1、Agricultural policy monitoringAgricultural policy monitoringfor eight countries for eight countries in Eastern Europe,Caucasus in Eastern Europe,Caucasus and Central Asiaand Central AsiaAgricultural policy monitoringAgricultural policy monitoringfor eight countries for eight countries in Eastern Eur

2、ope,Caucasus in Eastern Europe,Caucasus and Central Asiaand Central AsiaFood and Agriculture Organization of the United NationsRome,2022The designations employed and the presentation of material in this information product do not imply the expression of any opinion whatsoever on the part of the Food

3、 and Agriculture Organization of the United Nations(FAO)concerning the legal or development status of any country,territory,city or area or of its authorities,or concerning the delimitation of its frontiers or boundaries.The mention of specific companies or products of manufacturers,whether or not t

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11、ucts are available on the FAO website(www.fao.org/publications)and can be purchased through publications-salesfao.org.Requests for commercial use should be submitted via:www.fao.org/contact-us/licence-request.Queries regarding rights and licensing should be submitted to:copyrightfao.org.Cover photog

12、raph:FAO/Anatolii StepanovRequired citation:FAO.2022.Agricultural policy monitoring for eight countries in Eastern Europe,Caucasus and Central Asia.Rome https:/doi.org/10.4060/cc2637eniiiPreparation of this document In support of FAO,the Agricultural Institute of Slovenia(AIS)coordinated the data co

13、llection and carried out the calculations of the indicators.A country expert was assigned for each country to assist with the data collection process and provide background information on markets and policies to help interpret the indicators.FAO provided methodological support to the AIS team and th

14、e country experts.Focal points nominated by the governments of the eight countries provided additional support for the data collection and interpretation efforts and peer-reviewed country results and various drafts of the report.A methodology workshop with the country experts was held in Tbilisi,Geo

15、rgia on 56March2018.The first preliminary results were presented at a scientific conference organized by the Leibniz Institute of Agricultural Development in Transition Economies(IAMO)and the Institute of Scientific Research on Economic Reforms(ISRER)in Baku,Azerbaijan on 67September2018.A review se

16、ssion with the government focal points and other experts from the region was conducted at the fourth annual meeting of the Agricultural Trade Expert Network in Europe and Central Asia in Odessa,Ukraine on 1113September2018.The results of the first round of indicator calculations were presented to,an

17、d peer-reviewed by,government officials from the eight countries at a final workshop held in Minsk,Belarus on 23October2019.At the workshop,participating officials indicated the strong interest of their respective governments in the continuation of quantitative policy monitoring in support of eviden

18、ce-based decision-making processes at the country level.The results of the subsequent1 indicator estimates in 2020 were presented and validated by country experts and government focal points at a workshop held virtually in October 2020.This report is organized as follows.Chapter 1 provides a brief o

19、verview of key developments in agricultural trade in the eight countries and agricultural and trade policies in the region.Chapter 2 describes the data requirements and the methodological approach used to calculate the policy indicators.Chapter 3 presents the key results at the regional level,while

20、Chapter 4 examines the detailed study results by country.1 Tajikistan and Uzbekistan presented their first round of indicators,as they entered the study at a later point than the other countries.vContentsPreparation of this document .iiiForeword .ixAcknowledgements.xAcronyms,abbreviations and symbol

21、s.xiExecutive summary.xiiiChapter 1.Agricultural market and policy developments in the countries.11.1 Key market developments and main trading partners.11.2 Trade agreements.21.3 Trade and agricultural policies.3Chapter 2.Methodology.52.1 Calculation of the Nominal Rate of Protection(NRP)and Nominal

22、 Rate of Assistance(NRA).52.2 Selection of commodities and data requirements.72.3 Trade data analysis:net trade status and trade intensity.82.4 Determining the reference price.82.4.1 Alternative border prices.92.5 Budgetary transfers and their classification.10Chapter 3.Regional analysis of agricult

23、ural price distortions and budgetary transfers.133.1 Nominal Rates of Protection.133.2 Budgetary and other transfers to agriculture.16Chapter 4.Country analysis of agricultural price distortions and budgetary transfers.214.1 Armenia.214.1.1 Nominal Rates of Protection.214.1.2 Budgetary transfers.234

24、.2 Azerbaijan.244.2.1 Nominal Rates of Protection.254.2.2 Budgetary transfers.274.3 Belarus.284.3.1 Nominal Rates of Protection .284.3.2 Budgetary transfers.314.4 Georgia.324.4.1 Nominal Rates of Protection .324.4.2 Budgetary transfers.354.5 Kyrgyzstan.364.5.1 Nominal Rates of Protection.374.5.2 Bud

25、getary and other transfers to agriculture.384.6 Republic of Moldova.394.6.1 Nominal Rates of Protection.404.6.2 Budgetary transfers.434.7 Tajikistan.444.7.1 Nominal Rates of Protection.454.7.2 Budgetary and other transfers to agriculture.464.8 Uzbekistan.474.8.1 Nominal Rates of Protection.484.8.2 B

26、udgetary and other transfers to agriculture.51References .53Annexes .57Annex 1:Main trading partners of study countries by analysed commodities.57Annex 2:Key agricultural commodities analysed for each country .61Annex 3:Data sources by country .67Annex 4:Alternative reference prices at farm gate.69A

27、nnex 5:Nominal Rates of Protection(percent)by country and product.70viFiguresFigures1.Value of gross agricultural production and agrifood foreign trade,aggregate of the study countries,20052018.12.Net agricultural exports by country(constant 2015 billion USD),20152018.23.Average agricultural applied

28、 tariff rates,MFN simple average(percent),20052018.44.Weighted aggregate Nominal Rates of Protection by countries at farm gate(percent),calculated for the 20142016 period.145.Aggregate Nominal Rates of Protection by commodity group,livestock versus crops(percent,unweighted average),20142016 .156.Tot

29、al budgetary and other transfers to agriculture(BOT in million USD),20112018.167.The volume of budgetary and other transfers to agriculture(BOT in million USD and as a percent of value of production).178.Budgetary and other transfers to agriculture calculated per hectare of agricultural area(USD/ha)

30、.189.Composition of the total budgetary and other transfers(BOT)by economic group and the composition of transfers to producers by main categories(PSE BOT),average 20162018.1810.Armenia:Average aggregate Nominal Rate of Protection at farm gate(percent,weighted average),20072018.2211.Armenia:Nominal

31、Rates of Protection and Nominal Rate of Assistance by key commodities(percent),and prices of potatoes and grapes at farm gate(USD/tonne),20052019.2312.Armenia:Budgetary and other transfers to agriculture by economic group of beneficiaries,20072018.2413.Azerbaijan:Average aggregate Nominal Rates of P

32、rotection at farm gate(percent,weighted averages),20052016.2514.Azerbaijan:Nominal Rates of Protection and Nominal Rate of Assistancee by key commodities(percent),and prices of potatoes at farm gate(USD/tonne),20052018.2615.Azerbaijan:Budgetary and other transfers to producers,20072018.2716.Belarus:

33、Average aggregate Nominal Rates of Protection at farm gate(percent,weighted averages),20102018.2917.Belarus:Nominal Rates of Protection and Nominal Rate of Assistance by key commodities(percent),and prices of potatoes at farm gate(USD/tonne),20052018.3018.Belarus:Budgetary and other transfers to agr

34、iculture by economic group of beneficiaries,20112018.3119.Georgia:Average aggregate Nominal Rates of Protection at farm gate(percent,weighted average),20082018.3320.Georgia:Nominal Rates of Protection and by key commodities(percent),and prices of potatoes,grapes and eggs at farm gate(USD/tonne),2006

35、2018.3421.Georgia:Budgetary and other transfers to agriculture by economic group of beneficiaries,20072018.3522.Kyrgyzstan:Average aggregate Nominal Rates of Protection at farm gate(percent,weighted average),20102016.3723.Kyrgyzstan:Nominal Rates of Protection by key commodities(percent),and prices

36、of potatoes and sheep meat at farm gate(USD/tonne),20052019.3824.Kyrgyzstan:Budgetary and other transfers to agriculture by economic group of beneficiaries,20092018.3925.Republic of Moldova:Average aggregate Nominal Rates of Protection at farm gate(percent,weighted average),vii20052018 .4026.Republi

37、c of Moldova:Nominal Rates of Protection by key commodities(percent),and prices of potatoes and grapes at farm gate(USD/tonne),20052018.4227.Republic of Moldova:Budgetary and other transfers to agriculture by economic group of beneficiaries,20072018.4328.Tajikistan:Value of gross agricultural produc

38、tion and agrifood foreign trade,20052018.4429.Tajikistan:Average aggregate Nominal Rate of Protection at farm gate(percent,weighted average),20052012.4530.Tajikistan:Nominal Rates of Protection by key commodities(percent),and prices of potatoes;sheep meat and eggs at farm gate(USD/tonne),20052017.46

39、31.Tajikistan:Budgetary and other transfers to agriculture by economic group of beneficiaries,20072018.4732.Uzbekistan:Value of gross agricultural production and agrifood foreign trade,20052018.4833.Uzbekistan:Average aggregate Nominal Rate of Protection at farm gate(percent,weighted average),200520

40、18.4934.Uzbekistan:Nominal Rates of Protection by key commodities(percent),and prices of eggs at farm gate(USD/tonne),20052019.50Table1.Aggregate Nominal Rates of Protection(NRPs)by country(percent),20052018.13BoxesBox 1:The International Organisations Consortium for Measuring the Policy Environment

41、 for Agriculture.6Box 2:Limitations and future research.11AnnexesFigures A1 Figure 1.Main trading partners of Armenia by share in total trade value(percent),average 20152019.57A1 Figure 2.Main trading partners of Azerbaijan by share in total trade value(percent),average 20152019.57A1 Figure 3.Main t

42、rading partners of Belarus by share in total trade value(percent),average 20142018.58A1 Figure 4.Main trading partners of Georgia by share in total trade value(percent),average 20152019.58A1 Figure 5.Main trading partners of Kyrgyzstan by share in total trade value(percent),average 20142018.59A1 Fig

43、ure 6.Main trading partners of the Republic of Moldova by share in total trade value(percent),average 20142018.59A1 Figure 7.Main trading partners of Tajikistan by share in total trade value(percent),average 20142018(except milk,sheep meat and eggs:20102014).60viiiA1 Figure 8.Main trading partners o

44、f Uzbekistan by share in total trade value(percent),average 20152019.60Tables A2 Table 1.Agricultural commodities by country and their combined share in the total value of agricultural production .61A2 Table 2.HS codes/aggregates,used in the calculation of net trade status and trade intensity .62A2

