Report No: AUS0001585 Russian Federation— Agriculture Support Policies and Performance November 20, 2020 AGR 1 © 2020 The World Bank 1818 H Street NW, Washington DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org Some rights reserved This work is a product of the staff of The World Bank. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Rights and Permissions The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. Attribution—Please cite the work as follows: “World Bank. 2020. Russian Federation-Agriculture Support Policies and Perofrmance. © World Bank.� All queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@worldbank.org. 2 3 Contents ABBREVIATIONS AND ACRONYMS ......................................................................................... 8 ACKNOWLEDGMENT ............................................................................................................. 9 EXECUTIVE SUMMARY ......................................................................................................... 10 PREFACE: Agriculture Support Policies and Performance ...................................................... 22 I. Overview of Russia’s agriculture sector ........................................................................ 24 A. Production ........................................................................................................................... 24 B. The unevenness of agricultural growth in Russia, 2000-2018 ................................................. 28 C. Livestock ............................................................................................................................. 30 D. Land use .............................................................................................................................. 31 E. Prospects for land reutilization and its importance in Russia ................................................. 35 F. Productivity ......................................................................................................................... 36 G. Productivity by farm type ..................................................................................................... 41 H. Comparative advantage ....................................................................................................... 43 II. Russia’s State Program for the Development of Agriculture .......................................... 47 A. State Program budget support and total support to agriculture............................................. 48 B. Changes in budget support over time, 1995-2017 ................................................................. 50 C. The development of Russian State Agricultural Programs, 2005-2019 .................................... 51 D. Rural Development in the State Program .............................................................................. 54 E. Types and amounts of support in the State Program, 2013-25 ............................................... 57 F. Regional distribution of agricultural support payments in State Program, 2012-2020 ............. 60 1. The real value of producer support has been falling since 2013. ...........................................................60 2. Size-distribution of direct producer support by region has remained relatively constant, 2012-18. ....61 3. Eleven regions received 40 percent of subsidies available to producers, 2012-18. ...............................62 4. The performance of the largest subsidy recipient regions has been mediocre, 2012-18. .....................63 5. Correlates of subsidy distribution, 2012-18 ............................................................................................65 6. Distribution of producer subsidies by program, 2012-18 .......................................................................68 7. Conclusion ................................................................................................................................................69 G. The formal goals of the State Program .................................................................................. 71 1. Reducing dependence on imported food ................................................................................................72 2. Import substitution ..................................................................................................................................73 3. Increasing the competitiveness of Russian agriculture ..........................................................................74 4. Food security ............................................................................................................................................77 5. Increasing value added in agriculture .....................................................................................................78 6. Growth of agricultural exports ................................................................................................................79 7. Growth in capital investment ..................................................................................................................81 H. The informal goal of the State Program ................................................................................ 82 III. Comparative perspective on Russia’s agriculture support measures .............................. 84 A. The cost of agricultural support in Russia .............................................................................. 84 B. Distribution of agricultural support ...................................................................................... 85 1. The political process of defining and distributing support .....................................................................86 2. Outcomes for large farms ........................................................................................................................87 C. Vertical integration in Russia and OECD countries ................................................................. 89 D. Barriers to agricultural exports ............................................................................................. 92 1. Focus on private support rather than public goods ................................................................................93 4 2. Most producer support funded through higher domestic consumer prices ..........................................95 IV. Conclusion ................................................................................................................ 97 References........................................................................................................................... 99 5 Tables TABLE 1. VALUE OF RUSSIAN AGRI-FOOD EXPORTS, 2001-2018 (MILLION US DOLLARS IN CONSTANT 2018 PRICES) ..........................10 TABLE 2. RUSSIAN MEAT PRODUCTION, 2001, 2012 AND 2018 (1000 TONS, LIVEWEIGHT) ..........................................................12 TABLE 3. RUSSIAN NET EXPORTS OF AGRI-FOOD PRODUCTS, 2001, 2012, AND 2018 (MILLION CONSTANT 2018 US$) ......................13 TABLE 4. THE IMPORTANCE OF AGRICULTURE AND AGRO-PROCESSING, SELECTED COUNTRIES, 2018.................................................17 TABLE 5. PRODUCTION OF CROPS AND LIVESTOCK PRODUCTS, 1990, 2000 AND 2018 (1,000 TONS)..............................................17 TABLE 6. PROFITABILITY OF SELECTED CROPS AND LIVESTOCK PRODUCTS IN RUSSIA, 1990-97 (%) ...................................................24 TABLE 7. CROP PRODUCTION BY FARM TYPE, 1990-2018 (% OF TOTAL) ....................................................................................27 TABLE 8. LIVESTOCK PRODUCTION BY FARM TYPE, 1990-2018 (% OF TOTAL) ..............................................................................27 TABLE 9. PROFITABILITY OF SELECTED CROPS AND LIVESTOCK PRODUCTS IN RUSSIA, 2014-18 (%) ...................................................28 TABLE 10. REGIONS RESPONSIBLE FOR HALF OF AGRICULTURAL GROWTH IN RUSSIA, 2000-18 ........................................................29 TABLE 11. TEN REGIONS WITH LARGEST ABSOLUTE PRODUCTION DECLINES IN RUSSIA, 2000-2018 .................................................30 TABLE 12. AGRICULTURAL LAND IN RUSSIA, 1990-2019 .........................................................................................................31 TABLE 13. AGRICULTURAL LAND AND UTILIZATION BY FARM TYPE, 2006 AND 2016 ......................................................................32 TABLE 14. CHANGES IN SOWN AREA IN RUSSIA, 1990-2018 (1,000 HA) ...................................................................................33 TABLE 15. AVERAGE FARM SIZE IN RUSSIA, BY FARM TYPE, 1996-2016 (HA, AGRICULTURAL LAND) .................................................35 TABLE 16. TOTAL AVAILABLE CROPLAND FOR RE-CULTIVATION IN EUROPEAN RUSSIA .....................................................................36 TABLE 17. ANNUAL AVERAGE GROWTH OF PRODUCTIVITY INDICATORS IN RUSSIAN AGRICULTURE, 1990-2018 (%) ............................37 TABLE 18. CROP AND LIVESTOCK YIELDS: VALUE IN 22018 AND AND AVERAGE ANNUAL GROWTH, 1990-2018 .................................37 TABLE 19. CROP YIELDS: RUSSIA AND SELECTED LARGE PRODUCERS, 2018 (TONS PER HA) ..............................................................38 TABLE 20. LIVESTOCK YIELDS: RUSSIA AND SELECTED LARGE PRODUCERS, 2018............................................................................38 TABLE 21. AVERAGE GROWTH OF AGRICULTURAL TFP IN RUSSIA, 1994-2013 (%) ......................................................................40 TABLE 22. AVERAGE GROWTH OF AGRICULTURAL TOTAL FACTOR PRODUCTIVITY, BY FEDERAL DISTRICT, 1994-2013 (%) .....................41 TABLE 23. VALUE OF PRODUCTION PER HA, BY FARM TYPE, 2017-2019 (2019 RUB) ..................................................................42 TABLE 24. MEAT AND MILK PER ANIMAL, BY FARM TYPE, 2017-2018 (KG, SLAUGHTER WEIGHT PER ANIMAL; LITERS) .........................42 TABLE 25. CROP YIELDS BY FARM TYPE, 2018 (TONS PER HA)....................................................................................................43 TABLE 26. AGRI-FOOD PRODUCTS FOR WHICH RUSSIA HAD A COMPARATIVE ADVANTAGE (HS 4-DIGIT LEVEL) IN 2018 ........................45 TABLE 27. SUPPORT TO AGRICULTURE IN RUSSIA IN 2017 ........................................................................................................49 TABLE 28. MAIN ELEMENTS OF RUSSIAN AGRICULTURAL PROGRAMS, 2006-2020 .......................................................................51 TABLE 29. TARGETS AND FINANCING OF THE RUSSIAN NATIONAL PROJECT ON INTERNATIONAL COOPERATION AND EXPORT ...................53 TABLE 30. SOCIAL DEVELOPMENT OF THE VILLAGE TO 2013 AUTHORIZED FINANCING, 2003-2013 (BILLION RUB) ............................54 TABLE 31. THE STRUCTURE OF THE STATE PROGRAM FOR THE DEVELOPMENT OF RURAL TERRITORIES ..............................................55 TABLE 32. STATE PROGRAM FOR THE DEVELOPMENT OF RURAL TERRITORIES: PASSPORT BUDGET BY YEAR, 2020-2025 (BILLION RUB) 56 TABLE 33. STATE PROGRAM FOR THE DEVELOPMENT OF RURAL TERRITORIES: REVISED FINANCING, MARCH 3, 2020 (BILLION RUB) .....56 TABLE 34. FORESEEN (PASSPORT) FINANCING FOR THE STATE PROGRAM FOR DEVELOPMENT OF AGRICULTURE, 2013-2025 (BILLION RUB) ...................................................................................................................................................................57 TABLE 35. STATE PROGRAM, 2013-2018, FEDERAL EXPENDITURES BY TYPE................................................................................58 TABLE 36. CHARACTERIZATION OF STATE PROGRAM RUBRICS ...................................................................................................58 TABLE 37. STATE PROGRAM: FINANCIAL SUPPORT TO PRODUCERS IN 2017-18 ............................................................................59 TABLE 38. STATE PROGRAM FEDERAL AND REGIONAL FINANCING: PASSPORT AND SUPPORT AVAILABLE TO PRODUCERS (BILLION RUBLES) 60 TABLE 39. LARGEST PRODUCER SUPPORT RECIPIENT REGIONS, 2012-2018 .................................................................................64 TABLE 40. DISTRIBUTION OF PRODUCER SUPPORT BY REGION, REGRESSION RESULTS ......................................................................66 TABLE 41. CORRELATIONS BETWEEN THE SHARE OF PRODUCER SUPPORT (2012-18) AND INDEPENDENT VARIABLES FOR THE LARGER AND SMALLER SUPPORT RECIPIENT REGIONS ........................................................................................................................ 68 TABLE 42. DISTRIBUTION OF PRODUCER SUPPORT BY REGION AND SUBPROGRAM, 2012-18 ...........................................................69 TABLE 43. THE GOALS OF THE STATE PROGRAM FOR AGRICULTURAL DEVELOPMENT .....................................................................71 TABLE 44. FOOD SELF-SUFFICIENCY IN RUSSIA, 1990-2018 (%) ...............................................................................................72 TABLE 45. IMPORT SUBSTITUTION IN RUSSIA, 2013-2018.......................................................................................................74 TABLE 46. RUSSIA: CHANGES IN REVEALED COMPARATIVE ADVANTAGE FOR TOP 10 AGRI-FOOD EXPORTS IN 2018, 2013-2018...........75 TABLE 47. RUSSIA: CHANGES IN REVEALED COMPARATIVE ADVANTAGE FOR TOP 10 LIVESTOCK EXPORTS IN 2018, 2008-2018 ............75 TABLE 48. GROSS OUTPUT, INTERMEDIATE CONSUMPTION AND VALUE ADDED IN AGRICULTURE AND HUNTING, 2011-2018 ................78 TABLE 49. RUSSIA'S RANK IN WORLD AGRI-FOOD EXPORTS IN 2018 ...........................................................................................80 6 TABLE 50. RUSSIAN AGRICULTURAL ENTERPRISES, BY SUBSIDIES RECEIVED IN 2015 .......................................................................83 TABLE 51. THE LEVEL AND BURDEN OF AGRICULTURAL SUPPORT IN 2018, BY COUNTRY (%)............................................................85 TABLE 52. COMPARISON OF FARM POLICY LEGISLATION IN RUSSIA AND SELECTED OECD MEMBERS..................................................86 TABLE 53. SHARE OF LARGEST PRODUCERS IN RUSSIA (RF) AND US, BY SALES, AND THEIR GOVERNMENT PAYMENT RECEIPTS ................87 TABLE 54. THE IMPORTANCE OF AGROHOLDINGS IN RUSSIAN AGRICULTURE, 2006 AND 2016 ........................................................89 TABLE 55. SHARE OF AGROHOLDINGS IN THE GROSS AGRICULTURAL OUTPUT OF RUSSIAN REGIONS, 2016 .........................................90 TABLE 56. CONTRACT FARMING IN THE UNITED STATES, BY COMMODITY, 1996/97 AND 2017: SHARE OF VALUE OF PRODUCTION UNDER MARKETING OR PRODUCTION CONTRACTS .................................................................................................................... 91 TABLE 57. STRUCTURE OF TOTAL AGRICULTURAL SUPPORT IN SELECTED COUNTRIES, BY RECIPIENT, 2018 (%) ....................................93 TABLE 58. VETERINARY SITUATION IN RUSSIA, 2019 ...............................................................................................................94 TABLE 59. THE STRUCTURE OF PRODUCER SUPPORT IN SELECTED COUNTRIES, 2018 ......................................................................95 TABLE 60. PRODUCER NOMINAL PROTECTION COEFFICIENTS IN RUSSIA, 2013-2018 ....................................................................96 TABLE 61. PRODUCER NOMINAL PROTECTION COEFFICIENTS FOR MEATS AND CEREALS IN SELECTED COUNTRIES, 2018 .........................96 Figures FIGURE 1. INDEX OF EXPORT CONCENTRATION, SELECTED COUNTRIES, 2001-2019 .......................................................................19 FIGURE 2. GROSS AGRICULTURAL PRODUCTION IN RUSSIA, 1990-2019, BY SECTOR......................................................................24 FIGURE 3. AGRICULTURAL PRODUCTION IN RUSSIA BY FARM TYPE, 1990-2019 (%) .....................................................................25 FIGURE 4. PORTION OF LIVESTOCK PRODUCTION IN GAO BY FARM TYPE (%) ................................................................................26 FIGURE 5. THE UNEVENNESS OF AGRICULTURAL GROWTH IN RUSSIA, 2000-18 ............................................................................29 FIGURE 6. LIVESTOCK INVENTORIES BY FARM TYPE, COW UNITS, 1990-2018 ...............................................................................30 FIGURE 7. SOWN AREA BY CROP IN RUSSIA, 1990-2019 .........................................................................................................33 FIGURE 8. CHANGES IN SOWN AREA BY CROP GROUP AND FARM TYPE, 1990-2007 AND 2007-2018..............................................34 FIGURE 9. SOWN LAND BY FARM TYPE, 1990-2018 (%) .........................................................................................................34 FIGURE 10. GROSS AGRICULTURAL OUTPUT AND MAIN AGRICULTURAL INPUTS IN RUSSIA, 1990-2018 (1990=100) .........................37 FIGURE 11. ANNUAL GROWTH OF CROP AND LIVESTOCK YIELDS FOR SELECTED COUNTRIES, 2010-18 (%) .........................................39 FIGURE 12. NOMINAL AND REAL VALUE OF STATE BUDGET EXPENDITURES ON AGRICULTURE IN RUSSIA, 1995-2017...........................50 FIGURE 13. STATE PROGRAM SUPPORT AVAILABLE TO PRODUCERS, 2012-2020, BY FEDERAL DISTRICT (BILLION 2012 RUB) ...............61 FIGURE 14. DISTRIBUTION OF SUPPORT BY REGION, 2012-2020 (CUMULATIVE PERCENT) .............................................................62 FIGURE 15. THE DISTRIBUTION OF PRODUCER SUPPORT, PRODUCTION, LABOR AND SOWN AREA FOR 84 REGIONS OF RUSSIA, 2018 .......62 FIGURE 16. ELEVEN REGIONS WHICH HAVE RECEIVED 40 PERCENT OF PRODUCER SUPPORT, 2012-2020 (BILLION 2012 RUB).............63 FIGURE 17. SHARE OF PRODUCER SUPPORT AND LIVESTOCK PRODUCTION, BY REGION, 2012-2018 .................................................67 FIGURE 18. SHARE OF PRODUCER SUPPORT AND REGIONAL GAO IN AGROHOLDINGS, 2012-2018 ..................................................67 FIGURE 19. YEAR-TO-YEAR CHANGES IN SELF-SUFFICIENCY RATIOS IN RUSSIA (%) .........................................................................73 FIGURE 20. ECONOMIC ACCESS TO FOOD IN RUSSIA: HOUSEHOLD EXPENDITURES ON FOOD (% OF DISPOSABLE INCOME), 1997-2018 ...77 FIGURE 21. RUSSIAN AGRICULTURAL EXPORTS AND SHARE OF GAO EXPORTED, 2010-2018 (USD) ................................................79 FIGURE 22. RUSSIAN AGRI-FOOD EXPORTS, BY VALUE, 2019 ....................................................................................................79 FIGURE 23. INVESTMENT IN FIXED CAPITAL IN AGRICULTURE, HUNTING AND FORESTRY, 2005-2018 (BILLION RUB) ...........................81 7 ABBREVIATIONS AND ACRONYMS DAC Development Assistance Committee GAO Gross agricultural output GDP Gross domestic product GSSE General support service estimate GNI Gross National Income ha Hectare HS Harmonized Commodity Description and Coding System IMF International Monetary Fund MPS Market price support NPC National Protection Coefficient ODA Overseas Development Assistance OECD Organization for Economic Cooperation and Development OIE World Organization for Animal Health PSE Producer support estimate RCA Revealed comparative advantage RUB Ruble TFP Total factor productivity 8 ACKNOWLEDGMENT The report Russian Federation – Agriculture Support Policies and Performance was prepared under the overall guidance of Renaud Seligmann (Country Director, Russian Federation, World Bank). The task team leaders were Ulrich Schmitt (Lead Agriculture Economist) and Artavazd Hakobyan (Senior Agriculture Economist). The report was written by David Sedik (consultant, World Bank). Additional guidance was provided from the World Bank by Frauke Jungbluth (Practice Manager), Apurva Sanghi (Lead Economist), Irina Klytchnikova (Senior Agriculture Economist), Michael Lokshin (Lead Economist). Sheldon Lippman copy-edited the final report. Rosalie Quong Trinidad provided efficient administrative support during implementation. 9 EXECUTIVE SUMMARY The economic reforms initiated in Russia in the 1990s drove agricultural growth and exports in the years that followed. As Russian agriculture was leaving behind central planning and adjusted to market prices, competitiveness in cereal and oilseed production presented an opportunity for Russia to resume its earlier position as a sizeable grain exporter. Russia’s path to producing and exporting according to its natural comparative advantage involved a number of structural changes. The first of these was a move away from livestock husbandry towards cultivation of cereals and oilseeds. The second was a large reduction in utilized land area, as producers relinquished lands previously used for animal feed production. A corollary to the reduction in land use was the transfer of land from agricultural enterprises to a new class of family farms1. These changes led to greater production specialization, vertical integration in certain sectors and the growth in partial and total factor productivity. Declines in livestock inventories and increases in grain and oilseed production led to expansion of grain and oilseed exports consistent with Russia’s comparative advantage The move away from livestock can be seen in the substantial decrease in livestock inventories in Russia since 1990 which depressed domestic demand for cereals, oilseeds and other crops used for feed. Between 1990 and 2018, the number of livestock halved from nearly 80 million to about 40 million standardized cow units (Figure 6). Despite lower domestic demand for feed, production of cereals and oilseeds rose substantially in Russia, driven by favorable world prices (Rosstat, 2020). These increases enabled Russia to become one of the largest exporters of wheat, barley, maize and vegetables oils in the world. During the growth period of Russian exports between 2001 and 2018, the value of agri-food exports from Russia (in constant 2018 US dollars) increased by an average of 16 percent per year (Table 1). Cereals, vegetable oils and oilcakes exports exceeded this average rate. Table 1. Value of Russian agri-food exports, 2001-2018 (million US dollars in constant 2018 prices) 2001 2018 Annual growth (%) Value of exports 2,022 24,885 16 Of which Cereals 376 10,458 22 Fish and seafood 541 4,282 13 1 Family (peasant) farms trace their origins back to 1990 when the first (Soviet) law on peasant farms was passed largely due to the efforts of President Boris Yeltsin. The source of their initial land allotments was government land in reserve. After Russian collective and state farm share privatization began in 1992, family farms were able to receive more land. Household farms, in contrast, are the direct descendant of the Soviet ancillary plots, which were and are held by nearly every rural resident. These plots kept people alive during the Stalinist period of extreme rural poverty. After Stalin they were also vital to peoples’ well -being, in view of general food shortages. Now they are small garden plots with a cow around the house. Russian statisticians continue to count their production along with production by family (peasant) farms and agricultural enterprises. 10 Vegetable oils 85 2,669 22 Oilcake for feed 47 1,084 20 Oilseeds 77 763 14 Other 896 5,628 11 Source: ITC Trade Map, 2020. These structural changes in production and export reflect Russia’s comparative advantage in the production of grain and oilseeds (Liefert, 2002). According to the Balassa index of revealed comparative advantage calculated at the four-digit Harmonized Commodity Description and Coding System (HS) for 2018, the exports in which Russia has a comparative advantage include: • Cereals (wheat, buckwheat, barley, rye, maize); • Fish and aquatic invertebrates (e.g., crabs); • Oilseed (flax seed); • Vegetable oil products (rapeseed, sunflower seed, soybean oil, margarine, oilcake); • By-products of the sugar industry (beet pulp, yeast, molasses); • By-products of the milling industry (bran, wheat gluten, cereal germ). Changes in land use The halving of livestock inventories in Russia between 1990 and 2018 meant that farms required far less land to raise feed crops. With little incentive for agricultural enterprises or households to divest themselves of land, they instead left it fallow or abandoned it. Out of 222 million hectares of agricultural land registered in Russia in 2016, only 125 million hectares were actually in use (Uzun, 2017a; Rosstat-2016-Ag Census, 2018: v.3). Moreover, there was a substantial redistribution of land from enterprises to family (peasant) farms between 2006 and 2016. In 2006 agricultural enterprises held about 80 percent of agricultural land, a share which had shrunk to 63 percent by 2016 (Table 13). These changes are primarily due to the increasing land holdings of private family farms. The redistribution of land can be seen most clearly for sown land where the share held by family farms increased from zero to 30 percent between 1990 and 2018 (Table 13). These changes in the structure of land use also made for significant changes in the average size of farms in Russia (Table 15). Uzun and others (2019) used cadaster data to follow the size of each farm type in 1996, 2006, and 2016. During this period, family and household farms each grew two- to three-fold in size, while the mean size of enterprises shrank by 26 percent. Specialization by farm type and industrialization of some livestock production The three farm types–family (peasant) farms, household pots, and enterprises–have evolved distinct production profiles. Family farms produce primarily cereals and oilseeds, producing about 30 percent of cereals and one-third of sunflower seeds in Russia (Table 7). Households specialize in potatoes and vegetables, as well as beef and milk. In 2018, they produced 54 percent of beef, 39 percent of milk and 63 percent of potatoes and vegetables (Table 7, Table 8). Agricultural enterprises produce the majority of livestock products and field crops, such feed, cereals and oilseeds. In 2018, they produced 70 percent of cereals, 66 11 percent of sunflower seeds, over 90 percent of poultry meat, over 80 percent of pork and eggs, half of milk and one-third of beef in Russia (Table 7, Table 8). The high shares of production in enterprises of pork, eggs and poultry meat indicate the extent to which the value chains of these commodities have been vertically integrated into large farms subordinate to agroholding2 processors. Increases in crop production and trade have been driven largely by market forces, while changes in livestock production and trade have been partially policy driven While Russian agriculture has a comparative advantage in the production of grains and oilseeds, this does not mean that it does not or should not produce other products. In fact, between 2001 and 2018, Russian production of meat and poultry increased at a rate of 4.5 percent per year, due exclusively to increases in poultry and pork production as these industries have been restructured and industrialized (Table 2). Table 2. Russian meat production, 2001, 2012 and 2018 (1000 tons, liveweight) 2001 2012 2018 Total meat and poultry 7.0 11.6 14.9 --Beef and veal 3.3 2.9 2.8 --Poultry 1.3 4.9 6.7 --Pork 2.0 3.3 4.8 Source: Rosstat, 2020. Policy has played a role in these increases in meat production through two channels of support. First, about 70 percent of support to Russian agricultural producers is paid for by consumers through prices for food that are higher than world prices. Most of this support goes towards supporting livestock and sugar producers. Nominal protection coefficients (NPC) for 2018 indicate that while cereal and oilseed prices in Russia are between 2 and 15 percent less than world prices, sugar prices exceed world prices by nearly 80 percent, those for milk and beef exceed world prices by between 27 and 50 percent, while prices for pork and poultry meat exceed world reference prices by 8 to 10 percent (Table 60)3. These NPCs are consistent with Russian tariff policies. Russian import tariffs for meats, dairy products, and sugar are highly protective (Table 60). Average import tariffs for these products ranged from 9 to over 50 percent in 2018. In addition, Russia has banned imports of most food products from Australia, Canada, the European Union, Norway, Ukraine, and the United States since 2014 (FAO, 2014). These factors are responsible for Russian domestic prices for livestock and sugar products exceeding world market prices. The second policy instrument of support to the livestock sector has been direct budget payments. Between 2013 and 2018, 35 percent of Russian direct payments to agricultural producers (PSE-MPS) could be identified as supporting livestock, 23 percent supported crop 2 An agroholding is a group of agricultural enterprises owned by a holding company. The enterprises are registered as independent producers, but are managed by a single company, either a corporation or an individual. 3 Nominal protection coefficients are defined as the ratio of domestic prices to international reference prices, both evaluated at the farmgate. 