A Bayesian approach to identifying and interpreting regional convergence clubs in Europe

Manfred M. Fischer, James P. LeSage

    Publication: Working/Discussion Paper

    Abstract

    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. (authors' abstract)
    Original languageEnglish
    Place of PublicationVienna
    PublisherWU Vienna University of Economics and Business
    Publication statusPublished - 1 Oct 2012

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