When do universities own their patents?
An explorative study of patent characteristics and organizational
determinants in Germany
Anja Schoen1, Guido Buenstorf2
1
Corresponding author: TUM School of Management, Technische Universität München,
Email: schoen@wi.tum.de, Phone: +49 (89) 289 25746, Fax: +49 (89) 289 25742
2
Institute of Economics and INCHER-Kassel, University of Kassel,
Email: buenstorf@uni-kassel.de, Phone: +49 (561) 804 2506; Fax: + 49 (561) 804 2501
University-invented patents are often not owned by the university. Empirical knowledge
about factors affecting the ownership of university patents is limited and mainly focuses on
patent characteristics. To study how the ownership of German university patents (2006-2007)
relates to patent and university-level performance indicators, we matched PatStat data with a
register of German professors. Four to five years after the abolition of the professors’
privilege, universities on average owned more than half of all patents on faculty inventions.
General and technical universities differ in how patent ownership relates to patent and
university characteristics.
Keywords: university-owned patents; university-invented patents; technical universities;
organizations.
JEL codes: L31, O31, O34
1
1
Introduction
After decades of sometimes dramatic changes, technology transfer to the private sector is now
virtually taken for granted as a “third mission” of universities (Etzkowitz and Leydesdorff,
2000). Even though technology transfer operates through diverse channels (Cohen et al.,
2002), recent attention has focused on university patenting. Policy initiatives sought to
enhance transfer performance by re-allocating intellectual property rights in faculty inventions
to the university. In the same spirit, university-owned patents are frequently used as a key
indicator for the effectiveness and efficiency of technology transfer in policy-oriented
literature and discussions (e.g., OECD, 2003).
Prior empirical research (e.g., Saragossi and van Pottelsberghe de la Potterie, 2003; Lissoni
et al., 2009; Sterzi, 2012) shows, however, that a substantial share of university patents (i.e.,
patents (co-)invented by university faculty and staff) are not university-owned but have other
owners (mostly firms or public research organizations). In line with the literature, the latter
will be referred to as university-invented patents in what follows. Even though the share of
university-owned patents increased after the legal reforms, university-invented patents still
account for the majority of all university patents in Europe, e.g. in Denmark or France
(Lissoni et al., 2009; Della Malva et al., 2013). There is also evidence that differences in
ownership are associated with differences in patent and university characteristics (cf. Section
2 below).
In the present paper, we extend the prior work by studying patent- and university-level
determinants of patent ownership in more detail. Better understanding how university
characteristics relate to patent ownership is important to assess the effects of the legal reforms
on technology transfer outcomes. Are more research-intensive universities with better transfer
facilities more likely to own the inventions made by their faculty? Or are marginal and less
transfer-experienced universities perhaps pursuing their ownership rights most aggressively?
2
To answer these questions, we systematically match professor names with inventor names in
German patent applications filed in 2006-07, several years after the professors’ privilege was
abolished. In these years, German universities owned the majority of new university patent
applications. Individual universities differ substantially in their shares of university-owned
patents. Our findings are moreover suggestive of differences in the patenting regimes of
technical universities as compared to “traditional” research universities.
The paper is structured as follows. We next discuss extant literature related to university
patenting. Section 3 provides a brief introduction to the institutional context of public research
in Germany. Section 4 discusses the data. Empirical results are presented in Section 5 and
discussed in Section 6.
2
Literature review
Recent policy initiatives in several European countries, including Germany, aimed to improve
university-to-industry technology transfer based on intellectual property (IP) (Geuna and
Rossi, 2011). These reforms were motivated by the conjecture that European universities
often generate relevant findings but are not active enough in technology transfer, especially
when compared to their U.S. counterparts – a phenomenon often referred to as the “European
paradox” (OECD, 2003; Mowery and Sampat, 2005; Dosi et al., 2006). However, there was
little evidence to substantiate the allegations of insufficient European transfer activities, and
recent empirical studies have challenged these claims (e.g., Saragossi and van Pottelsberghe
de la Potterie 2003; Crespi et al., 2006; Lissoni et al., 2008, 2009; Della Malva et al., 2013;
Sterzi, 2013). These studies show that university-invented patents are more prevalent in
Europe than in the U.S., while the overall share of university patents among all patents is
similar. Only counting university-owned patents then gives a misleading impression.
Historical differences in the legal framework governing university patents are the main
cause of the observed U.S.–(continental) Europe discrepancies in ownership patterns. In the
3
U.S., the Bayh-Dole Act of 1980 gave universities blanket permission to patent inventions
that their faculty and staff had made in research sponsored by federal agencies (Mowery et al.,
2001). Compared to the earlier situation, this implied that IP had shifted closer to the inventor.
The Bayh-Dole Act has nonetheless remained controversial to date. Critics have proposed
switching to inventor ownership and letting faculty inventors decide themselves what to do
with their inventions (Kenney and Patton 2009, 2011). Somewhat ironically, inventor
ownership is exactly what Germany gave up in favor of a Bayh-Dole-like regime when the
professors’ privilege was abolished in 2002.
Prior research has started to investigate how the legislative changes affected ownership
patterns of university patents. For Germany, Frietsch et al. (2011) and von Proff et al. (2012)
find that patents filed after 2002 are much more likely to be university-owned. Similar results
were obtained by Della Malva et al. (2013) who analyzed the effect of the French Innovation
Act of 1999. Interestingly, Lissoni et al. (in this issue) observe an increase in the share of
university-owned patents after the new introduction of the professors’ privilege in Italy. They
show, however, that this was due to greater autonomy given to Italian universities in regard to
IP policies rather than the professors’ privilege.
These results suggest that effects of changes in intellectual property rights may not be as
straightforward as they seem. They resonate with evidence that the establishment of
technology transfer offices (TTOs), federal subsidies for new patent exploitation agencies,
and the increasing focus on entrepreneurial activities also played important roles in the rising
importance of university-owned patents in Germany (Geuna and Rossi, 2011; von Proff et al.,
2012). This raises the issue of how patent- and university-level characteristics are related to
the ownership patterns of university patents. Only a small number of prior studies have
focused on this question.
Thursby et al. (2009) study the ownership of university patents in the U.S. They
hypothesize that if university-invented patents resulted from faculty consulting, they should
4
be more incremental (measured by numbers of backward citations) than university-owned
patents. In contrast, inventors strategically circumventing the TTO should preferably do this
with more important (measured by numbers of claims and forward citations) and thus
presumably more profitable patents. University-owned patents are indeed found to be less
incremental. Results regarding importance are less conclusive. University-owned patents
receive less forward citations, but this is significant only in models without technology fixed
effects.
Recent work using European data challenges some of the results obtained by Thursby et al.
(2009). Lissoni et al. (in this issue) find that backward citations are not systematically
associated with ownership types of Italian university patents. Sterzi (2012) finds that firmowned academic patents by UK universities have more forward citations than universityowned patents, at least in the short term. For Germany, Czarnitzki et al. (2012) suggest firmowned patents to be more complex and to have higher blocking potential.
Turning to university-level determinants, Thursby et al. (2009) find a higher likelihood of
public universities to own faculty patents. University ownership is more likely when faculty
inventors receive larger shares of royalties in case of successful commercialization. Della
Malva et al. (2013) find that university size and type affect patent ownership in France. The
size effect diminishes, however, when the existence of a TTO is controlled for. Prior results
on the relevance of TTOs are inconclusive. For France, a positive effect of TTOs on the
likelihood of university ownership has been identified, while the presence of a TTO
apparently has no effect in Italy (Della Malva et al., 2013; Lissoni et al., in this issue).
