Venture capital investments and the technological performance of portfolio firms

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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

Venture

capital

investments

and

the

technological

performance

of

portfolio

ﬁrms

Henry

Lahr

a,b,1

_,

_Andrea

_Mina

c,∗

a_Centre_for_Business_Research,_Cambridge_Judge_Business_School,_University_of_Cambridge,_Trumpington_Street,_Cambridge_CB2_1AG,_UK

b_Department_of_Accounting_and_Finance,_The_Open_University,_The_Open_University_Business_School,_Walton_Hall,_Milton_Keynes_MK7_6AA,_UK

c_Cambridge_Judge_Business_School,_University_of_Cambridge,_Trumpington_Street,_Cambridge_CB2_1AG,_UK

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received11July2014

Receivedinrevisedform13May2015

Accepted6October2015

Availableonline16November2015

JELclassiﬁcation: O31 G24 L26 Keywords: Venturecapital Patenting Selection Signalling Innovation

a

b

s

t

r

a

c

t

Whatistherelationshipbetweenventurecapitalists’selectionofinvestmenttargetsandtheeffectsof

theseinvestmentsonthepatentingperformanceofportfoliocompanies?Inthispaper,wesetouta

modellingandestimationframeworkdesignedtodiscoverwhetherventurecapital(VC)increasesthe

patentingperformanceoffirmsorwhetherthiseffectisaconsequenceofpriorinvestmentselectionbased onfirms’patentoutput.WedevelopsimultaneousmodelspredictingthelikelihoodthatfirmsattractVC financing,thelikelihoodthattheypatent,andthenumberofpatentsappliedforandgranted.Fully accountingfortheendogeneityofinvestment,wefindthattheeffectofVConpatentingisinsignificant ornegative,incontrasttotheresultsgeneratedbysimplermodelswithindependentequations.Our findingsshowthatventurecapitalistsfollowpatentsignalstoinvestincompanieswithcommercially viableknow-howandsuggestthattheyaremorelikelytorationalise,ratherthanincrease,thepatenting outputofportfoliofirms.

1. Introduction

Newfirmscanrarelyrelyoninternalcashflowsintheirpursuit ofentrepreneurialopportunities.Amongthesourcesofexternal financeavailabletoentrepreneurs,venturecapital(VC)canprovide notonlythefinancialresourcestheyrequire,butalsoassistance toenhancethedesign,development,andperformanceofportfolio companies(Lerner,1995;BergemannandHege,1998;Gompers and Lerner,2001;De Clercqet al.,2006; Schwienbacher,2008; Cumming,2010).

Amongthedifferentdimensionsofentrepreneurialgrowththat theliteraturehasnoted,astrongassociationhasbeenidentified betweenVCinvestmentsandinnovation,oftenmeasuredbythe firm’spatentingoutput.Aprominentthesisisthatventure cap-italistsimproveinvesteefirms’innovativeperformancethrough theirabilityto‘coach’newbusinessesandtonurturethemto pro-ducegreatertechnologicaloutput(KortumandLerner,2000;Popov

∗ Correspondingauthor.Tel.:+441223765330.

E-mailaddresses:h.lahr@cbr.cam.ac.uk(H.Lahr),a.mina@jbs.cam.ac.uk

(A.Mina).

1 _Tel.:₊₄₄₁₉₀₈_332648.

and Roosenboom, 2012).An alternative argument hasreceived relativelylessattentionasyet,althoughitsvaliditymayleadto adifferentconclusion:thatventurecapitalistsareexceptionally goodatidentifyingnewfirmswithsuperiortechnological capabili-ties,whichtheyseeasthebestinvestmentopportunities.Seenfrom thisangle,themostdistinctivetraitofVC,andthereforethemost salientexplanationforthestrongertechnologicalperformanceof VC-backedfirmsrelativetootherfirms,wouldbetheventure capi-talists’superiorselectioncapabilities(BaumandSilverman,2004). Venturecapitalistsfacearesourceallocationproblem charac-terisedbyhighriskandstronginformationasymmetries.Inorder todecreasetheseinformationasymmetries–giventhatpotential investeeshavelittleornotrackrecordsofmarketperformance– investorshavetorelyonothersignalsoffirmquality.Theseinclude theexantepatentingperformanceofpotentialinvestees(Häussler etal.,2012;Contietal.,2013b;HsuandZiedonis,2013),so patent-ingcanbeseenasanantecedentofVCinvestmentdecisions,aswell asalikelyconsequence.Disentanglingtherelationshipbetween VCinvestment and firms’technologicalperformance involvesa significanttheoreticalaswellasempiricalchallengebecauseof endogeneityandreversecausationbetweentheinvestmentand innovationprocesses.

http://dx.doi.org/10.1016/j.respol.2015.10.001

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Thisisanimportantproblem,notonlyfromascholarly perspec-tivebutalsofromapolicyviewpoint.EventhoughtheVCsector financesonlyaminorityofnewfirms,itplaysaveryprominent roleinpoliciesdesignedtoovercomefinancegapsand togrow entrepreneurial,innovation-driveneconomies(OECD,2014).This rolehasnotgoneunquestioned:criticalissueshavebeenraised aboutscaleandskillsinthedemandandsupplyofventurefinance (Nightingaleet al.,2009), governance (Lerner, 2009), cyclicality andstagedistributionofinvestments(KaplanandSchoar,2005; Cummingetal.,2005;LahrandMina,2014),andtheoverallreturns andlong-termsustainabilityoftheVCinvestmentmodel(Mason, 2009;Lerner,2011;Mulcahyetal.,2012).Thesemakeitevenmore importanttogainaclearandaccurateunderstandingofthe VC-innovationnexus.

InthispaperwemodeltherelationbetweenVCandpatenting usingsimultaneousequationstoconsiderboththedeterminants ofVCinvestments,includingpatentsassignalsoffirmquality,and theeffectofVConfirms’post-investmentpatentingperformance, controllingfortheirpriorperformance.Weusedatafroman orig-inalsurveyof3669USandUKcompanies.Weextractinformation onthe940firmsthatsoughtfinancebetweentheyears2002and 2004andmatchthese recordswithpatent dataextractedfrom theEuropeanPatentOffice’sWorldwidePatentStatisticalDatabase (PatStat)fortheperiodsconcurrenttoandfollowingthesurvey years.Controllingforotherfirmcharacteristics(e.g.size,age,R&D expenditure,andmarketsize),weestimatesimultaneousmodels for(1) thelikelihood that firms’patenting activitiespredictVC investmentsand(2)thelikelihoodthatsuchinvestmentsleadto patentinginthefollowingperiod.Weemployabivariate recur-siveprobitmodelanddevelopasimultaneouszero-inflatedPoisson modelforcountdata,usingbothtocontrolfortheendogenous natureoftheselectionandcoachingprocesses.

Wedemonstrate that,oncewe accountfor endogeneity,the effectofVConthesubsequentpatentingoutputofportfolio compa-niesiseithernegativeorinsignificant.Theseresultsindicatethat, whileventurecapitalistspositivelyreacttopatentsassignalsof companieswithpotentially valuableknowledge,confirmingthe ‘selection’hypothesis,thereisnoevidenceofapositiveeffectofVC investmentonfirms’subsequentpatentingperformance.Itis plau-siblethatVCwillpositivelyinfluenceotheraspectsofnewbusiness growth(i.e.commercialisation,marketing,scalingup,etc.),butthe contributionofVCdoesnotseemtoinvolveincreasinginvestee firms’technologicaloutputs.Importantly,thefactthatthe techno-logicalproductivityofafirmmayslowdownafterVCinvestment doesnotimplythatthefirmwouldbebetteroffwithoutVC:onthe contrary,aninsignificantornegativeeffectofVConfirmpatenting suggeststhatventurecapitalistsrationalisetechnologicalsearches andfocusthefirm’sfiniteresources,includingmanagerial atten-tion,ontheexploitationofexistingintellectualproperty(IP)rather thanfurthertechnologicalexploration.

This paper advances our understanding of the financing of innovativefirms by modelling thedeterminants of investment choicesbyVCandthepatentingoutputoftheirportfolio compa-niesatthetimeofandafterVCinvestment.Insodoing,thepaper alsointroducesanoriginalmethodologythatcandisentanglethe endogenousrelationshipbetweenVCandpatentingefficiently,and hasthepotentialforfurtherusesintreatinganalogoustheoretical structures.

2. VCinvestmentsandpatenting:Theoryandevidence Investmentsinsmallandmedium-sizedbusinesses,andin par-ticularnew technology-basedﬁrms, posespeciﬁcchallenges to capitalmarketsbecausetheyinvolvehighrisksandstrong infor-mationasymmetries(Lerner,1995;Hall,2002).Fromaninvestor’s

viewpoint, theeconomic potentialof these firms is difficultto assess given their short history and the lack of external sig-nalsabouttheirquality(e.g.auditedfinancialstatements,credit ratings),orofmarketfeedbackaboutnewproductsandservices atthetimeofinvestment.Onlyfewinvestorsareableand will-ingtobackthesebusinesses.Theydosowiththeexpectationof satisfactoryreturnsbyapplyingaspecificsetofcapabilities,and oftensector-specificbusinessknowledge,thatenablethemtomake betterchoicesrelativetocompetinginvestors,handle technolog-icaland marketuncertainty,and activelyinfluencetheoutcome oftheirinvestments(Sahlman,1990;Gompers,1995;Hellmann, 1998;Gompersand Lerner,1999,2001;Kaplanand Strömberg, 2003,2004).