12 growing, and 39 percent were either uncertain or benefitted both sub-sectors (OECD STATS, 2020). Since 2005, many of the subprograms of the State Program for Agricultural Development have supported livestock production, import substitution and exports. Support policies under the National Priority Project (2006-2007) focused on meat and milk production in order to stem the decline of livestock inventories that continued in Russia through 2005. The first State Program (2008-2012) focused on debt relief for agricultural enterprises, much of which was due to unprofitable livestock production. The second State Program (2013-2020) marked the explicit integration of food self-sufficiency targets, and food self-sufficiency was expanded into a policy of import substitution in 2014 after the announcement of the Russian ban on food imports from 32 Western countries in reaction to financial sanctions. The food embargo originally covered bovine meat, pig meat, processed meats, poultry, fish and other seafood, milk and milk products, vegetables, fruits and nuts, but was later expanded to cover other food imports and countries. The State Program entered a new phase in December 2016 with the issue of a new priority project to support the export of high-value agricultural products through improving veterinary health and traceability and promoting Russian high-value products in foreign markets. These policy efforts, combined with falling consumer incomes in Russia since 2013, succeeded in reducing Russian net imports of meat and offal by over 80 percent since 2012. When combined with other import reductions, the overall Russian agri-food trade deficit was reduced from US$26 billion in 2012 to US$5 billion in 2018 (Table 3). Table 3. Russian net exports of agri-food products, 2001, 2012, and 2018 (million constant 2018 US$) Product label 2001 2012 2018 Agri-food product net exports -10,077 -26,333 -5,094 Major exports (net exports) -258 7,552 12,673 --Cereals 60 6,355 7,643 --Fish and seafood 250 143 2,866 --Vegetable oils -568 1,054 2,163 Major imports (net exports) -4,663 -23,411 -12,735 --Meat and offal -2,428 -8,094 -1,287 --Vegetables -253 -2,371 -1,362 --Beverages, spirits and vinegar -651 -2,832 -2,413 --Dairy -442 -3,294 -2,694 --Fruit and nuts -889 -6,820 -4,979 Other (net exports) -5,155 -10,474 -5,031 Source: ITC Trade Map, 2020. More widely, however, it is difficult to connect the State Program with its formal goals. The latest version of the Russian State Program outlines 7 formal goals, many of which concern food self-sufficiency and import substitution). Though many of these goals have been 13 achieved since 2013, other factors have played a role in these outcomes quite independently of the State Program. Falling consumer incomes, restrictions on food imports from many suppliers and the devaluation of the ruble have all contributed to rising self-sufficiency ratios for food products. Formal goals of the State Program Assessment Self-sufficiency ratios for many commodities have Goal 1: Ensure food independence according to the increased, particularly for meat. However, it is not food security program doctrine (Presidential decree, clear that the State Program has been responsible for January 30, 2010, no. 120) the increases in self-sufficiency. Russian agriculture and food processing have been most successful in substituting for imports in those areas where the sector was already enjoying robust growth before 2013. It is also not clear to what extent Goal 2: Import substitution for meat, milk, vegetables, the import substitution is an effect of policy or an seed potatoes, and berries effect of stagnating incomes. Though the Russian agri-food sector has shown Goal 3: Raise the competitiveness of Russian growth in total factor productivity and in indices of agricultural production on domestic and foreign revealed comparative advantage, it is not clear that markets the State Program has been responsible for these. Food security properly understood is not primarily dependent on growth in agricultural production. But food security in the State Program is understood as self-sufficiency. Self-sufficiency in a number of Goal 4: Ensure food security through agricultural products has increased, but the role of the State growth Program in this outcome is unclear. The share of VA in gross agricultural output has been falling slightly since 2011, not rising. But it is difficult Goal 5: Increase value added (VA) in agriculture to attribute this to the State Program. It is difficult to connect the phenomenal growth in agricultural exports with the State Program, if only because the main commodities exported are precisely Goal 6: Growth of agricultural exports those which are hardly supported under the Program. The real value of agricultural investment in fixed capital (in constant 2005 prices) has followed an overall pattern based on the booms and busts in the Russian economy as a whole. So, once again, it is Goal 7: Growth in capital investment in agriculture difficult to claim credit for the State Program. Source: Synthesis of the discussion of part II.G. Structural changes caused by economic reforms led to growing total factor productivity The organizational and structural changes in Russian agriculture of the past 30 years have led to growing total factor productivity, which grew by 1.7 percent per year between 2005 and 2013 (Table 21). TFP growth has been regional, focusing on the South (the primary grain-growing area) and Central (the primary livestock production region) federal districts (Table 22). The South has been responsible for most of the output and input growth in 14 Russia; TFP there grew by 3.6 percent per year between 1998 and 2013 and accounted for 69 percent of output growth between 1998 and 2013. The Central Federal District also saw positive growth in GAO and TFP after 2007. From 2007 to 2013, agriculture in the Central District grew by 4.3 percent per year and TFP by 2.9 percent per year. Issues for the future development of Russian agriculture Looking ahead, Russian agriculture faces a number of issues for the future. • First, as one of the leading global cereal exporters, what role can Russia play in global food security? • Second, are Russia’s protectionist trade policies for livestock products consistent with its ambitions to become a global exporter of high-value livestock products? • Third, can agriculture in Russia become the engine of diversification of the economy? • Fourth, is Russian state support foregoing opportunities for growth by focusing on enterprises in vertically integrated agroholdings? • Last, what will be the effects of climate change on Russian agriculture, and should the state now be preparing a response? What role does Russia play in global food security? The emergence of Russia as a substantial cereals exporter in the 2010s prompted some to reflect on the role Russia plays in global food security. Maksimychev (2019) argued that Russian global sustainable development policies have increased world food security by increasing the supply of cereals, making global markets more stable and cereals more accessible and affordable for the world; and by concluding long-term strategic support compacts with the United Nations food agencies4 to support global food security. Certainly, Russia has increased global cereal supplies by increasing exports. Table 1 indicated that cereal exports from Russia have increased from about US$400 million to US$10.4 billion (in 2018 constant US$) between 2001 and 2018. However, global food security is not primarily a function of the supply of food available in the world but is dependent on the stability of global food markets and on economic access to food at the household level, particularly in the poorest countries. Viewed in this way, Russia policies have often been at best ambivalent in supporting global food security. First, Russian, Ukrainian and Kazakh bans and quotas on cereal exports during periods of rising world prices in 2007-08 and 2010 contributed to instability in global cereal markets (Dollive, 2008; Welton, 2011; Fellman, and others, 2014; Sedik, 2017). Second, in 2018, least developed countries received only 5.4 percent of the Russian Federation’s gross bilateral overseas development assistance (ODA), compared with an average of 23.8 percent from Development Assistance Committee (DAC) members (OECD profiles, 2020) 5. Russia 4 The UN Food and Agriculture Organization (FAO), the World Food Programme (WFP) and the International Fund for Agricultural Development (IFAD). 5 The OECD Development Assistance Committee is an international forum of many of the largest providers of aid, including 30 members. The objective of the DAC for the period 2018-2022 is to promote development co- operation to contribute to implementation of the 2030 Agenda for Sustainable Development, including 15 contributes to global access to food by supporting poverty reduction programs in developing countries financed through overseas development assistance. In 2018, the Russian Federation provided US$999.1 million in total ODA, equivalent to 0.06 percent of gross national income (GNI). Thirty-seven percent of ODA consisted of Russian regular and voluntary contributions to UN and other international organizations. However, most (63 percent) of ODA was provided through bilateral channels, two-thirds of which was debt relief. The top recipients of Russian ODA in 2018 were Cuba, Kyrgyzstan, North Korea, Uzbekistan, Armenia and Tajikistan, all middle-income countries, except for North Korea (OECD, 2020; WB groups, 2020). Are Russia’s protectionist trade policies for livestock products consistent with its ambitions to become a global exporter of high-value livestock products? Russian import tariffs for meats, dairy products, and sugar are highly protective (Table 60). Average import tariffs for these products ranged from 9 to over 50 percent in 2018. In addition, Russia has banned imports of most food products from Australia, Canada, the European Union, Norway, Ukraine, and the United States since 2014 (FAO, 2014). These two factors have led to a situation where Russian domestic prices for livestock and sugar products exceed world market prices (Table 60). High domestic prices for producers have a detrimental effect on exports. If domestic prices offered for agricultural products are higher than in international markets, it is difficult to understand why Russian processors would export rather than sell their products domestically. Russian producers of these commodities are not used to the competition of world markets. In contrast to Russia, the domestic prices of the significant global meat exporters hover near world market prices, maintaining incentives for export (Table 61). The United States was the largest global exporter of beef and pork while Brazil was the largest exporter of poultry. Australia was the number two exporter of beef. Greater policy coherence and relaxation of self-imposed import restrictions could lead to better outcomes for Russian producers and consumers in the longer-term. Can agriculture in Russia become the engine of diversification of the economy? Economic diversification denotes a shift toward a more varied structure of production and trade which is often associated with increased productivity, innovation, job creation and sustained growth (OECD/WTO, 2019). Diversification can entail diversification across industries, diversification within industries and diversification of exports. It is difficult to make the case that agriculture has been an engine of diversification across industries within the Russian economy. As a developed country, agriculture represents a small share of the Russian economy (Table 4, col. 1). However, even a small raw materials sector of the economy like agriculture can be a driver of diversified production and exports through downstream links with processing. France is an example of a country where primary agriculture also accounts for a small share of the economy, but the agro-processing sector extends deep into agriculture, translating into substantial exports for the combined agriculture and food processing complex. inclusive and sustainable economic growth, poverty eradication, improvement of living standards in developing countries, and to a future in which no country will depend on aid (OECD DAC, 2020). 16 In Russia, the depth of food, tobacco and beverage processing is relatively shallow compared to the OECD countries in Europe and North America (Table 4, col. 2). Partially as a result, agriculture raw materials and processed food, tobacco and beverage exports are a relatively small share of total merchandise exports (Table 4, col. 3). Table 4. The importance of agriculture and agro-processing, selected countries, 2018 AGVA Depth of food Agriculture and food exports (% GDP) processing* (% of merchandise exports) (1) (2) (3) Russia 3.1 64 7.7 Hungary 3.6 51 8.3 Poland 2.1 130 14.1 Czech Rep 2.0 77 5.4 Latvia 3.6 51 29.1 Lithuania 2.9 110 19.3 Germany 0.8 199 8.2 France 1.7 116 20.2 EU 1.7 -- 11.1 US 0.9 193 11.8 Canada 1.3 96 17.0 OECD 1.4 -- 10.2 China 7.2 47 3.3 Notes: AGVA=agricultural value added. *The depth of food processing is measured as the ratio (in percent) of the value added of food, tobacco and beverages to the value added of primary agriculture, fisheries and forestry. For column 2, Czech Republic, 2013; US, 2017; Canada, 2016. Source: World Bank WDI, 2020. A second way that agriculture could be an engine of diversification is for agriculture itself to diversify. But it is difficult to argue that Russian agriculture has become significantly more diversified since 1990. Table 5 indicates that agriculture has become more specialized in oilseed, pork and poultry meat production. However, the country also produces more vegetables, melons and honey. Substantial diversification of production would likely require a deeper connection between agro-processing and agriculture. To be sure, agro-processing in Russia has deepened its roots in agriculture: in 2002, the depth of food processing was 40 percent, while in 2018 it had grown to 64 percent (World Bank WDI, 2020). This deepening is most likely the result of construction of new facilities for oilseed crush, poultry and pork production, but there simply is not enough information to make the case that Russian agriculture has innovated into new fields of diversified production. Table 5. Production of crops and livestock products, 1990, 2000 and 2018 (1,000 tons) 2018 1990 2000 2018 (% of 1990) Crops Cereals 116,676 65,420 113,255 97 Technical crops 37,109 18,594 61,636 166 17 --Sugar beets 32,327 14,051 42,066 130 --Oilseeds 4,662 4,473 19,525 419 Potatoes 37,619 29,465 22,395 60 Vegetables 10,328 10,822 13,685 133 Melons 1,116 537 1,970 177 Livestock products Meat (slaughter weight) 10,112 4,446 10,629 105 --Beef/veal 4,329 1,898 1,608 37 --Pigmeat 3,480 1,578 3,744 108 --Mutton/goatmeat 395 140 224 57 --Poultry meat 1,801 768 4,980 277 Milk 55,715 32,259 30,611 55 Eggs, million pieces 47,470 34,085 44,901 95 Wool 227 40 55 24 Honey, tons 46,091 54,248 65,006 141 Source: Rosstat, 2020. A third way that agriculture can be an engine of diversification in the economy is for the agri- food sector to diversify exports. Here the evidence is clear that Russian agri-food product exports are many times more concentrated than those of an average country, as well as more than selected developed exporters in Europe and North America (Figure 1). The Hirschman-Herfindahl Index (HHI) can be used as an index of the product concentration of exports. In Figure 1, the HHI is constructed from agri-food sector export trade data at the HS code 6-digit level. The index ranges from zero to one, with 1 indicating perfect product concentration and zero indicating perfect diversification. In Figure 1, the HHI has been normalized so that it is comparable between countries. Though the pattern of change in export concentration of most countries is similar (rising through 2007-08, falling thereafter), the level of concentration of exports in Russia remains consistently higher than the comparator countries considered. An alternative means of measuring export product diversification is to count the number of non-zero entries in the agri-food export list at the HS code 6-digit level. Using this simpler measure of agri-food diversification renders the same conclusion. The number of non-zero Russian product exports at the HS code 6-digit level over the period 2001 to 2019 averaged 33 percent less than the comparator countries and regions in Figure 1. Some concentration of exports by product is not a bad thing. After all, countries have a comparative advantage in certain products, so that concentration is inevitable. However, a diversity of exports is also an indirect measure of innovation in the sector. A more innovative agri-food sector tends to export a variety of products. Contrariwise, a less diverse product export assortment makes export revenues more vulnerable. 18 Figure 1. Index of export concentration, selected countries, 2001-2019 0.2000 0.1800 Normalized Hirschman-Herfindahl Index of concentration 0.1600 0.1400 0.1200 0.1000 0.0800 0.0600 0.0400 0.0200 0.0000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Russsia World United States European Union (27) France Canada Germany Note: The figure shows the normalized Hirschman-Herfindahl Index (HHI) of product concentration based on agri- food exports at the HS code 6-digit level. The HHI ranges from 0 to 1, with 0 indicating perfect diversification of product exports and 1 indicating perfect concentration. Source: Calculated from data from ITC Trade Map, 2020. Is Russian state support foregoing opportunities for growth by focusing on enterprises in vertically integrated agroholdings? Russian agriculture has performed admirably in the past few years but operates at below its potential. Disproportionate reliance on large agricultural enterprises belonging to vertically integrated agroholdings has restrained agricultural growth through: • limiting producer-processor relations to a single model of integration, • restricting the dissemination of technical change, • neglecting the development of a state-of-the-art system of veterinary services and control, and • subsidizing the largest, often less profitable, farms and putting smaller, more profitable and efficient farms at a competitive disadvantage. The distribution of State Program expenditures prioritizes direct financial support to the largest producers in agroholdings rather than broad public service investment. The State Program distorts competition within Russia, propping up the largest firms and putting smaller, more profitable and efficient farms at a competitive disadvantage. However, it does not have to be this way. The State Program can be modified to stimulate growth in agriculture more broadly for greater economic benefit. Reworking the State Program in this way would not necessarily diminish the dominance of agroholdings or slow the growth of the world-class efficient livestock producers in the country. These producers do not need government support to thrive since they already enjoy economies of scale and 19 up-to-date technology. But a modified State Program could benefit the smaller producers who, because they do not enjoy economies of scale, could benefit from government support. The first step toward more broad-based growth is acknowledging the issue. The lopsided support of large agroholdings does not stimulate broad-based agricultural growth. Rather, it drives out small and medium-size businesses in the sector, suppressing overall growth. More practically, the modification of the State Program toward more broad-based growth would benefit from taking the following changes: (a) Introduce limitations on maximum annual support payments for a single beneficiary from the State Program, similar to those found in OECD countries; (b) Reformulate the programmatic goals of the State Programs, emphasizing insurance against the systematic risks encountered by agriculture, available to all producers on a five-year programmatic basis; (c) Adjust interest rate subsidy programs, emphasizing a standard rate subsidy program for which all farm types and sizes are eligible; (d) Restrict the distribution of government support to federal government programs; (e) Increase the portion of federal support spending devoted to general support service programs intended to make agronomic and marketing support services available to all farms (this should include more support for Rossel’khoznadzor and Rospotrebnadzor); (f) Support efforts to work in networks with medium-size farming businesses as support earmarked for agroholdings decreases. None of these changes should affect the ability of the State Program to work toward achievement of the seven official goals of program—food independence, import substitution, raising competitiveness, ensuring food security, increasing value added, supporting the growth of exports, and capital investment. However, these changes would ensure that the State Program could more widely support these goals by embracing a broader-based vision of agricultural growth in Russia. What will be the effects of climate change on Russian agriculture, and should the state now be preparing a response? Climate change is happening in Russia. According to Roshydromet, mean temperatures in Russia have risen faster since the beginning of the twentieth century than the average for the world. In addition, Russia has seen more extreme weather events in the past few years, such as droughts, storms, heat and cold waves (Safonov and Safonova, 2013). The impact of climate change on Russian agriculture are potentially better than for many countries of the world. For Russia as a whole, the impact of climate change can be characterized as warming accompanied by increased dryness and an increase in the frequency of hot and dry days (Perelet, and others, 2007). Growing seasons in the northern areas of the country will become longer accompanied by higher precipitation, but some of the main crop growing areas of the country in the South and Caucasus will become hotter and drier, driving down yields. Agriculture in these regions will become more reliant on irrigation, pesticides, and more vulnerable to droughts and temperature extremes (National Intelligence Council, 2009). 20 The main costs of climate change in Russia involve the negative impacts on production and yields of the changes noted above and the cost of shifting of cultivation and resources between regions of the country as growing conditions change. Most forecasts of production changes for Russia have focused on cereals, because of their importance in Russia’s production and export profile. Belyaeva and Bokucheva (2017) project that if current grain growing areas were fixed at the 2012 level the production of winter and spring wheat and spring barley would decline by between 6 and 50 percent by 2080-2100 under various climate-warming scenarios. In 2001-2013, winter wheat, spring wheat and spring barley accounted for about 75 percent of total grain production. Rosstat projects 9 and 17 percent declines in cereal yields in Russia by 2030 and 2050 under what it considers a likely climate scenario (Safonov and Safonova, 2013). While the 17 percent fall in yields is an average for the country as a whole, cereal yields in the Northwest Federal District are projected to rise by 9 percent and those in the Volga and Urals Districts are projected to fall by over 30 percent compared to average yields between 2006 and 2010. However, it is likely that crop areas will move further north as growing conditions improve. Shifting crop areas will move some production out of the more fertile black-soil regions and to northern areas where soil productivity is lower and infrastructure, transport and logistics are less developed. The policy challenges posed by climate change will be to address the regional developmental inequities through forward-looking improvements in land productivity and investments in transportation between the new growing areas and Russia’s export terminals. Similarly, more efforts will be necessary to moderate the negative impacts of climate change on grain and oilseed production in the most productive regions of the South and North Caucasus. Drought and temperature spikes could considerably affect yields in these regions. Climate adaptation measures could include research on and adoption of more drought-resistant wheat varieties, irrigation and moisture preserving cultivation techniques (Belyaeva and Bokucheva, 2017). 21 PREFACE: Agriculture Support Policies and Performance Since 2013, the performance of Russian agriculture can fairly be described as remarkable. On average, Russian agriculture has been growing faster (3.3 percent per year) than gross domestic product (GDP) (0.9 percent per year). Increasing food exports and decreasing imports have led to a fall in the agricultural trade deficit from nearly US$27 billion in 2013 to about US$5 billion in 2018. As a result of this performance, Russian self-sufficiency targets have been met in all commodities except for milk production. Despite this positive trajectory, Russian agriculture is operating at less than its potential. Several studies have called attention to abandoned land as proof that Russian agriculture is underperforming. Currently, Russia uses just over half of registered agricultural lands (Uzun, 2017a). However, only 5-10 percent of total croplands could feasibly be brought back into production. Moreover, increases in gross agricultural output in OECD countries, as well as in Russia, come overwhelmingly from yield increases and not from increases in area. A more substantial source of underperformance pertains to the disproportionate reliance on production in agricultural enterprises in Russia often connected to large agroholdings. This is despite the fact that production in family (peasant) farms grew at rates that exceed those in enterprises for every major crop and livestock product except for pork between 2010 and 2018 (Rosstat, 2020). This is a missed opportunity for Russia that could contribute to higher rates of overall growth if addressed. The State Program for the Development of Agriculture contributes to the sector’s dependence on large agricultural enterprises. First, the distribution State Program for the Development of Agriculture is focused on support to large industrial livestock and grain producing enterprises belonging to large vertically integrated agroholdings (Table 40, Figure 17), while production in family (peasant) and household farms receive considerably less per ruble of sales (Table 42, Shagaida and Uzun, 2017). Second, the share of support in the State Program for agricultural public goods, a critical element for the support of family farms, has been consistently low, although this has improved lately (Table 42, Table 57). The emphasis on direct financial support to producers rather than broad public service investment has left Russia far behind in the control of zoonotic diseases (Information analytic center of Rossel’khoznadzor, 2019) which limits its export of high value livestock products. Russian agriculture also suffers from a critically undeveloped networking between large integrated farms-processors and small farms, and for this reason has a relatively undeveloped agricultural processing sector (World Bank, 2017). Last, it is not clear that the current distribution of support ensures a good financial return on public investment. Many of the enterprises in highest subsidy-recipient regions perform worse than the average for the country in sales profitability and on the share of agricultural enterprises which are unprofitable (Table 39). With a better enabling environment through greater attention to public goods and support for investment in family (peasant) farms the return on public investment in terms of growth and profitability could be improved. This report presents a general overview of Russian agriculture performance and policy, focusing on both the achievements of the past few years and the limits to that performance. It begins with a broad survey of production, land use, livestock, productivity and trade 22 (Chapter I). It then focuses on policies in the State Program for the Development of Agriculture in Russia, the types of support, distribution of subsidies, and the effectiveness of the Program (Chapter II). It then focuses on how Russian support differs from support in OECD and other countries (Chapter III). It ends with a conclusion on what can be done for Russian agriculture to operate closer to its potential (Chapter IV). 23 I. Overview of Russia’s agriculture sector Economic reforms in privatization, establishment of hard budget constraints, and price liberalization were the moving forces of structural changes that took place in Russian agriculture in the 1990s and beyond. In Russia, privatization and the establishment of hard budget constraints in agricultural enterprises moved more slowly than in other smaller countries of the region, and for this reason the structural changes they caused were stretched out over more than a decade, rather than a few years. The main structural changes caused by economic reforms were a move away from livestock, a rise of family (peasant) farms, specialization and industrialization of production, land abandonment, changes in farm size, and growth in partial and total factor productivity. A. Production Between 1990 and 1997, while the profitability of cereals and sunflower seeds remained positive, profitability for livestock products fell and then turned negative ( Table 6). The move away from livestock during the transition depression was due to falling subsidies, to changed relative pricing within agriculture after prices were liberalized, and to the fall in consumer incomes. Table 6. Profitability of selected crops and livestock products in Russia, 1990-97 (%) 1990 1991 1992 1993 1994 1995 1996 1997 Cereals 158 104 305 190 59 55 42 24 Sugar beets 26 -2 95 109 42 39 7 -5 Sunflower seeds 145 231 381 217 145 134 30 5 Milk and products 56 17 31 8 -26 -1 -34 -33 Beef/veal 22 23 57 64 -16 -20 -47 -55 Pork 23 15 37 52 2 -4 -31 -31 Wool 25 86 9 -30 -55 -52 -65 -68 Note: Profitability is for agricultural enterprises only and is calculated as profits divided by costs of production. Source: Goskomstat-agriculture, 1998; The move away from livestock is visible in the transition decline through 1998, when livestock production fell quite rapidly while crop production lessened at a much slower pace, causing a fall in the livestock portion of production (Figure 2). After 1998, gross agricultural production began to grow, with the two sub-sectors advancing in tandem. However, after the commodity price rises and the financial depression of 2007-2010, the value of crop production grew at a faster pace than that of livestock production, causing a further fall in the livestock portion of production. During this period of nearly 30 years, the portion of livestock in gross agricultural output (GAO) fell from a high of 63 percent in 1990 to 47 percent in 2019. Figure 2. Gross agricultural production in Russia, 1990-2019, by sector 24 7,000 70 6,000 60 Livestock as percent of total (%) Billion rubles (2019 prices) 5,000 50 4,000 40 3,000 30 2,000 20 1,000 10 0 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 20191) Crops Livestock Livestock (%) Source: Rosstat, 2020. The second structural change observed in Russian agriculture is the rise of family (peasant) farms at the expense of the household and enterprise sectors (Box 1). Figure 3 illustrates that family farms began to expand their portion of total GAO more rapidly after 2010 reaching 14 percent of GAO in 2019. In addition to expanding their portion of GAO, family farms were also partially responsible for the fall in the portion of livestock production in GAO after 2010 as shown in Figure 3. While the livestock portion of GAO in enterprises and household farms after 1998 stayed constant at about 50 percent, family farms, which began to emerge in 1992, have specialized in crop production from the beginning; and, as their numbers increased, they became more specialized in crop production (Figure 4). Figure 3. Agricultural production in Russia by farm type, 1990-2019 (%) 7,000 6,000 Billion rubles (2019 prices) 5,000 4,000 3,000 2,000 1,000 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 20191) Household farms Enterprises Family farms Source: Rosstat, 2020. 