Heterogeneous Convergence

Young, Andrew T.; Higgins, Matthew John; Levy, Daniel

doi:10.2139/ssrn.947843

Heterogeneous Convergence

Emory Law and Economics Research Paper No. 07-2

8 Pages Posted: 29 Nov 2006 Last revised: 29 Oct 2015

See all articles by Andrew T. Young

Andrew T. Young

Texas Tech University - Rawls College of Business

Matthew John Higgins

University of Utah - Department of Entrepreneurship & Strategy; National Bureau of Economic Research (NBER); Max Planck Law Network - Max Planck Institute for Innovation and Competition

Daniel Levy

Bar-Ilan University - Department of Economics; Emory University - Department of Economics; Rimini Center for Economic Analysis

There are 2 versions of this paper

Date Written: March 1, 2013

Abstract

We use U.S. county-level data containing 1,921 cross-sectional observations and up to 29 conditioning variables to estimate heterogeneity in convergence rates across 22 individual U.S. states. Applying GMM estimation, we find significant heterogeneity in the state-level convergence rates. For example, while the average convergence rate is 9.2 percent, the California estimate is 19.9 percent and the New York estimate is 3.3 percent. The estimated convergence rates are essentially uncorrelated with the income levels.

Keywords: Economic Growth, Conditional Convergence, County Level Data, Heterogeneity

JEL Classification: O40, O11, O18, O51, R11, H50, H70

Suggested Citation: Suggested Citation

Young, Andrew T. and Higgins, Matthew John and Levy, Daniel, Heterogeneous Convergence (March 1, 2013). Emory Law and Economics Research Paper No. 07-2, Available at SSRN: https://ssrn.com/abstract=947843 or http://dx.doi.org/10.2139/ssrn.947843