IntheextantstudiesthathaveaddressedthelinksbetweenVC andinnovation,onestreamhasfocusedontheabilityofventure capitaliststoassistportfoliocompaniesbygivingthemformaland informaladvice,thusaddingvalueinexcessoftheirfinancial con-tributions(GormanandSahlman,1989;Sapienza,1992;Busenitz etal.,2004;ParkandSteensma,2012).Asecondandmorerecent streamhasinsteademphasisedtheabilityofVCstousepatents assignalsoffirmqualityandtomakesuperiorchoices,relativeto otherinvestors,amongtheinvestmentoptionsthatareavailable tothem.Ifwhatmattersforthesubsequentperformanceof portfo-liocompaniesisthequalityoftheinitialinvestmentdecision,the sourceofventurecapitalists’competitiveadvantagerestsontheir selectioncapabilities,definedastheirabilitytoidentifytheinvestee companieswiththegreatestgrowthpotential(Dimovetal.,2007; Yangetal.,2009;Fitzaetal.,2009;ParkandSteensma,2012).In thefollowingtwosectionswereviewtheargumentsandevidence behindthesetwoperspectives.

2.1. TheeffectsofVConpatenting

Theproposition that venture capitalistsare ableto increase firmvaluebeyondtheprovisionoffinancialresourceshasgained considerable support in the literature (Gorman and Sahlman, 1989;Sahlman,1990;BygraveandTimmons,1992;Lerner,1995; KeuschniggandNielsen,2004;Croceetal.,2013),andisespecially clearwhentheyarecompared,forexample,tobanksinthesupply ofexternalfinancingtosmallandmedium-sizedenterprises(Ueda, 2004).Venturecapitalistscantakeactive rolesin manyaspects ofthestrategic andoperationalconductoftheirportfoliofirms, includingtherecruitmentofkeypersonnel,businessplan devel-opment,andnetworkingwithotherfirms,clientsandinvestors, oftenonthebasisofin-depthknowledgeoftheindustry(Florida and Kenney,1988; Hellmann and Puri,2000, 2002;Hsu, 2004; Sørensen,2007).

SeveralstudiesfindlinksbetweenVCinvestmentsandfirms’ patentingperformance,andgenerallyinterpretapositive associa-tionbetweenthetwoasaresultofthe‘value-adding’or‘coaching’ effectsofVC.Oneofthemostprominentstudiesonthistopicis KortumandLerner’s(2000)paper,inwhichtheauthorsmodeland estimateapatentproductionfunctioninaninvestmentframework. Aggregatingpatentnumbersbyindustry,theyfindapositiveand significanteffectofVCfinancingon(log)patentgrants1_._Ueda_and Hirukawa(2008)showthatthesefindingsbecomeevenmore sig-nificantduringtheventurecapitalboominthelate1990s.However, estimationsoftotalfactorproductivity(TFP)growthrevealthat thiswasnotaffectedbyVCinvestment,aresultthatcontrastswith

1_Both_patenting_and_venture_funding_could_be_related_to_unobserved

techno-logicalopportunities,therebycausinganupwardbiasinthecoefﬁcientonventure

capital,butregressionsthatuseinformationaboutpolicyshiftsinventurefund

legislationtoconstructaninstrumentalvariablealsoshowpositiveimpactsofVC

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Chemmanuretal.’s(2011)study,whichrevealsapositiveeffect ofVConTFP.PopovandRoosenboom(2012)alsoﬁndsimilar posi-tive,althoughweaker,resultsforsucheffectsinEuropeancountries andindustries.Furtherestimationsofautoregressivemodelsfor TFPgrowthand patentcountsbyindustryseemtosuggestthat TFPgrowthispositivelyrelatedtofutureVCinvestment,butthere isweaker evidencethat VCinvestmentsprecedeanincrease in patentingattheindustrylevel,andthereareindicationsthatlagged VCinvestmentsareoftennegativelyrelatedtobothTFPgrowthand patentcounts(HirukawaandUeda,2008).

Empirical firm-level studies on venture capital investments tendtoconfirmtheexistenceofapositiverelationbetweenVC andpatentingperformance(Arqué-Castells,2012;Bertonietal., 2010;Zhang,2009).Thispattern isnotonlyfoundfor indepen-dentbutalsoforcorporateventurecapital(Alvarez-Garridoand Dushnitsky,2012;ParkandSteensma,2012).Lerneretal.(2011) estimate various models, includingPoisson and negative bino-mial models, for patents granted and patent citations in firms thatexperiencedprivateequity-backedleveragedbuyouts(LBOs). Theyfindanincreasednumberofcitationsforpatentapplications post-LBOandnodecreaseinpatentoriginalityandgeneralityafter suchinvestments. Patentcounts do notseem tovary ina uni-formdirection.AstudybyEngelandKeilbach(2007)foundthat VC-backedfirmsapplyfortentimesasmanypatentsasmatched non-VCbacked firms: theauthorsusepropensityand balanced scorematchingtocompareventure-fundedtonon-VCfunded Ger-man firms in terms of theirtechnologicaloutputs and growth, althoughthisdifferencewasonlyweaklysignificant.Casellietal. (2009)useasimilarmatchingproceduretoassessthedifference inthepatentingandgrowthperformancesintheventure-backed IPOsofItalianfirms.Theirresultsshowahigheraveragenumber ofpatentsintheventure-backedfirmsthanintheircontrolgroup. Importantly,however,noneofthesestudiesprovidessolutionsto thefundamentalproblemoftheendogeneityofinvestmentrelative tofirms’technologicalperformance2_.

2.2. Investmentselection

The second hypothesis that might explain the correlation betweenVCinvestmentandfirms’technologicalperformanceis thatventurecapitalistshavedistinctiveselectioncapabilities.This impliesamodellingframeworkinwhichtheinnovativeprofilesof potentialinvesteefirmsaffecttheprobabilitythattheyreceiveVC investment.Fromthisperspective,patentscanfunctionassignals toinvestorsaboutfirmquality(BaumandSilverman,2004;Mann andSager,2007;Häussleretal.,2012;Audretschetal.,2012;Conti etal.,2013a,b;HsuandZiedonis,2013).

Thereareseveraldimensionstotheinvestmentevaluation pro-cessemployedbyventurecapitalists(Shepherd,1999),andthere isgrowinginterestinthetechnologicaldeterminantsofventure financing.BaumandSilverman(2004)exploredthelinksbetween VCfinancing,patentapplications,andpatentsgranted.Their find-ingssuggestthattheamountofVCfinanceobtaineddependson laggedpatentsgranted andappliedfor,R&D expenditures,R&D employees,governmentresearchassistance,theamountof sector-specificventurecapital,horizontalandverticalalliances,andthe investeefirmbeingauniversityspin-off.Ageisnegativelyrelated toventurecapital,asarenetcashflows,diversification,and

indus-2_To_the_best_of_our_knowledge,_Croce_et_al.₍₂₀₁₃₎_is_the_closest_attempt_to_date

toaddresssampleselectionproblemsinthecontextofthevalue-added

hypoth-esis.However,thisinterestingstudydoesnotconsideranyinnovationindicators

anditsanalysisofportfoliocompanies’productivitygrowthislimitedbytheuse

ofonlyasmallsetofbasicﬁrmcharacteristics.OurpaperdoesnotfocusonTFP

estimates—insteadweexploreinsomedetailthetechnologicaloutputofﬁrmsin

relationtoentrepreneurialﬁnancedecisions.

tryconcentration. Mannand Sager (2007) confirm thepositive impactofpatentingonVC-relatedperformancevariables,including thenumberoffinancingrounds,totalinvestmentandexitstatus. Similarly,astart-upfirm’spriorpatentingattractgreateramounts ofVCfundsinCaoandHsu’s(2011)studyofventure-backedfirms. Häussleretal.(2012)elaborateonSpence’s(2002)signalling theorytoarguethatthefoundersofentrepreneurialfirmsare bet-terinformedaboutthequalityoftheventurethanarepotential investors,andthattheyusepatentsascommunicationdevicesto bridgethisinformationgap.Patentsareeffectivesignalsof qual-ityonthegroundsthat, astheyareproducedat acost (inthis casethefeesassociatedwiththepatentingprocess),low-quality agentswilltendtobeweededout.Theirempiricalanalysis con-firmsthatpatentapplicationshaveapositiveeffectonthehazard rateofVCfundinginasampleofBritishandGermanbiotech com-panies. Thesefindings resonate withprior resultspresentedby EngelandKeilbach(2007),whoseprobitmodellingofVC invest-mentrevealsapositiveassociationwithpatentsandthefounder’s humancapital. Alonga similarlineof enquiry –albeit setin a broaderPenrosian framework thanthat usedby Häussleretal. (2012)–HsuandZiedonis(2013)analyseVC-financedstart-ups intheUSsemiconductorsector.Theyshowthat,bybridging infor-mationasymmetries,patentsincreasethelikelihoodofobtaining initialcapitalfromaprominentVC,andhavepositiveeffectson fundraisingandIPOpricing(conditionalonIPOexit).

Bybringingthesestreamsofcontributionstogether,weaimto answerthequestion:Doesventurecapitalpositivelycontributeto thepatentingperformanceoffirmsorisitaconsequenceof ven-turecapitalists’abilitytoidentifythebestcompaniesatthetimeof investment?Resultsbasedonfirm-levelinformationaremixed, which suggeststhatpositive findingscouldbeatleastpartially drivenbyVC’sselectionofcompaniesonthebasisoftheircurrent patentoutput.Wecanonlyshedlightontheeffectsofcoaching vis-à-vistheselectionfunctionofVCifwetakeintoaccountthe endogeneityoftherelationbetweenVCinvestmentandthe tech-nologicalperformanceoffirms.Ourresearchstrategyisthereforeto model,test,andevaluateinasimultaneoussetting(1)theeffectof VConthepatentingperformanceofportfoliofirmspostinvestment and(2)theeffectoffirms’patentingperformanceontheprobability ofattractingVC.