25 Box 1. Farm types in Russia Three legally defined types of agricultural producers exist in Russia today: (1) agricultural enterprises, (2) family (peasant) farms and (3) household farms. The first and last of these three types have their origins in the pre-reform agriculture of the USSR. Soviet agricultural producers included state and collective farms as well as household plots--small plots of land (usually less than 0.5 hectares and primarily used for subsistence) attached to a rural residence held in inheritable lifetime possession without the right to sell. Agricultural enterprises and household farms are basically the successors to these two farm types. The family (peasant) farm was first announced in a speech by General Secretary Mikhail Gorbachev at the 15- 16 March 1989 plenary session of the Central Committee of the Communist Party of the Soviet Union (CPSU). In his speech, Gorbachev indicated his intention to establish full legal rights to farm for a family (peasant) farm based on individual labor on leased or owned land. The Russian Law on Peasant Farms, adopted in November 1990, established the right of members and employees of collective and state farms to exit with a share of land and assets in order to begin a private farm. The November 1990 Law on Land Reform legalized private ownership of land by individuals effectively ending the limits on private land ownership that had existed under the Soviet system. Today there remain clear distinctions between the three types of farms in the Russian Federation. While family (peasant) farms and agricultural enterprises are juridical persons, household farms are physical persons. The average size of agricultural landholding of household, family (peasant) farms and agricultural enterprises in 2016 were 0.45 ha, 92 ha and 4,251 ha (Uzun, and others, 2019). Household farms still produce mainly for subsistence, while family (peasant) farms and agricultural enterprises are commercial farms. Of the three farm types, agricultural enterprises are the most unlike farms in Western countries. Corporate farms are quite rare in OECD countries. Even in the United States, according to the 2017 Census of Agriculture, only 5.7 percent of farms were corporate farms, of which 5.1 percent were family corporations and 0.6 percent were non-family corporations, comparable to Russian corporate farms (USDA-NASS Census 2017, 2018, Table 74). Moreover, corporate farms in Russia are far larger than those in the US. While the average size of non- family corporate farms in the US was 411 hectares in 2017, the average size in Russia was 4,251 hectares in 2016 (USDA-NASS Census 2017, 2018, Table 74; Uzun and others, 2019). Even the largest class of farms in the US by economic size (over US$1 million in sales per year) averaged 938 hectares in 2018, far smaller than the average size of corporate farms in Russia (USDA-ERS farms, 2019). Sources: Sedik and others, 2018; Brooks and Lerman, 1994. Figure 4. Portion of livestock production in GAO by farm type (%) 70 60 Portion of livestock in GAO (%) 50 40 30 20 10 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 20191) Enterprises Family farms Household far ms Source: Rosstat, 2020. 26 These farming tendencies (Table 7and Table 8) show specialization by farm type and by crop or livestock production. Family farms tend to specialize in cash crops (cereals and oilseeds) while enterprises focus on both cash crops and livestock products (Table 7 and Table 8). In 2018, enterprises produced 52 percent of crops but 61 percent of livestock products. Table 7. Crop production by farm type, 1990-2018 (% of total) Farm type 1990 2000 2010 2018 Enterprises 99.7 90.8 77.0 70.2 Cereals and legumes Family farms 0.0 8.4 21.9 29.0 Household farms 0.3 0.7 1.1 0.8 Enterprises 98.6 84.3 72.9 66.4 Sunflower seeds Family farms 0.0 14.5 26.4 33.3 Household farms 1.4 1.3 0.6 0.4 Enterprises 43.0 11.7 14.5 21.9 Potatoes and vegetables Family farms 0.0 1.6 8.8 15.0 Household farms 57.0 86.8 76.7 63.1 Enterprises 99.1 96.6 91.8 86.2 Feed crops Family farms 0.0 1.2 5.2 10.8 Household farms 0.9 2.2 3.0 3.0 Source: Rosstat, 2020. Table 8 also illustrates a third structural change that has emerged in Russian agriculture since 2000: the concentration of pork, poultry meat, and eggs in enterprises, a result of the industrialization of production of these products as part of vertically integrated agricultural holdings (Shagaida and Uzun, 2017). In contrast, neither beef nor milk has lent itself easily to this process. Beef production in Russian is still largely an outgrowth of the dairy industry (without specialized beef breeds), and 40 percent of milk continues to be produced in household farms. Table 9 illustrates that beef and wool production in enterprises are not profitable on average now, just as they were not profitable in the 1990s. However, milk and pork production went from unprofitable to profitable between 1997 and 2014. Cereal and sunflower seed production have been consistently profitable since the 1990s. Table 8. Livestock production by farm type, 1990-2018 (% of total) 1990 2000 2010 2018 Enterprises 87 43 33 36 Beef/veal Family farms 0 2 5 10 Household farms 13 55 62 54 Enterprises 66 28 53 85 Pork Family farms 0 2 3 1 Household farms 34 70 44 14 Poultry meat Enterprises 70 65 88 92 Family farms 0 0 1 1 27 Household farms 30 34 11 7 Enterprises 76 47 45 53 Milk Family farms 0 2 5 8 Household farms 24 51 50 39 Enterprises 78 71 77 81 Eggs Family farms 0 0 1 1 Household farms 22 29 22 18 Source: Rosstat, 2020. Table 9. Profitability of selected crops and livestock products in Russia, 2014-18 (%) Without budget subsidies With budget subsidies 2014 2015 2016 2017 2018 2014 2015 20126 2017 2018 Cereals 24.3 39.5 32.8 18.6 25.6 30.5 44.9 37.0 21.4 29.0 Sunflower seeds 48.6 90.9 70.5 42.0 33.2 52.8 94.1 73.1 42.2 33.3 Sugar beets 38.6 78.9 56.2 13.2 27.6 40.5 80.9 58.1 13.4 27.8 Milk and milk products 23.7 19.5 18.5 25 14.5 33.0 26.6 28.2 32.3 23.9 Beef -35.9 -27.6 -29.9 -30.8 -30.8 -33.0 -25.1 -27.3 -28.7 -28.5 Pork 36.6 28.5 19.7 23.8 35.2 37.3 29.0 19.0 24.1 35.8 Wool -56.3 -49.5 -39.5 -40.3 -37.1 -56.2 -46.0 -26.7 -27.8 -23.7 Eggs 12.8 17 13.5 5.8 9.2 13.6 17.5 13.9 6.6 10.2 Source: Rosstat-Agriculture, 2019. Profitability is for agricultural enterprises only. B. The unevenness of agricultural growth in Russia, 2000-2018 Growth in Russian agriculture has been spatially uneven over the two decades since 2000. Half of total growth in GAO between 2000 and 2018 came from 7 of 77 regions for which data is available. Ninety percent of the total value of production added over these 18 years occurred in 30 percent (23 of 77) of regions. Figure 5 illustrates the distribution of growth of GAO and crop and livestock production by region over the period 2000 to 2018. [Each dot represents a region; the cumulative growth of production is shown on the X-axis while the cumulative number of regions is shown on the Y-axis.]6 The curve bows out past the 100 6 To construct Figure 5, region-level GAO and crop and livestock production were calculated for 2000 and 2018 (in 2018 prices). The difference was calculated in region-level value of production between the two years. There was positive growth in GAO for about 66 percent of the regions between 2000 and 2018 as well as growth in crop production for 60 percent of regions and in livestock production for about 75 percent of regions. Figure 5 shows the cumulative difference between GAO, crop, and livestock production in 2018 and 2000 on the X- axis as regions are added, starting with the region that added the most to the total for the country. For example, the first dot for GAO is Belgorod oblast, which added 10.8 percent of total GAO change between 2000 and 2018; the first dot for crop production is Krasnodar krai, which added 13.5 percent of total crop production change between 2000 and 2018; and the first dot for livestock production is Belgorod oblast, which added 18.8 percent of total livestock production. After 33 (GAO), 21 (crop production), and 30 (livestock production) regions out of 77, 100 percent of total growth is reached. The remaining regions have small or zero positive and negative 28 percent mark for cumulative production growth because 22 regions experienced contractions in GAO, 33 regions experienced the same in crop production, and 20 regions had similar experience in livestock production over the 18-year period. The Russian Federation could have achieved the same total growth in GAO over this period with only 33 of the 77 regions (43 percent). In crop production, the same growth could have been achieved with only 21 regions (27 percent). Figure 5. The unevenness of agricultural growth in Russia, 2000-18 80 75 70 65 60 55 Cumulative number of regions 50 45 40 35 30 25 20 15 10 5 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 Cumulative change in GAO, crops and livestock production, 2000-2018 (%) GAO CROP PRODUCTION LIVESTOCK PRODUCTION Diagonal Source: Calculated from Rosstat-Social-economic, 2019. The high-performing regions are shown in Figure 10 along with their share of growth. These regions were responsible for half of all growth in GAO, crop and livestock production in Russia between 2000 and 2018. Table 10. Regions responsible for half of agricultural growth in Russia, 2000-18 GAO Percent Crops Percent Livestock Percent Belgorod oblast 10.8 Krasnodar krai 13.5 Belgorod oblast 18.8 Krasnodar krai 9.0 Rostov oblast 10.8 Chelyabinsk oblast 5.3 Rostov oblast 7.3 Voronezh oblast 8.3 Rep. of Tatarstan 5.2 Voronezh oblast 6.8 Stavropol krai 7.7 Voronezh oblast 5.1 Stavropol krai 5.4 Kursk oblast 6.2 Dagestan Rep. 5.1 Kursk oblast 5.4 Belgorod oblast 5.7 Kursk oblast 4.8 Rep. of Tatarstan 4.8 Tambov oblast 4.7 Leningrad oblast 4.0 growth. So, the regions with positive growth bend the curve out beyond 100 percent and the final regions with negative growth bring it back to 100 percent. 29 Source: Calculated from Rosstat-Social-economic, 2019. The regions where agriculture is marginal such as Arkhangelsk, Khabarovsk, Novosibirsk, Vologda, and Kurgan in the Northwest and Siberian Districts suffered large declines in production (Figure 11). Production declines in these regions represent the move toward areas more suitable for crop and livestock production. However, some of the largest production decreases took place in the oblasts in the Central Federal District, in Tver, Ivanovo, Yaroslavl, Vladimir, Kostroma, and in Moscow oblast itself. Other declining regions include Nizhnyi Novgorod, Kirov, Perm, and Samara in the Volga Federal District. These declines are difficult to explain since the Central and Volga Federal Districts also comprise some of the highest growing regions in Russia (i.e., Belgorod, Voronezh, and Tatarstan). Simultaneous large increases and decreases in production in the Central and Volga Districts can hardly be explained by marginal agro-climatic conditions. Table 11. Ten regions with largest absolute production declines in Russia, 2000-2018 Percent Percent Percent GAO decline Crops decline Livestock decline Kirov oblast -31.6 Moscow oblast -23.1 Kurgan oblast -41.9 Perm krai -25.2 Kirov oblast -50.8 Samara oblast -26.3 Vologda oblast -33.3 Novosibirsk oblast -26.2 Perm krai -21.9 Arkhangelsk oblast -55.0 Arkhangelsk oblast -65.4 Nizhny Novgorod oblast -18.4 Kostroma oblast -43.0 Vologda oblast -51.0 Vologda oblast -21.2 Khabarovsk oblast -34.9 Tver oblast -46.7 Kirov oblast -17.7 Ivanovo oblast -32.4 Kostroma oblast -56.9 Kostroma oblast -33.4 Moscow oblast -5.5 Khabarovsk krai -43.4 Arkhangelsk oblast -44.8 Vladimir oblast -17.2 Perm krai -32.3 Ivanov oblast -23.6 Kurgan oblast -10.7 Yaroslavl oblast -43.3 Khabarovsk krai -25.8 Source: Calculated from Rosstat-Social-economic, 2019. C. Livestock The decline in livestock production, reflected in a fall in livestock inventories (Figure 6), was reversed only after the introduction of the National Priority Project for Agriculture Development of 2006-07 (announced in 2005). The rapid development of the livestock sector was the largest single program within the National Priority Project. Figure 6. Livestock inventories by farm type, cow units, 1990-2018 30 90 80 70 60 Million Cow units 50 40 30 20 10 0 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 Enterprises Households Family Farms Source: Rosstat, 2020. Figure 6 reveals that the initial fall in livestock inventories was exclusive to agricultural enterprises. Inventories in household and family farms together remained about constant through 2006, declining slightly thereafter, while those in enterprises fell dramatically between 1990 and 2005, rising gradually thereafter. Enterprise livestock inventories found the largest declines in cattle, cows, and pigs, reflecting the negative profitability rates for milk, beef, and pork in these years (Goskomstat, 1998). D. Land use Both the moving away from livestock and the moving toward more profitable cereals and oilseeds have had important implications for agricultural land use. Decreases in land use were disproportionately due to abandonment of land formerly used for feed production. According to the State Federal Cadaster Registration Service, which records land by holder and owner, the area of agricultural land in Russia has changed little since 1990 (Table 12). Some 220 to 222 million hectares of agricultural land registered to owners, between 55 and 60 percent is arable, 40 percent is pasture and haylands and 1-3 percent is permanent crops and fallow. Table 12. Agricultural land in Russia, 1990-2019 Total agricultural Haylands and Permanent crops land (million ha) Arable (%) pasture (%) (%) Fallow (%) 1990 222.4 59 40 1 0 1995 222.0 59 40 1 1 2000 221.1 56 41 1 2 2005 220.7 55 42 1 2 2010 220.4 55 42 1 2 2015 222.1 55 42 1 2 2019 222.0 55 42 1 2 Sources: Rosreestr, 2017, 2020. 31 Another source for land information is the agricultural census, which has been conducted only twice by the Russian Federal State Statistical Service since 1897, in 2006 and 2016. The agricultural census is the authoritative source on land use. The 2016 agricultural census indicated that there were vast areas of land in farms registered but not used. Out of 222 million hectares of agricultural land registered in Russia in 2016, only 125 million hectares were indicated to actually be in use (Uzun, 2017a; Rosstat-2016-Ag Census, 2018: v.3). There are a number of reasons why land registered to farms and other entities was not being used, as recorded by the 2016 agricultural census. First, of the 97.2 million hectares of agricultural land that went unutilized, 29.1 million (30 percent) hectares were listed as not held by agricultural producers. This is because the land had either been abandoned or was stranded in the registration process for leasing or ownership. Uzun (2017a) believes the latter is responsible for most of the land not held by producers. The fate of the remaining 68.1 million hectares is more uncertain. About 17.7 million hectares were covered in the 2016 agricultural census but not “actually being used,� meaning that it is still held on the books of producers but not used. The remaining 50.7 million hectares were not covered by the census because there was no production on it. The 2006 and 2016 agricultural censuses show that the largest declines in land utilization came from abandonment of land formerly used for feed production. While arable land declined by 7 percent between 2006 and 2016, land in haylands and pastures declined by 25 percent over these 10 years (Table 13). This evidence could indicate that unutilized land is probably not distributed evenly between the main uses. Fifty-three percent of the decline in use of agricultural land covered by the censuses between 2006 and 2016 was haylands and pastures. Applying this ratio to overall land abandonment gives an estimate of 51.2 out of the 97.2 million hectares abandoned (Table 13). The census land data demonstrate a substantial redistribution of land from enterprises to family farms between 2006 and 2016. Table 13 illustrates that in 2006 agricultural enterprises held about 80 percent of agricultural land, arable land, and haylands and pasture and half of permanent crops. By 2016, their share of agricultural land had shrunk to 63 percent, while their portion of arable and haylands/pastures had fallen to 69 and 55 percent, respectively. These changes are primarily due to the increasing land holdings of private (peasant) farms. Table 13. Agricultural land and utilization by farm type, 2006 and 2016 Of which: Actually used: Agricultural Farm type land Haylands Agri land (ha) / Permanent Arable and Fallow % of agri land crops pastures 2006 Census 125.5 / Total (million ha) 166.0 102.1 49.1 0.8 13.9 76 Of which used by Agricultural 78 / 80 81 82 48 67 enterprises (%) 74 32 Family (peasant) 16 / 15 16 10 4 18 farms (%) 83 6/ Household farms (%) 6 3 8 49 15 78 2016 Census 125.0 / Total (million ha) 142.7 94.6 36.8 0.7 10.5 88 Of which used by Agricultural 64 / 63 69 55 44 45 enterprises (%) 89 Family (peasant) 29 / 28 28 30 8 14 farms (%) 92 7/ Household farms (%) 9 3 14 48 41 66 Sources: Calculated from Rosstat, 2008; and Rosstat, 2018. A third source for gathering land information is the Ministry of Agriculture, which monitors sown area, production, and many other indicators from businesses and households producing agricultural commodities in the Russian Federation. The area of land annually sown, along with permanent crops, is part of arable land, shown in Table 13. Figure 6 shows that sown areas in Russia declined by one-third between 1990 and 2019, that is from 118 to 80 million hectares. The decline was registered in all crop categories except for technical crops, which actually increased their sowing area. However, the bulk of decline in sown area is attributable to feed crops. Sown area of feed crops in 2019 was only 35 percent of its level in 1990. Figure 7. Sown area by crop in Russia, 1990-2019 140 120 100 Million ha 80 60 40 20 0 90 92 94 96 98 00 02 04 06 08 10 12 14 16 18 19 19 19 19 19 20 20 20 20 20 20 20 20 20 20 Cereals and legumes Technical crops Feed crops Potatoes, vegetables and other Source: Rosstat, 2020. A more detailed look at sown areas in Table 14 reveals that the main drivers of decline from 1990 through 2007 were coarse grains and feed crops. The main drivers of recovery since 2007 have been wheat, sunflower seeds, soybeans, and rapeseed. Oilseeds, particularly sunflower seeds, have been growing continuously since 1990. Table 14. Changes in sown area in Russia, 1990-2018 (1,000 ha) 33 1990-2007 2007-2018 Total sown area -43,008 4,936 Wheat 141 2,880 Coarse grains -18,942 -806 Technical crops, total 2,008 7,055 -- Sunflower seed 2,587 2,834 -- Soybeans 102 2,172 -- Rapeseed 400 918 Potatoes, vegetables, and other -1,189 -781 Feed crops -25,026 -3,411 Source: Rosstat, 2020. Family farms have been the drivers of nearly all increases in sown area for all crop groups except for technical crops (Figure 8). Enterprises have lost land continuously for all crop groups, except technical crops. Figure 8. Changes in sown area by crop group and farm type, 1990-2007 and 2007-2018 1990-2007 2007-2018 Feed crops Feed crops Potatoes, vegetables, other Potatoes, vegetables, other Technical crops Technical crops Coarse grains Coarse grains Wheat Wheat Total sown area Total sown area -60 -40 -20 0 20 -10 -5 0 5 10 Enterprises Family farms Household farms Enterprises Family farms Household farms Source: Rosstat, 2020. The land additions to family (peasant) farms have resulted in an increase in their holdings from 8 percent in 2000, to 21 percent in 2010 and 30 percent of sown land area in 2018 (Figure 9). Despite the gains made in landholding by family farms, they still accounted for only 19 percent of crop production in Russia in 2018. Enterprises, on the other hand, held 67 percent of sown land and produced 52 percent of crops. Household farms, with a mere 9 percent of agricultural land, produced 29 percent of crops in 2018. Figure 9. Sown land by farm type, 1990-2018 (%) 34 100% 90% 80% 70% Sown land area 60% 50% 40% 30% 20% 10% 0% 1990 1995 2000 2005 2010 2015 2018 Enterprises Family farms Household farms Source: Rosstat, 2020. These changes in the structure of land use resulted in significant changes in the average size of farms in Russia (Table 15). Uzun and others (2019) used cadaster data to follow the size of each farm type in 1996, 2006, and 2016. During this period, family and household farms each grew 2- to 3-fold in size, while the mean size of enterprises shrank by 26 percent. Table 15. Average farm size in Russia, by farm type, 1996-2016 (ha, agricultural land) Enterprises Family (peasant) Household farms farms 1996 5,739 44 0.18 2006 4,038 56 0.23 2016 4,251 92 0.45 Source: Shagaida and others 2019. E. Prospects for land reutilization and its importance in Russia The previous section on land use raised the issue of abandoned land in Russia. Having unutilized cropland means that Russia has the capacity to increase its crop production by bring back land into cultivation. However, this has not occurred at a large scale. Meyfroidt and others (2016) identified 31.4 million hectares of cropland abandoned since 1990 in European Russia. Of this total, Meyfroidt and others, Schiernhorn (2015) and Shagaida and others (2018) found that only between 17 and 36 percent of that land is suitable for re- cultivation (Table 16). This cropland that could be feasibly brought back into production was approximately 5-10 percent of total cropland in 2010. Moreover, the added GAO that could be gained from expanding cropland is overshadowed by the effects of increasing yields and changing crop mix. Using the actual current rate of cropland growth in Russia (2013-2019) and extending it out to 2025 and 2040, projections of utilized crop land in 2025 and 2040 indicate that only 2.1 or 5.0 million hectares of cropland will be added over levels in 2010 (Table 16). These 35 projections seem to indicate that either the potential for re-cultivation were overly optimistic or the process of bringing land back to cultivation is extremely slow. Table 16. Total available cropland for re-cultivation in European Russia Meyfroidt Shagaida Estimate of cropland to Schiernhorn be added by: and others and others (2015) (2016) (2018) 2025 2040 Total potentially available cropland 5.3 9.5 11.2 for re-cultivation (million ha) Cropland to be added by 2025 or 2040 at its current* rate of growth/a 2.1/b 5.0/b (since 2010) (million ha) Share of total abandoned cropland 16.9 30.3 35.6 6.7 15.9 (31.4 million ha) in 2009 (%) Share of total cropland in 2010 (%) 4.6 8.2 9.7 1.8 4.3 /a Current rate of growth in cropland is the annual rate calculated between 2013 and 2019. /b Forecast for cropland added since 2010 using forecasts for cropland utilized in 2025 and 2040 (using the current rate of cropland growth). Note: Cropland includes area for annuals, perennial grasses, and one-year fallow. Sources: Shagaida and others, 2018; Meyfroidt, 2016; Schiernhorn, 2015; MinAg-National Report, 2014-2019. In the end, the actual experience of crop production in Russia depends almost exclusively on improving yields rather than increasing land area. To exemplify how the added value of gross agricultural production is accounted for by increases in area versus increases in yields, consider the following: In 2013 there were 115.1 million hectares of cropland in Russia yielding 2.55 billion RUB of crops (in 2019 RUB). If the crop mix and yields were held constant at the 2013 level, the growth of crop production in Russia between 2013 and 2019 would have been more than 24.6 billion RUB. However, crop production in Russia between those years actually grew by 675.4 billion RUB. The implication is that 96.4 percent of the increase in the value of crop production in Russia was accounted for by increases in yields and changes in crop mix. F. Productivity Productivity has been central to the growth of Russian agricultural production. Partial productivity indicators—based on output per unit of input (e.g., livestock, land and labor)— reveal significant increases in productivity. However, this is only a partial accounting of productivity growth. The more inclusive measure of agricultural productivity growth, total factor productivity, grew after 1998 as well. 1. Growth of GAO and agricultural inputs, 1990-2018 Figure 10 shows how GAO as well as the main agricultural inputs have changed between 1990 and 2018. Gross agricultural output in Russia reached its nadir in 1998 and seems to have recovered to pre-independence levels (1990) only in 2019. Although GAO has been rising since 1998, the main agricultural inputs—livestock inventories, land, and labor—all 36 continued to fall into the 2000s. Moreover, after livestock inventories and sown land began to grow after 2005 and 2007, they increased very slowly. Figure 10. Gross agricultural output and main agricultural inputs in Russia, 1990-2018 (1990=100) 110 100 90 80 70 60 50 40 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Production Sown land Labor Livestock inventor ies Note: GAO is calculated in comparable 2019 prices. Source: Rosstat, 2020. 2. Partial productivity indicators The different trajectories of inputs and outputs have shaped the growth of the main productivity indicators in Russian agriculture (Table 17). All productivity indicators fell between 1990 and 1998, grew rapidly through the mid-2000s and then grew more slowly thereafter. The exception is labor productivity, which grew rapidly after 1998. Table 17. Annual average growth of productivity indicators in Russian agriculture, 1990-2018 (%) 1998 - 2005/07/b- 1990-98 1998-2018 ALL FARMS 2005/07/a 2018 Crop production per ha of sown land -0.92 6.25 2.16 3.98 (1,000 RUB per ha) [2019] Livestock production per cow unit -1.40 5.79 1.25 2.82 (1,000 RUB per cow unit) [2019} Ag production per worker -5.91 5.88 (1,000 RUB per worker) [2019] /aFor crops, 2007. /bFor livestock, 2005. Source: Calculated from Rosstat, 2020. Physical crop and livestock yields follow a similar path, falling through around 1998 and growing thereafter (Table 18). Table 18. Crop and livestock yields: value in 22018 and and average annual growth, 1990-2018 Growth Yield (% annual per year) (tons/ha) in Nadir year 2018 1990-Nadir Nadir-2018 Crops Wheat 1998 2.72 -5.37 3.56 Barley 1998 2.16 -9.13 2.34 37 Maize 1998 4.81 -7.91 5.56 Sunflower seeds 2001 1.60 -4.99 4.32 Sugar beets 1994 38.1 -13.31 4.39 Potatoes 1999 1.78 -0.85 3.04 Livestock Milk yields (kg/cow) 1996 4,492 -3.95 3.42 Sources: Calculated from Rosstat, 2020. Russian yields have grown quickly since the beginning of the 2000s, but both crop and livestock yields lag far behind other producers, except for pork. This is not necessarily a negative marker. Comparing yields in other countries, Canada us a good comparator with a climate similar to Russia. Russian average crop and livestock yields fall far below those in Canada, except for pork and chicken(Table 19). However, the highest yield regions in Russia exceed average yields in Canada for cereals and are at the level of the European Union (except for sugar beets). Sunflower yields in the best-performing Russian region surpass the average yields of all comparative countries. Table 19. Crop yields: Russia and selected large producers, 2018 (tons per ha) Flax Sugar Sunflower Cereals Wheat Barley Maize Potatoes seeds beets seeds China 6.1 5.4 4.0 6.1 18.8 1.3 55.9 2.9 European Union 5.3 5.4 4.6 8.4 30.3 1.4 68.5 2.5 Russian Federation 2.6 2.7 2.2 4.8 17.0 0.8 38.1 1.6 Russian Federation 5.3 30.0 1.4 49.0 3.4 (highest yield region) Canada 3.9 3.2 3.5 9.7 43.2 1.5 69.7 2.1 United States 8.7 3.2 4.2 11.9 49.8 1.5 67.8 1.9 Turkey 3.2 2.7 2.7 9.6 33.5 61.6 2.7 Ukraine 4.9 3.7 3.0 7.8 17.0 0.8 50.8 2.3 Kazakhstan 1.4 1.2 1.6 5.7 19.8 0.9 30.5 1.0 Source: FAOSTAT, 2020. The highest yield regions illustrate that highly performing agriculture on the level of OECD comparative countries exists in Russia. This is true not just in crops, but also in livestock. Russian average livestock yields in pork and chicken are nearly at the level of its comparators, and the highest productivity milk-producing region (Leningrad oblast) operates more efficiently than average milk production in Canada and the European Union (Table 20). Table 20. Livestock yields: Russia and selected large producers, 2018 Meat per slaughtered animal Milk per cow (kg) (kg) Cattle Hogs Chicken United States of America 10,463 363 96 2.14 Canada 7,358 335 99 1.72 38 European Union 7,088 296 92 1.65 Argentina 6,596 228 92 2.91 China 5,590 147 79 1.39 Ukraine 5,043 167 86 1.91 Russian Federation 4,511 203 90 1.76 Russian Federation (highest yield region) 8,590 Turkey 3,142 293 1.76 Kazakhstan 2,340 176 75 1.71 Brazil 2,069 250 89 2.38 Source: FAOSTAT, 2020. Russia is closing the gap between other countries in levels of crop and livestock yields at different rates. Yields have tended to grow fastest in those crops and livestock products in which Russia has the lowest yields. Figure 11 compares the pace of crop and yield growth in Russia between 2010 and 2018 with that of country comparators. Between 2010 and 2018, the annual growth of barley yields, for instance, has been rising at 3.1 percent per year in Russia. That is about average compared to the other comparators whose average growth ranged between -0.2 and 6.1 percent growth per year. Russia has done particularly well in raising yields of maize and potatoes in which it has the lowest yields of all the other countries. However, in hog yields, Russia is in the middle of the group and growing swiftly. Figure 11. Annual growth of crop and livestock yields for selected countries, 2010-18 (%) 12 10.7 10 8.5 Yield annual growth, 2010-18 (%) 8 7.8 7.1 6.9 6.9 6.6 6.7 6 6.1 6.0 5.9 4.5 4 3.1 2.6 2 2.2 2.2 1.6 1.6 1.9 1.1 0.9 0.4 0.6 0 -0.2 0.1 0.0 Barley Maize Potatoes Sugar beet Sunflower Wheat Milk Cattle Pigs -0.6 Chicken -0.9 seed -2 -1.8 -3.1 -4 High Low Russia Note: Country comparators are China, EU, Canada, United States, Russia, Argentina, Brazil, Ukraine, Kazakhstan, and Turkey. Crop yields are tons per hectare, livestock yields are milk or meat per animal. Source: FAOSTAT, 2020. 