Report No: AUS0002229 . Angola Agriculture Support Policy Review Realigning Agriculture Support Policies and Programs June 20, 2021 AGF GP 1 © 2021 The World Bank 1818 H Street NW, Washington, DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org Some Rights Reserved This work is a product of the staff of The World Bank. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the Executive Directors of the World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Rights and Permissions The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. Attribution—Please cite the work as follows: “World Bank. {YEAR OF PUBLICATION}. {TITLE}. © World Bank.� All queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@worldbank.org. 2 Contents Figures ........................................................................................................................................................... 5 Tables ............................................................................................................................................................ 6 List of Acronyms ............................................................................................................................................ 6 Executive Summary..................................................................................................................................... 10 Report Highlights .................................................................................................................................... 10 Recommendations .................................................................................................................................. 12 Introduction ................................................................................................................................................ 14 Country Context ...................................................................................................................................... 16 Sector Context......................................................................................................................................... 19 Overview of Agricultural Support in Angola ........................................................................................... 23 Objectives of Agriculture Support Policies and Programs in Angola .......................................................... 26 Sector Competitiveness .......................................................................................................................... 26 Food and Nutrition Security .................................................................................................................... 29 Climate Resilience ................................................................................................................................... 30 Conceptual Framework for Policy Review .................................................................................................. 33 Methodology: Rationale and Coverage .................................................................................................. 33 OECD Methodology: Technical Concepts and Calculation...................................................................... 35 Agriculture Support Estimates: Global Snapshot and Trends ................................................................. 38 OECD and Emerging Economies: A Comparison ..................................................................................... 39 Agriculture Support Estimates for Angola .................................................................................................. 42 Total Support Estimates (TSE) ................................................................................................................. 42 Support to Agricultural Producers (PSE) ................................................................................................. 44 Support to General Services for Agriculture (GSSE) ............................................................................... 50 Support to Consumers of Agricultural Products (CSE) ............................................................................ 53 Conclusions ................................................................................................................................................. 54 Main Findings .......................................................................................................................................... 54 Proposed Agriculture Policy Reform Agenda .......................................................................................... 56 Lessons from Angola for other Countries ............................................................................................... 59 Annex A: Supplementary Figures and Tables ............................................................................................. 61 Annex B: OECD Categories and Classification Criteria ................................................................................ 69 Annex C: Conceptual Note on Price Collection of Agricultural Producers .................................................. 72 Annex D: Public Programs and Budgetary Expenditure .............................................................................. 76 3 4 Figures Figure 1: Map of Angola .............................................................................................................................. 16 Figure 2: Budget Balance and Change in Debt, 2012–2022........................................................................ 18 Figure 3: Actual and Projected Poverty Rates and Consumption, 2018–2023 ........................................... 18 Figure 4: Benchmarking Size and Performance of Angola’s Agriculture Sector ......................................... 20 Figure 5: Angola’s Structural Transformation (2006–2017) ....................................................................... 21 Figure 6: Increase in Production of Selected Agricultural Products, 2012–2016 (%) ................................. 21 Figure 7: Benchmarking Maize and Wheat Yields, 2002–2017 .................................................................. 22 Figure 8: Employment by Sector, 2018–2019 ............................................................................................. 22 Figure 9: Poverty and Sectors of Employment ........................................................................................... 23 Figure 10: Share of Government Agriculture Expenditure in Total Public Expenditure (%), 2008–2018 ... 24 Figure 11: Performance on Enabling the Business of Agriculture (EBA) Indicators.................................... 25 Figure 12: Benchmarking Supermarkets and Grocery Retailers, 2017–2018 ............................................. 28 Figure 13: Benchmarking Retail Sales of Packaged and Ultra-Processed Food, 2017–2018 ...................... 28 Figure 14: Trends in Angola’s Performance on the Economist’s Global Food Security Index, 2020 .......... 29 Figure 15: Climate Projections and Sectoral Impacts ................................................................................. 31 Figure 16: GHG Emissions in 2014, by Sector ............................................................................................. 32 Figure 17: GHG Emissions in Agriculture .................................................................................................... 32 Figure 18: Coverage of OECD Methodology of Agriculture Support Estimates .......................................... 34 Figure 19: OECD Methodology—Main Indicators of Transfers, by Source ................................................. 35 Figure 20: Transfer to Specific Commodities (SCT%), 2017–2019 .............................................................. 38 Figure 21: Agriculture Support Trends (54 Countries) ................................................................................ 39 Figure 22: Agriculture Support Trends—OECD Countries .......................................................................... 40 Figure 23: Agriculture Support Trends—Emerging Economies .................................................................. 40 Figure 24: Transfer to Specific Commodities (SCT)—OECD, 2017–2019 .................................................... 41 Figure 25: Transfer to Specific Commodities (SCT)—Emerging Economies, 2017–2019 ........................... 41 Figure 26: GSSE Composition in OECD Countries, 1986–2019 ................................................................... 42 Figure 27: Benchmarking TSE as share of GDP, 2018–2019 (average) ....................................................... 43 Figure 28: Benchmarking TSE as a share of Agriculture GDP, 2018–2019 (average) ................................. 43 Figure 29: Benchmarking Angola’s TSE, by Source of Transfers ................................................................. 44 Figure 30: Benchmarking %PSE, 2018–2019............................................................................................... 45 Figure 31: Level and Composition of Angola’s PSE, 2018–2019 ................................................................. 46 Figure 32: Composition of Angola’s PSE, by Category of Support (2018–2019)......................................... 47 Figure 33: Benchmarking %SCT by Commodity, 2018–2019 (Average) ..................................................... 48 Figure 34: Benchmarking SCT% for Corn, 2018–2019 (average) ................................................................ 49 Figure 35: Benchmarking %SCT for Beans, 2018–2019 (average) .............................................................. 49 Figure 36: GSSE as a share of agriculture GDP, 2018–2019........................................................................ 50 Figure 37: GSSE as a share of Angola’s TSE, 2018–2019 (Average) ............................................................ 51 Figure 38: Composition of the General Services Support Estimate (GSSE) in Angola ................................ 52 Figure 39: Benchmarking GSSE by Component, 2018–2019 ...................................................................... 52 Figure 40: Benchmarking %CSE, 2018–2019 (Average) .............................................................................. 53 Figure 41: Agriculture Support and Value Added per Worker, 2018 – 2019 .............................................. 56 Figure 42: Support to Maize vs Yields, 2018 - 2019 (average).................................................................... 56 Figure 42: Sector-level Decomposition of Angola’s GDP, 2002–2018 ........................................................ 61 5 Figure 43: Distribution of Angola’s Surface Land and Arable Land............................................................. 61 Figure 44: Benchmarking the Size of Angola’s Agriculture Sector .............................................................. 61 Figure 45: Benchmarking Vegetable Yields, 2015–2019............................................................................. 62 Figure 46: Benchmarking Agriculture Labor Productivity, 2015–2019 ....................................................... 62 Figure 47: Import needs for key food staples, in tons ................................................................................ 63 Figure 48: Producer Support Estimate (PSE) and Sub-Categories .............................................................. 63 Figure 49: General Services Support Estimate (GSSE) and Sub-Categories ................................................ 64 Figure 50: Estimates of Support to Agriculture (54 Countries)................................................................... 65 Figure 51: Angola’s TSE in Kwanzaa, 2018–2019 ........................................................................................ 66 Figure 52: Disaggregation of Angola’s TSE, 2018 ........................................................................................ 67 Figure 53: Disaggregation of Angola’s TSE, 2019 ........................................................................................ 67 Figure 54: World Trade Organization Boxes ............................................................................................... 68 Figure 55: Budget PSE, by Category and Program ...................................................................................... 76 Figure 56: Budget GSSE, by Category and Project ...................................................................................... 80 Figure 57: Budget CSE ................................................................................................................................. 81 Tables Table 1: Top 5 Agricultural Import Commodities, 2019.............................................................................. 26 Table 2: Top 5 Agricultural Export Commodities, 2019 .............................................................................. 26 Table 3: Composition of PSE, 2018–2019 ................................................................................................... 45 Table 4: Composition of PSE Budgetary Payments ..................................................................................... 47 Table 3: WTO Classification of Angola’s TSE ............................................................................................... 53 List of Acronyms ADI Agricultural Development Institute AfCFTA Africa Continental Free Trade Area AGOA African Growth and Opportunity Act (of the United States) AgPERs Agriculture Public Expenditure Reviews AU African Union CAADP Comprehensive Africa Agriculture Development Program CAP Common Agriculture Policy CIF Cost, insurance and Freight CO2 Carbon Dioxide COVID-19 Coronavirus disease CPSD Country Private Sector Diagnostic CSA Climate Smart Agriculture CSE Consumer Support Estimate DRC Democratic Republic of Congo EBA Enabling the Business of Agriculture EU European Union FAO Food and Agriculture Organization FAS Food and Agriculture Service 6 FOB Free on Board FTA Free Trade Area GAO Gross Agricultural Output GDP Gross Domestic Product GHG Greenhouse Gases GSSE General Services Support Estimate IADB Inter-American Development Bank IFC International Finance Corporation IFPRI International Food Policy and Research Institute IMF International Monetary Fund INE National Institute of Statistics, Instituto Nacional De Estatísticas KPI Key Performance Indicator KZ Kwanza LDC Least Developed Country LMIC Lower-Middle Income Country LUCF Land-use Change and Forestry MAFAP Monitoring African Food and Agricultural Policies MFN Most Favored Nation MINAGRIP Ministry of Agriculture and Fisheries, Ministério da Agricultura e Pescas MINFIN Ministry of Finance MIT Massachusetts Institute of Technology MOSAP Market-oriented Smallholder Agriculture Project MPS Market Price Support MT Metric Tons NAFTA North American Free Trade Agreement NAIP National Agriculture Investment Plan NAPA National Adaptation Programme of Action NDC Nationally determined contributions NEPAD New Partnership for Africa’s Development NSmartAg Nutrition Smart Agriculture OECD Organisation for Economic cooperation and Development Agribusiness Products Acquisition Program/Programa de Aquisição de Produtos PAPAGRO Agro-Pecuários Commercial Agriculture Development Project/Projeto De Desenvolvimento Da PDAC Agricultura Comercial PER Public Expenditure Review Programme of Restructuring of the System of Logistics and Distribution of Basic PRESILD Commodities/Programa de Reestruturação do Sistema de Logística e Distribuição Program of Support for Production, Diversification of Exports and Substitution of PRODESI Imports/Programa de Apoio à Produção, Diversificação das Exportações e Substituição das Importações PSE Producer Support Estimate ROC Republic of Congo SADC Southern African Development Community SCT Single Commodity Transfer SME Small and Medium Enterprise 7 SOE State-Owned Enterprise SSA Sub-Saharan Africa TSE Total Support Estimate VAM Vulnerability Assessment and Monitoring VAT Value-Added Tax WB World Bank WFP World Food Programme WTO World Trade Organization 8 Acknowledgments The WBG team was led and coordinated by Diego Arias and Ashesh Prasann of the Agriculture and Food Global Practice. The team members included: Aniceto Bila, Hector Peña, Armando Gonzalez, Marcelino Ferreira, Izabela Leao, Bodomalala Rabarijohn, Nara Meli (Agriculture and Food, WB), Zenaida Hernandez Uriz (Finance, Competitiveness and Innovation, WB), John Keyser (Macroeconomic, Trade and Investment, WB), and Francisco Pereira Fontes (FAO). The WBG team would like to thank the Government of Angola for the support to undertake this review and the productive discussions, in particular Anderson Jeronimo and Helena Borges (Ministry of Agriculture). The WBG team would also like to thank colleagues that provided valuable input and guidance, in particular Shobha Shetty (Practice Manager, Agriculture and Food), Marc Lixi (Program Leader, Sustainable Development), Christian Derlagen and Valentina Pernechele (FAO), Gomes Cambuta, Paavo Eliste, and Madhur Gautam (Agriculture and Food), and Jean Saint-Geours (Private Sector Development). 9 Executive Summary This report assesses agriculture policy support estimates in Angola. These estimates represent the monetary value assigned to different agriculture support policies and programs using the OECD methodologyi for the years 2018–2019. The advantages of using the OECD methodology are that: (a) it provides a systematic and integrated view of agriculture support policies and programs (not limited to the more traditional public expenditure reviews or rate of protection); (b) given the large number of countries using this same methodology to measure support estimates, an immediate benchmarking is possible across a large set of comparatorsii; and (c) the methodology is simple and can be integrated into the agriculture public policy analysis conducted by the Government and other stakeholdersiii. The methodology also has some disadvantages and limitations, mainly: (a) while it quantifies the level of support provided to producers and consumers, it does not further disaggregate support received by type of agricultural producers (small-scale/large-scale; family farm/commercial) or consumers; (b) since the estimates are based on the monetary value of budget and price support, non-monetary support, like the quality of policies, is not captured (e.g., the methodology is able to identify how much policy/program support is invested in land administration efforts, but unable to qualify the impact (quality) of those policies/programs). This assessment aims to support the Angolan Government in reviewing its agriculture policies and programs, in particular to: (a) provide new estimates and a new approach to assess sector support for policy decision-making; (b) allow for benchmarking agriculture support policies with a large global database of countries using the same estimate methodology; and (c) help kickstart a policy dialogue on realigning agriculture policies and programs in Angola towards greater sector competitiveness and fast economic recovery from the coronavirus disease 2019 (COVID-19) pandemic, increased food security and nutrition outcomes, and climate sustainability through a build back better approach. Report Highlights • Angola allocated US$1.3 billion in annual support to the agriculture sector, representing 1.5 percent of GPD. Total Support Estimate (TSE) to agriculture from public policies and programsiv in Angola in 2018 and 2019 averaged US$1.315 billion per year. This was equivalent to 28.5 percent of the agriculture gross domestic product (GDP), much higher than in most developing countries (8.3 percent on average) and below OECD member countries (40.2 percent on average). A neighbor and close trading partner, South Africa, has a TSE of 9 percent of agriculture GDP and 0.4 percent of total GDP, much lower than Angola, while OECD countries’ support to agriculture represents 0.6 percent of total GDP. • Although total agriculture support in Angola is high compared to other developing countries, the portion of support going to public goods and services is relatively low. The Total Support Estimate (TSE) is composed of support to producers (measured as Producer Support Estimate, PSE), Consumer Support Estimate (CSE), and support to general agriculture public goods and services (General Services Support Estimate, GSSE)v. The analysis revealed that 94 percent of TSE was through producer support (largely in the form of market price support), while just 6 percent went to GSSEs. Benchmarking the TSE composition across countries where data is available, we observe that Angola’s investment in GSSE is the lowest, except for Indonesia and Mozambique. As a share of the agriculture GDP, GSSE accounted for just 1 percent, which is low compared to other developing 10 countries’ average (2.7 percent) and the OECD’s average (5.3 percent). This is consistent with the fact that Angola is one of the four countries with agriculture spending less than 1 percent of total public expenditure (between 2008 and 2018). Notably, Angola is a signatory of the African Union (AU) Malabo Declaration and has committed to allocating 10 percent of its public resources to agriculture under the Comprehensive Africa Agriculture Development Program (CAADP) targets. • Nearly half of gross farm receipts were accounted by Angola’s support to producers, more than twice the corresponding OECD average. On average, 46 percent of gross farm receipts in Angola came from agriculture support policies and programs in 2018 and 2019. Significantly, the corresponding producer support—measured by %PSE—averaged 12.5 percent for the OECD during the 2017–2019 period. Producer support in Angola was comparable with that of countries with high levels of support, such as Japan, Switzerland and Korea. • Agriculture producer support in Angola is overwhelmingly funded by policies that raise domestic agriculture prices. Ninety-eight percent of the support to agriculture producers (PSE) is funded by Market Price Support (MPS), while budgetary support only represents 2 percent (average between 2018 and 2019). These transfers occur due to public policies (mainly border measures) are making the domestic prices of agriculture and food products higher than the international prices (compared at farm gate). In other words, border measures are creating an “implicit tax� for food consumers in Angola and most beneficiaries of higher prices are agriculture producers that participate in market sales. MPS are thus monetary transfers from Angolan food consumers to Angolan producers. • The current structure of producer support only benefits a small number of large-scale, commercial producers, and does not enhance sector competitiveness. MPS is based on the amount of agriculture production that a farmer sells in the market, it is therefore poorly targeted and favors large producers who generate commercial surplus rather than smallholders with smaller surpluses or who only produce for self-consumption.vi Given that small-scale and subsistence-oriented family farms dominate in Angola—accounting for 80 percent of production and 90 percent of land—support to producers through higher prices benefits only a small proportion of producers. Furthermore, MPS distorts production decisions and investments in competitive agriculture products as it protects producers from international market prices. As Angola transitions to a liberal free trade environment by participating in the SADC Free Trade Area (FTA) and Africa Continental Free Trade Area (AfCFTA), the need for genuine competitiveness improvements will become all the more necessary. • Food consumers in Angola pay, on average, an implicit tax of about 8 percent. Support to food consumers (Consumer Support Estimate, CSE) is negative in Angola. CSE measures the support to (or tax on) food consumers arising from public agriculture policies. Although Angola does provide some support to food consumers in the form of food aid and school feeding programs, the overwhelming majority of the CSE is negative, due to public policies protecting domestic prices. CSE as a percentage of total food expenditures by food consumers averaged 8 percent in 2018 and 2019. This 8 percent implicit tax is a transfer from consumers to producers through higher domestic food prices. It is also a regressive tax since poor consumers spend a larger share of their income on food than high-income consumers. • Agriculture support to producers in Angola is concentrated in maize and beans and is relatively high for these commodities compared to other countries. Of the total gross revenues perceived by farmers producing maize, 45 percent came from agriculture public support policies and programs, 11 while beans had 28 percent support, averaged over 2018 and 2019 (commodity-specific support is measured by Single Commodity Transfers—SCT). In comparison, the %SCT in OECD countries was 11 percent for maize, 6 percent for bananas, and nearly zero for cassava in the same period (SCT for beans is not measured in the OECD). This large variation in agriculture public sector support—and therefore profitability—across commodities signals the distortions that farmers face when making production decisions. For example, maize and beans farmers received the equivalent of US$258/ha and US$126/ha respectively in 2019 (predominantly through MPS), while a banana farmer received US$45/ha (only input subsidies). The support to maize was nearly four times higher than in the US (US$72/ha) and nearly three times higher than in Canada for beans farmers (US$44/ha).vii • Angola is in the process of implementing public policies to diversify its economy, recovering from the COVID-19 pandemic, and moving towards a more competitive and sustainable agriculture sector. In the past, support consisted largely of price support (through border measures), without addressing underlying competitiveness bottlenecks. This approach will need to be phased out as Angola moves towards full participation in regional and continental free trade agreements. Programs like the Commercial Agriculture Development Project (PDAC) seek to improve the competitive position of the agriculture sector. Developing agribusinesses is high in the country’s development agenda, with an important private sector development program and technical assistance provided by the World Bank and IFC. The severe droughts of the last years and the COVID-19 pandemic have also renewed the urgency to focus on supporting the climate resilience and nutrition of the poorest households. This report presents some important recommendations for realigning agriculture support policies and programs towards competitiveness, climate resilience and nutrition and food security objectives. Competitiveness COVID-19 Recovery: Building back better Agriculture Policy Shift objective (diversification Nutrition—Food and trade integration) Climate Resilience Security PSE to GSSE MPS to non-distortionary PSE CSE (-) to CSE (+) SCT to non-commodity specific PSE Recommendations: ✓ Shift agriculture support from private towards public goods and services. Agriculture support in Angola is mainly geared towards private goods (subsidies and market price support) rather than towards investments in agriculture public goods and services: 60 percent of agriculture public expenditures (average for 2018 and 2019) went towards investments in private goods (subsidies), such as payments based on inputs—programs like land preparation support, seeds programs, and machinery subsidies. Angola should seek to shift its agriculture sector support towards investments in public goods and increase GSSE’s share of agriculture GDP from its current level of 1.2 percent to 12 at least the level of South Africa, or the average of developing countries (2.3 percent and 2.7 percent, respectively), given the overwhelming and long-standing evidence that public sector investments and support to agriculture public goods and services deliver higher economic returns than public sector investments in private goods (World Bank 2017viii; Lopez and Galinato 2007ix; Lopez 2005x; World Bank 2001xi). Commercial agricultural producers would benefit from the opportunities to supply the domestic market created by the recent floating of the kwanza, and the various Government programs for agribusiness development. ✓ Shift from distortive measures to competitive agriculture policy support. Given that an overwhelmingly large share of Angola’s agriculture support is MPS (or coupled to the production of specific agriculture products), a transition plan for agriculture to move towards a more competitive policy support environment is very much needed. In fact, Angola will likely be engaging in MPS reduction commitments in agriculture trade agreements such as the Africa Continental Free Trade Area (AfCFTA), so a complementary trade agenda is needed to support smallholders of protected agriculture product transition to face market prices and take advantage of trade.xii ✓ Shift from implicit taxation to positive support to food consumers. As the negative CSE estimates in this report demonstrate, Angolan food consumers are funding the bulk of agriculture support to the sector. A shift away from MPS, as suggested above, will reduce the implicit food tax to food consumers, consequently increasing the welfare of the poorest. However, other public policies and programs could be further enhanced to directly safeguard consumers from food insecurity and nutrition challenges, by targeting support through social protection programs (food aid, school feeding) and countercyclical safety nets. ✓ Shift support to promote environmental and nutrition security objectives. Given the country’s fiscal limitations and the implicit tax imposed by agriculture public policies on Angolan food consumers, producer support should be geared towards achieving objectives beyond supporting farmer incomes. Support can contribute towards food and nutrition security objectives, leveling the playing field for imported food products like maize and beans relative to bananas and cassava. As an example, a maize farmer receives almost six times more per hectare in support than a banana farmer, thus making a simple plate of food—as defined by the WFP “Counting the Beans� methodology— costlier.xiii Furthermore, Climate Smart Agriculture (CSA)xiv and Nutrition Smart Agriculture (NSmartAg)xv technologies and practices should be integrated into farmer input and technology support incentives, to promote productivity growth, and fulfill environmental and nutrition objectives. Moreover, decoupling producer support from specific agriculture products would enable farmers to make production decisions mainly on market opportunities (and not on the level of public sector support). 