Is geographic nearness important for trading ideas? Evidence from the US

Abstract

This paper studies the relative geographic scope of two different channels of knowledge flows, a market channel where knowledge diffuses via patent transactions and a non-market channel where knowledge spillovers operate via patent citations. While there is significant work on informal non-market channels of knowledge diffusion, formal market channels of knowledge transfer are less studied, primarily due to the lack of comprehensive data. Using a newly compiled dataset by the Office of the Chief Economist at the United States Patent and Trademark Office of transactions of US issued patents, we are able to provide novel insights on the spread of patent transaction flows across the states of the US. Our findings support that geographic proximity, in terms of distance and border, matters for the spread of knowledge for both channels; however, it is more essential to the operation of market based (patent trades) than to the operation of non-market based (citations) flows. Although both flows are highly localized, the geographic scope of knowledge flows based on citations is larger than that of traded patents. Intra-sectoral flows are also found to be very localized with Mechanical sector to exhibit the most geographically confined knowledge flows, while flows from information technology sectors, i.e., Electronics and Computers, are the most far reached compared to the knowledge flows from the rest of the sectors, both in the US and abroad. Finally, there is no nuance evidence that the importance of distance has declined over time, either at state or national level for both types of flows.

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Notes

  1. 1.

    During the last two decades, the value of patents and other intellectual property assets has surged to become a large part of the wealth portfolio of firms today. In the early 1980’s intangible assets represented 38 % of the portfolios of US firms, while in the mid 1990’s and 2000’s this share rose to 70 % (WIPO 2004). “The economic product of the United States”, as Alan Greenspan stated, has become ”predominantly conceptual” (Stanford Report 2004). Intellectual property forms part of those conceptual assets.

  2. 2.

    A recent study by Serrano (2011) shows that the transfer of patents has become an important source of adopting technology for US firms. The study develops and estimates models of costly technology transfer and renewal in the market for innovation and quantifies possible gains from trading patents as well as costs of adopting technology in the market for patents.

  3. 3.

    For an excellent and up-to-date review of knowledge flows and geography see Autant-Bernard et al. (2013).

  4. 4.

    See for a discussion the study of Criscuolo and Verspagen (2008).

  5. 5.

    The localization of knowledge flows has been considerably tested in the spillover literature, which almost unanimously documents that physical distance does matter and spillovers are constrained geographically (Jaffe et al. 1993; Peri 2005; Thompson 2006; Alcacer and Gittelman 2006; Belenzon and Schankerman 2011).

  6. 6.

    The ordinary least squares (OLS) estimation, in such models, yields inconsistent estimates (Santos Silva and Tenreyro 2006, 2010). For a formal development of negative binomial model, see Hausman et al. (1986).

  7. 7.

    In the US, when entities transfer US issued patents to other entities, they disclose such transactions to the USPTO. The latter are called assignments.

  8. 8.

    There is also a field in the assignment data in which entities can disclose the justification for the transfer. However, the justification, in most cases, is a generic one (i.e. assignment of assignor’s interest). Therefore, it is really difficult to extract information from that field.

  9. 9.

    For instance, in a potential litigation the courts will need to know clearly which firm or organization holds the intellectual property in question.

  10. 10.

    See http://www.freemaptools.com.

  11. 11.

    Patents re-assigned before 2002 have a lag between issue date and execution date of 1.41 years, while patents re-assigned after 2002 have a lag of 3.8 years. The difference is statistically significant.

  12. 12.

    One should be careful when reads the effect of the coefficients of the interaction terms of nonlinear models, like ours. For more detailed discussion, see Ai and Norton (2003).

  13. 13.

    A battery of additional robustness tests are also performed, but not presented here. For instance, we dropped from our sample the very distant states with the most zeros, Alaska and Hawaii. The exclusion of Alaska and Hawaii barely changes the results. Then, we excluded California, which in terms of patent performance could act as an outlier. Results, available upon request, mildly change, but overall conclusions drawn hold. A notable difference is that, the (long) distance effect, due to California, i.e., more patent transaction flows between distant states, disappears. Overall, results do not change in any significant way across different specifications and sub-samples.

