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A spatial panel data version of the knowledge capital model

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  • Christian Sommeregger
  • Christoph Hammer

    ()

  • Daniel Bekesi
  • Matthias Koch

    ()

Abstract

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.

Suggested Citation

  • Christian Sommeregger & Christoph Hammer & Daniel Bekesi & Matthias Koch, 2011. "A spatial panel data version of the knowledge capital model," ERSA conference papers ersa11p727, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa11p727
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    References listed on IDEAS

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    1. Göran Therborn & K.C. Ho, 2009. "Introduction," City, Taylor & Francis Journals, vol. 13(1), pages 53-62, March.
    2. 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," MPRA Paper 77786, University Library of Munich, Germany.
    3. Doraszelski, Ulrich & Jaumandreu, Jordi, 2006. "R&D and productivity: Estimating production functions when productivity is endogenous," MPRA Paper 1246, University Library of Munich, Germany.
    4. 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, August.
    5. J. Barkley Rosser, 2009. "Introduction," Chapters,in: Handbook of Research on Complexity, chapter 1 Edward Elgar Publishing.
    6. Bernard Fingleton, 2001. "Equilibrium and Economic Growth: Spatial Econometric Models and Simulations," Journal of Regional Science, Wiley Blackwell, vol. 41(1), pages 117-147.
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