13 Introduction 1. This report assesses agriculture policy support estimates in Angola. These estimates are the monetary value assigned to different agriculture support policies and programs using the OECD methodologyxvi for 2018 and 2019. The objective of undertaking this assessment is to support the Government in reviewing its agriculture policies and programs, and to: (a) provide new estimates and a new approach to assess sector support for policy decision-making; (b) allow for benchmarking agriculture support policies with a large global database of countries using the same estimate methodology; and (c) help kickstart a policy dialogue on realigning agriculture policies and programs in Angola towards greater sector competitiveness, food security and nutrition outcomes, and climate sustainability. 2. Previous work in other developing countries has shown policymakers the value of using such estimates in a process of transformation of the agriculture sector. The OECD methodology a complete picture of all public policies and programs supporting agriculture and food consumption, bringing the support from taxpayers and consumers alike. The advantages of using this methodology are that: (a) it provides a systematic view of agriculture support policies, programs (not limited to the more traditional public expenditure reviews or rate of protection), and incentives at different levels of the food system, allowing to envision policy reforms to improve sector competitiveness, reduce distortions and improve equality with trading partners; (b) given the large number of countries using this same methodology to measure support estimates, an immediate benchmarking is possible across a large set of comparatorsxvii; and (c) it is simple and can be integrated into the agriculture public policy analysis conducted by the Government and other stakeholdersxviii. The methodology also has some disadvantages and limitations, mainly: (a) few African countries have carried out agriculture support estimates with it, meaning Angola can only benchmark against South Africa and Mozambique; and (b) since the estimates are based on the monetary value of budget and price support, non-monetary support, like the quality of policies, are not captured (for example, the methodology is able to identify how much policy/program support is invested in land administration efforts, but not to qualify the impact (quality) of those policies/programs). 3. Agriculture support estimates are also expected to inform Angola’s upcoming trade negotiations on agriculture and food products in the Africa Continental Free Trade Area (AfCFTA), the Southern Africa Development Community (SADC), and other international trade agreements. These estimates enable Angola to benchmark against South Africa and Mozambique for the level and composition of agriculture support, which is key to successfully negotiating agriculture trade agreements and developing policy reforms that enhance agriculture trade competitiveness. Notably, this assessment builds on the Food and Agriculture Organization’s (FAO) recent support to Angola, which included budgetary data collection as part of an Agriculture Public Expenditure Review. This report fills existing coverage and price data gaps, expanding the scope of assessment from a public expenditure review to a comprehensive review of agricultural support.xix Given the current fiscal constraint faced by Angola and the need to diversify its economy, there is a window of opportunity for the Government of Angola to gradually open up the trade of agriculture inputs and products, while shifting public support policies and programs towards more targeted interventions that can achieve competitiveness objectives, as well as climate resilience and nutrition/food security. 4. As part of this assessment, four main activities were conducted between September 2020 and June 2021 as the basis for the drafting of this report: a. Training of in-country technical experts on the recognized OECD agriculture support estimate methodology. In December 2020, the World Bank (WB) team delivered a comprehensive training course (seven modules) to build capacity on data collection, processing and analysis among public 14 sector staff within the Ministries of Finance, Economy, and Agriculture and technical experts outside of the Government (independent consultants). The objective of the training was twofold: (a) to enable the national Government to update the estimates every two years following the OECD cycle to maintain benchmarking capacity; and (b) to help validate and discuss policy options based on the 2018–2019 estimates. b. Stocktaking of agriculture public support programs and policies impacting the agri-food system and technical analyses to produce quantitative estimates of agriculture support to producers (PSE), consumers (CSE), and to general services and support to agriculture (GSSEs). This activity also identified specific commodities, and classified the support per OECD categories to assess the level of distortion, while enabling an automatic benchmarking with other countries. The WB team collaborated with the trained staff to undertake a policy inventory to gain experience in the production of a detailed assessment on the nature and extent of public support. c. Discussion of preliminary estimates and options for policy and program reform with sector stakeholders: The team discussed and validated preliminary agriculture support estimates with relevant policymakers, private sector representatives, and other agri-food sector stakeholders in April 2021. This presentation included benchmarked indicators of agriculture support and draft policy conclusions. d. Database construction and institutionalization of future updates in Angola, to enable comparability with regional and global agriculture support estimates. The database of agriculture support estimates for Angola is expected to feed directly into the Government’s ongoing formulation of its first National Agriculture Investment Plan (NAIP), its reporting for the African Union’s CAADP Biennial Review Scorecard, and other regional and global initiatives targeted at capturing information on Angola’s support to the sector (such as MAFAP, Agrimonitor, OECD and others). 15 Country Context 5. Angola is a resource-rich country whose growth dynamics have been affected by the legacy of a long civil war and by natural resource exploitation. One of the largest countries in Africa, Angola has total area of 1,246,700 km2 and is bordered by Namibia in the south, the Democratic Republic of Congo (DRC) and Republic of Congo (ROC) in the north, and Zambia in the east. Endowed with a coastline of 1,650 km on the Atlantic Ocean in the west, the topography consists of a narrow plain along the coast which rises abruptly to a vast interior plateau. A low-middle income country (LMIC) with a Gross Domestic Product (GDP) per capita of US$3,111 (constant 2010 US$) in 2019, Angola is the fifth largest economy in Sub-Saharan Africa. The country had a total population of about 31.8 million people, of which 22 percent lives in the capital region of Luanda, and 34 percent resides in rural areas. As the second-largest oil producer in Africa, Angola’s economy is heavily reliant on the petroleum sector: oil constitutes 87.6 percent of exports, 50 percent of Government revenues, and with natural gas, 31 percent of GDP.xx Historically, oil represented 3/4th of government revenues, but this ratio has gone down to nearly 50 percent in recent years with the decline in production, prices and the strengthening of non-oil taxes, including the introduction of a value-added tax (VAT) in 2019. The country has a significant unrealized potential to develop industries in agriculture, Atlantic fisheries, and minerals.xxi However, more than 50 percent of the food consumed is imported, constituting 20 percent of merchandise imports in 2018.xxii Figure 1: Map of Angola 6. Despite its oil and mineral wealth, Angola’s poverty remains high and concentrated in rural areas. Despite the country’s LMIC status, poverty remains pervasive with 56.4 percent of the population living below the international poverty line in 2020.xxiii At the time of last household survey in 2018, the rural-urban divide was particularly large, with poverty incidence being 54.7 and 17.8 percent in rural and urban areas respectively.xxiv Poor rural infrastructure (paved roads, electricity grid, and energy sources) and lack of access to services—schooling, improved water and sanitation—exacerbate the non-monetary 16 dimensions of poverty. In particular, low access to paved roads acts as a major constraint for rural poverty reduction and is highly correlated to low market access, increased food insecurity, school dropouts, and low medical consultations. Life expectancy is 52.3 years and the country ranked 149 out of 188 on the Human Development Index in 2018.xxv Inequality is high and grew from 43 to 51 on the Gini index during the 2008–2018 period.xxvi 7. Economic structures from the colonial period and infrastructure decapitalization during the 27- year civil war have contributed to the persistence of rural poverty. During the colonial period, the agriculture sector consisted of a plantation and export economy, with most skilled work inaccessible to Angolans. After independence, most foreign companies and Portuguese settlers holding the managerial, technical, and administrative positions within commercial farms left the country without any systematic transfer of skills to Angolans, leading to a large-scale loss of technical knowhow. The widespread destruction of rural infrastructure like roads, bridges, irrigation systems, and warehouses also debilitated the agriculture sector during the 27-year civil conflict (1975–2002), a situation compounded by the presence of thousands of land mines in rural areas. Five hundred thousand to one million people died, and many more migrated to the cities, entrenching preexisting regional disparities.xxvii Given Angola’s natural endowments, history, and the agricultural GDP’s well-documented impact on rural poverty reduction—two to three times that of other sectorsxxviii—there is a clear case for greater investment in the sector. 8. Propelled by the end of the civil conflict and expansion of the oil sector, the Angolan economy experienced record growth in years of high oil prices; however, since 2016, the economy has been in recession.xxix Angola’s economy posted an average growth rate of 12.5 percent between 2004 and 2008, before being interrupted by the 2008–2009 global financial crisis. Economic growth resumed its fast pace in 2012 with a 7.6 percent growth rate, but was hit by another slump in oil prices, which declined by over 72 percent between March 2014 and March 2016. This led to important macroeconomic changes in the economy: a current account deficit—the first since 2008–2009; a scarcity of foreign currency; and local currency depreciation and inflationary pressures. In 2020, the total public debt reached about 128 percent of GDPxxx, despite fiscal surpluses recorded in 2018 and 2019. Close to 80 percent of public debt is denominated in foreign currencies and with a reduced oil price, external debt service in 2020 was estimated at 12.5 percent of GDP.xxxi Drastically reduced oil revenues have led the Government to implement large cuts in expenditures, including the near elimination of fuel subsidies, the cancelation of capital expenditures, and a reduction in the acquisitions of goods and services. 9. Macroeconomic instability has prompted the Government to embark on an ambitious reform program for a balanced growth model driven by the private sector. Since the presidential transition in 2017, the Government has prioritized fiscal consolidation, tight monetary policy, and exchange rate flexibility.xxxii Reflecting this commitment, the fiscal balance recorded a surplus of 2.5 and 0.7 percent of GDP in 2018 and 2019 respectively. As part of its Production Support, Export Diversification and Import Substitution Program (PRODESI), the Government has placed a strong emphasis on agricultural and light manufacturing products to boost exports and substitute imports in order to ease current account pressures.xxxiii It is also attempting to shift from large-scale public projects to private investment, by improving its competitiveness in non�oil sectors and undertaking structural reforms to change its challenging investment climate.xxxiv While the country has taken significant strides in both dimensions, it continued to rank low on the Ease of Doing Business Index (177th out of 190 economies in 2020)xxxv, World Economic Forum’s Global Competitiveness Index (136th out of 141 economies in 2019)xxxvi, and it lagged Sub-Saharan Africa’s (SSA) comparators on the 2019 Worldwide Governance Indicators.xxxvii 17 Figure 2: Budget Balance and Change in Debt, 2012–2022 10. COVID-19 is projected to prolong Angola’s recession into a fifth year, affecting labor incomes and increasing poverty due to food price inflation. Angola’s GDP is estimated to have contracted by 5.2 percent in 2020.xxxviii Agriculture (excluding fisheries) and commerce were the only two sectors that grew despite the pandemic, at 4.4 and 4.7 percent respectively. Significantly, agricultural output rose by 1 percent, driven by a growing production of food staples from a relatively small base.xxxix Both the oil and non-oil sectors are projected to decline due to spillover effects from lower oil prices and additional COVID- 19 impacts. Consequently, the recession will negatively impact labor incomes, particularly those of the poor working in vulnerable sectors such as services and tradables. With the pass-through from a 38 percent currency depreciation since 2019, inflation accelerated to 25.1 percent in 2020 (from 16.9 percent in 2019).xl Prices for food, much of it imported, rose by 31.4 percent and have likely pushed vulnerable families into poverty. Urban consumers have been hit especially hard, given that food comprises 44 percent of their expenditures.xli Responding to a 20 percent food inflation in the first three months of 2020, the Government also imposed an export ban on some food items in April 2020.xlii Figure 3: Actual and Projected Poverty Rates and Consumption, 2018–2023 18 11. Despite the dual COVID-19 pandemic and oil price shocks to Angola’s public finances, the Government has remained committed to fiscal consolidation while protecting social expenditures.xliii The steep fall in oil prices since March 2020, combined with reduced oil production, has led to a significant loss in oil revenues. Fiscal consolidation efforts in 2020 have focused on restraining expenditures through a nominal freeze on non-essential goods and services spending and a hiring freeze (except in education and health). Measures to raise non-oil revenues included changes to the personal income tax and broadening of the VAT base, though they aren’t expected to yield results before 2021. In parallel, the Government increased budget allocations for health (by 40 percent in 2019) and other social expenditures to mitigate the impact of the pandemic. Following the move to a floating currency in 2018, the exchange rate has served as a first line of shock absorption, depreciating considerably since the start of the pandemic.xliv With the COVID-19 shock to oil prices, the currency depreciated further (26 and 14 percent year-on-year by December 2020 in nominal and real effective terms, respectively). The parallel v.official exchange rate spread narrowed from 140 percent in January 2018 to about 20 percent by the end of 2020. Sector Context 12. The principal challenges and opportunities currently faced by Angola’s agriculture sector are the following: a. Economic diversification: Need to diversify Angola’s economy due to the macroeconomic conditions and reduction in the importance of the oil industry; given a growing domestic market for food and beverages due to urbanization and shifts in food demand, there are new opportunities for growth in value added by the agri-food system. b. Free Trade Agreements: Opportunity to capitalize on expanded market access through the AfCTA and SADC FTA by increasing sector competitiveness and exporting to the wider Africa region. c. Post-pandemic recovery: Opportunity to build back better the agriculture sector from the COVID-19 pandemic-induced impacts on food security and nutrition, and opportunity to build climate resilience within the new investments, addressing the challenges of natural disasters and climate change (particularly droughts). 13. Angola has an abundance of arable land, freshwater, and diversity of climatic conditions suitable to producing a variety of agricultural products, but this potential is underutilized. The growing period is long with a tropical and subtropical climate characterized by a hot and rainy season (August— May), and another dry and relatively cooler season.xlv Landscape diversity is high, with fertile soils in the northern and central highlands, and high average rainfall and rivers contributing to high freshwater availability in most regions.xlvi The main crops include cassava, maize, beans, potatoes, sweet potatoes, soy, and bananas, with other agricultural products being livestock, coffee, manioc, rice, vegetables, and fruits. Agricultural land accounts for 57 million ha—45.6 percent of total land—and the arable area is estimated to be 35 million ha. Both crop and livestock production have significant potential to grow on the extensive margin given that only 16 percent of arable area is cultivated and nearly half of it is unused (Annex, Fig. 43).xlvii On the intensive margin too, yields are low and just 3.5 percent of potentially irrigable land has been developed. 14. Agriculture was the motor of the colonial economy, but protracted conflict eroded the sector’s technical capacity, destroyed infrastructure, and isolated production areas from markets. In the colonial period, agriculture had a dual structure, with a commercial sector of about 6,400 farms (800,000 ha) 19 managed by Portuguese settlers using modern technologiesxlviii and a traditional sector composed of smallholder family farms cultivating about 3.4 million ha.xlix After independence, most Portuguese settlers left the country and many former commercial farms and plantations were converted into state farms, which have since been privatized. The civil war led to a collapse of commercial production, as large numbers of rural inhabitants either fled or reverted to subsistence agricultural production. Angola’s once thriving export of coffee, cotton, tobacco, and sugarcane all but ceased by the 1990s. Although the situation has improved with the rehabilitation of main roads and bridges and the clearance of mines, the agriculture sector has not yet fully recovered from the destruction and decapitalization of the conflict period. Figure 4: Benchmarking Size and Performance of Angola’s Agriculture Sector Angola's WDI outcomes and percentile comparison to the World SSA average Angola (2015-2019) Lower middle income average Percentile in World (RHS) Percent 97% Percentile 94% 94% 70 100% 86% 78% 60 66% 64% 75% 50 59% 40 46% 50% 30 20 25% 10 2% 9.7 4.9 47.5 35.5 1.4 47.6 21.4 51.3 0.4 16.1 0 0% Agriculture, Agriculture, Agricultural Rural Rural Poverty Poverty gap GINI index Food exports Food imports forestry, and forestry, and land (% of population population headcount at $1.90 a (World Bank (% of (% of fishing, value fishing, value land area) (% of total growth ratio at $1.90 day (2011 estimate) merchandise merchandise added (% of added population) (annual %) a day (2011 PPP) (%) exports) imports) GDP) (annual % PPP) (% of growth) population) Notes: All indicators are annual averages over the time period Source: Find my Friends tool using WDI data 15. In recent years, the share of agriculture in Angola’s economy has grown rapidly and has become the main opportunity for economic diversification and food security. Agriculture accounted for 9.7 percent of Angola’s GDP in the 2015–2019 period and was estimated to be 7.6 percent in 2020. In contrast to the rest of the economy, the sector growth averaged 4.9 percent in the 2015–2019 period.l Ranking in the 86th percentile globally, this rapid growth rate led to agriculture’s share of GDP increasing from 5.8 to 10 percent during the 2011–2017 period (Fig. 4, 5). The cultivation of main food crops has expanded rapidly, with cereal (maize), beans and oilseeds (peanuts), and tubers (cassava) production growing by 47, 42, and 14 percent respectively between 2012 and 2016 (Fig. 6). Fruits and vegetables grew at 12 and 4 percent respectively, with banana and sweet potatoes growing the fastest. Livestock products grew at the slowest rate, except chicken meat, which started from a low base (CPSD 2019).li The broad-based sector growth can be linked to currency devaluation (a natural incentive for domestic producers), cultivated area expansion by returning land to agriculture after the civil war, and the Government’s investments in public infrastructure, cooperatives, and fisheries.lii 20 Figure 5: Angola’s Structural Transformation (2006–2017) Figure 6: Increase in Production of Selected Agricultural Products, 2012 –2016 (%) 16. However, agricultural productivity remains low due to the dominance of subsistence-oriented family farms and low access to technologies, inputs, and services. The national agricultural output is well below demand, and Angola imports more than half of its food. The average yield for cereals (0.9 MT/ha) and vegetables (3.6 MT/ha) have been low and stagnant, currently at 1/4th and 1/5th of global averages respectively (Annex, Fig. 45).liii Maize yields are 1/4th of regional leader South Africa and wheat yields are 1/6th of its neighbor Namibia (Fig. 7). Yields for beans and soybeans are low relative to SSA comparators like Kenya, Ghana, and Zambia.liv lv Small-scale and subsistence-oriented family farms—averaging 2.3 ha in size—account for 80 percent of production and 92 percent of land under cultivation, with community holdings and commercial farms making up 5.8 percent of land. Almost one third of agricultural households are headed by women, who are responsible for 70 percent of traditional subsistence agriculture and 24 percent of commercial agriculture. Productivity growth is constrained by the use of poor agronomic practices and low access to improved technologies like climate-smart seeds, agrochemicals,lvi and mechanization. The domestic markets for seeds, fertilizers, tools, machinery, and other agricultural inputs are poorly developed, largely imported and unaffordable. Only 2 percent of farmers report access to credit, investments with high upfront costs are therefore limited.lvii Producer organizations could aggregate demand and enhance access to inputs and services, but they remain weak and collective decision-making is uncommon. Despite their large number, only a small fraction is registeredlviii, active, and commercially oriented, keeping access to input and output markets low. 21 Figure 7: Benchmarking Maize and Wheat Yields, 2002–2017 Maize Wheat 17. Agriculture is the largest source of jobs and livelihoods, but incomes from agriculture remain low, concentrating the rural poor in the sector. The agriculture and fisheries sector provides the main source of income to 90 percent of the 10.7 million Angolans living in rural areas and employs 45 percent of the workforce (Fig. 8).lix Over half of Angola’s poor are located in rural areas and depend almost exclusively on agriculture for their livelihood.lx However, agricultural labor productivity, measured in terms of value added per worker, is just US$1,216 (compared to a global average of US$20,916), which drives the low returns to labor in farming (Annex, Fig. 46).lxi Among economic sectors, agriculture offers the lowest median income at around 8,000 kz per month, even lower for women (approx. 6,000 kz per month).lxii Additionally, the concentration of low-skilled labor contributes to low incomes, with approximately 77 percent of the labor in the agriculture sector having less than primary education.lxiii Under its strategic objective of “Increasing the contribution of the agrarian sector to economic growth and social development� in the medium-term sector development plan, the Ministry of Agriculture and Fisheries (MINAGRIP) aims to raise the per capita income of agricultural households from US$01.2 per day to more than US$2.2 per day. Given the rural population’s heavy reliance on the sector, increasing both agricultural production and productivity is critical for improving livelihoods for the rural poor. Accompanied by complementary investments improving market connectivity, productivity gains will also contribute to food and nutrition security, poverty reduction,lxiv and ultimately help to diversify the economy. Figure 8: Employment by Sector, 2018–2019 22 Figure 9: Poverty and Sectors of Employment Overview of Agricultural Support in Angola 18. Over the last two decades, Angola has witnessed low and declining public spending on agriculture. The average share of agriculture in the national budget was 1.0 percent from 2010 to 2014, and fell to 0.6 percent in the subsequent five-year period (2015–2019).lxv lxvi Over the 2008–2018 period, Angola ranked near the bottom among SSA countries in terms of the share of agriculture in total public expenditure. It was one of only four countries that spent under 1 percent on agriculture over this period, far below the New Partnership for Africa’s Development (NEPAD) target of 10 percent (Fig. 10).lxvii Recently, the Government has been prioritizing investments in the sector through the PRODESI program and by raising budget allocations to 1.9 percent in 2020 and 2021. In addition to the need for greater public investment, is also a heightened need to improve the effectiveness and efficiency of public spending in the current fiscal environment. Sector spending as a share of agriculture GDP—a rough indicator of investment effectiveness—was 14.8 and 19 percent in 2017 and 2019 respectively, as reported by Angola to the CAADP Biennial Reviews.lxviii With disaggregated information on the composition of public spending limited to just one year and the absence of greater data coverage, assessments of investment trends and efficiency are not possible. Notably, no Agriculture Public Expenditure Reviews (AgPERs) have been conducted in Angola since 2007, leading to large evidence gaps in the understanding of public support to the sector.lxix 23 Figure 10: Share of Government Agriculture Expenditure in Total Public Expenditure (%), 2008 –2018 19. Agricultural support institutions are generally weak, hindered by weak public funding, unclear mandates, and limited human and technical capacities.lxx The responsibility for agricultural policy and programs (research, extension, technology transfers, and irrigation) falls under the Ministry of Agriculture and Fisheries (MINAGRIP), the Provincial Directories of Agriculture within the Provincial Governments, and the municipal Estação de Desenvolvimento Agrário. However, the Ministry is severely restricted in terms of human and financial resources. The provision of agricultural support services is insufficient and characterized by underfunded agriculture research—1 percent of agriculture GDP in 2018lxxi—and weak extension services provided by the Agricultural Development Institute (ADI).lxxii In Angola, clear evidence shows that yields of maize, potato and beans are on average higher for farmers who received extension services.lxxiii However, at present, just 700 agronomists and technicians serve about four million smallholders—a ratio of 1:5,722—low ratios relative to 1:280 in China and Vietnam, 1:1,000 in Nigeria, and 1:2,500 in Malawi. The insufficient coverage of extension workers and their limited technical capacities have contributed to lower sector productivity. 