TY  - BOOK
AU  - Huang,Luna Yue
AU  - Hsiang,Solomon M.
AU  - Gonzalez-Navarro,Marco
ED  - National Bureau of Economic Research.
TI  - Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs
T2  - NBER working paper series
PY  - 2021///
CY  - Cambridge, Mass.
PB  - National Bureau of Economic Research
N1  - July 2021; Hardcopy version available to institutional subscribers
N2  - 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
UR  - https://www.nber.org/papers/w29105
UR  - http://dx.doi.org/10.3386/w29105
ER  -