Probing the limits of mobile phone metadata for poverty prediction and impact evaluation

• 成果类型: Article
• 署名作者: Barriga-Cabanillas, Oscar; Blumenstock, Joshua E.; Lybbert, Travis J.; Putmand, Daniel S.
• 署名单位: The World Bank; University of California System; University of California Berkeley; University of California System; University of California Davis; University of Pennsylvania
• 刊物名称: JOURNAL OF DEVELOPMENT ECONOMICS
• ISSN/ISSBN: 0304-3878
• DOI: 10.1016/j.jdeveco.2025.103462
• 发表日期: 2025
• 关键词: poverty; Mobile phone data; Machine Learning; Cash transfers; targeting; HAITI

摘要

A series of recent papers demonstrate that mobile phone metadata can, together with machine learning, estimate the wealth of individual subscribers and accurately target cash transfer programs. In the context of an emergency cash transfer program in Haiti, we combine surveys and mobile phone call detail records (CDR) to test whether such methods can be used to estimate the program's impact on household expenditures. We find that CDR-based predictions of total and food expenditures are much less accurate than predictions of wealth-particularly when estimated on a relatively homogeneous sample of rural communities eligible for the program. While impact estimates based on conventional survey data are positive and statistically significant, estimates based on CDR predictions are not statistically significant. Ina postmortem discussion, we assess reasons for this failure and discuss the implications for using big data in poverty measurement and impact evaluation.