Join the Club! Knowledge Spillovers and the Influence of Social Networks on Firm Performance

JOIN THE CLUB!

KNOWLEDGE SPILLOVERS AND THE INFLUENCE OF

SOCIAL NETWORKS ON FIRM PERFORMANCE

DISSERTATION

to obtain

the degree of doctor at the University of Twente

under the authority of the rector magnificus,

prof. dr. H. Brinksma,

on account of the decision of the graduation committee,

to be publicly defended

on Friday 15th May 2009 at 15.00 hrs

by

Johannes Boshuizen

born on 21 August 1980

in Kampen

This dissertation has been approved by:

Prof.dr. Hans Th.A. Bressers (promotor)

Prof.dr. Anne Van der Veen (co-promotor)

Dr. Peter A.Th.M. Geurts (assistant promotor)

In the Clean Technology and Environmental Policy Series, the following dissertations considering various aspects of sustainability have been published:

Volume 1 De effectiviteit van gemeentelijke milieubeleidsplanning F.H.J.M. Coenen

Volume 2 Bevordering van milieumanagement in organisaties T.J.N.M. de Bruijn en K.R.D. Lulofs

Volume 3 The feasibility of Dutch environmental policy instruments Josee J. Ligteringen

Volume 4 25 jaar milieubeleid in Nederland: instrumenten, incidenten en effecten R.A. Van de Peppel, P-J. Klok en D. Hoek

Volume 5 The endurance of Mexican Amate Paper R. Citlalli López Binnquist

Volume 6 Sustained Diffusion of Renewable Energy Valentina Dinica

Volume 7 Water Governance and Institutional Change Stefan M.M. Kuks

Volume 8 Innovation and Institutional Change Peter S. Hofman

Volume 9 Transparancy in the Food Chain Agni Kalfagianne

Volume 10 Land Markets and Public Policy Wilbert Grevers

Volume 11 Corporate Social Responsibility and Public Policy-Making Arno Mathis

Volume 12 Private Equity; Public Principle David Regeczi

Volume 13 Understanding how actors influence policy implementation Katharine A. Owens

Volume 14 Geruisloos Beleid Derek Jan Fikkers Volume 15 The Power to Produce

Annemarije Kooijman-Van Dijk Volume 16 Join the Club!

Cover design: deel 4 ontwerpers, Jo Molenaar

Cover picture: Joselito Briones, http://www.istockphoto.com/attator Design of figures: TeresaW: http://teresaw.elance.com

Lay-out: Johannes Boshuizen

Published by: University of Twente / CSTM © Johannes Boshuizen, 2009

All rights reserved. No part of this publication may be reproduced in any form or by any means, without prior written permission of the author.

Table of Contents

ȱ ȱ

CHAPTERȱ1:ȱINTRODUCTION ... 1

1.1

ȱ

J

OINȱTHEȱCLUB

! ... 1

1.2

ȱ

B

ACKGROUND

... 3

1.3

ȱ

R

ESEARCHȱAIM

... 4

Researchȱquestion ... 4

1.4

ȱ

O

UTLINEȱOFȱTHEȱSTUDY

... 5ȱ

CHAPTERȱ2:ȱSPATIALȱECONOMICȱTHEORY... 7

2.1

ȱ

A

GGLOMERATIONS

... 7

2.2

ȱ

K

NOWLEDGEȱSPILLOVERS

... 7

2.3

ȱ

T

ACITȱKNOWLEDGE

... 8

2.4

ȱ

C

OMPOSITIONȱOFȱECONOMICȱACTIVITIES

... 9

2.5

ȱ

P

UBLICȱGOODȱORȱINDIVIDUALȱCOMPETITIVEȱADVANTAGE

? ... 9

Theȱpublicȱgoodȱnatureȱofȱspillovers ... 10

Spilloversȱasȱindividualȱcompetitiveȱadvantages ... 11

2.6

I

NTERACTIONSȱASȱMECHANISMSȱBEHINDȱSPILLOVERS

... 11ȱ

CHAPTERȱ3:ȱEXISTINGȱRESEARCHȱABOUTȱSPILLOVERS ... 13

3.1

ȱ

A

NECDOTALȱAPPROACHESȱTOȱINVESTIGATEȱCLUSTERS

... 13

3.2

ȱ

E

MPIRICALȱSTUDIESȱABOUTȱKNOWLEDGEȱSPILLOVERS

... 15

Distributionȱofȱknowledgeȱasȱanȱindicatorȱofȱspillovers... 15

Theȱconsequencesȱofȱclustersȱandȱknowledgeȱspillovers... 15

3.3

ȱ

T

YPOLOGIESȱANDȱIDEALȱTYPES

... 17

Theȱgeographicȱscaleȱofȱspillovers ... 17ȱ

CHAPTERȱ4:ȱTHEȱMICROȬMACROȱLINK ... 21

4.1

T

HEȱMICRO

Ȭ

MACROȱPROBLEM

... 21

4.2

ȱ

T

HEȱMICRO

Ȭ

FOUNDATIONȱOFȱKNOWLEDGEȱSPILLOVERS

... 22

4.3

ȱ

T

RANSITIONS

... 22

4.4

ȱ

S

IMULATIONȱTECHNIQUES

... 23

IIȱ

4.5

ȱ

S

OCIALȱNETWORKȱANALYSIS

... 24ȱ

CHAPTERȱ5:ȱSOCIALȱCAPITAL ... 27

5.1

ȱ

I

NTRODUCTION

... 27

5.2

ȱ

G

ROUP

Ȭ

LEVELȱSOCIALȱCAPITAL

... 27

GroupȬlevelȱsocialȱcapitalȱatȱtheȱregionalȱlevel ... 28

5.3

ȱ

I

NDIVIDUAL

Ȭ

LEVELȱSOCIALȱCAPITAL

... 29

Resources ... 29

StructuralȱPosition ... 29

5.4

ȱ

T

RUSTȱANDȱRECIPROCITY

... 30

5.5

ȱ

S

OCIALȱCAPITALȱMEASUREDȱVIAȱNETWORKS

... 31ȱ

CHAPTERȱ6:ȱSOCIALȱNETWORKȱTHEORY... 33

6.1

ȱ

I

NTRODUCTION

... 33

6.2

ȱ

T

IES

,

ȱBRIDGES

,

ȱANDȱINFORMATIONȱBENEFITS

... 34

6.3

ȱ

N

ETWORKSȱANDȱINNOVATION

... 38

6.4

ȱ

N

ETWORKSȱANDȱMARKETS

... 40

Informationalȱcues ... 40

6.5

ȱ

I

NTER

Ȭ

FIRMȱNETWORKȱSTUDIES

... 41

Descriptiveȱnetworkȱstudies ... 41

Networksȱ&ȱperformance... 42

6.6

ȱ

T

HEȱSPATIALȱDIMENSIONȱOFȱNETWORKS

... 43ȱ

CHAPTERȱ7:ȱNETWORKSȱANDȱKNOWLEDGEȱSPILLOVERS... 45

7.1

ȱ

N

ETWORKSȱANDȱCLUSTERS

:

ȱPATENTȱCITATIONS

... 45

7.2

ȱ

N

ETWORKSȱANDȱCLUSTERS

:

ȱSURVEYSȱANDȱ

I

NTERVIEWS

... 46

Linkingȱsurveyedȱnetworksȱwithȱoutcomeȱmeasures ... 47

7.3

ȱ

N

ETWORKSȱANDȱCLUSTERS

:

ȱSTRATEGICȱALLIANCES

... 48

7.4

ȱ

N

ETWORKSȱANDȱCLUSTERS

:

ȱLABOURȱMOBILITY

... 48

7.5

ȱ

N

ETWORKSȱANDȱCLUSTERS

:

ȱCO

Ȭ

PATENTING

... 49

7.6

ȱ

D

ESIGNȱISSUES

... 50

7.7

ȱ

H

YPOTHESES

... 51

Hypothesisȱ1 ... 52

Hypothesisȱ3 ... 53

Hypothesisȱ4 ... 54

Hypothesisȱ5a ... 54

Hypothesisȱ5b ... 54ȱ

CHAPTERȱ8:ȱRESEARCHȱDESIGNȱANDȱDATAȱCOLLECTIONȱ... 55

8.1

ȱ

R

ESEARCHȱSTRATEGY

... 55

8.2

ȱ

F

IRM

Ȭ

LEVELȱDATA

:

ȱHIGH

Ȭ

TECHȱFIRMS

... 56

Selectionȱofȱsectors ... 56

8.3

ȱ

R

EGIONALȱECONOMICȱDATA

... 57

8.4

ȱ

C

O

Ȭ

PATENTȱNETWORKS

... 57

Dataȱissues... 58

Matchingȱscript ... 59

Resultingȱpatentȱdata ... 60

8.4

ȱ

B

USINESSȱASSOCIATIONSȱNETWORKS

... 64

Samplingȱofȱregions ... 65

Collectionȱofȱmembershipȱdata ... 66

Mergingȱtheȱdata ... 67

Regionalȱnetworksȱinȱ11ȱregions ... 67ȱ

CHAPTERȱ9:ȱMODELȱANDȱOPERATIONALȱMEASURESȱ ... 77

9.1

ȱ

M

ODELȱANDȱRESEARCHȱMETHODOLOGY

... 77

9.2

ȱ

M

ULTILEVELȱANALYSIS

... 77

9.3

ȱ

R

ESEARCHȱMODEL

... 78

9.4

ȱ

F

IRMȱPERFORMANCEȱANDȱCONTROLS

... 78

Firmȱemploymentȱgrowth ... 78

Alternativeȱperformanceȱmeasures ... 79

Firmȱlevelȱcontrols ... 79

9.5

ȱ

R

EGIONALȱCONCENTRATION

... 79

9.6

N

ETWORKȱDATA

... 80

Regionalȱeffectsȱofȱnetworkȱcharacteristics ... 81

FirmȬlevelȱnetworkȱmeasures... 81

Diversityȱorȱhomogeneity ... 82ȱ

IVȱ

CHAPTERȱ10:ȱRESULTSȱ... 87

10.1

INTRODUCTION

... 87

10.2

BASICȱPARTȱOFȱTHEȱMODEL

... 87

10.3

E

FFECTSȱOFȱREGIONALȱPARTICIPATIONȱCHARACTERISTICS

... 89

10.4

F

IRM

Ȭ

LEVELȱRESULTS

... 93

Results:ȱPatentȱnetworks ... 93

Results:ȱNetworksȱofȱBusinessȱAssociations... 94

Combinationȱofȱpatentȱandȱmembershipȱdata ... 95ȱ

CHAPTERȱ11:ȱCONCLUSION... 99

ResearchȱQuestion ... 99

Designȱandȱdata... 100

Results ... 100

Discussion ... 102ȱ

REFERENCES ... 107

SUMMARYȱINȱDUTCHȱ/ȱNEDERLANDSEȱSAMENVATTING ... 119

EPILOGUEȱ/ȱNAWOORD ... 125

ȱ

ȱ

List of Figures

ȱ ȱ Figureȱ1.ȱ ExampleȱofȱaȱScienceȱPark ... 5 Figureȱ2.ȱ Theȱ“ColemanȬBoat”... 21 Figureȱ3.ȱ Granovetter’sȱIllustrationȱofȱtheȱStrengthȱofȱWeakȱTies... 35 Figureȱ4.ȱ StructuralȱHolesȱandȱWeakȱTies ... 37 Figureȱ5. PatentȱNetworkȱShowingȱtheȱEvolutionȱofȱTechnologyȱFields ... 39 Figureȱ6. TheȱMicroȬMacroȱProblemȱofȱKnowledgeȱSpillovers ... 52 Figureȱ7. GraphicalȱIllustrationȱofȱtheȱFiveȱHypotheses ... 53 Figureȱ8.ȱ TheȱDutchȱNUTSȬ3ȱRegions... 57 Figureȱ9.ȱ ResultsȱScreenȱofȱtheȱMatchingȱScript ... 61 Figureȱ10.ȱ NetworkȬGraphȱofȱHighȬTechȱFirmsȱandȱPatents ... 62 Figureȱ11.ȱ TheȱCoȬPatentingȱNetwork ... 63 Figureȱ12.ȱ AȱBusinessȱAssociationȱMeeting ... 64 Figureȱ13.ȱ SelectedȱNUTSȬ3ȱRegions... 65 Figureȱ14.ȱ ThreeȱExamplesȱofȱSelectedȱBusinessȱAssociations... 66 Figureȱ15.ȱ MergingȱMembershipȱData... 67 Figureȱ16.ȱ NetworksȱofȱtheȱTwenteȱandȱArnhem/NijmegenȱRegions ... 70 Figureȱ17.ȱ NetworksȱofȱtheȱDelftȱandȱLeidenȱRegions... 71 Figureȱ18.ȱ NetworksȱNorthȬDrentheȱandȱSouthwestȬFriesland ... 72 Figureȱ19.ȱ NetworksȱofȱEindhovenȱandȱHetȱGooiȱRegions ... 73 Figureȱ20. NetworksȱofȱDenȱHelderȱandȱAlkmaarȱregions... 74 Figureȱ21.ȱ NetworkȱofȱNorthwestȬOverijssel... 75 Figureȱ22. MultilevelȱModel ... 78 Figureȱ23.ȱ ExampleȱofȱanȱAffiliationȱNetwork ... 83 Figureȱ24.ȱ (TechȬ)ȱContactȱinȱOneȱandȱTwoȱSteps... 83 Figureȱ25.ȱ SummaryȱofȱIndividualȱNetworkȱVariables ... 84 Figureȱ26. MembersȱperȱEstablishmentȱandȱGrowth ... 90 Figureȱ27.ȱ AssociationsȱperȱEstablishmentȱandȱGrowth... 90 Figureȱ28. AverageȱNumberȱofȱMembershipsȱandȱGrowth... 93 ȱ