45、Table 3.HS codes/aggregates,used in the calculation of unit export/import values .64A2 Table 4.Commodities included in aggregate NRP calculations.66A3 Table 5.Sources of foreign trade data and HS tariff code level by country .67A3 Table 6.Sources of data on domestic producer prices of key commoditie

46、s by country .67A3 Table 7.Sources of data on production volumes of key commodities by country .67A3 Table 8.Sources of data on exchange rates by country .67A3 Table 9.Sources of data on inflation rates(consumer price index,annual average,percent change on previous year)by country .68A3 Table 10.Sou

47、rces of data on the value of agricultural production by country .68A3 Table 11.Sources of data on budgetary and other transfers by country.68A4 Table 12.Alternative reference prices at farm gate.69A5 Table 13.Nominal Rates of Production(percent)by country and product.70ixForewordAgricultural and tra

48、de policies affect prices and trade flows at the national and international levels.The 2030 Agenda for Sustainable Development and the Sustainable Development Goals(SDGs)recognize the importance of such policies in achieving food security.For instance,Target 2.b of SDG 2 commits countries to“correct

49、 and prevent trade restrictions and distortions in world agricultural markets,including through the parallel elimination of all forms of agricultural export subsidies and all export measures with equivalent effect,in accordance with the mandate of the Doha Development Round”.This target can only be

50、pursued effectively if the appropriate evidence base,in terms of data and analysis of the magnitude and effects of these distortions,is established.Since 2014,the Markets and Trade Division of the Food and Agriculture Organization of the United Nations(FAO)has monitored and documented changes in agr

51、icultural and trade policies in Eastern Europe,Caucasus and Central Asia(EECCA)countries through an annual Review of Agricultural Trade Policies in 12 EECCA countries.FAO has also established and is supporting the Agricultural Trade Expert Network(ATEN)in Europe and Central Asia.This network of expe

52、rts conducts research,carries out training programmes and advises governments and the private sector on issues related to agricultural trade and trade policy.In 2018,under the umbrella of FAOs strategic objective on enabling inclusive and efficient agricultural and food systems and to contribute to

53、FAOs Regional Initiative on Improving Agrifood Trade and Market Integration in Eastern Europe and Central Asia,1 the Markets and Trade Division conducted a pilot study to measure agricultural distortions in six EECCA countries.In this exercise,Armenia,Azerbaijan,Belarus,Georgia,Kyrgyzstan and the Re

54、public of Moldova were selected as pilot case study countries,given that they had undergone fundamental policy changes during the last two decades and did not have systematic and continuous policy monitoring in the past.Responding to the high demand expressed by governments for the continuation of s

55、uch quantitative policy monitoring in support of evidence-based decision-making,an update,expansion and revision of this study was conducted in 2020,covering additional key commodities and two more countries,namely Tajikistan and Uzbekistan.For these two new countries,the exercise was one of the fir

56、st such comprehensive monitoring efforts to quantify agricultural incentives,and an important part of the work was dedicated to testing the feasibility of the methodology and to exploring the availability of data required for conducting the analysis.The main objective of this study is therefore to r

57、eview the agricultural policy environment and provide quantitative indicators for policy incentives and disincentives to farmers for key commodity value chains in the eight study countries,utilizing the methodology aligned with the approach of the International Organisations Consortium for Measuring

58、 the Policy Environment for Agriculture(Ag-Incentives Consortium).This report describes the methodology and approach taken for the eight countries covered by this study and presents the key results and their interpretation in the policy and market contexts of the countries and the region.1 Since 202

59、1,this regional initiative has been renamed“Transforming food systems and facilitating market access and integration”.xAcknowledgementsThe report is a product of the Markets and Trade Division(EST)of FAO.The research and preparation of the report was led by Iryna Kobuta,Economist,EST,FAO.The researc

60、h and writing team included Sara Bele(AIS),Emil Erjavec(University of Ljubljana),Iryna Kobuta(FAO),Maja Koar(AIS),Ekaterina Krivonos(FAO),Luka Loar(AIS),Signe Nelgen(FAO),Tanja Travnikar(AIS)and Andrea Zimmermann(FAO).The data and information for the study countries were collected and validated by t

61、he country experts:Vasilina Akhramovich(Belarus,country expert),Zalina Enikeeva and Roman Mogilevskii(Kyrgyzstan,University of Central Asia),Ketevan Gachechiladze and Natali Kldiashvili(Georgia,Fund Georgian Center for Agribusiness Development GCAD),Vardan Urutyan(Armenian National Agrarian Universi

62、ty)and Hasmik Hovhanesian(Yerevan State University),Rashad Huseynov(Azerbaijan,The Khazar University),Darya Ilina(Uzbekistan,country expert),Parviz Khakimov(Tajikistan,country expert),Eugenia Lucasenco(the Republic of Moldova,National Institute for Economic Research).The results for each country wer

63、e validated by the respective country representatives.Signe Nelgen,FAO Consultant,significantly contributed to the report,including to the underlying data coordination and analysis.The research team is grateful to Valentina Pernechele,Economist at the FAO Agrifood Economics Division,ESA,for the tech

64、nical review,valuable comments and the methodological guidance provided.The research also benefited from contributions by Josef Schmidhuber,Deputy Director,and Georgios Mermigkas,Senior Economist,EST,FAO,through their review of an earlier version of the report.The authors thank Evelyne van Heck,Trad

65、e Specialist at EST,FAO,for her overall review of the report.They are also grateful to Jonathan Hallo and Ettore Vecchione for the report design,and to Araceli Cardenas for production support.Finally,the authors thank Boubaker Ben-Belhassen,Director of EST,FAO,for his overall guidance and support.xi

66、Acronyms,abbreviations and symbolsAIS Agricultural Institute of SloveniaATEN Agricultural Trade Expert Network in Europe and Central AsiaBOT commodity-specific public expenditure,measured as monetary units per quantity unitCCT Common Customs TariffCEPA Comprehensive and Enhanced Partnership Agreemen

67、t CIF cost,insurance and freight price of trade(imported commodity)CIS Commonwealth of Independent StatesCSE Consumer Support EstimateCSE BOT Budgetary transfers to consumersDCFTA Deep and Comprehensive Free Trade AreaEAEU Eurasian Economic UnionEECCA Eastern Europe,Caucasus and Central AsiaEU Europ

68、ean UnionFAO Food and Agriculture Organization of the United NationsFOB free on board price of trade(exported commodity)FTA Free Trade AreaGDP Gross domestic productGSP Generalized Scheme of PreferencesGSSE General Services Support EstimateGSSE BOT budgetary transfers to general servicesHS Harmonize

69、d Commodity Description and Coding System(Harmonized System)of tariff nomenclatureIAMO Leibniz Institute of Agricultural Development in Transition EconomiesIDB Inter-American Development BankIFPRI International Food Policy Research InstituteISRER Institute of Scientific Research on Economic ReformsM

70、AFAP Monitoring and Analysing Food and Agricultural PoliciesMi volume of imports of commodity ina not available/not applicableNRA Nominal Rate of AssistanceNRP Nominal Rate of ProtectionNRPi Nominal Rate of Protection for commodity iNRPg aggregate NRPNRPfg Nominal Rate of Protection at the farm gate

71、NTi net trade volumeOECD Organisation for Economic Co-operation and DevelopmentPfg domestic price at farm gatePSE Producer Support EstimatePSE BOT budgetary transfers to producersRPfg reference price at farm gatexiiRPfgi reference price of commodity i at the farm gateSDGs Sustainable Development Goa

72、lsSPS Sanitary and Phytosanitary MeasuresTI trade intensityTotal BOT total budgetary and other transfersTRQs tariff rate quotasUSD United States dollarVAT value-added taxVP value of production(agricultural output)WB World BankXi volume of exports of commodity iYi domestic production of commodity ixi

73、iiExecutive summaryTransparent and effective agricultural and trade policies are essential to meet growing demands for safe and nutritious food in a sustainable way.In most Eastern Europe,Caucasus and Central Asia(EECCA)countries,policy interventions such as agricultural subsidies are a defining fea

74、ture of food and agricultural markets.However,there has not been any systematic effort to quantify impacts of these policies.For three countries in the region Kazakhstan,the Russian Federation and Ukraine the Organisation for Economic Co-operation and Development(OECD)calculates agricultural policy

75、support indicators such as the well-established indicators of Producer and Consumer Support Estimates(PSE/CSE)and the General Services Support Estimates(GSSE).As part of the European Unions monitoring policy,OECD efforts also cover the three Baltic States:Estonia,Latvia,and Lithuania(OECD,2018).For

76、other countries in EECCA,there have been only a few agricultural policy monitoring and evaluation efforts to record and analyse the shift from centrally planned economies to market economies in the early 1990s,for example by K.Anderson and J.Swinnen(2008)in Distortions to Agricultural Incentives in

77、Europes Transition Economies,covering the period from 1991to2005.Comparable time series of policy indicators are not available for recent years for these countries.This study contributes to filling this gap by measuring agricultural policy support in eight EECCA countries,utilizing a combination of

78、methodologies used by FAOs Monitoring and Analysing Food and Agricultural Policies(MAFAP)programme and OECD to generate a set of indicators that is consistent with indicators for other countries.2 It also analyses agricultural support and taxation patterns in Armenia,Azerbaijan,Belarus,Georgia,Kyrgy

79、zstan,the Republic of Moldova,Tajikistan and Uzbekistan by reviewing and documenting policies that explain incentives and disincentives to agricultural producers.This study does not cover the Russian Federation,Kazakhstan or Ukraine,as the OECD develops a complete set of agricultural support indicat

80、ors for these countries.Nominal Rates of Protection(NRPs),and where possible,Nominal Rates of Assistance(NRAs)the two standard measures of policy-induced divergence of product-specific domestic prices from international prices are calculated for a set of six to ten key agricultural commodities per c

81、ountry,covering the time period from 2005to2019(product coverage varies by country and by year,depending on data availability).In addition,budgetary transfers and other supports to agriculture are analysed,in line with the OECD PSE/CSE classification(OECD,2010).Key findingsThe direction and magnitud

82、e of policy support to agriculture varies across the countries analysed in this report.Agricultural producers in the South Caucasus countries(Armenia,Azerbaijan and Georgia)received incentives throughout most of the analysed period.Among the Central Asian countries included in this study,strong pric

83、e disincentives were found at the aggregate level in Kyrgyzstan and Uzbekistan,whereas agricultural producers in Tajikistan in general received price incentives in the second part of the analysed period.For Belarus and the Republic of Moldova,the results show more moderate price disincentives compar

84、ed to the Central Asian study countries.Apart from divergent trends in the aggregate level of support across countries,there is also variation in support within individual countries across different commodities.These are explored in the country chapters.Substantial differences in agricultural polici

85、es across the eight EECCA countries are also reflected in the amounts of overall budgetary support available to the agricultural sector.While Azerbaijan and Belarus provide relatively large budgetary support to their agricultural sectors,Georgia,in comparison,provides a medium level of support,and A

