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Journal of the Knowledge Economy

, Volume 5, Issue 3, pp 464–480 | Cite as

Spatial Aspects of Innovation Activity in the US

  • Kyriakos DrivasEmail author
  • Claire Economidou
  • Sotiris Karkalakos
Article

Abstract

This paper studies the effects of spatial concentration of innovation activity on local production of patents in the US. In doing so, we augment the standard knowledge production function with a structure that allows for spatial effects, accounting along with bilateral also for multilateral influences across states. Our findings corroborate with past evidence on the important role of state’s own R&D stock and human capital in producing new inventions. In addition, external knowledge, via spatial interactions, is also a purveyor of local innovation production. The effect is stronger when we consider spatial influences from all states, in particular from the most innovative ones, and to a lesser extent from close neighboring states. Finally, spillovers are more likely to occur between states with similar technological specialization, which share common technological knowledge and pour similar technological effort.

Keywords

Patents Innovation Knowledge production Spatial 

Notes

Acknowledgments

We are grateful to George Dellas and to an anonymous referee for useful comments. Kyriakos Drivas gratefully acknowledges financial support from the National Strategic Reference Framework No: SH1_4083. The usual disclaimer applies.

References

  1. Aghion, P., & Howitt, P. (1997). Endogenous growth theory. Massachusetts: MIT Press.Google Scholar
  2. Anselin, L. (1988). Spatial econometrics, methods and models. Boston: Kluwer Academic.Google Scholar
  3. Anselin, L., Bera, A., Yoon, F.M. (1996). Simple diagnostic tests for spatial dependence. Regional Science and Urban Economics, 26(1), 77–104.CrossRefGoogle Scholar
  4. Arrow, K. (1962). The rate and direction of inventive activity: economic and social factors. In R. Nelson (Ed.), Economic welfare and the allocation of resources for invention (pp. 609–625). Princeton: Princeton University Press.Google Scholar
  5. Baltagi, B., & Lui, L. (2011). Instrumental variable estimation of a spatial autoregressive panel model with random effects. Economics Letters, 111(2), 135–137.CrossRefGoogle Scholar
  6. Blomstrom, L., & Kokko, A. (1998). Multinational corporations and spillovers. Journal of Economic Surveys, 12(3), 247–277.CrossRefGoogle Scholar
  7. Bode, E. (2004). The spatial pattern of localized r&d spillovers: an empirical investigation for germany. Journal of Economic Geography, 4(1), 43–64.CrossRefGoogle Scholar
  8. Bottazzi, L., & Peri, G. (2007). The international dynamics of r&d and innovation in the long run and in the short run. Economic Journal, 117(518), 486–511.CrossRefGoogle Scholar
  9. Branstetter, L.G. (2001). Are knowledge spillovers international or intranational in scope?: Microeconometric evidence from the U.S. and Japan. Journal of International Economics, 53(1), 53–79.CrossRefGoogle Scholar
  10. Cameron, G., Proudman, J., Redding, S. (2005). Technological convergence, r&d, trade and productivity growth. European Economic Review, 49(3), 775–809.CrossRefGoogle Scholar
  11. Coe, D., & Helpman, E. (1995). International R&D spillovers. European Economic Review, 39(5), 859–887.CrossRefGoogle Scholar
  12. Deltas, G., & Karkalakos, S. (2013). Similarity of r&d activities, physical proximity, and r&d spillovers. Regional Science and Urban Economics, 43(1), 124–131.CrossRefGoogle Scholar
  13. Griffith, R., Redding, S., van Reenen, J. (2004). Mapping the two faces of r&d: productivity growth in a panel of oecd industries. Review of Economics and Statistics, 86(4), 883–895.CrossRefGoogle Scholar
  14. Griliches, Z. (1979). Issues in assesing the contribution of r&d to productivity growth. Bell Journal of Economics, 10(1), 92–116.CrossRefGoogle Scholar
  15. Grossman, G., & Helpman, E. (1991). Innovation and growth in the global economy. Massachusetts: MIT Press.Google Scholar
  16. Guellec, D., & van Pottelsberghe de la Potterie, B. (2004). From r&d to productivity growth: do the institutional settings and the source of funds of r&d matter? Oxford Bulletin of Economics and Statistics, 66(3), 353–378.CrossRefGoogle Scholar
  17. Hall, B., & Ziedonis, R. (2001). The patent paradox revisited: an empirical study of patenting in the U.S. semiconductor industry, 1979–1995. RAND Journal of Economics, 32(1), 101–128.CrossRefGoogle Scholar
  18. Jacobs, J. (1969). The economy of cities. New York: Random House.Google Scholar
  19. Jaffe, A.B. (1986). Technological opportunity and spillovers of r&d: evidence from firms’ patents, profits, and market value. American Economic Review, 76(5), 984–1001.Google Scholar
  20. Kelejian, H., & Prucha, I. (1998). A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. Journal of Real Estate Finance and Economics, 17(1), 99–121.CrossRefGoogle Scholar
  21. Keller,W. (2002). Trade and the transmission of technology. Journal of Economic Growth, 7(1), 5–24.CrossRefGoogle Scholar
  22. Lee, L. (2003). Best spatial two-stage least square estimators for a spatial autoregressive model with autoregressive disturbances. Econometric Reviews, 22(4), 307–335.CrossRefGoogle Scholar
  23. Marshall, A. (1890). Principles of economics. London: Macmillan.Google Scholar
  24. Pace, R., & LeSage, J. (2007). Omitted variables biases of ols and spatial lag models. In A. Pez, J. Le Gallo, R. Buliung, S. Dallerba (Eds.), Progress in spatial analysis: theory and computation, and thematic applications. Berlin: Springer.Google Scholar
  25. Pakes, A., & Griliches, Z. (1980). Patents and r&d at the firm level: a first report. Economics Letters, 5(4), 377–381.CrossRefGoogle Scholar
  26. Parent, O., & LeSage, J. (2008). Using the variance structure of the conditional autoregressive specification to model knowledge spillovers. Journal of Applied Econometrics, 23(2), 235–256.CrossRefGoogle Scholar
  27. Peri, G. (2005). Determinants of knowledge flows and their effect on innovation. Review of Economics and Statistics, 87(2), 308–322.CrossRefGoogle Scholar
  28. Redding, S. (1996). The low-skill, low-quality trap: strategic complementarities between human capital and r&d. The Economic Journal, 106(435), 458–470.CrossRefGoogle Scholar
  29. Romer, P. (1986). Increasing returns and long-run growth. Journal of Political Economy, 94(5), 1000–1037.CrossRefGoogle Scholar
  30. Romer, P.M. (1989). Human capital and growth: theory and evidence. National Bureau of Economic Research Working Paper Series No. 3173.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Kyriakos Drivas
    • 1
    Email author
  • Claire Economidou
    • 1
  • Sotiris Karkalakos
    • 1
  1. 1.Department of EconomicsUniversity of PiraeusPiraeusGreece

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