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...
| Main Authors: | , , |
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2019
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| Subjects: | |
| 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 |
| 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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