Paper to be presented at the
35th DRUID Celebration Conference 2013, Barcelona, Spain, June 17-19
The role of geographical, social and cognitive proximity in collaborations
between high-tech entrepreneurial ventures and universities
Massimiliano Guerini
Università di Pisa - DESTEC
DESTEC
[email protected]
Andrea Bonaccorsi
Università di Pisa - DESTEC
DESTEC
[email protected]
Massimo Gaetano Colombo
Politecnico di Milano
DIG
[email protected]
Cristina Rossi Lamastra
Politecnico di Milano
DIG
[email protected]
Abstract
This paper explores whether and how geographical, social and cognitive proximity and university quality affect the
likelihood of collaboration between high-tech entrepreneurial ventures and universities. We use a sample composed by
79 Italian young high-tech entrepreneurial ventures that have collaborated with at least a Italian university in the period
2004-2008, resulting in 96 university-industry collaborations. We run a logistic regression where the dependent variable
is the probability that the entrepreneurial venture collaborates with a university. We also interact social, cognitive
proximity and university quality with geographical proximity. Results show that geography matters in shaping
university-industry collaborations. Moreover, results suggest that social and cognitive proximity are among the main
determinants of collaboration, while university quality do not play a significant role. Finally, we find that social proximity
actually reduces the negative effect of distance on the likelihood that the entrepreneurial venture collaborates with the
university.
Jelcodes:D21,M21
The role of geographical, social and cognitive proximity in
collaborations between high-tech entrepreneurial ventures and
universities
Abstract. This paper explores whether and how geographical, social and cognitive proximity and university
quality affect the likelihood of collaboration between high-tech entrepreneurial ventures and universities. We use
a sample composed by 79 Italian young high-tech entrepreneurial ventures that have collaborated with at least a
Italian university in the period 2004-2008, resulting in 96 university-industry collaborations. We run a logistic
regression where the dependent variable is the probability that the entrepreneurial venture collaborates with a
university. We also interact social, cognitive proximity and university quality with geographical proximity.
Results show that geography matters in shaping university-industry collaborations. Moreover, results suggest
that social and cognitive proximity are among the main determinants of collaboration, while university quality
do not play a significant role. Finally, we find that social proximity actually reduces the negative effect of
distance on the likelihood that the entrepreneurial venture collaborates with the university.
Keywords: university-industry collaborations, geographical proximity, social proximity, high-tech
entrepreneurial venture.
1
1 Introduction
In the last years, universities have significantly re-orientated their academic research towards
industrial applications and have shown a stronger propensity to collaborate with firms so as to
obtain research funds from them (Bonaccorsi et al 2012a). At the same time, collaborations
with universities allow high-tech entrepreneurial ventures to access scientific and
technological knowledge developed by universities (hereafter: university knowledge), thus
fostering the development and growth of these firms. Hence, the study of university-industry
collaborations is a highly relevant issue and has key policy implications. Indeed, scholars
agree that high-tech entrepreneurial ventures crucially contribute to the efficiency of the
economic systems (see, among many others, Audretsch 2007): these firms promote
technological changes, open new innovation routes, and are important sources of new
employment (Rothwell and Zegvel 1982; Oakey 1991; Baptista et al 2008).
Moving from these premises, in this paper we speculate on the differential role of
geographical, social and cognitive proximity and of university quality in enabling
collaborations between high-tech entrepreneurial ventures and universities. Available
empirical evidence highlights the importance of geographical proximity to access university
knowledge (e.g. Bonaccorsi et al 2012b; Belenzon and Schankerman 2012; Laursen et al
2011). However, we go beyond the existing studies by analyzing how the different forms of
proximity interact in determining the likelihood that the entrepreneurial venture collaborates
with the university. Specifically, we intend to answer the following research questions. Is
geographical proximity the main determinant of university-industry collaboration in high-tech
industries? Do other forms of proximity (social and cognitive) and university quality
substitute for geographical proximity? In other words, do young high-tech entrepreneurial
ventures collaborate with distant universities if social, cognitive proximity and university
quality are high?
