Kounetas, Kostas and Napolitano, Oreste and Stavropoulos, Spyridon and Burger, Martijn (2018): European Regional Productive Performance under a Metafrontier Framework. The role of patents and human capital on technology gap?
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Abstract
Assessing regional convergence is an important issue both at the national and at the supranational level, such as the level of European regions. Regional convergence and productivity growth are also principles of the European regional policy. This paper studies regional productivity convergence among 232 NUTS-2 European regions for the period 2003-2011. Despite the European regional policies implemented in the last two decades, the technology gap between European regions has only increased. The objective of this paper is to provide new evidence on production efficiency and the technology gap in European regions. We present a two-stage model of regional productive performance using a meta-frontier framework and a PVAR analysis. The main conclusion is that there exist significant differences in productive performance that confirm the North-South division in Europe. Finally, the results from the PVAR model provide robust evidence for the role played by human capital and innovation activity through patent realization in the technology gaps at the regional level in Europe.
Item Type: | MPRA Paper |
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Original Title: | European Regional Productive Performance under a Metafrontier Framework. The role of patents and human capital on technology gap? |
English Title: | European Regional Productive Performance under a Metafrontier Framework. The role of patents and human capital on technology gap? |
Language: | English |
Keywords: | Metafrontier, DEA bootstrap, PVAR, Spillovers, European Regions. |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General D - Microeconomics > D2 - Production and Organizations > D24 - Production ; Cost ; Capital ; Capital, Total Factor, and Multifactor Productivity ; Capacity O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O47 - Empirical Studies of Economic Growth ; Aggregate Productivity ; Cross-Country Output Convergence R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics > R11 - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes |
Item ID: | 88957 |
Depositing User: | Professor Konstantinos Kounetas |
Date Deposited: | 15 Sep 2018 07:19 |
Last Modified: | 02 Oct 2019 17:10 |
References: | Abrigo, M. R. M., Love, I. 2015. Estimation of panel vector autoregression in stata: A package of programs. http://paneldataconference2015.ceu.hu/Program/Michael-Abrigo.pdf Acs, Z. J., Anselin, L., Varga, A. 2002. Patents and innovation counts as measures of regional production of new knowledge. Research policy 31(7), 1069-1085. Aghion, Philippe, and Peter Howitt. Endogenous Growth Theory. Cambridge, MA: MIT Press, 1998. Andrews D., Lu B. 2001. Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models Journal of Econometrics 101 (1), 123-164. Arellano,M., O. Bover Another look at the instrumental-variable estimation of error-components models Journal of Econometrics 68 1995, 29-52. Arrow, A Economic welfare and the allocation of resources for invention. Collected papers of Kenneth J. Arrow, Vol. 5: Production and capital, Harvard University Press, Cambridge, MA (1962, 1985), 104-119. Barro, Robert J., and Xavier Sala-i-Martin. 