In a nutshell, the studies reviewed in this section shed some light on how patent
characteristics as well as of university-level variables affect ownership patterns of university
patents. However, determinants such as universities’ research and entrepreneurial
performance as well as their organizational identity are largely neglected in the discussion.
The present paper addresses this gap in the context of German universities. Knowledge about
5
these determinants will help to better understand differences between universities in
technology transfer and to assess the impact of the recent legal reforms.
3
The institutional context in Germany
Institutions of higher education in Germany can be distinguished into research universities
following the Humboldtian ideal of unity of research and teaching and Fachhochschulen
(universities of applied science or polytechnics) that primarily focus on teaching. German
public research is moreover characterized by strong non-university public research
organizations (PROs). In what follows, we concentrate on research universities. These can be
further differentiated into general research universities (henceforth GUs) and Technische
Universitäten (technical universities; henceforth TUs).
GUs encompass old universities such as Heidelberg (founded in 1386), and also newer
schools (there was a wave of new university founding in the 1970s) that nonetheless adhere to
the same ideal of curiosity-driven research and teaching. Within the German public research
system, GUs are strongholds of the humanities and social sciences. Many GUs also excel in
the sciences and in medical research. GUs are traditionally less focused on engineering and
have historically been reluctant to form strong ties to the private sector. In contrast, TUs were
originally founded to contribute to technological innovation; mostly by educating engineers
(König, 1996). Present-day TUs feature a broad spectrum of disciplines, even though there
still is a focus on the sciences and on engineering. They are research-intensive and tend to
pursue use-inspired basic research in Pasteur’s Quadrant (Stokes, 1997), often in close
interaction with private-sector partners. These differences may affect how patent/university
characteristics are associated to patent ownership at both types of universities. We probe into
this possibility in our empirical analysis.
Almost without exception, German universities (of both types) are publicly owned. Since
the mid-1970s they have faced substantial policy pressure to prove their “usefulness”. As part
6
of this pressure, universities were increasingly expected to foster technology transfer. This
culminated in the abolishment of the professors’ privilege (inventor ownership of patents),
which many observers saw as an obstacle to effective technology transfer (cf. von Proff et al.,
2012), in February 2002. Since then, German universities (GUs as well as TUs) have assumed
IP in the “service” inventions made by faculty and staff as part of their university
employment. As an incentive for faculty to disclose their inventions to the university, the law
uniformly allocates 30% of the gross revenue to the inventors. If universities fail to file
patents on disclosed inventions, property rights fall back to the inventors. Faculty also retain
the IP in “free” inventions outside their work for the university (e.g., those resulting from
consulting). Moreover, if research is sponsored fully or in part by external parties, these can
negotiate the ownership of the patent rights. Accordingly, the German ownership system of
university patents has been categorized as a “hybrid” system (Geuna and Rossi, 2011).
The legal reform was part of what the German federal government called its
Verwertungsoffensive (“exploitation offensive”). In this initiative policy makers also
supported the establishment of 21 regional Patentverwertungsagenturen (patent exploitation
agencies; PVAs) to enhance the technology transfer process. Each PVA assists the TTOs of
the regional universities in their patenting and commercialization activities (BMBF, 2001).
Already some years before, in 1998, the federal government had begun to support
entrepreneurial activities out of universities in the EXIST program. Since then, many
universities have stepped up their support of faculty and student entrepreneurship, often with
substantial financial support from EXIST.
The Verwertungsoffensive was initiated against the backdrop of tight public budgets, which
have also affected the funding of public universities. As institutional funding has declined in
real terms, universities have been forced to increase the share of program-based funding in
their budgets (Schubert and Schmoch, 2010). In addition, policy makers have made
substantial efforts to increase the university-level competition for academic reputation. In
7
2005 the federal government started the Exzellenzinitiative (“initiative for excellence”), a
paradigm change in that German policy makers for the first time openly endorsed uneven
university funding based on competitive principles (Kehm, 2006). In the first two rounds
funded with 1.9 billion euros for the time period 2006 to 2012, a national contest was
organized in which universities submitted proposals for graduate schools, topical or
disciplinary centers of excellence, and also strategic concepts for the entire university. Among
all submitted proposals, 39 graduate schools, 37 centers of excellence and nine universitylevel strategies were chosen for funding.
4
Data
4.1
Sample
University patenting leaves an imperfect paper trail. University-owned patents are easy to find
in patent databases. Identifying university-invented patents is more difficult, as it requires the
identification of university researchers among all inventors of patented inventions. Over the
past years, researchers have tried to solve this problem for a number of European countries
(e.g., Saragossi and van Pottelsberghe de la Potterie, 2003; Lissoni et al., 2008, 2009; von
Proff et al., 2012; Sterzi, 2012). However, the systematic identification of university-invented
patents requires access to a complete listing of potential academic inventors and engagement
in the “name game”, a difficult task especially in large countries.
Prior research on Germany has worked around these problems by searching for the
professor title in patent databases (e.g., Schmoch, 2007; Czarnitzki et al., 2007, 2012; von
Proff et al., 2012). It is not clear how many university-invented patents are missed by this
procedure, and how many non-university-invented patents erroneously enter the sample (for
example, because high-level R&D staff of industrial firms hold honorary professorships and
use their title in patent applications). Furthermore, the overall patent portfolio of individual
universities cannot be identified reliably, which frustrates comparisons between universities.
8
In this study, we adopt the approach previously used for other European countries and
systematically match professor names with inventor names in German patent data. Our dataset
is based on patent applications recorded in the PatStat database (version April 2010) provided
by the EPO. We matched information from PatStat to a list of professor names based on
Kürschners Gelehrtenkalender, a commercial directory of university researchers published
regularly since 1923. The matching and filtering algorithms are described in detail in S1.
Our final sample includes 665 professors listed as inventors in 2006-07 patent
applications.1 Of these, 60.8% have a single patent, 20% have two patents, 9.3% have three
patents, and 10.1% are listed on four or more patents in the two years under investigation.
29.9% of all patenting professors are active in engineering, 26.3% in medicine, 11.1% in
biochemistry, 12.5% in chemistry, 9.9% in physics, 5.1% in physical and technical chemistry,
and 3.9% in pharmacy. In total, the dataset contains 1,167 priority patents applied for in 200607.2 Due to various exclusions (e.g., honorary professors; see S1 for more details), we
consider this number as a conservative, lower-bound estimate of the patenting activities of
German university professors in these years. Of the 1,167 patents in the dataset, 691 (59.2%)
are university-owned and 476 (40.8%) are university-invented.3
4.2
Variables
Dependent variable: Our econometric analysis focuses on ownership types rather than
absolute numbers of university patents, which reflect differences in university size or research
specializations. The dependent variable takes the value one when a patent is (co-)owned by
1
We restricted the dataset to the period 2006-07 to be able to control for forward citations and to make sure that
the sample falls in a period in which the legal reform is implemented.
2
133 professors affiliated with the same university have co-invented 91 patents. To avoid double counting we
keep only distinct university-patent pairs.
3
A closer look at ownership types within the category of university-owned and -invented patents is given in S2.
