University Ownership, Patent Flow, and Signaling Effects of Licensing on Follow-on Research

University Ownership, Patent Flow, and Signaling Effects of Licensing on Follow-on Research Kyriakos Drivas*, Zhen Lei**, and Brian D. Wright*** * Department of Agricultural Economics & Rural Development Agricultural University Of Athens, Iera Odos 75, Athina 10447, Greece ** Department of Energy and Mineral Engineering and the EMS Energy Institute The Pennsylvania State University, 110 Hosler Building, University Park, PA 16802 ***Department of Agricultural and Resource Economics 207 Giannini Hall #3310, University of California at Berkeley, Berkeley, CA 94720 Abstract We know little about the effects of patent licensing because licensing information is notoriously difficult to find. Using publicly available data we construct a novel means of indirectly identifying academic patents that have been licensed to large corporations. We estimate the signaling effects of patent licensing on subsequent innovation. We find that after licensing patents have considerably more citations. The signaling effect of licensing by universities with a large flow of patents is similar for public and private universities. For universities with a small flow of patents, relative to Small public universities, a license of a patent owned by a private university to a large company leads to significantly more citations. The results suggest that licensing of small private university patents sends significantly stronger and more informative signal to out-of-state innovators who, influenced by the perceptions about university technology management practices across different types of universities, might be generally suspicious about the quality and potential of patents owned by small private universities. Keywords: academic patents, licensing, public universities, innovation sequence, patent citations, large entity status, patent renewal. 1

1. Introduction Universities are a critical component of a national innovation system and a key driver of economic growth (Jaffe, 1989; Adams, 1990; Berman, 2011). Since university inventions tend to be embryonic, in need of further research and development, 1 transfer of technology to the private sector is an essential step in a successful academic innovation program. In the United States, the Bayh-Dole Act of 1980 regularized the right of universities to patent and license inventions resulting from federally funded research (Eisenberg 1996). Most public and nonprofit research institutions in the United States have Offices of Technology Transfer (OTT) with responsibility for the patenting and licensing of inventions arising from their research, regardless of sponsorship. This model of university technology transfer in the U.S. has been emulated by many countries around the world (Mowery and Sampat 2005). There has been tremendous interest in, and a vast literature, on the intended and unintended effects of university patenting and licensing (Merrill and Mazza 2011). Much of the discussions have focused on the practice of university OTT and the nature of university ownership. There has been a well-established perception that OTTs differ tremendously across universities. OTTs in leading research universities with large portfolios of patents (hereafter large universities) are more experienced and functioning better than those with small portfolio of patents (hereafter small universities) that have a higher tendency to place a premium on revenue generation. OTTs with small portfolios of patents tend to be under pressure to generate license revenue. In particular, the university s size and experience with respect to technology transfer may influence the quality of patented inventions. Henderson et al (1998) found that since Bayh-Dole Act in 1980 the university patent quality decreased considerably; however, as the study by Mowery et al (2002) showed, the inexperienced universities were the ones that accounted for this decrease in early years. Another equally well established perception is that public universities differ from private universities in their technology transfer and management. Compared to private universities, public universities are considered to be more likely to embed the mission of serving the societal goals and public interests, including promoting local 1 Schacht 2012 p. 4. In a survey by Jensen and Thursby (2001 Table 1 p. 243) university technology transfer managers reported that 48% of inventions are proof of concepts but no prototype and 29% had only a laboratory-scale prototype. 2

and regional development, in their practice in technology transfer and licensing (Belenzon and Schankerman 2009). Public universities are also faced with more constraints in their patenting and licensing from their state governments. Such mission of public interests and constraints from the states might render public universities to have a more balanced practice in patenting and licensing. This paper studies how the market, with such established perceptions about the difference in university patenting and licensing across universities (large versus small universities, and public versus private universities), responds to a license deal where a university patent is licensed to a large company. In our previous study, we find that university licensing appears to be a signpost pointing out commercially relevant innovation pathways that the market follows with successful patented research: forward citations to an exclusively licensed university patent by nonlicensees actually increase following the license. In this study, we investigate whether such a signaling effect associated with university licensing differs across universities. The challenge in answering this research question is the availability of the patent level information on university licensing across universities. Such data is difficult, if not impossible, to collect, since this information is confidential and researchers can at best acquire access to data from one or a handful of universities. We overcome this data challenge by exploiting an interesting piece of information in the US patent system that is publicly available but has thus far not been utilized by researchers to infer the licensing status of university patents: switches from Small Entity Status (SES) to Large Entity Status (LES) in maintenance/renewals of university patents. When patent owners in the United States pay renewal fees at the fourth, eighth and twelfth year after patent grant to maintain the enforceability of a US patent, they need to specify their entity status, either as small entities (including non-profit institutions, small businesses with fewer than 500 employees and individuals) or large entities, and pay different maintenance fees. 2 For an unlicensed university patent, the university typically pays Small Entity Status (SES) fees. When the university licenses a patent to a large corporation, the university loses its small entity status for the particular patent and is obliged to pay LES fees at subsequent renewals of this particular patent. Thus, a switch from SES to LES status in patent 2 The fees of SES are approximately half the fees for LES. 3

