Date post: | 14-Apr-2018 |
Category: |
Documents |
Upload: | sher-azeem |
View: | 216 times |
Download: | 0 times |
of 46
7/27/2019 University Distance Function Sfa
1/46
1
Palma, September 2007II IBEW
Research Efficiency Analysis in a System of
University Technology Transfer Offices: AnEmpirical Analysis of the Spanish Case
DEMO Student: Maria Victoria Trujillo1
Tutor: Dr. Emili Grifell-Tatje
Co-tutor: Dr. Pablo Arocena
Abstract
The phenomenon of TTOs in Spain is relatively new and there is a need of assessingtheir efficiency. In this sense, we present quantitative evidence on the level of efficiencyof 50 Spanish TTOs for the period 2003-2005. An important methodological inside ofthe paper is the introduction of SFE with multi-output approach: Licenses and Spin-off.Our results suggest that an increment of invention disclosure and TTO size entails anincrement of the commercialization in the form of patents, licenses and Spin-off. Eventhough the evidence is weak, we also point out that public and technical universitieshave higher commercialization with respect to their counterparts, finally we find neitherexperience nor industrial cluster effects in Spanish TTOs.
Key Words: TTO, Technical efficiency, SFE, Licenses, Spin-off
JEL Codes: C23, D23, O31, O32.
1We would like to thanks, the REDOTRI Universidades who kindly lend us the data set of the questionnaires of the years 2003,2004 and 2005, and specially to Susana Camara. We also want to thank the TTO of the UAB and especially to Angela Serrano whogave us an interview to define the process of technology transfer in Spain.
7/27/2019 University Distance Function Sfa
2/46
2
1. Introduction.
The amount of innovation commercialized from the universities has increased
dramatically (Nelson, 2001; Mowery et al., 2001), not only in the US, also in other
countries where Universities have the same structure of ownership and decision rights.
This is the case of Spain (Geuna, et al., 2003). This phenomenon started with The Bayh-
Dole Act (1980) and the 1986 Federal Technology Transfer Act which moved the
property and commercialization rights on inventions achieved from federally funded
research to the universities. From then, the commercial success and the
commercialization of research is an important option to create wealth from universities
(Etzkowitz, 1998; Shane, 2002). It also has gone with an increasing interest of
academics about the role of University Transfer Technology Offices (TTO). This
phenomenon started in Spain almost 20 years ago with the First National Plan.
Consequently, most of the Spanish TTOs have achieved enough experience
transferring technology to the market and hence their efficiency might be analyzed in
depth.
In order to study their efficiency two methodologies are used in the literature. Firstly, a
non-parametric approach (Thursby and Kemp, 2002) well known as Data Envelopment
Analysis (DEA), and secondly a parametric approach (Siegel, et al., 2003) called
Stochastic Frontier Estimation (SFE). The second method gives statistical information
about the impact of independent variables but has the limitation when a multi-output
approach is required. We have made the effort of introducing a model of two outputs:
Licenses and Spin-off. Some of the results presented in the work are consistent with
previous research. In short, an increment of the inputs increases the amount of
commercialization done. Additionally, public and technical universities seem to be
more efficient (Siegel, et al., 2003). Besides, in contrast with Siegel et al., (2003) we
found neither experience nor cluster effects.
The order of the paper is as follows. In the next section, an extensive review of
international literature on the topic is done. In the third section, we analyze the
specificities of the Spanish system and develop the hypothesis. Subsequent sections
present data and results. Finally, concluding remarks close the work.
7/27/2019 University Distance Function Sfa
3/46
3
2. University to Industry Technology Transfer: a global review
In a more global view we can understand technology transfer as the process of
innovation that is presented, as the transference of intellectual capital and know-how
between organizations with the purpose to be used in the creation and development of
products and services commercially viable. (Rubiralta, 2003). Technology transfer is
usually thought of, as occurring within or across firms (Siegel, et al., 2003) such
information transfer is from one firm or institution to another, from one employee of
one division to another (intra-firm transfer of technology) or to one country to another.
- Insert Figure 1 -
The university to industry technology transfer or commercial transfer of scientific
knowledge from universities to firms has been a topic of interest in the literature since
the last decades (see Figure 1). In the following section we will review this literature
that can be divided under the following subjects:
2.1Historic perspective, growth in academic innovation transferred to the market
before and after Bayh-Dole Act of 1980 and statistics.
2.2Ownership and licensing.
2.3International and Regional comparisons through case studies.
2.4Outputs of university research. (patents, licenses, spin-offs)
2.5Efficiency of university Technology Transfer Offices. (TTOs).
2.1 Historic Perspective, Growth in academic innovation transferred to the market
before and after Bayh-Dole Act of 1980.
The University is an institution born in the XII century, with the mission to distribute
knowledge from professor to students, until the XIV century. In the XV and the XVI
centuries a declivity stage starts that continued all along during the XVII and XVIII
centuries, giving the role of knowledge and research to technical societies and
academies that made scientific research according to the needs of a society that was
becoming more technical. But the inefficiency of this societies and academies to
organize themselves in specialized ways, cause the reborn of the universities in the XIX
7/27/2019 University Distance Function Sfa
4/46
4
century, when Van Humboldt propose a new model were is combined the already
known educational function with a new function: research (Santamaria, et al., 2007).
The U.S research university and the organized pursuit of R&D in industry both
originated roughly 125 years ago, have grown in parallel throughout the 20th century
(Mowery and Rosenberg, 1998). Even though the research cooperation between
universities and the industry has a long story, recent changes like growth of university
patenting and licensing of new technologies to private firms, have concerned
considerable the attention of researches.
In 1810, the University of Berlin is founded and is the leader of change of many other
universities in the medieval era and also the inspiration to create new ones (Geuna, et
al., 1999). During XIX century many universities especially the American ones were
connected with the necessity to develop the local industry, offering guidance and
solving the practical problems of the industry. Before World War II, which supposed an
in flex point in the history of the science and the industry when the role of the university
starts to change, mainly in the American universities, with their relation with the
industry. During the war, the best scientists and technologists were incentive to create,
promoting big advances in fields like medicine and nuclear engineer. Projects like the
Manhattan one (atomic bomb) were the result of those efforts (Santamaria, et al., 2007).
At this point its important to recall the Vannevar Bush inform, Science: the Endless
Frontier, (1945) because when Bush was the director of the research and development
office in the U.S during the war; he established the need to finance the scientific
activities after the war and the importance of the basic research in the universities. He
established a model of the process of innovation that has been identified as the linear
model, were the basic research is considered as the source of technological innovation.
The increases in university patenting and licensing are frequently asserted to be directed
consequences of the federal policy initiative known as the Bayh-Dole Act of 1980
(Mowery, et al., 2001). This federal policy gives the ownership of commercialization of
federal funded research to universities. This right is usually managed by the Technology
Transfer Office (TTO). The Bayh-Dole act has increased dramatically the
commercialization of academic innovation. Godfarb and Henrekson, (2003) give partial
evidence to this fact. They compare the case of Sweden where the right of selling the
7/27/2019 University Distance Function Sfa
5/46
5
intellectual property belong to the inventor, with the case of US, where the right of
commercialize the intellectual property belong to the university and exploit these
resources through the figure of the TTO. They conclude that having Sweden a higher
relative amount of researchers than US, the income generated by licenses is relatively
larger in US, and therefore the ownership of the decision right for the University and the
presence of the TTO bring to a more efficient technology transfer process. This process
has not only been taking place in the US, but also in other countries where Universities
have the same structure of ownership and decision rights about inventions, like Spain,
Italy or UK (Geuna, et al., 2003).
Before the Bayh-Dole act2, U.S. public universities in the period from 1900s to 1940s
looked for the collaboration in great spread of research within the industry. The Second
World War transformed the role of U.S. universities as research performers, as well as
the sources of their research funding. Then during 1950s-1960s periods, the share of
industry findings declined because of great budgets of post war. During the 1960s the
National Science foundation allowed academic institutions to patent and license the
results of their research under the terms of Institutional Patent Agreements (IPAs).
Beginning in the 1970s, the share of industry funding within academic research began
to grow again. Most of the increase in industry funding occurred during the 1980s,
remaining roughly constant after 1990.
The Bayh-Dole act is contemporaneous with a sharp increase in U.S university
patenting and licensing activity (Mowery, et al., 2001). Table 1 reveals the large
increase.
- Insert Table 1
2.2 Ownership and Licensing
Universities ownership structure has two important players in the role of technology
transfer; one is the university, which many authors had claimed to have a new role in
society with respect to commercialization of research results, or entrepreneurial
science (Etzkowitz, 1998; Martin, 2003); and two is the universitys scientist or
2 To know more about USA patenting activity before 1980 see the National Science and Foundation Board Indicators. U.S.Government.
7/27/2019 University Distance Function Sfa
6/46
6
inventor. From the university perspective, the challenge becomes: to increase the extent
of commercialization, to visualize the contribution to economic development, and to
manage the relationship between commercialization and other core activities.
