Importance of knowledge networks within Canadian
biotechnology clusters1
Catherine Beaudry 2
Jean-Sébastien Beaucage
École Polytechnique de Montréal, Montréal, Canada
May 2006
Abstract
The paper studies the size and structure of collaboration networks within three Canadian
biotechnology clusters. Using information contained in 1915 biotechnology patents of
2669 scientists, we identified 416 networks of collaborators. In Montreal, it is found that
74 % of the co-inventors are also located in this cluster, while in Toronto, for instance,
this proportion drops to 63 %. In addition, although Toronto is the location of the scientist
with the most connections to other inventors, a greater number of prolific scientists work
in the Montreal cluster. The paper shows that in general, Montreal has a greater number
of scientists organized in networks more concentrated within its boundaries but that
Toronto, with a smaller number of scientists, benefits from more decentralised knowledge
networks.
Keywords: Inter-firm Networks, Clusters, Biotechnology, Patents
JEL Classification: R12, Z13, J24 and O18
1
We are grateful to the comments of Richard Shearmur and Nathalie de Marcellis-Warin,
examiners of the thesis upon which the results presented are based. We are responsible
for all remaining errors.
2
Corresponding author: Catherine Beaudry, Département de mathématiques et de génie
industriel, École Polytechnique de Montréal, C.P. 6079, Succ. Centre-ville, Montréal,
Québec, Canada, H3C 3A7. Tel.: +1 (514) 340-4711 ext. 3357, Fax: +1 (514) 340-4173,
E-mail: catherine.beaudry@polymtl.ca
1
INTRODUCTION
Clusters have been studied extensively during the last few decades. The success of
Silicon Valley has prompted many cities and state governments to try to reproduce its
exceptional environment for knowledge production by creating policies and tax breaks to
make their states and towns more attractive to enterprises. The attractiveness of a cluster
depends on many factors that have direct and indirect impacts on its innovation
production rate. Marshall (1920) identified three factors facilitating the agglomeration of
enterprises: a pool of skilled labour, a specialised intermediate goods industry, and
knowledge spillovers. A recent report by the Conference Board of Canada (Munn-Venn
and Voyer, 2004, thereafter referred to as the CBC survey) identified the importance of
knowledge flows as a critical element to the contribution of clusters to regional economic
growth. Their survey of clusters was triggered by the decision of the government’s
Innovation Strategy for Canada to create at least ten internationally renowned technology
clusters by 2010. A significant recommendation in the report was for government to
support the development of clusters, by investing in the knowledge infrastructure and by
promoting networking and research. In this paper, we aim to examine the structure of
innovation networks, a first step towards the understanding of their role in the production
of innovation within clusters.
In industries where new knowledge plays a crucial role, innovative activity tends to
cluster geographically (Audretsch and Feldman, 1996, Trajtenberg et al., 1997; Jaffe and
Trajtenberg, 1996). This is even more apparent in high technology fields such as
biotechnology that are strongly science-based. An interesting parallel can be drawn
between the photonic networks studied by Kéroack et al. (2004) and those of the
biotechnology domain regarding the dependence of an important local scientific base
within industrial clusters. The knowledge transmission in these fields is mostly tacit and
non-codifiable which require close geographical relation between researchers and implies
that firms collaborating need to be geographically close (Feldman and Audretsch, 1999).
Beaudry and Breschi (2003) showed that firms surrounded by innovative enterprises
tended to patent more than if they were located within a non innovative environment.
They studied 22 781 British firms and 35 108 Italian firms to show that while location in
a cluster densely populated by innovative firms positively affects the likelihood of
innovating, strong disadvantages arise from the presence of non-innovative firms in all
sectors. European Patent Office (EPO) data was used to provide information on British
and Italian innovation. In essence, these studies lay the groundwork for the current
research, the suggestion that something is going on within clusters that benefit firms’
growth and propensity to innovate; they strongly hint towards spillovers but do not
measure them specifically.
Studies have shown the attracting power of certain firms, universities or sectors to
industrial clusters (Wolfe and Gertler, 2004; Niosi and Bas, 2001; Beaudry, 2001; and
Swann et al., 1998). In their paper on drug discovery, Cockburn and Henderson (1998)
suggest that firms connected to the community of open science can increase their R&D
investments by tapping into the research of these institutions. Gertler and Vinaudrai
(2004) add to the argument by showing that universities are a key factor in attracting and
retaining the prolific researchers and students that are the roots of knowledge and
innovation production. Universities act as anchors of creativity; there is indeed a strong
2
link between the presence of universities involved in research in fields that require
complex science, public research laboratories and good diffusion of knowledge. Not only
are universities creating multidisciplinary networks of collaboration, they are also
creating pools of talent available to firms who locate within the cluster. Similarly, Zucker
et al. (1998b) and Prevezer (1997) show that in the biotechnology sector, firms tend to
cluster together in a few locations because of the presence of star-scientists who can
capitalise on their knowledge through firm start-ups. Audretsch and Feldman (2004)
indeed suggest that entrepreneurship is a spillover mechanism, and previous research has
shown the entry attraction power of certain firms or sectors within industrial clusters (on
anchor tenants see Wolfe and Gertler, 2004; on sub-sectoral entry attractors see Beaudry,
2001; Swann et al., 1998). Niosi et Banik (2005) add that Canadian biotechnology spinoffs remain relatively small because they concentrate their R&D activities in fundamental
research.
Wolfe and Gertler (2003) classify industrial clusters in two categories: in clusters of
type I, fundamental science is the principal knowledge generator while in clusters of type
II, the knowledge needed for successful innovation is transmitted trough market
transactions. According to Audretsch and Stephan (1996), university researchers not only
act as facilitators of knowledge transfers from research laboratories towards enterprises,
they also validate and signal the quality of the firm’s research to capital and resources
markets. One of the principal results of their paper is that contrarily to informal
knowledge diffusion for which geographical proximity is necessary (Jaffe et al., 1993),
when knowledge is transmitted formally between researchers and industries, this
proximity requirement disappears. Munn-Venn and Voyer (2004) also suggest that the
goal of collaboration within industrial clusters is essentially to share knowledge while
collaboration between clusters or out of clusters is principally aimed at commercialisation
(on knowledge sharing see also Breschi and Lissoni, 2001; Breschi and Malerba, 2001).
