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.
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.
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.
- 4.
We hold degrees in biomedical engineering and applied chemistry. In almost all the cases, the patent-paper pair assignment was unambiguous.
- 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.
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.
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.
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.
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.
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.
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.
In the likelihood-ratio test, H1: E(y it ) < var(y it ) is supported.
- 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.
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.
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.
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.
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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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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