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

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

No 21181, CEPR Discussion Papers from Centre for Economic Policy Research

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.

Keywords: Low and Middle Income Countries; Welfare Outcomes; Poverty estimation; Cash transfers; Food security; Mental health; Proxy means tests (search for similar items in EconPapers)
JEL-codes: C55 I32 I38 (search for similar items in EconPapers)
Date: 2026-02
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