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

July 1, 2008

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Format: Chicago

Camelia Minoiu, and Sanjay Reddy. Kernel Density Estimation Based on Grouped Data: The Case of Poverty Assessment, (USA: International Monetary Fund, 2008) accessed September 18, 2024

Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate

Summary

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.

Subject: Estimation techniques, Income distribution, Personal income, Population and demographics, Poverty

Keywords: Density estimate, Headcount ratio, Kernel density estimation, Poverty estimate, Standard deviation, WP

Publication Details

Pages:

34
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2008/183
Stock No:

WPIEA2008183
ISBN:

9781451870411
ISSN:

1018-5941

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