3. Total factor productivity Total factor productivity (TFP) is the real output produced over a period of time divided by the real inputs used over the same period of time. It is thus a more complete rendering of the productivity of an economy, sector, or enterprise than the partial productivity indicators discussed above. The primary interest here is in the growth of output and therefore the growth of total factor productivity. The percentage growth of TFP over time can be defined as: 39 %�TFP = %�Y - %�X (eq. 1) This growth accounting equation states that the percent annual change in TFP is equal to the percent annual change in aggregate output minus the percent annual change in an aggregate measure of inputs. It is actually the portion of change that is not explained by conventional inputs. Most studies of agricultural TFP that include Russia have involved multi-country comparisons (Lerman and others, 2003; Cungu and Swinnen, 2003; Swinnen and others, 2012; USDA-ERS-prod, 2020). These studies have concluded that TFP was a significant factor in the recovery and growth of Russian agriculture after 1998. The latest and most complete study concluded that TFP in Russian agriculture grew by 3.5 percent per year between 1994- 2013 (Rada and others, 2017; 2020). Rada and others (2017) point to two important factors that qualify these TFP results. First, they distinguish between TFP growth that results from declining input use coupled with low or even negative GAO growth on the one hand, and TFP growth resulting from increasing use of inputs along with GAO growth on the other. The study’s view is that only the second type of TFP growth can be considered as the productive application of inputs led by new technologies and efficiencies. Russia achieved TFP growth of the second type after 2005 when both inputs and outputs began to grow (Table 21). This view underlies Rada and his colleagues’ conclusion that TFP growth rate of 1.7 percent per year achieved since 2005 is a reasonable average expectation for the future. Table 21. Average growth of agricultural TFP in Russia, 1994-2013 (%) Period Inputs Output TFP 1994-2013 -2.2 1.3 3.5 1994-1998 -8.8 -4.6 4.2 1998-2005 -2.3 3.1 5.4 2005-2013 0.6 2.3 1.7 Source: Rada and others, 2017. Rada and his colleagues also found extreme differences among regional TFP growth in Russia. In only two regions were the authors able to identify TFP growth characterized by growing application of inputs leading to growth in GAO. The South (the primary grain- growing area) has been responsible for most of the output and input growth in Russia; TFP there grew by 3.6 percent per year between 1998 and 2013. The TFP growth accounted for 69 percent of output growth during that period in the South Federal District (31 percent was accounted for by input growth). The Central Federal District (the primary livestock production region) also saw positive growth in inputs and GAO after 2007. From 2007-13, agriculture in the Central District grew by 4.3 percent per year, inputs grew by 1.5 percent, and TFP by 2.9 percent per year. By 2013, the other regions of Russia had not seen growth of either inputs or GAO and thus, in the opinion of Rada and his colleagues, could not be characterized by the productive application of inputs led by new technologies and efficiencies (Table 22). The findings of Rada and others in their 2017 study are consistent with the previous analysis indicating the unevenness of growth in Russia. Of the regions responsible for 90 percent of 40 growth in Russia between 2000 and 2018, one-half are located in the Central or Southern Federal Districts identified in the 2017 study as experiencing robust TFP growth. Another 20 percent are located in the Volga Federal District. Rada and his colleagues analyzed the period 1994 to 2013, while the growth analysis presented previously encompassed GAO growth during the growth period (2000-2018) in Russian agriculture. Table 22. Average growth of agricultural total factor productivity, by federal district, 1994-2013 (%) District Inputs Output TFP National -2.2 1.3 3.5 South -0.1 3.8 3.9 Central -3.5 0.9 4.4 Volga -2.7 0.7 3.4 Northwest -3.9 -1.0 2.9 Urals -3.0 0.8 3.8 Siberia -2.2 1.0 3.1 Far East -3.0 -0.1 2.9 Source: Rada and others, 2017. Using their previous work on regional TFP growth from 1994 to 2013 and a regional dataset of policy variables, Rada and his colleagues decompressed TFP growth into two elements: (a) Technical progress, defined as pushing out the production possibilities frontier through general technical change, or (b) Efficiency gains, defined as farms performing closer to the production possibilities frontier due to relatively better performance. Both types of change could lead to rising TFP, but technical progress was more likely to be due to public policies related to investments in infrastructure and human capital. On the other hand, efficiency gains at the farm level could be due to “improvements induced by better organization, vertical integration, learning by-doing, private research, and other difficult to measure sources of innovation� (Rada and others, 2020). Rada and his colleagues found no evidence to support the thesis that public policies related to investments in infrastructure and human capital had pushed out the production possibilities frontier through widely shared technical change. Rather, they found that TFP growth occurred due to private efficiency gains, meaning better organization, vertical integration, learning by doing, and other efficiency-improving changes. This finding is consistent with the conclusions of the World Bank (2017) that the key distinguishing characteristic of government support policies has been their heavy reliance on support of private goods to the detriment of public goods. This issue will reappear when discussing the agricultural support and the State Program for Development of Agriculture in Russia. G. Productivity by farm type Three types of productivity indicators can be used to compare the performance of farms by farm type: (a) value of production per hectare of land, (b) physical output per animal, (c) and crop yields. 41 Under the first indicator, value of production per hectare of land, household farms consistently produce output valued at between 8 and 12 times per hectare as do enterprises and family farms. This is true regardless of the land used as the denominator but is particularly apparent for crop production per hectare of sown land (Table 23). Table 23. Value of production per ha, by farm type, 2017-2019 (2019 RUB) 2017 2018 2019 Value of production per ha of agricultural land (RUB per ha) Enterprises 27,059 27,803 29,940 Family farms 25,862 25,354 26,985 Household farms 247,559 235,964 223,281 Family and household farms 73,280 69,565 67,553 Value of crop production per ha of arable land (RUB per ha) Enterprises 19,832 20,462 23,269 Family farms 29,581 29,263 33,166 Household farms 166,347 163,186 152,275 Family and household farms 58,846 57,420 57,846 Value of crop production per ha of sown land (RUB per ha) Enterprises 27,346 28,520 Family farms 24,068 23,837 Household farms 339,813 343,698 Family and household farms 54,954 53,695 Note: Agricultural land includes arable land, fallow of more than one-year, permanent crops, haylands and pasture. Arable land is land under temporary crops and temporary (one-year) fallow. Sown land is land under temporary crops. Source: Rosreestr, 2020 (agricultural and arable land) and Rosstat, 2020 (sown land and value of agricultural production). Under physical indicators of output per animal, with the exception of poultry meat and milk, household farms produce more production per head of animal than other producers (Table 24). Table 24. Meat and milk per animal, by farm type, 2017-2018 (kg, slaughter weight per animal; liters) 2017 2018 2017 2018 Beef Sheep and goat Enterprises 66.0 71.5 3.9 4.7 Family Farms 57.9 61.8 5.6 6.0 Households 117.0 116.9 13.6 14.3 Pork Poultry Enterprises 146.8 153.0 9.9 10.2 Family Farms 103.6 115.7 5.3 5.7 Households 199.1 203.9 3.9 4.0 Milk Enterprises 4,727.1 4,947.8 Family Farms 1,923.9 1,933.7 Households 3,569.0 3,527.4 Note: Meat produced in slaughter weight and animal inventories. 42 Source: Calculated from data from Rosstat, 2020 Rosstat (2020) does not publish figures on the third indicator, crop yields, for household and family (peasant) farms, only for all farms and enterprises. From this information only the yields for family and household farms together can be calculated. A comparison of yields indicates that enterprises produce slightly more crops per hectare than family and household farms (Table 25). Table 25. Crop yields by farm type, 2018 (tons per ha) Family and Enterprises household farms Cereals 2.7 2.2 Wheat 2.9 2.4 Barley 2.3 1.8 Maize 5.2 4.1 Sugar beets 38.2 36.3 Sunflower seeds 1.7 1.5 Potatoes 25.6 15.8 Vegetables 29.2 25.0 Source: Calculated from Rosstat, 2020. How can household farm production per hectare exceed that in enterprises and family farms by such a wide margin when yields of family and household farms are less than those in enterprises? The answer is that the product mixes of households and enterprises are radically different. Household farms achieve their huge lead over other farms in value of output per hectare by producing more livestock products and a greater share of more intensive crops. For example, an agricultural enterprise might plant cereals and harvests 3 tons per hectare at 10 RUB per kilogram for which it receives 30,000 RUB per hectare. A household farm, on the other hand, that plants potatoes, might harvest 15 tons per hectare at 8 RUB per kilogram for which it receives 120,000 RUB per hectare. H. Comparative advantage Liefert (2002) took the first approach by directly computing cost-based measures of comparative advantage7. Social cost-benefit ratios are defined as the ratio of the value of all resources used to produce a good within a country (with tradable inputs valued at international prices), divided by the total foreign exchange, measured in domestic currency, that the good would cost to import. The social cost-benefit ration then gives an idea of the relative cost of producing domestically versus importing a good. If the social cost-benefit 7 Comparative advantage is based on Ricardian trade theory which suggests that patterns of trade between countries are guided by differences in factor endowments and productivity. Applied to agricultural production, if a particular good could be produced at a lesser opportunity cost than another good, for example, a country could have a comparative advantage in the production of that first good. The literature on comparative advantage in Russian agriculture provides two main approaches used to assess comparative advantage: direct cost-based measures and revealed comparative advantage (RCA). 43 ratio is greater than 1, then the opportunity cost of producing a good domestically exceeds that of importing it, and the country can be said to have a cost disadvantage in producing that good compared to world markets. If the social cost-benefit ratio is less than 1, then the country has a cost advantage in producing the good domestically. Using data from 1996-97, Liefert (2002) found that Russia had a relative cost disadvantage in the production of meats and a relative cost advantage in production of crops. Liefert believes that these results can help explain the major changes in Russian agricultural production and trade during transition, namely, the contraction of the livestock sector and the relative success of crop production. A second approach to assessing comparative advantage acknowledges that it may be difficult to directly measure differences in factor endowments and productivity that underlie comparative advantage directly. Therefore, it uses trade data to reveal the comparative advantage one country has over others in production of a given good. The Balassa (1965) index of revealed comparative advantage is defined as the ratio of two quotients. A particular country has a revealed comparative advantage in a given product when its ratio of exports of product-X to its total exports of all goods exceeds the same ratio for the world as a whole. So, for instance, because the ratio of the value of Russian wheat exports to total exports exceeds the same ratio for the rest of the world as a whole, Russia may be said to have a comparative advantage in wheat production. When a country has a revealed comparative advantage greater than 1 for a given product, it is believed to be a competitive producer and exporter of that product relative to an “average� country. The higher the value of a country’s revealed comparative advantage for product-X, the higher its export strength in product-X. Both the cost-based and trade-based measures of comparative advantage have limitations. First, they can be biased by national measures that distort trade such as tariffs, non-tariff measures, and subsidies. Therefore, they should ideally not be used to measure the trade competitiveness of products subject to such policies. In a world where tariffs and agricultural subsidies are ubiquitous, it is useful to keep this limitation in mind, but its practical application remains difficult. Second, the revealed comparative advantage properly pertains to net exports (exports- imports). However, the revealed comparative advantage deals only with exports, ignoring imports, which could lead to misleading results. In order to account for this, Vollrath (1991) and Lafay (1992) proposed trade-based indices that take into account the net trade aspect of comparative advantage. Ishchukova and Smutka (2013) used Vollrath and Lafay indices to analyze the comparative advantage of Russian agri-food net exports, but the results were consistent with those found using the Balassa index. Third, comparative advantage itself is a static concept, which suggests that countries specialize in what they can produce at lower relative costs. It says nothing about how countries trade under development when factors of production and technology change over time. For instance, the revealed comparative advantage does not take into account the possible positive externalities of moving up the value chain to export processed products rather than raw materials, in an effort to develop an advanced manufacturing base. Nor does the revealed comparative advantage take into account the negative risks involved in focusing exports too strongly on raw materials, oil and gas, whose prices are often subject to wild 44 fluctuations. In view of these limitations, the revealed comparative advantage should be used carefully and in combination with other measures as an indicator of competitiveness. Nevertheless, the index of revealed comparative advantage is perhaps the most widely used indicator of trade competitiveness and has the distinct advantage of being both easy to construct and intuitive. According to the revealed comparative advantage calculated at the 4-digit Harmonized Commodity Description and Coding System (HS) for 2018, the exports in which Russia has a comparative advantage include: • Cereals (wheat, buckwheat, barley, rye, maize); • Fish and aquatic invertebrates (e.g., crabs); • Oilseed (flax seed); • Vegetable oil products (rapeseed, sunflower seed, soybean oil, margarine, oilcake); • By-products of the sugar industry (beet pulp, yeast, molasses); • By-products of the milling industry (bran, wheat gluten, cereal germ). This list includes a few other minor products that can be seen in Table 26. Taken together, these products represented 73 percent of total agri-food exports in 2018 (ITC Trade Map, 2020). However, two of these products, wheat and frozen fish, are dominant, accounting for 44 percent of agri-food exports in 2018. This conclusion is consistent with those of Liefert (2002) indicating that the Russian Federation had a comparative advantage in grains and oilseeds and a comparative disadvantage in production of livestock products. Table 26. Agri-food products for which Russia had a comparative advantage (HS 4-digit level) in 2018 Comparative advantage at the 4-digit Harmonized Commodity Description and Coding System (HS) HS RCA Code Description Russia 1001 Wheat and meslin 8.81 1204 Flax seed 7.92 1512 Sunflower seed oil 6.56 1003 Barley 5.73 1002 Rye 4.74 0303 Frozen fish 4.40 2302 Bran, sharps and other residues 3.35 1703 Molasses 2.99 0507 Horns, antlers, hooves, nails, claws and beaks 2.89 1514 Rape, colza or mustard oil 2.45 1507 Soya-bean oil 2.08 2306 Oilcake 1.85 0308 Aquatic invertebrates other than crustaceans and mollusks 1.80 0510 Ambergris, castoreum, civet and musk 1.79 0306 Crustaceans 1.76 2303 Beet-pulp 1.63 1104 Cereal grains or germ hulled, rolled, flaked, pearled, sliced or kibbled 1.59 2102 Yeasts 1.57 0713 Dried leguminous vegetables 1.49 45 1109 Wheat gluten 1.42 1517 Margarine 1.38 1008 Buckwheat 1.34 2403 Manufactured tobacco 1.25 1005 Maize or corn 1.08 Source: ITC Trade Map, 2020. 46 II. Russia’s State Program for the Development of Agriculture The Russian State Program for the Development of Agriculture represents the budget support component of agricultural policy and accounts for about 35-39 percent of total agricultural support in Russia. The announcement of the National Priority Project in 2005 (the first state program for agriculture) began a period of rapid expansion in the real value of agricultural support spending in Russia. The increase was halted by the 2009 financial crisis, and the real value of agricultural support spending has been declining ever since. State programs for agricultural support in Russia have taken on various aims as the goals of agricultural policy have changed between 2006 and 2019. The main aim of the National Priority Project (2006-2007) was to restore livestock production in Russia. The State Program for the Development of Agriculture (2008-2012) sought to continue this aim, primarily through financial support to producers for debt relief and through interest rate subsidies. The second State Program (2013-2020) was the first to include specific targets for food self-sufficiency, and self-sufficiency and import substitution were the focuses of the program. Promotion of agricultural export was added to the State Program in 2016. In 2019, the State Program was extended to 2025. Since its beginning, the State Program has emphasized financial support to producers as opposed to public services for agriculture and rural development. However, the past two years have witnessed increased support to general services. Within the category of financial support, the State Program emphasized expenditures in support of import substitution, compensation for construction and modernization, and credit subsidies. The latest version of the Russian State Program outlines 7 formal goals: • Food independence, • Import substitution, • Increasing competitiveness, • Food security, • Increasing value added, • Increased agricultural exports, and • Growth of capital investment in agriculture. Since 2013, Russian agriculture has made great strides to improve its performance in some but not all of these goals. However, it is not always clear that the State Program played a critical role in contributing even to the goals where improvement can be observed. A further and more fundamental goal of the State Program has been to stimulate and support the dominance of vertically integrated, agro-industrial financial groups, so called agroholdings.8 This goal has been on the agenda of the state ever since Minister of Agriculture Gordeev first proposed “The Main Directions of Agro-industrial Policy of the Government of the Russian Federation for 2001-2010� (Gordeev 2000). In 2017, Shagaida and Uzun (2017) demonstrated what many had suspected for years: Farm support in Russia, 8 An agroholding is a group of agricultural enterprises owned by a holding company. The enterprises are registered as independent producers, but are managed by a single company, either a corporation or an individual. 47 distributed primarily at the regional level, is highly skewed toward support of the largest agricultural enterprises, most of whom are members of the largest agroholdings in Russia. A. State Program budget support and total support to agriculture Agricultural support in Russia is composed of three main elements: (1) Support from consumers who pay higher prices for food in Russia than in world markets; (2) Budget support for agricultural public services such as technical extension services, veterinary and phytosanitary services, land reclamation, and irrigation services; and (3) Budget support to producers. A fourth small element is budget support to agri-food consumers such as mill subsidies. The State Program covers only items (2) and (3) as well as rural development budget expenditures. The State Program for the Development of Agriculture (2013-2025) represents between 35-39 percent of total support to agriculture in the Russian Federation.9 There are four main sources of information on agricultural support in Russia: (1) OECD estimates (based on official information shared by the Government of Russia), (2) the budget expenditure data from Russian Federal State Statistical Service, (3) the figures on expenditures foreseen from the Decree on the State Program, and (4) the National Implementation Reports on the State Program published by the Ministry of Agriculture. The most complete estimate of support to agriculture available is that of OECD, which estimates agricultural support for 26 of the most important agricultural producing countries each year. In 2017, OECD estimated the total support to agriculture in Russia at 705.2 billion RUB (US$12.2 billion), which was 14 percent of gross agricultural output (GAO) or 0.77 percent of gross domestic product (GDP). The estimate of total support to agriculture covers support to producers (Table 27, line 3, 83 percent), support to public good agricultural services (Table 27, line 6, 16 percent) as well as support to consumers from taxpayers (Table 27, line 7, 1 percent) such as milling subsidies and school feeding programs. When estimating only support to producers, OECD considers two elements: market price support (Table 27, line 4, MPS) and budget payments (Table 27, line 5, PSE-MPS). The largest element of support to producers is market price support, the revenue transferred from consumers of food and taxpayers to producers as a result of differences between domestic and international prices. The major sources of this difference are import tariffs and non-tariff barriers to food imports. Removing market price support from total support to producers gives the budget payments to producers (line 5, PSE-MPS). Russian government budget payments also include not just transfers to producers but transfers to the whole of the sector through general agricultural support services (GSSE) (line 6). According to OECD 9 In the following comparisons all references are to the latest version of the State Program for the Development of Agriculture, 2013-2025 (Government of the Russian Federation, 2019). 48 accounts, based on Russian official sources, the total of budget payments to producers and general support services (line 8, (PSE-MPS)+GSSE) was 323.3 billion RUB in 2017. Table 27. Support to agriculture in Russia in 2017 Percent of Billion Total support RUB estimate GAO GDP OECD 1 Total support estimate to agriculture 705.2 100 13.8 0.77 2 Of which: 3 Support to producers (PSE) 584.0 83 11.4 0.64 4 -- Market price support (MPS) 371.6 53 7.3 0.40 5 -- Budget payments (PSE-MPS) 212.4 27 3.7 0.20 Support to general agricultural support 6 services (GSSE) 110.9 16 2.2 0.12 7 Support to consumers (TCT) 10.4 1 0.2 0.01 Budget payments to producers and general 8 support services [(PSE-MPS) + GSSE] 323.3 46 6.3 0.35 Rosstat Finances of Russia Expenditures on agriculture and fisheries from 9 the consolidated budget 343.8 52 6.7 0.37 10 Of which: 11 Federal* 214.1 32 4.2 0.23 12 Regional 270.9 41 5.3 0.29 Decree on State Program and National implementation report 13 1. Foreseen (in the Decree) 257.5 39 5.0 0.28 14 Of which: 15 Federal 215.9 32 4.2 0.24 16 Regional 29.0 4 0.6 0.03 17 Off-budget 12.6 2 0.2 0.01 18 2. Actual (from national implementation report) 19 Federal 233.8 35 4.6 0.25 *Note: The total figure from Rosstat-Finances (2018) nets out transfers between the federal and regional budgets. In 2017 the federal budget transferred 142.1 billion RUB (out of 214.1 billion) to regional budgets for co-financing of agricultural support projects. Sources: OECD-STATS (2020), Rosstat-Finances, 2018; GRF Decree 696, 2019; and MinAg-National Report, 2014-2019. Rosstat publishes the government budget accounts that include a section on expenditures on the national economy. Budget expenditures on agriculture and fisheries from the consolidated budget (Table 27, line 9) are similar in scope to OECD estimate of budget payments to producers plus GSSE (Table 27, line 8). Both include support from federal and regional budgets, and both include direct support to producers and public good agricultural services. The Rosstat figures are a bit larger than OECD figures because (1) OECD calculations do not include all commodities, only the main ones; and (2) OECD calculations 49 do not include rural development expenditures such as housing subsidies for rural teachers. The Rosstat figures also include expenditures for fisheries, though the gross value of production in the fisheries sector was only 8 percent of the gross value of agricultural production in 2017.10 Overall, the Rosstat and OECD estimates of state budget support for agriculture are quite consistent. The State Program expenditure data (line 13-19, Table 27) include the foreseen and actual expenditures, the so-called “passport� that indicates the authorized levels of funding for the State Program (GRF, 2012). However, the State Program is not an entitlement program in the sense that funding for it is not protected but is renegotiated every year; this is why the decree on the State Program is reissued every year with new budget figures. In 2017, federal spending on the State Program was foreseen at 215.9 billion RUB (Table 27, line 15). In fact, the national implementation report indicates that the federal government actually spent 233.8 billion RUB on the Program (Table 27, line 19). The difference is quite small and may be accounted for by support to the fisheries sector.11 It therefore appears that the State Program includes most or all of the Rosstat budget expenditures on agriculture, at least on the federal level. B. Changes in budget support over time, 1995-2017 The Rosstat budget figures (Table 27, line 9) can be used to trace the real value of state expenditures on agriculture since 1995. Figure 12 shows the nominal and real value of these expenditures taken from Rosstat’s Finances of Russia. After an initial decline from 1995, expenditures on agriculture began to rise in 2006 with the introduction of the National Priority Project of 2006-07 and continued to rise through 2009 under the State Program for the Development of Agriculture of 2008-12. The decline in state funding for agriculture began after the financial crisis of 2009, interrupted only by a spike upward in 2013, the year of imposed financial sanctions by some Western governments on Russian businesses and individuals in connection with the Ukraine crisis. The short series from 2013 to 2017 shows that federal State Program expenditures in real 1995 RUB have remained relatively constant (Figure 12). The difference between the Rosstat figures and the federal expenditures from the State Program is explained by regional budget expenditures on agriculture. Figure 12. Nominal and real value of state budget expenditures on agriculture in Russia, 1995-2017 10 According to Russian national accounts for 2017 (Rosstat-national accounts, 2019). 11 By subtracting the rural development expenditures (14.9 billion RUB) from the State Program expenditures (233.8 billion RUB), the total federal expenditures on agriculture and fisheries (214.1 billion RUB) are just about equal to the State Program expenditures (218.9 billion RUB). 