3. Dataandmethodology 3.1. Data

ThispaperbuildsonauniquecomparativesurveyofU.K.andU.S. businessescarriedoutjointlybytheCentreforBusinessResearch attheUniversityofCambridgeandtheIndustrialPerformance Cen-teratMITin2004–2005.ThebasisforthesamplingwastheDun& Bradsheet(D&B)database,whichcontainscompany-specific infor-mationdrawnfromvarioussources,includingCompaniesHouse, Thomson Financial, and press and trade journals3_. _The _sample coveredallmanufacturingandbusinessservicesectors,andwas stratifiedbysectorandemploymentsize(10–19;20–49;50–99; 100–499;500–999;1000–2999;and3000+),withlarger propor-tionstaken in the smallersize bands,as in both countries the vastmajority(over98%)offirmsemployfewerthan100people. ThedatawerecollectedviatelephonesurveysbetweenMarchand November2004(responserates:18.7%fortheU.S.and17.5%for

3_The_choice_of_the_Dun_&_Bradstreet_database_was_motivated_by_its_broad_coverage

ofsmallﬁrms,whicharekeyforstudyingventurecapitalinvestments.Good

cover-ageofSMEsisalsothereasonwhytheEuropeanCentralBankusesthisdatabasefor

itssurveyontheaccesstoﬁnanceofSMEsintheeuroarea(SAFE);seehttp://www.

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theU.K.),followedbyapostalsurveyoflargefirmsinspring2005 leadingtoatotalsampleof1540U.S.firmsand2129U.K.firms.

Werestrictoursampletofirmsthatactivelysoughtfinance dur-ingthetwo yearspriortobeinginterviewed,whichproduces a workingsampleof940firms(513intheU.S.and427intheU.K.). ThesurveylistsVCfundsandbusinessangelsaspossiblesources ofexternalfinance.Despitesomedifferencesinstagesandsizes ofinvestments,geographicproximity,andmotivationfor invest-ments(Ehrlichetal.,1994), bothventurecapitalistsandangels spendconsiderabletimewithfirms’managementteams,andmake substantialnon-financialcontributionsinadditiontotheir finan-cialcommitments,includingworkinghands-onintheirday-today operations(Kerret al., 2014;Haines et al.,2003; Harrison and Mason,2000).Sincebothformalandinformalventurecapitalists canperformsimilarfunctionsfromourstudy’sviewpoint,wepool observationsfor thesetwo classes of activeinvestors, although when we perform robustness tests with separate samples,the resultsareconsistentwithourmainanalyses(Section5.3). Infor-mationabouttheeventofaVCinvestmententersourmodelsasan endogenousbinaryvariable.

Firms answered the survey questions almost completely, althoughminorgapsinthedatawouldhavepreventedusfrom usingabout10% ofthesurvey responses.In ordertoavoid the lossofobservationsduetomissingvalues,weuserandom regres-sionimputationtoapproximatethem(GelmanandHill,2006).The numberofsuchimputationsisgenerallyverylow—alwayslessthan 2%pervariable.Wheredependentvariablevaluesaremissing,we dropthoseobservations.

PatentdataaretakenfromtheEuropeanPatentOffice’s(EPO) WorldwidePatentStatistical Database(PatStat), which contains informationon68.5millionpatent applicationsby17.3million assigneesandinventorsfrom1790to2010.Sincetherearenofirm identifiersavailableinPatStat,wematchpatentinformationtoour surveydatabyfirmname.Weconsiderfirms’globalpatent port-folios,andcountallapplicationstodifferentpatentauthoritiesfor similaroroverlappingknow-howasmultiplepatentingevents.To alignpatentdatawiththethreeyearperiodaddressedinthe sur-vey,wecountthenumberofpatentsappliedforandgrantedwithin athree-yearperiodpriortotheinterview(calculatedfromexact surveyresponsedates),anddetermineeachfirm’spatentingstatus fromthisnumber.Morespecifically,weuseapplicationfilingand publicationsdatesforthefirstgrantofanapplicationtodetermine thetimingsofpatentingevents.Forourdependentvariables,we countapplicationsandgrantsforthewholepost-surveyperiodin ordertocapturethelong-termeffectsofVCinvestment.Finally, weincludeadummyvariableforthefirmbeingbasedintheUSor theUKtocontrolfordifferentpropensitiestopatent–and likeli-hoodofgrant–indifferentdomesticinstitutionalenvironments. Weabstainfromusingforwardcitation-weightedindicators for patents,acontrolforpatentqualitythatisespeciallyusefulin stud-iesofperformance,becausesuchcitationsmaybeaffectedbythe likelihoodofinvestment,andmaythusintroduceafurthersource ofendogeneityintothisanalyticalcontextthatwouldbeimportant toavoid.

Table1showsdescriptivestatisticsforoursamplefirms’ patent-ingactivitiesandourindependentvariables.146firmsfromour sampleappliedforpatentsduringthethree-yearsurveyperiod(t), and168filedpatentapplicationsinthenextperiod(t+1).We iden-tifiedpatentgrantsin115and141firmsintheserespectiveperiods. 96firmsgainedventurecapitalorbusinessangelfinancinginabout equalproportionsinthetwoyearspriortothesurvey.Asimple cross-tabulationofindicatorsforVCfinancingandforpatenting activityatt(seeTable2)highlightsthestronglinkbetween ven-turecapitalandpatenting.Itshowsthat56.2%ofVC-financedfirms appliedforpatentsinanyoftheperiods,whereasonly18.2%of thosewithoutVCfundingdidso.Butthispicturebeginstolook

differentwhenweconsiderchangesinthepatentingstatusacross periods.Inthenon-VCfinancedgroup,firmsseemtostartpatenting attimet+1moreoftenthantheystopapplyingafterpatentingat timet.Incontrast,thenumbersoffirmsintheVC-financedgroup thatstart patentingat timet+1balancethose thatdiscontinue theirpatentingactivitiesafterperiodt4_._Including_additional con-trolvariablesinourmultivariateanalysesgivesusamuchmore preciseassessmentofthesestatetransitions.

Theinclusionofexplanatoryvariablesbuildsonpriorstudies intotherelationshipbetweenVCandpatenting,whichhaveoften usedaverylimitednumberofco-determinants,sometimesonly R&Dexpenditures.Weextendthescopeoftherelevantpredictors forthepropensitytopatent,ofwhichR&Dintensityisthepreferred choiceaccordingtostandard practiceintheliterature(Scherer, 1965,1983;PakesandGriliches,1980;Pakes,1981;Hausmanetal., 1984).Since prior research hasused various measures for this intensity,includingthelogofR&D expenditures,R&D expendi-turesscaledbysizevariables,orthenumberofR&Demployees, wechooseasuitablecombinationoftheseindicators.Weproxyfor sizebytakingthelogarithmofemploymentandcontrolforR&D intensitybyincludingthepercentageofR&Dstaffandadummy indicating thepresence ofR&D expenditures. Thisallows usto avoidtheuseofmultiplesize-dependentmeasures,sincevariables entertheexpectedmeaninPoissonspecificationsmultiplicatively. Furthervariablescontrolforage,countryandindustry.Following Scherer(1983),weusetheamountofinternationalsalestomeasure marketsizeandcontrolforindustryconcentrationbythenumberof competitors.WemeasureCEOeducationbyadummyvariable indi-catingwhethertheCEOhasauniversitydegreeornot.Thelength oftheaverageproductdevelopmenttimeinthefirms’principal productmarketisalsocontrolledfor,sinceitarguablyplaysarole inattractinginvestment(HellmannandPuri,2000).Finally,given thehighlycumulativenatureoftechnicalchange(Dosi,1988)we includelaggedpatentapplicationsandgrantsasproxiesforthe firm’sknowledgestocksthatitusestoproducenewpatents5_. 3.2. Modelsandestimation

The structure of firms’ patenting decisions presents several econometric challenges. Previous research shows that the vast majorityoffirmsdonotpatent,whichcausesobservationsofzero patentsin alargeproportion offirms leadingin turntomodel instabilityanderrordistributions thatdo notmeet themodel’s assumptions if these excess zeroes are not properly addressed (Boundetal.,1984;Hausmanetal.,1984).Atthesametime, unob-servableheterogeneityis highlylikelytobecorrelated between VC investment and patenting performance: for example, firms mightdisclosepatentingactivitiestoprospectiveinvestors,which increasesthelikelihoodthatweobserveVCinvestmentsin com-bination with more patenting in the future. When using VC investment to explain patenting, this endogeneity complicates modelestimationandmaymakeitanalyticallyintractable6_.

We suggest that patenting involves a two-step process, in which ﬁrmsﬁrstdecidewhethertousepatenting asa suitable IPprotectionstrategy and then producepatentsaccording toa Poisson orsimilardistribution (seeFig.1).Followingthis logic,

4_Less_{surprisingly,}_for_patent_grants_the_proportion_of_ﬁrms_that_start

receiv-inggrantscomparedwiththosethatstopafterreceivingatleastonegrantinthe

previousperiodishigherforVC-fundedﬁrms.

5_Inclusion_of_lagged_dependent_variables_also_helps_to_account_for_unobserved

heterogeneity.

6_A_similar_challenge,_also _associated _with _mixed_empirical _evidence,

char-acterisesstudiesofVCinvestmentsandtheﬁnancialperformanceofportfolio

companies.Confront,forexample,SteierandGreenwood(1995)andBusenitzetal.

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Table1

Descriptivestatistics.

Variable Mean Median Std.Dev. Min Max Description

Anyapplicationsint+1 0.179 0 0.383 0 1

Patentapplicationsint+1 4.118 0 34.241 0 779 Numberofpatentapplicationsbytheﬁrmintheperiodafter

thesurvey

Anygrantsint+1 0.150 0 0.357 0 1

Patentgrantsint+1 2.746 0 30.337 0 889 Numberofpatentgrantstotheﬁrmintheperiodafterthe

survey

Anyapplicationsint 0.155 0 0.362 0 1

Patentapplicationsint 3.266 0 25.347 0 526 Numberofpatentapplicationsbytheﬁrmintheperiodthree

yearspriortothesurvey

Anygrantsint 0.122 0 0.328 0 1

Patentgrantsint 1.176 0 9.972 0 273 Numberofpatentgrantstotheﬁrmintheperiodthreeyears

priortothesurvey

VCinvestment 0.102 0 0.303 0 1 Theﬁrmobtainedformalorinformalventurecapitalinthe

three-yearperiodpriortothesurvey

Age(log) 2.937 2.996 0.858 0.693 5.720 Thenaturallogarithmoftheﬁrm’sageinyears.

