|
|
|
|
| LEADER |
01871nmm a2200253 u 4500 |
| 001 |
EB002109556 |
| 003 |
EBX01000000000000001249646 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
221013 ||| eng |
| 100 |
1 |
|
|a Baez, Javier E.
|
| 245 |
0 |
0 |
|a Adaptive Safety Nets for Rural Africa
|h Elektronische Ressource
|b Drought-Sensitive Targeting with Sparse Data
|c Javier E. Baez
|
| 260 |
|
|
|a Washington, D.C
|b The World Bank
|c 2019
|
| 300 |
|
|
|a 59 pages
|
| 700 |
1 |
|
|a Skoufias, Emmanuel
|
| 700 |
1 |
|
|a Baez, Javier E.
|
| 700 |
1 |
|
|a Kshirsagar, Varun
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b WOBA
|a World Bank E-Library Archive
|
| 490 |
0 |
|
|a World Bank E-Library Archive
|
| 028 |
5 |
0 |
|a 10.1596/1813-9450-9071
|
| 856 |
4 |
0 |
|u http://elibrary.worldbank.org/doi/book/10.1596/1813-9450-9071
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 330
|
| 520 |
|
|
|a 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
|