ȱ

List of Tables

ȱ ȱ Tableȱ1. TheȱFourȱSourcesȱofȱData ... 55 Tableȱ2. DifferentȱNamesȱandȱMisspellingsȱinȱtheȱEPOȱDatabase ... 59 Tableȱ3. TheȱDistributionȱofȱPatentsȱperȱFirm ... 61 Tableȱ4. AverageȱStatisticsȱofȱtheȱ11ȱNetworks... 68 Tableȱ5.ȱ DescriptivesȱofȱTwenteȱandȱArnhem/Nijmegen. ... 70 Tableȱ6. DescriptivesȱofȱDelftȱandȱLeiden. ... 71 Tableȱ7. DescriptivesȱofȱNorthȬDrentheȱandȱSouthwestȬFriesland... 72 Tableȱ8. DescriptivesȱforȱEindhovenȱandȱHetȱGooi ... 73 Tableȱ9. DescriptivesȱofȱDenȱHelderȱandȱAlkmaar... 74 Tableȱ10. DescriptivesȱofȱNorthwestȬOverijssel... 75 Tableȱ11. Descriptiveȱstatisticsȱonȱfirmȱcharacteristics... 80 Tableȱ12. AȱRegionalȱConcentrationȱMeasure ... 80 Tableȱ13ȱ RegionalȬLevelȱNetworkȱCharacteristics ... 81 Tableȱ14. DescriptivesȱPatentȱData ... 84 Tableȱ15. DescriptivesȱMembershipȱData... 85 Tableȱ16. Correlations ... 85 Tableȱ17. ResultsȱofȱtheȱBasicȱMultilevelȱAnalysesȱ ... 88 Tableȱ18. ResultsȱofȱMultilevelȱAnalysisȱwithȱRegionalȱMeasuresȱ ... 92 Tableȱ19. MultilevelȱResultsȱwithȱPatentȱData ... 95 Tableȱ20. MultilevelȱResultsȱwithȱBusinessȱAssociationsȱNetworks... 96 Tableȱ21. MultilevelȱResultsȱwithȱPatentsȱandȱMembershipȱNetworks ... 97 ȱ

ȱ

Chapter 1:

Introduction

ȱ ȱ

1.1

ȱ

ȱ

J

OINȱTHEȱCLUB

!ȱ

Givenȱtheȱfinancialȱcrisisȱofȱ2008ȱandȱtheȱrelatedȱeconomicȱdownturn,ȱtheȱquestionȱofȱ howȱtoȱstimulateȱtheȱeconomyȱhasȱneverȱbeenȱmoreȱrelevant.ȱAȱrecentȱarticleȱinȱTheȱ FinancialȱTimes1_{ȱillustratesȱtheȱrelevanceȱofȱnetworkingȱinȱtimesȱofȱeconomicȱcrisis:ȱȱ} ȱ “Remainingȱcalmȱandȱflexibleȱisȱkeyȱtoȱsuccessȱinȱaȱdownturn,ȱaccordingȱtoȱourȱ experts.ȱ Duncanȱ Cheatle,ȱ founderȱ ofȱ Theȱ Supperȱ Club,ȱ anȱ entrepreneursȇȱ networkingȱ group:ȱ ‘Thereȱ areȱ aȱ fewȱ commonȱ factorsȱ amongȱ ourȱ membersȱ thatȱ areȱgrowingȱfast.ȱTypicallyȱtheyȱwillȱhaveȱaȱmoreȱinnovativeȱbusinessȱmodel,ȱsoȱ inȱaȱdownturnȱtheyȱareȱgrowingȱmarketȱshareȱwhileȱtheȱincumbentȱisȱstruggling.ȱ Flexibilityȱisȱalsoȱimportant.ȱOurȱmembersȱtendȱtoȱbeȱselfȬmade,ȱandȱhavingȱbuiltȱ theirȱ businessesȱ fromȱ scratch,ȱ theyȱ haveȱ learntȱ toȱ beȱ moreȱ flexible.ȱ Ifȱ theirȱ businessesȱ areȱ stillȱ smallȱ theyȱ canȱ alsoȱ beȱ moreȱ nimbleȱ thanȱ theirȱ competitors.ȱ Peopleȱthatȱhaveȱrisenȱupȱthroughȱaȱcompanyȱtoȱleadȱitȱtendȱtoȱtryȱtoȱdoȱtheȱsameȱ thingȱasȱbeforeȱwhenȱthingsȱgetȱtough.ȱTheyȱdonȇtȱseeȱwhatȱisȱcomingȱaheadȱsoȱ theirȱ businessesȱ startȱ toȱ struggle.ȱ Theȱ thirdȱ thingȱ isȱ thatȱ ourȱ guysȱ tendȱ toȱ beȱ betterȱnetworked.ȱBetterȱbusinessȱownersȱtendȱtoȱinvestȱtimeȱmeetingȱpeople.ȱInȱ theȱcorporateȱworld,ȱpeopleȱwillȱhaveȱtheirȱexistingȱrelationshipsȱwithȱsuppliersȱ andȱ customersȱ butȱ wonȇtȱ activelyȱ workȱ theirȱ networkȱ andȱ seekȱ outȱ newȱ inspiration’.”ȱȱ

ȱ

Thisȱnewspaperȱarticleȱillustratesȱwhyȱnetworkingȱactivitiesȱareȱgenerallyȱbelievedȱtoȱ strengthenȱ aȱ firm’sȱ capabilities.ȱ Entrepreneursȱ carryȱ outȱ manyȱ kindsȱ ofȱ networkingȱ activities:ȱ theyȱ discussȱ thingsȱ atȱ businessȱ meetings,ȱ andȱ membersȱ ofȱ businessȱ associationsȱjointlyȱvisitȱotherȱfirms,ȱetc.ȱSpeculatingȱaboutȱthis,ȱinȱnormalȱeconomicȱ times,ȱ itȱ isȱ seenȱ thatȱ entrepreneursȱ useȱ theseȱ activitiesȱ toȱ improveȱ theirȱ businessesȱ andȱstimulateȱdevelopment.ȱȱ

Inȱ timesȱ ofȱ economicȱ downturn,ȱ itȱ comesȱ downȱ moreȱ toȱ survival.ȱ Inȱ suchȱ times,ȱ discussingȱ andȱ developingȱ newȱ businessȱ opportunitiesȱ isȱ especiallyȱ crucial.ȱ Someȱ

1_{ȱIn:ȱFinancialȱTimesȱ(London,ȱEngland)ȱAugustȱ23,ȱ2008,ȱLondonȱEditionȱ1,ȱDonȇtȱ} Panic;ȱASKȱTHEȱEXPERTS,ȱByȱJonathanȱMoules,ȱp.ȱ30.ȱ

JoinȱtheȱClub!ȱKnowledgeȱSpilloversȱandȱtheȱInfluenceȱofȱSocialȱNetworksȱonȱFirmȱPerformanceȱ

2ȱ

companiesȱ doȱ notȱ focusȱ onȱ immediateȱ businessȱ opportunities,ȱ butȱ waitȱ outȱ theȱ recession,ȱ usingȱ thisȱ timeȱ toȱ developȱ newȱ productsȱ toȱ keepȱ upȬtoȬdateȱ andȱ toȱ beȱ readyȱ toȱ benefitȱ whenȱ theȱ economyȱ recovers.ȱ Therefore,ȱ inȱ additionȱ toȱ networking,ȱ manyȱcompaniesȱrecogniseȱtheȱneedȱtoȱremainȱinnovativeȱandȱkeepȱupȱwithȱsocietalȱ trendsȱ by,ȱ forȱ example,ȱ investigatingȱ cleanerȱ alternativesȱ toȱ currentȱ productionȱ methods.ȱLargeȱfirmsȱsuchȱasȱToyotaȱmayȱstartȱtoȱdownsize,ȱbutȱtheyȱalsoȱcontinueȱtoȱ investȱ inȱ innovationȱ because,ȱ asȱ withȱ manyȱ otherȱ companies,ȱ theyȱ wantȱ toȱ benefitȱ fromȱ increasingȱ consumerȱ interestȱ inȱ cleanerȱ technologies.ȱ “Afterȱ theȱ currentȱ shakeoutȱisȱover,ȱtheȱwinnersȱinȱtheȱindustryȱmostȱlikelyȱwillȱenjoyȱaȱperiodȱofȱbriskȱ growthȱ asȱ consumersȱ satisfyȱ pentȬupȱ demandȱ forȱ carsȱ andȱ seekȱ outȱ theȱ latestȱ fuelȬ savingȱtechnologies”,ȱsaidȱGaryȱA.ȱWilliams,ȱchiefȱexecutiveȱofȱwRatingsȱCorp2_.ȱ

Innovationȱhasȱgenerallyȱbeenȱfashionableȱforȱtheȱlastȱ20ȱyears.ȱGovernmentsȱlookȱatȱ theȱeconomicȱandȱintellectualȱpowerȱofȱregionsȱsuchȱasȱSiliconȱValleyȱwithȱenvyȱandȱ wantȱtoȱreplicateȱthisȱkindȱofȱspatialȱcluster.ȱOneȱrecognisedȱcharacteristicȱofȱSiliconȱ Valleyȱ isȱ theȱ strongȱ influenceȱ ofȱ interȬfirmȱ networkingȱ (Saxenian,ȱ 1990).ȱ Therefore,ȱ strengtheningȱ localȱ networksȱ isȱ oftenȱ seenȱ asȱ aȱ policyȱ toolȱ thatȱ canȱ beȱ utilizedȱ toȱ stimulateȱlocalȱeconomies.ȱ

ȱ

TheȱDutchȱeconomicȱpolicyȱagenda,ȱ“PiekenȱinȱdeȱDelta,”ȱaimsȱtoȱstrengthenȱexistingȱ regionalȱ successes,ȱ ratherȱ thanȱ spendingȱ moneyȱ onȱ regionsȱ thatȱ areȱ laggingȱ behindȱ (MinistryȱofȱEconomicȱAffairs,ȱ2009).ȱOneȱofȱtheȱpoliciesȱinȱthisȱagendaȱisȱtoȱstimulateȱ cooperationȱ amongȱ universities,ȱ researchȱ institutes,ȱ andȱ firmsȱ withinȱ regions,ȱ thusȱ enablingȱsoȬcalledȱ“knowledgeȱspillovers”.ȱInȱadditionȱtoȱtheȱregionalȱdevelopmentȱ agenciesȱthatȱworkȱtoȱachieveȱtheseȱgoals,ȱtheȱMinistryȱofȱEconomicȱAffairsȱcreatedȱaȱ governmentȱ agency,ȱ Syntens,ȱ designedȱ toȱ stimulateȱ innovationȱ inȱ smallȬȱ andȱ mediumȬsizedȱ firms.ȱ “Syntensȱ isȱ aȱ specialistȱ inȱ allȱ typesȱ ofȱ innovation,ȱ andȱ knowsȱ whereȱknowledgeȱandȱnetworksȱcanȱbeȱfound”ȱ(Syntens,ȱ2009).ȱ

ȱ

Theȱ beliefȱ thatȱ knowledgeȱ spilloversȱ tendȱ toȱ haveȱ aȱ spatialȱ dimensionȱ hasȱ inspiredȱ manyȱresearchersȱandȱpolicymakersȱinȱtheȱlastȱfewȱyears.ȱAlthoughȱmanyȱaspectsȱofȱ knowledgeȱspilloversȱhaveȱbeenȱresearched,ȱmanyȱthingsȱremainȱunclear.ȱTheȱmainȱ issueȱ isȱ howȱ theȱ macroȬlevelȱ successȱ ofȱ regionsȱ suchȱ asȱ Siliconȱ Valleyȱ canȱ beȱ understoodȱthroughȱmicroȬlevelȱmechanisms.ȱOften,ȱlocalizedȱknowledgeȱspilloversȱ areȱ seenȱ asȱ determiningȱ factorsȱ thatȱ occurȱ throughȱ socialȱrelationships.ȱHowever,ȱitȱ

2_{ȱQuotedȱin:ȱTheȱWashingtonȱTimesȱ(USA)ȱDecemberȱ14,ȱ2008,ȱU.S.ȱautomakersȱfaceȱ} consolidation,ȱByȱPatriceȱHill,ȱp.ȱ1.ȱ

Chapterȱ1:ȱIntroductionȱ

remainsȱ unclearȱ whetherȱ andȱ howȱ socialȱ relationshipsȱ enableȱ theseȱ spillovers.ȱ Thisȱ studyȱattemptsȱtoȱshedȱsomeȱlightȱonȱtheȱissueȱbyȱinvestigatingȱknowledgeȱspilloversȱ throughȱsocialȱnetworks.ȱȱ

ȱ

1.2

ȱ

ȱ

B

ACKGROUND

ȱ

Challengedȱ byȱ theȱ consequencesȱ ofȱ globalization,ȱ downsizing,ȱ andȱ outsourcing,ȱ governmentsȱatȱallȱlevelsȱrecognizeȱtheȱgreatȱopportunitiesȱthatȱnewȱtechnologiesȱandȱ knowledgeȬintensiveȱ labourȱ offer.ȱ Theyȱ seeȱ innovationȱ asȱ anȱ importantȱ factorȱ thatȱ canȱcontributeȱtoȱcompanies’ȱabilitiesȱtoȱsurviveȱincreasingȱinternationalȱcompetitionȱ (Porterȱ&ȱStern,ȱ2001).ȱ