Size(log(employees)) 3.848 3.714 1.059 1.099 6.804 Thenaturallogarithmofthenumberofemployeesinthemost

recentﬁnancialyear

U.S.ﬁrm 0.546 1 0.498 0 1 TheﬁrmhasitsheadquartersintheUnitedStates.Dummy

variable

Medium-hightechmanuf. 0.310 0 0.463 0 1 Theﬁrmisamedium-hightechmanufacturingﬁrmaccording

totheOECD(2005)Science,TechnologyandIndustry Scoreboard

Medium-lowtechmanuf. 0.381 0 0.486 0 1 Theﬁrmisamedium-lowtechmanufacturingﬁrm

R&Dservices&software 0.117 0 0.322 0 1 TheﬁrmisanR&Dserviceorsoftwareﬁrm.

Otherservices 0.153 0 0.360 0 1 TheﬁrmisaserviceﬁrmotherthanR&Dorsoftware

Otherindustry 0.039 0 0.195 0 1 TheﬁrmoperatesunderaSICcodenotcoveredabove

R&Dexpend.(yes/no) 0.732 1 0.443 0 1 TheﬁrmhasR&Dexpenditures.Dummyvariable

R&Dstaff 0.073 0 0.175 0 1 Full-timeR&Dstaffasaproportionoftotalstaff.

CEOhasadegree 0.637 1 0.481 0 1 Theﬁrm’sChiefExecutiveorMDhasadegree.Dummyvariable

Marketsize 1.747 2 0.912 0 3 Sizeoftheﬁrm’smarket.Codedasordinal0=local,

1=regional,2=national,3=international,treatedascardinal

Competitors(log) 1.987 1.792 1.012 0 6.909 Numberofcompaniesthattheﬁrmregardsasserious

competitorsplusone,inlogs

Productdev.time 0.971 1 1.048 0 4 Averagetimeittakestodevelopanewproductfrom

conceptiontothemarket.Codedasordinal0=lessthan6 monthsto4=morethan5years,treatedascardinal

Table2

Venturecapitalandpatentingstatus.

NoVC(n=844):Applicationsint+1 VC(n=96):Applicationsint+1 NoVC(n=844):Grantsint+1 VC(n=96):Grantsint+1

Applicationsint No(%) Yes(%) No(%) Yes(%) No(%) Yes(%) No(%) Yes(%)

No 81.8 6.0 43.8 11.5 85.4 4.7 51.0 15.6

Yes 3.4 8.8 11.5 33.3 2.7 7.1 6.3 27.1

Notes.Thistablepresentsthefractionoffirmsineachpresentandfuturepatentingstatusdependentonventurecapitalinvestment.Rowentriesarethenumberoffirms withanynumberofpatentapplicationsattimet.Columnsshowthenumberoffirmsapplyingfororbeinggrantedanynumberofpatents.

Fig.1.Modelframework.Notes.Dependentvariablesareventurecapitalinvestmentattimetandthenumberofpatentapplicationsorgrantsattimet+1.Inthebinary bivariatecase,“Patents(yes/no):”measureswhetherweobserveanynumberofpatentsforthefirmattimet+1.Inzero-inflatedPoissonmodelsthatalsoincludethenumber ofpatentsatt+1,thisvariableindicatesfirms’latentpatentingstatus.

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wemodelpatentingactivityasabinaryvariablethatdependson

ﬁrmandindustrycharacteristicsandaugmentourmodelswithan

endogenousbinaryvariablethatindicateswhetherornotaﬁrm

receivesventurecapitalﬁnancing.Insteadofrelyingon

propen-sityscorematchingorcomparablealgorithmstoidentifyacontrol

groupofnon-VCbackedﬁrms,wehavetheadvantagethatwecan

workwith‘treatment’and‘control’datathataregenerated

contem-poraneouslybythesurvey7_._Our_data_allow_us_to_identify_ﬁrms_that

soughtexternalfinance,andthoseofthemthatobtainedventure finance.Theexplicitconsiderationoffinance-seekingbehaviours, usuallyneglectedintheliterature,strengthensthequalityofour sampleandtheprecisionofourresults.

Weestimatetwo setsofsimultaneousequations:Inthefirst set,whichcontainstwoprobitequationsforpatentingandventure capitalinvestments,weignoreinformationaboutthenumberof patentsandtreatfirms’patentingbehavioursasbinaryoutcomes. Inthesecondsetweintroducethenumberofpatentsina zero-inflatedPoissonmodel.

Thepatentingequationintherecursivebivariatesystemis Pati,t+1=I

Xit0+1Patit+2ln (PatNit)+1VCit+εit>0

, (1) wherePatitisadummyvariableindicatingwhetherﬁrmiapplied foroneormorepatentsor,dependingoncontext,wasgrantedat leastonepatentperiodt.PatNitdenotesthenumberofpatent appli-cationsorpatentsgranted.TheindicatorfunctionI(·)equalsoneif theconditioninparenthesesholdsandzerootherwise.Sincepatent applicationsandgrantscanbezero–inwhichcasethenatural log-arithmwouldnotexist–wesetln(PatNit)tozeroanduseadummy variable(Patit)toindicatepatentingstatus.Endogenousventure capitalinvestmentiscapturedbyanindicatorvariable(VCit),and Xitrepresentsexogenousvariables.Thesimultaneouslydetermined venturecapitalinvestmentis:

VCit=I

Zitˇ0+ˇ1Patit+ˇ2ln (PatNit)+it>0

, (2)

whereZitisavectorofexogenousexplanatoryvariableswhichcan containsomeoralloftheelementsinXit.Endogeneityofventure capitalﬁnancingisaccountedforbyallowingarbitrarycorrelation betweentheerrorterms.Sincevarianceoferrortermsisnot iden-tiﬁedinbinarymodels,theerrortermsεitanditarenormalisedto haveavarianceofunity.

Asimilarsimultaneousmodelstructurecanbeusedtopredict thenumberofpatents.Sincepatentdatashowalargenumberof non-patentingfirms,wemodelthisempiricalregularity usinga zero-inflatedPoissondistribution.Inthismodel,firmsself-select intothepatentingregime,andathirdequationmodelsthenumber ofpatentapplicationsorgrantsproducedaccordingtoaPoisson distribution.AsinLambert’s(1992)zero-inflatedPoissonmodel, thenumberofpatentsisdistributedas:

PatNi,t+1 =

0 withprobability pit+ (1−pit)e−it k withprobability (1−pit)e−ititk/k!, k=1,2,... (3)

7 _This_approach_differs_from_Park_and_Steensma’s₍₂₀₁₂₎_and_Conti_et_al.’s_(2013b)_.

Incontrasttothesestudies,wehavedataonﬁrmsthatreceivedinvestmentandﬁrms

thatdidnot,becauseoursamplingwasnotguidedbyVCinvestmentevents.This

enablesustospecifyanappropriatetestforselection(investment)insteadofatest

forthetypeofinvestmentaﬁrmmayhavereceivedwhenalltheﬁrmsintheirsample

havereceivedsomeinvestments(i.e.wherethereisnocounterfactualof‘noVC

investment’).Contietal.(2013a),instead,haveno-VCinvestmentcounterfactuals

attheirdisposal,buttheirfocusisdifferent,astheyareinterestedintherelative

sensitivityofVCandbusinessangelinvestorstoheterogeneousinvestmentsignals.

Moreover,theyoptforamoretraditionaltwo-stageleastsquareslinearprobability

modeltotreatendogeneityofinvestment.

Thelikelihoodthataﬁrmchoosesnottopatentinthenextperiod is

pit=I

Xit0+1Patit+2ln (PatNit)+1VCit+εit

, (4) whiletheconditionalmeanofthePoissonprocessinthepatenting stateis it=exp

Xitı0+ı1Patit+ı2ln (PatNit)+NVCit+ωit

(5) Anovelfeatureofourmodelisthataﬁrm’slikelihoodof obtain-ingventurecapitalisdeterminedbyanadditionalequation: VCit=I

Zitˇ0+ˇ1Patit+ˇ2ln (PatNit)+it>0

(6) asinthebivariateProbitcaseabove.

Weallowforarbitrarycontemporaneouscorrelationbetween it and εit,as well asbetween it and ωit, which are assumed tofollowbivariatenormaldistributions.Specifyingthemodelin thiswayallowsforcorrelationbetweenheterogeneityinexpected meansofpatentcounts,thedecisiontopatentandVCfinancing.The varianceofindividual-levelerrors(ωit)introducesafree parame-terthataccountsforover-dispersioninPoissonmodels(Miranda andRabe-Hesketh,2006).Identificationinsemiparametric mod-elsofbinarychoicevariablesoftenreliesonexclusionrestrictions (Heckman,1990;Taber,2000)—inourparametriccase,however, thefunctionalformissufficientforidentification.Infact,imposing additionalrestrictionsonourmodelcouldcausespuriousresults, sincevariablesincludedintheVCequationbutexcludedfromthe patentingequationswouldaffecttheoutcomeequationthrough VCitif thosevariables werenottrulyindependentfrom patent-ing.Wethereforechoosetheexogenousvariablestobeidentical inallequations(Xit=Zit).Section5.4describesadditionalresults obtainedfromrobustnessteststhatuseexclusionrestrictionsin thepatentingequation(s).

Inthefollowingsectionwepresenttheresultsofbivariate recur-siveprobit modelsfor VCfinancing and patenting (i.e., results forthesimultaneousestimationofEqs.(1)and(2)andthenthe resultsweobtainfromthesystemofequationscompletewiththe zero-inflatedPoissonmodel(i.e.,Eqs.(3)to(6)),estimatedby max-imumsimulatedlikelihood(GouriérouxandMonfort,1996;Train, 2009)8_. _We _also_report _results _of _{zero-inflated} _Poisson _models thatincludeinformationaboutthenumberofpatents,butexclude simultaneousVCinvestmentasourbaselineresultsforthe com-pletesystemofequations(seeTable5).Astermsofcomparisons andtoshowhowkeyresultsdifferwhenendogeneityisnottaken intoaccount,weincluderesultsofindependent(single-equation) probitmodelsasrobustnesschecks(Table6).