86、rmenia,Kyrgyzstan,the Republic of Moldova and Tajikistan provide a relatively low level of budgetary support.For Uzbekistan,data on budgetary transfers to the agricultural sector was not available.2 Agricultural policy monitoring efforts are undertaken by the World Bank,FAO and a range of other orga

87、nizations,many of which are members of the Ag-Incentives Consortium established by the International Organisations Consortium for Measuring the Policy Environment for Agriculture(Ag-Incentives).http:/www.ag-incentives.org/.Agricultural policy monitoring for Eight countries in Eastern Europe,Caucasus

88、 and Central AsiaxivDuring the analysed period from 2005to2019,most of the eight countries were net importers of agrifood products.The exception countries were the Republic of Moldova,which was a net exporter during the entire period;Belarus,which was a net exporter in most of the studied years;and

89、Uzbekistan,which was a net exporter in the beginning of the analysed period.Belarus is by far the largest net exporter among the study countries,with growing exports throughout the analysed period.The main trading partners of the countries analysed in this report are their neighbouring countries,in

90、particular the Russian Federation,as it is the biggest market in the region.In addition,the European Union is an important export destination for Georgia and the Republic of Moldova,as their market access and overall trade relations with the European Union have been strengthened through the establis

91、hment of Deep and Comprehensive Free Trade Areas(DCFTA)in 2016.Armenias main exportable commodities are grapes,apricots and,in recent years,tomatoes.For Azerbaijan,they are hazelnuts,tomatoes,persimmons and cotton,while Belarus is an important net exporter of animal products(milk,bovine meat,poultry

92、 meat and eggs).Georgia exports hazelnuts and Kyrgyzstan exports dry beans,cotton,honey and milk.The Republic of Moldova mainly exports sunflower seed,wheat,maize and fruit(apples,grapes and plums)and Tajikistan and Uzbekistan are net exporters of cotton.Uzbekistan also exports sweet cherries,tomato

93、es and apricots.Domestic agricultural and trade policies do not appear to be the factors to influence the estimated price incentives and disincentives for agricultural producers in the eight EECCA countries.Macroeconomic,political and sectoral developments and other exogenous factors appear to affec

94、t substantially the estimates(see,for example,Mogilevsky,2017).The estimates of price distortion indicators are also influenced by the weak overall market functioning in most countries,which is common in many low-and middle-income countries.Factors that impede price arbitrage between domestic and in

95、ternational markets in these countries include limited market integration,asymmetric distribution of market power,lack of market institutions and underdeveloped physical infrastructure(MAFAP,2015).1Chapter 1.Agricultural market and policy developments in the countries1.1 Key market developments and

96、main trading partnersReal gross agricultural output of the analysed countries at an aggregate level,1 excluding Uzbekistan(for which FAOSTAT data was not available),grew at a compound annual growth rate of almost 2percent in the 20052018 period.At the same time,the agrifood foreign trade of all eigh

97、t study countries in real terms2 grew at a higher compound annual growth rate of 6percent.While both agrifood imports and exports increased during the analysed period,the countries in this study remained net importers of agrifood products during most of the period(Figure 1).Figure 1.Value of gross a

98、gricultural production and agrifood foreign trade,aggregate of the study countries,20052018aa Value of production:Uzbekistan is not included(FAOSTAT data not available).Source:FAO calculations using FAOSTAT data on agricultural production and trade.FAOSTAT.2020.Data on agricultural production,value

99、of agricultural production,agricultural trade by selected countries.Rome,FAO.http:/www.fao.org/faostat/en/#dataAmong the countries analysed in this study,only the Republic of Moldova was a net exporter of agrifood products throughout the entire 20052018 period(Figure 2).Belarus changed its trade sta

100、tus from being a net agrifood importer until 2009 to a net exporter(with the exception of 2015).Belarus is by far the largest net exporter among the eight study countries in terms of trade value during the analysed period,except in the 20052008 period when Uzbekistans net exports were higher.From 20

101、12,Uzbekistan was a net importer(with the exception of 2017).Azerbaijan and Tajikistan experienced increased net agrifood foreign trade deficits most notably Azerbaijan after 2014.After 2015,trade deficits decreased in Armenia,Georgia and Kyrgyzstan driven by substantial export growth.1 Measured in

102、constant 20142016 prices.2 Measure in 2015 prices.000620072008200920001620172018ExportImport10152025Value of agricultural production;without UzbekistanAgricultural production(billion USD;constant 20142016)Agrifood export and import(billion USD;2015=100)Agr

103、icultural policy monitoring for Eight countries in Eastern Europe,Caucasus and Central Asia2Figure 2.Net agricultural exports by country(constant 2015 billion USD),20152018 FAO calculations using FAOSTAT data on crop and livestock products trade.FAOSTAT.2018.Commodity Balances Crops Primary Equivale

104、nt.online.Rome,FAO.http:/www.fao.org/faostat/en/#data/BCThe main trading partners of the eight countries analysed in this report are other EECCA countries(Annex 1),however their trends with regard to trade openness and trade integration with other countries are divergent.Some countries are expanding

105、 their trade relations with the European Union,while others are focused on strengthening trade ties with the Russian Federation through the Eurasian Economic Union(EAEU)(FAO,2018b,2020).3 For all countries,grain imports(wheat,maize)are mainly sourced from the Russian Federation,while milk and dairy

106、products are imported from Ukraine,Belarus or the Russian Federation.Key export products analysed in this study,especially fruit and tomatoes,are destined for the Russian Federation or its neighbouring countries(such as China,Kazakhstan and Ukraine).Trkiye is the main export destination for cotton f

107、rom Azerbaijan and Kyrgyzstan,while China is the main destination for Uzbek cotton,and Islamic Republic of Iran for cotton from Uzbekistan and Tajikistan.Meat imports originate to a large extent from Ukraine or from other major global exporters,for example India or China(bovine meat),as well as Braz

108、il or the European Union(pig and poultry meat).1.2 Trade agreementsThe eight countries increasingly participate in multilateral,regional and bilateral trade agreements.Apart from the immediate effects on trade flows,participation in trade agreements has helped improve the countries institutional cap

109、acities for trade,increase adoption of international standards and align domestic policies and processes with these international standards(FAO,2018b).Five countries covered in this report are members of the World Trade Organization(WTO):Armenia(since 2003),Georgia(since 2000),Kyrgyzstan(since 1998)

110、,the Republic of Moldova(since 2001)and Tajikistan(since 2013).Azerbaijan,Belarus and Uzbekistan are currently granted observer status.3 The analysis of trade flows is for brevity reasons based on United Nations Comtrade data for the 20152019 period,or,in the case of unavailable data,for the 2014201

111、8 period(Annex 1).This period does not represent the entire studied period of the calculated indicators,which is 20052018/2019.1.510.500.511.5220062008200162018AremniaAzerbaijanBelarusGeorgiaKyrgyzstanRepublic ofMoldovaTajikistanUzbekistanNet trade;billion USD3Chapter 1 Agricultural marke

112、t and policy developments in the countriesArmenia,Belarus and Kyrgyzstan are,together with Kazakhstan and the Russian Federation,members of the EAEU,which came into force in 2015.Uzbekistan became an EAEU observer on 11 December 2020.The EAEU establishes free movement of goods,services,capital and l

113、abour,and members pursue coordinated policies in many sectors,including agriculture.EAEU members are harmonizing their national policies,including support to agriculture and sanitary and phytosanitary(SPS)regulation.EAEU members are committed to adopting the Common Customs Tariff(CCT),which,for Arme

114、nia and Kyrgyzstan presents a certain inconsistency with their WTO market access obligations,as for some agricultural products the CCT of the EAEU is higher(FAO,2016).With the exception of Georgia,all other countries in this study are signatories to the Commonwealth of Independent States Free Trade

115、Area(CIS FTA),which came into force in 2012.However,Georgia has bilateral free trade agreements with the CIS countries(FAO,2016).CIS FTA defines a free trade area and replaces several bi-and multilateral free trade agreements in the region between former republics of the Soviet Union.Georgia and the

116、 Republic of Moldova have signed Association Agreements and established free trade areas with the European Union DCFTAs which formally entered into force in 2016.Both countries are therefore harmonizing their national legal frameworks with those of the European Union,including those focusing on trad

117、e facilitation,technical regulation and SPS measures(FAO,2018b).In 2018,a Comprehensive and Enhanced Partnership Agreement(CEPA)between Armenia and the European Union entered into force.Since 2016,a preferential trade regime for Kyrgyzstans trade with the European Union(under the European Unions Gen

118、eralized Scheme of Preferences or GSP+)allows Kyrgyzstan to export some agrifood products to the European Union at zero or reduced tariff rates.Armenia is a beneficiary of the same regime granted by the European Union since 2014.Georgia and the Republic of Moldova were also benefiting from preferenc

119、es granted by the European Union through GSP(until 2005)and later GSP+,until replacing them with DCFTA in 2016.The member states of the European Free Trade Association(EFTA)(Iceland,Liechtenstein,Norway and Switzerland)signed a free trade agreement with Georgia in 2016,which entered into force in 20

120、18.The agreement provides for zero tariff rates for some agricultural goods.The eight countries analysed in this study participate in many other trade agreements,treaties and organizations.Azerbaijan and Kyrgyzstan are members of the Economic Cooperation Organization(ECO),which also includes Afghani

121、stan,the Islamic Republic of Iran,Kazakhstan,Pakistan,Tajikistan,Trkiye,Turkmenistan and Uzbekistan.An Economic Cooperation Organization Trade Agreement(ECOTA)aims to establish a free trade regime between the ECO members but has so far only been signed by Tajikistan.Moreover,the countries have sever

122、al bilateral trade agreements with each other and other countries in the region or with major global trading partners(for example,the free trade agreement between Georgia and China since 2016 or between the Republic of Moldova and Trkiye,in place since 2016).South Caucasus countries(Armenia,Azerbaij

123、an and Georgia)and the Republic of Moldova are also members of the Organization of the Black Sea Economic Cooperation(BSEC).The Republic of Moldova is a member of the Central European Trade Free Agreement(CEFTA).1.3 Trade and agricultural policiesAgricultural incentives or disincentives to farmers c

124、an be driven by a number of government policy interventions(such as,import tariffs or quotas;taxes or subsidies on domestic production;minimum prices;or other types of measures regulating agricultural markets).The most common types of agricultural support measures in the analysed countries include:t

125、ax concessions,investment support,subsidized interest rates/credit and input subsidies(for example,for seeds,fertilizers and fuel),leasing of machinery to farmers at reduced cost,subsidized insurance schemes,as well as market interventions such as government procurement from farmers and price contro

126、ls(e.g.administered prices)(FAO,2018b,2020).Border measures vary significantly by country.Among the countries in this study,Uzbekistan has the highest tariffs on agricultural imports(around 18percent on average in the 20052018 period;Figure 3),followed by Azerbaijan(almost Agricultural policy monito

127、ring for Eight countries in Eastern Europe,Caucasus and Central Asia413percent)and the Republic of Moldova(around 10percent).Average applied agricultural tariff rates are the lowest in Kyrgyzstan(7.2percent),Armenia(6.9percent)and Georgia(6.8percent).Countries that are members of the EAEU are in the

128、 process of aligning their national tariff schedules with the CCT.The new Customs Code of the EAEU entered into force on 1January2018 and replaced the Customs Code of the Customs Union that had been in place from mid-2010.Figure 3.Average agricultural applied tariff rates,MFN simple average(percent)

129、,20052018aa Data missing for certain years,most notably for Uzbekistan,Tajikistan and Azerbaijan.Source:World Bank.2020.World Integrated Trade Solution.https:/wits.worldbank.org/Tariff rate quotas(TRQs)are applied by EAEU members for beef,pig meat,poultry meat and edible offal of poultry(FAO,2018b).

