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 31751, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C55 I32 I38 (search for similar items in EconPapers)
Date: 2023-10
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