The Annals of Regional Science

, Volume 40, Issue 4, pp 693–721

Pan-European regional income growth and club-convergence

Insights from a spatial econometric perspective
Original Paper


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.

JEL Classification

C21 D30 E13 O18 O52 R11 R15 


  1. Anselin L (1988a) Spatial econometrics: Methods and models. Kluwer, DordrechtGoogle Scholar
  2. Anselin L (1988b) Lagrange multiplier test diagnostics for spatial dependence and spatial heterogeneity. Geogr Anal 20(1):1–18CrossRefGoogle Scholar
  3. Anselin L (1990) Spatial dependence and spatial structural instability in applied regression analysis. J Reg Sci 30(2):185–207CrossRefGoogle Scholar
  4. Anselin L (1999) SpaceStat, a software package for the analysis of spatial data. Version 190 BioMedware, Ann ArborGoogle Scholar
  5. Anselin L, Bera A (1998) Spatial dependence in linear regression models with an introduction to spatial econometrics. In: Ullah A, Giles D (eds) Handbook of applied economic statistics. Marcel Dekker, New York, pp 237–289Google Scholar
  6. Anselin L, Florax RJGM (1995) Small sample properties of tests for spatial dependence in regression models: Some further results. In: Anselin L, Florax RJGM (eds) New directions in spatial econometrics. Methodology, tools and applications. Springer, Berlin Heidelberg New York, pp 21–74Google Scholar
  7. Anselin L, Rey SJ (1991) Properties of tests for spatial dependence in linear regression models. Geogr Anal 23(2):112–131CrossRefGoogle Scholar
  8. Arbia G (1989) Spatial data configuration in statistical analysis of regional economic and related problems. Kluwer, BostonGoogle Scholar
  9. Armstrong HW (1995) Convergence among the regions of the European Union, 1950–1990. Pap Reg Sci 74(2):143–152CrossRefGoogle Scholar
  10. Azariadis C, Drazen A (1990) Threshold externalities in economic development. Q J Econ 105(2):501–526CrossRefGoogle Scholar
  11. Barro RJ, Sala-i-Martin X (1992) Convergence. J Polit Econ 100(2):223–251CrossRefGoogle Scholar
  12. Baumol WJ (1986) Productivity growth, convergence, and welfare: What the long-run data show. Am Econ Rev 76(5):1072–1085Google Scholar
  13. Baumont C, Ertur C, LeGallo J (2003) Spatial convergence clubs and the European regional growth process, 1980–1995. In: Fingleton B (ed) European regional growth. Springer, Berlin Heidelberg New York, pp 131–158Google Scholar
  14. Bernard AB, Durlauf SN (1996) Interpreting tests of the convergence hypothesis. J Econom 71(1-2):161–174CrossRefGoogle Scholar
  15. Boldrin M, Canova F (2001) Europe’s regions. Income disparities and regional policies. Econ Policy 16:207–253CrossRefGoogle Scholar
  16. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Chapman and Hall, New YorkGoogle Scholar
  17. Breusch T, Pagan A (1979) A simple test for heteroskedasticity and random coefficient variation. Econometrica 47:1287–1294CrossRefGoogle Scholar
  18. Burridge P (1980) On the Cliff–Ord test for spatial autocorrelation. J R Stat Soc, Ser B 42(1):107–108Google Scholar
  19. Chatterji M (1992) Convergence clubs and endogenous growth. Oxf Rev Econ Policy 8(4):57–69Google Scholar
  20. Chatterji M, Dewhurst JHL (1996) Convergence clubs and relative economic performance in Great Britain: 1977–1991. Reg Stud 30(1):31–40Google Scholar
  21. Cheshire P, Carbonaro G (1995) Convergence–divergence in regional growth rates: An empty black box? In: Armstrong H, Vickerman R (eds) Convergence and divergence among European regions. Pion, London, pp 89–111Google Scholar
  22. Chow GC (1960) Tests of equality between sets of coefficients in two linear regressions. Econometrica 28(3):591–605CrossRefGoogle Scholar
  23. Cliff A, Ord JK (1972) Testing for spatial autocorrelation among regression residuals. Geogr Anal 4:267–284CrossRefGoogle Scholar
  24. Cliff A, Ord JK (1973) Spatial autocorrelation. Pion, LondonGoogle Scholar
  25. Cliff A, Ord JK (1981) Spatial processes: Models and applications. Pion, LondonGoogle Scholar
  26. Dewhurst JHL, Mutis-Gaitan (1995) Varying speeds of regional GDP per capita convergence in the European Union, 1981–91. In: Armstrong HW, Vickerman RW (eds) Convergence and divergence among European regions. London, Pion, pp 22–39Google Scholar
  27. Durlauf SN, Johnson PA (1995) Multiple regimes and cross-country growth behaviour. J Appl Econ 10(4):365–384Google Scholar
  28. Durlauf SN, Quah DT (1999) The new empirics of economic growth. In: Taylor JB, Woodford M (eds) Handbook of macroeconomics, vol 1. Elsevier, Amsterdam pp 235–308Google Scholar
