Academic inventors: collaboration and proximity with industry
Academic inventors: collaboration and proximity with industry
Riccardo Crescenzi 0 1 2
Andrea Filippetti 0 1 2
Simona Iammarino 0 1 2
0 Centre for Innovation Management Research, Birkbeck College, University of London , London , UK
1 National Research Council, Institute for the Study of Regionalism, Federalism and Self- Government , Via dei Taurini, 19, 00185 Rome , Italy
2 Department of Geography and Environment, London School of Economics and Political Science , Houghton Street, London WC2A 2AE , UK
This paper addresses a number of fundamental research questions on universityindustry (U-I) collaborations. Are U-I collaborations intrinsically different from other forms of collaboration, such as inter-firm or inter-university collaborations? Are they more difficult to form? Is their output qualitatively different? What factors facilitate their development? By looking at the collaborative behavior of all Italian inventors over the 1978-2007 period, the empirical analysis shows that U-I collaborations are less likely to happen when compared to collaborations involving exclusively university partners of business partners, and suggests that they tend to generate patents of more general applicability in subsequent inventions-measured by forward-citations. As emphasized by the literature, geographical proximity plays an important role in facilitating all forms of collaboration. At the same time, it works as a possible substitute for institutional proximity, facilitating U-I collaborations. However, the involvement of 'star inventors' on both sides of the collaboration can play an equally important role in 'bridging' universities and industry.
University-industry collaboration; Institutional and geographical proximity; Innovation; Regions
& Andrea Filippetti
JEL Classification O31
Over the past 15 years university–industry (U–I) linkages have attracted increasing
attention from both scholars and policy makers. The progressive abandonment of the
‘linear model’ in favor of more sophisticated systemic and interactive approaches to the
genesis of innovation has produced a shift in both analytical and policy targets. The
spotlight moved from basic science, general purpose technologies and various forms of
Research and Development (R&D) efforts, to the relations and linkages between a variety
of agents (firms, public research centres, universities, etc.) collectively forming local,
regional, national or supra-national innovation systems (e.g. Archibugi 2001; Verspagen
2006) and contributing to regional development (Lawton Smith and Bagchi-Sen 2010).
Scholars and policy makers have come to the realization that leveraging public and private
investment in R&D is not necessarily leading to stronger regional or national innovation
performance, unless these efforts are supported by adequate systemic conditions. However,
notwithstanding the conceptual emphasis on the truly systemic multi-actor nature of the
innovation process, some specific components of the ‘innovation system’ have received a
disproportionate consideration: this is in fact the case of U–I linkages. Fostered by the
appeal of the ‘triple-helix’ approach (e.g. Etzkowitz et al. 2000), U–I collaborations have
become a mantra of innovation policies around the globe.
The strength and extent of U–I collaborations are now universally included among the key
indicators to capture the innovation performance of national and regional economies (see, for
example, the OECD Science and Technology Indicators or the EU Innovation Scoreboard).
U–I collaborations are top priorities in the innovation policy agendas of many governments.
When the OECD presented the latest available cross-country data on U–I collaborations1 in
June 2013, countries at the bottom of the ranking immediately reacted in order to make up for
their weakness. For example Australia—among the top-ten OECD countries for innovation
performance—ranked last (33rd) for the proportion of businesses collaborating with higher
education and public research institutions. This fuelled an intense internal debate that
culminated in March 2015 with the publication of a new innovation strategy report on ‘‘Ensuring
Australia’s Future Competitiveness through University–Industry Collaboration’’ (PwC
2015). But a similar faith in U–I collaborations has also been placed by countries in the middle
(e.g. the UK, ranked 19th) and lower (e.g. Italy, 26th) positions of the OECD ranking. Out of
seven key action-points summarizing the innovation policy of the UK Coalition Government
between 2010 and 2015, two are about U–I collaborations (Department for Business,
Innovation & Skills 2015). Even if Italy lacks an explicit national-level innovation strategy, a
significant amount of resources have been earmarked to U–I linkages in the framework of the
‘smart specialization strategy’, supported by both national strategies and the European
Structural Funds (European Commission 2012).
At both national and sub-national levels support for U–I linkages is presented as a
means to achieve two objectives simultaneously: (a) facilitate technology transfer and
increase technological intensity at the firm level; (b) create incentives for university
research to address relevant practical problems, generating market value.
A vast scholarly literature has aimed to assess the impact of U–I collaborations on
innovation, identify their drivers, and evaluate the corresponding policy tools. In this
1 OECD, based on Eurostat (CIS-2010) and national data sources, June 2013: doi:10.1787/888932891359.
context, special emphasis has been devoted to the role of geographical proximity and
spatial clustering in shaping knowledge transmission between science and business.
Empirical research has looked at three main channels, namely: collaborative research
projects (e.g. D’Este and Patel 2007; D’Este et al. 2013), scientific publications (e.g.
Gla¨nzel and Schubert 2004; Abramo et al. 2009a, b; Giunta et al. 2014), and patenting (e.g.
Balconi and Laboranti 2006). All these works have contributed to shed new light on the
functioning of U–I collaborations, often questioning the principles on which some of the
most common policy tools rest. However, existing research has given for granted the
‘special’ nature of U–I links as opposed to other possible forms of innovative collaboration
(e.g. inter-firm or inter-university collaborations). Even if the latter are also crucial
components of the relational dimension of any innovation system, existing research has focused
on the formation (or lack thereof) of U–I collaborations, failing to assess them against the
broader set of possible cooperative links. Therefore, the literature has so far failed to
provide empirical answers to fundamental conceptual questions that underlie the ongoing
scholarly and policy debate in this field. Are U–I collaborations more difficult to form (and
therefore deserving of special attention)? Are they more ‘valuable’ (Giuliani and Arza
2009) than other forms of collaboration? What factors make them more or less likely to
develop? Answers to these questions would provide a much needed justification for the
special attention (and funding) that existing innovation policies have devoted to U–I links
in a variety of countries.
This paper addresses these research questions by analyzing U–I as one of the possible
forms of collaboration between inventors. All collaborations are shaped by both
individuallevel characteristics and preferences, and by relational factors between possible
collaborators. Conditioned upon individual characteristics, the probability of collaboration is
shaped by geographical, institutional, social or cognitive proximity between the potential
team members involved. In this framework, for U–I collaborations to occur, agents have to
overcome the institutional distance between the business world and academia. We
contribute to this stream of research in two complementary ways. First, while most of the
existing research has only focused on actual collaborations between universities and the
business sector, we employ a novel counterfactual approach which allows us to compare
actual collaborations with a suitable counterfactual of potential collaborations that could
potentially happen—given the characteristics of the ‘partners’ involved—but are not
actually formed. The definition of a suitable counterfactual makes it possible to identify the
factors that facilitate/hamper collaboration (which is not possible by observing only actual
collaborations), assessing the impact of institutional distance (university vs. business) on
the probability to collaborate. Second, university–industry collaborations are here jointly
studied within the broader set of possible collaborations which include also those among
universities and within the business sector. This makes it possible to single out the intrinsic
differential features of U–I collaborations (if any), distinguishing them from the
characteristics of alternative forms of collaboration.
