Do inventors talk to strangers? On proximity and collaborative knowledge creation

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Riccardo Crescenzi

, Max Nathan,

Andrés Rodríguez-Pose

Do inventors talk to strangers? On proximity

and collaborative knowledge creation

Article (Published version)

(Refereed)

Original citation:

Crescenzi, Riccardo, Nathan, Max and Rodríguez-Pose, Andrés (2016)

Do inventors talk to

strangers? On proximity and collaborative knowledge creation.

Research Policy

, 45 (1). pp.

177-194. ISSN 0048-7333

DOI:

10.1016/j.respol.2015.07.003

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ContentslistsavailableatScienceDirect

Research

Policy

j ou rn a l h om ep a g e :w w w . e l s e v i e r . c o m / l o c a t e / r e s p o l

Do

inventors

talk

to

strangers?

On

proximity

and

collaborative

knowledge

creation

Riccardo

Crescenzi

a,b,∗

,

Max

Nathan

b,c

,

Andrés

Rodríguez-Pose

a,b a_Department_of_Geography_&_Environment,_London_School_of_Economics,_United_Kingdom

b_SERC,_London_School_of_Economics,_United_Kingdom

c_Birmingham_Business_School,_University_of_Birmingham,_United_Kingdom

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received26October2013

Receivedinrevisedform15July2015 Accepted16July2015 JELclassiﬁcation: O31 O33 R11 R23 Keywords: Innovation Patents Proximities Regions Knowledgespillovers Collaboration Ethnicity

a

b

s

t

r

a

c

t

ThispaperexaminesthecharacteristicsofthecollaborationsbetweeninventorsintheUnited King-dom(UK)bylookingatwhattypesofproximities–geographic,organisational,cognitive,social,and cultural–ethnic–betweeninventorsareprevalentinpartnershipsthatultimatelyleadtotechnological progress.UsinganewpanelofUKinventorsthispaperprovidesananalysisofassociationsbetween these‘proximities’andco-patenting.Theresultsshowthatwhilecollaborationwithinﬁrms,research centresanduniversitiesremainscrucial,externalnetworksofinventorsarekeyfeatureofinnovation teams.Theanalysisshowsthatexternalnetworksarehighlydependentonprevioussocialconnections, butaregenerallyunconstrainedbyculturalorcognitivefactors.Geographicalproximityisalsoweakly linkedwithexternalnetworks.Ourresultssuggestthatinnovationpoliciesshould,ratherthanfocuson spatialclustering,facilitatetheformationofopenanddiversenetworksofinventors.

1. Introduction

Theageoftheloneresearcher,ofthequixotic‘basement tin-kerer’(Rabinow,1976),orofthe‘garageinventor’(Seaborn,1979)is receding.TheromanticnotionthatanewNikolaTeslawillemerge fromthelabwiththenextACmotor(oradeathray)increasingly belongstoa bygoneera.Whileinthelate1970saround75%of EPOpatentapplicationsin theUnitedKingdom(UK) werefiled byindividualinventors,nowadaysthatfigureisbelow15%.More than80% ofall patentsareregisteredtomore than one inven-tor,suggestingthatcollaborationinresearchandinnovationhas becomethenorm. Increasingly largerteamsare formedwithin firmsorresearchcentres.Complexnetworksofresearchers involv-ingdifferentfirms,oftenincollaborationwithuniversities,public

∗ _{Corresponding}_author_at:_Department_of_Geography_and_Environment,

Lon-donSchoolofEconomics,HoughtonStreet,LondonWC2A2AE,UnitedKingdom. Tel.:+442079556720.

E-mailaddress:[email protected](R.Crescenzi).

agencies,andresearchcentresdrivetheworldofinventioninthe early21stcentury.AsSeaborn(1979:88)putsit,“bigscience[has] eclipsedthegarageinventor[...]Edisonhasbeensupersededbya teamofwhite-coatedtheoreticalphysicists”.

Whilethetrendtowardstheformationofever-largerresearch teams and inventor networks has been well documented, we knowmuchlessaboutthefeaturesoftheseteams.Whatarethe characteristicsof theinventors that decidetoworkin a team? Iscollaborativeresearchproducedbyinventorsthattalkto col-leagues,ortostrangers?Thesearethequestionsattheheartofthis paper,whichaimstoshednewlightonthepatternsofcollaboration observedamongUKinventors.

Inthepaper,collaborationbyinventorsiscapturedbymeans ofco-patentingoverthepastthreedecades.Weexplorethe indi-vidualcircumstancesthatmembersofaco-patentingteammay share andwhich, accordingtotheliterature oninnovationand proximity(Boschma,2005;BoschmaandFrenken,2009;Torreand

Rallet,2005),canbegroupedintodifferenttypesofproximities:

(a)geographic(thephysicaldistancebetweeninventors);(b) orga-nisational(whethertheinventors sharethesameorganisational

http://dx.doi.org/10.1016/j.respol.2015.07.003

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context,suchasthesameﬁrm,universityorresearchcentre);(c) social(whetherinventorshaveco-inventedinthepastorshare other co-inventors); (d) cultural–ethnic (whether co-patenting inventorssharethesamenational,cultural,and/orethnic back-ground);and(e)cognitive(thedistancebetweenthetechnology ﬁeldsoftheco-patentinginventors)proximity.

Toexploretheselinkages,wedevelopanewempirical strat-egy,whichbuildsonideasdevelopedinseminalpapersbyJaffe

et al. (1993), Singh (2005), and Agrawal et al. (2008). We use

this to builda new panel of EPO patents microdata from the KITES-PATSTATresourceandwethenanalysetheincidenceofthe differentproximitiesconsideredin co-patentingteams, control-lingforabroadsetofobservableandtime-invariantunobservable characteristics(theformerthroughavectorofindividual, organi-sational,andenvironmentalfactors;thelatterbymeansofﬁxed effects).TheempiricsalsoemploytheinnovativeONOMAPname classiﬁcation system to ascribe inventor ethnicity – and thus, ethnic/culturalproximity.Weusesocialnetworkanalysisto iden-tify the position of each inventor in pre-existingcollaboration networks.

Thepaperrepresents–tothebestofourknowledge–theﬁrst empiricalworkassessingtheincidenceofsuchalargesetof prox-imitiesincollaborationpatterns,andcontributestotheexisting literatureinseveraldifferentways.Itﬁndsthat,forinventorsasa whole,organisationalproximityisa keyfeatureofco-patenting teams together with cultural/ethnic diversity. Conversely, geo-graphicalproximityislinkedtoco-patentingincombinationwith otherproximities.

For‘multiplepatent’inventors,weﬁndthatorganisational prox-imityremainshighlyrelevant,whilecultural/ethnicfactorsarenot relevant.Socialnetworkandcognitiveproximitiesaremore impor-tantcharacteristicsoftheteamsformedbytheseinventors.The analysisalsoconﬁrmstheincidence of‘unconstrained’ (i.e.free fromethnicfactors)socialproximityandsocialnetworksin col-laborativeactivity.Forthiscategoryofinventorstheimportance ofgeographicalproximityonlyemergesaswellininteractionwith otherproximities.

Our results have important implications for the analysis of innovationdynamicsand, possibly, forthe targetingof innova-tionpolicies.Theempiricalanalysissuggeststhatknowledgeand keycompetencesforinnovationprocessesarecombined(and re-combined)withintheorganisationalboundariesofﬁrms,research centresand universities. These arethe key unitsof analysis of innovationdynamics.Assetsinternaltotheindividual organisa-tionalunitarecomplementedbyprocessesofexternalsearchthat takeplace withinexisting social networks that ‘bridge’various organisations.Theformation of thesenetworksremains largely unconstrainedbyculturalorcognitiveproximityconsiderations in order to ensurevariety and avoid lock-in situations. In this picturethedirectcontributionofgeographicalproximityand spa-tially mediated processes remains limited: it only emerges in theformofhyper-geographicalproximity(inventorsinthesame organisationarelikely tobeco-localisedinthesamepremises) andasareinforcement(orfacilitator)fornetwork-based interac-tions.Theseresultssuggestthatinnovationpoliciesshouldplace less emphasis onspatialclustering and localised collaborations and focus more on the capabilities internal to each ﬁrm and its ability to access ‘unconstrained’, openand diverse external networks.

Thepaperisstructuredasfollows.Section2reviewstheexisting literatureoncollaborativeworkingamonginventorsandoutlines aconceptualframeworkfortheanalysisofthedriversof collabo-rationsamonginventors.Section3introducesourdataandgives somestylisedfacts.Section4setsoutourempiricalstrategyand model.Theempiricalresultswithanumberofrobustnesschecks arediscussedinSection5.Section6concludes.

2. Collaborativeworkingamonginventorsandproximity relationships

Collaborativeinventioneffortshavebeenontheriseforquite some time. The number of co-authored scientific publications, bothinternational(Glänzel,2001;GlänzelandSchubert,2005)and withinspecificcountrieshasbeenincreasinginrecentdecades. IntheUS,forinstance,Adamsetal.(2005)finda50%riseinthe averagenumberofauthorsperacademicpaperduringtheperiod 1981–1999.Similarshiftscanbeseeninpatentingactivity.Inthe UK‘co-inventedpatents’rosefromaround100in1978(24.2%of allpatents)toover3300in2007(66.6%ofallpatents).Overthe periodasawhole,57.3%ofpatentshadmorethanoneinventor. Co-patentingalsoincreasedacrossallmajortechnologyfieldsand theshareofinventorsworkingalonefelldramatically.Duringthe periodofanalysisthemeansizeofpatentingteamsrosefromunder twotooverfour.

Thesetrendsaretheresultoftheevolutionofbothpublic pol-icyandcorporatestrategies.Nationalgovernmentshavesoughtto developtheformation ofinnovation‘ecosystems’.This,in com-binationwiththeinternationalisationoffirms’activitiesandthe tendencyofmultinationalfirmstocouplewithlocalpartnersin knowledge-intensiveactivities(Cantwell,2005;Yeung,2009),has encouragedtheformationofresearchteamswhich expandwell beyondthefirmortheirresearchcentre.University-industryjoint venturesand thegrowthof TripleHelix relationshipsinvolving firms,universities,andgovernment(D’EsteandIammarino,2010;

LeydesdorffandEtzkowitz,1998)havegraduallybecomethenorm.

