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

• 成果类型: Article
• 署名作者: Aiken, Emily; Bellue, Suzanne; Blumenstock, Joshua E.; Karlan, Dean; Udry, Christopher
• 署名单位: University of California System; University of California Berkeley; Institut Polytechnique de Paris; ENSAE Paris; Ecole Polytechnique; Northwestern University
• 刊物名称: JOURNAL OF DEVELOPMENT ECONOMICS
• ISSN/ISSBN: 0304-3878
• DOI: 10.1016/j.jdeveco.2025.103477
• 发表日期: 2025
• 关键词: poverty; Impact evaluation; Cash transfers; Machine Learning; Mobile phone data; Togo

摘要

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.