BUSINESS (NO PLAGARISM A+ WORK, ON TIME)
A STUDY OF IMPERFECT COMPETITION OF THE ASIAN DAIRY
MARKETS: THE IMPACTS OF DOHA ROUND AGRICULTURAL
NEGOTIATIONS AND FURTHER TRADE LIBERALIZATION
by
Tingjun Peng
A dissertation submitted in partial fulfillment of
the requirements for the degree of
Doctor o f Philosophy
(Agricultural and Applied Economics)
at the
UNIVERSITY OF WISCONSIN-MADISON
2006
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A dissertation entitled
A Study of Imperfect Competition of the Asian Dairy Markets: The Impacts of Doha Round Agricultural Negotiations
and Further Trade Liberalization
submitted to the Graduate School of the University of Wisconsin-Madison
in partial fulfillment of the requirements for the degree of Doctor of Philosophy
by Tingjun Peng
Date of Final Oral Examination: May 15,2006
Month & Year Degree to be awarded: December May August 2006
Approval Signatures of Dissertation Committee
Signature, Dean of Graduate School
r / t
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ABSTRACT
The objectives of this thesis are to address the knowledge gap between perfect competition
and imperfect competition in world dairy sector, and study the impacts of Doha round
agricultural negotiations on the Asian and/or world dairy markets. Using the conjectural
variation (CV) method, a model is built which allows any degree of market structure from
perfect competition to monopoly or monopsony. One spatial price arbitrage condition derived
from this model is estimated econometrically to test the existence of market power. Furthermore,
the differences between perfect competition and imperfect competition are evaluated in empirical
work in the context of Doha round negotiations on agriculture in the Asian and/or world dairy
markets by using the GAMS software. The separate impacts of implementing Doha round
commitments in 2009, Doha round boundary commitments in 2009 and full world liberalization
in 2009 are analyzed.
From a statistical point of view, it is found that the imperfect competition model is more
appropriate to explain China’s butter and skim milk powder (SMP) imports, Japan’s cheese,
butter and skim milk powder imports, and South East Asia’s skim milk powder imports. From
the empirical results, it is found that the impacts of imperfect competition are positively related
to trade distortions. That is, if there are more trade distortions, the impacts of imperfect
competition are more obvious, but if there are less trade distortions then the impacts of imperfect
competition become smaller. However, from the empirical results on market power, the use of
perfect competition assumptions to study world dairy sectors by current models is roughly
justified if imperfect competition only applies to a limited number of products in a few regions.
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ii
The impacts of different degrees of trade liberalization on world dairy sectors are similar to
each other with different magnitudes. Full dairy sector liberalization has significant impacts on
Japan, Korea and SEA dairy markets, but the impacts on China and India dairy markets are
small. Japan, Korea and SEA dairy producers suffer most from trade policy reform while
consumers benefit. For India, the opposite situation arises as dairy producers gain from trade
policy reform while suffer a loss from domestic support policy reform. For China, both trade and
domestic support policy reform have minimal impacts on producer surplus and consumer
surplus. As major exporters, Australia and New Zealand producers gain from trade polices
reform while suffer from domestic support policy reform due to the elimination of milk
production quota. EU dairy producers suffer the biggest losses from world dairy trade
liberalization, especially from domestic support policy reform. Compared with other regions, the
impacts of world dairy trade liberalization on the U.S. dairy market is generally moderate in the
medium term context. Dairy producers mainly suffer from domestic support policy reform.
Canada’s dairy producers also suffer significant losses from full dairy sector liberalization, and
trade polices and domestic support polices provide similar protection to dairy producers. Overall,
full dairy sector liberalization has little impact on world aggregate welfare, but it changes the
location of production and redistributes welfare across different countries.
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Table of Contents
ABSTRACT............................................................................................................ i
TABLE OF CONTENTS..................................................................................... iii
LIST OF FIGURES.................................................. viii
LIST OF TABLES.............................................................................................. xi
LIST OF BOXES................................................................................................ xiii
ACKNOWLEDGMENTS.................................................................................. xiv
TABLE OF CONTENTS
CHAPTER 1 INTRODUCTION............................................................... 1
1.1 MOTIVATION .................................................................................................................1
1.2 REVIEW OF LITERATURE............................................................................................. 9
1.3 STRUCTURE OF THE DISSERTATION........................................................................14
CHAPTER 2 ASIAN DAIRY SITUATION.......................................................16
2.1 DAIRY POLICIES................................ 16
2.1.1 China.......................................................................... 16
2.1.1.1 Domestic support policies......................................................................................16
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iv
2.1.1.2 Trade policies..........................................................................................................18
2.1.2 Japan.............................................................................................................................19
2.1.2.1 Domestic support policies.......................................................................................19
2.1.2.2 Trade policies......................................................................................................... 22
2.1.3 South Korea................................................................................................................. 23
2.1.3.1 Domestic support policies...................................................................................... 23
2.1.3.2 Trade policies.............................. 24
2.1.4 India..............................................................................................................................25
2.1.4.1 Domestic support policies...................................................................................... 25
2.1.4.2 Trade policies..........................................................................................................25
2.1.5 South East Asia ............................................................................................................26
2.1.6 Oceanic Countris ........... 27
2.1.7 EU ................................................................................................................................31
2.1.8 United States ...................................................................................................... 32
2.2 WTO COMMITMENTS AND IMPLIMENTATION................. 33
2.2.1 Market Access ..............................................................................................................34
2.2.2 Domestic Support ........................................................................................................39
2.2.3 Export Subsidy .............................................................................................................42
2.3 DAIRY PRODUCTS PRODUCTION, CONSUMPTION AND TRADE ..................... 43
2.3.1 China................... 45
2.3.2 Japan............................................. 50
2.3.3 Korea............................................................................................................................53
/
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V
2.3.4 India............................................................................................................................ 56
2.3.5 South East A sia........................................................................................................... 59
CHAPTER 3 CONCEPTUAL MODEL............................................................ 63
3.1 CURRENT MODELS TO STUDY WORLD DAIRY SECTOR..................................... 63
3.2 A SPATIAL EQUILIBRIUM MODEL TO STUDY PERFECT COMPETITION 68
3.3 A SPATIAL EQUILIBRIUM MODEL WITH IMPERFECT
COMPETITION.............. 71
3.4 APPLICATION TO THE ASIAN DAIRY MARKETS...........................;........................ .75
3.5 TEST OF MARKET POWER.......................................................................................... 81
3.6 SIMULATION PROCEDURE......................................................................................... 86
3.7 POLICIES MODELING............................. 87
3.7.1 Import Quota..................................... 88
3.7.2 Production Quota....................................................................................................... 95
3.7.3 Price Support Policies.................................................................................................. 96
3.8 PREDICTION.................................................................................................................. 97
3.9 DATA AND SOFTWARE........................................................ 99
Chapter 4 MARKET POWER...................................................................... 105
4.1 MEASUREMENT ERROR BIAS .................................................................................... 107
4.2 INSTRUMENTAL VARIABLES (IV )...............................................................................108
4.3 REDUCED FORM ............................................................................................................109
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4.4 IDENTIFICATION ........................................................................................................... I l l
4.5 GMM ESTIMATION....................................................................................................... 112
4.6 OVER-IDENTIFICATION TEST........................................................ 114
4.7 RESULTS..........................................................................................................................116
4.8 ROBUSTNESS............................................................................................. 125
4.9 DISCUSSION....................................................................................................................126
Chapter 5 THE IMPACTS OF TRADE LIBERALIZATION ON THE
ASIAN DAIRY MARKETS........................................................ 135
5.1 INTRODUCTION...................... 135
5.2 DOHA DEVELOPMENT AGENDA................................................................................137
5.3 POLICY SCENARIOS................. 149
5.4 SIMULATION RESULTS.................................................................................................157
5.4.1 Base Scenario.......................................................................................................... 157
5.4.1.1 Results................................................................................................................... 157
5.4.1.2 Sensitivity Analysis: perfect competition vs. imperfect competition....................159
5.4.2 The Short Term Impacts of Doha Development Agenda...........................................163
5.4.2.1 Central Doha Scenario ..........................................................................................163
5.4.2.2 Decomposing Central Doha scenario: market access, domestic support and export
subsidies............................................................ 169
5.4.3 The Long Term Impacts of Doha Development Agenda.............................................176
5.4.4 Comparing the Impacts of Individual Policies: Tariff, Quota, Export Subsidy, Trade
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Policies, Domestic Policies and Full Liberalization....................................................185
5.5 IMPACTS OF IMPERFECT COMPETITION UNDER DOHA SCENARIOS AND FULL
LIBERALIZATION.................................................. 198
5.6 DISCUSSION .................................................................................................................. 201
Chapter 6 CONCLUSIONS AND DISCUSSION........................................ 240
6.1 CONCLUSIONS.................................. 240
6.2 CONTRIBUTIONS OF THIS DISSERTATION ............................................................. 246
6.3 DISCUSSIONS AND SUGGESTIONS FOR FUTURE RESEARCH ............................ 247
REFERENCES.................................................................................................. 254
APPENDICES.................................................. 266
A. REGION DEFINITONS...................................................................................................... 266
B. COMMIDITY DEFINITIONS............................................................................................ 269
C. DATA SOURCES................................................................................................................ 272
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viii
LIST OF FIGURES
FIGURE 2.1 CHINA MILK PRODUCTION BY TYPE .............................................46
FIGURE 2.2 CHINA MILK PRODUCTION AND UTILIZATION....................................... 47
FIGURE 2.3 CHINA BUTTER PRODUCTION, CONSUMPTION AND TRADE.............. 48
FIGURE 2.4 CHINA CHEESE PRODUCTION, CONSUMPTION AND TRADE.............. 49
FIGURE 2.5 CHINA SMP PRODUCTION, CONSUMPTION AND TRADE....................... 50
FIGURE 2.6 JAPAN MILK PRODUCTION AND UTILIZATION....................................... 51
FIGURE 2.7 JAPAN BUTTER PRODUCTION, CONSUMPTION AND IMPORTS.......... 51
FIGURE 2.8 JAPAN CHEESE PRODUCTION, CONSUMPTION AND IMPORTS........... 52
FIGURE 2.9 JAPAN SMP PRODUCTION, CONSUMPTION AND IMPORTS................53
FIGURE 2.10 KOREA MILK PRODUCTION AND UTILIZATION ........................... 53
FIGURE 2.11 KOREA BUTTER PRODUCTION, CONSUMPTION AND IMPORTS.......54
FIGURE 2.12 KOREA CHEESE PRODUCTION, CONSUMPTION AND IMPORTS.......55
FIGURE 2.13 KOREA SMP PRODUCTION, CONSUMPTION AND IMPORTS............... 55
FIGURE 2.14 INDIA MILK PRODUCTION BY TYPE........................................................ 56
FIGURE 2.15 INDIA MILK PRODUCTION AND UTILIZATION..................................... 57
FIGURE 2.16 INDIA BUTTER PRODUCTION, CONSUMPTION AND IMPORTS......... 58
FIGURE 2.17 INDIA SMP PRODUCTION, CONSUMPTION AND IMPORTS................ 59
FIGURE 2.18 SEA MILK PRODUCTION BY TYPE. ................................................... 60
FIGURE 2.19 SEA MILK PRODUCTION AND UTILIZATION....................................... 60
FIGURE 2.20 SEA BUTTER CONSUMPTION AND IMPORTS....................................... 61
FIGURE 2.21 SEA CHEESE PRODUCTION, CONSUMPTION AND IMPORTS............62
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FIGURE 2.22 SEA SMP CONSUMPTION AND IMPORTS............................................... 62
FIGURE 3.1 THE IMPACTS OF IMPERFECT COMPETITION ON SUPPLY AND
DEMAND PRICES........................................................................................... 74
FIGURE 3.2 IMPACTS OF TRQS ON THE IMPORTING COUNTRY............................... 89
FIGURE 3.3 NONEQUIVALENCE OF IMPORT QUOTA AND TARIFFS: ECONOMIC
GROWTH............................................... 91
FIGURE 3.4 NONEQUIVALENCE OF IMPORT QUOTA AND TARIFFS: WORLD PRICE
INCREASES....................................................................................................... 93
FIGURE 3.5 TWO-TIER TARIFF-RATE QUOTA...............................................................94
FIGURE 3.6 SUPPLY CURVE WITH A PRODUCTION QUOTA...................................... 96
FIGURE 3.7 DEMAND CURVE WITH PRICE SUPPORT..................................................97
FIGURE 5.1 LINEAR REDUCTION MODALITY FOR EXPORT SUBSIDY.................. 155
FIGURE 5.2: AGGREGATE WELFARE IMPACTS (2009): WORLD................................. 214
FIGURE 5.3: AGGREGATE WELFARE IMPACTS (2009): CHINA................................... 214
FIGURE 5.4 AGGREGATE WELFARE IMPACTS (2009): JAPAN................ 215
FIGURE 5.5: AGGREGATE WELFARE IMPACTS (2009): INDIA.................................... 215
FIGURE 5.6: AGGREGATE WELFARE IMPACTS (2009): KOREA.................................. 216
FIGURE 5.7: AGGREGATE WELFARE IMPACTS (2009): SEA........................................ 216
FIGURE 5.8: AGGREGATE WELFARE IMPACTS (2009): E U ......................................... 217
FIGURE 5.9: AGGREGATE WELFARE IMPACTS (2009): AUSTRALIA........................ 217
FIGURE 5.10: AGGREGATE WELFARE IMPACTS (2009): NEW ZEALAND............... 218
FIGURE 5.11: AGGREGATE WELFARE IMPACTS (2009): U S ....................................... 218
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FIGURE 5.12: AGGREGATE WELFARE IMPACTS (2009): CANADA...........................219
FIGURE 5.13: AGGREGATE WELFARE IMPACTS UNDER DIFFERENT SCENARIOS
(2009): WORLD........................................................................................... 232
FIGURE 5.14: AGGREGATE WELFARE IMPACTS UNDER DIFFERENT SCENARIOS
(2009): CHINA............................................................................................. 232
FIGURE 5.15: AGGREGATE WELFARE IMPACTS UNDER DIFFERENT SCENARIOS
(2009): JAPAN............................................................................................. 233
FIGURE 5.16: AGGREGATE WELFARE IMPACTS UNDER DIFFERENT SCENARIOS
(2009): INDIA............................................................................................... 233
FIGURE 5.17: AGGREGATE WELFARE IMPACTS UNDER DIFFERENT SCENARIOS
(2009): KOREA.............................................................................................234
FIGURE 5.18: AGGREGATE WELFARE IMPACTS UNDER DIFFERENT SCENARIOS
(2009): SEA...................... 234
FIGURE 5.19: AGGREGATE WELFARE IMPACTS UNDER DIFFERENT SCENARIOS
(2009): E U .................................................................................................... 235
FIGURE 5.20: AGGREGATE WELFARE IMPACTS UNDER DIFFERENT SCENARIOS
(2009): OCEANIA.............................................................. 235
FIGURE 5.21: AGGREGATE WELFARE IMPACTS UNDER DIFFERENT SCENARIOS
(2009): U S ...................................................................... 236
FIGURE 5.22: AGGREGATE WELFARE IMPACTS UNDER DIFFERENT SCENARIOS
(2009): CANADA....................................................................................... 236
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LIST OF TABLES
TABLE 2.1 CHINA’S TARIFF RATES ON DAIRY PRODUCTS........................................19
TABLE 2.2 ASIA COUNTRY’S DAIRY TARIFFS............................................................. 37
TABLE 2.3 TARIFF RATE QUOTA ADMINISTRATION FOR ASIA COUNTRIES....... 38
TABLE 3.1 NOTATIONS USED IN THE CONCEPTUAL MODEL.................................... 84
TABLE 4.1 MARKET POWER OF BUTTER....................................................................... 128
TABLE 4.2 MARKET POWER OF CHEESE......................... 129
TABLE 4.3 MARKET POWER OF SM P................................................................................131
TABLE 4.4 EFFECTS OF DIFFERENT IV ON MARKET POWER..................................132
TABLE 4.5 DEMAND ELASTICITIES FOR DIFFERENT DAIRY PRODUCTS............ 133
TABLE 4.5 THE LERNER INDEX FOR DIFFERENT DAIRY PRODUCTS IN DIFFERENT
MARKETS............................................................... 134
TABLE 5.1 CUTS IN DOMESTIC SUPPORT UNDER A TIERED FORMULA
WITH 75 PERCENT CUTS IN HIGH-SUPPORTING COUNTRIES .....145
TABLE 5.2A 2002-04 BASE SOLUTIONS........................................................................... 206
TABLE 5.2B 2002-04 DATA SIMULATIONS..................................................................... 207
TABLE 5.3 PERFECT COMPETITION VS. IMPERFECT COMPETITION: BASE........208
TABLE 5.4 IMPACTS OF DDA: CENTRAL DOHA SCENARIO .............................. 209
TABLE 5.5 IMPACTS OF DDA: DOHA DOMESTIC SUPPORT SCENARIO................210
TABLE 5.6 IMPACTS OF DDA: DOHA MARKET ACCESS SCENARIO....................... 211
TABLE 5.7 IMPACTS OF DDA: DOHA EXPORT SUBSIDY SCENARIO..................... 212
TABLE 5.8 WORLD TRADE VOLUME UNDER ALTERNATIVE DOHA SCENARIOS ....213
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xii
.220
221
TABLE 5.11 IMPACTS OF DDA: DOHA MARKET ACCESS BOUNDARY SCENARIO...222
TABLE 5.12 IMPACTS OF DDA: DOHA EXPORT SUBSIDY BOUNDARY SCENARI0...223
TABLE 5.13 WORLD TRADE VOLUME UNDER ALTERNATIVE DOHA BOUNDARY
SCENARIOS.................................................................................................. 224
TABLE 5.14 WELFARE IMPACTS UNDER ALTERNATIVE DOHA BOUNDARY
SCENARIOS ..............................................................................................225
TABLE 5.15 IMPACTS ON MILK UNDER ALTERNATIVE SCENARIOS.................... 227
TABLE 5.16 IMPACTS ON CHEESE UNDER ALTERNATIVE SCENARIOS............... 228
TABLE 5.17 IMPACTS ON BUTTER UNDER ALTERNATIVE SCENARIOS............... 229
TABLE 5.18 IMPACTS ON SMP UNDER ALTERNATIVE SCENARIOS....................... 230
TABLE 5.19 FARM PRICE IMPACTS OF FULL DAIRY SECTOR LIBERALIZATION.^ 1
TABLE 5.20 PERFECT COMPETITION VS IMPERFECT COMPETITION: CENTRAL
DOHA SCENARIO........................................................................................... 237
TABLE 5.21 PERFECT COMPETITION VS IMPERFECT COMPETITION: CENTRAL
DOHA BOUNDARY SCENARIO/FULL LIBERALIZATION...................... 238
TABLE 5.22 COMPARISON OF MODEL SOLUTIONS AND ACTUAL DATA FOR 2004 ...239
TABLE 5.9 IMPACTS OF DDA: CENTRAL DOHA BOUNDARY SCENARIO .
TABLE 5.10 IMPACTS OF DDA: DOHA DOMESTIC SUPPORT BOUNDARY
SCENARIO............................................................. ..........................
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LIST OF BOXES
BOX 5.1 ALTERNATIVE APPROACHES TO REDUCING TARIFFS....................... !.......141
BOX 5.2 ELEMENTS OF THE DDA SCENARIO BASED ON JULY FRAMEWORK
AGREEMENT .................................................................................................145
BOX 5.3 MAJOR AGREEMENTS ON AGRICULTURE IN THE SIXTH MINISTERIAL
CONFERENCE.......................................................................................................... 148
BOX 5.4 DDA SCENARIO FOR SIMULATION...................................................................153
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ACKNOWLEDGMENTS
First of all, I would like to express my special appreciation to my advisor, Professor Thomas
Cox, for his great guidance, encouragement, understanding, tremendous patience and all-round
supports, without any of these, the completion of this dissertation is absolutely impossible. I also
want to thank Professor Thomas Cox for giving me father-kind caring during my study in U.S.. It
is him who makes my life in U.S. much more enjoyable. I am very thankful to Professor Jean-
Paul Chavas who amazingly responded to so many versions of my thesis. Talking to him has
always been refreshing and fruitful. I am very grateful to Professor Ian Coxhead for his insightful
comments on shaping the direction of this study. I also want thank him for providing me so many
background materials on the Doha round negotiations. It would be difficult to finish this study
without these materials. Professor Brian Gould has always shown his supports in spite of his
tight schedule. Professor Brian Yandell has also influenced my views of conducting this research.
To all of these helps, I am very thankful.
I thank all my other teachers at the University of Wisconsin-Madison, particularly Brad
Barham, Daniel Bromley, Kyle Stiegert, Guanming Shi, Michael Carter, Murray Clayton,
Douglas Bates, Robert Miller, Richard Johnson, Judith Grudzina and Kjell Doksum, from whom
I feel greatly benefited.
I thank Barbara Forrest for her administrative advice and helps during my 5-year study. She
is such a nice person to be always patient on any questions I raised. My appreciation also goes to
Davis Linda, Dawn Danz-Hale, Hilmanowski Nancy, Martin-Taylor Kathy, Munn Cindy, Olsen
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XV
Carol, Reid Vemetta, Waters Eliza for their administrative helps, and Eric Dieckman for his
computer supports.
I thank Mr. Joseph Burke for helping me understanding the data configuration of the GAMS
model. I would also thank my other fellow students, especially Du Ying, Cai Xiaowei, Li Muqun,
Cheng Xu and Lin Hua for their friendship and encouragement.
This work, and my graduate studies, would be impossible without the financial assistance
from the Department of Agricultural and Applied Economics, University of Wisconsin-Madison
and the U.S. Department of Agriculture.
Finally, my wife is of great support throughout my studies. I thank my wife for waiting in
Beijing to allow me to pursue my “selfish” career ambition in the past five years. Without her
love I would be broken down during my study. This work is dedicated to my parents and my
parents in law, thanks to all of them for their understanding.
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Chapter 1 Introduction
Spatial equilibrium models have frequently been applied to study interregional trade
problems in agriculture including regional competition issues associated with the dairy sector
(e.g., Chavas, Cox and Jesse; McDowell et al). Originally developed by Enke and by Samulson
and then refined by Takayama and Judge, spatial price equilibrium models operate mostly under
the perfect competition assumption. However, the perfect competition assumption is
questionable for some products trade. For example, Kawaguchi et al (1997) in the study of
Japanese milk market pointed out that “The Coumot-Nash equilibrium solutions were the most
similar to actual observations, and the actual interregional milk movements could be explained
by assuming imperfectly competitive behavior”. Is the assumption of perfect competition for
Asian and/or world dairy markets justified? Can Asian and/or world dairy markets be explained
by imperfect competition or some other market structure? The objective of this study is to
develop a model of Asian dairy markets allowing for imperfectly competitive market structure.
More specifically, I want to build a model that can incorporate any degree of market power from
perfect competition to monopoly or monopsony. I want to explore the empirical relevance of
perfect and imperfect competition for Asian and/or world dairy markets. This is done by
studying the functioning of dairy markets in Asia and the exercise of market power, with
implications for Doha round negotiations on agriculture in Asian and/or world markets.
1.1 Motivation
With over 60% of world population, Asia’s dairy products consumption has increased
rapidly in recent years. Asia also produces large amounts of milk reaching 206 million MT in
2003, which accounts for 31% of world total milk production. From 1989 to 2003 its
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consumption of dairy products (simple summation) increased from 90.8 million MT to 177
million MT, with an annual average growth rate of 5%. Asia accounts for 36.5% world total dairy
products consumption in 2003. Butter is the largest product category consumed, with
consumption exceeding 3.5 million MT in 2003. The consumption of whole milk powder (WMP),
skimmed milk powder (SMP), condensed evaporated milk (CEM) and cheese all exceeded 600
thousand MT. Except lactose, all other products consumption exceeds 170 thousand MT. The
annual average growth rate of butter, cheese and dry whey exceeds 5% during this period.
However, Asia is unevenly developed. There are developed countries and regions such as
Japan, Singapore, South Korea, Hong Kong and Taiwan. There are developing countries with
fast economic growth rates such as China, India, Thailand, Malaysia and Indonesia. There are
also least developed countries such as Nepal. Due to different stages of economic development,
consumer’s taste and farming styles, countries play a unique role in this region’s dairy
consumption. More than 90% of Asian regional casein, dry whey, lactose and residual dairy
products (fluid and soft products) are consumed by China, Japan, Thailand, South Korea,
Singapore, Philippines, Malaysia, Indonesia and India. At least 80% of other dairy products are
consumed by the above 9 countries, and there are large differences in the consumption share
among these countries. Japan consumes about 70% of regional casein and lactose, 50% of cheese
and 30% of skimmed milk power in Asia. China consumes more than 30% of regional cheese,
dry whey and residual products. India consumes about 60% of regional butter. Malaysia
consumes about 20% of regional condensed evaporated milk.
Asia’s per capita dairy consumption is still low compared with Western developed countries.
Per capita dairy consumption in China, India, Indonesia, Japan, Malaysia, Philippines, South
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Korea, Thailand, and Vietnam averages 4.5 kg, 35.7 kg, 2.1 kg, 44.2 kg, 7.7 kg, 2.4 kg, 35.2 kg,
9.8 kg, and 1.8 kg per capita, respectively, in the last decade, in contrast with 105 kg per capita in
the EU-15, 120 kg per capita in Australia, and 113 kg per capita in the U.S (Dong, 2005). This
also means that there is huge potential for further growth and development of the Asian dairy
markets.
The Asian markets play a more important role in dairy products imports than exports. Most
countries do not have exports at all. Only a few countries (China and India) export some dairy
products to neighboring countries. In 2003, Asia’s imports of milk equivalent are 25.8% of the
world total; Asia’s imports of butter, cheese and SMP accounts for 19%, 14% and 42% of world
imports (FAO, 2003), respectively. Asia’s exports of milk equivalent only accounts for 2.9% of
the world total in 2003. From 1989-2003, the annual average growth rates of imports of cheese,
dry whey, SMP and RES are 6%, 13.7%, 2.7% and 14.3%, respectively; if we exclude Japan
from the Asian dairy markets, the annual average growth rates of imports of cheese, dry whey,
SMP and RES are 11%, 15%, 5% and 14%, respectively. This suggests that cheese and SMP
imports have increased a lot in Asian countries other than Japan. This is reasonable as Japan
experienced an economic recession during this period. In 2003, China’s import of butter, WMP,
dry whey and residuals accounts for 14%, 21%, 40% and 35% of the Asian market, respectively;
Japan imports a lot of cheese (71%), SMP (9.2%) and dry whey (11%); South East Asia imports
a lot of butter (78%), WMP (44%), SMP (77%) and dry whey (38%); Korea mainly imports
cheese (11%) and dry whey (10%); India does not import much dairy products, but it exports
several dairy products (butter, WMP, SMP).
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Most of the Asian dairy product imports are from New Zealand and Australia. For example,
according to cheng et al (2002), during 1996-1998, China’s import of concentrated milk from
New Zealand and Australia account for 34% and 11% of its total imports, respectively; the
import of butter and dairy spread from New Zealand and Australia account for 50% and 25%,
respectively; the import of cheese and curd from New Zealand and Australia account for 30%
and 22%, respectively. Australia is the leading exporter of natural cheese to Japan (Campo and
Beghin, 2005). On a volume basis it accounts for 39.7 percent in 2001. Together with second-
place New Zealand (26.5 percent) exports from these two nations in Oceania account for nearly
66.2 percent of all Japanese natural cheese imports. Japan imports butter mainly from New
Zealand (Campo and Beghin, 2005), which had a share of 60.5 percent on a volume basis in
2001. Australia ranks second with a share of 20.7 percent.
Why this is the case? Is it because New Zealand and Australia have comparative advantage
against other countries (like US, Canada, EU, etc)? Before 2001, the New Zealand Dairy Board
acted as the single desk (monopoly) exporter of New Zealand’s dairy products and handled about
30 percent of world dairy product exports. Under pressure from WTO, the October 2001 merger
of the New Zealand Dairy Group and Kiwi Cooperative formed Fonterra. The merged
cooperatives then absorbed the New Zealand Dairy Board (NZDB), and now processes over 95
percent of the milk produced in New Zealand and has purchased interests in Australian dairy
companies (Armentano et al, 2004). This merger achieved processing economies and realized
efficiencies in dairy products exports. This is because the merger is based, in part, on the
following considerations (Armentano et al, 2004): (1) it was reasoned that coordination of
industry activities throughout the value chain would be facilitated by merging the NZDB with
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the New Zealand Dairy Group and Kiwi Cooperative; (2) while the Board had efficient
procedures for allocating production orders for export sales with New Zealand’s cooperatives,
the NZDB was unable to optimize New Zealand’s dairy export product mix when it operated
separately from the cooperatives. Understandably, the cooperatives produced milk to further
their own interests rather than those of the entire New Zealand dairy industry under the old
structure; (3) to some extent, the NZDB had come to be regarded as an unnecessary layer
between foreign buyers and New Zealand’s domestic processors. Moreover, New Zealand’s big
cooperatives had developed the ability to export dairy products on their own. The merger was
also to gain market power. This is because one of the current strategies of Fonterra is to enable
firms to be effective developers of integrated strategies for the four key regional markets of
China, Eastern Europe, India, and the economic grouping of Chile, Brazil, and Argentina. This
strategy appears to involve getting ahead of competitors for serving these major growth markets.
This could give New Zealand first mover advantage in these markets and expand its market share.
In this case, an imperfect competition model may be more appropriate to explain dairy trade in
these markets.
Australia had a similar organization with the Australian Dairy Corporation (ADC), created in
1975. The ADC worked at enhancing the production and marketing of dairy products for greater
profit for milk producers, and acted as the single desk exporter of Australian dairy products. Its
role, however, was limited to exports to Japan of all dairy products and to cheese sales to the EU,
and it ceased operation on June 30,2002 (Seyoum, 2004). This role has been more or less taken
over by dairy companies such as Murray Goulbum Cooperatives and Bonlac Foods which have
emerged as major dairy exporters. In addition to this, Australia and New Zealand also provide
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technical assistance to Asian countries. For example, both Australia and New Zealand have a
large number of programs to promote dairy cooperation with the Chinese government and firms
(Cheng, 2000). The aim of these programs is to give them first mover advantage in China’s dairy
market, such as (1) build good relationship with China’s firms to have the priority to sign export
contracts; (2) expand market share and establish brand recognition through technical assistance.
Before the 1990s China’s milk yield per cow was very low and China imported a lot of dairy
cows from EU and North American during the 1980s. During the 1990s, Australia and New
Zealand governments provide technical assistance to China and promoted dairy cow exports to
China. Now Australia and New Zealand have become the main markets to export dairy cows to
China.
The international dairy market is characterized by the pursuit of an active role in international
trade by large dairy companies and multinational enterprises (Seyoum, 2004). Many of the
world’s key dairy businesses have been involved in major mergers and acquisitions in the past
decades. Between May 2000 and June 2001 there were 150 mergers and acquisitions throughout
the world in large dairy companies (Rabobank International (2001)). In its report, Rabobank
International indicated that in some product categories, globalization has already taken place:
Nestle and Unilever dominate ice cream; Danone, Yoplait and Nestle dominate yoghurt; and
Kraft focuses primarily on cheese.
According to Handy and Henderson (1994), most food manufacturing firms rely more on
foreign investment than on exports as their major strategies are to access foreign markets.
Dobson (2001) indicated that the global food processing market is dominated by big companies
in the U.S., EU and Japan and he argued that because the prospects for further trade
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liberalization of the world dairy markets are unlikely, foreign direct investment in new markets is
a potentially attractive alternative to exporting. Many large companies are now moving
aggressively into Asia. For example, so far, all the world's top 20 dairy brands have entered the
Chinese market and four of them have built production plants in the country, including
Switzerland's Nestle and Italy's Parmalat. As multinational companies have the ability to
maximize total profit over several countries’ markets, they can gain efficiencies in dairy product
production. This is different from a national or regional company which can only maximize its
total profit over one country’s market. Moreover, if the Asian dairy markets are dominated by
several big companies, there may exist market power. In this case, oligopolistic competition may
be appropriate to explain Asian dairy products trade.
Dairy industries around the world are among the most distorted agricultural sectors. In order
to resolve the issue of trade distortion and promote trade liberalization of dairy products, GATT
concluded the Uruguay Round Agreements in late 1993 after eight years of painstaking
negotiations. As a component of the agreement, the GATT/WTO Agreement on Agriculture
(AoA) requires all GATT members to make reduction commitments on domestic support and
export subsidy, and broaden market access. The commitment would be fulfilled by developed
countries at the end of 2000 and by developing countries at the end of 2004, with 1986-1988 as
the base period for reduction. The least developed countries are not required any reduction
commitment. For market access commitments, ten dairy products exports to Japan are subject to
Tariff Rate Quota (TRQ) administration, five to South Korea, two and one to Malaysia and
Indonesia, respectively. Japan and Korea’s TRQs are allocated on global basis. For whey and
skim milk powder for other purposes, Japan’s TRQs are allocated to producers and producer
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organizations of mixed feed or sellers. For skim milk powder, whole milk powder, and other
milk and cream, Korea’s TRQs are allocated according to the highest price bidders at quota
auctions held by the Livestock Products Marketing Organization. Indonesia acts as the single
desk buyer.
When TRQs are binding, the question of how TRQs are allocated among the importing firms
is the core of element of competition. In case of limited access to import rights, the assumption
of perfect competition might be rather unrealistic. McCorriston (1996, p.372) has shown import
quota can create oligopsony power: “the results confirm that perfect competition among license
holders should be rejected”. De Gorter (1999, p.9) explains the situation where “it is possible for
one group to purchase the entire portion of the right to import (domestic or foreign), and then
withhold part of the license to maximize revenue”.
Therefore, there is circumstantial evidence that the Asian and/or world dairy markets are
subject to imperfect competition. In this paper, I want to explore this possibility in the context of
new round (Doha Round) negotiations for further trade liberalization on agriculture. Doha Round
negotiations started in 2001 and are scheduled to finish this year. These negotiations aim at
substantial expansion of market access and reduction in domestic support and export subsidy.
During the negotiations, it is difficult to find a consensus formula for reducing agricultural tariffs.
Real progress on agriculture was not evident until the early hours of 1 August 2004 with a set of
decisions in the General Council (sometimes called the July 2004 package). The Sixth
Ministerial Conference in Hong Kong, China, in December 2005, reaffirmed the Declarations
and Decisions adopted at Doha, as well as the Decision adopted by the General Council on
1 August 2004, and committed to give effect to them. As mentioned above, the dairy industry is
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an infant industry in Asia when compared to Western countries. However, it has huge potential
for further growth and development. Three issues are of particular interest to this study. First,
how will the outcome of the Doha round negotiations affect its development? Second, do Asian
countries have market power for dairy trade? Third, if market power exists, how does it impact
Asian dairy industry? All answers to these questions would help us to better understand the
development of Asian and/or world dairy sector in the future.
1.2 Review o f Literature
Several issues of Asian dairy markets have been studied. Dong (2005) studied the role that
demographics, income and prices played in Asian dairy markets by using the FAPRI
international dairy model, and pointed out that both Asian dairy consumption and supply show
upward trends over the next decade. Asian dairy demand growth in the next decade is mostly
driven by income and population growth. Given a 1% additional growth in income, cheese
consumption will increase 0.45% and WMP consumption will increase 0.39%. With
technological change which increases yield per cow, Asian domestic milk output is expected to
increase, implying a possible decease in imports.
Cheng et al (2002) studied the impacts of China’s accession into WTO on its dairy industry
and argued that China’s dairy production will increase with China's accession into WTO; China's
accession into WTO will increase its dairy consumption; China will remain a net dairy importing
country from 2000 to 2015, as accession into WTO will boost up its net dairy imports.
Fuller et al (2005) studied China’s dairy production efficiency and found that the share of
output growth contributed by total factor productivity (TFP) was the lowest for milk production
when compared to almost all other livestock sectors (with the exception of backyard hog
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10
production). Although TFP growth has been relatively slow, the contribution of technology has
played an important role in the growth of China’s milk production. Most of China’s productivity
growth in the specialized and commercial dairy sectors has been due to the adoption of improved
genetics, milking processes and other management methods. There were very small gains due to
scale economies.
Cheng (2000) based on her investigation of dairy enterprises and production farms argued
that China’s dairy industry won’t be disadvantaged with China’s accession into WTO, and
China’s dairy industry won’t increasingly depend on world dairy markets.
China’s Rural Economy Research Center analyzed the factors restricting China’s dairy
industry development and pointed out that China’s dairy production, consumption, and dairy
products quality and varieties would increase rapidly in the following 10-15 years.
Wattiaux, Guo and Frank (2002) based on their study of dairy production systems and dairy
farms management systems, found that an increasing number of dairy cows are in the hands of
private entrepreneurs; The transition in production systems from large state-owned farms to
(currently smaller) private farms create challenges and uncertainties about the future of domestic
production in China. Chinese small privately-owned farms were as profitable as much larger
collective or state-owned farms. Large, well-managed Chinese farms have a net farm income
approximately three times lower than Wisconsin farms of similar size.
Pratt et al (2005) pointed out that the success stories of Pakistan and India show a high and
growing demand for dairy products; a growing supply resulting from increased numbers of
milking animals and growing contribution of technical change; increased feed availability,
development of feed markets, and market institutions linking demand and supply effectively.
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Conversely, Bangladesh and Sri Lanka face supply constraints related to production of quality
feed, animal stock composition and yields. These two countries also face lower demand for dairy
products, depend more on imports and at least in the case of Sri Lanka, powdered milk is more
popular than traditional products among consumers. In both countries, development of
manufacture and growth of human capital have increased the opportunity cost of rural labor,
which together with lack of technical options at the farm level, has discouraged diversification
and increased the share of livestock production in mixed production systems. In other cases, like
in Nepal, constraints appear to be more related to policy and promotion of inadequate market
institutions (such as the pricing and distribution system).
Lee et al (2005) pointed out that trade liberalization would cause significant increases in
Korean dairy imports (milk fat increase by 27%, non-fat-solid (NFS) by 18.1%), lower prices of
processed dairy products for Korean consumers (raw milk price falls by 27.8%, milk fat by
23.7%, NFS by 29.6%). Despite these large shifts in percentage terms, the quantity of raw milk
produced falls by only 4.9 percent. Even in the free trade scenario analyzed, the prices of farmer
supplied inputs, labor and capital, fall by a modest 3.1 percent and 2.4 percent.
Campo et al(2005) pointed out that Japan’s dairy consumption patterns have evolved with
increasing individual consumption of cheese and fluid milk. The individual consumption of
butter and milk powder has been stagnating as butter is not widely used in cooking and as fluid
milk has been substituted for milk powder. This increase in per capita consumption is linked to a
decline in real dairy prices, rising household incomes, and changes in taste/information. Japanese
dairy supply is still isolated from world markets because of prohibitive tariffs, high
transportation cost and the perishability of fluid milk. Processors have been disadvantaged by
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12
their low effective protection and by a lack of scale economies (dairy farm size is small and
inefficient).
Rakotoarisoa et al (2005) pointed out that India’s dairy products, especially SMP during the
1990s, has become more competitive. Domestic supply for WMP and raw milk are relatively
inelastic but increases in world WMP price would significantly increase both India’s WMP export
and wholesale price of milk. These gains extend if domestic WMP supply becomes more elastic.
These finding imply that reduction of distortions in the world dairy markets, especially in the EU
and the US, leading to higher world prices would make India’s dairy market more competitive.
Such reform would have significant impacts on fresh milk price, thereby, small milk farmers.
There are several papers using perfect competition models to study world dairy sector which can
be categorized as: (1) based on Computable General Equilibrium (CGE) Models: ABARE (2004),
CARD (2004), World Bank LINKAGE; (2) based on Partial Equilibrium Econometric (Time-Series
or Equilibrium Displacement) models: OECD Aglink, FAPRI, ERS/Penn State WTO Model:
Langley et al (2003); Abler et al (2001) and the Guelph Model: Lariviere and Meilke (1999); (3)
based on Partial Equilibrium Programming models: Toulouse EU Dairy Sector Model, Bouamra-
Mechemeche, Chavas, Cox and Requillart (2002,2004) and the University of Wisconsin World
Dairy Model (UW-WDM) Hedonic Spatial Equilibrium: Cox, Coleman, Chavas and Zhu (1999);
Cox and Zhu (2004); Zhu, Cox and Chavas (1999).
However, none of the above approach explores the differences between perfect competition and
imperfect competition for Asian and/or world dairy markets. Is the perfect competition model
justified to study Asian and/or world dairy markets? There are several trade books studying the
impacts of imperfect competition.
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Helpman and Krugman (1989) pointed out that the theory of trade policy under imperfect
competition is full of paradoxes. For example, a tariff or import quota may reduce the output of the
protected industry, an import subsidy may improve the terms of trade, and export subsidy may raise
the subsidized firm’s profits by more than the subsidy itself, protection can raise the profits of
foreign as well as domestic firms, and a tariff can reduce internal prices. The basic reason for this
prevalence of unusual is that under imperfect competition firms react to more aspects of their
situation than they do under perfect competition. Instead of simply setting marginal cost equal to the
price, firms set it equal to perceived marginal revenue.
Krugman (1994) pointed out that, in the context of oligopoly competition and decreasing cost,
protecting the domestic firm in one market increases domestic sales, lowers foreign sales in all
markets, and increases the incentive for domestic RandD at foreign expense. This in turn translates
into a shift in relative production cost, which leads to increased domestic sales even in unprotected
markets.
Krishna (1989) pointed out that in the case of duopoly competition a quota can easily raise the
profits of both firms.
There are several papers that incorporate imperfect competition in spatial equilibrium models.
Nelson and McCarl developed a Cournot and conjectural variation model which could depict certain
forms of imperfect market structures, but they did not apply them empirically. Kawaguchi, Suzuki,
and Kaiser used the conjectural variation approach, similar to that used by Nelson and McCarl, and
applied it to the Japanese domestic milk market. But Kawaguchi et al. study exporters’ market
power and ignore potential importers’ market power. This is unrealistic because demand (imports)
more important than supply (exports) for trade flow in dairy products. Chen, McCarl, Chang and
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14
Hsu developed an imperfect competition model that can study any kind of market structure
specification allowing competitive behavior spanning from perfect competition to monopoly or
monopsony. They also considered both exporters’ and importers’ market power by applying the
model to world rice trade. But they focus only on the trade side and do not consider the domestic
supply/demand change associated with a change in countries’ exports/imports.
In this dissertation, I will develop a model to combine domestic supply/demand and
imports/exports into one unified system to study the importers and exporters’ market power by
allowing any degree of market structure from perfect competition to monopoly or monopsony, and
investigate which model is more appropriate for Asian and/or world dairy markets, with
implications for the impacts of Doha round negotiations.
1.3 Structure o f the Dissertation
The objectives of this dissertation are to better understand the Asian dairy situation, examine
whether or not there is market power for Asian dairy products trade, explore the differences
between perfect and imperfect competition for Asian dairy products trade, and investigate the
implications in the context of Doha round negotiations. I use five chapters to achieve this. Chapter 2
presents the status quo Asian dairy situation. I first review dairy policies (domestic support policies
and trade policies) for major Asian countries and their exporters (New Zealand, Australia, EU and
U.S.). I then study Asian countries WTO commitments and implementation from the perspective of
market access, domestic support and export subsidies. Lastly, I study Asian countries major dairy
products (butter, cheese and skim milk powder) production, consumption and trade in recent years.
Chapter 3 develops a model to combine domestic supply/demand and imports/exports into one
unified system to study the importers and exporters’ market power by allowing any degree of
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market structure from perfect competition to monopoly or monopsony. I first briefly introduce the
pros and cons of existing models to study world dairy markets, and then built an imperfect
competition model extending previous models of perfect competition (The UW-Madison World
Dairy Model (UW-WDM)).
Chapter 4 is used to study the market power of Asian countries and their major exporters for
dairy products trade. In this chapter I estimate an econometric model which can reveal and test
whether or not market power for importers and/or exporters in dairy products trade exists. It relies
on the spatial price arbitrage conditions derived under imperfect competition in chapter 3.
Chapter 5 studies the impacts of Doha round negotiations on the Asian dairy markets. First, I
introduce the Doha development agenda and its major achievements. Second, I set several
alternative scenarios based on the August 2004 Framework Agreement to study the potential
impacts of Doha round negotiations on Asian dairy markets. One scenario is the BASE scenario
which I use the average data from 2002-04.1 calibrate the endogenously generated dairy products
prices, production, consumption with 5-10% of the actual data for this period. Using this BASE
scenario, I explore the differences between perfect and imperfect competition for Asian and/or
world dairy markets. The results of other scenarios are compared to this BASE scenario to see their
individual impacts on dairy products prices, production, consumption and welfare. Third, I further
explore the difference between perfect and imperfect competition in the context of Doha round
negotiations and full dairy sector liberalization. Lastly, I discuss the needs for further research.
Further conclusions and discussions are developed in chapter 6. In particular, I discuss the
contributions, limitations and future tasks of this search.
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Chapter 2 Asian Dairy Situation
As Asian countries experience different stages of economic development, their dairy policies,
dairy product trade, production and consumption are consequently different from each other. In
this chapter, we review major Asian countries dairy policies, their WTO commitments, and dairy
products trade, production and consumption. In addition to major Asian countries, we also take a
look at their major exporters, i.e., EU, Australia, New Zealand and U.S.
2.1 Dairy policies
2.1.1 China
2.1.1.1 Domestic Supporting Policies
The dairy industry is regarded as an important industry by the government as it has a direct
impact on the nutrition and health of its people, and is identified as a key industry for state
support in the policy documents of the State Council. At the end of the 1990s, when there was a
surplus in the supply of pork and poultry, the market for dairy products remained rather stable.
At this time, the government was determined to improve the structure of the agriculture and
husbandry industry. In 1999, the Ministry of Agriculture (MOA) made it clear that China "put
more emphasis on the production of dairy products, stabalize the supply of pork and poultry, and
raise the output of herbivorous animals." In the 1990s, preferential policies were introduced to
promote foreign investment in the dairy industry and many international dairy enterprises have
established joint ventures in various parts of China. As a result, the quality of management,
operation, as well as marketing of dairy products has significantly improved. In its “Tenth-Five
Year” country economic development blueprint (2001-2005), dairy is identified as an important
industry. It has implemented “school student milk program” to provide subsidized milk for
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17
school students at selected cities. The program is expected to expand to other cities all over the
country.
To promote the development of the dairy industry, China reformed its dairy feed provision
and dairy products pricing policies in recent years. For feed provision policies, during the
transition from the planned economy to market economy (early 1990s), China implemented a
policy of fixed provision of grain. As a measure to ensure enough high quality feeds (including
feed grain, bran and oil meals) for the dairy industry, dairy farms and dairy farmers in suburbs
could exchange "feeds for milk". Three kilograms of milk could be exchanged for one kilogram
high quality feed at a subsidized price. In some places, high quality feeds were provided at a
subsidized price according to the number of milk cows. The measure aimed at providing price
subsidies (payment from local government) to farmers when the cost of raw milk production
exceeded the price of milk set by the state. Since the mid 1990s, with the development of the
market economy, China cancelled the fixed grain provision policy, liberalized the price of milk,
and abolished the measure of "milk for feeds".
For dairy products price policies, under the planned economic system, the price of milk was
set by the price bureau at provincial and local administrative levels and strictly controlled by the
state. From 1950s to the beginning of 1980s, the price of milk had been fixed at a certain level.
In some places the price was reduced in absolute terms to benefit the consumers. Such a price
system delayed the development of the dairy industry because most of the cities had to limit milk
consumption by issuing milk coupons. In the 1980s, restrictions on dairy products and feed were
lifted in a number of cities. In major cities such as Beijing, Tianjin and Shanghai, however, the
prices of dairy products are still controlled by the local government. As the market economy
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develops, the competitive mechanism of "good quality at a good price" was introduced in the
dairy industry. Many dairy enterprises now conduct strict inspection in the purchase and sale of
milk. They implement a pricing-according-to-quality system to encourage farmers to sell quality
milk and help avoid adulteration.
2.1.1.2 Trade Policy
Before the economic reform, China had closed its doors to the outside world. At that time,
China's dairy products were also of relatively low quality. Its policy was to resist imports and all
possible forms of international assistance. With the introduction of the economic reform, China
began to import dairy products from other countries to produce reconstituted milk. At the same
time, China began to accept international assistance from World Food Program (WFP)/Food and
Agriculture Organization (FAO) and EU in developing the dairy industry. The latter has helped
advance dairy processing technology and promoted the development of the industry as a whole.
Both China's market access and foreign investment policies in the dairy industry are lenient
and favorable. Now, China only has tariff measures and no quota limitation on dairy product
imports. The purpose of tariffs are to protect local products from competing head-to-head with
cheaper foreign imports. In recent years, in order to comply with the requirements of GATT and
later WTO, China’s import tariffs have been adjusted to lower levels. China’s commitment of
tariffs during its accession into WTO is shown in table 2.1.
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19
Table 2.1 China’s Tariff Rates (%) on Dairy Products
Description of goods TariffNo. Import duty rate in 1999
Binding tariff rate in 2004
Milk and cream, of a fat content, by weight, not exceeding 1% Milk and cream, of a fat content, by weight, exceeding 1%, but not exceeding 6%
04011000
04012000
25
25
20
20
Milk and cream, of a fat content, by weight, exceeding 6% Milk and cream in powder, granules or other
04013000 25 20
solid forms, of a fat content, by weight, not exceeding 1.5% Milk and cream, not containing sugar, in powder,
04021000 25 25
granules or other solid forms, of a fat content, by weight, exceeding 1.5% Milk and cream in powder, granules or other
04022100 25 25
solid forms, of fat content, by weight, exceeding 1,5%
04022900 25 25
Milk and cream not containing added sugar or other sweetening matter 04029100 50 40
Other milk and cream 04029900 50 25 Yogurt Other buttermilk, curdled milk and cream,
04031000 50 10
whether or not containing added sugar or other sweetening matter or flavored additives Whey and modified whey, whether or not
04039000 50 20
concentrated or containing added sweetening matter
04041000 6 6
Other whey and modified whey 04049000 50 20 Butter 04051000 50 30 Dairy spread 04052000 50 35 Other fat derived from milk 04059000 50 30 Fresh (unripened or uncured) cheese, including whey cheese, and curd 04061000 50 12
Grated or powdered cheese, of all kinds 04062000 50 12 Processed cheese, not grated or powdered 04063000 50 12 Blue-veined cheese 04064000 50 30 Other cheese 04069000 50 30 Source: The Schedule o f Management o f Imp-export o f P.R.C.- Tariff and Other Measures, MOFTEC, 1999.
2.1.2 Japan
2.1.2.1 Domestic Support Policies
Before 2001, Japanese dairy policy was composed of three basic programs: price supports for
milk used for manufactured dairy products, classified pricing and revenue pooling through
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2 0
prefectural milk marketing boards, and import quotas (Suzuki and Kaiser, 1994). Japan’s
government has set production ceilings in order to prevent market surpluses and price instability
since 1965. Dairy producers voluntarily organized a planned production system in 1979. Annual
production allowances based on demand were determined by the Japan Dairy Council, and were
allotted to each prefecture (nine in total). In turn, the authorities on the prefecture-level, set
production quotas for individual agricultural cooperatives and dairy farmers within the prefecture.