These‘global’high-leveltrendshavetakenplaceinacontext ofin-depthchangeintheindividual-levelincentivesfor collabo-ration.Theincreasingsophisticationof‘frontier’sciencereinforces thereturnstospecialisationandpromotescollaborationasameans tohandleagrowing‘burdenofknowledge’(Agrawaletal.,2014; Jones,2009),aswellasaformof‘risksharing’for high-risk/high-gainprojects.Atthesametime,theneedtogainaccesstoboth highlycomplexresearchinfrastructureand largerfundingpools viacollaborativegrants(Freeman,2014)strengthenstheincentives fortheformationoflargerteamsofresearchers.Finally,research projectsrequireincreasinglymorediversesetsofcomplementary skillsandcompetencestobesuccessful(Agrawaletal.,2008).

Scientists and researchers have responded tothese changes by makingcollaborative researchthe norm both in theUnited States(Jonesetal.,2008)andEurope(Brusonietal.,2007;Giuri

and Mariani,2012).However,collaborationcomesat acost for

allparties involved: thesearch for thebest possible collabora-tor(s)/team members – whether this decision is taken by the inventors themselvesor bymanagers withintheboundaries of aﬁrm–isanexpensiveprocessintermsoftimeandresources. Agentsfacebeneﬁtsandcostswhenconsideringpotential connec-tion/collaboration(seeJackson(2006)forarecentreview)anda numberofstudieshavedrawnonprincipal–agenttheorytolook atcontractformationandpartnerselectionattheindividuallevel (AckerbergandBotticini,2002;Sedikesetal.,1999).

Moreover,collaboration is by deﬁnition a social act and, in addition to economic considerations, it is shaped by personal preferencesandcircumstances(GiuriandMariani,2012),an indi-vidual’spositioninanorganisation,thenatureandcapacityofthose organisations,thetypeofworktheydo,andarangeofexternal cir-cumstances–suchaslegalandfundingframeworks,industryand policytrends.

Various ‘proximities’ assist in the creation of innovation networksbyreducingteamformationcostsandovercoming co-ordinationand control problems.Boschma(2005)distinguishes ﬁvetypes of proximity– cognitive,organisational,social, insti-tutional, and geographic. He suggests ﬁrst, that these factors may operate as substitutes or complements; and second, that

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proximitiesarenotalwaysbeneﬁcial(Boschma,2005).Excessive proximitymaycause‘lock-in’–alackofopennessandﬂexibility thatinhibitsinnovationbyleadingtotheformationofteamswhere redundantknowledgeprevails.

Cognitiveor ‘technological’ proximityallows agentsto com-municatein the sameresearch field (Seely Brown and Duguid, 2002). Organisational and social proximities lower transaction costsvia(respectively)contractsandsocialrelationships(Kaiser et al.,2011).However, hierarchicalorganisationalstructures or supplychainrelationshipsclosefirmsofffromthetechnological opportunitiesthat‘openinnovation’modelsprovide(VonHippel, 2005).Andsocialnetworksbasedon‘strongties’maybeless effec-tivethanlargernetworksof‘weakties’,iftheydonotadmitnew membersornewthinking(Granovetter,1973).Geographical prox-imity reducesthe costof knowledge sharing,transmission and monitoringbyenablingface-to-facecontactsbetweentheagents involved(StorperandVenables,2004).However,thespatial clus-teringofinnovativeactivities,ifnotconstantlyrenewedbyinflows andoutflowsofnewresourcesbymeansofmobility,caneasilylead tocognitivelock-in(Crescenzietal.,2007).Inaddition,an emerg-ingbodyofnewerliteraturehasshownthatco-ethnicanddiasporic networks(seeDocquierandRapoport(2012)forarecentsurvey) increasetrustandlowertransactionscosts,assistinginthe genera-tionanddiffusionofcollaborativeideas(KapurandMcHale,2005; KerrandLincoln,2010;SaxenianandSabel,2008).Justaswithother proximities,however,thecapacity ofdifferentco-ethnicgroups mayvarysubstantiallyandispotentiallylimitedbydiscrimination. Whilethere isconsensusin economicgeography,innovation studiesandmanagementontheimportanceofmultipleconditions for knowledge-intensivecollaborations, the empiricalchallenge remainshowtooperationalise,disentangleand‘weight’the rel-ativeimportance ofthese –oftencoexisting and overlapping – proximities.

Untilrecentlythegeneralviewwasthatgeographicalproximity trumpedallothers.Jaffeetal.(1993)werethefirsttousepatent citationsasawaytoprovidea‘papertrail’forknowledgeflows. Theyshowedthatgeographic proximityfacilitatedlocal knowl-edgeexchange.Morerecentanalyseshavetendedtore-statethe relevanceofphysicalproximity.LoboandStrumsky(2008)found thatspatialagglomerationofinventorsinUSMSAsismore impor-tantthanthedensityofinventorconnections(whicharenegatively correlatedtopatenting).Similarly,Flemingetal.(2007)have sug-gestedthatspatialproximityis acrucialenabling factorforthe effectivetransmissionofknowledgeflows.

Otherresearchhashoweversuggestedthattheroleof geograph-icalfactorsmayhavebeenoverstated(ThompsonandFox-Kean, 2005).Inparticular,theimportanceofsocialnetworksasa cata-lystforinventorcollaborationhascomeforwardinrecentyears (Singh,2005;Brusonietal.,2007;GiuriandMariani,2012).Froma socialnetworkperspective,Evansetal.(2011)haveuncovered evi-dencethathomophilyexplainsco-authorshipofscientificpapers, butthatinstitutionalandgeographicproximityplaybiggerroles inthisrespect.CassiandPlunket(2010)suggestthatspatialand socialproximityarehighlycomplementary,evenifindividual part-nersareindifferenttypesoforganisations.Singh(2005)confirmed thattheeffectofgeographyandfirm boundariesonknowledge flowsdiminishessubstantiallyonceinterpersonalnetworkshave beenaccountedfor. Priorsocial relationshipsbetweeninvestors alsoexplaincurrentcitationpatterns,evenafterspatial proxim-ityisalteredbymobilitydecisions(Agrawaletal.,2006).Overall, geographic and social proximity are considered to operate as substitutes(Agrawaletal.,2008).1

1_Micro-level_analyses_have_reached_similar_conclusions_on_the_simultaneous

interplayofavarietyofdriversforknowledgeexchangeandcooperativeinnovation

But,despitetheseefforts,ourunderstandingofwhicharethe mostimportanttypesofproximitiesfortheformationof collab-orative inventornetworks isstill rather poor(Torreand Rallet, 2005).A numberofunder-exploredareas remain.First, westill knowrelativelylittleaboutindividualinnovativeagents–most stud-iesaggregateoutcomestoﬁrms,citiesandregions.Second,while manystudiesexploreknowledgespillovers,orconsequencesof col-laboration,muchlessworkhasbeendoneontheintrinsicfeatures ofthesecollaborations(BoschmaandFrenken,2009).Third,due todataconstraints,therearefewstudiesthathavebeenableto exploretimeperiodsaboveadecade.Fourth,theroleofcultural andethnicproximityhasbeenparticularlyneglectedin quantita-tiveanalysisoutsidetheUS(seeKerr(2013)forareviewoftheUS literature).Asfarasweareawarethereisonlyonerelevantstudy forEurope(Nathan(2014),forUK‘minorityethnicinventors’).

Thesegapsraisethreeimportantresearchquestions:

(1)Whatformsofproximityareassociatedwiththeincidenceof collaborativeknowledgecreationattheindividuallevel? (2)Whatistheinteractionbetweendifferentproximities? (3)Howhasthesalienceoftheseproximitieschangedovertime?

Providinganswerstothesequestionsisofcrucialimportance totheunderstandingoftheprocessofinnovationand its diffu-sion:theyshednewlightonthephysicalorvirtualmilieuofthe innovationprocessandontherelevantunit(s)ofanalysisforits understanding.Ifinnovativecollaborationsarelargelyshapedby localisedandspatiallymediatedprocesses,thencities(andtheir functionalhinterland)arekeyunitsofanalysisandpossibletargets forinnovationpolicies.If,bycontrast,theprocessofsearchingand matchingofscientificcompetencesforinnovativeprojectstakes placewithintheorganisationalboundariesoffirmsandresearch centres,thefocus shouldmovetomicroanalysis.Finally, if cul-tural/ethnic,social and cognitivechannels are essential for the formationofinnovationnetworks,thentheemphasisshouldbeput onhowbothlocalitiesandfirmscanbeconnectedandre-connected beyondphysicalcontiguity.

Thispaperaimstoanswerthesequestionsbylookingatwhat inventors’characteristicsandrelationshipstoeachotherprevailin collaborativeknowledgecreation(speciﬁcallyco-patenting)teams. Todo this, weneed todisentanglerelational factorsfromeach other,andfromothercharacteristicsofcollaborationteams(i.e. ‘individual’,‘institutional’and‘environmental’factors).

3. Dataandstylisedfacts

OurdatasetcontainsEuropeanPatentOffice(EPO)patent micro-datafromthePATSTATdatabase,modifiedbytheKITESteamat Universita’Bocconi(hence‘KITES-PATSTAT’).Theraw dataruns from1978to2010,comprising116,325patentswithatleastone UK-residentinventor.173,180inventorsareassociatedwiththese patents,ofwhom133,610areUKresidents.Unlikestandardpatent data,KITES-PATSTAThasbeencleanedtoallowrobust identifica-tionofindividualinventors,theirspatiallocationandpatenting histories,aswellastheusualarrayofpatentandapplicant-level characteristics[seeLissonietal.(2006)fordetailsofthecleaning process].2

Patent datahave a number of advantages for ourpurposes. Theyproviderichdataoveralongtimeperiod,aswellasdetailed

projects.Seeforexample:Singh(2005),Agrawaletal.(2008),PaierandScherngell (2008),Lychaginetal.(2010),Grifﬁthetal.(2011),andD’Esteetal.(2013).

