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231006 ||| eng |
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|a Mahler, Daniel Gerszon
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| 245 |
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|a Nowcasting Global Poverty
|h Elektronische Ressource
|c Daniel Gerszon Mahler
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| 260 |
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|a Washington, D.C
|b The World Bank
|c 2021
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| 300 |
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|a 44 pages
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| 653 |
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|a Poverty Lines
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| 653 |
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|a Poverty Monitoring and Analysis
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| 653 |
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|a Machine Learning
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| 653 |
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|a Inequality
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| 653 |
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|a Nowcasting
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| 653 |
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|a Poverty Reduction
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| 653 |
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|a Poverty Measurement
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| 653 |
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|a Poverty
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| 700 |
1 |
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|a Newhouse, David
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| 700 |
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|a Castaneda Aguilar, R. Andres
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b WOBA
|a World Bank E-Library Archive
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| 028 |
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|a 10.1596/1813-9450-9860
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| 856 |
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|u http://elibrary.worldbank.org/doi/book/10.1596/1813-9450-9860
|x Verlag
|3 Volltext
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|a 330
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|a 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-a method that departs slightly from current World Bank practice-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
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