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

59 Pages Posted: 3 Dec 2019 Last revised: 4 Dec 2019

Date Written: December 2, 2019

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.

Suggested Citation

Baez, Javier and Kshirsagar, Varun and Skoufias, Emmanuel, Adaptive Safety Nets for Rural Africa: Drought-Sensitive Targeting with Sparse Data (December 2, 2019). World Bank Policy Research Working Paper No. 9071, Available at SSRN: https://ssrn.com/abstract=3497143

Emmanuel Skoufias

World Bank ( email )

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Washington, DC 20433
United States

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