1 56138 Global Distortions to Key Agricultural Commodity Markets Kym Anderson University of Adelaide kym.anderson@adelaide.edu.au Johanna L. Croser University of Adelaide johlou@gmail.com Signe Nelgen University of Adelaide signe.nelgen@adelaide.edu.au and Ernesto Valenzuela University of Adelaide ernesto.valenzuela@adelaide.edu.au Agricultural Distortions Working Paper 71, revised March 2009 This is part of a Working Paper series (see www.worldbank.org/agdistortions) that is designed to promptly disseminate the findings of work in progress for comment before they are finalized. It is a product of a research project on Distortions to Agricultural Incentives, under the leadership of Kym Anderson of the World Bank's Development Research Group. The authors are grateful for country case study authors for their spreadsheets and for help with data compilation by Esteban Jara and Marianne Kurzweil. Funding from World Bank Trust Funds provided by the governments of the Netherlands (BNPP) and the United Kingdom DfID) also is gratefully acknowledged. A revised version of this paper, without the Appendix, will appear as Ch. 12 in Distortions to Agricultural Incentives: A Global Perspective, 1955 to 2007, edited by K. Anderson, London: Palgrave Macmillan and Washington DC: World Bank (forthcoming 2009). The views expressed are the authors' alone and not necessarily those of the World Bank and its Executive Directors, nor the countries they represent, nor of the institutions providing funds for this research project. 2 Global Distortions to Key Agricultural Commodity markets Kym Anderson, Johanna Croser, Signe Nelgen and Ernesto Valenzuela The regional books that provided detailed estimates of distortion in developing economies 1 are all country focused. While they include commodity details for their particular country, they are not able to provide an overview for developing countries or high-income countries as a group, or for the world as a whole. This paper seeks to fill this gap. The paper begins by describing the overall project's coverage of 30 major commodities and their importance in regional and global agricultural production and trade. It then summarizes the nominal rates of assistance and consumer tax equivalents for twelve key covered products, together with their gross subsidy/tax equivalents in constant dollars. The policies generating the positive or negative NRAs and CTEs are often an attempt by government to not only raise or lower the trend level of domestic producer or consumer prices relative to those in international markets, but also to reduce price and quantity volatility in the domestic market for key farm products. Given that this issue became headline news again when international food prices briefly spiked in 2008, we examine whether domestic prices of key products have in fact been more stable than prices in international markets over the past fifty years. There is space to discuss this issue only briefly here, but we point to the scope that the Agricultural Distortions database provides for further in-depth study of the role of policies in influencing market volatility. The paper then examines seven largely non-traded food staples that are nonetheless important food items for poor people in low-income countries. Even though those commodities are only a small share of global production and exports of farm products, they can be crucial to the food security of large segments of developing country societies. The 1 Those regional books cover Africa (Anderson and Masters 2009), Asia (Anderson and Martin 2009), Latin America (Anderson and Valdés (2008) and Europe's transition economies (Anderson and Swinnen 2008). 3 Agricultural Distortions database lends itself to placing the policies affecting (or ignoring) those products in a broader perspective. The final part of the paper provides another new perspective on the project's database. It seeks to shed light on how relatively distorted are the various commodity markets from the viewpoint of global trade or welfare restrictiveness. This analysis draws on the theory outlined in the previous chapter, but switches the focus from countries to products. True, a global model of each commodity market (or a global economy wide computable general equilibrium (CGE) model) calibrated for a particular year of interest could provide such insights for that year: the NRA and CTE estimates for that product could be inserted in such a model to generate partial (or general) equilibrium estimates of the global trade and welfare effects of those distortionary policies. However, global models do not exist for many commodities, and global CGE models such as the one used by Valenzuela, van der Mensbrugghe and Anderson (2009) in the next chapter typically have to aggregate many of the smaller commodities into groups to keep the model tractable. Moreover, such models are calibrated to a particular year and so are incapable of providing a long time series of estimates of the global trade and welfare effects of distortionary policies affecting particular commodity markets. The global trade and welfare reduction indexes used here are calculated for each of twelve key agricultural commodities for each year over the past half century, based on NRA and CTE estimates for the project's sample of 75 countries. These two new indexes provide for each product the ad valorem trade tax rate which, if applied uniformly to that commodity in every country would generate the same partial equilibrium reduction in trade or economic welfare as the actual structure across countries of NRAs and CTEs for that tradable commodity. NRA and CTE Coverage of Key Farm Products This project has involved the estimation of annual nominal rates of assistance and consumer tax equivalents over the past five decades for 75 focus countries. In aggregate the coverage represents around 70 percent of the gross value of agricultural production in those focus countries, and just under two-thirds of global farm production valued at undistorted prices over the period covered. That generated about 30,000 NRA and CTE estimates covering more than 70 different products, with an average of 11 products per country. Not all countries 4 had data for the entire 1955-2007 period, but the average number of years covered is 41 per country. These NRAs cover more than three-quarters of global output of the 30 most valuable agricultural products in terms of their share of global farm production, and as much as five- sixths for grains and tubers. In this section we concentrate on just 12 main commodities: three meats plus milk (worth 55 percent of global output of these 12 products) three grains plus soybean (37 percent of this group's output), and three tropical products plus sugar (just 8 percent of global output of these 12 products, but of much greater significance to agriculture in developing countries). All but one-seventh of global production of these 12 products is covered by the project's NRAs and CTEs (table 1). That coverage is spread well across the five regions under study, as shown in table 2. Each region's share of the 12 key products varies considerably but, as a group, the NRA coverage of those 12 products ranges from one- third of agricultural production in our focus countries of Africa to one-half in Latin America and high-income countries (table 3). Taken together, these coverage statistics suggest the NRAs can be considered very representative of the regional and global agricultural economies. GSEs and NRAs by Product The gross subsidy equivalents (GSEs) of the NRAs for the 12 key products are summarized in figure 1 for developing and high income countries separately as well as for all of the study's focus countries. These estimates are obtained by multiplying the NRA by the value of production of each product at undistorted prices for each country, and summing across countries. The products attracting the largest subsidies in 2000-04 were the rice pudding ingredients of rice, milk, and sugar plus beef, with milk dominating by far (and even more so two decades earlier). Since for some countries the GSE for a particular product may be negative, it offsets the positive assistance in other countries. Rice is a case in point: it received positive assistance in both developing and high-income countries in 2000-04, but in 1980-84 developing countries in aggregate taxed rice production more than high-income countries subsidized it. That figure also shows that assistance fell for the majority of those 12 products in high-income countries over the past quarter century, becoming less positive, whereas in developing countries it rose, becoming less negative. Hence for all focus countries 5 as a group the picture is mixed: sugar was more assisted in 2000-04 than in 1980-84; wheat, beef and especially milk has become less assisted; and rice, maize and pigmeat have moved from being taxed in aggregate to being subsidized. Coffee, coconut and cotton are all less taxed now than in the early 1980s. The full time series of those GSEs is summarized in table 4 from 1965. Throughout that period, livestock assistance dominated crop assistance globally, and by a huge margin before the mid-1980s when crop assistance was negative in many years. Typically developing countries have not taxed milk so, unlike for meat earlier, that is not an offset to the positive assistance in high-income countries. Developing countries switched from negative to positive assistance during the 1980s for wheat and sugar, and during the 1990s for rice, maize and meat, while assistance for the three tropical crops of coconut, coffee and cotton remained slightly negative into the present century. These GSE values are the combined effect of output values reflected in tables 1 to 3 and NRAs shown in figure 