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...
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| Published in: | Science (American Association for the Advancement of Science) Vol. 350; no. 6264; p. 1073 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
27.11.2015
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| Subjects: | |
| ISSN: | 1095-9203, 1095-9203 |
| Online Access: | Get more information |
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| Abstract | 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. |
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| AbstractList | 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.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. 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. |
| Author | Cadamuro, Gabriel Blumenstock, Joshua On, Robert |
| Author_xml | – sequence: 1 givenname: Joshua surname: Blumenstock fullname: Blumenstock, Joshua email: joshblum@uw.edu organization: Information School, University of Washington, Seattle, WA 98195, USA. joshblum@uw.edu – sequence: 2 givenname: Gabriel surname: Cadamuro fullname: Cadamuro, Gabriel organization: Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA – sequence: 3 givenname: Robert surname: On fullname: On, Robert organization: School of Information, University of California, Berkeley, Berkeley, CA 94720, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26612950$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Copyright © 2015, American Association for the Advancement of Science. |
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| SubjectTerms | Cell Phone - statistics & numerical data Censuses Developing Countries - statistics & numerical data Family Characteristics Humans Poverty - statistics & numerical data Rwanda Social Class |
| Title | Predicting poverty and wealth from mobile phone metadata |
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