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Does Patent Strategy Shape the Long-Run Supply of Public Knowledge?

Evidence from Human Genetics

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Book cover Innovation and IPRs in China and India

Part of the book series: China-EU Law Series ((CELS,volume 4))

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Abstract

How do firms’ patent strategies, and the landscape of private property rights they collectively produce, influence the long-run production of public knowledge? Management scholars have paid close attention to the ways in which firms benefit from public knowledge—ideas disclosed through open commons institutions—by using it to generate private knowledge, which is protected by private property institutions such as patents (Cockburn and Henderson 1998; Cohen and Levinthal 1990; Fleming and Sorenson 2004; Powell et al. 1996). However, they have paid scant attention to the converse relationship: the impact of private knowledge on public knowledge production. Instead, legal and policy analyses dominate the study of this relationship (Heller 2008; Heller and Eisenberg 1998; Lessig 2004). This situation speaks to the importance of a management perspective linking policy and legal studies with organizational theory and strategy that can initiate a rich agenda examining the interaction between firm strategy and the institutional foundations of knowledge work.

Republished with permission of Academy of Management, from: Does patent strategy shape the long-run supply of public knowledge? Evidence from human genetics, Kenneth G. Huang and Fiona E. Murray, 52, 6, 2009; permission conveyed through Copyright Clearance Center, Inc.

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Notes

  1. 1.

    The US Supreme Court in its latest unanimous decision (on June 13, 2013) on the patentability on gene, namely, Association for Molecular Pathology vs. Myriad Genetics, Inc., ruled that naturally isolated DNA is not patentable but that synthetic DNA, such as the cDNA for the BRCA1 and BRCA2 genes, is patentable. (See, e.g., http://scopeblog.stanford.edu/2013/06/13/supreme-court-rules-on-myriads-gene-patenting-case/#sthash.9HwWEE9U.dpuf)

  2. 2.

    At least three measures have been used to capture the relationship between public and private knowledge streams: the number of publications cited in patents, or “science linkage” (Narin et al. 1997; Tijssen 2002); the patent and publication portfolios of firms (Gittelman and Kogut 2003; Lim 2000); and coauthorship and copatenting networks (Owen-Smith and Powell 2003; Powell et al. 1996; Zucker et al. 1998).

  3. 3.

    Exceptions to the “refusal to license” arise when the federal government exercises “march-in” rights (insisting on licensing ideas they funded), and when antitrust is binding (Lewis and Yao 1995; MacKie-Mason 2002).

  4. 4.

    We hold degrees in biomedical engineering and applied chemistry. In almost all the cases, the patent-paper pair assignment was unambiguous.

  5. 5.

    These observable characteristics include patent application and grant year, patent grant lag, number of national classes, type of national classes, number of claims, number of inventors, number of patentees, number of cited patent references, number of citing patent references, number of nonpatents cited, and several constructed patent measures based on Trajtenberg et al. (1997).

  6. 6.

    Number of inventors, classes, and nonpatents cited differ slightly: 2.6–3.3, 6.2–6.3, and 479–459 in the sample versus population, respectively. The actual differences in magnitude in all three cases are trivial.

  7. 7.

    First published in Futreal et al. (2004), this census summarizes more than two decades of searching. This census is updated on http://www.sanger.ac.uk/genetics/CGP/Census/.

  8. 8.

    As an additional test, we used two other variations of the citation data: (1) excluding organizational self-citations (defined as citations of papers written by any author from the same organization as the author of the focal paper) and (2) including author and organization self-citations. In both cases, the results remain essentially unchanged: the directions of the coefficients are similar and the differences in their magnitudes are very small.

  9. 9.

    We analyzed both the impact of increase in patent scope and the impact of increase in scope from the mean (or positive deviation). The regression results are similar for both procedures. Similarly, we analyzed both the impact of increases in patent strength and the impact of increases in strength from the mean (or positive deviation), again obtaining similar results with both procedures. We report the latter in Table 6.

  10. 10.

    As genes claimed by more than 10 patents (i.e., 11–20 patents) represent only about 1.7% of the total observations in our sample (or 0.02, rounded up to two decimal places), we have aggregated them into one category.

  11. 11.

    Again, we analyzed both the impact of increases in fragmentation and the impact of increase in fragmentation from the mean fragmentation (or positive deviation). The regression results are similar for both procedures. We report the latter in Table 7.

  12. 12.

    In the likelihood-ratio test, H1: E(y it ) < var(y it ) is supported.

  13. 13.

    Note also that the standard errors from the Poisson regression model can be biased downward, resulting in spuriously large z-values (Cameron and Trivedi 1986). The z-tests may overestimate the significance of the variables in the case of overdispersion in the data (Long 1997). The results of the Hausman (1978) test also supported the use of the fixed effects negative binomial regression model.

  14. 14.