renewals reveals whether and when a university patent is licensed to a large corporation. We examine how the market and firms respond to the signal sent by licensing of a university patent by a large company, for four groups of universities: (i) Large public (ii) Large private (iii) Small public and (iv) Small private. We focus on the set of university patents that switch from SES to LES at the eighth year after grant, so that we are able to employ a Dif-in-Difs strategy to compare the response from the market and firms to the signal of large firms licensing of university patents, across the four groups of universities. We measure firm response by forward citations to the licensed patent, as an indicator of follow-on research on the university patent. Our preliminary findings show that patents originating from Large public and Large private universities that switch to LES have approximately the same citation patterns in both the pre- and post-licensing periods. On the contrary, patents originating from small private universities experience a much bigger increase in their forward citations following a license deal to large companies, compared to patents originating from small public universities. The significant difference in forward citations between private small universities and public small universities is primarily due to citations from follow-on innovators that are out of the state where the university is located. There is no significant difference in citations from within-state innovators following the license deal. These results suggest that a licensing to a large company sends a strong signal about the quality and value of a patent owned by private universities with little patenting activity to out-of-state inventors. For innovators within the state, who are more likely to have direct contact with local universities, the signaling effect of licensing is no stronger for private universities with low patent flow. The next section describes the conceptual framework and how the key variables are constructed along with their limitations. Section 3 outlines the empirical strategy and section 4 the data. Discussion of the results follow and finally the paper concludes. 4

2. Concenptual Framework 2.1.Status Switch to LES in Patent Renewals: Indicator of Licensing by Large Firms One novel contribution of our study is to use the information on whether a university pays SES fees or LES fees in patent renewals to determine whether and when the patent is licensed to large companies. To claim SES for a patent, universities and academic research organizations must also certify that they have not assigned, granted, conveyed, or licensed, any rights in the patent to any person, concern, or organization that is not qualified as a person, small business concern, or a nonprofit organization. Thus, when a university patent enters into license agreement, it loses its small entity status, for the particular patent, and is obliged to pay all of the patent s subsequent renewal fees according to Large Entity Status (LES) patent fee schedule (see, Chapter 37 of the Code of Federal Regulations 1.27 (a)), which are typically double the SES fees ( 35 U.S. Code 41 (a),(b) and (d)(1)) and represent a significantly high cost of patent ownership and maintenance to universities. 3 2.2.Data Issues in Employing Switches to LES Although universities often have high incentives to claim SES(unless they have to do otherwise), there may still be significant noise in the data. There are two types of errors that could occur. First, a university may license or transfer rights to a large corporation but not switch the entity status from SES to LES. However, the probability of this error is likely very small given the ramifications it can have for the validity of the patent. Falsely claiming SES status in patent maintenance is considered fraud by the USPTO and can render a patent unenforceable and invalid. 4 Second, a university may pay LES fees for its patents even though it does not have to. The probability of the second noise is higher than the first noise, as the university might act this way just to be on the safe side. We find that only one university pays LES fees during filing for all of its 8 patents. 5 Further, only 6 universities pay LES fees during at for more than 60% of the 57 patents they own. Therefore, in their majority universities are not likely to always claim LES just to be on the safe side. In any case, it still could be the case that a division within the OTT or 3 Recently through the passage of the Leahy-Smith America Invents Act in 2011, there was an addition of a micro-entity status for issue and renewal fees purposes. However, this status does not enter our sample since it came in effect by the USPTO in March 19, 2013 (Federal Register, 2012). 4 see, Chapter 37 of the Code of Federal Regulations 1.27 (h) 5 This university has been excluded from the sample. 5

