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Research Collaboration in Co-inventor Networks:Combining Closure, Bridging and ProximitiesLorenzo Cassiab & Anne Plunketc

a Centre d'économie de la Sorbonne (CES), Université Paris 1, Maison des SciencesEconomiques, 106–112 Boulevard de l'Hôpital, F-75647 Paris Cedex 13, Franceb Observatoire des Sciences et des Techniques, 21 Boulevard Pasteur, F-75015 Paris,Francec Université Paris Sud Analyse des Dynamiques industrielles et Sociales (ADIS), 54,Boulevard Desgranges, F-92331 Sceaux Cedex, FrancePublished online: 05 Sep 2013.

To cite this article: Lorenzo Cassi & Anne Plunket (2015) Research Collaboration in Co-inventor Networks: CombiningClosure, Bridging and Proximities, Regional Studies, 49:6, 936-954, DOI: 10.1080/00343404.2013.816412

To link to this article: http://dx.doi.org/10.1080/00343404.2013.816412

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Research Collaboration in Co-inventor Networks:Combining Closure, Bridging and Proximities

LORENZO CASSI*† and ANNE PLUNKET‡*Centre d’économie de la Sorbonne (CES), Université Paris 1, Maison des Sciences Economiques,

106–112 Boulevard de l’Hôpital, F-75647 Paris Cedex 13, France. Email: lorenzo.cassi@univ-paris1.fr†Observatoire des Sciences et des Techniques, 21 Boulevard Pasteur, F-75015 Paris, France

‡Université Paris Sud, Analyse des Dynamiques industrielles et Sociales (ADIS), 54, Boulevard Desgranges,F-92331 Sceaux Cedex, France. Email: anne.plunket@u-psud.fr

(Received May 2011: in revised form April 2013)

CASSI L. and PLUNKET A. Research collaboration in co-inventor networks: combining closure, bridging and proximities, RegionalStudies. This paper investigates the determinants of co-inventor tie formation using micro-data on genomic patents from 1990 to2006 in France. In a single analysis, it considers the relational and proximity perspectives that are usually treated separately. In orderto do so, it analyses various forms of proximity as alternative driving forces behind network ties that occur within existing com-ponents (that is, closure ties) as well as those between two distinct components (that is, bridging ties). Thus, the paper investigatesnot only the respective impacts of network and proximity determinants, but also how they overlap, interact and possibly act assubstitutes or complements.

Social networks Relational perspective Proximity Co-patenting Network formation

CASSI L. and PLUNKET A. 共同发明者网络中的研究合作：结合闭合、桥连带与邻近性，区域研究。本文运用法国自

1990 年至 2006 年间的染色体专利微资料，探讨形成共同发明者连带的决定因素。本研究在单一分析中，考量经常被分开处理的相对性与邻近性视角。为了进行上述研究，本文将分析各种形式的邻近性，做为存在于既存元素（意即闭

合连带）之中、以及两个相异元素（意即桥连带）之间的网络连带背后的另类驱动力。因此，本文不仅探讨网络与邻

近性决定因素的各别影响，亦探讨这些影响如何相互交叠、互动，并可能相互替代或补充。

社会网络 相对性视角 邻近性 共同专利申请 网络形成

CASSI L. et PLUNKET A. La coopération en matière de recherche dans les réseaux de coinventeurs: combiner la fermeture, lepontage et la proximité, Regional Studies. À partir des données microéconomiques sur le brevetage du génôme en France de1990 à 2006, ce présent article cherche à examiner les déterminants de la création de liens entre les coinventeurs. En une seuleanalyse, on considère les points de vue relationnel et de proximité que l’on traite séparément d’habitude. Pour pouvoir le faire,on analyse les différentes formes de proximité comme forces motrices alternatives derrière les liens de réseau qui se produisentau sein des éléments en vigueur (à savoir, les liens de fermeture) ainsi que ceux qui relient deux éléments distincts (c’est-à-direles liens de pontage). Donc, l’article examine non seulement les retombées relatives aux déterminants des réseaux et de laproximité, mais aussi comment ils se chevauchent, interagissent et servent éventuellement de substituts ou de compléments.

Réseaux sociaux Point de vue relationnel Proximité Brevetage Création de réseaux

CASSI L. und PLUNKET A. Kooperative Forschung in Erfindernetzwerken: die Kombination von geschlossenen und überbrückendenVerbindungen mit Nähe, Regional Studies. In diesem Beitrag untersuchen wir die Determinanten der Bildung von Verknüpfungenzwischen gemeinsamen Erfindern mit Hilfe von Mikrodaten über genomische Patente im Zeitraum von 1990 bis 2006in Frankreich. Hierbei betrachten wir die Perspektiven der Beziehungen und Nähe, die normalerweise getrennt behandelt werden,in einer gemeinsamen Analyse. Zu diesem Zweck analysieren wir verschiedene Formen der Nähe als alternative treibende Kräftehinter Netzwerkverbindungen, die innerhalb von bestehenden Komponenten auftreten (d. h. geschlossene Verbindungen), undNetzwerkverbindungen, die zwischen zwei verschiedenen Komponenten auftreten (d. h. überbrückende Verbindungen). Auf dieseWeise untersuchen wir nicht nur die jeweiligen Auswirkungen der Netzwerk- und Nähedeterminanten, sondern analysieren auch,wie diese überlappen, sich gegenseitig beeinflussen und einander eventuell ersetzen oder ergänzen.

Soziale Netzwerke Relationale Perspektive Nähe Gemeinsame Patente Netzwerkbildung

CASSI L. y PLUNKET A. Investigación cooperativa en las redes de coinventores: la combinación de vínculos cerrados y vínculos deconexiones con la proximidad, Regional Studies. En este artículo investigamos los determinantes de la formación de vínculos entre

Regional Studies, 2015

Vol. 49, No. 6, 936–954, http://dx.doi.org/10.1080/00343404.2013.816412

© 2013 Regional Studies Associationhttp://www.regionalstudies.org

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coinventores mediante datos micro sobre las patentes genómicas de 1990 a 2006 en Francia. En un análisis único, consideramos lasperspectivas de relación y proximidad que generalmente se tratan por separado. Para ello, analizamos varias formas de proximidadcomo factores alternativos que impulsan los vínculos de redes que ocurren entre componentes existentes (es decir, vínculoscerrados) así como aquellos entre dos componentes distintos (es decir, vínculos de conexiones). Por tanto, en este artículoanalizamos no solamente los impactos respectivos de los determinantes de redes y proximidades, sino también cómo se solapan,interactúan y posiblemente actúan como sustitutos o complementos.

Redes sociales Perspectiva relacional Proximidad Copatentes Formación de redes

JEL classifications: D85, O31, R12, Z13

INTRODUCTION

The significance of social networks for innovation isnow widely acknowledged, and even considered atruism. A growing body of literature convincinglyargues that knowledge is far from being ‘in the air’and accessible to all actors but rather follows specificchannels between socially and personally linked individ-uals (BRESCHI and LISSONI, 2005, 2009; KNOBEN,2009). These ‘social proximity’ arguments strongly con-trast with previous studies on geographical proximitythat investigate agglomeration economies and arguethat knowledge circulates more or less freely amongco-located actors who benefit from a premium depend-ing upon their location (JAFFE, 1989; AUDRETSCH andFELDMAN, 1996; AHARONSON et al., 2008; BOUFADEN

and PLUNKET, 2008; KNOBEN, 2009).Although innovation and diffusion of knowledge do

not simply depend upon location (BOSCHMA, 2005;TORRE and RALLET, 2005), the formation of knowl-edge networks is highly spatially localized, at least inits earliest stages (PONDS et al., 2010), and mainlyfound within organizational and cognitive boundaries(SINGH, 2005). Understanding the dynamics ofnetwork formation is a major research objective forthe geographical analysis of innovative networks(BOSCHMA and FRENKEN, 2009). This debate raises anumber of questions. First, to what extent are geo-graphical and social proximity overlapping phenomena?Second, to what extent do networks enable one toreduce geographical, organizational and cognitiveboundaries and offer the opportunity to access non-local knowledge (GLUCKLER, 2007)?