130、Georgia,Tajikistan and Uzbekistan do not apply TRQs.In addition to import duties and TRQs,the countries of the region are actively applying non-tariff measures(in particular SPS requirements and technical regulations)to control imports of agricultural products to their territory.While Georgia and th

131、e Republic of Moldova already aligned their national legislation on SPS regulation with European Union legislation(FAO,2018b,2020),other countries are in the process of reviewing and modernizing their SPS systems to be more in line with international standards.Finally,Belarus,Tajikistan and Uzbekist

132、an maintained export duties on a limited number of agricultural products during the analysed period(FAO,2020).Uzbekistan also applied quantitative restrictions on exports of some agricultural products and even banned some exports,but this ban was lifted in May 2017.0510152025ArmeniaAzerbaijanBelarus

133、GeorgiaKyrgyzstanRepublicofMoldovaTajikistanUzbekistan2005200620072008200920001620172018%5NRPf g=*100Pf g RPf gRPf g*NRPi RPf gi Yi*RPf gi Yii=1i=ni=1i=nChapter 2.Methodology2.1 Calculation of the Nominal Rate of Protection(NRP)and Nominal Rate of Assistance(NRA)The main indica

134、tor used in the report,the NRP,captures price incentives(or disincentives)that agricultural producers receive due to domestic policies.It can be described as a farm-to-border-price ratio,a gap between the(possibly)distorted domestic farmgate price(the price that producers receive)and an internationa

135、l reference price.This reference price can be thought of as the price that would be in place in the absence of domestic price,market and trade policies(Anderson,2009).The reference price reflects the opportunity cost to domestic producers.In order to make the prices comparable,that is,take them to t

136、he same points in the value chain,information on exchange rate distortions,quality and quantity adjustments,marketing margins,and handling,transportation and processing costs need to be accounted for.Price distorting measures include government interventions at the national border(such as import tar

137、iffs,export subsidies,and import or export quotas),and at the domestic level(such as direct price administration,production quotas and public stockholding)(OECD,2016).Though aiming to measure exclusively the effects of policy-related distortions,the NRPs may also sometimes capture non-policy factors

138、,such as the impact of overall market performance on prices.Inefficient market functioning is common in many low-and middle-income countries and is characterized by factors that impede price arbitrage between domestic and international markets,such as limited market integration,asymmetric distributi

139、on of market power,lack of market institutions and underdeveloped physical infrastructure(MAFAP,2015).The NRP at farm gate(NRPfg)is defined as follows(adapted from MAFAP,2015):4 Eq.1 where Pfg is the domestic price at farm gate and RPfg is the reference price at farm gate.Expressed in percentage ter

140、ms,the NRP estimates by how much gross returns to farmers with government interventions exceed(positive NRPs)or fall below(negative NRPs)gross returns to farmers if no policy interventions are in place(Anderson,2009).NRPs at the farm gate are positive when the domestic price is higher than the refer

141、ence price,meaning there is a price distortion present and producers are incentivized to produce a commodity.NRPs at the farm gate are negative when reference prices exceed domestic prices,meaning that domestic market and trade policies,and possibly market performance,generate disincentives to agric

142、ultural producers.Commodity-specific indicators can be aggregated into product groups or country-level aggregates.Typically,the aggregate indicators are calculated as weighted averages based on each commoditys relative contribution to the total value of agricultural production:Eq.2 Where NRPg is the

143、 aggregate NRP for a subset of n commodities,NRPi is the NRP for commodity i,Yi is the volume of production in tonnes(or any other unit)of commodity i and RPfgi is the reference price of commodity i at the farm gate(MAFAP,2015).4 MAFAP(2015)distinguishes between observed and adjusted NRP.For this st

144、udy,observed NRPs have been estimated,and are based on the actual market and policy situation in the country.In comparison,the adjusted domain of indicators is based on the estimation of a fully efficient value chain setting.Agricultural policy monitoring for Eight countries in Eastern Europe,Caucas

145、us and Central Asia6*100NRA=+RPf g)BOTc(Pf g RPf g Box 1:The International Organisations Consortium for Measuring the Policy Environment for AgricultureAgricultural,market and trade policies affect trade flows,farm income and food prices at the national and international level.The pattern of incenti

146、ves to agriculture is continuously changing,with support to agriculture provided by a wide range of measures,and protection rates varying not only in response to explicit changes in policy,but also reflecting movements in world agricultural prices.In order to better understand and monitor policy imp

147、acts on trade and markets and support evidence-based decision making in the countries,a number of international organizations(FAO,IDB,IFPRI,OECD and WB)joined forces to establish the International Organisations Consortium for Measuring the Policy Environment for Agriculture(the Ag-Incentives Consort

148、ium).The Consortium builds on the individual efforts of the international organizations to improve the knowledge of agricultural policies.Its main aim is to provide a harmonized and continuously updated database of measures of agricultural support for countries worldwide(AgIncentives,2018).To date,t

149、he Nominal Rate of Protection(NRP)indicators are included in the database of the Ag-Incentives Consortium,as the core indicators on support provided by agricultural policies to producers.The Consortium has plans to also publish NRAs in the near future.The dataset currently covers 61 countries(coveri

150、ng the European Union,the United Kingdom of Great Britain and Northern Ireland members as single entity),representing close to 90percent of the global value of agricultural production.Indicators span from 2005 and are updated biannually.The NRPs and NRAs developed for the eight countries in this stu

151、dy are conceptually equivalent to the Consortiums methodology and can be compared to the NRPs of the countries that are already covered.Source:AgIncentives.2018.International Organisations Consortium for measuring the policy environment for agriculture.http:/www.ag-incentives.org/content/about-us.Ca

152、lculation of the Nominal Rate of Assistance(NRA)In addition to NRPs,and depending on the availability of data on commodity specific budgetary transfers,a construction of Nominal Rate of Assistance(NRA)is also possible.The NRA is an extension of the NRP and is calculated in a similar way,with the add

153、ition of public expenditure to the price gap at the farm gate.It is expressed as(adapted from MAFAP,2015):Eq.3 where Pfg is the domestic price at farm gate,RPfg is the reference price at the farm gate and BOTc is the commodity-specific public expenditure,measured as monetary units per quantity unit.

154、The difference between the two indicators is that in addition to the impact of domestic price distorting policies and overall market performance,which is already covered by the NRP,the NRA also measures the effects of public expenditure on the incentives received by producers.The NRA estimates by ho

155、w much government policies,including transfers,have increased or decreased gross returns to producers above or below the scenario without government interventions(MAFAP,2015).Adding budget information to the NRP provides a more complete picture of the prevalent price incentives/disincentives,especia

156、lly when budgetary payments cancel out the existing price disincentives to agricultural producers.It should be noted that the calculation of NRAs was only possible for a subset of the analysed commodities,as the majority of the budgetary support provided in the study countries was not commodity-spec

157、ific or its attribution to specific commodities was not possible due to limited information.Nevertheless,it is important to acknowledge that this type of support also influences production decisions and prices of the individual commodities and therefore NRAs provide a more complete picture of the ov

158、erall price distortions.7Chapter 2 Methodology2.2 Selection of commodities and data requirementsThe main criteria for selection of agricultural commodities for the analysis was their contribution to the total value of national agricultural production.The latest FAOSTAT data for gross agricultural ou

159、tput in constant 20042006 million USD was used(FAO,2018a),as available at the start of this study.The average value of production was taken for the 20142016 period to smooth out any unusual year-on-year fluctuations.Agricultural production in most of the analysed countries is highly diversified,and

160、therefore the initial objective to include all products that cumulatively account for at least 70percent of the total value of agricultural production(OECD,2016)was difficult to achieve,given the scarcity of data for individual commodities.Therefore,the threshold was lowered to 50percent.5 Methodolo

161、gical decision was made to limit the number of commodities per country to eight or ten.For Uzbekistan,the FAOSTAT data on the value of agricultural production by individual commodities was not available,therefore the six key commodities were selected solely based on consultation with the country exp

162、ert who participated in the analytical work.While the initial list of products was based on the value of production,the final product coverage was decided in coordination with country experts and the respective government focal points who saw value in prioritizing products of strategic importance(fo

163、r example,based on perceived export potential).The final criteria for the selection of products for the analysis was the availability of the product-specific data.This was a particular limitation for Tajikistan and Uzbekistan,which were added to the analysis at a later stage.For both countries,data

164、for several commodities was not available at the product level.For example,in Tajikistan the key meat products,as well as fruits and vegetables,are aggregated in national statistics.In Uzbekistan,data is aggregated for meat and milk.As a result,for Tajikistan and Uzbekistan,the study aimed to analys

165、e six key commodities.For four commodities,potatoes,grapes,sheep meat and eggs(with the exception of eggs in Belarus),the NRPs were not calculated.Instead,only domestic producer prices and reference prices at the farm gate are presented.The main reason is the low tradability of these commodities,whi

166、ch are typically produced by smaller farms that are not market-oriented and produce largely for own consumption or sporadic local sales.The law of one price,which is the underlying assumption on which the MAFAP approach for evaluating price incentives is based,is not applicable to non-tradable commo

167、dities(MAFAP,2015).For these commodities,it is more relevant to look at the price movements and market conditions than the actual NRPs.In total,21 different commodities(14 crop commodities and 7 livestock commodities)were selected for the analysis,six to ten commodities per study country,adding up t

168、o 66 country/commodity indicators in total.The final list of commodities and their representativeness in the value of agricultural production by country is shown in Annex 2.To calculate NRPs and NRAs for agricultural commodities,the following data is required:6 y foreign trade data to calculate trad

