Community Targeting for Poverty Reduction in Burkina Faso

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Abstract
This article develops a method for targeting antipoverty programs and public projects to poor communities in rural and urban areas. The method calls for constructing an extensive data set from a large number of sources and then integrating the entire set into a geographic information system. The data set includes demographic data from the population census; household-level data from a variety of surveys; community-level data on local road infrastructure, public facilities, water points, and so on; and department-level data on agroclimatic conditions. An econometric model that estimates the impact of household-, community-, and department-level variables on household consumption is used to identify the key explanatory variables that determine the standard of living in rural and urban areas. This model is then applied to predict poverty indicators for 3,871 rural and urban communities in Burkina Faso and to map the spatial distribution of poverty in the country. A simulation analysis assesses the effectiveness of village-level targeting based on these predictions. The results show that such targeting is an improvement over regional targeting in that it reduces leakage and undercoverage.

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