We analyze the performance of kernel density methods applied to grouped data to estimate poverty (as applied in Sala …-i-Martin, 2006, QJE). Using Monte Carlo simulations and household surveys, we find that the technique gives rise to biases in poverty … estimates, the sign and magnitude of which vary with the bandwidth, the kernel, the number of datapoints, and across poverty …

Current estimates of global poverty vary substantially across studies. In this paper we undertake a novel sensitivity … analysis to highlight the importance of methodological choices in estimating global poverty. We measure global poverty using … different data sources, parametric and nonparametric estimation methods, and multiple poverty lines. Our results indicate that …

a failure to follow the national trend in poverty reduction. Schooling costs appear to play a large role in this … relationship between poverty, schooling, and child labor. Extrapolating from our results, our estimates imply that roughly half of … India''s rise in schooling and a third of the fall in child labor during the 1990s can be explained by falling poverty and …