Innovationȱ isȱ noȱ longerȱ seenȱ asȱ aȱ processȱ ofȱ purelyȱ internalȱ researchȱ andȱ developmentȱ (R&D)ȱ (Rogers,ȱ 1995).ȱ Rather,ȱ theȱ innovationȱ processȱ isȱ seenȱ asȱ anȱ iterativeȱ processȱ inȱ whichȱ knowledgeȱ isȱ cooperativelyȱ exchangedȱ andȱ developedȱ (Vonȱ Hippel,ȱ 1994).ȱ Socialȱ networksȱ playȱ anȱ importantȱ roleȱ inȱ thisȱ processȱ becauseȱ theyȱenableȱtheȱdiffusionȱofȱinnovationsȱthroughȱsocietyȱ(Rogers,ȱ1995).ȱȱ

Innovationȱ isȱ notȱ onlyȱ studiedȱ atȱ theȱ firmȱ level.ȱ Spatialȱ scientistsȱ seeȱ localȱ cooperationȱandȱknowledgeȬsharingȱamongȱfirmsȱasȱbothȱtheȱmostȱimportantȱformȱofȱ spilloverȱandȱasȱtheȱkeyȱexplanationȱforȱtheȱsuccessȱofȱclustersȱ(e.g.ȱSaxenian,ȱ1994).ȱ Socialȱ (orȱ nonȬmarket)ȱ interactionsȱ areȱ seenȱ asȱ theȱ keyȱ mechanismsȱ drivingȱ theȱ successȱofȱspatialȱclustersȱ(Glaeser,ȱ2000).ȱȱ

Here,ȱ aȱ keyȱ assumptionȱ isȱ thatȱ firmsȱ insideȱ aȱ spatialȱ clusterȱ performȱ betterȱ thanȱ othersȱbecauseȱtheyȱbenefitȱfromȱlocalȱknowledgeȱspillovers.ȱInterȬfirmȱnetworksȱareȱ oftenȱ perceivedȱ asȱ theȱ pipesȱ andȱ prismsȱ throughȱ whichȱ localȱ knowledgeȱ spilloversȱ occurȱ(e.g.ȱZaheerȱ&ȱBell,ȱ2005).ȱȱ

Asȱ aȱ result,ȱ knowledgeȱ spilloversȱ amongȱ firmsȱ areȱ oftenȱ seenȱ asȱ economicȱ growthȱ drivers.ȱ Encouragedȱ byȱ theȱ provenȱ successȱ ofȱ famousȱ highȬtechȱ clusters,ȱ regional,ȱ national,ȱ andȱ supranationalȱ governmentsȱ haveȱ designedȱ policiesȱ toȱ stimulateȱ theȱ developmentȱofȱsuchȱclustersȱ(Enrightȱ&ȱFfowcsȬWilliams,ȱ2000).ȱȱ

ȱ

Anotherȱ reasonȱ forȱ theȱ prominenceȱ ofȱ clusteringȱ policiesȱ atȱ allȱ levelsȱ ofȱ economicȱ policymakingȱhasȱbeenȱMichaelȱPorter’sȱ(1998)ȱpromotionȱofȱclustersȱasȱpolicyȱtools.ȱ Accordingȱ toȱ Martinȱ andȱ Sunleyȱ (2003),ȱ policymakersȱ allȱ overȱ theȱ worldȱ adoptedȱ theseȱ clusteringȱ ideas.ȱ Today,ȱ policymakersȱ inȱ manyȱ countriesȱ andȱ regionsȱ areȱ interestedȱinȱstimulatingȱtheȱdevelopmentȱofȱinnovativeȱclustersȱ(Hospers,ȱ2006).ȱ ȱ

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4ȱ

Policiesȱdifferȱgreatly,ȱrangingȱfromȱhighȬtechȱclusterȱdevelopmentȱinȱlargeȱcitiesȱtoȱ theȱ developmentȱ ofȱ ruralȱ clustersȱ basedȱ onȱ agricultureȱ (Cooke,ȱ 2001;ȱ Martinȱ &ȱ Sunley,ȱ2003;ȱMcCannȱ&ȱFolta,ȱ2008;ȱRosenfeld,ȱ2001).ȱ

Twoȱ elementsȱ seemȱ toȱ beȱ centralȱ inȱ theȱ relatedȱ governmentȱ policies.ȱ First,ȱ regionalȱ developmentȱ agenciesȱ encourageȱ cooperationȱ amongȱ firmsȱ byȱ rewardingȱ cooperationȱ amongȱ firms—forȱ instance,ȱ byȱ subsidizingȱ jointȱ R&Dȱ projectsȱ (Cooke,ȱ 1992).ȱ Otherȱ cooperativeȱ activitiesȱ thatȱ canȱ beȱ stimulatedȱ includeȱ jointȱ marketing,ȱ production,ȱproblemȱsolving,ȱandȱpurchasingȱ(Rosenfeld,ȱ2001).ȱ

Second,ȱ spatialȱ policiesȱ focusȱ onȱ stimulatingȱ theȱ growthȱ ofȱ certainȱ industries.ȱ Aȱ frequentlyȱseenȱpolicyȱisȱreservingȱspaceȱforȱhighȬtechȱfirmsȱinȱ“scienceȱparks”.ȱTheȱ ideaȱisȱthatȱtheȱphysicalȱclosenessȱofȱfirmsȱinȱtheseȱparksȱwillȱstimulateȱcooperationȱ andȱknowledgeȱsharing.ȱȱ

ȱ

Theȱ degreeȱ toȱ whichȱ spilloversȱ workȱ atȱ theȱ microȬlevelȱ isȱnotȱ fullyȱ understoodȱ andȱ thereȱ isȱ aȱ ratherȱ limitedȱ amountȱ ofȱ firmȬlevelȱ evidenceȱ onȱ theȱ effectsȱ ofȱ agglomerationȱ economiesȱ inȱ general,ȱ andȱ localȱ knowledgeȱ spilloversȱ inȱ particularȱ (Henderson,ȱ 2007).ȱ Inȱ otherȱ words,ȱ itȱ isȱ notȱ certainȱ whether,ȱ andȱ underȱ whatȱ conditions,ȱlocalȱinteractionsȱleadȱtoȱenhancedȱfirmȱperformances.ȱȱ ȱ

1.3

ȱ

ȱ

R

ESEARCHȱAIM

ȱ

Theȱmainȱaimȱofȱthisȱresearchȱisȱtoȱopenȱtheȱ“blackȱbox”ȱofȱknowledgeȱspilloversȱbyȱ testingȱtheȱextentȱtoȱwhichȱsocialȱinteractionsȱamongȱfirmsȱinȱaȱregionȱcontributeȱtoȱ theirȱperformance.ȱAsȱsuch,ȱtheȱresearchȱquestionȱisȱphrasedȱasȱfollows:ȱ ȱ

Researchȱquestionȱ

AreȱinterȬfirmȱlinkagesȱaȱmechanismȱforȱknowledgeȱspillovers?ȱ ȱ

Toȱ answerȱ thisȱ question,ȱ establishedȱ networkȱ methodologyȱ isȱ appliedȱ inȱ twoȱ novelȱ ways.ȱFirst,ȱnetworkȱdataȱbasedȱonȱcooperativeȱpatentingȱ(coȬpatenting)ȱareȱutilized.ȱ Second,ȱ networkȱ dataȱ areȱ derivedȱ fromȱ businessȱ associationȱ memberships.ȱ Bothȱ approachesȱhaveȱbeenȱtriedȱbefore,ȱbutȱtheyȱhaveȱneverȱbeenȱappliedȱonȱaȱlargeȱscaleȱ andȱhaveȱneverȱincludedȱfirmȱperformanceȱmeasures.ȱCooperativeȱpatentingȱ(inȱcoȬ patents)ȱinvolvesȱcooperativeȱR&Dȱandȱisȱaȱratherȱformal,ȱratherȱcomplex,ȱandȱhardȬ toȬaccessȱ typeȱ ofȱ cooperation.ȱ Membershipȱ ofȱ businessȱ associations,ȱ onȱ theȱ otherȱ hand,ȱisȱlessȱformalȱandȱeasierȱtoȱestablish.ȱ

Chapterȱ1:ȱIntroductionȱ

Here,ȱ theȱ impactȱ ofȱ bothȱ typesȱ ofȱ networksȱ onȱ firmȱ performanceȱ isȱ studiedȱ inȱ anȱ attemptȱtoȱuncoverȱtheȱmicroȬfoundationsȱbeneathȱlocalȱknowledgeȱspillovers.ȱȱ ȱ

Figureȱ1.ȱȱ

ExampleȱofȱaȱScienceȱParkȱ

ȱ ȱ

1.4

ȱ

ȱ

O

UTLINEȱOFȱTHEȱSTUDY

ȱ

Theȱoutlineȱofȱthisȱdissertationȱisȱsummarizedȱasȱfollows:ȱȱ

Chaptersȱ 1ȱ toȱ 6ȱ discussȱ existingȱ researchȱ inȱ theȱ fieldȱ ofȱ spatialȱ clusters,ȱ knowledgeȱ spillovers,ȱandȱsocialȱnetworks.ȱInȱChapterȱ2,ȱspatialȱeconomicȱtheoryȱisȱoutlinedȱandȱ anȱ importantȱ componentȱ isȱ theȱ discussionȱ aboutȱ spilloversȱ asȱ aȱ publicȱ goodȱ asȱ againstȱ generatingȱ individualȱ competitiveȱ advantages.ȱ Inȱ Chapterȱ 3,ȱ theȱ existingȱ researchȱisȱdiscussedȱwithȱaȱfocusȱonȱtypologiesȱandȱidealȱtypesȱinȱresearchȱonȱspatialȱ clusters.ȱ Theȱ MarshallȬJacobsȱ controversyȱ concerningȱ theȱ constitutionȱ ofȱ economicȱ activityȱ isȱ discussed,ȱ andȱ alsoȱ theȱ debateȱ onȱ theȱ roleȱ ofȱ geographicalȱ scaleȱ inȱ knowledgeȱspillovers.ȱInȱChapterȱ4,ȱtheȱmicroȬmacroȱproblemȱonȱhowȱtoȱlinkȱmacroȬ phenomenaȱtoȱmicroȬbehaviourȱisȱdiscussed.ȱChapterȱ5ȱconsidersȱtheȱdiscussionȱasȱtoȱ whetherȱsocialȱcapitalȱshouldȱbeȱviewedȱasȱanȱindividualȱorȱasȱaȱcollectiveȱresource.ȱ Chapterȱ 6ȱ discussesȱ socialȱ networkȱ methodsȱ andȱ theory,ȱ andȱ theȱ advantagesȱ ofȱ specificȱlocationsȱinȱaȱnetworkȱsuchȱasȱaȱbridgingȱone.ȱȱ

Chapterȱ7ȱintroducesȱtheȱempiricalȱpartȱofȱtheȱstudyȱandȱcontainsȱtheȱresearchȱdesignȱ andȱ hypotheses.ȱ Theȱ followingȱ chaptersȱ considerȱ theȱ researchȱ designȱ andȱ theȱ collectionȱofȱdataȱ(Chapterȱ8),ȱtheȱresearchȱmodelȱandȱoperationalȱmeasuresȱ(Chapterȱ 9).ȱTheȱresultsȱareȱpresentedȱinȱChapterȱ10ȱandȱconclusionsȱdrawnȱinȱChapterȱ11.ȱȱ

ȱ

Chapter 2:

Spatial Economic Theory

ȱ ȱ

2.1

ȱ

ȱ

A

GGLOMERATIONS

ȱ

AgglomerationsȱareȱspatialȬboundedȱconcentrationsȱofȱeconomicȱactivitiesȱandȱserveȱ asȱrelevantȱphenomenaȱforȱpolicymakersȱandȱscholarsȱbecauseȱlabourȱandȱcapitalȱareȱ heavilyȱ concentratedȱ inȱ cities,ȱ andȱ specificȱ industriesȱ areȱ oftenȱ moreȱ likelyȱ toȱ beȱ concentratedȱ inȱ certainȱ regionsȱ thanȱ evenlyȱ spreadȱ overȱ aȱ countryȱ (Rosenthalȱ &ȱ Strange,ȱ 2004).ȱ Theȱ concentrationȱ processȱ ofȱ relatedȱ industriesȱ isȱ oftenȱ called:ȱ “clustering”.ȱ Especiallyȱ knowledgeȬintensiveȱ industries,ȱ whereȱ knowledgeȱ isȱ generatedȱ throughȱ industryȱ R&D,ȱ universityȱ R&D,ȱ andȱ skilledȱ labourȱ areȱ likelyȱ toȱ clusterȱinȱspaceȱ(Audretschȱ&ȱFeldman,ȱ1996;ȱMarkusen,ȱHall,ȱ&ȱGlasmeier,ȱ1986).ȱȱ ȱ

