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This report evaluates the outputs from the CGIAR Research Program on Policies, Institutions, and Markets (PIM) on national social accounting matrices (SAMs) and single-country computable general equilibrium (CGE) models. The study aims to identify what policies, programs, strategies, and expenditure decisions were informed by SAMs and single-country CGE models. The report seeks to document what decisions were made based on the contribution of SAM/CGE models’ outputs that effectively shaped policy work or what changes were made because of CGIAR PIM– supported economywide modeling being available to decision makers. Based on the above, the report makes recommendations for CGIAR PIM–supported single economywide modeling work on how to effectively increase decision makers’ use of SAM/CGE outputs.
In 2020, PIM findings contributed to seed policies in Nepal and Uganda, Malawi’s extension strategy and approval of insect-resistant cotton, a nationwide program aimed at improving the effectiveness of public service delivery in Uganda, social protection programs in Egypt, and school gardens for better nutrition in Papua New Guinea. At the global level, PIM research was used to shape strategic decisions of organizations such as the Bill & Melinda Gates Foundation, GIZ, the Inter-American Development Bank, the UK Government's Foreign, Commonwealth & Development Office, the World Bank and the World Health Organization. PIM tools were incorporated in universities’ curricula in India and South Africa. Examples of PIM innovations scaled up by partners in 2020 are private sector seed marketing in Ethiopia, picture-based insurance in Ethiopia, India and Kenya, and tools for inclusive governance of natural resources in India and Peru.
PIM had a productive final year centered on synthesizing findings while continuing to respond to demand on the impacts of COVID-19 and preparing the transition to the new CGIAR portfolio. PIM findings and engagement contributed to Myanmar’s response to COVID-19, South Africa’s policies on resilience to climate change, Tunisia's policies for pastoral development, a reform of Nigeria’s national agricultural research system, Ghana’s fish seed and farm certification system, gender strategies for three agricultural value chains in Honduras, and genome editing guidelines for the agricultural sector in four African countries. PIM research informed policy documents of FAO, IFAD, One CGIAR, the UK Government, the World Bank and the World Food Programme. PIM tools enabled more equitable co-management of 76 protected areas in Peru and informed World Bank social protection projects. Books on food security in Bangladesh and Malawi, trade in Latin America, African agricultural value chains and gender were published. 42 PIM synthesis briefs and notes were issued, summarizing research results in key thematic areas. PIM contributed 181 journal articles, 8 journal issues (on demand driven seed systems, China’s response to COVID-19, agriculture and food security in China under COVID-19, food loss and waste, landscape restoration, multistakeholder fora in forestry and two issues on gender), 15 book chapters and about 500 non-peer-reviewed outputs. 16 PIM webinars were organized. PIM’s contributions to the United Nations Food Systems Summit covered agricultural extension, food system innovations and digital technologies, the future of small farms, the science-policy interface, the cost of ending hunger by 2030, food waste and loss, management of the commons and gender. Building on past PIM investments in economywide modeling tools and social accounting matrices, PIM teams continued to assess the impacts of COVID-19 and policy responses at country level. Lessons learned from PIM country-level analyses on COVID-19’s impacts on food systems, poverty and diets are summarized in a chapter of the IFPRI 2022 book “COVID19 and global food security: Two years later”. A paper in partnership with the CGIAR COVID19 Hub reviewed the literature on agri-food value chains for evidence of fractures and resilience in response to the pandemic. The results of coordinated studies on the impacts of COVID-19 on value chains in different countries were published. Several cross-CGIAR outputs initiated by PIM speak to the fulfillment of PIM’s convening role as an integrating program: the CGIAR Foresight Report and CGIAR foresight website; several outputs produced through the CGIAR Community of Excellence on Seed Systems Development, and the CGIAR book “Advancing gender equality through agricultural and environmental research: Past, present, and future” are examples. Other examples of PIM global public goods produced in 2021 are 27 innovations at various stages of uptake, a cross-cutting effort to distill PIM lessons on migration; new or updated social accounting matrices for 25 countries; and lessons and tools on stakeholder platforms for natural resource governance. Independent reviews assessed the effectiveness of PIM’s partnerships and the use by partners of PIM’s work on economywide modelling, agricultural insurance, tenure and governance, and the Ag-Incentives database.