The paper is grounded into the micro-level literature on the different types of relational
factors, and in particular geographical, social, or organizational proximity among inventors
(e.g. Agrawal et al. 2008; Boschma and Frenken 2010; D’Este et al. 2013; Crescenzi et al.
2016) that shape collaborative behavior. The analysis looks at the case of Italy,
characterized by high heterogeneity in terms of both innovative dynamisms and attitude towards
cooperation (Crescenzi et al. 2013), and by the dominance of a ‘Personal Mode’ of
research collaboration that supposedly compensates for the limited technology transfer via
‘Institutional Mode’ (Bodas Freitas et al. 2013; Geuna and Rossi 2013).
The empirical strategy is based on the comparison between actual collaborations and a
control group of ‘virtual’ collaborations (i.e. teams that given their characteristics should
be formed but in fact are not).
We rely on co-patenting to detect collaboration. This implies that we only observe
collaborations that: i. are successful; ii. result in a patentable output; iii. are more likely to
occur in science and technology based sectors. In principle, scientific publications and
collaborative research projects are also good candidates to capture collaborations which
involve academia; however, they would significantly downplay the role of the private
sector. The use of patent data makes it possible for us to cover all types of collaboration in
the same analysis (building at the same time an appropriate counterfactual sample) but it
forces us to restrict the analysis to one dimension of collaborative work only.
The dataset covers all patents application filed by Italian inventors between 1978 and
2007 and identifies academic inventors by means of information provided by the Italian
Ministry of Education. The results confirm that collaborations between business and
academic inventors are indeed hindered by the lack of institutional proximity that instead
supports inventors within inter-firm or inter-university collaborative networks. The
analysis also suggests that, once established, U–I collaborations lead to patents of more general
applicability. Geographical proximity facilitates U–I collaborations, though the
involvement of ‘star inventors’ on both sides of the U–I collaboration can play an equally
important role in ‘bridging’ business and academia.
The nature and determinants of U–I linkages in the Italian context have been explored in
a number of studies. By using network analysis in the microelectronics industry, Balconi
and Laboranti (2006) point to three main features of Italian U–I collaborations: better
scientific performance is associated with stronger ties between industry and university;
cooperation relies substantially on face-to-face interaction; cross-border collaborative ties
tend to be driven by cognitive and social proximity (see also Abramo et al. 2009a, b;
Cesaroni and Piccaluga 2015). Giuliani et al. (2010) carry out a similar exercise for the
wine industry, comparing the case of Italy with that of two other countries, namely Chile
and South Africa. The authors find that what makes researchers central in U–I networks is
informal power based on personal networks, rather than influence based on formal
academic position or expertise. The crucial role of academic inventors within networks of
inventors is also a main finding of Balconi et al. (2004) who take co-patenting as a proxy of
In general, research in this area converges on the important role played by academic
inventors in research collaborative networks. Nevertheless, the role of proximity is still
ambiguous, particularly with respect to the extent of complementarity versus
substitutability among the various forms of proximity in different contexts (e.g. Bodas Freitas
et al. 2013). In this growing body of literature a number of relevant aspects of U–I links in
Italy still remain underexplored. First, to the best of our knowledge, the present study is the
first to take into account different types of proximity at the same time in the study of U–I
relationships in Italy. Second, studies at the inventor level are still rare in this field.
Perkmann et al. (2013) conclude that ‘‘individual discretion seems the main determinant of
academic engagement with industry’’ (433). Bodas Freitas et al. (2013) find that half of the
academics who engage in collaboration with industry use personal contractual
arrangements. Therefore, research based on ‘institutionalised’ forms of U–I linkages (such as joint
grants or research consortia) would overlook around 50% of the whole phenomenon. At the
same time, in a context such as Italy where the personal mode of U–I interaction still plays
a dominant role (Bodas Freitas et al. 2013; Geuna and Rossi 2013), firms tend to
appropriate the results of innovative collaborations with university: when patents are filed, the
applicants are very likely to be the former rather than the latter. Therefore, analyses
exclusively focused on firms would overlook the significant involvement of academic
scientists. Third, existing contributions have focused mainly on U–I collaborations, while
this paper explores a broader sample which includes all possible forms of collaboration—
between and within the two communities—making it possible to identify of the
specificities of U–I interactions.
The paper is organized as follows. The next section discusses the background literature
on U–I linkages and the different forms of proximity. Section three explains data and
empirical strategy, and provides some descriptive statistics. The results are presented in
section four, whilst the concluding section offers some implications for policy.
2 Proximities and innovative collaborations in university–industry linkages
Our analysis explores the factors that facilitate collaborative work among individuals in
different working environments (business vs. academia) focusing on the role of various
forms of proximity between innovators. Three forms of collaboration are analysed: i.
among inventors based in private companies (I–I collaboration); ii. among inventors based
in universities and public research centres (U–U collaboration); iii. between inventors
based in private companies and those affiliated with universities (or public research
centres) (U–I collaboration). These three forms of collaboration might differ in several ways.
Companies can establish one-off collaborations on a specific project or develop long-term
collaborations. For example, in the Japanese automotive industry Japan, specialized
suppliers collaborate with their leading companies on a long-term basis also on innovation
activities. Purely academic collaborations are also often based on one-off projects.
However, long-term collaborations are also common, and personal networks and friendship can
play a relevant role. It should also be noted that the greater autonomy of academic
researchers—compared to workers in the private sector—allows the former a greater
capacity to establish a multiplicity of collaborations and to experiment with new external
collaborations. Conversely, decisions to collaborate in the business sector tend to be more
structured, sometimes involving hierarchical processes and taking into account complex
issues linked with industrial secrecy and competition. Finally, U–I collaborations are
characterized by the relevant differences between the corresponding institutional
environments. On the one hand, companies collaborate with universities to benefit from the
competences of the latter in basic science and close-to-the-frontier research. On the other
hand, universities have been ‘pushed’ by policy makers to establish collaborations with the
industry in order to encourage technology transfer and the mobility of high-skilled human
capital from public research to the private centres. To sum up, collaborations outside the
boundaries of individual organisations can take different forms (short-term, long-term,
project-base, etc.), can be driven by different incentives, and can impinge on different
Collaborative work has to deal with two orders of problems: the identification of the
most suitable partner(s) and the efficiency of the resulting team. Individual inventors (or
the entrepreneurs or managers in charge of new projects/laboratories) have to identify the
most suitable collaborators/team members, dealing with information asymmetries and
signaling effects that increase the complexity of the search and matching process
(Ackerberg and Botticini 2002). Once the team is formed individual efforts are often
unobserved (or hard to observe) with free-riding, procrastination, and principal-agent
problems (Bonatti and Horner 2009). Therefore, the analysis of the collaborative behavior
of innovative agents has focused on the identification of individual-level (i.e. pertaining to
each agent), social (i.e. linked to the socio-economic environment in which individuals are
embedded) and relational (i.e. concerning the relative position of the agents in a cognitive
or relational space) characteristics enabling collaboration by solving such problems
(Breschi et al. 2007; Agrawal et al. 2008; Muscio and Pozzali 2013; Kerr and Kerr 2014;
Crescenzi et al. 2016). The relational factors that shape the collaboration between
innovators can be conceptualized by looking at five different ‘proximities’: geographic,
institutional, organisational, social and cognitive proximities are all likely to spur cooperative
behaviour (e.g. Boschma 2005; Torre and Rallet 2005; D’Este et al. 2013; Crescenzi et al.