SeveralȱfamousȱhighȬtechȱregionsȱhaveȱdevelopedȱswiftlyȱoverȱtheȱlastȱdecades.ȱSomeȱ examplesȱ areȱ Siliconȱ Valleyȱ (USA),ȱ EmiliaȬRomagnaȱ (Italy),ȱ BadenȬWürttembergȱ (Germany),ȱ andȱ theȱ Cambridgeȱ regionȱ (England)ȱ (Gordonȱ &ȱ McCann,ȱ 2005).ȱ Manyȱ scholarsȱrelateȱspatialȱagglomerationsȱlikeȱtheseȱtoȱeconomicȱgrowthȱ(Fujitaȱ&ȱThisse,ȱ 2002,ȱ p.ȱ 389),ȱ asȱ wellȱ asȱ toȱ theȱ innovationȱ andȱ competitivenessȱ ofȱ highȬtechȱ firmsȱ (Audretschȱ&ȱFeldman,ȱ2004;ȱDurantonȱ&ȱPuga,ȱ2004,ȱp.ȱ2098).ȱ

Theȱ successȱ ofȱ Siliconȱ Valleyȱ andȱ EmiliaȬRomagnaȱ inȱ particularȱ areȱ oftenȱ linkedȱ toȱ theȱ extensiveȱ networksȱ ofȱ regionalȱ knowledgeȱ exchange.ȱ However,ȱ manyȱ ofȱ theȱ studiesȱlinkingȱsuccessȱtoȱnetworksȱareȱanecdotalȱinȱnature.ȱDespiteȱincreasingȱeffortsȱ toȱ understandȱ theȱ microȬfoundationsȱ ofȱ theseȱ successfulȱ regions,ȱ muchȱ remainsȱ unknownȱaboutȱtheȱmechanismsȱthatȱunderlieȱtheȱsuccessȱofȱtheseȱregions.ȱȱ

ȱ

2.2

ȱ

ȱ

K

NOWLEDGEȱSPILLOVERS

ȱ

Marshallȱ(1920)ȱidentifiesȱthreeȱbenefitsȱofȱlocalizedȱeconomiesȱthatȱserveȱasȱdrivingȱ forcesȱ behindȱ agglomerations:ȱ theȱ advantageȱ ofȱ aȱ pooledȱ labourȱ market,ȱ theȱ availabilityȱ ofȱ specializedȱ inputsȱ andȱ services,ȱ andȱ theȱ possibilityȱ ofȱ knowledgeȱ spillovers.ȱ Heȱ isȱ oftenȱ referredȱ toȱ asȱ theȱ scholarȱ whoȱ originatedȱ theȱ ideaȱ ofȱ knowledgeȱspillovers.ȱDespiteȱaȱvastȱbodyȱofȱliteratureȱonȱtheȱrelationshipȱbetweenȱ knowledgeȱ spilloversȱ andȱ economicȱ growth,ȱ thereȱ isȱ littleȱ evidenceȱ onȱ howȱ theseȱ spilloversȱoccur.ȱInȱthisȱrespect,ȱHendersonȱ(2007,ȱp.ȱ506Ȭȱ507)ȱarguesȱthatȱ“…despiteȱ theȱ factȱ thatȱ knowledgeȱ spilloversȱ areȱ centralȱ toȱ notionsȱ ofȱ economicȱ growth,ȱ

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8ȱ

technologicalȱ progress,ȱ andȱ theȱ natureȱ andȱ characteristicsȱ ofȱ cities,ȱ researchȱ onȱ theȱ natureȱofȱsuchȱspilloversȱisȱsurprisinglyȱlimited“.ȱȱ

ȱ

Oneȱ mightȱ wonderȱ ifȱ faceȬtoȬfaceȱ contactȱ stillȱ playsȱ aȱ roleȱ nowȱ thatȱ soȱ muchȱ informationȱ isȱ availableȱ online,ȱ inȱ (scientific)ȱ journals,ȱ orȱ inȱ otherȱ documents.ȱ Nevertheless,ȱ localȱ knowledgeȱ spilloversȱ mayȱ indeedȱ stillȱ playȱ anȱ importantȱ roleȱ becauseȱsomeȱknowledgeȱisȱonlyȱavailableȱlocally.ȱThisȱtypeȱofȱknowledgeȱisȱknownȱ asȱ“tacitȱknowledge”.ȱ

ȱ

2.3

ȱ

ȱ

T

ACITȱKNOWLEDGE

ȱ

Polanyiȱ (1966)ȱ alreadyȱ arguedȱ thatȱ notȱ allȱ knowledgeȱ canȱ beȱ codified,ȱ andȱ thatȱ therefore,ȱitȱisȱimportantȱtoȱtakeȱtheȱtacitȱsideȱofȱknowledgeȱintoȱaccount.ȱMaskellȱandȱ Malmbergȱ(1999ȱp.ȱ172)ȱargueȱthatȱ“theȱmoreȱeasilyȱcodifiableȱ(tradable)ȱknowledgeȱ canȱ beȱ accessed,ȱ theȱ moreȱ crucialȱ doesȱ tacitȱ knowledgeȱ becomeȱ forȱ sustainingȱ orȱ enhancingȱtheȱcompetitiveȱpositionȱofȱtheȱfirm”ȱandȱthatȱ“...theȱmoreȱeasilyȱcodifiableȱ (tradable)ȱknowledgeȱcanȱbeȱaccessed,ȱtheȱmoreȱcrucialȱdoesȱtacitȱknowledgeȱbecomeȱ forȱ sustainingȱ orȱ enhancingȱ theȱ competitiveȱ positionȱ ofȱ theȱ firm”.ȱ Gertlerȱ (2003),ȱ identifiesȱ tacitȱ knowledgeȱ asȱ oneȱ ofȱ theȱ mainȱ determinantsȱ forȱ theȱ geographyȱ ofȱ innovativeȱactivity.ȱ

ȱ

Inȱ elaboratingȱ onȱ theȱ localȱ natureȱ ofȱ tacitȱ knowledge,ȱ Vonȱ Hippelȱ (1994)ȱ speaksȱ ofȱ “stickyȱ knowledge”.ȱ Forȱ closeȱ cooperationȱ betweenȱ firms—forȱ instance,ȱ betweenȱ aȱ productionȱ plantȱ andȱ aȱ developmentȱ lab—aȱ shortȱ distanceȱ betweenȱ theȱ twoȱ firmsȱ reducesȱtheȱcostȱofȱinformationȱtransfers.ȱCloseȱproximity,ȱinȱhisȱview,ȱmakesȱitȱeasierȱ toȱ iterativelyȱ adjustȱ theȱ productȱ thatȱ isȱ underȱ developmentȱ insteadȱ ofȱ firstȱ formalizingȱ(“unsticking”)ȱtheȱknowledge.ȱFaceȬtoȱfaceȱcontactȱthusȱmakesȱitȱeasierȱtoȱ startȱaȱprojectȱandȱadjustȱandȱexpandȱitȱiterativelyȱbyȱutilizingȱtrustedȱrelationsȱandȱ allowingȱforȱflexibleȱoutcomes.ȱȱ

Here,ȱnetworksȱemergeȱasȱchannelsȱthatȱenableȱtheȱexchangeȱofȱtheȱknowledgeȱandȱ informationȱ necessaryȱ toȱ accessȱ resources,ȱ basedȱ onȱ mutualȱ trustȱ andȱ reciprocityȱ (Dahlȱ&ȱPedersen,ȱ2004;ȱGiuliani,ȱ2007;ȱHansen,ȱ1992).ȱKnobenȱandȱOerlemansȱ(2006)ȱ argueȱ thatȱ theȱ spatialȱ dimensionȱ isȱ importantȱ becauseȱ physicalȱ closenessȱ facilitatesȱ faceȬtoȬfaceȱ contact,ȱ whichȱ makesȱ itȱ easierȱ forȱ firmsȱ toȱ establishȱ andȱ maintainȱ relationships.ȱ

Castillaȱ etȱ al.ȱ (2000)ȱ stressȱ theȱ importanceȱ ofȱ networkȱ contactȱ toȱ easeȱ theȱ labourȱ marketȱ throughȱ indirectȱ linkagesȱ thatȱ helpȱ toȱ bringȱ togetherȱ supplyȱ andȱ demand.ȱ

Chapterȱ2:ȱSpatialȱEconomicȱTheoryȱȱȱ

Accordingȱtoȱthem,ȱtheseȱcontactsȱalsoȱenhanceȱtheȱcapacityȱtoȱmobilizeȱcapitalȱandȱ findȱreliableȱinformationȱquickly.ȱȱ

ȱ

2.4

ȱ

ȱ

C

OMPOSITIONȱOFȱECONOMICȱACTIVITIES

ȱ

Inȱ regionalȱ economics,ȱ thereȱ isȱ anȱ ongoingȱ debateȱ aboutȱ howȱ theȱ compositionȱ ofȱ economicȱ activityȱ influencesȱ knowledgeȱ spilloversȱ (Feldmanȱ &ȱ Audretsch,ȱ 1999).ȱ Thereȱareȱtwoȱcontrastingȱviews,ȱandȱthisȱcontrastȱisȱsometimesȱcalledȱtheȱ“MarshallȬ Jacobsȱcontroversy”ȱ(VanȱderȱPanne,ȱ2004).ȱȱ

Inȱ Marshall’sȱ (1920)ȱ longstandingȱ viewȱ onȱ knowledgeȱ externalities,ȱ firmsȱ benefitȱ fromȱ aȱ regional,ȱ specialized,ȱ sectorȬspecificȱ knowledgeȱ base.ȱ Anȱ increasedȱ geographicȱ concentrationȱ ofȱ sectorȬrelatedȱ firmsȱ facilitatesȱ knowledgeȱ spilloversȱ acrossȱtheseȱfirms,ȱresultingȱinȱaȱpositiveȱeffectȱonȱtheirȱperformance.ȱȱ

ȱ

Jacobsȱ(1969),ȱinȱcontrast,ȱstressesȱtheȱimportanceȱofȱdiversity,ȱasȱitȱfostersȱtheȱcrossȬ fertilizationȱ ofȱ ideasȱ (Rosenthalȱ andȱ Strange,ȱ 2004).ȱ Inȱ Jacobs’ȱ view,ȱ spilloversȱ betweenȱdissimilarȱfirmsȱspecificallyȱleadȱtoȱmoreȱinnovationȱandȱfirmȱgrowth.ȱȱ Jacobsȱ (1969)ȱ theorisesȱ thatȱ urbanizationȱ externalitiesȱ referȱ toȱ theȱ advantagesȱ ofȱ aȱ locationȱwithinȱaȱdiverseȱurbanȱagglomeration.ȱIndustryȱspecialization,ȱinȱherȱview,ȱ isȱrisky.ȱInȱoneȱchapterȱofȱherȱfamousȱbookȱ“TheȱEconomyȱofȱCities”,ȱwhichȱisȱtitledȱ ‘Theȱ Valuableȱ Inefficienciesȱ andȱ Impracticalitiesȱ ofȱ cities’,ȱ Jacobsȱ describesȱ howȱ ‘modern’,ȱ specializedȱ citiesȱ likeȱ Manchesterȱ facedȱ significantȱ problemsȱ whenȱ theirȱ efficientȱ waysȱ ofȱ producingȱ spreadȱ acrossȱ theȱ worldȱ andȱ theirȱ comparativeȱ advantageȱdisappearedȱ(Jacobs,ȱ1969).ȱȱ

Bothȱ ofȱ theseȱ viewsȱ onȱ theȱ effectȱ ofȱ theȱ compositionȱ ofȱ economicȱ activitiesȱ onȱ knowledgeȱspilloversȱassertȱthatȱinteractionsȱamongȱfirmsȱareȱanȱimportantȱfactorȱinȱ theȱexchangeȱofȱknowledgeȱandȱinformation.ȱȱ

ȱ

2.5

ȱ

ȱ

P

UBLICȱGOODȱORȱINDIVIDUALȱCOMPETITIVEȱADVANTAGE

?ȱ

Marshallȱ(1920,ȱp.ȱ271)ȱdescribesȱknowledgeȱspilloversȱasȱadvantagesȱthatȱareȱ“inȱtheȱ air”.ȱ Therefore,ȱ economistsȱ concludedȱ thatȱ tacitȱ knowledgeȱ isȱ aȱ publicȱ good,ȱ anȱ assumptionȱ thatȱ isȱ notȱ beingȱ researched.ȱ Surprisingly,ȱ Marshallȱ didȱ notȱ explicitlyȱ distinguishȱ theȱ publicȱ natureȱ ofȱ knowledgeȱ spillovers.ȱ However,ȱ heȱ wasȱ probablyȱ oneȱofȱtheȱfirstȱwhoȱmentionedȱtheȱindividualȱadvantagesȱofȱinterpersonalȱexchangesȱ andȱ learning:ȱ “Goodȱ workȱ isȱ rightlyȱ appreciated,ȱ inventionsȱ andȱ improvementsȱ inȱ machinery,ȱ inȱ processesȱ andȱ theȱ generalȱ organizationȱ ofȱ theȱ businessȱ haveȱ theirȱ meritsȱpromptlyȱdiscussed:ȱifȱoneȱmanȱstartsȱaȱnewȱidea,ȱitȱisȱtakenȱupȱbyȱothersȱandȱ

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10ȱ

combinedȱ withȱ suggestionsȱ ofȱ theirȱ own;ȱ andȱ thusȱ itȱ becomesȱ theȱ sourceȱ ofȱ furtherȱ newȱideas”ȱ(Marshall,ȱ1920,ȱp.ȱ271).ȱ