1995. Economic Growth. New York: McGraw-Hill. Battese, G. E., Rao, D. S. P. O'Donnell, C. J. 2004. A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. Journal of Productivity Analysis 21, 91-103. Boldrin, M., Canova, F. 2001. Inequality and convergence in Europe’s regions: reconsidering European regional policies. Economic policy 16(32), 206-253. Bronzini, R, Piselli P 2009. Determinants of long-run regional productivity: The role of RD, human capital and public infrastructure. Regional Science and Urban Economics 39, 187–199. Canova, F., Ciccarelli, M. 2013. Panel Vector Autoregressive Models: A Survey The views expressed in this article are those of the authors and do not necessarily reflect those of the ECB or the Eurosystem. In VAR Models in Macroeconomics–New Developments and Applications: Essays in Honor of Christopher A. Sims (205-246). Emerald Group Publishing Limited. Cefis, E., L. Orsenigo 2001. The persistence of innovative activities: a cross-countries and cross-sectors comparative analysis Research Policy 30, 1139-1158. Cohen WM, Levinthal DA. 1989. Innovation and learning: the two faces of RD. Economic Journal 99, 569–596. Destefanis S., Sena V. 2005. Public capital and total factor productivity: new evidence from the Italian regions 1970–98 Regional Studies 39, 603-617. Dettori, B., Marrocu, E., Paci, R. 2012. Total factor productivity, intangible assets and spatial dependence in the European regions. Regional Studies 46, 1401–1416. Dosi, G., 1982. Technological paradigms and technological trajectories: A suggested interpretation of the determinants and directions of technical change. Research Policy 11, 147-162. Dyson, R.G., R. Allen, A.S. Camanho, V.V. Podinovski, C.S. Sarrico, E.A. Shale. 2001. Pitfalls and protocols in DEA. European Journal of Operational Research 132, 245-259. Duguet, E. 2003. Are RD subsidies a substitute or a complement to privately funded RD? Evidence from France using propensity score methods for non-experimental data. Enflo, K. Hjertstrand, P., 2009. Relative sources of European regional productivity convergence: a bootstrap frontier approach. Regional Studies 43, 643–659. Faber, J., Hesen, A. B. 2004. Innovation capabilities of European nations: Cross-national analyses of patents and sales of product innovations. Research Policy, 33(2), 193-207. Fagerberg, J., Verspagen, B. Caniëls, M., 1997. Technology, growth and unemployment across European Regions. Regional Studies 31, 457–466. Fagerberg, J B. Verspagen, B., 1996. Heading for divergence? Regional growth in Europe reconsidered Journal of Common Market Studies 34, 431-448. Wilson, P. W., 2008. FEAR: A software package for frontier efficiency analysis with R. Socio-economic planning sciences, 42(4), 247-254. Foddi, M., Usai, S., 2013. Regional knowledge performance in Europe. Growth and Change 44 (2), 258–286. Haas, A.D., Murphy, F.H., 2003. Compensating for non-homogeneity in decision-making units in data envelopment analysis European Journal of Operational Research, 144-154. Halkos, G.E., Tzeremes, G.N., 2012. Modelling the effects of national culture on multinational banks’ performance: A conditional robust nonparametric frontier analysis Economic modelling 28 (1–2) , 515-525. Holtz-Eakin, D., Newey, W., Rosen, H., 1988. Estimating vector autoregressions with panel data. Econometrica, 56, 1371-1395. Judson, R. A., Owen, A. L., 1999. Estimating dynamic panel data models: a guide for macroeconomists. Economics letters 65(1), 9-15. Kitson, M., Martin, R., Tyler, P. 2004. Regional competitiveness: an elusive yet key concept?. Regional Studies 38: 991–999. Kontolaimou, A., Tsekouras, K. 2010, Are the Cooperatives the weakest link in European Banking? A Non-parametric Metafrontier Approach, Journal of Banking and Finance 34(8), 1946-1957. Kontolaimou A., Kounetas K., Mourtos I., Tsekouras K., 2012 Technology gaps in European banking: Put the blame on inputs or outputs? Economic Modelling 29, 1798-1808. Kounetas, K., 2015. Heterogeneous technologies, strategic groups and environmental efficiency technology gaps for European countries Energy Policy 83, 277-287. Lin, W.T. and Chiang, C.Y., 2011. The Impacts of country characteristics upon the value of information technology as measured by productive efficiency. International Journal of Production Economics 132 (1), 13-33. Love, I., Zicchino, L., 2006. Financial development and dynamic investment behavior: Evidence from panel VAR. The Quarterly Review of Economics and Finance 46(2),190-210. Lütkepohl, H. 2005. New introduction