2016). The analysis of the drivers of collaboration patterns is then focused on
understanding which proximities are most important for different actors, and how they may or
may not interact/complement/substitute for each other.
In this framework, for university-based inventors to collaborate with firm-based
inventors (and vice versa) it is necessary to overcome the barrier of the lack of
‘institutional proximity’ that, instead, would facilitate individuals belonging to the same
institutional type (Kirat and Lung 1999; Hall et al. 2001, 2003). The latter refers to the
institutional conditions in which individuals operate2 and make decisions. Institutions
include both formal codes of behavior (such as laws and rules) as well as informal
arrangements (e.g. habits, norms, culture). While companies and universities based in the
same country share a similar national institutional framework, there are significant
differences in the rules governing business and academia. For instance, workers are recruited
and evaluated on the basis of completely different norms and regulations. The formal
system of incentives and career progression also differs radically. In addition, actors in
business and in academia show distinctive features along a number of informal institutional
dimensions such as habits, conventions, norms and culture (e.g. Merton 1973; Dasgupta
and David 1994).3 The two environments also differ in terms of the decision making
process that leads to the formation of collaborations. Academic researchers usually benefit
from greater autonomy (especially senior academics). Conversely, in the business sector
collaborations outside firm boundaries are part of more complex overarching strategies that
are shaped by a variety of factors often (but not always) outside the direct remit of
individual researchers. Different companies might balance hierarchy and horizontal
decision-making in different ways (see for example the case of many highly innovative
companies in IT that leverage an open and highly collaborative working environment to
attract the best talents). However, irrespective of the decision-making structure, all
wellfunctioning research teams still need to (self) select appropriate team members based on a
set of observable characteristics at the individual and relational level, in line with the
approach of our empirical model.
Other proximities between innovative agents co-exist with the institutional dimension.
The early literature on innovative collaborations has extensively focused on geographical
proximity as a key enabler for knowledge exchange and collaboration (Jaffe 1989; Jaffe
et al. 1993; Mansfield and Lee 1996; Feldman 1999; Arundel and Geuna 2004;
2 These should not be confused with relations at the micro level (e.g. friendship) which in turn relate to
social proximity (Boschma 2005).
3 Note that some authors have recently claimed that the Mertonian distinction between the academic and the
non-academic environments may hide differences within them and particularly within the former (Perkmann
et al. 2013).
Abramovsky et al. 2007; D’Este and Iammarino 2010; Feldman and Kogler 2010; Laursen
et al. 2011). Spatial proximity facilitates the exchange of new complex non-codifiable
knowledge via face-to-face contacts, making communication more effective due to trust
and social engagement (Storper and Venables 2004). These latter factors are clearly
relevant to both partner selection and the success and performance of the resulting
collaboration. However, as highlighted by an equally vast literature (e.g. Malmberg and Maskell
2002; Howells 2002; Gertler 2003; D’Este et al. 2013), geographical proximity can be
complemented or replaced by other proximities in supporting information and knowledge
sharing. The position of the actors in networks generates a social proximity that might spur
collaboration and knowledge exchange across institutional and spatial boundaries (Breschi
and Lissoni 2001). Cognitive proximity—defined as common knowledge bases, similar
and complementary bodies of knowledge that allow to understand, process, and exchange
new knowledge (Nooteboom et al. 2007)—is also important to reduce the ‘‘distance
between the academic and industrial realms’’ (Balconi et al. 2004, 128). Also important for
collaboration is organizational proximity: the set of relationships between and within
organization ‘‘connected by a relationship of either economic or financial dependence/
interdependence (between member companies or an industrial or financial group, or within
a network)’’ (Kirat and Lung 1999, 30).
Understanding the nature of U–I linkages is therefore based on the capacity to model
institutional proximity after controlling for other forms of proximity and inventor-level
characteristics and preferences. Conversely, in order to shed new light on the factors
facilitating or hindering U–I collaborations, it is necessary to explore the complementarity
or substitutability between institutional proximity and other proximities and/or inventors’
characteristics. It is true that for U–I collaborations to happen innovators have to overcome
‘institutional’ barriers, but it is also possible that other forms of proximity (or inventors’
characteristics) might ‘compensate’ for such obstacles. Shared habits and norms tend also
to show dynamic reinforcement processes and co-evolution at the local level,
compensating for University–Industry differences within the same national institutional
framework. In addition, geographical proximity might lead to ‘better’ U–I ties—‘‘more durable
or more likely to emerge from a prolonged search’’ (D’Este et al. 2013, 542)—or facilitate
local cumulative processes whereby existing U–I connections facilitate further links by
means of imitation effects and institutional learning. Conversely, the disadvantages
associated with initiating partnerships over geographical distance—e.g. uncertainty,
information asymmetry, lack of coordination, opportunism (e.g. Mora-Valentin et al. 2004;
Veugelers and Cassiman 2005) might be counterbalanced by the possibility to access
newer non-redundant knowledge that would not be available locally. While the economic
geography literature has focused on the interactions between U–I linkages and
geographical proximity, research in the field of innovation studies has placed more emphasis
on the importance of individual-level characteristics, and in particular on the prominent
role of star inventors (e.g. Azoulay et al. 2008; Bercovitz and Feldman 2010;
Subramaniana et al. 2013)– i.e. individuals with a long track-record of often highly influential
patents—who can often act as ‘bridges’ (Subramaniana et al. 2013) between different
communities and institutional contexts.