169、e status,trade intensity and border prices(for example,unit export/import values);y domestic prices at farm gate level(that is,farm gate or producer prices);y alternative reference prices at farm gate level;y production volumes and values;y exchange rates and inflation rates;y market access costs:fr

170、om the border to point of competition(usually the wholesale market)and from point of competition to farm gate;y budgetary and other transfers(BOT)to agriculture;and y quality and quantity adjustment factors(if required).5 The aggregate share of all commodities based on FAOSTAT data for the 20142016

171、average(in constant 20042006 prices;FAO,2018a)is exceeding the 50percent threshold for the majority of study countries.However,the share remains rather low for Armenia(45percent).Similarly,the cumulative share is also low for Tajikistan(44percent);however,this is somewhat expected,as the aim for the

172、 new study countries(Tajikistan,Uzbekistan)was to analyse up to six commodities in total.The aggregate shares of all commodities in terms of value of agricultural production according to national statistics data(presented in Annex 6),were generally lower than the aggregate shares based on the FAOSTA

173、T data,using the 20142016 average.6 All monetary values are given in US dollar.Agricultural policy monitoring for Eight countries in Eastern Europe,Caucasus and Central Asia8NTi=Xi Miif NTi 0 the country is a net exporterif NTi 0 the country is a net exporterTI=*100Xi+MiYi+Mi XiNational experts who

174、participated in this study provided descriptions of the selected value chains.This information revealed important insights into the structure of the market and the specifics of its functioning,identifying the main marketing channels,as well as relevant prices and access costs for selected commoditie

175、s.2.3 Trade data analysis:net trade status and trade intensityThe effects of market and policy interventions differ depending on whether a commodity is exported or import-competing,and if it is traded strongly or thinly.Therefore,trade data is analysed as a first step to determine the net trade stat

176、us and trade intensity of the selected commodities.7 The net trade position for a commodity was calculated using the following equation(MAFAP,2015):Eq.4 Where NTi is the net trade volume,Xi is the volume of exports of commodity i,and Mi is the volume of imports of commodity i.The concept of trade in

177、tensity was used to evaluate the relative share of trade over apparent domestic consumption of a commodity.Trade intensity was calculated for each year of the analysed period as follows(MAFAP,2015):Eq.5 Where TI is the trade intensity,Xi is the volume of exports of commodity i,Mi is the volume of im

178、ports of commodity i,and Yi is the domestic production of commodity i.In cases where the calculated trade intensity was very low(commodities were thinly traded),the robustness of the analysis could be affected,and therefore alternative reference prices at farm gate were considered in the calculation

179、 of the policy indicators.2.4 Determining the reference priceTo calculate the price gap for a commodity,two prices are needed,as shown in eq.1:producer price and the comparable reference price at the farm gate.While producer prices were provided by national experts(data sources are listed in Annex 3

180、),the reference prices were constructed from border prices,based on unit export and unit import values.The net trade status for a specific commodity determined which international border price was used in the analysis for each year(MAFAP,2015):y FOB price(free on board)for commodities with a net exp

181、ort status;and y CIF price(cost,insurance and freight)for commodities with a net import status.The FOB price is the cost of an exported commodity at the exit point of the country measured when the goods are loaded onto a ship or another means of transport.The CIF price is the landed cost of an impor

182、ted commodity on the dock or another entry point,including the cost of international freight and insurance.It excludes any charges after the imported good touches the dock,e.g.any domestic taxes,fees,duties or subsidies(MAFAP,2015).7 Each countrys net trade status and trade intensity for a specific

183、commodity were determined for each year of the analysed period and for HS 4-digit codes or HS 6-digit codes(for the countries that provided foreign trade data on 6-digit codes).9Chapter 2 MethodologyTo calculate reference prices at the farm gate and compare them to the corresponding producer prices,

184、access costs and adjustment factors need to be quantified.Access costs include all costs that are required to move the commodity from one point in the value chain to the other,for example,for an exported commodity,from farm gate to the point of competition and from the point of competition to the bo

185、rder.They cover all actual marketing costs and margins observed in the market pathway,whether officially paid for services(such as,transportation,taxes or profit margins for the involved agents)or not(illicit costs,such as bribes).Internal transport and related costs can provide a“natural”rate of pr

186、otection to imported commodities and implicit taxation to exported commodities(MAFAP,2015).Depending on trade status,access costs from the border to the point of competition and from the point of competition to the farm gate or vice versa are of importance.These costs were collected,where possible,f

187、or the following categories:transport,margins,processing,handling,taxes and fees,and others.Due to difficulties with obtaining reliable market access costs,some simplifications were applied.If access costs for an imported commodity were missing,incomplete or deemed unreliable,they were omitted from

188、the calculation of reference prices as different elements of access costs can be assumed to offset each other.8 Such simplification is consistent with the OECD approach(OECD,2016)and was applied in the following cases of imported commodities for which CIF prices were used for the calculation of the

189、reference prices:y Belarus:apples,maize,pig meat,poultry meat,wheat(2005to2008);y Republic of Moldova:milk,potatoes,pig meat,poultry meat;and y Tajikistan:all commodities except cotton.Moreover,no access costs were available for any commodity produced in Uzbekistan.For this reason,the results for Uz

190、bekistan are indicative only,and the analysis presented here should be treated as an exploratory step rather than definite findings.Next,quantity and quality adjustment factors were considered.A quantity adjustment factor is required if a commodity is processed or exposed to other physical treatment

191、 between two points in the value chain(OECD,2016),for example,when hazelnuts are shelled or raw milk is processed into milk powder.A quality adjustment factor is required if quality differences(such as,in terms of colour,size,oil,fat and protein content,etc.)exist between two points in the value cha

192、in(for example,domestic versus imported products)or if more than one quality of a product is exported(for example,milling versus feed quality wheat exports in Ukraine or higher-priced Arabica coffee versus lower-priced Robusta coffee in Brazil)(OECD,2016).Considering the adjustment factors in the ca

193、lculation of reference prices ensures that producer prices and reference prices are for the same commodity,and the gap between them accounts only for policy support or possible market performance issues.The calculations of policy indicators for cotton did not consider the domestic producer prices at

194、 the farm gate level,which refer to raw cotton,but prices at a higher level in the value chain in order to ensure the comparability(main reason being missing or unreliable ginning costs data).Therefore,the NRPs for cotton in this study reflect the price differences of cotton lint at the ginnery(poin

195、t of competition)level and at the border of a country.2.4.1 Alternative border pricesWhen available,unit values at the border were used in the calculation of reference prices for comparison with domestic prices to producers.In cases where prices based on unit values were not available or deemed not

196、sufficiently reliable for a specific country and commodity,alternative border prices were used(see Annex 4).Alternative border prices were constructed from FOB/CIF prices,of a key trading partner,neighbouring country or other relevant large player in the region(Russian Federation,European Union,Trki

197、ye or Ukraine),adjusted for insurance and freight costs.8 This approach is solely based on OECD approach and differs from the MAFAP methodology,where this practice is not used.Agricultural policy monitoring for Eight countries in Eastern Europe,Caucasus and Central Asia10%PSE BOTj=*100PSE BOT jVP2.5

198、 Budgetary transfers and their classificationThe budgetary support classification used in this study is based on the OECD methodology(OECD,2016).9 The budgetary transfers include(1)explicit support to agricultural value chain agents through budgetary expenditures,including direct payments,investment

199、 grants,co-financing of services and projects,and(2)support based on budgetary revenue forgone,such as tax concessions,preferential lending,debt concessions,and administered prices,among others(OECD,2016).These are classifying as follows(OECD,2016):1)Budgetary and other transfers to producers(PSE BO

200、T):A policy measure is included in PSE BOT if it(a)provides a transfer whose incidence is at the farm level and(b)is directed specifically to agricultural producers.Measures are classified into seven main categories(classified according to implementation criteria):A.support based on commodity output

201、;B.payments based on input use;C.payments based on current area/animal number/receipts/income,production is required;D.payments based on non-current area/animal number/receipts/income,production is required;E.payments based on non-current area/animal number/receipts/income,production is not required

202、;F.payments based on non-commodity criteria;andG.miscellaneous payments.2)Budgetary and other transfers to consumers(CSE BOT):If a policy measure provides positive transfers to first consumers of agricultural commodities(e.g.flour mills or meat-processing plants),it is included CSE BOT.Measures that

203、 support agriculture,e.g.distribution of government stocks acquired in the context of market interventions,are also included.These measures cannot be attributed to individual agricultural producers or general service support,but consumers have an indirect benefit from them.CSE BOT can be commodity s

204、pecific transfers to consumers and non-commodity specific transfers to consumers.3)Budgetary and other transfers to general services(GSSE BOT):The transfers to general services(GSSE BOT)are payments to eligible private or public services provided to agriculture generally,and include policies where p

205、rimary agriculture is the main beneficiary.This kind of support does not directly affect farm receipts(revenues)or consumption expenditure,although they may affect production or consumption of agricultural commodities in the longer term.GSSE measures are classified into 6 main categories,according t

206、o the nature of the service):H.agricultural knowledge and innovation system;I.food inspection and control;J.development and maintenance of rural infrastructure;K.marketing and promotion;L.cost of public stockholding;andM.miscellaneous.Budgetary support was compared between countries using relative(p

207、ercentage/ratio)indicators.The basic relative indicator used for comparison of the level of support was the value of transfers related to the value of agricultural production.This indicator was calculated at PSE/GSSE/CSE category level and then aggregated at higher levels using the following formula

208、s(OECD,2016):Eq.69 While the Producer Support Estimate(PSE)an indicator used by the OECD also includes market price support(MPS)to producers,for the purpose of this study the classification of budgetary transfer component of the PSE is used.11Chapter 2 Methodology%PSE BOT=%PSE BOTj%GSSE BOTj=*100GSS

209、E BOT jVP%GSSE BOT=%GSSE BOTj%CSE BOT=*100CSE BOT VP%Total BOT=%PSE BOT+%GSSE BOT+%CSE BOT Eq.7 Eq.8 Eq.9 Eq.10 Eq.11 Where j is the individual PSE or GSSE category,VP is the value of production(agricultural output),PSE BOT are budgetary and other transfers to producers,GSSE BOT are budgetary and ot

210、her transfers to general services,CSE BOT are budgetary and other transfers to consumers,and Total BOT are total budgetary and other transfers.Expressed as a share of value of production,the budgetary support indicators can be compared between countries at different levels of aggregation.Box 2:Limit

211、ations and future researchCalculation of agricultural policy support indicators requires very specific,detailed and high quality data,including data on domestic prices,access costs and budgetary transfers to producers.Accuracy of these data have significant implications for the quality and credibili

212、ty of results.Improvements in data quality and the methodology associated with obtaining them is an ongoing process.For example,this study provides greater granularity at product level by using 6-digit HS codes for almost all commodities,and increases the accuracy in terms of the selected alternativ