9
the university employing the identified inventor. This broad empirical conception of
university ownership reflects the theoretical focus of the present study, as we are primarily
interested in the characteristics of patents involving the university as an owner (indicating that
the university has exercised its ownership rights).
Independent variables:
Patent characteristics: We use a number of standard measures of patent characteristics
retrieved from PatStat. In addition to dummy variables distinguishing broad technological
fields (instruments, chemistry, mechanical engineering – electrical engineering and others is
the omitted reference category), the following indicators have been calculated for each patent:
EP/PCT: A dummy variable denotes patents filed at the EPO or under PCT.
Patent scope: Following Lerner (1994), the number of distinct industry classes (at the 4digit level) is used to proxy for the complexity of an invention (Harhoff and Wagner, 2009).4
Number of backward citations: We use the number of backward references as a measure of
how incremental a patent is. The larger the number of backward citations the more
incremental (less basic) is a patent (Thursby and Thursby, 2011).5
Number of forward citations (over a three-year window): Forward citations are commonly
used as a proxy of patent importance. We extracted three years’ forward citations from the
PatStat Version 2011. As our sample relates to only two years (2006-07), truncation problems
are of limited relevance.
4
The number of claims (total or per backward citation) could alternatively be used (Harhoff et al., 2003), but it
reflects idiosyncratic decisions by the author of the patent application (Reitzig, 2004).
5
The share of X and Y backward citations has been used as a complementary proxy for the inventive step of the
invention (Czarnitzki et al., 2012). As a robustness check we used the number of X and Y citations instead of the
overall number of backward citations. The same was done for forward citations, for which the share of X and Y
citations indicates a patent’s blocking potential (cf. Hall and Harhoff, 2001; Guellec et al., 2008). In both cases
this led to similar results as those reported here (available upon request).
10
Proximity to basic research: The number of references to non-patent literature, mostly
articles in scientific journals, is used as a proxy for patents’ proximity to basic research.
Number of inventors: The number of inventors is used as a proxy for the size of the
research team.
University characteristics: A first set of variables captures structural characteristics of the
inventors’ employer universities. In particular, we include the (ln) number of students (as
provided by the Federal Office of Statistics), (ln) university age (2006), as well as a dummy
indicating the presence of a medical school.
Several variables help characterize universities in terms of their technology transfer
experience and orientation.
Universities’ patenting experience: We use the (ln) number of applications filed by the
university prior to 2006 as a proxy of universities’ and TTOs’ experience in patenting, which
may give rise to learning effects (Baldini et al., 2006). The number is based on our own
calculations using PatStat.
Age of patent exploitation agencies (PVAen): We obtained detailed information from a
web survey about the age of the regional PVA responsible for the respective university and
formed three categories of PVA age: <5 years (omitted reference category), 5-10 years, and
above. Reorganizations were treated as continuations of the existing organizations, but the
clock was set at zero for newly established PVAs taking over patent management from other
agencies.
University-industry ties: We use the share of industrial research funding per total research
funding, provided by the Federal Office of Statistics, as a proxy for the intensity of universityindustry relations.
Finally, a variety of indicators is used to measure universities’ performance in different
realms as well as their responsiveness to the policy environment.
11
DFG funding: The 2005-2007 university rankings of Deutsche Forschungsgemeinschaft
(DFG) were used to construct a proxy of universities’ strength in basic research. DFG funding
is allocated on the basis of researcher and proposal quality. Both individuals and consortia of
varying size can apply for DFG funds. We use the inverse rank to have larger values indicate
better performance.
Exzellenzinitiative: A dummy variable indicates universities that successfully competed in
one of the three lines of the 2006 round of this initiative. Compared to DFG funding,
successful participation in the Exzellenzinitiative required more effort at the organizational
level, whereas individual researchers were limited in their potential contribution to success.
Accordingly, this variable is interpreted not only as a measure of research performance, but
also of universities’ policy responsiveness.
Publication ranking: A third quality measure is based on the SIR (Scimago Institutions
Rankings) world report 2012 which analyzes the period 2006-2010. Specifically, we use the
indicator for high quality publications6 as a proxy for scientific impact of a university. As
opposed to the Exzellenzinitiative, organization-level activities only indirectly affect the
publication performance of universities’ researchers.
Entrepreneurial orientation: Yet another ranking is utilized to proxy for the
entrepreneurial orientation of a university. Schmude and Heumann (2007) analyzed eight
dimensions of entrepreneurial orientation for 65 universities in Germany. A dummy variable
denotes the top 10 universities in this ranking.7
“Ratio of publications that an institution publishes in the most influential scholarly journals of the world; those
ranked in the first quartile (25%) in their categories as ordered by SCImago Journal Rank SJR indicator”
(SCImago Research Group, 2012).
6
7
Ranking outcomes are available for all but two (publication ranking) and five (entrepreneurial orientation)
universities.
12
5
Results
5.1
Descriptive statistics
Individual universities (NTU=17, NGU=44) strongly differ in the extent to which they own
the patents resulting from the inventions made by their faculty (cf. S3). This supports the
proposition that university characteristics are associated with patent ownership.8 Table 1
provides descriptive statistics (tests on the mean differences are reported in S4). Consistent
with their organizational histories, TUs seem to be more transfer-oriented than GUs. Prior to
2006, the average TU had more than twice as many university-owned patents as the GU
average. GU patents on average have broader scope and are more basic (measured by the
number of backward citations and non-patent-literature references). Interestingly, the share of
industrial third party funding is higher for GUs than for TUs, which in part reflects better
performance of TUs in the DFG ranking (implying a smaller share of industrial funding for
any given level). TUs outperform their counterparts in two performance indicators
(Exzellenzinitiative and entrepreneurial orientation), while GUs perform better in the
publication ranking.
[Insert Table 1 around here]
Turning to differences between university-owned and -invented patents, we find that the
latter, on average, receive more forward citations, cite less non-patent references, and list
more inventors. Universities with more patenting experience, as well as larger and older ones,
8
One worrisome explanation for this difference would be that firm ownership results from successful sales of
university-owned patents. Patent sales can be identified through commercial databases identifying the original
applicant of the patent. In our sample 21 university-invented patents were sold until 2011. However, only two of
these patents were originally applied for by a university.
13
seem to have a higher likelihood of university ownership. The same holds for universities that
successfully participated in the Exzellenzinitiative.
S5-7 provide the correlation matrix for the full sample as well as for the two university
types. For GUs correlations between the independent variables are generally low, except for
the correlation between DFG ranking and success in the Exzellenzinitiative. In contrast, the
performance indicators for TUs are quite strongly correlated, which induces us to enter these
indicators separately in the econometric model.
5.2
Probit estimations of university ownership
To analyze determinants of patent ownership, we estimated Probit models specified at the
level of individual patents, comparing university-owned patents to all other forms of
ownership for the full sample as well as for each university type. We report marginal effects
at the mean. Unobserved heterogeneity across groups prevents the simple comparison of
effects in nonlinear models (Allison, 1999; Hoetker, 2007). Long (2009) proposes using
predicted probabilities as they are not scaled by residual variation. To see how the key
variables in our analysis affect ownership patterns of university patents, and to what extent
this differs between TUs and GUs, we calculate predicted probabilities for individual
variations of each variable, keeping all other variables at their mean. (Detailed results can be
found in S8.)
[Insert Table 2 around here]
Models 1-4 (Table 2) differ in the included measures of university performance, but are
identical otherwise. In the following we will first discuss the results for the full sample before
we turn to differences between GUs and TUs.
