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Estimating impact with surveys versus digital traces: Evidence from randomized cash transfers in Togo

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  • Aiken, Emily
  • Bellue, Suzanne
  • Blumenstock, Joshua E.
  • Karlan, Dean
  • Udry, Christopher

Abstract

We study whether program impacts can be estimated using a combination of digital trace data and machine learning. In a randomized controlled trial of cash transfers in Togo, endline survey data indicate positive treatment effects on food security, mental health, and perceived economic status. However, estimates of impact based solely on predicted endline outcomes (generated using trace data and machine learning, which do successfully predict baseline poverty) are generally not statistically significant. When post-treatment outcome data are used in conjunction with predictions to estimate treatment effects, predicted impacts are similar to those estimated using surveys.

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  • Aiken, Emily & Bellue, Suzanne & Blumenstock, Joshua E. & Karlan, Dean & Udry, Christopher, 2025. "Estimating impact with surveys versus digital traces: Evidence from randomized cash transfers in Togo," Journal of Development Economics, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:deveco:v:175:y:2025:i:c:s0304387825000288
    DOI: 10.1016/j.jdeveco.2025.103477
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    1. Emily Aiken & Anik Ashraf & Joshua E. Blumenstock & Raymond P. Guiteras & Ahmed Mushfiq Mobarak, 2025. "Scalable Targeting of Social Protection: When Do Algorithms Out-Perform Surveys and Community Knowledge?," Cowles Foundation Discussion Papers 2443, Cowles Foundation for Research in Economics, Yale University.
    2. Klaus W. Deininger & Daniel Ayalew Ali, 2024. "Using Satellite Imagery and a Farmer Registry to Assess Agricultural Support in Conflict Settings : The Case of the Producer Support Grant Program in Ukraine," Policy Research Working Paper Series 10912, The World Bank.

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    More about this item

    Keywords

    Poverty; Impact evaluation; Cash transfers; Machine learning; Mobile phone data; Togo;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • 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

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