We explore the role of proximity in shaping university-industry collaborations by
using a sample composed by 79 Italian young high-tech entrepreneurial ventures that have
collaborated with at least a Italian university in the period 2004-2008, resulting in 96
university-industry collaborations. We combine data from a number of rich information
sources. The sample of young high-tech entrepreneurial ventures has been extracted from the
RITA (Research on Entrepreneurship in Advanced Technologies) database. This database
constitutes the most complete source of information available on Italian high-tech firms and
has been developed by Politecnico di Milano. Sample firms are young (less than 25 years
2
old), independent and operate in high-tech sectors in manufacturing and services. Data on
Italian universities have been extracted from the EUMIDA database. This database has been
developed under a European Commission tender and it is based on official statistics produced
by National Statistical Authorities in all 27 EU countries plus Norway and Switzerland (for
details see European Commission, 2010). Our final dataset consists in 6,241 entrepreneurial
venture-university dyads, which correspond to all possible combinations between the
collaborating entrepreneurial ventures and the Italian universities (79 entrepreneurial ventures
* 79 universities).
The dependent variable is the probability that the high-tech entrepreneurial venture
collaborates with a university. We use a logistic regression to estimate the effect of
geographic, cognitive and social proximity and of university quality on the likelihood of
collaboration between the high-tech entrepreneurial venture and the university. As a (inverse)
proxy for geographical proximity, we measure the distance between the high-tech
entrepreneurial venture and the university. In order to measure social proximity, we consider
if at least one of the founders of the entrepreneurial venture had a previous experience in the
university. As far as concerns cognitive proximity, we evaluate whether the university is
specialized in engineering sciences. Finally, order to measure university quality, we refer to
the Shanghai Ranking.
Results show that geography matters in shaping university-industry collaborations,
since the likelihood that a young high-tech entrepreneurial ventures collaborates with a
university decreases with the distance from that university. In other words, geographical
proximity is extremely relevant to access university knowledge. Moreover, results suggest
that social and cognitive proximity between the entrepreneurial venture and the university are
among the main determinants of collaboration, while university quality do not play a
significant role. Finally, we find that social proximity actually reduces the negative effect of
distance on the likelihood that the entrepreneurial venture collaborates with the university.
The structure of the paper is as follows. The next section reviews the extant literature
and provides the theoretical background for the empirical analysis. Section 3 describes data
and section 4 the methodology. Section 5 reports the results of the econometric estimates. The
final section provides conclusions and discussion.
3
2 Theoretical background
Research conducted within universities is a source of significant innovation-generating
knowledge for young high-tech entrepreneurial ventures. However, there is quite unanimous
agreement that the successful transfer of knowledge from universities to industry is affected
by geography. Various studies conducted in the US have found indeed that knowledge
transfers from universities to industry are driven by geographical proximity, being to a large
extent confined to the area in which the university is located (e.g. Jaffe 1989; Anselin et al
1997; Belenzon and Schankerman, 2012; for a similar evidence in European countries see,
among the others, Bottazzi and Peri 2003; Bonaccorsi et al 2012b).
The role of geographical proximity is explained by the importance of direct
interactions and face-to-face contacts for the exchange of tacit knowledge. In most cases,
knowledge developed within universities is not directly usable by firms for commercial
purposes, but it is still fluid and only partially formed (Storper and Venables 2004). Hence,
geographical proximity to university allows intense and frequent direct interactions with the
academic personnel that developed it (Morgan 2004; Bruneel et al 2010). Furthermore,
geographical proximity favors the establishment of common interests and aligns incentives
between collaborating entrepreneurial ventures and their academic partners. Put differently,
geographical proximity can compensate the lack of institutional proximity (Cooke et al 1997).
Institutional proximity helps bring organizations together through sharing similar values and
norms at the macro-level (Boschma 2005). If institutional proximity is missing, geographical
proximity helps to create a common background and a shared set of expectations and
understandings about the nature of the collaboration (Gertler 1995). In university-industry
relations it is generally acknowledged that institutional proximity is low and geographic
proximity plays an important role (e.g. Ponds et al 2007), allowing heterogeneous actors to
overcome the differences in institutional contexts. Moreover, it has been noted that
collaborations between firms and neighboring universities are more likely than distant
collaborations to generate impulses to innovation and create significant learning effects for
the collaborating firms (e.g., Broström 2010). Finally, recent contributions that focused on
this issue have found that distance from the university decrease the likelihood that a firm
collaborates with the university (e.g Laursen et al 2011; Hong and Su 2012). Accordingly, we
posit the following research hypothesis:
H1: The likelihood that the entrepreneurial venture collaborates with a university decreases
with the geographical distance from the university.