50 400 25 350 20 Real expenditures (billion 1995 R) Nominal expenditures (billion R) 300 250 15 200 10 150 100 5 50 0 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Expenditures in nomi nal R Expenditures in 1995 R Federal state pr og ram expenditures (1995 R) Source: Shagaida and Uzun, 2017; Rosstat-finances, 2002-2018; Goskomstat-yearbook, 1997, 1998; MinAg-National Report, 2014- 2019. C. The development of Russian State Agricultural Programs, 2005-2019 The National Priority Project for Agricultural Development, announced in a speech to the government, parliament, and regional leaders by President Putin on September 5, 2005, marked a turning point in government funding for Russian agriculture. “For the first time in modern Russian history agriculture became a priority sector for [state] social economic development policy� (RIA Novosti, 2007). Russia has now experienced three national programs that reflect government priorities placed on the development of agriculture. Table 28 indicates the planned spending of the three Russian agricultural programs and their main elements. A 2016 revision of the State Program for 2013-2020 integrated export promotion as a key goal. The revisions were incorporated into the State Program beginning in 2017 but became more significant beginning in 2019 (Table 29). In 2019, the State Program was extended to 2025. These changes between 2016 and 2019 have created a de facto new (fourth) State Program. Table 28. Main elements of Russian agricultural programs, 2006-2020 National Priority Project State Program State Program for 2006-2007 for 2008-2012 for 2013-2020 Total federal expenditures 47.8 653.2 2,162.2 foreseen (billions of nominal RUB) Average per annum spending (billions of 23.9 130.6 270.3 RUB) Program elements Rapid development of Rural development (7%) Development of crops (% of total livestock sector (31%) production, processing and expenditures) marketing (26%) 51 National Priority Project State Program State Program for 2006-2007 for 2008-2012 for 2013-2020 Stimulation of small farms General conditions for Development of livestock (33%) functioning of production, processing and agriculture (11%) marketing (16%) Provision of housing for Development of priority Development of beef cattle young rural specialists (8%) branches (14%) industry (4%) Additional areas (27%) Financial stability for Support to small forms of agriculture (64%) business (household and family farms) (5%) Market regulation (5%) Technological modernization and innovation (1%) State Program management (11%) Development of greenhouse vegetables and seed potatoes (2%) Development of dairy cattle (12%) Support to livestock breeding (4%) Development of wholesale market centers and infrastructure (4%) Development of the financial- credit system for the agro- industrial complex (4%) Other (11%) Sources: Sedik and others, 2018, based on National Priority Project (2005); GRF, 2007; GRF, 2012. The goals and instruments of the national programs evolved as the state goals of agricultural policy changed between 2004 and 2016. Support policies under the National Priority Project focused on meat and milk production in order to stem the decline of livestock inventories that continued in Russia through 2005. The first State Program announced in 2007 focused on the financial stability of agricultural enterprises, meaning debt relief, part of the managed bankruptcy process and recapitalization that took place during that period. The second State Program marked the explicit integration of food self-sufficiency targets (from the 2010 Food Security Doctrine). Food self-sufficiency was expanded into a policy of import substitution in 2014 with the announcement by President Putin on August 7, 2014, of a ban on food imports from 32 Western countries in reaction to financial sanctions. The food embargo originally covered bovine meat, pig meat, processed meats, poultry, fish and other seafood, milk and milk products, vegetables, fruits and nuts (GRF Government measures, 2014; FAO, 2014), but was later expanded to cover other food imports and countries. The embargo was followed by a road map for the practical implementation of the policy of import substitution, and the government subsequently approved a substantial increase in spending in order to support the achievement of import substitution targets. A new version of the State Program was issued with increased self-sufficiency targets in 2014. The State Program entered a new phase in December 2016 with the issue of a new priority project to support the export of agricultural commodities (GRF export, 2016). This was part 52 of a larger strategic goal of increasing the value of non-raw material and energy exports from US$135.1 billion in 2017 to US$250 billion in 2024. Agricultural export was one of 3 priority projects (together with systemic measures for export and industrial exports) adopted by the President’s Council on Strategic Development and Priority Projects. It took two years to work out the details of the National Project on International Cooperation and Export, which includes five subprojects—industrial, agricultural, and services exports; logistics of international trade; and systemic measures in support of trade. The project is slated to expend 950 billion RUB (approximately US$15 billion in 2020) over 7 years (2018-2024); the subproject on industrial exports would receive 44 percent; agriculture export, 43 percent; logistics, 2 percent; and systemic measures, 1 percent (Table 29). The Federal Project on the Export of Agricultural Products is thus the second largest subproject in the National Project on International Cooperation and Export. Table 29. Targets and financing of the Russian national project on international cooperation and export Base, 2018- Goal 2018 2019 2020 2021 2022 2023 2024 2017 2024 Targets: value of exports (billion $US) Annual value of exports of non- 135.6 149 160 167 181 202 226 250 X raw material, non-energy products, of which: From industrial production 114.0 126 136 142 153 168 185 205 X From agro-industrial complex 21.6 23 24 25 28 34 41 45 X Financing: by subproject (billion RUR) Total 1.3 91.0 79.4 128.8 233.5 226.1 197.7 956.9 Industrial export 0 36.5 25.5 46.2 114.8 107.9 92.9 423.9 Agri-food export 1.3 38.8 33.8 66.5 95 92.6 80 406.8 Logistics of international trade 0 2.4 7.8 5.5 4.5 1.1 0.2 21.6 Services export 0 0.8 0.8 0.8 0.8 1.2 1.3 5.8 Systemic measures 0 12.5 11.5 9.8 18.4 23.3 23.3 98.8 Source: Presidential Council on Strategic Development and National Projects, 2018. The Agricultural Export Project is also a part of the State Program for the Development of Agriculture, and accounts for about 5 to 10 percent of total state support for agriculture outlined there (GRF, 2012). The Agricultural Export Project includes plans to increase agri- food exports from US$21.6 billion in 2017 to US$45 billion by 2024 along with specific policies to support these targets (Table 29). It also includes region-level projects that spell out planning and support figures for federal subjects of the Russian Federation, and specific planning figures for commodities and countries. The Agricultural Export Project has two main areas for promoting the export of agricultural products. The first is targeted funding to improve veterinary health in Russia with plans to compartmentalize veterinary diseases; obtain World Organization for Animal Health (OIE) recognition of geographic disease-free zones; and obtain veterinary certificates for the export of poultry meat, pork, beef, milk products, and animal feed. The project includes the introduction of new plans for the containment and control of the main endemic veterinary diseases in Russia and the development of road maps for instituting crop, fisheries, and aquaculture traceability. Plans for disease control would focus on poultry, then pork, and followed by cattle. 53 The second area for promoting agricultural exports is to create a support system for positioning Russian agricultural products in foreign markets. This includes such measures as creating posts for 50 agricultural attachés, development of marketing strategies, business missions, a center for agricultural export, degustation demonstrations, and agri-food expositions in international trade exhibitions. D. Rural Development in the State Program Rural development has been the subject of federal programs in Russia explicitly since 2002. when government authorized per annum financing from the federal budget of between 1.8 and 8.9 billion RUB over the period 2003 to 2013 (Table 30). Table 30. Social development of the village to 2013 authorized financing, 2003-2013 (billion RUB) Total financing Per annum financing Stage I Stage II Stage III Stage I Stage II Stage III 2003-2005 2006-2010 2011-2013 2003-2005 2006-2010 2011-2013 Total 52.9 143 90.9 17.6 28.6 30.3 Federal 5.5 34.9 26.8 1.8 7.0 8.9 Regions 23.1 59.4 38.3 7.7 11.9 12.8 Off budget 24.3 48.7 25.8 8.1 9.7 8.6 Source: Government of the Russian Federation Decree no. 858 (December 3, 2002). The overall goal of the program was to decrease the rate of population decline in the countryside by improving infrastructure in rural areas. The infrastructure focus of the decree for “social development of the village to 2013� was on housing; education; culture; trade; internet access; rural public utilities (electricity, gas, plumbing); and rural road construction. This approach is seen as an example of the “exogeneous-sectoral model of rural development� by its detractors (Kostiaiev and others, 2019). The actual social, economic, and institutional disincentives of the Russian countryside during this period were and are considerably more negative than could be addressed through infrastructure development, particularly on such a small scale. The Russian countryside suffers not only from a deterioration of infrastructure but from aging of the rural population, a lack of rural jobs (leading to rising unemployment), abandonment of villages, and declining incomes of the rural population. These hardships have been aggravated by the predominant state development strategy for Russian agriculture that focuses on concentrating producers into large farms and agroholdings with supply, service, and marketing chains that bypass rural areas. As such, agricultural production strategy in Russia is not designed to promote effective development of rural areas and is likely to decrease rather than increase rural jobs. Moreover, the current state of the institutional development of agricultural product markets in the countryside is brutal for smallholders. Land rights are both uncertain and difficult to register, and marketing is disorganized with low prices for small producers. These social, economic, and institutional disincentives have led to outmigration and a declining rural population that infrastructure projects alone are unable to resolve. 54 The program on the “social development of the village to 2013� was followed by the state program on “sustainable development of rural territories, 2014-18 and to 2020� which, despite its name, differed little from its predecessor. In 2017, it was folded into the State Program for the Development of Agriculture and the Regulation of Markets, 2013-2020. Federal funding for the “sustainable development of rural territories, 2014-18 and to 2020� was between 5 and 7 percent of overall funding for the State Program for the Development of Agriculture and the Regulation of Markets between 2013 and 2018 (Table 31). Within the “sustainable development of rural territories� sub-program, only two measures stood out as different: a grant program entitled “grant support for local initiatives for rural citizens� and “support and popularization of the achievements in the area of the development of rural territories.� This “endogenous-territorial model� is fashioned after rural development programs in OECD countries such as USDA rural development programs based on grants, loan guarantees, and low interest loans to state and local governments; Cooperative Development Centers; cooperatives; educational institutions; and local banks, non-profits, rural utilities, and others, which in turn lend to rural citizens or businesses on a project basis. Such programs normally require recipient co-financing and a business plan (much as the Pillar II rural development programs of the EU Common Agricultural Policy). The endogenous-territorial model of rural development is seen as a step toward construction of rural development programs that respond to the real needs of the Russian countryside. Financing for the above two sub-programs over the period from 2014-2020 totaled 1,717 million RUB or 6.8 percent of the funding for the entire program (Kostiaiev and others, 2019). The State Program for the Development of Rural Territories, which is included in the State Program for the Development of Agriculture and the Regulation of Markets, 2013-2025, differs from its predecessors in four ways. First difference, the Program’s three goals focus on raising the welfare of rural households: • Keep the portion of the rural population at 25.3 percent or higher in 2025, • Raise the monthly available resources of rural households to 80 percent of that of urban households by 2025, • Increase the share of comfortable residential premises in the total residential area of rural areas to 50 percent by 2025. Though many of the measures of the program are similar to its predecessors, by focusing on rural housing and infrastructure, at least the goals focus on welfare indicators for rural households. However, this creates a disconnect between the measures that are primarily aimed at improving infrastructure and housing in rural areas (at least in terms of funding) and the goals of the program (Table 31). Table 31. The structure of the State Program for the Development of Rural Territories Sub programs Explanation Resources 2020-25 (billion RUB) Total 2,288.3 Analytic, normative, and methodological Monitoring, analytic, and information 0.8 support for the program support Creation of conditions for supplying the Housing construction and improvement, 1,870.8 rural population with comfortable housing low-cost mortgages for construction and 55 improvement, public utility service coverage improvement Development of the labor market in rural Job training programs for reducing rural 317.9 areas unemployment to 5.7% in 2025 Creation and development of the Gas, plumbing, drinking water, internet, 98.2 infrastructure of rural territories roads, development of medical clinics in rural areas, the appearance of today’s rural territories Source: GRF Decree 696, 2019. Second difference, the State Program for the Development of Rural Territories includes funding for monitoring, methodology, and informational support as well as funding for job training. These elements are presumably aimed at shaping the program to correspond to the endogenous-territorial model. A third difference with previous programs is the introduction of labor market training and small projects developed by local municipalities with a target of 42,250 projects throughout the Russian Federation of less than 2 million RUB each (about US$28,000 in 2020) for public area upgrades and internet access (2020-2025). A fourth difference of the State Program is the magnitude of funding. Per annum financing of rural development under previous programs from the federal government were on the order of 8 billion RUB per year, with a larger contribution expected from the regions. The State Program for the Development of Rural Territories, as it was originally budgeted, turned this funding relationship around. Now the federal government took a dominant position in funding the program. Moreover, the federal contribution was envisioned to be between 10 and 30 times what it had been previously (Table 32). Despite the ruble having been devalued since the early 2000s, the increase in funding is nevertheless significant. Table 32. State Program for the Development of Rural Territories: passport budget by year, 2020-2025 (billion RUB) 2020 2021 2022 2023 2024 2025 Total Program by year Total 228 363 413 414 426 444 2,288 Federal 79 161 193 201 209 218 1,061 Regions 16 24 28 31 35 39 174 Off budget 133 178 192 183 181 187 1,053 Source: GRF Decree 696, 2019. The State Program for the Development of Rural Territories still operates within the same social, economic, and institutional constraints as its predecessors. However, it is clear that there has been at least an attempt to integrate some aspects of the endogenous-territorial model into the program. Unfortunately, even before the first year of implementation of the State Program, it was cut by 60 percent (Table 33). The March 31, 2020, revised budget for the Program cut financing for the first three years, though it left subsequent years without change. Plans for these changes were made in October of 2019 (Uzun, 2019).12 Table 33. State Program for the Development of Rural Territories: revised financing, March 3, 2020 (billion RUB) 12 Because of the planning in 2019, the budget changes were not an effect of the coronavirus. 56 Program by year 2020 2021 2022 2023 2024 2025 Total Total 96.4 78.4 66.7 405.5 414.3 430.0 1,491 Federal 35.9 34.4 35.0 201.0 209.3 217.9 733 Regions 22.0 8.5 7.6 21.9 23.9 25.0 109 Off budget 38.4 35.5 24.1 182.6 181.2 187.1 649 Source: GRF Decree 391, 2020. E. Types and amounts of support in the State Program, 2013-25 It is important to understand the structure of financing in the State Program for 2013-25 in order to judge the realism of its funding forecasts. The government decree that describes and authorizes the State Program gives only overall figures on foreseen financing (Table 34)13. The total figures from 2013 through 2017 clearly fit within the trends described earlier. However, there is an abrupt break in 2018 when “off-budget funds� explode. “Off-budget funds� are an estimate of private funds attracted through co-financing of state measures (see Box 2 for examples). For analytical purposes, off-budget funding for the State Program has been ignored, with consideration given to only expenditures from federal and regional budgets (totaled in the last column of Table 34). Table 34. Foreseen (passport) financing for the State Program for Development of Agriculture, 2013- 2025 (billion RUB) Federal Regional Off-budget Federal + Total budget budgets funds Regional 2013 261.0 158.7 75.7 26.5 234.4 2014 262.1 170.1 73.4 18.6 243.5 2015 255.0 187.9 53.5 13.6 241.4 2016 295.9 237.0 47.4 11.6 284.4 2017 257.5 215.9 29.0 12.6 244.9 2018 1,165.6 242.0 45.0 878.7 287.0 2019 793.7 303.6 21.3 468.8 324.9 2020 742.1 294.8 21.4 425.9 316.2 2021 755.0 312.3 20.7 422.1 333.0 2022 860.3 365.8 22.1 472.3 387.9 2023 876.0 377.5 23.2 475.3 400.8 2024 881.1 377.4 25.4 478.3 402.8 2025 806.7 300.7 24.5 481.5 325.2 Source: GRF Decree 696, 2019. The difficulty in mapping the structure of State Program expenditures is because the overall budget and its structure is renegotiated every year. Moreover, for 2017 and 2018, the sub- programs and primary measures of the State Program were redesigned. The current structure of the State Program can be found only in the national implementation reports 13 From the revised budget figures in the government decree of December 18, 2019. 57 issued by the Ministry of Agriculture, which contain information only on federal expenditures and the latest of which has data for 2018. Box 2. Examples of off-budget funding in the State Program Off-budget funds in the State Program are an estimate of private funds attracted through co-financing of state measures. Two examples serve to clarify: Example 1: A milk processing cooperative decided to purchase equipment for milking cows. It submits a grant application to the Ministry of Agriculture for the development of a cooperative (under the State Program federal project “Creating a system of support for farmers and the development of agricultural cooperation�) , which includes the total cost of the investment, the grant requested and the investment by the cooperative. According to the rules of the grant, 40 percent of the funds must be contributed by the applicant, while 60 percent are contributed from federal and regional budgets. Thus, 40 percent of the cost of the investment are “extrabudgetary funds� which are mobilized through co-financing. Example 2: A young teacher arrives in a village. Under the subprogram “Sustainable development of rural territories� (and, from 2020, the State Program “Integrated development of rural territories of the Russian Federation�), the young teacher can apply for a subsidy for the purchase of housing. The cost of housing minus the subsidy is considered “off-budget funds� mobilized through co-financing. Source: personal communication. Table 35 shows the breakdown of funding in the State Program by rubrics based upon the sub-programs and main measures mentioned in the implementation reports (MinAg- National Report, 2014-19). The rubrics are approximate because the sub-programs sometimes include funding for items that do not seem to correspond to the overall sub- program. For instance, expenditures for veterinary and sanitary services of Rossel’khoznadzor are contained under “management of the state program�. Table 35. State Program, 2013-2018, federal expenditures by type 2013 2014 2015 2016 2017 2018 Total expenditures (billion RUB) 197.9 186.6 222.3 218.1 233.8 249.5 By element (%): 1. Management of the State Program 11.0 13.3 13.8 11.5 12.1 12.4 2. Land reclamation 3.3 4.1 3.5 3.4 4.8 4.5 3. Rural development 4.6 5.5 5.5 5.5 6.4 6.8 4. Agricultural financing support 0.0 0.0 5.4 3.7 2.1 0.0 5. General public support to the sector 0.0 0.0 0.0 0.0 7.4 7.7 6. Financial support to producers 81.1 77.2 71.8 75.9 67.3 68.6 Source: MinAg-National Report, 2014-19. The most complete characterization of the rubrics above are contained in the latest implementation report for 2018 that lists funding for both sub-programs and their main measures (Table 36). Table 36. Characterization of State Program rubrics Element Description (main measures, according to 2018 implementation report) Support to the Ministry of Agriculture for project management, improvements in 1. Management of the APK-information resources for ensuring food security and management, State Program government agricultural land use monitoring, Rossel’khoznadzor veterinary, and sanitary control 58 Support for regions’ land reclamation projects; scientific studies and experimental trials connected with land reclamation; construction and reconstruction of state 2. Land reclamation land reclamation facilities; and support to state land reclamation, including technical machinery and early warning services Public infrastructure and road construction support, rural housing support, grant 3. Rural development programs for rural initiatives, support and popularization of successes in rural development, and expert support for program implementation 4. Agricultural financing support Recapitalization of Rossel’khozbank and Rosagroleasing Commodity interventions, veterinary disease control, natural disaster relief, plant genetic resource preservation, scientific support for the development of the agro- 5. General public support industrial complex, creation of Center for Analysis of APK Export, formation of a to the sector system for supporting agri-food exports on foreign markets, and support to Rossel’khoznadzor for opening foreign markets Agroleasing subsidies and recapitalization, support to crop growing, milk sector support, support to regional programs for APK development, compensation for construction and modernization expenditures, investment credit subsidies, 6. Financial support to discounted credit subsidies, development of crop production, marketing and producers processing, livestock production, marketing and processing, development of cattle for meat production, development of small farms, vegetables and seed potatoes, development of dairy cattle, support for animal and seed breeding and selection, and development of wholesale distribution chains and public dining infrastructure Source: MinAg-National Report, 2014-2019. The last category on financial support to producers can be further divided for 2017 and 2018 (Table 37). Financial support to producers in the State Program accounts for between 67 and 69 percent of the entire Program. The rest of support in the State Program is for agricultural public services and rural development. The estimate of the portion of the State Program dedicated to financial support is close to the 2017 OECD portion of 66 percent, which pertains to the portion of budget transfers in the sum of budget transfers and general agricultural support services (line 8, Table 27). The largest category of direct support to producers in the State Program includes investment and discounted credit subsidies, which account for more than half of total financial support (Table 37). Table 37. State Program: financial support to producers in 2017-18 2017 Share of 2018 Share of total State total State 2017 2018 Program Program billion billion federal federal RUB RUB expenditures expenditures % % 1 Total financial support to producers, of which: 157.2 67 171.1 69 2 Agro-leasing: Leasing subsidies and recapitalization 15.9 7 14.0 6 Support for import substitution: to crop growing, 3 milk sector support, support to regional programs 58.3 25 64.1 26 for APK development Compensation for construction and modernization 4 expenditures, investment credit subsidies, 83.0 35 93.0 37 discounted credit subsidies Source: MinAg-National Report, 2014-2019. 59 F. Regional distribution of agricultural support payments in State Program, 2012-2020 The State Program provides financing to various recipients under its programs and sub- programs. Some financing is intended for federal organizations and ministries such as the Ministry of Agriculture for project management; for other ministries for project implementation; or for agencies such as Rossel’khoznadzor for veterinary, sanitary, and phytosanitary issues. Other financing is earmarked as direct aid to producers as support under various programs. The information available from the latest version of the State Program and from the website of the Ministry of Agriculture indicates that federal and regional financing for recipients other than producers has been rising since 2014. In 2020, federal and regional governments are to expend 300 billion RUB on agriculture, 144.4 billion RUB of which will be available as support payments for producers (Table 38). Thus, producers will receive direct support payments (or subsidies) for about half of funds under the State Program, down from nearly 80 percent in 2013. Table 38. State Program federal and regional financing: passport and support available to producers (billion Rubles) State Program financing Total State Program passport Direct support available to producers not earmarked for financing, total (billion RUB) (billion RUB) Year producers (billion RUB) Federal + Federal + Federal Regional Federal Regional Federal + Regional Regional Regional 2012 2013 234.4 158.7 75.7 184.7 121.2 63.5 49.7 2014 243.5 170.1 73.4 235.6 160.0 75.6 7.9 2015 241.4 187.9 53.5 222.4 150.7 71.6 19 2016 284.4 237.0 47.4 219.2 167.0 52.2 65.2 2017 244.9 215.9 29.0 198.2 151.9 46.3 46.7 2018 287.0 242.0 45.0 171.3 141.8 29.5 115.7 2019 324.9 303,6 21.3 114.2 90.3 23.9 210.7 2020 300.0 283.6 16.4 144.4 118.7 25.7 155.6 2021 306.4 291.2 15.2 2022 343.1 327.3 15.8 2023 345.7 328.9 16.8 2024 335.3 317.7 17.6 2025 246.3 228.5 17.8 Note: The Ministry of Agriculture updates estimates of the total annual subsidies available to producers each week during the year. The estimates here are those from the last date of the relevant year. The latest estimate for 2020 is from April 16, 2020. Source: GRF 2020; MinAg-Subsidies, 2020. 1. The real value of producer support has been falling since 2013. The nominal value of financing directly available to producers as subsidies has been falling since 2014 and will likely fall further in 2020 than that indicated here. On April 9, 2020, the Ministry of Agriculture website listed a total of 148.7 billion RUB available to producers, while on April 16, the total had shrunk to 144.4 billion RUB. However, in real terms (2012 RUB) the value of subsidies available to producers has been falling continually since 2013, reaching a low of 78.4 billion RUB (in 2012 RUB) in 2018 before recovering to 96 and then 94 billion RUB in 2019 and 2020, respectively (Figure 13). 60 Figure 13. State Program support available to producers, 2012-2020, by federal district (billion 2012 RUB) 250 200 Billions 2012 R South 150 Central Ural Siberia Northwest 100 N. Caucasus Volga Far East 50 0 2012 2013 2014 2015 2016 2017 2018 2019 2020 Note: Nominal values of subsidies were adjusted to 2012 RUB using GDP deflator from Rosstat-Finances (2002-2018), except for 2019 and 2020 which are IMF projections of GDP deflators (IMF, 2020). Source: MinAg-Subsidies, 2020. 2. Size-distribution of direct producer support by region has remained relatively constant, 2012-18. The distribution of producer support by federal district has remained relatively constant throughout the entire period, with the two largest recipient districts being the Volga and Central (Figure 13 and Figure 14). Moreover, the size-distribution of subsidies by region has been quite constant over the entire period 2012-2020. About 40 percent of subsidies is distributed to 11 regions and 70 percent of subsidies to 30 out of 82 regions. 61 Figure 14. Distribution of support by region, 2012-2020 (cumulative percent) 90 80 70 2012 Cumulative number of regions 60 2013 2014 50 2015 2016 40 2017 30 2018 2019 20 2020 10 0 0 20 40 60 80 100 Cumulative share of subsidies, by year (%) Source: MinAg-Subsidies, 2020. The size-distribution of subsidies in 2018 followed the distribution of agricultural labor in the country. The distribution of subsidies was more evenly distributed than production or sown area in 2018 (Figure 15). Ten regions in 2018 were eligible to receive about 35 percent of subsidies and 17 out of 84 received 50 percent of subsidies. The largest single recipient region in Russia was the Republic of Tatarstan with 4.9 percent of all subsidies. Figure 15. The distribution of producer support, production, labor and sown area for 84 regions of Russia, 2018 85 80 75 70 65 60 Cumulative number of regions 55 50 45 40 35 30 25 20 15 10 5 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Cumulative portion of variable in 2018 (%) SUBSIDIES PRODUCTION LABOR SOWN AREA Diagonal Source: Calculated from MinAg-Subsidies, 2020; Rosstat-Social-economic, 2019. 3. Eleven regions received 40 percent of subsidies available to producers, 2012-18. Over the entire period 2012 to 2020, 11 regions have consistently received relatively large shares of available subsidies totaling 40 percent of all subsidies (Figure 16). The largest recipient regions have been in the Central District (Belgorod, Bryansk, Voronezh, Kursk, Tambov); Volga District (Tatarstan, Bashkortostan); and South District (Krasnodar, Rostov). 62 Stavropol krai in the North Caucasus and Altai krai in Siberia are also large recipients of subsidies. Figure 16. Eleven regions which have received 40 percent of producer support, 2012-2020 (billion 2012 RUB) 100 90 Altai krai Tambov oblast 80 Bashkortostan Rep. 70 Kursk oblast Stavropol krai Billion 2012 R 60 Rostov oblast Krasnodar krai 50 Voronezh oblast 40 Bryansk oblast Belgorod oblast 30 Tatarstan Rep. 20 10 0 2012 2013 2014 2015 2016 2017 2018 2019 2020 Source: MinAg-Subsidies, 2020. 4. The performance of the largest subsidy recipient regions has been mediocre, 2012-18. Two basic expectations of subsidies that may apply to a sustained period of state support are growth in production and improved financial results (profits and profitability). While neither of these metrics alone is a definitive measure of performance, growth and profitability are two commonly used benchmark measures. While both growth and financial results may vary from year to year, it may be argued that six years is a substantial period to expect some return on a public investment. The large recipient regions seem to have done better in providing a return on state investment by the first metric (increased production) than by the second. Table 39 indicates that the 17 regions which received 50 percent of total subsidies available to producers between 2012 and 2018 all saw increases in production between 2012 and 2018. However, other regions that did not receive large subsidies also saw output increases. It might be expected that the highest subsidy regions would have higher growth than the average over the entire Russian Federation, but this is not the case. In 5 of the 17 highest subsidy recipients, the value of production in 2018 grew slower than the average for the entire country. The slower-than-average regions include the largest recipient of subsidies (Republic of Tatarstan) as well as Krasnodar krai, Leningrad, Sverdlov, and Orenburg oblasts. The results of high subsidies and slow growth can be seen in the column of Table 39 entitled “change in ruble output per ruble of subsidies, 2012-18.