2. Figure 2 confirms that the three rice pudding ingredients share the dubious honor of being the most assisted products in percentage terms in both developing and high-income countries. The time series since 1965 is shown in table 5 for all focus countries, where it is evident that it is not just the value of the livestock sector but also its high NRAs that contribute to its dominance in GSE terms, with milk the stand-out assisted product. We turn now to consider the distribution across countries of NRAs for these products individually, beginning with the most assisted. Rice, milk and sugar The first thing that is striking about figure 3 is that virtually all countries for which NRAs have been estimated for rice, milk and sugar assisted these three industries in 2000-04. The only exceptions are Ukraine and Egypt for milk and, for rice, Egypt, Zambia, Pakistan, and (very slightly) Thailand and China. Secondly, in virtually no country in the project's sample does the government not intervene in the market for these three products. And the third striking feature of figure 3 is the huge rates of assistance for these products in some countries, with the peak rate for each product exceeding 200 percent in 2000-04. This is far higher than the peak NRAs for the other products in our sample of 12, with the exception of poultry (and beef in Norway). These three features, and especially the third one, suggest there are characteristics that these industries have in common that influence 6 the political economy of support for them. One thing milk and sugar share with poultry is the need for immediate processing of the raw farm product before it is saleable to consumers, but that is also true of other products such as cotton. The definitive political economy paper on these products has yet to be written, but perhaps the availability of the project's NRA database will stimulate such an analysis. With high protection for these products in virtually all markets, their international price will have been depressed perhaps more than that of most other farm products. That would thus be very harmful for the main unsubsidized exporters of these products, notably Thailand for rice, New Zealand for milk products, and Brazil and Australia for sugar. Beef, pigmeat and poultry Meat industries too tend to be mostly assisted, except for beef where there are several countries still taxing its production (often implicitly, for example via export taxes or restrictions). The highest NRAs by far for these meats are in Norway, Switzerland and Northeast Asia. Since there are almost no pig and poultry industries with negative assistance, and since high-income countries dominate the upper part of figure 4, this suggests that those countries are overproducing these protein-rich foods. And since all but low-income countries produce these products using intensive feeding of grains and oilseed meals, it is also putting upward pressure on the prices of those crop products ­ except perhaps in countries where the assistance to these intensive livestock industries is just to compensate for the protection- induced high domestic price for feedgrains and oilseeds. Wheat, maize and soybean These three temperate crop products are grown more in high-income countries than in any of the other four studied regions (table 2). But where they are grown in developing countries (predominantly in the Southern Hemisphere), the NRAs tend to be negative. There are also lots of countries where these products' NRAs are close to zero, and the peak NRAs are in countries that grow very little of them (again mostly Norway, Switzerland and Northeast Asia ­ figure 5). Thus their contribution to global farm subsidies is quite modest even though these products account for a large share of the world's farm production (one-quarter of the 12 key products in focus here, according to table 1). 7 Coconut, cocoa, coffee and cotton Coconut NRAs are not shown in figure 6 because there are only three countries in the sample for which they were estimated. In each case the NRA by the turn of this century had become slightly positive: 10 percent in Indonesia, 14 percent in the Philippines and 17 percent in Sri Lanka for 2000-04. But in the last few decades of the 20th century this tree crop's exports were taxed heavily. Cocoa NRAs are available for six countries, all on the equator. In all cases production is still discouraged (negative NRAs), and most heavily in the major producing country of Cote d'Ivoire. Coffee is now produced in lots of countries, and the extent of government intervention in this product varies from heavy taxation (Cote d'Ivoire again and almost as much as for cocoa) to slight assistance in the case of Brazil and Columbia in 2000-04. Cotton more than any of our key products, and in sharp contrast to rice, milk and sugar, is simultaneously taxed heavily in developing countries and subsidized heavily in high-income countries. The United States had an NRA of 70 percent in 2000-04, 2 while several African countries have NRAs of around -70 percent ­ and several more were equally taxing of this industry in earlier decades (Baffes 2009). It also seems that some of the countries of Central Asia also still tax cotton producers substantially, although the data are not sufficiently robust to be able to estimate their NRAs with confidence (Pomfret 2008). This wide diversity of NRAs means that a freeing of cotton markets globally would lead to a big relocation of production from supporting countries, most notably the United States, to many poor countries, especially those in Africa and Central Asia that are currently underpricing raw cotton to growers. A recent study using an economy wide model of the global economy (Anderson and Valenzuela 2007) strongly supports that inference. CTEs by Product 2 Unfortunately the European Union does not show up in Figure 5 because a time series of its cotton NRA was not measured (being a relatively small crop produced only in southern Europe). Nor has the OECD measured its PSE. However, independent estimates for recent years show growers there receive an even higher NRA than the 70 percent received by US growers in 2000-04 (see Anderson and Valenzuela 2007). 8 The consumer tax equivalents of government intervention in nine of these key products are fairly similar to the NRAs in percentage terms (compare tables 5 and 6(a)), because intervention is mostly at the border and such measures affect producer and consumer prices equally. (The three tropical cash crops are not shown in table 6 because they are grown almost exclusively for export once they are lightly processed.) In cases where only border measures are used, the sign of the dollar equivalent of those taxes is the same as that of the GSE too. However the magnitude differs because these are heavily traded products and so countries differ in the extent to which they are net importers or exporters of each one. Table 6(b) shows that of the meats, only pigmeat consumption has been subsidized on a global basis. That was mostly due to China and was phased out by the mid-1990s. Among the grain crops, rice consumption was taxed in aggregate in the focus countries only up to the mid-1980s, soybean was taxed on net only in the 1970s, and maize consumption was taxed in aggregate only from the early 1990s. The Effects of Intervention on Price Variability Many governments intervene in commodity markets not only to alter the trend level of prices using long-term subsidies or taxes on farmers or food consumers, but also in an attempt to reduce price and quantity volatility in the domestic market for key farm products. The justification sometimes given for such intervention in poor countries is that credit markets are underdeveloped or inefficient because of local monopoly lenders, so low-income consumers and producers have difficulty smoothing their consumption over time as prices fluctuate. There and in higher-income countries the motive for intervention may be partly viewed also as a form of income insurance (Thompson et al. 2004), although it needs to be kept in mind that stabilizing prices is not the same as stabilizing incomes of the target households. It is also true that to achieve price stability through altering trade barriers is extraordinarily difficult. Indeed more than sixty years ago Hayek (1945) warned that such intervention is likely to lead to government failure that could reduce welfare more than the cost of the market failure it seeks to overcome, given the high cost of the information needed to do it well. There is a huge analytical literature on the economics of price stabilization. Its innate connection with trade policy was highlighted by Johnson (1975) following the upward spike in world food prices in 1973-74. His analysis of grain prices suggested that if free trade in 9 grain was in place in 1975, prices would be so much less variable ­ because trade could mitigate local supply variability ­ that only negligible quantities of carryover/storage would be profitable. A subsequent study of global food trade provided complementary results: using a stochastic model of world markets for grains, livestock products and sugar, Tyers and Anderson (1992, Table 6.14) found that instability of international food prices in the early 1980s was three times greater than it would have been under free trade in those products. Many countries vary their trade taxes and hence NRAs inversely with international prices, particularly for staple foods. Rice is perhaps the most obvious example. Anderson and Martin illustrate it for Southeast Asia in Chapter 9 in this volume, and figure 7 illustrates it for South Asia, where the domestic rice NRA moves in the opposite direction to the world rice price with a high correlation coefficient of -0.75 (which compares