Predicting poverty and wealth from 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…

• NS
  National Science Foundation

  Award: 1025103
• BA
  Bill and Melinda Gates Foundation

  Award: OPP1106936
• IF
  Institute for Money, Technology and Financial Inclusion

  Award: 2010-2366