Predicting Poverty and Wealth from Mobile Phone Metadata. Blumenstock, J.; Cadamuro, G.; and On, R. 350(6264):1073–1076.
Predicting Poverty and Wealth from Mobile Phone Metadata [link]Paper  doi  abstract   bibtex   
Predicting unmeasurable wealth In developing countries, collecting data on basic economic quantities, such as wealth and income, is costly, time-consuming, and unreliable. Taking advantage of the ubiquity of mobile phones in Rwanda, Blumenstock et al. mapped mobile phone metadata inputs to individual phone subscriber wealth. They applied the model to predict wealth throughout Rwanda and show that the predictions matched well with those from detailed boots-on-the-ground surveys of the population. Science, this issue p. 1073 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. Metadata from individuals’ phones can be used to predict aggregate-level characteristics such as access to electricity. Metadata from individuals’ phones can be used to predict aggregate-level characteristics such as access to electricity.
@article{blumenstockPredictingPovertyWealth2015,
  title = {Predicting Poverty and Wealth from Mobile Phone Metadata},
  author = {Blumenstock, Joshua and Cadamuro, Gabriel and On, Robert},
  date = {2015-11-27},
  journaltitle = {Science},
  volume = {350},
  pages = {1073--1076},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.aac4420},
  url = {https://doi.org/10.1126/science.aac4420},
  urldate = {2019-09-19},
  abstract = {Predicting unmeasurable wealth
In developing countries, collecting data on basic economic quantities, such as wealth and income, is costly, time-consuming, and unreliable. Taking advantage of the ubiquity of mobile phones in Rwanda, Blumenstock et al. mapped mobile phone metadata inputs to individual phone subscriber wealth. They applied the model to predict wealth throughout Rwanda and show that the predictions matched well with those from detailed boots-on-the-ground surveys of the population.
Science, this issue p. 1073
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.
Metadata from individuals’ phones can be used to predict aggregate-level characteristics such as access to electricity.
Metadata from individuals’ phones can be used to predict aggregate-level characteristics such as access to electricity.},
  eprint = {26612950},
  eprinttype = {pmid},
  keywords = {~INRMM-MiD:z-QIUP9B6T,data-transformation-modelling,mobile-communication,poverty,social-system,statistics},
  langid = {english},
  number = {6264}
}
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