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Adaptive Safety Nets for Rural Africa : Drought-Sensitive Targeting with Sparse Data / Javier E. Baez.
World Bank Open Knowledge Repository (formerly "World Bank E-Library Publications") Available online
View online- Format:
- Book
- Government document
- Author/Creator:
- Baez, Javier E.
- Series:
- Policy research working papers.
- World Bank e-Library.
- Language:
- English
- Subjects (All):
- Child Welfare.
- Climate Change.
- Early Child and Children's Health.
- Health, Nutrition and Population.
- Malnutrition.
- Nutrition.
- Poverty.
- Poverty Reduction.
- Safety Nets.
- Safety Nets and Transfers.
- Services and Transfers to Poor.
- Social Protection.
- Social Protections and Labor.
- Stunting.
- Targeting.
- Local Subjects:
- Child Welfare.
- Climate Change.
- Early Child and Children's Health.
- Health, Nutrition and Population.
- Malnutrition.
- Nutrition.
- Poverty.
- Poverty Reduction.
- Safety Nets.
- Safety Nets and Transfers.
- Services and Transfers to Poor.
- Social Protection.
- Social Protections and Labor.
- Stunting.
- Targeting.
- Physical Description:
- 1 online resource (59 pages)
- Other Title:
- Adaptive Safety Nets for Rural Africa
- Place of Publication:
- Washington, D.C. : The World Bank, 2019.
- System Details:
- data file
- Summary:
- 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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