Adaptive Safety Nets for Rural Africa

dc.creatorBaez, Javier E.
dc.creatorKshirsagar, Varun
dc.creatorSkoufias, Emmanuel
dc.date2019-12-13T20:58:27Z
dc.date2019-12-13T20:58:27Z
dc.date2019-12
dc.date.accessioned2026-07-01T00:41:11Z
dc.descriptionThis 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.
dc.formatapplication/pdf
dc.identifierhttp://documents.worldbank.org/curated/en/104851575303189267/Adaptive-Safety-Nets-for-Rural-Africa-Drought-Sensitive-Targeting-with-Sparse-Data
dc.identifierhttps://hdl.handle.net/10986/33014
dc.identifier10.1596/1813-9450-9071
dc.identifier.urihttp://hdl.handle.net/123456789/409126
dc.languageEnglish
dc.publisherWorld Bank, Washington, DC
dc.relationPolicy Research Working Paper;No. 9071
dc.rightsCC BY 3.0 IGO
dc.rightshttp://creativecommons.org/licenses/by/3.0/igo
dc.rightsWorld Bank
dc.subjectSAFETY NETS
dc.subjectPOVERTY
dc.subjectCHILD WELFARE
dc.subjectCLIMATE CHANGE
dc.subjectTARGETING
dc.subjectSOCIAL PROTECTION
dc.subjectMALNUTRITION
dc.subjectSTUNTING
dc.titleAdaptive Safety Nets for Rural Africa
dc.titleDrought-Sensitive Targeting with Sparse Data
dc.typeWorking Paper
dc.typeDocument de travail
dc.typeDocumento de trabajo

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