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According to AGRA's 2017 Africa Agriculture Status Report, smallholder farmers make up to about 70% of the population in Africa. The report finds that 500 million smallholder farms around the world provide livelihoods for more than 2 billion people and produce about 80% of the food in sub-Saharan Africa and Asia. Many development interventions and policies therefore target smallholder farm households with the goals of increasing their productivity and promoting agricultural transformation. Of particular interest for agricultural transformation is the degree to which smallholder farm households are commercializating their agricultural outputs, and diversifying their income sources away from agriculture. In this project, EPAR uses data from the World Bank's Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) to analyze and compare characteristics of smallholder farm households at different levels of crop commercialization and reliance on farm income, and to evaluate implications of using different criteria for defining "smallholder" households for conclusions on trends in agricultural transformation for those households.
The commercial alcohol industry in Africa may provide opportunities to increase market access and incomes for smallholder farmers by increasing access to agriculture-alcohol value chains. Despite the benefits of increased market opportunities, the high costs to human health and social welfare from increased alcohol use and alcoholism could contribute to a net loss for society. To better understand the tradeoffs between increased market access for smallholders and societal costs associated with harmful alcohol consumption, this paper provides an inventory of the societal costs of alcohol in Sub-Saharan Africa (SSA). We examine direct costs associated with addressing harmful effects of alcohol and treating alcohol-related illnesses, as well as indirect costs associated with the goods and services that are not delivered as a consequence of drinking and its impact on personal productivity. We identified resources using Google Scholar and the University of Washington libraries, and utilized the Global Burden of Disease (GBD) database by the Institute for Health Metrics and Evaluation (IHME) and the World Health Organization’s Global Information System on Alcohol and Health (GISAH) database. We also utilized FAOSTAT to retrieve raw data on national-level alcohol production and export statistics. We find that hazardous alcohol use contributes to early mortality and morbidity, loss of productivity, property damage, and other social costs and harms for drinkers and those around them. Drinking also affects vulnerable segments of the population disproportionately. Policymakers, local authorities, and donor agencies can use the information presented in this paper to plan and prepare for the higher consumption levels and subsequent social costs that may follow through agricultural development and economic growth in the region.
The purpose of this analysis is to provide a measure of marketable surplus of maize in Tanzania. We proxy marketable surplus with national-level estimates of total maize sold, presumably the surplus for maize producing and consuming households. We also provide national level estimates of total maize produced and estimate “average prices” for Tanzania which allows this quantity to be expressed as an estimate of the value of marketable surplus. The analysis uses the Tanzanian National Panel Survey (TNPS) LSMS – ISA which is a nationally representative panel survey, for the years 2008/2009 and 2010/2011. A spreadsheet provides our estimates for different subsets of the sample and using different approaches to data cleaning and weighting. The total number of households for Tanzania was estimated with linear extrapolation based on the Tanzanian National Bureau of Statistics for the years 2002 and 2012. The weighted proportions of maize-producing and maize-selling households were multiplied to the national estimate of total households. This estimate of total Tanzanian maize-selling and maize-producing households was then multiplied by the average amount sold and by the average amount produced respectively to obtain national level estimates of total maize sold and total maize produced in 2009 and 2011.