4
However, the importance of geographical proximity cannot be assessed in isolation,
but it should be analyzed in relation to other dimensions of proximity, that could facilitate the
coordination among individuals. In other words, other forms of proximity are supposed to be
necessary for successful collaboration and knowledge exchange. In particular, we focus here
on cognitive and social proximity. Cognitive proximity indicates the extent to which two
organizations share the same knowledge base while social proximity the extent to which the
members of the two organization have friendly relationships (Boschma, 2005).
Cognitive proximity is an essential ingredient for successful collaborations as it
provides a shared knowledge base for partners (Knoben and Oerlemans 2006; Nooteboom et
al 2007). As far as concern university-industry collaborations, the successful transfer of
university knowledge depends on the absorptive capacity of the entrepreneurial venture
(Cohen and Levinthal 1990). Accordingly, the cognitive base of the entrepreneurial venture
should be close enough to the new knowledge developed within the university. Specifically,
increasing the cognitive proximity enables the partners to successfully exchange and use
knowledge in their joint work building on shared technological experiences and knowledge
bases. We argue here that cognitive proximity is higher if the university has a strong
specialization in applied sciences. Using data from the Carnegie Mellon Survey of Industrial
R&D, Cohen et al (2002) find that the impact of public research, at least in most industries, is
exercised through engineering and applied science fields rather than through basic sciences.
Hence, we may expect that if cognitive proximity is high (i.e. the collaboration involves
universities specialized in applied sciences), the likelihood of collaborations increases.
Moreover, the effect of cognitive proximity might also overcome the negative effect of
distance in shaping university-industry collaborations. Specifically, we expect that
entrepreneurial ventures will tend to collaborate with universities specialized in applied and
engineering sciences, independently from the distance. Following this line of reasoning, we
posit the following research hypothesis:
H2a: The likelihood that the entrepreneurial venture collaborates with a university increases
with the cognitive proximity to university.
H2b: Cognitive proximity reduces the negative effect of geographical distance on the
probability that an entrepreneurial venture collaborates with the university.
5
Let us now focus on the role of social proximity. Boschma (2005) defined social
proximity in terms of socially embedded relations between agents at the micro level.
Specifically, the presence of previous relationships between the entrepreneurial venture and
the university increase trust between agents, reducing uncertainty, promoting effective
learning (Lundvall, 1993) and facilitating the transfer of tacit knowledge (Maskell and
Malmberg, 1999). Hence, we expect that high-tech entrepreneurial ventures in which at least
one of the founders has a previous experience in the university have an higher likelihood to
collaborate with that university. Moreover, we expect that social proximity not only increases
the likelihood of collaboration, but also has a moderating effect on the negative impact of
distance. In other words, the presence of social proximity increases university-industry
collaborations independently from distance. Accordingly, we posit the following research
hypotheses:
H3a: The likelihood that the entrepreneurial venture collaborates with a university increases
with the social proximity to university.
H3b: Social proximity reduces the negative effect of geographical distance on the probability
that an entrepreneurial venture collaborates with the university.
Finally, it is important to consider that not all universities are endowed equally with
resources and networks. The most valuable resources are likely to be concentrated in highquality universities. Therefore, given a choice of partners, firms will prefer to collaborate with
these universities. In a recent contribution, Laursen et al (2011) have shown that firms’
decisions to collaborate with universities are influenced by both geographical proximity and
university quality. Being located close (within 100 miles) to a high quality university
increases the propensity for firms to collaborate locally. Conversely, co-location with a low
quality university discourages local collaboration. In the absence of a high quality university
nearby, the second-best choice is to collaborate with a distant (more than 100 miles) high
quality university. In other words, firms appear to prefer university quality to geographical
proximity, in line with the view that the benefits of leveraging high quality knowledge
overcome the cost of long distance collaborations. Hence, we posit the following research
hypotheses:
6
H4a: The likelihood that the entrepreneurial venture collaborates with a university increases
with university quality.
H4b: University quality reduces the negative effect of geographical distance on the
probability that an entrepreneurial venture collaborates with the university.
