AEA Papers and Proceedings
ISSN 2574-0768 (Print) | ISSN 2574-0776 (Online)
Predicting Well-Being with Mobile Phone Data: Evidence from Four Countries
AEA Papers and Proceedings
(pp. 178–183)
Abstract
We provide systematic evidence on estimating household well-being from mobile phone data across four countries (Afghanistan, Côte d’Ivoire, Malawi, Togo). Using parallel, standardized machine learning experiments, we assess which welfare measures are most predictable and which data types most useful. Long-term poverty measures—wealth indices (Pearson’s ρ = 0.20–0.59) and multidimensional poverty (ρ = 0.29–0.57)—are predicted more accurately than consumption (ρ = 0.04–0.54); transient measures like food security are difficult to predict. Call and text behavior outperforms internet, mobile money, and airtime metadata. Nationally representative samples yield 20–70 percent higher accuracy than urban- or rural-only samples.Citation
Aiken, Emily, Joshua E. Blumenstock, Sveta Milusheva, and M. Merritt Smith. 2026. "Predicting Well-Being with Mobile Phone Data: Evidence from Four Countries." AEA Papers and Proceedings 116: 178–183. DOI: 10.1257/pandp.20261092Additional Materials
JEL Classification
- C45 Neural Networks and Related Topics
- I31 General Welfare; Well-Being
- I32 Measurement and Analysis of Poverty
- O12 Microeconomic Analyses of Economic Development
- O18 Economic Development: Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
- R23 Urban, Rural, Regional, Real Estate, and Transportation Economics: Regional Migration; Regional Labor Markets; Population; Neighborhood Characteristics