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

Luna Yue Huang, Solomon M. Hsiang and Marco Gonzalez-Navarro

No 29105, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C8 H0 O1 O22 Q0 R0 (search for similar items in EconPapers)
Date: 2021-07
New Economics Papers: this item is included in nep-agr, nep-big, nep-dev and nep-isf
Note: DEV EEE EFG PE
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Citations: View citations in EconPapers (5)

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Related works:
Working Paper: Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs (2021)
Working Paper: Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs (2021)
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