in this market segment.

In general, the export data reveal the extent of globalisation with the share of the

leading exporting countries declining over the 1985 to 2004 period. This illustrates that

more countries are contesting agricultural export markets and that more countries have

entered the global markets while existing competitors below the group in the top 20

increased their competitiveness and their share of the market. Overall, the share of exports

by OECD countries has declined in three of the four broad aggregates discussed (except for

horticultural markets). The data also reveal that despite the policy changes that have

occurred since the mid-1990s and the implementation of the URAA, agricultural trade

continues to be dominated by a relatively small number of countries, with the leading

20 exporting countries controlling more than 70% of the exports in each of the four

segments examined.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 43

1. OVERVIEW

Evolution of agricultural imports

Turning our attention to the flip side of the issue, the data show that growth of

agricultural trade based on imports is the same as that described above based on exports.

For example, agricultural imports increased at an average growth rate of 6.5% a year, but

still lagged behind that of all merchandise trade resulting in agriculture’s share of world

merchandise trade based on imports declining from 10% of the total in 1985 to slightly

above 6% in 2004. In terms of the composition of trade, import developments are also

similar to those for exports with the share of bulk commodities in total agricultural

imports falling and that for processed products increasing.

The Least Developed Countries seem to be more engaged in importing rather than

exporting agricultural goods as their share of world imports during the last 10 years has

been above 1% in contrast to less than 0.5% in exports. OECD countries share of imports fell

from more than 74% of the total at the beginning of the period to the low-60% in the later

years, while the import share of developing countries other than low income, increased

from around 13% at the beginning of the period to around 26% in the later years. Demand

for bulk commodities by the OECD countries has fallen particularly with its share in total

world imports of bulk products falling from 72% on average during the period between 1985

and 1989 to 51% for the 2000 to 2004 period. In contrast, import demand for bulk

commodities by developing countries expanded at a faster rate, increasing their share of

the market. Import demand increased the fastest among upper-middle-income countries, averaging 11.4% a year, followed by lower-middle-income countries with an annual growth rate of 9.1%.

The same trend prevailed for imports of processed products where imports by the

OECD countries grew at an annual rate of 8.2% compared to double digit rates for many developing countries. Consequently, the share in world imports of processed products by

OECD countries fell to 68% by 2004. Import demand by upper-middle-income countries increased at an average rate of 13.4% a year expanding their demand more than 10 times

from USD 1.7 billion in 1985 to USD 17.9 billion in 2004. Lower-middle-income countries also increased their demand at a double digit rate averaging 10.2% a year. Their demand

expanded more than 6 times from USD 2.8 billion in 1985 to USD 18.1 billion in 2004.

Low-income countries expanded their demand for this class of commodities about threefold from USD .9 billion in 1985 to USD 2.9 billion in 2004. (Tables B.2 to B.5 contain a

list of the leading importing countries for each of the four sub-sectors.)

It is noteworthy that import demand also expanded for the G20 countries, the group that is considered to have an export orientation at the WTO negotiations. Double digit

growth in import demand by the G20 countries was registered in each of the four

sub-sectors and their total imports of agricultural products grew by more than 11% per

year between 1985 and 2004, raising their share of total world imports from 10.8%

during 1985-89 to 17.2% during the 2000-04 period (compared to an average share of 17.7%

of world exports). This phenomenon was not confined to one or two large members, a

development that would lead to misleading interpretations. Rather large import demand

was exhibited by a majority of the members. Average imports for the 2000 to 2004 period by

three members, China, Indonesia, and Mexico, placed them among the leading 20 importing countries, while a total of 13 members were among the top 50 agricultural

importers. Furthermore, of these 13 important importers, 7 led by Mexico, Egypt and Venezuela were on average net importers of agricultural goods during the 2000 to 2004

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200744

1. OVERVIEW

period. It is particularly striking that the average growth in import demand for bulk

commodities by the G20 group of countries, 11.1% a year, outpaces that of global growth or

growth by OECD. Consequently, the G20 as a group switched from being net exporters of

bulk products on average during 1985-89 to being net importers during 2000-04. For the

other three sub-sectors however, strong growth in imports was more than offset by an even

stronger expansion in exports. As a result, the G20 maintained their net export position in

these set of commodities and in total agricultural trade.

Another group of countries that has joined ranks at the WTO negotiations is the G10.8

This group of countries is thought to have more of an import orientation in the

negotiations. While their agricultural imports indeed increased by 6.2% a year from 1985

to 2004, their share of total world imports declined from an average of 21.2% in the 1985-89

period to 18.4% in the 2000-04 period. And as total merchandise imports increased at an

even faster rate, averaging 8.8% a year, the agriculture share of total imports by these

countries fell from 11% in 1985 to 7% by 2004.

Most of the growth in agricultural imports by the G10 has occurred in processed

products. These grew at an annual average rate of 9.7%, increasing their share of

agricultural imports to almost half on average during 2000-04. On the other hand, import

demand for bulk commodities moderated during the 20-year period, growing by only 2.4%

a year. As a result their share in total agricultural imports declined from an average of 35%

of total in 1985-89 to 21% in 2000-04.

Summary

To summarize, between 1985 and 2004 trade in agriculture products (whether

measured by the value of exports or imports) increased substantially both due to an

expansion in trade by existing countries and due to new countries participating in the

globalisation of markets. Agricultural trade did not increase as fast as all merchandise

trade, resulting in a declining share of agriculture in world trade, to less than 10% in recent

years. This trend of a falling share of agriculture in total merchandise trade is persistent

across all income levels and geopolitical groupings, and is consistent with a similar pattern

of agriculture capturing a declining share of an economy’s income.

The trade data between 1985 and 2004 also show that even though there are more and

more countries participating in trade, a relatively small number of countries continue to

capture most of this trade whether one is referring to agriculture or non-agriculture goods.

The concentration ratio of the top 4 or top 20 exporting countries, although dropping

moderately over the 20-year period, is still rather high, with the top 20 exporting countries

accounting for almost 80% of total merchandise exports or 73% of total agricultural exports

in 2004. LDCs, the group of countries who are receiving special consideration in the Doha

Development Agenda are not very big participants in the expansion of agriculture trade,

accounting for less than 1% of the total. Members of the OECD continue to dominate

agriculture trade although their share of the total has declined somewhat over the 20-year

period. Most of the gains have been made by countries that are in the G20 and other

developing countries that are not LDCs.

The data suggest that the dynamics of agricultural trade is chiefly about trade in

processed products. The growth rate for this sector (8.5% a year) is comparable to the

growth rate of non-agricultural products and as a result this group of commodities has

steadily increased its share of agriculture trade, to 41% of total exports (45% of total

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 45

1. OVERVIEW

imports) in 2004. Trade in bulk products on the other hand is growing at the lowest rate

(3.7% a year based on exports and 2.6% a year based on imports) among the agricultural

sectors and as a result the share of bulk products in agricultural trade has declined from

37% to 19% of exports (from 34% to 21% of imports) during the 20 years since 1985.

Patterns in the exports of each of the four agriculture sub-sectors – bulk products,

horticulture, semi-processed products and processed products – follow those of agriculture

in general. The top 4 or 20 exporters continue to dominate but their share has declined

somewhat. The OECD countries continue to account for a majority of trade, and they tend

to dominate trade in processed products. Nevertheless, developing countries other than

LDCs have increased their importance in the trade of agricultural products in all the

sub-sectors but especially for bulk commodities.

Trade developments by some groups of countries are particularly striking given the

stance of these countries in the current Doha Round of trade negotiation. For instance,

agricultural exports for the G20 have decreased in importance as the share of agricultural

exports to total merchandise exports has declined from 28% of the total in 1985 to 9%

in 2004. Members of the G20 as a group have become net importers of bulk products while

remaining overall net exporters in agriculture.

The development in agricultural trade by the G10 group of countries is also

noteworthy. This group of countries has an import orientation in the current Doha

negotiation. However, both their share in world agricultural imports as the share of

agricultural imports in total imports by these countries has fallen over time.

Notes

1. These and other macroeconomic assumptions in this section are based on the OECD, World Bank and UN sources which are explained in detail in footnote a to Table A.1 of the Statistical Annex.

2. There appears to be an income threshold beyond which entry into export markets becomes more feasible. This in turn implies that the benefits from globalisation may depend on income levels.

3. See Regmi et al. (2005) for more details on the rationale for the product classification scheme.

4. All values are stated in nominal US dollars. Trade data are from UN COMTRADE.

5. Country classification by income is from the World Bank and is based on per capital gross national income as of 2005; http://siteresources.worldbank.org/DATASTATISTICS/Resources/CLASS.XLS.

6. If intra EU trade is included, the share of OECD countries in world trade is considerably higher, averaging 74% of the total in the last four years.

7. Members of the G20 are: Argentina, Bolivia, Brazil, Chile, China, Cuba, Egypt (Arab Republic of), Guatemala, India, Indonesia, Mexico, Nigeria, Pakistan, Paraguay, Philippines, South Africa, Tanzania, Thailand, Uruguay, Venezuela RB and Zimbabwe.

8. Members are: Bulgaria, Chinese Taipei, Iceland, Israel, Japan, Korea Republic, Liechtenstein, Mauritius, Norway and Switzerland.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200746

OECD-FAO Agricultural Outlook 2007-2016

© OECD/FAO 2007

ANNEX A

Statistical Tables

47

ANNEX A

2016

2.6

2.3

1.9

0.9

4.8

4.0

2.5

2.7

1.4

7.3

2.6

3.2

3.5

6.5

5.3

2.7

3.4

2.4

2.4

1.9

1.7

1.0

2.4

3.1

1.9

2.4

0.7

4.6

1.8

4.0

4.6

2.5

5.7

4.0

5.1

2.0

Table A.1. Economic assumptions

Calendar yeara Average 2001-05

2006 est. 2007 2008 2009 2010 2011 2012 2013 2014 2015

REAL GDPb

Australia % 3.2 2.6 3.0 3.4 3.0 3.0 2.9 2.7 2.6 2.6 2.6

Canada % 2.6 2.8 2.7 3.1 2.9 2.5 2.3 2.3 2.3 2.3 2.3

EU15 % 1.4 2.8 2.4 2.4 2.1 2.0 1.9 1.9 1.9 1.9 1.9

Japan % 1.5 2.8 2.0 2.0 1.6 1.4 1.3 1.1 0.9 0.9 0.9

Korea % 4.5 5.0 4.4 4.6 4.9 4.9 4.8 4.8 4.8 4.8 4.8

Mexico % 1.8 4.7 3.6 3.7 3.9 4.0 4.0 4.0 4.0 4.0 4.0

New Zealand % 3.4 1.5 1.3 2.0 2.9 2.5 2.5 2.5 2.5 2.5 2.5

Norway % 2.1 2.4 3.2 2.7 2.5 2.9 2.9 2.8 2.7 2.7 2.7

Switzerland % 1.1 3.0 2.2 2.0 1.6 1.4 1.4 1.4 1.4 1.4 1.4

Turkey % 4.5 6.1 5.3 6.3 7.5 7.1 7.1 7.3 7.3 7.3 7.3

United States % 2.4 3.3 2.4 2.7 2.8 2.7 2.7 2.7 2.6 2.6 2.6

Argentina % 2.3 7.7 5.6 4.0 4.6 4.4 4.1 3.9 3.7 3.4 3.2

Brazil % 2.2 3.5 3.4 3.8 3.8 3.7 3.7 3.6 3.6 3.5 3.5

China % 9.5 10.4 9.6 8.7 8.4 8.1 7.8 7.5 7.1 6.8 6.5

India % 7.0 8.7 7.7 7.2 6.9 6.7 6.4 6.1 5.9 5.6 5.3

Russia % 6.1 6.8 6.0 5.5 5.1 4.7 4.3 3.9 3.5 3.1 2.7

South Africa % 3.8 4.6 3.9 4.3 4.2 4.0 3.9 3.8 3.7 3.5 3.4

OECDc, d % 2.0 3.2 2.5 2.7 2.6 2.5 2.5 2.4 2.4 2.4 2.4

PCE DEFLATORb

Australia % 2.3 2.8 2.6 2.7 2.4 2.4 2.4 2.4 2.4 2.4 2.4

Canada % 2.6 1.4 1.3 1.7 1.8 1.9 1.9 1.9 1.9 1.9 1.9

EU15 % 2.0 2.1 1.9 1.8 1.8 1.7 1.7 1.7 1.7 1.7 1.7

Japan % –1.0 –0.4 0.4 0.8 0.9 1.0 1.0 1.0 1.0 1.0 1.0

Korea % 3.4 2.1 2.9 3.0 2.6 2.4 2.4 2.4 2.4 2.4 2.4

Mexico % 5.9 3.1 3.4 3.2 3.1 3.1 3.1 3.1 3.1 3.1 3.1

New Zealand % 1.4 2.7 2.7 2.0 1.8 1.9 1.9 1.9 1.9 1.9 1.9

Norway % 1.7 2.2 2.1 2.6 2.6 2.4 2.4 2.4 2.4 2.4 2.4

Switzerland % 0.8 1.4 0.9 1.2 0.9 0.7 0.7 0.7 0.7 0.7 0.7

Turkey % 27.1 10.2 7.3 6.0 5.1 4.6 4.6 4.6 4.6 4.6 4.6

United States % 2.2 2.8 2.2 2.2 2.0 1.9 1.8 1.8 1.8 1.8 1.8

Argentina % 10.6 9.8 9.7 9.0 8.2 7.5 7.0 6.4 5.8 5.2 4.6

Brazil % 9.1 5.5 5.2 5.2 3.9 4.7 4.6 4.6 4.6 4.6 4.6

China % 1.4 1.4 2.0 2.8 2.4 2.5 2.5 2.5 2.5 2.5 2.5

India % 3.6 6.2 5.8 5.0 5.7 5.7 5.7 5.7 5.7 5.7 5.7

Russia % 6.1 10.0 8.5 8.0 6.5 5.7 5.2 5.0 4.7 4.5 4.3

South Africa % 3.8 4.8 5.6 4.7 5.1 5.1 5.1 5.1 5.1 5.1 5.1

OECDc, d % 2.5 2.4 2.2 2.2 2.0 2.0 1.9 1.9 1.9 2.0 2.0

For notes, see end of the table. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200748

ANNEX A

2016

0.96

0.79

0.02

–0.15

0.16

0.96

0.57

0.47

0.09

1.04

0.80

0.88

0.98

0.51

1.18

–0.51

0.06

0.40

2016

1.42

1.15

0.77

105.6

0.83

11.91

1.50

3.67

3.25

7.82

64.69

31.0

9.39

60.85

ge rate ic data -term

re sent spects

hasing

Table A.1. Economic assumptions (cont.)

Calendar yeara 2006 est. (million)

2007 2008 2009 2010 2011 2012 2013 2014 2015

POPULATION

Australia % 20.5 1.03 1.01 1.01 0.99 0.98 0.98 0.96 0.97 0.96

Canada % 32.6 0.88 0.85 0.83 0.82 0.82 0.80 0.80 0.79 0.79

EU27 % 490.6 0.15 0.12 0.10 0.08 0.07 0.06 0.05 0.04 0.03

Japan % 127.9 0.08 0.06 0.03 0.01 –0.02 –0.05 –0.07 –0.10 –0.13

Korea % 48.5 0.33 0.31 0.29 0.27 0.25 0.23 0.22 0.20 0.18

Mexico % 105.4 1.17 1.13 1.10 1.08 1.06 1.04 1.01 1.00 0.98

New Zealand % 4.1 0.73 0.67 0.62 0.64 0.61 0.61 0.65 0.60 0.60

Norway % 4.6 0.47 0.47 0.45 0.47 0.46 0.46 0.46 0.46 0.48

Switzerland % 7.4 0.15 0.12 0.12 0.11 0.09 0.11 0.08 0.08 0.09

Turkey % 73.0 1.33 1.31 1.28 1.25 1.21 1.18 1.14 1.11 1.07

United States % 299.2 0.94 0.93 0.91 0.90 0.88 0.86 0.85 0.83 0.82

Argentina % 39.1 0.91 0.89 1.01 0.99 0.97 0.95 0.93 0.92 0.90

Brazil % 188.2 1.14 1.12 1.23 1.19 1.15 1.11 1.07 1.04 1.01

China % 1 301.2 0.63 0.63 0.58 0.58 0.58 0.58 0.58 0.56 0.53

India % 1 117.7 1.26 1.22 1.38 1.35 1.33 1.30 1.27 1.24 1.21

Russia % 142.5 –0.53 –0.53 –0.44 –0.45 –0.46 –0.47 –0.48 –0.49 –0.50

South Africa % 47.6 0.37 0.39 0.08 0.05 0.04 0.03 0.03 0.04 0.04

OECDc % 1 213.9 00.54 0.52 0.50 0.49 0.47 0.46 0.44 0.43 0.41

Calendar yeara Average 2001-05

2006 est. 2007 2008 2009 2010 2011 2012 2013 2014 2015

EXCHANGE RATE

Australia AUD/USD 1.60 1.33 1.31 1.32 1.33 1.34 1.35 1.37 1.38 1.39 1.40

Canada CAD/USD 1.41 1.13 1.14 1.13 1.13 1.13 1.13 1.13 1.14 1.14 1.15

European Union EUR/USD 0.93 0.80 0.78 0.78 0.77 0.77 0.77 0.77 0.77 0.77 0.77

Japan JPY/USD 116.2 116.5 118.1 115.9 114.2 112.8 111.5 110.3 109.1 107.9 106.7

Korea ‘000 KRW/USD 1.18 0.95 0.94 0.92 0.90 0.89 0.88 0.87 0.86 0.85 0.84

Mexico MXN/USD 10.39 10.91 10.92 10.99 11.08 11.18 11.31 11.42 11.54 11.67 11.79

New Zealand NZD/USD 1.84 1.55 1.52 1.50 1.50 1.49 1.49 1.49 1.49 1.49 1.49

Argentina ARS/USD 2.56 2.87 2.79 2.65 2.78 2.92 3.06 3.19 3.32 3.44 3.56

Brazil BRL/USD 2.78 2.47 2.60 2.73 2.79 2.85 2.91 2.98 3.04 3.11 3.18

China CNY/USD 8.24 7.85 7.64 7.44 7.46 7.50 7.55 7.60 7.65 7.71 7.76

India INR/USD 46.30 45.70 47.20 48.11 49.78 51.62 53.60 55.65 57.78 60.00 62.30

Russia RUB/USD 29.7 27.3 26.4 25.5 26.3 27.1 27.9 28.6 29.3 29.9 30.5

South Africa ZAR/USD 8.85 6.97 7.10 7.37 7.57 7.80 8.04 8.30 8.56 8.82 9.10

WORLD OIL PRICE

Brent crude oil price USD/barrel 34.18 65.22 67.16 65.50 61.31 58.38 55.59 54.64 56.13 57.66 59.24

a) For OECD member countries, historical data for population, real GDP, private consumption expenditure deflator and exchan were obtained from the OECD Economic Outlook, No. 80, December 2006. For non-member economies, historical macroeconom were obtained from the World Bank, November 2006. Assumptions for the projection period draw on the recent medium macroeconomic projections of the OECD Economics Department, projections of the World Bank, responses to a questionnai to member country agricultural experts and for population, projections from the United Nations World Population Pro Database, 2004 Revision (medium variant). Data for the European Union are for the euro area aggregates.

b) Annual per cent change. The price index used is the private consumption expenditure deflator. c) Excludes Iceland. d) Annual weighted average real GDP and CPI growth rates in OECD countries are based on weights using 1995 GDP and purc

power parities (PPPs). For a complete description of the technical assumptions made, please see the Methodology section. est.: Estimate. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 49

ANNEX A

16/17

183.2

138.2

326.0

299.6

200.8

613.9

242.5

308.6

260.9

297.7

480.3

142.3

160.6

361.2

111.3

177.5

404.7

222.6

307.3

251.7

Table A.2. World pricesa

Average 01/02-05/06

06/07 est. 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16

WHEAT

Priceb USD/t 152.0 204.0 204.5 197.5 191.8 186.1 184.6 184.5 183.1 181.7 182.4

COARSE GRAINS

Pricec USD/t 103.6 140.4 158.9 157.6 147.1 143.3 144.0 140.8 138.4 138.6 139.5

RICE

Priced USD/t 238.4 311.4 352.1 360.3 347.8 331.9 331.0 336.3 336.3 330.2 326.2

OILSEEDS

Pricee USD/t 266.0 289.8 310.4 311.7 306.5 300.8 297.4 297.7 295.4 295.1 298.4

OILSEED MEALS

Pricef USD/t 201.0 204.9 215.2 217.0 212.8 207.5 204.6 203.1 198.4 196.3 199.1

VEGETABLE OILS

Priceg USD/t 520.6 590.7 618.0 619.7 622.9 611.9 610.8 608.5 612.4 613.9 615.4

SUGAR

Price, raw sugarh USD/t 217.6 253.5 242.5 235.9 231.5 235.9 240.3 238.1 238.1 240.3 241.4

Price, refined sugari USD/t 269.7 360.5 341.7 330.7 319.7 319.7 319.7 314.2 310.9 310.9 309.7

BEEF AND VEAL

Price, EUj EUR/100 kg dw 244.1 285.2 250.4 258.4 258.0 257.0 259.2 258.8 258.7 259.4 259.8

Price, USAk USD/100 kg dw 282.0 303.6 303.4 299.0 297.1 288.5 285.0 279.4 279.0 286.2 293.8

Price, Argentinal ARS/100 kg dw 307.1 427.3 323.2 352.2 354.9 361.1 374.6 384.4 406.6 436.0 455.4

PIG MEAT

Price, EUm EUR/100 kg dw 134.9 141.4 142.4 153.6 148.6 139.2 143.9 137.3 136.7 138.2 139.9

Price, USAn USD/100 kg dw 136.4 145.0 126.2 154.4 165.4 165.4 157.8 160.8 162.5 158.8 161.3

Price, Brazilo BRL/100 kg dw 187.7 216.2 202.2 280.2 302.5 313.3 306.1 316.3 332.6 339.2 347.1

POULTRY MEAT

Price, EUp EUR/100 kg rtc 102.8 101.5 100.5 104.3 106.7 108.8 105.9 104.2 108.3 109.3 110.3

Price, USAq USD/100 kg rtc 141.8 140.9 159.5 164.8 171.5 179.3 183.0 182.1 180.4 176.6 178.0

SHEEP MEAT

Price, New Zealandr NZD/100 kg dw 390.1 330.0 325.4 333.8 343.6 351.9 361.0 370.0 378.8 387.5 396.1

BUTTER

Prices USD/100 kg 155.9 186.5 196.2 193.0 188.3 188.3 195.1 200.9 209.7 215.1 220.2

CHEESE

Pricet USD/100 kg 231.3 272.8 300.4 310.9 303.2 300.0 301.0 300.5 301.9 304.2 305.9

SKIM MILK POWDER

Priceu USD/100 kg 185.7 234.9 259.4 269.0 266.3 259.3 253.6 250.3 247.9 249.6 249.0

For notes, see end of the table. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200750

ANNEX A

16/17

253.1

89.2

439.4

ops are

Table A.2. World pricesa (cont.)

Average 01/02-05/06

06/07 est. 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16

WHOLE MILK POWDER

Pricev USD/100 kg 190.8 229.4 254.6 262.7 256.7 248.2 250.3 249.0 251.0 252.4 252.8

WHEY POWDER

Wholesale price, USAw USD/100 kg 50.6 74.5 79.0 83.5 90.7 91.3 92.4 91.4 90.6 90.3 89.0

CASEIN

Pricex USD/100 kg 438.8 486.0 480.3 481.9 468.2 457.9 442.5 451.0 440.2 444.9 439.8

a) This table is a compilation of price information presented in the detailed commodity tables further in this annex. Prices for cr on marketing year basis and those for meat and dairy products on calendar year basis (e.g. 05/06 is calendar year 2005).

b) No. 2 hard red winter wheat, ordinary protein, USA f.o.b. Gulf Ports (June/May); less EEP payments where applicable. c) No. 2 yellow corn, US f.o.b. Gulf Ports (September/August). d) Milled, 100%, grade b, Nominal Price Quote, NPQ, f.o.b. Bangkok (August/July). e) Weighted average oilseed price, European port. f) Weighted average meal price, European port. g) Weighted average price of oilseed oils and palm oil, European port. h) Raw sugar world price, New York, No. 11, f.o.b. stowed Caribbean port (including Brazil), bulk spot price. i) Refined sugar price, London, No. 5 , f.o.b. Europe, spot. j) Producer price. k) Choice steers, 1 100-1 300 lb lw, Nebraska – lw to dw conversion factor 0.63. l) Buenos Aires wholesale price linier, young bulls. m) Pig producer price n) Barrows and gilts, No. 1-3, 230-250 lb lw, Iowa/South Minnesota – lw to dw conversion factor 0.74. o) Producer price. p) Weighted average farm gate live chickens, first choice, lw to rtc conversion of 0.75, EU15 starting in 1995. q) Wholesale weighted average broiler price 12 cities. r) Lamb schedule price, all grade average. s) F.o.b. export price, butter, 82% butterfat, northern Europe. t) F.o.b. export price, cheddar cheese, 40 lb blocks, northern Europe. u) F.o.b. export price, non-fat dry milk, extra grade, northern Europe. v) F.o.b. export price, WMP 26% butterfat, northern Europe. w) Edible dry whey, Wisconsin, plant. x) Export price, New Zealand. est.: Estimate. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 51

ANNEX A

2016

127 971 25 278

104 505 10 511

118 227 49 143 84 887

3 663

37 954 6 020

31 676 6 750

93 597 31 799 69 278

141

67 867 34 559 34 255

72

58 450 16 050 43 094

2 866

9 337 4 610 4 040

324

5 901 3 297 2 134

71

10 588 2 669 6 620

593

878 155 492

22

1 876 944 741

47

2 203 82

2 128 167

1 244 244

1 111 12

Table A.3. World trade projections

IMPORTS Average 2001-05

2006 est. 2007 2008 2009 2010 2011 2012 2013 2014 2015

Wheat World trade kt 109 435 108 532 111 184 114 743 113 869 116 054 117 340 119 508 121 518 123 416 125 639 OECD kt 25 721 23 464 24 058 23 884 23 972 24 210 24 358 24 558 24 633 24 856 25 082 Developing kt 84 367 86 006 88 651 92 236 91 204 93 291 94 466 96 487 98 461 100 205 102 290 Least Developed Countries kt 8 121 7 298 8 025 8 207 8 396 8 762 8 974 9 279 9 618 9 942 10 204

Coarse grains World trade kt 104 995 106 537 102 205 107 308 108 919 108 723 109 776 111 531 113 116 114 841 116 115 OECD kt 50 254 50 243 45 898 48 230 49 334 49 193 48 622 48 501 49 000 49 230 49 072 Developing kt 72 159 72 214 70 810 74 307 74 977 75 101 76 785 78 773 79 816 81 184 82 751 Least Developed Countries kt 1 952 2 046 2 484 2 645 2 778 2 871 2 942 2 446 2 691 2 965 3 267

Rice World trade kt 29 810 29 300 30 979 31 831 32 849 33 512 34 097 34 352 35 259 36 074 37 093 OECD kt 4 951 4 661 4 989 5 290 5 445 5 573 5 640 5 695 5 790 5 907 5 967 Developing kt 24 595 24 490 25 714 26 391 27 287 27 790 28 285 28 479 29 303 29 995 30 910 Least Developed Countries kt 4 339 4 668 4 821 5 055 5 187 5 370 5 456 5 580 5 888 6 127 6 425

Oilseeds World trade kt 68 035 79 704 81 635 83 941 84 622 85 183 86 751 88 158 89 394 90 741 91 847 OECD kt 34 213 33 157 32 550 31 745 33 169 32 019 32 894 32 435 31 497 30 989 31 240 Developing kt 41 131 53 707 55 949 59 086 58 521 60 314 61 152 63 020 65 250 67 159 68 036 Least Developed Countries kt 90 90 112 113 117 121 124 128 133 137 140

Oilseed meals World trade kt 49 457 58 423 59 698 60 815 61 421 61 933 63 273 64 180 64 826 65 756 66 708 OECD kt 31 182 35 397 35 855 36 026 35 334 35 170 34 665 34 742 34 932 35 222 34 944 Developing kt 19 169 24 166 25 176 26 117 27 448 28 036 29 825 30 621 30 913 31 510 32 780 Least Developed Countries kt 95 93 76 64 63 65 68 69 70 72 72

Vegetable oils World trade kt 34 584 43 320 45 117 46 593 47 740 48 971 50 462 52 407 54 157 55 791 57 121 OECD kt 7 658 11 021 11 601 12 105 12 298 12 995 13 258 14 276 14 956 15 485 15 779 Developing kt 26 681 32 367 33 856 34 738 35 752 36 277 37 490 38 435 39 544 40 718 41 885 Least Developed Countries kt 1 701 2 061 2 131 2 207 2 280 2 370 2 451 2 535 2 615 2 697 2 780

Beefa World trade kt 5 963 6 424 6 969 7 176 7 584 7 851 8 062 8 321 8 613 8 843 9 097 OECD kt 3 578 3 230 3 677 3 840 3 974 4 050 4 171 4 268 4 381 4 491 4 554 Developing kt 2 176 2 691 2 924 3 008 3 215 3 372 3 466 3 587 3 764 3 829 3 979 Least Developed Countries kt 87 119 150 171 222 258 278 297 313 317 323

Pigmeata World trade kt 3 999 4 301 4 700 4 927 5 055 5 130 5 279 5 443 5 576 5 680 5 766 OECD kt 2 304 2 320 2 430 2 507 2 541 2 615 2 721 2 840 2 957 3 078 3 181 Developing kt 1 352 1 488 1 762 1 849 1 866 1 868 1 941 2 010 2 083 2 106 2 115 Least Developed Countries kt 33 44 57 75 64 65 74 83 85 78 71

Poultry World trade kt 7 563 7 479 8 060 8 353 8 541 8 783 9 077 9 475 9 741 9 948 10 180 OECD kt 1 994 1 908 2 044 1 964 1 769 1 866 2 031 2 193 2 335 2 464 2 511 Developing kt 4 247 4 129 4 562 4 887 5 255 5 410 5 517 5 819 6 027 6 177 6 321 Least Developed Countries kt 363 383 435 461 511 537 544 555 564 565 583

Butter World trade kt 697 708 760 799 818 833 842 852 855 863 867 OECD kt 145 129 133 137 138 140 144 147 150 152 153 Developing kt 399 430 452 460 459 471 474 479 478 482 486 Least Developed Countries kt 10 10 19 19 19 20 21 21 21 21 22

Cheese World trade kt 1 401 1 459 1 497 1 545 1 593 1 624 1 666 1 710 1 750 1 792 1 833 OECD kt 758 725 767 785 804 822 843 863 883 903 923 Developing kt 550 589 595 615 624 640 656 672 686 703 722 Least Developed Countries kt 15 18 27 32 34 35 36 38 39 42 45

Whole milk powder World trade kt 1 396 1 582 1 642 1 661 1 726 1 815 1 872 1 950 2 012 2 076 2 139 OECD kt 88 85 82 80 81 81 80 81 81 81 81 Developing kt 1 340 1 525 1 582 1 598 1 661 1 750 1 806 1 882 1 943 2 005 2 066 Least Developed Countries kt 86 112 115 120 126 132 138 144 150 155 161

Skim milk powder World trade kt 1 167 1 196 1 190 1 178 1 161 1 169 1 182 1 184 1 205 1 198 1 210 OECD kt 223 191 196 195 199 204 211 218 224 228 237 Developing kt 1 039 1 097 1 081 1 067 1 046 1 052 1 063 1 063 1 082 1 072 1 080 Least Developed Countries kt 42 23 22 23 24 25 26 22 18 16 14

For notes, see end of the table. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200752

ANNEX A

2016

127 971 86 082 19 759

141

118 227 83 047 27 141

1 819

37 954 5 394

33 100 1 975

93 597 33 760 53 755

19

67 867 11 613 54 670

12

58 450 1 441

57 119 84

9 337 4 088 6 086

2

5 901 4 202 2 251

5

10 588 3 898 6 602

4

878 603

70 1

1 876 1 339

373

2 203 1 563

807 4

1 244 841 157

2

Table A.3. World trade projections (cont.)

EXPORTS Average 2001-05

2006 est. 2007 2008 2009 2010 2011 2012 2013 2014 2015

Wheat World trade kt 109 435 108 532 111 184 114 743 113 869 116 054 117 340 119 508 121 518 123 416 125 639 OECD kt 72 970 73 916 72 143 76 376 77 540 78 916 79 610 81 394 82 447 83 183 84 641 Developing kt 19 282 17 789 18 549 19 203 18 301 18 511 18 693 18 885 19 171 19 374 19 555 Least Developed Countries kt 130 82 121 123 125 128 130 133 135 137 139

Coarse grains World trade kt 104 995 106 537 102 205 107 308 108 919 108 723 109 776 111 531 113 116 114 841 116 115 OECD kt 74 790 78 235 69 156 71 019 71 371 71 015 72 003 75 221 77 692 80 047 81 002 Developing kt 28 413 23 492 28 073 30 567 30 825 30 797 30 765 29 113 28 288 27 716 27 387 Least Developed Countries kt 1 377 2 028 3 518 3 303 2 937 2 887 2 916 1 962 1 888 1 871 1 873

Rice World trade kt 29 810 29 300 30 979 31 831 32 849 33 512 34 097 34 352 35 259 36 074 37 093 OECD kt 4 892 4 241 4 751 4 773 4 818 4 925 4 955 5 047 5 193 5 321 5 384 Developing kt 23 732 25 591 26 766 27 598 28 570 29 127 29 681 29 845 30 605 31 292 32 248 Least Developed Countries kt 496 855 1 716 1 656 1 868 1 995 1 887 1 748 1 778 1 649 1 744

Oilseeds World trade kt 68 035 79 704 81 635 83 941 84 622 85 183 86 751 88 158 89 394 90 741 91 847 OECD kt 35 632 38 699 40 964 39 117 35 847 32 979 32 461 32 320 32 555 33 139 33 521 Developing kt 30 446 36 660 36 083 39 935 43 413 46 973 49 045 50 486 51 290 51 991 52 511 Least Developed Countries kt 18 18 18 18 19 19 19 19 19 19 19

Oilseed meals World trade kt 49 457 58 423 59 698 60 815 61 421 61 933 63 273 64 180 64 826 65 756 66 708 OECD kt 8 256 10 336 10 495 10 684 11 417 11 218 11 467 11 474 11 394 11 409 11 447 Developing kt 41 978 47 122 48 109 48 914 48 819 49 421 50 461 51 329 52 001 52 874 53 736 Least Developed Countries kt 9 10 11 12 12 12 12 12 12 12 12

Vegetable oils World trade kt 34 584 43 320 45 117 46 593 47 740 48 971 50 462 52 407 54 157 55 791 57 121 OECD kt 2 691 2 286 2 120 1 794 1 749 1 597 1 674 1 984 2 041 1 874 1 654 Developing kt 33 005 41 332 43 581 45 254 46 396 47 755 49 105 50 705 52 360 54 119 55 624 Least Developed Countries kt 67 70 72 73 74 75 76 77 79 80 82

Beefa World trade kt 5 963 6 424 6 969 7 176 7 584 7 851 8 062 8 321 8 613 8 843 9 097 OECD kt 3 643 3 163 3 325 3 357 3 450 3 555 3 641 3 730 3 862 3 952 4 035 Developing kt 2 807 4 086 4 352 4 519 4 846 5 077 5 235 5 419 5 566 5 724 5 890 Least Developed Countries kt 2 1 2 1 2 2 2 2 2 2 2

Pigmeata World trade kt 3 999 4 301 4 700 4 927 5 055 5 130 5 279 5 443 5 576 5 680 5 766 OECD kt 3 202 3 831 3 785 3 840 3 839 3 871 3 926 4 001 4 097 4 141 4 170 Developing kt 1 094 1 228 1 125 1 351 1 547 1 651 1 777 1 899 1 951 2 028 2 103 Least Developed Countries kt 2

Poultry World trade kt 7 563 7 479 8 060 8 353 8 541 8 783 9 077 9 475 9 741 9 948 10 180 OECD kt 3 769 3 607 3 681 3 653 3 737 3 792 3 827 3 924 3 970 3 804 3 824 Developing kt 3 953 4 354 4 590 4 772 4 963 5 156 5 464 5 751 5 908 6 219 6 316 Least Developed Countries kt 6 5 3 3 3 3 3 3 3 4 4

Butter World trade kt 697 708 760 799 818 833 842 852 855 863 867 OECD kt 741 684 626 575 584 596 602 608 603 603 602 Developing kt 49 61 54 57 56 57 57 58 63 66 67 Least Developed Countries kt 1 1 1 1 1 1 1 1 1 1 1

Cheese World trade kt 1 401 1 459 1 497 1 545 1 593 1 624 1 666 1 710 1 750 1 792 1 833 OECD kt 1 196 1 186 1 150 1 146 1 159 1 177 1 206 1 240 1 265 1 291 1 314 Developing kt 137 198 239 274 293 305 318 326 339 351 361 Least Developed Countries kt

Whole milk powder World trade kt 1 396 1 582 1 642 1 661 1 726 1 815 1 872 1 950 2 012 2 076 2 139 OECD kt 1 185 1 245 1 245 1 235 1 269 1 330 1 359 1 414 1 453 1 489 1 526 Developing kt 382 499 570 593 625 654 682 705 728 755 781 Least Developed Countries kt 3 4 4 4 4 4 4 4 4 4 4

Skim milk powder World trade kt 1 167 1 196 1 190 1 178 1 161 1 169 1 182 1 184 1 205 1 198 1 210 OECD kt 970 803 771 766 739 745 740 753 756 782 813 Developing kt 108 137 167 158 169 171 188 177 200 168 150 Least Developed Countries kt 2 2 2 2 2 2 2 2 2 2 2

a) Excludes trade of live animals. est.: Estimate. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 53

ANNEX A

16/17

20

10

350

1.1

62

399

0.7

58

101

31

150

10

31

470

3 780

3 469

15.4

10.8

1 020

119

100

5 740

0.0

55

1 369

0

39

682

0

341

5.4

6 102

1.7

404

54

23

359

205

5

Table A.4. Main policy assumptions for cereal markets

Crop yeara Average

01/02-05/06 06/07 est. 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16

ARGENTINA

Crops export tax % 16 20 20 20 20 20 20 20 20 20 20

Rice export tax % 8 10 10 10 10 10 10 10 10 10 10

CANADA

Tariff-quotasb

Wheat kt 350 350 350 350 350 350 350 350 350 350 350

in-quota tariff % 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1

out-of-quota tariff % 62 62 62 62 62 62 62 62 62 62 62

Barley kt 399 399 399 399 399 399 399 399 399 399 399

in-quota tariff % 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7

out-of-quota tariff % 58 58 58 58 58 58 58 58 58 58 58

EUROPEAN UNIONc, d

Cereal support pricee EUR/t 101 101 101 101 101 101 101 101 101 101 101

Cereal compensationf, g EUR/ha 261 31 31 31 31 31 31 31 31 31 31

Rice support priceh EUR/t 239 150 150 150 150 150 150 150 150 150 150

Compulsory set-aside rate % 9 10 10 10 10 10 10 10 10 10 10

Set-aside paymentg EUR/ha 261 31 31 31 31 31 31 31 31 31 31

Direct payment for rice EUR/ha 564 470 470 470 470 470 470 470 470 470 470

Wheat tariff-quotab kt 2 587 3 780 3 780 3 780 3 780 3 780 3 780 3 780 3 780 3 780 3 780

Coarse grain tariff-quotab kt 3 349 3 469 3 469 3 469 3 469 3 469 3 469 3 469 3 469 3 469 3 469

Subsidised export limitsb

Wheat mt 15.4 15.4 15.4 15.4 15.4 15.4 15.4 15.4 15.4 15.4 15.4

Coarse grainsi mt 10.8 10.8 10.8 10.8 10.8 10.8 10.8 10.8 10.8 10.8 10.8

JAPAN

Rice land diversion program ’000 ha 1 008 1 020 1 020 1 020 1 020 1 020 1 020 1 020 1 020 1 020 1 020

Wheat support pricej ’000 JPY/t 138 119 119 119 119 119 119 119 119 119 119

Barley support pricek ’000 JPY/t 119 100 100 100 100 100 100 100 100 100 100

Wheat tariff-quota kt 5 740 5 740 5 740 5 740 5 740 5 740 5 740 5 740 5 740 5 740 5 740

in-quota tariff ’000 JPY/t 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

out-of-quota tariff ’000 JPY/t 55 55 55 55 55 55 55 55 55 55 55

Barley tariff-quota kt 1 369 1 369 1 369 1 369 1 369 1 369 1 369 1 369 1 369 1 369 1 369

in-quota tariff ’000 JPY/t 0 0 0 0 0 0 0 0 0 0 0

out-of-quota tariff ’000 JPY/t 39 39 39 39 39 39 39 39 39 39 39

Rice tariff-quotal kt 682 682 682 682 682 682 682 682 682 682 682

in-quota tariff ’000 JPY/t 0 0 0 0 0 0 0 0 0 0 0

out-of-quota tariff ’000 JPY/t 341 341 341 341 341 341 341 341 341 341 341

KOREA

Wheat tariff % 6.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4

Maize tariff-quota kt 6 102 6 102 6 102 6 102 6 102 6 102 6 102 6 102 6 102 6 102 6 102

in-quota tariff % 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7

out-of-quota tariff % 409 404 404 404 404 404 404 404 404 404 404

Barley tariff-quota kt 52 54 54 54 54 54 54 54 54 54 54

in-quota tariff % 23 23 23 23 23 23 23 23 23 23 23

out-of-quota tariff % 364 359 359 359 359 359 359 359 359 359 359

Rice quotal kt 185 205 205 205 205 205 205 205 205 205 205

in-quota tariff % 5 5 5 5 5 5 5 5 5 5 5

For notes, see end of the table. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200754

ANNEX A

16/17

10

8

10

1 332

0.0

0

0

2 501

50

194

5

50

115

101.0

76.8

16.9

10.3

7.5

3.8

3.7

14.5

1.6

0.0

0

0

0

9 636

2.3

65.0

2

7 200

3.7

41.7

5 320

2.3

51.7

Table A.4. Main policy assumptions for cereal markets (cont.)

Crop yeara Average

01/02-05/06 06/07 est. 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16

MERCOSUR

Wheat tariff % 11 10 10 10 10 10 10 10 10 10 10

Coarse grain tariff % 8 8 8 8 8 8 8 8 8 8 8

Rice tariff % 11 10 10 10 10 10 10 10 10 10 10

MEXICO

Cereal income paymentm MXN/ha 906 980 1 013 1 045 1 078 1 111 1 145 1 180 1 217 1 254 1 293

Wheat NAFTA tariff % 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Fidelist social program MXN mn 425 0 0 0 0 0 0 0 0 0 0

Tortilla consumption subsidy MXN mn 0 0 0 0 0 0 0 0 0 0 0

Maize tariff-quota kt 2 501 2 501 2 501 2 501 2 501 2 501 2 501 2 501 2 501 2 501 2 501

in-quota tariff % 50 50 50 50 50 50 50 50 50 50 50

out-of-quota tariff % 197 194 194 194 194 194 194 194 194 194 194

Barley tariff-quota kt 5 5 5 5 5 5 5 5 5 5 5

in-quota tariff % 50 50 50 50 50 50 50 50 50 50 50

out-of-quota tariff % 117 115 115 115 115 115 115 115 115 115 115

UNITED STATES

Wheat loan rate USD/t 100.5 101.0 101.0 101.0 101.0 101.0 101.0 101.0 101.0 101.0 101.0

Maize loan rate USD/t 76.8 76.8 76.8 76.8 76.8 76.8 76.8 76.8 76.8 76.8 76.8

Prod. flex. contract payment

Wheat USD/t 17.0 16.9 16.9 16.9 16.9 16.9 16.9 16.9 16.9 16.9 16.9

Maize USD/t 10.3 10.3 10.3 10.3 10.3 10.3 10.3 10.3 10.3 10.3 10.3

CRP areasn Mha 6.1 6.9 7.2 6.6 6.3 6.5 6.7 6.9 7.2 7.3 7.4

Wheat Mha 2.9 3.5 3.6 3.3 3.2 3.3 3.4 3.5 3.6 3.7 3.7

Coarse grains Mha 3.2 3.4 3.5 3.2 3.1 3.2 3.3 3.4 3.5 3.6 3.7

Subsidised export limitsb

Wheat Mt 14.5 14.5 14.5 14.5 14.5 14.5 14.5 14.5 14.5 14.5 14.5

Coarse grains Mt 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6

Wheat EEP paymento USD/t 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

CHINA

Wheat support price CNY/t 135 0 0 0 0 0 0 0 0 0 0

Coarse grains support price CNY/t 117 0 0 0 0 0 0 0 0 0 0

Rice support price CNY/t 440 0 0 0 0 0 0 0 0 0 0

Wheat tariff-quota kt 8 935 9 636 9 636 9 636 9 636 9 636 9 636 9 636 9 636 9 636 9 636

in-quota tariff % 1.8 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3

out-of-quota tariff % 65.9 65.0 65.0 65.0 65.0 65.0 65.0 65.0 65.0 65.0 65.0

Coarse grains tariff % 4 2 2 2 2 2 2 2 2 2 2

Maize tariff-quota kt 6 390 7 200 7 200 7 200 7 200 7 200 7 200 7 200 7 200 7 200 7 200

in-quota tariff % 2.9 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7

out-of-quota tariff % 45.8 41.7 41.7 41.7 41.7 41.7 41.7 41.7 41.7 41.7 41.7

Rice tariff-quota % 4 522 5 320 5 320 5 320 5 320 5 320 5 320 5 320 5 320 5 320 5 320

in-quota tariff % 1.9 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3

out-of-quota tariff % 53.7 51.7 51.7 51.7 51.7 51.7 51.7 51.7 51.7 51.7 51.7

For notes, see end of the table. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 55

ANNEX A

16/17

1 403

700

2 082

5 400

5 700

6 500

3 133

1 941

88

50

30

100

urrent med to

ber is ith the o rural

sed on ay be

world rential iff-rate

Table A.4. Main policy assumptions for cereal markets (cont.)

Crop yeara Average

01/02-05/06 06/07 est. 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16

INDIA

Input subsidy rate coarse grainsp INR/t 1 529 1 403 1 403 1 403 1 403 1 403 1 403 1 403 1 403 1 403 1 403

Input subsidy rate ricep INR/t 762 700 700 700 700 700 700 700 700 700 700

Input subsidy rate wheatp INR/t 2 002 2 082 2 082 2 082 2 082 2 082 2 082 2 082 2 082 2 082 2 082

Minimum support price

Maize INR/t 5 080 5 400 5 400 5 400 5 400 5 400 5 400 5 400 5 400 5 400 5 400

Rice INR/t 5 080 5 700 5 700 5 700 5 700 5 700 5 700 5 700 5 700 5 700 5 700

Wheat INR/t 6 240 6 500 6 500 6 500 6 500 6 500 6 500 6 500 6 500 6 500 6 500

Rice export subsidy INR/t 3 222 3 133 3 133 3 133 3 133 3 133 3 133 3 133 3 133 3 133 3 133

Wheat export subsidy INR/t 1 975 1 941 1 941 1 941 1 941 1 941 1 941 1 941 1 941 1 941 1 941

Wheat tariff % 88 88 88 88 88 88 88 88 88 88 88

Maize tariff % 50 50 50 50 50 50 50 50 50 50 50

Rice tariff % 30 30 30 30 30 30 30 30 30 30 30

Barley tariff % 100 100 100 100 100 100 100 100 100 100 100

a) Beginning crop marketing year – see Glossary of Terms for definitions. b) Year beginning 1 July. c) Prices and payments in market euro – see Glossary of Terms. d) EU farmers also benefit from the Single Farm Payment (SFP) Scheme, which provides flat-rate payments independent from c

production decisions and market developments. The total amount spent under the SFP scheme, before modulation, is assu increase from 26.9 billion euro in 2005 to 28.4 billion euro in 2008 for the total of the 15 former member states. The final num equivalent to 233 euro per hectare of eligible farm land on average. For the accession countries, payments are phased in w assumption of maximum top-ups from national budgets. Due to modulation, between 2.7% and 4.6% of the total SFP will go t development spending rather than directly to the farmers.

e) Common intervention price for soft wheat, barley, maize and sorghum. f) Compensatory area payments. g) Actual payments made per hectare based on program yields. h) Subject to a purchase limit of 75 000 tonnes per year. i) The export volume excludes 0.4 mt of exported potato starch. The original limit on subsidised exports is 10.8 mt. j) Government purchase price, domestic wheat. k) Government purchase price, barley, 2nd grade, 1st class. l) Husked rice basis. m) Applies to producers of wheat, maize and sorghum. n) Includes wheat, barley, maize, oats and sorghum. o) Average per tonne of total exports. p) Indian input subsidies consist of those for electricty, fertiliser and irrigation. Note: The source for tariffs and Tariff Rate Quotas is AMAD (Agricultural market access database). The tariff and TRQ data are ba Most Favoured Nation rates scheduled with the WTO and exclude those under preferential or regional agreements, which m substantially different. Tariffs are simple averages of several product lines. Specific rates are converted to ad valorem rates using prices in the Outlook. Import quotas are based on global commitments scheduled in the WTO rather than those allocated to prefe partners under regional or other agreements. For Mexico, the NAFTA in-quota tariff on maize and barley is zero, while the tar quota becomes unlimited in 2003 for barley and 2008 for maize. est.: Estimate. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200756

ANNEX A

16/17

291.6

230.3

54.5

381.0

443.7

125.4

672.6

674.0

179.9

183.2

622.2

587.9

84.9

562.1

587.0

140.9

184.3

174.9

225.8

138.2

22.1

22.7

7.0

447.0

446.0

80.0

469.0

468.6

86.9

326.0

Table A.5. World cereal projections

Crop yeara Average

01/02-05/06 06/07 est. 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16

WHEAT

OECDb

Production mt 253.5 236.3 265.5 271.5 271.9 276.5 279.0 281.9 284.7 287.0 289.1

Consumption mt 205.0 209.1 212.6 216.2 218.1 220.9 223.9 225.0 226.3 228.1 229.3

Closing stocks mt 56.6 44.1 48.9 51.6 51.9 52.8 52.6 52.7 53.3 53.9 54.1

Non-OECD

Production mt 341.0 359.7 362.8 368.8 368.0 369.5 370.6 373.6 375.4 378.3 378.9

Consumption mt 403.8 412.3 411.1 417.5 421.6 426.2 428.1 431.7 434.2 438.2 440.8

Closing stocks mt 138.5 117.1 118.7 124.2 125.8 125.5 124.9 125.4 126.0 126.2 125.5

WORLDc

Production mt 594.5 596.0 628.3 640.3 639.9 646.1 649.5 655.5 660.0 665.3 668.0

Consumption mt 608.8 621.4 623.7 633.8 639.7 647.1 652.0 656.6 660.5 666.2 670.1

Closing stocks mt 195.1 161.2 167.6 175.8 177.7 178.3 177.6 178.1 179.3 180.1 179.6

Priced USD/t 152.0 204.0 204.5 197.5 191.8 186.1 184.6 184.5 183.1 181.7 182.4

COARSE GRAINS

OECDb

Production mt 509.0 498.4 551.4 573.3 584.1 586.6 592.2 600.9 606.0 610.2 615.9

Consumption mt 479.9 510.2 534.9 549.7 558.6 564.5 569.1 573.8 576.9 579.6 584.1

Closing stocks mt 107.6 86.5 79.7 80.6 84.1 84.4 84.1 84.5 84.9 84.7 84.6

Non-OECD

Production mt 441.3 482.1 494.1 508.6 520.1 525.6 533.5 540.0 545.6 549.6 556.6

Consumption mt 468.8 505.3 514.5 521.1 532.8 540.3 549.7 558.0 566.0 572.4 580.0

Closing stocks mt 133.9 117.6 114.8 119.5 123.2 124.8 126.4 129.5 132.1 134.5 137.4

WORLDc

Production mt 950.2 980.5 1 045.5 1 081.9 1 104.3 1 112.2 1 125.6 1 140.9 1 151.6 1 159.7 1 172.5 1

Consumption mt 948.7 1 015.5 1 049.4 1 070.8 1 091.4 1 104.7 1 118.8 1 131.8 1 143.0 1 152.0 1 164.0 1

Closing stocks mt 241.5 204.1 194.5 200.1 207.3 209.2 210.5 214.0 217.0 219.1 222.0

Pricee USD/t 103.6 140.4 158.9 157.6 147.1 143.3 144.0 140.8 138.4 138.6 139.5

RICE

OECDb

Production mt 22.5 21.2 22.3 21.9 21.8 22.0 21.9 21.9 22.1 22.2 22.1

Consumption mt 22.4 22.2 22.4 22.5 22.7 22.7 22.8 22.8 22.7 22.7 22.7

Closing stocks mt 8.3 7.6 7.7 7.7 7.4 7.3 7.1 6.9 6.9 6.9 7.0

Non-OECD

Production mt 381.2 403.6 407.1 413.7 417.6 423.4 422.4 428.8 431.1 438.7 440.5

Consumption mt 395.8 404.6 408.4 411.3 414.6 421.4 423.0 427.4 429.8 436.5 440.5

Closing stocks mt 97.0 79.5 77.5 78.9 80.7 81.5 79.6 79.9 80.0 81.2 80.1

WORLDc

Production mt 403.6 424.8 429.4 435.6 439.4 445.4 444.3 450.8 453.2 460.9 462.7

Consumption mt 418.2 426.8 430.7 433.8 437.3 444.1 445.8 450.1 452.6 459.2 463.1

Closing stocks mt 105.2 87.1 85.2 86.5 88.1 88.8 86.7 86.8 86.9 88.1 87.1

Pricef USD/t 238.4 311.4 352.1 360.3 347.8 331.9 331.0 336.3 336.3 330.2 326.2

a) Beginning crop marketing year – see Glossary of Terms for definitions. b) Excludes Iceland but includes the 8 EU members that are not members of the OECD. c) Source of historic data is USDA. d) No. 2 hard red winter wheat, ordinary protein, USA f.o.b. Gulf Ports (June/May); less EEP payments where applicable. e) No. 2 yellow corn, US f.o.b. Gulf Ports (September/August). f) Milled, 100%, grade b, Nominal Price Quote, NPQ, f.o.b. Bangkok (August/July). est.: Estimate. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 57

ANNEX A

16/17

27.5

24.0

24.0

8.0

8.0

6.4

31

10

31

6.0

6.0

23.5

10.9

10.9

1 032

5

487

138

1 332

33.0

23.8

45.0

183.7

1.9

3.0

2.2

1.2

12.7

3.2

141

Table A.6. Main policy assumptions for oilseed markets

Crop yeara Average

01/02-05/06 06/07 est. 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16

ARGENTINA

Oilseed export tax % 19.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5

Oilseed meal export tax % 16.0 24.0 24.0 24.0 24.0 24.0 24.0 24.0 24.0 24.0 24.0

Oilseed oil export tax % 16.0 24.0 24.0 24.0 24.0 24.0 24.0 24.0 24.0 24.0 24.0

AUSTRALIA

Tariffs

Soybean oil % 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0

Rapeseed oil % 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0

CANADA

Tariffs

Rapeseed oil % 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.4

EUROPEAN UNIONc, d

Oilseed compensatione, f EUR/ha 261 31 31 31 31 31 31 31 31 31 31

Compulsory set-aside rate % 9.0 10 10 10 10 10 10 10 10 10 10

Set-aside paymentf EUR/ha 260.5 31 31 31 31 31 31 31 31 31 31

Tariffs

Soybean oil % 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0

Rapeseed oil % 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0

JAPAN

New output payments

Soybeans bn JPY 23.6 23.5 23.5 23.5 23.5 23.5 23.5 23.5 23.5 23.5 23.5

Tariffs

Soybean oil JPY/kg 10.9 10.9 10.9 10.9 10.9 10.9 10.9 10.9 10.9 10.9 10.9

Rapeseed oil JPY/kg 10.9 10.9 10.9 10.9 10.9 10.9 10.9 10.9 10.9 10.9 10.9

KOREA

Soybean tariff-quota kt 1 032 1 032 1 032 1 032 1 032 1 032 1 032 1 032 1 032 1 032 1 032

in-quota tariff % 5 5 5 5 5 5 5 5 5 5 5

out-of-quota tariff % 493 487 487 487 487 487 487 487 487 487 487

Soybean (for food) mark up ’000 KRW/t 183 142 155 150 147 144 142 141 139 138 138

MEXICO

Soybeans income paymentg MXN/ha 906 980 1 013 1 045 1 078 1 111 1 145 1 180 1 217 1 254 1 293

Tariffs

Soybeans % 33.4 33.0 33.0 33.0 33.0 33.0 33.0 33.0 33.0 33.0 33.0

Soybean meal % 25.4 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8 23.8

Soybean oil % 45.6 45.0 45.0 45.0 45.0 45.0 45.0 45.0 45.0 45.0 45.0

UNITED STATES

Soybeans loan rate USD/t 185.6 183.7 183.7 183.7 183.7 183.7 183.7 183.7 183.7 183.7 183.7

CRP area

Soybeans mha 2.1 2.3 2.4 2.2 2.1 2.2 2.1 2.0 2.0 2.0 1.9

Tariffs

Rapeseed % 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0

Soybean meal % 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2

Rapeseed meal % 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2

Soybean oil % 12.7 12.7 12.7 12.7 12.7 12.7 12.7 12.7 12.7 12.7 12.7

Rapeseed oil % 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2

Subsidised export limitsb

Oilseed oils kt 141 141 141 141 141 141 141 141 141 141 141

For notes, see end of the table. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200758

ANNEX A

16/17

058.7

2.4

6.3

9.0

7 998

3 360

100

100

100

100

125

100

100

45

45

300

300

198

urrent med to

ber is ith the o rural

sed on ay be

world rential 2003.

Table A.6. Main policy assumptions for oilseed markets (cont.)

Crop yeara Average

01/02-05/06 06/07 est. 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16

CHINA

Soybeans support price CNY/t 1 332.6 1 501.0 1 549.2 1 597.7 1 647.4 1 700.7 1 754.9 1 810.8 1 868.2 1 929.0 1 992.1 2

Tariffsb

Soybeans % 14.9 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4

Soybean meal % 8.0 6.3 6.3 6.3 6.3 6.3 6.3 6.3 6.3 6.3 6.3

Soybean oil in-quota tariff % 7.2 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0

Vegetable oil tariff-quota kt 6 426 7 998 7 998 7 998 7 998 7 998 7 998 7 998 7 998 7 998 7 998

INDIA

Input subsidy rate, oilseedsh R/T 3 596 3 360 3 360 3 360 3 360 3 360 3 360 3 360 3 360 3 360 3 360

Soybean tariff % 100 100 100 100 100 100 100 100 100 100 100

Rapeseed tariff % 100 100 100 100 100 100 100 100 100 100 100

Sunflower tariff % 100 100 100 100 100 100 100 100 100 100 100

Oilseed tariff % 100 100 100 100 100 100 100 100 100 100 100

Soybean meal tariff % 125 125 125 125 125 125 125 125 125 125 125

Rapeseed meal tariff % 100 100 100 100 100 100 100 100 100 100 100

Sunflower meal tariff % 100 100 100 100 100 100 100 100 100 100 100

Soybean oil tariff % 45 45 45 45 45 45 45 45 45 45 45

Rapeseed oil tariff % 45 45 45 45 45 45 45 45 45 45 45

Sunflower oil tariff % 300 300 300 300 300 300 300 300 300 300 300

Palm oil tariff % 300 300 300 300 300 300 300 300 300 300 300

Vegetables oil tariff % 198 198 198 198 198 198 198 198 198 198 198

a) Beginning crop marketing year – see Glossary of Terms for definitions. b) Calendar year, except for China and subsidised export limit in USA, beginning 1 July. c) Prices and payments in market euro – see Glossary of Terms. d) EU farmers also benefit from the Single Farm Payment (SFP) Scheme, which provides flat-rate payments independent from c

production decisions and market developments. The total amount spent under the SFP scheme, before modulation, is assu increase from 26.9 billion euro in 2005 to 28.4 billion euro in 2008 for the total of the 15 former member states. The final num equivalent to 233 euro per hectare of eligible farm land on average. For the accession countries, payments are phased in w assumption of maximum top-ups from national budgets. Due to modulation, between 2.7% and 4.6% of the total SFP will go t development spending rather than directly to the farmers.

e) Compensatory area payments, before penalties. f) Payments made per hectare based on regional yields. g) Weighted average of autumn/winter and spring/summer. h) Indian input subsidies consist of those for electricty, fertiliser and irrigation. Note: The source for tariffs and Tariff Rate Quotas is AMAD (Agricultural market access database). The tariff and TRQ data are ba Most Favoured Nation rates scheduled with the WTO and exclude those under preferential or regional agreements, which m substantially different. Tariffs are simple averages of several product lines. Specific rates are converted to ad valorem rates using prices in the Outlook. Import quotas are based on global commitments scheduled in the WTO rather than those allocated to prefe partners under regional or other agreements. For Mexico, the NAFTA tariffs on soybeans, oil meals and soybean oil are zero after est.: Estimate. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 59

ANNEX A

16/17

138.6

136.4

124.1

16.2

229.0

233.4

207.5

9.4

367.6

369.8

331.6

25.6

299.6

88.2

111.1

2.6

150.4

126.1

6.1

238.6

237.2

8.8

200.8

32.0

46.6

2.4

102.6

85.7

7.0

134.6

54.1

132.3

9.3

613.9

Table A.7. World oilseed projections

Marketing yeara Average

01/02-05/06 06/07 est. 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16

OILSEEDS

OECDb

Production mt 113.2 127.0 121.9 125.5 128.3 128.8 130.2 131.4 133.7 135.5 137.0

Consumption mt 110.5 118.8 120.5 122.8 126.4 128.2 130.8 131.7 132.5 133.2 134.6

Crush mt 99.3 105.8 107.8 110.3 113.9 115.7 118.3 119.2 120.1 120.9 122.3

Closing stocks mt 19.7 28.7 21.6 17.0 16.2 15.9 15.7 15.6 15.7 15.8 15.9

Non-OECD

Production mt 150.3 175.0 180.6 188.0 194.7 201.6 207.7 212.6 217.3 220.9 224.7

Consumption mt 152.4 183.0 191.5 197.6 199.8 205.0 209.6 215.0 220.7 225.4 229.3

Crush mt 127.3 157.2 166.7 173.1 175.2 180.2 184.6 189.8 195.3 199.8 203.6

Closing stocks mt 8.4 9.4 9.3 9.5 9.5 9.4 9.5 9.3 9.4 9.4 9.4

WORLDc

Production mt 263.5 302.0 302.5 313.5 322.9 330.5 337.9 344.0 350.9 356.4 361.7

Consumption mt 263.0 301.8 312.0 320.4 326.1 333.2 340.4 346.7 353.2 358.6 363.9

Crush mt 226.6 262.9 274.5 283.4 289.1 295.9 302.9 309.0 315.4 320.7 325.9

Closing stocks mt 28.0 38.1 30.9 26.5 25.7 25.3 25.2 24.9 25.1 25.2 25.4

Priced USD/t 266.0 289.8 310.4 311.7 306.5 300.8 297.4 297.7 295.4 295.1 298.4

OILSEED MEALS

OECDb

Production mt 71.5 75.7 77.0 78.6 81.0 82.3 84.0 84.7 85.4 85.9 86.9

Consumption mt 94.4 100.8 102.4 103.8 104.9 106.2 107.2 108.0 108.9 109.7 110.4

Closing stocks mt 2.6 2.4 2.3 2.4 2.4 2.5 2.5 2.5 2.5 2.6 2.6

Non-OECD

Production mt 92.3 113.5 120.6 125.3 126.8 130.4 133.6 137.5 141.5 144.8 147.5

Consumption mt 66.8 87.4 94.4 98.8 101.5 105.1 109.2 112.9 116.6 119.7 122.8

Closing stocks mt 4.8 5.0 4.8 4.8 5.0 5.2 5.3 5.5 5.7 5.8 6.0

WORLDc

Production mt 163.7 189.2 197.6 203.9 207.8 212.7 217.7 222.2 226.9 230.7 234.4

Consumption mt 161.2 188.2 196.8 202.6 206.4 211.3 216.4 220.9 225.5 229.4 233.1

Closing stocks mt 7.4 7.4 7.1 7.2 7.4 7.7 7.8 8.0 8.2 8.4 8.6

Pricee USD/t 201.0 204.9 215.2 217.0 212.8 207.5 204.6 203.1 198.4 196.3 199.1

VEGETABLE OILS

OECDb

Production mt 24.5 26.7 27.4 28.2 29.2 29.7 30.4 30.6 30.8 31.0 31.4

Consumption mt 29.4 35.7 37.2 38.6 39.8 41.1 42.0 42.9 43.8 44.7 45.6

Closing stocks mt 2.4 2.9 2.6 2.5 2.5 2.5 2.5 2.5 2.4 2.4 2.4

Non-OECD

Production mt 60.8 76.1 79.5 82.6 84.3 86.9 89.1 91.9 94.7 97.7 100.1

Consumption mt 53.3 65.2 67.9 70.0 71.6 73.2 75.3 77.4 79.6 81.7 83.7

Closing stocks mt 5.5 6.3 6.2 6.4 6.4 6.6 6.6 6.7 6.7 6.8 6.9

WORLDc

Production mt 85.3 102.9 106.9 110.8 113.5 116.6 119.5 122.6 125.5 128.7 131.5

of which: palm oil mt 31.0 39.2 40.4 42.1 43.2 44.7 45.8 47.5 49.1 51.0 52.5

Consumption mt 82.6 100.8 105.1 108.6 111.3 114.3 117.3 120.3 123.3 126.4 129.3

Closing stocks mt 7.9 9.2 8.8 8.8 8.9 9.0 9.0 9.1 9.1 9.2 9.2

Oil pricef USD/t 520.6 590.7 618.0 619.7 622.9 611.9 610.8 608.5 612.4 613.9 615.4

a) Beginning crop marketing year – see Glossary of Terms for definitions. b) Excludes Iceland but includes the 8 EU members that are not members of the OECD. c) Source of historic data is USDA. d) Weighted average oilseed price, European port. e) Weighted average meal price, European port. f) Weighted average price of oilseed oils and palm oil, European port. est.: Estimation. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200760

ANNEX A

2016

15

76

0

27

45

2

197

2.22

1.56

1.51

5.04

21.0

0

0

0

0

216

167

96

285

990

588

431

1 010

780

39

480

365

4

410

7

40

0

22

21

45

0

41

50

228

Table A.8. Main policy assumptions for meat markets

Average 2001-05

2006 est. 2007 2008 2009 2010 2011 2012 2013 2014 2015

ARGENTINA

Beef export tax % 4 15 15 15 15 15 15 15 15 15 15

CANADA

Beef tariff-quota kt pw 76 76 76 76 76 76 76 76 76 76 76

in-quota tariff % 0 0 0 0 0 0 0 0 0 0 0

out-of-quota tariff % 27 27 27 27 27 27 27 27 27 27 27

Poultry meat tariff-quota kt pw 45 45 45 45 45 45 45 45 45 45 45

in-quota tariff % 2 2 2 2 2 2 2 2 2 2 2

out-of-quota tariff % 197 197 197 197 197 197 197 197 197 197 197

EUROPEAN UNIONa, b

Beef basic pricec, d, e EUR/kg dw 2.38 2.22 2.22 2.22 2.22 2.22 2.22 2.22 2.22 2.22 2.22

Beef buy-in pricec, f EUR/kg dw n.a. 1.56 1.56 1.56 1.56 1.56 1.56 1.56 1.56 1.56 1.56

Pig meat basic priced EUR/kg dw 1.51 1.51 1.51 1.51 1.51 1.51 1.51 1.51 1.51 1.51 1.51

Sheep meat basic price EUR/kg dw 5.04 5.04 5.04 5.04 5.04 5.04 5.04 5.04 5.04 5.04 5.04

Sheep basic rateg EUR/head n.a. 21.00 21.0 21.0 21.0 21.0 21.0 21.0 21.0 21.0 21.0

Male bovine premiumh EUR/head 178 0 0 0 0 0 0 0 0 0 0

Adult bovine slaughter premiumi EUR/head 76 0 0 0 0 0 0 0 0 0 0

Calf slaughter premium EUR/head 37 0 0 0 0 0 0 0 0 0 0

Suckler cow premium EUR/head 156 0 0 0 0 0 0 0 0 0 0

Beef tariff-quota kt pw 216 216 216 216 216 216 216 216 216 216 216

Pig meat tariff-quota kt pw 167 167 167 167 167 167 167 167 167 167 167

Poultry meat tariff-quota kt pw 96 96 96 96 96 96 96 96 96 96 96

Sheep meat tariff-quota kt cwe 285 285 285 285 285 285 285 285 285 285 285

Subsidised export limitsd

Beefj kt cwe 990 990 990 990 990 990 990 990 990 990 990

Pig meatj kt cwe 588 588 588 588 588 588 588 588 588 588 588

Poultry meat kt cwe 431 431 431 431 431 431 431 431 431 431 431

JAPANk

Beef stabilisation prices

Upper price JPY/kg dw 1 010 1 010 1 010 1 010 1 010 1 010 1 010 1 010 1 010 1 010 1 010

Lower price JPY/kg dw 780 780 780 780 780 780 780 780 780 780 780

Beef tariff % 39 39 39 39 39 39 39 39 39 39 39

Pig meat stabilisation prices

Upper price JPY/kg dw 480 480 480 480 480 480 480 480 480 480 480

Lower price JPY/kg dw 365 365 365 365 365 365 365 365 365 365 365

Pig meat import systeml

Tariff % 4 4 4 4 4 4 4 4 4 4 4

Standard import price JPY/kg dw 410 410 410 410 410 410 410 410 410 410 410

Poultry meat tariff % 7 7 7 7 7 7 7 7 7 7 7

KOREA

Beef tariff % 40 40 40 40 40 40 40 40 40 40 40

Beef mark-up % 0 0 0 0 0 0 0 0 0 0 0

Pig meat tariff % 23 22 22 22 22 22 22 22 22 22 22

Poultry meat tariff % 21 21 21 21 21 21 21 21 21 21 21

MEXICO

Pig meat tariff % 46 45 45 45 45 45 45 45 45 45 45

Pig meat NAFTA tariff % 1 0 0 0 0 0 0 0 0 0 0

Poultry meat tariff-quota kt pw 41 41 41 41 41 41 41 41 41 41 41

in-quota tariff % 50 50 50 50 50 50 50 50 50 50 50

out-of-quota tariff % 231 228 228 228 228 228 228 228 228 228 228

For notes, see end of the table. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 61

ANNEX A

2016

480

15

40

502

15

40

1 252

25

697

5

26

16

16

15

19

100

100

92

87

150

6

20

96

urrent med to

ber is ith the o rural

at.

, from

d TRQ ments,

rates ated to tariff-

Table A.8. Main policy assumptions for meat markets (cont.)

Average 2001-05

2006 est. 2007 2008 2009 2010 2011 2012 2013 2014 2015

RUSSIA

Beef tariff-quota kt pw 458 463 468 474 480 480 480 480 480 480 480

in-quota tariff % 15 15 15 15 15 15 15 15 15 15 15

out-of-quota tariff % 58 40 52 50 40 40 40 40 40 40 40

Pigmeat tariff-quota kt pw n.a. 476 485 494 502 502 502 502 502 502 502

in-quota tariff % 15 15 15 15 15 15 15 15 15 15 15

out-of-quota tariff % 80 60 55 50 40 40 40 40 40 40 40

Poultry meat tariff-quota kt pw 618 1 131 1 171 1 212 1 252 1 252 1 252 1 252 1 252 1 252 1 252

in-quota tariff % 25 25 25 25 25 25 25 25 25 25 25

UNITED STATES

Beef tariff-quota kt pw 657 657 697 697 697 697 697 697 697 697 697

in-quota tariff % 5 5 5 5 5 5 5 5 5 5 5

out-of-quota tariff % 26 26 26 26 26 26 26 26 26 26 26

CHINA

Beef tariff % 23 16 16 16 16 16 16 16 16 16 16

Pig meat tariff % 17 16 16 16 16 16 16 16 16 16 16

Sheep meat tariff % 17 15 15 15 15 15 15 15 15 15 15

Poultry meat tariff % 19 19 19 19 19 19 19 19 19 19 19

INDIA

Beef tariff % 105 100 100 100 100 100 100 100 100 100 100

Pig meat tariff % 105 100 100 100 100 100 100 100 100 100 100

Sheep meat tariff % 96 92 92 92 92 92 92 92 92 92 92

Poultry meat tariff % 93 87 87 87 87 87 87 87 87 87 87

Eggs tariff % 150 150 150 150 150 150 150 150 150 150 150

SOUTH AFRICA

Sheepmeat tariff-quota kt pw 6 6 6 6 6 6 6 6 6 6 6

in-quota tariff % 20 20 20 20 20 20 20 20 20 20 20

out-of-quota tariff % 110 96 96 96 96 96 96 96 96 96 96

a) Prices and payments in market euro – see Glossary of Terms. b) EU farmers also benefit from the Single Farm Payment (SFP) Scheme, which provides flat-rate payments independent from c

production decisions and market developments. The total amount spent under the SFP scheme, before modulation, is assu increase from 26.9 billion euro in 2005 to 28.4 billion euro in 2008 for the total of the 15 former member states. The final num equivalent to 233 euro per hectare of eligible farm land on average. For the accession countries, payments are phased in w assumption of maximum top-ups from national budgets. Due to modulation, between 2.7% and 4.6% of the total SFP will go t development spending rather than directly to the farmers.

c) Price for R3 grade male cattle. d) Year beginning 1 July, except for E10 which is calendar year. Poland has a commitment on export subsidies on unspecified me e) Ending 1 July 2002, replaced by basic price for storage. f) Starting 1 July 2002. g) A supplementary payment of 7 euro per head is provided for Less Favoured Areas. h) Weighted average of all bull and steers payments. i) Includes national envelopes for beef. j) Includes live trade. k) Year beginning 1 April. l) Pig carcass imports. Emergency import procedures triggered from November 1995 to March 1996, from July 1996 to June 1997

August 2001 to March 2002, from August 2002 to March 2003 and from August 2003 to March 2004. Note: The source for tariffs and Tariff Rate Quotas (excluding Russia) is AMAD (Agricultural market access database). The tariff an data are based on Most Favoured Nation rates scheduled with the WTO and exclude those under preferential or regional agree which may be substantially different. Tariffs are simple averages of several product lines. Specific rates are converted to ad valorem using world prices in the Outlook. Import quotas are based on global commitments scheduled in the WTO rather than those alloc preferential partners under regional or other agreements. For Mexico, the NAFTA in-quota tariff on poultry meat is zero and the rate quota is unlimited from 2003. est.: Estimate. n.a.: Not available. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200762

ANNEX A

2016

27 647

28 168

866

15.5

293

261

298

480

39 052

37 946

813

23.3

142

161

40 848

39 617

1 169

27.4

111

178

2 922

2 501

428

1.7

357

148

405

67.9

Table A.9. World meat projections

Calendar year Average 2001-05

2006 est. 2007 2008 2009 2010 2011 2012 2013 2014 2015

OECDa

BEEF AND VEALb

Production kt cwe 26 564 26 918 27 182 27 119 26 859 26 717 26 615 26 674 26 933 27 216 27 500

Consumption kt cwe 26 481 26 930 27 542 27 601 27 381 27 211 27 143 27 209 27 450 27 752 28 017

Ending stocks kt cwe 1 021 876 865 864 865 865 865 866 866 866 867

Per capita consumption kg rwt 15.6 15.5 15.8 15.7 15.5 15.4 15.3 15.2 15.3 15.4 15.5

Price, Australiac AUD/100 kg dw 295 265 252 245 249 247 248 250 256 269 280

Price, EUd EUR/100 kg dw 244 285 250 258 258 257 259 259 259 259 260

Price, USAe USD/100 kg dw 282 304 303 299 297 289 285 279 279 286 294

Price, Argentinaf ARS/100 kg dw 307 427 323 352 355 361 375 384 407 436 455

PIG MEATg

Production kt cwe 36 399 37 455 37 770 37 593 37 787 37 936 38 065 38 208 38 313 38 529 38 799

Consumption kt cwe 35 271 35 787 36 232 36 099 36 307 36 498 36 673 36 860 36 977 37 269 37 614

Ending stocks kt cwe 780 832 827 808 814 818 823 821 821 818 813

Per capita consumption kg rwt 23.1 23.0 23.2 23.0 23.0 23.0 23.0 23.0 23.0 23.0 23.2

Price, EUh EUR/100 kg dw 135 141 142 154 149 139 144 137 137 138 140

Price, USAi USD/100 kg dw 136 145 126 154 165 165 158 161 162 159 161

POULTRY MEAT

Production kt rtc 35 857 37 302 37 616 37 960 38 463 38 926 39 243 39 577 39 871 40 090 40 489

Consumption kt rtc 34 069 35 696 35 922 36 271 36 494 36 999 37 447 37 846 38 236 38 749 39 175

Ending stocks kt rtc 1 127 1 105 1 162 1 162 1 163 1 164 1 165 1 166 1 166 1 167 1 168

Per capita consumption kg rwt 25.2 25.9 25.9 26.0 26.0 26.3 26.5 26.6 26.8 27.0 27.2

Price, EUj EUR/100 kg rtc 103 102 101 104 107 109 106 104 108 109 110

Price, USAk USD/100 kg rtc 142 141 159 165 171 179 183 182 180 177 178

SHEEP MEAT

Production kt cwe 2 793 3 252 2 769 2 799 2 806 2 868 2 902 2 898 2 888 2 846 2 896

Consumption kt cwe 2 428 2 883 2 449 2 474 2 464 2 518 2 549 2 535 2 512 2 456 2 490

Ending stocks kt cwe 520 560 559 558 557 554 546 533 515 491 462

Per capita consumption kg rwt 1.8 2.1 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.7 1.7

Price, Australial AUD/100 kg dw 332 291 287 294 303 310 318 326 334 342 349

Price, Australiam AUD/100 kg dw 173 98 103 107 112 117 122 127 133 138 143

Price, New Zealandn NZD/100 kg dw 390 330 325 334 344 352 361 370 379 387 396

TOTAL MEAT

Per capita consumption kg rwt 65.6 66.5 66.6 66.5 66.3 66.4 66.5 66.6 66.8 67.2 67.6

For notes, see end of the table. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 63

ANNEX A

2016

48 780

47 657

5.6

66

90 269

90 772

11.8

50

57 692

58 991

8.7

182

11 519

12 002

1.8

5

27.8

factors

Table A.9. World meat projections (cont.)

Calendar year Average 2001-05

2006 est. 2007 2008 2009 2010 2011 2012 2013 2014 2015

Non-OECD

BEEF AND VEAL

Productiono kt cwe 35 018 38 676 39 519 40 562 41 653 42 983 44 228 45 151 45 974 46 913 47 926

Consumption kt cwe 34 663 37 959 38 613 39 532 40 586 41 916 43 103 44 019 44 864 45 800 46 813

Per capita consumption kg rwt 4.7 5.0 5.0 5.1 5.2 5.3 5.3 5.4 5.4 5.5 5.5

Ending stocks kt cwe 78 66 66 66 66 66 66 66 66 66 66

PIG MEAT

Productiono kt cwe 60 572 69 964 73 027 74 295 76 128 78 478 79 241 81 642 83 596 85 170 87 454

Consumption kt cwe 61 332 70 834 74 344 75 528 77 256 79 512 80 192 82 518 84 438 85 903 88 077

Per capita consumption kg rwt 9.3 10.4 10.8 10.8 10.9 11.1 11.1 11.2 11.4 11.4 11.6

Ending stocks kt cwe 52 50 50 50 50 50 50 50 50 50 50

POULTRY MEAT

Production kt rtc 41 581 44 638 45 653 47 340 48 867 50 614 51 891 52 966 54 428 55 818 56 721

Consumption kt rtc 43 128 45 894 47 085 48 971 50 690 52 391 53 486 54 512 55 939 57 095 58 055

Per capita consumption kg rwt 7.4 7.6 7.7 7.9 8.1 8.3 8.3 8.4 8.5 8.6 8.6

Ending stocks kt rtc 244 165 174 174 176 177 178 179 180 182 182

SHEEP MEAT

Production kt cwe 8 541 9 311 9 548 9 784 10 007 10 232 10 463 10 676 10 897 11 106 11 319

Consumption kt cwe 8 867 9 657 9 914 10 179 10 418 10 652 10 893 11 112 11 340 11 558 11 782

Per capita consumption kg rwt 1.5 1.6 1.6 1.6 1.7 1.7 1.7 1.7 1.7 1.7 1.7

Ending stocks kt cwe 4 5 5 5 5 5 5 5 5 5 5

TOTAL MEAT

Per capita consumption kg rwt 23.0 24.6 25.1 25.5 25.8 26.3 26.4 26.7 27.0 27.2 27.5

a) Excludes Iceland but includes the 8 EU members that are not members of the OECD. Carcass weight to retail weight conversion of 0.7 for beef and veal, 0.78 for pig meat and 0.88 for sheep meat. Rtc to retail weight conversion factor 0.88 for poultry meat.

b) Do not balance due to statistical differences in New Zealand. c) Weighted average price of cows 201-260 kg, steers 301-400 kg, yearling < 200 kg dw. d) Producer price. e) Choice steers, 1 100-1 300 lb lw, Nebraska – lw to dw conversion factor 0.63. f) Buenos Aires wholesale price linier, young bulls. g) Do not balance due to consumption in Canada which excludes non-food parts. h) Pig producer price. i) Barrows and gilts, No. 1-3, 230-250 lb lw, Iowa/South Minnesota – lw to dw conversion factor 0.74. j) Weighted average farmgate live fowls, top quality, (lw to rtc conversion of 0.75), EU15 starting in 1995. k) Wholesale weighted average broiler price 12 cities. l) Saleyard price, lamb, 16-20 kg dw. m) Saleyard price, wethers, < 22 kg dw. n) Lamb schedule price, all grade average. o) Includes trade of live animals. est.: Estimate. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200764

ANNEX A

2016

5

80

7 728

6 451

0.00

20

1

246

9

45

144

2 464

1 747

90

103

71

412

332

323

10

31

2

35

733

116

16

210

0.3

24

316

Table A.10. Main policy assumptions for dairy markets

Average 2001-05

2006 est. 2007 2008 2009 2010 2011 2012 2013 2014 2015

ARGENTINA

Dairy export tax % 6 5 5 5 5 5 5 5 5 5 5

CANADA

Milk target priceb CADc/litre 62 71 72 75 76 76 77 77 78 78 79

Butter support price CAD/t 6 161 6 992 7 077 7 233 7 309 7 367 7 426 7 485 7 545 7 606 7 666

SMP support price CAD/t 5 227 5 834 5 760 6 044 6 180 6 165 6 151 6 181 6 237 6 308 6 377

Dairy subsidy CAD/hl 0.37 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Cheese tariff-quota kt pw 20 20 20 20 20 20 20 20 20 20 20

in-quota tariff % 1 1 1 1 1 1 1 1 1 1 1

out-of-quota tariff % 246 246 246 246 246 246 246 246 246 246 246

Subsidised export limitsc

Cheese kt pw 9 9 9 9 9 9 9 9 9 9 9

SMP kt pw 45 45 45 45 45 45 45 45 45 45 45

EUROPEAN UNIONd, e

Milk quotaf mt pw 139 143 143 143 144 144 144 144 144 144 144

Butter intervention price EUR/t 3 190 2 708 2 528 2 462 2 462 2 462 2 464 2 464 2 464 2 464 2 464

SMP intervention price EUR/t 2 014 1 798 1 747 1 747 1 747 1 747 1 747 1 747 1 747 1 747 1 747

Butter tariff-quotas kt pw 90 90 90 90 90 90 90 90 90 90 90

Cheese tariff-quota kt pw 103 103 103 103 103 103 103 103 103 103 103

SMP tariff-quota kt pw 71 71 71 71 71 71 71 71 71 71 71

Subsidised export limitsa

Butter kt pw 412 412 412 412 412 412 412 412 412 412 412

Cheese kt pw 332 332 332 332 332 332 332 332 332 332 332

SMP kt pw 323 323 323 323 323 323 323 323 323 323 323

JAPAN

Direct payments JPY/kg 11 10 10 10 10 10 10 10 10 10 10

Cheese tariffg % 31 31 31 31 31 31 31 31 31 31 31

Tariff-quotas

Butter kt pw 2 2 2 2 2 2 2 2 2 2 2

in-quota tariff % 35 35 35 35 35 35 35 35 35 35 35

out-of-quota tariff % 733 733 733 733 733 733 733 733 733 733 733

SMP kt pw 116 116 116 116 116 116 116 116 116 116 116

in-quota tariff % 16 16 16 16 16 16 16 16 16 16 16

out-of-quota tariff % 210 210 210 210 210 210 210 210 210 210 210

WMP t pw 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

in-quota tariff % 24 24 24 24 24 24 24 24 24 24 24

out-of-quota tariff % 316 316 316 316 316 316 316 316 316 316 316

For notes, see end of the table. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 65

ANNEX A

2016

0.4

40

89

1.0

20

176

0.6

40

176

0

9

50

125

90

0

125

377

20

15

22

0.0

2 316

1 764

13

10

112

135

12

87

21

68

Table A.10. Main policy assumptions for dairy markets (cont.)

Average 2001-05

2006 est. 2007 2008 2009 2010 2011 2012 2013 2014 2015

KOREA

Tariff-quotas

Butter kt pw 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

in-quota tariff % 40 40 40 40 40 40 40 40 40 40 40

out-of-quota tariff % 89 89 89 89 89 89 89 89 89 89 89

SMP kt pw 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

in-quota tariff % 20 20 20 20 20 20 20 20 20 20 20

out-of-quota tariff % 176 176 176 176 176 176 176 176 176 176 176

WMP kt pw 0.5 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6

in-quota tariff % 40 40 40 40 40 40 40 40 40 40 40

out-of-quota tariff % 176 176 176 176 176 176 176 176 176 176 176

MEXICO

Butter tariff % 1 0 0 0 0 0 0 0 0 0 0

Tariff-quotas

Cheese kt pw 9 9 9 9 9 9 9 9 9 9 9

in-quota tariff % 50 50 50 50 50 50 50 50 50 50 50

out-of-quota tariff % 127 125 125 125 125 125 125 125 125 125 125

SMP kt pw 90 90 90 90 90 90 90 90 90 90 90

in-quota tariff % 0 0 0 0 0 0 0 0 0 0 0

out-of-quota tariff % 127 125 125 125 125 125 125 125 125 125 125

Liconsa social programme MXN mn 264 377 377 377 377 377 377 377 377 377 377

RUSSIA

Butter tariff % 20 20 20 20 20 20 20 20 20 20 20

Cheese tariff % 15 15 15 15 15 15 15 15 15 15 15

UNITED STATESh

Milk support priceb USDc/litre 22 22 22 22 22 22 22 22 22 22 22

Target pricei USDc/litre n.a. 37.3 37.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Butter support price USD/t 2 121 2 315 2 315 2 315 2 315 2 315 2 315 2 315 2 315 2 316 2 316

SMP support price USD/t 1 864 1 764 1 764 1 764 1 764 1 764 1 764 1 764 1 764 1 764 1 764

Butter tariff-quota kt pw 13 13 13 13 13 13 13 13 13 13 13

in-quota tariff % 10 10 10 10 10 10 10 10 10 10 10

out-of-quota tariff % 112 112 112 112 112 112 112 112 112 112 112

Cheese tariff-quota kt pw 135 135 135 135 135 135 135 135 135 135 135

in-quota tariff % 12 12 12 12 12 12 12 12 12 12 12

out-of-quota tariff % 87 87 87 87 87 87 87 87 87 87 87

Subsidised export limitsa

Butter kt pw 21 21 21 21 21 21 21 21 21 21 21

SMP kt pw 68 68 68 68 68 68 68 68 68 68 68

For notes, see end of the table. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200766

ANNEX A

2016

80

40

40

20

4

20

81

mits in

urrent med to

ber is ith the o rural

target

Q data which s using ted to

Table A.10. Main policy assumptions for dairy markets (cont.)

Average 2001-05

2006 est. 2007 2008 2009 2010 2011 2012 2013 2014 2015

INDIA

Milk tariff % 80 80 80 80 80 80 80 80 80 80 80

Butter tariff % 47 40 40 40 40 40 40 40 40 40 40

Cheese tariff % 41 40 40 40 40 40 40 40 40 40 40

Whole milk powder tariff % 20 20 20 20 20 20 20 20 20 20 20

SOUTH AFRICA

Milk powder tariff-quota kt pw 4 4 4 4 4 4 4 4 4 4 4

in-quota tariff % 20 20 20 20 20 20 20 20 20 20 20

out-of-quota tariff % 89 81 81 81 81 81 81 81 81 81 81

a) Year ending 30 June. b) For manufacturing milk. c) The effective volume of cheese and SMP subsidized exports will be lower reflecting the binding nature of subsidized export li

value terms. d) Prices and payments in market euro – see Glossary of Terms. e) EU farmers also benefit from the Single Farm Payment (SFP) Scheme, which provides flat-rate payments independent from c

production decisions and market developments. The total amount spent under the SFP scheme, before modulation, is assu increase from 26.9 billion euro in 2005 to 28.4 billion euro in 2008 for the total of the 15 former member states. The final num equivalent to 233 euro per hectare of eligible farm land on average. For the accession countries, payments are phased in w assumption of maximum top-ups from national budgets. Due to modulation, between 2.7% and 4.6% of the total SFP will go t development spending rather than directly to the farmers.

f) Total quota, EU27 starting in 1999. g) Excludes processed cheese. h) Year beginning 1 January. i) The counter-cyclical payment is determined as a 45% difference in 2005 and a 34% difference in 2006 and 2007, between the

price and the Boston class I price. Note: The source for tariffs and Tariff Rate Quotas (except Russia) is AMAD (Agricultural market access database). The tariff and TR are based on Most Favoured Nation rates scheduled with the WTO and exclude those under preferential or regional agreements, may be substantially different. Tariffs are simple averages of several product lines. Specific rates are converted to ad valorem rate world prices in the Outlook. Import quotas are based on global commitments scheduled in the WTO rather than those alloca preferential partners under regional or other agreements. est.: Estimate. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 67

ANNEX A

2016

3 540

3 122

–28

7 375

7 916

10 914

11 038

–32

223

16 355

15 950

10

4 914

5 341

21 269

21 292

3

307

Table A.11. World dairy projections (butter and cheese)

Calendar yeara Average 2001-05

2006 est. 2007 2008 2009 2010 2011 2012 2013 2014 2015

BUTTER

OECDb

Production kt pw 3 694 3 592 3 627 3 580 3 549 3 536 3 545 3 544 3 536 3 537 3 536

Consumption kt pw 3 079 3 094 3 138 3 121 3 107 3 108 3 114 3 112 3 113 3 115 3 117

Stock changes kt pw 9 –28 28 25 –1 –24 –24 –26 –27 –28 –28

Non-OECD

Production kt pw 4 454 5 104 5 388 5 480 5 761 5 997 6 272 6 469 6 726 6 910 7 165

Consumption kt pw 4 890 5 547 5 891 6 009 6 300 6 544 6 823 7 023 7 272 7 454 7 707

WORLD

Production kt pw 8 148 8 696 9 014 9 060 9 310 9 533 9 816 10 013 10 262 10 446 10 701

Consumption kt pw 7 969 8 642 9 029 9 130 9 407 9 653 9 936 10 135 10 385 10 569 10 824

Stock changes kt pw 3 –25 27 24 –3 –26 –27 –29 –31 –31 –31

Pricec USD/100 kg 156 186 196 193 188 188 195 201 210 215 220

CHEESE

OECDb

Production kt pw 13 849 14 702 14 789 14 984 15 160 15 326 15 454 15 652 15 820 15 993 16 168

Consumption kt pw 13 410 14 218 14 470 14 660 14 825 14 978 15 098 15 277 15 436 15 601 15 770

Stock changes kt pw 0 22 –64 –38 –20 –7 –7 –2 2 5 7

Non-OECD

Production kt pw 3 787 4 047 4 076 4 151 4 252 4 358 4 456 4 549 4 644 4 735 4 827

Consumption kt pw 4 194 4 491 4 490 4 544 4 640 4 746 4 851 4 958 5 058 5 155 5 250

WORLD

Production kt pw 17 636 18 749 18 864 19 135 19 412 19 684 19 910 20 201 20 465 20 728 20 995

Consumption kt pw 17 605 18 709 18 960 19 204 19 465 19 723 19 949 20 235 20 494 20 755 21 020

Stock changes kt pw –8 15 –71 –45 –27 –14 –14 –9 –5 –2 0

Priced USD/100 kg 231 273 300 311 303 300 301 301 302 304 306

a) Year ending 30 June for Australia and 31 May for New Zealand in OECD aggregate. b) Excludes Iceland but includes the 8 EU members that are not members of the OECD. c) F.o.b. export price, butter, 82% butterfat, northern Europe. d) F.o.b. export price, cheddar cheese, 40 lb blocks, northern Europe. est.: Estimate. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200768

ANNEX A

2016

2 552

1 948

0

912

1 653

3 463

3 601

2

252

2 341

860

3 010

4 296

5 352

5 155

253

89

439

Table A.12. World dairy projections (powders and casein)

Calendar yeara Average 2001-05

2006 est. 2007 2008 2009 2010 2011 2012 2013 2014 2015

SKIM MILK POWDER

OECDb

Production kt pw 2 741 2 397 2 481 2 390 2 359 2 370 2 391 2 425 2 444 2 484 2 518

Consumption kt pw 1 984 1 783 1 852 1 762 1 771 1 792 1 824 1 858 1 887 1 912 1 929

Stock changes kt pw –30 –46 8 3 1 1 1 1 1 –1 0

Non-OECD

Production kt pw 742 727 771 791 826 844 885 900 931 920 925

Consumption kt pw 1 473 1 483 1 489 1 505 1 509 1 528 1 556 1 578 1 606 1 618 1 644

WORLD

Production kt pw 3 484 3 124 3 251 3 181 3 185 3 214 3 276 3 325 3 375 3 404 3 443

Consumption kt pw 3 457 3 266 3 341 3 268 3 280 3 320 3 381 3 437 3 493 3 530 3 573

Stock changes kt pw –30 –44 10 4 3 3 3 3 3 0 2

Pricec USD/100 kg 186 235 259 269 266 259 254 250 248 250 249

WHOLE MILK POWDER

OECDb

Production kt pw 1 819 1 946 1 945 1 946 1 990 2 057 2 093 2 154 2 205 2 250 2 295

Consumption kt pw 720 785 782 790 801 806 814 820 832 841 850

Non-OECD

Production kt pw 1 801 2 277 2 377 2 472 2 559 2 633 2 708 2 769 2 831 2 895 2 952

Consumption kt pw 2 712 3 245 3 352 3 432 3 551 3 687 3 791 3 907 4 007 4 108 4 200

WORLD

Production kt pw 3 620 4 223 4 321 4 418 4 549 4 690 4 801 4 923 5 036 5 145 5 246

Consumption kt pw 3 432 4 030 4 133 4 222 4 352 4 494 4 605 4 727 4 840 4 949 5 050

Priced USD/100 kg 191 229 255 263 257 248 250 249 251 252 253

WHEY POWDER

Non-OECD

Wholesale price, USAe USD/100 kg 51 74 79 83 91 91 92 91 91 90 89

CASEIN

Pricef USD/100 kg 439 486 480 482 468 458 442 451 440 445 440

a) Year ending 30 June for Australia and 31 May for New Zealand in OECD aggregate. b) Excludes Iceland but includes the 8 EU members that are not members of the OECD. c) F.o.b. export price, non-fat dry milk, extra grade, northern Europe. d) F.o.b. export price, WMP 26% butterfat, northern Europe. e) Edible dry whey, Wisconsin, plant. f) Export price, New Zealand. est.: Estimate. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 69

ANNEX A

16/17

35.0

39.8

35.0

35.0

25.0

27.7

35.4

1 954

15.0

50.0

50.0

20.0

404

16 188

1 431

531 660

340

419

40.0

750

60.0

25.0

40.0

150

71.8

103.1

18.0

17.0

4 705

4 705

Table A.13. Main policy assumptions for sugar markets

Crop yeara 05/06 06/07 est. 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16

MAIN ASSUMPTIONS FOR SUGAR MARKETS

ARGENTINA

Tariff, sugar ARS/t 35.0 35.0 35.0 35.0 35.0 35.0 35.0 35.0 35.0 35.0 35.0

BRAZIL

Cane allocation to sugar % 50.0 50.2 49.1 48.0 47.0 45.8 44.5 43.3 42.3 41.4 40.6

Tariff, raw sugar % 35.0 35.0 35.0 35.0 35.0 35.0 35.0 35.0 35.0 35.0 35.0

Tariff, white sugar % 35.0 35.0 35.0 35.0 35.0 35.0 35.0 35.0 35.0 35.0 35.0

Ethanol blending ratio with gazoline % 25.0 23.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0

CANADA

Tariff, raw sugar CAD/t 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7

Tariff, white sugar CAD/t 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4 35.4

CHINAb

TRQ sugar kt 1 954 1 954 1 954 1 954 1 954 1 954 1 954 1 954 1 954 1 954 1 954

Tariff, in-quota, raw sugar % 15.0 15.0 15.0 15.0 15.0 15.0 15.0 15.0 15.0 15.0 15.0

Tariff, in-quota, white sugar % 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0

Tariff, over-quota % 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0 50.0

DOMINICAN REPUBLIC

Applied tariff, white sugar % 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0

EUe

Reference price, white sugarc EUR/t 632 632 632 542 404 404 404 404 404 404 404

Effective quotad kt rse 18 803 15 910 17 427 17 485 17 143 17 057 16 924 16 773 16 583 16 361 16 108

Subsidised export limits

Quantity limit kt rse 1 431 1 431 1 431 1 431 1 431 1 431 1 431 1 431 1 431 1 431 1 431

Value limit 000 EUR 531 660 531 660 531 660 531 660 531 660 531 660 531 660 531 660 531 660 531 660 531 660

Tariff, raw sugar EUR/t 339 339 339 339 339 339 339 339 339 339 340

Tariff, white sugar EUR/t 419 419 419 419 419 419 419 419 419 419 419

FIJI

Applied tariff, white sugar % 40.0 40.0 40.0 40.0 40.0 40.0 40.0 40.0 40.0 40.0 40.0

INDIA

Intervention price, sugar cane INR/t 750 750 750 750 750 750 750 750 750 750 750

Applied tariff, raw sugar % 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0

INDONESIA

Tariff, white sugar % 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0

JAMAICA

Applied tariff, white sugar % 40.0 40.0 40.0 40.0 40.0 40.0 40.0 40.0 40.0 40.0 40.0

JAPAN

Minimum stabilisation price, raw sugar JPY/kg 150 150 150 150 150 150 150 150 150 150 150

Tariff, raw sugar JPY/kg 71.8 71.8 71.8 71.8 71.8 71.8 71.8 71.8 71.8 71.8 71.8

Tariff, white sugar JPY/kg 103.1 103.1 103.1 103.1 103.1 103.1 103.1 103.1 103.1 103.1 103.1

KOREA

Tariff, raw sugar % 18.0 18.0 18.0 18.0 18.0 18.0 18.0 18.0 18.0 18.0 18.0

MADAGASCAR

Applied tariff, white sugar % 17.0 17.0 17.0 17.0 17.0 17.0 17.0 17.0 17.0 17.0 17.0

MEXICO

Mexico common external tariff, raw sugar MXN/t 4 301 4 308 4 315 4 340 4 377 4 418 4 466 4 512 4 560 4 608 4 656

Mexico common external tariff, white sugar MXN/t 4 301 4 308 4 315 4 340 4 377 4 418 4 466 4 512 4 560 4 608 4 656

For notes, see end of the table. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200770

ANNEX A

16/17

75.7

340.0

397

504.9

1 080

49

339

357

0

105.0

25.0

40.0

16/17

39 429

2 236

43 271

12 954

47 971

43 774

42 736

69 142

87 400

86 007

82 096

242.5

308.6

Table A.13. Main policy assumptions for sugar markets (cont.)

Crop yeara 05/06 06/07 est. 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16

RUSSIA

Tariff, raw sugarf % 26.5 68.7 75.7 80.1 83.2 80.1 77.2 78.6 78.6 77.2 76.4

Tariff, white sugarf USD/t 340.0 340.0 340.0 340.0 340.0 340.0 340.0 340.0 340.0 340.0 340.0

UNITED STATESe

Loan rate, cane sugar USD/t 397 397 397 397 397 397 397 397 397 397 397

Loan rate, beet sugar USD/t 504.9 504.9 504.9 504.9 504.9 504.9 504.9 504.9 504.9 504.9 504.9

TRQ, raw sugar kt rse 2 549 1 853 1 755 1 080 1 080 1 080 1 080 1 080 1 080 1 080 1 080

TRQ, refined sugar kt rse 49 49 49 49 49 49 49 49 49 49 49

Raw sugar 2nd tier WTO tariff USD/t 339 339 339 339 339 339 339 339 339 339 339

White sugar 2nd tier WTO tariff USD/t 357 357 357 357 357 357 357 357 357 357 357

Raw sugar 2nd tier NAFTA tariff USD/t 100 67 33 0 0 0 0 0 0 0 0

SOUTH AFRICA

Tariff, raw sugar % 105.0 105.0 105.0 105.0 105.0 105.0 105.0 105.0 105.0 105.0 105.0

TANZANIA

Applied tariff, white sugar % 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0

VIET NAM

Applied tariff, white sugar % 40.0 40.0 40.0 40.0 40.0 40.0 40.0 40.0 40.0 40.0 40.0

a) Beginning crop marketing year – see the Glossary of Terms for definitions. b) Refers to mainland only. c) Reference price for consumers. d) Production that receives official support. Includes the 10 new member countries from May 2004, Bulgaria and Romania. e) In addition, price based special safeguard actions may apply. f) Assumes a wholesale price target of USD 470 per tonne as the basis for setting the floating tariff duty. The source for tariffs (except United States and Russia) is AMAD. The source for Russia and United States tariffs is ERS, USDA. est.: Estimate. rse: Raw sugar equivalent. Source: OECD and FAO Secretariats.

Table A.14. World sugar projections (in raw sugar equivalent)

Crop yeara Average

01/02-05/06 06/07 est. 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16

OECD

Production kt rse 41 974 38 769 38 360 38 099 38 395 38 471 38 273 38 522 38 780 38 999 39 206

EU bio-ethanol kL 889 973 921 1 047 1 132 1 288 1 463 1 621 1 777 1 930 2 082

Consumption kt rse 40 734 41 036 41 178 41 459 41 702 41 943 42 185 42 406 42 624 42 840 43 056

Closing stocks kt rse 18 755 17 323 17 337 16 443 16 033 15 649 14 787 14 156 13 668 13 328 13 193

NON-OECD

Production kt rse 103 147 123 023 124 850 123 881 122 870 127 670 132 744 137 145 138 893 142 201 145 136 1

Brazil bio-ethanol kL 13 943 19 398 21 486 23 573 25 661 28 248 30 836 33 423 36 011 38 599 41 186

Consumption kt rse 103 334 111 738 117 411 119 973 122 694 125 454 128 246 131 093 133 972 136 876 139 798 1

Closing stocks kt rse 45 094 55 795 60 401 61 843 59 121 58 250 59 699 62 498 64 063 65 887 67 511

WORLD

Production kt rse 145 120 161 792 163 210 161 980 161 265 166 141 171 017 175 667 177 673 181 200 184 343 1

Consumption kt rse 144 068 152 774 158 589 161 432 164 397 167 396 170 431 173 499 176 596 179 716 182 854 1

Closing stocks kt rse 63 848 73 118 77 738 78 286 75 155 73 899 74 486 76 654 77 731 79 215 80 704

Price, raw sugarb USD/t 217.6 253.5 242.5 235.9 231.5 235.9 240.3 238.1 238.1 240.3 241.4

Price, white sugarc USD/t 269.7 360.5 341.7 330.7 319.7 319.7 319.7 314.2 310.9 310.9 309.7

a) Beginning crop marketing year – see the Glossary of Terms for definitions. b) Raw sugar world price, New York No. 11, f.o.b. stowed Caribbean port (including Brazil), bulk spot price, September/August. c) Refined sugar price, London No. 5, f.o.b. Europe, spot, September/August. est.: Estimate. Source: OECD and FAO Secretariats.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 71

ANNEX B

ANNEX B

Trade Annex Tables

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200772

ANNEX B

Table B.1. Concordance of product groupings

Description HS code Description HS code

Primary bulk commodities Produce/horticulture Coffee 09011 Planting material 0601-0602 Tea 0902 Cut flowers/plants 0603-0604 Coffee mate 0903 Vegetables 0701-0709 Wheat 1001 Roots, tubers 0714 Rye 1002 Coconut 08011 Barley 1003 Brazilnut 08012 Oats 1004 Cashew nuts 08013 Corn 1005 Other nuts 08021-08025, 08029 Rice 1006 Fruit 0803-08010 Sorghum 1007 Frozen fruit 08119 Other grains 1008 Dried fruit 08131-08135 Soybeans 1201 Pepper 09041-09042 Peanuts 1202 Vanilla 905 Oilseeds 1204-1207 Cinnamon 09061-09062 Cotton linters 14042 Cloves 0907 Cocoa beans 1801 Nutmeg 09081 Tobacco 24011-24013 Mace 09082 Cotton 5201-5203 Cardamom 09083 Hemp 5302 Other seeds 09091-09095

Other spices 09101-09105 Semi-processed Spice mix 091091, 091099 Live animals 0101-0106 Hops 12101, 12102 Pig fat 0209 Stone fruit 12123 Hairs 0501-0503 Sugarbeet 121291 Animal products 0504-0511 Sugarcane 121292 Dried, shelled beans 0713 Coffee husks 09019 Processed Grain flours, groats 1101-1103 Fresh, chilled meats 0201-0208 Starch 11081 Processed meat 0210 Inulin 11082 Dairy products 0401-0406 Wheat gluten 1109 Eggs and products 0407-0408 Copra 1203 Honey 0409 Soy flour and meal 1208 Other animal products 0410 Sowing seeds 1209 Processed vegetables 0710-0712 Roots, seeds cut/crushed 1211 Processed fruit 0811-0812, 0814 Straw, husks, fodder 1213, 1214 Coffee 09012, 09014 Gum, lac, plant extracts 1301, 1302 Processed grains 1104-1107 Furnishing material 1401-1404 Other vegetables 1212 Animal fat 1501-1503, 1505, 1506 Fish and animal oils 1504, 1517 Vegetable oils 1507-1516 Prepared meats 1601-1603 Inedible fats, oils 1518 Sugar, sweeteners 1701-1704 Crude glycerol 1520 Chocolates 1806 Wax 1521 Flour preparations 1901 Degras 1522 Pasta 1902 Sugar 17011 Tapioca 1903 Cocoa products 1802-1806 Other preparations 1904-1905 Grain products 2301-2303 Prepared vegetables 2001-2005 Oilseed cake 2304-2306 Prepared fruit 2006-2009 Plant waste material 2308 Extracts, essences, broths 2101-2106 Pet food material 2309 Beverages 2201-2208 Glycerol/sorbitol/mannitol 2905 Vinegar 2209 Special plant oils 33011-33013, 33019 Tobacco products 2402-2403 Proteins/gelatins/starches 3501-3505 Other products 3502 Amylaceous substance 38091 Fatty acids, alcohols 3823-3824 Hides, skins 4101-4103 Fur 4301 Silk 5001-5003 Wool 5101-5103 Flax 530

Note: The Harmonized System (HS) provides a nomenclature for classifying internationally trade goods. The definitions of HS commodity groupings up to the 6-digit level are established by the World Customs Organization (www.wcoomd.org/ie/En/en.html).

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 73

ANNEX B

Table B.2. Top 20 exporters and importers of bulk products (excludes intra-EU)

Average bulk exports (1985-89) Average bulk imports (1985-89)

USD billion Share USD billion Share

United States 17.16 37.16 EU15 15.42 32.15

Canada 3.78 8.18 of which:

EU15 3.22 6.98 Germanya 3.96 8.26

of which: Netherlands 1.92 4.00

France 1.30 2.83 Italy 1.91 3.99

Germanya 0.52 1.13 Spain 1.57 3.26

United Kingdom 0.51 1.11 France 1.52 3.16

Australia 2.42 5.23 United Kingdom 1.48 3.09

Brazil 2.24 4.86 Belgium-Luxembourg 0.81 1.68

Colombia 2.00 4.32 Portugal 0.75 1.57

China 1.87 4.05 Japan 7.64 15.93

Argentina 1.82 3.93 United States 4.09 8.53

Thailand 1.39 3.01 Korea, Republic of 1.92 4.01

Côte d'Ivoire 1.36 2.95 Chinese Taipei 1.90 3.95

India 1.07 2.31 China 1.57 3.27

Pakistan 0.88 1.90 Mexico 1.04 2.17

Mexico 0.74 1.61 Algeria 0.85 1.77

Kenya 0.57 1.23 Egypt, Arab Republic of 0.75 1.57

Indonesia 0.57 1.23 Saudi Arabia 0.72 1.50

Zimbabwe 0.41 0.89 Brazil 0.67 1.40

Sri Lanka 0.39 0.84 Indonesia 0.67 1.39

Cameroon 0.37 0.79 Canada 0.61 1.27

Total of above 42.24 91.47 Total of above 37.84 78.91

Average bulk exports (2000-04) Average bulk imports (2000-04)

USD billion Share USD billion Share

United States 20.16 30.74 EU15 14.05 18.99

Brazil 5.58 8.50 of which:

Canada 4.13 6.30 Germanya 3.06 4.13

Australia 4.09 6.24 Netherlands 2.33 3.15

Argentina 3.88 5.91 Italy 1.95 2.64

EU15 3.25 4.96 Spain 1.72 2.33

of which: United Kingdom 1.19 1.61

France 1.19 1.82 France 1.13 1.53

Germanya 0.86 1.32 Japan 7.55 10.20

China 2.28 3.47 China 6.19 8.36

India 2.12 3.23 United States 3.84 5.19

Thailand 2.05 3.13 Mexico 3.60 4.87

Côte d'Ivoire 1.74 2.65 Korea, Republic of 2.67 3.61

Viet Nam 1.20 1.82 Indonesia 2.11 2.85

Colombia 0.88 1.35 Chinese Taipei 1.87 2.53

Indonesia 0.76 1.16 Brazil 1.71 2.31

Pakistan 0.74 1.13 Turkey 1.48 2.00

Russian Federation 0.71 1.08 Egypt, Arab Republic of 1.46 1.97

Ukraine 0.71 1.08 Iran, Islamic Republic of 1.36 1.84

Sri Lanka 0.69 1.05 Algeria 1.31 1.77

Kazakhstan 0.58 0.89 Saudi Arabia 1.28 1.73

Ghana 0.58 0.88 Canada 1.17 1.59

Total of above 56.13 85.57 Total of above 51.64 69.81

a) Excludes data for the German Democratic Republic.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200774

ANNEX B

Table B.3. Top 20 exporters and importers of horticultural products (excludes intra-EU)

Average horticulture exports (1985-89) Average horticulture imports (1985-89)

USD billion Share USD billion Share

United States 2.15 15.52 EU15 7.25 37.54

EU15 1.65 11.85 of which:

of which: Germanya 1.87 9.67

Netherlands 0.56 4.03 France 1.32 6.83

Italy 0.31 2.20 United Kingdom 1.04 5.38

Turkey 0.78 5.60 Netherlands 1.02 5.30

Thailand 0.75 5.37 Italy 0.51 2.62

Mexico 0.67 4.84 Belgium-Luxembourg 0.33 1.70

India 0.52 3.74 Sweden 0.28 1.47

Chile 0.47 3.39 Austria 0.23 1.20

China 0.44 3.15 Spain 0.20 1.02

Israel 0.42 3.06 United States 3.70 19.18

Colombia 0.39 2.82 Japan 1.83 9.46

New Zealand 0.39 2.77 Canada 1.57 8.14

Costa Rica 0.34 2.43 Switzerland 0.79 4.11

Indonesia 0.30 2.15 Hong Kong, China 0.59 3.06

Brazil 0.28 2.02 Singapore 0.44 2.27

Singapore 0.28 2.02 Saudi Arabia 0.32 1.65

Morocco 0.27 1.93 Norway 0.28 1.47

Honduras 0.27 1.91 Kuwait 0.23 1.21

Philippines 0.25 1.79 Chinese Taipei 0.21 1.08

Ecuador 0.24 1.72 Malaysia 0.18 0.92

Total of above 10.84 78.07 Total of above 17.40 90.10

Average horticulture exports (2000-04) Average horticulture imports (2000-04)

USD billion Share USD billion Share

United States 6.20 16.32 EU15 13.82 29.64

EU15 5.05 13.29 of which:

of which: Germanya 2.94 6.31

Netherlands 2.06 5.41 United Kingdom 2.26 4.85

Spain 0.79 2.09 Netherlands 2.08 4.46

Italy 0.71 1.86 France 1.76 3.78

Mexico 3.13 8.23 Belgium-Luxembourg 1.66 3.57

China 1.68 4.41 Italy 0.99 2.13

Turkey 1.56 4.10 Spain 0.96 2.07

Chile 1.47 3.87 United States 10.03 21.51

Ecuador 1.27 3.35 Japan 3.51 7.52

Colombia 1.13 2.98 Canada 2.99 6.42

Canada 1.05 2.75 Russian Federation 1.37 2.95

Costa Rica 1.00 2.63 Switzerland 1.31 2.80

India 0.96 2.54 Hong Kong, China 1.20 2.57

South Africa 0.83 2.19 Poland 0.75 1.62

New Zealand 0.74 1.96 Mexico 0.73 1.57

Iran, Islamic Republic of 0.72 1.89 China 0.66 1.42

Thailand 0.63 1.66 Singapore 0.65 1.39

Argentina 0.60 1.57 Saudi Arabia 0.56 1.19

Israel 0.59 1.54 Norway 0.53 1.15

Brazil 0.55 1.45 India 0.52 1.11

Total of above 29.17 76.74 Total of above 38.64 82.85

a) Excludes data for the German Democratic Republic.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 2007 75

ANNEX B

Table B.4. Top 20 exporters and importers of semi-processed products (excludes intra-EU)

Average semi processed exports (1985-89) Average semi processed imports (1985-89)

USD billion Share USD billion share

United States 8.02 19.55 EU15 14.24 32.08

EU15 6.68 16.30 of which:

of which: Germanya 2.75 6.20

Germanya 1.34 3.26 Italy 2.73 6.14

France 1.07 2.60 France 2.36 5.32

Netherlands 1.03 2.52 United Kingdom 1.72 3.87

Italy 0.69 1.69 Netherlands 1.65 3.73

United Kingdom 0.59 1.44 Spain 0.89 1.99

Australia 4.09 9.98 Belgium-Luxembourg 0.62 1.41

China 2.41 5.88 Japan 4.68 10.54

Malaysia 2.17 5.29 United States 4.33 9.75

Brazil 2.05 4.99 Korea, Republic of 1.84 4.15

New Zealand 1.68 4.10 Hong Kong, China 1.55 3.50

Argentina 1.44 3.52 Taiwan, China 1.42 3.20

Canada 1.41 3.45 China 1.28 2.89

Hong Kong, China 0.95 2.31 Canada 1.11 2.50

Singapore 0.91 2.23 India 0.94 2.11

Indonesia 0.91 2.21 Singapore 0.87 1.95

Turkey 0.73 1.77 Mexico 0.81 1.82

Japan 0.60 1.47 Algeria 0.71 1.59

Philippines 0.56 1.37 Switzerland 0.63 1.41

Chile 0.54 1.31 Egypt, Arab Republic of 0.62 1.39

Total of above 35.15 85.72 Total of above 35.01 78.86

Average semi processed exports (2000-04) Average semi processed imports (2000-04)

USD billion Share USD billion Share

EU15 13.40 16.77 EU15 17.70 20.72

of which: of which:

Germanya 3.13 3.97 Italy 3.19 3.73

Netherlands 2.14 2.71 Germanya 2.97 3.48

France 1.63 2.07 Netherlands 2.51 2.93

Denmark 1.02 1.29 France 2.39 2.80

Italy 1.42 1.80 United Kingdom 2.14 2.50

United Kingdom 0.97 1.22 Spain 1.62 1.89

Belgium 0.92 1.16 United States 9.01 10.55

United States 13.01 16.49 China 6.44 7.54

Malaysia 5.86 7.43 Japan 5.92 6.93

Argentina 5.76 7.30 India 3.13 3.67

Brazil 4.07 5.15 Korea, Republic of 2.66 3.12

Australia 3.98 5.05 Canada 2.59 3.03

Canada 3.95 5.01 Mexico 2.34 2.73

China 3.43 4.34 Hong Kong, China 2.23 2.61

Indonesia 3.30 4.18 Chinese Taipei 1.61 1.88

India 1.60 0.02 Poland 1.44 1.68

New Zealand 1.55 0.02 Turkey 1.38 1.62

Hong Kong, China 1.26 0.02 Thailand 1.24 1.45

Japan 0.97 0.01 Russian Federation 1.21 1.42

Peru 0.95 0.01 Indonesia 1.15 1.35

Total of above 63.10 74.20 Total of above 60.05 91.02

a) Excludes data for the German Democratic Republic.

OECD-FAO AGRICULTURAL OUTLOOK 2007-2016 – © OECD/FAO 200776

ANNEX B

Table B.5. Top 20 exporters and importers of processed products (excludes intra-EU)

Average processed exports (1985-89) Average processed imports (1985-89)

USD billion Share USD billion Share

EU15 18.31 36.93 United States 11.85 23.36

of which: EU15 8.73 17.21

France 4.40 8.88 of which:

United Kingdom 2.82 5.69 United Kingdom 2.28 4.49

Netherlands 2.67 5.38 Germanya 2.09 4.12

Germanya 1.93 3.90 France 1.28 2.53

Denmark 1.88 3.79 Italy 0.78 1.54

Italy 1.32 2.66 Netherlands 0.60 1.17

Ireland 0.92 1.85 Japan 7.82 15.40

Spain 0.80 1.62 Canada 2.25 4.43

United States 7.01 14.14 Hong Kong, China 2.06 4.06

Australia 2.93 5.91 Switzerland 1.46 2.88

New Zealand 2.32 4.67 Saudi Arabia 1.45 2.86

Brazil 2.04 4.11 Singapore 1.01 2.00

Canada 1.98 4.00 Chinese Taipei 1.01 1.98

China 1.40 2.83 Mexico 0.70 1.37

Chinese Taipei 1.19 2.40 Australia 0.65 1.27

Switzerland 1.08 2.18 China 0.63 1.24

Hong Kong, China 1.05 2.12 Malaysia 0.63 1.23

Thailand 1.02 2.05 Egypt, Arab Republic of 0.62 1.23

Argentina 0.78 1.57 Norway 0.53 1.04

Mexico 0.66 1.33 Korea, Republic of 0.49 0.96

Total of above 41.76 84.26 Total of above 41.88 82.52

Average processed exports (2000-04) Average processed imports (2000-04)

USD billion Share USD billion Share

EU15 40.08 27.21 United States 28.20 18.79

of which: Japan 20.07 13.37

France 7.86 5.34 EU15 19.69 13.12

United Kingdom 5.42 3.68 of which:

Netherlands 5.09 3.45 Germanya 5.15 3.43

Germanya 4.88 3.31 United Kingdom 4.72 3.14

Italy 4.30 2.92 France 2.13 1.42

Denmark 3.35 2.27 Netherlands 2.13 1.42

Belgium-Luxembourg 2.14 1.45 Canada 6.93 4.62

Spain 2.12 1.44 Russian Federation 5.80 3.86

United States 20.80 14.12 Mexico 5.12 3.41

Canada 8.59 5.83 Hong Kong, China 4.63 3.09

Brazil 7.98 5.42 Korea, Republic of 3.50 2.33

Australia 7.58 5.14 Switzerland 3.04 2.03

China 6.62 4.49 Saudi Arabia 2.65 1.77

New Zealand 5.75 3.90 Singapore 2.61 1.74

Mexico 3.88 2.63 Chinese Taipei 2.36 1.57

Thailand 3.61 2.45 Australia 2.19 1.46

Poland 2.72 1.84 China 2.10 1.40

Argentina 2.31 1.57 Malaysia 1.60 1.06

Hong Kong, China 2.21 1.50 Norway 1.28 0.85

Switzerland 2.17 1.47 Philippines 1.22 0.81

Total of above 114.29 77.60 Total of above 113.00 75.28

a) Excludes data for the German Democratic Republic.

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Glossary of Terms

AMAD Agricultural Market Access database. A co-operative effort between Agriculture and

Agri-food Canada, EU Commission-Agriculture Directorate-General, FAO, OECD, The World

Bank, UNCTAD and the United States Department of Agriculture, Economic Research

Service. Data in the database is obtained from countries’ schedules and notifications

submitted to the WTO.

Avian influenza Avian influenza is an infectious disease of birds caused by type A strains of the

influenza virus. The disease, which was first identified in Italy more than 100 years ago,

occurs worldwide. The quarantining of infected farms, destruction of infected or

potentially exposed flocks, and recently inoculation are standard control measures.

Atlantic beef/pigmeat market Beef/pigmeat trade between countries in the Atlantic Rim.

Baseline The set of market projections used for the outlook analysis in this report and as a

benchmark for the analysis of the impact of different economic and policy scenarios. A

detailed description of the generation of the baseline is provided in the chapter on

Methodology in this report.

Biofuels In the wider sense defined as all solid, fluid or gaseous fuels produced from biomass.

More narrowly, the term biofuels comprises those that replace petroleum-based road-

transport fuels, i.e. bio-ethanol produced from sugar crops, cereals and other starchy crops

that can be used as an additive to, in a blend with or as a replacement of gasoline, and

bio-diesel produced mostly from vegetable oils, but also from waste oils and animal fats,

that can be used in blends with or as a replacement of petroleum-based diesel.

Biomass Biomass is defined as any plant matter used directly as fuel or converted into other

forms before combustion. Included are wood, vegetal waste (including wood waste and

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crops used for energy production), animal materials/wastes and industrial and urban

wastes, used as feedstocks for producing bioproducts.

Bovine Spongiform Encephalopathy (BSE) A fatal disease of the central nervous system of cattle, first identified in the United

Kingdom in 1986. On 20 March 1996 the UK Spongiform Encephalopathy Advisory

Committee (SEAC) announced the discovery of a new variant of Creutzfeldt-Jacob Disease

(vCJD), a fatal disease of the central nervous system in humans, which might be linked to

consumption of beef affected by exposure to BSE.

Cereals Defined as wheat, coarse grains and rice.

CAFTA CAFTA is a comprehensive trade agreement between Costa Rica, the Dominican

Republic, El Salvador, Guatemala, Honduras, Nicaragua and the United States.

Common Agricultural Policy (CAP) The European Union’s agricultural policy, first defined in Article 39 of the Treaty of

Rome signed in 1957.

CAP reform The EU Commission has published a Communication on the Mid-Term Review on the

Common Agricultural Policy in July 2002, in January 2003 the Commission adopted a formal

proposal. A formal decision on the “CAP reform – a long-term perspective for sustainable

agriculture” was taken by the EU farm ministers. The reform includes far-reaching

amendments of current policies, including further reductions in support prices, partly offset by

direct payments, and a further decoupling of most direct payments from current production.

Coarse grains Defined as barley, maize, oats, sorghum and other coarse grains in all countries except

Australia, where it includes triticale and in the European Union where it includes rye and

other mixed grains.

Conservation Reserve Program (CRP) A major provision of the United States’ Food Security Act of 1985 and extended under

the Food and Agriculture Conservation and Trade Act of 1990, the Food and Agriculture

Improvement and Reform Act of 1996, and the Farm Security and Rural Investment Act

of 2002 is designed to reduce erosion on 40 to 45 million acres (16 to 18 million hectares) of

farm land. Under the programme, producers who sign contracts agree to convert erodable

crop land to approved conservation uses for ten years. Participating producers receive

annual rental payments and cash or payment in kind to share up to 50% of the cost of

establishing permanent vegetative cover. The CRP is part of the Environmental Conservation

Acreage Reserve Program. The 1996 FAIR Act authorised a 36.4 million acre (14.7 million

hectares) maximum under CRP, its 1995 level. The maximum area enrolled in the CRP was

increased to 39.2 million acres in the 2002 FSRI Act.

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Commonwealth of Independent States (CIS) The heads of twelve sovereign states (except the Baltic states) have signed the Treaty

on establishment of the Economic Union, in which they stressed that the Republic of

Azerbaijan, Republic of Armenia, Republic of Belarus, Republic of Georgia, Republic of

Kazakhstan, Kyrgyz Republic, Republic of Moldova, Russian Federation, Republic of

Tajikistan, Turkmenistan, Republic of Uzbekistan and Ukraine on equality basis

established the Commonwealth of Independent States.

Common Market Organisation (CMO) for sugar The common organisation of the sugar market (CMO) in the European Union was

established in 1968 to ensure a fair income to community sugar producers and self-supply

of the Community market. At present the CMO is governed by Council Regulation (EC)

No. 318/2006 (the basic regulation) which establishes a restructuring fund financed by

sugar producers to assist the restructuring process needed to render the industry more

competitive.

Crop year, coarse grains Refers to the crop marketing year beginning 1 April for Japan, 1 July for the European

Union and New Zealand, 1 August for Canada and 1 October for Australia. The US crop year

begins 1 June for barley and oats and 1 September for maize and sorghum.

Crop year, oilseeds Refers to the crop marketing year beginning 1 April for Japan, 1 July for the European

Union and New Zealand, 1 August for Canada and 1 October for Australia. The US crop year

begins 1 June for rapeseed, 1 September for soyabeans and for sunflower seed.

Crop year, rice Refers to the crop marketing year beginning 1 April for Japan, Australia, 1 August for

the United States, 1 September for the European Union, 1 October for Mexico, 1 November

for Korea and 1 January for other countries.

Crop year, sugar A common crop marketing year beginning 1 September and extending to 31 August,

used by FO Licht, the primary data source for sugar supply and demand balances for the

OECD’s World Sugar Model.

Crop year, wheat Refers to the crop marketing year beginning 1 April for Japan, 1 June for the

United States, 1 July for the European Union and New Zealand, 1 August for Canada and

1 October for Australia.

Decoupled payments Budgetary payments paid to eligible recipients who are not linked to current production

of specific commodities or livestock numbers or the use of specific factors of production.

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Direct payments Payments made directly by governments to producers.

Doha Development Agenda The current round of multilateral trade negotiations in the World Trade Organisation

that were initiated in November 2001, in Doha, Qatar.

Domestic support Refers to the annual level of support, expressed in monetary terms, provided to

agricultural production. It is one of the three pillars of the Uruguay Round Agreement on

Agriculture targeted for reduction.

Economic Partnership Agreements (EPAs) Trade negotiations currently being negotiated between the EU and the African,

Caribbean Pacific (ACP) group of developing countries. The outcome of the negotiations will

be a series of new Free Trade Agreements (FTA) replacing the Lomé system of preferential

access to the European market for the ACP countries from 2008.

Ethanol A biofuel that can be used as a fuel substitute (hydrous ethanol) or a fuel extender

(anhydrous ethanol) in mixes with petroleum, and which is produced from agricultural

feed-stocks such as sugar cane and maize.

Everything But Arms (EBA) The Everything But Arms (EBA) Initiative eliminates EU import tariffs for numerous

goods, including agricultural products, from the least developed countries. The tariff

elimination is scheduled in four steps from 2006/07 to 2009/10.

Export credits (with official support) Government financial support, direct financing, guarantees, insurance or interest rate

support provided to foreign buyers to assist in the financing of the purchase of goods from

national exporters.

Export restitutions (refunds) EU export subsidies provided to cover the difference between internal prices and world

market prices for particular commodities.

Export subsidies Subsidies given to traders to cover the difference between internal market prices and

world market prices, such as for example the EU export restitutions. Export subsidies are now

subject to value and volume restrictions under the Uruguay Round Agreement on Agriculture.

Foot and Mouth Disease (FMD) Foot and mouth disease is a highly contagious disease, which chiefly affects

cloven-hoofed animal species (cattle, sheep, goats and pigs). Its symptoms are the

appearance of vesicles (aphthae) on the animals’ mouths (with a consequent reduction in

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appetite) and feet. It is caused by a virus which may be found in the animals’ blood, saliva

and milk. The virus is transmitted in a number of ways, via humans, insects, most meat

products, urine and faeces, feed, water or soil. Although the mortality rate in adult animals

from this disease is generally low and the disease presents no risk for humans, because it

is highly contagious, infected animals in a given country are generally put down and other

countries place an embargo on imports of live animals and fresh, chilled or frozen meat

from the country of infection; in that case, only smoked, salted or dried meat and meat

preserves may be imported from the country concerned. In addition, given the possibility

of contagion between different species of cloven-hoofed animals, when foot and mouth

disease breaks out in one species in a given country, exports of meat from all four types of

animal are suspended

G10 Members of the G10 are: Bulgaria, Chinese Taipei, Iceland, Israel, Japan, Korea

Republic, Liechtenstein, Mauritius, Norway and Switzerland.

G20 Members of the G20 are: Argentina, Bolivia, Brazil, Chile, China, Cuba, Egypt (Arab

Republic of), Guatemala, India, Indonesia, Mexico, Nigeria, Pakistan, Paraguay, Philippines,

South Africa, Tanzania, Thailand, Uruguay, Venezuela RB and Zimbabwe.

FSRI Act, 2002 Officially known as the Farm Security and Rural Investment Act of 2002. This US farm

legislation replaces the FAIR Act of 1996, covering a wide range of commodity programmes

and policies for US agriculture for the period 2002-07.

Gur, khandasari Semi-processed sugars (plantation whites) extracted from sugarcane in India.

Industrial oilseeds A category of oilseed production in the European Union for industrial use (i.e. biofuels).

Intervention purchases Purchases by the EC Commission of certain commodities to support internal market

prices.

Intervention purchase price Price at which the European Commission will purchase produce to support internal

market prices. It usually is below 100% of the intervention price, which is an annually

decided policy price.

Intervention stocks Stocks held by national intervention agencies in the European Union as a result of

intervention buying of commodities subject to market price support. Intervention stocks

may be released onto the internal markets if internal prices exceed intervention prices;

otherwise, they may be sold on the world market with the aid of export restitutions.

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Inulin Inulin syrups are extracted from chicory through a process commercially developed in

the 1980s. They usually contain 83 per cent fructose. Inulin syrup production in the

European Union is covered by the sugar regime and subject to a production quota.

Isoglucose Isoglucose is a starch-based fructose sweetener, produced by the action of glucose

isomerase enzyme on dextrose. This isomerisation process can be used to produce

glucose/fructose blends containing up to 42% fructose. Application of a further process can

raise the fructose content to 55%. Where the fructose content is 42%, isoglucose is

equivalent in sweetness to sugar. Isoglucose production in the European Union is covered

by the sugar regime and subject to a production quota.

Least squares growth rate The least-squares growth rate, r, is estimated by fitting a linear regression trend line

to the logarithmic annual values of the variable in the relevant period, as follows:

Ln(xt) = a + r * t.

Loan deficiency payments (United States) Loan deficiency payments are a type of support whereby, for wheat, feed grain, upland

cotton, rice and oilseeds, a producer may agree to forgo loan eligibility and receive an

output subsidy, the rate of payment of which is the amount by which the applicable

county’s loan rate exceeds the marketing loan repayment rate. Producers may elect to

apply for this payment during the loan availability period on a quantity of the programme

crop not exceeding their loan-eligible production. This, combined with marketing loan

gains, represent the benefits made available to US farmers when commodity prices fall

relative to loan rates.

Loan rate The commodity price at which the Commodity Credit Corporation (CCC) offers non-

recourse loans to participating farmers. The crops covered by the programme are used as

collateral for these loans. The loan rate serves as a floor price, with the effective level lying

somewhat above the announced rate, for participating farmers in the sense that they can

default on their loan and forfeit their crop to the CCC rather than sell it in the open market

at a lower price.

Luxembourg agreement A formal decision on further “CAP reform – a long-term perspective for sustainable

agriculture” was taken by the EU Council of farm ministers meeting in Luxembourg on

26 June 2003. The reform includes far-reaching amendments of current policies, including

further reductions in support prices, partly offset by direct payments and a further

decoupling of most direct payments, such as the new single farm payment from current

production. The different elements of the reform will enter into force in 2004 and 2005. A

single farm payment will enter into force in 2005. If a member state needs a transitional

period due to its specific agricultural conditions, it may apply the single farm payment

from 2007 at the latest.

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Market access Governed by provisions of the Uruguay Round Agreement on Agriculture which refer to

concessions contained in the country schedules with respect to bindings and reductions of

tariffs and to other minimum import commitments.

Marketing allotments (US sugar programme) Marketing allotments designate how much sugar can be sold by sugar millers and

processors on the US internal market and were established by the 2002 FSRI Act as a way

to guarantee the US sugar loan programme operates at no cost to the Federal Government.

Marketing Assistance Loan Programme US loan programme, in operation since 1986 and designed to provide producers of

certain crops with financial assistance when prices are low while avoiding a disadvantage

of the traditional loan programme (see loan rate), i.e. the accumulation of government

stocks that depress prices when disposed of. The programme effectively guarantees

farmers a minimum price. Farmers can obtain payments in two ways. They can sell the

crop and repay the loan at the posted county price (a USDA estimate of the local market

price) and keep the difference known as “marketing gain”. They can also obtain a payment

without taking out a loan – see loan deficiency payments.

Marketing year, oilseed meal Refers to the marketing year beginning 1 October.

Marketing year, oilseed oil Refers to the marketing year beginning 1 October.

MERCOSUR A multilateral agreement on trade, including agricultural trade between Argentina,

Brazil, Paraguay and Uruguay. The agreement was signed in 1991 and came into effect on

1 January 1995. Its main goal is to create a customs union between the four countries

by 2006.

Market Price Support (MPS) Payment Indicator of the annual monetary value of gross transfers from consumers and

taxpayers to agricultural producers arising from policy measures creating a gap between

domestic market prices and border prices of a specific agricultural commodity, measured at

the farm gate level. Conditional on the production of a specific commodity, MPS includes

the transfer to producers associated with both production for domestic use and exports,

and is measured by the price gap applied to current production. The MPS is net of financial

contributions from individual producers through producer levies on sales of the specific

commodity or penalties for not respecting regulations such as production quotas (Price

levies), and in the case of livestock production is net of the market price support on

domestically produced coarse grains and oilseeds used as animal feed (Excess feed cost).

