Datasets

× This dataset contains the data and scripts required to reproduce the tables and figures in the study titled "Income, consumer preferences, and the future of livestock-derived food demand." R scripts were run using R version 4.0.5 on Windows 10 x64.
All the data and script should be placed in one folder. Add a R project into the folder (for example, "project_ldfDemand.Rproj"). Open the R project before running the scripts.
The scripts (extension .R) are ordered sequentially, and should be run sequentially for the first time. The script "22masterFile.R" is the master file that runs all scripts sequentially from start to finish.
The study generated simulation results in GAMS. The GAMS code is not part of the scripts in this dataset. Please direct any questions on the GAMS code and input data to Adam Komarek (a.komarek@uq.edu.au)

× The Tunisian regionalized SAM is built using the national accounts statistics, the Supply and Use Tables for 2015 that were produced by National Institute of Statistics (NIS). The regionalized matrix is constructed in three steps that are national, household and regional. The national 2015 SAM for Tunisia includes 46 sectors and 46 products. For the household SAM, factors of production and the household accounts are split, respectively into 13 and 15 factor and household categories. Labor is disaggregated across rural and urban areas and into four education-level categories. Capital is also disaggregated into four subcategories: crops, livestock, mining, and other; and land. Households are disaggregated by national per capita expenditure quintiles, and then split into rural farm, rural nonfarm, and urban households. 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. In total, the regional 2015 SAM includes 105 household groups and composed of 513 rows x 513 columns.

× This new regional SAM for Yemen includes 57 productive sectors – including 20 agricultural, 25 industrial, and 12 services activities – in four subnational regions: Highlands, Tihama, Aden, and Hadramaut. The regional SAM has 10 factors of production. Labor is differentiated by three educational levels and between the public and private sectors. Capital is disaggregated into four types: crops; livestock; mining; and other. Additionally, the new SAM has 60 representative households. Households were split according to per capita expenditure into five quintiles by rural farm, rural non-farm, and urban areas. These 15 household groups then were differentiated across the four subnational regions. The regionalization of the Yemen SAM for the year 2014 was done for sectoral production, i.e., agriculture, industry, and services; factor markets; and household groups. All of the other accounts in the SAM are at the national level. In total, the regional SAM for Yemen incorporates 379 rows x 379 columns.

× The Regional 2014/15 SAM for Egypt includes 62 sectors and 66 products. It also includes 13 factors of production, labor is disaggregated across rural and urban areas and into four education-based categories, capital is also disaggregated into four subcategories: crops, livestock, mining, and other; and land. Moreover, the new SAM has 105 household groups that are disaggregated by national per capita expenditure quintiles, then split into national rural farm, rural nonfarm, and urban households. Sectoral production, factor markets, and household groups are split into seven subnational regions: Greater Cairo, Delta, Alexandria, Northern Upper Egypt, Middle Upper Egypt, Southern Upper Egypt, and Suez Canal. In total, the regional 2015 SAM is composed of 646 rows x 646 columns.

× The new 2015 SAM for Jordan includes 56 sectors and 62 products. It also includes 13 factors of production, labor is disaggregated across rural and urban areas and into four education-based categories, capital is also disaggregated into four subcategories: crops, livestock, mining, and other; and land. Households were split according to per capita expenditure into five quintiles by rural and urban areas.

× The 2019 SAM for Egypt builds on the previous 2014/15 SAM that was built and published by Central Agency for Public Mobilization and Statistics (CAPMAS) with the support of the International Food Policy Research Institute (IFPRI). The updated SAM also relies on information from the Supply and Use Tables and the Economic Census for 2017/18 that were produced by CAPMAS. It includes 69 sectors and 73 products. The SAM also includes 13 factors of production in three broad categories: labor, land, and capital. Labor is disaggregated across rural and urban areas and into four education-based categories. Capital is disaggregated into four subcategories: crops, livestock, mining, and other. The SAM has ten household groups that are disaggregated by national per capita expenditure quintiles, then split into rural and urban households. Overall, the 2019 SAM is composed of 177 rows x 177 columns.

× Food policy research plays a crucial role in guiding agricultural transformation in developing countries. To achieve food security goals, countries need to strengthen their capacity to conduct food policy research. Strong local policy research institutions help shaping evidence-based policymaking. Measuring national capacity for food policy research is important for identifying capacity gaps in food policy research and guiding the allocation of resources to fill those gaps. “Food policy research capacity” is defined as the ability to do socioeconomic or policy-related research in the areas of food, agriculture, nutrition, or natural resources. To measure this capacity, the International Food Policy Research Institute (IFPRI) has developed a set of indicators for the quantity and quality of policy research at the country level.
IFPRI created the Food Policy Research Capacity Indicators (FPRCI) database in 2010 and has since continued to expand and refine it. Data are currently collected for 33 countries; data for Myanmar was added in 2017. A consistent methodology is followed to enable a comparison of values across time and countries. The database was most recently updated with numbers for 2019.
Analysts/researchers counts the professionals employed at local organizations whose work involves food policy research or analysis. To introduce some uniformity, IFPRI also presents a modified quantification of this headcount: full-time equivalent analysts/researchers with PhD. To obtain an indicator of per capita food policy research capacity, this research capacity is then divided by the country’s rural population (full-time equivalent researchers per million rural residents). This helps to illustrate the impact of local food policy research in a country. This indicator was last updated in 2015.
The quality of a country’s food policy research capacity is estimated by tallying the number of relevant international publications in peer-reviewed journals over a five-year period. IFPRI views this as a reflection of the local enabling intellectual environment for food policy research. This indicator allows for comparison across countries, as it ensures an internationally accepted standard of quality for publications. The final indicator is derived by dividing the number of international publications by the number of full-time equivalent researchers with a PhD, providing a measure of productivity.

× Increasing the efficiency of agricultural production—getting more output from the same amount of resources—is critical for improving food security. To measure the efficiency of agricultural systems, we use total factor productivity (TFP). TFP is an indicator of how efficiently agricultural land, labor, capital, and materials (agricultural inputs) are used to produce a country’s crops and livestock (agricultural output)—it is calculated as the ratio of total agricultural output to total production inputs. When more output is produced from a constant amount of resources, meaning that resources are being used more efficiently, TFP increases. Measures of land and labor productivity—partial factor productivity (PFP) measures—are calculated as the ratio of total output to total agricultural area (land productivity) and to the number of economically active persons in agriculture (labor productivity). Because PFP measures are easy to estimate, they are often used to measure agricultural production performance. These measures normally show higher rates of growth than TFP, because growth in land and labor productivity can result not only from increases in TFP but also from a more intensive use of other inputs (such as fertilizer or machinery). Indicators of both TFP and PFP contribute to the understanding of agricultural systems needed for policy and investment decisions by allowing for comparisons across time and across countries and regions.
The data include estimates of TFP and land and labor productivity measures for developing countries and regions for three-sub-periods between 2000 and 2016. These use the most recent data on outputs and inputs from the Economic Research Service of the US Department of Agriculture (ERS-USDA), an internationally consistent and comparable dataset on production and input quantities built using data from the FAOSTAT database of the Food and Agriculture Organization of the United Nations (FAO), supplemented with data from national statistical sources (for more on data and methodology- https://www.ers.usda.gov/data-products/international-agricultural-productivity/ ).