with -0.59 for Southeast Asia for the same period). This desire to stabilize does not seem to be diminishing, even though the trend rate of NRA for rice is rising as incomes grow (see figure 8). One consequence of such domestic market-stabilizing activities by governments is that the international market for food stables is `thinned'. As shown in table 7, food staples are traded much less than tropical products by developing countries ­ and the numbers in the high-income countries column of that table would be much lower too had intra-European Union trade been excluded from the data. For example, only 6.9 percent of global rice production was traded internationally in 2000-03, compared with 14 and 24 percent for maize and wheat, and prior to the 1990s the global share of rice traded was less than 4.5 percent. If this matters most in low-income countries where consumption smoothing through time is most unaffordable for poor households, the question arises as to how successful governments in that group of countries have been in keeping domestic price volatility below volatility in international markets. A recent attempt to test that, using the prices that generated the NRAs from this project, found that government intervention in low-income countries on average had de-stabilized prices relative to the international marketplace (Masters and Garcia 2009), apparently vindicating Hayek's concern cited above, and contrary to the general conclusion reached by Schiff and Valdés (1992, Ch. 3) using data up to the mid-1980s. That is, policies continue to seek to reduce fluctuations in domestic food prices and in the quantities available for consumption via fluctuations in barriers to trade. This beggar-thy- neighbor dimension of each national economy's food policy reduces the international public good role that trade between nations can play in bringing stability to the world's food markets. The more some countries insulate their domestic markets, the more other countries perceive a need to do likewise, exacerbating the effect on world prices so that even larger changes in 10 NRAs are desired--a classic collective action problem, and one that was illustrated yet again in 2007-08 when the imposition of export restrictions in key exporting countries in late 2007 and early 2008 certainly contributed to the sharp increases in world prices in the first half of 2008. This is an area requiring considerably more analysis of past government behavior, and for which the current project's Agricultural Distortions database is well suited, but space and time limitations preclude it from being included in this volume. What about Nontraded Food Staples of Low-income Countries? It was noted early in this chapter that the NRA coverage of the 12 key traded products discussed above represents only one-third of agricultural production in Africa, compared with one-half of farm output in Latin America and high-income countries (table 3). Part of the reason for the difference is that in low-income countries where rural infrastructure is weak, trade costs are relatively high and so a larger proportion of food production focuses on products that tend to be mostly not traded internationally. They include rootcrops such as cassava, potato, sweet potato and yams, grains such as millet, and fruits such as banana and plantain, its even-less-traded relative. Apart from potatoes, these crops are almost exclusively grown in hot developing countries, but with different degrees of specialization across regions (table 8). And apart from bananas, they are traded very little across borders, even with neighboring countries (table 9). Yet in terms of calories and protein, banana and plantain account for two-thirds of the fruit intake in Sub-Saharan Africa, and they with the four tubers and millet account for one-quarter of all Sub-Saharan African food intake, according to FAO food balance sheets in recent years. How much difference would it make if these products had been more-fully included in the Agricultural Distortions database? Despite their importance as a source of calories and protein, their share of the global value of production is quite low, because of their low prices. In aggregate those 7 products account for just 5 percent of the global value of farm output, when valued at domestic producer prices. In Africa, though, they account for a bit more than one-fifth of the regional value of agricultural production. For that reason, the project's African country authors typically included them in their sample of covered products. This can be seen from table 10, where row 4 shows that these products accounted in 1995-2004 for one-third (22 percentage points) of the 67 percent coverage ratio for the African sample. The 11 second set of rows in Table 10 shows that had all developing countries included all 7 of these products in their covered sample, it would have raised their coverage by no more than 6 percentage points. Those rows also show their inclusion in the African studies was nearly complete. Those facts together suggest that the fuller inclusion of those 7 staples in the covered product set would not have altered the developing countries' average NRA for covered products very much. But to test that assertion more formally, the NRA for covered products, shown in the third set of rows in table 10, was re-calculated to include any of the 7 staples that were missing, assuming the NRAs for those missing staples were zero (since the nature of the market for these products is such that they attract very little government intervention). The final set of rows in table 10 show that such inclusion would bring the NRA average for covered products only very slightly closer to zero (e.g., from 5.3 to 4.9 percent for developing countries as a group in 1995-2004). Moreover, their partial omission from the covered set makes no difference to the NRA average for all agriculture (including non-covered products), since in most cases the `guesstimated' NRA for non-covered non-tradable farm products in developing countries was zero anyway. Global commodity trade and welfare reduction indexes 3 This final part of the chapter provides yet another perspective on the project's database. It seeks to shed light on how relatively distorted are the various commodity markets from the viewpoint of global trade or welfare restrictiveness. This analysis draws on the theory outlined in the previous chapter, but with a focus on products rather than countries. It provides time series estimates for a pair of indexes that give more insights than NRAs or CTEs can provide into the likely impact of policies in restricting global trade in particular products and in reducing the contribution each product's market can make to global economic welfare. Certainly global models can estimate trade and welfare effects, but such models typically are calibrated to a particular year and so are incapable of providing a long time series of estimates of the global effects of distortionary policies affecting particular commodity markets. 3 This section draws heavily on Lloyd, Croser and Anderson (2009), who develop the theory summarized here and provide index estimates for a much larger sample of products than reported below. 12 These two new indexes can provide for each product the ad valorem trade tax rate which, if applied uniformly to that commodity in every country, would generate the same partial equilibrium reduction in trade or economic welfare as the actual structure across countries of NRAs and CTEs for that tradable commodity. If one is willing to assume that the domestic price elasticities of supply are equal across countries for a particular commodity, and likewise for the domestic price elasticities of demand for that commodity (as indeed many global commodity modelers do, for lack of country-specific econometric estimates), then there is no need to know the size of those elasticities in order to estimate the two new indexes. As in the previous chapter, we call these indicators the trade reduction index (TRI) and the welfare reduction index (WRI). One feature of the TRI is that it uses not a country's share of world production or consumption but rather its share of world trade in determining the global trade effect of price-distorting policies. And an important feature of the WRI is that it takes into account the fact that the welfare effect of a policy such as an import tariff is related to the square of the tariff rate, which is particularly important in global commodity markets with a wide dispersion of NRAs across countries. The theoretical literature that identifies ways to measure the welfare- and trade- reducing effects of international trade policy in scalar index numbers stems from the theoretical advances by Anderson and Neary (summarized in and extended beyond their 2005 book) and the partial equilibrium simplifications by Feenstra (1995). Notwithstanding these advances, to our knowledge no long time series of indexes had been estimated across countries for individual commodities until Lloyd, Croser and Anderson (2009) showed that the required theory is a straightforward variation of that summarized for countries in chapter 11 of this volume. They have applied the theory to estimate the indexes for 28 of the commodity markets included in the Agricultural Distortions database, summing across countries for each product in contrast to chapter 11 where the summation is across products for each country. Here we summarize their results for just the twelve key global markets that are the focus of this chapter, for each year since 1965, based on NRA and CTE estimates for the project's sample of 75 countries. Table 11 reports the time series of estimated global TRIs for each of the twelve agricultural commodities, and for the