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

31 Pages Posted: 6 Jun 2007

See all articles by Camelia Minoiu

Camelia Minoiu

Federal Reserve Board

Sanjay G. Reddy

The New School - Department of Economics

Multiple version iconThere are 2 versions of this paper

Date Written: July 5, 2007

Abstract

Kernel density estimation (KDE) has been prominently used to measure poverty from grouped data (Sala-i-Martin, 2006, QJE). In this paper we analyze the performance of this method. Using Monte Carlo simulations for plausible income distributions and unit data from several household surveys, we compare KDE-based poverty estimates with their true and survey counterparts. 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 data-points analyzed, and the poverty indicators used. We also demonstrate that KDE-based estimates of global poverty are highly sensitive to the choice of bandwidth. Depending on the choice of this parameter alone, the estimated proportion of '$1/day poor' in 2000 varies by a factor of 1.8, while the estimated number of '$2/day poor' in 2000 varies by 287 million people. These findings give rise to concern about the validity and robustness of kernel density estimation in poverty analysis.

Keywords: kernel density estimation, income distribution, grouped data, poverty

JEL Classification: I32, D31, C14, C15

Suggested Citation

Minoiu, Camelia and Reddy, Sanjay G., Kernel Density Estimation Based on Grouped Data: The Case of Poverty Assessment (July 5, 2007). Available at SSRN: https://ssrn.com/abstract=991503 or http://dx.doi.org/10.2139/ssrn.991503

Camelia Minoiu (Contact Author)

Federal Reserve Board ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

Sanjay G. Reddy

The New School - Department of Economics ( email )

Room 1116
6 East 16th Street
New York, NY 10003
United States

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