IDEAS home Printed from https://ideas.repec.org/a/aea/apandp/v116y2026p178-183.html

Predicting Well-Being with Mobile Phone Data: Evidence from Four Countries

Author

Listed:
  • Emily Aiken
  • Joshua E. Blumenstock
  • Sveta Milusheva
  • M. Merritt Smith

Abstract

We provide systematic evidence on estimating household well-being from mobile phone data across four countries (Afghanistan, Côte d’Ivoire, Malawi, Togo). Using parallel, standardized machine learning experiments, we assess which welfare measures are most predictable and which data types most useful. Long-term poverty measures—wealth indices (Pearson’s ρ = 0.20–0.59) and multidimensional poverty (ρ = 0.29–0.57)—are predicted more accurately than consumption (ρ = 0.04–0.54); transient measures like food security are difficult to predict. Call and text behavior outperforms internet, mobile money, and airtime metadata. Nationally representative samples yield 20–70 percent higher accuracy than urban- or rural-only samples.

Suggested Citation

  • Emily Aiken & Joshua E. Blumenstock & Sveta Milusheva & M. Merritt Smith, 2026. "Predicting Well-Being with Mobile Phone Data: Evidence from Four Countries," AEA Papers and Proceedings, American Economic Association, vol. 116, pages 178-183, May.
    • Handle: RePEc:aea:apandp:v:116:y:2026:p:178-183
    DOI: 10.1257/pandp.20261092
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/doi/10.1257/pandp.20261092
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.
    File URL: https://www.aeaweb.org/articles/materials/25183
    Download Restriction: no
    File URL: https://www.aeaweb.org/articles/materials/25184
    Download Restriction: no
    File URL: https://libkey.io/10.1257/pandp.20261092?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aea:apandp:v:116:y:2026:p:178-183. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.