This paper adopts a spatial econometric perspective to analyse regional convergence of per capita income in Europe in 1995 to 2000 and, moreover, relaxes the assumption of a single steady-state growth path which appears to be out of tune with reality of empirical dynamics. The two-club spatial error convergence model with groupwise heteroskedasticity is found to be most appropriate for the data at hand. Two empirical key findings are worthwhile to note. The first is that the data provide much support for unconditional ß-convergence in Europe. The second is that the usual convergence conclusions hold. But they do so for reasons that are not revealed by the classical test equation that is typical in mainstream economics literature. (authors' abstract)

Club-convergence analysis provides a more realistic and detailed picture about regional income growth than traditional convergence analysis. This paper presents a spatial econometric framework for club-convergence testing that relates the concept of club-convergence to the notion of spatial heterogeneity. The study provides evidence for the club-convergence hypothesis in cross-regional growth dynamics from a pan-European perspective. The conclusions are threefold. First, we reject the standard Barro-style regression model which underlies most empirical work on regional income convergence in favour of a two regime [club] alternative in which different regional economies obey different linear regressions when grouped by means of Getis and Ord's local clustering technique. Second, the results point to a heterogeneous pattern in the pan-European convergence process. Heterogeneity appears in both the convergence rate and the steady-state level. But, third, the study also reveals that spatial error dependence introduces an important bias in our perception of the club-convergence and shows that neglect of this bias would give rise to misleading conclusions.