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Estimating Impact with Surveys versus Digital Traces: Evidence from Randomized Cash Transfers in Togo

Author

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

Abstract

Do non-traditional digital trace data and traditional survey data yield similar estimates of the impact of a cash transfer program? In a randomized controlled trial of Togo’s COVID-19 Novissi program, endline survey data indicate positive treatment effects on beneficiary food security, mental health, and self-perceived economic status. However, impact estimates based on mobile phone data – processed with machine learning to predict beneficiary welfare – do not yield similar results, even though related data and methods do accurately predict wealth and consumption in prior cross-sectional analysis in Togo. This limitation likely arises from the underlying difficulty of using mobile phone data to predict short-term changes in wellbeing within a rural population with fairly homogeneous baseline levels of poverty. We discuss the implications of these results for using new digital data sources in impact evaluation.

Suggested Citation

  • Aiken, Emily & Bellue, Suzanne & Blumenstock, Joshua & Karlan, Dean & Udry, Christopher, 2026. "Estimating Impact with Surveys versus Digital Traces: Evidence from Randomized Cash Transfers in Togo," CEPR Discussion Papers 21181, Centre for Economic Policy Research.
    • Handle: RePEc:cpr:ceprdp:21181
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    2. 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.
    3. Karlan, Dean & Lowe, Matt & Osei, Robert & Osei-Akoto, Isaac & Roth, Benjamin N. & Udry, Christopher, 2026. "Social protection and social distancing during the pandemic: Mobile money transfers in Ghana," Journal of Development Economics, Elsevier, vol. 179(C).
    4. Kilic, Talip & Letta, Marco & Montalbano, Pierluigi & Petruccelli, Federica, 2026. "CLARE : A Causal machine Learning Approach to Resilience Estimation," Policy Research Working Paper Series 11292, The World Bank.
    5. 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.
    6. Letta, Marco & Montalbano, Pierluigi & Morales Opazo, Cristian & Petruccelli, Federica, 2025. "Measuring and testing vulnerability to food insecurity for prediction and targeting," Economics & Human Biology, Elsevier, vol. 59(C).

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    Keywords

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    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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