Adaptive Safety Nets for Rural Africa: Drought-Sensitive Targeting with Sparse Data

Javier Baez, Varun Kshirsagar and Emmanuel Skoufias

No 9071, Policy Research Working Paper Series from The World Bank

Abstract: This 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.

Date: 2019-12-02
New Economics Papers: this item is included in nep-agr, nep-big and nep-env
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Persistent link: https://EconPapers.repec.org/RePEc:wbk:wbrwps:9071

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