This seems to indicate that knowledge networks localised within industrial clusters lead
to the advancement of fundamental science while more remote links between networks
contribute mainly to the commercialisation of patented applications.
In their critical survey of the literature on spillovers, Breschi and Lissoni (2001)
suggest that most scholars in the domain do not prove the existence of local knowledge
spillovers but merely force this interpretation on the data they analyse. Their paper is an
echo to Krugman’s warning (1991) that empirical measurement of knowledge spillovers
was impossible since they are invisible and leave no paper trails. In trying to understand
what universities bring to local firms, one must therefore be careful not to assume that all
knowledge transfers are local knowledge spillovers. For instance, Mansfield (1995)
surveyed corporate R&D managers and showed that they consistently quoted the research
of academic researchers who played a role in the development of their new products and
processes. Although these cannot be deemed pure knowledge spillovers, they are
nevertheless a transmission of knowledge which contributes to the growth of the cluster
in which the research takes place. On the patent side, Jaffe et al. (2002) examine whether
the paper trail left by patent citations within the patent documents provides an indication
of knowledge spillovers or communication between the patentees and the authors of the
patents they cite. They show that this measure of spillovers is relatively noisy. Indeed,
about 30 % of the innovators surveyed indicated that they learned about the cited
invention after the patent was granted, implying that the citation was added by the patent
3
examiner. Caution is therefore needed when using patents and publications as measures
of spillovers. They are, however, the only existing, wide-spread objective measure of
innovation.
Zucker and Darby (1996) evaluated the capacity of a cluster to generate innovation by
looking at the production of its star-scientists. They define a star-scientist as an inventor
having made more than 40 genetic sequence discoveries or 20 or more articles reporting
genetic sequence discoveries. They noted that the scientific importance of those scientists
(and their collaborators) is considerable. Adding to their scientific contribution, the starscientists have also better results in their current projects. Moreover, Zucker et al. (1998a,
b) showed that the innovative performance of biotechnology firms was positively
associated with the number of scientific articles published by local university academics,
but that the explanatory power of this measure disappeared when a distinction was made
between articles written jointly with researchers from local firms and articles co-authored
by other academic scientists.
Breschi and Lissoni (2001) postulate that the long time interval between scientific
discoveries and industrial applications may suffice for the knowledge generated by the
former to be transmitted much further away from the cluster in which it is generated.
Breschi and Malerba (2001) indicate that a strong network of knowledge sharing is a key
to successful high technology clusters. The results of the CBC survey indeed suggest that
collaboration within a cluster is driven by knowledge while collaboration outside the
cluster may be more commercially oriented. If that were so, we would expect our
network of inventors to be involved more with basic research within clusters (basic
patents and scientific research) while the link between networks would represent a
majority of patents leading to commercial patent applications. The work of Trajtenberg et
al. (1997) supports this hypothesis as it shows that university research tends to be located
near the origin of the innovation path, and that it relies much more on non-patent sources
than its corporate counterpart. Jaffe and Trajtenberg (1996) found that university patents
are more highly cited than corporate patents, implying the basicness of the research being
granted intellectual property rights. Jaffe et al. (1993) also showed that early patent
citations are more likely to be localised than later ones as knowledge spreads further with
time. Very early citations though are more likely to emanate from the examiner than from
the inventor because of the lag between the patent application and the time the patent is
granted.
Regarding networks of innovation, there are numerous indications of a convergence of
interconnections between open science, typical of universities and public research
laboratories, and research targeted towards commercial performance arising from
industrial R&D. Balconi et al. (2004) constructed a bipartite graph of patents and
inventors using data on Italian patents from the European Patent Office. From their graph,
they built measures of the geodesic distance between inventors, i.e. the number of steps
that separates two inventors in the network. From a list of academic scientists they
identify the patents that are issued partly or completely to universities via their staff.
Balconi et al. found that networks of industrial inventors are much more fragmented than
networks of academic scientists, with the exception of the chemical sector. It is possible
that the missing link, or the cause of this fragmentation, may be found in the interaction
between industrial inventors and academic scientists not via the patenting system, but via
4
joint research between industry and academia, likely to be measured by scientific papers.
Consistent with this hypothesis, Balconi et al. found that academic inventors have a
tendency to work within larger teams, and for a larger number of assignees.
Cowan and Jonard (2003) also examine the phenomenon of collective invention in
which scientists and researchers share knowledge. The concept was first introduced by
Allen (1983) as an alternative to the three classical locations in which innovation takes
place, i.e. non-profit institutions, profit-seeking firms and the minds of individual
inventors. A typical application of the collective invention concept is the internet LINUX
community that shares all software code hence creating a giant software innovating entity
in which the gains from collaboration outweigh the costs associated with the loss of
intellectual property. They use the small-world network structure as defined by Watts
(1999) and Watts and Strogatz (1998) in which every agent is connected to a number of
other agents through which knowledge is broadcasted. Cowan and Jonard study the effect
of the relationship between inventors: what links a group of researchers to another group
or a researcher to another researcher, in order to show the advantages of public (free)
inventions to the global production of innovation. They model the formation of small
clusters, relational rather than geographical, and show that the more links there are
between mini clusters (or cliques), the greater the overall knowledge generation will be.
Their aim is to assess the influence of cliquishness (clustering) and path length between
each pair of agents on the diffusion of knowledge through the network. Too much
cliquishness or clustering have a negative effect when the cluster is completely closed
upon itself; this suggests that fresh ideas from outside may contribute to keeping a cluster
alive. There still exist the possibility of the creation of parasite networks within some
collaboration networks, i.e. non innovative free-riding scientists that just use the
knowledge produced somewhere else. These groups of non innovative researchers would
use other cluster’s knowledge (which they received by diffusion from the global network)
as a base for innovation. The authors show that the more complicated the knowledge is,
the shortest its transmission through the diffusion network will be.
