THESIS

1. INTRODUCTION

Cheap food has been taken for granted for almost 30 years. From their peak in the 1970s crisis, real food prices steadily declined in the 1980s and 1990s and eventually reached an all-time low in the early 2000s (Heady and Fan, 2010). Since 2003, the international prices of a wide range of commodities have surged upwards in dramatic fashion, often more than doubling within a few years, in some cases even within a few months (Heady and Fan, 2008).Food prices based on the International Monetary Fund (IMF) food price index increased by 9.5 % between April 2006 and April 2007 and by 45.6 % over the next 12 months. The increase has been particularly very sharp for staple foods. Rice prices doubled in the five months between November 2007 and March 2008, wheat prices increased more than twofold in the 12 months after March 2007 and maize prices doubled in one and half year after August 2006. These increases in prices of staple foods have led to emergencies and rationing in a large number of countries and there are frequent reports of food riots from various parts of the globe (Chand, 2008).In 2007 the food price index calculated by the Food and Agriculture Organization of the United Nations (FAO) rose by nearly 40 %, compared with 9 % 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 (von Braun, 2008). In 2007-08, world food prices reached record levels, rising 80% in 18 months. Following this peak, food prices fell, but since 2009 the cost of food has been climbing steadily in global markets, reaching record highs again in 2011. Over the last five years, the FAO food price index has risen by 92%, threatening the lives and livelihoods of millions of people (FAO, 2012).Although food prices are now lower than their 2008 peak, real prices have remained significantly higher in 2009 and 2010 than they were prior to the crisis, and various simulation models predict that real food prices will remain high until at least the end of the next decade. Needless to say, the stability and effectiveness of the world food system are no longer taken for granted (Heady and Fan, 2010).

Source: IMF, Data & Statistics

Figure 1.1. World Food Price Index (base 2005=100)

Although food commodities are not unique in undergoing such rapid price rises (energy and mineral prices have also surged), a sharp escalation in the price of basic foods has been a burden on the poor in developing countries, who spend roughly half of their household incomes on food. Sharply rising prices offer few means of substitution and adjustment, especially for the urban poor, so there are justifiable concerns that millions of people may be plunged into poverty by this crisis, and that those who are already poor may suffer further through increased hunger and malnutrition (Mitchell, 2008; Heady and Fan, 2010).

Yet surging food prices have caused panic and protest 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 (Mitchell, 2008; Heady and Fan, 2010).

In its 2008 state of food insecurity publication, FAO (2008) estimatesthat the number of chronically hungry people in 2007 increased by 75 millionover its estimate of 848 million undernourished in 2003–05, with muchof the increase attributed to high food prices. Equally grave concerns have been felt with respect to the impacts that rising food and fuel prices may have had on macroeconomic stability and economic growth. And although the food and fuel crises have largely abated since mid-2008 and have taken a back seat to the ongoing global financial crisis, food prices have remained high by historical standards and are predicted to stay high in the years to come (Heady and Fan, 2010).Moreover, the evidence of large household surveys since the 1970s generally indicates that food prices will have a negative impact on the welfare of not just urban areas, because many rural poor in developing countries are also net food consumers (World Bank 2008b). These facts have prompted some development agencies to suggest that rising food prices plunge millions more into poverty and deepen poverty still further for those already struggling (World Bank 2008a).

A wide range of research has examined the (potential) impacts of higher food prices on poverty (Ahmed et al., 2007; Aksoy and Isik-Dikmelik, 2010; Dessus et al., 2008; Headey and Fan, 2008; IMF, 2008; Ivanic and Martin, 2008; Wodon et al., 2008; Wodon and Zaman, 2008; World Bank, 2008; Zezza et al., 2008). Although we still do not know the accurate welfare effect of higher food prices on urban, rural and country level poverty, the findings of the studies suggest that the overall impact of higher food prices on poverty is generally adverse. Many of the countries in the study samples of Ivanic and Martin (2008) and Wodon et al. (2008) experience significant increases in poverty. However for some studies this conclusion is much more obvious for urban consumers, for some other studies changes in rural poverty are larger than those in urban poverty, especially in Africa. Surprisingly, the study by Ivanic and Martin (2008) shows that rural poverty increases more than urban poverty does, for two out of three countries examined. In Zambia, for example, the incidence of rural poverty increases three times as much as urban poverty. However Aksoy and Isik-Dikmelik (2010) analyze some of the same household surveys as Ivanic and Martin (2008), and they end up with different results. They conclude that (i) although most poor households are net food buyers, almost 50 % are marginal net buyers and (ii) net buyers typically have higher average incomes than net food sellers in eight of the nine countries surveyed, so that a rise in food prices would generally have progressive effects on income distribution.

The real welfare effect of higher food prices depends on whether the poor are net buyers or net sellers of food. Since rural populations also have large numbers of net buyers of food, it depends on whether they are marginal net buyers or sellers. Low prices on the world market benefit net buyers and hurt net sellers of the specific product. However, a straightforward implication of these basic principles is that urban consumers lose and rural farmers gain in post-2007 period, and vice versa in pre-2007 period (according to the Figure 1); media coverage of food crisis is limited to food price inflation (i.e. urban consumers).Since public officials react to media news because they see it as a reflection of public opinion (Kim, 2005), the media are important actors in policy-making. Paying a disproportionate amount of attention to the problems of urban consumers and yet not paying any attention to the problems of rural farmers (i.e. media bias) will translate into bad policies (i.e. policy bias).

2. THE CAUSES AND CONSEQUENCESOF FOOD CRISIS

2.2 The Causes of Food Crisis

The factors driving recent food price increases are complex. Notwithstanding, looking at the interplay of the forces driving food prices display a clearer picture for understanding the issue.

A wide range of research has attempted to identify which factors might have caused the recent surge in food prices (Abbott et al., 2009; Baltzer et al., 2008; Chand, 2008; Headey and Fan, 2008; Headey and Fan, 2010; Helbling et al., 2008; Mitchell, 2008; Schnepf, 2008; Trostle, 2008; von Braun, 2008).

In this study, driving factors are investigated by their impacts on the demand and supply of agricultural products.

2.2.1 Demand-side Factors

Numerous studies recognize biofuels production as a major driver of food prices. Biofuel production has surged since 2003, and consumed 25% of the US corn crop in 2007; two-thirds of global maize exports are from the US. This explanation is strong for corn, less so for wheat, despite the fact that substitution effects could account for rises in other products (Headey and Fan, 2008).

Another driving factor is rapid economic growth in developing countries, thus rising demand, especially from China and India. This factor partly explains rising oil prices and partly explains demand for oilseeds. The weakness of this explanation is thatChina and India are self-sufficient in most major grains, but have not increased imports of any staple foods (Headey and Fan, 2008).

Financial market speculation is a widely discussed explanation of the causes of food crisis. Increased financial market activity corresponds with the rise in food prices. Despite the fact that futures markets may have exacerbated the volatility in agriculturalmarkets, they are unlikely to be a main reason for the overall price surge, since there is little proof that these markets essentially impact “real” supply and demand factors. There is not yet clear evidence of a causal link (Headey and Fan, 2008).

Another explanation is the depreciation of the U.S. dollar (USD) over the last years, especially against the Euro. Real agricultural trade-weighted index for US depreciated 22% over 2002-2007; USD and commodity prices are covariate. Mitchell (2008) calculates that this factor probably increased dollar-denominated prices by 20% (Headey and Fan, 2008). Yet, Abbott et al. (2009) argue that causality is difficult to sort out since both prices and the exchange rate are determined simultaneously by macroeconomic performance and policy.

Another explanation of surging prices is that low real interest rates, especially in the US, have brought about a general price increase in a wide range of commodities. Low interest rates ought to increase demand for storable commodities, increase stocks, and shift investors from treasury bills to commodity contracts. The weakness of this explanation is that there is no clear evidence that futures markets are affecting spot prices. Gold and oil stocks are reasonably high, but stocks of staples are low (Headey and Fan, 2008).

2.2.1.1 Biofuels demand

Biofuels have pushed the demand for specific food crops up. An outstanding factor supporting the differencebetween this boom and earlier ones is the role of biofuels.High oil prices in recent years, together with generous policysupport in the United States and the European Union, have ledto a surge in the use of biofuels as a supplement to transportationfuels, particularly in the advanced economies (Helbling et al., 2008).

Historically, energy and agricultural markets were largely independent, each influenced by their relevant supply and demand situations. That is no longer the case. Since biofuels production surged in 2006, energy and agricultural markets became closely linked. Ethanol and biodiesel were linked as energy substitutes for gasoline and diesel. Usage of crops for these biofuels became large enough to influence world prices (Abbott et al., 2009).

Once oil prices reached $60 a barrel, biofuels became more competitive against oil. The increase in oil prices appears to have prompted the increase in biofuel demand (Schmidhuber, 2006). This dramatic increase in the prices of fossil fuels required a search for alternative sources of energy, and liquid biofuel is seen as an important alternative. Developed countries like the U.S. and EU took advantage of this situation. These countries can give support and subsidies to their producers for producing biofuel crops for domestic use without any problems with the WTO. The trend towards biofuel production helps in reducing subsidies and tariff as it leads to higher prices. Substitution of fossil by biofuel is also helpful in meeting the requirements of the Kyoto Protocol on climate change to reduce greenhouse gas emissions. As a long-term energy strategy, the U.S. is looking for energy security and is working hard to reduce its dependence for oil on the petroleum exporting countries, especially on Organisation of Petroleum Exporting Countries (OPEC). To achieve this goal, liquid biofuel is seen as a viable substitute (Chand, 2008).

In recent decades, small amounts of biofuels have been produced and used in several countries. Biofuel production grew slowly until after the turn of the century. Ethanol production in the U.S. began to increase more rapidly in 2003; biodiesel production in the EU began to increase more rapidly in 2005. The U.S. and Brazil are the global leaders for ethanol. They account for about threefourths of global ethanol production (Trostle, 2008). The main biofuels are ethanol from corn or sugarcane and biodiesel from oilseeds or palm. U.S. ethanol is mainly from corn while Brazil ethanol is mainly from sugarcane. The U.S. overtook Brazil as the leading ethanol producer in the world in 2007. However the European Union, China and India also produce ethanol, Brazil and the U.S. together covered most of global ethanol production. The EU is the major global player for biodiesel with more than threefourths of global production. The U.S. had 20 % of global production in 2006. The EU pays more attention to biodiesel than ethanol because a much higher percentage of the automobile fleet is diesel. On the other hand the U.S. pays more attention to ethanol because its fleet is predominantly gasoline. Rapeseed is the primary feedstock in the EU, whereas soybeans are used in the United States. Rapeseed contains about 40 % oil, and soybeans about 18 % (Abbott et al., 2009).

When the substitution effect is considered, the increase in biofuel production is a powerful explanation for the rapid rise in the price of many agricultural products, such as corn, soybeans and some oilseeds. Despite all the differences in approach, many studies(Abbott et al., 2009; Chand, 2008; Collins, 2008; Glauber, 2008; Helblinget al., 2008; Lipsky, 2008; Mitchel, 2008; Rosegrant, et al. 2008; Shnepfh, 2008; Trostle, 2008; van der Mensbrugghe, 2006)recognize biofuels production as a major driver of food prices.Glauber (2008)argues that the increase in farm prices of maize and soybeans is linked to biofuels production. Mitchell (2008) remarks that the increased demand for biofuels accounted for the increase in maize and soybean prices (Collins, 2008; Lipsky, 2008; Mitchell, 2008; Rosegrant, et al., 2008; van der Mensbrugghe, 2006). The estimations of the impact of biofuels on the price index of all food are differentiate in many studies. According to Mitchell (2008) these differences depend largely on how broadly the food basket is defined and what is assumed about the interaction between prices of maize and vegetable oils to prices of other crops such as rice through substitution on the supply or demand side (Mitchell, 2008).

