High-resolution poverty maps in Sub-Saharan Africa

Up-to-date poverty maps are an important tool for policymakers, but have been prohibitively expensive to produce and maintain ongoing accuracy. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the method for 25 Sub-Saharan African countries and validated against survey data. Our method can increase the validity of both single country and cross-country estimations, leading to more accurate poverty maps with a higher geographic precision for Sub-Saharan African countries. More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many low- and middle-income countries.