The aim of this paper is to investigate these questionsby analysing the determinants of scientific and techno-logical network collaborations using a longitudinalanalysis of French co-patenting data in the field of geno-mics between 1990 and 2006.

In order to disentangle network and proximityeffects, the paper considers the impact of various formsof proximity in establishing two different types ofnetwork ties. In the first case, individuals are at leastindirectly linked within the same network component.They form a closure tie, which enables them to increasecohesion, favour trust and facilitate the sharing ofresources (COLEMAN, 1988). In the second case, actors

belong to distinct components and have no networkconnection. They form a bridging tie that allows themto establish a channel across networks and distinctgroups of individuals, which facilitates access to differentresources or assets (BURT, 2004). This distinction expli-citly accounts for network effects through social proxi-mity and preferential attachment relative togeographical, technological and organizational proxi-mity (BOSCHMA, 2005) as driving forces behindnetwork formation. Considering both these determi-nants in the same framework allows one to investigatenot only their respective impacts on collaborations,but also how they overlap, interact and possibly act assubstitutes or complements.

The findings support the idea that within-networkeffects (that is, closure ties) occur among actors thatshare a strong organizational proximity and technologi-cal similarity. Moreover, social, geographical and organ-izational proximity act as substitutes in the sense thatgeographical proximity is less important when individ-uals are already connected through common acquain-tances or act under similar governance. In this sense,social connections allow actors to cross over geographi-cal and organizational boundaries. In contrast, across-network effects (that is, bridging ties) occur ratherwhen individuals seek some level of variety and diversityin collaboration, and this occurs mainly through inter-organizational ties for which technological distance ismore important.

The paper is organized as follows. The second sectionpresents the theoretical framework and stresses theelement of novelty in this work relative to the existingliterature. The third section provides a description ofthe data and an explanation of how networks are builtup. The fourth section describes the estimation designand variables, while the fifth section discusses theresults of the econometric analysis. The sixth sectionconcludes.

THE DETERMINANTS OF NETWORK TIEFORMATION

The formation of network ties may be explained bydifferent bodies of literature that offer two distinct per-spectives: (1) the relational perspective assumes that trust

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and knowledge access and control of information areconferred through the actors’ position within thenetwork; and (2) the proximity perspective focuses onthe relative position of economic actors in space,however defined (BOSCHMA, 2005).

These perspectives rely on different, though overlap-ping, mechanisms. The proximity determinants explainthe contexts in which people meet and may becomeconnected. Once connected, they are part of anetwork that offers opportunities to form new tiesand, in doing so, to cross over organizational and geo-graphic boundaries. While these perspectives havebeen developed more or less independently, researchersare increasingly concerned with how both patternsoverlap and interact.

The relational perspective

The relational perspective focuses on direct and indirectconnections among individuals; it is sometimes referredto as a ‘within the network’ approach, since the ‘focalpredictor of network change is hypothesized to be theshape and structure of the network in a prior timeperiod’ (RIVERA et al., 2010, p. 97). Two main expla-nations are identified: closure and preferential attach-ment. The former concerns the tendency of actors toform clusters, the latter deals with the actors’ propensityto link to the most connected individuals.

One of the characteristics that distinguishes socialfrom biological or technological networks is clustering(NEWMAN and PARK, 2003). That is, being embeddedin a very dense, interconnected, ‘cliquish’ network gen-erates benefits by enhancing the trust among individualsand thereby encouraging joint activities and the sharingof tacit and complex knowledge (COLEMAN, 1988).Consequently, the fact of sharing a mutual acquain-tance, that is, social proximity, increases the likelihoodof forming a dyad between indirectly connectedactors. Said differently, open triads tend to close overtime as actors become connected to one’s partner’spartner. Although social proximity strongly interactswith other forms of proximities (BOSCHMA, 2005;BRESCHI and LISSONI, 2009; TER WAL, 2013), forthe sake of analytical clarity social proximity is definedin a very restricted manner and refers to direct or indir-ect interpersonal connections between any two actors.

However, dense and strongly cohesive networks mayalso harm individuals in their search of new knowledgeand their learning processes. In fact, BURT (2004) arguesthat knowledge accessing is more efficient when indi-viduals occupy structural holes that enable one tobroker knowledge flows across unconnected groups(for example, GARGIULO and BENASSI, 2000). If clus-tering seems to be quite a general tendency, some stra-tegic reasons may lead actors to avoid theseconfigurations and instead seek out structural holes inorder to gain access to different or complementary

resources outside their close network (BAUM et al.,2012).

Skewed-degree (that is, the number of links pernode) distribution is another recurring feature of net-works. The main explanation initially proposed byBARABÁSI and ALBERT (1999) is the preferential attach-ment model; the rate at which actors acquire new ties isa function of the number of ties they already have,because they indicate some form of productivity.Thus, there is a tendency for the most connectedactors to connect amongst themselves; popular actorstend to attach to popular actors; likewise, low-degreeactors do so with their peers (NEWMAN and PARK,2003).

The proximity perspective

Geographical proximity is at the heart of the networkformation issue and often appears as one of its maindrivers, since many ties take place between actorslocated within a short distance (BOSCHMA andFRENKEN, 2009). Moreover, it is known that knowl-edge creation and innovation are spatially concentratedactivities for mainly two reasons. First, geographicalproximity facilitates information and knowledgesharing through frequent interactions, especially whenknowledge is tacit, complex and sticky (BATHELT

et al., 2004). This close proximity also contributes tosolving coordination problems through trust-buildingand inter-organizational learning. Second, the concen-tration of firms and universities in industrial clustersand large agglomerations offers a wide range of potentialpartners and more opportunities to meet and shareknowledge. These reasons largely explain why (1) indi-viduals, firms and universities collaborate primarily on alocal basis; (2) networks are locally embedded; and (3)knowledge spillovers are spatially bounded (MAGGIONI

et al., 2007). Networks are locally embedded to theextent that economic actors are geographically concen-trated. However, geographical proximity per se does notseem to be a necessary or sufficient condition for knowl-edge sharing and interactive learning (BOSCHMA, 2005,TORRE and RALLET, 2005), as opposed to being part ofthese networks (TER WAL, 2013). In explaining knowl-edge flows, AGRAWAL et al. (2008) as well as BRESCHI

and LISSONI (2009) show that patent citations aremore likely to occur among inventors who sharesocial proximity, held through co-ethnicity or labourmobility. In summary, geography seems more importantfor promoting initial connections; once these connex-ions exist, they enable one to overcome geographicalboundaries, and spatial proximity ultimately plays littleor no role in the formation of collaborations (MAG-

GIONI et al., 2007; AUTANT-BERNARD et al., 2007).Cognitive proximity means that actors share the same

knowledge base or technology. On the one hand, actorsare more likely to collaborate when they have verysimilar knowledge bases, since it makes communication,

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learning processes and knowledge sharing easier (JAFFE,1989). On the other hand, too much cognitive proxi-mity may harm collaboration and innovation assuggested in the recent proximity paradox debate(BROEKEL and BOSCHMA, 2012; HUBER, 2012). Theprocess of innovation requires some level of dissimilarityand complementarity in the knowledge base (NOOTE-

BOOM et al., 2007). Therefore, it is difficult to predictthe impact of cognitive distance on network tie for-mation, unless the types of ties are considered, as willbe discussed below.

Organizational proximity refers to the fact that‘relations are shared in an organizational arrangement,either within or between organizations’ (BOSCHMA,2005, p. 65). Organizational proximity is high whenindividuals share the same affiliation, in the presentcase when they patent for the same company or univer-sity (prior to tie formation). These ties are believed to bebeneficial for innovation collaborations because theyreduce the risk of uncertainty and opportunism(FLEMING and FRENKEN, 2007). In order to accessknowledge and financial resources, and despite theserisks, collaborations also occur between organizationsof the same type (between private companies orbetween research institutions) or of different types(between private companies and research institutions).In this latter case, different routines and incentiveschemes, and difficulties in coordinating labour andaccessing funds (PONDS et al., 2007) may hamper collab-oration. In summary, while high organizational proxi-mity clearly increases the likelihood of any tie, theimpact of inter-organizational relations is less easy topredict, and presumably depends on the type of tie, asis discussed in the following section.