According to Campo and Beghin (2005), allocation formulas varied among the different
prefecture councils. Until 1995, production quotas were under the control of the national and
prefectural councils. In 1996, however, a system allowing individual dairy farmers to adjust
production quotas among themselves was instituted.
Deficiency payments based on the difference between the average cost of producing one
kilogram of milk (the guaranteed price) and the price dairy producers receive for the same
quantity (the standard transaction price) were determined annually (Campo and Beghin, 2005).
Therefore, the price the dairy producers received was the guaranteed price (standard transaction
price paid by the milk processors plus the deficiency payments paid by the government). Also
before 2001, fluid milk prices were determined by negotiations between each marketing board
and the processors that it supplied (Campo and Beghin, 2005). Given the manufacturing milk
price, prefectural marketing boards usually obtained fluid milk premiums through their market
power (Suzuki and Kaiser, 1994).
The recent trend has seen steady price declines for fluid milk that progressed independently
of production costs to dairy farmers (Japan Dairy Council). The current dairy policy has been in
place since 2001. The production quota is still in effect and is determined as before. All
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2 1
government-regulated prices were abolished including (Campo and Beghin, 2005): (1) the
guaranteed manufacturing milk price for fanners; (2) the standard transactions price for
manufactures. The difference between guaranteed price and the standard transactions price used
to be paid to farmers as deficiency payments. According to Suzuki and Kaiser (2005), in the new
system, only the amount of the former deficiency payment, almost ¥ 10/kg, is maintained as a
fixed payment. The fixed payment level is reviewed every year considering the demand/supply
situation and farmers’ production costs. Now, the private market determines the manufacturing
milk prices. Regardless of the market price level, farmers receive the market price plus the fixed
payment.
However, the new policy has a built in security measure in case of a sudden price decline and
operates along the lines of a revenue insurance program. According to Campo and Beghin (2005),
a fund has been established that is charged by deductions of ¥0.4/kg of milk that is delivered and
by an additional ¥1.2/kg of milk quota for which farmers are getting subsidies. These funds are
then matched by government money. If the price for manufactured milk is below the past three-
year average, farmers can receive the 70 or 80% of the difference between the current price and
the past three-year average from the fund. Overall, it seems that the policy changes have been
cosmetic rather than of a fundamental nature. The essence of the policy is that it is paid by
consumers through market prices that are much higher than their international equivalents
(Campo and Beghin, 2005).
According to the OECD, the market price support (MPS) component indicates the producer
support obtained through market price distortions. In Japanese dairy production the MPS has
been about 90% of the producer support estimate (PSE) between 1986 and 2003, with very little
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2 2
change during this period. Still according to the OECD, consumer prices have been almost as
high as the price received by producers during the same period and this suggests that Japanese
dairy policy is mostly paid by consumers rather than by taxpayers. The level of support, as
measured by the PSE per unit of production in nominal terms, has been falling from ¥91,000/mt
in 1986 to a bit less than ¥66,000/mt, hence quite a substantial decrease, especially in real terms,
although the intervention level remains extremely high (Campo and Beghin, 2005).
2.1.2.2 Trade Policies
Japan’s domestic dairy market is highly protected. The imports of dairy products such as
milk powder, condensed milk, buttermilk powder, whey, and butter are managed by the
Livestock Industry Promotion Corporation, a state trading enterprise (STE). Ten dairy product
imports are subject to tariff rate quota (TRQ) administration, including skim milk powder, whey
and butter (see section 2.2.1 for details).
Japan distinguishes broadly between natural and processed cheeses (Campo and Beghin,
2005). Natural cheese includes soft cheeses (i.e. Camember or Mozzarella), semi hard (i.e.
Gouda), hard (i.e. Emmental and Gruyere) and extra hard cheeses (i.e. Parmesan). Processed
cheese is made out of one or more varieties of natural cheese. Today, most imported cheese
consists of natural cheese. According to Campo and Beghin (2005), on natural cheese imports
intended for direct consumption, Japan levies import tariffs ranging between 22.4 to 40 percent.
Natural cheese destined as an ingredient for processed cheese is imported through the “pooled
quota” and enters Japan duty free up to 2.5 times Japanese domestic natural cheese production
used for processed cheese. The over-quota tariff of 35% is applied to imports exceeding that
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volume. Importers must apply to the Ministry of Agriculture, Forestry and Fisheries (MAFF) for
the in-quota duty rate (Campo and Beghin, 2005).
To stabilize Japanese butter prices, all butter imports pass through a single importer channel,
the Agriculture and Livestock Industry Corporation (ALIC) (JETRO, 2002a). The state trading
importing regime of the ALIC basically ensures that a much higher domestic fluid milk price can
be maintained. Though it is possible to import butter through the “Pooled Quota” at the in-quota
rate of 35 percent, it is limited to butter for specific uses (for display at international trade fairs
and for airplanes on international flights etc.) (JETRO, 2002a). In order to apply for the primary
duty rate, importers must apply to MAFF and obtain a TRQ certificate. Ad valorem equivalents
for over quota rates for the butter TRQs range between 465.5 (Janet Nuzum) to 592 percent
(OECD). Clearly these rates are prohibitive in addition to the import monopoly by the ALIC
(Campo and Beghin, 2005).
2.1.3 South Korea
2.1.3.1 Domestic Support Policies
Korean dairy production is heavily supported by the government. The producer support
equivalent (PSE) for Korean dairy was 68 percent in 2003 (OECD, 2004). In addition to high
tariffs and tight tariff rate quotas, the government also provided a variety of domestic support
measures. According to Lee et al (2005), most importantly, the government requires that raw
milk be purchased from farmers at prices above the competitive market price. These prices are
set in reference to the production costs, so that, on average, farmers are guaranteed a positive net
margin. Further, although farm efficiency has improved, the guaranteed price has never been
adjusted downward.
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Given the high prices relative to production costs, management of dairy stocks has long been
a problem for the Korean government, especially after 1998 when the authorities raised the raw
milk price by 18.4%. According to Lee et al (2005), the result was a stockpile that grew from 8
percent of production in 2000 to 12 percent in 2002. To curtail milk production, the government
provided compensation to slaughter dairy cows, but the program was largely ineffective. With
mounting financial burden, the Dairy Committee began an informal two-tier price policy, which
pays farmers a price lower than the government-set base price for the raw milk that exceeds the
farmer’s “normal” production (based on previous years’ production). As a result, several large
producer cooperatives have left the Dairy Committee, reducing its raw milk market share more
than 70 percent to about 26 percent, making the Committee ineffective as a policy administrator
(Lee et al, 2005). Policy is unsettled and adjustments are under way. These include introduction
of formal marketing quotas and a gradual decline in the price of above-quota production (Cho et
al., 2002).
2.1.3.2 Trade Policies
Until 1994, Korea maintained strict import quotas for most dairy products. Under the
Uruguay Round WTO agreement, Korea formally opened the dairy market, providing minimum
access (MMA) quotas, relatively low within-quota tariff rates, and very high over-quota tariff
rates (Lee et al, 2005). Currently, five dairy product exports to Korea subject to TRQ
administration, which include skim milk powder, whole milk powder, whey powder and butter
(see section 2.2.1 for details). During the 10-year Uruguay round implementation period (1995-
2004), over-quota tariff rates fell each year, but the lower within-quota tariffs did not fall (Lee et
al, 2005). The tariff rates vary significantly across products. For example, according to Lee et al
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(2005), skim milk powder has a tight quota for which the lower tariff of 40 percent and an over
quota tariff of 176 percent apply (in 2004). Butter has an 89 percent over-quota tariff. At the
other end of the spectrum, formulated butter (which is about 70 percent milk fat) has a single
tariff of 8 percent. Cheese imports have a single tariff of 36 percent.
2.1.4 India
2.1.4.1 Domestic Support Policies
According to Rakotoarisoa and Gulati (2005), Indian dairy policies have always been aimed
at protecting the interests of both dairy farmers grouped in cooperatives as well as consumers
from the distorted world dairy markets. The dairy sector has attracted the attention of the
government, both from the point of view of rising demand, and as a means to provide
supplementary employment and income opportunities in the rural areas. Support for dairy sector
in India is included in the government expenditures in ‘dairying and animal husbandry’ but
constitutes less than one percent of government total agricultural subsidies. The support for milk
production in India is marked by three Operation Flood (OF) programs. Expenditures in the form
of subsidies of the first Operation Flood (OF-I) were included under the plan expenditures for the
period following 1970-71, the year OF-I started. The OF-II began in 1978-79 the overlapping
period of two years, 1978-79 and 1979-80. The OF-III started in 1987-88 and ended in 1996.
2.1.4.2 Trade Policies
Before 1990, India’s domestic dairy products production was heavily protected by import
restrictions, which include import quota and ‘canalization’ of imports of dairy products by the
Indian Dairy Corporation (IDC) (Rakotoarisoa and Gulati, 2005). But in the early 1990s, India’s
government initiated major trade policy reforms, which favored increasing privatization and
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26
liberalization of all sectors including the dairy sector. Current trade policies of India for 2002-
2007 are outlined in the ‘Export-Import’ or ‘Exim’ policies of 2002 (Rakotoarisoa and Gulati,
2005), which asked for the removals of restrictions on imports and exports of products including
all dairy products. Such policies were aimed at expanding dairy supply and trade.
2.1.5 South East Asia (SEA)
Southeast Asia countries do not produce sufficient fresh fluid milk to satisfy their fresh milk
needs. Although their governments have provided some supports, such as providing technical
assistance and financial support, those programs do not have expected results (Dong, 2005). For
example, the Malaysian government established and operated numerous large dairy enterprises
and a system of centralized milk collection centers (Dong, 2005). In the later 1990s, 60 percent
of milk produced was collected and sold through official milk collection centers, and about 65
percent of milk from the official milk collection centers was used by domestic dairy
manufacturers (Zhang et al, 2003). In addition, the Malaysian government supports the domestic
dairy industry by direct investment in farms, school milk programs, and restrictions on entry of
imported dairy products (Zhang et al, 2003). To develop the domestic dairy industry, the
Philippines National Dairy Authority has set up Herd Build-Up and Save-the-Herd Programs in
recent years to allow for the importation of bulls and cows from Australia, New Zealand, and
some Pacific Island states (Dong, 2005). The imported herds are adapted to the local climate and
bred with local cows and carabaos for milking purposes (USDA, 2002b). The infusion of the
imported dairy animals has resulted in marginal increases in local milk production. Moreover,
the Non Fat Dry Milk grant provided by the USDA has helped the purchase of imported dairy
cattle and the establishment of three new dairy zones which are expected to boost milk
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production (USDA, 2003c). In addition, a national school milk program was launched to create
the demand for locally produced milk and consequently stimulates the development of local
dairy industry (Dong, 2005). The Royal Thai Government has promoted raw milk production
through the price guarantees for raw milk, an import quota allocation for NFD and a school milk
program (Dong, 2005).
For trade polices, although Indonesia and Malaysia apply TRQs for some dairy products
imports SEA has relatively lower import tariffs than other Asian countries (see section 2.2.1 for
details).
2.1.6 Oceanic Countries
New Zealand is the only major milk producing country that has almost no government
intervention in dairy sector (Johnston, 1985) and it currently supplies dairy goods at world prices.
However, the New Zealand government does provide research and outreach support to the dairy
industry. According to Armentano et al (2004), Dairy InSight and the national government are
the primary sources of funding for dairy production RandD and dairy industry education. Dairy
InSight is a mandatory check-off program created in mid-2002. All dairy farmers contribute to
Dairy InSight through a mandatory levy of NZ$0,034 per kg milk solids. The levy is to be
reviewed every six years and voted on by dairy producers in a continuance referendum. Funding
is used for RandD, technology transfer, industry promotion (not milk promotion), education and
training, and research on animal health. Federal government funding is principally provided
through the Foundation for Research, Science and Technology (FRST). FRST supports research
within specified broad areas. Pertinent to dairy in 2002-03 were “Sustainable Development and
Biological Industries.” AgResearch (one of several Crown Research Institutes) is a major
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beneficiary of FRST funding, receiving NZ$54.4 million of its total 2002*03 budget of NZ$129
million from FRST. Most of AgResearch’s other funding is from commercial sources. It
specializes in basic research and commercialization of laboratory findings.
Before 2001, the New Zealand Dairy Board acted as the single desk (monopoly) exporter of
New Zealand’s dairy products and handled about 30 percent of world dairy product exports.
Under pressure from WTO, the October 2001 merger of the New Zealand Dairy Group and Kiwi
Cooperative formed Fonterra. The merged cooperatives then absorbed the New Zealand Dairy
Board and processes over 95 percent of the milk produced in New Zealand and has purchased
interests in Australian dairy companies. Fonterra is also the world’s largest exporter of dairy
products, exporting 95 percent of its two million metric tons of production to approximately 140
countries (Armentano et al, 2004). The merger is to achieve processing economies and realize
efficiencies in dairy products exports. This is because the merger is based, in part, on the
following considerations (Armentano et al, 2004): (1) it was reasoned that coordination of
industry activities throughout the value chain would be facilitated by merging the NZDB with
the New Zealand Dairy Group and Kiwi Cooperative; (2) while the Board had efficient
procedures for allocating production orders for export sales with New Zealand’s cooperatives,
the NZDB was unable to optimize New Zealand’s dairy export product mix when it operated
separately from the cooperatives. Understandably, the cooperatives produced milk to further
their own interests rather than those of the entire New Zealand dairy industry under the old
structure; (3) to some extent, the NZDB had come to be regarded as an unnecessary layer
between foreign buyers and New Zealand’s domestic processors. Moreover, New Zealand’s big
cooperatives had developed the ability to export dairy products on their own. The merger is also
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to gain market power. This is because one of the current strategies of Fonterra is to enable firms
to be an effective developer of integrated strategies for the four key regional markets of China,
Eastern Europe, India, and the economic grouping of Chile, Brazil, and Argentina. This strategy
appears to involve getting ahead of competitors for serving these major growth markets
(Armentano et al, 2004).
Current Australian government intervention in dairy sector is limited. On June 30,2000,
Australia’s government ended the country’s Domestic Market Support (DMS) program for
manufacturing milk producers and state market milk programs for fluid milk producers.
According to Armentano et al (2004), under state pricing systems, Australian farmers had
received prices for fluid milk during the 1990s that were approximately double those received by
manufacturing milk producers. The higher fluid milk prices that existed prior to deregulation in
mid-2000 were made possible in part by milk production quotas employed in New South Wales,
Queensland, and Western Australia. The DMS scheme, terminated in mid-2000, was a federal
program that placed levies on all fluid milk sold domestically (paid by fluid milk producers) and
all milk used to produce manufactured dairy products sold in Australia’s domestic market (paid
by processors). Proceeds from the levies were distributed to Australia’s manufactured milk
producers. A restructuring package was made available to Australian milk producers after
deregulation that helped them adjust to unregulated markets. The funds needed to finance the
restructuring package were provided by an AU$0.11 per liter government levy on all fluid milk
products (including imported items) sold in Australia’s domestic market. Restructuring payments
were approved for farmers amounting to about AU$0.46 per liter for producers of fluid milk and
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30
about AU$0.09 per liter for manufacturing milk produced in the 1998-99 base year. These
payments were to be made quarterly for eight years, beginning in mid- 2000.
It was estimated that the average milk producer in the relatively high fluid milk utilization
state of Queensland would receive about AU$110,000 to help him/her adjust to a deregulated
industry (Armentano et al, 2004). The Australian Dairy Council negotiated with banks to
establish an industry facility that permitted an individual farmer to obtain the discounted present
value of his/her quarterly payments as an upfront payment regardless of whether the farmer
planned to continue farming or leave the industry. In addition, Australia government also
provides low level research and outreach support to the dairy industry. The Commonwealth
Scientific and Industrial Research Organization (CSIRO) is the principal federal research agency.
Agriculture is only a small part of CSIRO’s research portfolio of nearly AU$1 billion. The
annual investment in meat, dairy and aquaculture research is about AU$60 million annually
(Armentano et al, 2004).
Australia has a TRQ for cheese. The in-quota tariff has been at around 3.5% on average
during 1995-2000 and is phased out now. The Australian Dairy Corporation (ADC) was created
in 1975. The Corporation works at enhancing the production and marketing of dairy products for
greater profit for milk producers. In the late 1990s the ADC acted as the single desk exporter of
Australian dairy products, but it does not currently play a significant role. They were limited to
exports to Japan of all dairy products and to cheese sales to the EU (Seyoum, 2004). It is ceased
operation on June 30,2002 (USDA, (2002a)). This role has been more or less taken over by
dairy companies such as Murray Goulbum Cooperatives and Bonlac Foods which have emerged
as major dairy exporters.
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2.1.7 European Union (EU)
EU has the highest domestic prices for dairy products while having a significant world dairy
market share. According to Colman (2002) the EU dairy policy regime has been one of the main
structures of the Common Agricultural Policy (CAP) since the creation of the European
Community in 1957 by its six founding member states. The CAP embodied classic protectionist
features in the form of external sliding tariffs on imports of dairy products, and internal support
in the form of intervention in member states to purchase butter and SMP at announced floor
prices (Seyoum, 2004). According to Zhu (1999), the dairy policies in the EU can be
summarized as:
(1) Government purchasing and subsidized disposal of surpluses. Government purchases
butter, skim milk powder, and certain varieties of cheese at announced intervention prices, which
in turn support a target price for raw milk. The surplus dairy products are directed into other food
industries with subsidies, or dumped into international markets through food aid programs or
subsidized exports.
(2) Production quotas. Persistent domestic production surpluses and budgetary difficulties
prompted the EU to control the milk production in its member countries. Milk production quotas
have been signed to each milk farmer since 1984. An almost prohibitive levy is imposed on over
quota milk production.
(3) Border policies. Allowing imports from non-EU countries would certainly undermine the
dairy farm support programs. The EU uses a variable import levy to keep foreign goods basically
out of the EU dairy markets and maintain domestic dairy prices. Export subsidies make EU dairy
products competitive in the world markets.
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(4) The EU Commission also runs a Private Storage Aid system to smooth out seasonal
disparities between the cycles of butter production and consumption patterns. This aid consists of
an interest rate subsidy and subsidies to the storage costs. In addition, the EU also subsidizes
skim milk powder for animal feeding and for casein production.
In March 1999, the heads of state of the EU agreed to reform the CAP under the banner
Agenda 2000. According to USDA (2005), the current EU CAP reform includes: reduces
intervention prices for butter (-25%) from 2004 to 2007 and skim milk powder (-15%) from
2004 to 2006; limits intervention buying of butter to 30,000 tons by 2008; moves milk quota
increases scheduled under Agenda 2000 back one year (beginning in 2006), and adds an extra
200,0001 quota for Greece; pays a dairy premium to dairy producers to compensate for the
intervention price cuts beginning in C Y2004, based on the milk quota per holding (reduced by
the amount by which total national quota have been increased since 1999/2000); allocates to
member states an ‘additional payment’ to be paid to dairy producers according to ‘objective
criteria.’ Both the dairy premium and the supplementary member state payment are to be
incorporated into the Single Farm Payment (SFP) beginning in 2007. (A member state can opt to
incorporate all or part of the additional payment into the SFP from 2005).
2.1.8 United States
The purpose of the U.S. dairy policies is to maintain a reasonable income level for dairy
farmers. According to Zhu (1999), the major policies are as follows:
(1) Price support for some manufacturing goods. The government agency, the Commodity
Credit Corporation (CCC) purchases surplus butter, skim milk powder, and cheddar cheese from
processors at specified prices, which in turn maintains a minimum milk support price at a
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33
targeted level. The current authority for the dairy price support program is the extension of the
Federal Agricultural Improvement and Reform Act of 1996. The 1996 program was supposed to
be terminated in 1999. However it was extended until May 2002 under different agriculture
appropriation acts (Chite, (2002)). Again the 2002 Farm Bill contains multi-year extensions of
the dairy price support program, which include:
(a) extension of the milk price support program at $9.90 per hundred pounds of 3.5% fat milk
through 2007;
(b) setting up of a system of direct payments based on the Boston area Class I milk price of
$16.94/cwt. It is ceased by September 2001.
(2) Import quotas and tariffs protecting domestic producers from the competition of other
countries and an export subsidy program (Dairy Export Incentive Program, or DEIP) to increase
the competitiveness of domestic dairy producers and maintain domestic market balance.
(3) Classified pricing under federal and state milk marketing orders. The U.S. milk marketing
orders are used to enhance milk producers’ income by discriminating against different markets
(Penn et al., 1998). That is, milk marketing orders implicitly transfer wealth from consumers to
producers.
2.2 WTO commitments and implementation
Government policies typically generate various trade distortions that imply departures from
competitive market equilibrium. According to the WTO Agreement on Agriculture (AoA) of the
Uruguay Round, these policies are classified as effecting market access, domestic agricultural
support and export subsidies. As China, Japan, Thailand, South Korea, Singapore, Philippines,
Malaysia, Indonesia and India are the main dairy products producers and consumers in Asia, we
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will focus our analysis on these countries in this section according to their notifications to the
WTO Committee on Agriculture. We also briefly mention the policies in EU, U.S., Canada,
Australia and New Zealand for the purpose of comparison.
2.2.1 Market access
Market access policies, usually designed to discourage imports, consist of tariffs and non
tariff barriers. They include, among others, import quotas, minimum import prices and
discretionary import licensing. Tariffs can be specific duties, ad valorem duties, or some
combination of the two. Specific duties are fixed currency amounts (using the importing
country’s currency) per unit of the imported commodity. Ad valorem tariffs are expressed as a
percentage of the value of the imported goods and are usually imposed on the exporting
country’s border price (f.o.b.) instead of the importing country’s border price (c.i.fi). As a result,
transportation costs (freight and insurance) are typically not subject to these duties. Specific
duties depend on the quantity imported whereas ad valorem tariffs depend on the import value.
Many countries use a combination of the two.
While tariff barriers have an indirect impact on import volumes through their price effects,
most non-tariff barriers restrict trade by directly affecting volumes. An important issue for trade
analysis involves the allocation of quota rents resulting from such restrictions (e.g., McCorriston
and Sheldon 1994). Governments may seek to collect these rents by selling the quota rights
through auctions (e.g., as done in the U.S. on some occasions), or give import authority to
government agencies (e.g., CONASUPO in Mexico, or LIPC in Japan). In general, quota rents
can have large effects on the welfare distribution of import policy.
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Many countries have used non-tariff barriers to exploit loopholes in the URAA. They have
sought ways to limit imports of some commodities while meeting their WTO obligations to
reduce tariffs. The URAA eliminated many non-tariff barriers and a special type of import
restriction, the tariff-rate quota (TRQ), is now used more extensively. TRQs allow a specified
volume (tariff-quota quantity) of commodities to enter a country at one tariff rate (the in-quota
rate), while imports above this quota level are subject to a higher tariff rate (the over-quota rate).
This is a two-tiered tariff schedule where the tariff-quota plays a pivotal role. As quantitative
import restrictions were prevalent in international agricultural trade, the URAA led to some
standardization of import policies through the use of this two-tiered tariff-rate quota. Note that
the tariff-rate quota would converge to a simple rate tariff as the difference between the two rates
decreases or as the quota level increases.
In order to expand market access opportunities, member countries promised to replace all
non-tariff border measures by tariffs that provide substantially the same level of protection. The
tariffs resulting from this process, known as “tariffication”, as well as other tariffs on agricultural
products, were to be reduced by an average 36 per cent in the case of developed countries and 24
percent in the case of developing countries, with minimum reductions for each tariff line being
required (15 percent for developed countries and 10 percent for developing countries).
Reductions were to be undertaken over six years in the case of developed countries and over ten
years in the case of developing countries starting in 1995. Least-developed countries are not
required to reduce tariffs. Concurrent with tariffication, provisions were also made for the
maintenance of current access opportunities and the establishment of minimum access tariff
quotas (at reduced-tariff rates) where current access was less than 3 percent of domestic
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36
consumption. These minimum access tariff quotas were to be expanded to 5 percent over the
implementation period.
Asia doesn’t have comparative advantage in dairy production, so the dairy industry is subject
to high protection levels. Many countries use tariff and Tariff Rate Quota (TRQ) to protect
domestic markets, and there are large differences in the tariffs they apply to dairy products. From
table 2.2 we can see that China, Japan, India and Korea use relatively higher tariffs than
Indonesia, Philippines, Malaysia and Singapore for dairy product imports. As shown in table 2.3,
ten dairy product exports to Japan are subject to TRQ administration, five to South Korea, two
and one to Malaysia and Indonesia, respectively. Japan and Korea’s TRQs are allocated on
global basis. In Japan, whey and skim milk powder for other purposes are allocated to producers
and producer organizations of mixed feed or sellers. For skim milk powder, whole milk powder,
and other milk and cream, Korea’s TRQs are allocated according to the highest price bidders at
quota auctions held by the Livestock Products Marketing Organization. Indonesia acts as the
single desk buyer. In Japan, the TRQ fill rates for skimmed milk powder, whey and butter are
around 50%. These low rates could be an indication of non-tariff trade barriers, such as
cumbersome TRQ-administration and allocation system (Campo and Beghin, 2005). South Korea
and Malaysia have higher fill rates, but real imports are still lower than TRQ. One interesting
thing is that for Indonesia, there exist out-of-quota imports; the TRQ fill rate is 100%. This
indicates that Indonesia’s over-quota tariffs are not prohibitive or are not enforced. As this
region’s dairy imports are distorted by tariff and TRQ, world dairy trade liberalization will
increase this region’s imports.
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Table 2.2 Asia Country’s Dairy Tariffs (%)
Tariff Heading
Description China Japan India Indonesia Phillipenes South Korea Malaysia
0401 Milk and cream, not concentrated nor containing added sugar or other sweetening matter
23.67 25 30 5 3 39.4 0
0402 Milk and cream, concentrated or containing added sugar or other sweetening matter
32.6 22.54 42 5 4.3 28 2
0403 Buttermilk, curdled milk and cream, yogurt, kephir and other fermented or acidified milk and cream, whether or not concentrated or containing added sugar or other sweetening matter or flavoured or containing added fruit, nuts or cocoa
43 30.35 30 5 6.8 39.4 16.67
0404 Whey, whether or not concentrated or containing added sugar or other sweetening matter; products consisting of natural milk constituents, whether or not containing added sugar or other sweetening matter, not elsewhere specified or included
25 22.88 30 5 3 22.43 0
0405 Butter and other fats and oils derived from milk; dairy spreads
44 34 33.3 5 7.7 40 4.17
0406 Cheese and curd 43.2 0 32 5 5.5 37.57 8.33
Note (1) tariff is the simple average under the same heading. (2) Singapore applies zero tariff to all dairy products, Thailand tariff data is not available.
Source'. APEC tariff database www.apectariff.org/_________________ _________________
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Table 2.3 Tariff Rate Quota Administration for Asia Countries (Metric tons >
Country Product 1995 1996 1997 1998 1999 2000
NOTRQ IMP NOTRQ IMP NOTRQ IMP NOTRQ IMP NOTRQ IMP NOTRQ IMP
Japan Skimmed milk powder (school
lunch)
7264 4245 7264 4615 7264 4066 7264 3783 7264 3808 7264 3592
Skimmed milk powder (other
purposes)
85878 41789 85878 34278 85878 37949 85878 32569 85878 33468 85878 33776
Evaporated milk 1585 663 1585 779 1585 823 1585 1429 1585 1459 1585 1470
Whey and modified whey (feeding
purposes)
45000 20456 45000 22463 45000 24255 45000 20913 15000 21686 45000 23999
Prepared whey (infant formula) 25000 7329 25000 8743 25000 10048 25000 8432 25000 10287 25000 10623
Butter and butteroil 1873 511 1873 375- 1873 430 1873 372 1873 347 1873 335
Mineral concentrated whey 14000 1944 14000 1465 14000 1543 14000 2185 14000 4654 14000 3559
Prepared edible fat 18977 18994 18977 18701 18977 18804 18977 18641 18977 18752 18977 18699
Other dairy products for general use 124640 114642 126500 117366 128360 127171 130220 120841 132080 129293 133940 131363
Designated dairy products for.. 137202 248275 137202 232471 137202 212514 137202 137022 137202 138266 137202 139270
Korea Skim milk powder... 621 621 667 649 713 713 759 756 804.6 804.6 850.5 743.3
Whole milk powder ... 344 344 369 16 395 395 420 80 445.8 445.8 471.2 60
Other milk and cream,
(Evaporated...)
78 78 84 50 90 0 95 0 101.1 0 106.9 19.2
Whey powder 23000 22250 26470 22973 29941 23367 33411 23642 36881 30644.5 40351 38752.2
Butter 250 250 269 268 288 288 307 307 325.6 325.6 344.5 344.5
Indonysia Milk and cream of fat and its products 414700 857413 414700 644916 414700 597838 414700 466806 414700 875112 414700 1150816.5
Malaysi
a
Milk and cream not concentrated.... 600000 58987 640000 1195412 640000 1195412
Milk and cream not concentrated... 90 0 92 696 92000 696000
Note (1) NOTRQ: Tariff rate quota notified to WTO Committee on Agriculture.
(2) IMP : actual imports.
(3) Thailand, Philipines, India and Singapore don't apply TRQ on dairy products
(4) During China’s WTO accession negotiation, China committed not to apply TRQ on dairy imports.
Source: WTO database http://www.wto.org/english/tratop_e/agric_e/agric_e.htm
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2.2.2 Domestic support
Based on differing trade impacts, the URAA contained a classification of domestic
agricultural support policies as “Green Box”, “Amber Box”, and “Blue Box” policies. Green Box
policies are those support measures with minimal impact on trade that can be used freely. They
include government services such as research and development, disease control, infrastructure
and food security. Payments made directly to farmers that do not stimulate production, such as
certain forms of direct income support, assistance to help farmers restructure agriculture, and
direct payments under environmental and regional assistance programs, are also included.
School lunch programs, which have dairy products as important parts, like those in the U.S.,
South Korea, China, the EU and Japan are examples.
“Amber Box” policies are those polices that distort international trade and have a direct
effect on production. They should be cut back under URAA. WTO members have calculated
how much support of this kind they were providing (using calculations known as “total aggregate
measurement of support” or “Total AMS”) for the agricultural sector per year in the base years
of 1986-88. Developed countries have agreed to reduce these Figures by 20% over six years
starting in 1995. Developing countries are making 13% cuts over 10 years. Least developed
countries are not required to make any cuts. These policies include consumption subsidy
programs and production or price supports.
Production, consumption and storage subsidies are commonly used instruments in dairy
policies around the world. For example, the EU uses production subsidies for casein (to absorb
surplus skim milk powder) and storage subsidies for butter. The EU also subsidized dairy
consumption in skim milk powder for animal feeding. New Zealand and Australia at one time
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40
subsidized fertilizer usage on dairy farms. A production subsidy shifts the supply curve down,
while a consumption subsidy shifts demand curves up. Both the prices and quantities in the
domestic markets, as well as world markets if the country is involved in international trade, are
affected. Price support programs generally provide minimum “floor prices”. Price support
systems often work together with other policy instruments, such as border measures, classified
pricing or production controls.
Blue box policies are those somewhere “between” the above two, and exempt from reduction.
Direct payments under production-limiting programs and certain government assistance
measures to encourage agricultural and rural development in developing countries are examples.
Some developed countries (e.g., Canada and the EU) have implemented direct production control
policies in their dairy sector as a means of dealing with market imbalances caused by price
support systems. Quotas, with significant over-quota penalties, are the simplest direct dairy
production control measure. Production quotas in major milk exporting countries have
significant effects on world dairy markets, as a substantial part of world dairy exports is surplus
disposal by countries with high domestic farm income support.
Price discrimination policies, often implemented through classified pricing systems, can also
have sizable effects on international trade. In developed countries, fluid milk markets are usually
less price elastic than manufactured dairy products. As a result, a classified pricing scheme that
increases the fluid milk price can increase total dairy revenue without involving a cost to the
taxpayers. Currently, the URAA has no explicit discipline on price discrimination applied to
domestic markets. Yet, these price discrimination policies have impacts on international trade
because they tend to lower the domestic price of manufacturing dairy products, products that
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41
dominate international trade. Cox and Chavas (1999) estimated that total elimination of the U.S.
federal and California classified pricing systems would induce a 5 cent per pound increase the in
cheese price and a 13 cent per pound increase in the skim milk power price. These results
indicated that the U.S. would be less competitive in world dairy markets without these price
discrimination schemes. In Canada, price discrimination has contributed to rapid increases in
exports of butter, cheese and other manufacturing products, which led the U.S. to challenge its
legality under the WTO agreement. (See papers included in Lyons, Meilke and Knutson 1996.)
From their notifications to WTO Committee on Agriculture, we find that in the Asian market
currently only Japan uses “Green Box” policies to support its domestic dairy market. In its
School lunch programs, Japan supplies rice, milk, and fruit juice for school children at
subsidized prices. Another potential user of this policy maybe China. In its “Tenth-Five Year”
country economic development blueprint (2001-2005), dairy is identified as an important
industry. It has implemented a “school student milk program” to provide subsidized milk for
school students in selected cities. The program is expected to expand to other cities all over the
country. Although Malaysia and Philippines have school milk programs (see section 2.1.5), it is
not shown in their notifications to the WTO Committee on Agriculture
Most countries in this region have a negative or de minimus aggregate measurement of
support for agriculture (i.e. amber box policy) except Japan and Korea. Japan uses price support
programs for certain dairy products (mainly butter and skimmed milk powder), and also gives
deficiency payments for calves and manufacturing milk. Japan’s price support program works
with production quota, which belongs to “Blue Box” policies. Production quota is under the
control of the national and prefectural councils, but farmers also have the right to adjust it. In
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42
2004, Japan’s milk producer support estimate (PSE) reaches $4.3 billion (OECD, 2004). Korea
also use a price support program for dairy products; its milk PSE reached $0.8 billion in 2004
(OECD, 2004). None of the other Asian countries use “Blue Box” policies to support their dairy
markets.
2.2.3 Export subsidies
Export policies include both export restrictions and export promotion instruments. While
most export policies were associated with food aid in earlier years, export promotion policies
have become a dominant feature of the current world market situation. Export subsidies
generally work together with other border instruments (e.g., tariffs and import quotas) to prevent
the products similar to the exported commodities from being shipped back to the original
exporting countries. Typically, the prices in world markets become more volatile and lower
under these policies. For instance, the EU uses world markets as a means of surplus disposal.
Without price support and surplus disposal policies, both the EU markets and the world markets
would absorb a supply shock. But with these policies, the shock is entirely absorbed by world
markets. Thus, dairy export policy in the EU increases fluctuations of world markets.
Developed countries were required to reduce the value of export subsidies by 36 per cent
from the 1986-90 base period level over the six-year implementation period starting in i995 and
the quantity of subsidized exports by 21 per cent over the same period. In the case of developing
countries, the reductions were two-thirds those of developed countries over a ten-year period.
Again, no reductions were required by the least-developed countries.
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43
As Asia plays a much more important role in world dairy imports than in dairy exports, no
country exports much dairy products to international markets except to regional Asian markets.
Therefore, no country in this region uses export subsidy to support dairy exports.
2.3 Dairy Products Production, Consumption and Trade
With over 60% of world population, Asia’s dairy products consumption increased rapidly in
recent years. Asia also produces large amounts of milk reaching 206 million MT in 2003, which
accounts for 31% of world total milk production. From 1989 to 2003 its consumption of dairy
products (simple summation) increased from 90.8 million MT to 177 million MT, with an annual
average growth rate of 5%. Asia occupies 36.5% world total dairy products consumption in 2003.
Butter is the largest product category consumed with consumption exceeding 3.5 million MT in
2003. The consumption of whole milk powder (WMP), skimmed milk powder (SMP),
condensed evaporated milk (CEM) and cheese all exceeded 600 thousand MT. Except lactose,
all other products consumption exceeds 170 thousand MT. The annual average growth rate of
butter, cheese and dry whey exceeds 5% during this period. However, Asia’s per capita dairy
products consumption is still low compared with Western developed countries. The per capita
dairy consumption in China, India, Indonesia, Japan, Malaysia, Philippines, South Korea,
Thailand, and Vietnam averages 4.5 kg, 35.7 kg, 2.1 kg, 44.2 kg, 7.7 kg, 2.4 kg, 35.2 kg, 9.8 kg,
and 1.8 kg per capita, respectively, in the last decade, in contrast with 105 kg per capita in the
EU-15,120 kg per capita in Australia, and 113 kg per capita in the U.S (Dong, 2005). This also
means that there is huge potential for further development of the Asian dairy market.
With the world’s largest population and area, Asia is a highly unevenly developed region.
There are developed countries and regions such as Japan, Singapore, South Korea, Hong Kong
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44
and Taiwan. There are developing countries with fast economic growth rates such as China,
India, Thailand, Malaysia and Indonesia. There are also least developed countries such as Nepal.
Due to different stages of economic development, consumer’s taste and farming styles, different
countries play quite a different role in this region’s dairy consumption. More than 90% of Asian
regional casein, dry whey, lactose and residual are consumed by China, Japan, Thailand, South
Korea, Singapore, Philippines, Malaysia, Indonesia and India. At least 80% of other dairy
products are consumed by the above 9 countries. There are large differences in the consumption
share among these countries. Japan consumes about 70% of regional casein and lactose, 50% of
cheese and 30% of skimmed milk power in Asia. China consumes more than 30% of regional
cheese, dry whey and residual products. India consumes about 60% of regional butter. Malaysia
consumes about 20% of regional condensed evaporated milk.
The Asian markets play a more important role in dairy products imports than exports. Most
countries do not have exports at all; only a few countries (China and India) export some dairy
products to neighboring countries. In 2003, Asia’s imports of milk equivalent are 25.8% of the
world total; Asia’s imports of butter, cheese and SMP accounts for 19%, 14% and 42% of the
world total imports (FAO, 2003), respectively. Asia’s exports of milk equivalent only accounts
for 2.9% of the world total in 2003. EU-15, Australia and New Zealand are the world major dairy
products exporters. The above three regions accounts for 65.6% of world total milk equivalent
exports in 2003 (the internal trade among EU members is excluded, as it is in the following
analysis), in which EU accounts for 36%, New Zealand accounts for 20% and Australia accounts
for 9.6% (FAO, 2003), respectively. EU and New Zealand are the world’s most important butter
exporters, each occupying 36% of the world butter market in 2003. Australia also occupies 8%
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45
of the world butter market. The EU accounts for 44% of the world cheese exports, and New
Zealand and Australia account for 12% and 10%, respectively. For SMP exports, New Zealand
accounts for 24% of the world market share, EU, Australia and U.S. account for 20%, 11% and
10%, respectively.
For the rest of this section, we will make a detailed analysis of dairy products production,
consumption and trade for major Asian countries. As there is high transportation cost for fluid
milk and high cost for keeping it frozen, we assume there is no trade of fluid milk in this section.
2.3.1 China
Before the 1970s, few people in China were engaged in milk farming except in a small
number of pastoral areas. In light of their unique geographical, economic and political positions,
major cities such as Beijing and Shanghai managed to establish dairy farms of a considerable
scale in their suburbs. But the milk produced was mainly consumed by the denizens, infants,
patients, and a handful of privileged people. After China’s open policy reform (late 1970s), its
dairy industry experienced rapid growth — milk production has increased about 10 times and cow
numbers have grown 14-fold since 1970. China produces milk from cows, buffaloes, goats, and
sheep. As shown in Figure 2.1, cows and buffalo are the two major sources for milk production,
occupy 81.4% and 12.4% in total milk production in 2003, respectively.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Figure 2.1 China Milk Production by Type
Sheep milkQoat milk Buffalo mil
12.4%
I
Cow milk 81.4%
Source: FAO data base 2003, http://faostat.fao.org/faostat/collections?subset=agriculture
As shown in Figure 2.2 China’s milk production in 2003 is more than 4-fold higher than in
1990, the annual growth rate is 12% during 1990-2003. However, in 2003, China’s milk yield
per cow (2,551 kg) is still much lower than that of EU (6,179 kg), Australia (5,153, kg), New
Zealand (3,788 kg) and U.S. (8,504 kg) (FAPRI Agricultural Outlook, 2005). The majority of
milk produced goes to manufacturing use (61.6%). China’s consumption of fluid milk in 2003
almost 3-fold higher than in 1990; the annual growth rate is 8.5%. China’s per capita
consumption of fluid milk is 6.32 kg in 2003, still far lower than that of EU (79.9 kg/person),
Australia (101.4 kg/person), New Zealand (91.1 kg/person) and U.S. (93 kg/person) (FARPI
Agricultural Outlook, 2005).
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47
Figure 2.2 China Milk Production and Utilization
24000 21000 18000
t 15000 -I e 12000 g 9000 * 6000
3000 0
1990 1992 1994 1996 1998 2000 2002
— Milk Production Manufacturing Use
—a- Fluid Milk Consumption
Source: FAPRI Agricultural Outlook various issues, http://www.fapri.iastate.edu/outlook2000 - 2005 /
Raw milk production in China displays apparent regional disparity. Generally speaking, milk
production in the North is much greater than that in the South. Currently, the top five producing
areas, Inner Mongolia, Heilongjiang, Hebei, Shandong, and Xinjiang, account for 60 percent of
China’s total cow milk production (USDA, 2004a). One characteristic of China’s milk
production is its small scale. About 60-80% of raw milk production originates from small
household farms, which typically have two to five cows each and low milk yields. With small
scale production, quality control becomes difficult. Quality concerns of domestic dairy products
have made foreign products more preferred by well-off consumers. Tight milk supply is another
constraint for the dairy processing sector. Coupling with sanitation problems, dairy companies
have extensively used imported milk powder as an important supplement to raw milk ((Dong,
2005).
Butter
As shown in Figure 2.3, China’s net butter imports increased rapidly during 1990-2003,
reaching 28 thousand MT in 2003 (the annual growth rate is 8.4%). According to cheng et al
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48
(2002), China’s butter is imported mainly from Oceania and the EU. New Zealand is the biggest
exporter (accounting for 48% of the total butter imports), followed by Australia (25%), Belgium
and Denmark (a combined share of 16%). China exports small amount (less than 0.5 thousand
MT) of butter to Hong Kong, North Korea and Taiwan. China’s butter production and
consumption in 2003 are 1.45-fold and 1.38-fold higher than in 1990, respectively. Currently
China’s per capita consumption of butter is 0.1kg, far lower than that of EU (4.6 kg/person),
Australia (3.0 kg/person), New Zealand (6.6 kg/person) and U.S. (2.0 kg/person).
Figure 2.3 China Butter Production, Consumption and Trade
120 -I 100 ■
e 60 ■ o
20 •
1990 1992 1994 1996 1998 2000 2002
Production Consumption Net imports
Source: FAPRI Agricultural Outlook various issues, httD://www.faDri.iastate.edu/outlook2000 - 2005 /
Cheese
As shown in Figure 2.4, China’s cheese consumption (annual growth rate is 3.3%) increased
faster than cheese production (annual growth rate is 2.8%) during 1990-2003. China relies on
imports to meet its demand; net imports of cheese in 2003 are 18 thousand MT, and the annual
growth rate of net cheese imports is 17.8% during 1990-2003. According to cheng et al (2002),
China’s cheese imports are mainly from New Zealand (30%) and Australia (22%). China also
exports less than 2 thousand MT of cheese to Hong Kong, Thailand and Japan. However,
China’s per capita consumption of cheese (0.2kg/person in 2003) is still far lower than that of
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49
EU (13.8 kg/person), Australia (12.0 kg/person), New Zealand (7.1 kg/person) and U.S. (13.9
kg/person).
Figure 2.4 China Cheese Production, Consumption and Trade
260 240 220 200
. 180 E 160 * 140 § 120 o 100
1990 1992 1994 1996 1998 2000 2002
Production Net imports
Consumption
Source: FAPRI Agricultural Outlook various issues, http://www.faDri.iastate.edu/outlook2000 - 2005 /
Skim Milk Powder (SMP)
China’s SMP consumption increased rapidly during 1990-2003 with an annual growth rate of
8.5% (Figure 2.5). Although China’s SMP production increased during the same period (the
annual growth of SMP production is 7.9%), it failed to meet the total demand. China’s net
imports of SMP are 50 thousand MT in 2003. According to cheng et al (2002), it is mainly from
New Zealand (34%), Australia (34%) and EU (23%). China also exports less than 2 thousand
MT of SMP to Hong Kong, Macao, Taiwan and some Southeast Asian countries. However,
China’s per capita consumption of SMP (O.lkg/person in 2003) is still far lower than that of EU
(2.4 kg/person), Australia (1.8 kg/person), New Zealand (1.3 kg/person) and U.S. (1.6 kg/person).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Figure 2.5 China SMP Production, Consumption and Trade
50
140 120 100
1990 1992 1994 1996 1998 2000 2002
Production Consumption Net imports
Source: FAPRI Agricultural Outlook various issues, http://www.fapri.iastate.edu/outlook2000 - 2005 /
2.3.2 Japan
All of Japan’s fluid milk is produced from milk cows. From Figure 2.6 we see that Japan’s
milk production and consumption are pretty stable during 1990 to 2003. In contrast to China,
about 60% of Japanese fluid milk is consumed directly (fluid milk consumption). Its per capita
consumption of fluid milk (39.6 kg in 2003) is much higher than that of China, but still much
lower than that of Western countries (EU, Australia, New Zealand and U.S.). Its milk yield per
cow is very high, 8,714 kg in 2003.
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51
Figure 2.6 Japan Milk Production and Utilization
10000
8000 ♦ • ♦ + • • • • • • ♦
2 6000 _ ̂ ^
8 4000 ■P »-----■— • ---- a— „----m--- »— a-----a----- a— a--- m m m 2000
0 - - - - - - - - - - - - - - 1- - - - - - - - - - - - - - 1- - - - - - - - - - - - - - 1- - - - - - - - - - - - - 1 i . . . . . . . . . . . . . . . i - - - - - - - - - - - - - - 1- - - - - - - - - - - - - - r ■■ ■ ~ " r ~ — — f ~ i i ■ - - i ■
1990 1992 1994 1996 1998 2000 2002
—♦—Cow Milk Production —*—Fluid Milk Consumption —■— Manufacturing Use
Source: FAPRI Agricultural Outlook various issues, http://www.fapri.iastate.edu/outlook2000 - 2005 /
Butter
Japan’s per capita consumption of butter (0.7 kg in 2003) is higher than that of China, but
much lower than that of Western countries. Its self-sufficient rate of butter is pretty high (90%).
Butter imports are insignificant (see Figure 2.7). According to Zhu et al. (1998), this is because
the imports of butter and butteroil are virtually prohibited after a certain amount in Japan.
Figure 2.7 Japan Butter Production, Consumption and Imports
120
100 ■
80 60 ■ 40 • 20 -
1990 1992 1994 1996 1998 2000 2002
Production Imports
Source: FAPRI Agricultural Outlook various issues, http://www.fapri.iastate.edu/outlook2000 - 2005 /
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52
Cheese
Unlike butter, Japan’s cheese consumption depends highly on the world market. Cheese
imports account for about 86% of total consumption. This indicates that the import restrictions
on cheese are much looser than those on butter. In 2003, Japan’s per capita consumption of
cheese is 1.8 kg, which is much lower than that of Western countries.
Figure 2.8 Japan Cheese Production, Consumption and Imports
240
140 oo
1990 1992 1994 1996 1998 2000 2002
Production Consumption Imports
Source: FAPRI Agricultural Outlook various issues, httD://www.faDri.iastate.edu/outlook2000 - 2005 /
Skim milk powder (SMP)
There is an obvious decreasing trend in Japan’s SMP consumption and imports in recent
years. However, Japan still imports much more SMP (43 thousand MT in 2003) than butter. In
additioh to SMP used for human consumption, Japan also imports SMP for animal feed. It’s per
capita consumption of SMP is 1.7 kg, which is very close to that of Western countries.
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53
Figure 2.9 Japan SMP Production, Consumption and Imports
1990 1992 1994 1996 1998 2000 2002
Production Consumption Imports
Source: FAPRI Agricultural Outlook various issues, htto://www.fapri.iastate.edu/outlook2000 - 2005 /
2.3.3 Korea
All of Korea’s fluid milk is produced from milk cow. From Figure 2.10 we see that Korea’s
milk production and consumption are pretty stable during 1990 to 2003. Similar to Japan, about
65% of Korea’s fluid milk is consumed directly (fluid milk consumption). Its per capita
consumption of fluid milk (32.1 kg in 2003) is very close to that of Japan, but still much lower
than that of Western countries. Its milk yield per cow is also very high, 9,375 kg in 2003.
Figure 2.10 Korea Milk Production and Utilization
2800 2400 -
I- 2000 - S 1600 • ♦- S 1200- i t ® 800 ■
400 • m ^ "U wr~
H I ■ -------■ -------
1990 1992 1994 1996 1998 I I I I
2000 2002
- Cow Milk Production - Manufacturing Use
Fluid Milk Consumption
Source: FAPRI Agricultural Outlook various issues, http://www.fapri.iastate.edu/outlook2000 - 2005 /
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Butter
Due to prohibitive over-quota tariffs and low import quota, Korea’s butter consumption is
highly self-sufficient (Figure 2.11). It’s per capita butter consumption is 1.2 kg in 2003, which is
higher than that of Japan but lower than that of Western countries. However, Korea’s total butter
consumption is lower than that of Japan and China.
Figure 2.11 Korea Butter Production, Consumption And Imports
60 i 50 -
I- 40- o 30- o P 20-
10
1994 2000 20021990 1992 1996 1998
Production Consumption Imports
Source: FAPRI Agricultural Outlook various issues, http://www.fapri.iastate.edu/outlook2000 - 2005 /
Cheese
Korea’s cheese consumption increased substantially in recent years (Figure 2.12). It’s per
capita cheese consumption is 1.1 kg in 2003, which is very close to that of Japan. This may be
explained by the fact that cheese is a luxury product for people when their income is lower than
some level (e.g., China); when their income exceeds some level (e.g., Japan and Singapore), it
turns out to be a normal product. As Korean’ per capita income increased a lot in recent years,
now cheese is a normal product in Korea. Unlike butter, Korea depends on imports to meet its
cheese consumption demand (imports account for 65% of the total cheese consumption).
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55
Figure 2.12 Korea Cheese Production, Consumption and Imports
60 -I 50 -
H 40 ■ o 3 0 -
10 ■
1990 1992 1994 2000 20021996 1998
Production Consumption Imports
Source: FAPRI Agricultural Outlook various issues, http://www.fapri.iastate.edu/outlook2000 - 2005 /
Skim milk powder (SMP)
Korea’s total SMP consumption is around 45 thousand MT in recent years. Its per capita
consumption is around 1 kg, which is close to that of New Zealand. Due to strict import control
measures, Korea’s SMP imports are insignificant. Its self-sufficient rate of SMP is as high as
88%.
Figure 2.13 Korea SMP Production, Consumption and Imports
60 50 • 40 • 30 ■ 20 ■
10 ■
1990 1992 1994 1996 1998 2000 2002
Production Consumption Imports
Source: FAPRI Agricultural Outlook various issues, http://www.fapri.iastate.edu/outlook2000 - 2005 /
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56
2.3.4 India
India has the world’s largest production of buffalo milk, which accounts for 55% of the total
milk production in India (Figure 2.14). Actually, it is the only country in the world where the
majority of milk is produced by buffalo. Goat milk also accounts for a small amount (3%) of
total milk production. India is also the world’s largest milk production country. No fewer than 70
million households are involved in the production of milk. These are mainly small and even
marginal cattle farmers, but also laborers without land, who have at most two dairy cows or
buffaloes tethered near their homes (Brouwers, 2006). Of these 70 million households, 11
million can be characterized as cattle farmers. These are dairy cattle farmers with an average of
two cows or buffaloes producing between 10 and 12 litres of milk per day. They are organized
into no fewer than 110,000 village dairy co-operatives or Dairy Co-operative Societies (DCSs).