� This is calculated as the difference between the value of production in 2018 and 2012 (in 2012 RUB) divided by the total value of subsidies received between 2012 and 2018 (in 2012 RUB). Using these 63 indicators, the Republic of Tatarstan, Leningrad, and Sverdlov oblasts all scored lower than the average for the Russian Federation as a whole. Table 39. Largest producer support recipient regions, 2012-2018 Change in annual net profit Change in RUB output per 2018 value of production (RUB) per RUB subsidies RUB subsidies, 2012-18 Crop Livestock Unprofitable Total subsidies profitability profitability enterprises, (2012= 100) 2012-18 2012-18 (%) (%) (%) Billion 2012 Cumula 2012 2018 2012 2018 2012 2018 RUB tive % Russian Federation 1,152.2 100 120 0.55 15.3 20.6 10.6 12.8 27 26 0.03 Rep. of Tatarstan 76.8 6.7 113 0.25 3.9 2.4 0.9 1.9 18 22 -0.03 Belgorod oblast 72.9 13.0 131 0.63 22.4 27.0 26.8 29.4 24 19 0.02 Voronezh oblast 48.6 17.2 122 0.55 17.9 22.2 11.5 14.2 19 34 0.08 Bryansk oblast 45.1 21.1 174 0.57 11.9 21.3 8.1 6.5 40 41 -0.18 Krasnodar krai 36.3 24.3 115 0.87 21.8 33.7 7.7 13.1 24 26 0.30 Rostov oblast 35.5 27.4 125 0.99 22.3 23.9 9.8 -9.1 14 22 -0.21 Stavropol krai 32.1 30.2 134 1.02 21.2 25.3 17.3 18.2 15 16 0.12 Kursk oblast 30.0 32.8 162 1.37 19.6 32.0 21.0 42.1 26 26 0.33 Rep. of Bashkortostan 29.8 35.3 122 0.75 4.3 3.7 5.4 2.8 16 23 -0.05 Tambov oblast 27.3 37.7 154 1.11 21.9 25.5 4.9 19.6 30 29 0.22 Altai krai 27.0 40.1 131 0.98 8.1 18.3 11.3 8.8 27 29 0.05 Lipetsk oblast 24.2 42.2 146 1.03 12.4 27.4 9.5 25.0 25 20 0.40 Leningrad oblast 23.0 44.1 106 0.16 5.4 6.6 11.4 9.6 28 21 -0.02 Sverdlov oblast 20.9 46.0 116 0.39 5.4 4.4 3.4 6.5 18 17 0.02 Orenburg oblast 20.6 47.8 117 0.56 2.6 2.4 9.2 0.1 34 35 -0.03 Chelyabinsk oblast 20.0 49.5 128 0.94 9.3 5.8 12.5 10.7 37 33 0.00 Rep. of Mordovia 19.2 51.1 127 0.55 10.5 10.1 12.0 21.2 19 16 0.09 Note: Relatively poor financial performance indicators are indicated in red italics. Source: MinAg-Subsidies, 2020. Financial indicators such as profitability on sales and share of unprofitable agricultural enterprises are lower where many of the highest subsidy-recipient regions perform worse than the average for the country as a whole. Thirteen out of 17 of the highest subsidy- recipient regions suffered from low or worsening profitability as measured by the profitability from enterprise crop or livestock sales or by the share of unprofitable agricultural enterprises (excluding medium-size and small enterprises). The poorest performer is the Republic of Tatarstan, which had overall low profitability for crop and livestock sales and also suffered a decrease in profitability for crop sales. The crop and livestock sales profitability measures are calculated to include subsidies. It seems likely that 64 without subsidies both crop and livestock sales in large enterprises in Tatarstan were unprofitable in these years. Worse performance according to the profitability measures may be one of the reasons for the rise in the share of unprofitable large agricultural enterprises in Tatarstan. Other relatively poor performers among high-subsidy regions are Bryansk oblast with declining livestock profitability on sales and an extraordinarily high (and growing) share of unprofitable large agricultural enterprises. The Republic of Bashkortostan also had worsening financial performance between 2012 and 2018, despite having received nearly 30 billion RUB (2012) of subsidies. The last column of Table 39 is entitled “change in annual net profits (in 2012 RUB) per ruble of subsidies extended between 2012 and 2018.� This ratio is calculated as the difference in net profits in 2012 and 2018 (in 2012 RUB) divided by the total subsidies granted between 2012 and 2018 (also in 2012 RUB). This can be interpreted as the return in terms of extra profits associated with the public investment in private agriculture between 2012 and 2018. On average, all 82 regions of the Russian Federation recorded an increase in annual net profits of 3 kopecks for every ruble of public subsidy invested. Among the highest subsidy regions, 6 (Tatarstan, Bryansk, Rostov, Bashkortostan, Leningrad, and Orenburg) actually registered less net profits from large enterprises in 2018 than in 2012, despite subsidies. Three additional regions registered profit increases less than the overall average for the Russian Federation. It is impossible to make definitive statements about the return on public investments when considering region-level financial indicators. It is entirely conceivable (and likely, given the value of public investment) that the high-subsidy regions are home to a small and growing segment of technologically advanced enterprises with globally competitive production and financial indicators and another segment of poorly performing, financially distressed enterprises. Aggregating one with the other is like taking the average temperature of patients in a hospital. However, the all-Russian average levels in Table 39 also reflect regions with better- and worse-performing enterprises. It seems a modest expectation that the high-subsidy regions would perform better than the all-Russian average. 5. Correlates of subsidy distribution, 2012-18 The State Program is elaborated each year with a distribution of producer subsidies by region. With the data from six years of producer subsidy distribution, it is reasonable to ask whether a systematic bias can be detected in the size distribution of producer subsidies, according to regional characteristics. A neutral hypothesis would be that subsidies are distributed by region according to the share of GAO produced. This hypothesis may gain support from Figure 15, which seems to indicate that the distribution over regions is near that of the distribution of GAO. Another hypothesis is suggested by Shagaida and Uzun (2017) who cite evidence showing that support is distributed disproportionately to large farm enterprises in agroholdings. Although data by farm is not available, it is possible to test this hypothesis indirectly by using data on GAO portion in each region produced in agricultural enterprises belonging to agroholdings. Yet another hypothesis is that subsidies are distributed not according to the share of GAO produced in a region, but according to the share of livestock production in a region. This 65 hypothesis has the advantage that it is consistent with the goal of the State Program to support import substitution and investment in new capital. To test these hypotheses, multiple regression analyses were run with the dependent variable as the share of total producer subsidies accorded to each region over the period 2012 to 2018, and the following independent variables: • Share of total GAO by region, 2012-18 (%) • Share of total livestock production by region, 2012-18 (%) • Share of total crop production by region, 2012-18 (%) • Share of the GAO produced in agroholdings by region, 2016 (%) Three regressions were run using logged variables. The results are shown in Table 40. Table 40. Distribution of producer support by region, regression results Model 1: Model 2: Model 3: GAO, agroholding Livestock, agroholding Crop, agroholding Coefficient t-stat Coefficient t-stat Coefficient t-stat Intercept -0.096 -1.464 -0.143 -2.403 -0.117 -1.328 GAO share 0.784 16.448 Livestock share 0.861 18.104 Crop share 0.545 11.104 Agroholding share 0.045 2.649 0.041 2.592 0.080 3.648 N 78 78 78 R2 0.843 0.865 0.726 Note: All coefficients were statistically significant at the 0.05 level. Model 2, in which the distribution of producer subsidies is explained by the regional share of total livestock production and the share of regional GAO produced in agroholdings, has the highest R2 indicating a better fit, followed by Model 1 and Model 3. In Model 2, the two independent variables explain over 85 percent of the variation in subsidy distribution. As an illustration, Figure 17 shows the plot of the dependent variable (the share of total producer subsidies by region, 2012-18) and the first independent variable of Model 2 (the share of livestock production by region, 2012-18). Labels are shown for the largest subsidy recipients accounting for a total of 50 percent of all producer subsidies between 2012 and 2018. Figure 17 shows that there is a close relationship between the regional portion of livestock production and the regional portion of producer subsidies distributed. The Republics of Tatarstan and Bashkortostan, as well as Bryansk and Voronezh oblasts, are relative outliers. Otherwise, regions are clustered rather tightly around the diagonal along which the share of producer subsidies is equal to the share of livestock production in each region. Figure 18 shows a trend that indicates a correlation between a higher share of regional GAO produced in agroholdings and a higher share of producer subsidies but there are many outliers for both the large and small recipient regions. Table 41 illustrates the different situation for the large and small recipient regions, presenting correlation coefficients between the share of producer subsidies on one side and the share of livestock production and regional GAO in agroholdings. Calculations are made separately for the large subsidy recipients and for the small. In Figure 17 the correlation 66 between the share of producer subsidies and livestock production for the larger subsidy recipient regions is lower than for the smaller subsidy recipient regions. This reflects the larger number of outliers for high-subsidy regions compared to the low-subsidy regions. For Figure 18, the correlation between the share of producer subsidies and the share of agroholdings in regional GAO is higher for the larger subsidy-recipient regions. This reflects the large number of outliers in the low-subsidy regions. Figure 17. Share of producer support and livestock production, by region, 2012-2018 7.0 Belgorod obl 6.5 6.0 5.5 5.0 Share of Livestock production, 2012-18 (%) 4.5 Rep Tatarstan Rep Bashkortostan 4.0 Krasnodar krai 3.5 Chelyabinsk obl Altai krai 3.0 Voronezh obl Leningrad obl Rostov obl 2.5 Stavropol krai Orenburg obl 2.0 Kursk obl Sverdlovsk obl Tambov obl Bryansk obl 1.5 Rep Mordoviia Lipetsk obl 1.0 0.5 0.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 Share of subsidies, 2012-18 (%) Source: MinAg-Subsidies, 2020; Rosstat-Social-economic, 2019. Figure 18. Share of producer support and regional GAO in agroholdings, 2012-2018 67 80.0 Belgorod obl 70.0 60.0 Share of regional GAO in agroholdings, 2016 (%) Bryansk obl Kursk obl 50.0 Chelyabinsk obl Lipetsk obl 40.0 Tambov obl Voronezh obl Leningrad obl Krasnodar krai 30.0 Rep Mordoviia Stavropol krai Sverdlovsk obl Rep Tatarstan 20.0 Rep Bashkortostan Altai krai Rostov obl 10.0 Orenburg obl 0.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 Share of subsidies, 2012-18 (%) Source: MinAg-Subsidies, 2020; GRF EMISS, 2020. Table 41. Correlations between the share of producer support (2012-18) and independent variables for the larger and smaller support recipient regions Regions Independent variables Livestock Agroholding production GAO share share (2012-18) (2016) Larger subsidy recipient regions (50% of total subsidies) 0.74 0.39 Smaller subsidy recipient regions (50% of total subsidies) 0.84 0.24 Source: MinAg-Subsidies, 2020; GRF EMISS, 2020; Rosstat-Social-economic, 2019. 6. Distribution of producer subsidies by program, 2012-18 The large majority of producer subsidies are programmed for financial and investment credit support (Table 42). Throughout Russia, on average, these private good subsidies were responsible for 86 percent of all subsidies between 2012 and 2018. With few exceptions, the large recipients of subsidies focused more on such private good subsidies than the average for the Russian Federation as a whole. A few regions – Rostov, Bashkortostan, Altai krai, Leningrad, Orenburg and Mordoviia—invested a bit more in public goods and development of peasant farms. Some of these regions have a high portion of GAO produced on family and household farms. More than half of all regional production in 2018 was produced in family and household farms in Rostov (52 percent), Bashkortostan (61 percent), and Orenburg (69 percent) (Rosstat-social-economic, 2019). Only Stavropol and Rostov—two of the largest grain-producing regions in the country—devoted over 1 percent of funds to land reclamation. Some regions went against the general trend and devoted a part 68 of their funds to the development of rural territories. Rostov, Bashkortostan, Tambov, Altai krai, Leningrad, Orenburg, and Mordoviia regions all devoted over 10 percent of funds to the development of rural territories. In 2017 the share of producer subsidies programmed for financial and investment credit support reached its peak of 89 percent for the Russian Federation, after which it began to fall to 85 percent (2018), 81 percent (2019) and 71 percent in 2020. At the same time, the portion utilized for land reclamation climbed to 5 to 7 percent and that for development of rural territories rose to 10 percent in 2018, 9 percent in 2019, and 20 percent in 2020. The regions which received 50 percent of total producer subsidies between 2012 and 2018 also decreased their use of funds for private goods, reducing that share after 2017: from 92 percent in 2017, 88 percent in 2018, 85 percent in 2019, and 77 percent in 2020. Expenditures on the development of rural territories increased from 6 percent in 2017 to 16 percent in 2020 (calculated from MinAg-Subsidies, 2020). Table 42. Distribution of producer support by region and subprogram, 2012-18 Share of total producer subsidies distributed to region, 2012-18 (%) Share of Private goods Public goods and peasant farms Region RF total Peasant (%) Financial Credit Land Rural Subtotal farms and Subtotal support support reclamation territories cooperatives Russian Federation 100 47 38 86 1 11 3 14 Tatarstan Rep. 6.7 38 52 89 0 9 1 11 Belgorod oblast 6.3 36 61 97 0 2 1 3 Voronezh oblast 4.2 46 48 95 1 4 1 5 Bryansk oblast 3.9 49 48 96 1 3 1 4 Krasnodar krai 3.2 56 36 91 1 6 1 9 Rostov oblast 3.1 42 42 84 2 12 3 16 Stavropol krai 2.8 54 35 89 2 5 3 11 Kursk oblast 2.6 38 57 94 0 5 0 6 Bashkortostan Rep. 2.6 45 32 76 1 20 3 24 Tambov oblast 2.4 31 56 87 1 11 2 13 Altai krai 2.3 52 30 82 0 16 2 18 Lipetsk oblast 2.1 41 49 90 1 8 1 10 Leningrad oblast 2.0 42 40 82 0 15 3 18 Sverdlov oblast 1.8 67 24 91 0 8 1 9 Orenburg oblast 1.8 57 27 84 1 12 3 16 Chelyabinsk oblast 1.7 39 56 95 0 4 1 5 Mordoviia Rep. 1.7 33 44 77 1 19 3 23 Source: Calculated from MinAg-Subsidies, 2020. 7. Conclusion This overview has established four conclusions through an analysis of the distribution of producer subsidies by region in the Russian Federation from 2012 to 2020, focusing primarily on the largest subsidy-recipient regions. First, from the point of view of providing a return on state investment, the largest support recipient regions seem to have done better in increasing production than in performing well financially. Though the regional approach does not afford a way to test for the effects of subsidies, there is still an expectation that the high-subsidy regions in the Russian Federation would perform better than the all-Russian average. This expectation may be justified, because mainly the same regions received high levels of producer subsidies every year. However, the performance of the high-support regions as a whole does not seem to exceed 69 that in other regions. Though most high-support regions increased production at a pace higher than the Russian national average, 5 of 17 of them grew more slowly than the national average (Table 39). In financial terms, agriculture in the high-subsidy regions had mediocre performance as a whole. Fourteen out of 17 high-subsidy regions had either a high proportion of unprofitable enterprises compared to the all-Russian average in 2018 or low profitability from crop and livestock sales. This mediocre regional-level agricultural performance is not necessarily connected with subsidies. The World Bank (2017) found that the most advanced agricultural enterprises for wheat, hogs, milk, and poultry operate at or nearly at technological and cost levels of their world competitors, but that these are islands of efficiency in a broader landscape of less efficient and less profitable farms. It is entirely conceivable that the high-subsidy regions are home to a small and growing segment of technologically advanced enterprises with globally competitive production and financial indicators and another segment of less well performing, perhaps financially distressed, enterprises. However, the all-Russian average levels in Table 39 also reflect regions with better and worse performing enterprises. The second main conclusion of this overview is the identification of two criteria that describe the distribution of subsidies by region between 2012 and 2018. The first is the share of the value of livestock production of the region in the total livestock production of the country. Figure 17 illustrates that, with the exception of a few outliers, the distribution of subsidies followed the share of livestock production by region quite closely. This means that it is the large, livestock-producing regions that received support. The focus on livestock production has been present in the State Program since its inception. A look at the sub-programs from the years 2012 to 2018 shows that livestock has been a consistent focus of the State Program. Some of the specific livestock-oriented programs were “state support for livestock branches (2012-2016),� “development of family livestock farms (2012),� “partial interest rate subsidies for the development of livestock, processing and sales (2013-2016),� “partial interest rate subsidies for investment to construct and reconstruct capital for meat production (2014),� “the development of livestock, processing and sales (2013-2016),� “development of milk production (2014),� “state support for economically significant regional programs in meat production (2013) and livestock (2013),� “raising productivity in milk production (2017),� and others. The second criterion was suggested by Shagaida and Uzun (2017). Their study of the distribution of agricultural support at the farm level concluded that farm support in Russia is distributed based on the size of the enterprise in an effort to support agroholdings. Along with the livestock share of the region, the share of regional GAO produced in agroholdings was also found to be a significant factor in the distribution of agricultural support. Taken together, these two criteria mean that the largest recipients of state support are large livestock-producing regions with enterprises that are members of agroholdings. This is a good description of regions in the Central Federal District such as Belgorod, Bryansk, Lipetsk, and Tambov. However, this criterion does not fit well for the Republics of Tatarstan and Bashkortostan where agroholdings produced only 20 percent of total GAO and 50 percent of enterprise production in 2016. The third main conclusion of this short overview is that support payments in Russia during this period heavily emphasized support of private goods through financial assistance and 70 subsidized credits, rather than public goods through rural territorial development and land reclamation, and the development of family farms. The tendency to stress financial support and subsidized credits was even more apparent for the large support recipients. However, this bias has changed after 2017, and fully 20 percent of total support is slated to go toward supporting the development of rural territories in 2020. A last conclusion concerns the role of the State Program in supporting uneven agricultural development in Russia. On one hand, support through the State Program is geographically more evenly distributed than growth. Eighty percent of subsidies were distributed to about 40 regions between 2012 and 2020 (Figure 14), while 80 percent of the increase in production between 2010 and 2018 occurred in less than 20 regions (Figure 5). Thus, the distribution of support in Russia would seem to be a mitigating factor on the uneven growth shown in Figure 5. However, the State Program supports the already lopsided distribution of production by farm type by principally supporting enterprises belonging to large vertically integrated agroholdings (Table 40, Figure 17). Such support contributes to the concentration of production into large agroholdings, while production in family (peasant) farms for the most part is unsupported. Shagaid and Uzun (2017) found that in 2015 for every 1 ruble of sales, 6.7 kopecks of support from the State Program were distributed to enterprises, 5.0 kopecks to family farms, and 0.3 kopecks to household farms. This bias may be a missed opportunity. Production in family farms grew at rates that exceed those in enterprises for every major crop and livestock product except for pork between 2010 and 2018 (Rosstat, 2020). With a better enabling environment through greater attention to public goods and support for investment in family (peasant) farms the return on public investment in terms of growth and profitability could be improved. It is not at all clear that the current strategy represents a good financial return on public investment. Many of the enterprises in highest subsidy-recipient regions perform worse than the average for the country in sales profitability and on the share of agricultural enterprises which are unprofitable. G. The formal goals of the State Program The goals of the State Program for Agricultural Development have been a moving target. The latest version divides the goals into two phases (Table 43). The first phase from 2013 to 2017 focuses on ensuring the food independence of Russia, import substitution, and raising the competitiveness of Russian agriculture. The second phase (2018-2025) focuses on ensuring food security, increasing value added in agriculture, growth of agricultural exports, and growth in capital investment in agriculture. Table 43. The goals of the State Program for Agricultural Development Phase Program Goals Goal 1: Ensure food independence according to the food security program Phase I doctrine (Presidential decree, January 30, 2010, no. 120) (2013-2017) Goal 2: Import substitution for meat, milk, vegetables, seed potatoes, and berries 71 Goal 3: Raise the competitiveness of Russian agricultural production on domestic and foreign markets Goal 4: Ensure food security through agricultural growth Phase II Goal 5: Increase value added in agriculture (2018-2025) Goal 6: Growth of agricultural exports Goal 7: Growth in capital investment in agriculture Source: GRF, 2019. The role of the State Program in furthering these goals is analyzed below without explicitly dividing the analysis into its two phases. 1. Reducing dependence on imported food The State Program 2013-2020 adopted the targets set in the Food Security Doctrine 2010 as the official goals on food self-sufficiency. The revision of the State Program in 2014 even increased some of these targets, and the revised Food Security Doctrine adopted in 2020 added commodity groups to the targets. In addition to the commodity groups in Table 44, the Food Security Doctrine contains targets for vegetable oil, salt, sugar, and seeds for the main crops. There is no question that Russia in 2018 was less dependent on food imports than it was in 2010 when the first Food Security Doctrine was issued. Table 44 indicates that Russian self- sufficiency in meat and its products, fish and products, milk and products, and vegetables has increased since 2010. Table 44. Food self-sufficiency in Russia, 1990-2018 (%) Vegetables Meat and Milk and and Fruits and Fish and Grains products products Potatoes melons berries products Target levels Food Security Doctrine, 2010 95 85 90 95 -- -- 80 Food Security Doctrine, 2020 95 85 90 95 90 60 85 Actual levels 1990 87 87 88 97 80 56 1995 95 73 86 100 89 53 2000 93 69 87 98 83 51 2005 98 61 81 98 77 37 2010 99 72 79 96 81 25 70* 2015 99 87 79 96 85 35 81 2018 100 92 82 95 86 36 81 Source: Calculated based on Rosstat, 2020 as 100-(ratio of imports to total resources), where total resources are defined as (imports+domestic production+beginning year stocks-end year stocks); President of the Russian Federation (Doctrine), 2010, 2020; *2011. It is certainly not clear, however, that the support of the State Program, though substantial, has been responsible for the increases in self-sufficiency. The focus of the State Program has 72 been on livestock products. The largest increases in self-sufficiency in livestock products came in 2014 and 2015 after Russia imposed a ban on many food imports from 32 Western countries (Figure 19). In addition to the import ban, there were three or four other critical factors in the years between 2013 and 2015 that drove down imports. First, from 2012 through 2015, the average ad valorem import tariffs for meat and meat offal rose from 6.5 percent in 2012, to 15.9 (2013), and to 20.7 percent in 2014 (WTO, 2020). Second, between 2013 and 2015, the ruble devalued greatly against the US dollar and the Euro. While in 2013, the US dollar and Euro were worth 32.7 RUB and 45 RUB, respectively, by 2015 the dollar had risen to 72.9 RUB and the euro to 79.7 (Rosstat-Finances, 2014, 2018). Third, real expenditures of households rose by only 1.7 percent in 2014 and fell by 8.3 in 2015 and 1.8 percent in 2016 (Rosstat-National accounts, 2019, 2017). Thus, tariffs, non-tariff policies, exchange rates, and incomes all played important roles in the evolution of the food self- sufficiency ratios in Table 44. Figure 19. Year-to-year changes in self-sufficiency ratios in Russia (%) 120 115 Change over previous year (%) 110 105 100 95 90 2012 2013 2014 2015 2016 2017 2018 Meat and products Mlk and milk products Fish and products Source: Calculated from data in Table 39. 2. Import substitution Russian agriculture and food processing have been most successful in substituting for imports in those areas where the sector was already enjoying robust growth before 2013. For instance, Table 45 indicates that pork and poultry meat have been the most successful in substituting for imports. However, pork and poultry meat production in Russia has been growing rapidly since the 1990s, with pork production growing at a rate of 5 percent (since 1999) and poultry at a rate of 10.3 percent (since 1997). Production of cheese, butter, and beef have also substituted for imports since 2013, though at a lesser rate than poultry and pork meat. The fall in imports across the board, except for dry milk and vegetable oils, is undoubtedly connected with the strong devaluation of the ruble, which has made all imported foods more expensive for consumers. Thus, the role of the State Program in promoting import substitution would seem to be minor in the case of pork and poultry meat production, though perhaps a more convincing case may be made for cheese, beef, and sugar. 73 Table 45. Import substitution in Russia, 2013-2018 Production Import 1,000 tons 1,000 tons Import substitution % of 2013 2018- 2018- 1,000 (production 2013 2018 2013 2018 2013 2013 tons + imports) Beef/veal 580 637 57 831 436 -394 57 4 Pork 1,666 3,010 1,344 746 66 -477 477 20 Poultry meat 3,748 5,059 1,311 550 224 -301 301 7 Butter 293 301 8 163 73 -54 8 2 Cheese and products 449 489 41 416 201 -231 41 5 Dry milk 117 153 37 175 93 2 0 Sugar 4,959 6,273 1,314 436 323 -114 114 2 Vegetable oil 3,939 5,950 2,011 914 1,298 384 0 Source: computed from Rosstat-Balances, 2014, 2017, 2019. 3. Increasing the competitiveness of Russian agriculture Though the concept of competitiveness is widely used, it is not easily defined. The OECD defines competitiveness as “the ability . . . to generate, while being and remaining exposed to international competition, relatively high factor income and factor employment levels on a sustainable basis� (Hatzichronologou, 1996, cited in Latruffe, 2010). This economy-wide “factor returns� version of competitiveness can be adapted to agriculture by identifying the most appropriate agricultural products for exploiting a country’s comparative advantage in order to maximize gains from trade and thus returns to domestic factors of production. In this section consideration is given to how the Balassa index of revealed comparative advantage has changed over the past 10 years as a core indicator of the increasing international competitiveness of Russian agriculture. A second approach to competitiveness emphasizes both the level and growth of productivity. The World Economic Forum defines competitiveness as “the set of institutions, policies, and factors that determine the level of productivity of a country� (Sala-i-Martin, 2009). In this section, the economy-wide “productivity� version of competitiveness is adapted to agriculture by analyzing the growth of total factor productivity and by comparing domestic firm costs vis-à-vis international comparators. (a) Changes in revealed comparative advantage Russian agriculture has increased its presence in international export markets over the past 10 years so that Russia is now one of the largest cereal exporters in the world. This in itself is a mark of the increasing competitiveness of Russian agriculture in world markets. More formally, the Balassa index of revealed comparative advantage in 2018 illustrates that Russia has a comparative advantage in the production and export of cereals and oilseeds and that that advantage is growing.14 Table 46 illustrates that the RCA index has grown quite 14 It is not clear that an increase in the RCA index actually indicates an increase in the “comparative advantage� of a country in a particular commodity per se. The index indicates “revealed� comparative advantage in the case when 74 substantially over the past ten years for a number of cereals (wheat, barley, rye, bran) and oilseed products (linseed, sunflower) as well as for frozen fish, molasses, and horns and hooves. For wheat, barley, and rye most RCA growth came after 2013; for the oilseeds, fish, and horns and hooves, the growth in comparative advantage started earlier. Table 46. Russia: Changes in revealed comparative advantage for top 10 agri-food exports in 2018, 2013- 2018 Annual growth in RCA HS RCA Index (%) Description Code 2008 to 2013 to 2008 2013 2018 2013 2018 1001 Wheat and meslin 2.18 2.54 8.80 3.1 28.2 1204 Linseed 1.38 6.55 7.88 36.5 3.8 1512 Sunflower-seed 3.04 5.16 6.52 11.2 4.8 1003 Barley 1.42 2.28 5.71 9.9 20.2 1002 Rye 0.64 0.85 4.70 5.8 40.9 0303 Frozen fish 0.72 3.26 4.42 35.4 6.2 2302 Bran 0.51 1.35 3.16 21.7 18.5 1703 Molasses 1.26 1.72 2.96 6.5 11.4 0507 Horns, antlers, hooves, nails, claws 1.19 2.45 2.90 15.5 3.5 1514 Rape, colza or mustard oil 0.47 1.30 2.44 22.7 13.5 Note: items are listed in order of RCA in 2018. Source: Computed from data in ITC Trade Map, 2020. In previous sections it was noted that one of the main reasons for the original establishment of the National Priority Project of 2006-07 was to support and restore the livestock sector in the Russian Federation. This focus remained to a great degree with the State Program 2008 to 2012 and in the current project-based program of 2013-2025. Table 47 illustrates that Russia does not seem to have a revealed comparative advantage in any livestock product with the exception of horns and hooves and fish (neither of which are supported under the State Program). In none of the other livestock products does Russia have a comparative advantage expressed in its trade with the rest of the world although the revealed comparative advantage for animal fat and poultry meat has risen rapidly over the past 10 years. Table 47. Russia: changes in revealed comparative advantage for top 10 livestock exports in 2018, 2008- 2018 Annual growth in RCA Index RCA (%) 2008 HS to 2013 to Code Description 2008 2013 2018 2013 2018 0303 Frozen fish 0.72 3.26 4.42 35.4 6.2 0507 Horns, antlers, hooves, nails, claws 1.19 2.45 2.90 15.5 3.5 0403 Buttermilk, curdled milk and cream, yogurt, kephir 0.90 0.78 0.72 -2.8 -1.6 RCA is greater than 1. However, focusing on the definition of the index, a value greater than 1 indicates that the commodity in question accounts for a larger share in total exports of Russia than in that of an “average� country. Therefore, an increase in the index indicates that the share in Russia is increasing vis-à-vis an average country. It may be more accurate therefore to say if RCA increases that Russia is becoming more specialized in the export of the particular commodity vis-à-vis an average country. 