Drought-sensitive targeting and child growth faltering in Southern Africa

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.