Rasmussen, et al., (2006), says that in addition to teaching and research, universities are
increasingly expected to take on technology transfer and commercialization as part of
their mission. They explore the initiatives provided by the universities to promote
commercialization of university knowledge. McAdam, et al., (2005) construct a model
with this initiatives analyzing licensing and business building process and suggest this
approach to innovation centers. The University Scientist or inventor has an academic
culture and it carries with an ambiguous relationship to commercial innovation and a
preference for basic research (Ndonzuau, et al., 2002). Most of his career recognition,
and consequently compensation, comes from his success in the basic research, therefore
it is a clear, and in some cases important, opportunity cost for the development of
commercial innovation. Aghion, et al., (2005) develop a model that clarifies the
respective advantages and disadvantages of academic and private-sector research and
they also examine when in the process of technological transfer is optimal to make the
transition from academia to the private sector. They found that innovative ideas can be
recognized by the private sector in a more early-stage. More recently Lowe, (2006)
proposes a theoretical model that shows how the inventor know-how affects the
decision to license his invention for development or star a spin-off with his invention.
Since the passage of the Bayh-Dole Act, proponents of this legislation argue that
industrial use of federally funded research would be reduced without university patent
licensing. This issue means if the commercial application and diffusion of inventions
from federally funded research critically depends upon allowing universities to retain
title to and license them. Jensen and Thursby, (2001), address this issue providingevidence of 62 U.S universities analyzing several related theoretical models. Behind a
moral-hazard framework, and taking into account that the effort of the inventor is not
observable, they stated that no development of an invention will be made unless
inventors receive incentive such as royalties or equity. Besides, following the
framework where firms have incomplete information of the quality of the inventions
Macho-Stadler, et al., (2005) developed a theoretical model explaining the specific role
of TTOs in licensing university inventions. They say that beyond the classical
economies of scale, a university wide TTO can be an instrument to reduce the
7/27/2019 University Distance Function Sfa
7/46
7
asymmetric information problem found in the scientific knowledge market. They
consider a model of technology transfer between a research institute (university) and the
industry (firm) and in parallel they develop a reputation argument for a Technology
Seller (TTO). Consequently TTOs exist as they reduce asymmetries of information
between academic entrepreneurs and established firms.
Ownership, income splits, stage of development, marketing, license policies and
characteristics, goals of licensing and the role of the inventor in licensing are studied by
Thursby, et al., (2001), as they describe their survey of licensing at 62 research
universities. Their most relevant find is that additional disclosures generate smaller
percentage increases in licenses, and those increases in licenses generate smaller
percentage increases in royalties.
2.3 International and Regional comparisons through case studies
There have been also a lot of studies comparing different approaches in the international
arena like the case of Goldfarb and Henrekson, (2003), as mentioned before, that
compare a subset of policies of the US and Swedish innovation systems that affect the
commercialization of university technology. Owen-Smith, et al., (2002) compared US
and European practices in terms of university industry relations.
Feldman, et al., (2002), explore also policy issues but in a regional perspective, using
data from U.S Carnegie I and Carnegie II universities. They estimate a model were they
consider equity as technology transfer mechanism and found that offers advantages to
university in revenues and ownership interests. Bercovitz, et al., (2001) studied the
influence of university organizational structure on technology transfer performance.
They treat the structure of the TTO as an independent variable that accounts, in part, for
measured inter-institutional differences in patenting, licensing, and sponsored research
activities. Using prior theories of distinct forms of organizational structure: U-Form, M-
Form and H-Form3; they analyze three major research universities John Hopkins
University, Pennsylvania State University and Duke University. They found that the
3 For more details about this three forms of organizational structure see the studies of Chandler (1990) and Williamson (1985).
7/27/2019 University Distance Function Sfa
8/46
8
three universities differ in their organizational structure4 and that this structure affects
performance in a predictable manner.
Di Gregorio and Shane, (2003) analyze cross-institutional variations in new firms
formation rates between university licensing offices (TLOs) over the 94-98 periods, in
other words they explore empirically why some universities generate more start-ups
than others. They say that several major recognized corporations had their origins as
TLO start-ups, thats why they are an important mechanism for economic activity.
Colyvas, et al., (2002) studied cases of commercialization of technologies. Feller, et al.,
(2002), studied ways in which academic R&D and education contribute to industrial
innovation. Beise and Stahl, (1999), find that in Germany high-technology does not
depend on co-location of public and private research.
Other studies focused on individual cases to explore similar issues. Zucker, et al.,
(2002) in a biotechnology case study looked at the efficiency of university technology
transfer process. Goldhor and Lund, (1982) made a study case of Massachusetts
Institute of Technology (MIT) that examines the events in the transfer on an advanced
technology (a text-to-speech reading machine) from the university to an industrial firm
seeking to exploit the innovation. They suggest important policy making issues and
important implications that latter gave birth to TTOs.
2.4 Outputs of University Research (patents, licenses, spin-offs)
The studies that focus on the outputs of the university research are widely focused on
spin-offs companies. The development of spin-offs is analyzed by Vohora, et al.,
(2004) they say that spin-offs has to pass through 5 phases of development in order tobe a successful venture. Clarysse, et al., (2005) explore the different incubation
strategies for spinning-out companies employed by European Research Institutes. They
use a two-stage approach where three distinct incubation models of managing the spin-
out process where identified: Low selective, Supportive and Incubator. Leitch and
Harrison, (2005) explore the spin-offs with a study case and they also examine the role
4 More specifically they found that John Hopkins most closely aligns with the H-Form, Duke with the U-Form and Pennsylvania hascreated an M-Form.
7/27/2019 University Distance Function Sfa
9/46
9
of the TTO in this context. They suggest that TTO should be assumed in a more
economic developer role.
Locket, et al., (2003) made a comparison of two groups of universities in the U.K. They
identify that more successful universities tend to create new ventures were the equity is
divided more equally between the TTO, the venture capitalist and the academic
entrepreneur. Chukumba and Jensen, (2005) develop and empirically tested a game-
theoretic model that explains why a university invention is commercialized in a spin-off
rather than in an established firm. The most relevant conclusion for the literature is that
they proof that when the invention, specially engineering, is of high quality universities
license more. And when the quality of the inventions is low universities make spin-offs.
In the U.S, legislative support to university patenting of federally funded research
results was largely motivated by expectations that such a policy would increase the level
of industry R&D; Mazzoleni, (2006) presents a theoretical model of R&D competition
based on a university invention. Patenting and licensing are studied. They found that the
results on patenting and licensing derive in increase of R&D investment and social
welfare, but they suggest this should be tested empirically. Macho-Stadler, et al., (1996)
analyze terms of contracts in licensing agreements, between Spanish and foreign firms.
They found that royalties are relative more important in contracts that transfer know-
how, and they explain this proposing a theoretical model where know-how is hard to
quantify so its difficult to include in a contract.
2.5 Efficiency of University TTOs
There have been a group of studies focusing on the use of tangible outputs to measurethe efficiency of TTOs. Markman, et al., (2005) studied the variable speed in 95 U.S
university technology transfer offices (UTTOs) and they find that faster UTTOs can
commercialize patent-protected technologies, greater licensing revenues and generate
more spin-offs. Chapple, et al., (2005) present evidence on the performance of TTOs in
the U.K. using Data Envelopment Analysis (DEA) and Stochastic Frontier Estimation
(SFE). They found that there is a need to increase capabilities and business skills of
TTO managers.
7/27/2019 University Distance Function Sfa
10/46
10
A series of studies built model to establish efficiency metrics and measure relative
productivity. Siegel, et al., (2003) present quantitative analysis of efficiency, measuring
the relative productivity of TTOs in the U.S using SFE measures. Their findings suggest
that TTO activity is characterized by constant return to scale and the variation in
performance is explained by environmental factors they use. They also present some
qualitative analysis. Thursby and Kemp, (2002) explore the increase in licensing
activity of U.S universities focusing on efficiency; employing DEA combined with
regression analysis. They find that licensing activity had increased over the years by
others factors than the relative size of the university. Anderson, et al., (2007) use a DEA
approach to examine efficient and inefficient TTOs within U.S. universities. As a result
efficiency in TTOs is found in many leading universities. Siegel and Phan, (2004)
analyze and describe the most important tools DEA and SFE as the most used technique
tools of evaluation.
3. Spanish legal environment and the Spanish Situation
3.1 TTO: Origin, Concept and Nature
The theoretical analysis of the process of technological transfer and its connection witheconomical growth is relatively new. Economic theory had always intuited the
importance of innovation and its effects in economic growth, but its until the decades
of the 1950 through 1960 that this variable started to be considered as exogenous.
The first theories not only had demonstrated the significant effect of innovation in
productivity, but also, they had demonstrated the existence of failures in the
transference of it to the market.
In the Spanish context, the public initiatives of promotion of innovation arrive with
remarkable delay with respect to other countries with stronger economies5. One of the
first laws, the Organic Law 11/1983 (LOU)6, where its principal objective is to regulate
the emerging relationships between the university and the firms, makes the role of the
first one, as the dynamic element of the innovative process. But the policies of R&D in
Spain have their in-flex point until 1986 when the law of Science 13/19867, takes effect.