3 Data
To test our conjectures, we take advantage of the RITA (Research on Entrepreneurship in
Advanced Technologies) database, which was developed by the RITA Observatory research
team at Politecnico di Milano and it is the most authoritative source of information presently
available on Italian high-tech entrepreneurial ventures. The Observatory aims at extending the
knowledge of Italian high-tech entrepreneurial ventures, by monitoring new start-ups, their
economic, financial and managerial characteristics and post-entry performances in terms of
innovation and growth. The RITA database was created at the beginning of year 2000 at
Politecnico di Milano and was updated and extended in the years 2002, 2004, 2007, and 2009.
The database contains information on 1,949 high-tech entrepreneurial ventures,
established in 1983 or later, independent at start-up time (i.e. not controlled by another
business organization even through other organizations may have held minority shareholdings
in the new firms) and operating in high-tech industries, both in manufacturing and services. In
order to obtain a measure of university-industry collaboration, we drew on the responses to 6
questions on the RITA 2009 survey that asked whether the entrepreneurial venture i) obtained
a license from the university; ii) has utilized technical (non patented) knowledge developed
within the university; iii) has financed joint R&D projects with the university; iv) has take
advantage of university laboratories and equipment provided by the university; v) has
received consulting services (technical, managerial, commercial) from the university and vi)
has received support from the university to obtain finance. We thus considered as
“collaborating” each entrepreneurial venture that declared that used at least 1 of the 6 forms of
collaborations defined above. The RITA 2009 survey also asked the name of the university
with which the entrepreneurial venture has collaborated. After cleaning data because of
missing information, we were able to identify 79 entrepreneurial ventures that collaborated
with at least a Italian university between 2004 and 2008. Since some entrepreneurial ventures
collaborated with more than a university, the total number of collaborations in our sample is
7
96. Table 1 reports the distribution of the 79 collaborating entrepreneurial ventures by
industry, foundation year and geographical localization.
[Table 1]
Data on collaborating entrepreneurial ventures from the RITA database are
complemented by data on Italian universities which have been extracted from the EUMIDA
database. This database has been developed under a European Commission tender and it is
based on official statistics produced by National Statistical Authorities in all 27 EU countries
plus Norway and Switzerland (for details see European Commission, 2010). For the purpose
of this paper, we extracted the data relating to the size of the university (measured by the
academic staff) and their scientific specialization. Moreover, in order to assess the quality of
the university, we use the Shanghai ranking.1
We then considered all the possible combinations between the 79 high-tech
entrepreneurial ventures in our sample and every Italian university (79 universities) with
which a high-tech entrepreneurial venture could potentially have established a collaboration.
Hence, our final dataset consists in 6,241 entrepreneurial venture-university dyads, which
correspond to all possible combinations between the collaborating entrepreneurial ventures
and the Italian universities (79*79).
In order to calculate distances between entrepreneurial ventures and universities, we
used the database of the Italian National Institute of Statistics (ISTAT) to obtain data on the
latitude and longitude of each municipality in which entrepreneurial ventures and universities
are located. Hence, we calculated the linear distance between the centroids of the
municipalities in which the entrepreneurial venture and the university are located.
Considering the 96 collaborations in our sample, the average distance of the collaboration is
91.26 km, ranging from 1km (the entrepreneurial venture and the university are in the same
municipality) to 854.21 km.
4 Method
We use a logistic regression to estimate the effect of geographic, cognitive and social
proximity and university quality on the likelihood of collaboration between the high-tech
entrepreneurial venture and the university. We estimate models of the type:
1
http://www.shanghairanking.com.
8
.
(1)
The dependent variable colli,j is a dummy variable that equals 1 if the entrepreneurial
venture i collaborates with the university j. Among covariates, as a (inverse) proxy for
geographical proximity, we consider the distance between the high-tech entrepreneurial
venture i and the university j. Following Sorenson and Stuart (2001), we use the logarithm of
distance (log(dist)i,j) to deal with the non-linearity between a distance and the time and money
consumed for that distance. In order to measure social proximity, foundi,j is a dummy variable
assuming value 1 if at least one of the founders of the entrepreneurial venture has a previous
experience in the university j. The variable engj is a dummy variable that equals 1 if the
university j is specialized in engineering sciences. We could expect that cognitive proximity is
higher if the university is specialized in engineering sciences. It has been constructed by first
calculating for each Italian university a Balassa Index (Balassa 1965), considering the share of
academic staff specialized in engineering sciences. Specifically, the Balassa Index has been
calculated as follows:
;
(2)
where eng_staffj is the size of the academic staff (full professors, assistant professors and
researchers) enrolled in the university j and specialized in engineering sciences and total_staffj
is the total academic staff. BIj ranges between zero and infinity with neutral value at 1. Values
higher than 1 mean that that the university j is more specialized in engineering than the
average Italian university. Hence, engj equals 1 if BIj is greater than 1. In order to measure
university quality, the variable qualityj is a dummy variable that equals 1 if the university j is
listed in the top 500 universities according to the Shanghai Ranking. In addition, in order to
assess whether social proximity reduces the impact of geographical proximity, we interact the
geographical distance from the university with foundi,j engj and qualityj.