  14. 14.

    A large volume of literature has documented the negative impact of geographic distance and borders on the flows of physical trade. See Wolf (2000) for a discussion on the impact of state border and distance on US trade of goods flows.

  15. 15.

    The patent classification in the six technology fields is based on their primary US Classification, which, in turn, relies on Hall et al. (2001).

  16. 16.

    Breschi and Lissoni (2004) apply a social network analysis to derive maps of social connectedness among patent inventors. The probability to observe a citation is positively influenced by social proximity of the inventors, as the authors argue.

  17. 17.

    The origin of citations, i.e., whether are included by inventors or examiners on the patent document may have, however, different implications for the geographic stretched of citation flows. A study by Criscuolo and Verspagen (2008) examines patents from the European Patent Office (EPO) and exploits the distinction the EPO provides about the source of patent citations (since 1979). The authors find that inventor-origin citations are more geographically localized than their examiner-origin counterparts as inventors tend to choose their citations from within a narrower geographical space than examiners do. Consequently, a more detailed analysis on the reach of citation flows and to the extent that they represent actual knowledge and not ‘noise’, it would require the distinction into inventor- versus examiner-origin citations. The USPTO, however, has allowed such distinction only since 2001 (Alcacer and Gittelman 2006; Thompson 2006). Performing such analysis considerably restricts the data set and scope of this paper and, therefore, left for future investigation.

  18. 18.

    Audretsch and Feldman (1996) studied interactions between university-based scientists and biotechnology firms based on disclosures in firms’ initial public offering documents about academic researchers’ roles in the firms.

  19. 19.

    We thank an anonymous referee who suggested such exploration.

  20. 20.

    See Kim et al. (2000), Marcon and Puech (2003), and Holmes and Stevens (2004) for a review.

  21. 21.

    The highest vales of \(LQ\) appear in the state of Pennsylvania for \(Chemical\), California for \(Computers\), Maryland for \(Drugs\), New York for \(Electronics\), Michigan for \(Mechanical\), and Illinois for \(Others\).

References

  1. Ai, C., & Norton, E. C. (2003). Interaction terms in logit and probit models. Economics Letters, 80(1), 123–129.

    Article  Google Scholar 

  2. Alcacer, J., & Gittelman, M. (2006). Patent citations as a measure of knowledge flows: The influence of examiner citations. Review of Economics and Statistics, 88(4), 774–779.

    Article  Google Scholar 

  3. Aldieri, L. (2011). Technological and geographical proximity effects on knowledge spillovers: Evidence from the us patent citations. Economics of Innovation and New Technology, 20(6), 597–607.

    Article  Google Scholar 

  4. Anton, J., & Yao, D. (1994). Expropriation and inventions: Appropriable rents in the absence of property rights. American Economic Review, 84(1), 190–209.

    Google Scholar 

  5. Arrow, K. (1962). Economic welfare and the allocation of resources for invention. In: The rate and direction of inventive activity (pp. 609–625). Princeton: Princeton University Press.

  6. Audretsch, D. B., & Feldman, M. P. (1996). R&d spillovers and the geography of innovation and production. The American Economic Review, 86(3), 630–640.

    Google Scholar 

  7. Autant-Bernard, C., Fadairo, M., & Massard, N. (2013). Knowledge diffusion and innovation policies within the european regions: Challenges based on recent empirical evidence. Research Policy, 42(1), 196–210.

    Article  Google Scholar 

  8. Belenzon, S., & Schankerman, M. (2011). Spreading the word: Geography, policy and knowledge spillovers. CEPR Discussion Paper No. 8002, Forthcoming in Review of Economics and Statistics.

  9. Breschi, S., Lissoni, F. (2004). Knowledge networks from patent data: Methodological issues and research targets. Centre for Knowledge, Internationalization and Technology Studies, University of Bocconi, KITeS Working Papers No. 150.

  10. Burhop, C., & Wolf, N. (2013). The german market for patents during the “second industrialization”, 1884–1913: A gravity approach. Business History Review, 87(1), 69–93.

