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

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  • Huang, Luna Yue
  • Hsiang, Solomon
  • Gonzalez-Navarro, Marco

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.
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Suggested Citation

  • Huang, Luna Yue & Hsiang, Solomon & Gonzalez-Navarro, Marco, 2021. "Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt1sp2w73b, Department of Agricultural & Resource Economics, UC Berkeley.
    • Handle: RePEc:cdl:agrebk:qt1sp2w73b
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    Cited by:

    1. Li, Kuanhong & Wang, Linping & Wang, Lianhui, 2024. "Consumption as the catalyst: Analyzing rural power infrastructure and agricultural growth through panel threshold regression and data-driven prediction," Applied Energy, Elsevier, vol. 365(C).
    2. Dickinson, Jeffrey, 2020. "Planes, Trains, and Automobiles: What Drives Human-Made Light?," MPRA Paper 103504, University Library of Munich, Germany.
    3. Imryoung Jeong & Hyunjoo Yang, 2021. "Using maps to predict economic activity," Papers 2112.13850, arXiv.org, revised Apr 2022.
    4. Jeffrey D. Michler & Dewan Abdullah Al Rafi & Jonathan Giezendanner & Anna Josephson & Valerien O. Pede & Elizabeth Tellman, 2024. "Impact Evaluations in Data Poor Settings: The Case of Stress-Tolerant Rice Varieties in Bangladesh," Papers 2409.02201, arXiv.org.
    5. Eugenia Go & Kentaro Nakajima & Yasuyuki Sawada & Kiyoshi Taniguchi, 2023. "Satellite-Based Vehicle Flow Data to Assess Local Economic Activities," CIRJE F-Series CIRJE-F-1209, CIRJE, Faculty of Economics, University of Tokyo.

    More about this item

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • H0 - Public Economics - - General
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development
    • O22 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - Project Analysis
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • R0 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General

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