Social Networks in High-Technology Local Economies: The Cases of Oxfordshire and Cambridgeshire

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SOCIAL NETWORKS IN HIGH-TECHNOLOGY LOCALECONOMIES

THE CASES OF OXFORDSHIRE AND CAMBRIDGESHIRE

Rupert WatersECOTEC Research and Consulting Ltd., UK

Helen Lawton SmithBirkbeck, London, UK

Abstract

The clustering of innovative industry both demandsand creates a highly skilled local labour market. Thegrowth of this agglomeration of labour, it has beenargued, benefits both individuals and firms by provid-ing the opportunity for matching labour demand withlabour supply, which is crucial to sustaining innovation.Additionally, mobility within the local labour market isargued to be of collective benefit as the movement ofthe highly skilled within the cluster is a key mechanismfor technology transfer and fostering of interfirmlinks. Social networks (social capital) are argued in theliterature to be the medium by which these activitiesare facilitated and the development of which is key toinnovation-based local economic development. Thisis exemplified by Silicon Valley.To examine the univer-sality of these assumptions, this article explores the

development of social networks among scientists andengineers in the high-technology local economies ofOxfordshire and Cambridgeshire. Drawing on theresults of a postal survey of the engineers, physicistsand chemists in the local labour markets of theseregions carried out between November 2000 andMarch 2001, the article considers the networkingbehaviour of the highly skilled, focusing on the compo-sition and spatial reach of their networks. It concludesthat the importance of local networks should not beoverstated on the basis that there are distinct differ-ences within two seemingly similar locations andwithin the professional associations.

KEY WORDS ★ Cambridge ★ clusters ★ high-technology ★ Oxford ★ social networks

Introduction

By their very nature, the competitiveness of high-technology firms is predicated on innovation. Theseare archetypical knowledge-creating companies(Nonaka and Takeuchi, 1995) and networkedorganizations (Castells, 1996). Definitionally, ‘high-technology’ refers to ‘firms and industries whoseproducts and services embody new and innovative,advanced technologies by the application of scientificand technological expertise’ (Keeble and Wilkinson,2000: 3). Once established as focal points oftechnological changes, the growth in the number ofhigh-technology firms contributes to an increasingconcentration of highly trained scientific and technicalemployees performing skilled work in research anddevelopment and advanced manufacturing (Scott andStorper, 1987). This growing concentration of human

capital, it is argued, increases the pool of labour whichprovides possibilities for recruitment for both theindividual and the firm (see Lawton Smith andWaters, 2005). It is also suggested that a density ofhighly skilled people collectively raises the overall levelof innovation in an area as individuals are moreproductive when they locate around others with highlevels of human capital (Florida, 2002). Innovationis also facilitated by the coordinating capacity ofsocial networks – sometimes known as social capital(Coleman, 1988; Putnam, 1995). Both formal(contractual, technical and economic) and informalpersonal (social) networks are proposed as explanatoryfactors for why some firms are more innovative than others and why some regional economies aremore innovative than others (Lever, 2002; Antonelli, 2003; Simmie, 2003; Pittaway et al., 2004).

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15(1): 21–3710.1177/0969776407081278Copyright © 2008 SAGE PublicationsLos Angeles, London, New Delhi and Singaporehttp://eur.sagepub.com

European Urbanand Regional

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It is argued here, however, that the literature onhigh-technology industries underplays labourmarket behaviour in explaining the economicdynamism of local economies and overplays theubiquity of networks, especially the embeddednessof local networks. Networks in economic geographyare most usually studied as part of understandingfirm-level competitiveness. Networking byindividuals for their own professional benefit ismuch less researched. Here we are interested in howthe shared opportunities derived from commonaccess to the rewards of accumulated skills are heldfirst by the person, second by the firm and thirdwithin the locality. We show how social andgeographical contexts are crucial to understandingwhy networks might be established and arenecessary, and how the specificities of particularprofessions are implicated in how local networksdevelop, function and are sustained.

The article then focuses on the connectionbetween how functional social ties or networks areformed and variations in different groupings withinthe highly skilled labour market and within andbetween places. In examining patterns of socialnetworks in the high-technology local economies ofOxfordshire and Cambridgeshire, we provideevidence from a recent survey of members of threeprofessional associations in each location on theexistence of social networks, how they are formedand maintained and the extent to which networksprovide career opportunities for individuals throughthe possibilities created for job matching. Thisdraws on earlier research on the Oxford andCambridge regions by Keeble et al. (1998) whichcompared interfirm links between regionallyclustered high-technology SMEs in Oxford andCambridge. The article provides a critique of theassumptions about the extent of local networkembeddedness and the direction of causality of hownetworks are formed. It highlights how differentmodels of professional labour markets coexist evenwithin highly skilled occupations in apparentlysimilar locations.

The article is in four sections. The first reviewsthe literature on the nature of networking,communities of practice and information-gatheringactivities at the level of the individual and the firm,and labour processes and job matching. The secondintroduces the case-study regions, highlighting theskill profile in each and local networking

institutions, and discusses previous research on thetwo regions which relates to the theme of thisarticle. The third reviews the survey evidence,finding that it is at odds with assumptions madeabout social networks in the literature andidentifying distinct differences in networks betweenthe two locations. In the final section someconclusions on these findings are drawn.