for some types of patents, the university always pays LES fees even though it is not necessary. While there might be these two types of noise in the legal status during patent renewals, it might not be a concern for our study, as we focus only on patents that pay LES at the eighth year after patent issuance while paid SES fees at the fourth year. For this group, the decision to switch to LES is a conscious one by the university that is clearly reflection of a license deal that occurred between the period of the fourth and eighth year renewals. 2.3.Proxy for Follow-on Research: Patent Citations Following the seminal work of Jaffe, Trajtenberg and Henderson (1993), we employ citations by subsequent patents as indicators of knowledge flows to the citing innovators. Several studies have argued that such forward patent citations, though noisy, are reasonably good proxies for knowledge flows, 6,7 although not without problems. 8 However these citations might include those generated by research devoted to duplicative inventing around the patent, as opposed to more socially useful follow-on research. Assuming any citing patents generated by inventing around a cited patent to share the same field, we can estimate at least an upper bound on citations related to inventing around rather than building on patented inventions. Since we are interested in knowledge flows related to follow-on research towards practical and commercial applications of university patents, patent citations are more appropriate and relevant indicators than paper citations for the purpose of this study. First, forward citations are generated only from subsequent patents that embody research output of practical utility and arise only from successful inventive activity worthy of commitment of thousands of dollars to patenting alone, in addition to the cost of the relevant research. Second, patent applications cite more parsimoniously than journal articles (Jaffe, Trajtenberg and Henderson 1993). 6 In a case study of NASA patents, Jaffe, Fogarty and Banks (1998) found that two thirds of citations are associated with knowledge spillovers; and a survey of patentees by Jaffe, Trajtenberg and Fogarty (2000) found that half of citations are or might be related to knowledge spillovers. 7 We should also note that patent citations, since the work by Trajtenberg (1990), have also been employed as a metric for patent value on the grounds that more influential patents are also more valuable. Harhoff et al (1999) and Bessen (2008) have shown that patent citations are correlated with patent private value, and Hall, Jaffe and Trajtenberg (2005) finds that citations received by a firm s patents are positively correlated with market value of the firm. 8 See Rosell and Agrawal (2009). Also, Roach and Cohen (2011) suggests patent citations are possibly biased downward as a measure of knowledge flows from the public sector (including academia) to private firms. 6

Furthermore, patent citations, relative to paper citations, tend to have greater legal and economic significance, since they are used to define the scope of the rights associated with the citing patent. 9 3. Empirical Strategy Our goal is to estimate the potential differential impact of licensing of university patents to large companies on knowledge diffusion and follow-on research that builds on university inventions, distinguished by size and type of universities. We employ the status switch from SES to LES during the renewals of a focal university patent to determine whether and when the patent is licensed to a large firm. 10 Specifically, US patents need to be renewed (maintained) at 4 th, 8 th and 12 th year after grant by paying maintenance fees. If we observe the legal status of a university patent is SES at one renewal but LES at the next renewal, we can infer that the patent is licensed to a large company at some point during the interval of these two renewals. We employ a Dif-in-Difs strategy to estimate the impacts of licensing of a university patent to a large company on follow-on research, indicated by forward citations to the patent. In order to do so, we need to observe forward citations that the patent receives both in the pre-licensing period and the post-licensing period after the patent is granted. Thus, the paper focus on patents in the data that switch to LES at the eighth year renewal. For this subsample of patents, we know that the licensing to large companies occurred between the fourth year and eighth year after grant. Hence, the pre-licensing period is before the fourth year since grant and the license deal occurs during the interval of the fourth and eighth renewals. We cannot use patents that switch to LES at the 4 th year renewal, for which there is no clear pre-licensing period as we only know that these patent were licensed at some point in time before the 4 th year renewal. Moreover, unfortunately, we cannot employ patents that switch to LES at the 12 th year renewal due to the size of this subsample. Studying this subsample of patents implies that we have to be able to observe their forward citations for at least twelve years after patent issuance. This 9 Patent forward citations will not, on the other hand, reflect the effects of exclusive licensing on follow-on development or commercialization of licensed patents that, as assumed in Kitch s (1997) prospect theory and in many discussions of the Bayh-Dole Act, do not involve further patented innovation. Patent citations might also miss information flows that are not reflected by subsequent patenting but still relevant to subsequent research. However, it is not obvious why such flows would be affected by licensing. 10 In Section 2.3, we discuss in details our method of identifying licensing to large firms. 7