× Advancing Research on Nutrition and Agriculture (AReNA) is a 6-year, multi-country project in South Asia and sub-Saharan Africa funded by the Bill and Melinda Gates Foundation, being implemented from 2015 through 2020. The objective of AReNA is to close important knowledge gaps on the links between nutrition and agriculture, with a particular focus on conducting policy-relevant research at scale and crowding in more research on this issue by creating data sets and analytical tools that can benefit the broader research community. Much of the research on agriculture and nutrition is hindered by a lack of data, and many of the datasets that do contain both agriculture and nutrition information are often small in size and geographic scope. ARENA team constructed a large multi-level, multi-country dataset combining nutrition and nutrition-relevant information at the individual and household level from the Demographic Health Surveys (DHS) with a wide variety of geo-referenced data on agricultural production, agroecology, climate, demography, and infrastructure (GIS data).
This dataset includes 60 countries, 184 demographic health surveys (DHS) surveys, and 122,473 clusters. Over one thousand geospatial variables are linked with DHS surveys. The entire dataset is organized into 13 individual files: DHS_distance, DHS_livestock, DHS_main, DHS_malaria, DHS NDVI, DHS_nightlight, DHS_pasture and climate (mean), DHS_rainfall, DHS_soil, DHS_SPAM, DHS_suit, DHS_temperature, and DHS_traveltime.

× Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM, also known as MapSPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, creating Africa South of the Sahara-wide grid-scape at the confluence between geography and agricultural production systems. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts.

× Increasing the efficiency of agricultural production—getting more output from the same amount of resources—is critical for improving food security. To measure the efficiency of agricultural systems, we use total factor productivity (TFP). TFP is an indicator of how efficiently agricultural land, labor, capital, and materials (agricultural inputs) are used to produce a country’s crops and livestock (agricultural output)—it is calculated as the ratio of total agricultural output to total production inputs. When more output is produced from a constant amount of resources, meaning that resources are being used more efficiently, TFP increases. Measures of land and labor productivity—partial factor productivity (PFP) measures—are calculated as the ratio of total output to total agricultural area (land productivity) and to the number of economically active persons in agriculture (labor productivity). Because PFP measures are easy to estimate, they are often used to measure agricultural production performance. These measures normally show higher rates of growth than TFP, because growth in land and labor productivity can result not only from increases in TFP but also from a more intensive use of other inputs (such as fertilizer or machinery). Indicators of both TFP and PFP contribute to the understanding of agricultural systems needed for policy and investment decisions by enabling comparisons across time and across countries and regions.

The data file provides estimates of IFPRI's TFP and PFP measures for developing countries for three-sub-periods between 1991 and 2014(1991-2000,2001-2010 and 2010-2014). These TFP and PFP estimates were generated using the most recent data from Economic Research Service of the United States Department of Agriculture (ERS-USDA), the FAOSTAT database of the Food and Agriculture Organization of the United Nations (FAO), and national statistical sources.

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and improving food security in a sustainable way. Modeling alternative future scenarios and assessing their outcomes can help inform policy choices. The International Food Policy Research Institute's IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for the exploration of such scenarios.

At IMPACT's core is a partial equilibrium, the multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policy makers at national, regional, and global levels. IMPACT benefits from close interactions with scientists at all 15 CGIAR research center through the Global Futures and Strategic Foresight (GFSF) program, and with other leading global economic modeling efforts around the world through Agricultural Model Intercomparison and Improvement Project (AgMIP).

This dataset summarizes results from the latest IMPACT projections to 2030 and 2050. Results are included for production, consumption, and for the population at risk of hunger, by region and for selected countries. The projections are for two "baseline scenarios"-one considers the impacts of climate change, while the assumes no climate change (for comparison).

× Policymakers recognize that increased investment in agricultural research is key to increasing agricultural productivity. Despite this, many low- and middle-income countries struggle with capacity and funding constraints in their agricultural research systems.

Agricultural Science and Technology Indicators (ASTI), facilitated by the International Food Policy Research Institute (IFPRI) within the portfolio of the CGIAR Research Program on Policies, Institutions, and Markets, works with national, regional, and international partners to collect time series data on the funding, human resource capacity, and outputs of agricultural research in low- and middle-income countries. Based on this information, ASTI produces analysis, capacity-building tools, and outreach products to help facilitate policies for effective and efficient agricultural research.

“Agricultural research” includes government, higher education, and nonprofit agencies, but excludes the private for-profit sector. Total agricultural research spending includes salaries, operating and program costs, and capital investments for all agencies, excluding the private for-profit sector, involved in agricultural research in a country. Expenditures are adjusted for inflation and expressed in 2011 prices. Purchasing power parities (PPPs) measure the relative purchasing power of currencies across countries by eliminating national differences in pricing levels for a wide range of goods. PPPs are relatively stable over time, whereas exchange rates fluctuate considerably. In addition to looking at absolute levels of agricultural research investment and capacity, another way of comparing commitment to agricultural research is to measure research intensity—that is, total agricultural research spending as a percentage of agricultural output (AgGDP).

“Total agricultural researchers” (excluding the private for-profit sector) are reported in full-time equivalents (FTEs) to account for the proportion of time researchers actually spend on research activities. A critical mass of qualified agricultural researchers is crucial for implementing a viable research agenda, for effectively communicating with stakeholders, and for securing external funding. Therefore, it is important to look at the share of PhD-qualified researchers. Gender balance in agricultural research is important, given that women researchers offer different insights and perspectives that can help research agencies more effectively address the unique and pressing challenges of female farmers. Age imbalances among research staff should be minimized to ensure the continuity of future research as researchers retire.

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and improving food security in a sustainable way. Modeling alternative future scenarios and assessing their outcomes can help inform their choices. The International Food Policy Research Institute's IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for the exploration of such scenarios.

At IMPACT's core is a partial equilibrium, the multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policy makers at national, regional, and global levels. IMPACT benefits from close interactions with scientists at all 15 CGIAR research center through the Global Futures and Strategic Foresight (GFSF) program, and with other leading global economic modeling efforts around the world through Agricultural Model Intercomparison and Improvement Project (AgMIP).

This dataset summarizes results from the latest IMPACT projections to 2030 and 2050. Results are included for production, consumption, and trade of major food commodity groups, by regions and country. The projections are for two "baseline scenarios"-one considers the impacts of climate change, while the assumes no climate change (for comparison).

