Adaptive Safety Nets for Rural Africa
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World Bank, Washington, DC
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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.
Palabras clave
SAFETY NETS, POVERTY, CHILD WELFARE, CLIMATE CHANGE, TARGETING, SOCIAL PROTECTION, MALNUTRITION, STUNTING