Until recently, the balance of consumption and production of the energy obtained from ethanol was negative. The energy used to achieve a unit of ethanol was higher than a unit of energy provided by ethanol. In the U.S. ethanol production has benefited from both the high protection and price support. Biodiesel produced from plant materials has enjoyed a greater subsidy than ethanol. Even with the higher subsidy, biodiesel generally is not profitable because soy oil prices have risen to the point that it cannot be economically converted to biodiesel in most circumstances. Soybean and palm prices have moved together and have increased proportionately more than corn prices (Abbott et al., 2009).According to Schmidhuber (2006) the U.S. corn ethanol has been competitive to crude oil on the price of about 58 US $ per barrel. However, this breakeven point reflects the instant corn prices; when raw material prices change this value also change. Many studies argue that diversion of the U.S. corn crop to biofuels is the largest biofuel demand, hence the largest demand-induced price pressure (Abbott et al., 2009; Mitchell, 2008; Schnepf, 2008; von Braun, et al., 2008).

The problem is not how much corn used in ethanol production, but it is how much soil withdrawn from food production to produce raw materials. A rough estimate by Trostle (2008) suggests that about 47.8 million acres were used to provide biofuel feedstocks in the 6 major producing countries in 2007. This would account for about 3-4 %t of arable land in these countries (Trostle, 2008). In the US, rapid expansion of maize area by 23 % in 2007 resulted in a 16 % decline in soybean area, which reduced soybean production and contributed to the 75 % rise in soybean prices from April 2007 to April 2008 (Mitchell, 2008). In Europe, other oilseeds displaced wheat for the same reason. Biofuels have contributed to substantially depleting grain stocks, especially in the U.S. (Helbling et al., 2008). On the other hand, there are studies which argue that aditional farm income to be generated by biofuel production may contribute to solutions of many problems, however biofuel production competes with food production.

2.2.1.2 Rapid economic growth in developing countries

Since the early 1990s economic growth has been strong for developing countries and since the late 1990s global economic growth has been strong. In addition to these growth in Asia has been exceptionally strong for more than a decade. With having nearly 40 % of the world’s population, rapid economic growth in China and India has provided a strong and sustained increase in the demand for agricultural products (Trostle, 2008).

Urbanisation accompanied with economic growth has led a dietary transition from traditional staplestowardhigher-value foods like meat and milk. Many reports (Abbott et al., 2009; Chand, 2008; Mitchell,2008; Schnepf, 2008; von Braun, et al., 2008) on the crisis have specifically referred to changing consumption patterns in China and India, particularly the rapid growth in meat and vegetable consumption.

In China, between 1990-2006, per capita annual urban household consumption of milk increased from 5 liters to 18 liters. However, the change is not limited to China. Likewise, milk consumption increased by 20% in India, 30% in Nigeria between 1990-2005. At the same time red meat consumption in Brazil increased by 70% ( von Braun, 2008).

There are two aspects in changes in dietary patters. The first is the increase in income per capita. The second issue is that there is an increased consumption of livestock products that purely by the independent decision of consumers. This can also be caused by a shift in preference from lowly priced caloric foods like cereal to more epensive caloric foods like meat and egg which ultmately leads to the increase in the consumption of cerealdue tothe price. The conversion ratio between feed to meat varies widely depending on the type of meat. For example int he U.S, approximately 7 kilograms of corn produces either one kilogram of beef or pork, chicken is considerably lower with one kilogram of corn producing 2.6 kg of chicken (ERS, 2008). This means that increased consumption of beef represents increase in consumption of cereal several fold. (Chand, 2008).

While China and India have received much attention, some studies differ with the others on the impacts of China and India. Abbott et al. (2009) argue that world prices are formed by those who trade. China and India have both followed policies aimed at agricultural self-sufficiency, and neither are major traders of most agricultural commodities. However, China’s rapidly growing oil imports have had an indirect effect on food prices by impacting world prices for crude oil. Mitchell (2008) argued that 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 oilseedprices as China increased soybean imports for its livestock and poultry industry. BothChina and India have been net grain exporters since 2000, although exports have declinedas consumption has increased. Global consumption of wheat and rice grew by only 0.8and 1.0 % per annum, respectively, from 2000 to 2007 while maize consumptiongrew by 2.1 % (excluding the demand for biofuels in the U.S.). This was slower than demand growth during 1995-2000 when wheat, rice and maize consumption increased by 1.4, 1.4 and 2.6 % per annum, respectively.

China imported less wheat in 2000-2007 (33.8 million metric tons) than it did in the preceding eight years (40.3 million mt), and its rice imports also declined slightly from already low levels (just over 5 million mt). Indian imports of wheat and corn have also been negligible, and India is generally a net exporter of rice. If China and India have contributed to the crisis, they have done so through very indirect channels, such as by influencing the demand for oil and global trends in stocks. The one agricultural commodity group for which China and India have sizably increased their demand is oilseeds, but this surge began in the mid 1990s. This increased oilseeds demand from Asia had had some effect on global markets. For example, soybean imports within the developing world rose from 20.4 to 33.5 million metric tons from the mid 1990s to the present, a trend which contributed to US farmers increasing their soybean production area by over 11 million hectares. (Headey and Fan, 2008).

Headey and Fan (2010) argue that this research theme was prominent prior to, and independent of the 2008 food crisis. However, surging demand from China and India turns out not to present any compelling linkages to the crisis. At the national level both countries are largely food secure, so they rarely rely on substantial food imports, except some oilseeds. It is true that both countries, especially China, have experienced greatly increased demand for energy and other minerals, but their demand is by no means the only cause of rising oil prices and is perhaps not even the main cause (Headeys and Fan, 2010).

2.2.1.3 Financial market speculation

Another factor that is said to have played a role in the current crisis is speculation in financial markets. This explanation has been widely discussed, but it is poorly understood and has been only superficially researched, with the exception of a recent Conference Board of Canada working paper (CBC, 2008) that provides an authoritative review of the issue (Headey and Fan, 2008).

Commodities-related financial markets have expanded rapidly and gained importance in recent years. The expansion of commodity financial markets creates new opportunities as well as challenges. On the one hand, financial markets can enhance the liquidity, depth, and fluidity of commodity trades, which helps price discovery. Commodity financial markets also contribute to the efficient allocation of risk. Financial hedging, as a form of insurance, can be used by commodity market participants to reduce risks associated with excessive commodity price volatility that complicate budgetary, financial, and investment plans. On the other hand, the simultaneous increase in prices and in investor interest, especially by speculators and index traders, in commodity futures markets in recent years can potentially magnify the impact of supply-demand imbalances on prices. Some have argued that high investor activity has increased price volatility and pushed prices above levels justified by fundamentals, thus increasing the potential for instability in the commodity and energy markets. Despite the the difficulty of identifying speculative and hedging-related trades, a number of recent studies seem to suggest that speculation has not systematically contributed to higher commodity prices or increased price volatility. Some studies show that speculative activity tends to respond to price movements, suggesting that the causality runs from prices to changes in speculative positions (IMF, 2006). In addition, the Commodity Futures Trading Commission has argued that speculation may have reduced price volatility by increasing market liquidity, which allowed market participants to adjust their portfolios, thereby encouraging entry by new participants. Finally, although many transactions are described as speculative, they may in fact reflect a precautionary desire to hedge exposures in the face of uncertainty (Helbling et al., 2008).

The role of speculation in futures markets has been much discussed as a cause for extreme shifts in agricultural and energy prices from 2006 to 2008. The factors identified played a more important role in price shifts than did speculation in futures markets. This does not infer that speculation may have played a role in the volatility, but it clearly was not the primary cause as some imply. Markets tend to overshoot—both by going up and going down—and additional funds in the commodity markets may have accentuated these short-term impacts (Abbott et al., 2009).

Several reports in the international media indicate that professional speculators and hedge funds drive up the prices of basic commodities in commodity futures following the collapse of the financial derivatives markets. These dealers are reported to be shifting investments out of equities and mortgage bonds and ploughing them into food and raw materials. However, the impact of such investments is expected to peter out in the long run with fundamentals assuming a determining influence on the market (Chand, 2008).

One of the principal reasons for concern over futures markets is that their emergence has brought the increasing participation of “noncommercial” participants in agricultural markets, or speculators. However, causal linkages between futures and spot prices are unclear (Headey and Fan, 2008).A reflection of speculative and investor activity was the quadrupling of thenumber of wheat futures contacts traded on the Chicago Board of Trade from 2002 to2006. However, the increase in futures contracts does not coincideclosely 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 activitychanges prices from the levels which would have prevailed without such activity (Mitchell, 2008 from Gilbert, 2007), however, they may change the rate of adjustment to a new equilibrium whenfundamental factors change (Mitchell, 2008).

Headey and Fan (2008) argue that speculation may be more a symptom of underlying volatility than a cause of that volatility. Also, many of the charges made against financial markets relate to their efficient function more than their effect on spot prices per se. Headey and Fan (2010) argue also that it is impossible to discern causality in the context of futures markets, even from time series econometrics, as futures-market variables represent expectations of the future. Thus the usual Granger-causality tests are potentially irrelevant, because expectations of price rises at time t might be noncausally associated with higher prices at time t + 1. However, whether or not futures market activities were a cause of the crisis, they find it unlikely that they were a driving force, if only because they have substantial confidence in several of the more tangible explanations of the crisis: oil prices, biofuels demand, a depreciating U.S. dollar, and various trade shocks, in particular.

Finally, what evidence there is of impacts on spot prices is largely anecdotal, and again, rarely indicative of causality. The contract price volatilities of corn and wheat futures price indexes have increased from 19.7% and 22.2% since 1980 to 28.8% and 31.4% in 2006–2007, respectively (Schnepf, 2008), and both the price level and volatility formost agricultural commodities have continued to rise in 2008. However, a study of the emerging lack of convergence between cash and futures prices has not identified any significant causal factor (Irwin et al., 2007). Other analysts have suggested that agricultural commodity markets are now playing a role traditionally reserved for gold and other precious metals—a safe haven for investors—but data from the U.S. Commodity Futures Trading Commission (CFTC) suggest that the balance between long (noncommercial) and short (commercial) positions has been more or less maintained. Another charge is that securitized foods have experienced more price volatility than nonsecuritized foods (van Ark, 2008). Yet several nonsecuritized foods have indeed experienced rapid price increases,4 and the fact that the securitized commodities may have been selected for futures markets precisely because of some distinguishing characteristics—for example, rising or less elastic demand, greater volatility, larger U.S. production—suggests that simple comparisons of securitized and nonsecuritized futures prices may not be valid in any case. In summary, although futures markets may have exacerbated the volatility in agriculturalmarkets, they are unlikely to be a leading cause of the overall price surge, since there is little evidence that these markets significantly influence “real” supply and demand factors (Headey and Fan, 2008).

2.2.1.4 Depreciation of the USD

Another commodity-wide explanation of surging prices is the depreciation of the U.S. dollar (USD) over the last years, especially against the euro. The effects of dollar depreciation became stronger when more currencies than just the euro began to appreciate against the dollar and, starting about August 2007 when the U.S. Federal Reserve Bank began to loosen monetary policy to fight impending recession, further weakening the dollar. Since July 2008, the dollar has appreciated against the euro and against many other currencies, especially those of developing countries. This has contributed to rapid agricultural commodity price declines in dollar terms, though less so in other currencies (Abbott et al., 2009). The U.S. dollar depreciated about 35% against the euro from January2002 to June 2008 (Mitchell, 2008). The depreciation of the USD can clearly account for the rise in dollar-denominated food prices in an arithmetical sense, cutting off 20-30% of the nominal dollar increase in the case of conversion from USD to Euros (Headey and Fan, 2008).

The depreciation of the dollar has been shown to increase dollar-denominated commodity prices with an elasticity of between 0.5 and 1.0 (Gilbert, 1989). Shane and Liefert (2007) argues thatthe elasticity should beless than 1.0, because the exchange rate does not pass-through completely in manycountries due to policies.If the elasticity istaken as the mid-point of the range from 0.5 to 1.0, the increase in food prices due to thedecline of the dollar would have been about 20 percent (with an elasticity of 0.75) between January2002 and June 2008 (Mitchell, 2008).

TheU.S. dollar exchange rate affects commodity pricesbecause most commodities—in particular, crude oil, preciousmetals, industrial metals, and grains such as wheatand corn—are priced in U.S. dollars. The effective dollardepreciation seen over the past few years therefore has madecommodities less expensive for consumers outside the dollararea, thereby increasing the demand for the commodities (Helbling et al., 2008). As Abbott et al. (2009) discuss, when the dollar weakens, agricultural exports (particularly grain and oilseeds) also increase, ceteris paribus. Abbott et al. (2009), find that the dollar depreciated 22% and the value of agricultural exports increased 54% from 2002 to 2007. Considering that the US is a large country in international agricultural markets, especially in the case of wheat, corn and soybeans, depreciation of the USD should lead to higher prices in the US, but lower prices in the rest of the world. Abbott et al. also show that in the current crisis the divergence between the dollar and many other currencies has been quite stark compared to previous increases in nominal dollar-denominated food prices.

