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Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning

Author

Listed:
  • Linden McBride
  • Christopher B. Barrett
  • Christopher Browne
  • Leiqiu Hu
  • Yanyan Liu
  • David S. Matteson
  • Ying Sun
  • Jiaming Wen

Abstract

Increasingly plentiful data and powerful predictive algorithms heighten the promise of data science for humanitarian and development programming. We advocate for embrace of, and investment in, machine learning methods for poverty and malnutrition targeting, mapping, monitoring, and early warning while also cautioning that distinct objectives require distinct data and methods. In particular, we highlight the differences between poverty and malnutrition targeting and mapping, the differences between structural and stochastic deprivation, and the modeling and data challenges of early warning system development. Overall, we urge careful consideration of the purpose and use cases of machine learning informed models.

Suggested Citation

  • Linden McBride & Christopher B. Barrett & Christopher Browne & Leiqiu Hu & Yanyan Liu & David S. Matteson & Ying Sun & Jiaming Wen, 2022. "Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 879-892, June.
    • Handle: RePEc:wly:apecpp:v:44:y:2022:i:2:p:879-892
    DOI: 10.1002/aepp.13175
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    2. Binh Tang & Yanyan Liu & David S. Matteson, 2022. "Predicting poverty with vegetation index," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 930-945, June.
    3. Li, Qing & Yu, Shuai & Échevin, Damien & Fan, Min, 2022. "Is poverty predictable with machine learning? A study of DHS data from Kyrgyzstan," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    4. Merfeld, Joshua D. & Newhouse, David & Weber, Michael & Lahiri, Partha, 2022. "Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes," IZA Discussion Papers 15390, Institute of Labor Economics (IZA).
    5. Ola Hall & Mattias Ohlsson & Thortseinn Rognvaldsson, 2022. "Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain," Papers 2203.01068, arXiv.org.
    6. Ola Hall & Francis Dompae & Ibrahim Wahab & Fred Mawunyo Dzanku, 2023. "A review of machine learning and satellite imagery for poverty prediction: Implications for development research and applications," Journal of International Development, John Wiley & Sons, Ltd., vol. 35(7), pages 1753-1768, October.
    7. Stefanía D’Iorio & Liliana Forzani & Rodrigo García Arancibia & Ignacio Girela, 2024. "Predictive power of composite socioeconomic indices for targeted programs: principal components and partial least squares," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(4), pages 3497-3534, August.
    8. Alessandra Garbero & Marco Letta, 2022. "Predicting household resilience with machine learning: preliminary cross-country tests," Empirical Economics, Springer, vol. 63(4), pages 2057-2070, October.
    9. Yujun Zhou & Erin Lentz & Hope Michelson & Chungmann Kim & Kathy Baylis, 2022. "Machine learning for food security: Principles for transparency and usability," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 893-910, June.

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