Abstract
This paper contributes to the current debate moderated by the European Commission on the territorial dimension of the economic and social cohesion, investigating the role of the CAP in the agricultural convergence process across a sample of 166 EU-15 regions at NUTS2 level from 1995–2005. The empirical study compares results from GWR and OLS models of absolute and conditional β-convergence where total transfers provided by the CAP and Structural funds expenditure related to agriculture, rural development and fishery are the conditioning variables. Furthermore, GWR approach allows detecting the parameters spatial non stationarity and the role of spatial dependence and heterogeneity of regions. The results provide useful insights on important policy sensitive issues difficult to be predicted with the traditional global estimate. They reinforce the prescriptions of the economic geography theory and new economic growth theory on convergence.
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References
Ali, K., Partridge, M. D., & Olfert, M. R. (2007). Can geographically weighted regressions improve regional analysis and policy making? International Regional Science Review, 30(3), 300–329.
Anselin, L. (1988). Spatial econometrics methods and models. Dordrecht: Kluwer.
Arbia, G. (2006). Spatial econometrics statistical foundations and application to regional convergence. New York: Springer.
Barro, R. J., & Sala-i-Martin, X. (1991). Economic growth in a cross-section of countries. Quarterly Journal of Economics, 106(2), 407–433.
Barro, R. J., & Sala-i-Martin, X. (1992). Convergence. Journal of Political Economy, 100, 223–251.
Bivand, R., & Brunstad, R. (2005). Further explorations of interactions between agricultural policy and regional growth in Western Europe: Approaches to nonstationarity in spatial econometrics. 45th Congress of the European Regional Science Association, Amsterdam.
Brunsdon, C., Fotheringham, A. S., & Charlton, M. (1998). Geographically weighted regression. Modelling spatial non stationarity. The Statistician, 47–3, 431–443.
Commission of the European Communities (2008a). Communication from the Commission to the Council, the European Parliament, the Committee of the Regions and the European Economic and Social Committee—Green paper on territorial cohesion—turning territorial diversity into strength. SEC (2008) 2550, COM(2008) 616 final, Brussels.
Commission of the European Communities (2008b). Accompanying the Green Paper on Territorial Cohesion—turning territorial diversity into strength. Commission Staff Working Document, SEC (2008), Brussels.
Délégation Interministérielle à l’Aménagement et la Compétitivité des Territoirs (2008). Reform of the Common Agricultural Policy and the European Cohesion Policy. Conference on Territorial Cohesion and the Future of the Cohesion Policy, Paris.
Döring, T., & Schnellenbach, J. (2004). What do we know about geographical knowledge spillovers and regional growth?—A survey of the literature. Research Notes, Working Paper Series, 14.
ESPON (2005). The territorial impact of CAP and rural development policy. http://www.espon.eu/mmp/online/website/content/projects/243/277/file_1322/fr-2.1.3_revised_31-03-05.pdf.
European Commission—DG Regional Policy (2008). EU cohesion policy 1988–2008: Investing in Europe’s future. http://ec.europa.eu/regional_policy/policy/history/.
Fingleton, B. (ed). (2003a). European regional growth. Berlin: Springer.
Fingleton, B. (2003b). Externalities. Economic geography and spatial econometrics. Conceptual and modelling development. International Regional Science Review, 26, 197–207.
Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships. West Sussex: Wiley.
Fotheringham, A. S., Brunsdon, C., & Charlton, M. E. (2006). Geographically weighted regression. West Sussex: Wiley.
Giudici, P. (2004). Data mining. West Sussex: Wiley.
Hurvich, C. M., Simonoff, J. S., & Tsai, C. L. (1998). Smoothing parameter selection in non-parametric regression using an improved Akaike information criterion. Journal of Real State Society, Series B (Statistic Methodology), 60(2), 271–293.
Kmenta, J. (1986). Elements of econometrics. London: Macmillan.
Kohonen, T. (1997). Self-organizing maps. Berlin: Springer-Verlag.
Pecci, F., & Sassi, M. (2008a). A mixed geographically weighted approach to decoupling and rural development in the EU-15. In L. Bartova, R. M’barek, & T. Ratinger (Eds.), Modelling agricultural and rural development policies. Luxembourg: Office for Official Publications of the European Communities.
Pecci, F., & Sassi, M. (2008b). Agricultural and economic convergence in the EU integration process: Do geographical relationships matter?. XII EAAE Congress on People, Food and Environments: Global trends and European Strategies, Gent (Belgium).
Rey, S. J., & Janikas, M. V. (2005). Regional convergence, inequality and space. Journal of Economic Geography, 5, 155–176.
Sala-i-Martin, X. (1995). The classical approach to convergence analysis. CEPR Papers, 1254.
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I would like to thank Carluccio Bianchi, Carlo Bernini Carri, John Doling and Nick Horsewood for useful discussions and comments.
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Sassi, M. OLS and GWR Approaches to Agricultural Convergence in the EU-15. Int Adv Econ Res 16, 96–108 (2010). https://doi.org/10.1007/s11294-009-9246-3
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DOI: https://doi.org/10.1007/s11294-009-9246-3