Does government funding complement or substitute private research funding to universities?

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ContentslistsavailableatSciVerseScienceDirect

Research

Policy

j o ur na l ho me p 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

Does

government

funding

complement

or

substitute

private

research

funding

to

universities?

Alessandro

Muscio

a,∗

_,

_Davide

_Quaglione

b,1

_,

_Giovanna

_Vallanti

c,2

a_Dipartimento_Di_Scienze_Economiche,_Matematiche_e_Statistiche_(DSEMS),_Università_degli_Studi_di_Foggia,_Largo_Papa_Giovanni_Paolo_II,_1,₇₁₁₀₀_Foggia,_Italy b_Dipartimento_di_Economia,_Università_“G._{d’Annunzio”}_di_Chieti_e_Pescara,_v.le_Pindaro,_42,₆₅₁₂₇_Pescara,_Italy

c_Dipartimento_di_Scienze_Economiche_e_Aziendali_(DPTEA),_Università_Luiss_Guido_Carli,_Viale_Romania,_32,₀₀₁₉₇_Roma,_Italy

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received9November2010

Receivedinrevisedform24March2012 Accepted23April2012

Available online 17 May 2012 JELclassiﬁcation: L24 L31 O32 O33 Keywords: University Collaboration

Technologyandknowledgetransfer Researchfunding

Thirdmission

a

b

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Thereisgrowingpoliticalpressureonuniversitiestointensifytheirinteractionwithindustryandto

enlargetheirownresearchfundingoptions,inacontextcharacterisedbyincreasingconstraintsonpublic

spending.However,whetherthesuccessfulachievementofsuchapoliticaldesiredoutcomeisconsistent

witharestrictionofgovernmentfundingisnotclearandrequiresfurtherinvestigation.Asamatterof

fact,thereisscantempiricalevidenceonwhetherandtowhatextentgovernmentfundingaffectsthe

externalfundingoptionsavailabletouniversities,inparticularthoserelatedtoresearchandconsulting

activities.Byusingasetofprobitandtobitpaneldatamodelsestimatedonﬁnancialdataforthewhole

populationofItalianuniversitydepartmentsengagedinresearchintheEngineeringandPhysical

Sci-ences,thispaperprovidesevidencethatgovernmentfundingtouniversitiescomplementsfundingfrom

researchcontractsandconsulting,contributingtoincreasinguniversities’collaborationwithindustry

andactivatingknowledgetransferprocesses.

1. Introduction

Thereisincreasingawarenessinindustrialisedcountriesofthe importanceofscientiﬁcresearchincreatingthefoundationsfor technologicalchangeandeconomiccompetitiveness.Historically, bringingresearchresultstomarkethasnotbeenofprime con-cern toacademicinstitutions. However,since thelate 1970s, a growingpressurehasbeenputonuniversitiestoproduceresearch that is valuable for industry3 _and _to _establish _closer _linkages

∗ Correspondingauthor.Tel.:+390881753730;fax:+390881775616. E-mailaddresses:al.muscio@unifg.it(A.Muscio)

d.quaglione@unich.it(D.Quaglione),gvallanti@luiss.it(G.Vallanti). 1 _Tel.:₊₃₉₀₈₅_4537610;_fax:₊₃₉₀₈₅_4537565.

2 _Tel.:₊₃₉₀₆_85225910;_fax:₊₃₉₀₆_85225949.

3_Although_the_literature_on_these_issues_make_systematic_reference_to_{“industry”} (whichevokesprivatemanufacturingfirms)asthetypicalrecipientoftheuniversity knowledgetransfer,thirdstreamactivitiesarebeneficialalsotoprivatefirms oper-atinginthetertiarysectoraswellastopublicinstitutionsinvolvedwiththesupply anddeliverof(marketandnon-market)goodsandservices.Intheviewexpressed inthispaper,“industry”,“businesses”and“university-industrycollaboration”are interpretedinawidersense,whichemphasisesastrongermarketorientationof universityresearch.

withthebusinesscommunityin ordertowidenthechancesof establishing collaborations.Early researchhastypically focused on technology transfer issues such as patenting, licensing and spin-offs.Morerecently,greaterattentionhasbeenpaidtoother university–industrycollaborationchannelssuchasresearch con-tractsandconsultingactivity,characterisedbyahigherdegreeof relationallinkages,capableofgeneratingstronglearningby inter-actioneffects(PerkmannandWalsh,2009).

At the same time, the creation of new channels of university–industry collaborationhasgained strategicrelevance touniversitiesprimarilybecauseoftheirpotentialassourcesof external funding(Cohen et al.,1998).There is nowsubstantial agreement in the economic literature that university–industry collaborationshouldbepromotedandthatgovernments should putinplaceallthenecessarymeasurestoeasethisprocess,thereby helpingtobringtheresultsofacademicresearchtomarket.Several empiricalworks,evenveryrecent(Gulbrandsenetal.,2011),have investigated the drivers of university–industry collaborations andbusinessfundingtouniversities.However,akeyfactor,and onethatmayhavebeenoverlooked,is theexistingrelationship betweengovernmentfundingandthefundingraisedby univer-sitiesthroughresearchcontracts,consultingand,moregenerally, 0048-7333/$–seefrontmatter © 2012 Elsevier B.V. All rights reserved.

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scientiﬁcactivitiestoorder.4 _There_is_extensive_evidence_on_the

effects offundingontheproduction of innovationsand onthe developmentofuniversity–industry networks.However,further investigationisneeded in ordertoassess whethergovernment fundingtouniversitiescomplementsorsubstitutesfundingfrom theseotherchannelsofcollaborativeinteraction.

AccordingtoarecentOECDreviewonuniversityfunding(OECD, 2010),Europeanuniversitiesareprimarilyfundedbythestate.The reviewestimatesthatinthelargemajorityofcasesthefunding proportionliesbetween60%and90%ofthetotalbudget.However, duringthelasttwodecades,theshortcomingsofthetraditional ‘input-orientated’fundingsystemwithrespectto performance-based management systems of public administrations pushed severalEuropeangovernments toimplementnumerousreforms ofresearchofuniversitysystems(McNabandMelese,2003).Many developedeconomieshavegraduallyreducedgovernmentfunding toresearchsystems.InItalyaswellasintheUSA,Japan,Germany, France,Canada,and theUK,governmentintervention hasbeen reduced,thusfavouringtheactionofmarketforces,whichhave becomemoreandmore importantinallocatingresources(Steil etal.,2002).

Fundingsystemsandespeciallyresourceallocationmechanisms forpublicfunds arean essential elementof reformsof univer-sitysystemsinseveralcountries.Despiteaninternationaltrend towards performance-basedfunding, the approaches that have beenimplementeddiffersigniﬁcantlyacrosscountries.According totheclassiﬁcationofuniversityfundingsystemspresented by SalmiandHauptman(2006),Italyhasafundamentallytraditional fundingsystemsimilartothatofmanyothercountriesinEurope (Strehletal.,2007):thelargestpartofuniversitybudgetsisbased on‘negotiatedbudgets’and‘fundingformulas’(basedonsizeof staffornumberofstudentsenrolled),butuniversitiesalso com-peteforresearchfundingonthebasisofpeer-reviewedproject proposals againsta set of objectives.Like in many other Euro-peancountries,othersourcesofuniversityfundingsuchasindustry fundingisbecomingincreasinglyimportantforItalianuniversities’ budgets.

Inthelightof thesearguments,thepurposeofthis paperis toinvestigatetheeffects thatgovernmentfundingforacademic researchactivityhasontheexternalfundingtouniversitiesraised throughresearchcontractsandconsultancies.Therationaleofour empiricalexerciseisstraightforwardin termsofpolicy implica-tions: we are interested in investigating whether the financial pressurethatuniversitieshavebeensubjectedtoinrecentyears –that in thespecificcase of Italy hasbeenamply reportedby themedia(Gandolfi,2009;Intravaia,2011;Magrini,2011;Tucci, 2010)–isdrivingtheseacademicinstitutionstolookforalternative sourcesof funding, stimulating university–industry interactions andcollaborations(substitutioneffect),or,conversely,itis hamper-ingtheircapabilitytocollectexternalfunding(complementarity effect)andtoactivateimportantknowledgetransferchannels.

Ourresearchdiffersfrompreviousstudiesinseveralrespects. Wecarryoutaneconometricanalysisbasedonhighly disaggre-gateddataonuniversitydepartments;wediscriminatebetween differentsources ofpublicfundingranging fromEUfundingto nationalandregionalsources;weusedisaggregateddataonprivate

4 _In_this_paper_the_terms_{“industry”,}_{“business”,}_{“private”}_and_“market”_funding mustbeunderstoodassynonymouslocutionsreferringtofundingthat universi-tiesraisefromscientiﬁcactivitiestoorder–i.e.theresearchactivities,consultancies andservicessoldonthemarket,carriedoninresponsetoanexclusiveinterestof thecommissioningentityindependentlyfromitspublicorprivatelegalnature– whichcanberegardedasaneffectiveproxyofcollaborationscapableofactivating knowledgetransferprocessesbasedonhighrelationality.Bycontrast,“government funding”(or“publicfunding”)isrelatedtoEuropeanCommission,nationalorlocal governmentsfundingforresearchprogramsontopicsdeﬁnedasofpublicinterest.

funding,testingourhypothesisconcerningthenatureofthe rela-tionshipbetweenpublicresearchfundingandresearchcontracts andconsulting activity(thusexcluding othersourcesof private fundingtouniversitiesnotrelatedtoknowledgetransferprocesses, suchasdonations).