This paper assesses the structure of Rwanda’s current and evolving agrifood system and its contribu-tion to national development. The paper reiterates the point that Rwanda’s agrifood system stretches well beyond primary agriculture and creates jobs and income opportunities throughout the economy. While off-farm components of Rwanda’s agrifood system have generally grown more rapidly than pri-mary agriculture in recent years, growth varies across value chains of the agrifood system in the stud-ied period. The growth diagnostic in this paper reveals that it is domestic markets that have driven the recent growth in Rwanda’s AFS other than exports. The paper’s forward-looking analysis assesses potentially differential impacts of value-chain develop-ment efforts on broad development outcomes. The analysis measures the synergies and trade-offs of value-chain development in the context of an inclusive agricultural transformation. Such analysis is conducted using the Rwanda Computable General Equilibrium (CGE) model – an adaption of IFPRI’s Rural Investment and Policy Analysis (RIAPA) model to the Rwandan context. The modeling results indicate that value chains differ considerably in their effectiveness in achieving development goals and there are significant trade-offs among different development goals from pro-moting a specific value chain. The value chains that make a larger contribution to growth or job crea-tion are not necessarily effective in reducing poverty or improving dietary quality – for example, value chains for coffee and tea – while value chains that play an important role in improving dietary quality may contribute less to job creation – such as vegetables or fruits. While there is no single value chain that can achieve all development goals effectively, it is possible to select a diversified set of value chains that complement each other in achieving different development goals. This latter strategy is a more realistic approach to growth and development.
Rwanda’s policy response to COVID-19 has been widely praised for its rapid, systematic, and comprehensive approach to containing the pandemic. Although the economic consequences are unavoidable, the country expects to return its economy to its high-growth trajectory as the pandemic subsides. We use economic modeling tools designed to estimate the short-term economywide impacts of the unanticipated, rapid-onset economic shocks of COVID-19 on Rwanda. - Results show that during the six-week lockdown that began in March, Rwanda’s GDP fell 39.1 percent (RWF 435 billion; USD 484 million) when compared to a no-COVID situation in the same period. - Results further show that Rwanda’s GDP in 2020 will be between 12 and 16 percent lower than a predicted no-COVID GDP, depending on the pace of the recovery. The losses in annual GDP are between RWF 1.0 and 1.5 trillion (USD 1.1–1.6 billion). - While GDP for the industrial and services sectors were estimated to have fallen during the lockdown period by 57 and 48 percent, respectively, exemptions of COVID-19 restrictions for the agricultural sector limited the decline in agricultural GDP to 7 percent compared to a noCOVID situation. - During the lockdown period, the national poverty rate is estimated to have increased by 10.9 percentage points as 1.3 million people, mostly in rural areas, fell into temporary poverty. Poverty rates are expected to stabilize by the end of 2020, increasing only by between 0.4 and 1.1 percentage points. While these figures may be encouraging, they mask the impacts on poor households of the sharp poverty spike during the lockdown and the inherent complexity of poverty dynamics post-lockdown. Looking forward, the speed and success of Rwanda’s recovery will depend critically on the expansion of Rwanda’s social protection programs, boosting enterprises of all sizes, support to the agri-food system, and restoration of international trade.
This new Social Accounting Matrix (SAM) for Jordan is a snapshot representation of the Jordanian economy in which productive activities, factors of production, and economic transactions between the main agents, including households, government, and the rest of the world, are illustrated in a circular flow. It has been constructed using IFPRI's Nexus format, which uses common data standards, procedures, and classification systems for constructing and updating national SAMs. This new SAM for Jordan is expected to be an important dataset for the Arab (Agricultural) Investment for Development Analyzer (AIDA), which is tool based on computable general equilibrium (CGE) model analyses. AIDA was developed to inform national and regional development strategies by providing evidence on the impact of agricultural investments on economic development.