Conceptual framework

The individual, social capital and locationalattractiveness

The development of social networks betweenindividuals has emerged as a key explanation for thelevels of innovation in particular locations. Emergingnetwork typologies reflect both the importance andthe diversity of the functions and geographies ofnetworks. The explanatory power of networks usedin effective technology transfer is linked conceptuallyto social capital (see Owen-Smith and Powell, 2004)which has strong resonances with Granovetter’s(1973) theory of weak and strong ties and Burt’s(1992) structural holes. Social capital is argued tohave a number of dimensions which are implicated inthe innovation process, such as in helping createaccess to necessary resources for innovation (e.g.information, ideas, business opportunities, financialcapital, power and influence, goodwill, trust andcooperation). Social capital also helps thedevelopment of human/intellectual capital andfacilitates cooperation and coordination by means ofreducing transaction costs (e.g. costs of negotiationand enforcement, imperfect information,opportunistic behaviour and unnecessarybureaucracy). Social capital is argued to enhancelearning processes by facilitating interaction betweencodified and tacit forms of knowledge inside the firmand through integration into wider learningcommunities (see Nahapiet and Ghoshal, 1998).

The distinction between bonding social capital(between similar types of people such as familymembers and close personal friends) and bridgingsocial capital – looser ties with colleagues which areimportant for ‘getting ahead’ (see Putnam, 2000) –helps further clarify emerging geographies ofinnovation. Putnam argues that bridging social

European Urban and Regional Studies 2008 15(1)

22 EUROPEAN URBAN AND REGIONAL STUDIES 15(1)



capital is more effective at acquiring information,while bonding social capital is better at generatingnormative, symbolic and cultural structures thataffect behaviour. The latter situation has been called‘high-network density’, with the implication beingthat the denser and more cohesive social networks arewithin a locality, the more effective is the process ofinnovation (see also Coleman, 1988; Szulanski, 1996).Such strong ties may, however, have the negativeeffect of lock-in (Olson, 1965), prioritizing someinterests over others and limiting access to moreextensive networks (Iyer et al., 2005). However,bridging social capital (i.e. direct and indirectnetwork ties) overcomes structural holes – the socialgap between two groups (Burt, 1992). It providesaccess to both people who can provide support and tothe resources those people can mobilize through theirown network ties. Granovetter’s (1973) theory of ‘thestrength of weak ties’ can be likened to the work ofBurt as both suggest that networks between ratherthan merely within groups (strong ties) brokerinformation flows (see Adler and Kwon, 2002). Oneof the benefits of weak ties, according to Granovetter,is that individuals’ acquaintances comprise a low-density network, which provides opportunities foremployment mobility by signposting opportunities.Conversely, ‘individuals with few weak ties will bedeprived of information from distant parts of thesocial system’ (Granovetter, 1983: 202). Iyer et al.(2005: 1020) argue that ‘deep-bonding social capitalwas an important impetus to the initial developmentof technology enterprises in the Cambridge area’, butas the high-tech economy has expanded, this type ofsocial capital has become less important, while that ofweak ties has increased. They propose that ‘thefailure to develop effectively sufficient bridging socialcapital is a constraint on the growth of high-technology industries in the Cambridge area’.

Granovetter (1992) later distinguished betweenstructural and relational embeddedness inrecognition that the concept of embeddedness is notunproblematic. The former being institutionally wellestablished, making networking easier, the latterhaving the opposite effect. How individuals interactis not only a function of the structure of the overallnetwork of relations, but also of dyadic (pairwise)relations. Relational embeddedness, which refers tothe strength of the relationship, has direct effects inindividual economic action; for example a workermight wish to stay in his or her job despite economic

advantages available elsewhere because of theattractiveness of the current working environment,particularly his or her social relationships.

Glaeser et al. (2002: 4) develop analysis ofindividual decision-making by examining theformation of social capital using a model ofindividual investment decisions. They define anindividual’s social capital as ‘a person’s socialcharacteristics including social skills, charisma andsize of his Rolodex – which enables him to reapmarket and non-market returns from interactionswith others’. As social capital expands, cooperationbecomes easier when individuals expect to interactmore in the future and in effect can substitute forlegal structures. The members of an individual’snetwork may not be well known to that individual;however, they must be associated with a particulartechnical competence or in a position to offer aservice to the network owner.

That networks not only contribute to anindividual’s actions with the firm and offer potentialfor career advancement through finding alternativeemployment is summed up by Granovetter. Informalchannels or networks are instrumental in sustaininghorizontal mobility. Networks, according toGranovetter, are the most effective means of jobmatching. In his study of job-finding methodsamong professional, technical and managerialworkers (i.e. SOCs 1–3) in Newark in the 1970s, hefound that ‘in the majority of cases, individuals hearabout a new job via personal contacts, and notthrough general announcements of vacancies’. Thisis consistent with the finding that the widespreaduse of friends, relatives and other acquaintances injob search has increased over time, but that thispattern varies by location and by industry(Calvo-Armengol and Jackson, 2007) and by level ofqualification (see Franzen and Hangartner, 2006 onnetworks and career prospects among graduates).Some socio-economic contexts therefore appearmore conducive than others to the productivity ofsuch networks. Early studies have suggested thathigh-technology economies are such conduciveenvironments. For example, Angel (1991) found thatsemiconductor firms in Silicon Valley fill at least 85percent of their vacancies from within the cluster,regardless of occupation. In contrast, Keeble et al.(1998) found that it was research staff rather morethan management staff who had been recruited fromwithin the local Oxford and Cambridge regions.


WATERS & LAWTON SMITH: SOCIAL NETWORKS IN HIGH-TECHNOLOGY LOCAL ECONOMIES 23



This suggests that there are occupational differencesas well as regional and industrial differences in thenetworking.

