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Objectives, Characteristics and Outcomes of
University Licensing:
A Survey of Major U.S. Universities
Jerry G. Thursby *
Purdue University
Richard Jensen **
University of Notre Dame
Marie C. Thursby
*
Purdue University and NBER
July 2000
* Department of EconomicsKrannert BuildingPurdue UniversityW. Lafayette, IN 47907
**Department of EconomicsUniversity of Notre DameSouth Bend, IN 46556
Abstract This paper describes results of our survey of licensing at 62 research universities. We considerownership, income splits, stage of development, marketing, license policies and characteristics, goals oflicensing and the role of the inventor in licensing. Based on these results we analyze the relationship be-tween licensing outcomes and both the objectives of the TTO and the characteristics of the technologies.
Patent applications grow one-to-one with disclosures, while sponsored research grows similarly with li-censes executed. Royalties are typically larger the higher the quality of the faculty and the higher the frac-tion of licenses that are executed at latter stages of development. Sponsored research is more likely to beincluded in a license if the new technology is at an early stage of development or if the TTO evaluates itas important. We find that additional disclosures generate smaller percentage increases in licenses, andthose increases in licenses generate smaller percentage increases in royalties.
JEL Classification: L2, L3, O3
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1. Introduction
University licensing has increased dramatically since the passage of the Bayh-Dole Act in 1980,
which gave universities the right to retain title to and license inventions resulting from federally-
sponsored research. The 1996 Survey of the Association of University Technology Managers(AUTM,
1998) reports that licenses executed increased 75% percent between 1991 and 1996, with 13,087 executed
over the entire period. Understanding this phenomenon is important because, while universities are con-
sidered critical to industrial innovation, our understanding of the way research is transferred to industry is
based largely on studies of spillovers.1
To this end, we recently conducted a survey of the technology transfer offices (TTOs) of 62 ma-
jor U.S. universities. We focused on policies related to ownership of inventions, the nature of university
inventions, license policies and strategies, as well as university objectives in licensing. For both pat-
entable and copyrightable inventions, we find that the majority of universities retain title to inventions,
but all universities split the income with inventors. We also find that the majority of inventions are at an
early stage of development when they are licensed, and that inventor involvement in the process is impor-
tant, not only for finding licensees, but also for further development once licenses are executed. Indeed,
almost half of the inventions licensed are only a proof of concept at the time of license. It is not surprising
then that the licenses executed include payment schemes that induce inventor involvement in develop-
ment and do not obligate the licensees to large up-front payments. That is, agreements almost always in-
clude running royalties and small up-front fees, often include sponsored research, and less frequently in-
clude equity positions in the licensee. Royalties generate the lion's share of the revenue generated by uni-
versity licensing.
Unlike the private sector, where success is measured by profits, university goals are more diverse.
In addition to generating royalties and sponsored research, universities (in particular, public universities)
are expected to contribute to economic development. To the extent that this goal is important, we expect
TTOs to value the signing of a license or the commercialization of an invention, regardless of the mone-
tary rewards. Indeed, the majority of TTO professionals responding to our survey indicated as much.
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There is a growing literature on university licensing (e.g. Jensen and Thursby 1999, Mowery et.
al1999, Mowery and Ziedonis 1999, Siegel, Waldman, and Link 1999, Thursby and Kemp 2000, and
Thursby and Thursby 2000a). Our survey contributes to that literature by providing evidence from a broad
spectrum of universities on their objectives, as well as a new evidence on the nature of inventions li-
censed. The first six sections of the paper present survey results. In Section 7, we examine an econometric
model that relates licensing outcomes (e.g., sponsored research, royalties, patents) to the stated objectives
of the TTO and invention characteristics. One interesting result is that invention characteristics are impor-
tant for several outcomes. In particular, royalties are lower and sponsored research is more likely when
the new technology is licensed at an early stage of development. Surprisingly, TTO objectives are signifi-
cantly related to only one outcome; sponsored research is less likely in a license agreement when the TTO
evaluates it as not very important.
We also find that the elasticity of licenses with respect to disclosures and royalties with respect to
licenses are both less than one, suggesting a declining marginal value of disclosed inventions. While this
could reflect a decrease in the importance of university inventions to industry (see Henderson, Jaffe and
Trajtenberg (1998) and Mowery and Ziedonis (1999) for evidence pro and con), it could be the result of
an increased willingness or propensity of faculty to disclose their research results (as shown by Thursby
and Thursby 2000a). Whether the increased propensity of faculty to disclose is simply a response to fi-
nancial incentives or an increase in the effectiveness of TTOs in inducing disclosure is an open question.
2. Survey Design and Characteristics of the Sample
Survey questionnaires were sent to the top 135 U.S. universities in terms of licensing revenue as
reported in the 1996AUTM Survey. The questionnaire was pretested on eleven experienced university
technology transfer professionals from both public and private universities. After they completed a draft
survey, these professionals were interviewed in an effort to remove ambiguities from the questionnaire.
Sixty-two universities responded, giving a response rate of 46%. The majority of universities re-
sponding were public, and of the public universities responding, 62% were land-grant institutions. Private
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universities accounted for 37% of the responses. Average industry sponsored research for universities in
the sample was $16.9 million in 1996, and federally sponsored research was $149.6 million. The average
TTO in the sample reported 26.3 licenses executed, 92.3 inventions disclosures, 30.1 new patent applica-
tions and $4.2 million in income for 1996. Compared to the 131 U.S. universities who responded to the
1996 AUTM survey, the respondents to our survey represent 68% of industry sponsored research, 75% of
federally sponsored research, 71% of royalty income, 74% of the licenses executed, 70% of the invention
disclosures and 48% of the new patent applications.
