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Estimating Economic Characteristics with Phone Data

Author

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  • Joshua E. Blumenstock

Abstract

Historically, economists have relied heavily on survey-based data collection to measure social and economic well-being. Here, we investigate the extent to which the "digital footprints" of an individual can be used to infer his or her socioeconomic characteristics. Using two different datasets from Afghanistan and Rwanda, we show that phone data can be used to estimate the wealth of individuals in two very different economic environments. However, we find that such models are relatively brittle, and that a model trained in one country cannot be used to estimate characteristics in another. These results suggest several promising applications and directions for future work.

Suggested Citation

  • Joshua E. Blumenstock, 2018. "Estimating Economic Characteristics with Phone Data," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 72-76, May.
    • Handle: RePEc:aea:apandp:v:108:y:2018:p:72-76
    Note: DOI: 10.1257/pandp.20181033
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    Citations

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    Cited by:

    1. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
    2. Mathieu J. P. Poirier & Karen A. Grépin & Michel Grignon, 2020. "Approaches and Alternatives to the Wealth Index to Measure Socioeconomic Status Using Survey Data: A Critical Interpretive Synthesis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 148(1), pages 1-46, February.
    3. Panle Jia Barwick & Yanyan Liu & Eleonora Patacchini & Qi Wu, 2023. "Information, Mobile Communication, and Referral Effects," American Economic Review, American Economic Association, vol. 113(5), pages 1170-1207, May.
    4. Aiken, Emily L. & Bedoya, Guadalupe & Blumenstock, Joshua E. & Coville, Aidan, 2023. "Program targeting with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan," Journal of Development Economics, Elsevier, vol. 161(C).
    5. Andreas Erlström & Markus Grillitsch & Ola Hall, 2022. "The geography of connectivity: a review of mobile positioning data for economic geography," Journal of Geographical Systems, Springer, vol. 24(4), pages 679-707, October.
    6. Marcel Fafchamps & Måns Söderbom & Monique van den Boogart, 2022. "Adoption with Social Learning and Network Externalities," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(6), pages 1259-1282, December.
    7. Blumenstock, Joshua & Aiken, Emily & Bellue, Suzanne & Udry, Christopher & Karlan, Dean, 2021. "Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance," CEPR Discussion Papers 16385, C.E.P.R. Discussion Papers.
    8. Guanghua Chi & Han Fang & Sourav Chatterjee & Joshua E. Blumenstock, 2022. "Microestimates of wealth for all low- and middle-income countries," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(3), pages 2113658119-, January.
    9. Erlström, Andreas & Grillitsch, Markus & Hall, Ola, 2020. "The Geography of Connectivity: Trails of Mobile Phone Data," Papers in Innovation Studies 2020/6, Lund University, CIRCLE - Centre for Innovation Research.
    10. Till Koebe & Alejandra Arias-Salazar & Timo Schmid, 2023. "Releasing survey microdata with exact cluster locations and additional privacy safeguards," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    11. Daniel Bjorkegren & Joshua E. Blumenstock & Samsun Knight, 2020. "Manipulation-Proof Machine Learning," Papers 2004.03865, arXiv.org.

    More about this item

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration

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