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

Journal of Economic Inequality, Forthcoming

29 Pages Posted: 24 Feb 2008 Last revised: 3 Mar 2012

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: February 22, 2012

Abstract

Grouped data have been widely used to analyze the global income distribution because individual records from nationally representative household surveys are often unavailable. In this paper we evaluate the performance of nonparametric density smoothing techniques, in particular kernel density estimation, in estimating poverty from grouped data. Using Monte Carlo simulations, we show that kernel density estimation gives rise to nontrivial biases in estimated poverty levels that depend on the bandwidth, kernel, poverty indicator, size of the dataset, and data generating process. Furthermore, the empirical bias in the poverty headcount ratio critically depends on the poverty line. We also undertake a sensitivity analysis of global poverty estimates to changes in the bandwidth and show that they vary widely with it. A comparison of kernel density estimation with parametric estimation of the Lorenz curve, also applied to grouped data, suggests that the latter fares better and should be the preferred approach.

Keywords: kernel density estimation, grouped data, income distribution, global 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 (February 22, 2012). Journal of Economic Inequality, Forthcoming . Available at SSRN: https://ssrn.com/abstract=1097182 or http://dx.doi.org/10.2139/ssrn.1097182

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

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
115
Abstract Views
873
rank
73,939
PlumX Metrics