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Methyl Tertiary Butyl Ether (MTBE) A chemical gasoline additive that can be used to boost the octane number and oxygen

content of the fuel, but can render contaminated water undrinkable.

Mid-Term Review See Luxembourg agreement on CAP reform.

Milk quota scheme A supply control measure to limit the volume of milk produced or supplied. Quantities

up to a specified quota amount benefit from full market price support. Over-quota volumes

may be penalised by a levy (as in the European Union, where the “super levy” is 115% of the

target price) or may receive a lower price. Allocations are usually fixed at individual

producer level. Other features, including arrangements for quota reallocation, differ

according to scheme.

Modulation A partial transfer of support from the first (support to agriculture) to the second pillar

(support to other rural activities) of the EU Common Agricultural Policy (CAP). With the

latest reform of the CAP, modulation was made compulsory, resulting in a gradual

reduction of payments directly to farmers with the aim of boosting rural development.

North American Free Trade Agreement (NAFTA) A trilateral agreement on trade, including agricultural trade, between Canada, Mexico

and the United States, phasing out tariffs and revising other trade rules between the three

countries over a 15-year period. The agreement was signed in December 1992 and came

into effect on 1 January 1994.

Oilseed meal Defined as rapeseed meal (canola), soyabean meal, and sunflower meal in all

countries, except in Japan where it excludes sunflower meal.

Oilseeds Defined as rapeseed (canola), soyabeans, and sunflower seed in all countries, except in

Japan where it excludes sunflower seed.

Pacific beef/pigmeat market Beef/pigmeat trade between countries in the Pacific Rim where foot and mouth

disease is not endemic.

PROCAMPO A programme of direct support to farmers in Mexico. It provides for direct payments

per hectare on a historical basis.

Producer Support Estimate (PSE) Indicator of the annual monetary value of gross transfers from consumers and

taxpayers to agricultural producers, measured at farm gate level, arising from policy

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measure, regardless of their nature, objectives or impacts on farm production or income.

The PSE measure support arising from policies targeted to agriculture relative to a situation

without such policies, i.e. when producers are subject only to general policies (including

economic, social, environmental and tax policies) of the country. The PSE is a gross notion

implying that any costs associated with those policies and incurred by individual

producers are not deducted. It is also a nominal assistance notion meaning that increased

costs associated with import duties on inputs are not deducted. But it is an indicator net of

producer contributions to help finance the policy measure (e.g. producer levies) providing a

given transfer to producers. The PSE includes implicit and explicit payments. The

percentage PSE is the ration of the PSE to the value of total gross farm receipts, measured

by the value of total production (at farm gate prices), plus budgetary support. The

nomenclature and definitions of this indicator replaced the former Producer Subsidy

Equivalent in 1999.

Purchasing Power Parity (PPP) Purchasing power parities (PPPs) are the rates of currency conversion that eliminate

the differences in price levels between countries. The PPPs are given in national currency

units per US dollar.

Recourse loan programme Programme to be implemented under the US FAIR Act of 1996 for butter, non-fat dry

milk and cheese after 1999 in which loans must be repaid with interest to processors to

assist them in the management of dairy product inventories.

Saccharin A low calorie, artificial sweetener used as a substitute for sugar mainly in beverage

preparations.

Scenario A model-generated set of market projections based on alternative assumptions than

those used in the baseline. Used to provide quantitative information on the impact of

changes in assumptions on the outlook.

Set-aside programme European Union programme for cereal, oilseed and protein crops that both requires

and allows producers to set-aside a portion of their historical base acreage from current

production. Mandatory set-aside rates for commercial producers are set at 10% until 2006.

Single Farm Payment With the 2003 CAP reform, the EU introduced a farm-based payment largely

independent of current production decisions and market developments, but based on the

level of former payments received by farmers. To facilitate land transfers, entitlements are

calculated by dividing the reference amount of payment by the number of eligible hectares

(incl. forage area) in the reference year. Farmers receiving the new SFP are obliged to keep

their land in good agricultural and environmental condition and have the flexibility to

produce any commodity on their land except fruits, vegetables and table potatoes.

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SPS Agreement WTO Agreement on Sanitary and Phyto-sanitary measures, including standards used

to protect human, animal or plant life and health.

Support price Prices fixed by government policy makers in order to determine, directly or indirectly,

domestic market or producer prices. All administered price schemes set a minimum

guaranteed support price or a target price for the commodity, which is maintained by

associated policy measures, such as quantitative restrictions on production and imports;

taxes, levies and tariffs on imports; export subsidies; and public stockholding.

Tariff-rate quota (TRQ) Resulted from the Uruguay Round Agreement on Agriculture. Certain countries agreed

to provide minimum import opportunities for products previously protected by non-tariff

barriers. This import system established a quota and a two-tier tariff regime for affected

commodities. Imports within the quota enter at a lower (in-quota) tariff rate while a higher

(out-of-quota) tariff rate is used for imports above the concessionary access level.

Uruguay Round Agreement on Agriculture (URAA) The terms of the URAA are contained in the section entitled the “Agreement on

Agriculture” of the Final Act Embodying the Results of the Uruguay Round of Multilateral

Trade Negotiations. This text contains commitments in the areas of market access, domestic

support, and export subsidies, and general provisions concerning monitoring and

continuation. In addition, each country’s schedule is an integral part of its contractual

commitment under the URAA. There is a separate agreement entitled the Agreement on

the Application of Sanitary and Phyto-sanitary Measures. This agreement seeks

establishing a multilateral framework of rules and disciplines to guide the adoption,

development and the enforcement of sanitary and phyto-sanitary measures in order to

minimise their negative effects on trade. See also Phyto-sanitary regulations and Sanitary

regulations.

Vegetable oil Defined as rapeseed oil (canola), soyabean oil, sunflower seed oil and palm oil, except

in Japan where it excludes sunflower seed oil.

Voluntary Quota Restructuring Scheme Established as part of the reform of the European Union’s Common Market Organisation

(CMO) for sugar in February 2006 to apply for four years from 1 July 2006. Under the scheme,

sugar producers receive a degressive payment for permanently surrendering sugar

production quota, in part or in entirety, over the period 2006-07 to 2009-10.

WTO World Trade Organisation created by the Uruguay Round agreement.

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OLS-Regression-[GD-Hutcheson].pdf

Hutcheson, G. D. (2011). Ordinary Least-Squares Regression. In L. Moutinho and G. D. Hutcheson, The SAGE Dictionary of Quantitative Management Research. Pages 224-228.

Ordinary Least-Squares Regression

Introduction Ordinary least-squares (OLS) regression is a generalized linear modelling technique that may be used to model a single response variable which has been recorded on at least an interval scale. The technique may be applied to single or multiple explanatory variables and also categorical explanatory variables that have been appropriately coded.

Key Features At a very basic level, the relationship between a continuous response variable (Y) and a continuous explanatory variable (X) may be represented using a line of best-fit, where Y is predicted, at least to some extent, by X. If this relationship is linear, it may be appropriately represented mathematically using the straight line equation 'Y = α + βx', as shown in Figure 1 (this line was computed using the least-squares procedure; see Ryan, 1997).

The relationship between variables Y and X is described using the equation of the line of best fit with α indicating the value of Y when X is equal to zero (also known as the intercept) and β indicating the slope of the line (also known as the regression coefficient). The regression coefficient β describes the change in Y that is associated with a unit change in X. As can be seen from Figure 1, β only provides an indication of the average expected change (the

observed data are scattered around the line), making it important to also interpret the confidence intervals for the estimate (the large sample 95% two-tailed approximation of the confidence intervals can be calculated as β ± 1.96 s.e. β).

In addition to the model parameters and confidence intervals for β, it is useful to also have an indication of how well the model fits the data. Model fit can be determined by comparing the observed scores of Y (the values of Y from the sample of data) with the expected values of Y (the values of Y predicted by the regression equation). The difference between these two values (the deviation, or residual as it is also called) provides an indication of how well the model predicts each data point. Adding up the deviances for all the data points after they have been squared (this basically removes negative deviations) provides a simple measure of the degree to which the data deviates from the model overall. The sum of all the squared residuals is known as the residual sum of squares (RSS) and provides a measure of model-fit for an OLS regression model. A poorly fitting model will deviate markedly from the data and will consequently have a relatively large RSS, whereas a good-fitting model will not deviate markedly from the data and will consequently have a relatively small RSS (a perfectly fitting model will have an RSS equal to zero, as there will be no deviation between observed and expected values of Y). It is important to understand how the RSS statistic (or the deviance as it is also known; see Agresti,1996, pages 96-97) operates as it is used to determine the significance of individual and groups of variables in a regression model. A graphical illustration of the residuals for a simple regression model is provided in Figure 2. Detailed examples of calculating deviances from residuals for null and simple regression models can be found in Hutcheson and Moutinho, 2008.

The deviance is an important statistic as it enables the contribution made by explanatory variables to the prediction of the response variable to be determined. If by adding a variable to the model, the deviance is greatly reduced, the added variable can be said to have had a large effect on the prediction of Y for that model. If, on the other hand, the deviance is not greatly reduced, the added variable can be said to have had a small effect on the prediction of Y for that model. The change in the deviance that results from the explanatory variable being added to the model is used to determine the significance of that variable's effect on the prediction of Y in that model. To assess the effect that a single explanatory variable has on the prediction of Y, one simply compares the deviance statistics before and after the variable has been added to the model. For a simple OLS regression model, the effect of the explanatory variable can be assessed by comparing the RSS statistic for the full regression model (Y = α + βx) with that for the null model (Y = α). The difference in deviance between the nested models can then be tested for significance using an F-test computed from the following equation.

F df p−df p+q ,df p+q =

RSS p−RSS p+q

 df p−df p+q   RSS p+q / df p+q 

where p represents the null model, Y = α, p+q represents the model Y = α + βx, and df are the degrees of freedom associated with the designated model. It can be seen from this equation that the F-statistic is simply based on the difference in the deviances between the two models as a fraction of the deviance of the full model, whilst taking account of the number of parameters.

In addition to the model-fit statistics, the R-square statistic is also commonly quoted and provides a measure that indicates the percentage of variation in the response variable that is `explained' by the model. R-square, which is also known as the coefficient of multiple determination, is defined as

R 2 = RSS after regression

total RSS and basically gives the percentage of the deviance in the response variable that can be accounted for by adding the explanatory variable into the model. Although R-square is widely used, it will always increase as variables are added to the model (the deviance can only go down when additional variables are added to a model). One solution to this problem is to calculate an adjusted R-square statistic (R2a) which takes into account the number of terms entered into the model and does not necessarily increase as more terms are added. Adjusted R-square can be derived using the following equation

R a 2 = R2−

k 1−R2  n−k −1

where n is the number of cases used to construct the model and k is the number of terms in the model (not including the constant).

An example of simple OLS regression A simple OLS regression model with a single explanatory variable can be illustrated using the example of predicting ice cream sales given outdoor temperature (Koteswara, 1970). The model for this relationship

(calculated using software) is

Ice cream consumption = 0.207 + 0.003 temperature.

The parameter for α (0.207) indicates the predicted consumption when temperature is equal to zero. It should be noted that although the parameter α is required to make predictions of ice cream consumption at any given temperature, the prediction of consumption at a temperature of zero might be of limited usefulness, particularly when the observed data does not include a temperature of zero in it's range (predictions should only be made within the limits of the sampled values). The parameter β indicates that for each unit increase in temperature, ice cream consumption increases by 0.003 units. The significance of the relationship between temperature and ice cream consumption can be estimated by comparing the deviance statistics for the two nested models in the table below; one that includes temperature and one that does not. This difference in deviance can be assessed for significance using the F-statistic.

Model deviance (RSS) df change in deviance

F-statistic P-value

consumption = a 0.1255 29 0.0755 42.28 <.0001

consumption = α + β temperature 0.0500 28

On the basis of this analysis, outdoor temperature would appear to be significantly related to ice cream consumption with each unit increase in temperature being associated with an increase of 0.003 units in ice cream consumption. Using these statistics it is a simple matter to also compute the R-square statistic for this model, which is 0.0755/0.1255, or 0.60. Temperature “explains” 60% of the deviance in ice cream consumption (i.e., when temperature is added to the model, the deviance in the Y variable is reduced by 60%).

OLS regression with multiple explanatory variables The OLS regression model can be extended to include multiple explanatory variables by simply adding additional variables to the equation. The form of the model is the same as above with a single response variable (Y), but this time Y is predicted by multiple explanatory variables (X1 to X3).

Y = α + β1X1 + β2X2 + β3X3

The interpretation of the parameters (α and β) from the above model is basically the same as for the simple regression model above, but the relationship cannot now be graphed on a single scatter plot. α indicates the value of Y when all vales of the explanatory variables are zero. Each β parameter indicates the average change in Y that is associated with a unit change in X, whilst controlling for the other explanatory variables in the model. Model-fit can be assessed through comparing deviance measures of nested models. For example, the effect of variable X3 on Y in the model above can be calculated by comparing the nested models

Y = α + β1X1 + β2X2 + β3X3

Y = α + β1X1 + β2X2

The change in deviance between these models indicates the effect that X3 has on the prediction of Y when the effects of X1 and X2 have been accounted for (it is, therefore, the unique effect that X3 has on Y after taking into account X1 and X2). The overall effect of all three explanatory variables on Y can be assessed by comparing the models

Y = α + β1X1 + β2X2 + β3X3

Y = α.

The significance of the change in the deviance scores can be assessed through the calculation of the F- statistic using the equation provided above (these are, however, provided as a matter of course by most software packages). As with the simple OLS regression, it is a simple matter to compute the R-square statistics.

An example of multiple OLS regression A multiple OLS regression model with three explanatory variables can be illustrated using the example from the simple regression model given above. In this example, the price of the ice cream and the average income of the neighbourhood are also entered into the model. This model is calculated as

Ice cream consumption = 0.197 – 1.044 price + 0.033 income + 0.003 temperature.

The parameter for α (0.197) indicates the predicted consumption when all explanatory variables are equal to zero. The β parameters indicate the average change in consumption that is associated with each unit increase in the explanatory variable. For example, for each unit increase in price, consumption goes down by 1.044 units. The significance of the relationship between each explanatory variable and ice cream consumption can be estimated by comparing the deviance statistics for nested models. The table below shows the significance of each of the explanatory variables (shown by the change in deviance when that variable is removed from the model) in a form typically used by software (when only one parameter is assessed, the F-statistic is equivalent to the t-statistic (F = √t) which is often quoted in statistical output).

deviance change

df F-value P-value

coefficient

price 0.002 1 F1,26= 1.567 0.222

income 0.011 1 F1,26= 7.973 0.009

temperature 0.082 1 F1,26= 60.252 <0.0001

residuals 0.035 26

Within the range of the data collected in this study, temperature and income appear to be significantly related to ice cream consumption.

Conclusion OLS regression is one of the major techniques used to analyse data and forms the basis of many other techniques (for example ANOVA and the Generalised linear models, see Rutherford, 2001). The usefulness of the technique can be greatly extended with the use of dummy variable coding to include grouped explanatory variables (see Hutcheson and Moutinho, 2008, for a discussion of the analysis of experimental designs using regression) and data transformation methods (see, for example, Fox, 2002). OLS regression is particularly powerful as it relatively easy to also check the model asumption such as linearity, constant variance and the effect of outliers using simple graphical methods (see Hutcheson and Sofroniou, 1999).

Further Reading Agresti, A. (1996). An Introduction to Categorical Data Analysis. John Wiley and Sons, Inc.

Fox, J. (2002). An R and S-Plus Companion to Applied Regression. London: Sage Publications.

Hutcheson, G. D. and Moutinho, L. (2008). Statistical Modeling for Management. Sage Publications.

Hutcheson, G. D. and Sofroniou, N. (1999). The Multivariate Social Scientist. London: Sage Publications.

Koteswara, R. K. (1970). Testing for the Independence of Regression Disturbances. Econometrica, 38:,97- 117.

Rutherford, A. (2001). Introducing ANOVA and ANCOVA: a GLM approach. London: Sage Publications.

Ryan, T. P. (1997). Modern Regression Methods. Chichester: John Wiley and Sons.

Graeme Hutcheson Manchester University

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Article Summative Analysis: A Qualitative Method for Social Science and Health Research Frances Rapport, PhD Head of the Qualitative Research Unit Professor of Qualitative Health Research Swansea University Swansea, Wales, United Kingdom © 2010 Rapport. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract In this paper the author describes a new qualitative analytic technique that she has been perfecting across a range of health research studies. She describes the summative analysis method, which is a group, collaborative analytic technique that concentrates on consensus-building activities, illustrating its use within a study of Holocaust survivor testimony that aimed to clarify how health and well-being were presented in Holocaust testimonials and what that might reveal about professional perceptions of trauma suffering. The author contextualizes the four stages of summative analysis with data from one Holocaust survivor’s health interviews. The Holocaust study is briefly described, as is the survivor’s background and experiences during the war. The author reflects on the study data and offers examples of individual and group analysis exercises to represent the method in practice. The author concludes with a consideration of the wider uses and implications of summative analysis within health and social scientific contexts.

Keywords: summative analysis, collaborative working, Holocaust data, health interviews, building consensus, qualitative methodology

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Purpose

The purpose of this paper is to describe a new qualitative research method, summative analysis, which the researcher has been developing over the past four years to effectively manage, organize, and clarify large bodies of qualitative, textual data. Summative analysis is a collaborative analytic technique that enables a wide range of researchers, academics, and scientists to come together through group analysis sessions to explore the details of textual data. It uses consensus-building activities to reveal major issues inherent in data. The method has purposefully been called summative analysis rather than collaborative analysis or any other term, in spite of its collaborative aspect, to emphasize the value placed on the form of working: Summative analysis prepares people to grasp an essentialized understanding of text.

To emphasize the versatility of the method, the author has worked with others to apply summative analysis across a variety of research studies, settings, and textual data types, such as

• focus groups with people at risk of breast cancer to consider the value of electronic decision aids for supporting their decisions for care (Rapport, Iredale, et al., 2006);

• biographies from general practitioners and community pharmacists to clarify situated practice and patient-centered approaches to work across community workspaces (Rapport, Doel & Elwyn, 2007; Rapport, Doel, & Wainwright, 2008); and

• Interviews: with gastroenterologists and nurse managers to examine innovation in endoscopy services across 40 National Health Service (NHS) trusts in England (Rapport, Jerzembek, et al., 2009).

The author begins the paper by describing the choice of summative analysis as a method and its various uses before presenting the most recent use of the method: a Holocaust study that aimed to explore ‘how notions of health and well-being were in evidence in survivor testimonies and what narrative forms they took’. The Holocaust study aimed to clarify what survivor testimonies might tell us about the ongoing health needs and expectations of people who have suffered extraordinary, traumatic events like the Holocaust. The overall objective of the study was to consider how health professionals might best support survivors and their families, through well- attuned understanding of their mental and physical health needs and through a greater understanding of the value of the health narrative in assisting with the clinical encounter.

This paper does not present the Holocaust study as an empirical study per se but, rather, in relation to the analytic method, using examples taken from the study to illustrate different stages of the method and to situate that knowledge in very practical and real terms. Having described the Holocaust study, the author describes the four stages of summative analysis using examples from the study. The paper concludes with a discussion of the wider implications of summative analysis for qualitative researchers and social scientists so that others might consider its place in their own research portfolio.

Choosing summative analysis

Qualitative researchers and social scientists now have an array of choices regarding the analysis of qualitative data and the representation of their data as results. Consequently, it is important that principled, informed, and strategic decisions are made, in line with the specific purposes of the research in question. Qualitative researchers must also reflect on the methodological issues related to the construction of each kind of data representation. For example, constructing a

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standard realist tale from data can often be effective, as Van Maanen (1998) has noted, if one wishes to take account of experiential authority, the research subject’s point of view (in the form of closely edited quotations), and the interpretive omnipotence of the researcher. Sparkes (2002) has commented that when well crafted, realist tales can provide useful, compelling, detailed, and complex depictions of social worlds. Summative analysis cannot offer interpreter omnipotence, nor can it enable the researcher to put to the background the ethnographer’s intent, whilst giving the author unchallenged authority in writing (Sparkes, 2002; Van Maanen, 1998). What it can offer, moving away from this notion of being overly deterministic with a text, are opportunities for analytic creativity.

As described earlier, the method is versatile and has been used across a range of studies. However, it should be noted that summative analysis is particularly useful for texts that are complex or cover sensitive topic areas. It is supportive of those studies where method choices are not readily made and where outcomes are not easily achieved. It works best with diverse data, whether snippets of text, or long, meandering tracts. It enables data that do not fit a mold to be considered, offering more flexibility of working, with sensitive support from others. It has the potential to generate a range of insights and reflections and can provide a careful approach to emotive materials that some researchers might be uncomfortable working with. It enables researcher to be aware of more nuanced and ambiguous aspects of text, where tales are not given up easily. It preserves the quality of the speaker’s voice irrespective of the mode of presentation, and it is fully involving of experts from, for example, across both physical and mental health care backgrounds, to consider issues beyond the researcher’s own knowledge, such as those relating to trauma presentation. It is inclusive of a wide variety of others’ views, revealed through both group-working and consensus-building activities, and as a consequence, one of its greatest strengths is its potential to enable an egalitarian approach: everyone’s view matters; all members are included. This has a strong leveling effect on working practice.

Summative analysis, when appropriately facilitated, offers the opportunity to embrace the research subject while involving teams of coresearchers (the term used in this paper though they can also be described elsewhere as research participants), who can join in with the researcher in the analysis process and who can help in the consideration of data representation. The term coresearcher has been chosen to emphasize the ability of others to work closely with the researcher in analyzing data. Whereas the researcher has overall accountability for the study, the coresearchers take on a commitment to be fully involved in all analysis sessions. By so doing, coresearchers must be aware of the importance of the collaborative aspect of the method and of developing a negotiated understanding of a text.

The group of coresearchers involved in summative analysis work can be selected for a variety of reasons: their homogeneity as a group who are either familiar with the topic area in question or with the people in question, their impartiality to the subject area or people in question, their expertise or insider knowledge, or their lack of expertise. The choice of coresearchers, as with other group working methods, is dependent on the individual study, the needs of the researcher, the research question, or study aims. In the case of this work, with stories taken from Holocaust survivors, six to nine people took part in 12 two-hour workshops, (some groups returned on a further two occasions to complete all three group-working sessions). Administrative processes related to summative analysis are not onerous and involve contacting coresearchers to organize the workshop sessions, providing them with the raw data and the group’s work in progress, and ongoing liaison across sessions. Coresearchers were chosen in this study to purposively cover as wide a range of research backgrounds and academic levels as possible, to encourage a wide spread of research experiences (they were all involved in academic or health research but had a breadth of knowledge, working across disciplinary groups, and spanning early-stage researchers

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to senior academics). Finally, the researcher wanted an approach that could accommodate the complexity of this kind of data and the “drama” that might be contained within it so that the strong emotional impact of the issues revealed would be reflected, rather than diminished or flattened by the outputs.

Summative analysis: the method

All analysis, including qualitative analysis has a reductive element to it (Miles & Huberman, 1994), and summative analysis is no exception. However, within the reductive process, as summative analysis undertakes a search for the essential elements of a text, it continues to consider the importance of the text as a whole and its impact on speaker and audience.

Essential text, for summative analysis purposes, refers to those elements of the text that offer a point of entry into the meaning of the whole text, and that give the text its import. These must be considered in their own right and can be discussed and clarified through group-working techniques. The essential elements are those without which, it could be argued, there could be no fully coherent understanding of the text. Essential text does not purport to an idea of an essential “truth” inhabiting the text but, rather, a range of truths, which can be negotiated by the group in question as those clear aspects of narrative that hold the key to understanding. Essential text, for summative analysis purposes, can include data regarding context, personal experience, emotional content, or the complex nature of human thought. In the case of a Holocaust survivor’s transcript, group members could seek to understand, first individually and then as a group, how the storyteller offers key ideas, descriptions of behaviors, conversations, and connections that appear to be integral pathways through research conversations with the researcher. These suggest a particular point of view, and when considering what they might be, the researcher and coresearchers take into account the way events are recounted within or without chronological order, and the way stories can provide vital insights into an experience like the Holocaust, as lived.

Coming to recognize the critical moments, also known as eureka moments or precious vignettes, that can be discovered in a story allow coresearchers to appreciate those elements that give a text its unique resonance. The essential qualities of a text can be found in all textual forms, be they interview transcripts, biographic accounts, or focus group transcripts, with marginalized or mainstream groups. It is up to the coresearchers, supported by the study researcher, to discover this for themselves during Stages 2 to 4 of the method (see below), having first undergone individual analytic work during Stage 1 (see below).

Undertaking summative analysis with a similar section of text but with more than one group of coresearchers, where similar outcomes are apparent, can help validate the group-working approach. In the case of the Holocaust study (see below) an excerpt of text from one survivor’s health interview was considered across all the workshops, with each coresearcher group providing a similar response to the text. This was recognized through the groups’ summative maps (see Stage 2 of the method below) and led to a clear understanding of the validity of this approach. Summative analysts also consider the relationship between the whole and parts of any text, and between the speaker, listener, and audience. With these issues apparent, the researcher can develop greater clarity regarding the key notions portrayed. This is an in-depth exercise, and in a sense these aspects of the method, particularly the search for a relationship between parts of the text and the whole text, is not dissimilar to the work of hermeneutic phenomenology.

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Hermeneutic phenomenologists consider the interplay between part and whole; reading parts of the text and testing out what might be contained within the whole of the text, before returning to the specific and the particular (Jasper, 2004; Thompson, 1981). They also strive to achieve an “essential” understanding of the phenomenon under review. However, in hermeneutic phenomenology the intention is to understand how objects, events, or situations are “given up” to consciousness to clarify their primal impression and the essential quality of knowing (Husserl, 1931), whereas in summative analysis it is not the experience of knowing that is so intriguing, nor how objects or experiences present themselves to consciousness. Rather, it is the meanings people give to those experiences that are sought and the way meaning is derived through the narrative presentation. In this respect, summative analysts try to find out more about people’s lived experiences by examining the meanings that they themselves give to their lives, meanings that are inherent in their ordinary language. This depends on consideration of the storytellers’ words and their ability to open up the story as well as the overall uniqueness of the story. Max van Manen (1990), through his “sententious approach” (the term van Manen uses), encourages us to understand both sections of text and the whole text by examining: “what it is that renders this or that particular experience its special significance . . . what constitutes the nature of this lived experience” (p. 32). However, in hermeneutic phenomenology this process is seen in terms of a continuous spiral where hermeneutic knowledge is never complete, but open for new understandings (The Hermeneutic Circle, Gadamer, 1977), whereas in summative analysis it is a journey toward knowing.

For the purpose of this paper, it is worth clarifying that the author considers the interviews that took place with one Holocaust survivor,1 Anka (the protagonist of this piece of work), as text. It has been argued that talk and text are two very different things and that one is not commensurate with the other, that they are produced according to different norms, for different purposes, and that some aspects of the performative experience are lost in the translation (Hammersley & Atkinson, 2001; Silverman, 2001). The author would agree that transcribing speech inevitably leads to further layers of interpretation and loss of some performative elements of telling, one stage further removed from the original. However, for analytic purposes there will always be some interpretation involved at whatever level, whether we are working directly from a tape recording of the original event, from notes, with speech itself or with a transcribed interview.

Summative analysis handles both concise and extensive texts. It also enables researchers to work with widely differing population groups; minority groups, disenfranchised groups, and dispersed populations as well as tightly knit communities. The method is flexible enough to be considered inclusive of a range of narratives, and encourages groups of coresearchers to tackle texts that as individuals they might consider too complex, or emotionally charged. Consequently, summative analysis lends itself to an accommodation of difference, difficulty, and dissimilation as opposed to similarity, ease of handing, and togetherness, and is particularly useful for researchers dealing with others’ trauma, sense of dislocation, or deep distress. It retains the richness and variety of different people’s views and voices while looking to group opinion to direct the analysis. It involves group activities that are highly participatory and works with knowledge that is cumulative, whereby coresearchers come to “know” the data intimately, by participating in in- depth discussion and debate. Understanding develops across meetings, and a commitment of two to three group meetings is usually necessary. Coresearchers support the main study researcher, who may also be the facilitator of the analysis sessions, lending their voice to analysis and becoming closely involved with the data under study. The main study researcher is ultimately accountable for the work and is expected to have a thorough understanding of all aspects of the study and its workings. Coresearchers may join an analysis group of six to nine people voluntarily in an unpaid role, for example, if they are members of a higher education institution where knowledge acquisition about methods development is expected and seen as part of their personal

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development, or they may be paid, if, for example, they are joining the analysis group from an external organization or taking part as a result of topic area expertise. The makeup of the group and financial reimbursement are dependent on the demands of each individual study, and it is up to the main study researcher to organize.

Coresearchers for this study, were chosen purposively from across a wide group of researchers, academics health professionals, and other professionals in one higher education institution according to people’s interests in learning about the method, their desire to learn more about the impact of traumatic events on health and well-being, their ability to take time to get involved with this particular body of work, their decision to dedicate three sessions to clarify the relationship between the analytic method and raw material. Coresearchers do not need to be from the same disciplinary background, nor must they have a similar, intimate understanding of the topic area. They do not necessarily have to be selected for their homogeneity as a research group, though they may be chosen for that very reason. Rather, the criteria for the group of analysts, as with many other qualitative, group-working methods, are sufficient interest in: the subject area, the research methodology and the group-working process, that lends itself to consensus-building. Summative analysis group-working meetings take between 1 and 2 hours, with the time period dependent on the coresearchers’ ability to voice their views, consider others’ views, discuss the richness of data, and recognize unique qualities inherent in the voice of the storyteller, an integral part of each group session. See Table 1 for key aspects of summative analysis.

Summative analysis was used most recently with a study of Holocaust survivors, which will now be presented, followed by an explanation of the four steps of the method. The Holocaust study is described first, as the method uses examples from this study to explain the approach in more detail. The study was granted ethical approval, in accordance with the ethical principles of the researcher’s higher education institution in the United Kingdom.

The Holocaust study

Holocaust survivors who consented to take part in the study agreed to tell the researcher their life stories, including life history and health history, work closely with the researcher throughout the course of the study, consider outputs from data analysis, and discuss these with the researcher during study output development. Plans for analysis of large quantities of textual data included

Table 1. Key aspects of summative analysis

Summative Analysis Consensus of opinion through group-working activities Participation from people with varying degrees of understanding and experience of qualitative methods Multidisciplinary group working to ensure wide-ranging and rich life experiences are brought to bear Analytic consideration of large or small sections of text including texts covering complex subject matter Wide range of potential research subjects providing narrative data, and an especially useful method for working with

sensitive or difficult data from the disenfranchised, peripheral, dispersed, or disempowered Involves consideration of content and context as well as the voice of speaker and her presentational style Stepped approach to analysis, across two to three workshop sessions, building understanding iteratively and over time Development of individual and group paragraphs describing and defining key aspects of the text’s content for group

discussion Individual and group work to clarify the fundamental meaning of a text and its properties Discloses a more nuanced and complex understanding than could be achieved using individualistic, self-contained

analytic techniques Begins with succinct presentation and work toward an elaboration of understanding (as opposed to other qualitative

methods)

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applying a range of techniques to different elements of the testimonies. Life histories, for example, were analyzed using ethnographic poetic methods, leading to ethnographic poetic representations and performative social science presentations (see, for example, Rapport & Sparkes, 2009). Health interviews were analyzed using summative analysis, which is described in detail in this paper. Since this work was conducted, 11 summative analysis workshops have been given across the United States and United Kingdom using excerpts from one of Anka’s health interview transcripts (see details in this article) to present the four phases of the method to mixed disciplinary groups of health researchers, social scientists, and methodologists and to ensure that the method was fully operational before using it across a range of other research studies. In this paper the researcher explicates the four methodological stages and the results of workshop outputs with the Holocaust data to enable others to use the method.

The origins of the Holocaust study were multiple research conversations that took place between a qualitative health researcher (FR) and three Holocaust survivors, the only three remaining survivors now resident in southeast Wales, United Kingdom. All three were female, in their 80s and early 90s, and, having spent time in Auschwitz-Birkenau concentration camp, all had left their Eastern European countries of origin to start a new life in the West.

All research coresearchers agreed to take part in this study and signed a consent form to that effect before any data were collected. Coresearchers were made aware of the process of data collection, were aware that others besides the researcher would be included in the group analysis work, and that coresearchers would be privy to elements of the stories. Coresearchers were also told that their involvement would be sought to view study outputs and comment on specific aspects of the work, to ensure their full agreement with the continued dialogue with the researcher. As Frank (2005) has remarked, the dialogical in research is much more than just listening and recording others’ life stories, it is neither static nor external to the process of telling, but deeply involving as an act of engagement:

The researcher, by specific questions, and even by his or her observing presence, instigates self-reflections that will lead the respondent not merely to report his or her life, but to change that life. (p. 968)

However, it is important to note that Anka and the other people who contributed their Holocaust narratives were not involved in the summative analysis work or in condoning or discouraging those activities, nor were they expected to take on the role of researcher or coresearcher. Although dialogical relationships are integral to the development of respect and understanding between researcher and subject (Frank, 2005), those who contribute stories are not likely to have the research experience or interest necessary to critique the complex research processes. However, the dialogical relationship that develops between storyteller and researcher allows the researcher to share insights into how the work is developing or to present the storyteller with the outcome of the work. In the case of the summative analysis work with Anka, she was informed during various stage of the process of how the study was developing, and she reviewed the group paragraph from Stage 3, in the same way that she read the ethnographic poetic representations produced from other analyses (Rapport, 2008). She was grateful at being included in these further stages of the work and delighted to describe to the researcher her recognition of the close relationship between the group-work and her actual story. Although researchers should not feel obliged to change the process or outcomes of their work, they bear a certain responsibility to their storytellers to share aspects of the work if the storyteller so wishes and, by so doing, respecting the shared relationship of taking and giving back in return.

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The study complied with the regulations of the higher education institution’s ethics committee where the researcher is based, which approved the study. Pseudonyms were not appointed, whereas first names were in agreement with research participants’ wishes. This was the result of participants’ dismay at the idea of their names not being linked to their stories. Indeed, they were adamant that as these were their stories, pseudonyms would detract from their impact and that including first names should be seen as a major element of the telling.

Anka, the protagonist of this paper, was one of the survivors. She presented the researcher with a rich oral testimony during lengthy conversations in the familiar surroundings of her own home. The research conversations lasted approximately 10 hours in total, generating 250 pages of transcript in which Anka disclosed a world of degradation and loss. They also provided insights into the impact of the Holocaust on her sense of self and her perception of health, illness and well-being. The summative analysis work that follows concentrates, however, on a section of text taken from one of her health interviews

Anka’s story

This section presents an abbreviated version of Anka’s life history (as she recounted her life story, from beginning to end). It derives from a number of life history interviews that took place between Anka and the researcher over the course of research conversations. As the life history took many hours to tell, each interview had as its focus a particular part of her life (childhood and adolescence, Holocaust camp years, postwar life, present day), and this section refers to Anka’s life as she told it, whereas her first-person account has been changed to a third-person account, in view of the fact that the researcher compiled this presentation from Anka’s words. This abbreviated life history is included here to enable the reader to understand something of the context within which her biographical journey took place. The summative analysis work that follows, however, concentrates on one aspect of her life history: her health and well-being, including mental well-being, with particular emphasis on her years in the camps.

The health interviews, from which information about her health and well-being were derived, formed an important element of her story, and she described her views on her own health and the health of those around her, including close family and friends, before, during, and after the camp experience. Anka’s health and well-being were a particularly significant aspect of her story for this researcher, whose study interest centered on a greater understanding of the relationship between health and extraordinary life events. Consequently, the summative analysis work in this paper involves an excerpt from one of the health interviews, to continue this theme. However summative analysis can be used for all aspects of a story and is flexible enough to be applied to a range of narrative presentations.

Born in Czechoslovakia at the time of the Austro-Hungarian Empire, Anka was brought up in a Jewish community within a wealthy, middle-class family. Although Jewish, her family believed that one’s religious leanings were unimportant, and her father was considered “a freethinker.” She was schooled in Czechoslovakia and was halfway through a law degree when Hitler closed the Czech universities and her life changed irrevocably. Following her marriage on May 15, 1940, to a German-Jewish refugee, Anka underwent voluntary relocation to follow her husband’s transport to Terezin concentration camp. At the beginning, it was presented by the Nazi regime as a sort of work camp for the able bodied and the young, but in time the full horrors of the camp became clear. When parents, siblings, and other family members joined her, they were less able to cope with the rigors of daily life. Three and a half years later, having lost her first child at 2 months to pneumonia, Anka was moved again, this time east to Auschwitz-

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Birkenau. Terezin was being evacuated, and only the bare minimum was to be left behind. Anka was later to realize that if her baby had not died, she would not have survived Terezin. As it was, her husband and family were never seen again.

In the early stages of a second pregnancy, Anka arrived in Auschwitz-Birkenau under the rule of Mengele. She lived in huge barracks with hundreds of other Jews, naked, heads shaven, and starving. There was a dreadful sense to the place: the chimneys, the smell, and nobody knowing what was happening or what was to be expected. Ten days from the day of arrival, having undergone innumerable roll calls, clothed in rags and with wooden clogs for shoes, she was moved again, this time west, to barracks in Freiberg, 10 miles from Dresden. There she was to make the V1 bomb, the Doodle Bug. In early April 1945, when her pregnancy was clearly visible, Anka was taken on a 3-week open-carriage train journey to Mauthausen concentration camp. Upon arrival she was placed upon a cart to ascend the hill to the camp. On the way back down, surrounded by people who had either died or were dying of typhoid fever and with millions of lice crawling around her, her second child was born. She named her Eva. It was three days before the end of the war, April 29, 1945.

Eva weighed three pounds, Anka weighed five stone; with no baby clothes to hand, the child was wrapped in paper found in the cart where Eva was born. Three days later, when the Americans arrived, declaring an end to the war and armed with chocolate bars, she and the baby went to Prague to live with her cousin. During the 3-year period she spent with Eva in Prague, Anka met her second husband, and following their marriage the family emigrated to the United Kingdom, where they have lived ever since.

Presentational effect

As the excerpts below and in Table 1 indicate, Anka’s oral testimony unfolded as life history (recounting her life story, from beginning to end), personal memoir (creating her own autobiography, recounting specific moments, events, people, circumstances that made up her lived experience of the Holocaust) (Muncey 2010), and social history (contextualizing her life story and autobiographical account through social and historical contexts), through a tapestry of complex political, contextual, and social threads. From early childhood to young adulthood in Czechoslovakia, from time spent under Nazi rule to freedom in the West, from a close and loving secular Jewish family to total separation and disjuncture in the camps, Anka’s story is remarkable. Sixty years later she still recalls the Holocaust in sharp focus, using a slow and thoughtful presentation style. In the same measured way, Anka describes her family and friends, the lengthy roll calls in the camps, and the attempts to stave off the cold, hunger, and despair that stemmed from lack of food, degrading and inhumane conditions, and the bitter Eastern European winter.

Through the telling, a relationship developed between Anka and the researcher, bound together by the enduring story. Anka’s voice predominated with few interruptions from the researcher. Anka’s speech lacked emotion, which was all the more shocking for the occasional fractured pause. However, Anka kept in close contact with the researcher following the conversations and was keen to welcome her into her home to talk about life, family, and the details of her story long after the initial research conversations were complete. The examples below emphasize Anka’s tempered style:

At first you think that you have got mad . . . We got into the barracks and one of my friends whose parents were in the same transport said: “when will I see my parents?” . . . That affected them and they started screaming, “you idiots they are in

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the chimney by now.” Well if somebody tells you that you think they are mad. And they thought we were mad. But you quickly come to realise that they were right and we were wrong. (Transcript 3, Section 146)

One girl was a mischling, meaning half Jewish half German. her father was a German, she was thought of being Jewish but she wasn’t, not quite. Her name was Hannalore something, terribly German, and she was a singer, a sort of popular singer. I can see her, Hannalore, whatever her name was, and she sang one of the popular songs of that time. That was apocalyptic, because you felt like listening to a song like you felt like going to the gas chamber. (Transcript 3, Section 467)

Auschwitz was twenty-four hours sort of thing so you were afraid all the time. But the one thing when we arrived in Mauthausen which is a beautiful village on the Danube and the sun was shining, and the greenery was starting to come out, it was the 29th April and I was sitting on those carts at the beginning of a baby coming and I looked at the beautiful countryside and the sunshine and the greenery and until today I remember how much I liked it. And I honestly had other worries other than looking at the sight seeing part of it. I really couldn’t explain to myself, “why did I look at the countryside and the Danube underneath and the Melke Abbey not far away?” (Transcript 3, Section 413)

The researcher joined in the research conversations to ask specific questions, seek clarification or offer support. This is illustrated in the excerpt below, where, with dark humor, Anka recalls the place of music in the camps:

Author: It was in the barracks, this so called entertainment took place? Anka: Yes, but the reason for it is totally . . . Author: Is that similar to playing in the orchestra as people were being taken . . .? Anka: That is a different story, the orchestras. The orchestras played every day,

when people went out to work and then when they came back. Author: They tried to keep people calm? Anka: Pretending that people go to work and will come back. (Transcript 3, Sections

480-485)

Table 2 offers an example of Anka’s health interview, indicating both Anka’s style of speech and the content of the research conversations. Unlike the life history, which provided an overall picture of life events, the health interviews were designed to enable the researcher to home in on aspects of her life as they related to her physical, emotional, and mental health and well-being. These interviews, which took place after the life history was told, concentrated on Anka’s responses to questions about her ongoing health and well-being, such as,

• What did good health mean to you during your time in the camps and what does good health mean to you now?

• How was your health and well-being affected by the camp experience?

• Were there ways in which your physical and mental health as a child and young woman impacted on your camp experience?

• How would you describe your current state of physical and mental health?

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These questions were designed to ascertain the relationship between health, well-being and the trauma of the Holocaust, to understand more clearly the continuum of health and well-being during the course of a lifetime that has been influenced by such an extraordinary event and to clarify the health narrative or interrupted health narrative as it relates to Holocaust events.

Undertaking summative analysis: Application of the process to the Holocaust text

Stage 1. Summative analysis begins with a written paragraph produced by each coresearcher (approximately 25 lines of text) that is written in response to the raw material provided by the research subject’s transcribed testimonial. The aim in Stage 1 is twofold: to introduce coresearchers to the text and to enable them to begin to consider what an “essentiality of text” might look like. Stage 1 is perhaps the most challenging aspect of the whole process. Coresearchers have to think not only about what to write in their brief paragraph but how to write in a rigorous and crystallized fashion, keeping the relationship between speaker and audience and between what might be contained in the whole text and what is contained in its part(s), in mind. This enables participants to capture the fundamental elements of an experience as a first attempt at understanding the work they are considering (see Table 2 for some examples) .

Coresearchers read and reread the raw material before undertaking any writing to familiarize themselves with the data and gain confidence in their response. Initial forays into the data can take some time, and the study brief is left purposefully broad to encourage limited researcher influence (Rose, 2000; Author’s name et al., 2008). Consequently, a number of stylistic and

Table 2. Excerpt from Anka’s health interview I never remember being ill. I don’t remember ever having any problem with my health, so I never thought of it. And I survived three and a half years in the camps, and the birth of two children. Shortly after we came back I caught a streptococcal infection in my leg. Apparently it happens if there is a small cut or abrasion: “Erysipelas, an acute infectious disease due to a specific streptococcus and characterized by diffusely spreading deep red inflammation of the skin or mucus membranes.” I seem to have been blessed with a very optimistic state of mind. That helps enormously. You can push things. I think about tomorrow, which I have done perhaps not all my life but through the three and a half years in the camps, and I carry on with it now. The opposite would be to suffer from things which perhaps are imaginary and not to count your blessings. But I think this “count my blessings” really would summarise my point of view. Everybody else was bent and I wasn’t. I am not going to give in. This is totally irrational because I really didn’t have any reason to think that nothing will happen to me, but, I knew that I will come back unscathed. I had a most happy childhood and young girlhood and my first one and a half years of being married, when we were still in Prague . . . Going to school was a pleasure. I loved that—elementary school, high school, university . . . I couldn’t have had a nicer childhood or teenage life, and the first two years of university was heaven. There were no problems. The physical was always there, so it was accepted as a norm; and the mental, well one had to face so many decisions that you couldn’t do anything about it. So many things happened and you still stood there and carried on. I always looked on the bright side, I always looked up and not down. My physical saved my life. When you arrived in Auschwitz on that ramp: “left right, left right.” Dr Mengele took one look at me and put me on the side that are fit, and my pregnancy didn’t show because that would have put me definitely on the other side. But then I went through I don’t know how many of these selections, running naked through Auschwitz. We are stark naked, sort of parading in front of him. I don’t think they looked at us as human beings: “is she healthy enough to work?” That was the only criterion. I was glad that we went on this side and not the other side. If it had happened that I went on this side which was life and my friend went to the other side, I was glad that it isn’t me and that it was somebody else. I am describing how low one can get. It’s only, “me, me, me,” and I am not a very selfish person, but if it’s life and death you choose life. I don’t know if you can understand because you have never been in a situation like that. You don’t do anything for it or against it, but you are relieved that you go this way and everybody else goes that way. It has nothing to do with the other people, but you have been chosen to live.”

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representational forms can result from Stage 1. Previous coresearchers have presented their short writing in a number of different ways, such as in the first person or in the voice of the storyteller, where the story is presented in its entirety but in a highly abbreviated fashion; in the third person; or more formally, with writing that touches on only certain sections of text while leaving others out entirely. Some people have summarized the issues in question, often doing so by placing them within an entirely new story of their own making. The researcher has also encountered responses in bullet-point form, people making comments using a memo of contents, or offering verbatim quotations to draw a pathway through the text. Whereas for some, their own words and thoughts are paramount, for others it is the storyteller’s words and thoughts that matter. In some cases, coresearchers are highly analytical, with few noticeable descriptors. In others, coresearchers depend solely on descriptive phrases, and in still others, coresearchers are interpretive, deciding what the storyteller was trying to put across and the meanings belying the text.

How to present the integral elements of text is up to the individual, although the researcher must carefully compile an overview of group meaning and presentational style in readiness for Stage 2. What should be apparent at the end of Stage 1 is that coresearcher presentations encapsulate the key issues within the text and, similarly, that the superfluous issues—those not included in individuals’ paragraphs but nevertheless present within the original raw material—lie outside the text’s essentiality. The superfluous issues are not without merit, but nevertheless there are some aspects of the raw material that are extraneous to the text’s irreducible essence.

During a 10-month period of work in the United States, between January and October 2009, seven groups of coresearchers comprising health practitioners, other professionals, researchers, and academics took part in workshops using summative analysis to clarify how notions of health, illness, and well-being were presented within a section of narrative from Anka’s health interview. The purpose of the workshops was both to explore narrative effect and to examine whether, and if so how, the Holocaust affected Anka’s health and well-being. Coresearchers displayed mixed levels of qualitative expertise, from little to extensive expertise. Coresearchers were also widely multidisciplinary, from lawyers and social scientists, to health care researchers, narrative specialists, psychiatrists, psychologists, and social workers. Coresearchers were involved on a voluntary basis, having either expressed an interest in the method or in the Holocaust data. Fifty coresearchers took part from across higher education institutions and professional bodies, including Harvard University, Brandeis University, Boston College, Boston University, the University of Massachusetts, and the University of Texas Medical Branch. Tables 2 to 4 offer examples of the different stages of summative analysis drawn from the U.S. workshops, with Table 2 illustrating three very different presentational styles from Stage 1.

Stage 2. On completion of Stage 1, the researcher re-stories the data in terms of the group’s findings and presentational styles, in readiness for Stage 2. This involves a careful crafting of data outputs, taking account of writing style, issues raised and coresearcher understanding. The researcher must be aware of the frequency with which notions are presented as this influences the way key points are grouped and listed under developing topic-oriented headings. In addition, the researcher must consider the relationship between concepts within and across paragraphs, similarities and differences between emergent themes, and resonance across and between groups. Thematic or topic-oriented headings are classified according to those containing issues that all coresearchers mentioned, those containing issues that most coresearchers mentioned, and those containing issues that only a few coresearchers mentioned (the outlier positions), a kind of directed and hierarchical map. However, although outlier positions might be perceived as at the end point of this hierarchy, their presence in the map is of no less significance, and it is up to the

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Table 3. Examples of coresearcher responses to Anka’s health interview Coresearcher A Anka survived three and a half years in three different concentration camps. Her account uses repetition, shifting pronouns, changing verb tenses, and quoted speech to try to convey an experience that is almost unbelievable and almost not understandable to anyone who was not there (or who has not been in a situation like that). Telling the tale is a way for her to ensure that the truth of the experience will be carried on even after all the survivors and their children have died, from the vantage point of someone who is almost 90 years old and anticipating her death. Anka divides the experience into a binary, starting with the first line: “I never remember being ill.” This is repeated throughout the account. But her account is filled with illnesses: streptococcal infection, scarlet fever, whooping cough, mental deprivation, malnutrition (starvation), premature labor and stillbirth. She disconnects her body and her mind: She had to be healthy to survive (life and death you choose; there was no room for anybody ill) and to make the cut (sent to the left or to the right), and to leave her body, use her mind to make decisions including the decision not to be ill (mind over matter; you could accept it or you go mad). She disconnects herself from others (It’s only me, me, me) and mourns what she can know because otherwise the experience is too big even for her to believe. The quotations provide a kind of inner dialogue about how she survived and what she sacrificed to survive, no past, no future, just the present and the need to know what she wants and doesn’t want and to will it into existence: “Is she healthy enough to work?” “Will I get through this time?” “Tomorrow, I will think about it tomorrow.” “This is it.” “You do some programme.” “Where was he?” Coresearcher B “I never remember being ill . . . And I survived three and a half years in the camps” “And the birth of two children” “I seem to have been blessed with a very optimistic state of mind” “That helps enormously . . . I think about tomorrow” “This ‘count my blessings’ really would summarize my point of view” “Everybody else was bent but I wasn’t . . . I always looked on the bright side” “I was glad that it isn’t me and that it was somebody else” “I am describing how low one can get . . . It’s only, ‘me, me, me’” “If it’s life or death you choose life . . . It has nothing to do with the other people” “But you have been chosen to live . . .” “The more you were in a camp the more you knew . . . how to live” “There was hunger, real hunger there . . . Only the pioneers knew where to turn” “You got frightened that it could happen any day . . . One had to cope with whatever came” “I think one had hope and it proved right . . . The fear was overpowering” “In these circumstances you have to make your choice, what’s important” “We didn’t look and we didn’t care . . . Because you get so selfish” “Even with those three and a half years which were anything but happy or normal” “I am here to tell the tale” “In the next two generations nobody will know what is really true” “The story should be believed . . . That’s what I try to say” Coresearcher C The essential quality of this text is the resilience of the human spirit/psyche; the human capacity to endure trauma; and not only to survive it but to thrive in its aftermath. Anka’s narrative reveals key values essential to appropriate coping and integration of traumatic experience necessary for health and well-being: the ability to externalize or defer negative information/experience [boundary management] [“I can push this thing off”]; the ability to maintain a positive attitude [gratitude, hope]; the ability to maintain one’s sensory integration abilities enough to feed/maintain that positive orientation [the purely sensory appreciation of the beauty upon arrival to Mauthausen]; the ability to focus on the essential tasks/choices, without distraction from essentially (in the context) superfluous input [life/death vs empathy, compassion or concern for others/parents]; the converse ability to retain/regain a porous nature, essential for meaningful human life [vs hermetically sealed, dissociated, psychopathic aftermaths]; the ability to adapt to the milieu [blend]; and the lasting ability to weave meaning from the experience in an manner which permits all of the above [reasons to remember]. Though she brought some of these qualities to her experience of trauma [“I don’t talk about my parents b/c they wouldn’t have burdened me with that”], increasing her chance of survival/thriving, many of these emotional/physical survival skills were forged as a result of her experience.

group to consider the positioning of items within the outlier group, rearranging them as necessary, subsuming them within other topic headings, or changing their position to enhance understanding. Indeed, it is often the outlier positions that hold the key to understanding other elements of the

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map as a result of their relationship to other topic headings and themes. The researcher must judge how best to group, classify, and clarify the work and, under working thematic headings, how to return to the group with a synthesis of their work that is both understandable and adaptive to further activity. This aspect of analysis is strongly dependent on the study aims and objectives and the desired outputs, which may differ on each occasion and with each new research study. For example, the aims of group work may be to develop policy or educational materials, to arrive at a specific endpoint for an empirical study or to consider theoretical aspects of the study. In this case, Holocaust workshops aimed to clarify whether, and if so how, health and well-being was present in the context of the survivor’s story. The researcher imagined that the effect of Anka’s time in the camps, especially her experiences of losing a child, hiding a second pregnancy, and then giving birth on an open, typhoid-infested coal wagon, might have had a strong influence on her health and her future health expectations.

During Stage 2it is important that the researcher/ facilitator avoid interpreting outputs, leaving that to the latter stages of analysis, but nevertheless the facilitator should keep in mind the interpretive stance of coresearchers thus far. Table 3 provides a detail from Stage 2 analysis from one of the U.S. workshops.

Stage 3. Stage 3 is the development of a single long, group paragraph approximately 30 to 35 lines in length that summarizes the researcher’s synthesis of the coresearchers’ work in relation to the ongoing, iterative account (see Table 4, for example). Stage 3 is detailed and deliberate and can take a number of meetings to achieve depending on the degree to which consensus is reached and the complexity of the issues involved. During Stage 3, all coresearchers work together to create a final paragraph, discuss each other’s work and the work of the facilitator, and outline what the group paragraph should contain. To achieve this, coresearchers refine emergent themes, reword or confirm thematic or topic-oriented headings, and consider the order of key concepts and their concomitant categories. This process continues until all members are in agreement that the essential aspects of text have not been lost and, furthermore, that they have been captured succinctly and convincingly. Not only must coresearchers come to know each other’s views, they must also be able to make careful choices regarding style and presentation of the group’s work so that wider audiences can recognize the evidence within. At its best, Stage 3 can be a highly effective, positive collaboration.

The researcher/facilitator, mindful of each coresearcher’s part in the discussion, should encourage people to be equally and effectively involved through open debate so that differences of opinion can be confidently aired and debated. In addition, coresearchers are encouraged to acquire the skills necessary to hone down qualitative data to a final paragraph. Of the themes pertaining to issues that all coresearchers raise, the most vital are considered in detail, whereas outlier themes might be considered more briefly unless they are repositioned, reordered, or removed entirely, according to the final “order of essentiality.” The researcher/facilitator’s role is to lead, probe, and suggest but not dictate to the group the content of the final paragraph, which should be a mixture of storied text and verbatim quotation. Consequently, the final paragraph is more than a sum of its parts. Rather than being seen as the researcher’s output alone, it pays homage to the collaborative strength of the method and the staged process. It indicates a depth of working and thought that has gone into the re-presentation of a participant’s story through both personal awareness and the collective voice.

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Table 4. Detail from Stage 2 analysis Everyone mentioned: Coping mechanisms Resourcefulness: finding ways to survive to “carry on” Postponement: “I will think about it tomorrow” Denial: denying her feelings this is in relation to the scope of the trauma Integrity v. despair: Eriksonian developmental stage (own health doesn’t alarm her) Muted emotional response: to brutality, loss and privation Detached descriptions: (detaching herself from her own feelings and experiences) Accepting: situation’s horror in order to focus energy on survival Shutting down: thoughts and believing in her own survival (selfishness, introspection) Limiting herself: looking out for herself and her camp friends (propped her up) Happy recall: childhood, supportive family, helped overcome major difficulties Surface thinking: thinking not too deeply protected mental and physical health Finding hope: looking to the future, believing in survival for her and her daughter Coping with whatever came: day-to-day functioning, fundamentals of survival Psychological resiliency: “I’ve been blessed” Avoidance: of certain memories (shoes of babies, glasses, people, number of dead) Active role: not passive recipient (not a victim, she has choice of what’s in her mind) Some people mentioned: Anka’s health Sickness expressed in a detached way, yet AB doesn’t say her physical health suffered from the experience Willed herself to remain healthy (execution was the alternative to work) Discussed health in terms of: work, hunger and deception Health = Community, bonds to friends, attachment to family Health = Social support necessary to maintain that one can still work Health = Maintaining the illusion of good health Health = Hiding her pregnancy and being seeing as able to work Becoming ill was not an option Anka’s lack of illness = Emotional well-being and making choices Concept of illness is fragmented (emphasis on the mind) Maintaining her mind will enable her to avoid illness (kidneys don’t work well, but “if I can get out with my mind still

working and my body saying not any more”) Anka “never remembers being ill” One or two people mentioned (outliers): Denial of food, housing, and personal dignity Enforcers were cruel, inhumane, tormenting Persecuted were tested on their own limits of humanity, justice, and integrity Starvation, minor illness, brutal anti-Semitism, physical and mental torture Parents would not have burdened her with complaints of their hunger Good physical health was linked to one’s age and transport group Concentrating on fundamental issues: basic survival, self-preservation Camp regimes were built on fear, repression, and humiliation Experience is considered before, during, and after the Holocaust Experience is presented in past, present, and future tenses (who will take this forward?) Looking back and being taken back (in a bodily sense) Tacking back and forth in the narrative between staggering events of Holocaust and determination to cope, accept, or

go mad (factual and personal approach?)

Stage 4. Two aspects of the method make summative analysis unique within the qualitative methodological analytic portfolio. First, summative analysis emphasizes the importance and value of coresearchers being open, honest, and mutually supportive of each other during the group- working activities, which encourages them to feel strongly involved and to want to achieve mutual understanding of the text. This encourages coresearcher concordance and the development of group understanding. Second, summative analysis ensures that, first as individuals and then as a group, coresearchers are supported in working together to home in on an understanding of the essential properties of a text. By moving through the method with clear guidance from the group

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facilitator, coresearchers arrive at a position of knowing the text in its significant aspects, and through further reflection, during Stage 4, offering detail to each of the significant aspects. This manner of working toward group understanding and clarifying the irreducible essence through its complex aspects is unlike any other qualitative analytic approach, which usually begins with the wider canvas before moving on to the irreducible essence. In addition, this approach enables people to work from the research question to the research output steadily and consistently through the group-working sessions, with the final group paragraph achieved within only two or three 2- hour group-working sessions.

Once the group paragraph has been written, coresearchers are encouraged to “explode” or “unpack” each of the elements that make up the paragraph, during a final group-working session during Stage 4. From the group paragraph to the elaborated elements, still ordered according to the final paragraph, coresearchers embellish their original group understanding. Where the essentiality of text is not clearly grasped during early stages of analysis, this process might be more difficult to achieve. However, summative analysis is particularly effective in enabling coresearchers to find meaning more readily, having gone through the previous three stages and having come to know the text intimately, as a group, through group discussion and reflection. This is also the stage where the coresearchers can take a more analytical or interpretive stance, presenting their views and opinions in relation to the storyteller’s narrative. Here the facilitator can bring to bear the interpretive considerations that were recognized in earlier stages of working. Stage 4 is the stage when nuance or ambiguity within the data, which may have arisen in relation to how meaning is derived, is given full consideration. For example, as the group paragraph presented in Table 5 illustrates, workshop coresearchers in this particular group discussed the ability of survivors such as Anka to mute their own emotional response to others’ suffering, to blinker down” or shut out negative thoughts, to guard against the full impact of the trauma

Table 5. Example of a group paragraph from Stage 3 analysis

Anka spent three and a half years in Terezin, Auschwitz, and Mauthausen concentration camps. In her “life and health story” she considers good physical health saved her life. She displays extensive psychological resilience and an indomitable will to survive, having the ability to see whatever is positive in life and describing herself as: “being blessed with a very optimistic frame of mind”. She applied techniques during the Holocaust such as forward thinking— “choosing life”, “looking up and not down”, and even in extremis, maintained a sensory appreciation of the world. She found pleasure in the countryside around her as she descended the hill from Mauthausen during her baby’s birth, on an open, typhoid-infested coal wagon, saying, “I really couldn’t explain—why did I look?” Her positive disposition meant that she was grateful for the good things: “this ‘count my blessings’ really would summarise my point of view”. During the Holocaust she managed to disconnect from others’ suffering. She describes this as a way of coping, muting her emotional response by shutting out negative thoughts that health professionals suggested later helped her minimize trauma. Whilst she sees this as perhaps “selfish,” she also recognises that it helped her concentrate on her own survival: “I will think about it tomorrow, like Scarlet O’Hara in Gone with the Wind.” Anka’s narrative indicates a clear interrelatedness between mind and body, but also a detachment from her own feelings and experiences: “Everybody tried to keep healthy because as soon as you weren’t healthy, you were gone.” She says that her body and mind saved her life—“Everyone else was bent and I wasn’t. I was not going to give in”—and her defiant attitude emphasises a resilience of human spirit and the human capacity to endure. Anka successfully pushed off the harsh realities of life, both mental and physical, and continues to have a strong sense of self-esteem, established through her early years: a comfortable childhood, loving parents, and good upbringing. During the camp years there was a strong social support system—her Transport group—a group of youngsters who came together, stayed together, and helped maintain good health and wellbeing. Whilst her own strength and coping mechanisms were fundamental to self-preservation, they still appear to influence her today. Anka says, for example, that she never remembers being ill—“I always tried to keep healthy”—but describes many illnesses, especially in later years. Using mind-over-matter tactics, she now wishes to look to the future, accept things as they are, and not give up hope. She wants to die in peace without being a burden to her daughter or herself. She wants to find a suitable ending to her life: “if I can get out with my mind still working and my body saying not anymore, well alright, I had a long life and a very healthy one”. She hopes that by telling her story, the memory of those who perished will be honoured and people will realize the full extent of what “one human being can do to another” so that the Holocaust can no longer be denied. That is fine!

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“perpetrated on others, and by so doing to find an appropriate personal coping mechanism. Although notions such as these have been psychologized extensively in the literature on trauma (see, for example, Candib, 2001), where the effects of somatization or posttraumatic stress disorder (PTSD) are said to lead to a range of complex coping mechanisms for dealing with human suffering, coresearchers in this group were in agreement that PTSD did not adequately convey the nuance of the coping experience. Nor did such labels illustrate the manner in which the weight of suffering is carried through an individual’s life, which the trauma narrative can more readily reveal. Coresearchers pointed to a host of anomalies within Anka’s story, which were identified as markers of her lifelong process of coping developed across her narrative that helped to clarify her muted emotional response. These included Anka seeing herself as behaving selfishly in the camps (“It was only me, me, me”), with a sense of shame that others were “sent to the other side.” She says, “I was glad we went on this side, the side that was fit” while recognizing that she needs support from others to find food for loved ones, to prop people up during roll calls, and to consider others’ best interests through community actions. Other anomalies included Anka’s response that one could choose to survive while acknowledging that all choices were taken out of one’s own hands. Her desire to look to the future and uphold a positive attitude, while considering how she “never thought about tomorrow,” the ability to switch off to survive while appreciating the necessity to be very aware and tuned in to life in the camps, the ability to conceive of the visibility of horror while wishing to remain invisible: These are just a few of the examples of seemingly contradictory statements embedded throughout Anka’s text. Having written the group paragraph, the coresearchers exploded Anka’s ability to mute her emotional response to suffering during Stage 4 to clarify whether these were indeed anomalies or paradoxes. Stage 4 enabled the group to agree on the notion of a sliding scale of experience that indicated nuance of coping with pain and suffering through this “range of experiences” rather than what was initially perceived as surprisingly contradictory statements.

Challenges of summative analysis

There are a number of challenges to the method worthy of note. First, it is important to ensure that no more than six to nine coresearchers participate in each workshop, though the researcher may choose to work through the four stages with more than one group. Too few or too many coresearchers can affect the length of the sessions and disturb the balance of discussions. With this number of coresearchers, the researcher/facilitator avoids being in the position of dominating too small a group or losing voice with too large a group. The role of the researcher/facilitator is to ensure that all workshop coresearchers have an equal say in discussing the text under review while recognizing the need to encourage quieter members of the group if necessary. Workshop groups of this size allow for individuals to appreciate each stage of the method and for the group to support a dynamic discussion that runs fluidly, with full coresearcher input. Second, the management of the workshop session is crucial to the success of the method. As with the management of face-to-face interviews or focus group meetings, the facilitator must be well versed in the method and familiar with its development across all four stages. The researcher/facilitator must be confident in supporting the coresearchers to achieve the method’s full potential, helping the group overcome any disagreements and resolve uncertainty with the aim of achieving overall consensus through group activities.

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Implications of the summative analysis method for a wider research audience

In this paper the author has concentrated on how summative analysis can be applied to one specific empirical research study. However, the implications of its use can be considered within a wider research context. It is particularly useful for documenting and describing others’ experiences, and can deal with difficult, traumatic, emotive, or sensitive material presented from individuals or from groups. In the case of groups, they may be disenfranchised, diverse, or mainstream. In addition, as the wide-ranging examples in the introduction section suggest, summative analysis is useful for addressing scientific problems in applied health research.

In terms of its use within a wider research context, first it is important to note that when dealing with others’ life stories in narrative form, whatever the data or field, there must be an appropriate fit between the study aims and, on the other hand, the data collection and analysis methods so that the story teller’s subjective understandings can be revealed. As Schiff and Noy (2006) have reminded us, for each story there is not only a research question that needs to be addressed but also a real life person behind the data:

Listening closely to talk, how tellers describe who they are and where they come from, life stories allow us to explore subjective understandings in great complexity and draw interpretations about how persons make sense of self and world. (p. 15)

Summative analysis takes stock of these relationships and the cultural aspects of experience (Schiff & Noy, 2006) within the context of the story being told and the questions being asked. It is particularly sensitive to the voice of the speaker, the speaker’s style of presentation, and the speaker’s intent.

Second, as the Holocaust testimonies remind us, what might today be considered autobiographical memory can quickly become “historical memory” (Bendix, 2004, p. 133). In terms of the Holocaust, only about a quarter of those who lived through the 1930s and 1940s in Europe are still alive. When the notion of “collective memory” becomes one that is “literally vanishing” (p. 133), there is an added urgency, an added imperative, perhaps even a moral imperative on the part of researcher to expose the story while paying close attention to the voice of the storyteller and his or her part in telling the story. That is to say, the researcher must be attentive to not only what is being spoken about, but how it is being spoken about and how this re-stories understanding. The researcher’s task is to value the oral nature of testimony; to see it as a living, breathing dynamic while recognizing that at some stage it will change to an historic artifice. In effect, collecting stories from survivors and analyzing them appropriately, although recognizing the importance of the face-to-face encounter, alerts researchers to the moral implications of their work and to their role in taking messages forward. In the case of this study, as Anka implies below, this might even affect the collective consciousness of future generations. The storyteller might be well aware of this, as in the case of Anka, but it is not always so:

Even with those three and a half years, which were anything but happy or normal, I am here to tell the tale. It was quite an experience, which not many people have lived through and perhaps it helps somehow to tell the tale, not to me but to the future generation, and who can tell the tale? It’s very important because there are so many people who deny it and as long as I am here . . . But I thought it might die with me. It might die with my daughter who carries the job on, but that’s not enough. So the more people that know it and know that it’s true, that it did happen, for no reason whatsoever. The number of people who deny it is growing. They should know it because that’s what one human being can do to another . . . As we will all die out

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within the next ten years, who will carry the torch? History is a subject you can turn this way and that, in the next two generations nobody will know what’s really true. The story should be believed, that’s what I try to say. (AB, Transcript 3, Section 533)

Expectations such as these can apply to a range of narrative contexts, where researchers are working with particularly pressing, sensitive, harrowing, or difficult stories. These narratives are never offered up lightly, and the researcher must be aware that there is often an expectation of something in return for an individual’s unburdening. This is akin to the research subject gifting their story to someone else’s care, and by accepting the gift, the researcher is agreeing to enter into a close relationship and take on the responsibility of the story (Mauss, 1990; Titmuss, 1997). Though the nature of the relationship might vary according to the nature of the gifted narrative, gifting offers a story an added sense of urgency and vitality: It makes a story real. As a result, it has an empowering effect, both on the storyteller and on the listener. To deal sensitively and appropriately with the expectations of a story gifted, researchers should apply an all- encompassing analytic technique that provides that something in return and even something to be continued. Through the use of summative analysis to uphold the speaker’s original voice inference while taking a story forward through a group-working process that recognizes the speaker’s original intent, researcher responsibility can be well earned. Summative analysis can accommodate varied and complex aspects of text toward a successful transformation and can keep the storyteller fully informed about the analytic process. This closely informative approach provides the person telling the stories a sense of ownership over them right up to the time of delivering study outputs.

Finally, the Holocaust study was designed to clarify whether notions of health and well-being were in evidence in survivor testimonies and what that might tell us about the ongoing needs and expectations of people suffering extraordinary, traumatic events and their family members. The purpose was to identify aspects of Holocaust narrative that might lead health care professionals to be better attuned to the particular needs and interests of this survivor group. Although the literature indicates great interdisciplinary interest in trauma and health research around suffering (Kansteiner, 2004), the literature is also quick to label survivor stories according to their psychosocial effect and in accordance with a symptom-oriented, medical model rather than in accordance with a wider social and emotional picture. Labels such as PTSD, “characteristic symptoms following exposure to an extreme traumatic stressor” (American Psychiatric Association, 2000, p. 463), can become a blanket cover for all things medical. This then becomes the challenge to the researcher: how to explore and present a story in its fullness but in a way that moves beyond psychosocial labels and symptom-driven accounts. Summative analysis allows people to grasp the compelling aspects of an account through re-presentation that is summative. It has the potential to extend understanding beyond the medical, psychiatric, legal, or theoretical expertise that at best can be considered lacking and at worst may contract or compete with the interests of the storyteller and their family.

Note

1. The terms protagonist, storyteller, and research participant refer to the Holocaust survivor who gave up her story to the author.

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Rapport, F., Doel, M., & Wainwright, P. (2008, May). “The doctor’s tale”: Enacted workspace and the general practitioner. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research (FQS), 9, Article 2. Retrieved from http://www.qualitative- research.net/fqs-texte/2-08/08-2-2-e.htm

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Rapport, F., & Doel, M., & Elwyn, G. (2007). Snapshots and snippets: Reflecting on professional space. Health & Place, 13, 532–544.

Rapport, F., Iredale, R., Jones, W., Sivell, S., & Edwards, A., Gray, J., et al. (2006). Decision aids for familial breast cancer: Exploring women’s views using focus groups. Health Expectations, 9, 232–244.

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Regression analysis with GRETL.pdf

Regression analysis in practice with GRETL

Prerequisites

You will need the GNU econometrics software GRETL installed on your computer

(http://gretl.sourceforge.net/), together with the sample files that can be installed from

http://gretl.sourceforge.net/gretl_data.html.

This is no econometrics textbook, hence you should have already read some econometrics text, such

as Gujarati’s Basic Econometrics (my favorite choice for those with humanities or social science

background) or Greene’s Econometric Methods (for those with at least BSc in Math or related

science).

1. Introduction to GRETL

1.1 Opening a sample file in GRETL

Now we open data3-1 of the Ramanathan book (Introductory econometrics wit applications, 5 th

ed.).

You can access the sample files in the File menu under Open data/Sample file…

Once you click on the sample files… you are shown a window with the sample files installed on your

computer. The sheets are named after the author of the textbook which the sample files are taken

from.

Choose the sheet named “Ramantahan” and choose data3-1.

You can open the dataset by left-clicking on its name twice, but if you right click on it, you will have

the option to read the metadata (info).

This may prove useful because sometime it gives you the owner of the data (if it was used for an

article), and the measurement units and exact description of the variables.

1.2 Basic statistics and graphs in GRETL

We have now our variables with descriptions in the main window.

You can access to basic statistics and graphs my selecting one (or more by holding down ctrl) of the

variables by right-click.

Summary statistics will yield you the expected statistics.

Frequency distribution should give you a histogram, but first you need to choose the number of bins.

With 14 observations 5 bins look enough.

You also have a more alphanumerical version giving you the same information:

You also ask for a boxplot.

Showing that the prices are skewed to the right (the typical price being less than the average) which

is often referred to as positive skew.

If you choose both variables, you have different options as they are now treated as a group of

possibly related variables.

The option scatterplot will give you the following:

Which already shows that a linear relationship can be assumed between the price of houses and

their area. A graphical analysis may be useful as long as you have a two-variate problem. If you have

more than two variables, such plots are not easy to understand anymore, so you should rely on 2D

representation of problems like different residual plots.

You can also have the correlation coefficient estimated between the two variables:

With a hypothesis test with the null hypothesis that the two variables are linearly independent or

uncorrelated. This is rejected at a very low level of significance (check out the p-value: it is much

lower than any traditional level of significance, like 0.05 (0.01) or 5% (1%)).

1.3 transforming variables

Transforming variables can be very useful in regression analysis. Fortunately, this is very easily done

in GRETL. You simple choose the variables that you wish to transform and choose the Add menu. The

most often required transformations are listed (the time-series transformations are now inactive

since our data is cross-sectional), but you can always do you own transformation by choosing “Define

new variable” .

Let us transform our price into a natural logarithm of prices. This is done by first selecting price, then

by left-clicking the Logs of selected variables in the Add menu. You will now have the transformed

price variable in you main window as well.

Now that you know the basics of GRETL, we can head to the first regression.

2. First linear regression in GRETL

2.1 Two-variate regression

You can estimate a linear regression equation by OLS in the Model menu:

By choosing the Ordinary Least Squares you get a window where you can assign the dependent and

explanatory variables. Let our first specification be a linear relationship between price and area:

After left-clicking OK, we obtain the regression output:

The intercept does not seem to be statistically significant (i.e. the population parameter is not

different from zero at 10% level of significance), while the slope parameter (the coefficient of the

area) is significant at even 1%. The R 2 is also quite high (0.82) signifying a strong positive relationship

between the area of houses and their prices. If a house had one square feet larger living area, its sale

price was on average higher by 138.75 dollar.

2.2 Diagnostic checks -heteroscedasticity

But this does not mean that we should necessarily believe our results. The OLS is BLUE (Best

Unbiased Linear Estimator) only if the assumptions of the classical linear model are fulfilled. We

cannot test for exogeneity (that is difficult to test statistically anyway), and since we have cross-

sectional data, we should also not care much about serial correlation. We can test heteroscedasticity

of the residual though. Heteroscedasticity means that the variance of the error is not constant. From

estimation point of view what really matters is that the residual variance should not be dependent

on the explanatory variables. Let us look at this graphically. Go to the Graph menu in the regression

output.

Plotting the residuals against the explanatory variable will yield:

You can observe that while the average of the residual is always zero, its spread around its mean

does seem to depend on the area. This is an indication of heteroscedasticity. A more formal test is a

regression of the square of the residuals on the explanatory variable(s). This is the Breusch-Pagan

test:

What you obtain after clicking on the Breush-Pagan test under Tests menu is the output of the test

regression. You can observe that the squared residuals seem to depend positively on the value of

area, so the prices of larger houses seem to have a larger variance than those of smaller houses. This

is not surprising, and as you can see, heteroscedasticity is not an error but rather a characteristic of

the data itself. The model seems to perform better for small houses than for bigger ones.

You can also check the normality of the residuals under the Tests menu:

Even though normality itself is not a crucial assumption, with only 14 observations we cannot expect

that the distribution of the coefficients is close to normal unless the dependent variable (and the

residual) follows a normal distribution. Hence this is a good news.

Heteroscedasticity is a problem though inasmuch as it may affect the standard errors of the

coefficients, and may reduce efficiency. There are two solutions. One is to use OLS (since it is still

unbiased), but have the standard errors corrected for heteroscedasticity. This you can achieve by

reporting heteroscedasticity robust standard errors, which is the popular solution. Go back to the

Model menu, and OLS, and have now robust standard errors selected:

While the coefficients did not change, the standard errors and the t-statistics did.

An alternative way is to transform you data so that the homoscedasticity assumption becomes valid

again. This requires that observation with higher residual variance are given lower weight, while

observations where the residual variance was lower are given a relatively higher weight. The

resulting methodology is called the Weighted Least Squares (WLS) or sometime it is also referred to

the Feasible Generalized Least Squares (FGLS). You can achieve this option in the Model menu under

“Other linear models” as “heteroscedasticity corrected”.

You may wonder which one is better? To transform your data or rather to have only you standard

errors corrected and stick with the OLS. You can see that there may be significant changes in the t-

statistics, while the coefficients are basically the same. Hence it is often the case that you do not gain

much by reweighting your data. Nevertheless, theoretically both are correct ways to treat

heteroscedasticity. The majority of articles report robust statistics and does not do WLS, partly for

convenience, and partly because there is some degree of distrust toward data that has been altered:

do not forget that once you weight your data it is not the same data anymore, but, in this particular

case, all your variables (including the dependent variable) will be divided by the estimated standard

error of the residual for that particular observation.

2.3 Alternative specifications

We may also try a different specification: since the prices are skewwed to the right, a logartihmic

transformation may just bring it more to the center. For a visuals look of the idea, let us look at the

histogram of log prices.

Indeed it look much more centered than prices. This may have improving effects on the model, even

though there is no guarantee. We need to estimate the alternative model and compare it with the

original one.

We obtain now a statistically significant constant term, saying that a building with null area would

sell at exp(4.9)=134.3 thousands dollar (this is a case when the constant seemingly has no deep

economic meaning, even though you may say that this is the price of location, or effect of fixed cost

factors on price), and every square feet additional area would increase the price by 0.04% on average

(do not forget to multiply by 100% if log is in the left-hand side). You may be tempted to say that this

log-lin (or exponential) specification is better than the linear specification, simply, because its R 2

exceeds that of the original specification. This would be a mistake though. All goodness of fit

statistics, including R 2 , the log-likelihood, or the information criteria (Akaike, Schwarz and Hannan-

Quinn) are dependent on the measurement unit of the dependent variable. Hence, if the dependent

variable does not remain the same, you cannot use these for a comparison. They can only be utilized

for model selection (i.e. telling which specification describes the dependent variable better) if the

left-hand side of the regression remains the same, albeit you can change the right-hand side as you

please.

In such situations when the dependent variable has been transformed, the right way to compare

different models is to transform the fitted values (as estimated from the model) to the same units as

the original dependent variable, and look at the correlation between the original variable and the

fitted values from the different specifications. Hence, now, we should save the fitted values from this

regression, than take its exponential, so that it is in thousand dollars again, and look at the

correlation with the dependent variable. Saving the fitted values is easy in GRETL:

Let us call the fitted values lnpricefitexp:

Now we have the fitted values from the exponential model as a new variable. Let us take the

exponential of it:

Let us also save the fitted values from the linear model as pricefitlinear. We can now estimate the

correlations:

We can observe that the linear correlation coefficient between price and the fitted price from the

linear model is 0.9058, while the correlation between the price and the fitted price from the

exponential specification is 0.8875. Hence the linear model seems better.

3. Multivariate regression

3.1 Some important motivations behind multivariate regressions

Life is not two-dimensional so two-variate regression are rarely useful. We need to continue into the

realm of multivariate regressions.

As you have seen in the lecture notes on OLS, multivariate regressions has the great advantage that

the coefficients of the explanatory variables can be interpreted as net or ceteris paribus effects. In

other words, the coefficient of variable x can be seen as the effect of x on the dependent variable y

with all other explanatory variables fixed. This is similar to the way of thinking behind comparative

statics. But beware! This is only true for variables which are included in the regression. If you omit

variables that are important and correlated with the variables that you actually included in your

regression, the coefficients will reflect the effect of the omitted variable too. Let us take the two-

variate regression from section 2 as an example. It is quite obvious that house prices depend not only

on the area of houses, but also on the quality of buildings, their distance from the city centre, or the

number of rooms, etc. By including the area only as explanatory variable, you do not really measure

the effect of area on the sale prices, but rather the total effect of area, including part of the effect of

other factors that are not in your model but are related to area. If, for example, bigger houses are

usually farther from the city centre, then the coefficient of the area will not only reflect that bigger

houses are more valuable, but also that bigger houses are further away from the centre and hence

their prices should be somewhat lowered because of this. The total effect of area on house prices

should then be lower than the net effect, which would be free of the distance effect). You can

observe this simply, by introducing new variables into a specification. You will have a big chance that

important additional explanatory variables will change the coefficient of other explanatory variables.

Be therefore very well aware of the problem of omitted variables and the resulting bias. You can only

interpret a coefficient as the net effect of that particular factor, if you have included all important

variables in your regression, or you somehow removed the effect of those omitted variables (panel

analysis may offer that).

3.2 Estimating a Mincer equation

We need now a different sample file: open wage2 of the Wooldridge datafiles.

You will find observations on socio-economic characteristics of 526 employees. The task is to find out

how these characteristics affect their wages. These type of models are called Mincer equation, after

Jacob Mincer’s empirical work. The basic specification is as follows:

0 1

2

ln k

i i j ji i

j

wage educ X u   

   

where the coefficient of the education (usually but not exclusively expressed as years of education) is

the rate of returns to education, and X denote a number of other important variables affecting wage

such as gender, experience, race, job category, or geographical position.

Let us estimate the coefficient with all available variables.

What we find is a reasonable R 2 , and a lot of statistically insignificant variables. We will discuss how

to reduce our model later, but let us first review the interpretation of the coefficients.

Since we have log wages on the left-hand side, the effect of explanatory variables should be

interpreted as relative effects. For example the educ coefficient is 0.047, that is, if we have two

employees who has the same gender, work in the same field, has the same experience and work at

their present employer for the same time, the one with one year more education will have 4.7%

higher salary on average. The female dummy is statistically significant and negative -0.268. The

interpretation is, that if all other factors are the same, being woman will cause the wage to be exp(-

0.268)-1=-0.235, that is 23.5% lower than for a man. We do not find a comparable result for race.

Another interesting feature of the model is the presence of squared explanatory variables, or

quadratic function forms. You can see that it is not only experience that is included but also its

square. This functional form is often used to capture non-linearities, i.e., when the effect of an

explanatory variable depends on its own values as well. For example, there is reason to believe that

experience has a large effect on wages initially, but at later phases this effect fades away. This is

simply because the first few years are crucial to learn all those skills that are necessary for you to be

an effective employee, but once you have acquired those skills, your efficiency will not improve much

simply by doing the same thing for a longer time. This is what we find here as well. The positive

coefficient of the experience suggests that at low levels of experience, any further years have a

positive impact on wages (2.5% in the first year), but this diminishes as shown by the squared

experience. If we have a specification as follows:

2

0 1 2i i i i y x x u      then the marginal effect of x can be calculated as follows.

1 2 2

i

dy x

dx   

We can use above expression to plot a relationship between the effect of experience on wage and

experience:

After a while, it may even be that the effect of experience is negative in the wage, even though this

may simple be because we force a quadratic relationship onto out data.

3.3 Model selection

Should we or should we not omit the variables that are not significant at at least 10%? This is a

question that is not easy to answer.

Statistically speaking, if we include variables that are not important (their coefficients are statistically

not significant) we will still have unbiased results, but the efficiency of the OLS estimator will reduce.

Hence, we can have that by including non-essential variables in our regression, an important variable

will look statistically insignificant. Hence purely statistically speaking removing insignificant variables

is a good idea. Yet, very often you will find that insignificant coefficients are still reported. The reason

is that sometimes having a particular coefficient statistically insignificant is a result by its own right,

or the author wishes to show that the results are not simply due to omitted variable bias, and hence

leave even insignificant variables in the specification to convince the referees (who decide if an

article is published or not). It may be a good idea though to report the original (or unrestricted)

specification and a reduced (or restricted) specification as well.

But which way is the best? Should you start out with a single explanatory variable and keep adding

new variables, or rather should you start with the most complex model, and reduce it by removing

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

0 10 20 30 40 50

experience (years)

insignificant variables until you have all variables statistically significant. This question can be

answered very simply. It should be the second way. Do not forget, that having unnecessary variables

does not cause a bias in your parameter estimates, while omitting important variables does. Hence if

you start out with single variable, you statistics on which you base your decision if you should add or

remove a variable will be biased too. Having less efficient but unbiased estimates is now the lesser

bad.

The standard method is to reduce you model by a single variable in each step. It is logical to remove

the variable with the highest p-value. The process can also be automatized such as in GRETL. The

omit variables option in the Tests menu allows you to choose automatic removal of variables.

This option allows you to assign the p-value at which a variables should be kept in the specification.

Choosing this value 0.10 means that only variables that are significant at at least 10% will be retained

in the specification.

The resulting specification is:

Schmidhuber.pdf

Impact of an increased biomass use on agricultural markets, prices and food security: A longer-term perspective

Josef Schmidhuber1

Abstract: This paper examines the impact of rising demand for bioenergy on agricultural markets and prices. It reviews quantitative assessments of bioenergy potential and concludes that demand for bioenergy is significant enough to create a change in the traditional paradigm for global agriculture, which has been characterized for decades by robust supply growth, slowing demand growth and falling real prices for agricultural produce. It then assesses the competitiveness of selected agricultural feedstocks in the transportation and heating fuel markets and identifies resulting floor and ceiling prices for agricultural products. Based on the likely price effects in food and energy markets, it assesses the impact of rising bioenergy production on food security, differentiating between effects on availability, access and stability of food supplies. 1. Introduction For decades, global agricultural markets have been characterised by steady production and productivity growth, slowing demand and as a result, falling real prices for agricultural produce. From 1973 to 2000, for instance, food prices fell by about 60% and agricultural prices by about 55% in real terms (World Bank, 2004, Figure 1). While the decline over the last four decades was particularly pronounced, it was part of a longer- term trend observed during the entire century not only in international markets but in regional and national markets as well. Over the last century, real prices for agricultural and food products in the US declined by 72 per cent and 76 percent, respectively (USDA).

Figure 1: Real prices for food and agriculture

This secular decline in real prices has rested on two main pillars. On the supply side, rapid technological progress in agriculture meant lower unit costs of production and, with competition in product markets, lower profit margins per unit of output and lower commodity prices. Together with declining product prices, the pressure on farmers to adopt the new technologies rose and competition in product markets effectively squeezed farmers’ profits. They accrued largely to

1 Josef Schmidhuber is Senior Economist with the Global Perspective Studies Unit of FAO. This paper was prepared for the “International symposium of Notre Europe”, Paris, 27-29 November, 2006. The views expressed in this paper reflect those of the author, not necessarily those of the Organization. The author greatly appreciates comments from Jelle Bruinsma and Chris Matthews, FAO.

buyers of food and agricultural products, with a consequent decrease in real costs of food and fibre products to consumers (Gardner 2002). In many countries, higher productivity in agriculture was accompanied by an intensification of production methods, i.e. higher applications of fertilizer, pesticides, and an expansion of irrigation. For most new high-productivity technologies only produced higher output if combined with higher levels of inputs. And finally, farmers tried to expand the production base. Lower profits margins per unit of output required increased volumes, more cropland and higher cropping intensities. The result was a massive increase not only in productivity but also in total output.

Figure 2: Cochran’s treadmill: traditional paradigm

On the demand side, a drastic slow-down in global population growth and increasingly saturated demand2 resulted in a steady slowdown in demand growth, first in developed countries and later, and rapidly so, in a growing number of developing countries. As mentioned above, it is consumers who have benefited the most from advances in agricultural productivity. In real terms food prices have declined to their lowest levels in history so that consumers today eat better while spending less and less of their budget on food. Clearly not all countries and regions have benefited from these advances. In parts of the developing word, notably in sub-Saharan Africa, they have yet to produce any meaningful impact. But in many developing countries progress towards providing more, better and cheaper food has been impressive. The rapid decline in real food prices has allowed consumers in developing countries to embark on food consumption patterns previously enjoyed only by consumers in industrialized countries at much higher gross domestic product (GDP) levels. Today, consumers in developing countries can purchase more calories than ever before -- and more than consumers in industrialized countries ever could at comparable income levels. In China, for instance, consumers today have about 3000 kcals/day and 50 kg of meat per year at their disposal -/ at less than US$1000 nominal income per year (Schmidhuber and Shetty, 2005). Since the early 1960s, global average calorie availability has increased from about 1950 to 2680 kcals/person/day while protein availability has nearly doubled from about 40 to 70 g/person/ day. With growing saturation levels, the growth rates of world demand steadily declined and are projected to fall further -- from 2.2% p.a. from 1969-1997/99 to 1.6% to 2015 and 1.4% for 2015 to 2030. The decline will even be more pronounced in developing countries where

2 Not only in developed and emerging markets, but also and rapidly so in many advancing developed countries. For a complete analysis of the driving forces see e.g. Schmidhuber and Shetty 2005.

growth in aggregate food demand is projected to fall from 4.0% p.a. to 2.2% and to 1.7% for the same periods (Bruinsma, 2003). FAO’s long-term outlook to 2050 suggests that the decline in growth will become even more pronounced after 2030, with growth rates for aggregate food demand declining to 0.9% for the world as a whole and 1.1% p.a. for the developing world (FAO, 2006). Even a rapid decline in the number of the 850 million now undernourished would not alter this outlook substantially3 in the longer term. The world could easily produce the additional food needed to feed them well, and do so without any significant “price stress” on world commodity markets. If today’s hungry are hungry it is because they are unable to purchase enough food and not because the world cannot produce enough. The potential demand is simply too small to challenge the spare production and productivity capacity of global agricultural production. Potential demand from energy markets could, however, be large enough to challenge the spare production capacity of world agriculture. How large the potential of energy markets is, how much and what type of agricultural produce is competitive in the energy markets and how this growing competition is changing price formation in commodity markets will be illustrated in this paper. It will also examine whether the non-food demand potential is large enough to stop the slow-down in overall demand growth or even reverse it. The size of the competitive potential will crucially depend on how much agricultural produce becomes a competitive source of energy in the overall energy market. At current energy prices, some agricultural feedstocks have indeed already become competitive sources of energy, at least under certain production environments. As a consequence, demand for these feedstocks has expanded and already supports prices for these commodities. Where demand was particularly pronounced as in the case of cane-based ethanol, bioenergy demand has created a quasi intervention system and an effective floor prices for agricultural produce – sugar in this case. With higher energy prices the range of products competitive in the energy markets has increased, strengthening the floor price effect for agriculture in general (Schmidhuber, 2005). In some countries, policy incentives to use and/or produce bioenergy further added to the demand for agricultural produce and lowered the parity price equivalent to a point where many otherwise uncompetitive feedstocks became economically viable in the energy market (Schmidhuber, 2006). The growing dependence of agriculture on energy markets has also created a growing concern that high and rising energy prices will create new or augment existing food security problems as a growing number of poor consumers are priced out of the food markets by rising energy demand or are exposed to more pronounced swings in food supplies and prices. This paper will show that higher prices can indeed add to food security problems, but that price increases are not open-ended and fears of a global neo-Malthusian scenario are unwarranted. The main reason for is an endogenous ceiling price effect (Schmidhuber 2006). As feedstock costs are the most important cost element of all (large-scale) forms of bioenergy use, feedstock prices (food and agricultural prices) cannot rise faster than energy prices in order for agriculture to remain competitive in energy markets (ceiling price effect). Barring massive subsidies for bioenergy, the need to maintain competitiveness should create an endogenous brake on food prices. 3 A third factor, albeit unrelated to agriculture, added to the decline in real prices over the last decade: Driven by globalization and growing competition in global manufactures markets, nominal prices for industrial goods and thus for the numéraire used to deflate agricultural prices have declined faster than nominal prices in agriculture (IMF, 2006).

Before floor and ceiling price effects are discussed in detail, it should be useful to estimate the potential size of bioenergy production and thus how far demand for agricultural produce can expand. The next section therefore examines the various potentials of bioenergy production, juxtaposes them with the overall energy markets and provides an idea as to what the regional distribution is likely to be. 2. How big is the potential for bioenergy? A number of studies have assessed the global and regional potential of bioenergy production. Their estimates differ considerably and the interpretation of the results presented has to be vetted carefully against the basic assumptions made (see e.g. Smeets et al. 2004, Fischer and Schrattenholzer, 2000). Assessments differ due to (i) different scopes in terms of countries and feedstock coverage, (ii) different assumptions made as to “reserve” resources (land, water, etc.) required to meet the world’s need for food, forest and fibre demand, (iii) different definitions of potentials (theoretical, technical, economic) or (iv) simply because they pursued completely different methodological approaches. To discuss the differences in the various studies in greater detail would exceed the scope of this paper and distract from its main purpose. Instead, the focus will be to give an idea of the magnitude of the potential and to illuminate the discussion by identifying the various forms of potentials. The analysis by Fischer and Schrattenholzer (Fischer and Schrattenholzer, 2000) helps illustrate some of the salient points that determine the various “potentials” and provides plausible estimates for their magnitude. The study is comprehensive in terms of country coverage, provides regional details and distinguishes five major possible sources of biomass, i.e. arable land, grasslands, forests, as well as animal and municipal wastes4. The study also distinguishes technical from economic potentials and takes account of cropland needs for food, forest and fibre production based on FAO’s long-term outlook for global agriculture (Bruinsma, 2003). It is therefore compatible with many other assumptions made in this paper. 2.1 The theoretical potential At the most general level, the global bioenergy potential is defined by the total amount of energy produced by global photosynthesis. Plants collect a total energy equivalent of about 3150 Exajoule, EJ [1018J/a] (Kapur, 2004) per year or nearly seven times the global current amount of energy used [total primary energy supply in 2004 was about 460 EJ (IEA, 2004a)]. While no doubt impressive, the photosynthesis potential as such is rather irrelevant for an assessment of global bioenergy potential. For one thing, it includes vast amounts of biomass that cannot be harvested because it is too inaccessible or because the cost of harvesting would be too high. For instance, nearly one third of photosynthesis, or about 1150 EJ/a, is produced as phytoplankton and other plants in the oceans (Kapur, 2004). Maritime photosynthesis products not only form the basis of the oceans’ food chains but are also difficult if not impossible to harvest. Similarly, much of what grows on land is either not harvestable (too remote, etc) or simply not available for energy use, being required for other purposes. The theoretical potential is not only limited by the global area suitable for photosynthesis production but also, and decisively so, but the low energy efficiency of photosynthesis. Plants are hugely inefficient converters of solar energy and will therefore face growing competition from more efficient methods of collecting and converting solar radiation. At around 0.5

4 Bioenergy crops and crop residues can come from both arable land and grasslands

Watt/m2, the power density rates of plants is extremely low and just a fraction of what solar energy can already provide, i.e., between 20-60 Watt/m2. This makes biomass a remarkably poor way of harvesting solar energy and means that (i) huge areas of land would be necessary to make a sizeable contribution to global energy supplies and (ii) that biomass production will have to compete increasingly with more efficient solar energy converters, particularly solar panels. It is therefore important to determine how much of the theoretical potential can be made technically available and how much of the latter is economically viable. Table 1: Agricultural and energy markets, potentials and actual use

9Exajoule/a [1018 Joule]/a million ha Energy source:

Potential and actual use

Year World OECD non-OECD World

19732 253 157(62.3%) 95(37.7%) 20042 463 231(49.8%) 232(50.2%) 20302 691 285(41.2%) 406(58.8%)

All sources (TPES)

20502 >850

Actual use 20042 4911 8 41 Theoretical

potential >>2000 Global photosynthesis: ~ 3150 EJ

Technical potential

19901 225 4812 17712

20501 400 8012 32012

Biomass

Economic potential

20501 158

Ethanol7 (actual) 20043

2006 0.84 1.01

0.34 0.51 9.524

Biodiesel7 (actual)

20033 0.06 0.04 0.02 0.474

Potential1 20501 5310

Biofuels

Use 2030 4.8(8.4)13 2.3(4.0)13 2.5(4.4)13

Resources: million ha Used for

agriculture 1506 658 848 8504/5

Total suitable

1997- 99

41888 14066 27826 2006 14 ~1% of land

Agricultural land8

Used for biofuels 2030 32.5 (57)13 ~2% of land

1.) Potential based on Schrattenholzer and Fischer, IIASA, 2000 2.) Based on IEA 2004b: Key energy statistics, 2006 (TPES), EIA (US) projections for 2030 are 761 EJ (in terms of consumption) 3.) Derived from http://www.earth-policy.org/Updates/2005/Update49.htm, Earth Policy Institute 4.) Assuming an average yield per hectare for ethanol of 4200 l (3000 l US maize, 5500 l Brazil cane, 6900 l France sugar beet) and of 3800 l/ha for biodiesel (average palm oil, rapeseed oil, etc.). Most recent yields are about 10% higher for cane and 20% higher for maize. 5.) 850 million ha would be required to meet 2002 road transport fuels needs (77 EJ) at current yields (l biofuel/ha), technology, and crop composition. 6.) Area for developed and developing countries, not OECD and non-OECD 7.) Assuming an energy content of 34 MJ/l for biodiesel and 21.1 MJ/l for ethanol 8.) Bruinsma (ed), World agriculture: towards 2015/2030, An FAO Perspective, 2003, total suitable land for rainfed agriculture 9.) 23.8845 Mtoe = 1 EJ 10.) IEA (2004a), “Biofuels for Transport”, table 6.8.; road transportation in 2030 about 120 EJ; total 132 EJ; EIA. 11.) 15-60 EJ: most biomass fuels are not traded on world markets, estimates of consumption are highly uncertain. 12.) Based on regional estimates from Schrattenholzer and Fischer, IIASA, 2000 13) The IEA Energy Outlook 2006 assumes a 4% share in road transportation in 2030 in the reference case, 7% in the alternative scenario 2.2 The technical potential The technical bioenergy potential is essentially that part of the theoretical potential that can be harvested in practice and thus be harnessed for practical energy use. Again a number of

studies have gauged the volume of biomass that can technically contribute to global energy supplies. Not surprisingly all estimates suggest that the technical potential is a relatively small fraction of the theoretical one. Fischer and Schrattenholzer for instance estimate that the global technical potential of bioenergy was about 225 EJ/a in 1990 and that it could increase to about 400 EJ/a by 2050. The near doubling in the potential from 1990 to 2050 largely reflects anticipated increases in crop yields and to a minor extent assumes growing amounts of municipal and agricultural wastes resulting from population growth and rapid urbanization. With about 177 EJ in 1990, non-OECD countries would account for the lion’s share of the global technical potential, Africa and Latin America together providing about 42% of that figure. In contrast, the technical potential of OECD countries, about 48 EJ, is rather limited and accounts merely for 21 percent of overall technical potential5. 2.3 The long-run economic potential More important than purely technical availability, however, is the question of how much technically-available bioenergy potential is economically viable. The two crucial parameters here are the prices of fossil energy and the costs of producing bioenergy. This means that the technical potential needs to be scaled down further to that part of the bioenergy stock that can compete with fossil energy after harvesting, transport and processing. These overheads can be substantial and accordingly reduce the amount of bioenergy that is profitable to use. Even more important is that increased use will mean that the costs of using bioenergy can and will increase rapidly at the margin of using an additional unit of biomass. How steeply long-term supply curves will increase is difficult to gauge. The increase in marginal costs of global sugar production illustrated in Figure A3 (Annex) is no doubt exaggerated by massive subsidies that keep high-costs producers in operation (right end of the curve). Though a massive increase in biomass use and thus growing competition for area should, however, also result in a rapid increase in marginal costs, once traditional production acreage is exhausted. Based on the IIASA-WEC A3 scenario, which assumes a continuation of high economic growth, rapid technological development and fossil energy prices in the middle of the existing long-term estimates, Fischer and Schrattenholzer find the economically viable potential to be in the range of 150 EJ/a globally (Fischer and Schrattenholzer, 2000). It is important to set this in the broader context of current and future energy needs. First, the 150 EJ/a should be seen in the context of future energy needs, projected at some 850 EJ/a by 2050, so that the contribution of bioenergy would “only” be around 17.5%, or only 7% above its current share of 10.6%. Second, the 150EJ/a are based on the use of biomass, not of biofuels. While second-generation biofuels should lower conversion losses and costs considerably, the 150 EJ/a would melt down to about 53 EJ/a in terms of biofuels at current conversion technologies and efficiencies (IEA, 2004a). 2.4 The current short-term economic potential The assessment of the long-term economic potential depends crucially on assumptions made about the prices of fossil energy, the development of agricultural feedstocks and future technological innovations in harvesting, converting and employing biofuels. In their current 5 It is interesting to note that the regional estimates for the potentials of bioenergy correspond almost exactly to current and actual use of biomass. This holds both for shares between OECD and non-OECD countries as well as for the distribution within OECD countries. Current use in the OECD countries for instance is about 8 EJ/a or 16% of total current biomass use. The potential is estimated to be 80 EJ/a or 20% of the total potential in 2050.

state all these factors also determine the competitiveness of the various forms of bioenergy and it should thus be useful to examine how they currently influence what feedstock is viable in what production or farming system. The key indicator in examining the question of short-term potential and economic viability is therefore the break-even point for the various forms of bioenergy and how sensitive this is with respect to fossil energy prices and most importantly agricultural feedstock prices. Break- even points are given by the so-called parity price, i.e. the price of fossil fuel per unit of energy at which the various forms of bioenergy become competitive. This indicator will be discussed in section 3 of this paper and key parity price levels will be presented. The main factor that determines the parity price of a particular form of bioenergy is the cost of the feedstock (market price adjusted for subsidies and other policy interventions that do not affect prices directly). At the industrial level of bioenergy production, feedstock costs account for the lion’s share of total costs and can exceed 80% of total production costs. As the energy market is large compared to the agricultural feedstock markets, prices of agricultural feedstocks are endogenous to changes in fossil energy prices. As also demonstrated in section 3 of this paper, large energy markets can create both a floor price for agriculture as well as ceiling price, i.e. the price for agricultural feedstocks that is still low enough to keep a given form of bioenergy in the energy market. As this price cannot be exceeded in the long-run, to keep a given feedstock viable in the energy market, the current economic potential of bioenergy is an endogenous potential that depends not only on the price changes in fossil energy markets but also, and crucially so, on the demand for and the price of the feedstocks. 2.5 Current, actual use of bioenergy The discussion of the biomass potentials suggests that the demand for biomass could create substantial demand for agricultural resources, but that the economically viable use of biomass is crucially dependent on prices for fossil energy, agricultural prices and the cost of converting biomass into marketable bioenergy. What still needs to be discussed is how much of the potential has already been reaped, what feedstocks are used and what forms of biomass are employed as the basis for bioenergy production. In 2004, global biomass6 use accounted for 49EJ or nearly 10.6% of total primary energy supply (TPES) (IEA, 2006, see also Table 1). Table 1 also shows that the importance of biomass use differs considerably across countries and groups of countries. In general, biomass is a more important contributor to energy supplies in developing countries where it accounted for nearly 19% of their TPES in 2004, equivalent to 41 EJ, while it is much less important, both in absolute terms and as a share of total energy supply, in OECD countries. In almost all developed countries it accounts for less than 5% of TPES and in 2004 represented a mere 3.4% for the OECD countries on average. While biomass accounts for small shares of TPES in almost all OECD countries, the importance varies widely across the various developing regions. Whereas biomass is entirely irrelevant for all oil and/or gas rich countries in the Near-East/North-Africa region, it is often the most important source of energy in most countries in sub-Saharan Africa. In some of these countries, bioenergy accounts for more than 90% of the TPES, examples are Tanzania (92%), Ethiopia (92.1%) and the Democratic Republic of Congo (93.5%) (for details see Figure 3).

6 Combustibles and waste

For discussion of the possible impacts of bioenergy on agricultural markets it is important to note that the major role it plays in developing countries is not a new phenomenon. The forms of bioenergy used in developing countries make that clear. They have little to do with the advanced and modern forms of bioenergy that have become en vogue in developed countries as a result of high fossil fuel prices and environmental concerns. The latter essentially reflect advanced development and demand for high-end environmental goods. In contrast, high biomass use in developing countries is often based on low-end products like charcoal, fuel wood or even cow dung and is often associated with environmental damage (deforestation) and health problems (fuel wood in India). The dominant role of these semi-marketable or non- marketable feedstocks, mostly based on forest products or by-products of agricultural production, means that the use of biomass in most developing countries has had no, or only limited, impact on international agricultural markets. The bioenergy that has most affected agricultural markets is probably biofuels, i.e. highly marketable bioenergy based on traded feedstocks such as cereals, sugar or cassava. Their use for energy production has created considerable public interest but their contribution to the energy markets is almost negligible. In 2006 they provided only about 1.1EJ or 1.3% of road transportation needs and thus less than 0.3% of total energy supplies (Table 1). As these feedstocks will play a dominant role in the supply of first-generation biofuels, the obvious question is how much land is necessary to make a sizeable contribution to current energy needs. Meeting global road transportation needs, which are about 77EJ/a or about 18% of global energy use, for instance. A mechanistic way to address this question is to assume current conversion efficiency, current yields, current feedstock composition (sugar cane, maize, rapeseed, etc.) and the proportions of bioethanol to biodiesel production and calculate the land area needed to produce 77 EJ in terms of biofuels. The resulting answer is around 850 million ha, equivalent to the total cropland currently used for food and fibre production in developing countries (Table 1). But this is unrealistic answer as it ignores the endogenous limits stemming from the fact that the demand for these feedstocks would drive up their prices and limit their use. Encroachment on existing land and expansion of overall crop land would consequently also be curtailed. The exercise is nonetheless useful as it shows that the energy market is “big” relative to the agricultural market and that energy prices will determine agricultural prices where agriculture is a competitive feedstock. When competitive, the energy market affects the agricultural markets and creates a floor price for agricultural produce but the contribution of agriculture would be too small to affect the energy market. How these floor price effects work in practice and how rising feedstock prices create a ceiling price effect for agricultural produce will be discussed in the next sections.

Share of biomass (combustbles and waste) in Total Primary Energy Supplies (TPES), non-OECD countries Source: IEA

0 10 20 30 40 50 60 70 80 90

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Figure 3: The share of current bioenergy use in developing countries

3 Price effects

3.1 Higher fossil fuel prices create a floor price for agricultural products

Agricultural prices have always been affected by energy prices. Hitherto, this price link was largely limited to the impacts of higher energy prices on the prices of agricultural inputs, i.e. the prices for fertilizer, pesticides, or diesel. Higher input prices often resulted in a rationalisation of production and thus lower output. This has changed. With rapidly rising energy prices and improved bioenergy conversion technologies, higher energy prices are also affecting agricultural output prices. As prices for fossil energy reach or exceed the energy equivalent of agricultural products, the energy market creates demand for agricultural products. Where demand from the energy sector is large/elastic and agricultural feedstocks are competitive in the energy market, a floor price effect for agricultural products results. The output price effect creates incentives to produce more rather than less.

How big is demand from the energy sector?

The effectiveness of this floor price mechanism strongly depends on the volumes of agricultural output/feedstocks that can be absorbed by the fuel market, i.e. on demand from the energy sector being sufficiently large. As illustrated in section 2, the volume of global demand for energy is indeed large compared with the energy that agricultural feedstocks can deliver. This means that demand for agricultural feedstocks should be elastic as long as biomass energy can be sold at prices that ensure coverage of total costs. In practice, the volumes depend, inter alia, on the degree of market integration.

What crops are competitive at what energy price ...?

The point where total costs for biomass-based energy production are covered by revenues from sales of bioenergy (ethanol, biodiesel, etc.) is referred to as the parity price of a given feedstock. This is the point where the costs (feedstock, upstream and downstream transport, conversion, wages, capital) of producing a unit of the bioenergy (ethanol, biodiesel) are equal to the costs of producing the same energy unit from fossil energy (petrol, diesel). In other words, this is the point where bioenergy producers break even. Figure 5 provides parity prices for a selection of agricultural feedstocks, farming systems and fuels (ethanol, diesel, BTL7).

The blue diagonal reflects a parity price line for the conversion from crude oil to petrol which allows mapping feedstock parity prices for crude oil into feedstock parity prices for refined petrol. To mention just a few of these break-even points, there lie at US$28/bbl for cane producers in Brazil’s south-centre region, at US$35/bbl for the average in Brazil, at US$38/bbl for large scale cassava-based ethanol production in Thailand, at US$45/bbl for palm oil-based biodiesel in Malaysia, US$58/bbl for maize-based ethanol in the US and can up to nearly US$100 for BTL production in Europe (for more detail see e.g. Schmidhuber 2005). It is important to note that these parity prices have been calculated for very specific production and conversion environments and may thus not necessarily apply to the same or similar feedstocks in different production environments. Likewise, they are based on the exchange rate to the US Dollar that applied for the underlying year of the calculations and may change for the same year and feedstock over time. The appreciation of the Brazilian Rais 7 BTL stands for Biomass to liquid and in practice refers to fuel products engendered by Fischer-Tropsch Synthesis on the basis of biomass conversion. Commercially, these products are known under such names as “Sunfuel” or “Synfuel”.

since 2004/05 has almost certainly raised the parity prices that have been derived for this period. And finally, these are parity price that are based on average feedstock prices of 2004- 05 and in some cases even at feedstock prices of 2000-2002.

Crude oil prices drive sugar prices

0

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20

30

40

50

60

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31 January 2000

31 January 2001

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Figure 4: Sugar prices track crude oil price above US$35/bbl

Parity prices for sugar, Brazil, im pact of carbon credits

0.00

5.00

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20 30 40 50 60 70 80 90 100 110

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Calculations based on data from a sugar m ill + CHP plant in Santa Adelia, Brazil The unit produces 150000 t of raw sugar and about 100000 m 3 ethanol. The project was awarded the title “Best CDM project 2004”, it has sold the CERs for electricity co-generation to a com pany in Europe. The CHP has a capacity of 100 MW , 65MW of which are fed into the local grid.

Figure 5: Parity prices for various first generation feedstocks

Figure 7: Parity prices (PP) for sugar and CDM effects on PPs Figure 6: Floor and ceiling price effect for Cassava, mega plant

... there is no complete market integration in practice ...

In practice, however, demand for biofuels is not driven by infinitely large demand. There are numerous constraints that limit the ability of the biofuel sector to reap the full demand potential, at least in the short run. For transportation fuels, availability for the final consumer and thus demand is circumscribed by bottlenecks in the distribution (lack of petrol stations), technical problems in transportation and blending systems (no pipeline transportation for ethanol due to corrosion problems), insufficient conversion capacities, delays in engine adjustments and development, and many more. In short, the entire “field-to-wheel” system is not yet fully developed for biofuels. For heating fuels, bottlenecks include logistical problems within households, lack of storage capacity given the much higher space requirements and the lower energy density of biofuels, unresolved emission problems (micro-particulates, NOx, CO, etc.), etc.

... except for cane-based ethanol in the Brazilian petrol market.

While most bioenergy markets are still in their infancy, there are a few that are already largely developed and integrated. The Brazilian sugar/ethanol market is probably the most developed and integrated as well as the most profitable one. Market integration is characterised by: (i) a high market penetration for cars that can run on ethanol or any blend of ethanol and petrol; (ii) a country-wide system of petrol stations that offer ethanol; (iii) a growing share of sugar mills that can flexibly switch between sugar and ethanol production; (iv) a small but also rising share of specialised ethanol plants; (v) high-tech conversion and energy production systems, e.g. an integrated energy production system with growing share of combine heat power plants and electricity co-generation.

The cumulative experience over the last 30 years of cane-based ethanol production has resulted in a sharp decline in production costs (see Goldemberg, et al 2004 for details). The integrated cane-based ethanol/electricity co-generation system in Brazil becomes a competitive energy provider at crude oil prices of about US$35/bbl. At this level, Brazilian sugar millers can produce ethanol cum electricity without subsidies. This also means that as of this threshold, prices for sugar should move in sync with petrol prices, and if markets are fully integrated, the co-movement of sugar and petrol prices should even hold for rather short-term changes. Figure 4 one depicts this co-movement of sugar and oil prices. It shows that (i): demand for ethanol has created a floor price for sugar at about US$35/bbl. Oil prices above US$35/bbl make cane-bioenergy (ethanol and electricity) competitive for the energy market and create a co-movement of energy and sugar prices. At (and above) this price level, a sugar/ethanol producer in Brazil will sell sugar only for a price that is at (and above) the energy price equivalent8. (ii) the co-movement of sugar and oil prices is strong, particularly over weeks and months but often even on a daily basis.

Higher use of cane for ethanol production reduces sugar availabilities for exports onto the world market (Figure A2) and these sugar exports in turn lift international sugar prices. The net result is a close link between energy prices (both for ethanol and crude oil) and sugar prices, i.e. that sugar prices closely track oil prices (Figure 4).

8 A price of US$35/bbl corresponds to about USc7/lb for raw sugar.

How does the energy-agriculture price transmission work in a fully integrated biofuel market?

Full flexibility on both the demand and the supply side ensure the tight price link in practice: On the demand side, the rapidly growing share of flex-fuel vehicles (FFVs)9 i.e. cars that can run on gasoline and any blend of hydrous or anhydrous ethanol up to 85 percent, allows consumers to shift almost instantaneously between the two energy alternatives (ethanol and petrol). By the end of 2005, almost 900,000 FFVs had already been sold. The share of these vehicles in new car registrations is expected to rise to 80% in 2006 and several car manufacturers have announced that in future they will only be producing FFVs (F.O. Licht, 2006). With the rapid increase in FFV sales, the share of ethanol in the Otto fuel market increased to 40% in by the end of 2005 (F.O. Licht, 2006). On the supply side, the growing share of sugar mills that can flexibly shift between sugar and ethanol allows producers to switch almost instantaneously between sugar and ethanol production10. As Brazil is not only the second-largest producer of sugar but also the by far the most important exporter, these shifts between sugar and ethanol determine the availability of sugar on the world markets and thus world market prices for sugar11. In short, the consumers ensure the link between oil and ethanol, the producers between ethanol and sugar prices; together they create a strong link between oil and sugar prices (Figure 4). (iii) However, the high and rising flexibility does not exclude that prices for sugar over or undershoot their energy parity price levels in the short run. But the high flexibility on both sides and an increasing awareness in the financial industry of the tight link ensures that the two prices move quickly back to the fundamental parity price relationships. Econometrically, this co- movement is corroborated when a co-integration regression (more precisely “threshold co- integration”) of the two series is undertaken. It can be shown that the price for sugar (Nymex, contract No 11) and oil (WTI, spot) are co-integrated with a threshold of about 35US$/bbl.

No other country and production system has a bioenergy market with the same level of market integration. There are, however, a number of markets where market integration and thus the price link has become increasingly tight. These include the wood pellets market and to a lesser extent the wood chips market in Austria, the prices for both have been following with a growing degree of correlation the prices for heating fuel in 2006 and 2007. A low level of market integration is not a phenomenon that is limited to the bioenergy segment of the energy markets. It also applies to the gas sector both vis-à-vis the oil market and for the gas market across continents. The lack of market integration between the European and the US gas market is largely a reflection of physical market separation, as gas can only be economically 9 The share of FFV in new car registrations in Brazil reached 34% in December 2005, (Sillas 2005) but increased to 80% by the end of 2006 (F.O. Licht). 10 There is full flexibility at the margin but not full flexibility for the average of cane processed by Brazil’s sugar mills. Most mills are able to make a maximum of about 55% of one product and 45% of the other. In the early weeks of a typical harvest and before the flow of cane has reached its peak, most mills tend to make more ethanol than sugar. “With alcohol stocks remaining low, the product is usually more attractive than sugar. The characteristics of cane which matures early in the harvest also make it more suitable for distilling into ethanol than refining into sugar. However, once harvesting has reached its mid-year peak and mills are working at full capacity, they have to make a similar amount of each product in order to keep pace with the flow of cane reaching them. It is only when the flow of cane slows towards the end of the harvest that mills can really choose whether to make more sugar or alcohol with relative prices and apparent prospects of each product at that time playing a key role” (F.O. Licht, 2006). 11 The close link between sugar prices and ethanol prices are corroborated by the strong correlation between Brazil’s sugar exports and ethanol prices.

shipped in liquid form. The growing importance of gas liquification and thus of improved transportation options is likely to bring these markets closer together and their price movements more into sync.

3.2 Fossil fuel prices also create a ceiling for agricultural prices

Energy prices not only can create a lower limit for agricultural prices, they can also create an upper boundary. In the long run, agricultural prices will not rise faster than energy prices. If they do, and irrespective of their doing so in the short run, agricultural feedstocks price themselves out of the energy market. Floor and ceiling prices together can thus create a price corridor for agricultural products, in which price fluctuations are (co-)determined by their energy equivalents and the current energy price. Figure 6 shows the ceiling price that can be paid by a cassava-based ethanol plant in Thailand. If cassava prices move above 1200 baht/t as they did in the late 1990s, only very high oil prices of US$70/bbl and above would keep cassava in the ethanol market. If cassava prices rise above this level, the feedstock loses its competitiveness in the bioenergy market, demand for it falls or ceases altogether and prices decline again. Figure 7 illustrates how additional payments/benefits (in this case through the “Clean Development Mechanism (CDM) of the Kyoto Protocol) can alter the parity price levels and affect the ceiling price effect.

The effect of a long-term price ceiling does not exclude short-term supply disruptions or speculative reasons creating short-term swings that exceed the parity price level. The very high sugar prices (which exceeded their parity price levels to oil by a margin of about 4ct/lb in late November 2006 are a case in point. The ceiling price effect has also become visible in other agricultural market. In Germany for instance, higher maize prices have made maize too expensive a feedstock for biogas production and resulted in lower demand and a situation where half of the plants are making losses on a total cost basis12. Similarly, lower oil prices and maize prices of US$4/bushel and more have squeezed the profit margins for maize-based ethanol production in the US which will, in the long-run, create a ceiling price somewhere in the vicinity of US$5/bushel. At these and higher price levels, particularly older ethanol plants should become unprofitable and this would – barring major subsidies – reduce maize demand and thus put a lid on maize price increases. Again, this does not mean that maize prices cannot increase further in the short run. Should, as some observers13 predict, US maize supplies become exhausted by June 2007, short-term price peaks of US$6-7/bushel are deemed possible. At these prices, however, a growing share of ethanol producers would not even meet variable costs14, thus cease production in the short-term and relieve upward price pressure in the US maize markets.

3.3 Price transmission from energy to agriculture: multiple channels and a growing number of agricultural markets/commodities

The price transmission from energy markets to agricultural markets takes place through a number of channels. First, there are direct, own price links on the supply side. When higher energy prices make an agricultural product competitive for the energy markets, the energy

12 Based on a presentation by Dr Broderson, Managing Director, DLG, at the DKB-Eliteforum Landwirtschaft 2006, Schloß Liebenberg, Germany, November 2006. 13 Randy Schnepf, Congressional Research Service of the US, Presentation at the SAF, SAF-agriculteurs de France, Prospective PAC Agro ressources et Politique agricole commune, 8 rue d’Athènes 75 009 PARIS, 15 March 2007. 14 The short-term production criterion.

market sucks up agricultural feedstock and thus raises feedstock prices. The link weakens again when demand has driven prices up to a point where agricultural feedstocks become too expensive as a source of energy for the energy market. Second, there is an indirect price transmission through substitutes on the supply side. Higher price for a given product (e.g. sugar) create increasing competition with other agricultural crops, thus reducing the availability of these products on the markets and driving up their prices. In addition, rising energy prices increase the number and the quantities of agricultural feedstocks that are competitive for bioenergy and can therefore no longer be supplied to food and feed markets. For instance, the use of cassava in Thailand for bioethanol production will reduce the availability of this product for exports and thus support cereal prices both in Thailand and, because of lower cassava exports, elsewhere. Likewise, the expansion of palm oil production in Malaysia has already created competition for other plantation crops and has reduced rubber and cocoa production. Third, there is price transmission through the demand side. Higher oil prices have already increased prices for nylon and other synthetic fibres and thus indirectly increased buttressed prices for cotton. Likewise, higher oil prices raise the prices for synthetic rubber and thus will increase the competitiveness of natural rubber. This provides room for natural rubber prices to rise.

3.4 Differential price impacts across agricultural markets

The co-movement of prices, however, is not a universal feature across all agricultural markets. Prices will neither increase unabatedly (open-ended) nor uniformly across all food products. The ceiling price effect discussed above is crucial for understanding why an increased bioenergy use is unlikely to create open-ended food price increases and thus a global, long- term food security problem. Importantly, and as demonstrated above, food prices cannot rise faster than energy prices simply because they would then lose their competitiveness in the energy market. The fact that there are different levels of competitiveness for individual feedstocks and that many feedstock contain both energy and protein means that the price effect will not be uniform. In the long-run and barring major policy distortions (subsidies, border measures, etc.), food products will only enter the bioenergy market if they are competitive feedstocks for conversion into fuel (heating or transportation). This means that feedstocks like sugar, cassava or palm oil could experience the strongest price rise, because they have the lowest break even points (Figure 5) and the highest level of competitiveness. The second reason for a non-uniform price increase is that the bioenergy demand is limited to the energy part of feedstocks and this selective demand creates a considerable amount of protein-rich by–products. This means that protein prices are likely to rise less rapidly than energy prices or could even fall in absolute terms. The importance of the selection of feedstocks and the impacts on energy versus protein prices is available from Table 1.

In general, prices for energy-rich crops (sugar, starch-rich crops or woody biomass) stand to benefit from the added energy demand, while those for protein-rich commodities and those for by-products of the bioenergy conversion process are likely to decline. Such negative price impacts have already been noticeable for some by-products such as glycerine, DDGS15, corn

15 The wet milling process renders for a tonne of maize used to produce ethanol a total of xx kg of CGM and xx kg of CGF. Alternatively, the dry milling renders 321 kg of Distillers Dried Grains Soluble (DDGS). CGM and CGF have a protein content of 60% and 21% respectively, and a protein content of about 75 MJ ME, i.e. somewhat less than maize but somewhat more that barley. DDGS on the other hand has a protein content of 27%, 11% fat and 9% fibre. In either case, these by-products are suitable substitutes for traditional protein-rich feedstuffs like soybean meal, even though their high phosphorous and crude fibre contents can put limits on their

gluten feed and soybean meal; Table 2 summarizes the impacts on international commodity prices for five different bioenergy scenarios. Price changes relate to long-term price changes in real terms relative to a scenario without agricultural feedstocks used for bioenergy.

The results from model-based simulations suggest that prices for protein may decline only relative to energy but also in absolute terms. As long as biofuels are produced from feedstocks that are providing energy without simultaneously creating protein-rich by-products (sugar cane, sugar beets, palm oil), downward pressure on protein prices will predominantly be relative to energy prices. Protein prices may slightly increase in absolute terms, as higher energy prices in feed rations would also result in a rise in protein prices, as the energy content of protein feedstuffs would substitute for some of the energy content in energy feedstuffs. At the same time, competition for acreage on the supply side should reduce acreage for protein crops which should also support protein prices. The basic idea of this outcome is depicted by column 1 of Table 2 where it is assumed that a limited amount of sugar (10 million tonnes of sugar in raw sugar equivalents) is absorbed by the biofuels market. In this case, the prices for protein rise, albeit in a very marginal way.

Table 2: Differential price effects of different bioenergy scenarios (Schmidhuber, 2005)

Model results also suggest that if biofuels are produced from feedstocks with high protein contents (oilseeds, notably soybeans, but also cereals), the downward pressure on protein prices is likely to be substantial. As expected the extent of the downward pressure strongly depends on the protein content of the feedstock. It is likely to be particularly pronounced in the case of biodiesel production from soybeans, where, for every litre of biodiesel, 4 kg of soybean meal will have to be absorbed by the market. In fact, soybeans have been the most maximum suitable shares in the feed rations. These limits depend on the type of livestock, and are relatively low for non-ruminants and but higher for ruminants. (Percentages according to K. Davis, 2001), http://www.distillersgrains.org/files/grains/K.Davis--Dry&WetMillProcessing.pdf

important feedstock for biodiesel in the US. The results model simulations in Table xx suggest that the price effect can be noticeable. For every additional 10 million tonnes of soybeans combined with every extra 10 million tonnes of Maize, protein prices would fall be 8%. Also derived products which require large quantities of protein feed such as poultry meat would experience a mild downward pressure on prices (2%).

There are, however, reasons to assume that this downward pressure on protein prices as well as the upward pressure on energy prices are unlikely to increase proportionally with higher quantities of the feedstocks used for bioenergy. For one, protein feedstuffs for animal feed rations also contain energy and the cheaper energy in the feed proteins will start to replace the energy from the increasingly expensive energy feedstuffs (within the relevant physiological limits). This effect has already been noted within the current, first generation use of bioenergy crops. For another, a growing shift towards the second generation bioenergy feedstocks will further strengthen the co-movement in protein and energy prices. The application of second- generation bioenergy technologies means that the entire crop will be used to produce bioenergy, in contrast with the present practice of utilizing only a (potentially small) part of the feedstock (energy) while the protein-rich rest is returned to agricultural markets. Both, the feed effect as well as the shift towards second-generation bioenergy technologies will stop protein prices from falling and energy prices from rising so that they move again in sync (Schmidhuber, 2005).

The third factor that can have an important effect on relative prices can emerge from support and protection policies. Subsidies and tariffs are important reasons today why cereals are used in the US and in Europe as feedstocks in the bioethanol industries of these countries (some of the most important trade barriers are illustrated in Figure A4, Annex). Whether protection and subsidies could be justified on grounds of energy security, infant industry protection, or environmental benefits is a debatable issue. For the analysis of price effects on agricultural markets it should suffice to note that these tariffs keep prices for feedstocks higher than they would otherwise be and thus increase, directly or indirectly, prices for food with consequent effects on food security. The next and final section of this paper will examine how the price effects (floor price effect, ceiling price effect and differential price changes) discussed in section 4 could affect food security. The price impacts on the various dimensions of food security, i.e. food availability, access to food, food utilization and stability will be discussed separately.

4 Impacts on food security

The FAO defines food security as a “situation that exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life” (FAO, 2002a). This definition comprises four key dimensions of food supplies: availability, stability, access and utilization. The first dimension relates to the availability of sufficient food, i.e. to the overall ability of the agricultural system to meet food demand. Its sub-dimensions include the agro- ecological fundamentals of crop and livestock production, as well as the entire range of socio- economic and cultural factors that determine where and how farmers perform in response to markets. The second dimension, stability, relates to individuals who are at high risk of temporarily or permanently losing their access to the resources needed to consume adequate food. This is either because these individuals cannot insure ex ante against income shocks or

because they lack enough “reserves” to smooth consumption ex post or both. The third dimension, access, covers access by individuals to adequate resources (entitlements) to acquire appropriate foods for a nutritious diet. Entitlements are defined as the set of all those commodity bundles over which a person can establish command given the legal, political, economic and social arrangements of the community of which he or she is a member. Thus a key element is the purchasing power of consumers and the evolution of real incomes and food prices (Schmidhuber and Tubiello, 2007). However, these resources need not be exclusively monetary but may also include traditional rights e.g. to a share of common resources. Finally, utilization encompasses all food safety and quality aspects of nutrition; its sub-dimensions are therefore related to health, including the sanitary conditions across the entire food chain. It is not enough that someone is getting what appears to be an adequate quantity of food if that person is unable to make use of the food because he or she is falling sick.

4.1 Access to food

Agriculture is not only a source of the commodity food but, equally importantly, also a source of income. In a world where trade is possible at reasonably low cost, the crucial issue for food security is not whether food is “available”, but whether the monetary and non-monetary resources at the disposal of the population are sufficient to allow everyone access to adequate quantities of food The key factors that affect changes in access to food are real incomes and real prices for food. A greater role of bioenergy has an effect on both.

Price effects: Higher prices will reduce the purchasing power of consumers with adverse effects on their food security. But as discussed prices will neither increase indefinitely nor uniformly across all food products. In the long-run, neither food energy nor protein prices can rise faster than fuel energy prices in order for these feedstocks to remain competitive in the fuel energy market. This means that a global long-term food security problem due to increased bioenergy use would only be credible when and if real energy prices continue to rise. And even if they did, it would only reduce access to food and increase food insecurity if real food prices rose faster than real incomes.

In the short-run and during first-generation bioenergy use, prices for energy will rise faster than prices for protein. In a food insecurity situation where protein rich feedstocks are in short supply, the extra amounts of protein at lower prices would attenuate the adverse impacts from higher food energy prices, and may even make food rations more nutritious and thus improve the quality of food. As discussed, generally lower protein prices would be the outcome of a bioenergy scenario that would be based on the use of protein-rich oilseeds such as soybeans or rapeseed perhaps combines with the use of cereals such as maize or wheat as feedstocks for ethanol production. As also discussed, while these feedstocks indeed play an important role today, their low energy efficiency and their low carbon sequestration effects suggest that they will give way to more efficient converters of sunlight such as sugar cane or ligno-cellulosic feedstocks such as straw, miscanthus, poplar, or willow. In the long-run, it is also unlikely that the wedge between protein and energy prices will continue to increase.16

Income effects: An increased use of bioenergy is likely to affect not only prices and price patterns but also levels and the distribution of incomes, particularly in developing countries. For farmers, bioenergy should boost their overall revenues by raising both the prices they

16 For the interpretation of these price effects it is important to bear in mind that they reflect changes at the margin. Higher use may not simply have a proportionally higher price effect.

fetch for their products and the volume of products that they can sell on the markets. The price effect was discussed above. The positive volume effect is due to the fact that bioenergy makes certain farm products such as straw or crop residues -- for which there is currently no market other than bioenergy -- marketable products. A higher use of these products means that farmers may also face higher prices for some of their inputs and they may need to buy inputs like feedstuffs which where previously produced on the farm. In the long-run, they may also face higher wages if and where bioenergy boosts overall rural incomes. They may also face higher resource costs, notably higher land prices, as higher price for agriculture tend to capitalise on these scarce resources. Overall and notwithstanding the long-term adjustment processes in costs for land and labour, the positive revenue effect will exceed the costs and increase net farm incomes. Higher wages in rural areas and more employment effects should also increased overall rural incomes (trickle-down effect). The net effect on incomes in rural areas in general and in agricultural incomes in particular should thus be positive. And this also holds for access to food and food security in rural areas, and thus for 70 percent of all poor and undernourished, globally. The income effects of an increased use of bioenergy will also depend on the type of bioenergy with respect to factor demand. Where bioenergy is labour- intensive, factor incomes from cheap labour could help engender higher incomes for the poor. Conversely, where bioenergy is capital-intensive and labour-saving, impacts on incomes and thus access to food could be negative. Particularly hard hit will be land-less rural households that are both net buyers of both food and energy, particularly if they fail to benefit from the macro-economic benefits that bioenergy can bring about (higher employment rates and higher wages). The exact effects of course require further empirical analysis.

While many rural areas stand to benefit, urban households will face higher prices for food. Important here is to recall that food prices and energy prices rise in tandem and that the strength of the link between the two increases with rising energy prices. For net buyers of food and energy, this would be particularly negative. At the household level, a poor urban household with a high expenditure share on food and energy would be particularly hard-hit. What types of households stand to benefit or lose from the parallel increase of food and energy prices needs to be examined empirically. At the country level, a first empirical analysis has already been undertaken. The results are summarized in Figure 9a-d. These 4 charts show countries with less than US$5000 GDP per capita sorted in four rubrics according to their net trade positions for food and energy imports. The graphs illustrate the per capita net-trade positions in nominal US Dollars of 2004 at different levels of oil prices, ranging from US$30/bbl to US$60/bbl. The energy imports include all forms of energy (oil, coal, gas, electricity). For the price changes in the energy sector it is assumed that the non-oil forms of energy increase in sync with oil prices. The revenues from agricultural exports refer to all agricultural products; the price links are endogenous and model-based. The strength of the link increases in a more than linear manner. This is due to the fact that higher energy prices make a growing number of commodities competitive for the energy market, and thus lifts their price with energy prices. It the long-run, higher levels of energy prices will also provide incentives for bioenergy investments and thus lead to a higher degree of market integration. Another consequence will be a co-movement of energy and agricultural prices for more products and in a firmer manner for each product.

The results of the analysis make it possible to categorise countries in four principal rubrics. Importers of agriculture and energy, these countries are in a lose-lose situation as they face higher current account deficits from both product rubrics and the deficit is likely to accelerate with rising energy prices. As discussed below, within this lose-lose rubric, two cases are to be distinguished. First, countries which can pass on the higher import expenditures for food and

energy to value-added export products; and second countries which import food and energy without being able to pass the extra costs onto their export sectors. In contrast, the positive extremes are countries that are traditional net exporters of both food and energy; these countries stand to benefit from price increases of both product categories and the increases in total current account surpluses are more than linear relative to the increases in oil price. Indonesia or Malaysia fall into this win/win rubric. And finally there are countries that export either food or energy and they tend to win or lose depending on the relative size of the food or energy exports and imports. They are in the upper left and lower left quadrant and are characterised as win/lose or lose/win cases.

The discussion of the results in the context of food security is limited to the lower left quadrant, i.e. the net importers of both food and energy. These are the countries that will experience the strongest negative effects on their current account as they face both higher expenditures for food and energy; and, as explained, the negative current account effect will accelerate as the link between food and energy prices get tighter as energy prices rise. These countries are likely to face a lose/lose situation not only for their current account but also as far as their food security situation is concerned.

The food trade deficit of LDCs is rapidly rising

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However, as for individual households, the impacts of higher food and energy prices will not be uniform across all countries in the lose/lose category. While all countries will experience a similar negative effect on their import expenditures, there are considerable differences with respect to their ability to pass these prices onto their exports and thus increase export revenues. Countries like to the Maldives or many of the Caribbean countries (St Lucia, Jamaica) should be able to pass at least a part of the input price increases onto their exports (tourism). At least in the long-run, their higher food and energy import expenditures should translate into higher export revenues. This is particularly so, where export demand for tourism is rather inelastic i.e. where tourists continue to holiday in their favourite spots even if prices have increased. The Maldives could be a case in point.

But there are also countries where the possibility of passing higher import costs onto exports does not exist, or to a much lower extent. These are countries like Jordan, Lebanon, etc., which would indeed face a double blow on their trade balance. Whether and to what extent

Food Exports Food Imports Food Net trade

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Figure 8a/b: Food and agricultural trade deficits of LCDs, 1961-2004, Source FAOSTAT

this translates into a food security problem depends of course on the overall income levels in these countries. Overall, the poorest of the poor may be particularly hard hit. Most LDCs are both net importers of food (43 of 52 in 2002/04), net importers of agricultural products in general (38 of 52 in 2002/04) and overall they have considerable and rapidly growing multi- billion trade deficit for both food (US$6 billion) and agriculture (US$6.9 billion, see Figure 8a/b). As they are also the countries with the lowest level of GDP, the adverse effect from higher energy and agricultural imports relative to national incomes is likely to be particularly strong. How strong this effect is in practice will depend on the possibilities of individual countries to substitute for energy and agricultural imports or to pass higher import prices onto value-added exports.