2_{KITES-PATSTAT}_also_provides_extensive_{applicant-level}_information,

particu-larlyforcorporate applicants:names/addressdetailsarematchedtocompany informationfromDunandBradstreet.

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informationonindividualinventorsandtheirpast/present collab-orators,thetypeofresearchtheyworkin(viadetailed‘technology ﬁeld’codes), andtheorganisations theyworkfor (typicallythe patentapplicant)(OECD,2009).Ontheotherhand,thedatahave twoinherentlimitations. Patentsmeasureinventionrather than innovation; andtheytendtoonlyobservesomeinventionsand inventors(forinstance,somemembersofaresearchteammaybe leftoffthepatentapplication).Otherlimitations,suchas patent-ing’smanufacturingfocusandvulnerabilitytopolicyshocks,are dealtwithusingappropriateindustrycontrolsandtimetrends,as discussedbelow.

Wemakesomebasiceditstothedatatomakeitﬁtforpurpose. First,thereistypicallyalagbetweentheapplicationandthe grant-ingofapatent.Thismeansthatinapanelofpatents,missingvalues appearinﬁnalperiods.FollowingHalletal.(2001),wetruncatethe datasetbythreeyearstoendin2007.Second,wegeo-locate UK-residentinventorsinUKTraveltoWorkAreas(TTWAs).TTWAsare designedtorepresentfunctionallabourmarketsandofferagood proxyforthelocalspatialeconomy.

Third,EPOpatentdatagatherspatentapplicationsthroughEU countries’nationalpatentofﬁces,throughEuropean-wide applica-tionstotheEPO(‘Euro-direct’),andinternationalapplicationsthat havereachedtheEuropeanexaminationstage(‘Euro-PCT’)(OECD, 2009).Assuch,ourdatasetmaynotincludeallPCTpatent appli-cations,whichcoverinternationalapplicationstomultiplepatent ofﬁces.SincePCTapplicationsareincreasinglythefavouredroute forinventorsseekingtoaccessinternationalmarkets,thereisarisk ofselectingoutsomecollaborativeactivity.However,theuseof PCTapplicationshasincreasedsubstantiallysincetheearly2000s, meaningthatthismayaffectonlypartofoursample.Bytruncating theendofthetimeseries,weminimisethePCTselectionissue.

WeusetheONOMAPsystem(Mateosetal.,2011)toobserve likelyinventor origin/ethnicity fromindividualinventors’ name information,buildingonworkbyNathan(2014)andkeyUS

stud-iesbyKerr(2010)andAgrawaletal.(2008).ONOMAPisdeveloped

fromaverylargenamesdatabaseextractedfromElectoral Reg-istersandtelephonedirectories,covering500,000forenamesand a million surnames across 28 countries. Classifying individuals accordingtomostlikely‘cultural–ethnic–linguistic’characteristics, ONOMAPalsoprovidesinformationonONSethnicgroups, geo-graphicaloriginandmajorlanguage.MoredetailsonONOMAPare giveninAppendixA.1.

3.1. Stylisedfacts

Collaborative research and invention have progressively increasedtheirimportanceovertimeandacrosstechnologyfields (Fig.1).Overthewholeperiod,57.3%ofpatentswere‘co-invented’. Co-inventionisthenorm:15.9%ofinventorsonlyworkalone(i.e. neverco-invent);4.7%sometimesco-invent;79.4%onlyco-invent. Patentswithfiveorfewerinventorscompriseover95%ofthe sam-ple,ofwhichoverhalfareco-invented.Mostco-inventedpatents havetwoorthreeinventors, withtwo being–bysomemargin –themode(26.2%ofpatents,versus14.7%ofpatentswiththree inventors).Threedistinctphasescanbeidentifiedwithinthe sam-pleperiod:fromthelate1970stothelate1980s;the1990s,with apeakinco-inventingin2000;andthenaplateauperiod,with aslightdeclineattheendofthepanel(probablyreflectingfewer grantedpatents).

Co-inventiontrendsvarysubstantiallyacrosspatentfields.Fig.2 showsthetrendsinco-inventedpatentsacrosssevenaggregated technologyfields(usingtheOSTreclassification).3_At_the_start_of

3 _This_{classiﬁcation}_is_used_for_illustrative_purposes_only._In_the_regression

anal-ysisthe30-foldtypologyisused,alongsidemoredetailedtypologiesof121IPC

Fig.1.Co-inventedpatents,1978–2007.

Fig.2.Co-inventedpatentsbytechnologyﬁeld,1978–2007.

thesampleperiod,sharesofco-inventingwerelow(from0.14in consumergoodsto0.22inelectricalengineering).Bytheendof theperiodshareswerehigher,althoughthevariationacross sec-torsincreased:from0.49inconsumergoodsto0.83inchemicals andmaterials.Insixoutofsevensectors,co-inventionshiftedfrom minoritytomajoritytype.

Inventors’behaviourhasalsochangedovertime.Fig.3shows thattheseaggregateshidelargechangeswithinthesample.Counts andsharesof‘onlyco-inventinginventors’rosesubstantively;in contrast,whilecountsof‘onlysolo’inventorsroseslightly,their relativesharesunderwentanextensivedecline.

Finally,welookatinventorteamcomposition.Fig.4showsthe trendinaverageteamsize.Thetrendlineisspikierthanthe co-inventiontrend,butthegeneralshapeisthesame.Wecanseethat theaveragepatentin1978had1.73inventors;by2007thishad risentojustoverfourinventors.

Takentogether,thesestylisedfactssuggestasubstantialrisein co-patentingacrosstechnologyfieldsbetweenthelate1970sand thelate2000s,involvingsignificantchangesininventorbehaviour. Intermsoftheinstitutionalandorganisationalcontextinwhichthe patentingprocesstakesplace,applicant-levelinformationreveals thatprivatefirmsandtheirresearchlabshavebeentheleading actorsbehindpatenting(with55%ofthetotalpatentsinthe sam-ple)while universities,otherpublicresearchcentresand NGOs

three-digitsub-classesand1851six-digitmainclassestogeneratetechnologyﬁeld ﬁxedeffects.

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Fig.3.Inventorbehaviour,1978–2007.

Fig.4. Inventorteamsize,1978–2007.

accountfor4.2%ofthetotalpatents,andindividualapplicantfor

6.7%(seeTableB.1intheAppendix).

4. Empiricalstrategy

4.1. Counterfactualbuild

The paper aims to compare thecharacteristics of inventors workingtogetherwiththosewhodonot,toidentifywhatfactors aresystematicallycorrelatedwiththeincidenceofco-invention. Asweareinterestedincollaborationsbetweeninventors,wemake theinventorpairtheunitofobservation.4

Wethereforeneedtoconsiderbothaplausiblesetofpossible pairs–inventorswhomighthaveworkedtogether–andtheset ofactualpairs(thosewhodidcollaborate). Todothis,we build a‘syntheticcounterfactual’consistingofa feasiblesetof poten-tialcollaborators.Wethenuseacase–controlstrategy,inwhich wedisentanglethecharacteristicsofactualpairsfromthefeasible possiblepairsthatmighthaveexisted.Thatis,weuncoverthe fac-torsassociatedwithcollaboration,andcontrolforitsunderlying

4_To_assess_why_{inventor-level}_analysis_is_appropriate,_consider_an_opposite

sce-nario:workingatthepatentlevel.Forexample,wecouldestimateamodelwhere thedependentvariablewouldbeadummytakingthevalue1foraco-invented patent;independentvariableswouldcovercharacteristicsfortheinventor/setof inventorsinvolved.Thishassomedesirablecharacteristics–notleast,allowingto examineallpatentsinoursample–butdiscardscrucialinventor-levelinformation. Anadditionalimportantchallengeforpatentswithmorethanoneinventoriswhere tolocatethepatentinspace.

incidence.Tomodel inventorcollaboration(asopposed to cita-tionpatterns)wemakesomechangestothecase–controlmethods deployedbyJaffeetal.(1993),ThompsonandFox-Kean(2005),

Agrawaletal.(2006)andothers.

Thefeasiblesetofpotentialcollaboratorsisconstructedas fol-lows.Wefirstspecifyafeasibilityframework.Forexample,itis highlyunlikelythatinventorsactiveinthefirstyearofourdata (1978)willbecollaboratingwiththoseactiveinthelastyear(2007). Moreover,collaborationsacrossentirelyunrelatedtechnology sec-tors areimplausible.5 _We _thus_assume_that _the_set_of _potential collaboratorsisbroadlydelineatedbytimeperiod(year)andsome high-levelIPC technologyfield (IPC1, 2or 3).We then useour mainvariablesofinterest–proximitiesvariables–asbinary ‘treat-ments’andlookatthetime-by-IPCfieldcombinationsthatcreate broadly‘balanced’‘treatment’and‘control’groupsamongtheset ofactualinventors.Thissuggestsyear-by-IPC3field isthemost feasiblecombination.6

Tomakethedatatractable,werandomlysample5%ofpatents. Thisallowstofuzzilyobservethereal-worldincidenceofactual andfeasiblecollaborations.7 _Given_the_boundaries_of_the_feasible collaboratorset,thisimpliesstratifyingthesamplebyyear,121 three-digittechnologyﬁelds,andinventorteamsize.8

Fortheactualinventorpairsineachyear-by-IPC3cellinthis5% sample,wegeneratethesetoffeasiblepossiblepairsinthatcell. Thatis,eachsetoffeasiblecollaboratorsisdirectlydeﬁnedfrom therelevantsetofactualcollaborators.Foreachyear,weappend theresulting‘cell-pairs’toformacross-section.Wethenappend theresultingyearlycross-sectionstocreateanunbalancedpanel.