    In our data, the goodness-of-fit test allowed us to reject the Poisson distribution assumption and indicated a zero-inflated distribution, showing further support for the negative binomial regression model.

  15. 15.

    To check and insulate our results against any possibility that the interaction effects in a nonlinear model were not the same as their cross-partial derivatives, we performed an additional regression similar to the one described in model 3 in Table 6 on split samples for each model in Table 7 (except model 5). For example, in model 1, Table 7, we performed the regression in the subsample with public assignee only (7718 observations) and then another regression on the subsample with no public assignees only (5112 observations). We repeated this procedure for the remaining models. These split-sample regressions yielded results that were consistent with those shown in Table 7 and equally robust, and our findings were unchanged across the models.

  16. 16.

    In our analysis of the impact of increase in fragmentation using the measure presented in Eq. 1, the regression result (available upon request) also showed a 7% significant decrease as fragmentation increased. Thus, our findings are consistent.

  17. 17.

    As an additional check against potential collinearity among the fixed effects, we also performed the fully interacted specification on regression models 3–6 in Table 6 and models 1–8 in Table 7. That is, instead of paper fixed effects, paper age fixed effects, and citation year fixed effects, we included paper fixed effects and the full set of paper year–paper age interaction dummies. Results were consistent with those shown in Tables 6 and 7 and equally robust; the coefficients have similar directions and almost identical magnitudes (results available upon request).

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Authors and Affiliations

  1. Division of Engineering and Technology Management & Department of Strategy and Policy, NUS Business School, National University of Singapore, Singapore, Singapore

    Kenneth Guang-Lih Huang

  2. Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA

    Fiona E. Murray

Authors
  1. Kenneth Guang-Lih Huang
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Corresponding author

Correspondence to Kenneth Guang-Lih Huang .

Editor information

Editors and Affiliations

  1. Academia Sinica, Institutum Iurisprudentiae, Taipei, Taiwan

    Kung-Chung Liu

  2. Department of Industrial and Management, Indian Institute of Technology Kanpur, Kanpur, India

    Uday S. Racherla

Appendix: Key Variable Definitions

Appendix: Key Variable Definitions

Citation year characteristics

Name

Definition

Source

Annual cite

Number of citations made by later papers to the (focal) paper previously published in a given year

ISI

Total cite

Total number of citations accruing to a paper over its lifetime

ISI

Citation year

The year in which the forward citation is received

ISI

Paper age

Age of paper when a citation is made

ISI

Paper characteristics

Paper year

Year when paper is published

ISI

Number of authors

Number of authors appearing on the paper

ISI

Number of addresses

Number of unique addresses appearing on paper

ISI

US address

Binary variable (1/0) denoting at least one US address

ISI

Public address

Binary variable (1/0) denoting at least one public address

ISI

Private address

Binary variable (1/0) denoting at least one private address

ISI

Impact factor

Impact factor for journal in which paper is published

ISI/ Journal Citation Report

Patent characteristics

Patent in force

Binary variable (1/0) set to 1 if citation is received in years after patent grant

USPTO

Patent window

Binary variable (1/0) set to 1 if citation is received in year of patent grant

USPTO

Patent grant lag

Number of years between patent application and grant

USPTO

Patent scope

Number of national patent classes

USPTO

Number of claims

Number of claims in the patent

USPTO

Number of inventors

Number of inventors appearing on patent

USPTO

Number of patentees

Number of patentees appearing on patent

USPTO

Public patentee

Binary variable (1/0) denoting at least one public patentee

USPTO

All public patentee

Binary variable (1/0) denoting all public patentee

USPTO

Private patentee

Binary variable (1/0) denoting at least one private patentee

USPTO

All private patentee

Binary variable (1/0) denoting all private patentee

USPTO

US patentee

Binary variable (1/0) denoting at least one US-based patentee

USPTO

Patent-gene characteristics

Gene patents

Count of the number of patents for any given gene

USPTO/ Jensen and Murray (2005)

Gene fragmentation (herfgene)

Herfindahl measure of concentration of ownership for a given gene using assignees on list for gene patents

USPTO/ Jensen and Murray (2005)

OMIM gene

Binary variable (1/0) set to 1 if gene is listed in OMIM

OMIM

Cancer gene

Binary variable (1/0) set to 1 if gene is listed in Wellcome Cancer Gene Census

Wellcome Trust

Disease gene

Binary variable (1/0) set to 1 if gene is OMIM OR Cancer

OMIM/ Wellcome Trust

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Huang, K.GL., Murray, F.E. (2016). Does Patent Strategy Shape the Long-Run Supply of Public Knowledge?. In: Liu, KC., Racherla, U. (eds) Innovation and IPRs in China and India. China-EU Law Series, vol 4. Springer, Singapore. https://doi.org/10.1007/978-981-10-0406-3_4

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