ȱ

Theȱpublicȱgoodȱnatureȱofȱspilloversȱ

Orthodoxȱ economistsȱ haveȱ adheredȱ toȱ theȱ ideaȱ thatȱ knowledgeȱ spilloversȱ offerȱ aȱ publicȱgoodȱadvantage.ȱKrugmanȱ(1991)ȱwasȱoneȱofȱtheȱfirstȱtraditionalȱeconomistsȱtoȱ renewȱ interestȱ inȱ regionalȱ agglomerationsȱ externalitiesȱ usingȱ “...simple,ȱ stylizedȱ modelsȱ designedȱ forȱ tractabilityȱ ratherȱ thanȱ realism...”ȱ (Krugman,ȱ 2004).ȱ Krugmanȱ arguesȱthatȱdespiteȱtheȱdeclineȱinȱtransportationȱandȱcommunicationȱcosts,ȱdistanceȱ stillȱplaysȱaȱveryȱimportantȱroleȱ(Krugman,ȱ2004).ȱȱ Inȱlocationalȱdynamicsȱmodels,ȱtransportationȱcostsȱandȱlandȱrentsȱareȱoftenȱusedȱasȱ explanatoryȱvariables.ȱTransportationȱcostsȱareȱseenȱasȱaȱcauseȱforȱconcentrationȱandȱ landȱrentsȱareȱviewedȱasȱaȱcentrifugalȱforce.ȱHowever,ȱtheseȱtwoȱfactorsȱwouldȱresultȱ inȱaȱrelativeȱevenlyȱdistributionȱofȱlandȱuse.ȱThisȱisȱwhereȱeconomiesȱofȱscaleȱenter.ȱȱ “...geographicȱ concentrationȱ isȱ clearȱ evidenceȱ ofȱ theȱ pervasiveȱ influencesȱ ofȱ someȱ kindȱofȱincreasingȱreturnsȱ[spillovers]”ȱ(Krugman,ȱ1991,ȱp.ȱ5).ȱInȱshort,ȱeconomiesȱofȱ scaleȱ representȱ theȱ advantagesȱ ofȱ beingȱ coȬlocated.ȱ Thereȱ mustȱ beȱ advantagesȱ toȱ beingȱlocatedȱinȱaȱclusterȱinȱorderȱtoȱovercomeȱtheȱdisadvantagesȱofȱhighȱlandȱrentsȱ andȱcongestion.ȱȱ

Theȱphraseȱ“economiesȱofȱscale”ȱcanȱreferȱtoȱeitherȱinternalȱorȱexternalȱeconomiesȱofȱ scale.ȱ Internalȱ economiesȱ ofȱ scaleȱ existȱ whenȱ aȱ firmȱ hasȱ lowerȱ productionȱ costsȱ becauseȱitȱcanȱserveȱaȱbiggerȱmarketȱandȱthereforeȱproduceȱmoreȱefficiently.ȱExternalȱ economiesȱ ofȱ scaleȱ areȱ advantageousȱ forȱ companiesȱ becauseȱ ofȱ theirȱ proximityȱ toȱ aȱ mainȱmarket.ȱȱ

Krugmanȱ alsoȱ citesȱ Marshall’sȱ (1920)ȱ knowledgeȱ spilloversȱ asȱ oneȱ ofȱ theȱ threeȱ reasonsȱforȱlocalization.ȱ“Evidentlyȱforcesȱforȱlocalizationȱotherȱthanȱthoseȱinvolvingȱ highȱ technologyȱ areȱ quiteȱ strong...Knowledgeȱ flows,…areȱ invisible;ȱ theyȱ leaveȱ noȱ paperȱ trailȱ byȱ whichȱ theyȱ canȱ beȱ measuredȱ andȱ tracked,ȱ andȱ thereȱ isȱ nothingȱ toȱ preventȱtheȱtheoristȱfromȱassumingȱanythingȱaboutȱthemȱthatȱsheȱlikes.ȱAȱsociologistȱ mightȱbeȱableȱtoȱhelpȱwithȱsurveyȱmethods;ȱbutȱIȱwouldȱlikeȱtoȱgetȱasȱfarȱasȱpossibleȱ withȱ drab,ȱ downȬtoȬearthȱ economicȱ analysisȱ beforeȱ turningȱ toȱ theȱ otherȱ socialȱ sciences”ȱ(Krugman,ȱ1991,ȱp.ȱ53Ȭ54).ȱȱ

ȱ

Besidesȱtheȱmeasurementȱissuesȱpertainingȱtoȱknowledgeȱspillovers,ȱKrugmanȱisȱalsoȱ scepticalȱaboutȱtheȱimportanceȱofȱknowledgeȱspilloversȱinȱgeneral:ȱ

Chapterȱ2:ȱSpatialȱEconomicȱTheoryȱȱȱ

deliberateȱeffortȱtoȱfightȱagainstȱfashionableȱideas...Ofȱcourse,ȱtheȱworldȱhasȱchanged,ȱ butȱ itȱ wasȱ aȱ prettyȱ remarkableȱ placeȱ beforeȱ theȱ comingȱ ofȱ largeȬscaleȱ integratedȱ circuits,ȱ andȱ evenȱ highȱ technologyȱ industriesȱ respondȱ toȱ oldȬfashionedȱ economicȱ forces”.

ȱ

“So,ȱwhileȱIȱamȱsureȱthatȱtrueȱtechnologicalȱspilloversȱplayȱanȱimportantȱroleȱinȱtheȱ localizationȱ ofȱ someȱ industries,ȱ oneȱ shouldȱ notȱ assumeȱ thatȱ thisȱ isȱ theȱ typicalȱ reason—evenȱinȱtheȱhighȱtechnologyȱindustriesȱthemselves”ȱ(Krugman,ȱ1991,ȱp.ȱ54).ȱ ȱ

Krugmanȱ hasȱ theȱ opinionȱ thatȱ thereȱ areȱ stillȱ manyȱ questionsȱ aboutȱ clusteringȱ mechanisms.ȱHowever,ȱheȱprefersȱtoȱstickȱtoȱeconomicȱmethodsȱbecause,ȱinȱhisȱview,ȱ theseȱmethodsȱcanȱbestȱexplainȱclustering.ȱOneȱrecentȱdevelopmentȱinȱthisȱfieldȱisȱtheȱ extensionȱofȱeconomicȱmodelsȱtoȱincludeȱspilloversȱandȱsocialȱinteractionsȱasȱexternalȱ effectsȱ (Beckerȱ &ȱ Murphy,ȱ 2001;ȱ Durlaufȱ &ȱ Young,ȱ 2001;ȱ Fujitaȱ &ȱ Thisse,ȱ 2002).ȱ AudretschȱandȱFeldmanȱ(2004,ȱp.ȱ2719)ȱargueȱthatȱ“socialȱinteractionsȱhaveȱeconomicȱ valueȱinȱtransmittingȱknowledgeȱandȱideas”.ȱTheyȱfurtherȱstateȱthatȱ“tacitȱknowledgeȱ isȱ inherentlyȱ nonȬrivalȱ inȱ nature,ȱ andȱ knowledgeȱ developedȱ forȱ anyȱ particularȱ applicationȱ canȱ easilyȱ spillȱ overȱ andȱ haveȱ economicȱ valueȱ inȱ veryȱ differentȱ applications”ȱ(Audretschȱ&ȱFeldman,ȱ2004,ȱp.ȱ2718Ȭ2719).ȱȱ

ȱ

Spilloversȱasȱindividualȱcompetitiveȱadvantagesȱ

Theȱ assumptionȱ thatȱ knowledgeȱ spilloversȱ leadȱ toȱ publicȱ advantagesȱ doesȱ notȱ discardȱtheȱpossibilityȱofȱinvestigatingȱlocalizedȱnetworksȱasȱcompetitiveȱbenefitsȱforȱ individualȱ firms.ȱ Ifȱ firmsȱ benefitȱ individually,ȱ localizedȱ knowledgeȱ spilloversȱ areȱ excludableȱ andȱ thereforeȱ notȱ aȱ publicȱ good.ȱ Boschmaȱ andȱ Terȱ Walȱ (2007,ȱ p.ȱ 196)ȱ argueȱ thatȱ knowledgeȱ spilloversȱ areȱ notȱ inȱ theȱ airȱ “...becauseȱ knowledgeȱ tendsȱ toȱ accumulateȱandȱremainȱinsideȱtheȱboundariesȱofȱfirmsȱandȱnetworks”.ȱIfȱknowledgeȱ spilloversȱ areȱ notȱ aȱ publicȱ good,ȱ butȱ theȱ resultȱ ofȱ socialȱ interactionsȱ amongȱ firms,ȱ firmsȱareȱtheȱbeneficiariesȱofȱthoseȱinteractionsȱ(Feldman,ȱ2003).ȱ Theȱnetworkȱmethodologyȱprovidesȱaȱwayȱtoȱempiricallyȱinvestigateȱtheȱindividualȱ advantagesȱofȱinteractionsȱinȱaȱnetworkȱandȱisȱseenȱasȱaȱmethodologyȱwithȱtheȱstrongȱ potentialȱtoȱinvestigateȱknowledgeȱspilloversȱ(TerȱWalȱ&ȱBoschma,ȱ2009).ȱȱ ȱ

2.6

I

NTERACTIONSȱASȱMECHANISMSȱBEHINDȱSPILLOVERS

ȱ

Gordonȱ andȱ McCannȱ (2000)ȱ linkȱ theȱ benefitsȱ ofȱ localizedȱ spilloversȱ toȱ interactionsȱ amongȱfirms.ȱInȱHansen’sȱviewȱ(1992),ȱinformalȱrelationshipsȱcanȱimproveȱaccessȱtoȱ allȱ kindsȱ ofȱ essentialȱ resourcesȱ andȱ facilitateȱ innovationȱ andȱ theȱ creationȱ ofȱ newȱ

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12ȱ

marketȱ activities.ȱ FaceȬtoȬfaceȱ contactȱ playsȱ anȱ importantȱ roleȱ inȱ establishingȱ andȱ maintainingȱ theseȱ informalȱ relationships.ȱ Accordingly,ȱ faceȬtoȬfaceȱ contactȱ fostersȱ trust.ȱTrustȬbasedȱrelationshipsȱreduceȱtransactionȱcostsȱsinceȱtheyȱreduceȱtheȱriskȱofȱ opportunismȱ (McCannȱ &ȱ Sheppard,ȱ 2003).ȱ FaceȬtoȬfaceȱ contactȱ isȱ easierȱ toȱ realizeȱ whenȱtheȱactorsȱareȱinȱcloseȱvicinityȱtoȱoneȱanother;ȱproximityȱmakesȱitȱeasierȱtoȱstartȱ andȱ maintainȱ reciprocalȱ relationshipsȱ (Glaeser,ȱ 2000).ȱ Thisȱ isȱ especiallyȱ trueȱ forȱ theȱ tacitȱknowledgeȱembodiedȱbyȱhighȬskilledȱworkersȱ(Storperȱ&ȱVenables,ȱ2004ȱp.ȱ367).ȱ Becauseȱ tacitȱ knowledgeȱ isȱ hardȱ toȱ grasp,ȱ itȱ isȱ oftenȱseenȱ asȱ anȱ importantȱ sourceȱ ofȱ competitiveȱadvantageȱforȱfirms.ȱȱ

ȱ

Chapter 3:

Existing Research about Spillovers

ȱ ȱ

Existingȱ researchȱ aboutȱ knowledgeȱ spilloversȱ andȱ clusteringȱ canȱ beȱ dividedȱ intoȱ anecdotalȱstudiesȱandȱstudiesȱthatȱareȱmoreȱempirical.ȱȱ

ȱ

3.1

ȱ

ȱ

A

NECDOTALȱAPPROACHESȱTOȱINVESTIGATEȱCLUSTERS

ȱ

Saxenianȱ(1990)ȱandȱFloridaȱ(2002)ȱandȱtheirȱfollowersȱuseȱanȱanecdotalȱapproachȱtoȱ investigateȱ spillovers.ȱ Accordingȱ toȱ theseȱ authors,ȱ thereȱ isȱ evidenceȱ ofȱ territorialȱ embeddednessȱ asȱ aȱ sourceȱ ofȱ innovationȱ andȱ competitiveness.ȱ Theyȱ offerȱ inȬdepthȱ descriptionsȱ ofȱ howȱ combinationsȱ ofȱ competition,ȱ cooperation,ȱ andȱ knowledgeȱ exchangesȱmakeȱclustersȱespeciallyȱsuccessfulȱ(Malmbergȱ&ȱMaskell,ȱ2006).ȱȱ

Marshallȱ (1920)ȱ wasȱ alreadyȱ aȱ clearȱ supporterȱ ofȱ cooperationȱ inȱ business:ȱ “Enoughȱ hasȱbeenȱsaidȱtoȱshowȱthatȱtheȱworldȱisȱonlyȱjustȱbeginningȱtoȱbeȱreadyȱforȱtheȱhigherȱ workȱofȱtheȱcooperativeȱmovement;ȱandȱthatȱitsȱmanyȱdifferentȱformsȱmayȱthereforeȱ beȱ reasonablyȱ expectedȱ toȱ attainȱ aȱ largerȱ successȱ inȱ theȱ futureȱ thanȱ inȱ theȱ past”ȱ (Marshall,ȱ1920,ȱp.ȱ307).ȱȱ

Hansenȱ alsoȱ describesȱ howȱ cooperationȱ andȱ intenseȱ competitionȱ coȬoccur.ȱ “Paradoxically,ȱ bothȱ cooperationȱ andȱ competitionȱ haveȱ beenȱ intensifyingȱ asȱ localȱ firmsȱlearnȱwithȱtheirȱcustomers,ȱsuppliers,ȱandȱcompetitorsȱaboutȱwhatȱtoȱmakeȱnextȱ andȱhowȱtoȱmakeȱit”ȱ(Hansen,ȱ1992,ȱp.ȱ102).ȱȱ