Eachinventorinapairisseparatelycodedbyaddressandby patentapplicant,permittingthespeciﬁcationofcontrolsandﬁxed effects oneach side of the pair. We build a 16-year panel for theyears1992–2007inclusive,reservingtheperiod1978–91to providehistoricinformationoninventors’patentingactivity(see Section5).Thisgivesusapanelof190,313observations,covering 187,997actualandpossibleinventorpairs,ofwhom3857(2.05%) areactualpairs.

Wealsolookatthesubsetof‘multiplepatent’inventors(who inventmorethanonceintheobservedtimespan).Intherawdata there are17,764 oftheseindividuals, activeon62,339 patents. Multiplepatentinventorsareworthgreaterscrutinyfortwo rea-sons.First,thevastmajorityofinventorsonlypatentonce,sothe ‘multiple’groupisanunusualminority.Theinventorlifecycle lit-eraturesuggestsmultiplepatentinventorsarelikelytobehighly productiveindividuals,ofteninseniorscientiﬁcpositions(Azoulay

etal.,2007; Leeand Bozeman,2005).Itis thereforeinteresting

to seeif theseinventors’ collaborations have different features whencomparedtothepooledsample.Second,lookingat multi-plepatentinventorsletsuslookatabroadersetofproximities.

5_This_is_also_{computationally}_intensive_–_for_example,_there_are_173,180_inventors

inourfullsample1978–2007,whichmakesfor1.49910_possible_pairs.

6_In_principle_possible_pairs_could_be_also_restricted_to_those_working_in_very

detailedtechnologyﬁelds(sayIPC6andabove).However,diagnostics suggest increasingincidenceofindividualspatentingacrosstechnologyﬁelds.More impor-tantly, wewant to explorewhether this‘cognitive distance’affects levelsof co-inventing,reservingthevariationforregressionsratherthanbuildintothe matchingprocess.

7_It_is_possible_that_some_inventor_pairs_who_did_not_patent_in_our_sub-sample

–whowedesignate‘feasible’pairs–didinfactpatentintherestofthedataset, forming‘actual’pairs.Ifthisprocesswasnon-randomitwouldbiasourestimates. Howeverandgivenoursamplingprocedure,asthereisnoreasontoexpectthis tobeanythingotherthanrandom,wetreatthisissueasgeneratingnoiseonly.In principle,analternativeapproachwouldhavebeentobuildacomplete counterfac-tualfromallpatentsandthensample,but,aswepointoutinfootnote5,thisisnot computationallyfeasible.

8_Given_the_size_of_the_original_population,_we_sample_without_replacement._We

seedthesamplesothatregressionresultsarereproducible.Werelaxthisin robust-nesschecks,totestwhethersampleconstructionaffectsourﬁndings.

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Speciﬁcally,socialandcognitiveproximitymeasuresneedtobe basedonhistoricbehaviourinordertoavoida mechanicallink betweendependentandindependentvariablesin themodel. In turn,thisrequiresthatweobservemorethanonepatentingevent. Includingtheseproximitiesthusinvolvesrestrictingoursampleto thesetofpatentswith(a)multiplepatentinventors,and(b)only multiplepatentinventors:thereare27,315ofthesepatents, involv-ing11,754 individuals. However,thesmallern allows a higher samplingrate.We sample25% of these patentsand stratify as before,thenbuildacounterfactualgroupusingthesameapproach asforthepooledsample.Thisresultsinasecondunbalancedpanel ofabout40,638observations.

4.2. Modelspeciﬁcation

Inordertoexplorethecharacteristicsofco-inventorpairs,we lookatlinksbetweencollaborativeactivityinaninventorpairijand therelationalcharacteristicsofij,whilecontrollingforindividual, institutionalandenvironmentalfactorsaffectingeachinventorin thatpair.Wethereforeestimatethefollowingempiricalmodel,for inventorpairijinapplicanto,areaa,yeargrouptandtechnology ﬁeldf:

Yijoatf =a+PROXbit,jt+CTRLSc(ioat,joat)f+eijoatf (1) Here,Yiseitheracollaborationdummy(takingthevalue1,if ijisanactualpair),oracontinuousvariablegivingthecountof collaborationsforij.Thedummyvariableallowsustostudythe characteristicsofindividualcollaborations,whileweusethe inven-torpairactivitycountinordertocapturethefeaturesofrepeated collaborations.CTRLSareeithervectorsofobservables,orﬁxed effects(seebelow).

Our variables of interest are given by PROX, a vector of proximities covering spatial, cultural–ethnic and organisational proximities(forthepooledsample),pluscognitiveandsocial prox-imities(formultiplepatentinventors).Theestimatedcoefﬁcients (b-hats)ofeachPROXvariableindicatethesalienceofa particu-larproximityinactualrather thanpossibleinventorpairs,after controllingforindividual,institutionalandmacro-environmental factors.Notethattheseareassociations,notcausallinks.

Spatialproximityiscalculatedastheinverseofthelinear dis-tancebetweenTTWAcentroidswhereeachinventorislocated;for inventorsindifferentcountriesthisissetaszero.Forrobustness checking,wealsoconstructamoresophisticatedinverselinear dis-tancefunction,withathresholdfunctiontocaptureknowledge spilloversdecay.Wealsobuildasimpledummytakingthevalue1 ifapairisresidentinthesameTTWA.

Organisationalproximityis capturedbymeansof adummy, whichtakesthevalue1ifiandjsharethesameapplicantand0 oth-erwise.Thecomputationofthisvariableisonlyproblematicwhere thenumberofinventorsexceedsthenumberofapplicantsinthe patent.Inthiscaseitismoredifﬁcultytoestablishaclear inventor-applicantassociation.Asaconsequence,wherethereismorethan oneapplicanttowhichaninventorcouldbeassigned(12.77%of thepatentsinthesample),weprobabilisticallyassigninventors and applicants.We do this in two ways:(a) weuse inventors’ patentinghistory(wheneveravailable)toassigninventorstotheir modalapplicantifthereisanambiguouspatent,thendrop dupli-catesfromthedataset(thisworksfor40%ofambiguouscases);(b) inthosecaseswhereapatentinghistoryisunavailable,we ran-domlyassigntheinventortooneapplicantortheotherandruna robustnesscheck,repeatingthere-assignmentprocedureto con-ﬁrmthatthisdoesnotchangetheresults.Wefurthercomputean additionalproxyfor‘scaled’organisationalproximity.Thisvariable takesthevalueof0(asabove),iftheapplicantisnotthesame;1, iftheapplicantisthesame,butthereismorethanoneapplicant onthepatent;2,iftheapplicantisthesameandthereisonlyone

applicantonthepatent.Theintuitionhereisthatinagiven collab-oration,organisationalproximitybetweentwopeopleinthesame companyisstrongerthantheircollaborationifanothercompanyis alsoinvolved.

EthnicproximityisdevelopedusingONOMAP(seeAppendix A.1).Ourpreferredmeasureisadummy,whichtakesthevalue1ifi andjsharethesame‘cultural–ethnic–linguistic’subgroup,ofwhich thereare67intheONOMAPclassiﬁcation.Inrobustnesscheckswe usedummiesbasedonnineethnicitygroupsbasedontheofﬁcial UKgovernmenttypologies,13geographicalorigincategories (cov-eringtheUK,Ireland,Europeanzonesandothercontinents),anda numberofmajorlanguages.

Formultiplepatentinventors,wearealsoabletoobserve cog-nitiveandsocial proximities.Cognitiveproximityis setupasa dummytakingthevalue1ifijhavepreviouslybothpatentedin thesameIPCtechnologyﬁeld.FollowingThompsonandFox-Kean

(2005)andSingh(2005),ourpreferredmeasureuseseachof1581

6-digitIPCﬁelds.WeusealessdetailedIPC3versioninrobustness checks.

Social proximity is defined as the inverse social distance between i and j, based on whether they have co-invented in thepast,haveco-authorsin common,or moreindirectlinksto actual/possiblepartners.Weassumethattiesdecayafterfiveyears. FollowingSingh(2005),foragivenyear,wethenmeasurethe num-berof‘steps’betweeninventorsiandjbasedontheiractivityinthe previousfive-yearperiod.Thisisthesocialdistancebetweeniandj. Wethentaketheinversedistancetogeneratethesocialproximity betweenthetwo.Forexample,ifiandjhaveco-inventedtogether inthepast,thenumberofstepsbetweenthemis0.Ifiandjhave notcollaborateddirectly,buthavebothworkedwithk,thenthere isonestepbetweenthem;ifiisconnectedtojthroughkandl,there aretwosteps;andsoon.Respectivedegreesofsocialproximityare then0,−1,and−2,throughtominusinfinity(nolink).

4.2.1. ‘Observables’versusﬁxedeffectsmodels

WeﬁrstestimatethemodelwithPROXalone,followedbythe modelwithvectorsofobservablecharacteristics,thenwitha bat-teryofﬁxedeffects.Theobservablesmodelis:

Yijoatf =a+PROXbit,jt+INDcit,jt+INSTdio,jo

+ENVe(iat,jat)f+eijoatf (2) ThevectorsIND,INSTandENVrespectivelycoverobservable characteristicsof inventors (both iand jin thepair), applicant organisations,andtime,technologyfieldandTTWA characteris-tics.MoredetailonthecontrolsisgiveninAppendixA.2.Thissetup handlesanumberofindividual,institutionalandmacrofeaturesof collaborations.However,itdoesnotdealwithunobservables,and thusourpreferredmodelisthefixedeffectsspecificationbelow, whichhandlestime-invariantfactors:

Yijoatf =a+PROXbit,jt+Iioa+Jjoa+Tt+TFf+uijoatf (3) Thisspeciﬁcationdoesnotcontrolforselectionissuesor time-varyingunobservables(seeSection4.4):so,asbefore,weinterpret bsasassociations,notcausaleffects.

