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Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan

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  • Aiken, Emily L.
  • Bedoya, Guadalupe
  • Blumenstock, Joshua E.
  • Coville, Aidan

Abstract

Can mobile phone data improve program targeting? By combining rich survey data from a “big push” anti-poverty program in Afghanistan with detailed mobile phone logs from program beneficiaries, we study the extent to which machine learning methods can accurately differentiate ultra-poor households eligible for program benefits from ineligible households. We show that machine learning methods leveraging mobile phone data can identify ultra-poor households nearly as accurately as survey-based measures of consumption and wealth; and that combining survey-based measures with mobile phone data produces classifications more accurate than those based on a single data source.

Suggested Citation

  • Aiken, Emily L. & Bedoya, Guadalupe & Blumenstock, Joshua E. & Coville, Aidan, 2023. "Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan," Journal of Development Economics, Elsevier, vol. 161(C).
    • Handle: RePEc:eee:deveco:v:161:y:2023:i:c:s0304387822001584
    DOI: 10.1016/j.jdeveco.2022.103016
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    Cited by:

    1. Beuermann, Diether & Hoffmann, Bridget & Stampini, Marco & Vargas, David & Vera-Cossio, Diego A., 2024. "Shooting a Moving Target: Choosing Targeting Tools for Social Programs," IDB Publications (Working Papers) 13359, Inter-American Development Bank.
    2. Al Kez, Dlzar & Foley, Aoife & Abdul, Zrar Khald & Del Rio, Dylan Furszyfer, 2024. "Energy poverty prediction in the United Kingdom: A machine learning approach," Energy Policy, Elsevier, vol. 184(C).
    3. Ginevra Buratti & Alessio D'Ignazio, 2024. "Improving the effectiveness of financial education programs. A targeting approach," Journal of Consumer Affairs, Wiley Blackwell, vol. 58(2), pages 451-485, June.
    4. Dang, Hai-Anh & Kilic, Talip & Hlasny, Vladimir & Abanokova, Kseniya & Carletto, Calogero, 2024. "Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment," GLO Discussion Paper Series 1392, Global Labor Organization (GLO).
    5. Ligon, Ethan & Trachtman, Carly, 2024. "Assessing Targeting Peformance: The Case of Ghana’s LEAP Program," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt2zk0m608, Department of Agricultural & Resource Economics, UC Berkeley.
    6. Chowdhury, Shyamal & Hasan, Syed & Sharma, Uttam, 2024. "The Role of Trainee Selection in the Effectiveness of Vocational Training: Evidence from a Randomized Controlled Trial in Nepal," IZA Discussion Papers 16705, Institute of Labor Economics (IZA).
    7. Letta,Marco & Montalbano,Pierluigi & Paolantonio,Adriana, 2024. "Climate Immobility Traps : A Household-Level Test," Policy Research Working Paper Series 10724, The World Bank.
    8. Paolo Verme, 2023. "Predicting Poverty with Missing Incomes," Working Papers 642, ECINEQ, Society for the Study of Economic Inequality.

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    More about this item

    Keywords

    Targeting; Machine learning; Mobile phone data; Afghanistan;
    All these keywords.

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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