Given its farm size, it is not surprising that India’s milk yield per cow is pretty low (1,010 kg in
2003)
Figure 2.14 India Milk Production by Type
Goat milk 3.0%
Buffalo milk 55.0%
Cow milk 42.0%
Source: FAO data base 2003, http://faostat.fao.org/faostat/collections?subset=agriculture
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57
From Figure 2.15 we see India’s milk production has increased during 1990-2003 with an
annual growth rate of 3.8%. Of the total milk production, no less than 65% is consumed
unpasteurised (Brouwers, 2006). Of this percentage, 44% is consumed in the rural area in which
it is produced, meeting the needs of cattle farmers and their families and sold, through the village
co-operatives, to others with no cows or buffalo. The remaining 21% of the unpasteurised milk is
sold to urban consumers. Of the 35% of the milk production that is pasteurised, 22% is processed
by the unorganised dairy sector. The majority (60%) of milk goes to manufacturing use. Fluid
milk consumption is quite stable during this period. India’s per capita consumption of milk is
33.5 kg, which is much higher than that of China and close to that of Japan and Korea. This is
because India has the tradition of drinking fluid milk.
Figure 2.15 India Milk Production and Utilization
90000 80000 - 70000 -
H 60000 - S 50000 - § 40000 - P 30000 -
20000 -
10000 -
0 1---- 1----- 1 1----- 1----- 1----- 1— 1990 1992 1994 1996 1998
1 1 " ‘ 1 1 1 2000 2002
—♦—Total Milk Production Manufacturing Use
—A— Fluid Milk Consumption
Source: FAPRI Agricultural Outlook various issues, http://www.fapri.iastate.edu/outlook2000 - 2005 /
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58
Butter
India is the world’s largest butter consumption country. Its butter consumption increased
rapidly during 1990-2003 (the annual growth rate is 7.8%). Almost all of India’s butter
consumption is produced domestically (Figure 2.16). India’s per capita consumption of butter is
2.4 kg in 2003, which is much higher than that of other Asian countries and close to that of U.S.
and Australia.
Figure 2.16 India Butter Production, Consumption And Imports
2800 2400
H 2000 z 1600 o 1200 ■ * 800
400
1990 1992 1994 1996 1998 2000 2002
Production Consumption Imports
Source: FAPRI Agricultural Outlook various issues, http://www.fapri.iastate.edu/outlook2000 - 2005 /
Skim milk powder (SMP)
India’s SMP production and consumption increased rapidly during 1990-2003, with annual
growth rates of 9.2% and 9.8%, respectively (Figure 2.17). However, its per capita consumption
of SMP is still pretty low (0.2 kg/person in 2003). It is very close to that of China and much
lower than that of Japan and Korea. India’s SMP trade is insignificant.
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59
Figure 2.17 India SMP Production, Consumption and Imports
2 40 220 200
140120100
-20 -I 1 9 9 0 19 9 2 19 9 4 1996 1998 2000 2002
Production *— Consumption Net Exports
Source: FAPRI Agricultural Outlook various issues, http://www.fapri.iastate.edu/outlook2000 - 2005 /
India’s cheese production and consumption is trivial, and we do not mention them here.
2.3.5 South East Asia (SEA)
In this section, we only discuss the major dairy production and consumption countries in
SEA, i.e., Malaysia, Indonesia, Philippine, Thailand and Vietnam. 80% of milk in this region is
cow milk, but goat milk and buffalo milk are also important (Figure 2.18). Goat milk is mainly
produced in Indonesia (accounting for 27% of Indonesia’s total milk production). Buffalo milk is
mainly produced in Malaysia and Vietnam, and accounts for 20% and 17% of their total milk
production, respectively.
From Figure 2.19 we see fluid milk production increased a lot in SEA during 1990-2003; the
annual growth rate is 5.8%. The majority (70%) of the fluid milk is consumed directly. Milk
production in SEA is in an early stage; most milk is supplied by dairy farmers with 2-3 cows
each. Average milk yield per cow is 1,823 kg in 2003. Thailand has the highest milk yield per
cow in this region (3,000 kg in 2003) while Malaysia has the lowest (428 kg in 2003). The
average per capita consumption of fluid milk is 2 kg in 2003, which is even lower than that of
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60
China. Thailand has the highest per capita fluid milk consumption (9.3 kg/person) in this region
while the Philippines has the lowest (0.5 kg/person).
Figure 2.18 SEA Milk Production by Type
S heep m ilk 4.0%
B uffalo m ilk 6.7%
G oat m ilk 9.3%
Cow m ilk 80.0%
Source: FAO data base 2003, http://faostat.fao.org/faostat/collections?subset=agriculture
Figure 2.19 SEA Milk Production and Utilization
1600 1400 • 1200 -
g 1000 - o 800 - © 600 -
400 ■ 200 -
0 1 1 1 I I 1 1 1990 1992 1994 1996
— i------1----- 1998
“1------1 1------1 1 2000 2002
—♦—Total Milk Production —m - Manufacturing Use
Fluid Milk Consumption
Source: FAPRI Agricultural Outlook various issues, http://vyww.faDri.iastate.edu/outlook2000 - 2005 / FAO data base 2003, http://faostat.fao.org/faostat/collections?subset=agriculture
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Butter
There is not much butter production in SEA. It mainly depends on imports to meet butter
consumption. Per capita butter consumption is 0.1 kg in 2003, which is very close to that of
China. The highest per capita consumption is in Malaysia (0.4 kg/person in 2003), and the lowest
per capita consumption is in Indonesia (0.05 kg/person in 2003).
Figure 2.20 SEA Butter Consumption and Imports
60 -I 50 - 40 - 30 - 20 -
10 -
ooo
1990 1992 1994 1996 1998 2000 2002
Consumption Imports
Source: FAPRI Agricultural Outlook various issues, httD://www.faDri.iastate.edu/outlook2000 - 2005 / FAO data base 2003, http://faostat.fao.org/faostat/collections?subset=agriculture
Cheese
Only Thailand has a small amount (about one thousand MT) of cheese production in this
region. It mainly depends on imports to meet its cheese consumption. Per capita cheese
consumption in this region is 0.07 kg in 2003. Malaysia and the Philippines have the highest per
capita consumption (0.2 kg in 2003), and Vietnam has the lowest (0.01 kg in 2003).
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62
Figure 2.21 SEA Cheese Production, Consumption and Imports
40 n
30 •
20 ■© 10 -
1990 1992 1994 1996 1998 2000 2002
Production Consumption Imports
Source: FAPRI Agricultural Outlook various issues, httD://www.fapri.iastate.edu/outlook2000 - 2005 / FAO data base 2003, http://faostat.fao.org/faostat/collections?subset=agriculture
Skim milk powder (SMP)
There is no SMP production in SEA. It depends on imports to meet its SMP consumption.
Per capita SMP consumption is 0.7 kg in 2003, which is higher than that of China and India. The
highest per capita consumption is in Malaysia (1.9 kg/person in 2003), and the lowest per capita
consumption is in Vietnam (0.26 kg/person in 2003).
Figure 2.22 SEA SMP Consumption and Imports
360 300 • 240 - 180 - 120
60 •
1990 1992 1994 1996 1998 2000 2002
Consumption Imports
Source: FAPRI Agricultural Outlook various issues, httD://www.fapri.iastate.edu/outlook2000 - 2005 / FAO data base 2003, http://faostat.fao.org/faostat/collections?subset=agriculture
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Chapter 3 Conceptual Model
3.1 Current models to study world dairy sector1
Current models to study world dairy sector can be categorized as: (1) based on Computable
General Equilibrium (CGE) Models: ABARE (2004), CARD (2004), World Bank LINKAGE; (2)
based on Partial Equilibrium Econometric (Time-Series or Equilibrium Displacement) models:
OECD Aglink, FAPRI, ERS/Penn State WTO Model: Langley et al (2003); Abler et al (2001)
and the Guelph Model: Lariviere and Meilke (1999); (3) based on Partial Equilibrium
Programming models: Toulouse EU Dairy Sector Model, Bouamra-Mechemeche, Chavas, Cox
and Requillart (2002,2004) and the University of Wisconsin World Dairy Model (UW-WDM)
Hedonic Spatial Equilibrium: Cox, Coleman, Chavas and Zhu (1999); Cox and Zhu (2004); Zhu,
Cox and Chavas (1999).
General equilibrium models are multi-sector models covering agriculture, manufacturing,
and services with various levels of sectoral disaggregation. These models have been used to
estimate impacts on incomes, relative prices and activity changes across sectors. Hence, they can
provide an overall picture where gains/losses to trade liberalization in one sector can be
considered in the context of gains or losses in other sectors. CGE models generally assume
somewhat stylized production technologies (Cobb-Douglas, generalized Leontief CRS, etc.) due
to the strong aggregation assumptions required to consistently aggregate/disaggregate individual
sectors from national accounts data. The breadth of coverage often comes at the expense of depth
of coverage on commodity, country, and policy detail that is often required to analyze distorted
sectors such as agriculture in general and the dairy sector, in particular.
1 This section is based on FAO Trade Policy Technical Note No. 11 on “Dairy - Measuring the impacts of reform”.
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64
Multi-commodity partial equilibrium models allow modelling interactions between
agricultural sectors, and often include detailed policy specifications for each sector. Examples
include modelling the impacts of feed grains or other livestock (e.g., beef, sheep) prices on dairy
production (supply shifters) and the impacts of dairy beef on the livestock sectors. Hence, partial
equilibrium models can analyze the tradeoffs within agricultural sectors due to trade and/or
domestic policy liberalizations and may allow for more detailed policy analyses than CGE
models. On the negative side, partial equilibrium models generally do not address the non-
agricultural sectors that can be an important part of trade negotiations. Key examples of these
limitations include modelling the income impacts of both agricultural and non-agricultural trade
liberalization and modelling the impacts of productivity growth and factor mobility (labour and
investment). In addition, partial equilibrium models themselves often lack commodity and policy
detail. Key examples of these shortcomings in dairy sector modelling include: lack of attention to
milk proteins and lactose as opposed to butter, SMP, cheese, and WMP commodity
specifications, and the absence of classified and other multi-tiered pricing and/or implicit export
subsidy schemes.
Econometric/time-series models generally estimate multi-region, multi-commodity trade
linkages with structural excess supply and demand, regional and world price linkage and quantity
balance equations. Econometric time series world trade models provide statistical estimates of
key structural relationships and parameters such as quantity and price linkage and balancing
equations, farm supply, processor derived demand for milk (and/or milk components), and
commodity supply and demand price response elasticities. This allows for statistical hypotheses
tests on both key structural and parameter specifications. Time-series based models also provide
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a recursive multi-year static policy simulation framework, allowing for dynamic simulations (via
time-series linkages) and Monte Carlo simulations to assess model-based distribution of impacts
utilizing the statistical distribution of key econometric/structural parameters (e.g., FAPRI’s
domestic United States policy simulations). On the negative side, estimation of excess
supply/demand curves under domestic and trade policy distortions is a difficult econometric
challenge. This is particularly true when there are multiple policy regimes over a time period of
sufficient length necessary for the reliable estimation of parameters. Time varying (as a function
of policy regime) parameter modeling often resorts to dummy variables to characterize the
different policy regimes and fails to capture these in a satisfactory way in the model’s structural
equations. A second key shortcoming is the explicit modelling of spatial and hedonic (milk and
product) characteristic linkages. Current dairy processing technology trends are likely to be quite
important to more fully characterizing and modelling the behaviour of increasingly large,
integrated, and often multi-national dairy processors. In this context, a multiple output/multiple
component (input) and scale sensitive cost function is one way to proceed. This type of approach
is crucial to better model trade and domestic policy-induced business structure and processing
technology innovation, where the size of the domestic-world price “margin” drives the economic
incentives to innovate, which is a key driving force in the world dairy sector. It is difficult to
estimate such cost functions using standard econometric techniques (for data availability reasons
alone) and hence, modelling processor commodity supply remains a key econometric challenge.
Mathematical programming, spatial equilibrium based world trade policy models are
alternatives to econometric time-series based models, although these models can often be
complementary to each other. One advantage of the spatial equilibrium approach is that it
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66
implicitly provides excess supply and demand curves with explicit detailing of domestic and
trade policy distortions. Specification of regional milk and/or milk component supply, processor
derived milk and/or component demands and commodity supply via a multiple output/multiple
component input cost function, and commodity demand functions imbedded in a regional
spatially based trade model with detailed domestic and trade policy distortions is possible. Two
tiered TRQs and applied versus bound rates on within- as well as over-quota imports, bilateral
and/or other preferential tariffs and quota regimes, are then applied directly to the spatial
(price/quantity) arbitrage conditions governing spatial trade flows. Implicit excess supply and
demand functions are then generated by the optimization modelling, including all of the policy
distortions in all of the potential trade markets. A second key advantage of the
programming/spatial equilibrium approach is the explicit modelling of spatial and hedonic (milk
and product) characteristic linkages. Generally, processing sector technology is characterized via
component balance constraints and explicit processor optimization behaviour. Such optimization
routines are commonly used to allocate milk (component) supplies to the highest valued
commodity utilization. Optimized interplant and interregional flows of dairy based ingredients
are common and increasingly important to attaining efficient milk component (procurement and
marketing) utilization. It is also somewhat easier to explicitly impose disaggregated and detailed
domestic and trade policy distortions (two tiered TRQ’s, in particular) in a spatial programming
model rather than using aggregated policy wedges such as the OECD’s Aggregate Measures of
Support (AMS) or Producer Subsidy Equivalent (PSE). However, severe data limitations,
particularly for disaggregated commodity, and detailed domestic trade policy modelling, remain
a key limitation. Reliable data on commodity and trade policy details (aggregating tariff/subsidy
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67
lines; maximum bound versus applied rates), country/regional GDP and exchange rate forecasts,
farmer versus processor versus retailer market power, and details on the increasingly important
dairy based ingredient markets are often difficult to obtain. Spatial models also require
parameterization of supply/demand price response (elasticities). These are generally borrowed
from econometric time-series models or their results, and, therefore, key price and behavioural
responses used in these models often import many of the shortcomings of the econometric time-
series approach. The University of Wisconsin World Dairy Model (UW-WDM) is an example of
a country/regional, commodity, and policy detailed spatial hedonic equilibrium, programming
model.
However, none of the above approach studies world dairy markets as an imperfectly competitive
market, and examine whether or not there exists market power for different players. There are some
papers that incorporate imperfect competition in spatial equilibrium models. Nelson and McCarl
developed a Cournot and conjectural variation model which could depict certain forms of imperfect
market structures, but they did not apply them empirically. Kawaguchi, Suzuki, and Kaiser used the
conjectural variation approach, similar to that used by Nelson and McCarl, and applied it to the
Japanese domestic milk market. But Kawaguchi et al study exporters’ market power and ignore
potential importers’ market power. This is unrealistic because demand (imports) more important
than supply (exports) for trade flow in dairy products. Chen, McCarl, Chang and Hsu developed an
imperfect competition model that can study any kind of market structure specification allowing
competitive behavior spanning from perfect competition to monopoly or monopsony. They also
considered both exporter’ and importer’ market power by applying the model to world rice trade.
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But they focus only on the trade side and do not consider the domestic supply/demand change
associated with a change in countries’ exports/imports.
In this chapter, we want to develop a model which combines domestic supply/demand and
imports/exports into one unified system to study the importer and exporter’s market power by
allowing any degree of market structure from perfect competition to monopoly or monopsony. With
this model we can test whether or not the assumption of perfect competition for Asian and/or world
dairy markets is justified. We begin with The UW-Madison World Dairy Model (UW-WDM), and
then incorporate imperfect competition into this model.
3.2 A spatial equilibrium model to study perfect competition
The U.S. dairy model is a spatial equilibrium model based on the work of Samuelson, and
Takayama and Judge (STJ). Samuelson (1957) and Takayama and Judge (1964) have developed
models of spatial resource allocation and competitive trade. Resources consist of primary
commodities and processed commodities, which can all be traded in markets assumed to be
competitive. The linkages between spatial markets involve trade and transportation costs.
Following Chavas, Cox and Jesse (1998) and Zhu (1999), we begin our model development with
some notation and definitions. Let N be the number of primary commodities, with Wj„ denoting
the quantity of the n-th primary commodity produced in the i-th region, and Xi„ being the quantity
of the n-th primary commodity used as an input in the production of processed commodities in
region i, n = 1 N, i = 1 J. Let K be the number of processed commodities, and denote
the production level of the k-th processed commodity in the i-th region by T)*>k = 1 K, i =
1 J. The consumption level of the k-th commodity in region i is denoted by z/*, k = 1 ........
K, i = l J. Production of the processed commodities will be influenced by interregional
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trade in the primary commodities and by processing technologies. The consumption of processed
commodities will be influenced by their production and by the interregional trade in them.
Denote by Tij >0 (tij > 0) the vector of export of primary (processed) commodities from region i
to region j. And let Cij (cij) be the vector of transportation and marketing cost per unit of primary
(processed) commodities traded from region i to region j. Using this notation, Tji„ > 0 is the
quantity of the n-th primary commodity that is both produced and used in the production of the
processed commodities within the i-th region. Similarly, tnk > 0 is the quantity of the k-th
processed commodity that is both produced and consumed in the i-th region.
Following Chavas, Cox and Jesse (1998), we assume that there are two kinds of inputs used
to produce the processed commodities y in each region: the vector of primary commodities x,
and other inputs denoted by the vector vi (e.g., labor, capital). In the i-th region, the use of inputs
vi must satisfy (vi, xi, yi) e 7!, where T\ is the production possibility set. Efficient use of the
inputs vi under perfect competition requires that they be chosen in a cost minimizing way:
Gi(xi, yi) = minv {ri’ vi: (vi, xi, yi) e Ti), (3.1)
where ri is the vector of market prices for vi in the i-th region. Gi(xi, yi) in (3.1) is a cost
function measuring the cost of optimal use of inputs vi, conditional on primary inputs xi and
output levels yi.
In the Samuelson-Takayama-Judge setting, market equilibrium is obtained through the
maximization of a net social payoff function given by the sum of producer and consumer surplus
across commodities as well as region, net of transportation and processing costs. In a vertical
sector involving more than one stage of production, the cost of transformation in each stage also
needs to be subtracted. This gives the following quasi- welfare function
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V(w, X , y, Z, T, t) = Si CSi(zi) - Si PCi(wi) - Si Gi(xi, yi) - Sij Tij Cij - Sij tij Cij. (3.2)
where CS', (z<) = £ P‘(^)d^ is the total benefits to consumers from purchasing the final
goods Zi, PC( (w( ) = P. (£)(!£, is the cost of producing primary commodity wi in region i, Gi(xi,
yi) is transformation cost in region i as given in (3.1). Assume that the quasi- welfare function
V(w, x, y, z, T, t) is concave and satisfies 5CSi(zi)/3zi = pi° and 5PCi(wi)/dwi = pis, where pic (pis)
is the vector of market prices for the processed (primary) commodities. This assumes that, under
competition, market prices reflect marginal benefits for consumers and marginal costs for
producers. In the presence of trade, the maximization of aggregate net social payoff is subject to
two sets of constraints: the trade flow constraints and non- negativity constraints. For the i-th
region, the trade flow constraints are
These restrictions state that exports plus domestic uses cannot exceed domestic production,
and that domestic consumption cannot exceed domestic production plus imports. This is true for
primary commodities (equations (3.3a) and (3.3b)) as well as processed commodities (equations
(3.3c) and (3.3d)). The optimization problem representing spatial competitive equilibrium then is
(3.3a) j
(3.3b) j
(3.3c) j
j (3.3d)
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subject to equations (3.3) and (w, x, y, z, T, t) > 0 (3.4)
In the absence of government intervention (i.e., no tax/subsidy and no quota), the
optimization problem (3.4) generates a Pareto efficient resource allocation. It also generates a
competitive market equilibrium where the Lagrange multipliers associated with constraints (3.3)
are interpreted as market prices (see Chavas, Cox and Jesse, 1998).
3.3 A spatial equilibrium model with imperfect competition
To develop an imperfect competition dairy trade model, we develop the ideas of Chen et al.,
Nelson and McCarl, Kawaguchi et al., Suzuki and Kaiser. To simplify our notation, we let X tj
denote the volume shipped from exporting country i to importing country j (we do not
differentiate processed or primary goods here, so it can be T y or ty ). Using this notation, X„ > 0
is the quantity of the commodity (primary or processed) that is produced in the i-th region. The
total demand in the importing region j is Dj,
As the quantity of exports from i to j is the same as the quantity of imports of j from i, we
define X i} = - X jni * j . Then the demand for region i can also be written as,
(3.5)
The total demand in the exporting region i is Dj f
X ^ - A - (3.6) j
*»-Z *«=A- (3.7) j* i
Let the inverse demand function in region i and j be,
(3.8)
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When X y > 0 and i * j , the profit maximization problem associated with trade flow X u is
M O X P jX y -P J y -T C y X y
or
M tff i j -b jD ^X y -(C, - d f i ^ X y -TCyXy AV
S.t D,=Yx»yj i
v - x . - Y . X f X i j*i
D j> 0,X y> 0,V i,j , (3.9)
where TCy is the unit transportation cost of shipping goods from region i to region j (we assume
constant marginal cost of trade), and TC„ = 0. The first order condition is
d Y x . dYx. . . Q T > J d X 9
—— = (a, - b p , ) - b, (1 + )X„ - (c, - d,D,) + d, ( ^ -1 — ^ ---)X„ - TC„ BXy J J ^ ^ dXy 9 V ' ' ^ ^dXy dXy " "
e y x... • 9 r)Y
= Pj - b j ( l+ry)Xy -(c, - d ,D , ) - d , { 1 + - ^ - ------ ^ ) X y ~TCy
= Pj -bj(l+ry)Xy - Pi- d P + Ry)Xy ~TCy=0 (3.10)
or
Pj =P,+bJ (l + ry)Xy+TCy+di(l + Ry)Xy. 0.1Oc')
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73
Equation (3.10c') tells us that the profits associated with trade flow are shared by exporting
d Y X... f-i. v qx and importing countries. The term Ry = J *'J------------- is the conjectural variation (CV) for
dXy 8Xy
importing country j , which gives the change in trade to country j ’ from exporting country i
d Y X...f—1. 'J QX ( J*IJ ), and the supply change in country i (— - ) caused by a change in the amount
dX0 dXy
imported by importing country j from exporting country i.
a Y x . ! . ‘J
Conversely, the term ru = —1— is the exporting country i’s conjectural variation dX-IJ
dX. regarding changes in all other regions’ exports (for region j this is the supply change (— —)) to
dXy
market j caused by a change in region V s exports.
A wide variety of market behavior can be reflected through the conjectural variation terms:
Ry and ry. If both equal -1, then exporter i and importer j would be acting as perfect competitors
as in the Takayama and Judge model. If ry equals zero while Ry equals -1, then exporting country
i acts as an imperfect competitor who will not change exports in response to other exporters (/ ’)
action in a Coumot-Nash context while importer j behaves as a price-taker. If all exporters’
conjectural variations are positive and importer’s conjectural variation is - 1 , then it implies that
collusion or cooperation exists among exporting countries. Similar statements can be made on
the import side.
Finally, if the exporter and importer’s conjectural variations are not simultaneously equal to -
1, then both markets are imperfectly competitive. The term dt(l + Ry)Xy is the price mark-up by
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74
importing country j, and the term bj {\'\-rtJ)XiJ is the price mark-up by exporting country i.
Therefore, the existence of market power can increase the importing country’s consumption
price by increasing importing cost. It can also decrease the exporting country’s export price
(F.O.B price). To explain this, we refer to figure 3.1. In figure 3.1, ED is the excess demand of
importing country and ES is the excess supply of exporting country. Under perfect competition,
both the demand and supply prices should both equal to Pw and trade volume is Q°, and the
profits associated with trade flow are zero. However, the existence of market powder lowers
trade volume to Q1. Now the importing country’s demand price (importing cost) is PD and
exporting countries supply price (F.O.B price) is Ps. Clearly, PD is greater than Pw and Ps is less
than Pw. This makes trade profitable, with unit market rent of Ps - PD, shared by the importing
and exporting countries.
Figure 3.1 The Impacts of Imperfect Competition on Supply and Demand Prices
ES
PD
Pw
ps ED
Q1
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75
Note that (3.10) can also apply to the case of X H(production in region i ), where from (3.10c1)
we know R #(.= rH = -1 . Therefore, from now on we do not differentiate whether i equals to j or
not. To maximize the net social welfare it is still required to subtract the associated production
costs. Given our specification in section 3.2, we can extend equation (3.4) to an imperfect
competition optimization problem as,
w,x,y,z,T,< i i i i j i j
- Z S A/ 1+' i ) j W - I l ¥ 1+' P j ' A i j i j
- E E 4 0 + * » > i w - Z 5 > , o ■ + v \<A) i J i j
subject to equations (3.3); (w, x, y, z, T, t) ^0}. (3.4')
3.4 Application to the Asian dairy markets
The analysis will consider 6 separate regions of the Asian dairy market, including China,
India, Japan, Korea, South-East Asia, other south Asia. In addition, we aggregate the dairy
market for the rest of the world into 15 regions including the US, Canada, Mexico, Australia,
New Zealand, Western Europe, Eastern Europe and the Former Soviet Union (FSU). In the
context of the dairy sector, the primary commodities (five types of farm milk) can be
transformed into eight processed dairy products (cheese, butter, whole milk powder, skim milk
powder, dry whey, casein, evaporated/condensed milk, and other dairy products). The crucial
linkages between primary and processed products are the milk components (milk fat, casein,
whey protein, and other milk solids) that are rearranged by dairy processing plants. In each
region, the total amount of components found in processed products must come from the primary
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76
products. To the extent that each product has fixed composition, this means that the processing
technology can be represented by a Leontief technology with respect to milk components. Let ajS
(bis) denote the matrix of quantities of the s-th component per unit of the primary (processed)
commodities in the i-th region. Then the transformation relationship between primary and
processed goods in region i must satisfy
bis’ y i < a i s ’ xi, s= 1 ,..., S, (3.11a)
where S is the number of components. This is a Lancasterian specification establishing a
fixed proportion relationships between products and their components, where the components are
perfect substitutes across commodities. For the optimization problem (3.4') above we need to add
equation (3.1 la) as another constraint. We can also let Af = ats, Bf = ajs, and write equation
(3.11a) as Bfy, < Af x, .
We also need to consider the effects of tariffs, tariff rate quota and export subsidy. Let IItf be
the tariffs (specific duties) imposed by region j on imports of primary commodities from region i
( n,. = 0 , there is no tariff for intraregional trade); ny be the tariffs (specific duties) imposed by
region j on imports of final commodity from region i (nu = 0 , there is no tariff for intraregional
trade); Ay be the export subsidy rates on primary commodity exports from region i to region j. if
Ay = 0, there is no consumption subsidy in i* region; let Sy be the unitary export subsidy rates
on final commodity exports from region i to region j. if Su = 0 , there is no consumption subsidy
in i* region; let Qj denote j* region’s import quota on primary goods; and let qj denote j*
region’s import quota on final commodities. Then we have
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i* j
Z ' * ^ r i*j
Therefore the social welfare maximization problem will be
A 4 t a { Z « . ( * i ) - £ r c , ( » l ) - 2 ; G )( W , ) - E r , ( c , + n , - a , ) - £ / , <c, + * w ,x ,y ,z ,T ,l i i j i j i j
i j i j
- S S ‘W + ^ ) f W - £ £ 4 < 1 +J!» )J 'A > i j i j
subject to equations (3.3), (3.11) and (w, x, y, z, T, t) > 0}.
The Lagrangean of equation (3.12) is
L^CSXz,)-'ZPCl(wl)- 'Z a i{x,,yl) -Y 1T,1(C,+n u-A,)-'Zt,(cll+xll- I J I J
- E & 0 + ' V ) f W - I £ * y 0 + .V)J v * , i j i j
- E £ 4 < 1+*#> J W - I l 4 0 + * » ) j < A i j i j
i i j
+ Z ^ 0 ' / - Z ^ + Z m Z ' * - z-) ' j i j
+2 > / e , - £ ' 9 ) + 2 > . , ( « , - B , ) •*j j i* j
(3.11b)
(3.11c)
h - Sii)
(3.12)
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where a>0, P>0, y>0, and X>0, are Lagrange multipliers corresponding to constraints (3.3), and
(p>0 are the Lagrange multipliers corresponding to constraints (3.11). The K-T conditions
associated with the primary (farm) sector are:
Given wj >0, (3.13a) implies that at the optimum a, = P*, the market supply price vector of Wj.
(3.13b) states that at the optimum the market prices of primary commodities at the
manufacturing level ( $ > 0 ) are equal to their component values as inputs ( f tA f > 0 ) whenever
the elements of x, are positive.
The K-T conditions associated with the final (processing) sector are:
= 0, Wj > 0, (3.13a)
= 0, x(. > 0, (3.13b)
(3.13c)
8L _ dCS, - ^ < 0 , ^ = 0 ,
dzj dzt
(3.13d)
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79
^ = 4 x ,-B ?y ,*0 . 'P ,= 0 ,d%
= 0 , ^ > 0 , (3.13e)
(3.13c) states that, at the optimum, the marginal values of final commodities (y ,> 0) are equal
to their marginal cost (i.e., material input costs (̂ p,Bf) plus other input costs ( > 0 ) ) dyt
whenever yiare positive. Given y, > 0 , (3.13c) implies y ,.= i f are the market supply prices for
yr (3.13d) states that, at the optimum, the market prices of the final commodities (A, > 0) are
DCSequal to their marginal values ( ----- - > 0) whenever z;are positive. Hence, given zi > 0 , (3.13d) dz,
implies = P*, the market demand price for zr (3.13e) states the regional component balancing
restrictions that formalize the interdependence in dairy manufacturing processes. q>, is the vector
of regional shadow prices of milk components.
The K-T conditions associated with the trade flows are:
dL dT:: = - C u ~ n u + A u + P j ~ a > ~ b i 0 + ri j ) Tij ~ d i0 + R y ) T>j ~ V j j t j ̂0>Ty =
ij
= 0,Ty>0, (3.13f)
dL — = -Cy-7Ty+Sj +J l j -y i - b j ( l + r y ) t y -di(l + Ry)ty - 0 j Mj <0 , t y =0 ,
y
= > 0 , (3.13g)
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In equation (3.13f) and (3.13g), tariff rate ( I l 9 / 7ty), export subsidy rate (A y /Sy) and quota rents
( T]jMj / Ojj+j) are created by countries’ trade policies. If Ry =ry =-1, then (3.13f) and (3.13g)
indicate that under perfect competition the demand price in region j (/?. / Ay) is equal to the
supply price in region i (a , / y, ) plus transportation cost (Cy/cy), associated tariff rate ( I lff / ntJ)
and quota rents ( r]jMj / 0jJmj) minus the export subsidy rate ( Au / Sy). But under imperfect
competition, there is an additional market rent by the exporter/importer. Exporting country i’s
price mark-up is bj ( 1 + rtj )Ty / b} ( 1 + rtJ )ty for primary/processed goods, while an importing
country j ’s price mark-up is d,( 1 + Ry)Tyld,(\ + Ry)ty for primary/processed goods. Again, if ry
equals zero while Ry equals -1, then exporting country i acts as an imperfect competitor who will
not change exports in response to other exporters ( /) action in a Coumot-Nash context while
importer j behaves as a price-taker. If all exporters’ conjectural variations are positive and
importer’s conjectural variation is - 1 , then it implies that collusion or cooperation exists among
exporting countries. Similar statements can be made on the import side. Finally, if the exporter
and importer’s conjectural variations are not simultaneously equal to - 1 , then both markets are
imperfectly competitive. The term </,(! + Ry)Xy is the price mark-up by importing country j, and
the term bj( 1 + r^Xy is the price mark-up by exporting country i.
Note that problem (3.12) only applies to the case where market power is exerted by the
county or a single firm. If market power is exerted by several firms and there is imperfect
domestic competition, then there will be additional mark up by firms at the processing stage.
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81
Marginal cost no longer equals to marginal revenue, and the equation (3.13c) can not hold.
Instead, the following condition holds
|L =—(|L +,yfxi+W)+y,so. y,=o, ty, ty,
= ( U > 0 , (3.13c')
where i//f is the mark up by firms at the processing stage. When i=j, from equation (3.13g) we
get Xj =Xt = Yi, i.e., P? = X, = y( = P*. Market demand price of processed products no longer
equals marginal cost, and firms get an additional monopoly profit (-^-L + from ty,
imperfect competition. Hence, the objective function would be changed to
MaxfEcsA)-Erc<(”.)-Ia + W X (w ,)-Z r»(c«+n, - a,)-E»»(<v+*» -s,) w,x,y,z,T,t i i i i j i j
- I £ » /0 + r ,) J W - E 5 > ,0 + 'V > J 'A i J i j
- £ £ < * . 0 + *s) P a - E 2 > . ( '+ * ,) fv*»> i j i j
subject to (l + ̂ /f)Bfyj <Afxn and equations (3.3), (3.11b), (3.11c) and (w, x, y, z, T, t)
>°}.
3.5 Test of Market power
When Tyytg > 0 we can use first order conditions (3.13f) and (3.13g) to test econometrically
for the existence of market power. For example, to test market power for the processed product,
we have A, = Yi +cij+nij- 6 ¥+0JM + bJ(\ + rij)tij + </,(! + Ry)ty. (3.13g’)
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As the data for quota rents 0jMJ is difficult to get, we still can not empirically estimate this
equation. But note y( + cu +nij - S o+0JJltj is the importing cost of country j for one unit
processed product imports from country i. We let y, +cij+7tiJ- S = PJ!, where PJ! is the
import price of country j for one unit imports of processed product from country i. We know
Xj = P j , so if we know the trade flow data ty, then we can estimate equation (3.13g') by,
P j= P ; +bjQ + r,yt, + di(l + Rij)tiJ (13g")
If we do not know the trade flow data ty, we can sum across / € (ty > 0) for equation (3.13g')
I ' i r E J ’M M ' n ) ' # + £ (3.14) < 6 //y > 0 i & y > 0 l e / y > 0 i & y > 0
Let Mj =^JdI(tiJ > 0), and divide equation (3.14) by this to get 1
E ^ + E iJ(1 +r,V (, + 2 </,(! + v .0 ie/,y>0 0 (3.15)
Mj M}
note — = X:, —-------= Pj where P, is the average import price of country j. If we assume Mj Mj
there is an average market power of ( 1 + r, ) by the rest of the world, and country j has an average
- - - L dimarket power of d(\ + Rj) for its imports (where d = — — ), then Mj
£ ' » £ XJ =PJ+bJ( l+rj y ^ - +d(i+RJ)'-S ^ - (3.16)
M j M j
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where — is country j ’s average import from an exporting country i. MJ
We can also use equation (3.13c') to test for the existence of firms’ market power at the
dg ■ 7■ processing stage, but as the marginal cost (MC, MC, = -=■*- + ) is not observable, we need to
estimate it. By definition, MC is a function of material input costs (<p,Bf) and other input costs
dg. ( — > 0). Therefore, we can let MC, = f, (rawm,, labor,, prawi ,K,), where rawmi is the input of
raw milk, praw, is the price of raw milk, laborx\ is the labor input, and K is the capital input.
Then we need to estimate the function
M cl(i+iirf)=p;
or
P,s =fi (rawntj, labor,, prow,, K, ) ( 1 + y/f). (3.13")
As the notations used in the conceptual model are extensive, I summarize them in table 3.1
for future references.
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Table 3.1 Notations Used in the Conceptual Model
Symbol Description N Number of primary commodities J Number of regions i i= 1........ J Wi„ The quantity of the n-th primary commodity produced in the i-th region Xin The quantity of the n-th primary commodity used as an input in the
production of processed commodities in region i K The number of processed commodities k k = 1 ........ K Yik The production level of the k-th processed commodity in the i-th region Zik The consumption level of the k-th commodity in region i Tij >0 (tij>0) The vector of export of primary (processed) commodities from region i to
region j Cij (Cij) The vector of transportation and marketing cost per unit of primary
(processed) commodities traded from region i to region j
? *
IV IV
° the quantity of the n-th primary commodity that is both produced
(processed) and used in the production of the processed commodities (consumed) within the i-th region
Vi other inputs except x Ti the production possibility set Gi(xi, yi) cost function measuring the cost of optimal use of inputs vi, conditional on
primary inputs xi and output levels yi C S i(z i) CSj(zt)= £ P‘(J;)dg is the total benefits to consumers from purchasing
the final goods z\ PCi(Wi) PCjiw,) = P P*(%)d% is the cost of producing primary commodity wi in
region i V(w, x, y, z, T, t) The quasi- welfare function
Pic(pis) Vector of market prices for the processed (primary) commodities Denote the volume shipped from exporting country i to importing country
J
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Continued Dj Total demand in the importing region j
Pi (Pi) Demand price in region i (j) di(bj) The slope of the demand function in country i (j)
ai (Ci) The intercept of the demand function in country j and i TCa The unit transportation cost of shipping goods from region i to region j
rU dZ x 0 rtj = ——----- is the exporting country i’s conjectural variation regarding
dXy
changes in all other regions’ exports (for region j this is the supply change dXn
(— — ) ) to market j caused by a change in region j’s exports dXy
Rii d Y X...f - f . « Ry = - ------------ is the conjectural variation (CV) for importing
dXy dXy
country j , which gives the change in trade to country j ’ from exporting d Y X...
,j q x .. country / ( J*'J---- ), and the supply change in country i (— - ) caused
dXy dXy
by a change in the amount imported by importing country j from exporting country i
ais(bjs) the matrix of quantities of the s-th component per unit of the primary (processed) commodities in the i-th region, Af =ais, Bj = ajs
s The number of components n iMy) The tariffs (specific duties) imposed by region j on imports of primary
(processed) commodities from region i A M ) The export subsidy rates on primary (final) commodity exports from
region i to region j
Qi(qi) j* region’s import quota on primary (final) goods quota rents for primary (final) commodity
Pj IXj At optimum, equal to demand price in region j ajy, At optimum, equal to the supply price in region i
TV, Price mark up by firms at the processing stage
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Continued pm ji The import price of country j for one unit imports of processed product
from country i
'pj The average import price o f country j
Mj = £ /« ,> < > ) 1
di(l + RiJ)XiJ The price mark-up by importing country j bjQ + r ^ The price mark-up by exporting country i
3.6 Simulation procedure
The conjectural variations in Chen, Nelson and McCarl and Kawaguchi, Suzuki, and Kaiser’s
models are assumed to be constants. We follow the same assumption in this study. The use of
this approach depends on a set of reliable data; hence, we will only apply the imperfect
competition model to butter, cheese and nonfat dry milk. For the other dairy products, we follow
Chavas, Cox and Jesse and assume the perfect competition model is appropriate. The
optimization problem (3.12) can be solved by using General Algebraic Modeling System
(GAMS) software. To study imperfect competition, we apply the following steps:
Step 1: from p=a + bq, we have 3p/dq=b, 3p/Sq (q/p)=b*(q/p), then b=p/(q e), where e=dq/dp
(p/q), the demand elasticity. Then a =p -bq=p(l- dp/3q *(q/p)=p(l- 1/e). We can get
- L di, so we can calculate c/ = — — . J J 1 ' Mj
A A
Step 2: If trade flow data is available, estimate equation (13g") to get ry and Rtj. Otherwise, we
estimate equation (3.16'). For M} = I{ti} > 0), we only focus on the major exporters,
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such as EU, Australia and New Zealand (in this case Mj = 3). Estimate equation (3.16') to
get Vj and R, , and see whether they are significantly different from -1 or not. If they are
significantly different from - 1 , we assume the imperfect competition model is more
appropriate and use these parameters in the GAMS model.
A T
Step 3: estimate equation (3.13") to get , and see whether they are significantly different from
zero or not. If they are significantly different from zero, we assume the imperfect
competition model is more appropriate and use these parameters in the GAMS model. In
this study, as the production cost data is very difficult to get, we only focus on the case
that market power is exerted by one country or a single trade firm, and assume perfect
domestic competition. Therefore, we ignore this step.
Step 4: Calibrate the base scenarios (2002-04) to replicate dairy products prices, production and
consumption observed during the same period until the difference is within tolerance
level (say 5-10%).
3.7 Policy Modeling
Dairy policy modeling is quite complicated, different programmer may end up with different
methods. As mentioned above, it is somewhat easier to explicitly impose disaggregated and
detailed domestic and trade policy distortions (two tiered TRQ’s, in particular) in a spatial
programming model rather than using aggregated policy wedges such as the OECD’s Aggregate
Measures of Support (AMS) or Producer Subsidy Equivalent (PSE). For example, to model
specific tariffs, Anderson and Martin (2005) first converted specific tariffs to ad valorem
equivalent by using GTAP model. However, the UW-WDM models specific tariffs directly by
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\
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using Ihe GAMS software. This section introduces how three types of important dairy policy
(import quota, production quota and price support policies) are modeled in this study.
3.7.1 Import Quota
Equivalence of import quota and tariffs
As the quota rents 0jMJ is one component of country j ’s importing cost ( P" ), we want to
know how the change of import quota is associated with change of demand and supply in
importing country j. We follow the ideas of the traditional three panel trade diagram to examine
it. From figure 3.2, we see that if there is no import quota then the importing country will import
product at world price (Pw) and import quantity Q4 -Q1. However, if there exists binding import
quota (Q3 -Q2 and Q3 -Q2 < Q4 -Q1), then the import price (also the domestic consumption price)
would raise to Pd. So the quota rent is P d - Pw- However, the existence of import quota does not
change the domestic demand and supply behavior. In other words, the a, b, c, d parameters in our
model specification of section 3.3 do not change. If we let import tariff rate (t) equal quota rent,
from static point of view, then for any import quota we can find an import tariff which provides
equal protection to the domestic market.
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Figure 3.2 Impacts of TRQs on the Importing Country
Pd
Pw
Nonequivalence of import quota and tariffs: rent seeking
However, import quota and tariffs are very different in other ways. One very important
difference occurs in the manner in which imports and government revenue is collected and
redistributed. In the case of import quotas, it is sometimes difficult to develop a rational plan to
properly handle quota rents, and to also distribute the commodity to the nation. Generally, quota
rents are collected by the agency which sells import quota and money is funneled into the general
fund. Industry or consumer groups do not always see the entitlement that is attributed to tariff
revenue. Sometimes, grossly inefficient behavior is rationalized within the context of quota
policies. For example, consider the case of the Taiwan flour milling industry (Stiegert and Peng).
In this example, the Taiwan Flour Association purchases wheat from the world market using a
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90
quota limit set by the government. Internal prices of flour and subsequently bread and noodles
are maintained at a very high level to protect the price of the domestic production sector. The
Taiwan Flour Milling Association (TFMA) simply allocates all wheat to the milling industry
based on individual Arm level capacities. But because mills could obtain this wheat at essentially
the world price (pw) and sell flour at a very high protected price, competition for the wheat
emerged in the form of excess milling capacity. For most of the period from 1950-1995, the
Taiwan milling industry rationally operated at 30-40% of capacity. This is an example of what
the literature dubs “rent seeking” activity. There are many examples of rent seeking behavior
related to quotas. Rent seeking occurs in relation to tariffs, but it is not as prevalent. Other forms
of rent seeking might include lobbying efforts, political bargaining for quota license, funneling
quota rents to research and development activities that benefit the home industry.
Nonequivalence of import quota and tariffs: quotas creates more price and import
demand distortions than tariffs
Although there exists a tariff equivalent for any quota, it is simply a static concept. Once
economic conditions change, the equivalence link is commonly broken. As Asian countries dairy
markets are relatively small, we consider the small country case here. If market power does exist,
the country is not a price taker in world markets, and the change would be more complicated.
But similar conclusions should still hold.
Case 1) Economic growth
Consider the case of growing demand in the price taking home country that shifts the Excess
Demand (ED) curve to the right (see figure 3.3).
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Figure 3.3 Nonequivalence of Import Quota and Tariffs: Economic Growth
91
ES
ED’ "e d
X , Xt
The assumption of a small country would imply that internal economic growth would not
influence the price faced by producers and consumers. What we show here is that under a tariff,
such a condition holds but under quota, this condition is violated. Begin with the excess demand
curve ED (net imports). If there were no trade distortions, the home country would import Xf at
Pw. Presume Home has either an import quota or an import tariff that equally restricts imports to
level Xi, so domestic consumption price (Pd) is the same as tariff price (Pt) and quota price (Pq),
or ( (Pd=Pq=Pt). Now, suppose the economy at Home grows and the demand curve shifts right
from D to D’ in figure 3.2. Under the quota, the import limitation is now more binding; therefore, *
price must increase to Pq to ration supply. Under the per unit tariff, price has not changed (Pq=Pt
=Pt) but imports have increased from Xi to Xt. Thus economic growth that increases demand for
this product has no effect on price under the tariff, which is consistent with a free market
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92
adjustment for a good that fits a small country status. Note, additionally, that after economic
growth the quota generates considerable deadweight losses (a+b+c+d+e), while under a tariff,
the deadweight loss is only area (b+e).
Case 2) A change in world price
Now consider a change in the world price. Obviously, a free market small country should see
its price change by the same amount. What we find under a quota is that the quota binding price
is shielded from world price fluctuation. Under a tariff, consumers and producers face price
fluctuations similar in magnitude to what they would observe in a non-distorted market. Here we
consider the case where world price increases (similar analysis can be done when world price
decreases)
Beginning with the Excess Demand (ED) curve and a distorted price Pd caused either by an
import quota at import volume Xq or an import tariff equal to Pd - Pw (see figure 3.4). Now
consider the case when the price of good X rises on world market from pw to pw\ In the quota
policy regime, quantities remain restricted at Xq. Because home demand is unchanged, the
internal price need not be altered to ration supply. Thus, Pq’ = Pq =Pd. In the case of tariff,
increasing the world price leads to the same increase in the home price. Thus price increase from
Pt to Pt’ and trade volume decrease to X t\ Therefore, under import quota, domestic consumption
price is shielded from world price change and can not reflect world production costs change.
Consequently, under tariffs, domestic consumption price will adjust according to world price
changes.
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Figure 3.4 Nonequivalence of Import Quota and Tariffs: World Price Increases
W
ES
ED
Xt
Tariff Rate Quota (TRQ)
As non-tariff measures bring more market distortions than tariff, the Uruguay Round of
GATT/WTO Negotiations require member countries to convert their non-tariff measures into
tariffs of more or less equivalent effects and bind them (this process is called tariffication). All
tariffs are subject to further reduction, and a special protection measure—Tariff Rate Quota
(TRQ) is introduced. TRQ is a combination of tariff and quota restrictions. TRQ’s allow a
specified volume (tariff-quota quantity) of commodities to enter a country at one tariff rate (the
in-quota rate), whereas imports above this quota level are subject to higher tariff rate (the over
quota rate) (see figure 3.5). This is a two-tier tariff schedule with the quota playing a pivotal role.
As quantitative import restrictions were prevalent in international agricultural commodity trade
before the Uruguay Round of GATT negations, the GATT agreements requires member
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94
countries to standardize their import policies by replacing all their border measures with this type
of two-tier tariff-rate quota. When a country using this tariff-rate quota scheme imports less than
the quota, the in-quota tariff applies; when the imports more than the quota, both in-quota and
over-quota rates apply. On the one hand, the tariff-rate quota can converge to a simple rate tariff
as the difference of the two rates decreases and as quota level increases. On the other hand, the
simple rate tariff schedule can be treated as a special case of tariff-rate quota with the same in
quota and over-quota rates and an infinite quota level. In GAMS, there are various ways to deal
with TRQ. One way is to break imports into in-quota imports and over-quota imports, and use
these as variables in the objective function and directly apply the corresponding tariffs.
Figure 3.5 Two-Tier Tariff-Rate Quota
Tariff Rate
▲
Over-quota tariff rate ------------------ -j----------------- i j iI
In-quota tariff rate -------------------------- 1
Quota
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95
3.7.2 Production Quota
As mentioned in section 2.2.2, some developed countries (Canada and the EU) have
implemented direct production control policies in their dairy sector as a means of dealing with
market imbalances caused by price support systems and export subsidies. One commonly used
policy is milk production quota over which milk production is penalized (subject to over-quota
levies) or strongly discouraged by other policy instruments. Production quotas in major milk
exporting countries have significant effects on world dairy markets, as a substantial part of world
dairy exports currently is surplus disposal by the countries with high farm income support (Zhu,
1999). According to Zhu, milk production quotas are handily modeled in the spatial equilibrium
framework by adding quantity constraints to the milk supply curve if over-quota production is
prohibitive. If over-quota taxes are not high enough to be prohibitive, then alternative approaches
must be used. One option is to use stepwise supply functions. In figure 3.6, the piece-wise linear
curve ABE is the supply curve without supply control, i.e., the marginal cost curve. The section
BE reflects that the over-capacity production is subject to additional costs. If the production
quota restrictions are prohibitive and Qo is the production quota, then the supply curve is AML.
Note that the milk price is the marginal cost plus quota rent (MN, the shadow value of one unit
of quota). The milk supply curve estimated from price, production and supply elasticity only
(CDE) is not the actual supply curve. In order to obtain the supply curve, CD must shift down by
the amount of quota rent (MN). If the penalty on over-quota production is not prohibitively high,
the quota constraint is not a vertical line, and the supply curve becomes AMK. Again, the quota
rent must be taken into account in order to obtain the true supply curve.
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Figure 3.6 Supply Curve with a Production Quota
Marginal Cost
Market Price
E
A Qo
3.7.3 Price Support Policies
Price support policies refer to the situation where the government guarantees specific price
levels of certain products. In order to do this, government must purchase the product at the
support price so that the price will never be allowed to fall below the support level. A price
support system usually works together with other policy instruments, such as border measures or
production controls (Zhu, 1999).
Price supports can be modeled either by setting a price floor as a bound or introducing a
government sector with perfectly elastic demand (Cox, 1993). In the latter case, the combined
demand curve for a commodity with a price support will be an inclined L-shaped. In figure 3.7,
the curve of total demand is flat at the right end. As the price hits the price floor supported by the
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97
government, the total demand in a market is Q2 , of which the amount Q2 - Qi is purchased by the
government.
Figure 3.7 Demand Curve with Price Support
Price
Support Price
Qi Q2
For more discussion on the modeling of trade and domestic policies in GAMS, see Zhu
(1999). Besides policy modeling, to evaluate impacts in the future, it is also important to
understand the method used by the model to forecast into the future. The forecast method in this
study is introduced in section 3.8.
3.8 Prediction
In addition to studying current impacts of policies changes on the Asian dairy markets, it is
also attractive to see these impacts in the near future (3-5 years ahead). Therefore, predictions of
the Asian dairy market situation for 3-5 years require taking into account demand and supply
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98
changes in the future. To accomplish this, demand and supply shifters are added into the model
depending on macro economic conditions.
