Predicting poverty and wealth from mobile phone metadata

By Joshua Blumenstock, Gabriel Cadamuro, Robert On. Science, 2015

703 citations recorded by the source corpus.

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 Metadata from individuals’ phones can be used to predict aggregate-level characteristics such as access to electricity. 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.

Subfields: Epidemiology, Information Systems, Transportation

View Paper: https://www.semanticscholar.org/paper/5f5fd2f61904895e7c7e5b42ea0c2e12a56e3179

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Citations in the corpus

Machine Learning: An Applied Econometric Approach (2017, background): “Machine learning also yields economic predictions from large-scale network data; for example, Blumenstock, Cadamuro, and On (2015) use cell-phone data to measure wealth, allowing them to quantify poverty in Rwanda at the individual level.”
Mobile Money and the Economy: A Review of the Evidence (2018, background): “There is a problem of interpreting a treatment effect when intervention depends also on the type of training information provided (see Aker and Blumenstock (2015)).”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, positive): “However, ground truth measurements of poverty are lacking for much of the developing world, since gathering them requires conducting expensive surveys in the field (Blumenstock et al., 2015; Xie et al., 2016; Jean et al., 2016; Yeh et al., 2020).”
Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States (2017, background): “(6) used mobile phone metadata to predict poverty rates in Rwanda.”
The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good (2016, background): “ing Crime hotspot prediction Perpetrator(s)/victim(s) identication [67, 72, 91] [10, 11, 32, 85] [64] Finance & Economy Wealth & deprivation mapping Spending behavior proling Credit scoring [8, 49, 39, 76, 88] [74] [71, 73] Public Health Epidemiologic studies Environment and emergency mappin”
Fine-Grained Car Detection for Visual Census Estimation (2017, methodology): “And Blumenstock et al used mobile phone data to predict poverty rates in Rwanda (Blumenstock, Cadamuro, and On 2015).”
Quantifying China's Regional Economic Complexity (2017, background): “Moreover, online social networks [3,16,73], mobile phone data [1,11,74], satellite imagery [12,75], geo-tagged images [13] and web queries [76,77] have also been applied to reveal economic status, infer economic development, forecast unemployment, predict poverty, map inequality, quantify trading be”