Thepaperisorganisedasfollows.Section2setsthetheoretical backgroundtouniversityfundinganduniversity–industry interac-tions.Section3presentsourempiricalresultsforthedeterminants ofindustryfundingtouniversities.Section4discussestheresults andtheirimplicationsforpolicy.

2. Theoreticalbackground

2.1. Universityfundinganduniversity–industryinteraction

Knowledgeisconsideredtobeaprimaryresourceforwealth creation and economic growth (Drucker, 1993; Nonaka and Takeuchi,1995;Florida,1995;Romer,1993,1995;Leonard-Barton, 1995)andintellectualcapitalacrucialresourceofeconomic advan-tage intheknowledge economy(Stewart, 1997;Edvinsson and Malone,1997).Theroleoftheuniversityasaneconomicandsocial institutionhasbecomeincreasinglyimportant(FloridaandCohen, 1999).Universitieshave long beeninvolved in so-called ‘third-stream’activities(GeunaandMuscio,2009),andthereisevidence thattheyhavesigniﬁcantlycontributedtoeconomicdevelopment andﬁrmcompetitiveness.However,adeeperconnectionbetween universityandindustryisbeingseenasessential,andthisrequires astructuralchangeintheroleofuniversitieswithinthenational innovationsystemaswellasamodernisationoftheirmanagerial andorganisationalskills(EuropeanCommission,2009).The expec-tationisthatuniversitiesnotonlyproducenewknowledge,but thatthisknowledgeberelatedtoestablishedsocialandeconomic targets(Laredo,2007).Inthisview,universitiesshould(a) inten-sifytheirinvolvementintheeconomicandsocialdevelopment; (b)increasethecommercialisationofresearchresults,patenting andlicensingactivities;(c)institutionalisespinoffactivities;(d) introducemanagerial andattitudinal changesamong academics withrespecttocollaborativeprojectswithindustry(VanLooyetal., 2004).

Sincethe1980s, manypolicymakershave beenpushingfor this ‘second revolution’ in academia, above all having in mind thepossibleenlargementofexternalfundingoptionsfor universi-tiesandtheconsequentreliefforgovernmentbudget.Now,while some countries are in the process of rethinking the role (and funding)ofresearchinstitutionswithintheirnationalinnovation systems(Arnoldetal.,2006), severalEuropeancountry govern-mentsare applyingincreasingpressure foruniversities toraise researchfundingfromindustryandtocontributeactivelyto indus-trial innovation. As Geuna(1999) notes, since the early 1980s Europeangovernments have beenintervening more directly in termsofguidingnationalresearchsystems.Thisinterventionhas takendifferentforms indifferentcountries, but isbeingdriven by similar overall targets, which are promoting a contractual-orientedapproachtouniversityresearchfunding,aimedatindirect controlofthebehaviourofuniversitiesthroughtheintroduction of(quasi-market)financialincentiveschemes.Thesepoliciesare meanttoimprovetheefficiencyofresearchfundsand increase theaccountabilityofuniversitiesaswellasthepressuretoreduce theircosts,thislatterobjectivebeingcrucialasaconsequenceof theconstraintsonpublicbudgetsresultingfromtheenforcement oftheMaastrichtcriteria(Sörlin,2007).Confirmingthis,Auranen andNieminen(2010)comparethefundingenvironmentsof uni-versity researchin eight countries, investigating whethermore competitivefundingschemesresultinamoreefficientproduction ofscientificpublication.Theauthorsfindthattheideaofoutput

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andcompetition-basedincentivespromotingproductivityin sci-enceismorecomplexthangenerallybelievedandthattheuseof fundingincentivesandcompetitionmustbecarefullypondered. Atthesametime,insuchacontextuniversitiesthemselveshave anincentivetoallowtheirstafftoundertakeappliedresearchand consultancies,thusopeningupchannelsthroughwhicheasetheir budgetconstraints(Beathetal.,2003).

Thenewacademicfundingrationaleandthepromotionof exter-nalorientationofacademicresearchhaveraisedseveraltheoretical andpolicyquestionsaboutthefutureroleofuniversitiesandtheir futurefundingoptions.Thereisalargestreamofliteraturefocusing ontheassessmentofthepotentialadvantagesanddisadvantages that sucha shiftcan implyon theway universitiescreate and transferknowledge.Someauthorspointtothepotentiallynegative effectsofthisshifttowardindustryfundingonacademic activi-ties(Geuna,2001;Elzinga,1985;FloridaandCohen,1999).Strehl etal.(2007),intheirextensiveOECDreportonuniversity fund-ingsystemsandtheireffectonhighereducationsystems,evidence thenegativeeffects of theimplementationof newinstitutional strategies in universitiesthat involvethe searchfor alternative resourcestogovernmentfunding,increasingconsultingand mar-ketingactivities.TheOECDreportpresentsnumerouscountrycase studiesbasedonstakeholdersinterviewsanditinvestigateshow institutionsreacttocontingencies.Theauthorshighlightthat uni-versitystakeholdersperceiveasnegativeeffectsderivingfromcuts inuniversityfundingtheneglectofbasicR&D, lowerqualityof researchandlessvarietyinteachingcourses.Moreover,increased competitionbetweenuniversitiesresultsincooperationproblems. Otherauthorsshowthatuniversity–industrycollaborationperse has little negative impact onacademic activities(Thursby and Thursby,2011)andthatbothuniversitiesandbusinessesmay ben-efitfromcollaboration(Gulbrandsenand Smeby,2005).In fact, assuggested by Perkmannand Walsh (2009)industry involve-mentwithuniversitiesundercertainconditions(i.e.theirdiscipline isassociatedwiththe‘sciencesoftheartificial’;theyarehighly research-driven;andtheyhaveaportfolioofdifferenttypesof rela-tionshipswithindustry)willbenefittheproductionofscientific research.

2.2. Doprivateandpublicfundingtouniversitiescomplementor substituteeachother?

Less captured and understood are the possible effects of a cutingovernmentfundingtouniversitiesontheircapabilityof establishingcollaborativeformsofinteractionwithﬁrms,in par-ticular those through which knowledge can be transferred to industry(asin thecase ofhighly relationalforms of collabora-tionssuchasresearchcontractsandconsultanciesintheirvarious forms), aswell as ontheoptions available for raising external funding.

The existence of a form of complementarity betweenthese publicandexternalfundingtouniversitieswouldimplythat uni-versitiesneed governmentfundingtoincrease theincidenceof highlyrelationalcollaborationchannelswithindustry(Mansﬁeld, 1995;Cohenetal.,1998;PerkmannandWalsh,2008;Jensenetal., 2010;Dechenauxetal.,2011)aswellastheirexternalfundraising options.

Fromatheoreticalpointofview,thegroundsfortheexistence ofarelationshipbetweenpublicandprivatefundingtoresearch withinuniversitiescanbefoundintheliteratureaddressingthe issueofcomplementaritybetweenpublicandprivateR&D,which mostlyfocuses,recallingthetheoreticalbasisprovidedbyArrow (1962),ontheimpactofpublicR&Dcontractsandgrantsupon pri-vateR&Dinvestmentbymanufacturingﬁrmsandindustries(David andHall,2000;Davidetal.,2000).

Theissueofthepositive directandindirecteffects ofpublic R&D onfirms’productivityand privateR&D hasbeenanalysed thoroughlyoverthepast50years.However,itisonlythemore recent literature that provides some theoretical and empirical insightsonthenatureandthecharacteristicsoftherelationship between publicand private research,addressing the questions of (1) whetherpublic and private R&D investmentsare linked and (2) under what conditions they behavelike substitutes or complements. David and Hall (2000) contribute to this debate by analysingpossible complementarities of public and private fundingof R&D conductedby firms.Through a simplified two-sectormodel,theauthorsfindthatcomplementarityeffectstend to be dominant when “the government R&D sector is relatively high,thelaboursupplyofscientistsandengineersiselastic, orthe marginal product R&D curve is relatively flat (that is, the effect of R&Donproductivity doesnot fallveryquickly asR&D budgets increase)” (David and Hall, 2000, p. 1180). David et al. (2000) emphasise that complementarities between public and private R&Dinvestmentscan(a)derivefromlearningandtrainingeffects of publicly subsidised R&D activities that acquaint enterprises withthelatestadvancesinscientificknowledge,thusincreasing theirefficiencyintheirownR&Dprograms;(b)emergewhenever publicfundingismadeavailablefortheconstructionoftest facil-ities, fortheacquisition ofdurableresearchequipmentand for thebearingofthefixedcostsofassemblingspecialisedresearch teams,sinceallthesecircumstanceslowertheincrementalcosts of firms’ R&D projects; (c) be in place every time govern-mentcontract R&Dsignalfuturepublicsectorproductdemand, becauseof a coordination effect which mayraise theexpected marginalratesofreturnoninnovationstargetedtothesignalled market.