4. Results

Wefindthatthecorrelationsbetweenventurecapital invest-ment and subsequent patenting are substantial and highly significant, ranging between 0.21 for (log) patent applications and 0.26 for a dummyvariablemeasuring whethera firm was grantedanypatentsafterreceivingVCinvestment.Thispositive linkcouldbeduetotechnologicalcoachingorselection.Aswe con-structincreasinglycompletemodelsfortherelationbetweenVC investmentandpatenting,thecoachingeffectdisappears.Table3 presentstheresultsfromoursimultaneousmodelthatjointly pre-dictspatentingandventurecapitalinvestment.

8_We_use_subscripts_t_and_t₊₁_to_distinguish_between_the_periods_concurrent

toandfollowingthesurvey.Weuse200randomdrawsinallmodelsestimated

bymaximumsimulatedlikelihood.Furtherestimationdetails,includinglikelihood

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Table3

PatentingandVCinvestment—simultaneousequations.

Dependentvariableinpatentingequation Applicationsint+1 Grantsint+1 Grantsint+1

Model 1 2 3

Dependentvariableinpatentingequation Patenting(yes/no) VCinvestment Patenting(yes/no) VCinvestment Patenting(yes/no) VCinvestment

VCinvestment −0.673(0.34)** _−0.175_(0.52) _−0.444_(0.37)

Patentapplications(log) 0.455(0.12)*** _−0.095_(0.08) _0.568_(0.13)*** _−0.098_(0.08)

Patentapplications>0 1.059(0.21)*** _0.609_(0.20)*** _1.635_(0.22)*** _0.614_(0.20)***

Patentgrants(log) 0.534(0.16)*** _−0.170_(0.12)

Patentgrants>0 1.206(0.22)*** _0.434_(0.23)*

Age(log) 0.083(0.08) −0.344(0.09)*** _0.010_(0.11) _−0.344_(0.09)*** _0.088_(0.10) _−0.342_(0.09)***

Size(log(employees+1)) 0.155(0.06)*** _0.197_(0.07)*** _0.058_(0.07) _0.213_(0.07)*** _0.027_(0.07) _0.206_(0.07)***

U.S.ﬁrm 0.053(0.14) −0.334(0.13)** _0.279_(0.17)* _−0.327_(0.14)** _0.320_(0.18)* _−0.332_(0.14)**

Medium-hightechmanuf. 0.095(0.29) −0.226(0.29) −0.084(0.34) −0.203(0.30) −0.254(0.27) −0.251(0.28)

Medium-lowtechmanuf. −0.223(0.30) −0.375(0.29) −0.463(0.35) −0.389(0.29) −0.566(0.28)** _−0.400_(0.29)

R&Dservices&software −0.131(0.33) 0.271(0.30) −0.152(0.40) 0.276(0.30) −0.260(0.35) 0.267(0.29)

Otherservices 0.068(0.32) −0.098(0.31) −0.757(0.40)* _−0.127_(0.31) _−0.782_(0.36)** _−0.132_(0.31)

R&Dexpend.(yes/no) 0.468(0.18)*** _0.424_(0.21)** _0.318_(0.20) _0.439_(0.21)** _0.467_(0.21)** _0.425_(0.22)*

R&Dstaff(in%) 0.356(0.35) 0.701(0.33)** _0.755_(0.44)* _0.838_(0.32)*** _−0.134_(0.44) _0.650_(0.33)**

CEOhasadegree 0.145(0.14) 0.363(0.17)** _−0.201_(0.16) _0.347_(0.17)** _−0.214_(0.17) _0.344_(0.17)**

Marketsize 0.260(0.09)*** _0.191_(0.09)** _0.319_(0.10)*** _0.203_(0.09)** _0.280_(0.11)*** _0.206_(0.09)**

Productdev.time 0.160(0.06)*** _0.043_(0.07) _0.138_(0.06)** _0.055_(0.07) _0.120_(0.07) _0.045_(0.07)

Competitors(log) −0.125(0.06)** _−0.084_(0.08) _−0.119_(0.06)* _−0.087_(0.08) _−0.203_(0.07)*** _−0.075_(0.07) Intercept −3.070(0.46)*** _−1.845_(0.44)*** _−2.313_(0.54)*** _−1.903_(0.43)*** _−2.408_(0.52)*** _−1.905_(0.44)*** Observations 940 940 940 Log-Likelihood −486.4 −454.7 −393.3 Chi-sq.test 354.1 296.1 374.8 P-value 0.000 0.000 0.000 (vit,εit)

p-Valuefor(Waldtest) 0.010 0.130 0.002

Pseudo-R2 _0.334 _0.357 _0.444

Notes.Thistablepresentsbivariaterecursiveprobitmodelsforpatentapplications,patentgrantsandforthelikelihoodofobservingventurecapitalinvestments.Robust standarderrorsareinparentheses.

Signiﬁcancelevels:***_p_<_0.01;**_p_<_0.05;*_p_<_0.1.

4.1. Patenting

Venture capital does not increase patenting activity

(Table 3—first column of Models 1–3)—rather, it decreases thelikelihoodofafirmfilingpatentapplicationsafterthe invest-ment, in contrastto prior firm level (e.g.Arqué-Castells, 2012) and industry level studies (e.g.Kortum and Lerner, 2000) that foundpositiveassociations.Theeffectonfuturepatentgrantsis insignificant.Correlationsofunobservedeffects inthepatenting and selection equations are positive, supporting ourmodelling strategy.

Wefindstrongpersistenceinpatenting,forbothapplications andgrants.Iffirmspatentinoneperiod,ittendstodosointhe next.Anindicatorforprior-periodpatentingissignificantinall specifications,whileapplyingfororreceivingalargenumberof patentsinoneperiodincreasesthelikelihoodofobservingatleast onepatentinthenext.Theseeffectscanbeinterpretedintwoways: Ontheonehand,priorpatentingcanbeaproxyforunobserved heterogeneitybetweenfirmsin theirabilitytoproduce innova-tions(othervariablesinourmodelsmightnotcaptureallaspectsof firms’internalprocessesandexternalmarketcharacteristicsthat leadtopatentingbehaviour).Ontheotherhand,knowledge–in theformofexistingpatents–isaninputfornewpatents. Exist-ingpatentscansignalthesizeoffirms’knowledgestocks,which areotherwisedifficulttomeasure.Astheseproductivecapacity stocksdepreciateovertime,itisreasonabletoassumethatrecent additions tothepatent stockare thebestpredictorsof present and future patenting activities, which is essentially what we find.

StrongevidenceoftheproductivityeffectsofR&Dexpenditures isconsistentwithpriorstudies(Cohen,2010).Thepercentageof R&Dstaffweaklypredictspatentingactivity,onlyshowing posi-tivecoefﬁcientsinmodel2.Humancapital–asmeasuredbythe CEO’seducation– doesnotappear toincreasethelikelihoodof

patent applicationsorgrants.Firmage doesnot seem toaffect patenting9_,_while_firm_size_has_a_positive_effect_on_future appli-cations,butnoeffectongrants,asBoundetal.(1984)found.Other variablesdonotexplainthevariationsinpatentingthatsizewould explainiftheywereexcluded.Collinearity islowinourmodels (varianceinflationfactorsarewellbelow5),anddropping signif-icantvariablesfromthemodelsdoesnotsignificantlychangethe effectofsize.Industryeffectscollectivelyexplainpatenting,but ontheirownareonlyweakpredictors.SignificantWaldtests con-firmtheimportanceofcontrollingforindustryeffects.However, individualeffectsarerarelysignificantinourpatentingmodels, asmightbeexpected,sinceourestimationsincludedetailed firm-level variables suchasR&D and humancapital. Unsurprisingly, firmscategorisedasmedium-hightechnologymanufacturingtend toapplyforpatentsmoreoftenandobtaingrantsmorefrequently thanlow-techmanufacturingandservicefirms.

FirmsbasedintheU.S.exhibitahigherchanceofsuccess(in termsoftheirapplicationsbeinggranted)thanthoselocatedin theU.K.,anindicationofknowninstitutionaldifferencesbetween the two countries’ patenting regimes10_. _As _expected,_patenting activityisstronglyassociatedwithproductmarketcharacteristics. Firmsthatoperatenationallyorinternationallyaremorelikelyto engageinformalIPprotectionthanlocalorregionalﬁrms.Thereis

9_If_patenting_was_to_depend_on_ﬁrm_age,_we_would_expect_a_start-up_effect_early_in

thelifeofﬁrmsthatarefoundedtoexploitsometechnologicalopportunity.Wetried

adummyvariableindicatingwhetheraﬁrmwasonlyfoundedduringthesample

periodbutfoundnoinﬂuenceonpatentingactivity.

10_The_{non-obviousness}_standard_in_U.S._patent_law_at_the_application_stage_has

beenweakened,leadingtothegrantofpatentsonincreasingnumbersoftrivial

inventions(Barton,2003;Gallini,2002).Structuraldifferencesinpatenting

pro-cessesalsoaffectpatentopposition,re-examinationandrevocationrates,which

aresigniﬁcantlyhigherforEuropeanandU.K.patentsthanforU.S.patents(Harhoff

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littledifferencebetweenmodelsforfutureapplicationsandgrants. Productsthatneedlongleaddevelopmenttimesaremoreoften protectedbypatentsthanthosewithashorttimetomarket.Again, thisisreasonablefromtheviewpointofafirmthatneedsmore pro-tectionoverlongerR&Dcycles.Finally,protectionfromimitation shouldbemoreprominentinindustriescharacterisedbyintense competition, although it is possible that firms in concentrated marketstrytodeterentrythroughthestrategicuseofpatenting (Scherer,1983).WhileScherer(1983)onlyfindsevidenceforalink betweenindustryconcentrationandthenumberofpatentsin mod-elsthatdonotcontrolforsectors,BaumandSilverman(2004)find fewerpatentsinconcentratedindustries.Incontrast,theeffectof highcompetitiononpatentingisnegativeinourmodels11_. 4.2. VCinvestment

Aﬁrm’sknowledgestockisagoodpredictorofventurecapital investment(seeTable3).PatentingattractsVCinvestments—more speciﬁcally,itisthefactthatacompanyispatent-active,notthe number of applicationsor grants, that predicts VC investment. Resultsareparticularlystrongfortheapplicationindicator,which signalsstronginnovationpotentialinportfoliocompanies.