5 OTRI: entre la relacin y el mercado. Available in www.redotriuniversidades.net, Biblioteca, Libros, Capitulo 2.6 Ley Orgnica de Reforma Universitaria del 11/1983.7 Ley de Fomento y Coordinacin General de la Investigacin Cientfica y Tcnica del 13/1986.
7/27/2019 University Distance Function Sfa
11/46
11
Until this date, we cannot talk about the existence of a scientific and technological
policy. This law defines a new organizational framework where the most important
instrument of planning and execution would be the National Plan of R&D8 that would
be implemented, followed and coordinated by the Inter-ministerial Commission of
Science and Technology (Comisin Interministerial de Ciencia y Tecnologa-CICYT-).
The law of Science in its 5 article says: that one of the objectives of the National Plan
of R&D is to promote research and development activities within the firms and the
collaboration between the firms and the public centres of research (law 13/86). In this
context, at the end of 1988, with the beginning of the first National Plan of R&D 1988
1991, starts-up the program of creation of the TTOs9 (in Spain Oficina de Transferencia
de Resultados de Investigacin-OTRI).
Following the previous approach of the Spanish context and the increasing flow of new
technologies in the recent scenario is important to view in a more general view the
process of technology transfer in two perspectives: (Rubiralta, 2003) the transference
that is produced between firms (horizontal transference) and the transference that is
developed between the agents (universities and public organisms) that are generators of
knowledge and the industry (vertical transference). The principal objectives of the TTO
are as follows (Conesa, 1997):
- To elaborate the databank about the infrastructure and the supply of R&D.
- To identify the transfer of results generated by the active groups of investigation
and to directly spread them between the firms or in collaboration with the next
units of interface.
- To facilitate the transference of those results to the firms, or in other case, the
correct assimilation of foreign technologies.
- To collaborate and participate in the negotiations of the contracts of research,
technical assistance, consultancy, license of patents, etc., between the groups of
research and the firms.
- To manage, with the support of the respective administrative teams, the
contracts to carry out.
8 Plan Nacional de I+D Espaa.9 To see the evolution and the way that have followed the Spanish TTOs until now see the documents of CICYT of 1989.
7/27/2019 University Distance Function Sfa
12/46
12
- To inform about the European programs of R&D and to facilitate technically the
elaboration of the projects, and also to manage the transaction of such a projects.
The first empirical study about the Spanish TTOs showed that they were very good
welcome and start to spread widely, but they well in that moment in process of
consolidation (Fernandez de Lucio y Conesa, 1996), because of their small size (2
technicians and 2 support persons) and they were more oriented to the university only,
which the authors qualified as a default in the mission to consolidate the relations
between universities and firms. Rubiralta, (2005) in his study found that the weakness in
the productivity growth in Spanish regions has generated a low technological demand of
universities R&D, making the TTOs to establish new strategies and goals. Macho-
Stadler, et al., (1996) analyze the contract terms of licensing agreements between
Spanish and foreign firms and found empirical evidence that royalties are relative more
important in contracts that transfer know-how. The intuition behind this is that because
know-how is hard to quantify and cannot be included in a contract, therefore, the license
agreement would be more credible when the scientist is interested in transfer know-how
and his profits depends on the sale of the license. Serarols, et al., (2007) analyze the
evolution, objectives, resources and activities of a specialised unit Technological
Trampoline and create some implications and recommendations to both university andTTOs.
Perez-Castrillo, (2005) says that one of the weaknesses of the TTOs is that, besides the
high enrolment of administrative employees, they should hire professionals highly
qualified, that can be able to establish a connexion between firms and the specialized
group of scientists researchers. The intuition behind this is that the participation of the
researchers in the process of technology transfer is crucial, and to make this happen isimportant that the TTO foments an incentive scheme that enforce the invention
disclosure and the collaboration of the research group with the firm that signs a license
contract. Following this approach we can derive our first empirical hypothesis10 for our
work:
10 Notice that this approach is consistent with the work of Jensen and Thursby (2001) and Jensen et al. (2003) presented in theprevious section.
7/27/2019 University Distance Function Sfa
13/46
13
Hypothesis 1: The more the number of invention disclosures more
commercialisations (patents, licenses and/or spin-offs) will be done.
As we announced before Macho-Stadler, et al., (2005) suggest that the main objective of
the TTOs is to reduce asymmetries of information between parties, it is worth to notice
that to accomplish this objective the TTOs must achieve a critical size in order to be
able to build a reputation11. The intuition behind this is; if the TTO has a work force big
enough to control the flows of inventions that arrive constantly they can control the
quality of commercial inventions they offer to the industry and this is transformed into
higher reputation. We construct our second hypothesis from this statement:
Hypothesis 2: The size of the TTO is important to achieve a degree of efficiency.
Vendrell and Ortin, (2006) explore the process of technological transfer from
universities. As a result they develop empirical implications. In particular they suggest
that more efficient TTOs help to increase the number of commercial innovations 12.
Following this intuition we can develop our third hypothesis:
Hypothesis 3: More efficient TTOs help to increase the number of commercial
innovations. We expect a positive relation between efficiency and the amount of
inventions commercialized.
3.2 Situation of the Spanish TTOs
In Spain, as in other countries there has been an increase, but in a medium rate, inSpanish universities patenting and licensing activity. The actual society has been
demanding the university a deeper commitment with the economy of the country;
claiming a higher involvement of the institution that traditionally has only been
academic. This new involvement is what Rubiralta, (2003) called technology transfer
that know is part of the mission of the new roll of the universities.
11 Extracted from Vendrell and Ortin (2006) implication 2 (page 11).12 Siegel et al. (2003) in their results support this empirical implication for the U.S.A case.
7/27/2019 University Distance Function Sfa
14/46
14
According to the 2006 annual Survey of RedOTRI Universidades one of the most
important indicators to measure the degree of interaction between the university and
industry is the number of contracts of R&D signed. We can see the evolution in Graphic
1.
- Insert Graphic 1 -
One of the traditional indicators of reference to measure the degree of interaction
between the university and the R&D firm is the quantity and the nature of the contracts.
With the data available by the RedOTRI, we can observe a tendency of rise in the total
volume of hiring, measure in euros, for the activities of R&D, consultancy or technical
services and other agents as shown in Graphic 1.
In other hand, the evolution of University patents also shows an important increase in
the last years, with a growth of almost 50% in only three years (see Graphic 2). The
international extensions have increasingly growth in the same interval of time which
shows a major interest of obtaining more utilities from the inventions disclosed. But
also shows that in spite of the effort, the bet for the international extension in the
Spanish arena is still growing.
- Insert Graphic 2 -
On of the most relevant items to reflect the increase in the patenting activity in the
Spanish university is the license agreements (see Graphic 3). In the last three years the
number of license agreements has been almost tripled, in the year 2005, 106 license
agreements were signed.
- Insert Graphic 3 -
The income generated by license agreements (see Graphic 4) have multiplied in the last
years, but its interesting to note that the volume of income generated by these
agreements do not evolve as expected, which indicates that it is not exploited in its
maximum of possibilities.
- Insert Graphic 4 -
7/27/2019 University Distance Function Sfa
15/46
15
Finally, another important aspect in the transference of technology is the university
based off firms or spin-offs (see Graphic 5). In Spain this aspect is still in an embryonic
stage in spite that there have been a lot of initiatives that are starting up from different
approaches.
- Insert Graphic 5 -
According to the data of the RedOTRI Universidades, in the year 2001 only 39 spin-offs
were created but after the 2003 the growth has been constant. There is a regular creation
of around 90 Spin-offs per year.
4. Determinants of the Process of University to Industry Technology Transfer
4.1 Internal Inputs, External Outputs and Environmental Factors.
In terms of organizational structure, the existence of a TTO inside a university is shown
as a fundamental instrument for the development of good relations with the industry
(Perez-Castrillo, 2005). In order to define the suitable inputs in the process oftechnology transfer from a university to a firm or entrepreneur it is important to describe
the principal steps of this process for universities that have a TTO. First we would make
a description of the process as follows taking into account the wisdom of specialized
administrative personnel13 and the literature. We will focus in the transference of
technologies through a license contract. This linear model (see figure 1) does not
represent that all technologies are transferred in the same way in all Spanish universities
this can be a topic for further research. The steps that follow the process of transferenceof technologies that results in a spin-off or start-up are very similar14.
- Insert Figure 1 -
The first step, necessary to transfer knowledge, is the research of innovations. This
process is done in laboratories or departments that are in charge of groups that work in
13 The interview was made in the TTO of the UAB to the person in charge of contracts and licence agreements.14 If you want a more detail specification of the steps for the creation of a spin-off or start-up that is of a result from an exploitedinnovation see Vendrell and Ortin (2006).
7/27/2019 University Distance Function Sfa
16/46
16
the same research line. This is generally a decentralized process where the researcher is
free to make research in those lines of his/her interest ( more near to their knowledge
areas) or that are considered more promising. In any case the lines of interest in research
are influenced by the possibility of obtaining financial aids.
