Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning
Linden McBride, Christopher B. Barrett, Christopher Browne, Leiqiu Hu, Yanyan Liu, David S. Matteson, Ying Sun, Jiaming Wen
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.