Within sucha context, the publiclyfunded researchcarried outinuniversitiesmakesnoexception,aslongastheadvances intheknowledgebaseandthebuildingof(technicalandhuman capital)facilitiesitallows,canstimulateandcomplement indus-trial innovation. As a consequence, a link between public and privateresearchfundingtouniversitiescanbeexpected when-ever ﬁrms,in order todevelop theirinnovations, must rely on the material infrastructure the university has built and/or the expertiseresearchershaveaccumulatedthankstopublicresearch funding.

As for the nature of the relationship between public and privatefunding touniversities,government and industry fund-ing forresearchwithin universitiescanbe strategicsubstitutes orcomplements.Thescantliteraturespecificallyaddressingthis issue focuses on consultancies as the reference form of uni-versity industry interaction. Jensen et al. (2010) centre their analysisonthemodellingoftheactualmechanismsunderlying the spillovers fromacademic research toindustry, witha spe-cific focus on research-driven consulting activities. They show that,undercertainsufficientconditions,governmentand indus-tryfundingforuniversityresearchactasstrategiccomplements everytime “an increase ineither type offundingincreases the marginaleffectoftheotherontheprobabilitythattheresearcher’s universityprojectwillbesuccessful”(p.3),independently from the faculty reputation or quality. In their model, consultan-ciesare theonly sourceofspillovers, while signallingplays no role.

Otherpapersrelyinsteadonsignallingandreputationeffects toexplaincomplementarities.Connolly(1997)hypothesisesthat externalsponsorswishtoallocatefundingtothoseuniversitiesthat dothehighestqualityresearch,andthatfundingfromeithersource providesinformationaboutquality.Similarly,Blume-Kohoutetal. (2009)maintainthatfederalfundingmayserveasasignalof uni-versityquality,duetotheextensivepeerreviewconductedbythe NIHandotherfederalagencies.

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To summarise the theoretical literature predicts the exis-tence of complementarities when: knowledge spillover effects are present; firms have the possibility to free ride on univer-sity’salready built equipmentand staff(e.g.in research-driven andopportunity-drivenconsulting);firmsareinneedtocapture uncodifiedknowledgewhenvaluableexpertisetendstobetacit andcomplex(e.g.incommercialisation-driven consulting); gov-ernmentfundingworkasasignallingandreputationmechanism. Onthecontrary,substitutabilitymayemergewhenspillovereffects areabsentorinpeculiarsituationsrelatedtotheacademics’work supplyfunction(operatingboundedtimeconstraintsorbackward bendingsupply).

As a consequence, understanding the final effects of gov-ernment funding on universities and, eventually, whether it substitutesorcomplementsexternalfundingfromresearch con-tracts and consulting activity, is mainly an empirical question. Theobviouspolicyimplicationsofthesubstitutioneffectwould be that government could reduce public funding to universi-ties and cut public spending without hampering universities’ financialsustainability,collaborationswithindustry and knowl-edgetransfer,whilethecomplementarityeffectwouldimplythat public funding would be indispensable, not only to guarantee thesurvival and thefinancial sustainability of universities, but alsoto consolidate universities’role as an engineof economic growth.

Therelevanceofthisissueforthepublicopinionwasalready stressedbyDiamondwhoarguedthat:“proposedcutbacksin gov-ernmentsciencefundinghavecausedmanytoexpectthattherate ofscientific progress willsignificantly decline. Beforethe mag-nitude of thedecline can beestimated, a preliminaryquestion mustbeanswered:“whatistheextenttowhichprivatefunding ofsciencemaybeexpectedtofillthegapleftbythedeclining gov-ernmentfunding?”(Diamond,1999,p.423).Inthesamelineof thinking,inthecaseoftheUSithasbeenobservedthat:“A pri-ori,therecouldbeanegative(substitution)effect,eitherdueto crowdingoutofprivateinvestmentorbecauseresearcherscould stopseekingothersourcesoffundingoncetheyreceivefederal funding.Ontheotherhand,federalR&Dfundingcouldalsohave apositive impactdue tocomplementarity orsignalling effects” (Blume-Kohoutetal.,2009,p.30).

Moreover,accordingtoGeunaandNesta(2006)thereduction instructural (government)fundswould producegreat financial difficultiesformostuniversitieswhilebenefitingonlyafew.For mostuniversitiesrevenuesfromresearchcontractsand consultan-ciescouldbeinsufficienttocompensatethedecreaseinstructural fundsandthus beingdetrimentaltobasicresearchactivityand teaching.Considering that researchcontractsand consultancies are mainly demandedby organizations located in the proxim-ityofuniversities(Capellari,2011), thisproblemisexacerbated inthe case ofsmalleruniversities locatedin lessindustrialised regions.

In linewith the theoreticaland empirical argumentsraised in the related literature, we argue that government funding complementexternalfundingandthencontributestostimulate universities’researchactivitieswhose resultscan beeventually transferredtoindustry.5 _We_argue_that_lack_of_government

sup-porttoacademicinstitutionsmayhamperuniversities’capability ofattractingexternalfunding,withpossiblenegativeconsequences onknowledgetransferactivities.

5 _In_support_of_this,_Bozeman_and_Gaughan₍₂₀₀₇₎_provide_evidence_that_academic researcherswhohaveresearchgrantsandcontractsworkmoreextensivelywith industrythanthosewithoutgrantsorcontracts.Furthermore,scientistswhohave industrycontractsinteractwithindustrytoagreaterdegreethanthosewhoare exclusivelyfundedbygovernments.

Table1

Samplecompositionﬁledbyscientiﬁcarea.

Code Scientiﬁcarea Frequency Percent 1MAT-INF Mathematics&ComputerScience 85 7.23

2FIS Physics 53 4.51

3CHIM Chemistry 81 6.89

4GEO Geology 36 3.06

5BIO Biology 120 10.21

6MED Medicine 422 35.91

7AGR-VET Agriculture&Veterinary 121 10.30 8ICAR CivilEngineering&Architecture 121 10.30 9INGIND-INF IndustrialEngineering 136 11.57

Total 1232 100.00

Source:Authors’calculationsonMIURdata.

Researchhypothesis:governmentfundingtouniversities

com-plementfundingfromresearchcontractsandconsultingactivity.

3. Empiricalanalysis

3.1. Datadescription

Theempiricalanalysisisbasedonﬁnancialdatafromthewhole

populationofuniversitydepartmentsinItalyengagedinresearch

inallnineScientiﬁcAreas(SA)oftheEngineeringandPhysical

Sci-ences(EPS).6 _The_data_were_provided _by_the_Italian_Ministry_of

UniversityandResearch(MIUR).Weobtainedﬁnancialdataforthe wholeperiod2005–2009,for1175EPSdepartments7_from₅₉

pub-licuniversities(4ofthemarepolytechnicuniversities)locatedin 48municipalities.Weconsideredintheanalysisalldepartments forwhichﬁnancialdatawasavailableforatleastthreeconsecutive yearsovertheperiod2005–2009.

Thedatabaseprovidesinformationonvolumeandsourcesof universityfunding,staffcomposition,presenceattheinstitutionof anofﬁcemanagingEuropeanpatents.Thesedatawerematchedto dataonresearchratingsandgeographicalcharacteristicsobtained fromothersources(seeTable3).

Table1reportsthedistributionofdepartmentsacrossthenine EPSSA.MostofthesedepartmentsareintheﬁeldofMedicine, which counts 422unitsand represents35.9% of thetotal pop-ulation. Industrial engineering accounts for 11.6% and Biology, Agriculture&Veterinary,CivilEngineering&Architectureaccount foraround10%each.Overthefour-yearperiodconsideredthere wasasubstantialincreaseinresearchstaff(+14.7%)especiallyata juniorlevel(+19%assistantprofessors).

3.2. RecentchangesinresearchfundinginItaly

Despite the rather slow emergence of initiatives to support knowledgetransferinItaly(MuscioandOrsenigo,2010),the polit-icalpressuretocommercialise theresultsofacademicresearch hasincreased,8 _prompting_several_universities _to_develop _plans

tosupportthecommercialexploitationofscientiﬁcresearch.By 2000–2005 the majority of Italian universities had Technology Transfer Ofﬁces(TTO),someof which hadbeen establishedfor severalyears.

6_The_National_University_Council_(CUN)_{classification}_of_scientific_areas_is_similar tothatappliedbytheOECDinitsFrascatiManual(OECD,2002).Thescientificareas consideredherecorrespondtotheareasidentifiedbytheFrascatiManualas:1. NaturalSciences,2.Engineering&Technology,3.MedicalSciences,4.Agricultural Sciences.