The second stage of the model is the scientific discovery. When a group of researchers
find an innovation is fundamental that the university have knowledge that the
innovation exists. In this second stage, the TTO staff must encourage the scientific
members to disclose the inventions. Besides the decentralization in the process of
research, the knowledge that the innovation exist cannot be supposed. There should be a
system of information transmissionfrom the research group to the TTO, and this is what
we know as invention disclosure.
Once the invention is formally disclosed, the TTO in the third stage with a specialized
team evaluates the potentiality of the technology and decides whether to patentor not
the innovation. If the TTO considers that the innovation represents a step forward in the
scientific arena and its possible it has a commercial value, they will start the transaction
to obtain a patent that protects the innovation. The origin of the regulations of the
universities technology transfer starts with the approval of Bayh-Dole Act in the United
States. Its prime objective was to stronger the interaction between the universities and
the industry, the result of this law was that the universities, by the figure of the TTO,
could retain the property of the technologies and grant licenses on them to companies,
always giving preference to the PYMES. In Spain the Foundation University-Industry
(FUE Fundacin Universidad Empresa) and the TTO have been contributing for years
to facilitate the collaboration in this sector. The Organism that regulates in Spain the
FUE and the TTO is the RED OTRI. The RED OTRI has its roots when the National
Plan of Scientific Research and Technological Development (PNID) start activities
(1988).
Its very important to take into account that to request and maintain a patent is very
expensive and the TTO have limited resources to fill; thats why only potential
innovations or the interest of the industry in the technology are the only criteria that are
taken into account. Then the university decides to apply to domestic or internationalpatent protection.
7/27/2019 University Distance Function Sfa
17/46
17
If the patent is awarded, then in the fourth stage of the process the TTO would often
attempt to market the technology, and the scientific members are involved in the
marketing process because their technical knowledge makes them a natural partner for
the firms. The TTO would search for potential buyers to license the technology. The
most active and wide experienced TTOs that usually are the big ones, making the size of
the TTO an important input, usually are the ones that have a portfolio of possible
clients. But the researcher is usually a very important source of information of the
industry because usually they know firms that work and commercialise with these
products. In the webpages of the Spanish TTOs there is only information of the
available patents. Jensen and Thursby, (2001) report in their study, and Siegel et al
(2003) confirmed in their field research, that many firms will license a technology
before it is patented. This means that a key input of the university to industry transfer is
invention disclosure, because is the portfolio of available technologies for licensing.
In the final stage of the model if a firm or individual entrepreneurs are interested in the
patent, also the TTO is the one in charge to negotiate and redact a contract or license
agreement for the transference of technology. This process can also derive in the
constitution of a spin-off or start-up but this is sub stage (Chukumba and Jensen, 2005).
In resume we assume that the following internal factors are inputs of the university to
industry technology transfer: invention disclosure, and the size of the TTO (labour
employed by the TTO). And that the following external factors are outputs: Licence
agreements, and patents. This would be our TTO production function where the
inputs are under the control of the producer in this case the TTO director.
The technology transfer activity may also depend on some institutional factors. For
example, being near an industrial zone may facilitate the commercialization of
innovations. For instance we can recall the case of Standford University being near an
industrial cluster like Silicon Valley15 or for the case of Spain the great industrial
clusters are located in the big cities like Barcelona, Madrid, etc. Another important
factor is the public status of the university. Private universities may have less rigid
policies and public universities may have more financial aids from the state. The year of
15 For more information about Silicon Valley see Hayes (1989).
7/27/2019 University Distance Function Sfa
18/46
18
creation of the TTO or its age can be a relevant factor too. We can assume that TTO
with more experience may be more efficient that the young ones.
5. Data Construction
Our data is based on the RedOTRI survey for the years 2003, 2004 and 2005. We
complement this information from data of a survey made by DGPYME and ICO16 to the
different to several TTOs and some Webpage information of some TTOs we needed to
complement information17. We could cover a complete and balanced panel for 50
Spanish universities18 over the period 2003-2005. We also conduct an in depth
interview with a responsible of the TTO of the Autonomous University of Barcelona,
our objective was to understand the objectives of the Spanish TTOs.
We consider university outputs19 of the TTOs those that imply the commercialisation
of one invention previously developed in the University. In this sense, we differentiate
in two different outputs. The Licenses are the sum of patents and licenses and are
considered the commercialisation through the market that gives an immediate cash-flow
to the TTO and the inventors. We also consider the number of university Spin-offscreated as an output. Those firms need time to get positive profits and sometimes these
returns do not go to the TTO and remain to the academic entrepreneurs and/or investors
(usually, equity holders as venture capitalists or debt-holders as Banks). We define two
different inputs. In this sense, we have Inv.disclosure that is the inventions received to
the TTO that has potential commercialisation to the market. We also have the Size of the
TTO, which are the number of employees in each office. Finally we define 4
environmental variables consistent with the Spanish model. In this sense, we havecreated four dummy variables. Technical measures if the university is based on
technological studies (16%), Public determines the property of the university being
either public or private (84%), we also differentiate between new and old TTOs. The
16 For more detail on the survey or the institutions see Ortin et al. (2007).17 We check the webpage of the following universities: U. Alcala de Henares, U. Politecnica de Madrid, U. de las Islas Baleares, U.de Salamanca, U. de Sevilla, U. de Valencia y U. de Zaragoza.18
In Spain there are 61 universities. So we have information of 82% of the population. Moreover, we have information of theuniversities that traditionally has been more active in innovation and commercialisation, remaining out of our sample thoseuniversities with minor impact on innovation. The universities are listed in table 5.19 As we have some zeros we might sum 1 to all the inputs and outputs.
7/27/2019 University Distance Function Sfa
19/46
19
new (52%) ones are those created after the National Plan (1988). We also define those
universities that are near to industrial areas 20(44%).
Table 2 shows the mean of all those variables for the period 2003-2005. We observe
that Inv.Disclosure has dramatically increased between 2004 and 2005. Probably this
fact explains also the increment of licenses and patents21. We see that the size of the
TTOs remaining constant in the range of 13-14 employees. The number of Spin-off per
year is around 65 in 2004 and 100 in 200322.
- Insert Table 2 -
6. Methodology and Results
6.1 The treatment of Efficiency
In the previous section, we identified a set of potential determinants of the process of
university to industry technology transfer; which includes internal inputs, external
outputs and environmental/institutional factors.
The microeconomic literature defines efficiency of a production unit as the comparison
between observed and optimal values of its output and inputs. The technical efficiency
is defined as the ability to obtain the maximum potential output obtainable from the
given inputs or the ratio of minimum potential to observe inputs required to produce the
given output.
In the economic theory two approaches are recognized to construct frontiers: DEA and
SFE. In this work is proposed an application of models of the distance function
proposed by Shephard (1953) and Coelli and Perelman (1996) to analyze technical
efficiency. The notion of distance to the frontier proposed by Shephard (1953) can be
used to calculate the efficiency of a set of units of production in a scene of multiple
outputs. DEA first developed by Charnes et al. (1978) is a non-parametric estimation
20
We consider those universities near Barcelona, Madrid, Sevilla, Bilbao and Valencia as universities near an industrial centre.21 Notice the patents are on average 75% of the sum of patents and licenses.22 For more detail in the evolution of Spanish Spin-offs see Ortin et al. (2007). Our data is consistent with their results. Theyestimate a creation of 90 university Spin-offs per year.
7/27/2019 University Distance Function Sfa
20/46
20
technique that has been used extensively to compute relative productivity in services
industries. SFE method first developed by Aigner, et al., (1977) and Meussen and Van
den Broeck (1977), are extensions of the tradition regression model, based in the
microeconomic premise that the production function represent an ideal: the maximum
output that can be obtained with a set of inputs. This interpretation as pointed by Green
(1993 (b)) recalls naturally under an econometric analysis, in which the inefficiency is
identified with the errors of the regression model.
6.2 Multi-output approach
Thursby and Kemp, (2002) use DEA to assess the relative efficiency of TTOs using a
multi-output approach. Multi-output approach is used when is assumed that producer
use multiple inputs to produce multiple outputs. DEA is a mathematical programming
approach that does not require the specification of a functional form for the production
function; but DEA doesnt allow the study of the relations of causality between
variables of resource and production. SFE, introduces this last approach but introduces
the problem that it is only design to incorporate only one endogenous variable, that
represents the output obtained by the productive system and is explained by a set of
production factors.
One possible solution to the problem is to add the outputs in a single one dependent
variable by the calculation of an indicator that gathers the set of outputs by their prices
(Shadow Prices) or by a set of assigned weights subjectively. In the public
administration arenas, that is the case of the TTOs, is not advisable to use shadow prices
because the sell of the services is not observed so its hard to get the shadow prices to
obtain a structure to weight.