× The Statistics on Public Expenditures for Economic Development (SPEED) database is a resource of the International Food Policy Research Institute (IFPRI) that contains information on agricultural and other sectoral public expenditures in 147 countries from 1980 to 2016.

Policymakers, researchers, and other stakeholders can use this robust database to examine both historical trends and the allocation of government resources across sectors. It also allows for comparisons with other countries within a region or at a similar level of development. Because the SPEED database covers many countries for a long time period, it allows analysts of government spending to examine national policy priorities, as reflected in the allocation of public expenditures, and track development goals and the cost-effectiveness of public spending both within and across countries.

Indicators reported in this data study include total agricultural expenditure, agricultural spending per capita, and the ratio of agricultural spending to the agricultural gross domestic product (GDP) for years 1995, 2000, and 2016. IFPRI researchers have compiled data from multiple sources, including the International Monetary Fund, World Bank, United Nations, and national governments, and conducted extensive data checks and adjustments to ensure consistent spending measurements over time that are free of exchange-rate fluctuations and currency denomination changes.

× Food policy research plays a crucial role in guiding the agricultural development of countries. To achieve food security goals, countries need to strengthen their capacity to conduct food policy research. Strong local policy research institutions help in shaping an evidence-based policy-making process. Measuring national capacity for food policy research is important for identifying capacity gaps in food policy research and guiding the allocation of resources to fill those gaps. Food policy research capacity is defined as any socioeconomic or policy-related research capacity in the area of food, agriculture, or natural resources. To measure this capacity, the International Food Policy Research Institute (IFPRI) developed a set of indicators of the quantity and quality of policy research at the country level.

IFPRI created a database for food policy research capacity in 2010 and has continued to expand and refine it. The data presented are currently collected for 33 countries; data for Myanmar were added in 2017. A consistent methodology is followed to enable comparison of values across time and countries. The database was most recently updated with numbers for 2018.

“Analysts/researchers” is a head count of professionals employed at local organizations whose work involves food policy research or analysis. To introduce some uniformity, IFPRI also presents a modified quantification of the headcount: "full-time equivalent analysts/researchers with the Ph.D. equivalent." To obtain an indicator of per capita food policy research capacity, this research capacity is then divided by the country’s rural population ("full-time equivalent researchers per million rural residents"). This helps to illustrate the impact of local food policy research in a country. This indicator was last updated in 2015.

The quality of a country’s food policy research capacity is estimated by tallying the number of relevant international publications in peer-reviewed journals over a five-year period. IFPRI views this as a reflection of the local enabling environment for food policy research. This indicator allows for comparison across countries, as it ensures an internationally accepted standard of quality for publications. The final indicator ("publications per full-time equivalent researcher") is derived by dividing the number of international publications by the number of full-time equivalent researchers with a Ph.D., providing a measure of productivity.

× Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, creating a global gridscape at the confluence between geography and agricultural production systems. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts

× Using a variety of inputs, IFPRI's Spatial Production Allocation Model (SPAM) uses a cross-entropy approach to make plausible estimates of crop distribution within disaggregated units. Moving the data from coarser units such as countries and sub-national provinces, to finer units such as grid cells, reveals spatial patterns of crop performance, creating a global grid-scape at the confluence between geography and agricultural production systems. Improving spatial understanding of crop production systems allows policymakers and donors to better target agricultural and rural development policies and investments, increasing food security and growth with minimal environmental impacts.

× The 2019 Statistics on Public Expenditures for Economic Development (SPEED) database contains public expenditure data for 164 countries from 1980 to 2017 for ten sectors: agriculture, communication, education, defense, health, mining, social protection, fuel and energy, transport, and transport and communication combined as one sector. Indicators reported include percentage of sector expenditure in total expenditure, percentage of total expenditure to total gross domestic product, and per capita sector and total expenditure in constant prices.

The data were compiled from multiple sources, including the International Monetary Fund, the World Bank, and national governments, and conducted extensive data checks and adjustments to ensure consistent spending measurements over time that are free of exchange-rate fluctuations and currency denomination changes.

SPEED is a user-friendly tool that could help governments to better allocate their resources to be consistent with their policy objectives, and citizens’ needs and priorities. Because of the wide coverage of time periods, countries, and sectors, it could help policymakers and researchers to better understand the linkages between different types of public expenditure and development. It could also help examine historical trends and compare those to other countries.

× The Statistics on Public Expenditures for Economic Development (SPEED) database is a resource of the International Food Policy Research Institute (IFPRI) that contains information on agricultural and other sectoral public expenditures in 147 countries from 1980 to 2014.

Policy makers, researchers, and other stakeholders can use this robust database to examine both historical trends and the allocation of government resources across sectors. It also allows for comparisons with other countries within a region or at a similar level of development. Because the SPEED database covers many countries for a long time period, it allows analysts of government spending to examine national policy priorities, as reflected in the allocation of public expenditures, and track development goals and the cost-effectiveness of public spending both within and across countries.

Indicators reported in this data study include total agricultural expenditure, agricultural spending per capita, and the ratio of agricultural spending to agricultural gross domestic product (GDP) for years 1980, 1995, and 2014. IFPRI researchers have compiled data from multiple sources, including the International Monetary Fund, World Bank, United Nations, and national governments, and conducted extensive data checks and adjustments to ensure consistent spending measurements over time that are free of exchange-rate fluctuations and currency denomination changes.

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and improving food security in a sustainable way. Modeling alternative future scenarios and assessing their outcomes can help inform their choices. The International Food Policy Research Institute's IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for exploration such scenarios.

At IMPACT's core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policy makers at national, regional, and global levels. IMPACT benefits from close interactions with scientists at all 15 CGIAR research centers through the Global Futures and Strategic Foresight (GFSF) program, and with other leading global economic modeling efforts around the world through Agricultural Model Intercomparison and Improvement Project (AgMIP).

This dataset summarizes results from the latest IMPACT projections to 2030 and 2050. Results are included for total demand and demand index of various agricultural commodities, by region and for selected countries. The projections are for two "baseline scenarios"-one considers the impacts of climate change, while the assumes no climate change (for comparison).

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and improving food security in a sustainable way. Modeling alternative future scenarios and assessing their outcomes can help inform their choices. The International Food Policy Research Institute's IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for exploration such scenarios.

At IMPACT's core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policy makers at national, regional, and global levels. IMPACT benefits from close interactions with scientists at all 15 CGIAR research centers through the Global Futures and Strategic Foresight (GFSF) program, and with other leading global economic modeling efforts around the world through Agricultural Model Intercomparison and Improvement Project (AgMIP).

This dataset summarizes results from the latest IMPACT projections to 2030 and 2050. Results are included for production, consumption, and for the population at risk of hunger, by region and for selected countries. The projections are for two "baseline scenarios"-one considers the impacts of climate change, while the assumes no climate change (for comparison).

