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

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dc.creatorMcbride, Linden
dc.creatorBarrett, Christopher B.
dc.creatorBrowne, Christopher
dc.creatorHu, Leiqiu
dc.creatorLiu, Yanyan
dc.creatorMatteson, David S.
dc.creatorSun, Ying
dc.creatorWen, Jiaming
dc.date2022-06
dc.date2024-04-12T13:37:18Z
dc.date2024-04-12T13:37:18Z
dc.date.accessioned2026-06-27T15:06:11Z
dc.descriptionIncreasingly 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.
dc.identifierhttps://hdl.handle.net/10568/141111
dc.identifier.urihttp://hdl.handle.net/123456789/94604
dc.languageen
dc.publisherAgricultural and Applied Economics Association
dc.relationhttps://doi.org/10.1371/journal.pone.0255519
dc.rightsLimited Access
dc.sourceMcbride, Linden; Barrett, Christopher B.; Browne, Christopher; Hu, Leiqiu; Liu, Yanyan; Matteson, David S.; Sun, Ying; and Wen, Jiaming. 2022. Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning. Applied Economic Perspectives and Policy 44(2): 879-892. https://doi.org/10.1002/aepp.13175
dc.subjectdata
dc.subjecthumanitarian organizations
dc.subjectmachine learning
dc.subjectcapacity development
dc.subjectearly warning systems
dc.subjectmalnutrition
dc.subjectpoverty
dc.subjectbig data
dc.titlePredicting poverty and malnutrition for targeting, mapping, monitoring, and early warning
dc.typeJournal Article

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Instituto Internacional de Investigación sobre Políticas Alimentarias (IFPRI)