In exploring the links between the labour marketfor the highly skilled, the local high-technologyeconomy and industrial organization, Crevoisier andMaillat (1991) stress the importance of job-matchingpotential for the continued development of atechnical culture within high-technology localeconomies, so that individuals can find a range ofjobs broad enough to allow them to acquire know-how and to satisfy professional aspirations in thelocality. They argue that since individuals are mobileand employment is the main factor integrating theminto a high-technology local economy, ‘onlyattractive jobs are capable of retaining labour’. Theyargue further that, beyond a single job, it isimportant to have the possibility of a range of jobscapable of enabling individuals to achieve theirambitions. Therefore, while internal markets are themain course of career progression where largecompanies predominate, with external labourmarkets giving chains of ‘horizontal mobility’ insituations such as in an innovative milieu wherecompany size is smaller, chains of ‘vertical mobility’are necessarily focused on the external labourmarket. This gives a richer and more varied set oflabour market activities in the latter than in theformer scenario. Likewise, in an examination of joband geographical mobility among professionalswithin high-technology localities, Malecki andBradbury (1992) suggest that within a labour marketthere are frequently advantages and incentives tomove from one employer to another rather thanrelying on promotion within the internal labourmarket of a single firm. Moreover, migration is animportant factor in career advancement withgeographic mobility being positively related toincome and occupational status.

Being stressed here, therefore, is that both thenature and quality of jobs available to an individual,as well as the opportunities those jobs provide, arethe key elements in the development of that high-technology local economy. This also depends on thequalities of the individuals in those jobs and theirexpertise, hence the capacity of high-technologyfirms to absorb new information and to retain theindividuals who are accessing new information (seeSong et al., 2003). It follows that the larger the locallabour market for particular skills, the more efficient

search activity (for employers as well as employees)tends to be due to the concentration of opportunityin geographical space.

The firm

Networks have for some time been identified asbeing key to innovation for the individual firm. Forexample, Powell and Smith-Doerr (1994: 372) findthat the network literature ‘shares the commonassumption that structure of social relations shapesthe flow of information and opportunities in theworkplace’. Evidence suggests that networks have atleast three beneficial effects for firms. First,networked businesses are likely to be moresuccessful than non-networked businesses.Membership of networks is linked to small businesssurvival and, through networking with competitors,results in firms having a greater knowledge of theirown strengths and weaknesses and a greaterknowledge of the industry (Besser et al., 2006).Mutual support networks enable their members tobecome more competitive through improvedmarketing and innovation, sharing of best practice,and access to current research, collective action andinfrastructures. Second, networked firms are moreinnovative. A review of networking and innovationin the UK by Pittaway et al. (2004) confirmed thatnetworks and networking among firms plays apivotal role in innovation and that this has becomemore relevant as technologies become morecomplex. The use of networks was cruciallyimportant during venture formation and for smallgrowing firms. Third, not only do individual firmsbenefit directly, but networks also act as ‘open gates’bringing in new ideas and practices to the localeconomy as a whole (Eradin and Armatli-Koroglu,2005). Hence, for the high-technology firm whichrelies on external sources of information, flows ofinformation such as those which take place throughthe networks created by employment mobility haveonly beneficial effects. This line of argument isconsistent with the general assumption thatnetworks benefit both the individual firm and thosein close proximity, being indicative of a well-functioning innovative milieu (Camagni, 1991).

The archetypical Silicon Valley model impliesstructural embeddedness, as its success is strongly





attributed to a free movement of ideas andindividuals which generates a ‘cross-pollination’ ofideas and innovation (Saxenian, 1994). Likewise,Keeble et al. (1998) argued, based on a study ofCambridgeshire and Oxfordshire, that localinterfirm movement of skilled staff plays animportant role in the intraregional transmission ofexpertise and fostering of interfirm links, bringing invaluable external technological and managerialexpertise. This effect has been shown empirically.Almeida and Kogut (1999: 905) show that theinterfirm mobility of engineers influences the localtransfer of knowledge. They find that ‘the flow ofknowledge is embedded in regional labor networks’.In Silicon Valley, numerous networking associationswhich support social interaction increase thepossibilities of this happening (Benner, 2003).

However, the increased knowledge which flowsthrough them might work to the disadvantage of thefirm. For example, Carnoy et al. find that: (1997, 43):

From the standpoint of high-technology companymanagers ... the main problem is an undesirably highrate of turnover among their most skilled (and valuable)employees ... these human resource managers try todesign compensation and training schemes that willreduce turnover – thus in effect trying to make thehighly skilled labour force less flexible. (1997: 43)

In effect they are trying to deepen bonding socialcapital. Investments in skills formation by firms isexpensive, risky and realized only in the mediumterm, and therefore firms making such investmentstake steps to ensure that it is they and not theircompetitors who realize the benefits (Peck, 1996).Moreover, Carnoy et al. also suggest that, ‘Middle-and lower-end labour may well want much more jobsecurity than high-technology firms want to give,while highly skilled workers want less job securitythan is optimal to the firms’. These notes of cautionbeg the question of whether rate of turnover mightbe too fast for firms to extract enough utility fromeach worker before they move on.

Hence interfirm mobility may work to thedisadvantage of the firm, even if it is of benefit tothe individual and the local economy. Therefore,while powerful, external economies may exist wherefirms are dependent on externalized skill formation,as larger labour markets for the types of skillsdemanded permits the socialization of the costs ofskill formation, there can also be negative effects.

Agglomeration may initiate a second set of forcesraising the level of interfirm competition for labour,which may trigger wage inflation within thatagglomeration and lead to labour recruitment andretention difficulties for firms with a weak purchaseon their labour supply (Peck, 1996).

Segmentation in labour markets, knowledgeflows and localities

While much of this discussion focuses on thepresent in these studies, networks and the formsthey take have their own history, geography andpractice, all of which are critical in understandinghow and why they might differ. As Grabher (1993)points out, networks, as dynamic relationships,develop so that exchange partners do not start fromscratch every day, but rather from some previouslyattained common understanding so that in thismanner networks develop their own set of acceptedpractices and norms. Brown and Duguid (2000)suggest that industrial know-how is situated ininformal ‘communities of practice’ which areconstituted within a local labour market. Suchpractices and norms have been described as invisiblecolleges (de Solla Price, 1969) but have also beenidentified as having particular characteristicsdepending on the structure of segmentation withinthe wider labour market (see Peck, 1996).