14
With respect to patent characteristics, we find for the full sample that forward citations and
proximity to basic research enter significantly in the regression. The results indicate that less
important and more “scientific” patents are more likely to be university owned. Patents with
more inventors are less likely to be university-owned, possibly because the likelihood of nonacademic co-inventors increases with the overall number of inventors. Turning to universitylevel determinants, university age and size are positively associated with the likelihood of
university ownership, whereas the presence of a medical school and PVA age reduce this
likelihood. We find weak evidence (significant only in Model 2) that the more dependent the
university is on industry funding (university-industry ties), the lower the likelihood of
university ownership. Contrary to expectations, universities’ patenting experience seems to
have no influence on the likelihood of university ownership.
In Models 1-4, various dimensions of university performance are alternatively proxied by
(inverse) ranks in DFG funding (Model 1), successful participation in the Exzellenzinitiative
(Model 2), publication quality according to SRI publication ranking (Model 3), and
entrepreneurial orientation as measured by Schmude and Heumann (2007) (Model 4). The
strongest association is obtained between university ownership and success in the
Exzellenzinitiative. This association is significantly positive for the full sample. In contrast,
we find a negative association between patent ownership and performance in the publication
rankings (Model 3). Ranks in DFG funding and entrepreneurial orientation do not enter the
regression significantly.
The variable denoting patents from technical universities is insignificant in three of the
four models, indicating no systematic difference in the likelihood of university ownership
between TUs and GUs after controlling for patent- or organization-related factors. It is
conceivable, though, that these factors differently affect the likelihood of university
ownership for TUs and GUs. To test this conjecture, we split the sample by university type
and re-estimate Models 1-4.
15
In all models we find that university-owned patents from GUs have fewer backward and
forward citations. (Marginal effects are significant at the 5% or 10% level.) This result
indicates that, in line with the findings of Thursby et al. (2009), GU-owned patents are on
average more basic and less important than GU-invented patents. No such differences are
found for TU patents. At the same time, only university-owned patents from GUs cite more
non-patent literature than those with other owners, suggesting they have a lower
commercialization potential. University size and age are associated with a higher likelihood of
university ownership for GUs only. For TUs, university age is associated negatively with
patent ownership, whereas patenting experience increases their likelihood of university
ownership.
To explore these differences in more detail, we turn to the predicted probabilities (Long,
2009) presented in S8. Predicted probabilities suggest that, holding all other variables
constant, TUs have a higher likelihood of owning patents. The predicted differences between
university types are often substantial even though, similar to the findings for the indicator of
TUs in the full sample, they are mostly not or only weakly significant. We furthermore find
some differences in how patent and university characteristics relate to the likelihood of
patents being owned by either type of university. GU patents that are cited once have a 3.5
percentage points lower predicted probability of being university-owned than non-cited ones.
Raising the number of backward citations from zero to five likewise lowers the likelihood of
university ownership by 4 percentage points for GU patents. In contrast, for TU patents,
increasing numbers of forward or backward citations leave the predicted probabilities
virtually unchanged. Ceteris paribus, the difference in predicted probabilities between both
types of universities turns (weakly) significant when at least one patent cites the patent in
question or the patent itself cites at least five documents. The difference in predicted
probabilities with regard to proximity to basic research turns out to be (weakly) significant,
while differences in university age are not. Moreover, the (significant) difference in predicted
16
probabilities between both types of universities gets larger as they accumulate patenting
experience.
Turning to performance indicators, a significant positive association of university
ownership with successful participation in the Exzellenzinitiative is found only for the TUs,
whereas publication performance is negatively related to university ownership only for the
GUs. The difference in predicted probabilities is significant and larger for successful
participants in the Exzellenzinitiative. For TUs, success in the Exzellenzinitiative is associated
with an about five percentage point higher predicted probability of university ownership.
Similar but less reliable results are obtained for DFG funding. In contrast, with regard to
publication rankings the difference in predicted probabilities is not significant and varies little
for changes in the quality of universities’ publication output. Further differences between the
university types emerge from Model 4 analyzing support for entrepreneurship. Point estimates
of the marginal effects differ in sign, and the (weakly) significant difference in predicted
probabilities is larger for universities performing well in the ranking. The latter is due both to
an increased likelihood of university ownership among successful TUs and a decreased
likelihood in the case of successful GUs.
6
Discussion and conclusion
This paper presented an analysis of ownership patterns of German university patents using
data from the time period after the professors’ privilege was abolished in 2002. We found that
about 60% of all patents in the sample are university-owned, which indicates relatively quick
adaptation to the new legal framework. We also found that the share of university-owned
patents varies strongly across universities, suggesting the importance of university-level
factors in shaping patterns of patent ownership. This brings us back to the question asked in
the introduction: Are large shares of university-owned patents characteristic of capable
universities with well-established transfer facilities?
17
Our results indicate that the answer may vary between (types of) universities. While we
find little evidence of systematic differences in the propensity of the two types of universities
to own inventions made by their faculty and staff, TUs and GUs differ in how organizational
and patent characteristics relate to ownership patterns. Similar to what Thursby et al. (2009)
found for the U.S., GU-owned patents seem to be more basic (as well as “scientific”) and at
the same time less important than GU-invented patents, indicating that GUs end up owning
those patents that are more difficult to commercialize. In contrast, characteristics of TUowned patents are very similar to those of TU-invented patents, and shares of TU-owned
patents increase with patenting experience. Within the group of TUs, larger shares of
university-owned patents are associated with better performance in the Exzellenzinitiative. We
also found that the predicted probability of patent ownership tends to be higher for TUs with
stronger support for entrepreneurship, while the opposite seems to hold in the case of GUs.
A possible interpretation of these patterns is that TUs, with their longer history of privatesector interaction, find it easier to acquire the IP in faculty inventions, without having to settle
for the commercially less attractive patents. One factor underlying these differences may be
that technology transfer and commercialization activities are more easily squared with the
organizational identities (Hannan et al., 2006) of TUs. In contrast, that larger shares of GUowned patents are not systematically associated with better patenting performance, research
performance or support for entrepreneurship could be interpreted as outcomes of isomorphic
tendencies (DiMaggio and Powell, 1983; Baldini et al., 2006), in which organizations emulate
activities of other successful organizations to enhance their own legitimacy.
Further research will be required to test the validity of these conjectures and to generate
more conclusive results. However, notwithstanding the limitations of the above analysis, in
light of our findings it seems highly problematic to use university-owned patents as a
university-level indicator of successful technology transfer (as some practitioners and policy
makers do). Not only does this neglect other university-invented patents. It also masks the
18
fact–which finds support in the heterogeneity characterizing the above empirical results–that
different views are possible about how university ownership of patents relates to university
performance in technology transfer.
19
Tables
Table 1: Descriptive Statistics
Full sample
EP/PCT (d)
Patent scope
# of backward citations
# of forward citations
Proximity to basic research
# of inventors
university age
# of students
Patenting experience
Presence of medical school (d)
PVA age <5 (d)
PVA age >5 & <10 (d)
PVA age >10 (d)
University-industry ties
DFG funding
Exzellenzinitiative (d)
Publication ranking
Entrepreneurial orientation (d)
Notes: (d) indicates a dummy variable.
(N = 1,167)
Mean
Std. Dev.
