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

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Listed:
  • Emily Aiken
  • Suzanne Bellue
  • Joshua Blumenstock
  • Dean Karlan
  • Christopher R. Udry

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

  • Emily Aiken & Suzanne Bellue & Joshua Blumenstock & Dean Karlan & Christopher R. Udry, 2023. "Estimating Impact with Surveys versus Digital Traces: Evidence from Randomized Cash Transfers in Togo," NBER Working Papers 31751, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:31751
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    More about this item

    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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