R&Dexpenditures and R&D stafflevelsare both strong pre-dictors of VC investments, as is the CEO’s education level. VC involvementismorelikelytobefoundinyoungfirms,echoingthe findingsofpriorresearch.Interestingly,however,venturecapital fundsappeartoinvestinlargerfirmsmoreoftenthaninsmaller ones.Thisfindingcanbeexplainedbyinterpretingsizeasa mea-sureofinvestmentrisk,withverysmallfirmstypicallybeingmore opaquethanlargerones.Butitisalsoimportanttobearinmindthat oursampleincludesfirmswith10to1000employees,andis there-foreasampleofSMEs,asdemandedforastudyofVCinvestment. Industryeffectspointtoapreferenceamongventureinvestorsfor R&Dservicesorsoftware.Firmsoperatinginlarger(international) marketsseemtobeattractiveinvestments,whilecoefficientsfor theintensityofcompetitionareinsignificant.Firmswithlong prod-uctdevelopmenttimesare neithermorenorless likelyto gain venturecapital12_.

4.3. Two-stagepatenting—Patentcounts,patenting,andventure capital

The large number of zeroes in patent counts suggests that patentingisatwo-stageprocess,consistingofthebinarydecision whethertousepatentingasanIPprotectionstrategyandadecision abouthowmanypatentstoapplyfor.Twopopularmethodsusedto modelthenumberofpatentsproducedbysuchprocessesarebased onazero-inflatedPoissondistributionandazero-inflatednegative binomialdistribution.Inorder tofurtherrefineourfindingswe integrateazero-inflatedPoissonprocessinoursystemofequations, whichnowincludesanequationforpatentcounts,onefor patent-ing,andoneforventurecapitalinvestment.Table4presentsthe resultsfortheseestimations.Forcompleteness,weincluderesults

11 _We_also_test_the_hypothesis_that_competition_is_more_relevant_if_the_ﬁrm

opera-tesinternationally,butdonotﬁndthatthisinteractioneffectissigniﬁcant.Prior

studieshavefoundconﬂictingevidenceontheimpactofproﬁtabilityonpatenting:

Bertonietal.(2010)showapositiverelationbetweennetcashﬂowandpatents,

whereasBaumandSilverman(2004)reportanegativeone.Wealsotriedusinga

proxyforproﬁtabilityconstructedfrompre-taxproﬁtsscaledbyassets,butdidnot

ﬁndsigniﬁcantresults.Consequently,wedecidedtodropthisvariablefromour

models,duetothelargeamountofmissingvaluesinsurveyresponsesonproﬁts.

12 _Although_collinearity_is_not_a_problem_in_these_models,_there_is_some_correlation

betweenproductdevelopmenttimeandR&Defforts,whichcausesdevelopment

timetobecomeasigniﬁcantpredictorofVCinvestmentsifR&Dvariablesare

droppedfromtheregression.

fromamodelwithonlythepatentcountsandpatentingequations asTable5,whichprovidesthebaselineforthefull(three-equation) modeldiscussedinthissection.

Thepositiveeffectonﬁrms’latentpatentingstates,whichwould beexpectedifventurecapitalistsadded totheirpatenting per-formance,disappearsacrossallmodels,whilethenegativeeffect onthenumberofgrantedpatentsremains.Moreover–andasin thesimultaneousbinarypatentingmodels–thenumberofpatent applicationsdropsafterVCinvestments.

Ifwelookatthenumberofpatentsbeingappliedfororgranted, ourresultssupporttheviewthatVCfinancefollowspatentsignals toinvestincompanieswithexistingcommerciallyviable know-how.WhiletheeffectofVCinvestmentonboththeuseofpatents andontheirnumbersseemsnegligible,VChasanegativeimpacton patentgrantsandapplicationsinsomemodels.Venturecapitalists areattractedtofirmsthatproducepatents,butdonotcontributeto theexpansionoffirms’knowledgestocks—instead,theyarelikely toshiftfirmresourcesfromproducingnewpatentapplicationsto exploitingexistingknowledge.

Controlvariables for future patent counts behave mostly as expected,andgivefurtherinsightsintofirms’patentingdecisions. While manufacturingfirms andservice firms appear– perhaps counterintuitively–equallylikelytopatent(seemodel1inTable3), wefindthatbeingamanufacturingorR&Dfirmincreasesthe num-berofpatentapplicationsrelativetootherservicefirms(seemodel 1 in Table4).The estimation algorithm for three simultaneous equationspickstherelevantequationsforourtwoR&Dvariables: TheexistenceofR&Dprogrammesmainlypredicts patentingin general,whiletheproportionofR&Dstaffexplainsthenumberof applicationsandgrantsawarded.InlinewithBaumandSilverman’s (2004)results,wefindthatcompetitionhasanegativeimpacton thedecision topatent (inbivariatemodelsin Table3)andthe numberofpatentsappliedfororgranted(intrivariatemodelsin Table4).However,firmstendtoprotecttheirpositioninthe mar-ketbychoosingtopatentiftheirmarketsarelargeorhavelong productdevelopmenttimes.

EstimatedmodelparametersprovidesupportformodellingVC investment,patenting,andthenumberofpatentssimultaneously. Inmostofthemodelstestedforpatentapplicationsandgrants, errorcorrelationsbetweenthefirst(VC)equationandthesecond and third are substantial and significant. External shocks lead-ingtoVCinvestmentcorrelatewiththelikelihoodtopatentwith theexpectedpositivesign(andnegativesignfornotpatenting). EstimatederrorcorrelationsbetweenVCinvestmentandpatent numbersareagainlargeandsignificant.Wealsotestmodel stabil-itybycheckinginfluentialobservationsandcross-tabulationsfor firmsthatstartorstoptheirpatentingactivitiesdependingonVC investment,butfindnoabnormalities.

5. Robustnesstests 5.1. Independentequations

Ourfirstrobustnesscheckshowstheadvantagesofour estima-tionstrategyoversimpleralternatives.Wecomparetheresultsof oursimultaneousestimationswiththoseobtainedfrom indepen-dentregressionsforVCinvestmentand patenting(seeTable6). Whilethereis nochangeintheVCequation,thereisastriking differenceinthepatentingequations(Table6,columns1–3): With-outcontrollingforendogeneity,venturecapitalappearstoincrease thelikelihoodofpatentsbeinggranted.Thisarguablybiasedresult notonlydisappearsinthesimultaneousestimationstrategy,but thefindingemergesthatVCinvestmentcanreducethelikelihood thatfirmsapplyfornewpatentsintheperiodimmediatelyafter theinvestment.Wecanruleoutthatreducedeffectsofventure

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Table4

Patenting,patentnumbers,andVCinvestment.

Dependentvariableinpatentingequations Applicationsint+1 Grantsint+1 Grantsint+1

Model 1 2 3

Dependentvariable Notpatenting

(zeroinﬂation)

VCinvestment Notpatenting (zeroinﬂation)

VCinvestment

VCinvestment 0.186(0.58) −0.976(0.66) −0.815(0.85)

Patentapplications(Log) −0.313(0.22) −0.083(0.08) −0.371(0.18)** _−0.103_(0.08)

Patentapplications>0 −1.779(0.51)*** _0.560_(0.20)*** _−2.460_(0.43)*** _0.591_(0.20)***

Patentgrants(Log) −0.586(0.21)*** _−0.173_(0.13)

Patentgrants>0 −1.110(0.27)*** _0.400_(0.24)*

Age(Log) −0.161(0.12) −0.346(0.10)*** _−0.233_(0.12)* _−0.343_(0.09)*** _−0.111_(0.14) _−0.338_(0.09)***

Size(log(employees+1)) −0.167(0.09)* _0.206_(0.07)*** _−0.006_(0.08) _0.203_(0.07)*** _0.022_(0.10) _0.201_(0.07)***

U.S.ﬁrm 0.075(0.20) −0.342(0.13)** _−0.400_(0.18)** _−0.318_(0.14)** _−0.351_(0.24) _−0.342_(0.14)**

Medium-hightechmanuf. −0.151(0.44) −0.216(0.29) −0.061(0.38) −0.125(0.30) 0.162(0.36) −0.228(0.29)

Medium-lowtechmanuf. 0.188(0.45) −0.375(0.29) 0.295(0.39) −0.346(0.30) 0.465(0.34) −0.384(0.29)

R&Dservices&software 0.159(0.49) 0.280(0.30) −0.094(0.48) 0.320(0.30) −0.111(0.42) 0.266(0.30)

Otherservices −0.652(0.57) −0.107(0.31) 0.634(0.46) −0.074(0.32) 0.711(0.49) −0.116(0.31)

R&Dexpend.(yes/no) −0.613(0.25)** _0.425_(0.21)** _−0.288_(0.23) _0.440_(0.21)** _−0.487_(0.28)* _0.432_(0.21)**

R&Dstaff(in%) 0.359(0.51) 0.752(0.32)** _−0.485_(0.56) _0.881_(0.32)*** _1.123_(0.67)* _0.665_(0.33)**

CEOhasadegree −0.282(0.22) 0.345(0.17)** _0.313_(0.22) _0.333_(0.17)* _0.355_(0.23) _0.341_(0.17)**

Marketsize −0.366(0.14)*** _0.186_(0.09)** _−0.299_(0.13)** _0.199_(0.09)** _−0.388_(0.15)*** _0.194_(0.09)**

Productdev.time −0.176(0.09)* _0.045_(0.07) _−0.208_(0.10)** _0.052_(0.07) _−0.226_(0.11)** _0.050_(0.07)