three groups of commodities (grains and oilseeds, tropical crops, and livestock products). Generally those TRIs are somewhat above the NRAs reported in Table 5, and especially for tropical products where the trade-reducing effects of import taxes of some high-income countries are reinforced by the export taxes of 13 some lower-income countries. By contrast, for a few products the global average TRI is less than the NRA, reflecting the fact that export subsidies have been in place for some higher-income countries or import subsidies for some lower-income countries. The most trade-distorted products are sugar, milk and rice. Among the grains it is rice trade that has been taxed most since the 1970s, while among the oilseeds and tropical crops it is sesame and sugar trade, respectively, that are taxed most. Maize and soybean trade has been taxed least among those crops shown, and at very low rates compared with livestock products, especially milk. Note, however, that the extent of distortions to trade has diminished more for livestock products than for crops since the 1980s when agricultural price and trade reforms began to be implemented in numerous countries. Table 12 similarly reports the global WRI estimates. These are substantially above the NRAs, with 5-year averages across the twelve commodities between 1965 and 2004 in the range of 55 to 85 percent compared with the 6 to 24 percent range for the comparable NRA averages. This greater size is partly because the welfare cost is proportional to the square of the NRA, and partly because some NRAs are negative and so offset positive NRAs in the process of averaging them whereas the welfare cost of those negative and positive NRAs are additive. Figure 9 shows that the most distorted among the twelve commodities in 2000-04 in terms of both their global welfare cost and their trade restrictiveness are rice, sugar, milk and beef. A useful way of summarizing the WRI and TRI estimates for particular products is provided in figure 10, which shows their movement since many of the indexes peaked in the late 1980s. The indexes would suggest policies for a particular commodity market were not reducing either trade or welfare if the product were located at the zero point of both axes, that is, in the bottom left corner of the diagram (the `sweet spot'). Nearly all of the farm commodities shown have moved towards that spot since 1985-89, and very substantially so for the outliers, namely milk and coffee, but considerably also for wheat and maize. The countries that contribute most to the global TRI are shown in figure 11 for the 5 most-distorted products. These shares are related to not only the size of the index but also the contribution of the country to global trade in that product. In the case of sugar,milk and beef, many countries protect their domestic producers highly and so the contributions are relatively evenly spread across lots of countries. By contrast, rice trade restrictions are due mostly to a few Asian countries, notably, India, Japan and Taiwan. And cotton trade distortions are even more concentrated, with subsidies in the United States the main contributor. 14 Similar summary information for the country contributions to the global commodity WRIs are presented in figure 12. In this case Japan is prominent in reducing world welfare in the markets of not just rice but also milk and beef. For cotton, distortionary policies not only in the United States but also in Turkey and several large developing countries are dominant contributors, where it is the size of the country in global cotton production/consumption that interacts with the percentage WRI to determine the aggregate contribution of each nation. In short, this application of these two additions to the family of so-called trade restrictiveness indexes provides very different indicators of distortions to global agricultural markets than the NRAs and CTEs (and even more so than the OECD's producer and consumer support estimates, which are expressed as a percentage of distorted rather than undistorted prices and so are smaller than their NRA and CTE counterparts). More specifically, the TRI offers a much truer indication of the world trade effects of government interventions in the markets for traded products, by properly accommodating trade subsidies alongside trade taxes; and the WRI offers a much truer indication of the global welfare effects of government interventions in the markets for traded products, by also properly taking into account the fact that the welfare cost of a price distortion is proportional to the square of the tax or subsidy rate. These two indexes have been calculated with the help of a number of simplifying assumptions, most notably that each country is small and that its price elasticity of supply (demand) for a particular product is the same as that for every other country, and that cross- price elasticities are zero. However, that is what trade negotiators typically assume when they attempt to calculate the trade effects of market access `concessions' they are considering exchanging. It is also commonly what would be assumed when calculating, for the Arbitrator of a trade dispute settlement case, the magnitude of the trade damage from a violation of commitments under a trade agreement. Models of the global market for particular farm products often have to make such assumptions too, for want of reliable or agreed econometric estimates of those elasticities for each country. Moreover, these indexes have the advantage over formal supply/demand models in that they can be expressed in time series form and thereby reveal trends and fluctuations over long periods, rather than just providing a snapshot at a point in time. References 15 Anderson, J.E. and J.P. Neary (2005), Measuring the Restrictiveness of International Trade Policy, Cambridge MA: MIT Press. Anderson, K. and Associates (2009), Distortions to Agricultural Incentives: A Global Perspective, 1955 to 2007, London: Palgrave Macmillan and Washington DC: World Bank. Anderson, K. and J. Croser (2009), "National and Global Agricultural Trade and Welfare Reduction Indexes, 1955 to 2007", World Bank, Washington DC, available from April on the Database page at www.worldbank.org/agdistortions. Anderson, K., M. Kurzweil, W. Martin, D. Sandri and E. Valenzuela (2008), "Methodology for Measuring Distortions to Agricultural Incentives," Agricultural Distortions Working Paper 02, World Bank, Washington DC, revised January. Anderson, K. and W. Martin (eds.) (2009), Distortions to Agricultural Incentives in Asia, Washington DC: World Bank. Anderson, K. and W. Masters (eds.) (2009), Distortions to Agricultural Incentives in Africa, Washington DC: World Bank. Anderson, K. and J. Swinnen (eds.) (2008), Distortions to Agricultural Incentives in Europe's Transition Economics, Washington DC: World Bank. Anderson, K. and A. Valdés (eds.) (2008), Distortions to Agricultural Incentives in Latin America, Washington DC: World Bank. Anderson, K. and E. Valenzuela (2007), `The World Trade Organization's Doha Cotton Initiative: A Tale of Two Issues', The World Economy 30(8): 1281-1304, August. Anderson, K. and E. Valenzuela (2008), "Estimates of Global Distortions to Agricultural Incentives, 1955 to 2007", World Bank, Washington DC, available from October on the Database page at www.worldbank.org/agdistortions. Baffes, J. (2009), `Benin, Burkina Faso, Chad, Mali and Togo', Ch. 18 in Anderson and Masters (2009). Feenstra, R.C. (1995) "Estimating the Effects of Trade Policy" in Handbook of International Economics, vol. 3, edited by G.N. Grossman and K. Rogoff, Amsterdam: Elsevier. Hayek, (1945), `On the Use of Information in Society', American Economic Review 35(4): 519-30, September. Johnson, D.G. (1975), "World Agriculture, Commodity Policy, and Price Variability", American Journal of Agricultural Economics 57(5): 823-28, December. 16 Lloyd, P.J., J.L. Croser and K. Anderson (2009), "How Do Agricultural Policy Restrictions to Global Trade and Welfare Differ Across Commodities?" Policy Research Working Paper 4864, World Bank, Washington DC, March. Masters, W.A. and A.F. Garcia (2009), "Agricultural Price Distortion and Stabilization", in Political Economy of Distortions to Agricultural Incentives, edited by K. Anderson (forthcoming). OECD (2008), PSE-CSE Database (Producer and Consumer Support Estimates, OECD Database 1986­2007), Organisation for Economic Co-operation and Development. www.oecd.org/document/55/0,3343,en_2649_33727_36956855_1_1_1_1,00.html Pomfret, R. (2008), `Tajikistan, Turkmenistan and Uzbekistan', Ch. 8 in Anderson and Swinnen (2008). Schiff, M. and A. Valdés (1992), The Political Economy of Agricultural Pricing Policy, Volume 4: A Synthesis of the Economics in Developing Countries, Baltimore: Johns Hopkins University Press for the World Bank. Thompson, S.R., P.M. Schmitz, N. Iwai and B.K. Goodwin (2004), `The Real Rate of Protection: The Income Insurance Effects of Agricultural Policy", Applied Economics 36: 1-8. Tyers, R. and K. Anderson (1992), Disarray in World Food Markets: A Quantitative Assessment, Cambridge and New York: Cambridge University Press. Valenzuela, E., D. van der Mensbrugghe and K. Anderson (2009), "General Equilibrium Effects of Distortions on Global Markets, Farm Incomes and Welfare", Ch. 13 in Anderson and Associates (2009). 17 Figure 1: Gross subsidy equivalents of assistance to farmers globally, by product, 1980-84 and 2000-04 (constant 2000 US$ million) (a) Developing countries (b) High-income countries (c) World Rice Milk Milk Rice Milk Rice Beef Sugar Beef Sugar Poultry Pigmeat Poultry Wheat Poultry Pigmeat Pigmeat Sugar Maize Maize Maize Wheat Soybean Coffee Soybean Rapeseed Coconut Wheat Barley Soybean 2000-04 Cotton 2000-04 2000-04 Cotton Cotton 1980-84 Barley 1980-84 1980-84 Coffee Beef Rapeseed Coconut -50000 0 50000 100000 -50000 0 50000 100000 -50000 0 50000 100000 Source: Anderson and Valenzuela (2008), based on estimates reported in the project's national country studies. 