20. Sector support includes subsidized credit, input subsidies, and free veterinary services; however, no systematic review has been undertaken to quantify the size and impact of public support. The Government has recently promoted several programs to support the agricultural sector including subsidized credit, fertilizer, lending for material and equipment, processing facilities, irrigation and draught power, and free veterinary services for smallholder farmers. Imported certified seeds are also subsidized by State-owned Enterprises (SOEs) or a small group of private firms selected by the Government for the multiplication of certified seeds. However, the rules governing multiplication are not well known and the Government lacks the resources to inspect, monitor, and certify the quality of both imported and locally produced seeds. Agriculture’s share of overall credit remains less than 5 percent and only 2 percent of farmers report having access to credit.lxxiv Agri-credit programs have historically shown high rates of non-performing loans and there was political interference in credit allocation in the past. While the World Bank is supporting investments in crop and livestock censuseslxxv, there remains an urgent need for investments in agricultural information systems (agrometeorological systems, periodic production statistics, value chain information, high-frequency market price monitoring, etc.), to provide decision support systems for policymakers, and end users such as private investors, extension services, and farmers’ organizations. 24 21. Angola ranked 95th out of 101 countries on the Enabling the Business of Agriculture (EBA) index, reflecting regulatory gaps and the disabling environment faced by farmers and agribusinesses. Angola trailed the SSA average on six of the eight dimensions scored by the EBA methodology in 2019, with food trade and access to water being exceptions (Fig. 11). This poor score shows that previous programs have considered the State—and not the private sector—as a leading investor, without paying sufficient attention to improving the investment climate for the private sector. For example, the Government has a national irrigation plan (PLANO IRRIGA) focused on the development of major irrigation infrastructure on the main river basins that is managed by the national Directorate of Hydraulics at the Ministry of Agriculture (MINAGRIP). However, to date there is no policy for smaller scale, gravity-fed irrigation systems (which have significant potential in the Central region), and no mechanism or policy instrument in place to allow for their concession and operation.lxxvi Under the program for the purchase of agricultural produce (Programa de aquisição de produtos agropecuários (PAPAGRO)), starting in November 2013, the State created 16 centralized purchasing entities to buy the output of small farmers and a fleet of 86 trucks for transporting it to large commercial establishments in 15 Angolan provinces. Designed to cut post- harvest losses, which can be as much as 50 percent of agricultural output, the program is now widely considered a failure and has been discontinued.lxxvii Figure 11: Performance on Enabling the Business of Agriculture (EBA) Indicators 25 Objectives of Agriculture Support Policies and Programs in Angola 22. The recent oil price shocks have brought to the forefront the need for Angola to address its oil dependence, economic diversification, and oil revenue volatility. They prompted the Government to accelerate efforts to diversify the economy and reduce the dependence on food imports. The Government’s macroeconomic adjustment plan and national development plan place strong emphasis on agriculture as an immediate way to increase domestic production and to reduce imports. The Agricultural Development Plan 2018–2022 is the main Government instrument aimed at increasing agricultural production and productivity. The plan has identified areas of focus that include improvements in inputs supply, production, agro-processing, and rural trade.lxxviii The PRODESI program is a key instrument to incentivize domestic production and investment in the sector, covering a wide range of agricultural products.lxxix The recent natural disasters (severe drought in the South of the country) and the COVID-19 pandemic have raised the environmental, nutrition and food security agendas high in the Government and donors’ priorities in Angolalxxx. This has triggered recent public policies and investments for building back better, addressing environmental, food security and nutrition challenges through agriculture interventions (i.e., changes in trade policy, direct support to agribusinesses, increasing food security programs). Sector Competitiveness 23. Angola is a large net food importer and remains highly dependent on imports despite growing production. As Angola’s population has grown, low and flat agricultural productivity has contributed to structural food deficits and a large share of the key staples consumed in the country being imported (Annex, Figure 30). In 2019, agricultural products accounted for 22.5 percent of merchandise imports and 2.4 percent of GDP.lxxxi Top food imports in the recent past have been poultry meat, cereal-based products (rice, wheat, wheat flour, pasta), palm oil, and sugar (Table 1). Historically, Portugal, Brazil, and South Africa were Angola’s main food suppliers, but recent import origins have been more diversified, including the U.S., Thailand, and France. The high share of imported food offers the opportunity to scale up production for a number of crops and livestock products to meet local demand. Notably, Angola is self- sufficient in sweet potatoes, cassava, bananas, eggs, and peanuts. On the other hand, agricultural exports have dwindled from erstwhile cash crops like coffee—which accounted for half of country’s exports in the 1960s—to virtually nothing. In fact, agricultural exports accounted for just 3.6 percent of imports in 2019 and were mainly restricted to fish products, bran, banana, and coffee (Table 2).lxxxii Table 1: Top 5 Agricultural Import Commodities, 2019 Commodity Value, million US$ Origin Poultry meat 262 United States Rice 193 Thailand Palm oil 162 Malaysia Raw sugar 145 Brazil Wheat 98.4 France Agricultural Imports 2,141 World Source: Authors’ calculations using MIT Observatory of Economic Complexity Table 2: Top 5 Agricultural Export Commodities, 2019 Commodity Value, million US$ Destination Non-fillet frozen fish 27.8 Benin Crustaceans 21.7 Spain 26 Bran 12.2 Spain Bananas 3 Portugal Coffee 1.4 Portugal Agricultural Exports 76.6 World Source: Authors’ calculations using MIT Observatory of Economic Complexity 24. Agriculture trade policy aims to foster import substitution and is characterized by tariffs and non-tariff barriers, which raise the cost of joining regional or global value chains.lxxxiii Trade barriers and procedural obstacles significantly increase the cost of trade in Angola, affecting trade and country competitiveness.lxxxiv The customs tariffs on agricultural imports range from 2 to 50 percentlxxxv and an additional 10 percent of consumer tax was levied on most products until October 2019, when it was replaced by a 14 percent VAT.lxxxvi Consequently, the agriculture sector has the highest average “Most Favored Nation� (MFN) duties at 23.3 percent. While tariff exemptions exist in theory for some farm inputslxxxvii and food items in a “basic basket,�lxxxviii they often face non-tariff barriers that lead to substantial costs and bureaucratic delays. In 2019, the Government reviewed proposed substantial reduction in duties for some products, as Angola’s moves towards participation in the SADC Free Trade Areas (SADC FTA) and Africa Continental Free Trade Area (AfCFTA).lxxxix The review of import tariffs for farm inputs and non-tariff barriers (e.g., licensing, permits, and laboratory fees) is required to identify opportunities to reduce the cost of importing agriculture inputs.xc This review also included policy measures to allocate scarce forexxci that appeared to have unintentionally concentrated market power among importers, who play an important role in importing seeds, fertilizers, and other farm inputs.xcii This issue has eased since the exchange rate regime was liberalized in October 2018.xciii 25. As Angola progresses towards a free trade environment, it has an opportunity to repurpose agriculture support to align with its strategic objectives of diversification and competitiveness. The Government recognizes that international trade can be an important catalyst for economic diversification. In the last three years, Angola has adopted the PRODESI program, ratified and deposited its instruments of participation in the AfCFTAxciv, and presented its tariff offer for the Free Trade Zone of SADC.xcv In the next stages of AfCFTA implementation, Angola will negotiate the terms of the agreement by submitting tariff offers, agreeing on tariff reductions, rules of origin, and commodity coverage.xcvi Angola also needs to prepare to potentially lose preferential access in developed markets due to the graduation from Least Developed Country (LDC) status in 2024, and that African Growth and Opportunity Act (AGOA) will likely expire in 2025. 26. Angola’s high urbanization is driving shifts in food demand and the emergence of commercial agriculture to supply modern retail and supermarkets. Angola is one of the most urbanized countries in SSA, with 66 percent of its population living in urban areas.xcvii A small but growing agribusiness sector is developing linked to higher incomes and rising demand in urban centers, especially the Luanda market.xcviii The growth in food demand, especially for packaged and processed food, is exemplified by: i) the high density of supermarkets and grocery retailers relative to Central Africa (Fig. 12); and ii) higher sales of packaged and ultra-processed food than Central Africa (Fig. 13). Supermarket chains have started developing commercial partnerships with SMEs and producers to reduce imports and increase freshness and control over quality. Domestic off-takers of fresh products, agro-processing and manufacturing companies have expressed interest undertaking upstream investments in agribusiness, especially in higher value horticultural products where demand is taking off.xcix Significantly, most large-scale projects have historically been Government-sponsored, financed with bilateral lines of credit, and in some cases under concession to private management since all rural land and some urban land is state-owned.c While fiscal constraints have slowed down public investment in such projects recently, the development of commercial agriculture nevertheless requires a close alignment with the new Government’s expressed priorities, portfolio of agricultural land assets, and strategic public investments.ci 27 Figure 12: Benchmarking Supermarkets and Grocery Retailers, 2017 –2018 Figure 13: Benchmarking Retail Sales of Packaged and Ultra-Processed Food, 2017–2018 27. However, poor transport and energy infrastructure and insecure access to land are major constraints to the development of commercial agriculture and sector competitiveness. The state of rural infrastructure like irrigation, rural roads, warehouses, and cold storage remains poor in Angola, with the country scoring 24 on the Economist’s Agricultural Infrastructure Index, relative to the global average of 53.cii Market links are constrained by poor road connectivity, storage, and commercial infrastructure. The total road network in Angola is about 76,000 km (one of the lowest densities in Southern Africa), much of which needs to be rehabilitated.ciii The Government has invested heavily in improving the transport network over the last decade, including roads and railways, but limited budget has been available for rural roads. Further, the limited reach of the electricity grid in rural areas and the high cost of operating gas generators hamper the competitiveness of agro�processing, irrigated agriculture, and livestock-based agribusiness SMEs. The absence of a registry or database identifying agricultural land for private tenure and investment has further hindered commercial agriculture, by increasing the potential for disputes.civ This situation is compounded by tenure insecurity due to the incompatibility of the legal framework with the socially accepted norms of land governance.cv However, even in this difficult environment, private investments in agriculture and agribusiness are being undertaken, with actors securing land tenure rights to ensure land utilization and security to safeguard investments and reduce conflict. 28 Food and Nutrition Security 28. Angola is one of the lowest-ranked countries in terms of food and nutrition security. Food insecurity remains a persistent phenomenon in Angola despite the recent growth in agricultural production and food availability. In 2020, the country ranked 97th out of 113 countries on the Economist’s Global Food Insecurity Indexcvi, and scored below average on 18 of the 24 indicators of food availability, food affordability, food quality and safety, and natural resources and resilience.cvii Similarly, Angola ranked 93rd out of 107 countries on IFPRI’s Global Hunger Index in 2020, and its score has trended downward since 2000.cviii Significantly, while the growth in agricultural production has contributed to a steady increase in food availability and a decline in undernourishment over the last two decades, the country still lagged African and global averages for the consumption of milk, calcium, vegetables, nuts and seeds, and whole grain in 2016.cix Consequently, child malnutrition indicators have stagnated or declined very slowly in the last decade (FAO State of Food Security and Nutrition 2020).cx Moreover, Angola’s adult population faces a malnutrition burden with the country being off course to meet targets for anemia in women of reproductive age, birth weight, diabetes, and obesity. Urgent action is required to improve food and nutrition security by targeted programs enhancing availability, access, and use. Figure 14: Trends in Angola’s Performance on the Economist’s Global Food Security Index, 2020 OVERALL FOOD SECURITY ENVIRONMENT 1) AFFORDABILITY 2) AVAILABILITY 100 100 100 75 75 50 50 75 25 25 0 0 2012 2013 2014 2015 2016 2017 2018 2019 2020 2012 2013 2014 2015 2016 2017 2018 2019 2020 50 3) QUALITY AND SAFETY 4) NATURAL RESOURCES & RESILIENCE 100 100 75 75 25 50 50 25 25 0 0 0 2012 2013 2014 2015 2016 2017 2018 2019 2020 2012 2013 2014 2015 2016 2017 2018 2019 2020 2012 2013 2014 2015 2016 2017 2018 2019 2020 Score increased, 2020 compared with 2012 Score decreased, 2020 compared with 2012 No change in score, 2020 compared with 2012 Average score for all 113 countries included in this index 29. Pandemic-induced food inflation and ongoing drought in south-western provinces have combined to increase the prevalence of food insecurity in 2020. Food shortages faced by Angolan households were correlated with poverty, drought (rural areas), and food inflation (urban areas) in 2018 and 2019.cxi During 2020, these preexisting vulnerabilities were deepened due to the pandemic, drought, and food price volatility.cxii According to the World Food Programme’s recent Vulnerability Assessment and Monitoring (VAM), an estimated 3.8 million people had inadequate food consumption in 29 January 2021. Sixty-two percent of Angolan households were resorting to crisis or emergency livelihood coping strategies like spending savings and reducing the consumption of essential non-food items. The highest prevalence of food insecurity is reported in the south-western provinces, reflecting the localized production shortfalls in 2020 and high prices of food staples. The situation is expected to worsen due to the anticipated drought-reduced 2021 agricultural output and the economic downturn associated to the COVID-19 pandemic. A weak national currency, which lost about 30 percent of its value against the US dollar between February 2020 and 2021, has contributed to high prices of imported food and as in the recent past, urban consumers are more likely to report food price inflation and shortages.cxiii In order to support access to food for vulnerable households that were most affected by the pandemic, the Government initiated the first phase of a cash transfer program in May 2020.cxiv 30. Ensuring food and nutrition security is one of the Government’s central objectives for the agriculture sector. Recognizing the severity of the situation, the Government has set targets to improve Angola’s performance on global food and nutrition security indices. Under its medium-term plan for agricultural development, the first strategic objective is meeting the needs of its population and MINAGRIP has explicitly included Key Performance Indicators (KPIs) that target score improvements on the Global Hunger Index and the Food Security Index over the 2018–2022 period. Further, under the second strategic objective of raising agriculture’s contribution to economic growth, the plan also targets greater self-sufficiency by including a KPI focused on halving the agricultural trade deficit. Lastly, the fourth strategic objective of capacity building and infrastructure development includes KPIs for facilitating access to improved technologies, farm inputs, mechanization, and rural infrastructure, which are expected to increase food availability.cxv Climate Resilience 31. The agriculture sector is extremely vulnerable to climate change and requires adaptation policies and investments to protect rural livelihoods and national food security. The average temperature in Angola is projected to rise by 1.2-3.2°C by the 2060s and according to the country’s National Adaptation Programme of Action (NAPA) (2011), the major expected climate change threats and impacts are floods, soil erosion, drought episodes, and rise in sea levels.cxvi Some climate scenarios project a reduction in crop yields by 2030 and increased livestock morbidity and mortality due to prolonged drought which requires longer transhumance migrations for feed and water, and also reduce milk and meat production.cxvii In fact, the Angolan economy has already experienced several climate change impacts in recent years: an ongoing prolonged drought which has contributed to high food prices and livestock deathscxviii, damaging flash floods, forest fires, and reduced crop production, water and fishing resources, among others. Over the last thirty years, climate variability has contributed to extreme events which not only pose serious risks to livelihoods and public healthcxix, but also threaten the economy and national food security. The economic sectors identified as most vulnerable and prioritized for adaptation include agriculture, coastal zones, land use, forests, ecosystems and biodiversity, water resources, and health. While the Government has established policies and regulations to address climate change adaptation, progress to fulfill the activities envisioned in the National Implementation Strategy has been limited.cxx 30 Figure 15: Climate Projections and Sectoral Impacts 32. Emissions from agriculture and land-use change and forestry (LUCF) account for nearly as much as energy; achieving the country’s mitigation targets requires urgent action in both sectors. Angola’s greenhouse gases (GHG) emissions in 2014 were 38.02 million MtCO2e, totaling 0.52 percent of global GHG emissions. The energy sector served as the predominant source of emissions in Angola, with 49.4 percent of emissions from energy, 37.4 percent from LUCF, 11.7 percent from agriculture, and the remaining 1.5 percent from waste and industrial processes.cxxi On the mitigation side, Angola’s nationally determined contributions (NDC) commitment is a 50 percent relative GHG emission reduction by 2030, at an overall cost of US$14.7 billion.cxxii To achieve this ambitious target, the agriculture and forestry sectors have a key role, given existing trends in deforestation and the growth of cultivated agricultural land.cxxiii In agriculture, GHG emissions largely stem from animal production and burning on the savanna (Fig. 17). Drawing on Brazil’s example, the country intends to promote the production and use of biofuels like ethanol as an alternative to fossil fuels through the cultivation of sugar.cxxiv The adoption of triple-win climate smart approaches (productivity and income enhancing, sustainable adaptations, and mitigation) integrating crops and agroforestry efficiently also prevents runoff and limits soil loss, while increasing water use efficiency, and resilience of farming systems to climate variability. These approaches will enable Angola to meet its commitments. 31 Figure 16: GHG Emissions in 2014, by Sector Figure 17: GHG Emissions in Agriculture 32 Conceptual Framework for Policy Review Methodology: Rationale and Coverage 33. Each year since 1987, the OECD has measured monetary transfers associated with agricultural policies in a growing number of countries using a standard method. The OECD agriculture support estimates were developed in order to monitor and evaluate agricultural support policies and programs using a common and easy-to-use methodology for policy dialogue among countries, and to provide economic data to assess the effectiveness and efficiency of policies. The estimates were mandated by OECD Ministers in 1987, and have since been calculated for the OECD and an increasing number of non- OECD countries, and are widely referred to in the public domain. 34. The objectives of agricultural policies in OECD countries have evolved over time—from overcoming food shortages or surpluses in the post-war period to securing food safety, environmental quality, and preservation of rural livelihoods. Policy instruments have also changed, reflecting changes in domestic political and economic settings and, progressively, developments in international economics. Given this diversity, the OECD has developed a methodology—referred to as Producer Support Estimate (PSE) in the literature—to compute support indicators measuring transfers to the agriculture sector and enabling comparability over time and across countries.cxxv PSE indicators provide insights into the burden that agricultural support policies place on consumers (i.e., market price support) and taxpayers (budgetary transfers). This is the most widely and systematically used methodology to monitor support to the agriculture sector in the world. The results, published annually, provide important contributions to the international policy dialogue on agriculture and trade.cxxvi 35. There are at least three clear benefits to adopting this methodology for reviewing agriculture policies at a global level: a. Monitoring and evaluation of agricultural policies developments: This includes policy reforms achieved by countries over time, through specific reform efforts (e.g., the U.S. Farm Bills and EU Common Agriculture Policy (CAP) reforms), as well as progress towards achieving international commitments agreed to by countries (EU, CAADP).cxxvii b. Establishment of a common base for policy dialogue: By using a consistent and comparative method to evaluate the nature and incidence of agricultural policies, countries are able to engage in trade negotiations and common agriculture policy discussions (WTO, World Bank, IMF, and FAO). They are also useful for farming and non-government organizations, and research institutions in the discussions on differentiated impact of agriculture policies. Mexico, Colombia, Central America and the Andean countries used these estimates to develop their transition into the FTAs with the U.S. and the EU. c. Undertaking research on policy impacts: The data serves as an input into modeling to assess the effectiveness and efficiency of policies in delivering the outcomes for which they were designed and to understand their effects on production, trade, income, the environment, etc. While the indicators cannot by themselves quantify these impacts, the economic information upon which they are based is an important building block for further analysis. The World Bank is undertaking an analysis with IFPRI at a global level, modeling the repurposing of agriculture support policies and programs towards climate change mitigation/adaptation objectives. 33 Figure 18: Coverage of OECD Methodology of Agriculture Support Estimates Note: The map represents all the countries using the OECD methodology with at least one year of estimates for agriculture support. The OECD has tracked a subset of countries over multiple years. 36. There are strong advantages, but also some limitations, to using the OECD methodology for undertaking the agriculture policy review for Angola. The advantages are that: (a) it provides a systematic and integrated view of agriculture support policies and programs (not limited to the more traditional public expenditure reviews or rate of protection); (b) given the large number of countries using this same methodology, an immediate benchmarking is possible across a large set of comparatorscxxviii; and (c) the methodology is simple and can be integrated into the agriculture public policy analysis conducted by the Government and other stakeholderscxxix. The methodology also has some disadvantages and limitations, mainly: (a) Only two African countries have carried out agriculture support estimates with it, meaning Angola can only benchmark against South Africa and Mozambique, and (b) since the estimates are based on the monetary value of budget and price support, non-monetary support, like the quality of policies, are not captured. As an example, the methodology is able to identify how much policy/program support is invested in land administration efforts, but unable to qualify the impact (quality) of those policies/programs. Relatedly, while SOEs in agriculture are relatively few and small in Angola, and that sector spending on them is partially captured in the public budget and estimates produced for this analysis, the impact of these SOEs on sector performance and market distortions may be significant, as the WB-IFC Country Private Sector Diagnostic (2019) analysis documents.cxxx 37. This report produces indicators covering a range of agricultural support, and is expected to inform upcoming trade negotiations and policy reforms enhancing sector competitiveness and economic diversification. In particular, the indicators of support are expected to be relevant to AfCTA trade negotiations on agriculture and food products. This estimation will enable Angola to benchmark against trading partners and comparator countries like South Africa, in relation to the level and composition of agriculture support. Given the current fiscal constraint and the need to diversify its 34 economy, there is a window of opportunity for the Government of Angola to gradually open up the trade of agriculture inputs and products, while shifting public spending towards more targeted interventions. However, in the absence of comprehensive estimates of agriculture support, the evidence base for capitalizing on this opportunity does not currently exist. OECD Methodology: Technical Concepts and Calculation 38. According to the OECD methodology, agricultural support is defined as gross transfers to agriculture from consumers and taxpayers, arising from public policies that support agriculture. This definition covers both budgetary and non-budgetary expenditures such as credit concessions and direct subsidies (electricity, fuel, water, farm inputs). It also includes implicit support arising from border trade (tariffs, taxes) and domestic market measures (e.g., minimum support prices). Overall, the methodology enables a computation of total transfers to producers (PSE), consumers (CSE), and general services (GSSE) respectively, with a clear identification of transfer sources (domestic and international taxpayers, consumers) (Fig. 20).cxxxi The OECD methodology also allows the calculation of disaggregated PSE for each product considered. The different levels of support are reflected in the Producer Single Commodity Transfers (SCT), a measure of commodity-specific agricultural policies indicating policy flexibility for producers in their choices of product mixes. Figure 19: OECD Methodology—Main Indicators of Transfers, by Source 39. The main indicators of support are grouped into three categories—producers, consumers, and general support. Box 1 and 2 below show how indicators are defined and computed. Annex B provides further details on classification of support across OECD categories: Box 1. OECD indicators of support to agriculture • Indicators of Support for Producers Producer Support Estimate (PSE): The absolute annual monetary value of gross transfers from consumers and taxpayers to agricultural producers, measured at the farm gate levelcxxxii, arising from policy measures that support agriculture, regardless of their nature, objectives, or impacts on farm production or income. The PSE includes market price support and budgetary payments. Specifically, PSE includes gross transfers from consumers and taxpayers to agricultural producers arising from policy measures based on current output, input use, area planted/animal numbers/receipts/incomes (current, non-current), and non-commodity criteria (considered one of the least distortive). 