A substantial portion (35%) of respondents indicated that their office had been reorganized since
1990. This is not surprising given the dramatic growth in licensing activity. Few (13%) of the TTOs re-
ported that their office is part of a foundation, and 20% reported that the university has a foundation that
provides support for the TTO. Only 15% of the TTOs are corporations that are separate from their univer-
sities, and 4.8% are for-profit. On average, 42% of the support for the TTOs is based on a line item in
the university budget and 43% comes from royalties/license fees. Most (80%) of the TTOs report directly
to an academic university official (typically, the vice president for research) rather than a university busi-
ness or finance official. More than 40% use brokers or consultants to aid the TTO.
3. Policies on Ownership and Income
A potentially important element in examining university objectives in licensing is the ownership
of inventions. Since the passage of the Bayh-Dole Act of 1980, universities can elect to retain title to pat-
entable inventions resulting from federally funded research. For both patentable and copyrightable inven-
tions, we asked Who owns inventions and materials made or developed by faculty or other personnel in
your university? Respondents were given three choices: university, inventor and other. All but one
university in the sample indicated that the university owns patentable inventions/materials. For copyright-
able inventions, 66% indicated that the university was the owner, and 48% and 15% said ownership re-
sided with the inventor or other, respectively. Note that a number of respondents indicated several owners
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of copyrightable inventions; this follows from different policies for different types of copyrightable mate-
rials; for example, most universities retain rights to copyrightable software but not to books.
While the university typically retains ownership, they nonetheless split net income from inven-
tions with the inventor. Respondents were asked to give the percent of net income for each of the catego-
ries patentable inventions/materials and copyrightable materials going to each of the following groups:
university, inventors, inventor department or school, technology transfer office and other.
For patentable inventions/materials, the universities in our sample gave an average of 40% of net
income to the inventors and 16% to the inventor's department or school. Departments and schools often
return their portion to the inventor's lab. For some universities, it is possible for as much as 75% of net
income to be under the control of an inventor.2On average, central administrations and TTOs take 26%
and 11%, respectively, of the income from licensing. Nearly 30% of the respondents indicated that the
central administration receives no royalty income, but those universities generally allocate income to the
TTO. Over 30% of the universities allocate a portion of income directly to the TTO and these universities
allocate, on average, over one third of income to the TTO. Finally, 8% of income is allocated to other.
For copyrightable materials, the average amounts are similar though the inventors receive about 5% more
of net income. Proceeds from the liquidation of equity are distributed differently from other revenue
sources in 23% of the universities responding.
To provide perspective on the income figures, consider the AUTM 1997 survey results (AUTM,
1997) on income and cashed-in equity. One hundred and twenty-four U.S. universities (including solely
university hospitals) reported a total of around $375 million in license income received net of legal fees
and income paid to other universities. If we assume 40% of that accrues to inventors, then the faculty in-
ventors at those 124 universities received license income of almost $150 million in 1997. Not surpris-
ingly, this income is highly skewed over inventors; our respondents report, on average, that 76% of li-
cense revenue is attributed to their top five inventions. Fourteen of the universities in the 1997 AUTM
survey report $21 million in cashed-in equity; 108 universities report $0 from cashed-in equity.
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Finally, 90% of the universities in our sample allow faculty to establish and operate businesses
based on technology owned by the university but developed in the course of the faculty's own research.
4. Nature, Source and Marketing of University Technologies
University research, particularly that which is federally funded, is usually considered more basic
than applied. For this sample, 67% of inventions disclosed in the prior five years were the result of feder-
ally sponsored research and only 19% came from industry sponsored research. Thus we would expect
inventions licensed to be basic or nascent as compared to inventions that are ready for use or sale. There
are, however, a number of ways to characterize the nature of inventions. Prior studies have tended to
characterize university technologies in terms of their end use or the academic field contributing to the
technology. Cohen et. al(1998), Adams (1998), and Rosenberg (1992), for example, point to industrial
use of research techniques and instruments developed in universities. In their analysis of spillovers
through publication citation, Adams (1990) and Jaffe (1989) focus on the role of particular science and
engineering disciplines. Similarly, Mansfield (1995) and Mansfield and Lee's (1996) survey work focuses
on the disciplines that contribute most to new products and processes. With the exception of chemistry
and mathematics, these studies tend to highlight the contributions of the more applied disciplines, particu-
larly those in engineering.
In our survey, we asked about the percentage of inventions disclosed by faculty in particular
schools within the university. We found that 33 percent of the inventions disclosed came from medical
schools, 29 percent from engineering schools, 22 percent from schools of science, 6 percent from schools
of agriculture, with the remainder scattered across schools of business, education, and liberal arts.
For our purposes, a more relevant characterization of inventions is in terms of distance from ap-
plication. We therefore asked a series of questions about the stage of development of inventions when
they are licensed. Respondents were asked What percentage of the inventions that were licensed in the
last five years were in the following stages of development at the time the license was negotiated? The
stages listed are standard categories used in TTO evaluation of invention disclosures. The results, shown
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in Table 1, overwhelmingly support the notion that university technologies are embryonic by any stan-
dards. Manufacturing feasibility of the technologies licensed was known for only 15% of the inventions
licensed, and fewer (12%) were ready for practical or commercial use. The overwhelming majority were
either a proof of concept stage (45%) or a lab scale prototype (37%).