Figures 9a-d: Impacts of higher energy prices on net trade values in energy and agriculture

4.2 Food availability

Food availability is the net effect of changes in production, net trade and stocks17. The analysis here will focus on production and trade aspects, as availabilities from stocks do not matter in the long, notwithstanding their crucial role for short-run food supplies.

In general, higher use of agricultural produce for non-food purposes should lower domestic food availability. The extent of lower food production would, however, depend on the type of feedstock used to produce bioenergy. Where bioenergy production is based on agricultural by- products (straw, molasses, crop residues, cow dung, etc.) or an increased use of forestry products and by-products (wood chips, saw dust, etc.) that have been used for other industrial purposes (e.g. the paper and pulp industry) the impact on domestic food availability is likely to be small. This the case for traditional bioenergy use in developing countries, which was based on by-products such as straw, crop residues or dung and could be the case for second- generation bioenergy which is likely to be based on ligno-cellulosic feedstocks such as wood or straw. The current, first-generation bioenergy feedstocks by contrast are largely based on food commodities which indeed compete either directly with food on the utilization side and indirectly on the production side for the resources needed (land, water, labour, capital) to produce food.

Higher domestic production of non-food products affects availability from trade, both directly and indirectly. Directly, as higher levels of non-food production (wood, etc.) are likely to lower the availability of food products for exports. Indirectly, as higher non-food exports could increase trade revenues and thus increase the purchasing power needed for food imports. This indirect effect would be particularly pronounced if and when the employment and income effect of a booming domestic bioenergy industry raises the purchasing power of people with low purchasing power and low food consumption levels. In this case, higher import availability would simply be a manifestation of enhanced access. As noted before, whether more food becomes available will therefore crucially depend on the distribution of the additional incomes generated by a burgeoning bioenergy industry.

4.3 Stability of food supplies

The stability element of food security relates to the risk of losing temporarily or permanently access to the resources needed to consume adequate food. While this risk has numerous components, important here is the risk that arises from possible swings in food prices that are pronounced enough to price poorer and food insecure segments of a population out of the food market. The basic question therefore is whether the rising demand for bioenergy makes agricultural prices more or less volatile. The impacts on price variability work through numerous channels and depend on many factors and a quantitative answer would have to be model-based. Without any quantitative results, this paper can only discuss some of the principal issues.

A priori, a rising non-food demand should reduce the size of the food market and make this smaller market more susceptible to exogenous shocks. Fewer producers would make supply less elastic and thus less able to compensate for such a shock. What is more, demand for energy could be very inelastic in the short-run, particularly in rich industrial countries. This

17 Food availability=food production + net trade (imports-exports) – stock changes defined as (ending stocks- beginning stocks)

could mean that energy consumers in rich countries price food consumers in poorer countries out of the food market. However, there is also reason to assume that the expectations of a marked increase in price variability may not be justified and that prices may even be less variable in an agricultural market with higher bioenergy use. First, and as discussed in section 3 of this paper, the overall energy market will not only create a floor price for agricultural produce but also a ceiling price. This ceiling price effect is due to the need for agricultural produce to stay below the energy price equivalent in order to remain competitive. This should put a cap on price hikes particularly in the long-run. There are also reasons to assume that this very mechanism will even be effective in the short(er)-run, particularly if prices increase in a pronounced way. The main reason for the limits on short-run price peaks is that feedstocks account for a large share of total costs and this share of course rises further if feedstock costs increase. In large ethanol production plants, for instance, feedstock costs can account for about 70-80% of total costs. This means that the short and long-term production criterion for profitability in such plants will be close to each other and that they will cease converting food into bioenergy altogether when feedstock prices become too expensive. In other words, when variable costs cannot be covered, plants will stop producing in the short run and thus help stabilise prices. The importance of feedstock costs is illustrated in Table 3 where simulations for two cassava-based ethanol plants with different feedstock prices and different plant sizes are depicted.

Table 3: Impacts of size of the processing plant and feedstock costs on the share between fixed costs and variable costs (source: own calculations based on project proposals)

Ethanol plant capacity Feedstock price for cassava In baht/t farm gate

200,0001 l/d (1,300t cassava/d)

2,000,0002 l/d (12,800 t cassava/d)

share of capital costs % 713 29 21 1000 23 17 1500 17 12

1.) This plant will be located in the Chok Chai district of Nakhon Ratchasima, Thailand’s main cassava- producing province. It is a joint venture of the Agricultural Co-operative Federation of Thailand and O.C.T. Land & Energy L.C., USA. The former is 4000 member farm co-operative and will hold 60% of the registered capital, the latter is an affiliate of O.C.T. Fiberglass Products, based in Wichita, Kansas and will hold 40% of the capital. 2. A feasibility study endorsed by Thai Oil and “strategic partners” found an ethanol plant of this size appropriate and necessary to match the company’s fossil fuel refinery capacity and its blending needs for gasohol production. The investment needs have been pegged at US$150-250 million, daily output would be about 1.5-2 million l. It should be noted that the plant would consume, at an extraction rate of 167 l ethanol/ cassava a total amount of about 4.3 million tonnes of cassava per annum. This is about 25% of total cassava production (2000-04 average) and equivalent to the entire output of the Nakhon Ratchasima district, Thailand’s main cassava producing area. It is interesting to note that this investment project has not been implemented but is likely to be substituted by three smaller cassava-based ethanol plants.

Another important factor that affects the magnitude of possible price swings is the extent of short-term substitutability in using feedstocks for food and non-food uses. High substitutability between food and non-food markets would enlarge the overall market volume and make prices c.p. less variable18. Brazil’s sugar-based ethanol production is a case in point.

18 The positive effects of an enlarged market on the stability of food prices are well researched for feed/food substitution. For details see e.g. FAO 2002b, online at: http://www.fao.org/docrep/004/y3557e/y3557e09.htm#o

Given the high market integration of this market and its significant size both in domestic energy and international sugar markets, the non-food use of sugar works like a giant buffer stock for the sugar market that releases sugar on the market when it becomes too expensive for ethanol production and sucks it up when sugar is too cheap and it is more profitable to produce bioenergy out of the same feedstock. It can already be shown that not only the price levels of sugar but also the variability of the sugar prices follows closely the variability of energy prices; with the growing integration of the sugar-ethanol market, magnitude and frequency of sugar price variations closely trace those in crude oil.

The high degree of integration in the sugar market is however not (yet) characteristic of other agricultural feedstock markets. In most bioenergy markets substitutability is still low and rising utilization of agricultural feedstocks for bioenergy eats into the volumes of the corresponding food markets. This is particularly the case for many perennial crops (miscanthus, poplar, willow, etc.) where the limited or completely missing substitutability in conjunction with a multi-year area allocation to non-food production makes it more difficult to shift from non-food use to food use and vice versa. A massive shift towards such feedstocks may make overall food markets more susceptible to price shocks.

The discussion of the impacts of an increased bioenergy use shows that higher agricultural and energy prices can provide both a threat to but also an important opportunity for improving food security. At the country level, the short-term static effects of the likely price changes for food and energy will crucially depend on the net trade position for these products and the ability of a country to pass on higher import prices to higher export values for derived products. Similarly, the effects at the individual household level will depend on whether a household is a net buyer of these products. In general, it can be expected that individual households will either be in lose/lose or in a win/win situation. In general, rural households stand to benefit from both higher food prices and higher volumes of marketable produce which they can sell as bioenergy feedstocks. Urban households stand to lose as net buyers of both food and energy.

Policies can play an important role in mitigating the adverse effects on net buyers of food and energy and ensure that net sellers of both are able to fully harness the benefits. If and where the right policies are in place, the use and production of bioenergy affords rural areas the chance of a renaissance. It could help attract resources back into the countryside, mitigate urbanisation pressures and initiate a new rural dawn. But a lot more work is needed to analyse the appropriate companion policies in order to maximise the benefits for rural areas without causing massive problems for urban dwellers.

Summary, conclusions and outlook

The paper illustrated energy markets are affecting agricultural markets and showed how. It briefly introduced nature and size of bioenergy potentials, examined price and market effects and discussed possible impacts on food security. The key findings and the main conclusions that follow from these findings can be summarized as follows:

1. Rising prices for fossil energy have made a growing number of agricultural feedstocks competitive feedstocks for the energy market. The extra demand has resulted in a global increase in agricultural commodity prices and the creation of a floor price effect for competitive feedstocks. The potential demand from the overall energy market is so large that it could result in a change in the overall paradigm of rapidly rising supply, increasingly saturated demand and falling real prices that has governed international agricultural markets over the last 40 years.

2. Higher real prices in agriculture will have numerous effects on rural areas, rural industries and food security. They create opportunities but also new challenges. Higher real prices can help revitalise rural areas and help reduce rural poverty. The combination of higher prices and more marketable produce will raise overall revenues for agricultural households. In tandem, rural, non-agricultural households could benefit from new employment opportunities and higher wages and thus higher incomes. The positive income effect should be particularly pronounced where bioenergy production and processing is labour-intensive and access to land is relatively equitable. Overall, the effect could be a global renaissance of agriculture and a revitalisation of rural areas.

3. A new bust after the first generation boom? While bioenergy has the potential to arrest the long-term downward trend in real prices for food and agriculture, the effect may be limited in time and size and even a longer interruption in falling real prices may not mean a complete and permanent departure from the century-long downward trend. Episodes of rising real prices are not new and the long-term price decline over the last century was characterised by three periods or rising real prices (1900-18, 1933-48, 1973-80, 2000- 2007). These periods lasted more than a decade and they were typically followed by pronounced bust cycles. High-price periods have led farmers to expand and intensify production, invest in land and technology and assume debt to an extent that has later proven unsustainable. What is more, much of the increased price was typically capitalized in the price of land rather than resulting in the longer-term profitability of farm operations. Higher values of collaterals, high short-term profitability followed by pronounced bust cycles led to large amounts of non-performing loans in agriculture and periods of widespread financial distress in farming. The US "farm crisis" of the 1980s is the most recent example (Gardner, 2003).

4. The current bioenergy-triggered boom could also be followed by a marked bust cycle. It could be ushered when the second generation biotech feedstocks enters the market on a large scale. Second-generation technologies could make many of the first generation feedstocks (i.e. the traditional agricultural and food commodities) unprofitable and result in a demand and price shift from food commodities to forests commodities. This shift could make not only first-generation feedstock production unprofitable, but the entire production chain as well because second-generation processing technologies will be entirely different. For food prices, this should result in less demand and possibly a return to falling real prices.

5. New support and protection policies in developed and developing countries for bioenergy, combined with new policy initiatives (CDM, JI, GEF, etc.) at the international level and a growing engagement by International Financial Institutions could add to possible over-

investments in bioenergy production. The simultaneous commitment to investing in the same sector could result in a global “fallacy of composition” problem. As more efficient first-generation plants come on stream and as second-generation technologies enter the bioenergy markets, a lot of investments in first-generation bioenergy could turn sour or remain only profitable if real prices for fossil energy remain high and rising. The first signs of such problems are already visible in the low profitability of maize-based biogas production in Germany and of maize ethanol plants in the US given currently rising maize prices.

6. The growing dependence of agricultural prices on energy prices also means that there will be an endogenous cap on food price increases. In order to remain competitive for the energy market, agricultural feedstock prices cannot rise faster than energy prices in order to remain competitive as a source of energy. This creates an implicit ceiling price effect, which is given by the energy equivalent of an agricultural feedstock. The ceiling price effect is crucial for understanding that agricultural prices and markets will not continue the recent boom sparked by the spike in oil prices but also to understand that concerns about a looming global neo-Malthusian scenario are unwarranted19. Only if energy prices continue to rise will agricultural prices follow. Overall, floor and ceiling price effects are creating a new equilibrium for an increasing number of agricultural commodity markets. With rising energy prices and a growing degree of market integration between energy and agricultural feedstock markets, both the levels and variability of agricultural commodities will increasingly be determined by those of energy prices.

7. The impact on food security needs to be analysed in the context not only of higher food prices and lower availability but also in terms of rising incomes for farmers and rural areas as well as changing price variability. A priori, competition with food production will result in lower availability and an increase in food expenditures for the poor. Particularly net buyers of both food and energy would suffer from the parallel increase in food and energy prices. As net buyers of food and energy, the poorest of the poor could be particularly hard hit. At the country level, many developing economies are currently facing a double burden on their current account balance through higher expenditures for food and energy imports. The same holds for individual households that are both net buyers of food and energy. However, many rural households stand to benefit both through higher prices for their produce and higher volumes of marketable production. As 70 percent of the poor live in rural areas, the overall net effect on food security could be positive. While rural households stand to benefit as sellers of food and energy, urban households stand to suffer from higher expenditures for both. Importantly, food expenditures rises more than linearly as the price link between energy and food prices tightens with rising energy prices. While the inter-country effects have been quantified for this paper, the intra- country effects would need to be gauged by a detailed analysis of household balance sheet effects.

8. One of the challenges for the orientation of development policy is to design and implement policy measures that help ensure that the growing use of bioenergy is conducive to reducing poverty and hunger, i.e. that “bioenergy becomes pro-poor”; in theory, this will the case, the closer the factor demand of bioenergy is complementary to the factor endowment of the poor; in general this is the case when bioenergy use and processing are labour- intensive, capital-saving, and technology-saving. Pro-poor policies would help to adapt bioenergy use and processing accordingly so that they are based on

19 The predictions of a massive dooms day scenario caused by an increase of use of agricultural commodities for biofuel are numerous and massive. For instance, BBC International quotes the Cuban Fidel Castro of claiming that the US ethanol programme alone will cause 3 billion deaths from hunger, i.e. half the world’s population. http://news.bbc.co.uk/2/hi/americas/6505881.stm

little capital and simple technologies and abundant low-skilled labour. Pro-poor policies will also promote and foster access to technology and capital (e.g. through the CDM and CDM like mechanisms), provide access to land (land reform, etc.).

9. Functioning institutions that make an important contribution to making bioenergy pro- poor. Co-operatives for instance can bundle the interests of the poor, accumulate and attract capital for the necessary investments, organise feedstock supplies in large quantities and qualities and create a countervailing power to the high power concentration of firms operating in the energy market. While this paper did not explicitly discuss options, some of the examples used to assess the profitability of such co-operative operations have been presented (see e.g. Cassava-based ethanol production in the Chok Chai district of Nakhon Ratchasima).

10. FAO and other international organizations have an important role to play in providing information and analyses that help create the basis for investments in bioenergy in developing countries. They may also have an even more important role in ensuring that the policy distortions of developed countries agricultural markets’, which were slowly and painfully reduced in the 1990s, will not be re-introduced through the “bioenergy backdoor”. Failure here would not only impede the development of the bioenergy income potentials in developing countries but also compound and prolong a possible bust period after the current boom cycle. A simple first step to level the bioenergy playing field would be a noticeable reduction in tariffs for biofuels in developed countries, noticeable for bioethanol. As depicted in Figure A4 (Annex), these entry hurdles in developed countries can be considerable.

References

BBC International: “Castro hits out at US biofuel use”, Thursday, 29 March 2007, online at: http://news.bbc.co.uk/2/hi/americas/6505881.stm

Bruinsma, J. (Ed.) (2003), World agriculture: Towards 2015/2030, An FAO Perspective. Rome: FAO and London: Earthscan.

Davis, K (2001), “Corn Milling, Processing and Generation of Co-products”, presentation at the Minnesota Nutrition Conference, Minnesota Corn Growers Association, Technical Symposium, September 11, 2001, http://www.distillersgrains.org/files/grains/K.Davis-- Dry&WetMillProcessing.pdf.

Earth Policy Institute (2005), http://www.earth-policy.org/Updates/2005/Update49.htm

FAO, 2002a: The State of Food Insecurity in the World 2001. Rome, 2002.

FAO, 2002b: World Agriculture: towards 2015/2030, Summary Report, Rome 2002.

FAO, 2006, World agriculture: towards 2030/2050, Interim report, Rome, 2006.

FAOSTAT, http://faostat.fao.org/default.aspx, various data bases.

F.O. Licht (2006), “World Ethanol Markets, A special Study”, The Outlook to 2015, Published and distributed by F.O. Licht, 80 Calverley Road, Tunbridge Wells, Kent TN1 2UN, UK

Fischer, G. and Schrattenholzer, L. (2001), “Global bioenergy potentials through 2050”, in Biomass and Bioenergy 20, p151-159, Elsevier publishing.

Gardner, Bruce L. (2003) American Agriculture in the Twentieth Century: How It Flourished and What It Cost. Cambridge, MA: Harvard University Press, 2002.

Gardner, Bruce. L. (2002), "U.S. Agriculture in the Twentieth Century". EH.Net Encyclopedia, edited by Robert Whaples. March 21, 2003. URL http://eh.net/encyclopedia/article/gardner.agriculture.us

Goldemberg J.; Coelho S.T.; Nastari P.M.; Lucon O. (2004), Ethanol learning curve-the Brazilian experience” in: Biomass and Bioenergy, Volume 26, Number 3, March 2004, pp. 301-304(4).

International Energy Agency (IEA, 2004a), Biofuels for transportation, Paris 2004

International Energy Agency (IEA, 2004b), “Key World Energy Statistics”, Paris, 2004.

International Energy Agency (IEA, 2006), World Energy Outlook 2006, Paris 2006

IMF (2006), World Economic Outlook, Financial Systems and Economic Cycles, Chapter 5. “The Boom in NonFuel Commodity Prices: Can It Last?”, Washington, September 2006.

Kapur, J.C. (2004), “Available energy resources and environmental imperatives”, http://www.worldaffairsjournal.com/article1.htm, World Affair, Issue No, V10 N1.

E. Smeets, A. Faaij, I. Lewandowski, (2004), A quickscan of global bio-energy potentials to 2050 – an analysis of the regional availability of biomass resources for export in relation to underlying factors, Report prepared for NOVEM and Essent, Copernicus Institute – Utrecht University, NWS-E-2004-109, March 2004. Pp. 67 + Appendices.

Schmidhuber, J and P. Shetty (2005), The nutrition transition to 2030. Why developing countries are likely to bear the major burden”, Acta Agriculturae Scand Section C, 2005; 2: 150-166.

Schmidhuber, J. (2005), “The nutrition and the energy transition of world agricultural markets”, Plenary presentation at the German Association of Agricultural Economists (GEWISOLA), Göttingen, October 2005.

Schmidhuber, J. (2006) Die Auswirkungen der Biomassenutzung auf die Weltagrarmärkte“ Kurzfassung des Vortrages zum Fachsymposium zu den „Perspektiven der energetischen Biomassenutzung“ Bad Hersfeld, 21. März 2006

Schmidhuber, J. and F.N. Tubiello (2007), “Climate Change and Global Food Security: Socio-economic dimensions of vulnerability”, paper accepted for publication in the Proceedings of the National Academy of Sciences (PNAS), 2007.

Sillas Oliva Filho (2005) The use of Ethanol in Brazil” Presentation at the UN World Environment day, San Francisco 2005.

World Bank (various issues), “Pink Sheets” World Bank Price Indicators, online available at: http://web.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTDECPROSPECTS/0,,conten tMDK:20268484~menuPK:556802~pagePK:64165401~piPK:64165026~theSitePK:476883,0 0.html.

World Bank (2004), World Development Indicators. CD-ROM.

Abbreviations

1. Units bbl Barrel, 159 litre

bushel (maize) 25.4012 kg

EJ Exajoule, 1018 joule

Mtoe (million tonne oil equivalent) = 41.868 PJ

PJ Petajoule, 1015 joule

2. Organizations FAO Food and Agriculture Organization of the United Nations

IEA International Energy Agency

IIASA International Institute for Applied Systems Analysis

NYMEX New York Mercantile Exchange

OECD Organization for Economic Co-operation and Development

USDA United States Department of Agriculture

3. Technical Terms

CO Carbon Monoxide

BTL Biomass to liquid

Fischer Tropsch The Fischer-Tropsch process is a catalyzed chemical reaction in which carbon monoxide and hydrogen are converted into liquid hydrocarbons of various forms.

LDC Least Developed Country

NOx Nitrogen oxide

TPES Total Primary Energy Supply

WTI West Texas Intermediate

Annex

Figure A1: Long-term real prices for agriculture in the US, Source, Gardner 2003.

Figure A2: Ethanol prices track sugar exports in Brazil

Figure A3: Supply curve of sugar and potentials for biofuels

Figure A4: Tariffs and production costs for ethanol in the EU, Brazil and the US

  • IMF (2006), World Economic Outlook, Financial Systems and Economic Cycles, Chapter 5. “The Boom in NonFuel Commodity Prices: Can It Last?”, Washington, September 2006.
    • Schmidhuber, J. and F.N. Tubiello (2007), “Climate Change and Global Food Security: Socio-economic dimensions of vulnerability”, paper accepted for publication in the Proceedings of the National Academy of Sciences (PNAS), 2007.

schnepf_CRS_CommodityPrices.pdf

Order Code RL34474

High Agricultural Commodity Prices: What Are the Issues?

May 6, 2008

Randy Schnepf Specialist in Agricultural Policy

Resources, Science, and Industry Division

High Agricultural Commodity Prices: What Are the Issues?

Summary

Prices for nearly all major U.S. agricultural program crops — corn, barley, sorghum, oats, wheat, rice, and soybeans — have exhibited extreme price volatility since mid-2007, while rising to record or near-record levels in early 2008. Several international organizations have announced that the sharply rising commodity prices are likely to have dire consequences for the world’s vulnerable populations, particularly in import-dependent, less developed nations. In the United States, high commodity prices have pushed farm income to successive annual records and have sharply lowered government farm program costs, but they have also stoked the flames of food price inflation and have raised costs for livestock producers and food processors. In addition, high, unexpectedly volatile prices have increased the risk and costs associated with grain merchandising. In particular, they have dramatically increased the cost of routine hedging activities (i.e., pricing commodities for purchase, delivery, or use at some future date) at commodity futures exchanges and, as a result, have diminished “forward contracting” opportunities for grain and oilseed producers who are eager to take advantage of record high market prices.

For some crops (particularly for wheat and rice), the price increases are likely to be relatively short-term in nature and are due to weather-related crop shortfalls in major producer and consumer countries, a weak U.S. dollar that has helped spark large increases in U.S. exports, a bidding war among major U.S. crops for land in the months leading up to spring planting in 2008, and the often perverse price effects resulting from international policy responses by several major exporting and importing nations to protect their domestic markets. Assuming a return to normal weather, these factors will likely self-correct within two growing seasons as global supplies are replenished and prices moderate. For coarse grains (corn, sorghum, barley, oats, and rye), oilseeds, and oilseed products (e.g., vegetable oil and meal), the price increases have also been due to strong, sustained demand deriving from two sources: robust income growth in developing countries (e.g., China and India), which has contributed to increased demand for meat products and the feed grains needed to produce that meat; and growing agricultural feedstock demand to meet large increases in government biofuel-usage mandates or goals in the United States, the European Union, and other countries.

Market analysts, including the United Nations’ Food and Agricultural Organization (FAO), are predicting record global grain and oilseed production in 2008 in response to the high market prices. However, given the overall strength in demand growth, most market analysts predict that when commodity supplies eventually recover and prices moderate from current high levels, the new equilibrium prices will be significantly higher than has traditionally been observed during periods of market balance.

This report examines the causes, consequences, and outlook for prices of the major U.S. program crops, and provides references for more detailed information. It will be updated as events warrant.

Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Global Food Crises Declared by United Nations . . . . . . . . . . . . . . . . . . . . . . 2 Not All Commodities Are Equal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 High Prices: A Case of Deja Vu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

U.S. and Global Stocks Near Historic Lows for Several Crops . . . . . . . . . . . . . . . 5

Many Commodity Price Records Established in 2008 . . . . . . . . . . . . . . . . . . . . 10 International Index of Export Prices Record High . . . . . . . . . . . . . . . . . . . . 10 U.S. Farm Prices Projected Record High for Several Crops . . . . . . . . . . . . 11 Several Futures Prices Set All-Time Highs . . . . . . . . . . . . . . . . . . . . . . . . 12

Factors Behind the High Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Widespread Weather-Related Crop Shortfalls . . . . . . . . . . . . . . . . . . . . . . . 16 Strong Economic Growth in Developing Countries . . . . . . . . . . . . . . . . . . 17 Weak U.S. Dollar Lowers Cost of U.S. Exports . . . . . . . . . . . . . . . . . . . . . 17 Government Biofuels Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Foreign Government Policies to Limit Exports . . . . . . . . . . . . . . . . . . . . . . 21 High Energy Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Macroeconomic Linkages Reinforce Price Rises . . . . . . . . . . . . . . . . . . . . 24

Implications of High Commodity Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 U.S. Farm Income Record High . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Lower Government Farm Program Outlays . . . . . . . . . . . . . . . . . . . . . . . . . 25 Crop Insurance Premiums Costs Surge in 2008 . . . . . . . . . . . . . . . . . . . . . . 25 Sharply Higher Feed Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Futures Market Dilemma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Expanded, More Intensive Agricultural Production . . . . . . . . . . . . . . . . . . 28 Converting Conservation Acres to Production . . . . . . . . . . . . . . . . . . . . . . 29 Rising Food Price Inflation Impacts Consumer Budgets . . . . . . . . . . . . . . . 31 Shortages, High Prices Hurt International Food Aid Prospects . . . . . . . . . . 34

Farm Commodity Market Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Positive Short-Run Outlook, Especially for Food Crops . . . . . . . . . . . . . . . 35 Long-Term Outlook Hinges on Productivity Gains . . . . . . . . . . . . . . . . . . . 37

U.S. and International Policy Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 U.S. Industry Groups Decry Rising Costs of Grain as an Input . . . . . . . . . . 37 U.S. Congressional Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Immediate International Food Crises Response . . . . . . . . . . . . . . . . . . . . . . 40 Long-Term Agricultural Productivity Response . . . . . . . . . . . . . . . . . . . . . 42 Possible World Trade Organization (WTO) Implications . . . . . . . . . . . . . . 43

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List of Figures

Figure 1. Monthly International Export Prices for Corn, Wheat, and Rice: January 1990 to April 2008 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Figure 2. U.S. Season Average Farm Prices for Corn, Soybeans, Wheat, and Rice: 1960/61 to 2007/08 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Figure 3. All Wheat: U.S. Season-Average Farm Price vs. Stocks-to-Use Ratio . 7 Figure 4. Corn: U.S. Season-Average Farm Price vs. Stocks-to-Use Ratio . . . . . 8 Figure 5. Barley: U.S. Season-Average Farm Price vs. Stocks-to-Use Ratio . . . . 8 Figure 6. Soybeans: U.S. Season-Average Farm Price vs. Stocks-to-Use Ratio . 9 Figure 7. Rice: U.S. Season-Average Farm Price vs. Stocks-to-Use Ratio . . . . . 9 Figure 8. Cotton: U.S. Season-Average Farm Price vs. Stocks-to-Use Ratio . . 10 Figure 9. U.N. FAO Agricultural Export Price Index: 1990 to 2008 . . . . . . . . . 11 Figure 10. Rough Rice July 2008 Futures Contract Sets All-Time High

of $24.85 per 100 lbs. on April 23, 2008 . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Figure 11. Corn July 2009 Futures Contract Sets All-Time High

of $6.584 per bushel on May 2, 2008 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Figure 12. Eleven-Month Moving Average of Price Volatility Index for

Corn and Wheat Futures Prices Since 1980 . . . . . . . . . . . . . . . . . . . . . . . . . 16 Figure 13. The U.S. Dollar Has Steadily Weakened Against the Euro,

the Canadian Dollar, and the Australian Dollar Since 2001 . . . . . . . . . . . . 18 Figure 14. Energy Costs: Annual Average Prices Since 1975 for Gasoline,

Diesel, and Crude Oil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Figure 15. Food Price Index: Month-to-Month Change versus 11-Month

Moving Average (11-mo MA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

List of Tables

Table 1. Summary of Global and U.S. 2007/2008 Ending Stocks . . . . . . . . . . . . 6 Table 2. U.S. Farm (or Wholesale) Prices: Projected versus the Previous

Five-Year Average and Prior Record . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Table 3. Futures Contract Price Highs, Selected Commodities and Months,

versus the Recent Five-Year Average Farm Price (AFP) . . . . . . . . . . . . . . 14 Table 4. FAPRI Projections of U.S. Biofuel Policy Impacts . . . . . . . . . . . . . . . 21

1 Note that all data are in metric tons unless otherwise stated. These prices are from “World Food Situation website,” Food and Agricultural Organization (FAO), United Nations, at [http://www.fao.org/es/esc/en/index.html]. 2 An additional source for more detailed market and policy information for major program crops may be found at the online briefing rooms maintained by the Economic Research Service (ERS) of USDA, available at [http://www.ers.usda.gov/briefing].

High Agricultural Commodity Prices: What Are the Issues?

Introduction

U.S. and international markets for major grains and oilseeds are presently experiencing a period of tight supplies, strong demand, and high prices not seen since the mid-1990s (Figures 1 and 2). While agricultural commodity prices rose sharply during 2007, they have jumped precipitously in early 2008. For example, export prices for the world’s two major food crops — wheat and rice, rose by 81% and 21%, respectively, during 2007, but have surged even higher in early 2008. Wheat prices (HRW No. 2, f.o.b., U.S. Gulf ports) rose 44% between November 2007 and March 2008 — rising from $334.6 per ton to $481.5 — before falling back slightly in April.1

Rice export prices (100% Grade B, f.o.b. Bangkok) have more than doubled since November 2007, rising from $358.3 per ton to $873.25 in late April 2008 — an increase of nearly 144%.

This report identifies the predominant factors behind the current (2007/2008 crop year) market conditions for major agricultural commodities, with a focus on U.S. farm program crops. In addition, it briefly discusses how higher, more volatile commodity prices have impacted farm incomes, government farm programs, hedging activities, the livestock and food processing sectors, food prices, and the international food security situation. It reviews both the near- and longer-term commodity price outlook, and finally, it discusses various viewpoints and policy options that have been suggested as possible responses to the perceived causes and consequences of the unusually high commodity prices.

Because supply and demand circumstances vary widely across these crops — particularly in terms of their seasonality, their price elasticity, and the derived nature of their end products — readers are encouraged to review the brief commodity overviews provided in CRS Report RL33204, Price Determination in Agricultural Commodity Markets: A Primer, for background information on the underlying nature of the different commodity markets.2

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Global Food Crises Declared by United Nations

While the high market prices have been a boon for producers and owners of agricultural commodities, they represent a drastically worsening food security outlook for low-income households, particularly those in poor, import-dependent countries. A global crisis was signaled when, on March 20, 2008, Josette Sheeran,

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3 “WFP letter of appeal to Government donors to address critical funding gap,” WFP News Room, March 20, 2008, at [http://www.wfp.org/]. 4 “UN chief: Food crisis in now emergency,” by Edith M. Lederer, Breitbart.com, April 14, 2008, at [http://www.breitbart.com/article.php?id=D901PT181&show_article=1]. 5 “Food Price Crisis Imperils 100 Million in Poor Countries, Zoellick Says,” The World Bank , News Release, April 14, 2008, at [http://www.worldbank.org/html/extdr/foodprices/]. 6 Crop Prospects and Food Situation report, No. 2, April, 2008, FAO, U.N., at [http://www. fao.org/giews/english/cpfs/index.htm]. 7 “Food Crisis Is Depicted as ‘Silent Tsunami’,” by Kevin Sullivan, Washington Post, April 23, 2008.

the executive director of the United Nations’ World Food Program (WFP), issued an appeal for $500 million from donor countries to close an immediate gap in the WFP’s normal food distribution commitments resulting from rising commodity prices.3 This was followed on April 14, 2008, by a warning from U.N. Secretary General Ban Ki-moon that a rapidly escalating global food crisis had reached emergency proportions and threatened to wipe out seven years of progress in the fight against poverty.4 World Bank president Robert B. Zoellick announced that the surge in food prices could push 100 million people living in low-income countries into deeper poverty.5 That same month the U.N.’s Food and Agricultural Organization (FAO) identified 37 countries in food crisis requiring external assistance — 21 of them in Africa.6 Then, on April 22, 2008, barely a month after her first announcement, executive director Sheeran announced that the WFP’s operation funding gap had now risen to $755 million, up from the earlier estimate of $500 million, due to continuing increases in commodity prices since mid-March.7

In addition to global food security concerns, higher commodity prices have stoked the flames of food price inflation and its potentially deleterious effect on lower-income households while raising costs for livestock feeders and food processors. Because the rising prices have been associated with unexpectedly large price volatility, they also have increased the risk and costs of grain merchandising all along the marketing chain. Finally, the high, volatile commodity prices have dramatically increased the cost of routine hedging activities (i.e., pricing commodities for purchase, delivery, or use at some future date) at commodity futures exchanges and thereby diminished “forward contracting” opportunities for grain and oilseed producers who are eager to take advantage of record high market prices.

Not All Commodities Are Equal

The specific circumstances leading to high market prices — e.g., weather- related supply shortfalls, unexpected surges in demand, market-distorting government policies — vary in important ways for each of the major U.S. program crops. For wheat, a combination of international weather-related crop failures over the past two years that has resulted in historically low U.S. and global stock levels is the primary impetus behind high prices. Government policies by several key foreign producers to limit exports in favor of domestic markets also have contributed to higher prices. For coarse grains and oilseeds, a combination of growing demand bolstered by rapid income growth in developing markets and government biofuels

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8 For a brief description of these earlier periods, see “Global Grain Markets in 1996: Shades of 1972-74?” by Pete Riley, Agricultural Outlook, September 1996, pp. 2-6.

mandates are the key drivers. For rice, the combination of population-driven demand growth outpacing crop yields over several years, and recent government policies by several major rice exporting countries to limit exports, are the primary catalysts. For cotton, where global supplies remain relatively abundant, the general “bull market” mentality that currently dominates global markets for nearly all commodities has likely been a major contributor to what are otherwise unusually high prices given cotton’s current supply and demand balance.

Of course, no single event or circumstance fully explains high prices for any single commodity. Global economic growth, in general, reinforces demand for all agricultural commodities. Lack of sustained investment in the agricultural sector diminishes long-term productivity potential, dampens producer incentives, and contributes to the slow erosion of food supply availability. High prices for one crop spill over into markets for other crops that compete for the same agricultural land. A ban on rice or wheat exports by one country ripples through all commodity markets that compete for the consumer’s food budget. And, as a backdrop, record oil prices have raised costs all along the various commodity marketing chains from field to kitchen table.

High Prices: A Case of Deja Vu

The last period of similarly high commodity prices occurred in the 1995-1996 period, when several years of government stock reductions were followed by an unusual combination of global supply-reducing weather events and strong international demand.8 However, current commodity market conditions for major U.S. farm program crops — which have occurred simultaneously with dramatic price rises in coffee, cocoa, and tea markets, as well as in non-agricultural markets (e.g, petroleum, gold, silver, platinum, copper, aluminum, iron ore, and coal) — appear more reminiscent of the 1972-1974 period, when increasing inflation, gasoline shortages, and fears of widespread resource depletion appeared to place constraints on economic growth and food production.

In the current farm commodity bull market, global stocks-relative-to-use ratios for vegetable oils and several grain crops are projected to reach historic lows by mid- 2008 (Table 1). As a result, commodity prices in both cash and futures markets have approached or surpassed historic highs (Tables 2 and 3) while exhibiting heightened sensitivity to crop prospects across the globe this year. This sensitivity has translated into record price volatility in agricultural markets. The full consequences of historically high, but unpredictably volatile, commodity prices are only beginning to emerge, but clearly they have raised the cost of doing business and such costs have not been spread evenly among market participants.

On the positive side, high commodity prices have contributed to record U.S. farm income in 2007 and the outlook for even higher returns in 2008, while dramatically reducing government outlays for price-contingent commodity

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9 For more information, see CRS Report RS21970, The U.S. Farm Economy. 10 Pipeline ranges are derived by CRS from various sources.

programs.9 On the other hand, the outlook for sustained high commodity prices has contributed to the concerns of the U.S. livestock sector and food processors about the continued timely availability of grain and oilseed supplies, and the impact such high input prices have had on their profitability. Historic high price levels and volatility have sharply increased the costs of routine hedging activities of commercial elevators, grain merchandisers, and food processors. In addition, as commodity prices have risen in tandem with food prices, consumers from low-income households and import-dependent nations have expressed concerns, often in the form of riots, about food price inflation, domestic and international food aid, and the ability of agricultural producers to meet projections for continued strong demand growth.

U.S. and Global Stocks Near Historic Lows for Several Crops

U.S. and global stocks for several major U.S. program commodities are expected to be at or near historically low levels — particularly when measured as a share of total usage — prior to the next harvest this coming summer and fall of 2008 (Table 1 and Figures 3-8). For example, global end-of-year stocks for coarse grains and wheat are projected to drop by mid-2008 to the lowest levels since 1977, while ending stocks of total grains fall to the lowest level since 1981. More importantly, their respective stocks-to-use ratios are all projected to reach record lows. Similarly, the stocks-to-use ratios for global corn and vegetable oils are projected to be the tightest since the early 1970s. Global rice stocks, as well as the stocks-to-use ratio, are projected up slightly from the previous year at 77.2 million tons and 18.2% in 2007/2008. However, the previous year’s stocks-to-use ratio of 18.1% was the lowest since 1976. Current rice stock levels represent a halving of available supplies from the year 2000, when global ending stocks peaked at 147.1 million tons.

A certain amount of stocks at the end of the marketing year are necessary to provide a continuous flow of grain to processors and exporters before the new crop is harvested — such stocks are referred to as pipeline supplies. Although there is no hard and fast rule on what volume of stocks represents desirable pipeline levels for the major grain and oilseed crops, whenever stocks approach historically low levels market analysts speculate about what pipeline-stock levels might be. For wheat, U.S. pipeline stocks are estimated to be in a range of 9.5 to 11 million tons (350 to 400 million bushels); for corn, 10 to 12 million tons (400 to 500 million bushels); and for soybeans, about 4 to 5.5 million tons (150 to 200 million bushels).10 Whenever USDA ending stock projections approach these levels, market prices become very sensitive to unexpected market news and prices tend to be more volatile than during periods of abundant stocks.

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Table 1. Summary of Global and U.S. 2007/2008 Ending Stocks Ending Stocks Stock-to-Use Ratio

Commodity Million

tons Lowest since

Change from 2006/07 %

Lowest since

Globala

Total Grainsc 317.2 1981 -6% 13% on record

Coarse Grainsd 127.5 1977 -7% 11% on record

Wheat 112.5 1977 -10% 16% on record

Corn 103.0 1983 -5% 12% 1973

Rice 77.2 2006 1% 17% 2006e

Soybeans 49.3 2004 0% 16% 2003

Cotton 13.0 2004 -3% 36% 2003

Vegetable Oil 8.9 2003 -3% 5% 1972

United Statesb

Total Grainsc 43.1 1996 -13% 10% 1995

Coarse Grains 35.9 1996 -1% 10% 1995

Wheat 6.6 1947 -47% 10% 1946

Corn 32.6 2003 -2% 10% 2003

Rice 0.7 1980 -45% 9% 1974

Soybeans 4.4 2003 -72% 5% 2003

Cotton 2.1 2006 2% 51% 2005

Vegetable Oil 1.5 2004 -7% 13% 2004

Source: USDA, PSD data base, April 9, 2008.

Note: The 2007 crop year covers the period from the start of the 2007 harvest to the start of the 2008 harvest. Thus, ending stocks for the 2007 crop represent supplies available in 2008 just prior to the harvest for the 2008 crop. Similarly, the stocks-to-use ratio for the 2007 crop is a measure of available supplies relative to use just prior to the harvest of the 2008 crop.

a USDA’s PSD database for global commodities extends back to 1960; thus, “lowest on record” means the lowest data point” since 1960.

b USDA domestic data extends back prior to 1900 for most commodities. c Total grains include coarse grains, wheat, and rice. d Coarse grains include corn, sorghum, barley, oats, and rye. e Rice stocks in 2006 were the lowest since 1976.

U.S. wheat ending stocks for 2007/2008 are projected to fall to their lowest level (242 million bushels or 6.6 million tons) since 1947 — well below their pipeline range. U.S. soybean stocks of 4.4 million tons are projected at the lower end of their pipeline range. U.S. corn ending stocks, although projected at what would appear to be an ample level, are low in historical global supply-to-use terms. Furthermore, the multi-year outlook for corn supplies is strongly impacted by the biofuels usage mandate in the Energy Independence and Security Act of 2007 (P.L. 110-140), which suggests that corn supplies will continue to tighten through 2015. Among the major program crops, cotton is the principal exception, with global and U.S. ending stocks projected at relatively abundant levels.

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11 For more information, see CRS Report RL33204, Price Determination in Agricultural Commodity Markets: A Primer by Randy Schnepf.

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Figure 3. All Wheat: U.S. Season-Average Farm Price vs. Stocks-to-Use Ratio

Ending stocks are calculated as the difference between total supplies (beginning stocks plus production plus imports) and total disappearance (all domestic uses plus exports). As such, season-ending stocks of an annually produced commodity summarize the effects of both supply and demand factors during the marketing year. Expected ending stocks — expressed as a ratio over expected total use — are frequently used as an indicator of a commodity’s expected price outcome by USDA and other market observers.11 For most seasonal commodities, annual prices tend to have a strong negative correlation with their ending stocks-to-use ratio (Figures 3-8). As a result, expectations for high stocks relative to use typically result in lower prices, while expectations for low stocks relative to use tend to raise prices.

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Figure 7. Rice: U.S. Season-Average Farm Price vs. Stocks-to-Use Ratio

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12 World Food Situation, Food Price Indices, FAO, April 2008, at [http://www.fao.org/ worldfoodsituation/FoodPricesIndex]. 13 “Poorest countries’ cereal bill continues to soar, governments try to limit impact,” FAO Newsroom, FAO, April 11, 2008.

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Figure 8. Cotton: U.S. Season-Average Farm Price vs. Stocks-to-Use Ratio

Many Commodity Price Records Established in 2008

The tight supply situation for many agricultural commodities has sparked higher commodity prices throughout the global marketing chain — farm gate, futures markets, major international ports of call, wholesale distribution points, and finally to retail prices.

International Index of Export Prices Record High

According to the United Nations’ Food and Agricultural Organization (FAO), export prices for major agricultural commodities rose 23% during 2007 following a 34% rise in 2006 (Figure 9).12 Furthermore, the FAO’s food price index indicates that food prices in the international marketplace have jumped nearly 18% during the first three months of 2008, driven largely by price rises in cereals (up 27%) and oils (up 26%). This rapid price rise is evidenced by the direction of major export prices for wheat, corn, and rice in Figure 1. FAO predicts that the cereal import bill for the world’s poorest countries will rise by 56% in 2007/08, following a 37% increase in 2006/2007.13

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U.S. Farm Prices Projected Record High for Several Crops

In light of the commodity price increases of the past several months, USDA is projecting record high season-average farm prices for wheat, corn, sorghum, barley, soybeans, and soybean products (soybean oil and soybean meal) for the current 2007/2008 crop year, while farm prices for rice are expected to be the highest since 1973 (Table 2). Prices for minor oilseeds (e.g., sunflower and rapeseed), oats, and hay crops also are projected to approach or surpass previous record highs.

USDA’s farm price estimates are weighted by monthly marketings. Since a large portion of each crop is marketed within two to three months of harvest when seasonal prices are generally at their lowest level, the season average farm price (SAFP) is weighted downward by a large volume of lower-priced early marketings. Also, USDA reports farm prices on a monthly basis, not daily like the major commodity futures exchanges. As a result, the monthly farm prices reported by USDA do not exhibit the same degree of volatility as that of the futures prices reported in the news media. For examples, see the discussion in the next section on futures contract prices and compare the record futures contract prices for the major program crops as reported in Table 3 with the farm prices reported in Table 2.

While the establishment of record highs for grain and oilseed program crops is noteworthy, the extent to which the 2007/2008 prices deviate from both the average prices of the preceding five-year period and the previous record SAFPs has evoked concern and even alarm from consumer and hunger advocates. For example, the 2007/2008 corn SAFP of $4.30 per bushel is projected to be nearly 94% above the previous five-year average farm price of $2.22 and about 33% above the previous record high $3.24 achieved in 1995/1996. Most grains and oilseeds are projected at least 56% to 94% above the previous five-year average price. Soybean oil is projected 121% above its five-year average. Even cotton, a relatively abundant commodity, is projected 25% above its previous five-year average price.

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Table 2. U.S. Farm (or Wholesale) Prices: Projected versus the Previous Five-Year Average and Prior Record

Crop Units

Prior record 5-Year Avg.

(5YA) Priceb

Proj. 2007/08c Proj. 2007/08

to 2017/18e

Pricea Crop Year Pricea

% of 5YA

Avg. Price

% of 5YA

All Wheat $/ bu. $4.55 95/96 $3.56 $6.65 187% $5.51 155%

Corn $/ bu. $3.24 95/96 $2.22 $4.30 194% $3.93 177%

Sorghum $/ bu. $3.29 06/07 $2.18 $4.15 191% $3.70 170%

Barley $/ bu. $2.89 95/96 $2.60 $4.05 156% $3.96 152%

Soybeans $/ bu. $7.83 83/84 $6.03 $10.25 170% $10.24 170%

Soy Oil ¢ /lb. 31.6¢ 73/74 23.6¢ 52.0¢ 221% 54.6¢ 232%

Soy Meal $/s.t. $270.7 96/97 $193.61 $325.0 168% $237.3 123%

Rice $/cwt $13.80 73/74 $6.81 $12.20 179% $11.54 169%

Upl. Cotton ¢ /lb. 76.5¢ 95/96 46.0¢ 57.3¢d 125% 62.0¢ 135%

Notes: s.t. = short ton; cwt = hundred pounds.

a Season average farm price received (SAFP) for all wheat, corn, sorghum, barely, soybeans, rice, and upland cotton, National Agricultural Statistics Service (NASS), USDA; season average annual wholesale prices for soybean oil and soybean meal, Decatur, Illinois, Agricultural Marketing Service (AMS), USDA.

b Simple average of SAFPs or wholesale prices for the 2002/03 to 2006/07 period. c Mid-point of projected price range; WASDE Report, April 9, 2008, WAOB, USDA. d Projection from U.S. Baseline Briefing Book, FAPRI-MU Report #03-08, March 2008. e Avg. for the 10-year projection period, 2007/08 to 2017/18, FAPRI-MU Report #03-08, Mar 2008.

Recent (March 2008) long-run commodity price projections from the Food and Agricultural Policy Research Institute (FAPRI) suggest that, when commodity markets return to equilibrium, the long-run average price for major program crops will settle at levels that are 23% to 132% above the recent five-year average.

Several Futures Prices Set All-Time Highs

Unlike cash markets which deal with the immediate transfer of goods, a futures exchange provides the facilities for buyers and sellers to trade commodity futures contracts — that is, contracts to buy (or sell) a specified volume of a commodity, subject to detailed quality conditions, at a fixed price for potential physical delivery (or acquisition) at some future date. Commodity futures exchanges are important barometers of commodity price movements — both the general level as well as the volatility — because they function as a central exchange for domestic and international market information. Market participants are able to respond with buy or sell orders within seconds upon receiving new information. As a result, futures contract prices react almost instantaneously to new information regarding commodity supply and demand expectations. This futures market activity (e.g., price, volume, open interest) is then reported electronically by the major exchanges through their

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14 For more information on agricultural futures exchanges see pp. 7-10 of CRS Report RL33204, Price Determination in Agricultural Commodity Markets: A Primer. 15 CBOT daily futures contract price quotes are available at [http://www.cbot.com]. 16 As of May 5, 2008. 17 The price volatility index is a measurement of the day-day change in price. It is expressed as a percentage and computed as the annualized standard deviation of the percentage change in daily price. 18 Chicago Mercantile Exchange (CME) Group, Datamine, Historical Volatility Measures, [http://www.cmegroup.com/].

own news media, as well as through national and international news media.14 As a result of this transparency, futures exchanges have served two critical roles — price discovery and risk management — in facilitating the marketing of agricultural commodities.

The market circumstances of the first few months in 2008 have clearly manifested themselves in the commodity futures exchanges, where prices for many commodities have hit historic all-time highs (Table 3). For example, at the Chicago Board of Trade (CBOT), prices for nearby futures contracts for corn, wheat, soybeans, soybean oil, and rice reached all-time highs in early March 2008.15 Corn and rice contracts have remained particularly active, pushing to new contract highs on almost a daily basis during April (Figures 10 and 11). Finally, the July 2009 futures contract for corn set a new all-time high of $6.584 per bushel on May 2, 2008.16

Heightened Commodity Price Volatility Since 2005. As commodity price levels have moved higher over the past two years in response to the gradual tightening of global supplies, they also have exhibited unprecedented volatility in the range of daily price movements, swinging rapidly up and down in response to the arrival of new market information. For example, according to a CBOT volatility index (of day-to-day price movements converted to an annual basis), corn and wheat futures contract price volatility have averaged 19.7% and 22.2% since 1980 (Figure 12).17 However, in 2006 and 2007 both corn and wheat price movements have produced successive record annual volatility measures of 28.8% and 31.4% for corn, respectively, and 30.4% and 32.7% for wheat.18

Both the price level and volatility for most agricultural commodities have continued to rise in 2008. During March 2008, CBOT’s monthly average price volatility (expressed on an annualized basis) for wheat was 73%, corn 41%, soybeans 54%, soybean oil 57%, soybean meal 65%, and rough rice 35%.

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Table 3. Futures Contract Price Highs, Selected Commodities and Months,

versus the Recent Five-Year Average Farm Price (AFP)

Commodity

Futures Exchanges

a Unit Contract:

Month / YR Date Intra-day

High Priceb 5-Year AFPd

Wheat: HRS MGEX bushels March 08 2/25/08 $25.00c $3.70 May 08 2/27/08 $19.00

Previous high May 96 5/10/96 $7.32

Wheat: HRW KCBOT bushels March 08 2/27/08 $13.70c $3.54 May 08 2/27/08 $13.70

Previous high May 96 4/26/96 $7.44

Wheat: SRW CBOT bushels March 08 2/27/08 $13.35 $3.28 May 08 2/27/08 $13.50c

Previous high July 96 3/20/96 $7.50

Corn CBOT bushels March 08 3/11/08 $5.72 $2.22

May 08 4/9/08 $6.16

July 09 5/2/08 $6.584c

Previous high July 96 7/12/96 $5.55

Soybeans CBOT bushels March 08 3/3/08 $15.71 $6.03 May 08 3/3/08 $15.86

July 08 3/3/08 $15.95c

Previous high July 73 6/5/73 $12.90

Soybean oil CBOT pounds March 08 3/3/08 $0.708 $0.236e

May 08 3/3/08 $0.708

March 09 3/3/08 $0.721c

Previous high October 74 10/1/74 $0.510

Soybean meal CBOT short March 08 3/3/08 $385.70 $193.6e

May 08 3/3/08 $392.90

July 08 3/3/08 $393.00

Previous high July 73 6/5/73 $451.00

Rice CBOT cwt March 08 3/13/08 $19.55 $6.81 May 08 4/23/08 $24.46

July 08 4/23/08 $24.85c

Previous high March 97 1/31/97 $12.45 Source: Futures contract prices are reported daily by the various futures exchanges and reprinted in the Wall Street Journal; farm prices are from NASS, USDA; and cash prices are from AMS, USDA.

a MGEX = Minneapolis Grain Exchange; KCBOT=Kansas City Board of Trade; and CBOT=Chicago Board of Trade; cwt = hundredweight (i.e., 100 lbs.). b Record price for each contract month with the exception of the July soybean meal futures contract. c Record price for any futures contract month for this commodity at this exchange as of May 5, 2008. d Simple average of monthly farm prices received for the five-year period 2002/03 through 2006/07. e Simple average of monthly cash prices; Decatur, Illinois.

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Note: Futures market price data is presented as weekly price ranges with the left tick representing the week’s opening price and the right tick representing the week’s final settlement price.

Figure 10. Rough Rice July 2008 Futures Contract Sets All-Time High of $24.85 per 100 lbs. on April 23, 2008

Figure 11. Corn July 2009 Futures Contract Sets All-Time High of $6.584 per bushel on May 2, 2008

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19 Statement given at the “Inaugural Ceremony: 30th Anniversary Session of IFAD’s Governing Council,” by Josette Sheeran, Executive Director, WFP, February 13, 2008. 20 For more information, see CRS Report RL33204, Price Determination in Agricultural Commodity Markets: A Primer, “Price-Inelastic Demand and Supply,” p. 23.

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Figure 12. Eleven-Month Moving Average of Price Volatility Index for Corn and Wheat Futures Prices Since 1980

Factors Behind the High Prices

Rising food prices, which are affecting millions of people, are rooted in what Josette Sheeran, Executive Director of the WFP, has described as a “perfect storm” of increasing demand for food from emerging economies, competition between biofuels and food production, high fuel prices, and increasing climatic shocks such as droughts and floods.19 Further contributing to high commodity prices have been a series of international government policies to limit domestic export supplies that have heightened fears of shortage and a weak U.S. dollar that has made U.S. exports more competitive in international markets. The market effects of these factors have been particularly acute for agricultural commodities because of the inelastic nature of both supply and demand.20

Widespread Weather-Related Crop Shortfalls

Global grain production declined in both 2005 and 2006 — primarily due to declining global productivity — cutting into existing stocks and reducing exportable supplies. A major tipping point occurred in 2007 when Australia — traditionally a major wheat and barley exporter — suffered a second consecutive year of sharply lower grain production due to drought. With stocks already low, Australia’s 2007 grain exports were dramatically curtailed for a second year. Meanwhile, grain crops

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21 For more information on GDP and population growth in the developing world, see USDA Agricultural Projections to 2017, OCE-2008-1, USDA, pp. 12-13. 22 Rising Food Prices: What Should Be Done?, Joachim von Braun, International Food Policy Research Institute Policy Brief, April 2008. 23 For more information, see CRS Report RL31985, Weak Dollar, Strong Dollar: Causes and Consequences by Craig K. Elwell.

in the United States, Canada, European Union (EU), Eastern Europe, and some countries of the former Soviet Union were also reduced by weather conditions. In the EU, declining grain supplies forced livestock producers to import substantial volumes of wheat and other feed grains for feed rations. As a result, the EU switched from its traditional status as a major net exporter of grains into a net importer in 2007. The cumulation of these events severely drew down global grain supplies (Table 1).

Strong Economic Growth in Developing Countries

A steadily increasing world population, boosted by robust growth in purchasing power, especially in developing countries such as China and India, has contributed to a permanent increase in global demand for more and different kinds of food.21 As households improve their incomes and food purchasing power, they shift their demand away from traditional staples and toward higher-value foods like meat and dairy products.22 This dietary shift is leading to increased demand for grains used to feed livestock.

Weak U.S. Dollar Lowers Cost of U.S. Exports

When the U.S. dollar declines in value in international exchange markets relative to the currency of our export competitors (e.g., Canada, Australia, or the EU) or importing nations (e.g., Japan, Taiwan, etc.), it makes U.S. export products cheaper and, therefore, more competitive. Since January 2002, the U.S. dollar has lost over 44% of its value against the EU’s euro and the Australian dollar, and nearly 37% against the Canadian dollar (Figure 13).23

A key result of the declining value of the U.S. dollar has been a dramatic surge in U.S. exports of agricultural products, particularly of bulk commodities. Record exports in the face of historically high commodity prices seems somewhat counterintuitive. However, the decline in the foreign exchange value of the U.S. dollar has been so dramatic that, in some cases, it has completely offset the rise in commodity prices, thereby making U.S. grains and oilseed very attractive.

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24 Outlook for U.S. Agricultural Trade, AES-57, February 21, 2008, at [http://usda.mannlib. cornell.edu/usda/current/AES/AES-02-21-2008.pdf]. Bulk shipments include wheat, rice, feed grains, soybeans (and other oilseeds), cotton and linters, and tobacco. 25 For more information, see CRS Report RL32712, Agriculture-Based Renewable Energy Production.

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Figure 13. The U.S. Dollar Has Steadily Weakened Against the Euro, the Canadian Dollar, and the Australian Dollar Since 2001

Total U.S. agricultural exports in FY2008 are estimated at a record $101 billion, including record bulk shipments of $44.7 billion and 135.8 million tons.24 In terms of year-to-year export volumes, U.S. corn exports are projected up nearly 18% to a record 2.5 billion bushels (63.5 million tons) in the 2007/2008 marketing year, wheat exports are projected up 40%, sorghum exports 82%, and rice exports 23%. These record shipments of grains and oilseeds have helped to draw down U.S. stocks and fuel higher commodity prices.

Government Biofuels Policy

Concerns over high oil prices, energy security and climate change have prompted governments to take a more proactive stance towards encouraging the production and use of agriculture-based biofuels.25 Several countries have set standards or targets for use of biofuels. The largest biofuels programs are in the United States, Brazil, and the EU. Brazil requires a minimum use of 20%-25% of sugar-cane-based ethanol (E20-E25) in its national gasoline supply, and has subsidized the establishment of a national distribution network that includes pumps for 100% ethanol in addition to the E20-E25 blend. In addition, Brazil requires that all diesel oil contain a 2% blend of biodiesel by 2008 rising to a 5% blend by 2013. The EU has established a goal of 5.75% of motor fuel use from biofuels by 2010, rising to 10% by 2020. The EU’s program is primarily focused on vegetable-oil- based biodiesel production. As a result of its biofuels policy, EU farm policy

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26 For more information, see CRS Report RL32712, Agriculture-Based Renewable Fuels, and CRS Report RL34265, Selected Issues Related to an Expansion of the Renewable Fuel Standard (RFS). 27 An exception to the tariff exists under the Caribbean Basin Initiative; see CRS Report RS21930, Ethanol Imports and the Caribbean Basin Initiative. 28 “Rising Food Prices: Policy Options and World Bank Response,” World Bank, undated mimeo, at [http://www.worldbank.org/].

incentives have generally favored the expansion of rapeseed production at the expense of wheat, barley, and other grain crops. Thailand, India, and China also have established biofuel mandates that hinge on the expansion of agriculture-based biofuels. However, these three countries have at least temporarily suspended their biofuels programs in light of the current high commodity prices and global food crisis.

U.S. Biofuels Mandate. In the United States, the Energy Independence and Security Act of 2007 (EISA; P.L. 110-140) extended and substantially expanded the existing Renewable Fuel Standards (RFS).26 The RFS is a usage requirement mandating that an increasing volume of biofuels be blended with conventional fuels. Under EISA, the RFS mandates the use of at least 9 billion gallons of biofuel in U.S. fuel supplies in 2009, but grows quickly to 20.5 billion gallons by 2015 and to 36 billion gallons by 2022. The U.S. biofuels sector is also supported by a tax credit (TC) of $0.51 for every gallon of ethanol blended in the U.S. fuel supply ($1.00 per gallon of virgin-oil-based biodiesel), and an import tariff of $0.54 per gallon of imported ethanol.27 In addition, several federally-subsidized grant and loan programs assist biofuels research and infrastructure development.

Current U.S. biofuel production is almost entirely corn-based ethanol — nearly 6.5 billion gallons of corn-ethanol were produced in 2007, compared with an estimated 450 million gallons of biodiesel. The RFS for corn-based ethanol is capped at 15 billion gallons in 2015. However, additional mandates for biodiesel and for cellulosic and other non-corn ethanol continue to expand to a total RFS of 36 billion gallons by 2022. This mandate places tremendous pressures on U.S. and global crop production systems. This crop year (2007/2008), USDA estimates that about 24% of the U.S. corn crop will be used to produce ethanol; however, this share is projected to grow to 33% next year. This rapid, “permanent” increase in corn demand has directly sparked substantially higher corn prices to bid available supplies away from other uses — primarily livestock feed. Higher corn prices, in turn, have forced soybean, wheat, and other grain prices higher in a bidding war for available crop land.

This bidding war is being played out in global markets as traditional corn users search for alternative feed grain supplies. According to the World Bank, increased biofuel production has been one of the principal causes of the dramatic rise in food prices — almost all of the increase in global corn production from 2004 to 2007 (the period when grain prices rose sharply) went for biofuels production in the United States.28

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29 “The Energy Independence and Security Act of 2007: Preliminary Evaluation of Selected Provisions,” FAPRI-MU #01-08, Jan. 2008. 30 “The Outlook for Corn Prices in the 2008 Marketing Year,” Iowa Ag Review, Spring 2008, Vol. 14, No. 2, pp. 4-5; and “Ethanol, Mandates, and Drought: Insights from a Stochastic Equilibrium Model of the U.S. Corn Market,” by Lihong Lu McPhail and Bruce A. Babcock, Working Paper 08-WP 464, CARD, Iowa State University, March 2008.

Economic Analysis of U.S. Biofuels Mandate. A recent study by the Food and Agricultural Policy Research Institute (FAPRI) attempts to measure the pure and joint price effects of the U.S. biofuels RFS and the tax credits (TC) (Table 4).29 FAPRI’s study suggests that implementation of EISA’s RFS (in the absence of the TC) will raise corn price by about 19% once the new long-run equilibrium has been established. The FAPRI study also estimates that the ethanol tax credit (TC) of $0.51 per gallon (in the absence of the RFS) supports corn prices by a slightly smaller 11%. Because of interactions between the two subsidies, it is estimated that joint implementation of both the RFS and TC supports corn prices by about 20%.

Strong effects were also observed by FAPRI for other commodities, particularly soybean oil whose wholesale price is projected 73% higher under the joint RFS-TC scenario (Table 4). A substantial portion of corn price effects are likely transmitted to the soybean market via competition for land, primarily in the Corn Belt where soybeans and corn are both widely grown. The biofuels price effects would also transmit to regions outside of the Corn Belt (where wheat, cotton, and other major grain and oilseeds are produced) as farmers reconfigure their planting decisions and opt for greater soybean and corn production to maximize returns.

A similar study by the Center for Agricultural Research and Development (CARD) found that, jointly, the RFS and TC supported the price of corn by a slightly smaller 16%.30 Both of these studies found the results to be highly dependent on the price of petroleum (or gasoline). Higher petroleum prices substitute for government incentives and diminish the relative impact of such incentives on corn prices. Neither study evaluated the effect of the U.S. import tariff of $0.54 per gallon on imported ethanol from Brazil, although the CARD study pointed out that the corn price impacts would be greater if the tariff on Brazilian ethanol were eliminated. Nor did either study include the effects of the various grants and subsidized loans that have been made available to the U.S. biofuels sector for research and infrastructure development.

The CARD study also investigated the potential combined impact of the biofuels mandate with a major drought in the Corn Belt. The study replicated the 1988 drought when corn yields fell almost 25% below trend levels. Study results suggest that weather-reduced corn supplies confronted by a biofuels blending mandate would place severe pressures on the U.S. and global corn market — with the mandate in place, corn prices would increase to an eye-popping $8.62 per bushel compared with $7.28 without the mandate. Thus, the study projects that corn prices would be over 18% higher with the mandate in place if a severe 1988-like drought occurred.

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31 Nathanial Gronewold, “Food prices hampering U.N. agency’s ability to head off ‘new face of hunger,’” E&E News PM, April 24, 2008, at [http://www.eenews.net].

Table 4. FAPRI Projections of U.S. Biofuel Policy Impacts Scenario: Projections and % Change

Units No RFS No TC

No RFS TC

RFS No TC

RFS TC

Production

Ethanol Bil. gal. 8.11 11.7 44% 14.1 74% 14.5 79%

Biodiesel Bil. gal. 0.23 0.51 122% 0.96 317% 0.96 317%

Price

Corn $/bu. 2.81 3.11 11% 3.33 19% 3.37 20%

Soybeans $/bu. 6.15 6.64 8% 7.21 17% 7.25 18%

Wheat $/bu. 4.03 4.19 4% 4.31 7% 4.33 7%

Soy Meal $/s.t. 179.99 166.23 -8% 138.29 -23% 137.98 -23%

Soy Oil $/lb. 26.89 34.34 28% 46.29 72% 46.64 73%

Source: “The Energy Independence and Security Act of 2007: Preliminary Evaluation of Selected Provisions,” FAPRI-MU #01-08, Jan. 2008.

Note: The numbers presented in this table are the averages of the scenario projections for the 2011 to 2016 period, thus, they are an estimate of the long-run equilibrium values. The “No RFS; TC extended” scenario represents FAPRI’s December 2007 baseline projections.

Foreign Government Policies to Limit Exports

Foreign government policy responses to the high commodity prices have, for the most part, had the perverse effect of reinforcing higher prices thereby contributing to, rather than alleviating, the current market supply-and-demand conditions.

Since late 2007, several traditional wheat and rice exporting countries — in an effort to ensure domestic food availability and temper rising internal inflation — have instituted policies designed to limit exports of domestic supplies. These policies, albeit implemented to dampen internal prices for domestic consumers, have had exactly the opposite effect on international market prices, pushing them higher than supply and demand conditions would otherwise dictate by limiting access to available supplies by international buyers. On April 24, 2008, the WFP claimed that more than 40 food-exporting countries had placed some kind of restriction or outright ban on many crop exports in an attempt to stabilize prices within their borders.31

For example, in the international rice market, the traditional exporters including Vietnam, India, China, and Egypt, (the world’s second-, fourth-, sixth-, and eighth- leading rice exporters last year), along with minor exporter Cambodia, have all set in place policies to limit exports. The combined effect of these bans is to remove over a third of available export supplies from world markets and to drive

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32 “Wheat Jumps on Supply Concerns,” Stevenson Jacobs, Washingtonpost.com, Feb. 25, 2008. 33 “Argentina suspends wheat exports indefinitely,” Commodity Online, April 22, 2008. 34 “Farmers’ Strike in Argentina Is Suspended for Negotiations,” by Vinod Sreeharsha and Alexei Barrionuevo, New York Times, April 3, 2008. 35 “Argentine Soybean Output May Slip; Protests May Pause,”by Heather Walsh and Eliana Raszewski, Bloomberg News, March 19, 2008. 36 Crop Prospects and Food Situation, No. 2, April 2008, FAO, U.N., at [http://www. fao.org/worldfoodsituation].

international rice prices sharply higher. Similar action in the international wheat market includes both Ukraine and Argentina which, in the spring of 2007, initiated wheat export restrictions in efforts to control food price inflation. Pakistan placed taxes on wheat exports. By late February 2008, Kazakhstan officials set in place policies to slow their country’s wheat export pace (via higher custom duties), also due to declining supplies.32 By mid-April, Kazakhstan had converted its export slowdown into an outright ban.

Argentina’s government policy of banning wheat and beef exports, slowing corn exports through procedural barriers at customs, and heavily taxing exports of soybeans33 (both to limit exports and to raise government revenues) is particularly noteworthy for three reasons. First, the export controls resulted in a nationwide farmers’ strike whereby producers refused to bring to market any agricultural products. Second, although the strike (after lasting three weeks) was temporarily suspended for 30 days starting on April 3, 2008,34 in an agreement between farmers and government that essentially left the export controls in place, the uncertainty surrounding Argentina as a reliable export supplier resulted in a substantial amount of purchase contracts being diverted to U.S. suppliers. Thus, already huge U.S. export numbers were further bolstered by the shift in demand from Argentinean to U.S. commodities. Third, Argentina’s strict export controls have had a major impact on that country’s agricultural prospects, at least in the short run, as most market analysts are now predicting a significant decline in Argentina’s planted area for wheat (and possibly corn and soybeans) in 2008.35

Alternately, on the demand side, several countries that either depend on imports to meet an important share of their domestic food needs, or have large groups of nutritionally vulnerable people, began to remove long-standing barriers to imports.36

For example, in late March 2008, India authorized duty-free imports of rice. Several other Asian, African, Latin American, and Caribbean nations have also lowered or eliminated tariffs on a range of imported foodstuffs. In addition, many countries froze internal commodity prices at below market levels and issued temporary income subsidies to help consumers meet their food needs. While these consumer-oriented actions may be laudable, they have had the effect of increasing international demand at the same time that international supplies are being restricted.

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37 Grain Transportation Report, Agricultural Marketing Service, USDA, April 17, 2008. 38 Nathanial Gronewold, “Food prices hampering U.N. agency’s ability to head off ‘new face of hunger’,” E&E News PM, April 24, 2008, at [http://www.eenews.net].

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Figure 14. Energy Costs: Annual Average Prices Since 1975 for Gasoline, Diesel, and Crude Oil

High Energy Costs

Petroleum prices have doubled since January 2007 when the monthly spot market price for West Texas Intermediate (WTI) at Cushing, Oklahoma, averaged $53.70 per barrel (Figure 14). On April 22, 2008, the price for WTI exceeded a record $119 per barrel and have since surpassed $120. Since gasoline, diesel fuel, and other energy products are either directly or indirectly derived from petroleum, high petroleum price have increased operating costs all along the marketing chain for agricultural inputs and outputs, thus inflating prices everywhere. Energy-driven higher marketing costs accumulate at the retail outlet where they translate directly into higher consumer food prices.

High petroleum prices and strong demand for ocean shipping drove ocean rates for shipping bulk commodities to record levels in 2007, nearly doubling the previous record set in 2004, and adding to the imported cost of internationally traded products.37 WFP executive director Sheeran has suggested that high oil prices may be the single most important factor in driving up food costs because, in addition to its effect on raising energy costs throughout the marketing chain, it has boosted the popularity of biofuels.38

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39 “Open Market Operations,” Federal Open Market Committee, U.S. Federal Reserve Board, at [http://www.federalreserve.gov/fomc/fundsrate.htm]; see also CRS Report 98-856, Federal Reserve Interest Rate Changes: 2000-2008, by Marc Labonte and Gail E. Makinen. 40 “Some blame speculators for worsening global food crisis,” RB, Greenwire, April 24, 2008. 41 Nathanial Gronewold, “Commodity ‘feedback loop’ fueling price surge,” Greenwire, March 11, 2008, at [http://www.eenews.net/Greenwire/]. 42 For more information, see CRS Report RS21970, The U.S. Farm Economy, by Randy Schnepf.

Macroeconomic Linkages Reinforce Price Rises

Several economists have stated that U.S. fiscal policies intended to stave off economic recession have contributed indirectly to the U.S. and global “food crises” by resulting in a weaker U.S. dollar. Between September 2007 and April 2008, the U.S. Federal Reserve’s Open Market Committee cut a key interest rate (the federal funds rate) by 3.25 percentage points to 2%, primarily due the escalating financial crisis related to the rise in defaults on subprime mortgages and the overall weak U.S. economy.39 Lower U.S. interest rates contribute to a decline in the value of the dollar relative to other currencies which, in turn, contributes to higher U.S. commodity exports and higher oil prices.

Since oil is priced in U.S. dollars in international markets, a weakening dollar is generally perceived as contributing to higher oil prices via two mechanisms. First, oil exporters must raise the dollar price per barrel to retain the same level of purchase power against appreciating non-U.S. currencies. Second, oil importers — whose currencies have generally strengthened against the dollar — drive the dollar price of oil higher when they bid the same price per barrel in their own currency. Since the United States is the world’s largest oil importer, surging oil import costs pressure the U.S. trade deficit and ripple through the U.S. economy further slowing economic activity. The weakening U.S. economy has impacted capital and stock market prices. As a result, the bullish commodity markets have become an attractive market for mutual funds and investors seeking more profitable investment opportunities. This “speculative” investment money — valued in the hundreds of billions of dollars — has been accused of reinforcing rising prices.40 Some analysts suggest that agricultural commodity markets are now playing a role traditionally reserved for gold and other precious metals — a safe haven for investors.41

Implications of High Commodity Prices

U.S. Farm Income Record High

The past six years are the six highest farm income years on record, but the past two years have seen significant gains from previous records.42 According to USDA’s Economic Research Service (ERS), national net cash income — a key indicator of U.S. farm well-being — is expected to rise to a record $96.6 billion in 2008, over 10% above the previous year’s record ($87.6 billion) and 26% above the four-year

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43 Ibid. Note, these farm income and government outlay projections were made in early February 2008, and are likely to be revised downward due to significant price rises since then. The next USDA farm income update is scheduled for August 28, 2008. 44 For more information on commodity programs, see CRS Report RL33271, Farm Commodity Programs: Direct Payments, Counter-Cyclical Payments, and Marketing Loans. 45 “Selected direct government payments,” U.S. Baseline Briefing Book, FAPRI-MU Report #03-08, FAPRI, March 2008, p. 59. 46 Gary Schnitkey, “Crop Insurance Decisions: Why Not the Same as Last Year?” Illinois AgriNews, Dept of Agriculture and Consumer Economics, University of Illinois, Feb. 2008. 47 The base price for corn is equal to the average price during the first half of February, of

(continued...)

average of $76.5 billion for 2003 through 2006, all on the strength of higher commodity prices. Farm revenue gains are expected to easily outpace rising input expenses (up 34% versus 29%, respectively, from the 2000-2006 period average), leaving many farm communities flush with cash.

Lower Government Farm Program Outlays

In February, USDA forecast government direct payments at $13.4 billion in 2008, up slightly from $12.0 billion in 2007 but well below the four-year (2003- 2006) average of $17.4 billion.43 Government direct payments peaked at $24.4 billion in 2005. Higher projected market prices are expected to limit payments under the two major price-triggered programs — counter-cyclical payments (CCP), and marketing loan benefits (loan deficiency payments, marketing loan gains, and certificate exchange gains).44 Fixed direct payments, whose payment rates are fixed in legislation and are not affected by the level of program crop prices, are estimated up slightly at $5.3 billion.

USDA’s farm income and government program outlay forecasts, which were released in February 2008, were based on prices that persisted in late 2007. However, commodity prices have increased unexpectedly since then. In March, FAPRI released more current estimates of government direct payments for 2008/2009 at $5.8 billion, down slightly from a revised forecast of $6.0 billion in 2007/2008.45

Under FAPRI’s projections, price contingent outlays on CCP and LDP are about $0.5 billion, while fixed direct payments of $5.2 billion comprise the majority of government subsidy expenditures.

Crop Insurance Premiums Costs Surge in 2008

Commodity prices are a key ingredient in the formula for calculating crop insurance premiums.46 Higher, more volatile prices lead to higher insurance premiums, but they also provide the opportunity for producers to lock in unusually high per-acre returns. Both volatility and absolute prices levels are substantially higher in 2008 than in 2007 for all major program crops.

For example, the base price for corn was $4.06 per bushel in 2007 compared with $5.25 in 2008 — an increase of $1.19 or 29%.47 The higher price level plus this

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47 (...continued) the harvest-time (i.e., December) futures contract at the Chicago Board of Trade. 48 For more information on crop insurance programs, see CRS Report RL34207, Crop Insurance and Disaster Assistance: 2007 Farm Bill Issues, by Ralph Chite. 49 “Crop Insurance,” U.S. Baseline Briefing Book, FAPRI-MU Report #03-08, FAPRI, March 2008, p. 59. 50 “Farm Income and Costs: 2008 Farm Sector Income Forecast” briefing room, ERS, USDA updated March 7, 2008; at [http://www.ers.usda.gov/Briefing/FarmIncome/]. 51 World Agricultural Supply and Demand Estimates, World Agricultural Outlook Board, USDA, April 9, 2008.

year’s higher volatility (Figure 12) translate into higher crop insurance premiums. Premiums vary by crop and location, as well as the farm’s production history. However, a higher base price also means that the per acre returns being insured are substantially higher in 2008, especially since most crop insurance policies sold are revenue products that allow farmers to insure a target level of revenue rather than just yields.48 Many farmers on traditionally high-yielding farms in the Corn Belt will be able to guarantee corn revenues in the $500 per acre range for a 75% coverage level. Guarantees over $600 are possible with higher coverage levels. Similar high insurance premiums and revenue guarantees will be available for most major program crops in most major producing areas.

Unlike government commodity program outlays, which decline when prices rise, government support for federal crop insurance rises with higher prices. This is because the federal government subsidizes the premiums by an average of 50% to 60% of the total premium, depending on the coverage level. In addition, the government reimburses the insurance companies for a share of their administrative and operating expenses incurred in delivering the crop insurance policies. The reimbursement share of administrative and operating expenses is based on a percentage of total premiums; thus, it also rises with rising crop prices. FAPRI projects net federal outlays (including premium subsidies, excess indemnity payments, and administrative and delivery costs) at $4.71 billion in 2008 and at $7.1 billion in 2009.49 These net outlays compare with an estimated $3.6 billion in 2007.

Sharply Higher Feed Costs

In February, ERS projected U.S. livestock feed costs for 2008 at a record $45 billion, up nearly $7 billion or over 18% from the previous year’s record.50

Meanwhile, USDA projects that wholesale prices for nearly all livestock product categories (with the exception of poultry and eggs) will decline in 2008.51 Rising feed costs (primarily grains and protein meals) have cut into profit margins of all livestock sectors (beef, dairy, pork, and poultry) and, in the case of hogs have rendered many operations unprofitable. For example, on April 28, 2008, Tyson Food — a major producer, distributor, and marketer chicken, beef, and pork products — reported its first loss in six quarters and said that its corn and soybean costs would

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52 Steven Mueson, “A Costly Link Between Food and Fuel,” Washington Post, April 30, 2008. 53 Letter to EPA Administrator Stephen Johnson, by Texas Governor Perry, April 25, 2008, at [http://www.governor.state.tx.us/]. 54 For more information on farm risk management strategies, see “Risk Management Strategies” at the ERS Farm Risk Management Briefing Room, at [http://www.ers.usda.gov/ Briefing/RiskManagement/Strategies.htm].

increase by $600 million in 2008.52 Texas Governor Rick Perry — whose state is the U.S. leader in beef production and ranks in the top 10 for production of poultry, eggs, and dairy — has stated that rising corn prices have been particularly harmful to Texas livestock producers. According to Governor Perry, every one-cent rise in corn prices costs his state’s livestock sector over $6 million.53

The U.S. livestock sector will have to work through many issues related to the changing nature of feed supplies as feedstock demand from biofuels production lowers grain supplies and replaces them in part with higher protein supplies. Feed supply logistics and feed ration composition are likely to remain unsettled for several years if the biofuels industry continues to expand.

Futures Market Dilemma

Because of their transparency and their traditionally strong relationship with cash markets, futures contract prices are often the primary basis for price determination in many wholesale and cash markets, as well as for managing the risk associated with the ownership (current or anticipated) of a large volume of an agricultural commodity that is actively traded on a futures exchange. However, the rapid, volatile escalation in agricultural futures prices that has evolved since 2005 appears to be diminishing the effectiveness of the futures market as a device for both price discovery and risk management. The financial demands associated with routine hedging operations (primarily in the form of increased margin requirements) have risen in tandem with commodity prices, thereby placing severe strains on market participants. In addition, increasing evidence of lack of convergence between cash and futures contract prices for some commodities in some markets (observed primarily for corn, soybeans, and wheat contracts at the CBOT) is increasing the risk of futures-price-based forward contracts for the grain buyers that offer them.

These developments are of particular concern to traditional commercial interests — such as grain and oilseed elevators, food processors, grain merchandisers, and other participants in the marketing chain for agricultural products — who are likely to see their costs of operations rise with any decline in the efficiency of the futures market. Agricultural producers are equally concerned because, as grain and oilseed buyers refrain from offering forward contracts, producers are increasingly unable to take advantage of the current high prices. Forward contracting has traditionally been one of the primary risk management strategies employed by U.S. producers.54 It is not clear to what extent, if any, high commodity prices have had on the perceived lack of convergence.

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55 “Overview of Agricultural Futures Markets For Congressional Staff ,” by John Fenton, Deputy Director for Market Surveillance, Div. of Market Oversight, CFTC, April 2, 2008. 56 For example, see “The Performance of Chicago Board of Trade Corn, Soybean, and Wheat Futures Contracts After Recent Changes in Speculative Limits,” by Scott H. Irwin, Philip Garcia, and Darrel L. Good, Dept. of Ag and Consumer Economics, Univ. of ILL, Urbana-Champaign, IL, May 2007, at [http://www.farmdoc.uiuc.edu/irwin/research/CBOT FuturesPerformance.pdf]. 57 For more information, visit the CFTC website at [http://www.cftc.gov].

The Commodity Futures Trading Commission (CFTC) — the government agency responsible for oversight and regulation of U.S. futures exchanges — has said that an examination of futures trading data has yet to show any visible evidence that hedging operations are declining as a result of the rising financial obligations associated with hedging.55 In addition, economists have studied the emerging lack of convergence between cash and futures prices and have yet to identify any significant causal factor.56 Despite any current lack of evidence, some agricultural interests affected by these issues have accused the growing pool of speculative money that has been invested in agricultural futures markets in recent years of artificially increasing prices and their volatility, and sharply raising the costs of standard hedging operations. As a result, these parties have called for greater federal regulation and monitoring over speculative participation in futures markets, as well as for more stringent trading limits on speculative funds. As a general rule, this type of speculative investment in futures markets is considered to add necessary liquidity to commodity markets.

Several other issues related to the efficient functioning of futures markets that have emerged in recent years — for example, electronic trading and the standard practice of raising limits on the daily movement of contract prices when prices settle at their limits — have been accused (rightly or wrongly) of aggravating the dilemma surrounding the rising cost and declining viability of routine hedging operations in agricultural futures markets. On April 22, 2008, the CFTC held a special public “Round Table” to publicly discuss the issues confronting commodity futures exchanges and to hear from market participants.57 While no policy positions were recommended or adopted from the session, the Round Table represents an awareness of the importance of the efficient functioning of agricultural futures markets to the U.S. agricultural sector and a willingness to heighten monitoring by the CFTC of these emerging issues.

Expanded, More Intensive Agricultural Production

Since most of the increase in demand is considered permanent, commodity prices will likely return to lower levels only through an expansion in aggregate supply (from either domestic or foreign sources) that outpaces demand growth. This, in turn, may be accomplished by increases in either yields or cultivated area. The strong market price signals received by the world’s farmers during the past six months are expected to engender a response in both planted area and yield per unit of planted area.

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58 “Crops winning out over conservation for some landowners,” James MacPherson, Associated Press, WashingtonPost.com, April 30, 2008. 59 Acreage, National Agricultural Statistics Service (NASS), USDA, June 29, 2007. Major program crops include corn, sorghum, oats, barley, winter wheat, rye, durum wheat, other spring wheat, rice, soybeans, peanuts, sunflower, cotton, dry edible beans, potatoes, sugarbeets, canola, proso millet, hay, tobacco, and sugarcane. 60 For more information on the CRP, see CRS Report RS21613, Conservation Reserve Program: Status and Current Issues, by Tadlock Cowan.

Agricultural Productivity. Yield increases generally accumulate slowly over time via more intensive use of fertilizers, pesticides, improved seeds, and adoption of better farming practices (which themselves are generally the product of investments in research, extension, and infrastructure). The availability and cost of fertilizers and chemicals can be a limiting factor on short-term yield gains. Furthermore, adoption of more intensive cultivation practices may contribute to potentially harmful environmental consequences such as possible water quality degradation from fertilizer and chemical runoff, and increased soil erosion.

Expanded Cropped Area. Area increases for a given crop can occur more quickly than yield increases by shifting land use among different crops, by altering rotational tillage-fallow cultivation practices, or by bringing marginal, less- productive soils into cultivation. Land-use shifts imply winners and losers among crops, while altering rotational patterns and farming marginal lands all imply potentially harmful environmental consequences such as reduced wildlife habitat, lower soil fertility, increased erosion, possible water quality degradation from nutrient and sediment loads in rural waterways, and lost carbon sequestration. Most analysts agree that the current high commodity prices are likely to entice some marginal land back into production in 2008.58

In 2007, about 321 million acres were planted to the principal crops in the United States, of which 315 million acres were for the major program crops.59 In addition, 62 million acres of hay were harvested in 2007. In 2008, USDA estimates that about 4 million additional acres will be planted to principal crops (up 1.2%), while program crop area will expand by 6.2 million acres (up 2%). Thus, more than a 2-million-acre shift from minor crops to program crops is anticipated. In addition, nearly 1 million acres is expected to shift from hay to program crop area.

Converting Conservation Acres to Production

In the United States, the Conservation Reserve Program (CRP) represents the most visible bank of potential crop land available for re-entry into agricultural production.60 The CRP provides payments to farmers to take highly erodible or environmentally sensitive cropland out of production for ten years or more to conserve soil and water resources. In February 2008, national enrollment in the CRP was 34.6 million acres. Each year, a portion of enrolled CRP acres is eligible for renewal or removal depending on the owner’s preferences which, in turn, are likely influenced by market conditions. Farmers also have the option of paying a penalty for early withdrawal, but the penalty (which includes full repayment of all benefits received) is generally prohibitive except in the case of recently enrolled land which

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61 “Options for the Conservation Reserve Program,” by Bruce A. Babcock and Chad Hart, Iowa Ag Review, Spring 2008, Vol. 14, No. 2, pp. 6-7. 62 Ibid. 63 For example, see the AAGC mission statement and its letter of policy recommendations for USDA at [http://www.cmcmarkets.org/files/18_mission_&_prin.pdf] and [http://www. nopa.org/content/newsroom/2005/aug/081205_lettertochuckconnor_CRP.pdf]. 64 “Options for the Conservation Reserve Program,” by Bruce A. Babcock and Chad Hart, Iowa Ag Review, Spring 2008, Vol. 14, No. 2, pp. 6-7.

has yet to accumulate many benefits. In March 2007, then-Secretary of Agriculture Mike Johanns announced that there would be no penalty-free release of acreage from the CRP in 2007. USDA estimates that, in 2007, about 130,000 acres of CRP that were under contract were withdrawn early and were subject to penalty. Secretary Schafer has reiterated the no-penalty-free-release-of-CRP position for FY2008, but has said that USDA will make a decision concerning FY2009 in August or September 2008.

CRP is perceived as providing multiple environmental services — for example, critical wildlife habitat, wind and soil erosion control, wetlands protection, forestry restoration, carbon sequestration, and water quality gains via filter strips and buffer acreage. As a result, the public interest in seeing lower crop prices via expanded cropland must be weighed against the public interest in maintaining the substantial environmental benefits of land in CRP.61

Between September 2007 and February 2008, 2.1 million acres opted out of the CRP as their contracts expired. Another 27.8 million acres under CRP contracts will expire by 2010. Contracts for approximately 23 million (83%) of these acres have been renewed or extended. High commodity prices, however, may discourage future re-enrollments and contract extensions. Environmental and wildlife organizations are major advocates for maintenance and/or expansion of current CRP levels. Livestock groups, the milling and baking industry, and other food processors favor reducing or eliminating early-out penalties for CRP to maximize the amount of land that is cropped.62 The Alliance for Agriculture Growth and Competitiveness (AAGC) — a group representing the beef, poultry, pork, and grain and feed industries — has been lobbying USDA since 2005 to allow landowners to pull out of CRP contracts without penalty.63 According to the Center for Agricultural Research and Development (CARD), if CRP policy is unchanged, than as much as 2 million acres of CRP land per year will be brought back into crop production over the next 10 years.

CRP Policy Options. USDA’s March Planting Intentions report estimates about a 2% increase in program crop planted acreage in 2008 in response to the high commodity prices. Given that USDA’s projected year-to-year farm price increases for 2007/2008 range from 20% to 60% for the major program crops, economists at CARD suggest that such a low area response, if realized, implies that U.S. farmers’ ability to respond to high commodity prices is constrained by a lack of viable cropland.64 As a result of this land constraint, CARD suggest that USDA consider “re-optimizing” CRP through a combination of penalty elimination and aggressive

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65 Changing Structure of Global Food Consumption and Trade, Anita Regmi, Editor, WRS- 01-1, ERS, USDA, May 2001. 66 “Table 97 — Expenditures on Food, by Selected Countries, 2002,” Briefing Room: Food CPI, Prices and Expenditures, ERS, USDA, at [http://www.ers.usda.gov/Briefing/ CPIFoodAndExpenditures/]. 67 “Food Price Outlook, 2008,” Briefing Room: Food CPI, Prices, and Expenditures, ERS, USDA, March 21, 2008. See also CRS Report RS22859, Food Price Inflation: What are the Issues? by Tom Capehart and Joe Richardson. 68 “Table 1. Consumer Price Index for All Urban Consumers (CPI-U): U.S. city average, by expenditure category and commodity and service group,” U.S. Dept. of Labor, BLS; as observed on April 22, 2008.

rebidding of its entire CRP holdings. By eliminating penalties on CRP contracts that expire in the next three years, more productive land could return to production earlier, while the freed up CRP payment funds could be used to offer further protection to the more environmentally sensitive land that offers the greatest environmental benefits. Similarly, CARD suggests that USDA, by rebidding its entire CRP land portfolio, could ensure that the most vulnerable land is retained while allowing less vulnerable land to return to production.

Rising Food Price Inflation Impacts Consumer Budgets

The rise in agricultural prices, combined with high energy costs, have contributed to higher food inflation in the United States and around the world. In general, higher food price inflation impacts consumers’ dietary choices as relative prices vary across foods that compete for food expenditure dollars.65 The overall impact to consumers from higher food prices depends on the proportion of income that is spent on food — households that spend a much greater proportion of their income on food have less flexibility to adjust expenditures in other budget areas to accommodate increasing food costs.

International comparisons of household budgetary expenditures indicate that rich industrial nations spend 6% to 20% of their annual budgets on food compared with 20% to 30% for middle income countries, and 30% to nearly 80% for low income countries.66 These differences suggests that food price increases represent a potentially far more serious hardship for households in low income countries, particularly those nations that depend on imports for an increasing share of their domestic food needs.

U.S. Food Price Inflation. In the United States, food prices increased by 4.2% during 2007, the highest one-year rise since 1989. USDA predicts that food price inflation for 2008 will be in the range of 4% to 5%.67 During the first three months of 2008, the U.S. Bureau of Labor Statistics (BLS) reports that food prices have climbed by 1.3% for an annual rate of 5.2%.68 Figure 15 displays the monthly rate of change in the BLS food price index compared with its more stable 11-month moving average. Since 2005, the general trend has been upward and in June 2007, the 11-month moving average reached its highest point (0.43%) since June 1990.

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69 For example, see “The Relative Impact of Corn and Energy Prices in the Grocery Aisle,” John M. Urbanchuk, Director, LECG LLC, June 14, 2007. 70 “Price Spreads from Farm to Consumer,” Briefing Room: Food Marketing System in the U.S., ERS, USDA, at [http://www.ers.usda.gov/Data/FarmToConsumer/marketingbill.htm]. 71 Statement by Joseph Glauber, Chief Economist of USDA, at a Hearing entitled “How Are High Food Prices Impacting American Families?” before the Joint Economic Committee U.S. Congress, May 1, 2008, p.15, at [http://www.jec.senate.gov/].

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Figure 15. Food Price Index: Month-to-Month Change versus 11-Month Moving Average (11-mo MA)

Despite the sharp increases in commodity prices in 2007, most economists agree that energy costs, particularly fuel prices, have played a larger role in food price inflation than have commodity prices.69 In general, retail food prices are much less volatile than farm-level prices and tend to rise by a fraction of the change in farm prices. This is because the actual farm product represents only a small share of the eventual retail price (20% on average), whereas transportation, processing, packaging, advertising, handling, and other costs — all vulnerable to higher fuel prices — comprise the majority of the final sales price.70

Because food expenditures represent a relatively small share of consumer spending for most U.S. households, food price increases are absorbed relatively easily in the short run. On average, in 2006 U.S. households spend about 6% of their total disposable personal income on food consumed at home and another 4% on food consumed away from home for a total food outlay of about 10.5%. 71 However, even in a wealthy nation such as the United States, household income variations suggest that the impact of food price inflation can vary widely and can result in painful

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72 Ibid. 73 For more information on U.S. food assistance programs, see “Federal Spending for Domestic Assistance Programs,” in CRS Report RS22859, Food Price Inflation: What are the Issues? by Tom Capehart and Joe Richardson. 74 Shahla Shapouri and Stacey Rosen, “Energy Price Implications for Food Security in Developing Countries,” Food Security Assessment, 2006, GFA-18, ERS, USDA. 75 International Monetary Fund, World Economic Outlook: Globalization and Inequality. October 2007. Washington. 76 “Poorest Countries’ cereal bill continues to soar, governments try to limit impact,” FAO Newsroom, FAO, U.N., April 11, 2008. For detailed information on international food supply and demand projections see Crop Prospects and Food Situation, No. 2, April 2008, FAO, U.N., at [http://www.fao.org/worldfoodsituation].

spending choices at the household level. In 2006, U.S. families with less than $20,000 in income spent over 20% of their after-tax income on food.72

The United States has several food assistance programs that are designed to assist households in meeting their minimum food needs.73 The two largest programs, Food Stamps and child nutrition programs, operate as entitlement programs that make specified payments to all qualifying beneficiaries. However, in the case of Food Stamps the burden is upon the eligible individuals to seek out the benefits. In general, rising food prices result in higher federal spending on Food Stamps and child nutrition programs because participation expands and because the benefits under most federal food assistance programs are indexed to some type of consumer food basket. In 2007, $33.2 billion was spent on the Food Stamp program while the average food stamp recipient received $95.63 per month in benefits and the average participating household received $214.69 per month.

International Price Rises Dim Food Security Prospects. Due to market and trade linkages, high commodity prices ripple through international markets where impacts vary widely based on a country’s grain import dependence and its financial ability to respond to higher commodity prices. For example, both Japan and Mauritania are dependent on imports for a substantial portion of domestic food needs; however, Japan has the financial means to better accommodate rising food import prices.

Import-dependent developing country markets are put at greater food security risk due to the higher cost of imported commodities. Lower-income households in many foreign markets where food imports are an important share of national consumption, and where food expenses represent a larger portion of the household budget, may be affected quite severely by higher food prices.74 Humanitarian groups have expressed concern for the potential difficulties that higher grain prices imply for developing countries that are net food importers.75 According to the U.N.’s Food and Agricultural Organization (FAO), the grain import bill for Low-Income Food-Deficit Countries (LIFDCs) — those nations identified as the most vulnerable to international food price changes — is forecast to increase by 56% in 2007/2008 following a 37% rise the previous year.76 For low-income food-deficit countries in

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77 Grain Transportation Report Agricultural Marketing Service, USDA, April 10, 2008. 78 For an example, see “Rice dealers hoard lucrative crop, intensifying shortage,” by RB, Greenwire, April 18, 2008. 79 “Food price anger sparks protests,” Reuters News, April 21, 2008. 80 U.N. food agency facing worst crisis, director says,” Nathanial Gronewold, E&E News PM, April 22, 2008, at [http://www.eenews.net]. 81 “Food Crisis Is Depicted As ‘Silent Tsunami’,” by Kevin Sullivan, Washington Post, April 23, 2008.

Africa, the cereal bill is projected to increase by 74% due to the sharp rise in international cereal prices, freight rates, and oil prices.

The cost of food imports is compounded by bulk ocean freight rates which were record high in 2007.77 Ocean freight rates are expected to retreat slightly in 2008 as the supply of ships expands, however, the continual rise of crude oil prices to new highs in early 2008 suggest that shipping costs are unlikely to experience a significant decline, and could possibly rise with higher fuel costs.

The political consequences of food shortages can be severe. Since January 2008, the emerging food crisis has sparked reports of hoarding and theft.78 Civil unrest over food prices have been reported around the globe including Egypt, Indonesia, the Philippines, and much of Africa (Burkina Faso, Cameroon, Ivory Coast, Mauritania, Mozambique, Senegal, and South Africa).79 In Haiti, two days of food-price rioting toppled the prime minister.80

Shortages, High Prices Hurt International Food Aid Prospects

Higher commodity and food prices reduce the international community’s ability to provide food aid to other countries without additional appropriations. This is because most international food aid activities (the United States included) are fixed in value by annual appropriations; thus, the amount of commodities that can be purchased declines with rising food prices.

In 2007, the U.N.’s World Food Program (WFP) — the world’s leading source for international food aid — estimated it would need $2.9 billion to cover its 2008 approved project needs which included feeding 73 million people in 78 countries. However, on March 20, the WFP made an emergency appeal for an additional US$500 million to twenty heads of government to offset the increased price of food commodities which had since raised its operating needs to $3.4 billion. Then, barely a month later on April 22, 2008, Josette Sheeran, the executive director of WFP, announced that the WFP needs $755 million in additional funding to meet its current operating needs due to continued rises in commodity prices since mid-March.81 One of the difficulties facing the WFP, according to Sheeran, is that rice suppliers that had signed earlier grain delivery contracts with the U.N. when prices were lower are now

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82 Food prices hampering U.N. agency’s ability to head off ‘new face of hunger’,” Nathanial Gronewold, E&E News PM, April 24, 2008, at [http://www.eenews.net]. 83 For more information, see CRS Report RL33553, Agricultural Export and Food Aid Programs by Charles Hanrahan. 84 P.L. 480 is the principal U.S. food aid program. It is administered by USAID. 85 White House News Release, Office of the Press Secretary, April 14, 2008. For more information on the Bill Emerson Humanitarian Trust, see CRS Report RS21234, The Bill Emerson Humanitarian Trust: Background and Current Issues, by Charles Hanrahan.

finding it more profitable to pay a 5% penalty to break the contract, and then sell their rice at the current higher market price.82

International food aid is the United States’ major response to reducing global hunger.83 In 2006, the United States provided $2.1 billion of such assistance, which paid for the delivery and distribution of more than 3 million tons of U.S. agricultural commodities. The United States provided food aid to 65 countries in 2006, more than half of them in sub-Saharan Africa. The U.S. Agency for International Development (USAID) indicated that rising food and fuel prices would result in a significant reduction in emergency food aid in 2008. According to press reports in March 2008, USAID expects a $200 million shortfall in funding to meet emergency food aid needs. For FY2008, Congress appropriated $1.2 billion for P.L. 480 food aid, the same as FY2007.84 For FY2009, the President’s budget again requested $1.2 billion. However, in six out of ten years since 1999, supplemental funding for P.L. 480 Title II food aid has been appropriated.

Since February, President Bush has been under increasing pressure from international hunger advocacy groups, as well as the U.S. milling and baking industry and other food industry groups, to open grain supplies held in the Bill Emerson Humanitarian Trust (BEHT) — which was estimated to hold $177 million in cash and about 33 million bushels of wheat in early 2008 — as a short-term means of dampening grain prices while augmenting international supplies. On April 14, 2008, President Bush directed the Secretary of Agriculture to access grain supplies from the BEHT valued at $200 million to meet emergency food aid needs abroad.85

Farm Commodity Market Outlook

Positive Short-Run Outlook, Especially for Food Crops

For some crops (particularly for wheat and rice), the price increases are likely to be relatively short-term in nature and are due to weather-related crop shortfalls in major producer and consumer countries, a weak U.S. dollar which has helped spark large increases in U.S. exports, a bidding war among major U.S. crops for land in the months leading up to spring planting in 2008, and the often perverse price effects resulting from international policy responses by several major exporting and importing nations to protect their domestic markets.

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86 Crop Prospects and Food Situation, No. 2, Global Information and Early Warning System on Food and Agriculture (GIEWS), FAO, U.N., April 2008. 87 “Field and specialty crops: Seeded area,” Statistics Canada, April 21, 2008. 88 “Set aside suspended by European Union,” by Bruno Waterfield and Charles Clover, ©Telegraph Media Group Limited 2008, September 26, 2007; [http://www.telegraph.co.uk].

Substantial recovery is expected to occur by late 2008, especially in wheat and rice markets, as global supplies rebuild. FAO forecasts that world cereal production will expand 2.6% to a record 2.164 billion tons in 2008.86 Most of the increase is expected to come from wheat with rice and coarse grains showing modest gains. If realized, FAO predicts that the expanded production will help to ease the current tight global cereal supply situation. However, any recovery remains weather-dependent and commodity prices are likely to remain highly volatile until the 2008 harvests are “in the bin.” More immediate price moderation can be achieved if government policies to limit or ban exports of available grain and oilseed supplies are repealed. Such export-prohibiting policies distort international market prices in the short-run by limiting access to export supplies, while dampening long- run productivity gains by artificially curtailing demand and thereby discouraging investments in domestic agriculture.

Biofuel feedstock demand has bid up the price of corn, soybeans, and other crops that compete for acres, especially in the United States. Both USDA’s and FAPRI’s outlook projections for 2008 include expanded acreage in the United States and worldwide, as high prices bring marginal land back into crop production. In the U.S., expectations for increased double cropping of winter wheat and soybeans in the Delta and Southeast, along with a return to crop production of a substantial portion of pasture land as well as nearly 2 million acres of former CRP, are expected to boost planted area for corn, soybeans, and wheat by nearly 7 million acres to 224.6 million acres.

Similar area expansion is expected to occur worldwide in response to strong price incentives and, in some countries, to government policy. For example, StatsCanada recently forecast Canadian wheat plantings to be up over 16% in 2008.87

In the EU, in September 2007 agriculture ministers suspended a 10% set-aside requirement that paid farmers to idle nearly 3.8 million hectares (9.4 million acres) of cropland annually. Mariann Fischer Boel, EU Agriculture Commissioner, expects the additional land re-entering crop production to bolster EU grain output by up to 17 million tons in 2008.88

Despite projections for record world grain production in 2008, only marginal global stock building is projected to occur. Relatively low stocks are expected to persist through 2008 as global production will be hard pressed to keep up with continued growth in demand (even with a return to normal weather and yields). This will leave commodity markets particularly vulnerable to news of poor harvests or demand shocks, and will very likely mean continued high price volatility.

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Long-Term Outlook Hinges on Productivity Gains

Projections for a steady rise in global population, accompanied by sustained income growth in the world’s developing economies, are expected to sustain growth in demand for livestock products and the feedstuffs — e.g., coarse grains and protein meals — needed to produce those products. In addition, the outlook for increased demand for agricultural feedstocks to meet large increases in government biofuel- usage policies, particularly in the United States and the European Union (EU), suggest that demand will increase strongly over the coming decade for corn (the primary feedstock for U.S. ethanol production), and vegetable oils (the primary feedstock for biodiesel production in the United States and the EU).

As a result, even with a return to normal crop growing conditions and successful harvests, commodity prices are expected to remain at significantly higher levels than experienced during the 1998-2006 period. FAPRI’s long-term 10-year projections suggest that prices for major U.S. program crops and products — corn, barley, sorghum, soybeans, soybean oil, soybean meal, wheat, and rice — will remain well above average prices of the recent five-year period 2002/2003 to 2006/2007 (Table 2).

A sustained period of agricultural output growth that surpasses the projected rise in demand is needed to produce a return to abundant supplies and moderate prices. Increased agricultural production implies some combination of increased land dedicated crop production and/or increased output per acre of planted land. For most countries, only marginal expansion of cropped area is possible and often this involves less-productive, more environmentally sensitive land. Agricultural productivity gains, on the other hand, require sustained long-term investment in agricultural research, extension, and marketing infrastructure. How commodity markets actually evolve will depend greatly on the policy choices that the U.S. and international community make concerning agricultural productivity and renewable energy.

U.S. and International Policy Response

Because the current U.S. and global commodity market dynamic affects so many aspects of agricultural markets including short-term consumer needs, as well as issues related to intermediate and longer-term agricultural productivity, the nature of the U.S. and international response will necessarily vary by targeted beneficiary as well as the relevant time period. It is unlikely that any single policy response will be able to address all issues simultaneously. This section briefly reviews some of the more salient policy responses being suggested or implemented by major players in the U.S. and international agricultural arena.

U.S. Industry Groups Decry Rising Costs of Grain as an Input

Interests from livestock and poultry sectors, food processors and retailers, and hunger advocacy groups, have advocated for changes in current U.S. agricultural and food policy. Many interests from within these sectors have advocated for the reversal

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89 For more information see the American Bakers Association, Press Releases, at [http://www.americanbakers.org/]. 90 For more information, see CRS Report RS22870, Waiver Authority Under the Renewable Fuel Standard (RFS), by Brent Yacobucci. 91 Transcript from interview with Scott Faber, vice president of government affairs, Grocery Manufacturer Association, E&ENews OnPoint, April 9, 2008, at [http://www.eenews.net/]. 92 “Renewable Fuels and Ethanol Production,” policy news, NCBA, at [http://www.beefusa. org/goveRenewableFuelsandEthanolProduction.aspx].

of U.S. biofuels policy, while some have also advocated for a reduction in crop land retirement under the CRP program.

The U.S. milling and baking industry, led by the American Bakers Association (ABA), held a march in Washington, D.C. in March, 2008, to highlight their concerns about the short grain supplies and sharp price rises in U.S. commodity markets. As part of their campaign, the ABA put forward a three-point congressional action plan.89

! First, the ABA claims that as much as one-third of CRP land could

be returned to agricultural production without environmental harm. They urge Congress to accept House Agriculture Committee Chairman, Colin Peterson’s proposal to decrease the CRP by 7 million acres; they encourage Congress and the USDA to support early out-of-contract provisions within the CRP; and they request that USDA and the Administration undertake an evaluation to identify viable cropland within the CRP to facilitate its return to production.

! Second, the ABA recommends that EPA use its waiver authority to waive the RFS annual requirements, and to drop all tariffs on imported ethanol.90

! Third, the ABA recommends that Congress and USDA consider the needs of the domestic food industry ahead of export markets whenever U.S. wheat stocks drop below a three-month-usage supply.

In April 2008, a spokesperson for the U.S. Grocery Manufacturers Association called on Congress, the EPA, and states to freeze and rollback the biofuels mandates due to their effect on food prices.91 In addition to interests from the food processing and grocery retail sectors, the U.S. livestock sector has expressed long-standing concerns about diverting feed crops away from commercial animal feeders and into biofuels production. The National Cattlemen’s Beef Association (NCBA) explicitly opposes the national biofuels RFS.92 NCBA members have called for a market-based approach for the production and usage of ethanol produced from livestock feedstuffs, and the NCBA supports sun-setting the existing biofuels tax credits and the ethanol import tariff as scheduled and not allowing for renewal in their current form. In April, a major international agriculture research group, the International Food Policy Research Institute (IFPRI), announced that a moratorium on global grain- and

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93 “Biofuels halt would ease food prices — ag group,” Missy Ryan, Guardian, April 29, 2008. 94 “Letter to EPA Administrator Stephen Johnson,” by Texas Governor Perry, April 25, 2008, at [http://www.governor.state.tx.us/]. 95 “National Chicken Council Commends Texas Governor for Filing First Request for Waiver of National Renewable Fuel Standard,” NCC Press Release, April 25, 2008. 96 “Sens. Hutchinson, McCain Urge Ethanol Mandate Relief from EPA,” news release, Sen. Hutchinson’s office, May 2, 2008, at [http://hutchinson.senate.gov/pr050208b.thml]. 97 For example, see the articles “Flat out Wrong About Food Prices,” and “RFA Opposes Waiver Request by Texas Governor,” at the RFA website at [http://www.ethanolrfa.org/]; or “Recipe for a Food and Fuel Smear Campaign,” Rick Tolman, Chief Executive Officer, National Corn Growers Association, at [http://www.ncga.com/]. 98 “Citing food costs, Texas Governor seeks waiver of fuel mandate,” Ben Geman, Greenwire, April 28, 2008, [http://www.eenews.net].

oilseed-based biofuels would help ease corn prices by up to 20% and wheat prices by 10% in the next few years.93

On April 25, 2008, Texas Governor Rick Perry, in a letter to Stephen Johnson, Administrator of the Environmental Protection Agency (EPA) — the federal agency responsible for administering the RFS — to request that EPA waive 50% of the RFS’ ethanol requirements to alleviate their impact on corn prices.94 Section 211(o) of the Federal Clean Air Act (as amended by EISA of 2007) provides the EPA Administrator, in consultation with the Secretary of Agriculture and the Secretary of Energy, with the authority to suspend for one year all or part of the RFS. EPA has 90 days to respond to Governor Perry’s petition for a waiver. The National Chicken Council’s President, George Watts, publicly commended Governor Perry’s action stating that ethanol has been a factor behind rising food costs.95

On May 2, 2008, 23 Republican senators sent a letter to EPA administrator Johnson to inquire about the status of regulations for states applying for an ethanol mandate waiver and urged that EPA take into consideration food inflation concerns related to the biofuels mandate.96 However, not all quarters agree with the blame being targeted on the biofuels sector. The Renewable Fuels Association, the National Corn Growers Association, and other interest groups that have benefitted from U.S. biofuels policy suggest that high oil prices, global production shortfalls, and foreign export controls are the main culprits and that it is unfair and incorrect to place the entire blame on the biofuels sector.97

U.S. Congressional Action

On April 29, 2008, Congressman Jeff Flake introduced a bill, H.R. 5911, that would repeal the RFS biofuels usage mandate, as well as the biofuels tax credit incentive and the ethanol import tariff — all to take effect immediately upon enactment of the bill. Senator Kay Bailey Hutchinson has also announced her intentions to introduce legislation that would freeze the RFS at its current level rather than allowing it to increase through 2022.98

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99 See CRS Report RL33934, Farm Bill Legislative Action in the 110th Congress. 100 “De-railing the Farm Bill,” Washington Trade Daily, Vol. 17, No. 87, April 30, 2008. 101 See CRS Report RL34145, International Food Aid and the 2007 Farm Bill. 102 For more information see CRS Report RL34451, Second FY2008 Supplemental Appropriations for Military Operations, International Affairs, and Other Purposes 103 “Bush Seeks $770 Million More in World Food Aid,” Dan Eggen, Washington Post, May 2, 2008.

Two additional legislative proposals currently being negotiated in Congress could contribute directly or indirectly to the U.S. response to the current global food crisis. These are a renewal of U.S. farm legislation, and a possible emergency supplemental appropriations bill.

The U.S. farm bill legislative process has been ongoing for nearly a year with considerable uncertainty surrounding a final outcome as of May 1, 2008. However, the eventual outcome has important implications for the level and timing of funding for several areas related to the U.S.’ ability to respond to domestic and international food concerns. For example, these include U.S. domestic nutrition programs, U.S. foreign food aid and agricultural assistance, the on-going debate over food versus fuel, agricultural land use incentives, and agricultural productivity issues. Different versions of new farm legislation (H.R. 2419) have been passed by both the House and the Senate. Both the House and Senate farm bills seek many of the same types of changes to existing legislation and programs, but there are numerous differences, particularly in the nutrition and energy titles. These differences will have to be worked out in conference.99 However, the Administration has objected to new restrictions on its management of emergency food aid funding that is being proposed within farm legislation.100 The President asserts that much of U.S. emergency assistance in the bill actually is earmarked for non-emergency situations, and that the bill prohibits use of U.S. funds to purchase foods in regions near a food emergency.101

Congress is presently considering supplemental appropriations for the Iraq war which could include some as-yet-undetermined funding for emergency response to the international food crisis.102 President Bush has requested $350 million in additional food aid as part of this emergency supplemental bill.

On May 1, 2008, President Bush announced a new, additional funding request of $770 million for emergency response to the international food crisis in FY2009.103

This funding is separate from the $350 million that he proposed for the current FY2008. According to White House officials, the $770 million would include about $395 million for direct food assistance; $150 million for agricultural development; and $225 million for local crop purchases, vouchers, and other special programs.

Immediate International Food Crises Response

On April 29, 2008, U.N. Secretary-General Ban Ki-moon announced that he will lead a task force to coordinate the efforts of the U.N. system in addressing the global crisis arising from the surge in food prices. The Task Force on the Global Food

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104 For more information on the WFP, see [http://www.wfp.org/]. 105 For more information on the FAO, see [http://www.fao.org/]. 106 “Information Note,” FAO’s Initiative on Soaring Food Prices, FAO, U.N., at [http://www.fao.org/newsroom/common/ecg/1000826/en/ISFP.pdf]. 107 Food prices hampering U.N. agency’s ability to head off ‘new face of hunger’,” Nathanial Gronewold, E&E News PM, April 24, 2008, at [http://www.eenews.net]. 108 “U.N. taskforce to tackle global food crisis,” Harvey Morris, Financial Times, April 30, 2008.

Crisis will bring together the heads of U.N. agencies, funds and programs, and the World Bank and International Monetary Fund, as well as experts within the UN and leading authorities from the international community.

The two principal agencies within the United Nations (U.N.) responsible for international agricultural development and food aid are the Food and Agricultural Organization (FAO) and the World Food Program (WFP). WFP is the U.N.’s front- line agency in the fight against global hunger via emergency operations in response to natural and man-made disasters, relief and rehabilitation projects, development projects where food aid is used for social and economic development, and special operations involving logistics to speed up the movement of food aid.104 In contrast, FAO has a longer-term focus. FAO is mandated to raise levels of nutrition, improve agricultural productivity, better the lives of rural populations and contribute to the growth of the world economy. FAO accomplishes this through in-country agricultural research and extension support, information dissemination, international workshops and conferences, etc. in order to help developing countries modernize and improve their agriculture, forestry and fisheries practices and ensure good nutrition for all.105

On December 17, 2007, the U.N.’s FAO launched its Initiative on Soaring Food Prices (ISFP) as a focal point for technical and policy assistance to those countries identified as the hardest hit by the sharp increase in food prices — referred to as Low-Income Food-Deficit Countries (LIFDCs).106 The international community is seeking to coordinate on-going work by the World Bank, the International Fund for Agriculture Development (IFAD), WFP, Regional Development Banks (e.g., the Asian Development Bank and the African Development Bank), and private foundations to integrate new projects and interventions. First, the WFP, FAO, and IFAD are scheduled to present their strategies for coping with the food-price crisis at a U.N. executive board meeting in Geneva.107 Then, the WFP and other international agencies will hear directly from governments on what they believe policy responses need to be at a gathering at U.N. headquarters in New York. These policy meetings are a prelude to the FAO’s “Summit on Food Security” in Rome scheduled for June 3-5, 2008. It is expected that the Rome summit will provide a forum for coordinating the global response to the current food crises. The list of current proposals by the U.N. and its agencies includes:108

! Establishment of a U.N. task force on food crisis. ! WFP request for $755 million of emergency food aid (above normal

program funds) to world’s poorest.

CRS-42

109 For example, see “Food Price Hikes Threaten Political Crises,” by John Baize, World Perspectives, Inc., April 9, 2008. 110 “Rising Food Prices: What Should Be Done?” Joachim von Braun, IFPRI Policy Brief, April 2008, at [http://www.ifpri.org/themes/foodprices/foodprices.asp].

! Emergency $1.7 billion initiative to provide poor food importers with seed and fertilizer.

! The World Bank is to explore a rapid financing facility for poor countries.

! The International Monetary Fund (IMF) proposes aid to countries facing balance of payments gaps.

! The U.N. calls for the lifting of food export restrictions.

Long-Term Agricultural Productivity Response

While the international donor community is responding to short-term food needs, other interest groups are encouraging both greater investment in international agricultural productivity and the phasing out or elimination of government policies that distort market signals and diminish agricultural producer’s incentives to respond to price signals. For each country, the appropriate policy response depends on the specific policy goal.109 Many suggestions are being offered by market watchers, but several recurring themes are present:

! Reverse protective country-level policies of export bans and/or limitations that have exacerbated the problem.

! Reduce or eliminate subsidies that divert agricultural land from food and feed production to the production of feedstocks for biofuels.

! Remove or phase out domestic policies that keep market prices low in favor of consumers, but at the expense of sustained investment in the agricultural sector.

! Remove barriers that have constrained the production and use of genetically-modified crops.

In April, the International Food Policy Research Institute (IFPRI) — a major international agriculture research agency that operates as part of the Consultative Group on International Agricultural Research (CGIAR) — issued a 2-page policy brief that enumerated several policy recommendations for dealing with high international commodity prices and their harmful effect on groups vulnerable to food insecurity. First, IFPRI calls for short-run reinforcement and expansion of social protection and nutrition programs targeted to vulnerable groups. Second, IFPRI calls for elimination of biofuel subsidies and mandates. Third, IFPRI recommends the elimination of trade barriers to reduce market distortions and thereby allow correct price signals to reach agricultural producers. Finally, IFPRI encourages long-term investment in agricultural research and extension, rural infrastructure, and market access for small farmers.110

The World Bank (WB) also has released a list of policy recommendations in response to the emerging global food crisis. The WB’s recommendation focus on those policy options designed to improve household food security. The WB recently

CRS-43

111 Rising Food Prices: Policy Options and World Bank Response,” Background note for the Development Committee, prepared by PREM, ARD, and DEC drawing from across the Bank; undated mimeo, at [http://www.worldbank.org/]. 112 “A Proposal on Food Export Restrictions,” World Trade Daily, Vol. 17, No. 87, April 30, 2008. 113 “Revised Draft Modalities for Agriculture,” TN/AG/W/4/Rev.1, WTO, Feb. 8, 2008.

released a policy option paper as a partial guide for government policy designed to respond to the current high commodity prices in international markets.111 As such, the WB prioritizes policy options by their effectiveness at reaching target groups, the equity of distribution of program benefits, and the degree of market distortions introduced. The WB lists country-level policy options under three broad classes: Targeted Safety Net Programs; Measures to Lower Domestic Food Prices; and Measures to Stimulate Medium-term Food Grain Production

Possible World Trade Organization (WTO) Implications

In an attempt to deal with its own food-import dependency while responding to the global food crisis and the proliferation of export restrictions, Japanese officials have announced that Japan will be offering a formal proposal in the WTO’s Doha Round of multilateral trade negotiations calling for stronger disciplines on exporting members.112

Doha agriculture negotiations chair, Crawford Falconer, has proposed eliminating all existing export restrictions by the end of the first year of the implementation of any new agreement.113 In addition, he has proposed that any new export restrictions and prohibitions be allowed only for a period of 12 months, extendable up to a maximum of 18 months, in consultations with affected importers.

swinnen.pdf

LICOS Discussion Paper Series

Discussion Paper 259/2010

The Right Price of Food

Johan F.M. Swinnen

Katholieke Universiteit Leuven LICOS Centre for Institutions and Economic Performance Huis De Dorlodot Deberiotstraat 34 – mailbox 3511 B-3000 Leuven BELGIUM TEL:+32-(0)16 32 65 98 FAX:+32-(0)16 32 65 99 http://www.econ.kuleuven.be/licos

1

The Right Price of Food

Johan F.M. Swinnen

LICOS Centre for Institutions and Economic Performance & Department of Economics University of Leuven (KUL)

& Centre for European Policy Studies

Brussels

Version: 6 May 2010

Abstract Only a few years ago the widely shared view was that low food prices were a curse to developing countries and the poor. The dramatic increase of food prices in 2006-2008 appears to have fundamentally altered this view. The vast majority of analyses and reports in 2008 and 2009 state that high food prices have a devastating effect on developing countries and the world’s poor. This reversal of opinion raises questions about the old and the new arguments and about the proposed remedies. It also raises questions about the causes of this dramatic turnaround in analysis and policy conclusions. In this paper I document these changes in perspective and I discuss potential implications and offer hypotheses on the cause of the change in views. I thank Mara Squicciarini for excellent research assistance and Kym Anderson, John Bensted-Smith, Luc Christiaensen, Tassos Haniotis, Tom Hertel, Michiel Keyzer, Andrzej Kwiecinski, Will Martin, Alan Matthews, Alessandro Olper, Manohar Sharma, Peter Timmer, Alan Winters and various other colleagues for discussions on the issues raised in this paper. The opinions expressed here are mine only. Contact: [email protected]

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Introduction

“Only Socrates knew, after a lifetime of unceasing labor, that he was ignorant. Now every high school student knows that. How did it become so easy ?

What accounts for our amazing progress ?”

Allan Bloom, 1987 1

In his famous book “Getting Prices Right”, Peter Timmer (1986, p.13) posed the

question “What is the “right” price for an agricultural commodity ?”. He goes on to

argue that we can only determine the right price of food if we take into account a wide

variety of effects of prices, both on efficiency and on income distribution.2 This

logic, obviously, assumes that we can in fact determine the impact of food on various

groups in a country and across the globe. Somewhat surprisingly, the 2007-2008 food

crisis seems to have challenged this assumption. The food crisis has led to a wide set

of reports and public statements analyzing and suggesting remedies for the crisis. The

puzzling thing about the post-crisis statements is that many seem to ignore pre-crisis

analyzes and to convey a dramatically opposed view.

Only a few years ago the widely shared view was that low food prices were a

curse to developing countries and the poor. The following statement from the Food

and Agricultural Organization (FAO) of the United Nations on the state of the world

food markets and its implications for developing countries represents the common

view as recently as 2005: “The long-term downward trend in agricultural commodity

prices threatens the food security of hundreds of millions of people in some of the

1 The Closing of the American Mind, New York: Simon and Schuster, 1987, p. 43 2 Timmer (1986, p.13) also explains that “[e]conomists have an easy answer to the question, but only in a world of perfect information, with competitive markets, without other government interventions … and without political concerns for the impact on income distribution.”

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world's poorest developing countries where the sale of commodities is often the only

source of cash.”3

The dramatic increase of food prices in 2006-2008 appears to have

fundamentally altered this view of the food system. The vast majority of reports in

2008 and 2009 state that high food prices have a devastating effect on developing

countries and the world’s poor. A typical example is the following statement from the

2008 annual report of the International Food Policy Research Institute (IFPRI): “In

2007, longstanding disruptions to the world food equation became widely evident and

rapidly rising food prices began to further threaten the food security of poor people

around the world. … The current food-price crisis can have long-term, detrimental

effects on peoples’ health and livelihoods, and can contribute to the further

impoverishment of many of the world’s poorest people.”4

This reversal of opinion – which, as I will document in this paper, was

widespread – raises questions about the correctness of the old and the new arguments

and about the proposed remedies. It also raises questions about the causes of this

dramatic turnaround in analysis and policy conclusions.

In this paper I review the positions of a variety of organizations active in the

food policy arena and review a series of hypotheses to explain their apparent change

of views as reflected in their public statements. More specifically, I start by

presenting a simple framework to assess welfare effects of food price changes. Then, I

document that many organizations have indeed changed their message, and quite

radically so. Next, I discuss some of the policy implications, and how they conflict

3 FAO newsroom, Agriculture commodity prices continue long-term decline, 15 February 2005, Rome/Geneva. http://www.fao.org/newsroom/EN/news/2005/89721/index.html

4 IFPRI, Annual Report 2007-08, p. 3

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with earlier arguments in food policy. In the last section I present some hypotheses to

explain the observed changes in food policy arguments.

Food Price Effects: Some Basic Principles

Before reviewing analyses and policy statements let us first present some basic

principles on the effects of food price changes – which I presume are generally known

but, given the variety of conclusions presented, it appears useful to start by setting the

framework.5 Consider first a simple model of an open economy with two groups,

producers and consumers of food, where prices are determined at the world market

with local production or consumption having no impact on global prices (i.e. the so-

called small country assumption in international trade theory). In this situation, a

change in world market prices (caused by some external factor which is exogenous to

the country) affects producers and consumers, but in opposing directions: consumers

gain and producers lose from a decline in prices, and vice versa when prices increase.

To make this model more realistic one can consider several extensions. First, in

reality the distinction between producers and consumers may not be so simple. Many

rural households in developing countries are both producers and consumers of food

and are thus affected in different ways by price changes. The net household effect

depends on their net consumption status. Second, the change in world market prices

may differ from the change in the local prices and the latter may even differ for local

producers and local consumers, as these changes are affected by various policies

(trade policy, taxes, …), by infrastructure and institutions, and by the industrial

organization of the food chain. Third, local production and consumption may also

5 For more elaborated and sophisticated models see e.g. the textbooks on agricultural, food, and development policy analysis of Bruce Gardner (1988), James Houck (1986), Peter Timmer (1986) and Elisabeth Sadoulet and Alain de Janvry (1995).

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affect local prices, in addition to exogenous external shocks. Fourth, the “exogenous”

shocks may be caused by nature (e.g. the weather) or by men (e.g. changes in trade

policies or consumption or production in other countries). Fifth, short-run effects may

differ from long-run effects, as pass-through may take some time.6

What is important for our purposes is that, first, all these extensions do not

fundamentally change the basic result of the simple model: when prices go up

consumers lose and producers gain, and vice versa. Hence, when rich countries

increase (reduce) export subsidies which leads to a decline (increase) in world

markets, this will benefit (hurt) urban consumers and net consuming rural households

in poor countries and hurt (benefit) net producing rural households in poor countries.

The size of the benefits/losses though will depend on various factors, such as local

policies, institutions, the food chain organization, time, etc..7

Second, the net benefits of price increases and decreases for a country should be

roughly symmetric. Countries that benefit most from price decreases (e.g. if they

consume lots of food but produce little) will lose most from price increases. The same

holds at the household level within a country. Households which only consume food

and do not produce food will be affected stronger when prices change than

households which both produce and consume food. Another implication is that

households which are directly affected by world market prices will gain or lose more

than those living in areas largely isolated from market transactions when world prices

change.

6 There are more factors that would need to be taken into account in a truly complete model. For example, not only “exogenous” shocks will affect producers and consumers, but also “endogenous” price changes, with the latter caused, for example, by faster productivity growth in agriculture. In addition, one would have to consider general equilibrium effects (considering not just food market effects but also effects through/on markets for labor, capital, services, other inputs and outputs). For example, as other prices (eg energy, fertilizer, etc.) changed together with food prices, this may need to be taken into account as well. 7 In extreme cases the size of the effects could actually be reduced to zero.

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A straightforward implication of these basic principles is that low food prices on

the world market in most of the pre-2005 period benefited consumers and hurt

farmers in developing countries, and vice versa in the 2006-2008 period.

Another implication is that households which suffered strongly in 2007 from

high food prices (e.g. they lived in urban market centers and produced little food

themselves) would have benefited significantly from low food prices prior to 2005.

Inversely, some rural households may not have benefited (much) from the high prices

in 2007 (e.g. because they live in remote places with poor pass-through of prices from

the world market or because they consume all their food production themselves).

These rural households would also have experienced limited negative welfare effects

from the low food prices prior to 2005.

Surprisingly, however, while these basic principles are well known, we do not

find them reflected in most arguments put forward in the food policy debate. For

example, there has been hardly any mentioning of the benefits of low food prices for

urban consumers and net consuming rural households during the pre-2006 low price

era, and there has been very little emphasis in more recent statements on the benefits

for producers in poor countries from high food prices.

A 180° Turnaround in Food Policy Analysis & Communication

Before trying to understand why this is the case, let me first document that this was

indeed the case, i.e. that there are conflicting analyses and communications prior to

2006 and afterwards, and that there is a lack of consistency in analysis and policy

recommendations. I will document my claims by a series of quotes from various

organizations’ own communications of analyses and policy recommendations.

Afterwards I will discuss whether these quotes are representative and address the

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critique that this approach may not be appropriate by taking quotes out of their

context.

Analyses from NGOs

To start, let us take a look at statements from some of the non governmental

organizations (NGOs) working in the area of food policy before and after the food

crisis. In 2005, Oxfam International argues that:

“US and Europe[‘s s]urplus production is sold on world markets at artificially low prices, making it impossible for farmers in developing countries to compete. As a

consequence, over 900 millions of farmers are losing their livelihoods.”8

Three years later, at the height of the food crisis, Oxfam International’s view is that:

“Higher food prices have pushed millions of people in developing countries further into hunger and poverty. There are now 967 million

malnourished people in the world….”9

To put it simply: this organization claims that whatever happens to prices -- either

decreasing (pre-2006) or increasing (post-2006) -- hundreds of millions of people will

end up in poverty.

Other NGOs share this analysis: prior to 2006 they claim that low food prices

are hurting the poor and creating food insecurity; after 2006 they claim that high

prices are hurting the poor and leading to food insecurity. To illustrate this, compare

the following statements from the Bread for the World Institute. In their 2005 annual

report they write that:

“The agricultural trade and subsidy policies in the United States, European Union and Japan are harming poor people in developing countries. The harm done by far

8 OXFAM International, International celebrities get dumped on at the WSF, 1 November 2005 (underlining added). http://www.oxfam.org/en/node/283 9 OXFAM International, Lessons from the food price crisis: Questions & Answers, 15 October 2008 (underlining added). http://www.oxfam.org/en/campaigns/agriculture/food-price-crisis-questions- answers

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exceeds the good done by development assistance… The net result is continuation of poverty, hunger and related misery.”10

In 2009 annual report the same organization states that:

“Food prices are soaring worldwide. For the world's poorest people in developing countries—who spend up to 80 percent of their income to buy food—the situation is

even more devastating.”11

The apparent contradiction in these statements is obvious. One justification for

the statements could be that they refer to different groups in society (farmers in one

case, urban consumers in the other case). However, the lack of emphasis on this and

the absence of recognition that other groups may benefit is striking.

These are not even the most extreme examples. In several cases the excuse

that the conflicting arguments are due to focusing on different groups (while

selectively ignoring other groups) cannot even be used. For example, consider the

following statements from Oxfam Solidarité from as recently as 2006:

“The prices of products traded at the world markets are too low and do not allow the majority to live decently. … As a consequence of this competition at low prices, local prices fall, worsening poverty … This causes poverty, migration and malnutrition.” 12

However, after the food crisis, the same organization claims that:

“The FAO predicts a new price increase in 2009. This crisis of agricultural prices affects in the first place the poorest populations, mostly rural, which spent more than

half their revenues to feed themselves.” 13

10 Bread for the World Institute, 2005 Annual Report, p.44 11 http://www.bread.org/learn/rising-food-prices/ or http://www.breadblog.org/hunger_in_the_news_1/ 12 Own translation. The original statement : « Les prix des produits échangés sur les marchés internationaux sont trop bas et ne permettent plus à la majorité de vivre décemment…Pour faire face à cette concurrence à bas prix, les prix locaux chutent, aggravant la pauvreté …. S’en suit alors pauvreté, exode et malnutrition. » (OXFAM Solidarité , Les revendications, 21 novembre 2006). http://www.oxfamsol.be/fr/Les-revendications,723.html. 13 Own translation. The original statement : « [L]a FAO prévoit une nouvelle hausse des prix en 2009. Cette crise des prix agricoles affecte en premier lieu les populations les plus pauvres, en majorité rurales, qui dépensent plus de la moitié de leurs revenus pour s’alimenter. » (OXFAM Solidarité, Agriculture : le G8 doit changer de cap!, 20 avril 2009). http://www.oxfamsol.be/fr/Agriculture-le-G8- doit-changer-de.html.

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This organization thus argues that the same group (poor rural people) is hurt by low

prices (in 2005) and hurt by high prices (in 2009).

One explanation for these observations could be that one should not expect

anything else from NGOs. One may argue that, after all, these are advocacy groups

and their primary objective is not to provide objective and carefully balanced

analyses, but rather to raise attention to problems and to pressure governments to do

something about it, or to raise funds for their own projects.14

Analyses from International Organizations

Let us therefore next consider the views of international institutions which are

not (expected to be) advocacy groups but which are expected to provide analyses and

recommendations to enhance social welfare, such as the FAO, IFPRI, OECD, the IMF

and the World Bank. Interestingly, these institutions seem to have adjusted their

analyses and policy communications similar to NGOs. Consider the following

examples from these organizations. In 2005, FAO writes that:

“The long-term downward trend in agricultural commodity prices threatens the food security of hundreds of millions of people in some of the world's poorest developing

countries.” 15 In 2008, the leading officials of FAO declare that :

“The number of hungry people increased by about 50 million in 2007 as a result of

high food prices” 16

“Rising food prices are bound to worsen the already unacceptable level of food deprivation suffered by 854 million people. We are facing the risk that the number of

hungry will increase by many more millions of people.” 17

14 For economic models of NGOs, see e.g. Aldashev and Verdier (2010), Andreoni and Payne (2001), Chau and Huysentruyt (2006); for an analyses of “what NGOs do”, see e.g. Werker and Ahmed (2008). See also further in this paper. 15 FAO newsroom, Agriculture commodity prices continue long-term decline, 15 February 2005, Rome/Geneva. http://www.fao.org/newsroom/EN/news/2005/89721/index.html 16 FAO Director-General Jacques Diouf, 3 July 2008, European Parliament Conference, Brussels. http://www.fao.org/newsroom/EN/news/2008/1000866/index.html

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Let us next compare the following statements from the IFPRI Annual Reports. The

first, from the 2002-03 Annual Report (p.22) states that:

“The combination of agricultural protectionism and subsidies in industrialized countries has limited agricultural growth in the developing world, increasing poverty

and weakening food security in vulnerable countries.”

In contrast, the 2007-08 IFPRI Annual Report (p.5) states that:

“In 2007, rapidly rising food prices began to further threaten the food security of poor people around the world. … The current food-price crisis can have long-term,

detrimental effects on peoples’ health and livelihoods, and can contribute to the further impoverishment of many of the world’s poorest people.”

Similarly, before the food crisis, reports from the OECD, the World Bank, and the

IMF discussing the effects of trade liberalization for developing countries typically

state that liberalization will help the poor by increasing world prices as rich countries

cut their agricultural subsidies. This is illustrated by the following quotes from the

OECD, the World Bank, and the IMF, respectively:

“Many (developed countries) continue to use various forms of export subsidies that drive down world prices and take markets away from farmers in poorer countries. …

Much of this support depresses rural incomes in developing countries while benefiting primarily the wealthiest farmers in rich countries.”18

“The combination of depressed world prices and developing country policies which tax agriculture relative to industry have discouraged farm output and hence lowered

rural incomes. Because the majority of the world’s poorest households depend on agriculture and related activities for their livelihood, this … is especially

alarming.”19

17 FAO Assistant Director-General Hafez Ghane, May 2008, Rome. http://www.fao.org/newsroom/EN/news/2008/1000845/index.html 18 OECD, Cancún and the Doha agenda: The key challenges, 10-14 September 2003 [Also repeated in the Declaration by the Heads of the IMF, OECD and World Bank, 4 September 2003] http://www.bfsb- bahamas.com/photos/old_images/Declaration.pdf 19 World Bank, Agricultural trade liberalization: implication for developing countries, 1990

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“The numbers leave no room for doubt. Industrial country protectionism in the agricultural sector inflicts considerable hardship on the citizens of developing

countries.”20

In contrast, during the food crisis of 2007-2008, these three organizations, like NGOs

and the FAO and IFPRI, communicate very different effects of food prices. This is

illustrated again by several quotes from the OECD, the World Bank and the IMF,

respectively:

“When food prices skyrocket this can quickly pose a threat to the lives of the poorest, particular in developing countries.” 21

“The situation (high food prices) …could set back welcome progress in many

developing countries towards growth, development and poverty reduction… poor people, particularly those living in urban areas, are already suffering”22

“The increase in food prices represents a major crisis for the world’s poor.”23

“Preliminary estimates suggest that up to 105 million people could become poor due

to rising food prices alone.”24

“Millions of consumers could fall into extreme poverty due to higher food prices, and millions more already under the poverty line are likely to experience a further

deterioration in their living standards.” 25

“The rapid increase in food prices has had an adverse impact on poverty, and effectively denied many poor people access to food.”26

20 IMF, Agricultural Trade Reform: The Role of Economic Analysis, 3-4 November 2004 http://www.imf.org/external/np/speeches/2004/110404.htm 21 OECD, Ensuring food security for the world’s poor: Questions and Answers, 07 May 2009. http://www.oecd.org/document/50/0,3343,en_2649_37401_42666830_1_1_1_1,00.html 22 Rising food prices and developing countries, Speech by Angel Gurría, OECD Secretary-General, 21 May 2008. http://www.oecd.org/document/11/0,3343,en_2649_33721_40651723_1_1_1_1,00.html 23 World Bank, Rising Food Prices in Sub-Saharan Africa: Poverty Impact and Policy Responses, Policy Research Working Paper 4738, October 2008, p. 1. http://www- wds.worldbank.org/external/default/WDSContentServer/IW3P/IB/2008/10/01/000158349_200810011 11809/Rendered/PDF/WPS4738.pdf 24 World Bank, Double Jeopardy: Responding to High Food and Fuel Prices, G8 Hokkaido-Toyako Summit, July 2008, p. 4. http://web.worldbank.org/WBSITE/EXTERNAL/NEWS/0,,contentMDK:21827681~pagePK:6425704 3~piPK:437376~theSitePK:4607,00.html 25 World Bank, Poverty Effects of Higher Food Prices. A Global Perspective, Policy Research Working Paper 4887, March 2009, p. 23

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In summary, (virtually) all the major international organizations that focus on

food and agricultural policy issues globally have shifted from emphasizing how low

food prices, often argued to have been caused by rich country agricultural policies,

cause poverty and food insecurity in developing countries in the pre-2006 period to

emphasizing that high food prices cause poverty and food security in the post-2006

period, without mentioning the benefits (in either period).