35 Box 1. OECD indicators of support to agriculture Percentage PSE (%PSE): %PSE represents monetary gross transfers to producers as a share of gross farm receipts. As it is neither affected by inflation nor by the size of the sector, it allows comparisons in the level of support to be made over time, products, and between countries. %PSE is the OECD’s key indicator to measure support to agricultural producers, as it provides insights into the burden that agricultural support policies place on consumers (i.e., market price support) and taxpayers (budgetary transfers). Producer Single Commodity Transfers (producer SCT) : The annual monetary value of gross transfers from consumers and taxpayers to agricultural producers, measured at the farm gate level, arising from policy measures linked to the production of a single commodity that the producer must produce to receive the transfer. Producer Percentage Single Commodity Transfers (producer %SCT) : The commodity SCT as a share of gross farm receipts for the specific commodity. • Indicators of Support to Consumers Consumer Support Estimate (CSE): The annual monetary value of gross transfers from (to) consumers of agricultural commodities, measured at the farm-gate level, arising from policy measures that support agriculture, regardless of their nature, objectives or impacts on the consumption of farm products. If negative, the CSE measures the burden on consumers (implicit tax). Percentage CSE (%CSE): CSE as a share of consumption expenditure (measured at farm gate) net of taxpayer transfers to consumers. It estimates the transfers as a share of consumption expenditure on agricultural commodities (at farm-gate prices), net of taxpayer transfers to consumers. The %CSE measures the implicit tax (or subsidy, if CSE is positive) placed on consumers by agricultural price policies. • Indicators of Support to General Services for Agriculture General Services Support Estimate (GSSE): The annual monetary value of all transfers from taxpayers to policy measures and programs supporting general agriculture public goods and services such as rural infrastructure, animal and plant health, research and development, promotion of agriculture, agriculture schools, arising from policy measures that support agriculture, regardless of their nature, objectives and impacts on farm production, income, or consumption. The GSSE does not include any transfers to individual producers or activities related to a particular agriculture commodity.cxxxiii Percentage GSSE (%GSSE): GSSE as a share of Total Support Estimate (TSE). • Indicators of Total Support to Agriculture Total Support Estimate (TSE) : The annual monetary value of all gross transfers from taxpayers and consumers arising from policy measures that support agriculture, net of the associated budgetary receipts, regardless of their objectives and impacts on farm production and income, or the consumption of farm products. Percentage TSE (%TSE): TSE transfers as a share of GDP. Box 2. Calculation of PSE for Angola Broadly, the PSE has two main components: market price support and budgetary allocations. 1) Market Price Support (MPS) MPS is the annual monetary value of gross transfers from consumers and taxpayers to agricultural producers arising from policy measures that create a gap between the domestic market price and the 36 Box 2. Calculation of PSE for Angola border price (without tariffs/import taxes) of a specific agricultural commodity, measured at the farm gate level. Policies creating a price gap include domestic measures, such as administered pricing or market interventions. These policies include trade measures such as import tariffs, import quotas, tariff quotas, export subsidies, export taxes, as well as quantitative restrictions on exports. In some cases, the gaps between domestic and international prices are also explained by factors that are not strictly policy-related, e.g., deficiencies in physical infrastructure, inadequate information, and weak market institutions. MPS is financed by consumers through higher prices. In Angola, MPS is calculated based on the following information: Period covered: 2018 and 2019 Products covered: Cassava, banana, maize and dry beans. These four commodities account for 72.6 percent of the total value of gross agricultural output (GAO) in Angola for 2018 (73.9 percent in 2019) cxxxiv. For the purpose of the PSE estimation, maize and dry beans are treated as net imports (M) and cassava and banana are net exports.cxxxv Producer prices: These are average prices received by producers at the farm gate level. This information has been provided by a local consultant, sourced from producer surveys, farmer cooperatives and the National Institute of Statistics of Angola (INE) (See Annex C for technical details).cxxxvi External reference prices: Average import/export prices were used for the products considered in this analysis.cxxxvii Prices were adjusted (added) with international transportation cost and other processing costs in order to make reasonable comparisons with domestic prices. cxxxviii In the case of cassava and banana, we used the average export unit price (FOB) of Angola adjusted (subtracted) with estimated transportation and processing costs from the border to the production zone. Data for FOB prices was provided by FAOSTAT and transport and processing cost provided by surveys to local producers. In the case of maize and beans, we used average import unit prices (CIF) at the border adding transport cost to the production zone and subtracting processing costs. Data for CIF prices was provided by FAOSTAT and transport and processing cost by surveys to local producers. Marketing margins: Marketing margins are estimations of processing and handling costs for a given commodity. Marketing margin adjustment to the reference prices is required to compare them with domestic prices measured at the farm gate. For products, margins data was provided by surveys to local producers. Price gap estimates: The “zero price gap� was used when negative gaps were obtained between producer prices and adjusted reference prices (farm level), as the estimated negative price gaps reflect factors other than agricultural policies. This adjustment considers transport costs from border to farm gate and the costs of processing farm products into exported products. 2) Budgetary Support Budgetary support is funded by taxpayers (government revenues). Budgetary information for 2018 and 2019 was provided by FAO and complemented by line-item data sourced from the Ministry of Finance (MINFIN), the Ministry of Agriculture (MINAGRIP), and consumption subsidies from the Ministry of Social Action (see Annex D for complete list).cxxxix 40. In Angola’s data-poor setting, the quality of price information collected is a potential limitation of the OECD methodology. Like other measures used to compute indicators of agriculture supportcxl, the OECD methodology has limitations associated with the availability, quality, and nature of market information in general, and prices in particular. Since an official source of producer prices and other 37 parameters used in the analysis did not exist, a survey of producers and exporters was conducted by the study team. To validate this data, the survey results were reviewed by the Government’s technical staff and were found to have yielded credible price levels within plausible bounds. The team’s previous experience in other countries has shown that the use of this methodology allows dialogue between parties and the construction of better public policies, and there are incentives to systematize the generation of key information and develop a key public good. It is noteworthy that one of the results of these estimates indicates an underinvestment in market information systems in Angola, despite its positive externalities for market development. Agriculture Support Estimates: Global Snapshot and Trends 41. In the 2017–2019 period, the 54 countries monitored by the OECD provided net total transfers of US$619 billion to their agriculture sectors annually. According to the OECD’s Agriculture Policy Monitoring and Evaluation Report (2020)cxli, the net transfers or total support to agriculture (TSE) included US$708 billion of gross support, offset by an implicit taxation of farmers worth more than US$89 billion in countries like Argentina and India, which used measures that depressed the domestic prices of some commodities. US$425 billion of total transfers constituted budgetary spending for various support programs, and the rest was market price support (MPS). About US$536 billion, comprising 72 percent of TSE, was in the form of support to producers (PSE). 42. Over half of producer support was provided via policy instruments most likely to distort agricultural production and trade. The OECD methodology identifies support based on commodity output—MPS and subsidies linked to output or the unconstrained use of variable inputs—as having the strongest potential to distort agricultural production and trade. During the 2017–2019 period, the effective prices received by producers were 6 percent higher than world prices, with the largest price gaps for sugar and rice. Correspondingly, Single Commodity Transfers (SCT) represented above 50 percent of PSE and sugar and rice had the highest share of SCT in commodity gross farm receipts. MPS is the main component of the SCTs in most cases. On the other hand, the expenditures financing general services to the sector (GSSE) reached an annual average of US$106 billion in 2017–2019, with financing of infrastructure projects, agricultural knowledge and innovation, and public stockholding accounting for US$45 billion, US$26 billion, and US$21 billion, respectively. Figure 20: Transfer to Specific Commodities (SCT%), 2017 –2019 38 43. The changes in the structure of support were relatively moderate over the last two decades, when averaged over all countries covered by the methodology. During the 2017–2019 period, producer support represented 12.5 percent of gross farm receipts (%PSE), a reduction from 18.4 percent in 2000– 2002 (Fig. 22). Comparing the same periods, the share of the most distorting forms of transfers has declined slightly from 72 to 69 percent of gross producer transfers in absolute terms. In terms of aggregate gross farm receipts, this share has declined from 13 percent in 2000–2002 to 8 percent in 2017–2019. Notably, while distortionary transfers based on output are shrinking in relative terms, those based on unconstrained input use have increased. Among the remaining forms of producer support, payments based on areas planted, animal numbers, and historical parameters not requiring production are significant, accounting for 18 percent of all producer support. Notably, payments decoupled from current production and therefore less distorting, have increased significantly and represent 14 percent of all producer support (Annex, Figure 50). On average, relative expenditures for GSSE (%GSSE) have declined as agricultural GDP has grown more rapidly. Conversely, the total support to agriculture as a share of GDP (%TSE) has declined slightly over time, mainly driven by the smaller relative size of the sector within overall economies. Figure 21: Agriculture Support Trends (54 Countries) OECD and Emerging Economies: A Comparison 44. Despite a strong decline in the OECD area, producer support has continued to account for double the share of gross farm receipts, relative to emerging economies. The numbers below show that overall there are substantial variations at the country and commodity levels in both groups (OECD and emerging economies). During the 2017–2019 period, the total support to agriculture (TSE) in OECD countriescxlii was US$319 billion and the corresponding figure for emerging economiescxliii was US$295 billion. While TSE as a share of GDP had declined to nearly half of the 2000–2002 level in the OECD, it had only marginally declined in emerging economies. The support provided to producers individually (PSE) was nearly identical in the OECD and in emerging economies, at 72 and 71 percent of the TSE respectively. However, OECD producer support accounted for 17.6 percent of gross farm receipts (%PSE), twice that of emerging economies at US$89 billion (8.5 percent), partly due to the implicit taxation 39 of producers due to a large negative MPS in Argentina and India. The %PSE indicator of producer support has trended upward in emerging economies, growing from 4.2 percent, even as it has declined from 29 percent in the OECD since 2000–2002 (Fig. 23, 24). The effective prices received by producers were 9 percent higher than the world prices, on average, but showed a declining trend over the last three decades. In contrast, effective prices were 5 percent higher than the world prices in emerging economies, rising from 1 percent in 2000–2002. Figure 22: Agriculture Support Trends—OECD Countries Figure 23: Agriculture Support Trends—Emerging Economies 45. Single Commodity Transfers (SCT) accounted for more than half of PSE in both OECD and emerging economies; sugar remained among the most supported commodities in both groups. SCT represented about 51 percent of total PSE in OECD and emerging economies, with MPS accounting for the largest component in both groups. There was significant variation across commodities in the OECD, with domestic prices for rice being more than twice the world price in 2017–2019, accounting for the largest share of gross farm receipts. Sugar, sunflower, milk, and beef prices were 35 percent, 30 percent, 13 percent and 13 percent above world prices. In emerging economies, SCT witnessed a falling trend in recent years partly due to more negative SCTs in India and Argentina and the extended direct income scheme in India. Rapeseed, sugar, maize, rice and wheat had the highest share of SCT in commodity gross farm receipts, while SCTs were negative for barley, oilseeds, milk and oats. 40 Figure 24: Transfer to Specific Commodities (SCT)—OECD, 2017–2019 Figure 25: Transfer to Specific Commodities (SCT)—Emerging Economies, 2017–2019 46. The composition of support has shown a larger shift towards fewer distortionary policies in the OECD, relative to emerging economies. In contrast with the long-term OECD decline in the share of transfers based on output and input use,cxliv the shares of less distorting forms of support such as payments decoupled from commodity criteria but linked to environmental services and animal welfare objectives have grown.cxlv Over the 2017–2019 period, they account for 3.5 percent of gross farm receipts and a fifth of PSE. GSSE had also grown in nominal terms, with infrastructure financing recording a small increase and expenditures on agriculture knowledge and innovation growing by two thirds, and inspection and control services also doubling (Annex, Fig. 50).cxlvi Fig. 27 below illustrates the growth in the share of the agricultural knowledge, inspection and marketing category (from 20.2 to 32.2 percent) and corresponding decline in the share public stockholding (from 22.5 to 1.6 percent) over the 1986–2019 period. 41 Figure 26: GSSE Composition in OECD Countries, 1986–2019 Share of miscellaneous Share of cost of public stockholding Share of marketing and promotion Share of development and maintenance of infrastructure Share of inspection and control Share of agricultural knowledge and innovation system, in total GSSE 0.0 10.0 20.0 30.0 40.0 50.0 2019 % 1998 % 1986 % 47. On the other hand, the share of output and input-based transfers remains high at 83 percent in emerging economies, having declined from 89 percent in 2000–2002. In terms of gross farm receipts, they have grown from 4 to 7 percent, but remain below the OECD average. Payments based on areas and animal numbers were almost non-existent in 2000–2002 but reached close to 13 percent of aggregate support to producers in 2017–2019. In turn, the relative importance of support for investments, often related to irrigation, has declined over time, now representing some 9 percent of PSE. GSSE reached an annual average of US$64 billion, with infrastructure projects, again largely irrigation-related, accounting 40 percent of expenditures. Public stockholding and agricultural knowledge and innovation accounted for 31 and 13 percent respectively (Annex, Fig. 50). Agriculture Support Estimates for Angola Total Support Estimates (TSE) 48. Angola’s total support to agriculture averaged 1.4 percent of GDP in the 2018–2019 period, more than twice the OECD average. The level of total support provided to agriculture (TSE) in between 2018 and 2019 averaged US$1.32 billion, equivalent to 1.4 percent of GDP, and more than twice the OECD average of 0.6 percent. Representing the sum of PSE, GSSE, and CSE (Annex, Fig. 48, 49), Angola’s TSE as a share of GDP was comparable to Korea and China, but remained lower than Mozambique in the SSA region, which ranked highest in the world (Fig. 28). As a share of agriculture GDP, Angola’s TSE was equivalent to 29.5 and 27.3 percent in 2018 and 2019, higher than South Africa but similar to Canada and Iceland (Fig. 29). Measured in proportion to producer income, this level of %PSE is relatively high (47.7 percent in 2018 and 45 percent in 2019), compared to OECD countries (12.5 percent) over the 2017–2019 period. 42 Figure 27: Benchmarking TSE as share of GDP, 2018–2019 (average) Figure 28: Benchmarking TSE as a share of Agriculture GDP, 2018–2019 (average) 97.2% 92.3% 77.6% 48.3% 44.7% 42.0% 41.0% 40.2% 31.9% 29.4% 28.4% 24.7% 22.8% 22.4% 18.3% 17.9% 16.2% 15.2% 12.8% 8.9% 8.3% 7.5% 7.3% 7.2% 7.1% 4.4% 1.7% Developing… Nam New Zealand Mexico Indonesia China U.S Russia Australia Argentina Costa Rica Colombia India Chile Brazil South Africa EU28 Japan Mozambique Norway Philippines Korea OECD Israel Switzerland Iceland Kazakhstan Canada Angola -9.8% Viet -41.0% 49. On average, MPS accounted for 94.5 percent of Angola’s TSE in 2018–2019, reflecting the relatively small role of budgetary transfers in relation to total agriculture support. In 2018, MPS alone accounted for 96 percent of TSE and budgetary transfers were 4 percent. Within budgetary transfers, GSSE and the support to farmers were nearly equal and accounted for 2 percent. However, in 2019 the GSSE share of TSE grew to 6.1 percent, with MPS declining to 93 percent due to changes in agricultural programs (Fig. 30). It is worth noting that the sources of transfers were mainly consumers, who provided 93 percent of the support. Taxpayers contributed the remaining 7 percent.cxlvii This pattern is in stark contrast to OECD countries, where taxpayers are the ones generating the most transfers compared to consumers. 43 Figure 29: Benchmarking Angola’s TSE, by Source of Transfers TSE by source of transfers 8% 47% 93% 53% Angola (2018-19) OECD (2011-18) Consumer Transfers Taxpayers Transfers Support to Agricultural Producers (PSE) 50. Angola provides one of the highest levels of support to agricultural producers among developing and developed countries monitored by the OECD and the Inter-American Development Bank (IADB). Utilizing the primary data collected for this report, Angola’s %PSE—producer support as a share gross farm receipts—was estimated to be 46 percent (US$1.09 billion) and 45 percent (US$1.41 billion) in 2018 and 2019 respectively. Benchmarked internationally, Angola’s %PSE was nearly 4 times the OCDE average of 12.5 percent in this period and was comparable to Japan (41 percent) and Switzerland (47 percent) in 2018. However, it is noteworthy that this analysis aggregates all producers of specific agriculture commodities, and does not empirically disaggregate the targeting of the support by typology of agricultural producers; i.e., support actually received by subsistence-oriented versus commercial farmers or small-scale versus large-scale operators. 44 Figure 30: Benchmarking %PSE, 2018–2019 51. Market price support comprised nearly all of producer support in Angola in 2018 and 2019. On average, MPS accounted for 97.8 percent of Angola’s PSE during the study period, over budgetary support (Table 3).cxlviii In fact, Angola ranked top among the countries monitored by the OECD in terms of MPS share of PSE in both 2018 and 2019 (Fig. 33). Given the dominance of direct agricultural support—i.e., coupled to commodity output, inputs, and financed by consumers—it is likely to be highly distortionary for domestic food production, consumption, and trade decisions. This type of support also imposes additional costs on domestic food consumerscxlix and distorts farmer production decisions as it changes domestic relative prices, reducing the exposure of farmers to international prices. Global trends, but also regional free trade agreements (like the AfCFTA) will likely reduce MPS over time for Angola, so such support is unlikely to be sustainable. MPS tends to be regressive, as it favors large producers who generate commercial surplus rather than smallholders, who tend to have smaller commercial surpluses or only produce for self-consumption. It also generates a regressive tax on low-income food consumers since a relatively large share of their income is spent in food, compared to high-income consumers. Table 3: Composition of PSE, 2018–2019 Category 2018 (US$ Mill) 2018 (%) 2019 (US$ Mil) 2019 Producer Support Estimate (PSE) 1409.7 100.0 1091.0 (%) 100.0 (A+B+C+D+E+F+G) A.1 MPS 1382.1 98.0 1061.8 97.3 A.2 Payments based on output 4.1 0.3 15.4 1.4 B. Payments based on inputs 21.1 1.5 12.3 1.1 45 Category 2018 (US$ Mill) 2018 (%) 2019 (US$ Mil) 2019 C. Payments based on current production1 0.1 0.0 0.0 (%) 0.0 D. Payments based on Non-current production2 0.0 0.0 0.0 0.0 E. Payments based on Non-current production3 0.0 0.0 0.0 0.0 F. Payments based on non-commodity criteria 2.3 0.2 1.4 0.1 G. Miscellaneous 0.0 0.0 0.0 0.0 Source: WB Estimates. 1: Production required; 2: Production required; 3: Production not required. Figure 31: Level and Composition of Angola’s PSE, 2018–2019 500,000 47% 45% 50% MPS and Budgetary support, 400,000 10,940 40% Millions local currency 5,590 300,000 30% % PSE 200,000 357,516 20% 398,023 100,000 10% 0 0% Budgetary Transfer (left scale) Support based on commodity Output (left scale) % Producer Support Estimate (right scale) 52. Exchange rate liberalization and other factors outside of agricultural policies could also partially explain MPS estimates. MPS is generated by a price gap between domestic and external reference prices. This differential is commonly related to border measurescl or direct market prices interventions (regulated prices)cli that generate the differential. In Angola, border measures in the form of import tariffs on products like maize and beans at least partially explain the high share of MPS.clii However, other factors that are not derived from domestic agricultural policies, such as market structure (monopolies/monopsonies), exchange rate movements, and support policies in other countries, could also explain variations in MPS estimates. Given the general price increase observed in 2019 after the foreign exchange liberalization in Angola, it’s clear that the exchange rate depreciation contributed to higher food prices and PSE estimates in analysis.cliii However, the indicators of support were calculated for both 2018 and 2019 and the similarity in estimates across both years gives greater confidence in the findings; while exchange rate movements may affect MPS, it’s unlikely to do so over a period of two years. An additional, in-depth marginal analysis to disaggregate the effects of each potential factor affecting the MPS estimate is possible, but was determined to be beyond the original scope of this study. 46 Figure 32: Composition of Angola’s PSE, by Category of Support (2018–2019) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% MPS Production Non-current A/An/R/I, production not required Non-current A/An/R/I, production required Inputs Miscellaneous Non-commodity criteria Payments on commodity outputs 53. Budgetary support directly benefiting farmers averaged just 2 percent of PSE, with input-based payments comprising the largest share. As part of this analysis, data was also collected on a diverse range of government programs financed by taxpayers and executed by the Government of Angola (Ministry of Agriculture, other ministries, or public agencies) at the national and subnational levels. Following the PSE methodology, expenditure on programs like the Second Market-oriented Smallholder Agriculture Project (MOSAP-II), the Commercial Agriculture Development Project (PDAC), seed production programs, extension services, etc. were allocated to PSE categories based on their characteristics. Considering only budgetary payments, it was observed that payments based on inputs—like land preparation subsidies, seeds programs, and machinery subsidies—comprised the largest share, accounting for 76.5 percent and 42.2 percent of PSE budgetary payments in 2018 and 2019 (Table 4). Next, output-based payments increased from 15 to 52.8 percent in this period, reflecting the effect of programs like Programa Fomento da Producao Agricola and Programa Fomento da Produção Agrícola/Intensificação Cultivo Do Milho. Lastly, payments based on non-commodity criteria—including the Instituto Desenvolvimento Forestal’s public resources—represented 8.2 and 4.9 percent in 2018 and 2019 respectively.cliv Table 4: Composition of PSE Budgetary Payments Category 2018 (US$, Mill.) 2018 (%) 2019 (US$ Mill.) 2019 (%) Budgetary Payments (A.2+B+C+D+F+G) 27.5 100 29.2 100.0 A.2 Payments based on output 4.1 15.0 15.4 52.8 B. Payments based on inputs 21.1 76.5 12.3 42.2 47 Category 2018 (US$, Mill.) 2018 (%) 2019 (US$ Mill.) 2019 (%) C. Payments based on current production1 0.9 0.3 0 0.0 D. Payments based on Non- current production2 0 0.0 0 0.0 E. Payments based on Non- current production3 0 0.0 0 0.0 F. Payments based on non- commodity criteria 2.3 8.2 1.4 4.9 G. Miscellaneous 0 0.0 0 0.0 Source: WB Estimates. 1: Production required; 2: Production required; 3: Production not required. 