Closure, bridging and proximity interactions

Following AMBURGEY et al. (2008), it is possible to clas-sify each new link according to the connectivity to theoverall network. Taking two individual inventors asthe focal point, they can become connected throughfour categories of links, as represented in Fig. 1: (1) alink bridging two components; (2) a link determining

the creation of a new component; (3) a pendant to anexisting component; or (4) an intra-component link.The formation of each type of link has different impli-cations for the overall network structure, as summarizedin Table 1.

Bridging ties allow for the linking of separate groupsof inventors and establishing channels that facilitate theaccess to resources or other assets, while intra-com-ponent ties allow for the establishment of a direct linkbetween actors already (indirectly) connected. In thedata, 84% of intra-component ties are formed betweeninventors who are indirectly linked at a distance (thatis, the shortest path between two individuals within anetwork) smaller or equal to 3. These ties allow individ-uals to make their local network denser and, forexample, close the so-called ‘triadic closure’. For simpli-city, all intra-component ties are labelled as closure ties.

By construction, these network ties differ in the sensethat social proximity only plays a role for establishingclosure ties. This may have two consequences, whichare tested in this article: first, bridging ties enableactors to gain access to different organizations andknowledge resources; and second, social proximitymay act as a moderator for geographical, technologicaland organizational proximity. Concerning geographicalproximity, physical propinquity is expected to explainthe formation of network links, whether bridging orclosure ties. Since networks and geography are stronglyoverlapping phenomena, and since they endorse similarroles of reinforcing the bonds of trust, reducing uncer-tainty and finally facilitating knowledge sharing andinteractive learning (TORRE and RALLET, 2005;BOSCHMA, 2005), social proximity and geography areexpected to act as substitutes (AGRAWAL et al., 2008;BRESCHI and LISSONI, 2009). In other words, theimpact of geography may be less important for closureties, that is, when social proximity is very close.

As discussed above, the impact of cognitive proxi-mity on collaboration may be difficult to predictbecause of the trade-off between similarity and comple-mentarity (NOOTEBOOM et al., 2007). As a conse-quence, it is expected that actors will look for similarbodies of knowledge, search for partners in their closenetworks and rather form closure ties. However,when they search for complementary and dissimilartypes of knowledge, they may be inclined rather to

Fig. 1. Type of network tiesSource: AMBURGEY et al. (2008)

Table 1. Consequence of tie formation

Type of linkSize of thenetwork

Number ofcomponents

Size ofcomponents

1. Bridging links ↔ ↓ ↑2. New component

links↑ ↑ ↑

3. Pendant links ↑ ↔ ↑4. Intra-component

links↔ ↔ ↔

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look for partners outside their close networks and formbridging ties. However, if cognitive distance increasesand if there is no social proximity (as in bridging ties),actors may need to rely on other forms of proximity,such as being part of the same organization or beinglocated in the same region. Therefore, their interactionis expected to be complementary.

Finally, the likelihood of forming any tie is expected tobe greater when two actors have a high organizationalproximity (that is, they have already patented for thesame organization). In this case, organizational and geo-graphical proximity may act as substitutes. When actorshave previously patented for different organizations, thelikelihood of collaboration is expected to be smaller forclosure ties and larger for bridging ties, especially foractors belonging to different organizational types,namely company–research institution collaborations.The role of geography is also expected to be more impor-tant in order to compensate for the lack of organizationalproximity, as argued by PONDS et al. (2007).

PATENT NETWORKS IN GENOMICS

Description of the data and network formation

The dataset under investigation is composed of all thegenomic patents published at the European Patent Office(EPO) between 1990 and 2006, with at least one inventorreporting a French postal address and their co-inventors,whatever their location within or outside France.

The database was built during a recent researchproject carried out by Analyse des Dynamiques indus-trielles et Sociales (ADIS)-Paris Sud, Laboratoired’Études et de Recherches en Economie – InstitutNational de la Recherche Agronomique (LERECO-INRA) and the Observatoire des Sciences et des Tech-niques (OST) supported by the Agence National pour laRecherche (ANR – French National ResearchAgency). The EPO Worldwide Patent Statistical Data-base (PATSTAT) was searched using genetics and geno-mics keywords in order to define the genomic field(LAURENS et al., 2010).

Genetics stricto sensu is the science of gene heredity andvariation of organisms by looking at single genes […] incontrast, genomics typically looks at all the genes or atleast at large fractions of a genome as a dynamic system,over time, to determine how they interact and influencebiological pathways, networks and physiology, in amuch more global sense.

(LAURENS et al., 2010, p. 649)

A number of experts were asked to validate the lexicalquery for filtering genomics out of genetics and ulti-mately the field delineation and the border areas.

The final database was a subsample of 2104 patents filedby 496 applicants and 4456 inventors. These represent7976 patent–inventor couples, among which 6034report a French postal address and 1942 a foreign address.

Every patent provides information on inventors,their name and postal address. The patent also offersinformation on applicants, for which it was determinedwhether they were private companies, research institutesand universities, non-profit organizations or individuals.Each patent’s International Patent Classification (IPC)codes were also known; these identify their technologi-cal fields. All this information was used in order todefine the inventor’s individual characteristics, such asgeographical location, technological specialization andaffiliation. The affiliation is, in this case, the organizationfor which the patent is filed and not necessarily theemployer. For instance, it may happen in a number ofcases that academic inventors file patents for a privatecompany instead of their own university.

In order to build the network,1 a link (edge) betweenany two inventors (nodes) who file a patent togetherwas assigned. The actors that co-patent form small com-ponents which increase over time and eventuallyconnect to other components through new co-patent-ing activities. Networks may thus be described asbundles of actors that are connected, but all the actorswithin a network are not necessarily linked.

The aim was to understand the formation of dyadsbetween co-inventors. These new links are explainedby the network structure and the inventors’ individualcharacteristics. In order to avoid simultaneity biases, alldeterminants with a lag of one period were considered.For this reason, links among already active actors, that is,bridging and closure ties, can only be investigated.Another reason for investigating these links comesfrom the specificity of patents as compared with publi-cations (FAFCHAMPS et al., 2010; PONDS et al., 2007);co-inventors of a given patent have, by definition, thesame affiliation2 and technological field (IPC codes).For this reason, this information cannot be used to high-light organizational or technological determinants.

Finally, since ties may die out after a certain period oftime, a five-year moving window is used to obtain amore realistic picture of the network for any givenyear. Therefore, for instance, the network in 1994 com-prises all the patents published between 1990 and 1994.Accordingly, an inventor is considered as active (forexample, in 1994) if he/she has at least one patentover the 1990–1994 period. The observed co-patentsand the potential co-patents actually used for theregressions as controls start in 1996 and go through to2006.

Networks structural and dynamic properties

Fig. 2 displays the number of active inventors over time.At the beginning of 2000 the number of inventorsclearly grows and then stabilizes around 2004.

More striking is the time-varying pattern depicted bythe giant component. First, it appears to be relativelysmall throughout the period compared with the size insimilar studies (for example, FLEMING and FRENKEN,

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2007). Second, it reaches its maximum in the year 2002,and starts decreasing before reaching a plateau.

While previous analyses focus on the giant com-ponent, the present paper tracks the network dynamicby considering all subcomponents (BAUM et al., 2003;FLEMING and FRENKEN, 2007). It is interesting to con-sider the formation of the giant component over time andto understand why some network subparts become con-nected and grow over time, whereas others do not.

Fig. 3 illustrates the evolution of the first four largestcomponents in the 1998 network. The first component(137 inventors in 1998, around 13% of active inventors)is mainly composed of inventors located in the Parisregion, Ile-de-France (the same holds for the secondand partially for the fourth component), while thebulk of the third component is located in the Rhône-Alpes region. The components also differ in terms ofpatent applicants. The first component includes severalbig corporations (for example, Aventis and Centillion)and foreign universities; the second mainly includespublic actors such as the Centre National de la

Recherche Scientifique (CNRS), Institut national dela Santé et de la Recherche médicale (INSERM) andcertain Parisian universities as well as biotechnologicalfirms (for example, Neurotech SA). Finally, the thirdcomponent revolves around one main applicant: BioMerieux, while the fourth component is mainly com-posed of inventors working for a spin-off of the Austra-lian Commonwealth Scientific and ResearchOrganization (CSIRO), an Australian governmentresearch agency, and for a French firm located in thecentral region of Auvergne. Most striking is that the‘public’ component, that is, the second one, breaks upduring the first years (which is represented in Fig. 3 bythe fact that the second line disappears in 2001), whilethe other components converge into a giant com-ponent. Finally, in the most recent years (2005), thesize of the giant component decreases with itsmembers splitting into three subgroups. In summary,examination of the giant component formation con-firms the usefulness of analysing the specific role of brid-ging ties and their determinants.