75 1601 Sausages and similar products 0.69 0.67 0.54 -0.6 -4.2 2105 Ice cream 0.37 0.43 0.46 3.1 1.4 1518 Animal or vegetable fats and oils 0.01 0.08 0.43 51.6 40.0 0407 Birds' eggs, in shell 0.18 0.20 0.35 2.1 11.8 0206 Edible offal 0.00 0.05 0.32 45.0 0204 Meat of sheep or goats 0.00 0.00 0.32 0207 Meat and edible offal of poultry 0.01 0.08 0.30 51.6 30.3 Note: Items are listed in order of RCA in 2018. Source: Computed from data in ITC Trade Map, 2020. It is certainly not clear that support of the State Program has been vital for the increases in comparative advantage shown here. The commodities with the highest comparative advantage (grains and oilseeds) held that status far before the State Program began in 2013. It may be more accurate to say that Russia has increased its specialization of export since 2008 in the commodities in which it has a comparative advantage. However, the driving force for this specialization probably lies more with the constant profitability of cereals and oilseeds, rather than in the State Program. Furthermore, it does not appear that the substantial resources of the State Program invested in livestock production and processing have been enough to fundamentally change the comparative disadvantage of Russian agriculture in livestock products. (b) Total factor productivity growth The second core indicator of the competitiveness of Russian agriculture is the total factor productivity (TFP) of the sector. As discussed before, Rada and others (2017) found that total factor productivity in Russian agriculture grew by 3.5 percent per year between 1994 and 2013 and 1.7 percent between 2005 and 2013 but suggested that only TFP growth during the later period can be considered as the productive application of inputs led by new technologies and efficiencies. A USDA-ERS (2020) productivity study of international TFP growth found that total factor productivity in Russian agriculture grew at an even higher rate of 3.1 percent per year between 2005 and 2013. Either of these growth rates during this period is similar to some of the top agricultural exporters. According to the USDA-ERS study, while total factor productivity in Brazil, Russia, and China grew by 3.8, 3.1, and 3.6 percent respectively, per year, TFP growth in Germany, Canada, United States, Turkey and Ukraine grew by 1.8, 1.0, 1.4, 1.7, and 2.0 percent, respectively. It is difficult to say to what degree the State Program has played a role in the growth in total factor productivity in Russia. Certainly, the main issue in agricultural TFP growth has been the fundamental economic and institutional reforms in Russian agriculture that have spurred the recovery (Shagaida and Uzun, 2019). Rada and others (2020) have found that TFP growth in the Russian Federation has not been induced by policy-related expansion of the production possibilities frontier through public investment in infrastructure and human capital but rather reflects improvements induced by better organization. In summary, with little evidence based on aggregate data on State Program expenditures (as opposed to distribution of State Program expenditures discussed below) there is no clarity on whether or not State Program subsidies have played an active role in raising total factor productivity. 76 4. Food security The State Program cites the indicators of the Food Security Doctrine as the official goals of the program in the area of food security.15 Both versions of the Food Security Doctrine use the level of food self-sufficiency as the main indicator of food security of the country. The most commonly used definition of food security is one proposed by the UN Food and Agriculture Organization from the 1996 World Food Summit: “Food security, at the individual, household, national, regional, and global levels [is achieved] when all people, at all times, have physical and economic access to sufficient, safe, and nutritious food to meet their dietary needs and food preferences for an active and healthy life.� This definition is usually interpreted to include 4 aspects of “food security�: (a) availability of food, (b) economic access to food, (c) utilization of food (nutrition), and (d) stability of access to food. The most relevant of these aspects relative to agricultural policy is economic access. The impact of agricultural policy on economic access to food over time can be assessed in part by focusing on the behavior of the share of household expenditures on food from the regular household budget surveys. Figure 20 shows that food security in Russia in the late 1990s was truly precarious, with households spending nearly 60 percent of their disposable incomes on food. The devaluation and financial crisis of 1998 raised expenditures on food by households from 52 to 58 percent of total expenditures, an indication of the profound impact of these events on consumer budgets. Robust economic growth during the early 2000s caused the share of food expenditures in total disposable income to decline steadily through 2007, when it reached 34 percent, a result of increasing incomes and falling food prices as the ruble appreciated against other currencies. During the financial crisis of 2009, the growth of disposable income per capita in Russia slowed to 1.8 percent and the share of food in household budgets rose slightly. It then declined to 33 percent in 2014. Figure 20. Economic access to food in Russia: household expenditures on food (% of disposable income), 1997-2018 60 58 58 55 53 53 52 50 48 Percent 45 43 43 40 39 37 36 35 37 37 36 35 35 35 34 34 34 33 34 30 2011 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2012 2013 2014 2015 2016 2017 2018 Source: Rosstat-Social, 2003-2019. 15 President of the Russian Federation (Doctrine), 2010, 2020. 77 The Russian food embargo introduced in August 2014 and the abrupt devaluation of the ruble raised food prices again in 2015 and 2016. The percentage increase on the share of household disposable income spent on food was even larger than in the 1998 devaluation and financial crisis. Expenditures on food soared from 33 percent in 2014 to 37 percent of total disposable incomes in 2016. The consumer nominal protection coefficients—the ratio of the domestic to international reference prices—increased from 1.42 in 2013 to 1.70 in 2014 for pork, and for milk and milk products from 1.0 in 2013 to 1.11 in 2014, to 1.24 in 2015, and 1.52 in 2016 (OECD STATS, 2020). Such was the cost of these abrupt policy changes for the Russian consumer. While these changes are undoubtedly a result of agricultural policy, the State Program, which includes budget support only, did not play a part. 5. Increasing value added in agriculture The State Program attempts to raise value added in agriculture by investing primarily in high-value products (namely, livestock). As a reminder, the OECD estimate of support to producers nearest to State Program expenditures is all payments to producers without market price support (refer to Table 27). According to OECD PSE accounts, between 2013 and 2018, 35 percent of Russian direct payments to agricultural producers (PSE-MPS) could be identified as supporting livestock, 23 percent supported crop growing, and 39 percent were either uncertain or benefitted both sub-sectors (OECD STATS, 2020). However, it is hardly justified to analyze agricultural policy in Russia without noting the large role played by import tariff policy and non-tariff barriers to food imports. Thus, if the effects of tariff and non-tariff barriers are included to food imports in the analysis, the focus in OECD estimates should include both direct budget payments and market price support to the benefit of producers. Including market price support significantly changes the analysis of the bias of agricultural, tariff, and non-tariff policies: Between 2013 and 2018, 66 percent of Russian (PSE, including MPS) payments to agricultural producers could be identified as supporting livestock, only 3 percent supported crop growing, and 31 percent were either uncertain or benefitted both sub-sectors (OECD STATS, 2020). What has been the effect on value added of this support to livestock? If the goal of the State Program has been to increase value added as a share of total agricultural output through subsidizing primarily livestock development, then it has not achieved this goal (Table 48). Value added fell after 2015 and in 2018 was just under 50 percent of gross output. Moreover, it is far from certain that a policy of supporting livestock is a good strategy for increasing value added as a share of gross output. The share of livestock in gross output has been falling in Russia since 2010 (Figure 2). Moreover, the share of livestock in total gross output changes independently of the share of value added in gross agricultural output. The correlation between the two between 2011 and 2018 was low (0.31) and not statistically significant. Table 48. Gross output, intermediate consumption and value added in agriculture and hunting, 2011- 2018 GAO… …of which: Share of Share of Year (billion RUB) (billion RUB) value added value of livestock 78 in GAO production Intermediate Value (%) to GAO consumption added (%) 2011 3.58 1.73 1.84 51.6 49.4 2012 3.74 1.76 1.97 52.8 52.8 2013 4.05 1.95 2.09 51.7 50.0 2014 4.50 2.08 2.42 53.8 50.7 2015 5.37 2.47 2.90 54.0 48.1 2016 5.74 2.78 2.96 51.6 47.0 2017 5.77 2.86 2.91 50.4 49.1 2018 5.80 2.92 2.88 49.7 48.5 Sources: Rosstat-National accounts, 2017, 2019. 6. Growth of agricultural exports Since 2010, GDP in Russia has risen at a disappointing 1.25 percent per year. Russian agriculture has been a bright spot in the economy, increasing output at 4.2 percent per year. However, agricultural exports have outpaced even the healthy growth of GAO by growing at the phenomenal rate of 16 percent per year. In 2018, Russia exported nearly one-third of agricultural production (Figure 21). Figure 21. Russian agricultural exports and share of GAO exported, 2010-2018 (USD) 30,000 35 25,000 30 Share of GAO exported (%) 25 Exports, million USD 20,000 20 15,000 15 10,000 10 5,000 5 - - 2010 2011 2012 2013 2014 2015 2016 2017 2018 Exports Ag exports/GAO (%) Source: Rosstat, 2020; ITC Trade Map, 2020. Exports have been led by cereals, fish, and vegetable oils, with other exports lagging far behind (Figure 22). In fact, Russia was the number-one exporter of wheat, linseed oil, and beet pulp for animal feed, and the number-2 exporter of cereals, sunflower oil, and rapeseed oil in 2018 (Table 49). Figure 22. Russian agri-food exports, by value, 2019 79 Meat Cereal prepsBeverages 3% 3% 3% Misc Cocoa 3% 3% Oil seeds Cereals 5% 37% Food industry waste 6% Vegetable oils 16% Fish 21% Source: ITC Trade Map, 2020. Table 49. Russia's rank in world agri-food exports in 2018 Share of world Exporter export (%) rank Horns, hooves 6.8 3 Cereals 9.3 2 Wheat 20.5 1 Barley 13.3 3 Buckwheat 21.5 3 Flaxseed 18.4 1 Sunflower oil 15.2 2 Rapeseed oil 5.7 2 Molasses 6.9 3 Bran 7.4 3 Beet pulp 26.1 1 Source: ITC Trade Map, 2020. It is difficult to connect this phenomenal growth in agricultural exports with the State Program, if only because the main commodities exported are precisely those which are hardly supported under the Program. Instead, soaring wheat and oilseed exports are primarily a result of soaring profits to be made from specializing in cereals and oilseeds (refer to Table 6 and Table 9). 80 7. Growth in capital investment The State Program supports agricultural capital investment primarily through subsidies on interest rates for agricultural producers. The overwhelming majority of agricultural investment in 2018 was financed by farm own funds (52 percent) or through loans (48 percent) (Rosstat, 2020). The State Program supports agricultural investment through loans. In the fourth quarter of 2019, interest rates in Russia for farms averaged about twice that in the United States.16 In the United States during this period, farm loan interest rates averaged between 4.5 and 5.5 percent per year (Federal Reserve Bank of Kansas City, 2020) while interest rates on loans to small and medium-size nonfinancial organizations in Russia averaged about 9.5 to 10 percent during the same period (Central Bank of RF, 2020). Subsidized interest rates cut the Russian rate to 1 to 5 percent per year (Uzun, 2017b). However, Uzun (2018) found that only about one-sixth of credits advanced to agriculture were subsidized. Interest rate subsidies for farms make investment cheaper and, in that sense, they raise investment. However, the real value of agricultural investment in fixed capital (in constant 2005 prices) has followed an overall pattern based on the booms and busts in the Russian economy as a whole (Figure 23). Investment expanded rapidly as agricultural commodity prices were rising from 2005 to 2008, coinciding with the National Priority Project of 2006- 07. Investment then fell in 2009 and 2010 as the financial crisis hit Russia. The years 2011- 2013 saw a recovery of investment, followed by a decline in 2014-2015 as the economy slowed. The years 2016-2018 have seen a steady rise in investment. Figure 23. Investment in fixed capital in agriculture, hunting and forestry, 2005-2018 (billion RUB) 800 700 600 500 billion R 400 300 200 100 0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Investment (constant 2005 prices) Investment (nominal prices) Source: Rosstat, 2020. 16 Loan rates for agricultural borrowers tend to be higher than for other businesses. This is therefore the interest rate at the largest 30 banks for small and medium nonfinancial organizations, which is higher than for larger businesses in the fourth quarter of 2019. 81 H. The informal goal of the State Program While the overall level of agricultural investment may be higher as a result of State Program subsidies, a more important question is: Has agricultural support in the State Program contributed to the growth of the sector or has it been used to prop up underperforming farms? Funds for support are limited. Thus, there is a method for rationing these funds, which can be observed by analyzing the distribution of support. Support for agriculture in Russia is, by law, a function of the regions of the Russian Federation. The funds allocated by the federal budget of the Russian Federation are partially spent directly on implementation of certain measures (i.e., land reclamation, veterinary services), but much of federal spending is actually transferred to regional budgets as co- financing for regional expenses. For instance, in lines 9-12 of Table 27, in 2017, 72.9 billion RUB were administered and spent by the federal government for these purposes while the federal budget transferred 142.1 billion RUB (out of 214.1 billion) to regional budgets for co- financing of agricultural support projects. So, in 2017, the federal government administered only 72.9 billion RUB of support, while regional governments administered 270.9 billion RUB. In the situation of excess demand for support, regional governments themselves decide how to distribute support between producers (Shagaida and Uzun, 2017). Shagaida and Uzun analyzed the distribution of support by farm type, by region, and by size in 2015. Their analysis suggested, first, that support is skewed toward supporting agricultural enterprises. For every 1 ruble of sales, 6.7 kopecks of support were distributed to enterprises, 5.0 kopecks to family farms, and 0.3 kopecks to household farms. Household farms receive nearly no support under the State Program. Second, the regional distribution of support was quite uneven, and seemed to be designed to compensate producers with high costs of production. Producers in some northern regions such as Sakhalin or Sakha (Siberia) received 35.4 and 20 kopecks, respectively, per ruble of sales; while in Krasnodar krai, the heart of efficient Russian cereal production, producers received only 2.3 kopecks per ruble of sales. This distribution seems to indicate that the State Program is being used to offset the high cost of production disadvantages for farms in areas where there perhaps should not be any farming at all. Next, Shagaida and Uzun (2017) arrayed agricultural enterprises into three categories by value of subsidies received per farm (Table 50). The highest category, where each farm received over 101 million RUB (US$1.39 million in 2020) or higher, constituted 1.2 percent of agricultural enterprises. These 248 largest enterprises, most of which belonged to large agroholdings, received 41 percent of all state support distributed to enterprises. The sales per enterprise in this category were a whopping 2.9 billion RUB per farm (US$39.3 million in 2020).17 The lowest category of enterprises, where each farm received up to 11 million RUB per farm (US$141,000), constituted 86.4 percent of all enterprises and received only 19 percent of all state support distributed to enterprises. These were relatively small farm enterprises by Russian standards, with an average of 43.2 million RUB of sales per farm (US$555,000). In the United States, these farms would qualify as mid-size farms. The middle 17 This is considerably larger than farms in the United States. In the United States, the largest category of farms, very large farms, had sales of over US$5 million per farm and constituted 0.3 percent of the total number of farms. Altogether, they received less than 5 percent of payments for program crops in 2018 (USDA-ERS-farms, 2019). 82 category of enterprises, where each farm received between 11 and 101 million RUB, constituted 12.4 percent of enterprises and received 40 percent of subsidies. These mid- sized enterprises sold on average 351.3 million RUB per farm (US$4.8 million). In the United States, these farms would qualify as large farms, just shy of the very large farm cutoff of US$5 million. Table 50. Russian agricultural enterprises, by subsidies received in 2015 Subsidies per enterprise, million RUB Total 0-11 11-101 >101 Number of enterprises 17,501 2,502 248 20,254 --Share of total (%) 86.4 12.4 1.2 100 State support (million RUB) 32,528 69,637 70,833 172,998 --Share of total (%) 18.8 40.3 40.9 100 --Per 1 ruble of sales (RUB) 0.04 0.08 0.10 0.07 Sales (million RUB) 755,599 880,090 710,712 2,346,401 -- Share (%) 32.2 37.5 30.3 100 --Per enterprise (million RUB) 43.2 351.8 2,865.8 115.8 Profit before taxes (million RUB) 139,069 166,443 83,340 388,852 -- Share of total (%) 35.8 42.8 21.4 100 Profitability, without state support* (%) 16.4 12.4 1.8 10.1 Profitability, with state support* (%) 23 23 13 20 Note: 2,846 enterprises received no agricultural support in 2015 (14.1 percent of enterprises). Sources: Shagaida and Uzun, 2017; Uzun, 2017c. *Profitability is (profits/cost of production) X 100. Perhaps the most interesting fact of the Shagaida and Uzun (2017) analysis is that the category of recipients of state support that received the most per farm were the least profitable of all enterprises. Their profitability before state support was only 1.8 percent. On the other hand, the category which received the least support per farm was the most profitable with a profitability of 16.4 percent before support. This is further evidence that state support in the State Program is used to support unprofitable large farms. Shagaida and Uzun concluded that farm support in Russia is distributed predominantly based on the size of the enterprise. The largest enterprises, which make up the largest agroholdings in Russia, are favored by the regional authorities that distribute support. They conclude: “It is for this reason that growth in agriculture is ensured predominantly by the largest companies.� It is not that these are the most efficient or more competitive farms. They survive and thrive directly as a result of the state policy aimed at supporting them over more efficient and more profitable smaller farms. 83 III. Comparative perspective on Russia’s agriculture support measures Many governments around the world support agriculture for a variety of reasons. The Russian Government has its own set of desiderata in the State Program. Some of these goals differ from those in other countries. For instance, OECD countries do not focus policy so heavily on reducing dependence on imported food as their predominant interpretation of food security. However, these countries do have programs designed to support agricultural exports, just as in the Russian Federation. It is sometimes argued that the level of agricultural support in OECD countries far exceeds that in the Russian Federation and that this gives the large exporting countries an unfair advantage. Though total support to agriculture as a portion of agriculture value added is higher for some countries, this is not universally true. Moreover, the burden of agricultural support (as a share of GDP) in Russia tends to be higher than that in North America, though at the same level as in the European Union. But this the comparison is between 0.3, 0.5, and 0.7 percent of GDP, and it is difficult to say that these differences in level make a significant difference in their effect on agriculture. Institutional differences in how agricultural support programs are structured and administered are a source of much more significant differences between Russia and OECD countries. This section will focus on three institutional and structural features that distinguish agricultural support in the Russian Federation from that in OECD countries: distribution of agricultural support, vertical integration, and to what extent agricultural support policies support agricultural export. A. The cost of agricultural support in Russia The Russian Ministry of Agriculture often cites the portion of agricultural support in OECD countries as a share of agricultural value added in order to illustrate how OECD countries spend far larger sums than Russia to support agriculture. Indeed: total support to agriculture in 2018 as a portion of agricultural value added in the United States and the European Union was about twice as high as in Russia. In Canada, it was about 50 percent higher (Table 51). The degree of distortion caused by agricultural policies increases when the sector shrinks as a portion of GDP and incomes increase. However, the issue of which countries have higher support for agriculture is not as straightforward as this. The burden of agricultural support as a share of GDP was highest in China and Turkey, followed by Kazakhstan, Russia, and the European Union. This share was far lower (by about 40-50 percent) in the United States, Canada, and Brazil in 2018. So, the richer OECD countries with smaller agricultural sectors were able to support agriculture at higher levels than middle- (or high-) income countries with larger agricultural sectors. The burden of that support is often higher in the middle-income countries than in the richer OECD countries (though not always). As discussed earlier, support to agriculture can be delivered through higher domestic versus world prices or through budget transfers. Column (3) of Table 51 shows the burden of support to producers (including general services support which are budget transfers) 84 delivered as budget transfers as a share of GDP. On this measure of the burden of support, Russia is much closer to the United States, Canada, and Brazil than to middle-income countries such as China, Kazakhstan, and Turkey. Table 51. The level and burden of agricultural support in 2018, by country (%) Budget support to Total support estimate, producers Total support Agriculture value % of agricultural value (including general estimate, % of GDP added, % of GDP added services), percent of GDP* (1) (2) (3) (4) China 24.8 1.8 0.82 7.2 Turkey 21.7 1.3 0.56 5.8 Kazakhstan 20.3 0.9 0.66 4.4 Russia 23.2 0.7 0.30 3.1 EU 44.6 0.7 0.53 1.5 India 4.3 0.6 1.99 14.6 United States 55.8 0.5 0.19 0.9 Canada 31.1 0.3 0.20 1.1 Brazil 7.0 0.3 0.17 4.4 Ukraine 0.1 0.0 0.41 10.1 Argentina -1.7 -1.7 0.06 10.0 *OECD producer budget support as a share of GDP [(GSSE+PSE-MPS)/GDP]. Sources: OECD-STATS, 2020 (support); World Bank WDI, 2020; Statistics Canada, 2020; USDA-ERS-VA, 2020 (agricultural value added); and IMF, 2020 (GDP). In summary, total Russian support for agriculture as a portion of agricultural value added is about half that in OECD countries in the European Union, and in the United States (column 1, Table 51). However, the burden that such support presents for the economy is higher than in the United States, Canada, and Brazil, although lower than in China and Turkey (column 2). But Russian support is much more in line with that in the United States and Canada and far less than that in Turkey, China, and even Kazakhstan with regard to the share of overall producer support that is reflected in in the government budget. B. Distribution of agricultural support The goals of agricultural policy are the result of political decisions, and the rules for the distribution of agricultural support are the results of a political process. OECD countries, including Canada, the European Union, and the United States have set policy and budgets for agriculture every 5 to 7 years to avoid annual negotiations. The rationale behind such policy planning is that farmers, who are already subject to highly variable weather and commodity prices, can benefit from policy stability over the 5-year term. Stability is also achieved by ensuring that most farm spending is distributed according to rules-based programs with explicit eligibility requirements. 85 The distribution of agricultural support payments in the Russian Federation differs from this model. First, annual budget appropriations to a large extent dominate the five-year authorizing legislation, meaning that agricultural budgets are renegotiated every year to a greater degree than in OECD countries. Second, support payments in the Russian Federation, though they are authorized within specific programs, seem to be distributed in a more discretionary manner than the rules-based programmatic spending that takes place in OECD countries. 1. The political process of defining and distributing support When the State Program 2013-20 was first announced, the government issued a so-called “passport� which indicated the authorized levels of funding for the Program (GRF, 2012). This is similar to the United States “authorizing legislation;� but unlike in the United States, the actual spending levels specified in this legislation are subject to year-to-year negotiation. In Canada, the European Union, and the United States, a high share of resources authorized are actually mandatory, meaning that during implementation of the legislation, they are not subject to year-to-year budgetary discussions (Table 52). Moreover, in Canada, the European Union, and the United States support is distributed according to federal and provincial (in the case of Canada) programmatic payments. If a producer qualifies for payments and submits an application, the payments are made according to the rules of the program. Shagaida and Uzun (2017) maintain that subsidy payments in Russia, though they may be part of federal programs, are in fact rationed by regional authorities. In other words, some producers may qualify for payments but do not receive them due to limits. Table 52. Comparison of farm policy legislation in Russia and selected OECD members Russia US Canada EU How is farm Annual federal budget Authorizing Authorizing Authorizing legislation legislation appropriations federal federal and proposed by the budgeted? dominate; Authorizing legislation provincial European Commission legislation every 5 every 5 years legislation every and approved by years 5 years Council every 7 years Mandatory No High portion High portion High portion spending? Participation of Yes No Yes No regions in financing of budget payments? Distribution of Predominantly at Federal Federal, By member countries support? regional level via programmatic provincial via programmatic (informal) rationing payments programmatic payments payments Do authorities Yes No No No impose limits on payments under the programs (besides program eligibility)? Source: Adapted from Shagaida and Uzun, 2017; Uzun, 2017c; Novak and others., 2015; Van Kooten, 2018; European Parliament, 2020. 