Out of Context ?

An obvious critique on my arguments here is that I am making false claims by

taking statements out of context and that I am just selecting one element of a broader

and more complex message and that the full analyses are more complete and nuanced.

It is of course true that these quotes are taken out of their context – that’s why they are

quotes to begin with – and that reading the full documents may provide more nuance.

However, I would still argue that these quotes quite accurately represent the

key arguments in the context of the current debate. First, in the vast majority of the

cases these quotes summarize quite well the key message of the reports. This

argument is particularly important because “key messages” are crucial for these

organizations as target audiences (political decision-makers and the general public)

have no time to read long reports. Therefore, the organizations spend effort and time

in developing “key messages” and their communication strategy is typically focused

on such key messages. The rest of the policy document is typically to substantiate, but

26 IMF Food Security and the Increase in Global Food Prices, Speech by Mark Plant, IMF Deputy Director, Policy Development and Review Department, 19 June 2008. http://www.imf.org/external/np/speeches/2008/061908.htm

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not to deviate from the key message27. Hence, the quotes as I have listed them here

do represent the key arguments made.

Second, even if one reads the full report and one takes on board all the

nuances, there remain striking differences in the pre- and post-2006 analyses and

conclusions. For example, in very few of the reports published before the recent food

price increases is there any mention of the fact that urban consumers in developing

countries benefit (and the few that do mention it do not consider it as a major

element). Neither is the argument made that many poor rural households are net

consumers, and may thus benefit from low food prices.

Yet, the (mirror versions of these) arguments are emphasized very strongly in

all the post-2006 reports. All the attention there goes to the losses of these two

groups. Paradoxically, at the same time very little attention is paid to benefits for

poor farmers in these reports. Both observations are in total contrast with the pre-2006

arguments.

Do Poor Farmers Benefit from High Food Prices ?

A potential justification of this bias in focus and arguments is that poor

farmers were hurt by low agricultural prices before 2006 but that consumers did not

benefit from low food prices and that during the 2006-2008 period of high prices poor

farms did not benefit from high prices but that poor consumers did get hurt.

It is well known that in developing countries there exist a variety of market

imperfections and transaction costs which may influence the extent to which

consumers and producers are affected by price changes. Some have used such

27 In fact, any academic researcher who starts working for such organizations is reminded from day one to move complicated analyses and sophisticated messages to the appendix and the footnotes and to focus on bringing out the key messages in simple, easy to understand, sentences. I have extensive personal experiences on this.

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arguments to argue, for example, that consumes were strongly negatively affected by

the food crisis, while prices for farmers increased very little, if at all.

I find these arguments not convincing as an explanation for the bias in policy

messages. First, if farmers in rural areas are not (very much) affected by the high

prices, then how can one argue that many poor rural households are negatively

affected by the price increases since they are net consumers? If prices do not benefit

(net producing) farmers, they should not harm net food consuming households living

in the same rural areas.

Second, the problems of imperfect price pass-through between farms and

consumers (domestic or international) is of course not new and not restricted to a

period of high prices. These problems are due to a combination of institutional,

policy, and infrastructural constraints. If they are important – as they are in many

regions of developing countries – they should have also have limited the pass-through

of low prices to farmers in the pre-2006 period. Continuing along the same logic, as

urban consumers would have been more directly affected, this should then have

caused more positive aggregate (net) effects of low food prices than generally argued

in the pre-2006 period.28

Policy Implications (What’s the Problem ?)

An explanation of these observations could be that the objective of the organizations

is to assist those in need, i.e. those who are negatively affected by shocks. When

prices fall (are low) farmers are negatively affected and, therefore, aid and policy

focus should be targeted towards them. When prices increase (are high) consumers

are negatively affected and they should attract most policy attention and aid. Hence,

28 Additional arguments are that farmers may not have benefited because their costs (in particular fertilizer and energy) went up by more than the price of their output (food). This is an important point, but to draw conclusions one should take into account the extent to which poor farmers rely on external inputs.

15

when conditions change, attention will shift from one group to another depending on

how they are (relatively) affected, i.e. who is benefiting and losing from the change.

If this is indeed the case, instead of worrying about this, one may instead appreciate

the change in policy attention and international organizations’ re-focus towards those

who are in need. Hence: what’s the problem ?

The problem is the policy messages that are being communicated and

recommended, and the fact that they seem to ignore that there are always winners and

losers. In addition, one should expect a good policy framework to be coherent and be

relevant and correct both when prices go up and when they go down.

Recommendations to assist developing country consumers, such as cutting

export restrictions in food exporting countries, would hurt poor farmers in food

importing countries and food consumers in food exporting countries. In fact, export

restrictions in food exporting countries with many poor people (such as India and

Thailand) have been blamed by international organizations for hurting poor

consumers in food importing countries. However they have benefited poor consumers

in food exporting countries – and some experts have argued that these governments

have indeed made the right policy choice.29 Similarly, policy recommendations

intended to help developing country farmers prior to 2005 -- such as cutting export

subsidies from the EU – typically ignored that they would hurt consumers in

developing countries.

In summary, even if the objective of NGOs and international organizations is

to assist those who are negatively affected by food price changes, this is no excuse for

overly simplistic policy analyses and conclusions. Quite the contrary, also then the

29 See e.g. Timmer (2009).

16

policy choices are difficult and involve trade-offs, and would benefit from careful

analyses and nuanced messages.

Back to the Future or Forward to the Past ?

The Old CAP as a Model for Global Food Security ?30

To illustrate the importance of the analyses of NGOs and international

organizations for actual policy debates and choices, I refer to some ongoing policy

debates. The considerations which I explained above have led me to make some

ironically-intented suggestions at several occasions (including once in a World Bank

informal expert group discussion and at an FAO expert workshop). I argued that

those who do believe that poor farmers in developing countries are not affected by

changes in world market prices and that poor country’ consumers are affected

severely by the high global food prices should maybe recommend the EU (and the

US) to turn back their agricultural policy reform efforts of the past 20 years and re-

install their old policies. In the EU’s case this means turning back their reforms to

decouple agricultural subsidies and re-installing the old Common Agricultural Policy

(CAP) with high intervention prices, import tariffs and export subsidies. Under this

policy system, EU farmers were protected from imports by high tariffs and EU

farmers received prices much higher than the world market, stimulating EU

agricultural production and surpluses. World market prices were pushed down by the

EU import tariffs and by the export of surpluses with export subsidies. My ironical

30 There is a related but distinct discussion on the role of governments to stabilize markets in volatile food markets. The European Commission is emphasizing the importance of stable CAP payments in a volatile market environment. Timmer (2009) discusses the trade-off in domestic benefits for 3 billlion rice consumers in Asia (as countries such as China, India and Indonesia have isolated their rice economies from the recent turmoil in global markets through trade and domestic policies) versus the resulting increase of market instability for 500 million rice consumers in the rest of the world – in particular to Africa and other poor countries.

17

claim was that everybody would then presumably be better off: many European

farmers would love it since they returned to their high price system; EU taxpayers

would no longer have to pay decoupled farm payments; poor country consumers

would have low prices again; and poor country farmers would not care since they

were unaffected by world market price changes according to this logic. A Pareto

improvement if ever there was one … .31

After generations of economists have dismissed the old CAP as a highly

distortive system – both domestically and internationally -- and have asked for its

reform or outright dismissal and after almost twenty years of consecutive EU CAP

reforms that have substantially reduced the trade distortions, I presumed the irony was

obvious and the implications clear. We need a much more balanced and nuanced set

of analyses and policy recommendations of the current situation which recognizes

both the costs and benefits of price changes and policy actions.

Yet, the irony is not obvious to everybody apparently. Instead some have

embraced the new policy focus and communications for advocating certain policies.

For example, inside the EU various groups have actually started using “food security”

(including global food security) as an argument to defend the 50 billion euro of

subsidies which are each year paid to EU farmers from the EU budget. In fact the

main EU farm lobby (COPA-COGECA) and the EU association of land owners

(ELO) argue that food security should be a key motivation for continuing the

subsidization of EU agriculture in the future.32

31 To be fully Pareto improving, one should also consider the negative impact on EU consumers. However, in the same ironical logic, since they are supposedly rich (actually many are not) and since price declines are typically assumed to be captured by the big retail chains (actually they are not) this would probably not be considered an issue by those following this logic. 32 Statement by Mr. Pekkonen, DG of COPA-COGECA, during debate in Brussels on 19 November 2009 (at the launch of the economists CAP reform call) and various ELO reports.

18

Moreover, recently the United Nations explicitly praised the attractiveness of

the old CAP as a model:

“While the establishment of the EU Common Agricultural Policy (CAP) in 1962 had ‘many negative externalities’, … the policy is a good example of how to

achieve food security in a given area.”33 That the old CAP raised EU food prices, thereby hurting urban consumers in

the EU and thus lowering food security in the “given area” and that the “negative

externalities” have been attacked by all international organizations (literally from

“left” to ”right”, i.e. from Oxfam to the IMF) as being detrimental for poor country

farmers -- see all the pre-2006 quotes above as an illustration -- does not seem to be a

major concern to those who are making such statements.

The Political Economy of (Food) Policy Analysis and Communication

In this last section I discuss some potential explanations – in addition to the arguments

made earlier (in the policy implications section) – for the puzzling observations that I

have outlined above.

Scientific Progress (Analysis vs Communication)

Maybe the simplest explanation is that the analyses and arguments in the past

were wrong and the recent food crises, in combination with improved economic

modeling and better data, has contributed to better analysis and improved insights.

There certainly has been significant progress in economic models and data to measure

the impact of global price changes and policies on developing country households,34.

The simulation results of the most recent economic models are more reliable, more

33 UN special rapporteur on the right to food, Interview with EurActiv on 26 November 2009 (www.euractiv.com) 34 There are a series of improvements in data and models in this area, including in models run by OECD, FAO, IFPRI, GTAP, the World Bank, etc.

19

precise and more detailed in their impact assessments. For example, recent studies

based on the integration of global trade models and household data come to very

nuanced conclusions on the effects of liberalization and price changes. Hertel et al

(2007) find that the reduction of export subsidies and domestic support of rich

countries, on average, increase poverty in their sample of 15 developing countries

because these reforms raise world prices for staple foods, including wheat, maize,

dairy and rice. At the same time rich countries’ tariff reductions reduce poverty in

developing countries because they improve the revenues of farms. The net effect of

liberalization in their analysis is a reduction in poverty.35

However, the issue is not the outcomes of the models, but instead the

communication of their results and the policy messages that have been derived from

them. In the same way that benefits for poor consumers from low market prices have

not been emphasized in the past,36 benefits for poor farmers from high prices are not

emphasized now. In fact, several organizations published analytical reports with

detailed findings and carefully nuanced interpretations and conclusions around the

same time when their communication departments released communications on the

food price issues which demonstrated the shift in emphasis (bias) which I have

documented above.37 For example, in the light of the careful modelling work and

analyses of trade liberalization which, among others, Kym Anderson, Tom Hertel,

Will Martin, Alan Winters and their colleagues of the World Bank have done over the 35 See also the interesting exchange on this issue between Dani Rodrik, Tom Hertel and Will Martin on Dani Rodrik’s weblog. 36 Very few pre-2006 studies emphasize the benefits of low food prices for the poor. Note also that many model runs of trade liberalization in agriculture show that the impact for Africa is negative, precisely because Africa is a net consuming region and is benefiting more from low food prices (as consumer) than it is losing (as producer). 37 See, for example, Anderson et al (2010), Christiaensen and Demery (2007), Hertel and Winters (2006) and the 2008 World Development Report, all published by the World Bank, Sarris and Morrison (2010) published by FAO and the policy analyses in various OECD reports on the state of agricultural markets and policies over the past decade.

20

years,38 consider two major World Bank reports intended for wide distribution, one

after/during the food crisis (the 2008 World Development report) and one before the

food crisis (the World Bank’s 2002 Rural Development Strategy “Reaching the Rural

Poor”) and what they communicate in their overview and executive summaries on

trade policy.

The 2008 World Development Report (Overview, p 10) states that in

developing countries “liberalization of imports of food staples can also be pro-poor

because often the largest number of poor, including smallholders, are net buyers. But

many poor net sellers (sometimes the largest group of poor) will lose …” The

emphasis is on how low prices benefit the poor. Better than many reports of other

organizations, it also explicitly recognizes the losses for households/farms who are net

sellers.

Now compare this to the 2003 Rural Development Strategy report (Executive

Summary, p xvii): “A major reason both for the limited growth of agricultural trade

and for the inability of developing countries to enlarge their share of this trade is high

protection in the large markets of the industrial world. High subsidies and other

forms of trade protection impair developing countries’ ability to compete in global

markets with farmers from the industrial world. They also encourage surpluses that

have been sold on world markets, depressing world prices and undermining the

potential contribution of agriculture to global prosperity. … It is crucial that the

industrial countries liberalize their agricultural markets by removing access for

developing countries’ products and by phasing out subsidies.” The argument and

emphasis here is very different: there is no mention of a difference between staple

foods and other agricultural commodities. The entire message is about how depressed 38 For surveys and overviews of model improvements and their insights, see e.g. Anderson and Martin, 2007; Anderson et al 2010; Ivanic and Martin, 2008; Hertell et al 2007, 2009; Hertell and Reimer, 2005; Winters et al 2004.

21

world market prices (and rich country subsidies) hurt developing country farmers.

There is no mention whatsoever of the benefits for consumers, or how a reduction in

rich country export subsidies would benefit the urban or rural poor net consumers.

(And neither is there in the rest of the report.) I would argue that these statements,

taken from two major strategic reports of the World Bank, are fully consistent with

the argument I make in the paper.

In summary, the problem does not appear to be (lack of) scientific progress or

quality of analysis, but the interpretation and communication of the results of the

scientific studies. In fact, some colleagues involved in research in or for these

organizations – when confronted with the arguments made in this paper – reacted that

they sometimes hardly recognized the relationship between their analytical work and

the policy messages sent by the communications departments to the external world

and the media. They wondered where, when, and why the policy nuances and careful

analytics had been left behind.

Urban Bias and Relative Incomes

For decades the poor situation of African farmers has been caused at least

partially by policies which were said to be “urban biased”, i.e. favoring urban interests

and at the detriment of rural farmers through (implicit) taxes. This, in fact was one of

the main conclusions from the famous Krueger, Schiff and Valdes (1992) study of the

World Bank, which contributed to the motivation for structural adjustment programs

in the 1990s. These programs have contributed to reduce taxation of developing

country farmers, as documented by the recent World Bank study led by Kym

Anderson (Anderson, 2009).

22

The 2007-2008 food crisis has led to a surge in attention to food policy caused

by pressure from urban interests.39 As soon as urban protests reached the streets and

the media, international organizations have reacted much like local politicians and

paid a disproportionate amount of attention to the problems of urban consumers.

There are a variety of explanations for the urban bias in developing countries.

Urban consumers, when hit by a negative relative income shock, such as an increase

in food prices, will react politically, e.g. through demonstrations. 40 Since they are

concentrated in cities and are easier to mobilize (lower transportation and lower

organization and communication costs) than dispersed farmers in distant rural areas,

they may receive disproportionate attention and policy favors from policy-makers.41 It

may be that a similar urban bias effect plays a role in drawing reactions and policy

attention from international organizations, e.g. through global media markets.

Fundraising and Legitimacy

If one wants to help the poor or stimulate development, funding is needed.

NGOs need to invest in fundraising activities in an environment where various NGOs

compete for attention and funding of donors (e.g. Andreoni and Payne, 2003; Rose-

Ackermann, 1982). In this perspective, the statements listed above could be

interpreted as part of a marketing strategy by NGOs.

39 See Hendrix et al (2009) and Maas and Matthews (2009) for empirical political economy analyses on the determinants of protests and riots against the food price increases. 40 This shift in policy attention reflects the relative income effect, which is widely observed to be a determinant of food and trade policy. When prices fall (are low) farmers are negatively affected and aid and policy focus is targeted towards them. When prices increase (are high) consumers are negatively affected and they attract most policy attention and focus. Hence, when conditions change, attention will shift from one group to another depending on how they are (relatively) affected, i.e. who is benefiting and losing from the change. The relative income effect in agricultural and food policy was emphasized by, for example, de Gorter and Tsur (1991), Swinnen and de Gorter (1993) and Swinnen (1994). 41 The organization cost argument was made first by Olson (1965) and has been applied to agricultural and food policy by, for example, Anderson and Hayami (1986) and Gardner (1987).

23

While academic studies analyzing this have focus on NGOs, the general

argument to focus on the costs and ignore the benefits of price changes as a marketing

strategy may apply more widely.42 All international organizations -- be it NGOs or

IFPRI, the World Bank, FAO – use to some extent funds from public or private

donors to operate and implement their projects – or subgroups within these

organizations have to compete internally for funding. While their funding sources

may differ, in a world where financial means are limited and where there is

continuous pressure to demonstrate relevance and importance of budget spending on

particular items, projects or divisions within large organizations, all these

organizations face a demand to demonstrate the importance of their work. Focusing

their reports and analyses on those hurt by price changes may fit in such strategy to

show relevance and importance – and may thus help in securing and raising funds.

A closely related argument is that, with mass media reports focusing on those

hurt by changing food prices – in particular consumers post 2006 – the donor

community, the organizations’ shareholders, and the public at large may expect (or

even demand) that these organizations focus their attention on those who are suffering

from price changes. If they would not publicly react to the reported problems, then it

would hurt their legitimacy as development organizations. This could undermine

overall support for their existence.

For some organizations discussed here, the objective is directly linked with

addressing negative welfare consequences. Others, however, should be expected to

focus more on the overall (aggregate) welfare effects. Hence for the first group of

42 Most academic research on the behaviour of international organizations has focused on their lending strategies and much less on their communication or fundraising strategies (see e.g. Aldenhoff (2007); Dreher, Sturm and Vreeland (2009), Vaubel et al. (2007)).

24

organizations, the incentive to bias their message may be stronger, both for

fundraising purposes and for their legitimacy.

Mass Media and Policy Communication

The arguments above already point at the important role of the media in inducing

organizations to act, either in order to preserve their legitimacy, to raise funds, or as a

consequence of pressure from the public at large or their stakeholders.

There are two important, but distinct, mechanisms at work in the interaction

between these organizations and the mass media.43 The first mechanism is the impact

of stories that appear in the mass media on the actions (analysis and policy focus) of

the organizations. The second mechanism is the desire of the organizations to appear

in mass media in order to achieve their objectives.44

Several characteristics of mass media are relevant to explain these

mechanisms (McCluskey and Swinnen, 2010). First, the agenda setting effect of the

media in international and aid policy, has sometimes been referred to as the “CNN

factor” (Hawkins, 2002). It refers to the process by which the media influences policy

by invoking responses in their audiences through concentrated and emotionally based

coverage, which in turn applies pressure to governments to react. Similarly, the

absence of media coverage reduces priority in agenda-setting (Jakobson, 2000). In

this logic, public officials react to media news because they see it as a reflection of

43 A rapidly growing literature documents other effects of mass media on development such as its effect on political accountability (e.g. Besley and Burgess (2001), Djankov et al (2003)) and its impact on reducing corruption in public policy (Francken et al (2008), Reinikka and Svensson (2005)). 44 The latter is analyzed in detail by Cottle and Nolan (2007) who conclude that “aid agencies have become increasingly embroiled in the practices and predilections of the global media and can find their organizational integrity impugned and communication aims compromised. These developments imperil the very ethics and project of global humanitarianism that aid agencies historically have done so much to promote.“(p862).

25

public opinion (Kim, 2005).45

Several studies have analyzed the impact of media coverage of poverty,

humanitarian crises, and natural disasters on humanitarian and foreign aid flows. Van

Belle, Rioux and Potter (2004) and Kim (2005) find that a higher level of media

attention to developing countries problems leads to more aid in several developed

countries. Eisensee and Stromberg (2007) argue that disaster relief decisions and aid

allocations are driven by media coverage of disasters but that other newsworthy

events may crowd out this news coverage.

Second, media attention is typically concentrated around “events” or “shocks”

(Swinnen and Francken, 2006).46 Hence, sudden changes with dramatic effects, such

as the 2008 food crisis, not only present important challenges to the international

organizations in addressing these, but also important opportunities for development

organizations to capture media attention and signal their relevance and importance to

their donors and the public.

A third factor is that the public at large will be more interested in media

reports concentrating on negative (development) effects. This follows from the so-

called “bad news hypothesis”. Media consumers in general tend to be more interested

in negative news items than in positive news items, ceteris paribus. This demand

effect of the media market drives mass media to pay more attention to “bad news”

(McCluskey and Swinnen 2004).

In combination, these factors create a set of incentives for international

organizations to emphasize the negative welfare implications in their analysis and

45 Some have questioned the importance of these effects (Natsios, 1996) and argue that the media is more likely to follow politics than lead it (Strobel, 1996). A more nuanced argument is forwarded by Robinson (2001) who explains that the media can be a powerful source in leading policy makers but primarily when there is great uncertainty or limited information. 46 For example, Swinnen and Francken (2006) find that virtually all the attention to globalization, trade and development issues in mass media is concentrated around ‘international summits’.

26

policy communications, and to de-emphasize the positive effects around the food

crisis in 2007-2008. In doing so, they were more likely to attract media coverage on

their work and, in turn, more likely to reach a wide audience and to influence policy-

makers. Such a media strategy could have a direct effect in influencing public and

private donations and policies of governments in the short run and an indirect effect in

encouraging appreciation and legitimacy for their work and the organizations

themselves – which could lead to support in the long run.

Some Concluding Comments

As discussed above, there are several reasons/motivations which may explain why

policy messages of NGOs and international organizations may be biased by

emphasizing the negative welfare effects changes and ignoring positive welfare

effects.

Policy Bias

The main question, of course, is to what extent this bias in focus and

communication of effects is affecting policy-making, and ultimately welfare and

development. The answer to this question is difficult since it depends on various

assumptions regarding (a) the processing of these sets of information by voters,

policy-makers and the organizations themselves, (b) the type of welfare function one

has in mind, and (c) the political economy of policy decisions –at various levels. That

said, it is likely that (ultimately) a bias in the analysis and the policy messages does

influence policy-making, and, thus welfare and development.

Land Grabbing and Headline Grabbing

27

Finally, the issues discussed here are relevant beyond the food price debate.47

The analysis and policy communication on issues such as the effects of

biotechnology, foreign investment in developing countries, including the so-called

“land grabbing” debate, the supermarket revolution etc have been influenced by

similar mechanisms.

Another example is the recent debate on foreign investment in land in Africa

has been captured by the term “land grabbing” – a concept which in itself emphasizes

the potentially negative implications. This is somewhat remarkable given the

empirical evidence on the huge benefits that farmers in other parts of the world have

gained from foreign investments in the food system. In fact, I and several colleagues

have argued at several occasions that foreign investment in the agri-food system has

been a crucial factor behind the post-1995 growth in agricultural productivity and

performance in Eastern Europe, with major positive spillovers for small and large

farms (Dries and Swinnen, 2004; Gow and Swinnen, 1998; Swinnen, 2002), as it

appears to be at least in some places in Africa (Maertens et al 2009).

However, from a media strategy and communications perspective, coining the

process by the term “land grabbing” has been a remarkable success as the term is now

widely used to describe the process and its risks.48 The potential problem of course is

47 The food crisis itself also affected the communication on other policy issues. For example, for most of the 1980s and 1990s, biofuels and biochemicals were seen as a potential source for enhancing farm incomes. As an alternative outlet for agricultural commodities, they were seen as potentially providing an opportunity to stop the long-run downward trend in prices for farmers. This perspective has changed totally with the recent food crisis to the extent that biofuels have been called “a crime against humanity” by a UN special rapporteur on food in 2007.

48 Some examples of reports by international organizations and media which have picked up the concept:

 Cotula L., Vermeulen S., Leonard R. and Keeley J., 2009. "Land grab or development opportunity? Agricultural investment and international land deals in Africa", FAO, IIED and IFAD.

 Von Braun J. and Meinzen-Dick, R., 2009. “Land Grabbing” by Foreign Investors in Developing Countries: Risks and Opportunities, IFPRI Policy Brief 13, April 2009

 FAO, 2009. “From Land Grab to Win-Win”, FAO Economic and Social Perspectives, Policy Brief 4, June 2009 ftp://ftp.fao.org/docrep/fao/011/ak357e/ak357e00.pdf

28

that with such a negative connotation it has become much more difficult to

communicate an unbiased evaluation of the benefits and costs, the pros and cons, of

foreign investment in land in Africa. If evidence would show that such investment

would be beneficial for the local population, it is now certainly more difficult to

overcome opposition to “land grabbing”.

Similarly, the focus on one side of the effects in the food policy debate both

before and after the recent food crisis is likely to have a cost in terms of policy-

making and thus in terms of welfare and poverty reduction.

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  • voorblad 259.pdf
    • Katholieke Universiteit Leuven

SWINNEN_mass_media_Eur Rev Agric Econ-2011-Swinnen-1-.pdf

The food crisis, mass media and the political economy of policy analysis and communication

Johan F. M. Swinnen *, Pasquamaria Squicciarini , and Thijs Vandemoortele

Catholic University of Leuven (KUL), Belgium

Received February 2011; final version accepted April 2011

Review coordinated by Thomas Heckelei

Abstract

Few years ago, the widely shared view was that low food prices were a curse to devel- oping countries. The dramatic increase in food prices in 2006 – 2008 appears to have fundamentally altered this view. The vast majority of analyses and reports in 2008 and 2009 state that high food prices have a devastating effect on developing countries. In this paper, we (i) document these changes in perspective; (ii) develop a model of policy communication to explain the cause of the change in views; and (iii) review the policy recommendations of the organisations that shifted their communication.

Keywords: food prices, political economy, bias, NGOs, intergovernmental organisations, mass media

JEL classification: E31, P16, D23, L31, D83

1. Introduction

Few years ago, the widely shared view was that low food prices were a curse to developing countries and the poor. The following statement from the Food and Agricultural Organization (FAO) of the United Nations on the state of the world food markets and its implications for developing countries represents the common view as recently as 2005: ‘The long-term downward trend in agri- cultural commodity prices threatens the food security of hundreds of millions of people in some of the world’s poorest developing countries where the sale of commodities is often the only source of cash’ (FAO, 2005).

The dramatic increase in food prices in 2006 – 2008 and in 2010 – 2011 appears to have fundamentally altered this view of the food system. The vast majority of recent reports state that high food prices have a

*Corresponding author: Waaistraat 6 Box 3511, B-3000 Leuven, Belgium. E-mail: jo.swinnen@econ.

kuleuven.be

European Review of Agricultural Economics Vol 38 (3) (2011) pp. 409–426 doi:10.1093/erae/jbr020 Advance Access Publication 10 June 2011

# Oxford University Press and Foundation for the European Review of Agricultural Economics 2011; all rights reserved. For permissions, please email [email protected]

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devastating effect on developing countries and the world’s poor. Typical examples are a statement from the 2008 annual report of the International Food Policy Research Institute (IFPRI): ‘ . . . rapidly rising food prices began to further threaten the food security of poor people around the world. . . . The current food-price crisis can have long-term, detrimental effects on peoples’ health and livelihoods, and can contribute to the further impoverish- ment of many of the world’s poorest people’ (Braun, 2008: 3), and a statement from Oxfam UK (2011): ‘High global food prices risk hunger for millions of people. Poor people in developing countries spend up to 80% of their income on food.’

This reversal of opinion was widespread (see further) and raises questions about the correctness of the old and the new arguments and about the proposed remedies. It also raises questions about the causes of this dramatic turnaround in analysis and policy conclusions.

As such, the increase in food prices in recent years provides an interesting ‘natural experiment’ on potential bias in policy analysis and communication. Development organisations, charities, aid agencies, non-governmental organ- isations (NGOs) and other institutions whose formal objective is to enhance welfare and reduce suffering around the world are regularly accused of provid- ing biased analyses on the state of the world and to have their actions guided by their private benefits – such as attracting media attention, raising funds or personal fame – rather than the public goods they are supposed to be after.

Bias in policy communication is an important issue. Policy communication by these organisations does influence policy thinking, government strategies, development priorities and aid flows. For example, an important element of the current WTO negotiations (the so-called Doha Development Round) is to reduce the depressing effect of rich countries’ agricultural policies on global food prices – a perspective which until the recent food crisis was widely seen as negative for developing countries. Similarly, the food crisis has clearly influenced discussions on the reform of the Common Agricultural Policy.

Some have explicitly linked the (bias in) policy communication of develop- ment and aid organisations to capturing media attention and fund-raising. For example, Cottle and Nolan (2007) argue that the humanitarian aim of some has become compromised as the focus of organisations is on their communi- cation process: the ‘media logic’ of packaging information and images has become institutionalised inside aid agencies. The Lancet (2010: 253) – an internationally renowned medical journal – at the height of the humanitarian crisis in Haiti makes an even stronger statement: ‘Polluted by the internal power politics and the unsavory characteristics seen in many big corporations, large aid agencies can be obsessed with raising money through their own appeal efforts. Media coverage as an end in itself is too often an aim of their activities. Marketing and branding have too high a profile.’

This issue has, so far, received little attention in the academic literature. There is a burgeoning literature on bias in communication in mass media and its impact on government policy (see McCluskey and Swinnen, 2010,

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for a review). However, studies on international organisations, such as the World Bank and the IMF, have focused on their lending and project implementation activities (see, e.g., Aldenhoff, 2007; Vaubel et al., 2007; Dreher et al., 2009). Studies on fund-raising by NGOs focus on the trade-off in allocating resources (time or funds) to fund-raising and the impact of public and private funding on NGO activities and strategies (e.g. Andreoni and Payne, 2003; Chau and Huysentruyt, 2006; Aldashev and Verdier, 2010). None of these studies addresses the nature of and possible bias in communi- cation by these organisations.

In this paper, we review the analysis and communication of organisations active in the food policy arena and review a series of hypotheses to explain their apparent change of views as reflected in their public statements. In par- ticular, we analyse how communications to potential donors in fund-raising affects the overall communication strategy of the organisation. We explain how policy organisations’ (POs) competition for donors’ funding may lead to ‘bias’ in their policy communications. Bias in policy communication may draw in larger revenues through fund-raising, but it may have negative welfare effects if it induces suboptimal behaviour by various other agents who use this advice for their decision-making. The last section of the paper considers alternative explanations.

2. Food price effects: some basic principles

Before reviewing analyses and policy statements, let us first present some basic principles on the effects of food price changes which are useful to set the framework. There is a long literature on the role of agriculture in economic development and the impact of high versus low agricultural prices, including the work by famous economists such as T. W. Schultz, John Mellor, Irma Adelman, Hans Binswanger, etc. The issue is well captured in the book Food Policy Analysis of Timmer et al. (1983: 11):

The dual role of food prices – determining food consumption levels, especially among poor people, and the adequacy of food supplies through incentives to farmers – raises an obvious dilemma for food policy analysts. . . . The incomes of the poor depend on their employment opportunities, many of which are created by a healthy and dynamic rural sector. Incentive prices for farmers are, in the long run, important in gen- erating such dynamism and the jobs that flow from it. But poor people do not live in the long run. They must eat in the short run, or the prospect of long-run job creation will be a useless promise. . . . [F]ood policy analy- sis directs attention to the decision-making environment that creates the dilemma and to the potential interventions that can bridge the short-run and long-run effects.

Several economists working on the Doha Round have also pointed out the ambiguous effects of a price change because of the combination of effects on consumption costs and production income. For example, Bureau et al. (2006)

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and Panagariya (2005) stress the complex effects of price changes which result from trade liberalisation with different effects for net importing countries versus net exporting countries; and for net consuming versus producing house- holds inside countries – and identify some ‘fallacies’ in the public discourse on the subject.

1

It would lead us too far (and take too much space) to review these models in detail. Instead, we use a simple framework to identify key analytical issues which are important to interpret the policy messages. Consider therefore a simple model of an open economy with two groups, producers and consumers of food, where prices are determined at the world market with local production or consumption having no impact (i.e. the so-called small country assumption in international trade theory). A change in world prices (caused by some exogenous factor) affects producers and consumers, but in opposing direc- tions: consumers gain and producers lose from a decline in prices, and vice versa when prices increase.

To make this model more realistic, one can consider several extensions. First, rural households in developing countries are both producers and consu- mers of food. The net household effect depends on their net consumption status. Second, world market prices may differ from local prices and the latter may differ for producers and consumers, as they are affected by policies, infrastructure, institutions and the organisation of the food chain. Third, local production and consumption may also affect local prices, in addition to exogenous external shocks. Fourth, the ‘exogenous’ shocks may be caused by nature (e.g. the weather) or by men (e.g. changes in trade policies or con- sumption or production in other countries). Fifth, short-run effects may differ from long-run effects, as pass-through may take some time.

However, these extensions do not fundamentally change the basic result of the simple model: when prices go up, consumers lose and producers gain, and vice versa. Hence, when rich countries increase (reduce) export subsidies, which leads to a decline (increase) in world market prices, this will benefit (hurt) urban consumers and net consuming rural households in poor countries and hurt (benefit) net producing rural households in poor countries. The size of the benefits/losses though will depend on various factors, such as local policies, institutions, the food chain organisation, time, etc.

Second, the net benefits of price increases and decreases for a country should be roughly symmetric. Countries that benefit most from price decreases (e.g. if they consume lots of food but produce little) will lose most from price increases. The same holds at the household level within a country. Households which only consume food and do not produce food will be affected stronger when prices change than households which both produce and consume food. Another implication is that households which are directly affected by world market prices will gain or lose more than those living in areas largely isolated from market transactions when world prices change.

1 See Naylor and Falcon (2010) for a review on the different effects of recent food price changes.

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Implications of these basic principles are that low food prices on the world market in most of the pre-2005 period benefited consumers and hurt farmers in developing countries, and vice versa in the 2006 – 2008 and 2010 – 2011 periods; and that households which in the last years suffered strongly from high food prices (e.g. urban households) would have benefited significantly from low food prices earlier. Inversely, if rural households did not benefit (much) from the high prices after 2007 (e.g. because they live in remote places with poor pass-through of prices from the world market or because they consume all their food production themselves), they would also have experienced limited negative welfare effects from the low food prices prior to 2005.

3. The food crisis and policy communication

Surprisingly, while these basic principles are well known, we do not find them reflected in the food policy debate. For example, there has been hardly any mentioning of the benefits of low food prices for urban consumers and net con- suming rural households during the pre-2006 low price era, and there has been very little emphasis on the benefits for producers in poor countries from current high food prices.

In 2005, Oxfam International argues that

US and Europe[’s s]urplus production is sold on world markets at artifi- cially low prices, making it impossible for farmers in developing countries to compete. As a consequence, over 900 millions of farmers are losing their livelihoods.

(Oxfam International, 2005)

Three years later, at the height of the food crisis, Oxfam International’s view is that

Higher food prices have pushed millions of people in developing countries further into hunger and poverty. There are now 967 million malnourished people in the world . . . .

(Oxfam International, 2008)

Other NGOs share this analysis: prior to 2006, they claim that, low food prices were hurting the poor and creating food insecurity; after 2006, they claim that, high prices are hurting the poor and leading to food insecurity.

One justification for the statements could be that they refer to different groups in society (farmers in one case, urban consumers in the other case). If so, then the lack of emphasis on this and the absence of recognition that other groups may benefit remains striking. Moreover, in several cases, NGOs claim that the same group (e.g. poor rural people) is hurt by low prices (in 2005) and hurt by high prices (in 2009) (Swinnen, 2011).

Another potential justification of this bias is that poor farmers were hurt by low prices before 2006 but that consumers did not benefit and that after 2006 poor farms did not benefit from high prices but that poor consumers did get

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hurt, due to market imperfections and transaction costs which may influence how consumers and producers are affected by price changes. These arguments are not convincing as an explanation for the bias in policy messages. First, if prices do not benefit (net producing) farmers, they should not harm net food consuming households living in the same (rural) areas. Second, imperfect price pass-through between farms and consumers (domestic or international) is important in times of low and high prices. Hence, they also affect the pass- through of low prices to farmers in the pre-2006 period, and as urban consu- mers would have been more directly affected, this should then have caused more positive aggregate (net) effects of low food prices than generally argued.

A last explanation for these observations could be that NGOs are advocacy groups and their primary objective is not to provide objective and carefully balanced analyses, but rather to raise attention to problems and to pressure governments to do something about it, or to raise funds for their own projects.

2

However, international institutions which are not (expected to be) advocacy groups seem to have adjusted their analyses and policy communications similar to NGOs. Before the food crisis, reports from FAO, IFPRI, the OECD, the World Bank and the IMF typically claim that low agricultural prices hurt developing countries and state that liberalisation will help the poor by increasing world prices as rich countries cut their agricultural subsidies.

The long-term downward trend in agricultural commodity prices threatens the food security of hundreds of millions of people in some of the world’s poorest developing countries. (FAO, 2005)

Many (developed countries) continue to use various forms of export subsi- dies that drive down world prices and take markets away from farmers in poorer countries. . . . Much of this support depresses rural incomes in devel- oping countries. (OECD, 2003)

In contrast, during the recent food crisis, these organisations communicate very differently:

When food prices skyrocket this can quickly pose a threat to the lives of the poorest, particular in developing countries. (OECD, 2009)

. . . up to 105 million people could become poor due to rising food prices alone. (World Bank, 2008: 4)

The rapid increase in food prices has had an adverse impact on poverty, and effectively denied many poor people access to food. (IMF, 2008)

An obvious critique on our arguments is that these quotes may be taken out of context and ignore more complex and nuanced full analyses. While they are indeed only ‘quotes’, we argue that they quite accurately represent the key

2 See the exchange on this point on Dani Rodrik’s weblog: http://rodrik.typepad.com/ (accessed

20 April 2011).

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messages of the organisations. This is important because ‘key messages’ are crucial for target audiences (decision-makers and the public) who have no time to read long reports. The organisations’ communication strategy is typi- cally focused on such key messages. The rest of the documents is to substanti- ate, but not to deviate from the key message. Moreover, also in the full reports, there are striking differences. In very few of the main reports published before the recent food price increases is there any mention of the fact that urban con- sumers in developing countries benefit (and the few that do mention it do not consider it as a major element). Neither is the argument made that many poor rural households are net consumers, and may thus benefit from low food prices. Yet, the (mirror versions of these) arguments are emphasised very strongly in the post-2006 reports.

What is different though is the results from the actual policy analyses and model outcomes. In fact, several organisations published background studies and working papers with detailed findings and carefully nuanced interpret- ations and conclusions around the same time when their communication departments released communications on the food price issues which demon- strated the shift in emphasis (bias) as documented here.

3

In summary, international organisations in their main external communi- cations have shifted from emphasising how low food prices, often argued to have been caused by rich country agricultural policies, cause poverty and food insecurity in developing countries in the pre-2006 period to emphasising that high food prices cause poverty and food security in the post-2006 period, without mentioning (emphasising) the benefits (in either period).

4. Fund-raising and the market for policy communications

Another perspective is to interpret the statements listed above at least partly as part of a fund-raising strategy. If one wants to help the poor or stimulate devel- opment, funding is needed. NGOs need to invest in fund-raising activities in an environment where various NGOs compete for attention and funding of donors (e.g. Rose-Ackermann, 1982; Andreoni and Payne, 2003). While academic studies analysing this issue have focused on NGOs, the general argument to focus on the costs and ignore the benefits of price changes as a marketing strat- egy may apply more widely.

4 All international organisations – be it NGOs or

IFPRI, the World Bank, FAO – use, to some extent, funds from public or private donors to operate and implement their projects – or subgroups within

3 See, for example, Anderson et al. (2010), Christiaensen and Demery (2007), Hertel and Winters

(2006) and the 2008 World Development Report, all published by the World Bank, Sarris and

Morrison (2010), published by FAO, and the policy analyses in various OECD reports on the

state of agricultural markets and policies over the past decade. See Swinnen (2011) for a more

detailed analysis of the relationship between the policy analysis of the research departments

and policy communication of the organisation.

4 Most academic research on the behaviour of international organisations has focused on their

lending strategies and much less on their communication or fund-raising strategies (see, e.g.,

Aldenhoff, 2007; Vaubel et al., 2007; Dreher et al., 2009).

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these organisations have to compete internally for funding. While their funding sources may differ, in a world where financial means are limited and where there is continuous pressure to demonstrate relevance and importance of budget spending on particular items, projects or divisions within large organis- ations, all these organisations face a demand to demonstrate the importance of their work. Focusing their reports and analyses on those hurt by price changes may fit in such strategy to show relevance and importance – and may thus help in securing and raising funds.

4.1. A model

To analyse potential bias in ‘policy communication’, we develop a formal model of the interaction between ‘POs’ and ‘donors’. Our model builds on the seminal work of Mullainathan and Shleifer (2005) on bias in mass media and of Andreoni and Payne (2003) on fund-raising by charity organis- ations. We define ‘policy communication’ as communication of advice and results of analyses of important public policy issues. It includes rapid com- munications (such as interviews or press releases) and more extensive exter- nally released reports on certain issues. It does not include news or reports from commercial media sources or internal reports of organisations.

We use the term ‘policy organisations’ to represent all organisations who are communicating public policy analyses and advice and who obtain a sig- nificant share of their funding from various external sources (‘donors’). POs can include organisations as diverse as international NGOs (such as Oxfam, Greenpeace, etc.), intergovernmental organisations (such as the World Bank, FAO, etc.) and various national organisations. In our model, POs do not include commercial companies or organisations representing specific interest groups with single source funding (such as labour unions or associations of companies).

POs engage in both analysis and communication. The purposes of the POs’ analysis (i.e. fact-finding and various types of research) are multiple: their analysis serves to support internal decision-making on funding and project implementation. Analysis also provides the basis for policy communication. The POs’ communication strategy has two objectives. The first objective is policy advice, i.e. to influence others (e.g. governments) to implement or reform certain policies. The second objective is fund-raising, i.e. to influence donors to contribute funds to the POs. Policy communication by the POs is influenced by both objectives and by the agents they interact with (donors and those targeted with advice).

POs receive a significant share of their funding from a variety of ‘donors’. Donors may include public entities (such as governments) or private entities (such as foundations or households). POs have to raise funds and compete for attention and funding of donors. This assumption is consistent with the aforementioned observation that all international organisations compete to some extent for funds from public or private donors to operate and implement their projects.

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Formally, POs, indexed by j, collect data on the state of the economy t, e.g. the impact of global warming or rising food prices. We assume that POs perform correct analyses, i.e. they gather the necessary information and use appropriate methodologies to arrive at the correct conclusion d about the state of the economy (d ¼ t). The POs communicate their conclusions to the external world by means of ‘reports’, containing information nj. We assume POs may introduce an amount of slanting sj in their policy communications, so their reports contain information nj ¼ d + sj.

Donors, indexed by i, hold certain beliefs bi about the state of the economy t, and these beliefs may be biased. If bi . 0, donor i has an optimistic belief about the state of the economy, whereas if bi , 0, the donor holds a pessimistic belief.

5 Following Andreoni and Payne (2003), we assume that donors have a

latent demand to donate. This implies that a donor does not give money to a PO unless solicited. A donor selects which PO to support based on a comparison of the contents, nj, of POs’ reports and their requested donations Dj.

In line with Mullainathan and Shleifer (2005), we assume that, on the one hand, donors dislike slanted reports because it is costly both in effort and time to read a slanted report and ‘to figure out the truth’. On the other hand, donors get disutility from reading reports that are inconsistent with their beliefs. Formally, a donor i’s utility of reading the report of PO j and consequently donating to PO j is:

Uij = uw(nj) − ms2j − f(nj − bj)

2 − Dj if donor i reads the report of, and donates to, PO j;

0 if the donor does not donate,

⎧ ⎨ ⎩

(1)

where u w (nj) is the ‘warm glow’ a donor receives from donating (Andreoni

and Payne, 2003). If a donor does not read any report, and consequently does not donate, he receives zero utility. The constant m . 0 is a measure

for a donor’s sensitivity to slanting; therefore, ms2j represents the disutility

from reading a slanted report issued by PO j. f . 0 represents a donor’s pre- ference for reading a report consistent with his beliefs, where consistency is modelled as the squared distance between the report’s content nj and the

donor’s beliefs bi, i.e. nj − bj ( )2

. 6

It is likely that donors draw more ‘warm glow’ if their donations have larger welfare impacts, for example more from helping the victims of an earthquake that killed thousands of people and made millions of poor homeless than from

5 These beliefs are assumed to be exogenously determined and may be based on donors’ prior

information or their political leaning. Swinnen et al. (2010) analyse the case of endogenous

beliefs where donors may update their beliefs with the content of POs’ reports.

6 We use squared differences to ensure that positive and negative bias is treated symmetrically.

The convex functional form makes the derivations easier but is not essential to our model’s out-

come. Any distance measure, for example absolute values, would qualitatively yield the same

results.

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helping the victims of a local flood that made a few rich people having to leave their house for a few days. We therefore assume that the ‘warm glow’ depends on the severity of the problems on which POs report and solicit donations for. Formally, the ‘warm glow’ component equals uw(nj) = uw0 − anj, where uw0 is the baseline ‘warm glow’ from donating to a PO, and a is a scalar which measures the donors’ ‘warm glow’ from supporting a PO that addresses more severe problems. Hence, if a report is more negative than the actual situation (nj , 0 and larger in absolute value), donors’ utility from donating to PO j is higher.

As discussed before, the POs’ policy communication has two objectives. On the one hand, POs’ reports serve the purpose of fund-raising; on the other hand, they aim at improving government policy through their reports. The PO chooses its slanting strategy sj and the donation Dj it requests to maximise its objective function Wj(nj), which is the weighted sum of revenues, Rj(nj), and policy impact, Ij(nj). The objective function of PO j is

Wj(nj) = vRRj(nj) + vI Ij(nj), (2)

where vR and vI are the respective weights of revenues and policy impact. The revenues Rj(nj) are the funds collected from donors who decide to donate to PO j after reading its report. The policy impact, Ij(nj), is specified as Ij(nj) = G − (nj − t)2, with G . 0 being the policy impact of a report that is not slanted. We abstract from the complexities of the decision-making process of governments and assume they choose better policies when receiving better (i.e. less slanted) information from the reports. The policy impact is decreasing in the squared distance between the report’s contents nj and the true situation t.

7

For homogenous donor beliefs (bi ¼ b for all i), the equilibrium amount of slanting s∗j in this market can be derived as

8 :

s∗j = vR

vR(m + f) + vI fb −

a

2

[ ] . (3)

4.2. Key results

It is clear from equation (3) that slanting will depend on donors’ utility para- meters, on the weights given to revenues versus policy impacts and on the dis- tribution of beliefs among donors. Our analysis shows that POs do not slant their reports only under very restrictive, and unrealistic, conditions. Only when (i) donors’ beliefs are homogenous; (ii) donors’ beliefs are unbiased and (iii) donors’ utility is not affected by the severity of the problem, POs

7 We use a squared distance measure for the same reasons as explained in footnote 6.

8 The sequence of the underlying game is as in Mullainathan and Shleifer (2005). We refer to Swin-

nen et al. (2010) for technical derivations, details and proofs and summarise here some key

insights.

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do not slant their reports. However, in all other cases POs will slant their communications.

If donors’ beliefs are homogenous and biased bi = b = t( ), POs slant their policy communication in the direction of the donors’ beliefs. The level of the optimal slanting is affected by several factors, such as the relative importance of respectively fund-raising (vR) and policy impact (vI) in the POs’ objective function, and the donors’ sensitivity to slanting (m) and to reading reports that are inconsistent with their beliefs (f). All these factors are affecting the outcome in the intuitive direction.

When accounting for problem severity, i.e. when donors prefer donating to POs that (claim to) address more severe problems (a . 0), POs will depict situations as being more negative than they actually are, even when donors’ beliefs are unbiased.

With heterogeneity in donors’ beliefs, POs differentiate themselves in the policy communication market. They slant their reports in different directions to increase donations from a subgroup of the population.

These results hold even when assuming that donors’ beliefs are exogen- ously determined. However, it is more realistic to consider that donors’ beliefs are not static. People may change their opinion on a certain issue, for example, because they receive additional information that is not in line with their beliefs. Such new information may come from the POs’ reports. When donors’ beliefs are endogenous, this affects slanting.

Our model demonstrates that when donors update their beliefs with the policy communications of the organisations, both donors’ beliefs and the POs’ slanting converge to a biased equilibrium. A very important finding is that donors’ initial beliefs do not matter in the long run as they are increasingly affected by POs’ reports. Moreover, even if these initial beliefs were correct, they become biased over time.

5. Endogenous beliefs and the mass media

Donor beliefs may not only be influenced by POs’ reports but also by other information sources, in particular by mass media. This will also influence POs’ communications and slanting.

There are two important, but distinct, mechanisms at work in the interaction between POs’ communications and the mass media. The first mechanism is the impact of stories that appear in the mass media on the communications of the organisations. Several characteristics of mass media are relevant to explain this mechanism (McCluskey and Swinnen, 2010). Mass media may play an important role in influencing donors’ beliefs, in particular initial beliefs, and thus POs’ communication. Media attention is typically concen- trated around ‘events’ or ‘shocks’.

9 Mass media’s impact on donors’ beliefs

is determined by its broad audience and the relative speed of mass media

9 For example, Swinnen and Francken (2006) find that virtually all the attention to globalisation,

trade and development issues in mass media is concentrated around ‘international summits’.

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coverage. Typically mass media can bring news reports much faster than a report from a PO that may require substantially more time for a thorough analysis of the situation and corresponding policy communications. When initial beliefs are influenced by mass media reports, these mass media reports induce POs to slant their reports. In the long run, this impact disap- pears if donors update their beliefs with information from other sources.

This agenda-setting effect of the media in policy has sometimes been referred to as the ‘CNN factor’ (Hawkins, 2002). It refers to the process by which the media influences policy by invoking responses in their audiences through concentrated and emotionally based coverage, which in turn applies pressure to governments to react. Similarly, the absence of media coverage reduces priority in agenda-setting (Jakobson, 2000). In this logic, public offi- cials react to media news because they see it as a reflection of public opinion (Kim, 2005).

10

The second mechanism is the desire of the organisations to appear in mass media in order to achieve their objectives (Cottle and Nolan, 2007). With mass media reports focusing on those hurt by changing food prices – in particular consumers post-2006 – the donor community, the organisations’ shareholders and the public at large may expect (or even demand) that these organisations focus their attention on those who are suffering from price changes. If they would not publicly react (‘communicate’) on these problems, it would hurt their legitimacy as development organisations. This could undermine overall support for their existence. Hence, sudden changes with dramatic effects, such as the 2008 food crisis, not only present important challenges to the international organisations in addressing these, but also important opportunities for development organisations to capture media attention and signal their relevance and importance to their donors and the public.

A related factor is that the public at large is more interested in media reports concentrating on negative (development) effects. This follows from the so-called bad news hypothesis. Media consumers in general tend to be more interested in negative news items than in positive news items, ceteris paribus. This demand effect of the media market drives mass media to pay more attention to ‘bad news’ (McCluskey and Swinnen, 2004).

In combination, these factors create a set of incentives for international organisations to emphasise the negative welfare implications in their analysis and policy communications, and to put less emphasis on the positive effects. In doing so, they are more likely to attract media coverage on their work and, in turn, more likely to reach a wide audience and to influence policy-makers. Such a media strategy could have a direct effect in influencing public and private donations and policies of governments in the short run and an indirect effect in encouraging appreciation and legitimacy for their work and the organisations themselves – which could lead to support in the long run.

10 Van Belle et al. (2004) and Kim (2005) find that a higher level of media attention to developing

countries’ problems leads to more aid. Eisensee and Strömberg (2007) argue that disaster relief

and aid allocations are influenced by media coverage of disasters.

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The causal relationship of mass media affecting donors’ beliefs and conse- quently influencing POs’ policy communications depends on the nature of the problem/policy. For longer term or structural problems, mass media may not play a role in affecting initial conditions. On such issues, it may rather be that the mass media report on an issue because of a PO’s report. In this case, the donors’ initial beliefs would not be affected by the mass media, and neither would the POs’ initial policy communications.

6. Bad news and good policies?

A final important issue is that, while all these organisations have changed their communications, they may have not changed their basic policy advice. In terms of our model, it would imply that POs would be able to separate ‘donor communications’ from ‘policy communications’ – and that ∂Ij/∂nj = 0 and Ij ¼ G. In this case, they could use the price shocks to attract attention and support of donors. This is related to the aforementioned ‘bad news hypothesis’. If donors tend to pay more attention to negative news or policy communications, and POs are able to separate their communi- cations partially or completely from their policy advice, POs have an incentive to portray events more negatively in their communications while keeping the same policy advice. Accounting for this ‘bad news hypothesis’ in our model would be largely similar to the ‘problem severity’ effect (a . 0) and would result in more negatively slanted reports of POs ∂s∗j /∂a . 0. This bad news

effect could work both directly in the PO – donor communications or indirectly through the mass media.

When POs are able to separate their communications from their policy advice, one should expect organisations that consider global underinvestment in agriculture a problem to forward this as a remedy, whether prices are high or low – and just use somewhat differently framed arguments to make this point. Similarly, one should expect some of the organisations – those that believe in the benefits of free markets – to emphasise the benefits that free markets bring – whether prices are high or low; and the organisations that believe in government regulation of markets to emphasise the importance of regulations – whether prices are high or low.

In Squicciarini and Swinnen (2011), we have analysed in detail the policy recommendations of the organisations whose communication shift we docu- mented earlier in this paper. Table 1 summarises some of the key findings, before and after the food crisis. We conclude that the evidence is mixed.

First, there is a significant shift in the attention to food consumer concerns, and in the emphasis of rural households as net food consumers.

Second, most organisations have not changed their perspectives on trade policy. Organisations like the World Bank, OECD, FAO and IFPRI have con- tinued to emphasise the importance of trade liberalisation and of concluding the Doha Round both before and after 2006; while NGOs, such as Oxfam and ActionAid, have continued to recommend the cut of rich country subsidies

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Table 1. Elements of policy communication before and after the food crisis

ActionAid Oxfam FAO IFPRI OECD World Bank

Before

Poor farmers are net consumers 0 0 0 0 0 0

Liberalisation in rich countries ++ and *** + + and *** + + and ** + + and ** + + and ** + + and *** Liberalisation in poor countries 2 2 and *** 2 2 and *** + and ** + and ** + and ** ++ and *** Elimination of exports bans (in poor countries) 0 0 0 0 0 ++ and * Investment in infrastructure ++ and * ++ and * ++ and ** ++ and * ++ and ** ++ and ** Target smallholders and marginal areas ++ and *** ++ and ** ++ and ** ++ and ** ++ and ** ++ and ** Support for biofuels production 0 0 0 + and ** 0 0 Reduce price volatility 0 0 + and * 0 + and * ++ and *

After

Poor farmers are net consumers ** *** *** *** * **

Liberalisation in rich countries ++ and *** + + and *** + and * + and ** + + and * ++ and ** Liberalisation in poor countries 2 2 and *** 2 2 and *** + and * + and ** + and * ++ and ** Elimination of exports bans (in poor countries) 0 + and ** ++ and ** ++ and ** ++ and ** ++ and ** Investment in infrastructure ++ and ** ++ and ** ++ and ** ++ and ** ++ and ** ++ and ** Target smallholders and marginal areas ++ and *** ++ and *** ++ and ** ++ and *** ++ and ** ++ and *** Support for biofuels production 2 2 and *** 2 2 and *** 2 and ** 2 2 and ** 2 and * 2 and *

Reduce price volatility + and * ++ and ** + and ** ++ and ** ++ and *** ++ and ** Source: Squicciarini and Swinnen (2011). Note: +, agree; ++, fully agree; 2, disagree; 2 2, fully disagree; *, low emphasis; **, medium emphasis; ***, strong emphasis; 0, not mentioned.

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and the importance of government regulation and protection of poor countries’ agri-food markets. However, there is a clear shift in trade policy focus from arguing to remove import constraints to export constraints.

Third, there is some change in the emphasis on supply and productivity-enhancing measures and the need to support small farmers. Most organisations have much strongly emphasised the importance to invest in agriculture in the post-2007 period than before.

Fourth, there is a substantial change in the emphasis and preference on biofuel policies, emergency aid and measures to reduce price volatility. In recent communications, great emphasis has been put on price volatility and on the design of new policies to respond to it; food aid, already a contentious and delicate issue, has now gained primary importance; the provision and implementation of safety nets has also gained higher importance on the policy table; finally, the role of biofuels and their effects on food security have become crucial in the current debate and in the policy advices of the organisations. In summary, it appears that the organisations have partly main- tained their core policy message and partly adjusted it.

A final, but important, issue is that consistent policy advice may be associ- ated with more, rather than less, slanting in communication by POs. As our model shows, this (partial) decoupling of POs’ communication and policy advice has potentially large consequences for the bias in POs’ communi- cations. For example, in the extreme case where communication would be completely separate from policy advice, the policy impact term Ij in the PO’s objective function would be constant, i.e. independent from policy com- munication. In that case, the term vI would drop from the equilibrium outcome in equation (3) and POs’ slanting would be higher (in absolute terms). Hence, somewhat paradoxically, while decoupling policy advice from (slanted) communications may be welfare-improving because then policy advice is no longer biased (that is, if policy advice induces welfare-enhancing policies), it results in a communication strategy that is even more slanted towards negative issues.

Acknowledgements

We thank Kym Anderson, Rick Barichello, John Bensted-Smith, Luc Christiaensen, Walter

Falcon, Tassos Haniotis, Tom Hertel, Michiel Keyzer, Andrzej Kwiecinski, Will Martin, Alan

Matthews, Alessandro Olper, Scott Rozelle, Manohar Sharma, Kostas Stamoulis, Stefan Tanger-

mann, Peter Timmer, Alan Winters and participants in conferences in Berkeley (IATRC) and

Campobasso (SIDEA) for comments and discussions on the issues raised in this paper.

The research was financially supported by the K. U. Leuven (Methusalem) and the FWO.

The opinions expressed here are ours only.

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The food crisis, mass media and the political economy of policy analysis and communication

Johan F. M. Swinnen *, Pasquamaria Squicciarini , and Thijs Vandemoortele

Catholic University of Leuven (KUL), Belgium

Received February 2011; final version accepted April 2011

Review coordinated by Thomas Heckelei

Abstract

Few years ago, the widely shared view was that low food prices were a curse to devel- oping countries. The dramatic increase in food prices in 2006 – 2008 appears to have fundamentally altered this view. The vast majority of analyses and reports in 2008 and 2009 state that high food prices have a devastating effect on developing countries. In this paper, we (i) document these changes in perspective; (ii) develop a model of policy communication to explain the cause of the change in views; and (iii) review the policy recommendations of the organisations that shifted their communication.

Keywords: food prices, political economy, bias, NGOs, intergovernmental organisations, mass media

JEL classification: E31, P16, D23, L31, D83

1. Introduction

Few years ago, the widely shared view was that low food prices were a curse to developing countries and the poor. The following statement from the Food and Agricultural Organization (FAO) of the United Nations on the state of the world food markets and its implications for developing countries represents the common view as recently as 2005: ‘The long-term downward trend in agri- cultural commodity prices threatens the food security of hundreds of millions of people in some of the world’s poorest developing countries where the sale of commodities is often the only source of cash’ (FAO, 2005).

The dramatic increase in food prices in 2006 – 2008 and in 2010 – 2011 appears to have fundamentally altered this view of the food system. The vast majority of recent reports state that high food prices have a

*Corresponding author: Waaistraat 6 Box 3511, B-3000 Leuven, Belgium. E-mail: jo.swinnen@econ.

kuleuven.be

European Review of Agricultural Economics Vol 38 (3) (2011) pp. 409–426 doi:10.1093/erae/jbr020 Advance Access Publication 10 June 2011

# Oxford University Press and Foundation for the European Review of Agricultural Economics 2011; all rights reserved. For permissions, please email [email protected]

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devastating effect on developing countries and the world’s poor. Typical examples are a statement from the 2008 annual report of the International Food Policy Research Institute (IFPRI): ‘ . . . rapidly rising food prices began to further threaten the food security of poor people around the world. . . . The current food-price crisis can have long-term, detrimental effects on peoples’ health and livelihoods, and can contribute to the further impoverish- ment of many of the world’s poorest people’ (Braun, 2008: 3), and a statement from Oxfam UK (2011): ‘High global food prices risk hunger for millions of people. Poor people in developing countries spend up to 80% of their income on food.’

This reversal of opinion was widespread (see further) and raises questions about the correctness of the old and the new arguments and about the proposed remedies. It also raises questions about the causes of this dramatic turnaround in analysis and policy conclusions.

As such, the increase in food prices in recent years provides an interesting ‘natural experiment’ on potential bias in policy analysis and communication. Development organisations, charities, aid agencies, non-governmental organ- isations (NGOs) and other institutions whose formal objective is to enhance welfare and reduce suffering around the world are regularly accused of provid- ing biased analyses on the state of the world and to have their actions guided by their private benefits – such as attracting media attention, raising funds or personal fame – rather than the public goods they are supposed to be after.

Bias in policy communication is an important issue. Policy communication by these organisations does influence policy thinking, government strategies, development priorities and aid flows. For example, an important element of the current WTO negotiations (the so-called Doha Development Round) is to reduce the depressing effect of rich countries’ agricultural policies on global food prices – a perspective which until the recent food crisis was widely seen as negative for developing countries. Similarly, the food crisis has clearly influenced discussions on the reform of the Common Agricultural Policy.

Some have explicitly linked the (bias in) policy communication of develop- ment and aid organisations to capturing media attention and fund-raising. For example, Cottle and Nolan (2007) argue that the humanitarian aim of some has become compromised as the focus of organisations is on their communi- cation process: the ‘media logic’ of packaging information and images has become institutionalised inside aid agencies. The Lancet (2010: 253) – an internationally renowned medical journal – at the height of the humanitarian crisis in Haiti makes an even stronger statement: ‘Polluted by the internal power politics and the unsavory characteristics seen in many big corporations, large aid agencies can be obsessed with raising money through their own appeal efforts. Media coverage as an end in itself is too often an aim of their activities. Marketing and branding have too high a profile.’

This issue has, so far, received little attention in the academic literature. There is a burgeoning literature on bias in communication in mass media and its impact on government policy (see McCluskey and Swinnen, 2010,

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for a review). However, studies on international organisations, such as the World Bank and the IMF, have focused on their lending and project implementation activities (see, e.g., Aldenhoff, 2007; Vaubel et al., 2007; Dreher et al., 2009). Studies on fund-raising by NGOs focus on the trade-off in allocating resources (time or funds) to fund-raising and the impact of public and private funding on NGO activities and strategies (e.g. Andreoni and Payne, 2003; Chau and Huysentruyt, 2006; Aldashev and Verdier, 2010). None of these studies addresses the nature of and possible bias in communi- cation by these organisations.

In this paper, we review the analysis and communication of organisations active in the food policy arena and review a series of hypotheses to explain their apparent change of views as reflected in their public statements. In par- ticular, we analyse how communications to potential donors in fund-raising affects the overall communication strategy of the organisation. We explain how policy organisations’ (POs) competition for donors’ funding may lead to ‘bias’ in their policy communications. Bias in policy communication may draw in larger revenues through fund-raising, but it may have negative welfare effects if it induces suboptimal behaviour by various other agents who use this advice for their decision-making. The last section of the paper considers alternative explanations.

2. Food price effects: some basic principles

Before reviewing analyses and policy statements, let us first present some basic principles on the effects of food price changes which are useful to set the framework. There is a long literature on the role of agriculture in economic development and the impact of high versus low agricultural prices, including the work by famous economists such as T. W. Schultz, John Mellor, Irma Adelman, Hans Binswanger, etc. The issue is well captured in the book Food Policy Analysis of Timmer et al. (1983: 11):

The dual role of food prices – determining food consumption levels, especially among poor people, and the adequacy of food supplies through incentives to farmers – raises an obvious dilemma for food policy analysts. . . . The incomes of the poor depend on their employment opportunities, many of which are created by a healthy and dynamic rural sector. Incentive prices for farmers are, in the long run, important in gen- erating such dynamism and the jobs that flow from it. But poor people do not live in the long run. They must eat in the short run, or the prospect of long-run job creation will be a useless promise. . . . [F]ood policy analy- sis directs attention to the decision-making environment that creates the dilemma and to the potential interventions that can bridge the short-run and long-run effects.

Several economists working on the Doha Round have also pointed out the ambiguous effects of a price change because of the combination of effects on consumption costs and production income. For example, Bureau et al. (2006)

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and Panagariya (2005) stress the complex effects of price changes which result from trade liberalisation with different effects for net importing countries versus net exporting countries; and for net consuming versus producing house- holds inside countries – and identify some ‘fallacies’ in the public discourse on the subject.

1

It would lead us too far (and take too much space) to review these models in detail. Instead, we use a simple framework to identify key analytical issues which are important to interpret the policy messages. Consider therefore a simple model of an open economy with two groups, producers and consumers of food, where prices are determined at the world market with local production or consumption having no impact (i.e. the so-called small country assumption in international trade theory). A change in world prices (caused by some exogenous factor) affects producers and consumers, but in opposing direc- tions: consumers gain and producers lose from a decline in prices, and vice versa when prices increase.

To make this model more realistic, one can consider several extensions. First, rural households in developing countries are both producers and consu- mers of food. The net household effect depends on their net consumption status. Second, world market prices may differ from local prices and the latter may differ for producers and consumers, as they are affected by policies, infrastructure, institutions and the organisation of the food chain. Third, local production and consumption may also affect local prices, in addition to exogenous external shocks. Fourth, the ‘exogenous’ shocks may be caused by nature (e.g. the weather) or by men (e.g. changes in trade policies or con- sumption or production in other countries). Fifth, short-run effects may differ from long-run effects, as pass-through may take some time.

However, these extensions do not fundamentally change the basic result of the simple model: when prices go up, consumers lose and producers gain, and vice versa. Hence, when rich countries increase (reduce) export subsidies, which leads to a decline (increase) in world market prices, this will benefit (hurt) urban consumers and net consuming rural households in poor countries and hurt (benefit) net producing rural households in poor countries. The size of the benefits/losses though will depend on various factors, such as local policies, institutions, the food chain organisation, time, etc.

Second, the net benefits of price increases and decreases for a country should be roughly symmetric. Countries that benefit most from price decreases (e.g. if they consume lots of food but produce little) will lose most from price increases. The same holds at the household level within a country. Households which only consume food and do not produce food will be affected stronger when prices change than households which both produce and consume food. Another implication is that households which are directly affected by world market prices will gain or lose more than those living in areas largely isolated from market transactions when world prices change.

1 See Naylor and Falcon (2010) for a review on the different effects of recent food price changes.

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Implications of these basic principles are that low food prices on the world market in most of the pre-2005 period benefited consumers and hurt farmers in developing countries, and vice versa in the 2006 – 2008 and 2010 – 2011 periods; and that households which in the last years suffered strongly from high food prices (e.g. urban households) would have benefited significantly from low food prices earlier. Inversely, if rural households did not benefit (much) from the high prices after 2007 (e.g. because they live in remote places with poor pass-through of prices from the world market or because they consume all their food production themselves), they would also have experienced limited negative welfare effects from the low food prices prior to 2005.

3. The food crisis and policy communication

Surprisingly, while these basic principles are well known, we do not find them reflected in the food policy debate. For example, there has been hardly any mentioning of the benefits of low food prices for urban consumers and net con- suming rural households during the pre-2006 low price era, and there has been very little emphasis on the benefits for producers in poor countries from current high food prices.

In 2005, Oxfam International argues that

US and Europe[’s s]urplus production is sold on world markets at artifi- cially low prices, making it impossible for farmers in developing countries to compete. As a consequence, over 900 millions of farmers are losing their livelihoods.

(Oxfam International, 2005)

Three years later, at the height of the food crisis, Oxfam International’s view is that

Higher food prices have pushed millions of people in developing countries further into hunger and poverty. There are now 967 million malnourished people in the world . . . .

(Oxfam International, 2008)

Other NGOs share this analysis: prior to 2006, they claim that, low food prices were hurting the poor and creating food insecurity; after 2006, they claim that, high prices are hurting the poor and leading to food insecurity.

One justification for the statements could be that they refer to different groups in society (farmers in one case, urban consumers in the other case). If so, then the lack of emphasis on this and the absence of recognition that other groups may benefit remains striking. Moreover, in several cases, NGOs claim that the same group (e.g. poor rural people) is hurt by low prices (in 2005) and hurt by high prices (in 2009) (Swinnen, 2011).

Another potential justification of this bias is that poor farmers were hurt by low prices before 2006 but that consumers did not benefit and that after 2006 poor farms did not benefit from high prices but that poor consumers did get

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hurt, due to market imperfections and transaction costs which may influence how consumers and producers are affected by price changes. These arguments are not convincing as an explanation for the bias in policy messages. First, if prices do not benefit (net producing) farmers, they should not harm net food consuming households living in the same (rural) areas. Second, imperfect price pass-through between farms and consumers (domestic or international) is important in times of low and high prices. Hence, they also affect the pass- through of low prices to farmers in the pre-2006 period, and as urban consu- mers would have been more directly affected, this should then have caused more positive aggregate (net) effects of low food prices than generally argued.

A last explanation for these observations could be that NGOs are advocacy groups and their primary objective is not to provide objective and carefully balanced analyses, but rather to raise attention to problems and to pressure governments to do something about it, or to raise funds for their own projects.

2

However, international institutions which are not (expected to be) advocacy groups seem to have adjusted their analyses and policy communications similar to NGOs. Before the food crisis, reports from FAO, IFPRI, the OECD, the World Bank and the IMF typically claim that low agricultural prices hurt developing countries and state that liberalisation will help the poor by increasing world prices as rich countries cut their agricultural subsidies.

The long-term downward trend in agricultural commodity prices threatens the food security of hundreds of millions of people in some of the world’s poorest developing countries. (FAO, 2005)

Many (developed countries) continue to use various forms of export subsi- dies that drive down world prices and take markets away from farmers in poorer countries. . . . Much of this support depresses rural incomes in devel- oping countries. (OECD, 2003)

In contrast, during the recent food crisis, these organisations communicate very differently:

When food prices skyrocket this can quickly pose a threat to the lives of the poorest, particular in developing countries. (OECD, 2009)

. . . up to 105 million people could become poor due to rising food prices alone. (World Bank, 2008: 4)

The rapid increase in food prices has had an adverse impact on poverty, and effectively denied many poor people access to food. (IMF, 2008)

An obvious critique on our arguments is that these quotes may be taken out of context and ignore more complex and nuanced full analyses. While they are indeed only ‘quotes’, we argue that they quite accurately represent the key

2 See the exchange on this point on Dani Rodrik’s weblog: http://rodrik.typepad.com/ (accessed

20 April 2011).

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messages of the organisations. This is important because ‘key messages’ are crucial for target audiences (decision-makers and the public) who have no time to read long reports. The organisations’ communication strategy is typi- cally focused on such key messages. The rest of the documents is to substanti- ate, but not to deviate from the key message. Moreover, also in the full reports, there are striking differences. In very few of the main reports published before the recent food price increases is there any mention of the fact that urban con- sumers in developing countries benefit (and the few that do mention it do not consider it as a major element). Neither is the argument made that many poor rural households are net consumers, and may thus benefit from low food prices. Yet, the (mirror versions of these) arguments are emphasised very strongly in the post-2006 reports.

What is different though is the results from the actual policy analyses and model outcomes. In fact, several organisations published background studies and working papers with detailed findings and carefully nuanced interpret- ations and conclusions around the same time when their communication departments released communications on the food price issues which demon- strated the shift in emphasis (bias) as documented here.

3

In summary, international organisations in their main external communi- cations have shifted from emphasising how low food prices, often argued to have been caused by rich country agricultural policies, cause poverty and food insecurity in developing countries in the pre-2006 period to emphasising that high food prices cause poverty and food security in the post-2006 period, without mentioning (emphasising) the benefits (in either period).

4. Fund-raising and the market for policy communications

Another perspective is to interpret the statements listed above at least partly as part of a fund-raising strategy. If one wants to help the poor or stimulate devel- opment, funding is needed. NGOs need to invest in fund-raising activities in an environment where various NGOs compete for attention and funding of donors (e.g. Rose-Ackermann, 1982; Andreoni and Payne, 2003). While academic studies analysing this issue have focused on NGOs, the general argument to focus on the costs and ignore the benefits of price changes as a marketing strat- egy may apply more widely.

4 All international organisations – be it NGOs or

IFPRI, the World Bank, FAO – use, to some extent, funds from public or private donors to operate and implement their projects – or subgroups within

3 See, for example, Anderson et al. (2010), Christiaensen and Demery (2007), Hertel and Winters

(2006) and the 2008 World Development Report, all published by the World Bank, Sarris and

Morrison (2010), published by FAO, and the policy analyses in various OECD reports on the

state of agricultural markets and policies over the past decade. See Swinnen (2011) for a more

detailed analysis of the relationship between the policy analysis of the research departments

and policy communication of the organisation.

4 Most academic research on the behaviour of international organisations has focused on their

lending strategies and much less on their communication or fund-raising strategies (see, e.g.,

Aldenhoff, 2007; Vaubel et al., 2007; Dreher et al., 2009).

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these organisations have to compete internally for funding. While their funding sources may differ, in a world where financial means are limited and where there is continuous pressure to demonstrate relevance and importance of budget spending on particular items, projects or divisions within large organis- ations, all these organisations face a demand to demonstrate the importance of their work. Focusing their reports and analyses on those hurt by price changes may fit in such strategy to show relevance and importance – and may thus help in securing and raising funds.

4.1. A model

To analyse potential bias in ‘policy communication’, we develop a formal model of the interaction between ‘POs’ and ‘donors’. Our model builds on the seminal work of Mullainathan and Shleifer (2005) on bias in mass media and of Andreoni and Payne (2003) on fund-raising by charity organis- ations. We define ‘policy communication’ as communication of advice and results of analyses of important public policy issues. It includes rapid com- munications (such as interviews or press releases) and more extensive exter- nally released reports on certain issues. It does not include news or reports from commercial media sources or internal reports of organisations.

We use the term ‘policy organisations’ to represent all organisations who are communicating public policy analyses and advice and who obtain a sig- nificant share of their funding from various external sources (‘donors’). POs can include organisations as diverse as international NGOs (such as Oxfam, Greenpeace, etc.), intergovernmental organisations (such as the World Bank, FAO, etc.) and various national organisations. In our model, POs do not include commercial companies or organisations representing specific interest groups with single source funding (such as labour unions or associations of companies).

POs engage in both analysis and communication. The purposes of the POs’ analysis (i.e. fact-finding and various types of research) are multiple: their analysis serves to support internal decision-making on funding and project implementation. Analysis also provides the basis for policy communication. The POs’ communication strategy has two objectives. The first objective is policy advice, i.e. to influence others (e.g. governments) to implement or reform certain policies. The second objective is fund-raising, i.e. to influence donors to contribute funds to the POs. Policy communication by the POs is influenced by both objectives and by the agents they interact with (donors and those targeted with advice).

POs receive a significant share of their funding from a variety of ‘donors’. Donors may include public entities (such as governments) or private entities (such as foundations or households). POs have to raise funds and compete for attention and funding of donors. This assumption is consistent with the aforementioned observation that all international organisations compete to some extent for funds from public or private donors to operate and implement their projects.

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Formally, POs, indexed by j, collect data on the state of the economy t, e.g. the impact of global warming or rising food prices. We assume that POs perform correct analyses, i.e. they gather the necessary information and use appropriate methodologies to arrive at the correct conclusion d about the state of the economy (d ¼ t). The POs communicate their conclusions to the external world by means of ‘reports’, containing information nj. We assume POs may introduce an amount of slanting sj in their policy communications, so their reports contain information nj ¼ d + sj.

Donors, indexed by i, hold certain beliefs bi about the state of the economy t, and these beliefs may be biased. If bi . 0, donor i has an optimistic belief about the state of the economy, whereas if bi , 0, the donor holds a pessimistic belief.

5 Following Andreoni and Payne (2003), we assume that donors have a

latent demand to donate. This implies that a donor does not give money to a PO unless solicited. A donor selects which PO to support based on a comparison of the contents, nj, of POs’ reports and their requested donations Dj.

In line with Mullainathan and Shleifer (2005), we assume that, on the one hand, donors dislike slanted reports because it is costly both in effort and time to read a slanted report and ‘to figure out the truth’. On the other hand, donors get disutility from reading reports that are inconsistent with their beliefs. Formally, a donor i’s utility of reading the report of PO j and consequently donating to PO j is:

Uij = uw(nj) − ms2j − f(nj − bj)

2 − Dj if donor i reads the report of, and donates to, PO j;

0 if the donor does not donate,

⎧ ⎨ ⎩

(1)

where u w (nj) is the ‘warm glow’ a donor receives from donating (Andreoni

and Payne, 2003). If a donor does not read any report, and consequently does not donate, he receives zero utility. The constant m . 0 is a measure

for a donor’s sensitivity to slanting; therefore, ms2j represents the disutility

from reading a slanted report issued by PO j. f . 0 represents a donor’s pre- ference for reading a report consistent with his beliefs, where consistency is modelled as the squared distance between the report’s content nj and the

donor’s beliefs bi, i.e. nj − bj ( )2

. 6

It is likely that donors draw more ‘warm glow’ if their donations have larger welfare impacts, for example more from helping the victims of an earthquake that killed thousands of people and made millions of poor homeless than from

5 These beliefs are assumed to be exogenously determined and may be based on donors’ prior

information or their political leaning. Swinnen et al. (2010) analyse the case of endogenous

beliefs where donors may update their beliefs with the content of POs’ reports.

6 We use squared differences to ensure that positive and negative bias is treated symmetrically.

The convex functional form makes the derivations easier but is not essential to our model’s out-

come. Any distance measure, for example absolute values, would qualitatively yield the same

results.

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helping the victims of a local flood that made a few rich people having to leave their house for a few days. We therefore assume that the ‘warm glow’ depends on the severity of the problems on which POs report and solicit donations for. Formally, the ‘warm glow’ component equals uw(nj) = uw0 − anj, where uw0 is the baseline ‘warm glow’ from donating to a PO, and a is a scalar which measures the donors’ ‘warm glow’ from supporting a PO that addresses more severe problems. Hence, if a report is more negative than the actual situation (nj , 0 and larger in absolute value), donors’ utility from donating to PO j is higher.

As discussed before, the POs’ policy communication has two objectives. On the one hand, POs’ reports serve the purpose of fund-raising; on the other hand, they aim at improving government policy through their reports. The PO chooses its slanting strategy sj and the donation Dj it requests to maximise its objective function Wj(nj), which is the weighted sum of revenues, Rj(nj), and policy impact, Ij(nj). The objective function of PO j is

Wj(nj) = vRRj(nj) + vI Ij(nj), (2)

where vR and vI are the respective weights of revenues and policy impact. The revenues Rj(nj) are the funds collected from donors who decide to donate to PO j after reading its report. The policy impact, Ij(nj), is specified as Ij(nj) = G − (nj − t)2, with G . 0 being the policy impact of a report that is not slanted. We abstract from the complexities of the decision-making process of governments and assume they choose better policies when receiving better (i.e. less slanted) information from the reports. The policy impact is decreasing in the squared distance between the report’s contents nj and the true situation t.

7

For homogenous donor beliefs (bi ¼ b for all i), the equilibrium amount of slanting s∗j in this market can be derived as

8 :

s∗j = vR

vR(m + f) + vI fb −

a

2

[ ] . (3)

4.2. Key results

It is clear from equation (3) that slanting will depend on donors’ utility para- meters, on the weights given to revenues versus policy impacts and on the dis- tribution of beliefs among donors. Our analysis shows that POs do not slant their reports only under very restrictive, and unrealistic, conditions. Only when (i) donors’ beliefs are homogenous; (ii) donors’ beliefs are unbiased and (iii) donors’ utility is not affected by the severity of the problem, POs

7 We use a squared distance measure for the same reasons as explained in footnote 6.

8 The sequence of the underlying game is as in Mullainathan and Shleifer (2005). We refer to Swin-

nen et al. (2010) for technical derivations, details and proofs and summarise here some key

insights.

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do not slant their reports. However, in all other cases POs will slant their communications.

If donors’ beliefs are homogenous and biased bi = b = t( ), POs slant their policy communication in the direction of the donors’ beliefs. The level of the optimal slanting is affected by several factors, such as the relative importance of respectively fund-raising (vR) and policy impact (vI) in the POs’ objective function, and the donors’ sensitivity to slanting (m) and to reading reports that are inconsistent with their beliefs (f). All these factors are affecting the outcome in the intuitive direction.

When accounting for problem severity, i.e. when donors prefer donating to POs that (claim to) address more severe problems (a . 0), POs will depict situations as being more negative than they actually are, even when donors’ beliefs are unbiased.

With heterogeneity in donors’ beliefs, POs differentiate themselves in the policy communication market. They slant their reports in different directions to increase donations from a subgroup of the population.

These results hold even when assuming that donors’ beliefs are exogen- ously determined. However, it is more realistic to consider that donors’ beliefs are not static. People may change their opinion on a certain issue, for example, because they receive additional information that is not in line with their beliefs. Such new information may come from the POs’ reports. When donors’ beliefs are endogenous, this affects slanting.

Our model demonstrates that when donors update their beliefs with the policy communications of the organisations, both donors’ beliefs and the POs’ slanting converge to a biased equilibrium. A very important finding is that donors’ initial beliefs do not matter in the long run as they are increasingly affected by POs’ reports. Moreover, even if these initial beliefs were correct, they become biased over time.

5. Endogenous beliefs and the mass media

Donor beliefs may not only be influenced by POs’ reports but also by other information sources, in particular by mass media. This will also influence POs’ communications and slanting.

There are two important, but distinct, mechanisms at work in the interaction between POs’ communications and the mass media. The first mechanism is the impact of stories that appear in the mass media on the communications of the organisations. Several characteristics of mass media are relevant to explain this mechanism (McCluskey and Swinnen, 2010). Mass media may play an important role in influencing donors’ beliefs, in particular initial beliefs, and thus POs’ communication. Media attention is typically concen- trated around ‘events’ or ‘shocks’.

9 Mass media’s impact on donors’ beliefs

is determined by its broad audience and the relative speed of mass media

9 For example, Swinnen and Francken (2006) find that virtually all the attention to globalisation,

trade and development issues in mass media is concentrated around ‘international summits’.

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coverage. Typically mass media can bring news reports much faster than a report from a PO that may require substantially more time for a thorough analysis of the situation and corresponding policy communications. When initial beliefs are influenced by mass media reports, these mass media reports induce POs to slant their reports. In the long run, this impact disap- pears if donors update their beliefs with information from other sources.

This agenda-setting effect of the media in policy has sometimes been referred to as the ‘CNN factor’ (Hawkins, 2002). It refers to the process by which the media influences policy by invoking responses in their audiences through concentrated and emotionally based coverage, which in turn applies pressure to governments to react. Similarly, the absence of media coverage reduces priority in agenda-setting (Jakobson, 2000). In this logic, public offi- cials react to media news because they see it as a reflection of public opinion (Kim, 2005).

10

The second mechanism is the desire of the organisations to appear in mass media in order to achieve their objectives (Cottle and Nolan, 2007). With mass media reports focusing on those hurt by changing food prices – in particular consumers post-2006 – the donor community, the organisations’ shareholders and the public at large may expect (or even demand) that these organisations focus their attention on those who are suffering from price changes. If they would not publicly react (‘communicate’) on these problems, it would hurt their legitimacy as development organisations. This could undermine overall support for their existence. Hence, sudden changes with dramatic effects, such as the 2008 food crisis, not only present important challenges to the international organisations in addressing these, but also important opportunities for development organisations to capture media attention and signal their relevance and importance to their donors and the public.

A related factor is that the public at large is more interested in media reports concentrating on negative (development) effects. This follows from the so-called bad news hypothesis. Media consumers in general tend to be more interested in negative news items than in positive news items, ceteris paribus. This demand effect of the media market drives mass media to pay more attention to ‘bad news’ (McCluskey and Swinnen, 2004).

In combination, these factors create a set of incentives for international organisations to emphasise the negative welfare implications in their analysis and policy communications, and to put less emphasis on the positive effects. In doing so, they are more likely to attract media coverage on their work and, in turn, more likely to reach a wide audience and to influence policy-makers. Such a media strategy could have a direct effect in influencing public and private donations and policies of governments in the short run and an indirect effect in encouraging appreciation and legitimacy for their work and the organisations themselves – which could lead to support in the long run.

10 Van Belle et al. (2004) and Kim (2005) find that a higher level of media attention to developing

countries’ problems leads to more aid. Eisensee and Strömberg (2007) argue that disaster relief

and aid allocations are influenced by media coverage of disasters.

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The causal relationship of mass media affecting donors’ beliefs and conse- quently influencing POs’ policy communications depends on the nature of the problem/policy. For longer term or structural problems, mass media may not play a role in affecting initial conditions. On such issues, it may rather be that the mass media report on an issue because of a PO’s report. In this case, the donors’ initial beliefs would not be affected by the mass media, and neither would the POs’ initial policy communications.

6. Bad news and good policies?

A final important issue is that, while all these organisations have changed their communications, they may have not changed their basic policy advice. In terms of our model, it would imply that POs would be able to separate ‘donor communications’ from ‘policy communications’ – and that ∂Ij/∂nj = 0 and Ij ¼ G. In this case, they could use the price shocks to attract attention and support of donors. This is related to the aforementioned ‘bad news hypothesis’. If donors tend to pay more attention to negative news or policy communications, and POs are able to separate their communi- cations partially or completely from their policy advice, POs have an incentive to portray events more negatively in their communications while keeping the same policy advice. Accounting for this ‘bad news hypothesis’ in our model would be largely similar to the ‘problem severity’ effect (a . 0) and would result in more negatively slanted reports of POs ∂s∗j /∂a . 0. This bad news

effect could work both directly in the PO – donor communications or indirectly through the mass media.

When POs are able to separate their communications from their policy advice, one should expect organisations that consider global underinvestment in agriculture a problem to forward this as a remedy, whether prices are high or low – and just use somewhat differently framed arguments to make this point. Similarly, one should expect some of the organisations – those that believe in the benefits of free markets – to emphasise the benefits that free markets bring – whether prices are high or low; and the organisations that believe in government regulation of markets to emphasise the importance of regulations – whether prices are high or low.

In Squicciarini and Swinnen (2011), we have analysed in detail the policy recommendations of the organisations whose communication shift we docu- mented earlier in this paper. Table 1 summarises some of the key findings, before and after the food crisis. We conclude that the evidence is mixed.

First, there is a significant shift in the attention to food consumer concerns, and in the emphasis of rural households as net food consumers.

Second, most organisations have not changed their perspectives on trade policy. Organisations like the World Bank, OECD, FAO and IFPRI have con- tinued to emphasise the importance of trade liberalisation and of concluding the Doha Round both before and after 2006; while NGOs, such as Oxfam and ActionAid, have continued to recommend the cut of rich country subsidies

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Table 1. Elements of policy communication before and after the food crisis

ActionAid Oxfam FAO IFPRI OECD World Bank

Before

Poor farmers are net consumers 0 0 0 0 0 0

Liberalisation in rich countries ++ and *** + + and *** + + and ** + + and ** + + and ** + + and *** Liberalisation in poor countries 2 2 and *** 2 2 and *** + and ** + and ** + and ** ++ and *** Elimination of exports bans (in poor countries) 0 0 0 0 0 ++ and * Investment in infrastructure ++ and * ++ and * ++ and ** ++ and * ++ and ** ++ and ** Target smallholders and marginal areas ++ and *** ++ and ** ++ and ** ++ and ** ++ and ** ++ and ** Support for biofuels production 0 0 0 + and ** 0 0 Reduce price volatility 0 0 + and * 0 + and * ++ and *

After

Poor farmers are net consumers ** *** *** *** * **

Liberalisation in rich countries ++ and *** + + and *** + and * + and ** + + and * ++ and ** Liberalisation in poor countries 2 2 and *** 2 2 and *** + and * + and ** + and * ++ and ** Elimination of exports bans (in poor countries) 0 + and ** ++ and ** ++ and ** ++ and ** ++ and ** Investment in infrastructure ++ and ** ++ and ** ++ and ** ++ and ** ++ and ** ++ and ** Target smallholders and marginal areas ++ and *** ++ and *** ++ and ** ++ and *** ++ and ** ++ and *** Support for biofuels production 2 2 and *** 2 2 and *** 2 and ** 2 2 and ** 2 and * 2 and *

Reduce price volatility + and * ++ and ** + and ** ++ and ** ++ and *** ++ and ** Source: Squicciarini and Swinnen (2011). Note: +, agree; ++, fully agree; 2, disagree; 2 2, fully disagree; *, low emphasis; **, medium emphasis; ***, strong emphasis; 0, not mentioned.

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and the importance of government regulation and protection of poor countries’ agri-food markets. However, there is a clear shift in trade policy focus from arguing to remove import constraints to export constraints.

Third, there is some change in the emphasis on supply and productivity-enhancing measures and the need to support small farmers. Most organisations have much strongly emphasised the importance to invest in agriculture in the post-2007 period than before.

Fourth, there is a substantial change in the emphasis and preference on biofuel policies, emergency aid and measures to reduce price volatility. In recent communications, great emphasis has been put on price volatility and on the design of new policies to respond to it; food aid, already a contentious and delicate issue, has now gained primary importance; the provision and implementation of safety nets has also gained higher importance on the policy table; finally, the role of biofuels and their effects on food security have become crucial in the current debate and in the policy advices of the organisations. In summary, it appears that the organisations have partly main- tained their core policy message and partly adjusted it.

A final, but important, issue is that consistent policy advice may be associ- ated with more, rather than less, slanting in communication by POs. As our model shows, this (partial) decoupling of POs’ communication and policy advice has potentially large consequences for the bias in POs’ communi- cations. For example, in the extreme case where communication would be completely separate from policy advice, the policy impact term Ij in the PO’s objective function would be constant, i.e. independent from policy com- munication. In that case, the term vI would drop from the equilibrium outcome in equation (3) and POs’ slanting would be higher (in absolute terms). Hence, somewhat paradoxically, while decoupling policy advice from (slanted) communications may be welfare-improving because then policy advice is no longer biased (that is, if policy advice induces welfare-enhancing policies), it results in a communication strategy that is even more slanted towards negative issues.

Acknowledgements

We thank Kym Anderson, Rick Barichello, John Bensted-Smith, Luc Christiaensen, Walter

Falcon, Tassos Haniotis, Tom Hertel, Michiel Keyzer, Andrzej Kwiecinski, Will Martin, Alan

Matthews, Alessandro Olper, Scott Rozelle, Manohar Sharma, Kostas Stamoulis, Stefan Tanger-

mann, Peter Timmer, Alan Winters and participants in conferences in Berkeley (IATRC) and

Campobasso (SIDEA) for comments and discussions on the issues raised in this paper.

The research was financially supported by the K. U. Leuven (Methusalem) and the FWO.

The opinions expressed here are ours only.

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SWINNEN-Self-Reported Food Insecurity in Africa.pdf

Electronic copy available at: http://ssrn.com/abstract=1995538

LICOS Discussion Paper Series

Discussion Paper 303/2012

Self-Reported Food Insecurity in Africa During the Food Price Crisis

Marijke Verpoorten, Abhimanyu Arora and Johan F.M. Swinnen

Katholieke Universiteit Leuven LICOS Centre for Institutions and Economic Performance Waaistraat 6 – mailbox 3511 3000 Leuven BELGIUM TEL:+32-(0)16 32 65 98 FAX:+32-(0)16 32 65 99 http://www.econ.kuleuven.be/licos

Electronic copy available at: http://ssrn.com/abstract=1995538

 

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Self-Reported Food Insecurity in Africa During the Food Price Crisis

Marijke Verpoorten, Abhimanyu Arora and Johan Swinnen LICOS Centre for Institutions and Economic Performance

& Department of Economics University of Leuven (KUL)1 www.econ.kuleuven.be/licos 

Version: 4 January, 2012

Abstract

This article analyzes data on self-reported food insecurity of more than 50,000 individuals in

18 Sub-Saharan African countries over the period 2005 to 2008, when global food prices

increased dramatically. The average level of self-reported food insecurity was high but

remarkably stable, at about 54%. However, this average hides large heterogeneity, both

within countries and across countries. In eight of the sample countries, self-reported food

security improved, while it worsened in the ten other countries. Our results suggest that

heterogeneous effects in self-reported food security are consistent with economic predictions,

as they are correlated with net food consumption (both at the household and country level)

and economic growth. Specifically, self-reported food security improved on average in rural

households, while it worsened in urban households. Improvements in food security were

positively correlated with net food exports and GDP per capita growth. We estimate that over

the period 2005-2008 between 5 and 12 million people in the 18 SSA countries became more

food secure. While the self-reported indicator used in this paper requires further study and one

should carefully interpret the results, our findings suggest the need for a critical evaluation of

the currently used data in the public debate on the food price crisis, which makes mention of

hundreds of millions of additional food insecure.

Keywords: Food policy, food insecurity measurement, Sub-Saharan Africa, food crisis JEL classification codes: Q18, I32, O55        

                                                             1 We thank Derek Headey for valuable input. The usual disclaimer applies.

 

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 1.Introduction The dramatic increases in food prices since 2006 have raised many concerns on the

impacts on the world’s poor. Poor people, who spend a large share of their income on

food, are said to suffer severely from food price increases. This, it is argued, is not

only the case for poor urban consumers, but also for poor rural households and

farmers, many of whom are argued to be net consumers and therefore suffering from

higher prices (Barret and Dorosh, 1996; Weber et al., 1988). FAO, USDA and World

Bank estimates of the welfare impact of the 2007/2008 global food crisis conclude

that somewhere between 75 to 160 million people were thrown into hunger or poverty

(de Hoyos and Medvedev, 2009; USDA, 2009). Several studies focusing on Sub-

Saharan Africa have also concluded strong negative welfare effects (Arndt et al.,

2008; Wodon and Zaman, 2009).

These claims, however, have not gone without challenge. A first critique

relates to the apparent inconsistency of these claims with earlier arguments that poor

farmers and rural households in developing countries suffered heavily from low

global food prices, partly as a consequence of rich countries’ agricultural subsidies

(Swinnen, 2011). A second critique relates to the fact that other factors than food

prices can be more important determinants of food consumption in developing

countries (Banerjee and Duflo, 2011). A third critique relates to the actual

measurement of hunger and food security which underlies these arguments, and their

determinants (Easterly, 20102; Headey, 2011). A crucial issue with the hunger

calculations is that they are typically not based on actual measurement but on

simulations. One drawback of the simulation-based studies is that their country                                                              2 ”Spot the made-up world hunger numbers” on Aidwatchers, 15th September, 2010 (can be accessed

at: http://aidwatchers.com/2010/09/spot-the-made-up-world-hunger-numbers/) 

 

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coverage may be limited and possibly not representative, in particular when the

models do not include some large countries with many poor people. Another potential

drawback is the ceteris paribus nature of the analyses.

While some of these studies have carefully pointed out the limitations of the

simulations exercises, many of those who have used the results of these studies in the

policy debate have not. For example, Will Martin and Hassan Zaman, in a recent

response to Headey on Dani Rodrik’s website correctly point out the ceteris paribus

nature of studies they and others have been involved in (see e.g. Ivanic and Martin,

2008; Wodon and Zaman, 2009) and they also stress the limitations of looking at

hunger or food security as welfare indicators, with poor people making costly

adjustments to make up for increasing food prices3. Indeed, it has been extensively

demonstrated that, in order to meet their consumption needs, rural households may

sell their productive assets such as seeds and livestock, thereby jeopardizing their

future earnings prospects (Dercon, 2004; Poulton et al., 2006).

However, in much of the policy debates on the food crisis, the discussions

have ignored the ceteris paribus assumption and the complex interactions between

food prices and welfare, and have used the numbers from the simulation models as if

they were actual changes in food security or poverty (see Swinnen (2011) for a review

of statements and public arguments).

In a recent review of the issue, Headey (2011) not only points out important

deficiencies but also proposes considering alternative measures which are based on

ex-post observations instead of simulating from ex ante data. He analyzes self-

                                                             3 “Was the food price crisis of 2008 really a myth?”, posted by Martin and Zaman on June 16, 2011 can

be accessed at http://rodrik.typepad.com/dani_rodriks_weblog/2011/06/was-the-food-price-crisis-of-

2008-really-a-myth.html

 

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reported food insecurity from the Gallup World Poll (GWP), a survey that has

covered almost 90% of the developing world population over the period 2005-2010.

His findings are strikingly different from those so far claimed. The GWP data suggest

that although there was large variation across countries, global self-reported food

insecurity fell sharply from 2005 to 2008, with estimates ranging from 60 to 340

million people (Headey, 2011). The discrepancy between this finding and the results

of the more commonly used simulation-based approach is likely to be due to the

combination of the ceteris paribus assumption and the under-sampling of fast-growing

large economies - China, India and Brazil - in the latter approach.

Given the dramatically different conclusions, and the potential implications for

food and economic policies, it is important to further investigate this issue. In this

paper we follow up on Headey’s (2011) approach by using a self-reported food

insecurity indicator from a different data source: the Afrobarometer (AB) surveys.4

Analyzing a similar indicator from a different source is important to see to what

extent these data confirm Headey’s GWP data findings, or not.

There is an additional contribution. A limitation of the GWP data used by

Headey (2011) is their aggregate nature, i.e. they provide only one figure for each

country-year in the sample. This makes it impossible to distinguish between, for

example, rural and urban areas, and to control for a number of individual level

characteristics that may influence self-reported food insecurity. In contrast to the

GWP data, the AB data is available at the individual level. The survey rounds of 2005

                                                             4 Afrobarometer is a research project funded by Institute for Democracy in South Africa, the Ghana

Centre for Democratic Development and the Department of Political Science at the Michigan State

University. The project seeks to explore public attitudes towards governance and socio-economic

scenarios.

 

 

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and 2008 include observations on approximately 50,000 representative individuals

across 18 different Sub-Saharan African (SSA) countries, making it represent 56.3%

of the population in SSA.

The next section gives an overview of the AB data on self-reported food

insecurity in 2005 and 2008. In section 3, we study the change in self-reported food

insecurity over this period and discuss the within- and between- country heterogeneity

in the results. In section 4, we verify the robustness of these results by means of a

multivariate regression analysis. Section 5 provides further discussion on three fronts.

First, we assess the economic significance of the changes in self-reported food

insecurity by providing estimates on the number of millions falling into or escaping

food insecurity over the period under study. Second, we assess the external validity of

the results by comparing characteristics of the in-sample SSA countries with the out-

of-sample SSA countries. Finally, as a test of the reliability of the AB self-reported

food insecurity indicator, it is compared with a similar measure, i.e. the self-reported

food insecurity in the GWP data, used in the paper of Headey (2011).

2. The Afrobarometer Data

Figure 1 shows the dramatic change of the real food price over the past decade. The

average annual international food price index increased dramatically from its (annual

average) baseline level of 100 in 2005 to 167 in 2008. To assess how rising food

prices affected self-reported food insecurity in SSA, we use the AB household survey

data for the years 2005 and 2008.

Eighteen countries, displayed in Table 1, have nation-wide respondents for

these two rounds. Respondents are selected using a random, stratified, multistage,

national probability sample representing adult citizens aged 18 years or older. There

 

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are between 1,104 and 2,400 respondents for each country-year, allowing inferences

to national adult populations with a margin of sampling error of no more than plus or

minus 2.5% with a confidence level of 95%. In total, across the two survey rounds for

the 18 countries, the dataset includes information of 50,710 respondents, of which the

majority (62%) resides in the rural sector.

In the AB surveys, the question on food insecurity is formulated as follows:

“Over the past year, how often, if ever, have you or anyone in your family gone without

enough food to eat? 0=Never, 1=Just once or twice, 2=Several times, 3=Many times,

4=Always”

The exact phrasing of the question is important, an issue that is extensively

discussed by Headey (2011). Phrases of interest include “over the past year”, “you or

anyone in your family”, and “enough food to eat”. The latter may be subjective,

depending on the diet the respondent is accustomed to, which may include meat and

other more expensive items for well-off individuals and may consist exclusively out

of staple foods for poorer individuals. In principal, the phrase “you or anyone in your

family” makes the question sensitive to unequal intra-household distribution of food.

Finally, for the purpose of linking the self-reported measure with events that occurred,

it is important to take account of the 12 month recall period (“over the past year”).

The timing of the interview varied within and across survey rounds.

Depending on factors including the size of the country, each survey was implemented

in periods ranging from nine days (Cape Verde, May 2008) to 125 days (Nigeria,

August-December 2005). In a regression analysis (reported in section 4), we

determine for each individual separately the 12-month recall period and the

corresponding food price inflation, and find qualitatively the same results as those on

the basis of summary statistics across 2005 and 2008 (reported in section 3).

 

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To facilitate reading the data, we derive three binary indicator variables from

the categorical answers. Foodinsecurity1 indicates whether or not the individual

experienced food shortages over the past year; it takes the value 1 for the categories 1-

4 (1=Just once or twice, 2=Several times, 3=Many times, 4=Always) and zero

otherwise, i.e. if the household never experienced hunger. A second indicator,

foodinsecurity2, takes the value 1 for categories 2-4 and zero otherwise, capturing

when individuals had gone without enough food more frequently than “just once or

twice”. A third indicator variable, foodinsecurity3, equals 1 when the households

reported having gone without enough food “many times” or “always”.

Changes in foodinsecurity1 can be read as changes in the incidence of food

insecurity, while  increases in foodinsecurity3 are likely to result from individuals

moving from less severe (rather than no food insecurity) to more severe food

insecurity. Therefore, to some extent, increases in foodinsecurity3 can be interpreted

as increases in the depth of food insecurity.

3. Self-Reported Food Insecurity, 2005-2008

3.1. General numbers

Table 2 summarizes the frequency distribution of the answers. The descriptive

statistics yield several important conclusions. First, in both years, more than half of all

the respondents reported having gone without food at least once in the past 12 months

prior to the interview and 16% to 17% did “many times” or “always”.

Second, food insecurity is considerably larger for rural than for urban

respondents. Only 39% - 41% for rural respondents never went without food,

compared to 55% - 57% of urban respondents. Furthermore, rural respondents

answered “many times” or “always” in approximately 20% of cases compared to only

 

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10% for their urban counterparts. This is consistent with information from other

sources indicating that food insecurity is much higher in rural than in urban areas

(Weber et al., 1988).

Third, remarkably there are no dramatic shifts during the period of food crisis.

Between 2005 and 2008, the share of respondents who were never without food

decreased by only two percentage points (from 47.3% to 45.4%). Furthermore, this

decrease was almost entirely due to a shift to the category of going without food just

once or twice (increasing from 15.8% to 17.3%). In fact, very severe food insecurity

slightly declined over the period, with the share of respondents reporting going

without food “many times” or “always” decreasing from 17.3% to 16.3%.

Thus, it seems that the incidence of food insecurity increased slightly, while

the depth of food insecurity decreased slightly. This is also the pattern that emerges

from Table 3, which summarizes the descriptive statistics for the three binary food

insecurity variables, in Panel A, B and C, respectively. In line with Table 2, we find

that foodinsecurity1 slightly increased, foodinsecurity2 remained basically unchanged

and foodinsecurity3 slightly decreased.

There may be several reasons why, in a period with high international food

price inflation, we find only a relatively moderate increase (foodinsecurity1,

foodinsecurity2) or even a decrease in the share of food insecure (foodinsecurity3).

One potential reason is that many poor people are farmers who may have benefited

from the high food prices. Another reason may be the influence of other important

determinants of food security. Most importantly, as is evident from Table 4, the

countries in our sample experienced strong income growth, with above 3% annual

average GDP per capita growth over the 2005-2008 period.  

 

9   

In the words of Headey and Fan (2008, p. 387) “Essentially, the welfare effect

of rising food prices at the urban, rural, or country level depends upon the number of

people who are poor and vulnerable (just above the poverty line), whether those

people are net buyers or net sellers of food, and whether they are marginal net

sellers/buyers or significantly so.” Although conceptually very transparent, at the

micro-level the difficulty lies in measuring the extent to which rural and urban

households are net sellers/buyers of food. This measurement issue, in particular the

likely underreporting of household income in rural regions, may explain some

contrasting results of recent cross-country poverty simulations.5 Given these

contrasting results, it is useful to further explore the heterogeneity in the change in

self-reported data on hunger.

3.2 Heterogeneous results

3.2.1. Rural versus urban areas

The results in Table 3 suggest that rural respondents are on average benefiting from

high food prices and experience an improvement in food security, whereas urban

households are worse off. The rural-urban difference in the change in self-reported

food insecurity is largest for very severe food insecurity (foodinsecurity3), for which

the figures indicate a decrease of 9.2% in rural areas and an increase of 7.8% in urban

areas, suggesting that the depth of food insecurity increased substantially in urban                                                              5  For example, for two African countries, Ivanic and Martin (2008) find larger increases in rural than in

urban poverty upon food price increases. These results are challenged by Aksoy and Isik-Dikmelik

(2008), who assume that many of the poorest in rural areas are mostly net food sellers. Several detailed

country case studies also reach the conclusion that, while the effect of world food prices is likely to be

relatively modest for the country as a whole, the urban poor –rather than the rural poor– may be hardest

hit by high food prices (Godsway et al. (2010) for Ghana; Ferreira et al. (2011) for Brazil; Arndt et al.

(2008) for Mozambique).

 

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areas while decreasing substantially in rural areas. For foodinsecurity2, the difference

is smaller, with a drop of -0.2% in rural areas and a rise of 5.2% in urban areas.

Foodinsecurity1 increased in both rural and urban areas, although more so in the latter

(4.4% versus 3.4% in rural areas).

Figure 2a visualizes the rural-urban difference in the change in foodinsecurity1

during the period 2005-2008, depicting the change of foodinsecurity1 in urban areas

on the horizontal and the change in rural areas on the vertical axis. On average, urban

food insecurity increased more than rural food insecurity, as is shown by the fitted

line which lies below the 45°-line. However, many country data points lie close to the

45°-line, indicating that, in those countries, the proportional change in foodinsecurity1

was similar across rural and urban areas. Noteworthy exceptions, where food

insecurity increased much more in urban areas, are Benin and Senegal (denoted by the

ISO-codes BJ and SE respectively).

Figures 2b and 2c depict the rural-urban difference for the change in

foodinsecurity2 and foodinsecurity3, respectively. The average result is qualitatively

the same as in Figure 2a: the fitted line is flatter than the 45° line indicating that, on

average, the depth of food insecurity increased more in urban than in rural areas.

Again, the results are very different across countries with some of the country data

points lying close to the 45° line, while others (e.g. Senegal and Benin) lying far

below the 45° line.

3.2.2. Food importing versus food exporting countries

A second interesting distinction can be made between net food-importing and net-

food exporting countries. We classify a country as a net food-importer (-exporter) if

the value of annual food imports was larger (smaller) than the value of annual food

 

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exports, on average for the period 2005-2008.6 Table 3 indicates that, on average,

self-reported food insecurity increased considerably for food importers, across all

three binary variables, while respondents in food exporting countries reported stable

(foodinsecurity1) or declining food insecurity (foodinsecurity2 and foodinsecurity3).

This pattern is consistent with the intuition of increasing prices benefiting net

exporters more (or hurting less) than net importers.

Table 4 shows that, in the sample countries, net food imports as a share of

GDP over the 2005-2008 period was on average negative (-1%), but varied between -

9.9% in Ghana and 13% for Cape Verde. The importance of food import reliance is

depicted in Figure 3a, which shows a strongly positive relationship between the

change in foodinsecurity1 between 2005 and 2008 and the share of net food imports in

GDP. The relationship is also positive for foodinsecurity2 and foodinsecurity3, be it

less strong (see Figures 3b and 3c).

3.2.3. GDP per capita growth

Table 4 shows that, in our sample of countries, annual GDP per capita growth  over

2005-2008 ranged between a low of 0.9% (or -5.3% when including Zimbabwe) and a

high of 5.3% for Uganda. Given this heterogeneity across countries, and the

presumption that income matters for food insecurity, we expect to find large cross-

country variation in the change in self-reported food insecurity.

We indeed find large differences. For instance, Benin, a poor growth

performer in the period under study (0.9%), tops the list for the change in

foodinsecurity1 with a 41.7% increase in the share of respondents going without food

                                                             6 Except for Lesotho for which, due to unavailability of data, we use the figures for the period 2000- 2004.

 

12   

at least once. Mozambique, which had a very good growth record (5.2%), displays the

largest decrease in foodinsecurity1 (-22.5%). Figure 4a shows the relationship

between GDP per capita growth and the change in food insecurity for all countries in

our sample. The relationship is strongly negative, indicating that, on average,

foodinsecurity1 increased in countries with relatively low economic growth. Some

country data points lie far above the fitted line, indicating that food insecurity

increased much more than predicted based on the average relationship. Not

surprisingly, these data points include Senegal, Lesotho and Cape Verde, the three

countries with the highest reliance on food imports in our sample.

Figure 4b shows a very similar pattern for the relation between GDP per capita

growth and changes in foodinsecurity2. However, Figure 4c shows only a very weak

correlation between GDP growth and the change in foodinsecurity3. This suggests

that, at least in the short run, GDP per capita growth is more strongly correlated with

the change in the incidence of hunger than with the change in the depth of hunger.

3.2.4. Education level

Educational attainment of the respondent is yet another significant aspect lending

heterogeneity to food insecurity. In the absence of income data in the AB, educational

attainment provides a useful cue for income as the relation is shown to be strong and

highly significant (Griliches and Mason, 1972). About six in ten respondents have

completed primary education, while four in ten have received no formal education or

only some primary education. Figure 5a compares the change in foodinsecurity1 by

these categories, which are depicted on the horizontal and vertical axis, respectively.

Figures 5b and 5c provide the same comparisons but for the subsamples of rural and

urban respondents respectively. In all three figures (5a, 5b, 5c) the fitted line lies

 

13   

below the 45°-line, indicating that self-reported food insecurity increased more among

the educated respondents. This pattern is qualitatively the same when looking at

foodinsecurity2 and foodinsecurity3 (not reported).

One explanation may be that there are more net-food consumers among the

relatively well educated, while net-food producers make up the majority of the

uneducated. However, this explanation can only apply to the rural areas, not to the

urban areas, where basically everybody – both educated and uneducated – are net-

food consumers. Another explanation may relate to the subjective nature of the

measure of self-reported food insecurity, i.e. the educated may be more likely to self-

report a change in food insecurity, for reasons related to behavior and social norms, or

because their consumption basket consists of more traded food items.

3.2.5. Gender

Finally, we study heterogeneity across the gender of the respondent. A recent study by

Kumar and Quisumbing (2011) on the gendered impact of the 2007-08 food price

crisis on poverty perception and consumption by Ethiopian households finds that

female-headed households are more vulnerable to food price changes and are more

likely to have experienced a food price shock in 2007–08, mainly because they have

fewer resources, have fewer years of schooling, and have smaller networks. Our

findings from the self-reported food insecurity measure are in line with their study.

Figure 6 demonstrates that the change in food insecurity during the period 2005-8 for

all severity levels was greater for female headed than for male headed households.

 

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4. Regression analysis

We check whether the above results hold in a multivariate analysis as well as when

properly accounting for the 12-month recall period.

As explained above, the question on food insecurity was asked with respect to

“the past year”, and the timing of the interviews varied within each survey round. We

now account for the 12-month recall period, by determining for each individual the

exact survey date, 12-month recall period and the corresponding mean food prices. In

a probit model, we regress self-reported food insecurity on the average food price

index over the past year from the date of interview. Table 5, Panel A displays the

results of this regression, which confirms our previously reported descriptive

statistics. In particular, the coefficient on the 12-month average price index is

significantly positive when explaining foodinsecurity1 and significantly negative

when explaining foodinsecurity2 and foodinsecurity3. For example, a 1% increase in

the mean price level of the past year decreases the probability of a respondent being

severely food insecure by 0.05%.7

In panel B of Table 5, we check the robustness of our rural-versus-urban result

when controlling for the fact that urban households have a higher educational

attainment (which may result in them reporting more easily a change in food

insecurity). In particular, the regression analysis controls for whether the respondent

is urban or rural, the respondent’s education category as well as the interaction terms

of these two variables with the 12-month average of the food price index. We indeed

find confirmation that urban respondents are more vulnerable to an increase in food

prices. In particular, the interaction term between the urban indicator variable and the

                                                             7 This effect is the average marginal effect (over all countries) derived from changes in the z-score of the probit regressions.

 

15   

food price index indicates that, for a 1% increase in the food price level the

probability of becoming food insecure (foodinsecurity1) is on average 3% higher for

an urban respondent than for a rural respondent8. For foodinsecurity2 and

foodinsecurity3 this difference in probability is 5.3% and 7.3%, respectively. These

regression results are in line with our descriptive results, and it tilts the balance in

favor of the claims that rural respondents on average benefited (suffered less) from

the food crisis, even though they might be worse off in level terms to begin with.

Finally, since the country-level covariates, i.e. real GDP per capita growth and

the share of net food imports as percentage of GDP, are positively correlated (with a

correlation coefficient of 0.15), we check the robustness of our results with respect to

these variables by including them jointly as explanatory variables, as well as their

interaction terms with the food price index. The results displayed in Panel C of Table

5 are in line with our claims above, i.e. growth in income moderates the increases in

food insecurity upon a rise in food prices, whereas a higher dependence on food

imports of a country increases the chances of its respondent becoming more food

insecure, on average, with rising prices.

We also perform a joint regression in which we include all the regressors from

Panel B and C of Table 5. Our conclusions remain valid (results not reported).

                                                             8  Due to the estimation model being non-linear, the effect of the interaction term (of being in an urban

region with the mean price over the past year) may differ in sign and magnitude across observations (Ai and Norton, 2003). Here the minimum over all observations is positive and significant for all severity levels of food insecurity. 

 

16   

5. Discussion 

5.1 Accounting for population size

How can we translate these figures in numbers of people falling into or escaping food

insecurity? Table 4 shows that the sample countries are very different in terms of

population size, varying from less than one million for Cape Verde to more than 140

million inhabitants in Nigeria. In the above individual-based analyses, the size of the

respondent’s country, in terms of population, is disregarded. In order to assess the

numbers of people falling into or escaping food insecurity, we need to account for

population size.

First, we repeat the same analysis as above - calculating the proportional

change in self-reported food insecurity – but now using population-weights that give a

higher weight to more populous countries. Second, we estimate the changes in terms

of absolute numbers of people falling into (or escaping) food insecurity, both when

assuming zero population growth, and when taking population growth between 2005

and 2008 into account.9

The results are given in Table 6. The first column gives the proportional

change in food insecurity for the unweighted data, repeating the results reported in the

last column of Table 3. The second column gives the population-weighted

proportional change in food insecurity. The results are very different. The population-

weighted data show a much stronger decrease in food insecurity than the unweighted

data: Weighting by population leads to a proportional decrease of 2.3%, 6.8% and

                                                             9 The population weights used reflect the number of people residing in a country in 2005. The

calculations for the urban and rural subsamples make use of information on the size of the urban and

rural population of the country, taken from the WDI 2010.

 

17   

17.3%, compared to a proportional increase of 3.6% and 1.4% and decrease of 5.2%

for foodinsecurity1, foodinsecurity2 and foodinsecurity3, respectively. This results

from the fact that especially countries with a large population experienced an

improvement in food security. For instance, Nigeria weighs heavily in our sample

with its 140 million inhabitants, and experienced a strong reduction in all three binary

food insecurity indicators (-4.9%, -9.2%, -19.3%).

The population-weighted results imply that several millions of people escaped

food insecurity: -5.62 million, -10.60 million and -12.20 million for foodinsecurity1,

foodinsecurity2 and foodinsecurity3 respectively. These figures, reported in the third

column of Table 6, are obtained by multiplying the population-weighted relative

changes of column 2 by the 2005 population in our sample countries.

5.2 Accounting for population growth

So far, in our calculation – as in other studies estimating the effect of price changes on

food security -- we have assumed constant population over the period 2005-8. In

reality, population grew on average by 2.2% annually across our sample countries. To

account for population growth, we take the difference between the share of food

insecure in 2008 (derived using population weights) times the population in 2008 and

the corresponding term but for the year 2005. The results are reported in column four.

Logically, both the absolute number of food secure and the absolute number of

food insecure people increased, as both groups increased with population growth.

However, despite population growth, we find that the number of people who

experienced regular food shortages (measured by foodinsecurity2 and

foodinsecurity3) was lower in 2008 than in 2005 – with -0.75 million and -8.4 million

respectively. For foodinsecurity1, we find that the number of food insecure increased

 

18   

with 9.99 million. However, the absolute number of food secure increased by even

more, given their increasing share in the population and the fact that they make up

almost half of the population. Hence the net effect was negative, as reflected in the

declining share.

5.3 External validity of the results

Another important question is how representative these results are for the whole of

SSA? Sub-Saharan Africa counts 47 countries, totaling a population of 759 million

(on average in 2005-2008). The AB sample across the 18 countries is representative

for a population of 427 million, or around 56% of the SSA total population.10 To

gauge whether this is a particular subsample of the SSA population, we compare a

number of relevant characteristics of the included and excluded countries.

Table 7 shows that the sample represents SSA to a good extent in the relevant

economic characteristics: GDP per capita, GDP per capita growth, malnutrition

prevalence and depth of hunger.11 However, the in-sample countries had higher

inflation and were on average relying more heavily on food imports than the out-of-

sample SSA countries, suggesting that the in-sample countries may have suffered

more (benefited less) from the food price crisis than the out-of-sample countries. On

the other hand, the latter had on average a much larger number of conflict events over

the period, which are likely to have negatively affected food security. This might

explain why they were not surveyed in the first place.

                                                             10 All population figures are calculated from WDI indicators (World Bank, 2008) 11  Depth of hunger indicates how much food-deprived people fall short of minimum food needs in

terms of dietary energy. The food deficit, in kilocalories per person per day, is measured by comparing

the average amount of dietary energy that undernourished people get from the foods they eat with the

minimum amount of dietary energy they need to maintain body weight and undertake light activity

 

19   

5.4 Comparison with Gallup World Poll (GWP) data

The results above show that the changes of the self-reported indicators between

countries (e.g. food importing - food exporting) and within countries (e.g. urban-rural)

are consistent with intuition. As a further test of the reliability of our self-reported

food insecurity indicators, the AB self-reported food insecurity measure is compared

with a similar measure, i.e. the self-reported food insecurity in the GWP data, used by

Headey (2011). The 2005 GWP data does not include information for SSA countries,

but the 2008 GWP provides aggregate country-level data for 15 SSA countries that

are also included in the AB.

The food insecurity question included in the 2008 GWP is very similar to the

one in the AB survey (Headey, 2011): “Have there had been times in the past 12

months when you did not have enough money to buy the food that you or your family

needed?” A simple yes/no answer is recorded. We compare this measure with the

foodinsecurity1 indicator derived from the 2008 AB.

The comparison results in very similar figures of average food insecurity

across both data sources, with on average 56% of respondents reporting being food

insecure in the 2008 AB sample and 59% in the 2008 GWP sample. The correlation

coefficient between the AB and GWP country figures of self-reported food insecurity

is as high as 0.78. Figure 7 illustrates the strong relationship between both measures

with a plot of the AB and GWP country-level data points.

In summary, the comparison shows that the indicators from both surveys are

very similar and thus provides some support to the reliability of a self-reported food

security measure.

 

20   

6. Conclusion

This paper has studied the evolution of self-reported food insecurity in SSA across the

period 2005-2008, a period with a dramatic global rise in food prices. Despite a strong

increase of the food price index we have found only a small increase in the incidence

of food insecurity (as measured by foodinsecurity1), and even decreases in the depth

of food insecurity (as measured by foodinsecurity3).

This finding does not support the widely advocated view that the food price

spikes of recent years have led to strongly increased food insecurity in developing

countries. In particular, food security apparently improved for net food-producers in

our sample, both at the micro-level (among the rural households) and macro-level

(among the net food exporting countries). Although rural respondents report much

higher food insecurity than urban respondents in all three survey years, the rural-

urban gap became narrower over the period 2005-2008, as urban food insecurity

increased and rural food insecurity declined on average.

We also find that it is highly likely that strong GDP growth over the recent

years has improved food security in a large number of SSA countries, compensating a

possible negative impact of food price increases even on net food consuming

households.

This paper may raise more questions than it answers. There is uncertainty on

the quality and accuracy of our key indicator of food security. This needs further

analysis. However, the fact that our analysis suggests that food security did not

increase (dramatically) - even in very poor African countries - supports the argument

that there is need for a careful re-evaluation of existing measures and methodologies,

and of the use of the results of various approaches in the public debate.

 

21   

To precisely identify the contributions of various factors (food prices, income

growth, energy prices, etc.) on food security in the world, one probably needs a

combination of various methodologies. The advantage of simulation models is that it

allows showing the mechanism of how various factors affect food security, welfare

and poverty. The advantage of survey based approaches is that they allow to measure

actual changes in food security levels. Both have their role and should be used

appropriately.

 

22   

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25   

Figure 1. The real international food price 

 

Notes: The food price data is obtained from the FAO Food Price Index database. In calculating the FAO 

food price index, the FAO classifies 55 commodity quotations into 5 groups‐meat, dairy, cereals, oil & 

fat and sugar and takes the average of these indices, weighting them by their average export shares 

over 2002‐2004. 

 

   

8 0

1 0 0

1 2 0

1 4 0

1 6 0

1 8 0

In te

rn a ti o

n a

l f o

o d

p ri ce

in d

e x

(a ve

ra g

e o

ri ce

in 2

0 0

5 =

1 0

0 )

2000m1 2002m1 2004m1 2006m1 2008m1 2010m1 Time

 

26   

Figure 2. The change in urban and rural food insecurity over 2005‐2008 

a. foodinsecurity1 

  b. foodinsecurity2 

  c. foodinsecurity3 

  Note: The ISO‐codes, listed in Table 4, are used to label the country data points; the dashed line is the 

45° degree line.  

 

27   

Figure 3. The share of net food imports in GDP and the 2005‐2008 change in food insecurity 

a. foodinsecurity1 

  b. foodinsecurity2 

  c. foodinsecurity3 

  Note: The ISO‐codes, listed in Table 4, are used to label the country data points; the dashed line is the 

45° degree line. Net food imports are defined as in Table 4. 

 

 

28   

Figure 4. GDP per capita growth and the 2005‐2008 change in food insecurity 

a. foodinsecurity1 

  b. foodinsecurity2 

  c. foodinsecurity3 

  Note: The ISO‐codes, listed in Table 4, are used to label the country data points; the dashed line is the 

45° degree line. Zimbabwe is left out because it has data available only for 2005. 

 

   

 

29   

Figure 5. Change across 2005‐2008 in food insecurity (foodinsecurity1) by education 

a. Total sample 

  b. Rural Respondents 

  c. Urban Respondents 

  Note: The ISO‐codes, listed in Table 4, are used to label the country data points; the dashed line is the 

45° degree line. The category “uneducated” include those with no, informal or incomplete primary 

education. 

 

30   

 

Figure 6. Change in food insecurity across 2005‐2008 by gender of the household head 

a. foodinsecurity1 

  b. foodinsecurity2 

  d. foodinsecurity3 

 

  Note: The ISO‐codes, listed in Table 4, are used to label the country data points; the dashed line is the 

45° degree line 

BJ

BW

CV

GH

KE

LS

MG

MW

ML

MZ

NA

NE

SE

ZA

TZ

UG ZM

ZW

-. 2

0 .2

.4 .6

% c

h a n g e s

h a re

o f

fo o d in

se cu

re a

m o n g m

a le

h e a d e d h

o u se

h o ld

s

-.2 0 .2 .4 .6 % change share of food insecure among female headed households

BJ

BW CV

GH KE

LS

MG

MW

ML

MZ

NA

NE

SE

ZA TZ

UG

ZM

ZW

-. 5

0 .5

1 %

c h a n g e s

h a re

o f

fo o d in

se cu

re a

m o n g m

a le

h e a d e d h

o u se

h o ld

s

-.5 0 .5 1 % change share of food insecure among female headed households

BJ BW

CV

GH

KE

LS

MG

MW

ML MZ

NA

NE

SE

ZATZ

UG

ZM

ZW

-1 0

1 2

3 %

c h a n g e s

h a re

o f

fo o d in

se cu

re a

m o n g m

a le

h e a d e d h

o u s e h o ld

s

-1 0 1 2 3 % change share of food insecure among female headed households

 

31   

 

Figure 7. Comparison of country‐wise changes in 2008 self‐reported food insecurity: AB 

versus GWP 

   

   

 

32   

Table  1. Afrobarometer sample observations across country‐years 

 

   2005  2008 

   obs  % rural  obs  % rural 

Benin  1,198  0.58  1,200  0.59 

Botswana  1,200  0.57  1,200  0.45 

Cape Verde  1,256  0.53  1,264  0.4 

Ghana  1,197  0.53  1,200  0.55 

Kenya  1,278  0.71  1,104  0.78 

Lesotho  1,161  0.66  1,200  0.74 

Madagascar  1,350  0.76  1,350  0.76 

Malawi  1,200  0.86  1,200  0.85 

Mali  1,244  0.73  1,232  0.73 

Mozambique  1,198  0.57  1,200  0.68 

Namibia  1,200  0.6  1,200  0.64 

Nigeria  2,363  0.51  2,324  0.51 

Senegal  1,200  0.59  1,200  0.55 

South Africa  2,400  0.39  2,400  0.34 

Tanzania  1,304  0.77  1,208  0.74 

Uganda  2,400  0.7  2,431  0.8 

Zambia  1,200  0.63  1,200  0.63 

Zimbabwe  1,048  0.68  1,200  0.63 

Total  25,397  0.62  25,313  0.62 

   

 

33   

 

Table 2. Share of respondents reporting having gone without enough food 

 

 % 

Total sample Rural Urban

2005  2008  2005  2008  2005  2008 

Never  47.3  45.4  41.3  39.3  57.1  55.2 

Just once or  twice  15.8  17.3  15.6  17.7  16.2  16.6 

Several times  19.6  21  21.4  23.3  16.6  17.3 

Many times  12.8  12.7  15.9  15.4  7.7  8.4 

Always  4.5  3.6  5.8  4.3  2.4  2.6 

Obs  25,342  25,254  15,676  15,635  9,666  9,619 

Column %  100  100  100  100  100  100 

 

   

 

34   

Table 3. Self‐reported food insecurity, binary variables 

 

    2005  2008  % 

change 

Panel A: food insecurity 1 (at least once)       

Total  0.53  0.55  3.6%    

Urban  0.43  0.45  4.4% 

Rural  0.59  0.61  3.4%    

Net food imports > 0% GDP  0.49  0.53  9.9% 

Net food imports < 0% GDP  0.55  0.55  0.4% 

Panel B: food insecurity 2 (at least several times)    

Total  0.37  0.37  1.4%    

Urban  0.27  0.28  5.2% 

Rural  0.43  0.43  ‐0.2%    

Net food imports > 0% GDP  0.34  0.36  4.7% 

Net food imports < 0% GDP  0.39  0.38  ‐0.3% 

Panel C: food insecurity 3 (at least many times or always)     Total  0.17  0.16  ‐5.2%    

Urban  0.10  0.11  7.8% 

Rural  0.22  0.20  ‐9.2%    

Net food imports > 0% GDP  0.14  0.16  11.4% 

Net food imports < 0% GDP  0.19  0.17  ‐13.0% 

 

   

 

35   

Table 4. Characteristics of sample countries 

 

 

   

 

Average 

annual GDP 

per capita 

growth 05‐08 

(%)

Net food imports as 

percent of GDP 

(average across 05‐08)

Total 

population 

in 2005 

(millions)

Average annual 

population growth 

05‐08(%)

ISO code 

Benin 0.9 2.6 7.87 3.2 BJ

Botswana 1.8 3.1 1.84 1.4 BW

Cape Verde 5.1 13.0 0.48 1.5 CV

Ghana 4.1 ‐9.9 21.92 2.1 GH

Kenya 2.5 ‐3.4 35.82 2.6 KE

Lesotho 3.5 19.2 c

2.00 0.9 LS

Madagascar 2.9 0.0 17.61 2.7 MG

Malawi 4.3 ‐9.8 13.65 2.8 MW

Mali 2.3 ‐0.4 11.83 2.4 ML

Mozambique 5.2 1.9 20.83 2.4 MZ

Namibia 2.5 ‐1.9 2.01 1.9 NA

Nigeria 3.5 2.3 140.88 2.4 NG

Senegal 1.4 4.5 11.28 2.6 SN

South Africa 3.3 ‐0.3 46.89 1.2 ZA

Tanzania 4.2 ‐3.6 39.01 2.8 TZ

Uganda 5.3 ‐4.8 28.70 3.3 UG

Zambia 3.4 ‐0.6 11.74 2.4 ZM

Zimbabwe ‐5.2 a

‐9.8 12.48 ‐0.1 ZW

Average 3.3 b

‐1.0 23.71 2.2

Notes: The country level variables are calculated using World Bank's WDI indicators.  a Data available only for 2005 b Zimbabwe excluded

c Data available only for 2000‐04

 

36   

  Table 5: Regression analysis 

    (1)  (2)  (3) 

Dependent variable:  foodinsecurity1  foodinsecurity2  foodinsecurity3 

Panel A: Accounting for individual‐level 12‐month recall period       

Mean (log) price past year  0.0691**  ‐0.0576**  ‐0.196***  (0.0278) (0.0283)  (0.0331)

Constant  Yes  Yes  Yes 

Observations  50,596  50,596  50,596 

Panel B: Analyzing the differential impact on urban and rural respondents 

Mean log price past year  0.137***  ‐0.102**  ‐0.322***  (0.0488) (0.0478)  (0.0526)

Urban  ‐0.611**  ‐0.992***  ‐1.652***  (0.295) (0.307)  (0.377)

Urban X Mean log price past year  0.0737  0.150**  0.284***  (0.0607) (0.0632)  (0.0776)

At least primary  ‐0.322  ‐0.957***  ‐1.452***  (0.296) (0.297)  (0.346)

At least primary X   ‐0.0150  0.109*  0.217***  Mean log price past year  (0.0608) (0.0611)  (0.0712)

Constant  Yes  Yes  Yes 

Country fixed effects  Yes  Yes  Yes 

Observations  50,470  50,470  50,470 

Panel C: Analyzing the effect of relevant country‐level variables 

Mean log price past year  0.345***  0.771***  ‐0.109  (0.127) (0.128)  (0.147)

Net food imports/GDP (%)  ‐0.184***  ‐0.146**  ‐0.149**  (0.0632) (0.0643)  (0.0754)

Mean log price past year X  Net food imports/GDP  (%)  0.0391***  0.0310**  0.0335** 

(0.0122) (0.0124)  (0.0145)

Per capita GDP growth  0.476**  1.337***  0.155  (0.189) (0.191)  (0.221)

Mean log price past year X Per capita GDP growth  ‐0.0968**  ‐0.275***  ‐0.0389  (0.0381) (0.0386)  (0.0447)

Constant  Yes  Yes  Yes 

Country‐fixed effects  Yes  Yes  Yes 

Observations  41,014  41,014  41,014 

Notes: Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 

 

 

 

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Table 6. Changes in food insecurity over the period 2005‐2008, taking population into 

account   

 

   Proportional change (%)  Changes in absolute numbers  

Population weighted  No  Yes  Yes  Yes 

Population growth taken into  account 

No  No  No  Yes 

Panel A: food insecurity 1 (at least once)          

      Total  3.6  ‐2.3  ‐5,620,035  9,990,000 

              

Urban  4.4  0.2  ‐84,856  7,930,000 

Rural  3.4  ‐2.9  ‐4,758,582  2,140,000 

Panel B: food insecurity 2 (at least several times)       

      Total  1.4  ‐6.8  ‐10,600,000  ‐752,000 

              

Urban  5.2  0.6  120,645  4,890,000 

Rural  ‐0.2  ‐9.0  ‐10,209,799  ‐5,770,000 

Panel C: food insecurity 3 (at least many times or always) 

              

Total  ‐5.2  ‐17.3  ‐12,200,000  ‐8,450,000 

              

Urban  7.8  ‐0.9  ‐176,667  1,480,000 

Rural  ‐9.2  ‐21.5  ‐11,653,631  ‐9,930,000 

Notes: In the scenario where population growth is assumed to be zero (column 3), the change in the  population‐weighted fraction of food insecure is multiplied by the corresponding population in 2005  (total, urban or rural) and summed over all countries. When population growth is taken into account  on the other hand, we take the difference between the share of food insecure in 2008 (derived using  population weights) times the population in 2008 (total, urban or rural) and the corresponding term  but for the year 2005.          