3 Empirical strategy
Patents have been extensively used as a proxy for innovation activities, despite their
wellknown limitations (e.g. Archibugi 1992). This paper uses the dataset KITES-PATSTAT on
Italian patents developed by Bocconi University, that includes all patents for the pre-crisis
period 1978–2007 with information on applicants and inventors (Lissoni et al. 2006). The
dataset includes all information on patents (i.e. publication number, title, abstract, priority date,
application year, and technological class), their applicants (i.e. name, address, city, country) and
inventors (i.e. name, surname, address, city, province, region, and country). In addition, it is
possible to identify a sub-sample of 1297 academic inventors (AI) by relying on information
from the Italian Ministry of Education. Information includes, for each academic inventor,
academic affiliation, career status—i.e. the Italian equivalent for full, associate, and assistant
professor—and scientific field of expertise. The AI database is matched with the patent database
making it possible to univocally identify all academic inventors and their patents.4
3.2 Methodology and unit of analysis
The empirical strategy follows Crescenzi et al. (2016) and models collaborations at the
individual (inventor) level, where the units of observation are inventor pairs. In order to
control for a number of personal characteristics of the individual inventors we are forced to
focus our attention on the sub-sample of multi-patent inventors, therefore excluding from the
analysis all inventors that have patented only once in our sample. Our sample includes all
academic and business inventors for the 1987–2007 period, hence including both inventors
who have collaborated (patents with at least two inventors) and inventors who have not
collaborated (patents with only one inventor). This allows us to study the factors influencing
the probability of collaboration avoiding the problem of self-selection which potentially
affects other studies focusing only on actual collaborations. In the real economy, some
employees of private companies do not patent, as well as there are academics that do not
generate any patent: both these groups of non-patenting individuals are not captured by patent
data. However, as far as this ‘sample selection’ affects both groups (i.e. academic and
business inventors) in the same way, there is no bias in the results based on the systematic
comparison between these two groups. In studying what influences collaboration between
inventors, we rely on a comparison between actual pairs—pairs of inventors that have
actually collaborated—and virtual pairs—pairs of inventors that could have
collaborated/coinvented given their characteristics but in fact did not. The latter group forms the ‘control
group’ in order to identify the differential factors that lead to actual collaborations. In other
words, the ‘virtual’ pairs are collaborations that would have been possible given their
characteristics but that did not actually occur. For all pairs (actual and virtual) we compute the
‘distance’—or proximity—between individuals in the pair along institutional, geographical,
organizational and social dimensions. The model controls for individual, institutional and
4 Unfortunately, the database includes only personnel with permanent positions in Italian universities or
public research centres, while it does not include PhDs and post-docs. In any case post-docs and PhDs would
be a confounding factor in the analysis, given that they can be based in university or private labs depending
on the source of funding of their scholarship. In any case, it is highly unlikely that PhD students or
PostDocs are listed as inventors in a patent without their supervisors being also mentioned among the inventors.
The inclusion of their academic supervisor in the patent record ensures that the U-I collaboration is correctly
captured in the dataset.
socio-economic factors that might influence the propensity to cooperate over and above the
proximities between partners. We also control for the overall size of the inventing team each
couple belongs to in order to account for the overall team structure.
Three complementary dependent variables are employed in the analysis: i. a dummy
variable indicating whether the pair is an actual pair (actually collaborating) or a virtual
pair; ii. a continuous count of the number of collaborations per pair, proxing the
performance of the actual collaborations once they are established; and iii. a citation-weighted
count of the patents generated by the actual collaboration as a proxy for the
scienceintensity or generality of the innovation output of the established collaborations.5
In principle, it is possible to study all possible pairs in the sample, along with the subset of
actual inventor pairs. This approach poses two challenges: first, it is hard to think that an
inventor active in the 1970s could collaborate with an inventor active at the end of the 2000s;
second, the potential number of pairs which can be observed over different decades makes the
calculation computationally extremely intensive. We therefore follow a sampling strategy:6 we
first randomly sample 10% of patents, stratified by year, 121 three-digit technology fields and
inventor team size; second, we create a set of possible pairs (pairs who might have co-invented
but did not) and a set of actual pairs (pairs who actually co-invented). Increasing the number of
virtual pairs, up to several millions, would not affect the results to the extent that i. virtual pairs
are generated on the basis of characteristics that make them comparable to actual pairs; ii.
several robustness checks are performed to verify whether the results are robust to different
sampling strategy, both in terms of choosing a different 10% sample, or by choosing a larger
(e.g. 15% and 20%) sample (these robustness tests are presented in the empirical section).
We end up with an unbalanced panel of 595,983 observations, of which 38,957 (5.6%)
are actual pairs.
We build a panel for the years 1987–2007, divided into two 10-year periods, 1987–1996
and 1997–2007. We use the first 10 years 1978–1986 to provide information on inventors’
patenting activity which is used to control for unobserved heterogeneity in individuals in
the period 1987–1996; similarly, we use data of the period 1987–1996 to control for
individual heterogeneity during the years 1997–2007.
3.3 The model
The empirical model is specified in Eq. 1 below. For inventor pair ij in the 10-year period t,
and technology field f, the specification is:
Yijtf ¼ a þ PROXbijtf þ GEOcij þ INVdij þ INSTfij þ TEAMgij þ zf þ kt þ eijtf
where Y is either a dummy for an actual/possible co-inventing pair (DCOINVENT), or the
count of a pair’s co-invented patents (#COINVENT), or a citation weighted count
(CITATIONS) in a given 10 year period. The variable DCOINVENT takes value 1 if the pair of
inventors has patented together, and value 0 if it has not. Instead, the variable #COINVENT is
a continuous variable recording the number of co-invented patents. When looking at simple
patent counts as dependent variable, each co-invented patent is counted as 1 independently on
the importance and scope of the invention (Tajtenberg 1990). As it is extremely relevant to
investigate whether innovations generated by U–I collaboration differ qualitatively from
those coming either from a solely business-based team or from a purely academic team, we
5 For the second and third dependent variables the virtual pairs have always a value equal to 0, since they
have not co-patented.
6 See also, for similar strategies, Sorenson et al. (2006), and D’Este et al. (2013).
rely on a variable of forward citations of the patents generated by each couple (CITATIONS).
Forward citations are correlated with both the technological impact and market and social
value of innovation (Tajtenberg 1990; Hall et al. 2005). Patents involving academic partners
are more likely to be the outcome of basic research, while patents in which only private
companies are involved tend to be more ‘applied’ in nature. Leaving aside the huge
difficulties in distinguishing between basic and applied research (e.g. Stokes 1997; OECD 2002),
and taking into consideration the fact that also private companies need to perform basic
research (e.g. Rosenberg 1990; Pavitt 1993), university-based patents tend to be broader in
terms of underlying scientific and technological knowledge. We therefore employ a measure
of forward citations which is meant to capture the basic-science intensity and the influence of
each patent on future innovations (Trajtenberg 1990; OECD 2009).
To sum up, our model looks first at the factors which influence the matching (DCOINVENT)
and subsequently at two different measures of the performance of the collaborations that are
eventually formed, i.e. the number of co-invented patents (#COINVENT) and a measure of forward
citations of the patents generated by each couple (CITATIONS). Note that for the latter two
specifications the dependent variables—#COINVENT and CITATIONS—range between 0 (for
the virtual pairs that have no joint patents and therefore no citations) and 1…n (for actual pairs that
can produce any number of patents from 1 to n and attract any number of citations). As a result, all
these additional estimations are based on the full specification of the model and on the full sample.