Demand Shifters
Income and population growth are generally considered the most important determinants for
aggregate demand. Following Zhu (1999), the linkage between income/population changes and
demand shifters is the income elasticity (using per capita income) and population elasticity. It is
assumed that the population elasticity for all dairy products is one, i.e., 1 % population growth
leads to a 1% increase in total demand. As the individual effects of population are already take
into account, only the net income growth can drive demand growth. That is, income growth due
to population growth can not increase demand (it is already incorporated into the model by the
population shifter). Therefore, the income shifter = income elasticity*(income growth rate -
population growth rate). In a partial equilibrium analysis, macro economic parameters (income
and population changes) are treated as exogenous demand shifters. In the linear demand function
setting, parallel demand curves shifts are assumed, which means slopes of demand curves are
fixed during the shifts. Zhu (1999) showed that how the demand shifts (i.e., rotating or parallel)
is not very important in this kind of model setting.
Supply Shifters
The major determinants of milk supply are technological change and governmental policy
reform. The overall technological progress depends on scientific findings and knowledge
accumulation, either exogenously or interactively with economic activities through increasing
return to scale and externality effects (Solow, 1958, Lucas, and Romer). To model these effects,
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another variable (a) is added to the production function in addition to price. That is, the milk
supply function is
Q = f (P ,a )
where Q is the quantity produced, P is the price, and a is the supply shifter embodying the
technological changes, weather changes and government policies changes. A change in Q can be
expressed as
AQ = — AP +— Aa dP da
The supply shift is thus
^ A a = A Q - ^ A P = A\nQ-Q-riPA\nP-Q da dP
where tjp is the supply price elasticity. Therefore, the total contribution from a is measured as a
residual. Given historical data for percentage changes in Q and P, and tjp , one can estimate the
contribution of A a , the supply shifter.
In addition to demand and supply shifter, transportation costs are assumed increase 7.8% per
year for the prediction period. The main reasons for this assumption are the huge increase in
demand for raw materials from China, including steel, coal, scrap iron etc, and increasing oil
prices.
3.9 Data and Software
The model used for this analysis is based on a perfect competition model developed at the
University of Wisconsin and previously used to assess the impacts of full deregulation and
extending the URAA dairy modalities an additional 5 years, from 2000 to 2005 (Cox, et al.). An
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100
earlier version was used for a World Bank study of the impacts of Developed Economy dairy
policies on the Developing Economies (Zhu, et al.). From perfect competition to imperfect
competition, the data used in the models are very similar to each other. We follow Zhu (1999)
for most of the data description. However, we changed the data sources for some parameters to
get better data.
1. Base year production of primary commodities, production and consumption of processed
dairy products. For EU, Australia, New Zealand, China, U.S., Japan, Korea, India and some
other countries, the data comes directly from FAPRI outlook. For others regions, the data is
from FAO database. When consumption is not available (for others regions), it is calculated
as production + imports - exports + beginning stocks - ending stocks. All quantities are
measured in metrics tons (MT).
2. Import and export prices, and prices for primary and processed products. Most of the
Import and export prices are calculated as (import/export) value / (import/export) quantity;
these data are from FAO data base. The prices data for primary and processed products are
very difficult to get; they are from different sources for different countries (see appendix C).
3. Regional milk composition. Milk from all sources (i.e., cow, buffalo, sheep, goat and camel)
is modeled as the dairy supply and demand in many regions comes from several different
sources. Milk fat, protein and other solids data are obtained from a variety of sources
including OECD. Data of composition of non-cow milk are obtained from Wong et al. (1988)
or Webb et al. (1974).
4. Processed dairy product composition. While the current model allows farm level milk to
vary in composition, the composition of dairy products except for the residual (RES) and
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butter (regions differ in the combination of butter and ghee) are fixed at U.S. standards of
identity following Selinsky et al. (1992), Wong et al. (1988) and the Australian Dairy
Corporation.
5. Regional wholesale sector value-added matrix (farm-wholesale processing and
distribution costs). Quality information on these crucial data is scanty. The results presented
here use the processing cost data based on a survey by the Boston Consulting Group prepared
for the Dairy Research and Development Corporation and the Australian Dairy Industry
Council (1993). Several studies by U.S. researchers also provide limited information about the
manufacturing costs of making cheese, butter, milk powder and fluid milk (e.g., Hughes, 1976;
Ling and Schwenk, 1993). Sensitivity analysis regarding the possibility of using flat
processing cost across regions indicate that while aggregate price, consumption changes, and
import/export volume are fairly robust, regional production and the interregional trade flows
are clearly sensitive to the specification of these costs (Zhu, 1999).
6 . Interregional transportation costs. Distances between regions (ports) are obtained from
Defense Mapping Agency data. In the absence of better information, we use flat
transportation costs when calibrating the 2002-04 base: $0.018/MT/Nautical mile (one
nautical mile is approximately 1.15 miles) for non-refrigerated products (WMP, SMP, casein,
evaporated and condensed Milk, and dry whey); $0.027/MT/Nautical mile for refrigerated
products (cheese and butter), and a very high rate for fresh milk products (results in no fluid
milk trade).
7. Regional supply and demand elasticities. The model generates linear regional supply and
demand curves using these elasticities and base level prices and quantities. Most of the
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102
elasticities are from the FAPRI database, or derived from the USDA SWOPSIM (Roningen,
etal.).
8 . Regional income elasticities. Data come from the FAPRI database and the USDA SWOPSIM
for major countries. For other countries, we compute income elasticities under the assumption
that countries having similar development status should have similar demand characteristics.
9. Population and GDP growth rates. Annual population and GDP growth rates come from the
World Bank reports (or the IMF database). Not only does the World Bank provide the
historical data on GDP growth rate for each country, but it also projects the growth rate for
several years ahead. For population growth rates in the future, we use the most recent 5-year
moving average as the forecast.
10. Regional trade distortions. These data (regional export subsidies, import tariffs and quotas,
etc.) are obtained from URA of GATT/WTO (International Dairy Arrangement, 1994). For
non-WTO members, U.S. Dairy Export Council and APEC database provides tariff and
import quota information for some countries.
Although the production and trade data for residual products category (RES) can be obtained
from the FAO database, the actual RES data used in the model is calculated from equation
(3.11a). That is, assuming non waste of milk components, the total availability of fat, casein,
whey protein, and other milk solids from raw milk is balanced with component utilization
associated with wholesale dairy production for all products except the residual category. This
residual is then converted into total solids (40% fat and 60% SNF) milk equivalent basis. Thus,
the regional base level starting values satisfy a supply/demand component balance, as do the
resulting simulation solutions via equation (3.11a).
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The model is solved using the General Algebraic Modeling System (GAMS), a popular
software to solve large and complex mathematical optimization problems. A nice feature of
GAMS is the flexibility that users can choose from a number of different optimization solvers
that embody different algorithms depending on their particular needs. Currently, there are three
families of NLP algorithms available, CONOPT, MINOS and SNOPT. CONOPT is available in
three versions, the old CONOPT1 and CONOPT2 and the new CONOPT3. All algorithms
attempt to find a local optimum. The algorithms in CONOPT, MINOS, and SNOPT are all based
on fairly different mathematical algorithms, and they behave differently on most models. This
means that while CONOPT is superior for some models, MINOS or SNOPT will be superior for
others.
The algorithm used in CONOPT is based on the GRG algorithm first suggested by Abadie
and Carpentier (1969). It is designed for models with smooth functions, but it can also be applied
to models that do not have differentiable functions. It also designed for large and sparse models.
This means that both the number of variables and equations can be large. It has a preprocessing
step in which recursive equations and variables are solved and removed from the model.
Therefore, it has a fast method for finding a first feasible solution that is particularly well suited
for models with few degrees of freedom.
MINOS (Murtagh and Saunders, 1977) is a specially adapted version of the solver that is
used for solving linear and nonlinear programming problems in a GAMS environment. It is
designed for large-scale nonlinear optimization problems. The nonlinear functions in a problem
must be smooth (i.e., their first derivatives must exist). It solves such problems using a reduced-
gradient algorithm combined with a quasi-Newton algorithm.
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For both linearly and nonlinearly constrained problems, SNOPT applies a sparse sequential
quadratic programming (SQP) method, using limited-memory quasi-Newton approximations to
the Hessian of the Lagrangian. It is designed for large-scale constrained optimization problems.
In general, SNOPT requires fewer evaluations of the functions than the nonlinear algorithms in
MINOS. It is suitable for nonlinear problems with thousands of constraints and variables, but not
thousands of degrees of freedom.
There are a lot of options for each solver, the options are designed to save solving time and
improve the final solutions. Choosing of different solvers depends on the problem to be solved
and the solving time. However, if one problem can be solved by two or more solvers, the
solutions should be very close to each other. In this study, we use MINOS to solve the model.
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Chapter 4 An Analysis of Market Power
Chapter 1 and 2 revealed that most of Asian dairy product imports are from New Zealand and
Australia. For example, during 1996-1998, Oceania accounts for 45%, 75% and 52% of China’s
imports of concentrated milk, butter and dairy spread, and cheese and curd, respectively. Oceania
accounts for 66.2% and 81.2% of Japan’s natural cheese and butter imports in 2001. As well,
chapter 1 and 2 observed that world dairy products trade markets are dominated by Australia,
New Zealand and EU. For example, in 2003, the above three regions account for 65.6% of world
total milk equivalent exports. More specifically, they account for 80%, 66% and 55% of world
butter, cheese and SMP exports. Therefore, oligopolistic competition may be more appropriate to
explain world dairy trade. On the one hand, exporters may use Fonterra (in New Zealand) or
large dairy companies (such as Murray Goulbum Cooperatives and Bonlac in Australia) to create
market power. On the other hand, Asian countries may use tariff rate quota or other domestic
market distortions as means to create market power.
One spatial price arbitrage condition derived under imperfect competition in chapter 3 is
Pj =P]i +bj (l + rij)tij+ d i(\ + RiJ)tij (equation (3.13g")). Recall thatP*is the importing country’s
demand price, P" is the importing cost of country j from country i, ttJ is country j ’s import of
d Y X . , IJ dX--
processed product from country i, Ry = —J*'J----------- is the conjectural variation (CV) for dXy dXy
X,J importing country j, ry = ——---- - is the exporting country i’s conjectural variation. If both Ry
dXy
and ry equal -1, then exporter i and importer j would be acting as perfect competitors as in the
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106
Takayama and Judge model. If the exporter and importer’s conjectural variations are not
simultaneously equal to -1, then both markets are imperfectly competitive. The term
dl (1 + Ry )Xtj is the price mark-up by importing country j , and the term bj (1 + rtj )Xy is the price
mark-up by exporting country i. Therefore, if the country to country trade flow data is available,
equation (3.13g") can be estimated econometrically to see whether or not there exists market
power for the related products trade. The econometric model would be
Pj =C + P” + by (1 + ry )ty + d, (1 + Ry )ty + e , where C is constant and e is the error term. If the
country to country trade flow data is not available, then
p i = Pj+bj(l+rj) ^ ~ — + d(l + ^ /) ^ ^ — (equation (3.16’)) is demonstrated to be an Mj Mj
alternative representation of equation (3.13g"). Hence, the existence of market power could also
be tested by evaluating the econometric model
I <« £ p ‘ = C + PJ+f t / l + 0 ) ^ - + d(l +
The importing cost of country j from country i ( P” ) or the average importing cost of country
j from major exporters (Pj) is needed for econometric estimation. But only the simple average
importing cost of country j from the rest of the world ( P” ) is available from secondary data.
Therefore, there could be some measurement error bias if country j ’s average importing cost is
used directly. To be more specific, for equation (3.13g"), chapter 3 illustrated
that Pj” = yi + Cy +7ty-8 y +0jj*j • That is, P" is the sum of exporting country’s domestic
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107
consumption price ( y, ), country to country transportation cost (ci}), import tariffs ( ), quota
rents ( BJMJ), and minus export subsidy ( S y). But PJ1 is calculated by the importing country’s
total import value divided by total import quantity. Therefore, P" is a proxy of P" . There is a
clear difference between these two. If P" is correlated with the error term, then there exists
measurement error bias and consequently may cause serious problems for the estimates.
However, the problem of measurement error bias can be solved by instrumental variables. That is,
one can simulate country j ’s importing cost from country i or major exporters by using some
instrumental variables which are correlated with P" / P} but orthogonal to the error term, and use
Generalized Method of Moment (GMM) to estimate the parameters. In this chapter, we will first
briefly introduce the ideas of measurement error bias, instrumental variables, GMM estimation
and the associated over-identification tests, and then the estimation results and discussions. We
follow Bruce E. Hansen for the introduction of measurement error bias, instrumental variables,
GMM estimation and the associated over-identification tests.
4.1 Measurement error bias
Suppose that (y i, z * ) are joint random variables, where yi is 1 * 1 and Zj* is k x 1, E (y i | Z j * )
= Zj* p is linear, p is the parameter of interest with dimensions k x 1, and Zj* is not observed.
Instead we observe zt = z* + Sj where <5, is a k * 1 measurement error ( E(St) = 0 ), independent
of yi and Zi*. Then
yi =z]'p + ei
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108
= ( z - 8 i)'l3 + ei
(4.1)
where
The problem is that since
(4.2)
there is endogeneity1 between z, and tj, , if P ^ 0 and Eidfi]) * 0. It follows that if /? is the
Ordinary Least Squares (OLS) estimator, then
This is called measurement error bias. In this case, the OLS estimator is not a consistent
estimator of p. An alternative estimation method is needed to solve this kind of problem, such as
using instrumental variables.
4.2 Instrumental Variables (IV)
Let the equation of interest be
where z, is k x 1, and assume that Efaej) ^ 0 so that there is endogeneity. Equation (4.4) is called
the structural equation. In matrix notation, this can be written as
P fiT = p - ( E ( z izi))E(SiS'i) P * p . (4.3)
yi =z]p+ei (4.4)
Y = Zp + e (4.5)
where Y is n x 1, Z is n x k, p is k x 1.
We say that there is endogeneity in the linear model y(. = ztP + e( if p is the parameter of interest and E^eO 4- 0.
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Definition The ix l random vector xt is an instrumental variable for (4.4) if E (xjeO = 0.
In a typical set-up, some regressors in z, will be uncorrelated with ei (for example, at least the
intercept). Thus we make the partition
z,= V
(4.6)zu \ zvJ
where zn is ki x 1, Z2i is kj x 1, E(znej) = 0 yet Efajed i10. z\\ is exogenous in the sense that it
is orthogonal to the error term, and zi\ is endogenous in the sense that it is correlated with the
error term. By the above definition, z;, is an instrumental variable for (4.4), so should be included
in Xj. So one has the partition
x, = (7 ̂z li
v*2,y (4.7)
where zu = xu are the included exogenous variables, and (I2 x 1) are the excluded exogenous
variables. All the / variables in Xj are predetermined in the sense that they are all orthogonal to
the current error term, zn is also called the predetermined regressor, and z* is called the
endogenous regressor. With instrumental variables, the structural form is
yi =zifi + ei
£(x(.e,.) = 0. (4.4’)
4.3 Reduced Form
The reduced form relationship between the variables or “regressors” z1 and the instruments x,
is found by linear projection. Let
T = E^x])-'EiX'Z,)
be the / x k matrix of coefficients from a projection of z\ on xj, and define
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110
u, = z ,-x ,r
as the projection error. Then the reduced form linear relationship between Zi and Xj is
z, =Y'x,+ui . (4.8)
In matrix notation, (4.8) can be written as
Z = XT + u (4.9)
where u is n x k.
By construction,
E{xiui) = 0 ,
so (4,8) is a projection and can be estimated by OLS:
A A
Z = X Y+ u
A
Y = ( x ' x y \ x ' z ) .
Substituting (4.9) into (4.5), find
Y = (XY + u)/3 + e
= XA + v , (4.10)
where
JL = Yfi (4.11)
and
v = ufi + e .
Observe that E{x,vt) = E{xiul)fi + E(xfa) = 0. Thus (4.10) is a projection equation and may be
estimated by OLS. This is
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I l l
A A A
Y = XX+v
A
X = ( X X y ' ( X Y ) .
The equation (4.10) is the reduced form representation of the structural form (4.4’).
4.4 Identification
The structural parameter P relates to (X, T) through (4.11). The parameter P is identified,
meaning that it can be recovered from the reduced form. The rank condition for identification
is that the / x k matrix E{xizi) is of full column rank (i.e., its rank equal k, the number of its
columns), or
rank(T) = k. (4.12)
Assume that (4.12) holds. If / = k, then p = T~xX , If I > k, then for any W > 0,
P = (T W r y lT W X . If (4.12) is not satisfied, then P cannot be recovered from (X , T). This is the
case of identification failure. In this case, there is no method to get consistent estimate of P. The
parameters ( X ) in the reduced form are always identified; however, identification failure can
happen to P . A necessary but not sufficient condition to avoid identification failure is the order
condition for identification:
/ (= #predetermined variables) > k (= # regressors).
The model is just-identified if the rank condition is satisfied and I = k (h = h), over-identified if
the rank condition is satisfied and / = k(l2 > kp, and under-identified (or not identified) if the
order condition is not satisfied (i.e., if I < k).
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4.5 GMM Estimation
The model can be written as
yi =zip ^ e i
E(xA ) = 0 (4.13)
or
Eg(wi,p ) = 0
g(wi,J3) = xi(yi -z]fi) (4.13’)
where the dimension of z\ and Xj are k * 1 and / x 1. This is a moment condition model.
Define the sample analog of (4.13’)
g.(0) = - £ g , W = -z,P) = -(XY-XZfi) n m n
The method of moments estimator for (3 is defined as the parameter value which sets
g„(P)= 0»but this is generally not possible when I > k. The idea of the generalized method of
moments (GMM) is to define an estimator which sets g„(P) “close” to zero.
For some I x l weight matrix W„ > 0, let
This is a non-negative measure of the “length” of the vector gn(P) . For example, if W„ = I, then,
= n • £„(/?) g„{P) = n' I g„(P) |2»the square of the Euclidean length. The GMM estimator
A A
)8gmm minimizes Jn ( fi) , that is, P GMM =argmin/„(/?). So, P
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p GMMH z ' x w nx ' z r z ' x w nx 'Y .
While the estimator depends on W„, the dependence is only up to a scale factor, for if Wn is
A
replaced by cWn for some c > 0, PGMM does not change.
Let Wbe I x k and Wn ->p W > 0 . Let Q = E{xizi) and Q = E ix^e f) = E{gjgi) . Then it can
A
be shown that >I^(PGMM- P ) - ^ d N(0, V) , where V = (Q'WQ)~l (QWQ WQXQWQT'■ W0 is the
optimal weight matrix in the sense that it minimizes V. This turns out to be W0 = f i_1. This yields
the efficient GMM estimator: Pgmm = (Z'XQ-'Z'Z)-1 Z'XQT'X Y and
■ f r i i m c e ' f i - ' a r ' ) ■
WQ = Q_1 is not known in practice, but it can be estimated consistently. For any Wn -+p W0,
A A
Pgmm is the efficient GMM estimator, as it has the same asymptotic distribution as P which is
estimated by using W0 = Q"‘.
A
Given any weight matrix W„ > 0, the GMM estimator PGMM is consistent yet inefficient (in
the sense of not achieving the smallest possible mean-squared error among feasible estimators).
A
However, a consistent and efficient PGMM can be obtained by first using the Two-Stage-Least
Squares (2SLS) to get the weight matrix W„. The steps are:
A A a -
(1) regress Z on X, in equation (4.9), T = (X 'X y '(X Z) and Z = X T
A a a * a A '
(2) regress Y on Z , p 2SLS = (Z Z )'1 Z Y = (Z’X (X X)~l X Z)~l Z X ( X X)~l X Y .
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114
A A
(3) calculate e = Y - Z P 1SLS •
A A
(4) calculate gt = x, e,
(5) calculate Wn = ( - £ g , g, )"'
By Central Limit Theory, W„ = ( - £ g , g, )_1 £(& & )'' = Q '1 = PF0- Therefore, p GMM is n i=i
asymptotically efficient.
4.6 Over-Identification Test
Overidentified models (/ > k) are special in the sense that there may not be a parameter value
0 such that the moment condition
Eg(Wi,P) = 0
holds. Thus the model — the overidentifying restrictions — are testable. This is equivalent to
testing the specification of the model and/or the validity of the instrumental variables. For
example, one can specify the linear model yx = 0xzu + yd2z2i + e(., where zn is endogenous and
is exogenous. There are two sets of instrumental variables, i.e., xn, and xn and x^. If xu is chosen
as an instrumental variable, then it is assumed that E{xXiej) = 0 . If xu and X2 i are chosen as
instrumental variables, then it is assumed that £(x1(.e(.) = 0 and E(x2iei) = 0. However, from
given information, the true model is yx = 0xzxj + ex with /?2 = 0. It is possible that the
estimated = 0 , so that the linear equation may still be written as yx = 0xzxj + e,. In this case, the
orthorgonality condition E(xue,) = 0 and E{xljei) = 0 still hold. However, it is possible that the
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115
estimated 0, and in this case E(xu (y, - zXiPx - z2ip2)) = 0 and E(x2l (yx - zXiPx - zvP2)) = 0
hold simultaneously. But it would be impossible to find a value of Px so that both
E(xXi(yj - zxifix)) = 0 and E{x2j{yi - zXiPx)) = 0 hold simultaneously. In other words, the assumed
orthogonality between instrumental variables and error term is not true. In this sense an
exclusion restriction can be seen as an overidentifying restriction.
Note that g„ -> Egt , and thus g„ can be used to assess whether or not the hypothesis that
Egi = 0 is true. The criterion function at the parameter estimates is
which is a quadratic form in gn and is thus a natural test statistic for Ho: Egt = 0. If the equation
is just identified, then it is possible to choose P so that all the elements of the sample moments
g„ are zero and the distance J is zero. If the equation is overidentified, then the distance cannot
be set zero exactly, but one would expect the minimized distance to be close to zero. It turns out
that, if the weight matrix W„ is chosen optimally so that p\\mWn = E ig & y 1 = Q"1 = W0, then
the minimized distance is asymptotically distributed as chi-squared. The degrees of freedom of
the asymptotic distribution are the number of overidentifying restrictions. Formally,
If the statistic J exceeds the chi-square critical value, the model can be rejected. This means
that either the orthogonality conditions or the other assumption (or both) are likely to be false.
Generally, if the error terms are independent of each other, the lagged variables of the
(4.14)
J=J(P)->dxlk (4.15)
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116
endogenous regressors are good choices for instruments. If die error terms are correlated with
each other, things become more complicated. Theoretically, variables with any lags are
correlated with the error term. But as the correlation diminishes when the number of lags
increases, the more the number of lags the higher the probability that the correlation may be
insignificant2. In this case, a lagged two period variable may turn out to be one valid candidate
instrument. The GMM overidentification test is a very useful by-product of the GMM
methodology, and it is advisable to report the statistic J whenever GMM is the estimation method.
4.7 Results
An intercept term is added to equation (3.13g") and (3.16') to represent the effects from other
factors. The constant is orthogonal to the error term, bj (and d,) are calculated by b=p/(q s),
where p is the importing/exporting country’s consumption price, q is the importing/exporting
country’s domestic consumption and s is the demand elasticities (see table 4.5). As e is obtained
from other databases, bj / dt are calculated exogenously for given p and q. In addition, ty are
given exogenously. Hence, bjty and djy should be orthogonal to the error term. So we can use
the other explanatory variables except for P" (i.e., constant, b f9 anddfy) as the included
exogenous instrumental variables. The excluded exogenous instrumental variables should be
orthogonal to the error term and correlated with the related regressor. As P" or Pj includes the
Transportation Cost, Insurance and Freight price (CIF), to simulate them we choose crude oil
2 For example, let y, = a + fiyt_x + e, and e, = pe,_x + u, , where E(e,) = E(u,) = 0 , and u, are i.i.d.
E{y,_xe,) — E((a + /3yt_2 + e,_, )(pet_x + ut)) = per1, where E(et_xet_x) = cr2. Similarly, it can be shown
that E(y,_2et ) = p 2a 2 .As 0 < p < 1, then p 2a 2 < p a 1. Although it is still subject to formal test to see
whether or not /02cr2 significantly different from zero, the likelihood is smaller than that of p a 2.
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117
price, exporting countries’ domestic production / exporting countries’ total exports to the rest of
the world, and P" 2 as the candidate excluded exogenous instrumental variables.
P" is chosen based on the reasons discussed above (a formal test of serial correlation is
not conducted in this research, see section 4.7 for more details). Crude oil price is determined by
macro economic development and can be expected to be uncorrelated with the error term.
Exporting country’s dairy product production is determined by domestic consumption and export
demand. If exports account for most of internal production, it may affect the importing country’s
dairy product price and, hence be correlated with the error term. However, as exports are the
exporting country’s total exports to the rest of the world, if the country to country exports
account for only a small part of the total exports, it still can be deemed as uncorrelated with the
error term. For example, although New Zealand’s exports of butter and dairy spread account for
50% of China’s imports, it only accounts for 3.9% of New Zealand’s total exports. Given this
small percentage, it is unlikely that New Zealand’s exports and production of butter and dairy
spread are correlated with the price in China. Therefore, it is assumed that the exporting
country’s domestic production and exports are uncorrelated with the error term. Crude oil price
reflects transportation costs, while domestic production or total exports reflect the Free on Board
price (FOB). That is, if the crude oil price increases then transportation cost should increase, and
if the domestic production / total exports increase it will consequently decrease domestic
consumption price and FOB price.
The country to country trade flow data are very difficult to get; it is available only for some
of China and Japan’s dairy product imports. Therefore, we can only estimate equation (3.13g")
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118
for the imports of China’s butter, cheese and SMP, and Japan’s cheese imports. We estimate
equation (3.16') for the dairy product imports of other countries.
The annual data is from 1980 to 2003. The Gauss software is used for the estimation and the
GMM estimates are shown in table 4.1-4.3. The signs of most conjectural variation (CV)
estimates are as expected. The residual plot is checked for each fit and the Normal distribution
assumption seems to be appropriate. The instrumental variables are chosen depending on over
identification tests. For example, for Japan’s cheese imports from New Zealand, if only P" t_2
and New Zealand’s cheese production are used as excluded exogenous instrumental variables,
then J = 6.03 and p-value is 1.4%. This suggests there are some problems with the validity of the
instruments. Although we assumed P" t and New Zealand’s cheese production are orthogonal
to the error term, they may turn out to be weakly correlated with it. If we add New Zealand’s
total exports as another instrumental variable, then J = 4.57 and p-value is 10.2%. Hence, the
correlation effects may be diluted by this additional variable. Anyway, two excluded exogenous
instrumental variables are enough for most models ( 1=5), which means the degree of freedom
for most x 1 tests are one. For some models, we need three excluded exogenous instrumental
variables. All over-identification tests are not significant at the 5% level, which indicates the
orthogonality condition holds and the instrumental variables are appropriate at this level.
Butter Imports
The economic results for Asia’s butter trade (table 4.1) indicate that the imperfect
competition model is more appropriate to explain China and Japan’s butter import. Specifically,
three conclusions can be drawn from these results.
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First, for China’s butter imports, New Zealand acts as an imperfect competitive exporter and
EU acts as a weakly imperfect competitive exporter while China acts as an imperfect competitive
importer for its butter imports from New Zealand and Australia. Moreover, oligopolistic
competition may be more appropriate to explain China’s butter imports. New Zealand acting as
an imperfect competitive exporter in China’s butter import market is because the estimate of ri}
A
is -0.77 (asl + zv = 0.23), which is significantly greater than -1 at the 5% statistical level. This
means New Zealand has price mark-up in its butter exports to China. The significant Lemer
index3 (L=0.15, see table 4.6) also suggests that New Zealand has market power in China’s
A A
butter market. As Rtj = -0.13 (as 1 + Ri} =0.87) when i is New Zealand and is significantly
different from -1, China acts as an imperfect competitive importer for its butter imports from
New Zealand. China also acts as an imperfect competitive importer for its butter imports from
3 The price-cost margin is the natural measure of a market’s competitiveness. The conjectural variations theory is motivated by rewriting the Cournot first-order condition as
p(Q) + <Jj —̂ — MC, = p(Q) + Qfl, — MCj = 0 , where the term —— (= 9,) is suppose to represent oQ oqt oQ dq,
firm »’ s (refer to country here) “conjecture” about the response of total industry output to increases in its own output. If each firm / anticipates that its rival’s aggregate output is some function i?_((^() and R_, (q,) = r_,, firm f s fio.c.
is p(Q) + (1 + r , )qt - MCj = 0, where 6? = 1 + r_t . Then the Lemer Index is dQ
t P(Q)~MCj - (1 +r_,)q, dp (l + r ,) s , L, = --------------------------- = -, where s* is the market share of firm i, and E is the price
P(Q) P(Q) dQ e elasticity of demand. Varying r_(. generates the entire range of outcomes from the perfectly competitive to the monopolistic or joint-profit-maximizing outcome. In particular, a conjecture of r_(. = —1 corresponds to the
competitive model. A conjecture of r_t = 0 yields the Cournot model and = N — 1 corresponds to a model of joint maximization. The greater the difference between price and marginal cost, the larger the Lemer Index and the greater the market power.
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Australia (Ry= 0.54 when i is Australia). The Lemer index also suggests that China has market
power in its butter market (L=0.50). However, the Lemer index for China seems to be too high.
The high Lemer index is mainly contributed by the large market share (78% in 2003). However,
in reality, as China has a large number of firms currently, the market share controlled by large
firms should not be that high and consequently the Lemer Index. China acts as a price taker for
A
its butter imports from EU, but EU acts as a weakly imperfect competitor (rg = -0.22) in China’s
butter import market and the Lemer index is 0.15. This is reasonable as China’s butter imports
from New Zealand, Australia and EU account for 50%, 25% and 16% of its total butter imports,
respectively (Cheng et al, 2002). The larger trading quantity between two countries is more
likely to trigger imperfect competition.
The Hershman-Herfindahl Index4 (HHI) tells us that China’s butter imports market is highly
concentrated (HHI=3381 for these top three countries). Therefore, oligopolistic competition may
be more appropriate to explain China’s butter imports. China uses its market as a means to exert
market power (China’s consumption of butter is only next to India in Asia, but it imports much
more butter than India). New Zealand and EU also use their dominance in the world butter
market to exert market power (as mentioned in chapter 2, these two economies account for 40%
of world butter exports).
Second, Japan acts as a price taker for its butter imports, but its exporters (New Zealand,
Australia and EU) act as imperfect competitors in its butter import market. This is because
4 Hirshman-Herfindahl Index (HHI) provides a measure of market concentration. HHI = £j=i"(Sj)2, Si is the market share of firm i, generally computed with the share of the first four largest firms (refer to countries here). If HHI < 1000, then the market is classified as unconcentrated; if 1000<= Ihh <=1800, then the market is classified as moderately concentrated; if IHh >1800, then the market is classified as highly concentrated.
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r. = -0.53 and significantly different from -1 at the 5% statistical level. This is reasonable as
Japan’s butter imports from the above three countries accounts for about 90% of its total butter
imports. On average, the Lemer index for each country is 0.11 and significantly different from
zero, and HHI = 2025. All these indicate that Japan’s butter imports market is highly
concentrated and market power is likely to exist for its exporters. Although Japan does not have
price mark-up for its butter imports, it may already get it from its border measures (such as tariffs
and quota rents) as Japan has relatively high effective protection rates for its butter imports.
Third, the econometric results fail to reject the hypothesis that Korea and South East Asia
(SEA) butter import markets are perfectly competitive. None of the av and J?, for Korea and SEA
are significant at the 5% statistical level. This seems to be contrary to the fact that Korea’s butter
imports are highly restricted by import quota and other border measures. One explanation is that,
unlike Japan where import quotas are allocated to producer organizations of mixed feed or sellers,
Korea’s import quotas are auctioned (see chapter 2.2.1). In this way, the government gets the
quota rents instead of firms. This may be also because Korea and SEA’s butter imports are more
diversified.
Cheese Imports
The CV estimates of Asia’s cheese imports (table 4.2) reveal that only Japan’s cheese import
markets are imperfectly competitive. In particular, three conclusions can be drawn from these
results.
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First, China’s cheese import market is perfectly competitive. None of the Rtj are
significantly different from -1 at the 5% statistical level, which indicate China is a price taker for
A
its cheese imports. None of the ri} are significantly different from -1 at the 5% statistical level,
which suggests there is no price mark-up for cheese exports to China by exporters. Compared to
China’s butter imports, there are two possible explanations to this. First, China’s cheese imports
are more diversified. China’s cheese imports from New Zealand, Australia and EU account for
30%, 22% and 16% of its total cheese imports, respectively (Cheng et al, 2002). This is lower
than that of butter imports. Second, China’s cheese imports are smaller in quantity than its butter
imports. In 2003, China’s cheese imports were 18,186 MT while its butter imports were 28,197
MT.
Second, Japan acts as an imperfect competitive importer for its cheese imports from New
Zealand while EU acts as an imperfect competitive exporter. New Zealand does not mark up its
price for cheese exports to Japan, but Japan gets some market rent from its cheese imports from
A
New Zealand ( Ry = -0.5 ). Australia is the leading cheese exporter to Japan (the market share is
about 33%), and New Zealand ranks second (the market share is about 30%). This may be
because, under Japan’s TRQ administration system for cheese imports, the Japanese cheese
market is a so called “designated market” for New Zealand (Campo and Beghin, 2005). Fonterra,
New Zealand’s largest dairy cooperative, has exclusive rights to export cheese to Japan. In 2001,
New Zealand’s share of natural cheese imports under the TRQ was 43.9 percent, larger than that
of Australia (39.1 percent) (Campo and Beghin, 2005).
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123
While Japan acts as price taker for its cheese imports from EU, EU marks up its price for
A
cheese exports to Japan (r& = -0.98). EU ranks as the third largest cheese exporter to Japan (the
market share is about 25%). Its Lemer index (L = 0.02) is also significantly different from zero.
While the market power for Australia and New Zealand are not statistically significant, the
significance of EU’s market power may be caused by product differentiation. As we mentioned
in chapter 2, Japan distinguishes broadly between natural and processed cheeses. Natural cheese
includes soft cheeses (i.e. Camember or Mozzarella), semi hard (i.e. Gouda), hard (i.e. Emmental
and Gruyere) and extra hard cheeses (i.e. Parmesan). Currently, most of Japan’s cheese imports
consists of natural cheese which are mainly exported by Australia and New Zealand (accounting
for 66.2 percent of the market share). EU may distinguish itself from Australia and New Zealand
by exporting high valued cheese products.
Third, Korea and South East Asia (SEA) cheese import markets are perfectly competitive.
None of the rv and /?, for Korea and SEA are significant at the 5% statistical level. Similar to '\
butter imports, this may also be explained by the fact that Korea’s cheese import quotas are
auctioned and government collects the quota rents instead of firms. This may also suggest that
Korea and SEA’s cheese imports are more diversified than China or Japan.
SMP Imports
Table 4.3 indicates that the imperfect competition model is more appropriate to explain
China, Japan and SEA’s skim milk powder (SMP) imports, and three conclusions can be drawn
from these results.
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124
First, for China’s SMP imports, Australia and New Zealand act as imperfect competitive
exporters while China acts as an imperfect competitive importer for its SMP imports from New
Zealand and EU (with weak evidence). New Zealand acts as an imperfect competitive exporter in
A
China’s SMP import market (ry = -0.8, which is significantly greater than -1 at the 5% statistical
level). China also acts as an imperfect competitive importer for its SMP imports from New
A
Zealand ( Ry = -0.8). China acts as a price taker for its SMP imports from Australia, but
A
Australia has price mark-up for its SMP exports to China (rjJ = -0.78). From a statistical point of
view, imperfectly competitive behavior is more apparent for China’s SMP imports from New
Zealand than from Australia (p=0.008 for New Zealand versus p=0.02 for Australia). This makes
sense as 34% of China’s SMP imports are from New Zealand while only 11% are from Australia
(Cheng et al, 2002). There is weak evidence to show that China gets market rent for its SMP
imports from EU (Ry = -0.06) while the EU acts as a perfect competitive exporter. The
statistically significant Lemer index for China, New Zealand and Australia are 0.18,0.10 and
0.04, respectively. This indicates that all these countries have market power in China’s SMP
market. The Hershman-Herfindahl Index (HHI = 1806 for New Zealand, Australia and EU) also
indicates that China’s SMP imports market is highly concentrated, and oligopolistic competition
is more appropriate to explain market behavior.
Second, there is strong evidence to show that SEA’s SMP import market is imperfectly
A
competitive. As Ry = -0.8 (and significantly different from -1), SEA gets market rent from its
SMP imports from the rest of the world (the Lemer index is 0.12). As we already mentioned in
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125
section 2.3.5, SEA has no SMP production and depends on imports to meet its demand. This may
give SEA some bargaining power and market rent from its SMP imports.
Third, there is weak evidence suggesting that Japan gets some market rent from its SMP
A
imports ( Ry = 0.44). This may reflect the fact that Japan’s SMP imports are subject to TRQ
administration and managed by the Livestock Industry Promotion Corporation, a state trading
enterprise (STE). The statistically significant Lemer Index (L = 0.16) also suggests that Japan
has market power in its SMP import market. But as there is no detailed data, we can not tell
which country or which combination of countries of New Zealand, Australia, EU and U.S. act as
imperfect competitors in Japan’s SMP import market.
4.8 Robustness
In addition to the excluded exogenous instrumental variables in section 4.5, there are other
alternative excluded exogenous instrumental variables (such as exporting country’s milk cow
number, milk production and milk utilization, etc). One natural question is whether or not the
final results depend on the instrumental variables we have chosen. In other words, does the
conclusion of imperfect competition in section 4.5 still hold if the instrumental variables change?
To see this, alternative excluded exogenous instrumental variables for each model were
evaluated. Table 4.4 summarizes the results of this sensitivity analysis. As the results for the
alternative excluded exogenous instrumental variables are similar to each other, we report only
one of them. Note that if only one of an exporting country’s production and export quantity
variables are included in table 4.1 - 4.3, we regard the remaining one as an alternative excluded
exogenous instrumental variable as well.
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126
Two conclusions can be drawn from table 4.4. First, if the CV estimates are significant at the
5% statistical level in section 4.5 then most of them would still be significant at the 5% Statistical
A A
level by using different instrumental variables. The estimates of Ry and ru for China’s butter and
SMP imports from New Zealand, for China’s SMP imports from Australia, for Japan’s butter
imports from the rest of the world, for Japan’s cheese imports from New Zealand, and for SEA’s
SMP imports from the rest of the world are very close to what was found in section 4.5. The
A A
Ry’s or ry ’s are still significant at the 5% statistical level if they are significant in section 4.5.
The two exceptions are Japan’s cheese imports from EU and China’s butter imports from
A A
Australia. Now the estimate of ry I Ry is weakly significant;
A A
Second, for the estimates of Ry’s or ry’s which are weakly significant in section 4.5, some
of them may become insignificant by using different instrumental variables. For example, EU no
longer has price mark-up for its butter exports to China, and China no longer gets market rent for
its SMP imports from EU. But if we change the instrumental variable from EU’s total production
to EU’s total exports or other instruments, it fails the over-identification test for EU’s butter
exports to China. But some of them are still weakly significant by using different instrumental
variables. For example, Japan’s SMP imports from the rest of the world.
4.9 Discussion
By using the GMM method, the imperfect competition model is found to be more appropriate
to explain China’s butter and skim milk powder imports, Japan’s cheese, butter and skim milk
powder imports, and South East Asia’s skim milk powder imports. However, these results are
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tentative, and are meant to be illustrative rather than definitive due to some limitations for this
study. First, these results depend on other studies for information on elasticities. To avoid
identification problems, bj (and d,) are calculated by b=p/(q e). The elasticities are from the
FAPRI database, or the USDA SWOPSIM model whenever it is not available from the FAPRI
database. Therefore, if these elasticities are wrong, this would lead to measurement errors in bj /
dt and contribute bias to the final results. Second, the country specific price information for many
dairy products is difficult to get. We get most of them from the FAPRI database. But some are
from various sources (such as OECD or FAO databases), or inferred from available information
(based on a food price index, etc). This may also lead to measurement errors in importing and
exporting country’s prices and affect our final results. We could also use instrumental variables
and the GMM method to solve the measurement errors associated with bj / dt and prices. But this
would make our estimation much more complicated. Third, as the CV estimates were found to be
reasonable, the choice of P” 2 as candidate excluded exogenous instrumental variables are not
justified by formal serial correlation tests. Formal test of serial correlation is needed for future
research.
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128
Table 4.1 Market Power of Butter
Country Estimates Excluded
Exogenous Instrumental
Variables
Over identification
test (J) Import (j) Export (i) constant pm
j bh i d h
China New
Zealand
-1133
(997)
1.46
(0.51)
0.23
(0.11)
0.87
(0.37)
pm j> 1-2
total production
J=0.41
P=52.3%
China Australia -1179
(2437)
2.11
(1.12)
-2.77
(1.84)
1.54
(0.50)
pm J< 1-2
total production
J=0.06
P=80.5%
China EU -332
(515)
1.73
(0.25)
0.78
(0.40)
-0.35
(1.75)
pm j ’ 1-2
total production
J=2.1
P=14.5%
Japan ROW 3965
(3324)
-3.38
(2.63)
0.47
(0.18)
-0.42
(0.27)
pm 1-2
ROW exports
J=2.5
P=11.4%
Korea ROW -32
(41)
1.5
(0.01)
0.24
(0.36)
0.14
(0.23)
pm J< 1-2
ROW exports
J=1.32
P=25%
SEA ROW -3267
(1348)
50
(56)
-14
(17)
-0.46
(0.48)
pm j ’ 1-2
ROW exports
J=0.21
P=64.5%
Note: (1) numbers in parenthesis are standard errors. (2) ROW is the world major butter exporters, i.e., New Zealand, Australia and EU.
A A
(3) the coefficients associated with b}tj and d fj are 1 + ri} and 1 + Ry , respectively.
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129
Table 4.2 Market Power of Cheese
Country Estimates Excluded
Exogenous Instrumental
Variables
Over
identification
test (J) Import (j) Export (i) constant pm
J bh d h
China New
Zealand
700
(1440)
1.6
(0.53)
0.61
(3.35)
0.47
(1.35)
pm J’ t-2
total production,
crude oil price
J=4.15
P=12.57%
China Australia -982
(1472)
1.69
(0.54)
-0.14
(2.8)
-0.36
(25.7)
pm J- 1-2
total production,
total exports,
crude oil price
J=5.06
P=16.7%
China EU 0.41
(3178)
1.4
(1.17)
9.2
(57.5)
-59
(250)
pm J’ 1-2
crude oil price
J=2.38
P=12.3%
Japan Aus 6
(10)
4.58
(0.03)
0.002
(0.003)
-0.0009
(0.006)
pm J< t-2
crude oil price
J=2.5
P=11.3%
Japan NZL -6416
(1084)
3.86
(2.63)
0.27
(0.26)
0.50
(0.22)
pm j . t-2
total exports, total
production
J=4.57
P=10.2%
Japan EU 42
(28)
4.47
(0-07)
0.02
(0.008)
0.08
(0.27)
pm i< t-2
total exports,
crude oil price
J=0.9
P=63.7%
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130
Continued
Country Estimates Excluded Exogenous
Instrumental
Variables
Over identification
test (J) Import (j) Export (i) constant pm
j bh d h
Japan US 3.4
(6.4)
4.6
(0.02)
0.02
(0.02)
0.57
(0.95)
pm J’ 1-2
total export
J=1.95
P=16.3%
Japan Canada -20
(36)
4.7
(0.10)
-0.05
(0.11)
-0.15
(0.24)
pm J. 1-2
total exports
J=0.5
P=47.9%
Korean ROW -3109
(2121)
-107
(150)
148
(202)
8.6
(7.2)
pm J. t-2
ROW production
crude oil price
J=0.94
P=62.5%
SEA ROW -2339
(291)
6.6
(7.0)
-3.3
(5.3)
-0.68
(1.0)
pm j . 1-2
ROW production
J=2.0
P=15.6%
Note: (1) numbers in parenthesis are standard errors. (2) ROW is the world major cheese exporters, i.e., New Zealand, Australia, EU and U.S.
A A
(3) the coefficients associated with b}ti} and djy are 1 + ru and 1 + R.j , respectively.
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131
Table 4.3 Market Power of SMP
Country Estimates Excluded Exogenous
Instrumental Variables
Over
identification test (J)
Import (j) Export (i) constant pm j
bh d h
China Australia -495
(69)
1.46
(0.03)
0.22
(0.09)
0.004
(0.01)
pm J. 1-2
total production
J=1.53
P=21.5%
China New
Zealand
-413
(53)
1.45
(0.02)
0.20
(0.07)
0.20
(0.06)
pm J> t-2
total production
J=0.98
P=32.1%
China EU 3.5
(27)
1.28
(0.01)
-0.25
(0.16)
1.94
(1.10)
pm J' t-2
total production
J=1.43
P=23.1%
China US 621
(155)
1.19
(0.05)
0.58
(111)
2.3
(6.7)
pm j . t-2
total production
J=0.06
P=80.5%
Japan ROW -8823
(2154)
8.22
(1-81)
-2.0
(2.28)
1.44
(0.78)
pm J< t-2
ROW production
J=0.11
P=74.3%
Korea ROW -1342
(608)
3.1
(0.4)
1.11
(8.24)
0.09
(3.23)
pm J- t-2
ROW production
Crude oil price
J=2.59
P=27.4%
SEA ROW -1331
(152)
-25
(21)
14
(12)
0.20
(0.06)
pm J' t-2
ROW production
J=1.9
P=17.4%
Note: (1) numbers in parenthesis are standard errors. (2) ROW is the world major SMP exporters, i.e., New Zealand, Australia, EU and U.S.
A A
(3) the coefficients associated with bjtj and dtttJ are 1+ry and 1 + RtJ, respectively.
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132
Table 4.4 Effects of Different IV on Market Power
Country Estimates Excluded Exogenous
Instrumental
Variables
Over
identification
test (J) Import (j) Export (i) constant pm
J bh d h
Butter
China New
Zealand
-1551
(858)
1.68
(0.44)
0.25
(0.12)
0.81
(0.36)
pm J< 1-2
total exports
J=0.57
P=45%
China Australia -4941
(7962)
3.97
(3.84)
-2.36
(3.46)
1.73
(0.55)
pm J. t-2
total exports
J= 0.00007
P=99%
China EU -242
(1404)
1.7
(0.66)
0.94
(0.74)
-1.09
(3.87)
pm J, t-2
total exports
J=4.6
P=3.2%
Japan ROW 64444
(5509)
-5.98
(4.65)
0.62
(0.27)
-0.52
(0.37)
pm J< t-2
ROW. production
J=2.03
P=15.3%
Cheese
Japan New
Zealand
-6835
(849)
4.66
(2.01)
0.16
(0.19)
0.62
(0.19)
pm J. t-2
total exports, total
production, crude
oil price
J=2.9
P= 16.48%
Japan EU 45
(34)
4.45
(0.09)
0.02
(0.01)
0.13
(0.35)
pm J’ 1-2
total exports, total
production
J=0.95
P=62.1%
SMP
China Australia -525
(77)
1.47
(0.03)
0.26
(0.11)
0.0001
(0.01)
pm J’ t-2
total exports
J=0.66
P=41.6%
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133
continued
Country Estimates Excluded Exogenous
Instrumental Variables
Over
identification test (J)
Import (j) Export (i) constant pm J
bh dfi}
SMP
China New
Zealand
-407
(63)
1.44
(0.05)
0.18
(0.06)
0.20
(0.05)
pm J. 1-2
total exports
J=2.56
P=ll%
China EU -75
(52)
1.31
(0.02)
0.04
(0.20)
0.12
(1.34)
pm j' 1-2
total exports
J=0.08
P=77.7%
Japan ROW -10947
(3446)
9.78
(2.64)
-3.6
(2.79)
2.09
(1.08)
pm J< 1-2
ROW exports
J=0.04
P=83.8%
SEA ROW -1253
(169)
-17
(17)
10.2
(9.5)
0.18
(0.07)
pm J’ 1-2
ROW exports
J=0.93
P=33.3%
Note: (1) numbers in parenthesis are standard errors. (2) ROW is the world major cheese exporters, i.e., New Zealand, Australia, EU and U.S.
A A
(3) the coefficients associated with bfo and dfy are 1 + /v and 1 + Ry , respectively.
Table 4.5 Demand Elasticities for Different Dairy Products
Country Butter Cheese SMP China -0.20 -0.05 -0.30 Japan -0.14 -0.12 -0.17 Korea -0.20 -0.40 -0.12 SEA -0.15 -0.24 -0.11 New Zealand -0.11 -0.76 -0.46 Australia -0.10 -0.37 -0.28 EU -0.30 -0.18 -0.24 U.S. -0.28 -0.33 -0.50
SOURCE: FAPRI database http://www.fapri.iastate.edu/tools/elasticitv.asDx USDA SWOPSIM Model (Roningen et al., 1991)
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134
Table 4.6 The Lerner Index for Different Dairy Products in Different Markets
Markets Butter Cheese SMP
China
China 0.50 0.18
New Zealand 0.15 0.10
Australia 0.04
EU 0.15
Japan
Japan 0.11 0.16 EU 0.02
ROW 0.11
SEA
SEA 0.12 Note: (1) 2003 data are used for calculation.
(2) as testing the significance of the Lerner Index is the same as testing the significance of the CV, the Lerner Index is calculated and reported only for those significant CV. The others should not be significantly different from zero.
(3) for importers, except for China’s butter imports, the simple average Of significant CVs is used for the calculation of the Lerner Index.
(4) ROW is the world major butter exporters, i.e., New Zealand, Australia and EU. (5) as China’s butter Lerner index is too high if the simple average of significant CVs is used, the
Lerner index is calculated by using the CV estimates from the econometric model with China versus the rest of the world. It is still too high. The high Lerner index is mainly contributed by the large market share (78% in 2003). However, in reality, as China has a large number of firms currently, the market share controlled by large firms should not be that high and consequently the Lerner Index.
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135
Chapter 5 the Impacts of Trade Liberalization on the Asian
Dairy Markets
5.1 Introduction
The goal of this chapter is to evaluate the impacts of trade liberalization, in particular, the
Doha round negotiations on the Asian dairy markets. To accomplish this goal, the Doha
development agenda is reviewed first and the major achievements are sketched. The Central
Doha scenario will be based on this. In addition, some variations to this Central Doha scenario
are formulated to evaluate the individual effects of different policy changes. The impacts from
different scenarios can be assessed when comparing these results to that of the Base scenario.
The Base scenario simulates the dairy situation during 2005-09. In this scenario, domestic
support, market access and export subsidy are assumed unchanged after 2005 (see section 5.3).
The latest available data is for 2004, and, to avoid undue annual fluctuations, the average data
during 2002-04 is used as the base and forecasted out to 2009. The base year model (2002-04) is
calibrated to make the endogenously generated production, consumption and prices to be very
close to the actual data. So it can be a reasonable benchmark for policy analysis. It should be
borne in mind that the information on commodity prices is incomplete. Except for raw milk, the
complete price data are only available for several major countries and major products (butter,
cheese and skim milk powder). For these unknown prices, it depends on the model to search for
a reasonable solution.
In addition to the Central Doha scenario, Doha Domestic Support, Doha Market Access and
Doha Export Subsidy scenarios are evaluated. The Doha Domestic Support scenario evaluates
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136
the effects if domestic support is reduced alone following the Doha agreement on agriculture.