0.19
0.39
1.84
1.04
3.82
4.55
0.32
0.86
4.22
10.57
3.72
1.96
240.68
191.68
22,210.38
9,956.65
202.49
239.82
0.64
0.48
0.19
0.39
0.56
0.50
0.25
0.44
0.73
0.11
0.11
0.20
0.44
0.50
53.05
7.63
0.19
0.39
University patents of
GUs
University patents of
TUs
University-owned
patents
(N = 723)
Mean
Std. Dev.
0.24
0.43
1.91
1.07
3.59
4.65
0.30
0.87
5.61
12.75
3.68
1.95
286.79
227.61
23,672.08
9,475.42
130.17
119.98
0.80
0.40
0.23
0.42
0.55
0.50
0.22
0.42
0.76
0.10
0.07
0.09
0.41
0.49
57.14
6.05
0.11
0.31
(N = 444)
Mean
Std. Dev.
0.10
0.30
1.73
0.98
4.20
4.35
0.36
0.84
1.95
4.56
3.77
1.98
165.60
56.12
19,830.17
10,268.10
320.26
324.77
0.39
0.49
0.11
0.32
0.58
0.49
0.30
0.46
0.69
0.11
0.16
0.30
0.48
0.50
46.39
4.67
0.33
0.47
(N = 690)
Mean
Std. Dev.
0.16
0.37
1.87
1.00
3.66
3.98
0.28
0.73
4.82
11.08
3.53
1.75
251.81
193.28
23082.51
9,958.43
218.03
257.68
0.64
0.48
0.20
0.40
0.57
0.50
0.23
0.42
0.73
0.11
0.11
0.20
0.49
0.50
53.04
8.00
0.20
0.40
University-invented
patents
(N = 477)
Mean
Std. Dev.
0.23
0.42
1.79
1.09
4.06
5.25
0.39
1.01
3.35
9.73
3.99
2.21
224.58
188.38
20948.79
9,828.23
180.01
209.55
0.65
0.48
0.16
0.37
0.55
0.50
0.29
0.46
0.74
0.10
0.10
0.20
0.35
0.48
53.06
7.08
0.18
0.39
20
Table 2: Estimates university-owned vs. university-invented patents (Probit; marginal effects)
Full sample
Marginal
SE
Effects
-0.084
0.074
0.015
0.064
0.060
0.069
0.036
0.073
-0.157***
0.059
0.007
0.015
-0.006
0.004
-0.029*
0.016
0.006***
0.002
-0.035***
0.010
-0.193***
0.060
0.053**
0.022
0.129**
0.052
0.011
0.042
-0.127*
0.076
-0.213***
0.082
-0.430
0.334
0.135
0.089
Model 1
GU
Marginal
SE
Effects
TU
Marginal
SE
Effects
Technical university (d)
Instruments (d)
0.051 0.080
0.018 0.097
Chemistry (d)
0.170* 0.092
-0.126 0.107
Mechanical engineering (d)
0.142 0.127
-0.056 0.073
EP/PCT (d)
-0.134** 0.066
-0.254** 0.116
Patent scope
-0.012 0.017
0.030 0.037
# of backward citations
-0.010** 0.005
0.000 0.005
# of forward citations
-0.045* 0.024
-0.012 0.028
Proximity to basic research
0.005** 0.002
0.006 0.008
# of inventors
-0.034*** 0.012
-0.032** 0.015
Presence of medical school (d)
-0.271*** 0.056
-0.243* 0.132
university age
0.070*** 0.017
-0.125*** 0.038
# of students
0.130* 0.068
0.049 0.051
Patenting experience
-0.038 0.043
0.141** 0.058
PVA age >5 & <10 (d)
-0.151* 0.087
-0.138** 0.059
PVA age >10 (d)
-0.125 0.107
-0.310*** 0.086
University-industry ties
-0.381 0.387
-0.497 0.348
1/DFG ranking
0.317 0.428
0.210* 0.108
Exzellenzinitiative (d)
Publication ranking
Entrepreneurial orientation (d)
Observations
1,167
723
444
Number of cluster
61
44
17
Log likelihood
-734.22
-432.76
-272.22
Prob>Chi2
0.000
0.000
0.000
McFadden's Pseudo R2
0.070
0.111
0.099
Notes: *** p<0.01, ** p<0.05, * p<0.1; Standard errors (SE) are clustered on university level.
Full sample
Marginal
SE
Effects
-0.077 0.055
0.021 0.066
0.063 0.073
0.048 0.074
-0.163*** 0.060
0.007 0.016
-0.006 0.004
-0.028* 0.016
0.006*** 0.002
-0.035*** 0.009
-0.168*** 0.053
0.052*** 0.019
0.098** 0.050
-0.014 0.036
-0.119* 0.071
-0.174** 0.079
-0.586* 0.326
0.134***
1,167
61
-729.01
0.000
0.076
0.050
Model 2
GU
Marginal
SE
Effects
TU
Marginal
Effects
0.064
0.183**
0.151
-0.134**
-0.012
-0.010**
-0.044*
0.005**
-0.033***
-0.266***
0.070***
0.136**
-0.039
-0.156*
-0.112
-0.435
0.081
0.090
0.124
0.065
0.017
0.005
0.023
0.002
0.012
0.064
0.018
0.061
0.044
0.082
0.106
0.393
0.009
-0.130
-0.048
-0.256**
0.030
-0.001
-0.009
0.006
-0.030**
-0.249***
-0.116***
0.011
0.081***
-0.105
-0.240***
-0.627***
0.091
0.104
0.073
0.116
0.036
0.005
0.029
0.008
0.015
0.069
0.041
0.036
0.029
0.071
0.074
0.207
0.052
0.067
0.231***
0.055
723
44
-432.83
0.000
0.111
SE
444
17
-270.49
0.000
0.105
21
Table 2: continued
Full sample
Marginal
SE
Effects
-0.129* 0.073
0.004 0.063
0.059 0.066
0.014 0.076
-0.145** 0.059
0.010 0.015
-0.006 0.004
-0.031** 0.016
0.006*** 0.002
-0.038*** 0.009
-0.134** 0.053
0.062*** 0.024
0.170*** 0.053
0.007 0.040
-0.136* 0.074
-0.211** 0.085
-0.332 0.327
Model 3
GU
Marginal
SE
Effects
TU
Marginal
SE
Effects
Technical university (d)
Instruments (d)
0.036 0.076
0.026 0.100
Chemistry (d)
0.164* 0.085
-0.115 0.100
Mechanical engineering (d)
0.092 0.146
-0.054 0.076
EP/PCT (d)
-0.113* 0.064
-0.253** 0.116
Patent scope
-0.010 0.017
0.034 0.036
# of backward citations
-0.011** 0.005
0.000 0.005
# of forward citations
-0.051** 0.023
-0.009 0.029
Proximity to basic research
0.006*** 0.002
0.006 0.008
# of inventors
-0.039*** 0.011
-0.032** 0.015
Presence of medical school (d)
-0.248*** 0.049
-0.046 0.105
university age
0.082*** 0.017
-0.097*** 0.036
# of students
0.206*** 0.065
0.074 0.058
Patenting experience
-0.032 0.043
0.087*** 0.033
PVA age >5 & <10 (d)
-0.191** 0.075
-0.106* 0.061
PVA age >10 (d)
-0.108 0.118
-0.242*** 0.076
University-industry ties
-0.203 0.339
-0.502 0.348
1/DFG ranking
Exzellenzinitiative (d)
Publication ranking
-0.010** 0.005
-0.015*** 0.005
-0.011 0.010
Entrepreneurial orientation (d)
Observations
1,167
723
444
Number of cluster
61
44
17
Log likelihood
-731.42
-427.25
-272.79
Prob>Chi2
0.000
0.000
0.000
McFadden's Pseudo R2
0.073
0.123
0.097
Notes: *** p<0.01, ** p<0.05, * p<0.1; Standard errors (SE) are clustered on university level.