× The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally, regionally, and by country. Each year, the International Food Policy Research Institute (IFPRI) calculates GHI scores in order to assess progress, or the lack thereof, in decreasing hunger. The GHI is designed to raise awareness and understanding of regional and country differences in the struggle against hunger.

Since 2015, GHI scores have been calculated using a revised and improved formula. The revision replaces child underweight, previously the sole indicator of child undernutrition, with two indicators of child undernutrition—child wasting and child stunting—which are equally weighted in the GHI calculation. The revised formula also standardizes each of the component indicators to balance their contribution to the overall index and to changes in the GHI scores over time.

The 2017 GHI has been calculated for 119 countries for which data on the four component indicators are a

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and improving food security in a sustainable way. Modeling alternative future scenarios and assessing their outcomes can help inform their choices. The International Food Policy Research Institute's IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for exploration such scenarios.

At IMPACT's core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policy makers at national, regional, and global levels. IMPACT benefits from close interactions with scientists at all 15 CGIAR research centers through the Global Futures and Strategic Foresight (GFSF) program, and with other leading global economic modeling efforts around the world through Agricultural Model Intercomparison and Improvement Project (AgMIP).

This dataset summarizes results from the latest IMPACT projections to 2030 and 2050. Results are included for production, consumption, and trade of major food commodity groups, by regions and country. The projections are for two "baseline scenarios"-one considers the impacts of climate change, while the assumes no climate change (for comparison).

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and improving food security in a sustainable way. Modeling alternative future scenarios and assessing their outcomes can help inform their choices. The International Food Policy Research Institute's IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for exploration such scenarios.

At IMPACT's core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policy makers at national, regional, and global levels. IMPACT benefits from close interactions with scientists at all 15 CGIAR research centers through the Global Futures and Strategic Foresight (GFSF) program, and with other leading global economic modeling efforts around the world through Agricultural Model Intercomparison and Improvement Project (AgMIP).

This dataset summarizes results from the latest IMPACT projections to 2030 and 2050. Results are included for production, consumption, and trade of major food commodity groups, by regions and country. The projections are for two "baseline scenarios"-one considers the impacts of climate change, while the assumes no climate change (for comparison).

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and improving food security in a sustainable way. Modeling alternative future scenarios and assessing their outcomes can help inform their choices. The International Food Policy Research Institute's IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for exploration such scenarios.

At IMPACT's core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policy makers at national, regional, and global levels. IMPACT benefits from close interactions with scientists at all 15 CGIAR research centers through the Global Futures and Strategic Foresight (GFSF) program, and with other l

× Policy makers, analysts, and civil society face increasing challenges to reducing hunger and improving food security in a sustainable way. Modeling alternative future scenarios and assessing their outcomes can help inform their choices. The International Food Policy Research Institute's IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for exploration such scenarios.

At IMPACT's core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policy makers at national, regional, and global levels. IMPACT benefits from close interactions with scientists at all 15 CGIAR research centers through the Global Futures and Strategic Foresight (GFSF) program, and with other leading global economic modeling efforts around the world through Agricultural Model Intercomparison and Improvement Project (AgMIP).

This dataset is an extended set of results from IMPACT version 3.2.1 generated for the analysis originally presented in Sulser et al (2015) and covers “baseline scenarios” of different socioeconomic assumptions, climate change, and no climate change from 2010 to 2050.

× Spatially-explicit data is increasingly becoming available across disciplines, yet they are often limited to a specific domain. In order to use such datasets in a coherent analysis, such as to decide where to target specific types of agricultural investment, there should be an effort to make such datasets harmonized and interoperable. For Africa South of the Sahara (SSA) region, the HarvestChoice CELL5M Database was developed in this spirit of moving multidisciplinary data into one harmonized, geospatial database. The database includes over 750 biophysical and socio-economic indicators, many of which can be easily expanded to global scale. The CELL5M database provides a platform for cross-cutting spatial analyses and fine-grain visualization of the mix of farming systems and populations across SSA. It was created as the central core to support a decision-making platform that would enable development practitioners and researchers to explore multi-faceted spatial relationships at the nexus of poverty, health and nutrition, farming systems, innovation, and environment. The database is a matrix populated by over 350,000 grid cells covering SSA at five arc-minute spatial resolution. Users of the database, including those conduct researches on agricultural policy, research, and development issues, can also easily overlay their own indicators. Numerical aggregation of the gridded data by specific geographical domains, either at subnational level or across country borders for more regional analysis, is also readily possible without needing to use any specific GIS software. See the HCID database ( http://dx.doi.org/10.7910/DVN/MZLXVQ ) for the geometry of each grid cell. The database also provides standard-compliant data API that currently powers several web-based data visualization and analytics tools.

× Yield gap of nine priority commodities of the African Development Bank was assessed and aggregated at two levels across the Africa continent: 1) agro-ecological zones and 2) agro-ecological zones by country. In this rapid assessment, the yield gap was defined as the percentage difference between the actual yield estimated from spatially-disaggregated crop production statistics database and the potential yield retrieved from the FAO's Global Agro-Ecological Zones database, both at the 10-km pixel level.

× This data study includes disaggregated social accounting matrix (SAM) for the Egyptian economy for year 2010/11. This new SAM builds on the previous SAM 2010/11 built and published by Central Agency for Public Mobilization and Statistics (CAPMAS) with the support of the International Food Policy Research Institute (IFPRI). This SAM was constructed with a special focus on the agriculture sector and on income distribution amongst households. It is composed of 52 activity sectors, 49 commodity sectors, three types of factors of production: labor (unskilled labor, semiskilled labor, and skilled labor), land, and capital; a government account, as well as, enterprises, households, savings and investment, and the rest of the world (ROW). The household sector is divided spatially into urban and rural households, with each disaggregated into 10 deciles according to expenditure. The disaggregated SAM allows for analyzing agricultural issues at the detailed crop level and to better understand the potential impacts of policy changes for both better off and more vulnerable households.

× The Tunisia Social Accounting Matrix (SAM), 2012 was built with a focus on analyzing the structure and importance of the agriculture and trade in the Tunisian economy. This SAM helps to understand the linkages between agricultural production, factor income distribution, and households' incomes and expenditures. The 2012 Input-Output (I-O) and the supply-use table were the two main data sources used in building the disaggregated activity sector and commodity accounts. One of the most important added values of this SAM is the estimation of agricultural technologies.

× The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally, regionally, and by country. Each year, the International Food Policy Research Institute (IFPRI) calculates GHI scores in order to assess progress, or the lack thereof, in decreasing hunger. The GHI is designed to raise awareness and understanding of regional and country differences in the struggle against hunger.

Since 2015, GHI scores have been calculated using a revised and improved formula. The revision replaces child underweight, previously the sole indicator of child undernutrition, with two indicators of child undernutrition—child wasting and child stunting—which are equally weighted in the GHI calculation. The revised formula also standardizes each of the component indicators to balance their contribution to the overall index and to changes in the GHI scores over time.