Oliver (2004) hypothesises about the necessary duality of collaboration and
competition in collaborative biotechnology networks. The capacity of a cluster to
generate sustainable competitive advantages for R&D firms within its geographical
frontier is directly linked to its networks of researchers. Not only will the cluster give
quicker access to the knowledge developed by some of its scientists but it will give
potential access to collaborations outside the geographical frontier of the cluster, hence
making the tacit information available to firms located outside the cluster. Furthermore,
Oliver has associated the absence of alliances or collaborative ventures to firm death;
hence corroborating the idea that interorganisational alliances are essential to the survival
of biotechnology firms (Lane et Lubatkin, 1998; Powell et al., 1996). Alliances may even
become a necessary condition to the transformation of technological know-how into
commercial products. In the biotechnology sector, alliances are crucial as development
costs of new pharmaceutical products require financial amounts that far outreach the
capabilities of the majority of small firms (Audretsch, 2001). Niosi (2003) showed that
alliances cannot explain all the growth of biotechnology firm with very rapid growth, and
that the moment chosen for the alliance is crucial. These works thus seem to indicate the
importance of alliances in the success of biotechnology firms.
5
The goal of this paper is to understand the structure of networks of inventors and its
relationship with high technology clusters. Our research will therefore examine the
diffusion of knowledge through networks of scientists. Knowledge generation is a
mixture of social and human factors; this paper will therefore examine the structure of
these factors of the technology creation chain, i.e. the collaboration networks of
innovators. We will study the technological innovation creation process and its evolution.
To do so, we will use quantitative variables such as the output of innovation (measured
here by the number of patents produced in each cluster) and qualitative variables such as
the strength of the collaborative relations within networks and within clusters. As Cowan
and Jonard (2003) showed, we believe that in order to learn from somebody else’s work,
a scientist must have worked with him at least indirectly to really be able to integrate his
work. This implies that biotechnology knowledge is tacit to a very high level and
therefore limits the expansion of codified knowledge diffusion. Indeed, it is crucial to a
cluster’s performance to be able to count on very knowledgeable collaborators with a
great number of contacts in many research fields. More than ever, the value of a
researcher is not only measured by what he knows but by who he knows and has worked
with. Our research will therefore evaluate the impact of the geographical distance and the
cognitive distance (as defined by Balconi et al., 2004) on the net production of
innovation. We believe that geographical (cluster) and cognitive (network) distances
should be closely linked; which would mean that the closest a researcher works from
another researcher, the more chances he has to work with him directly or indirectly. The
works of Cowan and Jonard (2003) and that of Balconi et al. (2004) will help direct the
construction of our network models in the biotechnology sector.
The paper is organised as follows: section 2 introduces the data used in this study,
section 3 presents the basic statistics regarding patent inventors, section 4 presents the
bases of our methodology to construct and analyse networks of inventors, section 5
presents the core of our preliminary analysis, and finally, section 6 concludes.
DATA
A number of Canadian biotechnology firms are involved in alliances with other
enterprises (larger or smaller) or institutions (universities, hospitals or government
laboratories). In 2003, 272 alliances were the result of a need to access outside scientific
expertise, and 246 alliances were necessary because the knowledge was not available
internally to the firm. Furthermore, academic institutions, hospitals and government
laboratories or agencies were partners in 417 alliances with biotechnology firms in 2003.
These numbers suggest that R&D alliances contribute to about 40 % of collaborative
arrangements between firms and other entities, whether other firms or research
institutions. Table 1 shows the evolution of various measures of collaborative ventures of
Canadian biotechnology firms between 1999 and 2003. For instance, in Ontario,
biotechnology firms form more alliances per firm for a smaller number of enterprises. As
more firms enter the biotechnology market, the proportion of firms involved in alliances
is diluted, this phenomenon is much more apparent in Quebec where the number of
biotechnology firms has increased by 84.81 % between 1997 and 2003. These new
enterprises will need a few years before venturing into formal alliances.
In light of the large proportion of alliances related to scientific expertise and
knowledge, this paper aims to understand the structure of biotechnology knowledge
6
networks and their relationships with three clusters in Canada: Montreal, Toronto and
Vancouver. In this paper, we will measure knowledge networks as those created from the
collaboration of inventors on particular biotechnology patents. Arguably, this eliminates
basic science from the network equation as well as further alliances developed for clinical
trials and commercialisation. While the former are essential to the evolution of the field,
the latter does not necessarily contribute in the same way.
Table 1 – Alliances of Canadian biotechnology firms
Proportion of innovative biotechnology firms involved in alliances*
1999
2001
2003
Quebec
73.83 %
60.77 %
48.63 %
1999
2001
2003
Average number of alliances per firm
Quebec
Ontario British Columbia
3.4
3.0
3.4
5.4
5.3
5.7
3.7
6.6
3.4
1999
2001
2003
Ontario
42.34 %
57.43 %
45.74 %
British Columbia
67.61 %
66.67 %
50.55 %
Elsewhere in Canada
72.46 %
57.33 %
60.48 %
Canada
3.1
5.1
4.1
Proportion of alliances with academic institutions or hospitals
Total
30.41 %
194
24.74 %
284
30.75 %
317
Proportion of alliances with government laboratories or agencies
Total
16.77 %
107
1999
11.06 %
127
2001
10.67 %
110
2003
*
Source: Statistics Canada, Biotechnology Use and Development Survey – 1999, 2001
and 2003
We collected patent data from the USPTO database available online. The aim is to
evaluate the dynamics of innovation creation in the three largest biotechnology clusters in
Canada, i.e. Montreal, Toronto and Vancouver. We limited our study to certain fields
within biotechnology; we only collected patents in the classes C-12-N, C-07 and
C-08-K-L (representing respectively micro-organism or enzyme, organic chemistry and
pharmaceutical preparation). These classes provide a representative sample of the total
number of patents attributed in biotechnology in recent years. Data was collected on all
patents granted from 1979 to February 2005 in these international classes but only for
patents that had at least one inventor living in one of the three major biotechnology
clusters in Canada: Montreal, Toronto and Vancouver. The geographical area of each
cluster was defined as a circle of about 100 km diameter around the center of the
metropolitan area of Montreal, Toronto and Vancouver. The database constructed
7
contains information about patents (year of application and approval, international
classification, name, assignee, assignee addresses, patent citations, all the other
references, references in other patents) and their authors (name and complete addresses).
