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

Camelia Minoiu () and Sanjay Reddy

No 08/183, IMF Working Papers from International Monetary Fund

Abstract: 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.

Keywords: Poverty; Economic models; Income distribution; Data analysis (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: Written 2008-07-22
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