The potential emergence of a difference in the nature of the patents resulting from U–I
collaboration with respect to those resulting from other types of collaboration, i.e. within
industry or university, can add important qualitative insights in this field, as well as more
tailored policy prescriptions. The development of an indicator of forward patent citations has
to deal with two operational challenges. First, older patents are—ceteris
paribus—automatically more cited than newer ones, thus making it necessary to include a control for the
priority date of the patent and year dummies for temporal effects. Second, patent citations
tend to differ across technological classes (Hall et al. 2005). Forward citations are therefore
normalized looking at the share of citations within each patent’s technological class (based on
a thirty-sector classification): our dependent variable is the share of forward citations within
the technological class of the patent generated by each pair of inventors (2-digit International
patent classification IPC). Finally, when looking at this indicator our controls include the type
of organization in which inventors work: since virtual pairs do not necessarily share the same
patent, this step of the analysis is based on actual pairs only (with no random sampling).
The independent variables are defined as follows:
Proximities (PROX)—The vector PROX includes the key variables of
interest—institutional and geographical proximity—and controls for other relevant forms of proximities
between the inventors.
Institutional Proximity: a dummy variable taking value 1 if inventors in a pair belong to
the same type of institution, i.e. both work either in a university or in the private sector
(i.e. business firm); the dummy takes value 0 when one of the inventors is based in a
company and the other in a university. The latter case identifies U–I linkages.7
7 U-I linkages are identified by the diversity of the type of institution the inventors belong to (affiliation
with a private company vs. university), while the applicant (assignee) of the patent can be either the
university or the company. Therefore both patents whose applicant/assignee is a university and patents
whose applicant/assignee is a company can be identified as U-I linkages to the extent that there are both
business inventors and academic inventors in the same patent. What makes it possible to univocally identify
academic inventors is the merge of the patent dataset—as discussed in the data section of the paper—with
the exhaustive list of all Italian Academics provided by the Italian Ministry of Education.
In order to capture the capability of ‘star inventors’ to bridge (or not) institutional
distance, a set of three additional dummy variables is built, taking value 1 if: i. there is at
least one star inventor in the pair; ii. there is at least one academic star inventor in the
pair; iii. there is a least one business star inventor in the pair. An inventor is a ‘star’—
therefore the variable takes a value of 1—if she invented a number of patents above 75%
(third quartile) of the entire distribution of patents. The time frame used to compute the
total number of patents is the total time coverage of our database. The accumulation of a
significant number of patents over the life-time of some inventors is a signal for their
patenting experience, inherent quality and inventive productivity that can make them
more ‘interesting’ counterparts for joint projects.
Geographical Proximity: the inverse of the linear physical distance expressed in
kilometers between two inventors measured in logarithm and based on their residential
addresses. The distance is calculated on the base of the province of residence of the
inventor. Italy is divided into 110 provinces.
We also control for the following proximities:
Organisational Proximity: this dummy variable is a proxy for the likely embeddedness
of the inventors’ couple into the same organization8 and takes value 1 if both inventors
work in the same company or in the same university, research center, or other types of
Social Proximity/Position in co-invention network: a set of dummies is included in the
model, taking value 1 if: i. inventors’ pair co-invented in the previous period; ii.
inventors’ pair has worked for the same organization in the previous period; iii.
inventors’ pair shared a co-inventor in the previous period (i.e. the current collaboration
is the closure of a triad).
In addition, in order to single out the role of various proximities and their interactions, a
number of other inventor characteristics that might influence collaboration choices are
Geographical Factors (GEO)—The vector GEO takes into account the place of
residence (i.e. macroregion) of inventors, i.e. whether they live in the North, Center, or South
of Italy. The vector also includes a dummy variable that considers whether at least one of
the inventors lives in a large city with major universities (i.e. Milan, Rome, Turin, Naples).
Inventor characteristics (INV)—The vector INV takes into account the patenting
behavior of each inventor in the previous 10-year period. Two sets of dummy variables are
included in the equation and equal 1 when: i. the inventor patented in the previous period;
ii. the inventor patented always alone, always in team, or both ways.
Institutional Factors (INST)—The vector INST provides information on the type of
organization (firm, university, other) behind the inventor. A set of dummy variables is
included in order to identify whether the inventor works in a private business firm, a
university or a public research center, or a foundation/NGO/consortium, and whether the
inventor works in a foreign company. This information is based on the applicant of the
Team Factors (TEAM)—Since our unit of analysis is the couple, two different
situations can occur. A co-invented patent can include only the two inventors of the couple, or it
can include more than two inventors. In this latter case the inventors in a couple are part of
8 Throughout the paper the term ‘organization’ refers to a company, a university, a research center, an
a larger team. A dummy variable that takes into account if the pair is part of a large team
has been therefore also added to the model.9
Finally, patent technological classes (z) (2-digits) and year dummies (k) are included in
the estimates. Appendix 1 reports all the variables included in the model.
3.4 Descriptive statistics
Figure 1 plots the share of co-invented patents on the total over the entire period of
analysis, showing how collaborative invention has progressively become the norm among
Italian inventors (in line with the general trend worldwide—see Lee and Bozeman 2005;
Jones et al. 2008). Figure 2 shows the percentage of inventors: i. who have always
coinvented with others over their entire career (team); ii. who have always invented on their
own (solo); iii. who have both invented in team and on their own (mix). The share of
inventors always co-inventing rises over time from 60 per cent up to about 70 per cent of
the total, whilst that of solo inventors declines from 40 per cent to less than 30 per cent.
The proportion of ‘mix behavior’ inventors remains low and stable over time. Overall, this
suggests that inventors’ preferences in terms of collaboration choices tend to remain
relatively stable over their life-time, with team-invention progressively becoming the norm
for younger generations.
As discussed in the previous paragraph, one of the key strengths of our dataset is the
possibility to clearly identify academic inventors.10 Figure 3 shows the share of
co-invented patents on the total by macro-region, confirming the general strength of the Italian
northern regional system of innovation, where collaborative linkages and innovation
networks are far more entrenched than in the rest of the country, and particularly in the South.
This picture is broadly confirmed in Fig. 4—which reports the share of academic patents
by macro-region. However, here the weight of the central regions is much more prominent,
due to the major role played by the capital region, Lazio, and Rome as location of
universities, public and private research institutes, and large (often foreign-owned)
sciencebased firms (Iammarino 2005).