One of the main problems in extracting information on Canadian inventors from the
USPTO database is its use of the abbreviation CA for both Canada and California. The
consequent lack of discrimination between the state of California and the Canadian
provinces may cause the misattribution of inventors to a particular cluster or region. All
entries relating to California were therefore removed manually and each ambiguity (for
instance when the same city name exists both in California and in Canada) was further
verified with the full address of the inventor using Microsoft MapPoint North America.
In total, we collected 1915 patents granted to 2669 authors. For the same period, the
Canadian biotechnology industry generated a total of 6122 patents involving
10 161 authors. The sample collected therefore represents nearly 30 % of all Canadian
biotechnology patents, a sample of sufficient size to understand biotechnology
collaboration networks between and within each cluster.
PATENT INVENTORS STATISTICS
Before constructing the networks, basic statistics for the inventors localised in the
three main Canadian clusters under analysis are presented in Tables 2 to 5. Table 2 shows
the distribution of the 2669 inventors within the three main Canadian clusters, Montreal,
Toronto and Vancouver, and elsewhere in the world3. Out of the 960 authors located
outside the three Canadian clusters, 221 are located in Canada, of which 109 are in
Ontario but not in the Toronto cluster, 40 in Quebec (mainly Quebec City) and 8 in
British Columbia. Let us first introduce two concepts that will be used throughout the
paper: links and collaborations between co-inventors. In this paper, we distinguish the
links between co-inventors regardless of the number of times they contributed jointly to
the production of patents from the collaborations between these co-inventors that
specifically take into consideration repeated co-authorship of patents.
In Canada, the inventor that has collaborated with the highest number of co-inventors
is located in Toronto and has worked with 70 fellow scientists, for a total of 318
collaborations (see Table 3 for details). In comparison, in Quebec, the most collaborative
inventor has worked with 44 co-inventors for a total of 137 collaborations. Although
Toronto is the location of the scientist with the most connections to other inventors, the
majority of the most prolific scientists work in the Montreal cluster: 24 of them are
named-inventors on more than 20 patents and 48 of them are authors of 10 to 19 patents.
In addition to the fact that Toronto has a smaller number of star-scientists, inventors
worked with a lower average number of co-inventors in this city than in Montreal. Table
4 shows that in Quebec, each inventor has worked on average with 6.53 other scientists
while in Toronto and Vancouver, inventors collaborated with 4.06 and 4.37 individuals.
When the number of times inventors have collaborated with other scientists for the
purpose of a patent is taken into consideration, these collaboration values more than
double for the scientists located in Montreal.
3
The reader will recall that this is restricted database that does not account for all biotechnology patents in
the world and consider the category “elsewhere in the world” accordingly.
8
Table 2 - Geographic location of patent inventors per country, province and cluster
Inventor
cluster
Montreal
Toronto
Vancouver
Number
of
inventors
764
600
345
Country
Number
of
inventors
Province
Ontario
Alberta
Quebec
221
Saskatchewan
British Columbia
Elsewhere in Canada
533
60
36
24
18
68
Canada
Elsewhere
Total
USA
960 France
Japan
UK
Germany
Others
2669
Number
of
inventors
109
41
40
11
8
12
Table 3 - Statistics relating to the most prolific and collaborative inventors
Number of coinventors of the
most connected
inventor
Three main clusters
Montreal Toronto
Vancouver
44
70
32
Outside the three main clusters
Quebec
Ontario British Columbia
Elsewhere in Canada
16
27
13
27
Number of
collaborations
of the most
connected
inventor
Three main clusters
Montreal Toronto
Vancouver
137
318
70
Outside the three main clusters
Quebec
Ontario British Columbia
Elsewhere in Canada
35
58
36
68
Number of starinventors with:
10 to 19 patents
> 20 patents
Montreal
48
24
Three main clusters
Toronto
Vancouver
18
0
5
1
Elsewhere in Canada
3
0
Surprisingly, there appears to be a clear difference between the inventors located in
Montreal and those in the other two clusters studied. In Montreal, 74.23 % of the coinventors are also located in this cluster, while in Toronto for instance, this proportion
drops to 62.96 %. There are two possible explanations for this gap. In Montreal, the
research leading to patents may have a more fundamental aspect and therefore requires
the proximity of universities and public laboratories, while in Toronto research alliances
9
may be related to patents that are closer to commercialisation and therefore the coinventors need not be located within the same cluster, as suggested by Audretsch and
Stephan (1996). In any case, it remains to be shown that too much apparent agglutination
in Montreal may have a negative effect as suggested by Cowan and Jonard (2003). Let us
now turn to the construction and analysis of the innovation networks.
Table 4 - Average inventor performance per region and cluster
Average
number of coinventors per
inventor
Montreal
6.53
Average
number of
collaborations
per inventor
Montreal
13.94
Average
number of
patents per
inventor
Montreal
3.77
Quebec
4.28
Quebec
6.78
Quebec
1.68
Three main clusters
Toronto
Vancouver
4.06
4.37
Outside the three main clusters
Ontario British Columbia
Elsewhere in Canada
4.05
3.25
4.18
Three main clusters
Toronto
Vancouver
7.97
6.67
Outside the three main clusters
Ontario British Columbia
Elsewhere in Canada
6.10
6.12
7.22
Three main clusters
Vancouver
Toronto
2.60
1.93
Outside the three main clusters
Ontario British Columbia
Elsewhere in Canada
1.90
1.25
1.94
Table 5 - Average proportion of patent co-authorship within cluster and province
Three main clusters
Average proportion of co-inventors within the
same cluster as the inventor
Average proportion of collaborations within
the same cluster as the inventor
Average proportion of co-inventors within the
same province as the inventor
Average proportion of collaborations within
the same province as the inventor
Montreal
Toronto
Vancouver
74.23 %
62.96 %
65.45 %
74.90 %
63.23 %
65.64 %
81.65 %
72.35 %
71.18 %
82.06 %
72.59 %
71.39 %
NETWORK CONSTRUCTION METHODOLOGY
In order to illustrate the construction methodology of the network database, let us
imagine the following five patents and the seven individuals listed as their inventors
(summarised in Table 6). The corresponding network built from the links between the
individuals who collaborated on these joint patents is given in Figure 1. With the
exception of the pairs of inventors 5-6 and 6-7 who together contributed to two patents
respectively, all other authors only worked once with each other.