Fig. 2. Size of the network (active inventors) and of the giant component

Fig. 3. Evolution of the first four 1998 componentsNote: The evolution of the first four components in terms of size as measured in 1998 is displayed. The fact that two

lines converge (as lines 2 and 3 in 1999) means that two components have been merged by a bridging link

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Table 2 reports the number and share of new linksrelative to the period 1995–2006.

Most ties happen to involve new inventors througheither the formation of new components or pendantlinks. Indeed, the most adopted strategy to enter intoa network is forming a new component. The corollaryis that one should already have patented (that is, sent asignal) before attaching to some active inventor.

A fortiori, this implies a more central role for bridgingties. If the majority of inventors enter into a networkestablishing a new component, the overall network’sconnectivity depends mainly upon actors’ ability to linkalready existing components (that is, a bridging link)rather than inventors’ ability to attach themselves directlyto already active inventors (that is, a pendant link).

Moreover, descriptive statistics (Table 3) suggest thatintra-component ties are to a large extent formed withinthe same applicant or with subsidiaries, whereas bridgingties are formed by different types of applicants, namelybetween academia and private companies.

ESTIMATION DESIGN AND VARIABLES

To address the question of how network configurationand proximity affect network formation, two differentestimation procedures – a conditional logit and a multi-nomial logit – are used.

Dependent variable and estimation

The conditional logit approach.3 The first estimation pro-cedure considers the likelihood of forming a closure tie

or a bridging tie versus no tie. For two inventors i and j,the probability of forming a tie pij follows a conditionallogit distribution given by (CAMERON and TRIVEDI,2005):

pij = exp(x′b)∑mexp(x′b) with m = B,C,No tie

where x represents a vector of covariates; whereas β is avector of parameters to be estimated. If the tie is observed,the dependent variable takes the value of 1; and is 0 other-wise.Three cases are consideredwhether onedistinguishesbetween closure (C), bridging (B) or No tie.

In order to estimate this model, all realized and poten-tial ties between any two pairs of inventors are first com-puted. This generates around 4 million observations,whereas the realized links only represent a marginalportion of all possible ties. Since this gap raises importantdifficulties of estimation, a case-control approach isadopted (SORENSON et al., 2006). For any realized tieand its related co-inventors, five possible but not realizedco-inventors who have filed a patent in the same year asthe observed tie, which provides five controls for eachco-inventor, are randomly selected. In summary, foreach realized tie there are ten controls. Each realized tieand its controls represent a group and the estimation isrealized within this group; a cluster robust procedure isused to adjust standard errors for intra-group (matchedcase control) correlation. The corollary is that variablescharacterized by a constant within-group effects, suchas year dummies, cannot be estimated. The sampleis composed by 2684 bridging ties (that is, 244observed dyads + 244*10 controls) and 2123 (that is,193*(10 + 1)) intra-component ties. But since onlyEuropean inventors are being considered, the numberof observations was dropped and the sample becomes2421 and 1604 observations, respectively.4 The samesample is then used for the multinomial estimations.5

The multinomial probit approach. The multinomialprobit model6 is equivalent to a series of pairwise

Table 3. Organizational relationships among types of ties

Bridging ties Closure ties

Whole sample Regressions Whole sample Regressions

Total % Total % Total % Total %

Organizational proximityWithin the same applicant 77 31.56 74 32.03 148 76.68 121 73.33Among academics 12 4.92 12 5.19 1 0.52 1 0.61Among firms 43 17.62 38 16.45 21 10.88 20 12.12

Organizational distanceBetween firms and academics 112 45.90 107 56.32 23 11.92 23 13.94

Total 244 100 231 100 193 100 165 100

Note: Definition and coding of variables are explained in the text and summarized in Appendix A.

Table 2. New link: type of networks ties

Links Total number %

1. Bridging links 244 1.882. New component links 8723 67.033. Pendant links 3853 29.614. Intra-component links 193 1.48

Total 13 013 100

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probit regressions, except that the whole sample is usedin order to reduce the potential biases that may arisefrom dropping part of the observations. In this frame-work, it is supposed that inventors choose betweenthree outcomes: forming a bridging tie (B), a closuretie (C) or not forming any tie (No tie). In this case,‘closure tie’ is chosen as the ‘reference category’ inorder to estimate if proximity and relational variablesexplain differences between closure and bridging ties.

Let yij be the dependent variable with J nominal out-comes that are not ordered. Pij is the probability of observ-ing outcome B given explanatory variables vector x.

The probability may be written as follows(CAMERON and TRIVEDI, 2005):

pij = exp(x′b)∑mexp(x′b) with J = B,No tie

Independent variables

Two sets of variables are considered according to the rela-tional and proximity perspectives. The relational per-spective is tested using social proximity and degreecentrality measures in order to grasp the closure effects.Social proximity is computed as the inverse of the geodesicdistance dij between two inventors i and j. This measure isonly appropriate for closure ties since the geodesic dis-tance is infinite between unconnected nodes as in brid-ging ties. The impact of social proximity is estimatedthrough dummy variables given geodesic distance:‘social proximity (= k)’ is equal to 1 when the geodesicdistance is k (k= 2 or 3); and 0 otherwise (see Table A1in Appendix A for variables’ definitions).

Since inventors cannot manage an increasing numberof collaborations, the likelihood of forming a tie isexpect to increase with the number of common partnersup to a certain threshold, and then decrease again. Thisinverted ‘U’-shaped curve was tested through theimpact on collaboration of four dummy variables –‘common (= 1, 2, 3 and 4) – according to the factthat co-inventors have one, two, three or fourcommon partners with a geodesic distance of 2.

To account for preferential attachment, the degreecentrality measure was considered. This measure mustbe examined for both inventors and the average �nijand the difference Δnij of both inventors’ degreesshould be considered (FAFCHAMPS et al., 2010):

�nij = (ni + nj)2

Dnij = |ni − nj|

For each type of tie, a different sign is expected. In par-ticular, the average measure is expected to be positiveand the difference is expected to be negative forclosure ties, and vice versa for bridging ties. When

actors belong to the same sub-network, individualstend to link to partners similar to themselves in termsof degree, thus the difference in the number of partnersshould tend to 0. When individuals are searching for aneffective collaboration that enables them to access newand different resources, it is likely that similarity is lessimportant or even plays a negative role. In this case, agreater difference would have a positive effect on tie for-mation and, consequently, a negative effect of theaverage degree should also be expected.

The proximity perspective is assessed through geo-graphical, technological and organizational proximity.The ‘geographical proximity’ is calculated in kilometresdivided by 100 based on the latitude and longitudecoordinates of each NUTS-3 centroid7 correspondingto the inventors’ postal address.8 All European inventorsare identified this way; the non-European inventorswere dropped from the regressions.9

Technological proximity is computed as JAFFE’s (1989)index tij, which is a proximity measure rangingbetween 0 and 1, depending on the degree of overlapbetween the co-inventors’ prior patent IPC codes:

tij =∑Kk=1

fikfjk�������������∑Kk=1

f 2ik∑Kk=1

f 2jk

√

where fik and fjk represent each inventor’s i and j techno-logical position.

In order to account for organizational proximity,three different organizational settings are distinguished:Same applicant takes the value of 1 when inventorshave patented for the same organization prior to tie for-mation, and 0 otherwise; Same type takes the value of 1when inventors have patented for the same organiz-ational type (firms or academia), and 0 otherwise; andDifferent type, different applicant is the reference group,in this case university–industry relationships.

These variables are interacted with geographical andsocial proximity in order to test if they might have sub-stitutable or complementary impacts on network tie for-mation. The hypothesis is that inventors will chooseclosure ties when they require similar competencesthat may be found in a close neighbourhood. Theywill choose bridging links when they need distinctskills that may not be found in their own environments.