implies that we can consider only patents issued before the mid-nineties. As data will show in the next Section, we will not have enough sample size to perform a robust econometric analysis. 4. Data Our study involves a careful multi-step process of compiling the analytic data from various data sources. First, we identify US patents that are owned by US universities. Second, we collect information on the maintenance/renewal events of the university patents we identified, in particularly on whether and when they change their status that we use to determine their licensing to large companies. Third, we collect detailed information on patent forward citations that the university patents in the data receive during the relevant years after patent grant. Below, we describe how we constructed our dataset and, in particular, provide a discussion over the measurement issues of the novel part of the dataset; i.e. switches from SES to LES as an indication of technology transfer. 4.1.Data Construction Our first step is to identify patents that were assigned to US universities. Our source of data for this first step is the Patent Data Project, sponsored by the National Bureau of Economics Research (NBER). 11 The NBER data identifies and classifies patent assignees to types of entities. We collect patents that are identified as being assigned to a US University, either to a single US university or multiple US universities. Patents that have co-assignees that are not US universities are excluded. Further, we focus on universities that have more than 10 patents issued between 1990-2006, excluding those that might have yet set up a formal technology transfer system. To characterize the universities in the sample, we define a university as Small if it has less than 150 patents between 1990-2006 and as Large otherwise. We also determine whether a university is a Public or Private institution by manually checking them online. As we use forward citations that a focal patent receives after grant as the indicator of the knowledge flow from and follow-on research on the patent, we focus on patents issued between 1990-1999 so that we can observe enough long years after 11 https://sites.google.com/site/patentdataproject/ 8

patent grant. Overall, we consider 13,684 patents assigned to 161 universities. While our sample of interest is a subset of the above, this is our point of departure. Overall, in the baseline sample we have 6,697 patents assigned to 29 Large public universities, 4,138 patents assigned to 21 Large private universities, 1,817 patents assigned to 73 Small public universities and 1,032 patents assigned to 38 Small private universities. Our second step is to collect information on patent maintenance renewal fee events for the patents in our sample. The data source is the Patent Maintenance Fee Event Data from the Google bulk downloads, a dataset created and updated weekly by the USPTO. 12 This dataset includes all renewal events for all utility patents issued by the USPTO filed on or after December 12, 1980, which are subject to the USPTO rule of patent renewal and maintenance fees. For each university patent in the data, we assembled the event codes in the patent maintenance event dataset indicating whether the university has paid SES fees or LES fees at the renewal events of the patent. We accessed the dataset on February 15, 2013, enabling us to study the entire renewal history of all university patents issued until 1999. The Google bulk download maintenance fee dataset does not provide information on the kind of status entities claimed at the time of filing. This information was graciously supplied to us by the Office of the Chief Economist at the USPTO. This final addition to the data enabled us to analyze SES/LES status information over the entire lifecycle of an issued university patent during the timeframe of the study. Out third step is to acquire information on patent forward citations. The data source is from the Fung Institute 13,14, which also contains disambiguated assignee names (with a unique identifier assigned to each assignees including the universities in the sample). With unique patent assignee identifiers, we are able to distinguish between self-citation and other citations, and identify new versus old citers. The data also gather geographic information for patent inventors, allowing us to distinguish citations by inventors within the state, outside the state or outside the country. Our fourth step is to acquire information on variables of patent characteristics. The number of claims (Claims), and technology field (TechnologyField) are extracted 12 http://www.google.com/googlebooks/uspto-patents-maintenance-fees.html 13 https://github.com/funginstitute/downloads 14 We would like to thank Gabe Fierro for his guidance in accessing the data and understanding the dataset architecture. 9