Similarly, the Doha Market Access scenario and Doha Export Subsidy scenario are to evaluate
the individual effects of market access alone and export subsidy alone following the Doha
agreement on agriculture.
As 2009 is the second year after the implementation of the Doha round agreement on
agriculture (we assume it begins in 2007), the impacts on world dairy sectors may not be so
obvious. It would be attractive to see what happens to the world dairy sectors if the tentative final
commitments are implemented, i.e., the long term impacts. Therefore, four alternatives scenarios
which are similar to the above scenarios are analyzed, i.e., Central Doha Boundary scenario,
Doha Domestic Support Boundary scenario, Doha Market Access Boundary scenario and Doha
Export Subsidy Boundary scenario. The only difference in the boundary scenarios is that the
final Doha commitments are assumed to be effective.
As tariff rate quota and milk production quota system are not touched in the above scenarios,
it would be interesting to evaluate the impacts of these policies on the world dairy sectors.
Moreover, it is attractive to explore the foremost potential of individual policies reform on the
world dairy sectors. Although there may exist significant interactions among individual policies,
these simulations provide upper bound measure on the potential impacts of individual policies
reform and serve as a supporting analysis for future WTO negotiations. To achieve this, three
other scenarios are analyzed: World No Domestic Support, World No Trade Policies and World
Full Liberalization. For World No Trade Policies, there are three sub scenarios: No Tariff, No
Quota and No Export Subsidy.
To evaluate the effects of imperfect competition, results under imperfect competition and
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137
perfect competition are compared and contrasted for the Central Doha scenario, Central Doha
Boundary scenario and World Full Liberalization.
The rest of this chapter is organized as follows: section 5.2 is an introduction of the Doha
development agenda, section 5.3 describes the different scenarios, section 5.4 summarizes the
simulation results and analyzes the impacts of trade liberalization under different scenarios,
section 5.5 analyzes the effects of imperfect competition, and further discussions are developed
in section 5.6.
5.2 Doha Development Agenda
The November 2001 declaration of the Fourth Ministerial Conference in Doha, Qatar,
provides the mandate for negotiations on a range of subjects and other work, including issues
concerning the implementation of the present agreements. Agriculture is part of the single
undertaking in which virtually all the linked negotiations are supposed to end by January 1,2005.
The declaration reconfirms the long-term objective already agreed in the present WTO
Agreement: to establish a fair and market-oriented trading system through a programme of
fundamental reform. The programme encompasses strengthened rules, and specific commitments
on government support and protection for agriculture. The purpose is to correct and prevent
restrictions and distortions in world agricultural markets.
Without prejudging the outcome, member governments commit themselves to
comprehensive negotiations aimed at:
• market access: substantial reductions.
• exports subsidies: reductions of, with a view to phasing out, all forms of these.
• domestic support: substantial reductions for supports that distort trade.
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138
The declaration makes special and differential treatment for developing countries integral
throughout the negotiations, both in countries' new commitments and in any relevant new or
revised rules and disciplines. It says the outcome should be effective in practice and should
enable developing countries to meet their needs, in particular in food security and rural
development.
The ministers also take note of the non-trade concerns (such as environmental protection,
food security, rural development, etc) reflected in the negotiating proposals already submitted.
They confirm that the negotiations will take these into account, as provided for in the Agriculture
Agreement.
The negotiations were scheduled to finish in three phases: phase 1 (2000-2001), phase 2
(2001-2002) and phase 3 -- preparations for modalities and the frameworks (2002-2004). Phase
1 negotiations begin in March 2000, under Article 20 of the WTO Agriculture Agreement, and
ended in March 2001. The major results are:
• 7 meetings.
• 45 proposals from 121 countries (counting the EU as 16, i.e. the 15 countries plus the
EU as a group) or 85% of the WTO’s membership.
• 4 documents described as notes, technical submissions, discussion papers.
• Secretariat background papers.
The second phase (May 2001-Feb 2002) consists of detailed discussions on the many issues
raised in the first phase, organized by topic. The meetings are largely “informal”, meaning that
there is no official record except for chairperson’s summaries presented at the formal meetings.
Papers presented so far have not been official WTO documents. They are usually off-the-record
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“non-papers”. Despite the increased complexity, developing countries continue to participate
actively.
The third phase (March 2002-Aug 2004) is “Preparations for modalities and the
frameworks”. This stage should determine the shape of the negotiations’ final outcome. Under
the Doha Development agenda, the negotiations should have a March 31,2003 deadline to set
“modalities” or targets (including numerical targets) for achieving the objectives set out in the
Doha Ministerial Declaration: “substantial improvements in market access; reductions of, with a
view to phasing out, all forms of export subsidies; and substantial reductions in trade-distorting
domestic support”.
During the negotiations, it has been difficult to find a consensus formula for reducing
agricultural tariffs. On one side are those groups (U.S.A. and Cairns Group) that want an
ambitious outcome in terms of substantial tariff reductions, especially in tariff peaks, and
improvement in market access. On the other are those (EU, G101 and most developing countries)
wanting the flexibility to reduce tariffs modestly on sensitive products, i.e. those products often
protected by high tariffs. At the same time, a large number of developing countries want special
products related to food security, livelihood and rural development concerns to be largely exempt
from tariff reductions, while others would argue against tariff reductions that result in preference
erosion (many key products can be chosen to be exempt from tariff reductions). This divergence
in views is illustrated with respect to one proposed reduction approach - the Uruguay Round
(UR) formula. Initially in the UR, the United States and the Cairns Group felt that this formula
1 The G10 includes Switzerland, Norway, Japan, Korea, Bulgaria, Mauritius, Bulgaria, Israel, Liechtenstein,
Chinese Taipeh.
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was not ambitious enough, while others, notably the G10, found the formula was too ambitious.
The counterproposal of the United States was to use the Swiss formula, with a coefficient of 25,
to harmonize tariff levels across countries and bring all tariffs down to less than 25 percent. In
addition to the above two approaches, there are three alternative approaches: banded approach,
blended approach and tiered approach (see box 5.1).
The Fifth Ministerial Conference in Cancun, Mexico, in September 2003, was intended as a
stock-taking meeting where members would agree on how to complete the rest of the
negotiations. But the meeting was soured by discord on agricultural issues, including cotton, and
ended in deadlock on the “Singapore issues2”. Real progress on the Singapore issues and
agriculture was not evident until the early hours of 1 August 2004 with a set of decisions in the
General Council (sometimes called the July 2004 package). The original 1 January 2005 deadline
was missed. After that, members unofficially aimed to finish the negotiations by the end of 2006.
2 The 1996 Singapore Ministerial Declaration mandated the establishment of working groups to analyze issues
related to investment, competition policy, transparency in government procurement and trade facilitation.
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BOX 5.1 Alternative approaches to reducing tariffs
Negotiations related to the reduction of agricultural tariffs have focused on five main
formulae or approaches: the Uruguay Round formula, the Swiss formula, the Banded approach,
the Blended approach and the Tiered approach.
Uruguay Round formula
The Uruguay Round formula requires the negotiation of an average percentage reduction in
tariffs over a number of years with the flexibility of a smaller minimum reduction for individual
tariff lines. The formula applied is Z = C.X where X is the initial tariff rate, C is a constant
proportion of the original rate to which the tariff is reduced and Z is the resulting lower tariff rate
(end of period). The average reduction is obtained by averaging the Z’s applied to each tariff line
and not by a reduction in the average X. The combination of average and minimum reduction
figures allows countries the flexibility to vary their actual tariff reductions on individual products
Swiss formula
The Swiss formula is a harmonizing formula where a much narrower gap between high and
low tariffs is achieved with a built-in maximum tariff. It uses a single mathematical formula to
produce a narrow range of final tariff rates from a wide set of initial tariffs and a maximum final
rate, no matter how high the original tariff. A key feature is A coefficient, A that determines the
maximum final tariff rate below which all tariff rates will be reduced.
Z = AX/(A+X) where X is the initial tariff rate, A is a coefficient and maximum final tariff
rate and Z is the resulting lower tariff rate. Banded approach
The Banded approach, proposed in the Harbinson draft modalities in March 2003,
categorizes tariffs into a number of bands on the basis of their initial values. In each band, the
UR formula would be applied using different average and minimum cuts in each band. These
bands differ for developing and developed countries as shown below.
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Continued
Initial tariff level Average cut(%) Minimum cut (%)
Developed countries
Greater than 90% 60 45
15-90% 50 35
0-15% 40 25
Developing countries
Greater than 120% 40 30
60-120% 35 25
20-60% 30 20 0-20% 25 15 Special products 10 5
Blended approach
The Blended approach, as proposed in the Cancun draft framework, separates products into
three groups with tariffs for each of the groups subject to a different type of cut, namely: (1) a
Uruguay Round approach with the average and minimum cuts to be negotiated and tariff quotas
to provide market access if tariffs remain high; (2) a Swiss formula application; and (3) products falling into the third group being bound at a zero rate, in other words, duty-free. Countries would choose which tariffs were allocated to which group.
Tiered approach
The Tiered approach proposed in the August 2004 framework agreement reverts, in part, to
the strategy of the Banded approach by characterizing products according to the height of their
initial tariff. However, the Tiered approach leaves the option open for the application of any
formula approach in any of the tiers. Although both the tiers (number and width) and the
formulae (type and coefficients) remain to be negotiated, it was agreed that higher tiers would face steeper cuts.
Source: FAO Trade Policy Technical Notes on “Tariff Reduction Formulae”.
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The August 2004 Framework Agreement proposed an approach similar to Harbinson’s
(chairman of the agriculture negotiations) Banded approach but with no requirement to use the
UR formula in each tier. The August WTO Framework Agreement also provides some guidance
as to how disciplines over the use of domestic support measures might be re-shaped by the
current negotiations. The Agreement states that the final bound Total AMS will be substantially
reduced using a tiered approach, implying that countries with higher Total AMS will be required
to make greater cuts. In addition, the product-specific AMS will be capped. The Agreement also
proposed the addition of a criterion for the Blue Box, which will be extended to include both
direct payments under production-limiting programmes and direct payments that do not require
production if such payments are based on fixed and unchanging bases and yields; or livestock
payments made on a fixed and unchanging number of head; and such payments are made on 85
percent or less of a fixed and unchanging base level of production. It is also agreed that Blue Box
support will not exceed 5 percent of a country’s average total value of agricultural production
during a historical period, to be established in the negotiations. This ceiling will apply to any
actual or potential Blue Box user from the beginning of the implementation period. In cases
where a Member already has a large percentage of its trade-distorting support in the Blue Box,
some flexibility will be provided. The Framework Agreement states that Green Box criteria will
be reviewed and clarified with a view to ensuring that measures have no, or at most minimal,
trade-distorting effects or effects on production. Such a review and clarification “will need to
ensure that the basic concepts, principles and effectiveness of the Green Box remain and take due
account of non-trade concerns” (see FAO Trade Policy Technical Notes on “Domestic Support”
for more detail).
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To sum up current achievements, we follow Hertel and Winters (2005). Based on July 2004
Framework Agreement (adopted by the General Council on 1 August 2004), the key elements of
the Doha negotiations on agriculture can be summarized as in box 5.2. For import tariffs, the
Central Doha scenario uses a tiered formula with inflexion points at 15 and 90 percent for
developed countries, and marginal cuts are 45 percent for the lowest agricultural tariffs, 70
percent for tariffs in the middle range and 75 percent marginal cuts for the highest tariffs. For
developing countries, the inflexion points were placed at 20,60 and 120 percent and the marginal
cuts at 35,40, 50 and 60 percent. Also consistent with the Special and Differential Treatment
provisions in the framework, Least-Developed Countries (LDCs) are not required to undertake
any reduction commitments. For domestic support, the central Doha scenario assumes that
industrial countries with domestic support in excess of 20 percent of production cut their bound
AMS commitments by 75 percent, while others cut by 60 percent. Developing countries are
assumed to cut their AMS by 40 percent. Even with these ambitious reductions, only six WTO
members would be required to reduce actual support, based on 2001 notifications: Australia, EU,
Iceland, Norway, Thailand and USA (see table 5.1). As the other countries have minimal
domestic support to their dairy industries, the required reduction only applies to EU and USA.
The Doha Framework does envisage complete elimination of export subsidies.
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Box 5.2 Elements of the DDA Scenario Based on July Framework Agreement
Market access: use non-linear (tiered) formula (as with progressive income tax):
• For developed: marginal rates (45,70 and 75%) change at 15,90% tariffs
• For developing: marginal rates (35,40, 50,60%) change at 20,60,120%
tariffs
• LDCs: no cuts to tariffs
Aggregate Measure of Support: apply tiered formula:
• For developed: marginal rates of 60% (AMS less than 20%) and 75%
• For developing: marginal rate of 40%
• LDCs: no cuts to domestic subsidies
Export subsidies abolished
Table 5.1 Cuts in Domestic Support under a Tiered Formula with 75 Percent Cuts in High-supporting Countries
Countries Required cut in domestic support (%)
United States 28
EU 16
Iceland 1
Australia 10
Norway 18
Thailand 30 Source: Henrik and Zobbe (2C05, Tables 9.6 and A9.1).
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The Sixth Ministerial Conference in Hong Kong, China, in December 2005, reaffirmed the
Declarations and Decisions adopted at Doha, as well as the Decision adopted by the General
Council on 1 August 2004, and committed to give effect to them. It also decided to complete the
Doha Work Programme fully and to conclude the negotiations launched at Doha successfully
in 2006. From the Ministerial Declaration, the major agreements on agriculture can be
summarized in box 5.3.
Chapter 4 found that an imperfect competition model is more appropriate to explain China’s
butter and skim milk powder (SMP) imports, Japan’s cheese, butter and skim milk powder
imports, and South East Asia’s skim milk powder imports. There exist price markups for either
importer or exporter. The disciplinary effects of foreign competition on domestic markups, so-
called “pro-competitive effects,” have been a frequent topic in the theoretical trade literature
(Markusen, 1981; Hertel, 1994) and in simulation studies (Harris, 1984; Devarajan and Rodrik,
1991; Jensen and Krishna, 1996). Indeed, the latter have often asserted that these reductions in
markups are a very important source of gain from trade liberalization (Ianchovichina et al, 2000).
Ignoring pro-competitive effects when studying trade liberalization may lead to serious bias. For
example, using a short-run model of imperfect competition and trade with the assumption of
static, non-cooperative firm behavior and no entry, Ianchovichina et al (2000) found that
economists analyzing tariff cuts in heavily protected industries (such as the Australian
automotive industry) would overstate the short-run adjustment in output by 80% if the estimated
pro-competitive effects are ignored. In other words, imperfect competition can create serious
negative impacts on a heavily protected industry. In this study, in addition to studing the impacts
of Doha round agricultural negotiations on the Asian and/or world dairy markets, we also hope to
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find some evidence that further trade liberalization in world dairy sectors can reduce market
power. If so, then Doha round negotiations have double meaning. It not only increases world
aggregate welfare by encouraging world trade due to expanded market access opportunities. It
also increases aggregate welfare by the reductions in non-competitive markups.
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Box 5.3 Major Agreements on Agriculture in the Sixth Ministerial Conference
Market access
Note the progress made on ad valorem equivalents. It adopts four bands for structuring
tariff cuts, recognizing the need to agree on the relevant thresholds — including those
applicable for developing country Members. It recognizes the need to agree on treatment
of sensitive products, taking into account all the elements involved.
Domestic Support:
There will be three bands for reductions in Final Bound Total AMS and in the overall
cut in trade-distorting domestic support, with higher linear cuts in higher bands. In both
cases, the Member with the highest level of permitted support will be in the top band, the
two Members with the second and third highest levels of support will be in the middle
band and all other Members, including all developing country Members, will be in the
bottom band. In addition, developed country Members in the lower bands with high
relative levels of Final Bound Total AMS will make an additional effort in AMS
reduction.
Export subsidies
Ensure the parallel elimination of all forms of export subsidies and disciplines on all
export measures with equivalent effect to be completed by the end of 2013. This will be
achieved in a progressive and parallel manner, to be specified in the modalities, so that a
substantial part is realized by the end of the first half of the implementation period.
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5.3 Policy scenarios
Base scenario: The BASE scenario simulates the 2005-09 world dairy situations. It includes
domestic supports, tariffs, import quotas and export subsidies from the GATT/WTO. Developed
Economies are assumed to fulfill their 2000 GATT/WTO commitments, which means during the
simulation period (2005-09) their domestic support, tariffs and export subsidy will be the same
as their final AoA commitments. Developing economies are assumed to fulfill their final
commitment of AoA in 2005 and stay at this level till 2009. Domestic policies (intervention
prices, milk quotas, production subsidies, classified pricing) and trade policies (tariffs, two-tiered
import quotas, export subsidies) are formulated explicitly in the model. Regional average
production, price and trade data for 2002-04 from FAO are used as the starting point of the
model. However, FAPRI or OECD data are used wherever possible, especially for regional
prices. The CV estimates in chapter 4 for imperfect competition are formulated explicitly in the
model for butter, cheese and skim milk power.
As the latest available data is for 2004, and, to avoid undue annual fluctuations, the average
data during 2002-04 is used as the base and forecasted out to 2009. Five years is believed to be a
reasonable time period to do these forecasts. As mentioned in chapter 3, the model assumes
intermediate run (3-5 year) supply/demand response, and solves for the regional production,
consumption and trade of milk and dairy products that maximizes producer and consumer
welfare net of processing and transport costs. Demand/supply shifters are introduced to the
supply/demand functions. In this way, consumption is shifted by regional GDP/Population
growth and supply is shifted by 5 year moving average growth rates. The forecast can also be
extended to 2010 or 2015, but more assumptions on macro economy are to be made (countries’
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economic growth rate, oil prices, exchange rates, etc). These assumptions may themselves be
misleading, especially when the forecast is extended to 2015. Another reason for just forecasting
to 2009 is that our main interests are to see the impacts of tariffs, tariff rate quota and domestic
policies on the Asian and world dairy markets. After 2004, both developed and developing
countries’ implementation periods are finished, their final commitments on tariffs, tariff rate
quota and domestic support will be binding till a new agreement is reached. Once the trade and
domestic policies are unchanged, the conclusions on their impacts should be similar to each other
no matter five or ten years are studied into the future.
One difficulty in the base scenario is to model the impact of Japanese domestic support
policies. Japan provides subsidy to its milk producers through a price support program, which is
working with production quota. Unlike EU and Canada, Japan did not make an initial and final
commitment for its milk production quota during WTO negotiations. Hence, there is no formula
to predict its change. But as farmers have the right to adjust production quotas, it is deemed as
decided by market demand. Therefore, the impacts of domestic support policies are modeled
through quota rents. That is, quota rents is determined by the direct subsidy per unit of milk
production (¥8/kg), and market demand for raw milk is set to be equal to production quota.
In revision to the previous the UW-World Dairy Model (WDM) ((Zhu, et al), two domestic
policy changes are added into the model: (1) US MILC (target price/deficiency payment)
program; (2) EU CAP reform starting in 2005. EU CAP reform reduces intervention prices for
butter (-25%) from 2004 to 2007 and skim milk powder (-15%) from 2004 to 2006; limits
intervention buying of butter to 30,000 tons by 2008; moves milk quota increases scheduled
under Agenda 2000 back one year (beginning in 2006), and adds an extra 200,0001 quota for
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Greece; pays a dairy premium to dairy producers to compensate for the intervention price cuts
beginning in C Y2004, based on the milk quota per holding (reduced by the amount by which
total national quota have been increased since 1999/2000); allocates to member states an
‘additional payment’ to be paid to dairy producers according to ‘objective criteria.’ Both the
dairy premium and the supplementary member state payment are to be incorporated into the
Single Farm Payment (SFP) beginning in 2007. (A member state can opt to incorporate all or
part of the additional payment into the SFP from 2005) (USDA, 2005). We also add the US-
Australia free trade agreement (starting from 2005), and the Australia-New Zealand free trade
agreement into the model.
Another change to the previous the UW-World Dairy Model is EU expansion. In 2004, ten
new members integrated into European Union, and there are now 25 members in the EU. Among
the new members, Poland is an important player in the world dairy markets. To take this into
account, free trade between EU and Poland are allowed. But as the current EU CAP reform
information is for the original EU-15, the impacts of it on Poland are not considered.
For the purpose of calibration, the model also assumes transportation costs increase 7.8%
every year. The main reasons are the huge increase in demand for raw materials from China,
including steel, coal, scrap iron etc, and increasing oil prices.
After a calibration exercise, the BASE 2002-04 was able to replicate the actual data within 5-
10% for most regions and product categories, and provide a reasonably good representation of
the world dairy markets. As a result, the world dairy situation is forecasted out to 2009 based on
BASE 2002-04 and used as a benchmark to compare results from other simulations. Economic
distortions generated by various domestic and trade policy instruments are introduced into the
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model. Regional milk and commodity prices as well as dairy products production and
consumption, producer and consumer welfare are computed under the alternative policy
scenarios. The results are compared with the BASE scenario to assess the ceteris paribus changes
induced by the new policy context.
Central Doha scenario: To evaluate the impacts of Doha round negotiations on the Asian
dairy markets, our scenario is based on the July Framework Agreement (Box 5.2) and
agreements on agriculture during the Sixth Ministerial Conference (Box 5.3). Compared to the
Base scenario, the changes of the central Doha scenario can be summarized in Box 5.4.
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Box 5.4 Doha Development Agenda Scenario for Simulation
Market access: use non-linear (tiered) formula (as with progressive income tax):
• For developed: marginal rates (45,70 and 75%) change at 15,90% tariffs
• For developing: marginal rates (35,40, 50,60%) change at 20,60,120%
tariffs
• LDCs: no cuts to tariffs
Aggregate Measure of Support: apply tiered formula:
• For developed: marginal rates of 60% (AMS less than 20%) and 75%
• For developing: marginal rate of 40%
• LDCs: no cuts to domestic subsidies
The above principle implies that the reduction in dairy sector only applies to EU
(16%) and USA (26%).
Export subsidies: eliminated by the end of 2013. This will be achieved in a
progressive and parallel manner, to be specified in the modalities, so that a substantial part
is realized by the end of the first half of the implementation period.
Similar to the Uruguay Round agreement, except for export subsidies, the implementation
periods for developed and developing countries are assumed to be 6 and 10 years, respectively,
and both start at 2007. It is important to note that there is no agreement on the issues of tariff rate
quota and production quota in the current Doha round negotiation. They are kept at the same
levels as 2005. But if the over-quota tariffs are reduced, the import quota should be less
restrictive.
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To decompose the impacts of the Central Doha scenario, the Doha Domestic Support
scenario, Doha Market Access scenario and Doha Export Subsidy scenario are used to evaluate
the individual impacts of domestic support, market access and export subsidy.
Doha Domestic Support scenario: In this case, market access and export subsidy polices are
retained as in the BASE scenario but domestic supports are reduced following the Doha
development agenda. That is, EU and USA are supposed to reduce domestic support to then-
dairy industry by 16% and 26% at the end of 2012, respectively.
Doha Market Access scenario: In this case, domestic support and export subsidy polices are
retained as in the BASE scenario but dairy product import tariffs are reduced following the Doha
development agenda. That is, dairy product import tariffs, are cut by using a tiered formula, with
marginal cuts changing at 15 and 90 percent of bound tariff rates. The marginal cuts are 45
percent for the lowest agricultural tariffs, 70 percent for tariffs in the middle range and 75
percent marginal cuts for the highest tariffs. For developing countries, the inflexion points were
placed at 20,60 and 120 percent and the marginal cuts at 35,40,50 and 60 percent. Also
consistent with the Special and Differential Treatment provisions in the framework, Least-
Developed Countries (LDCs) are not required to undertake any reduction commitments.
Doha Export Subsidy scenario: In this case, domestic support and market access polices are
retained as in the BASE scenario but dairy product export subsidies are reduced following the
Doha development agenda. That is, all dairy products export subsidies are abolished by the end
of 2013. As the Doha development agenda requires a substantial part to be realized by the end of
the first half of the implementation period, the linear reduction modality are used. The linear
reduction modality assumes that the export subsidy is reduced linearly during the implementation
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period (see figure 5.1). Therefore, each year’s export subsidy level is moving along the straight
line.
Figure 5.1 Linear Reduction Modality for Export Subsidy
Export subsidy level
Initial
Final 0 2007 2013
As 2009 is the second year after the implementation of the Doha agreement, the impacts on
the world dairy sectors may not be so obvious. It would be interesting to see what happens to the
world dairy industry if the tentative final commitments take effect, i.e., the long term impacts.
Therefore, four alternatives which are similar to the above scenarios are analyzed, i.e., Central
Doha Boundary scenario, Doha Domestic Support Boundary scenario, Doha Market Access
Boundary scenario and Doha Export Subsidy Boundary scenario. The only difference in the
boundary scenarios is that the final commitments are assumed to be effective. That is, for
developed countries, the levels for import tariffs and domestic support are those for 2012, and the
levels for export subsidies are those for 2013 (i.e., eliminated).
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As trade policies and domestic support policies are reduced but not eliminated in the Central
Doha scenario, the impacts of trade policies and domestic support policies on world dairy sectors
may not be totally understood, especially for EU as its domestic support cut is only 16%. Three
scenarios are set to evaluate the separate impacts of trade policies, domestic support policies, and
trade and domestic support policies on the world dairy sectors, i.e., WTO 2009/World No Trade
Policies, WTO 2009/World No Domestic and WTO 2009/World Liberalization.
WTO 2009 /World No Trade Policies: In this scenario, which could be called a free dairy
trade situation, the elimination of all trade policy distortions in all regions during the study
period is modeled. All export subsidies and import TRQs (quotas, within and over quota tariffs)
are eliminated while domestic support policies are retained as in the BASE scenario. In such a
case, we would expect an increase in world dairy trade, increased marginal dairy product prices,
and considerable strain to be placed on several domestic support policies (intervention price
program costs, in particular) in the protected dairy sectors.
WTO 2009 /World No Domestic Support: In this case, trade polices are retained as in the
BASE scenario but all domestic supports in all regions are eliminated. The support measures
eliminated include: intervention/support prices for the EU (SMP), Canada (butter and SMP), the
U.S. (butter, SMP, and cheese) as well as in other countries; elimination of classified pricing in
the U.S. and Canada (modeled as a price wedge/premium for residual (fluid, soft and frozen)
products over manufactured products); and removing milk production/marketing quotas in the
EU and Canada.
WTO 2009 /Full Liberalization: The BASE scenario assumptions are held except all of the
trade and domestic support policies in all regions are eliminated.
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WTO 2009 /No Tariff /No Quota /No Export Subsidy: for each scenario, either import
tariffs, or import quotas, or export subsidies are eliminated, but the other policies are retained as
in the Base scenario.
S.4 Simulation Results
5.4.1 Base Scenario
5.4.1.1 Results
Table 5.2A and 5.2B summarize the endogenously generated solutions for prices, production
and consumption, and their deviation from the 2002-04 average data. For the prices of milk,
butter, cheese and skim milk powder, most of the model solutions are within 5% deviations to
the actual data for major regions (EU, China, Japan, India, Korea, South East Asia, Australia,
New Zealand, USA and Canada). For the prices of other dairy products, as their actual prices are
unknown/unreliable, we should not concern too much about their large percentage deviations to
the actual data. For unknown prices/unreliable prices, the input data is just serving as the starting
point. The model generated a set of prices based on the shadow value of the milk components
(component prices). As it is assumed there is no trade in unprocessed raw milk, component
prices partially reflect the availability of milk components in each region’s dairy manufacturing
sector and are determined by implicit regional demand and supply for milk nutrients, as well as
regional technological differences in dairy manufacturing. Chavas et al. highlight the hedonic
aspects of dairy industry in a spatial equilibrium framework and its implications for government
dairy policies.
Compared to price information, the data for dairy product production and consumption are
more reliable. Most solutions for dairy product consumption converge pretty well to the actual
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data (within 5%), especially for products consumption with larger quantity. For example, the
model simulation shows that New Zealand cheese consumption is 33% lower than the actual
data. But as New Zealand base level of cheese consumption is low (27 thousand MT), the
quantity difference is not that large (9 thousand MT). If Oceania countries (Australia and New
Zealand) are taken as one region (see below for reasons), the deviation of total cheese
consumption to actual data is less than 6%.
The simulation of regional production, however, is not as good. According to Zhu (1999),
this problem partially stems from the nature of mathematical programming sector models in
which “over-specification” is a common problem3. The causes of over-specification solutions
include assumptions of homogenous products, neglect of marketing and other transaction costs,
lack of consideration of risk and production capacity adjustments, etc. However, if we focus on
the production of milk, butter, cheese and skim milk powder for major regions, the solutions are
still 5-10% of the actual data. If we focus on aggregate regional results, model solutions are even
closer to actual data. For example, the model suggests New Zealand has overproduction of
cheese (+6%) and Australia does the opposite (-2%). If these two countries are treated as a single
region, namely Oceania, the overall deviation of cheese production from data is 1%. The reason
is that there are zero tariffs between these two countries under Australia-New Zealand Closer
Economic Relations trade policy and they are geographically close. Any small difference in
manufacturing costs between these two countries will lead to specialization in production. Due to
this, these two countries are analyzed as a single region in the following sections. The good news
is that most of those large percentage deviations of production solutions to actual data happen for
3 In many unsophisticated linear programming sector models, comer solutions can often prevail (McCarl and Spreen, 1998).
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dairy products with small production quantities. For example, during 2002-04, Canada’s average
whole milk powder production is 4 thousand MT, the model solution is 16 thousand MT, and the
deviation is 300%. As the non-linear program model is solved by GAMS software, the fraction
change maybe tolerated by the software. But it may still create large deviations to the data.
Overall, the world aggregated dairy products production and consumption generated by the
model are very close to the actual data except for dry whey (-17%) and casein (-13%). We do not
worry too much about this as dry whey is byproduct of cheese production even if sometimes
whey has little or even negative value, and world total production and consumption of casein are
relatively small (269 thousand MT) compared to other dairy products.
Dairy product net imports are calculated as consumption - production - stock change, where
stock change = beginning stocks - ending stocks. As stock change is kept constant when solving
the model, if the endogenously generated dairy product production and consumption are close
enough to the actual data, the deviations of dairy product net imports to data will be small
consequently (they are not shown here).
5.4.1.2 Sensitivity Analysis: perfect competition vs. imperfect competition
Assumptions about market structure and scale economies are important in determining how
large the gains from dairy trade liberalization will be. Often, the effects of perceived market
imperfections are captured in models in a broad brush fashion by inferring an assumption about
the returns to scale. In this study, dairy product production, because of its atomistic structure, is
assumed to be characterized by constant returns to scale (CRS). In addition, constant marginal
cost of trade is assumed. In this context, Cournot competition is assumed to be appropriate and
market power is captured by conjectural variation (CV). From chapter 4 revealed that imperfect
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competition is more appropriate to explain China’s butter and skim milk powder imports, Japan’s
cheese, butter and skim milk powder imports, and South East Asia’s skim milk powder imports.
The difference between perfect competition and imperfect competition is significant/weakly
significant from a statistical point of view. We want to see whether this makes any difference in
practice.
Table 5.3 summarizes the deviations of dairy product prices, production and consumption
under perfect competition from those under imperfect competition. There are three main changes.
First, switching from imperfect competition to perfect competition decrease importing countries
dairy product prices while increase exporting countries prices. China’s butter price and skim
milk powder price decline 25% and 20%, respectively. Japan’s cheese price and butter price
decline 16% and 14%, respectively. The skim milk powder price of South East Asia decline 11%.
Oceania’s prices of cheese, butter and skim milk powder increase 4%, 3% and 8%, respectively.
This is because the profits associated with dairy product trade flow under imperfect competition
are shared by both importing countries and exporting counties (see section 3.2). They are
collected by putting a market rent on each unit of trade flow. For exporting countries, this
decreases export prices (F.O.B prices) and domestic production prices consequently. For
importing countries, this increases importing costs and gives extra protection to domestic
production. Switching to perfect competition, both countries no longer collect market rent and
average world dairy product prices decrease, as do importing countries dairy product prices. But
marginal dairy product prices increase, and exporting countries dairy product prices increase
consequently. Note that there is little impact on Japan’s skim milk powder price. This may be
because the CV parameter estimate is only weakly significant for Japan’s skim milk powder
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imports (P-value is 0.08). Although EU has market power for its cheese exports from a statistical
point of view, it has little impact on its cheese price. This may be because the CV estimate is
small (0.02) and hence does not pose any impacts on its cheese exporting price, or because the
effects are dominated by the effects from other countries (Australia and New Zealand) in the
international market.
Second, the impacts on dairy product production are relatively small when compared to dairy
product prices. Switching from imperfect competition to perfect competition, Japan’s cheese
production decrease 26%, Oceania’s production of cheese, butter and skim milk powder increase
5%, 3% and 10%, respectively. This is not surprising given their relative prices changes.
However, the large price decreases for China’s butter and skim milk powder, Japan’s butter and
South East Asia’s skim milk powder do not lead to their production decrease. This suggests that
domestic production of these products is heavily protected by other measures.
Third, the impacts on dairy product consumption are small. Switching from imperfect
competition to perfect competition, South East Asia’s skim milk powder consumption increases
6%, Japan’s butter consumption increases 2%, and China’s consumption of butter and skim milk
powder increase 4% and 2%, respectively. This is consistent with their prices change. But the
percentage change is small when compared to the percentage decline in prices. One explanation
is that the domestic dairy product market is heavily protected and the change of trade flow is not
large enough to pose bigger impacts on dairy product consumption. Actually, Japan’s imports of
cheese increase 10 thousand MT, South East Asia’s skim milk powder imports increase 20
thousand MT, China’s imports of butter and skim milk powder increase 3 thousand MT and 2
thousand MT, respectively.
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There are also some spillover effects on the prices, production and consumption of other
dairy products for other regions. This is because we are solving a multilateral nonlinear
equilibrium problem, and the prices changes in the international market may lead to changes in
individual countries domestic production as dairy products are competing for the utilization of
milk components in their production. Overall, eliminating market power has little impact on
average world dairy product prices, world dairy product production and consumption. Average
world prices for butter and skim milk powder decrease 1% and 2%, respectively. World dairy
product production and consumption remain unchanged except for lactose (-1%). World total
trade volume of cheese, butter and skim milk powder slightly increase (+1.5%, +0.5%, and
+1.4%, respectively). The impacts on world welfare are also negligible; producer surplus
increases $10 million, consumer surplus increases $17 million and total welfare increase $76
million. These welfare changes are insignificant when compared to their absolute value. For
example, the base level total welfare is $1,878,504 million. This is reasonable as we are dealing
with a multi-region (21 regions) and multi-product (10 products) optimization problem while
imperfect competition only applies to six regions and three dairy products. In this way, the
effects of imperfect competition may be diluted by perfect competition. In other words, although
the CV estimates are significant from a statistical point of view, they are not big enough to
change the trade flow in the context of multilateral equilibrium. If it turns out that market power
exists for most region and dairy products, the conclusions may be different.
The impacts of imperfect competition under various degrees of trade liberalization are
studied in section 5.5.
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5.4.2 The Short Term Impacts of Doha Development Agenda
5.4.2.1 Central Doha Scenario
If current agreement on agricultural market access, domestic support and export subsidy is
implemented in 2009, its potential impacts on world dairy product prices, production and
consumption are summarized in table 5.4. The impacts on world trade volume are summarized in
table 5.8, and the impacts on producer surplus, consumer surplus, government treasury and total
welfare can be seen from figures 5.2-5.13. In addition to Asian countries, information for major
exporters is also provided. Generally speaking, producers in more protected regions (EU and
U.S.) suffer a loss and producers in low cost exporting countries (Oceania and India) gain, and
the impacts on other Asian countries are minimal.
Further trade liberalization in world dairy markets helps to decrease the average world milk
price by 2% over the BASE. The world average prices for cheese, butter, dry whey, casein,
condensed and evaporated milk, and lactose fall (-1%, -2%, -1%, -5%, -1% and -1%,
respectively), and the world average prices for whole milk powder and skim milk powder
increase 2%. World total production and consumption of milk, cheese and butter does not
change. World total production and consumption of dry whey, casein, condensed evaporated
milk and lactose increase by 1%, 2%, 1% and 1%, respectively. World total production and
consumption of whole milk powder and skim milk powder both decrease 1%4. Overall, world
producer surplus decreases $4.3 billion, consumer surplus increases $2.6 billion, government
revenues increase $2.1 billion, and total welfare increases $508 million.
4 This seems to be inconsistent with their prices increases. There are two explanations to this phenomenon. One is that 2009 is second year after the implementation of the agreement, and the effects from trade liberalization is not so obvious in the short (2 year) run. Another explanation is that there are substitution effects among dairy products as they compete for the utilization of milk components; hence, the production increase of one product may lead to the production decrease of another.
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The world net imports of cheese and skim milk powder increase 6.1% and 2.2%,
respectively. But the net imports of butter and whole milk powder decline 7.7% and 3.2%,
respectively. For cheese and skim milk powder, this means that the impacts of tariff reduction
outweigh the impacts of domestic support reduction and export subsidy reduction. For butter and
whole milk powder, the opposite situation arises (see section 5.4.3 for more detail). The net
imports of casein, on the other hand, decreases 19.9%. This decline can be attributed to the
substitution effect between cheese and casein production. These two products compete with each
other in the utilization of the milk component, casein protein. Thus, in the Central Doha scenario
context, more casein protein is more profitably utilized in cheese.
As 2009 is the second year after the implementation of the agreement, the impacts on China’s
milk price is negligible. China’s cheese and casein prices decrease by 3% and 4%, respectively.
This is because China’s cheese import tariff is relatively high (see table 2.2), its production is
disadvantaged, and there are substitution effects between casein and cheese production.
However, further trade liberalization in world dairy markets also helps to increase marginal dairy
prices, and China benefits from this. The prices for butter, whole milk powder, skim milk
powder and dry whey increase by 1%, 4%, 5% and 1%, respectively. Most of China’s dairy
products production do not change except for whole milk powder (+2%), butter (-3%) and skim
milk powder (-10%). Most of China’s dairy products consumption does not change except for
whole milk powder (-1%) and skim milk powder (-1%). Given the production and consumption
changes, it is not surprising that China’s net imports of butter and skim milk powder increase 5
thousand MT and 10 thousand MT, respectively; its net import of whole milk powder decrease
22 thousand MT. Overall, China’s net imports of dairy product decrease 8 thousand MT. The
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impacts on China’s welfare change are negligible5; producer surplus increases $21 million,
consumer surplus decrease $36 million and total welfare decrease $16 million.
The Central Doha scenario has minimal impacts on the prices of Japan’s milk, cheese, whole
milk powder, skim milk powder and lactose. This is because Japan’s domestic dairy product
production is heavily protected by high tariffs and tariff rate quotas, and small reductions in these
measures have little impact on Japan’s dairy market. Japan’s increase of net imports of dairy
product is not big (5 thousand MT). However, this liberalization scenario helps to decrease the
prices of butter, dry whey, casein and condensed evaporated milk (-1%, -1%, -3% and -1%,
respectively). Japan’s production of whole milk powder and condensed evaporated milk decrease
by 2% while the production of skim milk powder increases 1%. The production decrease of
WMP and CEM is consistent with their price decrease. The increase of SMP may be driven by
the increase of its consumption (+1%). The impacts on other dairy products production are
trivial. In addition to the consumption of SMP, the consumption of DWH and CAS also increase.
The consumption of WMP slightly decrease (1%). The welfare impacts are negligible.
India’s prices for cheese, butter, WMP and SMP increase 1-11% under the Central Doha
scenario. This suggests that India is a potential competitive exporter in the world dairy markets.
Its dairy products production expands, especially for WMP (188%) as its base production is low
(7 thousand MT). Its dairy products consumption of DWH and CAS increase 2% while the
consumption WMP and SMP decrease 2%. Thus, its net dairy product exports increase 36
thousand MT. The increase in dairy product prices leads to increased producer surplus ($76
5 As this is a non-linear optimization problem, there may be some fractional change which is tolerated by the software. But these changes should be insignificant when compared to its total welfare.
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million) and decreased consumer surplus ($83 million), but the total welfare change is negligible
($7 million).
Similar to Japan, most of Korea’s dairy product prices do not change in response to small
reductions in its heavily protective border measures. The price of casein decrease 5%. This
suggests the import of casein is less restrictive than cheese. The substitution effects between
casein and cheese lead to the increase of casein imports (2 thousand MT), and lower casein price
consequently. The substitution effects can also be seen from dairy products consumption change
(cheese consumption decrease 1% and casein consumption increase 2%, respectively). Dairy
products production change is trivial under the Central Doha scenario. The welfare impacts are
also negligible.
The milk price in South East Asia declines by 1%, and the prices of dry whey and casein
decrease by 3% and 8%, respectively. The prices of cheese, butter, WMP and SMP increase by
3%, 1%, 4% and 3%, respectively. The price increase of cheese, WMP and SMP will lower their
consumption (decrease by 2%, 1% and 1%, respectively). But the consumption of dry whey and
casein increase by 1% and 3%, respectively. As there is little change in dairy products
production, the net imports of cheese, WMP and SMP decline and the net imports of dry whey
and casein increase corresponding to their consumption changes. South East Asia’s net imports
of dairy product decrease 3 thousand MT. The price increases also lead to consumer surplus
losses of $33 million, producer surplus also slightly declines ($6 million) due to the milk price
decrease, and total welfare loss is $37 million (see figure 5.7).
The impacts of the Central Doha scenario on EU is substantial, resulting in a 9% decrease in
milk price over the Base scenario and similar decreases (1-12%) in the prices of other dairy
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products. This indicates that EU domestic dairy market is heavily protected; any small step
toward further trade liberalization can result in large decreases in dairy product prices. Due to
EU’s milk production quota system, milk production remains unchanged. But the production of
cheese and WMP decrease by 2% and 19%, respectively. The production of butter, SMP, dry
whey, CEM and lactose increase 1-4%. As the base production level of casein is low (18
thousand MT), there is large increase in its production (317%). This can also be explained by the
substitution effects between cheese and casein. All dairy products consumption increase under
Central Doha scenario. As a consequence, EU’s dairy products exports decline 317 thousand
MT. EU producers suffer a loss of $4.2 billion, consumers gain $3.9 billion, government
revenues also gains from the reduction of export subsidy, and total welfare increases $527
million.
As major world dairy product exporters, Oceania (Australia and New Zealand) gains from
the Central Doha scenario. The milk prices of Australia and New Zealand increase by 2% and
1%, respectively. Similarly, most of the dairy products prices increase for both countries. If we
take these two countries as one region, then all dairy products prices increase except for casein.
This is because trade liberalization lowers average world market dairy product prices but
increases marginal dairy product prices. Australia and New Zealand benefit from this process as
competitive dairy product exporters. The price changes in cheese and casein are passed on to
their production; Oceania cheese production increases 14% and casein production decreases
21%. This suggests cheese production is more lucrative than casein production under the Central
Doha scenario; hence, more casein is utilized in cheese production. The consumption of cheese,
WMP, SMP, DWH, CEM and lactose decrease 1-2%. This is not surprising as more dairy
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products are exported to other countries (Oceania’s dairy products exports increase 64 thousand
MT). Both Australia and New Zealand producers gain under Central Doha scenario ($49 million
and $38 million, respectively), their consumer lose ($23 million and $17 million, respectively),
and total welfare increases ($46 million and $21 million, respectively). This may explain why
Australia and New Zealand strongly support dairy products trade liberalization.
As a less heavily protected dairy sector than EU, the U.S. milk price declines 1% under the
Central Doha scenario, as does the price of casein (-3%). But the prices for other dairy products
increase 1-8%. The production of cheese decreases 1%. As dry whey is a byproduct of cheese
production, it is not surprising that its production also declines 3%. The production of whole
milk powder decreases 28%. As whole milk powder production historically has not been
important in the U.S., the overall impacts of the Central Doha scenario on the U.S. dairy sector
can be considered very small. Due to price increases, the consumption of cheese, butter, skim
milk powder, dry whey and condensed evaporated milk decline 1-4%. Unlike EU, the Central
Doha scenario helps to increase dairy product net exports of the U.S. by 46 thousand MT.
Overall, U.S. producers lose $686 million, and consumers lose $404 million due to increased
dairy product consumption prices. As government revenues gain $1.2 billion from the reduction
of export subsidy and saving of domestic support programs (MILC program and target price
support program), U.S. total welfare increases $131 million.
Except for lactose and cheese, the prices of dairy product in Canada decrease by 1-8% under
the Central Doha scenario. The production of dry whey increases 2%, and the production of
whole milk powder and condensed evaporated milk decrease 5% and 2%, respectively. Price
decreases help to increase dairy products consumption; the consumption of skim milk powder
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increase 28% and all other dairy products consumption increase 1-4% except for cheese and
lactose. The supply shortage is filled by the increase of imports (27 thousand MT). However, as
the base levels production and consumption are relatively low, the impacts on welfare are trivial.
Producers lose $54 million, consumers gain $82 million, and the total welfare increases $3
million.
In summary, the Doha development agenda helps to increase world total welfare in the dairy
sector. But in Asia, only India producers gain significantly from it and the impacts on other
Asian producers are negligible. As major exporters, Australia and New Zealand producers gain
at the cost of EU and U.S. producers. The overall changes in milk production and trade volume
are generally quite small although trade patterns change noticeably.
5.4.2.2 Decomposing Central Doha scenario: market access, domestic support and
export subsidies
In general, the effects of each policy instrument depends on the economic and policy context
where implemented, In particular, there can exist significant interactions between the various
policy instruments. To evaluate these interactions and the influence of alternative policy
proposals, the separate impacts of import tariffs, domestic support and export subsidies are
analyzed.
Tables 5.5-5.7 summarize the impacts on prices, production and consumption under the Doha
Domestic scenario, Doha Market Access scenario and Doha Export Subsidy scenario. World
average dairy product prices do not change much under the Doha domestic Support scenario. But
most dairy product prices decrease 1% under the Doha Market Access scenario, and the prices
for butter, whole milk powder and skim milk powder increase 1-2% under the Doha Export
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Subsidy scenario. This suggests that small reduction of domestic support alone can not change
world average dairy product prices, but the reduction Of import tariffs can decrease world
average dairy product prices and the reduction of export subsidy can increase world average
prices for some dairy products. This is because:
(1) small reduction of domestic support alone can bring down dairy product prices in some
heavily protected economies (EU and U.S.). Decreased prices lead to decreased production and
consequently exports. Actually, domestic support reduction alone decreases world total net
imports of dairy product, especially for cheese (-5.6%) and casein (-3.6%). But the price
decreases in domestic markets are not passed on to the world market.
(2) the reduction of export subsidy alone can increase net importing countries dairy product
prices and decrease imports consequently. All dairy products net imports decrease except for dry
whey under the Doha Export Subsidy scenario, and the size is larger than that from the reduction
of domestic support (cheese -7.7%, butter -5.0%, casein -9.7%). Note export subsidy reduction
alone can also decrease the prices of cheese, dry whey, casein, condensed evaporated milk and
lactose by 1-3%. This is because the internal supply in countries using export subsidy now
increases (some of the production destined as exports are now consumed in domestic market).
(3) the reduction of import tariffs decrease importing costs and increase trade opportunities
for low cost exporters, and as a consequence, bring down world average dairy product prices.
Domestic support reduction alone or import tariffs reduction alone do not have much impact
on world aggregate dairy product production and consumption. But export subsidy reduction
alone decreases the production of skim milk powder and whole milk powder by 1%, and
increases the production of dry whey, casein, condensed evaporated milk and lactose by 1-2%.
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This may reflect that subsidized exports are frequently used as foreign donations. On the one
hand, without export subsidy, exporters would decrease their exports and consequently domestic
production. On the other hand, without foreign donations, importers need to change then-
domestic supply to meet their demand. Tariff reduction alone or domestic support reduction
alone makes world producers lose about $1.3 billion, but export subsidy reduction alone makes
world producers lose $2.5 billion. In the case of tariff reduction, this means exporting countries’
producers gain is lower than importing countries’ producers loss; in the case of domestic support
reduction, this is because heavily protected countries’ producers suffer big losses. In the case of
export subsidy reduction, this suggests that the exporting countries’ producer losses due to
lowering subsidized production outweigh importing countries’ producers gains. Consumers gain
$1.3 billion from tariff reduction. This is not surprising as tariff reduction increase trade
opportunities and helps to bring down world average dairy product prices. For export subsidy
reduction, net importing countries’ consumers suffer a loss of $304 million when they can no
longer enjoy subsidized dairy products (their producers gain $118 million), but world total
consumer surplus increases $2.3 billion. As domestic support reduction alone can not increase
trade opportunities, it is not surprising there are little impact on consumer surplus.
Either tariff reduction alone, domestic support reduction alone or export subsidy reduction
alone have little impact on China’s milk price. Export subsidy reduction alone helps to increase
the prices of China’s butter, whole milk powder and skim milk powder by 3%, 3% and 2%,
respectively, and lower the prices of cheese and casein by 3%. Tariff reduction alone or domestic
support reduction alone has not much impact on the prices of other dairy products in China.
Therefore, the impacts on China’s dairy product prices under the Doha Central scenario are
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mainly contributed by export subsidy reduction. This is because export subsidy reduction can
increase some importing countries dairy product prices, and China finds exports more lucrative
than domestic consumption.
This can be seen from China’s dairy products consumption change; tariff reduction alone or
domestic support reduction alone have little impact on domestic consumption of dairy products,
but export subsidy reduction alone lower domestic consumption of butter and skim milk powder
by 1%. As there is not much impact on dairy product production under the three different
scenarios, the consumption decrease under the Doha Export subsidy scenario implies that some
of the production are used as exports. Actually, China’s net dairy product exports increase 5
thousand MT under the Doha Export subsidy scenario and has little change under the other
alternative scenarios. Therefore, producers gain ($16 million) from export subsidy reduction and
consumers lose ($24 million). The contributions from the other scenarios to China’s total
producer surplus and consumer surplus change under the Central Doha scenario are negligible.
Japan’s milk price does not change under any of these scenarios. Domestic support reduction
alone or import tariff reduction alone has not much impact on other dairy product prices, but
export subsidy reduction alone increases the price of skim milk power by 9%. This implies that
Japan imports some subsidized skim milk powder, export subsidy reduction lowers domestic
supply and drives up consumption price. Under the Doha Export Subsidy scenario, Japan’s skim
milk power production increases 1%, consumption decreases 2%, and imports decline 4 thousand
MT consequently. Dairy product production and consumption changes under the other scenarios
are quite small. The welfare changes under the Central Doha scenario is mainly contributed by
export subsidy reduction, but the changes are negligible under any scenario.
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India’s milk price does not change under any scenario, but the price of skim milk powder,
whole milk powder and cheese increase 7%, 3% and 2% under the Doha Export Subsidy
scenario, respectively. The price of skim milk powder also increases 3% under the Doha Market
Access scenario. This is because tariff reduction or export subsidy reduction can increase export
opportunities for India. Actually, India’s dairy products exports increase 25 thousand MT and 6
thousand MT under the Doha Export Subsidy scenario and Doha Market Access scenario,
respectively. Dairy products consumption does not change much under any of the three
scenarios. From figure 5.5, it is obvious that the welfare changes under the Central Doha
scenario come from tariff reduction and export subsidy reduction. Either tariff reduction alone or
export subsidy reduction alone can increase producer surplus and decrease consumer surplus,
and domestic support reduction alone has negligible impacts on welfare change.
