Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning. Issue 2 (15th July 2021)

• Record Type: Journal Article
• Authors: McBride, Linden; Barrett, Christopher B.; Browne, Christopher; Hu, Leiqiu; Liu, Yanyan; Matteson, David S.; Sun, Ying; Wen, Jiaming
• Is Part Of: Applied economic perspectives and policy. Volume 44:Issue 2(2022)
• Page Start: 879
• Page End: 892
• Publication Date: 2021-07-15
• Subjects: big data -- humanitarian assistance -- machine learning -- poverty mapping -- poverty prediction; Agriculture -- Economic aspects -- Periodicals; Agriculture and state -- Periodicals; 338.105
• DOI: 10.1002/aepp.13175
• Languages: English
• ISSNs: 2040-5790

Abstract

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.