54. Producer support was highest for maize and beans, with a rising trend in the former and a falling trend in the latter. Disaggregating Angola’s PSE at the product-level, this analysis examined the producer support provided to major crops through commodity-specific policies. In 2018, the SCT was calculated to be US$575 million for maize, US$246 million for beans, US$196 million for bananas, and US$12 million for cassava. In 2019, the corresponding estimates were US$631 million (maize), US$112 million (beans), US$13 million (cassava) and US$7 million (bananas) (see Annex, Fig. 52, 53). Expressed in terms of share of gross receipts, %SCT was also calculated all four agricultural products. The results show that %SCT for maize and beans was 45 percent (rose from 29 percent) and 28.5 percent (declined from 39 percent) respectively in 2019, principally reflecting MPS through border and price measures. In contrast, the %SCT for cassava and bananas was 0.5 percent (rose from 0.3 percent) and 0.2 percent (declined from 6 percent) respectively in 2019, implying that all the support was budgetary. Figure 33: Benchmarking %SCT by Commodity, 2018 –2019 (Average) Corn 37.1% Beans 33.8% Banana 3.1% Cassava 0.4% 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 48 55. Angola’s support to maize and beans is significantly higher than its OECD comparators . Of the total revenues perceived by farmers who produce maize, 37 percent came from support policies and programs in 2018–2019, significantly higher than the OECD average of 3.7 percent (Fig. 35). Similarly, 34 percent of the revenue received by beans farmers was due to support policies in this period. While OECD countries do not measure specific commodity support (SCT) for beans, the corresponding support in Canada and Mexico was 2.5 percent and 3.2 percent respectively (Fig. 36). However, other commodities like cassava received minimum support while bananas have an SCT of 6 percent (average 2018–2019). This large variation in public sector support among agriculture commodities has an impact on the domestic market by distorting incentives and consequently, the production decisions made by farmers. To illustrate the difference, a maize farmer in Angola received the equivalent of US$258/ha in 2019 while a beans farmer received half the support (US$126/ha) and a banana farmer received US$45/ha. Significantly, the support to maize was nearly four-times higher than in the U.S. (US$72/ha) and the support to beans was nearly three-times higher than in Canada (US$44/ha). Figure 34: Benchmarking SCT% for Corn, 2018–2019 (average) 43.2 50.0 42.4 41.1 36.9 40.0 32.5 23.2 30.0 21.2 15.6 14.5 20.0 11.5 6.7 10.0 4.1 3.4 2.8 2.0 1.7 0.6 0.0 0.0 0.0 0.0 0.0 0.0 Ukraine Viet Nam Indonesia New Zealand Mexico Russia China Argentina OECD - Total Philippines Turkey Switzerland Colombia South Africa Brazil India Chile EU28 Mozambique Canada United States Angola Averange Non OECD Kazakhstan -10.0 -20.0 -18.0 -20.7 -30.0 Non-OECD Economies OECD Figure 35: Benchmarking %SCT for Beans, 2018–2019 (average) 34.0 2.5 3.2 Canadá México Angola 49 Support to General Services for Agriculture (GSSE) 56. Agriculture public expenditures are mainly allocated to investments in private goods (subsidy- PSE) rather than public goods (GSSE). Financed by taxpayers in the form of budgetary payments, GSSE support activities providing general benefits or goods with public characteristics, i.e., agricultural innovation (R&D and education), animal/plant health services, marketing and promotion, rural infrastructure, and public stockholding. Positively correlated with country income-level, agricultural growth and competitivenessclv, GSSE represented only 1.2 percent of agricultural GDP, on average, in 2018 and 2019. On the other hand, the corresponding averages for OECD, developing countries, and regional comparator South Africa were 5.3 percent, 2.7 percent, and 2.3 percent respectively (Fig. 37). Similarly, GSSE accounted for 4.2 percent of TSE, less than one third of the averages for OECD (13.2 percent) and developing countries (14.3 percent) (Fig. 38). Most public expenditures went towards investments in private goods (subsidies), such as payments based on inputs—programs like land preparation support, seeds programs, and machinery subsidies, accounting for 76.5 and 42.2 percent of budgetary payments in 2018 and 2019. Figure 36: GSSE as a share of agriculture GDP, 2018–2019 20% 15% 10% 5.3% 5% 2.7% 1.2% 0% 50 Figure 37: GSSE as a share of Angola’s TSE, 2018–2019 (Average) 80% 70% 60% 50% 40% 30% 14.3% 13.2% 20% 4.2% 10% 0% Developing… Viet Nam Iceland Ukraine Angola Kazakhstan Canada Indonesia Mexico New Zealand China Russia Argentina Norway USA Colombia Turkey Costa Rica Japan Australia Mozambique EU28 Switzerland OECD Korea South Africa Brazil Israel Philippines Chile -10% -20% -30% 57. GSSE grew between 2018 and 2019, but remains one of the lowest among the countries monitored by the OECD. In 2018, 54 percent of the relatively small GSSE outlay (US$25 million) was allocated to agricultural infrastructure and maintenance, i.e., irrigation infrastructure, agro-meteorology, mechanization in land preparation, and support for adapted research. Forty-two percent was allocated to agricultural knowledge and innovation systemsclvi and 4.2 percent to marketing and promotion, with the remainder in other categories. In 2019, GSSE rose to US$70 million, driven by additional spending on agricultural infrastructure like rural roads (building and maintenance), reinforcement of agro-statistical information and expenditure through of PRODESIclvii. Benchmarking against other countries, Angola’s GSSE was 1.66 percent of agricultural GDP in 2019, one third of the OECD average (5.3 percent) and ¾ of South Africa (2.3%) (Fig. 40). 51 Figure 38: Composition of the General Services Support Estimate (GSSE) in Angola 2018 2019 K. Marketing and M. promotion Miscellane 0% ous 1% J. H. Infrastructure Agricultural Development Knowledge and J. Infrastructure 8% Maintenance, Development I. 53.8% and Maintenance Inspection 87% and Control 4% H. Agricultural Knowledge, K. Marketing 42.0% and promotion, 4.2% Figure 39: Benchmarking GSSE by Component, 2018–2019 58. During the study period, approximately 94 percent of Angola’s total support could be classified as belonging to the WTO amber box and be subject to countervailing measures by its trade partners. In WTO’s terminology, agricultural support is classified in three boxes: green (measures with no distortive effect on production and trade)clviii, amber (distorting measures of production and trade), and blue (“amber box with conditions�) subsidies that are tied to programs limiting production (Annex, Fig. 54). Notably, amber box support is subject to reduction commitments and is actionable by importing countries. On the other hand, blue and green box measures are not subject to reduction commitments and are non-actionable (“Peace Clause�). Based on an approximate classification, more than 94 percent 52 of Angola’s TSE could be classified as belonging in the amber box given that they include measures to support prices, or subsidies directly related to production quantities (Table 3). A large share of Angola’s agriculture support—because of its reliance on MPS—is therefore subject to reduction commitments and is actionable by importing countries, i.e., they may apply countervailing measures. The blue box was not included since it carried commitments to reduce support. Since OECD and WTO methodologies do not fully correspond, this analysis is intended to be indicative and instructive for policymakers. Table 5: WTO Classification of Angola’s TSE Total according to WTO Box As % of TSE Box 2018 (US$, Mill.) 2019 (US$, Mill.) 2018 (%) 2019 (%) Amber box 1,407.2 1,089.7 96.4 92.4 Green Box 39.8 71.2 2.7 6.0 Note 3: The shares do not sum up to 100%, because OECD estimations include other support (mainly consumer support) not considered by WTO. Support to Consumers of Agricultural Products (CSE) 59. Angolan consumers have borne the cost of MPS, paying an implicit food consumption tax equivalent to 7.5 and 8 percent of food basket value in 2018 and 2019, respectively. The Consumer Support Estimate (CSE) measures the cost to consumers arising from agricultural policies.clix Similar to the PSE, CSE can be expressed in relative terms as a share of consumption value (%CSE). The CSE% for Angola was estimated to be -7.5 percent in 2018, and grew to -8 percent in 2019, indicating that policies supporting agriculture (particularly through domestic producer prices) acted as an implicit tax. Consequently, consumers paid higher domestic prices than international prices and their consumption expenditure rose. Comparing across countries, this aggregate tax on consumers in Angola is similar the OECD average of -7 percent and lower than other African countries like South Africa or Mozambique. Figure 40: Benchmarking %CSE, 2018–2019 (Average) 23.8 28.6 11.3 6.8 1.2 1.0 0.0 -0.3 -1.0 -1.1 -2.8 -3.5 -3.7 -4.0 -5.0 -7.0 -7.3 -7.6 -7.6 -9.2 -9.3 -11.7 -16.5 -17.4 -27.2 -27.4 -27.8 -36.5 -39.2 -42.4 -45.0 53 Conclusions Main Findings 60. Based on the assessment of the agriculture support estimates, the main findings to be considered for agriculture policy decisions going forward are the following: • Angola allocated US$1.3 billion in annual support to the agriculture sector, representing 1.5 percent of GPD. Total Support Estimate (TSE) to agriculture from public policies and programsclx in Angola in 2018 and 2019 averaged US$1.315 billion per year. This was equivalent to 28.5 percent of the agriculture gross domestic product (GDP), much higher than in most developing countries (8.3 percent on average) and below OECD member countries (40.2 percent on average). A neighbor and close trading partner, South Africa, has a TSE of 9 percent of agriculture GDP and 0.4 percent of total GDP, much lower than Angola, while OECD countries’ support to agriculture represents 0.6 percent of total GDP. • Although total agriculture sector support in Angola is relatively high, the portion of support going to agriculture public goods and services is low. TSE is composed of direct support to producers (measured as Producer Support Estimates—PSE), consumer support (CSE) and support to general agriculture public goods and services (GSSE)clxi. Analyzing the composition of support in Angola, it is overwhelmingly (94 percent) direct producer support, while a very small fraction (6 percent) of total agriculture support (TSE) goes to GSSEs. Benchmarking the TSE composition across countries where data is available, we observe that Angola’s investment in GSSE is the lowest, except for Indonesia and Mozambique. As a share of agriculture GDP, GSSE accounted for just 1 percent, which is low compared to other developing countries average (2.7 percent) and the OECD’s average (5.3 percent). This level of GSSE (% of Ag GDP) is consistent with Angola being one of just four countries with agriculture spending being less than 1% of total public expenditure (2008–2018). • Angola’s support to agriculture producers was more than twice the OECD average. On average, 46 percent of gross farm receipts in Angola came from agriculture support policies and programs in 2018 and 2019. Significantly, the corresponding producer support—measured by %PSE—averaged 12.5 percent for the OECD during the 2017–2019 period. Producer support in Angola was comparable with that of countries with high levels of support, such as Japan, Switzerland, and Korea. • Agriculture producer support in Angola is overwhelmingly funded by policies that raise t domestic agriculture prices. The support to agriculture producers (PSE) is 98% funded by Market Price Support (MPS), while budgetary support only represents 2 percent (average of 2018–2019). MPS are monetary transfers from Angolan food consumers to Angolan producers. These transfers occur due to public policies (mainly border measures) that make the domestic prices of agriculture and food products higher than the international prices (compared at farm gate). In other words, food consumers in Angola are paying higher prices (or an implicit tax), due to agriculture policies leading to positive support to agriculture producers. • The current structure of producer support benefits a small number of large-scale, commercial producers, and does not enhance sector competitiveness. MPS is based on the amount of agriculture production that a farmer sells in the market, it is therefore poorly targeted and favors large producers who generate commercial surplus rather than smallholders, with smaller surpluses or who only produce for self-consumption. Given that small-scale and subsistence-oriented family 54 farms dominate in Angola—accounting for 80 percent of production and 90 percent of land—support to producers through higher prices benefits only a small proportion of producers. Furthermore, MPS distorts production decisions and investments in competitive agriculture products as it protects producers from international market prices. As Angola transitions to a liberal free trade environment by participating in the SADC Free Trade Area (FTA) and Africa Continental Free Trade Area (AfCFTA), the need for genuine competitiveness improvements will become all the more necessary. • Food consumers in Angola pay, on average, an implicit tax of about 8 percent. Support to food consumers (Consumer Support Estimate, CSE) is negative in Angola. CSE measures the support (or tax) to food consumers arising from public agriculture policies. Although Angola does provide some positive support to food consumers in the form of food aid and school feeding programs, the overwhelming majority of the CSE is negative, due to public policies protecting domestic prices. CSE as a percentage of total food expenditures by food consumers averaged 8 percent in 2018 and 2019. This 8 percent implicit tax is a transfer from consumers to producers through higher domestic food prices. • Agriculture support to producers in Angola is concentrated in maize and beans, and is relatively high for these commodities compared to other countries. Of the total gross revenues perceived by farmers producing maize, 45 percent came from agriculture public support policies and programs, while beans had a 28 percent support (commodity-specific support is measured by Single Commodity Transfers—SCT) averaged over 2018 and 2019. In comparison, the %SCT in OECD countries was 11 percent for maize, 6 percent for bananas, and nearly zero for cassava in the same period (SCT for beans is not measured in the OECD). This large variation in agriculture public sector support—and therefore profitability—across agriculture commodities signals to the distortions that farmers face when making production decisions. • Angola’s agriculture support to producers is associated with the lowest agricultural productivity among comparators. Comparing Angola to regional (South Africa, Mozambique) and aspirational peers (USA, Brazil), we observe that sector productivity – measured as value added per worker – remained low despite high producer support (%PSE) in 2018 and 2019 (Fig. 41). At the commodity level, we again observe that the combination of support to maize (%SCT) and corresponding yields are only better than Mozambique, which bears a higher cost for the crop but witnesses even lower productivity. While causation cannot be established given data limitations, it’s evident that there exists significant potential for improving sector productivity in Angola by repurposing its significant support to the sector. 55 Figure 41: Agriculture Support and Value Added per Worker, 2018 – 2019 100,000 Output per Worker, Constant 2010 USD 90,000 USA USA 80,000 70,000 60,000 50,000 40,000 30,000 Brazil 20,000 South Africa 10,000 Angola Angola Brazil Mozambique 0 South Africa 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% %PSE Figure 42: Support to Maize vs Yields, 2018 - 2019 (average) 12.0 United States of America 10.0 8.0 Yield (MT / Ha) 6.0 Brazil South Africa 4.0 2.0 AngolaMozambique 0.0 0 10 20 30 40 50 %SCT Proposed Agriculture Policy Reform Agenda 61. Angola is in the process of implementing public policies geared towards diversifying its economy, recovering from the COVID-19 pandemic, and moving towards a more competitive, integrated, and sustainable agriculture sector. Several public policies and programs, like PRODESI and the Commercial Agriculture Development Project (PDAC), are seeking to improve the competitive position of the agriculture sector and the development of agribusinesses is high in the country’s development agenda, with an important private sector development program and technical assistance being provided by the World Bank and IFC. Furthermore, the severe droughts of the last years and the COVID-19 pandemic have renewed the urgency of the country to focus on climate resilience and nutrition support to the poorest households. It is in this context that this report presents some important recommendations 56 for realigning agriculture support policies and programs towards Government competitiveness, climate resilience and nutrition and food security objectives. The three main recommended policy shifts are summarized here below and are tagged for competitiveness (COMP), climate resilience (CC) and nutrition/food security (NFS) objectives; as well as identified as policy reforms to be undertaken in the short (ST) and long term (LT). Fiscal implications need to be taken into account when considering such policy shifts, as well as the international experience with such transitions (see Box 1 for a summary of studies of country experiences). Competitiveness COVID-19 Recovery: Building back better Agriculture Policy Shift Objective (diversification Nutrition—Food and trade integration Climate Resilience Security PSE to GSSE MPS to non- distortionary PSE CSE (-) to CSE (+) SCT to non-commodity specific PSE ✓ Shift agriculture support from private towards public goods and services [COMP; LT]: Agriculture support in Angola is mainly geared towards private goods (subsidies and market price support) rather than towards investments in agriculture public goods and services. Given the overwhelming and long-standing evidence (World Bank, 2001clxii; Lopez, 2005clxiii) that public sector investments and support to agriculture public goods and services yield higher economic returns than public sector investments in private goods, Angola should seek to shift its agriculture support, and increase its share of GSSE as % of agriculture GDP from its current level of 1.2 percent to at least the level of South Africa or the average of developing countries (2.3 percent and 2.7 percent respectively). Sixty percent of agriculture public expenditures (average 2018–2019) went towards investments in private goods (subsidies), such as payments based on inputs—programs like land preparation support, seeds programs, and machinery subsidies. Shifting agriculture sector support towards investments in public goods is a critical step for commercial agricultural producers to benefit from the opportunities to supply the domestic market created by the recent floating of the kwanza and the various government programs for agribusiness development. ✓ Shift from distortive measures to competitive agriculture policy support. [COMP; LT]. Given that an overwhelmingly large share of Angola’s agriculture support is MPS (or coupled to the production of specific agriculture products), a transition plan for agriculture to move towards a more competitive policy support environment is very much needed. In fact, Angola will likely be engaging in MPS reduction commitments in agriculture trade agreements such as the Africa Continental Free Trade Area (AfCFTA), so a complementary trade agenda is needed to support smallholders of protected agriculture product transition to face market prices and take advantage of trade. As Angola moves forward with its accession to AfCTA and SADC FTA, it will undertake removal of tariff and non-tariff barriers. However, it would be important to establish a strategy to transition the sector towards market prices, improving competitiveness, and supporting the transition of “losers� of commodities like maize and beans that will see farm-gate prices being reduced over time. In Mexico, this strategy was adopted with the reduction of MPS share of PSE being accompanied with the increase in direct income payments to producers, which supported rural incomes during economic crises (1995; 2008 – 2009).clxiv In some cases, it’s noteworthy than 57 tariffs on agricultural inputs (e.g., improved seeds, fertilizers, machinery) and non-tariff barriers could be gradually eliminatedclxv, thus lowering unit costs for producers without reducing output prices, thus maintaining producer margins. ✓ Shift from implicit taxation to positive support to food consumers [NFS; LT]. As the negative CSE estimates in this report demonstrate, Angolan food consumers are funding the bulk of agriculture support to the sector. A shift away from MPS, as suggested above, will reduce the implicit food tax to food consumers, consequently increasing the welfare of the poorest. However, other public policies and programs could be further enhanced to directly safeguard consumers from food insecurity and nutrition challenges, by targeting support through social protection programs (food aid, school feeding) and countercyclical safety nets. ✓ Shift support to promote environmental and nutrition security objectives [COMP, CC. NFS; ST]. Given the fiscal limitations of Angola and the implicit tax imposed by agriculture public policies on Angola food consumers, the producer support should be geared towards achieving objectives beyond supporting farmer incomes. Support can contribute towards nutrition objectives, leveling the playing field for food products and thus making the cost of a nutritious dietclxvi more affordable. Furthermore, Climate Smart Agriculture (CSA)clxvii and Nutrition Smart Agriculture (NSmartAg)clxviii technologies and practices should be integrated into farmer input and technology support incentives, to promote productivity growth, and fulfill environmental and nutrition objectives. Moreover, decoupling producer support from specific agriculture products would enable farmers to make production decisions mainly on market opportunities (and not on the level of public sector support). Box 3. Strategies for shifting from MPS to decoupled support Several studies and experiences point to potential pathways for Angola to transition from protecting a few commodities and producers through market prices, to supporting a more competitive agriculture sector and poor households through targeted and decoupled support. The implementation of such agriculture policy reform strategy is urgent and opportune as it can help provide a building back better recovery from COVID-19, but also take advantage of SADC and AfCFTA. Parikh et al. (1995) studied several agriculture sector trade liberalization post GATT (Uruguay Round). The conclusions point out that the policy package that has shown superior growth, welfare and distribution effects, without raising taxes, includes: (a) switching from agriculture input subsidies to safety nets (reducing PSE and increasing CSE); and (b) increasing public investments in public goods and services (rural infrastructure).clxix Various studies show how agriculture trade liberalization and discontinuity in policy reforms can lead to negative impacts in the most vulnerable farming population. Nyairo et al. (2010) point to the mixed experience of some African countries in agriculture trade liberalizationclxx and McCorrston et al. (2013) to the mixed experience of a global set of 34 countries, finding clear drawbacks from “stop-go� policy reform programs, and results depending on the way food security and other impact variables are assessed. Uganda is one of the interesting cases of a mixed experience in shifting from MPS to direct farmer support. Reforms did not automatically translate into an increased value of agriculture exports, largely because world prices are beyond the control of small-country exporters. Often, the anticipated benefits from reducing MPS do not materialize because only limited or partial reforms are actually implemented, i.e., there is no significant increase in incentives for diversifying and/or exporting. This is especially true of many SSA countries. Furthermore, even when significant trade reforms are implemented, important constraints remain. Several reasons explain the limited agricultural supply response towards higher competitiveness following a reduction in MPS. In particular, farmers’ ability to increase production and exports to respond to increased incentives will be constrained by farming practices, limited access to inputs, credit and new technologies (McKay et al. 1997). Poor infrastructure and natural barriers act as a tax, often very high, on building a competitive agribusiness and engaging in exports (Milner et al. 2000). Delays in implementing 58 policy and institutional reforms to support the competitive transition of farmers have been suggested as one factor limiting export supply response in Uganda. Another case is Mexico, analyzed by Henriques et al. (2003) clxxi and UNCTAD (2014), pointing out to the sector gains and losses following the country entering the FTA with the USA and Canada (NAFTA) in 1994. Mexico negotiated a 15-year gradual tariff reduction for sensitive crops like maize. The total value of agriculture production and agriculture exports increased, including the production of maize. However, some smallholder farmer support shifted mainly from MPS to decoupled payments (per hectare payments and social safety nets). This made them shift out of agriculture rather than investing in improving their production system. Particular attention must be paid to the food security and transition strategy of smallholder farmers in accessing the needed public sector support and incentives to embark in an agriculture transition path to increased competitiveness, in particular investing in agriculture public goods and services. Finally, a successful case of policy shifts in the context of reduction of MPS is Brazil, as documented by the World Bank (2014). In thirty years, it went from a food-importing country (as most SSA countries), with mainly subsistence farmers, to a food exporting powerhouse, through a combination of public policy reforms including (a) direct support to vulnerable households through safety nets; (b) direct support to farmers through incentives for technology adoption (through credit programs); and (c) large investments in agriculture public goods and services (mainly agriculture innovation systems).clxxii Lessons from Angola for other Countries Box 4: Lessons for Capacity Building and Database Institutionalization As part of this review, the joint World Bank-FAO team undertook capacity building of technical counterparts in Angola with the objective of institutionalizing the use of OECD indicators into the country’s policy analysis and policymaking process. The key lessons and experiences from this approach are summarized below. Given the low but growing coverage of this methodology in SSA, these lessons are intended to serve as a guide for other countries that are considering similar reviews of their agricultural support. i. Identification of key responsible staff, often within the Ministry of Agriculture, is critical for following the data collection and analysis methodology correctly and ensuring institutional memory within the Government. ii. To widen the pool of expertise, it is also important to target not only staff from the Ministry of Agriculture and of Finance, but also from NGOs, Universities and private consultants that may want to use the estimates for further policy analysis. To the extent possible, the training modules should be delivered in local languages to maximize retention and learning outcomes. iii. The development of partnerships with technical organizations like FAO (MAFAP) is key for building on existing agricultural policy databases and ensuring the sustainability of this review. In particular, the integration of PSE updates with national data sourcesclxxiii can leverage higher quality data and increase the depth and granularity of agricultural support estimates. iv. Use a phase-in phase-out approach to the capacity building: Since this exercise is only repeated annually (or every two years), it is advisable to organize refresher courses and to ensure that national counterparts are gradually able to implement the methodology independently. 59 v. Linking the results to policies and outcomes that matter to Government: To institutionalize the OECD indicators in country-level policy analysis, it is key that the mid- and senior management in the Ministry of Agriculture and other ministries appreciate the full range of its applications. 60 Annex A: Supplementary Figures and Tables Figure 43: Sector-level Decomposition of Angola’s GDP, 2002–2018 Quadro 3.1 - Participação das classes e atividades no valor acrescentado a preços básicos - 2002 a 2017* Valor acrescentado a preços básicos (%) Classes e Actividades 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017* 2018** 01 Agropecuária 5.89 6.22 6.22 5.05 5.20 5.21 4.90 6.62 6.18 5.85 6.07 6.51 7.55 9.12 9.83 10.02 8.61 Agropecuária e Silvicultura 4.07 4.38 4.58 3.71 3.61 3.67 3.39 4.60 4.43 4.11 4.12 4.39 4.81 5.72 6.15 6.33 5.63 Pesca 1.82 1.84 1.65 1.34 1.59 1.54 1.51 2.03 1.75 1.74 1.95 2.11 2.74 3.40 3.69 3.69 2.98 02 Indústria 56.53 53.27 54.40 60.27 58.24 61.46 61.46 44.06 52.37 56.03 56.92 53.36 46.20 41.93 42.64 42.17 47.93 Extracção e Refinação de Petróleo Bruto e Gás Natural 44.45 40.58 41.30 48.21 45.31 48.24 49.70 27.50 37.47 42.66 40.92 35.63 27.85 22.26 20.89 20.93 29.86 Extracção de Diamantes, de Minerais Met. e de Outros Minerais não Metálicos2.55 2.63 2.44 2.26 1.62 1.34 0.98 0.64 0.77 0.71 0.60 0.59 0.61 0.60 0.68 0.56 0.78 Transformadora, exceto Refinação de Petroleo Bruto 3.69 4.01 4.66 3.90 3.62 3.38 3.53 5.11 4.54 4.17 4.40 4.83 4.76 5.69 6.75 6.58 6.06 Produção e Distribuição de Eléctricidade e �gua 0.39 0.64 0.56 0.29 0.82 1.09 0.56 0.69 0.55 0.42 0.39 0.45 0.43 0.51 0.55 0.45 0.52 Construção 5.45 5.40 5.44 5.61 6.87 7.40 6.69 10.11 9.03 8.07 10.61 11.86 12.55 12.89 13.77 13.65 10.71 03 Serviços 38. 