The Annals of Regional Science

, Volume 44, Issue 3, pp 467–491 | Cite as

Neoclassical theory versus new economic geography: competing explanations of cross-regional variation in economic development

Original Paper

Abstract

This paper uses data for 255 NUTS-2 European regions over the period 1995–2003 to test the relative explanatory performance of two important rival theories seeking to explain variations in the level of economic development across regions, namely the neoclassical model originating from the work of Solow (Q J Econ 70:65–94, 1956) and the so-called Wage equation, which is one of a set of simultaneous equations consistent with the short-run equilibrium of new economic geography (NEG) theory, as described by Fujita et al. (The spatial economy. Cities, regions, and international trade. The MIT Press, Cambridge, 1999). The rivals are non-nested, so that testing is accomplished both by fitting the reduced form models individually and by simply combining the two rivals to create a composite model in an attempt to identify the dominant theory. We use different estimators for the resulting panel data model to account variously for interregional heterogeneity, endogeneity, and temporal and spatial dependence, including maximum likelihood with and without fixed effects, two stage least squares and feasible generalised spatial two stage least squares plus GMM; also most of these models embody a spatial autoregressive error process. These show that the estimated NEG model parameters correspond to theoretical expectation, whereas the parameter estimates derived from the neoclassical model reduced form are sometimes insignificant or take on counterintuitive signs. This casts doubt on the appropriateness of neoclassical theory as a basis for explaining cross-regional variation in economic development in Europe, whereas NEG theory seems to hold in the face of competition from its rival.

JEL Classification

C33 O10 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abdel-Rahman H, Fujita M (1990) Product variety, Marshallian externalities and city size. J Reg Sci 30: 165–183CrossRefGoogle Scholar
  2. Baltagi BH (2005) Econometric analysis of panel data, 3rd edn. Wiley, ChichesterGoogle Scholar
  3. Baltagi BH, Bresson G, Pirotte A (2006) Panel unit root tests and spatial dependence. Center for Policy Research, working paper no. 88, Maxwell School of Citizenship and Public Affairs, Syracuse UniversityGoogle Scholar
  4. Bond SR, Hoeffler A, Temple J (2001) GMM estimation of empirical growth models. Centre for Economic Policy Research (CEPRS), Discussion paper no. 3048Google Scholar
  5. Chang Y (2002) Nonlinear IV panel unit root tests with cross-sectional dependency. J Econom 110: 261–292CrossRefGoogle Scholar
  6. Choi I (2002) Combination unit root tests for cross-sectionally correlated panels. In: Corbae D, Durlauf SN, Hansen BE (eds) Econometric theory and practice. Cambridge University Press, Cambridge, pp 311–333Google Scholar
  7. Davis DR, Weinstein DE (2003) Market access, economic geography and comparative advantage: An empirical test. J Int Econ 59: 1–23CrossRefGoogle Scholar
  8. Dixit AK, Stiglitz JE (1977) Monopolistic competition and optimum product diversity. Am Econ Rev 67(3): 297–308Google Scholar
  9. Elhorst JP (2003) Specification and estimation of spatial data models. Int Reg Sci Rev 26: 244–268CrossRefGoogle Scholar
  10. Feldstein M, Horioka C (1980) Domestic saving and international capital flows. Econ J 90(358): 314–329CrossRefGoogle Scholar
  11. Fingleton B (2005) Towards applied geographical economics: modelling relative wage rates, incomes and prices for the regions of Great Britain. Appl Econ 37: 2417–2428CrossRefGoogle Scholar
  12. Fingleton B (2006) The new economic geography versus urban economics: an evaluation using local wage rates in Great Britain. Oxf Econ Pap 58: 501–530CrossRefGoogle Scholar
  13. Fingleton B (2007) New economic geography: some preliminaries. In: Fingleton B (eds) New directions in economic geography. Edward Elgar, Cheltenham, pp 11–52Google Scholar
  14. Fingleton B (2008a) A generalized method of moments estimator for a spatial panel model with an endogenous spatial lag and spatial moving average errors. Spatial Econ Anal 3: 27–44CrossRefGoogle Scholar
  15. Fingleton B (2008b) Competing models of global dynamics: evidence from panel models with spatially correlated error components. Econ Model (available online from 7 November, 2007) (forthcoming)Google Scholar
  16. Fingleton B, McCann P (2007) Sinking the iceberg? On the treatment of transport costs in new economic geography. In: Fingleton B (eds) New directions in economic geography. Edward Elgar, Cheltenham, pp 168–203Google Scholar
  17. Fingleton B, Le Gallo J (2008) Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances: finite sample properties. Pap Reg Sci 87: 319–339CrossRefGoogle Scholar
  18. Fischer MM, Stirböck C (2006) Pan-European regional income growth and club-convergence. Ann Reg Sci 40(4): 693–721CrossRefGoogle Scholar
  19. Fujita M, Krugman P, Venables AJ (1999) The spatial economy. Cities, regions, and international trade. The MIT Press, CambridgeGoogle Scholar
  20. Head K, Mayer T (2006) Regional wage and employment responses to market potential in the EU. Reg Sci Urban Econ 36: 573–594CrossRefGoogle Scholar
  21. Jones CI (1997) Convergence revisited. J Econ Growth 2: 131–153CrossRefGoogle Scholar
  22. Kapoor M, Kelejian HH, Prucha IR (2007) Panel data models with spatially correlated error components. J Econom 140: 97–130CrossRefGoogle Scholar
  23. Kennedy P (2003) A guide to econometrics, 5th edn. Blackwell, OxfordGoogle Scholar
  24. Koch W (2008) Development accounting with spatial effects, spatial economic analysis 3(3) (forthcoming)Google Scholar
  25. LeSage JP, Fischer MM (2009) Spatial growth regressions: model specification, estimation and interpretation. Spatial Econ Anal 4 (forthcoming)Google Scholar
  26. Mankiw NE, Romer D, Weil DN (1992) A contribution to the empirics of economic growth. Q J Econ 107(2): 407–437CrossRefGoogle Scholar
  27. McCann P (2005) Transport costs and new economic geography. J Econ Geogr 6: 1–14CrossRefGoogle Scholar
  28. Mion G (2004) Spatial externalities and empirical analysis: the case of Italy. J Urban Econ 56: 97–118CrossRefGoogle Scholar
  29. Pesaran MH (2007) A simple panel unit root test in the presence of cross-sectional dependence. J Appl Econom 22: 265–312CrossRefGoogle Scholar
  30. Phillips PCB, Sul D (2003) Dynamic panel estimation and homogeneity testing under cross-section dependence. Econom J 6: 217–259CrossRefGoogle Scholar
  31. Redding S, Venables AJ (2004) Economic geography and international inequality. J Int Econ 62: 53–82CrossRefGoogle Scholar
  32. Rivera-Batiz F (1988) Increasing returns, monopolistic competition, and agglomeration economies in consumption and production. Reg Sci Urban Econ 18(1): 125–153CrossRefGoogle Scholar
  33. Roos M (2001) Wages and market potential in Germany. Jahrbuch für Regionalwissenschaft 21: 171–195Google Scholar
  34. Solow RM (1956) A contribution to the theory of economic growth. Q J Econ 70: 65–94CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Department of EconomicsUniversity of StrathclydeGlasgowScotland, UK
  2. 2.Institute for Economic Geography and GIScienceVienna University of Economics and BAViennaAustria

Personalised recommendations