18 Figure 2: Nominal rates of assistance, key covered productsa, high-income and developing countries, 1980-84 and 2000-04 (percent) (a) Developing countries (b) High-income countries 387 Sugar Rice Milk Sugar Rice Milk Poultry Beef Wheat Poultry Maize Cotton Pigmeat Pigmeat Coffee Soybean Soybean 2000-04 Maize 2000-04 1980-84 1980-84 Beef Wheat Coconut Barley Cotton Rapeseed -150 -100 -50 0 50 100 -150 -50 50 150 250 Source: Anderson and Valenzuela (2008), based on estimates reported in the project's national country studies. a. Product nominal rates of assistance (NRAs) are averages of country NRAs weighted by the value of production at undistorted prices. 19 20 Figure 3: Nominal rates of assistance, rice, milk and sugar, by country, 2000-04 (percent) (a) Rice (b) Milk (c) Sugar Japan 607 Iceland Swit z e r l a nd Japan B a ngl a de s h Korea UK Nor way T aiwan Switzer land Swe de n T urkey Kor ea Spa i n P or tuga l Canada Dominican Ne t he r l a nds Romania Colombia Colombia Ita ly Ir e l a nd Malaysia Slovenia Ge r ma ny US Hungar y Fr a nc e Mexico Fi nl a nd Philippines De nma r k US Nicaragua Slovakia Aus t r i a R oma ni a Ecuador UK Hunga r y Mexico Sweden Li t hua ni a Spain Vi e tna m Ghana P or tugal J a pa n Cote d'Ivoire Nether lands La t vi a Suda n Mozambique Italy T ur ke y Ir eland Vietnam US Ger many India Fr ance Sl ove ni a Colombia Indonesia Finland T a nz a ni a Spain Denmar k Moz a mbi que Austr ia P a kis t a n Portugal Me xi c o Czech Rep. Italy Tur key P ol a nd P hil i ppi ne s France Poland Sl ova ki a Estonia Uganda C z e c h Re p. India R us s i a T anzania Sudan Indone s i a Brazil Russia South Af r i c a Bulga r ia Nigeria China Nic a r a gua P akistan Madagascar Indi a Latvia Senegal Bulgar ia Ke nya Ukr a i ne Sri Lanka Ecuador Chile Bangladesh Chile China Lithuania Uga nda Australia Nicar agua E gypt China New Zealand E c ua dor T ha il a nd T hailand Ar gentina Ka z a khs t a n Austr alia Pakistan Domi ni c a n Kazakhstan Br azil Zambia Egypt Aus t r a l i a Egypt Ukr aine Ma da ga s c a r -100 0 100 200 300 400 -100 0 100 200 300 400 -100 0 100 200 300 400 Source: Anderson and Valenzuela (2008), based on estimates reported in the project's national country studies. 21 Figure 4: Nominal rates of assistance, beef, pigmeat and poultry, by country, 2000-04 (percent) (a) Beef (b) Pigmeat (c) Poultry Nor wa y 333 Switzer land Switzer land 503 S wit z e r la nd Taiwan 498 Iceland Kor e a Nor way Nor way 341 J a pa n Kor ea Taiwan 280 Tur ke y Iceland Kor ea UK Romania Kazakhs tan S we de n Latvia S pa in Ecuador Indones ia P or t uga l Latvia Lithuania Ne t he r la nds Lithuania Slovenia It a ly Slovakia J apan Ir e la nd Slovenia Rus s ia Ge r ma ny Rus s ia UK Fr a nc e Romania Sweden Finla nd Hungar y Spain De nm a r k P or tugal Aust r ia UK Nether lands Ic e la nd Sweden Italy S love nia Spain Ir eland Ta iwa n P or tugal Ger many Rom a nia Nether lands Fr ance Russia Italy Finland Ec ua dor Ir eland Denmar k Bulga r ia Ger many Aus tr ia Ka z a khst a n ` Ukr aine P hilippine s Fr ance Es tonia Chile Finland Slovakia Br a z il Denmar k Bulgar ia Ca na da Aus tr ia Ne w Hungar y Ze a la nd Czech Rep. Czech Rep. Egypt Es tonia P hilippines US Vietnam Tur key Aust r a lia J apan Mexico Ukr a ine Ukr aine New Zealand Cz e c h Re p. Bulgar ia P oland Ar ge nt ina Nicar agua Me xic o Mexico Ecuador Colombia Canada Thailand La t via Br azil South Lit hua nia Af r ica S out h New Zealand Canada Af r ic a US Br azil S lova kia China Vietnam Hunga r y Dominican Aus tr alia Nic a r a gua Rep. Thailand US Est onia P hilippines China S uda n P ola nd Aus tr alia P oland - 100 0 100 200 -100 0 100 200 -100 -50 0 50 100 150 200 Source: Anderson and Valenzuela (2008), based on estimates reported in the project's national country studies. 22 Figure 5: Nominal rates of assistance, wheat, maize and soybean, by country, 2000-04 (percent) ( a) Wheat (b) Maize (c) Soybean Ko rea Swit zerland No rway Nig eria K or ea 757 J ap an P hilip p ines Tanzania R o mania J apan Slo venia Ecuad o r Swit zerland Turkey M exico T hai l and Ghana Kenya M ad ag as car Ro mania Chi na Sp ain Ind ia Turkey P o rt ug al US Zamb ia Net herland s Sud an It aly E c uador Co lo mb ia Germany Po land F rance Lit huania Romani a Aus t ria Chile C o lo mb ia Es t o nia Col ombi a S lo venia So ut h Africa Hung ary Nicarag ua I ndi a US Eg yp t UK Ind ia Canada Swed en C hina Sp ain Ind o nes ia Po rt ug al I ndones i a M o zamb iq ue Net herland s C anad a It aly Spai n US Ireland B ulg aria Germany C hile I t al y France Kenya Finland Denmark Po land Ger many Aus t ria Aus t ralia China New Zealand Fr anc e Eg yp t C amero o n Slo vakia Ug and a A us t r al i a Canad a Thailand B razil B razil B r az i l New Zealand Tanzania Aus t ralia R us s ia M ex i c o Lat via M exico Bang lad es h Bulg aria Hung ary Zambi a Czech Rep . Et hio p ia Kazakhs t an Ukraine A r gent i na Et hio p ia So ut h Africa Rus s ia S lo vakia Ni c ar agua Pakis t an P akis t an Arg ent ina Arg ent ina Ukraine Zi mbabwe Zamb ia Zimb ab we Zimb ab we -100 -50 0 50 100 150 -100 -50 0 50 100 150 -1 0 0 -50 0 50 100 150 Source: Anderson and Valenzuela (2008), based on estimates reported in the project's national country studies. 23 Figure 6: Nominal rates of assistance, cotton, cocoa and coffee, by country, 2000-04 (percent) (a) Cotton (b) Cocoa (c) Coffee US 70 Colombia Sudan Ma la ysia Brazil India Brazil Indonesia Pakistan Ec ua dor Colombia Ecuador Cameroon Tanzania Mali Australia Ca me roon Uganda Uganda Burkina Faso Cameroon China Nige ria Kenya Mozambique Chad Et hiopia Benin Senegal Ma da ga sc a r Viet nam Togo Nicaragua Cote d'Ivoire Turkey Dominican Gha na Rep. Egypt Zambia Mexico Zimbabwe Madagascar Tanzania Cot e d'Ivoire Nigeria Cot e d'Ivoire -100 -80 -60 -40 -20 0 20 40 -100 -80 -60 -40 -20 0 20 -100 -80 -60 -40 -20 0 20 Source: Anderson and Valenzuela (2008), based on estimates reported in the project's national country studies. 24 Figure 7: Rice NRA and international rice price, South Asian region, 1970 to 2005 (left axis is int'l price in USD, right axis is NRA in percent) 600 30 20 500 10 0 400 -10 NRA % USD 300 -20 -30 200 -40 -50 100 -60 - -70 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 Pw S Asia Correlation coefficient is -0.75 Source: Authors' compilation based on data in Anderson and Valenzuela (2008) 25 Figure 8: Nominal rates of assistance for rice and per capita income, 1955 to 2007 rice 800 600 400 NRA (%) 200 0 -200 6 7 8 9 10 11 Ln real GDP per capita Developing (with Taiwan and Korea) R^2 = 0.27 HICs and ECA R^2 = 0.25 Total R^2 = 0.26 Source: Using NRA estimates in Anderson and Valenzuela (2008) that are based on estimates reported in the project's national country studies. 26 Figure 9: Trade and welfare reduction indexes for twelve key covered products, 2000-04 (percent) 27 Source: Derived from estimates in Anderson and Croser (2009), based on NRA and CTE estimates in Anderson and Valenzuela (2008). Figure 10: Global Trade and Welfare Reduction Indexes for covered tradable farm products, by commodity, 1985-89 and 2000-04 (percent) (a) Beef, milk, rice, sugar and wheat 200 Milk 150 Rice Rice 100 WRI Sugar Beef Sugar Beef Milk Wheat 50 Wheat 0 0 25 50 75 100 125 150 TRI 1985-89 2000-04 (b) Coconut, coffee, cotton, maize and pigmeat 28 50 Maize Cotton Pigmeat 40 Cotton Coffee 30 Pigmeat W I R Coconut 20 Maize Coffee Coconut 10 0 -10 0 10 20 30 40 TRI 1985-89 2000-04 Source: Derived from estimates in Anderson and Croser (2009), based on NRA and CTE estimates in Anderson and Valenzuela (2008). Figure 11: Country Share of the Global Commodity-Specific TRI for Rice, Sugar, Beef, Cotton and Milk, 2000­04 (a) Sugar (b) Milk (c) Rice (d) Beef (e) Cotton (51 countries global (46 countries global (36 countries global (47 countries global (19 countries global TRI = 54.8) TRI = 44.5) TRI = 42.9) TRI = 32.0) TRI = -4.1) 29 Mexico Japan France Canada Italy India Germany Indonesia Brazil US Korea Colombia Japan Slovenia Germany US Ukraine US Japan India Czech Rep China France Germany Spain Pakistan China Switzerland India Turkey Brazil Lithuania France Russia India Japan Philippines Colombia Ukraine Cote ... Taiwan Zambia Russia Canada US Pakistan UK Vietnam Colombia Zimbabwe Rep S outh ... Mexico Korea Argentina Tanzania UK Italy Suda E gypt China Italy Netherlan... Nicaragua Nigeria Turkey Ukraine US Poland Turkey 0 5 10 -20 0 20 0 20 40 -50 0 50 100 -1000 0 1000 Source: Derived from estimates in Anderson and Croser (2009), based on NRA and CTE estimates in Anderson and Valenzuela (2008). Notes: The decomposition over the 5-year period can be greater than or less than 100, even though the decomposition sums to 100 in any one year. We have scaled the 5-year averages, so that the decompositions sum to 100. Focus countries have been omitted where the decomposition share has an absolute value of less than 2. 30 Figure 12: Country Share of the Global Commodity-Specific WRI for Rice, Sugar, Milk, Beef and Cotton, 2000­04 (a) Rice (b) Sugar (c) Milk (d) Beef (e) Cotton (51 countries global (46 countries global (36 countries global (47 countries global (19 countries global WRI = 140.9) WRI = 86.7) WRI = 72.8) WRI = 68.1) WRI = 44.7) US Germany France Japan Colombia Sudan Japan France Lithuania Korea UK Italy Taiwan Italy Japan Germany Japan Indonesia US UK US Vietnam India Switzerland Poland Turkey Korea S pain Canada Turkey Nigeria US Bangladesh India Spain China China Pakistan Germany Mexico E gypt India Turkey France Slovenia Zimbabwe 0 20 40 0 5 10 0 50 0 20 40 0 50 Source: Derived from estimates in Anderson and Croser (2009), based on NRA and CTE estimates in Anderson and Valenzuela (2008). Note: The decomposition over the 5-year period can be greater than or less than 100, even though the decomposition sums to 100 in any one year. We have scaled the 5-year averages, so that the decompositions sum to 100. Focus countries have been omitted where the decomposition share has a value of less than 2. 