Description:
Social safety net programs focus on a subset of the population, usually the poorest and most vulnerable. However, in most developing countries there is no administrative data on relative wealth of the population to support the selection process of the potential beneficiaries of the social safety net programs. Hence, selection into programs is often multi-methodological approached and starts with geographical targeting for the selection of program implementation areas. To facilitate this stage of the targeting process in São Tomé and Príncipe, this working paper develops High Resolution Satellite Imagery (HRSI) poverty maps, providing both estimates of poverty incidence and program eligibility at a highly detailed resolution (110 m x 110 m). Furthermore, the analysis combines poverty incidence and population density to enable the geographical targeting process. This working paper shows that HRSI poverty maps can be used as key operational tools to facilitate the decision-making process of the geographical targeting and efficiently identify entry points for rapidly expanding social safety net programs. Unlike HRSI poverty maps based on census data, poverty maps based on satellite data and machine learning can be updated frequently at low cost supporting more adaptive social protection programs