213、e border prices compared to the first round of calculations performed in the initial stage of this project.Of course,as in all quantitative analyses,certain assumptions and imputations had to be made,especially for missing or low-quality data.It should also be noted that it is not always possible to

214、 capture explicitly some specific agricultural price distorting factors(for example,market imperfections and weak infrastructure)in access costs computations,and this fact may influence the NRP results.Therefore,data-related and methodological improvements,such as increased commodity coverage and re

215、finement of the derivation of reference prices and access costs can raise the quality of the analysis and reliability of the results in future studies.Valuable additional insight may also result from a detailed analysis of the fluctuations of national currencies against the currencies of key trading

216、 partners.Further investment into the existing regional expert network supported by FAO and development of national capacities for agricultural policy monitoring and measurement would improve the sustainability of the analysis and enhance evidence-based policymaking at different levels.13Chapter 3.R

217、egional analysis of agricultural price distortionsand budgetary transfers3.1 Nominal Rates of ProtectionThe price incentives indicators that were developed and analysed for the eight EECCA countries suggest a high degree of heterogeneity in policy support to farmers across countries and across produ

218、cts.Country-specific findings are discussed in detail in Chapter 4.Aggregated NRPs by country,for those years where the same products could be included in the calculations,are shown in Table 1.The list of products included in the calculations is shown in Annex 2(Table 5).The analysis of the results

219、is clustered using a geographical classification dividing the countries into South Caucasus countries(Armenia,Azerbaijan and Georgia),Central Asian countries(Kyrgyzstan,Tajikistan and Uzbekistan)and Eastern European countries(Belarus and Republic of Moldova).Table 1.Aggregate Nominal Rates of Protec

220、tion(NRPs)by countrya(percent),20052018 Country2005200620072008200920001620172018Armenia/-8%-1%-9%-2%6%1%-9%6%8%24%26%49%Azerbaijan-16%-5%-8%5%39%35%22%31%24%32%29%-10%/Belarus/-17%-34%-9%-14%-17%-9%-19%-13%-8%Georgia/2%44%-1%14%13%10%17%13%25%24%20%Kyrgyzstan/-14%-26%-21%-22%-

221、23%-18%6%/Republic of Moldova19%-1%27%22%28%4%-3%17%-3%-5%2%-3%-4%-4%Tajikistanb4%10%-31%-15%10%5%-18%10%/Uzbekistanb-71%-75%-68%-72%-66%-55%-64%-50%-49%-43%-36%-43%-53%-60%Legend:/-Aggregate NRP not presented due to different set of key commodities.a The NRPs are shown only for cases where the same

222、 products were included for each year in the calculation of the aggregate NRPs for the individual country.These products are listed in Table 5 of Annex 2.Chapter 4 contains NRPs for additional products that were not included in the aggregation.b The results for Tajikistan and Uzbekistan should be tr

223、eated with caution given the significant data limitations,in particular with regard to access costs,as described in section 2.4.1 and country sections 4.7 and 4.8.Source:Authors calculations.On average over the 20052018 period,agricultural producers in the South Caucasus countries received substanti

224、ally higher domestic prices than the comparable reference prices.The aggregate national NRPs for Georgia seem surprisingly high in most years,given its open economy and limited policy measures in support of agriculture.10 Further analysis reveals that other factors,such as the weak overall market fu

225、nctioning and therefore protection for import-competing products,could be driving these positive NRPs as they were not captured explicitly in the access costs.In Armenia,mostly negative aggregate NRPs(indicative of price disincentives to farmers),were observed in the first part of the period until 2

226、010,while the indicator became positive for all years from 2014 onwards.For Azerbaijan,NRPs were positive from 2008until2015,turning negative in 2016,possibly driven by the declining global oil prices and currency devaluation.In Central Asia,aggregate NRPs are strongly negative in Kyrgyzstan and Uzb

227、ekistan,implying high price disincentives for agricultural producers throughout the analysed period,while Tajikistan shows a more irregular pattern of aggregate NRPs,moving between positive 10 It should be taken into account that there was a change in the methodology to collect production volume dat

228、a from 2014 in Georgia.For the 20062013 period,the main source of the sample frame of surveys was the 2004 Agricultural Census in Georgia.The sample frame for the 20142019 period has been updated and is based on the 2014 Agricultural Census.Agricultural policy monitoring for Eight countries in Easte

229、rn Europe,Caucasus and Central Asia14and negative NRPs.In Uzbekistan,strong negative NRPs range from-75percent in 2006 to-36percent in 2015,showing the strongest estimated price disincentives among the analysed countries.11In the two Eastern European countries(Belarus and the Republic of Moldova),pr

230、ice disincentives are also observed in many of the analysed years,indicating implicit taxation of agricultural producers.Domestic prices were lower than comparable reference prices at the aggregate level for all years.Whereas in the Republic of Moldova,incentives to agricultural producers are observ

231、ed in some years,though with an irregular pattern.NRPs fluctuated substantively from-4percent to 28percent in the analysed period.Changes in exchange rates affect agricultural incentives and disincentives in the region.For example,highly overvalued currencies implicitly lower the domestic prices of

232、exportable products.A devaluation of the national currency would provide an incentive to producers and especially exporters of agricultural products.However,other policies may be adjusted at the same time and amplify or mitigate the exchange rate effects.For example,if a devaluation is accompanied b

233、y a reduction of subsidies to farm inputs,this may outweigh the effects of a devaluation(Anderson and Swinnen,2008).Figure 4.Weighted aggregate Nominal Rates of Protection by countries at farm gate(percent),calculated for the 20142016 period12Note:Tajikistan not included due to lack of data for the

234、20142016 period.Information on which commodities are included in the calculation of aggregate NRPs for each study country is shown in Annex 2(Table 5).Source:Authors calculations.11 Tajikistan and Uzbekistan entered the study at a later point than the other countries,which may impact comparability i

235、n terms of data quality.12 Weighted by value of production.Please note that,subject to data availability,the number of commodities included in the calculation of aggregate NRPs differs by country.60%40%20%0%20%40%60%ArmeniaAzerbaijanBelarusGeorgiaKyrgyzstanRepublicofMoldovaUzbekistan13%17%15%18%11%2

236、%41%15Chapter 3 Regional analysis of agricultural price distortions and budgetary transfers Figure 5.Aggregate Nominal Rates of Protection by commodity group,livestock versus crops(percent,unweighted average),2014201613 Note:Livestock:Tajikistan and Uzbekistan not included;crops:Tajikistan not inclu

237、ded.Source:Authors calculations.The results of the NRP calculations show that producers of livestock in the region received a higher level of price support than producers of crops over the analysed period,which is also the case for the Russian Federation,one of the major trading partners of all the

238、analysed countries.An exception to this observation is Belarus,where,due to a system of administered prices,policy support was negative for both crops and livestock commodities.In comparison,on average for all countries monitored by the OECD,rice,sugar and sunflowers are the most supported products,

239、followed by milk,beef and veal and other meat products(OECD,2020a).At the country level,NRPs for crops were on average positive only in Azerbaijan,Georgia and Kyrgyzstan.The overall higher support for the studied livestock commodities can be explained by higher levels of import tariffs for these pro

240、ducts and often weaker market integration.Although in theory,the NRPs are understood to be solely policy indicators,there appears to be a relatively strong contribution to agricultural price incentives by additional implicit drivers.These include market inefficiencies and imperfections,such as the e

241、ffects on prices of uneven market power within value chains,asymmetric information and high trading(access)costs that include bribes and that are difficult to measure and quantify(see Swanidze et al.,2019).Prevalent subsistence farming with low productivity,low quality and limited market integration

242、 are additional aspects that are difficult to quantify as part of NRP calculations.In the current study,these drivers of price incentives were not explicitly analysed.Accounting for these market imperfections requires further research to quantify their magnitude and effects on prices(see an example

243、of such research in MacDonald,2012).13 Due to the data constraints explained earlier,Tajikistan is not included in this comparison and Uzbekistan only for crops.20LivestockCrops201420152016%Agricultural policy monitoring for Eight countries in Eastern Europe,Caucasus and Central Asia163.2

244、 Budgetary and other transfers to agricultureThe absolute volume of budgetary support to the agricultural sector in the region varies considerably over the analysed period(Figure6)14.The fluctuations are driven mainly by the changes in budgetary transfers in Azerbaijan and Belarus that led to a stea

245、dy increase in the total level of transfers in the eight countries from 2011to2013,followed by a decline until 2017.The slight increase from 2017to2018 is mainly driven by transfers in Azerbaijan.Figure 6.Total budgetary and other transfers to agriculture(BOT in million USD),2011201815 Source:Author

246、s calculations.Data on budgetary support was collected for the period 20052018 for almost all countries(Armenia,Azerbaijan,Georgia,Republic of Moldova and Tajikistan).Exceptions are Belarus(datasets cover 20112018)and Kyrgyzstan(datasets cover 20092018).Data for Uzbekistan was not collected during t

247、his study,therefore the budgetary and other transfers to agriculture could not be analysed.It should also be added,that for some countries and/or some years,the information on budgetary support is not complete.For example,in some cases,donor-funded projects were excluded.The specific data issues are

248、 explained in the respective country chapters.Taking a closer look at the countries in terms of their budgetary support to the agricultural sector,in minimal terms and as a share of the value of agricultural production(Figure 7),considerable variation across a countries and over time can be observed

249、.To compare recent developments with longer-term country trends,averages of budgetary transfers were taken for the most recent period for which data was available,2016to2018,and the decade 20092018.14 Budget data for Uzbekistan was not collected during the study,therefore the budgetary and other tra

250、nsfers to agriculture could not be analysed.Country abbreviations in the figures in this chapter:Armenia;Azerbaijan;Belarus;Georgia;Kyrgyzstan;Republic of Moldova;Tajikistan;Uzbekistan.15 Total budgetary support includes support to producers,to general services and to consumers.In order to analyze t

251、he overall development with comparable data for most countries,this time period was chosen.05001 0001 5002 0002 5003 0003 5002000172018ArmeniaAzerbaijanBelarusGeorgiaKyrgyzstanRepublic of MoldovaTajikistanTotal budgetary and other transfers(Total BOT,million USD)17Chapter 3 Reg

252、ional analysis of agricultural price distortions and budgetary transfers As a share in the value of production,budgetary transfers range from very low(around 1percent)in Kyrgyzstan to relatively high(12.6percent)in Azerbaijan,followed by Belarus(9.6percent).In Armenia,the Republic of Moldova and Taj

253、ikistan,16 this share is much lower,at around 3percent(Figure 7).17 Figure 7.The volume of budgetary and other transfers to agriculture(BOT in million USD and as a percent of value of production)a Belarus:BOT data available for the 20112018 period.Source:Authors calculations.If the analysis is exten