De Backere and Rappa (1994), for example,distinguished between scientific and engineeringcommunities, arguing that social and professionalrelationships differed between the two groups. Morerecently, Lam (2000) has argued that the nature ofemployment relationships within the firm influencesboth the knowledge base and learning capabilities ofthe firm by determining the extent to whichexpertise is developed outside or inside the firm, andfor the individual, determining career mobility andincentives which in turn influence the capability ofthe firm in acquiring and accumulating differenttypes of knowledge; hence shaping the individual’scareer and social identity. Lam finds different typesof societal models of knowledge and learning andcorresponding labour markets. Of relevance to thisdiscussion is the ‘Professional Model’ which Lamdefines as a narrow, elitist education based on a highdegree of formalization of knowledge and the





‘Occupational Community Model’ which is rootedin a region-based occupational labour marketsurrounding a cluster of interdependent occupationsand firms. While the former implies a static pictureof information exchange and one not necessarilyanchored to particular locations, the latter ischaracterized by a high rate of mobility ofindividuals which fosters the development of socialnetworks and the transmission of knowledge.Belanger (2003) also identifies that different labourmarket processes operate in highly skilled labourmarkets compared to other kinds of labour markets.He finds that unlike in manufacturing sectors wherethe emphasis is on the upgrading of skills, inindividualized employment in leading sectors of thenew economy (biotechnology, medical products,telecommunications, etc.) much of the emphasis ison the attraction and retention of high-qualityhuman capital.

Decisions of individuals – e.g. those of graduatesto stay in the locality following graduation, or ofuniversity or company scientists making a careermove – depend on employment opportunities.Studies have shown geographically different patternsof university graduate retention rates. Belt et al.(2000) show that of the English regions, graduateretention tends to be highest in Greater London andthe Rest of the South East, in part reflecting thesuperior job and career prospects in these regions.Within the Eastern region, while the region as awhole has low rates of retention, in Cambridge wherethe high-technology economy offers more jobopportunities, rates are substantially higher (Gray et al., 2006). For the individual, the decision to stayin a location depends not only on whether suitablejobs are available, but also on whether the place isphysically and socially attractive. Keeble (1989)argued that the locational preferences of scientificand engineering professionals explained whyCambridge in the UK became established as a high-technology economy. Malecki and Bradbury’s (1992)analysis of desirable locations for high-technologyfirms reached similar conclusions.

That highly skilled labour markets do notfunction in the same way as other labour marketshas also been identified by Massey (1995). Sheargues that a better understanding of processes canbe obtained by examining collective activity inhigh-technology labour markets. She describes how

in such highly differentiated labour markets, adynamic of competition is created in which it isnecessary for both employers and employees tohave detailed knowledge of the market. Like Angel(1991), Massey finds that scientific workers changejobs frequently. She also finds that they belong to aprofessional community which extends beyond andcuts across the confines of the firms which employthem. The professional labour market does notoperate in terms of simple supply and demand, nordoes it operate in terms of cost. She argues that itis a ‘very individualistic labour market’ (1995: 135)in which particular people are sought, singled outby their possession of knowledge; a monopoly, sheclaims, they have developed through the separationof conception from execution. She notes thatscientists are highly mobile, and that to a largeextent they choose their employers, so that workfollows them and not vice versa. Massey argues thatthe clustering effect caused by the locationalpreferences, for example of electronic engineers(citing Keeble, 1976), is reinforced by the way inwhich the labour market for their skills operates.She states that: ‘What is being bought on thelabour market is not just labour power but scientificknowledge. Poaching between companies iscommon.’

In sum, the literature throws up a number ofinteresting propositions which we use as a basis forcomparison with patterns in the Oxford andCambridge study. First, we would expect a highdensity of very skilled scientists and engineers,given these regions’ reputations as rapidly growinghigh-technology economies. Second, we wouldexpect to find individuals’ social networks arestrongly locally embedded in the two high-technology economies, particularly given theexemplar of Silicon Valley. Third, as aconsequence, we would expect to find that labourmarket mobility would be the key factor in thecreation of networks. Fourth, we would expectsuch networks to be influenced by a series ofcontingencies relating to the specifics of sector, thenature of personal relationships withinorganizations, and of professional associations.Fifth, we would expect networks to be instrumentalin job matching. It is these propositions andcontingencies which we now seek to explorethrough the case-studies of the two regions.





The case-studies and the organization ofscientific employment

Oxfordshire and Cambridgeshire are two of the mostimportant centres of the knowledge economy inBritain. The two counties, although not the largestconcentrations of high-technology activity in theUK, are among the fastest growing centres and havedense populations of research activity and highlyskilled people (see Lawton Smith et al., 2003). Theirimportance in the UK’s knowledge economy as awhole, and in particular sectors such as bioscience,has been recognized by central government (see e.g.Sainsbury Report, 1999; Trends Business Research,2001 for the Department of Trade and Industry; andthe HM Treasury’s Lambert Review, 2003).Furthermore, both Oxford and Cambridge areamong only 15 locations in Europe to have beenawarded a European Label of Excellence inrecognition of the quality of their economic,scientific and technological base and proven ability tocreate and sustain start-up companies. In April 2002,Oxfordshire received its second Award of Excellence.

The two counties are both located about 50 milesfrom London, Cambridgeshire to the north andOxfordshire to the west. They are similar in size and

population. Both have historic and influentialuniversities and the residential attractions of culturalcentres. Scientific and technological resources areconcentrated in their university and governmentlaboratories, providing centres of knowledge capableof supporting high-technology commercial activity.In line with the first proposition, we find high levelsof human capital (measured as the proportion ofresidents holding degree-level qualifications).