The 2016 GHI has been calculated for 118 countries for which data on the four component indicators are available and where measuring hunger is considered most relevant. GHI scores are not calculated for some higher income countries where the prevalence of hunger is very low. The GHI is only as current as the data for its four component indicators.

This year's GHI reflects the most recent available country-level data and projections available between 2011 and 2016. It therefore reflects the hunger levels during this period rather than solely capturing conditions in 2016. The 1992, 2000, 2008, and 2016 GHI scores reflect the latest revised data for the four component indicators of the GHI. Where original source data were not available, the estimates of the GHI component indicators were based on the most recent data available.

The four component indicators used to calculate the GHI scores draw upon data from the following sources:

1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1992, 2000, 2008, and 2016 GHI scores. Undernourishment data and projections for the 2016 GHI are for 2014-2016.

2. Child wasting and stunting: The child undernutrition indicators of the GHI—child wasting and child stunting—include data from the joint database of United Nations Children's Fund (UNICEF), the World Health Organization (WHO), and the World Bank, and additional data from WHO's continuously updated Global Database on Child Growth and Malnutrition; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS) reports; and statistical tables from UNICEF. For the 2016 GHI, data on child wasting and child stunting are for the latest year for which data are available in the period 2011-2015.

3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1992, 2000, 2008, and 2016 GHI scores. For the 2016 GHI, data on child mortality are from 2015.

× The 2015 Global Nutrition Report Dataset contains data for all the indicators that were used in Global Nutrition Report 2015: Actions and Accountability to Accelerate the World's Progress on Nutrition . The data are compiled from secondary sources including United Nations Children's Fund (UNICEF), World Health Organization (WHO), and the World Bank (WB) among many others. The dataset broadly contains information on adult and child nutrition, economic demography, nutrition intervention coverage, and policy legislation in the nutrition sector.

× The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally, regionally, and by country. Each year, the International Food Policy Research Institute (IFPRI) calculates GHI scores in order to assess progress, or the lack thereof, in decreasing hunger. The GHI is designed to raise awareness and understanding of regional and country differences in the struggle against hunger.

This year, GHI scores have been calculated using a revised and improved formula. The revision replaces child underweight, previously the sole indicator of child undernutrition, with two indicators of child undernutrition—child wasting and child stunting—which are equally weighted in the GHI calculation. The revised formula also standardizes each of the component indicators to balance their contribution to the overall index and to changes in the GHI scores over time.

The 2015 GHI has been calculated for 117 countries for which data on the four component indicators are available and where measuring hunger is considered most relevant. GHI scores are not calculated for some higher income countries where the prevalence of hunger is very low. The GHI is only as current as the data for its four component indicators.

This year's GHI reflects the most recent available country-level data and projections available between 2010 and 2016. It therefore reflects the hunger levels during this period rather than solely capturing conditions in 2015. The 1990, 1995, 2000, 2005, and 2015 GHI scores reflect the latest revised data for the four component indicators of the GHI. Where original source data were not available, the estimates of the GHI component indicators were based on the most recent data available.

The four component indicators used to calculate the GHI scores draw upon data from the following sources:

1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1990, 1995, 2000, 2005, and 2015 GHI scores. Undernourishment data and projections for the 2015 GHI are for 2014-2016.

2. Child wasting and stunting: The child undernutrition indicators of the GHI—child wasting and child stunting—include data from the joint database of United Nations Children's Fund (UNICEF), the World Health Organization (WHO), and the World Bank, and additional data from WHO's continuously updated Global Database on Child Growth and Malnutrition; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS) reports; statistical tables from UNICEF; and the latest national survey data for India from UNICEF India. For the 2015 GHI, data on child wasting and child stunting are for the latest year for which data are available in the period 2010-2014.

3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1990, 1995, 2000, and 2005, and 2015 GHI scores. For the 2015 GHI, data on child mortality are for 2013.

× Public expenditure is a powerful instrument for governments to use in achieving sustainable growth, poverty reduction, and transformation. Understanding the linkages between different types of public expenditure and development can help governments to better allocate their resources in a manner consistent with their policy objectives and citizens’ needs and priorities. Development practitioners, donors, and the general public have increasingly requested expenditure accountability and transparency in the use of public resources. Transparency in public spending allows governments to better track, monitor, and evaluate the impacts of investment decisions and to invest in the provision of public goods and services that benefit the rural poor (such as agricultural research and extension, health, education, and social protection) and provide a conducive environment for private-sector investments.

Against this background, the International Food Policy Research Institute (IFPRI) launched and made publicly available the Statistics on Public Expenditures for Economic Development (SPEED) database in 2010. The database aims to provide policymakers, researchers, and the broader development community with the most comprehensive public expenditure information. This is the third major update of the dataset since 2010 (the second major update was made available in 2013). This third update includes an expanded time coverage, 1980 to 2012, and an additional economic sector, fuels and energy. While data were not always available for all the 147 countries included in the dataset, significant efforts were made in updating the data for as many countries as possible.

This 2015 version includes a total of ten sectors: agriculture, communication, education, defense, health, mining, social protection, fuel and energy, transport, and transport and communication (as a group). Prior to 1990, the International Monetary Fund (IMF) presented the expenditures on transport and communications as one combined sector. Since 1990, the expenditures have been reported separately as two sectors. The new version of the SPEED dataset presents the expenditures on the two sectors separately as well as combined for completeness of information.

× This data study includes Yemen Social Accounting Matrix (SAM) for the year 2012. The national SAM is built using the 2012 national accounts, government budgets, balance of payments, and 2007 input-output tables. It provides a detailed representation of Yemen economy. It separates 24 activities and commodities; 10 agricultural sectors, fisheries and forestry, 2 for mining, 4 for manufacturing, 1 for electricity,water, construction and trade and transport, and 3 service sectors. Information on labor is drawn from 2005/06 household budget survey (HBS) and dis-aggregates labor into three categories: unskilled, semi-skilled and skilled. Finally the SAM identifies government investments and foreign accounts. Therefore, this database can be a very good input for undertaking economy wide impact assessments, SAM-based multiplier analysis and computable general equilibrium (CGE) modeling.

× This data study includes Iraq Social Accounting Matrix (SAM) for the year 2011. The SAM includes 15 agricultural sectors (including livestock), 2 mining sectors (oil and rest), 3 industrial sectors, and 6 service sectors. This database draws information on income, expenditure and labor from Iraq Household Socio-Economic Survey (IHSES) and separates out households into 12 groups and production factors into 10 groups. The households are classified based on the gender of the household head , per capital expenditure quintiles of the households, and whether the household resides in rural or urban area. Similarly, production factors were dis-aggregated into 6 categories of labor ( 3 levels of skill and gender), 3 types of capital (agriculture, oil and rest) and land. This SAM gives the detailed representation of Iraqi economy and is an ideal database for conducting further analysis such as economy wide impact assessment, multiplier analysis, and trade flow.