7_The_list_of_the_Italian_departments_is_available_at:_{www.cineca.it}_.

8_E.g.,_National_laws_D.L._27/7/1999_no.₂₉₇_and_D.M._8/8/2000_no.₅₉₃_encourage andregulatethecreationofuniversityTTO.Art.65oftheCodicedeiDirittidi Propri-etàIndustriale,10/02/2005,grantsintellectualpropertyrightstoscientistsfortheir scientiﬁcdiscoveries.

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Table2

Departmentfunding,2006–2009(Euros,meanvalues).

Sourceofrevenues 2009 2009(%) 2006(%) 2006–2009 (var.%)a 2006–2009per capita(var.%) MIUR 102,714 10.46 20.21 −43.24 −46.65 Europeancommission 132,218 13.47 9.17 61.07 51.39

Publicresearchinstitutions 33,728 3.44 2.81 33.80 25.75

Otherpublicbodies 141,545 14.42 10.84 45.77 37.01

Enterprises 38,725 3.95 4.71 −8.17 −13.69

Not-for-proﬁtorganisations 42,680 4.35 3.64 31.01 23.13

Foreignresearchinstitutions 6,655 0.68 0.68 9.98 3.37

Foreignprivateorganisations(enterprisesand not-for-proﬁtorganisations)

12,579 1.28 0.67 109.41 96.81

Ownuniversity 138,577 14.12 16.24 −4.70 −10.43

Othersources 23,442 2.39 3.46 −24.37 −28.92

Researchcontractsandconsultanciesfrom publicandprivateorganisationsb

308,683 31.45 27.58 24.99 17.47

Total 981,546 100.00 100.00 9.60 3.01

Source:Authors’calculationsonMIURdata. Notes:

a_2006–2009_growth_rates._Starting_from₂₀₀₆_MIUR_adopted_a_more_detailed_{classiﬁcation}_of_sources_of_funding._Therefore,_it_was_preferred_to_report_growth_rates_for_the period2006–2009.

b_Research_contracts_and_{consultancies}_for_public_and_private_{organisations}_are_deﬁned_by_the_National_Law_382/1980.

Thefinancial data reportedin Table 2 confirma substantial cutinresearchfundingintheperiod2006-09.Ifin2006the pri-marysourceoffundingforuniversitydepartmentswasprovided byMIURwhichrepresentedmorethan20%oftheentireresearch budget,in2009theprimarysourcesoffundingwereresearch con-tractsandconsultancies(31.45%),fundingfrompublicbodiesother thanMIUR(e.g.regionalandlocaladministrations)(14.42%), trans-fersfromtheuniversity(14.12%)andfinallycompetitiveresearch fundingfromMIUR(10.46%).Overtheperiodconsidered,research fundingfromMIURwasreducedbyasubstantial43.24%whilst rev-enuesfromthemajorityofothersourcesincreased.Thisreduced fundingfromMIURhasbeenoffsetbyanincreaseinthetransfers fromotherpublicbodiessuchasregionalandlocalgovernment institutions.

Arguably,this shifttowards regional funding hasbeen par-tiallycausedbythereformoftheConstitutionalLawintroduced in2001that,withtheintroductionoftheprincipleofsubsidiarity (establishedinEUlawbythe1992TreatyofMaastricht),decreed theformallegislativeautonomyofregionsinseveralpolicyareas, includingindustrialpolicy,innovationandscientiﬁcresearch(e.g. before2002regionswerenotfreetocreatetheirownregional agen-ciesforinnovation,laboratoriesorresearchnetworks,ordevelop theirowninnovationstrategies).However,thisincreaseinregional politicalautonomyhasnotyetbeensupportedwithasubstantial increaseinﬁnancialresourcesandexpenditurecapacity(Muscio andOrsenigo,2010).

ECfundingviacompetitiveprogrammessuchastheFramework Programmes,alsoincreasedby61.07%overtheperiodconsidered. ThereductioninMIURfundingisevenmorestrikingifconsidered intermsofthousandsofEurospercapita:totalrevenuesremained basicallystable(+3.01%),whilstMIURfundingshrankby46.65% thoughthiswasoffsetbyincreasesinallotherareas.Similartrends arecommontoothernationalpublicresearchbodiessuchas,for example,theNationalResearchCouncil(CNR)(CocciaandRolfo, 2008).9

9_The_CNR_is_a_major_public_research_body_similar_in_mission_to_the_French Cen-treNationaldelaRecercheScientiﬁqueortotheGermanMax-PlanckGesellschaft. Aspartoftworeformsintroducedin1999and2003CNRbudgetwas substan-tiallyreducedwithtwomainconsequences:publicfundsarenotsufﬁcienttocover currentexpensesandsince2001staffrecruitmenthasbeenstopped.

MIURcategorisesprivatefundingtodepartmentsasrevenues fromenterprisesandrevenuesfromresearchcontractsand con-sultancies.Since2006,fundingfromenterprisesshrankby8.17% andrevenuefromcontractsincreasedby27.58%.Thisisconﬁrmed empiricallybyMuscio(2010)whoprovidesevidenceofarelevant increase offrequencyofuniversity–industry research collabora-tionsinrecentyears.

3.3. Econometricspeciﬁcation

Table3presentsinformationonthevariablesusedinthe anal-ysis. The dependent variable is the amount of funding raised byuniversitydepartments asa resultofresearchtoorder (con-tracts and consultancies) commissioned by public and private organisations and subject to university regulations. Following Perkmannand Walsh (2008),research canbe distinguishedin: research-driven consulting(contract research commissionedby ﬁrms);opportunity-drivenandcommercialisation-driven consult-ing(researchoradvisoryservicesprovidedbyindividualacademic researcherstoindustryclients).ThedependentvariableFPRIVATE doesnotincludebusinessfundingtodepartmentsthatisnot com-pensated by research results in return (such as in the case of privatecontributionforconferencesandevents,scholarshipsand prizesforproﬁcientyoungresearchers,etc.).Theseresourcesare in factaccounted asa separatesourceof revenue(see Table2, under“Enterprises”),whichingeneralis relativelysmall. More-over, FPRIVATE does not account for funding from research programmes/contracts thatdo notallowincome distributionto researchstaff.

Theexplanatoryvariablesareindicatorsofsourcesofrevenue, departmentanduniversitycharacteristicsandregionaleconomic indicatorsasproxiesforexternaldemandofuniversityknowledge. Finally, weinclude yearlydummyvariables tocontrolfor ﬁxed temporaleffects.Table4reportssomedescriptivestatisticsforthe variablesincludedintheregressions.

Asalargepartofthedepartmentshavenoprivatefunding,our dependentvariableispartlycontinuouswithapositiveandlarge probabilitymassatzero.Hence,wemodelsucharesponsevariable inordertoaccountforthepresenceofacornersolutionoutcome. Wealsoallowforpersistenceintheprocessofcollectingprivate ﬁnance by introducing a 1-year lag of the dependent variable, inordertoinvestigatewhetheranevidenceofanaccumulation

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Table3

Variableusedintheregressions.

Variable Deﬁnition Datasource

Departments’sourceofrevenue

F PRIVATE Volumeoffundingfromresearchcontractsandconsultanciesfrompublicand privateorganisationsraisedinthelastﬁnancialyear(2006–2009)

MIUR

FPRIVATEY FPRIVATE(yes/no) MIUR

F EC ResearchfundingfromtheEC(2005–2009) MIUR

FMIUR ResearchfundingfromMIUR(2005–2009) MIUR

FUNI Researchfundingfromownuniversity(2005–2009) MIUR FADMIN Researchfundingfromothernationalandregionalgovernmentalbodies

(2006–2009)

MIUR Departments’characteristics

P TOT Numberofadministrativestaff,researchstaff(fullprofessors,associate professors,assistantprofessors,researchofﬁcers)andPhDstudents (2005–2009)

MIUR

PRESEARCHS Shareofresearchstaff(2005–2009) MIUR

P PHDS ShareofPHDstudents(2005–2009) MIUR

RES RATING ResearchratingpublishedbyMIURin2007,basedontheevaluationof researchoutputcarriedoutovertheperiod2001–2003.Thiscomposite indicatortakesintoaccountpeerreviewevaluationsofresearchactivity carriedoutatacademicinstitutions(patents,impactfactorofjournalarticles, etc.)