As far as concern control variables, Zi,j includes the age of the entrepreneurial venture
agei, the size of the university, measured by the academic staff (staffj) and the dummy
variable southi, that equals 1 if the entrepreneurial venture is located in the South of Italy.
Finally, we also include industry dummies and fixed effects at the entrepreneurial venture
level. Table 2 reports the summary statistics and the correlation matrix of the variables used in
the regressions.
9
[Table 2]
5 Results
Table 3 shows the results from the logistic regression of equation (1). Specifically, the first
column shows the results concerning the role of the various types of proximity (geographical,
cognitive and social) and of university quality on the likelihood that the entrepreneurial
venture i collaborates with the university j, without interacting the distance between the
entrepreneurial venture and the university with other proximity variables and university
quality. Results on the regressions obtained by adding interactive terms are shown in columns
2-5.
[Table 3]
Before coming to the discussion of the variables of interest, let us briefly provide a
comment on the control variables. We find evidence of a positive impact of the size (staffj) of
the university on the likelihood to collaborate. Conversely, neither the age of the
entrepreneurial venture (agei) or its location in the South of Italy affect the likelihood to
collaborate with a university.
When looking at the role of proximity and university quality, results clearly highlight
that geographical proximity shapes the likelihood that a high-tech entrepreneurial venture
collaborates with a university. Indeed, the coefficient of log(dist)i,j is negative and statistically
significant in all estimates at 99% confidence level. This result thus confirms that high-tech
entrepreneurial ventures are less likely to collaborate with distant universities, confirming H1.
Moreover, when looking at the other types of proximity, we find strong evidence that both
social and cognitive proximity positively affect the likelihood of collaboration. Indeed, the
coefficients of foundi,j and engj are positive with strong statistical significance (again at 99%
confidence level). Quite surprisingly, we do not detect any significant effect of university
quality (qualityj is positive but not significant at conventional confidence levels).
Let us now turn attention to the interaction terms. Results shown in columns 2 and 5
suggest that social proximity contributes to reduce the negative effect of distance on the
likelihood of collaboration. The coefficient of foundi,j
log(dist)i,j is indeed positive and
statistically significant at 90% in column 2 and at 95% in column 5. Conversely, both
cognitive proximity and university quality do not moderate the negative effect of distance.
However, for a better interpretation of the non-linear interaction model, the left hand side of
10
Figure 1 illustrates the probabilities of collaboration depending on the distance, for different
values of social, cognitive proximity and university quality.2 Specifically, from top to bottom,
the left hand side of Figure 1 shows the probability of collaboration depending on distance
when foundi,j = 0 (solid line) and foundi,j =1 (dashed line), when qualityj = 0 (solid line) and
qualityj = 1 (dashed line), and when engj = 0 (solid line) and engj = 1 (dashed line),
respectively. Moreover, the 3 graphs on the right hand side of Figure 1 show the difference
between the 2 curves in the left hand side. Finally, the dotted line means that the difference is
significant at 99% confidence level. The significance has been estimated by Zelner’s method
(2009).
[Figure 1]
Starting from the top of Figure 1, the left hand side clearly shows that entrepreneurial
ventures are more likely to collaborate if cognitive proximity with the university is high.
However, if we look at the difference in the probability of collaboration (the graph on the topright), we observe that for low values of distance, the difference in probability is positive and
significant, while for values of log(dist)i,j higher than 5 (approximately) the difference is no
more significant. These results suggest that the entrepreneurial venture is more prone to
collaborate with the university if cognitive proximity with that university is high. However,
the effect of cognitive proximity is not so strong to reduce the negative effect of distance
when the university is located too far. In other words, when the distance between the
university and the entrepreneurial venture is high, the difference in the probability of
collaboration between is not statistically different from zero. These results are thus consistent
with H2a but not with H2b.