As further evidence on the early stage of university inventions, 71% of licensed inventions are
viewed as requiring inventor cooperation for commercial success. Respondents said that specialized fac-
ulty knowledge is generally considered necessary for firms to be willing to license and develop early
stage technologies that are typically years away from commercial application. While faculty do not gen-
erally own inventions, they are clearly an important stakeholder not only because it is their decision
whether or not to disclose, but it also their decision whether or not to cooperate in further development of
inventions.
Evidence on patent protection at the time of licensing can also be interpreted as a measure of the
embryonic nature of technologies, since patents are often applied for when commercial viability is known.
In response to the question, How often are the technologies that you license protected by a patent issued
or a copyright registered at the time you negotiate the license agreement?, only 12% of respondents said
almost always and 13% said often, whereas 48% said sometimes and 28% said rarely.
The embryonic nature of these inventions affects their marketing, the types of firms to whom they
are licensed, and the characteristics of license agreements. In terms of the types of companies that license
early stage inventions, 40% of the respondents could not tell a difference in the size of companies that
license early and late stage inventions. On the other hand, 60% indicated that small companies were more
likely to take early stage technologies and large companies were more likely to take late stage. None of
the respondents indicated that early stage technologies tended to go to large firms and late stage to small
firms. These results accord well with results from the literature on agency costs, which suggests that small
firms may have advantages in innovative research (see, for example, Holmstrom 1989)
We asked TTOs an open ended question about the procedures used to market inventions. The
practices given fall into six categories and are listed in Table 2. The role of inventors is apparent here,
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with 58% of the respondents listing inventor contacts as useful for marketing. It is also likely that some of
the 75% of TTOs who listed personal contacts as important were referring to the personal contacts of fac-
ulty. In a recent survey of businesses who license-in university technologies, Thursby and Thursby
(2000b) found that 46% of respondents said that personal contacts between their R&D staff and university
personnel (i.e., faculty) were extremely important in identifying technologies to license. Our results also
accord with Jansen and Dillon's (1999) report that 56% of the primary leads for license adoptions in 1100
licenses they examined came from faculty. These results on the importance of the faculty in finding licen-
sees follows, we believe, from the generally early stage of university technologies since, for such tech-
nologies, it is the faculty who are able best to articulate the value and nature of such technologies.
Products and processes based on early stage technologies are often years away from commer-
cialization. Further, it is difficult to specify royalty income based on sales (i.e., running royalties) for very
early stage technologies since the nature of the final product is often unknown, and it is difficult to define
a royalty schedule for a product whose final nature is diffuse. These factors can discourage firms from
entering into license contracts with universities. We asked respondents how often more than one company
expresses interest in a technology by signing confidentiality agreements or by bidding for a license. Re-
sults are in Table 3. Note that it is common for multiple companies to examine a technology, but that it is
much less frequent for multiple companies to become involved in license discussions (see also Ziedonis
(1999)). This follows in large part, we believe, from a thin market for early stage technologies. Further
evidence for this can be found in the results of a survey of industry licensing executives reported in
Thursby and Thursby (2000b). Of 300 respondents, nearly two-thirds did not license from universities
and, of these, 49% cited the early stage of development of university technologies as an important reason
for not licensing from universities.
In part, we asked these questions in order to determine whether results from the theoretical litera-
ture on optimal patent licensing are useful in explaining the observed characteristics of university li-
censes. The main result of that literature is that licensor revenue is maximized by the use of a lump-sum
fee determined by an auction and paid up-front (see Kamien, 1992). Our survey results indicate that li-
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censes are rarely determined by an auction, primarily because the early nature of university inventions
makes it difficult to find more than one potential licensee. As discussed in the next section, the early stage
of development has other implications for license characteristics.
5. License Payment Policies and Characteristics
A number of survey questions dealt with terms of payment. These and their responses are found
in Table 4. As noted above, the theoretical literature concludes that an up-front fee is the optimal method
of licensing. That analysis, however, assumes that the value of the invention is certain and obvious to all.
In that case, the TTO and licensee can both determine the value of the license, so it is easy to see how
they can reach an agreement that allows the university to collect the entire payment immediately, rather
than wait for royalties to accumulate over time. In fact, in this certainty case, it is likely that there will be
more than one firm interested in the technology, so the university can simply auction off the license.
However, Jensen and Thursby (1999) analyze licensing policies when there is substantial uncertainty
about the value of the invention, as is the case for embryonic university technologies. They show that, in
this uncertainty case, the TTO should use both output-based payments, such as royalties or equity, and
up-front fees, and that these fees should tend to be smaller the more uncertain is the technology.
Their results correspond quite well with the responses in Table 4. The overwhelming payment of
choice is running royalties (payments based on the output or sales of the licensee) which are included
almost always in licenses by 81% of respondents and often by 16% of respondents. After royalties,
the most common form of payment is patent fee reimbursement, included almost always by 68% and of-
ten by 21%. Up-front fees are also used, included almost always by 66% and often by 26% of respon-
dents, while annual or minimum royalty fees are included almost always by 57% and often by 32% of
respondents.
Evidence on the embryonic nature of university inventions is reported in Section 4. Regarding
payment issues for early stage technologies, we asked For technologies which are in an early stage of
development (e.g., proof of concept but no prototype, or prototype at lab scale only), which forms of
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payment are most difficult to negotiate? The overwhelming response was up-front fees, cited by nearly
64% of all respondents.
As noted above, Jensen and Thursby (1999) find that the up-front fees that a TTO can obtain will
be smaller the more uncertain is the technology being licensed. This finding is supported by the survey
results. We asked What do you think is the most important factor in determining a licensee's willingness
to pay a substantial up-front fee? By a large margin, the most frequent choice was the value perceived by
the licensee, noted by nearly 46% of respondents. After this, the most common responses were the viabil-
ity of the technology at 28%, the degree of development at 26%, and the size of the market with 21%.
