期刊
SCIENCE
卷 350, 期 6264, 页码 1073-1076出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.aac4420
关键词
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资金
- NSF [1025103]
- Institute for Money, Technology, and Financial Inclusion [2010-2366]
- Gates Foundation [OPP1106936]
- Bill and Melinda Gates Foundation [OPP1106936] Funding Source: Bill and Melinda Gates Foundation
- Direct For Social, Behav & Economic Scie [1025103] Funding Source: National Science Foundation
- Divn Of Social and Economic Sciences [1025103] Funding Source: National Science Foundation
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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