× The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally and by region and country. Calculated each year by the International Food Policy Research Institute (IFPRI), the GHI highlights successes and failures in hunger reduction and provide insights into the drivers of hunger, and food and nutrition security. The 2014 GHI has been calculated for 120 countries for which data on the three component indicators are available and for which measuring hung er is considered most relevant. The GHI calculation excludes some higher income countries because the prevalence of hunger there is very low.

The GHI is only as current as the data for its three component indicators. This year's GHI reflects the most recent available country level data for the three component indicators spanning the period 2009 to 2013. Besides the most recent GHI scores, this dataset also contains the GHI scores for four other reference periods- 1990, 1995, 2000, and 2005. A country's GHI score is calculated by averaging the percentage of the population that is undernourished, the percentage of children youn ger than five years old who are underweight, and the percentage of children dying before the age of five. This calculation results in a 100 point scale on which zero is the best score (no hunger) and 100 the worst, although neither of these extremes is reached in practice. The three component indicators used to calculate the GHI scores draw upon data from the following sources:

1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1990, 1995, 2000, 2005, and 2014GHI scores. Undernourishment data for the 2014 GHI are for 2011-2013.

2. Child underweight: The "child underweight" component indicator of the GHI scores includes the latest additions to the World Health Organization's (WHO) Global Database on Child Growth and Malnutrition, and additional data from the joint data base by the United Nations Children's Fund (UNICEF), WHO and the World Bank; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey reports; and statistical tables from UNICEF. For the 2014 GHI, data on child underweight are for the latest year for which data are available in the period 2009-2014.

3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1990, 1995, 2000, and 2005, and 2014 GHI scores. For the 2014 GHI, data on child mortality are for 2012.

× The 2014 Global Nutrition Report Dataset contains data for all the indicators that were used in Global Nutrition Report 2014: Actions and Accountability to Accelerate the World's Progress on Nutrition . The data are compiled from secondary sources including United Nations Children's Fund (UNICEF), World Health Organization (WHO), and the World Bank among many others. The dataset broadly contains information on adult and child nutrition, economic demography, nutrition intervention coverage, and policy legislation in the nutrition sector.

× This dataset includes national-level time series data on researcher capacity by qualification level, age bracket, discipline mix, and commodity, as well as a detailed breakdown of agricultural research investment across government, higher education, nonprofit, and (where possible) private for-profit agricultural research agencies. These data were derived through a series of primary survey rounds conducted by IFPRI’s Agricultural Science and Technology Indicators (ASTI) and in close collaboration with a large network of national collaborators. The ASTI data constitute a powerful resource for national and regional research managers, policymakers, donor organizations, and other stakeholders. ASTI’s key indicators provide both a diagnostic tool for assessing the allocation and use of existing resources and an advocacy tool for increasing resources and improving the efficiency and effectiveness of resource use. The ASTI website offers interactive pages that allow users to access country-level time series data, make cross-country comparisons, create graphs, and download country datasets and publications as well as detailed institutional information on agencies involved in agricultural research. The interactive benchmarking tool on the ASTI website is a convenient map-based instrument allowing users to make cross-country comparisons and rankings based on a wide set of financial and human resource indicators. The detailed ASTI datasets are available in an easy-to-use data download tool. Detailed information on definitions, methodology, and calculation procedures are available at www.asti.cgiar.org

× This dataset includes national-level time series data on researcher capacity by qualification level, age bracket, discipline mix, and commodity, as well as a detailed breakdown of agricultural research investment across government, higher education, nonprofit, and (where possible) private for-profit agricultural research agencies. These data were derived through a series of primary survey rounds conducted by IFPRI’s Agricultural Science and Technology Indicators (ASTI) and in close collaboration with a large network of national collaborators. The ASTI data constitute a powerful resource for national and regional research managers, policymakers, donor organizations, and other stakeholders. ASTI’s key indicators provide both a diagnostic tool for assessing the allocation and use of existing resources and an advocacy tool for increasing resources and improving the efficiency and effectiveness of resource use. The ASTI website offers interactive pages that allow users to access country-level time series data, make cross-country comparisons, create graphs, and download country datasets and publications as well as detailed institutional information on agencies involved in agricultural research. The interactive benchmarking tool on the ASTI website is a convenient map-based instrument allowing users to make cross-country comparisons and rankings based on a wide set of financial and human resource indicators. The detailed ASTI datasets are available in an easy-to-use data download tool. Detailed information on definitions, methodology, and calculation procedures are available at www.asti.cgiar.org

× This dataset includes national-level time series data on researcher capacity by qualification level, age bracket, discipline mix, and commodity, as well as a detailed breakdown of agricultural research investment across government, higher education, nonprofit, and (where possible) private for-profit agricultural research agencies. These data were derived through a series of primary survey rounds conducted by IFPRI’s Agricultural Science and Technology Indicators (ASTI) and in close collaboration with a large network of national collaborators. The ASTI data constitute a powerful resource for national and regional research managers, policymakers, donor organizations, and other stakeholders. ASTI’s key indicators provide both a diagnostic tool for assessing the allocation and use of existing resources and an advocacy tool for increasing resources and improving the efficiency and effectiveness of resource use. The ASTI website offers interactive pages that allow users to access country-level time series data, make cross-country comparisons, create graphs, and download country datasets and publications as well as detailed institutional information on agencies involved in agricultural research. The interactive benchmarking tool on the ASTI website is a convenient map-based instrument allowing users to make cross-country comparisons and rankings based on a wide set of financial and human resource indicators. The detailed ASTI datasets are available in an easy-to-use data download tool. Detailed information on definitions, methodology, and calculation procedures are available at www.asti.cgiar.org

× This dataset includes national-level time series data on researcher capacity by qualification level, age bracket, discipline mix, and commodity, as well as a detailed breakdown of agricultural research investment across government, higher education, nonprofit, and (where possible) private for-profit agricultural research agencies. These data were derived through a series of primary survey rounds conducted by IFPRI’s Agricultural Science and Technology Indicators (ASTI) and in close collaboration with a large network of national collaborators. The ASTI data constitute a powerful resource for national and regional research managers, policymakers, donor organizations, and other stakeholders. ASTI’s key indicators provide both a diagnostic tool for assessing the allocation and use of existing resources and an advocacy tool for increasing resources and improving the efficiency and effectiveness of resource use. The ASTI website offers interactive pages that allow users to access country-level time series data, make cross-country comparisons, create graphs, and download country datasets and publications as well as detailed institutional information on agencies involved in agricultural research. The interactive benchmarking tool on the ASTI website is a convenient map-based instrument allowing users to make cross-country comparisons and rankings based on a wide set of financial and human resource indicators. The detailed ASTI datasets are available in an easy-to-use data download tool. Detailed information on definitions, methodology, and calculation procedures are available at www.asti.cgiar.org