86 The priority of regional authority distribution according to size considerations and the lack of stable, guaranteed programmatic funding have profoundly shaped not just the State Program, but Russian agriculture. Shagaida and Uzun (2017) argue that the State Program distorts competition within Russia, propping up the largest firms and putting smaller, more profitable and efficient farms at a competitive disadvantage. 2. Outcomes for large farms It is well-known that support payments to producers in OECD countries are distributed predominantly to larger producers because the size of payments is connected with the physical size of the farm. For instance, in the European Union in FY2017, 24 percent of beneficiaries received 84.9 percent of direct aids to farms (European Commission, 2019). Presumably, the farms with the highest sales received this aid, but the European Union does not publish tables by the economic size of the farm but rather by the size of direct payments. However, it is possible to construct a standardized table for United States and Russian farms by economic size in order to assess the portion of support payments distributed to the largest farms. Table 53. Share of largest producers in Russia (RF) and US, by sales, and their government payment receipts Farms responsible for: Total number of farms 10% of total 25% of total 50% of total sales sales sales RF US RF US RF US RF US 1 Total number of farms 70,680 2,109,303 15 466 101 4,326 715 33,330 2 -- % of total farms 0.02 0.02 0.1 0.2 1.0 1.6 3 Sales (billion USD, billion RUB) 1,537 395 151 39 383 99 768 197 4 -- % of total sales 10 10 25 25 50 50 5 Number of farms receiving 6,629 811,387 15 76 101 1,702 703 20,753 government payments 6 -- % farms receiving 9 38 0.2 0.01 1.5 0.2 10.6 2.6 7 Government payments (million 210.9 8,053.3 8.8 3.2 34.0 96.3 72.9 1,019.9 USD, billion RUB) 8 -- % of total payments 4.2 0.04 16.1 1.2 34.6 12.7 Source: Uzun, 2017c, based on the 2012 US Agricultural Census, table 44 (NASS Census 2012, 2020) and Ministry of Agriculture of Russia database for 2013 [family (peasant) farms and enterprises only]. Table 53 illustrates that the distribution of government payments to farms in Russia is considerably more skewed in favor of economically large farms than in the United States. The table compares total number of farms, total sales, number of farms receiving government payments, and amount of these payments for the United States and the Russian Federation. Farms are arrayed into three cumulative groups that encompass the largest farms by economic size and which together account for 10, 25, and 50 percent of total sales of the sector. 87 The universe of farms considered here are those that are potentially eligible for government payments. In the United States, these are all farms covered under the census; while in Russia, this is only family (peasant) farms and enterprises, without household farms (shown under “Total number of farms� in Table 53). Household farms in Russia numbered 17.5 million according to the 2016 agricultural census (Rosstat, 2018), and are excluded from this analysis. Of the universe of farms in the two countries, 9 percent of farms in Russia received support and 38 percent of farms in the United States (line 5 as a share of line 1). The second two columns of the table cover the largest farms in each of the two countries accounting for 10 percent of total sales. In Russia, these farms received 4.2 percent of government payments, in the US 0.04 percent. The third pair of columns of the table indicate that farms responsible for 25 percent of total sales in Russia received 16.1 percent of government payments, while in the US this figure was 1.2 percent. Finally, farms responsible for 50 percent of sales in Russia received 34.6 percent of government payments, while in the US this share was 12.7 percent.18 Uzun (2017c) cited numerous instances of a small number of agricultural enterprises receiving huge shares of agricultural subsidies under multiple programs. In 2016, the Bryansk Meat Company within the agroholding Mirotorg received 90.7 percent of subsidized credits allotted under the program on meat livestock farming. The remaining credits were distributed among three other meat producers. Uzun also shows that the overall inequality in the distribution of state support has been increasing every five years between 1995 and 2015. Moreover, since 2017, plans for subsidy distribution have included earmarks for individual producers. For instance, the 2017 investment program for small businesses includes distribution allotments that can only be understood as pertaining to individual or a small number of businesses. How is it that the largest farms in the United States and European Union have not secured the largest share of government payments? First, this is neither an explicit goal of support policies in the United States nor a goal of support policies in the European Union. Although the goals of the US Farm Bill tend to change with each iteration, its main overall purposes are to insure producers against risks, whether due to weather, price, or even trade policy (Novak and others, 2015). For the European Union, the 1957 founding Treaty of Rome noted stabilization of markets, increasing agricultural productivity, a fair standard of living, and food availability as important objectives of the Common Agricultural Policy (Thomson, 2018). Second, both the United States and the European Union have introduced limits on payments to individual producers. The rationale for such limits is that larger farms are in less need of support since they enjoy the benefits of economies of scale. The United States has had limits on the size of government payments to individual producers since 1970 (CRS, 2019). Currently, persons with a farm and non-farm adjusted gross income in excess of US$900,000 18 The 2017 US agricultural census showed even less concentration of government payments. In that year, the largest farms responsible for 10 percent of total sales received 0.05 percent of payments, those responsible for 25 percent of sales received 0.7 percent, and those responsible for 50 percent received 9 percent of government payments (NASS Census 2017, 2020). 88 are excluded from most farm programs. In addition, most programs have a payment limit to individuals of US$125,000 (CRS, 2019). In the European Union, a 2013 regulation directed Member States to reduce “by at least 5%� the part of the basic payment to be granted to farmers that exceeds 150,000 EUR (Publications Office of the European Union, 2013). In 2020, Russia has no limitations on government payments to individual recipients. According to Uzun (2017c), agricultural policy through the concentration of subsidies has created an exclusive competitive advantage for a select group of large farms, leading to the crowding out of the market of small and medium-size businesses. C. Vertical integration in Russia and OECD countries The preceding characterization of the distribution of agricultural support in Russia implies that it is no accident that agroholdings increased in importance between 2006 and 2016 (Table 54). In 2006, enterprises inside agroholdings were responsible for 26.5 percent of total sales in Russia. By 2016, this share had doubled to 53.5 percent. Agroholdings also increased their share of agricultural employees from 23 to 41 percent and landholdings from 20 to 30 percent. Moreover, state support seems to be skewed in their favor: 17 percent of enterprises in agroholdings received 50 percent of state support, and support per agroholding enterprise was five times higher than that outside of agroholdings. Table 54. The importance of agroholdings in Russian agriculture, 2006 and 2016 Share of enterprises in Agricultural enterprises Independent agroholdings among all in agroholdings enterprises agricultural enterprises (%) 2006 2016 2006 2016 2006 2016 Number of agricultural 3,491 3,366 13,365 16,226 20.7 17.2 enterprises Number of employed (1,000) 505 528 1,660 754 23.3 41.2 Agricultural land (1,000 ha) 17,378 25,195 68,638 58,093 20.2 30.3 Sales revenue (billion RUB) 142.4 1,364.3 394.0 1,184 26.5 53.5 Sales per worker (RUB) 282 2,584 237 1,570 Profits (billion RUB) 14.7 273.6 43.8 217.4 25.1 55.7 Profits per worker (RUB) 29 518 26 288 State support (billion RUB) n.a. 86.8 n.a. 85.7 n.a. 50.3 State support per enterprise 25.8 5.3 (million RUB) Profitability (%) 11.5 25.1 12.5 22.5 (profit/production costs) Source: Shagaida and others (2019). It is not possible to say that agroholdings have performed badly altogether. It is clear from Table 54 that, on average, enterprises in agroholdings had higher labor productivity (sales 89 revenue per worker) and higher profits per worker in both 2006 and 2016. While this was marginally true in 2006, by 2016 labor productivity in agroholdings was twice that outside of them. However, in 2016, profitability was only marginally higher in agroholdings. Why is the dominance of agroholdings in Russian important? The movement toward concentration of production in a smaller number of large farms is not problematic per se. OECD countries themselves are moving toward larger farms as well though not as agroholdings and not through policies that promote support for the largest farms. In the United States, the portion of farms with cropland of over 2,000 acres (809 hectares) increased from 24 to 34 percent between 2001 and 2011 (McDonald and others 2013). However, the movement toward larger farms in OECD countries is mostly driven by market forces and not by policy. There are two issues which should concern policymakers in Russia that are associated with the rise of agroholdings. First, an analysis of Russian regions arrayed by the share of total production in agroholdings in 2016 showed that those regions with the highest portion of output produced in agroholdings had the highest level of loss of rural population and employment between 2006 and 2016 (Table 55). This is consistent with the data of Table 54. Enterprises in agroholdings tend to lay off labor in the regions where they predominate. However, with higher labor productivity, agroholdings may be able to offer higher wages. Table 55. Share of agroholdings in the gross agricultural output of Russian regions, 2016 Share of agroholdings in GAO of the region (2016) >10% 10-25% 25-50% >50% Total Number of regions 27 24 21 8 80 Share of agroholdings in gross agricultural 5.4 16.9 34.2 60.9 26.9 output of region (%) Rural population in 2016 (2006=100) 107.6 93.3 96.4 88.5 97.6 Agricultural employment in 2016 90.2 74.1 74.8 62.5 76.9 (2006=100) Source: Shagaida and Uzun, 2019. Second, agroholdings are vertically integrated organizations which combine agricultural producers into a centralized system of supply of agricultural inputs and marketing of agricultural outputs. Vertical integration in agroholdings thus solves a number of problems of agriculture. In an environment where farmers are unskilled at operating within a market, an agroholding can provide a vertical system that allows an enterprise to produce without being concerned about buying and selling. Agroholdings can also bring much needed capital to agriculture from outside the sector. In a situation when agriculture was starved for capital in the 2000s, agroholdings provided a way to bring in new capital. However, full vertical integration, which involves company-owned farms, is only one way of improving the supply of agricultural inputs and marketing of outputs. Another means widely practiced in Western market agriculture is contract farming (Rehber, 2000). Contract farming is agricultural production carried out according to an agreement between a buyer (integrator) and farmers, which establishes conditions for the production and marketing of a farm product or products. Contract farming differs from full vertical integration in the 90 sense that the contracting farms are not owned by the integrator. However, contract farming also differs from spot markets in which farmers produce and sell on markets each year without contracts. The latter is similar to the situation of smallholders in many countries and in Russia as well. In contract farming, a producer pledges to provide an agreed quantity of an agricultural product according to quantity and quality standards and at a time set by the purchaser. In turn, the buyer commits to purchase the product and to support production through supplying technical advice, inputs, and other services (such as land preparation and harvesting). In the United States, 34 percent of commodities were produced under contract farming; and in Japan, 89 percent of chicken was produced under contract farming as early as 1989 (Rehber, 2000). Contract farming is particularly important, even dominant, in livestock production. Table 56 shows that the highest value products—sugar beets, fruits, vegetables and particularly livestock—tend to be produced under contract farming while low-value standardized products such as wheat, soybeans and corn are produced on spot contracts. Livestock tend to be produced under contracts in which the contractor owns the commodity during production (the chicks, the hog, or eggs), and the farmer is remunerated for “growing out� services. The contractor often provides raw materials and technical advice, and contracts usually have incentive clauses that specify a sliding price scale based on the productivity performance of the grower. Table 56. Contract farming in the United States, by commodity, 1996/97 and 2017: Share of value of production under marketing or production contracts % of value production Commodity 1996/97 2017 All commodities 32 34 All crops 23 21 Wheat 9 9 Soybeans 14 17 Corn 13 13 Vegetables 39 39 Fruits 57 46 Sugar beets 75 89 Tobacco 0 90 All livestock 45 49 Cattle 17 32 Dairy 58 41 Hogs 34 63 Poultry and eggs 84 90 Source: USDA-ERS-farms, 2018. 91 The decision between company-owned farms and contract farming depends on balancing off transaction costs. Company-owned farms may be preferable when it is impossible to procure a reliable supply of raw materials of an acceptable cost and quality. However, company- owned farms, particularly large ones in Russia, have immense monitoring costs and diminished incentives for quality production compared to family farms. Contract farming for family farms have the advantage of high incentives and low monitoring costs. They are preferred by processors because they allow greater specialization of processes with consequent cost reductions. Farmers and processors are allowed to do what they do best. The agroholding model would not seem to exclude contract farming. The addition of contract farming would only seem to provide an additional source of raw materials that could be gradually expanded as demand dictates. The contract farming model has additional advantages over the full vertical integration model. It could facilitate a more widespread dissemination of technical change in agriculture, increased opportunities for smaller family farms and thus higher incomes for rural communities and wider improved links between processing and farming. World Bank (2017) found that agro-processing in Russia is surprisingly underdeveloped. The value added of food processing in Russia is about half the value added of agriculture while this portion is much higher among competitors in Europe and North America. This is peculiar because Russian food processing seems to have good growth prospects since its productivity growth exceeds productivity growth in the manufacturing sector as a whole. World Bank (2017) found two factors holding back the development of the food processing sector in Russia. First, there is a shortage of skilled labor in the Russian countryside, where many food processors are located. Second, domestic value chains and backward linkages to agriculture are considerably weaker than expected. As a result, the food processing sector relies heavily on imported raw materials. In short, instead of concentrating the returns to capital in large agroholdings, contract farming has the potential to allow opportunities for more widespread economic prosperity within the countryside. Though the State Program has a (relatively small) component on territorial development, such programs themselves are not enough to ensure thriving rural communities. D. Barriers to agricultural exports The State Program emphasizes the export of agricultural commodities, adopting the strategic goal of increasing the value of agri-food exports from US$21.6 billion in 2017 to US$45 billion in 2024. The National Project on International Cooperation and Export emphasizes the export of high value, processed products. While Russian agriculture was on target with US$24.8 billion of exports in 2019, agri-food exports are dominated by cereals and the share of processed products was about 30.5 percent in 2019 (ITC Trade Map, 2020). The National Project also focuses on improving veterinary health to compartmentalize veterinary diseases; obtain World Organization for Animal Health (OIE) recognition of geographic disease-free zones; and obtain veterinary certificates for the export of poultry meat, pork, beef, milk products, and animal feed. These are long-overdue changes in policy that will support Russia’s ambitions of becoming a world-class exporter of agri-food 92 products. However, two agricultural policies will likely make the move toward high-value products a challenge: (1) predominance of private support rather than public good support in agricultural support and (2) high domestic prices for sugar and livestock products. 1. Focus on private support rather than public goods OECD provides an overall estimate of the total support to agriculture, which is composed of support to producers; support to agriculture through funding public services such as extension, research, sanitary and phytosanitary inspection, and other services; and budget support to consumers (of agricultural commodities) such as mill subsidies, food stamps, school milk and other child nutrition programs. Producer support includes both direct budget transfers to producers and the value of implicit support to producers afforded by domestic prices that exceed world prices (so-called market price support). The analysis of Table 27 showed that the sum of State Program expenditures was near to producer subsidies without market price support. Table 57 shows total support to agriculture by recipient. The second column represents the share of total support received by producers, the third column shows the share devoted to agricultural public services, and the fourth shows the share of total support transferred from taxpayers (state budgets) to consumers of agricultural commodities. Russian support to general services in Table 57 is somewhere in the middle between the traditional agricultural exporters such as the United States and the European Union and the newer exporter, Brazil. The overwhelming majority of total support in Russia goes to producers, just as in the European Union, China, and Canada. This conclusion was corroborated in the analysis of Table 42 based on the direct producer support data from the Ministry of Agriculture for 2012-2020. Table 57. Structure of total agricultural support in selected countries, by recipient, 2018 (%) Budget support Funding for Support to producers to consumers of agricultural public goods (PSE) agricultural commodities (GSSE) (TCT) US 44.6 9.3 46.1 EU 89.4 10.2 0.4 China 85.0 15.0 0.0 Russia 83.0 15.2 1.8 Canada 73.1 26.5 0.4 Brazil 40.3 39.0 20.7 Source: OECD-STATS, 2020. Note: PSE: Producer support estimate; GSSE: general service support estimate; TCT: support to consumer For a country with plans to increase exports of high-value goods to new markets, the portion of support expenditures on general services may appear low. In fact, the level of GSSE investment in Russia has been consistently low compared to other new exporters outside of the Eurasian region such as Brazil. The neglect of investments in agricultural public goods limits the beneficial effects of private state subsidies and stands in contrast to the success stories of other countries. Most countries that have achieved significant increases in agri- food productivity did this through large investments in public goods such as agricultural 93 research and development, education, agricultural advisory services, and veterinary health. While advanced Russian agricultural enterprises in pork, milk, and corn/soybean production are catching up with international comparators in terms of costs and productivity, private good subsidies have not yet resulted in broad-based productivity gains for the whole industry (World Bank, 2017). Veterinary issues are a significant obstacle for Russian exporters to many countries. By end of 2019, only 12 countries allowed the import of Russian livestock products (Rossel’khoznadzor, 2018).19 According to the information analytical center of Rossel’khoznadzor, the epidemiological situation of the animal population is unfavorable for 14 animal diseases and uncertain for another 2. Furthermore, 8 livestock diseases, including African Swine Fever, Avian influenza, Newcastle Disease, Brucellosis, and Tuberculosis, are endemic in the Russian Federation; and one (Classical Swine Fever) is endemic in Primorskii krai (Table 58). Table 58. Veterinary situation in Russia, 2019 Veterinary disease Animal Epidemiological Disease prevalence state of animal population Newcastle disease cattle Unfavorable Endemic Lumpy skin disease cattle Unfavorable Since 2015 Foot and mouth cattle Unfavorable OIE official status, zone free disease of FMD without vaccination Tuberculosis cattle Unfavorable Endemic Cervical leukemia cattle Unfavorable Endemic Blue tongue cattle Unfavorable Anthrax cattle, sheep, goats, Unstable Stable presence due to soil camels sources of infection African swine fever hogs Unfavorable Endemic since 2007 Classical swine fever hogs Unfavorable Endemic in Primorskii krai Aujesky’s disease hogs Unstable Endemic Equine influenza horses Unstable No known cases, 1965 to 2006 Avian flu poultry Unfavorable Endemic Brucellosis Ruminants: cattle, Unfavorable Endemic sheep, goats Sheep pox Sheep and goats Unfavorable In 2018, 12 outbreaks Rabies Wild and domestic Unfavorable Prevalence is trending down animals since 2004 Leptospirosis Wild and domestic Unfavorable Endemic animals Source: Information analytic center of Rossel’khoznadzor, 2019. The problem is not only the presence of animal disease in the Russian Federation. According to Rossel’khoznadzor, regional-level veterinary services of the country are not able to meet the international standards and requirements set by the World Organization for Animal Health (OIE). Furthermore, there is a need to change current Russian legislation on veterinary issues to create a state registry of exporter enterprises; ensure snap inspections 19 Japan, Vietnam, Lebanon, South Korea, Saudi Arabia, Bahrain, Qatar, UAE, Serbia, Morocco, Brazil, and Singapore. 94 of processors, farms, and other enterprises under the authority of Rossel’khoznadzor or Rospotrebnadzor; and to develop national programs for the elimination of particularly dangerous animal diseases (Rossel’khoznadzor, 2019). Finally, there is no traceability system for the use of veterinary drugs in the Russian Federation (Rossel’khoznadzor, 2019b). 2. Most producer support funded through higher domestic consumer prices About 70 percent of support to Russian agricultural producers is paid for by consumers through higher prices for food. Although this share of producer support paid by consumers is similar to that of other new exporters such as Turkey, Brazil and China, it is far higher than the share in the European Union and the United States (Table 59). Table 59. The structure of producer support in selected countries, 2018 Canada Russia US EU China Brazil Turkey Producer support (PSE), total 100 100 100 100 100 100 100 Of which: (1) MPS 54.1 69.0 31.7 21.3 63.9 61.9 66.9 Budget transfers: (2) Payments based on 0.0 2.5 17.4 0.5 2.4 0.1 11.7 output (3) Payments based on input 8.9 21.7 19.9 13.7 10.9 36.5 9.2 (4) Payments based on area, 36.8 6.5 25.9 63.4 21.8 1.4 12.2 animal numbers, etc. (5) Other payments 0.3 0.3 5.0 1.1 1.1 0.0 0.0 Subtotal: Highly production- and trade-distorting support 63.0 93.2 69.0 35.4 77.1 98.6 87.8 [% of producer support in (1), (2), and (3)] Source: OECD-STATS, 2020. Russian import tariffs for meats, dairy products, and sugar are highly protective (Table 60). Average import tariffs for these products ranged from 9 to over 50 percent in 2018. In addition, Russia has banned imports of most food products from Australia, Canada, the European Union, Norway, Ukraine, and the United States since 2014 (FAO, 2014). These two factors have led to a situation where Russian domestic prices for livestock and sugar products exceed world market prices (Table 60)). High domestic prices for producers have a detrimental effect on exports. If domestic prices offered for agricultural products are higher than in international markets, it is difficult to understand why Russian processors would export rather than sell their products domestically. Table 60 shows the producer nominal protection coefficient—the ratio between the average domestic price received by producers (at farm gate), including subsidy payments per ton of current output, and the international price (measured at farm gate). For livestock products and sugar, for which Russia is a net importer, the international price is the import price. For cereals and sunflower seeds, for which Russia is a net exporter, the international price is the export price. Table 60 illustrates that producers of sugar, milk, beef 95 and veal, pork, and poultry meat do not have incentives to export since the price they can receive domestically is higher than they would receive on international markets. Table 60. Producer nominal protection coefficients in Russia, 2013-2018 Ave tariff 2013 2014 2015 2016 2017 2018 rate, 2018 Wheat 0.99 0.86 0.92 1.00 0.95 0.92 4.7 Barley 0.87 0.82 0.83 0.99 0.96 0.84 4.7 Maize 1.02 0.90 0.93 0.88 0.97 0.98 0.1 Sunflower seeds 0.78 0.79 0.91 0.90 0.89 0.93 2.6 Refined sugar 1.16 1.64 1.40 1.17 1.56 1.77 21.5 Milk 1.05 1.15 1.28 1.58 1.27 1.50 9.2 Beef/veal 1.61 1.23 1.37 1.21 1.20 1.27 17 Pork 1.46 1.70 1.27 1.20 1.18 1.10 41.4 Poultry meat 1.22 1.01 1.07 1.04 1.14 1.08 51.3 Note: The nominal protection coefficient of a commodity is the ratio of the domestic producer price to the international reference price measured at the farm gate. Source: OECD STATS, 2020; ITC Trade Map, 2020 (average tariff rate). The high nominal protection coefficients for meat and sugar mean that Russian producers of these commodities are not used to the competition of world markets. The domestic prices of the significant global meat exporters hover near world market prices, maintaining incentives for export (Table 61). The United States was the largest global exporter of beef and pork while Brazil was the largest exporter of poultry. Australia was the number two exporter of beef. The low nominal protection coefficients for wheat, barley, and maize indicate that Russia is highly competitive in these products. Table 61. Producer nominal protection coefficients for meats and cereals in selected countries, 2018 Australia United States Brazil Russia Beef and veal 1.00 1.00 1.00 1.27 Pork 1.00 1.01 1.00 1.10 Poultry meat 1.00 1.00 1.00 1.08 Wheat 1.00 1.00 1.02 0.92 Barley 1.00 1.00 0.84 Maize 1.00 1.00 0.98 Source: OECD STATS, 2020. 96 IV. Conclusion Russian agriculture has performed admirably in the past few years but operates at below its potential. Disproportionate reliance on large agricultural enterprises belonging to vertically integrated agroholdings has restrained agricultural growth through: • limiting producer-processor relations to a single model of integration, • restricting the dissemination of technical change, • neglecting the development of a state-of-the-art system of veterinary services and control, and • subsidizing the largest, often less profitable, farms and putting smaller, more profitable and efficient farms at a competitive disadvantage. This report has laid out the history of the State Program for Development of Agriculture in Russia and detailed the ways in which it plays a key role in the reliance on large agricultural enterprises belonging to vertically integrated agroholdings. The distribution of State Program expenditures prioritizes direct financial support to the largest producers in agroholdings rather than broad public service investment. The State Program distorts competition within Russia, propping up the largest firms and putting smaller at a competitive disadvantage. However, it does not have to be this way. The State Program can be modified to stimulate growth in agriculture more broadly. Adjusting the State Program in this way would not necessarily diminish the dominance of agroholdings or slow the growth of the competitive and efficient livestock producers in the country. These producers do not need government support to thrive since they already enjoy economies of scale and up-to-date technology. But a modified State Program could benefit the smaller producers who do not enjoy such economies of scale. The first step toward more broad-based growth is acknowledging the issue. The currently lopsided support of agroholdings does not stimulate broad-based agricultural growth. Rather, it drives out small and medium-size businesses in the sector, suppressing overall growth. In practical terms, while not exhaustive, the following modifications of the State Program could support a shift toward more broad-based growth in agriculture: • Increase the portion of federal support spending devoted to general support service programs intended to make agronomic and marketing support services available to all farms (this should also include more support for Rossel’khoznadzor and Rospotrebnadzor); • Introduce limitations on maximum annual support payments for a single beneficiary from the State Program, similar to those found in OECD countries; • Review and adjust interest rate subsidy programs towards broader access to finance for all farm types and sizes; • Reformulate the programmatic goals of the State Programs, emphasizing insurance against the systematic risks, including climate change risk, encountered by agriculture, available to all producers on a five-year programmatic basis; • Restrict the distribution of government support to federal government programs; 97 • Support efforts to work in networks with medium-size farming businesses as support earmarked for agroholdings decreases. 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