CIVRVTR(MIUR,2007)

Scientiﬁcareas Predominantdepartmentalscientiﬁcresearcharea MIUR-CINECA a1 SAMathematics&ComputerScience

a2 SAPhysics

a3 SAChemistry

a4 SAGeology

a5 SABiology

a6 SAMedicine

a7 SAAgriculture&Veterinary a8 SACivilEngineering&Architecture a9 SAIndustrialEngineering Universitycharacteristics

SIZE UNI1-4 Sizeoftheacademicinstitutionwherethedepartmentislocated.University sizeisexpressedintermsofnumberofstudents:1small(<10,000);2medium (10,000–15,000);3large(15,000–40,000);4mega(>40,000)

MIUR(2007)

EPOMNGMT PresenceattheuniversityofanofficemanagingEuropeanpatents.Normally thistaskiscarriedoutbyofficesforvalorisationofresearchresultsorbyTTOs. Theseofficeshavethemissionofsupportingresearchstaffincommercialising theresultsofscientificresearchestablishingcollaborationsandmediating betweenagents

MIUR

POLYTECH Locationofthedepartmentinapolytechnicuniversity(fourinItaly) Universitywebsite Indicatorsoflocaldemandfortechnology

GEOS,GEOC,GEONE,GEONW GeographicallocationofthedepartmentrespectivelyinSouthern,Central, North-EastandNorth-WestItaly

LOCAL SIZE MNF Averagesizeofmanufacturingcompaniesintheadministrativeprovince wherethedepartmentislocated

ISTAT2001Census LOCALEPO NumberofEuropeanpatentsgrantedtoindustrialresearchersresidentinthe

administrativeprovincewherethedepartmentislocatedduringtheperiod 2000–2006

PATSTATdatabaseelaborated byCentroKITES,Università Bocconi

advantageemergesalongthelinesoftheMattheweffectargument

(Merton,1968).

Denotebyyitdepartmenti’sprivatefundingcollectedattime t,thedynamic panelTobitmodel withdepartmentunobserved effectsis:

y∗

it=xitˇ+ci+ct+uit, i=1,...,N, t=1,...,T yit=max(0,y∗_it)

wherexitisasetofdepartmentspeciﬁccharacteristicsincludingthe amountofgovernmentfunding(namelyEuropeanUnion,MIUR, otherpublicbodies,andinternaluniversitytransfers)receivedin previousyears,ci arethe (random)department-speciﬁc effects, ct aretheyeareffects,uitistheerrorterm.Theyeareffectsare included to account for cyclical variation in private funding.10

In order to handle the initial condition problem in dynamic, non-linearunobservedeffectsmodel,wefollowthemethodology

10 _See_Tables₃_and₄_for_a_more_detailed_deﬁnition_of_the_controls_used_in_the modelandsomesummarystatistics.

suggestedbyWooldridge(2005).11_We_also_report_the_results_for

aprobitmodelwherethedependentvariableisabinaryvariable thatisequalto1ifthedepartmentreceivedanyprivatefundingin thereferenceyear,and0otherwise.

Inboth thetobitand probitestimatestheregressorsrelated togovernment fundingare laggedupto two years in orderto capturethedynamiceffectsonprivatefunding.Thevectorxit con-tains,apartfrompublicﬁnancialresources,asetofcovariatesthat mightbecorrelatedtothedepartmentcapabilitiestocollectprivate funding, such as departmentsize (both in term of administra-tiveandresearchstaff),quality/reputation,management,location,

11_The_approach_suggested_in_Wooldridge₍₂₀₀₅₎_can_be_easily_implemented_for_the TobitandProbitregressionsthroughastraightforwardestimationusingstandard econometricsoftware.Thismethodologyimpliestouseasadditionalregressorsthe longitudinallyaveragedexplanatoryvariablesforeachdepartmentandtheinitial outcomevalues.Thecoefﬁcientsoflongitudinallyaveragedexplanatoryvariables arenotreportedintheTableoftheresultsandareavailablefromtheauthorsupon request.

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Table4

Descriptivestatistics.

Variable Mean Std.dev. Min Max FPRIVATE09 286.61 524.45 0.00 5608.00 F PRIVATE 08 245.61 402.77 0.00 3707.00 F PRIVATE07 216.99 363.43 0.00 3660.00 FPRIVATE06 217.81 382.43 0.00 4743.00 FPRIVATEY 0.85 0.36 0.00 1.00 FEC09 122.77 460.48 0.00 8924.00 FEC08 81.54 243.28 0.00 3857.00 F EC 07 111.32 398.49 0.00 7585.00 F EC 06 74.64 219.90 0.00 4062.00 F EC05 78.19 287.74 0.00 5721.00 FMIUR09 95.37 180.58 0.00 1689.00 FMIUR08 113.75 216.57 0.00 2423.00 FMIUR07 137.96 228.64 0.00 3295.00 F MIUR 06 163.91 310.04 0.00 4000.00 FMIUR05 159.63 269.74 0.00 3064.00 F UNI09 128.67 154.05 0.00 1781.00 FUNI08 126.20 178.37 0.00 2268.00 F UNI 07 118.42 143.91 0.00 1214.00 FUNI06 130.83 203.97 0.00 1984.00 F UNI05 119.57 156.58 0.00 2020.00 FADMIN09 131.43 350.50 0.00 6652.00 FADMIN08 104.58 271.84 0.00 4365.00 FADMIN07 90.72 198.35 0.00 2468.00 FADMIN06 86.14 204.30 0.00 2316.00 P TOT 09 84.49 60.16 4.00 552.00 PTOT08 83.34 58.33 0.00 507.00 P TOT07 81.68 58.01 0.00 532.00 PTOT06 82.14 59.77 1.00 544.00 PRESEARCHS09 0.42 0.16 0.00 1.00 P RESEARCH S 08 0.42 0.16 0.00 1.00 PRESEARCHS07 0.44 0.17 0.10 1.00 P RESEARCH S06 0.43 0.16 0.08 1.00 PPHDS09 0.23 0.18 0.00 0.95 P PHDS08 0.24 0.17 0.00 0.97 PPHDS07 0.24 0.17 0.00 0.79 PPHDS06 0.23 0.18 0.00 0.75 RES RATING 0.79 0.08 0.20 1.00 a1 0.07 0.26 0.00 1.00 a2 0.05 0.21 0.00 1.00 a3 0.07 0.26 0.00 1.00 a4 0.03 0.17 0.00 1.00 a5 0.10 0.30 0.00 1.00 a6 0.36 0.48 0.00 1.00 a7 0.10 0.30 0.00 1.00 a8 0.10 0.30 0.00 1.00 a9 0.11 0.32 0.00 1.00 SIZEUNI1 0.08 0.27 0.00 1.00 SIZEUNI2 0.07 0.26 0.00 1.00 SIZEUNI3 0.44 0.50 0.00 1.00 SIZEUNI4 0.41 0.49 0.00 1.00 EPOMNGMT 0.86 0.35 0.00 1.00 POLYTECH 0.06 0.24 0.00 1.00 GEOS 0.32 0.46 0.00 1.00 GEOC 0.28 0.45 0.00 1.00 GEONE 0.20 0.40 0.00 1.00 GEONW 0.20 0.41 0.00 1.00 LOCALSIZEMNF 7.45 2.36 3.11 11.78 LOCALEPO 11.71 17.95 0.00 58.70

researchareasanduniversitystructuralcharacteristics12_and

exter-nalspillovers. 4. Results

Table5reportstheestimationresultsforthetobitmodel. Col-umn(1)referstoalinearmodelthatignoresthepresenceofacorner solution.Column(2)reportstheresultforthepooledtobitmodel

12 _The_results_obtained_using_university_dummies_to_control_for_university_effects ratherthanspeciﬁccontrolsforuniversitystructuralcharacteristicsarenot sub-stantiallydifferentandareavailablefromtheauthorsuponrequest.

thatignoresforthepresenceofunobservedrandomeffects,while column(3)focusesontheunobservedeffectsdynamictobitmodel whichis ourpreferredspeciﬁcation.Themarginaleffects atthe meanvaluesonbothE(yit|xit,ci)andE(yit|xit,ci,yit>0)arereported incolumns(4)and(5)respectively.Table6showstheresultsfor theprobitmodel.

Asexpected,thereissomepathdependencytoaccessingprivate funding.Forboththetobitandprobitregressionsthecoefficients ofthelaggeddependentvariablesarepositiveandhighly signifi-cant.Inotherwords,accessingprivatefundingincreasesboththe probability of furtherfundingfrom business and itsvolume in thefuture,whichisconsistentwiththeexistenceofaMatthew effect-like(Merton,1968)accumulationpattern.Thisresult con-firms thefindings of Arvanitiset al.(2008) who provethat, in thecaseofSwitzerland,instituteswhichhavealreadycollaborated withindustry–asreflectedbyahighshareofexternalfundsin aninstitute’sbudget– aremorelikelytobeinvolvedintooverall knowledgetransferactivities.

