Baez, Javier E.Kshirsagar, VarunSkoufias, Emmanuel2019-12-132019-12-132019-12https://hdl.handle.net/10986/33014This paper combines remote-sensed data and individual child-, mother-, and household-level data from the Demographic and Health Surveys for five countries in Sub-Saharan Africa (Malawi, Tanzania, Mozambique, Zambia, and Zimbabwe) to design a prototype drought-contingent targeting framework that may be used in scarce-data contexts. To accomplish this, the paper: (i) develops simple and easy-to-communicate measures of drought shocks; (ii) shows that droughts have a large impact on child stunting in these five countries -- comparable, in size, to the effects of mother's illiteracy and a fall to a lower wealth quintile; and (iii) shows that, in this context, decision trees and logistic regressions predict stunting as accurately (out-of-sample) as machine learning methods that are not interpretable. Taken together, the analysis lends support to the idea that a data-driven approach may contribute to the design of policies that mitigate the impact of climate change on the world's most vulnerable populations.CC BY 3.0 IGOSAFETY NETSPOVERTYCHILD WELFARECLIMATE CHANGETARGETINGSOCIAL PROTECTIONMALNUTRITIONSTUNTINGWorking PaperWorld Bank10.1596/1813-9450-9071