Similar to Japan, Korea’s milk price does not change much under any of these scenarios, and
the prices of cheese, whole milk powder and skim milk power increase 2-3% under the Doha
Export Subsidy scenario. Import tariff reduction alone, domestic support reduction alone or
export subsidy reduction alone have little impact on Korea’s dairy product production and
consumption. This is because Korea’s dairy industry is also heavily protected by border
measures, and the size of its dairy sector is relative small even compared to Japan. Figure 5.6
shows that any of the three scenarios has little impact on Korea’s producer surplus and consumer
surplus.
Tariff reduction alone, domestic support reduction alone or export subsidy reduction alone
have little impact on South East Asia’s milk price. But export subsidy reduction alone does
increase the prices of cheese, butter, whole milk powder and skim milk powder 2-3%. As a
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consequence, these products consumption decrease 1-2% under the Doha Export Subsidy
scenario. Dairy products production does not change much under any of the three scenarios.
Therefore, export subsidy reduction alone helps to increase South East Asia’s dairy products
exports by 7 thousand MT, producer surplus slightly increases ($4 million), and consumer
surplus decreases $40 million. The welfare changes under the other scenarios are negligible.
As a heavily protected dairy sector, the impacts of tariff reduction alone, domestic support
reduction alone or export subsidy reduction alone on EU are much bigger than that on any Asian
country. In the short term, EU suffers substantially from export subsidy reduction; milk price
decreases by 7%, butter price decreases 9%, and other dairy product prices decrease by 2-6%.
This is because some of the production destined for exports is now pressured to be consumed in
the EU domestic market. Actually, EU net exports decrease 246 thousand MT under the Doha
Export Subsidy scenario. Hence, export subsidy reduction alone makes EU consumption of dairy
products increase 1-4%, consumer surplus increases $3.0 billion, and producer surplus decreases
$3.0 billion. Compared to tariff reduction alone and domestic support reduction alone, the
impacts on welfare changes under the Central Doha scenario mainly come from export subsidy
reduction. Either tariff reduction alone or domestic support reduction alone lowers EU milk price
(by 3% or 1%, respectively). Other dairy product prices also decline under either of the above
two scenarios, but the size is smaller than that under the Doha Export Subsidy scenario.
However, compared to Asian countries, the impacts on EU producer surplus and consumer
surplus are still large under the Doha Market Access scenario or Doha Domestic Support
scenario.
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Import tariff reduction alone or export subsidy reduction alone increase Oceania’s milk price
by 2%, but domestic support reduction alone has not much impact. From figure 5.9-5.10, it is
obvious that both Australia and New Zealand producers benefit from import tariff reduction and
export subsidy reduction. This means Oceania dairy production has comparative advantage in the
world dairy market. Actually, Oceania countries dairy products exports increase 59 thousand MT
and 36 thousand MT under the Doha Export Subsidy scenario and Doha Market Access scenario,
respectively. But Oceania producers do not gain from domestic support reduction alone
(producer surplus decreases slightly). This is because domestic support reduction in heavily
protected economies lowers dairy product prices in their domestic markets and may be partially
passed on to international markets. As a consequence, Oceania is pressured to lower its dairy
product prices in order to compete in international market. For consumer surplus, the opposite
situation arises. Overall, import tariff reduction and export subsidy reduction increase Oceania’s
total welfare while domestic support reduction alone decreases it.
Import tariff reduction alone or export subsidy reduction alone has not much impact on the
U.S. milk price, but domestic support reduction alone decreases the U.S. milk price by 1%. In
other words, domestic support policies have more impacts on the U.S. dairy industry than trade
policies. This is obvious from figure 5.12; producer surplus decreases $756 million and
consumer surplus increases $359 million under the Doha Domestic Support scenario, and
welfare changes under the Doha Market Access scenario and Doha Export Subsidy scenario are
negligible in comparison. About 90% of the producer surplus and consumer surplus changes
under the Central Doha scenario are contributed by domestic support reduction alone. Domestic
support reduction alone decreases U.S. cheese and skim milk powder production by 1%. Import
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tariff reduction alone increases U.S. butter and skim milk powder production by 1% and 3%,
respectively. As dairy product consumption decreases more under the Doha Market Access
scenario than under the Doha Domestic Support scenario, die U.S. gains more export
opportunities (48 thousand MT) from import tariff reduction alone.
Import tariff reduction alone or export subsidy reduction alone decreases Canada’s milk price
by 1%, with similar decreases (1-8%) in the prices of other dairy products. Canada’s dairy sector
is heavily protected by domestic support policies, but as domestic support reduction only applies
to EU and U.S. under the Central Doha scenario, there is not much impact on Canada’s milk
price under the Doha Domestic Support scenario. Overall, as the base levels of production and
consumption of Canada’s dairy product are relatively small, the impacts on producer surplus and
consumer surplus are likewise small (see figure 5.13).
In summary, trade policy reform (import tariff and export subsidy) has more impacts on
Asian countries’ dairy industry than domestic support policy reform. Oceania gains from trade
policy reform and slightly loses from domestic policies reform. Domestic support policies have
more impacts on the U.S. dairy industry than trade policies. In the short term (1-2 years), trade
policies have more impacts on the EU dairy sector than domestic support policies. Trade policy
reform has small impacts on Canada’s dairy sector. As domestic support reduction only applies
to EU and U.S. under the Central Doha scenario, domestic policies reform also has minimal
impacts on Canada’s dairy sector.
5.4.3 The Long Term Impacts of Doha Development Agenda
If the tentative final commitments on agricultural market access, domestic support and export
subsidy are implemented in 2009, the simulated impacts on world dairy product prices,
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Ill
production and consumption are summarized in table 5.9. The impacts from import tariffs
reduction alone, domestic support reduction alone and export subsidy reduction alone are
summarized in tables 5.10-5.12. The impacts on world trade volume are summarized in table
5.13, and the impacts on producer surplus, consumer surplus, government treasury and total
welfare are summarized in table 5.14. Generally speaking, similar to section 5.4.2.1, producers in
more protected regions (EU and U.S.) suffer a loss and producers in low cost exporting countries
(Oceania and India) gain. But the magnitudes are bigger here. The impacts on other countries are
still minimal.
Similar to the Central Doha scenario, the Central Doha Boundary scenario also decreases
world average dairy product prices and with a larger magnitude. Now world average milk price
declines 4% instead of 2%. The world average prices for cheese, butter, dry whey, casein,
condensed and evaporated milk fall (-4%, -2%, -3%, -10% and -2%, respectively), and the world
average prices of whole milk powder, skim milk powder and lactose increase 2-5%. As larger
cuts should lead to larger changes, these results are not surprising. The prices changes are mainly
contributed by import tariffs reduction and export subsidy reduction. Import tariffs reduction
alone or export subsidy reduction alone lowers world average milk price by 2% while domestic
support reduction alone lowers it by 1%.
As under the Central Doha scenario, world total production and consumption of whole milk
powder and skim milk powder both decrease by 1-2%. But the production and consumption of
other dairy products increase except for lactose. Therefore, as mentioned in section 5.4.2.1, the
substitution effects among dairy products likely explains the production decrease of whole milk
powder and skim milk powder. Overall, world producer surplus decreases $9.8 billion, consumer
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surplus increases $6.4 billion, government revenues increase $3.4 billion, but total welfare
slightly decreases ($79 million). Although the total welfare decrease should be insignificant
when compared to the base level total welfare ($1,138.4 billion), it is still contrary to the
common perception that trade liberalization should tend to increase total welfare. One
explanation is that this is the second best results. That is, in the imperfect competition context,
the existence of market power creates some production efficiency loss and welfare loss
consequently. When this welfare loss outweighs the welfare gains from trade liberalization, the
total welfare decreases. Actually, although switching from imperfect competition to perfect
competition has small impacts on dairy product prices, production and consumption under the
Central Doha Boundary scenario (see section 5.5.1 for more detail), it still has some fractional
welfare gains ($89 million). Furthermore, if the perfect competition model is used as the BASE,
then world total welfare increases some $820 million under the Central Doha Boundary scenario.
From the decomposition of the welfare changes under three alternative scenarios it is found that:
(1) welfare changes are mainly contributed by trade policy reform, i.e., import tariffs reduction
and export subsidy reduction; (2) only trade policy reform increases world total welfare, and
domestic support policy reform alone lowers world total welfare. This is reasonable as trade
policy reform changes trade flows and helps to lower world average production cost; domestic
support reduction lowers producer prices but consumer prices may not lower as much if there is
no pressure (foreign competition) to reduce them.
Compared to previous scenarios, the impacts on trade flows are obvious from the reduction
of import tariffs and export subsidies. Import tariffs reduction alone increases the net imports of
cheese, butter, condensed evaporated milk, dry whey and whole milk powder by 14.7%, 15.3%
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and 12.8%, 5.8% and 5.6%, respectively. Export subsidies reduction alone decreases the net
imports of cheese, butter, skim milk powder, whole milk powder, casein and condensed
evaporated milk by 9.5%, 9.0%, 9.0%, 6.8%%, 15.1% and 13.0%, respectively. However, the
impacts on trade flows under the Doha Domestic Support Boundary scenario are very similar to
those under the Doha Domestic Support scenario. Overall, the net imports of cheese and dry
whey increase 14.9% and 8.3%, respectively. As well, most of the other dairy products trade
volume increases. But due to the substitution effects between cheese and casein, the trade of
casein decreases 22.8%.
Although China’s milk price does not change much in the short run, it decreases 1% in the
long run. The prices of butter, whole milk powder and skim milk powder also increase 6-7%.
Dairy product production does not change much, but the consumption of butter, whole milk
powder and skim milk powder decrease 1-2%. Consequently, China’s dairy products exports
increase 85 thousand MT. This means China is a potential competitive exporter of dairy
products, and the increase of marginal world dairy product prices resulting from further trade
liberalization will increase China’s producer surplus. Actually, China’s producer surplus
increases $41 million while consumer surplus decreases $65 million and total welfare decreases
$47 million. From the decomposition analysis, export subsidy reduction alone has more impact
on China’s dairy product prices than import tariff reduction alone and domestic support
reduction alone; milk price increases 1% by export subsidy reduction, as do the prices of whole
milk powder and skim milk powder (+11% and +14%, respectively). Abolishing export subsidy
increases China’s dairy products exports by 24 thousand MT. Hence, China’s producer surplus
increases $66 million and consumer surplus decreases $104 million under the Doha Export
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Subsidy Boundary scenario. The producer surplus and consumer surplus changes from other
scenarios are negligible.
Although small steps toward further trade liberalization has little impact on Japan’s milk
price, the final commitments on agriculture decrease Japan’s milk price by 1%. As well, the
prices of other dairy products decrease 3-22% except for condensed evaporated milk and lactose.
These prices changes can be attributed to import tariffs reduction; the prices of milk, cheese,
butter, skim milk powder, whole milk powder, dry whey and casein decrease 4%, 22%, 9%,
10%, 13% and 5% under the Doha Market Access Boundary scenario, respectively. Under the
Central Doha Boundary scenario, the production of cheese, butter, whole milk powder and skim
milk powder decrease 2-4% while the consumption of cheese, butter, WMP, SMP, dry whey and
casein increase 3-7%. This is reasonable as Japan’s domestic dairy market is heavily protected
by high tariffs and tariff rate quotas; thus, substantial cuts in import tariffs increase Japan’s dairy
products imports (19 thousand MT) and lowers their prices. Overall, Japan’s producer surplus
declines $50 million and consumer surplus increases $48 million. It is not surprising that most of
the welfare changes are contributed by import tariff reduction.
As a potential competitive exporter, India’s dairy products producers benefit from further
trade liberalization; India’s milk price increase 1%, and the price of whole milk powder and skim
milk powder increase 13% and 29%, respectively. As the base level production of whole milk
powder is relative low, its production increases more than 3 fold. The consumption of whole
milk powder and skim milk powder decrease 5-6%. India’s total dairy products exports increase
139 thousand MT. As a consequence, India’s producers gain $405 million and consumers lose
$409 million. The changes in producer surplus and consumer surplus can be explained by import
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tariffs reduction and export subsidy reduction; import tariffs reduction alone and export subsidy
reduction alone increase producer surplus by $260 million and $301 million, and decrease
consumer surplus by $262 million and $308 million, respectively.
There is still not much impact on Korea’s milk price even if the final commitments are
applied. But the prices of cheese, whole milk powder, skim milk powder, dry whey and casein
decrease 3-13%, and their consumption increases 1-6% consequently. The production of dairy
products is unchanged except for butter and skim milk powder (decrease 2%). Hence, Korea’s
net dairy products imports increase 9 thousand MT. However, the impacts on producer surplus
and consumer surplus are still negligible. A decomposition analysis finds that the decline of
dairy product prices can be explained by the reduction of import tariffs; import tariffs reduction
alone decrease the milk price by 1%, and decrease the prices of cheese, whole milk powder, skim
milk powder, dry whey and casein 2-18%. This is not surprising as Korea’s dairy industry is
heavily protected by import tariffs.
The milk price in South East Asia declines 5%, and the prices of cheese, dry whey, casein
and condensed evaporated milk decrease 2-17%. The prices of butter, WMP, SMP and lactose
increase 17%, 7%, 10% and 5%, respectively. The price increase of butter, WMP, SMP and
lactose will lower their consumption (decrease 7%, 3%, 4% and 3%, respectively). But the
consumption of cheese, dry whey, casein and condensed evaporated milk increase by 2-7%. As a
consequence, South East Asia’s total imports of dairy products increase 15 thousand MT. The
above changes can be attributed to the reduction of import tariffs and export subsidy. Milk price
declines 5% by import tariffs reduction alone while increases 2% by export subsidy reduction
alone. Overall, South East Asia’s producer surplus decreases $59 million and consumer surplus
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decreases $63 million. This seems to contradict our intuition (producer surplus and consumer
surplus generally moves in opposite direction). This may because we are dealing with several
products here (some of their prices increase and some of their prices decrease), and consumer
surplus losses resulted from price increases outweigh its gain resulting from price decreases.
Anyway, these changes should not be significant when compared to its 2009 base level producer
and consumer welfare ($1 billion and $7.4 billion, respectively).
EU’s milk price decreases 20%, the prices of cheese, butter, whole milk powder, casein and
condensed evaporated milk decrease 11-21%, with decreases (2-6%) in the prices of skim milk
powder and dry whey. The price decreases can be attributed to the reduction of import tariffs and
export subsidy; import tariffs reduction alone lowers milk price by 13%, and export subsidy
reduction alone lowers milk price by 15% while the reduction of domestic support alone only
lowers milk price by 1%. This suggests that 16% cuts in EU domestic support to its dairy sector
has no significant impacts on its milk price unless opening of EU domestic market is also
emphasized. Corresponding to the price decreases, consumption of all dairy products increases
(2-11%) except for lactose. The production of cheese, whole milk powder, skim milk powder
and condensed evaporated milk declines (1-31%) while the production of other dairy products
increases (3-411%). The impacts on EU dairy product trade are significant (total dairy product
exports decrease 747 thousand MT) and can be explained by the reduction of export subsidy and
import tariffs; export subsidy reduction alone reduces EU dairy product exports by 821 thousand
MT, and import tariffs reduction alone reduces EU dairy product exports by 374 thousand MT.
As a result, EU producer surplus loses $9.9 billion, consumer surplus gains $8.1 billion, and total
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welfare loss is $195 million. Most of the welfare changes are contributed by the reduction of
export subsidy and import tariffs.
Oceania’s milk price increases 4%, as do most other dairy product prices (1-16%). The
production of milk increases 2%, and the production of cheese and whole milk powder increase
16% and 11%, respectively. Other products production falls (1-32%), but their consumption also
falls (1-5%). As a result, Oceania’s exports of cheese and whole milk powder increase 125
thousand MT and 93 thousand MT, respectively. This suggests that the trade of cheese and
whole milk powder is more profitable than other products for Oceania. Oceania’s producers gain
$255 million, consumers also slightly gain ($46 million), and total welfare increases $374
million. However, Oceania only benefits from the reduction of import tariffs and export subsidy.
Import tariff reduction alone increases milk price by 8%, and export subsidy reduction alone
increases milk price by 4% while domestic support reduction alone lowers milk price by 3%.
Import tariffs reduction alone and export subsidy reduction alone increase total dairy product
exports by 162 thousand MT and 100 thousand MT, respectively. But domestic support
reduction alone lowers total dairy product exports by 51 thousand MT. Producer surplus
increases under import tariffs reduction alone ($405 million) and export subsidy reduction alone
($225 million), but decreases under domestic support reduction alone ($145 million).
U.S. milk price decreases 2%, as does the price of casein (-8%). But the prices of butter and
cheese increase 6% and 9%, respectively. The prices of dry whey, condensed evaporated milk
and lactose also increase by 3-19%. The production of cheese and skim milk powder decrease
2%, and the consumption of cheese, butter and skim milk powder decrease 2%, 4% and 16%,
respectively. Consequently, U.S. dairy product exports increase 114 thousand MT. As a result,
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U.S. producers lose $1.3 billion and consumers lose $484 million, but as government revenues
increase $2.3 billion, total welfare increases $454 million. Unlike EU, domestic support
reduction is the main reason for the changes in the U.S. dairy sector. Domestic support reduction
alone lowers U.S. milk price by 2%, and increase the prices of cheese and lactose by 7% and
18%, respectively; it lowers the production of cheese, butter and skim milk powder by 1-3%, and
lower the consumption of them by 1-2%; it lowers producer and consumer surplus by $1.4
billion and $421 million, respectively. This implies that 26% cuts in the U.S. domestic support to
its dairy sector has significant impacts on its dairy industry while 16% cuts has no significant
impacts on EU dairy industry. One explanation is that the EU production quota system belongs
to “Blue Box” policies, not subject to further reduction, and EU farmers get extra protection
from production quota rents. In order to see the overall impacts from domestic policies, we need
to run one scenario that removes all domestic policies (see section 5.4.4). Also unlike EU, U.S.
benefits from the reduction of import tariffs and export subsidy; import tariffs reduction alone
increases U.S. milk price by 1%, increases total dairy product exports by 81 thousand MT and
producer surplus by $168 million; export subsidy reduction alone increases U.S. total dairy
product exports by 121 thousand MT, and increases producer surplus by $49 million.
Canada’s milk price declines 18%, and the prices of cheese and butter decrease 11% and
22%, respectively. As well, similar decreases (1-23%) happen to other dairy products except for
lactose. All dairy products consumption increases except for lactose. But the production of
cheese, whole milk powder, dry whey and condensed evaporated milk decrease by 3%, 100%,
62% and 25%, respectively. Therefore, Canada’s total dairy products imports increase 119
thousand MT, producer surplus decreases $658 million and consumer surplus increases $818
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million. Most of the above changes can be explained by the reduction of import tariffs and export
subsidy. Import tariffs reduction alone lowers Canada’s milk price by 15%, increases total dairy
products imports by 77 thousand MT and decreases producer surplus by $546 million. Export
subsidy reduction alone lowers Canada’s milk price by 3%, increases total dairy product imports
by 32 thousand MT and decreases producer surplus by $124 million. Domestic support reduction
alone has minimal impacts on Canada’s dairy sector — producer surplus and consumer surplus
increase $31 million and $79 million, respectively. This is also because domestic support
reduction under this scenario only applies to EU and U.S.
Therefore, if the tentative final commitments of the Doha development agenda on agriculture
are implemented in 2009, similar conclusions can be drawn as if the commitments are only
implemented for two year (see section 5.4.2). But the longer term impacts are bigger than those
found in section 5.4.2.
5.4.4 Comparing the Impacts of Individual Policies: Tariff, Quota, Export Subsidy,
Trade Policies, Domestic Policies and Full Liberalization
Section 5.4.2 and 5.4.3 analyzed the short term and long term impacts of Doha round
negotiations on the world dairy sectors, but some policy changes were not discussed, such as
tariff rate quota and production quota system in EU and Canada. In this section, we want to
explore the foremost potential of individual policies reform in the world dairy sectors. Although
there may exist significant interactions among individual policies, this exercise provides an
upper bound measure on the potential impacts of individual policies reform and serves as a
supporting analysis for future WTO negotiations. Instead of discussing all dairy products in all
regions, we focus on key dairy products (milk, cheese, butter and skim milk powder) in major
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regions (EU, China, Japan, India, Korea, South East Asia, Australia, New Zealand, USA and
Canada) in this section. Tables 5.15-5.18 provide a cross-scenario summary of various degrees of
trade liberalization impacts on the world dairy markets. Figures 5.13-5.23 summarize the welfare
impacts under individual policies reform. Generally speaking, producers in Japan, Korea and
SEA suffer big losses from the elimination of trade measures, producers in EU and U.S. suffer
big losses from the elimination of domestic support measures, and producers in low cost
exporting countries (Oceania and India) benefit most from the elimination of trade measures.
Full dairy sector liberalization decreases the world average prices of milk (-16.5%), cheese (-
13.7%), butter (-15.6%) and skim milk powder (-5.2%). Domestic support policies have more
impacts on world average dairy product prices than trade policies (includes tariffs, TRQs and
export subsidies): milk price (-15.5% versus -5.8%); cheese price (-10.5% versus -7.7%); butter
price (-14.3% versus -4.9%); and skim milk powder price (-5.9% versus -2.8%). This is because
the elimination of production quota in EU and Canada can largely increase dairy product
production and lower prices consequently (see below for more detail). There are also interactions
between trade policies and domestic support policies - the impacts of full liberalization are
smaller than the combined separate impacts of trade polices and domestic support policies. For
trade policies, tariff reduction alone or import quota reduction alone lowers world average prices
of milk, cheese, butter and SMP; export subsidies reduction alone drives up the price of SMP but
lowers the prices of milk, cheese and butter. This seems to contradict the common perception
that export subsidies reduction tends to increase world average prices. One explanation is that
some exporters’ (especially EU) domestic supply increases sharply without export subsidies
(some of the production originally destined for exports now have to be consumed domestically).
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As a consequence, their domestic prices decline substantially, and the price decline effects
outweigh the price increase effects in some dairy product importing markets. Combining tariffs,
TRQs and export subsidy generate results that are larger than any change separately, but they are
not additive. This reflects the fact that tariff rate quotas with increased quota access at prohibitive
tariff levels basically act like quotas. If tariff reductions are not sufficient to generate over-quota
imports, then the impacts of tariff liberalization will be small. Conversely, expanding quotas at
prohibitive within-quota tariffs will also have small impacts (Cox et al, 2004).
World aggregate production and consumption of milk, cheese and butter increase 1.4-2.8%.
But the production and consumption of skim milk powder decline 3.8% and 4.2%, respectively.
Domestic support policies contribute more to the changes of dairy products
production/consumption than trade policies: milk production (+1.5% versus +0.4%); cheese
production (+2.0% versus +1.5%); butter production (+2.8% versus +0.4%); and SMP
production (-2.7% versus -3.5%). As trade policy reform increases trade opportunities, decreases
prices and increases consumption consequently, the production increase under No Trade Policies
is not surprising. The production increase under domestic support reduction can be attributed to
the elimination of milk production quotas in EU and Canada, and the butter buy-in ceiling in EU;
EU and Canada’s milk production increase 16.1% and 6.0%, and EU’s butter production increase
30%. The decline of SMP production may be due to the fact that the trade is less lucrative than
that of butter and cheese (they compete with each other for the utilization of milk components).
A decomposition analysis of the impacts of trade policies on dairy product production and
consumption finds that:
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(1) tariff reduction alone, import quota reduction alone or export subsidy reduction alone can
not increase milk production or consumption. This is because we assume there is no fluid milk
trade in the model due to perishability and transportation costs. The change of milk production /
consumption can only be made through the utilization of milk components.
(2) the production and consumption changes of cheese and butter are similar to each other.
Both of them increase about 1% under tariffs reduction and export subsidy reduction, but the
elimination of import quota has smaller impacts on them. As discussed above, this indicates that
the in-quota tariffs for butter and cheese are so high that expanding quotas has little impact on
their imports.
(3) export subsidy reduction alone or import quota reduction alone increases SMP production
and consumption by about 1%, but tariffs reduction alone decreases it by about 0.6%. The
production change under No Export Subsidy is driven up by its price increase. The production
change under No Tariff and No Quota can be explained by substitution effects among dairy
products. For example, under No Tariff, the production of cheese and butter increase more than
that under No Quota, and hence, the production of SMP is reduced.
World producer surplus decreases $32.4 billion and consumer surplus increases $25.5 billion,
but total welfare does not change much (slight decrease, $1.7 billion). Although total welfare
deceaseis only 0.15%, it is still a cause of concern. This can also be explained as a second best
results. Under this circumstance, switching from imperfect competition to perfect competition,
although there is little impact on dairy product prices, production and consumption (see section
5.5.1 for more detail), total welfare increases are $1.75 billion. Moreover, if the perfect
competition model is used as the BASE, then world total welfare increases some $1 billion under
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189
Full Liberalization. Note the welfare losses created by the existence of market power increases
from the Central Doha scenario, and Central Doha Boundary scenario to Full Liberalization. This
is reasonable as dairy products production expands under these scenarios and consequently the
production efficiency losses. Although producer surplus losses under No Trade Policies and No
Domestic Support are not additive, most of the producer surplus losses under Full Liberalization
are from the reduction of domestic support. This is not surprising when we consider the domestic
support to dairy industries by EU, U.S. and Canada (see below for more details). Trade policy
reform also generates producer surplus losses as world average dairy product prices decrease.
Tariffs reduction generates more producer surplus loss than import quota reduction or export
subsidies reduction. This indicates that import tariffs provide more protection to producers on
average than other trade policy instruments. This is reasonable because if both in-quota and over
quota tariffs are low enough then import quotas are no longer binding. But net importing
countries producers benefit from the reduction of export subsidies (gain $464 million). Of
course, consumers benefit from any kind of liberalization in the world dairy sectors. Most of the
consumer surplus gains are from the reduction of domestic support. This suggests that most of
the government treasury to support producers by using domestic support policies are transferred
from consumers. For trade policies, consumers gain most from the reduction of import tariffs.
Full dairy sector liberalization decreases China’s milk price (-1.0%) and butter price
(-23.7%), and increases cheese price (+12.1%), but there is not much impact on the price of SMP
(+0.7%). Domestic policies reform has larger impacts on the prices of milk, cheese and butter
than trade policy reform. In contrast, for SMP, domestic policies reform increases its price
(1.4%) while trade policy reform decreases it (-0.9%). Consistent with prices changes, China’s
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190
consumption of butter increase 4.7%, consumption of cheese and SMP decrease 0.4% and 0.2%,
respectively. The production of cheese, butter and SMP decrease 0.4-19.4%. Given the
consumption increase and production decrease of butter, China’s butter imports increase 14 MT.
It is interesting to find that China’s cheese price decreases 6.8-9.1% under No Quota and No
Export Subsidy. At the same time, its consumption slightly increases while its production does
not change much. As China does not apply import quota administration for its dairy products
imports and uses export subsidy for its dairy products exports, this reflects the fact that China’s
cheese exports face more intense competition in foreign markets which apply import quotas or
receive export subsidy. As a consequence, some of China’s cheese production originally destined
for exports are now forced to be consumed domestically. Therefore, domestic consumption
increases and price decreases. China also benefits from the elimination of butter and SMP export
subsidy; the prices increase 2.1-14.3%, the production increases 2.4-4.8%, and consumption
decreases 0.4-3.9%, respectively. Hence, China’s butter and SMP exports increase under No
Export Subsidy. Under No Tariff, China’s prices of butter and SMP decrease (-18.6% and -2.9%,
respectively) while the price of cheese increases (+5.4%). This means China’s cheese producers
benefit from the marginal price increase of cheese while butter and SMP producers suffer from
the lowering of world average prices of butter and SMP.
The overall impacts on China’s welfare are small. Producer surplus slightly decreases (-$50
million) and consumer surplus slightly increase (+$50 million), and total welfare declines (-$72
million) under Full Liberalization. Elimination of import quota and export subsidy increases
China’s producer surplus, but the other scenarios decreases China’s producer surplus. For
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191
consumer surplus, the opposite situation arises. However, as under the Central Doha Boundary
scenario, the welfare changes are negligible under all scenarios.
Under Full Liberalization, Japan’s prices of milk, cheese and butter decrease 41.1%, 35.1%
and 76.9%, respectively. But the price of SMP slightly increases 0.7%. For cheese and butter, the
price decline under No Trade Policies is much larger than under No Domestic Support policies:
cheese price (-31.6% versus -17%); butter price (-75.3% versus -1.6%). As discussed in chapter
2, this reflect the fact that Japan’s domestic cheese and butter production are heavily protected by
import tariffs and quotas: under No Tariff, Japan’s cheese and butter price decrease 17.1% and
75%, respectively; under No Quota, Japan’s cheese and butter price decrease 2.4% and 68.3%,
respectively; under No tariff, Japan’s production of cheese and butter decrease 52.8% and
15.7%, respectively. Japan enjoys export subsidies on its cheese, butter and SMP imports. This
can be seen from the prices changes under No Export Subsidy -- the prices of cheese, butter and
SMP increase 1.8-14%. Hence, the price increase of SMP under Full Liberalization are largely
driven up the elimination of export subsidy. Overall, Japan’s producer surplus decreases $2
billion (close to OECD’s PSE -- $4.3 billion in 2004) and consumer surplus increases $2.2
billion under Full Liberalization. But total welfare change is negligible (decrease $45 million).
Trade policies are seen to provide much more protection to producers than domestic support
policies. Multilateral elimination of domestic support policies has small impacts on the farm
milk price, producer and consumer surplus. However, the multilateral elimination of trade
policies generates a farm milk price and milk production decrease of 26.8% and 9.9%,
respectively, and producer losses of some $1.4 billion. Most of the producer surplus losses are
from the elimination of import tariffs - decreases some $1.3 billion under No Tariff. Conversely,
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consumers gains most from trade policy reform. This indicates that Japan’s domestic protection
for its dairy products producers are at the cost of its dairy products consumers.
As a potential competitive exporter, under Full Liberalization, India’s milk price increases
slightly (+0.2%), and its prices of butter and SMP also increase (+2.7% and +25.2%,
respectively). As India’s cheese production and consumption are trivial, they are not discussed
here (see chapter 2). From the production and consumption changes of butter and SMP, we see
India gains some export opportunities for these products: butter production increases 0.1% while
consumption decreases 1.2%; SMP production decreases 4.1% while consumption decreases
5.2%. India’s producers benefit from trade policy reform while suffer from total elimination of
domestic support policies - the production of milk, butter and SMP increase under No Trade
Polices while decrease under No Domestic Support policies. Its producers benefit most from the
elimination of import tariffs, and also benefit from the elimination of import quota and export
subsidy. Overall, India’s producers gain $1 billion from trade policy reform. However, unlike
under the Doha scenarios, India’s producer surplus losses are significant ($819 million) under No
Domestic Support policies. As mentioned above, this is because the production expansion in
heavily protected economies, such as EU and Canada. Hence, unlike under Central Doha
Boundary scenario, the welfare impacts under Full Liberalization are minimal.
Unlike under the Doha scenarios, Korea’s milk price decreases significantly (-21.4%) under
Full Liberalization. The prices of cheese, butter and SMP also decline significantly (-18.2%, -
10.5% and -47.6%, respectively). Trade policy reform contributes more to the price decline than
domestic support policies reduction: milk price (-21.7% versus -0.9%); cheese price (-15.8%
versus -1.2%); butter price (-8.5% versus +2.6%); SMP price (-54.4% versus -13.6%). Korea’s
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production of milk, cheese, butter and SMP decrease 5.4%, 20.8%, 7.6% and 28.9%, respectively.
Similar to the price declines, trade policy reform contributed more to the production declines
than domestic support policies reduction: milk production (-5.5% versus -0.2%); cheese
production (-20.8% versus 0.0%); butter production (-7.6% versus -1.5%); SMP production (-
28.9 versus -2.2%). Given the prices changes, it is not surprising that Korea’s consumption of
cheese, butter and SMP increase 1.2-5.8% under Full Liberalization. The impacts on welfare are
significant now. Under Full Liberalization, producer surplus loses $217 million (OECD’s PSE is
0.8 billion in 2004), consumer surplus gains $240 million, and the total welfare change is
negligible (decrease $23 million). Akin to Japan, most of Korea’s producer surplus losses are
from trade policy reform, especially from import tariffs reduction, and the opposite situation
arises for consumer surplus. Domestic support policies elimination has minimal impact on
Korea’s welfare.
South East Asia’s milk price declines 20.4% under Full Liberalization. As well, the prices of
cheese, butter and SMP decrease 16.8%, 9.8% and 44.4%, respectively. This is not surprising as
South East Asia mainly depends on imports to meet its demand for cheese, butter and SMP (see
chapter 2). Trade policy reform has more impact on milk price than domestic policies reduction
(-18.7% versus -2.9%). This is because as the prices of other dairy products fall, and so does the
shadow value of milk components. A similar story holds for cheese price (-13.7% versus
-0.8%). But the opposite situation arises for the prices of butter and SMP: butter price (+12.3%
versus -8.2%); SMP price (-2.2% versus -5.1%). Consistent with prices changes, the
consumption of cheese, butter and SMP increase 12.2%, 4.0% and 1.9%, respectively. Overall,
South East Asia’s producer surplus decreases $238 million and consumer surplus increases $243
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194
million, but government revenues lose $277 million by lowering tariffs. South East Asia is more
sensitive to import tariffs reduction than to other trade policy instruments reform: import tariffs
reduction decreases the prices of milk, cheese, butter and SMP by 18.9%, 14.1%, 13.8% and
2.0%, respectively; producer surplus loses $221 million, consumer surplus gains $276 million,
and government revenues loses $277 million.
EU dairy producers suffer the biggest losses from full dairy sector liberalization, with a farm
milk price decline of 49.5% and the prices of cheese, butter and SMP decline 23.5%, 43.0%, and
9.4%, respectively. As a consequence, EU producers lose around $22 billion, consumers gain
about $15 billion, but even adding government revenue gains due to liberalization, EU total
welfare falls around $2 billion. These results suggest some of potential reasons for EU resistance
to dairy trade liberalization. Note the farm milk price decline in this study is larger than that from
some previous studies which vary from -5 percent to -26 percent (see table 5.19). This is because
EU CAP reform is added to the model. Contrary to price changes, EU production of milk, cheese
and butter increase instead of decrease. This is because of the elimination of milk production
quota - EU milk production increase 16.1%. In this study, it is assumed that the quota rents are
collected by farmers. Without quota rents, dairy product consumption prices decrease and
consequently dairy product demand increases. As a consequence, milk production increases. As
milk components supply increase, the production of cheese and butter increase consequently
(+16.7% and +21.8%, respectively). Domestic support policies provide more than double the
protection to EU producers compared to trade policies. Without domestic support policies, EU
producers face losses of about $24 billion and farm milk price falls by 52.8%, and the prices of
cheese, butter and SMP fall by 23.9%, 49.5% and 14.8%, respectively. If only dairy trade
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policies are eliminated, EU producers suffer a loss of some $10 billion and farm milk price
decreases by 21.7%, and the prices of cheese, butter and SMP decrease by 12.5%, 19.7% and
6.3%, respectively. Note that in both cases, EU milk production does not decrease (+15.6% and
0%, respectively) due to the effects of production quota; but the effects resulting from the price
decline dominate any effects resulting from production increase; consequently total producer
surplus decreases. Given the size of the dairy product prices change, it is not surprising EU
consumer surplus increases—by $16.7 billion under No Domestic Supports and $8.8 billion
under No Dairy Trade distortions, respectively.
Oceania’s milk price increases 5.7% under Full Liberalization. As well, the prices of cheese,
butter and SMP increase 6.8%, 12.7% and 23.2%, respectively. Note, except for the price of
SMP, all other prices’ increase is lower than previous studies, especially for butter - previous
studies shows price increases from 46 percent to 60 percent. This is because the EU milk price
decreases so much in the model that it sharply lowers world average milk price, and as a
consequence, sharply lower world average prices for other dairy products. Although we
mentioned that imperfect competition tends to lower exporters prices, it is not the reason for the
smaller price decline in this study (see section 5.5 for detail). An inspection of welfare changes
suggests Oceania (Australia and New Zealand) only benefit from the elimination of trade
policies, with net welfare gains of $609 million (after adding government revenue changes) and
suffer a loss of about $141 million from multilateral elimination of domestic support policies.
This result is driven mainly by the increase in EU and Canadian milk supply with the elimination
of production quotas. Increased milk supplies with no additional market access and sharply lower
domestic prices makes the EU and Canada less attractive export destinations, and generates
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negative spillovers on competitive exporters. For trade policies, Oceania benefits most from the
elimination of import tariffs - producer surplus gains $839 million, farm milk prices increase
15.6% and net welfare increases $589 million. This is reasonable, as discussed above, import
tariffs create more distortions to world dairy sectors than import quota and export subsidies.
Full dairy sector liberalization decreases U.S. milk price by 11.5%, but increase the prices of
cheese (+7.2%), butter (+2.7%) and SMP (+9.5). U.S. milk price decline is close to that of
OCED (2005) and Cox and Zhu (2004). Overall, U.S. producers lose $3.4 billion, consumers
gain $2.1 billion, and net welfare increases $1 billion. This suggests that the U.S. is moderately
competitive and not too heavily distorted by current dairy policies relative to the rest of the world.
The simulation results suggest that U.S. dairy producers get more protection from domestic
support policies than from trade policies. Without domestic support policies, American
producers suffer a loss of $3.5 billion as farm milk price and milk production decreases 11.5
percent and 5.0 percent, respectively. Due to expanding market demands, the multilateral
elimination of trade policies suggests an increase in marginal world dairy product prices and a
gain to U.S. producers of about $430 million with farm milk price and milk production increases
of 2.9 percent and 1.2 percent, respectively. Consumer surplus moves in the opposite direction
in the two cases, increasing about $2.1 billion under No Domestic Supports and declining around
$0.9 billion under No Trade Policies distortions. For trade policies, similar to Oceania, U.S.
producers benefit most from the elimination of import tariffs - producer surplus gains some $443
million and farm milk prices increase 2.9%.
Canada’s milk price decreases 44.7% under Full Liberalization, as do the prices of cheese
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(-30.9%), butter (-48.9%) and SMP (-36.3%). The milk price decline is close to previous studies.
But due to the elimination of production quota, Canada’s dairy products production does not
decrease: milk (+6.0%); cheese (+7.2%); butter (11.9%); and SMP (+14.3%). Canada’s producer
surplus loses $1.6 billion and consumer surplus gains $1.5 billion, but the impacts on total
welfare is minimal (decrease $18 million). Domestic support policies and trade policies provide
similar protection to Canada’s producers. Without domestic support policies, Canada’s producers
suffer a loss of about $1.2 billion and farm milk prices decrease by 34.2%, while without dairy
trade policy distortions producer surplus losses are $1.3 billion and farm milk prices decline
34.4%. Canada’s consumer surplus gains are $1.13 billion and $1.08 billion for the two scenarios,
respectively. For trade policies, import tariffs and import quota provide similar protection to
Canada’s producers. Without import tariffs, Canada’s producers suffer a loss of $990 million and
farm milk prices decrease by 26.6%, while without import quota producer surplus losses are
$953 million and farm milk prices decline 25.6%.
In summary, full dairy sector liberalization has significant impacts on Japan, Korea and SEA
dairy markets, but the impacts on China and India dairy markets are small. Japan, Korea and
SEA dairy producers suffer most from trade policy reform while consumers benefit. For India,
the opposite situation arises as dairy producers gain from trade policy reform while suffer a loss
from domestic support policy reform. For China, both trade and domestic support policy reform
have minimal impacts on producer surplus and consumer surplus. As major exporters, Australia
and New Zealand producers gain from trade polices reform while suffer from domestic support
policy reform due to the elimination of milk production quota. EU dairy producers suffer the
biggest losses from world dairy trade liberalization, especially from domestic support policy
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198
reform. Compared with other regions, the impacts of world dairy trade liberalization on the U.S.
dairy market is generally moderate in the medium term context. Dairy producers mainly suffer
from domestic support policy reform. Canada’s dairy producers also suffer significant losses
from full dairy sector liberalization, and trade polices and domestic support polices provide
similar protection to dairy producers. Overall, full dairy sector liberalization has little impact on
world aggregate welfare, but it changes the location of production and redistributes welfare
across different countries.
5.5 Impacts of Imperfect Competition under Doha Scenarios and Full
Liberalization
In previous sections we see the impacts of Doha round negotiations on Asia and world dairy
sectors in the context of imperfect competition. Does it make any difference if we switch to
perfect competition? This section explores the difference between perfect and imperfect
competition by reevaluating the impacts on Central Doha scenario, Central Doha Boundary
scenario and Full Liberalization. The difference on prices, production and consumption are
summarized in table 5.20 - 5.21.
From table 5.20 we can see that the differences between perfect competition and imperfect
competition under Central Doha scenario are very similar to that from the BASE 2002-04
scenario, but the magnitudes are smaller. Switching from imperfect competition to perfect
competition, China’s butter and SMP price decrease 22% and 4%, respectively; Japan’s cheese,
butter, and SMP price decline by 15%, 6% and 8%, respectively; Oceania’s cheese and SMP
prices increase 1% and 9%, respectively. But there are no impacts on SEA’s SMP price and
world average dairy product prices now. The impacts on production and consumption are even
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smaller. China’s butter and SMP production fall by 8% and 16%, respectively, Japan’s cheese
production falls by 6% while Oceania’s butter and SMP production increase 1-2%. China’s
butter consumption increases 4%, Japan’s cheese, butter and SMP consumption increase 1-2%
while Oceania’s SMP consumption decreases 1%. World total welfare slightly increases $47
million. There are no impacts on world aggregate dairy products production and consumption.
Table 5.21 tells us that there is not much difference between perfect and imperfect competition
under the Central Doha Boundary scenario and Full Liberalization.
Therefore, table 5.3,5.20 and 5.21 suggest that the impacts of imperfect competition are
positively related to trade distortions. That is, if there are more trade distortions (such as the
BASE 2002-04 scenario) the impacts of imperfect competition are more obvious, but if there are
less trade distortions than the impacts of imperfect competition become smaller. This is
reasonable. Recall that import quota, import tariffs and export subsidies can all be used as
strategies to create market power and consequently imperfect competition. In other words,
market power tends to be hidden behind quota rents, import tariffs and export subsidies. As
market power is modeled explicitly by using the CV estimates from chapter 4, there may be
significant interactions between CV and trade policies. For example, for Japan’s butter price,
under the BASE scenario, when Japan’s butter import tariffs are high (high over-quota tariff
suggesting prohibitive import quota), the impact of imperfect competition is 16 percent; under
the Central Doha scenario, once the import tariffs are cut back and import quotas are less
restrictive, the impact of imperfect competition is 6 percent; under the Central Doha Boundary
scenario/Full Liberalization, once the import tariffs are cut back substantially/eliminated and
consequently import quotas are not restrictive, the impacts of imperfect competition is negligible.
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This example also tells us that once these are cut back or abolished, the interaction effects should
diminish and imperfect competition may converge to perfect competition. The only difference is
that the existence of CV can create some production inefficiency and fractional change in world
total welfare. As mentioned above, the welfare losses from the pure effects of CV are $89
million and $1.75 billion under the Central Doha Boundary scenario and Full Liberalization,
respectively.
At any rate, from table 5.3,5.20 or 5.21 we can conclude that there is no significant
difference between imperfect competition and perfect competition in world dairy sectors in
practice. Note this does not contradict the findings in chapter 4. Although the CV estimates are
significant from a statistical point of view, they are not big enough to change trade flows in the
context of multilateral equilibrium.
Therefore, from the empirical results on market power, the use of perfect competition
assumptions to study world dairy sectors by current models is roughly justified if imperfect
competition only applies to a limited number of products in a few regions.
The finding that the impacts of imperfect competition are positively related to trade
distortions and that there may be significant interactions between CV and trade policies can serve
as a complementary example of the finding of Ianchovichina et al (2000). As mentioned above,
they found that economists analyzing tariff cuts in heavily protected industries (such as
Australian automotive industry) would overstate the short-run adjustment in output by 80% if the
estimated pro-competitive effects are ignored. In other words, they found that there exists
significant interaction in a heavily protected industry (Australian automotive industry) between
tariff and price markups. All these findings suggest that Doha round negotiations are in the right
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201
direction for the purpose of increasing world aggregate welfare - substantial cuts in import tariffs
and quotas can increase world aggregate welfare by trade expansion and reductions in markups.
The welfare increase by reductions in markups is due to the finding that if there are less trade
distortions than the impacts of imperfect competition become smaller.
5.6 Discussion
The impacts of Doha round negotiations on the Asian and world dairy markets were
discussed in great detail in this study. However, we should bear in mind that results of this
analysis are just suggestive, and are meant to be illustrative rather than definitive, as there are
still many issues that need to be addressed.
First is the problem of tariff overhang and tariff water. Tariff overhang is defined as the
difference between WTO scheduled bound tariffs and current applied tariffs. Tariff water is
defined as the difference between the applied tariff rate, and the tariff equivalence of market
price support (Cluff and Vanzetti, 2006). Tariff overhang in developing countries is more
prevalent than in developed countries. For example, agricultural bound tariffs in developing
countries average 48% while applied tariffs average only 21%. In the case of the least developed
countries, the respective figures are 78% and 13%! Even in the EU (21% binding vs. 12%
applied) and USA (6% binding vs. 3% applied) there is substantial binding overhang in
agriculture. So for many countries / products, bound tariffs can be cut deeply with no impact on
applied protection and hence international trade (Hertel and Winters, 2005). This is because
developing countries had the right to set their tariff bindings without reference to previous levels
of protection during Uruguay Round negotiation, under the so-called ceiling binding option.
Tariff water in developed countries is more prevalent than in developing countries, and
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particularly in those with complex support programs having bound and applied tariffs which
exceed the tariff equivalence of their market support regimes. This is because negotiators used a
highly-protected base period (1986-88) and many members used so-called “dirty tariffication” to
set their tariff rates well above the previously-prevailing average applied tariffs during Uruguay
Round negotiation (Hathaway and Ingco 1996). Both tariff overhang and tariff water provide
some indication of room for reduction in bound rates that would not necessarily affect applied
rates or possibly existing policy as applied. The size of tariff “room” may also affect whether
and how countries use either the Special or Sensitive product clauses (Cluff and Vanzetti, 2006).
As it is very difficult to get data for boundary tariff, applied tariff and tariff equivalence of
market price support, we assume all countries use their maximum and/or minimum commitments
under the URA. The simulated impacts from trade liberalization would be overestimated if the
final commitments are not binding.
Second, there are a lot of other proposed scenarios besides the scenarios studied here,
especially the U.S. and EU proposal. The U.S. proposal is more aggressive than what was
studied here. For “Market Access”, using the “tiered formula” identified in the July 2004
framework and building on the elements proposed by the G-20, the U.S. calls for the following
to be phased-in over five years:
• Progressive tariff reduction: Developed countries cut their tariffs by 55-90%. Lowest
tariffs are cut by 55%, with cuts ranging to 90% for highest tariffs.
• Tariff rate caps: Establish a “tariff cap” ensuring no tariff is higher than 75%.
• Sensitive products: Limit tariff lines subject to “sensitive product” treatment to 1 % of
total dutiable tariff lines. For these lines, ensure full compensation by expanding
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TRQs where they exist, and find other means to address sensitive products where
TRQs are not in place.
• Special provisions for developing countries: Create special and differential treatment
provisions for developing countries to provide real improvements in access while
ensuring import-sensitive sectors in those countries are afforded appropriate
protection.
For “Domestic Support”, the United States calls for substantial reductions in trade-distorting
domestic support, with deeper cuts by countries with larger subsidies. The United States
proposes the specific elements to be enacted within five years:
• Overall goals: Reduce overall levels of trade-distorting support by 53% for the
United States and 75% for the European Union.
• Amber box: Cut Aggregate Measurement of Support (AMS) by 60% for the United
States and 83% by the European Union, with product-specific AMS caps based on
1999 - 2001 period.
• Blue box: Cap partially decoupled direct payments at 2.5% of the value of
agricultural production.
• De minimis: Cut “de minimis” allowances for trade-distorting domestic support by
50% (from 5% of the value of production to 2.5%)
The EU proposal is more conservative than what was studied here. For import tariffs, using
the “tiered formula”, EU suggests inflexion points at 30,60 and 90 percent for developed
countries, and marginal cuts at 35,45 percent, 50 and 60 percent. For developing countries, the
inflexion points were placed at 30,60 and 90 percent and the marginal cuts at 25, 30 percent, 35
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and 40 percent. Tariff rate caps are 100 percent for developed countries and 150 percent for
developing countries. Although these proposals were not studied here, the boundary impacts of
further trade liberalization on the world dairy sectors were explored. From the analysis of the
Central Doha scenario, Central Doha Boundary scenario and Full Liberalization, the impacts of
trade liberalization with different degrees of freedom on the world dairy sectors were found to be
quite similar to each other, but the magnitude varies. Full Liberalization provides an upper bound
measure on the potential impacts of dairy trade liberalization, and the Central Doha scenario
provides a close to lower bound measure on the potential impacts of dairy trade liberalization.
The impacts of the U.S. proposal should fall between the interval of Full Liberalization and the
Central Doha Boundary scenario while the impacts of the EU proposal should be smaller than
those of the Central Doha scenario.
Third, how well does this model behave? Although the BASE 2002-04 solutions were
calibrated to be very close to the actual average data of 2002-04, it is useful to know how the
model forecasts works and how useful is the base level prediction for 2009. To achieve this goal,
another base scenario was calibrated by using data for 2002 and forecasting out to 2004. The
predicted prices, production and consumption of 2004 and their deviations from the actual data
o f2004 are presented in table 5.22. For milk, cheese, butter and SMP, the endogenously
generated production and consumption data by the model are very close the actual data for major
regions, and most of the deviations are less than 5-10%. However, the simulation of dairy
product prices are not as good. This is because 2002 has a relatively higher price for SMP and
some dairy product prices are “soft” (questionable) in the BASE 2002 model. Anyway, for most
regions, the simulated prices are within 10% of the actual data.
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Fourth, how will the categories of “sensitive” and “special” products affect WTO
negotiations and subsequent implementation by member countries. The July 2004 Framework
Agreement included clauses defining Special Products and Sensitive Products, and may affect
both the potential for and the nature of future dairy reform. According to Clufif and Vanzetti
(2006), the Special Products (SSP) clause may enable developing countries to exempt dairy
products from tariff reduction, where the dairy sector may be considered of fundamental
importance to “food security, livelihood security and rural development needs”. The Sensitive
Product (SPP) clause would enable all countries to deviate from formula tariff reductions for a
limited (negotiated) number of tariff lines. The clause achieves “substantial improvement” in
market access through tariff rate quota expansion that takes “into account deviations from the
tariff formula”. For example, for those countries that may choose dairy product tariff lines as
sensitive, a key question is how might the opening of, or increase in, tariff rate quotas be
negotiated, and what would be the potential impact on markets compared to a tariff reduction
required by the tariff formula.