Fewer than 10% of respondents mentioned the size of the company and the exclusivity of the license. Ob-
viously, the TTO can obtain a nontrivial up-front fee only if the invention is far enough along in the de-
velopment process that its viability is reasonably well-established, and the licensee expects it to have sub-
stantial value. We find it interesting that, in the absence of this likelihood of a substantial payoff, the offer
of an exclusive license is not enough to induce the licensee to make a substantial up-front payment.
We also asked Have you ever negotiated a license agreement in which the only form of payment
was an up-front fee? Although 69% of respondents replied yes, most of these undoubtedly involved
software or reagent materials with evident viability and value. Rather, we find it remarkable that 31% of
respondents had never been able to negotiate an agreement that used only an up-front fee.
Another interesting aspect of these responses is that license agreements that involve the university
taking equity in the licensee are not very common. Note in Table 4 that no TTO responded that their li-
cense agreements almost always include equity, while 8% responded often and only 32% even responded
sometimes. Conversely, 42% and 18% responded their agreements rarely or never include equity.
However, when equity is included, it is used, to some extent, as a substitute for royalties and fees. When
we asked how often agreements with equity also included running royalties and license fees, the percent-
age of respondents who replied almost always declined to 69% for royalties and 44% for fees.
The lack of equity is particularly surprising in light of the finding by Jensen and Thursby (1999)
that equity not only provides the same development incentives as royalties (because both are based on
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output/sales), but also generates greater revenue. Nevertheless, our survey found some resistance to the
use of equity by some universities. We asked Does your university place restrictions on the use of equity
in a license agreement? Interestingly, two-thirds responded that there were no restrictions, and restric-
tions varied among the remaining third. Four respondents said they cannot take equity positions. Limits
on the percent of equity that the university can take were most common, varying from 5% to less than a
controlling interest, with 10% the most common limit. Restrictions to avoid conflict of interest are also
typical.
Our final question regarding license payment terms has to do with sponsored research. Results are
in Table 4. Agreements with sponsored research are quite common, as 40% of respondents said their li-
censes include it often and another 39% said theirs include it sometimes. Although only 6% said their li-
censes almost always include sponsored research, only 14% said their licenses rarely or never included it.
The use of sponsored research as a license payment is typically to assist in the development of an embry-
onic technology to viable commercial stage, and in Section 7 below we provide evidence on the use of
sponsored research with early stage technologies. Finally, 74% of respondents note that, when licenses do
include sponsored research, they almost always involve the option to negotiate an exclusive license.
6. TTO Licensing Objectives
Unlike the private sector where profits are the ultimate objective, university objectives are more
diverse.3We asked about the importance of five objectives. We asked respondents How important to you
are the following as measures of success? The outcomes listed were: (1) royalties/license fees generated,
(2) sponsored research funds, (3) number of licenses/options signed, (4) number of patents awarded and
(5) number of inventions commercialized. For each objective, the TTO could indicate not very impor-
tant, moderately important or extremely important. Respondents were also given an opportunity to
indicate that an outcome was Not applicable. Results can be found in Table 5.
The most important objective to the TTO is clearly royalties and fees generated, as 71% of re-
spondents said they are extremely important, and only one respondent said they are not important. The
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next most important objective is the number of inventions commercialized, with 61% of respondents indi-
cating extremely important and 31% moderately important. The number of licenses signed follows
closely, with 49% and 44% indicating extremely and moderately important. Next was sponsored research
funds, with only 34% of respondents reporting extremely important and 13% not important. Finally, the
number of patents awarded was the least important outcome to the TTO, with 53% reporting moderately
important and 31% not important.
7. Licensing Outcomes
In this Section, we examine several econometric models that relate our survey results on inven-
tion characteristics and licensing objectives to actual license outcomes. As dependent variables, we con-
sider four of the five outcomes considered in Section 6: royalties, sponsored research, patents and licenses
executed. Inventions commercialized are excluded since there are no measures available. Because of the
long lags that often occur between the application and issuance of patents, we use new applications as our
outcome measure for patents rather than patents issued. Further, new patent applications are a better
measure of a universitys interests in patents.
Data on the numbers of new patent applications, licenses executed and the amounts of royalties
and sponsored research tied to a license are from the AUTM surveys (AUTM, various years) for the years
1994-96. We use average values over these years. For royalties, we use license income net of income paid
to other institutions and inclusive of reimbursed legal fees. For sponsored research, we also consider the
frequency, as reported in our survey, of license agreements which include sponsored research. Data for
this measure are responses to a question as to how often their license agreements included sponsored re-
search. Possible responses were almost always, often, sometimes, rarely and never. Only two
TTO's responded never, so that we aggregate the never and rarely responses to a single category.
Independent variables in the analysis are the importance the TTO attaches to the outcome, the
types of inventions produced by the faculty and some measure(s) of the size of the university's licensing
operation and/or potential. With the exception of indicator variables, we use the logs of the dependent and
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independent variables. For all models except the frequency of sponsored research, we use ordinary least
squares with heteroscedastic robust standard errors. An ordered probit regression is used to estimate the
model based on the frequency of sponsored research.
Licenses. For licenses executed, we include two size variables: the number of disclosures (IN-
VDIS) and the number of licensing professionals (TTOSIZE). The greater the number of disclosures
made to the TTO, the greater the potential for executed licenses. The greater the number of licensing pro-
fessionals, the more attention that can be paid to each disclosure. A number of TTO professionals indi-
cated to us that they did not have sufficient staff in their offices to adequately market all technologies that
were disclosed.