10
Table 6 - Patents and co-inventors example
Inventors
1
2
3
4
5
6
7
Patents
B C D
A
X
X
X
E
X
X
X
X
X
X
X
X
X
X
X
1
A
C
7
2
C, D
C
A
C
6
A
C
E
3
C, E
B
4
E
5
Figure 1 - Network built from the inventor-patent combination from Table 6
In this particular network, inventor 1 worked once with two co-inventors and therefore
is attributed two collaborations, while inventor 6 worked with four co-inventors but is
assigned six collaborations, having worked twice with inventors 5 and 7. In total, this
network would have 12 links between co-inventors, and 14 collaborations.
Following the construction of the networks, a number of basic statistics can be
calculated. The first and most obvious is the density of co-authorship of each network,
shown in its simplest version in equation (1).
Density n = ∑
i∈n
2CI i
(I n )(I n − 1)
(1)
where CIi represents the number of co-inventors of inventor i in network n and In is the
total number of inventors in network n. The maximum number of links between all
inventors in a network is given by the denominator divided by 2. This density measure
11
therefore shows the degree of interrelations between the scientists of a network. For
instance, the density of the network given as an example in Figure 1 is 0.57, implying that
there are more than half of the possible number of links between inventors in the
network. The closer this number is to 1, the denser is the network, with everyone having
worked with everyone else. In contrast, the greater the number of patents in the network
and the longer the distance between individuals, the smaller is the density.
A similar measure to the density is the number of links or collaborations between coinventors per author I in network n, where CIi is the number of co-inventors of inventor i,
and Ci is the number of collaborations of inventor i in network n.
Nb _ links n = ∑
i∈n
CI i
In
; or
Nb _ collaborations n = ∑
i∈n
Ci
In
(2)
Another measure widely used in economics to measure the degree of diversity
amongst a group is the Herfindahl index. Equation (3) shows the basic formulation of this
index.
Herfindahl n = ∑
i∈n
X i2


∑ Xi 
 i

(3)
2
where Xi represents either the number of co-inventors (CIi) or the number of
collaborations (Ci) of inventor i in network n. A value of the Herfindahl index close to 1,
for instance, would imply a high degree of diversity amongst inventors in terms of their
number of collaborators, while a lower value, closer to 0, would suggest a uniformly
distributed number of collaborators amongst inventors. In the example described above,
the Herfindahl index using the number of collaborations of each inventor is 0.168
implying a relatively uniform network.
These three equations can easily be modified to include proportions of co-authorship
or collaborations within clusters and provinces. The distinction between entire networks,
within-cluster and within-province networks will be presented in the next section. In
addition to these relatively scale-effect free measures, the proportion of inventors, links
and collaborations between co-inventors in the cluster or province will be examined to
evaluate the differences in the network fauna in these regions. Measures that are directly
affected by size of the cluster such as the number of authors per network, number of
patents per network, number of links per network, number of collaborations per network
will also be calculated.
NETWORK MEASURES
Applying this network construction method to the 1915 patents extracted for the
purpose of this study yields 416 networks, of which 234 are one patent networks, i.e.
none of the inventors have indirect links with other scientists outside their patent coauthors (see Table 6 for details), and 51 are one author “networks”, in other words, this
last category cannot be deemed a network per se. Table 7 shows the distribution of the
networks in the four regions studied with the sizes of the largest networks. In total, 167
networks composed by 1988 scientists will be examined, excluding the one-author and
12
one-patent “networks”. The number of inventors per network varies between 2 and 587.
This large range in the network sizes is due to the nature of biotechnology research. It is
possible to realise successful research in small or large networks depending on the exact
nature and complexity of projects. Moreover, most networks, even if they clearly belong
to a specific cluster, are not geographically confined to one cluster only. Many of them
have links to other clusters, such as Ottawa or Boston. In each network, the proportion of
researchers located in each of the three main Canadian clusters (Montreal, Toronto and
Vancouver) was calculated. If the majority of inventors were located within a particular
cluster, the network was assigned to this cluster. If the majority of inventors were located
outside of Toronto, Montreal and Vancouver, the network’s location was classified as
“elsewhere”. This proportion of the numbers of co-inventors and collaborations between
inventors will be further examined later in this section of the paper.
Table 7 - Basic network measures per cluster
Number of networks in each cluster
Number of one-patent networks
Number of one-author “networks”
Number of networks considered
Number of co-inventors
Number of patents
Number of links between coinventors
Number of collaborations between
co-inventors
Networks in three main clusters
Montreal Toronto Vancouver Elsewhere
117
129
70
100
62
74
37
61
13
24
14
50
48
30
39
876
888
561
412
237
142
314
189
2 981
1 445
557
1 432
12 576
5 990
1 784
3 740
587
247
41
39
11
57
39
31
Maximum number of co-inventors
per network
Second largest network in each
cluster
Let us first turn to some more specific measures of clusters, such as density and
Herfindahl indices, both of which control for differences in network sizes. Table 8
presents six types of Herfindahl indices, three for the number of links between coinventors and three for the number of collaborations between these inventors. These
results may appear in contradiction to the earlier measures of the importance of starinventors in each of the three main clusters in Canada. We have shown earlier that
Toronto is the location of the most prolific scientist of our sample, but that the number of
star-inventors is greater in Montreal than it is in the rest of the country. This fact is also
shown in Table 8 with Herfindahl indices for Toronto generally higher than those for
Montreal or Vancouver. A greater number of star-inventors in a region will tend to
diminish the value of the Herfindahl index as is observed for Montreal in comparison
with Toronto. Indeed, applied research in Montreal appears to revolve around a number
of star-inventors that generate knowledge and act as catalysers of innovation, while in
13
Toronto, and Vancouver to a lesser extent, these prolific inventors play a less central role.
