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

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 protot...

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Main Authors:	Baez, Javier E., Kshirsagar, Varun, Skoufias, Emmanuel
Format:	Working Paper
Language:	English
Published:	World Bank, Washington, DC 2019
Subjects:	SAFETY NETS POVERTY CHILD WELFARE CLIMATE CHANGE TARGETING SOCIAL PROTECTION MALNUTRITION STUNTING
Online Access:	http://documents.worldbank.org/curated/en/104851575303189267/Adaptive-Safety-Nets-for-Rural-Africa-Drought-Sensitive-Targeting-with-Sparse-Data http://hdl.handle.net/10986/33014

id	okr-10986-33014
recordtype	oai_dc
spelling	okr-10986-330142022-09-20T00:14:13Z Adaptive Safety Nets for Rural Africa : Drought-Sensitive Targeting with Sparse Data Baez, Javier E. Kshirsagar, Varun Skoufias, Emmanuel SAFETY NETS POVERTY CHILD WELFARE CLIMATE CHANGE TARGETING SOCIAL PROTECTION MALNUTRITION STUNTING 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. 2019-12-13T20:58:27Z 2019-12-13T20:58:27Z 2019-12 Working Paper http://documents.worldbank.org/curated/en/104851575303189267/Adaptive-Safety-Nets-for-Rural-Africa-Drought-Sensitive-Targeting-with-Sparse-Data http://hdl.handle.net/10986/33014 English Policy Research Working Paper;No. 9071 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper Africa Sub-Saharan Africa Malawi Mozambique Tanzania Zambia Zimbabwe
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
language	English
topic	SAFETY NETS POVERTY CHILD WELFARE CLIMATE CHANGE TARGETING SOCIAL PROTECTION MALNUTRITION STUNTING
spellingShingle	SAFETY NETS POVERTY CHILD WELFARE CLIMATE CHANGE TARGETING SOCIAL PROTECTION MALNUTRITION STUNTING Baez, Javier E. Kshirsagar, Varun Skoufias, Emmanuel Adaptive Safety Nets for Rural Africa : Drought-Sensitive Targeting with Sparse Data
geographic_facet	Africa Sub-Saharan Africa Malawi Mozambique Tanzania Zambia Zimbabwe
relation	Policy Research Working Paper;No. 9071
description	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.
format	Working Paper
author	Baez, Javier E. Kshirsagar, Varun Skoufias, Emmanuel
author_facet	Baez, Javier E. Kshirsagar, Varun Skoufias, Emmanuel
author_sort	Baez, Javier E.
title	Adaptive Safety Nets for Rural Africa : Drought-Sensitive Targeting with Sparse Data
title_short	Adaptive Safety Nets for Rural Africa : Drought-Sensitive Targeting with Sparse Data
title_full	Adaptive Safety Nets for Rural Africa : Drought-Sensitive Targeting with Sparse Data
title_fullStr	Adaptive Safety Nets for Rural Africa : Drought-Sensitive Targeting with Sparse Data
title_full_unstemmed	Adaptive Safety Nets for Rural Africa : Drought-Sensitive Targeting with Sparse Data
title_sort	adaptive safety nets for rural africa : drought-sensitive targeting with sparse data
publisher	World Bank, Washington, DC
publishDate	2019
url	http://documents.worldbank.org/curated/en/104851575303189267/Adaptive-Safety-Nets-for-Rural-Africa-Drought-Sensitive-Targeting-with-Sparse-Data http://hdl.handle.net/10986/33014
_version_	1764477809000972288

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

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