31 Table 1: Coverage of gross value of agricultural production at undistorted prices, for twelve key covered products, 2000-04 (percent) NRA coverage Product's (%) of share of global product's production global value of value of 12 key production products Grains and oilseeds 93 37 Rice 92 13 Wheat 89 10 Maize 94 9 Soybean 96 5 Tropical crops 80 8 Sugar 87 3 Cotton 82 3 Coconut 60 1 Coffee 75 1 Livestock products 82 55 Milk 83 15 Beef 69 14 Pigmeat 91 16 Poultry 81 10 All above products 86 100 Source: Authors' calculations based on the Anderson and Valenzuela (2008) database and FAO commodity balance and production data. 32 Table 2: Share of global agricultural production for key covered products, by region,a 2000- 04 (percent) Regional shares (%) of global gross value of agric production Covered products in focus countries Residual World Africa Asia LAC ECA HIC All Grains+oils 11 39 5 6 23 84 16 100 Rice 3 81 2 0 5 92 8 100 Wheat 6 32 4 14 33 89 11 100 Maize 11 26 13 5 40 94 6 100 Soybean 0 15 37 0 43 96 4 100 Tropical crops 10 36 12 5 11 74 26 100 Sugar 5 43 17 6 16 87 13 100 Cotton 11 30 5 14 22 82 18 100 Coconut 0 60 0 0 0 60 40 100 Coffee 11 12 52 0 0 75 25 100 Livestock products 3 21 6 7 36 72 28 100 Milk 3 21 4 12 43 83 17 100 Beef 6 1 16 5 41 69 31 100 Pigmeat 0 49 3 6 34 91 9 100 Poultry 2 27 9 5 38 81 19 100 Source: Authors' calculations based on the Anderson and Valenzuela (2008) database and FAO commodity balance and production data. a. The group averages refer to 30 key products, and in total there are more than 70 products covered by the project, even though only 12 are shown separately here. 33 Table 3: Shares of regional agricultural production for major covered products,a by region, 2000-04 (percent) Covered product shares of regional gross value of agricultural production of focus countries Africa Asia Latin Eastern High- America Europe income and CIS countries Grains + oils 16 23 13 12 16 Rice 2.8 13.6 1.9 0.1 1.0 Wheat 4.7 4.6 3.0 10.2 5.6 Maize 8.4 3.3 8.3 2.7 6.2 Soybean 0.0 1.1 13.3 0.0 3.6 Tropical crops 4.3 3.8 8.0 3.3 1.6 Sugar 1.2 1.9 3.8 1.3 0.8 Cotton 2.3 1.0 0.9 2.1 0.9 Coconut na 0.8 na na na Coffee 0.8 0.1 3.3 na na Livestock products 12 19 28 24 33 Milk 3.5 4.5 4.4 11.8 11.1 Beef 6.8 0.2 14.7 4.6 9.1 Pigmeat na 10.6 3.0 6.6 8.9 Poultry 1.6 3.6 5.9 2.9 6.2 Total of above 12 32 45 49 39 51 All covered 68 66 70 61 72 Non-covered 32 34 30 39 28 All agric 100 100 100 100 100 Source: Authors' calculations based on the Anderson and Valenzuela (2008) database and FAO commodity balance and production data. a. The product group averages refer to 30 key products, and in total there are more than 70 products covered by the project, even though only 12 are shown separately here. 34 Table 4: Gross subsidy equivalents of assistance to farm industries, by focus country group,a 1965 to 2007 (a) All focus countries (constant 2000 US$ per year) 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Grains and oilseeds 12911 -2700 11442 -10561 44083 38345 37908 36655 Rice 2009 -3700 9851 -15006 23352 23491 24633 28189 Wheat 8365 -881 974 7010 16094 12663 7993 3489 Maize 2477 1995 1300 -2259 5507 1770 3347 4124 Soybean 61 -114 -682 -307 -869 422 1935 853 Tropical crops 7053 -7289 -6757 -6521 2092 3468 5716 10683 Sugar 8287 -6247 547 3134 7816 7211 8958 10750 Cotton -94 927 -2008 -2230 -1422 -2151 -1297 228 Coconut -110 -543 -256 -841 -841 -1117 -1017 -273 Coffee -1030 -1425 -5040 -6584 -3462 -476 -928 -21 Livestock products 61368 66214 105824 77798 97486 94166 86491 76757 Milk 35581 39518 72029 73126 73973 59982 46208 43974 Beef 7350 7364 10554 17018 27272 21052 19998 13986 Pigmeat 15792 15132 17550 -19655 -9729 3382 9874 8927 Poultry 2644 4201 5691 7309 5969 9750 10411 9869 All of above 81332 56225 110509 60716 143661 135979 130116 124095 35 Table 4 (continued): Gross subsidy equivalents of assistance to farm industries, by region,a 1965 to 2007 (b) Developing countries (constant 2000 US$ per year) 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Grains and oilseeds -6971 -15204 -14214 -38079 -3883 -7934 9494 15235 Rice -9278 -14804 -11835 -32706 -5435 -7230 2513 12245 Wheat 1574 108 -115 -453 3191 1361 4720 2297 Maize 673 -396 -1579 -4611 -28 -2092 1106 1222 Soybean 61 -112 -685 -308 -1611 27 1155 -529 Tropical crops 331 -8680 -12538 -12092 -5003 -3181 -503 4222 Sugar 2980 -6249 -4819 -1982 889 440 2273 5103 Cotton -1509 -462 -2424 -2684 -1590 -2028 -832 -586 Coconut -110 -543 -256 -841 -841 -1117 -1017 -273 Coffee -1030 -1425 -5040 -6584 -3462 -476 -928 -21 Livestock products -755 -1814 11494 -22745 -2035 7066 14248 12930 Milk 263 55 9639 11242 13198 6443 5610 8684 Beef -1914 -2793 -307 -298 1583 -608 1926 -965 Pigmeat 671 883 1352 -36180 -16910 -1207 3323 2125 Poultry 225 41 810 2491 94 2438 3388 3085 All of above -7396 -25699 -15258 -72916 -10921 -4049 23238 32387 36 Table 4 (continued): Gross subsidy equivalents of assistance to farm industries, by region,a 1965 to 2007 (c) High-income countries (constant 2000 US$ per year) 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-07 Grains and oilseeds 19889 12523 25672 27495 47967 46277 28416 21431 15359 Rice 11291 11128 21696 17685 28791 30717 22126 15955 11431 Wheat 6795 -991 1095 7459 12894 11300 3272 1192 1351 Maize 1804 2387 2879 2349 5542 3862 2243 2905 2408 Soybean 0 -2 2 1 739 397 776 1379 169 Tropical crops 10304 3555 11135 6948 15900 14940 8037 6595 5120 Sugar 5301 1 5373 5117 6914 6775 6682 5645 2819 Cotton 1414 1390 416 455 168 -119 -467 816 1992 Barley 3563 2135 5324 1359 7154 7175 1858 129 307 Rapeseed 26 29 22 17 1664 1110 -36 6 2 Livestock products 62126 68044 94370 100534 99476 87122 72259 63791 34486 Milk 35312 39462 62441 61852 60757 53568 40626 35260 13117 Beef 9277 10171 10854 17324 25661 21648 18062 14953 8519 Pigmeat 15121 14249 16196 16534 7176 4590 6543 6802 7206 Poultry 2416 4162 4879 4824 5882 7316 7027 6777 5643 All of above 92319 84122 131176 134976 163343 148339 108712 91817 54964 Source: Anderson and Valenzuela (2008), based on estimates reported in the project's national country studies. a Does not include non-product-specific or decoupled assistance, nor and assistance provided by non-focus countries. 37 Table 5: Nominal rates of assistance, twelve key covered farm products,a all focus countries, 1965 to 2004 (percent) 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 b Grains and oilseeds 11 6 5 -3 21 16 14 17 Rice 6 11 12 -10 26 25 23 39 Wheat 22 7 2 9 32 23 12 6 Maize 8 5 2 -3 12 3 6 7 Soybean 1 0 -2 -1 -2 1 7 4 Tropical cropsb 34 -5 -9 -8 4 7 11 27 Sugar 157 -4 9 15 39 28 39 60 Cotton 0 9 -9 -12 -8 -10 -6 3 Coconut -24 -8 -3 -11 -19 -34 -22 -8 Coffee -31 -33 -43 -43 -31 -8 -10 0 Livestock productsb 46 39 50 30 42 35 30 27 Milk 97 91 140 138 151 85 62 53 Beef 14 12 13 25 43 29 31 23 Pigmeat 47 36 31 -16 -11 4 10 10 Poultry 20 26 26 29 21 26 20 19 All of above 29 18 21 10 28 24 21 23 b All covered products 24 15 18 6 16 18 16 16 Source: Anderson and Valenzuela (2008), based on estimates reported in the project's national country studies. a. The group averages refer to 30 key products, and in total there are more than 70 products covered by the project, even though only 12 are shown separately here b. Weighted averages using value of production at undistorted prices. 38 Table 6: Consumer tax equivalents of policies assisting producers of covered farm products, per cent and by value, all focus countries, 1965 to 2007 (a)Percent 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-07a b Crops 10 -3 4 5 19 17 13 16 22 Rice -14 -11 4 1 24 25 22 38 137 Wheat 19 2 3 12 27 16 6 2 5 Maize 11 7 8 2 5 -3 -2 -2 3 Soybean 1 -3 -1 3 1 0 7 4 8 Sugar 175 1 13 19 40 42 44 63 79 Livestock productsb 46 39 50 32 41 29 27 25 19 Milk 98 89 137 130 140 69 54 46 23 Beef 16 14 16 25 47 30 36 31 21 Pigmeat 47 35 30 -12 -10 0 7 8 19 Poultry 23 28 27 28 18 21 18 19 16 b All of above 28 16 25 17 30 23 20 21 21 (b)Aggregate value (constant 2000 US$ per year) 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-07a Crops 13090 -20549 10751 12591 46803 49139 38465 40780 34167 Rice -7405 -15615 4303 -511 21994 23714 22973 27390 15748 Wheat 7020 -2812 1113 7545 14412 9339 3720 1420 2418 Maize 3399 2396 3992 1218 2249 -1755 -1487 -998 1465 Soybean 60 -452 -323 703 -11 123 2557 1518 2280 Sugar 10016 -4065 1667 3636 8159 17718 10702 11450 12255 Livestock products 62252 66748 106150 80323 96353 83687 82670 76759 41500 Milk 34929 38158 70180 69282 69593 52186 41196 40069 13019 Beef 8622 8945 12604 17432 30110 23143 24990 18906 12589 Pigmeat 15702 15119 17544 -13400 -8648 -82 6971 7487 9676 Poultry 2998 4526 5822 7009 5297 8440 9513 10298 6217 All of above 75342 46200 116901 92914 143156 132826 121135 117539 75667 Source: Anderson and Valenzuela (2008), based on estimates reported in the project's national country studies. a. The estimates for the period 2005-07 refer only to high-income country policies. b. Weighted averages based on the value of consumption at undistorted prices. 