A Bayesian Approach to Identifying and Interpreting Regional Convergence Clubs in Europe
Manfred M. Fischer
Vienna University of Economics and Business - Institute for Economic Geography and GIScience, Department of Socioeconomics
James P. LeSage
Texas State University - McCoy College of Business Administration
October 3, 2012
This study suggests a two-step approach to identifying and interpreting regional convergence clubs in Europe. The first step involves identifying the number and composition of clubs using a space-time panel data model for annual income growth rates in conjunction with Bayesian model comparison methods. A second step uses a Bayesian space-time panel data model to assess how changes in the initial endowments of variables (that explain growth) impact regional income levels over time. These dynamic trajectories of changes in regional income levels over time allow us to draw inferences regarding the timing and magnitude of regional income responses to changes in the initial conditions for the clubs that have been identified in the first step. This is in contrast to conventional practice that involves setting the number of clubs ex ante, selecting the composition of the potential convergence clubs according to some a priori criterion (such as initial per capita income thresholds for example), and using cross-sectional growth regressions for estimation and interpretation purposes.
Number of Pages in PDF File: 44
Keywords: dynamic Space-time panel data model, Bayesian model comparison, European regions
JEL Classification: C11, C23, O47, O52working papers series
Date posted: May 21, 2012 ; Last revised: October 4, 2012
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