to multiple time series analysis. Springer Science Business Media. Lucas, R. E. 1998. On the mechanics of economic development. Econometric Society Monographs 29, 61-70. Matawie, K. M., Assaf, A., 2010. Bayesian and DEA efficiency modelling : an application to hospital food service operations. Applied Statistics 37(6),945-953. Matawie K. M., Assaf A 2008. A metafrontier model to assess regional efficiency differences. Journal of Modelling in Management 3(3), 268-276. Mas, M., Maudos, J., Pérez, F., Uriel, E. 1996. Infrastructures and productivity in the Spanish regions. Regional Studies 30(7), 641-649. Nickell, S., 1981 Biases in Dynamic models with fixed effects. Econometrica 49, 1417–1426. O’Donnell, C.J., Rao, D.S.P., Battese, G.E. 2008. Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical Economics 34, 231–255. O’Donnell, O., E. van Doorslaer, and A. Wagstaff. 2006. “Decomposition of Inequalities in Health and Health Care.” In The Elgar Companion to Health Economics, ed. A. M. Jones. Cheltenham, United Kingdom: Edward Elgar. Oughton, C., Landabaso, M., Morgan, K. 2002. The regional innovation paradox: innovation policy and industrial policy. The Journal of Technology Transfer 27(1), 97-110. Percoco M 2004 Infrastructure and economic efficiency in Italian regions. Network Spatial Economics 4, 361–378. Rao, P., O’Donnell, C., Battese, G., 2003. Metafrontier Functions for the Study of Inter-group Productivity Differences. CEPA Working Paper Series No. 01/2003, School of Economics, University of New England, Armidale. Romer, P.,1990. Endogenous technological change Journal of Political Economy, 98 S71-S102. Rosenberg, N., 1963. Technological change in the machine tool industry, 1840–1910. The Journal of Economic History 23, 414–443. Samoilenko, S., Osei-Bryson , K. 2010. Determining source of relative inefficiency in heterogenous samples:Methodology using Cluster Analysis, DEA and Neural Networks. European Journal of Operation Research 206 (10), 479-487. Simar, L., Wilson, P. W. 2007. Estimation and inference in two-stage, semi-parametric models of production processes. Journal of econometrics 136(1), 31-64. Simar, L., P. Wilson 2000. Statistical inference in nonparametric frontier models: the state of the art Journal of Productivity Analysis, 13, 49-78. Simar, L., and P. W. Wilson. 1999b. "Of Course We Can Bootstrap DEA Scores! But Does It Mean Anything? Logic Trumps Wishful Thinking." Journal of Productivity Analysis 11, 93-97. Simar, L. and P.W. Wilson 2008, Statistical Inference in Nonparametric Frontier Models: recent Developments and Perspectives, in The Measurement of Productive Efficiency, 2nd Edition, Harold Fried, C.A.Knox Lovell and Shelton Schmidt, editors, Oxford University Press, 2008. Tzeremes NG 2014. The effect of human capital on countries' economic efficiency. Economics Letters, 124(1), 127-131. Tsekouras, K., Chatzistamoulou, N., Kounetas, K., Broadstock, D. C. 2016. Spillovers, path dependence and the productive performance of European transportation sectors in the presence of technology heterogeneity. Technological Forecasting and Social Change 102, 261-274. Tsekouras, K., Chatzistamoulou, N., Kounetas, K. 2017. Productive performance, technology heterogeneity and hierarchies: Who to compare with whom. International Journal of Production Economics 193, 465-478. Tsekouras, K., Papathanassopoulos, F., Kounetas, K., Pappous, G. 2010. Does the adoption of new technology boost productive efficiency in the public sector? The case of ICUs system. International Journal of Production Economics 128(1), 427-433. Wang, Q., Zhao, Z., Zhou, P., Zhou, D. 2013. Energy efficiency and production technology heterogeneity in China: a meta-frontier DEA approach. Economic Modelling 35, 283-289. Zabala-Iturriagagoitia, J.M., P. Voigt, A. Gutierrez-Gracia, F. Jimenez-Saez 2007. Regional innovation systems: how to assess performance Regional Studies 41 (5), 661-672 |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/88957 |