    Article  Google Scholar 

  11. Coe, D., & Helpman, E. (1995). International R&D spillovers. European Economic Review, 39(5), 859–887.

    Article  Google Scholar 

  12. Criscuolo, P., & Verspagen, B. (2008). Does it matter where patent citations come from? inventor vs. examiner citations in european patents. Research Policy, 37(10), 1892–1908.

    Article  Google Scholar 

  13. Disdier, A., & Head, K. (2008). The puzzling persistence of the distance effect on bilateral trade. Review of Economics and Statistics, 90(1), 37–48.

    Article  Google Scholar 

  14. Ellison, G., & Glaeser, E. (1997). Geographic concentration in U.S. manufacturing industries: A dartboard approach. Journal of Political Economy, 105(5), 889–927.

    Article  Google Scholar 

  15. Fujita, M., & Thisse, J.-F. (2002). Economics of agglomeration. cities, industrial location and regional growth. Cambridge: Cambridge University Press.

    Google Scholar 

  16. Furman, J. L., & Stern, S. (2011). Climbing atop the shoulders of giants: The impact of institutions on cumulative research. American Economic Review, 101(5), 1933–1963.

    Article  Google Scholar 

  17. Gawer, A., & Cusumano, M. (2002). Platform leadership: How intel, palm, cisco and others drive industry innovation. Cambridge, MA: Harvard Business School Press.

    Google Scholar 

  18. Griffith, R., Lee, S., & van Reenen, J. (2011). Is distance dying at last? falling home bias in fixed-effects models of patent citations. Quantitative Economics, 2(2), 211–249.

    Article  Google Scholar 

  19. Hall, B., Jaffe, A., & Trajtenberg, M. (2001). The nber patents citations data file: Lessons, insights and methodological tools. NBER Working Paper No. 8498.

  20. Hausman, J., Hall, B., & Gril, (1986). Econometric models for count data with an application to the patents—R&D relationship. Econometrica, 52(4), 909–938.

    Article  Google Scholar 

  21. Holmes, T., & Stevens, J. (2004). Spatial distribution of economic activities in North America. In J. Vernon Henderson & Jacques-François Thisse (Eds.), Hand book of urban and regional economics: Cities and geography. Amsterdam: Elsevier.

  22. Jaffe, A. B., Trajtenberg, M., & Henderson, R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics, 108(3), 577–598.

    Article  Google Scholar 

  23. Keller, W. (2002). Geographic localization of international technology diffusion. American Economic Review, 92(1), 120–142.

    Article  Google Scholar 

  24. Kim, Y., Barkley, D., & Henry, M. (2000). Industry characteristics linked to establishment concentrations in nonmetropolitan areas. Journal of Regional Science, 40(2), 231–259.

    Article  Google Scholar 

  25. Krugman, P. (1991). Geography and trade. Cambridge: MIT Press.

    Google Scholar 

  26. Lai, R., Amour, A.D., Yu, A., Sun, Y., Torvik, V., Fleming, L. (2011). Disambiguation and co-authorship networks of the U.S. patent inventor database (1975–2010). http://hdl.handle.net/1902.1/15705 UNF:5:9kQaFvALs6qcuoy9Yd8uOw== V1 [Version].

  27. Li, Y. (2009). Borders and distance in knowledge flows: Dying over time or dying with age? Evidence from patent citations. CESifo Working Paper Series No. 2625.

  28. Lucas, R. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–42.

    Article  Google Scholar 

  29. Marcon, E., & Puech, F. (2003). Evaluating the geographic concentration of industries using distance-based methods. Journal of Economic Geography, 3(4), 409–428.

    Article  Google Scholar 

  30. Martin, R., & Sunley, P. (2003). Deconstructing clusters: Chaotic concept or policy panacea? Journal of Economic Geography, 3(1), 5–35.

    Article  Google Scholar 

  31. Marx, M., Strumsky, D., & Fleming, L. (2009). Mobility, skills, and the michigan non-compete experiment. Management Science, 55(6), 875–889.

