Nowcasting Global Poverty. (6th October 2022)

• Record Type: Journal Article
• Authors: Mahler, Daniel Gerszon; Castañeda Aguilar, R Andrés; Newhouse, David
• Is Part Of: World Bank economic review. Volume 36:Number 4(2022)
• Page Start: 835
• Page End: 856
• Publication Date: 2022-10-06
• Subjects: poverty -- nowcasting -- machine learning -- measurement; Economic development -- Periodicals; Developing countries -- Economic conditions -- Periodicals; Developing countries -- Economic policy -- Periodicals; 330.91724
• DOI: 10.1093/wber/lhac017
• Languages: English
• ISSNs: 0258-6770

Abstract

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+ 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.