Figures for the time that the study wasundertaken show that 27 percent of Oxfordshireresidents and 25 percent of Cambridgeshireresidents are qualified to degree level (NVQ Level4+) to rank as the second and sixth most qualifiedcounties respectively in England and Wales (Table 1).These figures mask the range of performancerecorded for the local authorities which make up thecounties. Cambridge City is the strongest performerwith 41 percent of residents qualified to degree levelto rank eighth of the 376 local authorities of Englandand Wales; while in Oxford City the proportion is36.8 percent to rank twelfth. Outside the cities,South Cambridgeshire, which surrounds CambridgeCity, also has a highly qualified workforce, ranking29th in England and Wales with 29.8 percent ofresidents holding degree-level qualifications. East



Table 1 Educational attainment in Cambridgeshire and Oxfordshire

NVQ NVQ Level 3 NVQ NVQ Level 4/5 NVQ NVQ Level 3 NVQ NVQ Level 4/5 Level 3 Rank (of 376) Level 4/5 Rank (of 376) Level 3 Rank (of 376) Level 4/5 Rank (of 376)

East of England 7.9 4 (of 9) 18.1 4 (of 9) 308,581 4 (of 9) 704,743 4 (of 9)Cambridgeshire 9.5 4 (of 49) 25.2 6 (of 49) 38,458 21 (of 49) 102,528 17 (of 49)Cambridge 18.0 2 41.2 8 15,324 40 35,145 45East Cambridgeshire 7.2 210 20.2 136 3,842 313 10,712 278Fenland 5.0 370 9.5 370 3,014 339 5,681 358Huntingdonshire 8.0 142 20.0 140 9,096 111 22,726 98South Cambridgeshire 7.6 176 29.8 29 7,182 152 28,264 67

South East of England 9.2 2 (of 9) 21.7 2 (of 9) 530,682 1 (of 9) 1,253,917 2 (of 9)Oxfordshire 10.8 1 (of 49) 27.7 2 (of 49) 48,177 15 (of 49) 123,323 14 (of 49)Cherwell 7.7 169 20.0 139 7,329 147 19,169 128Oxford 19.0 1 36.8 12 19,763 21 38,301 38South Oxfordshire 8.8 95 28.3 40 8,222 129 26,261 76Vale of White Horse 8.7 106 28.2 41 7,234 151 23,576 87West Oxfordshire 8.2 131 23.3 79 5,629 213 16,016 169

England 7.9 18.8 2,962,282 7,072,052England & Wales 8.3 19.8 3,110,135 7,432,962

Source: Authors’ analysis of Census, ONS (2001).



Cambridgeshire is the next strongest performer inthe county with a national ranking of 136th. InOxfordshire, the fall is not so sharp betweendistricts, with all of the county’s local authoritiesscoring above the national figure. Oxford City andCambridge City rank respectively first and second inEngland and Wales for the proportion of residentsqualified to NVQ Level 3 with 19 percent and 18percent respectively.

The institutional milieu with regard toopportunities for networking for high-technologyfirms in Oxford is based around formal initiativessuch as sectoral support programmes. Oxfordshirenow has some 62 business-to-business networks.These range from specialist technology-focusednetworks in IT and biotech to investment networksto breakfast clubs (Lawton Smith et al., 2007).These include the Oxford Bioscience Network andOxford Investment Opportunity Network (OION);and VentureFest, an annual two-day event designedto bring entrepreneurs and finance providerstogether. Oxfordshire Chamber of Commerce’sResearch Business Group (RESBIG) providesseminars for high-technology firms at the OxfordScience Park. The Oxfordshire BiotechNet(established under the DTI’s BiotechnologyMentoring and Incubation Challenge and led byThe Oxford Trust, a local charitable trust), however,has not survived. Initially a network, it thenestablished the Oxford BioBusiness Centre,Littlemore Park. In 2005, this closed following thedecision by the regional development agency,SEEDA, to fund a new incubator site at MiltonPark. This illustrates that networks and social capitalby themselves are insufficient to sustain a businessenvironment, even in network-rich places such asOxfordshire.

In Cambridgeshire, Waters and Lawton Smith(2002), building on work by Keeble et al. (1999),identified a similar range of network-buildingorganizations often based on the St John’sInnovation Centre, which provides a physicalfocus not found in Oxfordshire. These include theCambridge Network, the Eastern RegionBiotechnology Initiative (ERBI), the AngliaEnterprise Network and the EuropeanTechnology Club. The Cambridge Network Ltd isan attempt at replicating networking opportunitiesin Silicon Valley. Its mission is ‘to link likeminded people from business and academia to

each other and to the global high technologycommunity for the benefit of the Cambridgeregion’.1 As in Oxford, the Cambridge Chamberof Commerce has recently engaged with the high-technology community (see Keeble et al., 1999).While orchestrated networks are established inboth locations, they are of a much smaller scope and scale than the networking infrastructure which has grown up in SiliconValley (Benner, 2003).

The earlier study of the two regions, Keeble et al. (1998), compared interfirm links betweenregionally clustered high-technology SMEs inOxford and Cambridge based on a survey of 50firms in each location. They found that 60 percentof the firms reported the existence of close linkswith other firms within the local milieu –predominantly supplier and service provision. Atthe same time, they found that Oxford andCambridge clusters appeared to differ in thenature and extent of their local interfirm links.Cambridge high-technology SMEs showed agreater propensity to form links than those inOxford. This is explained as reflecting both thegreater number of SMEs in Cambridge (hence thescope to find suitable links) and also the sizestructure of firms in the Cambridge region, whichhas a lower density of large firms, with smallerfirms in Cambridge needing to link with others toperform tasks not possible within the firm itself. Ahigher proportion of SMEs in Oxford, however,regarded their local links as especially important.This again could be explained by sectoraldifferences, namely Oxfordshire’s greaterorientation to manufacturing as opposed to servicefirms, or could be related to the maturity thesis –that smaller and newer firms – as in Oxford –depend more on local customers, suppliers andresearch collaborators. With regard to innovation,two-thirds of Oxford SMEs gave customers in andoutside the UK as important sources of innovation(twice as high as in Cambridge and twice as high asuniversities within the UK). None of the firms’innovative ideas came from suppliers in the region.However, the study also found evidence of well-developed informal links and social contacts, withover 80 percent of firms with interfirm linkagesreporting occasional or frequent informal meetingswith managers or professionals from othercompanies in the local milieu.