Recall from Section 6 that in indicating the importance of outcomes, respondents were asked to
indicate whether an objective was not very important, moderately important and extremely impor-
tant. Based on the response, we form two indicator variables. For licenses, TTOEVAL_1 is set to one if
the TTO indicates that licenses are not very important (zero, otherwise) and TTOEVAL_2 is set to one
if the TTO indicates moderately important (zero, otherwise).
To capture the types of inventions in a university, we include measures of stage of development,
the quality of the faculty, as well as whether the university has a medical school. With regard to the num-
ber of licenses executed, we expect a negative relationship between the stage of development and the like-
lihood of license. Thus we include as regressors the percentage of licensed disclosures that were only
Proof of concept but no prototype (PROOF) and the percentage where there was a Prototype available
but only lab scale (further development needed) (PTYPE). Inventions in either state PROOF or PTYPE
are considered as early stage, with PROOF being earlier than PTYPE. Note that we are using the per-
centage of licensed disclosures in these stages, whereas the percentage of all disclosures in these stages
would be preferable; however, we did not collect that information.
Previous studies have found that the presence of a medical school is related to productivity meas-
ures of university licensing (see Thursby and Kemp (2000) and Thursby and Thursby (2000a). It is not
clear whether this presence influences outcomes via different preferences of medical school faculty versus
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other faculty or via generally different characteristics of medical technologies. In this analysis, the prefer-
ences of the TTO are held constant (and presumably are related to the preferences of the faculty), so that
the presence or absence of a medical school (MEDSCHL = 1 if there is a medical school, 0 otherwise) is
included here to account for possible differences in types of technologies and their marketability.
The academic quality of the faculty (QUAL) is included since different quality of faculties may
result in different invention characteristics (stage of development, novelty, etc.). As our measure of qual-
ity, we use the 1993 National Research Council's (NRC 1995) survey results regarding the academic qual-
ity of Ph.D. granting departments. One problem with this measure is that it considers only the quality of
Ph.D. granting departments. However, apart from medical schools, it is plausible that substantial research
programs are difficult to sustain in the sciences and engineering without Ph.D. students. Thus with the
exception of medical schools, the quality of Ph.D. granting departments in the engineering and sciences
should reflect the quality of the departments from which disclosures emanate. We use a weighted average
of the department quality scores where the weights are faculty size. Note that the NRC rankings do not
include all institutions, hence some of our respondents must be excluded. The quality scores range from 0
to 5 where 5 indicates a distinguished department.
We have omitted a number of characteristics of university offices that one might expect to be re-
lated to outcomes. For example, we omit public/private status, the portion of the TTO's operating ex-
penses that derive from licenses and the background of the TTO. We do so, not because we believe they
are unimportant but because we expect them to influence outcomes via their influence on the evaluation
the TTO places on different objectives.
Royalties. Licenses are modelled as functions of several size regressors. Royalties, on the other
hand, are not expected to be related to the number of licensing professionals in the TTO or to the number
of invention disclosures. Rather, the only size variable that we expect might be related to royalties is the
number of licenses executed (LICENSES). We include the importance the TTO attaches to royalties.
Only one TTO indicated that royalties were not very important so we aggregate responses to two
groups: extremely important and other. TTOEVAL_2 is set to one if the TTO indicates other (zero,
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otherwise). Characteristics of inventions, for the reasons outlined above for licenses executed, can influ-
ence their market acceptance so that we also include PROOF, PTYPE, MEDSCHL and QUAL.
Patents. The measure of patents we use is the number of new patent applications, and our only
measure of size for patents is the number of inventions disclosed (INVDIS). For inventions that are only a
proof of concept, it may be difficult to convince the patent office of the utility of the invention. Simi-
larly, inventions that are no more than a lab scale prototype may not work when scaled up. Thus we might
expect our measures of stage of development to be negatively related to the patent outcome. Further, uni-
versities often seek patent protection only when commercial potential is clear, and this is often not the
case for early stage technologies. Note that the measure of stage of development appropriate to patenting
is the stage of development of disclosures; however, PROOF and PTYPE are the fraction of licensedin-
ventions in these stages.
Faculty quality, QUAL, is included since higher quality faculties are expected, in general, to pro-
duce more novel inventions. The importance attached by the TTO to patents is included. TTOEVAL_1 is
set equal to one if the TTO responded that patents are not very important (zero, otherwise), and TTO-
EVAL_2 is set to one if the TTO indicated that patents are moderately important (zero, otherwise).
Sponsored Research. Sponsored research is measured both as the amount of sponsored research
tied to a license (SPONRES) and the frequency, as reported by the TTO, that sponsored research is in-
cluded in a license agreement (SPONFREQ). For SPONFREQ we assign 3 to the response almost al-
ways, 2 to often, 1 to sometimes and 0 to rarely or never. In an ordered probit model the actual
values assigned do not matter so long as they properly reflect the ordinal ranking. Since sponsored re-
search tied to a license is often used to support further development of the invention licensed, we include
the stages of development PROOF and PTYPE. We also include QUAL and measures of the valuation the
TTO places on sponsored research. TTOEVAL_1 is set equal to one if the TTO responded that sponsored
research is not very important (zero, otherwise), and TTOEVAL_2 is set to one if the TTO indicated
moderately important (zero otherwise). A size variable (LICENSES) is used for SPONRES.
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Results. Results for the five regressions are in Table 6. Since ordered probit coefficients are dif-
ficult to interpret, we consider first the four least squares regressions dealing with numbers of licenses and
new patent applications and amounts of royalties and sponsored research funds. We then turn to the or-
dered probit results for SPONFREQ. Recall that we are using logs of all variables except the indicator
variables, so that the coefficients of the least squares regressions are elasticities.
