Measuring poverty is notoriously difficult. The collection of detailed data on households is time-consuming and expensive. But the marriage of machine learning techniques to lighter collection instruments may transform how the World Bank and its development partners approach poverty measurement. Predicting a household¡¯s poverty status with a handful of easy-to-collect qualitative variables lowers costs, decreases turnaround times, and, ultimately, creates a more solid empirical foundation for policy.
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