Lastly, the dairy trade policy is not negotiated in isolation. Impacts of trade liberalization on
other agricultural sectors (grains, oilseeds, and livestock products) can have significant influence
on negotiators' multi-commodity bargaining positions. Exploring these multi-commodity impacts
under alternative liberalization proposals can provide useful additional insights on the policy
making process. The linkages between the agricultural sector and the macro-economy can also
be quite important (e.g., monetary policy, exchange rates, etc.). Further work is needed to
explore these linkages.
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Table 5.2A 2002-04 BASE Solutions Prices (SUS/MT)
milk res che but wmp smp dwh cas cem lac eu 333 488 4,716 3,688 2,872 2,309 654 6,060 1,007 526 eeu 217 348 2,710 1,975 2,245 1,881 643 5,830 864 601 China 269 480 3,103 2,818 2,695 2,378 836 6,077 989 768 Japan 716 785 4,449 11,868 4,784 2,878 1,416 6,069 1,399 646 India 232 371 3,863 2,015 1,955 1,631 1,207 4,855 785 901 Korea 399 474 3,628 3,377 3,908 3,817 1,279 6,670 1,223 671 sea 283 469 3,557 2,203 2,638 2,517 995 7,407 953 670 osa 183 290 2,290 1,866 3,344 3,508 1,091 10,479 1,708 869 Ociana 154 346 2,613 1,783 2,193 1,635 718 5,685 874 485 aus 157 346 2,631 1,850 2,245 1,678 725 5,696 868 524 nzl 150 345 2,596 1,716 2,140 1,592 711 5,674 879 445 Canada 470 554 3,897 4,395 3,891 3,736 1,723 5,808 1,209 457 USA 299 493 3,168 2,907 1,113 2,061 673 5,810 1,045 389 mex 354 546 4,413 2,465 3,750 3,211 741 8,219 1,224 563 samn 345 415 3,856 2,884 3,354 2,666 1,299 7,348 1,116 594 sams 196 302 2,573 1,957 2,176 1,908 618 5,749 880 477 Aggregate 294 432 3,796 2,724 2,991 2,345 788 6,067 1,039 537
PRODUCTION (1000 MTs)
milk res che but wmp smp dwh cas cem lac eu 118,933 57,551 5,324 1,755 704 1,135 928 53 1,168 239 eeu 17,032 12,458 453 125 27 n o 37 4 104 0 China 20,812 13,496 244 98 700 82 0 0 138 0 Japan 8,382 5,776 31 83 49 185 0 0 38 0 India 88,982 68,975 0 2,606 1 238 0 11 171 0 Korea 2,409 1,512 20 57 6 40 0 0 6 0 sea 2,387 1,440 41 12 0 0 0 0 356 0 osa 32,012 28,135 13 585 5 0 0 0 8 0 Ociana 24,784 7,271 692 529 780 486 130 108 37 47 aus 10,689 4,233 379 152 200 201 103 13 34 0 nzl 14,095 3,038 313 377 580 285 27 95 3 47 Canada 7,914 4,380 326 85 16 100 33 0 64 0 USA 77,241 40,781 4,016 587 15 726 497 0 654 228 mex 9,899 7,082 145 80 95 25 0 0 174 0 samn 35,887 29,032 205 108 509 3 0 0 289 2 sams 11,832 5,859 454 100 232 67 6 11 41 3 Aggregate 602,641 394,575 14,897 8,193 3,340 3,843 1,717 235 3,901 611
CONSUMPTION (1000 MTs)
milk res che but wmp smp dwh cas cem lac eu 114,262 57,551 5,268 1,770 320 1,065 781 109 939 240 eeu 16,365 12,458 447 104 23 67 28 1 92 4 China 23,042 13,496 244 105 776 120 180 1 166 14 Japan 10,730 5,776 280 85 49 232 39 11 40 59 India 88,805 68,975 0 2,606 1 235 1 2 140 6 Korea 3,043 1,512 59 61 6 43 42 2 6 10 sea 8,462 1,440 68 109 200 354 162 3 356 21 osa 32,878 28,135 13 585 103 39 4 0 8 3 Ociana 10,626 7,271 245 82 23 39 29 3 8 137 aus 6,928 4,233 227 56 22 34 14 3 5 108 nzl 3,698 3,038 18 26 1 5 15 0 3 29 Canada 8,299 4,380 342 87 30 108 42 10 45 10 USA 71,644 40,781 4,008 597 24 397 209 72 658 68 mex 12,894 7,082 211 125 150 181 68 8 176 15 samn 37,270 29,032 196 128 586 27 46 1 289 11 sams 10,571 5,859 359 83 90 41 115 1 13 3 Aggregate 601,753 394,575 14,901 8,176 3,349 3,686 1,832 235 3,901 708
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Table 5.2B 2002-04 DATA Simulations PRICE •/. CHANGE (SOLUTION vs. DATA)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU 0 10 1 4 3 1 13 24 6 17 EEU 0 6 4 -13 7 4 0 11 5 -3 China 0 13 3 2 15 -4 1 12 6 0 Japan 0 -5 3 1 14 -3 33 27 9 -17 India 0 3 1 -1 3 3 -1 12 6 -3 Korea 0 4 -3 -7 7 -2 1 12 5 0 SEA 0 8 0 -4 15 -4 1 12 7 2 OSA 0 1 13 1 15 -7 -1 10 0 -3 Ociana 0 26 2 1 12 4 44 42 28 -20 AUS 0 20 2 1 20 4 45 25 30 -19 NZL 0 33 1 0 5 5 42 64 26 -20 Canada 0 1 2 4 2 1 -1 11 4 -1 USA 0 12 4 -3 -25 7 55 34 23 -35 MEX 0 10 8 21 14 9 1 12 11 0 SAMN 0 6 21 -13 6 -10 1 12 4 -3 SAMS 0 6 25 -34 16 -32 -2 11 5 -10 Aggregate 0 7 -3 5 11 0 22 25 9 -5
PRODUCTION •/• CHANGE (SOLUTION vs. DATA)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU -5 -1 -5 -8 -13 9 A -22 A -2 EEU 0 -1 -1 5 -18 10 -20 100 20 -90 China 0 -2 6 9 1 8 -90 -90 21 -90 Japan 0 1 -6 1 -8 1 -90 -90 -7 -90 India 0 0 0 0 0 4 -90 450 20 -90 Korea 0 0 0 0 50 0 -90 -90 0 -90 SEA 0 -1 5 0 -90 0 -90 -90 -6 -90 OSA 0 0 18 0 0 -90 -90 30 100 -90 Ociana 0 -3 1 0 2 -4 19 3 23 4 AUS 0 -3 -2 3 5 -7 20 63 21 -100 NZL 0 -4 6 -1 1 -2 17 -2 50 12 Canada 0 0 -4 4 300 9 -21 -100 -14 -100 USA 0 -1 2 1 -50 4 0 -90 -20 21 MEX 0 -2 11 4 -10 39 -90 -90 10 -100 SAMN 0 -1 -1 1 8 200 -90 -90 -13 -33 SAMS 0 -1 -3 20 -13 20 20 2,100 17 0 Aggregate -1 -1 -1 -1 -4 6 -17 -13 -6 2
CONSUMPTION % CHANGE (SOLUTION vs. DATA)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU -1 -1 0 1 -1 5 -22 -12 -5 *8 EEU -1 -1 -1 2 0 -1 0 0 -1 0 China -2 -2 -1 -5 -3 -5 -1 -6 -1 0 Japan 1 1 9 -7 -9 6 -13 -15 -7 9 India 0 0 0 0 0 3 1 0 -1 0 Korea 0 0 0 3 -14 0 0 0 0 0 SEA A -1 0 2 -7 -7 -1 0 A 0 OSA 0 0 0 -1 -7 3 0 -5 0 0 Ociana -3 -3 -6 -1 A -3 -29 -3 -27 13 AUS -2 -3 -3 -2 A -3 -30 0 -29 13 NZL -5 A -33 0 -2 0 -29 -32 -25 16 Canada 4 0 -3 -5 0 13 0 -17 -6 0 USA -3 -1 -1 1 14 -3 -22 -17 -19 26 MEX -2 -2 -1 -2 -1 -1 0 0 -2 0 SAMN -1 -1 -13 9 -5 8 0 0 -1 0 SAMS -2 -1 -15 24 -13 28 2 -6 -7 0 Aggregate -1 -1 -1 -1 A 6 -16 -13 -6 2
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Table 5.3 Perfect Competition vs. Imperfect Competition: BASE PRICE CHANGE (•/.)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU 0 0 0 0 0 0 -1 0 1 -1 EEU 0 0 0 -1 0 -3 -1 0 -2 -2 China -2 0 -2 -25 1 -20 1 0 -1 0 Japan -2 1 -16 -14 -1 0 1 0 1 0 India -1 -2 1 0 0 -1 -1 0 -3 -2 Korea 0 0 1 0 0 2 1 0 0 0 SEA 1 0 1 0 1 -11 -1 0 1 0 OSA 0 0 1 0 1 3 -1 0 0 -2 Ociana 2 0 4 3 1 8 4 0 0 -3 AUS -1 -1 5 4 1 8 1 0 -2 -6 NZL 4 0 4 2 1 9 7 0 1 0 Canada 0 0 0 0 0 1 0 0 1 -1 USA 0 0 0 0 0 2 2 0 0 -1 MEX -2 0 -1 0 0 2 2 0 0 0 SAMN 0 0 -5 0 0 3 -1 0 0 -2 SAMS -3 1 1 0 1 1 -2 0 -1 4 Aggregate -1 0 0 -1 1 -2 0 0 0 -2
PRRODUCTION CHANGE (*/•)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU 0 0 0 0 -5 0 1 7 10 -2 EEU 0 0 0 0 0 -1 51 75 0 0 China 0 0 0 0 1 0 0 0 -4 0 Japan 0 0 -26 -2 0 0 0 0 0 0 India 0 0 0 0 0 -1 0 -20 0 0 Korea 0 0 0 0 0 0 0 0 0 0 SEA 0 0 0 0 0 0 0 0 0 0 OSA 0 0 0 0 0 0 0 0 0 0 Ociana 0 0 5 3 -11 10 -19 2 -36 -11 AUS 0 0 -4 -3 17 2 -17 -31 0 0 NZL 0 0 17 5 -20 15 -26 6 -84 -11 Canada 0 0 0 0 0 0 0 0 0 0 USA 0 0 0 0 17 1 -1 0 0 0 MEX 0 0 0 1 1 0 0 0 1 0 SAMN 0 0 0 0 0 0 0 0 0 -100 SAMS 0 0 -3 -8 30 0 0 -23 -71 -33 Aggregate 0 0 0 0 0 0 0 0 0 -1
CONSUMPTION CHANGE (•/.)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU 0 0 0 0 0 -1 1 0 -1 1 EEU 0 0 0 0 0 4 0 0 0 0 China 0 0 0 4 0 2 -1 0 0 0 Japan 0 0 0 2 0 0 -2 0 0 -2 India 0 0 0 0 0 3 0 0 1 0 Korea 0 0 0 0 0 0 0 0 0 0 SEA 2 0 -1 0 0 6 0 0 0 0 OSA 0 0 0 0 -1 -3 0 0 0 0 Ociana 0 0 0 1 -4 0 -2 0 10 -1 AUS 0 0 0 2 -4 0 -5 0 17 0 NZL 0 0 0 0 -1 0 0 0 0 -4 Canada 0 0 0 -2 0 1 0 0 -2 0 USA 0 0 0 0 0 -1 -23 0 0 0 MEX 0 0 0 0 0 0 -1 0 1 0 SAMN 0 0 5 0 0 -4 -2 0 0 -8 SAMS -1 0 -1 1 -2 5 0 0 0 -33 Aggregate 0 0 0 0 0 0 0 0 0 -1
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Table 5.4 Impacts of DDA: Central Doha Scenario PRICE •/. CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU -9 -5 -5 -12 -8 -3 -3 -5 -5 -1 EEU 0 0 -1 1 7 4 -3 -4 0 0 China 0 -1 -3 1 4 5 1 -4 0 0 Japan 0 -1 2 -1 2 9 -I -3 1 0 India 0 0 3 1 5 II -3 -8 0 -1 Korea 0 0 3 0 1 2 0 -5 0 0 SEA -1 -1 3 1 4 3 -3 -6 0 0 OSA 0 0 0 0 3 4 -3 -8 0 -1 Ociana 2 0 4 1 4 4 1 -2 0 I AUS 2 -1 5 1 3 5 -3 -1 0 1 NZL 1 1 3 2 5 4 5 -3 1 1 Canada -1 -2 0 -8 -2 -1 -2 -3 -2 1 USA -1 0 2 8 0 0 1 -3 1 1 MEX 0 0 -1 1 1 3 1 -6 0 0 SAMN 0 0 0 0 0 3 0 -4 0 0 SAMS 6 0 -1 5 9 4 1 -4 4 0 Aggregate -2 -1 -1 -2 2 2 -1 -5 -1 -1
PRODUCTION •/. CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU 0 1 -2 4 -19 1 4 317 1 4 EEU 0 0 0 -2 46 -5 0 -50 0 0 China 0 0 0 -3 2 -10 0 0 0 0 Japan 0 0 0 0 -2 1 0 0 -2 0 India 0 0 0 0 188 -10 0 -3 3 0 Korea 0 0 0 0 0 0 0 0 0 0 SEA 0 0 0 0 0 0 0 0 0 0 OSA 0 0 0 0 0 0 0 0 0 0 Ociana 1 0 14 -4 -1 0 23 -21 0 -16 AUS 1 0 29 -10 -19 I 40 -82 0 -33 NZL 1 0 0 -1 3 0 -18 -2 0 -3 Canada 0 0 0 0 -5 0 3 0 -2 0 USA 0 0 -1 0 -28 0 -3 0 -1 1 MEX 0 0 0 0 0 11 0 0 0 0 SAMN 0 0 0 0 0 0 0 0 0 0 SAMS 2 0 2 -2 12 -4 -99 -9 0 0 Aggregate 0 0 0 0 -1 -1 1 2 1 1
CONSUMPTION % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU 1 1 1 4 2 1 3 3 5 1 EEU 0 0 0 0 -3 -1 1 2 0 0 China 0 0 0 0 -1 -1 0 2 0 0 Japan 0 0 0 0 -1 -2 1 2 0 0 India 0 0 0 0 -2 -2 2 2 0 0 Korea 0 0 -1 0 0 0 0 2 0 0 SEA 0 0 -2 0 -1 -1 1 3 0 0 OSA 0 0 0 0 -1 -2 1 4 0 0 Ociana 0 0 -2 0 -1 -1 -1 1 0 -1 AUS 0 0 -2 0 -1 -1 2 1 0 -1 NZL 0 0 -3 0 -2 -2 -3 0 -1 -1 Canada 2 0 0 4 1 28 1 2 2 0 USA -1 0 -1 -4 0 -4 -1 2 -1 0 MEX 0 0 0 0 0 0 0 3 0 0 SAMN 0 0 0 0 0 -1 0 2 0 0 SAMS -1 0 0 -1 -6 -1 -1 2 0 0 Aggregate 0 0 0 0 -1 -1 1 2 1 1
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210
Table 5.5 Impacts of DDA: Doha Domestic Support Scenario PRICE % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU -1 -I -1 -1 -1 -1 -1 -1 -1 -1 EEU 0 0 0 -1 0 0 -1 -1 0 -1 China 0 0 0 -2 0 1 0 -1 0 5 Japan 0 0 0 0 0 0 0 -1 0 5 India 0 0 1 0 0 -1 -1 0 -1 Korea 0 0 1 0 0 0 0 -1 0 5 SEA 0 0 1 -2 0 0 0 -1 0 4 OSA 0 0 0 0 0 -1 -1 0 -1 Ociana 0 0 I 3 0 -2 -1 ■1 0 5 AUS -1 -2 1 6 0 -4 -1 ■1 0 5 NZL 0 1 1 -2 0 0 -1 -1 0 5 Canada 0 -2 1 -1 0 1 1 -1 1 2 USA -1 0 2 0 0 0 0 -I 1 5 MEX 0 0 0 -2 0 0 0 0 8 SAMN 0 0 0 -2 0 0 0 -1 0 -1 SAMS 0 0 -1 -1 0 0 1 -1 -1 -1 Aggregate 0 0 0 0 0 0 0 -1 0 1
PRODUCTION •/* CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU 0 0 -1 1 -2 1 -1 56 1 3 EEU 0 0 0 0 0 0 0 0 0 0 China 0 0 0 -1 0 -2 0 0 0 0 Japan 0 0 3 0 0 0 0 0 0 0 India 0 0 0 0 150 -8 0 -3 1 0 Korea 0 0 0 0 0 0 0 0 0 0 SEA 0 0 0 0 0 0 0 0 0 0 OSA 0 0 0 0 0 0 0 0 0 0 Ociana 0 0 4 0 -4 0 25 -3 0 -12 AUS 0 0 9 -1 -21 0 35 -11 0 -25 NZL 0 0 0 0 0 0 0 0 0 -3 Canada 0 0 0 0 5 0 0 0 0 0 USA 0 0 -1 0 -99 -1 0 0 -1 -2 MEX 0 0 0 0 0 0 0 0 0 0 SAMN 0 0 0 0 0 0 0 0 0 0 SAMS 0 0 1 -1 1 0 -99 -5 -36 0 Aggregate 0 0 0 0 0 0 0 0 0 -1
CONSUMPTION % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU 0 0 0 0 0 0 1 0 1 1 EEU 0 0 0 0 0 0 0 1 0 0 China 0 0 0 0 0 0 0 2 0 -2 Japan 0 0 0 0 0 0 0 1 0 -3 India 0 0 0 0 0 0 1 0 0 0 Korea 0 0 0 0 0 0 0 0 0 -2 SEA 0 0 -1 1 0 0 0 0 0 -2 OSA 0 0 0 0 0 0 0 0 0 0 Ociana 0 0 -1 0 0 1 0 1 0 -4 AUS 0 0 0 -1 0 1 0 1 0 -4 NZL 0 0 -1 0 0 0 0 0 0 -4 Canada 0 0 0 0 0 -3 0 0 -1 -1 USA 0 0 -1 0 0 -2 1 1 -1 -1 MEX 0 0 0 0 0 0 0 0 0 -2 SAMN 0 0 0 1 0 0 0 1 0 0 SAMS 0 0 0 0 0 0 -1 1 0 0 Aggregate 0 0 0 0 0 0 0 0 0 -1
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211
Table 5.6 Impacts of DDA: Doha Market Access Scenario PRICE % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM EU -3 -2 -2 -5 -3 -1 -1 -1 -2 EEU 0 0 0 2 1 3 0 0 0 China 0 0 0 -1 0 1 1 -2 0 Japan 0 0 -1 0 0 0 0 -2 0 India 0 0 0 0 1 3 -1 -1 0 Korea 0 0 -1 0 0 1 1 -2 0 SEA 0 0 -1 2 0 2 -1 -1 0 OSA 0 0 0 0 0 -1 -1 -2 0 Ociana 2 2 1 -1 1 -1 -1 -2 0 AUS 3 5 2 -4 1 -4 -1 -1 0 NZL 1 0 -1 3 1 3 -1 -2 0 Canada -1 -1 0 -8 -2 0 -4 -2 0 USA 0 0 0 9 0 0 3 -2 0 MEX 0 0 0 1 0 0 2 -1 0 SAMN 0 0 0 1 0 2 1 -1 0 SAMS 1 0 0 1 1 3 1 -1 0 Aggregate -1 0 -1 -1 0 1 0 -1 -1
PRODUCTION % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM EU 0 0 0 -1 0 -3 0 -22 2 EEU 0 0 0 0 0 0 0 0 0 China 0 0 0 -1 0 -2 0 0 0 Japan 0 0 0 0 0 0 0 0 0 India 0 0 0 0 -75 4 0 0 2 Korea 0 0 0 0 0 0 0 0 0 SEA 0 0 0 0 0 0 0 0 0 OSA 0 0 0 0 0 0 0 0 0 Ociana 1 0 2 0 2 -3 30 3 0 AUS 1 -1 26 -12 2 -6 43 -79 0 NZL 0 0 -22 6 2 -1 0 27 0 Canada 0 0 0 -1 5 -2 0 0 0 USA 0 0 0 1 0 3 -3 0 0 MEX 0 0 0 0 0 0 0 0 0 SAMN 0 0 0 0 0 0 0 0 0 SAMS 0 0 -1 2 3 0 -99 9 -36 Aggregate 0 0 0 0 0 0 0 0 0
CONSUMPTION % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM EU 1 0 0 2 1 0 1 0 2 EEU 0 0 0 0 0 -1 0 0 0 China 0 0 0 0 0 0 -1 2 0 Japan 0 0 0 0 0 0 0 1 0 India 0 0 0 0 0 -1 1 0 0 Korea 0 0 0 0 0 0 0 1 0 SEA 0 0 0 -1 0 -1 1 0 0 OSA 0 0 0 0 0 0 1 0 0 Ociana 0 0 -1 0 0 1 1 1 0 AUS 0 -1 -1 0 0 1 1 1 0 NZL 0 0 1 0 0 -1 0 0 0 Canada 1 0 0 5 1 2 1 1 0 USA 0 0 0 -4 0 -1 -3 1 0 MEX 0 0 0 0 0 0 0 1 0 SAMN 0 0 0 0 0 -1 -1 1 0 SAMS 0 0 0 0 -1 -1 -1 0 0 Aggregate 0 0 0 0 0 0 0 1 0
LAC -1 -3 -1 -1 0 -1 0 0 0 0 0 1 1 1 1
LAC 3 0 0 0 0 0 0 0
-16 -33 -3
-100 1 0
-100 0 1
LAC 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
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212
Table 5.7 Impacts of DDA: Doha Export Subsidy Scenario PRICE */• CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM EU -7 -4 -4 -9 -6 -3 -3 -4 -4 EEU 0 0 -1 2 5 4 -4 -4 0 China 0 -1 -3 3 3 2 -1 -3 0 Japan 0 0 1 -2 1 9 0 -3 1 India 0 0 2 1 3 7 -3 -5 0 Korea 0 -1 2 -1 2 3 -1 -3 0 SEA 0 0 2 3 3 3 -2 -4 0 OSA 0 0 0 0 3 5 -3 -4 0 Ociana 2 2 2 8 2 2 0 -3 0 AUS 3 3 2 12 1 2 -3 -3 0 NZL 1 1 2 3 3 3 2 -4 0 Canada -1 -1 0 -4 -2 -1 -3 -3 -2 USA 0 0 0 0 0 0 -1 -3 0 MEX 1 0 -2 2 3 4 -1 -4 0 SAMN 0 0 0 2 0 3 -1 -4 0 SAMS 5 0 -1 5 7 3 1 -4 6 Aggregate -1 -1 -1 1 2 2 -2 -3 -1
PRODUCTION % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM EU 0 1 -1 3 -18 1 4 156 -1 EEU 0 0 0 0 0 0 0 0 0 China 0 0 0 -1 1 -2 0 0 1 Japan 0 0 0 1 -2 1 0 0 -2 India 0 0 0 0 172 -10 0 -3 0 Korea 0 0 0 0 0 0 0 0 0 SEA 0 0 0 0 0 0 0 0 0 OSA 0 0 0 0 0 0 0 0 0 Ociana 1 0 10 -3 1 0 7 -15 0 AUS 1 0 20 -7 ■6 0 16 -57 0 NZL 0 0 0 -1 3 -1 -14 -2 0 Canada 0 0 0 1 -5 1 3 0 -2 USA 0 0 0 0 -99 0 0 0 0 MEX 0 0 0 0 0 11 0 0 0 SAMN 0 0 0 0 0 -100 0 0 0 SAMS 2 0 2 -2 12 -6 -99 -5 0 Aggregate 0 0 0 0 -1 -1 2 2 1
CONSUMPTION */• CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM EU 1 1 1 3 1 0 4 2 4 EEU 0 0 0 0 -2 -1 1 2 0 China 0 0 0 -1 0 -1 0 2 0 Japan 0 0 0 1 -1 -2 0 2 0 India 0 0 0 0 -1 -1 2 2 0 Korea 0 0 -1 0 -1 0 0 1 0 SEA 0 0 -2 -1 -1 -1 1 2 0 OSA 0 0 0 0 -1 -2 1 0 0 Ociana 0 0 -1 -1 0 -1 0 2 0 AUS 0 0 -1 -1 0 -1 2 2 0 NZL 0 0 -2 0 -1 -1 -1 0 0 Canada 1 0 0 2 1 17 1 2 1 USA 0 0 0 -1 0 -3 1 2 0 MEX 0 0 0 0 0 0 0 2 0 SAMN 0 0 0 -1 0 -2 0 2 0 SAMS -1 0 0 -2 -5 -1 -1 2 0 Aggregate 0 0 0 0 -1 -1 2 2 1
LAC -2 -2 -1 -1 -2 -1 -3 -2 -3 -4 -3 -2 -2 -2 -2 -2 -2
LAC 4 0 0 0 0 0 0 0 3 17 -6 0 -1 0 0 0 1
LAC 1 1 1 1 1 1 2 1 3 3 3 1 1 1 1 1 1
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213
Table 5.8 World Trade Volume under Alternative Doha Scenarios
Net Imports of Dairy Products (1000 MT)
l Base
Central Doha Scenario
Doha Domestic Support Scenario
Doha Market Access Scenario
Doha Export Subsidy Scenario
Cheese 697 739 658 732 643 Butter 462 427 459 464 439 Whole Milk Powder 1496 1448 1488 1500 1465 Skim Milk Powder 917 937 915 930 900 Dry Whey 641 645 638 636 646 Casein 247 198 238 252 223 CEM 443 445 445 448 439
Change from Base (%)
Central Doha Scenario
Doha Domestic Support Scenario
Doha Market Access Scenario
Doha Export Subsidy Scenario
Cheese 6.1 -5.6 5.1 -7.7 Butter -7.7 1 o 0.5 -5.0 Whole Milk Powder -3.2 -0.5 0.3 -2.1 Skim Milk Powder 2.2 -0.2 1.4 -1.8 Dry Whey 0.6 -0.4 -0.8 0.7 Casein -19.9 -3.6 2.1 -9.7 CEM 0.4 0.4 1.1 -0.9
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214
Figure 5.2: Aggregate Welfare Impacts (2009) World
5,000 ------------------------------------------------------------------------------------------------------------------------------
■ ^Reduction ^ ^ ■ r i f f reduction ^ ^ ■ e s tic Reduction ^ ^ K p o r t Subsidy
-5,000
■Producer Surplus ■ConsumerSurplus ■Revenues ■Total Welfare
Figure 5.3: Aggregate Welfare Impacts (2009) China
100 ---------------------------------------------------------------------------------- 80
60
^ 4 0<»
-40
-60
-80
-100
■Producer Surplus ■Consumer Surplus ■Revenues ■Total Welfare
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215
Figure 5.4 Aggregate Welfare Impacts (2009) Japan
I Producer Surplus ■ Consumer Surplus
I Revenues ■ Total Welfare
100
80
60
£ 40
| 20 9* 0£« F Tariff reduction Domestic Reduction > -20
-40
-60
-80
-100
Figure 5.5: Aggregate Welfare Impacts (2009) India
(Producer Surplus I Consumer Surplus I Revenues BTotal Welfare
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216
Figure 5.6: Aggregate Welfare Impacts (2009) Korea
100
£ Full Reduction Tariff reduction Domestic Reduction Export Subsidy
-80
-100
■Producer Surplus ■ Consumer Surplus ■ Revenues iT o ta l Welfare
Figure 5.7: Aggregate Welfare Impacts (2009) SEA
100 '■ 80
60
S ' 40 V3 S» 20
-60
-80
-100
■Producer Surplus ■Consumer Surplus ■Revenues ■ Total Welfare
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
217
Figure 5.8: Aggregate Welfare Impacts (2009) EU
5,000
•5,000
II ReductHra riff reduction m i a t k Reductiontion
I Producer Surplus ■Consumer Surplus ■ Revenues ■ Total Welfare
150 ■
3 e £TJ £
o
Figure 5.9: Aggregate Welfare Impacts (2009) Australia
Tariff reductkn
I Producer Surplus ■ Consumer Surplus ■Revenues I Total Welfare
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218
Figure 5.10: Aggregate Welfare Impacts (2009) New Zealand
150 ------------------------------------------------------------------------------------------------------------------------
FflHWUuction Ta^^Hduction Export Subsidy
-150
■Producer Surplus ■Consumer Surplus ■Revenues ■ Total Welfare
Figure 5.11: Aggregate Welfare Impacts (2009) US
1,500
Export Subsidy
-1,500
■Producer Surplus ■Consumer Surplus ■Revenues ■ Total Welfare
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219
Figure 5.12: Aggregate Welfare Impacts (2009) Canada
100
J - i J L TJOmestic Reduction ^ ^ ■ p o r tH M H ^ ^iff reduction
-100
■Producer Surplus ■Consumer Surplus ■Revenues ■Total Welfare ___________________________________________________________
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220
Table 5.9 Impacts of DDA: Central Doha Boundary Scenario PRICE % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM EU -20 -10 -11 -20 -21 -6 -2 -11 -12 EEU 6 3 1 18 17 21 7 -9 7 China 1 -1 -7 6 7 6 1 -10 0 Japan -1 2 3 -22 -6 11 -14 -9 4 India 1 1 -3 0 13 29 -10 -15 1 Korea 0 1 -3 5 -4 -8 -7 -13 -1 SEA -5 -3 -2 17 7 10 -6 -17 -5 OSA 0 0 0 1 0 -4 -9 -25 0 Ociana 4 -5 7 16 12 2 5 -4 1 AUS -1 -8 7 7 10 -10 -1 0 0 NZL 10 -2 6 29 15 19 12 -8 2 Canada -18 -21 -11 -22 -23 -1 -15 -8 -12 USA -2 -2 6 9 0 0 4 -8 3 MEX 0 0 -2 13 1 -1 1 -18 -1 SAMN 0 0 -3 10 0 3 -10 -15 -1 SAMS 13 1 2 26 20 18 1 -8 26 Aggregate -4 -2 -4 -2 2 5 -3 -10 -2
PRODUCTION % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM EU 0 1 -1 4 -31 -10 16 411 -8 EEU 2 0 4 6 50 11 -29 -50 9 China 0 0 0 0 0 -2 0 0 1 Japan 0 0 3 -2 4 -2 0 0 -5 India 0 0 0 0 313 -14 0 9 35 Korea 0 0 0 -2 0 -2 0 0 0 SEA -1 0 0 -8 0 0 0 0 -5 OSA 0 0 0 0 0 0 0 0 0 Ociana 2 1 16 -6 11 -1 -6 -32 0 AUS 0 1 18 -19 18 -4 0 -99 0 NZL 4 0 15 0 9 1 -21 -11 0 Canada 0 3 -3 8 -100 23 -62 0 -25 USA -1 0 -2 0 148 -2 -4 0 -2 MEX 0 0 0 1 -3 33 0 0 0 SAMN 0 0 -1 0 0 0 0 0 0 SAMS 4 0 12 -10 12 9 -99 -64 325 Aggregate 0 0 1 1 -1 -1 1 5 2
CONSUMPTION % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM EU 3 1 3 9 5 6 2 6 11 EEU -I 0 0 -3 -7 -6 -2 5 -1 China 0 0 0 -1 -1 -2 -1 7 0 Japan 0 0 0 6 4 -3 7 5 -3 India 0 0 0 0 -5 -6 5 4 0 Korea 0 0 1 -1 2 1 3 6 1 SEA -1 0 2 -7 -3 -4 3 7 3 OSA 0 0 0 -1 0 2 4 8 0 Ociana 0 1 -3 -1 -3 2 -5 0 -1 AUS 0 1 -3 •1 -3 3 1 0 0 NZL 0 0 -6 -3 -7 -7 -8 3 -1 Canada 9 3 3 28 10 60 5 4 11 USA -2 0 -2 -4 0 -16 -6 4 -3 MEX 0 0 0 -2 0 0 0 8 0 SAMN 0 0 2 -5 0 -1 7 7 0 SAMS -2 0 -1 -7 -13 -5 -1 4 -2 Aggregate 0 0 1 1 -1 -2 1 5 2
LAC 0 0 2 5 -2 4 5 -1 6 16 -3 12 19 7 4 5 4
LAC 3 0 0 0 0 0 0 0
-17 -71 21 0 -1 0 0 0 -2
LAC 0 0 -1 -3 1
-2 -3 1 1
-13 3 -7 -5 -2 -2 -2 -2
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Table 5.10 Impacts of DDA: Doha Domestic Support Boundary Scenario PRICE •/* CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU -1 0 0 0 0 O' 1 0 0 3 EEU 0 0 0 0 0 1 2 0 0 7 China 0 0 0 0 1 1 0 -1 0 13 Japan 0 0 1 0 0 1 0 -1 0 13 India 0 0 2 0 1 1 1 0 0 7 Korea 0 0 2 0 1 2 0 -1 0 12 SEA 0 0 2 0 1 1 1 0 0 10 OSA 0 0 0 0 1 1 1 0 0 7 Ociana -3 -6 2 5 1 -1 1 -1 0 10 AUS -7 -14 2 8 1 -4 1 -1 0 14 NZL 0 1 2 0 1 2 1 -1 0 7 Canada 1 -5 2 0 2 0 14 -1 2 10 USA -2 -2 7 0 0 0 0 -1 3 18 MEX 0 0 0 0 0 1 0 0 0 21 SAMN 0 0 0 0 0 1 0 0 0 7 SAMS 1 0 0 1 1 1 1 0 0 7 Aggregate 0 0 2 0 0 1 1 -1 1 7
PRODUCTION •/. CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU 0 0 0 0 0 1 -2 -11 0 4 EEU 0 0 0 0 0 0 0 0 0 0 China 0 0 0 0 0 -1 0 0 0 0 Japan 0 0 3 0 -2 0 0 0 0 0 India 0 0 0 0 84 -3 0 2 1 0 Korea 0 0 0 0 0 0 0 0 0 0 SEA 0 0 0 0 0 0 0 0 0 0 OSA 0 0 0 0 0 0 0 0 0 0 Ociana -1 1 -3 0 -4 2 0 2 0 -24 AUS -3 2 -6 -1 -21 4 0 7 0 -58 NZL 0 0 0 0 0 0 0 0 0 0 Canada 0 1 0 0 0 1 -6 0 -4 0 USA -1 0 -2 -1 -99 -3 0 0 -3 -9 MEX 0 0 0 0 0 0 0 0 0 0 SAMN 0 0 0 0 0 0 0 0 0 0 SAMS 0 0 1 -1 1 0 -99 -9 0 0 Aggregate 0 0 -1 0 0 0 -1 0 -1 -4
CONSUMPTION % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU 0 0 0 0 0 0 -2 0 0 -3 EEU 0 0 0 0 0 0 -1 0 0 -3 China 0 0 0 0 0 0 0 2 0 -6 Japan 0 0 0 0 0 0 0 1 0 -8 India 0 0 0 0 0 0 -1 0 0 -3 Korea 0 0 -1 0 0 0 0 0 0 ■6 SEA 0 0 -1 0 0 0 0 0 0 -6 OSA 0 0 0 0 0 0 -1 0 0 -4 Ociana 0 1 -1 -1 0 1 -1 1 0 -7 AUS 1 2 -1 -1 0 1 -1 1 0 -11 NZL -1 0 -2 0 0 -1 -1 0 0 -7 Canada 0 1 -1 0 -1 -1 -5 1 -2 -6 USA -1 0 -2 -1 0 -1 0 1 -3 -5 MEX 0 0 0 0 0 0 0 0 0 -7 SAMN 0 0 0 0 0 -1 0 0 0 -3 SAMS 0 0 0 0 0 0 -1 0 0 -3 Aggregate 0 0 -1 0 0 0 -1 0 -1 4
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222
Table 5.11 Impacts of DDA: Doha Market Access Boundary Scenario PRICE % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM EU -13 -7 -7 -20 -11 -3 0 -5 -7 EEU 7 3 4 19 14 8 1 -4 2 China 0 0 -1 4 -1 -3 3 -5 -1 Japan -1 3 -4 -22 -9 0 -13 -5 4 India 1 0 -2 1 6 12 -9 -6 1 Korea -1 3 -2 5 -10 -18 -6 -8 -2 SEA -5 -3 -1 18 0 1 -5 -10 -5 OSA 0 0 0 0 -6 -17 -7 -19 0 Ociana 8 2 7 21 5 0 2 -4 1 AUS 7 6 7 16 4 -3 2 -4 1 NZL 8 -1 7 30 6 5 2 -4 1 Canada -15 -11 -12 -22 -28 -1 -16 -4 -10 USA 1 0 -1 12 0 0 6 -4 0 MEX -2 -1 3 14 -6 -16 2 -12 -2 SAMN -1 -1 -2 11 -6 -8 -9 -11 -1 SAMS 7 0 3 21 7 6 1 -4 2 Aggregate -2 -1 -4 -2 -4 -1 -1 -5 -3
PRODUCTION % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM EU 0 1 0 -1 -3 -5 3 28 6 EEU 2 0 6 6 50 3 12 -50 -1 China 0 0 0 -1 0 -1 0 0 1 Japan 0 0 3 -2 5 -2 0 0 -5 India 0 0 0 0 231 -11 0 3 24 Korea 0 0 0 -2 0 -2 0 0 0 SEA -2 0 0 -8 0 0 0 0 -5 OSA 0 0 0 0 0 0 0 0 0 Ociana 3 0 14 2 2 0 35 -6 0 AUS 3 -1 29 -7 -5 -3 49 -68 0 NZL 3 0 0 6 3 2 4 13 0 Canada 0 1 -3 8 -87 22 -66 0 17 USA 0 0 0 1 0 2 -3 0 0 MEX 0 0 -1 1 -2 22 0 0 -3 SAMN 0 0 -4 0 -2 0 0 0 -3 SAMS 2 0 6 -2 5 21 -99 -27 0 Aggregate 0 0 1 1 1 1 -1 3 2
CONSUMPTION % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM EU 2 1 2 9 3 1 0 3 7 EEU -1 0 -1 -3 -5 -2 0 2 0 China 0 0 0 -1 0 1 -1 4 0 Japan 1 0 0 6 6 0 6 3 -3 India 0 0 0 0 -2 -3 5 2 0 Korea 0 0 1 -1 4 2 2 4 1 SEA 0 0 1 -7 0 0 2 4 3 OSA 0 0 0 0 3 8 3 8 0 Ociana 0 0 -3 -2 -1 1 -1 2 0 AUS -1 -1 -3 -1 -1 1 -1 2 0 NZL 1 0 -7 -3 -3 -2 -2 0 -1 Canada 6 1 3 24 13 35 5 2 9 USA 0 0 0 -5 0 0 -8 2 0 MEX 0 0 0 -2 0 2 0 6 0 SAMN 1 0 1 -5 4 4 6 6 0 SAMS -1 0 -2 -6 -4 -2 -1 2 0 Aggregate 0 0 1 1 1 1 -1 3 2
LAC 2 -3 •1 2 0 2 '
-7 1
-11 -2 -19 11 17 3 4 5 2
LAC -20 0 0 0 0 0 0 0 14 -25 41 100 8 0
300 0 -1
LAC -2 1 0 -1 0 -1 4 0 16 2 18 -7 -5 -1 -2 -2 -1
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Table 5.12 Impacts of DDA: Doha Export Subsidy Boundary Scenario PRICE •/. CHANGE (FROM BASE 2009)
MILK RES CHE EU -15 -9 -9
BUT -20
EEU 0 0 -4 6 China 1 -2 -9 2 Japan 0 -2 11 2 India 1 0 3 1 Korea 1 -3 3 0 SEA 2 1 3 8 OSA 0 0 0 0 Ociana 4 2 3 1 AUS 4 7 2 -5 NZL 4 -2 3 10 Canada -3 -2 -3 -4 USA 0 0 0 1 MEX 4 2 -7 8 SAMN 0 0 -1 8 SAMS 9 1 -2 10 Aggregate -2 -1 -4 -3
PRODUCTION •/. CHANGE (FROM BASE 2009)
EU MILK 0
RES 1
CHE 1
BUT 7
EEU 0 0 -2 0 China 0 0 0 2 Japan 0 0 6 0 India 0 0 0 0 Korea 0 0 0 0 SEA 0 0 0 0 OSA 0 0 0 0 Ociana 2 0 11 -8 AUS 2 -1 15 -19 NZL 2 0 8 -3 Canada 0 0 0 1 USA 0 0 0 0 MEX 0 0 3 2 SAMN 0 0 1 1 SAMS 3 0 8 -5 Aggregate 0 0 1 1
WMP SMP DWH CAS CEM LAC -18 -6 -5 -10 -13 -2 21 22 23 -10 6 -3 11 14 -1 -9 1 0 4 14 0 •8 0 0 12 27 -3 -15 1 -2 11 19 -1 -10 2 0 11 17 -3 -11 1 0 10 21 -3 -12 0 -2 11 7 2 -5 0 0 9 -3 -5 -2 -1 0 13 21 9 -8 1 0 -4 -1 -1 -8 -6 -1 0 0 -1 -8 0 -2 13 22 -1 -11 2 -1 1 18 -1 -9 0 -3 17 18 1 •8 25 -2 7 8 -2 -9 -1 0
WMP SMP DWH CAS CEM LAC -56 6 17 278 -8 4 54 3 -50 0 9 0 -1 5 0 0 0 0 -4 0 0 0 0 0
294 -13 0 5 9 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 18 -5 -17 -32 0 -21 54 -12 0 -89 0 -46 9 0 -57 -14 0 -3 -5 0 0 0 -27 0
592 0 -3 0 1 5 -1 44 0 0 -1 0 0 100 0 0 0 0 12 7 -99 -36 107 0 -3 1 2 5 2 0
CONSUMPTION % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU 4 1 2 9 4 23 5 5 12 2 EEU 0 0 1 -1 -9 -6 -7 6 -1 1 China 0 0 0 0 -2 -4 0 6 0 0 Japan -1 0 -1 0 -3 -3 0 4 0 0 India 0 0 0 0 -5 -6 2 4 0 1 Korea 0 0 -1 0 -4 -2 0 5 -1 0 SEA -3 0 -2 -3 -4 -7 1 5 0 0 OSA 0 0 0 0 -5 -10 I 4 0 1 Ociana 0 0 -1 0 -2 0 -3 1 -1 0 AUS -1 -1 -1 0 -2 1 4 1 0 0 NZL 0 0 -3 -1 -6 -8 -6 3 -1 0 Canada 2 0 1 2 2 17 0 4 5 1 USA -1 0 0 -2 0 -13 1 4 0 0 MEX 0 0 1 -1 -1 -2 0 5 0 0 SAMN 0 0 1 -4 -1 -10 0 4 0 1 SAMS -1 0 1 -3 -11 -5 -1 4 -2 1 Aggregate 0 0 1 1 -3 1 2 5 2 0
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224
Table 5.13 World Trade Volume under Alternative Doha Boundary Scenarios
N et Im p o rts o f D airy P ro d u cts (1000 M T)
Base
Central Doha Boundary Scenario
Doha Domestic Support Boundary Doha M arket Access Doha Export Subsidy
Scenario Boundary Scenario Boundary Scenario Cheese 697 800 662 799 631 Butter 462 463 461 533 421 Whole Milk Powder 1496 1482 1490 1579 1395 Skim Milk Powder 917 936 913 929 835 Dry Whey 641 694 639 678 670 Casein 247 191 240 246 210 CEM 443 453 442 500 386
Change from Base (% )
Central Doha Boundary Scenario
Doha Domestic Support Boundary Doha M arket Access Doha Export Subsidy
Scenario Boundary Scenario Boundary Scenario Cheese 14.9 -4.9 14.7 -9.5 Butter 0.2 -0.2 15.3 -9.0 Whole Milk Powder -0.9 -0.4 5.6 -6.8 Skim Milk Powder 2.1 -0.5 1.3 -9.0 Dry Whey 8.3 -0.4 5.8 4.6 Casein -22.8 -3.0 -0.4 -15.1 CEM 2.3 -0.2 12.8 -13.0
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225
Table 5.14 Welfare Impacts under Alternative Doha Boundary Scenarios
Producer Surplus
Consumer Surplus Treasury Total Welfare
EU Full Reduction -9854 8056 1603 -195
Tariff reduction -5815 5645 232 62
Domestic Reduction -1156 -6 629 -533
Export Subsidy -6939 6861 692 614
China
Full Reduction 41 -65 -23 -47
Tariff reduction -2 2 -22 -22
Domestic Reduction 3 -7 1 -3
Export Subsidy 66 -104 -3 -41
Japan
Full Reduction -50 48 -15 -17
Tariff reduction -63 113 -28 22
Domestic Reduction 0 -15 4 -11
Export Subsidy 9 -96 21 -66 India
Full Reduction 405 -409 -1 -5
Tariff reduction 260 -262 -1 -3
Domestic Reduction 83 -83 0 0
Export Subsidy 301 -308 0 -7
Korea
Full Reduction -4 16 -10 2
Tariff reduction -11 23 -9 3
Domestic Reduction 2 -5 0 -3
Export Subsidy 16 -23 -1 -8
SEA
Full Reduction -59 -63 -68 -190
Tariff reduction -64 49 -74 -89
Domestic Reduction 2 -16 1 -13
Export Subsidy 20 -213 19 -174
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226
Continued Producer Surplus
Consumer Surplus Treasury Total Welfare
Oceania Full Reduction 255 46 73 374
Tariff reduction 405 -130 12 287
Domestic Reduction -145 126 0 -19
Export Subsidy 225 -69 72 228
Australia
Full Reduction -22 42 73 93
Tariff reduction 236 -3 0 233
Domestic Reduction -156 136 0 -20
Export Subsidy 99 -88 72 83
New Zealand
Full Reduction 277 4 0 281
Tariff reduction 169 -127 12 54
Domestic Reduction 11 -10 0 1
Export Subsidy 126 19 0 145 USA
Full Reduction -1327 -484 2265 454
Tariff reduction 168 -70 -35 63
Domestic Reduction -1422 -421 2030 187
Export Subsidy 49 -81 287 255 Canada
Full Reduction -658 818 -95 65
Tariff reduction -546 591 -76 -31
Domestic Reduction 31 79 0 110
Export Subsidy -124 135 -2 9
World Full Reduction -9841 6378 3384 -79
Tariff reduction -4656 5278 -426 196
Domestic Reduction -2471 -525 2649 -347
Export Subsidy -4922 3999 1135 212
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227
Table 5.15 Impacts on Milk under Alternative Scenarios
PRICE % CHANGE (FROM BASE 2009)
No Tariff No Quota No Export
Subsidy EU -20.4 -5.4 -15.3 China -0.4 0.7 1.3 Japan -24.6 -18.2 0.2 India 3.2 0.9 1.0 Korea -20.5 -14.0 1.5 SEA -18.9 2.8 1.7 OSA 1.0 0.0 0.0 Ociana 15.6 7.8 4.4 AUS 13.3 9.0 4.3 NZL 18.2 6.4 4.5 Canada -26.6 -25.6 -3.4 USA 2.9 0.2 0.4 Aggregate -5.2 -1.9 -2.2
PRODUCTION % CHANGE (FROM BASE 2009)
EU 0.0 0.0 0.0 China -0.1 0.1 0.3 Japan -9.1 -6.7 0.1 India 0.6 0.2 0.2 Korea -5.2 -3.6 0.4 SEA -5.3 0.8 0.5 OSA 0.1 0.0 0.0 Ociana 6.4 3.0 1.8 AUS 5.3 3.6 1.7 NZL 7.2 2.6 1.8 Canada 0.0 0.0 0.0 USA 1.3 0.1 0.2 Aggregate 0.5 0.2 0.3
CONSUMPTION % CHANGE (FROM BASE 2009)
EU 2.9 0.6 3.9 China 0.1 -0.3 -0.4 Japan 4.1 2.3 -0.6 India -0.5 -0.1 -0.1 Korea 3.3 1.8 -0.1 SEA 2.1 -1.5 -2.7 OSA -0.1 -0.1 -0.1 Ociana -1.4 -0.4 -0.5 AUS -1.9 -1.1 -0.7 NZL -0.6 0.7 -0.1 Canada 9.7 9.9 1.9 USA -2.5 0.1 -0.9 Aggregate 0.3 0.1 0.2
No Trade No Domestic Full Policeis Support Liberalizaion
-21.7 -52.8 -49.5 -0.3 -0.7 -1.0 -26.8 -7.8 -41.1 3.7 -2.8 0.2
-21.7 -0.9 -21.4 -18.7 -2.9 -20.4 0.8 0.0 0.4 14.3 -9.2 5.7 10.4 -9.1 -4.6 18.7 -9.3 17.0
-34.4 -34.2 -44.7 2.9 -11.9 -11.5 -5.8 -15.5 -16.5
0.0 15.6 16.1 -0.1 -0.1 -0.2 -9.9 -2.9 -15.2 0.7 -0.6 0.0 -5.5 -0.2 -5.4 -5.3 -0.8 -5.7 0.1 0.0 0.0 6.0 -3.7 3.1 4.2 -3.7 -1.8 7.4 -3.7 6.8 0.0 7.2 6.0 1.2 -5.2 -5.0 0.4 1.5 1.6
2.6 7.7 6.1 0.1 0.2 0.2 4.5 0.3 5.3«n©a 0.0 -0.1 3.4 0.0 3.2 2.2 2.1 3.3 -0.1 0.1 0.1 -1.2 1.7 0.1 -1.7 1.8 0.7 -0.5 1.5 -0.9 5.5 3.4 5.8 -2.4 -0.9 -1.5 0.3 1.4 1.4
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228
Table 5.16 Impacts on Cheese under Alternative Scenarios
PRICE % CHANGE (FROM BASE 2009)
No Export No Trade No Domestic Full No Tariff No Quota Subsidy Policeis Support Liberalizaion
EU -12.3 -2.5 -8.7 -12.5 -23.9 -23.5 China 5.4 -6.8 -9.1 5.7 10.6 12.1 Japan -17.1 -2.4 10.8 -31.6 -17.0 -35.1 India -16.1 4.9 3.3 -15.7 -0.6 -18.4 Korea -16.2 5.1 2.9 -15.8 -1.2 -18.2 SEA -14.1 5.1 3.3 -13.7 -0.8 -16.8 OSA 0.0 0.0 0.0 0.0 0.0 0.0 Ociana 11.2 5.6 2.5 11.8 -2.0 6.8 AUS 11.0 5.5 2.5 11.5 -2.0 5.4 NZL 11.5 5.7 2.6 12.1 -2.0 8.4 Canada -27.9 -31.9 -2.9 -27.8 -26.5 -30.9 USA 3.0 -0.4 0.5 2.8 9.2 7.2 Aggregate -7.2 -1.8 -3.8 -7.7 -10.5 -13.7
PRODUCTION % CHANGE (FROM BASE 2009)
EU -6.2 0.4 0.7 -5.5 11.5 16.7 China 0.0 0.0 0.0 0.0 0.0 -0.4 Japan -52.8 16.7 5.6 -100.0 -100.0 -100.0 India 0.0 0.0 0.0 0.0 0.0 0.0 Korea -16.7 -16.7 0.0 -20.8 0.0 -20.8 SEA 0.0 0.0 0.0 0.0 0.0 0.0 OSA 0.0 0.0 0.0 0.0 0.0 0.0 Ociana 27.0 14.2 11.4 34.8 46.3 31.7 AUS 28.5 28.5 15.3 28.5 11.6 21.5 NZL 25.6 0.0 7.6 41.0 80.9 41.9 Canada -15.3 -47.0 0.3 -3.7 8.6 7.2 USA 2.5 0.3 0.0 2.4 -13.6 -13.3 Aggregate 1.3 0.4 0.8 1.5 2.0 2.8
CONSUMPTION % CHANGE (FROM BASE 2009)
EU 3.4 0.7 2.4 3.4 6.6 6.4 China -0.2 0.2 0.3 -0.2 -0.3 -0.4 Japan 1.9 0.3 -1.2 3.5 1-9 3.9 India 6.3 0.0 0.0 6.3 0.0 6.3 Korea 5.1 -1.6 -0.9 5.0 0.4 5.8 SEA 10.2 -3.7 -2.4 9.9 0.6 12.2 OSA 0.0 0.0 0.0 0.0 0.0 0.0 Ociana -4.8 -2.4 -1.1 -5.0 0.9 -2.6 AUS -4.1 -2.0 -0.9 -4.3 0.7 -2.0 NZL -12.0 -6.0 -2.7 -12.6 2.2 -8.8 Canada 7.8 8.9 0.8 7.7 7.4 8.6 USA -0.8 0.1 -0.1 -0.8 -2.6 -2.0 Aggregate 1.3 0.4 0.8 1.5 2.0 2.8
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229
Table 5.17 Impacts on Butter under Alternative Scenarios
PRICE % CHANGE (FROM BASE 2009)
N o E x p o r t N o T ra d e N o D o m estic F u ll N o T a r i f f N o Q u o ta S u b sid y P o lice is S u p p o r t L ib e ra liz a io n
EU -19.7 -13.6 -19.7 -19.7 -49.5 -43.0 China -18.6 7.7 2.1 -19.6 -28.4 -23.7 Japan -75.0 -68.3 1.8 -75.3 -1.6 -76.9 India 6.2 0.5 0.7 4.7 7.1 2.7 Korea -7.3 13.5 0.3 -8.5 2.6 -10.5 SEA -13.8 7.2 8.3 12.3 -8.2 -9.8 OSA 9.4 0.0 0.0 7.8 0.0 5.1 Ociana 30.2 19.8 1.3 15.9 2.5 12.7 AUS 23.2 14.3 -5.0 0.4 11.3 -2.5 NZL 40.4 27.7 10.3 38.2 -10.2 34.5 Canada -23.8 -23.8 -4.3 -11.3 -14.7 -48.9 USA 6.1 3.6 1.2 4.8 13.4 2.7 Aggregate -3.8 -2.3 -3.0 -4.9 -14.3 -15.6
PRODUCTION % CHANGE (FROM BASE 2009)
EU 3.7 -1.2 6.8 3.8 30.5 21.8 China -15.4 -0.8 2.4 -10.6 -10.6 -8.9 Japan -15.7 -15.7 0.0 -14.5 0.0 -44.6 India 0.7 -0.1 0.3 0.9 -1.9 0.1 Korea -7.6 -7.6 0.0 -7.6 -1.5 -7.6 SEA -23.1 7.7 0.0 -23.1 -7.7 -30.8 OSA 0.1 0.0 0.0 0.1 0.0 0.1 Ociana -2.2 0.5 -8.2 -7.5 -19.3 -1.4 AUS -6.3 -6.8 -19.5 -18.4 -14.2 -15.8 NZL -0.3 4.0 -2.8 -2.3 -21.7 5.5 Canada 25.0 54.8 1.2 4.8 9.5 11.9 USA 1.8 0.2 -0.2 2.2 -2.2 -1.2 Aggregate 0.8 0.5 0.9 0.4 2.8 2.8
CONSUMPTION % CHANGE (FROM BASE 2009)
EU 9.1 4.7 9.1 6.7 16.9 14.7 China 3.7 -1.6 -0.4 3.9 5.7 4.7 Japan 19.6 17.8 -0.5 19.6 0.4 20.1 India -1.7 -0.1 -0.2 -1.3 -1.9 -1.2 Korea 0.8 -1.5 0.0 0.9 -0.3 1.2 SEA 5.6 -5.0 -3.3 -5.0 3.3 4.0 OSA -4.7 0.0 0.0 -3.9 0.0 -2.5 Ociana -2.7 -1.7 0.0 -1.1 -0.5 -0.8 AUS -2.2 -1.4 0.5 0.0 -1.1 0.2 NZL -3.7 -2.5 -0.9 -3.5 0.9 -3.2 Canada 39.8 70.2 2.5 13.5 8.3 27.7 USA -3.7 -3.1 -2.3 -3.4 -5.4 -2.8 Aggregate 0.8 0.5 0.9 0.4 2.8 2.8
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230
Table 5,18 Impacts on SMP under Alternative Scenarios
PRICE % CHANGE (FROM BASE 2009)
No Export No Trade No Domestic Full No Tariff No Quota Subsidy Policeis Support Liberalizaion
EU -6.3 2.7 -6.3 -6.3 -14.8 -9.4 China -2.9 3.8 14.3 -0.9 1.4 0.7 Japan 4.7 8.2 14.0 2.8 -3.6 2.2 India 33.2 14.7 27.0 34.7 10.6 25.2 Korea -55.4 -52.3 19.2 -54.4 -13.6 -47.6 SEA -2.0 3.4 16.7 -2.2 -5.1 -4.5 OSA -46.0 5.4 20.5 -44.8 -8.0 -44.4 Ociana 16.0 7.2 7.2 12.1 5.5 23.2 AUS 13.3 6.7 -2.6 4.1 -2.3 7.9 NZL 19.7 7.9 20.5 22.9 16.1 44.3 Canada -0.6 -0.6 -0.6 -0.6 -33.3 -36.3 USA 3.8 0.0 0.4 6.3 -2.7 9.5 Aggregate -3.3 -1.5 8.4 -2.8 -5.9 -5.2
PRODUCTION % CHANGE (FROM BASE 2009)
EU -5.4 0.7 5.5 -12.1 0.3 -2.3 China -35.5 -2.4 4.8 -24.2 -22.6 -19.4 Japan -21.7 -22.2 0.0 -22.8 -12.2 -63.5 India 0.1 -2.3 0.6 1.0 -5.3 -4.1 Korea -26.7 -26.7 0.0 -28.9 -2.2 -28.9 SEA 0.0 0.0 0.0 0.0 0.0 0.0 OSA 0.0 0.0 0.0 0.0 0.0 0.0 Ociana 0.9 2.0 -5.2 -4.8 10.0 12.5 AUS 2.4 2.4 -12.1 -14.1 10.9 5.2 NZL -0.3 1.6 0.3 2.6 9.4 18.4 Canada 29.6 9.2 0.0 16.3 7.1 14.3 USA 5.6 0.1 0.2 6.2 -12.7 -9.4 Aggregate -0.6 0.6 1-3 -3.5 -2.7 -3.8
CONSUMPTION % CHANGE (FROM BASE 2009)
EU 10.1 -0.5 23.3 1.2 2.9 1.9 China 0.8 -1.1 -3.9 0.3 -0.4 -0.2 Japan -1.1 -1.9 -3.3 -0.7 0.8 -0.5 India -6.9 -3.0 -5.6 -7.2 -2.2 -5.2 Korea 6.7 6.3 -2.3 6.6 1.6 5.8 SEA 0.8 -1.4 -7.1 0.9 2.1 1.9 OSA 21.5 -2.5 -9.6 20.9 3.8 20.7 Ociana -4.4 -2.1 -0.4 -2.2 -0.2 -4.3 AUS -4.0 -2.0 0.8 -1.2 0.7 -2.3 NZL -7.5 -3.1 -7.9 -8.8 -6.1 -16.9 Canada 43.0 16.0 17.4 5.9 -24.7 -24.4 USA -22.2 6.1 -12.8 -23.1 -19.9 -24.2 Aggregate -0.7 0.7 1.4 -3.8 -3.0 -4.2
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231
Table 5.19 Farm Price Impacts of Full Dairy Sector Liberalization
(percentage change from base scenarios)
Canada EU US Oceania World Dairy Prices (Oceania)
Cheese SMP Butter
Cox and Zhu
(2004)
-43.8 -22.6 -12.2 25.9 22.3 19.9 46.0
Langley et al
(2003)
-35.0 -5.0 -8.0 26.6 33.0 10.0 60.0
Lariviere and
Meike (1999)
-36.0 -18.0 0.0 “ - “
OECE (2005) -27.9 -9.8 -12.7 28.4 34.5 21.5 57.4
Zhu, Cox and
Chavas (1999)
-32.0 -25.8 -0.4 35.5 20.3 22.1 46.2 x
Source: FAO Trade Policy Technical Notes “ Dairy - Measuring the impacts of reform”
(2004)
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232
Figure 5.13: Aggregate Welfare Impacts under Different Scenarios (2009): World
35,000
Full :ralization
T ariff io Quota ■> Export Subsidy
lee Dairy fTrade
Domesfl upports
-35,000
B Producer Surplus B Consumer Surplus B Revenues B T otsI Welfare
Figure 5.14: Aggregate Welfare Impacts under Different Scenarios (2009): China
500
No ;port Free D a^B > a d e No Domestic ;idy Supports
luota
-500
■ Producer Surplus ■Consumer Surplus ■ Revenues ■ Total Welfare
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233
Figure 5.15: Aggregate Welfare Impacts under Different Scenarios(2009): Japan
3,000
£ a
o Tariff Quota No Export Subsidy
•airy Trade I Domestic Supports
-3,000
■ Producer Surplus ■ Consumer Surplus ■ Revenues ■ Total Welfare
Figure 5.16: Aggregate Welfare Impacts under Different Scenarios (2009): India
2,000
VI3 © £a
Full Liberalization
Free Domestic upports
luota W p o i t Subsidy
- 2,000
■ Producer Surplus ■Consumer Surplus ■ Revenues ■ Total Welfare
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234
Figure 5.17: Aggregate Welfare Impacts under Different Scenarios (2009): Korea
500
Cfl I £
Libel ithm [o Quota No Export Subsidy rude No Domestic Supports
-500
■Producer Surplus ■Consumer Surplus ■ Revenues ■ Total Welfare
Figure 5.18: Aggregate Welfare Impacts under Different Scenarios (2009): SEA
500
(A £ £ £ 1
-500
No Domestic Supports
I Producer Surplus ■ Consumer Surplus ■ Revenues ■ Total Welfare
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235
Figure 5.19: Aggregate Welfare Impacts under Different Scenarios (2009): EU
25,000
£ Vi
a a
lo Quota irt Subsidy iiry Trade Domea upports
-25,000
■Producer Surplus ■Consumer Surplus ■ Revenues ■Total Welfare
Figure 5.20: Aggregate Welfare Impacts under Different Scenarios (2009): Oceania
1,000
2 a<3
larifT No Export Subsidy
Freeiuota Trade iDomefl upports
- 1,000
■Producer Surplus ■Consumer Surplus ■Revenues ■Total Welfare
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236
Figure 5.21: Aggregate Welfare Impacts under Different Scenarios (2009): US
4,000
& ta
Full raltzation
ariff No Quota No Export Subsidy
'airy Domestic upports
-4,000
■ Producer Surplus ■ Consumer Surplus ■Revenues ■Total Welfare
Figure 5.22: Aggregate Welfare Impacts under Different Scenarios (2009): Canada
2,000 i
</>P £ a
Full iralization
No Export Subsidy
e Dairy frade
Domestic ipports
- 2,000
■ Producer Surplus ■ Consumer Surplus ■Revenues ■Total Welfare
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237
Table 5.20 Perfect Competition vs Imperfect Competition: Central Doha Scenario
PRICE •/• CHANGE
MILK RES CHE BUT WMP SMP DWH CAS CEM EU 1 0 0 2 0 0 -1 0 0 EEU 0 0 0 0 0 0 0 0 0 China 0 0 6 -22 0 4 0 0 3 Japan 0 3 -15 -6 -4 -8 0 0 1 India 0 0 0 0 1 1 0 0 0 Korea 0 -1 0 -1 3 5 0 0 0 SEA 0 0 0 0 0 0 0 0 0 OSA 0 0 0 0 0 6 -1 0 0 Ociana 1 -4 1 0 0 9 3 1 0 AUS 1 1 1 0 0 0 -1 1 0 NZL 2 -7 0 0 1 22 6 0 0 Canada 0 0 0 0 0 0 0 0 1 USA 0 0 0 0 0 0 0 0 0 MEX 0 0 0 0 0 0 0 0 0 SAMN 0 0 0 0 0 4 0 0 0 SAMS 1 0 0 0 0 4 0 0 4 Aggregate 0 0 0 0 0 0 0 0 0
PRODUCTION •/. CHANGE
MILK RES CHE BUT WMP SMP DWH CAS CEM EU 0 0 1 0 -6 0 3 -15 -2 EEU 0 0 0 0 3 0 0 0 0 China 0 0 0 -8 3 -16 0 0 0 Japan 0 0 -6 2 2 1 0 0 0 India 0 0 0 0 3 0 0 0 0 Korea 0 0 0 0 0 0 0 0 0 SEA 0 0 0 0 0 0 0 0 0 OSA 0 0 0 0 0 0 0 0 0 Ociana 1 0 0 1 1 2 -14 0 0 AUS 0 0 0 0 5 2 -12 -40 0 NZL 1 1 0 1 0 2 -22 2 0 Canada 0 0 0 1 0 0 -3 0 0 USA 0 0 0 0 0 0 1 0 0 MEX 0 0 0 0 0 0 0 0 0 SAMN 0 0 0 0 0 0 0 0 0 SAMS 0 0 1 0 0 5 0 0 0 Aggregate 0 0 0 0 0 0 0 0 0
CONSUMPTION •/. CHANGE
MILK RES CHE BUT WMP SMP DWH CAS CEM EU 0 0 0 -1 0 0 1 0 0 EEU 0 0 0 0 0 0 0 0 0 China 0 0 0 4 0 -1 0 0 -1 Japan 1 0 2 1 3 2 0 0 -1 India 0 0 0 0 0 0 0 0 0 Korea 0 0 0 0 -1 -1 0 0 0 SEA 0 0 0 0 0 0 0 0 0 OSA 0 0 0 0 0 -3 0 0 0 Ociana 0 0 0 0 0 -1 -2 -1 0 AUS 0 0 0 0 0 0 0 -1 0 NZL 1 1 0 0 -1 -9 -4 0 0 Canada 0 0 0 0 0 0 0 0 -1 USA 0 0 0 0 0 -1 0 0 0 MEX 0 0 0 0 0 0 0 0 0 SAMN 0 0 0 0 0 -2 0 0 0 SAMS 0 0 0 0 0 -1 0 0 0 Aggregate 0 0 0 0 0 0 0 0 0
LAC 0 0 -1 -1 0 -1 -1 0 -2 -1 -2 0 -1 -2 0 0 0
LAC 0 0 0 0 0 0 0 0 8 0 12 0 -1 0 0 0 0
LAC 0 0 0 1 0 0 0 0 2 1 2 0 0 0 0 0 0
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Table 5.21 Perfect Competition vs Imperfect Competition: Central Doha Boundary Scenario/Full Liberalization
PRICE % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU 0 0 0 0 0 0 0 0 0 0 EEU 0 0 0 0 0 0 0 0 0 0 China 0 0 0 0 0 0 0 0 0 0 Japan 0 0 0 0 0 0 0 0 0 0 India 0 0 0 0 0 0 0 0 0 0 Korea 0 0 0 0 0 0 0 0 0 0 SEA 0 0 0 0 0 0 0 0 0 0 OSA 0 0 0 0 0 0 0 0 0 0 Ociana 0 0 0 0 0 0 0 0 0 0 AUS 0 0 0 0 0 0 0 0 0 0 NZL 0 0 0 0 0 0 0 0 0 0 Canada 0 0 0 0 0 0 0 0 0 0 USA 0 0 0 0 0 0 0 0 0 0 MEX 0 0 0 0 0 0 0 0 0 0 SAMN 0 0 0 0 0 0 0 0 0 0 SAMS 0 0 0 0 0 0 0 0 0 0 Aggregate 0 0 0 0 0 0 0 0 0 0
PRODUCTION % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU 0 0 0 0 0 0 0 0 0 0 EEU 0 0 0 0 0 0 0 0 0 0 China 0 0 0 0 0 0 0 0 0 0 Japan 0 0 0 0 0 0 0 0 0 0 India 0 0 0 0 0 0 0 0 0 0 Korea 0 0 0 0 0 0 0 0 0 0 SEA 0 0 0 0 0 0 0 0 0 0 OSA 0 0 0 0 0 0 0 0 0 0 Ociana 0 0 0 0 0 0 0 0 0 0 AUS 0 0 0 0 0 0 0 0 0 0 NZL 0 0 0 0 0 0 0 0 0 0 Canada 0 0 0 0 0 0 0 0 0 0 USA 0 0 0 0 0 0 0 0 0 0 MEX 0 0 0 0 0 0 0 0 0 0 SAMN 0 0 0 0 0 0 0 0 0 0 SAMS 0 0 0 0 0 0 0 0 0 0 Aggregate 0 0 0 0 0 0 0 0 0 0
CONSUMPTION % CHANGE (FROM BASE 2009)
