Testing Convergence in Income Distribution*

Authors


  • *

    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.

Abstract

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.

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