Nowcasting Global Poverty

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.

Bibliographic Details

Main Authors:	Mahler, Daniel Gerszon, Aguilar, R. Andrés Castañeda, Newhouse, David
Format:	Journal Article biblioteca
Language:	English en_US
Published:	Published by Oxford University Press on behalf of the World Bank 2022-10-06
Subjects:	POVERTY, NOWCASTING, MACHINE LEARNING, MEASUREMENT,
Online Access:	http://documents.worldbank.org/curated/en/099658312112338950/IDU0519867a504b6f04e4c0bf3d0e3b9b7b70d3b https://hdl.handle.net/10986/41135
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