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

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

Journal of Development Economics, 2025, vol. 175, issue C

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.

Keywords: Poverty; Impact evaluation; Cash transfers; Machine learning; Mobile phone data; Togo (search for similar items in EconPapers)
JEL-codes: C55 I32 I38 (search for similar items in EconPapers)
Date: 2025
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Working Paper: Estimating Impact with Surveys versus Digital Traces: Evidence from Randomized Cash Transfers in Togo (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:deveco:v:175:y:2025:i:c:s0304387825000288

DOI: 10.1016/j.jdeveco.2025.103477

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