When the U.S. dollar declines in value in international exchange markets relative to the currency of. export competitors of the U.S. (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.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. The record shipments of grains and oilseeds have helped to draw down U.S. stocks and fuel higher commodity prices (Schnepfh, 2008).

On the supply side, the declining profits in local currencyfor producers outside the dollar area have put price pressureson the commodities. A decline in the effective value ofthe dollar also reduces the returns on dollar-denominatedfinancial assets in foreign currencies, which can make commodities a more attractive class of “alternative assets” toforeign investors. Finally, dollar depreciation canlead to monetary policy easing and lower interest rates inother economies, especially in countries whose currenciesare pegged to the dollar, which also raises the demand forcommodities (Helbling et al., 2008).

Causality is difficult to sort out since both the exchange rate and commodity prices are determined simultaneously by macroeconomic performance and policy in the United States and abroad. Macroeconomic forces, such as global recession and financial crisis, are critical to explaining the recent evolution of the dollar, crude oil prices, and agricultural commodity prices, although market specific factors also matter in each case (Abbott et al., 2009).

2.2.1.5 Low Interest Rates

Another explanation is that low real interest rates, especially in the US, have caused a general price increase in a wide range of commodities. With the rapid expansion of commodity financial markets in recent years, many commodity prices are more directly exposed to various macrofinancial shocks. The main reason is that spot prices of a growing number of commodities are determined in exchange-based trading. Although such trading has long existed for some agricultural commodities such as grains, it has recently become more prevalent for other commodities. Moreover, with many futures contracts settled in cash rather than through the delivery of the underlying commodity, investors outside the commodity business can now use commodities to diversify their portfolio, thereby more closely linking futures markets for commodities with other financial markets. This has opened up new opportunities for market participants but also led to challenges (Helbling et al., 2008).

Low interest rates increase the demand for storable commodities, increase the desires of firms to carry inventories, and encourage speculators to shift out of treasury bills and into commodity contracts. All three of these mechanisms work to increase the market price of commodities, in what is often known as “carry trade.” It is questionable, however, whether this explanation is actually consistent with the evidence. One inconsistency is that agricultural inventories are low rather than high. Moreover, the diversion of assets from treasury bills and the like to commodities may have influenced agricultural futures prices,but the jury is still out as to whether this has had a substantial effect on spot prices (Heady and Fan, 2008).

Low interest rates can spur aggregate demand, which would increase the demand for commodities. Besides this growthrelated effect, the favorable liquidity conditions associated with low interest rates also tend to increase both asset demand for commodities (partly because low-yielding treasury bills are less attractive) and incentives for holding commodity inventories by lowering holding costs, everything else being equal (Helbling et al., 2008).

2.2.2 Supply-Side Factors

Increased demand alone cannot explain the large and persistent rise in commodity prices seen in recent years.Supply factors also play a role. Slow supply responses have amplified price pressures. The slow supply response in the initial phases of this primarily demand-driven boom did not come as a surprise, given limits to production increases in the short term. Excess demand is accommodated by inventorydrawdowns while prices increase - a pattern that was seen in many commodity markets in recent years. Because the demand for commodities tends to be price inelastic - that is, a large change in the prices of commodities leads to only a small change in the demand for them, especially in the short term - the feedback effects of rapid price increases on demand during these phases tend to be limited, which partlyexplains the large spikes often seen in commodity markets. Besides initial supply-response problems, however, a new key feature that has emerged in the current broad-based commodity market boom is the increasingly prominent role of the slow supply adjustment to increased demand. Such structural problems have been particularly acute in the case of oil, where capacity growth in response to persistently higher prices has been disappointing in recent years. And, as the pessimistic prospects for capacity growth have seemed more certain, these expectations have further fueled price pressures. This was particularly the case in 2007. Key handicaps have been the declining average size of fields and the technological challenges involved in the increasing reliance on exploiting nonconventional fields. These supply rigidities, together with soaring demand for oil equipment and services, have pushed up costs dramatically (Helbling et al., 2008).

Sharply risenoil prices have a direct impact on cereal prices, especially on wheat and corn prices in several ways – through an increase in prices of fertilisers and agriculture chemicals used as inputs, through an increase in the cost of operation of farm power and machinery, and through an increase in transport cost and also an indirect impact through substitution effects.This explanation has no critical weaknesses, even thought there are several authors who expect the effects of rising oil prices on food prices to be more delayed and to have a larger impact via biofuel demand (Heady and Fan, 2008).

Weather-related production shortfalls offer another commodity-specific explanation for world cereals price rises, specifically for wheat (Headey and Fan, 2008), especially in Australia, U.S., EU, Canada, Russiaand Ukraine. Australian wheat production was 50- 60% below trend growth rates in 2005 and 2006; there were also moderately poor harvests in US, Russia and Ukraine. The weakness of this explanation is that it only explains wheat prices (Heady and Fan, 2008).

Another supply-side explanation is decline of stocks, which is traditionally associated with increased sensitivity to shocks; stocks of all major cereals declined prior to the price surge. This might constitute a crop-specific explanation, especially given that stocks have declined for maize, wheat and rice. The weaknesses of this explanation are that netting out China makes the decline in stocks less dramatic and unless stock declines result from policies, declines only represent the effects of other factors (Heady and Fan, 2008).

Export bans and restrictions which are imposed by Argentina, India, Kazakhstan,Pakistan, Ukraine, Russia and Vietnam fueled the price increases by restricting access to supplies.Price rises for rice were preceded by export restrictions in countries that account for 40% of global rice exports. The weaknesses of this explanation are that wheat, maize and soybean price rises generally preceded restrictions, and the biggest players did not impose restrictions (Heady and Fan, 2008).

2.2.2.1. Rising Oil Prices

The increase in prices of crude oil, gas and such sources of energy affects almost all sectors. It has a direct impact on cereal prices in several ways – through an increase in prices of fertilisers and agriculture chemicals used as inputs, through an increase in the cost of operation of farm power and machinery, and through an increase in transport cost. Between 2004 and 2007, crude oil prices increased by 89 % (Chand, 2008). Higher oil prices have also had an important effect on other commodities, not only through the traditional cost-push mechanism (because oil is used as an input in agriculture and the production of metals such as aluminum) but also through substitution effects. For example, natural rubber prices have risen because its substitute is petroleum-based synthetic rubber. Uranium price increases have been driven by demand for nuclear energy, whereas coal prices have recently risen because of utilities’ switching from more expensive fuel oil to coal for power generation. And, of course, biofuels are substitutes for gasoline and diesel at the margin (Helbling et al., 2008). Historically, energy and agricultural markets were largely independent, each influenced by their respective supply and demand situations. That is no longer the case. Since 2006, energy and agricultural markets became closely linked as biofuels production surged. Ethanol and biodiesel were linked as energy substitutes for gasoline and diesel, and usage of crops for these biofuels became large enough to influence world prices (Abbott et al., 2009). Energy and agricultural prices have become increasingly intertwined. 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 (von Braun, 2008). The long-run association between the prices of crude oil and food can be seen fromFigure 2.1by Chand (2008) using IMF and USDA’s data. This presents the index of food and crude oil prices with base 2005 = 100 and shows that fluctuations in crude oil prices are much higher and bigger than fluctuations in food prices.

Source: Chand, 2008

Figure 2.1. Crude Oil Price Index and Food Price Index (base 2005=100)

Food prices are not affected by small fluctuations in crude prices, but a large and consistent decrease or increase exerts a very strong influence on food prices. This is evident from the correlation between crude and food prices in different phases of the trend in crude prices. When crude prices fluctuated around a flat trend then food prices followed an almost independent trend, affected by other factors. This was in the period 1987 to 1999 (with a correlation of 0.244). However, when crude prices followed a sharp decline for a couple of years, then food prices also declined, though less sharply than crude prices (1980 to 1986). Conversely, when crude oil prices rise sharply for couple of years, food prices also increase sharply as is evident from the correlation for the period 2000 to 2007, which was as high as 0.95. The impact varies across commodities, regions and farming practices (Chand, 2008).

Mitchell (2008) argues that high energy prices have contributed about 15-20% to higher U.S. food commodities production and transport costs. Production costs per acre for U.S. corn,soybeans and wheat increased 32.3, 25.6 and 31.4 %, respectively, from 2002 to2007.However, yield increases during this period reduced the per bushel costincreases to 17.0, 24.1 and 6.7 %, respectively. The contribution of the energy intensivecomponents of production costs—fertilizer, chemicals, fuel, lubricants andelectricity—were 13.4 % for corn, 6.7 % for soybeans and 9.4 % forwheat per bushel. The production-weighted average increase in the cost of productiondue to these energy-intensive inputs for these crops was 11.5 % between 2002 and 2007. In addition to the increase in production costs, transport costs also increased due tohigher fuel costs and the margin between domestic and export prices reflect this cost. Thus, the combined increase inproduction costs and transport costs for the major U.S. food commodities—corn, soybeans and wheat—was at most 21.7 %, 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 % between 2002 and 2007 (Mitchell, 2008).

Finally, the energy used in agricultural production is mostly oil-related, and oil prices have risen faster than the prices of other energy sources. Moreover, US food production– which dominates world food production and export markets– is especially oil-intensive. Oil prices also affect the prices of fertilizers and other chemicals used in crop production. For wheat and corn, fertilizer prices alone account for over a third of total operating costs and 15-20% of total costs (Headey and Fan, 2008). Factoring in the rising costs of fuel, fertilizers and other oil-related farm productions, Headey and Fan (2008) estimate that oil prices increased the costs of US production of corn, wheat and soybeans by 30-40% over 2001-2007 relative to a baseline scenario in which oil-related prices only increased by the inflation of the US GDP deflator. These fuel-based cost increases correspond to about 8% of the observed corn price increases, 11% of soybean price increases, and about 20% of wheat price increases.

2.2.2.2 Weather-Related Production Shortfalls

Weather shocks offer another commodity-specific explanation for World cereals price rises, specifically for wheat (Headey and Fan, 2008), especially in Australia, U.S., EU, Canada, Russiaand Ukraine (OECD-FAO, 2007).

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 (Schnepfh, 2008). The back-to-back droughts in Australia in 2006 and 2007 reduced grain exports by an average of 9.2 million tons per year compared with2005 (Mitchell, 2008). Meanwhile, grain crops in the U.S., Canada, EU, Eastern Europe, and some countries of the former Soviet Union were also reduced by weather conditions (Schnepfh, 2008). Poor crops in the EU and Ukraine reduced their exports by an additional 10million tons in 2007 (Mitchell, 2008). 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 (Schnepfh, 2008).

Theproduction shortfall was most significant in wheat, where global production declined 4.5percent in 2006 and then increased only 2 percent in 2007. Global oilseed production rose5.4 percent in 2006/07 and declined 3.4 percent in 2007/08 (Mitchell, 2008). Australian wheat production was 50-60% below trend growth rates in 2005 and 2006. The U.S. also experienced a poor harvest in 2006, some 14% lower than the previous year, and more modest declines were seen in Russian and Ukrainian production. However, a closer inspection of the data suggests that this intuitively attractive explanation is not as convincing as it first appears. The main problem is that annual production shortfalls are a normal occurrence in agricultural production in general and in wheat production in particular. Global wheat production declined by 5% in 2006/07, but it also declined by 11% in 2000/01 and 6% in 1993/94. The U.S. wheat production fell by bigger margins in 1991/92 (27%), 2001/02 (13%) and 2002/03 (18%), and a closer inspection of Australia’s wheat production since 1990 shows other years when harvests were well below trend: by 51% in 2002, and by 50-100% from 1993 to 1995. Moreover, the output declines seen in several countries in 2007 were offset by large crops in Argentina, Kazakhstan, Russia and the U.S., whose wheat exports increased by around 13% (or an additional 7.5 million mt) compared to those in 2006. Therefore, while overall global grain production declined by 1.3% in 2006, it then increased 4.7% in 2007 (Headey and Fan, 2008).

