Format:
1 Online-Ressource (59 Seiten)
Series Statement:
World Bank E-Library Archive
Content:
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
Additional Edition:
Erscheint auch als Druck-Ausgabe Javier E. Baez Adaptive Safety Nets for Rural Africa: Drought-Sensitive Targeting with Sparse Data Washington, D.C : The World Bank, 2019
Language:
English
DOI:
10.1596/1813-9450-9071
URL:
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