We must however state the possibility that the Montreal star-scientists are directors of
large research groups and include their names on all patents emerging from their
laboratories. This will need to be investigated in further analysis of the database.
Table 8 - Average network Herfindahl indices per cluster4
measured as the number of:
co-inventors per author
co-inventors per author in the same cluster
co-inventors per author in the same province
Networks in three main clusters
Montreal Toronto
Vancouver
0.151
0.207
0.152
0.174
0.226
0.201
0.170
0.203
0.201
collaborations per author
collaborations per author in the same cluster
collaborations per author in the same province
0.098
0.117
0.114
0.127
0.160
0.135
0.094
0.134
0.135
Table 9 – Number of star-inventors per network
Network
number
size
1
587
26
247
28
43
59
89
128
39
57
16
6
14
154
22
266
290
8
10
Total
Number starinventors with:
10 to 19 patents
> 20 patents
10 to 19 patents
> 20 patents
> 20 patents
10 to 19 patents
10 to 19 patents
10 to 19 patents
10 to 19 patents
10 to 19 patents
> 20 patents
10 to 19 patents
10 to 19 patents
10 to 19 patents
> 20 patents
Networks in three main clusters
Montreal Toronto Vancouver
53
26
7
4
1
3
Elsewhere
1
1
1
2
1
1
2
56
26
13
5
0
1
2
0
Obviously, because of the way the networks are constructed, one would expect most
star-inventors to be part of the largest network in the cluster in which he or she is located.
Table 9 presents the number of inventors of 10-19 patents and with more than 20 patents
that belong to specific clusters. Note that in this table, some star-scientists located in a
4
Note that the network Herfindahl indices are omitted for the “elsewhere” network category. Without a
complete analysis of all patents produced outside of our three main Canadian clusters, these figures are
relatively meaningless and inaccurate. They represent the portion of patents involving Canadian inventors
and hence only account for part of the networks that exist outside Montreal, Toronto and Vancouver.
14
particular cluster may appear in another because of the network to which they are
associated. For instance, the Toronto networks have “lost” 5 star-inventors to the benefit
of Montreal. While 72 star-inventors reside in Montreal, 82 of them are associated with
the Montreal networks, of which only three are not part of Montreal’s largest network. In
Toronto, the less numerous star-inventors are only slightly better spread through the
network population.
In terms of average network density however, Table 10 does not show a significant
difference between the different clusters, with an average measure of 73 % of the possible
connections between inventors in the cluster of Toronto and Montreal and around 68 % in
Vancouver. The large majority of networks are relatively small with all inventors having
worked with one another – this is true for 69 out of the 167 networks under study. In fact,
only 36 networks have a co-authorship density of less than 50 % and all the large
networks are in this category. For example, the density of the largest Toronto network is
2.5 %. A weighted mean would considerably lower the value of the density but would not
represent a better picture than what is shown in Table 10 and is consequently not
presented here.
Table 10 – Network density per cluster
Networks in three main clusters
Montreal Toronto Vancouver
Average network density per cluster as measured as
the number of:
co-inventors per author
co-inventors per author in the same cluster
co-inventors per author in the same province
0.730
0.744
0.741
0.727
0.744
0.747
0.684
0.722
0.721
Number of networks with a coauthorship density of:
< 50 %
50 – 99 %
100 %
11
17
22
11
17
20
8
11
11
Number of networks with a coauthorship density in the same cluster
of:
< 50 %
50 – 99 %
100 %
8
38
4
8
34
6
6
23
1
Number of networks with a coauthorship density in the same
province of:
< 50 %
50 – 99 %
100 %
8
38
4
7
35
6
6
23
1
Let us briefly turn to measures that strongly depend on the size of the network, the
number of links between co-inventors and collaborations per author. On average, the
clusters of Montreal and Vancouver appear relatively similar, with a greater number of
links and collaborations per author than the Toronto networks.
As an echo to the geographical repartition of co-authorship and collaborations between
co-inventors seen earlier (see Table 5), Table 12 shows the average geographical
composition of networks resulting from the association of each network to a particular
15
cluster according to the location of the majority of its inventors. The most geographically
concentrated networks are those associated with British Columbia, with 77.64 % of their
inventors localised in the same province. An interesting phenomenon is illustrated in this
table, except for the inventors localised elsewhere, the third largest group of inventors is
localised westward to the province of the network. For instance, networks associated with
Quebec include 11.19 % inventors from Ontario and 0.46 % from British Columbia,
while Ontario networks are composed of 12.48 % scientists from British Columbia, but
only 2.50 % from Quebec. It is surprising because networks from British Columbia only
include 2.11 % and 0.42 % inventors from Quebec and Ontario respectively. One would
have expected the second or third largest group of inventors to be localised mainly in
Ontario.
