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Regional Growth in Western Europe: An Empirical Exploration of Interactions with Agriculture and Agricultural Policy

  • Roger S. Bivand
  • Rolf J. Brunstad
Part of the Advances in Spatial Science book series (ADVSPATIAL)

Abstract

Much of the analysis of regional growth in Europe in recent years has concentrated on the concept of convergence, whether in the neo-classical model, or in alternative formulations. Empirical work has shifted from more confirmatory nonspatial estimation to the acknowledgement of the importance of spatial factors, including spillovers, and to exploratory spatial data analysis. This has occurred in conjunction with similar work in North America, and has led to a better understanding of the difficulties involved in relying simply on the convergence model without augmentations.

Keywords

Agricultural Policy Geographically Weighted Regression Convergence Model Geographically Weighted Regression Model Spatial Data Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Anselin L (1988) Spatial econometrics: methods and models. Kluwer, DordrechtCrossRefGoogle Scholar
  2. Anselin L, Bera AK (1998) Spatial dependence in linear regression models with an introduction to spatial econometrics. In: Ullah A, Giles DEA (eds) Handbook of applied economic statistics. Marcel Dekker, New York, pp. 237–289Google Scholar
  3. Anselin L, Bera AK, Florax R, Yoon MJ (1996) Simple diagnostic tests for spatialdependence. Regional Science and Urban Economics 26: 77–104CrossRefGoogle Scholar
  4. Arbia G (2001) The role of spatial effects in the empirical analysis of regional concentration. Journal of Geographical Systems 3: 271–281CrossRefGoogle Scholar
  5. Baltagi BH, Li D (2001) LM tests for functional form and spatial error correlation. International Regional Science Review 24: 194–225CrossRefGoogle Scholar
  6. Brunsdon C, Fotheringham AS, Chariton ME (1996) Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis 28: 281–298CrossRefGoogle Scholar
  7. Brunsdon C, Fotheringham AS, and Charlton, ME (1998) Spatial nonstationarity and autoregressive models. Environment and Planning A 30: 957–973CrossRefGoogle Scholar
  8. Brunsdon C, Fotheringham AS, and Charlton, ME (1999) Some notes on parametric signficance tests for geographically weighted regression. Journal of Regional Science 39: 497–524CrossRefGoogle Scholar
  9. Brunsdon C, Fotheringham AS, Charlton ME (2000) Geographically weighted regression as a statistical model. (Unpublished working paper, Department of Geography, University of Newcastle)Google Scholar
  10. Brunstad RJ, Gaasland I, Vårdal E (1999) Agricultural production and the optimal level of landscape preservation. Land Economics 75: 538–546CrossRefGoogle Scholar
  11. Cahill C, Legg W (1989) Estimation of agricultural assistance using producer and consumer subsidy equivalents: theory and practice. OECD Economic Studies 13: 13–43Google Scholar
  12. Cleveland WS (1979) Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association 74, 829–836.CrossRefGoogle Scholar
  13. Cliff AD, Ord JK (1981) Spatial processes. Pion, LondonGoogle Scholar
  14. Colman D (2001) The Common Agricultural Policy. In: Artis M, Nixson F (eds) The economics of the European Union. Oxford University Press, Oxford, pp.97–124Google Scholar
  15. DG REGIO (2001a) Study on the impact of community agricultural policies on economic and social cohesion. (http://www.inforegio.cec.eu.int/wbdoc/docgener/studies/pac_en.htm, accessed 2 Jan 2002)Google Scholar
  16. DG REGIO (2001b) Unity, solidarity, diversity for Europe, its people and its territory: Second report on economic and social cohesion. (http://www.inforegio.cec.eu.int/wbdoc/docoffc/official/reports/contentpdf en.htm, accessed 2 Jan 2002)Google Scholar