  29. Ertur C, LeGallo J, LeSage JP (2004) Local versus global convergence in Europe: A Bayesian spatial economic approach. REAL Working Paper, No. 03-T-28. University of Illinois at Urbana-Champaign, Urbana, IllinoisGoogle Scholar
  30. European Commission (1999) 6th Periodic report on the social and economic situation of the regions of the EU. Official Publication Office, BrusselsGoogle Scholar
  31. Fagerberg J, Verspagen B (1996) Heading for divergence? Regional growth in Europe reconsidered. J Common Mark Stud 34(3):431–448CrossRefGoogle Scholar
  32. Fingleton B (1999) Estimates of time to economic convergence: An analysis of regions of the European Union. Int Reg Sci Rev 22(1):5–34CrossRefGoogle Scholar
  33. Fingleton B (2001) Equilibrium and economic growth: Spatial econometric models and simulations. J Reg Sci 41(1):117–147CrossRefGoogle Scholar
  34. Fischer MM, Scherngell T, Jansenberger E (2006) The geography of knowledge spillovers between high-technology firms in Europe. Evidence from a spatial interaction modelling perspective. Geogr Anal 38 (in press)Google Scholar
  35. Florax RJGM, Folmer H, Rey S (2003) Specification searches in spatial econometrics: The relevance of Hendry’s methodology. Reg Sci Urban Econ 33(5):557–579CrossRefGoogle Scholar
  36. Fujita M, Thisse J-F (2002) Economics of agglomeration. Cities, industrial location, and regional growth. Cambridge University Press, CambridgeGoogle Scholar
  37. Galor O (1996) Convergence? Inferences from theoretical models. Econ J 106:1056–1069CrossRefGoogle Scholar
  38. Getis A (2005) Spatial pattern analysis. In: Kempf-Leonard K (ed) Encyclopedia of social measurement, vol 3. Academic Press, Amsterdam, pp 627–632Google Scholar
  39. Getis A, Ord JK (1992) The analysis of spatial association by use of distance statistics. Geogr Anal 24(3):189–206CrossRefGoogle Scholar
  40. Islam N (1995) Growth empirics: A panel data approach. Q J Econ 110(4):1127–1170CrossRefGoogle Scholar
  41. Islam N (2003) What have we learnt from the convergence debate? J Econ Surv 17(3):309–362CrossRefGoogle Scholar
  42. Jarque CM, Bera AK (1987) A test for normality of observations and regression residuals. Int Stat Rev 55(2):163–172CrossRefGoogle Scholar
  43. Kelejian HH, Prucha IR (1999) A generalized moments estimator for the autoregressive parameter in a spatial model. Int Econ Rev 40(2):509–533CrossRefGoogle Scholar
  44. Koenker R, Bassett G (1982) Robust tests for heteroskedasticity based on regression quantiles. Econometrica 50(1):43–61CrossRefGoogle Scholar
  45. LeGallo J, Dall’erba S (2005) Evaluating the temporal and spatial heterogeneity of the European convergence process 1980–1999. Paper presented at the Spatial Econometrics Workshop April 8–9, 2005. Kiel Institute for World Economics, KielGoogle Scholar
  46. López-Bazo E, Vayá E, Mora A, Suriñach J (1999) Regional economic dynamics and convergence in the European Union. Ann Reg Sci 33(3):343–370CrossRefGoogle Scholar
  47. López-Bazo E, Vayá E, Artís M (2004) Regional externalities and growth: Evidence from European regions. J Reg Sci 44(1):43–73CrossRefGoogle Scholar
  48. Magrini S (2004) Regional (di)convergence. In: Henderson J, Thisse J-F (eds) Handbook of regional and urban economics. Elsevier, Amsterdam, pp 2741–2796Google Scholar
  49. Martin R (2001) EMU versus the regions? Regional convergence and divergence in Euroland. J Econ Geogr 1(1):51–80CrossRefGoogle Scholar
  50. Ord JK, Getis A (1995) Local spatial autocorrelation statistics: Distributional issues and an application. Geogr Anal 27(4):286–305CrossRefGoogle Scholar
  51. Quah DT (1996) Empirics for economic growth and convergence. Eur Econ Rev 40(6):1353–1375CrossRefGoogle Scholar
  52. Rey SJ, Montouri BD (1999) US regional income convergence: A spatial econometric perspective. Reg Stud 33(2):143–156CrossRefGoogle Scholar
  53. Sala-i-Martin XX (1996) The classical approach to convergence analysis. Econ J 106:1019–1036CrossRefGoogle Scholar
  54. Solow RM (1956) A contribution to the theory of economic growth. Q J Econ 70(1):65–94CrossRefGoogle Scholar
  55. Upton GJ, Fingleton B (1985) Spatial data analysis by example, vol 1, Point pattern and quantitative data. Wiley, New YorkGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.Institute for Economic Geography and GIScienceVienna University of Economics and Business AdministrationViennaAustria
  2. 2.Deutsche Bundesbank Germany

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