BothEUfunding(FEC)andMIUR(FMIUR)haveapositiveand significantimpactondepartmentalcapabilitytoraiseprivate fund-inginboththetobitandprobitregressions.Whenthepresenceof randomeffectsistakenintoaccount(column3),theoveralleffect ofbothEUandMIURfundingisslightersmallerinsizebutremain significantatthestandardlevel.Toquantifytheestimated over-alleffect,foreveryeuroof domesticpublicsupport(MIUR) the departmentreceivesaround0.025eurooffundingfromprivate contractsandtheimpliedelasticityisaround0.02.Thelongrun coefficientcalculatedkeepingintoaccountthehighpersistenceof privatefundingisaround0.04.ThetobitregressionsshowthatEU fundingismoreeffectiveinenhancingprivatefundingthanother sourcesofdomesticfinancing(for1euroincreaseintheEU fund-ing,privatefundingincreasesof0.04,whiletheeffectof1additional euroofMIURfundingisabout0.025increaseinprivatefunding). Theseresultscouldbeexplainedconsideringthattheabilityofthe departmenttosecureresearchgrantsfromtheEU,whichare gen-erallyallocatedonafiercelypeer-reviewedcompetitivebasisat aninternationallevel,istakenasasignalofitsresearch capabil-itiesandthusraisesthechancetosecurefurtherprivatefunding fromexternalcollaborationsandconsultancies.Moreover,asthe EUresearchgrantsaimtopromotepartnershipsbetween universi-tiesandtheindustry,theytendtofundresearchprojectsthat,even iffocusedonbasicresearch,lateronwillhaveahigherlikelihoodof generatingpositivespilloversfortheindustry.Theestimatedeffect isnotlargebutinlinewiththeevidencefoundinpreviousempirical works(Jensenetal.,2010).

In terms ofprobability (Table 6), thedecline ingovernment researchgrantsexperiencedbyItaliandepartmentsinthelastﬁve yearshasreducedtheprobabilityofgettingprivatefundingofabout 2.5percentagepointswhiletheincreaseinEUfundingavailability hasincreasedtheprobabilityof3percentagepoints.

Publicfundingfromothernationalandregionalgovernmental bodies(FADMIN)complementstheamountofprivateresources collectedbydepartmentsandthesizeofcoefficientisinlinewith thepreviousformofprivatefinancing,but ithasnotsignificant effectsontheprobabilityofobtainingprivatefunding.

Notsurprisingly,theeffectofuniversityinternaltransfersto departmentsisnotsigniﬁcantlydifferentfromzero(FUNI).Thisis becauseinternaltransfersareaimednotatpromotingresearchbut theyaremainlyusedtoprovidegeneralpurposeresourcesor antici-patefundingalreadyallocatedtodepartmentsforthenextyear/s.13

13_Internal_transfers_of_resources_have_no_effect_on_a_{department’s}_capability_to attractfundingfromindustrybecauseapartfromanybudgetformallyassignedto researchactivities,inmostcasesthisfundingisdesignedtocoverexpensessuchas purchaseofhardwareandsoftware,andresearchers’subsistenceforattendanceat

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Table5

Paneldatatobitregressions.

Dependentvariable:FPRIVATE(t) (1) (2) (3) (4) (5) OLS Pooleddynamictobit Unobservedeffectsdynamictobit

Coefficients Coefficients Coefficients Marginaleffects(1) Marginaleffects(2)

FPRIVATE(t−1) 0.841 0.860 0.661 0.386 0.530 (0.041)*** _(0.014)*** _(0.038)*** _(0.023)*** _(0.031)*** FEC(t−1) 0.031 0.036 0.032 0.019 0.026 (0.025) (0.019)* _(0.019)* _(0.011)* _(0.0151)* FEC(t−2) 0.011 0.009 0.009 0.005 0.007 (0.035) (0.019) (0.019) (0.011) (0.0149) FMIUR(t−1) −0.054 −0.047 −0.054 −0.031 −0.043 (0.052) (0.022)** _(0.022)** _(0.013)** _(0.0178)** F MIUR(t− 2) 0.088 0.094 0.084 0.049 0.067 (0.043)** _(0.021)*** _(0.021)*** _(0.012)*** _(0.0169)** F UNI(t− 1) −0.035 −0.034 −0.019 −0.011 −0.015 (0.040) (0.034) (0.034) (0.019) (0.027) F UNI(t−2) 0.023 0.031 0.022 0.013 0.018 (0.042) (0.035) (0.035) (0.020) (0.028) FADMIN(t−1) 0.021 0.036 0.037 0.022 0.030 (0.027) (0.021)* _(0.022)* _(0.013)* _(0.017)* PTOT(t−1) 0.649 0.777 0.810 0.473 0.649 (0.139)*** (0.110)*** (0.116)*** (0.067)*** (0.092)*** PRESEARCHS(t−1) 167.395 107.917 117.880 68.852 94.426 (50.620)*** _(63.770)* _(66.981)* _(39.137)* _(53.665)* P PHD S(t− 1) −77.649 −68.221 −73.651 −43.018 −58.996 (27.032)*** _(32.874)** _(34.328)** _(20.056)** _(27.501)** RES RATING 104.020 138.453 169.893 99.232 136.089 (52.482)** (78.827)* (83.533)** (48.79)** (66.908)** a2 −11.756 −24.148 −28.942 −16.475 −22.760 (25.212) (28.967) (30.738) (17.042) (23.707) a3 4.040 42.586 47.368 28.773 38.940 (17.658) (25.249)* _(26.823)* _(16.913)* _(22.577)* a4 17.235 64.502 63.010 38.940 52.359 (19.475) (31.893)** _(33.879)* _(22.081)* _(29.084)* a5 1.436 20.482 20.853 12.378 16.888 (15.711) (23.604) (25.011) (15.083) (20.470) a6 26.475 44.288 47.926 28.388 38.743 (15.568)* _(20.727)** _(21.980)** _(13.199)** _(17.917)** a7 43.880 90.986 94.503 59.265 79.181 (19.790)** _(25.736)*** _(27.313)*** _(18.290)*** _(23.769)*** a8 61.815 106.624 112.092 71.191 94.590 (21.909)*** _(24.365)*** _(25.953)*** _(17.784)*** _(22.850)*** a9 129.443 171.912 181.519 120.337 156.572 (25.331)*** _(23.641)*** _(25.400)*** _(18.763)*** _(23.103)*** SIZEUNI1 28.232 33.359 38.068 22.932 31.129 (22.517) (22.088) (23.446) (14.566) (19.549) SIZEUNI2 −36.012 −67.505 −75.555 −41.372 −57.655 (18.610)* _(21.558)*** _(22.883)*** _(11.706)*** _(16.527)*** SIZE UNI3 14.227 21.119 21.115 12.366 16.944 (10.871) (12.455)* _(13.186) _(7.743) _(10.599) EPOMNGMT −4.389 −18.560 −15.448 −9.121 −12.466 (13.485) (15.894) (16.821) (10.039) ()13.673 POLYTECH 112.338 105.578 115.490 74.185 98.112 (31.834)*** _(25.081)*** _(26.551)*** _(18.611)* _(23.675)*** GEOS −13.722 −49.095 −42.682 −24.542 −33.802 (24.294) (25.177)* _(26.714) _(15.111)* _(20.899)* GEOC −23.638 −30.529 −29.242 −16.864 −23.211 (18.262) (21.943) (23.286) (13.261) (18.309) GEONE 8.135 −2.637 4.309 2.523 3.458 (17.258) (18.553) (19.719) (11.583) (15.851) LOCALSIZEMNF −6.109 −3.959 −4.790 −2.798 −3.837 (4.013) (3.685) (3.893) (2.274) (3.119) LOCALEPO 1.106 0.887 1.067 0.623 0.855 (0.509)** _(0.497)* _(0.528)** _(0.309)** _(0.423)** Constant −190.221 −235.434 −271.247 – – (81.840)** _(94.629)** _(100.063)*** _– _–

Yeardummies Yes Yes Yes – –

Observations 3293 3293 3293 – –

No.ofgroups – – 1175 – –

Log-likelihood – −19853.210 −19832.810 – –

R-squared/pseudoR-squared 0.710 0.710 0.710 – –

Ho:rho=0a 2₌_5.96,_p-value₌_0.007

Standarderrorsinparentheses. Marginaleffects(1):␦E(y|x,c)/␦xj. marginaleffects(2):␦E(y|x,c,y>0)/␦xj.

a_{Likelihood-ratio}_test_comparing_the_random_effects_model_with_the_pooled_(tobit)_model. * _Signiﬁcant_at_10%.

** _Signiﬁcant_at_5%. ***_Signiﬁcant_at_1%.

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Overall,theeconometricresultsforﬁnancialvariablessuggesta clearevidenceofspilloversfromgovernmentsupporteduniversity researchtomoreindustryorientedresearchandconsulting.

As an additional check, Table 7 presents the results of a Probit model, where the dependent variable is time invariant andassumesvalue 1ifthedepartmenthasobtainedsome pri-vate fundinginat leastoneyear and0 otherwise. Inthis way, we test if there is any effect of public funding on the initial decision of a department to access toprivate funding at all.14

Thismaybe thecase, for example,of departments highly spe-cialised in basic research, which is generally underfunded by private sources and are not involved in the consultant activ-ity. For thesedepartments, we expect no relationship (neither complementarily nor substitution) between private and public sourcesofﬁnancing.Accordingly,theresultsoftheProbit regres-sioninTable7showthattheprobabilityofaccessingtoprivate fundingdoes not dependon theavailability of publicresearch fundingoncewecontrol forthedepartmentscientiﬁc areaand size.

