Working Paper: NBER ID: w29105
Authors: Luna Yue Huang; Solomon M. Hsiang; Marco Gonzalez-Navarro
Abstract: 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.
Keywords: antipoverty programs; satellite imagery; deep learning; household welfare; cash transfers
JEL Codes: C8; H0; O1; O22; Q0; R0
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Cash transfers (F16) | Building footprint (R33) |
| Cash transfers (F16) | Area of tin roofs (L61) |
| Cash transfers (F16) | Night light (Y60) |