Notealsothatafixedeffectsspecificationseparately control-ling for individuals, organisations, areas, time and technology field would be unwieldy. To keep regressions tractable, there-fore,wesetupinventor-applicant-area‘spells’foreachinventor iandjinapair,followingAbowdetal.(2002:48)andAndrews

et al. (2006:49). These ‘spell’ variables take unique values for

each inventor-applicant-TTWA combination in thepanel. Since ourvariablesofinterestareatthepairlevel,thisallows control-lingfornon-time-varyingunobservablesoneachsideofthepair, whilekeepinginpair-levelcharacteristics.Inaddition,weﬁtyear

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dummiesandanIPC6technologyfield‘groupingvariable’tocover timetrendsandtechnologyfield-specificshifts.

4.3. Estimation

Wehavea‘limiteddependentvariables’setting.Inprinciplewe shouldadoptnon-linearestimators,sincelinearregressionwould assumethewrongfunctionalform.AsAngristandPischke(2009) pointout,incertaincasesnon-linearmodelsshouldbepreferred on‘curve-fitting’grounds.Forinterpretation,however,resultsneed tobeconvertedintomarginaleffects,andwhenthisisdone,slope coefficientsare verysimilartoOLSestimates.Practically, linear applicationsarealsomoreparsimoniousandeasiertohandle, espe-cially whenusing paneldataand when largenumbersof fixed effects are required (Schmidheiny, 2013). In ourcase, the esti-mationofthecoefficientsofinterestisconditionalonbeingable toobservetime-varyingand invariantpair-levelcharacteristics. Fitting pair-level fixedeffects is therefore not feasible, and fit-tingindividual-leveldummiesasfixedeffectswouldtendtoyield biasedestimatesaswellasbeingcomputationallychallenging(over 150,000variables). Thatis, ‘correct’ functionalformcomesat a substantialcost.

Our preferred approach is toﬁt a linear model withrobust standarderrors,whichforthebinaryoutcomevariableissimply alinearprobabilitymodel.Wethencompareestimatesfrom non-linearestimatorsinrobustnesschecks.9_For_the_counts_model,_we compareOLSwithnegativebinomialestimates(wehaveover90% zeroesandPoissonconditionsarenotmet).Weareonlyableto achieveconvergencewithaPROXplusobservablesmodel,asnoted above.ResultsaregiveninTableC.4.Forthebinarymodel,excess zeroesmeanthatastandardlogitmodelwillnotconverge.Weare abletoachieveconvergenceusingarareeventsmodel(Boschma etal.,2013;KingandZeng,1999):inthiscase,convergenceisonly feasibleforareduced-formobservablesmodelwithoutENV,with resultsverysimilartoequivalentOLSestimates.

4.4. Otherissues

Webrieflyreviewfourotherissuesthatmightaffectourresults. Thefirstisendogenouspartnerselection(AckerbergandBotticini, 2002).ConsideracontractdecisionbetweenaprincipalPandan agentA.IdeallyPandAobserveeverythingabouteachother, reach-ingtheoptimalcontract.Inreality,thereareunobservablequalities ofPandAwhichaffecttypeofcontractchosen.Our‘observables’ specificationfacesaversionofthisproblem;thefixedeffects spec-ificationhandlesnon-time-varyingunobservablecharacteristics, butdynamicunobservablefactorsareleftunobserved.Asweset outabove,thismeansourresultsaredescriptiveassociations.

Asecondissueconcernsthepresenceofthirdparties.Sofarwe havelookedatthecharacteristicsofco-inventingteams assum-ingthatA’sdecisiontoco-inventwithB(ornot)isnotaffectedby thepresenceofCorD.ButasSedikesetal.(1999)pointout,this assumptionmaynothold.AdecisiontopartnerwithAratherthan BmaybeaffectedbythepresenceofC,whichshiftsrelative pos-itionsofA-B-Conspeciﬁcdecisionaxes.Inthiscase,patentdata givesusalimitedviewoncollaborationstructurebyallowingusto seeinventorteamsize.Weusethistogenerateateamsizecount variable.Wealsoﬁtafurthervariablegivingtheaveragenumber ofco-inventorsforeachinventorinagivenyear:theintuitionhere isthatinventorsmayseekoutteamsofagivensize.

9_We_estimate_using_the_user-written_Stata_command_reg2hdfe:_this_uses_an

iter-ativeprocedureoneachcoefﬁcienttoachieveconvergence,followingGuimaraes andPortugal(2010).Importantly,thisapproachprovidesaclearinterpretationof theﬁxedeffects,avoidingarbitrary‘holdouts’/referencecategories.

Third,ourdatastructureimpliesthatwedonotactuallyobserve inventorswhentheyarenotﬁlingpatents:theymaybeworkingon otherinventions,ormaybeinactive.Iftherearestructuralpatterns here,thismayleadtoomittedvariablebias.Wecouldtherefore setinventorpairactivitytozeroincellswherepatentingdoesnot occur;or,moreconservatively,blank allcells inwhich inventor pairsarenotactive.Inarelatedpaper,Nathan(2014)testsboth approachesonasubsetofmultiplepatentinventors–bothdeliver identicalresults.Wethereforefeelconﬁdentwithazero-basing assumptionforouranalysis.

Fourth,itisalwayspossiblethatco-patentingisdriving peo-pletomoveinclosergeographicalproximityortobecomepart of thesame ﬁrm.Without anexogenous source ofvariation in these proximities,selection issues are harder todeal with. We testtheincidenceofphysicalmovementfollowingtheprocedure

inAgrawaletal.(2006),whoidentify14.2%ofinventorsaslikely

moversacrossTTWAs,aswellasusingamorecautiousstrategy developed byCrescenziand Gagliardi(2015),who report5%of likelymoversamongUKinventorsduringthe1992–2007period. This impliesspatial movement is unlikely toaffect results, but weareunabletoeasilydevelopparalleltestsforotherselection channels.Forexample, accordingtothePatValsurvey,34.7%of UKinventorswhohadpatentedchangedjobatleastonce,albeit duringthewholelengthoftheircareers(Brusonietal.,2007), mak-ingjob-to-jobmobilityapotentialadditionalchannelofselection beforeresearcherscanobserveanypatentingactivity.Thisimplies that,onceagain,ourresultsaretreatedasassociations,ratherthan causaleffects.

5. Results

The regressionanalysis is organisedinto threesections. The ﬁrstsectionlooksattheresultsforallinventors(largersample, butmorelimitedsetofexplanatoryvariablesintermsof proximi-ties),whilethesecondsectionlooksatthesub-sampleofmultiple patentinventors.Thethirdsectionincludesanumberofrobustness checks.

5.1. Allinventors

For thefullsample ofinventors itis possibletoexplore the incidenceofgeographic,organisationalandcultural/ethnic prox-imities.ResultsforthecollaborationdummyaregiveninTable1, andforco-inventioncountsinTable2.Columns1to3includethe basicspecificationwithgeographic,cultural/ethnic,and organisa-tionalproximitiesalone(Column1),plusobservables(Column2), andplusfixedeffects(Column3).Columns4and5lookatthetime splitforthe1990sand2000s.Intheinterpretation,wefocusonthe relativesignandsignificanceoftheproximitiesvariables,rather thanunpackingspecificpointestimates.10

5.1.1. Collaboration

TheresultspresentedinTable1pointtodiversiﬁed associa-tionsbetweenproximitiesandco-patenting.Co-patentingteams areformedpredominantlywithintheboundariesofthesame orga-nisation in culturally and ethnically diversegroups. Teams are formedbypreferablyrelyingonlong-distancemeansof commu-nicationinordertoincludecollaboratorswiththenecessaryskills andpossiblytapintotheknowledgeofremotelocations.

10_For_the_full_sample,_Table_B.1_in_Appendix_B_gives_summary_statistics_(ﬁrst_panel)

andcorrelationsmatricesoftheproximitiesvariables(secondpanel).Correlation matricesindicatethatourvariablesofinterestarefreeofcollinearityproblems.The resultsareconﬁrmedinVIFtests.Othermodelﬁtstatisticsarealsosatisfactory,with

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Table1

Allinventors,co-inventiondummy,1992–2007.

(1) (2) (3) (4) (5)

Proximities Proximitieswith controls Proximitieswith fixedeffects Proximitieswith fixedeffects 1990s Proximitieswith fixedeffects 2000s Geographicproximity −0.00612* ₋_0.0207*** ₋_0.0320*** ₋_0.0526*** ₋_0.0236*** (0.003) (0.002) (0.001) (0.002) (0.001) Cultural/ethnicproximity −0.00426*** ₋_0.00922*** ₋_0.0158*** ₋_0.0209*** ₋_0.0137*** (0.001) (0.001) (0.001) (0.002) (0.001) Organisationalproximity 0.304*** _0.118*** _0.120*** _0.152*** _0.107*** (0.004) (0.004) (0.001) (0.002) (0.001)

IND,INST,ENVcontrolsforij N Y N N N

Spellsﬁxedeffectsforij N N Y Y Y

Observations 190,313 117,456 190,313 54,415 135,898

F 1729.627 39.960 27.508 19.286 32.871

R2 _0.271 _0.526 _0.662 _0.660 _0.666

Allmodelsusetimedummies.Robuststandarderrorsinparentheses.

INDvectorincludespre-1992patentcountanddominantpre-92patentingstylefori,j.

INSTvectorincludesapplicanttypedummiesfori,j.

ENVvectorincludesIPC6groupingvariable,TTWAdummiesfori,jandyeardummies.

* _p_<_0.1.

**p<0.05.

***_p_<_0.01.

Column1suggestsarobustpositiveassociationbetween orga-nisational proximityand inventorpairs’ tendency toco-invent. Bycontrast,cultural/ethnicproximityisstronglynegatively con-nectedtothepropensitytoco-inventandgeographicalproximity isonlymarginallysignificant.Theintroductionofcontrolsfor indi-vidual,institutional,andenvironmentalconditions(Column2),as wellasfixedeffects inColumn3reducesthemagnitudeofthe organisationalproximitycoefficient,whileincreasingthenegative coefficientofculturalethnicproximity.Thesecontrolsalsomake thecoefficientforgeographicalproximityhighlysignificant.The resultsalsoindicatenoparticularchangeintheincidenceofall proximitiesbetweenthe1990s(1992–1999,Column4)andthe 2000s(2000–2007,Column5),exceptforamarginaldecreasein theirsize.