The propensity of academic inventors to collaborate is highly heterogeneous across
scientific disciplines.11 In ‘basic science’ disciplines (e.g. Urology, Neuropsychiatry or
Pediatric surgery) patents tend to include only academic inventors. On the contrary, in
‘applied’ academic disciplines (e.g. Chemistry and Engineering) academics patent more
with inventors from the business sector Also, it should be considered that research in
medical disciplines (and related fields) is often pursued in public academic hospital, while
other disciplines (such as chemistry or engineering) are more common among academic
When looking at our full sample of 595,983 collaboration pairs, 79.3% are
collaborations (pairs) between inventors both based in a private firm (firm–firm collaboration),
1.67% are collaborations involving exclusively university partners (uni–uni
collaborations) and 19% are collaborations between academic and firm-based inventors (uni-firm
9 Note that this variable is not included in the model with the first dependent variable (actual versus virtual
pair) since it would predict exactly the actual pairs.
10 Although ‘academic patents’ can have multiple inventors from different types of organisations, the
definition refers to patents in which there is at least one inventor based in a university.
11 Note that these are different from the patent technology classes: academic positions in Italy are classified
according to a pre-defined set of ‘scientific disciplines’ that identify the macro area of expertise of the
postholder for both teaching and research purposes.
Inventor behaviour 1978-2007
Fig. 2 Shares of inventors patenting alone, in team, or both for every year
collaborations). Out of the 38,957 actual pairs, 95.11% are firm–firm collaborations,
0.63% are uni–uni collaborations, and 4.26% are uni-firm collaborations. Among all the
pairs, 3.87% include an academic star, of which 1.5% are actual pairs.
In terms of the job title of the academic inventors, in the whole sample 57.37% are full
professors, 25.29% associate professors, and 17.34% assistant professors. By looking at the
overall actual pairs, a rise in the share of full and assistant professors clearly emerges,
Fig. 3 Share of co-invented
patents by macro-regions in
Fig. 4 Share of academic
patents by macro-regions in
associated to a decline of associate professors. This is also reflected by looking at the actual
pairs between an academic inventor and a firm inventor—i.e. U–I linkages—whereas full
professors play a greater role in establishing collaborations with the business sector.
When considering inventor-level data, academic patenting is a relevant practice in Italy.
In a study comparing Italy, Sweden and France, Lissoni et al. (2008) show that: i. in the
three countries academic patenting has been increasing since 1978; ii. over 60% of
academic patent applications in France are owned by business companies, which account also
for almost 74% of Italian academic patents and 82% of Swedish ones (interestingly this
same figure drops to 24% in the US); iii. French, Italian, and Swedish academic patents are
respectively around 3, 4 and 6%.
4.1 Types of proximity and types of collaboration
Table 1 includes the key results for the estimation of Eq. 1 for the 1987–2007 period.
Columns 1 to 3 show the results with respect to the probability of collaboration
(DCOINVENT) and are based on probit estimates12; column 4 reports the findings for the
count of each pair’s co-invented patents (#COINVENT) on the basis of negative binomial
estimates13; columns 5 and 6 present results for the citation-weighted patent count
(CITATIONS), using negative binomial and Tobit estimates respectively.14
Column 1 shows the baseline results for the key variable of interest: institutional
proximity. The positive and highly significant coefficient suggests that institutional
proximity—i.e. belonging to the same type of institution, either academia or business—
facilitates collaboration among inventors. This implies that—ceteris paribus—University–
Industry collaborations are more ‘difficult’ and less likely to occur than the other forms of
In principle, since in U–I collaborations are less affected by competitive behaviors
between the partners involved in innovative projects, the reliance on secrecy should be
diminished and collaboration fostered. Our results can depend on the presence of different
sets of incentives, norms and practices regulating activities and acting as barriers to
collaborative behavior. For instance, private firms might find it difficult to anticipate the
potential commercial application of academic research, with associated high search costs
for the identification of the best possible partner(s) in a new project. Symmetrically,
academics may find it easier to collaborate with other academics whose ‘quality’ is clearly
assessable on the basis of common indicators (e.g. publications or academic reputation).
All other coefficients in the model behave as expected: organizational proximity (i.e.
being affiliated with the same university or the same company) facilitates collaboration.
The position in the social network of inventors is also important for collaboration: having
co-invented in the previous period has a positive association with current collaborations,
while having worked for the same organization seems—ceteris paribus—to discourage
inventive cooperation. If inventors are part of the same organization and do not collaborate
it is very unlikely that they will collaborate on future occasions once they leave this
organization. Having had a co-inventor in common in the past (i.e. closing a triad with a
new collaboration) does not affect the probability of collaboration: in other words, the
12 All estimates presented have been computed also by using OLS yielding similar results (see Appendix 2).
Note also that introducing the control variables one group at a time does not affect the results. We therefore
report only the results with all controls, while the main regressors are included in a stepwise way. In order to
test the robustness of the results in terms of our sampling strategy we have performed the following further
estimations: i. the same models have been re-estimated using ten new different random samples at 10%, one
new sample at 15% and one new sample at 20%; ii. the same models have also been re-estimated on a
sample at 10% in which the only criterion for building the virtual pairs was the time frame. The results based
on the ten samples at 10% are reported in Appendix 3. As expected other results are qualitatively unchanged
and therefore they are not reported in the paper but available upon request.
13 Since the dependent variable in this case is a count variable, it would be possible to rely on either Poisson
or negative binomial estimates. After tested the goodness for both, we opted for the negative binomial model
with robust standard errors. Instead, we ruled out zero inflated types of modes since in principle all inventors
can form a couple, i.e. decide to collaborate. Also in the case of the negative binomial estimates results are
robust to using an OLS specification with robust standard errors.
14 Note that in this case the number of observations drops considerably due to the presence of several
missing among the citations.
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t IN ** 40 ** 70 ** 38 ** 45 39 25 91 67 ** 30 96 79 79 60 79 30
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In G In In In In In A A A T P T Y
degrees of separation between two inventors in the co-invention network do not exert a
statistically significant influence on new collaborations. The geographical macro-regional
dummies confirm the well-known dualism of the Italian innovation system, with the South
of Italy, and to a lesser extent the Centre, suffering from less collaborations among
inventors than the North (reference category for the dummy variables). The location of
inventors in major urban areas increases the probability of collaboration.
In column 2 the focus of the analysis shifts to the role of geographical proximity
between inventors: the positive and highly significant coefficient suggests that—ceteris
paribus—geographical proximity facilitates innovative collaboration, in line with the
existing evidence from the geography of innovation literature. Column 3 reports results
when both institutional and geographical proximity are included together in the model,
with no relevant changes in their coefficients’ size and significance: both proximities play a
relevant role in shaping collaborations.
Column 4 looks at the ‘performance’ of inventive partnerships, counting the number of
patents produced by actual collaborations. Even after introducing an additional control for the
overall structure of the patenting team, the same factors that facilitate collaborations also
influence performance. The lack of institutional proximity that characterizes U–I linkages
reduces the number of patents produced by these collaborations, once they are formed.
