  Up-to-date poverty maps are an important tool for policy makers, but until
now, have been prohibitively expensive to produce. We propose a generalizable
prediction methodology to produce poverty maps at the village level using
geospatial data and machine learning algorithms. We tested the proposed method
for 25 Sub-Saharan African countries and validated them against survey data.
The proposed method can increase the validity of both single country and
cross-country estimations leading to higher precision in poverty maps of 44
Sub-Saharan African countries than previously available. 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.