5.1.2. Inventorpairactivity

In Table 2 we look at whether the results attained when

assessing the characteristics of individual collaborations stand whenconsideringthenumberofpatentsactuallygeneratedbythe ‘actual’inventors’pairs.Thestructureoftheregressionspresented

inTable2isthesameasthatdescribedforTable1.Theresultsin

Table2arealsobroadlysimilartotheco-inventiondummy

mod-els,suggestingthatorganisationalproximityandculturaldiversity areessentialelementsforcollaborationbetweeninventorpairs. The coefficient for thevariable depicting geographical proxim-ity turns negative only after the introduction of fixed effects: geographicalproximityiscorrelatedwitha numberof unobser-vablefactorsthat,ifnotproperlyaccountedfor,arelikelytobias estimatedcoefficients.Onceagain,nonoticeabledifferencesare evidentbetweenthe1990sand2000s.

Overall,theseresultssuggestthatﬁrmsandresearchcentres aretheloci ofknowledgecombination andre-combination,the fundamentalunitsofanalysisoftheinnovationprocess.Itiswhen inventorsarewithinthesameorganisationalboundariesthat prob-lems of signalling (e.g. in terms of the quality of the possible collaborators), free riding and procrastination (once teams are formed),andsecrecy aresuccessfullydealtwith. Bysharingthe samesetofinternalroutines,rulesandnormsinventorsareable toworkinteamsinthemostproductivefashion.Atthesametime, withintheboundariesofthesameorganisation,inventorscanmore Table2

Allinventors,co-inventioncounts,1992–2007.

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Proximities Proximitieswith controls Proximitieswith fixedeffects Proximitieswith fixedeffects 1990s Proximitieswith fixedeffects 2000s Geographicproximity 0.0197** _0.000539 ₋_0.0180*** ₋_0.0349*** ₋_0.0118*** (0.009) (0.007) (0.003) (0.007) (0.003) Cultural/ethnicproximity −0.00517*** ₋_0.00964*** ₋_0.0190*** ₋_0.0263*** ₋_0.0160*** (0.001) (0.001) (0.002) (0.005) (0.002) Organisationalproximity 0.343*** _0.0996*** _0.116*** _0.169*** _0.0927*** (0.009) (0.014) (0.003) (0.007) (0.003)

IND,INST,ENVcontrolsforij N Y N N N

Spellsﬁxedeffectsforij N N Y Y Y

Observations 190,313 117,456 190,313 54,415 135,898

F 584.899 22.622 5.893 3.805 7.471

R2 _0.090 _0.169 _0.296 _0.277 _0.311

Allmodelsusetimedummies.Robuststandarderrorsinparentheses.

IND,INSTandENVcontrolvectorsasinTable1. *p<0.1.

** _p_<_0.05. ***_p_<_0.01.

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easilysolveconﬁdentialityproblems,facilitatingknowledge circu-lation.

Conversely, the results signal that the direct incidence of geographical proximity is complex and diversified. Given that inventorsinthesameorganisationarelikelytobeco-localisedin thesamepremises,organisationalproximityalsoshedslight on therelevanceofhyper-geographicalproximitywithinthefirms’ boundaries.UKinventorsfirstseekthecollaborationof–orare coercedtocollaboratewith–otherinventorswithinthe bound-ariesoftheirorganisations.Inventorteamsarethencomplemented byresearchersandspecialistswhocanprovideanadditionaledge totheteam.Thissearchforadequateco-inventorsisbynomeans constrainedbygeographicalboundariesandtendstofollow non-spatiallymediatedchannels.Giventhehighercostsofsearching outsidethefirms’boundariesaswellastheotherfrictions that affectcollaborationsacrossorganisations,thesearchforthe opti-malpartnertendsnottobelimitedbyspatialproximity(Fitjarand

Rodríguez-Pose,2011).

ContrarytosomerecentUSstudies(KerrandLincoln,2010;

SaxenianandSabel,2008),ourmainresultsalsosuggestthat,on

average,UK-basedinventorsseekcultural/ethnic/linguistic diver-sityratherthantheproximity,trustandlowertransactioncosts which may be possibly associated with collaborating with co-nationalsorwithindividualsofthesamecultural/ethnicorigin. Thetypicalinventorseemslesslikelytoworkwithpeoplefrom thesame national and/or culturalbackgroundeven when they arephysicallyclose.11_Ethnic_proximity_is_likely_to_{‘inﬂuence’}_the optimalsearchprocessofinventorsbeyondgeographicproximity, eventuallyreducingtheincentivestocollaborateandincreasing theriskofcognitivelock-in,inparticularwhenthisiscoupledby geographicproximity.Theseresultsechotheevidenceonthe neg-ativeinﬂuenceof‘bondingsocialcapital’oninnovation(Crescenzi etal.,2013):‘strongties’increasetheriskofexchangingredundant knowledgesimplybecausetheyconnectknowledgeseekerswith otherindividualsthataremorelikelytodealwith‘known’/familiar informationandknowledge(LaursenandMasciarelli,2007;Levin

and Cross,2004;Ruef,2002).Thisevidenceeffectively

comple-mentsourpreviousresults:once thesearchprocessovercomes ﬁrms’organisationalboundaries,itfollowstrajectoriesthatarenot constrainedbyotherproximities.Onthecontrary,it seemsthat teamformationtendstoprivilegediversityintermsofnon-local knowledge/skills/competences,aswellasavarietyofculturaland ethnicbackgrounds.

5.2. Multiplepatentinventoranalysis

Dothesepatternsstandwhenweconsiderthesubsetof multi-plepatentinventorsindetail?Arevariousproximitiesbalancedin thesamewaybythesehighlyinnovativeindividuals?Thefocuson multiplepatentinventorsmakesitpossibletoreducethepotential ‘whitenoise’generatedinpatentdataby‘hobbyistinventors’,who patentonlyonceintheirlifewithlimitedimpactontheir tech-nologyﬁeld(Lettletal.,2009).Inaddition,focusingonmultiple patentinventorsallowsustotestdifferenttypesofproximities– socialandcognitiveproximity–impossibletooperationaliseinthe singleinventoranalysis.12_The_possibility_to_explore_a_wider_set_of

11 _This_may_not_be_the_case_for_all_inventors;_Nathan_{(forthcoming)}_ﬁnds_evidence

ofco-ethniclinkstopatentingforsomeminorityethnic‘stars’.Ourresultisalso sensitivetothepreciseethnicityproxyused–seerobustnesschecksinSection5.3.

12 _Table_B.2_in_Appendix_B_gives_summary_statistics_(ﬁrst_panel)_and_correlations

matricesoftheproximitiesvariables(secondpanel).Inthiscase,becausethesocial andcognitiveproximityvariablesarebasedonpastinventorbehaviour,pairwise correlationsbetweentheseproximitiesanddependentvariablesarehigherthan desirable(between0.5and0.54),butdonotindicatefatalcollinearityproblems. Withthepatentsamplingbaseincreasedfrom5to25%,modelﬁtstatisticsare

non-spatialproximitiesfacilitatesthecomparisonoftheincidence ofnetworksbasedoncultural/ethnicproximitywithpotentially less‘constrained’andmore‘diverse’collaboration-searchpatterns basedonsocialandcognitiveproximity.Theresultsaregivenin Tables3and4.

5.2.1. Collaboration

Collaborationresultsformultiplepatentinventorsarecovered

inTable3.Column1introducesﬁxedeffects,asinColumn3in

theprevioustables.Social andcognitiveproximityareincluded inColumn2,whileColumns3to7interacteachproximitywith geographicaldistance.Finally,Columns8and9splitthesample intothe1990sand2000stimeperiods.

Themoststrikingresultsfromtheanalysisofmultiplepatent inventorsarethatorganisationalproximityremainsmainfeatureof co-patentingcollaborationswhilecultural/ethnicproximityloses significance and geographical proximity emerges as a relevant characteristicforco-patentingteams,wheninteractedwithother proximities(Table3).Thecoefficientfor organisational proxim-ityisalwayspositiveandsignificantatthe1%level.Thisimplies that multiple patent inventors do not become less constrained by organisationalboundariesthan occasionalinventors. Indeed, theircollaborationstendtohavepreciselythesame characteris-tics,suggestingthattheyprefertoworkwithcolleaguesfromthe sameorganisationwhendecidingtocollaborateorcreatenetworks. Potentiallymore‘senior’andexperiencedindividualswithlonger patentingexperiencestillleveragetheirorganisationsasthe fun-damental milieufor theirinnovativeactivities.Thisrelianceon organisationalproximityincreasesinthe2000s(Table3,Column 9).Thesharingofhighlysophisticatedandconfidentialmaterial viainformationandcommunicationtechnologieshasreinforced theimportanceofplatformsandsystemsinternaltothefirm.

Iftheformationofteamstakesplacelargelywithinthesame organisationalunit,external searchpatternsarealsoimportant. Social proximity is always positively and signiﬁcantly associ-atedwithrepeatedinvention.UKmultiplepatentinventorsmore frequentlypatentwithotherinventorswithwhomtheyhad previ-ouslyestablisheddirectorindirectcollaborations(Table3,Column 2).Cognitiveproximity,measuredonitsown,bycontrastseems to be negatively associated withcollaboration by serial

inven-tors(Table3,Column2).Multiplepatentinventorsworktogether

withindividualswithwhomtheyhaddevelopedpre-existing con-nectionsvia social networks, rather than withthose in similar cognitive areas. Connections mediated by social proximity are strongerthanthose‘constrained’bycultural/ethnicfactorsand cog-nitiveproximity.Indeed,serialinventors’teamsareassociatedwith cognitivediversityandcomplementaryknowledgebases.Theneed toavoidlock-inandsearchforcognitivediversityand complemen-taryknowledgedrivetheselectionofcollaboratorswithin‘known’ networks,soastominimisesearchcosts,whilepreservingdiversity andaccesstonon-redundantknowledge.

