Estimating Poverty and Inequality from Grouped Data: How Well Do Parametric Methods Perform?

24 Pages Posted: 23 Aug 2006 Last revised: 14 Aug 2014

See all articles by Camelia Minoiu

Camelia Minoiu

Federal Reserve Board

Sanjay G. Reddy

The New School - Department of Economics

Date Written: June 25, 2008

Abstract

Poverty and inequality are often estimated from grouped data as complete household surveys are neither always available to researchers nor easy to analyze. In this study we assess the performance of functional forms proposed by Kakwani (1980a) and Villasenor and Arnold (1989) to estimate the Lorenz curve from grouped data. The methods are implemented using the computational tools POVCAL and SimSIP, developed and distributed by the World Bank. To identify biases associated with these methods, we use unit data from several household surveys and theoretical distributions. We find that poverty and inequality are better estimated when the true distribution is unimodal than multimodal. For unimodal distributions, biases associated with poverty measures are rarely larger than one percentage point. For data from multi-peaked or heavily skewed distributions, the biases are likely to be higher and of unknown sign.

Keywords: grouped data, Lorenz curve, poverty, inequality, income distribution, POVCAL, SimSIP

JEL Classification: C13, C14, C15, C16, D31, D63, I32

Suggested Citation

Minoiu, Camelia and Reddy, Sanjay G., Estimating Poverty and Inequality from Grouped Data: How Well Do Parametric Methods Perform? (June 25, 2008). Journal of Income Distribution, Vol. 18, No. 2, 2009. Available at SSRN: https://ssrn.com/abstract=925969

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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