Lookingatdepartmentanduniversitycharacteristics,bothtobit andprobitregressionsshowthatstructuralcharacteristicshavean impactonbusinessfundingtodepartments.Departments’ capac-ity ofrising resources fromprivatesources largely depends on thetypeofresearchcarriedoutinsidethedepartments. Depart-mentsassignedtoresearchareasa9(IndustrialEngineering)and a8(Civil Engineeringand Architecture) and toa less extent a7 (Agriculture and Veterinary) are more involved in consulting activity.15

Departmentsin medium sized universitiesare less likely to attract business funding.16 _There_are _positive _effects _of _critical

massinlargeacademicinstitutionsonbusinessfundingexpressed interms ofuniversity reputation,visibilityand sizeofresearch teams.Ontheotherhand,smallacademicinstitutionstendtohave fewer,highlyspecialiseddepartmentsthatarearguablymore efﬁ-cient(lessbureaucraticprocedures,moredirectcontactwithlocal businesses,higher motivationtoachievevisibility) inattracting businessfunding.

UniversitieswithmoreEPSdepartments,suchaspolytechnic universities,arelikelytohavemorereﬁnedpracticesandbetter servicesfortechnologicalcollaborations withindustry,and this affectspositivelythevolumeofbusinessfundingatadepartment level.Ontheotherhand,theexistenceintheuniversityofanofﬁce tomanageEuropeanpatents(EPOMNGMT)doesnotseemtohave anyimpactontheprobabilityofattractingprivatefundingnorits

conferencesandscientificmeetings.Thepercapitaamountsofthesetransfersare typicallycappedatwellbelowtheamountrequiredtofinancestructuredresearch activitiesthatarelikelytoattractfirmsandpromotecollaboration.

14 _In_our_sample,₅₇_departments_report_of_having_received_no_funding_from exter-nalcollaborationandconsultantactivityovertheobservedperiod.Theyaccountfor 15%ofthedepartmentsoperatingintheﬁeldofMathematicsandComputerScience, 13%inPhysics,and10%inBiology.

15 _As_a_robustness_check,_we_extend_the_analysis_by_including_in_the_sample_the departmentsactiveinallscientificdisciplines.Theeffectofpublicfundingonprivate fundingisstillpositiveandsignificantthoughabitlessquantitativelyrelevant. Thisgeneralisationsuggeststhatalltheresearchfieldsbenefitfromalargerpublic contributiontoresearchactivityintermsoftheircapabilityofattractingprivate resources.However,sucheffectisstrongerwhentheresearchismore “industry-intensive”.Resultsareavailablefromtheauthorsuponrequest.

16 _In_our_sample,_the_research_departments_operating_in_medium_sized Universi-tiesreportanaverageamountoffundsfromexternalcollaborationsofaround6 thousandseuroperresearchereveryyear,whichaccountsfor22%oftotalfunding receivedbythedepartment.Indepartmentsoperatinginsmallandlarge Universi-tiestheamountoffundingfromexternalcollaborationsismorethan8thousands eurorepresentingthe24%and29%respectivelyofthetotalresources.The depart-mentsinthemediumsizeUniversitiesarealsolesseffectiveincollectingfundsfrom EUresearchgrants,whiletherearenotsubstantialdifferencesfortheothersources ofﬁnancing.

Table6

Paneldataprobitregressions.

Dependentvariable:FPRIVATEY Unobservedeffectsdynamicprobit Coefﬁcients Marginaleffects FPRIVATE(t−1) 0.002 0.0001 (0.000)*** _(0.000)*** FEC(t−1) 0.001 3.3E−05 (0.000)* _(0.0000)* FEC(t−2) 0.000 3.32E−05 (0.000) (0.0000) FMIUR(t−1) −0.000 −2.64E−06 (0.000) (0.0000) FMIUR(t−2) 0.001 2.35E−05 (0.000)** _(0.0000)** F UNI(t− 1) −0.000 −8.44E−08 (0.000) (0.0000) F UNI(t−2) 0.000 1.31E−05 (0.000) (0.0000) FADMIN(t−1) 0.000 4.05E−08 (0.000) (0.0000) PTOT(t−1) 0.007 0.0003 (0.002)*** _(0.0000)*** P RESEARCH S(t− 1) −0.341 −0.0013 (0.704) (0.0261) P PHD S(t−1) −0.262 −0.0098 (0.355) (0.0142) RESRATING 0.103 0.0039 (0.893) (0.0336) a2 −0.361 −0.0194 (0.317) (0.0233) a3 1.356 0.0186 (0.337)*** _(0.0052)*** a4 1.943 0.0173 (0.490)*** _(0.0049)*** a5 0.765 0.0160 (0.274)*** _(0.0048)*** a6 0.539 0.0178 (0.232)** _(0.0079)** a7 1.480 0.0215 (0.321)*** _(0.0058)*** a8 1.490 0.0216 (0.315)*** _(0.0059)*** a9 1.657 0.0240 (0.350)*** _(0.0062)*** SIZEUNI1 0.183 0.0059 (0.296) (0.0081) SIZEUNI2 −0.620 −0.0419 (0.248)** _(0.0268) SIZEUNI3 0.261 0.0096 (0.159) (0.0061) EPOMNGMT −0.096 −0.0034 (0.189) (0.0062) POLYTECH 0.066 0.0023 (0.368) (0.0122) GEOS −1.250 −0.0894 (0.364)*** _(0.043)** GEOC −0.351 −0.0159 (0.323) (0.0176) GEONE −0.288 −0.0132 (0.277) (0.0154) LOCAL SIZEMNF 0.048 0.0018 (0.048) (0.0017) LOCALEPO −0.006 −0.0002 (0.007) (0.0002) Constant 0.592 – (1.107) –

Yeardummies Yes –

Observations 3293

No.ofgroups 1175

Log-likelihood −997.751

PseudoR-squared 0.090

Standarderrorsinparentheses. *_Significant_at_10%. **_Significant_at_5%. ***_Significant_at_1%.

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Table7

Probitregressionsrobustnesscheck.

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

Pooledprobit Year=2007 Year=2008 Year=2009

FEC(t−1) 0.000 −0.000 0.001 −0.000 (0.000) (0.001) (0.001) (0.001) FEC(t−2) −0.000 −0.000 −0.001 −0.000 (0.000) (0.000) (0.001) (0.001) FMIUR(t−1) 0.000 0.000 −0.000 −0.000 (0.000) (0.000) (0.001) (0.001) F MIUR(t− 2) 0.000 −0.001 0.000 −0.000 (0.000) (0.000)** _(0.000) _(0.001) FUNI(t−1) 0.000 0.001 −0.000 0.002 (0.000) (0.000) (0.001) (0.001)* FUNI(t−2) 0.000 0.001 0.001 −0.001 (0.000) (0.000) (0.001)** _(0.001) F ADMIN(t− 1) 0.000 0.001 0.000 −0.000 (0.000) (0.001) (0.000) (0.000) P TOT(t−1) 0.000 0.002 −0.000 0.002 (0.000)** _(0.001)* _(0.001) _(0.002) P RESEARCH S(t− 1) −0.084 −0.407 −1.094 −0.498 (0.028)*** _(0.530) _(0.539)** _(0.242)** PPHDS(t−1) 0.006 −0.062 0.083 0.014 (0.013) (0.257) (0.262) (0.301) RESRATING 0.092 1.143 2.009 2.711 (0.030)*** _(0.619)* _(0.715)*** _(0.832)*** a2 −0.025 −0.242 −0.073 0.058 (0.017) (0.196) (0.195) (0.162) a3 0.022 0.099 0.159 0.348 (0.004)*** _(0.187) _(0.202) _(0.046)*** a5 0.011 −0.051 −0.084 0.198 (0.005)** _(0.165) _(0.181) _(0.128) a6 0.020 0.028 0.214 0.362 (0.006)*** _(0.129) _(0.121)* _(0.139)*** a7 0.029 0.251 0.345 0.382 (0.004)*** _(0.102)** _(0.050)*** _(0.047)*** a8 0.028 0.165 0.292 0.356 (0.004)*** _(0.138) _(0.061)*** _(0.055)*** a9 0.029 0.174 0.188 0.309 (0.004)*** _(0.177) _(0.146) _(0.067)*** SIZEUNI1 −0.009 0.170 −0.251 −0.045 (0.014) (0.151) (0.362) (0.224) SIZEUNI2 −0.014 −0.206 −0.115 0.013 (0.012) (0.202) (0.178) (0.174) SIZEUNI3 0.007 0.119 0.063 −0.032 (0.005) (0.103) (0.112) (0.114) EPO MNGMT −0.007 −0.231 −0.073 0.066 (0.005) (0.095)** _(0.123) _(0.171) POLYTECH −0.014 −0.111 0.272 0.105 (0.019) (0.256) (0.082)*** _(0.232) GEOS −0.068 −0.264 −0.444 −0.370 (0.031)** _(0.321) _(0.278) _(0.269) GEOC −0.027 −0.193 −0.443 −0.337 (0.019) (0.321) (0.322) (0.288) GEONE −0.064 −0.341 −0.542 −0.449 (0.026)** _(0.248) _(0.204)*** _(0.219)** LOCALSIZEMNF 0.003 −0.002 −0.055 −0.032 (0.002) (0.039) (0.041) (0.040) LOCALEPO −0.000 −0.006 −0.007 −0.004 (0.000)* _(0.006) _(0.007) _(0.007) Observations 3191 1051 1096 1044 Log-likelihood −527.67 −173.17 −173.82 −167.73 PseudoR-squared 0.209 0.218 0.222 0.242

Standarderrorsinparentheses.

