Regional efficiency in generating technological knowledge
There is broad consensus among economists that regionsâ€šÃ„Ã´ competitiveness heavily relies on their ability to produce innovative goods and services (Baumol 1967, Romer 1990, Grossman and Helpman 1991, Barro and Sala-i-Martin 1997, Los and Verspagen 2006). Main drivers of innovation include, but are not limited to, human and cognitive capital (Quelle), R&D expenditures (Quelle), industrial clusters and structure (Quelle) and foreign direct investments (Quelle). Most empirical studies confirm the presumed positive correlation of these inputs and regional innovativeness, measured for example by patent applications. At the same time, regions operating at similar input level show significant differences in the degree of innovativeness. These differences can, to some extent, be explained by the regions efficiency in using their available input factors (Quelle). The presented paper aims, in a first step, to identify this efficiency by using an outlier robust enhancement of the data envelopment analysis (DEA), the so-called order-Å’Â±-frontier analysis (Daouia and Simar 2005, Daraio and Simar 2006), for a sample of more than 200 EU regions (NUTS 2). The findings of this model suggest that the regionsâ€šÃ„Ã´ efficiency is partly affected by a spatial factor. Therefore, the study foresees to decompose regional efficiency into a spatial and non-spatial part by introducing a geoadditive regression analysis based on markov fields. The spatial part reveals differences of the efficiency for greater areas. Regions located in efficient areas, for example, are likely to be efficient as well, since they benefit by the efficiency of neighboring regions. In contrast, the non-spatial effect gives an idea on a regionâ€šÃ„Ã´s efficiency compared to the neighboring and nearby regions.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Jensen, Morten Berg & Johnson, Bjorn & Lorenz, Edward & Lundvall, Bengt Ake, 2007.
"Forms of knowledge and modes of innovation,"
Elsevier, vol. 36(5), pages 680-693, June.
- Morten Jensen & Bjorn Johnson & Edward Lorenz & B.-A. Lundvall, 2007. "Forms of knowledge and modes of innovation," Post-Print halshs-00483642, HAL.
- Ludwig Fahrmeir & Stefan Lang, 2001. "Bayesian inference for generalized additive mixed models based on Markov random field priors," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 201-220.
- Hashimoto, Akihiro & Ishikawa, Hitoshi, 1993. "Using DEA to evaluate the state of society as measured by multiple social indicators," Socio-Economic Planning Sciences, Elsevier, vol. 27(4), pages 257-268, December.
- Daouia, Abdelaati & Simar, Léopold, 2005. "Robust nonparametric estimators of monotone boundaries," Journal of Multivariate Analysis, Elsevier, vol. 96(2), pages 311-331, October.
- Zvi Griliches, 1998. "Patent Statistics as Economic Indicators: A Survey," NBER Chapters,in: R&D and Productivity: The Econometric Evidence, pages 287-343 National Bureau of Economic Research, Inc.
- Griliches, Zvi, 1990. "Patent Statistics as Economic Indicators: A Survey," Journal of Economic Literature, American Economic Association, vol. 28(4), pages 1661-1707, December.
- Zvi Griliches, 1990. "Patent Statistics as Economic Indicators: A Survey," NBER Working Papers 3301, National Bureau of Economic Research, Inc.
- Aragon, Y. & Daouia, A. & Thomas-Agnan, C., 2005. "Nonparametric Frontier Estimation: A Conditional Quantile-Based Approach," Econometric Theory, Cambridge University Press, vol. 21(02), pages 358-389, April.
- Hartman, Linda & Hossjer, Ola, 2008. "Fast kriging of large data sets with Gaussian Markov random fields," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2331-2349, January.
- Romer, Paul M, 1990. "Endogenous Technological Change," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 71-102, October.
- Paul Romer, 1989. "Endogenous Technological Change," NBER Working Papers 3210, National Bureau of Economic Research, Inc.
- Paul M Romer, 1999. "Endogenous Technological Change," Levine's Working Paper Archive 2135, David K. Levine.
- repec:fth:harver:1473 is not listed on IDEAS
- David B. Audretsch, 1995. "Innovation and Industry Evolution," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262011468, July.
- Barro, Robert J & Sala-i-Martin, Xavier, 1997. "Technological Diffusion, Convergence, and Growth," Journal of Economic Growth, Springer, vol. 2(1), pages 1-26, March.
- Barro, Robert J. & Sala-i-Martin, Xavier, 1995. "Technological Diffusion, Convergence and Growth," CEPR Discussion Papers 1255, C.E.P.R. Discussion Papers.
- Sala-i-martin, X. & Barro, R.J., 1995. "technological Diffusion, Convergence and Growth," Papers 735, Yale - Economic Growth Center.
- Robert J. Barro & Xavier Sala-i-Martin, 1995. "Technological Diffusion, Convergence, and Growth," NBER Working Papers 5151, National Bureau of Economic Research, Inc.
- Robert J. Barro & Xavier Sala-i-Martin, 1995. "Technological diffusion, convergence and growth," Economics Working Papers 116, Department of Economics and Business, Universitat Pompeu Fabra.
- Bart Los & Bart Verspagen, 2006. "The Evolution Of Productivity Gaps And Specialization Patterns," Metroeconomica, Wiley Blackwell, vol. 57(4), pages 464-493, November.
- Los, Bart & Verspagen, Bart, 2002. "The evolution of productivity gaps and specialization patterns," CCSO Working Papers 200301, University of Groningen, CCSO Centre for Economic Research.
- Athanassopoulos, Antreas D., 1996. "Assessing the comparative spatial disadvantage (CSD) of regions in the European Union using non-radial data envelopment analysis methods," European Journal of Operational Research, Elsevier, vol. 94(3), pages 439-452, November.
- Charnes, Abraham & Cooper, William W. & Li, Shanling, 1989. "Using data envelopment analysis to evaluate efficiency in the economic performance of Chinese cities," Socio-Economic Planning Sciences, Elsevier, vol. 23(6), pages 325-344.
- Boussofiane, A. & Dyson, R. G. & Thanassoulis, E., 1991. "Applied data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 52(1), pages 1-15, May.
- Li, Xibao, 2009. "China's regional innovation capacity in transition: An empirical approach," Research Policy, Elsevier, vol. 38(2), pages 338-357, March.
- Cazals, Catherine & Florens, Jean-Pierre & Simar, Leopold, 2002. "Nonparametric frontier estimation: a robust approach," Journal of Econometrics, Elsevier, vol. 106(1), pages 1-25, January.
- Bottazzi, Laura & Peri, Giovanni, 2003. "Innovation and spillovers in regions: Evidence from European patent data," European Economic Review, Elsevier, vol. 47(4), pages 687-710, August.
- Laura Bottazzi & Giovanni Peri, "undated". "Innovation and Spillovers in Regions: Evidence from European Patent Data," Working Papers 215, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
- Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
- E. E. Kammann & M. P. Wand, 2003. "Geoadditive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 1-18. Full references (including those not matched with items on IDEAS)
When requesting a correction, please mention this item's handle: RePEc:wiw:wiwrsa:ersa10p1108. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Gunther Maier)
If references are entirely missing, you can add them using this form.