  17. Fagerberg J, Verspagen B, Caniels M (1997) Technology, growth and unemployment across European regions. Regional Studies 31: 457–466CrossRefGoogle Scholar
  18. Fingleton B (1999a) Estimates of time to convergence: an analysis of the regions of the European Union. International Regional Science Review 22: 5–34Google Scholar
  19. Fingleton B (1999b) Spurious spatial regression: some Monte Carlo results with a spatial unit root and spatial cointegration. Journal of Regional Science 39: 1–19CrossRefGoogle Scholar
  20. Fingleton B (2000) Spatial econometrics, economic geography, dynamics and equilibrium: a ‘third way’? Environment and Planning A 32: 1481–1498CrossRefGoogle Scholar
  21. Fingleton B (2001) Equilibrium and economic growth: spatial econometric models and simulations. Journal of Regional Science 41: 117–147CrossRefGoogle Scholar
  22. Fingleton B, McCombie JSL (1998) Increasing returns and economic growth: some evidence for manufacturing from the European Union regions. Oxford Economic Papers 50: 89–105CrossRefGoogle Scholar
  23. Fotheringham AS, Brunsdon C, Charlton ME (2000) Quantitative geography: perspectives on spatial data analysis. Sage, LondonGoogle Scholar
  24. de Graff T, Florax RJGM, Nijkamp P, Reggiani A (2001) A general misspecification test for spatial regression models: dependence, heterogeneity, and nonlinearity. Journal of Regional Science 41: 255–276CrossRefGoogle Scholar
  25. Heckelei T, Britz W (2000) Concept and explorative application of an EU-wide, regional agricultural sector model (CAPRI project). (http://www.agp.uni-bonn.de/agpo/rsrch/capri/eaaecapri.pdf, accessed 2 Jan 2002)Google Scholar
  26. Johnston J, DiNardo J (1997) Econometric methods. McGraw-Hill, New YorkGoogle Scholar
  27. Leung Y, Mei CG, Zhang WX (2000a) Statistical tests for spatial non-stationarity based on the geographically weighted regression model. Environment and Planning A 32: 9–32CrossRefGoogle Scholar
  28. Leung Y, Mei CG, Zhang WX (2000b) Testing for spatial autocorrelation among the residuals of the geographically weighted regression. Environment and Planning A 32: 871–890CrossRefGoogle Scholar
  29. López-Bazo E, Vayá E, Mora AJ, Suriñach J (1999) Regional economic dynamics and convergence in the European Union. Annals of Regional Science 33: 343–370 OECD (2000) Eurostat’s EAA97: Main changes and the impact on the EAA-data. OECD Statistics Directorate STD/NA/AGR(2000)10 (http://wwwl.oecd.org/std/AGRMeet2000/docs/naa00_10.pdf, accessed 2 Jan 2002)CrossRefGoogle Scholar
  30. Paci R (1997) More similar and less equal: economic growth in the European regions. Weltwirtschaftliches Archiv 133 : 609–634CrossRefGoogle Scholar
  31. Paci R, Pigliaru F (1999) Is dualism still a source of convergence in Europe? Applied Economics 31: 1423–1436CrossRefGoogle Scholar
  32. Pons-Novell J, Viladecans-Marsal E (1999) Kaldor’s Laws and spatial dependence: evidence for the European regions. Regional Studies 33: 443–451CrossRefGoogle Scholar
  33. Rey SJ (2001) Spatial empirics for economic growth and convergence. Geographical Analysis 33: 195–214CrossRefGoogle Scholar
  34. Rey SJ, Montouri BD (1999) US regional income convergence: a spatial econometric perspective. Regional Studies 33: 143–156CrossRefGoogle Scholar
  35. Tondl G (2001) Regional policy. In: Artis M, Nixson F (eds) The economics of the European Union. Oxford University Press, Oxford, pp. 180–212Google Scholar
  36. Vayá E, López-Bazo E, Artis M (1998) Growth, convergence and (why not?) regional externalities. (Unpublished working paper E98/31, “Anàlisi Quantitativa Regional” Research Group, University of Barcelona)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Roger S. Bivand
    • 1
  • Rolf J. Brunstad
    • 1
  1. 1.Norwegian School of Economics and Business AdministrationNorway

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