A Longitudinal Cross-Country Dataset on Agricultural Productivity and Welfare in Sub-Saharan Africa

Investment Rating - The report does not provide a specific investment rating for the agricultural sector in Sub-Saharan Africa. Core Insights - The agricultural sector is crucial for the labor force and economic output in low-income countries, particularly in Sub-Saharan Africa, where it accounts for about 50% of the labor force [6] - The World Bank's LSMS-ISA program has been instrumental in collecting high-quality, nationally representative data on agricultural productivity and household welfare across seven Sub-Saharan African countries from 2008 to 2021 [2][7] - The harmonized panel dataset includes over 200,000 agricultural plot observations, over 400,000 individuals, and about 59,000 households, enabling in-depth analysis of agricultural dynamics and welfare outcomes [2][8] Summary by Sections Background and Summary - The agricultural sector is vital for poverty alleviation, food security, and economic development in Sub-Saharan Africa, where many extreme poor rely on agriculture for income [6] - The LSMS-ISA program addresses the historical lack of high-quality data necessary for informed research and policy interventions [7] Data Collection and Structure - The harmonized panel dataset (HP) covers seven countries: Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania, and Uganda, representing 39% of the population and nearly a third of the poor in Sub-Saharan Africa [8] - The HP consists of four datasets: household, individual, agricultural plots, and crops on each plot, allowing for detailed analysis of agricultural productivity and household welfare [9][40] Survey Methodology - The HP includes 29 waves of longitudinal surveys conducted between 2008 and 2021, with varying timeframes across countries [12] - Surveys were designed to be nationally representative, employing a stratified two-stage probability sampling approach [18] Data Processing and Harmonization - The HP datasets were created by cleaning and harmonizing nearly 150 indicators, ensuring they are ready for analysis [9][41] - Key identifiers and geospatial variables are included to facilitate longitudinal tracking and integration with geospatial data [42][55] Agricultural and Household Variables - The datasets include a wide range of agricultural variables, such as crop production, labor inputs, and agricultural practices, as well as household welfare indicators like consumption and asset ownership [66] - The household-level dataset captures critical variables for analyzing livelihood outcomes, including access to electricity and household consumption values [66] Individual-Level Insights - The individual-level dataset provides demographic information, work and employment data, and anthropometric measurements for children, allowing for comprehensive analysis of household members' welfare [69]