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Kernel Density Estimation Based on Grouped Data: The Case of Poverty Assessment

Kernel Density Estimation Based on Grouped Data: The Case of Poverty Assessment by Camelia Minoiu
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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 lines. Depending on the chosen bandwidth, the $1/day poverty rate in 2000 varies by a factor of 1.8, while the $2/day headcount in 2000 varies by 287 million people. Our findings challenge the validity and robustness of poverty estimates derived through kernel density estimation on grouped data.
International Monetary Fund; July 2008
34 pages; ISBN 9781452786414
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Title: Kernel Density Estimation Based on Grouped Data: The Case of Poverty Assessment
Author: Camelia Minoiu; Sanjay Reddy