The Keeble et al. (1998) study also foundconsiderable local recruitment, but found as wellclear differences between the two locations.Exactly one-third of all research staff recruitmentreported by respondents came from within thelocality. For management staff, the respectiveregional proportion was not far short of this, at30 percent. The study also showed that the labourmarkets, although similar, are constructed fromdifferent sources. A slightly higher proportion offirms in Oxford (18 percent) than in Cambridge(14 percent) recruited research staff from theirrespective universities. Cambridge firms recruitedmanagerial staff from other local firms andorganizations more frequently than Oxford firms(22 percent compared to 18 percent). In bothplaces recruitment of research staff was twice ashigh from non-local – and often overseas – firms/organizations (one-third of firms in eachlocation). Management staff were also much morelikely to be recruited from outside the region,particularly in Oxford (34 percent Cambridge,50 percent Oxford). However, far more Oxfordfirms (72 percent) had an explicit policy to recruitlocally than was the case in Cambridge(48 percent), and slightly more Oxford firmsreported links because of the movement ofpersonnel between firms (58 percent compared to46 percent). In Cambridge, recruitment seems tobe more locally embedded as local recruitment ismore often justified in terms of region-specificfactors such as high-quality environment orhaving appropriate skills, and links are valuedmore strongly than in Oxford. Compared toAngel’s research in Silicon Valley, the extent oflocal recruitment is very much lower in bothregions. Unlike in studies which stress theimportance of such mobility in maintainingnetworks (e.g. Crevoisier and Maillat, 1991), onlyhalf of these firms overall claimed that this was infact the case, although these links were more

important in Cambridge, suggesting a stronger‘innovative milieu’ than in Oxford.

Methodology

Against that background, we report on data collectedfrom three postal surveys of the highly skilled carriedout between November 2000 and August 2001. A totalof 6,099 questionnaires were sent to the members ofthe Institute of Electrical Engineers (IEE), theInstitute of Physics (IOP) and the Royal Society ofChemistry (RSC) in the two case-study areas – thecounties of Oxfordshire and Cambridgeshire in theUK. The survey yielded 831 responses, a responserate of 14 percent. The response rate of 14 percent istypical for this type of survey, providing sufficientlylarge samples for both case-study areas and institutemembership to allow analysis by both place andbranch of science, and it compares favourably withprevious studies of high-technology local economies.For example, Angel’s (1991) study of Silicon Valleywas based on a postal survey of 67 firms (representinga 14 percent response rate), Segal Quince Wicksteed’s(2000) update to the Cambridge Phenomenon wasbased on a study of 12 firms, while Henry and Pinch(2000) based their study on the careers of 100engineers employed in Motorsport Valley.

The questionnaires were distributed by postcodeof home residence and then screened to ensure thatthe places of work were within the case-study areas.The respondents were very highly qualified with themajority having an undergraduate degree and a halfalso having a doctorate (Table 2). It is important tonote, however, that there are entry requirements tothese institutes, which serve to maintain quality levels.

The sample is not evenly split by gender, with86 percent of respondents being male and only13 percent female. The average age of respondentswas 41.7 years (39.5 in Cambridgeshire and 43.8 in



Table 2 Qualifications profile of the survey population (percent)

Oxfordshire Cambridgeshire

Degree Master’s PhD Total Degree Master’s PhD Total22.8 14.6 53.7 91.1 21.6 21.6 44.2 87.3

Source: Author’s survey 2000/01.



Oxfordshire). In the Cambridgeshire sample,the largest single cohort was 26–30-year-olds,accounting for 15.6 percent of respondents;while in Oxfordshire, the most common cohortwas 51–5-years-olds, who accounted for12.7 percent of respondents.

Over 30 percent of respondents were in researchpositions in both case studies, with managementpositions accounting for 25 percent and 27 percentof respondents in Cambridgeshire and Oxfordshirerespectively. In both case-studies the majority ofrespondents were employed in private companies.In Cambridgeshire, 28 percent of respondents gavetheir place of work as the University of Cambridge,while in the Oxfordshire sample 19 percent wereemployed at Oxford University. Retiredrespondents were included in both samples to allowcomplete careers as well as those in progress to beconsidered, accounting for 5 percent ofCambridgeshire respondents and 4 percent ofOxfordshire ones.

In the next section we review the evidence forexistence of networks, identifying different types ofnetworks in each location. This is followed by acloser examination of sectoral patterns. Finally weconsider methods of developing networks. The data

therefore charts what networks look like inparticular locations. We use this evidence to considerwhat this tells us about networks for individuals,firms and the locality.

Social networks in Oxfordshire andCambridgeshire

The conceptual discussion suggests that we wouldfind a number of factors which influence the extent towhich networks are pervasive among the highly skilledin concentrations of high-technology activity. In thissection we present the findings from the survey of thehighly skilled. There are two broad findings that donot accord with the literature. First, large numbers ofrespondents reported having no networks; and second,where networks were reported, the highly skilledreported more networks beyond their counties thanwithin. The rest of this section presents findings fromthe survey of the highly skilled and contrasts thesewith the situation that the literature reviewed in theprevious sections would lead us to expect.

