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 WorldDevelopment Indicators and Google Earth Engine. The methods are evaluated by withholding measured poverty rates anddetermining how accurately the methods predict the held-out data. A simple approach that scales the last observedwelfare distribution by a fraction of real GDP per capita growth—a method that departs slightly from current WorldBank practice—performs nearly as well as models using statistical learning on 1,000+ variables. This GDP-basedapproach 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 learningtechniques to a large set of variables yields only marginal improvements in accuracy.