   

 

38   

Table 7. Comparison of country characteristics 

 

Average, 2002‐2008 (unless otherwise indicated)  In‐sample SSA  countries 

Out‐of‐sample  SSA countries 

Real GDP/capita (constant 2000 US$)  928.9  987.2 

GDP per capita growth (Annual %)  2.62  2.05 

Inflation, GDP deflator (Annual %)  18.04  10.07 

Net food imports/GDP (in percentage terms)  0.2  1.6 

Number of years of civil war 2002‐2008  7  37 

Population (2005, in million)   427  332 

Malnutrition prevalence, height for age (% of children under 5)  43.96  41.50 

Malnutrition prevalence, weight for age (% of children under 5)  21.96  26.85 

Depth of hunger (kilocalories per person per day) a   234.72  269.62 

Number of countries  18  29 

Number of low income countries  9  17  a  Mean of 2002 and 2006 (2007 & 2008 missing)                                   

SWINNEN-The Right Price of Food.pdf

Development Policy Review, 2011, 29 (6): 667-688

© The Author 2011. Development Policy Review © 2011 Overseas Development Institute. Published by Blackwell Publishing, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

The Right Price of Food

Johan Swinnen∗ Only a few years ago the widely shared view was that low food prices were a curse to developing countries and the poor. Their dramatic increase in 2006-8 appears to have altered this view fundamentally. High food prices are now judged to have a devastating effect on developing countries and the world’s poor – a reversal of opinion that raises questions about the old and the new arguments and the proposed remedies, and also about the causes of this dramatic turnaround in analysis and policy conclusions. This article puts these changes in perspective and discusses their potential implications. Key words: Food price, political economy, communication

1 Introduction

Only Socrates knew, after a lifetime of unceasing labor, that he was ignorant. Now every high school student knows that. How did it become so easy? What accounts for our amazing progress? (Bloom, 1987: 43)

In his famous book Getting Prices Right, Peter Timmer (1986: 13) posed the question ‘What is the ‘right’ price for an agricultural commodity?’. He goes on to argue that we can only determine the right price of food if we take account of a wide variety of the effects of prices on both efficiency and income distribution.1 This logic, obviously, assumes that we can in fact determine the impact of food on various groups in a country and across the globe. Somewhat surprisingly, the 2007-8 food crisis seems to have challenged this assumption. It has led to a wide set of reports and public statements analysing and suggesting remedies for the crisis. The puzzling thing about the post- crisis statements is that many seem to ignore pre-crisis analyses and convey a dramatically opposed view.

∗ LICOS (Centre for Institutions and Economic Performance), Deberiotstraat 34, Leuven 3000, Belgium ([email protected]). This research was supported by the KU Leuven Research Fund (Methusalem). Mara Squicciarini provided excellent research assistance. Kym Anderson, Rick Barichello, John Bensted-Smith, Luc Christiaensen, Tassos Haniotis, Tom Hertel, Michiel Keyzer, Andrzej Kwiecinski, Will Martin, Alan Matthews, Alessandro Olper, Scott Rozelle, Manohar Sharma, Kostas Stamoulis, Stefan Tangermann, Peter Timmer, Thijs Vandemoortele, Alan Winters, and participants in conferences in Brussels (CEPS), Berkeley (IATRC) and Campobasso (SIDEA) provided useful comments. The opinions expressed here are those of the author only.

1. Timmer (1986: 13) also argues that ‘[e]conomists have an easy answer to the question, but only in a world of perfect information, with competitive markets, without other government interventions … and without political concerns for the impact on income distribution’.

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Only a few years ago the widely shared view was that low food prices were a curse to developing countries and the poor. The following statement from the Food and Agriculture Organisation (FAO) on the state of world food markets and its implications for developing countries represented the common view as recently as 2005: ‘The long- term downward trend in agricultural commodity prices threatens the food security of hundreds of millions of people in some of the world’s poorest developing countries where the sale of commodities is often the only source of cash.’2 The dramatic increase in food prices in the following three years appears to have fundamentally changed this view of the food system. Reports in 2008 and 2009 state that high food prices have a devastating effect on developing countries and the world’s poor, a typical example being the following statement from the International Food Policy Research Institute:

In 2007, longstanding disruptions to the world food equation became widely

evident and rapidly rising food prices began to further threaten the food security of poor people around the world. … The current food-price crisis can have long-term, detrimental effects on peoples’ health and livelihoods, and can contribute to the further impoverishment of many of the world’s poorest people. (IFPRI, 2008:3)

This reversal of opinion – which, as I shall document in this article, was widespread – raises questions about the correctness of the old and the new arguments and about the proposed remedies, and also about the causes of this dramatic turnaround in analysis and policy conclusions.

This article reviews the positions of a variety of organisations active in the food- policy arena and also a series of hypotheses to explain their apparent change of views as reflected in their public statements. More specifically, it starts by presenting a simple framework to assess the welfare effects of food-price changes, and documents that many organisations have indeed changed their message, quite radically so. It then discusses some of the policy implications, and how they conflict with earlier arguments in food policy. The final section presents some hypotheses to explain the observed changes in food-policy arguments.

2 Food-price effects: some basic principles

Before reviewing analyses and policy statements, let us first present some basic principles on the effects of food-price changes. Presumably these are generally known but, given the variety of conclusions presented, it would seem useful to start by setting the framework.3 Consider first a simple model of an open economy with two groups, producers and consumers of food, where prices are determined on the world market with local production or consumption having no impact on global prices (i.e. the so- called small country assumption in international trade theory). In this situation, a change

2. FAO newsroom, ‘Agriculture commodity prices continue long-term decline’, 15 February 2005,

Rome/Geneva. http://www.fao.org/newsroom/EN/news/2005/89721/index.html 3. For more elaborated and sophisticated models see, for example, the textbooks on agricultural, food, and

development policy analysis of Gardner (1988), Houck (1986), Timmer (1986) and Sadoulet and de Janvry (1995).

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in world-market prices (caused by some external factor which is exogenous to the country) affects producers and consumers, but in opposing directions: consumers gain and producers lose from a decline in prices, and vice versa when prices increase.

To make this model more realistic one can consider several extensions. First, in reality the distinction between producers and consumers may not be so simple. Many rural households in developing countries are both producers and consumers of food and are thus affected in different ways by price changes. The net household effect depends on their net consumption status. Second, the change in world-market prices may differ from the change in the local prices and the latter may even differ for local producers and local consumers, as these changes are affected by various policies (trade policy, taxes, etc.), by infrastructure and institutions, and by the industrial organisation of the food chain. Third, local production and consumption may also affect local prices, in addition to exogenous external shocks. Fourth, the ‘exogenous’ shocks may be caused by nature (for example, the weather) or by humans (for example, changes in trade policies or consumption or production in other countries). Fifth, short-run effects may differ from long-run effects, as pass-through may take some time.

4 What is important for our purposes is that, first, all these extensions do not

fundamentally change the basic result of the simple model: when prices go up consumers lose and producers gain, and vice versa. Hence, when rich countries increase (reduce) export subsidies which leads to a decline (increase) in world markets, this will benefit (hurt) urban consumers and net consuming rural households in poor countries and hurt (benefit) net producing rural households in poor countries. The size of the benefits/losses, however, will depend on various factors, such as local policies, institutions, the food-chain organisation, time, etc.5

Second, the net benefits of price increases and decreases for a country should be roughly symmetrical. Countries that benefit most from price decreases (for example, if they consume lots of food but produce little) will lose most from price increases. The same holds at the household level within a country. Households which only consume food and do not produce food will be affected more severely when prices change than those which both produce and consume food. Another implication is that households which are directly affected by world-market prices will gain or lose more than those living in areas largely isolated from market transactions when world prices change.

A straightforward implication of these basic principles is that low food prices on the world market in most of the pre-2005 period benefited consumers and hurt farmers in developing countries, and vice versa in the 2006-8 period. Another implication is that households which suffered severely in 2007 from high food prices (for example, they lived in urban market centres and produced little food themselves) would have benefited significantly from low food prices prior to 2005. Inversely, some rural households may not have benefited (much) from the high prices in 2007 (for example, because they live

4. There are more factors that would need to be taken into account in a truly complete model. For example,

not only ‘exogenous’ shocks will affect producers and consumers, but also ‘endogenous’ price changes, with the latter caused, for example, by faster productivity growth in agriculture. In addition, one would have to consider general equilibrium effects (considering not just food-market effects but also effects through/on markets for labour, capital, services, other inputs and outputs). For example, as other prices (energy, fertiliser, etc.) change together with food prices, this may need to be taken into account as well.

5. In extreme cases the size of the effects could actually be reduced to zero.

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in remote places with poor pass-through of prices from the world market, or because they consume all their food production themselves). These rural households would also have experienced limited negative welfare effects from the low food prices prior to 2005.

Surprisingly, however, while these basic principles are well known, we do not find them reflected in most arguments put forward in the food-policy debate. For example, there has been hardly any mention of the benefits of low food prices for urban consumers and net consuming rural households during the pre-2006 low price era, and there has been very little emphasis in more recent statements on the benefits for producers in poor countries from high food prices.

3 180° turnaround in food-policy analysis and communication Before trying to understand why this is the case, let us first document that this was indeed the case, that is, that there are conflicting analyses and communications prior to 2006 and thereafter, and that there is a lack of consistency in analysis and policy recommendations. Our claims are documented by a series of quotations from various organisations’ own communications of analyses and policy recommendations. We shall then discuss whether these quotes are representative and address the criticism that this approach may not be appropriate by taking quotes out of their context. 3.1 Analyses from NGOs To begin, let us take a look at statements from some of the non-governmental organisations working in the area of food policy before and after the food crisis. In 2005, Oxfam International argued that: ‘The US and Europe[‘s] [s]urplus production is sold on world markets at artificially low prices, making it impossible for farmers in developing countries to compete. As a consequence, over 900 million farmers are losing their livelihoods’ (Oxfam International, 2005, emphasis added.) Three years later, at the height of the food crisis, its view is that: ‘Higher food prices have pushed millions of people in developing countries further into hunger and poverty. There are now 967 million malnourished people in the world’ (Oxfam International, 2008, emphasis added). To put it simply: this organisation claims that whatever happens to prices – either decreasing (pre-2006) or increasing (post-2006) – hundreds of millions of people will end up in poverty.

Other NGOs share this analysis: prior to 2006 they claim that low food prices are hurting the poor and creating food insecurity; after 2006 they claim that high prices are hurting the poor and leading to food insecurity. To illustrate this, compare the following statements from the Bread for the World Institute. In their 2005 annual report they write: ‘The agricultural trade and subsidy policies in the United States, European Union and Japan are harming poor people in developing countries. The harm done by far exceeds the good done by development assistance … The net result is continuation of poverty, hunger and related misery’ (Bread for the World, 2005: 44).

In 2009 the annual report of the same organisation states: ‘Food prices are soaring worldwide. For the world’s poorest people in developing countries – who spend up to

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80 percent of their income to buy food – the situation is even more devastating.’6 The contradiction is obvious. One justification for the statements could be that they refer to different groups in society (farmers in one case, urban consumers in the other). However, the lack of emphasis on this and the absence of recognition that other groups may benefit are striking.

These are not even the most extreme examples. In several cases the excuse that the conflicting arguments are due to focusing on different groups (while selectively ignoring others) cannot be used. For example, consider the following statement from Oxfam Solidarité from as recently as 2006: ‘The prices of products traded on the world markets are too low and do not allow the majority to live decently … As a consequence of this competition at low prices, local prices fall, worsening poverty … This causes poverty, migration and malnutrition.’7 However, after the food crisis, the same organisation claims that: ‘The FAO predicts a new price increase in 2009. This crisis of agricultural prices affects in the first place the poorest populations, mostly rural, which spent more than half their revenues to feed themselves.’8 This organisation thus argues that the same group (poor rural people) is hurt by low prices (in 2005) and by high prices (in 2009).

One explanation for these observations could be that one should not expect anything else from NGOs. One may argue that, after all, these are advocacy groups and their primary objective is not to provide objective and carefully balanced analyses, but rather to raise attention to a problem and to put pressure on governments to do something about it, or to raise funds for their own projects.9 3.2 Analyses from international organisations Let us therefore next consider the views of international institutions which are not (expected to be) advocacy groups but which are expected to provide analyses and recommendations to enhance social welfare, such as the FAO, IFPRI, the OECD, the IMF and the World Bank. Interestingly, these institutions seem to have adjusted their analyses and policy communications in a way similar to NGOs. For instance, in 2005, the FAO issued the statement quoted in the Introduction to this article, and then in 2008 its Director-General declared that: ‘The number of hungry people increased by about 50

6. http://www.bread.org/learn/rising-food-prices/ or http://www.breadblog.org/hunger_in_the_news_1/ 7. Own translation. The original statement: ‘Les prix des produits échangés sur les marchés internationaux

sont trop bas et ne permettent plus à la majorité de vivre décemment … Pour faire face à cette concurrence à bas prix, les prix locaux chutent, aggravant la pauvreté … S’en suit alors pauvreté, exode et malnutrition’ (OXFAM Solidarité, Les revendications, 21 November 2006 http://www.oxfamsol.be/fr/ Les-revendications,723.html).

8. Own translation. The original statement: ‘[L]a FAO prévoit une nouvelle hausse des prix en 2009. Cette crise des prix agricoles affecte en premier lieu les populations les plus pauvres, en majorité rurales, qui dépensent plus de la moitié de leurs revenus pour s’alimenter’ (OXFAM Solidarité, Agriculture: le G8 doit changer de cap!, 20 April 2009. http://www.oxfamsol.be/fr/Agriculture-le-G8-doit-changer-de.html).

9. For economic models of NGOs, see for example, Aldashev and Verdier (2010), Andreoni and Payne (2001), Chau and Huysentruyt (2006); for an analysis of ‘what NGOs do’, see for example, Werker and Ahmed (2008). See also later in this article.

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million in 2007 as a result of high food prices’,10 and its Assistant Director-General that ‘Rising food prices are bound to worsen the already unacceptable level of food deprivation suffered by 854 million people. We are facing the risk that the number of hungry will increase by many more millions of people.’11

We can also compare the statement from the IFPRI 2002-3 Annual Report (p.22) that: ‘The combination of agricultural protectionism and subsidies in industrialized countries has limited agricultural growth in the developing world, increasing poverty and weakening food security in vulnerable countries’ with that from the 2007-8 Report quoted in the Introduction.

Similarly, before the food crisis, reports from the OECD, the World Bank, and the IMF discussing the effects of trade liberalisation for developing countries typically claim that liberalisation will help the poor by increasing world prices as rich countries cut their agricultural subsidies. This is illustrated by the following quotes from the OECD, the World Bank and the IMF, respectively:

Many (developed countries) continue to use various forms of export subsidies

that drive down world prices and take markets away from farmers in poorer countries … Much of this support depresses rural incomes in developing countries while benefiting primarily the wealthiest farmers in rich countries.12

The combination of depressed world prices and developing country policies which tax agriculture relative to industry have discouraged farm output and hence lowered rural incomes. Because the majority of the world’s poorest households depend on agriculture and related activities for their livelihood, this … is especially alarming. (World Bank, 1990: 19)

The numbers leave no room for doubt. Industrial country protectionism in the agricultural sector inflicts considerable hardship on the citizens of developing countries.13

In contrast, during the food crisis of 2007-8, these three organisations, like NGOs and the FAO and IFPRI, communicate very different effects of food prices. This is again illustrated by a number of quotes from the OECD, the World Bank and the IMF, respectively:

10. FAO Director-General Jacques Diouf, European Parliament Conference, Brussels, 3 July 2008

http://www.fao.org/newsroom/EN/news/2008/1000866/index.html 11. FAO Assistant Director-General Hafez Ghane, Rome, May 2008, http://www.fao.org/newsroom/

EN/news/2008/1000845/index.html 12. OECD, ‘Cancún and the Doha Agenda: The key challenges’, press release for conference 10-14 September

2003. (Also in the Declaration by the Heads of the IMF, OECD and World Bank, 4 September 2003, http://www.bfsb-bahamas.com/photos/old_images/Declaration.pdf).

13. IMF, ‘Agricultural Trade Reform: The role of economic analysis’, 3-4 November 2004, http://www.imf.org/ external/np/speeches/2004/110404.htm

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When food prices skyrocket this can quickly pose a threat to the lives of the poorest, particular in developing countries.14

The situation (high food prices) … could set back welcome progress in many

developing countries towards growth, development and poverty reduction … poor people, particularly those living in urban areas, are already suffering.15

The increase in food prices represents a major crisis for the world’s poor.

(World Bank, 2008a:1) Preliminary estimates suggest that up to 105 million people could become

poor due to rising food prices alone. (World Bank, 2008b: 4)

Millions of consumers could fall into extreme poverty due to higher food

prices, and millions more already under the poverty line are likely to experience a further deterioration in their living standards. (World Bank, 2009: 23)

The rapid increase in food prices has had an adverse impact on poverty, and

effectively denied many poor people access to food.16 In summary, (virtually) all the major international organisations that focus on food

and agricultural policy issues globally have shifted from emphasising how low food prices, often argued to have been caused by rich-country agricultural policies, caused poverty and food insecurity in developing countries in the pre-2006 period to emphasising that high food prices cause poverty and food security in the post-2006 period, without mentioning the benefits (in either period).17 3.3 Out of context? An obvious critique of the arguments being made here is that I am making false claims by taking statements out of context and selecting just one element of a broader and more complex message, and that the full analyses are more complete and nuanced. It is of course true that these quotes are taken out of their context – that is why they are quotes to begin with – and that reading the full documents may provide more nuance.

14. OECD, ‘Ensuring Food Security for the World’s Poor: Questions and Answers’, 7 May 2009,

http://www.oecd.org/document/50/0,3343,en_2649_37401_42666830_1_1_1_1,00.html 15. ‘Rising Food Prices and Developing Countries’, Speech by Angel Gurría, OECD Secretary-General, 21

May 2008, http://www.oecd.org/document/11/0,3343,en_2649_33721_40651723_1_1_1_1,00.html 16. IMF, ‘Food Security and the Increase in Global Food Prices’, Speech by Mark Plant, IMF Deputy

Director, Policy Development and Review Department, 19 June 2008, http://www.imf.org/external/ np/speeches/2008/061908.htm

17. The food crisis itself also affected the communication on other policy issues. For example, for most of the 1980s and 1990s, biofuels and biochemicals were seen as a potential source for enhancing farm incomes. As an alternative outlet for agricultural commodities, they were seen as potentially providing an opportunity to stop the long-run downward trend in prices for farmers. This perspective has changed totally with the recent food crisis to the extent that biofuels have been called ‘a crime against humanity’ by a UN special rapporteur on food in 2007.

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However, I would still argue that these quotes represent quite accurately the key arguments in the context of the current debate. First, in the vast majority of cases they summarise quite well the key message of the reports. This argument is particularly important because ‘key messages’ are crucial for these organisations, as target audiences (political decision-makers and the general public) have no time to read long reports. Therefore, the organisations spend effort and time in developing ‘key messages’ and their communication strategy is typically focused on such messages. The rest of the policy document is typically to substantiate, but not to deviate from, the key message.18 Hence, the quotes as I have listed them here do represent the key arguments made.

Second, even if one reads the full report and takes on board all the nuances, there remain striking differences in the pre- and post-2006 analyses and conclusions. For example, in very few of the reports published before the recent food-price increases is there any mention of the fact that urban consumers in developing countries benefit (and the few that do mention it do not consider it a major element). Nor is the argument made that many poor rural households are net consumers, and may therefore benefit from low food prices.

Yet, the (mirror versions of these) arguments are emphasised strongly in all the post-2006 reports, with all the attention going to the losses of these two groups. Paradoxically, at the same time very little attention is paid to benefits for poor farmers. Both observations are in total contrast with the pre-2006 arguments.

3.4 Do poor farmers benefit from high food prices?

A potential justification of this bias in focus is that poor farmers were hurt by low agricultural prices before 2006 but that consumers did not benefit from low food prices, and that during the 2006-8 period of high prices poor farmers did not benefit from them but that poor consumers did get hurt. It is well known that in developing countries there are a variety of market imperfections and transaction costs which may influence the extent to which consumers and producers are affected by price changes. Some have used such arguments to claim, for example, that consumers were strongly negatively affected by the food crisis, while prices for farmers increased very little, if at all.

I do not find these arguments convincing. First, if farmers in rural areas are not (much) affected by the high prices, then how can one argue that many poor rural households are negatively affected by the price increases since they are net consumers? If prices do not benefit (net producing) farmers, they should not harm net food- consuming households living in the same rural areas.

Second, the problems of imperfect pass-through of prices between farms and consumers (domestic or international) are of course not new and not restricted to a period of high prices. These problems are due to a combination of institutional, policy, and infrastructural constraints. If they are important – as they are in many developing countries – they should also have limited the pass-through of low prices to farmers in

18. In fact, any academic researcher who starts working for such organisations is reminded from day one to

move complicated analyses and sophisticated messages to the appendix and the footnotes and to focus on bringing out the key messages in simple, easy to understand, sentences. I have extensive personal experience of this.

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the pre-2006 period. Continuing the same logic, since urban consumers would have been more directly affected, this should then have caused more positive aggregate (net) effects of low food prices than was generally argued in the pre-2006 period.19

Finally, one may argue that the price impacts were of a different nature (structurally low for a long period before 2006 and rapid increases (‘shocks’) in 2007-8) and that therefore the impact would be different. However, many of the arguments in the communications do not refer to the speed or extent of price changes, but are really about structural factors and how they imply certain effects. Hence, while volatility is an important policy issue in itself, it does not diminish the arguments presented here.20 4 Policy implications (what’s the problem?) An explanation of these observations could be that the objective of the organisations is to assist those in need, that is, those who are negatively affected by shocks. When prices fall (are low) farmers are negatively affected, and aid and policy focus should therefore be targeted on them. When prices rise (are high) consumers are negatively affected and they should attract most policy attention and aid. Hence, when conditions change, attention will shift from one group to another depending on how they are (relatively) affected, i.e. who is benefiting or losing from the change. If this is indeed the case, instead of worrying about this, one may instead appreciate the change in policy attention and the refocusing of international organisations on those who are in need. Hence: what’s the problem?

The problem is the policy messages that are being communicated and recommended, and the fact that they seem to ignore that there are always winners and losers. In addition, one should expect a good policy framework to be coherent, and relevant and correct both when prices go up and when they go down. To illustrate the importance of the analyses of NGOs and international organisations for actual policy debates and choices, let us refer to some ongoing policy debates. 4.1 Export restrictions and the WTO During the pre-2006 period attention was focused on policies bringing world-market prices down: import tariffs, production and export subsidies, etc.; recommendations were to remove policies such as rich-country export subsidies (‘dumping’) which pushed prices down on international markets. This has changed importantly with the

19. Additional arguments are that farmers may not have benefited because their costs (in particular for

fertilisers and energy) went up by more than the price of their output (food). This is an important point, but to draw conclusions one should take into account the extent to which poor farmers rely on external inputs.

20. This is also reflected in a related but distinct discussion on the role of governments in stabilising volatile food markets. The European Commission emphasises the importance of staple subsidies to farmers in a volatile market environment. Timmer (2009) discusses the trade-off in domestic benefits for 3 billion rice consumers in Asia (as countries such as China, India and Indonesia have isolated their rice economies from the recent turmoil in global markets through trade and domestic policies) versus the resulting increase of market instability for 500 million rice consumers in the rest of the world – in particular in Africa and other poor countries.

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price increases. Policy attention and communication have shifted to removing (or restricting the use of) export restrictions in food-exporting countries, with proposals to constrain tariffs, bans, quotas, etc. under the WTO rules. However, there are also costs and benefits here. Preventing these governments from using such export restrictions would hurt farmers in food-importing countries and food consumers in food-exporting countries. In fact, export restrictions in food-exporting countries with large numbers of poor people (such as India and Thailand) have been blamed by international organisations for hurting poor consumers in food-importing countries. However, they have benefited poor consumers in food-exporting countries – and some experts (for example, Timmer, 2009) have argued that these governments have indeed made the right policy choice. Similarly, policy recommendations intended to help developing- country farmers prior to 2005 – such as cutting the European Union’s export subsidies – typically ignored that they would hurt consumers in developing countries. 4.2 Back to the future or forward to the past? The old CAP as a model for

global food security?

The considerations explained above have led me to argue ironically21 that those who believe that poor farmers in developing countries are not affected by changes in world- market prices and that poor countries’ consumers are affected severely by the high global food prices should maybe recommend the EU (and the US) to reverse their agricultural policy-reform efforts of the past 20 years and re-install their old policies. In the EU’s case this means decoupling agricultural subsidies and re-installing the old Common Agricultural Policy (CAP) with its high intervention prices, import tariffs and export subsidies. Under this system, EU farmers were protected from imports by high tariffs and received prices much higher than on the world market, thus stimulating EU agricultural production and surpluses. World prices were pushed down by the import tariffs and the export of surpluses with export subsidies. My ironical claim was that everybody would then presumably be better-off: European farmers would love it since they returned to their high price system; European taxpayers would no longer have to pay decoupled farm payments; poor-country consumers would have low prices again; and developing-country farmers would not care since they were unaffected by price changes on the world market – a Pareto improvement if ever there was one!

22 After generations of economists have dismissed the old CAP as highly distortive –

both domestically and internationally – and have asked for its reform or outright rejection, and after almost twenty years of CAP reforms that have substantially reduced the trade distortions, I presumed the irony was obvious and the implications clear. A much more balanced and nuanced set of analyses and policy recommendations is needed which recognises both the costs and benefits of price changes and policy actions.

21 Including once in a World Bank informal sector group discussion and an FAO expert workshop. 22. To be fully Pareto improving, one should also consider the negative impact on EU consumers. However,

in the same ironical logic, since they are supposedly rich (actually many are not) and since price declines are typically assumed to be captured by the big retail chains (actually they are not), this would probably not be considered an issue by those following this logic.

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Yet, the irony is not obvious to everybody apparently. Instead, some have embraced the new policy focus and communications for advocating certain policies. For example, inside the EU various groups have started using ‘food security’ (including global food security) as an argument to defend the 50 billion of subsidies paid each year to EU farmers from the EU budget. In fact, the main EU farm lobby (COPA- COGECA) and the EU association of landowners (ELO) argue that food security should be a key motivation for continuing the subsidisation of EU agriculture in the future.23

Moreover, recently the United Nations explicitly praised the old CAP as a model: ‘While the establishment of the EU Common Agricultural Policy (CAP) in 1962 had “many negative externalities”, … the policy is a good example of how to achieve food security in a given area.’24 That the old CAP raised EU food prices, thereby hurting urban consumers and thus lowering food security in the ‘given area’ and that the ‘negative externalities’ have been attacked by all international organisations (literally from ‘left’ to ‘right’, i.e. from Oxfam to the IMF) as being detrimental to developing- country farmers – see all the pre-2006 quotes above as illustration – does not seem to be a major concern to those making such statements.

4.3 Land grabbing and headline grabbing

The issues discussed here are relevant beyond the food-price debate. The analysis and policy communication on issues such as the effects of biotechnology, foreign investment in developing countries, including the so-called ‘land grabbing’ debate, and the supermarket revolution, etc. have been influenced by similar factors. A striking example is the recent debate on foreign investment in land in Africa, which has been captured by the term ‘land grabbing’ – a concept which in itself emphasises the potentially negative implications. This is somewhat remarkable, given the empirical evidence on the huge benefits that farmers in other parts of the world have gained from foreign investments in the food system. In fact, foreign investment in the agri-food system has been a crucial factor behind the post-1995 growth in agricultural productivity and performance in Eastern Europe, with major positive spillovers for small and large farms (Dries and Swinnen, 2004; Gow and Swinnen, 1998; Swinnen, 2002), and similar positive effects exist at least in some sectors and regions in Africa (Maertens et al., 2011).

However, from a media strategy and communications perspective, coining the process by the term ‘land grabbing’ has been a remarkable success as the term is now widely used to describe the process and its risks.

25 The potential problem, of course, is

23. Statement by Mr Pekkonen, DG of COPA-COGECA, during a debate in Brussels, 19 November 2009 (at

the launch of the economists’ CAP reform call), and various ELO reports. 24. UN special rapporteur on the right to food, Interview with EurActiv, 26 November 2009

(www.euractiv.com). 25. Some examples of reports by international organisations and media which have picked up the concept:

Cotula, L. et al. (2009) Land Grab or Development Opportunity? Agricultural investment and international land deals in Africa. London/Rome: FAO, IIED and IFAD; Von Braun, J., and Meinzen- Dick, R., (2009) ‘“Land Grabbing” by Foreign Investors in Developing Countries: Risks and Opportunities’, IFPRI Policy Brief 13, April; FAO (2009) ‘From Land Grab to Win-Win’, FAO Economic and Social Perspectives, Policy Brief 4, June, ftp://ftp.fao.org/docrep/fao/011/ ak357e/ak357e00.pdf;

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that, with such a negative connotation, it has become much more difficult to communicate an unbiased evaluation of the benefits and costs, the pros and cons, of foreign investment in land in Africa. If evidence were to show that such investment would be beneficial for the local population, it is now certainly more difficult to overcome opposition to ‘land grabbing’.

4.4 Conclusion In summary, even if the objective of NGOs and international organisations is to assist those who are negatively affected by food-price changes, this is no excuse for exclusively simplistic policy analyses and conclusions. Quite the contrary; also the policy choices are then difficult and involve trade-offs, and would benefit from careful analyses and nuanced messages.

An important question, of course, is to what extent this bias in focus and communication is affecting policy-making, and ultimately welfare and development. The answer is difficult since it depends on various factors, such as on how the communication and policy advice of organisations differs (see next section); on the processing of these sets of information by voters, policy-makers and the organisations themselves; on the type of welfare function one has in mind; and on the political economy of policy decisions – at various levels. In addition, it is empirically difficult to measure such ‘impact’ in actual decision-making. However, observations on current policy debates – as illustrated above – do suggest that a bias in the analysis and the policy messages does influence policy-making, and, thus, welfare and development.

5 The political economy of (food) policy analysis and

communication This section discusses some potential explanations – in addition to the arguments made earlier (in Section 4) – for the puzzling observations outlined above. 5.1 Scientific progress (analysis vs communication) Maybe the simplest explanation is that the analyses and arguments in the past were wrong, and the recent food crises, in combination with improved economic modelling and better data, have contributed to better analysis and improved insights. There certainly has been significant progress in economic models and data to measure the impact of global price changes and policies on developing-country households.26 The simulation results of the most recent models are more reliable, more precise and more

Cotula, L. and Vermeulen, S. (2009) ‘“Land grabs’ in Africa”: can the deals work for development?’, IIED briefing, September; Shepard, D. and Anuradha, M. (2009) ‘The Great Land Grab. Rush for the World’s Farmland Threatens Food Security for the Poor’, Oakland, CA: The Oakland Institute, http://www.oaklandinstitute.org/pdfs/LandGrab_final_web.pdf; Borger, J. ‘Rich Countries Launch Great Land Grab’, Daily Nation, 12 January 2009; Brandford, S., ‘Food Crisis Leading to an Unsustainable Land Grab’, The Guardian, 22 November 2008.

26. There are a series of improvements in data and models in this area, including in models run by the OECD, FAO, IFPRI, GTAP, the World Bank, etc.

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detailed in their impact assessments. For example, recent studies based on the integration of global trade models and household data come to very nuanced conclusions on the effects of liberalisation and price changes. Hertel et al. (2007) find that rich countries’ reduction of export subsidies and domestic support, on average, increases poverty in their sample of 15 developing countries because these reforms raise world prices for staple foods, including wheat, maize, dairy products and rice. At the same time, rich countries’ tariff reductions reduce poverty in developing countries because they improve farm revenues. The net effect of liberalisation in their analysis is a reduction in poverty.27

However, the issue is not the outcomes of the models, but instead the communication of their results and the policy messages that have been derived from them. In the same way that benefits for poor consumers from low market prices have not been emphasised in the past,28 benefits for poor farmers from high prices are not emphasised now. In fact, several organisations published analytical reports with detailed findings and carefully nuanced interpretations and conclusions around the same time as their communication departments released communications on the food-price issues which demonstrated the shift in emphasis (bias) documented above.29 For example, in the light of the careful modelling work and analyses of trade liberalisation which, among others, Kym Anderson, Tom Hertel, Will Martin, Alan Winters and their colleagues at the World Bank have carried out over the years,30 consider two major World Bank reports intended for wide distribution, one after/during the food crisis (the 2008 World Development Report) and one before the food crisis (the Bank’s 2002 Rural Development Strategy, Reaching the Rural Poor) and what they communicate in their overview and executive summaries on trade policy.

The 2008 World Development Report (Overview, p.10) states that in developing countries ‘liberalization of imports of food staples can also be pro-poor because often the largest number of poor, including smallholders, are net buyers. But many poor net sellers (sometimes the largest group of poor) will lose …’ The emphasis is on how low prices benefit the poor. Better than many reports of other organisations, it also explicitly recognises the losses for households/farms that are net sellers.

Now compare this with the 2002 Rural Development Strategy report (Executive Summary, p.xvii):

27. See also the interesting exchange on this issue between Dani Rodrik, Tom Hertel and Will Martin on Dani

Rodrik’s weblog. 28. Very few pre-2006 studies emphasise the benefits of low food prices for the poor. Note also that many

model runs of trade liberalisation in agriculture show that the impact for Africa is negative, precisely because Africa is a net consuming region and is benefiting more from low food prices (as consumer) than it is losing (as producer).

29. See, for example, Anderson et al. (2010), Christiaensen and Demery (2007), Hertel and Winters (2006) and the 2008 World Development Report, all published by the World Bank, Sarris and Morrison (2010) published by FAO and the policy analyses in various OECD reports on the state of agricultural markets and policies over the past decade.

30. For surveys and overviews of model improvements and their insights, see Anderson and Martin, (2007); Anderson et al. (2010); Ivanic and Martin, (2008); Hertel et al. (2007), (2009); Hertel and Reimer, (2005); Winters et al. (2004).

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A major reason both for the limited growth of agricultural trade and for the

inability of developing countries to enlarge their share of this trade is high protection in the large markets of the industrial world. High subsidies and other forms of trade protection impair developing countries’ ability to compete in global markets with farmers from the industrial world. They also encourage surpluses that have been sold on world markets, depressing world prices and undermining the potential contribution of agriculture to global prosperity … It is crucial that the industrial countries liberalize their agricultural markets by removing access for developing countries’ products and by phasing out subsidies.

The argument and emphasis here are very different. There is no mention of a difference between staple foods and other agricultural commodities. The entire message is about how depressed world-market prices (and rich-country subsidies) hurt developing- country farmers. There is no mention whatsoever of the benefits for consumers, or how a reduction in rich-country export subsidies would benefit the poor urban or rural net consumers. (And neither is there in the rest of the report.) I would argue that these statements, taken from two major strategic reports of the World Bank, are fully consistent with the argument I am making in this article.

In summary, the problem does not appear to be (lack of) scientific progress or quality of analysis, but the interpretation and communication of the results of the scientific studies. In fact, some colleagues involved in research in or for these organisations – when confronted with the arguments made in this article – reacted that they sometimes barely recognised the relationship between their analytical work and the policy messages sent by the communications departments to the outside world and the media. They wondered where, when, and why the policy nuances and careful analytics had been left behind.

5.2 Urban bias and relative incomes For decades the poor situation of African farmers has been caused at least in part by policies which were said to be ‘urban-biased’, i.e. favouring urban interests and to the detriment of rural farmers through (implicit) taxes. This, in fact, was one of the main conclusions from the famous study by Krueger et al. (1992) of the World Bank, which contributed to the motivation for structural adjustment programmes in the 1990s. These programmes have contributed to reducing taxation of developing-country farmers, as documented by the recent World Bank study led by Kym Anderson (Anderson, 2009).

The 2007-8 food crisis has led to a surge in attention to food policy caused by pressure from urban interests.31 As soon as urban protests reached the streets and the media, international organisations reacted much like local politicians and paid a disproportionate amount of attention to the problems of urban consumers. There are a variety of explanations for the urban bias in developing countries. Urban consumers, when hit by a negative relative income shock, such as an increase in food prices, will

31. See Hendrix et al. (2009) and Maas and Matthews (2009) for empirical political economy analyses of the

determinants of protests and riots against food-price increases.

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react politically, for example, through demonstrations.32 Since they are concentrated in cities and are easier and cheaper to mobilise than farmers dispersed in distant rural areas, they may receive disproportionate attention and policy favours from policy- makers.33 It may be that a similar urban-bias effect plays a role in drawing reactions and policy attention from international organisations, for example, through global media markets.

5.3 Fundraising and legitimacy If one wants to help the poor or stimulate development, funding is needed. NGOs need to invest in fundraising activities in an environment where various NGOs compete for attention and funding from donors (see, for example, Andreoni and Payne, 2003; Rose- Ackermann, 1982). In this perspective, the statements listed above could be interpreted as part of a marketing strategy by NGOs.

While academic analysis has focused on NGOs, the general argument to focus on the costs and ignore the benefits of price changes as a marketing strategy may apply more widely.34 All international organisations, whether NGOs or IFPRI, the World Bank, the FAO, to some extent use funds from public or private donors to operate and implement their projects – or subgroups within these organisations have to compete internally for funding. While their funding sources may differ, in a world where financial means are limited and where there is continuous pressure to demonstrate the relevance and importance of spending on particular items, projects or divisions within large organisations, all these organisations face a demand to demonstrate the importance of their work. Focusing their reports and analyses on those hurt by price changes may fit in such a strategy and thus help in securing and raising funds.35

A closely related argument is that, with mass media reports focusing on those hurt by changing food prices – in particular consumers post-2006 – the donor community, the organisations’ shareholders, and the public at large may expect (or even demand) that these organisations also focus their attention on them. If they do not react publicly to the problems reported, their legitimacy as development organisations will be damaged. This could undermine overall support for their existence.

For some organisations discussed here, the objective is directly linked with addressing negative welfare consequences. Others, however, should be expected to focus more on the overall (aggregate) welfare effects. Hence for the first group of organisations, the incentive to bias their message may be stronger, both for fundraising purposes and for their legitimacy.

32. This shift in policy attention reflects the relative income effect, emphasised by, for example, de Gorter and

Tsur (1991), Swinnen and de Gorter (1993) and Swinnen (1994). 33. The organisation cost argument was made first by Olson (1965) and has been applied to agricultural and

food policy by, for example, Anderson and Hayami (1986) and Gardner (1987). 34. Most academic research on the behaviour of international organisations has focused on their lending

strategies and less on their communication or fundraising strategies (see, for example, Aldenhoff (2007); Dreher et al. (2009), Vaubel et al. (2007)).

35. See Swinnen et al. (2010) for a formal political economic analysis of this process.

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5.4 Raising attention with (semi-)consistent policy advice Another argument is that, while all these organisations may have changed their communications – as is obvious from this article – they may not have changed their basic policy advice. Instead, they may use the price shocks to attract the attention of donors and governments to emphasise the importance of the policy package they are advocating. In this perspective, one should expect organisations which see global underinvestment in agriculture as a problem to put investment forward as a remedy, whether prices are high or low – and just use somewhat differently framed arguments to make their point. Similarly, one should expect organisations which believe in the benefits of free markets and those which believe in government regulation of markets to emphasise these respective benefits, whether prices are high or low.

Analysing this argument thoroughly would require a more elaborate empirical analysis than is possible in the framework of this article, but a preliminary evaluation of the policy recommendations of the organisations whose communication shift we documented earlier suggests that the evidence on this is mixed (see Squicciarini and Swinnen, 2011 for more details). It is indeed the case that most organisations have emphasised the importance of investing in agriculture, but have done so much more strongly in the post-2007 period than before. It is also true that organisations like the World Bank, the IMF and IFPRI continued to emphasise the importance of trade liberalisation and of concluding the Doha Round both before and after 2006, while Oxfam has continued to recommend the cutting of rich-country subsidies and the importance of government regulation of poor countries’ agri-food markets. However, there is also evidence that the emphasis put on specific policies has changed considerably pre- and post-2006. In addition, as explained above, the focus on trade policies shifted from removing import constraints to removing export constraints. Hence, it appears that the organisations have partly maintained their core policy message and partly adjusted it.

6 Mass media and policy communication The arguments above already point to the important role of the media in inducing organisations to act, either in order to preserve their legitimacy, or to raise funds, or as a consequence of pressure from the public at large or their stakeholders. There are two important, but distinct, mechanisms at work in the interaction between these organisations and the mass media.36 The first is the impact of stories that appear in the mass media on the actions (analysis and policy focus) of the organisations. The second is the desire of the organisations to appear in the mass media in order to achieve their objectives.37

36. A rapidly growing literature documents other effects of the mass media on development, such as their

effect on political accountability (e.g. Besley and Burgess, 2001; Djankov et al., 2003) and their impact on reducing corruption in public policy (Francken et al., 2008; Reinikka and Svensson, 2005).

37. The latter is analysed in detail by Cottle and Nolan (2007) who conclude that ‘aid agencies have become increasingly embroiled in the practices and predilections of the global media and can find their organizational integrity impugned and communication aims compromised. These developments imperil

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Several characteristics of the mass media are relevant to explain these mechanisms (McCluskey and Swinnen, 2010). First, the agenda-setting effect of the media in international and aid policy has sometimes been referred to as the ‘CNN factor’ (Hawkins, 2002). It refers to the process by which the media influence policy by invoking responses in their audiences through concentrated and emotionally based coverage, which in turn applies pressure on governments to react. Similarly, the absence of media coverage reduces priority in agenda-setting (Jakobson, 2000). According to this logic, public officials react to media news because they see it as a reflection of public opinion (Kim, 2005).38

Several studies have analysed the impact of media coverage of poverty, humanitarian crises, and natural disasters on humanitarian and foreign aid flows. Van Belle et al. (2004) and Kim (2005) find that a higher level of media attention to developing countries’ problems leads to more aid in a number of developed countries. Eisensee and Strömberg (2007) argue that disaster relief decisions and aid allocations are driven by media coverage of disasters, but that other newsworthy events may crowd-out this news coverage.

Second, media attention is typically concentrated on ‘events’ or ‘shocks’ (Swinnen and Francken, 2006).39 Hence, sudden changes with dramatic effects, such as the 2008 food crisis, not only present important challenges to the international organisations in addressing these, but also important opportunities for development organisations to capture media attention and signal their relevance and importance to their donors and the public.

A third factor is that the public at large will be more interested in media reports concentrating on negative (development) effects. This follows from the so-called ‘bad news hypothesis’. Media consumers in general tend to be more interested in negative than in positive news items, ceteris paribus, driving the mass media to pay more attention to ‘bad news’ (McCluskey and Swinnen, 2004).

In combination, these factors create a set of incentives for international organisations to emphasise the negative welfare implications in their analysis and policy communications, and to de-emphasise the positive effects around the food crisis of 2007-8, in this way making them more likely to attract media coverage of their work and, in turn, to reach a wider audience and influence policy-makers. Such a media strategy could have a direct effect in influencing public and private donations and policies of governments in the short run and an indirect effect in encouraging appreciation and legitimacy for their work and for the organisations themselves, which could lead to support in the long run.

the very ethics and project of global humanitarianism that aid agencies historically have done so much to promote’(p. 862).

38. Some have questioned the importance of these effects (Natsios, 1996) and argue that the media are more likely to follow politics than to lead it (Strobel, 1996). A more nuanced argument is put forward by Robinson (2001) who explains that the media can be a powerful source in leading policy-makers but primarily when there is great uncertainty or limited information.

39. For example, Swinnen and Francken (2006) find that virtually all the attention to globalisation, trade and development issues in the mass media is concentrated on ‘international summits’.

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7 Some concluding comments This article has documented how the dramatic increase in food prices in 2006-8 appears to have reversed views on the impact of food prices on global poverty. There are several reasons/motivations that may explain why the policy messages of NGOs and international organisations may be biased by emphasising the negative welfare effects and ignoring the positive effects. These include scientific advances, an urban bias in global policy attention, fundraising and legitimacy motivations of policy organisations, the pressure and technological developments in the global mass media, and the use of crises to bring a (semi-)consistent policy message to the attention of policy-makers, donors and the public at large.

An important question following this analysis is, of course, to what extent this bias in focus and communication of effects is affecting actual policy-making, and ultimately welfare and development. Answering it is difficult since, conceptually, it depends on various assumptions regarding the relationship between analysis, communication and policy prescription; the processing of these sets of information by voters, policy-makers and the organisations themselves; the type of welfare function one has in mind; and the political economy of policy decisions, at various levels. Empirically, this is also difficult to assess because measuring the impact of communications on the actual decision- making process is very complicated.

The question is not fully answered in this article; it is the subject of ongoing research. This article has, however, used several cases to illustrate how the communication shifts have potentially major implications for policy. The discussions on the impact of export restrictions and the need to deal with these policy measures in the WTO, the ongoing discussions and negotiations on the reform of the CAP, and the impact of foreign investment in developing countries’ rural land and food systems, the so-called ‘land grabbing’ debate, all seem to have been influenced significantly by the shift in communication. While more research on this is certainly required, it is likely that a bias in the analysis and the policy messages does influence policy-making, and, thus, welfare and development.

Hence, what is successful from a media strategy and communications perspective may conflict with an unbiased evaluation of the benefits and costs, the pros and cons of certain policies. In the particular case discussed in this article, the focus on one side of the effects in the food-policy debate both before and after the recent food crisis may have a cost in terms of sub-optimal policy-making and thus in terms of welfare and poverty reduction.

first submitted May 2010 final revision accepted February 2011

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TIMMER.pdf

ADB Economics Working Paper Series

Causes of High Food Prices

C. Peter Timmer No. 128 | October 2008

ADB Economics Working Paper Series No. 128

Causes of High Food Prices

C. Peter Timmer October 2008

C. Peter Timmer is a Visiting Professor with the Program on Food Security and the Environment, Stanford University, and Non-Resident Fellow with the Center for Global Development, Washington. The author would like to thank William James of the Economics and Research Department, Asian Development Bank for coordinating the writing of this paper; Robin Kraft from the Center for Global Development, and Wally Falcon and Roz Naylor of the Program on Food Security and the Environment, Stanford University, for their helpful comments; and Shiela Camingue and Juan Paolo Hernando of the Asian Development Bank for their excellent research assistance while in Manila. This paper was originally published in the Asian Development Outlook 2008 Update. It augments that publication’s contents by providing an extensive, new empirical appendix.

Asian Development Bank 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org/economics

©2008 by Asian Development Bank October 2008 ISSN 1655-5252 Publication Stock No.: _______

The views expressed in this paper are those of the author(s) and do not necessarily reflect the views or policies of the Asian Development Bank.

The ADB Economics Working Paper Series is a forum for stimulating discussion and eliciting feedback on ongoing and recently completed research and policy studies undertaken by the Asian Development Bank (ADB) staff, consultants, or resource persons. The series deals with key economic and development problems, particularly those facing the Asia and Pacific region; as well as conceptual, analytical, or methodological issues relating to project/program economic analysis, and statistical data and measurement. The series aims to enhance the knowledge on Asia’s development and policy challenges; strengthen analytical rigor and quality of ADB’s country partnership strategies, and its subregional and country operations; and improve the quality and availability of statistical data and development indicators for monitoring development effectiveness.

The ADB Economics Working Paper Series is a quick-disseminating, informal publication whose titles could subsequently be revised for publication as articles in professional journals or chapters in books. The series is maintained by the Economics and Research Department.

Contents

Abstract v

I. Introduction 1

II. What has Caused Commodity Prices to Increase since 2000? 6

A. �ayers of CausationA. �ayers of Causation 7 B. The Biofuel Debate 10 C. The Rice Difference 15 D. Summing up the Factors Causing High Food Prices 18

III. Transmission of World Commodity Prices into Domestic Economies 19

A. Exchange Rates 2A. Exchange Rates 21 B. Transmission to Domestic Economies 22 C. Consumer Prices of Rice: Pass-Through is Incomplete 23 D. Price Movements in Early 2008 25 E. Summary of Price Transmission Results 26

IV. Country Results: Contrasting Experiences of Rice Importers and Exporters 27

V. Can Anything be Done about High Food Prices? 32

Technical Appendix 34

Appendix 1. The Analytics of What Causes High Food Prices 3Appendix 1. The Analytics of What Causes High Food Prices 34 Appendix 2. The Supply of Storage Model and Short-run Price Behavior 40 Appendix 3. Testing for Granger Causality across Exchange Rates and Commodities 42

References 48

Abstract

Since mid-2007 basic food prices have rocketed with disastrous consequences for poor consumers. The spike in international market prices through the first half of 2008 has now subsided. Still prices of rice, wheat, corn (maize), and edible oils remain well above the levels of just a year ago and are likely to remain elevated and volatile for years to come. Two separate dynamics need to be understood in order for countries to make necessary adjustments. A gradual rise in food prices has been under way since at least 2004 with three general and fundamental factors at work: rapid economic growth in the People’s Republic of China and India especially put upward pressure on prices as demand simply outpaced supply; a sustained decline in the United States dollar since mid-decade added to the pressures on dollar-denominated international market prices; and a combination of high and rising fuel prices coupled with legislative mandates to increase production of biofuels has established a firm link between petroleum prices and food prices. The causes of price spikes are crop-specific. Drought and disease in 2007 caused wheat prices to jump, and supplies of edible oil were reduced as farmers in the United States shifted acreage out of soybeans into corn for nonfood uses (ethanol). Rice is the clearest example of crop-specific causes—the price spike was driven by export bans that were aimed at helping contain domestic food price inflation in exporting countries, but had the unintended effect of setting off panic as supplies to the already thin world rice market were sharply reduced. Asia will need several years of good rice harvests in order to stabilize the situation and reduce the exposure of the poor to another shock in food prices. This will not be easy to achieve as input costs are driven higher by high energy prices. Thus, it seems unlikely that world food prices will return to the declining trend seen between the mid-1970s and the first few years of this century.

I. Introduction

Are food grain prices high? The answer depends on the commodity, the period of comparison, and whether the prices are in nominal or real terms. Even from the perspective of just two decades, deflated prices are not exceptionally high for corn (maize) and wheat—only rice seems to be going off the top end of the scale (Figure 1).

800

600

400

200

00

Figure 1: Short-Run Movements in Real Prices of World Grains

($ per metric ton)

1986 1990 1994 1998 2002 2006

Wheat

Rice

Corn (maize)

Note: 2008 represents data for the first 5 months. The world prices of corn (maize), rice, and wheat are based on US No. 2 Yellow, free on board Gulf of Mexico; Thailand white milled 5% broken, free on board Bangkok; and No. 1 Hard Red Winter, ordinary protein, free on board Gulf of Mexico, respectively. Prices were deflated by the US consumer price index, with 2007 prices as the base.

Source: International Monetary Fund website (imf.org/external/np/res/commod/index.asp), downloaded 15 August 2008.

A longer-run view, from 1950 to the present, is even more surprising. Price trends over more than half a century reveal that even the highest price levels experienced in 2007 and 2008 are substantially below the peaks in the previous world food crisis in 1973–1974. Indeed, real prices in mid-2008 for corn, wheat, and rice remain well below what was considered “normal” until the full impact of the green revolution was felt after 1980 (Figure 2).

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($ per metric ton) 2400

1800

1200

600

0

Figure 2: Long-Run Movements in Real Prices of World Grains

1950 1960 1970 1980 1990 2000 2008

Wheat

Rice

Corn (maize)

Note: 2008 represents data for the first 5 months. The world prices of corn (maize), rice, and wheat are based on US No. 2 Yellow, free on board Gulf of Mexico; Thailand white milled 5% broken, free on board Bangkok; and No. 1 Hard Red Winter, ordinary protein, free on board Gulf of Mexico, respectively. Prices were deflated by the US consumer price index, with 2007 prices as the base.

Source: International Monetary Fund website (imf.org/external/np/res/commod/index.asp), downloaded 15 August 2008.

But most policy makers, consumers, and producers have shorter memories than implied by Figure 2. Recent price movements have been very sharp and disruptive, with an especially heavy impact on poor consumers and low-income food-importing countries. Rapid increases in food prices are adding to inflationary pressures in most of developing Asia, bringing into prospect monetary tightening and slower economic growth. After several decades of stability in world grain markets, and even steady price declines, the world looks very different in mid-2008 (Figure 3). Scarcity is back, hunger is growing, and rapid economic growth is threatened (ADB 2008b). These are difficult times.

(2000 = 100) 600

400

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Figure 3: Grain Price Indexes

Jan 2005 Jul Jan 2006 Jul Jan 2007 Jul JulJan 2008

Wheat

Rice

Corn (maize)

Note: The world prices of corn (maize), rice, and wheat are based on US No. 2 Yellow, free on board Gulf of Mexico; Thailand white milled 5% broken, free on board Bangkok; and No. 1 Hard Red Winter, ordinary protein, free on board Gulf of Mexico, respectively.

Source: International Monetary Fund, International Financial Statistics online, downloaded 15 August 2008.

Causes of High Food Prices | �

These high food prices have attracted a great deal of attention in policy, media, and academic circles. The run-up in corn prices since mid-2007 has fueled a sharp debate over the ethanol subsidy program in the United States (US). High vegetable oil prices have raised similar questions over biodiesel mandates in Europe. High wheat and rice prices may significantly undermine the gains in poverty reduction in the past two decades. The world community has mobilized new resources to feed the poor, including a doubling of the budget for the World Food Program, from $3 billion a year to over $6 billion for 2008.

A combination of decent weather in most growing regions, vigorous response from farmers, and announcement of a small but timely release in May of imported rice stocks by Japan seem to have stopped the price panics seen early in 2008.1 Market psychology has clearly turned negative (and Viet Nam has aggressively cut export prices for rice in an effort to regain market share from Thailand). But price levels remain well above long- run trends and significant micro- and macroeconomic adjustments are in the works. To understand these adjustments and to assess their impact, it is necessary to understand the causes of high food prices and their likely duration. That is the purpose of this paper.

The new price environment has now existed long enough to move beyond journalistic coverage (some of it quite insightful) and to have generated a preliminary flow of analysis and policy perspectives. These range from thoughtful essays that reflect on previous world food crises and the distinguishing features of this one (Naylor and Falcon 2008), to urgent appeals to ramp up food aid funding and support for agricultural research (von Braun 2008). The most useful and balanced assessment appeared in the Farm Foundation Issue Report (FFIR) in July 2008. Authored by three distinguished agricultural economists based at Purdue University, the report concludes that falling grain stocks since 2000 have gradually changed world commodity markets from surplus to deficit and have provided the supply–demand fundamentals for sharply higher prices (Abbot, Hurt, and Tyner 2008).

These changing fundamentals can be seen in an especially compelling way when one compares rates of population growth in Asia with rates of growth in rice yields (Figure 4). The green revolution produced a surge in rice production and rice surpluses, but the rate of growth has been on a falling trend for the last two decades.

The trigger for the higher prices depends on individual commodities, but significant depreciation of the US dollar, high oil prices, and demand for biofuels have been the main drivers, although even these basic forces are interrelated. Because the FFIR covers the drivers of high food prices in detail, from both a macroeconomic and a commodity-specific perspective, it provides the basic foundation for the more specialized analysis here � Interestingly, as of end-August, the rice had not actually been shipped from Japan to the Philippines, although the

Japanese Ministry of Agriculture, Forestry, and Fisheries insists that it will be when all the details are agreed to by the Philippines. Obviously, what was important to the market in May was the signal that additional supplies would become available, at which point market psychology reversed.

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(Abbot, Hurt, and Tyner 2008). In particular, the FFIR stresses the distinction between short- and long-term responses of supply and demand to a new price environment, and the pervasive impact of changes in exchange rates on commodity prices. Both these factors are investigated in some detail in this paper.

(percent) 5

4

3

2

1

0

Figure 4: Growth of Rice Yield and Population

1970 1975 1980 1985 1990 1995 20052000

Population

Rice yield

Note: Growth is calculated using successive rolling 5-year period data on rice yield and population in 24 rice-producing developing Asian economies. For example, growth for 1970 is the change between 1966–1970 and 1961–1965.

Sources: Food and Agriculture Organization of the United Nations Statistics (FAOSTAT ) website (faostat.fao.org/site/291/default.aspx); World Development Indicators online, both downloaded 3 September 2008.

A major policy issue has been the extent to which “outside” financial speculation—by pension and hedge funds, or newly created commodity index funds available to small investors—has been driving up prices for key staple foods (and petroleum). India, for example, has banned futures trading in important food staples. Nearly all economists and market analysts agree that financial speculation cannot drive up prices in the long run—over a decade or longer. Only the fundamentals of supply and demand can do that.

But there is much more controversy over the role of new speculative activity on price formation in the short run, and especially the potential for such speculation to create “spikes” in prices, or bubbles, that disconnect the market price from underlying fundamentals (OECD 2008). It is very difficult to explain the creation of such spikes across a wide range of commodities without a significant role for financial speculation based on expectations of higher prices. Indeed, the sharp sell-off in many commodity markets since mid-July 2008 has convinced many doubters that financial speculation played a significant role in the rapid price run-ups seen since mid-2007. This paper also brings to bear new empirical analysis that sheds light on this role.

Causes of High Food Prices | �

The key results are as follows. First, the distinction between short-run responses of supply and demand to price changes and longer-run responses is crucial. This is a result familiar to agricultural economists, who have used Nerlovian-type distributed lag models of farmer and consumer behavior for half a century (Nerlove 1958). A simple model developed here that captures this distinction suggests that much of the recent gradual increase in the prices of food commodities—from 2002 to 2007—is a direct result of sharply declining prices a decade ago. We are paying a high price, literally, for the destocking of grains since the mid-1990s, a process that pushed down prices (see Appendix 1).

Simultaneously, this destocking was a rational response to falling grain prices. The simultaneity between stock levels and price expectations—emphasized in the theory of the supply of storage (Brennan 1958, Williams and Wright 1991)—is another neglected aspect of most analyses of current high food prices (see Appendix 2). Considerable insight comes from remedying that neglect, simply by recognizing that in market economies, stock changes do not happen “exogenously” from price formation.

Second, the pervasive impact of exchange rates on commodity prices is confirmed even in the very short run (a result compatible with the FFIR perspective but additional to it). It is important to remember, as the report stresses, that exchange rates are financial variables conditioned by their macroeconomic and trade context. Almost inherently, then, commodity prices will be linked to financial markets, even in the long run (Frankel 2006). Price formation in organized commodity markets depends on financial factors as well as “real” supply and demand factors.

Finally, the short-run price linkages among exchange rates, oil prices, and the prices of important food commodities are tested with Granger causality techniques (see Appendix 3). These linkages are almost certainly driven by the intermediation of financial markets, i.e., speculators engaged in commodity futures (and other derivatives) markets who have no physical connection to the commodity businesses themselves. These results provide tantalizing, but preliminary, evidence of the role of financial speculation in short- run price behavior, but the role is not nearly as uniform and pervasive as most critics seem to think. Speculative pressures come and go, for reasons not yet apparent from the data. Understanding these reasons—which are perhaps no more than “animal spirits”—is the next goal of the research reported here. Any progress in such understanding will move the discussion forward a great deal.

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II. What has Caused Commodity Prices to Increase since 2000? When compared with the long-run decline in most commodity prices visible in Figure 5, the run-up in prices since 2000 appears to be a reversal of historical trends. The timing of the rise varies by commodity, so some commodity-specific stories will be needed to explain the patterns. But there seem to be common elements to the rise as well. This section will attempt to assess the role both of the general drivers and of the commodity- specific dimensions of the commodity price boom. A formal model is developed in Appendix 1 that attempts to illustrate how the general drivers and commodity-specific dimensions of price formation are related.

(2002 = 1) 10

8

6

4

2

0

Figure 5: Long-Run Real Commodity Price Movements since 1970

1970 1975 1980 1985 1990 1995 20052000

Metals

Corn

Soybeans

Crude oil

All commodities

Note: The commodity price index includes both fuel and nonfuel price indices. The oil price index is the simple average of the spot prices of dated Brent, West Texas Intermediate, and Dubai. Corn (maize) and soybeans, respectively, refer to US No. 2 Yellow, free on board Gulf of Mexico; and US No. 2 yellow, Chicago Soybean futures contract (first contract forward). The metals price index comprises copper, aluminum, iron ore, tin, nickel, zinc, lead, and uranium price indexes. Prices were deflated using the US consumer price index, with 2000 prices as the base.

Source: International Monetary Fund, International Financial Statistics online, downloaded 1 September 2008.

The general patterns since 2000 are clear enough in Figure 6. From 2000 to 2004 all the tracked commodities moved more or less in tandem, and by relatively small amounts. Soybean prices spurted in 2004 after production problems in the US, but returned to normal levels in 2005. From then until early 2007 prices of wheat, corn, and soybeans remained flat, but rice prices had already started a steady rise from their historical low in 2001. Crude oil prices and metals—which together make up a large share of the International Monetary Fund commodity price index—had also started a steady rise by 2004. Clearly, by the mid-2000s, commodity prices were beginning to show signs of life not seen for a decade. Something had changed.

Causes of High Food Prices | �

(2002 = 1) 6

4

2

0

Figure 6: Short-Run Nominal Commodity Price Movements since 2000

2000 2002 2004 2006 2008

Corn

Crude oil

Soybeans

Rice

Wheat

Commodity price index

Note: The commodity price index includes both fuel and nonfuel price indices. The oil price index is the simple average of the spot prices of dated Brent, West Texas Intermediate, and Dubai. The bases for the price of maize, rice, soybeans, and wheat, respectively, are as follows: US No. 2 Yellow, free on board Gulf of Mexico; Thailand white milled 5% broken, free on board Bangkok; US No. 2 yellow, Chicago Soybean futures contract (first contract forward); and No. 1 Hard Red Winter, ordinary protein, free on board Gulf of Mexico.

Source: International Monetary Fund, International Financial Statistics online, downloaded 29 August 2008.

The change is most apparent in crude oil and the metals-heavy International Monetary Fund index. Food staples, except rice, remained stable until 2007. Such a pattern is best explained by the accelerating demands for industrial raw materials and energy as the economies of the People’s Republic of China (PRC) and India consolidated their momentum of very rapid growth after the turn of the millennium. As the authors of FFIR point out, however, the PRC and India are not large factors in global grain markets, and their rapid economic growth did not spill over directly into higher prices for wheat, corn, and soybeans. The rising prices for rice need a special explanation, detailed below. By 2006, however, it was clear that rapid growth in the developing world, especially the PRC and India, could move global commodity markets. This realization set the stage for new expectations among commodity traders in particular and the broader investment community in general. By 2006, expectations of higher commodity prices were well established.

A. Layers of Causation

It is useful to think about the factors causing high food prices in terms of cumulative layers of causation (Timmer 2008a). Five basic drivers seem to be stimulating rapid growth in demand for food commodities:

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(i) Rising living standards in PRC, India, and other rapidly growing developing countries lead to increased demand for improved diets, especially greater consumption of vegetable oils and livestock products (and the feedstuffs to produce them). The PRC is a major importer of soybeans for both meal and oil and India is a significant importer of vegetable oils. However, wheat and rice consumption in the PRC and India are not rising significantly and both countries are largely self-sufficient in both commodities.

(ii) The rapid depreciation of the dollar against the euro and some other important currencies drives up the price of commodities quoted in dollars for both supply and demand reasons (see below). The depreciation of the dollar also causes investors “long” in dollars (i.e., most US-based investors, but holders of dollars globally as well) to seek hedges against this loss of value, with commodities being one attractive option.

(iii) Mandates for corn-based ethanol in the US (and biodiesel fuels from vegetable oils in Europe) cause ripple effects beyond the corn economy, which are stimulated by inter-commodity linkages (Naylor et al. 2007; Timmer, Falcon, and Pearson 1983). There is active debate about whether legislative mandates or high oil prices are driving investments in biofuel capacity (Abbot, Hurt, and Tyner 2008), but no doubt about the increasing quantities of corn and vegetable oil being used as biofuel feedstocks (Elliott 2008).

(iv) Massive speculation from new financial players searching for better returns than in stocks or real estate has flooded into commodity markets. The economics and finance communities are unable to say with any confidence what the price impact of this speculation has been, but virtually all of it has been a bet on higher prices.

(v) Underneath all these demand drivers is the high price of petroleum and other fossil fuels.

Figure 7 provides a graphical representation of how the first four factors listed above have contributed to the recent escalation in food prices. The figure also illustrates the tail end of the long-run declining trend in prices that prevailed over the last 200 years or so. A moderate recovery from the trough earlier in this decade was motivated by long-run demand and supply responses to the protracted period of falling prices (i.e., a huge expansion in demand and limited additions in supply in reaction to declining prices gradually bid prices back up again; see Appendix 1). Nevertheless, the sharp acceleration in food prices generally began in late 2006, but the appeal of food commodities to speculative investors seems to have begun only toward the middle of 2007 (Timmer 2008a).

Causes of High Food Prices | �

Figure 7: Factors Contributing to Food Price Formation since 2000

2000 2002 2004 2006 2008

Long-run decline Recovery

Demand in India/PRC, etc.

Depreciation of the US dollar

Mandates for biofuels in the US/Europe

Speculation

Source: Based on Timmer (2008a).

Each of the four demand-driven causes is a little different for each basic commodity, but the “structural” forces—rapid demand growth in developing countries and depreciation of the dollar—are similar for all the commodities of interest here (again, with rising oil prices as a foundation). These factors have been in play for years and have been fairly predictable, driven as they are by macroeconomic fundamentals. The two “top” layers, however, have come on the scene much more recently and have the potential to change the price formation equation rapidly and unexpectedly. Table 1 summarizes this perspective for supply and demand drivers according to their “predictability,” i.e., whether the drivers are low variance (and easy to predict) or high variance (and very difficult to predict).

Table 1: External Drivers of Food Prices

Supply Demand

Low variance Seed technology Population growth

Irrigation Income growth

Total harvested area Dietary changes and tastes

Climate change Meat and livestock economy

Knowledge and management skills

High variance Weather Exchange rates

Diseases Speculation

Crop-specific harvested area Biofuels (but predictable from mandates; not predictable from oil prices)

Fuel costs Panic or hoarding

Fertilizer costs Government trade and inventory policies

Source: Author.

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B. The Biofuel Debate

Biofuels are enormously controversial, and this paper is not the place to review the debate over their full economic and environmental impact (see Elliott 2008, Collins 2008, and Runge and Johnson 2008 for useful, if sobering, reviews). Very senior and experienced commodity analysts place the share of biofuels’ contribution to the run-up in grain prices since mid-2007 at between 60% (Collins, the former chief economist for the United States Department of Agriculture, analyzing only corn) and 75% (Mitchell 2008, the senior commodity economist at the World Bank, analyzing all grain markets). More academic analysts relying on large-scale models tend to place the share at between 25% and 35%—the latter figure from Rosegrant’s (2008) use of the International Model for Policy Analysis of Agricultural Commodities and Trade or IMPACT developed by the International Food Policy Research Institute. FFIR agrees that biofuel demand for corn was a main driver of higher corn prices, but argues that most of this demand was driven by high oil prices, not Congressional mandates.

The problem is that none of the formal models fully capture the cross-commodity supply and demand linkages between corn—the primary grain used to make ethanol—and other commodities such as soybeans, wheat, and other feed grains. As a simple example, increased planting of corn led to reductions in soybean acreage in 2007 in the US. The reduced output of soybeans meant that soy oil production was also lower, which caused increased demand for palm oil in Asia, and a spike in prices. Although the PRC is not a significant importer of corn, it is a huge importer of soybeans to crush for both soymeal and soy oil. With reduced supplies of soybeans available—a ripple effect of the increased acreage devoted to corn—the PRC turned to Asian-produced palm oil to meet its growing demand for vegetable oils (Naylor et al. 2007). India, too, is a substantial importer of vegetable oils and of palm oil, in particular.

Corn is the quintessential “multi-end-use” commodity, and the economics of which end use is “driving” market prices depends on the supply and demand structure of all the alternative commodities, as well as on macroeconomic conditions and trade policies in importing and exporting countries. Modeling this is difficult. In the precise language of Chen, Rogoff, and Rossi (2008), the multiple end uses lead to “parameter instability” in the relationship between supply, demand, and price.

It is entirely possible that in one month demand for corn to make ethanol is driving up the price of corn, soybeans, and palm oil, whereas in another month price formation across these commodities can be completely delinked, depending simply on each commodity’s own supply and demand situation (or on other forces). Thus not only would the parameters of a “multi-end-use commodity price model” vary from period to period, so too would the entire structure of the model. Perhaps it is not surprising that different analysts and different models produce very different estimates of what is causing high food prices. Parameter instability is the fundamental reason that careful analysts, such as Abbot, Hurt,

Causes of High Food Prices | 11

and Tyner (2008), argue that it is impossible to place quantitative weights on the causes of higher food prices, or at least weights that would have continuing validity over time and across commodities.

It is possible actually to “see” this parameter instability and changing structure if price data are available with sufficiently high frequency. Appendix 3 uses daily price data from 31 December 1999 to 2 July 2008 to test the structure of price interaction across exchange rates and commodities, and the structure clearly changes frequently. As one example of such data, Figure 8 plots daily prices of palm oil for 31 December 1999 to 2 July 2008. The sudden take-off around mid-2006, when corn prices also started to increase, suggests a new set of drivers in the formation of palm oil prices.

($ per metric ton) 1600

1200

800

400

0

Figure 8: Palm Oil Price Movements

2000 2002 2004 2006 20082007200520032001

Note: Price refers to Malaysian oil palm (Rotterdam). Source: Datastream, downloaded 28 August 2008.

Not all the action has been on the demand side. Supplies of some food commodities have generally been marked by shocks from adverse weather conditions and crop disease. Wheat is a clear example. A shock on wheat supplies would usually trigger some price increase, but would be quickly addressed by stock drawdowns and increased production that would damp the upward price movements. However, the bad harvest in 2007 happened at a time of extremely low wheat stocks (Figure 9). As a result, the price response was exaggerated. In the same vein, the rebound in Australia’s wheat harvest in 2008 brought about a marked drop in wheat prices after April (Figure 3 above).

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50

40

30

20

10

Figure 9: World Wheat Stock-to-Use Ratio

1960 1970 1980 1990 2000 20051995198519751965

Note: The stock-to-use ratio indicates the level of stocks held at the end of the period as a share of total use.

Source: Foreign Agricultural Service, United States Department of Agriculture, Production, Supply and Distribution online (fas.usda.gov/psdonline/psdHome.aspx), downloaded

2 September 2008.

Declining stock-to-use ratios for corn since the late 1990s are the main rationale offered by analysts who see corn-based ethanol demand as the main driver of higher prices for staple food grains (Figure 10). Because corn has multiple end uses that are economically efficient at normal prices, a shift in demand from one of the end uses (e.g., biofuels) can create ripple effects throughout many other commodity markets. Corn is a primary feedstuff for livestock, but competes in this end use with wheat. But wheat and rice are consumption substitutes in many parts of Asia. In another direction, corn oil competes with soy oil and palm oil. Rapid growth in vegetable oil demand in Asia can indirectly stimulate corn production in the US.

50

40

30

20

10

Figure 10: World Corn Stock-to-Use Ratio

1960 1970 1980 1990 2000 20051995198519751965

Note: The stock-to-use ratio indicates the level of stocks held at the end of the period as a share of total use.

Source: Foreign Agricultural Service, United States Department of Agriculture, Production, Supply and Distribution online (fas.usda.gov/psdonline/psdHome.aspx), downloaded

2 September 2008.

Causes of High Food Prices | 1�

Competition and substitution can also take place on the supply side. Corn and soybeans compete directly for acreage in much of the US. Increased demand for corn for biofuel production can reduce soybean acreage, causing soymeal and soy oil prices to rise. Thus there are many mechanisms by which higher demand for corn to convert into ethanol might have an impact on a wide range of food commodity prices around the world. With stock-to-use levels for corn so low in the mid-2000s (Figure 10), it was these mechanisms that led analysts such as Mitchell (2008) and Collins (2008) to single out rising demand for ethanol in the US as the trigger for higher food prices across the board.

Whether the demand was from Congressional mandates or from high gasoline prices, establishing a direct link between energy prices and food prices is a “game changer” in global commodity markets. The outlook for continued high crude oil prices (Asian Development Outlook Part 2 in ADB 2008a) thus has direct implications for the outlook for staple food prices. Most knowledgeable analysts of the US biofuel industry feel that corn- based ethanol will be economically competitive if crude oil stays above $80 a barrel (in 2008 prices) and if corn is available to local refiners at less than $5–6 a bushel. As noted, because of its multiple end uses in consumption, and area competition with soybeans (and to a lesser extent, with wheat) in the US, high-priced corn (specifically) means high- priced food (generally), including even rice in the long run.

The price trajectory for vegetable oils is similar to the basic path for staple food grains (see Figure 8 for palm oil prices since 2000). The connections are established from both their food uses and their industrial uses. Figure 11 shows food uses of vegetable oils on an exponentially increasing path, led especially by rapid growth in demand in the developing world. But industrial use, after growing very slowly for decades, has also started an exponential increase since 2000. This growth is almost entirely due to the use of vegetable oils to make biodiesel fuels. Rapeseed oil and palm oil are used for this purpose in Europe and some soy oil is used for biodiesel in the US (Figure 12). Again, once a price connection is established between vegetable oils and liquid fuels, the price dynamics for vegetable oils will be driven largely by the world market for petroleum. All the evidence suggests that these connections are well established at petroleum prices over $80 per barrel and thus are likely to be permanent features of vegetable oil price dynamics for the foreseeable future, whatever happens to legislative mandates (Elliott 2008; Abbot, Hurt, and Tyner 2008).

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(million metric tons) 90

60

30

0

Figure 11: World Vegetable Oils—Food versus Industrial Uses

1964 1974 1984 1994 2004 20081998198819781968

Food uses

Industrial

Note: Vegetable oil use refers to soy, palm, and rapeseed oil consumption. Source: Foreign Agricultural Service, United States Department of Agriculture, Production,

Supply and Distribution online (fas.usda.gov/psdonline/psdHome.aspx), downloaded 2 September 2008.

(percent) 30

20

10

0

Figure 12: World Vegetable Oils—Industrial Use as Share of Total

1964 1974 1984 1994 2004 20081998198819781968

Rapeseed

Palm

Soy

Note: Vegetable oil refers to soy, palm, and rapeseed oil. Total use is the sum of industrial, food, and feedwaste uses.

Source: Foreign Agricultural Service, United States Department of Agriculture, Production, Supply and Distribution online (fas.usda.gov/psdonline/psdHome.aspx), downloaded

2 September 2008.

Causes of High Food Prices | 1�

C. The Rice Difference

For rice, the story is more complicated. The actual production–consumption balance for rice has been relatively favorable since 2005, with rice stock-to-use ratios improving slightly. This stock buildup was a rational response to the very low stocks seen in the middle of the decade and to gradually rising rice prices. Short-run substitutions in both production and consumption between rice and other food commodities are limited, and until late 2007 it seemed that the rice market might “dodge the bullet” of price spikes seen in the wheat, corn, and vegetable oil markets. The lack of a deeply traded futures market for rice also made financial speculation less attractive.

But the world rice market is very thin, trading just 6–7% of global production. While this is a significant improvement over the 4–5% traded in the 1960s and 1970s, it still leaves the global market subject to large price moves from relatively small quantity moves.

The global rice market is also relatively concentrated, with Thailand, Viet Nam, India, US, and Pakistan (in order of their share of rice exports) routinely providing nearly four fifths of available supplies. Only in the US is rice not a political commodity from a consumer’s perspective (although it certainly is a political commodity for producers there). All Asian countries show understandable concern over access of their citizens to daily rice supplies. Both importing and exporting countries watch the world market carefully for signals about changing scarcity, while simultaneously trying to keep their domestic rice economy stable.

As concerns grew in 2007 that world food supplies were limited and prices for wheat, corn, and vegetable oils were rising, several Asian countries reconsidered the wisdom of maintaining low domestic stocks of rice. The Philippines, in particular, tried to build up stocks to protect itself against shortages in the future. If every other country, household, or individual does the same thing, panic will grip the market. This will lead to commodity shortages and subsequent price surges. Such price panics have been fairly common over the past 50 years, but the hope was that deeper markets, more open trading regimes, and wealthier consumers able to adjust more flexibly to price changes had made markets more stable. This was wishful thinking, as the price record for rice shows (Figures 1–3 above).

After the acceleration in the gradual price increases that had been seen for half a decade started in September 2007, concern over the impact of higher rice prices in exporting countries, especially India, Thailand, and Viet Nam, started to translate into talk, and then action, on export controls.2 Importing countries, especially the Philippines, started 2 It is almost amusing that Indonesia announced a ban on rice exports early in 2008, before its main rice harvest

started in March. Historically, Indonesia has been the world’s largest rice importer, surpassed only recently by the Philippines, and no one in the world rice trade was looking to Indonesia for export supplies. But there was a rationale to the announcement by the minister of trade—it signaled that Indonesia would not be needing imports and was thus not vulnerable to the skyrocketing prices in world markets. The calming effect on domestic rice market participants meant that little of the hoarding behavior seen in the Philippines and Viet Nam was evident in Indonesia.

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to scramble for supplies. Fears of shortages spread and a cumulative price spiral started that fed on the fear itself.

The panic was set off by the complex interlinkages among certain commodities. In 2007, wheat harvests in India, as in other parts of the globe, were damaged by drought and disease. This left the Food Corporation of India with inadequate wheat supplies for its public distribution system. The Government of India could have imported as much wheat as it did in 2006 (about 7 million metric tons) to meet the shortfall, but while importing was an option, it would have been too costly (both economically and politically), as wheat prices were already elevated at the time. The Food Corporation of India instead decided to substitute rice for wheat and announced increased procurement of rice from domestic producers. Restrictions were imposed on rice exports in September 2007, and by February 2008, an outright ban on non-basmati rice exports was in place. (India is the world’s third-largest rice exporter, supplying 4.1 million metric tons in 2007.)

As rice prices picked up, other rice-exporting countries followed India’s actions. Thailand’s newly elected populist government, for instance, openly discussed similar export restraints on rice to avoid a sharp increase in domestic retail prices. (Thailand is the world’s top rice exporter, supplying 10.0 million tons in 2007.)

These actions by two large rice exporters caused rice prices to jump to $750 per metric ton on 28 March 2008. Prices continued to surge, breaching $1,100 per metric ton in April. All because of panic.

Dwindling global stocks have generally been recognized as the major trigger for the rise in prices, and indeed rice consumption has been significantly outstripping production since 2000 (Figure 13). Over the past decade, rice stocks in the PRC have been shrinking in response to declining world prices and to increased reliance on trade for a ready supply. However, in the rest of the world, there has been relatively little change in rice stocks—just small increases in the stock-to-use ratio since 2005. Since holding large stocks of rice in tropical conditions is extremely costly, a dependable flow of rice in international trade can sharply reduce outlays. With the recent experience of exporting countries readily putting bans on rice exports to protect their own consumers, importing countries will now be forced to accumulate significant domestic stockpiles. That is a tragedy for poor consumers and takes a toll on economic growth, since capital is used to fund large inventories rather than being allocated to investment that would foster productivity and growth.

The psychology of hoarding behavior is important in explaining why rice prices suddenly shot up from late 2007. Financial speculation seems to have played only a small role (partly because futures markets for rice are very thinly traded). Instead, decisions by millions of households, farmers, traders, and some governments sparked a sudden surge in demand for rice and changed the gradual increase in rice prices from 2002

Causes of High Food Prices | 1�

to 2007 into an explosion: this was “precautionary” demand even if not “speculative” demand (see Appendix 2).

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Figure 13: Rice Stock-to-Use Ratio

1960 1970 1980 1990 2000 20051995198519751965

World

India

China, People’s Rep. of

Note: The stock-to-use ratio indicates the level of stocks held at the end of the period as a share of total use.

Source: Foreign Agricultural Service, United States Department of Agriculture, Production, Supply and Distribution online (fas.usda.gov/psdonline/psdHome.aspx), downloaded

2 September 2008.

A rough calculation of the effect of household hoarding of rice shows the potential. Assume that 1 billion households each consumes 1 kilogram of rice a day (for a total consumption of 365 million metric tons, for the year, which is the right order of magnitude).

Assume that they keep a 1-week supply in their pantry, or 7 kilograms per household, which is 7 million metric tons of household stocks in total. This quantity probably varies by income class, with the very poor buying hand to mouth, and better off households storing more just for convenience. When prices start to rise, or the media start talking about shortages of rice, each household, acting independently, decides to double its own storage, thus buying an additional 7 kilograms. This means that the world rice market— the source of marginal supplies (and demand) for many countries—needs to supply an additional 7 million metric tons of rice over a short period (perhaps a few weeks). But this quantity is about one quarter of total annual international trade in rice (recent levels have been 27–30 million metric tons per year).

And this is just the added demand from households. Farmers, traders, rice millers, and even governments will also want to hold more stocks in these circumstances. As an example, the Government of Malaysia announced in July that it was doubling the size of the national buffer stock held by Padiberas Nasional Berhad, even though it had to pay extremely high prices to do so. The Philippines is seeking to increase its government-

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held stocks. Indonesia has announced plans to triple its level of buffer stocks to 3 million metric tons.

Now, put realistic short-run supply and demand parameters (from the analytical model developed in Appendix 1) into the price determination mechanism: –0.1 for demand and 0.05 for supply. With a 25% (sudden) increase in short-run demand on the world market, the world price will have to rise by 167% to get a new equilibrium. That is close to what happened—panicked hoarding caused the rice price spike.

Fortunately, a speculative run can be ended by “pricking the bubble” and deflating expectations. Once the price starts to drop, the psychology reverses on hoarding behavior by households, farmers, traders, and even governments. When the Government of Japan announced in May, after considerable international urging, that it would sell 300,000 tons of its surplus “World Trade Organization” rice stocks to the Philippines, prices in world rice markets started to fall immediately (Slayton and Timmer 2008, Mallaby 2008). By late August, medium-quality rice for export from Viet Nam was available for half what it sold for in late April.

D. Summing up the Factors Causing High Food Prices

Three fundamental factors, all interrelated, combined to drive up food prices. First, rapid economic growth, especially in the PRC and India, put pressure on a variety of natural resources such as oil, metals, timber, and fertilizers. Demand simply increased faster than supply for these commodities.

Second, a sustained decline in the dollar since mid-decade added to the upward price pressure on dollar-denominated commodity prices directly, and indirectly fueled a search for speculative hedges against the declining dollar. Increasingly from 2006, these hedges were found first in petroleum, then in other widely traded commodities, including wheat, corn, and vegetable oils.

Third, the combination of high fuel prices and legislative mandates to increase production of biofuels established a price link between fuel prices and ethanol/biodiesel feed stocks—corn in the US and vegetable oils in Europe. Because of intercommodity linkages in both supply and demand, food prices now have a floor established by their potential conversion into biofuel. These linkages are not always tight or effective in the short run—rice and corn prices can be disconnected for some time, as the discussion above indicated (and as the Granger causality results in Appendix 3 demonstrate quantitatively). But the long-run forces for substitution in both production and consumption are very powerful. If high fuel prices are here to stay, high food prices are, too.

To complicate matters, in the short to medium run, the specifics of individual commodity dynamics can produce divergent price paths. Rice is the clearest example, as large

Causes of High Food Prices | 1�

Asian countries act for their own short-run political interests with little or no regard to consequences for the international market or traditional trading partners. Without significant hope for binding international agreements between rice exporters and importers, this source of unique instability seems likely to last a long time.

III. Transmission of World Commodity Prices into Domestic Economies�

A key question is the extent to which changes in world market prices have been transmitted to domestic economies in recent years, especially for cereals. The extent of transmission is important for two reasons. First, domestic prices affect the welfare of poor consumers and farmers, not world prices. Second, the magnitude of price transmission will influence the extent to which adjustments by producers and consumers help stabilize world price movements. These adjustments (reduced consumption, increased production) will only take place if world prices are transmitted to domestic prices (see also Imai, Gaiha, and Thapa 2008). It is obvious from Figure 14 that world rice prices are not immediately transmitted into Indonesia and the Philippines, two important rice importers. Figure 15, however, shows that price transmission for exporters is quicker and more complete, despite Viet Nam’s efforts to insulate domestic rice prices from the run-up in world prices.

($ per metric ton) 1200

800

400

0

Figure 14: World versus Domestic Rice Prices of Importers

2002 2004 2006 2008200720052003

World

Philippines

Indonesia

Note: World rice refers to Thailand 100% grade B; Indonesian rice refers to the retail price in Jakarta; and Philippine rice refers to ordinary (C-4) rice.

Sources: CEIC Data Company Ltd.; International Monetary Fund, International Financial Statistics online, both downloaded 22 August 2008.

� This section relies heavily on Dawe (2008a).

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($ per metric ton) 1200

800

400

0

Figure 15: World versus Domestic Rice Prices of Exporters

2002 2004 2006 2008200720052003

World Viet Nam

Thailand

Note: World rice refers to Thailand 100% grade B; Thailand rice refers to the retail price of white rice 5%; and Viet Nam rice refers to the retail price of ordinary rice in An Giang province.

Sources: CEIC Data Company Ltd.; International Monetary Fund, International Financial Statistics online, both downloaded 22 August 2008; Information Center for

Agricultural and Rural development, Institute of Policy and Strategy for Agricultural and Rural Development website (agro.gov.vn).

The extent of price transmission is a function of three key variables: the exchange rate at which dollar prices are converted to domestic currency prices; trade policy barriers at the border, which restrict (or enhance) the flow of commodities across the border; and the time horizon of adjustment. Normal marketing lags as well as policy interventions delay the immediate transmittal of international prices into domestic economies, but the longer there is a substantial difference between the two prices, the more pressure there is for convergence. Accordingly, Imai, Gaiha, and Thapa use an error-correction model (to allow for lags in price convergence) to test for price transmission of important foodstuffs into the PRC and India. They summarize their findings as follows (Imai et al. 2008, 1):

This paper examines the extent to which changes in global agricultural commodity price[s] are transmitted to domestic prices in India and PRC. The focus is on short and medium-run adjustment processes using an error correction specification. In particular, we show that the extent of adjustment in the short and medium- run (from 0 to 3 years) is generally larger in PRC than in India. Second, the adjustment is larger for wheat, maize and rice than for fruits and vegetables in both India and PRC. In fact, the adjustment is the weakest for vegetables in both countries. Third, while most of the domestic commodity prices co-move with global prices, the transmission is incomplete presumably because of distortionary government interventions (e.g., subsidies for agricultural commodities) and failure to exploit spatial arbitrage. So potential benefits to farmers of higher food prices —especially in India—may be restricted, as also the supply response.

Causes of High Food Prices | �1

Figure 16 shows that Thai wholesale prices for rice adjust very quickly to world prices. The core of the analysis carried out by Dawe (2008a) is a very basic calculation of cumulative changes in international and domestic prices in real (inflation-adjusted) terms between various points in time. A base year of 2003 is used because international oil, cereal, and fertilizer prices were relatively stable during the course of that year.

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Figure 16: Real Price Movements of Rice: World versus Wholesale Price in Thailand (Baht per kilogram)

Jan 2003 Jan 04 Jan 06 Jan 08Jan 07JulJan 05 Jul Jul JulJulJul

World

Thailand

Note: World rice refers to Thailand 100% grade B, while the wholesale price of rice for Thailand refers to white rice 5% new. Prices were deflated by the US consumer price index, with December 2007 prices as base.

Sources: CEIC Data Company Ltd; International Monetary Fund, International Financial Statistics online, both downloaded 28 August 2008.

A. Exchange Rates

Even before the dramatic surge in prices in 2008, world market prices had increased substantially in real dollar terms in recent years. Comparing Q4 2007 with Q4 2003, world market prices increased by 56% for rice, 91% for wheat, 40% for corn, and 107% for urea (a source of nitrogen and the main fertilizer used by Asian farmers). During that time, however, the dollar depreciated substantially against many currencies.4 Figure 17 shows the percentage appreciation of the real exchange rate for the seven countries included in the analysis.

� In fact, this depreciation is one cause of the recent high commodity prices.

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−20

Figure 17: Real Exchange Rate Appreciation of Domestic Currencies versus US Dollar (percent)

Bangladesh India Viet NamPhilippines ThailandIndonesiaPRC

Q4 2003 vs Q4 2007 Q4 2003 vs Q1 2008 Q4 2003 vs Q2 2008

Sources: CEIC Data Company Ltd.; International Monetary Fund, International Financial Statistics online, both downloaded 29 August 2008.

Real exchange rate appreciation vis-à-vis the dollar, to the extent that it occurs, will neutralize some of the impact of increased prices in dollar terms. Because the magnitude of real exchange rate appreciation varies from country to country, changes in world market prices in real domestic currency (DC) terms will also vary from country to country, even for the same commodity. A comparison of columns 1 and 2 of Table 2 shows that, for a substantial group of Asian countries, world market rice prices did not in effect increase by as much as was commonly believed (the figure in column 1). For some countries, however, such as Bangladesh, world price increases were substantial because the real exchange rate was approximately constant.5

B. Transmission to Domestic Economies

The extent to which international prices of rice have been transmitted into domestic markets in developing Asia has been influenced by movements of exchange rates. This can readily be seen by comparing columns 1 and 2 in Table 2. The appreciation of Asian currencies against the US dollar (the currency in which international prices are set) means that, in domestic currency terms, the percentage increase is less than in US dollar terms.

� In some countries, the exchange rate may be partially determined by world commodity price movements when the commodity in question is a major share of that country’s international trade, as is the case for oil in some African countries. The value of international cereal trade in the Asian countries analyzed here is relatively small, however, compared with the size of their foreign exchange markets and compared with total exports and imports (this is true even at current high price levels). Thus, exchange rate changes in these countries are taken as exogenous for the purposes of discussing commodity price transmission.

Causes of High Food Prices | ��

C. Consumer Prices of Rice: Pass-Through is Incomplete

Table 2 column 3 shows that not all the change in the international price of rice measured in domestic currency was passed through to domestic markets. Dawe (2008a) uses wholesale prices rather than retail prices to measure pass-through. This seems a valid procedure because rice at the wholesale level is milled and packaged and is quite close to that sold in the retail market.

Table 2: Cumulative Changes in Real Rice Prices, Q4 200� to Q4 2007 (percent) Country World Price

(US$) (1)

World Price (DC) (2)

Domestic Price (DC) (3)

DC Pass through (%) = (3)/(2)

(4) Bangladesh 56 55 24 44 a

China, People’s Rep. of 56 34 30 88 b

India 56 25 5 20 a

Indonesia 56 36 23 64 Philippines 56 10 3 30 a

Thailand 56 30 30 100 b

Viet Nam 56 25 3 12 a

DC = domestic currency. a “Stabilizers.” b “Free traders.” Sources: Dawe (2008a), author’s calculations.

There is quite a range of pass-through shown in column 4 of Table 2, and this indicates that some countries made a major effort to shield consumers from the spike in prices. The countries (indicated by “a” in column 4) with the low pass-through percentages are referred to by Dawe (2008a) as “stabilizers” while those for which pass-through exceeds 75% are called “free traders.” Thus Bangladesh, India, Philippines, and Viet Nam are classified as “stabilizers” and the PRC and Thailand as “free traders.” Implicitly this classification excludes the exchange rate policies of the countries and only considers commodity-specific policies, such as procurement, public distribution and subsidies, and international trade restrictions.

For “stabilizers,” domestic prices should move with less volatility and variance than international prices. This turns out to be the case for Bangladesh, India, Philippines, and Viet Nam but not for Indonesia. Rice prices in India are representative of “stabilizer” behavior (Figure 18). Price signals from the international market are not getting through to consumers and farmers in these countries, but are being muted. This is likely to have costs in terms of supply responses and consumer behavior.

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Figure 18: Real Price Movements of Rice—World versus Wholesale Price in India (Rupees per kilogram)

Jan 2003 Jan 04 Jan 06 Jan 08Jan 07JulJan 05 JulJulJul

World

Thailand

Note: World rice refers to Thailand 100% grade B, while the domestic price for India is the average retail price of rice in four large cities: Calcutta, Delhi, Bangalore, and Mumbai. Prices were deflated by the US consumer price index, with December 2007 prices as the base.

Sources: CEIC Data Company Ltd.; International Monetary Fund, International Financial Statistics online, both downloaded 28 August 2008.

Jul Jul

In contrast, the PRC’s and Thailand’s rice prices have moved closely with international prices, and although there are some trade restrictions and government interventions, this means that consumers and producers are getting full price signals from the international market.

Indonesia has traditionally tried to stabilize domestic rice prices (Timmer 1986 and 1996) but this policy was abandoned in 2004 when imports were curtailed and domestic prices rose well above global prices. Since then, Indonesian rice prices have tended to be more volatile than international prices and thus the country cannot be classified as a “stabilizer.”

The conclusion that emerges from the above discussion is that the real increase in domestic rice prices has averaged only about one third of the increase in international prices in real dollar terms. This indicates that the pass-through of international to domestic rice prices was muted though the end of 2007. Have things changed in 2008?

Causes of High Food Prices | ��

D. Price Movements in Early 2008

World market rice prices rose from 2003 to end-2007, but this increase was relatively steady and gradual. Thus in October 2007, prices were $335 per ton for Thai 100% grade B, just 5% higher in real terms than in October 2006. Prices began to increase more rapidly in November and December, but it was not until 2008 that prices surged, reaching a peak of more than $1,000 per ton in April and May (more than triple the level seen in the previous October). To what extent were these large price increases transmitted to domestic economies?

Table 3 shows that, again, less than half of these most recent price increases on world markets were transmitted to domestic economies, with the exception of Thailand and, barely, Viet Nam. The simple average pass-through of dollar prices to domestic prices, excluding Thailand and Viet Nam, was lower, at about 17%, than the average of 49% from Q4 2003 to Q4 2007. Given the much larger price increase on the world market, however, domestic prices increased substantially in several countries. In Bangladesh, Philippines, Thailand, and Viet Nam, real prices increased by nearly 50% or more in the span of 1 year, whereas prices did not increase more than 30% in any country in the 4 years between Q4 2003 and Q4 2007. Such large rises have serious repercussions for household food security, and often for domestic politics as well.

Table �: Cumulative Changes in Real Rice Prices, “Early” 2007 to “Early” 2008 (percent) Country World Price

(US$) (1)

World Price (DC) (2)

Domestic Price (DC) (3)

DC Pass through (%) = (3)/(2)

(4) Bangladesh 203 171 54 32 China, People’s Rep. of 144 115 5 4 India 203 178 15 8 Indonesia 203 174 −5 −3 Philippines 144 104 46 44 Thailand 203 169 131 78 Viet Nam 202 158 85 54

DC = domestic currency. Note: With regard to “early”, all calculations compare a month in the first half of 2008 with the same month in 2007 to control

for seasonality, although the months are different across countries. The chosen month for a given country is that month between April and June for which data are available, and where column � is largest (to capture different peak months in different countries). For Bangladesh, India, Indonesia, and Thailand, that month is April. For Viet Nam it is May, and for the PRC and the Philippines it is June.

Source: Author’s calculations.

There have been substantial differences across countries during the past year with respect to the extent of price transmission, just as there were in 2003–2007. The obvious question is: Why did prices increase so much in some countries, but much less in others? There is no general answer: individual country analyses are required.

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E. Summary of Price Transmission Results

There are two important reasons for wanting to understand the extent of price transmission from world markets to domestic markets. First is to understand the impact on consumers, especially those who must buy most of or all their staple foods from the market. Second is to understand the impact on incentives facing farmers. If high world prices are passed through to domestic producers, a more vigorous production result will be forthcoming than otherwise.

Working against a supply response, however, are increases in input prices, especially for fertilizer, fuel, and seeds (prices of the last input are likely to follow the same trend as output prices). Before the recent surge in prices, the value of these inputs accounted for perhaps one sixth of the value of gross output in Asian rice farming (labor, land, and returns to management usually account for well over half the gross value of production). The ratio of one sixth suggests that the negative effect on farmer incentives of a 60% increase in fertilizer prices will be offset by just a 10% increase in output prices.

If fuel and fertilizer are the only inputs whose prices have increased in real terms, even if they have doubled, it seems likely that incentives for farmers have improved on balance. Especially in rice-exporting countries where world prices have been transmitted to a substantial extent, even after the depressing effect of higher fertilizer prices is taken into account, farmers will have substantially enhanced incentives to expand production. If wages and land rents have also increased, incentives from higher output prices could be muted (although land-owning farmers providing most of their own labor will see these higher factor prices as higher incomes). Unfortunately, up-to-date data on prices for labor and land are not easily available. Early evidence from Asian rice harvests through August 2008—especially in India, Indonesia, Thailand, and Viet Nam—suggests that farmers are responding quite enthusiastically to higher rice prices.

Still, the magnitude of the improved incentives is much less than the price increases reported on world markets due to less than perfect transmission of world prices to domestic markets, and to increases in input prices. Thus, the ultimate supply response is still subject to a great deal of uncertainty in both the short and long run.

Causes of High Food Prices | ��

IV. Country Results: Contrasting Experiences of Rice Importers and Exporters

Policies are complex and differ from one country to another. The recent experiences of two exporters—Thailand and Viet Nam—and two importers—Indonesia and the Philippines—are discussed in this section to show the dramatic impact of diverse policy approaches.

In the broadest terms, there were three alternative policy approaches pursued by these four countries. Despite much internal political discussion after the new government was elected in early 2008, Thailand kept its border open and did not restrict rice exports. It did not release any of the 2.1 million metric tons of government-owned rice stocks that had accumulated since a farm-price support program began in 2005 (despite strong internal and external pressures), but it did not prevent private traders from selling into the world market.

At the other extreme, Indonesia stayed resolutely out of the world rice market. It had maintained very high rice prices since 2004, with sharp price run-ups late in 2005 and again in 2006 (Figure 19). These high prices were tolerated in the name of “food security”, and the implied political support from rice farmers.

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Figure 19: Food Price In�ation

Jan 2005 Jan 06 Jan 08 JulJan 07 JulJulJul

Philippines (50%)

Thailand (36.1%)

Indonesia (36.2%)

Viet Nam (47.4%)

Note: Figures in parentheses indicate the weights for food in overall inflation. Sources: CEIC Data Company Ltd.; International Monetary Fund, International Financial

Statistics online, both downloaded 28 August 2008.

The Philippines and Viet Nam seem to be tied at the waist by their mutual export-import relationship. Both countries sought to stabilize their domestic rice prices, and they engaged in very extensive rice trade with each other, on government account. Figure 14 above has already shown that rice prices increased rapidly in the Philippines, and Table 3

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above has shown that domestic rice prices increased by 44% between early 2007 and early 2008.

Similarly, despite a ban on rice exports initiated early in 2008 and not lifted until July, Vietnamese rice prices shot up by 85% over the same period (Table 3 and Figure 15 above). What can explain such bizarrely unstable prices in the face of such active, and expensive, efforts to stabilize them? The only credible explanation is that price expectations changed on the part of key participants in the rice economy of both countries, partly because the two countries were so actively, and publicly, engaged with each other in the rice trade.6

These changed expectations then led to precautionary hoarding on the part of farmers, traders, and consumers. (A “run” on retail rice supplies in Ho Chi Minh City supermarkets in May showed how powerful this hoarding mentality could be.)

Neither the Philippines nor Viet Nam were short of supplies during this time. While government rice stocks were a bit on the low side in the Philippines, private sector stocks account for most of total stocks, and these stocks were ample. Domestic production in 2008 was forecast to be substantially above that in 2007, and there were no adverse climatic shocks at the time. �arge import contracts were being negotiated, so domestic supplies were adequate in quantity terms. Viet Nam typically exports about 20% of domestic production and the export bans it put in place should have ensured ample local supplies.

Supplies were adequate in both countries and neither allows the private sector to arbitrage prices between domestic and international markets.7 Thus the most likely explanation for the surge in domestic prices was speculation and panic on the part of domestic farmers, traders, and consumers in those two countries, who were well informed about the trades on the international market between the Philippines and Viet Nam in early 2008. Of course, once retail prices started to rise, this behavior became self- reinforcing.

The contrast with Indonesia and Thailand is striking. In the end, after much political debate—even talk of establishing a rice exporters’ cartel like the Organization of the Petroleum Exporting Countries—Thailand allowed exports to continue and domestic prices to follow world prices. For several months Thailand was the only country with significant exportable supplies, and picked up customers from India and Viet Nam. Although domestic rice prices shot up—by 131% from early 2007 to early 2008—the � The Office of the President in the Philippines routinely made public statements on the extent of necessary imports

and the need to obtain them from Viet Nam. 7 While the private sector does participate in international rice trade in both countries, it is the government that

decides the quantities of imports or exports; private traders are not free to make this decision.

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overall impact on the rate of inflation in Thailand was modest, as food and beverages make up only 36.1% of the overall consumer price index (Figure 20), and rice is a relatively small part of that. As Figure 21a shows, food price inflation surged to more than 10% early in 2008, but nonfood inflation also rose sharply. Inflation was more of a macroeconomic phenomenon than a food phenomenon in Thailand.

Partly because rice prices were already so high in Indonesia, none of the run-up in world prices was passed into domestic prices (indeed, Indonesian rice prices actually fell slightly between early 2007 and early 2008 in the wake of an excellent harvest, stimulated by high producer prices and very good rains from La Niña weather pattern— see Table 3 and Figure 19 above). Much of the food price inflation seen in Figure 21b was due to rising palm oil prices (despite efforts to stablize domestic palm oil prices through higher export taxes) and the cost of tahu and tempe, both derived mostly from imported soybeans, and a staple of Indonesian diets. However, food price inflation in early 2008 in Indonesia was roughly double the rate of that in Thailand, despite the radically different pass-through of rice prices from the world market to domestic consumers.

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Figure 20: Rice Prices in Indonesia (Rupiah per kilogram)

Jan 2000 Jan 01 Jan 03Jan 02 Jan 04 Jan 06 Jan 07Jan 05 Jan 08

Jakarta wholesale price

Farmgate rice price

Retail price

Note: Farmgate rice prices are quoted in terms of wet paddy (gabah kering panen). After drying and milling, 100 kilograms of wet paddy produce roughly 55 kilograms of rice. Rp 2,500 per kilogram of wet paddy is therefore equivalent to Rp 4,545 per kilogram of rice.

Sources: Retail price from Ministry of Trade of the Republic of Indonesia, wholesale price from PT Food Station, and farmgate price from Badan Pusat Statistik, Republic of Indonesia (adapted from Rosner 2008).

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Figure 21a: Food versus Nonfood Price In�ation, Thailand (percent)

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Sources: CEIC Data Company Ltd., International Monetary Fund, International Financial Statistics online, all downloaded 28 August 2008.

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Figure 21b: Food versus Nonfood Price In�ation, Indonesia (percent)

Jan 2005 Jan 06 Jan 07 JulJan 08JulJulJul

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Nonfood

Sources: CEIC Data Company Ltd., International Monetary Fund, International Financial Statistics online, all downloaded 28 August 2008.

The parallels between Viet Nam and the Philippines can be seen in Figures 21c and 21d. In contrast to Thailand, both countries showed more than threefold increases in the rate of food price inflation (although from a much lower base in the Philippines than in Viet Nam). Efforts at food price stabilization in both countries clearly failed. By contrast, Indonesia managed to stabilize rice prices—at extremely high levels—but failed to contain food price inflation in other important commodities. Thailand, with the most open border and the biggest runup in rice prices, did best in overall food price stability. What a paradox, and also what a guideline to current and future trade policy makers!

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Figure 21c: Food versus Nonfood Price In�ation, Viet Nam (percent)

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Sources: CEIC Data Company Ltd., International Monetary Fund, International Financial Statistics online, all downloaded 28 August 2008.

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Figure 21d: Food versus Nonfood Price In�ation, Philippines (percent)

Jan 2005 Jan 06 Jan 07 JulJan 08JulJulJul

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Sources: CEIC Data Company Ltd., International Monetary Fund, International Financial Statistics online, all downloaded 28 August 2008.

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V. Can Anything be Done about High Food Prices?

The main explanatory factors behind the gradual run-up in food prices from the early 2000s to mid-2007 were spillover from the broad resource demands generated by rapid demand growth, the declining dollar, and the lagged effect of earlier declines in real food prices and their (endogenous) impact on stock-to-use ratios. But these factors do not explain the sharp run-up in many staple food prices from mid-2007 to mid-2008. The explanation for this varies by commodity and period, but in addition to the broad factors affecting all commodity markets just noted—especially high oil prices and the declining dollar—new end uses for food grains and vegetable oils as biofuels, bad weather and diseases, and political decisions by food exporters to insulate their consumers from world prices led to the sharp increases in food prices. Panicked hoarding on the part of countries and individuals clearly played a role in the spike in rice prices, and financial speculation may have contributed to spikes in other commodities, especially oil, wheat, corn, and vegetable oils.

The longer-term issue is whether supply responses can meet the outlook for the rapid growth in demand. In the past, when food prices spiked and talk of an impending Malthusian crisis arose, output responded to bring world food prices to their long-run downward trend, though with a lag (Figure 2 above). This time, however, expectations are that such a benign output response may not be forthcoming, for the following reasons:

(i) �ittle additional high-quality agricultural land is now available for farming.

(ii) Yields of existing agricultural technologies have essentially been unchanged for decades because of the paucity of investment in research during this time. Thus raising yields from actual farmer practices to the present technology potential is the only source of increased output until new agricultural technologies are developed. New technologies, however, are at least a decade away. Moreover, the yield gap to full potential has largely been closed except for Africa,

(iii) The costs of essential inputs—fuel, fertilizer, and water—to obtain greater yields are both high and growing rapidly. In addition, prolonged periods of high grain prices could raise land rents and rural labor costs.

In view of these difficulties, it seems unlikely that basic food prices will return to their real long-run downward trend, seen so clearly in Figure 2 above. Instead, a return to the real average prices seen in 2007 would be considered a major accomplishment from the perspective of late August 2008. That is, when the panic subsides and the financial speculators move on to “greener pastures”, the new equilibrium price for rice, for example, is likely to be in the $500–600 range, not in the $300–400 range (in 2007 prices). Other basic food commodities are likely to exhibit similar patterns.

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Should policy makers try to do anything about this new equilibrium? Clearly, it was appropriate to do everything possible to prick the speculative price bubbles, especially for rice, since reversing the dynamics of rising price expectations, and the private hoarding that exacerbated them, brought dramatic price relief in just a few months. It is unfortunate that the world does not have any internationally mandated mechanism for stabilizing grain prices, or for keeping large countries from destabilizing them. But that is the world we live in. Domestic policies will trump international cooperation whenever politicians see a short- run advantage in closing borders or subsidizing trade. The world was lucky that Japan had 1.5 million metric tons of unwanted rice imports in storage and received a World Trade Organization waiver from the US to reexport some of it to the Philippines. The deal marked the turning point in world rice prices (even though the rice has yet to be shipped as of early September—thus emphasizing again the importance of expectations in short- run price formation).

Equally, it was also appropriate for the international community to rally resources on behalf of increased food aid to the most affected populations. Safety nets for poor consumers are essential in a world of highly unstable food prices. But no one should be fooled into thinking that such safety nets are a solution to poverty, or even high food prices, in more than a transitory way. The only sustainable solution for these households is inclusive, or pro-poor, economic growth that provides reliable real incomes and stable access to food from home production or in local markets.

The appropriate policy response to high food prices, then, is to find ways to stimulate such growth. Much of the action is likely to be in the agriculture sector, especially in investments to raise productivity of basic food crops (see, for example, Brahmbatt and Christiaensen 2008). High food prices now provide plenty of incentives to make those investments, but many of those investments—especially in research and extension— would have paid off at the prices of a decade ago if donors and governments had recognized the full social value of rising agricultural productivity (Timmer 1995 and 2008b). These are political decisions that are driven only indirectly by market realities. Perhaps it is good news that the market is sending very clear signals on what to do.

Technical Appendix

Appendix 1. The Analytics of What Causes High Food Prices

Understanding causation implies an empirically refutable model of mechanisms of action. For food prices, this means an analytical model based on supply and demand mechanisms with equilibrium prices derived from basic competitive forces. There are many such models in existence (International Food Policy Research Institute, Food and Agricultural Policy Research Institute, Food and Agriculture Organization, United States Department of Agriculture, and World Organization for Agriculture), but none that address the specific issues in this paper (Munier 2008, Trostle 2008).

Here we seek to understand the contribution from a wide range of basic causes to high prices of important food commodities—rice, wheat, corn, and palm oil. Some of these causes may be exogenous, e.g., weather shocks or legislated mandates for biofuel usage. But many will be endogenous, e.g., responses of producers and consumers to prices themselves, perhaps even policy responses of governments to prices. Export bans for rice as a way to prevent domestic food price inflation are an obvious example (Brahmbhatt and Christiaensen 2008).

The model of price formation developed here attempts to incorporate all of these factors in a rigorous enough way to bring data to bear on answering the key question: what caused the recent run-up in world market prices for these basic commodities? For several of the factors, the answers remain more impressionistic than statistical, but we push the statistical approach as far as it will go (perhaps too far; see the Granger Causality tests in Appendix 3).

A Simple Model of Price Formation to Use as a Heuristic Device

Consider the most basic model of commodity price formation that is capable of illuminating our problem.

D f a P sr P lr a P Pt t t d t n d t t

sr t n lrd d= =− −( , , , ),

S g b P sr P lr b P Pt t t s t n s t t

sr t n lrs s= =− −( , , , ),

where Dt = demand for the commodity during time t; St = supply of the commodity during time t; f and g = functional forms for demand and supply functions, respectively; at = time-dependent shifters of the demand curve; bt = time-dependent shifters of the supply curve; Pt = equilibrium market price during time t; Pt-n = market price during some previous time period t-n; and, srd, srs, lrd and lrs = indicators that demand and supply responses will vary depending on whether they are in the short run sr or long run lr. In the specification below, these will be short-run and long-run supply and demand elasticities.

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In short run equilibrium, Dt = St. For simplicity (and the ability to work directly with supply and demand elasticities), assume the demand and supply functions are Cobb-Douglas. Then

log log log log log loga sr P lr P b sr P lr Pt d t d t n t s t s t n+ + = + +− −

Solving for the equilibrium price P,

log [log log ] /[ ] log [ ] /[ ]P b a sr sr P lr lr sr srt t t d s t n s d d s= − − + − −−

Taking first differences to see the factors that explain a change in price from t-1 to t reveals a somewhat complicated result:

d P b b a a sr srt t t t t d slog {[log log ] [log log ]} /[ ]= − − − − +− −1 1 [log log ][ ] /[ ],( )P P lr lr sr srt n t n s d d s− − +− − −1

where d Ptlog = the percentage change in price from time period t-1 to time period t (for relatively small changes). This is what we are trying to explain. What “causes” changes in d Ptlog ? Why are food prices high?

To answer these questions, it helps to simplify the equation. �et SR = the net short-run supply and demand response sr srd s− , which is always negative because srd < 0 and srs > 0 . �et LR = the net long-run supply and demand response lr lrs d− , which is always positive, for similar reasons (note that the demand coefficient is subtracted from the supply coefficient in this case, the opposite from the short-run coefficients above). Let d b b bt t tlog log log= − −1 , which for small changes is the percentage change in the supply shifters. �et d a a at t tlog log log= − −1 , which for small changes is the percentage change in the demand shifters. Finally, let d P P Pt n t n t nlog log log ( )− − − += − 1 , which for small changes is the percentage change in the commodity price for some specified number of time periods in the past, for example, 5 or 10 years (after which the long-run producer and consumer responses to price have been realized).

Combining all of these new definitions, we have a simpler equation explaining percentage changes in commodity prices:

Percent change in Pt = [percent change in bt - percent change in at]/SR + [percent change in Pt-n] LR/SR

The “surprising” result is how simple the answer appears to be. There are four key drivers:

(i) the relative size of changes in at to bt, i.e., factors shifting the demand curve relative to factors shifting the supply curve;

(ii) the relative size of short-run supply and demand elasticities (srs and srd);

(iii) the relative size of long-run supply and demand elasticities (lrs and lrd); and

(iv) how large the price change was in earlier time periods.

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Why the Analytics Matter

A simple numerical example, with plausible parameters, shows the power of this “explanatory” equation. Assume the following numerical parameters for purposes of illustration:

srd = −0.10

srs = +0.05

lrd = −0.30

lrs = +0.50

These values imply that SR = −0.15 and LR =0.80.

The short-run elasticities assumed here are quite low, but realistic for annual responses. Demand responds 1% for a 10% change in price; supply only responds by half a percentage point to a similar 10% price change (the signs, of course, are negative for demand and positive for supply responses).

The long-run elasticities are also on the low side of econometric estimates, but again, seem realistic for a world facing increasing resource constraints. Although some estimates of long- run supply response are quite high—approaching unity or higher; these were estimated for time periods when acreage expansion was significant and fertilizer usage was just becoming widespread (Peterson 1979).

Assume, as seems to be the case since the early 2000s, that demand drivers have been larger than supply drivers, with demand shifting out by 3.0% per year and supply shifting out just 1.5% per year (an example of such a growing imbalance is shown in Figure 4). Finally, assume that prices in the past have been “low”, so the change in Pt-n is –10.0%. What do all these parameters mean for current price change?

Plugging these values into the price change equation yields the following result:

Percent change in Pt = [1.5% − 3.0%]/−0.15 + [−10.0%]0.80/−0.15 = [10.0%] + [53.3%] = 63.3% higher.

This is a very dramatic result. The imbalance between “current” supply and demand drivers causes the price to rise by 10%, but the historically low prices (and “only” a 10% decline in the earlier period) cause current prices to be 53% higher, as the long-term, lagged response from producers and consumers to these earlier low prices has a very large quantitative impact. Much of the slow run-up in food prices from 2003 to 2007 would seem to be caused by producers and consumers gradually responding (i.e., reflecting their “long-run” responses) to earlier episodes of low prices, especially from the late 1990s until about 2003. For example, between 1996 and 2001 the real price of rice declined by 14.7% per year!

Over long periods of time, the first driver is clearly most important—how fast is the demand curve shifting relative to the supply curve? At the level of generality specified in this model, the actual underlying causes of these shifts do not matter. All that matters is the net result. If the demand

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curve is shifting outward by 3% per year, and the supply curve is shifting out by just 1.5% per year, the difference of 1.5% per year will push prices higher, by an amount determined by net short-run supply and demand elasticities with respect to price. The “simple” fact is that commodity price changes are driven by the net of aggregate supply and demand trends, not their composition.

It is important to realize that the analytical model of price formation makes a sharp distinction between factors that shift the demand and supply curves (the at and bt coefficients), and the responsiveness of farmers and consumers to changes in the market price (the srs and srd coefficients), which show up as movements along the supply and demand curve. Analytically, the distinction is very clear, but empirically it is often hard to tell the difference. If farmers use more fertilizer in response to higher grain prices, should this count as part of the supply response or as a supply shifter? If governments and donor agencies restrict their funding of agricultural research because of low grain prices, is the resulting lower productivity potential a smaller supply shifter a decade later or a long-run response to prices? Whatever the labels, it is important to understand the causes.

The Composition of Changing Demand and Supply Trends

This ambiguity can be a serious problem, because it is the composition of changing demand and supply trends that we are seeking to understand, even quantify, as a way to understand the causes of high food prices. The list of possible factors is long. For demand, it includes (in order of predictability):

1. Population (driven by demographic transition, fertility, mortality, famine)

2. Income growth (driven by economic policy, trade, technology, governance) (i) Direct consumption (ii) Indirect consumption through livestock feeding or industrial utilization

3. Income distribution (driven by globalization, food prices, agricultural growth, structural transformation)

4. Biofuel demands (driven by political mandates and the price of petroleum) (i) Direct demand for maize and vegetable oils (ii) Ripple effects on other commodities

5. US dollar depreciation (most commodities on world markets are priced in dollars)

6. Food prices (endogenous, driven by supply/demand balance and technical change; impact felt through the demand elasticities)

7. Private stockholding (i) Commercial (driven by price expectations and supply of storage) (ii) Household (driven by price panics and hoarding)

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8. Public stockholding (driven by buffer stock policy) (i) Trade policy (ii) Procurement policy

9. Financial speculation (i) Futures/options markets and “sophisticated” speculators (ii) Role of commodity index funds available to general investors

For supply, the list is not so long, but the factors may be even more difficult to understand and quantify:

1. Area expansion (i) Irrigation and cost of water (ii) Deforestation and environmental costs (iii) “Benign” area expansion in Africa and �atin America?

2. Yield growth (i) Availability and costs of inputs (a) Fertilizer costs (b) Energy costs (c) Sustainability issues

(ii) Seed technology and the GMO debate (iii) Management improvements/farmer knowledge

3. Variability (i) Weather (ii) Climate change

The original goal of this paper was to put quantitative weights on each of the supply and demand factors in terms of their role in causing the current high levels of food prices for key commodities in developing Asia: rice, wheat, corn, and palm oil. Other researchers are attempting to do the same thing for other regions or for global markets. The main debates have been over how much biofuels and financial speculation have caused the run-up in food and oil prices. A paper by Mitchell (2008), for example, caused a furor when it was “leaked” to the press in July: his finding was that perhaps three quarters of the run-up in grain prices was caused by US policy toward ethanol production from corn. At the same time, the US Secretary of Agriculture was arguing publicly, at the FAO Food Summit in June, that biofuel production played only a minor role in high food prices: 2–3%. Somebody is wrong.

The point is that these are contentious issues with no clearly accepted methodology for resolving them, a point also stressed by Abbot, Hurt, and Tyner (2008):

The factors driving current food price increases are complex. We make no attempt to calculate what percentage of price changes are attributable to the many disparate causes, and, indeed, think it is impossible to do so (Abbot et al. 2008, 8; emphasis added).

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The simple model here reveals why. If, for example, population growth is adding 1.5% per year to demand for a staple food grain, income growth is adding 0.5% per year to direct demand for that grain, and indirect demand via livestock feeding is adding 1.0 % per year, demand is growing by 3% per year. If, at the same time, supply is growing by 1.5% per year (0.5 % from area expansion and 1.0% from annual yield growth, for example), the net result is that aggregate demand growth exceeds aggregate supply growth by 1.5% per year, putting upward pressure on the equilibrium price of this food grain. Even if lagged prices had been in long-run equilibrium until demand shifters started to outstrip supply shifters, just this imbalance of 1.5% per year leads to price increases of 10% per year with the assumed short-run supply and demand elasticities.

Conclusion

There is no meaningful way to say what element of demand is growing “too fast” so long as each of the components of demand growth is growing relatively steadily. Indeed, the “blame” for the rising grain price can equally be laid at supply growth that is “too slow.” Market clearing prices are driven by the aggregate of supply and demand in that market at a point in time. Prices themselves cannot reveal the underlying composition of those supplies and demands (the origin of the classical “identification problem”).

This perspective on formation of market prices presents a conundrum. The “slow and steady” shifters of both supply and demand can explain gradual increases in prices, such as seen from the mid-2000s until late 2007 (see Figure 3). The lagged response to earlier periods of low prices can explain some acceleration in these prices, especially for rice and wheat. But the explosion in food prices late in 2007 and in the first half of 2008 clearly requires additional explanation involving factors not incorporated in the simple model of price formation just outlined. Much of the additional “analytical” explanation of short-run price movements will be provided from the supply of storage model, with its focus on links between inventory movements and price expectations in futures markets.

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Appendix 2. The Supply of Storage Model and Short-run Price Behavior

The link between the supply of grain held in storage and prices in both spot and futures markets has long been the subject of analytical attention (Working 1933, 1948, 1949; Keynes 1936; Kaldor 1939; Telser 1958, Brennan 1958; Cootner 1960, 1961; Weymar 1968; Williams and Wright 1991). The basic “supply of storage” model that has emerged from this theoretical and empirical work is the foundation for understanding short-run price behavior for storable commodities (Houthakker 1987). It stresses the interrelated behavior of speculators and hedgers as they judge inventory levels in relation to use. The formation of price expectations is the key to this behavior.

The basic supply of storage model is a simple extension of the supply/demand model already used here. The formulation here follows Weymar’s presentation, with three behavioral equations and one identity (error terms are omitted for simplicity):

C f P Pt c t t

L= ( , ) (1)

H f P Pt h t t

L= ( , ) (2)

( ) ( )*P P f It t p t− = (3)

I I H Ct t t t= + −−1 (4)

where C = consumption, P = price, PL = lagged price, H = production (harvest), I = inventory, and P*= expected price at some point in the future.

The first two equations, indicating the dependency of consumption and production on current and/or lagged price, reflect traditional micro economic theory. While other variables may appear in these relationships (e.g., consumer income, government support levels), their exclusion here will not affect the discussion that follows. [The third equation] represents the “supply of storage” curve … and reflects the notion that the amount of a commodity that people are willing to carry in inventory depends on their expectations as to future price behavior. If they feel that the price will increase substantially, they will be willing to carry heavier inventories (supply more storage) than would otherwise be the case. Because the inventory level is in fact determined by the identity expressed in [the fourth equation], the supply of storage function can be used to explain the gap between the current price and price expectations in terms of the current inventory level (Weymar 1968, 28; emphasis added).

Thus the relationship between current inventories and current price helps explain price expectations, and vice versa. These price expectations can then be expressed in prices on futures markets. The actual working out of this theory empirically requires a close understanding of the behavior of market participants—farmers, traders, processors, and end users (consumers)—in their role as hedgers or speculators. The current controversy over the role of “outside” speculators— investors who are not active participants in the commodity system—has many precursors in the history and analysis of commodity price formation on futures markets (see, for example, the Telser- Kaldor debate reviewed by Cootner 1960).

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The empirical difficulty in using the supply of storage model to understand short-run price behavior is having current information on inventory levels. This is not such a severe problem when virtually all the commodity storage is in commercial hands, as with cocoa or wheat, and stock levels for such commodities can be estimated fairly accurately. For a commodity such as rice however— which is mostly grown by smallholders, is marketed by a dense network of small traders and processors, and is purchased by consumers in a readily storable form (milled rice)—stock levels can change at any or all levels of the supply chain, and there is virtually no data available on these inventory levels.

For the purposes here, then, the main advantage of the supply of storage model is its ability to build conceptual links between long-run supply and demand trends, where basic models of producers and consumers provide operational guidelines to decision making and price formation, and very short-run movements in prices that often seem totally divorced from supply and demand fundamentals. Because long-run trends are gradually built up from short-run observations, these links are crucial for understanding price behavior even in the long run.

The key, then, to making the supply of the storage model operational in the short run, is to use it to gain insight on formation of price expectations. In the very short run, from day-to-day or week- to-week, these expectations seem to be driven by a combination of price behavior for commodities broadly, and by the specifics of individual commodities. Broad commodity price trends are captured by the International Monetary Fund commodity price index, the Economist price index, or the Goldman-Sachs commodity price index, for example. Thus, traders operating in any one specific commodity market, such as oil, corn or wheat, will be following closely the broader price movements for all commodities (Sanders and Irwin 2008). As the main body of this report stresses, these broad price movements seem to be driven by basic macroeconomic forces such as rates of economic growth, the value of international currencies, especially the US dollar, and relative inflation rates.

But traders are also following closely the specifics of the commodity as well. Here inventories (especially relative to actual use for consumption) are the key to price formation, once the harvest/ supply situation for the crop is established. Clearly, the analytics of price behavior for oil or metals begin to look quite different from the analytics of food commodities at this stage, as seasonal production and the inherent need to store the commodity for daily use throughout the year drive inventory behavior via the supply of storage.

Typically, commodities for which inventory data are reasonably reliable tend to have their prices driven by unexpected supply behavior, whereas commodities with poor data on inventories, especially where significant inventories can be in the hands of millions of small agents—farmers, traders, consumers—tend to have their extremes in price behavior generated by rapidly changing price expectations themselves, and consequent hoarding or dishoarding. The short-run price dynamics for rice thus look significantly different from wheat or corn, partly because of the different industrial organization of the respective commodity systems. There are surprisingly few studies of individual commodity systems that are set within this broader macroeconomic and organizational framework (see Timmer 1987 for an exception). The world food crisis in 2008 provides ample rationale for major new studies within this framework for all of the major food commodities.

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Appendix �. Testing for Granger Causality across Exchange Rates and Commodities8

It is possible to examine the changing relationships for price formation across commodities in a formal way using the methodology of Granger Causality. Simply put, variable X is said to “Granger Cause” variable Y if time series information on variable X adds to the explanation of variable Y over and above the ability of past values of variable Y to explain the current value. Econometrically, vector autoregressive (VAR) techniques are used to determine how much of variable Y can be explained using just lagged values of variable Y itself, after which lagged values of variable X are added to the regression. If these lagged values are statistically significant in contributing additional explanatory power to variable X, then variable X is said to “Granger cause” variable Y. Reverse causation is routinely tested as well, and with many macroeconomic variables, direct and reverse causality are often found simultaneously.

A plausible interpretation of the visual model in Figure 7 would suggest that the depreciating US dollar might cause oil prices to rise. Through a biofuels connection, higher oil prices might then cause corn (maize) prices to rise (the main mechanism analyzed in the Farm Foundation report; see Abbot, Hurt, and Tyner, 2008). Higher corn prices might then spill over to other commodities through both supply and demand linkages, thus causing wheat, rice, or palm oil prices to rise. Using Granger causality methods, it is possible to test certain elements of this interpretation. In the first instance we are seeking very short-run linkages that are most likely mediated through futures and other financial markets, so daily price movements are required to observe such short- run effects. Indeed, given the split-second decision making on most trading floors where these “investments” are being made, even daily prices might aggregate away some of the effects we wish to observe.

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1 Jan 2002

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Appendix Figure 3.1: Euro/US$⇒Brent Crude

Note: The Granger Causality test is applied on a daily (with ��-day lag) rolling �-month data starting from �� December �999 to 2 July 2008. The symbol “⇒” indicates the direction of causality.

Source: Author’s calculations.

8 This part of the Technical Appendix is very much research in progress and thus raises far more questions than it answers.

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Appendix Figure 3.1 shows a startling result for the Granger test that the exchange rate between the Euro and the US dollar “causes” the price of oil (Brent). A 15-day lag is specified and the model is run on (daily) rolling 6-month horizons, starting on 31 December 1999 and ending on 2 July 2008. Each observation in Appendix Figure 3.1 is thus the outcome of a Granger regression on 6 months of price data, resulting in 2090 regressions. The vertical axis is the probability that the null hypthosis of no Granger causation is rejected. Values between 0.95 and 1.00 reflect a very high probability that Granger causation in the direction specified is significant.

As Appendix Figure 3.1 demonstrates, there are several lengthy intervals when the exchange rate seems to be “causing” the price of oil (which was shown in Figures 5 and 6)—at least seven intervals of more than 2 months just between 2000 and 2008. But there are also many intervals when there seem to be no linkages at all between the two markets. If the question is, “did the depreciation of the US dollar cause high oil prices”, the answer seems to be, “it depends on when you look.” No model that assumes a stable relationship between the two variables can possibly capture this behavior. To understand it, we almost certainly need to understand behavior in financial markets and especially the formation of price expectations on the part of traders in these markets, including markets for commodities.

From this perspective, the most volatile element in the sudden and sharp run-up in food commodity prices is likely to be the “hot money” in search of the next investment boom, after the crash in tech stocks and then real estate derivatives. The source of this hot money is the massive liquidity infusion provided by the US Federal Reserve System as it seeks to stave off a recession caused by collapsing real estate values and subsequent threats to the nation’s financial system itself (see Frankel 2006). This money has to go somewhere. Thus the real trigger for the recent spike in food prices seems to be speculative behavior on the part of large investment/ hedge funds with hundreds of billions of dollars looking for the next price bubble. The combination of a rapidly falling dollar, movement of investment funds into commodities, especially petroleum, and then on to other commodities is the trigger needed for the food market to explode. The Bank of International Settlements in Basel estimates that hundreds of billions of dollars are now invested in commodity funds, and until recently, they all were betting on higher prices.

Exchange Rates Driving Food Commodity Prices

Of course, the depreciating dollar can be reflected directly in prices of food commodities. In the medium run, both supply and demand adjustments by producers and consumers to changes in the value of the US dollar relative to their own domestic currency cause the US dollar price of most commodities to rise when the dollar falls. In the very short run, however, in daily price formation, a declining dollar seems likely to stimulate financial speculation into commodity markets, thus establishing a direct price link even before producers and consumers have had a chance to adjust. Appendix Figure 3.2 and Appendix Figure 3.3 show how these connections come and go between the Euro/US$ rate and corn and hard wheat prices, respectively. We still do not know why these short-run speculative connections get established for shorter or longer periods of time, and then disappear altogether for extended periods of time.

It is especially difficult to explain these short run price linkages for rice (see Appendix Figure 3.4). For long periods of time the Euro/US$ rate seems to drive the price of Thai rice. This may simply be a factor of the Thai baht being linked to the appreciation of the Euro, with the US dollar price of Thai rice being converted directly from the baht wholesale price.

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Appendix Figure 3.2: Euro/US$⇒Corn (Maize)

Note: The Granger Causality test is applied on a daily (with ��-day lag) rolling �-month data starting from �� December �999 to 2 July 2008. The symbol “⇒” indicates the direction of causality.

Source: Author’s calculations.

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Appendix Figure 3.3: Euro/US$⇒Hard Wheat

Note: The Granger Causality test is applied on a daily (with ��-day lag) rolling �-month data starting from �� December �999 to 2 July 2008. The symbol “⇒” indicates the direction of causality.

Source: Author’s calculations.

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Appendix Figure 3.4: Euro/US$⇒Rice

Note: The Granger Causality test is applied on a daily (with ��-day lag) rolling �-month data starting from �� December �999 to 2 July 2008. The symbol “⇒” indicates the direction of causality.

Source: Author’s calculations.

Cross-Commodity Linkages

One broad hypothesis underlying the various explanations for sharply higher food prices on world markets has been the link between oil prices and food commodity prices. As the main body of the report puts it, if high oil prices are here to stay, high food prices are here to stay. The logic of this connection, through biofuel production, depends on medium- to long-run responses by producers and consumers to the profitability of converting corn or vegetable oils into ethanol or biodiesel. But again, financial speculators can see this longer-run potential and convert it into short-run price behavior by investing in futures markets (and other more exotic derivatives). Appendix Figure 3.5 and Appendix Figure 3.6 show how the oil price drives the daily formation of maize and palm oil prices. Again, we need to understand why the periods of strong price linkages come and go.

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Appendix Figure 3.5: Oil (Brent)⇒Corn (Maize)

Note: The Granger Causality test is applied on a daily (with ��-day lag) rolling �-month data starting from �� December �999 to 2 July 2008. The symbol “⇒” indicates the direction of causality.

Source: Author’s calculations.

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Appendix Figure 3.6: Oil (Brent)⇒Palm Oil

Note: The Granger Causality test is applied on a daily (with ��-day lag) rolling �-month data starting from �� December �999 to 2 July 2008. The symbol “⇒” indicates the direction of causality.

Source: Author’s calculations.

Most commodity analysts think the main connection between the maize market and wheat market comes through livestock feeding, with soft wheat serving as a very close substitute for maize in many feed rations. Appendix Figure 3.7 and Appendix Figure 3.8 test which direction this linkage tends to run in the very short run. Visually, it seems like soft wheat had more of an impact on maize prices before 2004 (Appendix Figure 3.7), with maize having more of an impact on soft wheat after then (Appendix Figure 3.8). Such a change would be consistent with the argument that biofuel demand for maize in the US after 2005 became a much more important driver of maize prices. Formal confirmation of this hypothesis is part of the ongoing research.

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Appendix Figure 3.7: Soft Wheat⇐Corn (Maize)

Note: The Granger Causality test is applied on a daily (with ��-day lag) rolling �-month data starting from �� December �999 to 2 July 2008. The symbol “⇐” indicates the direction of causality.

Source: Author’s calculations.

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Appendix Figure 3.8: Corn (Maize)⇒Soft Wheat

Note: The Granger Causality test is applied on a daily (with ��-day lag) rolling �-month data starting from �� December �999 to 2 July 2008. The symbol “⇒” indicates the direction of causality.

Source: Author’s calculations.

What explains rice price behavior, in terms of cross commodity linkages? Normally, rice behaves as a “special” commodity, driven mostly by national and international balances for the commodity itself, with relatively weak connections to other commodities (Dawe 2008b, c, d). Rice is not used for livestock feed or biofuel production, except in very unusual circumstances. The Japanese, for example, allow their imported rice required by WTO commitments to deteriorate in storage, and then feed it to livestock.

But there are substantial regions in Asia where rice competes with wheat in consumption. Over the long run, commodity analysts expect rice and wheat prices to reflect this substitution and exhibit a relationship that captures the opportunity cost of producing each commodity (at the long-run margin). Although this relationship is likely to be stable only in the long run, with very substantial divergences from year to year, it is apparently important enough for short-run commodity traders to factor wheat prices into expectations about rice prices, and vice versa. Appendix Figure 3.9 and Appendix Figure 3.10, respectively, show the episodes when short-run prices of hard wheat drive rice prices, and when rice prices are driving the prices of hard wheat.

Although the timing of the linkages across all these commodities is not yet understood, it is clear that financial markets must be the main integrator of these markets in the very short run, for daily price formation. The Granger Causality results already show that there are episodes when the rice market is connected to the hard wheat market (in both directions). The wheat market (mostly via the market for soft wheat, which competes at both the production and consumption margin with hard wheat) is connected to the maize market. All of these commodity markets are linked at times to the market for oil and to the rate of exchange between the Euro and the US dollar.

Understanding the timing of these linkages, and what causes their strength to come and go, is the purpose of the next stage of research.

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Appendix Figure 3.9: Hard Wheat⇒Rice

Note: The Granger Causality test is applied on a daily (with ��-day lag) rolling �-month data starting from �� December �999 to 2 July 2008. The symbol “⇒” indicates the direction of causality.

Source: Author’s calculations.

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Note: The Granger Causality test is applied on a daily (with ��-day lag) rolling �-month data starting from �� December �999 to 2 July 2008. The symbol “⇐” indicates the direction of causality.

Source: Author’s calculations.

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Causes of High Food Prices | �1

About the Paper

C. Peter Timmer writes about the causes of high food prices, focusing on staple grains—rice in particular—and edible oils. He shows that although food prices have come down from the spikes of early 2008, they are likely to remain higher than they were in early 2007 for years to come. The paper explores the implications for policy and includes a technical appendix that provides the analytical framework of this analysis.

About the Asian Development Bank

member countries substantially reduce poverty and improve the quality of life of their people. Despite the region's many successes, it remains home to two thirds of the world's poor. Six hundred million people in the region live on $1 a day or less. ADB is committed to reducing poverty through inclusive economic growth, environmentally sustainable growth, and regional integration.

Based in Manila, ADB is owned by 67 members, including 48 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. In 2007, it approved $10.1 billion of loans, $673 million of grant projects, and technical assistance amounting to $243 million.