Lets assume that and represent the input and
output vectors at time
),,...,( 1t
M
ttyyy =
Tt ,...,1=
{ }tttt xfromobtainableisyxP =)(
+= KtKtt Rxxx ),,...,( 1
7/27/2019 University Distance Function Sfa
21/46
21
Shephards distance function avoids having to establish a priori shadow prices. The
output Distance function is defined by Shephard, (1970) as:
(1)
The parametric approach of the distance function parts from the theory of the
homogeneous functions to introduce the assumption of multiple outputs obtained by
multiple inputs. The method was introduced by Aigner and Chu, (1968) for the
assumption of one single output and multiple inputs and a Cobb-Douglas function. Here
is used that approximation to adapt it to the assumption of a translogaritmic function,
that is based in a Cobb-Douglas function, but more flexible than this because its partial
derivatives are not constant. The expression proposed of the distance translog function
for the assumption of M output and K inputs is the following:
)2(,...,2,1lnln2
1
lnln2
1lnlnln
2
1lnln
1 1
,
1 11 1 11
00
= =
= == = ==
=+
++++=
K
K
M
m
mikikm
K
k
K
l
likiki
M
n
M
m
ki
K
k
knimimnmi
M
m
mni
Niyx
xxyyyD
WheremiY is the production of output m and kiX the quantity of input k for the
productive unit i. ,, are the parameters to estimate and iD0ln is the term of
inefficiency of the evaluated unit.
Additionally two constraints of homogeneity are required. Homogeneity of degree +1 in
outputs and homogeneity of degree +1 in inputs:
=
=
==
==
M
n
km
M
n
mn
Kk
Mm
1
1
,...2,1,0
,...,2,1,0
Homogeneity of degree +1 in outputs is imposed in order to obtain an output oriented
radial distance function. Homogeneity of degree +1 in inputs implies constant returns
to scale technology, an assumption necessary to accurately measure productivitychange.
)}()/(:min{),( xPyyxD o =
7/27/2019 University Distance Function Sfa
22/46
22
The third constraint is of symmetry of the parameters:
.,...,2,1,.,...2,1, klkandMnm lkklnmmn ====
A case of a homogeneous function of degree +1, where is accomplished:
,0),(*)*,(0 >= gaforyxDgygxD o
If we select arbitrarily one of the M outputs and we consider Myg /1= , then:
MM yyxDyyxD /),()/,( 00 =
And the translogaritmic function would be transformed into:
= = ==
=
=
= =
=+
++++=
K
k
K
k
M
m
mikikm
K
l
kiki
M
m
M
n
M
m
K
k
kiknimimnmimnMioi
Niyxxlix
xyyyyD
1 1 11
1
1
1
1
1
1 1
0
,...,2,1,*lnln2
1lnln
2
1
lnln*ln2
1*ln)/ln(
)3(
Where Mim yyy /* = , this means, the ratio between each one of the outputs and the
output selected as a reference to transform g.
The translog can be expressed as follows:
.,...,2,1),,,/,()/ln( NiyyxfyD miiimio ==
And this is equal to:
),,,/,()ln()ln( miiimio yyxfyD =
Or
imiiimi UDwhereDyyxfy == )ln(),ln(),,,/,()ln( 00
7/27/2019 University Distance Function Sfa
23/46
23
This is equivalent to identify the term of error with the logarithm of the distancefunction.
The stochastic approximation in this case adapts the translog form to the assumption
that the decomposition of the error term of the model of two stochastic terms: itV , which
is assigned a normal distribution ),0( N , with mean equal 0 and constant standard
deviation, that represents the deviations of the values of production with respect to the
frontier by factors affected by uncertainty; not controlled by the participants in the
productive process. A second component:
itUD = )ln( 0 , that represents the deviations of the observations in the sample with
respect to the efficient frontier, because of the inefficiency of the economic agents
represented by them. Coelli and Perelman, (1996) considered this second component as
the product of two terms: a term that is a deterministic function dependent of time23.
This function represents the change experimented with the pass of time of the economic
agents represented by the sample used. The other term is a random variable that has
been assigned a normal truncated distribution in the value24 0. Beside is allowed that the
mean of this second component is zero and that the standard deviation to be constant,
but different from the term itV .
It should be noticed that the value of 0D is not directly observable because it forms part
of the composed error termititit UVE += . The estimation of that value is made by the
expected value of the errors due to inefficiencies conditioned to the composed error:
[ ]ititoi EUED /)exp(=
That is the expected value of the degree of inefficiency of the correspondingobservation, obtained by the comparison of the value of production of this with the
value of efficient production, for its levels of available productive resources.
For the translog function we use the program FRONTIER 4.1 (Coelli, 1996).
23 It can be exponential, linear or quadratic depending of witch has been the temporal evolution of the degree of inefficiency of theobservations.24 With this the level of efficiency is not negative.
7/27/2019 University Distance Function Sfa
24/46
24
Our equation (4) as indicated before, to construct the distance function is precisely to
choose one of the outputs to normalize the function, and the expression of the adjusted
function is:
)4(
6.3 Single output approach
In order to asses relative productivity in the process of technology transfer and using a
single output approach, following the methodology of Siegel, et al., (2003) we use SFE.
SFE creates a production frontier (Aigner et al., 1977, & Meussen and Van den Broeck,
(1977)) with a stochastic error term that is composed by: a conventional random error
and term that represents relative inefficiency (deviations from the frontier).
Following Aigner et al. a production function for a given university, say the ith, its
estimated:
);( ii xfy = ii Xy = ( )5
Here i denotes the ith university, iy is the maximum output obtainable from ix , a vector
of (non-stochastic) inputs and is an unknown parameter.
In order to characterize differences in output among universities with identical input
vectors or to explain how a given universitys output lies below the frontier, );( ixf
a disturbance term is assumed.
In an attempt to give them a statistical basis, Schmidt (1976) explicitly added a one-
sided disturbance which yields the model,
iii xfy += );( Ni ,......,1= Where 0i .
,iii UV += Ni ,......,1=
)ln(
)ln(
)/ln(
)(2/1)(2/1)(2/1
2
1
1
122111112112
2222
2111
2111221111
sizeX
isclosureinventiondX
licencesspinoffY
YXYXXX
XXYXXYLnLicences
=
=
=
++++++++++=
7/27/2019 University Distance Function Sfa
25/46
25
i is an error term with two components. The error component
iv represents the
symmetric disturbance: iV are assumed to be independently and identically distributed
as ).,0(
2
vN The error component iU is assumed to be distributed independently of iv ,
and to satisfy .0iu The non-positive disturbance iu reflects the fact that each
university output must lie on or below its frontier.
To estimate the technical efficiency of a producer, distributional assumptions are
required: The Normal-Exponential Model, the Normal Truncated Normal Model, and
the Normal-Gamma Model which are the ones that can be considered. In this model we
would use The Normal-Half Normal Model, considering the stochastic production
frontier model we make the following distributional assumptions:
regressorstheofandothereachoftlyindependenddistributeareuandviii
normalhalfenonnegativaasisthatNiiduii
Niidvi
ii
ui
vi
,)(
),,0()(
),0()(
2
2
+
The inefficiency term iU is assumed to have a half normal distribution. The log
likelihood function for a sample of ith universities is:
+=
i
i
i
iItconsL2
22
1lnlntanln
(6)
Subsequent with some SFE models25, within current time, have been created to allow
that the technical inefficiency term can be expressed as a function of a vector of
environmental and organizational variables. This is consistent with our assumption that
relative inefficiency is related to environmental/institutional factors. So following the
studies of Reifschneider and Stevenson (1991) and Siegel et al. (2003), we presume
that26 the inefficiency disturbance is composed of two factors, a factor reflecting
systematic influences and a random factor:
iiii wZgU += )( ( )7
25 For more details see Reifschneider and Stevenson (1991).26
iU (universities on or below the frontier) are independently distributed as truncations at zero of the ),( 2uimN
7/27/2019 University Distance Function Sfa
26/46
26
Where Zis a vector of firm specific inefficiency explanatory variables and is a
parameter vector. iw is the unexplained component of inefficiency error and has the
same assumed normal distribution.
Using the program FRONTIER 4.1 (Coelli, 1996), we obtain maximum likelihood
estimates27 of the parameter vectors and from the estimation of the production
function and inefficiency term equations.
iiii UVSizedisclosureInvLicenseLn +++= )ln().ln()( 210 ( )8
Our equation (8) is based on the model28of Siegel et al. (2003) usingLicenses as a proxy
of the process of technology transfer output and relating to two inputs: Invention
Disclosure and Size; assuming a three-factor log-linear Cobb-Douglas production
function.
And the technical inefficiency ( )iU term expressed as:
++=k
iki INSTENVU /0
Where ENV/INST is a vector of environmental and institutional factors, and is a
disturbance term. Thus, our equation (8) is:
iiiiii CLUSTERNATIONALPUBLICTECHU +++++=
43210 )9(
Consistently with our interview in depth and according to Chukumba and Jensen (2005)
licenses and patents are important outputs for universities. In this sense, these outputs
generate immediate cash-flows, and universities do not have to pay the opportunity cost
of renouncing to academics. Moreover, Chukumba and Jensen suggest that TTOs try to
commercialise the projects through established firms and consider Spin-offs as a second
27See Battese and Coelli (1995).28Siegel et al. developed their equation (page 32) based on the knowledge production function framework developed by Griliches(1979).