254、ded to budgetary transfers per hectare of agricultural area,the results are similar(Figure 8).Azerbaijan and Belarus continue to stand out strongly at the higher end of support,with one hectare of agricultural land receiving support of around USD100on average during the 2016to2018 period.This suppor

255、t in other countries ranged from around USD10to USD40per hectare.For comparison,total European Union support to agriculture in 2017 was much higher,amounting to approximately USD500per hectare and covering almost 20percent of the volume of production(Erjavec et al.,2020).16 For Tajikistan:Value of a

256、gricultural production was not available for 2017and2018 when the indicators were calculated.17 Budget data for Uzbekistan was not collected during the study,therefore the budgetary and other transfers to agriculture could not be analysed.Country abbreviations in the figures in this chapter:Armenia

257、AM;Azerbaijan AZ;Belarus BY;Georgia GE;Kyrgyzstan KY;Republic of Moldova MD;Tajikistan TJ;Uzbekistan UZ.005006007008009001 0001 1001 200ArmeniaAzerbaijanBelarusGeorgiaKyrgyzstanRepublic of MoldovaTajikistan-201416ArmeniaAzerbaijanBelarusGeorgiaKyrgyzstanRepublic of

258、 MoldovaTajikistan2.6%12.6%9.6%6.4%1.4%3%3.2%2%13.9%12.5%5.1%0.7%3.8%1%-2018aamillion USD%Total volume of the BOT(million USD)Budgetary indicator:BOT as percent of VPAgricultural policy monitoring for Eight countries in Eastern Europe,Caucasus and Central Asia18Figure 8.Budgetary and ot

259、her transfers to agriculture calculated per hectare of agricultural area(USD/ha)18 Source:Authors calculations.Transfers to individual agricultural producers(PSE BOT)dominate total support in almost all countries(Figure 9).Considering all analysed countries together,around 77percent of the total sup

260、port was provided to individual agricultural producers,amounting to USD1.25billion per year on average in 20162018.General services generally constitute a relatively small share in the total BOT.As an exception to this,support for general services is relatively high in Tajikistan,where it accounted

261、for 65percent of all transfers during 20162018.This share is also high in Georgia and Kyrgyzstan,where GSSE BOT accounted for almost half of all transfers in the same period.Figure 9.Composition of the total budgetary and other transfers(BOT)by economic group19 and the composition of transfers to pr

262、oducers by main categories(PSE BOT),average 20162018Source:Authors calculations.18 Data on hectares of agricultural area:average of the years,for which the data is available.19 Total BOT composition for Azerbaijan needs to be interpreted with caution as the data was available only for transfers to i

263、ndividual agricultural producers(PSE BOT).0204060800180ArmeniaAzerbaijanBelarusGeorgiaKyrgyzstanRepublic of MoldovaTajikistan-2018USD/ha0%20%40%60%80%100%120%ArmeniaAzerbaijanBelarusGeorgiaKyrgyzstanRepublic of MoldovaTajikistanTransfers to producers(PSE BOT)Transfers to gene

264、ralservices(GSSE BOT)Transfers to consumers(CSE BOT)0%20%40%60%80%100%120%ArmeniaAzerbaijanBelarusGeorgiaKyrgyzstanRepublic of MoldovaTajikistanOther producersupport(DEF+G)Transfersreducing theon-farminvestment cost(B2)Subsidies tovariable inputsand services(B1+B3)Payments basedonarea/animal/receipt

265、s/income(C)Payments basedon output(A2)BOT composition(20162018)PSE BOT composition(20162018)19Chapter 3 Regional analysis of agricultural price distortions and budgetary transfers While large differences in the composition of the total budgetary support to agriculture are observed across countries,t

266、he shares of the main categories of transfers to individual agricultural producers are similarly diversified across the countries(Figure 9).Subsidies to variable inputs and services(provided,for example,by subsidizing the purchase of fertilizers or seeds)accounted for about 90percent or more of tran

267、sfers to producers in Armenia and Azerbaijan,whereas in Kyrgyzstan,the Republic of Moldova and Tajikistan measures for reducing on-farm investment cost(fixed capital formation,such as the purchase of mechanization equipment and investments in land operation)accounted for around 95percent.Other types

268、 of measures(for example,payments based on output or area)provide a negligible share of support in all countries.21Chapter 4.Country analysis of agricultural price distortions and budgetary transfers4.1 ArmeniaArmenias agricultural sector contributed 12percent to the countrys total GDP in 2019 and e

269、mployed 30percent of its workforce,constituting a decrease compared to 39percent of employment in 2009(World Bank,2020).Armenia can be characterized as a liberal and open economy,with low levels of agricultural policy support until recently(FAO,2017)when it joined the EAEU.Before joining the EAEU,Ar

270、menia had zero or very low import duties on agricultural products.The country is now slowly increasing tariffs for products imported from non-EAEU countries to match the external tariff of the EAEU.Real gross agricultural output20 in Armenia grew at a compound annual growth rate of 1percent over the

271、 20052018 period.At the same time,agrifood trade21 grew at a much higher compound annual growth rate of 9percent,in real terms.While both agrifood imports and exports increased during the analysed period,Armenia was on average a net importer of agrifood products over the 20052018 period(FAO,2020).In

272、 the latest five-year period with available data(20152019),Armenia was a net importer of wheat,milk,pig meat,bovine meat,potatoes and apples,and a net exporter of tomatoes,grapes and apricots.During this period,on average 97percent of Armenian imports of wheat by value were sourced from the Russian

273、Federation.Dependency on the Russian market is also reflected in Armenias exports:Tomatoes(the most important commodity in terms of export value in recent years),apricots and grapes were almost exclusively exported to the Russian Federation.More than half of all bovine meat was imported from India a

274、nd almost a third from Ukraine,while more than three quarters of pig meat were imported from Brazil.On average,around one quarter of imports of milk and dairy products originated from New Zealand,another quarter from Ukraine,and around 15percent from Belarus(UN Comtrade,2020).4.1.1 Nominal Rates of

275、ProtectionAt the aggregate level,the results indicate considerable price incentives for agricultural producers in Armenia in the period 20142018,with NRPs gradually increasing from 6percent in 2014 to 49percent in 2018(Figure 10).Prior to that,in the period 2005-2013,aggregate NRPs were predominantl

276、y negative,indicative of price disincentives for agricultural producers.Exchange rates fluctuations in Armenia,for example the rapid appreciation of the national currency vis-vis the Russian ruble in 2014/15,contributed to the increasing NRPs from 2014,in addition to the overall low level of market

277、integration,fragmented land structure and a large share of smallholder farms that are not well integrated into markets:In Armenia 89percent of all farms are smaller than 3 hectares in size(FAO,2020).20 Measured in constant prices of 20142016.21 Measured in 2015 prices.Agricultural policy monitoring

278、for Eight countries in Eastern Europe,Caucasus and Central Asia22Figure 10.Armenia:Average aggregate nominal rate of protection at farm gate(percent,weighted average),a 20072018a Commodities include apples,apricots,bovine meat,milk,pig meat,tomatoes and wheat.Source:Authors calculations.Armenias NRP

279、s by commodity are shown in Figure 11.For wheat,apples,apricots,bovine meat,pig meat and milk,the positive NRPs indicate that in the analysed period(20072018),domestic producer prices for these commodities were substantially above the comparable reference prices for most years.Looking at the potenti

280、al drivers of price incentives for producers of the above-mentioned commodities,a 20percent value-added tax(VAT)is applied to imported wheat,most of which is sourced from the Russian Federation.While local producers cannot compete with the imported wheat(that is often considered to be of better qual

281、ity),the mills often match the price paid to local farmers to the price paid on imported wheat at the point of delivery at the mills.Given that wheat in Armenia is one of the commodities for which commodity-specific budgetary measures could be identified,NRAs for wheat are shown in Figure 11.Since N

282、RAs reflect additional incentives that wheat producers in Armenia received,these provide a more complete picture of price incentives and disincentives compared to NRPs.However,this indicator is only slightly higher than the NRPs and only in the 2010-2013 period,which is attributed to a programme imp

283、lemented to distribute high quality seeds.The effect of recent tariff increases for meat and milk,resulting from the alignment of Armenias tariff schedule with the EAEU,appear to be reflected in the increase of NRPs in 2018.Over the last decade,tomatoes have become one of Armenias most important agr

284、icultural commodities in terms of export value and the countrys only key commodity receiving price disincentives throughout the entire period.While smaller producers sell at the local wholesale market and have no storage infrastructure,the bulk of exports comes from large producers that export strai

285、ght from their greenhouses or sell to supermarkets.One possible explanation for the negative price incentives for tomatoes could be the highly competitive production conditions.The production costs in Armenia are low due to favorable climatic conditions and lower input costs,while yields remain low(

286、Urutyan,Yeritsyan and Mnatsakanyan,2015).The NRPs for apricots fluctuate over the analysed period,which can partially be explained by the variations in annual production volumes due to weather conditions(such as in 2010),by the fluctuations in demand in Armenias main export destination the Russian F

287、ederation as well as in exchange rate fluctuations(such as the currency appreciation against the Russian ruble in 2014/15).60%40%20%0%20%40%60%2007200820092000%1%9%2%6%1%9%6%8%24%26%49%Armenia:Aggregate NPR23Chapter 4 Country analysis of agricultural price distortion

288、s and budgetary transfersFigure 11.Armenia:Nominal Rates of Protection and Nominal Rate of Assistance by key commodities(percent),and prices of potatoes and grapes at farm gate(USD/tonne),20052019a a Due to the low tradability of potatoes,which are typically produced by smaller farms for self-consum

289、ption and are not market-oriented,NRPs are not shown in this graph.For grapes,besides being a thinly traded commodity,there have been issues with the comparability of producer and reference prices.Source:Authors calculations.4.1.2 Budgetary transfersIn Armenia,the absolute level of budgetary transfe

290、rs to agriculture has been increasing since 2011,with the only exception in 2017(when some measures were discontinued)(Figure 12).Total budgetary support reached USD58.1million in 2018,of which about USD55.7million(96percent)were intended for producer support and USD2.4million(4percent)for the finan

291、cing of general services in agriculture.The share of budgetary transfers to individual producers has been increasing steadily throughout the entire 2007-2018 period.There was no budgetary support to consumers in Armenia.On average over the last three-year period(20162018),the total budgetary support

292、 to agriculture(total BOT)was equivalent to 2.6percent of the value of agricultural production.The budget allocation to agriculture is relatively low compared to other analysed countries.100%50%0%50%100%200520062007200820092000019NRPNRA100%50%0%50%100%200620072008200

