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

This study suggests a two-step approach to identifying and interpreting regional convergence clubs in Europe. The first step calculates Bayesian probabilities for various assignments of regions to two clubs using a general stochastic space-time dynamic panel relationship between growth rates and initial levels of income as well as endowments of physical, knowledge and human capital. The second step uses the club assignments in a dynamic space-time panel data model to assess long-run dynamic direct and spillover responses of regional income levels to changes in initial period endowments for clubs that were identified. We find different dynamic partial derivative responses to endowments by regions in the two clubs that appear consistent with low- and high-income regions as clubs.