ȱ

Floridaȱ (2002)ȱ stressesȱ theȱ importanceȱ ofȱ theȱ “creativeȱ class”,ȱ whichȱ heȱ seesȱ asȱ anȱ engineȱ forȱ economicȱ development.ȱ Creativeȱ professionalsȱ andȱ knowledgeȱ workers,ȱ rangingȱ fromȱ paintersȱ andȱ webȱ designersȱ toȱ chemistsȱ andȱ lawyers,ȱ belongȱ toȱ thisȱ class;ȱ inȱ fact,ȱ allȱ collegeȬeducatedȱ workersȱ areȱ partȱ ofȱ thisȱ class.ȱ Inȱ Florida’sȱ view,ȱ socialȱ interactionsȱ withinȱ thisȱ creativeȱ classȱ leadȱ toȱ newȱ ideas,ȱ innovation,ȱ andȱ economicȱsuccess.ȱȱ

ȱ

AsȱIȱdiscussedȱearlier,ȱsomeȱfamousȱregions,ȱsuchȱasȱSiliconȱValley,ȱhaveȱprovenȱtoȱ beȱ stronglyȱ innovative—muchȱ moreȱ innovativeȱ thanȱ otherȱ regions.ȱ Saxenianȱ (1994)ȱ analyzesȱ theȱ differencesȱ betweenȱ Siliconȱ Valleyȱ andȱ theȱ Routeȱ 28ȱ area.ȱ Whileȱ theȱ boundariesȱbetweenȱfirmsȱandȱsocietyȱwereȱblurredȱinȱSiliconȱValley,ȱtheȱboundariesȱ ofȱ companiesȱ wereȱ strictlyȱ definedȱ inȱ theȱ Routeȱ 28ȱ region.ȱ Thisȱ differenceȱ wasȱ alsoȱ reflectedȱbyȱtheirȱproductionȱchains.ȱInȱSiliconȱValley,ȱfirmsȱspecializedȱthemselves,ȱ

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whereasȱ theȱ Routeȱ 28ȱ firmsȱ followedȱ theȱ traditionȱ ofȱ verticalȱ integrationȱ byȱ integratingȱ suppliersȱ (Saxenian,ȱ 1994).ȱ Saxenian’sȱ analysesȱ revealȱ howȱ socialȱ interactionsȱ areȱ theȱ explanationȱ forȱ theȱ successȱ ofȱ Siliconȱ Valley,ȱ comparedȱ toȱ theȱ declineȱ ofȱ theȱ routeȱ 28ȱ region.ȱ Sheȱ describesȱ theȱ specificȱ cultureȱ ofȱ Siliconȱ Valley,ȱ whereȱ entrepreneursȱ seeȱ socialȱ relationshipsȱ asȱ aȱ crucialȱ aspectȱ ofȱ theirȱ businesses.ȱ Informalȱ networksȱ provideȱ firmsȱ withȱ technicalȱ andȱ marketȱ informationȱ andȱ alsoȱ functionȱasȱveryȱeffectiveȱjobȱsearchȱandȱrecruitmentȱnetworksȱ(Saxenian,ȱ1994).ȱȱ Businessȱassociationsȱplayedȱanȱimportantȱroleȱinȱfacilitatingȱthisȱinformalȱnetwork.ȱ TheyȱnotȱonlyȱprovidedȱaȱwayȱtoȱdiffuseȱknowledgeȱaboutȱstateȬofȬtheȱartȱideasȱandȱ technologiesȱinȱdesign,ȱproduction,ȱandȱmarketing,ȱbutȱalsoȱprovidedȱaȱmoreȱformalȱ representationȱofȱtheȱfirmsȱtoȱtheȱregionalȱgovernmentȱ(Saxenian,ȱ1994).ȱAminȱ(1999)ȱ alsoȱstressesȱtheȱimportanceȱofȱparticipationȱinȱassociationsȱasȱanȱimportantȱculturalȱ aspectȱthatȱexplainsȱtheȱsuccessȱofȱtheȱItalianȱEmiliaȬRomagnaȱregion.ȱCastillaȱetȱal.ȱ (2000)ȱ likewiseȱ agreeȱ uponȱ theȱ importanceȱ ofȱ associations.ȱ Theyȱ describeȱ howȱ individualsȱ andȱ industriesȱ thatȱ areȱ notȱ activeȱ inȱ theȱ mainȱ highȬtechȱ industryȱ ofȱ SiliconȱValleyȱbecameȱinvolvedȱthroughȱassociations.ȱȱ

ȱ

Severalȱ scholarsȱ concludeȱ thatȱ thereȱ isȱ evidenceȱ ofȱ localȱ interactionsȱ asȱ aȱ sourceȱ ofȱ innovationȱandȱcompetitivenessȱ(Cooke,ȱ2001;ȱCooke,ȱ2002;ȱHess,ȱ2004).ȱAccordingȱtoȱ Sorensonȱ (2003),ȱ toȱ startȱ aȱ newȱ firm,ȱ aȱ potentialȱ entrepreneurȱ needsȱ informationȱ toȱ findȱ aȱ suitableȱ marketȱ nicheȱ inȱ orderȱ toȱ makeȱ profit.ȱ Moreover,ȱ theȱ entrepreneurȱ needsȱ capital,ȱ skilledȱ labour,ȱ andȱ knowledgeȱ toȱ exploitȱ thisȱ opportunity.ȱ “Socialȱ relationshipsȱ playȱ aȱ crucialȱ roleȱ inȱ acquiringȱ tacitȱ informationȱ andȱ inȱ convincingȱ resourceȱholdersȱtoȱjoinȱtheȱfledglingȱventure”ȱ(Sorenson,ȱ2003,ȱp.ȱ514).ȱȱ

Sorensonȱstressesȱtheȱimportanceȱofȱnearbyȱsocialȱcontactsȱ(networks),ȱandȱpointsȱoutȱ thatȱbusinessȱcontactsȱfollowȱthisȱpattern.ȱTheseȱcontactsȱareȱimportantȱconduitsȱforȱ communicationȱ flowsȱ (Sorenson,ȱ 2003).ȱ Saxenianȱ (1994,ȱ p.ȱ 46)ȱ underlinesȱ thisȱ idea:ȱ “theȱ paradoxȱ ofȱ Siliconȱ Valleyȱ wasȱ thatȱ competitionȱ demandedȱ continuousȱ innovation,ȱwhichȱinȱturnȱrequiredȱcooperationȱamongȱfirms”.ȱ

ȱ

CurranȱandȱBlackburn’sȱ(1994)ȱresearchȱpresentsȱcompletelyȱdifferentȱresults.ȱBasedȱ onȱ 400ȱ interviewsȱ inȱ sevenȱ Britishȱ regions,ȱ thisȱ researchȱ concludesȱ thatȱ theȱ importanceȱ ofȱ bothȱ networksȱ andȱ theȱ regionȱ areȱ declining,ȱ andȱ thatȱ firmsȱ haveȱ aȱ moreȱ flexibleȱ wayȱ toȱ establishȱ relationships.ȱ Thoseȱ relationshipsȱ areȱ lessȱ oftenȱ permanentȱ and,ȱ becauseȱ ofȱ modernȱ communicationȱ equipment,ȱ moreȱ oftenȱ

Chapterȱ3:ȱExistingȱResearchȱaboutȱSpilloversȱȱ

conductedȱatȱsignificantȱdistances.ȱHowever,ȱtheirȱviewȱdoesȱnotȱreceiveȱwidespreadȱ supportȱinȱtheȱareaȱofȱeconomicȱgeographicȱresearch.ȱ

ȱ

3.2

ȱ

ȱ

E

MPIRICALȱSTUDIESȱABOUTȱKNOWLEDGEȱSPILLOVERS

ȱ

Thereȱ areȱ moreȱ systematicȱ empiricalȱ approachesȱ thatȱ investigateȱ knowledgeȱ spilloversȱ andȱspatialȱ distributionȱ ofȱ industries.ȱAboutȱ halfȱ ofȱ themȱ areȱ descriptive,ȱ whileȱothersȱlinkȱspilloversȱtoȱfirmȬlevelȱoutcomes.ȱ

ȱ

Distributionȱofȱknowledgeȱasȱanȱindicatorȱofȱspilloversȱ

Prevezerȱ(1997)ȱstudiedȱtheȱlocationȱpatternȱofȱU.S.ȱBiotechȱfirmsȱandȱfoundȱthatȱthisȱ industryȱ isȱ highlyȱ clusteredȱ inȱ space.ȱ Vanȱ Oort’sȱ (2002b)ȱ resultsȱ wereȱ similar.ȱ Heȱ discoveredȱ thatȱ innovativeȱ activityȱ inȱ theȱ Netherlands,ȱ measuredȱ withȱ labourȱ costsȱ forȱ R&D,ȱ isȱ spatiallyȱ clustered.ȱ Thisȱ showsȱ thatȱ scienceȬbasedȱ industriesȱ areȱ moreȱ concentratedȱthanȱotherȱindustries.ȱ

Patentsȱ areȱ mostlyȱ relatedȱ toȱ knowledgeȬintensiveȱ workȱ andȱ thereforeȱ seenȱ asȱ outcomeȱ measuresȱ forȱ regionalȱ innovation.ȱ Verspagenȱ andȱ Schoenmakers’ȱ (2004)ȱ resultsȱshowȱthatȱR&Dȱisȱconcentratedȱinȱaȱsmallȱnumberȱofȱregions.ȱTherefore,ȱtheyȱ concludeȱ thatȱregionalȱ innovationȱ systemsȱ inȱ Europeȱ areȱ stillȱ factorsȱ inȱ theȱ locationȱ decisionsȱ ofȱ firms.ȱ Similarly,ȱ Pondsȱ andȱ Vanȱ Oortȱ (2008)ȱ findȱ thatȱ scienceȬbasedȱ industriesȱ areȱ clusteredȱ inȱ space,ȱ andȱ suggestȱ thatȱ thisȱ mightȱ beȱ aȱ resultȱ ofȱ theȱ advantagesȱofȱlocalizedȱknowledgeȱspillovers.ȱTheȱdistributionȱofȱpatentsȱisȱthereforeȱ oftenȱusedȱtoȱidentifyȱknowledgeȱspilloversȱinȱtheȱspatialȱdistributionȱofȱpatents.ȱ ȱ

Theȱconsequencesȱofȱclustersȱandȱknowledgeȱspilloversȱȱ

Studiesȱlinkingȱtheȱlocalizationȱpatternȱofȱindustriesȱtoȱoutcomeȱmeasuresȱhave,ȱuntilȱ now,ȱshownȱvariousȱeffects.ȱHendersonȱ(2003)ȱconfirmsȱinȱhisȱresearchȱthatȱhighȬtechȱ firmsȱ benefitȱ inȱ termsȱ ofȱ productivityȱ fromȱ localizationȱ economiesȱ (theȱ resultȱ ofȱ clusteringȱofȱsimilarȱfirms).ȱHenderson’sȱ(2007)ȱlaterȱresearchȱconfirmsȱtheseȱresults.ȱ Rosenthalȱ andȱ Strangeȱ (2003)ȱ demonstrateȱ similarȱ outcomes.ȱ Basedȱ onȱ industryȱ sharesȱofȱemploymentȱandȱtheȱbirthȱofȱnewȱfirms,ȱtheyȱfindȱstrongerȱsupportȱforȱtheȱ localizationȱhypothesisȱthanȱtheȱdiversityȱhypothesis.ȱȱ

RosenthalȱandȱStrangeȱ(2003)ȱalsoȱfindȱthatȱaȱpositiveȱeffectȱonȱtheȱdiversityȱofȱfirmsȱ isȱ theȱ birthȱ ofȱ newȱ firms.ȱ Thisȱ positiveȱ effectȱ ofȱ diversityȱ isȱ confirmedȱ byȱ manyȱ researchers.ȱGlaeserȱetȱal.ȱ(1992)ȱlikewiseȱfindȱthatȱtheȱdiversityȱofȱfirmsȱfostersȱfirmȱ growthȱ atȱ theȱ regionalȱ level.ȱTheyȱ findȱ aȱ negativeȱ effectȱ ofȱ specialization.ȱ Vanȱ Oortȱ (2002a;ȱ 2007)ȱ concludesȱ thatȱ JacobsȬrelatedȱ sectorȱ varietyȱ isȱ theȱ dominantȱ conditionȱ

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forȱagglomerationȱexternalitiesȱinȱtheȱNetherlands.ȱHisȱanalysesȱareȱbasedȱonȱdataȱforȱ industrialȱ firmsȱ andȱ municipalȱ employmentȱ growth.ȱ Vanȱ Stelȱ andȱ Nieuwenhuijsenȱ confirmȱ theseȱ results,ȱ justȱ likeȱ Feldmanȱ andȱ Audretschȱ (1999),ȱ whoȱ alsoȱ affirmȱ theȱ diversityȱ hypothesisȱ andȱ showȱ thatȱ diversityȱ inȱ economicȱ activitiesȱ promotesȱ innovativeȱoutputs.ȱȱ

