Publication: Nowcasting Global Poverty
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Date
2022-10-06
ISSN
0258-6770 (print)
1564-698X (online)
1564-698X (online)
Published
2022-10-06
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Abstract
This paper evaluates different methods for nowcasting country-level poverty rates, including methods that apply statistical learning to large-scale country-level data obtained from the World Development Indicators and Google Earth Engine. The methods are evaluated by withholding measured poverty rates and determining how accurately the methods predict the held-out data. A simple approach that scales the last observed welfare distribution by a fraction of real GDP per capita growth performs nearly as well as models using statistical learning on 1,000 plus variables. This GDP-based approach outperforms all models that predict poverty rates directly, even when the last survey is up to five years old. The results indicate that in this context, the additional complexity introduced by applying statistical learning techniques to a large set of variables yields only marginal improvements in accuracy.
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“Mahler, Daniel Gerszon; Aguilar, R. Andrés Castañeda; Newhouse, David. 2022. Nowcasting Global Poverty. World Bank Economic Review. © World Bank. http://hdl.handle.net/10986/41135 License: CC BY-NC-ND 3.0 IGO.”
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World Bank Economic Review
1564-698X
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- Cited 5 times in Scopus (view citations)
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