Thus theproduction shortfall in grains would not, by itself, have been a major contributor to theincrease in grain prices. But when combined with large increases in biofuels production,land use changes, and stock declines it undoubtedly contributed to higher prices (Mitchell, 2008).

2.2.2.3Decline of Stocks

Another supply-side explanation is stock declines, which could influence price volatility by determining the stability of supply. This might constitute a crop-specific explanation, especially given that stocks have declined for maize, wheat and rice. World stocks for maize, for example, declined from 26% of usage over 1990-2000 to just 14% of consumption from 2005-2008, but excluding China from the global figures suggests that world stocks remained the same over the two periods, at just 12%. Nevertheless, a large number of major producing and exporting countries have incurred substantial stock declines in recent years (Headey and Fan, 2008). The period from 1998 to 2005 was one of high stocks and low prices. The world was reducing stocks as production dropped below usage in most of those years. By 2006, excess grain and oilseed stocks had been eliminated. Low world production in 2006 and 2007, in combination with on-going food demand growth and large added demands for biofuels, drove global stocks to extremely low levels by mid-2008 with expectations of continued low stocks until 2009. Going into the spring of 2008, expectations were for dangerously low stocks. High prices helped stimulate production with larger area and greater use of inputs. Over time, high prices also reduced world usage. Revisions in supply and use estimates since June 2008 have generally increased world output and reduced usage (Abbott et al., 2009).

Recent data and strong historical covariance between prices and stocks superficially suggest that stock declines could substantially account for recent price movements. However, there are some significant caveats to this conclusion. Most importantly, declining stocks might simply reflect increased demand or reduced production levels. Biofuel production offers a promising explanation for declines in maize stocks, and bad weather, stagnating production growth and low prices seem to account for the almost pervasive decline in wheat stocks (Headey and Fan, 2008). Headey and Fan (2008) argue that for stock declines to be causally related to the current crisis, they must therefore be associated with exogenous policy decisions, or other forces. And they see three such policy decisions that could support a causal relationship. First, it may be that stocks were so high and prices were so low prior to 2000 that there appeared to be a need to reduce stocks. Second, the increasing use of just-in-time inventory systems may have led to lower stocks. These two explanations are plausible but generally difficult to prove.

Headey and Fan (2008) also argue that stock declines are consistent with rising prices but not as causally convincing as it might appear at first glance, partly because they are a symptom of deeper causes, and partly because their effects on prices are enacted through interactions with other factors. It is also possible that excessively high stocks in the 1990s (and before the 1974 crisis) were actually an underlying cause of the crisis: The use of stocks to satisfy increasing demand may have delayed price rises that would otherwise have provided a stronger signal of rising demand. In terms of policy implications, it is therefore not altogether clear that increasing stocks once more would prevent further food crises.

2.2.2.4 Export Bans and Restrictions

Export bans and restrictions fueled the price increases by restricting access to supplies.A number of countries have imposed export restrictions or bans on grainexports to contain domestic price increases. These include Argentina, India, Kazakhstan,Pakistan, Ukraine, Russia and Vietnam (Mitchell, 2008). 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 (Schnepfh, 2008).

For rice, in particular, export restrictions are a very compelling explanation, first because a number of important exporting countries that imposed restrictions, and second because rice is much more thinly traded relatively to other staples, with only around 7% of global production being traded over the last five years (Heady and Fan, 2008). In the international rice market, the traditional exporters including Vietnam, India, China, and Egypt, (the world’s second-, fourth-, sixth-, and eighthleading rice exporters in 2007), 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 international rice prices sharply higher (Schnepfh, 2008).

A closer look at the timing of export restrictions and rice price increases also suggests causality. From August 2005 until November 2007, rice prices increased steadily and significantly, by about 50% (in real terms) from an all-time low in 2005. In November of 2007, India imposed the first major export restriction. This appears to have been the turning point for rice prices. From November 2007 to May 2008, rice prices increased by 140%, despite an all time production high in 2007, the complete absence of any significant increase in demand, and fairly stable rice stocks (with the exception of non-trading China). In early 2008, panic ensued as the rise in other commodity prices began to attract much more concern in Asian markets. This prompted further export restrictions from Vietnam, Cambodia and Egypt, and precautionary rice purchases by the Philippines, which imported 1.3 million metric tons of rice in just the first four months of 2008 (an amount that exceeded their entire import bill of 2007). This surge continued until May, when Japan released 200,000 tons of rice to the Philippines. Prices fell almost immediately. This was followed by further price declines after Cambodia lifted its export ban in June. Hence, it appears that the remarkable and very costly surge in rice prices in 2008 was largely due to the traders’ reactions to export restrictions, plus hoarding by a number of important players in what was already an unusually thin market (Heady and Fan, 2008).

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. By mid-April, Kazakhstan had converted its export slowdown into an outright ban. According to Schnepfh (2008) Argentina’s government policy of banning wheat and beef exports, slowing corn exports through procedural barriers at customs, and heavily taxing exports of soybeans 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 was temporarily suspended for 30 days starting on April 3, 2008, 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 in 2008 (Schnepfh, 2008).

Similar outcomes were observed in the 1974 crisis as a result of export restrictions on soybeans, wheat, rice and fertilizers. The tragedy of these restrictions is that they effectively sacrifice international price stability for the sake of domestic price stability, as Johnson (1975) noted after the 1974 crisis (Heady and Fan, 2008).

2.2.3. The Consequences of The Crisis

A number of factors suggest that the recent surge in food prices had-an may still be having- a severe impact on the poorer populations of the world, perhaps even throwing more than 100 million people into poverty (Headey and Fan, 2008 from Ivanic and Martin, 2008; World Bank, 2008). All assumptions and predictions concerning the concequences of sharply rising food prices require much closer examination and often significant qualification. The group most vulnerable to rising food prices is generally the urban poor, but this group is also far more vociferous than the rural poor (Headey and Fan, 2008 from Bezemer and Headey, 2008). Thus, protests may be evidence of suffering, but not of net suffering: price changes always create winners and losers, and judging who is who requires accurate data and careful analysis at both the macro and micro level (Heady and Fan, 2008).

Several studies estimated the impacts of rising food prices, but according to Heady and Fan (2010) they often generated unconvincing results, and most were limited by the absence of general equilibrium effects, country-specific price changes, and other relevant shocks, such as rising fuel prices. Moreover, judging who is negatively affected requires accurate data and careful analysis of food dependency, poverty/vulnerability, and price changes at both the micro and macro levels. A great deal of progress has already been made especially in micro-level, but a large gap still exists between micro- and macroassessments of the consequences of the crisis. A further distinction must also be made between the short and long terms. In the short term the adjustment costs of responding to rapid price changes may be prohibitively high and painfully slow. But even in the long term the ability of the poor to make adjustments depends on their access to productive assets and on national and international policies aimed at raising agricultural output or successfully pursuing other strategies to increase food security (Heady and Fan, 2010).

Ideally, a full assessment of the short-term impacts of the crisis on poverty requires consideration of both macroeconomic impacts and transmissions, as well as household and intrahousehold effects, for both food and fuel price increases. However to partially bridge this gap, Heady and Fan (2010) analyze the Global Information and Early Warning System (GIEWS 2009) dataset on food prices in developing countries, the impacts on farmers remain unclear.

OECD (X) reports that 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. 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 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 (very probably less than 35%), as is the proportion of disposable income spent on food (For many OECD countries its between 10 to 15 %). 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 (OECD, 2008).

2.2.3.1. Macroeconomic impacts

Many studies on the impacts refer solely to food prices, but any comprehensive assessment of current poverty trends needs to incorporate changes in a range of prices, including fuel costs and fertilizers. By effecting transport costs, exchange rates, foreign reserves, and domestic inflation oil prices have a pervasive effect on a country’s vulnerability to the recent crisis. That is why the most relevant macroeconomic assessments of the crisis incorporate the effects of rising oil prices (Heady and Fan, 2010).

This section reviews the studies which cover the macroeconomic impacts such as Headey and Fan’s (2010) review of impact studies; Dawe’s (2008) study on the strength of transmission from international to domestic prices and IMF report (2008) and Aksoy and Ng’s (2008) study on the impact of rising food prices on import bills.

2.2.3.1.1. Import bills

Aksoy and Ng (2008) calculate both food and general agricultural net import bills for low-, middle-, and high-income countries. While doing this, they disaggregate within each category by oil exporters, conflict states, small islanders, and “normal” countries. According to their study, once the three special groups are omitted, the typical “normal” low- and middle-income country has gone from being a net food importer in 1980/1981 to being a net food exporter in 2004/2005. However, Africa still contains a large number of oil exporters and conflict states, as well as other exceptions, meaning that most African countries (35 of 47) are still net importers of food, even though most are also net exporters of all agricultural goods (32 of 47). Moreover, only six low-income countries have food deficits that are more than 10 % of their imports, so most net food importing developing countries are marginal net food importers. Aksoy and Ng (2008) also identify countries with considerable potential to switch from being net exporters of non-food agricultural products to net exporters of food.This switch is much less relevant to the short-term impacts of the crisis, since switching from cash crops to food production may be time-consuming and costly. According to Aksoy and Ng (2008) the severity of food dependence is often overstated.

One might regard the rise in oil prices as a more serious threat to macroeconomic stability in developing countries (Headey and Fan, 2010). According to IMF (2008), oil import costs are 2.5 times larger than food imports for low-income countries and twice as large for middle-income countries, so the impact of commensurate price increases is much greater for oil. The study argues that the absence of policy responses, the impacts of oil prices are considerably larger than those of increases in food prices. It estimates that for 33 net food-importing countries with available data, the adverse balance of payments impact of the increase in food prices from January 2007 to April 2008 is 0.5 % of 2007 annual GDP. During the same period, the impact of the increase in oil prices in 59 net oil-importing countries is estimated to be 2.2 % of GDP (IMF, 2008).

Finally, although we have no data on overall terms-of-trade movements that factor into other commodities, we know that many net exporters of other minerals have also benefited from rising commodity prices to some extent, as have countries that are net exporters of labor to oil-producing countries (Headey and Fan, 2010).

2.2.3.1.2. Exchange rate movements and foreign reserves

As noted before, a number of currencies have appreciated against the U.S. dollar, the currency in which food and oil exports prices are usually denominated. The euro area and the West African franc zone (which is pegged to the euro) have appreciated against the U.S. dollar by 80 % from 2002 to 2008. Thus the adverse effects of rising commodity imports for many West African countries should, all else being equal, be limited from a macroeconomic perspective. In contrast, the Central American and Caribbean area, which consists of currencies largely pegged to the U.S. dollar, and quite a large number of other countries around the world have experienced a stable rate of exchange with the dollar, while a few countries have experienced depreciation.In either case it is clear that although most countries have appreciated against the dollar, considerable variation remains in terms of countries’ vulnerability to rising dollar-denominated food and oil prices (Headey and Fan, 2010).

By using commodity-specific, trade-weighted U.S. dollar exchange rate index of USDA (2008), Headey and Fan (2010) calculate that relative to dollar-denominated prices, export prices denominated in the currencies of other exporters of maize, wheat, and rice have declined by about 15–17 % from 2002 to 2008. According to them, the relationship between dependency on U.S. food imports and exchange rate movements should be investigated, because countries that are both dependent on the United States and have not benefited from appreciation against the dollar may be particularly vulnerable in a macroeconomic sense. Since the United States is the only highly dominant cereal exporter in the world, it is therefore worth investigating the relationship between dependency on U.S. food imports and exchange rate movements against the U.S. dollar. Such an analysis suggests that the regions that are most dependent on the United States as a source of food imports are Central America, the Caribbean, and some of the more northern countries of South America. A few other countries and regions are fairly dependent on the United States for food imports, but many such countries are either wealthy or experienced large real appreciations against the U.S. dollar.As for foreign exchange reserves, the International Monetary Fund has enumerated the months of imports as of the first quarter of 2008. Disconcertingly, the Caribbean and Central American countries appear to be highly vulnerable in this dimension as well. This evidence therefore suggests that, so far, it is the Central American and Caribbean countries that have been most vulnerable to rising U.S. dollar–denominated export prices because of their dependence on U.S. exports and their lack of any major compensating currency movements. Another indirect indicator that countries are suffering as a result of rising prices is the change in cereal imports in 2007 and 2008. USDA data suggest that most developing regions experienced declines of 10–20 % in 2007 or 2008 (Headey and Fan, 2010).