    Article  Google Scholar 

  32. Mowery, D., & Ziedonis, A. (2001). The geographic reach of market and non-market channels of technology transfer: Comparing citations and licences of university patents. NBER Working Paper No. 8568.

  33. Peri, G. (2005). Determinants of knowledge flows and their effect on innovation. Review of Economics and Statistics, 87, 308–322.

    Article  Google Scholar 

  34. Perkins, R., & Neumayer, E. (2011). Transnational spatial dependencies in the geography of non-resident patent filings. Journal of Economic Geography, 11(1), 37–60.

    Article  Google Scholar 

  35. Portes, R., & Rey, H. (2005). The determinants of cross-border equity flows. Journal of International Economics, 65(2), 269–296.

    Article  Google Scholar 

  36. Portes, R., Rey, H., & Oh, Y. (2001). Information and capital flows: The determinants of transcations in financial assets. European Economic Review, 45(4–6), 783–796.

    Article  Google Scholar 

  37. Rivera-Batiz, L., & Romer, P. (1991). Economic integration and endogenous growth. Quarterly Journal of Economics, 106(2), 227–244.

    Article  Google Scholar 

  38. Romer, P. (1986). Increasing returns and long-run growth. Journal of Political Economy, 94(5), 1000–1037.

    Article  Google Scholar 

  39. Santos Silva, J. M. C., & Tenreyro, S. (2006). The log of gravity. Review of Economics and Statistics, 88(4), 641–658.

    Article  Google Scholar 

  40. Santos Silva, J. M. C., & Tenreyro, S. (2010). On the existence of the maximum likelihood estimates for poisson regression. Economics Letters, 107, 310–312.

    Article  Google Scholar 

  41. Saxenian, A. (1994). Regional advantage: Culture and competition in Silicon Valley and Route 128. Cambridge, MA: Harvard University Press.

    Google Scholar 

  42. Serrano, C. (2011). Estimating the gains from trade in the market for innovation: Evidence from the transfer of patents. NBER Working Paper No. 17304.

  43. Spulber, F. D. (2008). Innovation and international trade in technology. Journal of Economic Theory, 138(1), 1–20.

    Article  Google Scholar 

  44. Stanford Report. (2004). Intellectual property the next big thing, Stanford Report, March 3. Stanford University.

  45. Thompson, P. (2006). Patent citations and the geography of knowledge spillovers: Evidence from inventor- and examiner-added citations. The Review of Economics and Statistics, 88(2), 383–388.

    Article  Google Scholar 

  46. WIPO. (2004). Intellectual property: A power tool for economic growth. World Intellectual Property Organization.

  47. Wolf, H. C. (2000). Intra-national home bias in trade. Review of Economics and Statistics, 82(4), 555–563.

    Article  Google Scholar 

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Acknowledgments

We are grateful to Stuart Graham, Alan Marco, Kirsten Apple, Saurabh Vishnubhakat, Galen Hancock and the entire staff of the Office of the Chief Economist for their assistance and generous support. We also thank Dietmar Harhoff, Karin Hoisl, and seminar participants at the Center for Advanced Management Studies at Ludwig Maximilian University and at the 7th Annual EPIP Conference for their useful insights. Finally, we appreciate valuable comments provided by Sotiris Karkalakos, Zhen Lei, Timothy Simcoe, Brian D. Wright, and two anonymous referees. Kyriakos Drivas gratefully acknowledges financial support from the National Strategic Reference Framework No: SH1_4083. The usual disclaimer applies.

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Correspondence to Kyriakos Drivas.

Appendix

Appendix

Table 8 Summary statistics per state
Table 9 Indices of concentration per state

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Drivas, K., Economidou, C. Is geographic nearness important for trading ideas? Evidence from the US. J Technol Transf 40, 629–662 (2015). https://doi.org/10.1007/s10961-014-9360-0

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Keywords

  • Patent transactions
  • Citations
  • Knowledge flows
  • Localization
  • Distance

JEL Classification

  • F10
  • F23
  • O33