Asian Development Bank 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org/economics ISSN: 1655-5252 Publication Stock No.: Printed in the Philippines

  • Abstract
  • I. Introduction
  • II. What has Caused Commodity Prices to Increase since 2000?
    • A. Layers of Causation
    • B. The Biofuel Debate
    • C. The Rice Difference
    • D. Summing up the Factors Causing High Food Prices
  • III. Transmission of World Commodity Prices into Domestic Economies
    • A. Exchange Rates
    • B. Transmission to Domestic Economies
    • C. Consumer Prices of Rice: Pass-Through is Incomplete
    • D. Price Movements in Early 2008
    • E. Summary of Price Transmission Results
  • IV. Country Results: Contrasting Experiences of Rice Importers and Exporters
  • V. Can Anything be Done about High Food Prices?
  • Technical Appendix
  • Appendix 1. The Analytics of What Causes High Food Prices
  • Appendix 2. The Supply of Storage Model and Short-run Price Behavior
  • Appendix 3. Testing for Granger Causality across Exchange Rates and Commodities
  • References

trostle_Global Agricultural Supply and.pdf

United States Department of Agriculture

www.ers.usda.gov

A Report from the Economic Research Service

Abstract

World market prices for major food commodities such as grains and vegetable oils have risen sharply to historic highs of more than 60 percent above levels just 2 years ago. Many factors have contributed to the runup in food commodity prices. Some factors refl ect trends of slower growth in production and more rapid growth in demand that have contributed to a tightening of world balances of grains and oilseeds over the last decade. Recent factors that have further tightened world markets include increased global demand for biofuels feedstocks and adverse weather conditions in 2006 and 2007 in some major grain- and oilseed-producing areas. Other factors that have added to global food commodity price infl ation include the declining value of the U.S. dollar, rising energy prices, increasing agricultural costs of production, growing foreign exchange holdings by major food-importing countries, and policies adopted recently by some exporting and importing countries to mitigate their own food price infl ation. This report discusses these factors and illustrates how they have contributed to food commodity price increases.

Keywords: Agricultural prices, food prices, prices, supply, demand, global supply, global demand, food infl ation, energy prices

Acknowledgments

The report was improved by comments, questions, and suggestions, from Mike Dwyer of the Foreign Agricultural Service, Carol Goodloe of the Offi ce of the Chief Economists, Dave Stallings of the World Agricultural Outlook Board, and Joy Harwood of the Farm Service Agency. Special thanks go to Paul Westcott, Bill Coyle, and Janet Perry of the Economic Research Service for numerous substantive contributions and for helping incorporate reviewers’ comments on a compressed schedule. Cynthia Ray produced the fi nal report on a much shortened schedule.

Ronald Trostle

WRS-0801

May 2008

July 2008 (Revised)

Contents

Approved by USDA’s World Agricultural

Outlook Board

Introduction . . . . . . . . . . . . .2

Long-Term Trends . . . . . . .5

Increased Meat Consumption Means In- creased Demand for Grain and Protein Feeds . . . . . . .12

Developments Since 2000. . . . . . . . . . . . . . . . . . .13

The Role of Biofuels . . . . .15

Further Developments . . .20

Policy Responses to Rising Food Prices. . . . . . . . . . . . .23

Implications for Food Security . . . . . . . . . . . . . . 25

Food Price Infl ation Impact on Social Unrest . . . . . . . . .27

Summary of Factors Contributing to Higher Food Prices. . . . . . . . . . . . .28

Prospects for the Future . .29

Global Agricultural Supply and Demand: Factors Contributing to the Recent Increase in Food Commodity Prices

ERS
Revision to this report (July 2008)
This revised report adds a timeline of factors behind rising food prices (see: “A Mix of Short- and Long Term Factors Are Contributing to Higher Food Prices” on page 6), and new information and data on land use associated with the production of biofuels (see: “Update on Global Land Use in Biofuel Feedstock Production” on page 19). The new information on biofuel land use replaces figure 22 in the original report. Several typographical and formatting errors have also been corrected.
lking
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2 Global Agricultural Supply and Demand: Factors Contributing to the Recent Increase in Food Commodity Prices/ WRS-0801

Economic Research Service/USDA

World market prices for major food commodities such as grains and vege- table oils have risen sharply to historic highs—more than 60 percent above levels just 2 years ago. Retail food prices in many countries have also risen in the last 2 years, raising concerns around the world.

No one factor has been the cause of the price runup in food commodity prices. Some factors refl ect underlying trends in supply and demand for agricultural commodities that began more than a decade ago. Other devel- opments that have contributed to the price increase have occurred more recently. Some factors refl ect signifi cant structural changes in supply and demand relationships; others can be interpreted as short-term shocks to global supply and demand for agricultural products.

Figure 1 shows an index of monthly prices for food commodities, e.g., grains, vegetable oils, meats, seafood, sugar, bananas, and various other commodities that are the basis for human consumption of staple foods. Although prices, measured in nominal dollars, trended slightly downward between 1980 and 2002, there were several short periods (1980, 1983, 1988, and 1996) when prices did rise from the previous year. After 2001, prices began to rise slowly and by 2004 reached the level that they had been in the mid-1980s. In early 2006, commodity food prices began to rise more quickly. During the last 2 years, prices of these commodities rose sharply to a new high, more than 60 percent above what they were 2 years ago.

Figure 2 puts the evolution of the food commodity price index into broader perspective. Monthly price indices for wheat, rice, corn, and soybeans back to 1970 have been added to the index for food commodity prices. Wheat and rice account for much of the world food consumption of grains. Corn is used for both food and animal feed. Soybeans provide vegetable oil for human

Introduction

Figure 1

Food commodity prices rose more than 60 percent in the last 2 years

Index: January 1992 = 100

Source: International Monetary Fund: International Financial Statistics.

1980 82 84 86 88 90 92 94 96 98 2000 02 04 06 08 50

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This revised report adds a timeline of factors behind rising food prices (see: “A Mix of Short- and Long Term Factors Are Contributing to Higher Food Prices” on page 6), and new information and data on land use associated with the production of biofuels (see: “Update on Global Land Use in Biofuel Feedstock Production” on page 19). The new information on biofuel land use replaces fi gure 22 in the original report. Several typographical and formatting errors have also been corrected.

3 Global Agricultural Supply and Demand: Factors Contributing to the Recent Increase in Food Commodity Prices/ WRS-0801

Economic Research Service/USDA

consumption and protein feed for animals. Combined, the four crops account for a large share of the staple foods that are consumed globally.

Two general patterns are especially signifi cant in fi gure 2. First, the index of average food commodity prices (data only available back to January of 1980) closely tracks the prices of the four major crops (wheat, rice, corn, and soybeans), although in a somewhat dampened manner. Second, there have been periodic spikes in the prices of the four crops during the last 38 years. Although some of the price spikes focused on only one of the crops, in general the prices of all four crops rise and recede in a similar pattern. This occurs because buyers can substitute among these or other commodities, whether for food use or animal feed use, and purchase whichever is cheaper. With the exception of the early 1970s, each period of rapidly rising prices was followed by a retreat back to their pre-spike level.

The question on the minds of many consumers around the world is, “Will food prices drop again this time?” Or, stated another way, “Is the current price spike any different from those of the past, and if so, why?”

Before we begin to explore the factors contributing to the most recent rise in food commodity prices, two more additions to the graph provide an even broader perspective on the current increase in food commodity prices.

Figure 3 charts the price index for food commodities along with an index for the average of all commodities and an index for crude oil. Although the food commodity index has risen more than 60 percent in the last 2 years, the index for all commodities has also risen 60 percent and the index for crude oil has risen even more.

Since mid-1999, when all three indices were at about the same level (and were about where they had been 10 years earlier), food commodity prices have risen 98 percent (as of March 2008); the index for all commodities has

Figure 2

Food commodity price spikes since 1970

Index: January 1992 = 100

Source: International Monetary Fund: International Financial Statistics.

1970 74 78 82 86 90 94 98 02 06 0

50

100

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Wheat Soybeans

Rice

Corn

Food commodity

4 Global Agricultural Supply and Demand: Factors Contributing to the Recent Increase in Food Commodity Prices/ WRS-0801

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risen 286 percent; and the index for crude oil has risen 547 percent. In this perspective, the recent rise in food commodity prices might not seem so severe after all. However, because an increase in the price of food—a basic necessity—causes hardships for many lower income consumers around the world, food-price infl ation is socially and politically sensitive. That is why much of the world’s attention is now focused on the increase in food prices more so than on the more rapid increase in prices of other commodities.

Figure 3

Prices of many commodities rose

Index: January 1992 = 100

Source: International Monetary Fund: International Financial Statistics.

Monthly 1992 1994 1996 1998 2000 2002 2004 2006 2008 0

50

100 150 200 250

300 350 400

450 500 550 600

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All commodities

Food commodities

5 Global Agricultural Supply and Demand: Factors Contributing to the Recent Increase in Food Commodity Prices/ WRS-0801

Economic Research Service/USDA

A number of long-term, slowly evolving trends have affected the global supply and demand for food commodities. The impact of these trends has been to slow growth in production and to strengthen demand. The resulting tightening of the global supply and demand balance has gradually put upward pressure on agri- cultural prices. Many of these long-term trends have been exacerbated by the more recent developments that have put additional upward pressure on world prices by further reducing supplies and increasing demand.

The annual growth rate in the production of aggregate grains and oilseeds has been slowing. Between 1970 and 1990, production rose an average 2.2 percent per year. Since 1990, the growth rate has declined to about 1.3 percent. USDA’s 10-year agricultural projections for U.S. and world agricul- ture see the rate declining to 1.2 percent per year between 2009 and 2017.1

Growth in productivity, measured in terms of average aggregate yield, has contributed much more to the growth in production globally than has expan- sion in the area planted to grains and oilseeds. Global aggregate yield growth averaged 2.0 percent per year between 1970-1990, but declined to 1.1 percent between 1990 and 2007. Yield growth is projected to continue declining over the next 10 years to less than 1.0 percent per year.

The growth rate for area harvested has averaged only about 0.15 percent per year during the last 38 years. In USDA’s agricultural projections, crop prices do not decline much over the next decade. The continued higher prices provide the incentive for producers to respond by increasing the area allo- cated to crops during the coming decade. Some of this expanded area planted will come from land converted to cropland from non-cropland uses, such as pasture and forest. Area harvested will also increase as a result of more inten- sive use of existing cropland, generally from double-cropping and reduced fallow area.

Reduced agricultural research and development by governmental and inter- national institutions may have contributed to the slowing growth in crop yields. Stable food prices during the last two decades have led to some complacency about global food concerns and to a reduction in R&D funding levels. Although private sector funding of research has grown, private sector research has generally focused on innovations that private companies could sell to producers. These have often been cost-reducing rather than yield- enhancing technological developments. Publicly-funded research might be more likely to focus on innovations that would increase yields and produc- tion, particularly in parts of the world where farmers are unable to pay royal- ties for new varieties of seeds.

1USDA’s 10-year agricultural projec- tions are a Departmental consensus on a longrun scenario for the agricul- tural sector. The projections are not a USDA forecast of what the future will be, but instead are a description of what would be expected to happen with a continuation of current farm legislation and under very specifi c assumptions regarding the macro- economy, trade policies, weather, and international developments. The projections provide a neutral back- drop, reference scenario that provides a point of departure for discussion of alternative farm sector outcomes that could result under different domestic or international assumptions. The pro- jections referred to in this report were prepared in October through December 2007 and refl ect a composite of model results and judgment-based analyses. See the documentation of the baseline process at http://www.ers.usda.gov/Briefi ng/ Baseline/ .

Long-Term Trends

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Other trends show an even longer history of gradually slowing production growth.

• For decades, each year a small percentage of the world’s agricultural land has been converted to nonagricultural uses.

• The ability to obtain more water for agricultural uses has gradually become more diffi cult, either because gravity-fl ow irrigation systems are

A number of factors have contributed to the tight market conditions that set the stage for the sharp increase in food commodity prices since 2002. Some factors refl ect underlying trends in supply and demand for agricultural commodities that began more than a decade ago. Trends of more rapid expansion in demand and slower growth in production began in the 1990s, and contributed to declining global demand for stocks of grains and oilseeds since 2000. Then, rising crude oil prices and changing biofuel policies provided incentives to expand biofuel production in some coun- tries. Also, since the early 2000s, the declining value of the dollar and the foreign accumulation of foreign exchange reserves (U.S. dollars) enabled some coun- tries to increase food commodity imports, even as world prices denominated in dollars reached record highs. On the supply side, largely due to rising energy prices, production costs for most of the world’s farmers

were increasing and, in 2006 and 2007 adverse weather in a number of countries reduced global production of grains and oilseeds.

Together, these factors resulted in declining global stock-to-use ratios for aggregate grains and oilseeds which, by 2007, fell to the lowest levels since 1970. Importers faced declining market supplies and many countries experienced politically sensitive increases in domestic food prices, leading some to contract aggres- sively for future imports, even at world record prices. Finally, in late 2007 and early 2008, various exporters of food commodities imposed restrictions on exports in an attempt to moderate domestic food price infl ation. These actions, combined with the already tight market conditions, set the stage for the further rapid increases in food prices in late 2007 and early 2008.

A Mix of Short- and Long-Term Factors Are Contributing to Higher Food Prices

Factors contributing to higher food commodity prices

Strong growth in demand based on:

Increasing population + Rapid economic growth + Rising per capita meat consumption

Slowing growth in agricultural production

Rapid expansion biofuels production

Dollar devaluation

Large foreign exchange reserves

Adverse weather

Exporter policies

Importer policies

Aggressive purchases by

importers

Declining demand for stocks of food commodities

Escalating crude oil price

Rising farm production costs

Supply factors in blue

Demand factors in red

1996 1998 2000 2002 2004 2006 2007 2008

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more diffi cult and expensive to develop, or because irrigation wells have to be dug deeper as water tables decline.

These factors are changing slowly and likely played a negligible role in the recent increase in world prices. Additionally, although climate change has increasingly become a concern, its impact on crop production is unclear.

The demand for agricultural commodities has also been affected by some long-term trends. Over the last decade, strong global growth in average income combined with rising population to increase the demand for food, particularly in developing countries. As per capita incomes rose, consumers in developing countries not only increased per capita consumption of staple foods, they also diversifi ed their diets to include more meat, dairy prod- ucts, and vegetable oils, which in turn, amplifi ed the demand for grains and oilseeds.

Global economic growth has been strong since the late 1990s (fi g.5). For developing countries, growth has been quite strong since the early 1990s. Growth in Asia has been exceptionally strong for more than a decade. Unusually rapid economic growth in China and India, with nearly 40 percent of the world’s population, has provided a powerful and sustained stimulus to the demand for agricultural products.

Rapid economic growth in developing countries has also resulted in very rapid growth in the demand for energy for electricity and industrial uses, as well as for transportation fuel. The associated increase in petroleum use in developing countries has contributed to rapidly rising oil prices since 1999. The oil imports of China alone grew more than 21 percent per year from 194 million barrels in 1996 to 1.37 billion barrels in 2006.

Figure 4

Total world grain & oilseeds1 Production, yield, area harvested, population & per capita production

Index: 1970 = 100

1Total oilseeds = soybeans + rapeseed + sunflowers.

Source: USDA Agricultural Projections to 2017.

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 80

100

120

140

160

180

200

220

240

260 Production

Yield

Population

Per capita production Area harvested

Exponential trend growth rates: 1970-90 90-07 2009-17 Production 2.2 1.3 1.2 Yields 2.0 1.1 0.8 Area 0.15 0.14 0.39

Population 1.7 1.4 1.1 Per capita 0.56 0.11 0.02

production

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The world’s population growth rate has been trending down since before the 1970s (fi g. 6). This declining trend applies to nearly all countries and regions of the world. However, the number of people on earth is still rising by about 75 million (1.1 percent) per year. This rising population adds to the global demand for agricultural products and energy. The impact on demand is amplifi ed because the most rapid population growth rates tend to be in devel-

Figure 5

Strong economic growth Average real GDP growth rates

Percent

Source: USDA Agricultural Projections to 2017.

World Developed Developing China India United States

0

2

4

6

8

10

12

1975-90

1990-2000

2000-07

1990-2000

World Developed Developing Middle East

Africa Latin America

USA 0

1

2

3

4

Figure 6

Population growth rates decline But still high in developing countries

Percent (by period)

Source: USDA Agricultural Projections to 2017.

1975-90

2000-07

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oping countries. Many of these have rapidly rising incomes, again particu- larly important for agricultural demand due to diet-diversifi cation.

Figures 7-12 illustrate how the rapid increase in global demand for agri- cultural products is facilitated by growth in imports. Note that much of the demand growth comes from developing countries.

Figure 7

Global soybean oil imports

Million metric tons

Source: USDA Agricultural Projections to 2017.

1European Union, former Soviet Union, and other Europe. 2Asia excluding India and China. 3Includes Mexico.

1990 1995 2000 2005

0

2

4

6

8

10

12

India

China

Other Asia2

N. Africa & M. East

Latin America3

EU, FSU, & OE1 Rest of world

1990 1995 2000 2005

0

5

10

15

20

25

30

35

Figure 8

Global rice imports

Million metric tons

Source: USDA Agricultural Projections to 2017.

1European Union, former Soviet Union, and other Europe. 2Includes Mexico.

Other Asia

Other

EU, FSU, & OE1

N. Africa & M. East

Latin America2

Sub-Saharan Africa

Indonesia

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Figure 9

Global coarse grain imports

Million metric tons

Source: USDA Agricultural Projections to 2017.

1EU-27 excludes intra-trade after 2002, EU-15 intra-trade before 2003, Slovenia before 1992. 2Former Soviet Union and other Europe; prior to 1999, includes Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia.

1990 1995 2000 2005

0

30

60

90

120

150

FSU & OE2

East Asia

Latin America

Mexico

Africa & Middle East

China & HK EU−271

Other

Figure 10

Global soybean imports

Million metric tons

Source: USDA Agricultural Projections to 2017.

1Includes Mexico. 2EU-27 excludes intra-trade after 2002, EU-15 intra-trade before 2003, Slovenia before 1992.

1990 1995 2000 2005

0

10

20

30

40

50

60

70

80

Other

China & Hong Kong

N. Africa & M. East

Latin America1

East Asia

European Union2

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Figure 11

Pork imports1

Million metric tons

Source: USDA Agricultural Projections to 2017.

1Selected importers.

China & Hong Kong

1990 1995 2000 2005

0

1

2

3

4

United States

China & Hong Kong

Russia

East Asia

Mexico

Figure 12

Poultry imports1

Million metric tons

Source: USDA Agricultural Projections to 2017.

1Selected importers. 2EU-27 excludes intra-trade after 2002, EU-15 intra-trade before 2003, Slovenia before 1992.

European Union2

1990 1995 2000 2005

0

1

2

3

4

5

Other N. Africa & M. East Russia

East Asia

China & Hong Kong Saudi Arabia

Mexico

European Union2

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Global consumption of meat has been growing much more rapidly than consumption of grains and oilseeds. Between 1985 and 1990, production of meat (beef, pork, chicken, and turkey) rose more than 3 percent per year. Since this was well above the world’s population growth rate of 1.7 percent per year, per capita consumption was able to climb by 1.4 percent per year. Although the average growth rates in production and per capita consumption of meat have declined somewhat since 1990, they are still well above the growth rates for aggregate use of grains and oilseeds.

As the demand for meat rises, the demand for grain and protein feeds used to produce the meat grows proportionally more quickly. Feed-to-meat conver- sion rates vary widely depending on the class of animal and the production practices used to produce the meat. The feed-to-product conversion factors below show an upper bound of how much the demand for feed increases for every 1-pound increase in meat consumed using the typical U.S. production system.

Feed-to-meat conversion rates Pounds of feed needed to Class of animal produce 1 pound of meat

Chicken 2.6 Pork 6.5 Beef 7.0

Source: Ephraim Leibtag, “Corn Prices Near Record High, But What About Food Costs?” In Amber Waves, February 2008. http://www.ers.usda.gov/AmberWaves/February08/Features/CornPrices.htm

Index: 1971 = 100

Exponential trend growth rates: 1975-90 90-07 2009-17 Production 2.2 2.5 2.1 Population 1.7 1.4 1.1 Percapita use 1.4 1.1 1.0

Figure 13

Global meat1 Production, per capita consumption, and population

Source: USDA Agricultural Projections to 2017.

Production

Per capita consumption

Population

1971 1976 1981 1986 1991 1996 2001 2006 2011 2016 50

100

150

200

250

300

350

400

1Total meat = beef + pork + chickens & turkeys.

Increased Meat Consumption Means Increased Demand for Grain and Protein Feeds

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As the new century began, the trends discussed above resulted in slowing growth in production and increasing growth in demand. At the same time, policy decisions in China led to a reduction of its grain stocks. And else- where, there were incentives for governments and the private sector to reduce stocks. Government-held buffer stocks were deemed to be less important after nearly two decades of low and stable food prices. For the private sector, the cost of holding stocks, use of “just-in-time” inventory management, and years of readily available global supplies provided incentives to reduce stock holding. Over the last decade, the shift toward more liberalized trade reduced trade barriers and facilitated trade, which in turn reduced the need for indi- vidual countries to hold stocks.

As a result of these factors, global consumption of aggregate grains and oilseeds exceeded production in 7 of the 8 years since 2000 (fi g. 14). And since 1999, the global stocks-to-use ratio for the aggregate of grains and oilseeds declined from about 30 percent to less than 15 percent currently— the lowest level on record since 1970 (fi g. 15). The resulting low level of world stocks in 2007 has caused importing countries to become anxious about being able to obtain their future food needs.

In 2000, the price of crude oil began to rise—slowly at fi rst (see fi g. 3). The underlying trends of rapid economic growth and demand for energy led to rapidly rising use of crude oil in developing countries.

Beginning in 2002, the U.S. dollar began to depreciate, fi rst against OECD country currencies, and later against many developing countries’ currencies. As the dollar lost value relative to the currency of an importing country, it reduced that country’s cost of importing. Since the United States is a major source of many agricultural commodities, foreign countries’ imports of commodities from the United States began to rise. This put upward pressure

Developments Since 2000

Figure 14

Total world grain & oilseeds Production and total use

Million metric tons

Source: USDA PS&D Database.

1970 1975 1980 1985 1990 1995 2000 2005 1,000

1,500

2,000

2,500

Production

Total use

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on U.S. prices for those commodities. Further, since the world price of major crops are typically denominated in U.S. dollars, the depreciation of the dollar also raises prices (measured in dollars).

Crude oil is also denominated in U.S. dollars, and the declining value of the dollar enabled importing countries to increase their oil imports. This increase in global demand for oil (in addition to the underlying trend resulting from rapid economic growth in developing countries) put additional upward pres- sure on the world price of crude oil, and in 2004 oil prices began to rise more rapidly than in prior years.

Figure 15

Total world grain & oilseeds Stocks and stocks-to-use ratio

Million metric tons

Source: USDA PS&D Database.

Ending stocks

1970 1975 1980 1985 1990 1995 2000 2005 0

100

200

300

400

500

600

700

800

0

5

10

15

20

25

30

35

40

Stocks/use

Stocks/use (percent)

Figure 16

Value of U.S. dollar declines after 20021

Index values, 2000=100

Source: ERS International Macroeconomics Dataset.

1970 1975 1980 1985 1990 1995 2000 2005 60

70

80

90

100

110

120

130

1Real U.S. agricultural trade-weighted dollar exchange rate, using U.S. agricultural export weights, based on 192 countries.

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Biofuels have been produced and used in small amounts in several countries in recent decades. Production generally grew slowly until after the turn of the century. U.S. ethanol production began to rise more rapidly in 2003; EU biodiesel production began to increase more rapidly in 2005.

Brazil and the United States account for most of the world’s ethanol produc- tion. Brazil uses sugarcane as a feedstock, while the United States uses nearly all corn. A number of other countries have policy initiatives designed to increase ethanol production, but so far the total augmentation in produc- tion capacity has been small relative to the combined capacity of Brazil and the United States. In 2006, China reversed its decision to invest in facili- ties to produce more ethanol from grain. Given its food policies, China is now focusing on using cassava and sweet potatoes as feedstocks for future increases in ethanol production.

The European Union is the largest biodiesel producer, and rapeseed oil is its main feedstock. The EU has mandated that biofuels account for 10 percent of transportation fuel use by 2020. The EU cannot produce suffi cient rape- seed to fi ll the mandate and will have to import either some feedstocks for producing biodiesel, or some biodiesel. Russia and the Ukraine are increasing rapeseed production destined for export to the EU as rapeseed, rapeseed oil, and perhaps as biodiesel. Brazil and Argentina are using soybean oil as a feedstock to expand biodiesel production. Brazil’s biodiesel will mostly be produced in the Center West part of the country and will replace petrol-diesel traditionally trucked in from the coast. Most of Argentina’s biodiesel produc- tion is destined for the export market. Canada is expanding biodiesel produc- tion in the Prairie Provinces using rapeseed as the feedstock.

The Role of Biofuels

Figure 17

Ethanol production Mostly from grain feedstocks except for Brazil

Million gallons

Source: USDA Agricultural Projections to 2017.

0

5,000

10,000

15,000

20,000

25,000

30,000

USA

EU

Canada

China

Brazil (from sugarcane)

2004 2006 2008 2010 2012 2014 2016

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U.S. ethanol production began to expand rapidly in 2003. There were several incentives for expanding ethanol production: the increasing price of petro- leum; concerns about the reliability of some traditional exporters; concerns about the pollution effects of methyl tertiary butyl ether (MTBE) and initial switching from MTBE to ethanol; and an environmental objective to increase the use of cleaner burning fuels. Without these developments, the increase in U.S. and world biofuels production would not have been nearly as great.

Corn used for ethanol rose from about 1 billion bushels in 2002/03 to a projected 3.1 billion bushels in the current (2007/08) crop year. With this increase, corn used for ethanol production now accounts for about 24 percent of total U.S. corn disappearance, up from 10 percent in 2002/03. This

Figure 18

Biodiesel production

Million gallons

Source: USDA Agricultural Projections to 2017.

USA

Brazil

Argentina

Ukraine & Russia

Canada

2004 2006 2008 2010 2012 2014 2016 0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

EU

Figure 19

U.S. corn use

Billion bushels

Source: USDA Agricultural Projections to 2017.

1Food, seed, and industrial less ethanol.

European Union2

1990 1995 2000 2005 2010 2015

0

2

4

6

8

10

12

14

16

Feed & residualFeed & residual

Exports

FSI less ethanol1

Ethanol

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increase was facilitated because U.S. corn production rose in response to increased demand and prices, and, in general, other uses of U.S. corn (food, feed, non-ethanol industrial uses, and exports) did not decline.

Figures 20 and 21 provide perspectives about the importance of grain used to produce ethanol relative to the total demand for grain used for all purposes over 1980-2002 and over the most recent 5 years. For both charts, average contributions to the markets, as well as marginal contributions to recent growth are discussed.

Figure 20

Global wheat and coarse grains use, 1980/81– 2002/03 U.S. ethanol accounted for 7 percent of historical global growth

Million metric tons

100%

1980/81 85/86 90/91 95/96 2000/01 0

250

500

750

1,000

1,250

1,500

1,750 Total

Food and other nonfeed (except U.S. corn ethanol)

Feed

Crop year

49%

44%

7% U.S. corn ethanol

Note: Category’s share of the change in total use from 1980/81to 2002/03 shown at the right.

Source: USDA PS&D Database.

Figure 21

Global wheat and coarse grains use, 2002/03 – 2007/08 U.S. ethanol has accounted for 30 percent of recent global growth

Million metric tons

Note: Category’s share of the change in total use from 2002/03 to 2007/08 shown at the right.

Source: USDA PS&D Database.

Crop year

2002/03 03/04 04/05 05/06 06/07 07/08 0

250

500

750

1,000

1,250

1,500

1,750

Total

Feed

Food and other non-feed (except U.S. corn ethanol)

U.S. corn ethanol

100%

44% 27%

30%

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Historically, the amount of grain used to produce ethanol has been a small percentage of the global total used for all purposes. Furthermore, during the 1980s and 1990s, the increase in grain used to produce ethanol accounted for a small percentage of the total increase in demand. Between 1980 and 2002 (before the more rapid growth in ethanol production in the United States began), the amount of corn used to produce ethanol in the United States rose by 24 million metric tons. During the same period, global feed use of wheat and coarse grains increased 144 million metric tons, and food and other non-feed uses (besides U.S. corn for ethanol) increased by 160 million tons. Of the total increase in the demand for wheat and coarse grains (corn, barley, sorghum, rye and oats), ethanol accounted for 7 percent, feed use for 44, and food and other non-feed use, except for U.S. ethanol, for 49 percent. During this period, the strong growth in global demand for food and feed far surpassed the demand for industrial uses of grain. Biofuels was only one of several rising industrial uses of grain (fi g. 21).

Ethanol output increased rapidly after 2002, and from the perspective of global market changes from 2002 onward, provides a somewhat different picture. Between 2002 and 2007, the quantity of U.S. corn used to produce ethanol rose by 53 million metric tons. This accounted for 30 percent of the global growth in wheat and feed grains use. Feed use grew by 48 million tons and accounted for 27 percent of the increase in total use. Food and other non- feed uses climbed 79 million tons and accounted for 44 percent of the global increase in wheat and coarse grains use.

The data suggest that while U.S. corn used for ethanol production had only a small effect on global markets in the 1980s and 1990s, the increase in U.S. ethanol production over the past 5 years and the related signifi cant changes in the structure of the U.S. corn market have had a more pronounced impact on the world’s supply and demand balance for total coarse grains recently. Importantly, since the United States is the world’s largest corn exporter, some of the higher prices resulting from increased U.S. demand has spilled over onto world markets.

Most feedstocks used to produce biofuels come from annual crop produc- tion. Perennial crops, such as oil palm and coconut, as well as previously used vegetable oils and fats, that are feedstocks for biodiesel are the primary excep- tions. Use of crops for biofuel may divert some cropland away from producing crops used for food, feed, and non-biofuel industrial uses. However, in some cases, coproducts such as distiller’s grains (a byproduct when producing ethanol from corn) or soybean meal (a joint product in producing soybean oil from soybeans), continue to be available for food or feed use when biofuels are produced. Also, because global total area harvested is rising, increases in land used to produce biofuel feedstocks have not led to equivalent declines in area planted to traditional food and nonfood uses.

A rough estimate suggests that about 47.8 million acres were used to provide biofuel feedstocks in the 6 major producing countries in 2007 (see box). This would account for about 3-4 percent of arable land in these countries.

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Since the initial release of this report in May 2008, ERS has compiled additional information for estimating 2006/07 land used for biofuel feedstock production in other countries for 2007, as well as fi nal data for updating estimates for the United States. These estimates cover the 6 major countries producing biofuel—either ethanol or biodiesel—together accounting for about 95 percent of global biofuel output in 2007. Estimates of biofuel production for 2007 are combined with crop yields and feedstock-biofuel conversion factors for 2006/07 crop years to derive implied harvested areas of the major feedstocks used in each country (see table). These esti- mates do not include land used for minor feedstocks in these countries, or biofuel feedstock production in other countries, such as Thailand, India, and Indonesia.

Despite rapid global expansion in biofuel production, total land cultivated in biofuel feedstocks amounted to about 47.8

million acres in 2006/07, or 3-4 percent of arable land, in the top six producing countries.* The United States accounted for about 46 percent of the global total, followed by the EU and Brazil. Per acre biofuel yields (combining both crop yields and feedstock-biofuel conversion factors) in 2007 range from from 66 gallons for U.S. soybeans, to 140 gallons for EU rapeseed, to 403 gallons for U.S. corn, to 710 gallons for Brazilian sugarcane. With higher yields from sugarcane, Brazil produced about 76 percent more ethanol per acre of land in 2007 than the United States.

*Land used to produce biofuel feedstocks may also produce food or feed coproducts. Examples include distiller’s grains (produced when corn is converted to ethanol by the dry-mill method) and soybean meal (a joint product of processing of soybeans to produce soybean oil, a biodiesel feedstock). These calculations do not include deduc- tions for the area equivalent of coproducts.

Update on Global Land Use in Biofuel Feedstock Production

Biofuel Production and Land Use by Major Producing Countries, 2006/07

Country Biofuel production3

Biofuel feedstocks4 Biofuel yield5 Implied feedstock area6 Arable land7

Ethanol Bio-

diesel Ethanol

Bio- diesel

Ethanol Bio-

diesel Ethanol Bio-

diesel Coun- try total

Area Biofuel share

Million gallons Gallons/acre Million acres Percent

Argentina -- 117 -- soy (100%)

-- 65 -- 1.8 1.8 70 2.5

Brazil 5,284 105 sugarcane

(100%) soy

(66%) 710 65 7.4 1.1 8.5 146 5.8

Canada

159 27 corn (70%)

-- 370 -- 0.3 -- 0.7 113 0.6

wheat (30%)

-- 115 -- 0.4 -- -- -- --

China1

469 30 corn (70%)

-- 215 -- 1.6 -- 2.4 354 0.7

wheat (30%)

-- 185 -- 0.8 -- -- -- --

EU-27

488 1,480 wheat (48%)

rape (64%)

182 140 1.3 6.8 12.3 281 4.4

sugarbeet (29%)

soy (16%)

550 60 0.3 3.9 -- -- --

United States2

6,485 509 corn (98%)

soy (74%)

403 66 15.7 5.7 22.1 431 5.1

sorghum (2%)

146 -- 0.7 -- -- -- --

Totals 12,884 2,267 28.5 19.3 47.8 1,395 3.4

Note: When countries import/export feedstock for processing, calculations based on biofuel production overstate/understate feedstock area in that country. 1China ethanol production for 2007 from USDA/FAS GAIN report. 2U.S. ethanol production for 2007 from Renewable Fuel Association (http:// www.ethanolrfa.org/). 3Unless otherwise noted, biofuel production data for 2007 are from FO Licht, various publications. 4Percentages indi- cate feedstock shares of ethanol or biodiesel production; 100 percent is assumed when shares of other feedstocks are small. 5Biofuel yields based on 2006/07 crop yields from the USDA PS&D database and biofuel conversion factors based on USDA estimates for the United States and various USDA/FAS GAIN reports for foreign countries. 6Implied area estimates are adjusted for share of biofuel production from the major feedstocks indicated. 7Arable land data are from FAO.

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Developments in 2004

In 2004, agricultural production costs began to rise, especially for energy- related inputs such as fertilizer, fuel, and pesticides. Although there was a lag between the increase in petroleum prices and when farmers began to pay more for fertilizer, fertilizer prices have risen sharply. In the long run, farmers must cover their costs of production. Farm output prices will increase because of reductions in output, until production again becomes profi table, or because of offsetting price increases due to demand strength.

Developments in 2005/06

In early 2006, food commodity prices began to rise more rapidly than in previous years. This increase refl ected many diverse and not necessarily related factors.

During 2006, hedge funds, index funds, and sovereign wealth funds became more involved in agricultural commodity markets. The investors in these funds were not so much interested in agricultural commodities as they were in using commodities to diversify their fi nancial portfolios. The funds held an increasingly large percentage of open interest in the futures market for agricultural commodities, as well as of nonagricultural commodities such as metals and energy. These investors only had a fi nancial interest in the markets and did not intend to take delivery of the agricultural commodi- ties. Indeed, it is likely that in general, neither the investors nor the fi nancial managers that directed the funds’ investments knew much about the funda- mentals of agricultural commodity markets. It is unclear to what extent the effect these new investor interests had on prices and the underlying supply and demand relationships for agricultural products. However, computerized trend-following trading practices employed by many of these funds may have increased the short-term volatility of agricultural prices

The U.S. Energy Policy Act of 2005 mandated that renewable fuel use in gasoline reach 7.5 billion gallons by calendar year 2012. Additionally, the legislation did not provide liability protection for effects of methyl tertiary butyl ether (MTBE), an oxygenating gasoline additive that has been found to contaminate drinking water. As a result, blenders sharply reduced use of MTBE by May 2006 and switched to ethanol as a fuel additive.2

Adverse weather reduced crop production in some countries in 2006. Russia and Ukraine had yield losses due to drought. Australia was in the second year of a severe drought. South Africa also experienced drought. These droughts resulted in lower world production of grains and oilseeds, contributed to a further decline in the global stock-to-use ratio for aggregate grains and oilseeds, and contributed to rising prices. In September 2006, corn prices began a signifi cant rise to a new high.

2Paul Westcott, “U.S. Ethanol Expansion Driving Changes Throughout the Agricultural Sector,” Amber Waves, U.S. Department of Agriculture, Economic Research Service, September, 2007.

Further Developments

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Figure 22

Total world grain and oilseeds1 Production, yield, and area harvested

Index: 1970=100

Source: USDA Agricultural Projections to 2017.

1Total oilseeds = soybeans + rapeseed + sunflowers.

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 80

100

120

140

160

180

200

220

240

260

Production Yield

Area harvested

Developments in 2007

In 2007, a number of adverse weather events affected yields across the globe, including:

• Northern Europe had a dry spring and harvest-time fl oods.

• Southeast Europe experienced a drought.

• Ukraine and Russia experienced a second year of drought.

• A large area of the U.S. hard red winter wheat area had a late, hard, multi-day freeze that killed some of the crop and reduced yields over large areas.

• Canada’s summer growing season was hot and dry, resulting in lower yields for wheat, barley, and rapeseed.

• Northwest Africa experienced a drought in some of its major wheat- and barley-growing areas.

• Turkey had a drought that reduced yields in its nonirrigated production areas.

• Australia was in the third year of the worst multiyear drought in a century. Grain yields were very low and exports plummeted.

• Argentina had a late freeze followed by drought that reduced corn and barley yields.

The result of adverse weather in 2007 was a second consecutive drop in global average yields for grains and oilseeds (fi g. 22). In historical perspec- tive, two sequential years of lower global yields occurred only three other times in the last 37 years. The lower production caused yet another decline in the global stocks-to-use ratio and created a world market environment

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characterized by concern among importers about the future availability of supplies.

In May of 2007, soybean prices began a rapid upward trend. Corn prices were already at record highs.

By late summer 2007, some importers were aggressively contracting for imports of grains and oilseeds. Even though prices were at record highs, importers were buying larger volumes, not less. Some countries that usually imported suffi cient quantities of grain to meet their needs for the following 3-4 months began to contract for imports to meet their needs for the following 5-10 months.

Large foreign exchange reserves held by some major importing countries enabled them to contract for their import needs regardless of how high the world price rose. There have been very large accumulations of foreign exchange reserves held by oil-exporting countries (OPEC and Russia) and by countries with large non-oil trade surpluses (China, Japan, and other Asian countries). Countries holding these large foreign exchange reserves are able to import large volumes of food commodities in order to meet their consump- tion needs and allay their domestic food price infl ation. In essence, they can bid supplies away from other traditional importers that do not hold signifi cant foreign exchange reserves.

In August 2007, world wheat prices began a sharp upward trend. Rice prices jumped sharply later in the fall.

Figure 23

Foreign exchange reserves

0

200

400

600

800

1,000

1,200

1,400

1,600

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

$ billion

Emerging Asia excluding China

Source: Oxford Economics / Haver Analytics

China

OPEC Russia

Japan

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Policy Responses to Rising Food Prices

The rapidly increasing world prices for food grains, feed grains, oilseeds, and vegetable oils caused domestic food prices at the consumer level to rise in many countries. In response to rising food prices, some countries began to take protective policy measures designed to reduce the impact of rising world food commodity prices on their own consumers. However, such measures typically force greater adjustments and higher prices onto global markets.

In the fall of 2007, some exporting countries made policy changes designed to discourage exports so as to keep domestic production within the country. The objective was to increase domestic food supplies and restrain increases in food prices. A partial list of these policy changes follows:

Eliminated export subsidies:

• China eliminated rebates on value-added taxes on exported grains and grain products. The rebate was effectively an export subsidy that was eliminated.

Export taxes:

• China, with food prices still rising after eliminating the value-added tax rebate, imposed an export tax on a similar list of grains and products.

• Argentina raised export taxes on wheat, corn, soybeans, soybean meal, and soybean oil.

• Russia and Kazakhstan raised export taxes on wheat.

• Malaysia and Indonesia imposed export taxes on palm oil.

Export quantitative restrictions:

• Argentina restricted the volume of wheat that could be exported even before raising export taxes on grains.

• Ukraine established quantitative restrictions on wheat exports.

• India and Vietnam put quantitative restrictions on rice exports.

Export bans:

• Ukraine, Serbia, and India banned wheat exports.

• Egypt, Cambodia, Vietnam, and Indonesia banned rice exports. India, the world’s third largest rice exporter, banned exports of rice other than basmati, signifi cantly reducing global exportable supplies.

• Kazakhstan banned exports of oilseeds and vegetable oils.

Early in 2008, importing countries also began to take protective policy measures to combat rising food prices. Their objective was to make high- cost imports available to consumers at lower prices. A partial list of policy changes follows:

The following countries reduced import tariffs:

• India (wheat fl our)

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• Indonesia (soybeans and wheat; streamlined the process for import- ing wheat fl our)

• Serbia (wheat)

• Thailand (pork)

• EU (grains)

• Korea and Mongolia (various food commodities)

Subsidizing consumers:

• Some countries, including Morocco and Venezuela, buy food com- modities at high world prices and subsidize their distribution to consumers.

Other decisions by importers:

• Iran imported corn from the United States, something that has oc- curred rarely—only when they could not procure corn elsewhere at reasonable prices.

The policies adopted by importing countries also changed price relationships in world markets. Their policy changes increased the global demand for food commodities even when world prices were already rapidly escalating.

The policies adopted by exporting countries to reduce food price infl ation within their own countries resulted in lower supplies available to the rest of the world. Importers who want to buy food commodities now have fewer sources. This heightened concerns among importing countries, stimulating them to buy additional supplies, even at record high prices. The combination of reduced supplies and increased demand meant that world market adjust- ments had to be made by the smaller number of countries trading in the world market that had not changed their trade policies.

The combination of reduced supplies from traditional exporters and increased demand from importers, at a time when the global stocks-to-use ratio was unusually low, increased importers’ concerns about future availabilities to meet consumption needs. This boosted world market prices even more. These contributions to higher world prices in April 2008 exacerbated an already tight supply and demand situation.

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Implications for Food Security

Rising food commodity prices tend to negatively affect lower income consumers more than higher income consumers. First, lower income consumers spend a larger share of their income on food. Second, staple food commodities such as corn, wheat, rice, and soybeans account for a larger share of food expenditures in low-income families. Third, consumers in low-income, food-defi cit countries are vulnerable because they must rely on imported supplies, usually purchased at higher world prices. Fourth, coun- tries receiving food aid donations based on fi xed budgets receive smaller quantities of food aid.

A number of factors affect how much of an increase in world food commodity prices passes through to consumers’ budgets: the percentage of income spent on food, the percentage of retail food expenditures spent on staple foods, government trade and domestic food policies. A simplifi ed comparison of the impact of higher food commodity prices on consumers in high-income countries and on consumers in low-income, food-defi cit coun- tries illustrates these differences.

Impact of Higher Food Commodity Prices On Consumers’ Food Budgets*

High-income Low-income countries food-defi cit countries

I. Base scenario Income $40,000 $800 Food expenditure $4,000 $400 Food costs as % of income 10.0% 50%

Disaggregate retail food spending (staples vs. non-staples) Staples as % of total food spending 20% 70% Expenditures on staples $800 $280 Expenditures on non-staples $3,200 $120

II. Scenario: 50% price increase in staples, partial pass through on staples Assumed % pass through 60% 60% Increase in cost of staples $240 $84 New cost of staples $1040 $364 New total food costs $4,240 $484 Food costs as % of income 10.6% 60.5%

*These are illustrative food budgets that characterize the situations for consumers in high- and low-income countries.

Source: As compiled by ERS.

This illustrative comparison shows that for a consumer in a high-income country, a 50-percent increase in staple food prices causes retail food expen- ditures to rise 6 percent ($240). This results in the percentage of income spent on food rising from 10 to 10.6 percent—less than 1 percentage point. For a consumer in a typical low-income food-defi cit country, food expendi- tures increase only $84, but that is a 21-percent increase in total food expen- ditures. Furthermore, this $84 increase means that the percentage of income spent on food climbs from 50 to more than 60 percent.

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For highly import-dependent or highly food-insecure countries, any decline in import capacity stemming from rising food prices can have challenging food security implications. Foreign food aid donations have provided supple- mental assistance to lower income consumers in many low-income, food- defi cit countries. However, food aid donations have stagnated during the last two decades, and food aid’s share has declined relative to total food imports of low-income countries.3 Higher food commodity prices negatively affect the ability to provide food aid donations. Most food-aid donors budget a fi xed annual amount to fund procurement of food aid commodities. When prices rise, their fi xed budget buys less food to donate. Additionally, higher petroleum prices have been a major factor in the sharp increase in ocean freight rates. This further increases the cost of getting food aid donations to the recipient countries.

3Stacey Rosen and Shahla Shapouri, “Rising Food Prices Intensify Food Insecurity in Developing Countries,” Amber Waves, U.S. Department of Agriculture: Economic Research Service, February 2008.

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Food Price Infl ation Impact on Social Unrest

The recent price spike has led to social unrest in a number of countries.4

Peaceful protests have been held in Malaysia (millers & bakers), Indonesia (markets selling soybeans and meats), and Pakistan (wheat marketers). Peruvian farmers blocked rail lines to protest rising fertilizer costs. In South Africa, members of the National Labor Federation demonstrated against higher food and electricity prices.

Less peaceful demonstrations of consumers’ anger and fear over higher food prices (generally referred to in the news media as riots) have occurred in a variety of countries including:

Guinea Mauritania Morocco Senegal Cameroon Mexico Uzbekistan Yemen Niger Burkina Faso Egypt Haiti Ethiopia Philippines Thailand Mozambique Ivory Coast Bangladesh Indonesia

Most of these incidents have occurred in low-income, food-defi cit countries.

4Incidents gleaned from news media reports.

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Food prices, and particularly the prices for basic food commodities, have risen sharply during the last 2 years. Many factors contributed to these price increases. Long-term trends that led to slower growth in production and rapid growth in demand contributed to a sharp downward trend in world aggregate stocks of grains and oilseeds that began in 1999. Recent factors that have further tightened world markets include increased global demand for biofuels feedstocks and adverse weather conditions in 2006 and 2007 in some major grain- and oilseed-producing areas.

Additional recent developments that have put upward pressure on food commodity prices by further restricting available supplies or increasing demand for food commodities include the devaluation of the U.S. dollar, rising energy prices, increases in agricultural costs of production, growth in foreign exchange holdings by major food-importing countries, and protective policies adopted by some exporting and importing countries.

As a result of these market factors, stocks of grains and oilseeds in the world have fallen to levels that make the global aggregate stock-to-use ratio for grains and annual oilseeds the lowest since 1970. Stocks in major exporting countries are particularly low. All of these factors have contributed to higher world prices for food commodities.

Summary of Factors Contributing to Higher Food Prices

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Prospects for the Future

In assessing prospects for the future, there are a number of uncertainties and concerns:

Global economic growth: If rapid growth continues, particularly in developing countries, it will continue to put upward pressure on food commodity prices through increases in food demand.

Energy prices: If petroleum prices continue to rise, costs of agricultural production will rise, as will the cost of processing, and the cost of trans- porting products to markets both within a country and exporting to other countries. Continued high petroleum prices will also sustain the global incentives to produce more biofuels.

Biofuels production: Global growth in grains- and oilseeds-based biofuels production is expected to slow in the next several years from the rapid gains of the past several years, even with the higher mandates in the United States under the Energy Independence and Security Act of 2007. This will lessen further demand pressures on agricultural markets and likely will result in some reductions in grain and oilseed prices. Nonetheless, with sustained higher levels of biofuels-related demand, world food commodity prices are not projected to retreat to past levels. However, several years into the future, the underlying long-term trend in rapidly increasing global demand is expected once again to be the primary contributor to future upward pressure on food commodity prices.

Supply response capacity of the global agricultural production system:

• Cost of inputs: Continued increases in production costs, especially in energy-related costs, will restrain the world’s production response. Higher costs for fertilizer, fuel, and seeds could cause farmers with- out access to credit to plant less than they otherwise would have, or to shift to crops requiring fewer inputs.

• Additional cropland (quantity and quality): What will be the longrun impact of higher world food commodity prices on the amount of land used to produce the crops? What is the productivity of the land that will be used to increase production?

• Water shortages: How quickly will constraints on the amount of water available for agricultural production become more widespread?

• New seed varieties and use of biotechnology: Will higher food prices encourage some countries to adopt the use of biotechnology, especially genetically modifi ed seed for crops? Will future research focus more on yield-enhancing varieties rather than cost-reducing innovations?

• Biophysical response to climate change: How will climate change affect agricultural production? How will it change temperatures, pre- cipitation, the length of growing seasons, and variability of yields? How, and under what circumstances, will climate change increase and/or reduce production? In affected regions, how diffi cult will it

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be for producers to shift to different crops, to adopt new cropping patterns, and to adjust production practices to the new environment?

With such low world stocks of food commodities, food prices are vulnerable to a production shortfall in one or more major production areas. If a signifi - cant shortfall occurs this year due to weather or disease, food prices might continue to rise sharply from the current high level. Although trade fl ows can mitigate some of these effects, new or existing trade restrictions or barriers can exacerbate price impacts. However, if good crop production conditions exist in the Northern Hemisphere during the next 6 months, food commodity prices could retreat signifi cantly from their current highs.

USDA-depr. of usd.pdf

United States Department of Agriculture

Electronic Outlook Report from the Economic Research Service and Foreign Agricultural Service

www.ers.usda.gov www.fas.usda.gov

Outlook for U.S. Agricultural Trade FY 2008 Export Forecast Raised to Record $101 Billion; Imports Increased to $76.5 Billion

AES-57 February 21, 2008

Fiscal 2008 agricultural exports are forecast at $101 billion, up $10 billion from November’s forecast and 19 billion above 2007. Higher unit values for wheat, feed grains, and soybeans and products account for about $6 billion, or just over half of the overall increase since November. Grain and feed exports rise to a record $32.7 billion; oilseeds and products are expected to reach a record $18.9 billion. Foreign demand remains remarkably strong given sharply higher prices. Tight competitor stocks boost demand for U.S. wheat and corn, with a similar story unfolding for soybeans. Total bulk commodity exports rise 5 million tons to near-record levels, mostly on gains for wheat and corn. High-value animal and horticultural product exports, both setting new records, boost the export forecast $2 billion. Shipping volumes for most meats are raised on strong demand, a weak dollar, and adequate domestic supplies. Similar market conditions benefit horticultural exports.

Approved by the World Agricultural Outlook Board.

Contents

Economic Outlook Export Products Regional Exports Import Products Regional Imports Top Trading Partners Have Changed Dramatically Contact Information Tables Commodity Exports Regional Exports Commodity Imports Regional Imports Reliability Tables Web Sites U.S. Trade Data FAQ & Summary Data Articles on U.S. Trade ---------------- The next release is May 29, 2008

Fiscal 2008 agricultural imports are forecast at a record $76.5 billion, up $1 billion from November and $6.5 billion above 2007. Vegetable oils, dairy products, and grain products account for the largest upward adjustments from the November forecast. Horticultural product imports are forecast to rise $2.6 billion above 2007, with two- thirds of that gain from fruits and vegetables. Grains and feeds, grain products, and oilseeds and products account for 37 percent of the overall increase, mainly the result of higher prices.

Table 1--U.S. agricultural trade, fiscal years 2003-2008, year ending September 30

Item 2003 2004 2005 2006 2007 Nov. Feb.

$ billion Exports 56.0 62.4 62.5 68.6 81.9 91.0 101.0 Imports 45.7 52.7 57.7 64.0 70.0 75.5 76.5 Balance 10.3 9.7 4.8 4.6 11.9 15.5 24.5

Reflects forecasts in the February 8, 2008, World Agricultural Supply and Demand Estimates report.

Source: Compiled by USDA using data from Census Bureau, U.S. Department of Commerce.

Forecast Fiscal 2008

Economic Outlook

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Economic Outlook As energy prices stay high, the U.S and world equities markets retreat from record levels, and the impact of the home mortgage crisis is felt globally, world growth slows. As the dollar weakens further, the world economy is more favorable to growth in U.S. exports and farm exports than expected late last year. U.S. gross domestic product grew 2.2 percent in 2007 with growth between 1.4 and 1.8 percent expected in 2008, down sharply from prior forecasts. Growth slowdowns in North America, Europe, and Japan will keep world economic growth at 3 percent in 2008, down modestly from 2007, but essentially the same as the prior forecast. Growth in Asia, particularly China, and the transition economies is expected to be below 2007, in part due to higher raw material prices and lower growth in Europe and North America. Crude oil prices in 2008 are expected to be up over 10 percent from 2007, and gasoline, diesel, and heating oil prices may rise 8, 10 and 12 percent, respectively. Farm fertilizer prices will be up 7-9 percent in 2008, as wholesale natural gas prices rise in 2008. The macroeconomic picture has deteriorated from late 2007 as growth in 2008 in the U.S., Japan, and Asia is slower than previously expected. Strong but slower growth in China should limit the size of the slowdown in non-Japanese East Asian growth, which would be expected from a U.S. growth slowdown. U.S. growth will slow in 2008 due to a sharp decline in housing construction due to falling home real estate sales and financial market disturbances due to difficulties in sub-prime mortgages and high energy prices. Adjustments to the financial and housing situation are expected to continue into 2009, despite lower interest rates. Rising farm and nonfarm exports will be growth areas in 2008. China’s GDP is expected to grow 10.5 percent in 2008, bringing growth in the rest of East Asia to 4.5 percent. The rest of Asia is slated to grow over 5 percent, with India growing more rapidly, somewhat below prior expectations. Strong international goods trade continues to support near-trend world growth and good growth supports surging trade, continuing a cycle beginning in 2003. This robust trade growth will likely overcome the drag of industrial material prices and greater financing difficulties in keeping the impact of the growth slowdowns in Europe and North America modest, particularly on developing economies. The dollar exchange rate is an important determinant of agricultural trade. Relative to 2007, the dollar, adjusted for relative inflation rates, is expected to depreciate 7 percent against the euro, 6 percent against the yuan, and 8 percent against the Brazilian real in 2008. The dollar is forecast to be up 2 percent versus the yen, unchanged against the Canadian dollar, down 2 percent against the Mexican peso, and down 6 percent against the Argentinean peso. The weaker dollar will partly offset the impact of slowing world growth on U.S. exports. Compared with the November forecast, the economic environment is now perceived as positive to U.S. farm exports as the rest of the world’s growth weakens modestly and the U.S. dollar is weaker in most major markets. The oil market appears to have had only modest economic effects on most major U.S. trading partners, with Canada (as a major energy exporter) better off than expected given the large amount of trade with the United States. The likelihood of recent financial market volatility and continued high energy and input prices triggering a larger slowdown in world growth is modest.

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Export Products The fiscal 2008 forecast for grain and feed exports is raised to a record $32.7 billion, up $5.2 billion from the November forecast and $8.5 billion above last year. The revision is mostly due to surging unit values for wheat and coarse grains, reflecting reduced competitor exportable supplies and strong demand. The forecast for fiscal 2008 wheat exports is up $2.6 billion to $10.1 billion, and mostly reflects higher unit value although volume is also increased. U.S. wheat shipments have started to slow in the last few months as many buyers bought ahead in the fall. Sales are also slowing due to limited exportable U.S. supplies and other competitors entering the market such as Kazakhstan, Argentina, and Australia. The forecast for coarse grain exports is raised to 70 million tons, up 2 million tons since November due mostly to higher unit value. Corn and sorghum exports are up $2.4 billion from November. Coarse grain exports are forecast at 14.1 billion, $4.3 billion above last year’s level. While competitor exportable supplies of corn and other feed grains are limited, U.S. supplies have remained ample due to a record corn crop. Record prices have not yet had any significant dampening effect on importers. In addition, Argentina’s corn export registration ban, now in place for nearly one year, allowed the United States to sell to nontraditional corn importers. China’s corn exports have remained minimal. With much higher sales than last year, the forecast for sorghum exports is unchanged from November; higher imports from the EU offset lower demand from Mexico. Rice exports are unchanged at 3.8 million tons, but higher unit value boosts sales $100 million to $1.7 billion. Stronger sales to Saudi Arabia and Turkey more than offset an expected slowdown to Mexico and countries in Central America, which completed purchases earlier in anticipation of further price increases. The fiscal 2008 export forecast for oilseeds and products is a record $18.9 billion, up $2.6 billion from the November estimate and $5.2 billion higher than fiscal 2007. The revision is mostly due to higher unit values for soybeans (although volume is also raised), but soybean meal and oil forecasts are raised as well. Compared with last year, soybean export volume is still forecast 2.9 million tons lower, but unit values are much higher, reflecting strong demand for feed and non- feed uses. Soybean exports are raised $1.5 billion since November to $11.9 billion, or $3.4 billion above last year. Growing sales to China, which are expected to top 40 percent of U.S. exports, continue to drive export volume. Meal shipments are increased on improved sales to Asia, while rising soybean oil exports are attributed to China and North Africa. The forecast for fiscal 2008 cotton exports is lowered $200 million from the November estimate to $5.6 billion, but still remains a record. This reduction is due to lower export volume and reflects decreased import demand and greater export competition since November, making it more difficult for the United States to reduce its large carryover stocks. The revised estimate is the result of a 2 percent decrease in forecast world consumption due to lower mill use in China, Pakistan, India, and Turkey. These four markets accounted for nearly 60 percent of U.S. exports last year. Since November, estimates of more production and less consumption in India have expanded exportable supplies in competition with U.S. cotton.

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Exports of livestock, poultry, and dairy products are forecast at a record $18 billion in fiscal 2008, up $900 million from the November estimate and $1.7 billion higher than last year. Increased pork and broiler meat shipments and higher prices for animal fats account for most of the change since November. Beef shipments are noticeably higher than last year, but there is little change in the U.S. beef export forecast as traders continue to face BSE-related restrictions in Asian and other markets. Pork exports are raised 140,000 tons since November to a record 1.2 million tons. Expanding pork shipments are supported by abundant domestic supplies, a competitive dollar, and firm demand from key importers Japan and Canada. The revised poultry meat forecast reflects higher broiler shipments and unit value due to firm foreign demand resulting from the weaker dollar. Shipments to key markets Russia and China should continue strong. The forecast for animal fats is raised mostly due to higher unit value as prices rise in vegetable oil markets. Horticultural product exports are increased $1.1 billion from November to a record $19.7 billion, up $1.8 billion from last year. This outlook is supported by a competitive dollar and strong foreign demand for healthful and convenience foods. In general, export value is rising on both volume gains and higher unit value, and sales are surging to our top market Canada. Fresh fruits and vegetables are forecast at a record $5.1 billion, up $200 million from the previous forecast. A larger orange crop more than offsets some decline in grapefruit production. California’s orange crop is projected to increase 40 percent over last year’s crop to 2.1 million tons. Deciduous fruit export value is forecast higher mainly due to higher table grape and pear shipments and a tighter apple market, which should raise apple prices. Mexico and Canada, our largest pear markets, are more than offsetting weaker apple shipments to the United Kingdom. Fresh vegetable exports are forecast higher due to strong growth to Canada and emerging markets like Taiwan. Processed fruit and vegetables are forecast to reach a record $5 billion in fiscal 2008, up $500 million from November. Food service chains and strong demand for fruit juices, dried fruit, and frozen fruits and vegetables support this expansion. Florida’s orange crop, which is used for juice, is expected to be 29 percent larger than last year’s hurricane-damaged crop, up to 6.8 million tons. Whole and processed tree nuts are forecast at $3.3 billion, up $300 million from the previous forecast. Year-to-date almond export volume, mostly to the EU, is well above trend and expected to reach a new record with a record harvest in 2007. Almond prices, which fell in 2006 and 2007, should remain relatively firm as stocks are somewhat lower. The walnut crop is smaller than last year’s crop, and prices are expected to remain strong. Almond exports to the EU continue to increase with the implementation of the Voluntary Aflatoxin Sampling Plan. Other horticultural products including wine, essential oils, and miscellaneous food preparations are all forecast higher.

Table 2--U.S. agricultural exports: Value and volume by commodity, 2006-2008

Commodity Fiscal year 2006 2007 2007 Nov. Feb.

VALUE

Grains and feeds 1/ 5.293 8.940 24.175 27.5 32.7 Wheat 2/ 1.106 3.071 6.365 7.5 10.1 Rice 0.285 0.405 1.279 1.6 1.7 Coarse grains 3/ 2.384 3.537 9.794 11.7 14.1 Corn 2.191 3.024 8.922 10.2 12.4 Feeds and fodders 0.806 1.032 3.489 3.9 4.1 Oilseeds and products 4/ 4.242 6.253 13.669 16.3 18.9 Soybeans 2.967 4.488 8.483 10.4 11.9 Soybean meal 5/ 0.465 0.681 1.933 2.2 2.9 Soybean oil 0.158 0.309 0.611 0.7 1.0 Livestock, poultry, and dairy 3.895 5.100 16.342 17.1 18.0 Livestock products 2.615 2.988 10.106 10.8 11.4 Beef and veal 6/ 0.420 0.531 1.894 2.4 2.3 Pork 6/ 0.693 0.819 2.625 2.7 3.1 Beef and pork variety meats 6/ 0.207 0.266 0.830 0.8 1.0 Hides, skins, and furs 0.490 0.496 2.159 2.3 2.1 Poultry and products 0.825 1.143 3.777 3.7 4.0 Broiler meat 6/ 7/ 0.523 0.783 2.477 2.4 2.7 Dairy products 0.455 0.970 2.459 2.6 2.6 Tobacco, unmanufactured 0.401 0.466 1.144 1.1 1.3 Cotton 0.592 0.876 4.294 5.8 5.6 Seeds 0.276 0.353 0.946 1.0 1.2 Horticultural products 8/ 4.826 5.348 17.911 18.6 19.7 Fruits and vegetables, fresh 1.211 1.380 4.776 4.9 5.1 Fruits and vegetables, processed 8/ 1.086 1.257 4.402 4.5 5.0 Tree nuts, whole and processed 1.040 1.160 2.938 3.0 3.3 Sugar and tropical products 9/ 0.854 1.004 3.455 3.7 3.7 Major bulk products 10/ 22.644 12.843 31.359 38.0 44.7 Total 11/ 20.382 28.342 81.947 91.0 101.0

VOLUME Wheat 2/ 5.541 9.711 28.718 28.7 31.0 Rice 0.783 0.966 3.317 3.8 3.8 Coarse grains 3/ 15.773 19.888 59.104 68.0 70.0 Corn 14.567 17.267 54.095 60.0 62.0 Feeds and fodders 2.762 3.220 11.655 12.1 12.4 Soybeans 11.746 11.247 30.319 26.5 27.4 Soybean meal 5/ 2.092 2.126 7.971 7.5 7.9 Soybean oil 0.256 0.328 0.857 0.7 0.9 Beef and veal 6/ 0.101 0.123 0.442 0.5 0.5 Pork 6/ 0.275 0.322 1.004 1.1 1.2 Beef and pork variety meats 6/ 0.130 0.154 0.506 0.5 0.6 Broiler meat 6/ 7/ 0.637 0.731 2.525 2.5 2.7 Tobacco, unmanufactured 0.062 0.069 0.180 0.2 0.2 Cotton 0.436 0.590 3.104 3.6 3.4 Major bulk products 10/ 34.342 42.472 124.743 130.8 135.8 Total may not add due to rounding. 1/ Includes corn gluten feed and meal, and processed grain products. 2/ Excludes wheat flour. 3/ Includes corn, barley, sorghum, oats, and rye. 4/ Excludes corn gluten feed and meal. 5/ Includes soy flours made from protein meals. 6/ Includes chilled, frozen, and processed meats. 7/ Includes only federally inspected product. 8/ Includes juices. 9/ Includes coffee and cocoa products, tea, and spices. 10/ Includes wheat, rice, coarse grains, soybeans, cotton, and unmanufactured tobacco. 11/ Includes cotton linters. Source: Compiled by USDA using data from Census Bureau, U.S. Department of Commerce.

Forecast Fiscal 2008October-December

---Million metric tons---

---Billion dollars--

5 Outlook for U.S. Agricultural Trade/AES-57/February 21, 2008

Economic Research Service, USDA

6 Outlook for U.S. Agricultural Trade/AES-57/February 21, 2008

Economic Research Service, USDA

Regional Exports Much of the $10 billion increase in the forecast is being driven by broadly higher export prices, so USDA has raised its 2008 forecast to virtually every regional market. The forecast for U.S. agricultural exports has been increased to Asia (up $2.8 billion), Africa (up $1.9 billion), North America (up $1.8 billion), Middle East (up $1.4 billion), Latin America (up $800 million), EU-27 (up $700 million), and the Former Soviet Union (up $500 million). Asia U.S. agricultural exports to Asia in 2008 are forecast at a record $35 billion, up $2.8 billion from the November forecast and $5.7 billion higher than 2007. Much of the upward revision is due to stronger expected exports to Japan (up $600 million), China (up $600 million), and Southeast Asia (up $1 billion, primarily due to an improved outlook for the Philippines and Indonesia). For 2008, Asia is expected to account for almost 35 percent of total U.S. agricultural exports, down slightly from its share in 2007. U.S. exports to China are forecast to reach a record $8.4 billion, up $600 million from the previous forecast and almost $1.4 billion from 2007 levels. Much of the improved outlook is due to an increase in expected sales of soybeans and a wide variety of high value products. China continues to be the world’s largest and fastest growing soybean importer with total imports forecast to reach 34 million tons in 2008, an increase from the previous forecast and 18 percent, or 5.3 million tons, higher than 2007 levels. This—coupled with the U.S. being China’s largest foreign supplier and historically high U.S. export prices—means the value of U.S. soybean exports will significantly exceed previously projected levels and comfortably reach new record highs. Likewise, exports of U.S. consumer-oriented high value products to China continue to expand at impressive rates. In 2007, exports reached a record high of almost $1 billion, up 44 percent from the previous year. With China’s economy expected to be among the fastest growing in the world in 2008, the country’s rapidly growing middle class should boost U.S. high value exports by a comparable amount this year, led by higher sales of poultry meat, red meats, and dairy products. U.S. exports to Japan are forecast to reach $11 billion in 2008, up $600 million from the previous forecast and the highest since the Asian financial crisis in 1997. Much of this increase in export value is due to higher expected commodity prices in 2008, which boosts the value of major bulk commodity exports such as soybeans, corn, and wheat. In addition, Japan is a major export market for U.S. high value products. For 2008, exports of these products are expanding faster than previously expected—benefiting from a growing Japanese economy and a stronger yen. This is boosting Japanese consumers’ purchasing power and increasing the competitiveness of a wide range of U.S. high value exports such as meats, horticultural products, and processed foods and beverages. Fiscal 2008 exports to Southeast Asia are forecast to reach $5.8 billion, up $1 billion from the previous forecast and up $1.5 billion from 2007 levels. Most of the gain is due to revisions for the Philippines (up $400 million) and Indonesia (up $300 million). Likewise, exports to Vietnam are expanding rapidly. In fact, Vietnam is the fastest growing market in the region, and for 2008, exports should exceed $500 million. Wheat, soybeans, cotton, feeds and fodders, and a wide

7 Outlook for U.S. Agricultural Trade/AES-57/February 21, 2008

Economic Research Service, USDA

variety of consumer-oriented high value products (particularly dairy products) dominate U.S. agricultural exports to Southeast Asia. For 2008, U.S. exports of these products are expected to benefit from continued strong growth in consumer incomes and a growing middle class plus local currencies which have appreciated significantly against the U.S. dollar over the past two years. Europe, Africa, and the Middle East U.S. agricultural exports to the markets in this region in 2008 are forecast at $25 billion, up $4.6 billion from the previous forecast and up $6.8 billion from 2007. Like Asia, the revisions since November are broad-based with all major markets being revised higher—Middle East (up $1.4 billion), North Africa (up $1.2 billion), Sub-Saharan Africa (up $700 million), EU-27 (up $700 million), and Russia (up $500 million). For 2008, these markets are expected to account for roughly 25 percent of total U.S. agricultural exports, up from 22 percent in 2007. Upward revisions in 2008 forecasts have been particularly noteworthy for the Middle East and North Africa with export strength across the board in bulk commodities (particularly grains) and high value products. Exports to this region are typically dominated by sales to Egypt, Turkey, and Saudi Arabia. Fiscal 2008 exports to Egypt are projected to reach $2.2 billion (up $400 million from the previous forecast), while exports to Turkey are forecast to reach $1.8 billion (up $300 million) and exports to Saudi Arabia reach the $1 billion mark (up $400 million)—with all three markets easily reaching new record highs. However, other markets in the region are also expected to do well. USDA does not provide specific forecasts for any other markets in the Middle East and North Africa. However, based on export performance in the first quarter of the fiscal year, 2008 is shaping up to be a particularly strong year for U.S. agricultural exports to Morocco, Algeria, Iraq, and Israel. Western Hemisphere U.S. agricultural exports to Western Hemisphere markets in 2008 are forecast at a record $39.7 billion, up $2.6 billion from the previous forecast and up $6.5 billion from 2007. While there have been revisions in the export forecasts across most of the markets in the region, 70 percent of the increase is due to higher expected shipments to Canada and Mexico. For 2008, exports to Western Hemisphere markets are expected to account for almost 40 percent of total U.S. agricultural exports, down slightly from 2007. Canada and Mexico are the United States’ top two markets worldwide. Exports to both are forecast to continue the impressive growth shown over the past 15 years. Exports are forecast to reach a combined $30.2 billion in 2008—up $1.8 billion from November and up almost $5 billion from 2007. This means that our two NAFTA partners currently account for 30 cents out of every dollar in worldwide U.S. agricultural exports—up from just 20 cents on the dollar when NAFTA went into effect 15 years ago. Exports to Canada are projected to reach a record $15.7 billion, up $1 billion from the previous forecast and up $2.5 billion from 2007, due largely to higher exports of meats, horticultural products, and corn. Exports to Mexico are forecast to reach $14.5 billion in 2008, up $800 million from the previous forecast and up $2.2 billion from 2007, due largely to increased exports of wheat, soybeans (including products), dairy products, and fresh fruit.

Table 3--U.S. agricultural exports: Value by region, 2006-2008

Country and region 1/ Fiscal year 2006 2007 2007 Nov. Feb.

Asia 7.210 10.243 29.321 32.2 35.0 East Asia 6.013 8.292 23.952 26.3 28.0 Japan 2.303 2.714 9.693 10.4 11.0 China 1.980 3.243 7.051 7.8 8.4 Hong Kong 0.311 0.397 1.082 1.3 1.4 Taiwan 0.726 0.906 2.932 3.3 3.5 South Korea 0.691 1.030 3.178 3.5 3.7 Southeast Asia 1.002 1.633 4.338 4.8 5.8 Indonesia 0.290 0.458 1.375 1.5 1.8 Philippines 0.229 0.391 0.950 1.1 1.5 Malaysia 0.100 0.154 0.508 0.6 0.7 Thailand 0.227 0.310 0.786 1.0 1.1 South Asia 0.195 0.319 1.031 1.1 1.2 Western Hemisphere 8.021 10.054 33.145 37.1 39.7 North America 6.201 7.392 25.516 28.4 30.2 Canada 3.109 3.907 13.206 14.7 15.7 Mexico 3.093 3.486 12.311 13.7 14.5 Caribbean 0.614 0.803 2.399 2.9 2.9 Central America 0.551 0.737 2.187 2.4 2.6 South America 0.656 1.122 3.042 3.4 4.0 Brazil 0.068 0.104 0.375 0.4 0.5 Colombia 0.276 0.383 1.115 1.4 1.5 Venezuela 0.096 0.181 0.518 0.6 0.8 Europe/Eurasia 2.960 3.963 9.824 11.0 12.3 European Union-27 2/ 2.562 3.279 8.053 8.9 9.6 Other Europe 3/ 0.093 0.154 0.331 0.4 0.5 FSU-12 4/ 0.304 0.529 1.440 1.7 2.2 Russia 0.230 0.436 1.122 1.3 1.8 Middle East 1.027 1.763 4.224 4.7 6.1 Turkey 0.266 0.392 1.363 1.5 1.8 Saudi Arabia 0.100 0.273 0.537 0.6 1.0 Africa 0.835 1.785 4.246 4.7 6.6 North Africa 0.523 1.152 2.628 2.9 4.1 Egypt 0.309 0.465 1.645 1.8 2.2 Sub-Saharan Africa 0.312 0.633 1.619 1.8 2.5 Oceania 0.221 0.285 0.899 1.0 1.0 Transshipments via Canada 5/ 0.108 0.248 0.288 0.3 0.3

Total 20.382 28.342 81.947 91.0 101.0 Total may not add due to rounding. 1/ Projections are based primarily on trend or recent average growth analysis. 2/ The former EU-25 plus Romania and Bulgaria who acceded in January 2007. 3/ Major countries include Switzerland, Norway, Iceland, and former Yugoslav states. 4/ The former 15 Republics of the Soviet Union minus the three Baltic Republics. 5/ Transshipments through Canada have not been allocated to final destination, but are included in the total. Source: Compiled by USDA using data from Census Bureau, U.S. Department of Commerce.

Forecast Fiscal 2008October-December

---Billion dollars--

8 Outlook for U.S. Agricultural Trade/AES-57/February 21, 2008

Economic Research Service, USDA

Import Products

9 Outlook for U.S. Agricultural Trade/AES-57/February 21, 2008

Economic Research Service, USDA

Like a large locomotive that is slowing, it may take some time before U.S. agricultural import growth significantly reduces pace after averaging 11 percent since 2003 and 9 percent in 2007. Although import volume growth slowed from 8 percent in 2006 to 5 percent in 2007 and is expected to slow further in 2008, the price escalation of most farm products over the past year will raise the value of U.S. agricultural imports to an estimated $76.5 billion in fiscal year 2008. Despite higher food and fuel prices, sluggish domestic economic activity and the weak dollar, Americans’ eating habits and choices will keep food products flowing in at a brisk pace. Although grains, feeds, grain products, oilseeds, and oilseed products collectively amount to only 16 percent of the $76.5 billion import bill, their projected $2.4- billion gain in 2008 represents 37 percent of the overall $6.5-billion import increase from 2007. The $2.6 billion additional imports of horticultural crops and products in 2008 contribute 40 percent of the total import gain. And close to two-thirds of the $2.6 billion additional horticulture imports are fruits and vegetables. Thus, the bulk of U.S. agricultural import gains in 2008 is from crop products, not livestock or dairy products, which are expected to increase by only $300 million from 2007. About $1.7 billion of the horticulture import gain in 2008 is from fruits and vegetables and $400 million from wine and beer. Although imported wine and beer have consistently increased in both volume and value each year, American per capita consumption has stabilized between 23.7 and 23.8 gallons annually, down from 26 to 27 gallons in the early 1980s. On average, Americans consume close to 800 pounds of fruits, nuts, and vegetables per capita per year, which represents 36 percent of total annual per capita food consumption of 2,230 pounds. Of this consumed amount of fruits and vegetables, 183 pounds or 23 percent are imported. For wine and beer, the combined import share of U.S. consumption is 20 percent. Imports of beef and veal for 2008 are reduced to $3.2 billion from $3.6 billion in November due to 140,000 fewer tons of expected foreign shipments. The strong Canadian dollar and higher U.S. cattle slaughter rate have discouraged import demand for beef. Also, pork imports were reduced by 25,000 tons, which amounts to an $80-million decline. Instead, imported pork is partly supplanted by more shipments of slaughter hogs from Canada. A record 11 million head of Canadian swine are expected due to disincentives to slaughter hogs and process meat in Canada, including higher feed costs, a less competitive meat processing sector, and the higher exchange rate. As the number of swine shipments is more than offset by lower beef and pork imports, total livestock and meat import value drops by about $300 million. Nevertheless, the estimated $8.9 billion import value for livestock and meats in 2008 is unchanged from the 2007 amount. When dairy products are added to the total, however, the import value for all animal products rises $300 million from more imported cheese and other dairy products. Because U.S. wheat production is projected up 14 percent in 2008, wheat import volume is expected to decline 26 percent. However, since imported wheat prices are 50-percent higher than in the past year, the value of wheat imports is anticipated to rise 15 percent. Although rice import volume is projected up 4 percent in 2008, a 15-percent price hike will boost import value by 20 percent. Higher prices for imported feed grains—up 30 percent on average—plus the 12-percent larger import

10 Outlook for U.S. Agricultural Trade/AES-57/February 21, 2008

Economic Research Service, USDA

volume will likewise boost import value by around 40 percent. Wheat products and other food grain products drive the $300-million projected increase in imported processed grain products. Bulk grain imports add another $200 million, plus $100 million for feeds and fodders. The $1.6-billion jump in imports of oilseed products is attributed to higher prices of tropical oils and larger expected shipments. Import value of oilseeds and products is estimated to be up by 21 percent from 2007, largely due to a 48-percent jump in vegetable oils. Prices of coffee and cocoa beans, as well as rubber and natural gums, are higher in 2007 than in 2006, whereas sugar prices are lower. The prices of imported palm oil, palm kernel oil, and coconut oil were up 44 percent, 60 percent, and 66 percent, respectively, in 2007. Import volume of oilseeds and products is estimated to be up by 14 percent from 2007, largely due to a similar jump in vegetable oil shipments. Prices of other tropical imports, however, such as coffee and cocoa beans and products, sugar, rubber, and gums have been volatile in the past year. The lower import volumes of cocoa and coffee beans thus far are offset by higher prices such that they help drive the import value of tropical crops. Overall, the average prices for imported food, feeds, and beverages were up 8 percent in 2007. The prices of imported commodities in 2007 will impact import values during at least the first half of fiscal 2008 due to the time gap between order and shipment dates.

11 Outlook for U.S. Agricultural Trade/AES-57/February 21, 2008

Economic Research Service, USDA

Table 4--U.S. agricultural imports: Value and volume by commodity, fiscal years 2006-2008 Forecast

Commodity October-December Fiscal year Fiscal year 2008 2006 2007 2007 Nov. Feb.

VALUE

Livestock, dairy, & poultry 3.071 3.371 12.021 12.3 12.3 Livestock and meats 2.264 2.402 8.906 9.2 8.9 Cattle and calves 0.476 0.675 1.698 1.8 1.8 Swine 0.159 0.166 0.646 0.6 0.7 Beef and veal 0.803 0.704 3.386 3.6 3.2 Pork 0.313 0.289 1.211 1.2 1.2 Dairy products 0.707 0.854 2.653 2.7 3.0 Cheese 0.322 0.353 1.077 1.1 1.2 Grains and feed 1.490 1.762 5.993 6.6 6.8 Grain products 1.019 1.145 3.917 4.2 4.3 Oilseeds and products 0.902 1.325 4.018 4.8 5.6 Vegetable oils 0.633 0.939 2.774 3.5 4.1 Horticulture products 7.660 8.264 32.391 35.2 35.0 Fruits, fresh 1.097 1.165 5.406 6.0 5.8 Fruits, processed 0.725 0.888 3.418 4.0 4.1 Fruit juices 0.316 0.426 1.618 2.0 2.1 Nuts and preparations 0.291 0.336 1.079 1.2 1.2 Vegetables, fresh 0.938 1.020 4.165 4.4 4.5 Vegetables, processed 0.781 0.853 3.149 3.4 3.4 W ine 1.239 1.353 4.544 4.8 4.8 Malt beer 0.927 0.843 3.686 4.0 3.8 Essential oils 0.582 0.631 2.427 2.5 2.5 Cut flowers & nursery stock 0.363 0.380 1.531 1.6 1.6 Sugar & tropical products 3.460 3.712 14.141 15.0 15.1 Cane and beet sugar 0.215 0.232 0.814 0.9 0.9 Confections 1/ 0.319 0.318 1.221 1.3 1.3 Cocoa and chocolate 1/ 0.692 0.725 2.593 2.7 2.7 Coffee beans & products 0.811 0.927 3.654 3.8 3.9 Rubber, natural 0.499 0.531 2.087 2.2 2.2 Other imports 2/ 0.277 0.327 1.472 1.6 1.7

Total agricultural imports 16.860 18.760 70.037 75.5 76.5

VOLUME

W ine 3/ 0.235 0.235 0.869 1.0 0.9 Malt beer 3/ 0.889 0.802 3.535 3.7 3.5 Fruit juices 3/ 1.025 1.206 4.794 4.5 4.9 Cattle and calves 4/ 0.696 0.892 2.320 2.6 2.6 Swine 4/ 2.338 2.869 9.474 9.7 11.0 Beef and veal 0.235 0.203 1.026 1.1 1.0 Pork 0.108 0.098 0.424 0.4 0.4 Fruits, fresh 1.868 1.910 8.791 9.3 9.2 Fruits, processed 5/ 0.323 0.340 1.442 1.5 1.5 Vegetables, fresh 0.987 1.109 4.384 4.6 4.8 Vegetables, processed 5/ 0.761 0.757 2.954 3.2 3.0 Vegetable oils 0.644 0.799 2.637 2.8 3.1 Cocoa and chocolate 0.301 0.274 1.140 1.4 1.2 Coffee beans 0.326 0.312 1.370 1.5 1.4 Rubber, natural 0.224 0.248 1.005 1.0 1.1 1/ Confections are consumer-ready products that contain sugar. Cocoa and chocolate are intermediate products. 2/ Tobacco, planting seeds, and cotton. 3/ Liquid volume is in billion liters. 4/ Million head; includes bison. 5/ Excludes juices. Source: Compiled by USDA using data from Census Bureau, U.S. Department of Commerce.

---Billion dollars--

---Million metric tons---

Regional Imports

12 Outlook for U.S. Agricultural Trade/AES-57/February 21, 2008

Economic Research Service, USDA

The largest source of imported grains, grain products, and feeds is Canada, supplying 3 times more than the European Union. For oilseeds and products, 36 percent of imports are shipped from Canada, and 21 percent are supplied by the EU. With respect to imported dairy products, however, the EU is the principal source, shipping as much as the next top sources (New Zealand and Canada) combined. And because of tropical oil price inflation, Malaysia, the Philippines, and Indonesia are among the chief import sources of oilseed products. Among the major suppliers of U.S. agricultural imports, China ranks first in the pace of shipments—24 percent on average in value annually over the past 5 years. Nevertheless, it still lags far behind Mexico, which supplies the U.S. with more than 3 times the products. Fishery and horticultural products are China’s leading food exports to the U.S., combining for 71 percent of total shipment value. Among the other major sources of U.S. imports after China are Australia, Brazil, Indonesia, Chile, and New Zealand. The next tier of suppliers includes Colombia, Thailand, Costa Rica, India, and Guatemala. The higher prices of many tropical crops, especially vegetable oils, offset to a large extent the dollar’s lower exchange rate.

Table 5--U.S. agricultural imports: Value by region, fiscal years 2006-2008 Share

Country and region Fiscal year of total 2006 2007 2007 2007 Nov. Feb.

Percent

Western Hemisphere 8.614 9.560 37.073 52.9 39.5 39.9 Canada 3.636 4.181 14.701 21.0 15.7 15.8 Mexico 2.170 2.424 9.916 14.2 10.3 10.4 Central America 0.570 0.663 3.112 4.4 3.4 3.4 Costa Rica 0.229 0.252 1.214 1.7 1.3 1.3 Guatemala 0.182 0.221 1.028 1.5 1.1 1.1 Other Central America 0.159 0.190 0.869 1.2 1.0 1.0 Caribbean 0.085 0.085 0.451 0.6 0.5 0.5 South America 2.153 2.208 8.893 12.7 9.6 9.8 Brazil 0.625 0.745 2.525 3.6 2.7 2.8 Chile 0.340 0.258 1.922 2.7 2.2 2.2 Colombia 0.375 0.396 1.519 2.2 1.6 1.6 Other South America 0.813 0.810 2.928 4.2 3.1 3.2 Europe and Eurasia 4.134 4.450 15.544 22.2 16.4 16.4 European Union-27 1/ 3.998 4.298 14.987 21.4 15.7 15.7 Other Europe 0.120 0.126 0.490 0.7 0.6 0.6 Asia 2.480 3.058 10.813 15.4 12.6 13.2 East Asia 0.895 1.018 3.766 5.4 4.5 4.6 China 0.636 0.754 2.800 4.0 3.4 3.5 Other East Asia 0.259 0.264 0.965 1.4 1.1 1.1 Southeast Asia 1.291 1.678 5.834 8.3 6.7 7.2 Indonesia 0.440 0.583 1.939 2.8 2.3 2.5 Thailand 0.343 0.352 1.498 2.1 1.6 1.6 Other Southeast Asia 0.508 0.743 2.398 3.4 2.8 3.0 South Asia 0.294 0.362 1.213 1.7 1.4 1.4 India 0.263 0.332 1.094 1.6 1.2 1.2 Oceania 1.059 1.125 4.399 6.3 4.5 4.5 Australia 0.680 0.704 2.608 3.7 2.7 2.7 New Zealand 0.353 0.386 1.700 2.4 1.7 1.7 Africa 0.387 0.351 1.392 2.0 1.6 1.6 Sub-Sahara 0.353 0.307 1.178 1.7 1.4 1.4 Ivory Coast 0.161 0.128 0.482 0.7 0.6 0.6 Middle East 0.186 0.217 0.816 1.2 0.9 0.9 Turkey 0.113 0.139 0.478 0.7 0.6 0.6

Total 16.860 18.760 70.037 100.0 75.5 76.5 Totals may not add due to rounding. 1/ The former EU-25 plus Romania and Bulgaria who acceded in January 2007. Source: Compiled by USDA using data from Census Bureau, U.S. Department of Commerce.

Forecast October-December Fiscal 2008

--- Billion ------ Billion ---

13 Outlook for U.S. Agricultural Trade/AES-57/February 21, 2008

Economic Research Service, USDA

Economic Outlook

14 Outlook for U.S. Agricultural Trade/AES-57/February 21, 2008

Economic Research Service, USDA

Top Trading Partners Have Changed Dramatically U.S. agricultural exports continue climbing to new highs. A new record has been set each of the last 5 years, with indications that growth will continue. A number of factors underlie these developments. Foreign economic growth, particularly in developing countries, is essential for strong trade demand. Also, a lower dollar makes U.S. exports less expensive in foreign markets. Since 1990, the leading destinations for U.S. exports have changed tremendously. Japan and the European Union were historically our top markets, but the share of U.S. exports going to these markets has been declining since the mid 1990s. The inception of NAFTA in 1994 led to tremendous growth in exports to Canada and Mexico, which have since become our leading destination markets. Growth in exports to developing Asia is also trending upward, with a dip in 1997-99 related to the Asian financial crisis.

Leading export destinations

0%

10%

20%

30%

40%

1990 1993 1996 1999 2002 2005 2008F

Developing Asia Canada & Mexico Japan EU-27

Share of exports

Some of the shift is related to policy, some is related to demand growth. Historically, bulk products were the predominant exports, but in 1991 high-value product exports exceeded bulk exports for the first time. High-value product values have continued to climb since then and have exceeded bulk exports ever since. For example, part of the shift is moving from soybeans to soybean oil. The soybean oil share of all agricultural exports has risen 14 percent since 1990, on average, while the share of soybean exports has risen less than 1 percent. There has also been a large increase in shares for dairy products, poultry, and red meat exports. Fruits, nuts, and vegetables are also up 6 percent from 1990.

Share of U.S. agricultural export value

0

20

40

60

80

1975 1979 1983 1987 1991 1995 1999 2003 2007

Bulk

HVP

Percent

15 Outlook for U.S. Agricultural Trade/AES-57/February 21, 2008

Economic Research Service, USDA

The value of U.S. agricultural imports has been growing steadily since 1988. Latin America (other than Mexico) was the leading source of our imports, but its share has declined over time. Imports from the EU have been mostly steady, exceeding Latin America in 1999. Imports from Mexico have risen rather steadily since 1997, but are still below the rest of Latin America. Canada has seen the most growth, becoming the leading source in 2001, only to fall back below the EU in 2003 with the emergence of bovine spongiform encephalopathy. This has now been largely resolved and trade has started to increase again. Together, our NAFTA partners have accounted for more than 30 percent of our imports since the trade agreement was signed in 1994.

Leading import sources

0%

10%

20%

30%

1990 1993 1996 1999 2002 2005 2008f

European Union-27

Canada

Mexico

Latin America less Mexico

Share of imports

Most agricultural imports are high-value products—exceeding 90 percent of import value for at least the last 3 decades. Horticulture and sugar and tropical products account for roughly 60 percent. The most dramatic increase has been for essential oils—products used in flavorings (citrus and mint oils), carbonated beverages, and personal care products—with a 16-percent increase in share since 1990. Grains and feeds have increased their share by 9 percent over time. These are mostly grain products such as biscuits, wafers, and grain products such as flours and other milled grains. Wine imports continue trending up while malt beverage imports have tapered off somewhat since 2002. Although the share of animals and products (such as wool and hides) has declined over the period, they still account for nearly 20 percent. Fruits, nuts, and vegetables have also retained a 20 percent share.

Horticulture dominates imports

0

10

20

30

40

50

1990 1993 1996 1999 2002 2005 2008F

Horticulture

Sugar & tropical Animals & products

Oilseeds

Grains & feeds

Share of import value

Reliability Tables

16 Outlook for U.S. Agricultural Trade/AES-57/February 21, 2008

Economic Research Service, USDA

Table 6--Reliability of quarterly U.S. export projections, by commodity and quarter

Average forecast errors Forecast accuracy Commodity fiscal 2005-07 fiscal 2005-07 Forecast

Aug 1 Nov Feb May Aug 2 Aug 1 Nov Feb May Aug 2 accuracy Export value Percent "X" if error ? 5% Percent Grains and feeds 12 9 7 4 2 - - - X X Wheat 12 9 9 7 6 - - - - - Rice 8 13 13 8 5 - - - - X 2 Coarse grains 23 9 9 3 2 - - - X X Corn 23 9 9 2 2 - - - X X Feeds and fodders 13 9 5 7 3 - - X - X Oilseeds and products 11 11 8 5 3 - - - X X 40 Soybeans 13 9 10 6 4 - - - - X 20 Soybean meal 26 25 17 15 6 - - - - - Soybean oil 25 31 14 6 6 - - - - - Livestock, poultry, and dairy 15 10 7 6 3 - - - - X 20 Livestock products 13 8 5 4 2 - - X X X Beef and veal 27 21 7 12 6 - - - - - Pork 12 2 2 4 2 - X X X X 8 Beef and pork variety meats 13 6 0 0 5 - - X X X Hides, skins, and furs 13 8 6 3 0 - - - X X Poultry and products 13 11 10 7 5 - - - - X 20 Broiler meat 12 13 9 9 3 - - - - X 20 Dairy products 24 19 12 12 5 - - - - X 20 Tobacco, unmanufactured 0 3 6 6 3 X X - - X Cotton 14 14 7 6 1 - - - - X 20 Planting seeds 7 4 4 4 0 - X X X X 8 Horticultural products 4 4 1 1 0 X X X X X 10 Fruits and vegetables, fresh 4 4 2 2 2 X X X X X 10 Fruits & veget., processed 6 6 4 5 2 - - X X X Tree nuts 14 11 6 5 2 - - - X X 40 Sugar and tropical products 9 8 4 2 1 - - X X X Major bulk products 5 6 6 2 1 X - - X X Total agricultural exports 9 7 5 4 2 - - X X X

Average error & accuracy 13 10 7 5 3 14% 17% 34% 55% 86% 41

Export volume Wheat 9 7 7 5 4 - - - X X Rice 9 12 9 3 2 - - - X X Coarse grains 9 8 7 2 2 - - - X X Corn 10 9 8 3 2 - - - X X Feeds and fodders 5 16 3 5 2 X - X X X 80 Oilseeds and products 11 11 3 0 1 - - X X X 60 Soybeans 6 7 5 4 2 - - X X X Soybean meal 21 16 14 7 4 - - - - X 20 Soybean oil 27 23 14 7 13 - - - - - Beef, pork, other red meats 15 12 6 3 3 - - - X X 40 Beef and veal 17 29 13 0 0 - - - X X 40 Pork 10 0 10 5 0 - X - X X 60 Beef and pork variety meats 20 0 0 0 7 - X X X - Broiler meat 6 6 1 4 3 - - X X X Tobacco, unmanufactured 0 0 0 0 0 X X X X X 10 Cotton 15 11 8 5 3 - - - X X 40 Major bulk products 3 1 3 2 2 X X X X X 10

Average error & accuracy 11 10 7 3 3 18% 24% 41% 88% 88% 52

40 0 0

40 40 40

0 0

60 0 0

60 40

60

0 0 0

60

60 60 60

40 40 40 40

60

0

60 60

0

0

1 Forecast made for following fiscal year, with 15 months out. 2 Forecast made for current fiscal year, with 3 months remaining in current fiscal year. - = Error exceeds 5 percent.

Table 7--Reliability of quarterly U.S. export projections, by country and quarter Average forecast errors Forecast accuracy

Country/region fiscal 2005-07 fiscal 2005-07 Forecast Aug 1 Nov Feb May Aug 2 Aug 1 Nov Feb May Aug 2 accuracy

Export value Percent "X" if error ? 5% Percent

Asia 10 7 7 4 1 - - - X X East Asia 8 7 8 3 1 - - - X X 50 Japan 14 5 4 3 2 - X X X X 1 China 6 17 17 3 6 - - - X - 25 Hong Kong 9 7 10 7 3 - - - - X 25 Taiwan 14 13 13 10 5 - - - - X 25 South Korea 22 11 9 8 5 - - - - X Southeast Asia 19 21 13 12 4 - - - - X 25 Indonesia 36 14 5 9 6 - - X - - Philippines 20 19 12 3 8 - - - X - 25 Malaysia 0 8 0 0 0 X - X X X 75 Thailand 25 32 19 10 0 - - - - X 25 South Asia 40 24 24 20 11 - - - - - 0 Western Hemisphere 9 7 4 2 1 - - X X X 75 North America 7 6 3 1 1 - - X X X Canada 6 4 3 1 1 - X X X X 100 Mexico 8 8 5 3 2 - - X X X Caribbean 8 6 5 3 3 - - X X X 75 Central America 14 10 5 4 3 - - X X X 75 South America 20 11 14 11 7 - - - - - Brazil 25 0 25 8 8 - X - - - 25 Colombia 18 10 4 0 3 - - X X X 75 Venezuela 20 23 8 0 15 - - - X - 25 Europe and Eurasia 15 8 3 4 4 - - X X X 75 European Union-27 19 7 5 5 4 - - X X X 75 Other Europe 100 36 29 24 24 - - - - - FSU-12 21 27 19 13 12 - - - - - 0 Russia 9 13 10 13 7 - - - - - Middle East 24 14 12 10 5 - - - - X 25 Turkey 29 23 16 8 8 - - - - - 0 Saudi Arabia 0 7 11 11 11 X - - - - 0 Africa 24 10 9 7 2 - - - - X 25 North Africa 38 12 11 11 5 - - - - X 25 Egypt 38 15 15 12 8 - - - - - Sub-Sahara 0 7 4 9 7 X - X - - 25 Oceania 22 23 18 4 8 - - - X - 25 Transshipments via Canada 67 56 56 22 39 - - - - - 0

Average error & accuracy 21 14 12 8 6 8% 8% 35% 46% 57% 36

50

00

25

25

75

75

0

0

0

0

1 Forecast made for following fiscal year, with 15 months out. 2 Forecast made for current fiscal year, with 3 months remaining in current fiscal year. - = Error exceeds 5 percent.

17 Outlook for U.S. Agricultural Trade/AES-57/February 21, 2008

Economic Research Service, USDA

Table 8--Reliability of quarterly U.S. import projections, by commodity and quarter Average forecast errors Forecast accuracy

Commodity fiscal 2005-07 fiscal 2005-07 Forecast Aug 1 Nov Feb May Aug 2 Aug 1 Nov Feb May Aug 2 accuracy

Import value Percent "X" if error ? 5% Percent

Livestock, dairy, and poultry 9 5 7 6 2 - X - - X Livestock and meats 10 6 9 7 2 - - - - X Cattle and calves 22 16 21 11 4 - - - - X 20 Swine 11 9 6 6 2 - - - - X Beef and veal 11 9 8 9 2 - - - - X Pork 5 5 8 5 0 X X - X X Dairy products 9 7 6 6 1 - - - - X Grains and feed 6 6 2 2 0 - - X X X Grains 11 7 7 18 4 - - - - X 20 Grain products 3 3 2 1 0 X X X X X 1 Grain products and feed 8 6 5 1 1 - - X X X Oilseeds and products 15 15 8 7 3 - - - - X Vegetable oils 16 15 6 10 6 - - - - - Horticulture products 5 5 3 2 1 X X X X X 1 Fruits and preps., and juices 11 9 5 3 13 - - X X - Fruits, fresh 14 12 8 5 2 - - - X X Fruits, preserved 12 7 5 2 1 - - X X X Fruit juices 13 15 8 3 2 - - - X X Nuts and preparations 19 14 12 4 5 - - - X X 40 Vegetables and preparations 4 3 4 1 1 X X X X X 1 Vegetables, fresh 3 3 5 2 1 X X X X X 1 Vegetables, processed 4 3 2 2 1 X X X X X 1 W ine and malt beer 5 5 4 3 1 X X X X X 1 W ine 4 3 2 2 3 X X X X X 1 Malt beer 3 4 5 5 2 X X X X X 1 Essence oils 10 6 4 6 3 - - X - X Cut flowers & nursery stock 6 5 5 0 0 - X X X X Sugar and tropical products 7 9 6 4 2 - - - X X Cane and beet sugar 30 31 21 6 4 - - - - X 20 Confections 8 4 4 4 4 - X X X X Tobacco, unmanufactured 8 13 18 14 9 - - - - - Cocoa and chocolate 14 11 13 2 3 - - - X X 40 Coffee beans and products 10 9 6 4 3 - - - X X Natural rubber 19 16 21 10 4 - - - - X 20 Spices, natural drugs, tea 13 7 9 11 0 - - - - X 20 Tobacco, seeds, other veget. 16 10 16 21 3 - - - - X 20 Other imports 5 5 4 2 0 X X X X X 1 Total agricultural imports 5 4 2 1 1 X X X X X 1

Average error & accuracy 10 8 8 5 3 29% 37% 45% 61% 92%

Import volume W ine (HL) 8 7 4 1 1 - - X X X Malt beverages (HL) 5 4 4 2 1 X X X X X 1 Cattle and calves 7 9 29 15 10 - - - - - Swine 7 6 1 4 1 - - X X X Beef and veal, fresh 20 20 5 5 5 - - X X X Pork, fresh 33 33 29 17 0 - - - - X 2 Fruits, fresh 4 4 3 2 1 X X X X X 1 Fruits, preserved 15 8 15 8 8 - - - - - Vegetables, fresh 3 3 3 1 1 X X X X X 1 Vegetables, processed 15 15 8 8 - - - - - - - - Oilseeds and products 8 7 10 5 2 - - - X X 40 Vegetable oils 11 10 7 6 4 - - - - X Cocoa and chocolate 11 10 12 5 5 - - - X X 40 Coffee beans 10 7 7 3 4 - - - X X Rubber, natural 9 9 6 10 6 - - - - -

Average error & accuracy 11 10 10 6 4 12% 12% 23% 35% 42% 43

18 Outlook for U.S. Agricultural Trade/AES-57/February 21, 2008

Economic Research Service, USDA

40 20

20 20 80 20 60

00 60 20

0 00 40 40 60 40

00 00 00 00 00 00 40 80 40

80 0

40

00 00 53

60 00

0 60 60

0 00

0 00

0

20

40 0

1 Forecast made for following fiscal year, with 15 months out. 2 Forecast made for current fiscal year, with 3 months remaining in current fiscal year. - = Error exceeds 5 percent. - - - = No forecast available.

Table 9--Reliability of quarterly U.S. import projections, by country and quarter Average forecast errors Forecast accuracy

Country/region fiscal 2005-07 fiscal 2005-07 Forecast Aug 1 Nov Feb May Aug 2 Aug 1 Nov Feb May Aug 2 accuracy

Import value Percent "X" if error ? 5% Percent

Western Hemisphere - - - 4 2 2 1 - - - X X X X 100 Canada - - - 6 4 4 1 - - - - X X X 75 Mexico - - - 5 5 3 1 - - - X X X X 100 Central America - - - 3 6 3 1 - - - X - X X 75 Costa Rica - - - 9 7 1 0 - - - - - X X 50 Guatemala - - - 11 11 11 0 - - - - - - X 25 Other Central America - - - 0 0 6 6 - - - X X - - 50 Caribbean - - - 5 12 12 8 - - - X - - - 25 South America - - - 5 3 3 2 - - - X X X X 100 Brazil - - - 13 12 6 3 - - - - - - X 25 Chile - - - 2 6 5 1 - - - X - X X 75 Colombia - - - 6 5 0 2 - - - - X X X 75 Other South America - - - 8 4 3 3 - - - - X X X 75 Europe and Eurasia - - - 7 5 3 2 - - - - X X X 75 European Union-27 - - - 6 4 4 2 - - - - X X X 75 Other Europe - - - 31 24 15 13 - - - - - - - 0 Asia - - - 8 7 3 1 - - - - - X X 50 East Asia - - - 3 2 0 2 - - - X X X X 100 China - - - 6 3 1 2 - - - - X X X 75 Other East Asia - - - 7 11 4 4 - - - - - X X 50 Southeast Asia - - - 13 11 3 3 - - - - - X X 50 Indonesia - - - 13 14 4 2 - - - - - X X 50 Thailand - - - 7 10 8 3 - - - - - - X 25 Other Southeast Asia - - - 5 8 0 0 - - - X - X X 75 South Asia - - - 8 12 6 3 - - - - - - X 25 India - - - 9 7 7 5 - - - - - - X 25 Oceania - - - 7 8 3 2 - - - - - X X 50 Australia - - - 9 8 3 3 - - - - - X X 50 New Zealand - - - 7 11 6 4 - - - - - - X 25 Africa - - - 9 14 3 6 - - - - - X - 25 Sub-Sahara - - - 0 29 0 4 - - - X - X X 75 Ivory Coast - - - 21 24 6 3 - - - - - - X 25 Middle East - - - 8 14 0 5 - - - - - X X 50 Turkey - - - 12 17 0 4 - - - - - X X 50

Average error & accuracy - - - 8 9 4 3 - - - 29% 31% 69% 86% 55 1 Forecast made for following fiscal year, with 15 months out. 2 Forecast made for current fiscal year, with 3 months remaining in current fiscal year. - = Error exceeds 5 percent. - - - = No forecast available.

19 Outlook for U.S. Agricultural Trade/AES-57/February 21, 2008

Economic Research Service, USDA

Contact Information

20 Outlook for U.S. Agricultural Trade/AES-57/February 21, 2008

Economic Research Service, USDA

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Coordinators (area code 202) Nora Brooks/ERS, 694-5211, [email protected] Ernest Carter/FAS, 720-2922, [email protected] Forecast Contacts (area code 202) Nora Brooks/ERS, 694-5211, [email protected] Andy Jerardo/ERS, 694-5266, [email protected] Ernest Carter/FAS, 720-2922, [email protected] Commodity Specialist Contacts (area code 202) Grains and Feeds: Richard O’Meara/FAS, 720-4933 Coarse Grains: Richard O’Meara/FAS, 720-4933 Edward W. Allen/ERS, 694-5288 Wheat: Joshua Lagos/FAS, 690-1151 Edward W. Allen/ERS, 694-5288 Rice: Nathan Childs/ERS, 694-5292 Rob Miller/FAS, 720-8398 Oilseeds: Mark Ash/ERS, 694-5289 Bill George/FAS, 720-6234 Cotton: James Johnson/FAS, 690-1546 Steve MacDonald/ERS, 694-5305 Livestock, Poultry & Dairy Products: Claire Mezoughem/FAS, 720-7715 Beef & Cattle: Claire Mezoughem/FAS, 720-7715 Michael McConnell/ERS, 694-5158 Pork & Hogs: Julie Morin/FAS, 720-4185 Mildred Haley/ERS, 694-5176 Poultry: Dave Harvey/ERS, 694-5177 Michelle DeGraaf/FAS, 720-7285 Dairy Products: Paul Kiendl/FAS, 720-8870 Horticultural & Tropical Products: Lashonda Mcleod/FAS, 720-6086 Fruits & Preparations Deciduous Fresh Fruit: Heather Vethuis/FAS, 720-9792 Fresh Citrus and Juices: Reed Blauer/FAS, 720-0898 Vegetables & Preparations: Shari Kosco/FAS, 720-2083 Tree Nuts: Lashonda Mcleod/FAS, 720-6086 Essential Oils: Tony Halstead/FAS, 720-1592 Sugar and Tropical Products: Bob Knapp/FAS, 720-4620 Sugar: Ron Lord/FAS, 720-6939 Macroeconomics Contact (area code 202) David Torgerson/ERS, 694-5334 Special article – Top Trading Partners Have Changed Dramatically Nora Brooks/ERS, 694-5211

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van Dijk.pdf

Ideology and discourse A Multidisciplinary Introduction

Teun A. van Dijk Pompeu Fabra University, Barcelona

 An earlier version of this book was used as an internet course for the Universitat Oberta de Catalunya (Open University) in 2000.

 A Spanish version of this book has been published by Ariel, Barcelona, 2003.

 An Italian version of this book, with the title “Ideologie. Discorso e costruzione sociale del pregiudizio” (translated by Paola Villano) was published by Carocci, Roma, 2004.

.. About the author

Teun A. van Dijk was professor of discourse studies at the University of Amsterdam until 2004, and is at present professor at the Universitat Pompeu Fabra, Barcelona. After earlier work on generative poetics, text grammar, and the psychology of text processing, his work since 1980 takes a more critical perspective and deals with discursive racism, news in the press, ideology, knowledge and context. He is the author of several books in most of these areas, and he edited The Handbook of Discourse Analysis (4

vols, 1985) the introductory book Discourse Studies (2 vols., 1997) as well as the reader Discourse Studies (5 vols., 2007). He founded 6 international journals, Poetics, Text (now Text & Talk), Discourse & Society, Discourse Studies, Discourse & Communication and the internet journal in Spanish Discurso & Sociedad (www.dissoc.org), of which he still edits the latter four. His last monographs in English are Ideology (1998) and Racism and discourse in Spain and Latin America (2005), and his last edited book (with Ruth Wodak), Racism at the Top (2000). He is currently completing a new interdisciplinary study in 2 vols. on the theory of context, and planning a new book on discourse and knowledge. Teun van Dijk, who holds two honorary doctorates, has lectured widely in many countries, especially also in Latin America. With Adriana Bolivar he founded the Asociación Latino-americana de Estudios del Discurso (ALED), in 1995. For a list of publications, recent articles, resources for discourse studies and other information, see his homepage: www.discourses.org. E-mail: [email protected]

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Contents

0. Summary

1. Defining ideology

2. Ideology as Social Cognition 2.1. The structure of ideologies 2.2. From ideology to discourse and vice versa 2.3. Mental Models 2.4. From Mental Models to Discourse 2.5. Context Models

3. Ideologies in Society

4. Racism

5. Ideological Discourse Structures 5.1. Meaning 5.2. Propositional structures 5.3 Formal structures 5.4 Sentence syntax 5.5 Discourse forms 5.6 Argumentation 5.7 Rhetoric 5.8 Action and interaction

6. Examples

7. Conclusion

Appendix

- 4 -

0. Summary

What is ideology? We all use the notion of ideology very often, and so do newspapers and politicians. Most of the time, we do not use it in a very positive sense. We may speak of the ideologies of communism, or neo- liberalism, pacifism or consumerism, and many other -isms, but seldom qualify our own ideas as an "ideology". But what are ideologies exactly?

Ideology in cognition, society and discourse In this course, a multidisciplinary introduction to the notion of "ideology" is presented --involving cognitive and social psychology, sociology and discourse analysis. The cognitive definition of ideology is given in terms of the social cognitions that are shared by the members of a group. The so- cial dimension explains what kind of groups, relations between groups and institutions are involved in the development and reproduction of ideolo- gies. The discourse dimension of ideologies explains how ideologies influ- ence our daily texts and talk, how we understand ideological discourse, and how discourse is involved in the reproduction of ideology in society.

Racism Racism is one of the major problems of contemporary European societies. To illustrate the theoretical discussion, we shall therefore specifically pay attention throughout the course to the example of racist ideology and how it is expressed by discourse.

Discourse Structures Discourse plays a fundamental role in the daily expression and reproduc- tion of ideologies. This course therefore pays special attention to the ways ideologies influence the various levels of discourse structures, from intona- tion, syntax and images to the many aspects of meaning, such as topics, coherence, presuppositions, metaphors and argumentation, among many more.

- 5 -

Chapter 1

Defining 'ideology'

This book provides a multidisciplinary introduction to the notion of 'ideology' and especially focuses on how --for instance racist-- ide- ologies are expressed, construed or legitimated by discourse.

'Ideology' as a vague and controversial notion

The notion of 'ideology' is widely being used in the social sciences, in poli- tics, and in the mass media. There are thousands of articles and books writ- ten about it since the notion was invented by French philosopher Destutt de Tracy at the end of the 18th century.

This is how Destutt de Tracy begins his famous book, which is explicitly addressed to young people, because --he says-- the minds of established scholars are already full of "fixed ideas" that are very difficult to change:

- 6 -

Eléments de Idéologie

Par A.L.C. Destutt-Tracy

Jeunes gens, c' est à vous que je m' adresse ; c' est pour vous seuls que j' écris (…) La première fois qu' il arrivera à un de vos camarades de s' attacher obstinément à une idée quelconque qui paraîtra évidemment absurde à tous les autres, observez-le avec soin, et vous verrez qu' il est dans une disposition d' esprit telle qu' il lui est impossi- ble de comprendre les raisons qui vous semblent les plus claires : c' est que les mêmes idées se sont arrangées d' avance dans sa tête dans un tout autre ordre que dans la vô- tre, et qu' elles tiennent à une infinité d' autres idées qu' il faudrait déranger avant de rectifier celles-là. C' est pour vous préserver de l' un et de l' autre que je veux dans cet écrit, non pas vous enseigner, mais vous faire remarquer tout ce qui se passe en vous quand vous pensez, parlez, et raisonnez. Avoir des idées, les exprimer, les combiner, sont trois choses différentes, mais étroitement liées entre elles. Dans la moindre phrase ces trois opérations se trouvent : elles sont si mêlées, elles s' exécutent si rapidement, elles se renouvellent tant de fois dans un jour, dans une heure, dans un moment, qu' il paraît d' abord fort difficile de débrouiller comment cela se passe en nous.

As we see in this quotation, for Destutt de Tracy ideology was nothing less than a general "science of ideas" (the study of "how we think, speak and argue…"), something what today would be called psychology or even 'cognitive science.

Despite the huge scholarly attention paid to the study of ideology since Destutt de Tracy's book, the notion remains one of the vaguest and most "contested" concepts of the social sciences. So, I shall begin with a defini- tion of what in this book I understand by 'ideology'.

Ideology as a system of beliefs

As we already see in Destutt de Tracy's writings, ideologies have some- thing to do with systems of ideas, and especially with the social, political or religious ideas shared by a social group or movement. Communism as well as anti-communism, socialism and liberalism, feminism and sexism, racism and antiracism, pacifism and militarism, are examples of wide- spread ideologies. Group members who share such ideologies stand for a number of very general ideas that are at the basis of their more specific be- liefs about the world, guide their interpretation of events, and monitor their social practices.

Instead of the rather vague and ambiguous notion of 'ideas' we shall hence- forth use the term that is mostly used in psychology to refer to 'thoughts' of any kind: beliefs. We thus get the following very general working defini- tion of ideology:

- 7 -

Ideologies are the fundamental beliefs of a group and its members.

In this book, I shall develop this conception of ideology in more detail.

'Ideology' as 'false consciousness' or 'misguided beliefs'.

Note that there are many definitions and approaches to ideology. For En- gels' interpretation of Marx, and hence in many directions within Marxism, ideologies were forms of 'false consciousness', that is, popular but mis- guided beliefs inculcated by the ruling class in order to legitimate the status quo, and to conceal the real socioeconomic conditions of the work- ers.

Until quite recently, this negative concept of ideology --namely, as systems of self-serving ideas of dominant groups-- has been prevalent in the social sciences, where it was traditionally being used in opposition to true, scien- tific knowledge.

This negative notion of 'ideology' has also become the central element in the commonsense and political uses of the term, namely as a system of false, misguided or misleading beliefs. For instance, in the ideology of anti-communism that for decades dominated politics and even scholarship in much of the Western World, ideology was typically associated with communism.

More generally, this negative use of the notion presupposes the following polarization between Us and Them:

WE have true knowledge, THEY have ideologies.

We shall encounter this social polarization between ingroup and outgroup very often throughout this book.

'Ideology' as a general notion

Although the legitimization of dominance is an important function of many ideologies, we shall propose a more general notion of ideology. This

- 8 -

will also allow us to study 'positive' ideologies, such as those of feminism and anti-racism in the same way, namely as systems that sustain and le- gitimatize opposition and resistance against domination and social ine- quality. Karl Mannheim called such positive or oppositional ideologies 'utopias'. "Anti-ideologies" such as those of anti-racism, thus, are not just opposing racism and racist ideologies, but have their own (e.g., humanitar- ian) ideology -- just as feminist ideologies are not merely anti-sexist.

In the same way as ideologies need not be negative, they need not be dominant -- there are also non-dominant ideologies that are often widely considered to be 'negative', such as those of religious sects or right-wing extremists. In other words, a general theory of ideology allows a broader and more flexible application of the notion. This does not exclude, a criti- cal account of negative or dominant ideologies, simply because critical analysis is directed against all forms of power abuse and dominance, and will henceforth also focus on the ideological basis of dominance. In the same way, it is useful to have a general notion of power which need not imply a negative evaluation, as long as we are able to critically study power abuse or dominance. Hence we do not agree with those scholars who claim that a general notion of ideology does not allow critical study.

'Ideology' as the basis of social practices

As systems of ideas of social groups and movements ideologies not only make sense in order to understand the world (from the point of view of the group), but also as a basis for the social practices of group members. Thus, sexist or racist ideologies may be at the basis of discrimination, pacifist ideologies may be used to protest against nuclear weapons, and ecological ideologies will guide actions against pollution. Often, ideologies thus emerge from group conflict and struggle, and they thus typically pitch Us against Them.

However, although ideologies and social practices of group members are closely related, we shall make clear that these are two different notions, and that ideologies cannot simply be reduced to 'ideological practices.'

The role of discourse

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One of the crucial social practices influenced by ideologies are language use and discourse, which in turn also influence how we acquire, learn or change ideologies. Much of our discourse, especially when we speak as members of groups, expresses ideologically based opinions. We learn most of our ideological ideas by reading and listening to other group members, beginning with our parents and peers. Later we 'learn' ideologies by watch- ing television, reading text books at school, advertising, the newspaper, novels or participating in everyday conversations with friends and col- leagues, among a multitude of other forms of talk and text. Some discourse genres, such as those of catechism, party rallies, indoctrination and politi- cal propaganda indeed have the explicit aim of 'teaching' ideologies to group members and newcomers.

We shall pay special attention to these discursive dimensions of ide- ologies. We want to know how ideologies may be expressed (or con- cealed!) in discourse, and how ideologies may thus also be repro- duced in society.

An example: racist ideologies

Given the current situation in Europe and North America, where xenopho- bic ideologies against immigrants and minorities have grown rapidly, we shall pay special attention to 'racist' ideologies and discourses, also in the examples. The general term 'racism' shall be used to refer to related but different ideologies such as those of anti-Semitism, eurocentrism, ethni- cism and xenophobia.

A multidisciplinary framework: Discourse, Cognition, and Society

The theoretical framework that underlies this book is multidisciplinary. Ideology and discourse are not notions that can be adequately studied in one discipline: They require analysis in all disciplines of the humanities and the social sciences. However, we shall reduce this large number of po- tential disciplines to three main clusters, namely those involved in the study of Discourse, Cognition and Society.

Thus, language use, text, talk, verbal interaction, and communication will be studied under the broad label of 'discourse'. The mental aspects of ide- ologies, such as their nature as ideas or beliefs, their relations with opin- ions and knowledge, and their status as socially shared representations,

- 10 -

will all be covered under the label of 'Cognition'. And the social, political, cultural and historical aspects of ideologies, their group-based nature, and especially their role in the reproduction of, or resistance against, domi- nance, will be examined under the broad label of Society. Note that these conceptual distinctions are merely analytical and practical. They do of course overlap: Discourse for instance is part of society, and so are the socially shared ideas of group members. We make the distinction, however, because the concepts, theories and methods of analysis are rather distinct for these three areas of inquiry.

- 11 -

Chapter 2

Ideologies as social cognition

Whatever the differences may be between the many definitions of ideology throughout the history of the social sciences, they all have in common that they are about the ideas or beliefs of collectivities of people. Strangely, it is this central 'mental' character of ideologies that has been studied much less than their social and political functions. Indeed, compared to those in the social sciences, and until today, detailed psychological studies of ide- ology are quite rare, or reduced to studies of political beliefs.

In order to explain the proper nature of ideologies and their relations to social practices and discourse, we first need some insight into their mental or cognitive dimension. Traditional terms such as 'false consciousness' and commonsense, everyday terms such as 'ideas' are simply too vague to be able to serve for the definition of what mental objects ideologies are.

Types of beliefs

Contemporary cognitive and social psychology make a distinction between many types of 'beliefs'. Thus, beliefs may be personal vs. social, specific vs. general, concrete vs. abstract, simple vs. complex, rather fleeting or more permanent, about ourselves or about others, about the physical or the social world, and so on. Similarly, we distinguish between knowledge and opinions, or between knowledge and attitudes, depending on whether the beliefs have an evaluative element or not. And we may have beliefs such as norms and values that are the basis of such evaluations in opinions and at- titudes. Ideologies often have such an evaluative dimension.

In the same way that we do not speak of individual languages, we do not have individual ideologies. So ideologies consist of shared, social beliefs, and not of personal opinions. Moreover, they are often about important social and political issues, namely those issues that are relevant for a group and its existence, rather than about trivial everyday things like the color of our car, or the brand of our computer. Ideologies are about life and death, birth and reproduction, as the conflicting attitudes about abortion and euthanasia show. They are about people and their health in relation to their natural environment, as is obvious in ecological ideologies. They are about class, about being poor or rich, having power or having nothing, about the redistribution of wealth and resources, as socialist or communist ideologies profess. They are fundamentally about gender, being a woman or a man, as

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feminist or sexist ideologies show, or about race and ethnicity, as is the case for racist and antiracist ideologies.

In sum, our first step is to recognize that ideologies consist of socially shared beliefs that are associated with the characteristic properties of a group, such as their identity, their position in society, their interests and aims, their relations to other groups, their reproduction, and their natural environment. This is one of the reasons why we provisionally defined ide- ologies in terms of the socially shared basic beliefs of groups. Since this notion of 'group' is still pretty vague, we'll have to come back to it later.

Types of Memory and Representations

Psychologists often associate different beliefs with different types of mem- ory, or with different systems of cognition. Well-known is the distinction between Short Term Memory (STM) and Long Term Memory (LTM), to which we briefly shall come back below. The ideological beliefs we have encountered above are usually 'located' in LTM. But we need to distin- guish between various kinds of 'beliefs', for instance the following ones:

Episodic memories.. When beliefs are more personal and based on experi- ences, they are often called 'episodic.' Together, these episodic beliefs de- fine what is usually called 'episodic memory.' This memory is personal, autobiographic and subjective: it registers our personal experiences. This is the kind of 'memory' we speak about in everyday life. Episodic memory is the location of the things we 'remember'. Since episodic memories are about individual people themselves, Self plays a central role in them.

Thus, we have episodic memories of our breakfast this morning, of our last vacation or the first time we met the person we are in love with. Given the multitude of our daily experiences, activities and encounters, it is not sur- prising that the majority of these episodic memories are no longer accessi- ble after some time. After some years one is likely to remember this unique and exotic vacation, but not that I bought croissants at the bakery this morning.

Since ideologies are basic and socially shared, we would not typically look for them in episodic memory, which is personal, subjective and consist of specific experiences. Yet, this does not mean that ideologies do not influ- ence our personal beliefs. We shall later see how ideologies may influence the beliefs in our episodic memory.

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Sociocultural knowledge. People not only have personal beliefs about per- sonal experiences, but also share more general beliefs with others, such as other members of the same group, or even with most others in a whole so- ciety or culture. Our sociocultural knowledge is perhaps the most crucial example of such shared beliefs: We would be unable to understand each other, nor would we be able to speak or to interact with others, without sharing a large amount of knowledge about all aspects of the world and our daily lives. From birth to death people thus acquire an enormous amount of knowledge, beginning with their language(s) and the principles of interac- tion, the people and groups they interact with, the objects around them, the institutions of society, and later, often through various forms of media or educational discourse, about the rest of the world. We shall assume that these socially shared beliefs form what may be called social memory, and that sociocultural knowledge is a central system of mental representations in social memory.

Knowledge is what WE think is true and for which we have reasons (crite- ria) to believe it is true. Of course, other people may think that what we think we 'know' are merely beliefs, or opinions, prejudice, or fantasies, or - -indeed-- ideologies. So, obviously, the notion of knowledge is relative, and dependent on the beliefs of our group, society or culture. What was knowledge in the Middle Ages may be described as superstition today, and conversely, some originally controversial opinions of scholars (and Galileo Galilei is merely one of them) later turned out to become widely accepted scientific 'fact', that is, knowledge that has passed the scientific criteria of truthfulness, and even accepted as knowledge in everyday life.

Common ground. Although what is knowledge or 'mere belief' may thus vary for different groups or cultures, also within the same group or culture, people usually make a distinction between knowledge and belief, between fact and opinion. There is an enormous body of knowledge nobody ever disputes, and that is accepted by virtually all competent members of a cul- ture. This knowledge may simply be called the sociocultural common ground of a group or culture. These are the kind of beliefs people presup- pose to be known in their everyday interaction and discourse, and hence the beliefs that need not be expressed, unless when to teach or recall to those who don't know them yet, like children and immigrants from other cultures. Discourse, as we shall see, presupposes vast amounts of such be- liefs in order to be comprehensible.

Opinions and attitudes. On the other hand, there are beliefs of which we are not certain, that are controversial, about which we have different views,

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and which in general can therefore not be presupposed and tacitly be as- sumed to be true. These beliefs may be personal, and hence represent our personal opinions associated with our episodic beliefs. But such beliefs may also be socially shared by groups of people, as is the case for our atti- tudes about say abortion, immigration or nuclear energy. These are the be- liefs that typically need to be asserted, contended and defended, especially also in interaction with members of other groups. Of course, within the group, typical group opinions and attitudes may also be taken for granted, and therefore no longer asserted or defended. Since these group opinions are social, we also prefer to associate them with social memory, as was the case for knowledge.

Ideology as social representations

If ideologies are the basic beliefs shared by groups, we need to locate them in what we have just defined as social memory, alongside with social knowledge and attitudes. Indeed, we shall assume that ideologies are the basis of the social memory shared by groups. Thus, because within the same society or culture there are many ideologies, we need to restrict ide- ologies to groups or social movements. That is, unlike common ground knowledge, ideologies are not sociocultural, and cannot be presupposed to be accepted by everyone. On the contrary, as is the case for attitudes, ide- ologies typically give rise to differences of opinion, to conflict and strug- gle. Yet, the same 'ideological group' may be defined precisely by the fact that its members share more or less the same ideology, as is the case for socialists, feminists or anti-racists as groups. There are of course sub- groups with variants of the general ideology, and individual members of a group may again have individual opinions on certain issues.

We called ideologies 'basic systems' of beliefs because other, more specific beliefs, may depend on them or be organized by them. Thus, a racist ideol- ogy may organize many prejudices or racist attitudes, e.g. about immigra- tion, about the intellectual capacities of minorities, about the role of immi- grants on the labor market, on the relation between immigration and crime, and so on. These different attitudes, pertaining to different areas of society, may be organized by some basic beliefs about the negative properties of the Others.

In sum, ideologies form the basic social representations of the beliefs shared by a group, and precisely function as the framework that defines the overall coherence of these beliefs. Thus, ideologies allow new social opin- ions to be easily inferred, acquired and distributed in a group when the

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group and its members are confronted with new events and situations, as was the case for large scale immigration during the last decades in Europe.

Ideologies and values

Among the mental representations typically associated with our social memory, we finally should mention the norms and values that organize our actions and evaluations. They basically define what is good and bad, per- mitted or prohibited, and the fundamental aims to be striven after by indi- viduals, groups and societies alike. Thus, freedom, independence, and autonomy may be values for groups, whereas intelligence, beauty or pa- tience are typically values for people.

Given the close relationships between ideologies and evaluative beliefs such as attitudes, it is not surprising that there is also a connection between ideologies and values. Indeed, both are fundamental for social memory. However, whereas ideologies are typical for groups, and may determine group conflict and struggle, values have an even more general, more basic, cultural function, and in principle are valid for most competent members of the same culture. Indeed, whatever our ideology, few of us are against freedom or equality, and those who do explicitly place themselves beyond the boundaries of the socially acceptable. In a sense thus, the system of so- ciocultural norms and values is part of what we have called the Common Ground above. That is, they are beliefs which are not usually disputed within the same culture.

However, although norms and values may be very general, and culturally accepted, they may be applied in different areas and in ways about which controversy is fundamental. When that happens we witness the 'translation' of values into component of ideological beliefs. Thus, we may all be for freedom, but the freedom of the market will typically be defended in a lib- eral ideology, the freedom of the press in the professional ideology of jour- nalists, and the freedom from discrimination in a feminist or antiracist ide- ology. Similarly, equality is a value that will be prominent in most oppositional ideologies, such as those of socialism, feminism and antirac- ism. And individualism and personal responsibility are again prominent in conservative and liberal ideologies. In other words, it is the specific, group-related and interest-defined, interpretation of values that forms the building blocks of ideological beliefs.

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2.1. The structure of ideologies

We now have a provisional, but still rather informal framework for a the- ory of ideology, in which ideology is defined as a form of social cognition, and more specifically as the basic beliefs that underlie the social represen- tations of a social group.

However, this is of course far from adequate when we really want to un- derstand the nature and functions of ideologies in society. Indeed, we have not even asked the crucial question what ideologies actually look like. We provisionally described them in terms of (systems of) 'basic social beliefs', but we don't know yet what such beliefs, as mental representations, look like, how they are mutually related into 'systems', how they interact, and so on. In brief, we need to examine the structure of beliefs in the same way as we later need to examine the structures of discourse.

Propositional format for ideological beliefs

Unfortunately, despite the vast number of studies on ideology, we as yet have very few ideas about the ways ideologies should be represented in memory. As clusters of beliefs in social memory, they might be represented first of all in the same formal terms as other beliefs, for instance as propo- sitions (see definition).

However, propositions provide merely a convenient format. They make it easier to speak or write about beliefs in some natural language. However, they are not exactly an ideal format to represent mental rep- resentations. We might also represent them as a network of conceptual nodes or in other formats that bear some resem- blance to the neural network of the brain. Although it is cer- tainly not arbitrary for a theory of ideology how they are organ- ized, we shall not further con- sider this question of format,

Propositions are units of meaning, traditionally defined as those meanings that express a 'complete thought', or in philosophy as something that can be true or false. Propositions are typically expressed in simple clauses, such as Women and men are equal or Harry and Sally are friends. In the same philosophical tradition, propositions are usually said to be composed of a predicate and one or more arguments, as in beats(John, Mary). Such a simple proposition may then further be modified in various ways, for instance by modalities ('it is possible that' , 'it is known that'). In our sample analysis below, we shall say some more about the ways ideologies may be expressed in propositions. For the moment, think of propositions simply as units of meaning that are typically expressed as a simple clause.

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and simply assume that the general beliefs of ideologies can be represented by propositions such as 'Men and women should have equal rights', or 'All citizens have the right to elect their representatives'. We should only re- member that these propositional elements of ideologies are not linguistic units, such as sentences.

The organization of ideologies

Whatever their format, ideological beliefs are most probably not organized in an arbitrary way. All we know about the mind and about memory, sug- gests order and organization, although sometimes in ways we still do not understand. Thus, we shall also assume that ideologies somehow form 'sys- tems' of beliefs, as was said in the beginning of this book.

As many other complex representations in memory, ideologies may have a 'schema-like' nature, that is, consist of a number of conventional categories that allow social actors to rapidly understand or to build, reject or modify an ideology.

The categories that define the ideological schema should probably be de- rived from the basic properties of the social group. That is, if ideologies underlie the social beliefs of a group, then the identity and identification of group members must follow a more or less fixed pattern of basic catego- ries, together with flexible rules of application.

Thus, we briefly assumed above that the following categories reflect rather fundamental categories of group life and identity, categories that may be good candidates for the schema that organizes the ideologies of the same group:

Categories of the ideology schema

Membership criteria: Who does (not) belong? Typical activities: What do we do? Overall aims: What do we want? Why do we do it? Norms and values: What is good or bad for us? Position: What are the relationships with others? Resources: Who has access to our group resources?

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We thus arrive at a schema of six categories which not only organize col- lective and individual action, but which also organize the ideologies of our mind. Overall, these categories in fact define what it means to feel a mem- ber of a group, and to jointly feel as "one" group. In that respect they de- fine a 'group self-schema'. This is how it should be, because an ideology in a sense is a form of self- (and Other) representation, and summarizes the collective beliefs and hence the criteria for identification for group mem- bers. That is, an ideology is one of the basic forms of social cognition that at the same time define the identity of a group and hence the subjective feelings of social identity (belonging) of its members.

Of course, this schematic structure is purely theoretical. We can only make it plausible when it explains social practices, including discourse. For in- stance, if people speak as group members, their discourse should somehow systematically display these categories. For instance, if they speak about themselves and others, then category number 5 will typically appear as some form of ingroup-outgroup polarization, which we find in the pronoun pair US vs. THEM and in a host of other discourse structures. Below we shall deal in more detail with the ways ideologies (both as to their content and as to their structures) may control the discourse of group members.

2.3. From ideology to discourse and vice versa

Just like other forms of social cognition, ideologies are by definition rather general and abstract. They need to be, because they should apply in a large variety of everyday situations. Thus, racist ideologies embody how WE think about THEM in general, and individual group members may (or may not, depending on the circumstances) 'apply' these general opinions in con- crete situations, and hence in concrete discourses.

In other words, there may be a wide gap between the abstract, general ideologies on the one hand, and how people produce and understand dis- course or engage in other social practices on the other hand.

One exception to this are of course the discourses that are explicitly ideo- logical, such as those that teach or explain ideologies to new group mem- bers or that defend ideologies against attacks from outsiders. Such dis- courses may feature rather general formulations about what WE stand for, as is the case for political propaganda, religious teachings, or the pam- phlets of social movements.

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Ideological attitudes

More often than not however, abstract ideologies only indirectly appear in text and talk. This means that we need 'intermediary' representations be- tween ideologies and discourse. Thus, we already have seen that attitudes, while also being forms of social cognition, may embody ideological propo- sitions as applied to specific social domains. For instance we may 'apply' a feminist ideology in the area of the labor market, in education, or in the area of reproduction or sexuality. It is in this way that we may have femi- nist or antifeminist attitudes about abortion.

Ideological knowledge?

Similarly, group ideologies may affect knowledge. This seems contradic- tory, because knowledge has traditionally often been defined precisely as free from ideology. Ideological knowledge is often seen as a contradiction in terms, and was often seen merely as some form of 'ideological belief'. Thus, if some racist psychologists hold that Blacks are less intelligent than Whites, they might see this as knowledge, while obtained by what they see as scientific evidence, but others may well see this as a form of racist prejudice, based on biased argumentation and misguided application of scientific method.

More generally, then, we shall accept that also knowledge may be affected by ideology, because those who hold such beliefs think these beliefs are true by their standards, and hence consider them to be knowledge and not ideological beliefs. There are many examples where we would say that group-knowledge is dependent on group-ideology, and such dependence may be evaluated more or less positively or negatively. What once was considered by scholars to be scientific knowledge about women or blacks, now often will be seen and rejected (also by scientists) as biased, preju- diced beliefs or stereotypes.

On the other hand, knowledge may also be controlled by more positive ideological principles. Thus, much of the knowledge we today have about pollution is undoubtedly formulated under the influence of ecological ide- ologies. This will probably be the case for many forms of critical knowl- edge that opposes traditional views. Thus, it is also beyond doubt that the feminist movement, and hence feminist ideologies, are at the basis of many of the insights that today are widely accepted as characterizing gender rela- tions in society. Thus, much insight into domination and inequality will, at

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least initially, be based on ideologies of resistance, and only later be ac- cepted by other groups and by society at large.

Note that we do not claim, as some scholars may do, that all our knowl- edge or all our beliefs are ideological. That would make the notion of 'ide- ology' rather useless, because it precisely needs to distinguish between ideological and other beliefs. Thus, by definition, Common Ground beliefs are non-ideological within a given society or culture, precisely because there is no controversy about these beliefs, no opposition, no struggle, no WE-THEM groups, no conflict of interest, no conflicting views of the world. Indeed, a table is a table for all social groups in our culture, and its properties or functions hardly a matter of deep-rooted controversies.

Of course, what we now accept to be non-ideological Common Ground beliefs in our own society or culture, may later, or from the point of view of another culture, become ideological beliefs. This is typically the case for a religion like Christianity, which say 500 years ago was nearly generally accepted as 'true belief' by most members of European societies, but which now is associated with the ideological beliefs of just one group of people. And conversely, what once was controversial belief (for instance about the form and position of the earth) is now generally accepted Common Ground belief.

We see that in order to relate ideology to discourse, this may first happen through other forms of social cognition, such as socially shared opinions (attitudes) or through various forms of group knowledge. But these are still general and abstract, and we hence need a more specific interface between social cognition and discourse.

Representing our discussion so far in a simple schema, we may represent the relation between social cognition and discourse as follows:

Interaction/Discourse

Social Cognition Group Knowledge

Group Attitudes

Group Ideology

Socio-cultural Knowledge (Common Ground

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2.3. Mental models

Above we have seen that it makes sense to make a distinction between so- cial memory on the one hand, and more personal, individual, autobio- graphical memory on the other hand. The latter was called 'episodic' be- cause it is made up of the mental representations of the episodes that give rise to our daily experiences, from the moment we wake up in the morning, until we fall asleep at night. These episodic representations of the daily events we participate in, witness (in reality or on TV), or read about, are called (mental) models. We may thus have models of events, actions, situa- tions, as well as of their participants, of which the autobiographical models of the events we participate in ourselves are a specific case.