Looking at the effect of social proximity, Figure 1 (middle-left) highlights that
entrepreneurial ventures are more likely to collaborate if among the founders there is at least
one with a previous experience in that university. When we turn attention to the difference in
probability between entrepreneurial venture with and without an academic founder from that
university (middle-right), we observe that for low values of distance, social proximity reduces
the negative effect of distance on the likelihood of collaboration. However, when the distance
from the university increases, the difference in the likelihood of collaboration between an
entrepreneurial venture with and without an academic founder decreases. However, the
2
All other variables are held at their mean.
11
difference is always significant at 99% confidence level. This result support H3a and partially
H3b, since for very high values of distance the effectiveness of social proximity tends to
become lower.
Finally, let us now to the bottom of Figure 1. Looking at the role of university quality,
we do not detect any significant difference between entrepreneurial ventures that collaborates
with high and low quality universities. Looking at the right hand side of Figure 1, we find
indeed that the difference in the two curves shown on the left hand side is not significant.
Hence we do not find support for H4a and H4b.
6 Discussion and conclusion
In this work, we have offered empirical evidence on the role of different forms of proximity
(geographical, cognitive and social) and of university quality on the likelihood of universityindustry collaborations for young high-tech entrepreneurial ventures. In accordance with the
recent literature that deals with knowledge spillovers from university (e.g. Bonaccorsi et al
2012b; Belenzon and Schankerman 2012; Laursen et al 2011), our results show that
geographical proximity is extremely relevant to access university knowledge. Moreover,
results suggest that social and cognitive proximity between the entrepreneurial venture and
the university are among the main determinants of collaboration, while university quality do
not play a significant role. This latter result is quite surprising, contradicting the UK evidence
(Laursen et al 2011). Finally, we also find that social proximity actually reduces the negative
effect of distance on the likelihood that the entrepreneurial venture collaborates with the
university. However, when the distance from the university is too high, the effect of social
proximity is reduced.
This paper contributes to the recent literature on university-industry collaborations
along several dimensions. First, we use a survey based measure in order to detect the
collaborations. A recent study by Hong and Su (2012) analyzes similar issues but using
patents, which could represent only a small fraction of the phenomenon. Moreover, with
respect to other survey based studies on university-industry collaborations (e.g. Laursen et al
2011, using CIS data) we know the name of the collaborating university, allowing a better
assessment on the role of geographical proximity. Finally, we know whether the founders of
the entrepreneurial venture have a previous experience in a specific university. This allows a
fine grained evaluation on the role of social proximity.
12
We are aware that the work has some limitations, which leave room for further
inquiring. First, the limited size of the sample raises some doubts on the generalizability of
results. Second, other form of proximity might be investigated, such as organizational or
institutional. Third, universities can have a proactive strategy towards technology transfer and
devote substantial resources to this activity (e.g. through technology transfer offices). Here we
are not able to detect the effect of different technology transfer policies conducted by Italian
universities. Finally, our study refers to the period 2004-2008, just before the current global
crisis. It would be interesting to replicate this study in downturn periods so as to assess
whether university-industry collaborations are influenced by macroeconomic conditions.
Despite the aforementioned limitations, our results have important policy implications.
By investigating whether and how the various forms of proximity affect university-industry
collaborations, we contribute to the policy debate on the design of the national university
system. Our data clearly indicate that distance is still an issue. Therefore, our results support
the design of policy schemes aimed at increasing frequent and intense direct interactions
between academic personnel and entrepreneurs, through the organization of dedicated
meetings and workshops and the improvements in information and communication
technologies (Agrawal and Goldfarb 2008). This would allow to increase the social proximity
between the entrepreneurial ventures and the universities.
13
References
Anselin, L., Varga, A. & Acs, Z. (1997). Local geographic spillovers between university
research and high technology innovations. Journal of Urban Economics, 42(3), 422–448.
Audretsch, D.B. (2007), “Entrepreneurship capital and economic growth”, Oxford Review of
Economic Policy, 23(1), 63-78.
