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

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

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

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

  10. See http://www.freemaptools.com.

  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. 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. 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. 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. 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. 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. 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. 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. We thank an anonymous referee who suggested such exploration.

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

  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 \(Others\).

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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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  1. Department of Economics, University of Piraeus, Piraeus, 185 34, Greece

    Kyriakos Drivas & Claire Economidou

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  1. Kyriakos Drivas
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Correspondence to Kyriakos Drivas.

Appendix

Appendix

Table 8 Summary statistics per state
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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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