Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs

The rigorous evaluation of anti-poverty programs is key to the fight against global poverty. Traditional approaches rely heavily on repeated in-person field surveys to measure program effects. However, this is 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 a recent anti-poverty program in rural Kenya. Leveraging 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 generates inexpensive and timely insights on program effectiveness in international development programs. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.