author = { Baez, Javier E. AND Kshirsagar, Varun AND Skoufias, Emmanuel },

title = { Adaptive safety nets for rural Africa drought-sensitive targeting with sparse data },

publisher = {World Bank Group, Poverty and Equity Global Practice},

keywords = { Graue Literatur },

year = {December 2019},

abstract = {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},

booktitle = {Policy research working paper ; 9071},

booktitle = {World Bank E-Library Archive},

address = { [Washington, DC, USA] },

url = { http://slubdd.de/katalog?TN_libero_mab2 }