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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.


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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.


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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


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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


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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.


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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


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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).


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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.


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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.


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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.


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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.


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- 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.


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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.


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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.


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.


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.



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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.


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).


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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.


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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).


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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.


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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.


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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


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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 =


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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


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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.


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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

=

=

=

++++++++++=


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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


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).


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)


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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).


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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.


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


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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.


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32

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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


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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


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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



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Graphic 5. Evolution of the Spin-off created



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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%


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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%


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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



0,623 9

Other 0,646 141

Total 0,644 150

0,7218

From Model 4



0,917 9

Other 0,834 141

Total 0,839 150

0,0018


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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

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