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

Luna Yue Huang, Solomon Hsiang and Marco Gonzalez-Navarro

Papers from arXiv.org

Abstract: 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 development programs.

Date: 2021-04
New Economics Papers: this item is included in nep-big
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Citations: View citations in EconPapers (5)

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http://arxiv.org/pdf/2104.11772 Latest version (application/pdf)

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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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2104.11772

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