2.2.3.1.3. Transmission in domestic markets and the impact on inflation

The steps of the transmission of rising international prices into domestic markets are the conversion of dollar-denominated international prices into local currency prices and domestic policies that alter the local price of foods. However, the effects of these factors are generally bundled together as a residual which reflects a range of other factors. Thus it may be possible that the change in domestic prices is very high, even though there is little transmission of international prices (Heady and Fan, 2010).

Heady and Fan (2010) argues that both commodity-specific prices and consumer price indexes can be used to assess to what extent the international prices be transmitted to domestic markets. According to them, commodity-specific approaches are useful for assessing transmission proper, including the impacts of exchange rate movements. In principle, the food consumer price index (CPI) is more comprehensive and should be a better indicator of welfare costs, especially when it is deflated by the non-food CPI to look at the terms of trade for food, or real food-price trends.

FAO’s study (Dawe, 2008) on the recent crisis reanalyzes the extent of price transmission in seven large Asian countries from the fourth quarter of 2003 to the fourth quarter of 2007, a period which admittedly does not capture the full international price increase, especially in rice. Overall transmission—measured as the ratio of LCU (local currency units) denominated retail price changes to U.S. dollar-denominated export prices—varied considerably among the seven countries. In India, the Philippines, and Vietnam, the pass through was just 6–11%, but in the remaining countries it was 41–65%. Interestingly, movements in the real exchange rate explain more than half of the price difference between U.S. dollar-denominated export prices and LCU denominated local currencies, the main exception being Bangladesh. Dawe (2008) also found that transmission of wheat prices appeared to be partial in India and Indonesia, but fully transmitted in Bangladesh (Dawe, 2008).

Price changes ca be determined from recent trends on inflation such as the food inflation (WHO, 2008) for 2007 and the beginning of the year 2008. Other researchers such as Headey and Fan (2008) base their data regionally and with it to show non-food inflation. The results that they present is from estimates on householf food expenditures. To determine food price change they calculate the difference betweent food and non food inflation. Food inflation in all aread was 9.5% to 18% which is a very high percentage. This is not an accurate representation however. Averagely, Africa has experienced lower food inflation in comparison to non food inflation compared to other continents. To extract the inflation data from the the regions that are constitute Outliers, the reserchers grouped smaller regions in the ecompasing area and used inflation data from the year 2005 to 2008. For example,in one case they used data from 5 key expoters in Africa. The data derived from this was consistent to estimates made earlier. The first consistency was that price rose in countries with domestic issues like weather probles and conflict; those countries were Kenya, Myanmar and Ethiopia. Ghana is considered an outlier country but its also a country whose rise in transmission tell a different story. Although this country imports basic cereal like wheat, rice and maize, the dietary patterns are very diverse and the ghanaian currency has experienced appreciation against the universally accepted Dollar. For Ghana as a country, the inflation that it experiences is due to large remitances, oil prices and government spending. Yemen is a perfect example of a country that is price increase sensitive since it is almost entirely dependent on imports. A total of five mineral exporting countries faced inflation die to rising export earnings and what is commonly refered to as the Dutch Disease. Nigeria is an example of a country which has non food inflation passing specific food inflation. Dependence on oil and rice imports and reduced chances of opportunities of exchanging currencies have caused inflation in some Ssouth Asian countries. Central Asian countries have also suffered due tothe Dutch Disease. Low earning countries in central and caribbean countries have also experienced high inflation naturally. In african countries, the selected groups have recorded iflation of between 6-8 percent per year with West Africa registering the lowest inflation perhaps due to being tied closely to the Euro. This means that althoughh the African countries are vulnerable to economic dynamism, food inflation is cushioned by the fact that many African Households are net food buyers which means that the actual food prices have not had such an impact in Africa (Headey and Fan, 2008).

The buffer that is provide by the depreciation of the dollar currency is not permanent when it comes to the larger price transmissions. There is a school of thought that suggests that the Euro is highly overvalued which suggests that the dollar might be on the rise soon. The result of this is that ultimately west African countries will have a faster rate of price transmission. Secondly, transmissions may be low due to government involvement which will be most likely discourage price rises. The World Bank (2008) shows that as many as 84 countries have reduced significantly the taxes imposed on food while another 30 countries have put up export restrictions in various forms. The Imternational Monetary Fund estimates that these action have reduced signifintly the price of food and fuel. It is estimated that the iposed taxes and subsidies transfer the burden from the markets to the government and in fact, this burden is estimated to incresed the percentage deficits by at least 1 percent. Faulure to do this means that government would have to cut back on other exenditures that are important on for the poor such as health expenditure. Whether the mechanisms that are employed by the governments through taxes, subsidies and export restrictions are to absorb price rises is not clear but due to its importance, it merits further investigations. The second aspect that is noteworthy is the tax and transfer programs implications on distribution. Essama-Nssah (2008) give a clearer picture with a conceptional analysis and review . Arndt et al. (2008) gives an analysis of the implication of rising food prices in Mozambique. Due to the complexity of transmission mechanisms, the full transmission of the international prices is time consuming. Whilst food prices can be imported, food exporters also experience increase in prices. For example,in a country like Uganda, the increase in food prices is not due to diersity in dietary patters but due to rising food prices in neighbouring price in countries like Kenya and Tanzania. The reason for this is the fact prices in neighbouring countries put pressure on prices of the local markets (Benson, 2008). It is worth noting that not all regions within a country are affected the same. It is often the case that rural areas are not affected as much as urban areas when in comes to international price changes (Codjoe et al., 2008; Ulimwengu et al., 2008). This further shows that there is need to conduct additonal reserch that combines factors affecting both macro and micro economic issues (Headey and Fan, 2008).

2.2.3.2. Microeconomic vulnerability to rising food prices

Several papers follow Ivanic and Martin’s (2008) study of 9 countries across several continents in using microeconomic data to simulate the impacts of rising food prices on household poverty (Heady and Fan, 2010). This section reviews the studies which cover a range of countries and use quite similar methodologies despite their different sample sizes and scopes. These studies are the study by Ivanic and Martin (2008); the study by Wodon et al. (2008) of 12 West African countries; and the study by Dessus et al.(2008) of the urban sector of 73 developing countries. The basic approach in these papers follows Deaton (1989) in estimating the change in food welfare (ΔWFood) as the product of the food net-benefit ratio (NBRFood) and the change in food prices (ΔPFood):

ΔWFood = ΔPFood × NBRFood = ΔPFood × (YFood/YTotal – CFood/CTotal),

where YFood/YTotal is the ratio of food sales and own-production to total household monetary income, and CFood/CTotal is the ratio of food expenditure and own-consumption to total household expenditure. Notice that, by definition, own-production equals own-consumption, and because each enters into YFood/ YTotal and CFood/CTotal, respectively, the consumption of food produced by the household is netted out of NBRFood. Hence the main issues with microeconomic assessments of the poverty impacts concern the size of price changes, the numbers of net buyers and sellers, and the choice of poverty line (Heady and Fan, 2010).

None of these papers assesses the most likely impact on poverty because: a) They simulate results from admittedly quite recent macroeconomic surveys, b) They assume domestic food price changes for lack of actual data, c)They assume a limited range of behavioral responses by consumers and producers, d) Their surveys may overestimate net food consumers (see Aksoy and Isik-Dikmelik, 2008), e) They do not consider rising oil prices as a simultaneous shock to income and revenue streams, f) They do not factor in strong economic growth, which characterizes several developing countries that have been benefiting from strong commodity prices, g) The absence of better measures. They use international poverty lines which can potentially make a considerable difference to the results(Heady and Fan, 2010).

According to Heady and Fan (2010), in addition to these general limitations, the individual studies have some specific limitations. Wodon et al. (2008) consider different food items for different countries, which generally constitute dissimilar shares of total consumption, casting some doubt on whether their results are highly comparable. Dessus et al. (2008) only examine the effects of aggregate food consumption on urban poverty, assuming constant shares of food expenditures and fixed food/non-food elasticities across countries. Moreover, their estimates often contradict findings from the other two studies. These cross-country microeconomic studies point to the possibility of marked increases in hunger, but they do not provide reliable indications of the actual effects of rising food prices on poor and vulnerable people (Heady and Fan, 2010).

The net effect created by these stimulations implies those that would be vulnerable to soaring prices and not the populations that experiance difficulties as a result of the rapidly increasing food prices. since not one of the expreiments incorporated real price changes. As indicated above, the shifting nature of food prices is substantially variant from one country to the other, but the assumption made in this study is that only the common price varies across countries. However, these studies have a way of providing perspective and empirically applicable data for highlightinng vulnerability to shifting prices across countries and subnational clusters( e.g urban and rural)

Three studies clrearly point out the poverty change incidences(poverty headcounts) and also the level of the changes(for instance poverty gaps). Particularly the studies by Ivanic and Martin (2008) and Wodon et al. (2008) have greatly influenced the aspect of framing the responses of the Worldbank toward the crisis(World Bank, 2008) and as well enhancing other institutions' support. These four studies as well have incorporated useful comparisons, including similar simulation techniques microeconomic surveys. Some of the similarities listed are ,firstly, four of the papers focus on actuel shifts in food prices and not entirely all prices of fertilizers, although the soaring oil prices could much more contribute to poverty than food prices would. Secondly, the studies have explored the aspect of the short-run effects through precluding the producer and consumer behavioral responses, as well as the general or partial equilibrium consequences on prices of food -all of which are all very crucial aspects.

The four studies clearly agree with this concept and in addition the studies by Martin and Ivanic and Zezza et al. (2008) as well compute some partial equilibrium consequences on the income of households like robust tests. However neither of the studies come to a significantly varying finding from the rest, but not in the case of redistribution of the negative effects between rural and urban households by Ivanic and Martin’s computation of unskilled wage effects.

That notwithstanding , it is a possibiliyy for the existence of behavioral responses to the shifting food prices even in the short-span period. For instance, many households that a re pooor oftenly undertake a diversified range of income sources and as such ma substantially broaden the scope of their farm-based activities even with the soaring food prices. Summarily, the major methodological framework applied is such that all studies have similiarities based on the approach of Deaton (1989)

Most importantly, the prevalent consequences of the shifting food prices is dependent upon the number ogf the poor and vulnerable a.k.a above poverty line , regardless of whether these peple are net food buyers or sellers : marginal or not, but whether they are close to this characteristic. This calculus results into disturbing expectations through which the groups are expected to face difficulty should the food prices increase. The urban population is composed of an overwhelming number of net buyers but who are better off thatn their rural counterparts. However, we expect equally large number of net buyers in rural areas attributed to landlessness, low productivity, cash crop production as well as the prevalent number of nonfarm workers(Ahmed et al., 2007).

One way in which we can say that the studies by Ivanic and Martin’s are suprisingly insightful is by considdering the fact that they implore that the rate of poverty increase in rural areas is higher than in urban areas – in all the three countries where the survey was conducted. For instance in Zambia, the rate of poverty increase in rural areas is three times the urban poverty rate, regardless of the fact that the two rates were initially at par(NB: poverty rates to not incorporate the vulnerable population). Much of consequences on rural poverty that arise from these simulations do not agree both with our initial intuitions as well as miscellaneous evidence on these aspects. According to the analysis on similar surveys as the ones by Ivanic and Martin (2008), done by Aksoy and Isik-Dikmelik (2008), the conclusions drawn were such that a)As much as poor households are more of net food buyers , approximately half of them are net marginal buyers and b) The average income of net buyers is usually higher than the one of net sellers in 8 out of the 9 countries in the study.

An alternative explanation to the variations of rural poverty is the underestimation of the level of hown tmuch net sellers households are since the measure of consumption is usually hinged on production. Based on the same, rural household income cannot be calculated the same as in urban regions. As such, its possible that, some survey biases greatly affect these stimulations, although the strength bias is still unknown to us.