216 234 175 41.30 40.17 35.45 37.24 34.37 35.02 48.99 41.89 39.08 39.60 42.78 46.81 48.66 46.98 46.76 43.21 Comércio 10.68 12.14 12.22 10.99 9.39 9.39 8.97 12.60 11.53 10.78 11.14 11.99 15.50 15.75 16.40 17.91 16.91 Transportes e Armazenagem 1.83 1.99 1.84 1.61 1.75 1.94 2.39 3.14 2.68 2.31 2.26 2.28 2.40 2.70 2.56 2.34 1.97 Correios e Telecomunicações 1.01 1.06 0.81 1.34 1.42 1.42 1.61 2.28 1.78 2.44 2.30 2.50 2.53 2.61 2.09 1.76 1.40 Intermediação Financeira e de Seguros 1.84 1.78 1.74 1.45 1.39 1.63 1.74 2.39 2.01 1.79 1.74 1.68 1.50 1.94 2.04 1.96 2.33 Administração Pública, Defesa e Segurança Social Obrigatória 8.95 10.69 9.62 8.56 8.63 8.87 9.62 13.70 10.56 9.76 9.91 10.75 11.25 12.28 10.61 9.03 7.60 Serviços Imobiliários e Aluguer 5.89 4.97 6.25 4.37 4.23 4.32 3.77 5.31 4.45 3.52 3.83 3.89 3.71 4.16 4.89 4.50 4.15 Outros Serviços 8.03 8.67 7.68 7.12 10.44 6.8 6.93 9.57 8.89 8.47 8.42 9.69 9.92 9.22 8.39 9.27 8.84 Sifim (-) 0.64 (-) 0.78 (-) 0.79 (-) 0.78 (-) 0.68 (-) 1.04 (-) 1.38 (-) 2.00 (-) 1.52 (-) 1.45 (-) 1.24 (-) 1.30 (-) 1.10 (-) 1.58 (-) 1.91 (-) 1.49 (-) 1.79 Total 100.00 100.00 100.00 100.00 100.00 100.00 100.00 97.67 98.91 99.50 101.36 101.36 99.47 98.13 97.55 97.46 97.96 Impostos 4.80 3.83 2.96 2.59 2.83 3.01 3.47 2.88 2.85 2.23 Subsídios (-) 2.47 2.74 2.46 3.94 4.19 2.47 1.61 0.43 0.31 0.19 PIB 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 Figure 44: Distribution of Angola’s Surface Land and Arable Land Figure 45: Benchmarking the Size of Angola’s Agriculture Sector Angola's Agriculture, forestry, and fishing, value added (% of GDP) 35.0 comparison to Lower middle income, Oil Exporter: Latest Available 31.5 28.8 30.0 outcome 25.0 19.8 21.2 SSA average 17.6 20.0 14.4 14.7 12.8 15.0 10.0 7.1 10.0 5.0 0.0 Source: Find My Friends using the World Development Indicators 61 Figure 46: Benchmarking Vegetable Yields, 2015 –2019 Figure 47: Benchmarking Agriculture Labor Productivity, 2015–2019 62 Figure 48: Import needs for key food staples, in tons Figure 49: Producer Support Estimate (PSE) and Sub-Categories 63 Figure 50: General Services Support Estimate (GSSE) and Sub-Categories 64 Figure 51: Estimates of Support to Agriculture (54 Countries) Source: OECD (2020), “Producer and Consumer Support Estimates�, OECD Agriculture statistics (database), http://dx.doi.org/10.1787/agr-pcse-data-en. Statlink 2 https://doi.org/10.1787/888934143603 65 Figure 52: Angola’s TSE in Kwanzaa, 2018–2019 Unidade 2018 2019 I. Valor total da produção (no portão da fazenda) Kw Mill 772,988.3 897,362.2 1. Dos quais, participação de commodities PSE padrão (%) % 72.6 73.9 II. Valor total de consumo (no portão da fazenda) Kw Mill 4,730,474.1 5,201,941.8 1. Dos quais, commodities PSE padrão Kw Mill 3,433,851.2 3,842,674.4 III.1 Estimativa de Suporte ao Produtor (PSE) Kw Mill 364,637.7 408,963.4 A.1 Suporte ao preço de mercado Kw Mill 357,516.0 398,023.2 1. Dos quais, commodities PSE padrão Kw Mill 259,520.9 294,019.7 A.2 Pagamentos com base na produção Kw Mill 1,066.9 5,779.0 Estimativa de Apoio do Produtor B. Pagamentos com base no uso de insumos Kw Mill 5,449.1 4,621.6 1. Com base no uso de entradas variáveis Kw Mill 317.5 852.9 2. Com base no uso de insumos fixos Kw Mill 3,357.0 2,091.7 3. Com base no uso de serviços Kw Mill 1,774.6 1,677.0 C. Apoios com base na produção A /An/ I. Produccion necessária Kw Mill 22.5 0.0 1. Com base na receita Kw Mill 0.0 0.0 2. Com base na área ou número de animais Kw Mill 22.5 0.0 D. Apoios com base em A / AN / I Não Atual. Produção necessária Kw Mill 0.0 0.0 E. Apoios com base em A / AN / I Não Atual. Produção Não necessária Kw Mill 0.0 0.0 1. Taxas variables Kw Mill 0.0 0.0 2. Tasas Fixas Kw Mill 0.0 0.0 F. Apoios com base em critérios de não relacionados a commodities Kw Mill 583.2 539.6 1. Recurso de longo prazo Kw Mill 528.5 491.3 2. Um produto não commodity específico Kw Mill 54.8 48.3 3. Outros critérios não relativos a commodities Kw Mill 0.0 0.0 G. Apoios Diversos Kw Mill 0.0 0.0 III.2 Percentagem Estimativa de Apoio do Productor (PSE) % 46.7 45.0 Estimativa de Apoio de IV. Estimativa de Apoios de Serviços Gerais (GSSE) Kw Mill 9,723.6 26,144.4 Serviços Gerais (GSSE) H. Conhecimento Agrícola Kw Mill 3,904.5 2,086.4 I. Inspeção e Controle Kw Mill 710.6 1,070.9 J. Desenvolvimento e Manutenção de Infraestrutura Kw Mill 4,740.7 22,710.1 K. Marketing e promoção Kw Mill 367.8 28.4 L. Custo de ações públicas Kw Mill 0.0 0.0 M. Diversos Kw Mill 0.0 248.6 V.1 Estimativa de Apoio ao Consumidor (CSE) Kw Mill -356,481.5 -394,093.5 Consumidor (CSE) Total N. Transferências de consumidores para produtores (-) Kw Mill -357,479.0 -398,023.2 Estimativa de 1. Dos quais, commodities PSE padrão Kw Mill -259,494.0 -294,019.7 Apoio ao O. Outras transferências de consumidores (-) Kw Mill -2,104.0 -2,860.3 1. Dos quais, commodities PSE padrão Kw Mill -1,527.3 -2,112.9 P. Transferências de contribuintes para consumidores Kw Mill 3,101.5 6,789.9 V.2 Porcentagem de CSE % -7.5% -7.6% VI.1. Estimativa de Apoio Total (TSE) Kw Mill 377,462.8 441,897.7 Estimat iva de Apoio (TSE) Q. Transferências de consumidores Kw Mill 359,582.9 400,883.4 R. Transferências de contribuintes Kw Mill 19,983.8 43,874.5 S. Receitas do orçamento (-) Kw Mill -2,104.0 -2,860.3 66 Figure 53: Disaggregation of Angola’s TSE, 2018 Figure 54: Disaggregation of Angola’s TSE, 2019 67 Figure 55: World Trade Organization Boxes 68 Annex B: OECD Categories and Classification Criteria From the definition of the PSE, a policy measure will be included in the estimation of agricultural support if it: (a) provides a transfer whose incidence is at the farm level; and (b) is directed specifically to agricultural producers or treats agricultural producers differently from other economic agents in the economy. Support for farm product prices, or direct payments based on agricultural production or agricultural area, are clearly agricultural and producer-specific, and are included in the PSE indicator. Similarly, a payment reducing the price of fertilizer or pesticide for application on farm land, or a payment compensating for yield loss as a result of practicing organic farming, is clearly agricultural and producer specific and are also included in the PSE. The impact of policy measures on variables such as production, consumption, trade, income, employment and the environment depend, among other factors, on the way policy measures are implemented. Therefore, to be helpful for policy analysis, policy measures to be included in the PSE are classified according to implementation criteria. For a given policy measure, the implementation criteria are defined as the conditions under which the associated transfers are provided to farmers, or the conditions of eligibility for the payment. Here are the main criteria used to classify programs according to OECD categories: I. PSE CATEGORIES A.1. Market price support (MPS)—transfers from consumers and taxpayers (consumption subsidies) to agricultural producers arising from policy measures that create a gap between domestic market prices and border prices of a specific agricultural commodity, measured at the farm gate level. A.2. Payments based on output—transfers from taxpayers to agricultural producers from policy measures based on current output of a specific agricultural commodity B.1 Payments based on variable input use—transfers reducing the on-farm cost of a specific variable input or a mix of variable inputs. B.2. Fixed capital formation—transfers reducing the on-farm investment cost of farm buildings, equipment, plantations, irrigation, drainage and soil improvements. B.3. On-farm services—transfers reducing the cost of technical, accounting, commercial, sanitary and phyto-sanitary assistance, and training provided to individual farmers. C. Payments based on current production, production required transfers from taxpayers to agricultural producers arising from policy measures based on current area, animal numbers, receipts or income, and requiring production. Category C is further broken into two subcategories: C.1. Based on current receipts/income—including transfers through policy measures based on receipts or income. 69 C.2. Based on current area/animal numbers—including transfers through policy measures based area/animal numbers D. Payments based on non-current A/An/R/I, production required Transfers from taxpayers to agricultural producers arising from policy measures based on non-current (i.e., historical) area, animal numbers, receipts or income, with current production of any commodity required. E. Payments based on non-current production, production not required: transfers from taxpayers to agricultural producers arising from policy measures based on non-current (i.e., historical or fixed) area, animal numbers, receipts or income, with current production of any commodity not required but optional. Category E is further divided in two sub-categories according to the nature of payment rates used: E.1. Variable rates—transfers using payment rates which vary with respect to levels of current output or input prices, or production/yields and/or area. E.2. Fixed rates—transfers using payment rates which do not vary with respect to these parameters. F. Payments based on non-commodity criteria: transfers from taxpayers to agricultural producers arising from policy measures based on: F.1. Long-term resource retirement—transfers for the long-term retirement of factors of production from commodity production. The payments in this subcategory are distinguished from those requiring short- term resource retirement, which are based on commodity production criteria. F.2. A specific non-commodity output—transfers for the use of farm resources to produce specific noncommodity outputs of goods and services, which are not required by regulations. F.3. Other non-commodity criteria—transfers provided equally to all farmers, such as a flat-rate or lumpsum payment. G. Miscellaneous payments: transfers from taxpayers to farmers for which there is insufficient information to allocate them to the appropriate categories. II. General Services Support Estimates Policy measures included in the General Services Support Estimate (GSSE) are classified into one of seven categories according to the nature of the services provided to agriculture in general (and not to individual producers or consumers). The transfers in the GSSE are payments to eligible private or public services provided to agriculture generally. Unlike the PSE and CSE, the GSSE transfers are not destined to individual producers or consumers, and do not directly affect farm receipts (revenue) or consumption expenditure, although they may affect production or consumption of agricultural commodities in the longer term. Services that benefit primary agriculture but whose initial incidence is not at the level of individual farmers: for example, agricultural education, research, marketing and promotion of agricultural goods, general infrastructural investment relating to irrigation, and inspection services beyond the farm gate. 70 While implementation criteria are used to distinguish whether the transfer is allocated to PSE or GSSE, the definition of the categories in the GSSE and the allocation of policy measures to these categories is according to the nature of the service, as the following: A. Research and development: budgetary payments financing research and development activities improving agricultural production. Includes payments to institutions for research related to agricultural technologies and production methods. In most cases, these payments include the financing of public research institutions B. Agricultural schools: budgetary payments financing agricultural training and education. Includes the public funding of education and training targeted specifically on the agricultural sector. C. Inspection services: budgetary payments financing control of quality and safety of food, agricultural inputs and the environment. Includes payments to finance institutions for the control of food quality, animal health, and agricultural inputs. In most cases, these services are financed by public (governmental) organizations, and hence the budgets of these organizations are included. If the unpaid services are provided on farms (e.g., animal vaccinations), the corresponding costs should be allocated to the PSE. D. Infrastructure: budgetary payments financing improvement of off-farm collective infrastructure. Includes public expenditure financing the development of production-related infrastructure in rural areas. It is important to distinguish support between on- and off-farm infrastructures. E. Marketing and promotion: budgetary payments financing assistance to marketing and promotion of agro-food products. This category includes forms of government assistance to increase sales of primary agricultural commodities, such as agricultural exhibitions, fairs, promotional campaigns, advertising, and publications F. Public stockholding: budgetary payments meeting the costs of storage, depreciation and disposal of public storage of agricultural products. Includes budgetary expenditures that finance investments and operating cost for off-farm storage and other market infrastructure facilities related with handling or marketing agricultural products (silos, docks, etc.) G. Miscellaneous: budgetary payments financing other general services that cannot be disaggregated and allocated to the above categories due, for example, to a lack of information III. Consumer Support Estimate The CSE includes price transfers from consumers, which is the inverse value of Market Price Support. A component of the CSE is transfers associated with market price support for the production of commodities that are consumed domestically; these are called price transfers from (to) consumers. These transfers are the same as those included in the PSE under category Market Price Support, but they are given an opposite sign in the CSE and adjusted to apply to quantities consumed. Another type of payment classified under the CSE is budgetary transfers to consumers of agricultural commodities, where these are provided specifically to offset the higher prices resulting from market price support. Finally, consumption subsidies in cash or in kind (their monetary equivalent) associated with programs of market price support for domestic producers are also included in the CSE. This component includes, for example, domestic food aid programs. 71 Annex C: Conceptual Note on Price Collection of Agricultural Producers I. Introduction • This Note aims to describe procedures used to collect information on farm prices for agricultural products under the Agriculture Support Policy Realignment Consultancy and Programmes to help Total Support Estimation (TSE) and other indicators of Support for Agriculture, such as Producer Support Estimation (PSE), General Service Support Estimation (GSSE) and Consumer Support Estimation (CSE). • The crops under analysis are: Corn, Cassava, Beans and Bananas. • Producer Price research and transport costs associated to their commercialization II. A brief description of the methodology used II.1 Period of time in which the survey was lifted The period of consultation with the producers was to last approximately eight weeks that is, from the start of January 2021 to end of February 2021. However, due to lack of time of some farmers in the first contact, it was necessary to schedule a second contact and in some cases farm owners provided other contacts to provide more information. II.2 Geographical Coverage The choice of the regions below was made based on provinces with greater representativeness in terms of total production quantities, according to data from the agricultural campaign report 2018/2019. Product Quantities of the total Location produced (2018/2019) (%) Bananas 60.1 Benguela, Cuanza Sul, Bengo, Uíge, Cabinda Beans 71.7 Malanje, Cuanza Norte, Cuanza Sul, Uíge, Huíla, Huambo. Corn 75.3 Huíla, Huambo, Bié, Benguela, Cuanza Sul Cassava 50.7 Cuanza Sul, Malanje, Bié, Bengo, Uíge II.3 Strategies for raising producer price information • The first way to raise information was carried out by publishing official results generated by MINAGRIP and the INE. • Surveys of representative producers were also carried out in various regions of the country. II.3.1 MINAGRIP, Office of Studies, Planning and Statistics: This Office, through its Statistics Department, monitors the prices charged by agricultural producers, through annual consultation of producers and prices in the main markets, from which producers flow their 72 agricultural production. In this sense, we have been provided with a table of benchmarks for estimated national average producer prices, practiced by agricultural producers on the market. By the force of legal instruments, this Ministerial Body is considered ODINE (Delegated Body of the National Institute of Statistics) and the data that it has processed and produced, constitutes legal-based information for analysis, studies, and policies. https://www.ine.gov.ao/Arquivos/Geral/Manual-de-Procedimentos-sobre-o-Relacionamento- Institucional-e-Fluxos-de-Informao-dos-rgos-Delegados-no-Quadro-do-SEN.pdf II.3.2 National Statistical Office “ INE�, Department of Economic and Financial Statistics The consultation with this body was to obtain information on the Producer Price Index (PPI) in the Agricultural sector. Despite being a challenge and goal for 2022 of this department, Angola does not have studies related to Agricultural PPI. The only report disseminated by the INE refers to the PPI on activities of Transformation of Intermediate Goods, Consumer Goods and Energy Goods). E.g.: https://www.ine.gov.ao/Arquivos/arquivosCarregados//Carregados/Publicacao_637531377595944058. pdf II.3.2 National Producer Portal This is a tool from PRODESI (Export Diversification and Import Substitution Program), created and coordinated by the Ministry of Economy and Planning—MEP, to disseminate the national production of essential goods and services to the real economy. The National Producer Portal (NPP) allows producers to register their existing production in stock, discloses their sales price, as provides contact details to establish commercial relationships. The NPP functions as a virtual market, providing metrics for quantities and average prices of products in stock, and also allowing searchability of producers for products and production areas. II.3.4 Producer Surveys a) Universe According to the NPP, in 2021 there were 6,682 registered producers: ✓ 2,605 Corn Grain Producers; ✓ 1,360 Cassava producers; ✓ 2,070 Bean Producers; ✓ 647 Banana Growers. b) Sample selection Considering the production reported by the Agricultural Campaign 2017/2018 and 2018/2019 and stocks reported in the NPP for Cassava, Corn, Beans and Bananas, 200 producers located in Cuanza Norte, Cuanza Sul, Benguela, Huambo, Bié, Huíla, Malanje, Uíge end Bengo were contacted by telephone. Based in their historical activity, the producers selected have local market specific importance. 73 Product Number of Number of Surveyed/Registered Location producers producers (%) surveyed registered in NPP Banana 40 647 6.18 Benguela, Cuanza Norte, Cuanza Sul, Bengo, Uíge Beans 50 2,070 2.42 Luanda, Malanje, Cuanza Sul, Uíge, Huíla, Huambo, Lunda Sul. Corn 50 2,605 1.92 Huíla, Huambo, Bié, Benguela, Cuanza Sul Cassava 60 1,360 4.41 Cuanza Sul, Malanje, Bié, Bengo, Uíge Total 200 6,682 c) Capturing information Telephone consultation with farmers allowed the collection of information on producer prices for 2018 and 2019 agricultural year. Additionally, the following information was questioned (and informed): ✓ Production quantity; ✓ Price of the product on the farm (producer price) without transportation ✓ Price of the product on the farm (producer price) with transportation ✓ Market where the product flows According to the information obtained by the producers, the transport price is fixed according to the collection area and the place of unloading and as follows: ✓ Total truck rental, in this case the price per ton of product transportation was determined by the rental value divided by the truck’s capacity. The rental price refers to the distance between the provinces where the producers are located for the markets in the province of Luanda. The province of Luanda is the main market for the disposal of products. ✓ Payment of the transport price per package. Bananas are transported mostly in 30 kg plastic boxes, beans in 50 kg bags, maize and cassava in 150 kg bags. In this case, the price per ton was determined by finding the price of the kilogram and multiplied by 1,000. d) Information Analysis The analysis of the results presented was determined by calculating the average price/ton reported by the farmers, that is, the sum of the prices per ton, divided by the total number of farmers who provided information. The cost of transport was calculated by using the difference in average price per ton between the provinces where the farmers are located, and the markets in the province of Luanda. According to the information provided by producers, in the case of export products, the transport costs are similar to the export port in Luanda. The research allowed to observe the following functioning of the market and disposal of agricultural products: 74 • All producers contacted by telephone, inform that generally for higher yields of their production, they sell directly in the markets, their prices dictate better positions of gain. It implies that in many cases the producer is also a distributor of the products on the market. • National agricultural products are mostly traded in formal markets, as they still constitute higher places of great consumption and demand for these products. e) Final comments: For the purposes of the PSE calculation exercise, the prices and transport cost used for cassava and bananas were derived from surveys. For corn and beans, data from the Ministry of Agriculture was used, considering that the selected sample was not representative. 75 Annex D: Public Programs and Budgetary Expenditure Figure 56: Budget PSE, by Category and Program A. Apoio com base na produção de commoditie A.2 Apoios com base na produção Origem Unidade 2018 2019 Apoio com base na produção de commodities MOSAP II (Apoyo Directo). Para 2019 dice financiamiento Programa De Desenvolvimento Da Agricultura Familiar MINAGF Kw Mill 33.58 5351.00 externo A.2 Apoios com base na produção Programa De Desenvolvimento Da Agricultura Familiar MINAGF Kw Mill 1362.14 MOSAP II (Comercializacion) 50% aquí y 50% en extension Fomento Da Produção Agrícola MINAGF Kw Mill 2379.70 PAPAGRO MIISTERIO COMERCIO Kw Mill 38.99 Total Kw Mill 1395.73 7769.68 Asignacion Apoios (todos) Origem Unidade 2018 2019 Mandioca Kw Mill 462.47 2,586.52 Bananas Kw Mill 246.18 1,450.00 Milho Kw Mill 199.50 1,138.91 Feijões Kw Mill 104.96 564.41 Total asignado Kw Mill 1,013.11 5,739.84 Outras Kw Mill 382.62 2,029.85 Apoios produtos SELECCIONADOS Origem Unidade 2018 2019 Mandioca MINAGF Kw Mill 22.49 5.25 Incluye PDAC (https://pdac.ao/) Banana MINAGF Kw Mill Milho MINAGF Kw Mill 20.18 16.97 Feijoes MINAG Kw Mill 11.12 16.97 Incluye PDAC Total alocado para produtos selecionados Kw Mill 53.79 39.19 Total A.2 1,449.52 7,808.87 76 B. Apoios com base no uso de insumos B. 1. Com base no uso de insumos variáveis Apoios Diversos Insumos Origem Unidade 2018 2019 Programa De Desenvolvimento Da Agricultura Familiar Governo Provincial Do Cuanza-Sul Kw Mill 45.10 708.89 MOSAPII Preparacion de tierras y otros Fomento Da Produção Agrícola Governo Provinciales Kw Mill 32.68 PDAC Desenvolvimento Da Agricultura Familiar MINAGF Kw Mill 401.04 MOSAP II Programa 4 MINEF Kw Mill Programa 5 MINEF Kw Mill Programa 6 Otro Kw Mill Total Kw Mill 45.10 1142.60 Asignacion Apoios Unidade 2018 2019 Mandioca Kw Mill 14.94 380.37 B. 1. Com base no uso de insumos variáveis Bananos Kw Mill 7.95 213.24 Maize (Maiz) Kw Mill 6.45 167.49 Frijoles Kw Mill 3.39 83.00 Total Asignado Kw Mill 32.74 844.09 Otros Kw Mill 12.36 298.51 Apoios SEMENTES Origem Unidade 2018 2019 Programa De Fomento Da Actividade Produtiva Agrícola Governo Provincial Do Uíge Kw Mill 3.79 11.12 Fortalecimento Da Cadeia De Produção De Sementes Programa De Fomento Da Actividade Produtiva Agrícola Ministério Da Agricultura E Florestas Kw Mill 31.10 0.65 Produção De Sementes Programa De Fomento Da Actividade Produtiva Agrícola Ministério Da Agricultura E Florestas Kw Mill 100.00 Produção De Sementes Programa De Fomento Da Actividade Produtiva Agrícola Ministério Da Agricultura E Florestas Kw Mill 0.01 Produção De Sementes Programa De Fomento Da Actividade Produtiva Agrícola Ministério Da Agricultura E Florestas Kw Mill 250.35 Produção De Sementes Total Kw Mill 385.25 11.77 Asignación Apoios SEMILLAS Unidade 2018 2019 Mandioca Kw Mill 130.01 3.99 Bananos Kw Mill 69.21 2.24 B. Apoios com base no uso de insumos Maize (Maiz) Kw Mill 56.08 1.76 Frijoles Kw Mill 29.51 0.87 Total Asignado Kw Mill 284.80 8.85 Otros Kw Mill 100.46 2.92 Total B1 Kw Mill 430.35 1154.37 B 2. Com base no formação de capital fixo Capital Fijo Origem Unidade 2018 2019 Programa De Fomento Da Actividade Produtiva Agrícola Governo Provincial Do Bengo Kw Mill 33.32 Drenagem E Sist.Captação Fomento Da Prod. Agrícola/Cabinda Programa De Fomento Da Actividade Produtiva Agrícola Governo Provincial Do Bengo Kw Mill 4.77 Incluye apoyos bienes duraderos Programa De Fomento Da Actividade Produtiva Agrícola Governo Provincial Do Bengo Kw Mill 1.74 Incluye apoyos bienes duraderos B 2. Com base no formação de capital fixo Programa De Investigação E Desenvolvimento TecnológicoPrograma De Reforço Da Capacidade Institucional Kw Mill 0.09 Incluye apoyos bienes duraderos Programa De Desenvolvimento Da Agricultura Comercial MINAGF Kw Mill 0.02 PDAC Incluye equipo de transporte Programa De Fomento Da Actividade Produtiva Agrícola Governo Provincial Do Namibe Kw Mill 0.50 8.55 maquinaria Programa De Desenvolvimento Da Agricultura Familiar MINAGF Kw Mill 791.58 MOSAP maquinaria Projecto De Mecanização Agrícola (Aquisição) MINAGF Kw Mill 3,792.84 maquinaria Desenvolvimento Local E Combate À Pobreza MINAGF Kw Mill 2,822.84 Equipo de transporte+maquinaria+otros consumo duradero Total 4,624.85 2,831.40 Asignacion Apoios (todos) Unidade 2018 2019 Mandioca Kw Mill 1532.44 942.57 Bananos Kw Mill 815.75 528.40 Maize (Maiz) Kw Mill 661.05 415.04 Frijoles Kw Mill 347.79 205.68 Total Asignado Kw Mill 3357.03 2,091.69 Otros Kw Mill 1267.82 739.71 Total B2 Kw Mill 4,624.85 2,831.40 B 3. Serviços on-farm Apoios EXTENSION Origem Unidade 2018 2019 Programa De Desenvolvimento Da Agricultura Familiar MINAG Kw Mill 1,362.14 MOSAP (Extension) Prog.De Desen.Da Act.Com.E Das Infraest.Comerciais Básicas MIN COMERCIO Kw Mill 1,082.59 B 3. Serviços on-farm Programa De Desenvolvimento Da Agricultura Familiar Programa De Desenvolvimento Da Agricultura Familiar Kw Mill 278.60 MOSAP (Extension) Apoio A Extenção Rural gobiernos provinciales Kw Mill 2,270.0 Total Kw Mill 2,444.74 2,270.00 Asignación Apoios Extension Origem Unidade 2018 2019 Mandioca Kw Mill 810.06 755.68 Bananos Kw Mill 431.21 423.63 Maize (Maiz) Kw Mill 349.43 332.75 Frijoles Kw Mill 183.85 164.90 Total Asignado Kw Mill 1,774.55 1,676.96 Otros Kw Mill 670.18 593.04 Total B3 Kw Mill 2444.74 2270.00 77 C. Apoios com base na produção A /An/ I. Produccion necessária C. 1. Com base na receita C. Pagamentos com base na produção A /An/ I. Produccion necessária Origem Unidade 2018 2019 Programa 1 MINAG Kw Mill Programa 2 MINAG Kw Mill Programa 3 MINAG Kw Mill C. 1. Com base na receita Programa 4 MINEF Kw Mill Total Kw Mill 0.00 0.00 Asignacion Apoios (todos) 0 2018 2019 Mandioca Kw Mill 0.0 0.0 Bananos Kw Mill 0.0 0.0 Maize (Maiz) Kw Mill 0.0 0.0 Frijoles Kw Mill 0.0 0.0 Total asignado Kw Mill 0.00 0.00 Otros Kw Mill 0.00 0.00 Total C1 0.00 0.00 C. 2. Com base na área ou número de animais Exemplo: Apoios Desastre Origem Unidade 2018 2019 C. 2. Com base na área ou Apoio Em Situações De Calamidades Naturais MINAGF Kw Mill 30.97 Bienes y servicios número de animais Total Kw Mill 30.97 0.00 Asignacion Apoios (todos) Unidade 2018 2019 Mandioca Kw Mill 10.3 0.0 Bananos Kw Mill 5.5 0.0 Maize (Maiz) Kw Mill 4.4 0.0 Frijoles