× This dataset includes national-level time series data on researcher capacity by qualification level, age bracket, discipline mix, and commodity, as well as a detailed breakdown of agricultural research investment across government, higher education, nonprofit, and (where possible) private for-profit agricultural research agencies. These data were derived through a series of primary survey rounds conducted by IFPRI’s Agricultural Science and Technology Indicators (ASTI) and in close collaboration with a large network of national collaborators. The ASTI data constitute a powerful resource for national and regional research managers, policymakers, donor organizations, and other stakeholders. ASTI’s key indicators provide both a diagnostic tool for assessing the allocation and use of existing resources and an advocacy tool for increasing resources and improving the efficiency and effectiveness of resource use. The ASTI website offers interactive pages that allow users to access country-level time series data, make cross-country comparisons, create graphs, and download country datasets and publications as well as detailed institutional information on agencies involved in agricultural research. The interactive benchmarking tool on the ASTI website is a convenient map-based instrument allowing users to make cross-country comparisons and rankings based on a wide set of financial and human resource indicators. The detailed ASTI datasets are available in an easy-to-use data download tool. Detailed information on definitions, methodology, and calculation procedures are available at www.asti.cgiar.org

× This dataset includes national-level time series data on researcher capacity by qualification level, age bracket, discipline mix, and commodity, as well as a detailed breakdown of agricultural research investment across government, higher education, nonprofit, and (where possible) private for-profit agricultural research agencies. These data were derived through a series of primary survey rounds conducted by IFPRI’s Agricultural Science and Technology Indicators (ASTI) and in close collaboration with a large network of national collaborators. The ASTI data constitute a powerful resource for national and regional research managers, policymakers, donor organizations, and other stakeholders. ASTI’s key indicators provide both a diagnostic tool for assessing the allocation and use of existing resources and an advocacy tool for increasing resources and improving the efficiency and effectiveness of resource use. The ASTI website offers interactive pages that allow users to access country-level time series data, make cross-country comparisons, create graphs, and download country datasets and publications as well as detailed institutional information on agencies involved in agricultural research. The interactive benchmarking tool on the ASTI website is a convenient map-based instrument allowing users to make cross-country comparisons and rankings based on a wide set of financial and human resource indicators. The detailed ASTI datasets are available in an easy-to-use data download tool. Detailed information on definitions, methodology, and calculation procedures are available at www.asti.cgiar.org

× This dataset includes national-level time series data on researcher capacity by qualification level, age bracket, discipline mix, and commodity, as well as a detailed breakdown of agricultural research investment across government, higher education, nonprofit, and (where possible) private for-profit agricultural research agencies. These data were derived through a series of primary survey rounds conducted by IFPRI’s Agricultural Science and Technology Indicators (ASTI) and in close collaboration with a large network of national collaborators. The ASTI data constitute a powerful resource for national and regional research managers, policymakers, donor organizations, and other stakeholders. ASTI’s key indicators provide both a diagnostic tool for assessing the allocation and use of existing resources and an advocacy tool for increasing resources and improving the efficiency and effectiveness of resource use. The ASTI website offers interactive pages that allow users to access country-level time series data, make cross-country comparisons, create graphs, and download country datasets and publications as well as detailed institutional information on agencies involved in agricultural research. The interactive benchmarking tool on the ASTI website is a convenient map-based instrument allowing users to make cross-country comparisons and rankings based on a wide set of financial and human resource indicators. The detailed ASTI datasets are available in an easy-to-use data download tool. Detailed information on definitions, methodology, and calculation procedures are available at www.asti.cgiar.org

× This database provides estimates of the burden of undernutrition and its costs to societies around the world. To derive these estimates, various health outcomes were attributed to undernutrition, as suggested in the literature. The health outcomes that are covered in the World Health Organization's (WHO) Global Burden of Disease (GBD) study and that relate directly to undernutrition are: protein –energy malnutrition, iodine deficiency, vitamin A deficiency, and iron-deficiency anemia. Furthermore, 18 percent of diarrheal diseases and 41 percent of lower respiratory infections can be attributed to zinc deficiency, and 5 percent of maternal mortality can be attributed to iron-deficiency anemia. Aggregating the Disability Adjusted Life Years (DALYs) lost due to these various health outcomes (and using 2011 population figures to generate DALYs estimates for 2011 from the WHO's data for 2004) yields results for the burden of hunger in all its forms in terms of DALYs lost in 2011 at the global and country level. Globally, 44 million DALYs are lost due to micronutrient malnutrition (hidden hunger) and 21 million DALYs are lost due to undernourishment (chronic hunger). While the same individual can be both undernourished and suffering from one or another micronutrient deficiency, in the calculation of DALYs the various health outcomes are clearly differentiated by cause; that is, DALYs lost are summable (which is one of their conceptual strengths). Therefore, added together, the global burden of hunger in all its forms amounts to 65 million DALYs lost — about 4 percent of the total burden of disease. All these calculations use the latest data on DALYs lost at the country–level provided by the WHO. In addition, another set of estimates of the cost of undernutrition at the global level is derived from more recent, but less disaggregated data from the Institute for Health Metrics and Evaluation (IHME).

× The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally and by region and country. Calculated each year by the International Food Policy Research Institute (IFPRI), the GHI highlights successes and failures in hunger reduction and provide insights into the drivers of hunger, and food and nutrition security. The 2013 GHI has been calculated for 120 countries for which data on the three component indicators are available and for which measuring hung er is considered most relevant. The GHI calculation excludes some higher income countries because the prevalence of hunger there is very low.

The GHI is only as current as the data for its three component indicators. This year's GHI reflects the most recent available country level data for the three component indicators spanning the period 2008 to 2012. Besides the most recent GHI scores, this dataset also contains the GHI scores for four other reference periods- 1990, 1995, 2000, and 2005. A country's GHI score is calculated by averaging the percentage of the population that is undernourished, the percentage of children youn ger than five years old who are underweight, and the percentage of children dying before the age of five. This calculation results in a 100 point scale on which zero is the best score (no hunger) and 100 the worst, although neither of these extremes is reached in practice. The three component indicators used to calculate the GHI scores draw upon data from the following sources:

1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1990, 1995, 2000, 2005, and 2013 GHI scores. Undernourishment data for the 2013 GHI are for 2010-2012.
2. Child underweight: The "child underweight" component indicator of the GHI scores includes the latest additions to the World Health Organization's (WHO) Global Database on Child Growth and Malnutrition, and additional data from the joint database by the United Nations Children's Fund (UNICEF), WHO and the World Bank; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey reports; and statistical tables from UNICEF. For the 2013 GHI, data on child underweight are for the latest year for which data are available in the period 2008-2012.
3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1990, 1995, 2000, and 2005, and 2013 GHI scores. For the 2013 GHI, data on child mortality are for 2011.