Table 11 - Number of co-inventors and collaborations per author per network per
cluster
measured as the number of:
mean
max
mean
max
mean
max
co-inventors per author
co-inventors per author in the same cluster
co-inventors per author in the same province
mean
max
mean
collaborations per author in the same cluster
max
mean
collaborations per author in the same province
max
collaborations per author
Networks in three main clusters
Montreal Toronto Vancouver
1.482
1.285
1.410
4.175
3.965
5.385
1.146
0.953
1.174
4.458
3.154
4.294
1.186
1.029
1.173
4.470
3.500
4.294
4.682
18.620
3.714
22.778
3.802
22.667
4.164
15.320
3.141
14.697
3.396
14.200
4.501
24.769
3.817
18.353
3.807
18.353
Table 12 - Average network proportion of inventors within the cluster or province
of the network
Network cluster or
province
Montreal
Toronto
Vancouver
Elsewhere
Quebec
Ontario
British Columbia
Elsewhere
Inventor cluster or province
Montreal Toronto
Vancouver Elsewhere
62.44 %
8.45 %
0.34 %
28.77 %
2.14 % 55.44 %
12.48 %
29.95 %
2.11 %
0.42 %
76.79 %
20.68 %
16.24 %
8.92 %
2.55 %
72.29 %
Quebec
64.73 %
2.50 %
2.11 %
18.79 %
Ontario
11.19 %
59.89 %
0.42 %
15.61 %
16
British Columbia Elsewhere
0.46 %
23.63 %
12.48 %
25.13 %
77.64 %
19.83 %
2.87 %
62.74 %
Table 13 - Average network proportion of co-authorship and collaboration within
the cluster or province
measured as the number of:
co-inventors per author in the same cluster
co-inventors per author in the same province
collaborations per author in the same cluster
collaborations per author in the same province
Networks in three main clusters
Montreal
Toronto
Vancouver
66.13 %
58.89 %
71.68 %
69.14 %
66.33 %
71.80 %
67.34 %
70.36 %
58.85 %
67.08 %
73.12 %
73.20 %
While Montreal holds the highest proportion of co-inventors within its cluster (see
Table 5 above), when these co-inventors are considered within the location of their
network, Vancouver presents the largest proportion of network co-authorship and
collaboration within the cluster or province. This can be explained by the fact that the
inventors that have links with co-inventors from other clusters are generally associated
with networks of other clusters, while this is true to a lesser extent in Montreal and
Toronto. As was hinted above, 12.48 % of inventors from Vancouver are associated with
networks from the Toronto cluster.
CONCLUSION
The attractiveness of a cluster to a firm depends on many other factors that have direct
and indirect impacts on the creation of innovation. The dynamics of new innovative
creation in high technology clusters is very complex. Collaboration networks are
certainly a key factor of knowledge generation underlying new innovation within
biotechnology clusters. In this paper, we have highlighted the size and structure of
collaboration networks within three Canadian biotechnology clusters: Montreal, Toronto
and Vancouver. Using the information contained in specific biotechnology patents of
Canadian scientists, we identified 416 networks of collaborators. In general, we found
that the networks located in the Montreal biotechnology cluster are more geographically
concentrated than those from the Toronto cluster which, with a smaller number of
scientists, benefits from more decentralized knowledge networks. We were also able to
establish that the collaboration networks that lie within the cluster of Montreal are in
general larger that those from Toronto and Vancouver, in terms of the number of
collaborators and collaborations between co-inventors. We have also shown that the
networks associated with the Toronto cluster have wider geographical limits than that of
Montreal and Vancouver which are generally more concentrated in one location. This
could mean that the Montreal cluster have better knowledge diffusion within a smaller
range. In contrast, the Toronto cluster benefits from wider sources of information,
therefore, a smaller quantity of information can circulate and arrive to the cluster of
Toronto but knowledge is of a greater variety.
The paper also examined the role played by star-scientists, i.e. the way in which they
influence the structure of information networks. The Montreal networks tend to be more
centralized and based on the work of these key researchers than those associated with
Toronto. In fact, the creation process in Toronto relies mostly from the work of several
good researchers even in the presence of a few really prolific researchers. Each network
17
characteristic taken individually yields some interesting results, but the combination of all
these factors allows a better insight into their true nature. Basic Herfindahl indices
regarding the network population within each cluster and the average number of
collaboration per researcher show similar results for all clusters implying a relatively
homogeneous network population. In contrast, jointly considering the average number of
patents per inventor and the number of star-scientists outlines the dominance of Montreal.
These last results taken individually appear to favour Montreal but the comparison of all
measures indicates that scientists associated with the Montreal cluster mostly work with
the same group of inventors hence the similar ratio of collaboration per inventor
measured for all clusters. It is therefore quite possible that a network where most
scientists collaborate with each other appears more “closed” to new scientists and is
consequently more difficult to join, or could benefit from outside knowledge flows.
As mentioned before, this paper is a first step towards the understanding of the
influence of knowledge networks on the innovative activities of firms located within high
technology clusters. The question as to the exact role played by networks and their
importance in the chain of knowledge creation however requires the construction of a
formal model that would globally represent knowledge creation. In this paper, we have
set the bases for the realisation of this model. We have collected relevant information
about the biotechnology collaboration networks in Canada, this information will be the
foundation of our full model. There remain many avenues to explore. The first will be to
expand our sample to the entire biotechnology sector and to other clusters in North
America and to compare the innovation production of networks within and between
clusters.
A second avenue will be to include patent citations not to simply add more
contributors to the network, but as a way to measure the value of patents and therefore the
value of network knowledge production. The measure used in the paper excluded patent
citation indexes which arguably are a more precise measure of patent value. For instance,
one invention could be useless or isolated and another one could lead to fantastic
scientific discoveries. The role played by star-inventors may take a completely new
direction when citation indexes are added to the model. In order to assess the importance
of the star-scientists within each important network from various regions and clusters, we
plan to map the network using the link between researchers/regions. This will help us
identify the inventors whose dominant work had the most influence on the development
of science. An important consideration though when constructing networks of academic
and industry scientists using patent data and academic journal articles is that academics
may cite a friend for no specific scientific reason while an inventor doing the same in a
patent application would reduce the scope of the monopoly power granted by the patent
(Jaffe et al., 1993).
A third very important avenue of research will study the relationship intensity between
scientists and inventors measured from academic articles, patent co-authorship and
citations, as links between firms and research institutions. This approach is an extension
of the work of Balconi et al. (2004) who included the networks arising from scientific
contributions in the overall network of scientists.
18
REFERENCES
Allen, R. (1983) “Collective Invention”, Journal of Economic Behavior and Organization
4, 1-24.
Audrestch, D. B. and Feldman, M. P. (1996) “R&D spillovers and the geography of
innovation and production”, American Economic Review 86, 253-273.
Audrestch, D. B. and Feldman, M. P. (2004) “Knowledge Spillovers and the Geography
of Innovation”, in Vernon, J. and Thisse, J., ed. Handbook of Urban and Regional
Economics, Volume 4, North Holland Publishing: Amsterdam.
Audrestch, D. B. and Stephan, P. E. (1996) “Company-scientist location links: the case of
biotechnology”, American Economic Review 86, 641-652.
