Publication: Adaptive Safety Nets for Rural Africa: Drought-Sensitive Targeting with Sparse Data
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2019-12
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2019-12-13
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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.
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“Baez, Javier E.; Kshirsagar, Varun; Skoufias, Emmanuel. 2019. Adaptive Safety Nets for Rural Africa: Drought-Sensitive Targeting with Sparse Data. Policy Research Working Paper;No. 9071. © World Bank. http://hdl.handle.net/10986/33014 License: CC BY 3.0 IGO.”
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