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Kernel Density Estimation Based on Grouped Data: The Case of Poverty Assessment
Author:
Ms. Camelia Minoiu
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and
Sanjay Reddy null

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Series:
IMF Working Papers
Volume/Issue:
Volume 2008: Issue 183
Language:
English
Publication Date:
01 Jul 2008
DOI:
https://doi.org/10.5089/9781451870411.001
ISBN:
9781451870411
ISSN:
1018-5941
Pages:
34
Keywords:
WP; poverty estimate; density estimate; kernel density estimation; headcount ratio; standard deviation
Legal-disclaimer:
IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
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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.
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Cover IMF Working Papers
IMF Working Papers
ISSN:
1018-5941
Publisher:
International Monetary Fund