Therefore, University–Industry collaborations are both more difficult to establish and
quantitatively less productive than other forms of cooperation among inventors. However,
columns 5 and 6 suggest that, when it comes to the forward-citations attracted by the
patents generated by inventors’ collaborations, institutional proximity has a negative
influence. Collaborations involving exclusively either academics or firm-based inventors
attract less forward citations than University–Industry collaborations. Therefore, once
established, the latter links tend to produce qualitatively different patents that, because of
science-intensity and degree of generality tend to find application in a larger number of
subsequent innovations. Geographical proximity still exerts a positive influence on
citations suggesting that face-to-face contacts are also supportive of patents’ capacity to
influence subsequent innovative projects. However, belonging to the same organization has
the opposite effect, as well as (although with at a lower significance level) having
collaborated on a previous occasion: these findings might be interpreted in the light of a higher
propensity of collaborations within-organization and/or repeated over time to bring about
cognitive lock-in, therefore having a weaker impact on future discoveries.
4.2 How proximities complement or replace each other
In order to explore the degree of complementarity (or substitutability) among the various
forms of proximity here considered, in Table 2 a number of interaction terms are included
into the model to estimate the probability of collaboration. In columns 1 and 2 we explore
the interaction between institutional proximity and geographical space, while in columns 3
and 4 we look at the interaction between the former and various measures of inventors’
quality and reputation as potential ‘bridges’ between university and industry. For
robustness checks see Footnote 12 and Appendix 4.
The interaction term between institutional and geographical proximity shows a negative
and highly significant coefficient, indicating that geographical proximity works as a
substitute for institutional proximity. It is possible that in the absence of institutional proximity
(as in U–I linkages) physical propinquity might be able to spur innovative collaboration.
The possibility to interact with potential collaborators within the same locality makes it
easier for both universities and firms to ‘signal’ the quality and relevance of their research,
Table 2 Results—dependent variable: dummy variable: co-invented patent dummy
Institutional prox. * geographical
Pair with at least an academic star
Institutional proximity with academic
Pair with at least a business star
Institutional proximity in large cities
* p \ 0.10; ** p \ 0.05; *** p \ 0.01
facilitating the matching process beyond institutional barriers. It should also be noted that
when it comes to geographical proximity companies might be more reluctant to collaborate
with each other due to stronger competition in the same local market. This would also
depend on the nature of inter-firm relationships. Firms collaborating along the value chain
(vertical types of collaboration) exhibit more intense collaborations when they are
colocated, as in the case of the industrial districts in the North-East of Italy. By contrast,
horizontal types of collaborations—occurring between firms operating at the same value
chain stage—might be discouraged by geographical proximity due to higher
substitutability and the interaction between up-stream and down-stream competition.15 In
column 2 we also consider micro-geographic proximity—i.e. the inventors are located in the
same major city—which per se does not influence the probability of collaboration, even if
it does interact negatively with institutional proximity. This may indicate that it is easier to
bridge the institutional gap in large urban agglomerations (e.g. Iammarino and McCann
2006), where the higher diversity of cognitive and skill bases makes it easier to identify the
‘right’ complementarities, irrespective of the affiliation of the individual inventors.
Overall, this evidence seems to support the importance of geographical proximity for
the formation of U–I collaborations. However, in line with some recent empirical literature
(e.g. D’Este et al. 2013), the second set of interaction terms suggests that other factors
15 We are indebted to an anonymous referee for this fundamental point.
might facilitate the identification of the relevant collaborators: in particular, the reputation
and experience of the individual inventors. Column 3 shows that the presence of an
academic ‘star’ (i.e. an academic inventor with a substantial patenting track-record)
facilitates collaboration in general, but it can also compensate for the lack of institutional
proximity (negative and significant coefficient of the interaction term). Column 4 shows
symmetric results for ‘business star inventors’: the latter have a direct positive effect on the
probability of collaboration, and a substitution effect with institutional proximity. Star
inventors—thanks to their reputation and patenting history—are easier to trust for
counterparts belonging to different communities (business world vs. academia) and are also
more effective in signaling the nature and commercial applicability of their knowledge/
research/innovative activities, facilitating matching. This is true for both academic and
business stars, suggesting that ultimately what matters for University–Industry
collaboration is the possibility to efficiently deal with information asymmetries and uncertainty at
both ends of the partnership. Geographical proximity is only one possible means to address
this information problem in collaborators’ matching. Scientists with the capability to
translate their research into patentable ideas—signaling relevance and applicability of their
innovative activities—can pursue an equally important function.
4.3 Proximity and collaboration for single inventors
In this final section we look at proximity and collaboration for the sample of single
inventors. The latter are defined as inventors that patent only once over the period of
analysis. The major drawback of this extension is that, while we can cover the behavior of
‘occasional inventors’, we cannot observe the ‘historical’ behavior of these individuals
(since we only observe them once in the data). However, this analysis can still provide
interesting insights for policy makers that might differ from those for multiple inventors.
Table 3 reports the results of the baseline model in line with those reported in Table 1.
All specifications mirror those reported above for multiple inventors. Also in this case U–I
linkages are harder to establish than collaborations that only involve either private
companies or universities alone. Geographical proximity increases the likelihood of
collaboration also in the case of single inventors. However, when analyzing at the number of
citations of the corresponding patents, the results are no longer statistically significant
(columns 5 and 6). In the case of single inventors, U–I collaborations do not seem to lead to
qualitatively different patents in terms of citations. This can be interpreted into two ways.
First, it could depend on the fact that only stable relationships between universities and
private companies lead to more ground-breaking forms of innovation; second, occasional
inventors may be regarded as qualitatively different from multiple inventors.
Table 4 reports the estimates for the interaction terms for single inventors. In this case
the only key difference concerns the role of spatial proximity as a mediating factor for U–I
collaboration (column 1). In this case the coefficient is not statistically significant, although
cities still encourage U–I collaborations.
This paper has explored the factors that characterize collaborations between inventors.
Specifically, the paper has focused on the lack of ‘institutional’ proximity as a key barrier
that University–Industry collaborations have to overcome, exploring its interaction with
s V * ) * * 6 * ) * )
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t E * *
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tteadp )3 ICON .48*0* .00739 .28*7* .00658 .72*3* .00847 .30*4* .)0150 .0608 .00651 .0417 .00253 .0224 .00028
n ( D 0 ( 2 ( 3 ( 0 ( - ( - ( - (
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I G I P A A A # P S O P *
Table 4 Results for single inventors sample—dependent variable: dummy variable: co-invented patent
Institutional prox. * geographical
Pair with at least an academic star
Institutional proximity with academic
Pair with at least a business star
Institutional proximity in large cities
geographical proximity and other relevant collaboration drivers. The results suggest that
U–I collaborations are less likely to happen when compared to other cooperative links:
institutional differences between business and academia hinder the signaling process
necessary for an effective search and match of potential collaborators. However, once U–I
collaborations are established they tend to generate patents of more general applicability in
technological terms, i.e. more likely to form the basis for subsequent technological
applications, although this result does not hold for single inventors. Geographical
proximity plays an important role in facilitating all forms of collaboration (again with the
exception of single inventors). At the same time, it works as a possible substitute for
institutional proximity, facilitating U–I collaborations. The reputation and patenting
curricula of the inventors on both sides of the partnership is also a relevant ‘bridge’ between
universities and industries.
