TY - BOOK ID - 136792557 TI - Estimating Impact with Surveys versus Digital Traces : Evidence from Randomized Cash Transfers in Togo AU - Aiken, Emily. AU - Bellue, Suzanne. AU - Blumenstock, Joshua. AU - Karlan, Dean. AU - Udry, Christopher R. AU - National Bureau of Economic Research. PY - 2023 PB - Cambridge, Mass. National Bureau of Economic Research DB - UniCat UR - https://www.unicat.be/uniCat?func=search&query=sysid:136792557 AB - 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. ER -