Mental models are subjective

In other words, the way we perceive, understand or interpret our daily real- ity takes place through the construction or reconstruction (updating or modification) of such models. Models are therefore personal and subjec- tive: They represent the way I see and understand events. Such a represen- tation is often influenced by previous experiences (old models), and the ways these may bias my current perceptions and interpretations. Models also embody opinions about the events we participate in, witness or read and hear about. Thus, reading the newspaper about the civil wars in Bosnia or Kosovo, we not only form mental models of the events, but probably also associate these with negative opinions about the war crimes and 'eth- nic cleansing' being perpetrated in these wars.

The structure of mental models

We have only speculative ideas about what these mental models in epi- sodic memory look like. If they are about events, they probably feature a rather general, abstract schema that we use in the interpretation of the mil- lions of events we have experienced in our lives. Such a schema should on the one hand be relatively simple, that is, consist only of a few, fixed cate- gories, but on the other hand, it should be rather flexible and allow applica- tion to less current situations with which we are confronted in everyday life. Thus, we may assume that model schemata for events feature catego- ries such as Setting (Time, Place), Participants (Things, People) and some occurrence. Models of actions more specifically feature participants who are actors in various roles (agents, patients, etc.). Such schemata allow fast, strategic processing of relevant information and (provisional) interpreta-

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tion. Closer inspection or interpretation may reveal that we need to correct our "first impression" of the event.

The interesting property of mental models is not only that they represent personal, subjective and possibly biased information about the events we experience in our everyday lives. Mental models also feature 'instantia- tions' (specifications, examples) of more general, abstract beliefs, includ- ing social cognitions. Reading about a specific event in the civil war in Bosnia or Kosovo, may involve specific instantiations of our general, so- cially shared knowledge about civil wars, about war, about armies and arms, about atrocities, and so on. These need not all be spelled out (ac- tively thought about) in the mental model. They only must be present in the background, pointing to more general knowledge, from which they may be inferred when actually needed to understand an event. In the interpretation of (a discourse about) a current event, we may only need to activate a small fragment of our knowledge, for instance, that the use of guns may kill peo- ple, without activating all we know about guns. Thus, we shall assume that models only feature the relevant instantiations of general knowledge.

Personal models and social representations

Although separately represented as general social representation, knowl- edge, attitudes and indirectly ideologies may affect the structures and the contents of the mental models we construct of specific events. This also means that we are able to 'translate' general ideologies to specific experi- ences as embodied in mental models. If WE oppose the immigration of more people from Africa, as part of an anti-immigration attitude controlled by a racist ideology, then the mental model I --as group member-- may have of a recent arrival of immigrants may feature the more specific (situa- tion dependent) opinions derived from the general ideology.

Note though that the ideological influence on mental models is not purely automatic. People are not (fully) dependent on their ideologies, and may of construe their everyday models on the basis of earlier personal experi- ences, or on the basis of other knowledge and ideologies. Thus, although I may share an anti-immigration attitude, my personal experiences with Af- rican immigrants may be positive, and that will probably affect my future models of events in which such immigrants appear as participants. Or I may at the same time have a socialist ideology, based on principles of equality, and such ideological principles may contradict those of racist atti- tudes I have about immigration.

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Ideological conflict

This means, that at the level of personal experiences, people may be con- fronted by ideological conflict and confusion. We may identify with sev- eral social groups or formations at the same time, and these may lead to different ideological positions. You may be a woman, and at the same time a mother, and a professional journalist, and a socialist, and a feminist and an atheist, and so on, and the representations of your personal life experi- ences may require opinions or a perspective that is not always compatible with these various identities and ideologies.

This is also what we find in empirical research on opinions, attitudes and ideologies: individuals may express a wide variety of conflicting opinions about an issue. So much so, that many scholars have concluded that there are no such things as stable attitudes or ideologies. Rather, they argue, people construct their opinions ad hoc, on the spot, in each context, and do so typically when talking or writing to other people. These scholars con- clude that there is no need to postulate general, abstract, social cognitions.

Social representations cannot be reduced to mental models

In the theory of ideology presented here, however, we do not take that po- sition. We agree that in everyday situations people may live, express or enact different ideologies, and that these expressions or interactions are unique. Mental models account for such uniqueness, and for the contextual nature of the ideological opinions expressed. However, there is no doubt that also across different situations, not only one social actor, but many social actors, may have and express and use the same or very similar opin- ions.

This similarity cannot simply be explained by similar circumstances, but need to be accounted for by more permanent mental structures, shared with others, as they are represented in social memory. Social knowledge, atti- tudes and ideologies precisely need some form of permanence and continu- ity across different situations, otherwise we would be unable to communi- cate, interact, talk and cooperate in a group. We need to have at least some shared world knowledge, and some general attitudes, norms and values that monitor our actions, and allow us to predict what others expect of us, and how they will probably evaluate what we do or say. This is also the reason why in concrete situations we will often do or say different things than we would like to -- we know that shared social cognitions and partici-

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pation in a group require us to act and talk as competent and cooperative members.

In sum, despite the multiplicity of factors involved in the construction of the mental models of everyday life experiences, and despite the personal and contextual variations these may imply, mental models also at the same time exhibit fragments of socially shared ideologies. This explains why we are often able to ideologically categorize and recognize actors or speakers as being progressive or conservative, feminist or anti-feminist, racist or anti-racist.

2.4. From mental models to discourse

We now have found the most important interface between ideologies and discourse: mental models as represented in episodic memory. If affected by ideological-based opinions, we'll say that such models are ideologically 'biased': they represent or construct events from the perspective of one (or more) ideological groups.

Such mental models are not only important for the representation of our personal experiences. They are also the basis of the production and com- prehension of action and discourse. That is, if I want to tell about an event, I need to use my event model in which I have represented that event. And conversely, if I listen to a story, what I try to do is to construct a mental model (mine!) which allows me to understand the story. In other words, speaking involves the expression of mental models, and understanding the construction (or updating) of mental models.

How does this happen?

One way to explicitly connect models with discourse is to derive the meanings of a discourse (its semantic representation) from the propo- sitions of the model.

Note however that models are usually much richer in information than dis- courses. A model may feature the information that one can kill people with guns, but since we all know that, we need not express that information as part of the semantic representation in our discourse. Indeed, such informa- tion may be left implicit in discourse production, thus giving rise to what we usually call presuppositions.

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In this sense discourses are like icebergs of which only a small amount of meanings (propositions) are actually expressed, and of which most other information may be tacitly presupposed, and hence remain implicit, simply because recipients of the same culture are able to supply this information themselves in the construction of their own models of an event. After all, speakers and recipients often share the same Common Ground, and are therefore able to instantiate such social beliefs in the models they are con- structing during discourse comprehension. In other words:

The semantic representations that define the 'meaning' of discourse are only a small selection of the information represented in the model that is used to understand such discourse.

Let us now try to represent the theory just discussed again in a schema that shows how various kinds of cognition are related to discourse:

2.5. Context models

The crucial question now is: How do speakers know what information to include in a discourse, and what information to leave implicit?

Discourse/Interaction

Episodic

Memory

Social

Memory Knowledge

Attitudes

IDEOLOGY

Common Ground

Mental Model

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Beliefs about mutual beliefs

Apparently, speakers must have beliefs about the beliefs of recipients. This is trivially the case when we speak about the socially shared beliefs that belong to the Common Ground, which precisely presuppose that we have beliefs (knowledge, attitudes) in common with other members of the same culture. Also, knowing other people personally and intimately, such as par- ents, children, spouses or friends, mostly implies that we know what more specific (model) information they already have, so that also that informa- tion need not be expressed in discourse.

This is also the case for the socially shared but specific information about events as it is distributed (and presupposed) by the mass media, knowledge which we may call 'historical'. In this sense, models are not always per- sonal and private, and limited to face to face encounters, but may also be public, and for the same reason as for the general, sociocultural beliefs of our Common Ground, such specific public beliefs may be presupposed in the models that are the basis of our discourse. To wit: A newspaper article need not explain to its readers what the Second World War or the Holo- caust are. This is information that simply may be presupposed.

This means that we not only need general information about social beliefs, but also about who we are talking to or writing for. That is, we need to rep- resent the other participants in the current situation, as well as their prob- able specific and general beliefs. At the same time, we may need to know whether our recipients actually want to get the information they are lack- ing. Our communicative intentions may vary accordingly, whether we are journalists writing for a newspaper or teachers in front of a classroom.

We must conclude for these arguments that what is still lacking in the link between social cognition and discourse is what we may simply call a model of the communicative situation. These context models (or simply: contexts) are models like those of any other event, as explained above, with the difference that they represent the current, ongoing communicative event in which you and I are now being involved as participants.

Mental Context Models vs. Social Situations

Note that the notion of context defined here is a cognitive notion, namely defined as a mental model, whereas the actual situation of the communica- tive event is a social notion, featuring 'real' social actors as participants.

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The mental model of that situation (that is, the context or context model) is merely a subjective construct of that social situation, and features all in- formation that is relevant for the interpretation of the ongoing discourse.

Since speaking/writing are ongoing activities, context models must be dy- namic: They evolve, and change with each word being said or written -- thus making all previously uttered and understood text or talk automati- cally part of the (known) context. Speakers and writers thus may adapt what they say constantly to what they believe the recipients to know al- ready, and will construct their discourse meanings accordingly. But also the social relations between the participants, the presence of certain ob- jects, the time, and other elements of the communicative situation may have changed, thus leading to continuously updated context models.

Thus defined, context models operate as some kind of overall control mechanism in discourse processing. They keep track of our intentions and goals, they let us know what we believe our recipients to know already, what the current social relations are between the participants, where we are now, and what time it is, and in what social situation we are now, e.g., in a classroom, courtroom or pressroom, and engaging in the overall genre of a lesson, a plea or news-report, within the general domains of (say) educa- tion, law, or the media.

These and several other categories of the context model are required to be able to engage in adequate, situationally sensitive, discourse. We may therefore assume that these categories are standard elements of the schema that defines context models: This is the way we routinely analyze, under- stand and represent communicative events.

Not all categories may always be relevant. Thus, as part of the Social Role category of the context model it may sometimes be relevant to represent ourselves or others as man or woman, as professor or student, as commu- nist or anticommunist, whereas in other situations such representations are irrelevant. This is why it was emphasized that a context model is a repre- sentation of what is relevant-for-discourse in the current communicative situation.

Without this kind of contextualization, we would be unable to adapt event models or social cognition to the requirements of everyday interaction, talk or text. In that sense, context models are not only about relevance, but also about people's ability to adapt themselves to current situations on the basis

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of a combination of old information and the capacity to analyze current situations.

Context models and style

As we shall see in more detail below, such discursive adaptation especially shows in our ability to adapt the style of our discourse to the current com- municative context: We may be more or less formal, more or less polite, and may choose one word rather than another, as a function of where, when and with whom we speak, and with what intentions. We are aware of the current context by the choice of deictic expressions, such as I, you, he, she, here, there, today, tomorrow, representing current participants and space-time coordinates. Context models enable us to represent the social relations that allow us to distinguish between different kinds of recipients, and therefore to use Usted or Tu in Spanish, or the technical vocabulary used in court or the classroom, and the political vocabulary used by politi- cians and the media.

Ideological Context Models

We have argued that models may be ideologically biased. Context models have the same property. As speaker I may categorize myself and other par- ticipants as members of various social groups. I may speak as a man, sexist or racist, as a professor or student, and this will not only affect the things I speak about (as represented in event models), but also the beliefs and opin- ions I may have about the current situation, for instance about other par- ticipants in the communicative event. Thus, men may not only speak dero- gatorily about women, but also address them in that way. Similarly, professional ideologies of teachers will of course influence the context models of their didactic discourse, and media ideologies of journalists similarly control their ways of writing or editing news, background stories or editorials.

In other words, ideologies not only may control what we speak or write about, but also how we do so.

With the discussion of the role of context models, we have completed our sketch of the cognitive part of a theory of ideology. In the next section we offer the important societal basis for this cognitive fragment -- after all, group members do not merely exist as disconnected minds, and in order to

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acquire and use an ideology we need social actions and discourses of real people in the real world of society and politics.

Before we start with our discussion on the social basis of ideology, how- ever, let us summarize what we have so far in an overall schema:

Episodic

Memory

Social Memory

Group Knowledge Group Attitudes

IDEOLOGY COMMON GROUND

Event model

Context Model

Social situation

Interaction/Discourse

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Chapter 3

Ideologies in society

Contrary to most earlier work in the social sciences, we have emphasized that ideologies also have an important cognitive dimension: They may be studied as structures represented in the minds of members of groups, just like knowledge.

It would however be very misguided if we would limit a general theory of ideology to such a cognitive approach. It has been stressed from the start that ideologies are essentially also social. Even in the cognitive account we spoke of social cognition, social memory and of the shared social repre- sentations of the members of a group. This means that ideologies are not merely acquired and represented by individuals, but socially learned and collectively represented by a group of people, as is also the case for lan- guage. It makes sense to speak of ideologies only in this combined sense of being at the same time cognitive and social.

At one level of theoretical description ideologies are part of the minds of individual people (because only individuals have minds), but at another level they are a joint representation, distributed over the minds of the members of a group, something they have in common. Thus, although groups of course do not have a brain-based mind, we may still say they have something 'mental' in common, as a group, when they share an ideol- ogy. There are unresolved theoretical and philosophical issues involved here, but these will not be further discussed here.

Ideology and social interaction

The social dimensions of ideology are not limited to an account of social cognition, however. If we want to understand the emergence and functions of ideologies in society, we need to deal with many other aspects of social structure.

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The social aspects of ideolo- gies may be defined both at the macro and the micro level of society.

Instead of beginning the so- cial account of ideologies at the abstract macro-level of groups and group relations, let us begin at the micro- level where we may witness how ideologies actually manifest themselves, namely in the social practices of everyday life, that is, among social actors who are par- ticipants in various forms of interaction. One crucial form of that everyday inter- action is discourse, both as monological text as well as

in dialogical conversation. Given the fundamental role of discourse in the expression and reproduction of ideologies, we shall deal with discourse separately and in more detail below.

Many of our everyday social practices are imbued by ideologies. Women and men interacting may exhibit various gender ideologies, such as those of sexism or feminism. Members of different ethnic or 'racial' groups may manifest racist, ethnicist or antiracist ideologies. Class ideologies will af- fect many aspects of the interactions between the rich and the poor. People of different ages will often show ageist ideologies. Professors and students may have opposing ideologies about education, and this will also reveal itself in their daily interaction in the classroom. Professionals have their typical professional ideologies and also will exhibit those with other pro- fessionals (as politicians and journalists may do), as well as with their cli- ents, customers, readers or constituents.

In sum, as soon as people act as members of social groups, they may bring to bear their ideologies in their actions and interaction. Thus, men may dis- criminate against women, whites against blacks, the young against the aged, and the rich against the poor. This may happen by text and talk, as

Macro and micro in sociology. In sociology as well as in other disciplines — such as dis- course studies — one distinguishes often be- tween the macro level and the micro level of description or analysis, although this is merely a practical distinction that has led to much controversy. In reality macro and micro as- pects of society are often intermingling. At the micro-level one usually describes social ac- tors, and the social interaction between these actors in social situations. The macro level (or rather several macro levels, intermediate or meso-levels) is more abstract: Here we talk about groups of social actors, institutions, or- ganizations, whole states or societies, and their relationships, such as those of power. Since ideologies are shared by a group they socially speaking belong to a macro-level of description, whereas the individual opinions of a social actor at a given moment would be- long to the micro level of description.

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we shall see below. But ideologies may also be expressed in the many 'paraverbal' activities that accompany talk, for instance in gestures, facial expressions, body posture and distance, and so on: Also in these -- sometimes very subtle-- ways we may show whether we consider someone to be an equal, superior or inferior. Every woman knows how men many show their sexism/machismo only by the way they look, by their tone of voice, gestures or proximity.

The same is true for the social practices that define people's everyday life in the family, at work, during study, at leisure, and so on. Women may be discriminated by the daily tasks their husbands expect them to fulfill, as well as by numerous forms of sexual and other harassment, violence, ex- ploitation, and so on, both by their own spouses, as well as by their male bosses and colleagues as well as other men. And when not with such overt and blatant forms of sexism, the everyday lives of women are replete with the more subtle and indirect ways of being treated unequally. Sexist ide- ologies imbue virtually all aspects of the everyday interactions between women and men.

Similar remarks may be made for the social practices defining the relations between members of different ethnic, racial, religious or political groups. Whether controlled by relationships of power or resistance, the everyday actions of group members interacting with group members of other (and especially opposed) groups, will show in many ways the underlying ide- ologies that characterize these groups.

In this way, group members may typically marginalize, exclude, or prob- lematize the members of other, dominated, groups, in infinitely subtle ways. Thus, they may do so by paying no (or too much) attention to them; by not admitting them to their country, city, neighborhood, company or house; by not giving them a job or not promoting them even when quali- fied; by criticizing them without any grounds, as well as by many forms of physical rudeness, harassment or violence. Many of these forms of ideo- logical based discrimination will also appear in discourse, and we shall therefore come back to these in more detail below.

Groups

If we now move to the macro-level account of ideology, we first need to say a bit more about the notion of a 'group'. The basic idea is here that not each collectivity of people constitutes a social group that may have an ide-

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ology. The 'group' of people waiting for a bus, is not typically the kind of social group that shares an ideology. Hence, for a collectivity of social ac- tors to form a group that may develop an ideology, we probably need some criteria, such as the relative permanence of the group, and maybe some common goals that go beyond one situation or event.

In our account of the categories that define the ideological schema, some of these social dimensions of groupness seem to be represented -- as we may expect when we define ideologies basically as some kind of group self-schema. Thus, also social groupness may be defined in terms of mem- bership criteria (origin, appearance, language, religion, diplomas or a membership card), typical activities (as is the case for professionals), spe- cific goals (teach students, heal patients, bring the news), norms, group relations and resources, as discussed above. That is, also in social terms we may define a number of the properties that people routinely use to identify themselves and others as ingroup and outgroup members, and to act ac- cordingly. Sometimes these group criteria will be quite loose and superfi- cial, e.g., when based on preferred dress or music styles, sometimes they organize virtually all aspects of the life and activities of the members of a group, as may be the case for gender, ethnicity, religion and profession.

The close relationship between ideology, social identity, group self- schemata and the social construction of the group suggests that groupness may be inherently linked to having an ideology. This would mean that all social groups have an ideology. Although this is a position that might be defended (depending on how we define a group), this conclusion may be too bold. But it is certainly true that the identification with a group not only manifests itself in a number of social practices (like professional ac- tivities, discrimination, resistance, demonstrations, and a host of other ac- tivities), but also in joint social representations, such as common goals, beliefs and values. As we have seen, these may be organized by underlying ideologies. On the other hand, groups may be formed quite loosely only on the basis of a common goal of a shared attitude, and these need not (yet) have a broader ideological basis.

Groups are themselves often structured. They may have ordinary members, who may be more or less officially part of the group (e.g., having a mem- bership card), but also individuals or subgroups who fulfill specific posi- tions or have special roles. We have leaders and followers, teachers and ideologues, as well as offices that have similar functions. This kind of or- ganization of the group is vital for the acquisition, spreading, defense or inculcation of ideologies. Thus, new members need to learn the ideology of

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a group. This ideology may have to be defended or legitimated in the pub- lic sphere. New members may have to be recruited by various forms of propaganda. Leaders or ideologues may have to teach and preach and keep the ideology alive. Books and other media may be used to help doing so. In other words, the "ideological life" of a group may be based on a complex organization of functions, organizations and institutions and their daily practices, as is obvious from churches, political parties as well as the femi- nist, the environmental, human rights and pacifist movements.

Ideological institutions

These last remarks also show that an efficient reproduction of ideologies usually requires more than just a couple of people who have a common goal and shared attitudes, values or ideological principles. Indeed, group organization as well as institutionalization may be crucial, as the history of the Catholic Church, or the efficiency of some current NGO's such as Am- nesty International or Greenpeace have shown.

The same is more generally true of the most influential ideological institu- tions of modern society: the school and the mass media. People may ac- quire partial ideologies through the imitation of everyday activities of other group members (as would be the case for male chauvinist forms of vio- lence and harassment against women), but ideologies are largely acquired as such -- and not merely as a specific form of 'behavior' or action -- through discourse. More than most other institutions, the school and the mass media fulfill that role, as it was once fulfilled by the church.

The reasons we mention the institutional nature of ideologies also in rela- tion to discourse and its reproduction is that it is not merely text and talk that does the job. The ideological dimension of public discourse is also shaped by (and shapes) the many non-verbal practices, the organizational structures, and other aspects of companies or institutions. For instance, the ideology of news reporting is not only limited to content and style of news reports, but imbues all aspects of news gathering, attending to sources, in- teraction with other journalists as well as news actors, and the organization of the professional activities of journalists (meetings, deadlines, etc). Pro- fessional as well as other social (gender, ethnic, class, age, etc) ideologies of journalists fundamentally control who will be searched for, who will be covered, listened to, interviewed, or cited. Thus, the multitude of activities that define daily news- and program making in the newspaper or on televi- sion may themselves be ideologically based, and fundamentally influenced by social actors participating as members of various social groups.

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Similar remarks may be made for the daily, institutional organization of education in schools, in lessons, teaching, textbooks, curricula, and teacher-student interactions. Ideologies not only show up in educational discourse, but in the whole organization of school life, in which also gen- der, age, ethnicity and class, among other affiliations will play a role be- sides the professional ideologies of the teachers.

Ideology and power

The fundamental social question for a theory of ideology is why people develop ideologies in the first place. Cognitively, as we have seen, ideolo- gies may be developed because they organize social representations. At the level of groups, this means that people are better able to form groups based on identification along various dimensions, including sharing the same ideology. Since ideologies indirectly control social practices in general, and discourse in particular, the obvious further social function of ideolo- gies is that they enable or facilitate joint action, interaction and coopera- tion of ingroup members, as well as interactions with outgroup members. These would be the social micro-level functions of ideologies.

At the macro-level of description, ideologies are most commonly described in terms of group relations, such as those of power and dominance. Indeed, ideologies were traditionally often defined in terms of the legitimization of dominance, namely by the ruling class, or by various elite groups or or- ganizations.

Thus, if power is defined here in terms of the control one group has over (the actions of the members of) another group, ideologies function as the mental dimension of this form of control. That is, ideologies are the basis of dominant group members' practices (say of discrimination). They pro- vide the principles by which these forms of power abuse may be justified, legitimized, condoned or accepted.

In other words, ideologies are the beginning and end, the source and the goal, of group practices, and thus geared towards the reproduction of the group and its power (or the challenge towards the power of other groups). Traditionally the term 'dominant ideologies' is used when referring to ide- ologies employed by dominant groups in the reproduction or legitimization of their dominance.

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It is also in this sense that ide- ologies are often related to group interests, that is, the set of arrangements, processes, activities, rules, laws and re- sources that favor the group in any way, thus increasing (or maintaining) its power, and the resources on which these are based (strength, capital, income, as well as knowledge, education or fame). Ideologies may thus be geared especially towards the formulation of the principles by which a group 'deserves' such advantages over other groups. For in- stance, opposition to immigra- tion will often be legitimated by claiming that WE were 'here' first, and therefore that WE have priority over scarce social resources such as citi- zenship, housing or work. Note that such interests need not at all be merely material, as was the case in traditional class-based ideologies. Many modern ideologies are rather oriented toward symbolic re- sources and aims, or to those having to do with lifestyle, sexuality, health, and so on.

Power. If there is one notion often related to ideology it is that of power, as we also see throughout this course. As is the case for many very general and abstract notions in the social sciences and the humanities, there are many definitions and theories of power. Here we only speak of social power, that is, the power of a group A over another group B. This power may be defined in terms of control. Usually this means the control of action: A is able to control (limit, prohibit) the actions of B. Since discourse is also a form of action, such control may also be exercised over dis- course and its properties: its context, its topic, or its style. And because such discourse may also influence the mind of the recipients, pow- erful groups may --indirectly, for instance through the mass media -- also control the minds of other people. We then speak of per- suasion or manipulation. In terms of our cogni- tive theory this means that powerful discourse may influence the way we define an event or situation in our mental models, or how we rep- resent society in our knowledge, attitudes and ideologies. Power needs a 'power base', such as scarce social resources such as force, money, real estate, knowledge, information or status. One of the important social resources of much contemporary power is the access to public discourse. Who controls public discourse, indi- rectly controls the minds (including the ideolo- gies) of people, and therefore also their social practices. We shall often encounter this rela- tion between social power, discourse, the mind and control. In a more critical approach to power, we are especially interested in power abuse or dominance, and how ideologies may be used to legitimate such dominance.

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Ideology, society and culture

If ideologies are typically defined for social groups, it would be strange if we would also define them for whole societies and cultures. The point is that ideologies develop as mental forms of group (self-) identification, and often in relation to other groups. This means that if there is no conflict of goals or interests, no struggle, no competition over scarce resources, nor over symbolic resources, then ideologies have no point. That is, it is only within and between groups that ideologies make sense, and not at the level of society as a whole. This would only be the case if a whole society would be related to another one, as two countries at war might be, as and nation- alist ideologies would typically show.

The same is true for whole cultures. Although ideologies and cultures are often compared (when they characterize groups or organizations), we pro- pose to distinguish between the two for the same reasons as we did refrain from assigning ideologies to whole societies. Cultures may have a shared Common Ground, as well as shared norms and values, but not a generally shared ideology as we have defined it. This would at most be a relevant notion when we again compare competing cultures, and when these (and their members) would interact and vie for power. This is sometimes said for Western- and Non-Western, Christian and Muslim cultures, which we would define in terms on political or religious ideologies, rather than in "cultural ideologies".

Although the social analysis of ideology and the ways ideologies are ac- quired and used by social groups may be detailed with many further obser- vations, we now have the basic social notions that will be needed to study the relations between ideology and discourse.

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Chapter 4

Racism

Ideologies may in one sense be mental objects, systems of socially shared ideas of a group, but we have just argued that they do not exist in a social vacuum. On the contrary, they emerge, are being used and reproduced as inherent part of social life, and are related to groups and social movements, with power, dominance and struggle. Thus, it is impossible to fully under- stand socialist or communist ideologies without knowing something about the history of class struggle and the dominated position of the workers in capitalist societies.

The same is true for the feminist movement and hence for the various ide- ologies of feminism: They arise in the broader societal context of male chauvinism, gender inequality and the institutional arrangements that have supported and perpetuated the subordinate position of women. That is, ide- ologies are so to speak the 'cognitive' counterpart of social struggle and inequality. They are not only shaped by these social structures, but largely also sustain and reproduce them by monitoring the discourses and other social practices of group members, which at the micro-level realize the structures of inequality, domination and resistance.

Thus, when we want to study racist ideologies in more detail, and espe- cially examine how discourse expresses and reproduces ethnic or 'racial' inequality, we need to know a little bit more about these social dimensions of racism. Indeed, no relevant ideological analysis of racist discourse is possible without a thorough insight of the broader context of racism in contemporary societies. We therefore briefly provide a theoretical frame- work that also explains the role of racist ideologies and discourse in soci- ety.

Racism as system of social inequality

As is the case for inequality of class and gender, also racism is a complex system of social inequality, in which some groups (in this case "white" Europeans) have more power than other (non-white, non-European, etc.) groups in society -- and indeed, in the whole world. This power difference essentially shows in differential access to scarce social resources, such as having less of most material goods, but also having less access to or con- trol over symbolic resources, such as education, knowledge, information and status, among a host of other resources. In Western Europe and North

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America today, this also means that immigrants have less access to the country, and have less residence rights. And once they are within the coun- try, they will have worse neighborhoods, worse housing, and worse jobs, if any at all.

This overall system of social inequality in which Europeans have more power than non-Europeans, is sustained as the 'micro-level' by a host of everyday discriminatory practices. If minorities or immigrants have fewer jobs, this is also because they have a harder time to get hired or promoted, and very often their work tends to be valued less than that of other work- ers. The same may be true for immigrant children at school, who for a vari- ety of reasons may also be problematized, if only by the textbooks which until today often are biased, if they take the present of non-European chil- dren into account at all.

Throughout society, thus, non-European minorities are daily confronted with a sometimes subtle system of inequities, in their neighborhoods, at work, at school, in shops, public transport as well as in the mass media. This system is called 'everyday racism' in order to stress that racism only occurs occasionally and in very blatant forms that are reported by the me- dia. The overall consequence of these forms of problematization, margin- alization and exclusion at the micro-level is social inequality at the macro level.

It should be emphasized that racially or ethnically based social inequality need not be very explicit, blatant or overt. Although racist violence is a daily phenomenon, and much more widespread than most white people think, also in today's Europe, it is not the main characteristic of contempo- rary European racism. Everyday racism, as suggested, may be subtle and indirect and appear in sometimes minor forms of daily interaction -- in such a way that someone of the European majority treats someone of the non-European minority in a way in which he or she would not treat another European person. In that respect, everyday racism is a violation of norms -- treating someone differently and more negatively than one should.

Everyday racism

Although this may also happen among white people, the typical character- istic of racism is that this may happen to minority group members every- day, so that the inequities accumulate and thus become a massive system of psychological and social stress if not oppression. At the same time, this

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everyday nature of subtle racism has become so natural that it seems to be taken for granted. Racist slurs, jokes, harassment and marginalization are so common that they no longer raise much concern among most members of the dominant white group. It is only the more overt, more explicit and more extremist form of racism that is being noticed and written about in the paper, and of course officially condemned. Ordinary racism is simply part of everyday life for minorities in Europe and North-America.

These everyday social practices that define racism at the micro-level of course have a cognitive foundation. That is, other people can only be treated differently if they are being perceived and categorized as being dif- ferent. And they are treated more negatively, they are problematized, mar- ginalized and excluded if they are being evaluated as being "less" on all relevant dimension of social evaluation. In other words, discrimination as unequal treatment can only be subjectively justified when dominant group actors believe that such treatment is normal or otherwise legitimate. For instance, a European employer may not give a Moroccan immigrant a job because he thinks that the newcomer is less intelligent, less competent or less diligent, or simply because he or she prefers to hang out with his friends. In other words, the everyday social practices of discrimination pre- suppose a cognitive basis of negative beliefs about the Others: stereotypes, prejudices, racist attitudes or other socially shared negative opinions as they are organized by racist ideologies.

In other words, racist ideologies are not some kind of an abstract system that floats over European society. On the contrary, they are beliefs that are historically, socially and culturally deeply ingrained in the social mind of many Europeans, and that more or less subtly control their beliefs about non-European others. Such attitudes may for instance show up in the fact that at present more than on average two thirds of the population in West- ern Europe opposes further immigration. This need not be (although in practice it often still is) a feeling of ethnic or racial superiority -- but more often than not, negative treatment of the others implies at least one form of negative categorization. Through the complex structures of everyday life and culture in Europe, thus, people of African descent, as well as other non-Europeans are thus routinely being perceived and evaluated not only as different, but also as deviant, problematic if not as dangerous. It is in this profound way that racist ideologies hold sway over social attitudes in many domains of life in multicultural Europe and North America. This is also true for those European groups and in those institutions where this is most resolutely denied: among the elites, that is, in politics, in the mass media, in scholarship, in education, in the courtroom, in the ministries, and

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so on. In other words: racist ideologies are the socially shared foundations of the ethnic/racial beliefs that enable the daily discrimination defined as everyday racism. We shall see below how fragments of such racist ideolo- gies also show up in discourse.

Summary. Racism is a system of ethnic/racial inequality, reproduced by discriminatory social practices, including discourse, at the local (micro) level, and by institutions, organizations and overall group re- lations on the global (macro) level, and cognitively supported by rac- ist ideologies.

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Chapter 5

Ideological discourse structures

We now have a first impression of what ideologies are, how they affect the other mental structures that are involved in the production and understand- ing of discourse and how ideologies function in society. That is, we have an elementary theory of ideological discourse processing and the begin- ning of a social theory of the role of ideologies in the life of groups and the relations between groups.

This is however only a first step. It does not tell us much about the ways these ideologies, attitudes and biased models actually are being expressed in discourse and what role discourse plays in the social functions of ide- ologies. To clarify these matters, we now finally turn to the more detailed study of the ways ideologies manifest themselves in discourse.

Which structures?

Discourse is very complex, featuring many levels of structures, each with their own categories and elements, which may be combined in innumerable ways. As we have seen, ideologies may be expressed explicitly and then are easy to detect, but this may also happen very indirectly, implicitly, con- cealed or in less obvious structures of discourse, such as an intonation, a hesitation or a pronoun.

In this section, then, we shall explore some of the structures that typically exhibit underlying ideologies. We have reason to believe that ideology may exhibit in virtually all structures of text or talk, but on the other hand, we also believe that this may be more typical for some than for other struc- tures. Thus, semantic meaning and style will more likely be affected by ideology than morphology (word-formation) and many aspects of syntax (sentence-formation), simply because the latter are much less context de- pendent: In English and Spanish the article precedes the noun, and no ideo- logical influence will change that. But whether we call someone a 'freedom fighter' a 'rebel' or 'terrorist' is a lexical choice that is very much dependent on our opinion of such a person, and such an opinion in turn depends on our ideological position, and the attitudes we have about the group that person belongs to. In other words, we need to look for those properties of discourse that most clearly show the ideological variations of underlying context models, event models and social attitudes.

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A practical, general strategy of ideological analysis

Since discourse is so complex, and hence ideological structures can be ex- pressed in so many different ways, it is useful to have a more practical 'heuristic', a method to 'find' ideology in text and talk. To formulate such a heuristic, let us go back for a moment to the nature of ideologies. These were represented as some kind of basic self-schema of a group, featuring the fundamental information by which group members identify and catego- rize themselves, such as their membership criteria, group activities, aims, norms, relations to others, resources, etc. These categories typically organ- ize information of the following kind:

- Membership: Who are we? Who belongs to us? Who can be admitted? - Activities: What are we doing, planning? What is expected of us? - Aims: Why are we doing this? What do we want to achieve? - Norms: What is good or bad, allowed or not in what we do? - Relations: Who are our friends or enemies? Where do we stand in soci-

ety? - Resources: What do we have that others don't? What don't we have what

others do have?

These then are the kind of questions that typically are associated with group identity and hence also with ideologies. We see that much of this information is about Us vs. Them. Indeed, ideologies typically organize people and society in polarized terms. Group membership first of all has to do with who belongs or does not belong to Us, and how we distinguish ourselves from others by our actions, aims and norms, as well as our re- sources. Socially fundamental is what position we have relative to the Oth- ers -- whether we are in a dominant or dominated position, or whether we are respected or marginalized, etc. as is typically the case in chauvinist vs. feminist, racist vs. anti-racist ideologies. Many social ideologies of groups and movements have these properties. Some other ideologies, such as the ecological ones, combine these social views with views about nature and how people should interact with nature, whereas religious ideologies in addition will feature propositions about people's relation to God.

Given this informal rendering of 'typical' ideologies and their typical con- tents, we may try to formulate the heuristic that tries to combine such un- derlying social beliefs to their expression in discourse.

Basically, the overall strategy of most ideological discourse is a very gen- eral one:

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- Say positive things about Us - Say negative things about Them

This form of positive self-presentation and negative other-presentation is not only a very general characteristic of group conflict and the ways we interact with opposed groups, but also characterizes the way we talk about ourselves and others.

Now, this overall strategy typically applies to meaning (content), and would therefore be rather limited. Thus, we need to extend it in some ways so that also other discourse structures can be characterized by it. But first, we need to complement it with its opposite meanings:

- Do not say negative things about Us - Do not say positive things about Them.

As formulated, the strategy is too absolute and too general. So in order to enable a more subtle ideological analysis that also applies to others struc- tures in the expression of ideology, we modify the four principles as fol- lows:

 Emphasize positive things about Us.  Emphasize negative things about Them.  De-emphasize negative things about Us.  De-emphasize positive things about Them.

This four of possibilities form a conceptual square, which may be called the 'ideological square'. It may be applied to the analysis of all levels of discourse structures. As to their content, they may apply to semantic and lexical analysis, but the use of the opposing pairs 'emphasize' and 'de- emphasize' allows for many forms of structural variation: we may talk at length or briefly about our good or their bad things, prominently or not, explicitly or implicitly, with hyperbolas or euphemisms, with big or small headlines, and so on. In other words, discourse has many ways to empha- size of de-emphasize meanings, and as soon as these have an ideological basis, we are able to analyze the expression of ideology on many levels of discourse, of which we shall now give some examples.

5.1. Meaning

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We have argued that ideology may in principle show up anywhere in dis- course. Yet, ideological 'content' is most directly expressed in discourse meaning. So we shall pay special attention to the semantics of ideological discourse. Since the meaning of words, sentences and whole discourses is extraordinarily complex, we'll make a selection of its most relevant as- pects. These will only be briefly and informally characterized, without a lengthy theoretical summary of its properties.

Topics. The meaning of discourse is not limited to the meaning of its words and sentences. Discourse also has more 'global' meanings, such as 'topics' or 'themes'. Such topics represent the gist or most important infor- mation of a discourse, and tell us what a discourse 'is about' , globally speaking. We may render such topics in terms of (complete) propositions such as 'Neighbors attacked Moroccans'. Such propositions typically ap- pear in newspaper headlines.

Incidentally, in order to avoid confusion, we distinguish here between topics --as they can be represented by a proposition-- and more abstract Themes, typically expressed by single words, such as 'Immigration' , 'Discrimination' or 'Education' which are broad categories that may de- fine classes of texts with many different (specific) topics.

Topics typically are the information that is best recalled of a discourse. Al- though topics abstractly characterize the meaning of a whole discourse or of a larger fragment of discourse, they may also be concretely formulated in the text itself, for instance in summaries, abstracts, titles or headlines.

The ideological functions of topics directly follow from the general princi- ples mentioned above: If we want to emphasize our good things or their bad things, the first thing we do is to topicalize such information. And con- versely, if we want to de-emphasize our bad things and their good things, then we'll tend to de-topicalize such information. For instance, in much public discourse in multicultural society this means that topics associated with racism are much less topicalized than those related to the alleged crimes, deviance or problems allegedly caused by minority groups.

For a research project on conversations on minorities, carried out in the Netherlands and in California, we found that the preferred topics white autochthonous people speak about may be categorized by the following three concepts characterizing the Others:

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Difference Deviance, transgression Threat

Level of description. Degree of detail. Once a topic is being selected, language users have another option in the realization of their mental model (= what they know about an event): To give many or few details about an event, or to describe it at a rather abstract, general level, or at the level of specifics. We may simply speak of 'police violence', that is, in rather gen- eral and abstract terms, or we may 'go down' to specifics and spell out what precisely the police did. And once we are down to these specifics, we may include many or few details. As is the case for topicalization, it hardly needs much argumentation that we will usually be more specific and more detailed about our good things and about the bad things of the others, and vice versa -- remain pretty vague and general when it comes to talk about our failures.

In much public discourse in Europe, and especially in the conserva- tive press, one finds much detail about the deviance and crimes of mi- norities, but very little detail about the everyday forms of racism to which they are submitted -- and if something is said about Our racism at all, it will typically be at a fairly high level of abstraction, for in- stance in terms of popular "resentment".

Implications and presuppositions. It has been explained that discourse production is based on mental models we have about some event, and that for many reasons (such as the knowledge a recipient already has) we need only express part of the information in such a model. When necessary, missing information may thus be inferred by the recipients, namely from their model for a discourse or their general sociocultural knowledge. All propositions that appear in a model but not in the discourse may thus be called the 'implied' meaning of a discourse.

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In ideological discourse analysis making explicit the meanings im- plied by a sentence or text fragment may be a powerful instrument of critical study.

The option to express information or leave it explicit, is not ideologically neutral, however. It is easy to predict that within our general schema, peo- ple tend to leave information implicit that is inconsistent with their positive self-image. On the other hand, any information that tells the recipient about the bad things of our enemies or about those we consider our outgroup will tend to be explicitly expressed in text and talk.

A well-known move is to presuppose information that is not generally shared or accepted at all, and thereby introduce it so to speak through the backdoor). For instance, if the police declares to "worry about the high crime rate of young immigrant boys" then such a declaration tacitly pre- supposes that young immigrant boys indeed do have a high crime rate. This may not be true, or may be true for all young boys who have no jobs, so that the presupposition is misleading, and should rather be about the unemployed.

Local Coherence. One of the typical characteristics of discourse meaning is coherence: The meanings of the sentences (that is, their propositions) of a discourse must be related in some way. Such coherence may be global or local. Global coherence may simply be defined in terms of the topics we discussed above: a discourse (or discourse fragment) is globally coherent if it has a topic.

Going down to the local meanings of discourse, however, we deal with what may be called 'local coherence'. Although it is not easy to define this notion very precisely, we shall simply assume that a sequence of proposi- tions is locally coherent if it is about a sequence of actions, events or situa- tions that are mutually related, for instance by relations of causality or en- ablement. In even more succinct (but formally impeccable) terms we may say that a discourse sequence is coherent if it has a model. In more intui- tive terms this means that we may call a discourse (or discourse fragment) coherent if we can imagine a situation in which it is or in which it could be true. Because this kind of coherence is defined in terms of the 'facts' re- ferred to, we may call this referential coherence. There is also coherence that is defined for relations between propositions themselves, for instance

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when one has the function of being a Specification, a Generalization, an Example or a Contrast of another: we may call this functional local coher- ence.

Now, what are the ideological options language users have in the manage- ment of discourse coherence? Obviously, not very many, because coher- ence is a very general condition of discourse, and whether one is left or right, man or woman, racist or antiracist, one needs to respect some basic conditions of coherence in order to be meaningful.

More in general we may say that if discourse structures are obligatory, and hence do not change under the influence of context, they also cannot vary with the ideology of the speaker.

And yet, coherence is ideologically controlled, namely via the mental mod- els on which it is based. These may feature a causal relation between to facts F1 and F2 that explains why proposition P1 and P2 are locally coher- ent. But such a model of a situation may very much depend on one's opin- ions, attitudes or ideologies.

See also the kind of coherence that presupposes certain assumptions to be true, and in such well-known examples as "He is from Nigeria, but a very good worker", a sentence that presupposes that workers from Nigeria

Synonymy, paraphrase. Whereas coherence is defined for relations be- tween propositions in a discursive sequence or in a model, there are many other semantic properties of discourse defined in terms of relations be- tween propositions, such as synonymy and paraphrase. Since these rela-

I often observed a very typical case in the discourse of employers in the Netherlands, for whom high minority unemployment is primarily due to lacking abilities of minorities, and not to discriminatory em- ployment practices of employers themselves. The conditions of coher- ence for a discourse that explains minority unemployment in the Netherlands thus completely depends on the model one has of the causes of such unemployment, models that may be more or less racist or anti-racist. In other words, coherence is relative, and this relativity also has an ideological dimension.

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tionships are not defined in a different way for different contexts, ideology does not seem to have grip on them: a synonym is one whether one votes communist or conservative. But note that strict synonymy does not exist, and that paraphrases are typically expressions that have more or less the same meaning, but not quite, and are usually formulated in different words. And different words means lexical and stylistic variation, which is depend- ent on context. Thus, we may of course speak about immigrants in terms of many expressions and descriptions that are more or less synonymous, but whose meanings-of-use and ideological implicatures are different. Thus, to speak of 'foreigners' in Western Europe today usually implies reference to ethnic minorities or immigrants and not to 'real' foreigners. Moreover, de- pending on context, the use of the word may sound more negative than for instance 'ethnic minorities'.

Contrast. Ideologies often emerge when two or more groups have con- flicting interests, when there is social struggle or competition, and in situa- tion of domination. Cognitively and discursively, such opposition may be realized by various forms of polarization, as the well-known pronoun pair Us and Them illustrates. We have already seen that the overall strategy of ideological discourse is to emphasize Our good things and Their bad things, a form of polarization that is semantically implemented by con- trast. In racist discourse, for instance, we discover many statements and stories that are organized by this form of contrast: We work hard, They are lazy; They easily get jobs (housing etc), and we do not, and so on. It is pre- cisely this kind of recurrent discursive contrast that suggests that probably also the underlying attitudes and ideologies are represented in polarized terms, designating ingroups and outgroups.

Examples and illustrations. More generally discourse about Us and Them, and hence also racist discourse, is characterized by examples and illustrations, often in the form of stories, about Our good deeds and Their bad behavior. Functionally, such propositions (or whole stories) serve to support another, mostly previously expressed proposition, for which it may give proof or evidence (as we have seen above). In other words, stories may serve as premises in an argumentation. In racist discourse, thus, we may find a general opinion statement, for instance about how They break the rules, do not adapt, are deviant or even criminal. But, to prevent nega- tive evaluation by the hearer, speakers usually feel obliged to give some example or illustration of a general statement that is negative about immi- grants. A very credible story in that case provides the experiential 'evi- dence' for the general statement.

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Disclaimers. Very typical of any type of prejudiced discourse is the se- mantic move of the disclaimer, of which the Apparent Negation is the best known: I have nothing against X, but… We call this an Apparent Negation because it is only the first clause that denies adverse feelings or racism against another group, while the rest of the discourse may say very nega- tive things about the others. The negation in such a case primarily serves as a form of positive self-presentation, of face keeping: Speakers want to avoid that the recipients have a negative opinion about them because of what they say about immigrants. Note that in those cases where speakers are really ambivalent about their attitudes about minorities, we do not typi- cally find such disclaimers but discourses that are ambivalent throughout, with positive or neutral and negative parts.

DISCLAIMERS Apart from the well-known Apparent Denial, there are many types of disclaimers, such as: Apparent Concession: They may be very smart, but…. Apparent Empathy: They may have had problems, but… Apparent Apology: Excuse me, but… Apparent Effort: We do everything we can, but… Transfer: I have no problems with them, but my clients… Reversal, blaming the victim: THEY are not discriminated against, but WE are!

All these disclaimers combine a positive aspect of our own group, with negative ones of the Others, and thus directly instantiates the contradic- tions in ideological based attitudes.

Propositional structures

Local discourse meaning is (theoretically speaking) organized in proposi- tions: One sentence expresses one or more propositions -- things that may be true or false, or which (intuitively speaking) express one complete 'thought'. In the same way as the meaning of sequences of sentences and whole discourses are constituted by propositions, also the propositions themselves have internal structures. Indeed, the traditional philosophical

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and logical analysis of propositions assigned them the well-known Predi- cate(Argument, Argument, Argument….) structure.

For our ideological analysis, the structures of propositions have some in- teresting properties which however we shall deal with only briefly. For one, the predicates of propositions may be more or less positive or nega- tive, depending on the underlying opinions (as represented in mental mod- els). Thus, in British tabloids as well as in conservative political discourse, we may typically find propositions such as 'Refugees are bogus', and simi- lar negative evaluations may be found in virtually any kind of discourse about minorities, immigrants or refugees. We here deal with the core of discursive racism: the selection of words that express underlying negative predicates about the Others.

Actors. The arguments of a propositions may be about actors in various roles, namely as agents, patients, or beneficiaries of an action. Since ideo- logical discourse is typically about Us and Them, the further analysis of actors is very important. More specifically, in racist or anti-racist dis- course, we may want to examine in detail, how immigrants are being repre- sented. Actors may thus appear in many guises, collectively or individu- ally, as ingroup ('we') or outgroup members ('they'), specifically or generally, identified by their name, group, profession or function; in per- sonal or impersonal roles, and so on.

Depending on text and context, discourse that is controlled by racist attitudes and ideologies will have the tendency to represent minorities or immigrants first of all as Them, that is, as belonging to some out- group. Instead of talking individually and specifically, all Others are being homogenized, for instance in terms of generalized or generic expressions ('the Turks', 'the Turk'). In other words, actor descriptions that are ideologically based are semantically reflecting the social dis- tance implied by racist ideologies.

Modality. Propositions may be modified by modalities such as 'It is neces- sary that' , 'It is possible that' or 'It is known that'. For instance, a proposi- tion such as "Many African refugees have arrived in the country' may also have the following form: "It is well-known that many African refugees have arrived in the country. We already have seen that these modalities have something to do with the way we represent the world and its events. Representing (say) police brutality as 'necessary' may imply some kind of

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legitimization for such violence, as is often the case in newspaper accounts of 'race riots'.

Evidentiality. Speakers are accountable for what they say. Thus, if they express a belief, they are often expected to provide some 'proof' for their beliefs, and engage in a debate with those who deny it. Of course, each genre, context and culture has its own evaluation criteria for what is good, acceptable or bad 'evidence'. Scholarly proof in the natural sciences, social sciences or humanities may require different types of evidence, and the same is true for 'proof' in everyday life, which may range from "I have seen it with my own eyes" to more or less reliable hearsay. In contemporary so- ciety the media are a prominent criterion of evidentiality: "I have seen it on TV" or "I read it in the newspaper" are rather powerful arguments in eve- ryday conversations.

In discourse about immigrants, most knowledge is borrowed from the me- dia. So media information forms an important part of the evidentiality strategy people use. Since the use that may be made of media messages may be biased, such "evidence" may also be ideologically based. Rather typical for instance is to support claims about the alleged criminality of immigrants with reference to the mass media: "You read about it in the newspaper everyday". Since the newspapers indeed often provide the eth- nic background of criminals, even when such information is irrelevant, se- lective attention and reporting in the media is thus reproduced and magni- fied by the public at large. And selective attention and recall for the crimes of outgroups makes such news items more salient.

Hedging and vagueness. A powerful political and ideological tool is the management of clarity and vagueness, as the well-known example of dip- lomatic language shows. We may hedge or be vague when we do not know a precise answer to a question, and yet do not want to appear ignorant. But we may also hedge a discourse for political reasons, for instance when pre- cise statements are contextually inappropriate or simply "politically incor- rect". A politician or journalist may oppose immigration, but may hedge such an opinion lest he or she be accused of racism. And both in the media and in political discourse, we may precisely witness the use of vague terms such as "popular discontent" or "resentment" instead of using the more specific term racism. Obviously, vagueness may imply mitigation, euphe- mism and indirectly also a denial.

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Topoi

Halfway between semantics and rhetoric, we may find the well-known 'to- poi' (Greek: places; as in common places; Latin: loci communes). They are like topics as earlier defined, but they have become standardized and pub- licized, so that they are typically used as 'ready-mades' in argumentation. Ideological discourse in general, and racist discourse in particular, is usu- ally replete with such topoi. Thus, refugees and other immigrants are rec- ommended to stay in their own country -- to help build it up. Or even more cynically: To stay in their own country , because of widespread discrimina- tion and prejudice in our country.

In much official discourse against immigration, we find topoi that empha- size that They are a "burden" for our country (economy, social services, education, etc), if not a "threat" of the welfare state, or of Our Western Culture. Equally standard is the topos of (large) numbers, which character- izes much media reports on immigration -- but only the influx is thus quan- tified and emphasized: the media very seldom report how many people have left.

Note that topoi not only define racist text and talk, but also anti-racist dis- course. Thus, the claim that we should not close our borders, not to be too strict with immigration rules, and so on, are usually based on topoi that refer to general humanitarian values (equality, tolerance, hospitality, broth- erhood and sisterhood, and so on). One of the discursive implications of the use of topoi is that as standard arguments they need not be defended: They serve as basic criteria in argumentation.

5. 3. Formal structures

I have argued before that content or meaning is the most obvious dis- course level for the expression of ideology. It is here that the general and specific propositions of models and social representations can be most di- rectly exhibited.

This does not mean, however, that ideological analysis should be limited to semantics. On the contrary, although often more indirectly, but therefore also more subtly, underlying ideologies may also affect the various formal structures of text and talk: the form of a clause or sentence, the form of an argument, the order of a news story, the size of a headline, and so on.

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Since forms 'as such' have no meaning, their ideological function can only be exercised together with meaning or (inter)action. Given the ideological square that we have found to characterize discourse, this means that dis- course forms are typically deployed to emphasize or de-emphasize mean- ings.

There are many types of discourse forms. In sentence syntax alone there are dozens of possible structural forms that might be used to emphasize or de-emphasize meaning. The same is true for the overall schematic forms of discourse, such as argumentative or narrative structures, or the conven- tional schemata of a conversation, a news article or a scholarly article in a psychological journal.

In all such cases, syntactic or schematic (superstructural) form consists of a number of categories that appear in a specific hierarchical or linear order, following some rules or other general principles. Some of these rules are obligatory, so that there is no possible contextual variation of structure. For instance, as was already said above, in English and Spanish (but not in Scandinavian languages) the article always precedes the noun: a table, the table, una mesa, la mesa. This is true independent of context, and hence independent of speaker, and hence independent of groups and ideologies. This means that article placement generally is not the kind of structure one would study in an ideological analysis.

On the other hand, all forms that may change as a function of some context feature, such as the social role, position, belief or opinion of the partici- pants, may in principle also have an ideological function. For instance, the well-known variation between Spanish 'tu' and 'Usted' is based on the so- cial relation between speaker and recipient, and may therefore in principle be deployed ideologically. Thus, a white person may use familiar 'tu' when addressing a black person who because of social position would normally have been addressed with 'Usted'. That is, such 'biased' pronoun use could be seen as a form of derogation, and hence as an expression of underlying racist ideologies.

5.4. Sentence Syntax

As suggested above, many sentence structures are not contextually variable and hence cannot be used to ideologically 'mark' discourse sentences. However, others do allow at least some variation, such as word order, ac- tive and passive sentences, and nominalizations. Words may be put up front through so called 'topicalization', or they may be 'downgraded' by

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putting them later in a clause or sentence, or leaving them out completely. The canonical (standard, preferred) order in English and Spanish is to match semantic agents with syntactic subjects, which are typically in first position, for instance, "The police arrested the demonstrators." But we may make the agency of the police in this example less prominent, by moving the expression 'the police' towards the back of the sentence, for instance by using a passive construction: "The demonstrators were arrested by the po- lice", or by using a cleft sentence that topicalizes the demonstrators: "It was the demonstrators who the police arrested". Indeed, the agent may be completely left implicit, for instance in such sentences as "The demonstra- tors were arrested", or using the nominalization (verb turned into a noun): "The arrest of the demonstrators". In other words, by using different sen- tence forms, the order of words may signal whether the meaning expressed by some words is more or less emphasized, and it needs little argument that such emphasis or lack of emphasis has ideological implications, as shown above.

5.5. Discourse forms

What is true for the expression of meanings in variable syntactic forms, is also true for whole propositions at the level of the whole discourse: some propositions may be expressed in sentences that are put up front, and oth- ers in sentences at the end of text or talk. This kind of sentence order in discourse has many functions, including ideological ones. In general, as is the case for sentences, information that is expressed in the beginning of a text thus receives extra emphasis: it is read first and therefore will have more control over the interpretation of the rest of the text than information that is expressed last. Headlines and leads in newspapers, and titles and abstracts in scholarly articles, are characteristic examples. Thus, more gen- erally, word and sentence meaning in discourse may become foregrounded or backgrounded by their position in the semantic structure as it is ex- pressed by sentences order in the discourse. Again, this fundamental prop- erty of discourse meaning and its associated forms closely corresponds to the ideological square that assumes ingroup favoritism and outgroup dero- gation: Sentences that express positive meanings about us, and negative meanings about them, will typically appear up front -- if possible in head- lines, leads, abstracts, announcements or initial summaries of stories. And conversely, meanings that embody information that is bad for our image will typically tend to appear at the end, or be left implicit altogether.

This overall strategy controlling the order of discourse may also affect the various categories that conventionally define the schematic structure of

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text or talk. Thus, the conventional categories of Headline, Title or Sum- mary are typically realized at the beginning of a text, and thus tend to be filled by propositions that express the most important meaning. This will be so for news stories in the press, for scholarly articles, for everyday sto- rytelling and so on. Sometimes, the most important information comes last, for instance as a Summary, as Conclusions or as Recommendations -- but the basic idea is that importance of information is related to importance of meaning which in turn is related to prominence of position (first, last, on top, etc.). And it is this general principle that may be interpreted as ideo- logically relevant.

For instance, in news reports about minorities, we thus may expect that negative information about the Others will typically be expressed first and on top, that is, in the Headlines and in the Leads, as is often the case. In- deed, even the intertextual order in the newspaper obeys this principle: such news will tend to be placed higher on the page, and more towards the front of the paper, if not on the fist page. The opposite will be true for bad information about Us, such as reports about racism, or any other informa- tion that violates the norms and values we find important in our culture.

5.6. Argumentation

Many discourse genres have argumentative structures, for instance editori- als in the press, letters to the editor, scholarly articles, an everyday fight of a couple or parliamentary debates. Typical of such genres is that partici- pants (or speakers and addressees) have different opinions, different stand- points or points of view. In the argumentative discourse of such a situation one or more of the participants then tries to make his or her standpoint more acceptable, credible or truthful by formulating 'arguments' that are purported to sustain the chosen point of view. That is, such a discourse may be conventionally divided into two main categories: Arguments and a Conclusion, or Standpoint and Arguments, depending on what comes first.

As is the case for many formal structures, also argumentative structures as such do not appear to vary with ideology. The content of an argumentation may depend on our ideologies, but the argumentation structure itself is probably independent of our ideological position. And 'good' and 'bad' ar- gumentation is rather something that varies with individual speakers than with group membership. Of cours, like any genre, various argumentative genres may be learned, and be associated with a profession and hence with professional ideologies: An experienced politician, scholar, journalist, lawyer or teacher probably is more experienced in 'good' argumentation

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than those who do not have such professional training and experience. But this is as close as one may get to relate (expertise in) discourse structures with groups, namely through education, training and experience. But this still does not link discourse structures, such as those of argumentation, with ideology.

As is the case for other discourse genres and schematic structures, argu- mentation is controlled by a number of normative rules, interaction princi- ples and efficient strategies of actual performance. Note that these are not the same. One may break the rules of argumentation, for instance by using fallacies, but still respect interaction principles (for instance of respect or cooperation) or still be a very efficient arguer. In other words, as is the case for any structure, there is some variation here, and hence the theoreti- cal possibility of ideological interference.

Not quite trivially, the very choice of a standpoint rather than another one might already be seen as one of the means language users have to empha- size meaning and hence underlying beliefs. Thus, one may choose to op- pose the immigration of more refugees, and focus the whole text, both in content and form, on the defense of such a position. That is, the main point of view, usually expressing a prominent opinion, has a function that is similar to that of a headline, which also represents the most important in- formation of a text, and whose content also globally controls the produc- tion of the rest of the discourse. Given the rather direct links between standpoint and opinion, which in turn may be linked to shared group atti- tudes, we see that argumentation structures may be powerful signals of the underlying structures of ideological attitudes.

For instance, one may oppose immigration mainly because of possible la- bor market problems, and that would signal the ways ethnic ideologies are combined with labor ideologies in specific attitudes about minorities on the labor market. Of course, when some of the underlying ideologies are politically incorrect, for instance when the speaker specifically does not want the immigration of African refugees or laborers, then the arguments involved may of course be hidden, or rationalized in terms of more 're- spectable' arguments about the labor market or lack of housing, or cultural problems. Note though that these variations of ideology are expressed in the meaning or content of the argument, not specifically in its structures.

Fallacies, very generally defined, are breaches of argumentation rules and principles. Thus, interaction principles are violated when we do not let

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others speak their mind, interrupt them, threaten them, or in any other way obstruct or prevent argumentative interaction.

And argumentation rules are broken if, for instance, we use an irrelevant argument, play on people's emotions, ask the opponent to show I am wrong, argue that something must be true because everybody thinks so, or because some authority says so. Similarly, we engage in fallacies when we overgeneralize, use false analogies, are begging the question, or assume that from bad one necessarily goes to worse.

The question now is whether these and other fallacies may be ideologically variable. Does the left prefer some fallacy and the right another? Are some fallacies typical for racist talk, as are many disclaimers ('I have nothing against X, but…')? Quite superficially one might say that it is typically 'fascist' to use force to prevent an argument, but that assumes that violence --in argumentation or elsewhere-- is a privilege of fascism only. Or, a bit less superficially we might hold that the fallacy of authority is typically used by authoritarian people. But again, any ideological group and its members defends points of views by referring to leaders, heroes and credi- ble authorities. And not only socialists will have recourse to an argument "ad populum" -- populism is also something of the right. In sum, as far as our analysis goes, we need to conclude that there is no direct link between fallacies or ways of arguing and ideology. Where these links exist, they are only semantic: The contents of arguments are of course related to ideologi- cal attitudes.

5.7. Rhetoric

What about the kind of structures typically described in classical rhetoric in terms of 'figures of style'? Are alliterations, metaphors, similes, irony, euphemisms, litotes, and many other figures of style ideologically vari- able? Having reviewed the arguments made above for other formal struc- tures, such as those of argumentation, the ideological nature of rhetoric seems implausible: the left and the right, racists and anti-racists, feminists as well as male chauvinists, they probably all use all forms of rhetoric. True, racist discourse may feature many euphemisms when it refers to eth- nic inequality, racism or discrimination, but may not do so when talking about the Others alleged misdeeds. It depends on which opinions are for- mulated about whom.

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What does happen among speakers of various groups, though, is the rhe- torical emphasis on our good things and their bad ones, as we have seen before, but again, that is a matter of meaning and content, not of form. And it is true that the left and the right, racists and anti-racists may use different metaphors, as the Nazis used special metaphors (dirty animals, etc.) to de- note its opponents and victims. But again, that is a question of meaning, content and cognition, not of form, not the choice of a figure of style rather than another.

Thus, a rhetorical study of ideological discourse will generally follow the same principles as above: It will focus on those figures of style that can be deployed to emphasize our good things and their bad things, and vice versa for our bad things and their good things, such as hyperbolas, euphemisms, and so on. To know what ideological implications such figures of style have, we again need to examine the meanings they organize.

5.8. Action and interaction

Discourse is roughly defined by three main components, two of which we have examined above: Meaning and Form. We now need to introduce the third, and most social dimension: Action and interaction. Thus, discourses when uttered in a specific situation may accomplish the speech act of an assertion, of a question, accusation, promise or threat.

Do these speech acts differ by speaker or social group? Hardly. Except from a few institutional speech acts, such as to marry or baptize someone, virtually all speech acts can be used by all people. True, it may be so that members of dominant groups, when talking to members of dominated groups, may have more often recourse to commands or threats, given the social conditions of such speech acts. However, that presupposes that members of dominated groups, among each other, never engage in com- mands or threats, which is clearly implausible.

In a broader sense of social action (actions that are not only accomplished by language, but may be accomplished that way) there are many acts that are part of the very definition of dominance: discrimination, delegitimiza- tion, slurs, derogation, problematization, marginalization, and so on. They might be associated with power, and power groups in society, but again not with specific ideologies.

What about conversational interaction? Could one say that turn taking, pauses, interruptions, self-presentation, closing conversation, laughing,

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and so on, and so on, are acts one ideological group typically engages in more than another? In general terms, this does not seem likely: These are interactional resources that happen to be available to the whole commu- nity, and children learned to use them even before any ideological group affiliation. On the other hand, in some contexts some (ideological) group may engage in specific acts more often than others, for instance when they have the power to do so, for instance when the conservative parties in the French Assemblée Nationale use many more interruptions than the social- ists. In other words, such differences need not (only) be ideologically based, but may depend on who happens to have the power, or the majority.

The same is true for macro-level actions that are largely accomplished by discourse, such as education, legislation or governing the country. These are of course imbued by ideology, but largely as to their "content" , not as macro-actions: Both a conservative and a socialist government by defini- tion 'govern' a country, and legislation takes place in any parliament. In other words, both locally at the micro-level, as well as globally, at the macro-level, discursive acts such as speaking, debating, quarreling or man- aging a company, among many others, may be being carried out by social actors with any ideology. It is only what they say, what they decide or how they speak or govern that is monitored by ideologies: It is here that they may do so in a democratic, authoritarian, conservative, progressive, neo- liberal, socialist, chauvinist or feminist, racist or anti-racist way.

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Chapter 6

Examples

Now we have dealt with the theoretical aspects of the relations between discourse and ideology, let us have a look at some examples. These will be taken from a debate in the British Parliament (House of Commons), held on March 5, 1997.* The debate especially deals with the issue of benefits for specific categories of asylum seekers, after an earlier discussion about whether certain inner city boroughs of London (such as Westminster) will have to pay for the extra costs for reception of those refugees who are enti- tled to benefits. The debate is interesting because it nicely shows the vari- ous political and ideological positions being taken by right-wing conserva- tives, more moderate conservatives and Labour MPs (Labour was still in the opposition then). That is, on the one hand we find an anti-immigrant attitude which we associate with a form of political racism, and on the other hand various humanitarian, or anti-racist ideologies that control more tolerant attitudes about immigration.

To make the examples as practical as possible for future reference (so you can search for discourse properties by name), we have not ordered them by level as we did above, but by name of the relevant structural category, also because some categories belong to various levels of analysis. Of each of the categories we first classify it by one or more levels of analysis, then we briefly summarize its definition, if necessary repeating some of the theory given above, indicate what ideological functions it may have, and finally give one or more examples. Since the examples come from one debate, not each category can be illustrated with an example of course -- but for com- pleteness we mention it anyway, even without an example. Sometimes the examples are summarized in the description of the category and (to save space) not actually quoted. In the description of a category sometimes other categories are mentioned, and these will then be written with capi- tals, so that you know that that category is defined elsewhere in the list.

Apart from an alphabetically ordered set of analytical categories that are used to illustrate the ideological based properties of discourse structures, the following may also be taken as a brief summary of some properties of political (and especially parliamentary) discourse and rhetoric. That is, as we have seen above, ideologies usually translate into more specific social opinions and then to discourse within a specific social domain, such as

* The complete text of the debate can be found in the Appendix.

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politics, media, work, business, education, research or the law. In our ex- amples then racist and anti-racist ideologies especially are articulated in the crucial political domain -- crucial because it is here where it is decided who will be able to (legally) enter the country or not.

The examples are followed by C for a Conservative speaker, and by L for a Labour speaker. Many of the quotes come from the lengthy speech of Ms. Gorman (Conservatives) who took the initiative of the debate, and whose populist speech pitches the poor British "rate-payer" (tax-payer) against foreign refugees whom she largely defines in negative terms. Indeed, as we shall see in many of her and other conservative interventions, the overall discursive strategy based on racist ideology is that of positive self- presentation and negative other-presentation, where WE are the (white, original) British, and THEM are immigrants, refugees, and minorities, and by extension those who defend them (like Labour, and specifically the "Loony Left").

Categories of ideological analysis (alphabetical)

ACTOR DESCRIPTION (MEANING). All discourse on people and ac- tion involves various types of actor description. Thus, actors may be de- scribed as members of groups or as individuals, by first or family name, function, role or group name, as specific or unspecific, by their actions or (alleged) attributes, by their position or relation to other people, and so on. Since this debate is on asylum seekers, this is also true in our examples. The overall ideological strategy is that of positive self-presentation and negative other-presentation. Descriptions of Others may be blatantly racist, or they may more subtly convey negative opinions about refugees. In anti- racist discourse, the opposite will be true, and asylum seekers will primar- ily be described as victims of oppressive regimes abroad or of police offi- cers, immigration officials and more generally of prejudice and discrimina- tion at home. Besides this characterization of THEM, ingroup-outgroup polarization will typically reverse that role for ingroup members when con- servative speakers describe "our own" people as victims (see VICTIMI- ZATION). That is, descriptions are never neutral, but have semantic, rhe- torical and argumentative functions in the expression of opinions and standpoints about the (il)legitimacy of immigration. Of the large number of actor descriptions in this debate, we cite a typical one in which negative other-presentation and positive self-presentation are combined so as to emphasize the contrast:

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(1)† In one case, a man from Romania, who came over here on a coach tour for a football match--if the hon. Member for Perth and Kinross (Ms Cunningham) would listen she would hear practical examples--decided that he did not want to go back, declared himself an asylum seeker and is still here four years later. He has never done a stroke of work in his life. Why should someone who is elderly and who is scraping along on their basic income have to support people in those circumstances? (Gor- man, C).

AUTHORITY (ARGUMENTATION). Many speakers in an argument, also in parliament, have recourse to the fallacy of mentioning authorities to support their case, usually organizations or people who are above the fray of party politics, or who are generally recognized experts or moral leaders. International organizations (such as the United Nations, or Amnesty), scholars, the media, the church or the courts often have that role. Thus, also Ms. Gorman thanks a colleague (a "honourable friend") for supporting her, and adds: "He is a great authority on the matter". And for a concrete example of a woman who has stayed illegally in the country, she refers to the Daily Mail, which also shows that Authority often is related to the se- mantic move of Evidentiality, and hence with Objectivity and Reliability in argumentation. And Mr Corbyn (L) attacks Ms. Gorman, who claims that Eastern European countries are democratic now and hence safe, by ironi- cally asking whether she has not read the reports of Amnesty and Helsinki Watch. Similarly, he refers to the "Churches of Europe" who have drawn attention to the exploitation of asylum seekers. Precisely because the over- all strategy of Labour is to attack conservative immigration in moral terms, it is especially progressive discourse on minorities and immigration that often has recourse to the support of morally superior authorities.

BURDEN (TOPOS). Argumentation against immigration is often based on various standard arguments, or topoi, which represent premises that are taken for granted, as self-evident and as sufficient reasons to accept the conclusion. In this debate, which focuses on benefits for asylum seekers, and on local councils that may have to pay for such benefits, the main to- pos is that of a financial burden: We can't afford to pay the benefits or other costs of immigration and reception. In other words, anti-immigrant ideologies may be expressed in discourse by emphasizing that the Others are a (financial) burden for us:

† These examples can be found in the text of the debate in the examples by searching for the number of the example between parentheses.

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(2) (...) an all-party document that pointed out that it was costing about £200 million a year for those people (Gorman, C).

(3) It is wrong that ratepayers in the London area should bear an undue proportion of the burden of expenditure that those people are causing (Gorman, C).

(4) The problem of supporting them has landed largely on the inner London boroughs, where most of those people migrate as there is more to do in central London (Gorman, C).

The burden-topos not only has a financial element, but also a social one, as the following examples show, although even then the implication is often financial:

(5) There are also about 2,000 families, with young children who must be supported (Gorman, C)

(6) Presumably, if those people are here for long enough under such terms, they will have to be provided with clothing, shoe leather and who knows what else (Gorman, C)

Note that the burden-topos is one of the "safest" anti-immigration moves in discourse, because it implies that we do not refuse immigrants for what they are (their color, culture or origin), nor out of ill will, or because of other prejudices, but only because we can't. It is not surprising, therefore, that it is widely used in EU political discourse that opposes immigration, and not only on the right.

CATEGORIZATION (MEANING). As we also know from social psy- chology, people tend to categorize people, and so do speakers in parlia- ment, especially when Others (immigrants, refugees, etc.) are involved. Once groups have thus be distinguished and categorized (with lexically variable terms, see below), they can be attributed positive or negative char- acteristics (see below). Most typical in this debate is the (sub)categorization of asylum seekers into "genuine" political refugees, and "bogus" asylum seekers, a categorization formulated in the following ways:

(7) There are, of course, asylum seekers and asylum seekers (Gorman, C).

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(8) I entirely support the policy of the Government to help genuine asy- lum seekers (Gorman, C).

(9) ... those people, many of whom could reasonably be called economic migrants and some of whom are just benefit seekers on holiday, to re- main in Britain (Gorman, C)

(10) The Government's reasoning was the same then as it is now: they still talk about economic migrants and benefit scroungers (Gerrard, L).

(11) But the escalating number of economic and bogus asylum seekers who have come here, not because of persecution but because of the eco- nomic situation in this country and the benefits it affords them, has caused great concern (Burns, C)

COMPARISON (MEANING, ARGUMENTATION). Different from rhetorical similes, comparisons as intended here typically occur in talk about refugees or minorities, namely when speakers compare ingroups and outgroups. In racist talk, such comparisons typically imply the negative score of the outgroup on the criteria of the comparison, as in the typical everyday argument: "If we go abroad we learn another language" in an ar- gument or story in which "foreigners" are accused of not wanting to learn "our" language. In anti-racist talk about refugees such comparisons may favor the outgroup or their case, e.g., when the speaker claims that, com- pared to "our own" daily experiences, those of refugees have been incom- parably worse. Similarly in anti-racist discourse, "our" own country may be compared negatively (e.g., as to their hospitality for asylum seekers) with other countries. Another well-known comparative move is to compare cur- rent immigrations (refugees, or anti-immigration policies) with similar situations in the past. Typically, the refusal to accept refugees will be com- pared to the refusal to help the Jews during the Second World War. Here is another example of a comparison that explains why not all asylum seekers can talk about their experiences upon arrival in the UK:

(12) Many soldiers who were tortured during the second world war found it difficult to talk about their experiences for years. That is no dif- ferent from the position of people who have been tortured in Iran, Iraq, west Africa or anywhere else. The issue is not simple. They feel a sense of failure, a sense of humiliation and a sense of defeat. (Corbyn, L).

CONSENSUS (POLITICAL STRATEGY). One of the political strate- gies that are often used in debates on issues of "national importance" --and

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immigration is often defined as such--is the display, claim or wish of "con- sensus". This means that racist ideologies often combine with nationalist ones, in which the unity and the interests of the nation are placed before any internal, political divisions among US. In other words, ingroup unifi- cation, cohesion and solidarity (WE English) against Them. Facing the "threat" of immigration, thus, the country should "hold together", and deci- sions and legislation should ideally be non-partisan, or bipartisan as in the UK or the USA. This is a very typical political-ideological move in argu- ments that try to win over the opposition. In this case it is a means to per- suade the (Labour) opposition that earlier immigration policies or regula- tions were developed together, so that present opposition to new legislation is unwarranted and a breach of earlier consensus politics, for instance about illegal immigration:

(13) The Government, with cross-party backing, decided to do some- thing about the matter (Gorman, C).

COUNTERFACTUALS (MEANING, ARGUMENTATION). "What would happen, if...", is the standard formula that defines counterfactuals. In argumentation they play an important role, because they allow people to demonstrate absurd consequences when an alternative is being considered, or precisely the compellingness of a story about refugees and their experi- ences when WE would be in the same position. As a warning or advice, counterfactuals are relevant in political debate in parliament to show what would happen if we would NOT take any measures or formulate policies or a law. In our debate, counterfactuals typically occur on the left, and sup- port the viewpoint of Labour to soften immigration law. Here are a few more extensive examples that clearly show the argumentative role of coun- terfactuals, for instance by eliciting empathy when people are put in the place of others. They are clear examples of what me might call a humani- tarian ideology:

(14) I suggest that he start to think more seriously about human rights issues. Suppose he had to flee this country because an oppressive regime had taken over. Where would he go? Presumably he would not want help from anyone else, because he does not believe that help should be given to anyone else (Corbyn, L).

(15) If that happened in another country under a regime of which we disapproved, the British Government would say that it was a terrible in- dictment on the human rights record of that regime that prisoners were

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forced to undertake a hunger strike to draw attention to their situation (Corbyn, L).

(16) Even if we accepted the Government's view--which I do not-- that only a tiny proportion of people who claim asylum are genuine refugees, we cannot defend a policy that leaves genuine refugees destitute (Ger- rard, L).

DISCLAIMERS (MEANING). A well-known combination of the ideologically based strategy of positive self-presentation and negative other-presentation, are the many types of disclaimers. Note that disclaimers in these debates are not usually an expression of attitudinal ambiguity, in which both positive and negative aspects of immigration are mentioned, or in which humanitarian values are endorsed on the one hand, but the "burden" of refugees is beyond our means. Rather, disclaimers briefly save face by mentioning Our positive characteristics, but then focus rather exclusively, on Their negative attributes. Hence our qualification of the positive part of the disclaimer as 'Apparent', as in Apparent Denials, Concessions, Empathy, etc.:

(17) I understand that many people want to come to Britain to work, but there is a procedure whereby people can legitimately become part of our community (Gorman, C). [Apparent Empathy]

(18) The Government are keen to help genuine asylum seekers, but do not want them to be sucked into the racket of evading our immigration laws (Gorman, C). [Apparent Benevolence]

(19) I did not say that every eastern European's application for asylum in this country was bogus. However... (Gorman, C) [Apparent Denial]

(20) Protesters may genuinely be concerned about refugees in detention, but the fact is that only a tiny proportion of applicants are detained (Wardle, C). [Apparent Concession].

DISTANCING (MEANING, LEXICON). One of the ways US-THEM polarization may be expressed in talk is by words that imply distance be- tween ingroup speakers refer to outgroup speakers. This familiar socio- cognitive device may for instance be expressed by the use of demonstrative pronouns instead of naming or describing the Others. Also in this debate, thus, Conservatives will often refer to refugees as "those people".

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DRAMATIZATION (RHETORIC). Together with hyperbolas, dramati- zation is a familiar way to exaggerate the facts in one's favor. Positions in immigration debates, thus, tend to represent the arrival of a few thousand refugees as a national catastrophe of which we are the victims (see VIC- TIMIZATI0N). Thus, Ms. Gorman claims to feel "great worry" about La- bour's aim to change the current law, and finds such an aim "extremely ir- responsible."

EMPATHY (MEANING). Depending on their political or ideological perspective, MPs will variously show sympathy or empathy with the plight of refugees or the ingroup (the poor taxpayer). In disclaimers (see DIS- CLAIMERS), the expression of empathy my be largely strategic and serve especially to manage the speaker's impression with the audience (e.g. "I understand that refugees have had many problems, but..."). In that case, the apparent nature of the empathy is supported by the fact that the part of the discourse that follows "but" does not show much empathy at all, on the contrary. Empathy in that case will be accorded to ingroup members, rep- resented as victims (see VICTIMIZATION). In anti-racist and pro- immigration points of view, empathy appears to be more genuine, espe- cially since the experiences of political refugees may be demonstrably hor- rendous. In the same discourse, we will typically encounter accusations of lacking empathy of the Government with respect to refugees. Both ingroup and outgroup empathy may be in a generalized form, or in the form of an EXAMPLE. Again, we give an example of both forms of empathizing, the second example at the same time illustrating a form of ingroup-outgroup COMPARISON:

(21) Many of those people live in old-style housing association Peabody flats. They are on modest incomes. Many of them are elderly, managing on their state pension and perhaps also a little pension from their work. They pay their full rent and for all their own expenses (Gorman, C).

(22) So far as I am aware, no hon. Member has been woken up by the police at 4 am, taken into custody with no rights of access to a judicial system, and, with his or her family, forced to flee into exile for their own safety. It is not an experience that most British people have had, and we should think very carefully about what a major step it would be to un- dertake such a journey (Corbyn, L).

EUPHEMISM (RHETORIC; MEANING). The well-known rhetorical figure of euphemism, a semantic move of mitigation, plays an important role in talk about immigrants. Within the broader framework of the strat-

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egy of positive self-presentation, and especially its correlate, the avoidance of negative impression formation, negative opinions about immigrants are often mitigated, especially in foreign talk. The same is true for the negative acts of the own group. Thus, racism or discrimination will typically be mitigated as "resentment" (in our debate used by Nicholson, C), or "un- equal treatment", respectively. Similarly Ms. Gorman in this debate uses the word "discourage" ("to discourage the growing number of people from abroad...") in order to refer to the harsh immigration policies of the gov- ernment, and thus mitigates the actions of the conservative government she supports. Similarly, the Labour (Corbyn) opposition finds the condemna- tion of oppressive regimes by the Government "very muted" instead of us- ing more critical terms. Obviously, such mitigation of the use of euphe- misms may be explained both in ideological terms (ingroup protection), as well as in contextual terms, e.g., as part of politeness conditions or other interactional rules that are typical for parliamentary debates.

EVIDENTIALITY (MEANING, ARGUMENTATION). Claims or points of view in argument are more plausible when speakers present some evidence or proof for their knowledge or opinions. This may happen by references to AUTHORITY figures or institutions (see above), or by vari- ous forms of Evidentiality: How or where did they get the information. Thus people may have read something in the paper, heard it from reliable spokespersons, or have seen something with their own eyes. Especially in debates on immigration, in which negative beliefs about immigrants may be heard as biased, evidentials are an important move to convey objectiv- ity, reliability and hence credibility. In stories that are intended to provoke empathy, of course such evidence must be supplied by the victims them- selves. When sources are actually being quoted, evidentiality is linked to INTERTEXTUALITY. Here are a few examples:

(23) According to the magistrates court yesterday, she has cost the Brit- ish taxpayer £40,000. She was arrested, of course, for stealing (Gor- man).

(24) This morning, I was reading a letter from a constituent of mine (..) (Gorman).

(25) The people who I met told me, chapter and verse, of how they had been treated by the regime in Iran (Corbyn, L).

EXAMPLE/ILLUSTRATION (ARGUMENTATION). A powerful move in argumentation is to give concrete examples, often in the form of a

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vignette or short story, illustrating or making more plausible a general point defended by the speaker. More than general 'truths' concrete exam- ples have not only the power to be easily imaginable (as episodic event models) and better memorable, but also to suggest impelling forms of em- pirical proof (see also EVIDENTIALITY). Rhetorically speaking, concrete examples also make speeches more 'lively', and when they are based on the direct experiences (stories of constituents) of MPs, they finally also imply the democratic values of a speaker who takes his or her role as representa- tive of the people seriously. As such, then, they may also be part of popu- list strategies. In anti-racist discourse, examples of the terrible experiences of refugees may play such a powerful role, whereas the opposite is true in conservative discourse, where concrete examples precisely contribute to negative other-presentation. Note also, that the concrete example often also implies that the case being told about is typical, and hence may be general- ized. In sum, giving examples has many cognitive, semantic, argumenta- tive and political functions in debates on asylum seekers. Here are two fragments that illustrate both the conservative and Labour type of storytel- ling, respectively:

(26) The Daily Mail today reports the case of a woman from Russia who has managed to stay in Britain for five years. According to the magis- trates court yesterday, she has cost the British taxpayer £40,000. She was arrested, of course, for stealing (Gorman, C).

(27) The people who I met told me, chapter and verse, of how they had been treated by the regime in Iran--of how they had been summarily im- prisoned, with no access to the courts; of how their families had been beaten up and abused while in prison; and of how the regime murdered one man's fiancee in front of him because he would not talk about the secret activities that he was supposed to be involved in (Corbyn, L).

EXPLANATION (MEANING, ARGUMENTATION). Characteristic of anti-racist discourse is the (empathetic) explanation of possibly illegal acts of asylum seekers or other immigrants. Social psychology uses the notion "Ultimate Attribution Error," according to which negative acts of ingroup members tend to be explained (away), whereas the negative acts of out- group members tend to be explained in terms of inherent properties of such actors (e.g., because they are unreliable or criminal) . The inverse is true in anti-racist talk, which focuses on the terrible circumstances of their flight which leave asylum seekers often no choice but to break the rules or the law, as is the case in the following example:

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(28) If one has grown up in Iraq and has always been completely terri- fied of anyone wearing any type of uniform, it is fairly unlikely that-- after managing to steal oneself out of Iraq, possibly using false docu- mentation, aliases, guides and other measures--one will trust a person wearing a uniform whom one encounters when first arriving at the air- port. It is more likely that one would first get out of the airport and then think about the next step (Corbyn, L).

FALLACIES (ARGUMENTATION). Parliamentary debates, just like any other dispute about contested points of view and opinions, are riddled with normative breaches of 'proper' argumentation, that is, with fallacies. These may pertain to any element of the argumentative event, namely to the nature of the premises, the relations among the premises and the con- clusion, the relations between speaker and recipients, and so on. There are numerous fallacies, which cannot all be specified here. Thus, as we see have seen above, claiming the support for one's standpoint by referring to an AUTHORITY (incorrectly) implies that one's point is true because someone else says so. Similarly, the relations between premises and a con- clusion may be faulty as in a non-sequitur, as in the following example where the availability of work in the cities seems to be a sufficient condi- tion for refugees to work illegally:

(29) I am sure that many of them are working illegally, and of course work is readily available in big cities (Gorman).

Another fallacy quite typical in these debates is that of extreme case for- mulation. An action or policy is deemed to be condemned but only because it is formulated in starkly exaggerated terms. Here is a typical example, which has become so conventional, that it is virtually a standard-argument or TOPOS (We can't take them all in):

(30) We must also face the fact that, even in the case of brutal dictator- ships such as Iraq, we cannot take in all those who suffer (Shaw, C).

GENERALIZATION (MEANING, ARGUMENTATION). Most de- bates involve forms of particularization, for instance by giving EXAM- PLES, and Generalization, in which concrete events or actions are general- ized and possibly abstracted from, thus making the claim broader, while more generally applicable. This is also the way discourse may signal the cognitive relation between a more concrete example as represented in a mental model, and more general opinions such as those of social attitudes or ideologies.The problem of examples, even when persuasive and compel-

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ling stories, is that they are open to the charge of exceptionality. It is there- fore crucial that it be shown that the given examples are not exceptional at all, but typical or representative, so that they may be generalized. This may happen with standard expressions, such as quantifiers for nouns ("most", "all"), or expressions of time and frequency ("always", "constantly") or place ("everywhere"). These properties of the dynamics of singularity and generalization are also typical of immigration debates, since it is politically crucial that negative examples as reported by the press, constituents or the police may be shown to be typical and of a general nature, so that effective policies can be developed. The same is true for the opposite case, in which negative experiences of asylum seekers in their own countries or in their new countries can be generalized, so as to support the argument for empa- thy and policies to help them. Note also that (over)generalization of nega- tive acts or events are the basis of stereotyping and prejudice. Of course, the opposite may also be true as part of positive self-presentation: Current acts or policies that are found beneficial are generalized, typically in na- tionalist rhetoric, as something 'we' always do. Here are a few examples:

(31) Such things go on and they get up the noses of all constituents (Gorman, C).

(32) In the United Kingdom there has been a systemic erosion of peo- ples' ability to seek asylum and to have their cases properly determined (Corbyn, L).

(33) If someone has a legitimate fear of persecution, they flee abroad and try to seek asylum (Corbyn, L).

(34) I heard about many other similar cases (Corbyn, L).

(35) First, it matters crucially that this country honours, as it always has, its obligations under the Geneva convention (Wardle, C).

HISTORY AS LESSON (TOPOS). As we have found also for COM- PARISON, it is often useful in an argument to show that the present situa- tion can be relevantly compared to earlier (positive or negative) events in history. Such comparisons may be generalized to the more general topos of the "Lessons of history", whose argumentative compellingness are taken for granted, as were it a law of history:

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(36) History shows that unless we stand up for human rights wherever they are abused around the world, eventually it will come back and our human rights will be abused (Corbyn, L).

HUMANITARIANISM (TOPOS, MACROSTRATEGY). Whereas the overall strategy on the right is to limit immigration and benefits for refu- gees, and in particular to derogate (bogus) asylum seekers, the overall strategy of the left could be summarized in terms of its overall underlying ideology: humanitarianism, that is, the defense of human rights, critique of those who violate or disregard such rights, and the formulation of general norms and values for a humane treatment of refugees. Since in argumenta- tion of various kinds this may be a conventional, recognizable strategy, we may also categorize this argument as a topos (in the same way as "law and order" would be one for the right). There are many ways humanitarianism is manifested in parliamentary debates. One basic way is to formulate NORMS, in terms of what 'we' should or should not do. Secondly, recipi- ents are explicitly recommended to pay more attention to human rights, show empathy for the plight of refugees, condemn policies that infringe the rights of refugees, making appeals to our moral responsibility, showing understanding for and listening to the stories of refugees, denouncing hu- man rights abuses, praising people who stood up for human rights, explic- itly antiracist opinions, reference to authorities, international bodies, agreements, and laws that deal with human rights, and so on.

HYPERBOLE (RHETORIC). As is the case for DRAMATIZATION, hyperboles are semantic rhetorical devices for the enhancement of mean- ing. Within the overall strategy of positive self-presentation and negative other-presentation, we may thus expect in parliamentary debates about immigrants that the alleged bad actions or properties of the Others are ex- pressed in hyperbolic terms (our bad actions in mitigated terms), and vice versa. Sometimes such forms of hyperbole are implied by the use of special METAPHORS, as we observe in Ms. Gorman's use of "opening the flood- gates" in order to refer to the arrival of many asylum seekers. Similarly, to emphasize that asylum requests take a long time to handle by the courts, she will call such a procedure "endless". And conversely, on the left, La- bour speakers will of course emphasize the bad nature of authoritarian re- gimes, and like Mr. Corbyn, will call them "deeply oppressive", and the conditions of refugees coming from those countries "appalling". Similarly, within the House he also deems a racist question of a conservative MP "to- tally ludicrous". Note though that, as with many moves studied here, their interpretation may depend on political point of view: What is exaggerated

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for one group, may be the simple and objective truth, and the "correct" way of referring to an issue, for another group.

IMPLICATION (MEANING). For many 'pragmatic' (contextual) rea- sons, speakers do not (need) to say everything they know or believe. In- deed, large part of discourse remains implicit, and such implicit informa- tion may be inferred by recipients from shared knowledge or attitudes and thus constructed as part of their mental models of the event or action repre- sented in the discourse. Apart from this general cognitive-pragmatic rule of implicitness (Do not express information the recipients already have or may easily infer), there are other, interactional, socio-political and cultural conditions on implicitness, such as those monitored by politeness, face- keeping or cultural norms or propriety. In debates about immigration, im- plicitness may especially be used as a means to convey meanings whose explicit expression could be interpreted as biased or racist. Or conversely, information may be left implicit precisely because it may be inconsistent with the overall strategy of positive self-presentation. Negative details about ingroup actions thus tend to remain implicit. Thus, when Ms. Gor- man says that many refugees come from countries in Eastern Europe who have recently been "liberated", she is implying that people from such coun- tries cannot be genuine asylum seekers because democratic countries do not oppress their citizens (a point later attacked by the Labour opposition). And the same is true when she describes these refugees as "able-bodied males", which implies that these need no help from us.

ILLEGALITY (ARGUMENTATION). For many conservative speakers, most refugees are or remain in the country as "illegals", or otherwise break the law or do not follow procedures. This also means that such law and or- der arguments may be part of the strategy of negative other-presentation, and in particular of criminalization. Such criminalization is the standard way minorities are being characterized in racist or ethnic prejudices:

(37) I am sure that many of them are working illegally, and of course work is readily available in big cities (Gorman, C).

(38) It is equally important that abuse of the asylum rules by the large number of people who make asylum applications knowing that their po- sition as illegal immigrants has no bearing on the Geneva convention should be debated openly, so that it is fully understood and tackled (Wardle, C).

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(39) ....because there are many attempts at illegal immigration using asy- lum techniques, fraudulent documents or other methods (Shaw, C).

INTERACTION AND CONTEXT. Whereas most other categories of analysis discussed here deal with structural properties of discourse, e.g., at the levels of meaning, style, argumentation and rhetoric, and apply espe- cially to the way asylum seekers are being talked ABOUT, it is obvious that the debate is also a form of interaction between MPs, or between MPs and representatives of the government. Large part of the properties of this debate therefore can only be described and explained in an interactional framework, that is as inherent part of a context consisting of overall politi- cal action categories (legislation), setting (session of parliament), various forms of interaction (discussing a bill, opposing the government), partici- pants in many different roles (speaker, recipients, MPs, representatives of their districts, member of a government or opposition party, and so on), as well as their cognitive properties (knowledge, beliefs, prejudices, biases, goals, aims, etc.). An analysis of all acts and interactions in this debate, yields the following (alphabetical) list of interactional elements and con- text features -- and many of these acts are ideologically based, in the same way as many social practices may be controlled by ideologies; thus an 'at- tack' or an 'accusation' in parliament usually is directed against the politi- cal and hence the ideological opponent. In other words, many of the fol- lowing actions not only characterize political interaction in a parliamentary debate, but also what may be called 'ideological' interaction:

-Accusing other MPs -Addressing the whole House -Agreement and disagreement with MP -Answering a question -Asking a (rhetorical) question -Attacking (member) of other party -Calling other MP to attention -Challenging other MPs -Collective self-incitement ("Let us…") -Congratulating other MP -Criticizing the Government -Defending oneself against attack of other MP -Denying a turn, refusing to yield the floor -Disqualifying a contribution of other MP -Formulating goals of legislation -Formulating the aims of a speech -Interrupting a speaker

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-Praising a member of own party -Recommendation to Government -Recommending a policy, etc. -Reference to one's own role as representative of district -Reference to parliamentary procedure -Reference to present time and place -Reference to previous debates -Referring to (un)desirable consequences of current policies -Remind MPs of something -Requesting a turn -Self-obligation of MPs -Suggesting MPs to do something -Supporting own party member -Supporting the Government -Thanking other MP

IRONY (RHETORIC). Accusations may come across as more effective when they are not made point blank (which may violate face constraints), but in apparently lighter forms of irony. There is much irony in the mutual critique and attacks of Conservatives and Labour, of course, and these characterize the proper interactional dimension of the debate. However, when speaking about immigrants, irony may also serve to derogate asylum seekers, as is the case for the phrase "suddenly discover" in the following example, implying that such a "sudden discovery" can only be bogus, since the asylum seekers allegedly knew all along that they came to the country to stay:

(40) Too many asylum seekers enter the country initially as family visi- tors, tourists, students and business people, and then suddenly discover that they want to remain as asylum seekers (Shaw, C).

LEGALITY (ARGUMENTATION). Part of the arguments that support a standpoint that opposes immigration, is to have recourse to the law or regulations -- which is of course a standard argument (and hence a topos) within a legislative body like parliament:

(41) (…) there is a procedure whereby people can legitimately become part of our community (Gorman, C).

(42) The Asylum and Immigration Act 1996 stated that people whose application to remain in Britain had been turned down could no longer

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receive the social security and housing benefit that they had previously enjoyed (Gorman, C).

(43) In order to try to subvert the legislation, a case was recently brought before our courts and to the High Court which sought to overturn the provisions that the Government intended (Gorman, C).

LEXICALIZATION (STYLE). At the local level of analysis, debates on asylum seekers need to express underlying concepts and beliefs in specific lexical items. Similar meanings may thus be variably expressed in different words, depending on the position, role, goals, point of view or opinion of the speaker, that is, as a function of context features. In conservative dis- course opposing liberal immigration policies, this will typically result in more or less blatantly negative expressions denoting refugees and their ac- tions, thus implementing at the level of lexicalization the overall ideologi- cal strategy of negative other-presentation. Thus, also in this debate, we may typically find such as expressions as "economic immigrants", "bogus asylum seekers", or "benefit scroungers", as we also know them from the tabloid press in the UK. On the other hand, lexicalization in support of refugees may focus on the negative presentation of totalitarian regimes and their acts, such as "oppression", "crush", "torture", "abuse" or "injustice". Depending on the political or ideological perspective, both ingroup and outgroup members may be empathetically (see EMPATHY) described in emotional terms, such as "poor people in the UK scraping along on their basic income", "modest income". Note also, that context (parliamentary session) requires MPs to be relatively formal, so they will speak rather of "destitution" than of "poverty". On the other hand, precisely to emphasize or mark expressions, the stylistic coherence of formality may be broken by the use of informal, popular expressions, for instance to use "not to have a penny to live on", or to use "rubbish" to defy an invalid argument or state- ment of fact.

METAPHOR (RHETORIC). Few semantic-rhetorical figures are as per- suasive as metaphors, also in debates on immigration. Abstract, complex, unfamiliar, new or emotional meanings may thus be made more familiar and more concrete. Virtually a standard metaphor (if not a topos) is the use of flood-metaphors to refer to refugees and their arrival, symbolizing the unstoppable threat of immigration, in which we would all "drown". Even more than numbers, thus, flood metaphors symbolize dangerous if not le- thal quantities, as is also the case for the military metaphor of the "inva- sion" used to refer to dangerous "aliens". Thus, Ms. Gorman warns for

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changes in the present law by saying that such changes would "open the floodgates again". And once the refugees are here, they may be accused of fraud, of "milking the taxpayers", and of being "addicted" to the social ser- vices (Ms. Gorman, C). Most of these metaphors are negative, and thus fall under the overall strategy of negative other-description. This is especially the case when metaphors become explicit forms of derogation, e.g., when asylum seekers are called "parasites" (Gorman) of the social system, that is, associated with dangerous or otherwise threatening or dirty animals, as was also the case in Nazi-propaganda about the Jews.

NATIONAL SELF-GLORIFICATION (MEANING). Especially in par- liamentary speeches on immigration, positive self-presentation may rou- tinely be implemented by various forms of national self-glorification: Posi- tive references to or praise for the own country, its principles, history and traditions. Racist ideologies may thus be combined with nationalist ideolo- gies, as we have seen above. This kind of nationalist rhetoric is not the same in all countries. It is unabashed in the USA, quite common in France (especially on the right), and not uncommon in Germany. In the Nether- lands and the UK, such self-glorification is less explicit. See, however, the following standard example -- probably even a topos:

(44) Britain has always honoured the Geneva convention, and has given sanctuary to people with a well-founded fear of persecution in the coun- try from which they are fleeing and whose first safe country landing is in the United Kingdom (Wardle, C).

NEGATIVE OTHER-PRESENTATION (SEMANTIC MACRO- STRATEGY). As the previous examples have shown, the categorization of people in ingroups and outgroups, and even the division between 'good' and 'bad' outgroups, is not value-free, but imbued with ideologically based applications of norms and values. Whereas 'real' political refugees are de- scribed in neutral terms in conservative discourse, and in positive or em- pathic terms in Labour interventions, "economic" refugees are extensively characterized by the Conservatives in starkly negative terms, namely as "benefit seekers" and "bogus". Since the latter group is defined as a finan- cial burden (see BURDEN) or even as a threat to the country or to Us, they are defined as the real Outgroup. At many levels of analysis, for instance in lexical and semantic terms, their representation is influenced by the overall strategy of derogation or "negative other-presentation", which has been found in much earlier work on the discourse about minorities and immi- grants.

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NORM EXPRESSION. Anti-racist discourse is of course strongly norma- tive, and decries racism, discrimination, prejudice and anti-immigration policies in sometimes explicit norm-statements about what 'we' (in parlia- ment, in the UK, in Europe, etc.) should or should not do:

(45) We should have a different attitude towards asylum seekers (Cor- byn, L).

(46) We should think a bit more seriously about how we treat those peo- ple (Corbyn, L).

(47) Attitudes towards asylum seekers need to be changed (Corbyn. L).

(48) It is wrong to force them into destitution or to throw them out of the country, often with no access to lawyers or anyone else (Corbyn, L).

(49) Europe must stop its xenophobic attitude towards those who seek a place of safety here and adopt a more humane approach.

NUMBER GAME (RHETORIC, ARGUMENTATION). Much argu- ment is oriented to enhancing credibility by moves that emphasize objec- tivity. Numbers and statistics are the primary means in our culture to per- suasively display objectivity. They represent the "facts" against mere opinion and impression. Especially in discourse about immigration, also in the mass media, therefore, the frequent use of numbers is well-known. The very first attribute applied to immigrants coming to the country is in terms of their numbers. These are usually given in absolute terms, and when speaking of X thousand asylum seekers who are arriving, a speaker makes a stronger impact than when talking about less than 0,1 percent of the population. Similarly, when arguing against immigration and the reception of refugees, as in this debate, we may expect a lot of figures about the costs of benefits. Ms Gorman's main point in this debate is to show, with many numbers (see also financial BURDEN), that local councils can't pay for so many refugees:

(50) It would open the floodgates again, and presumably the £200 mil- lion a year cost that was estimated when the legislation was introduced (Gorman, C).

OPENESS, HONESTY (ARGUMENTATION). Nearly a topos because of its increasingly conventional nature in current immigration debates is the argumentative claim (or norm) that "we should talk openly (honestly)

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about these things". This move presupposes that dishonesty, or rather eva- sion or mitigation may be seen as the normatively base rate, namely to avoid making a negative impression on the recipients. Breaking these norms has increasingly been advocated during the last years as a "refresh- ing" view on the "cramped" debate on immigration. Thus, speakers suggest that their argument satisfies the positive values of honesty and openness, while at the same time indulging in negative other-presentation or even blatant derogation. This reversal of the anti-racist norm in increasingly more intolerant values, is characteristic of contemporary conservative posi- tions and discourses about minorities, race relations and immigration. Here is a typical example:

(51) It is equally important that abuse of the asylum rules by the large number of people who make asylum applications knowing that their po- sition as illegal immigrants has no bearing on the Geneva convention should be debated openly, so that it is fully understood and tackled. (Wardle, C).

POLARIZATION, US-THEM CATEGORIZATION (MEANING). Few semantic strategies in debates about Others are as prevalent as the ex- pression of polarized cognitions, and the categorical division of people in ingroup (US) and outgroup (THEM). This suggests that especially also talk and text about immigrants or refugees is strongly monitored by underlying social representations (attitudes, ideologies) of groups, rather than by mod- els of unique events and individual people (unless these are used as illus- trations to argue a general point). Polarization may also apply to 'good' and 'bad' sub-categories of outgroups, as is the case for friends and allies on the one hand, and enemies on the other. Note that polarization may be rhetori- cally enhanced when expressed as a clear contrast, that is, by attributing properties of US and THEM that are semantically each other's opposites. Examples in our debate abound, but we shall only give two typical exam- ples:

(52) Now they are going to be asked to pay £35 to able-bodied males who have come over here on a prolonged holiday and now claim that the British taxpayer should support them (Gorman, C).

(53) It is true that, in many cases, they have made careful provision for themselves in their old age, have a small additional pension as well as their old-age pension and pay all their rent and their bills and ask for nothing from the state. They are proud and happy to do so. Such people

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should not be exploited by people who are exploiting the system (Gor- man, C).

POSITIVE SELF-PRESENTATION (SEMANTIC MACROSTRAT- EGY). Whether or not in combination with the derogation of outgroups, group-talk is often characterized by another overall strategy, namely that of ingroup favoritism or "positive self-presentation". This may take a more individual form of face-keeping or impression management, as we know them from familiar disclaimers ("I am not a racist, but..."), or a more col- lective form in which the speaker emphasizes the positive characteristics of the own group, such as the own party, or the own country. In the context of debates on immigration, such positive self-presentation will often manifest itself as an emphasis of own tolerance, hospitality, lack of bias, EMPA- THY, support of human rights, or compliance with the law or international agreements. Positive self-presentation is essentially ideological, because they are based on the positive self-schema that defines the ideology of a group. Some examples:

(54) I entirely support the policy of the Government to help genuine asy- lum seekers, but...(Gorman, C).

(55) I understand that many people want to come to Britain to work, but... (Gorman, C)

(56) A lot of brave people in this country have stood up for the rights and needs of asylum seekers (Corbyn, L).

POPULISM (POLITICAL STRATEGY). One of the dominant overall strategies of conservative talk on immigration is that of populism. There are several variants and component moves of that strategy. The basic strat- egy is to claim (for instance against the Labour opposition) that "the peo- ple" (or "everybody") does not support further immigration, which is also a well-known argumentation fallacy. More specifically in this debate, the populism-strategy is combined with the topos of financial burden: Ordi- nary people (taxpayers) have to pay for refugees. Of the many instances of this strategy, we only cite the following:

(57) It is wrong that ratepayers in the London area should bear an undue proportion of the burden of expenditure that those people are causing (Gorman, C).

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(58) £140 million a year, which is a great deal of money to be found from the council tax budget (Gorman, C).

(59) Why should someone who is elderly and who is scraping along on their basic income have to support people in those circumstances? (Gorman, C).

PRESUPPOSITION (MEANING). A specific type of semantic implica- tion is presupposition, which by definition is true whether or not the cur- rent proposition is true or false. In this indirect way, propositions may be conveyed whose truth value is taken for granted and unchallenged. This will be generally the case for all forms of shared (common ground) knowl- edge and opinions, but in this kind of debates more often than not it is stra- tegically used to convey controversial beliefs about immigrants. Thus, in the first example, the speaker presupposes that the recipient (Mr. Corbyn) is able to have British people share their citizenship with foreigners, whether the characteristic second example presupposes that the asylum rules are being abused of and that the position as illegal immigrants has no bearing on the Geneva convention:

(60) I wonder whether the hon. Gentleman will tell the House what mandate he has from the British people to share their citizenship with foreigners? (Gill, C).

(61) It is equally important that abuse of the asylum rules by the large number of people who make asylum applications knowing that their po- sition as illegal immigrants has no bearing on the Geneva convention should be debated openly, so that it is fully understood and tackled. (Wardle, C).

PSEUDO-IGNORANCE (MEANING, ARGUMENTATION). As is the case for vagueness and hedging, speakers may feign not to have specific knowledge, but implicitly suggest nevertheless that they do know, thus making claims that need not be substantiated -- a well-known fallacy. Such forms of apparent knowledge typically appear in disclaimers, such as "I don't know, but..." which despite the professed ignorance claims the but- clause to be true -- which is also a form of impression management. In our debates, these forms of pseudo-ignorance are typically used to derogate asylum seekers without any evidence, in the following case expressed in the form of a rhetorical question following an ironical accusation:

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(62) In addition to the breakfast that comes with the bed-and-breakfast accommodation, they have to be given a packed lunch, presumably in case they decide to go shopping in the middle of the day or to do a bit of work on the black economy--who knows? (Gorman, C).

REASONABLENESS (ARGUMENTATION MOVE). A familiar move of argumentative strategies is not only to show that the arguments are sound, but also that the speaker is 'sound', in the sense of rational or rea- sonable. Such a move is especially relevant when the argument itself may seem to imply that the speaker is unreasonable, or biased. Therefore the move also has a function in the overall strategies of positive self- presentation and impression management:

(63) (...) those people, many of whom could reasonably be called eco- nomic migrants (Gorman, C).

REPETITION (RHETORIC). As a general rhetorical device, repetition is of course hardly specific to debates on immigration. However, it may of course play a specific role in the overall strategy of emphasizing Our good things and Their bad ones. Thus, throughout this debate we find numerous literal or semantic repetitions of the accusation that (most) refugees are bogus, not genuine, illegal or otherwise break norms, rules or the law. Or conversely, specifically for this debate, that poor English taxpayers should pay for this. This may be so within individual speeches, or across speeches when respective MPs support the opinions of previous speakers. In some cases repetitions take a more 'artistic' form, for instance when Ms. Gorman presents two parallel forms of exploitation, that of the system and of the people: "Such people should not be exploited by people who are exploiting the system."

SITUATION DESCRIPTION (MEANING). Of course, debates on refu- gees are not limited to the description of Them in relation to Us. Also the actions, experiences and whole situations need to be described. Indeed, 'definitions of the situation' are crucial to make a point, because the way they are described may suggest implications about causes, reasons, conse- quences and evaluations. In this and similar debates on immigration, we encounter many forms of situation descriptions, for instance short narrative vignettes, or generalizations of what refugees "have to go through". Here are two characteristic examples:

(64) Let us return to the issues facing people fleeing areas of oppression. Currently if they arrive here, seek asylum and are refused, they have lost

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all access to benefits. They then have to undergo an appeal process, which can take a very long time. During the appeal process, what on earth are they supposed to do unless they are declared destitute and con- sequently supported by a local authority? (Corbyn, L).

(65) Those people came to this country and applied for asylum. Their applications were refused, and they appealed. They are now living a life of virtual destitution, while the Home Office ponders on what to do for them. Those people stood up for their communities against an oppres- sive regime (Corbyn, L).

VAGUENESS (MEANING). Virtually in all contexts speakers may use 'vague' expressions, that is, expressions that do not have well-defined ref- erents, or which refer to fuzzy sets. Vague quantifiers ('few', 'a lot'), ad- verbs ('very') nouns ('thing') and adjectives ('low', 'high'), among other ex- pressions may be typical in such discourse. Given the normative constraints on biased speech, and the relevance of quantification in immi- gration debates, we may in particular expect various forms of Vagueness, as is the case for "Goodness knows how much", and "widespread" in the following examples:

(66) Goodness knows how much it costs for the legal aid that those peo- ple invoke to keep challenging the decision that they are not bona fide asylum seekers (Gorman, C).

(67) Is she aware that there is widespread resentment? (Nicholson, C).

VICTIMIZATION (MEANING). Together with DRAMATIZATION and POLARIZATION, discourse on immigration and ethnic relations is largely organized by the binary US-THEM pair of ingroups and outgroups. This means that when the Others tend to be represented in negative terms, and especially when they are associated with threats, then the ingroup needs to be represented as a victim of such a threat. This is precisely what happens, as we also have observed in conversations about "foreigners" in which ordinary speakers apply the move of inversion order to emphasize that not the Others are discriminated against, but WE are. When used in an argument, this would typically be a type of topos. In this debate, the ordi- nary and especially the poor and elderly taxpayers are systematically repre- sented as the real victims of immigration policies, because they have to pay for them. Here is a detailed example of this move:

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(68) Many of those people live in old-style housing association Peabody flats. They are on modest incomes. Many of them are elderly, managing on their state pension and perhaps also a little pension from their work. They pay their full rent and for all their own expenses. Now they are going to be asked to pay £35 to able-bodied males who have come over here on a prolonged holiday and now claim that the British taxpayer should support them.

Final comments on the examples

The categories analyzed above show something about the reality of discourse and racism -- and anti-racism-- in Europe. They show how powerfully the ideo- logically based beliefs of Europeans about immigrants may impact on dis- course, for instance through the polarization of Us vs. Them and the strategy of positive self-presentation and negative other presentation which largely control all properties of racist discourse. Antiracist discourse precisely tries to undo some of this harm not only by avoiding such discourse, but by reversing the strategies, for instance instead of generalizations of negative properties, it will argue that one can NOT generalize, or that there are explanations of some ob- served deviance.

Through our brief analyses of the various categories and the examples we have obtained some insight in the ideologically base of political (parliamentary) dis- course and its specific structures and moves, and how such discourse plays a role in the broader social-political issues of immigration. On the conservative side, thus, we witness how refugees may be marginalized and criminalized, and further immigration restrictions recommended by playing the populist trick of wanting to protect the "own people". This move is especially ironic when we realize how little the Conservatives would normally be concerned about poor old people. Detailed and systematic analysis of discursive strategies in parlia- mentary debates may thus uncover at the same time some of the subtleties of politics, policy-making and populism.

The definition of the categories and the examples also have shown how ideolo- gies impinge on (in this case political) discourse. Generally speaking, the cate- gories studied are not themselves ideological: We may find populism, meta- phors or euphemism both on the left and on the right. Yet, some discourse structures seem more typical of right-wing and racist talk, for instance group polarization and negative other-description, whereas humanitarian discourse typically has recourse to forms of (real, and not apparent) empathy. More gener- ally, however, it is mostly the "content" of the various structures described above that is ideologically controlled.

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Chapter 7

Conclusion

In this introductory book on discourse and ideology, we started with a mul- tidisciplinary definition of ideology, according to which ideologies are the fundamental beliefs that form the basis of the social representations of a group. They are represented in social memory as some kind of 'group- schema' that defines the identity of a group. The fundamental propositions that fill this schema monitor the acquisition of group knowledge and atti- tudes, as hence indirectly the personal models group members form about social events. These mental models are the representations that control so- cial practices, including the production and comprehension of discourse.

It is in this theoretically complex way that we are able to link ideologies as forms of social cognition, with social practices and discourse, at the micro- level of social situations and interactions, on the one hand, and with groups, group relations, institutions, organizations, movements, power and dominance, on the other hand.

Please note however, that this is merely a very general picture of the nature and the role of ideology in the mind, in discourse and society. Many di- mensions of the theory of ideology remain unexplored or obscure. For in- stance, we only have vague ideas about the internal organization of ideolo- gies, or how they monitor the development of other socially shared representations of a group. We do not even know how to represent the "content" of ideologies, even when we provisionally adopted the classical representation in terms of propositions. We assume that basic norms and values are involved in the formation of ideologies, but how exactly this happens, we don't know. One basic assumption is that ideologies are de- fined for social groups, and not for individuals or arbitrary collectivities of people, but what social conditions a group must satisfy in order to be able to develop an ideology, we don't know exactly. Indeed, the very fact that a collectivity of people has an ideology and other shared social representa- tions may precisely define the identity that makes them a social group. In other words, as is often the case for complex theories, we may have gener- ated more questions than answers, and new developments in psychology and sociology may change our theoretical framework considerably.

It is also within this, still speculative, multidisciplinary theory of ideology that we examined the ways discourses express, confirm, instantiate or con-

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stitute ideologies. We have seen that both in production and comprehen- sion of discourse, ideologies usually operate indirectly, namely at first via attitudes and group knowledge for special social domains (such as politics, education or the labor market), and at the level of individual discourses of group members, via their ideologically biased mental models of social events and social situations. These personal representations of events fi- nally interact with (possibly also ideologically biased) context models par- ticipants dynamically construct of a communicative situation, and both kind of models then give rise to the ongoing production of ideological text and talk.

Finally, we examined how such underlying, socially shared representations as well as personal models may influence the structures of discourse. Most clearly this happens at the level of content or meaning of discourse, that is, in what people say: The topics they select or avoid, the standard topoi of their argumentation, the local coherence of their text or talk, what infor- mation is left implicit or expressed explicitly, what meanings are fore- grounded and backgrounded, which details are specified or left unspeci- fied, and so on for a large number of other semantic properties of discourse.

The overall ideological, group-based principle we found operative here is that information that is favorable for or about the own group or unfavor- able for the outgroup will tend to be topical, important and explicit. Infor- mation that portrays us in a negative light (or the Others in a too positive light) will tend to remain implicit, not topicalized, hidden, vague and little detailed.

The same general principle explains how also the other, formal, levels of discourse may be involved in expressing or rather in 'signaling' ideologies, namely through processes of emphasizing or de-emphasizing ideological meanings. Intonation and stress of words and sentences may thus make meanings more or less salient, as may do visual structures such as page lay-out, size and type of letters, color, photographs or film. Syntactic struc- tures by definition are about the order and hierarchy of words, clauses and sentences and hence -- where they allow optional variation -- they are able to emphasize or de-emphasize meanings, such as the agency and responsi- bility for specific actions. Similar remarks hold for global schematic struc- tures, such as the overall formats of conversations, stories, news reports or scholarly articles, whose conventional categories may be deployed in such a way (order or hierarchy) that they emphasize of de-emphasize the ideo- logical meanings they organize.

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And finally, at the level where discourse is defined as structures of local and global speech acts, as sequences of turn taking and interruptions, as false starts and repairs, as agreeing and disagreement, as storytelling and argumentation, in sum, as action and interaction, ideologies also operate at the level of 'meaning', that is, in what is being done. The abstract forms of talk, debate and interaction may be quite general, and independent of ide- ology, but what is being done and how may well depend on group mem- bership and hence on ideology.

Note finally that the links between discourse and ideology run both ways. Not only do ideologies influence what we say and how we say it, but also vice versa: We acquire and change ideologies through reading and listen- ing to large amounts of text and talk. Ideologies are not innate, but learnt, and precisely the content and form of such discourse may be more or less likely to form intended mental models of social events, which finally may be generalized and abstracted to social representations and ideologies. In- deed, in specific discourses (such as catechisms and propaganda) we may learn some fundamental ideological propositions more directly. The social function of ideologies is to control and coordinate the social practices of a group and between groups.

Discourse is the most crucial of these social practices, and the only one that is able to directly express and hence convey ideologies. A theory of ideology without a theory of discourse is therefore fundamentally incom- plete. And conversely, to understand the role of discourse in society, we also need to know their fundamental role in the reproduction of social rep- resentations in general, and of ideologies in particular.

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Bibliographies

NB. To limit the potentially vast size of these bibligraphies, only a selection of books in English are included.

Basic Bibliography

Billig, M. (1988). Ideological dilemmas: A social psychology of everyday thinking. London Newbury Park: Sage Publications.

Eagleton, T. (1991). Ideology. An introduction. London: Verso Eds.

Fowler, R., Hodge, B., Kress, G., & Trew, T. (1979). Language and Control. London: Routledge.

Larraín, J. (1979). The concept of ideology. London: Hutchinson.

Thompson, J. B. (1990). Ideology and modern culture: Critical social theory in the era of mass communication. Stanford, Calif.: Stanford University Press.

Van Dijk, T. A. (1993). Elite discourse and Racism. Newbury Park, CA: Sage.

Van Dijk, T. A. (1998). Ideology. A multidisciplinary Approach. London: Sage.

Wodak, R. (Ed.). (1989). Language, power, and ideology. Studies in political discourse. Amsterdam Philadelphia: J. Benjamins Co.

Wodak, R., & Van Dijk, T. A. (Eds.). (2000). Racism at the top. Parliamentary discourses on ethnic issues in six European countries. Klagenfurt: Drava.

Further Reading on discourse and ideology

Apple, M. W. (2004). Ideology and curriculum. New York: RoutledgeFalmer.

Aronowitz, S. (1988). Science as power. Discourse and ideology in modern society. Houndmills, Basingstoke, Hampshire: Macmillan Press.

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Bastow, S., & Martin, J. (2003). Third way discourse. European ideologies in the twentieth century. Edinburgh: Edinburgh University Press.

Burton, F., & Carlen, P. (1979). Official discourse. On discourse analysis, government publications, ideology and the state. London Boston: Routledge & Kegan Paul.

Dant, T. (1991). Knowledge, ideology & discourse. A sociological perspective. London: Routledge.

De Saussure, L., & Schulz, P. (Eds.). (2005). Manipulation and ideologies in the twentieth century. Discourse, language, mind. Amsterdam Philadelphia: J. Benjamins Pub. Co.

Dirven, R. (Ed.). (2001). Language and ideology. Amsterdam Philadelphia: J. Benjamins Co.

Fowler, R. (1991). Language in the news. Discourse and ideology in the British press. London: Routledge.

Fox, R., & Fox, J. (2004). Organizational discourse : a language-ideology- power perspective. Westport, Conn.: Praeger.

Freeden, M. (1996). Ideologies and political theory. A conceptual approach. Oxford: Clarendon Press.

Gee, J. P. (1996). Social linguistics and literacies. Ideology in discourses. London Bristol, PA: Taylor & Francis.

Larsen, I., Strunck, J., Vestergaard, T. (Eds.). Mediating ideology in text and image. Amsterdam: Benjamins.

Lazar, M. M. (Ed.). (2005). Feminist critical discourse analysis. Gender, power and ideology in discourse. Houndmills, Basingstoke, Hampshire New York: Palgrave Macmillan.

Leach, R. (2002). Political ideology in Britain. New York: Palgrave.

Malrieu, J. P. (1999). Evaluative semantics. Language, cognition and ideology. London New York: Routledge.

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Mumby, D. K. (1988). Communication and power in organizations. Discourse, ideology, and domination. Norwood, N.J.: Ablex Pub. Corp.

Pêcheux, M. (1982). Language, semantics, and ideology. New York: St. Martin's Press.

Pütz, M., Neff-van Aertselaer, & Van Dijk, T. A. (Eds.). (2004). Communicating ideologies. Multidisciplinary Perspectives on Language, Discourse and Social Practice. Frankfurt: Lang.

Oberschall, A. (1993). Social movements. Ideologies, interests, and identities. New Brunswick, NJ: Transaction

Schäffner, C., & Kelly-Holmes, H. (Eds.). (1996). Discourse and ideologies. Clevedon Philadelphia: Multilingual Matters.

Talshir, G., Humphrey, M., & Freeden, M. (Eds.). (2006). Taking ideology seriously. 21st century reconfigurations. London New York: Routledge.

Thompson, J. B. (1984). Studies in the theory of ideology. Berkeley: University of California Press.

Van Dijk, T. A. (1996). Discourse, racism and ideology. La Laguna: RCEI.

Wetherell, M., & Potter, J. (1992). Mapping the language of racism: Discourse and the legitimation of exploitation. New York: Columbia University Press

Wuthnow, R. (1989). Communities of discourse: Ideology and social structure in the Reformation, the Enlightenment, and European socialism. Cambridge, Mass.: Harvard University Press.

Further reading on racism and discourse

Back, L., & Solomos, J. (Eds.). (2000). Theories of Race and Racism. A reader. London: Routledge.

Blommaert, J., & Verschueren, J. (1998). Debating diversity: Analysing the discourse of tolerance. New York: Routledge.

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Bulmer, M., & Solomos, J. (Eds.). (1999). Racism. Oxford New York: Oxford University Press.

Cashmore, E. (2003). Encyclopedia of race and ethnic studies. London New York: Routledge.

Essed, P., & Goldberg, D.T. (Ed.). (2002). Race critical theories text and context. Malden, Mass.: Blackwell Publishers.

Goldberg, D. T., & Solomos, J. (Ed.). (2002). A Companion to racial and ethnic studies. Malden, Mass.: Blackwell.

Hecht, M. L. (1998). Communicating prejudice. Thousand Oaks, Calif.: Sage Publications.

Lauren, P. G. (1988). Power and prejudice: The politics and diplomacy of racial discrimination. Boulder: Westview Press.

Reisigl, M., & Wodak, R. (Eds.). (2000). The semiotics of racism. Approaches in critical discourse analysis. Wien: Passagen.

Reisigl, M., & Wodak, R. (Eds.). (2001). Discourse and discrimination. Rhetorics of racism and antisemitism. London New York: Routledge.

Smitherman-Donaldson, G., & Van Dijk, T. A. (Eds. ). (1987). Discourse and discrimination. Detroit, MI: Wayne State University Press.

Van der Valk, I. (2002). Difference, deviance, threat? Mainstream and Right-Extremist Political Discourse on Ethnic Issues in the Netherlands and France (1990-1997). Amsterdam: Aksant.

Ter Wal, J., & Verkuyten, M. (Eds.). (2000). Comparative Perspectives on Racism. Aldershot: Ashgate.

Van Dijk, T. A. (1984). Prejudice in discourse an analysis of ethnic prejudice in cognition and conversation. Amsterdam Philadelphia: J. Benjamins Co.

Van Dijk, T. A. (1987). Communicating racism: Ethnic prejudice in thought and talk. Newbury Park, CA: Sage Publications, Inc.

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Van Dijk, T. A. (2005). Racism and discourse in Spain and Latin America. Amsterdam: Benjamins.

Wetherell, M., & Potter, J. (1992). Mapping the language of racism: Discourse and the legitimation of exploitation. New York: Columbia University Press.

Some references on analyzing parliamentary discourse

Bayley, P. (Ed.). (2004). Cross-cultural perspectives on parliamentary discourse. Amsterdam: Benjamins.

Wodak, R., & Van Dijk, T. A. (Eds.). (2000). Racism at the top. Parliamentary discourses on ethnic issues in six European countries. Klagenfurt: Drava.

Some books on Critical Discourse Analysis

Blommaert, J. (2005). Discourse. Cambridge: Cambridge University Press.

Caldas-Coulthard, C. R., & Coulthard, M. (Eds.). (1995). Texts and practices: Readings in critical discourse analysis. London: Routledge.

Chilton, P. (2004). Analysing political discourse. London: Routledge.

Fairclough, N. (1989). Language and Power. London: Longman.

Fairclough, N. (1995). Critical discourse analysis. The critical study of language. London: Longman.

Fowler, R. (1991). Language in the news: Discourse and ideology in the British press. London: Routledge.

Fowler, R., Hodge, B., Kress, G., & Trew, T. (1979). Language and control. London: Routledge & Kegan Paul.

Kramarae, C., Schulz, M., & O’Barr, W. M. (Eds.). (1984). Language and Power. Beverly Hills, CA: Sage.

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Lazar, M. (Ed.). (2005). Feminist Critical Discourse Analysis. Gender, Power and Ideology in Discourse. Houndsmills, UK: Palgrave MacMillan.

Lemke, J. L. (1995). Textual politics. Discourse and social dynamics. London: Taylor & Francis.

Toolan, M. J. (Ed.). (2002). Critical discourse analysis. Critical concepts in linguistics. New York: Routledge.

Van Dijk, T. A. (1993). Elite discourse and racism. Newbury Park, CA: Sage.

Van Dijk, T. A. (Ed.). (1997). Discourse Studies. A multidisciplinary introduction. London: Sage. Volume 2: Discourse as Social Interaction.

Van Leeuwen, T. (2005). Introduction to social semiotics. London: Routledge.

Wodak, R. (Ed.). (1989). Language, power and ideology. Studies in political discourse. Amsterdam: Benjamins.

Wodak, R. (Ed.). (1997). Gender and Discourse. London: Sage.

Wodak, R., & Meyer, M. (Eds.). (2001). Methods of critical discourse analysis. London: Sage.

Weiss, G., & Wodak, R. (Eds.). (2003). Critical discourse analysis. Theory and interdisciplinarity. Houndmills, UK: Palgrave Macmillan.

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Appendix

Commons Hansard: 5 March 1997‡

Mrs. Teresa Gorman (Billericay): I want to bring to the attention of the House the particular difficulties faced by the London boroughs because of the problems of asylum seekers.

(7) There are, of course, asylum seekers and asylum seekers. (8,54) I entirely support the policy of the Government to help genuine asylum seekers, but to discourage the growing number of people from abroad who come to Britain on holiday, as students or in some other capacity and, when the time comes for them to leave, declare themselves to be in need of asylum.

The matter was adequately dealt with by the Social Security Committee report on benefit for asylum seekers, which was (2) an all-party document that pointed out that it was costing about £200 million a year (63) for those people, (9) many of whom could reasonably be called economic migrants and some of whom are just benefit seekers on holiday, to remain in Britain. (3,57) It is wrong that ratepayers in the London area should bear an undue proportion of the burden of expenditure that those people are causing.

( 15,55) I understand that many people want to come to Britain to work, but (41) there is a procedure whereby people can legitimately become part of our community. People who come as economic migrants are sidestepping that.

(13 )The Government, with cross-party backing, decided to do something about the matter. (42) The Asylum and Immigration Act 1996 stated that peo- ple whose application to remain in Britain had been turned down could no longer receive the social security and housing benefit that they had previously enjoyed. That is estimated to have cut the number of bogus asylum seekers by about a half.

It is a great worry to me and many others that the Opposition spokesman for home affairs seems to want to scrap the legislation and return to the previous situation. I would consider that extremely irresponsible. (50) It would open the floodgates again, and presumably the £200 million a year cost that was esti- mated when the legislation was introduced would again become part of the charge on the British taxpayer.

(43) In order to try to subvert the legislation, a case was recently brought be- fore our courts and to the High Court which sought to overturn the provisions that the Government intended. (18) The Government are keen to help genuine asylum seekers, but do not want them to be sucked into the racket of evading our immigration laws.

‡ The numbers between parentheses in the text refer to the numbers of the examples given above.

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The judges effectively, although not directly, overturned the decision that the Act produced and said that those who declare themselves destitute must be given assistance under the National Assistance Act 1948. (4) The problem of supporting them has landed largely on the inner London boroughs, where most of those people migrate as there is more to do in central London. (29,37) I am sure that many of them are working illegally, and of course work is readily available in big cities.

The London councils have a particular problem. They are now providing for 3,000 single males, many of whom are from east European countries recently liberated from oppressive regimes. They cannot by any means be said to be from countries where they would find themselves in grave political difficulties if they had stayed at home.

(5) There are also about 2,000 families, with young children who must be sup- ported. The cost of that to Westminster council is estimated to be £2 million a year, but over London as a whole, the cost is running at about (58) £140 mil- lion a year, which is a great deal of money to be found from the council tax budget.

Mr. Peter Brooke (City of London and Westminster, South): I would not want my hon. Friend to mislead the House. She should point out that the figure that she has just quoted represents the net expenditure which will fall on the city council. There is a great deal of further expenditure, which is paid for by grant.

Mrs. Gorman: I thank my right hon. Friend. He is a great authority on the matter, as he represents Westminster city council. I know that he has an impor- tant contribution to make.

(66) Goodness knows how much it costs for the legal aid that those people invoke to keep challenging the decision that they are not bona fide asylum seekers.

(26) The Daily Mail today reports the case of a woman from Russia who has managed to stay in Britain for five years. (23) According to the magistrates court yesterday, she has cost the British taxpayer £40,000. She was arrested, of course, for stealing. I do not know how people who are not bona fide asylum seekers and whose applications have been rejected time and again manage to remain in this country for so long at the expense of the British public, but the system clearly needs tightening up.

A number of London boroughs--Hammersmith and Fulham, Lambeth and Westminster--are to challenge the judges' decision, as it has placed an enor- mous financial burden on the taxpayers in central London. Before that deci- sion, Westminster had five applications from asylum seekers for help, but since the judges' decision in October, the number has increased to 300. At pre- sent Westminster city council is accommodating 66 families with children and 338 single adults, half of whom come from eastern Europe and are able-bodied males.

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Westminster is in a unique position because, being the centre of the capital city, it must also accommodate many other homeless people who find their way to London and take up temporary accommodation places. That means that the alleged asylum seekers whom the council is obliged to support often have to be put in expensive accommodation. There is a limit to the number of cheap bed-and-breakfast places in the centre of a city like London. Much of the ac- commodation is in hotels, which can charge a great deal more for a week's bed and breakfast than the sum that the council considers adequate, and certainly more than the sum that might be adequate in outer London boroughs or in other parts of the country. Therefore we have this unique situation, which Westminster has to deal with.

The Government have announced--this is most welcome--that they are to con- tribute £165 a week for each asylum seeker while their requests for asylum are being endlessly considered. Of course, in some parts of Britain, that may be adequate, but in Westminster it is not. It has done detailed homework and it can prove that, on average, the cost for the council is £215 a week for a single adult--and that is based on shared bed and breakfast accommodation, not on very expensive flats.

The National Assistance Act says that the assistance given to these people must be provided in kind, which means that Westminster city council has to use its meals on wheels service to take food to them, wherever they are placed, whether in the centre of London or in outer boroughs. (62) In addition to the breakfast that comes with the bed-and-breakfast accommodation, they have to be given a packed lunch, presumably in case they decide to go shopping in the middle of the day or to do a bit of work on the black economy--who knows? They also have to be provided with an evening meal and snacks to keep them through the day because the assumption is that they have no money--they have declared themselves destitute.

In addition, the council has to provide those people with a hygiene pack, which must include a toothbrush, toothpaste, soap, a flannel and deodorants. For a family of half a dozen, six sets of those commodities must be provided. (6) Presumably, if those people are here for long enough under such terms, they will have to be provided with clothing, shoe leather and who knows what else. All that cost falls on the British taxpayer and particularly on Westminster residents. The council estimates that, in addition to what the Government are proposing, about £35 a year will fall on each council tax payer in Westminster.

Again and again in the House, we hear the Opposition spokesman on housing, the hon. Member for Holborn and St. Pancras (Mr. Dobson), assert for the umpteenth time that all the residents in Westminster are terribly well off, so they can easily afford those extra charges. Nothing is further from the truth. Part of his act--because it is an act; he does it every time he gets the chance--is to cite people living in Mayfair and Belgravia, which we all know are two of the most expensive neighbourhoods in Britain.

The truth is that, out of 100,000 households in Westminster, only 1,500 are in Mayfair and only 3,000 are in Belgravia. (21,68) Many of those people live in

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old-style housing association Peabody flats. They are on modest incomes. Many of them are elderly, managing on their state pension and perhaps also a little pension from their work. They pay their full rent and for all their own expenses. (52) Now they are going to be asked to pay £35 to able-bodied males who have come over here on a prolonged holiday and now claim that the British taxpayer should support them.

(1) In one case, a man from Romania, who came over here on a coach tour for a football match--if the hon. Member for Perth and Kinross (Ms Cunningham) would listen she would hear practical examples--decided that he did not want to go back, declared himself an asylum seeker and is still here four years later. He has never done a stroke of work in his life. (59) Why should someone who is elderly and who is scraping along on their basic income have to support people in those circumstances?

Mr. David Nicholson (Taunton): My hon. Friend is exploiting a rich seam and she is doing so assiduously. (67) Is she aware that there is widespread re- sentment? (24) This morning, I was reading a letter from a constituent of mine, who has fallen into a catch 22 situation between health and social ser- vice provision, about the assistance that is available to people who do not have the right to reside in Britain, yet are milking not only the taxpayers, but the caring services, on which so many others depend.

Mrs. Gorman: My hon. Friend is entirely right. In my constituency at the weekend, I had the case of a woman who has managed to remain here for five years by playing the system. She has given birth to two children while she has been here and she is so addicted to the social services that, when she needs to go shopping in Basildon, she telephones her social service assistant worker and asks for a minicab to take her there because she cannot bring back her shopping. That is a fact, which I will and could demonstrate if I had to. (31) Such things go on and they get up the noses of all constituents, including those of Opposition Members, who seem to think it is funny that elderly British people, who are managing to live on their modest incomes, should fork out for alleged asylum seekers, who are simply parasites.

As I have said, Westminster has a particular problem and particular expenses. My purpose in bringing this matter to the attention of the House is to say to my hon. Friend the Under-Secretary of State for Health that Westminster's special circumstances should be given special treatment. Best of all, we would ac- knowledge that, although this matter has to be dealt with, it is a national prob- lem and should not be landed on the doorstep of a relatively small group of residents in the centre of London, who have many other problems associated with residence in London and who need to be given special care and help.

This matter needs to be aired because I am talking largely about Westminster. Of the 100,000 households in Westminster, more than half are on below aver- age incomes. Westminster has inherited many Greater London council estates such as Mozart and Lissom Green, which are given special estate assistance grants by the Government to help the low-income people living there, who have particular problems, but those people are all part and parcel of the com-

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munity charge scheme. In addition, about 16,000 households live in either Guinness Trust or Peabody estates, which again cater specially for people on modest incomes. They provide good quality homes, but, like everyone else, the people who live there pay their rates and 50 per cent. or perhaps more are eld- erly people on modest incomes.

As I was a member of Westminster city council, I have many friends among the residents in those places--people who used to be my constituents. (53) It is true that, in many cases, they have made careful provision for themselves in their old age, have a small additional pension as well as their old-age pension and pay all their rent and their bills and ask for nothing from the state. They are proud and happy to do so. Such people should not be exploited by people who are exploiting the system.

In Britain, about 70,000 alleged asylum seekers are going through umpteen appeals against deportation. All of them can exploit the loophole provided by the National Assistance Act. It is an extremely important matter. I have out- lined some of the costs in Westminster, but the people are distributed through- out Britain and other council areas will be grateful for the assistance that the Government have already announced. However, it ill-behoves Opposition Members to laugh at this and to treat it as a joke. We know what they would do because we have heard it from the Opposition Front-Bench spokesman: they would sweep away the measures that the Government have tried to intro- duce and reinstate the previous position.

Dr. Norman A. Godman (Greenock and Port Glasgow): Will the hon. Lady give way?

Mrs. Gorman: Would the hon. Gentleman forgive me because I want to sit down soon and let others into the debate?

The cost will again be landed on the doorsteps of British taxpayers, and par- ticularly on the doorsteps of Westminster city ratepayers. They do not deserve to have to pay those costs out of their own pockets.

11.19 am

Mr. Jeremy Corbyn (Islington, North): This debate is welcome in the sense that it provides an opportunity to talk about the problem of asylum seekers and the situation facing local authorities. However, I think that the hon. Member for Billericay (Mrs. Gorman)--who, today, appears to be batting for Westmin- ster council--should pause for a moment to think about why people seek asy- lum. Britain is a signatory of the 1951 Geneva convention, which requires that if someone is genuinely and legitimately in fear of persecution for political, religious or social reasons, they should be guaranteed a place of safety in the country to which they flee. That principle should be adhered to.