Competitors(Log) 0.109(0.09) −0.081(0.07) 0.148(0.09) −0.087(0.07) 0.164(0.11) −0.082(0.07)

Intercept 3.655(0.70)*** _−1.859_(0.44)*** _2.780_(0.62)*** _−1.897_(0.44)*** _2.703_(0.65)*** _−1.867_(0.44)***

Dependentvariable Applications(number) Grants(number) Grants(number)

VCinvestment −0.780(0.15)*** _−0.136_(0.25) _−0.886_(0.29)***

Patentapplications(Log) 0.815(0.05)*** _0.962_(0.05)***

Patentapplications>0 −0.160(0.23) −0.411(0.32)

Patentgrants(log) 0.615(0.06)***

Patentgrants>0 0.820(0.23)***

Age(log) −0.032(0.06) −0.441(0.12)*** _−0.042_(0.11)

Size(log(employees+1)) 0.017(0.05) 0.186(0.09)** _0.014_(0.10)

U.S.ﬁrm 0.289(0.10)*** _−0.061_(0.15) _0.119_(0.19)

Medium-hightechmanuf. −0.027(0.34) −0.246(0.27) 0.074(0.40)

Medium-lowtechmanuf. 0.071(0.36) −0.282(0.32) 0.130(0.44)

R&Dservices&software −0.106(0.34) −0.799(0.30)*** _−0.199_(0.45)

Otherservices −1.136(0.53)** _−0.664_(0.46) _−0.285_(0.59)

R&Dexpend.(yes/no) 0.224(0.42) −0.128(0.41) 0.544(0.32)*

R&Dstaff(in%) 1.215(0.12)*** _0.850_(0.30)*** _1.059_(0.37)***

CEOhasadegree −0.158(0.17) 0.012(0.34) 0.031(0.20)

Marketsize −0.055(0.09) 0.166(0.11) −0.211(0.12)*

Productdev.time 0.096(0.05)** _−0.103_(0.06) _−0.054_(0.07)

Competitors(log) −0.210(0.05)*** _0.042_(0.14) _−0.239_(0.09)*** Intercept 0.259(0.62) 0.914(0.53)* _0.531_(0.53) Var(ωit) 1.307(0.12)*** 0.656(0.10)*** 0.483(0.07)*** (vit,εit) −0.338(0.24)* 0.076(0.29) −0.092(0.44) (vit,ωit) 0.290(0.03)*** −0.108(0.06)** 0.444(0.11)*** Observations 940 940 940 Waldtest 25697 25356 8424 p-Value 0.000 0.000 0.000 Log-likelihood −968.4 −846.0 −762.8

Notes.Thistablepresentszero-inﬂatedPoissonmodelsforpatentapplicationsandpatentgrantsduringtheperiodfollowingthesurveyperiod,includinganendogenous equationforventurecapitalinvestment.Robuststandarderrors(estimatedusingthesandwichestimator)areshowninparentheses.

capitalarecausedbyestimationuncertaintyduetotheadditional

parameterforerrorcorrelationbetweenequations.Waldtestsof

thejointsigniﬁcanceofthiscorrelationandthecoefﬁcientof

ven-turecapitalonpatentingaresigniﬁcantattheﬁvepercentlevelin

allmodelsinTable3.Theimpactofpositiveerrorcorrelationscan

beseeninthecoefﬁcientsforventurecapital,whichchange consid-erablywhenestimatedsimultaneously.Introducingcross-equation correlationalsoharmonisescoefﬁcientsforsomevariablesacross modelsand causesno majorchanges in theresultsfor control variables.

Whetherornotafirm obtainsfinancecouldhaveanimpact onitsabilitytostartorsustainpatentingactivities.Sincewe per-formourregressionsonasample offirmsthat soughtexternal finance,ratherthanonlyonthosethatobtainedit,weaddasetof robustnesstestsforthissubsample.Resultsofseparateregressions

(not reported here) confirm ourfindings in Table 3. However, two smallchangesappear inthepatenting equations.First, the effectsize ofdevelopmenttime decreasesslightly and losesits significance.Second,coefficientsonproductmarketcompetition allincreaseinmagnitude,andtheonepredictingfuture applica-tionsbecomesslightlysignificant.Estimatingourmodelsonthe fulldataset(includingthose96firmsthatdidnotobtainexternal finance)hastwoadvantagesoverthesmallersample.First,adding theseobservationsincreasesthestatisticalprecisionofourresults. Second,ourfindingsareconservative,thatis,theeffectofobtaining venturecapitalonpatentingcanbeupwardbiasedifitincludesa (positive)effectofobtaininganykindoffinance,whichwouldbe ignorediffirmsgainingnofinancewereexcluded.Inthissense,the negativeorinsignificantcoefficientsforventurecapitalrepresent upperboundsforthe‘true’effect.

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Table5

Patentingandpatentnumbers—zero-inﬂatedPoissonmodel.

Dependentvariablein patentingequations

Applicationsint+1 Grantsint+1 Grantsint+1

Model 1 2 3

Dependentvariable Patents(number) Notpatenting

(zeroinﬂation)

Patents(number) Notpatenting (zeroinﬂation)

VCinvestment −0.5030.29)* _−0.342_(0.22) _−0.287_(0.22) _{−0.689(0.22)}*** _−0.696_(0.26)*** _−1.130_(0.36)***

Patentapplications(log) 0.805(0.10)*** _−0.448_(0.14)*** _0.990_(0.09)*** _−0.419_(0.18)**

Patentapplications>0 −0.756(0.34)** _−1.150_(0.27)*** _−0.916_(0.43)** _−2.372_(0.37)***

Patentgrants(log) 0.682(0.11)*** _−0.545_(0.16)***

Patentgrants>0 0.314(0.30) −1.167(0.24)***

Age(log) −0.098(0.17) −0.177(0.08)** _−0.318_(0.17)* _−0.143_(0.10) _0.143_(0.15) _−0.078_(0.14)

Size(log(employees+1)) −0.125(0.14) −0.150(0.07)** _0.244_(0.14)* _0.019_(0.07) _−0.138_(0.15) _−0.015_(0.10)

U.S.ﬁrm 0.401(0.34) −0.073(0.16) 0.583(0.24)** _−0.262_(0.16) _0.164_(0.17) _−0.381_(0.22)*

Medium-hightechmanuf. 0.260(0.32) −0.159(0.34) −0.510(0.58) −0.041(0.37) −0.269(0.46) 0.101(0.34)

Medium-lowtechmanuf. 0.126(0.36) 0.133(0.34) −0.162(0.59) 0.390(0.37) −0.223(0.47) 0.372(0.33)

R&Dservices&software 0.392(0.40) 0.255(0.38) −0.443(0.62) 0.163(0.42) −0.459(0.47) −0.128(0.40)

Otherservices −0.498(0.43) −0.232(0.37) −0.415(0.67) 0.726(0.42)* _−0.684_(0.62) _0.696_(0.45)

R&Dexpend.(yes/no) 0.470(0.39) −0.420(0.19)** _−0.100_(0.57) _−0.284_(0.23) _0.389_(0.45) _−0.484_(0.26)*

R&Dstaff(in%) 1.324(0.72)* _−0.020_(0.37) _0.752_(0.48) _−0.474_(0.46) _1.551_(0.42)*** _1.292_(0.62)**

CEOhasadegree −0.048(0.28) −0.105(0.16) −0.193(0.32) 0.234(0.18) 0.249(0.35) 0.459(0.25)*

Marketsize 0.055(0.32) −0.243(0.09)*** _0.487_(0.22)** _−0.233_(0.11)** _−0.157_(0.17) _−0.336_(0.15)**

Productdev.time −0.225(0.10)** _{−0.189(0.07)}*** _−0.072_(0.10) _−0.161_(0.08)** _−0.248_(0.09)*** _−0.281_(0.11)**

Competitors(log) −0.163(0.15) 0.101(0.07) −0.307(0.18)* _0.046_(0.09) _−0.568_(0.22)** _0.069_(0.14) Intercept 1.674(1.21) 3.451(0.50)*** _0.498_(1.11) _2.385_(0.55)*** _1.974_(0.90)** _2.967_(0.65)*** Observations 940 940 940 Waldtest 2204.7 469.0 480.9 p-Value 0.000 0.000 0.000 Log-likelihood −1537.8 −982.8 −819.3

Notes.Thistablepresentszero-inflatedPoissonmodelsforpatentapplicationsandpatentgrantsduringtheperiodafterthesurvey.Whencomparingcoefficientsfromthe patentingequationwithpriormodelsforthelikelihoodtopatent,allsignsmustbereversedasthe“patenting”equationinthistablepredictsthelikelihoodofnotpatenting. Asarobustnesstest,wetriedzero-inflatednegativebinomialmodels.Testsforoverdispersionareallinsignificantinthesemodels,whileVuongtestsagainstthealternative hypothesisofastandardPoissonprocessarehighlysignificant.Robuststandarderrorsareinparentheses.

Table6

Patentingactivityandventurecapitalinvestment—independentequations.

