  The rigorous evaluation of anti-poverty programs is key to the fight against
global poverty. Traditional evaluation approaches rely heavily on repeated
in-person field surveys to measure changes in economic well-being and thus
program effects. However, this is known to be costly, time-consuming, and often
logistically challenging. Here we provide the first evidence that we can
conduct such program evaluations based solely on high-resolution satellite
imagery and deep learning methods. Our application estimates changes in
household welfare in the context of a recent anti-poverty program in rural
Kenya. The approach we use is based on a large literature documenting a
reliable relationship between housing quality and household wealth. We infer
changes in household wealth based on satellite-derived changes in housing
quality and obtain consistent results with the traditional field-survey based
approach. Our approach can be used to obtain inexpensive and timely insights on
program effectiveness in international development programs.