Two types of controls are introduced. The distinc-tion between French located inventors and foreignersis first controlled for. Since being a foreigner is stronglycorrelated with geographical distance, it is preferable toconsider the specific case of foreigners located in bordercountries by introducing a dummy for inventors locatedin one of the French border countries, that is, Spain,Germany, Italy, Switzerland and Belgium. The impactis expected to be positive.

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The number of years since the first tie is also con-sidered in order to control for experience with thepatent process. Again, in order to account for the sym-metric relation, the difference and average value of bothinventors’ experiences, namely Experience – absolutedifference and Experience – average difference are introduced.

All variables are considered and computed for theperiod prior to the tie formation for which the likelihoodis estimated. Year fixed effects in the conditional logitmodel cannot be controlled for since, by definition, themodel includes group fixed effects for the inventors andtheir controls.10 In order to control for changesthrough time, year fixed effects were introduced intothe multinomial probit estimation. However, introdu-cing year fixed effects does not change the overall results.

ESTIMATION RESULTS AND DISCUSSION

Explaining network tie formation

Table 4 presents the results from a series of conditionallogit models; models 1–5 demonstrate the impact ofrelational and proximity variables on the likelihood offorming closure ties, and models 6–8 test the same vari-ables on bridging ties. Across models, variables and con-trols remain overall consistent in sign and magnitude,suggesting that they are rather robust to the introductionof additional variables.

Since social proximity is infinite by definition in thecase of separate components, and in order to enable com-parison between closure and bridging ties, the impact ofnetworks was first tested through degree centrality in allmodels. The results show distinct patterns of dissimilaritybetween both types of ties. As expected, the inventors’relative position within the network explains closure tieformation; the difference in degrees has a negativeimpact, whereas average degree has a positive impact.This confirms that the likelihood of forming such tiesdecreases when inventors are more dissimilar and itincreases when they have high degrees, namely whenthey are more visible and attractive within the network.Yet, these impacts are only slightly significant asopposed to the bridging ties for which the signs are oppo-site but highly significant, suggesting that these ties aredriven by a search for diversity. The absolute numberof years since the first patent does not seem to play animportant role in the formation of network ties, asopposed to average years of experience. This impact isespecially strong for closure ties, which depend onwithin-network relationships that are built over time.

Regarding the proximity mechanisms, all the sourcesof similarity impact collaborations, as expected. Thelikelihood of forming a tie is larger when co-inventorsshare similar technological fields and work in close geo-graphical distance. The impact is even twice as large forclosure ties in the case of technological proximity.Given that they occur within a short social distance(84% of closure ties have geodesic distance of 2 or 3),

knowledge bases are highly overlapping if not redun-dant. Organizational proximity is also strongly signifi-cant and positive; the likelihood of forming a tieincreases when inventors patent for the same applicant,even in the case of bridging ties. This confirms the factthat inventors patent first of all with individuals whobelong to their own organization (SINGH, 2005).

The interaction term Geographical proximity × Sameapplicant is strongly negative for closure ties, suggestingthat geographical proximity matters less when inventorsalready patent for the same organization. The inter-action is negative although non-significant for bridgingties, which implies that the facilitating role of geographi-cal proximity is as important for inventors patenting forthe same or for a different organization, presumably tocompensate for the lack of social proximity.

In contrast, proximity in organizational type has anegative impact probably because of the risks due toopportunism and competition given that the largemajority of these collaborations occur between privatecompanies (Table 3). The interaction of Same typewith Geographical proximity is positive, confirming thefindings of PONDS et al. (2007) that geographical proxi-mity compensates for organizational distance.

Finally, forming a tie with a foreigner located incountries bordering France has a positive impact onnetwork tie formation, although slightly less positivefor bridging ties.

When introducing dummies for the number ofcommon partners, previous results remain consistentoverall in sign and significance, although the magnitudeof coefficients is reduced for all proximity variables.Since these dummy variables represent the number ofcommon partners, they explicitly account for triadicclosure, and it appears that social proximity and otherproximity variables partly overlap. The results do notconfirm the expected inverted ‘U’-shape for thenumber of common partners. Signs remain overall posi-tive; coefficients first become larger for two partners incommon, they subsequently become smaller for threepartners in common and finally they become insignifi-cant after four common partners.

Table 5 further explores the impact of social proximityon closure ties. Social proximity appears as a strong deter-minant of network tie formation, and other proximityvariables become less important, technological proximitybecoming even insignificant. In a sense this confirms thatclosure ties occur among a close community of inventorswho share similar knowledge bases. The interactions withgeographical and organizational proximity are very nega-tive and highly significant for very close social proximity(that is, geodesic distance = 2), meaning that they act assubstitutes in facilitating collaborations. Geographicaland organizational proximity matter less when inventorshave one partner in common.

The opposite impact occurs among similar organiz-ations; only very close social proximity will facilitate col-laboration among similar types of organizations. This

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Table 4. Conditional logit – determinants of network ties

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8Closure Closure Closure Closure Closure Bridge Bridge Bridge

Geographical proximity 1.205*** 2.031*** 1.783*** 0.969*** 0.847*** 1.334*** 1.308*** 1.339***(0.187) (0.258) (0.325) (0.205) (0.226) (0.143) (0.156) (0.162)

Technological proximity 2.704*** 2.678*** 1.753+ 2.529*** 1.779* 1.430** 1.427** 1.430**(0.770) (0.771) (0.916) (0.751) (0.905) (0.456) (0.458) (0.457)

Same applicant 2.221*** 1.470*** 1.164** 2.289*** 1.870*** 1.306*** 1.402*** 1.304***(0.307) (0.327) (0.396) (0.312) (0.363) (0.210) (0.281) (0.213)

Geographical proximity × Same applicant –1.789*** –1.674*** 0.191(0.326) (0.418) (0.276)

Same type –0.575+ –0.342 0.011 0.053 0.303 –0.678*** –0.678*** –0.691*(0.348) (0.356) (0.389) (0.429) (0.500) (0.194) (0.193) (0.302)

Geographical proximity × Same type 0.872** 0.737+ –0.016(0.329) (0.395) (0.264)

Border 1.487*** 0.898* 0.408 1.414*** 0.897+ 0.637* 0.649** 0.638*(0.396) (0.454) (0.547) (0.403) (0.518) (0.251) (0.248) (0.251)

Degrees – absolute difference –0.254+ –0.281* –0.212 –0.233 –0.145 0.288* 0.290* 0.288*(0.146) (0.143) (0.180) (0.146) (0.185) (0.130) (0.131) (0.131)

Degrees – average 0.383 0.417+ 0.014 0.337 –0.066 –0.504** –0.501** –0.505**(0.239) (0.237) (0.342) (0.242) (0.333) (0.188) (0.189) (0.189)

Experience – absolute difference 0.163 0.238 0.268 0.191 0.159 –0.048 –0.045 –0.049(0.285) (0.294) (0.365) (0.286) (0.355) (0.191) (0.194) (0.191)

Experience – average –0.365* –0.396* –0.400+ –0.369* –0.342+ –0.170 –0.167 –0.169(0.169) (0.176) (0.205) (0.173) (0.207) (0.114) (0.115) (0.114)

Number of common partners (= 1) 2.454*** 2.513***(0.410) (0.399)

Number of common partners (= 2) 3.103*** 3.257***(0.646) (0.648)

Number of common partners (= 3) 2.171** 2.220**(0.679) (0.731)

Number of common partners (= 4) –0.875 –0.815(1.177) (1.225)

Number of observations 1604 1604 1604 1604 1604 2421 2421 2421Log-likelihood –185.703 –173.642 –124.896 –183.211 –130.087 –393.782 –393.597 –393.780Pseudo-R2 0.504 0.536 0.666 0.510 0.652 0.273 0.274 0.273

Note: Cluster robust standard errors are shown in parentheses: +p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001. Dependent variable: closure tie (model 1–5) or bridging tie (model 6 and 8) versus no tie.