The R2in several of the equations is quite high (for example, LICENSES has an R2of 0.84). This
is, however, an artifact of including the size regressors TTOSIZE, LICENSES and INVDIS. Note that
each of these size regressors is significantly different from zero and each is positive (as they are expected
to be). For the patents and sponsored research equations, only the size variables (INVDIS and LI-
CENSES, respectively) are significantly different from zero. The elasticities of patents with respect to
disclosures and sponsored research with respect to licenses are each close to one, and, further, they are not
significantly different from one. Patents and sponsored research appear to grow nearly lock-step with
disclosures and licenses, respectively.
In the licenses executed equation, only TTOSIZE, INVDIS and MEDSCHL are significantly dif-
ferent from zero and each is positive. The MEDSCHL result is expected given the generally greater mar-
ketability of medical inventions (see, for example, Thursby and Kemp (2000)); however, this does not
carry over to royalties as MEDSCHL is not significant in that regression. Also not surprising is the result
that larger TTOs execute more licenses and more inventions disclosures lead to more inventions. Below
we consider the significance of the small elasticity of licenses with respect to disclosures.
In the royalty equation, PTYPE, QUAL and LICENSES are each positive and significantly dif-
ferent from zero while PROOF is negative and significant. Earlier we noted that royalties are hard to de-
fine for early stage technologies given the inherent uncertainty about their ultimate commercial viability.
That fact appears to manifest itself in our findings for royalties with respect to PROOF and PTYPE. The
greater the percent of licenses executed in the earliest stage of development (PROOF) the lower are royal-
ties, and royalties grow as the percent of licenses in the next stage of development (PTYPE) increases.
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Finally, the positive, significant coefficient of QUAL suggests that higher quality faculty tend to produce
inventions with greater commercial viability (perhaps because of novelty).
The elasticities of licenses with respect to invention disclosures and royalties with respect to li-
censes are each smaller than one. It is also the case that they are significantly smaller than one (at a 10%
level in the license equation and a 5% level in the royalty equation). Thursby and Thursby (2000a) find
results which suggest that universities are delving more deeply into the available pool of inventions so
that there is declining commercial appeal for the marginal disclosure. Our finding that the elasticities of
licenses with respect to invention disclosures and royalties with respect to licenses each are less than one
reinforces the Thursby and Thursby result. If there is declining commercial appeal of the marginal disclo-
sure then the likelihood of finding a licensee should be declining and this would result in an elasticity
smaller than one. The same point holds for royalties and licenses.
Table 6 gives the ordered probit coefficients for the sponsored research frequency equation (that
is, the equation explaining the frequency with which licenses include sponsored research). Ordered probit
coefficients are difficult to interpret, so we also provide in Table 7 the conditional probabilities and elas-
ticities associated with the ordered probit results. Recall that the dependent variable SPONFREQ is set
equal to 3 if the TTO responded that sponsored research is included in licenses almost always, 2 if they
responded often, 1 if they responded sometimes and 0 if they responded rarely or never. To in-
terpret ordered probit coefficients we need to calculate the effects of changes in regressors on changes in
the probability of observing different values of SPONFREQ. For example, a positive, significant coeffi-
cient only indicates that increases in the independent variable will increase the probability of observing a
score of 3 and will decrease the probability of a 0. By themselves, the coefficient estimates do not reveal
either the direction of effects on observing scores of 1 or 2 or the magnitude of the probability of observ-
ing a particular score. For a more meaningful understanding of the coefficients we substitute them into the
probability model and calculate elasticities for the continuous regressors and for the discrete regressors
we calculate probabilities of each of the four frequencies conditional on each possible value of the dis-
crete regressors.
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The SPONFREQ equation is the only one in which the TTO's evaluation of an outcome is signifi-
cant. If the TTO views sponsored research as not very important, then the probability that a license will
include sponsored research is lower than if the TTO values sponsored research more highly. However,
there is not a significant difference between a TTO valuation of moderately important and extremely
important. In the first column of the first panel of Table 7 the value 0.704 is the probability that a univer-
sity will rarely or never include sponsored research in a license agreement conditional on their valuation
of sponsored research to be not important. If the TTO values sponsored research as either moderately or
extremely important the probability of rarely or never is less than 0.08. The probability of sometimes
having sponsored research in a license is roughly similar regardless of the TTO's valuation of sponsored
research. When the TTO values sponsored research moderately or extremely important the probability
rises to greater than 0.5 that a license agreement will include sponsored research often.
Both PROOF and PTYPE are significantly different from zero, with the former having a positive
effect and the latter a negative effect on the frequency of sponsored research. This result is expected, as
was the relation between stage of development and royalties. The earlier the stage of development, the
more likely is sponsored research to support further development and the less likely are royalties. In the
royalty equation, we found the expected result of increasing royalties as the stage of development in-
creased. In the sponsored research frequency equation, we also find the expected result that the earlier the
stage of development of inventions the more likely it is that sponsored research will appear in a license; in
general, such sponsored research would be for further development of the technology. The second panel
of Table 7 gives the elasticities of the probability of a license including sponsored research with respect to
PROOF and PTYPE. For example, the value 2.554 indicates that a 1% increase in PROOF will lead to a
2.554% fall in the probability of rarely or never having a license include sponsored research.
Finally, universities with higher quality faculty are less likely to include sponsored research in a
license; recall that we also find that such universities are more likely to have greater amounts of royalty
income. One hypothesis that explains these results is that higher quality faculty have less difficulty, in
general, obtaining sponsored research funds so that licenses are used more to generate income.