The analysisof theinteraction effects of thedifferent prox-imities consideredinrelationtogeographical distanceprovides someinteresting nuancestothis picture.On theone hand,the relevanceofcultural/ethnicproximitiesseemstoemergeonlyat closequarters.MultiplepatentinventorsintheUKtendtowork morewithcolleaguesfromthesameculturaland/orethnic back-ground,onlyiftheyarebasedinthesameorinnearbylocations (positiveinteractionterminTable3,Column3).Asimilartendency emergesforbothorganisationalandsocialproximity:theincidence oforganisational(Column4)andsocial(Column5)proximitiesin collaborationsis furtherreinforced bygeographicproximity.By

substantiallyhigherthanforthefullpanel,withtheR2_rising_to_about_0.8_with

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Table3

Multiplepatentinginventors,co-inventiondummy,1992–2007.

(1) (2) (3) (4) (5) (6) (7) (8) (9) Basewith ﬁxedeffects Social& cognitive Interaction terms 1990s 2000s Geographicproximity 0.00462*** _0.00383*** _0.00154 _0.00148 _0.00262*** _0.00390*** ₋_0.00107 ₋_0.00311* _0.000557 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.002) (0.002) Cultural/ethnicproximity −0.000343 −0.000420 −0.000741 −0.000442 −0.000377 −0.000419 −0.000711 −0.00200** _0.0000191 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Organisationalproximity 0.0237*** _0.0215*** _0.0213*** _0.0160*** _0.0210*** _0.0215*** _0.0171*** _0.0120*** _0.0239*** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.002) (0.002) Socialproximity 0.0625*** _0.0625*** _0.0618*** _0.0351*** _0.0627*** _0.0356*** _0.0261*** _0.0441*** (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.004) (0.005) Cognitiveproximity −0.00498*** ₋_0.00498*** ₋_0.00468** ₋_0.00522*** ₋_0.00430** ₋_0.00284 ₋_0.00172 ₋_0.00406 (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.003) (0.003) Geographicprox.*cultural/ethnicprox. 0.00437*** _0.00435*** _0.0101*** ₋_0.0000359

(0.001) (0.001) (0.002) (0.002) Geographicprox.*organisationalprox. 0.0128*** _0.00886*** _0.00588** _0.00894***

(0.002) (0.002) (0.003) (0.003) Geographicprox.*socialprox. 0.0666*** _0.0663*** _0.0725*** _0.0812***

(0.005) (0.005) (0.007) (0.008) Geographicprox.*cognitiveprox. −0.00576 −0.0187*** ₋_0.0321*** ₋_0.00287

(0.006) (0.006) (0.008) (0.009)

Observations 40,638 40,638 40,638 40,638 40,638 40,638 40,638 19,710 20,928

F 27.691 28.266 28.269 28.295 28.432 28.261 28.451 34.030 20.916

R2 _0.821 _0.824 _0.824 _0.824 _0.825 _0.824 _0.825 _0.865 _0.762

Allmodelsusetimedummiesandspellsﬁxedeffects.Robuststandarderrorsinparentheses.

IND,INSTandENVcontrolvectorsasinTable1.

* _p_<_0.1. ** _p_<_0.05. ***_p_<_0.01.

contrast,geographicproximitydoesnotreinforcetherecurrence ofcollaborationsinthesametechnologyfield(Column6).A neg-ativeinteractiononlyappearsinColumn(7)whereallinteraction termsareincludedsimultaneouslyandthedirecteffectbecomes insignificant.ThisresultisinlinewithRigby(2013),whofindsa

trendintheUSforcitiestobuildinventioncompetencearounda rangeofrelatedtechnologies–iftheyarebasedindistant loca-tions,suggestingthatgeographicandcognitiveproximitytendto besubstitutesratherthancomplements.However,theseresultsare mainlyaconsequenceoftrendsinthe1990s.Inthe2000sneither

Table4

Multiplepatentinginventors,co-inventioncounts,1992–2007.

(1) (2) (3) (4) (5) (6) (7) (8) (9) Basewith ﬁxedeffects Social& cognitive Interaction terms 1990s 2000s Geographicproximity −0.0108 −0.0134* ₋_0.0512*** ₋_0.000288 ₋_0.0139* ₋_0.0183** ₋_0.0418*** ₋_0.0697*** ₋_0.0193* (0.008) (0.008) (0.011) (0.009) (0.008) (0.008) (0.011) (0.021) (0.011) Cultural/ethnicproximity 0.00524 0.00526 −0.0000429 0.00538 0.00528 0.00521 0.00000667 0.00611 −0.00518 (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.006) (0.011) (0.006) Organisationalproximity 0.0701*** _0.0598*** _0.0578*** _0.0902*** _0.0596*** _0.0600*** _0.0922*** _0.154*** _0.0118 (0.011) (0.011) (0.011) (0.014) (0.011) (0.011) (0.014) (0.023) (0.014) Socialproximity 0.249*** _0.0881*** _0.0866*** _0.0881*** _0.0389** _0.0366* _0.0760** ₋_0.0210 (0.024) (0.018) (0.018) (0.018) (0.019) (0.019) (0.034) (0.019) Cognitiveproximity 0.0882*** _0.249*** _0.253*** _0.238*** _0.232*** _0.236*** _0.380*** _0.0269 (0.018) (0.024) (0.024) (0.031) (0.024) (0.031) (0.052) (0.034) Geographicprox.*cultural/ethnicprox. 0.0721*** _0.0727*** _0.0983*** _0.0467***

(0.014) (0.014) (0.025) (0.013) Geographicprox.*organisationalprox. −0.0712*** ₋_0.0797*** ₋_0.178*** _0.0759***

(0.020) (0.021) (0.035) (0.021) Geographicprox.*socialprox. 0.0276 −0.000994 −0.221*** _0.253***

(0.048) (0.049) (0.081) (0.055) Geographicprox.*cognitiveproximity 0.422*** _0.424*** _0.867*** ₋_0.0960*

(0.056) (0.057) (0.097) (0.058) Observations 40,638 40,638 40,638 40,638 40,638 40,638 40,638 19,710 20,928

F 4.711 4.750 4.757 4.753 4.749 4.767 4.777 3.931 6.564

R2 _0.438 _0.440 _0.441 _0.440 _0.440 _0.441 _0.442 _0.425 _0.501

Allmodelsusetimedummiesandspellsﬁxedeffects.Robuststandarderrorsinparentheses.

IND,INSTandENVcontrolvectorsasinTable1.

* _p_<_0.1. ** _p_<_0.05. ***_p_<_0.01.

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thecoefficientfortheinteractionbetweengeographicproximity andcultural/ethnicproximity,norbetweentheformervariableand cognitiveproximityremainsignificant.Onlythestrongconnections betweensocialandgeographicproximityandbetweentheformer andorganisationalproximitystaysignificantinthe2000s(Column 9).Technologicaldevelopmentplaysanancillaryrolein suppor-tingtheprocessofsearchalongorganisationalandsocialnetworks trajectories.

Hence,as inthe case of occasional inventors,organisational proximityisanessentialcharacteristicofco-patentingteams,while social proximityemerges asanother cruciallyimportant factor. MultiplepatentinventorsbasedintheUKpatentmuchmorewith otherinventorsincloseorganisationalproximityandwithin estab-lishedsocialnetworks,withspacemediatingtherelevanceofall typesofproximitiesconsidered.

Teamsformedbymultiplepatentinventorscollaboratemore withotherinventorswhomtheymaybeabletomeetonamore frequentbasisinthesameorganisationandwithwhomtheyhave collaboratedinthepastandmaybeeasilyaccessibleviaanumberof telecommunicationschannels.Thisimpliesthatideasarenot nec-essarilyintheair–asimpliedinMarshallianandagglomeration approaches–butthattheyflowinsideverywell-structured orga-nisationalandsocialpipelines(FitjarandRodríguez-Pose,2015). Nevertheless,thefunctioning of thesecommunicationchannels improvesinclosegeographicalproximity.Similarconclusionsare reachedbyCassiandPlunket(2010),whostudygenomicspatents inFranceandsuggestthatspatialproximityishighly complemen-tarytosocialproximity,evenifindividualpartnersareindifferent typesoforganisations.Singh(2005)confirmsthatthegeography andfirmboundariesinteractwithinterpersonalnetworksin shap-ingknowledgeflows.However,ourresultscontrastwiththoseof

Agrawalet al.(2008),who concludethatgeographic and social

proximityoperateassubstitutesintheUSA. 5.2.2. Multiplepatentinventorpairactivity

Table 4 covers inventor pair activity. As for the full set of

inventors,theresultsforfrequencyofcollaborationsbymultiple patentinventorslargelyresemblethesimplecollaboration analy-sisdiscussedabove.Socialand,aboveall,organisationalproximity remainhighlysignificantinallspecifications(Columns1and2). However,inthecaseofco-inventioncounts,cognitiveproximity showsapositiveandsignificantsign,suggestingmultiple collab-orationshappeningmorefrequentlywithinthesametechnology class.

Whiletheinteractionbetweencultural/ethnicand geographi-calproximitiesremainsunchanged(Column3),otherinteraction terms (Columns4–7) depicta slightly differentstorythan pre-viousresults. Organisationalandgeographicalproximitydisplay significantcoefficientswiththeexpectedsign,buttheir interac-tionis not significant. Thissuggeststhat theydo notreinforce oneanother,hintingatpotentialsubstitution,ratherthan comple-mentarities(Column5).Finally,theinteractionbetweencognitive andspatialproximitiesispositive andsignificant,meaningthat frequent patenting by serial inventors is potentially associated withspecialisedspatialclusters. Timesplits(Columns8 and9) indicatethatthesedifferenceswiththecollaborationdummyare most prevalentin the 1990s. For the 2000s the signs and sig-nificance oftheinteraction termsare in linewiththe previous table.