ȱ

Theȱ negativeȱ consequencesȱ ofȱ firmsȱ beingȱ locatedȱ inȱ aȱ clusterȱ ofȱ relatedȱ sectorsȱ areȱ discoveredȱbyȱBaumȱandȱMeziasȱ(1992),ȱwhoȱproveȱthatȱclusteredȱhotelsȱhadȱhigherȱ ratesȱofȱfailureȱthanȱotherȱhotels.ȱShaverȱandȱFlyerȱ(2000)ȱexplainȱthisȱbyȱpointingȱoutȱ thatȱ forȱ theȱ bestȱ firmsȱ inȱ aȱ cluster,ȱ theȱ disadvantageȱ ofȱ employeesȱ movingȱ toȱ smallȱ spinȬoffsȱisȱlargerȱthanȱtheȱadvantagesȱofȱagglomerating.ȱFoltaȱetȱal.ȱ(2006)ȱshowȱthatȱ thereȱ isȱ anȱ inverted,ȱ UȬshapedȱ relationshipȱ betweenȱ clusterȱ sizeȱ andȱ firmȱ performance.ȱAnotherȱfindingȱwasȱthatȱtheȱlargerȱtheȱsizeȱofȱaȱcluster,ȱtheȱmoreȱlikelyȱ theȱfirmȱisȱtoȱfailȱ(Foltaȱetȱal.,ȱ2006).ȱȱ

ȱ

Onȱ theȱ contrary,ȱ Feldmanȱ andȱ Floridaȱ (1994)ȱ confirmȱ thatȱ innovationȱ clustersȱ geographicallyȱ inȱ areasȱ withȱ geographicȱ concentrationsȱ ofȱ specializedȱ (highȬtech)ȱ resources.ȱ Theyȱ alsoȱ foundȱ thatȱ theȱ spatialȱ concentrationȱ ofȱ theseȱ resourcesȱ reinforcedȱ theȱ innovativeȱ capacitiesȱ ofȱ firms.ȱ Vanȱ derȱ Panneȱ (2004)ȱ confirmȱ theseȱ resultsȱ andȱ throughȱ theirȱ researchȱ onȱ newȱ regionalȱ productȱ announcementsȱ asȱ dependentȱ variables,ȱ theyȱ showȱ thatȱ theȱ Marshallȱ hypothesisȱ holds,ȱ especiallyȱ forȱ R&DȬintensiveȱandȱsmallȱfirms.ȱȱ

ȱ

Theȱ spatialȱ distributionȱ ofȱ knowledgeȬintensiveȱ firmsȱ andȱ theirȱ patentsȱ reflectsȱ theȱ spilloverȱ phenomenonȱ onȱ theȱ macroȬlevel.ȱ Itȱ doesȱ notȱ provideȱ insightȱ intoȱ microȬ levelȱ exchanges.ȱ Theȱ previouslyȱ mentionedȱ resultsȱ ofȱ studiesȱ aboutȱ theȱ MarshallȬ Jacobsȱcontroversyȱ(VanȱderȱPanne,ȱ2004)ȱshowȱthatȱtheȱresultsȱareȱnotȱconsistentȱandȱ thatȱstudiesȱdifferȱinȱtheirȱapproaches.ȱHendersonȱ(2007)ȱpointsȱoutȱthat:ȱ“Despiteȱtheȱ factȱ thatȱ knowledgeȱ spilloversȱ areȱ centralȱ toȱ notionsȱ ofȱ economicȱ growth,ȱ technologicalȱ progress,ȱ andȱ theȱ natureȱ andȱ characteristicsȱ ofȱ cities,ȱ researchȱ onȱ theȱ natureȱofȱsuchȱspilloversȱisȱsurprisinglyȱlimited”.ȱȱ

However,ȱinsteadȱofȱtestingȱexistingȱtheoriesȱandȱfocusingȱonȱtheȱmicroȬlevel,ȱseveralȱ researchersȱfollowedȱMarshall’sȱoriginalȱideasȱandȱdevelopedȱtheirȱownȱtypologies.ȱȱ ȱ

Chapterȱ3:ȱExistingȱResearchȱaboutȱSpilloversȱȱ

3.3

ȱ

ȱ

T

YPOLOGIESȱANDȱIDEALȱTYPES

ȱ

Aȱtypologyȱisȱaȱsummaryȱofȱtheȱintersectionsȱofȱtwoȱorȱmoreȱvariablesȱ(Babbie,ȱ1998).ȱ Idealȱtypesȱneverȱmatchȱreality,ȱwhereasȱonȱaȱcontinuousȱscale,ȱtheȱdimensionsȱofȱaȱ typologyȱalwaysȱmatchȱtoȱaȱcertainȱdegreeȱ(inȱthisȱcase,ȱforȱexample,ȱtheȱdiversityȱofȱ firmsȱinȱaȱregion).ȱȱ

Theȱ Marshallȱ versusȱ Jacobsȱ discussionȱ isȱ anȱ exampleȱ ofȱ theȱ obscuringȱ useȱ ofȱ typologiesȱinȱknowledgeȱspilloverȱresearch.ȱBothȱtheȱMarshallȱandȱJacobsȱhypothesesȱ areȱidealȱtypesȱstressingȱdifferentȱaspectsȱthatȱareȱnotȱnecessarilyȱmutuallyȱexclusive.ȱ Theyȱ bothȱ describeȱ theȱ constitutionȱ ofȱ regionsȱ atȱ theȱ macroȬlevelȱ (Rosenthalȱ &ȱ Strange,ȱ 2004;ȱ Vanȱ derȱ Panne,ȱ 2004).Rosenthalȱ andȱ Strangeȱ (2004)ȱ showȱ howȱ theȱ differenceȱbetweenȱabsoluteȱandȱrelativeȱspecializationȱcomplicatesȱtheȱinterpretationȱ ofȱ specializationȱ variables.ȱ Becauseȱ aȱ relativelyȱ smallȱ cityȱ sizeȱ canȱ coincideȱ withȱ aȱ largeȱ absoluteȱ sectorȱ size,ȱ differentȱ specializationȱ measuresȱ generateȱ differentȱ outcomes.ȱDurantonȱandȱPugaȱ(2004ȱp.ȱ2110)ȱmakeȱaȱsimilarȱpointȱandȱargueȱthatȱ“Itȱ isȱ veryȱ difficultȱ toȱ conceiveȱ howȱ interactionsȱ withinȱ ‘anȱ armyȱ ofȱ clones’ȱ couldȱ generateȱsufficientȱbenefitsȱtoȱjustifyȱtheȱexistenceȱofȱmodernȱcities“.ȱ

Thisȱillustratesȱthatȱtheȱidealȱtypesȱofȱhomogeneityȱandȱheterogeneityȱofȱfirmsȱareȱnotȱ sufficientȱ toȱ explainȱ clusters.ȱ Therefore,ȱ itȱ isȱ importantȱ toȱ knowȱ whatȱ degreeȱ ofȱ diversityȱandȱsimilarityȱisȱimportant.ȱResearchersȱcouldȱavoidȱthisȱissueȱbyȱfocusingȱ onȱtheȱmicroȬlevelȱinȱorderȱtoȱinvestigateȱtheȱtwoȱdimensionsȱseparately.ȱ

ȱ

Insteadȱ ofȱ buildingȱ onȱ existingȱ researchȱ andȱ focusingȱ onȱ theȱ microȬlevelȱ usingȱ empiricalȱresearch,ȱresearchersȱhaveȱcomeȱupȱwithȱvariousȱ“new”ȱclusteringȱconceptsȱ atȱtheȱmacroȬlevel.ȱMartinȱandȱSunleyȱ(2003)ȱargueȱthatȱconceptsȱsuchȱas:ȱindustrialȱ districts,ȱ industrialȱ complexes,ȱ newȱ industrialȱ spaces,ȱ territorialȱ productionȱ complexes,ȱ neoȬMarshallianȱ nodes,ȱ regionalȱ innovationȱ milieux,ȱ technolopoles,ȱ technologyȱ districts,ȱ Italianȱ industrialȱ districts,ȱ hubȬandȬspokeȱ districts,ȱ satelliteȱ platformȱ districts,ȱ hotȱ spots,ȱ networkȱ regions,ȱ stickyȱ places,ȱ regionalȱ systemsȱ ofȱ innovation,ȱlearningȱ regions,ȱandȱPorter’sȱcompetitiveȱdiamondȱareȱallȱreinventionsȱ ofȱMarshall’sȱideasȱandȱallȱrepresentȱnewȱidealȱtypesȱ(Bunnellȱ&ȱCoe,ȱ2001a;ȱGordonȱ &ȱMcCann,ȱ2000;ȱMarkusen,ȱ1996;ȱMartinȱ&ȱSunley,ȱ2003).ȱ

ȱ

Theȱgeographicȱscaleȱofȱspilloversȱ

Anotherȱ issueȱ thatȱ remainsȱ unsolvedȱ isȱ theȱ geographicȱ scaleȱ inȱ whichȱ knowledgeȱ spilloversȱoccur.ȱSuggestedȱscalesȱrangeȱfromȱaȱradiusȱofȱoneȱmileȱtoȱcities,ȱregions,ȱ statesȱ orȱ evenȱ countriesȱ (Martinȱ &ȱ Sunley,ȱ 2003;ȱ McCannȱ &ȱ Folta,ȱ 2008).ȱ Asȱ inȱ theȱ

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18ȱ

previousȱ case,ȱ knowledgeȱ canȱ spillȱ overȱ atȱ shortȱ distancesȱ andȱ alsoȱ acrossȱ largeȱ distances.ȱDueȱtoȱtheȱlackȱofȱempiricalȱresearch,ȱthereȱisȱlittleȱconsensusȱonȱthisȱissue.ȱ Rosenthalȱ andȱ Strangeȱ (2003)ȱ showȱ thatȱ agglomerationȱ economiesȱ ofȱ aȱ certainȱ industryȱareȱlargestȱinȱcloseȱvicinities,ȱdecreaseȱrapidlyȱwithȱtheȱadditionȱofȱtheȱfirstȱ fewȱ milesȱ andȱ thenȱ attenuateȱ muchȱ moreȱ slowly.ȱ Theyȱ suggestȱ thatȱ informationȱ spilloversȱ mightȱ beȱ responsibleȱ forȱ mostȱ localȱ advantagesȱ becauseȱ faceȬtoȬfaceȱ contactȱisȱimportant,ȱespeciallyȱforȱthoseȱtypesȱofȱspillovers.ȱȱ

Onȱtheȱotherȱhand,ȱtheȱadvantageȱofȱtranslocalȱlinkagesȱinȱaccessingȱnewȱinformationȱ isȱ stressedȱ (Bunnellȱ &ȱ Coe,ȱ 2001a;ȱ Markusen,ȱ 1996).ȱ Malmbergȱ andȱ Maskellȱ (2006)ȱ pointȱoutȱthatȱlocalizedȱandȱdistantȱlearningȱareȱnotȱmutuallyȱexclusive.ȱToȱsupportȱ theȱlocalȱknowledgeȱspilloverȱhypothesis,ȱtheyȱargueȱthat,ȱdespiteȱtheȱadvantagesȱofȱ distantȱ links,ȱ itȱ isȱ notȱ necessaryȱ forȱ localȱ interactionsȱ toȱ dominateȱ overȱ extraȬlocalȱ linksȱ(Malmbergȱ&ȱMaskell,ȱ2006,ȱp.ȱ9).ȱȱ

ȱ

Again,ȱ Johanssonȱ andȱ Quigleyȱ (2004)ȱ adoptȱ aȱ differentȱ position.ȱ Theyȱ argueȱ thatȱ proximityȱ becomesȱ lessȱ importantȱ becauseȱ networksȱ mayȱ substituteȱ forȱ proximity.ȱ Theyȱargueȱthatȱconnectionsȱbetweenȱfirmsȱ“mayȱleadȱtoȱpreciselyȱtheȱsameȱexternalȱ benefitsȱ thatȱ ariseȱ fromȱ agglomerationsȱ andȱ forȱ preciselyȱ theȱ sameȱ reasons”ȱ (Johanssonȱ&ȱQuigley,ȱ2004,ȱp.ȱ166).ȱ

Giulianiȱ (2005b)ȱ showsȱ thatȱ accessȱ toȱ informationȱ isȱ essentialȱ andȱ thatȱ theȱ localȱ dimensionȱ isȱ lessȱ important.ȱ Appoldȱ (1995)ȱ confirmsȱ this:ȱ “...researchersȱ haveȱ systematicallyȱ ignoredȱ theȱ possibilityȱ thatȱ businessȱ organizationsȱ thatȱ haveȱ similarȱ internalȱstructuresȱandȱlinkagesȱtoȱotherȱproducersȱbutȱthatȱareȱnotȱlocatedȱinȱdenseȱ concentrationsȱ mightȱ neverthelessȱ beȱ justȱ asȱ productive,ȱ innovative,ȱ andȱ capableȱ ofȱ adjustingȱtheirȱoperationsȱtoȱrapidlyȱchangingȱmarketȱconditions”ȱ(Appold,ȱ1995,ȱp.ȱ 28).ȱ Oinasȱ (1999)ȱ agreesȱ andȱ assertsȱ thatȱ learningȱ isȱ theȱ resultȱ ofȱ aȱ combinationȱ ofȱ proximateȱandȱdistantȱinteractions.ȱ

ȱ

Whenȱ investigatingȱ theȱ relevantȱ spatialȱ scaleȱ forȱ spillovers,ȱ someȱ researchersȱ argueȱ thatȱtheȱgeographicalȱdimensionȱofȱexchangeȱnetworksȱamongȱfirmsȱshouldȱcompriseȱ theȱunitȱofȱanalysis.ȱBreschiȱandȱLissoniȱ(2001b,ȱp.ȱ270):ȱ“Theseȱ(networks)ȱareȱlikelyȱ toȱ beȱ aȱ muchȱ moreȱ fruitfulȱ unitȱ ofȱ observationȱ thanȱ theȱ regionȱ orȱ theȱ stateȱ asȱ such,ȱ sinceȱtheyȱareȱanȱorganisationalȱarrangementȱthatȱallowȱfirmsȱbothȱtoȱcirculateȱandȱtoȱ internaliseȱ manyȱ knowledgeȱ flows.ȱ Inȱ particular,ȱ anȱ explicitȱ linkȱ shouldȱ beȱ establishedȱ betweenȱ theȱ geographicalȱ dimensionȱ ofȱ knowledgeȱ flowsȱ andȱ theȱ researchȱ onȱ allȱ theȱ contractualȱ arrangementsȱ thatȱ allowȱ firmsȱ andȱ individualsȱ toȱ

Chapterȱ3:ȱExistingȱResearchȱaboutȱSpilloversȱȱ

appropriateȱ theirȱ knowledgeȱ rents,ȱ asȱ wellȱ asȱ theȱ disclosureȱ rulesȱ foreseenȱ inȱ thoseȱ arrangements”.