Kw Mill 2.3 0.0 Total asignado Kw Mill 22.48 0.00 Otros Kw Mill 8.49 0.00 La asiganción fue hecha de acuerdo a la participación de cada producto en el valor de la produccion agropecuaria. Total C2 30.97 0.00 D. Apoios com base em A / AN / I NÃO Atual. Produção necessária D. Apoios com base em A / AN Ejemplo:+ Origem Unidade 2018 2019 / I NÃO Atual. Produção Programa 1 MINAG Kw Mill Total 0.00 0.00 necessária Asignacion Apoios (todos) Unidade 2018 2019 Mandioca Kw Mill 0.0 0.0 Bananos Kw Mill 0.0 0.0 Maize (Maiz) Kw Mill 0.0 0.0 Frijoles Kw Mill 0.0 0.0 Total asignado Kw Mill 0.00 0.00 Otros Kw Mill 0.00 0.00 Total D. 0.00 0.00 E. Apoios com base em A / AN / I NÃO Atual. Produção Não necessária E.1. Taxas variables E. Apoios com base em A / AN / I NÃO Atual. Produção NO necessária Programa Origem Unidade 2018 2019 E.1. Taxas variables Kw Mill Total Kw Mill 0.00 0.00 Asignacion Apoios (todos) Unidade 2018 2019 Mandioca Kw Mill 0.00 0.00 Bananos Kw Mill 0.00 0.00 Maize (Maiz) Kw Mill 0.00 0.00 Frijoles Kw Mill 0.00 0.00 Total Asignado Kw Mill 0.00 0.00 Otros Kw Mill 0.00 0.00 Total E1 Kw Mill 0.00 0.00 E.2 Tasas Fixas Exemplo: Apoio directo Renta Origem Unidade 2018 2019 Kw Mill Kw Mill E.2 Tasas Fixas Total 0.00 0.00 Asignacion Apoios (todos) Unidade 2018 2019 Mandioca Kw Mill 0.00 0.00 Bananos Kw Mill 0.00 0.00 Maize (Maiz) Kw Mill 0.00 0.00 Frijoles Kw Mill 0.00 0.00 Total Asignado Kw Mill 0.00 0.00 Otros Kw Mill 0.00 0.00 Total E2 Kw Mill 0.00 0.00 78 F. Apoios com base em critérios de não relacionados a commodities F.1. Recurso de longo prazo F.1. Recurso de longo prazo Apoios Reforestacion Origem Unidade 2018 2019 Instituto De Desenvolvimento Florestal Instituto De Desenvolvimento Florestal Kw Mill 728.07 665.00 Total Kw Mill 728.07 665.00 F. Apoios com base em critérios de não commodities Asignacion Apoios Energia (todos) Unidade 2018 2019 Mandioca Kw Mill 241.25 221.38 Bananos Kw Mill 128.42 124.10 Maize (Maiz) Kw Mill 104.07 97.48 Frijoles Kw Mill 54.75 48.31 Total Asignado Kw Mill 528.48 491.27 Otros Kw Mill 199.59 173.73 Total F1 Kw Mill 728.07 665.00 F.2. Um produto não commodity específico Origem Unidade 2018 2019 commodity específico F.2. Um produto não Kw Mill Total Kw Mill 0.00 0.00 Unidade 2018 2019 Mandioca Kw Mill 0.00 0.00 Bananos Kw Mill 0.00 0.00 Maize (Maiz) Kw Mill 0.00 0.00 Frijoles Kw Mill 0.00 0.00 Total Asignado Kw Mill 0.00 0.00 Otros Kw Mill 0.00 0.00 Total F2 Kw Mill 0.00 0.00 F.3. Outros F.3. Outros critérios não relativos a commodities relativos a critérios não Total F3 Kw Mill 0.00 0.00 G. Otros Otr os G. Total G Kw Mill 0.00 0.00 79 Figure 57: Budget GSSE, by Category and Project H. Conhecimento Agrícola Programa Origem Unidade 2018 2019 H. Conhecimento Agrícola Capacitacion MINAGF Kw Mill 2,159.45 301.69 Aquí 50% y 50% infraestructura (EASG). El segundo va completo Educacion MINAGF Kw Mill 182.52 Investigación MINAGF Kw Mill 62.10 Instituto De Investigação Agronómica Instituto De Investigação KwAgronómica Mill 633.27 591.70 Instituto De Investigação Veterinária Instituto De Investigação KwVeterinária Mill 470.57 570.64 Instituto Nacional Dos Cereais Instituto Nacional Dos Cereais Kw Mill 208.10 385.24 Instituto Superior De Tecnologias Agro-Alimentar Instituto Superior De Tecnologias 188.49 Kw Mill Agro-Alimentar 165.23 PRODESI MINAGF Kw Mill 6.70 Enseñanza y formacion Desenvolvimento Local E Combate À Pobreza Gobiernos provinciales Kw Mill 65.20 TOTAL Conhecimento Agrícola Kw Mill 3,904.50 2,086.40 I. Inspeção e Controle Program Origem Unidade 2018 2019 I. Inspeção e Controle Programa De Saúde Pública Veterinária Gobiernos Provincias Kw Mill 100.44 99.71 Prevenção, Controlo E Erradicação Da Peripneumonia Contagiosa Bovina MINAGF Kw Mill 301.64 Instituto Dos Serviços De Veterinária Instituto Dos Serviços De KwVeterinária Mill 610.20 669.51 TOTAL Inspeção e Controle Kw Mill 710.64 1070.86 J. Desenvolvimento e Manutenção de Infraestrutura Program Origem Unidade 2018 2019 J. Desenvolvimento e Manutenção de Transporte Kw Mill 1,347.0 Meteorologia Kw Mill 11.6 Riego Kw Mill 1,026.5 Need more information of program Others Kw Mill 2,355.5 Need more information of program Infraestrutura Construção Infraestruturas Para O Desenvolvimento Da Agricultura Comercial Kw Mill 133.3 Aquí 50% y 50% Capacitacion (EAP) Desenvolvimento Local E Combate À Pobreza Estruturação EconómicaKw E Produtiva Mill Das Comunidades Rurais 22,128.7 Construção E Reabilitação De Infraestruturas Rodoviárias Gobiernos provinciales Kw Mill 111.4 Reforço Das Estatísticas Agrícolas Kw Mill 112.0 Implementação Plano De Cadastro De Direito De Superfície Governo Provincial De Luanda Kw Mill 5.1 PRODESI Serviços De Estudo, Fiscalização Kw Mill E Consultoria 141.4 PRODESI TELECOMUNICACIONESKw Mill 3.7 Asociaciones de Poductores Kw Mill 74.6 TOTAL Desenvolvimento e Manutenção de Infraestrutura Kw Mill 4,740.7 22,710.1 K. Marketing e promoção Comercialização e Program Origem Unidade 2018 2019 promoção Promocion Agência De Investimento KwPrivado 367.79 Mill E Promoção 26.26 Das Exportações PRODESI. Promocion de inversion 0.18 K. Feira De Produtos Nacionais/Malange Gobiernos provincial Malange Kw Mill Feira Provincial Agropecuaria/Huambo gobierno provincial Huambo Kw Mill 2.00 TOTAL Marketing and Promotion Kw Mill 367.79 28.44 L. Custo de ações públicas L. Organismo público Program Origem Unidade 2018 2019 Kw Mill TOTAL Organismo público Kw Mill 0.00 0.00 M. Diversos M. Diversos Program Origem Unidade 2018 2019 PRODESI Ministrio de Economia Kw Mill 248.56 Incluye "otros servicios" Total Miscelllaneous Kw Mill 0.00 248.56 80 Figure 58: Budget CSE Apoyos Origem Unidade 2018 2019 Merienda Escolar MZ Mill 3,083.34 9,191.15 Ministério Programa Integrado De Desen.Rural E Combate A Pobreza Da Acção Social, Família E Prom. MZ Mill Da Mulher 1,189.42 MZ Mill Total MZ Mill 4,272.76 9,191.15 Asignacion Apoios (todos) Unidade 2018 2019 Mandioca MZ Mill 1,415.77 3,059.72 Banana MZ Mill 753.65 1,715.28 Milho MZ Mill 610.72 1,347.27 Feijoes MZ Mill 321.31 667.67 Total Asignado MZ Mill 3,101.45 6,789.95 Otros MZ Mill 1,171.30 2,401.21 i See methodology manual at: http://www.oecd.org/agriculture/topics/agricultural-policy-monitoring-and- evaluation/documents/producer-support-estimates-manual.pdf ii At present, the OECD methodology for agriculture support estimates covers 109 countries. This includes OECD countries, non-OECD EU Member States (subject to data availability), and a number of developing countries where monitoring is done by the OECD, IADB, and FAO’s MAFAP unit. The 54 countries monitored by the OECD are Argentina, Australia, Brazil, Canada, Chile, China, Colombia, Costa Rica, the European Union (Austria, Belgium, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Poland, Portugal, Spain, Slovakia, Slovenia, Sweden, the United Kingdom), India, Indonesia, Iceland, Israel, Japan, Kazakhstan, Korea, Mexico, New Zealand, Norway, the Philippines, the Russian Federation, South Africa, Switzerland, Turkey, Ukraine, the United States and Vietnam. iii As part of this assessment, a training of more than 15 public sector staff was undertaken to build capacity and allow for Government to update the estimates going forward. iv Agriculture support was estimated using the OECD methodology (https://www.oecd.org/agriculture/topics/agricultural-policy-monitoring-and-evaluation/documents/producer- support-estimates-manual.pdf ). The total support estimate measure (TSE) is the annual monetary value of all gross transfers from taxpayers and consumers arising from public policy measures that support agriculture, net of the associated budgetary receipts, regardless of their objectives and impacts on farm production and income, or consumption of farm products. v GSSEs include agriculture public goods and services such as innovation systems (agriculture R&D and education), animal and plant health services, food safety, infrastructure, agriculture promotion, land administration, and other public services. vi In some settings, other value chain actors (such as input suppliers) also capture part of the transfers. It’s conceivable that in those settings, they benefit more than even large-scale producers. vii Authors calculations, based on OECD data viii Goyal, Aparajita; Nash, John. 2017. Achieving Better Results: Public Spending Priorities for Productivity Gains in African Agriculture. Africa Development Forum;. Washington, DC: World Bank and Agence Francaise de Développement. © World Bank. https://openknowledge.worldbank.org/handle/10986/25996 License: CC BY 3.0 IGO ix López, R., and G. I. Galinato. 2007. “Should Governments Stop Subsidies to Private Goods? Evidence from Rural Latin America.� Journal of Public Economics 91:1071—94. x Lopez, Ramon. Under-investing in public goods: evidence, causes, and consequences for agriculture development, equity and the environment. Journal of Agriculture Economics, Volume 32, Issue 1. January 2005: https://onlinelibrary.wiley.com/doi/full/10.1111/j.0169-5150.2004.00025.x 81 xi World Bank. World Development Report 2001: https://elibrary.worldbank.org/doi/pdf/10.1596/0-1952-1606-7 xii An update to the World Bank’s 2006 Diagnostic Trade Integration Study (DTIS) is under preparation and is expected to take on these questions in more detail. xiii Based on an extrapolation from the World Food Programme (WFP)’s measurement of the cost of a minimum diet globally. This methodology defines a simple plate of food to consist of pulses, a local carbohydrate—such as rice, bread, maize meal—vegetable oil, tomatoes, onions and water. https://cdn.wfp.org/2018/plate-of-food/ However, Angola has not yet made it into the database and this qualitative assessment assumes that maize will be considered part of Angola’s plate of food. xiv For a definition and approach to CSA, see: https://www.worldbank.org/en/topic/climate-smart-agriculture xv For a definition and approach to NSmartAg see: https://www.worldbank.org/en/topic/agriculture/publication/nutrition-smart-agriculture-when-good-nutrition-is- good-business xvi See methodology manual at: http://www.oecd.org/agriculture/topics/agricultural-policy-monitoring-and- evaluation/documents/producer-support-estimates-manual.pdf xvii At present, the OECD methodology for agriculture support estimates covers 109 countries. This includes OECD countries, non-OECD EU Member States (subject to data availability), and a number of developing countries where monitoring is done by the OECD, IADB, and FAO’s MAFAP unit. The 54 countries monitored by the OECD are Argentina, Australia, Brazil, Canada, Chile, China, Colombia, Costa Rica, the European Union (Austria, Belgium, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Poland, Portugal, Spain, Slovakia, Slovenia, Sweden, the United Kingdom), India, Indonesia, Iceland, Israel, Japan, Kazakhstan, Korea, Mexico, New Zealand, Norway, the Philippines, the Russian Federation, South Africa, Switzerland, Turkey, Ukraine, the United States and Vietnam. xviii As part of this assessment, a training of more than 15 public sector staff was undertaken to build capacity and allow for Government to update the estimates going forward. xix Under FAO (MAFAP)’s support to Angola, data was collected for an Agriculture Public Expenditure Review. However, the budgetary data was collected for just two years (2018, 2019) and does not include any data on prices which are required for the OECD methodology. This ASA builds on FAO’s database ( excepting line-item data) to fill the coverage and data gaps and deploys the OECD methodology to produce a comprehensive review of agricultural support. xx World Bank. 2020. Angola Poverty Assessment. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/34057 License: CC BY 3.0 IGO xxi Project Appraisal Document, “Commercial Agriculture Development Project� (2018); Project Appraisal Document, “Smallholder Agriculture Development and Commercialization Project� (2016) xxii MIT Observatory of Economic Complexity xxiii World Bank Angola Macro Poverty Outlook (October 2020) http://pubdocs.worldbank.org/en/873551492188150493/mpo-ago.pdf xxiv World Bank. 2020. Angola Poverty Assessment. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/34057 License: CC BY 3.0 IGO xxv World Bank. 2020. Angola Poverty Assessment. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/34057 License: CC BY 3.0 IGO xxvi World Development Indicators xxvii World Bank. 2020. Angola Poverty Assessment. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/34057 License: CC BY 3.0 IGO; Project Appraisal Document, “Commercial Agriculture Development Project� (2018); Project Appraisal Document, “Smallholder Agriculture Development and Commercialization Project� (2016) xxviii Goyal, Aparajita, and John Nash. 2017. Reaping Richer Returns: Public Spending Priorities for African Agriculture Productivity Growth. Africa Development Forum series. Washington, DC: World Bank. doi:10.1596/978-1-4648- 0937-8. License: Creative Commons Attribution CC BY 3.0 IGO World Bank. 2007. World Development Report 2008: Agriculture for Development. Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/5990 License: CC BY 3.0 IGO xxix World Development Indicators; World Bank Angola Macro Poverty Outlook (April 2020) xxx World Bank Macro-Poverty Outlook https://pubdocs.worldbank.org/en/873551492188150493/mpo-ago.pdf 82 xxxi World Bank-IMF Annual Meetings 2020 xxxii As part of its Production Support, Export Diversification and Import Replacement Program (PRODESI), the Government has placed a strong emphasis on agricultural and light manufacturing products to boost exports and substitute imports in order to ease current account pressures. Initial policy measures outlined in the Government’s six�month plan (Plano Intercalar) aimed at: (a) continuing the fiscal consolidation process and ensuring public debt sustainability; (b) consolidating and strengthening monetary and foreign exchange policies; (d) strengthening the financial sector; (e) improving the business environment and the productivity and competitiveness of local companies, and attracting foreign direct investment. The Government has followed on these measures, including fiscal consolidation and rebalancing of the foreign exchange market by un�pegging the currency from the U.S. dollar and allowing the Angolan kwanza to depreciate (CADP Project Appraisal Document). xxxiii The former includes bananas, coffee, grains, wood, ornamental stones, textiles, cement, fisheries and tourism. The latter comprise agricultural and light manufactured goods where some national production exists already. Specific products identified by the Government for import substitution include rice, vegetables, beans, fresh fruit, palm oil, tomato paste, mineral water, barbed wire and medicines. In total, PRODESI prioritizes 54 products. xxxiv Revising the investment law, removing restrictions to foreign investment, easing visa requirements, opening markets to competition, launching a privatization program, and fighting corruption are important steps that the Government is taking to increase investor confidence in Angola. Building on previous efforts to improve business start�ups and labor laws, the Government is stepping up business environment reforms. The Government is also launching a program to support diversification and improve competitiveness, including cross�cutting and sector� specific measures (Programa de Apoio à Produção, Diversificação das Exportações e Substituição das Importações, PRODESI). xxxv https://www.doingbusiness.org/content/dam/doingBusiness/country/a/angola/AGO.pdf xxxvi Global Competitiveness Report http://www3.weforum.org/docs/WEF_TheGlobalCompetitivenessReport2019.pdf xxxvii The five indicators are Voice and Accountability, Political Stability, Regulatory Quality, Control of corruption, and Rule of law. https://info.worldbank.org/governance/wgi/Home/Reports xxxviii Total real GDP was Kz 1.457 billion, down from Kz 1.537 billion in 2019. INE published seasonally adjusted national accounts data for Q4 and 2020. xxxix INE published seasonally adjusted national accounts data for Q4 and 2020. xl WB Country Brief, Africa Agriculture Policy Inventory 2021 xli World Bank Angola Macro Poverty Outlook (October 2020) http://pubdocs.worldbank.org/en/873551492188150493/mpo-ago.pdf xlii World Bank Internal COVID-19 and Food Security Update (October 2020). xliii Fiscal consolidation efforts in 2020 focused on restraining expenditures through a nominal freeze on non-essential goods and services spending and a hiring freeze (except in education and health). Measures to raise non-oil revenues included changes to the personal income tax and broadening of the VAT base, though those are expected to yield results not before 2021. At the same time, the Government increased budget allocations for health (by 40 percent over 2019) and other social expenditures to mitigate the impact of the pandemic. xliv Exchange rate reforms have entailed a progressive move towards market determination of the exchange rate through regular spot futures auctions. With foreign exchange liberalization beginning in 2018, the exchange rate depreciated 56 and 41 percent until the end of 2019 in nominal and real terms, respectively. xlv Savanna covers much of the country, with humid savanna forest accounting for about 18 percent of the total land area. Humid tropical forests are largely restricted to the northern provinces and the enclave of Cabinda. FAO, 2013b xlvi The soils are generally fertile in the north and the central highlands (Bié, Huambo, and Malanje provinces) and average rainfall exceeds 1,000 mm per year. The country has enormous potential to increase cropped area, raise crop yields, and exploit the potential of its diverse agroclimatic regions to increase agricultural and food production. xlvii World Bank Group. 2018. Angola Country Economic Memorandum: Towards Economic Diversification. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/31898 License: CC BY 3.0 IGO; National Rice Development Strategy xlviii International Monetary Fund. (1995). “Angola�. In Angola: Recent Economic Developments. USA: International Monetary Fund. doi: https://doi.org/10.5089/9781451800470.002 xlix Project Appraisal Document, “Commercial Agriculture Development Project� (2018) 83 l World Development Indicators li World Bank and IFC. 2019. Country Private Sector Diagnostic: Creating Markets in Angola. Washington. DC. lii UNCTAD (2018). National Green Export Review of Angola Wood, Fish, and Coffee: Baseline Report. liii Food Systems Dashboard https://foodsystemsdashboard.org/countrydashboard liv FAOSTAT lv Project Appraisal Document, “Commercial Agriculture Development Project� (2018); Project Appraisal Document, “Smallholder Agriculture Development and Commercialization Project� (2016) lvi World Bank: Agriculture Policy Note, Angola, 2017. Analyses showed that the yields of cereals, potato, and beans were generally higher for farmers who received such services. lvii World Bank (2017). “Republic of Angola: Selected Policy Notes for Incoming Administration of Angola� lviii While a very small number are registered as cooperatives under the 2015 law, they might be registered with the Provincial governments. lix World Development Indicators lx The poor are more likely to work in the agriculture sector with more than 70% of poor employees. World Bank Poverty Assessment 2020. lxi Food Systems Dashboard lxii In construction and commerce, males earn twice as much as females. The highest wages are in the financial services sector with females earning more than males. Female’s remuneration in financial services is high and more than twice males (250 vs 108 thousand kz per month). World Bank Poverty Assessment 2020. lxiii This figure reverses for non-agriculture sector where 73 percent of the employees have primary education or more. lxiv The WB Poverty Assessment (2020) has provided strong empirical evidence that longer distances to the nearest markets are associated with poverty. lxv Source: WB Africa Agriculture Policy Inventory (2021). Agriculture’s share of the national budget declined from 1.10 percent in 2013 (US$702 million) to 0.41 percent (US$544.0 million) in 2015. It is important to note that the budget allocations for the agriculture sector not only fall under the Ministry of Agriculture (MINAGRIP), but also under the Ministries of Commerce, Industry, and Transport. World Bank (2017). “Republic of Angola: Selected Policy Notes for Incoming Administration of Angola�. lxvi Preliminary FAO estimates show that this share rose then to 0.5 and 0.9 percent in 2018 and 2019 respectively. MAFAP Presentation to MINAGRIF, October 2020 lxvii IFPRI 2019 lxviii Note: The authors calculated amounts for public expenditure on agriculture to be $1.47 billion and $1.66 billion in 2016 and 2018 by multiplying share of agriculture GDP (CAADP AATS Scorecards) by agriculture GDP (WDI). In absolute terms, this would indicate a large jump in resources allocated to the sector relative to 2014 and 2015. lxix At the time of the last AgPER (2007), total the investment budget was overwhelmingly directed towards irrigation projects (70%) and mechanization (21%) largely due to the priorities of external donors. The spatial concentration of these investments was also quite concentrated and did not reflect overall agricultural potential. A large portion of agricultural investment was off budget entirely, being funded from various external sources. Also, a huge amount of public expenditure, both on and off budget, is devoted to improving roads, bridges and railroads, expenditures that directly benefit both producers and consumers by bringing down the cost of transporting both inputs and outputs. don https://openknowledge.worldbank.org/bitstream/handle/10986/7648/397100v20ER0P01disclosed0Feb0602008.p df?sequence=1&isAllowed=y lxx World Bank Project Appraisal Document (2016). Smallholder Agriculture Development and Commercialization (SADC) Project lxxi Africa Agriculture Transformation Scorecard (AATS) 2019. CAADP Biennial Review lxxii Within MINAGRIP, the ADI is responsible for development of smallholder agriculture and extension services. As defined in the medium-term Agricultural Development Program (ADP) 2013–2017, ADI’s mandate is to support smallholders across the country. ADI is present in 128 out of 131 municipalities in the country through the EDAs. Under extension services, ADI is responsible for promoting agricultural practices and technology generation to increase smallholder agricultural production and productivity. However, ADI faces institutional constraints such as 84 weak human resource capacity and poor housing conditions for staff in municipalities and communes that they serve. ADI’s staff total 699, of whom 104 have a university degree and 595 are medium-level technicians. lxxiii World Bank (2017). “Republic of Angola: Selected Policy Notes for Incoming Administration of Angola� lxxiv Overall, commercial banks have shown a reduced appetite to lend during the economic slowdown. In this context, agriculture’s share of overall credit remains less than 5 percent and only 2 percent of farmers have access to agriculture credit. Although the availability of agriculture credit has increased recently with the Angola Development Bank’s lines of credit and the creation of the Agriculture Development Fund in 2016, it has not reached the smallholder segment. World Bank (2017). “Republic of Angola: Selected Policy Notes for Incoming Administration of Angola� lxxv Despite the importance of the agricultural sector, timely and accurate data on its structure and trends of the development of agricultural activities are very limited in Angola. The last agricultural census dates back to the early 1970s. Since the country’s independence in 1975, no nationally representative agricultural survey has been implemented. This lack of reliable and up-to-date data is a major constraint for policy makers and investors in the sector as well as for national statistics like the national accounts which currently have to rely on outdated data and estimates. Source: Project Appraisal Document, “Angola Statistics Project� (2016). lxxvi World Bank (2017). “Republic of Angola: Selected Policy Notes for Incoming Administration of Angola� lxxvii World Bank (2018). Africa Agriculture Policy Inventory lxxviii The policies, guidelines and strategies for the Ministry of Agriculture and Forestry (MINAGRIF) are defined in the PDMPSA (Medium Term Development Plan for the Agrarian Sector) 2018 –2022 and are based on four main strategic objectives: (i) Promote a broad professional training and technology transfer campaign to optimize agricultural production and productivity; (ii) Implement a process of agricultural products and rural transport for the development of family agriculture, corporation and public-private partnership; (iii) Establish an efficient mechanism for coordination and synergy among different sectors and other stakeholders in rural areas, highlighting the participation of society in the National Development Process; (iv) Support the industrialization process of the country. lxxix The products defined, within the scope of PRODESI, for Agriculture, are: Cereal Culture Products: Rice, Corn, Wheat and Massambala; Legume and Oilseed Culture Products: Soy, Beans, Ginguba, Cashew Nuts, Sunflower and Palm Oil; Products of Root and Tuber Culture: Rena Potato, Sweet Potato and Cassava; Vegetable Culture Products: Onions, Garlic, Carrots, Peppers, Tomatoes, Cabbage, Cabbage, Ginger, Lettuce, Cucumbers and Jindungo; Coffee; Sugar cane; Beekeeping; Beef and Dairy Cattle; Pigs; Goats; Broiler and laying poultry; Forestry. Source: https://prodesi.ao/fileiras/agricultura lxxx A summary of the 2020 policy measures can be found here: https://www.imf.org/en/News/Articles/2020/09/18/na-angola-confronting-the-covid-19-pandemic-and-the-oil- price-shock lxxxi MIT Observatory of Economic Complexity lxxxii See National Development Plan (NDP) 2013–2017 and its predecessor. Angola has the potential to export building materials, coffee, honey, fishery products and byproducts, woods, iron ore, alcoholic and nonalcoholic beverages, vegetables, and tubers. lxxxiii Angola has been a member of the WTO since November 1996 and is also a member of two regional economic communities: the Economic Community of Central African States (ECCAS) and the Southern African Development Community (SADC). Angola withdrew from COMESA in 2007. According to WTO, Angola has not ratified the SADC Trade Protocol or signed the SADC draft Protocol on Trade in Services but has signed the SADC harmonized seed policy. Angola is part of several bilateral trade agreements, which has increased from 30 to 38 framework or cooperation agreements. Angola participated, as part of the SADC group, in the negotiations for an Economic Partnership Agreement (EPA), but did not sign the Agreement concluded by the European Union with the six other SADC members in July 2014. Angola is a beneficiary of the United States’ African Growth and Opportunity Act (AGOA) and as a least developed country, of the GSP schemes of other countries. However, due to its lack of competitiveness, Angola has not taken advantage of preferential access under AGOA. Under the Global System of Trade Preferences among Developing Countries (GSTP), Angola has conducted negotiations with Mozambique and Cuba. For the time being, however, the country accords no trade preferences. lxxxiv The average tariff is close to 12 percent with double-digit tariffs in all sectors of the economy. The procedures for international trade are complicated. The time for exports border and documentary compliance is respectively 32 85 percent and 75 percent higher than the average time in Sub-Saharan Africa. The time for imports border and documentary compliance is according to the World Bank Doing Business Report 76 percent and 133 percent higher respectively than the average time in Sub-Saharan Africa. Angola ranks 159 out of 160 countries on the World Bank ’s 2018 Logistic Performance Index (LPI) and the country’s performance in the area of logistics deteriorated relative to the LPI in 2014. lxxxv According to the 2007 Diagnostic Trade Integration Study, the nominal average rate of tariff protection for “agriculture, forestry, hunting and fishing� as defined in the International Standard Industrial Classification (ISIC) is 10.3 percent, with average rates for the subsectors “agriculture and hunting,� “logging,� and “fishing,� at 8.2 percent, 20 percent, and 18.9 percent, respectively. Peak rates of 30 percent are applied to items in Harmonized System chapters 5 (products of animal origin), 9 (coffee, tea, maté, and spices), 21 (miscellaneous edible preparations), 22 (beverages, spirits, and vinegar), and 44 (wood and articles of wood). lxxxvi https://www.orbitax.com/news/archive.php/Angola-Consumption-Tax-Replace-39832 lxxxvii Agricultural inputs (farm tools and machinery) are subject to multiple small tariffs (e.g. stamp tax; custom service duty; port charges; transport charges etc.), which cumulatively present a significant tax burden. In principle some agricultural inputs are exempt from some taxes, but in practice even “automatic� exemptions require substantial delays and bureaucratic footwork. Source: Kyle, S. C. (2010). Angola’s Macroeconomy and Agricultural Growth. lxxxviii Presidential Decree 23/19 restricts the import of 54 goods including some “cesta basica� products that are tariff exempt, but can only be imported with a special license after showing that they cannot be purchased locally or the importer is also investing in local production. They may be imported free of duty and consumption tax if they are included in the “basic basket� or in the event of a shortage on the domestic market. lxxxix Import tariffs overall remain in a range of 2–50 percent, but some reductions have been proposed for meat and live animals which have a current level of duty of 20 percent plus a consumption tax of 10 percent for meat. The Government is proposing to cut the duty to 2 percent and to maintain the consumption tax. Irish potatoes seed is subject to a 50 percent import tax and the current revision proposes a reduction to 30 percent. xc Angola’s 2005 Customs schedule bans imports of animals and byproducts from areas affected by epizootic diseases, of plants from areas affected by epiphytic diseases, and of genetically modified or transgenic seeds or grains, except those supplied for food aid programs. Decree No. 92/04 of December 2004 strengthened Angola’s legislation regarding genetically modified organisms (GMOs) supplied for food aid. The decree says that the Ministry of Agriculture must grant permission for such imports and that GM grains and seeds entering the country as food aid must be milled immediately on arrival. An export tax of 20 percent is levied on hides and skins, and a tax of 10 percent on exports of unworked ivory. Angola is not a member of CITES. Exports of animals, parts, and animal products are subject to permission from the “competent authorities,� and exports of fodder are subject to export permits. Source: Kyle, S. C. (2010). Angola’s Macroeconomy and Agricultural Growth . xci According to the US State Department, foreign exchange availability in the market during 2016 averaged US$890.5 million per month, a substantial decrease, almost half of the 2015 monthly average of US$1.46 billion per month average and 2013/2014 US$1.6 billion monthly averages. Per the February 24, 2016, Presidential Decree No. 40/16, foreign exchange availability in 2016 was expected to meet only 63 percent of demand.