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...
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INFORMATION
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2021/11/01
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Document de travail de recherche sur les politiques
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WPS9860
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1
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1
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2022/03/24
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Disclosed
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Nowcasting Global Poverty
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Income and Expenditures Household Survey
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