from the NBER. 15 Given the unique identifier for each university, we are able to calculate the number of patents that each assignee has at each point in time (AssigneeActivity). The rest of the control variables namely, all types of citations (BackwardPat, BackwardNonPat), number of inventors (Inventors), application length (ApplicationLength), and grant year (GrantYear), are obtained from the database of Lai et al. (2011). 16 4.2. Validity of Using Switch to LES as indicator of patent licensing To further confirm the validity of using the switch to LES in patent renewals to determine patent licensing, we show that patents that switch to LES are indeed of higher quality than other patents, we consider three groups of patents: (i) patents that pay LES fees at 3.5 years after grant but not earlier, (ii) patents that pay LES fees at 7.5 years after grant but not earlier, (iiii) patents that never pay LES fees. 17,18 Figure 1 displays the average forward citations whereas Figure 2 the cumulative average forward citations for each group. The first observation is that the patents that switch to LES ((i) and (ii)) have considerably higher citations than patents that never pay LES fees. 19 This result supports our argument that a switch to LES indicates an event of patent licensing The validity of our approach is further corroborated if one takes into account that among patents that never pay LES fees there are also a subset that have been licensed to small businesses and startups. 15 We first classify each patent according to its primary US Classification, in one of the 37 technology fields, as defined in Hall et al. (2001). The latter study had categorized US classifications in 36 broad technology fields; however, in the 2006 NBER update, there was an addition of a 37th technology field in the area of Computers and Communication Technologies. 16 Information on the data is provided at http://thedata.harvard.edu/dvn/dv/patent 17 There is also the group of patents that pay LES at 11.5 years but not earlier. However, this is a small group (N=380) and similar to (i) and (ii) in terms of citations. For simplicity we exclude it from the analysis. 18 A 23.5% of patents in the data pay LES at patent filing. This group of patents has more citations than patents that never pay LES fees, but lower citations than patents that switch to LES during patent renewals later. The group of patents paying LES at filing is likely a mix of patents that were eventually were not licensed to large corporations but paid LES fees regardless and patents of patents that were licensed, thus leading to an average citation rate that is between groups i and ii and the group iii. Since it is unclear how this group can be interpreted we exclude it from the rest of the analysis. 19 As a robustness check, we exclude patents that were not renewed to full maturity to ensure that the difference between patents that pay LES fees and those that do not is not driven by patents that were not renewed. Figure A1 of the appendix shows findings Figure 1. 10

4.3.Descriptive Statistics Table 1 describes the propensity of paying LES fees by type (public vs. private) and size of university (big vs. small) for the baseline sample. First of all the propensity for paying LES fees is larger for universities with larger patent flows. In particular, 46% of patents from Large private universities are licensed; for patents from Large public universities the figure is are 41%. For Small universities, the likelihoods are 33% and 36% for public and private, respectively. University type appears to make a small but significant difference in Large universities in that patents by large private universities are slightly more likely to be licensed to large companies. Provided that a switch to LES does not count licensing to small businesses and startups, one potential explanation for the difference between large private and public universities is that the latter might be more committed to local development and therefore more inclined to license to small businesses. Even though we observe the difference in LES between small public and small private, it is small (roughly three percentage units) and significant only at the 10% level. Focusing on the 612 patents that switch to LES at the eighth year renewal, our focal group, we first examine whether indeed this group receives more citations than patents that never switch to LES. Figure 3A shows that for small universities, regardless of the type of ownership, the focal group receives more citations than patents that never switch to LES. Figure 3B shows a similar pattern for Large universities. Figures A.2A and A.2B of the Appendix show the respective graphs by drawing the cumulative forward citations stressing this way more any differences that may arise. The 612 patents in the focal group are associated with 96 universities. As shown in Table 2, patents from large private universities have 2.2 non-self citations per year while large public 2.36; however difference is not statistically significant. On the other hand, examining smaller universities, the difference between citations is considerable. Patents from private universities receive 2.92 citations while patents from public universities 1.46. This difference holds for all geographic types of citations (within and outside the state and outside the country). To check whether this difference in forward citations to patents that switch to LES at the eighth year renewal between small private and small public universities is due to the technological characteristics of patents. We re-visit Figures 3A and 3B which show the citation patterns. For patents by small universities, the pattern in 11

forward citations is parallel and similar between private and public universities until the fourth year after grant, and since then there is a considerable divergence. The start of the divergence coincides with the timing of licensing that occur during the interval between the fourth year and eighth year renewal. The similar pattern in forward citations before the fourth year suggest that the focal patents are similar between small private universities and small public universities.. There is virtually no difference for Large universities until ten years after grant; while there is a divergence after period ten, this difference is not a significant one. 5. Results Table 3 shows the results for Small private versus Small public universities. Column 1 shows the differential effects of licensing to large companies on non-self citations to the license patent. Private takes the value of 1 for periods 4-14 and zero otherwise. The coefficient of the private variable of key interest can be interpreted in the following way: patents assigned to private universities that switch to LES at the eighth year renewal receive exp(.558) - 1~= 75% more citations per period after the fourth year after grant, compared to patents owned by public universities. This result suggests that small private universities patents may be in general perceived as patents of low quality and an unanticipated license deal sends stronger signal about the quality of the licensed patent more than in the case of small public universities. Although overall licensing is associated positively with follow-on patent citations, licensing of patents owned small private universities yields a much greater boost in patent citations than patents owned by small public universities. In Columns 2-4, we decompose non-self patent citations by the location of the first inventor of a citing patent and investigate whether small private universities and small public universities differ in terms of the effects of patent licensing on citations from within state, out of state and out of country, respectively. The results suggest that the difference between small private universities vis-à-vis small public universities is mostly attributable to citations from outside the state. Local inventors may have more direct contact with their local universities, both private and public ones, and have a better understanding about the technology management practice in those universities. Thus a licensing by a local small private university does not draw 12