Figure 1 shows the prevalence and types of networkdeveloped by the highly skilled in both Oxfordshire



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and Cambridgeshire. The patterns are similar in bothcounties, although Oxfordshire’s scientists andengineers are more ‘network rich’ than theirCambridgeshire counterparts, with 8 percentcompared to 12 percent of respondents reportinghaving no networks. In both locations, informalnetworks are the most commonly recorded, accountingfor more than all the other options combined.2

In both Oxfordshire and Cambridgeshire, thereare more non-local than in-county networksreported (53 percent of respondents reporting non-local networks and 39 percent reporting localnetworks in Oxfordshire compared to 49 percent and38 percent in Cambridgeshire). In both cases the twomost common forms of networks are informal, withnon-local (reported by 27 percent of respondents inboth locations) being more common than local (20percent in both locations), followed by non-localtechnical (15 percent in Oxfordshire and 13 percentin Cambridgeshire) and in county technical(12 percent in Oxfordshire and 10 percent inCambridgeshire). In all cases, non-local networkswere reported as being more common than the

corresponding local linkage. These findings suggestthat although networks are a feature of these localeconomies, the reach of networks is extensive and inmany cases can be seen to extend beyond the cityand even county boundaries. Moreover, theincidence of local linkages in this study is less thanwould have been expected from both the earlierOxford/Cambridge study and studies of SiliconValley. Therefore, for the second proposition there isevidence of a relatively low level of bonding socialcapital, but rather more of bridging social capital.The latter is argued to be important in thelocalization of knowledge in this particular place(Almeida and Kogut, 1999).

The way in which networks are developed ispresented in Figure 2. The distribution of networksis similar in the two case-study areas, with no majordifferences in orders of magnitude. In support of thesecond proposition, labour market movement is themost common way that networks are formed: linkswith previous colleagues is by far the highest returnwith 28.5 percent of respondents having formednetworks this way in both Oxfordshire and



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Cambridgeshire. This is not a surprising result butis consistent with Granovetter’s (1995) finding thatchurn in the labour market is a crucial means ofestablishing and maintaining networks. Without thatprocess, either networks would be underdevelopedor other mechanisms would need to be established tocreate them if networks are as important as the 2001Clusters Report for the DTI would suggest.

In the case of Cambridgeshire, this finding isconsistent with that of Segal Quince Wicksteed(2000: 72), who found that ‘through individualemployees, leading-edge high-technology activitywithin the Cambridge sub-region is ensconcedwithin much wider spatial networks’, so thatalthough Cambridgeshire is where knowledge is putto work, the high-technology local economy isbenefiting from know-how from a much larger area.It also highlights that another important means bywhich networks are established occurs through theindividual’s career path, especially the universityattended. The university remains an important wayof developing contacts, and is slightly moreimportant for Cambridge people than Oxford ones.The contractual elements of doing business are moreimportant than meeting places as returns are higherfor customers, suppliers and contractors than forbusiness clubs. This is consistent with the findingsfrom the earlier Oxford and Cambridge study which

showed strong patterns of links with customers andsuppliers. Business clubs do not appear to make animportant contribution to local networking,suggesting that both Oxford and Cambridge havevery different social and organizational structures toSilicon Valley (cf. Benner, 2003).

Figure 2 is most surprising in that it shows a highproportion of respondents have not developedlinkages. Thus, people with high levels of humancapital (qualifications) may have low levels of socialcapital. This implies that the firm is missing out ontechnology transfer opportunities by a lack ofbridging social capital, while the localities as a wholelose out from spillover effects of such knowledgetransfer. There is, however, a marked sectoraldistribution within this general pattern whichsuggests that this is contingent upon the professionalcommunity to which the individual belongs (DeBackere and Rappa, 1994; Lam, 2000). These datamake this point more explicit (see also Figure 5).

Given the importance attached in the literatureto labour market networks and movement in thelabour market as a way of improving knowledge flowwithin high-technology local economies, we turnnow to the process of finding jobs in the case-studyregions. Figure 3 shows the methods of findingcurrent employment. Although networks arecommon in both high-technology local economies,



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the responses to this question show that they hadnot been directly put to use in finding employment,with respondents most commonly having beenappointed following a response to a printedadvertisement (34 percent and 27 percent inOxfordshire and Cambridgeshire respectively).Although word of mouth is the second mostcommon source of employment, in 19 percent and24 percent of cases respectively, it is the actions ofthe applicant that have led to respondents enteringinto new employment (i.e. through printedadvertisements, employment agencies, making adirect application or becoming self-employed),rising to 72 percent and 70 percent for respondentsclaiming not to have developed networks(see Figure 4). In comparatively few cases did thereputation of an individual lead to them beingoffered new employment. This finding is in sharpcontrast with what might have been predicted andsuggests that relational rather than structuralembeddedness is more prevalent in these twolocations. It also suggests that communities ofpractice (Brown and Duguid, 2000), if they aredeveloped, do not serve to facilitate the job matchingprocess. Thus, for most individuals, the locationdoes not appear to offer substantial benefits in theway of job matching opportunities. This may alsowork to the detriment of the firm if it is looking torecruit on the basis of personal recommendation.

However, the study also shows that there aredifferences between the two locations. Although theOxfordshire respondents reported a higherincidence of networks, job matches facilitated bysocial networks or local reputation were higher inCambridgeshire than Oxfordshire for all thesurveyed groups (physicists, chemists andengineers). Figure 4 presents findings with regard tothe proportion of highly skilled respondents whohad recently told someone about an opening in theircompany. This shifts the analysis from the supply tothe demand side. Whereas comparatively fewrespondents used their networks to aid job matchingfor themselves, a far higher proportion had recentlytold someone of an opening in their company. Thissuggests that respondents have been more willing toacknowledge the supply than the demand side ofsocial networks.