MILK RES CHE BUT WMP SMP DWH CAS CEM LAC EU 0 0 0 0 0 0 0 0 0 0 EEU 0 0 0 0 0 0 0 0 0 0 China 0 0 0 0 0 0 0 0 0 0 Japan 0 0 0 0 0 0 0 0 0 0 India 0 0 0 0 0 0 0 0 0 0 Korea 0 0 0 0 0 0 0 0 0 0 SEA 0 0 0 0 0 0 0 0 0 0 OSA 0 0 0 0 0 0 0 0 0 0 Ociana 0 0 0 0 0 0 0 0 0 0 AUS 0 0 0 0 0 0 0 0 0 0 NZL 0 0 0 0 0 0 0 0 0 0 Canada 0 0 0 0 0 0 0 0 0 0 USA 0 0 0 0 0 0 0 0 0 0 MEX 0 0 0 0 0 0 0 0 0 0 SAMN 0 0 0 0 0 0 0 0 0 0 SAMS 0 0 0 0 0 0 0 0 0 0
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239
Table 5.22 Comparison of Model Solutions and Actual Data for 2004 PRICES (MODEL SOLUTION FOR 2004, US $/MT) PRICE*/* CHANGE (FROM 2004 DATA)
MILK CHE BUT SMP MILK CHE BUT SMP EU 319 4596 3515 2247 EU -4 -2 -1 -2 China 344 2776 2220 2257 China 4 8 0 12 Japan 675 15612 7828 4686 Japan -6 -2 -5 0 India 257 3311 2023 1796 India 11 -14 -1 0 Korea 450 3179 2930 4201 Korea 13 -15 -27 8 SEA 249 3098 1945 2221 SEA -12 6 -15 4 OSA 166 2258 1838 3977 OSA -9 11 -1 5 O dana 157 2231 1522 1725 O dana 2 5 -2 10 Australia 162 2258 1597 1762 Australia 3 -7 4 10 New Zealand 151 2204 1448 1687 New Zealand 0 21 -8 11 Canada 433 4035 3851 3104 Canada -8 19 3 0 USA 292 2657 2500 1999 USA -2 -10 -6 9 Aggregate 295 3560 2509 2296 Aggregate 0 -9 -4 -2
PRODUCTION (MODEL SOLUTION FOR 2004, IN 1000 MTS) PRODUCTION % CHANGE (FROM 2004 DATA)
MILK CHE BUT SMP EU 119013 7029 1822 1136 China 11440 238 98 78 Japan 8566 36 87 188 India 38248 0 2520 227 Korea 2538 19 61 44 SEA 2384 47 10 0 OSA 34434 13 634 0 O dana 25715 730 540 508 Australia 11562 440 141 216 New Zealand 14153 290 399 292 Canada 8090 343 81 93 USA 79324 4133 624 705 Aggregate 551248 16948 8268 3878
CONSUMPTION (MODEL SOLUTION FOR 2004, IN 1000 MTS)
MILK CHE BUT SMP EU 110476 6886 1651 840 China 12422 239 107 118 Japan 10686 263 92 228 India 38020 0 2520 223 Korea 3030 58 63 46 SEA 8190 76 110 376 OSA 35477 13 634 37 O dana 11039 243 83 40 Australia 6606 220 56 35 New Zealand 4433 23 27 5 Canada 8361 354 87 57 USA 74287 4121 592 411 Aggregate 546508 16893 8141 3572
EU MILK -2
CHE -5
BUT 0
SMP 10
China -6 3 6 3 JAP 2 3 6 2 India 3 0 -2 0 Korea 5 -5 7 5 SEA 0 2 0 0 OSA 8 18 9 -90 O dana 4 6 2 1 Australia 8 11 -5 0 New Zealand 0 -2 5 1 Canada 2 1 -1 1 USA 3 5 8 1 Aggregate -10 12 0 7
CONSUMPTION % CHANGE (FROM 2004 DATA)
EU MILK
-5 CHE -2
BUT 0
SMP -6
China 1 -3 -2 -6 Japan 1 2 1 5 India 1 0 -3 -2 Korea 0 -2 7 6 SEA -7 -5 3 -1 OSA 8 1 8 -2 O dana 1 -7 0 -1 Australia -6 -6 . -2 0 New Zealand 14 -16 5 -1 Canada 5 0 -5 13 USA 1 1 0 0 Aggregate -10 12 -1 3
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Chapter 6 Conclusions and discussion
6.1 Conclusions
This study explores the empirical relevance of perfect and imperfect competition for Asian
and/or world dairy markets. This is done by studying the functioning of dairy markets in Asia
and the exercise of market power, with implications for Doha round negotiations on agriculture
in Asian and/or world dairy markets. Extending the previous UW-Madison World Dairy Model,
we develop a model to combine domestic supply/demand and imports/exports into one unified
system to study the importer and exporter’s market power by allowing any degree of market
structure from perfect competition to monopoly or monopsony. Using the conjectural variation
method, one spatial price arbitrage condition derived under imperfect competition is estimated \
econometrically to test the existence of market power. Furthermore, we evaluate the differences
between perfect competition and imperfect competition in empirical work in the context of Doha
round negotiations on agriculture in the Asian and/or world dairy markets by using the GAMS
software. We analyze the separate impacts of implementing Doha round commitments in 2009,
Doha round boundary commitments in 2009 and full world liberalization in 2009. For the
impacts from Doha round negotiations, we analyze the separate impacts of domestic support
reduction, market access reduction and export subsidy reduction. For the impacts from frill
liberalization, we analyze the separate and combined impacts of changing import quotas, tariffs,
export subsidies and domestic policies. This provides justification of using perfect competition
model for world dairy trade, and useful insights on the effects of further trade liberalization on
the Asian dairy sector. One unifying theme across these simulation results concerns the impacts
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241
of dairy trade liberalization on Asian economies and their major exporters (New Zealand,
Australia, EU, U.S. and Canada).
From the estimates of conjectural variation (CV) and a statistical point of view, we find that
the imperfect competition model is more appropriate to explain China’s butter and skim milk
powder (SMP) imports, Japan’s cheese, butter and skim milk powder imports, and South East
Asia’s skim milk powder imports. Specifically, for China’s butter imports, New Zealand acts as
an imperfect competitive exporter and EU acts as a weakly imperfect competitive exporter while
China acts as an imperfect competitive importer for its butter imports from New Zealand and
Australia. For China’s SMP imports, Australia and New Zealand act as imperfect competitive
exporters while China acts as an imperfect competitive importer for its SMP imports from New
Zealand and EU (with weak evidence). Japan acts as a price taker for its butter imports, but its
exporters (New Zealand, Australia and EU) act as imperfect competitor in its butter import
market. Japan acts as an imperfect competitive importer for its cheese imports from New
Zealand while EU acts as an imperfect competitive exporter. Japan acts as an imperfect
competitive importer for its SMP imports from the rest of the world (with weak evidence). SEA
acts as an imperfect competitive importer for SMP imports from the rest of the world. The
associated Lemer index also indicates that the above imperfect competitive behaviors exist, and
the Hershman-Herfindahl Index suggests that oligopolistic competition may be more appropriate
to explain the above imperfectly competitive markets.
By using the average dairy products prices, production and consumption from 2002-04 as the
BASE, switching from imperfect competition to perfect competition, we find that the impacts of
imperfect competition on the price of relevant imperfect competitors can be substantial. China’s
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242
butter price and skim milk powder price decline 25% and 20%, respectively. Japan’s cheese
price and butter price decline 16% and 14%, respectively. The skim milk powder price of South
East Asia decline 11%. Oceania’s prices of cheese, butter and skim milk powder increase 4%,
3% and 8%, respectively. In contrast, the impacts of imperfect competition on dairy product
production are relatively small compared to dairy product prices. Japan’s cheese production
decrease 26%, Oceania’s production of cheese, butter and skim milk powder increase 5%, 3%
and 10%, respectively. However, the large price decreases for China’s butter and skim milk
powder, Japan’s butter and South East Asia’s skim milk powder do not lead to their production
decrease. This maybe because that domestic production of these products is heavily protected by
other measures (import quotas etc.). Lastly, the impacts of imperfect competition on dairy
product consumption are small. South East Asia’s skim milk powder consumption increases 6%,
Japan’s butter consumption increases 2%, and China’s consumption of butter and skim milk
powder increase 4% and 2%, respectively. One explanation is that the domestic dairy product
market is heavily protected and the change of trade flow is not sufficiently large to pose bigger
impacts on dairy product consumption.
However, eliminating market power has little impacts on average world dairy product prices,
world dairy product production and consumption. Average world prices for butter and skim milk
powder decrease 1% and 2%, respectively. World dairy product production and consumption
remain unchanged except for lactose (-1%). World total trade volume of cheese, butter and skim
milk powder slightly increase (+1.5%, +0.5%, and +1.4%, respectively). The impacts on world
welfare are also negligible; producer surplus increases $10 million, consumer surplus increases
$17 million and total welfare increases $76 million. This is reasonable as we are dealing with a
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multi-region (21 regions) and multi-product (10 products) optimization problem while imperfect
competition only applies to six regions and three dairy products. In this way, the effects of
imperfect competition may be diluted by perfect competition. In other words, although the CV
estimates are significant from a statistical point of view, they are not big enough to change the
trade flow in the context of multilateral equilibrium. If it turns out that market power exists for
most region and dairy products, the conclusions may be different.
Moreover, if we compare the impacts from different degrees of market distortion for world
dairy trade, we find that the impacts of imperfect competition are positively related to trade
distortions. That is, if there are more trade distortions, the impacts of imperfect competition are
more obvious, but if there are less trade distortions then the impacts of imperfect competition
become smaller. For example, the empirical results suggest that there is no significant difference
between imperfect and perfect competition modeling of the world dairy sectors under the Central
Doha Boundary scenario and Full Liberalization. This is because import quota, import tariffs and
export subsidies can all be used as strategies to create market power and consequently imperfect
competition. In other words, market power is hidden behind quota rents, import tariffs and export
subsidies. As we modeled market power explicitly by using the CV estimates, there may be
significant interactions between CV and trade policies. For example, for Japan’s butter price,
under the BASE scenario, when Japan’s butter import tariffs are high (high over-quota tariff
suggesting prohibitive import quota), the impact of imperfect competition is 16 percent; under
the Central Doha scenario, once the import tariffs are cut back and import quotas are less
restrictive, the impact of imperfect competition is 6 percent; under the Central Doha Boundary
scenario/Full Liberalization, once the import tariffs are cut back substantially/eliminated and
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consequently import quotas are not restrictive, the impacts of imperfect competition is negligible.
This example also tells us that once these measures (import tariffs, import quota and export
subsidies, etc.) are cut back or abolished, the interaction effects should diminish and imperfect
competition may converge to perfect competition. The only difference is that the existence of CV
can create some production inefficiency and fractional change in world total welfare.
Therefore, from the empirical results on market power, the use of perfect competition
assumptions to study world dairy sectors by current models is roughly justified if imperfect
competition only applies to a limited number of products in a few regions.
The finding that the impacts of imperfect competition are positively related to trade
distortions and that there may be significant interactions between CV and trade policies can serve
as a complementary example of the finding of Ianchovichina et al (2000). As mentioned above,
they found that economists analyzing tariff cuts in heavily protected industries (such as
Australian automotive industry) would overstate the short-run adjustment in output by 80% if the
estimated pro-competitive effects are ignored. In other words, they found that there exists
significant interaction in a heavily protected industry (Australian automotive industry) between
tariff and price markups. All these findings suggest that Doha round negotiations are in the right
direction for the purpose of increasing world aggregate welfare - substantial cuts in import tariffs
and quotas can increase world aggregate welfare by trade expansion and reductions in markups.
The welfare increase by reductions in markups is due to the finding that if there are less trade
distortions than the impacts of imperfect competition become smaller.
The impacts of different degrees of trade liberalization on world dairy sectors are similar to
each other with different magnitudes. The impacts from world multilateral trade liberalization on
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245
Japan, Korea and SEA are significantly different from other Asian countries. Japan, Korea and
SEA’s producers suffer a loss of some $2 billion, $217 million and $238 million, respectively.
Their farm milk price declines sharply (decrease more than 51%, 21% and 20%, respectively).
But their consumers will benefit some $2.2 billion, $240 million and $243 million, respectively.
From a decomposition analysis we find that Japan, Korea and SEA’s producers get more
protection from trade restrictions than from domestic subsidy, as they suffer smaller losses from
eliminating domestic subsidy than from elimination of trade policies. For trade policy
instruments, import tariff provides more protection to their dairy product producers than import
quota and export subsidy.
As a potential competitive exporter, India’s dairy producers gain from further trade
liberalization. But they only gain from trade policy reform and suffer a loss from domestic
support policy reform. For China, both trade and domestic support policy reform have minimal
impacts on producer surplus and consumer surplus.
Overall, the WTO 2009/Full liberalization scenario indicates that the order of
competitiveness of Asian dairy economies from least competitive to most competitive is Japan,
Korea, South East Asia, China and India.
As major exporters, Australia and New Zealand producers are found to gain from trade
policy reform but suffer from domestic support policy reform due to the elimination of milk
production quota in EU and Canada. EU dairy producers are found to suffer the biggest losses
from world dairy trade liberalization, especially from domestic support policy reform. Compared
with other regions, the impacts of world dairy trade liberalization on the U.S. dairy market are
found to be generally moderate in the medium term context. US dairy producers mainly suffer
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246
from domestic support policy reform. Canada’s dairy producers also suffer significant losses
from full dairy sector liberalization, and trade polices and domestic support polices are found to
provide similar protection to Canadian dairy producers. Overall, full dairy sector liberalization
has little impacts on world aggregate welfare, but it changes the location of production and
redistributes welfare across different countries.
6.2 Contributions of this dissertation
The major contributions of this thesis are that it addresses the knowledge gap between perfect
competition and imperfect competition in the world dairy sectors, and studies the impacts of
Doha round agricultural negotiations on the Asian and/or world dairy markets. Most current
models of world dairy markets are based on the assumption of perfect competition. The
assumption of imperfect competition is more difficult for applied work. However, as world dairy
product export markets are dominated by some major exporters, market power may exist and
imperfect competition may be a better assumption. Using the conjectural variation (CV) method,
we built a model which allows any degree of market structure from perfect competition to
monopoly or monopsony. Although CV has been criticized for lack of strong economic
foundation, it is analytically convenient. It provides us one way to understand the differences
between perfect competition and imperfect competition modelling in the world dairy sectors.
To study the impacts of Doha round negotiations on the Asian and/or world dairy markets,
we review the Doha Development Agenda and assess current literature to obtain insights on the
achievements of agricultural negotiations and their potential impacts on world dairy markets.
Using the appropriate model, we evaluate the impacts of further trade liberalization on major
Asian dairy products importers and their exporters. This helps us to understand the losses and
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gains of individual countries and the world from further trade liberalization in the world dairy
sectors, and provides us a supporting analysis for the formulation of dairy policies in individual
countries and the world. For example, under Full Liberalization, it is found that the developed-
economy distorted dairy sectors (EU and US) are more protected by domestic support and other
types of policies and programs than by trade policies, but competitive exporters (Oceania and
India) gain more from elimination of dairy trade distortions than from eliminating domestic
support or other types of policies. In a post-Cancun WTO discussion context, this suggests
potential “win-win” dairy trade negotiations may involve more rapid liberalizing of dairy trade
policy (minimizing impacts on protected developed economies and maximizing benefits to
competitive exporters) while allowing for longer adjustment periods in reforming domestic dairy
policies and programs.
6.3 Discussions and Suggestions for Future Research
We discussed in great detail market power and the impacts of Doha round negotiations on the
Asian and/or world dairy markets in this study. However, we should bear in mind that results of
this analysis are just suggestive, and are meant to be illustrative rather than definitive. There are
still some limitations for this study.
First is the limitation of data. The data requirements of this study are extensive and the
reliability of many parameters we use in the model is questionable. The production, trade
volume, and raw milk prices data are obtained from FAO. However, FAPRI or OECD data are
used wherever possible, especially for regional prices. FAO data are based on each country’s
government reporting. Despite FAO guidelines about product definitions and reporting methods,
the understanding and interpretation of these guidelines seems to vary significantly. This raises
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questions concerning the reliability of some of the FAO data. FAO’s dairy products classification
is also not quite suitable for our hedonic approach. Cheese, for example, consists of all types of
cheese and curd including fresh cheese, which has a significantly different composition from
hard cheese. Moreover, FAO/FAPRI only reports production and/or consumption data for major
dairy products, and many “small” dairy products are ignored. For example, there are no
production data for lactose, which should anchor the shadow price of a major milk component
with the same name if it was included in the model.
The price data is not so reliable and is obtained from various sources. Converting local
currencies into a uniform currency (the U.S. dollar) is problematic given the fact that currency
exchange rates are subject to substantial distortions and fluctuations.
The data on applied import tariffs and export subsidies are difficult to get. We assume all
countries use their maximum and/or minimum commitments under the URA. This would
overestimate the impacts of further trade liberalization on the Asian and/or world dairy sectors if
applied rates are less than their respective maximum (or minimum) bound rates.
Data for demand and supply elasticities are from the FAPRI database or the USDA
SWOPSIM model whenever it is not available from FAPRI database. If these estimates are
wrong, this would lead to errors in this study. However, according to Zhu (1999), sensitivity
analysis shows that small changes in elasticity parameters have small impact on the BASE
scenario results.
Second, the conjectural variation method is questionable. A typical empirical paper in this
literature takes an agnostic stance toward the behavioral model governing imperfect competition
in the market in question and simply interprets the empirical results as indicating that the result
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of that behavior is as competitive ‘as-if the firms were in fact playing a conjectural variations
game with the estimated conjectural variations parameter (Steigert, 2003). The problem is,
however, that such inferences may be invalid. Without stipulating the true nature of the behavior
underlying the observed equilibrium, no inference about the extent of market power can be made
from analysis of the observed variables, as suggested by Corts (1999). Corts show that if
observed equilibrium behavior results from efficient supergame collusion, the mismeasurement
can be severe. In particular, the estimated conduct parameter underestimates the degree of
market power if demand shocks are not fully permanent, and may fail to detect any market
power whatsoever when demand shocks are completely transitory, even if average price-cost
margins are near the monopoly level. Moreover, in this study, the CV estimates are the same
across all scenarios. This may be the reason why the simulated impacts of imperfect competition
are found to be insignificant in world dairy trade. However, the conjectural variation method is
analytically convenient. Genesove and Mullin (1995) assessed the efficacy of the conjectural
approach by making a comparison of it in the U.S. sugar refining industry against using full cost
information. They concluded that the CV approach yielded estimates of the industry conduct
parameters that were close to the direct measures derived from a full cost function. They also
indicated that CV methods did underestimate the conduct parameter, but the deviation was
minimal in the sugar refining industry.
Third, the WTO 2009 results reflect "pine" separate policy impacts by holding other policies
constant at BASE levels. Supply/demand trends are held constant at base levels and changes
across scenarios are ignored. Hence, these results are not "forecasts" of the year 2009 dairy
sector, but rather ceteris paribus assessments of the impacts of changes in individual policies.
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Our welfare estimates reflect, in large part, the intermediate run scenarios we analyze. To the
extent that supply/demand response may be larger in the longer run than we assume in our
modeling, estimated welfare impacts would increase in the longer run (Cox, 1999).
Fourth, the present model is static by holding the parameters (elasticities) for linear demand
and supply functions constant. Despite trade barriers, Asian dairy markets are dynamic both on
the supply and demand sides (Cox and Zhu, 2004; Nin Pratt, Staal, and Jabbar, 2005) and have
much growth potential. Innovations in food processing and structural changes in industrial
organization also contribute to the sector’s dynamism, as documented by Fuller et al. (2005) for
China, with new value-added opportunities such as dry whey and lactose, for which trade
barriers are low. Innovations have also expanded trade opportunities for traditional milk products
such as milk powder and butter-oil, which are transformed into final products after importation
to circumvent protection on finished products (Cox and Zhu, 2004).
According to Beghin (2005), concentration in processing and vertical integration are
emerging in several Asian markets and are important sources of economies in procurement,
processing, and logistics and lead to significant levels of foreign direct investment. The latter is
conditioned by countries’ macroeconomic policies, political stability, and investment climate.
China is the best example of these supply dynamics, with its double-digit production growth rate
and improving production techniques, processing technology, and marketing arrangements.
These changes in dairy industries have been fostered by a transformation of food retailing, with
large retailers creating a new interface between producers and consumers. They have become a
driving force in several Asian countries such as China and India, but this phenomenon is just
starting in other countries such as Indonesia, although there the upper-middle income class in
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urban areas is leading dairy consumption growth (Barichello, 2005; and Fabiosa, 2005). These
patterns confirm the conjecture of Reardon et al. (2003) on the spread of this new interface in
many regions of the globe. Asian dairy consumption, especially in developing Asia, has been
expanding dramatically with income growth, changing demographics (population growth,
urbanization), and dietary changes in many (but not all) countries (Fuller et al., 2005; Pingali,
2004; Schluep, Campo and Beghin, 2005; and Watanabe, Suzuki, and Kaiser, 1999). Income
growth and demographic changes explain 60 percent or more of dairy consumption expansion in
Asia (Dong, 2005).
Fifth, as we discussed in chapter 5, dairy trade policy is not negotiated in isolation. Further
work is needed to explore these linkages between the agricultural sector and the macro-economy.
In other words, we use a partial equilibrium (PE) approach instead of a general equilibrium (GE)
approach. Partial equilibrium models investigate the impact of changes within certain sectors of
the economy on those sectors, and macro economic variables (productivity, GDP growth rate,
etc) are assumed to be exogenous in these models. This is common in most partial equilibrium
modeling and to the extent that more general (non-dairy) liberalization generates GDP induced
dairy demand growth, the impacts of dairy sector liberalization will be understated under these
assumptions (FAO trade policy technical notes - dairy, 2004). By contrast, computable general
equilibrium models attempt to account for effects of reform in, and on, the wider economy. They
are essentially concerned with determining how changes in resource allocation within and across
sectors contribute to increases in welfare through improvements in allocative efficiency. This is
not possible within the partial equilibrium framework because cross price effects across sectors
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252
are largely ignored, as are overall resource (e.g. land, labour, capital) limitations and budget
constraints (see FAO Trade Policy Technical Notes No. 13 (2005) for more details).
Grant et al (2006) compared and contrasted sub-sector (PE) results which include full policy
details focusing on the U.S. dairy sector with GE liberalization results which do not. By nesting a
fully disaggregated partial equilibrium model inside a GE framework, they explicitly modeled
detailed US dairy policies at the tariff line level (24 HS-6 product lines), and then integrated the
US dairy sector into a standard-sized GE model (GTAP) of global trade reform of 14 regions and
15 sectors. They found that the aggregate GTAP model did a remarkably good job of predicting
the aggregate welfare impacts of dairy trade reforms - at both the US and global levels.
However, when it comes to predicting the global allocation of output in the dairy industry, the
GE model performs more poorly. In general, it greatly understates the change in industry output
that arises when the reform is analyzed at the sub-sector level and then aggregated up (PE/GE
approach). This is reasonable as the PE model puts no constraint on the resources used in dairy
production while dairy production is constrained by the production in other sectors in the GE
model. Consequently, the marginal cost of dairy production is higher in the PE model than in the
GE model. The differences between the two models are even more striking when one focuses on
bilateral trade flows. Here, the GE model does a good job of predicting sub-sector trade flows in
the base case that includes identical Armington elasticities, and elastic supply relative to demand.
However, when they doubled the disaggregated Armington elasticities, as might well be justified
in a product-line model, the GE model under predicts the bilateral changes by a factor of six.
Using a general equilibrium model, Anderson et al. (2006) found that world total welfare gains
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from global agricultural liberalization in 2015 are $182 billion, with high income countries
receiving about 70 percent of these gains.
Lastly, there are some other alternative policy proposals during Doha round negotiations and
the issues of “sensitive” and “special” products. Analyzing these issues would offer further
applications of the model and better understanding of the Asian and/or world dairy markets.
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254
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Appendices
A. Region Definitions
266
France
Liechtenstein
Spain
Czech Rep.
Slovenia
1. WEST EUROPE: WSU (21)
Austria Belgium Denmark Finland Faeroe Is
Germany Greece Iceland Ireland Italy
Luxembourg Malta Netherlands Norway Portugal
Sweden Switzerland United Kingdom
2. EAST EUROPE: EEU (12)
Albania Bosnia & Herzegovina Bulgaria Croatia
Hungary Macedonia Poland Romania Slovakia
Yugoslavia
3. FORMER USSR: FSU (15)
Ukraine Belarus . Estonia Latvia Lithuania
Russia Armenia Azerbaijan Georgia Kazakhstan
Tajikistan Uzbekistan Turkmenistan
4. China
China, Mainland China, Taiwan China, Hong Kong Macao Mongolia
FAO reports data for P.R. China and Taiwan as one country. Hong Kong and Macao are unified
with China in 1997 and 1999, respectively. Mongolia does not have a significant trade activity of
dairy products, and its milk production is very similar to that of Inner Mongolia of China.
5. JAPAN: JAP
Moldova
Kyrgyzstan
6. KOREA: KOR
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267
7. SOUTHEAST ASIA: SEA (10)
Brunei Cambodia Indonesia Laos
Myanmar Philippines Singapore Thailand
These countries are geographically close and belong to ASEAN.
8. INDIA: IND
9. OTHER SOUTH ASIA: OSA (7)
Afghanistan Bangladesh Bhutan
Pakistan Sri Lanka
10. AUSTRALIA: AUS
11. NEW ZEALAND: NZL
12. MIDDLE EAST: MDE (15)
Bahrain Cyprus Gaza Strip
Israel Jordan Lebanon
Qatar Saudi Arabia
United Arab Emirates Yemen
13. NORTH AFRICA: NAF (5)
Algeria Egypt Libya
14. SOUTH AFRICA: SAF
15. CANADA: CAN
16. UNITED STATES: USA
17. MEXICO: MEX
Maldives
Iran
Kuwait
Syria
Morocco
18. CENTRAL AMERICA & CARIBBEAN: CAM (27)
Malaysia
Vietnam
Nepal
Iraq
Oman
Turkey
Tunisia
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Antigua Barb Bahamas Barbados Belice Bermuda
Costa Rica Cuba Dominica Dominican Republic
Grenada Guadeloupe Guatemala Haiti Honduras
Martinique Montserrat Netherlands Antilles Nicaragua
Saint Lucia St. Kitts-Nevis St. Vincent Trinidad and Tobago
19. SOUTH AMERICA (NORTH): SAMN (11)
Bolivia Brazil Colombia Ecuador Falkland I.
Guyana Paraguay Peru
20. SOUTH AMERICA (SOUTH): SAMS (3)
Argentina Chile Urugay
21. REST OF THE WORLD: ROW
all other countries
Suriname Venezuela
Cayman Is
EL Salvador
Jamaica
Panama
Virgin Is.
French Guiana
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269
B. Commodity Definitions
0. MILK: whole raw milk
In FAO database, production of animal milk from five species is reported. They are cow milk,
buffalo milk, sheep milk, goat milk, and camel milk. The raw milk contains 80 to 90% of water,
depending on species of animal, season, lactation period, and other factors. The solid part of milk,
which decides the value of milk, includes milk fat, protein, lactose (milk sugar or carbohydrate),
and ash.
1. CHE: cheese
Cheese is the curd of milk coagulated by rennet separated from the whey and pressed and
molded into a more or less solid mass. FAO data on cheese relate, unless otherwise stated, to all
kinds of cheese, from full fat cheese to low fat cheese; hard and soft cheese, ripe and fresh
cheese, including cottage cheese and curd. Cheese contains little carbohydrate, and the content of
water, protein and fat varies considerably from one to the other.
2. BUT: butter, butter oil and anhydrous milk fat (AMF)
Butter is solid emulsion of milk fat and water made to coalesce by churning the cream
obtained from milk. Fat content if about 80 percent. Ghee is liquid butter clarified by boiling,
produced chiefly in countries of the Far East. Butter oil if butterfat melted and clarified thus has
higher fat content than butter. Butter oil is an important input for milk recombination.
3. WMP: whole milk powder
Whole milk powder is made by evaporating milk that has had some of the cream removed.
The evaporated milk is concentrated and dried either by roller or spray process to form a powder.
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The milk fat content of WMP is about 26%. WMP is basic input for milk reconstitution. One
reason of making condensed and evaporated milk is easy transport and storage.
4. SMP: skim milk powder and buttermilk powder
Skim milk powder is byproduct of butter production. After separating cream from whole
milk, the residue, skim milk, is evaporated and spray dried to produce skim milk powder. The
normal fat content of SMP is 1-1.5%. A liquid by-product of butter making the process of
churning cream is called buttermilk. Buttermilk can be evaporated and spray dried into
buttermilk powder (BWP). Because of compostitional similarity of SMP and BMP, some reports,
as FAO, put these two products into one category.
5. DWH: dried whey
Whey is the serum or watery part of milk that is separated from the curd in the process of
making cheese. It contains almost all water (more than 90 percent), but also lactose, minerals and
protein; very little fat, if any. It is used, for food and feed, fresh, concentrated and dried;
Concentrated whey and dried whey are usually traded internationally because their role as
ingredients in food industry.
6. CAS: casein
Casein, named also lactoprotein, is the main protein of milk, containing more than 20
individual amino acids. It is obtained mainly from skimmed milk. Some food use (meat and
bakery products, confectionary, etc.) and large non-food use (glues, leather industry, plastics,
pharmaceutical products, etc.) Caseinate is a compound of casein with a metal.
7. CEM: condensed and evaporated milk
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271
Raw whole milk or skim milk can be concentrated by evaporation with the aim of reducing
their water content while maintaining more or less intact their content of protein, fat and lactose.
The products resulting from a modest or medium reduction of water are evaporated and
condensed milk, with or without sugar added. Fat content of products made from milk ranges
from 8 to 15 percent; protein content, from 7 to 8.5 percent.
8. RES: residual
This group of products covers fluid milk, soft products, such as yogurt, frozen products, such
as ice cream, lactose, and all other diary products that are not covered in above seven categories.
Fluid milk can be whole milk, semi-skim milk, and skim milk. The difference of fluid milk from
raw milk is also due to some value-added cost embodied in it. In most developed countries, the
direct human consumption of raw milk is very small, and the directly consumed raw milk usually
has high quality. The bulk undergoes more or less complex processes to obtain either products
which are still liquid milk (standardized milk, pasteurized milk, partly skimmed milk, skimmed
milk, buttermilk, etc.).
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272
C. Data Sources
(i) Dairy production. Includes all types farm milk, whole milk powder, skim milk powder,
cheese, butter (including ghee and butteroil), casein, dry whey, condensed and evaporated milk,
and other dairy products.
Sources:
FAO database httt>://faostat.fao.org/faostat/collections?subset=agriculture
OECD database http://www.oecd.org/dataoecd/55/44/32980897.xls
FAPRI database http://www.fapri.iastate.edu/outlook2000-05/
(ii) Dairy trade. Includes total volume and value of annual imports and exports of dry milk,
cheese, butter, casein, dry whey, condensed and evaporated milk, fresh milk products, and others
dairy products.
Sources:
FAO database http://faostat.fao.org/faostat/collections?subset=agriculture
OECD statview v2.0 Documentation.
USDA, Foreign Agriculture Service: Dairy, Livestock,
and Poultry: World dairy situation, various issues.
FAPRI database http://www.fapri.iastate.edu/outlook2000-05/
(iii) Dairy prices
1. Regional prices for raw milk. Include producer prices of farm milk from different species.
2. Regional prices for processed dairy products. Include prices for dry milk, cheese, butter,
casein, dry whey, condensed and evaporated milk, and others dairy products.
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273
3. import and export prices for processed dairy products. Include prices for dry milk, cheese,
butter, casein, dry whey, condensed and evaporated milk, and others dairy products.
Sources:
FAO database http://faostat.fao.org/faostat/collections?subset=agriculture
FAO, unofficial data from internet inquires.
USDA, dairy markets, various issues.
Australian Dairy Cooperation, Dairy Compendium in 1994 and 1995.
UN economic commission for EU, the milk and dairy markets, various issues.
FAPRI database http://www.fapri.iastate.edu/outlook2000-05/
Statistical Yearbook of China, various issues.
Japan Statistical Yearbook, various issues.
Korea Statistical Yearbook, various issues.
Yearbook of Australia, various issues.
(iv) Dairy stock changes
FAO database, FAO Yearbook.
USDA, dairy markets, various issues.
(v) Dairy consumption
Computed from the formula: consumption = product + net import - stock change. Can also
calculated from the formula: total consumption = per capita consumption * population.
Sources:
FAO database http://faostat.fao.org/faostat/collections?subset=agriculture
FAPRI database http://www.fapri.iastate.edu/outlook2000-05/
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274
(vi) Income elasticities, supply and demand price elasticities.
FAPRI database http://www.fapri.iastate.edu/tools/elasticity.aspx
Roningen et al. documentation of the Static World Policy Simulation (SWOPSIM).
Modeling Framework. U.S. Department of Agriculuture, Economic Research Service,
Agriculture and Trade Analysis Division, Washington, DC, 1991.
(vii) Composition of Milk and Dairy Products. Includes composition of raw and dairy
products, and combination of dairy products in each category in U.S.
Sources:
Robert, G. Jensen, Eds (1995), Handbook o f Milk Composition, San Diego: Academic Press.
Selinsky, R., T.L. Cox, and E.V. Jesse (1992), “Eestimation of U.S. Dairy Product
Component Yields,” U.W. Agricultural Economics Staff Paper 355.
Webb, B., A. Johnson, J. Alford (1974), Fundamentals o f Dairy Chemistry, 2nd edition. ACI,
Westport, 1974.
Wong N. P., R. Jenness, M. Keeney, and E.H. Marth (1998), Fundamental o f Dairy
Chemistry, 3rd edition, New York: Van Nostrand Reinhold Co.
(viii) Distance between ports
1. Main regional ports. List of regions, country and ports for each region.
2. Distance between ports. Contains a matrix with distances between ports in nautical miles
2. main travel routes between ports. List of some travel routes used to calculate the distance
between ports.
Sources:
Defense Mapping agency (1985), distance between ports, hydrographic/Topographic Center,
Fifth edition.
(ix) GDP and exchange rate
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275
The exchange rate and GDP growth rate data used in the model comes from the International
Monetary Fund (IMF). Exchange rate data is found in International Financial Statistics, a
monthly publication of the IMF. The IMF publishes GDP growth rate data for all countries in the
World Economic Outlook. This volume is published about every six months, in April and
October.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.