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. This approach produces club assignments that are unconditional on specific parameter estimates. 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. Correctly determining the dynamic partial derivative impacts of changes in initial endowments on regional income levels is an important contribution of our study. The dynamic trajectories of regional income levels over time allow us to draw inferences regarding the timing and magnitude of regional income responses to changes in (physical capital, human capital and knowledge capital) endowments for the clubs that have been identified in the first step. We find different responses to endowments by regions in two clubs that appear consistent with low- and high-income regions as clubs.

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