Audretsch, D. B. (2001) “The Role of Small Firms in U.S Biotechnology Clusters”, Small
Business Economics 17, 3-15.
Balconi, M., Breschi, S. and Lissoni, F. (2004) “Networks of Inventors and the Role of
Academia: An Exploration of Italian Patent Data”, Research Policy 33, 127-145.
Beaudry, C. (2001) “Entry, Growth and Patenting in Industrial Clusters: A Study of the
Aerospace Industry in the UK”, International Journal of the Economics of Business 8,
405-436.
Beaudry, C. and Breschi, S. (2003) “On clustering of innovative firms: empirical
evidence from Italy and the UK”, Economics of Innovation and New Technology 12,
325-42.
Breschi, S. and Lissoni, F. (2001) “Knowledge Spillovers and Local Innovation Systems:
A Critical Survey”, Industrial and Corporate Change 10, 975-1005.
Breschi, S. and Malerba, F. (2001) “The Geography of Innovation and Economic
Clustering: Some Introductory Notes”, Industrial and Corporate Change 10, 817-833.
Cockburn, I. M. and Henderson, R. (1998) “Absorptive Capacity, Coauthoring behavior
and the Organization of Research in Drug Discovery”, The Journal of Industrial
Economics 66, 157-182.
Cowan, R. and Jonard, N. (2003) “The Dynamics of Collective Invention”, Journal of
Economic Behavior and Organization 52, 513-532.
Feldman, M. P. and Audretsch, D. B. (1999) “Innovation in Cities: Science-Based
Diversity, Specialisation and Localized Competition”, European Economic Review.
43, 409-429.
Gertler, S. M. and Vinodrai, T. (2004) “Anchors of creativity: How do Public University
Create Competitive and Cohesive Communities?”, Presented at Building Excellence:
Graduate Education and Research, University of Toronto, December 2004.
Jaffe, A. B. and Trajtenberg, M. (1996) “Flows of Knowledge from Universities and
Federal Laboratories: Modelling the Flow of Patent Citations over Time and across
Institutional and Geographic Boundaries”, Proceedings of the National Academy of
Science 93, 12671-12677.
19
Jaffe, A. B., Trajtenberg, M. and Henderson, R. (1993) “Geographic Localization of
Knowledge Spillovers as Evidenced by Patent Citations”, Quarterly Journal of
Economics 108, 577-598.
Jaffe, A. B., Trajtenberg, M. and Fogarty, M. S. (2002) “The Meaning of Patent
Citations: Report on the NBER/Case-Western Survey of Patentees”, in Patents,
Citations and Innovations: A Window on the Knowledge Economy, Jaffe, A. B. and
Trajtenberg, M. (eds.), Cambridge: MIT Press, pp.379-401.
Kéroack M., Ouimet, M. and Landry, R. (2004) “Networking and innovation in the
Quebec optics/photonics cluster”, in Wolfe, D. A. and Lucas, M. éd. Clusters in a cold
climate – innovation dynamics in a diverse economy, Queen’s University School of
Policy Studies.
Krugman, P. (1991) Geography and Trade, MIT Press: Cambridge.
Lane, J. P. and Lubatkin, M. (1998) “Relative absorptive capacity and interorganizational
learning”, Strategic Management Journal 19, 461-477.
Mansfield, E. (1995) “Academic Research Underlying Industrial Innovations: Sources,
Characteristics and Financing”, Review of Economics and Statistics 77, 55-65.
Marshall, A. (1920) Principles of Economics, Macmillan: London.
Munn-Venn, T. and Voyer, R. (2004) “Clusters of Opportunity, Clusters of Risk”, Report
from the Conference Board of Canada, 21 p.
Niosi, J. (2003) “Alliances are not enough explaining rapid growth in biotechnology
firms”, Research Policy 32, 737-750.
Niosi, J. and Banik, M. (2005) “The evolution and performance of biotechnology
regional systems of innovation”, Cambridge Journal of Economics 29, 343-357.
Niosi, J. and Bas, T. G. (2001) “The competencies of regions – Canada’s clusters in
biotechnology”, Small Business Economics 17, 31-42.
Oliver, A. L. (2004) “On the duality of competition and collaboration: network-based
knowledge relations in the biotechnology industry”, Scandinavian Journal of
Management 20, 151-171.
Powell, W. W., Koput, K. W. and Smith-Doerr, L. (1996) “Interorganizational
collaboration and the locus of innovation: Networks of learning in biotechnology”,
Administrative Science Quarterly 41, 116-145.
Prevezer, M. (1997) “The Dynamics of Industrial Clustering in Biotechnology”, Small
Business Economics 9, 255-271.
Swann, G. M. P., Prevezer, M. and Stout, D. (eds.) (1998) The Dynamics of Industrial
Clusters: International Comparisons in Computing and Biotechnology, Oxford
University Press: Oxford.
Trajtenberg, M., Henderson, R. and Jaffe A. B. (1997) “University versus Corporate
Patents: A Window on the Basicness of Invention”, Economics of Innovation and New
Technology 5, 19-50.
20
Watts, D. (1999) “Networks, Dynamics, and the Small-World Phenomenon”, American
Journal of Sociology 105, 493-527.
Watts, D. and Strogatz, S. (1998) “Collective Dynamics of Small-World Networks”,
Nature 393, 400-403.
Wolfe, D. A. and Gertler, M. S. (2004) “Clusters from the Inside and Out: Local
Dynamics and Global Linkages”, Urban Studies 41(5/6), 1071-1093.
Zucker, L. G. and Darby, M. R. (1996) “Star Scientist and institutional Transformation:
Patterns of Invention and Innovation in the Formation of the Biotechnology Industry”,
Proceedings of the National Academy of Science 21, 12709-12716.
Zucker, L. G., Darby, M. R. and Armstrong, J. (1998a) “Geographically Localised
Knowledge: Spillovers or Markets?”, Economic Inquiry 36, 65-86.
Zucker, L.G., Darby, M. R. and Brewer, M. (1998b) “Intellectual Human Capital and the
Birth of US Biotechnology Enterprises”, American Economic Review 88, 290-306.
21