more attention from local inventors than a licensing by a local small private university. By contrast, inventors, influenced by the established perception about large versus small universities and about private versus public ones, would be more shocking in learning that a patent license deal by an out-of-state small private university than by an out-of-state small public university, both of which is far away and unfamiliar to the inventor. Finally Column 5 shows that there is a significantly greater increase in citations by innovators following patent licensing from small private universities, relative to small large universities. In Table 4, we study the impact of licensing on non-self citations for patents owned by Large universities. Here, the results are distinctively different than those for Small universities. Table 4 Column 1 shows that licensing from large private universities has no statistically different impacts on overall non-self citations than licensing from large public universities. Decomposing non-self citations by location of the first inventor, we don t observe any pronounced differences between large private versus large public universities. See Columns 2-4 of Table 4. Further, there is also no difference if we only consider citations from new citers (Column 5 of Table 4). Tables A1and A2 of the appendix re-estimates the results of Tables 3 and 4, by excluding the fourth year through the eighth year after patent issuance, during which period the patents in the sample are licensed to large companies. This allows us to have a clearly identified post-licensing period and to have a conservative estimate about the effects of patent licensing. Results are qualitatively robust. In further robustness checks, we include the years between the fourth and eighth year after grant but include an additional dummy that takes the value of 1 for private universities from periods 4 through 8 and 0 otherwise. Results hold. Finally, we exclude outliers; i.e. patents that receive more than 100 citations. All these patents are owned by private universities. There is still a statistically significant difference between the small private universities and small public universities. 6. Conclusion Patent licensing is one of the most important channels of transferring knowledge and technology from academia to the industry, and there have been debates on the impacts of university patenting and licensing. However, it has been 13

difficult to collect licensing data at the invention and/or patent level as license deals are confidential in nature; and researchers have thus far, at best, gained access to data in one or a handful of universities to perform empirical analyses. We overcome this problem by using publicly available data on entity status during patent renewals, namely, switch from SES to LES, to infer whether and when university patents are licensed to large corporations. Our descriptive findings show that university patents that have switched from SES to LES are cited considerably more than university patents that do not, lending support to our approach of using such switch as an indicator for university technology transfer. We apply this methodology and examine the impacts of licensing of a university patent to large companies on follow-on research on the licensed patent, distinguishing universities along the two dimensions that have been widely discussed in the literature: university ownership (public vs. private) and patenting volume of university OTTs (large vs. small) that deal with large or small patent portfolios. Public universities, with more commitment to societal goals and public interests and more constraints from their state governments, are believed to have a more-balanced practice in patenting and licensing than private universities. On the other dimension, OTTs with small portfolio of patents are less experienced, and under pressure to generate license revenue, tend to pursue patents of lower quality. We find that for patents owned by small private universities, licensing to large companies sends a stronger signal about the potential of the licensed patents to innovators, in particular to those out of state, than for patents owned by small public universities. These innovators then follow up with successful subsequent innovations that cite the licensed patent. Thus, small private universities patents receive considerably more citations than small public universities patents following licensing to large firms, even though their citation patterns are similar prior to the licensing. Such difference between private versus public universities does not exist for large universities. Our findings come with several caveats. First and foremost, this methodology captures licensing only to large corporations. Therefore, to the extent that licensing to startups and small businesses is different across universities, we cannot shed any light. However, we have observed that the highest quality, in terms of citations, patents are in the groups of those that switch to LES. 14