Full sample
Marginal
SE
Effects
-0.072 0.063
0.015 0.061
0.059 0.067
0.037 0.072
-0.155*** 0.059
0.009 0.015
-0.006 0.004
-0.030* 0.016
0.006*** 0.002
-0.035*** 0.009
-0.193*** 0.057
0.073*** 0.028
0.129** 0.053
-0.011 0.039
-0.113 0.080
-0.200** 0.088
-0.345 0.350
0.091 0.077
1,167
61
-734.15
0.000
0.070
Model 4
GU
Marginal
SE
Effects
TU
Marginal
SE
Effects
0.055
0.174*
0.141
-0.129**
-0.012
-0.010**
-0.044*
0.005**
-0.034***
-0.279***
0.071**
0.153**
-0.035
-0.166*
-0.123
-0.359
0.036
-0.108
-0.039
-0.252**
0.035
0.000
-0.011
0.006
-0.031**
-0.115
-0.102**
0.056
0.080*
-0.100
-0.251**
-0.522*
0.080
0.092
0.129
0.065
0.017
0.005
0.023
0.002
0.012
0.058
0.033
0.066
0.044
0.086
0.111
0.410
-0.011 0.125
723
44
-433.41
0.000
0.110
0.092
0.100
0.073
0.115
0.037
0.005
0.028
0.008
0.015
0.100
0.047
0.048
0.046
0.096
0.100
0.317
0.018 0.105
444
17
-273.38
0.000
0.095
22
Acknowledgements The authors are grateful for comments received from the participants at the
Name Game Workshops in Brussels and Leuven as well as at the EPIP conference in Leuven.
Financial support by the European Science Foundation (project ESF-APE-INV) is gratefully
acknowledged.
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27
When do universities own their patents?
An explorative study of patent characteristics and organizational
determinants in Germany
-Supplementary Material-
Anja Schoen, Guido Buenstorf
Version: June 2013
Supplement 1: Creation of the dataset9
To create a dataset of university patents we used two data sources:
1. the PatStat database (the European Patent Office (EPO) Worldwide Patent Statistic
Database, version April 2010) provided by the EPO;
2. a list of professor names from Kürschners Gelehrtenkalender, a commercial directory of
university researchers published regularly since 1923. (The print version is currently in its
23rd edition.)
The latter records individual researchers after they completed their Habilitation (the academic
degree traditionally required to be named professor in Germany) or when they assume a
professorship at a research university. Retired professors remain listed as long as they are alive. We
obtained from the publisher a dataset encompassing the subset of entries for all researchers working
at German universities as of April 2010, a total of 45,307 entries. To minimize homonym problems
in the identification of academic inventors, we restricted the list of professor names to those
9
The matching and filtering processes as well as concomitant methodology issues are discussed in greater detail in
Schoen et al. (2013).
28
professors working in disciplines with a high expected patenting propensity (specifically, biology,
chemistry, engineering, medicine, (unspecified) natural sciences, pharmaceutics, physics, and
physical and technical chemistry).
The PatStat database contains information on more than 28 million inventors.10 Again to
minimize homonym problems, we excluded inventors based on the following criteria:
1. inventors` addresses outside Germany (inventors with no specified country are included);
2. inventors without a specified country and a different name11 than inventors with an address
in Germany;
3. inventors with no patent application in 2006 and 2007.
These restrictions leave us with a list of 16,046 professor names and 369,284 inventor names.
We then applied a matching algorithm (described in Schoen et al. (2013) in more detail) consisting
of the following procedures (depicted in Figure 1).
Step 1: Cleaning of the database
Step 2: Name comparison:
Comparison of professor and inventor names to obtain groups of inventor names
for each professor that are potentially identical (inventor “name group(s)” per
professor). The comparison is based on the simple string matching algorithm, the 2gram algorithm, and the Jaccard similarity coefficient.
Step 3: Inventor-inventor filtering:
Comparison of the inventors within the “name group” and assignment of a unique
id to inventors considered to be identical.
Step 4: Professor-inventor filtering:
Comparison of each professor with each inventor (group).
10
The PatStat database is not harmonized; consequently, this number does not refer to individuals.
11
Identical names are identified by PatStat’s doc_std_name_id.
29
Figure 1: Overview of matching and filtering process
3.step: inventorinventor filtering
professor A matched to
inventor 1-5
inventor 1 and 2 as
well as 3 and 4 are
identical
inventor
1
ALBER
TO
EINSTEIN
inventor
2
ALBER
TO F
EINSTEIN
ALBER
TO F
EINSTEIN
inventor
3
ALEBR
T
EINSTEIN
inventor
4
A
EINSTEIN
inventor
5
ALBER
T
EINSTEINS
Source: Schoen et al. (2013)
inventor
3
ALEBR
T
EINSTEIN
inventor
4
A
EINSTEIN
inventor
5
ALBER
T
EINSTEINS
1. step: cleaning
inventor
1
ALBER
TO
EINSTEIN
inventor
2
Prof. A
"ALEBRT
EINSTEIN"
4. step: professorinventor filtering
professor A is
matched to unique
inventor b (inventor 3
and 4)
uniqu
e
inventor
a
uniqu
e
inventor
a
uniqu
e
inventor
b
uniqu
e
inventor
b
uniqu
e
inventor
c
uniqu
e
inventor
c
/
5. step: manual control
2. step: name
comparison
Prof. A
"ALBERT
EINSTEIN"
/
Through this procedure we are able to identify 8,076 inventors matched to 1,647 professors.
After limiting the dataset to priority patents12 applied in 2006 or 2007 independently of the
receiving patent office, the identified professor-inventor matches (1,259 professors, 2,157
inventors) were manually controlled (step 5). Again this involved several steps:
1. We manually controlled if the identified professor-inventors are correctly listed in the professor
names list. We excluded 277 professors matched to 450 inventors based on the following
criteria :
12
Priority patent is the first patent describing the invention. We count all priority patents independently of the receiving
patent office (see de Rassenfosse et al. 2013a, b for a detailed discussion on the worldwide count of priority patents).
30
a. person below the rank of professor (including Lecturers (Privatdozenten));
b. professors affiliated with an university and a public research organization simultaneously;
c. retired professors;
d. individuals who are not full-time employees of German research universities, mostly
Honorarprofessoren and physicians employed at university-affiliated Lehrkrankenhäuser.
2. We manually checked the names of professors and the matched inventors. This step excluded 14
professors matched to 333 inventors.
3. We checked manually all professor-patent pairs. The dataset was reduced by 288 professors and
583 inventors. Criteria applied to this manual check were:
a. fit of patent title and professor’s research field;
b. overlap of co-inventor(s) and co-author(s);
c. all patents of the respective professor were applied for by a single company and professor’s
research field does not fit the content of the patent application (in this case the professorinventor pair was dropped from the dataset because the inventor is likely to be employed at
this company).