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Table 5. Conditional logit – determinants of network ties with social proximity

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7Closure Closure Closure Closure Closure Closure Closure

Social proximity (= 2) 3.432*** 2.214*** 5.138*** 3.828*** 2.226*** 3.045*** 1.975***(0.431) (0.480) (0.583) (0.634) (0.478) (0.460) (0.517)

Social proximity (= 3) 2.189*** 1.680** 2.727*** 1.986* 1.667** 2.032*** 1.632**(0.436) (0.557) (0.734) (0.844) (0.556) (0.482) (0.594)

Technological proximity 1.366 1.010 1.283 0.966 1.030 1.030 0.966(0.995) (1.001) (1.056) (1.074) (0.993) (1.040) (1.008)

Geographical proximity 0.889*** 1.968*** 0.731** 1.854*** 2.016*** 0.921*** 2.009***(0.210) (0.499) (0.233) (0.559) (0.506) (0.216) (0.529)

Social proximity (= 2) × Geographical proximity –3.939** –3.500** –3.898*** –3.471**(1.203) (1.300) (1.175) (1.224)

Social proximity (= 3) × Geographical proximity –0.877 –0.624 –0.902 –0.843(0.697) (0.689) (0.694) (0.688)

Same applicant 1.136** 1.031** 2.651*** 2.333*** 1.178*** 1.703*** 1.441***(0.395) (0.369) (0.645) (0.588) (0.300) (0.359) (0.343)

Social proximity (= 2) × Same applicant –3.164*** –2.837***(0.808) (0.839)

Social proximity (= 3) × Same applicant –1.587+ –0.977(0.918) (0.898)

Same type –0.227 –0.297 –0.271 –0.268(0.397) (0.429) (0.556) (0.568)

Social proximity (= 2) × Same type 2.121** 1.472*(0.733) (0.707)

Social proximity (= 3) × Same type –0.304 –0.636(0.943) (0.949)

Degrees – average –0.311 –0.380 –0.207 –0.398 –0.431 –0.352 –0.448(0.374) (0.383) (0.400) (0.398) (0.384) (0.384) (0.383)

Degrees – absolute difference 0.044 0.149 –0.079 0.080 0.163 0.049 0.169(0.197) (0.217) (0.198) (0.219) (0.215) (0.215) (0.227)

Border 1.177** –0.267 1.331** 0.186 –0.183 1.343** 0.230(0.446) (1.065) (0.468) (1.168) (1.030) (0.458) (1.088)

Experience – absolute difference –0.040 0.431 0.285 0.826+ 0.475 0.141 0.556(0.357) (0.380) (0.411) (0.462) (0.370) (0.366) (0.386)

Experience – average –0.391+ –0.470* –0.360 –0.396 –0.479* –0.429* –0.454*(0.205) (0.216) (0.237) (0.246) (0.214) (0.210) (0.220)

Number of observations 1604 1604 1604 1604 1604 1604 1604Log-likelihood –121.052 –106.132 –110.711 –98.367 –106.311 –116.141 –103.903Pseudo-R2 0.677 0.716 0.704 0.737 0.716 0.690 0.722

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result is explained by the fact that for the 165 closureties, only twenty-one occur among similar organiz-ations, and all but one concern private and distinct com-panies. This supports the view that having a partner incommon creates sufficient trust to compensate for therisk of opportunism. Social propinquity and similarorganizational types appear as complements since theformer moderates the negative sign of the latter whenexplaining collaborations.

Bridging versus closure ties

Until nowthe regressions have considered thedeterminantsof bridging and closure ties as opposed to not forming anytie. The results have revealed that behaviours are rathersimilar as regards geographical, technological and organiz-ational proximity, although some differences appear inthe coefficients that are slightly smaller for bridging ties.

In order to investigate these differences further, a multi-nomial probit was estimated (Table 6). These resultsprovide direct evidence for the argument that bridging tiesoccur outside organizational boundarieswith some techno-logical diversity. Geographical, technological and organiz-ational proximity have all negative signs, which means thatmore proximity leads to closure ties rather than bridging

ties. It can be inferred from this result that bridging tiesoccur when inventors cross local networks (no social dis-tance), organizational and technological boundaries. Theinteraction term is positive,which confirms that geographi-cal proximity is more important when individuals have nosocial proximity. In other words, when there is no socialproximityasinbridgingties,geographicalandorganizationalproximity complement each other.

Fig. 4 displays the probabilities of forming bridgingand closure ties for three levels of technological distance(that is, none, average and large) given the co-inventors’geographical and organizational distances.

It appears that closure ties are preferred when inventorsbelong to the sameorganization and share the same researcharea. Within organizational boundaries and with no tech-nological distance, geographical distance can be overcome(Fig. 4, upper left). When technological distance reachesan average level, closure ties are still preferred whateverthe geographical distance. For greater geographical dis-tances, even within organizational boundaries, inventorswill use bridging ties, but the differences in probability aremarginal. The picture becomes sharperwhen technologicaldistance becomes larger as well. Bridging ties appear to bedominant when there is organizational distance, namelyfor academia–firm linkages, whatever the level of

Table 6. Multinomial probit – bridging and no tie versus closure ties

(1) (2) (3) (4)

Network tie Network tie Network tie Network tie

Bridge No tie Bridge No tie Bridge No tie Bridge No tie

Geographical proximity –0.068 –0.804*** –0.397** –1.140*** –0.068 –0.804*** 0.039 –0.690***(0.126) (0.111) (0.149) (0.132) (0.126) (0.111) (0.146) (0.131)

Technological proximity –0.707 –1.356*** –0.690 –1.343** –0.707 –1.356*** –0.689 –1.340***(0.460) (0.407) (0.463) (0.411) (0.460) (0.407) (0.459) (0.407)

Same applicant –1.149*** –2.002*** –0.871*** –1.681*** –1.149*** –2.002*** –1.177*** –2.030***(0.178) (0.159) (0.205) (0.184) (0.178) (0.159) (0.182) (0.163)

Geographical proximity × same applicant 0.715** 0.765***(0.234) (0.202)

Same type –0.366+ 0.048 –0.428* –0.016 –0.366+ 0.048 –0.594* –0.201(0.198) (0.167) (0.205) (0.176) (0.198) (0.167) (0.271) (0.221)

Geographical proximity × Same type –0.352 –0.369+

(0.237) (0.197)Border –0.156 –0.593** 0.122 –0.311 –0.156 –0.593** –0.083 –0.518*

(0.248) (0.210) (0.283) (0.252) (0.248) (0.210) (0.255) (0.218)Experience – absolute difference –0.175 –0.256+ –0.176 –0.254+ –0.175 –0.256+ –0.177 –0.257+

(0.177) (0.145) (0.179) (0.147) (0.177) (0.145) (0.178) (0.145)Experience – average 0.149 0.297** 0.169 0.319** 0.149 0.297** 0.159 0.307**

(0.112) (0.099) (0.115) (0.102) (0.112) (0.099) (0.114) (0.100)Degrees – absolute difference 0.204* 0.058 0.211* 0.068 0.204* 0.058 0.205* 0.060

(0.100) (0.075) (0.102) (0.077) (0.100) (0.075) (0.100) (0.075)Degrees – average –0.608*** –0.172 –0.623*** –0.192 –0.608*** –0.172 –0.615*** –0.180

(0.171) (0.131) (0.174) (0.134) (0.171) (0.131) (0.171) (0.131)Constant 2.411*** 3.809*** 2.211*** 3.598*** 2.411*** 3.809*** 2.452*** 3.857***

(0.523) (0.447) (0.525) (0.451) (0.523) (0.447) (0.526) (0.450)

Number of observations 4025 4025 4025 4025Log-likelihood –1174.32 –1167.20 –1174.32 –1172.81Likelihood ratio (LR) Chi square 699.08 602.98 699.08 682.21

Note: Robust standard errors are given in parentheses: +p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001. Comparison group: closure ties – yeardummies included.

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technological distance. The probability of forming closureties in this case decreases as technological distance increases,and it becomes nearly null when there is no technologicaloverlap between inventors. These results are somewhatcounterintuitive because it would be expected that socialproximity facilitate crossing over geographical boundaries,but this does not seem to be the case. On the contrary,social proximity seems very much correlated to geographi-cal, technological and organizational boundaries. The like-lihood of interregional bridging ties increases withtechnological distance and different applicants. These tiesare formed outside one’s component and in other regionsin order to find different technological skills that are noteasily found in close technological, geographical andorgan-izational neighbourhoods.