References Adams, J. "Fundamental Stocks of Knowledge and Productivity Growth." Journal of Political Economy 98(1990): 673-702. Belenzon, S., and M. Schankerman. "University Knowledge Transfer: Private Ownership, Incentives, and Local Development Objectives." Journal of Law & Economics 52, no. 1(2009): 111-144. Berman, E. P. Creating the Market University: How Academic Science Became an Economic Engine. Princeton University Press, 2011. Bessen, J. "The Value of US Patents By Owner and Patent Characteristics." Research Policy 37, no. 5(2008): 932-945. Boldrin, M., and D. K. Levine. "The Case Against Patents." Journal of Economic Perspectives 27, no. 1(2013): 3-22. Eisenberg, R. S. "Public Research and Private Development: Patents and Technology Transfer in Government-Sponsored Research." Virginia Law Review 82, no. 8(1996): 1663-1727. Hall, B. H., A. Jaffe, and M. Trajtenberg. "Market Value and Patent Citations." Rand Journal of Economics 36, no. 1(2005): 16-38. Hall, B., A. Jaffe, and M. Trajtenberg. "The NBER Patents Citations Data File: Lessons, Insights and Methodological Tools." NBER Working Paper No. 8498. (2001). Harhoff, D., F. Narin, F. M. Scherer, and K. Vopel. "Citation Frequency and the Value of Patented Inventions." Review of Economics and Statistics 81, no. 3(1999): 511-515. Henderson, R., A. B. Jaffe, and M. Trajtenberg. "Universities as a Source of Commercial Technology: A Detailed Analysis of University Patenting, 1965-1988." Review of Economics and Statistics 80, no. 1(1998): 119-127. Jaffe, A. "Real Effects of Academic Research." American Economic Review 79(1989): 957-970. Jaffe, A. B., M. S. Fogarty, and B. A. Banks. "Evidence From Patents and Patent Citations on the Impact of NASA and Other Federal Labs on Commercial Innovation." Journal of Industrial Economics 46, no. 2(1998): 183-205. Jaffe, A. B., M. Trajtenberg, and M. S. Fogarty. "The Meaning of Patent Citations: Report on the NBER/Case-Western Reserve Survey of Patentees." National Bureau of Economic Research Working Paper Series No. 7631(2000). Jaffe, A., M. Trajtenberg, and R. Henderson. "Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations." Quarterly Journal of Economics 108, no. 3(1993): 577-598. 15

Jensen, R., and M. Thursby. "Proofs and Prototypes for Sale: The Licensing of University Inventions." American Economic Review 91, no. 1(2001): 240-259. Kitch, E. "The Nature and Function of the Patent System." Journal of Law and Economics 20, no. 2(1977): 265-290. Merrill, S. A., and A. M. Mazza. Managing University Intellectual Property in the Public Interest: National Academies Press, 2011. Mowery, D. C., and B. N. Sampat. "The Bayh-Dole Act of 1980 and University Industry Technology Transfer: A Model for Other OECD Governments?" Journal of Technology Transfer 30(2005): 115-127. Mowery, D. C., B. N. Sampat, and A. A. Ziedonis. "Learning to patent: Institutional experience, learning, and the characteristics of US university patents after the Bayh- Dole Act, 1981-1992." Management Science 48, no. 1(2002): 73-89. Murray, F., and S. Stern. "Do Formal Intellectual Property Rights Hinder the Free Flow of Scientific Knowledge? An Empirical Test of the Anti-Commons Hypothesis." Journal of Economic Behavior & Organization 63, no. 4(2007): 648-687. Roach, M., and W. M. Cohen. "Lens or Prism? A Comparative Assessment of Patent Citations as a Measure of Knowledge Flows from Public Research." (2011). Rosell, C., and A. Agrawal. "Have University Knowledge Flows Narrowed? Evidence From Patent Data." Research Policy 38, no. 8(2009): 1-13. Schacht, W. H. "The Bayh-Dole act: selected issues in patent policy and the commercialization of technology." Library of Congress Washington DC Congressional Research Service DTIC Document(2012). Thompson, N. C., D. C. Mowery, and A. A. Ziedonis. " Does University Licensing Facilitate or Restrict the Flow of Knowledge and Research Inputs among Scientists?" UC Berkeley and University of Oregon, manuscript (2013). Trajtenberg, M. "A Penny for Your Quotes: Patent Citations and the Value of Innovations." RAND Journal of Economics 21, no. 1(1990): 172-187. Williams, H. L. "Intellectual Property Rights and Innovation: Evidence From the Human Genome." Journal of Political Economy, 2013, 121(1), 1-27. 16

Figure 1. Forward citation patterns of university patents (1990-1999). Distinguish by whether they switch entity status. Figure 2. Cumulative Forward citation patterns of university patents (1990-1999). Distinguish by whether they switch entity status. 17

Figure 3A. Forward citation patterns of Large universities patents (1990-1999). Distinguish by whether they switch entity status. Figure 3B. Forward citation patterns of Small universities patents (1990-1999). Distinguish by whether they switch entity status. 18

