Using Satellite Imagery and Deep Learning to Evaluate the Impactof Anti-Poverty Programs
- Author(s): Huang, Luna Yue;
- Hsiang, Solomon;
- Gonzalez-Navarro, Marco
- et al.
Published Web Locationhttps://doi.org/10.26085/C3T59V
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 developmentprograms.