A spatial panel data version of the knowledge capital model
This paper attempts to analyze the impact of knowledge and knowledge spillovers on regional total factor productivity (TFP) in Europe. Regional patent stocks are used as a proxy for knowledge, and TFP is measured in terms of a superlative index. We follow Fischer et. al (2008) by using a spatial-spillover model and a data set covering 203 regions for six time periods. In order to estimate the impact of knowledge stocks we use a spatial autoregressive model with random effects, which allows for three kinds of spatial dependence: Spatial correlation in the innovations, the exogenous and the endogenous variables. The results suggest that there is a significant positive impact of knowledge on regional TFP levels, and that knowledge spills over to neighboring regions. These spillovers decay exponentially with distance at a rate of 8%. Using Monte Carlo simulations we calculate the distribution of direct and indirect effects. The average elasticity of a region's TFP with respect to its own knowledge stock is 0.2 and highly significant. The average effect of all other regions' TFP is about 50% higher, which confirms that the cross-country externalities are important in the measuring of the impact.
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- Göran Therborn & K.C. Ho, 2009. "Introduction," City, Taylor & Francis Journals, vol. 13(1), pages 53-62, March.
- Fischer, Manfred M. & Scherngell, Thomas & Jansenberger, Eva, 2005.
"The Geography of Knowledge Spillovers between High-Technology Firms in Europe. Evidence from a Spatial Interaction Modelling Perspective,"
77786, University Library of Munich, Germany.
- Manfred M. Fischer & Thomas Scherngell & Eva Jansenberger, 2005. "The Geography of Knowledge Spillovers between High-Technology Firms in Europe - Evidence from a Spatial Interaction Modelling Perspective," ERSA conference papers ersa05p5, European Regional Science Association.
- Doraszelski, Ulrich & Jaumandreu, Jordi, 2006. "R&D and productivity: Estimating production functions when productivity is endogenous," MPRA Paper 1246, University Library of Munich, Germany.
- Doraszelski, Ulrich & Jaumandreu, Jordi, 2008. "R&D and Productivity: Estimating Production Functions when Productivity is Endogenous," CEPR Discussion Papers 6636, C.E.P.R. Discussion Papers.
- Jaumandreu, Jordi & Doraszelski, Ulrich, 2007. "R&D and productivity : estimating production functions when productivity is endogenous," UC3M Working papers. Economics we078652, Universidad Carlos III de Madrid. Departamento de Economía.
- Bernard Fingleton & Julie Le Gallo, 2008. "Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances: Finite sample properties," Papers in Regional Science, Wiley Blackwell, vol. 87(3), pages 319-339, 08.
- J. Barkley Rosser, 2009. "Introduction," Chapters,in: Handbook of Research on Complexity, chapter 1 Edward Elgar Publishing.
- Bernard Fingleton, 2001. "Equilibrium and Economic Growth: Spatial Econometric Models and Simulations," Journal of Regional Science, Wiley Blackwell, vol. 41(1), pages 117-147. Full references (including those not matched with items on IDEAS)
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