5.3. Robustnesschecks

Section4highlights a numberofpotentialchallenges toour empiricalstrategy.Todealwiththese,we subjectourmodelto aseriesofrobustnesschecks(reportedinAppendixC).Resultsnot shownherearegiveninanonlineappendix(AppendixD).

Wefirsttestformeasurementandspecificationissues.TableC.1 covers three key tests (the others are available in the online appendix).Column1refitsthemainresultforthecollaboration dummy.Column2removesallpatentswhereinventorsand appli-cants are probabilistically matched(see Section 4.2).The betas changeslightlybuttheoverallpatternofresultsremainsthesame. Thissuggeststhatthepatentassignmentprocessdoesnotaffect ourresults.

Column3introducesascaledorganisationalproximitymeasure. Proximityisassignedgreaterweightforsingleapplicantpatents thanforco-assignedpatents.Thecoefficientispositiveand sig-nificant,confirmingourintuitionthatthereisastrongerlinkto co-patentingintheformercasesthanwhenotherapplicantsare involved,evenifbothmembersofthepairworkforthesame orga-nisation.

Column4includesadummyforapplicantspatialproximity, tak-ingthevalue1ifapplicantssharethesameTTWA.Wefitthisin ordertotryanddisentangleindividualandapplicantlocationissues (asexploredinLychaginetal.(2010)amongothers).Thisvariable isfuzzybyconstruction,sinceitcanonlybedirectlyobservedfor actualpairsonco-assignedpatents,aselectedsub-sample:for pos-siblepairs,applicantinformationistakenfromanyotherpatentand maynotcorrespondtoaninventor’s‘real’applicantinanygiven year.Assuchitmaybeacaseof‘badcontrol’(AngristandPischke, 2009)andneedstobetakenwithsomecaution.Here,the coeffi-cientispositivesignificant,andthebetaforgeographicproximity dropsslightly.Takenatfacevalue,thisimpliesthatwhile work-ingforthesameorganisationistheprinciplelinktoco-patenting, workinginco-locatedapplicantsalsohasasmaller,independent connection.

Inothertests(shownintheonlineappendix,TableOA1),we reﬁtthecollaborationdummymodelwithalternativemeasures forgeographicproximity(alineardistancethresholdandaTTWA dummyforinventorsinapair)andcultural/ethnicproximity(same ONSethnicgroup,samegeographicalorigin,samemajorlanguage). Theoverallpatternoftheresultsdoesnotchangefromthemain speciﬁcation.Wealsorunallofthesetestsforthecountmodel, withlittleornochangefromourmainestimates.Furtherchecks exploretheroleofinventorteamsize,andsuggestthatwhilesize isarelevantcharacteristicofco-patentingteams,itdoesnot‘knock out’proximities(TableOA2).

TableC.2repeats thesechecks formultiplepatentinventors,

andaddssomefurthertests.Aswiththepooledsample,rescaling organisationalproximitysuggeststhatorganisationalproximityis strongest whenpatentshavesingleapplicants (Column2). Col-umn3 fitsanalternative social proximitymeasure, rescaledas an ordinal variable; Column 4 refits cognitive proximity using largerIPC3technologyfieldsinsteadofIPC6.Neitherchangesour mainresults.Column5fitsapplicantspatialproximity,with sim-ilarresultstothepooledsample.Column6removesco-assigned patents,alsowithlittlechange.Asbefore,wealsorefitgeographic andcultural–ethnicproximities(TableOA3).Specifyingthelatter as‘samemajorlanguage’generatesamarginallysignificantpositive coefficient: this is weak evidencethat linguistic ties may mat-terforrepeatcollaboratorsinawaythatbroadercultural–ethnic groupingsdonot.Otherre-specificationsdonotchangeourmain estimates.Resultsforcollaborationcountsarealsoavailableonline (TableOA4).

A second set of concerns centres on estimation issues. We beginbyshiftingfromrobuststandarderrorstoerrorsclustered on inventor pairs: results are identical (Table OA5). Next, we tighten thefixedeffectspecification toincludeIPC6technology field-by-yearfixedeffects,allowingshockstovaryovertimeand technologyspacesimultaneously(TableC.3).ComparedtoColumn 1,themainspecificationinColumn2,geographicproximity,isnow closetozeroandnon-significant.Estimatesforotherproximities

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barelychange.These resultssuggest that geographic proximity maybesensitivetofixedeffects specifications:however,model fit substantially decreases, implying a potentially sub-optimal specification.

TableC.4compareslinear and non-linear estimatorsfor the

countsmodel.AsdiscussedinSection4,wecontrastthelinear esti-matorwithanegativebinomialspecification,usinganobservables modelwithoutfullfixedeffects.Asallunobservedfactorsare omit-ted,thetestsimplyillustratesdifferentfunctionalforms.Column 1givesthelinearmodel,Column2therawcoefficientsfromthe negativebinomial,andColumn3themarginaleffects.Thelatter showsthatwithanon-linearestimator,geographicproximityis positivebutonlymarginallysignificant,comparedtozerointhe linearmodel.Coefficientsforotherproximitieskeepthesamesign andsignificance.Thisimpliesthatthechoiceofalinearestimator doesnotbiasourresults.

Finally,thereistheconcernthatourresultsderivefroma partic-ularsub-sampleofpatentsandinventors.Sincesamplingpatents andinventorsisthebasisofourempiricalstrategy,itis impor-tanttotestthis.Todoso,werebuildthemainpanelfivetimes usingdifferentsamplesofpatents.Wethenre-runthe collabora-tiondummymodeloneachpanelinsequence;whilecoefficient sizesvary slightly, thesign and significance of thePROX vari-ablesremainunchanged(TableOA6),indicatingthatourresults arerobusttosamplechoice.

6. Conclusions

Innovationhasbecomeanincreasinglycollaborativeactivityin recentyears. Inventorscooperatemorethan everin the inven-tionprocess,butourknowledgeofthefactorsthatdeterminethe formationofinventorteamsisstillincomplete.

Thispaper has lookedat the characteristics of co-patenting teamsinordertoexploretheincidenceofdifferentproximities. Usingarichmicrodataset,wehavebeenabletofocuson indi-vidualinventors acrossthe full range of technology ﬁelds and indifferenttimeperiods,examininggeographic,cultural/ethnic, organisational,social, and cognitive factors,individually and in combination.Indoingso,weaddressempiricallyanumberofissues whichhavegenerallybeenconsideredfromamoretheoretical per-spectiveintheliterature.

Ourresults–whichcontrolforﬁxedeffectsandarerobustto multiplecross-checks–containa numberofimportantﬁndings whichinpartchallengetheexistingliteratureandpolicyconsensus ontheimportanceofspatialclusteringforknowledgeexchangeand innovation.

The analysis of the features of UK patenting teams shows thata signiﬁcant partof innovationactivitiesare characterised byorganisationalproximity.Thatis,theytakeplacewithinthe boundariesofprivateﬁrms(thataccountforthelargestshareof allapplicants),universitiesandotherorganisations. Sharingthe sameapplicant capturesthesimultaneousimportanceof hyper-geographical proximity(inventorsin thesame organisationare likely to be co-localised in the same premises) and organisa-tionalproximity.Second,theresultsshowthatethnicandcultural diversityarealsoimportantfeaturesofinventingteamsandthat social networksrepresent thebuildingblocks forcollaboration. Third,geographicproximityentersthepictureonlyindirectly:it interactswithotherproximitiesincreasingtheirassociationwith collaborativework.Inventorsseemtorelyongeographical prox-imitytoformtheirteamswhenitiscoupledwithotherformsof ‘advantage’.

Overall, theresults highlightimportant differences between theproximityrelationshipslinkingUKinventorsoveralongtime span.However,ithastobeborneinmindthatourresultssuffer fromsomelimitations.First,withoutexogenousvariationsinthe

observedproximities,selectioneffectscannotbecontrolledfor,and resultshavetobeinterpretedasassociations.Secondpatentdata canonlycapturecollaborationsthatleadtoapatentedoutput.We donotobserveunproductivecollaborations(ornot-yet-productive ones).Third,wecannotcapturecollaborationsthatleadto non-patentableoutput–forexampleintheformofprocessinnovation orinnovationinservices(animportantpartofUKinnovation activ-ityashighlightedinCrescenzietal.,2015).Finally,therelianceon patentdatamightleadtoanover-estimationoftheimportanceof organisationalproximity.Thetendencyofinventorstosharethe sameapplicantinevitablyreﬂectsfundamental incentivesabout disclosureversussecrecyinR&Dprojects.

Havingacknowledgedtheselimitations,ourresultsmake inno-vativecontributionstotheexistingliteratureoninnovationand itsdriversonseveralfronts.First,theanalysisofinnovationand itsgeography cannotbe restricted tothestudy of spatial rela-tionships between innovation agents: organisational structures andﬁrms’boundariesplayakeyrole–oftenunderestimatedin theexistingliterature–inshapinginnovativecollaborationsand knowledgediffusion.Second,wheninventorssearchfor collabo-ratorsoutsidetheboundariesoftheirorganisations,ethnictiesor cognitiveproximityarenotnecessarilythepreferredsearch chan-nel.Contrarytowhatpartoftheexistingliteraturesuggests(e.g.

DocquierandRapoport,2012oncultural/ethnicproximityintheUS

orKogleretal.,2013;Rigby,2013oncognitiveproximity),

‘uncon-strained’socialnetworksthatallowfordiversitytoemergeand avoidcognitivelock-inareprevalentinUKpatentingteams.Third, theinnovativeapproachofthispaper–combiningfortheﬁrsttime alargesetofdifferentproximitiesinthesameinventor-level analy-sis–hasuncoveredhithertounobservedsynergiesandsubstitution effectsbetweenvariousproximities.Thefocusoftheexisting lit-eratureonspeciﬁcspatialunitsofanalysis(e.g.cities)oronmore limitedsub-setsofproximitieshasoftenneglectedtheunderlying complexityofthephenomenaunderanalysis.

The three-fold innovative contribution of our paper has importantimplicationsfo