Britain has among the smallest numbers of asylum seekers of any European country. Compared to most other continents, Europe has one of the smallest numbers of asylum seekers. The real burden of the world's refugee crisis falls not on western Europe but on Mexico, Jordan, India and on other countries

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that are near to places where there has been great civil strife or which have Governments who are deeply oppressive towards their own people. So the idea that there is a huge flood of people trying to get into western Europe and into Britain, and particularly into Westminster city council accommodation, is slightly over-egging the pudding. It is also missing the point.

It is a major step for someone with a legitimate fear to seek refuge in exile. (22) So far as I am aware, no hon. Member has been woken up by the police at 4 am, taken into custody with no rights of access to a judicial system, and, with his or her family, forced to flee into exile for their own safety. It is not an experience that most British people have had, and we should think very care- fully about what a major step it would be to undertake such a journey.

When asylum seekers arrive in the United Kingdom, they must apply for asy- lum. Under the new legislation, if they do not apply immediately at the port of entry, their chances of being granted asylum are severely diminished. (28) If one has grown up in Iraq and has always been completely terrified of anyone wearing any type of uniform, it is fairly unlikely that--after managing to steal oneself out of Iraq, possibly using false documentation, aliases, guides and other measures--one will trust a person wearing a uniform whom one encoun- ters when first arriving at the airport. It is more likely that one would first get out of the airport and then think about the next step.

(32) In the United Kingdom there has been a systemic erosion of peoples' abil- ity to seek asylum and to have their cases properly determined. There has also been a vindictiveness against asylum seekers--it has been parroted in this de- bate by some Conservative Members--which has been promoted by some newspapers, particularly the Daily Mail. For very many years, that newspaper has had a long and dishonourable record on this issue.

Mr. Christopher Gill (Ludlow): (60) I wonder whether the hon. Gentleman will tell the House what mandate he has from the British people to share their citizenship with foreigners?

Mr. Corbyn: I am unsure how one answers such a totally ludicrous question. (33) If someone has a legitimate fear of persecution, they flee abroad and try to seek asylum. Many people sought asylum from Nazi Germany. Presumably the hon. Gentleman, on the basis of his comment, believes that they should not have been admitted to the UK, and that people fleeing from oppression in any regime should not be admitted. He talks utter nonsense. (14) I suggest that he start to think more seriously about human rights issues. Suppose he had to flee this country because an oppressive regime had taken over. Where would he go? Presumably he would not want help from anyone else, because he does not believe that help should be given to anyone else.

(64) Let us return to the issues facing people fleeing areas of oppression. Cur- rently if they arrive here, seek asylum and are refused, they have lost all access to benefits. They then have to undergo an appeal process, which can take a very long time. During the appeal process, what on earth are they supposed to do unless they are declared destitute and consequently supported by a local

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authority? We need to restore benefit rights for all people pending the outcome of their appeal. Not to do so is a gross abuse of individual human rights. Moreover, removing benefit is not saving any money because, in many cases, it costs far more to look after the children involved by placing them in foster care than by allowing their families to look after them in the normal and proper way.

We should consider the experiences of people who have fled countries. A couple of weeks ago, I spent several hours talking to a group of asylum seekers from Iran. That regime--despite the fatwa against Salman Rushdie and numer- ous other human rights abuses--is beginning to be cosied up to by the British Government and by the rest of western Europe, because they now prefer to support Iran rather than Iraq. (25) The people who I met told me, chapter and verse, of how they had been treated by the regime in Iran— (27) of how they had been summarily imprisoned, with no access to the courts; of how their families had been beaten up and abused while in prison; and of how the re- gime murdered one man's fiancee in front of him because he would not talk about the secret activities that he was supposed to be involved in. (34) I heard about many other similar cases.

(65) Those people came to this country and applied for asylum. Their applica- tions were refused, and they appealed. They are now living a life of virtual destitution, while the Home Office ponders on what to do for them. Those people stood up for their communities against an oppressive regime. I remind the House that merely because a regime calls itself democratic does not mean that human rights are guaranteed. Around the world, many regimes call them- selves democratic and have a multi-party democracy, but that does not mean that human rights are universally respected or that people are safe.

The hon. Member for Billericay said that no one in eastern Europe has any justification for seeking asylum. That is a sweeping statement. I presume that she has not had an opportunity to read the papers from Amnesty International or from Helsinki Watch on what is happening in Albania.

Mrs. Gorman: Will the hon. Gentleman give way?

Mr. Corbyn: I will in a moment.

Perhaps the hon. Lady has not had a chance to consider what is happening in Romania, where homosexuality is a criminal act, or in Bulgaria and other places. All is not well merely because there is multi-party democracy and a market economy. Perhaps events in Albania are not a credit to the market eco- nomic system?

Mrs. Gorman: (19) I did not say that every eastern European's application for asylum in this country was bogus. However, many countries that were in the former Soviet sphere of influence have now established democracies, and some people from those countries come here to claim asylum. Of those claim- ing benefit from Westminster city council, about 50 come from countries in which there is no longer oppression.

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Is the hon. Member for Islington, North (Mr. Corbyn) aware that--in a report signed by Labour Members--the all-party Social Security Select Committee, which considered the matter, stated:

"Any responsible Government would want to examine ways of control- ling expenditure of £200 million a year, when it is known that well over 90 per cent. of people who claim asylum turn out not to be genu- ine.

Genuine applicants, such as those described by the hon. Gentleman, are frus- trated and suffer delayed applications because of those who are not genuine.

Mr. Corbyn: The hon. Lady seems to have moved on a bit from the cant and prejudice that she produced in her earlier speech. However, she does not deal with the point. I am a member of the Social Security Select Committee and took part in that inquiry. I did not sign that section of the report, although I produced a minority opinion, which I am sure that she would disagree with profoundly. However, that is up to her.

I merely want the hon. Lady and the House to understand that democracy does not always follow multi-party elections. The UK, for example, prides itself on its close relationship with Turkey, yet many Kurdish people have fled Turkey and appealed for a place of safety here. Many of them have died trying to get out of Turkey because they have a point of view that is different from that of the Turkish Government. I think that there is a foreign policy implication and potential initiative in that situation.

Since last year, people from the Ivory Coast have sought asylum in the UK. I recall a discussion with the Home Office about the safety of people from the Ivory Coast. The Minister told me that he was assured that everything was okay in the Ivory Coast. The students whom I met who had sought asylum in this country from the Ivory Coast told me that their Government were so keen on carrying out the economic wishes of the International Monetary Fund and others that they were crushing anyone who opposed them--they crushed trade unions and they crushed student opposition, sending troops into various uni- versities and closing them down. Is that how a democratic Government should behave? No. We must recognise that those people from the Ivory Coast are justifiably seeking asylum.

Dr. Godman: I hesitate to intervene in the debate, because I come across few asylum seekers--an experience that I suspect that I share with the hon. Member for Perth and Kinross (Ms Cunningham). I have come across a few at Green- ock prison. One concession was offered a few months ago by the Minister of State, Home Office, the right hon. Member for Maidstone (Miss Widde- combe)--a promise that those women seeking to avoid the infliction of genital mutilation would be given sympathetic consideration when seeking asylum. That is at least one concession in this picture of unrelieved gloom.

Mr. Corbyn: At least the Minister was forced into that concession during a debate in this Chamber. I wonder whether those who make decisions on refus-

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ing people asylum, refusing them benefits and forcing them into destitution have ever taken the trouble to sit down and listen to the stories of people who have been tortured and abused.

The process depends on refugees applying at the point of entry. That is often difficult to do, for reasons that I have already outlined. It is also often difficult for people to talk about the torture experiences that they have been through. (12) Many soldiers who were tortured during the second world war found it difficult to talk about their experiences for years. That is no different from the position of people who have been tortured in Iran, Iraq, west Africa or any- where else. The issue is not simple. They feel a sense of failure, a sense of humiliation and a sense of defeat. (45) We should have a different attitude towards asylum seekers.

Almost uniquely among European countries, this country routinely puts in prison people who seek asylum. There are nearly 900 people in British prisons who have sought asylum. It costs £20 million a year to keep them in prison. I have been given a letter from several people who are being held in the Home Office holding centre at Haslar. They complain about their treatment and the way in which the immigration service carries out its duties. They say:

"Another problem, literally fatal for certain detainees, is deportation without prior notice of the date being given. Those under notice for many months are often collected from Haslar for deportation at a week-end when it is quite impossible to have recourse to their solici- tors or other help."

(46) We should think a bit more seriously about how we treat those people.

For the past few weeks, there has been a hunger strike at Her Majesty's prison in Rochester. I understand that that hunger strike is not continuing at the mo- ment. When I raised the issue on a private notice question, the Home Office Minister was dismissive. She appeared to have no understanding of the moral force of people undertaking a hunger strike to draw attention to their problems. Hon. Members should stop and think for a moment about the circumstances of those who come to this country seeking asylum, go to prison with no direct access to the courts and then, thinking that they have been badly treated and fearful of what will happen, undertake a hunger strike and, in some cases, a refusal to take fluids. (15) If that happened in another country under a regime of which we disapproved, the British Government would say that it was a ter- rible indictment on the human rights record of that regime that prisoners were forced to undertake a hunger strike to draw attention to their situation. In this country, people who say that get routine abuse from Home Office Ministers and Conservative Members. Stop and think for a moment about the moral courage of those who have undertaken a hunger strike to ensure that their case is at least looked at.

(47) Attitudes towards asylum seekers need to be changed. Routine imprison- ment should end. Access to benefits should be restored for those applying for asylum. If they are refused asylum but are undertaking their legitimate right of

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appeal, they should continue receiving benefits until the appeal has been de- termined. ( 48 ) It is wrong to force them into destitution or to throw them out of the country, often with no access to lawyers or anyone else.

The Government's regime on asylum seekers is creating a serious situation, with a class of destitute people that is paralleled across Europe. Those who have applied for asylum, have been refused and are fearful of deportation end up going into hiding in the poorest areas of Paris, Frankfurt, Madrid, Berlin, London or Amsterdam. They are subject to the worst kind of exploitation by rogue employers, drugs and prostitution. They cannot reveal their identity be- cause they would be deported. Only the churches around Europe have drawn attention to the issue and tried to do something about it. I hope that we shall recognise that we should have a slightly more humane approach towards asy- lum seekers in this country.

Last year, the Churches Commission for Racial Justice held a conference called, "Why Detention?". A report of the conference has been published. There was universal condemnation of the principle of imprisoning asylum seekers and a plea for a more understanding approach. (49) Europe must stop its xenophobic attitude towards those who seek a place of safety here and adopt a more humane approach.

There is also a foreign policy agenda. Where is the outright condemnation from the Government of the denial of human rights in Iran, Iraq, the Ivory Coast and many other countries? I find it very muted on many occasions. They seem more interested in trade and selling arms to those regimes than in de- fending human rights. (36) History shows that unless we stand up for human rights wherever they are abused around the world, eventually it will come back and our human rights will be abused. (56) A lot of brave people in this country have stood up for the rights and needs of asylum seekers. Local authorities are being told that they should pay a large share of the bill. I do not want them to have to do that. Central Government should give more support to local au- thorities to ensure that asylum seekers do not live in destitution. Above all, I want a change in attitude and a more humane approach to this serious problem of the victims of injustice from around the world.

11.36 am

Mr. Peter Brooke (City of London and Westminster, South): I shall be briefer than my hon. Friend the Member for Billericay (Mrs. Gorman) and the hon. Member for Islington, North (Mr. Corbyn), because this is a short debate and I want others to get in. I congratulate my hon. Friend on securing the de- bate.

The problem that we are discussing arises from the autumn of 1995, when various announcements were made at the Conservative party conference about the Government's intentions. There was evidence through the autumn of that year of a lack of interaction between Government Departments. Brussels often praises Whitehall for having better co-ordination between Departments than any other Government in the European Union, but that co-ordination was not

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in evidence in this case. The Social Security Advisory Committee wrote a hos- tile report on the Government's intentions. I suspect that once the Home Office had legislative cover and clearance for its Bill, it washed its hands of the con- sequences, which would fall on other Departments.

On Second Reading of the Asylum and Immigration Bill, in December 1995, I alluded to some of the problems that I could foresee. I mentioned in particular the problems of unaccompanied children coming to Westminster and other central London boroughs. Perhaps as a consequence of that debate, there was a delay in bringing forward the amendments to the benefit regulations, quaintly named the Social Security (Persons from Abroad) Miscellaneous Amendments Regulations 1996. The Opposition were satisfied with a 90-minute debate. Some Conservative Members felt that that was inadequate time to discuss the regulations. I was the last to speak before the replies to the debate and was allowed three minutes. I said that the drama that I foresaw would be played out on the streets of my constituency rather than those of some of my right hon. and hon. Friends on the Front Bench who were introducing the measures.

A legal case went against the Government in the summer, as a result of which they had to amend the Bill in the House of Lords with primary rather than sec- ondary legislation. As has been said, on 8 October the decision was taken that obliged local authorities to provide assistance to single adult asylum seekers. That decision was challenged in the Court of Appeal, and the appeal was de- feated. That series of legal defeats reflects rather badly on the degree of co- ordination involved in the preparation of the legislation before its introduction. Like my hon. Friend the Member for Billericay, I am briefed primarily by Westminster city council, but I shall allude to other areas of central London later. At the heart of the problem is the fact that it is being dealt with on a piecemeal, rather than a co-ordinated, basis.

My hon. Friend referred to the £165 per week grant provided by central Gov- ernment. That is an average figure drawn from estimates that the Government received, which ranged from £95 for cold weather shelter provision to £290. That scatter of figures derives from outer and inner London areas. As the Bishop of London reminded us during the centenary service for the King's Fund only yesterday, costs outside central London are quite different from those in inner London. For two reasons the £95 for cold weather shelter is an unrealistic figure for provision in central London. First, the rough sleepers initiative has absorbed so much of the accommodation that might be used for that purpose that the central London boroughs no longer have access to it. Secondly, asylum seekers are specifically excluded from cold weather shelters.

Westminster pays £175 for accommodation alone, before the addition of extra sums that it must provide. The rough sleepers initiative, co-ordinated by cen- tral Government in conjunction with the voluntary sector, has been a great success. The number of those sleeping rough in central London has fallen from more than 1,000 to below 400 in the past six or seven years. Central Govern- ment would render major assistance if they took over that co-ordination in conjunction with the voluntary sector, upon which a great deal of the burden of the problem falls. That would instantly reduce the average unit cost. The

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piecemeal approach adopted at present increases the likelihood of fraud.

It is recognised widely that the burden of the problem falls on local authorities in London, and primarily on those in inner London. I freely acknowledge that Westminster is not the only authority involved: the borough of Islington is affected in the same way. I alluded to the problem of unaccompanied children during the Second Reading of the Asylum and Immigration Bill in December 1995. This year, Westminster will spend £1.2 million on unaccompanied chil- dren. There is no logical reason why Westminster and one or two other bor- oughs should uniquely absorb that problem. Unaccompanied children--who come to this country extremely well prepared--simply go to a handful of au- thorities in central London about which they have heard or to which they have been directed, and the council tax payers in those areas must foot the bill.

There is a hazard to community and race relations in central London if such costs continue to fall heavily on council tax. The burden constitutes a risk to the quality of community and race relations in those areas and, in that respect, I endorse my hon. Friend's comments. At the margin, community care budgets are being diverted to this problem and away from council tax payers.

I put it to my hon. Friend the Minister--for whom I have some sympathy--first, that all unavoidable costs resulting from the programme should be reimbursed to local authorities that are acting on behalf of the nation as a whole. Secondly, it would be immensely desirable if the Government would announce their grant levels for 1997-98. It is now 5 March and the fiscal year ends within a month. However, local authorities do not yet know what level of grant the Government will provide.

I hope that the Home Office--in this respect I make common cause with the hon. Member for Islington, North--can improve the speed with which it proc- esses these cases. Between December 1995 and May 1996, applicants under the legislation prior to 1993 waited an average of 43 months for initial deci- sions. Between October and December 1996, the waiting time increased to more than 48 months. The comparable statistics for those who were treated under the legislation that was introduced in 1993 are 10.7 months in the earlier period and 12.2 months in the second period. The time taken by the immigra- tion appellate authority to determine appeals lengthened from eight to 10 months in the same period. Outstanding appeals increased from 14,000 in Feb- ruary 1996 to nearly 22,000 at the end of last year. So the burden on local au- thorities is being extended because the process of handling applications is slowing down rather than accelerating.

I said that I sympathise with my hon. Friend the Minister, who will come to the Dispatch Box on behalf of the Department of Health as much of the ex- penditure flows through that Department. However, I am not sure that the De- partment of Health should necessarily take the lead in co-ordinating this proc- ess. It originates in the Home Office, and I believe that it would be desirable if that Department took the lead--not least because a lack of co-ordination at the end of 1995 led to this situation. I promised that I would be brief, Mr. Deputy Speaker, and I now sit down within 10 minutes.

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Mr. Deputy Speaker (Sir Geoffrey Lofthouse): Order. Five hon. Members hope to catch my eye in the 25 minutes before the winding-up speeches begin. With the co-operation of the House, I hope that they will all be successful.

11.46 am

Mr. Neil Gerrard (Walthamstow): I shall try to be brief. The right hon. Member for City of London and Westminster, South (Mr. Brooke) has dis- cussed this subject on several occasions and raised the issue of responsibility. His speech contrasted considerably with that of the hon. Member for Billericay (Mrs. Gorman) at the beginning of the debate. I must admit that I was one of those who laughed at some of the things that she said, not because I do not take the subject seriously, but because it was obvious that she does not have the slightest clue about who asylum seekers are, the circumstances in which they find themselves, and what happens to them.

I agree that London boroughs should not carry the responsibility for asylum seekers, but what are the alternatives? The right hon. Gentleman suggested that the Government should shift the responsibility somewhere else. The hon. Member for Billericay seemed to endorse the Government's option of appeal- ing the court decisions and returning to their favoured position of removing benefits completely and leaving asylum seekers with absolutely nothing. I re- mind the House that the measure applies to asylum seekers who apply in coun- try, and not to those who apply at the port of entry. That is despite the fact that the success rate for asylum applications of people who apply in country is at least as great as--and sometimes greater than--that of people who apply at the port of entry.

In the first four months of last year, 775 people were awarded refugee status, 610 of whom were in-country applicants--precisely the people who have been denied benefits. The Government were warned about the repercussions from the beginning. The Social Security Advisory Committee warned the Govern- ment not to change the social security regulations in 1995, and pointed to the likely consequences of that action.

(10) The Government's reasoning was the same then as it is now: they still talk about economic migrants and benefit scroungers. Anyone who deals with asy- lum seekers knows the reality. It is rubbish to say that people come this coun- try because the benefits here are more than the average wages in the countries from which they have come. They may be, but we should consider what that means in real terms, and what standard of living people have had in their own countries.

An Algerian asylum seeker told me that he had been a general practitioner in Algeria and that his wife had been a vet, but people were telling him that he had come here to live on benefits. I have known an 18-year-old Somali girl for a couple of years. She is struggling to look after six children younger than her- self. They all live in a bedsit, and she showed me photographs of her house in Somali, which has a mosque in the back garden that her father built. Yet we tell those people that they have come here to live on a few pounds a week in

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benefits.

The people who manage to get to this country are usually not the poorest or most downtrodden. The poorest people are in refugee camps in neighbouring countries: that is where the majority of refugees end up. How many of the 20 million refugees worldwide are trying to get to Europe, never mind the United Kingdom?

The Government lost the court case on the benefit regulations. At the last min- ute, they included these provisions in the Asylum and Immigration Act 1996. Time and again, those of us who served on the Committee considering that Bill and who participated in the debates asked what would happen and who would have ultimate responsibility. We said that local authorities would be stuck with the problem of having to deal with children under the Children Act 1989 and with homeless people on the streets. We did not know then that the courts would decide that the National Assistance Act 1948 could be used. We pointed out the problems and said that council tax payers would have to pick up the bill.

(16)Even if we accepted the Government's view--which I do not--that only a tiny proportion of people who claim asylum are genuine refugees, we cannot defend a policy that leaves genuine refugees destitute. The hon. Member for Billericay defended the Government's position. Even if only a small number of cases are genuine, how can anyone defend such callousness? Genuine asylum seekers will be left without a penny to live on. Only one other country in Europe has such a policy, and that is Italy. On the outskirts of large towns such as Naples one sees shanty towns full of asylum seekers. That is the logical consequence of the Government's policy.

It is a disgrace to any civilised society even to consider leaving genuine asy- lum seekers without a penny to live on. That is what we should be debating, not the financial position of a few local authorities that have been dropped into this mess by the Government, who want to leave them in that mess. Hon. Members should read the Refugee Council's report, which shows the impact that having to live on nothing has on the lives of asylum seekers. People have to walk miles to soup kitchens to get a meal.

As the right hon. Member for City of London and Westminster, South said, delays should be eliminated. Why are people having to wait four or five years for a decision on their case? Why are the queues getting longer? In 1993, we were told that the Asylum and Immigration Appeals Act 1993 would make things better, and we were told last year that the 1996 Act would makes them better, but waiting times are getting longer. If we want to encourage people to make bogus applications, the way to do so is to let the queues get longer, but that penalises the genuine asylum seeker. I believe that the majority of appli- cants are genuine: I do not believe the 90 per cent. figure.

Long queues encourage the bogus applicant, so the Home Office and the Lord Chancellor's Department should do something about it. Why has the number of cases awaiting appeal gone from 13,000 to 21,000? Many of those people

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will have to await their appeal--which they may well win--without a penny, because their benefits have been cut off. Do not tell me that that is what hap- pens to people who are refused benefits through the social security system. Few people who are refused social security benefits are left destitute without a penny. The people who are refused benefit tend to be those claiming a particu- lar benefit to which they are not entitled.

We should not treat in such a way people who come here to escape from ap- palling conditions. They may have been in gaol and may have been tortured. To put them on the streets without a penny is a disgrace to any society that calls itself civilised.

11.55 am

Mr. Charles Wardle (Bexhill and Battle): I congratulate my hon. Friend the Member for Billericay (Mrs. Gorman) on securing this debate. The topic of asylum seekers is fundamentally important for two obvious reasons. (35) First, it matters crucially that this country honours, as it always has, its obligations under the Geneva convention. (38,51,61) It is equally important that abuse of the asylum rules by the large number of people who make asylum applications knowing that their position as illegal immigrants has no bearing on the Geneva convention should be debated openly, so that it is fully understood and tack- led.

Bearing in mind the fact that year in, year out the number of people found to be genuine Geneva convention cases ranges from 1,000 to 3,000, it stands to reason that the other tens of thousands of applicants include people who know- ingly abuse the system. Those people do a disservice to genuine refugees, who are held up in the queue, to which the hon. Member for Walthamstow (Mr. Gerrard) alluded, and do not receive the treatment and care that should come their way.

Mr. Corbyn: Will the hon. Gentleman give way?

Mr. Wardle: I shall not give way. The hon. Gentleman and I have often dis- cussed this matter, but I am aware of the time, and I would like to make pro- gress.

(44) Britain has always honoured the Geneva convention, and has given sanc- tuary to people with a well-founded fear of persecution in the country from which they are fleeing and whose first safe country landing is in the United Kingdom. The only occasion that I know of when our proud record under suc- cessive Governments of honouring the convention was sullied was the recent Al Masari case. Reference to the primacy of British business interests in Saudi Arabia brought the integrity of our asylum criteria into question, and, when the Government lost the appeal, a thoroughly undesirable person was allowed to remain in this country and continue his political activity.

I want to make three points on detention, the asylum queue and the wider issue of asylum, the European Union and broader immigration policy. Much of what is said about detention is confused or misleading. (20) Protesters may genu-

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inely be concerned about refugees in detention, but the fact is that only a tiny proportion of applicants are detained. In virtually every case--not in 100 per cent. of cases, but in almost all of them--a detainee is someone whose appeal has been refused, who is waiting to be removed from the country and is only temporarily in detention, or whose application has been refused and is await- ing appeal but is considered likely to abscond. However, it is a tiny proportion of the number of people concerned.

Mr. Tony Marlow (Northampton, North): Will my hon. Friend give way?

Mr. Wardle: If my hon. Friend will allow me, I must make some progress.

My next point concerns the asylum queue. As I have already said and as is widely known, there are people in the queue who have arrived in this country and been welcomed as visitors but who have then overstayed that welcome, found work and assimilated themselves into the local population, quite unlaw- fully. When apprehended and questioned, they are frequently advised by im- migration lawyers or advisers to apply for asylum because, once they are in the queue they can stay here and qualify for social security. As my right hon. Friend the Member for City of London and Westminster, South (Mr. Brooke) said, it may take four years to resolve the case.

Recently, Ministers have pointed to the fall in the number of asylum applica- tions and to the success of the Asylum and Immigration Act 1996. It is a wel- come development if some bogus applicants are no longer applying, but it does not deal with the underlying problem of the queue. In December 1995, on Second Reading of the 1996 Act, I explained what I felt was the only way to tackle the problem, which was not simply to pass more legislation--Bills do not resolve what is fundamentally an administrative problem--but to process the queue swiftly.

On Second Reading my right hon. and learned Friend the Home Secretary said that some 75,000 people were in the asylum queue at the end of 1995. He es- timated the cost to be about £200 million a year. I said that I had every reason to believe that he was grossly underestimating the costs and that when the fig- ures for social security, housing, school places, the health service and so on were added to that figure, the cost was likely to be closer to £500 million or even £750 million a year. I recommended that he should think again about his promise to spend £37 million on the appeals section of the asylum division and on the Lord Chancellor's Department and that he should spend about £150 million a year for two years to process the queue. As the hon. Member for Walthamstow said, once the queue is gone, the attraction of making a bogus claim disappears. At the same time, it would help the genuine applicants be- cause they could be dealt with promptly.

Mr. Marlow: My hon. Friend said that everyone is concerned about people going into detention and many people do not go into detention. The Home Office is unable to give me an answer to my question, but perhaps my hon. Friend will have some idea. Does he know how many people who do not go into detention but who are bogus asylum seekers disappear and do not turn up

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ever again?

Mr. Wardle: I cannot give my hon. Friend an exact answer. Undoubtedly many people who are not detained but are in the queue and see their appeal coming closer to resolution, disappear into the undergrowth. That is unlawful and wrong and should not happen. It is all very well to talk about new legisla- tion and new measures, but while the queue exists, the temptation to join it as a bogus applicant is there. That is fundamentally wrong. We must process the queue and ensure that those who do not qualify for leave to remain in this country are removed from here, including those who have absconded. That is being missed in all the headline chasing about new Bills every other year. That is not what is needed. We need competent administrative action.

I should like to raise the link between asylum and the European Union and the wider but directly related issue of immigration and border controls. Under the third pillar of co-operation in the EU, there has for several years been har- monisation of asylum policies--the Dublin convention is one example of that. The European Commission wants to go much further--it is perfectly open about its ambitions. It wants to take the third pillar into treaty competence and that includes asylum policy. The Government have said that they will resist that and I am sure that they are right to do so. The cornerstone of that resis- tance is not to allow Britain's border controls to be dismantled, as is required by the existing European treaty. The moment those border controls are gone, the ability to determine where a person has landed as the first safe country becomes confused.

There was recently a welcome announcement by the Dutch Government that they now recognise--the operative word is "now"--that no future British Gov- ernment will willingly relinquish border controls. I should like to believe that it is significant that, until I made a fuss about this two years ago there were no Government speeches or great policy statements on the subject of our border controls. There was only the occasional furtive and uneasy answer to parlia- mentary questions. Undoubtedly, Ministers in other EU member states and their officials all assumed that, sooner or later, Britain would cede its border controls when required to do so by the European Court. That position has changed, but the battle is not yet over.

The best thing that the Government can do is to be open and frank about the legal threat to our position as it now stands. There has been some progress with the recognition by the Dutch, but the problem is still there. By rehearsing the nature of the problem openly rather than glossing over it, the full force of British public opinion, including people of all ethnic origins, would be brought to bear to persuade the Commission that this country will not wish to change its stance.

Unfortunately, time and again Ministers have given Parliament the strong im- pression that the Government consider that they have a sound defence against the requirement in article 7A to dismantle border controls. The Government, effectively, seem to face both ways because they have said that they will never give away the border controls, but then say that we have an adequate defence.

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It might be as well for Ministers to remind themselves of "Questions of Proce- dure for Ministers" which states:

"Ministers have a duty to give Parliament and the public as full account as possible about the policies, decisions and actions of the Government and not to mislead Parliament and the public."

They should also remember the Scott report, which said:

"If the account given by a Minister to Parliament withholds informa- tion on the matter under review, it is not a full account."

Time and again we have not been given a full account on this subject in Par- liament. While the Government gloss over our vulnerability but assert, at the same time, as my right and learned Friend the Foreign Secretary has done, that the Government will not break European law, we are not getting to the bottom of the problem. The only way to do that is to be open with Parliament and the British public and to ensure that, with the force of British public opinion be- hind them, these matters can be dealt with to British satisfaction at the forth- coming intergovernmental conference. To do that, would put our asylum and immigration policies into the proper framework. This is a subject to which I fully intend to return in the next Parliament.

12.7 pm

Mr. David Shaw (Dover): I speak as the Member of Parliament for Dover, which is a port of entry, and which has many immigration officers who have to carry out difficult work. They enforce our border controls with great difficulty, (39) because there are many attempts at illegal immigration using asylum techniques, fraudulent documents or other methods. They face a difficult bat- tle. There are police officers and special branch people at the port, as well as five social security benefit fraud investigators to deal with many of those who try to get into this country to take advantage of our system, either to claim benefits or to gain residency here.

Although many of us may support the Geneva convention and want to see people with a legitimate fear of persecution being able to come to this country for protection, we do not want people to take advantage of our compassion, and many of them who come here are doing that. When the recent hunger strike at Rochester was investigated, it was found that nearly all, if not all, the people involved were not genuine asylum seekers but illegal immigrants who were being detained with a view to being deported. Many people want to take advantage of this country.

The world is full of economic migrants, who can travel more easily than ever before. I accept that there are trouble spots, but there are not as many as asy- lum seekers would have us believe. (30) We must also face the fact that, even in the case of brutal dictatorships such as Iraq, we cannot take in all those who suffer. I would like to help all those people who suffer from Saddam Hussein's actions, but we cannot do so. Almost the whole population of Iraq is perse-

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cuted and oppressed, and we could not take them all in.

Mr. Marlow: My hon. Friend has cited the example of Iraq. If people are des- perate to get out of Iraq, why do they not go to Jordan or somewhere else in the middle east? Why do such people come all the way here? Is it because they are seeking the economic benefits of this country? Why do people have to traverse a continent to get away, instead of going to the country next door?

Mr. Shaw: My hon. Friend raises the question of how so many migrants, who seek asylum or become illegal immigrants, reach this country.

Mr. Gerrard: Will the hon. Gentleman give way?

Mr. Shaw: I cannot give way again, because of the shortage of time. (40) Too many asylum seekers enter the country initially as family visitors, tourists, students and business people, and then suddenly discover that they want to remain as asylum seekers. That is why the Social Security Select Committee produced a report on the Government's proposals. I accept that the report was not unanimous, but we had no difficulty in saying that the Government's ac- tions were right.

The problem is that far too many people have jumped on the asylum band- wagon. There is an industry supporting people who try to remain in this coun- try when they cannot justify their presence. I have recently come across the Migrant Training Company. Labour councillors in Camden have apparently been involved in a £1 million fraud with taxpayers' money, and European grants have gone astray. I understand that a Labour parliamentary candidate has also been involved. There is a serious possibility that Labour councillors in Camden will have to be surcharged as a result of that fraud.

We have to face the fact that real problems are caused by asylum policies and immigration. We cannot go on meeting the bill, which at one stage was £200 million a year, for attempts by 40,000 people to seek asylum. Many of those people are not genuine. My hon. Friend the Member for Billericay (Mrs. Gor- man) mentioned a lady from Russia, who is an arts graduate and claims that she had problems at her university. That is not a good enough reason to cost the British taxpayer £40,000. The situation cannot continue.

I have much sympathy for Westminster council, which has had to bear consid- erable costs. Outrageous accusations have been made that the resources that Westminster receives from the Government are unfair, but it bears many costs that should properly be borne by the whole country. It is the central authority in London. I also have sympathy for Kent, which also bears some of the cost of asylum seekers. Dover district council has also had to bear the costs of some cases. It is unfair for local authorities to have to bear the costs, when the Geneva convention is a national policy.

It is also unfair that Camden council, and other Labour councils involved in the Migrant Training Company, are abusing the system and engaging in fraud. The Government have a serious problem, because they cannot tell councils that they will take over 100 per cent. of the bill, but allow Labour councils to

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take advantage by setting up fraudulent companies, such as the Migrant Train- ing Company, for the benefit of Labour councillors and a Labour parliamen- tary candidate.

Mr. Corbyn: Where is the evidence?

Mr. Shaw: The evidence is sitting in the Department for Education and Em- ployment, which has a European Court of Auditors' report showing that the company has been involved in serious fraud. That is a disgrace, and the La- bour councillors and members involved should be exposed. The Government have the right approach, but I have much sympathy for the councils that incur unreasonable expense.

12.15 pm

Ms Ann Coffey (Stockport): I congratulate the hon. Member for Billericay (Mrs. Gorman) on obtaining her Adjournment debate. The issues she has raised concern a number of London boroughs, but I am not sure that some of her general comments were helpful. I remind her that it is easy to raise and exploit fears about immigration, but the challenge in a multiracial society is the maintenance of good race relations.

The Government's defence is that the current shambles over payments under section 21 of the National Assistance Act 1948 is not their fault, but the fault of the judges. The Government claim that the judges have put local authorities in an invidious position, and that they have rushed to the rescue with a special grant to help out the local authorities.

I am not sure that that is a correct assessment of the judgment. The judges in the Court of Appeal said that, because asylum seekers were disqualified from assistance under the Asylum and Immigration Act 1996, they automatically qualified under the National Assistance Act 1948 for assistance from local authorities. As the 1948 Act had not been repealed by Parliament, the judges interpreted the general will of Parliament as a desire to continue to provide for those in need. That is the principle that has been behind the poor law for 350 years.

The present situation of local authorities is not the fault of the judges, in the stark way that the Government claim, but arises from the confusion caused by two conflicting Acts of Parliament. Clearly, the legal advice received by Min- isters was not entirely sound. The local authorities had to appeal, because the Government refused to reimburse them for payments they made under section 21 of the 1948 Act. It was clear that the local authorities would not be reim- bursed without a legal ruling that would enable Ministers to blame the judges for the Government having to pay for an alternative benefits system for asylum seekers, administered at a high cost by the local authorities.

I might add that the Asylum and Immigration Act 1996 did not remove asylum seekers' entitlement to national health service treatment. Asylum seekers would be admitted to hospital if they became physically ill through lack of funds, suffered hypothermia from sleeping on the streets or contracted a dis-

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ease. If asylum seekers become mentally ill as a result of stress and depression, they would be entitled to treatment under the mental health Acts. It would be interesting to see the after-care programme for such cases.

Yesterday, when we discussed the special grant of £165 for each asylum seeker that the Government are giving local authorities, I asked about cash payments. The Department of Social Security has ruled that such payments are not lawful under the National Assistance Act 1948, and would not be eligible to be reimbursed, although the expenditure is lawful under general local gov- ernment powers.

I understand that there is conflicting legal advice, but the present situation is absurd. Social workers' time is being used to deliver groceries and take people shopping. One silly example is that people cannot be given money for toothbrushes, because they have to be bought for them. The hon. Member for Billericay gave the example of the use of the meals on wheels service to pro- vide food, when the service is already under much pressure. Local authorities could meet their responsibilities in a more cost-effective way if they could make direct cash payments. That idea should be pursued.

The recent Refugee Council report, "Just Existence", tracked 15 asylum seek- ers who had lost entitlement to benefit and were being offered various kinds of help by local authorities. No one reading that report could fail to be struck by the desperation of those people's lives and circumstances. Whatever the even- tual judgment on their status, each personally saw overwhelming reasons for not being able to return to their country of origin, and would endure any condi- tions in this country rather than face that alternative. That is the reality that must be taken into account.

The importance to those people of resolving their status as quickly as possible is also clear. Several hon. Members have already talked about the delays, and I have a constituent who, after nearly five years in this country, has not yet had his appeal against refusal of refugee status heard. That is totally unacceptable.

The delays in the legal process need tackling. If the fundamental problem is not addressed, local authorities face the prospect of having to administer an alternative benefit system for asylum seekers, and to support them in hotels, bed-and-breakfast accommodation, hostels, flats and shelters. The administra- tion will be costly, and will undermine local authorities' ability to perform their other statutory functions.

I know that the Government propose changes, as yet unannounced, in social services departments, but I would not have thought that the role of poor law administration was something that even the present Government had in mind for them. Of course, I could be wrong. Perhaps Ministers foresee the prospect, if a Conservative Government are re-elected, of an extended role for social services departments in dealing with destitution.

Mr. Marlow: Will the hon. Lady give way?

Ms Coffey: I cannot, because of the shortage of time.

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As a civilised society, we should offer refuge to genuine asylum seekers; we must also be aware of our humanitarian responsibilities. Our objection to the Asylum and Immigration Act 1996 is that it used the withdrawal of benefits to establish who was and who was not a genuine asylum seeker. That was always bound to cause undue hardship.

I understand that a further appeal will be made to the House of Lords, and clearly, if the Lords uphold the judgment of the Court of Appeal, the practice will cease to be an option, even for the present Government. We must there- fore consider the best way of giving assistance and benefits to people entitled to them, whatever legislation that process falls under. The assistance must be fair and consistent, and must not carry high administrative costs.

12.21 pm

The Parliamentary Under-Secretary of State for Health (Mr. Simon Burns): I start by congratulating my hon. Friend the Member for Billericay (Mrs. Gorman) on initiating this important debate. I assure the House that I have listened extremely carefully to the variety of points made by my right hon. and hon. Friends, as well as by Opposition Members.

Clearly there will not be time for me to deal with all the points that have been raised. My hon. Friend the Member for Bexhill and Battle (Mr. Wardle) raised several issues concerning the Home Office in connection with immigration and asylum policy, and I shall ensure that his comments are drawn to the ap- propriate Ministers' attention, so that he can be given answers. I shall also write to other hon. Members to deal with any other points that I am unable to raise during the short time available.

I must first make it plain that this Government and this country have a justifi- able reputation for welcoming to our shores genuine asylum seekers escaping persecution and torture. (11) But the escalating number of economic and bo- gus asylum seekers who have come here, not because of persecution but be- cause of the economic situation in this country and the benefits it affords them, has caused great concern.

There has been an abuse of the asylum system, as several of my hon. Friends have said. In 1988 there were 4,000 asylum applications; in 1995, the number had risen to a staggering 44,000. Yet by 1996, as a result of the changes that we made to benefits, it had fallen to 28,000.

Although there was an increase in the number of asylum seekers recognised as refugees--from 628 in 1988 to 2,240 in 1996--the proportion of successful applicants granted refugee status as a result of genuine applications fell from 23 per cent. to 6 per cent.

Mr. Marlow: Will my hon. Friend give way?

Mr. Burns: I am sorry, but I hope that my hon. Friend will understand that I have only seven minutes left.

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As hon. Members will know, asylum seekers who claim asylum at the point of arrival in this country are entitled to social security benefits that cover hous- ing, food and other necessities. Rights to benefits have been withdrawn only from those who claim asylum after they have entered this country. It is those people who now pose such an onerous problem for local authorities.

It is worth looking briefly at how that happened. As some of my hon. Friends have said, the situation arose in early August, when a small number of people who had claimed asylum after entering the country, and so had been denied benefits, approached social services departments for aid. After social services provision was refused, four of the asylum seekers sought judicial review against the local authorities concerned, and an interim court order obliged the local authorities to accommodate them while proceedings were pending.

On 8 October 1996, the High Court ruled that local authorities had a duty un- der section 21(a) of the National Assistance Act 1948 to provide services as a safety net of last resort to those who, by reason of their circumstances, were unable to fend for themselves.

My right hon. Friend the Secretary of State for Health, with the local authori- ties concerned--Westminster, Hammersmith and Fulham, and Lambeth-- appealed against that ruling; the appeal was dismissed on 17 February. We are currently seeking leave to appeal to the House of Lords, because we do not accept that the National Assistance Act should apply to adult asylum seekers who are not elderly, infirm or disabled, and who have no need for community care services.

The judgment has had serious consequences for many social services authori- ties, especially in London. It has imposed a new duty on them to support peo- ple for whom they have never before had to provide services. Although the number of people claiming asylum in this country has fallen since the removal of benefits, thus suggesting that the intended disincentive to economic mi- grants is working, the numbers remain high, and the burden for local authori- ties is substantial.

On 21 February, 3,501 adults were being accommodated by London authori- ties, and at least a further 200 outside London. It is not right that such a finan- cial burden should be imposed on council tax payers, or that services for local people should suffer as a result of the court ruling.

It is precisely because the Government are so concerned about the impact on local authorities of having to house asylum seekers that we are now making a new special grant available to help them to carry the burden. As the House will know, three types of grant are being made available: one for unaccompanied children, one for children accompanied by adults, and the grant for adult asy- lum seekers, which we approved in Standing Committee yesterday afternoon,

That last grant will allow claims from local authorities up to the equivalent of £165 per person per week, averaged over the relevant period, to help meet the costs of those individuals. In addition, authorities will be able to claim up to

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£10 per person per week for documented costs incurred in commissioning new premises for housing asylum seekers.

The local authority associations and individual authorities, including West- minster, were consulted on the details of the grant, and have been given guid- ance on how to claim reimbursement. I certainly accept that Westminster, which has featured prominently in the debate, has a very high number of asy- lum seekers--292 at the most recent inquiry--but it is not alone in that.

Two other London boroughs currently accommodate more asylum seekers than Westminster, and there are about eight authorities with similarly high numbers. We have listened to what they have said, and we consider that the special grant is a fair and reasonable response to their concerns about adults without children.

The House may be interested to know that the figures from the local authori- ties show that most of the London authorities are spending less than the £165 per week that we allow. The sums range from a low, in Ealing, of £90 per week, to a high, in Redbridge, of £164 per week. However, two authorities are excluded from that range--Newham, which says that it is spending £205 a week, and Westminster, which is spending about £226 a week.

It must be borne in mind that Westminster is being charged about £226 a week, and the neighbouring borough, Kensington and Chelsea, which is in many ways a similar local authority, about £119 a week. It would be wrong not to take an average figure rather than giving different amounts to different au- thorities, which would clearly not be any more cost-effective or efficient for the taxpayer. We have no plans to change the existing policy.

Mrs. Gorman: Is my hon. Friend aware that Kensington and Chelsea is giving cash benefits at the moment, which allows it to save about £30 a head? The hon. Member for Stockport (Ms Coffey) seems to agree with me that that is illegal.

Mr. Burns: Our legal advice is that it is illegal, but even--

Mr. Deputy Speaker (Mr. Michael Morris): Order. We must now move on to the next debate.

VAN DIJK3.pdf

5 Ideologies, Racism, Discourse: Debates on Immigration and Ethnic Issues TEUN A. VAN DIJK

Aims

This paper studies some of the ideological properties of political discourse on immigration and minorities in contemporary Europe. It combines results of my current work on the theory of ideology (Van Dijk, 1998a) with those of an earlier large project on the discursive reproduction of racism (Van Dijk, 1984, 1987, 1991, 1993a). More specifically, the framework of this discussion is an international project, directed by Ruth Wodak1 and myself, which examines and compares the way leading politicians in seven EU countries speak and write about immigration and ethnic issues.

Although there are obvious contextual differences between immigration, race relations, and hence between talk about these issues in the various

countries, the overall theoretical framework for their analysis is essentially the same. This framework, which will only be briefly summarised here, combines elements from the following multidisciplinary triangle: (a) an elite theory of racism as a form of ethnic dominance and inequality, (b) a socio-cognitive approach to (racist, nationalist) ideologies and other social representations, and (c) a complex multi-level analysis of text and talk in context, in general, and of parliamentary debates, in particular.

Thus, although file examples analysed in this paper are taken from a debate in the British House of Commons oil asylum seekers, it is assumed that many of the properties of this debate may also be found in immigration debates in other western European countries. Earlier analyses and comparisons of debates on immigration and ethnic issues in Western Europe shows that there are differences of style (e.g., in the UK, France and Germany, MPs may interrupt, heckle and shout, which is much less the case in Spain and the Netherlands), and of nationalist rhetoric (especially in France), but that the main topics, argumentation strategies and especially the standard arguments (topoi) against immigration are very much comparable (Van Dijk, 1993a).

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Another difference exists between countries where immigration has been taking place for some decades now (the UK, France, the Netherlands), and the countries where immigration is a more recent phenomenon (Italy, and especially Spain). In the first group of countries, issues of affirmative action, integration, minority policy, and other topics related to multicultural societies are more prominent. In the latter countries, the main topic and concern of talk is still often that of new Immigration and their reception and integration. In the countries where large-scale immigration goes back several decades, there are also MPs who belong to immigrant communities (see also Hargreaves and Leaman, 1995). Common to nearly all cuuntrics is the current preoccupation with a flood of asylum seekers, a topic common to many debates in most western European countries.

Discourse and Racism

The issue to be theoretically dealt with here is the relations between ideology, racism and discourse. My first thesis about these relations is that both racism and ideology are prominently reproduced by social practices and especially by discourse. I am interested in these processes of societal reproduction and how exactly text and talk are involved in such processes. More specifically, from a more critical perspective, I want to know how discourse reproduces systems of dominance and social inequality, such as racism. That is, the broader framework of my investigation is the type of analytical discourse research now commonly designated as critical discourse analysis or CDA (Fairclough, 1995; Fairclough and Wodak, 1997: Van Dijk, 1993b).

This way of framing my problem entails that I do not equate racism with ideology, as is often done in the literature (Miles, 1989). Racism does have an ideological basis, but cannot be reduced to it alone. As a form of dominance and social inequality, racism also needs to be defined in terms of various types of social practice, such as discriminatory discourses and other acts of interaction, at the micro-level. At the same time it requires analysis at the macro-level, through analysis of institutional arrangements, organisational structure, and group relations of power abuse (for details. see the vast literature on the social and institutional dimensions of racism, e.g., Essed, 1991; Feagin and Sikes, 1994: Marable, 1995; Omi and Winant, 1994; Solomos and Wrench, 1993: Van Dijk, 1991, 1993a; Wellman, 1993).

Although I do analyse ideologies in terms of the social cognitions of social groups, other social representations are also involved in this cognitive domain of analysis, such as knowledge, opinions or attitudes (such as preju- d i c e s ) ( D o v i d i o a n d G a e r t n e r , 1 9 8 6 ; F i s k e a n d T a y l o r , 1 9 9 1 ; F a r r a n d

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Moscovici, 1984: Spears, Oakes, Ellemers and Haslam, 1997). Moreover, whereas this is true for groups, 1 finally also needed what I term mental models of individual group members in order to be able to account for individual discourses and acts of discrimination, and hence for personal variation in the social system of racism.

In sum, racism is a complex system of social inequality in which at least the following components are combined:

a) ideologically based social representations of (and about) groups b) group members mental models of concrete ethnic events

c) everyday discriminatory discourse and other social practices d) institutional and organisational structures and activities e) power relations between dominant white and ethnic minority groups.

Without this complex, multidisciplinary framework it is impossible to understand many of the structural and functional properties of talk in western European parliaments, as I analyse it below. Not only is it necessary to describe what parliamentarians say in such debates, and how they do so, but also why these political elites speak the way they do, and what functions such properties have in the overall system of racist inequality characterising western European societies and their ideological underpinnings.

However, space limitations allow only the highlighting of some of these features of racism and its reproduction, namely the relations between racist cognition (ideologies, representations and models) on the one hand and (political, parliamentary) discourse on the other.

Ideology

The multidisciplinary theory of ideology that inform this analysis of parlia- mentary debates, is markedly different from prevailing, largely sociological, political-economic and philosophical approaches (see Larrain, 1979; Eagleton, 1991). Instead of using vague notions such as prevailing ideas , belief systems , or (false) consciousness , as they are used in the traditional litera- ture, I propose to found a new theory of ideology based on a more explicit socio-cognitive theory, in which ideologies are first defined as fairly general and abstract mental representations which govern the shared mental represen- tations (knowledge and attitudes) of social groups. Second, the societal dimen- sion of the theory makes explicit which groups, group members, or institutions, are actually involved in the formation, confirmation, reproduction, or change of such ideologies. As is the case for the reproduction of racism, I

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assume, for instance, that specific elite groups, such as politicians, journalists, teachers, scholars, and their institutions, are greatly involved in this process of ideological reproduction. Third, as suggested above, I assume that these societal (and historical) processes of ideological formation and change are enacted by group members through social practices in general, but especially in many forms of institutional talk and text (for detail, see Van Dijk, 1998a).

To summarise this complex theory of ideology as a form of social cognition, I highlight the following main points (see Figure 3.1); some details will be elaborated later where I deal with racist ideologies and discourse.

(1) An ideology is a type of belief system (Seliger, 1976). This implies that they should he characterised in cognitive terms, and not be contused with, or reduced to. Social practices or discourses, or societal structures of any kind. One may however say that such practices or discourses are expressions or enactments of underlying ideologies.

(2) Ideological belief systems. however, are at the same time social, and defined for social groups, and hence are forms of shared, societal cognition (Fraser and Gaskell, 1990). Although individuals, as group members, may have ideologically based opinions, ideologies as such are not individual.

(3) Unlike classical theories of ideologies, I do not assume that ideologies are limited to dominant classes, groups or formations; dominated groups may have, for example, ideologies underlying their resistance to domination. Under specific social conditions, any social group or social movement may develop an ideology.

(4) In addition to organising the shared social representations and social identity of a group, ideologies control intra-group action and cooperation, as well its inter-group perception and interaction of group members.

(5) Ideologies are not just any kind of socially shared belief systems, but should he located it a more fundamental or basic level. They are less specific than. for instance, social attitudes (e.g., about abortion, the death penalty, or immigration), but form the axiomatic basis of numerous attitudes and much knowledge (about various social domains) as shared by group members.

(6) I distinguish between group knowledge, that is, beliefs held to be true by a group according to its own truth criteria, and the more general, culturally shared knowledge that is taken for granted, undisputed, and generally (and discursively) presupposed, across groups, within a given culture or historical period. I call this latter Kind of knowledge common ground knowledge. Of course. what counts as knowledge within one group may be seen as ideo- logically based beliefs from the perspective of another group. Similarly, common ground beliefs may. and generally will, alter over time (and may even be reduced to the knowledge of specific groups), whereas the knowledge of specific groups (e.g., scholars) may later enter the common ground.

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(7) The essential distinction between group knowledge and cultural common ground knowledge allows us to distinguish between ideological and non-ideological beliefs in u given culture. Thus, contrary to most other approaches, I hold that beliefs which are taken for granted and undisputed within a given culture are by definition not ideological within that culture (they may, of course, later or from another perspective be seen as ideological). In informal terms: ideologies presuppose competition, conflict, struggle, or differences of opinion and knowledge between groups.

(8) Ideologies are themselves constituted by basic propositions that represent what is good or bad for the group. They are, thus, based on the values and norms that each social group develops or borrows from more general cultural values (freedom, liberty, autonomy, truth, reliability, etc.).

(9) Virtually neglected in traditional approaches, a socio-cognitive theory of ideology also focuses on the internal structures or organisation of ideologies. Discursive and experimental evidence suggests that ideologies tend to he polarised, e.g., as propositions about Us and Them, as is also suggested by the conflictual or competitive social basis of ideologies.

(10) I go on to assume that ideologies are organised by fixed ideological schema gradually learned and applied by social actors during their socialisa- tion and identification with various social groups. Categories in this schema are, e.g., membership criteria, typical actions, goals, norms and values, group position (relations with other groups), and specific group resources. These categories and their contents are some kind of group self-schema, defining the basics of their socio-cognitive identity.

(11) Ideologies along with the knowledge and attitudes they control are general, social, and shared by group members. However, ideological practices and hence discourses are engaged in by individual group members and in specific social situations, and are therefore unique. To describe and explain that uniquenses, I therefore need a cognitive Interface between social repre- sentations of groups and real action, the text or talk of individual social actors, namely mental models. These models are subjective representations (in episodic memory) of specific events or situations in which or about which social actors communicate or act (John son-Laird, 1983; Oakhill and Garnham, 1996: Van Dijk and Kintsch, 1983).

(12) Because mental models not only feature biographical representations of personal experiences but also instantiations of shared social, representation, Ideologies may indirectly influence mental models. Because such biased

mental models are the cognitive structures on which social practices and discourse are based, this brings me, finally, to an explicit way of relating ideology with text and talk.

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Figure 5.1 Political Discourse and Political Cognition

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(13) Discourse is not only based on mental models of events that people think or speak about, but also on mental models of the communicative situation in which they speak, write, read or listen (Van Dijk, 1998b). It is this personal, subjective representation of the relevant features of the social situation that defines the notion of context. In other words semantic event models and pragmatic context models together give shape to the contents and variable Structures of a discourse in production or to their appropriate understanding in comprehension. Since context models may also be ideologically influenced (e.g., in the ways interacting participants are represented as own or other group members), also contextually controlled structures of discourse may be ideologically based.

Taken together. this provides the cognitive basis for a theory of contextualisation.

Racist Ideologies

Given this theoretical framework, it is possible to make the next step, and examine the nature of racist ideologies (for some earlier studies, see Barker, 1981; Guillaumin. 1973; Jager, et al., 1998; Yeboah, 1988). How do the main properties of ideologies distinguished above specifically apply to a theory of racist ideologies? Again, I merely summarise the main features (and problems) of such a theory.

(1) Since, by definition, each ideology must be shared by a social group, racist ideologies would be based in the group of racists. For several reasons, however, this group is not only ill-defined, but its identification would also imply a clear distinction between racists and non-racists, a distinction which is highly problematic. Moreover, this group is hardly self-defined as such. I therefore prefer to speak of racist practices that some members of a group (e.g., white Europeans) may engage in more or less frequently or intensely. In other words, the social basis of racist ideologies is a problem that requires further analysis.

(2) For the same reason, the definition of the ideological self-schema of racists

is problematic, because racists seldom identify themselves as such. However, even when not self-categorised as racist, this self-schema may feature the following categories: (a) member-ship devices: by color, race or nationality, e.g., We white people , We Dutch people , etc.; (h) activities: racist practices/discourse (talking negatively about minorities, discrimination, differentiation, exclusion, inferiorisation and problematisation etc.); (c) goals: Keeping them down and out. ; (d) values: e.g., the purity and priority of the own

group; (e) position: superiority and dominance over Others; (f) resources:

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our territory. space, nation, and white color, and preferential access to all social resources.

In other words, for those group members (e.g., white Europeans) who have an ideology featuring these categories and their related propositions, one would say they have a racist ideology (whether or not they identify with such a group or self-define themselves as such).

(3) Racist ideologies govern other shared social representations, and espe- cially racist attitudes, prejudices, etc. (Dovidio and Gaertner, 1986; Spears, Oakes, Ellemers and Haslam, 1997). These attitudes are negative opinions on (the role of) minorities in various social domains: immigration, housing, welfare, work or education. Given the structure of the ideological schema above, thus, we may expect that for each of these domain we find specific opinions such as keeping Them out of the country, the neighborhood, the job, etc. or assigning priority to ourselves in situations in each of these domains.

From Racist Cognition to Racist Discourse

As suggested before, these underlying racist ideologies and ideologically controlled ethnic/racial prejudices may finally be expressed in text and talk. For concrete news reports, everyday stories, or discussions about specific cases, this will occur on the basis of ideologically controlled mental models, that is, by biased definitions of the situation (for concrete case studies, see Jaiger. 1992; Wetherell and Potter. 1992; Van Dijk, 1984, 1987, 1991, 1993; Wodak, Nowak, Pelikan, Gruber, de Cillia and Mitten, 1990). Ideological propositions may also be expressed directly as generic expressions about Us and Them in other discourses, especially those of politics, scholarship and education. Us and Them may be the prototypical form for racist propaganda.

Given these underlying structures, and those of specific contexts that may favor or prohibit them (the same people will speak differently about the Others in different situations), we may expect overall interactional, pragmatic, semantic and stylistic strategies that select or emphasise positive information about Us, and negative information about Them (or avoid negative information about Us and positive information about Them). The effects of these ideologically based strategies of positive self-presentation and negative Other-presentation may he observed at all levels of discourse structures in text and talk. Conversely, these biased discourse structures may in turn lead to the desired biased mental model about ethnic events or general representations about ethnic minorities among the recipients. Note again, though, that, both in production and in comprehension, this process of racist discourse processing i s a l s o a f u n c t i o n o f c o n t e x t . S o c i a l d o m a i n , s e t t i n g , s i t u a t i o n , o t h e r

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participants as well as their roles and goals, among other things, may favor, modify or block racist text or talk.

Parliamentary Debates

Parliamentary debates are also partly defined by their complex, institutional context. Indeed, people sometimes say something very similar in other genres (in e.g., a school textbook, the classroom, or the media), for instance, about immigration, but only when uttered in the context of a congress or parliament will such discourse be part of a political debate.

Many of the structures of a political debate are a function of the context: Chair-controlled allocation of speaking time, duration and order, as well as interjections; the reading of prepared speeches; a strict etiquette of address; formalised rules of interjection; as well as overall strategies of persuasion, and the polarisation between government and opposition speakers.

According to my theory and analysis, parliamentary debates are, by definition, ideologically based. MPs do not speak as individuals but as group (party) members. Parties are the quintessential ideological groups, because party formation is largely ideological. This (theoretically) implies that contri- butions to a debate are a function of the ideology of the party as interpreted by the speaker. In other words, the social representations of MPs are one of the cognitive categories that form part of a context (that is, of a context model).

This is not merely an analytical category, but also a practical, group member s category. Not only will MPs express (intentionally or not) their ideologically based mental models of a particular event (e.g., the immigration of asylum seekers), but other MPs (and the public) will typically hear such discourse as partisan and hence as ideological. Conversely, parliamentary discourses may also contribute to the changes of context, such as the relations between groups, for example, between government and opposition parties.

I will show that even such variable forms as those controlled by the context may have an ideological basis, much the same as for the variation between the emphasis on Our good actions as opposed to Their bad ones. Indeed, as mental models, contexts by definition inherit some of the ideological orientations of the group with which the speaker identifies (e.g., MP Conservative, male, white. Western, middle class, representative of a specific region, etc.). Such ideological mental models will, therefore, also control the ideological structures of a discourse. Members of dominant elite groups, for

instance, may use specific language forms to derogate dominated minority groups or to highlight their own expertise, credibility, moral standing or status, as well as doing the same for their constituents, their fellow MPs or the media.

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Semantics

Members of a parliament engage, then, in parliamentary debates constrained by the context models they have for each specific session of parliament. They do so first by selecting or constructing relevant propositions from the event models, that is, of all they know about an issue, such as the immigration of a specific group of asylum seekers. Of course, given the severe time constraints, only small fractions of event models may he relevant for expression. Generally speaking, each selection of meaning should be a function of the political aims, rules or strategies that define a debate. Among other things, they should enhance the role and prestige of the speakers or their parties. Thus, polarisation of content may be the result. Moreover, both the global and the local meanings of a discourse should he a function of the politico-ideological aim of the debate as a whole, such as legislating on immigration restrictions.

The global topics of parliamentary debates are often stereotypical (see also Reeves, 1983; Van Dijk, 1993a): (a) Some social or political phenomenon has been noted, and will now be defined according to the ideology of the speech participants, usually as a problem. Such problem-definitions of the situation may also apply to current policies (or Bills of law) of the government. (b) The (usually negative) consequences of such events (or policies) will be spelled out if no appropriate action (policy, legislation) is taken. (c) Government parties or, more critically, opposition parties will positively or negatively deal with current policy and action. After examining the problems associated with such actions in the past, or conversely having blasted the policies of the opponents, they will make proposals and argue for an expedient policy, extolling the beneficiary consequences of new policies or laws or of the actions recommended by an MP. At this stage, other MPs may intervene or interrupt. and participate in the ensuing debate.

The semantic macro-structures (thematic Structures) that organise such debates are (mentally) selected from event models providing a speaker s definition of the situation. In principle, these main topics are selected from the high-level propositions of the speaker s models, but in some cases, for contex- tual (political) reasons. lower level details may be focused on, for example, when these are detrimental to the opponent(s) (the government and/or opposi- tion party, or a particular politician). Such biased definitions of the situation (event models) are based on attitudes and hence are ideological; indeed, the contextually monitored selection of main topics of a debate may also be ideo- logically controlled. For example, if current or proposed legislation harms asylum seekers, anti-racist speakers may apply their anti-racist ideologies and attitudes to call such legislation racist . This may also apply to apparently irrelevant details, such as with whom an MP has been seen having lunch.

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In general, then, ideological topic selection in most discourse, and even more so in parliamentary discourse, will involve the selection of any topic that contributes to the formation of positive models and social representations of the ingroup, and negative ones about the outgroup (that is, the opposition or the outgroup(s) under discussion, for instance, asylum seekers).

An Example

Let us examine a concrete example. On March 5, 1997, a debate on asylum seekers was held in the British House of Commons. The debate was initiated by Mrs. Teresa Gorman (Conservative MP for Billericay, Essex), who sets out to speak about what she defines in her own words as:

the particular difficulties faced by the London boroughs because of the problems of asylum seekers. (P1)

As expected, a phenomenon (arrival of asylum seekers) is introduced, defined as a problem, and its consequences for Us are highlighted. Note that the ambiguous phrase problems of asylum seekers for her clearly means the problems caused by asylum seekers . Her speech in no way expresses any understanding for the problems experienced by asylum seekers.

Other topics (macro-propositions) that control her speech are:

a) We Should distinguish between genuine and bogus asylum seekers. b) A recent document found that asylum seekers cost 200 million pounds per year. c) London ratepayers should not have to pay For this. d) Many asylum seekers are illegal immigrants. e) Previous legislation [by the Conservative government] cut benefits and thus halved the number of bogus asylum seekers. f) Current proposals for legislation [by the Labour opposition] aim to reverse these measures, and will cause massive immigration of asylum seekers. g) Recent court decisions have resulted in many millions of extra expenditure for London borough councils. h) Some illegal immigrants cost the taxpayer a lot of money. i ) Some illegal immigrants are involved in crime. j) Especially the borough of Westminster has to pay a lot lox- everyday living costs of asylum seekers. k) Contrary to what is sometimes said, people in Westminster are not very rich. l) Elderly people should not have to pay for the up keep of illegal asylum seekers. m) Some bogus asylum seekers are playing the system . n) This is 1 national problem, and these costs should be paid nationally.

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o) Many people in Westminster have below average incomes. p) Asylum seekers go through many appeals against deportation. q) The opposition should he serious and not change current legislation.

Further reduction of these topics would globally define the topics of her speech as:

A) Many asylum seekers are bogus and break the law. B) We (Westminster) have no money to pay for them.

C) The law that reduced the number of bogus asylum seekers should not be abolished.

It is obvious from this example that the selection of topics is ideological, and controlled by racist attitudes about asylum seekers (from outside of Europe) being bogus, frauds, and criminals. Throughout her speech thus, also at the local level, Mrs. Gorman will enumerate examples and engage in descriptions that are negative about asylum seekers.

However, following the positive ideological self-image, she presents Us first as MPs who should uphold current law, second as caring representatives of a constituency (Westminster), and third, more implicitly as (white) English threatened by massive immigration ( this would open the floodgates again ).

Part of the strategy of positive self-presentation is in the form of a number of pity-moves, in which poor elderly (British) people are contrasted with bogus asylum seekers who play the system . The racist nature of her speech (its tile next speaker also points out) is exclusively based on a focus on a few negative examples, and the whole orientation towards asylum seekers as causing problems and difficulties, costing lots of money, or entering the country illegally.

Her main Labour opponent, Jeremy Corbyn (Islington South, a seat in London) on the other hand, speaks from the position of an anti-racist, humanitarian ideology. The topics of his speech include:

a) We should think about why people seek asylum. b) The Geneva Convention guarantees people a safe place. c) Britain has few asylum seekers compared to other countries, especially outside of Europe. d) It is an exaggeration to say that we have too many asylum seekers. e) Asylum seekers, who already have terrible experiences, now also have these in Britain, IN, instance, gained front the immigration authorities and tile police. f) Seeking asylum is becoming increasingly difficult. g) Many people. also Conservative MPs, have negative opinions about them.

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h) (Replying to interruption) People seek asylum like those who fled from Nazi Germany. It is nonsense not to admit them. i ) Asylum seekers are destitute Mid should he able to live during appeal processes. j) We should listen to tile terrible experiences of asylum seekers. k) Many regimes (also democratic ones in Eastern Europe) violate human right. l) (After an interruption by Mrs. (Gorman claiming that over 90 per cent of asylum

seekers are not genuine) Some democratic regimes violate human rights and thus cause people to flee, for example, Kurds from Turkey or people from (the) Ivory Coast.

m) Many people here do not listen to the terrible stories of asylum seekers. n) In Britain many asylum seekers are put in prisons. o) Many asylum seekers are badly treated. p) That there has been a hunger strike by asylum seekers shows that their problems are serious. q) This is a blemish on the Human Rights record of Britain. r) Asylum seekers should be helped and respected. s) The Government s regime creates serious problems for asylum seekers. t) Many (e.g., Churches) have been protesting against imprisonment of asylum seekers. u) Why does tile British government not protest against human rights violations in

many countries

Obviously, these topics derive from a definition of the situation (a mental model) of asylum seekers in Britain that is ideologically opposed to that of the previous speaker. Instead of presenting asylum seekers in a had light, their plight is highlighted, and the British authorities and Government are criticised for their policies and actions. The basic ideological and attitudinal propositions expressed here deal with the imperative of international law, with Human Rights principles, and with humanitarian principles to help those in need. The main value expressed here is that of Solidarity with the oppressed. The complex attitude that inspired the mental model being conveyed here features propositions about how asylum seekers are tortured and otherwise persecuted by oppressive regimes; how in this country they are badly treated by immigration authorities and police, as well as being put in prison: and how they need financial support to live. The specific model of the current situation features further details about specific countries, and specific events y hunger strike) and examples of had treatment.

That is, the selection of topics is largely controlled by an ideologically based model and general social representations organised by anti-racist and humanitarian ideologies, as they are usually associated with a more progressive (Labour) position.

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Local Semantics

Ideologies not only monitor the overall, global meaning (or topics) of discourse, but also their more local meanings, as they are actually expressed in, and implied by a debate s words and sentences. Again, in the racist contribution to this debate, we may thus expect to find many concrete examples of negative Other-presentation.

A first move of this strategy is to properly define or categorise the relevant sub-groups, namely as bogus asylum seekers and as genuine ones respectively (numbers indicate paragraphs from which the examples are taken):

There are, of course, asylum seekers and asylum seekers (P2) Genuine asylum seekers... [vs.] ...economic migrants... benefit seekers on holiday (P2-3)2

alleged asylum seekers (P16) Genuine applicants ...are frustrated and suffer from delayed applications because of those who arc not genuine (P47)

We see in the last example that the negative presentation of bogus asylum seekers is enhanced by emphasising how they hurt genuine ones. This is also part of the strategy of positive self-presentation, because it implies first that We are not simply against (all) asylum seekers, and secondly that We care for the genuine asylum-seekers.

Once the bogus asylum seekers are properly defined, identified and categorised, the speaker will proceed to describe who the bogus asylum seekers are and what they do, thereby focusing first on the problems and difficulties They create, and what they cost Us:

[dil-ficullies1 ...because of the problems of asylum seekers (Pl ) the burden of expenditure that those people are causing (P3) £200 million a year cost ...[that] would again become part of the charge on the British taxpayer (P6) The problem of Supporting them (P8) She coat the British taxpayer £40,000 (P14) at tile expense of the British public (P14) an enormous financial burden on the tax payers (P15) [they have to be housed in] expensive accommodation (P16)

Note the important strategy of creating a pitiful image of poor, old ratepayers, in order to emphasise that these cannot share the burden, a populist

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move which also enhances the problematic nature of immigration, and further contributes to the positive self-image of the speaker as caring for poor old people. Note also the semantic ploy of using presuppositions. That immigrants cause difficulties It is not asserted as a debatable opinion, but simply presupposed as a fact.

The next move in the strategy of negative Other-presentation, appears in the following examples, where the Others are seen to break Our norms, if not the law.

[A man from Romania] He has never done a stroke of work in his life (P22) [they] are milking the social services (P23) playing the system ...addicted to the social services (P24) racket of evading our immigration laws (P7) I am sure that many of them are working illegally, and of course work is readily available in big cities (PR) She was arrested. of course, for stealing (P14) or to do a bit of work on the black economy (P18)

Note that where the speaker refrains from (over)generalising, she uses vague quantifiers such as many . Obviously, the attributes focused on here are consistent with prevailing stereotypes and prejudices about Others (especially non-Europeans): They are lazy, They cheat, They abuse Our system, They work illegally and They steal. Basically, They violate Our laws and morals. That is, racist ideologies articulate evaluations in relation to Our values, and will derogate the Others its fundamentally different from Us: They are inconsistent with whatever We stand for.

On the other hand, positive self-presentation of the (British) ingroup may be observed in the following example. of which the first, positive part introduces a disclaimer:

The Government are keen to help genuine asylum seekers, but... (P7)

Apart from emphasising the good We do for Them, part of this strategy of positive self-presentation is a move that euphemises Our less positive actions:

To discmn-age the growing number of people from abroad (P2)

Severe restrictions on immigration and institutional harassment of asylum- seekers are thus mitigated by the word discourage .

Another well-known move in the strategy of positive self-presentations is what we may call the move of apparent empathy. Here the speaker seems to

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show positive feelings about a group, but (as is usual with disclaimers) this is followed by but and u statement that implies something negative about Them:

I understand that many people want to come to Britain to work, but there is a procedure whereby people can legitimately become part of our community. (P4)

Note that in this example another ideology is emerging, namely that of legalism: Whatever happens, the law must be respected, including the rules and regulations of immigration. This formulation also implies that when the Others break those laws, We can legitimately take action against Them.

To avoid allegations of bias, speakers routinely engage in the well-known apparent negation disclaimer, which begins by denying a negative self- characteristic, but continues negatively about Them:

I did not say that every eastern European s application for asylum in this country was bogus. However... (P46)

The point of analysing these semantic moves is to show that all properties of meaning of a discourse may be affected by some ideological component of event and context models. Positive self-presentation applied to speakers and their groups is n strategy that is based on context models, and aims at manag- ing the impression speakers make on recipients. Thus, avoiding an impression of being racist can only be explained in terms of social representations and ideologies speakers have about racism representations which may, of course, he inconsistent with those of a critical recipient or analyst, who may see such denials of racism precisely as a marker of racist speech.

Anti-Racist Ideology

Note that what has been argued for local semantics as controlled by racist ideologies, also applies to the influence of anti-racist, humanitarian, and other progressive ideologies. Here, instead of negative Other-presentation, one would expect to find various moves of positive Other-presentation, as well as genuine empathy with and sympathy for asylum seekers:

It is a major step for Someone with legitimate fear to seek refuge in exile (P34) They are now living [al life of virtual destitution (P41 ) I wonder whether those who make decisions on refusing people asylum... have ever taken the trouble to sit down and listen to the stories of the people who have

Comparative Perspectivas on Racism 107

been tortured and abused (P52) It is difficult for people to talk about torture experiences (P53) Hon. Members Should stop and think for a moment about the circumstances of those who cone to this Country seeking asylum (P55)

Similarly, various semantic functional relations are to be expected, such as the provision of more general examples, for instance, about the terrible plight of asylum seekers:

[No MP] has been woken up by the police at 4a.m., taken into custody (P34) If one has grown up in Iraq and has always been completely terrified of anyone wearing any type of uniform (P35) areas of oppression (P39) summarily imprisoned... beaten up etc. (P40) We Should consider the experiences of people who have fled countries (P40). [in the Ivory Coast] they crushed trade unions and they crushed student opposition, sending troops into various universities (P50)

Note that these examples are not merely expressions of knowledge about the horrors asylum seekers have lived through. They are also selected and formulated as a function of underlying ideologies and social representations, for instance, critical ones about the police, the military, or oppressive regimes. Harassment, imprisonment, beatings, and crushed oppositions are part and parcel of the social representations of oppressive groups or institutions, as organised by a progressive, anti-racist or humanitarian ideology. Such negative representations may also be relevant for self-critical discourse about Our people, institutions or country:

In the United Kingdom there has been a systematic erosion of people s ability to seek asylum (P3(,) The UK, for example, prides itself on its close relationship with Turkey, yet... (P49) Almost uniquely among European countries, this country routinely puts in prison people who seek asylum (P54) In this country, people who say that get routine abuse from Home Office Ministers and Conservative Members (P55) The Government s regime on asylum seekers is creating a serious situation (P57)

Note though that the ideological polarisation between Us and Them in these anti-racist examples is not between Us-English and Them-Foreigners, but presupposes a split within the ingroup itself. The speaker is not merely

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exercising self-critique, but criticising ideological Others, namely Conserva- tives. The political implications of such ideologically based accusations are obvious when conveyed by a Labour speaker attacking a previous Conservative speaker.

As is the case in racist negative Other-presentation, also the Conservative Others in anti-racist talk are represented as violating basic rules, norms, principles or values, such as the norm (in fact: a law) that asylum seekers cannot be sent back, the norm not to have close relationships with oppressive regimes (a corollary of a democratic ideology), that innocent people should not be put in prison, that power abuse by the authorities is wrong, and so on. In other words, from another perspective, such anti-racist discourse presents the Conservative Others as violating the Moral Order, specifically all the principles of Human Rights. In the following examples, several of these principles are formulated even more explicitly:

I suggest that he [the MP] start to think more seriously about human rights issues (P38) [on benefit rights] Not to do so is a gross abuse of individual human rights (P39) democracy does not always follow multi-party elections (P49) Is that how a democratic Government should behave? (P50) Attitudes towards asylum seekers need to be changed (P56) Routine imprisonment should end (P56) I hope that we shall recognise that we should have a slightly more humane approach towards asylum seekers in this country (P57) Europe must stop its xenophobic attitude towards those who seek a place of safety here and adopt a more humane approach (P58) Where is the outright condemnation from the Government of the denial of human rights in ...It seem [s] more interested in trade and selling arms to those regimes than in defending human rights (P59)

Although specifically applied to the present, British, situation, these are examples that could easily be drawn, almost directly, from anti-racist and democratic social representations and ideologies. That is, they are generic statements, and not specific ones based on unique personal mental models. Apart from being critical statements, these examples are, at the same time, moral imperatives and exhortations. Finally, such critique may, of course, explicitly address the Others in terms of racist accusations:

There also has been a vindictiveness against asylum seekers it has been parroted in this debate by some Conservative Members which has been promoted by some newspapers, particularly the Daily Mail (P36)

Comparative Perspectivas on Racism 109

The Hon. Lady seems to have moved on a bit from the cant and prejudice that she Produced in her earlier speech.

Rhetoric

What is true for semantics also holds for rhetorical semantic figures. Generally, emphasising and de-emphasising meanings are rhetorical operations conventionalised as figures of speech such as metaphor, hyperbole, euphemism, and the like. It is, then, not surprising that the choice of such semantic figures of speech is also controlled by underlying ideological models, social representations, and ideologies. Thus, in order to emphasise the threat of immigration, the speaker will hyperbolically refer to opening the floodgates (P6). Similarly, the negative characteristics attributed to the Others may be enhanced by specific metaphors, such as:

they are milking not only the taxpayers but the caring services (P23) They are simply parasites (P24)

Especially the implicit comparison of outgroups with threatening or disgusting animals, like parasites in the last example, is a standard meta- phorical way to derogate minorities, and was also a familiar ploy in Nazi propaganda about the Jews.

Such metaphors are not merely discursive, rhetorically persuasive ways of expressing properties of mental models. They may be associated more deeply with thought and judgment. If minorities or Others are thus associated with proper-ties of animals, we may assume that also the social representations about minorities are connected to representations of animals. This may imply that in racist ideologies, the Others are basically also represented as less human, so that attitudes and specific models about minorities will tend to be associated with the appropriate animals: If asylum seekers are seen as a threat, they may he thought of in terms of threatening animals, and if they are seen as living off us , then parasitic animals may be the appropriate cognitive

association. In other words, ideologies may influence the very thought processes that underlie discourse.

Another well-known rhetorical ploy found in discourse on immigrants and minorities is the mmther,s game: the use of figures to speculate about the number of new people entering the country. Often used by the press, its further rhetorical function is usually the same as that of hyperbole, i.e., by emphasising the numbers of immigrants (or what they cost) their threatening or problematic nature may be enhanced:

Comparative Perspectivas on Racism 110

There are about 2000 families... the cost is estimated to be 2 million a year... but for London as a whole...£140 million a year (P10). Over 90 per cent of People who claim asylum turn out not to he genuine (P47)

In sum, throughout the discourse, and at all levels, structures, strategies and moves are all geared towards the most effective expression and persuasive communication of ideologically based mental models and social representations. Whatever else may be said, the overall strategy is to present the Others, or their arrival and immigration, in a negative light.

The choice of these negative characteristics may be ad hoc, and tied to a unique model, but often it is controlled by the contents of ideological stereo- types, prejudices and ideologies. The same is true for the representation of Us, or the relation between Us and Them. For instance, also in this speech, We (or at least some of Us) are represented as victims of the asylum seekers, and more generally, if the flood of asylum seekers does not threaten to drown us, they are, at the very least, a financial burden to us.

All these meanings derive from socially shared representations about minorities and immigrants, and are not merely the unique, contextually specific constructions of an individual speaker. And since many recipients share these representations, such discourse will also be eminently recognis- able, and thus very likely to coincide with and the ethnic prejudices and ideologies that recipients may already have, or otherwise persuasively Contribute to their development.

The same is true, mutatis mutandis, for the representation of the Others (the Conservatives) in dissident, anti-racist text and talk. Violating human rights and the moral order will be similarly emphasised. In the following concrete example, metaphor, hyperbole, and comparison are used to emphasize the negative actions and policies of the Conservative Government:

Britain has among the smallest number of asylum seekers of any European country (P33) Many people sought asylum from Nazi Germany (P38) History shows that unless we stand up for human rights ( P59)

Argumentation

Of the many properties of parliamentary discourse, argumentation structures are paramount. That ideological positions are defended and attacked can be seen in the discursive moves which are made, some of which have already been examined above in terms of semantic or rhetorical structures. Globally, the Conservative argument is that the uncontrolled immigration of the many

Comparative Perspectivas on Racism 111

bogus asylum seekers places a financial burden on the community, and that therefore the current restrictions should remain in place. Conversely, the Labour argument is that international legal, and moral imperatives do not allow us to prohibit asylum seekers to enter the country, and that therefore the restrictive law should be changed.

These global arguments and conclusions are supported, more locally, by a host of specific arguments. These provide, for example, evidence that many asylum seekers are indeed bogus , abuse the system, and break the law, as well as why (London) councils cannot bear the financial burden, or, con- versely, that those refugees applying for asylum are doing so legitimately. Similarly, restrictions on immigration are judged untenable on account of basic Moral principles of human rights, as well as international law. Where the Conservative argument makes a rational appeal to practical consequences, lack of money, as well as more principled arguments that our poor and elderly should not bear the brunt of the cost of taking care of asylum seekers. Again, these arguments all derive from general social representations of asylum seekers, oppressive regimes, the elderly, and so on.

More specifically, we find argumentational moves, characteristic of discussions about immigration, on both sides of the debate (sometimes criti- cally categorised as fallacies). Thus, both main speakers will have recourse to arguing by authority. The Labour speaker supports his argument with recourse to the moral authority of, e.g., Amnesty or the Churches:

the opportunity to read (he papers from Amnesty International or from Helsinki Watch (Pd2) The Churches Commission for Racial Justice... (P58)

On the other hand, the Conservative speaker refers to the conclusions of a bipartisan (and hence not-partisan) committee that established the costs of receiving asylum seekers.

Similarly, both speakers will make appeals to the emotions of the recipients by starkly emphasising the situation of those they speak for, viz., the elderly, poor tax payers, and asylum seekers, respectively:

Many of these people live in old-style housing...They are on modest incomes. Many of them are elderly... with a little pension from their work. They pay their full rent and for all their own expenses (P21) I wonder whether those who make decisions on refusing people asylum ...have ever taken the trouble to sit down and listen to the stories of the people who have been tortured and abused (P52)

Comparative Perspectivas on Racism 112

A typical fallacy of racist argumentation is to generalise from single

examples, as also the Conservative speaker does when she gives concrete examples in order to claim or imply that asylum seekers are lazy, or criminal:

[A man from Romania] He has never done a stroke of work in his life (P22) She was arrested, of course, for stealing (P14) Similarly, racist discourse will attribute negative characteristics to Others, typically by arguing from impressions and not evidence: I am sure that many of them are working illegally, and of course work is readily available in big cities (P8)

Conversely, the anti-racist speaker will typically resort to the ad hominem argument of accusing the conservative speaker of racism.

These few examples of argumentational moves (there are many others not dealt with here), also show that the nature of argumentation is ideologically controlled. It is true that, whatever the ideological position of speakers they may have recourse to the same types of moves and strategies. Both sides of a debate may exaggerate, use populist arguments, appeal to emotions, or invoke authorities when arguing. In that respect, argumentation, just like other discourse structures, is ideologically neutral. However, the specific contents being chosen for arguments and conclusions are obviously ideologically based, for example when the Conservative speaker sets out to prove that asylum seekers break the law and our norms, or when she argues that poor ratepayers should not have to pay for able asylum seekers.

On the other hand, there are also argumentational strategies and moves that, as such, appear to be more typical (though seldom exclusively so) of conservative, progressive, racist or anti-racist speakers. Racist discourse typically engages in unwarranted generalisations from individual negative examples of an immigrant breaking the law or violating Our norms. Thus, a fallacy is the argumentational counterpart of the cognitive fallacy of generalising from models to social representations, as is the case for prejudice formation. Conversely, anti-racist discourse may resort to ad hominem arguments, attacking speakers as racists instead of arguing against their positions.

Conclusions

This paper has explored some of the relations between discourse, racism, and ideology. Within the framework of a new theory of ideology, I have argued that ideologies should be properly analysed in terms of social cognition, and

Comparative Perspectivas on Racism 113

may he defined as the basic structures which organise the social repre- sentations of a group. In this respect, ideologies differ from the general, culturally-shared common ground of undisputed knowledge and attitudes. Ideologies may be represented in terms of ingroup self-schemata, featuring categories that define the basic characteristics of a social group, such as their membership criteria, activities, goals, norms, values, relations to specific other groups, and resources. Ideologies, and the social representations organised by them, may become specific in mental models of concrete events and situations, which in turn are the basis of discourse and other social practices.

Racism has often been defined in terms of (racist) ideologies. However, I argued here that this would be a reduction of the more complex notion of racism as a system of social inequality and dominance. This system has both a mental-level of analysis and reproduction, featuring racist ideologies and social representations of a group, as well as a social-level of analysis, featuring everyday discriminatory interaction and discourse, on the one hand, and group relations and institutions, on the other. As is the case for other ideologies, also racist ideologies are largely (though not uniquely) reproduced by text and talk.

These ideas were applied in a succinct analysis of a debate in the British House of Commons, in order to show that many properties of such political discourse are controlled by underlying ideological models and social repre- sentations. I emphasised, though, that this is only the case in a particular con- text, where I defined context as a mental model of the discursively relevant properties of the communicative situation. For instance, the context here defined the nature and the aims within the specific genre of a parliamentary debate, the intentions of the speakers, and ultimately the political functions of the turn- taking, structures, meanings and other characteristics of the debate.

Ideological positioning occurred at all levels of discourse, for instance, in the choice of topics, local meanings, disclaimers, implications and presuppos- itions, descriptions, metaphors, hyperbole, and argumentation. The overall strategy in the speech of racist speakers is to focus on the negative character- istics of the Others, find to represent Us as the victims of these Others. Anti- racist discourse on the other hand, will focus on the plight of the asylum seekers, and on fundamental norms, laws and principles of human rights, and hence has a basically moral slant. Thus, we see how fundamental prejudices occur in racist discourse, in topics as well as in other structures, about non- European immigrants as being lazy, criminal, cheating, untrustworthy, etc., and can be marshaled to argue against less severe immigration law. Anti-racist speakers on the other hand, make extensive use of humanitarian, human rights ideologies, operate with positive attitudes about asylum seekers, and negative ones about conservatives, as may be expected.

Comparative Perspectivas on Racism 114

My main point was thus, to show what racist ideologies are and how they

may be expressed in discourses of social and political interaction, and how racist ideologies may thus he propagated and reproduced in society.

Notes

1 Ruth Wodak is Professor of Applied Linguistics at the University of Vienna. 2 All omissions and additions to the text of each MP s speech are mine. The text is from Hansard. The numbering given at the end of each line is my own.

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von braun.pdf

THE WORLD FOOD SITUATION New Driving Forces and Required Actions

International Food Policy Research Institute Washington, D.C.

December 2007

Joachim von Braun

Copyright © 2007 International Food Policy Research Institute. All rights reserved. Sections of this report may be reproduced for noncommercial and not-for-profit purposes without the express written permission of but with acknowledgment to the International Food Policy Research Institute.

ISBN 10-digit: 0-89629-530-3 ISBN 13-digit: 978-0-89629-530-8

DOI: 10.2499/0896295303

Contents Acknowledgments vi

The World Food Equation, Rewritten 1

Outlook on Global Food Scarcity and Food-Energy Price Links 6

Poverty and the Food and Nutrition Situation 11

Conclusions 13

Notes 14

References 15

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Tables 1. China: Per capita annual household consumption 2

2. Change in food-consumption quantity, ratios 2005/1990 2

3. Expected impacts of climate change on global cereal production 4

4. Consumption spending response (%) when prices change by 1%

(“elasticity”) 6

5. Changes in world prices of feedstock crops and sugar by 2020

under two scenarios compared with baseline levels (%) 9

6. Net cereal exports and imports for selected countries

(three-year averages 2003–2005) 10

7. Purchases and sales of staple foods by the poor

(% of total expenditure of all poor) 10

8. Expected number of undernourished in millions, incorporating

the effects of climate change 12

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Figures 1.World cereal production, 2000–2007 (million tons) 3

2.World cereal stocks, 2000–2007 3

3. Annual growth rate of high-value agriculture production, 2004–2006 (percent) 3

4. A “corporate view” of the world food system: Sales of top 10 companies

(in billions of US dollars), 2004 and 2006 4

5. Global supply and demand for cereals, 2000 and 2006 5

6. Commodity prices (US$/ton), January 2000–September 2007 6

7. Domestic and world prices of maize in Mexico (January 2004 = 100) 7

8. Producer and consumer prices of wheat in Ethiopia (2000 = 100) 7

9. Brazil: Ethanol and sugar prices, January 2000–September 2007 7

10. Meat and dairy prices (January 2000 = 100) 8

11. Calorie availability changes in 2020 compared to baseline (%) 8

12. Modeling the actual price change of cereals, 2000–2005

and scenario 2006–2015 (US$/ton) 10

13. Prevalence of undernourishment in developing countries, 1992–2004

(% of population) 11

14. Changes in the Global Hunger Index (GHI) 12

15.Trends in the GHI and Gross National Income per capita

(1981, 1992, 1997, 2003) 12

Acknowledgments The research cooperation and assistance for the development of this paper by Bella Nestorova,Tolulope Olofinbiyi,Rajul Pandya-Lorch,Teunis van Rheenen, Mark Rosegrant, Siwa Msangi, and Klaus von Grebmer—all at IFPRI—is gratefully acknowledged.

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The world food situation is currently being rapidly redefined by new driving forces. Income growth, cli-mate change, high energy prices, globalization, and urbanization are transforming food consumption, production, and markets.The influence of the private sector in the world food system, especially the leverage

of food retailers, is also rapidly increasing. Changes in food availability, rising commodity prices, and new pro-

ducer–consumer linkages have crucial implications for the livelihoods of poor and food-insecure people.

Analyzing and interpreting recent trends and emerging challenges in the world food situation is essential in

order to provide policymakers with the necessary information to mobilize adequate responses at the local,

national, regional, and international levels. It is also critical for helping to appropriately adjust research agendas

in agriculture, nutrition, and health. Not surprisingly, renewed global attention is being given to the role of agri-

culture and food in development policy, as can be seen from the World Bank’s World Development Report,

accelerated public action in African agriculture under the New Partnership for Africa’s Development

(NEPAD), and the Asian Development Bank’s recent initiatives for more investment in agriculture, to name

just a few examples.

The World Food Equation, Rewritten

Demand driven by high economic growth and population change Many parts of the developing world have experienced high economic growth in recent years. Developing Asia, especially China and India, continues to show strong sustained growth. Real GDP in the region increased by 9 percent per annum between 2004 and 2006. Sub-Saharan Africa also experi- enced rapid economic growth of about 6 percent in the same period. Even countries with high incidences and preva- lences of hunger reported strong growth rates. Of the world’s 34 most food-insecure countries,1 22 had average annual growth rates ranging from 5 to 16 percent between 2004 and 2006. Global economic growth, however, is pro- jected to slow from 5.2 percent in 2007 to 4.8 percent in 2008 (IMF 2007a). Beyond 2008, world growth is expected to remain in the 4 percent range while developing-country growth is expected to average 6 percent (Mussa 2007).This growth is a central force of change on the demand side of the world food equation. High income growth in low- income countries readily translates into increased consump- tion of food, as will be further discussed below.

Another major force altering the food equation is shift- ing rural–urban populations and the resulting impact on spending and consumer preferences.The world’s urban pop- ulation has grown more than the rural population; within the next three decades, 61 percent of the world’s populace

is expected to live in urban areas (Cohen 2006). However, three-quarters of the poor remain in rural areas, and rural poverty will continue to be more prevalent than urban poverty during the next several decades (Ravallion, Chen, and Sangraula 2007).

Agricultural diversification toward high-value agricul- tural production is a demand-driven process in which the private sector plays a vital role (Gulati, Joshi, and Cummings 2007). Higher incomes, urbanization, and changing prefer- ences are raising domestic consumer demand for high-value products in developing countries.The composition of food budgets is shifting from the consumption of grains and other staple crops to vegetables, fruits, meat, dairy, and fish.The demand for ready-to-cook and ready-to-eat foods is also rising, particularly in urban areas. Consumers in Asia, espe- cially in the cities, are also being exposed to nontraditional foods. Due to diet globalization, the consumption of wheat and wheat-based products, temperate-zone vegetables, and dairy products in Asia has increased (Pingali 2006).

Today’s shifting patterns of consumption are expected to be reinforced in the future.With an income growth of 5.5 percent per year in South Asia, annual per capita consump- tion of rice in the region is projected to decline from its 2000 level by 4 percent by 2025. At the same time, con- sumption of milk and vegetables is projected to increase by 70 percent and consumption of meat, eggs, and fish is pro- jected to increase by 100 percent (Kumar et al. 2007).

In China, consumers in rural areas continue to be more dependent on grains than consumers in urban areas (Table 1). However, the increase in the consumption of meat, fish and aquatic products, and fruits in rural areas is even greater than in urban areas.

In India, cereal consumption remained unchanged between 1990 and 2005, while consumption of oil crops almost doubled; consumption of meat, milk, fish, fruits, and vegetables also increased (Table 2). In other developing countries, the shift to high-value demand has been less obvious. In Brazil, Kenya, and Nigeria, the consumption of some high-value products declined, which may be due to growing inequality in some of these countries.

World food production and stock developments Wheat, coarse grains (including maize and sorghum), and rice are staple foods for the majority of the world’s population. Cereal supply depends on the production and

availability of stocks.World cereal production in 2006 was about 2 billion tons—2.4 percent less than in 2005 (Figure 1). Most of the decrease is the result of reduced plantings and adverse weather in some major producing and exporting countries. Between 2004 and 2006, wheat and maize production in the European Union and the United States decreased by 12 to 16 percent. On the positive side, coarse grain production in China increased by 12 percent and rice output in India increased by 9 percent (based on data from FAO 2006b and 2007b). In 2007, world cereal production is expected to rise by almost 6 percent due to sharp increases in the production of maize, the main coarse grain.

In 2006, global cereal stocks—especially wheat— were at their lowest levels since the early 1980s. Stocks in China, which constitute about 40 percent of total stocks, declined significantly from 2000 to 2004 and have not recovered in recent years (Figure 2). End-year cereal stocks in 2007 are expected to remain at 2006 levels. 2

As opposed to cereals, the production of high-value

2

Table 1—China: Per capita annual household consumption

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Urban Rural

1990 2006 2006/1990 1990 2006 2006/1990 Product (kg) (kg) ratio (kg) (kg) ratio

Grain 131 76 0.6 262 206 0.8

Pork, beef, and mutton 22 24 1.1 11 17 1.5

Poultry 3 8 2.4 1 4 2.8

Milk 5 18 4.0 1 3 2.9

Fish and aquatic products 8 13 1.7 2 5 2.4

Fruits 41 60 1.5 6 19 3.2

SOURCE: Data from National Bureau of Statistics of China 2007a and 2007b.

Table 2—Change in food-consumption quantity, ratios 2005/1990

Type India China Brazil Kenya Nigeria

Cereals 1.0 0.8 1.2 1.1 1.0

Oil crops 1.7 2.4 1.1 0.8 1.1

Meat 1.2 2.4 1.7 0.9 1.0

Milk 1.2 3.0 1.2 0.9 1.3

Fish 1.2 2.3 0.9 0.4 0.8

Fruits 1.3 3.5 0.8 1.0 1.1

Vegetables 1.3 2.9 1.3 1.0 1.3

SOURCE: Data from FAO 2007a.

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agricultural commodities such as vegetables, fruits, meat, and milk is growing at a fast rate in developing countries (Figure 3).

Climate-change risks will have adverse impacts on food production, compounding the challenge of meeting global food demand. Consequently, food import dependency is projected to rise in many regions of the developing world (IPCC 2007).With the increased risk of droughts and floods due to rising temperatures, crop-yield losses are imminent. In more than 40 developing coun- tries—mainly in Sub-Saharan Africa—cereal yields are expected to decline, with mean losses of about 15 percent by 2080 (Fischer et al. 2005). Other estimates suggest that although the aggregate impact on cereal pro- duction between 1990 and 2080 might be small—a decrease in production of less than 1 percent—large reductions of up to 22 per- cent are likely in South Asia (Table 3). In con- trast, developed countries and Latin America are expected to experience absolute gains. Impacts on the production of cereals also dif- fer by crop type. Projections show that land suitable for wheat production may almost disappear in Africa. Nonetheless, global land use due to climate change is estimated to increase minimally by less than 1 percent. In many parts of the developing world, espe- cially in Africa, an expansion of arid lands of up to 8 percent may be anticipated by 2080 (Fischer et al. 2005).

World agricultural GDP is projected to decrease by 16 percent by 2020 due to global warming. Again, the impact on developing countries will be much more severe than on developed countries. Output in developing countries is projected to decline by 20 per- cent, while output in industrial countries is projected to decline by 6 percent (Cline 2007).

Carbon fertilization3 could limit the severity of climate-change effects to only 3 percent. However, technological change is not expected to be able to alleviate output losses and increase yields to a rate that would keep up with growing food demand (Cline 2007). Agricultural prices will thus also be affected by climate variability and change.Temperature increases of more than 3ºC may cause prices to increase by up to 40 percent (Easterling et al. 2007).

The riskier climate environment that is expected will increase the demand for inno-

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Source: Data from FAO 2003, 2005, 2006b, and 2007b.

Note: Data for 2007 are forecasts.

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Figure 2—World cereal stocks, 2000–2007

Source: Data from FAO 2003, 2005, 2006b, and 2007b.

Note: Data for 2007 are forecasts.

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Figure 3—Annual growth rate of high-value agriculture production, 2004–2006 (percent)

Source: Data from FAO 2007a.

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Developed countries Developing countries

vative insurance mechanisms, such as rainfall-indexed insur- ance schemes that include regions and communities of small farmers.This is an area for new institutional exploration.

Globalization and trade A more open trade regime in agriculture would benefit developing countries in general. Research by the International Food Policy Research Institute (IFPRI) has shown that the benefits of opening up and facilitating mar- ket access between member countries of the Organisation for Economic Co-operation and Development (OECD) and developing countries—as well as among developing coun- tries—would bring significant economic gains. However, large advances in poverty reduction would not occur except in some cases (Bouet et al. 2007). Multilateral discus- sions toward further trade liberalization and the integration of developing countries into the global economy are cur- rently deadlocked.The conclusion of the World Trade

Organization (WTO) Doha Development Round has been delayed due to divisions between developed and developing countries and a lack of political commitment on the part of key negotiating parties. In the area of agriculture, developed countries have been unwilling to make major concessions. The United States has been hesitant to decrease domestic agricultural support in its new farm bill, while the European Union has been hesitant to negotiate on its existing trade restrictions on sensitive farm products. Deep divisions have also emerged regarding the conditions for nonagricultural market access proposed in Potsdam in July 2007.

In reaction to the lack of progress of the Doha Round, many countries are increasingly engaging in regional and bilateral trade agreements.The number of regional arrange- ments reported to the WTO rose from 86 in 2000 to 159 in 2007 (UNCTAD 2007). Increasingly, South-South and South-North regional initiatives have emerged—such as the Central American Free Trade Agreement (CAFTA) between the United States and Central America and the negotiations between the African, Caribbean, and Pacific (ACP) states and the European Union—and they may create more opportunities for cooperation among developing countries and for opening up their markets.

Another development has been the improvement of the terms of trade for commodity exporters as a result of increases in global prices.The share of developing countries in global exports increased from 32 percent in 2000 to 37 percent in 2006, but there are large regional disparities. Africa’s share in global exports, for example, increased only from 2.3 to 2.8 percent in the same period (UNCTAD 2007).

Changes in the corporate food system The growing power and leverage of international corpora- tions are transforming the opportunities available to small agricultural producers in developing countries.While new

prospects have arisen for some farmers, many others have not been able to take advantage of the new income-generating opportunities since the rigorous safety and quality stan- dards of food processors and food retailers create high barriers to their market entry.

Transactions along the corporate food chain have increased in the past two years. Between 2004 and 2006, total global food spending grew by 16 percent, from US$5.5 trillion to 6.4 trillion (Planet Retail 2007a). In the same period, the sales of food retailers increased by a disproportionately large amount compared to the sales of food processors and of companies in the food input industry (Figure 4).The sales of the top food processors and traders grew by 13 per- cent, and the sales of the top 10 companiesT

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Table 3—Expected impacts of climate change on global cereal production

1990–2080 Region (% change)

World –0.6 to –0.9

Developed countries 2.7 to 9.0

Developing countries –3.3 to –7.2

Southeast Asia –2.5 to –7.8

South Asia –18.2 to –22.1

Sub-Saharan Africa –3.9 to –7.5

Latin America 5.2 to 12.5

SOURCE: Adapted from Tubiello and Fischer 2007.

2004 2006

Agricultural input industry

Food processors and traders

Food retailers

37 40363 777 409 1,091

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producing agricultural inputs (agrochemicals, seeds, and traits) increased by 8 percent.The sales of the top food retailers, however, soared by more than 40 percent.While supermarkets account for a large share of retail sales in most developed and many developing countries, independent grocers continue to represent 85 percent of retail sales in Vietnam and 77 percent in India (Euromonitor 2007).

The process of horizontal consolidation in the agricultural-input industry continues on a global scale.The three leading agro- chemical companies—Bayer Crop Science, Syngenta, and BASF—account for roughly half of the total market (UNCTAD 2006). In con- trast, the top five retailers do not capture more than a 13-percent share of the market. Global data, however, mask substantial differ- ences between countries; while the top five retailers account for 57 percent of grocery sales in Venezuela, they represent less than 4 percent of sales in Indonesia (Euromonitor 2007).Vertical integration of the food supply chain increases the synergies between agricultural inputs, pro- cessing, and retail, but overall competition within the dif- ferent segments of the world food chain remains strong.

The changing supply-and-demand framework of the food equation The above-mentioned changes on the supply and demand side of the world food equation have led to imbalances and drastic price changes. Between 2000 and 2006, world demand for cereals increased by 8 percent while cereal prices increased by about 50 percent (Figure 5). Thereafter, prices more than doubled by early 2008 (com- pared to 2000). Supply is very inelastic, which means that it does not respond quickly to price changes.Typically, aggre- gate agriculture supply increases by 1 to 2 percent when prices increase by 10 percent.That supply response decreases further when farm prices are more volatile, but increases as the result of improved infrastructure and access to technology and rural finance.

The consumption of cereals has been consistently higher than production in recent years and that has reduced stocks. A breakdown of cereal demand by type of use gives insights into the factors that have contributed to the greater increase in consumption.While cereal use for food and feed increased by 4 and 7 percent since 2000, respectively, the use of cereals for industrial purposes— such as biofuel production—increased by more than 25 percent (FAO 2003 and 2007b). In the United States alone, the use of corn for ethanol production increased by

two and a half times between 2000 and 2006 (Earth Policy Institute 2007).

Supply and demand changes do not fully explain the price increases. Financial investors are becoming increas- ingly interested in rising commodity prices, and speculative transactions are adding to increased commodity-price volatility. In 2006, the volume of traded global agricultural futures and options rose by almost 30 percent. Commodity exchanges can help to make food markets more transparent and efficient.They are becoming more relevant in India and China, and African countries are initi- ating commodity exchanges as well, as has occurred in Ethiopia, for example (Gabre-Madhin 2006).

Figure 5—Global supply and demand for cereals, 2000 and 2006

Source: Data from FAO 2003, 2005, 2006b, 2007b, and 2007c.

Notes: Supply and demand of cereals refer to the production and consumption of wheat, coarse grains, and rice.

153

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Due to government price policies, trade restrictions, and transportation costs, changes in world commodity prices do not automatically translate into changes in domestic prices. In the case of Mexico, the margin between domestic and world prices for maize has ranged between 0 and 35 percent since the beginning of 2004, and a strong relationship between domestic and world prices is evi- dent (Figure 7). In India, the dif- ferences between domestic and international rice prices were greater, averaging more than 100 percent between 2000 and 2006.4 While domestic price- stabilization policies diminish price volatility, they require fiscal resources and cause additional market imperfections. Govern- ment policies also change the relationship between consumer and producer prices. For instance, producer prices of wheat in Ethiopia increased more than consumer prices from 2000 to 2006 (Figure 8).

Though international price changes do not fully trans- late into equivalent domestic farm and consumer price changes because of the different policies and trade positions adopted by each country, they are in fact transmitted to consumers and producers to a considerable extent.

The prices of commodities used in biofuel production are becoming increasingly linked with energy prices. In Brazil, which has been a pioneer in ethanol production since the 1970s, the price of sugar is very closely connected to the price of ethanol (Figure 9). A worrisome implication of the increasing link between energy and food prices is that high energy-price fluctuations are increasingly translated

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Outlook on Global Food Scarcity and Food-Energy Price Links

Cereal and energy price increases

World cereal and energy prices are becoming increasingly linked. Since 2000, the prices of wheatand petroleum have tripled, while the prices of corn and rice have almost doubled (Figure 6). The impact of cereal price increases on food-insecure and poor households is already quite dramatic.

For every 1-percent increase in the price of food, food consumption expenditure in developing countries

decreases by 0.75 percent (Regmi et al. 2001). Faced with higher prices, the poor switch to foods that have

lower nutritional value and lack important micronutrients.

Figure 6—Commodity prices (US$/ton), January 2000–September 2007

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Source: Data from FAO 2007c and IMF 2007b; in current US $.

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Table 4—Consumption spending response (%) when prices change by 1% (“elasticity”)

Low-income High-income countries countries

Food -0.59 -0.27

Bread and cereals -0.43 -0.14

Meat -0.63 -0.29

Dairy -0.70 -0.31

Fruit and vegetables -0.51 -0.23

SOURCE: Seale, Regmi, and Bernstein 2003.

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Figure 5—Global supply and demand for cereals, 2000 and 2006

Source: Data from FAO 2003, 2005, 2006b, 2007b, and 2007c.

Notes: Supply and demand of cereals refer to the production and consumption of wheat, coarse grains, and rice.

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37 40363 777 409 1,091

Figure 8—Producer and consumer prices of wheat in Ethiopia (2000 = 100)

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Sources: Data from Central Statistical Agency of Ethiopia 2007 and Ethiopian Grain Trade Enterprise 2007.

Note: Consumer prices represent wholesale prices in Addis Ababa, and producer prices are national farmgate prices.

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Figure 7—Domestic and world prices of maize in Mexico (January 2004 = 100)

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(J an

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Figure 9—Brazil: Ethanol and sugar prices, January 2000–September 2007

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Sources: Data from CEPEA 2007.

Notes: Fuel ethanol prices in Brazil refer to averages for the São Paulo market (mills, distilleries, distributors, intermediaries). Hydrous ethanol is used as a substitute for gasoline and Anhydrous ethanol is mixed with gasoline.

Anhydrous ethanol

Sugar (right scale)

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into high food-price fluctuations. In the past five years, price variations in oilseeds and in wheat and corn have increased to about twice the lev- els of previous decades.5

The increasing demand for high-value com- modities has resulted in surging prices for meat and dairy products (Figure 10), and this is driv- ing feed prices upward, too. Since the beginning of 2000, butter and milk prices have tripled and poultry prices have almost doubled.

The effects of price increase on consump- tion are different across different countries and consumer groups. Consumers in low-income countries are much more responsive to price changes than consumers in high-income coun- tries (Table 4). Also, the demand for meat, dairy, fruits, and vegetables is much more sensi- tive to price, especially among the poor, than is the demand for bread and cereals.

Scenario analyses of the determinants of prices and consumption

The effect of biofuels When oil prices range between US$60 and $70 a barrel, biofuels are competitive with petro- leum in many countries, even with existing technologies. Efficiency benchmarks vary for different biofuels, however, and ultimately, pro- duction should be established and expanded where comparative advantages exist.With oil prices above US$90, the competitiveness is of course even stronger.

Feedstock represents the principal share of total biofuel production costs. For ethanol and biodiesel, feedstock accounts for 50–70 percent and 70–80 percent of overall costs, respectively (IEA 2004). Net production costs—which are all costs related to produc- tion, including investments—differ widely across countries. For instance, Brazil produces ethanol at about half the cost of Australia and one-third the cost of Germany (Henniges 2005). Significant increases in feedstock costs (by at least 50 percent) in the past few years impinge on comparative advantage and competitiveness. The implication is that while the biofuel sector will contribute to feedstock price changes, it will also be a victim of these price changes.

Food-price projections have not yet been able to fully take into account the impact of bio- fuels expansion.When assessing potential devel- opments in the biofuels sector and their consequences, the OECD-FAO outlook makes assumptions for a number of countries, including the United States, the European Union, Canada, and China. New biofuel technologies and policies

are viewed as uncertainties that could dramatically impact future food prices (OECD-FAO 2007). The Food and Agricultural Policy Research Institute (FAPRI) con- ducts a detailed analysis of the potential impact of policy on bio- fuels and links between the ethanol and gasoline markets, but its extensive modeling is limited to the United States.

A new, more comprehensive global scenario analysis using IFPRI’s International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) examines current price effects and estimates future ones. In view of the dynamic world food situation and the rap- idly changing biofuels sector, IFPRI continuously updates and refines its related models, so the results presented here should be viewed as work in progress. Recently, the IMPACT model has incorporated 2005/06 develop- ments in supply and demand, and has generated two future scenarios based on these developments:

• Scenario 1 is based on the actual biofuel investment plans of many countries that have such plans and assumes biofuel expansions for identified high- potential countries that have not specified their plans.

• Scenario 2 assumes a more drastic expansion of biofuels to double the levels used in Scenario 1.

Under the planned biofuel expansion sce- nario (Scenario 1), international prices increase by 26 percent for maize and by 18 percent for oilseeds. Under the more drastic biofuel expan- sion scenario (Scenario 2), maize prices rise by 72 percent and oilseeds by 44 percent (Table 5).

Under both scenarios, the increase in crop prices resulting from expanded biofuel produc- tion is also accompanied by a net decrease in the availability of and access to food, with calorie consumption estimated to decrease across all regions compared to baseline levels (Figure 11). Food-calorie consumption decreases the most in Sub-Saharan Africa, where calorie availability is projected to fall by more than 8 percent if biofu- els expand drastically.

One of the arguments in favor of biofuels is that they could positively affect net carbon emissions as an alterative to fossil fuels.That added social benefit might justify some level of

subsidy and regulation, since these external benefits would not be internalized by markets. However, potential forest conversion for biofuel production and the impact of biofuel production on soil fertility are environmental concerns that require attention. As is the case with any form of agricultural production, biofuel feedstock production can be managed in sustainable or in damaging ways. Clear environment-related efficiency criteria and sound process standards need to be

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Figure 10—Meat and dairy prices (January 2000 = 100)

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Source: Data from FAO 2007c.

Notes: Beef = USA beef export unit value; poultry = export unit value of broiler cuts; butter = Oceania indicative export prices, f.o.b. Milk = Oceania whole milk powder indicative export prices, f.o.b.

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Figure 11—Calorie availability changes in 2020 compared to baseline (%)

Source: IFPRI IMPACT projections.

Notes: N America = North America; SSA = Sub-Saharan Africa; S Asia = South Asia; MENA = Middle East & North Africa; LAC = Latin America and the Caribbean; ECA = Europe & Central Asia; EAP = East Asia and Pacific.

-9 -6 -3 0

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Biofuel expansion Drastic biofuel expansion

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established that internal- ize the positive and negative externalities of biofuels and ensure that the energy out- put from biofuel pro- duction is greater than the amount of energy used in the process. In general, subsidies for biofuels that use agricultural production resources are extremely anti- poor because they implicitly act as a tax on basic food, which represents a large share of poor people’s consumption expendi- tures and becomes even more costly as prices increase, as shown above (von Braun 2007).

Great technological strides are expected in biofuel production in the coming decades. New technologies converting cellulosic biomass to liquid fuels would create added value by both utilizing waste biomass and by using less land resources.These second-generation technologies, however, are still being developed and third-generation technologies (such as hydrogene) are at an even earlier phase. Even though future technology development will very much determine the competitiveness of the sector, it will not solve the food–fuel competition problem.The trade-offs between food and fuel will actually be accelerated when biofuels become more competitive relative to food and when, consequently, more land, water, and capital are diverted to biofuel production.To soften the trade-offs and mitigate the growing price burden for the poor, it is necessary to accelerate investment in food and agricultural science and technologies, and the CGIAR has a vital role to play in this. For many developing countries, it would be appropriate to wait for the emergence of second-generation technologies, and “leapfrog” onto them later.

Attempts to predict future overall food price changes How will food prices change in coming years? This is one of the central questions that policymakers, investors, speculators, farmers, and millions of poor people ask.Though the research community does its best to answer this question, the many uncertainties created by supply, demand, market functioning, and policies mean that no straightforward answer can be given. However, a number of studies have analyzed the forces driving the current increases in world food prices and have predicted future price developments.

The Economic Intelligence Unit predicts an 11-percent increase in the price of grains in the next two years and only a 5-percent rise in the price of oilseeds (EIU 2007).The OECD- FAO outlook has higher price projections (it expects the

prices of coarse grains, wheat, and oilseeds to increase by 34, 20, and 13 percent, respectively, by 2016–17).The Food and Agricultural Policy Research Institute (FAPRI) expects increases in corn demand and prices to last until 2009–10, and thereafter expects corn production growth to be on par with consumption growth. FAPRI does not expect biofuels to have a large impact on wheat markets, and predicts that wheat prices will stay constant due to stable demand as population growth offsets declining per capita consumption. Only the price of palm oil—another biofuel feedstock—is projected to dramatically increase by 29 percent. In cases where demand for agricultural feedstock is large and elastic, some experts expect petroleum prices to act as a price floor for agricultural commodity prices. In the resulting price corridor, agricultural commodity prices are determined by the product’s energy equivalency and the energy price (Schmidhuber 2007).

In order to model recent price developments, changes in supply and demand from 2000 to 2005 as well as biofuel developments were introduced into the IFPRI IMPACT model (see Scenario 1).The results indicate that biofuel pro- duction is responsible for only part of the imbalances in the world food equation. Other supply and demand shocks also play important roles.The price changes that resulted from actual supply and demand changes during 2000–2005 capture a fair amount of the noted increase in real prices for grains in those years (Figure 12).6 For the period from 2006 to 2015, the scenario suggests further increases in cereal prices of about 10 to 20 percent in current U.S. dollars. Continued depreciation of the U.S. dollar—which many expect—may further increase prices in U.S.-dollar terms.

The results suggest that changes on the supply side (including droughts and other shortfalls and the diversion of food for fuel) are powerful forces affecting the price surge at a time when demand is strong due to high income growth in developing countries. Under a scenario of continued high income growth (but no further supply shocks), the prelimi- nary model results indicate that food prices would remain at

Table 5—Changes in world prices of feedstock crops and sugar by 2020 under two scenarios compared with baseline levels (%)

SCENARIO 1 SCENARIO 2

Biofuel Drastic biofuel Crop expansiona expansionb

Cassava 11.2 26.7

Maize 26.3 71.8

Oilseeds 18.1 44.4

Sugar 11.5 26.6

Wheat 8.3 20.0

SOURCE: IFPRI IMPACT projections (in constant prices).

aAssumptions are based on actual biofuel production plans and projections in relevant countries and regions.

b Assumptions are based on doubling actual biofuel production plans and projections in relevant countries and regions.

high levels for quite some time.The usual sup- ply response embedded in the model would not be strong enough to turn matters around in the near future.

Who benefits and who loses from high prices? An increase in cereal prices will have uneven impacts across countries and population groups. Net cereal exporters will experience improved terms of trade, while net cereal importers will face increased costs in meeting domestic cereal demand.There are about four times more net cereal-importing countries in the world than net exporters. Even though China is the largest producer of cereals, it is a net importer of cereals due to strong domestic consumption (Table 6). In contrast, India—also a major cereal producer—is a net exporter. Almost all countries in Africa are net importers of cereals.

Price increases also affect the availability of food aid. Global food aid represents less than 7 percent of global official development assistance and less than 0.4 percent of total world food production.7 Food aid flows, however, have been declining and have reached their lowest level since 1973. In 2006, food aid was 40 percent lower than in 2000 (WFP 2007). Emergency aid continues to constitute the largest portion of food aid. Faced with shrinking resources, food aid is increasingly targeted to fewer countries—mainly in Sub- Saharan Africa—and to specific beneficiary groups.

At the microeconomic level, whether a household will benefit or lose from high food prices depends on whether the household is a net seller or buyer of food. Since food accounts for a large share of the poor’s total expenditures, a staple-crop price increase would translate into lower quantity and quality of food consumption. Household surveys provide insights into the potential impact of higher

food prices on the poor. Surveys show that poor net buyers in Bolivia, Ethiopia, Bangladesh, and Zambia purchase more staple foods than net sellers sell (Table 7).The impact of a price increase is country and crop specific. For instance, two-thirds of rural households in Java own between 0 and 0.25 hectares of land, and only 10 percent of households would benefit from an increase in rice prices (IFPP 2002).

In sum, in view of the changed farm-production and market situation that the poor face today, there is not much supporting evidence for the idea that higher farm prices would generally cause poor households to gain more on the income side than they would lose on the consumption–expenditure side. Adjustments in the farm and rural economy that might indirectly create new income opportunities due to the changed incentives will take time to reach the poor.

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Figure 12—Modeling the actual price change of cereals, 2000–2005 and scenario 2006–2015 (US$/ton)

Source: Preliminary results from the IFPRI IMPACT model, provided by Mark W. Rosegrant (IFPRI). In constant prices.

0

100

200

300

2000 2005 2010 2015

Rice Wheat Maize

Oilseeds Soybean U

S $/

to n

Table 6—Net cereal exports and imports for selected countries

(three-year averages 2003–2005)

Country 1000 tons

Japan –24,986 Mexico –12,576 Egypt –10,767 Nigeria –2,927 Brazil –2,670 China –1,331 Ethiopia –789 Burkina Faso 29 India 3,637 Argentina 20,431 United States 76,653

SOURCE: Data from FAO 2007a.

Table 7—Purchases and sales of staple foods by the poor (% of total expenditure of all poor)

Bolivia Ethiopia Bangladesh Zambia Staple foods 2002 2000 2001 1998

Purchases by all poor net buyers 11.3 10.2 22.0 10.3

Sales by all poor net sellers 1.4 2.8 4.0 2.3

SOURCE: Adapted from World Bank 2007a.

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Many of those who are the poorest and hungriest today will still be poor and hungry in 2015, the targetyear of the Millennium Development Goals. IFPRI research has shown that 160 million people live in ultra poverty on less than 50 cents a day (Ahmed et al. 2007).The fact that large numbers of people continue

to live in intransigent poverty and hunger in an increasingly wealthy global economy is the major ethical, eco-

nomic, and public health challenge of our time.

Poverty and the Food and Nutrition Situation

The number of undernourished in the developing world actually increased from 823 million in 1990 to 830 million in 2004 (FAO 2006a). In the same period, the share of undernourished declined by only 3 percentage points— from 20 to 17 percent.The share of the ultra poor—those who live on less than US$0.50 a day—decreased more slowly than the share of the poor who live on US$1 a day (Ahmed et al. 2007). In Sub-Saharan Africa and Latin America, the number of people living on less than US$0.50 a day has actually increased (Ahmed et al. 2007). Clearly, the poorest are being left behind.

Behind the global figures on undernourishment, there are also substantial regional differences (Figure 13). In East Asia, the number of food insecure has decreased by more than 18 percent since the early 1990s and the prevalence of undernourishment decreased on average by 2.5 percent per annum, mostly due to economic growth in China. In

Sub-Saharan Africa, however, the number of food-insecure people increased by more than 26 percent and the preva- lence of undernourishment increased by 0.3 percent per year. South Asia remains the region with the largest num- ber of hungry, accounting for 36 percent of all undernour- ished in the developing world.

Recent data show that in the developing world, one of every four children under the age of five is still under- weight and one of every three is stunted.8 Children living in rural areas are nearly twice as likely to be underweight as children in urban areas (UNICEF 2006).

An aggregate view on progress—or lack thereof—is given by IFPRI’s Global Hunger Index (GHI). It evaluates manifestations of hunger beyond dietary energy availability. The GHI is a combined measure of three equally weighted components: (i) the proportion of undernourished as a percentage of the population, (ii) the prevalence of under-

weight in children under the age of five, and (iii) the under-five mortality rate.The Index ranks countries on a 100-point scale, with higher scores indicating greater hunger. Scores above 10 are considered serious and scores above 30 are considered extremely alarming.

From 1990 to 2007, the GHI improved significantly in South and Southeast Asia, but progress was limited in the Middle East and North Africa and in Sub-Saharan Africa (Figure 14).The causes and manifestations of hunger differ substantially between regions. Although Sub-Saharan Africa and South Asia currently have virtually the same scores, the prevalence of underweight chil- dren is much higher in South Asia, while the proportion of calorie-deficient people and child mortality is much more serious in Sub-Saharan Africa.

Figure 13—Prevalence of undernourishment in developing countries, 1992–2004 (% of population)

Source: Data from FAO 2006a and World Bank 2007b. Note: The size of the bubbles represents millions of undernourished people in 2004. EAP—East Asia and the Pacific, LAC—Latin America and the Caribbean, SA—South Asia, SSA—Sub-Saharan Africa, MENA—Middle East and North Africa, ECA—Eastern Europe and Central Asia.

0

5

10

15

20

25

30

35

-4 -3 -2 -1 0 1 2

P re

va le

n ce

o f

u n

d er

n o

u ri

sh m

en t

20 04

( %

)

SA

Annual change in prevalence of undernourishment 1992-2004 (%)

SSA

MENA ECA

EAP

LAC

52 37

213

300

227

23

12

Table 8—Expected number of undernourished in millions, incorporating the effects of climate change

Region 1990 2020 2050 2080 2080/1990 ratio

Developing countries 885 772 579 554 0.6

Asia, Developing 659 390 123 73 0.1

Sub-Saharan Africa 138 273 359 410 3.0

Latin America 54 53 40 23 0.4

Middle East & North Africa 33 55 56 48 1.5

SOURCE: Adapted from Tubiello and Fischer 2007.

G lo

b al

H u

n ge

r In

d ex

Figure 15—Trends in the GHI and Gross National Income per capita (1981, 1992, 1997, 2003)

Source: Analysis by Doris Wiesmann (IFPRI) based on GHI data from Wiesmann et al. 2007 and gross national income per capita data from World Bank 2007b.

Note: Gross National Income per capita was calculated for three-year averages (1979–81, 1990–92, 1995–97, and 2001–03, considering purchasing power parity). Each triangle represents one of the four years: 1981, 1992, 1997, and 2003.

0

10

20

30

40

50

0 2,000 4,000 6,000 8,000

Gross National Income per capita

Ethiopia

India

Ghana

China Brazil

Figure 14—Changes in the Global Hunger Index (GHI)

Source: Adapted from Wiesmann et al. 2007.

Note: GHI 1990 was calculated on the basis of data from 1992 to 1998. GHI 2007 was calculated on the basis of data from 2000 to 2005, and encompasses 97 developing countries and 21 transition countries.

0

10

20

30

1990 2007

proportion of calorie-deficient people

prevalence of underweight in children

under-five mortality rate

South Asia East Asia & Pacific

Middle East & N. Africa

L. America & Caribbean

Sub-Saharan Africa

Contribution of components to the GHI

1990 20071990 20071990 20071990 2007

In recent years, countries’ progress toward alleviating hunger has been mixed. For instance, progress slowed in China and India, and accelerated in Brazil and Ghana (Figure 15). Many countries in Sub-Saharan Africa have considerably higher GHI values than countries with similar incomes per capita, largely due to political instability and war. Index scores for Ethiopia moved up and down, increasing during times of war and improving considerably between 1997 and 2003.

Climate change will create new food insecurities in coming decades. Low-income countries with limited adaptive capacities to climate variability and change are faced with significant threats to food security. In many African countries, for example, agricultural production as well as access to food will be negatively affected, thereby increasing food insecurity and malnutrition (Easterling et al. 2007).When taking into account the effects of climate change, the number of undernour- ished people in Sub-Saharan Africa may triple between 1990 and 2080 under these assumptions (Table 8).

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Conclusions

The main findings of this update on the world food situation are:

• Strong economic growth in developing countries is a main driver of a changing world food demand toward high-value agricultural products and processed foods.

• Slow-growing supply, low stocks, and supply shocks at a time of surging demand for feed, food, and fuel have led to drastic price increases, and these high prices do not appear likely to fall soon.

• Biofuel production has contributed to the changing world food equation and currently adversely affects the poor through price-level and price-volatility effects.

• Many small farmers would like to take advantage of the new income-generating opportunities presented by high-value products (meat, milk, vegetables, fruits, flowers).There are, however, high barriers to market entry.Therefore, improved capacity is needed to address safety and quality standards as well as the large scales required by food processors and retailers.

• Poor households that are net sellers of food benefit from higher prices, but these are few. Households that are net buyers lose, and they represent the large majority of the poor.

• A number of countries—including countries in Africa—have made good progress in reducing hunger and child malnutrition. But many of the poorest and hungry are still being left behind despite policies that aim to cut poverty and hunger in half by 2015 under the Millennium Development Goals.

• Higher food prices will cause the poor to shift to even less-balanced diets, with adverse impacts on health in the short and long run.

Business as usual could mean increased misery, especially for the world’s poorest populations. A mix of policy actions that avoids damage and fosters positive responses is required.While maintaining a focus on long- term challenges is vital, there are five actions that should be undertaken immediately:

1. Developed countries should facilitate flexible responses to drastic price changes by eliminating trade barriers and programs that set aside agriculture resources, except in well-defined conservation areas. A world confronted with more scarcity of food needs to trade more—not less—to spread opportunities fairly.

2. Developing countries should rapidly increase investment in rural infrastructure and market institutions in order to reduce agricultural-input access constraints, since these are hindering a stronger production response.

3. Investment in agricultural science and technology by the Consultative Group on International Agricultural Research (CGIAR) and national research systems could play a key role in facilitating a stronger global production response to the rise in prices.

4. The acute risks facing the poor—reduced food availability and limited access to income-generating opportunities—require expanded social-protection measures. Productive social safety nets should be tailored to country circumstances and should focus on early childhood nutrition.

5. Placing agricultural and food issues onto the national and international climate-change policy agendas is critical for ensuring an efficient and pro- poor response to the emerging risks.

14

Notes

1. The most food-insecure countries include the 20 countries with the highest prevalence of undernourishment and the 20 countries with the highest number of undernourished people as reported in FAO 2006a. Six countries over- lap across both categories.

2. The data on stocks are estimates that need to be interpreted with caution since not all countries make such data available.

3. Carbon fertilization refers to the influence of higher atmospheric concentrations of carbon dioxide on crop yields.

4. Calculation based on data from Government of India 2007 and FAO 2007b.

5. The coefficient of variation of oilseeds in the past five years was 0.20, compared to typical coefficients in the range of 0.08–0.12 in the past two decades. In the past decade, the coefficient of variation of corn increased from 0.09 to 0.22 (von Braun 2007).

6. The weather variables are partly synthesized because complete data are not available, so turning points on prices will not be precise, but the trend captures significant change.

7. Calculations are for 2006 and are based on data from OECD 2007, FAO 2007a, and WFP 2007.

8. With height less than two standard deviations below the median height-for-age of the reference population.

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INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE 2033 K Street, NW Washington, DC 20006-1002 USA Telephone: +1-202-862-5600 Fax: +1-202-467-4439 Email: [email protected]

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von braun-2008rising food prices.pdf

The sharp increase in food prices over the past couple of years has raised serious concerns about the food and nutrition situ-ation of poor people in developing countries, about inflation, and—in some countries—about civil unrest. Real prices are still below their mid-1970s peak, but they have reached their highest point since that time. Both developing- and developed-country gov- ernments have roles to play in bringing prices under control and in helping poor people cope with higher food bills.

In 2007 the food price index calculated by the Food and Agriculture Organization of the United Nations (FAO) rose by nearly 40 percent, compared with 9 percent the year before, and in the first months of 2008 prices again increased drastically. Nearly every agricultural commodity is part of this rising price trend. Since 2000—a year of low prices—the wheat price in the international market has more than tripled and maize prices have more than doubled. The price of rice jumped to unprecedented levels in March 2008. Dairy products, meat, poultry, palm oil, and cassava have also experienced price hikes. When adjusted for inflation and the dollar’s decline (by reporting in euros, for example), food price increases are smaller but still dramatic, with often serious consequences for the purchasing power of the poor.

National governments and international actors are taking vari- ous steps to try to minimize the effects of higher international prices for domestic prices and to mitigate impacts on particular groups. Some of these actions are likely to help stabilize and reduce food prices, whereas others may help certain groups at the expense of others or actually make food prices more volatile in the long run and seriously distort trade. What is needed is more effective and coherent action to help the most vulnerable populations cope with the drastic and immediate hikes in their food bills and to help farm- ers meet the rising demand for agricultural products.

The Sources of Current Price Increases The combination of new and ongoing forces is driving the world food situation and, in turn, the prices of food commodities. One emerging factor behind rising food prices is the high price of energy. Energy and agricultural prices have become increasingly intertwined (see figure). With oil prices at an all-time high of more than US$100 a barrel and the U.S. government subsidizing farmers to grow crops for energy, U.S. farmers have massively shifted their cultivation toward biofuel feedstocks, especially maize, often at the expense of soybean and wheat cultivation. About 30 percent of U.S. maize production will go into ethanol in 2008 rather than into world food and feed markets. High energy prices have also made agricultural production more expensive by raising the cost of mechanical cultivation, inputs like fertilizers and pesticides, and transportation of inputs and outputs.

At the same time, the growing world population is demanding more and different kinds of food. Rapid economic growth in many developing countries has pushed up consumers’ purchasing power,

generated rising demand for food, and shifted food demand away from traditional staples and toward higher-value foods like meat and milk. This dietary shift is leading to increased demand for grains used to feed livestock.

Poor weather and speculative capital have also played a role in the rise of food prices. Severe drought in Australia, one of the world’s largest wheat producers, has cut into global wheat production.

The Impacts of High Food Prices Higher food prices have radically different effects across countries and population groups. At the country level, countries that are net food exporters will benefit from improved terms of trade, although some of them are missing out on this opportunity by banning exports to protect consumers. Net food importers, however, will struggle to meet domestic food demand. Given that almost all countries in Africa are net importers of cereals, they will be hard hit by rising prices. At the household level, surging and volatile food prices hit those who can afford it the least—the poor and food insecure. The few poor households that are net sellers of food will benefit from higher prices, but households that are net buyers of food—which represent the large majority of the world’s poor—will be harmed. Adjustments in the rural economy, which can create new income opportunities, will take time to reach the poor.

The nutrition of the poor is also at risk when they are not shielded from the price rises. Higher food prices lead poor people to limit their food consumption and shift to even less-balanced diets, with harmful effects on health in the short and long run. At the household level, the poor spend about 50 to 60 percent of their overall budget on food. For a five-person household living on US$1 per person per day, a 50 percent increase in food prices removes up to US$1.50 from their US$5 budget, and growing energy costs also add to their adjustment burden.

IFPRI Policy Brief • April 2008

RISING FOOD PRICES What Should Be Done?

Joachim von Braun

World Commodity Prices, January 2000–February 2008 (US$/metric ton)

Sources: FAO international commodity prices database 2008, and IMF world economic outlook database 2007.

For more information and to provide feedback, please visit www.ifpri.org/themes/foodprices/foodprices.asp.

Policy Responses So Far Many countries are taking steps to try to minimize the effects of higher prices on their populations. Argentina, Bolivia, Cambodia, China, Egypt, Ethiopia, India, Indonesia, Kazakhstan, Mexico, Morocco, Russia, Thailand, Ukraine, Venezuela, and Vietnam are among those that have taken the easy option of restricting food exports, setting limits on food prices, or both. For example, China has banned rice and maize exports; India has banned milk powder exports; Bolivia has banned the export of soy oil to Chile, Colombia, Cuba, Ecuador, Peru, and Venezuela; and Ethiopia has banned exports of major cereals. Other countries are reducing restrictions on imports: Morocco, for instance, cut tariffs on wheat imports from 130 percent to 2.5 percent; Nigeria cut its rice import tax from 100 percent to just 2.7 percent.

How effective are these responses likely to be? Price controls and changes in import and export policies may begin to address the problems of poor consumers who find that they can no longer af- ford an adequate diet for a healthy life. But some of these policies are likely to backfire by making the international market smaller and more volatile. Price controls reduce the price that farmers receive for their agricultural products and thus reduce farmers’ incentives to produce more food. Any long-term strategy to stabilize food prices will need to include increased agricultural production, but price con- trols fail to send farmers a message that encourages them to pro- duce more. In addition, by benefiting all consumers, even those who can afford higher food prices, price controls divert resources toward helping people who do not really need it. Export restrictions and import subsidies have harmful effects on trading partners dependent on imports and also give incorrect incentives to farmers by reducing their potential market size. These national agricultural trade policies undermine the benefits of global integration, as the rich countries’ longstanding trade distortions with regard to developing countries are joined by developing countries’ interventions against each other.

Sound Policy Actions for the Short and Long Term The increases in food prices have a dominant role in increasing inflation in many countries now. It would be misguided to address these specific inflation causes with general macroeconomic instru- ments. Mainly, specific policies are needed to deal with the causes and consequences of high food prices. Although the current situation poses policy challenges on several fronts, there are effective and coherent actions that can be taken to help the most vulnerable people in the short term while working to stabilize food prices by increasing agricultural production in the long term.

First, in the short run, developing-country governments should expand social protection programs (that is, safety net programs like food or income transfers and nutrition programs focused on early childhood) for the poorest people—both urban and rural. Some of the poorest people in developing countries are not well connected to markets and thus will feel few effects from rising food prices, but the much higher international prices could mean serious hard- ship for millions of poor urban consumers and poor rural residents who are net food buyers, when they actually are exposed to them. These people need direct assistance. Some countries, such as India and South Africa, already have social protection programs in place that they can expand to meet new and emerging needs. Countries that do not have such programs in place will not be able to cre- ate them rapidly enough to make a difference in the current food

price situation. They may feel forced to rely on cruder measures like export bans and import subsidies. Aid donors should expand food- related development aid, including social protection, child nutrition programs, and food aid, where needed.

Second, developed countries should eliminate domestic biofuel subsidies and open their markets to biofuel exporters like Brazil. Biofuel subsidies in the United States and ethanol and biodiesel subsidies in Europe have proven to be misguided policies that have distorted world food markets. Subsidies on biofuel crops also act as an implicit tax on staple foods, on which the poor depend the most. Developed-country farmers should make decisions about what to cultivate based not on subsidies, but on world market prices for various commodities.

Third, the developed countries should also take this opportunity to eliminate agricultural trade barriers. Although some progress has been made in reducing agricultural subsidies and other trade- distorting policies in developed countries, many remain, and poor countries cannot match them. This issue has been politically diffi- cult for developed-country policymakers to address, but the political risks may now be lower than in the past. A level playing field for developing-country farmers will make it more profitable for them to ramp up production in response to higher prices.

Fourth, to achieve long-term agricultural growth, developing- country governments should increase their medium- and long-term investments in agricultural research and extension, rural infrastruc- ture, and market access for small farmers. Rural investments have been sorely neglected in recent decades, and now is the time to re- verse this trend. Farmers in many developing countries are operating in an environment of inadequate infrastructure like roads, electric- ity, and communications; poor soils; lack of storage and processing capacity; and little or no access to agricultural technologies that could increase their profits and improve their livelihoods. Recent unrest over food prices in a number of countries may tempt policy- makers to put the interests of urban consumers over those of rural people, including farmers, but this approach would be shortsighted and counterproductive. Given the scale of investment needed, aid donors should also expand development assistance to agriculture, rural services, and science and technology.

Conclusion World agriculture is facing new challenges that, along with existing forces, pose risks for poor people’s livelihoods and food security. This new situation calls for policy actions in three areas:

1. comprehensive social protection and food and nutrition initia- tives to meet the short- and medium-term needs of the poor;

2. investment in agriculture, particularly in agricultural sci- ence and technology and in market access, at a national and global scale to address the long-term problem of boosting supply; and

3. trade policy reforms, in which developed countries would revise their biofuel and agricultural trade policies and devel- oping countries would stop the new trade-distorting policies with which they are hurting each other.

In the face of rising food prices, both developing and developed countries have a role to play in creating a world where all people have enough food for a healthy and productive life.

Joachim von Braun is director general of IFPRI.

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