Thedependentvariableistimeinvariantandtakesvalue1ifthedepartmenthasbeenengagedinexternalfundraisinginanyyearofthesampleperiod,0otherwise.Sincea4 differentfrom0predictssuccessperfectly,thescientiﬁcareaa4(Chemistry)isdroppedand102observationsarenotusedintheregression.Thecoefﬁcientsarethemarginal effects

* _Significant_at_10%. ** _Significant_at_5%. ***_Significant_at_1%.

volume.Therehasbeenasubstantialincreaseinpublicandprivate investmentinTTOs(LinkandScott,2007)andthereisagrowing empiricalliteratureontheircontributiontothetechnologytransfer process(Muscio,2010).

The analysis of the impact of the department’s geographi-callocation onitscapability toraisebusiness fundingprovides mixedresults.Boththetobitandprobitregressionsshowthatlow levelsof industrialisation,and poorinfrastructure, e.g. thecase

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ofsouthernItaly(GEOS),negativelyaffectadepartment’s capa-bilityto establishcollaborationwith industry and raise private funding. Academic institutions in southern Italy are disadvan-tagedwithrespecttoinstitutionslocatedelsewhereinthecountry. Moreover, the proxy for local absorptive capacity for research services(LOCALEPO)ispositiveand significant.Thisresultisin linewithpreviousempiricalfindings.Forexample,Chappleetal. (2005)findthatuniversitiesinregionswithhigherR&Dandgross domesticproduct(GDP)levelsappeartobeefficientinknowledge transferactivities,implying theexistenceof regionalspillovers. FriedmanandSilberman(2003)findthatinregionswitha con-centrationofhightechnologyfirms,morepatentexploitationand co-patentingcanbeexpected.Mansfield(1995)providesevidence thatuniversitiesconductinghigherqualityresearch,andwhichare locatedclosetoinnovatingcompanies,makeagreater contribu-tiontoindustrialinnovation.Firmstendtotradeofffacultyquality againstgeographicproximity,particularlyinthecaseofapplied R&D.

Thereis alsoevidence thatinnovative firmsfavourresearch producedbyhighqualityresearchuniversitiesand publishedin peer-reviewedjournals(BrunoandOrsenigo,2003;Pavitt,2001; Hicks et al., 2000). In our analysis we use a research perfor-manceindicatortocontrolfirstforwhetherhighqualityresearch generates valuable intellectual property that can be passed to industryandsecondforwhetherresearchperformanceprovides asignaltoindustryofthebestuniversitydepartments.Wefind thatresearchperformance(RESRATING)hasalargeand signifi-cantimpactonbusinessfundingtouniversities,whichmeansthat bothMattheweffectsandsignalling/reputationmechanismsareat work.

Instead,wehavenoinformationtoallowustotesttheimpact ofresearchqualityonthefrequencyofinteractionsandthe appli-cabilityoftheresearch,onfunding.

BrunoandOrsenigo’s(2003)findingsfortheimpactof depart-mentsizeonindustryfundingareconfirmed.Thenumberofstaff (bothadministrativeandresearchstaff)hasapositiveand signif-icanteffect onboth thevolumeof privatefundingcollectedby departmentsandontheprobabilityofaccessingprivatefunding. Moreover,controllingforthenumberofpeopleemployedinthe department,the capacity of collectingprivateresearch funding increaseswiththeshareofresearchersinvolvedinresearch activ-ities,confirmingthatdepartmentsneedtodevelopcriticalmass inresearchactivitiesinordertoattractbusinesses.Departments withlargernumbersofresearchstaffwillbenefitfromgreater vis-ibility,greaterspecialisationofdepartmentalresearchandmore efficientproceduresfortheestablishmentandmanagementof col-laborations.Finally,thecapabilityofattractingprivatefundingis negativelyrelatedtotheshareofPh.D.studentstoresearchstuff. Thisfinalmeasure reflectsthefactsthat,ceterisparibus,Ph.D.s areobviouslylesseffectivethanseniorresearchersinattracting privatefunding.Moreover,allelseequal,thehigherthePhD stu-dent/researchratio,thegreatertheadvisingrolesofthemoresenior researchersandthenlessthetimedevotedtoresearch(Jensenetal., 2010).17

17 _We_also_tested_the_robustness_of_our_results_in_several_ways._We_used_an alter-nativeproxyofresearchqualitymeasuredasthenumberofMIUR“PRIN”projects grantedtodepartmentsobtainingsimilarresults.Wealsotestedforthepresence ofnon-linearityintheeffectofdepartmentsizeonprivateﬁnancingusingamore ﬂexiblepolynomial(secondandthirdorder)structure.Thelinearityassumptionis notrejectedbythedata.

5. Discussionandconclusions

This paper investigates the relationship between different formsofpublicfundingandprivatefundingtouniversity depart-ments. We provide empirical evidence that public funding to universitiescomplementsprivatesourcesoffundingprovidedvia researchcontractsandconsultancies.Wefindsignificantevidence of complementarities betweenpublicand private fundingwith anestimated private/publicfundingelasticity(bothEU funding anddomesticfunding)ofabout0.04.Wealsoconfirmtheresults of previous studies providing evidence that the ability to pro-ducegood researchisthepre-conditionforthedevelopmentof astrongroleofuniversitiestowardsindustry(BrunoandOrsenigo, 2003).

Wecanformulatesomeconcludingremarksandrelated pol-icy implications to contribute to the on-going debate on the public/privatefundingofuniversities.Thisdebateemergesfrom severalrecentstatementsinEuropeandintheUS,which under-linetheneedforgreatereffortsbyuniversitiestoincreaseprivate fundingalsothroughlargercommercialisationofresearch activ-ity. In the Italian case this is in linewith themore pragmatic aim of a cut in government fundingto universities, thus indi-rectlyimplyingthatpublicandprivatefundingtouniversitiesare substitutes.

Our empirical analysis does not support the “substitution hypothesis”.Publicfundingandfundingfromresearchcontracts and consultanciesare generally positivelylinked, meaningthat thesetwo formsof fundingare strategicallycomplementary.In otherwords,publicfundingcanplayanimportantrolein stim-ulatingthoseuniversity–industryinteractions.Asaconsequence, currentinitiativesaimedatcuttingpublicfundingtouniversities maynegativelyaffectuniversity–industrycollaborationandtheir externalfundraisingcapacity.Moreover,ouranalysisshowsahigh degreeofpersistenceofprivatefundingovertime,implyingthata reductioninpublicfundingtouniversitieswouldprobablyresult inafurtherwideningofthecurrentlyexistinggapbetweenthose departments that are capableof attracting privatefundingand thosethatarenot.

Finally,ourresultsarelikelytounderestimatetheextentofthe complementaritybetweenpublicandprivatesourcesasourdatado notcapturetheactualvolumeofconsultingactivity(andtherefore theimpactofpublicfundingontheirsheervolume)asmanydirect engagementsarenotdisclosedtouniversityadministrators.

InthespeciﬁccaseofItalythedecreaseincompetitiveresearch fundingfromMIURhasbeenatleastpartiallyoffsetbyanincrease infundingfromotherpublicsources.Thisispartiallyduetothe gradual implementation of the principle of subsidiarity. Inter-estingly, our results show that EU funding is more effective in enhancing private funding than other sources of domestic ﬁnancing.Thisimpliesthat,inordertofosteruniversity–industry interactions,themodalitiesthroughwhichpublicfundsaregranted and/ortheprojectstobefundedareselectedcouldplayacrucial role.

Ourempiricalresultscanbeextendedtothecountriesthathave notimplemented(yet?)moreadvancedperformance-based sys-tems (see:AuranenandNieminen, 2010;Strehl etal.,2007)in whichthelinkbetweengovernmentfundingtouniversityandthe interactionwithindustryisbasedoncompetitivecriteria.

Finally, thedatasetusedin thispaper imposessome limita-tionsontheavailability of measuresand proxiesof potentially relevantvariablescapturingresearchers’incentivestocollaborate withindustry,suchasownershipofintellectualpropertyrights, revenuesdistributiontoacademicstaffortheestablishmentof aca-demictariffsfortestsandanalyses.Theimpactofthesefactorson university–industryinteractionsisaninterestingtopicforfuture research.