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

Linden McBride, Christopher Barrett, Christopher Browne, Leiqiu Hu, Yanyan Liu, David S. Matteson, Ying Sun and Jiaming Wen

Applied Economic Perspectives and Policy, 2022, vol. 44, issue 2, 879-892

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.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.1002/aepp.13175

Related works:
Working Paper: Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:apecpp:v:44:y:2022:i:2:p:879-892

Access Statistics for this article

More articles in Applied Economic Perspectives and Policy from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().