There were again large differences betweenlocations and between institutes in answering thisquestion, showing that there are different kinds ofprofessional communities. The study showed thatmembers of the IEE were most likely to havesignposted openings to their network. Moreover,respondents from Oxfordshire were more likely tohave told others about openings in their companiesthan those in Cambridgeshire. The comparativelysmall number of respondents telling others aboutopenings in their company among chemists may be a



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reflection of the possible lack of critical mass of verysimilar industries in the case-study locations.

Considering survey responses by instituterather than location suggests that networks arequantitatively different by discipline. This evidenceshows that it cannot be assumed that networksoperate uniformly within highly skilled labourmarkets, and that not only are these differencesbetween technological and scientific communities(De Backere and Rappa, 1994), but also thatdisciplines vary in the extent to which networks areformed and in the interaction between members ofthose disciplines. Figure 5 shows that physicistshave the least well-developed methods ofdeveloping contacts outside of their university.Moreover, they are by far the most likely not tohave any networks. Engineers have the mostnetworks based on previous colleagues, and slightlymore networks with suppliers and contractors thando members of the other institutes. Theimportance of universities in the creation ofnetworks noted above is further illustrated byFigure 5, particularly for physicists.

Conclusions

We have set out to explore networks of the highlyskilled in two high-technology locations. Theconsensus in the literature is that networks andmobility within local clusters are key elements of thecompetitiveness in clusters, as key methods fortechnology transfer and for the fostering of interfirmlinkages. Oxfordshire and Cambridgeshire are classicexamples of concentrations of the highly skilled,used as exemplars of such in both academic andpolicy literature. Their concentrations of skills andnetworks has grown up over time as the high-technology local economies have developed. At thesame time career opportunities have been created forthe highly skilled as the number of firms has grownand firm size has increased.

This study, in contrast with others, particularly ofSilicon Valley, suggests that the importance of localnetworks should not be overstated, neither shouldtheir existence be assumed – a sizeable proportion ofrespondents reported having no networks in bothOxfordshire and Cambridgeshire. Furthermore, in



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Figure 5 Method of developing networks by institute



both case-study locations, the proportion ofrespondents having each type of network, hencebridging social capital, was higher non-locally thanlocally (bonding social capital); suggesting thatalthough these locations remain privileged ones forscience and engineering, networks more commonlyextend beyond the centre of the respective clustersdrawing on a more dispersed range of contacts.Thus, knowledge exploited in each location andcontributing to the competitiveness of the clusterscan be seen to be drawn from a wider geography.This finding is consistent with the earlier Oxford andCambridge study which showed that with respect toinnovation, non-local links – especially withcustomers – were more important than local links.This is a similar pattern to that found in Almeida andKogut’s (1999) study of engineers. This hasimportant policy implications because of thecomplex mechanisms by which social capital has animpact on innovation and economic growth andwhich vary across space and time (Iyer et al., 2005;Calvo-Armengol and Jackson, 2006).

However, marked disciplinary differences werefound which indicates that Lam’s (2000)professional model is more diverse than shesuggests. Engineers were the most likely to haveinformal linkages (consistent with Massey’s [1995]observations) and physicists the least, althoughphysicists have the highest level of contacts withpeople they meet at university. This implies thatthe physicists do less well out of other types ofinformal social networks and therefore that thereare different processes involved in the waynetworks are formed in different groupings ofpeople in particular places. This might be cultural,as Saxenian (1994) found when comparing thecontrasting histories of Silicon Valley and Route128, or due to the relative importance of scientificand/or technological communities (De Backere and Rappa, 1994) in different disciplines whichoperate locally and more extensively. The evidence here suggests that there are differentkinds of professional communities operating within the two locations. This has implications for policy in that intervention to overcome barriersto innovation needs to take account of how theinnovation process is conducted locally, nationally and internationally and the extent towhich activities are localized and can be

expected to contribute to local spillovers whichhave a positive impact on productivity of otherindividuals’ productivity.

Nor should it be assumed that local networksare the main means for job matching. Despite thedensity of networks, job matching through printedadvertisements accounts for the largest share ofresponses in both locations. Word of mouth,however, is a strong second, particularly inCambridgeshire. This is in contrast withGranovetter’s (1995) study and assumptions about the pervasiveness and benefits ofstructural embeddedness in high-technologylocalities. Hence, for individuals, in general it canbe assumed that maintaining networks external tothe locality through previous colleagues and their universities is of far more importance than local contacts in obtaining information of professional benefit.

For firms, the increasing concentration of thehighly skilled must be seen as increasing the size ofthe local pool of labour from which can be obtainedpeople with externally and locally acquiredexpertise. Networks external to the county can bebeneficial to the individual, the firm and the localityas they bring in new information through thenetworks of individuals. Firms, however, are incompetition with other local firms for the highlyskilled. In this study, the lower incidence ofnetworks for physicists suggests that physics-basedfirms may be less likely to lose key staff thanchemical and engineering-based firms. This groupof scientists can be argued to exhibit relationalembeddedness because of the lower levels ofnetworks. Further research, therefore, is needed onlonger-term effects of recruitment and networks oninnovation and the competitive advantage of firmsand localities.

Notes

1 Cambridge Network Ltd: [www.cambridgenetwork.co.uk].2 Formal networks can be described as technical,

contractual and suppliers, where technical networksconsist of communities of interest; contractual networkscomprise subcontracting and collaborative arrangements;and supplier networks exist between actors at differentstages of the supply chain.





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Correspondence to:

Helen Lawton Smith, School of Management andOrganizational Psychology, Birkbeck, Malet Street,London WC1E 7HX, UK. [email: [email protected]]





Documents

Social Networks in High-Technology Local Economies: The Cases of Oxfordshire and Cambridgeshire