Table 1. Propensity of switching to LES by university s patenting experience and type of ownership. Variable Large Private Large Public Small Private Small Public LES 46% 41% 33% 36% Switch0 56% 55% 59% 68% Switch4 27% 27% 24% 18% Switch8 12% 10% 12% 10% Switch12 5% 8% 5% 5% Table 2. Summary statistics of citations by type of university. Variable Large Private (n=3,465) Large Public (n=4,140) Small Private (n=600) Small Public (975) Mean Std.Dev Mean Std.Dev Mean Std.Dev Mean Std.Dev NonSelf 2.21 4.39 2.36 4.48 2.92 4.80 1.46 2.29 WithinState 0.28 1.02 0.33 1.23 0.30 0.84 0.08 0.47 OutOfState 1.27 3.12 1.38 2.91 1.88 3.73 0.95 1.80 OutOfCountry 0.67 2.00 0.65 1.80 0.73 2.33 0.42 0.93 NewCitors 1.00 1.74 1.07 1.97 1.24 2.00 0.75 1.29 19

Table 3. Impact of licensing for Small universities. (1) (2) (3) (4) (5) VARIABLES NonSelf WithinState OutOfState OutOfCountry NewCitors private 0.558*** 0.274 0.701** 0.368 0.515** (0.212) (0.456) (0.294) (0.339) (0.201) Constant 1.712*** -1.265* 1.830*** -1.266** 0.652 (0.313) (0.741) (0.393) (0.509) (0.413) Observation s 1,485 1,485 1,485 1,485 1,485 Notes: private takes the value of 1 for periods 4-14 and zero otherwise. As citations are counts, our estimation method is negative binomial. Period dummies, year dummies and patent dummies are included in all specifications. Standard errors are clustered at the patent level. *** p<0.01, ** p<0.05, * p<0.1. Table 4. Impact of licensing Large small universities. (1) (2) (3) (4) (5) VARIABLES NonSelf WithinState OutOfState OutOfCountry NewCitors private -0.0955-0.299-0.0398 0.457-0.0175 (0.0935) (0.214) (0.116) (0.290) (0.109) Constant -0.227* -0.639* -20.32 0.244 0.0574 (0.129) (0.368) (20.05) (0.286) (0.178) Observations 7,605 7,605 7,605 7,605 7,605 Notes: private takes the value of 1 for periods 4-14 and zero otherwise. As citations are counts, our estimation method is negative binomial. Period dummies, year dummies and patent dummies are included in all specifications. Standard errors are clustered at the patent level. *** p<0.01, ** p<0.05, * p<0.1. 20

APPENDIX Table A1. Impact of licensing for Small universities. Exclude periods 4-8. (1) (2) (3) (4) (5) VARIABLES NonSelf WithinState OutOfState OutOfCountry NewCitors private 0.574** 0.875 0.720* 0.264 0.524* (0.271) (0.793) (0.395) (0.388) (0.279) Constant 0.972*** -0.508 0.905*** -2.682*** -0.145 (0.237) (0.559) (0.271) (0.493) (0.332) Observations 990 990 990 990 990 Notes: private takes the value of 1 for periods 9-14 and zero otherwise. As citations are counts, our estimation method is negative binomial. Period dummies, year dummies and patent dummies are included in all specifications. Standard errors are clustered at the patent level. *** p<0.01, ** p<0.05, * p<0.1. Table A2. Impact of licensing for Large universities. Exclude periods 4-8. (1) (2) (3) (4) (5) VARIABLES NonSelf WithinState OutOfState OutOfCountry NewCitors private -0.160-0.411-0.0925 0.356-0.0899 (0.122) (0.295) (0.157) (0.292) (0.117) Constant -0.154-0.502-18.82 0.201 0.0246 (0.139) (0.401) (17.56) (0.280) (0.182) Observations 5,070 5,070 5,070 5,070 5,070 Notes: private takes the value of 1 for periods 9-14 and zero otherwise. As citations are counts, our estimation method is negative binomial. Period dummies, year dummies and patent dummies are included in all specifications. Standard errors are clustered at the patent level. *** p<0.01, ** p<0.05, * p<0.1. 21

Figure A.1. Forward citation patterns of university patents (1990-1999). Distinguish by whether they switch entity status. Consider only patents that are renewed to full maturity. Figure A.2A. Cumulative Forward citation patterns of Large universities patents (1990-1999). Distinguish by whether they switch entity status. 22

Figure A.2B. Cumulative Forward citation patterns of Small universities patents (1990-1999). Distinguish by whether they switch entity status. 23