39 Table 7: Shares of production exported (100X/Q) and of consumption imported (100M/C) for major covered products,a by region, 2000-03 (percent) Africa Asia LAC ECA HIC Grains X/Q 2 7 11 13 29 M/C 17 9 22 11 27 Rice X/Q 6 6 1 2 32 M/C 28 1 12 59 15 Wheat X/Q 4 3 46 13 49 M/C 44 4 51 6 27 Maize X/Q 4 8 15 10 20 M/C 14 5 14 9 5 Tropical X/Q 52 38 45 32 47 crops M/C 13 18 12 42 42 Sugar X/Q 27 12 40 20 31 M/C 20 9 4 46 25 Cottonb X/Q 29 1 5 2 31 M/C 2 4 9 21 3 Coconut X/Q - 9 - - - M/C - - - - - Coffee X/Q 77 78 73 - - M/C 2 3 5 - - Livestock X/Q 1 4 10 7 20 M/C 8 6 5 9 14 Pigmeat X/Q - 1 12 6 21 M/C - 2 8 12 20 Milk X/Q 0 0 5 2 7 M/C 1 1 5 1 3 Beef X/Q 1 2 10 6 23 M/C 9 48 5 14 20 Poultry X/Q 1 10 16 7 19 M/C 9 11 5 28 10 Total of above 12 X/Q 16 22 19 11 24 products M/C 13 14 12 11 19 Source: Authors' calculations based on and FAO commodity balance and production data. a. The group averages refer to 30 key products, and in total there are more than 70 products covered by the project, even though only 12 are shown separately here. These data include intra-European Union trade which, if excluded, would have lowered substantially the numbers in the HIC column. LAC, ECA and HIC refer to Latin America and the Caribbean, Europe and Central Asia's transition economies, and high-income countries. b. Excluding data for the 5 cotton countries of Benin, Burkina Faso, Chad, Mali and Togo. 40 Table 8: Focus countries' shares of global production of seven mostly-nontraded staple crops, by region, 1995-2004 (percent) Regional shares of global volume of crop production Focus countries Non- World Africa Asia LAC All HIC+ All focus DCs ECA countries Cassava 37 28 14 79 0 79 19 100 Potato 2 29 4 35 52 87 13 100 Sweet potato 5 89 1 95 1 96 4 100 Yams 88 0 1 89 0 89 11 100 Millet 31 45 0 76 5 81 19 100 100 Banana 6 48 26 80 1 81 19 100 Plantain 56 2 13 70 0 70 30 100 ALL 7 crops 23 41 11 75 11 86 14 100 Source: Authors' calculations based on and FAO production data. 41 Table 9: Average of focus developing countries' self-sufficiency ratios for seven mostly- nontraded staple crops, by region, 1961 to 2005 (production divided by production plus net imports) 1961-69 1970-79 1980-89 1990-99 2000-05 Africa Cassava 1.00 1.00 1.00 1.00 1.00 Potato 1.02 1.03 1.02 1.03 1.02 Sweet potato 1.00 1.00 1.00 1.00 1.00 Yam 1.00 1.00 1.00 1.00 1.00 Millet 1.00 0.99 1.00 1.00 1.00 Banana 1.23 1.13 1.05 1.09 1.13 Plantain 1.00 1.00 1.00 1.00 1.00 Asia Cassava 1.04 1.10 1.18 1.13 1.04 Potato 1.00 1.00 1.00 1.00 1.00 Sweet potato 1.00 1.00 1.00 1.00 1.00 Yam 1.00 1.00 1.00 0.98 0.87 Millet 1.00 1.00 1.00 1.00 1.00 Banana 1.04 1.08 1.06 1.04 1.04 Plantain 1.00 1.00 1.00 1.00 1.00 Latin America Cassava 1.00 1.00 1.00 1.00 1.00 Potato 1.00 0.99 1.00 0.99 0.99 Sweet potato 1.00 1.00 1.01 1.01 1.01 Yam 1.00 1.02 1.04 1.02 1.01 Millet 4.21 2.21 2.15 1.97 1.03 Banana 1.23 1.21 1.25 1.48 1.52 Plantain 1.00 1.00 1.00 1.03 1.06 Source: Authors' calculations based on and FAO production and trade data. 42 Table 10: Additional contribution of 7 non-covered staplesa to values of agricultural production (VOP) and to aggregate NRAs in focus developing countries, 1966 to 2004 (percent at undistorted prices) s countries of: Africa Asia LAC All DCs Covered products' share of regional VOP (with 7 staples' share in brackets) 1966-1974 71 (16.5) 63 (1.3) 58 (1.2) 64 (2.7) 1975-1984 69 (18.2) 71 (1.1) 69 (0.4) 71 (3.5) 1985-1994 67 (18.6) 76 (1.3) 66 (0.5) 73 (4.0) 1995-2004 67 (22.1) 69 (2.6) 69 (0.9) 69 (5.8) Non-covered 7 staples' share of regional VOP 1966-1974 1.7 4.6 7.2 4.5 1975-1984 2.4 5.9 8.3 5.8 1985-1994 2.4 5.4 10.1 5.8 1995-2004 2.7 5.7 10.3 6.0 Covered products' weighted average NRA 1966-1974 -20.1 -0.2 -19.8 -8.1 1975-1984 -16.2 -10.7 -17.1 -13.8 1985-1994 -5.8 -9.9 -6.7 -9.1 1995-2004 -7.7 8.3 1.8 5.3 Covered plus 7 non-covered wted. av. NRAb 1966-1974 -19.6 -0.2 -17.6 -7.6 1975-1984 -15.7 -9.9 -15.3 -12.8 1985-1994 -5.6 -9.2 -5.8 -8.4 1995-2004 -7.4 7.7 1.6 4.9 a The staples considered here are banana, cassava, millet, plantain, potato, sweet potato and yam. The undistorted prices for these products are assumed to be the domestic producer prices. b Assumes the NRA and CTE for each of the 7 staples is zero. Source: Authors' calculations based on FAO production data and on NRAs from the project's national country studies as summarized in Anderson and Valenzuela (2008). 43 Table 11: Global Trade Reduction Indexes, by commodity, 1965 to 2004 (percent) 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Grains and oilseeds 18 15 15 20 19 18 15 9 Rice 50 58 42 41 58 53 32 43 Wheat 15 1 1 9 28 20 11 4 Maize 8 4 9 -3 9 10 2 3 Soybean 1 0 6 8 11 8 6 6 Tropical Crops 34 26 36 42 32 31 19 9 Sugar 143 27 40 47 56 44 41 55 Cotton 2 13 14 1 13 4 9 -4 Coconut 24 8 3 12 21 35 23 9 Coffee 30 31 37 46 33 13 12 2 Livestock products 53 37 48 53 49 37 23 25 Milk 83 79 133 131 125 63 53 45 Beef 20 17 18 32 47 32 33 32 Pigmeat 37 28 25 47 25 11 9 8 Poultry 22 29 26 24 27 27 18 18 All of above 29 21 23 30 30 29 19 15 Source: Derived from estimates in Anderson and Croser (2009), based on NRA and CTE estimates in Anderson and Valenzuela (2008). 44 Table 12: Global Welfare Reduction Indexes, by commodity, 1965 to 2004 (percent) 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Grains and oilseeds 44 42 47 49 89 82 61 58 Rice 65 86 75 75 150 152 116 141 Wheat 45 36 30 30 59 47 29 20 Maize 29 23 29 30 48 29 21 20 Soybean 6 10 16 28 31 27 24 25 Tropical crops 97 48 47 49 63 58 52 59 Sugar 224 58 68 72 99 76 77 87 Cotton 46 47 32 29 39 38 34 45 Coconut 24 12 14 19 24 38 27 12 Coffee 32 35 44 50 38 31 22 15 Livestock products 83 76 91 89 88 68 54 52 Milk 161 149 218 182 191 111 83 73 Beef 43 42 47 66 93 76 72 68 Pigmeat 79 66 59 70 42 33 27 28 Poultry 43 54 48 50 48 54 46 45 All of above 67 58 65 66 86 73 57 55 Source: Derived from estimates in Anderson and Croser (2009), based on NRA and CTE estimates in Anderson and Valenzuela (2008). 45 Appendix Figure 1: Global Trade Reduction Indexes, 1960 to 2004 (percent) (a) Grains 160 140 120 100 80 60 40 20 0 -20 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Rice Wheat Maize Barley Sorghum (b) Oilseeds 160 140 120 100 80 60 40 20 0 -20 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Soybean Groundnut Palmoil Rapeseed Sunflower Sesame 46 Appendix Figure 1 (continued). Global Trade Reduction Indexes, 1960 to 2004 (percent) (c) Tropical crops 160 140 120 100 80 60 40 20 0 -20 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Sugar Cotton Coconut Coffee Rubber Tea Cocoa (d) Livestock products 160 140 120 100 80 60 40 20 0 -20 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Pigmeat Milk Beef Poultry Egg Sheepmeat Wool Source: Derived from estimates in Anderson and Croser (2009), based on NRA and CTE estimates in Anderson and Valenzuela (2008). Appendix Figure 2: Global Welfare Reduction Indexes, 1960 to 2004 (percent) (a) Grains 47 250 200 150 100 50 0 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Rice Wheat Maize Barley Sorghum (b) Oilseeds 250 200 150 100 50 0 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Soybean Groundnut Palmoil Rapeseed Sunflower Sesame 48 Appendix Figure 2 (continued): Global Welfare Reduction Indexes, 1960 to 2004 (percent) (c) Tropical crops 250 200 150 100 50 0 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Sugar Cotton Coconut Coffee Rubber Tea Cocoa (d) Livestock products 250 200 150 100 50 0 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Pigmeat Milk Beef Poultry Egg Sheepmeat Wool Source: Derived from estimates in Anderson and Croser (2009), based on NRA and CTE estimates in Anderson and Valenzuela (2008). 49 Appendix Table 1: Summary of NRA estimates by major product, Africa, Asia and Latin America, 2000-04 (a) Africa Number Weighted Gross of average Value of Product countries NRA Prod'na Countries included (by ISO code) Apple b 1 0.3 0.15 ZA Banana 1 1.1 0.08 CM Bean 3 -25.1 0.49 MZ, TZ, UG Beef 3 -26.0 5.89 EG, ZA, SD Camel 1 87.7 0.10 SD Cashew 2 -9.9 0.06 MZ, TZ Cassava 13 -2.6 8.45 BJ, BF, CM, TD, CI, GH, MG, ML, MZ, NG, TZ, TG, UG Chat 1 -39.5 0.07 ET Clove 1 -18.7 0.05 MG Cocoa 5 -35.8 2.59 CM, CI, GH, MG, NG Coffee 7 -12.0 0.70 CM, CI, ET, KE, MG, TZ, UG Cotton 16 -46.1 1.94 BJ, BF, CM, CI, TD, EG, ML, MZ, NG, SN, SD, TZ, TG, UG, ZM, ZW Fruit & veg b 1 0.0 0.14 KE Grape b 1 7.4 0.21 ZA Groundnut 8 -40.3 1.72 GH, MZ, NG, SN, SD, UG, ZM, ZW Gumarabic 1 -67.1 0.02 SD Hides & skins 1 -48.4 0.03 ET Maize 13 -5.4 7.24 CM, EG, ET, GH, KE, MG, MZ, NG, ZA, TZ, UG, ZM, ZW Milk 2 14.6 2.99 EG, SD Millet 13 -2.3 1.79 BJ, BF, CM, TD, ML, MZ, NG, SN, SD, TZ, TG, UG, ZM Oilseed 1 -39.4 0.08 ET Orange b 1 8.4 0.23 ZA Roots &tubers 1 0.0 0.38 CM Palmoil 1 -12.6 0.73 NG Pepper 1 -10.2 0.00 MG Plantain 5 -0.1 1.93 CM, CI, GH, TZ, UG Potato 2 0.0 0.07 MZ, TZ Poultry 1 2.7 1.36 ZA Pulse 1 -20.4 0.16 ET Pyrethrum 1 -47.7 0.00 TZ Rice 10 -5.5 2.45 CI, EG, GH, MG, MZ, NG, SN, TZ, UG, ZM Sesame 1 -38.1 0.20 SD Sheepmeat 2 -21.4 1.57 ZA, SD Sisal 1 0.0 0.01 TZ Sorghum 13 20.7 2.13 BJ, BF, CM, TD, ML, MZ, NG, SD, TZ, TG, UG, ZM, ZW Soybean 2 -54.2 0.04 ZM, ZW Sugar 8 43.7 1.03 EG, KE, MG, MZ, ZA, SD, TZ, UG Sunflower 3 -3.5 0.15 ZA, ZM, ZW Sweet potato 4 -0.2 0.34 MG, MZ, TZ, UG Tea 3 -16.4 0.58 KE, TZ, UG Teff 1 -7.1 0.37 ET Tobacco 4 -63.0 0.51 MZ, TZ, ZM, ZW Vanilla 1 -12.8 0.06 MG Wheat 8 -1.1 4.03 EG, ET, KE, ZA, SD, TZ, ZM, ZW Yam 12 -3.3 5.73 BJ, BF, TD, CI, GH, ML, NG, TG All covered products 21 -8.9 58.8 50 Appendix Table 1 (continued): Summary of NRA estimates by major product, Africa, Asia and Latin America, 2000-04 (b) Asia Product Number Weighted Gross Value Countries included of Average of (by ISO Code) Countries NRA Productiona Banana 1 0.0 0.47 PH Barley 1 562.8 0.04 KP Beef 3 85.2 1.00 KP, PH, TW Cabbage 1 27.6 0.39 KP Cassava 1 -10.0 0.42 TH Chickpea 1 18.7 1.43 IN Chillies 1 67.2 0.03 LK Cocoa 1 0.0 0.02 MY Coconut 3 -7.9 3.80 ID, MY, PH, LK Coffee 2 -1.7 0.68 ID, VN Cotton 3 5.1 4.79 CH, IN, PK Egg 2 51.3 0.64 KP, TW Fruit & veg 1 -8.9 23.10 IN Fruits 1 0.0 9.23 CH Garlic 1 122.6 0.26 KP Groundnut 1 12.9 1.79 IN Jute 1 -38.7 0.18 BD Maize CH, IN, ID, PK, PH, 