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8. Conclusion
This paper describes the results of our survey of TTOs at 62 major research universities. Roughly
two-thirds of the respondents were public universities, and more than half of those were land-grant
institutions. For fiscal year 1996 each of these received, on average, about $150 million in federally
sponsored research funds and earned $4 million in income. Patentable inventions are typically owned by
the university, though the inventor's share of license income averages 40%, and inventors often have
control over an even larger share. Medical schools disclose the most inventions, though engineering and
science schools disclose nearly as many. Most inventions which evolve from university research are
disclosed at a very early stage of development, and so have generally uncertain market potential and
require substantial additional development before they can be brought to the market (if they ever make it
that far).University objectives in the licensing process are diverse. The most important objective to the
TTO is royalties and fees generated, but other objectives are important. Licenses executed almost always
include royalties and up-front fees, often include sponsored research, but less frequently include equity
shares in the licensee.
Finally, given this information, we analyzed the relationship between licensing outcomes and
both the objectives of the TTO and characteristics of the new technologies. It appears that patent applica-
tions grow on a one-to-one basis with disclosures, while sponsored research grows similarly with licenses
executed. More licenses are executed at universities with large TTOs and medical schools. Royalties gen-
erated are typically larger the higher the quality of the faculty and the higher the fraction of licenses that
are executed at latter stages of development. Sponsored research is more likely to be included in a license
agreement if the new technology is at an early stage of development or if the TTO values it as important.
We also find evidence that, at the margin, additional disclosures generate smaller percentage increases in
licenses, and those increases in licenses generate smaller percentage increases in royalties. That is, TTOs
generally have been effective at tapping the pool of available technologies in their universities.
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Acknowledgment
We express appreciation to Don Siegel and Arvids Ziedonis for comments on an earlier draft. We grate-
fully acknowledge support from the Sloan Foundation and the National Bureau of Economic Research
under the NBER Project on Industrial Technology and Productivity. Jerry Thursby and Marie Thursby
thank the Purdue Technology Transfer Initiative for support.
Endnotes
1The bulk of this literature has focused either on the role of patents and publications in the transfer proc-
ess (see Adams 1990, Henderson, Jaffe, and Trajtenberg 1998, and Jaffe, Trajtenberg, and Henderson
1993) or on consulting, sponsored research or institutional ties (see Cohen et al.1998, Mansfield 1995,
and Zucker, Darby and Armstrong 1998 and Zucker, Darby and Brewer 1998).
2For 26% of our sample it is university policy that the inventor can direct department, university and/or
TTO shares. For other universities, it is department policies which determine whether the inventor can
direct department shares.
3For more on the multiplicity of university objectives see Thursby and Kemp (2000) and Parnes, Omenn
and Brock (2000).
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References
Adams, J., 1990, Fundamental Stocks of Knowledge and Productivity Growth, Journal of Po-litical Economy 98, pp. 673-702.
Adams, J., 1998, Endogenous R&D Spillovers and Industrial Research Productivity, manu-script, University of Florida.
Association of University Technology, Inc., 1996, 1997. AUTM Licensing Survey.Cohen, W. M., R. Florida, L. Randazzese, and J. Walsh, 1998, Industry and the Academy: Un-
easy Partners in the Cause of Technological Advance, in Roger Noll (ed), Challenges to Research Uni-versities, Washington, D.C.: The Brookings Institution, pp. 171-199.
Henderson, R., A. Jaffe and M. Trajtenberg, 1998, Universities as a Source of CommercialTechnology: A Detailed Analysis of University Patenting, 1965-1988, Review of Economics and Statis-tics, pp. 119-127.
Holmstrom, B., 1989, Agency Costs and Innovation, Journal of Economic Behavior and Or-ganization, 12, pp. 305-327.
Jaffe, A., 1989, Real Effects of Academic Research, American Economic Review, 79 (5), pp.957-70.
Jaffe, A., M. Trajtenberg and R. Henderson, 1993, Geographic Localization of Knowledge
Spillovers as Evidenced by Patent Citations, Quarterly Journal of Economics, 108, (3), pp. 577-598.Jansen, C. and H. Dillon, 1999, Where do the Leads Come From? Source Data from Six Institu-
tions, The Journal of the Association of University Technology Managers, 11.Jensen, R. and M. Thursby, 1999. Proofs and Prototypes for Sale: The Licensing of University
Inventions, American Economic Review, forthcoming.Kamien, M., 1992, Patent Licensing, in R. Auman and S. Hart (eds.), Handbook of Game The-
ory, Amsterdam: North Holland.Mansfield, E., 1995, Academic Research Underlying Industrial Innovations: Sources, Character-
istics, and Financing, The Review of Economics and Statistics, 77, pp.55-65.Mansfield, E. and Y. Lee, 1996, The Modern University: Contributor to Industrial Innovation
and Recipient of Industrial R&D Support, Research Policy, 25, pp. 1027-1058.Mowery, D., R. Nelson, B. Sampat, A. Ziedonis, 1999, The Effects of the Bayh-Dole Act on
U.S. University Research and Technology Transfer: An Analysis of Data from Columbia University, theUniversity of California, and Stanford University, Research Policy, forthcoming.
Mowery, D. and A. Ziedonis, 1999, The Effects of the Bayh-Dole Act on U.S. University Re-search and Technology Transfer: Analyzing Data from Entrants and Incumbents, Research Policy, forth-coming.
National Research Council, Research Doctorate Programs in the United States, 1995, M. Gold-berger, B. Maher and P. Flattau (eds.), Washington, D.C.: National Academy Press.