Balassa B (1965), “Trade Liberalisation and “Revealed” Comparative Advantage”, The
Manchester School, 33(2), 99-123
Baptista, R., V. Escária and P. Madruga (2008), “Entrepreneurship, regional development and
job creation: the case of Portugal”, Small Business Economics, 30(1), 49–58.
Belenzon, S. and Shankerman, A. (2012) “Spreading the Word: Geography, Policy and
University Knowledge Diffusion”, The Review of Economics and Statistics, forthcoming.
Bonaccorsi, A., Secondi, L., Ancaiani, A. and Setteducati, E. (2012a), “Exploring the role of
third party research in Italian universities”, Working paper.
Bonaccorsi, A., Colombo, M.G., Guerini, M. and Rossi-Lamastra, C. (2012b), “The spatial
range of university knowledge and the creation of knowledge intensive firms”, Working
paper.
Boschma, R. (2005). “Proximity and innovation: A critical assessment”, Regional Studies,
39(1), 61-74.
Bottazzi, L. and Peri, G. (2003), “Innovation and spillovers in regions: Evidence from
European patent data”, European Economic Review, 47(4), 687-710.
Broström, A. (2010), “Working with distant researchers—Distance and content in university–
industry interaction”, Research Policy, 39(10), 1311–1320.
14
Bruneel, J., D’Este, P. and Salter, A. (2010) “Investigating the factors that diminish the
barriers to university–industry collaboration”, Research Policy, 39(7), 858–868.
Cohen, W. M. and Levinthal, D. A. (1990), “Absorptive Capacity: A New Perspective on
Learning and Innovation”, Administrative Science Quarterly, 35(1), 128-152.
Cohen, W. M., Nelson, R. R. & Walsh, J. P. (2002), “Links and Impacts: The Influence of
Public Research on Industrial R&D”, Management Science, 48(1), 1-23.
Cooke, P. Uranga, M. G. and Etxebarria G. (1997), “Regional innovation systems:
Institutional and organisational dimensions”, Research Policy, 26(4–5), 475–491.
European Commission (2010). Feasibility Study for Creating a European University Data
Collection
[Contract
No.
RTD/C/C4/2009/0233402].
Technical
report.
http://ec.europa.eu/research/era/docs/en/eumida-final-report.pdf. Accessed 6 February 2012.
Gertler, M.S. (1995) “"Being There": Proximity, Organization, and Culture in the
Development and Adoption of Advanced Manufacturing Technologies”, Economic
Geography, 71(1), 1-26.
Hong, W. and Su, Y. (2012), “The Effect of Institutional Proximity in Non-Local UniversityIndustry Collaborations: An Analysis Based on Chinese Patent Data”, Working Paper.
Jaffe, A. B. (1989), “Real Effects of Academic Research”, The American Economic Review,
79(5), 957-970.
Knoben, J., Oerlemans, L.A. (2006), “Proximity and inter-organizational collaboration: A
literature review”, International Journal of Management Reviews, 8(2), 71-89.
Laursen, K., Reichstein, T. and Salter, A. (2011), “Exploring the Effect of Geographical
Proximity and University Quality on University–Industry Collaboration in the United
Kingdom”, Regional Studies, 45(4), 507-523.
15
Morgan, K. (2004), “The exaggerated death of geography: learning, proximity and territorial
innovation systems”, Journal of Economic Geography, 4(1), 3–21.
Nooteboom, B. , Van Haverbeke, W., Duysters, G., Gilsing, V. and van den Oord, A. (2007),
“Optimal cognitive distance and absorptive capacity”, Research Policy, 36(7), 1016–1034.
Oakey, R. (1991), “High technology small firms: their potential for rapid industrial growth”,
International Small Business Journal, 9(4), 31-42.
Ponds, R., Van Oort, F. and Frenken, K. (2007), “The geographical and institutional
proximity of research collaboration”, Papers in Regional Science, 86(3), 423–443.
Rothwell, R. and Zegveld, W. (1982), Innovation and the small and medium sized firm. Their
role in employment and in economic change, London, UK: Frances Pinter.
Storper, M. and Venables, A. (2004), “Buzz: face-to-face contact and the urban economy”,
Journal of Economic Geography, 4(4) , 351-370.
Zelner, B. A. (2009), “Using simulation to interpret results from logit, probit, and other
nonlinear models”, Strategic Management Journal, 30(12), 1335–1348.