Patenting(yes/no) VCinvestment

Model 1 2 3 4 5 6

Dependentvariable Applicationsint+1 Grantsint+1 Grantsint+1 VCinvestment VCinvestment VCinvestment

VCinvestment 0.267(0.20) 0.598(0.20)*** _0.445_(0.25)*

Patentapplications(Log) 0.513(0.13)*** _0.627_(0.14)*** _−0.094_(0.08)

Patentapplications>0 0.980(0.22)*** _1.594_(0.23)*** _0.574_(0.20)***

Patentgrants(log) 0.584(0.16)*** _−0.166_(0.12)

Patentgrants>0 1.182(0.22)*** _0.393_(0.23)*

Age(log) 0.164(0.07)** _0.073_(0.09) _0.167_(0.09)* _−0.331_(0.09)*** _−0.345_(0.09)*** _−0.339_(0.09)***

Size(log(employees+1)) 0.123(0.06)** _0.028_(0.06) _−0.012_(0.07) _0.202_(0.07)*** _0.205_(0.06)*** _0.193_(0.07)***

U.S.ﬁrm 0.132(0.14) 0.344(0.15)** _0.409_(0.18)** _−0.327_(0.14)** _−0.324_(0.14)** _−0.323_(0.14)**

Medium-hightechmanuf. 0.177(0.32) −0.045(0.34) −0.202(0.27) −0.235(0.29) −0.138(0.29) −0.142(0.29)

Medium-lowtechmanuf. −0.127(0.32) −0.407(0.35) −0.500(0.28)* _−0.405_(0.29) _−0.351_(0.30) _−0.347_(0.29)

R&Dservices&software −0.235(0.36) −0.229(0.39) −0.352(0.36) 0.259(0.29) 0.312(0.30) 0.312(0.30)

Otherservices 0.122(0.34) −0.761(0.40)* _−0.786_(0.36)** _−0.127_(0.31) _−0.084_(0.31) _−0.099_(0.31)

R&Dexpend.(yes/no) 0.430(0.18)** _0.278_(0.20) _0.432_(0.22)** _0.419_(0.21)** _0.442_(0.21)** _0.464_(0.21)**

R&Dstaff(in%) 0.170(0.36) 0.563(0.46) −0.352(0.46) 0.696(0.33)** _0.878_(0.32)*** _0.874_(0.31)***

CEOhasadegree 0.085(0.14) −0.261(0.16)* _−0.288_(0.18) _0.335_(0.17)** _0.339_(0.17)** _0.362_(0.17)**

Marketsize 0.239(0.09)*** _0.300_(0.10)*** _0.260_(0.11)** _0.198_(0.09)** _0.196_(0.09)** _0.214_(0.09)**

Productdev.time 0.163(0.06)*** _0.136_(0.07)** _0.119_(0.08) _0.044_(0.07) _0.053_(0.07) _0.057_(0.07)

Competitors(log) −0.119(0.06)* _−0.111_(0.07)* _−0.202_(0.08)*** _−0.077_(0.07) _−0.089_(0.07) _−0.092_(0.07) Intercept −3.311(0.46)*** _−2.448_(0.52)*** _−2.588_(0.52)*** _−1.883_(0.44)*** _−1.887_(0.44)*** _−1.889_(0.44)*** Observations 940 940 940 940 940 940 Log-Likelihood −252.3 −218.3 −159.3 −235.2 −237.1 −238.9 Chi-sq.test 235.7 186.4 265.8 111.0 114.6 101.5 p-Value 0.000 0.000 0.000 0.000 0.000 0.000 Pseudo-R2 _0.428 _0.451 _0.599 _0.241 _0.235 _0.229

Notes.Thistablepresentsprobitmodelsforthelikelihoodofobservinganynumberofpatentapplicationsorgrants,respectively,incolumns1–3andprobitmodelsforthe likelihoodofobservingventurecapitalinvestmentsincolumns4–6.Robuststandarderrorsareinparentheses.

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Table7

Likelihoodofbecomingamergeroracquisitiontarget.

Dependentvariable M&ATarget M&ATarget M&ATarget M&ATarget

Model 1 2 3 4

VCinvestment×applications(log) −0.013(0.19)

VCinvestment×grants(log) 0.303(0.26)

VCinvestment×(applications>0) −0.433(0.38) −0.406(0.55)

VCinvestment×(grant>0) −0.215(0.39) −0.700(0.55)

VCinvestment 0.619(0.24)** _0.547_(0.23)** _0.619_(0.24)** _0.537_(0.23)**

Patentapplications(log) 0.020(0.09) 0.024(0.11)

Patentapplications>0 0.509(0.26)** _0.502_(0.27)*

Patentgrants(log) 0.183(0.12) 0.108(0.13)

Patentgrants>0 0.184(0.28) 0.302(0.28)

Age(log) −0.181(0.09)** _−0.191_(0.09)** _−0.181_(0.09)** _−0.190_(0.09)**

Size(log(employees+1)) 0.266(0.07)*** _0.257_(0.06)*** _0.266_(0.07)*** _0.257_(0.06)***

U.S.ﬁrm −0.124(0.15) −0.143(0.15) −0.125(0.14) −0.145(0.15)

Medium-hightechmanuf. −0.157(0.33) −0.134(0.33) −0.157(0.33) −0.171(0.33)

Medium-lowtechmanuf. −0.257(0.34) −0.234(0.34) −0.258(0.34) −0.258(0.34)

R&Dservices&software −0.289(0.37) −0.294(0.36) −0.292(0.36) −0.278(0.36)

Otherservices −0.525(0.37) −0.514(0.37) −0.525(0.37) −0.527(0.37)

R&Dexpend.(yes/no) −0.507(0.18)*** _−0.474_(0.18)*** _−0.506_(0.18)*** _−0.475_(0.18)***

R&Dstaff(in%) 0.218(0.44) 0.133(0.46) 0.216(0.44) 0.127(0.46)

CEOhasadegree 0.132(0.17) 0.150(0.18) 0.133(0.17) 0.146(0.18)

Marketsize 0.119(0.08) 0.129(0.08) 0.118(0.08) 0.139(0.08)*

Productdev.time 0.072(0.08) 0.067(0.08) 0.072(0.08) 0.066(0.08)

Competitors(log) −0.055(0.08) −0.051(0.08) −0.055(0.08) −0.050(0.07) Intercept −2.004(0.43)*** _−1.958_(0.42)*** _−2.001_(0.43)*** _−1.952_(0.43)*** Observations 940 940 940 940 Log-Likelihood −170.713 −171.065 −170.711 −170.518 Chi-sq.test 65.850 64.510 66.040 65.380 p-Value 0.000 0.000 0.000 0.000 Pseudo-R2 _0.151 _0.149 _0.151 _0.152 Signiﬁcancelevels:***_p_<_0.01;**_p_<_0.05;*_p_<_0.1.

5.2. Sampleattritionbias

Sampleattritioncanbeaproblemifﬁrmsdisappearinginperiod

t+1aresystematicallythoseforwhichventurecapitalinvestment

hadapositiveeffectonpatenting.Tolimittheriskofattritionbias

weinvestigatethemergerandacquisitionhistoryoftheﬁrmsinour

samplebyretrievingtherelevantrecordsfromThomsonReuters’

SDCPlatinumdatabase.Weusethisinformationtoestimatethe

probabilitythat ﬁrmsare acquireddepending onwhetherthey

patentandwhethertheyreceiveVCfunding.Table7showsthat

firmsaremorelikelytobeacquirediftheypatentinperiodtandif theyexperienceVCinvestment.Thisresultisnotsurprisingsince acquirersmayfollowthesamepatentsignalsthatcauseventure capitaliststoinvest—ormayevenbeventurecapitaliststhemselves. Note,however,thattheinteractioneffectsforVCinvestmentand patentingarenotsignificant.Sampleattritionduetomergersand acquisitions,ifthesefirmsactuallyleavethesample,isthusunlikely tosystematicallyreducethemeasuredeffectofVConpatenting.

AfterinspectingtheM&ArecordsintheSDCdatabase,ifafirm isthetargetofanykindoftransaction,wecorrectthenumberof patentapplicationsandgrantsinthefollowingway.Ifafirmretains itsidentityandremainsactiveunderitsnameinthesample,we keeptheoriginalnumberofpatents.Anexampleofthistypeof transactionisaleveragedbuyout,inwhichthefirm’smanagement acquiresthefirm,buttheoperatingbusinessremainsunchanged. If,instead,thefirmismergedintotheacquirer,wecheckwhether itisstillpatentingattheoriginallocation,butundertheacquirer’s name,andifthisisthecaseweaddthesepatentstothesample.If thefirmdisappearsasalegalentityafterthemerger,weaddallthe acquirer’spatentstotheoriginalfirm’spatents,whichthen estab-lishesanupperboundaryforthefirms’patentingactivityint+1.Of 128transactionsweidentified,weadjustedthepatentapplication orgrantnumbersfor15firms.Theresultsofthebinarymodels pre-sentedinTable8areunchanged,whilethoseofthezero-inflated PoissonmodelspresentedinTable9showaninsignificanteffectof

VConpatentinginmodels1and3,asonemightconfidentlyexpect afterattributingmergedfirms’technologicaloutputsintothoseof acquiringfirms.

5.3. Formalvs.informalventurecapital

PoolingformalandinformalVCinvestmentsinourmodelmay affectresultsifthetwoclassesofinvestorsbehavedifferentlyin relationtothetechnologicalprofilesoftheirinvesteefirms.Outof 96firmsthatobtainVCfinancinginoursample,66firmsreceive formalVCfunding,while41firmsattractVCinvestors.We there-foredisaggregateformalVCinvestmentsfromthoseofinformal venturecapitalists(suchasbusinessangels)inTable10,butfind theresultsforthetwosub-samplestobeverysimilar.EffectsofVC involvementarenegativeinbothcases,albeitinsignificant,while theeffectofformalVConpatentingseemstobemorenegative thanthatofinformalVC.Whiletheseeffectsdonotseemtodiffer betweentypesofinvestors,theirlackofsignificanceisanexpected consequenceofthereducednumberofobservationsineachbinary modelestimation.

ItshouldalsobepointedoutthattheeffectsobservedinTable10 maybeduetothefactthatthecontrolgroupsforfirmsthatgained formal(orinformal)venturecapitalincludefirmsfinancedby infor-mal(orformal)venturecapital.Hence,wewould(tosomeextent) becomparingfirmsreceivingformaland informalventure capi-talandnot,forexample,firmsgainingformalventurecapitalwith those receivingneithertype of investment.Ifwe excludefirms fundedbyinformalVCfromthecontrolgroupfortheformalVC models,andviceversa,wefindthattheseeffectsremain insignifi-cantandqualitativelyunchangedfromthosepresentedinTable10. Theserobustnesschecks,whichtakeintoaccountthetypeofVC investor,contrastwithContietal.’s(2013a)results,where busi-nessangelsarefoundnottobeassensitiveasVCtopatentsignals, butareconsistentwithresultspresentedbyAudretschetal.(2012), whoseestimationsofprobitmodelsfortheprobabilityofreceiving