The paper develops a method for targeting anti-poverty programs and public projects on poor communities in rural and urban areas. The method is based on the application of an extensive data-set from a large number of sources and the integration of the entire data-set in a Geographical Information System. This data-set includes data from the population census, household-level data from a variety of surveys, community-level data on the local road infrastructure, public facilities, water points, etc., and department-level data on the agro-climatic conditions. An econometric model that estimates the impact of household-, community-, and department-level variables on households’ consumption has been used to identify the key explanatory variables that determine the standard of living in rural and urban areas. This model was applied to predict poverty indicators for 3871 rural and urban communities across the country and to provide a mapping of the spatial distribution of poverty in Burkina Faso. Simulation analysis was subsequently conducted to assess the effectiveness of village-level targeting based on these predictions of the poverty indicators. The results show that village-level targeting based on these predictions provides an improvement over regional targeting by reducing the leakage of the targeted program and the percentage of the population that remains undercovered
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