The complete dataset contains 628 professors with 1,167 priority applications worldwide in 2006
and 2007.
31
Supplement 2: Ownership types
A closer look at ownership types within the category of university-owned patents reveals that
167 patents are co-owned by the university and another party/other parties (cf. Table 1). To a large
extent (45% for both types, 45% for TUs), co-owned patents are applied for by a university and a
company; another 42 patents (25% for both types, 33% for TUs) are owned by a university and a
PRO.
Table 1: Ownership categorization of co-owned patents with university participation, N=167 (NTU=42)
Company
PRO
Individua
l
Foreign
university
Company
75 (19)
PRO
5 (2)
42 (14)
Individual
1 (0)
1 (0)
5 (2)
Foreign university
0
1 (0)
0
4 (2)
8 (0)
1 (0)
Notes: Numbers for TUs are presented in parentheses.
0
0
Another German university
Anoth
er
German
university
24 (3)
Among the university-invented patents 69% (68%) are owned by a company, 13% (14%) are
privately owned, and 6.7% (8%) are owned by a public research organization (PRO). This relatively
small number of PRO patents presumably reflects our exclusion of researchers jointly appointed by
a university and a PRO. Moreover, we find 26 (10) patents owned by a different German university
than the home university of the identified professor. The observed ownership types of universityinvented patents are illustrated in Table 2.
Table 2: Ownership categorization of university-invented patents, N=478 (NTU=187)
Company
Company
PRO
Individual
Foreign university
Another German university
331
(127)
6 (2)
10 (1)
0
PRO
32 (15)
5 (2)
0
2 (2)
4 (2)
Notes: Numbers for TUs are presented in parentheses.
Individua
l
Foreign
university
61 (26)
0
0
0
0
Anoth
er
German
university
26
(10)
32
Supplement 3: Universities’ share of university-owned patents
University
Halle-Wittenberg GU
Clausthal TU
Bayreuth GU
Düsseldorf GU
Magdeburg GU
Marburg GU
Duisburg-Essen GU
Greifswald GU
Lübeck GU
Ulm GU
Bochum GU
Hannover TU
Wuppertal GU
Kaiserslautern TU
Braunschweig TU
Darmstadt TU
Stuttgart TU
München TU
Heidelberg GU
Weimar GU
Münster GU
Chemnitz TU
Kiel GU
Hamburg-Harburg TU
Jena GU
Saarbrücken GU
Bremen GU
Freiberg TUBergAk
Aachen TH (TU)
Karlsruhe U KIT
(TU)
Regensburg GU
Leipzig GU
Frankfurt am Main
GU
Augsburg GU
Berlin FU
Bielefeld GU
Bonn GU
Köln GU
Tübingen GU
Dortmund TU
Mainz GU
Dresden TU
# of
university
patents per
university
3
15
4
16
15
39
38
16
12
18
23
30
5
12
31
19
25
29
20
2
26
22
20
9
39
14
26
39
49
Share of university-owned patents (in
%)
per university
0.00%
20.00%
25.00%
25.00%
26.67%
28.21%
28.95%
31.25%
33.33%
33.33%
34.78%
40.00%
40.00%
41.67%
41.94%
42.11%
44.00%
48.28%
50.00%
50.00%
53.85%
54.55%
55.00%
55.56%
56.41%
57.14%
57.69%
58.97%
59.18%
25
5
18
60.00%
60.00%
61.11%
28
3
24
3
18
9
41
7
26
94
64.29%
66.67%
66.67%
66.67%
66.67%
66.67%
70.73%
71.43%
73.08%
73.40%
33
Erlangen-Nürnberg
GU
Oldenburg GU
Potsdam GU
Rostock GU
University
Würzburg GU
Göttingen GU
Berlin TU
Cottbus TU
München GU
Freiburg GU
Siegen GU
Kassel GU
Berlin Humboldt GU
Ilmenau TU
Bamberg GU
Giessen GU
Hamburg GU
Konstanz GU
Paderborn GU
39
4
4
38
# of
university
patents per
university
9
14
17
7
15
23
18
10
13
14
1
8
10
2
4
74.36%
75.00%
75.00%
76.32%
Share of university-owned patents (in
%)
per university
77.78%
78.57%
82.35%
85.71%
86.67%
86.96%
88.89%
90.00%
92.31%
92.86%
100.00%
100.00%
100.00%
100.00%
100.00%
34
Supplement 4: t-Tests on mean difference
UniversityUniversity patents
invented vs.
of GU vs. university
university--owned
patents of TU
patents
EP/PCT (d)
0.0641**
0.141***
Patent scope
-0.0765
0.185**
# of backward citations
0.398
-0.601*
# of forward citations
0.106*
-0.0566
Proximity to basic research
-1.465*
3.661***
# of inventors
0.456***
-0.0915
University age
-27.24*
121.2***
# of students
-2133.7***
3841.9***
Patenting experience
-38.02**
-190.1***
Presence of medical school (d)
0.0122
0.412***
PVA age <5 (d)
-0.0450*
0.118***
PVA age >5 & <10 (d)
-0.0224
-0.037
PVA age >10 (d)
0.0674*
-0.0814**
University-industry ties
7.829
-81.26***
DFG funding
-0.0132
-0.0891***
Exzellenzinitiative (d)
-0.140***
-0.0726*
Publication ranking
0.0194
10.75***
Entrepreneurial orientation (d)
-0.0126
-0.225***
Notes: The numbers in the table reflect the mean differences between the respective
groups (compare to Table 1 in the paper).
The asterisks *** (**, *) denote a 1% (5%, 10%) significance level of t-tests on mean
differences.
(d) indicates a dummy variable.
35
Supplement 5: Correlation matrix for the full sample (N = 1,167)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
EP/PCT (d)
Patent scope
# of backward
citations
# of forward
citations
Proximity to basic
research
# of inventors
University age
# of students
Patenting
experience
Presence of
medical school (d)
PVA age >5 &
<10 (d)
PVA age >10 (d)
Universityindustry ties
DFG funding
Exzellenzinitiative
(d)
Publication
ranking
Entrepreneurial.
orientation (d)
(1)
1.00
-0.01
(2)
(3)
(4)
(5)
(6)
(7)
(8)
-0.05
0.05
1.00
-0.02
0.00
0.02
1.00
0.13***
0.09**
0.09**
-0.07*
1.00
0.02
0.01
0.15***
0.04
0.07*
0.05
0.07*
0.06*
-0.06
0.04
0.07*
-0.01
-0.07*
-0.07*
0.03
0.10***
0.04
-0.02
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
0.17***
0.01
0.09**
1.00
0.03
-0.03
1.00
0.09**
1.00
0.01
-0.06*
0.00
0.34***
0.23***
1.00
0.01
0.01
0.08**
-0.01
0.23***
0.47***
0.23***
1.00
-0.05
-0.01
-0.04
-0.02
0.01
-0.31***
0.20***
-0.08**
-0.01
1.00
-0.03
0.05
-0.02
0.04
0.04
0.02
0.26***
-0.16***
0.03
-0.16***
-0.66***
1.00
0.11***
0.07*
-0.05
-0.01
0.11***
-0.06
-0.14***
0.18***
-0.36***
0.10***
-0.32***
0.16***
1.00
0.06
0.06*
0.03
0.01
-0.03
0.01
0.08**
0.28***
0.04
0.22***
0.06*
-0.08**
0.10***
1.00
0.11***
0.04
0.01
-0.03
0.03
-0.03
0.07*
0.47***
0.26***
0.19***
0.07*
-0.20***
0.21***
0.42***
1.00
0.22***
0.11***
-0.05
-0.04
0.18***
-0.07*
0.21***
0.50***
-0.03
0.62***
-0.12***
0.00
0.33***
0.00
0.26***
1.00
-0.01
-0.08**
0.00
-0.02
-0.06*
-0.01
-0.34***
0.27***
0.41***
0.21***
0.19***
-0.28***
-0.20***
-0.02
0.25***
-0.10**
(17)
1.00
1.00
Notes: The asterisks *** (**, *) denote a 1% (5%, 10%) significance level.