This working paper identifies agricultural activities and value chains in Ghana whose expansion is most effective at generating economic growth, reducing national and rural poverty, creating jobs, and improving nutrition by diversifying diets. The Rural Investment and Policy Analysis (RIAPA) model of the Ghanaian economy is used to estimate how increasing production in different agricultural sectors leads to changes in national and household outcomes.1 RIAPA captures linkages between sectors and rural-urban economies, as well as changes throughout the agriculture-food system (AFS).
The Central Agency for Public Mobilization and Statistics (CAPMAS) is pleased to present the first regionalized social accounting matrix (SAM) for Egypt. This SAM marks a major milestone in “localizing” development research and policy in Egypt and is expected to be most valuable for the development, implementation, and assessment of Egypt’s national and local development plans, like Egypt’s Sustainable Development Strategy 2030. In addition to standard SAMs, which combine many national datasets from institutions such as Central Banks, Ministries of Finance and Agriculture, and Statistical Bureaus, this regionalized SAM makes extensive use of sub-national level information such as the Economic Survey and Household Income and Expenditure Survey (HIECS) produced by CAPMAS, regional GDP estimates by the Ministry of Planning, Monitoring and Administrative Reform, and the Agricultural Bulletins with information at the Governorate level produced by the Ministry of Agriculture and Land Reclamation. With this data it becomes possible to provide a detailed, socioeconomic status update for different regions within Egypt. As such, the disaggregated SAM allows for analyzing developmental issues at regional level and to better understand the potential impacts of policy changes at the local level.
A Social Accounting Matrix (SAM) is a representation of an economy that shows the circular flow of all transfers and real transactions between sectors and institutions. The SAM, which is a square matrix, describes the flows of incomes from activities, taking the form of factor remunerations, that are consequently received by the households for consumption on goods and services. The accounts in the SAM are the production activities, commodities, institutions, and factors of production. According to data availability, the production activities can be further disaggregated to include more detailed information on sub-sectoral or regional production. Similarly, the factors of production could be differentiated by the level of skills or the location of employment. Households can be disaggregated by income quintiles or by rural and urban residence.
A Social Accounting Matrix (SAM) is an accounting framework that gives a quantitative overview of the structure of the economy over a given time period. It records all transactions between economic agents, while respecting the principles of circularity of flows and balance between revenues and expenditures for each account. The level of disaggregation of accounts in the matrix varies according to the analyses to be undertaken and data availability. The accounts in a national SAM generally are production activities, commodities, institutions, and factors of production. For economic analyses and planning, a more detailed SAM is constructed. These involve disaggregation of activities, households, and factors of production from the more general national SAM. In such matrices, the national economy often will also be disaggregated into sub-national regions. Such SAMs provide rich datasets to help decision-makers in developing, designing, and evaluating regional economic and investment policies. As part of the technical cooperation within the (Arab) Agricultural Investment Development Analyzer (AIDA) project, which aims to develop tools for planning and evaluating investment projects in the agricultural sector, the Institut Tunisien de la Compétitivité et des Etudes Quantitatives (ITCEQ – the Tunisian Institute of Competitiveness and Quantitative Studies), in collaboration with the International Food Policy Research Institute (IFPRI), have built a regionalized SAM of the Tunisian economy with detailed disaggregation at the sector, product, household, and regional levels. This SAM has been constructed using IFPRI's Nexus format, which uses common data standards, procedures, and classification systems for constructing and updating national SAMs. The regionalized SAM for Tunisia was built using national accounts statistics for the country, the Supply and Use Tables for 2015, which are produced by the National Institute of Statistics (NIS). The regionalized matrix is constructed in three steps – national, household, and regional. • The national 2015 SAM for Tunisia includes 46 sectors and 46 products. • For the household SAM, factors of production are split into 13 categories. Capital is disaggregated into four subcategories: crops, livestock, mining, and other. Land is a separate factor of production category. Labor is disaggregated into four education-level categories and across rural and urban areas. For the household SAM, household accounts are split into 15 categories by rural farm, rural nonfarm, and urban categories and then by national per capita expenditure quintiles. • For the regionalized SAM, sectoral production, production factors, and household groups are disaggregated into seven subnational regions: Greater Tunis, North East, North West, Center East, Center West, South East, and South West. The regional 2015 SAM in total has 105 household groups and is composed of 513 rows x 513 columns.