293、92000019100%50%0%50%100%200520062007200820092000019100%50%0%50%100%200520062007200820092000%50%100%150%200%250%200520062007200820092000019100%50%0%50%100%20052006200720

294、0820092000019100%50%0%50%100%150%200%250%200620072008200920000052006200720082009200000400500600Domestic price at farm gateReference price at farm gate-alternative2005200620072008200920102011

295、2000006008001 0001 200Domestic price at farm gateReference price at farm gate-alternativeUSD/tonneUSD/tonneArmenia,wheatArmenia,applesArmenia,bovine meatArmenia,pig meatArmenia,milkArmenia,apricotsArmenia,grapesArmenia,tomatoesArmenia,potatoesAgricultural policy moni

296、toring for Eight countries in Eastern Europe,Caucasus and Central Asia24Figure 12.Armenia:Budgetary and other transfers to agriculture by economic group of beneficiaries,20072018Source:Authors calculations.Agricultural producers receive support through the subsidization of irrigation costs by cubic

297、meter of water.Arable land is very limited in Armenia and requires irrigation for an adequate yield(ICARE,2012),which us provided through a subsidy.Since 2012,agricultural producers have also received support through the provision of mineral fertilizers and diesel fuel at reduced prices.Until 2013 a

298、nd from 2018 onwards,the government provided support to purchase of seeds for all crops,and separate support specifically for wheat producers(such as the provision of elite wheat seeds between 2010and2013).In the 20072009 period,some budgetary support was given in the form of per hectare payments ai

299、med at bringing non-utilized agricultural land back into cultivation.Under general service support,the largest share of budgetary funds was spent on inspection and control measures(such as the financing of veterinary and anti-epidemic activities,plant protection,food safety and sanitary services)and

300、 knowledge generation and transfer measures(such as animal breeding and seed varieties improvement,education and extension services).In 2018,these two categories of support captured 93percent of the total general service support.The remaining 7percent of funds were earmarked for infrastructure servi

301、ces targeting improvement and maintenance of agricultural land.Support to agriculture is provided through the programmes managed by the Ministry of Agriculture,other government agencies and through cooperation with international organizations(such as The International Fund for Agricultural Developme

302、nt IFAD,the World Bank Group and FAO).4.2 AzerbaijanIn Azerbaijan,the share of employment in the agricultural sector is very high at 36percent in 2019,while the sector contributed 5.7percent to the GDP(World Bank,2020).Azerbaijan has made significant progress toward transformation to a market-based

303、economy.At the same time,some reform initiatives are unfinished,and structural inefficiencies are slowing down long-term growth.This applies particularly to sectors not related to oil,which Azerbaijans economy is highly dependent on(Aksoy et al.,2017).Real gross agricultural output22 in Azerbaijan g

304、rew at a compound annual growth rate of 2percent during the 20052018 period.At the same time,real agrifood trade23 grew at higher rate of 7percent.Azerbaijan was a net importer of agrifood products throughout the analysed period(FAOSTAT,2020).With regard to specific commodities,Azerbaijan was a net

305、importer of wheat,potatoes,bovine meat,poultry meat and milk in the 20152019 period,and a net exporter of hazelnuts,tomatoes,persimmons and cotton.Azerbaijans main trading partner for most of the 22 Measured in constant 20142016 prices.23 Measured in 2015 prices.0007 08 09 10 11 12 13 14

306、15 16 17 18Producers individually(PSE BOT)Agriculture collectively(GSSE BOT)0%10%20%30%40%50%60%70%80%90%100%07 08 09 10 11 12 13 14 15 16 17 18million USD25Chapter 4 Country analysis of agricultural price distortions and budgetary transfersanalysed commodities is the Russian Federation.On average d

307、uring 20152019,around 87percent of wheat imports originated from the Russian Federation,while most imported meat originated from Ukraine(with shares for bovine meat of 72percent and poultry meat of 60percent).More than half of the milk and dairy products were imported from three countries:the Russia

308、n Federation(27percent),followed by Ukraine(14percent)and Belarus(12percent).Tomatoes,with the highest share among the analysed commodities in terms of export value in recent years,were almost entirely exported to the Russian Federation,as were persimmons.Some 90percent of cotton was exported to Trk

309、iye.Hazelnuts,another important export commodity for Azerbaijan,were mostly exported to the Russian Federation(35percent)and the European Union(33percent)(UN Comtrade,2020).4.2.1 Nominal Rates of ProtectionAggregate NRPs for Azerbaijan suggest strong price incentives for agricultural producers betwe

310、en 2009and2015(Figure 13).Azerbaijan is not a member of the WTO or the EAEU,and as such had relatively high levels of border protection,one of the highest among the countries in this study,throughout the analysed period.Other factors that may be contributing to the overall high estimates of NRPs pri

311、or to 2015 are the fragmented farm structures,causing limited supply and high production costs,and the increasing purchasing power of the population(Volk et al.,2015),with robust demand pushing up prices to producers.Before 2008 and in 2016,the average aggregate NRPs were negative,indicating price d

312、isincentives for agricultural producers at the aggregate level.While tomatoes and,to a lesser extent,hazelnuts might be driving this result prior to 2008,the decrease of the aggregate NRPs in 2016 could be caused by the lasting effect of the currency devaluation in 2015,24 which was associated with

313、a drastic decline in global oil prices and Azerbaijans high dependence on oil exports(see in Mogilevsky,2017).Figure 13.Azerbaijan:Average aggregate Nominal Rates of Protection at farm gate(percent,weighted averages),a 20052016 a Commodities include bovine meat,cotton,hazelnuts,milk,poultry meat,tom

314、atoes and wheat.Source:Authors calculations.NRPs at the individual commodity level reveal that producer prices were predominantly above the comparable international prices(Figure 14)for wheat,cotton,poultry meat and milk in the 20052018 period.For other commodities,such tomatoes and hazelnuts that a

315、re exported,the NRPs are more volatile,changing between positive and negative NRPs throughout the period(often negative before 2008 and after 2015).Persimmon and bovine meat producers faced negative NRPs throughout the entire period.The national currency devaluation in 2015 is likely to have contrib

316、uted to the decrease in the measured NRPs for all selected commodities,except hazelnuts.For tomatoes,the domestic price are below the reference price only in 2005/06and2015/16,while it is above the reference price(and the NRPs are therefore positive)from 2007until2014.Import duties increased to USD0

317、.4per kg in 2016(the bound rate had been 15percent until then),but while this measure was implemented to protect local production,the negative NRPs in the last three years 24 Azerbaijans national currency experienced two major devaluations during 2015(against the US dollar),and appreciation against

318、the Russian ruble in 20112015.60%40%20%0%20%40%60%2005200620072008200920001616%5%8%5%39%35%22%31%24%32%29%10%Azerbaijan:Aggregate NRPAgricultural policy monitoring for Eight countries in Eastern Europe,Caucasus and Central Asia26covered indicate that it may not have been effect

319、ive.Hazelnut prices are typically very volatile,given that harvests heavily depend on weather conditions and their exports are subject to stringent phytosanitary and food safety rules.Hazelnuts exported from Azerbaijan to the European Union were found to contain aflatoxins above permitted levels in

320、some of the analysed years and could have affected the domestic price.Persimmons are exported mostly fresh and mainly to the Russian Federation.NRPs are shown only for the 20122018 period due to the lack of trade data.25 The appreciation of the national currency against the Russian ruble and the dep

321、reciation of the Russian ruble,the main trading partner for Azerbaijans persimmons,against the US dollar are presumed to be contributing to the negative NRPs.The NRPs for cotton are positive and high for most years.Rather than the effect of policies,it could be the result of cotton farmers in Azerba

322、ijan facing poor irrigation infrastructure,unreliable access to machinery and labour shortages during harvesting(Prikhodko et al.,2019),keeping the domestic prices high.More in-depth analysis of the cotton value chain would be needed to identify the exact drivers.The NRPs for wheat were also positiv

323、e and high during the 20092015 period.In 2010,wheat production fell by around 40percent(primarily caused by flooding)compared to 2009,dropping 20percent below the longer term average of the 20052009 period.This resulted in higher domestic prices relative to the reference price,which was still the ca

324、se during the subsequent years.The high NRPs are most likely not the result of trade protection(as no such measures were reported),but driven by a combination of other factors,such as the fluctuation of exchange rates(Hasanov and Huseynov,2009),e.g.the depreciation against the US dollar in 2015,and

325、higher production costs due to a fragmented farm structure(Volk et al.,2015).Figure 14.Azerbaijan:Nominal Rates of Protection and Nominal Rate of Assistance by key commodities(percent),and prices of potatoes at farm gate(USD/tonne),20052018a a Due to low tradability of potatoes,which are typically p

326、roduced by smaller farms for self-consumption and are not market-oriented,the NRPs are not shown in this graph.Source:Authors calculations.25 The HS code for persimmons in the UN Comtrade database has been in use only since 2012.100%50%0%50%100%200520062007200820092000162017201

327、8NRPNRA100%50%0%50%100%20052006200720082009200016NRPNRA100%50%0%50%100%150%20052006200720082009200000%50%0%50%100%20052006200720082009200000%50%0%50%100%200018100%50%0%50%100%200520062007200

328、82009200000%50%0%50%100%20062007200820092000005200620072008200920000500600700Domestic price at farm gateReference price at farm gate-alternative100%50%0%50%100%200520062007200820092010201120

329、0018USD/tonneAzerbaijan,wheatAzerbaijan,hazelnutsAzerbaijan,bovine meatAzerbaijan,poultry meatAzerbaijan,milkAzerbaijan,persimmonsAzerbaijan,cottonAzerbaijan,potatoesAzerbaijan,tomatoes27Chapter 4 Country analysis of agricultural price distortions and budgetary transfers4.2.2 B

330、udgetary transfersFor Azerbaijan,data on budgetary support to agriculture is available only for a limited number of producer support measures.From 2007until2014,direct support to producers was increasing,but dropped significantly after 2014 due to the impact of monetary policy changes(such as the de

331、valuation of the national currency)(Figure 15).In 2018,budgetary support amounted to USD579.5million.About 82percent of these payments were provided in the form of input subsidies related to specific variable inputs and 11percent as transfers that reduced on-farm investment costs(leasing machinery a

332、nd equipment at a discounted price).The rest was granted in the form of area payments(3percent)and in the form of output payments(3percent).The total budgetary support to agricultural producers in Azerbaijan represented on average around 13percent of the total value of agricultural production in the

333、 20162018 period(11percent in 2018).26 Figure 15.Azerbaijan:Budgetary and other transfers to producers,20072018Source:Authors calculations.Tax concessions(covering almost 40percent of the total BOT)are very important instruments used to support agriculture in Azerbaijan.Agricultural producers are exempt from all taxes,except the land tax.Other measures for reducing variable input costs of agricult