Robustness check

Since the proportion of ties in the sample (11%) is muchhigher than the proportion of ties in the population (lessthan 0.005%), logistic regressions may be biased (KING

and ZENG, 2001; SORENSON et al., 2006). For thisreason, rare event logisticmodelsmay bemore appropri-ate to estimatemodels based on a case-control design. Asa robustness check to the conditional logit modelimplemented in this paper, a rare event logistic modelwas also estimated using the method proposed byKING and ZENG (2001) and implemented through theReLogit Stata routine proposed by TOMZ (1999). Thestrategy is to select all the ‘cases’ for which the event isrealized (pij = 1, a realized tie in the population as well

Fig. 4. Relative probabilities of forming bridging versus closure ties given different technological distancesNote: These probabilities correspond to a multinomial logit estimation with all the variables set at their mean, exceptfor: geographical distance which ranges from 0 to 800 km; and the technological distance, which is set to 0, its average

and its extreme value depending on whether one considers no, average or large technological distances

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Table 7. Rare events logit of the likelihood of a network tie

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8Closure Closure Closure Closure Closure Bridge Bridge Bridge

Geographical proximity 1.060*** 1.850*** 1.372*** 0.789*** 0.507* 1.118*** 1.106*** 1.087***(0.218) (0.243) (0.246) (0.240) (0.214) (0.117) (0.134) (0.136)

Technological proximity 1.531* 1.457* 0.569 1.455* 0.554 0.947* 0.942* 0.955*(0.692) (0.648) (0.626) (0.670) (0.659) (0.379) (0.380) (0.382)

Same applicant 2.121*** 1.490*** 1.013** 2.211*** 1.695*** 1.261*** 1.291*** 1.269***(0.256) (0.256) (0.323) (0.273) (0.304) (0.182) (0.231) (0.184)

Geographical proximity × same applicant –1.686*** –1.517*** 0.049(0.322) (0.335) (0.280)

Same type –0.563* –0.385 –0.567+ 0.065 –0.247 –0.521** –0.522** –0.442+

(0.279) (0.281) (0.318) (0.358) (0.402) (0.169) (0.169) (0.264)Geographical proximity × Same type 0.909** 0.597+ 0.093

(0.326) (0.332) (0.249)Border 1.243*** 0.379 –0.751 1.052** –0.036 0.652*** 0.654*** 0.644***

(0.352) (0.430) (0.490) (0.381) (0.434) (0.177) (0.174) (0.176)Degrees – absolute difference –0.172 –0.192+ –0.086 –0.167 –0.062 0.241* 0.242* 0.240*

(0.117) (0.115) (0.170) (0.115) (0.169) (0.112) (0.112) (0.112)Degrees – average 0.214 0.175 –0.136 0.205 –0.104 –0.517** –0.517** –0.515**

(0.189) (0.191) (0.278) (0.188) (0.271) (0.161) (0.162) (0.161)Experience – absolute difference 0.476** 0.560** 0.764*** 0.497** 0.701*** 0.060 0.063 0.059

(0.184) (0.182) (0.206) (0.182) (0.200) (0.116) (0.117) (0.116)Experience – average –0.381* –0.404* –0.466* –0.390* –0.444* –0.221* –0.220* –0.222*

(0.159) (0.157) (0.181) (0.160) (0.181) (0.107) (0.107) (0.107)Number of common partners (= 1) 2.702*** 2.783***

(0.317) (0.328)Number of common partners (= 2) 3.599*** 3.732***

(0.596) (0.618)Number of common partners (= 3) 2.079** 2.144**

(0.742) (0.790)Number of common partners (= 4) 1.357 1.215

(1.003) (0.994)Constant –11.609*** –11.180*** –10.964*** –11.730*** –11.480*** –8.799*** –8.810*** –8.823***

(0.644) (0.610) (0.706) (0.649) (0.750) (0.360) (0.365) (0.369)

Number of observations 1604 1604 1604 1604 1604 2421 2421 2421

Note: A total of 72 801 dyads (intra-component ties: 11% realized ties versus 0.00429% in the population; and bridging ties: 11% realized ties versus 0.005424% in the population). *p < 0.05; **p < 0.01;***p < 0.001.

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as in the sample is observed) and a random selection ofcontrols is considered (pij = 0, the tie is potential butnot realized). Using this sampling method, the fractionsof ones in the population are known; in this case, it isknown that there are 244 bridging ties and 193 closureties. To estimate the rare event logit, the prior correctionprocedure is implemented, which involves computingthe usual logistic regression and correcting the estimatesusing prior information about the fraction of ones in thepopulation. In doing so it is possible to correct the esti-mation by taking in account the difference between theprobability of a positive case observed in the sample andthe rarity of the event actually observed in the popu-lation. In this case, the fraction of ones in the populationis computed by dividing the number of realized ties bythe number of potential ties,11 which corresponds to0.005425% for bridging ties and 0.004290% forclosure ties. The number of realized dyads in thesample is 11% since there are ten controls for eachobserved dyad.

These regressions (Table 7) lead to similar results andconfirm those obtained previously with the conditionallogit procedure.

CONCLUSION

The aim of the paper is to investigate the dynamics ofnetwork formation using data on research collaborationsidentified through co-patenting in the field of genomicsin France over the last two decades. It contrastednetwork and proximity mechanisms in two distinctnetwork configurations as whether these collaborationsoccur within the same network through closure ties oracross separate network components through bridgingties. This framework enables one to investigate notonly the respective impact of network and proximityon collaborations, but also how they overlap, interactand possibly act as substitutes or complements.

The analysis shows that geographical, technological andorganizational proximity strongly determine the likeli-hood of forming network ties. However, once networkties are established, social proximity becomes predominantand it acts as a substitute for geographical and organiz-ational proximity for further tie formation. This confirmsprevious studies that conclude that geographical andorganizational proximity become less important withinnetworks (MAGGIONI et al., 2007; AUTANT-BERNARD

et al., 2007; AGRAWAL et al., 2008; BRESCHI andLISSONI, 2009). However, this result is only valid fortriadic closure. For higher geodesic distances and forinter-organizational relationships, geographical proximityis again more important because it allows for compen-sation of risk and uncertainties (PONDS et al., 2007).However, the advantages of closure disappear as techno-logical distance increases because then individuals mayhave to cross over their close networks through bridgingties. These bridging ties are explained by a different

dynamic,mainly driven by organizational and technologi-cal diversity. These ties enable the crossing over of organ-izational boundaries in search of some technologicalvariety, but they mainly occur within a certain geographi-cal proximity, at least when they occur. This result may beexplained by two facts. First, the data aremainly composedby dyads among French inventors, thus geographical dis-tances are overall limited. Second, in France, genomicshas benefited from large public and private funding thathas enabled the creation offive regional-basedGenopoles.This has largely favoured the development of publicresearch, private spin-offs and ultimately science–univer-sity research projects.However, this reduces the generalityof the results and asks for further investigations within awider context, in both national and technological terms.

The role of bridging versus closure ties as it appears inthis analysis may also advance some explanations regard-ing industrial clustering and specialization effects. Itappears that local clustering is mainly based on within-network closure ties that facilitate collaborationsbetween academic and non-academic organizationswithin similar technological fields, thus contributing tothe increase of local specialization effects. While thecluster increases over time, different technologicalresources are needed, and these are brought to thenetwork through bridging ties, which enable connect-ing communities that are technologically separate.This is clearly related to the debate on ‘local buzz andglobal pipelines’ (BATHELT et al., 2004).

The main limitations of this study fall under threecategories. The first concerns the fact that time is notexplicitly taken into account although it is capturedthrough the path-dependent effect of prior networkconnections and network structural position. Furtherresearch is needed to investigate how the interplaybetween the different forms of proximities and networkschanges over time (BALLAND, 2012).

The second limitation is related to the present defi-nition of social proximity, which captures only a subsetof relevant interpersonal relations related to the patentingactivity. An extension could be to supplement socialproximity with additional data such as collaborationsthrough publications to have a broader picture ofnetwork connections (BRESCHI and CATALINI, 2010).