7/27/2019 University Distance Function Sfa
27/46
27
option. Consequently in this section we analyze the efficiency of TTOs considering on
licenses as an output (Siegel, Waldman and Link, 2003). Besides, we also consider spin-
off as a single-output to make the analysis more robust (See equation (10)).
iiiiUVSizedisclosureInvoffSpinLn +++= )ln().ln()( 210 )10(
6.4 Results and Comments
Table 3 contains two sets of parameters estimates of the Multi-output distance function
outlined in the previous section (equation (4)) for the dependent variable licences
(licences + patents). Models
29
1 and 2, (with and without environmental factorsrespectively), are presented in the first two columns. Across all variants, the estimated
elasticity of Y2/Y1 (Y1) with respect to invention disclosure is positive and significant
in model 1. This means that if more new innovations are disclosed then more licences
agreements are commercialized. The estimated elasticity of Y1 with respect to size is
positive and significant in model 1 and 2. It appears that hiring additional staff for the
TTO increments the commercialization of licences agreements. Consequently, the extra
information that we can extract from this analysis come from the relation of the outputs.
We see that there is a quadratic effect between the ratio Y2/Y1 (Y1) and the amount of
licenses (Y1). In this sense from Model 1 there is an optimal ratio that maximises the
level of licenses commercialised. Operating30 this optimal ratio is Y2/Y1 = 0,7639.
- Insert Table 3 -
Removing logarithms we get that in the optimal situation Spin-offs equal to
Licenses0,7639. Finally, from the positive sign and significance of the parameters
2 and
11 of the Models 1 and 2 we can accept for Hypothesis 1 and 2.
In the model 3 (licences) and 4 (spin-off) of the table 3 we show the results for panel
data analysis. We can see that invention disclosures affect positively and highly
significant the number of licenses and the number of spin-off created. In this sense,
29 Notice that variables x1, x2 and y2 are divided by y1. For homogeneity reasons this fact is not mentioned in the Table 3.30 We make a first order approach to get such result (Y2/Y1 = Y1 = 2,24/2,93 = 0,7639)
7/27/2019 University Distance Function Sfa
28/46
28
from the coefficient of Model 3 and 4, if we double the number of invention disclosures
the licenses would increase in 82%, and the spin-off would increase in 15%. The
relation between size of the TTOs is positive and highly significant for model 3 this
means that if we double the size of the TTO licences would increase in a 27%.
It is important to notice, that for the Model 1, we have significant results for the
environmental variables. So we can say that universities that have a technical profile
and public universities have higher licence activity. But the ones that are near an
industrial cluster reduce the licence activity. This last result might come because of the
high competition, in this sense, universities operating in small markets or cluster work
better as they have less competence. Besides, we do not find experience effect as 3
is
not significant. Notice that the results of Model 3 are consistent with the results
outlined.
The mean technical efficiency is consistent with Siegel et al. (2003). In particular, they
found that the mean of technical efficiency for US are closed to 0,75 very similar to the
ones found for model 1 (0,72) model 2 (0,68) and model 3 (0,64).
A set of aspects that are interesting and that can be studied in an analysis of this type of
models, are the ones referring to the structure of the term of error, dispersion of
efficiencies and the distributions of probabilities of the density function component of
the error term representing the degree of inefficiency. In the models 1, 2 and 4 results
highly significant the parameter bounded to sigma squared, that gathers the total
variance of the error term. Also the parameter associated to gamma, are significant in
the model 1 and 3, which represents the proportion of variance of the stochastic term of
inefficiency with respect to the total variance.
Besides, we check for specificities of technology based31 universities (UPC; UPV;
UPM). In order to see if technological based are more efficient a One-way ANOVA was
run. We use the technical efficiency from model 1, 3 and 4. We cannot reject the null
hypothesis that the mean are equal (see table 4) in model 1 and 3 at the traditional levels
of significance. Even though for model 4 (spin-off) there is a significant difference (this
31 In Spain they are called Polytechnics (Politcnica in Spanish).
7/27/2019 University Distance Function Sfa
29/46
29
means that for the spin-off there a difference between the TTOs) we consider that this
fact does not justify a separate analysis. Therefore, we can say that technology based
universities are not more efficient than their counterparts.
- Insert Table 4 -
Besides, the efficiency analysis at university level has also special interest; in particular,
the study of the efficiency of the technological based universities. To do so we use the
technical efficiency for each one of the university considering the three years. We have
the source of technical efficiency coming from Panel Data analysis: SFE with multi-
output (from Model 1) and SFE with single output (from Model 3). The results are
presented in Table 5.
- Insert Table 5 -
We can observe that the polytechnic universities are ranked in both models below the
mean, and this is consistent with the results we obtained from the ANOVA that we
explained before. We can extract from Table 5 that the universities that maintain the
first positions constant for the three years are University of Navarra or University of
Zaragoza; and the universities that maintain the worst position are the University of La
Corua.
6.4 What affects efficiency?
It is of special interest to explain which variables determine efficiency. In this sense, our
Hypothesis 3 states that there is a positive relation between efficiency and the amount of
inventions commercialized (Vendrell and Ortin, 2006). In a first attempt to see this
relation we use a one way ANOVA. First we divided the sample of licences in three
groups32. From table 6 we can observe that the mean efficiency is significantly different
between groups, being higher for the universities that have a greater number of licences.
From this result we can accept Hypothesis 3.
32 Group 0= between 0 and 1 licence, Group 1= between 2 and 9 licences, Group 2= 10 or more licences.
7/27/2019 University Distance Function Sfa
30/46
30
- Insert Table 6 -
In order to make the analysis more robust we propose a panel data with fixed effects
(Green, 1983; pp. 560-566). Our dependent variables are the scores presented in Table
5. Consequently, we run two different models taking into account the mean efficiency
of the models 1 and 3. The results are shown in the Models 5 and 6 of the Table 7.
- Insert Table 7
The results indicate that an increment of one license or patent entails a growth of 2% of
the technical efficiency (it ranges from 1,5% to 2,2%). Similarly creating a new spin-off
increase the technical efficiency around 1%. Ceteris Paribus, the expected effect of
increasing an input is a reduction of the technical efficiency. This fact explains the sign
of invention disclosure. A new invention disclosure produces a reduction of 2% in the
technical efficiency. Its important to recall that the effect of TTO size is diffuse
because it has a positive and not signficant impact on efficiency. Consequently TTO
size does not have the effect predicted by the theory on technical efficiency.
7. Conclusions, limitations and further research.
The efficiency of the university TTOs is an important discussion in the academic
literature (Siegel et al., 2003; Thursby and Kemp, 2002). In this sense, this paper fills
two existent gaps of the previous literature. First, set up evidence on Spanish TTOs.
Second, introduce an important methodological tool that has not been used in the
previous works. This important methodological inside of the paper is the use of SFE
with a multi-output approach.
From our analysis we shed light into several issues. The mean technical efficiency for
the Spanish TTOs ranges from 0,72 (SFE with multi output) to 0,64 (SFE with single
output). These results are consistent with the evidence found by Siegel et al. (2003) for
the case of US (around 0,75).Invention disclosure and TTO size increases the amount
of commercialisation done and hence the efficiency of the TTO. From this result we can
state an important advice for policy makers. TTO might increase their size and amountof invention disclosure. For this second variable it is important to look for good
7/27/2019 University Distance Function Sfa
31/46
31
incentives and information. In technical efficiency terms they should try to avoid
inventions without potential commercialisation. This is probably the case of technology
based universities (UPC, UPM and UPV).
We also look for the impact on environmental factors. Even the evidence is weak, the
results indicate that Public and technical universities are more efficient than their
counterparts. Additionally, we could find neither experience nor industrial cluster
effects.
The work has two important limitations related to the data base. First, our sample is
small what difficult the introduction of several independent variables required by SFE.
Second, we do not have information of a relevant output that would enrich our analysis.
In this sense, the introduction of research contracts could modify some of the results. As
a further research we recommend the extension of the sample with the TTOs of some
South-European countries similar to Spain such as Portugal, France or Italy. Moreover,
apart from the introduction of the amount of research contracts we also think that it is
important controlling for the quality of research as an environmental factor. A possible
proxy would be a relative amount of the papers published in top scientific journals.
7/27/2019 University Distance Function Sfa
32/46
32
References
Aigner, D., and Chu, S.F., 1968. On Estimating the Indistry Production Function.American Economic Review, 58, pp. 826-839.
Aigner, D., Lovell, C.A.K., Schmidt, P., 1977. Formulation and Estimation ofStochastic Frontier Production Function Models. Journal of Econometrics, 1, pp. 21-36.
Aghion, P., Dewatripont, M., Stein, J.C., 2005. Academic Freedom, Private-SectorFocus, and the Process of Innovation. NBER Working Paper Series. 11542.
