Testing Convergence in Income Distribution*
| Author | Yong Bao,Shatakshee Dhongde |
| Date | 01 April 2009 |
| Published date | 01 April 2009 |
| DOI | http://doi.org/10.1111/j.1468-0084.2008.00514.x |
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©Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2008. Published by Blackwell Publishing Ltd,
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OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 71, 2 (2009) 0305-9049
doi: 10.1111/j.1468-0084.2008.00514.x
PRACTITIONERS’ CORNER
Testing Convergence in Income DistributionÅ
Yong Bao† and Shatakshee Dhongde‡
†Department of Economics, Purdue University, West Lafayette, IN 47907, USA
(e-mail: ybao@purdue.edu)
‡Department of Economics, Rochester Institute of Technology, Rochester, NY 14623, USA
(e-mail: shatakshee.dhongde@rit.edu)
Abstract
The generalized method of moments (GMM) estimator is often used to test for con-
vergence in income distribution in a dynamic panel set-up. We argue that though
consistent, the GMM estimator utilizes the sample observations inefficiently. We
propose a simple ordinary least squares (OLS) estimator with more efficient use of
sample information. Our Monte Carlo study shows that the GMM estimator can be
very imprecise and severely biased in finite samples. In contrast, the OLS estimator
overcomes these shortcomings.
I. Introduction
Most versions of the neoclassical growth model imply convergence in the entire
distribution of income, not just in the mean income level (Benabou, 1996). States,
regions or countries with similar fundamentals and preferences tend to evolve toward
a common distribution of income, with falling (rising) inequality in economies of high
(low) inequality. In the past few years, there has been some work done to test conver-
gence in income distribution, see Benabou (1996), Panizza (2001), Bleaney and
Nishiyama (2003), Ravallion (2003), among others. We note that Panizza (2001)
follows the approach of Caselli, Esquivel and Lefort (1996), who point out that the
incorrect treatment of section-specific effects representing differences in technology
*We would like to thank the editor John Knight as well as an anonymous referee for their comments.
Yong Bao benefited from discussions with Monica Das,Thomas Fullerton, Daniel Henderson, Don Lien and
Melody Lo.
JEL Classification numbers: O15, C23, O18.
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