The third limitation is related to the motivation ofindividuals. The framework does not allow accountingexplicitly for the motivation or for the strategies ofindividuals in establishing their collaboration. For thisreason, the analysis proposes to disentangle the effectof different dimensions of proximity in establishingone type of tie rather than the other. Nothing couldbe inferred from this analysis in terms of individuals’strategic behaviour, nor was the effect of this collabor-ation on individuals’ productivity analysed. However,the former topic has been analysed in a different theor-etical context (see, for instance, CARAYOL and ROUX,2009, who explicitly model individuals’ choice and testtheir arguments using similar micro-data on co-

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invention). Concerning the second issue, it is thepresent authors’ intention to address the effect ofdifferent type of ties on individuals’ performance in afurther analysis.

Acknowledgement – This research was funded by a grantfrom the Agence Nationale pour la Recherche (ANR)Corpus Genomique project. The authors are grateful to the

seminar and conference participants of: Association deScience Régionale de Langue Française (ARSDLF) Clermont2009; Colloque proximité, Poitier 2009; Analyse des Dyna-miques industrielles et Sociales (ADIS) internal seminar,Fudan-Paris 1 joint seminar, June 2010; Toulouse Eurolio,2010; Dime workshop Utrecht, September 2010; andespecially to Koen Frenken for comments. The authorswish to thank Endri Nocka, Sounia Chanfi and especiallyOlivier Antelo for research assistance on this project.

APPENDIX A

Table A1. Variables’: definitions

Variables Definitions

Dependant variablesClosure tie Takes a value of 1 if two inventors already in the network form an intra-component tieBridging tie Takes a value of 1 if two inventors already in the network form a bridging tie

Network variablesCommon (= 1, 2, 3 or 4) Four categorical variables take the value of 1 if two inventors have respectively one, two, three or four

partners in common with a geodesic distance of 2Absolute difference in degree Absolute value of the differences between the co-inventors’ respective degree centralityAverage degree Average value of the co-inventors’ respective degree centralitySocial proximity (= 2 or 3) Social proximity takes the value of 1 if two inventors have a geodesic distance of 2 or 3

Proximity variablesGeographical proximity Inverse of the distance (km)/100 between NUTS-3 (Nomenclature des Unités Territoriales Statistiques)

regions prior to attachment (in logs) – very similar to the Euclidean distanceTechnological proximity Jaffe’s index using International Patent Classification (IPC) codes for each co-inventor’s patents prior to

attachmentSame applicant Takes the value of 1 when inventors have patented for the same organization prior to tie formation; and 0

otherwise; it is a proxy for close organizational proximitySame type Takes the value of 1 when inventors have patented for the same organizational type (firms or companies); and

0 otherwise. It is a proxy for proximity in organizational typeOther controlsAbsolute difference in experience Absolute value of the differences between each co-inventors’ number of years since the first patentAverage experience AverageBorder Takes a value of 1 if one of the co-inventors belong to a border country to France; 0 otherwise

Table A2. Variables: descriptive statistics

Variables Number of observations Mean Standard deviation (SD) Minimum Maximum

1 Geographical proximity 4069 –1.1482490 0.7218783 –2.584302 02 Technological proximity 4069 0.7295127 0.1953041 0 13 Border 4069 0.1162448 0.3205576 0 14 Same applicant 4069 0.1162448 0.3205576 0 15 Same type 4069 0.4885721 0.4999308 0 16 Absolute difference in degree 4069 1.6547260 0.8848728 0 4.0253527 Average degree 4069 1.9729930 0.5389898 0.6931472 3.6763018 Absolute difference in experience 4069 3.1446510 2.0904640 0 5.9661479 Average experience 4069 3.9202250 1.3382700 1.098612 5.97126210 Common (= 1) 4069 0.0287540 0.1671349 0 111 Common (= 2) 4069 0.0103219 0.1010837 0 112 Common (= 3) 4069 0.0044237 0.0663717 0 113 Common (= 4) 4069 0.0014746 0.0383764 0 114 Social proximity (= 2) 4069 0.0452200 0.2078118 0 115 Social proximity (=3) 4069 0.0292455 0.1685147 0 1

Note: All continues variables are in logs, except for Technological proximity, and are taken for the period prior to attachment.

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Table A3. Correlations

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 1.00002 0.1659* 1.00003 –0.2816* –0.1311* 1.00004 0.3147* 0.1359* –0.0143 1.00005 –0.2231* –0.0715* –0.0093 –0.3545* 1.00006 0.0299 0.0886* –0.0675* 0.0340* –0.0202 1.00007 0.0564* 0.2208* –0.0686* 0.0495* –0.0027 0.6941* 1.00008 –0.1423* –0.0319* –0.0552* –0.2762* 0.0741* 0.1806* 0.1407* 1.00009 0.1335* 0.1391* –0.1487* 0.1256* 0.0413* 0.3164* 0.4814* 0.2991* 1.000010 0.1902* 0.0858* –0.0486* 0.2725* –0.0858* 0.0247 0.0502* –0.2589* 0.0832* 1.000011 0.0893* 0.1020* 0.0540* 0.2057* –0.0755* –0.0124 0.0512* –0.1536* 0.0881* –0.0176 1.000012 0.0678* 0.0287 0.0105 0.1376* –0.0503* 0.0395* 0.0509* –0.1003* 0.0510* –0.0115 –0.0068 1.000013 0.0561* 0.0370* –0.0139 0.1060* –0.0376* 0.0057 0.0393* –0.0578* 0.0329* –0.0066 –0.0039 –0.0026 1.000014 0.2293* 0.1363* –0.0125 0.3860* –0.1299* 0.0301 0.0925* –0.3274* 0.1336* 0.7906* 0.4693* 0.3063* 0.1766* 1.000015 0.1559* 0.0897* –0.0447* 0.2556* –0.0909* 0.0204 0.0855* –0.2611* 0.1677* –0.0299 –0.0177 –0.0116 –0.0067 –0.0378* 1.0000

Note: *p < 0.05.

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NOTES

1. Social network analysis computation was programmed bythe authors themselves with SAS. The SPAM modulesdeveloped by James Moody (MOODY, 2000) were extre-mely helpful.

2. Even for industry–university collaborations, usually thereis only one affiliation for a given patent. For this reason,inventors of a given patent have the same affiliation evenif the applicant designated in the patent does not employthem.

3. The logit and probit models were used, as is usual in thisliterature, which assumes that each tie formation is inde-pendent of the ties created at the same period. However,it is recognized that this is a strong assumption since thedecision to form a link by an inventor is affected by thechoice of other collaborations and the simultaneous be-haviour of other individuals. The authors thank one ofthe referees who highlighted this point.

4. The Euclidean distance was also calculated and similarresults were obtained.

5. The estimations are limited to bridging and closure tiessince the authors cannot estimate geographical, or organ-izational and institutional distances for the pendant andnew component ties, because these ties are formed bynew inventors for which there is no information abouttheir characteristics in t – 1.

6. The multinomial probit model is preferred since itenables the independence of irrelevant alternatives (IIA)assumption to be relaxed. The Hausman–McFadden

and Small–Hsiao tests (available from the authors uponrequest) are not entirely conclusive and thus providelittle guidance to the violation of the IIA assumption(LONG and FREESE, 2003). However, similar results areobtained as for the multinomial logit.

7. See note 4.8. The latitude and longitude coordinates for the Earth’s

curvature were adjusted; thus, the distance (km)between two points A and B is computed as:

d(A,B) = 6371 × arccos[sin(latitude(A))× sin(latitude(B)) + cos(latitude(A))× cos(latitude(B)) × cos(|longitude(A)– longitude(B)|)]

9. In order to ensure that dropping all non-European inven-tors does not affect the regressions, all models were esti-mated with a proxy of the geographical distance to non-Europeans by introducing a geographical distance of 6000km for all North American inventors. Observations weresubstantially increased with 1,999 observations for closureties and 2671 for bridging ties. The results remain similaroverall for signs, magnitude and significance.

10. A logit model with cluster robust errors and year fixedeffects was also estimated, and similar results to the con-ditional logit were obtained. These tables can be providedby the authors upon request.

11. If n is the number of active inventors, n*(n – 1) is thenumber of potential ties between these inventors. Thisnumber is estimated to be approximately 3000.

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