Anderson, T.R., Daim, T.U., Lavoie, F.F., 2007. Measuring the Efficiency of UniversityTechnology Transfer. Technovation, doi:10.1016/j.technovation.2006.10.003.
Beise, M., and Stahl, H., 1999. Public Research and Industrial Innovations in Germany.
Research Policy, 28, pp. 397-422.
Bercovitz, J., Feldman, M., Feller, I., Burton, R., 2001. Organizational Structure as aDeterminant of Academic Patent and Licensing Behavior: An Exploratory Study ofDuke, Johns Hopkins, and Pennsylvania State Universities. Journal of TechnologyTransfer, 26, pp. 21-35.
Chandler, A., 1990. Scale and Scope: The Dynamics of Industrial Capitalism.Cambridge, MA: The Belknap Press of Harvard University Press.
Chapple, W., Lockett, A., Siegel, D., Wright, M., 2005. Assessing the relativeperformance of U.K. University Technology Transfer Offices: Parametric and non-parametric evidence. Research Policy, 34, pp. 369-384.
Charnes, A., Cooper, W.W., Lewin, A., Seiford, L.M., 1995. Data EnvelopmentAnalysis: Theory, Methodology and Applications. Kluver Nijhoff Publishing Boston.
Chukumba, C., and Jensen, R., 2005. University Invention, Entrepreneurship and Start-ups. NBER Working Series, 11475.
Clarysse, B., Wright, M., Lockett, A., Van de Velde, E., Vohora, A., 2005. Spinning
Out New Ventures: a typology of incubation strategies from European researchinstitutions. Journal of Business Venturing, 20, pp. 183-216.
Coelli, T.J., 1996A. A guide to Frontier Version 4.1: A Computer Program forStochastic Frontier Production and Cost Function Estimation. CEPA Working Papers.http://www.une.edu.au/econometrics/cepawp.htm
Coelli, T.J, and Perelman S., 1996. Efficiency Measurement, Mult-ioutput and DistanceFunctions: with application to European railways. CREPP WP 96/05. Centre deRecherche en Economie Publique et Economie de la Population, Universit de Lige.
7/27/2019 University Distance Function Sfa
33/46
33
Colyvas, J., Crow, M., Gelijns, A., Mazzoleni, R., Nelson, R.R., Rosenberg, N.,Sampat, B. N., 2002. How do University Inventions Get into Practice? ManagementScience, 48, pp. 61-72.
Conesa, F. 1997. Las Oficinas de Transferencia de Resultados de Investigacin en el
Sistema Espaol de Innovacin. Tesis doctoral. Universidad Politcnica de Valencia.
Di Gregorio, D., and Shane, S., 2003. Why do some Universities Generate more Start-ups than Others? Research Policy, 32, pp. 209-227.
Etzkowitz, H., 1998. The Norms of Entrepreneurial Science: cognitive effects of thenew university-industry linkages. Research Policy 27, pp. 823-833.
Fre, R.S., Grosskopf, M., Lovell, C.K., Yaisawarng, S., 1993. Derivation of ShadowPrices for Undesirable Outputs: A distance function approach. Review of Economicsand Statistics, 75, pp. 374-380.
Feldman, M., Feller, I., Bercovitz, J., Burton, R., 2002. Equity and the TechnologyTransfer Strategies of American Research Universities. Management Science, 48, pp.105-121.
Feller, I., Ailes, C.P., Roessner, D., 2002. Impacts of Research Universities onTechnological Innovation in industry: Evidence from Engineering Research Centers.Research Policy, 31, pp. 457-474.
Fernndez de Lucio, I and Conesa F., 1996. Estructura de Interfaz en el Sistema Espaolde Innovacin. Su papel en la difusin de Tecnologa. Centro de Transferencia deTecnologa. Universidad Politcnica de Valencia.
Friedman, J., and Silberman, J., 2003. University Technology Transfer: Do incentivesManagement, and Location Matter? Journal of Technology Transfer 28, pp. 17-30.
Geuna, A., 1999. Determinants of University Participation in EU-funded R&DCooperative Projects. Research Policy 26, pp. 677-687.
Geuna, A., Salter, A.J., Steinmueller, W.E., 2003. Science and Innovation Rethinkingthe Rationales for Public Funding. Edward Elgar. Cheltenham, UK.
Godfarb, B., Henrekson, M., 2003. Bottom-up versus top down policies towards thecommercialization of university intellectual property. Research Policy 32, pp. 639-658.
Goldhor, R.S., and Lund, R.T., 1982. University-to-Industry Advanced TechnologyTransfer: A case study. Research Policy, 12, pp. 121-152.
Greene, W.H. 1993 (a). Econometric Analysis. Prentice Hall (4th edition)
Greene, W., 1993 (b). The econometric approach to efficiency analysis, in Lovell K.and Schmidt S. (Eds.). The Measurement of Productive Efficiency: Techniques and
Applications. Oxford University Press, Oxford, 68-119.
7/27/2019 University Distance Function Sfa
34/46
34
Griliches, Z., 1979. Issues in Assessing the Contribution of R&D to ProductivityGrowth. Bell Journal of Economics 10, pp. 92-116.
Hayes, D., 1989. Behind the Silicon Curtain: the seductions of work in a lonely era.London: Free Association Books.
Jensen, R., Thursby M., 2001. Proofs and Prototypes for Sale: The licensing ofuniversity inventions. The American Economic Review, 91 (1), pp. 240-259.
Leitch, C.M., and Harrison, R.T., 2005. Maximizing the Potential of University Spin-Outs: the development of second-order commercialization activities. R&DManagement, 35, pp. 257-272.
Locket A., Wright M., Franklin S., 2003. Technology Transfer and Universities Spin-out Strategies. Small Business Economics, 20, pp. 185-200.
Lowe, R., 2006. Who Develops a University Invention? The impact of tacit knowledgeand licensing policies. The Journal of Technology Transfer, 31, pp. 415-429.
MacAdam, R., Keogh, W., Galbraith, B., Laurie, D., 2005. Defining and ImprovingTechnology Transfer Business and Management Process in University InnovationCentres. Technovation, 25, pp. 1418-1429.
Macho-Stadler, I., Martnez-Giralt, X., Prez-Castrillo, J.D., 1996. The Role ofInformation in Licensing Contract Design. Research Policy, 25, pp. 43-57.
Macho-Stadler, I., Castrillo-Prez, D., Veugelers, R., 2005. Licensing of UniversityInventions: The role of a technology transfer office. January 19. BBVA Working Paper;forthcoming in International Journal of Industrial Organization (2007).
Markman, G.D., Giadionis, P.T., Phan, P.H., Balkin, D.B., 2005. Innovation Speed:Transferring University Technology to Market. Research Policy, 34, pp. 1058-1075.
Martin, B.R., 2003. The Changing Social Contract for Science and the Evolution of theUniversity, in:
Mazzoleni, R. 2006. The effects of University Patenting and Licensing on Downstream
R&D Investment and Social Welfare. Journal of Technology Transfer, 31, pp. 431-441.
Meussen, W., and Van den Broeck, J., 1977. Efficiency Estimation from Cobb-DouglasProduction Function with Composed Error. International Economic Review, 18, pp.435-455.
Mowery, D.C., Nelson, R.R., Sampat, B.N., 2001. The Growth of Patenting andLicensing by U.S Universities: an assessment of the effects of the Bayh-Dole Act of1980. Research Policy, 30, pp. 99-119.
Mowery, D.C, Rosenberg, N., 1998. Paths of Innovation: Technological Change in 20 th
Century America. Cambridge University Press, New York.
7/27/2019 University Distance Function Sfa
35/46
35
Ndonzuau, F.N., Pirnay, F., Surlemont, B., 2002. A stage model of academic spin-offcreation. Technovation, 22, pp. 281-289.
Nelson, R., 2001. Observations on the Post-Bayh-Dole rise in patenting at American
universities. The Journal of Technology Transfer 26, 13-19.
Ortn, P., Salas, V., Trujillo, M.V., Vendrell, F. 2007. El spin-off universitario en Espaa comomodelo de creacin de empresas intensivas en tecnologa. Estudio DGPYME.http://www.ipyme.org/IPYME/es-ES/Publicaciones/estudios/
Owen-Smith, J., Riccaboni, M., Pammolli, F., Powell, W.W., 2002. A Comparison ofU.S and European university-industry Relations in the Life Science. ManagementScience, 48, pp. 24-43.
Perez-Castrillo, D., (Coordinadora Isabel Bussom) 2005. La innovacin en Catalua;
Las Oficinas de Transferencia de Tecnologia. Coleccin estudios CIDEM. Generalitatde Catalunya.
Rasmussen, E., Moen, O., Gulbrandsen, M., 2006. Initiatives to PromoteCommercialization of University Knowledge. Technovation, 26, pp. 518-533.
Reifschneider, D., and Stevenson, R., 1991. Systematic Departures from the Frontier: aframework for the analysis of firm inefficiency. International Economic Review 32, pp.715-723.