ȱ

Gordonȱ andȱ McCannȱ (2000)ȱ alsoȱ seeȱ socialȱ networksȱ asȱ anȱ alternativeȱ toȱ physicalȱ closeness.ȱ However,ȱ inȱ aȱ laterȱ study,ȱ Gordonȱ andȱ McCannȱ (2005)ȱ concludedȱ thatȱ thereȱisȱmoreȱsupportȱforȱtheȱorthodoxȱagglomerationȱeffectsȱdescribedȱbyȱMarshallȱ (1920).ȱ Inȱ theirȱ research,ȱ Gordonȱ andȱ McCannȱ findȱ thatȱ regionalȱ linksȱ withȱ customers,ȱ suppliersȱ andȱ jointȱ venturesȱ positivelyȱ affectȱ innovativeȱ behaviour.ȱ Thisȱ effectȱ doesȱ notȱ differȱ inȱ theȱ Greaterȱ SouthȬEastȱ andȱ theȱ Industrialȱ Heartland.ȱ Theȱ exactȱwayȱthatȱtheseȱorthodoxȱspilloversȱfunction,ȱhowever,ȱremainsȱunclear.ȱȱ ȱ JustȱlikeȱtheȱMarshallȬJacobsȱcontroversy,ȱtheȱdistinctionȱbetweenȱlocalȱandȱtranslocalȱ linkagesȱrepresentsȱanȱidealȱtypeȱdistinction.ȱTheȱfocusȱonȱtypologiesȱusesȱassumedȱ behaviourȱasȱaȱfoundationȱforȱknowledgeȱspilloversȱinsteadȱofȱfocusingȱonȱtheȱmicroȬ level.ȱAȱregionȱorȱfirmȱcanȱhaveȱaȱcombinationȱofȱboth.ȱMalmbergȱandȱMaskellȱ(2006)ȱ showȱ thatȱ theȱ riskȱ ofȱ thinkingȱ thisȱ wayȱ isȱ neglectingȱ toȱ focusȱ onȱ theȱ mechanismȱ drivingȱtheseȱtypologies.ȱȱ

Oneȱ ofȱ theȱ reasonsȱ forȱ theȱ increasingȱ focusȱ onȱ distantȱ tiesȱ liesȱ inȱ theȱ natureȱ ofȱ theȱ dataȱ usedȱ toȱ investigateȱ spillovers.ȱ Often,ȱ largeȱ firmsȱ areȱ overȬrepresented.ȱ Thisȱ is,ȱ forȱ example,ȱ theȱ caseȱ inȱ databasesȱ withȱ strategicȱ alliancesȱ basedȱ onȱ newspaperȱ articlesȱ andȱ annualȱ reports.ȱ Becauseȱ multinationalsȱ operateȱ onȱ aȱ largerȱ scaleȱ thanȱ smallerȱfirms,ȱtheirȱcooperativeȱbehaviourȱreflectsȱthisȱscale.ȱȱ

ȱ

Theȱ importanceȱ ofȱ distantȱ tiesȱ doesȱ notȱ automaticallyȱ meanȱ thatȱ localȱ tiesȱ areȱ unimportantȱinȱtermsȱofȱacquiringȱknowledgeȱandȱgainingȱcompetitiveȱadvantages.ȱIfȱ distantȱ tiesȱ areȱ anȱ advantageȱ forȱ allȱ firms,ȱ thanȱ localȱ tiesȱ canȱ makeȱ aȱ differenceȱ byȱ providingȱ anȱ advantageȱ thatȱ otherȱ firmsȱ cannotȱ achieveȱ becauseȱ theyȱ areȱ locatedȱ furtherȱ away.ȱ Therefore,ȱ aȱ moreȱ fineȬtunedȱ analysisȱ isȱ neededȱ toȱ investigateȱ theseȱ localȱsocialȱinteractions.ȱToȱciteȱGlaeserȱ(2000,ȱp.ȱ104):ȱ“Weȱcannotȱunderstandȱcitiesȱ andȱagglomerationsȱwithoutȱunderstandingȱnonmarketȱinteractions”.ȱȱ

ȱ

Chapter 4:

The Micro-Macro Link

ȱ ȱ

4.1ȱ

T

HEȱMICRO

Ȭ

MACROȱPROBLEMȱ

ȱ

TheȱquestionȱofȱhowȱtoȱlinkȱmacroȬlevelȱphenomenaȱwithȱmicroȬlevelȱdynamicsȱhasȱ puzzledȱ manyȱ researchersȱ inȱ differentȱ fieldsȱ forȱ someȱ time.ȱ Colemanȱ (1990,ȱ p.ȱ 6)ȱ definesȱtheȱmicroȬmacroȱproblemȱas:ȱ“theȱproblemȱofȱmovingȱfromȱtheȱlowerȱlevelȱtoȱ theȱsystemȱlevelȱtoȱexplainȱsystemȱbehaviourȱbasedȱonȱactionsȱandȱorientationsȱatȱtheȱ levelȱbelow”.ȱColemanȱ(1986)ȱarguesȱthatȱthisȱproblemȱisȱoftenȱmisleadinglyȱreferredȱ toȱasȱ“aggregation”.ȱForȱsocialȱscientistsȱinȱgeneral,ȱitȱisȱaȱmajorȱchallengeȱtoȱcomeȱupȱ withȱmicroȬfoundationsȱforȱmacroȬsocialȱphenomenaȱ(VanȱderȱVeen,ȱ2007).ȱȱ

“Inȱ economics,ȱ forȱ example,ȱ thereȱ isȱ microeconomicȱ theoryȱ andȱ thereȱ isȱ macroeconomicȱtheory;ȱandȱoneȱofȱtheȱcentralȱdeficienciesȱinȱtheȱeconomicȱtheoryȱisȱ theȱweaknessȱofȱtheȱlinkageȱbetweenȱthem,ȱaȱweaknessȱpaperedȱoverȱwithȱtheȱideaȱofȱ ‘aggregation’ȱ andȱ withȱ aȱ ubiquitousȱ conceptȱ inȱ macroeconomicȱ theory,ȱ thatȱ ofȱ theȱ ‘representativeȱagent’”ȱ(Coleman,ȱ1990,ȱp.ȱ6).ȱȱ

Colemanȱ(1990)ȱstressesȱtheȱimportanceȱofȱfocussingȱonȱindividualȱactorsȱtoȱexplainȱ macroȬphenomena.ȱ Aȱ macroȬlevelȱ phenomenonȱ isȱ notȱ aȱ simpleȱ aggregateȱ ofȱ theȱ individualȱ level,ȱ butȱ aȱ resultȱ ofȱ interactionsȱ amongȱ individualȱ actors.ȱ Inȱ hisȱ wellȬ knownȱ“ColemanȬBoat”,ȱheȱgraphicallyȱdepictsȱhowȱtheȱmicroȬȱandȱmacroȬȱlevelȱcanȱ beȱrelatedȱ(seeȱFigureȱ2).ȱ ȱ

Figureȱ2.ȱȱ

Theȱ“ColemanȬBoat”ȱȱ

ȱ (Coleman,ȱ1990,ȱp.ȱ702)ȱ

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22ȱ

Givenȱ theȱ increasingȱ computingȱ powerȱ ofȱ computers,ȱ theȱ possibilityȱ ofȱ examiningȱ interdependenciesȱ betweenȱ differentȱ levelsȱ ofȱ analysisȱ hasȱ expanded.ȱ Multilevelȱ analysisȱ hasȱ beenȱ developedȱ toȱ investigateȱ theȱ microȬmacroȱ problemȱ throughȱ regressionȱ analysis.ȱ Multilevelȱ analysisȱ canȱ beȱ usedȱ toȱ assessȱ bothȱ theȱ effectsȱ onȱ allȱ levels,ȱandȱtheȱinteractionsȱbetweenȱtheseȱlevels.ȱMultilevelȱanalysisȱprovidesȱaȱtoolȱ toȱ analyzeȱ thisȱ linkȱ empiricallyȱ (Snijdersȱ &ȱ Bosker,ȱ 1999).ȱ Moreover,ȱ multilevelȱ analysisȱ canȱ beȱ utilizedȱ asȱ anȱ alternativeȱ toȱ aggregation,ȱ whichȱ isȱ oftenȱ usedȱ inȱ spatialȱstudies.ȱ

ȱ

4.2

ȱ

ȱ

T

HEȱMICRO

Ȭ

FOUNDATIONȱOFȱKNOWLEDGEȱSPILLOVERS

ȱ

Knowledgeȱ spilloversȱ constituteȱ aȱ typicalȱ exampleȱ ofȱ theȱ aforementionedȱ microȬ macroȱproblem,ȱandȱtheȱeasiestȱsolutionȱforȱthisȱproblemȱisȱaggregationȱofȱtheȱmicroȬ level.ȱ However,ȱ itȱ turnsȱ outȱ thatȱ aggregationȱ doesȱ notȱ alwaysȱ leadȱ toȱ correctȱ predictionsȱofȱmacroȬlevelȱphenomena,ȱandȱitȱisȱnotȱclearȱwhatȱdoesȱprovideȱaȱgoodȱ prediction.ȱ Thisȱ isȱ aȱ fundamentalȱ methodologicalȱ reasonȱ thatȱ theȱ microȬfoundationȱ behindȱlocalizedȱspilloversȱremainsȱaȱmystery.ȱȱ

Inȱ theȱ lastȱ fewȱ decades,ȱ theoreticalȱ constructsȱ suchȱ asȱ agglomeration economies and localizationȱhaveȱbeenȱusedȱbyȱregionalȱeconomicȱtheoristsȱtoȱexplainȱwhyȱfirmsȱlocateȱ inȱ theȱ vicinityȱ ofȱ oneȱ anotherȱ andȱ why,ȱ onȱ oneȱ hand,ȱ theyȱ benefitȱ fromȱ companiesȱ conductingȱtheȱsameȱtypesȱofȱactivitiesȱand,ȱonȱtheȱotherȱ hand,ȱalsoȱgainȱ“fromȱtheȱ generalȱatmosphereȱinȱsuchȱaȱregion”ȱ(VanȱderȱVeenȱ&ȱOtter,ȱ2001,ȱp.ȱ147).ȱDespiteȱ theȱ developmentȱ ofȱ theseȱ constructs,ȱ theȱ resultsȱ ofȱ theȱ empiricalȱ researchȱ basedȱ onȱ theseȱconstructsȱhasȱbeenȱlessȱfertile,ȱleavingȱtheirȱmicroȬfoundationȱaȱblackȱboxȱ(Vanȱ derȱVeenȱ&ȱOtter,ȱ2001).ȱȱ

ȱ

Manyȱ socialȱ scientistsȱ haveȱ triedȱ toȱ investigateȱ theȱ missingȱ linkȱ betweenȱ theȱ microȬȱ andȱ macroȬȱ levels.ȱ Aȱ relativelyȱ newȱ branchȱ ofȱ researchȱ thatȱ triesȱ toȱ shedȱ lightȱ onȱ macroȬlevelȱchangesȱfromȱaȱmicroȬperspectiveȱisȱtheȱstudyȱofȱsoȬcalledȱtransitions.ȱ ȱ

4.3

ȱ

ȱ

T

RANSITIONS

ȱ

AccordingȱtoȱMartensȱandȱRotmansȱ(2005,ȱp.ȱ1136),ȱaȱtransitionȱcanȱbeȱdefinedȱasȱ“aȱ gradual,ȱ continuousȱ processȱ ofȱ societalȱ changeȱ whereȱ theȱ structuralȱ characterȱ ofȱ societyȱ (orȱ aȱ complexȱ subȬsystemȱ ofȱ society)ȱ transforms”.ȱ Theȱ termȱ “transition”ȱ isȱ sometimesȱusedȱtoȱdescribeȱtheȱdesirableȱdevelopmentȱofȱaȱmoreȱsustainableȱworld.ȱ Historicȱ transitions,ȱ suchȱ asȱ theȱ developmentȱ ofȱ steamȱ technology,ȱ areȱ studiedȱ toȱ understandȱ theȱ processesȱ behindȱ theseȱ transitionsȱ (Geelsȱ &ȱ Schot,ȱ 2007).ȱ Aȱ bottomȬ upȱapproachȱisȱcentral,ȱwhereinȱcooperativeȱinnovationȱnetworksȱatȱtheȱmicroȬlevelȱ