The last issue relates to the diversified nature of microeconomic vulnerabilities inter-countrywide. There are various factors that obviously influence household vulnerabilities to shifting food prices in different countries. According to Zezza et al. (2008), disaggregates vulnerability aspect among groupd, explaining vulnerability measures using OLS regressions. Of the 13 3rd world countries, they point out that the most vulnerable households are those : with less educated people, rural nofarm, with dependency on female laborers, landless and practicing modern agricultural practices.

As much intuitive as all the findings are, its noteworthy that the microeconomic evidence confirms our intuitions and also offering magnitudes to the most crucual household attributes in this study.

In summary, the studies make suggestions that poverty , in the short run, is bound to increase on condition that food prices rise substantially. Zezza et al. (2008) emphasizes on the household types most vulnerable to food price increase. The limitations of these stimulationare also crucial. Consequently, it is important to study about real price shifts and the adiitional consequences of fuel and fertilizer price increase as well as the short-term responses in the behaviors due to rising food prices and contributing effects of government policies to these outcomes.

3. MATERIALS AND METHODS

3.1. Methodology

This study derives categorical data from the qualitative data in order to quantitatively analyze different media coverage of urban consumers and rural producers under changes in relative incomes. Content analysis (CA), which is a method of chancing qualitative data into quantitative data, is chosen as a main method in the present study. Moreover, a summative approach to qualitative content analysis which goes beyond mere word counts to include latent content analysis (Hsieh and Shannon, 2005) is adopted in order to know the frequency of words that were used to refer to food crisis but also to understand the underlying contexts for the use of explicit versus implicit terms related with food crisis. A technique which combines summative approach to content analysis and regression analysis is a new technique in economics studies.

3.1.1. Content Analysis

Content analysis is a research method that uses a set of systematic, rule-guided techniques used to gather and analyze the content of text (Weber, 1990; Mayring, 2000). ‘‘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.’’ (Neuman, 1997). Berelson (1952) suggests that describing substance characteristics of message content; describing form characteristics of message content; making inferences to producers of content; makeing inferences to audiences of content; predicting the effects of content on audiences are main purposes of content analysis.

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. They differ, however, in the ways they generate categories and apply them to the data, and how they analyze the resulting data (Forman and Damschroder, 2008).

Comparing qualitative content analysis with its rather familiar quantitative counterpart may enhance the understanding of the method. Qualitative content analysis is used in order to explore the meanings underlying physical messages while quantitative content analysis is used widely in mass communication as a way to count manifest textual elements. Qualitative content analysis is mainly inductive, grounding the examination of topics and themes, as well as the inferences drawn from them while quantitative content analysis is deductive, intended to test hypotheses or address questions generated from theories or previous empirical research. samples for qualitative content analysis usually consist of purposively selected texts which can inform the research questions being investigated while 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. The qualitative approach usually produces descriptions or typologies, along with expressions from subjects reflecting how they view the social world while the quantitative approach produces numbers that can be manipulated with various statistical methods (Zhang and Wildemuth, 2009).

In real research work, the two approaches are not mutually exclusive and can be used in combination (Zhang and Wildemuth, 2009). As Weber (1990) points out, the best content-analytic studies use both qualitative and quantitative operations.

3.1.1.1 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. (Forman and Damschroder, 2008).

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 and Shannon, 2005). It is 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). It emphasizes an integrated view of speech/texts and their specific contexts. It 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 also allows researchers to understand social reality in a subjective but scientific manner (Zhang and Wildemuth, 2009).

In quantitative content analysis, data are categorized using predetermined categories that are generated from a source other than the data to be analyzed, applied automatically through an algorithmic search process (rather than through reading the data), and analyzed solely quantitatively (Forman and Damschroder, 2008 from Morgan, 1993). The categorized data become largely decontextualized. (Hsieh and Shannon, 2005).

Hsieh and Shannon (2005) discusses three approaches to qualitative content analysis, based on the degree of involvement of inductive reasoning. The first approach is conventional qualitative content analysis, in which coding categories are derived directly and inductively from the raw data. Conventional content analysis is generally used with a study design whose aim is to describe a phenomenon. Is is the approach used for grounded theory development. The advantage of this approach to content analysis is gaining direct information from study participants without imposing preconceived categories or theoretical perspectives. The second approach is directed content analysis, in which initial coding starts with an existing theory or prior research findings. Then, during data analysis, the researchers immerse themselves in the data and allow themes to emerge from the data.It is guided by a more structured process than in a conventional approach. The purpose of this approach usually is to support or extend a conceptual framework or theory.The third approach is summative content analysis, which starts with identifying and quantifying certain words or content in text with the purpose of understanding the contextual use of the words or 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 because it goes beyond mere word counts to include latent content analysis whichrefers to the process of interpretationof content (Hsiesh and Shannon, 2005)

3.1.1.2. Summative Approach to Qualitative Content Analysis

Summative analysis was recently introduced as a new qualitative analytic technique for social science. It is developed to effectively manage, organize, and clarify large bodies of qualitative, textual data that are complex or cover sensitive topic areas. 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 and enables researcher to be aware of more nuanced and ambiguous aspects of text (Rapport, 2010).

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. This quantification 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 (Hsieh and Shannon, 2005 from Potter and 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 (Hsieh and Shannon, 2005 from Kondracki and 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 of content (Hsieh and Shannon, 2005 from Holsti, 1969). In this analysis, the focus is on discovering underlying meanings of the words or the content (Hsieh and Shannon, 2005 from Babbie, 1992; Catanzaro, 1988; Morse & Field, 1995).

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. Counting is used to identify patterns in the data and to contextualize the codes (Hsieh and Shannon, 2005 from Morgan, 1993). It allows for interpretation of the context associated with the use of the word or phrase.

Qualitative content analysis usinf the summative content analysis has several advantages. The first advantage is that it is nonobtrsive and non reactive (Babbie, 1992). İts provides indepth meaning on how words are used. The disadvantage however is the inattention arising from the present data. The reason for this is that this research depends on trustworthiness. The best way to show credibility is to show evidence from text is similar with the iterpretetion (Weber, 1990).

Data analysis process is largely driven by the research question or aims of the research and begins with identifying and quantifying the core keywords in text. Word frequency counts for each identified keyword to classify textual data and identify patterns in it. The purpose is to understand the contextual use of the keywords, interpret the context associated with the use of the keywords and discover the content. Data analysis continues with quantitative analysis. The OLS regression model is applied to the categorical data derived from qualitative data. Finally, the results from qualitative and quantitative analysis are compared.

3.1.1.3. Computer Program for Support of Qualitative Content Analysis

ATLAS.ti, a qualitative data analysis software, is used to support the coding process, such as linking source documents with notes and memos, coding the texts, retrieving them based on keywords and picturing the relationships of codes.

ATLAS.ti is software for text analysis and model building. It handles graphical, audio, and video data files as well as text. With this package one can code and/or annotate text or media segments in a variety of ways, search/select segments by code, create hotlinks connecting segments, and display relationships among segments in diagrammatic format. One can use the automatic coding mode to code all similar segments according to defined patterns. Network diagrams, created with the built-in semantic network editor, can be exported to graphics and word processing packages and a built-in HTML generator creates web pages for sharing work with collaborators. Visually, annotations and links are made in a margin area of the computer display. However, ATLAS.tiis not a content analysis package per se, but rather a text management package lacking fundamental content analysis statistical functions.

3.1.2. The Ordinary Least Squares (OLS) Regression

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. 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'. 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 (the intercept) and β indicating the slope of the line (the regression coefficient). The regression coefficient β describes the change in Y that is associated with a unit change in X (Hutcheson, 2011).

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 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 operates as it is used to determine the significance of individual and groups of variables in a regression model (Hutcheson, 2011).

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) (Hutcheson, 2011).

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

R²= 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. 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

R²a = 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 (Hutcheson, 2011).

In this study, gretl which is a cross-platform software package for econometric analysis, written in the C programming language, free and open-source software is used for the OLS regression analysis.

3.1.2.1 The OLS Model and Hyphothesis

The analysis contiues with the Ordinary least-squares (OLS) regression model where ‘the code of food crisis’ (i.e. annual frequencies of global food crisis statements) is a dependent variable. The independent variables are the annual frequencies of codes which represent food price inflation, food riot, poverty, hunger, malnutrition, famine, farmer(negative), farmer(positive), africa food crisis, a dummy variable (year 2008), and time trend.

The regression equations for the three newspapers are:

=u (1)

=u (2)

=u (3)

whereY, and u are T-vectors, a, b, …, n are the T*k matrix of regressors, and x is the k-vector of the parameters. OLS minimizes the sum of the squared residuals.

It is hypothesized that the media will pay more attention to the consumers than to the producers when food prices change, and if this hypothesis is accepted, the results will be significant for the concepts which are related with consumers, such as poverty, hunger, food riots, etc., and they will have a positive relationship with food crisis; while the results will not be significant for the concepts which are related with farmers, such as farmer(negative), and farmer(positive).

3.2. Data Source

In this paper, the articles reported on the food crisis over the 13-year period are analyzed. These articles were published in three international newspapers, since January 2000 till mid-April 2013; more specifically, before, during and after the ‘global food crisis’. 78, 159 and 100 reports, respectively taken from the online versions of The Economist, The Guardian and The Mail & Guardian, are investigated. The reason for choosing these three newspapers as representatives of the entire group of newspaper magazines are their being highly ranked and creditable all over the world; having printed and online formats that give audiences the opportunity to search online; and being owned by companies, and not by goverments which shows that they represent the views of their stakeholders and the audiences.

Text selection was based on standard techniques, such as keyword computer searches: for instance, food crisis, world food crisis, global food crisis.

3.3. Data Pre-processing

The process of qualitative content analysis often begins during the early stages of data collection. This early involvement in the analysis phase helps to move back and forth between concept development and data collection, and helps direct subsequent data collection toward sources that are more useful for addressing the research questions (Zhang and Wildemuth, 2009 from Miles and Huberman, 1994).

After the articles reported on the food crisis over the 13-year period are saved as PDF documents, they are logged in the ATLAS.ti computer program. Three different ATLAS.ti projects created for analysis of each newspaper. All the articles to be examined are named as ‘‘primary documents’’ by ATLAS.ti.

Figure 3.1. Creating Primary Documents for the ATLAS.ti Project

3.3.1 Defining 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 (Zhang and Wildemuth, 2009 from De Wever et al., 2006). 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. 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,we primarily look for the expressions of an idea (Zhang and Wildemuth, 2009 from Minichiello et al., 1990). Doing this helps assigning a code to a text which represents a single theme or issue of relevance to the research questions (Zhang and Wildemuth, 2009).

3.3.2 Developing Categories and a Coding Scheme

Developing categories and a coding scheme is one of the most crucial steps in content analysis since the codes provide the classification system for the analysis of qualitative data. In quantitative content analysis, categories need to be mutually exclusive because confounded variables would violate the assumptions of some statistical procedures (Zhang and Wildemuth, 2009 from Weber, 1990). However, in reality, assigning a particular text to a single category can be very difficult. Qualitative content analysis allows to assign a unit of text to more than one category simultaneously (Zhang and Wildemuth, 2009 from Tesch, 1990). Even so, the categories in coding scheme should be defined in a way that they are internally as homogeneous as possible and externally as heterogeneous as possible (Zhang and Wildemuth, 2009 from Lincoln and Guba, 1985). The basic coding process is to reorganize large quantities of textual data in a way that enables retrieving data by much fewer content categories that are analytically useful to the study.

When sufficient consistency has been achieved, the coding rules can be applied to the entire corpus of text. During the coding process, the coding was needed to be checked repeatedly, to prevent “drifting into an idiosyncratic sense of what the codes mean” (Zhang and Wildemuth, 2009 from Schilling, 2006). Because coding proceeds while new data continue to be collected, it’s possible that new themes and concepts will emerge and will need to be added to the coding manual (Zhang and Wildemuth, 2009).

This type of content analysis starts with key words. Prewords are preselected depending on the interest of the researchers or the literature review but this is prior and during data analysis. (Hsieh and Shannon, 2005).

Once the theoretical framework is built, the research questions to be answered are formulated and the text to be analyzed is selected then the priori major codes, such as ‘food crisis, food prices, poverty, malnutrition, hunger…’ are identified. Preliminary coding consists of reading through the text, underlining passages that may be potentially important and relevant to the research questions.