Rubiralta, M., 2003. Transferencia a las empresas de la Investigacin Universitaria.
Academia Europea de Ciencias y Artes. Espaa.
Rubiralta, M., 2005. Transferencia a las Empresas de la Investigacin UniversitariaDescripcin de Modelos Europeos. COTEC.
Serarols, C., Urbano, D., Vaillant, Y., 2007. Technological Trampolines for newventure creation in Catalonia: The case of the University of Girona. Eighteenth IRMAInternational Conference (Information Resources Management Association) VancouverCanada.
Santamara, L., Barge, A., Modrego, A., 2007. Anlisis del Proceso de Transferencia
Tecnolgica Universidad-Empresa.http://otri.uc3m.es/docweb/pct/2007/N7-comercializacion-abril07-estudiotecnologico.pdf.
Shane, S., 2002. Selling University Technology: Patterns from MIT. Management Science 48,
122-137.
Shephard, R., W., 1953. Cost and Production Functions. Princeton University Press.
Shephard, R.W., 1970. Theory of Cost and Production Functions. Princeton UniversityPress.
7/27/2019 University Distance Function Sfa
36/46
36
Siegel, D.S., Waldman, D., Link, A., 2003. Assessing the Impact of OrganizationalPractices on the Relative Productivity of University Technology Transfer Offices: anexploratory study. Research Policy, 32, pp. 27-48.
Siegel, D.S., and Phan, P.H., 2004. Analyzing the Effectiveness of University
Technology Transfer: Implications for Entrepreneurship Education (No. 0426).Rensselaer Polytechnic Institute, Troy.
Thursby, J., Jensen, R., Thursby, M.C., 2001. Objectives, Characteristics and Outcomesof University Licensing: A Survey of Major U.S. Universities. Journal of TechnologyTransfer, 26, pp. 59-72.
Thursby, J.G., and Kemp, S., 2002. Growth and Productive Efficiency of UniversityIntellectual Property Licensing. Research Policy, 31, pp. 109-124.
Vendrell, F., and Ortin P., 2006. Technological Transfer from Universities: A
theoretical review and an empirical analysis of Spin-Offs in Spain. Working Paperpresented in I International Business Economics WorkshopUIB. Palma de Mallorca, September 7 and 8th 2006. demo.uib.es
Vohora, A., Wright, M., Lockett, A., 2004. Critical Junctures in the Development ofUniversity high-tech spin-out Companies. Research Policy 33, pp. 147-175.
Williamson, O., 1985. The Economic Institutions of Capitalism, New York Press, NY:The Free Press.
Zucker, L.G., Darby, M.R., Armstrong, J.S., 2002. Commercializing Knowledge:university science, knowledge capture, and firm performance in biotechnology.Management Science 48, pp. 138-153.
7/27/2019 University Distance Function Sfa
37/46
37
Figure 1: Linear model of the process of technology transfer from universities to firms.
Source: Siegel et al. 2003; Friedman and Silberman 2003.
Invention
Disclosure
Evaluation ofthe inventionfor patenting
Patent
Commercialization of the
technology tothe firm
Negotiation of
a license
License to thefirm (existent
or start-up
TTO
Group ofInvestigation
FIRM
7/27/2019 University Distance Function Sfa
38/46
38
Graphic 1. Evolution of the volume of R&D contracts signed*(in millions of Euros)
*Contracts of R&D and Consultancy (art. 83), services and other activities. Data of 51 of the 60 universities.Source: REDOTRI Universidades
Graphic 2. Evolution of the activity of Intellectual Property
Data of 52 of the 60 universities.Source: REDOTRI Universidades
7/27/2019 University Distance Function Sfa
39/46
39
Graphic 3. Evolution of the number of license agreements
Data of 48 of the 60 universities.
Source: REDOTRI Universidades
Graphic 4. Evolution of the income generated by license agreements
Data of 41 of the 60 universities.Source: REDOTRI Universidades
7/27/2019 University Distance Function Sfa
40/46
40
Graphic 5. Evolution of the Spin-off created
Data of 40 of the 60 universities.Source: REDOTRI Universidades
7/27/2019 University Distance Function Sfa
41/46
41
Table 1Utility Patents Issued to U.S Universities and Colleges, 1969-1997 year issue
Year Number of U.S patents
1969 1881974 2491979 264
Bayh-Dole Act 1980
1984 5511989 17801997 2436
Source: Extracted from Mowery et al. 2001
Table 2: Descriptive Statistics
Variables 2003 2004 2005
Inputs
Inv. Disclosure 10,8 10,1 32,6TTO Size 12,9 13,4 14
Outputs
Licenses 4,4 9,5 30,2Spin-off 2 1,3 1,4
Environmental factors
Technical 16%Public 84%Posterior National
Plan
52%
Near a Industrial
center
44%
7/27/2019 University Distance Function Sfa
42/46
42
Table 3: Results SFE with Panel Data
Inputs:Invention disclosure (x1), size (x2)
Outputs:Licenses (y1), Spin-off (y2)Environmental factors: Technical (z1), Public, Posterior National Plan (z3), Near an Industrial Centre (z4)
Variables and ParametersModel 1
Licenses &
Spin-off(lny1&lny2)
Model 2Licenses &
Spin-off(lny1&lny2)
Model 3Licenses
(lny1)
Model 4Spin-off
(lny2)
Intercept 0
-0,736 -0,596 -0,112 -0,141
Inputs ln x1
1 0,215* 0,036 0,820*** 0,153***
ln x2
2
0,785* 0,804* 0,271*** 0,188
(ln x1)2
11 0,361*** 0,374***
(ln x2)2
22 -0,218 -0,232
(ln x1)(ln x2)
12 -0,069 -0,048
Outputs ln y2
1 2,240*** 2,523***
(ln y2)2
11 -2,932*** -0,345***
Inputs-Outputs
(ln x1)(ln y2)
12 -0,165 0,064
(ln x2)(ln y2)
22 0,132 -0,014
Environmental z0
0
-9,692* -21,014* 0,014
z1
1 1,762** 3,692* 0,258
z2
2
6,620** 7,720** -0,215
z3
3 0,195 2,443* 0,275
z4
4 -1,182** -1,920** -0,271
Other ML
Parameter
2 0,192** 0,512*** 0,174* 0,474***
0,915*** 0,561** 0,403*** 0,698
Log-
Likelihood-123,51 -128,86 -165,14 -149,68
MeanEfficiency
0,72 0,68 0,64 0,94
Level of Statistical significance: *** 1%, ** 5%, * 10%
7/27/2019 University Distance Function Sfa
43/46
43
Table 4. Results of technology based universities vs. non-technology based universities.
From Model 1
Mean Efficiency Observations SignificanceProb>F
Technology Baseduniversity
0,648 9
Other 0,721 141
Total 0,717 150
0,1782
From Model 3
Mean Efficiency Observations SignificanceProb>F
Technology Baseduniversity
0,623 9
Other 0,646 141
Total 0,644 150
0,7218
From Model 4
Mean Efficiency Observations SignificanceProb>F
Technology Baseduniversity
0,917 9
Other 0,834 141
Total 0,839 150
0,0018
7/27/2019 University Distance Function Sfa
44/46
44
Table 5. Technical Efficiency in SFE
Technical Efficiency SFE Multi
Output (from Model 1)
Technical Efficiency SFE Multi
Output (from Model 3)
Year
2003
Year
2004
Yeat
2005
Rank
Mean
Year
2003
Year
2004
Yeat
2005
Rank
Mean
U. de Alcala de Henares 0,832 0,769 0,747 13 0,559 0,724 0,708 28
U. de Alicante 0,821 0,816 0,770 11 0,713 0,744 0,772 10
U. de Almera 0,703 0,715 0,768 26 0,543 0,679 0,719 29
U. Autnoma de 0,756 0,754 0,816 14 0,714 0,674 0,765 13
U. Autnoma de Madrid 0,722 0,783 0,708 23 0,663 0,736 0,655 21
U. de Barcelona 0,326 0,877 0,819 35 0,299 0,841 0,718 33
U. de Burgos 0,742 0,748 0,716 25 0,577 0,699 0,732 26
U. de Cdiz 0,697 0,787 0,252 44 0,662 0,723 0,247 44
U. de Cantabria 0,606 0,725 0,802 30 0,365 0,583 0,820 38
U. Carlos III de Madrid 0,581 0,481 0,801 41 0,240 0,505 0,658 48
U. de Castilla-La 0,876 0,622 0,451 390,750 0,799 0,489 23
U. Complutense de 0,690 0,776 0,698 27 0,690 0,727 0,649 20
U. de Crdoba 0,724 0,780 0,779 17 0,472 0,740 0,786 27
U. de da Corua 0,480 0,689 0,353 49 0,250 0,665 0,167 49
U. de Deusto 0,826 0,817 0,817 9 0,719 0,707 0,707 15
U. Europea de Madrid 0,911 0,877 0,842 3 0,798 0,806 0,637 9
U. de Extremadura 0,882 0,692 0,815 12 0,774 0,692 0,777 8
U. de