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.
Testing Convergence in Income Distribution†
Article first published online: 28 AUG 2008
© Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2008
Oxford Bulletin of Economics and Statistics
Volume 71, Issue 2, pages 295–302, April 2009
How to Cite
Bao, Y. and Dhongde, S. (2009), Testing Convergence in Income Distribution. Oxford Bulletin of Economics and Statistics, 71: 295–302. doi: 10.1111/j.1468-0084.2008.00514.x
- Issue published online: 22 FEB 2009
- Article first published online: 28 AUG 2008
- Final Manuscript Received: May 2008
The generalized method of moments (GMM) estimator is often used to test for convergence 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.