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Estimates of the impact of static and dynamic knowledge spillovers on regional factor productivity

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  • Manfred M. Fischer

    ()

  • James P. LeSage

Abstract

We develop an empirical approach to examine static and dynamic knowledge externalities in the context of a regional total factor productivity relationship. Static externalities refer to current period scale or industry-size effects which have been labeled localization externalities or region-size effects known as agglomeration externalities. Dynamic externalities refer to the relationship between accumulated or prior period knowledge and current levels of innovation, where past learning-by-doing makes innovation positively related to cumulative production over time. Our empirical specification allows for the presence of both static and dynamic externalities, and provides a way to assess the relative magnitude of spillovers associated with spillovers from these two types of knowledge externalities. The magnitude of own-region impacts and other-region (spillovers) can be assessed using scalar summary measures of the own- and cross-partial derivatives from the model. We find evidence supporting the presence of dynamic externalities as well as static, and our estimates suggest that dynamic externalities may have a larger magnitude of impact than static externalities.

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Bibliographic Info

Paper provided by European Regional Science Association in its series ERSA conference papers with number ersa11p31.

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Date of creation: Sep 2011
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Handle: RePEc:wiw:wiwrsa:ersa11p31

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  1. Rachel Griffith & Stephen Redding & John Van Reenen, 2000. "Mapping the Two Faces of R&D: Productivity Growth in a Panel of OECD Industries," CEP Discussion Papers dp0458, Centre for Economic Performance, LSE.
  2. Corinne Autant-Bernard, 2001. "The Geography Of Knowledge Spillovers And Technological Proximity," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 10(4), pages 237-254.
  3. Cem Ertur & Wilfried Koch, 2007. "Growth, technological interdependence and spatial externalities: theory and evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(6), pages 1033-1062.
  4. Rachel Griffith & Rupert Harrison & John Van Reenen, 2004. "How special is the special relationship? Using the impact of US R&D spillovers on UK firms as a test of technology sourcing," IFS Working Papers W04/32, Institute for Fiscal Studies.
  5. Corinne Autant‐Bernard & James P. LeSage, 2011. "Quantifying Knowledge Spillovers Using Spatial Econometric Models," Journal of Regional Science, Wiley Blackwell, vol. 51(3), pages 471-496, 08.
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Cited by:
  1. Raffaele Paci & Emanuela Marrocu, 2013. "Knowledge Assets and Regional Performance," Growth and Change, Gatton College of Business and Economics, University of Kentucky, vol. 44(2), pages 228-257, 06.
  2. Demidova, Olga, 2014. "Spatial-autoregressive model for the two groups of related regions (eastern and western parts of Russia)," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 34(2), pages 19-35.
  3. James LeSage & Yuxue Sheng, 2014. "A spatial econometric panel data examination of endogenous versus exogenous interaction in Chinese province-level patenting," Journal of Geographical Systems, Springer, vol. 16(3), pages 233-262, July.
  4. Brown, Jason & Lambert, Dayton, 2014. "Location decisions of natural gas extraction establishments: a smooth transition count model approach," Research Working Paper RWP 14-5, Federal Reserve Bank of Kansas City.

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