6 12.6 16.30 TH Milk 4 31.6 22.00 CH, IN, KP, PK, TW Onion 1 53.4 0.02 LK Palmoil 3 -2.6 6.66 ID, MY, TH Peppers 1 197.0 0.28 KP Pigmeat 6 4.2 52.10 CH, KP, PH, TW, VN Potato 2 6.2 0.44 BN, LK Poultry 7 12.2 17.50 CH, KP, PH, TW, VN Rapeseed 1 64.8 1.09 IN Rice BN, CH, IN, ID, KP, MY, PK, PH, LK, TW, 12 18.5 67.00 TH, VN Rubber 5 3.9 4.47 ID, MY, LK, TH, VN Sorghum 1 15.7 0.83 ID Soybean 5 16.9 5.22 CH, IN, ID, KP, TH Sugar TH, CH, IN, ID, PK, 8 43.1 9.18 PH, TH, VN Sunflower 1 14.6 0.26 IN Tea 3 -7.5 0.56 BN, ID, LK Vegetables 1 0.0 49.90 CH Wheat BN, CH, IN, KP, PK, 6 10.7 22.50 TW All covered products 12 10.4 324.6 51 Appendix Table 1 (continued): Summary of NRA estimates by major product, Africa, Asia and Latin America, 2000-04 (c) Latin America Product Number Weighted Gross Countries included of Average value of (by ISO Code) countries NRA, % a production Apple 1 -0.2 0.15 CL Banana 2 -24.3 0.69 DO, EC Barley 1 -6.8 0.18 MX Bean 3 -3.3 0.88 DO, MX, NI Beef 7 -1.3 14.30 AR, BR, CL, CO, EC, MX, NI Cassava 1 0.0 0.02 DO Cocoa 1 -6.7 0.08 EC Coffee 6 3.3 3.20 BR, CO, DO, EC, MX, NI Cotton 2 10.7 0.86 BR, CO Egg 1 -15.7 1.84 MX Garlic 1 361.9 0.00 DO Grape 1 -0.4 0.20 CL Groundnut 1 -34.5 0.04 NI Maize 7 -3.1 8.07 AR, BR, CL, CO, EC, MX, NI Milk 6 45.3 4.26 AR, CL, CO, EC, MX, NI Onion 1 74.0 0.01 DO Palmoil 1 47.4 0.14 CO Pigmeat 3 4.5 2.93 BR, EC, MX Poultry 5 18.8 5.78 BR, DO, EC, MX, NI Rice 6 33.7 1.87 BR, CO, DO, EC, MX, NI Sesame 1 -40.5 0.01 NI Sorghum 3 -10.3 0.87 CO, MX, NI Soybean 6 -9.9 13.00 AR, BR, CO, EC, MX, NI Sugar 7 26.5 3.71 BR, CL, CO, DO, EC, MX, NI Sunflower 1 -31.9 0.91 AR Tomato 2 -37.0 1.68 DO, MX Wheat 5 2.0 2.91 AR, BR, CL, CO, MX All covered products 8 2.7 68.6 Source: Drawn from estimates in Anderson and Valenzuela (2008). a. Annual average gross value of covered production at undistorted prices (US$billion). b. Even though apple, fruit and vegetables, grape and orange are covered only by one country, the weighted and simple averages differ because traded and nontraded products are treated separately. 52 Appendix Table 2: Nominal Rates of Assistance of Policies Assisting Producers of 28 Covered Farm Products, All 75 Focus Countries, 1960 to 2004 (percent) 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Grains and tubers 20 15 9 9 -1 25 20 14 17 Rice 39 6 11 12 -10 26 25 23 39 Wheat 15 22 7 2 9 30 23 12 6 Maize 4 8 5 2 -3 11 3 6 7 Cassava 0 0 -3 1 1 -1 -2 -4 -3 Barley 40 38 23 33 10 85 73 20 2 Sorghum 61 56 47 17 14 24 11 12 9 Millet -19 -6 -4 -1 1 0 1 -3 -2 Oat 38 52 33 69 12 54 45 28 0 Oilseeds -3 2 -3 -7 -2 10 8 2 1 Soybean 0 1 0 -2 -1 -2 1 7 4 Groundnut -21 2 -14 -27 -1 34 3 -10 -14 Palmoil -20 -24 -23 -15 -4 -5 8 -5 -3 Rapeseed 12 29 14 5 12 72 47 7 13 Sunflower 13 1 -9 -14 -23 46 19 -10 -12 Sesame -53 -64 -65 -68 -60 -48 -46 -49 -39 Tropical crops 1 22 -8 -13 -10 0 3 9 21 Sugar 78 157 -4 9 15 38 28 39 60 Cotton -10 0 9 -9 -12 -8 -10 -6 3 Coconut -29 -24 -8 -3 -11 -19 -34 -22 -8 Coffee -20 -31 -33 -43 -43 -31 -8 -10 0 Rubber -16 -14 -8 -19 -19 -14 -16 5 4 Tea -32 -31 -26 -26 -25 -24 -27 -19 -12 Cocoa -27 -50 -45 -56 -47 -32 -32 -31 -35 Livestock products 38 41 36 48 29 39 33 28 25 Pigmeat 33 47 36 31 -16 -12 4 10 10 Milk 96 97 91 140 138 152 85 62 53 Beef 15 14 12 13 25 42 29 31 23 Poultry 21 20 26 26 29 20 26 20 19 Egg -8 -3 -6 12 11 17 15 19 6 Sheepmeat 41 48 61 99 64 51 30 13 11 Wool 0 0 6 4 7 4 5 1 1 All of the above 28 commodities 26 27 17 19 9 27 23 19 20 Source: Anderson and Valenzuela (2008), based on NRA estimates reported in national studies covering 75 focus countries. Note: The countries for which there are NRA (and CTE) estimates of these commodities account on average for 77 percent of global production (85 percent for grains, 74 percent for oilseeds, 74 percent for tropical crops, and 72 percent for livestock products). 53 Appendix Table 3: Consumer Tax Equivalents of Policies Assisting Producers of 28 Covered Farm Products, All 75 Focus Countries, 1960 to 2004 (percent) 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Grains and tubers 23 7 1 7 4 20 15 10 13 Rice 42 -14 -11 4 1 24 25 22 38 Wheat 19 19 2 3 12 27 16 6 2 Maize 7 11 7 8 2 4 -3 -2 -2 Cassava 0 0 -1 -1 -2 -1 0 3 3 Barley 44 39 24 33 10 28 27 11 6 Sorghum 62 32 43 20 5 17 7 10 7 Millet -15 -4 -2 0 2 3 4 6 6 Oat 39 54 33 68 11 24 17 4 -3 Oilseeds -4 -2 -8 -8 0 3 2 4 2 Soybean 0 1 -3 -1 3 1 0 7 4 Groundnut -21 -8 -20 -30 -7 26 -6 -12 -15 Palmoil -19 -30 -35 -15 -7 -9 33 -2 -6 Rapeseed 3 13 7 5 9 13 15 5 11 Sunflower 10 1 -9 -17 -23 -2 -6 -5 -8 Sesame -43 -56 -58 -61 -51 -38 -36 -40 -26 Tropical crops 28 56 -2 -2 -1 11 19 15 27 Sugar 116 175 1 13 19 38 42 44 63 Cotton -8 0 3 -12 -15 -11 -18 -11 -6 Coconut -29 -24 -9 -3 -12 -22 -36 -25 -10 Coffee -16 -30 -30 -32 -49 -35 -18 -14 -4 Rubber -43 -52 -6 -19 -23 -19 -11 2 1 Tea -38 -41 -28 -26 -21 -21 -19 -21 -21 Cocoa -28 -29 -33 -50 -43 -29 -19 -22 -31 Livestock products 41 43 37 49 31 39 28 26 24 Pigmeat 34 47 35 30 -12 -11 0 7 8 Milk 96 98 89 137 130 139 69 54 46 Beef 19 16 14 16 25 46 30 36 31 Poultry 24 23 28 27 28 17 21 18 19 Egg -6 -1 -6 11 8 17 15 17 8 Sheepmeat 64 77 107 161 94 70 39 19 19 Wool 0 0 6 4 6 2 4 1 0 All of the above 28 commodities 32 26 15 23 15 26 21 18 19 Source: Anderson and Valenzuela (2008), based on CTE estimates reported in national studies covering 75 focus countries. 54 Appendix Table 4: Country Share of the Global Commodity-Specific TRI for Sugar, Milk, Rice, Beef and Cotton, 2000­04 (percent) Sugar Milk Rice Beef Cotton TRI Global Average 54.8 44.5 42.9 32.0 -4.1 Decomposition Argentina 0.1 -4.6 Australia 0.0 0.5 0.0 0.0 0.0 Austria 0.7 0.8 0.9 Bangladesh 1.5 0.0 Benin Brazil 0.7 0.2 5.2 16.5 Bulgaria 0.0 -0.1 0.0 Burkina Faso Cameroon 0.1 Canada 3.7 7.6 Chad Chile 0.4 0.1 0.1 China 4.8 1.3 5.9 109.0 Colombia 6.7 3.9 0.2 -3.8 0.0 Cote d'Ivoire 0.1 -3.7 Czech Rep 0.9 0.5 3.4 Denmark 0.7 1.1 0.7 Dominican Republic 0.1 0.1 Ecuador 0.2 0.1 1.4 0.5 Egypt 0.3 -0.4 -0.8 0.0 -101.4 Estonia 0.1 0.0 Finland 0.3 0.6 0.4 France 5.4 5.9 0.1 7.7 Germany 5.7 6.7 5.4 Ghana 0.0 Hungary 0.4 1.3 0.2 Iceland 0.1 0.4 India 9.4 10.8 36.8 -2.8 Indonesia 8.7 1.9 Ireland 0.4 1.3 1.6 Italy 2.6 3.0 1.0 6.0 Japan 5.4 18.3 16.5 21.1 Kazakhstan 0.6 0.0 -0.2 Continued over 55 Sugar Milk Rice Beef Cotton TRI Global Average 54.8 44.5 42.9 32.0 -4.1 Kenya 0.4 Korea 1.3 6.5 4.9 Latvia 1.6 0.0 0.0 Lithuania 3.5 -0.2 1.4 Madagascar 0.0 0.0 Malaysia 0.1 Mali Mexico 3.0 0.0 55.8 Mozambique 0.6 0.0 -0.1 Netherlands 1.5 2.6 1.7 New Zealand 0.1 0.9 Nicaragua 0.3 0.0 0.0 -11.3 Nigeria 0.0 -125.6 Norway 1.1 1.0 Pakistan 3.0 0.8 1.0 35.0 Philippines 3.4 1.4 0.2 Poland 1.2 1.8 -13.4 Portugal 0.4 0.5 0.1 0.7 Romania 0.2 1.5 0.3 Rep South Africa 2.8 -0.3 Russia 3.2 2.3 2.8 Senegal 0.0 -0.2 Slovakia 0.2 0.4 0.0 Slovenia 0.0 0.4 4.7 Spain 2.0 1.9 0.7 3.1 Sri Lanka 0.0 Sudan 1.5 1.3 -9.1 -0.7 Sweden 0.6 0.9 1.0 Switzerland 0.9 6.4 1.0 Taiwan 15.4 0.4 Tanzania 0.1 0.0 -30.9 Thailand 1.6 -2.0 Togo turkey 2.6 1.6 0.1 3.1 -530.3 Uganda 0.1 0.0 0.0 UK 2.7 3.7 4.3 Ukraine 0.9 -2.9 -2.7 Continued over 56 Sugar Milk Rice Beef Cotton TRI Global Average 54.8 44.5 42.9 32.0 -4.1 US 7.3 11.9 5.5 -3.2 769.3 Vietnam 1.5 7.6 Zambia 0.0 -8.3 Zimbabwe -26.0 Sum 100.0 100.0 100.0 100.0 100.0 Source: Derived from estimates in Anderson and Croser (2009), based on NRA and CTE estimates in Anderson and Valenzuela (2008). Note: the decomposition over the 5-year period can be greater than or less than 100, even though the decomposition sums to 100 in any one year. We have scaled the 5-year averages, so that the decompositions sum to 100. 57 Appendix Table 5: Country Shares of the Global Commodity-Specific WRI for Sugar, Milk, Rice, Beef and Cotton, 2000­04 (percent) Rice Sugar Milk Beef Cotton WRI global average 140.9 86.7 72.8 68.1 44.7 Decomposition Argentina 0.0 0.2 Australia 0.0 0.0 0.1 0.0 0.0 Austria 1.1 0.4 0.8 Bangladesh 0.0 2.8 Benin 0.0 Brazil 0.0 0.1 0.2 0.4 Bulgaria 0.0 0.0 0.0 Burkina Faso 0.1 Cameroon 0.0 Canada 4.3 0.0 Chad 0.0 Chile 0.1 0.0 0.0 China 3.9 2.4 0.4 8.2 Colombia 0.1 7.8 2.5 1.4 0.3 Cote d'Ivoire 0.0 0.1 Czech Rep 0.9 0.3 1.6 Denmark 1.0 0.6 0.6 Dominican Republic 0.0 0.1 Ecuador 0.4 0.1 0.0 0.1 Egypt 1.4 0.2 0.1 0.1 4.3 Estonia 0.0 0.0 Finland 0.4 0.3 0.4 France 0.1 8.0 3.3 6.8 Germany 8.4 3.7 4.8 Ghana 0.0 Hungary 0.7 0.7 0.5 Iceland 0.2 0.2 India 3.0 3.2 3.8 0.6 Indonesia 0.1 3.5 Ireland 0.6 0.7 1.5 Italy 0.3 3.9 1.6 5.3 Japan 27.8 7.0 46.9 21.8 Kazakhstan 0.1 0.0 0.2 Continued over 58 Rice Sugar Milk Beef Cotton WRI global average 140.9 86.7 72.8 68.1 44.7 Kenya 0.3 Korea 7.1 1.9 5.6 Latvia 1.8 0.0 0.0 Lithuania 5.2 0.2 0.5 Madagascar 0.0 0.0 Malaysia 0.0 Mali 0.1 Mexico 0.0 1.6 1.7 2.7 Mozambique 0.0 0.5 0.0 Netherlands 2.2 1.5 1.5 New Zealand 0.0 0.0 Nicaragua 0.0 0.1 0.0 0.9 Nigeria 0.0 17.0 Norway 1.6 2.1 Pakistan 1.5 2.5 0.2 0.2 Philippines 0.2 2.3 0.0 Poland 1.3 1.0 3.2 Portugal 0.1 0.6 0.3 0.6 Romania 0.3 1.4 0.2 Rep South Africa 1.7 0.1 Russia 1.8 0.7 0.8 Senegal 0.0 0.0 Slovakia 0.2 0.2 0.0 Slovenia 0.0 0.2 2.6 Spain 0.2 3.0 1.1 2.8 Sri Lanka 0.0 Sudan 1.5 0.5 19.9 0.2 Sweden 0.9 0.5 0.9 Switzerland 1.7 6.2 1.2 Taiwan 36.1 0.2 Tanzania 0.0 0.1 1.7 Thailand 0.6 0.2 Togo 0.0 Turkey 0.0 2.5 0.9 3.0 20.1 Uganda 0.0 0.0 0.0 UK 4.0 2.1 3.8 Continued over 59 Rice Sugar Milk Beef Cotton WRI global average 140.9 86.7 72.8 68.1 44.7 Ukraine 0.3 0.4 0.9 US 4.5 8.5 7.2 0.2 43.8 Vietnam 12.5 2.0 Zambia 0.0 0.3 Zimbabwe 2.6 Sum 100.0 100.0 100.0 100.0 100.0 Source: Derived from estimates in Anderson and Croser (2009), based on NRA and CTE estimates in Anderson and Valenzuela (2008). Note: the decomposition over the 5-year period can be greater than or less than 100, even though the decomposition sums to 100 in any one year. We have scaled the 5-year averages, so that the decompositions sum to 100.