Parnes, M., G. Omenn and E. Brock, 2000, A Case Study of System Complexity and RegionalApproaches to Technology Transfer, The Journal of Technology Transfer, forthcoming.
Rosenberg, N., 1992, Scientific Instrumentation and University Research, Research Policy, 21,pp. 381-390.
Siegel, D., D. Waldman and A. Link, 1999, Assessing the Impact of Organizational Practices on
the Productivity of University Technology Transfer Offices: An Exploratory Study, NBER Working Pa-per #7256.
Thursby, J. and S. Kemp, 1999, Growth and Productive Efficiency of University IntellectualProperty Licensing, Research Policy, forthcoming.
Thursby, J. and M. Thursby, 2000a, Commercializing the Ivory Tower: Propensity andProductivity, manuscript, Purdue University.
Thursby, J. and M. Thursby, 2000b. Industry Perspectives on Licensing University Technolo-gies: Sources and Problems, The Journal of the Association of University Technology Managers,forthcoming.
-
8/9/2019 Tech Trans Survey
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Ziedonis, A., 1999, Inward Technology Transfer by Firsm: The Case of University TechnologyLicenses, manuscript, University of Pennsylvania.
Zucker, L., M. Darby and J. Armstrong, 1998, Geographically Localized Knowledge: Spilloversor Markets, Economic Inquiry, 36, (1), pp. 65-86.
Zucker, L., M. Darby and M. Brewer, 1998, Intellectual Capital and the Birth of U.S. Biotech-nology Enterprises, American Economic Review, 88, pp. 290-306.
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Table 1. Stage of Development of Licensed Inven-tions
What percentages of the invention disclosures which have been licensed in the last five
years were in the following stages of development at the time the license was negiotiated?
Proof of concept but no prototype 45.1
Prototype available but only lab scale (further development needed) 37.2
Some animal data available 26.7
Some clinical data available 9.5
Manufacturing feasibility known 15.3
Ready for Practical or commercial use (e.g., software or reagent quality materials) 12.3
Table 2. Marketing Procedures
What procedure(s) does your office follow in marketing technologies whichare available for licensing?
Percentage of respondents
website 37.5
personal contacts 75.0
direct mailing/fax 52.5
trade shows 18.8
meetings 20.8
inventor contacts 58.3
Table 3. Interest by More Than One Firm in a Technology
AlmostAlways
Often Sometimes Rarely Never
In your experience, how often does more than one company
express interest in the same technology by
Signing a confidentiality agreement to look at the technology 18.0 55.7 24.6 1.6 0.0
Bidding or negotiating to license the technology 0.0 4.9 50.8 41.0 3.3
When only one company bids on a particular technology,
how often is the reason
an exclusive option from prior sponsored research 12.9 30.6 43.5 9.7 3.2
nature of the technology 26.2 42.6 29.5 1.6 0.0
market characteristics 18.0 39.3 29.5 8.2 4.9
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Table 4. License Payment
Almostalways
Often Sometimes Rarely Never
How often do the license agreements you negotiate include the following types of payments?
license issue or up-front fee 66.1 25.8 6.5 0.0 1.6running royalties 80.6 16.1 3.2 0.0 0.0annual fees or minimum royalty fees 56.5 32.3 9.7 1.6 0.0progress or milestone payments 29.5 42.6 21.3 4.9 1.6patent reimbursement 67.7 21.0 8.1 3.2 0.0Equity 0.0 8.1 32.3 41.9 17.7
For agreements that include equity, how often does the agreement also include
license fees 43.8 16.7 20.8 16.7 2.1running royalties 68.8 18.8 10.4 2.1 0.0
How often do your license agreements include sponsored research?
6.5 40.3 38.7 11.3 3.2
Table 5. TTO Valuations
We are interested in how you measure the success of your TTO. How important to you arethe following measues of success?
Extremely Moderately Not impt.Royalties/license fees generated 70.5 27.9 1.6Sponsored research funds 34.4 49.2 13.1Number of licenses/options signed 49.2 44.3 6.6Number of patents awarded 16.4 52.5 31.1
Number of inventions commercialized 60.7 31.1 8.2
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Table 6. Outcome Regressions
LICENSES ROYALTIES PATENTS SPONRES SPONFREQ
TTOEVAL_1 -0.231 NA -0.202 -0.964 -2.675 ***
TTOEVAL_2 0.223 0.466 -0.094 -0.289 -0.258
PROOF 0.017 -2.333 *** 0.171 1.248 2.759 **PTYPE 0.405 7.740 *** -0.536 -3.794 -6.707 **
QUAL -0.138 1.091 * 0.529 0.883 -1.987 **
INVDIS 0.788 *** 0.952 ***
LICENSES 0.567 ** 0.913 ***
MEDSCHL 0.405 ** 0.404
TTOSIZE 0.365 ***
R-Square 0.840 0.584 0.882 0.394 0.276
Number of Obs. 47 47 47 42 47
*** Significant at 1% level
** Significant at 5% level
* Significant at 10% level
Table 7. Conditional Probabilities and Elasticities forSponsored Research Frequency
Rarely orNever
Sometimes Often AlmostAlways
Conditional Probabilities
TTO Eval.
Not impor-tant
0.704 0.256 0.040 0.000
Moderatelyimportant
0.076 0.369 0.517 0.039
Extremely
important
0.049 0.315 0.577 0.059
Elasticities
PROOF -2.554 -1.091 1.205 4.259
PTYPE 6.209 2.652 -2.925 -10.354
QUAL 4.068 1.271 -1.667 -5.556
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