The paper is innovative in a number of respects. The analysis has looked simultaneously
at all possible types of inventive collaborations, identifying the specificities of U–I
relationships against a broad relational spectrum. Our approach has made it possible to shed
new light on the rationale for the special attention devoted to U–I links in both scholarly
and policy work.
The broader conceptual and empirical perspective offered by this approach is not free
from limitations. First, by looking at patents we only observe collaborations that: (a) are
successful (i.e. lead to an output); (b) result in a patentable output (while successful
collaborations may well lead to non-patentable forms of innovation). Second, the lack of an
exogenous variation in the proximity relations among inventors makes it difficult to
interpret our results in terms of causality. Third, our analysis cannot distinguish between
alternative channels of collaboration (informal collaborations, collaborative research grants
and projects, joint-ventures, consultancy projects, etc.). It is possible that these alternative
channels are more commonly used for U–I collaborations rather than for collaborations
involving only universities or private businesses. This would lead us to underestimate the
actual incidence of U–I links, with a downward bias of the estimated coefficients that
should be interpreted as lower-bound estimates. Given that systematic information on other
channels of collaboration is very difficult to collect for an equally long time-span and for a
similar geographical coverage, these lower-bound are still highly relevant to shed light on
this under-analysed phenomenom. In addition, patents make it possible to compare
homogenous forms of collaboration in a highly standardised (and internationally
comparable) manner, making it possible to place the case of Italy in a broader context.
Having acknowledged these limitations, our results offer relevant material for reflection
on the best targets for innovation policies. As discussed in the introduction, governments
around the world are devoting an increasing share of resources to the support of U–I links.
Very often these policies have been implemented in the context of actions aimed at
reinforcing the innovative performance of clusters, or have resulted in the development of
physical infrastructure (or subsidies for office/laboratory space) in order to foster spatial
proximity between universities and private firms. Our results confirm the ‘special’ nature
of U–I collaborations: they do face more difficulties/barriers than other collaborations,
providing a rationale for policies that try to minimize such barriers.
However, our results also suggest a number of relevant caveats. First, forms of support for
U–I linkages are only justified when trying to foster more ‘general purpose’ innovations. In
innovation systems where imitation and absorption are the norm (e.g. in less advanced
countries and regions) this type of inventions might not necessarily be realistic targets for
local innovative actors. Second, geographical proximity is not the only (and not necessarily
the most cost-effective) way to facilitate U–I collaborations. While spatial proximity
certainly facilitates collaborations, other mechanisms might produce equally beneficial effects
with less ambiguous side-effects. Particularly in less developed regions, improvements in
the quality of local universities and the simultaneous reinforcement of technological
capabilities of private firms are key to foster innovation linkages and networks in a systemic
fashion. Third, policy makers always have the option to focus their attention on the
framework conditions that facilitate all possible forms of collaborations, allowing individual
agents to choose the most suitable business or university partners depending on their
particular technological needs and on the conditions of their markets of reference.
Acknowledgements This research has been supported by the Marie Curie Intra-European Fellowship
project FP7-PEOPLE-2011-IEF-298167-REGIO_SPIN, under the EC Grant Agreement No:
PIEF-GA2011-298167. We also acknowledge support from the project on ‘‘Industry and University linkages’’ funded
by Roma Tre University (Italy). The authors would like to thank Francesco Lissoni, and the participants in
the research seminar at the Group of Theoretical and Applied Research in Economics (GREThA) of the
University of Bordeaux, May 2014; in the Workshop on University–Industry Linkages at the Department of
Economics of Roma Tre University, May 2014; and in the NARSC Conference 2014 in Washington. The
authors are solely responsible for any errors contained in the article.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if changes were made.
Appendix 1: List of variables
See Table 5.
Table 5 Variables definition
Institutional and geographical
Organisational and social
Individual control variables
DCOINVENT: Dummy variable—co-invented patent dummy
#COINVENT: Count variable—#co-invented patents per inventor pair
CITATIONS: Share of citations within the same technology class
digit International patent classification IPC)
Institutional proximity (dummy variable): inventor pairs share the same
type of organization (both inventors from universities or from the
business = 1; one inventor from university and one from
business = 0)
Geographical proximity (continuous variable): inverse of distance
Inventor pair work in the same organization (dummy variable)
Inventor pair has co-invented previously (dummy variable)
Inventor pair has worked in the same organization previously (dummy
Inventor pairs has co-invented with the same third inventor previously
# Of teams the inventor is part of (continuous variable)
Location of the inventor, macro region: i.e. north, centre, south
(categorical variable (1 = at least one inventor lives in the North;
2 = at least one inventor lives in the Center; 3 = at least one inventor
lives in the South)
City (dummy variable): at least one of the inventor resides in a large city
where there are major universities (Milan, Rome, Turin, Naples)
Inventors have always patented alone previously (dummy variable)
Inventors have always patented in team previously (dummy variable)
Inventors have patented both alone and in team previously (dummy
Star (dummy variable): inventor with a number of patents over 75%
Business star (dummy variable): business inventor with a number of
patents over 75%
Academic star (dummy variable): academic inventor with a number of
patents over 75%
Applicant is private (dummy variable)
Applicant is a public (dummy variable)
Applicant is no-profit (dummy variable)
Applicant is foreign (dummy variable)
A dummy variable that takes into account if the pair is part of a large
Technological classes (2-digit International patent classification IPC)
Appendix 2: Robustness checks
See Table 6.
Table 6 Robustness checks: same estimates as for Table 1 with OLS
Inventor pair with at 0.0106***
least one foreign (0.00112)
At least one inventor
lives in a large city
See Table 7.
* p \ 0.10; ** p \ 0.05; *** p \ 0.01
(udmm iiroxm (udmm irxpom (udmm iiroxm irxpom (cuno iroxm irxpom
ITVECNNDO tittilIsanuonp tirsseabvonO ITVECNNOD ilreacaogphG tirsseabvonO ITVECNNOD ittitlIsanuonp ilreacaogphG tirsseabvonO I#TVECNNO itttilIsanuonp ilreacaogphG itrsseabvonO ititssaonaC itttilIsanuon tirsseabvonO ititssaonaC itttilIsanuon tirsseabvonO
(dumm iiroxm (dumm irpoxm (dumm iiroxm irpoxm (conu iroxm irpoxm
ITVECNNOD ttitilIsanuonp tirsseabvonO ITVECNNOD ilreacagophG tirsseabvonO ITVECNNOD itttilIsanuonp ilreacagophG tirsseabvonO I#TVECNNO itttilIsanuonp ilreacagophG tirsseabvonO ittissoanaC itttilIsanuon tirsseabvonO
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