Figure 2a and 2b provide examples of coding process on ATLAS.ti

Figure 3.2a. An Example of Coding Process for the code ‘‘food crisis’’

Figure 3.2b. An Example of Coding Process for the code ‘‘food price inflation’’

The first step of coding process is deductive. After a preliminary coding, the process becomes inductive by close reading. New codes are identified and some priori codes are eliminated. The process was repeated several times for each code in order to minimize the subjectiveness of the coder.

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 (Zhang and Wildemuth, 2009 from Miles and Huberman, 1994; Weber, 1990). For all these reasons, the consistency of coding was rechecked after coding the entire data set.

When the coding is done, the next step is to quantify each code in order by the publication date of the reports that the codes were stated. The frequencies of the codes bring into being quantitative data for the statistical analysis.

Figure 3.4. Codes-Pdocs tables that are derived from ATLAS.ti projects (Frequencies of each code by years and newspapers)

4. RESULTS AND DISCUSSION

4.1. Key Findings

In this section, key findings of content and regression analyses are demonstrated. Furthermore they are compared with each other in order to ascertain whether quantitative analysis results substantiate qualitative analysis findings.

4.1.1. Key Findings of Content Analysis

As shown in Figure 1, the global food crisis has been argued in the media since 2007. The numbers of articles which were published in the pre-2007 period and also the frequencies of the code of “global food crisis” in these articles are too small to be ignored. In post-2007, especially in 2008, all the newspapers suddenly started to talk about a ‘food crisis’ and during all the post- period, they kept talking about it.

What was reasoning this sudden change in communication? The analysis shows that it was ‘food inflation’ what leads to ‘food crisis’ talks (Figure 2 and Table 1). There is a dramatic change in the emphasis on ‘food price inflation’ in the pre- and after- periods. The results show the change in emphasis on ‘food price inflation’ is more dramatic than the change in emphasis on ‘food crisis’ for pre- and post- periods. We should also take into consideration that in some articles, ‘food price inflation’ was used as a complement to or even instead of ‘food crisis’. ‘Food crisis’ has been mentioned in most of the articles where ‘food price inflation’ has been mentioned.

When food prices increased media started to cover the issues that are of interest to urban dwellers, such as protests and riots (Figure 2 and Table 1). The changes in media coverage of ‘food price inflation’, ‘food riots’ and ‘food crisis’ move in the same direction. For example, in 2010, a second increase in food prices after a shock increase in 2008 generated a new wave of riot and made the media talk about a ‘food crisis’ again. The media name the situation of the changes in prices, which starts to hurt consumers, as a ‘crisis’. Up to this point, the situation is analyzed only from the consumer’s side.

In Figure 3, positive farmer-side discourses, such as:

“…Farmers in developed countries are the winners of food crisis…,

…Farmers benefit from high food prices…”

refer to ‘farmers-positive’ while negative farmer-side discourses, such as:

“…Farmers who were already poor became poorer…,

…Poor farmers could not benefit from the high food prices…’’

refer to ‘farmers-negative’.

As said before, consumers will lose from price increases; producers will gain, and vice versa when price decrease. Since the food prices were very low in the pre-2007 period, one would expect the media to mention the losses of farmers; however, we do not find it in media arguments. In the pre- period, farmers were mentioned only 12 times which consist of 11 negative discourses and 1 positive discourse. We daresay that there was a lack of media coverage of the losses of the farmers. Additionally, one would expect the media to mention the benefits of farmers in the post-period, when the food prices increased dramatically. However positive discourses start to appear, negative discourses still exist, to an even larger extent. Surprisingly, negative discourses concerning farmers (99 statements) are much more than positive discourses (44 statements) in the post- period. Negative discourses refer to farmers in developing countries, while positive discourses refer to farmers in developed countries. Considering that some of the farmers in developing countries are net buyers of food and most of the farmers hurt by increasing energy prices, it is not surprising anymore that negative media coverage of farmers in the post- period is quite large. However, the emphasis on farmers in the post- period is much stronger than the pre- period (Figure 3 and Table 1); the overall emphasis on farmers is very little comparing to the overall emphasis on the concepts concerning consumers. In total media coverage of consumers is 6 to 8 times (depends on taking ‘‘food price inflation’’ into account) bigger than media coverage of farmers.

Table 1 demonstrates the key discourses which are important in the food crisis debate and summarizes some of the key findings prior to and after the food crisis. In 2008, in media discourses, the strongest emphases were on food price inflation, food crisis, poverty and hunger, which were practically not mentioned in the pre-2007 period. As previously discussed, ‘food crisis’ became a new storyline in the recent debate (Figure 1); the talks on food crisis in the pre-period were referring mostly to a local food crisis due to bad weather conditions and bad politics, mainly in African countries (Table 1). It is also remarked that ‘food price inflation’, having the strongest emphases in all in 2008, led to ‘food crisis’ talks (Figure 2 and Table 1). Increasing food prices helped the media to attach attention to poverty and hunger (Table 1). Throughout the post-period, more than 40 countries experienced food riots, mostly in the urban areas. So, food riots became an important issue in the post-2007 period (Table 1).

There is a vast amount of literature on the causes of the food crisis. One of the most important factors which caused increases in agricultural commodity prices was biofuel production (Abbott et al., 2009; Chand, 2008; Mitchell, 2008; Schnepf, 2008; USDA 2008; von Braun, et al., 2008;). Since 2006, biofuel production has surged with the rapidly rising energy prices and improved bioenergy conversion technologies. Energy and agricultural markets became closely linked (Abbott et al., 2009; Chand, 2008; Schmidhuber, 2006), and usage of crops for ethanol and biodiesel became large enough to influence world prices. There is a dramatic change in the emphasis on biofuels in pre- and post-periods (Table 1).

Some other discourses which are stated in Table 1, such as famines, food aid, malnutrition and Africa food crisis were not as ‘famous’ as the others in either period. Africa has always been suffering from hunger, poverty and malnutrition, and is often hit by many local food crises and famines.

4.1.2. Regression Analysis Results

As stated previously, it is hypothesized that the media will pay more attention to consumers than to producers when food prices change; if this hypothesis is accepted, the results will be significant for the concepts which are related with consumers, such as poverty, hunger, food riots, etc., and they will have a positive relationship with food crisis. Each explanatory variable in the model is assessed: Coefficient, Probability or Robust Standard Errors (HAC), and Variance Inflation Factor (VIF). The T-test is used to assess whether or not an explanatory variable is statistically significant. Each consumer-side explanatory variable is expected to be significant and the signs of the coefficients to be positive. In contrast, farmer-side explanatory variables are expected to be non-significant. The models do not include first order autocorrelation (i.e. the Durbin-Watson test statistics are very close to 2 in all models).The models for The Economist and The Mail & Guardian do not include collinearity problem (i.e. the variables which have VIF values bigger than 10.0 are omitted), while the other model includes a negligible collinearity problem (VIF value for food price inflation is 12.699). According to R-squared, in all models, all the explanatory variables modeled using regression explain more than 85% of the variation in the dependent variable (food crisis).

As shown in Table 2, the code of ‘hunger’ is significant with a positive coefficient for all the newspapers analyzed. The more we talk about hunger, the more we talk about food crisis. The other codes concerning consumers, such as ‘food price inflation’, ‘food riots’, ‘poverty’, ‘malnutrition’, and ‘famine’ are all significant for at least one of the newspapers and have positive coefficients, except malnutrition. The codes concerning farmers are not significant at all. The emphasis on ‘food crisis’ do positively change with respect to time. We can also say that the emphasis on ‘food crisis’ increased in 2008. African food crises are believed to be local or regional problems mostly caused by bad weather conditions and bad policies. The result from the analysis of The Economist show that the media attention shifted from African food crises to global food crisis by time. However it is not clearly shown by the quantitative results, The South African newspaper (The Mail & Guardian) differs in coverage of African problems and it kept reporting on ‘African food crises’ during the global food crisis.

Finally, the regression results substantiate the results from content analysis by supporting research hypotheses. In contrast with consumers, farmers do not play a significant role in the food crisis debate. Food crisis talks become popular when consumers are hurt by food price changes.

4.2. Discussion: What Might Lead To Media 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.” 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.”

A disproportionate amount of attention which has been paid to the problems of urban consumers can be explained by urban bias. According to (FAO, 2008) riots and civil disturbances, which have taken place in many low- and middle-income developing countries, signal the desperation caused by soaring food and fuel prices for millions of poor and also middle-class households.The groups who usually respond politically with strikes, protests or riots to the negative income effects of food price changes are urban consumers, not rural farmers; it is easier to mobilize the urban populations who are already concentrated in the cities. However, Hendrix et al. (2009) argues that farmers are acutely aware of the political gains that come from concentrating collective action in cities; we agree with von Grebmer et al. (2008) concerning the farmers, who usually live in distant rural areas, suffer silently for a while. A lack of protests may not correctly depict the severity of impact on the poorest of the poor (von Grebmer et al., 2008). It may be that a similar urban bias effect plays a role in drawing policy attention through global media (Swinnen, 2010), since dissatisfied urban consumers have an important role in driving government policy on the issue. Policymakers favor programmes that protect city dwellers from feeling the full brunt of food price increases in large part because these have a pacifying effect on this urban discontent (Cohen and Garrett, 2009).

Second, media attention is typically concentrated around “events”. Events trigger media attention by a combination of dynamic demand and supply effects. The importance of events is driven on the demand side by the increased interests of consumers, in some cases through their anticipation of important implications, and on the supply side by the availability or lower cost of stories, including pictures and illustrative material. Some of the forces that cause increased media attention on an issue at a certain moment also contribute to reduced attention to other moments, as other activities or events take over (Swinnen and Francken, 2006). After a period of decreasing food prices for almost 50 of the last 60 years, and low food prices during the last 20 years, food prices started to increase dramatically. Only three price spikes have been seen in the last 60 years, together lasting just about ten years (Aksoy & Beghin, 2004; Aksoy & Hoekman, 2010). This breaking news has been widely reported.

It is believed that media consumers tend to be more interested in negative coverage, and they choose to consume more negative stories than positive stories (the so-called “bad news hypothesis’’). McCluskey and Swinnen (2010) reported 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. This demand effect of the media market drives more negative coverage in reporting. Swinnen and Francken’s (2006) study on trade policy and globalization and Swinnen, McCluskey, and Francken’s (2005) study on food safety provide empirical support for negative coverage. Marks, Kalaitzandonakes, and Konduru (2006) find that reporting on globalization was positive early on but switched to more negative in recent years.

The importance of attracting large numbers of readers may in itself also lead to bias (McCluskey and Swinnen, 2010). Strömberg (2004) argues that increasing returns to scale in media markets induce the media to cover the issues that are of interest to larger groups. 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.

A combination of all these factors creates a bias in reporting. Moreover, this news bias will translate into a policy bias.

5. CONCLUSION AND RECOMMENDATIONS

As a consequence of increasing food prices, the poor who spend most of their income on food became poorer and the ones in the cities started to protest against high food prices. Hence, the media started to pay more attention to the consequences of high food prices on consumers, such as poverty, hunger and food riots. Eventually ‘food crisis’ discourse which was led by ‘food inflation’discourse became a new storyline in the post-2007 period. Even though there were poverty and hunger problems in the pre-2007 period and many of the rural farmers could not afford to buy food, there was an absence of media coverage for the food crisis.

Media pay a disproportionate amount of attention to the negative welfare effects of high food prices on consumers, especially in urban areas and ignore the negative welfare effects of low food prices on farmers. Moreover, the media are also selective in reporting the positive welfare effects of high food prices on farmers –at least for the ones in developed countries. There is a lack of media attention to farmers, regardless of how much and in which direction they are affected by food price changes. And there is a disproportionate amount of attention to consumers regarding the negative welfare effects. Selective media coverage contributes to an irrational allocation of short-term emergency relief because coverage is determined by factors other than humanitarian need (Jakobson, 2000). Small groups will receive less favorable policies because of the provision of information by mass media firms (Strömberg, 2004).

Another very important question based on the research findings is how big the effect of media bias on policy bias is. Swinnen (2010) argues that it is difficult to answer this question since it depends on various assumptions regarding the processing of these sets of information by voters, policy-makers and the organizations themselves, the type of welfare function one has in mind, and the political economy of policy decisions at various levels (Swinnen 2010).In Table 3, the main actors of food policy debate and their fears, hopes and possible actions are given. Any changes in food policy will depend on these four players’ actions and the balance of forces.