� See U.S. Department of State. Bureau of Economic and Business Affairs. 2017 Investment Climate Statements—Angola. Report. June 29, 2017 xcii Since 2015, the scarcity of foreign exchange appears to have accentuated market concentration in the supply of agricultural inputs, generating shortages and significant price increases. The Government has also conditioned access to foreign exchange on a commitment by importers to adhere to Government-determined retail prices in the domestic market. While this policy has improved product availability and lowered prices, it can negatively affect the market performance, as it potentially create barriers to entry for new bidders, facilitates collusion, and favors some competitors over others. “Programas Dirigidos� allocated around US$ 500 million in Forex to the acquisition of agriculture goods, including inputs, between 2016 and 2017. The program contemplated 691 companies in 2019 and planned to cover 950 in 2017.99 “Apenas em Moxico, relativamente à disponibilidade de factores de produção para o apoio a campanha agrícola 2016/2017, foram recepcionadas e distribuídas 29,990 kg de milho e 24,999,8 kg de NPK, em um programa do governo (Programa de Extensão e Desenvolvimento Rural (PEDR)� . Universidade Católica de Angola (2016). Relatório Económico de Angola 2016. pp 253-254. xciii The kwanza slumped to an all-time low against the U.S. dollar at the end of October after the National Bank of Angola (BNA) fully liberalized the foreign exchange regime. In mid-October, the Bank abandoned the trading band 86 that was in place since January 2018, thus allowing the currency to trade freely; its previous policy of controlled exchange rate adjustment prevented the kwanza from depreciating by more than 2.0% at currency auctions. On 8 November, the kwanza ended the day at 462.5 per USS$ marking a 18.1% depreciation over the same day in October. Furthermore, the currency was down 33.5% on a year-to-date basis and 33.0% in year-on-year terms. https://www.focus-economics.com/countries/angola/news/special/kwanza-dives-to-all-time-low-as-central-bank- floats-the-currency xciv Angola ratified and deposited its instruments of participation in the AfCFTA with the AU on November 8, 2020 making it the 30th country to become a full State Party to the continental agreement. https://www.tralac.org/resources/infographic/13795-status-of-afcfta- ratification.html#:~:text=The%20operational%20phase%20of%20the,Niger%20on%207%20July%202019.&text=It% 20has%20been%20indicated%20that,and%20Algeria%3B%20confirmation%20is%20pending xcv Angola has signaled its interest to join and submitted their tariff offer to the SADC FTA last year but is also not yet operational and is likely still to take some considerable time to come into full effect. https://www.tralac.org/documents/resources/sadc/4281-sadc-council-of-ministers-remarks-by-the-sadc- executive-secretary-h-e-dr-stergomena-tax-delivered-12-march-2021/file.html xcvi Later stages of the negotiations will focus on competition policy, intellectual property rights, dispute settlement, and other things. Thus, while the agreement is said to be “in force� it has not yet resulted in actual tariff reductions. xcvii https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS?locations=AO-ZG xcviii BMI Research. 2017 Angola Retail Report. Q2 2017. xcix Project Appraisal Document, “Commercial Agriculture Development Project� (2018); IFC “Creating Markets in Angola: Country Private Sector Diagnostic� (2019) c CPSD 2018 ci All non-urban and some urban land is ultimately under state ownership, but can be leased for 45 years to private entities; different land rights apply to different land use requirements. The provincial governor allocates areas less than 5,000 ha; the Minister of Agriculture allocates land areas of between 5,000 ha and 10,000 ha, and the President allocates areas greater than 10,000 ha. In 2004, the country enacted a new land law that sought to strengthen perceived areas of weakness in prior legislation related to land and natural resource management, for example the role of traditional rural communities.55 The law itself is not an obstacle to investment, but its application does not appear to create the security needed as the overall context for rural investment adds to uncertainties and high operational costs. cii Food Systems Dashboard https://foodsystemsdashboard.org/countrydashboard ciii Road density is 6 km per 100 km², one of the lowest in Southern Africa. The rural access index is 42 percent, which is the fourth lowest in the world after South�Sudan, Chad, and Mali (World Development Indicators). civ The Angolan Land Law establishes that all land belongs to the State. The law (Article 5 of the Land law 04/04) provides user rights to the community that have traditionally inhabited certain lands — known as (non-transferable) rights of useful customary domain. Economic operators may exploit land under concession contracts (land-use rights) with a maximum but renewable duration of 60 years. Foreign natural and legal persons may be granted land- use rights, provided they have a representative in Angola (Land Law of November 2004). However, the procedures for obtaining and transacting land-use rights (concessions) are cumbersome, expensive, and slow, and few transfers come with legally-defensible documents to prove that dwellers own the land they have acquired or been allocated. cv Information on available land is underdeveloped. The current state of land information in Angola constrains the ability of Government and investors to identify potentially promising land for investment. Most rural lands are not formally registered and absent from any database, and the practice of registering land rights or transactions is not common. No statistics are available on what percentage of land is under private tenure as opposed to communal tenure. There are no reliable records of customary land ownership, or use of land under concession. Furthermore, the boundaries of community land, and of land under concession, are generally not formally mapped and recorded, causing frequent disputes. cvi https://foodsecurityindex.eiu.com/Country/Details#Angola cvii Food security is a multi-dimensional concept. According to the definition adopted at the 1996 World Food Summit (FAO, 1996[3]), food security exists when “all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life.� As this definition makes clear, food security is not only a matter of food availability—people will only be food secure 87 when they have access to it, and when it leads to good nutritional outcomes. A fourth requirement is stability of these dimensions over time. cviii https://www.globalhungerindex.org/angola.html cix Angola Nutrition Profile: https://globalnutritionreport.org/resources/nutrition-profiles/africa/middle- africa/angola/ cx Food availability, as measured by per capita supply of calories on FAOSTAT http://www.fao.org/publications/sofi/2020/en/. Under-five wasting, and the food deficit have also reached historically low levels, albeit they remain high relative to comparators Cameroon, South Africa, and Zambia. cxi In rural areas, the most commonly cited cause for the food shortages is drought (29%), whereas urban dwellers are more likely to blame their food insecurity on prices (14%) (see Figure 86 NOFIGURE 86). These may, of course, be two different experiences of the same phenomenon, as droughts in rural areas are likely to lead to price increases in urban areas (World Bank Poverty Assessment 2020). cxii Volatility can have negative implications for producers and consumers alike as well as macroeconomic consequences for countries, especially those that are heavily dependent on imports or exports of the commodities affected by volatility. For consumers, food price volatility can lower food security causing poor people to reduce consumption or switch to cheaper, less nutritious foods. In 2008–2009, high commodity prices caused economic and social crisis in the world, particularly in developing and low-income countries. Volatility also impacts producers who face high uncertainty about future prices or lack adequate tools to manage risk —which is typically the case for smallholder farmers in developing countries. cxiii World Bank Poverty Assessment (2020) cxiv The Programme, which makes monthly disbursements of AOA 8 500 (about US$ 14) to vulnerable households, assisted 1.6 million households in 2020 and plans to support 700,000 households in 2021. GIEWS Country Profile http://www.fao.org/documents/card/en/c/CB4382EN cxv Plano de Desenvolvimentode Médio Prazo do Sector Agrário: 2018 -2022 cxvi IPCC scenarios (SRES A1B scenario as shown in the IPCC 4th Assessment Report) cxvii USAID (2018) Climate Risk Profile: https://reliefweb.int/sites/reliefweb.int/files/resources/2018_USAID-CCIS- Project_Climate-Risk-Profile-Angola.pdf cxviii Angola is currently facing the most severe drought of the last 38 years, which compounded by the outbreak of the pandemic could increase the number of vulnerable people to food security from 2.3 million to 7.4 million in the country. In the first months of 2019, a severe drought in the southern part of Angola caused a food security and nutrition crisis which affected 2.3 million people, including close to half a million children under the age of five. Water scarcity for crops and livestock led to a poor 2018–2019 harvest season. As a result, the cost of basic commodities—such as maize flour, beans, and sugar—increased by 25 percent. Small farmers and herders are also negatively impacted. More than 877,199 livestock (35 percent of the total) have died, a severe shock with both immediate and long-term impacts for rural households’ livelihoods cxix Malaria and diarrheal disease are leading causes of death in Angola. More frequent extreme rainfall and flooding could lead to increased incidence of waterborne diseases, and temperature shifts may alter the range of disease vectors and duration of malaria transmission seasons. For example, by 2030 areas currently not at risk of malaria transmission may become suitable for transmission 7–9 months out of the year, putting an additional 250,000 people at risk, primarily in the western provinces of Moxico and Bie and areas in the north. cxx In 2008, the Government approved a National Implementation Strategy for the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol. cxxi USAID 2019. Greenhouse Gas Emissions in Angola. https://www.climatelinks.org/sites/default/files/asset/document/2019_USAID_Angola%20GHG%20Factsheet.pdf cxxii The country plans to reduce GHG emissions up to 35% unconditionally by 2030 as compared to the Business as Usual (BAU) scenario (base year 2005). In addition, it is expected that through a conditional mitigation scenario the country could reduce an additional 15% below BAU emission levels by 2030. In achieving its unconditional and conditional targets Angola expects to reduce its emissions trajectory by nearly 50% below the BAU scenario by 2030 at overall cost of over US$ 14.7 billion . https://www4.unfccc.int/sites/ndcstaging/PublishedDocuments/Angola%20First/INDC%20Angola%20deposito.pdf 88 cxxiii According to WRI CAIT, LUCF emissions decreased by 13.8 MtCO2e from 1990 to 2014, due to lower emissions from burning biomass in 2014. Emissions from burning biomass have fluctuated over time and do not show a sustained decreasing trend.10 About 80% of Angola’s population utilizes forest biomass as firewood or charcoal to meet daily needs, such as heating water, cooking, and home lighting cxxiv The objective is to cultivate and harvest 34 thousand hectares of sugarcane in the province of Malange, to process a capacity of 2.25 million tons of raw material per season. The country intends to produce 23 million liters of ethanol and 170 GW of power through co-generation by 2019. The budget is between US$540 million and US$1 billion. cxxv As it is neither affected by inflation nor the size of the sector, it allows comparisons in the level of support to be made both over time and between countries. cxxvi OECD’S Producer Support Estimate and Related Indicators of Agricultural Support : Concepts, Calculations, Interpretation and Use (The PSE Manual) cxxvii This commitment stated that “agricultural trade should be more fully integrated within the open and multilateral trading system,�, and it called for OECD countries to pursue “a gradual reduction in protection a nd a liberalization of trade, in which a balance should be maintained as between countries and commodities.� Ministers also requested the OECD to develop a method to measure the level of protection in order to monitor and evaluate progress. cxxviii At present, the OECD methodology for agriculture support estimates covers 109 countries. This includes OECD countries, non-OECD EU Member States (subject to data availability), and a number of developing countries where monitoring is done by the OECD, IADB, and FAO’s MAFAP unit. The 54 countries monitored by the OECD are Argentina, Australia, Brazil, Canada, Chile, China, Colombia, Costa Rica, the European Union (Austria, Belgium, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Poland, Portugal, Spain, Slovakia, Slovenia, Sweden, the United Kingdom), India, Indonesia, Iceland, Israel, Japan, Kazakhstan, Korea, Mexico, New Zealand, Norway, the Philippines, the Russian Federation, South Africa, Switzerland, Turkey, Ukraine, the United States and Vietnam. cxxix As part of this assessment, a training of more than 15 public sector staff was undertaken to build capacity and allow for Government to update the estimates going forward. cxxx The OECD methodology’s GSSE category includes budgetary payments associated with public stockholding (storage) associated with SOEs. While SOE assets in agriculture were found to be a relatively small share of the government portfolio (less than 3.3 percent) in the CPSD, they have historically played a major role in the agriculture sector. In recent years, the government is restructuring or closing agricultural SOEs. Principally, Gesterra, the main entity managing medium- and large-scale government farms and public agriculture land is being restructured. Others being closed include Sociedade de Desenvolvimento de Perímetros Irrigados , in charge of irrigation perimeters; Empresa Nacional de Mecanização Agrícola (Mecanagro), founded to support land preparation, and rural civil engineering works; Empresa de Rebeneficiamento e Exportação de Café (Cafangol) involved in processing and export of coffee; and Sociedade de Desenvolvimento do Polo Agro-industrial de Capanda, in charge of managing the large Capanda development pole in Malanje province. cxxxi The PSE is an indicator that measures the annual monetary value of gross transfers from consumers and taxpayers to agricultural producers, measured at the farm-gate level, arising from policy measures that support agriculture, regardless of their nature, objectives or impacts on farm production or income. The GSSE is a proxy for public support to agricultural public goods such as research and extension, agricultural education and some infrastructure investments closely linked to agriculture. It is defined as the annual monetary value of gross transfers arising from policy measures that create the public goods and the enabling conditions for the primary agricultural sector through development of private or public services, and through institutions and infrastructures regardless of their objectives and impacts on farm production and income, or consumption of farm products. cxxxii The price paid to the farmer at the farm, which excludes transport costs to the market cxxxiii There are six main GSSE support categories and the amount of subsidies allocated under them is derived from public expenditure data. Considering the previous budget analysis made by FAO in Angola, we select each program according its characteristics and we classify it in the corresponding category (Agricultural research, public Infrastructure, Marketing and promotion, etc.). For example, subsidies under the program “Building and maintenance of rural roads" were considered under "Infrastructure GSSE category". Public resources of Instituto de Investigación Agronomica were considered under "Agricultural Research GSSE" category cxxxiv Source FAOSTAT, 2020 and own estimation. 89 cxxxv In the case of cassava—a thinly traded commodity—there is ambiguity on whether, on average, it is an import or export commodity for Angola. According to COMTRADE, for all years prior to 2018 Angola was a net importer (no data for 2019). According to FAOSTAT for all years Angola was a net exporter, but this is not official data. It is an estimate based on other sources. cxxxvi In the case of producer prices, the arithmetic annual average (at national level) was considered. Source: Local consultant survey. cxxxvii For a Representative Import Tariffs, the “Most Favored Nation Tariff� was considered for each product analyzed. Source: Tarifa de Pauta Aduanera. Diario de la Republica de Angola cxxxviii In the case of exchange rate, the arithmetic average “Sell� price was considered, as it better reflects the cost of US dollar to make local currency conversions. Source: Banco de Angola cxxxix FAO AgPER data was complemented with other information directly from Ministry of Finance (that was not included in FAO exercise) cxl Four widely known measures are used in various studies to estimate support: the nominal rate of protection (NRP), the nominal rate of assistance (NRA), the effective rate of protection (ERP) and the effective rate of assistance (ERA). The NRP measures the increase in gross receipts from the sale of the commodity; the NRA measures the increase in gross receipts including support not linked to the sale of the commodity. The ERP measures the increase in the value added from the sale of the commodity, i.e. taking into account the price of inputs; the ERA measures the increase in value added from both the sale of the commodity and support not linked to the sale of the commodity. cxli OECD (2020), Agricultural Policy Monitoring and Evaluation 2020, OECD Publishing, Paris, https://doi.org/10.1787/928181a8-en. cxlii The OECD total does not include the non-OECD EU Member States, nor Colombia which joined the OECD in April 2020. cxliii The Emerging Economies total includes Argentina, Brazil, People’s Republic of China, Costa Rica, India, Indonesia, Kazakhstan, Philippines, Russian Federation, South Africa, Ukraine and VietnNam, as well as Colombia which joined the OECD in April 2020. cxliv MPS, payments based on output and unconstrained use of variable inputs. cxlv Payments decoupled from current production, based on non-commodity criteria such as land set aside or payments for specific environmental or animal welfare outcomes. Payments based on current crop area and animal numbers have remained largely unchanged compared to 2000-02, and currently represent around 22% of total producer support. cxlvi The expenditures financing general services to the sector (GSSE) increased (in nominal terms) in the OECD area from US$ 36 billion per year in 2000-2002 to US$ 43 billion in 2017-2019. Most of these expenditures in 2017-2019 go to the financing of infrastructure (US$ 18.4 billion), recording a slight increase compared to 2000-2002, while the expenditures for agricultural knowledge and innovation (US$ 13 billion) have increased by two thirds. Expenditures for inspection and control services doubled, while spending for marketing and promotion activities and, more substantially, public stockholding declined over the same period, but all of these represented smaller shares of the GSSE expenditure. cxlvii This is a result of the high participation of MPS in total support. Consumers generate transfers through the payment of prices above international reference. cxlviii The aggregate value of MPS is the outcome of implicit taxation through negative price gaps for some commodities (a negative MPS) and price support of others (a positive MPS). Annual variations depend on movements in world prices, domestic prices and exchange rates, as well as changes in production levels. Major components of the MPS are the price differential (gap between domestic producer price and reference price) for products analyzed cxlix OECD, 2008 cl In general, border measures include import (export) tariffs or quotas and import (export) licen ses or other measures that constitute restrictions or supporting on trade. cli In Angola, the government “fixed prices� (utilities, fuel, etc.) and also “monitored prices� for 22 products including beans, maize flour and other agriculture goods. The government calculates a reference price based on cost of production plus, over which a 20% margin can be added. Retailers found to be selling at higher margin can be fined. It’s unclear how much it is enforced in practice. According to Executive Decree No 256/20, the government is committed to gradually phasing out the price regulations. clii The Most Favored Nation (MFN) import tariff was 10 percent during the study period (WTO). 90 cliii With foreign exchange liberalization beginning in 2018, the exchange rate depreciated 56 and 41 percent until the end of 2019 in nominal and real terms, respectively. With the passthrough from currency depreciation, inflation accelerated, to 25.1 percent in 2020 (from 16.9 percent in 2019). Prices for food, much of it imported, rose faster (by 31.4 percent). cliv A detailed information for each program and amounts are included in the PSE Excel calculations, which is part of this analysis. clv One interesting point is that in some countries that are currently referenced in international markets, highly market oriented and export leaders (New Zealand, Australia, Canada), the GSSE is the most important way to support their agricultural sector. clvi Training, R&D and resources for agricultural research institutes clvii Its mean Program to Support Production, Diversification of Exports and Substitution of Imports, approved in June 2018 and executed since 2019. The beginning of this program was a main difference in economic policy because concentrated several budgetary resources only in a specific program. clviii For example research, direct payments DECOUPLED from production, and infrastructure investment. clix In the PSE methodology, the consumer is understood to be the first buyer of agricultural products. clx Agriculture support was estimated using the OECD methodology (https://www.oecd.org/agriculture/topics/agricultural-policy-monitoring-and-evaluation/documents/producer- support-estimates-manual.pdf ). The total support estimate measure (TSE) is the annual monetary value of all gross transfers from taxpayers and consumers arising from public policy measures that support agriculture, net of the associated budgetary receipts, regardless of their objectives and impacts on farm production and income, or consumption of farm products. clxi GSSEs include agriculture public goods and services such as innovation systems (agriculture R&D and education), animal and plant health services, food safety, infrastructure, agriculture promotion, land administration, and other public services. clxii World Bank. World Development Report 2001: https://elibrary.worldbank.org/doi/pdf/10.1596/0-1952-1606-7 clxiii Lopez, Ramon. Under-investing in public goods: evidence, causes, and consequences for agriculture development, equity and the environment. Journal of Agriculture Economics, Volume 32, Issue 1. January, 2005: https://onlinelibrary.wiley.com/doi/full/10.1111/j.0169-5150.2004.00025.x clxiv At national level, the production and productivity of main agricultural products grew during that years MPS lost importance as mechanism of support. However, some lagged regions located in southern regions, characterized by rain-fed, small scale economic units, have not progressed to the same extent. As the MPS declined, public budget savings were generated which facilitated a better allocation of public resources. According to official statistics, the openness of trade has benefited consumers, as they have had access to better and more diversified prices, and in some cases, to better prices. On average, the food share of expenditure for average Mexican households has also diminished in the last 30 years. clxv However, at the present time, many of these inputs are indeed eligible for exemptions but since these exemptions must be applied for each time a shipment is made there are in fact fairly substantial bureaucratic costs involved. One recommendation might be to attempt to streamline this process by publishing a positive list of exempt items for which no additional paperwork is required. clxvi The World Food Program (WFP) measures the cost of a minimum diet globally, but Angola has not yet made it into the database: https://cdn.wfp.org/2018/plate-of-food/ clxvii For a definition and approach to CSA, see: https://www.worldbank.org/en/topic/climate-smart-agriculture clxviii For a definition and approach to NSmartAg see: https://www.worldbank.org/en/topic/agriculture/publication/nutrition-smart-agriculture-when-good-nutrition-is- good-business clxix Parikh, K., N. S. S. Narayana, Manoj Panda, & A. Ganesh Kumar. (1995). Strategies for Agricultural Liberalisation: Consequences for Growth, Welfare and Distribution. Economic and Political Weekly, 30(39), A90-A92. Retrieved May 28, 2021, from http://www.jstor.org/stable/4403270 clxx Nyairo, N. M., Kola, J., & Sumelius, J. (2010). Impacts of agricultural trade and market liberalization of food security in developing countries: comparative study of Kenya and Zambia (No. 308-2016-5085). clxxi Patel, R., & Henriques, G. (2003). Agricultural trade liberalization and Mexico. Food First Policy Brief, (7). 91 clxxii Correa, P., & Schmidt, C. (2014). Public research organizations and agricultural development in Brazil: how did Embrapa get it right?. Economic Premise, 145, 1–10. clxxiii In the near future, household-level price data will be available in Angola through the data on the Relatório da campanha agrária. 92