× In many developing countries, weak institutions and fiscal mismanagement often lead to poor access and weak delivery of public services, such as health, education, and basic infrastructure. Improving the efficiency of public spending is an important economic and political tool, along with good governance and a strong revenue stream, for improved public sector management. An efficient public sector serves an important role in a country 's economic development by promoting adequate and appropriate allocation of resources. Even small steps toward prudent fiscal management can benefit the poor and other disadvantaged groups by allowing the provision of effective and targeted public services. A better understanding of the linkages between public expenditure and development can provide insights for poverty reduction strategies and key development goals. With this objective, IFPRI has compiled the Statistics of Public Expenditure for Economic Development (SPEED) database, providing the most comprehensive and publicly available public expenditure information for 147 countries and 10 sectors: agriculture, communication, education, health, defense, mining, social protection, transport, transportation and communication as a group, and total expenditures for the time period 1980-2010. The SPEED database is available for use by researchers, policymakers, donors, and others in the development community for a variety of economic and policy applications.

× The 2012 GHI report focuses particularly on the issue of how to ensure sustainable food security under conditions of water, land, and energy stress. Demographic changes, rising incomes and associated consumption patterns, and climate change, alongside persistent poverty and inadequate policies and institutions, are all placing serious pressure on natural resources. In this report, IFPRI describes the evidence on land, water, and energy scarcity in developing countries and offers two visions of a future global food system—an unsustainable scenario in which current trends in resource use continue, and a sustainable scenario in which access to food, modern energy, and clean water improves significantly and ecosystem degradation is halted or reversed. Concern Worldwide and Welthungerhilfe provide on-the-ground perspectives on the issues of land tenure and title as well as the impacts of scarce land, water, and energy on poor people in Sierra Leone and Tanzania and describe the work of their organizations in helping to alleviate these impacts.

× In many developing countries, weak institutions and fiscal mismanagement often lead to poor access and weak delivery of public services, such as health, education, and basic infrastructure. Improving the efficiency of public spending is an important economic and political tool, along with good governance and a strong revenue stream, for improved public sector management. An efficient public sector serves an important role in a country 's economic development by promoting adequate and appropriate allocation of resources. Even small steps toward prudent fiscal management can benefit the poor and other disadvantaged groups by allowing the provision of effective and targeted public services. A better understanding of the linkages between public expenditure and development can provide insights for poverty reduction strategies and key development goals. With this objective, IFPRI has compiled the Statistics of Public Expenditure for Economic Development (SPEED) database, providing the most comprehensive and publicly available public expenditure information for 67 countries and six sectors: agriculture, education, health, defense, social protection, and transportation and communication, for the time period 1980-2007. The SPEED database is available for use by researchers, policymakers, donors, and others in the development community for a variety of economic and policy applications.

× Agroecological zones (AEZs) are geographical areas exhibiting similar climatic conditions that determine their ability to support rainfed agriculture. At a regional scale, AEZs are influenced by latitude, elevation, and temperature, as well as seasonality, and rainfall amounts and distribution during the growing season. The resulting AEZ classifications for Africa have three dimensions: major climate zone (tropics or subtropics), moisture zones (water availability) and highland/lowland (warm or cool based on elevation).

× The disaggregated Egypt Social Accounting Matrix (SAM) was constructed for 1997 for an analysis of Egypt's food subsidy system. It has 28 productive sectors (activities), 36 commodities, 10 households, 5 factors, a government account and a rest-of-the-world account. The SAM has a detailed treatment for agriculture, there are 14 agricultural sectors producing 22 agricultural commodities. Bread and flour are two commodities that are subsidized in Egypt. The SAM accounts for these commodities and splits them into subsidized and unsubsidized, a treatment that facilitates the analysis of food subsidy issues. The SAM also differentiates between urban and rural households by quintiles, allowing welfare, inequality and poverty analysis. In addition to land, the SAM distinguishes between agricultural and nonagricultural labor and capital. For a description of the construction and use of this dataset, download the Trade and Macroeconomics Division Discussion Paper Number 48 (PDF 220K).

× The aggregated Egypt Social Accounting Matrix (SAM) was constructed for 1997 and has 14 sectors, 10 households, 4 factors, a government account and a rest-of-the-world account. There are 6 agricultural sectors, 4 manufacturing sectors, with the rest as service sectors. The agricultural sectors are: food crops, rice, other food crops, fruits and vegetables, cotton, and animal agriculture. The manufacturing sectors are: food processing, oil, textiles, and other manufacturing. The remaining service sectors are: electricity, construction, government sectors, and other services. The SAM differentiates between urban and rural households by quintiles, allowing welfare, inequality and poverty analysis. In addition to land and capital, the SAM distinguishes between agricultural and nonagricultural labor. For a description of the construction and use of this dataset, download the Trade and Macroeconomics Division Discussion Paper Number 78: (PDF 136K)

× The 1997 questionnaire was administered to 2,500 households from 20 governorates using a two-stage, stratified selection process from March through May, 1997. Topics include household information, housing characteristics, access to facilities, migration, subsidized food expenses, all food expenses, non-food expenses, health, anthropometrics, maternity history, childcare, wage employment, farming, livestock ownership, non-farm enterprises, credit and savings, remittances and transfers, and other income. In addition to the household level data, data were collected on the overall characteristics of the community/villages containing the surveyed households. The EIHS 1999 survey was conducted on a sub-sample (348 households) of the EIHS 1997. The EIHS 1999 household questionnaire consists of the following sections or topics: household Information, housing characteristics, Income Sources, food expenses, non-food expenses, farming, livestock ownership, non-farm enterprises, non-farming assets, and other income sources, and subsidized food expenses (Tamwin sugar and oil, Balady bread).

× The questionnaire was administered to randomly selected wholesale and retail traders in 18 governorates (Cairo and Alexandria; Damietta, Dakahlia, Sharkia, Ismailia, Qalubia, Kafr El-Sheikh, Gharbia, Menufia, and Beheira in Lower Egypt; Giza, Fayum, Beni Suef, and Minia in Middle Egypt; Assiut and Sohag in Upper Egypt; and Matruh of the Frontier governorates) from June 1997 through the end of May 1998. Data on the daily purchase and sale price for both wholesale and retail traders were collected for domestic wheat grain, 82% extraction (baladi) flour, domestic 72% extraction (fino) flour, and imported 72% extraction (fino) flour.

× This SAM is distinguished by an explicit separation of activities and households into rural and urban. It has a detailed treatment of aspects that are linked to the rural economy. Among the 45 activities, 38 are rural and 7 urban. Most rural sectors are part of crop or livestock agriculture. The non-agricultural sectors are classified as rural or urban. The SAM has 34 commodities, 14 factors of production, 4 households, a government account and two rest-of-the-world accounts, EU and non-EU. The distinction in the latter account permits an analysis of Morocco's regional trade (imports and exports) with the EU and the rest of the non-EU world. For a description of the construction and use of this dataset, download the Trade and Macroeconomics Division Discussion Paper Number 41