This study sheds light on selective media coverage under food price changes, media bias on food crisis debate and possible factors which cause shifted media discourses between the pre-2007 and post-2007 periods. It will hopefully lead to further studies to explore the effects of selective media coverage on policy making, and hence, on welfare distribution and the future development process of nations.

REFERENCES

Abbott, P. C., Hurt, C., & Tyner, W. E. (2008). ‘‘What’s driving food prices?’’Issue Report. Oak Brook, Ill. U.S.A.:Farm Foundation.

———. (2009). ‘‘What's driving food prices?’’ March 2009 Update (No. 48495). Farm Foundation..

Ahmed, A. U., Vargas Hill, R., Smith, L. C., Wiesmann, D., Frankenberger, T. (2007). ‘‘The World’s Most Deprived: Characteristics and Causes of Extreme Poverty and Hunger’’ 2020 Discussion Paper 43. International Food Policy Research Institute (IFPRI), Washington DC.

Akaike, H. (1974). ‘‘A new look at the statistical model identification’’, IEEE Transactions on Automatic Control AC- 19: 716–723.

Aksoy, M.A., & Beghin J. C. (eds.), 2004.Global Agricultural Trade and Developing Countries. Washington, DC: World Bank.

Aksoy, M. A.,& Ng. F. (2008).Who are the net food importing countries? Policy Research Working Paper 4457. Washington, D.C.: World Bank.

Aksoy, M. A. & Hoekman, B. (2010). ‘‘Food Prices and Poverty: Introduction and Overview’’ In: Aksoy, M. A. & Hoekman, B. (eds), Centre for Economic Policy Research. Food prices and rural poverty.CEPR :1-24

Aksoy, M. A., & Isik-Dikmelik, A. (2010). ‘‘Are Low Food Prices Pro-Poor? Net Food Buyers and Sellers in Low- Income Countries’’ Centre for Economic Policy Research, 113.

Baltzer, K., Hansen, H. & Lind, K. M. (2008). ‘‘A Note on The Causes and Consequences of The Rapidly Increasing International Food Prices’’Institute of Food and Resource Economics, University of Copenhagen , May 2008.

Berelson, B. (1952). Content analysis in communication research. New York: Hafner.

CBC (Conference Board of Canada). (2008). ‘‘Is food commodity securitization worsening the world’s food problem’’Policy Brief. Ottawa, Ontario.

Chand, R. (2008). ‘‘The Global Food Crisis: Causes, Severity and Outlook’’ Review of Agriculture, Economic & Political Weekly, June 28, 2008: 115-123.

Cohen, M. & Garrett, J. (2009). ‘‘The Food Price Crisis and Urban Food (In)security’’ Human Settlements Working Paper Series, London, International Institute for Environment and Development.

Collins, K., (2008). 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. Report commissioned by Kraft Food Global.

Dawe, D. (2008). ‘‘Have recent increases in international cereal prices been transmitted to domestic economies? The experience in seven large Asian countries’’. ESA WorkingPaper 08-03. Rome: Agricultural Development Economics Division, Food andAgriculture Organization of the United Nations.

Deaton, A. (1989). ‘‘Rice Prices and income Distribution in Thailand: A Non-parametric Analysis’’ The Economic Journal, 99(395), 1-37.

Dessus, S., Herrera, S., & De Hoyos, R. (2008). ‘‘The impact of food inflation on urban poverty and its monetary cost: some back‐of‐the‐envelope calculations’’ Agricultural Economics, 39 (s1), 417-429.

ERS (2008). Corn Prices Near Record High, But What About Food Costs?,Amber Waves, TheEconomics of Food, Farming, Natural Resourcesand Rural Poor, Economics Research Service, USDA, February.

FAO (2008).The State of Food Insecurity in the World 2008.FAO, Rome.

FAO (2012).High and volatile food prices: Supporting farmers and consumers. CAADP Policy Brief 08, Future Agricultures Consortium.

Gilbert, C. (2007). “Commodity Speculation and Commodity Investments,” manuscript, October 2007.

Gilbert, C. (1989). “The Impact of Exchange Rate Changes and Developing Country Debt on Commodity Prices.” Economic Journal, 99: 773-784.

Glauber, J. (2008) USDA Chief Economist, in testimony before the Joint Economic

committee of Congress on May 1, 2008.

Headey, D. (2010). ‘‘Rethinking the global food crisis: The role of trade shocks.’’Discussion Paper 00958. Washington, D.C.: International Food Policy Research Institute.

Headey, D., & Fan, S. (2008). ‘‘Anatomy of a crisis: the causes and consequences of surging food prices’’ Agricultural Economics, 39(s1), 375-391.

Helbling, T., Mercer-Blackman V. & Cheng K. (2008). ‘‘Commodities Boom: Riding A Wave’’Finance & Development45 (1): 10–15, March 2008.

Hendrix, C., Haggard, S. and Magaloni, B. (2009).‘‘Grievance and opportunity: Food Prices, Political Regime, and Protest’’ Paper presented at the International Studies Association convention, New York, 15-18 February.

Hsieh, H. F. & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative health research, 15(9), 1277-1288.

Hodkinson, P. (2011). Media, Culture and Society: An Introduction. Sage Publication.

IEA (International Energy Agency). (2007). World energy outlook 2007: China and India insights. Paris: Organisation for Economic Co-operation and Development and IEA.

IMF(International Monetary Fund). (2008). ‘‘Food and fuel prices: Recent developments, macroeconomic impact, and policy responses’’ Fiscal Affairs, Policy Development and Review, and Research Departments, International Monetary Fund, Washington DC.

———.(2009). World economic outlook, April 2009: Crisis and recovery. Washington, D.C.

Ivanic, M., & Martin, W. (2008).‘‘Implications of higher global food prices for poverty in low‐income countries’’ Agricultural Economics, 39(s1), 405-416.

Ivanic, M.& Zezza, . (2008).

Jakobson, P. V. (2000). ‘‘Focus on the CNN effect misses the point: the real media impact on conflict management is invisible and indirect’’ Journal of Peace Research, 37(2), 131-143.

Kim, J. S. (2005). ‘‘Media coverage and foreign assistance: The effects of U.S. media coverage on the distribution of U.S. official development assistance (ODA) to recipient countries’’ Georgetown Public Policy Institute. Final Practicum.

Lipsky, J. (2008). Commodity prices and global inflation. Remarks by the first deputy managing director of the International Monetary Fund at the Council on Foreign Relations.

Macnamara, J. (2005). Media content analysis: Its uses, benefits and best practice methodology. Asia Pacific Public Relations Journal, 6(1), 1-34.

McCluskey, J.J. & Swinnen, J. F. M. (2010). ‘‘Media economics and the political economy of information’’ In: Coen, D., Grant W. & Wilson G. (eds), The Oxford Handbook of Government and Business. Oxford: Oxford University Press.

Mitchell, D. (2008). ‘‘A Note on Rising Food Prices’’ Policy Research Working Paper 4682, The World Bank Development Prospects Group, July 2008.

OECD-FAO (2007). “Agricultural Outlook 2007-2016,” 2007.

http://www.oecd.org/dataoecd/6/10/38893266.pdf

OECD (2008). Rising Food Prices Causes And Consequences

http://www.oecd.org/trade/agricultural-trade/40847088.pdf

Rapport, F. (2010).‘‘Summative analysis: A qualitative method for social science and health research’’ International Journal of Qualitative Methods, 9, 270–290.

Rosegrant, M. W., Zhu T., Msangi, S. & Sulser, T.. (2008). The impact of biofuels production on world cereal prices. International Food Policy Research Institute, Washington, D.C.

Schmidhuber, J. (2006). ‘‘Impact of an increased biomass use on agricultural markets, prices and food security: A longer-term perspective’’ International symposium of Notre Europe, Paris.

Schnepf, R. (2008). ‘‘High Agricultural Commodity Prices: What Are the Issues?’’ Congressional Research Service (CRS) Report for Congress, May 6, 2008.

Strömberg, D. (2004) ‘‘Mass media competition, political competition, and public policy,’’ Review of Economic Studies 71: 265-84.

Swinnen, J. (2010). ‘‘The right price of food: Reflections on the political economy of policy analysis and communication’’ (No. 259).LICOS Discussion Paper.

Swinnen, J. F., & Francken, N. (2006). ‘‘Summits, Riots and Media Attention: The Political Economy of Information on Trade and Globalisation’’ The World Economy, 29(5), 637-654.

The Economist (2002, May 9). Famine in Ethiopia: Drought, death and taxes.

http://www.economist.com/node/1316910

———. (2008, November 19). Old Macdonald gets some cash: After the year of food crisis, the year of the farmer.

http://www.economist.com/node/12494668

———. (2011, January 27). Hungry for votes: How much do rich governments really worry about feeding the world?

www.economist.com/node/18008010

The Guardian (2002, May 30). Fat is a fatuous issue.

http://www.guardian.co.uk/lifeandstyle/2002/jun/30/foodanddrink.maryriddell?INTCMP=SRCH

———. (2008, May 27). Crisis talks on global food prices.

http://www.guardian.co.uk/environment/2008/may/27/food.internationalaidanddevelopment?INTCMP=SRCH

———. (2008, November 25). Europe and the global food crisis.

http://www.guardian.co.uk/commentisfree/2008/nov/25/eu-food?INTCMP=SRCH

The Mail & Guardian (2005, January 28). Zimbabwe: 5,8m go hungry as food crisis worsens.

http://mg.co.za/article/2005-01-28-zimbabwe-58m-go-hungry-as-food-crisis-worsens

———. (2008, April 29) UN and World Bank to tackle food crisis.

http://mg.co.za/article/2008-04-29-un-and-world-bank-to-tackle-food-crisis

Timmer, P. (2011). ‘‘Managing price volatility: approaches at the global, national, and household levels’’ Center on Food Security and the Environment. Stanford Symposium Series on Global Food Policy and Food Security in the 21st Century(FSE Stanford), Stanford University, Stanford, CA.

Trostle, R. (2008). ‘‘Global Agricultural Supply and Demand: Factors Contributing to the Recent Increase in Food Commodity Prices’’ United States Department of Agriculture (USDA), WRS-0801, July 2008 (Revised)

U. S. Department of Agriculture (USDA) Interagency Agricultural Projections Committee (2008). USDA ‘‘Agricultural Projections to 2017’’ OCE-1008-1, Washington, DC.

von Braun, J. (2008). ‘‘Rising Food Prices: What Should Be Done?’’ IFPRI Policy Brief, The International Food Policy Research Institute (IFPRI).

von Grebmer, K., Fritschel, H., Nestorova, B., Olofinbiyi, T., Pandya-Lorch, R. & Yohannes, Y. (2008) ‘‘Global Hunger Index: The challenge of hunger 2008’’ Deutsche Welthungerhilfe, International Food Policy Research Institute and Concern Worldwide, Bonn, Washington, DC, and Dublin.

Wodon, Q., & Zaman, H. (2008).‘‘Rising food prices in sub-Saharan Africa: Poverty impact and policy responses’’ World Bank Policy Research Working Paper 4738, October, The World Bank, Washington DC.

Wodon, Q., C., Tsimpo, P. , Backiny-Yetna, G. , Joseph, F. Adoho & Coulombe, H. (2008). ‘‘Potential impact of higher food prices on poverty : summary estimates for a dozen west and central African countries’’ Policy Research Working Paper Series 4745, The World Bank, Washington DC.

World Bank (2008a).‘‘Addressing the Food Crisis: The Need for Rapid and Coordinated Action’’ Background paper for the Group of Eight Meeting of Finance Ministers, Osaka, June 13-14, 2008.The World Bank, Washington DC.

———. (2008b). World development report 2008: Agriculture for development. Washington, D.C.: World Bank.

Zezza, A., Davis, B., Azzarri, C., Covarrubias, K., Tasciotti, L., & Anriquez, G. (2008).‘‘The impact of rising food prices on the poor’’ Unpublished manuscript.Food and Agriculture Organization, Rome.

Zhang, Y. and Wildemuth, B. M. (2009). "Qualitative analysis of content."Applications of social research methods to questions in information and library science, 308-319.

29