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

Oscar Barriga-Cabanillas, Joshua E. Blumenstock, Travis J. Lybbert and Daniel Putman

Journal of Development Economics, 2025, vol. 174, issue C

Abstract: 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. In a postmortem discussion, we assess reasons for this failure and discuss the implications for using big data in poverty measurement and impact evaluation.

Keywords: Poverty; Mobile phone data; Machine learning; Cash transfers; Targeting; Haiti (search for similar items in EconPapers)
JEL-codes: C8 D0 O1 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304387825000136
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:deveco:v:174:y:2025:i:c:s0304387825000136

DOI: 10.1016/j.jdeveco.2025.103462

Access Statistics for this article

Journal of Development Economics is currently edited by M. R. Rosenzweig

More articles in Journal of Development Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().