Predicting Poverty and Wealth with Mobile Phone Metadata

Accurate and timely estimates of population characteristics are a critical input to social and economic research and policy. In industrialized economies, novel sources of data are
enabling new approaches to demographic profiling, but in developing countries, fewer sources of big data exist.We show that an individual’s past history of mobile phone use can
be used to infer his or her socioeconomic status. Furthermore, we demonstrate that the predicted attributes of millions of individuals can, in turn, accurately reconstruct the
distribution of wealth of an entire nation or to infer the asset distribution of microregions composed of just a few households. In resource-constrained environments where censuses
and household surveys are rare, this approach creates an option for gathering localized and timely information at a fraction of the cost of traditional methods.

Datalab Faculty
Joshua Blumenstock

Collaborators
Gabriel Cadamuro (University of Washington), Robert On (UC Berkeley), Nathan Eagle (MIT)

Status
Completed

Publications

• Blumenstock, J.E., 2016. Fighting poverty with data. Science 353, 753–754. https://doi.org/10.1126/science.aah5217
• Blumenstock, J.E., Cadamuro, G., On, R., 2015. Predicting poverty and wealth from mobile phone metadata. Science 350, 1073–1076. https://doi.org/10.1126/science.aac4420
• Blumenstock, J.E., 2014. Calling for Better Measurement: Estimating an Individual’s Wealth and Well-Being from Mobile Phone Transaction Records, in: The 20th ACM Conference on Knowledge Discovery and Mining (KDD ’14), Workshop on Data Science for Social Good. New York, NY.
• Blumenstock, J.E., Shen, Y., Eagle, N., 2010. A Method for Estimating the Relationship Between Phone Use and Wealth, in: 4th International IEEE/ACM Conference on Information and Communication Technologies and Development (ICTD 2010). London, U.K.