36
Supplement 6: Correlation matrix for the GU sample (N = 723)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(1)
EP/PCT (d)
1
(2)
Patent scope
# of backward
citations
# of forward
citations
Proximity to basic
research
# of inventors
University age
# of students
Patenting
experience
Presence of
medical school (d)
PVA age >5 &
<10 (d)
PVA age >10 (d)
Universityindustry ties
DFG funding
Exzellenzinitiative
(d)
Publication
ranking
Entrepreneurial
orientation (d)
-0,03
1
-0,04
0.03
1
0.00
-0.01
0.05
1
0.10**
0.07
0.11**
-0.08*
1
0.03
0.02
0.14***
0.03
0.09*
0.03
0.08*
0.08*
-0.06
0.01
0.09*
0.00
0.21***
0.01
0.05
1
0.00
-0.02
1
0.11**
1
0.03
-0.06
0.04
-0.02
0.00
-0.04
0.40***
0.21***
1
-0.01
-0.01
0.02
0.05
0.00
-0.01
0.35***
0.16***
0.38***
1
0.00
-0.05
-0.05
-0.05
-0.01
0.04
-0.41***
0.19***
-0.32***
-0.29***
1
0.02
0.07
0.03
0.04
0.10*
0.01
0.30***
-0.09*
0.13***
0.19***
-0.59***
1
-0.02
0.05
-0.04
0.00
0.06
-0.05
-0.13***
0.08*
-0.19***
-0.03
-0.21***
0.02
1
0.14***
0.03
-0.05
0.04
0.01
-0.02
0.27***
0.38***
0.22***
0.06
-0.28***
0.15***
0.20***
1
0.13***
0.03
-0.05
-0.03
0.04
-0.05
0.07
0.32***
0.08*
-0.19***
-0.09*
-0.09*
0.37***
0.52***
1
0.15***
0.09*
-0.03
-0.04
0.09*
-0.11**
0.32***
0.38***
0.27***
0.38***
-0.29***
0.22***
0.21***
0.33***
0.28***
1
0.01
-0.07
-0.08*
-0.05
-0.04
0.00
-0.56***
0.15***
0.05
0.07
0.14***
-0.19***
-0.14***
0.17***
-0.15***
-0.18***
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(17)
1
Notes: The asterisks *** (**, *) denote a 1% (5%, 10%) significance level.
37
Supplement 7: Correlation matrix for the TU sample (N = 444)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(1)
EP/PCT (d)
1
(2)
Patent scope
# of backward
citations
# of forward
citations
Proximity to basic
research
# of inventors
University age
# of students
Patenting
experience
-0.02
1
-0.06
0.11*
1
-0.05
0.00
-0.03
1
0.15**
0.10*
0.10*
-0.07
1
0.03
-0.10*
0.10*
0.07
0.03
0.05
0.06
-0.01
-0.02
0.09*
0.01
-0.01
0.12*
0.00
0.09
1
0.15**
-0.02
1
0.09*
1
-0.07
-0.01
-0.04
0.01
0.02
0.03
0.45***
0.48***
1
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(10)
Presence of
medical school (d)
0.10*
0.03
0.07
-0.01
0.06
0.00
0.08
0.67***
0.56***
1
(11)
PVA age >5 &
<10 (d)
-0.03
-0.05
0.06
-0.04
-0.02
-0.04
0.00
0.26***
0.20***
0.40***
1
(12)
PVA age >10 (d)
-0.08
0.05
-0.10*
0.03
-0.05
0.02
0.20***
-0.20***
-0.17***
-0.53***
-0.78***
1
(13)
Universityindustry ties
0.23***
0.03
0.00
0.00
0.11*
-0.05
-0.29***
0.16***
-0.37***
-0.03
-0.50***
0.42***
1
(14)
DFG funding
0.12*
0.13**
0.06
-0.01
0.03
0.02
-0.05
0.37***
-0.16***
0.52***
0.23***
-0.24***
0.21***
1
0.11*
0.08
0.10*
-0.03
0.11*
0.00
0.09
0.73***
0.49***
0.82***
0.32***
-0.39***
0.08
0.46***
1
0.07
0.04
0.04
0.03
0.08
-0.01
0.21***
0.69***
0.51***
0.76***
0.19***
-0.17***
0.12*
0.21***
0.74***
1
0.08
-0.06
0.05
-0.02
0.05
-0.03
-0.05
0.50***
0.61***
0.65***
0.25***
-0.47***
-0.11*
-0.17***
0.66***
0.58***
(15)
(16)
(17)
Exzellenzinitiative
(d)
Publication
ranking
Entrepreneurial
orientation (d)
(17)
1
Notes: The asterisks *** (**, *) denote a 1% (5%, 10%) significance level.
38
Supplement 8: University type differences in the probability of university ownership by independent variables with all other variables at
the mean
Figure 1: University-type differences in the probability of university ownership–patent scope
39
Figure 2: University-type differences in the probability of university ownership–# of backward citations
40
Figure 3: University-type differences in the probability of university ownership–# of forward citations
41
Figure 4: University-type differences in the probability of university ownership–proximity to basic research
42
Figure 5: University-type differences in the probability of university ownership–# of inventors
43
Figure 6: University-type differences in the probability of university ownership–presence of medical school (d)
44
Figure 7: University-type differences in the probability of university ownership–university age
45
Figure 8: University-type differences in the probability of university ownership–# of students
46
Figure 9: University-type differences in the probability of university ownership–patenting experience
47
Figure 10: University-type differences in the probability of university ownership–PVA age >5 & <10 (d)
48
Figure 11: University-type differences in the probability of university ownership–PVA age>10 (d)
49
Figure 12: University-type differences in the probability of university ownership–university-industry ties
50
Figure 13: University-type differences in the probability of university ownership–1/DFG ranking
51
Figure 14: University-type differences in the probability of university ownership–Exzellenzinitiative (d)
52
Figure 15: University-type differences in the probability of university ownership–publication ranking
53
Figure 16: University-type differences in the probability of university ownership–entrepreneurial orientation
54
References:
de Rassenfosse, G., Schoen, A., Wastyn, A. (2013a): Selection bias in innovation studies: A
simple test, Technological Forecasting and Social Change, forthcoming.
de Rassenfosse, G., Dernis H.; Guellec, D., Picci, L., van Pottelsberghe de la Potterie, B.
(2013b): The worldwide count of priority patents: A new indicator of inventive activity,
Research Policy, 42(3): 720-737.
Schoen, A., Buenstorf, G., Heinisch, D. (2013): Playing the “Name Game” to identify
university-invented patents in Germany. Mimeo.
55