16
Tables and figures
Table 1. Distribution of 79 collaborating entrepreneurial ventures by industry, foundation year and
localization
Number
of firms
Frequency
(%)
Energy and environmental services
6
7.59
ICT Manufacturing
26
32.91
Biotechnology & pharmaceutical
16
20.25
Software & Internet
18
22.78
Other
13
16.46
Total
79
100.00
10
12.66
Industry
Foundation year
1986-1991
1992-1997
6
7.59
1998-2003
22
27.85
2004-2008
41
51.90
Total
79
100.00
North
47
59.49
Center
15
18.99
South
17
21.52
Total
79
100.00
Localization
Table 2. Summary statistics and correlation matrix of regression variables
Variable
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Colli,j
log(dist)i,j
foundi,j
qualityj
engj
agei
staffj
southi
Mean
0.02
5.67
0.01
0.10
0.32
6.51
1170.82
0.22
Std.
Dev.
0.12
1.15
0.08
0.30
0.47
6.14
1074.26
0.41
Min
0.00
0.00
0.00
0.00
0.00
0.00
7.00
0.00
Max
(1)
(2)
(3)
(4)
(5)
1.00
1.00
7.01
-0.34 1.00
1.00
0.54 -0.30 1.00
1.00
0.11 -0.13 0.07 1.00
1.00
0.04 0.05 0.02 -0.05 1.00
22.00
0.00 0.00 -0.04 0.00 0.00
4704.00 0.10 -0.07 0.06 0.69 -0.02
1.00
0.00 0.13 0.01 0.00 0.00
(6)
(7)
(8)
1.00
0.00
0.06
1.00
0.00
1.00
17
Table 3. Results of the econometric estimates
1
log(dist)i,j
foundi,j
qualityj
engj
agei
staffj
southi
foundi,j log(dist)i,j
qualityj log(dist)i,j
engj log(dist)i,j
Constant
Industry dummies
Firm Fixed effects
N. Observations
Log-Likelihood
R2
-0.824
(0.080)
5.785
(0.865)
0.353
(0.450)
0.904
(0.286)
0.131
(0.113)
0.000
(0.000)
0.968
(2.396)
2
***
-0.846
(0.081)
4.535
(0.971)
0.322
(0.451)
0.911
(0.288)
0.105
(0.091)
0.000
(0.000)
0.976
(1.900)
0.793
(0.407)
3
***
-0.815
(0.089)
5.787
(0.865)
0.459
(0.646)
0.897
(0.288)
0.130
(0.112)
0.000
(0.000)
0.964
(2.379)
4
***
-0.759
(0.093)
5.846
(0.872)
0.305
(0.461)
1.586
(0.584)
0.134
(0.119)
0.000
(0.000)
1.155
(2.532)
5
***
-0.768
(0.100)
***
***
***
***
4.552
(0.983)
0.463
(0.647)
***
***
***
***
1.545
(0.568)
0.105
(0.093)
***
***
***
***
0.000
(0.000)
1.143
(1.955)
*
0.796
(0.406)
-0.030
-0.056
(0.132)
(0.134)
-0.170
-0.165
(0.126)
(0.124)
-3.639
-3.068
-3.688
-1.754
-1.699
(2.458)
(2.067)
(2.710)
(1.128)
(1.136)
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
6241
6241
6241
6241
6241
-239.799
-237.765
-239.772
-238.878
-236.783
0.517
0.521
0.517
0.518
0.523
***
***
***
***
**
Standard errors are in brackets. The asterisks, *, **, and *** indicate significance at the 10%, 5%, and 1% level, respectively. The dependent
variable (colli,j) is a dummy variable that equals 1 if the entrepreneurial venture i collaborates with the university j.
18
Figure 1
Figure 1 illustrates the probabilities of collaboration depending on the distance, for different values of social, cognitive proximity and
university quality. Specifically, from top to bottom, the left hand side shows the probability of collaboration depending on distance when
foundi,j= 0 (solid line) and foundi,j=1 (dashed line), when qualityj= 0 (solid line) and qualityj=1 (dashed line), and when engj= 0 (solid line)
and engj=1 (dashed line), respectively.The 3 graphs on the right hand side show the difference between the 2 curves in the left hand side. The
dotted line means that the difference is significant at 99% confidence level. The significance has been estimated by Zelner’s method (2009).
19