Drought-sensitive targeting and child growth faltering in Southern Africa

Javier Baez, Varun Kshirsagar and Emmanuel Skoufias

World Development, 2024, vol. 182, issue C

Abstract: We combine remote-sensed data and individual child, mother, and household level data for five countries in Sub-Saharan Africa (Malawi, Tanzania, Mozambique, Zambia, and Zimbabwe) to design a prototype drought-sensitive targeting framework that may be used in scarce-data contexts. To accomplish this we: i) develop simple and easy-to-communicate measures of drought shocks; ii) show that droughts have a large impact on child growth faltering in these five countries -- comparable, in size, to the effects of mother’s illiteracy, living in a house with a primitive roof, or to a fall to a lower wealth quintile; and iii) show that, in this context, decision trees and regressions predict growth faltering as accurately (out-of-sample) as machine learning methods that are not interpretable. Taken together, our analysis lends support to the idea that a data-driven targeting approach may contribute to the design of policies that alleviate the impact that climate change has on the world’s most vulnerable populations.

Keywords: Poverty; Child Welfare; Climate change; Poverty targeting; Social Protection; Child Malnourishment; Interpretable Machine Learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:wdevel:v:182:y:2024:i:c:s0305750x24001724

DOI: 10.1016/j.worlddev.2024.106702

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