Local versus global convergence in Europe: A Bayesian spatial econometric approach

Abstract

Numerous studies have pointed to the econometric problems introduced by heterogeneity in cross-sectional data samples used to explore convergence suggested by neo-classical growth models. We introduce a local concept of convergence along with a Bayesian locally linear spatial estimation method to address these problems. The method allows global and local β-convergence to be viewed in a continuous fashion. Inference regarding global convergence can be treated as a mix-ture distribution arising from local β-convergence estimates from each region in the sample. Taking this approach eliminates the need to specify sub-samples and regimes as well as parameter variation schemes that have been used to model heterogeneity. We illustrate the method using a sample of 138 European regions.