The strength of long ties and the weakness of strong ties: Knowledge diffusion through supply chain networks

ContentslistsavailableatScienceDirect

Research

Policy

j ou rn a l h om epa 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

The

strength

of

long

ties

and

the

weakness

of

strong

ties:

Knowledge

diffusion

through

supply

chain

networks

夽

Yasuyuki

Todo

a,∗

_,

_Petr

_Matous

b

_,

_Hiroyasu

_Inoue

c

a_{WasedaUniversity,1-6-1Nishi-Waseda,Shinjuku-ku,Tokyo,169-8050,Japan} b_{FacultyofEngineeringandIT,TheUniversityofSydney,NSW2006,Australia}

c_{GraduateSchoolofSimulationStudies,UniversityofHyogo,7-1-28Minatojima-minamimachi,Chuo-ku,Kobe,Hyogo650-0047,Japan}

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received25December2015 Receivedinrevisedform8May2016 Accepted26June2016

Availableonline9July2016 Keywords: Knowledgediffusion Supplychains Networks Productivity Innovation

a

b

s

t

r

a

c

t

Usingalargefirm-levelpaneldatasetforJapan,thispaperexaminestheeffectsofthestructureof sup-plychainnetworksonproductivityandinnovationcapabilitythroughknowledgediffusion.Wefind thattieswithdistantsuppliersimproveproductivity(asmeasuredbysalesperworker)morethanties withneighboringsuppliers,whichislikelybecausedistantfirms’intermediatesembodymore diversi-fiedknowledgethanthosefromneighboringfirms.Tieswithneighboringclientsimproveproductivity morethantieswithdistantclients,whichislikelybecauseneighboringclientsmoreeffectivelydiffuse disembodiedknowledgethandistantclients.Bycontrast,tieswithdistantsuppliersandclientsimprove innovativecapability(asmeasuredbythenumberofregisteredpatents),whereastieswithneighboring suppliersorclientsdonotaffectinnovativecapability.Inaddition,thedensityofafirm’segonetwork(as measuredbyhowdenselyitssupplychainpartnerstransactwithoneanother)hasanegativeeffecton productivityandinnovativecapability,implyingknowledgeredundancyindensenetworks.Theseresults suggestthataccesstodiversifiedtiesisimportantforimprovingproductivityandinnovationcapability throughknowledgediffusion.

©2016TheAuthor(s).PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBYlicense (http://creativecommons.org/licenses/by/4.0/).

1. Introduction

Growthintheproductivityandinnovationcapabilityoffirms issubstantiallyaffectedbythediffusionofknowledge, technol-ogy,andinformationfromotherfirms(Bloometal.,2013;Romer,

夽 Thisresearchwasconductedaspartofaprojectentitled‘Empirical Analy-sisonDeterminantsandImpactsofFormationofFirmNetworks,’undertakenat theResearchInstituteofEconomy,Trade,andIndustry(RIETI).Theauthorswould liketothankRIETIforprovidingthefirm-leveldatausedintheanalysis. Finan-cialsupportfromJSPSKakenhiGrant(No.25101003and26245037forTodoand Matous,andNo.24530506and15K01217forInoue)isgratefullyacknowledged. Commentsfromtwoanonymousrefereesthatsubstantiallyclarifiedtheargument ofthispaperaregratefullyacknowledged.Theauthorswouldalsoliketothank MartinEverett,MasahisaFujita,AkieIriyama,JohanKoskinen,ToshiyukiMatsuura, MasayukiMorikawa,GarryRobins,YukikoSaito,IichiroUesugi,RyuheiWakasugi andseminarparticipantsattheUniversityofTokyo,RIETI,WasedaUniversity,and theWesternEconomicAssociationInternationalAnnualMeetingsfortheirhelpful comments.Theopinionsexpressedandargumentsemployedinthispaperarethe soleresponsibilityoftheauthorsanddonotnecessarilyreflectthoseofRIETI, Uni-versityofHyogo,theUniversityofSydney,WasedaUniversity,oranyinstitution withwhichtheauthorsareaffiliated.

∗ Correspondingauthor.

E-mailaddresses:[email protected](Y.Todo),[email protected]

(P.Matous),[email protected](H.Inoue).

1990).Anevidentchannelofsuchknowledgediffusionisresearch collaboration(Ahuja,2000).Anotherlessevidentchannelis buyer-supplierrelationsbetweenfirmsbecausebuyersoftenprovidenew knowledgetotheirsupplierswhenseekingtoprocurehigh-quality products(DyerandNobeoka,2000).Inaddition,buyerscan ben-efitfromtheirsuppliersbecausetheproductivityofassemblersis higherwhentheyemployalargervarietyofintermediatesfrom differentsuppliersandutilizetheknowledgeembodiedin their products(DixitandStiglitz,1977).Supplychaintiesareoften asso-ciatedwithresearch collaborationfor thedevelopment ofnew intermediates(Uesugi,2015),whichpromotesknowledge diffu-sionbetweensuppliersandbuyers.

Knowledgediffusionthroughbuyer-supplierrelationshasbeen testedextensivelyintheempiricalliterature,inwhichan improve-mentin themeasuresof productivityand innovationcapability associated withsuchrelations is typically consideredto reflect knowledgediffusion.Forexample,whenfirmsimprovetheir pro-ductivitythroughexporting,itisassumedthatexportinghasledto newknowledgegains.Knowledgediffusionthroughinternational tradehasbeenfoundbyAmitiandKonings(2007),Crespietal. (2008a),LööfandAndersson(2010),andPiermartiniandRubínová (2014),amongmanyothers.Javorcik(2004)providesevidenceof

http://dx.doi.org/10.1016/j.respol.2016.06.008

knowledgespilloversfromforeign-ownedfirmstotheirupstream suppliers.

Otherstudiespaymoreexplicitattentiontosupplychain net-worksasachannelof knowledgediffusion.Forexample,Crespi etal.(2008b)usefirm-leveldatafortheUnitedKingdomtoshow thatboth thenumber ofregisteredpatentsandgrowthin total factorproductivity(TFP)arehigherwhenfirmsreportthatthey gainknowledgefromtheirsuppliers.Isakssonetal.(2016)analyze patentdataforUSfirmsinthehigh-techsectorsandfindevidence that buyers’innovationhasa positive effect ontheirsuppliers’ innovation.Inthesupplychainmanagementliterature,Flynnetal. (2010)usefirms’subjectivemeasurementsanddeterminethatthe strengthofrelationswithcustomershasapositiveeffectonfirm performancebutthatthestrengthofrelationswithsuppliershas aninsignificanteffect.Bozarthetal.(2009)findthatthenumber ofsuppliersorclientsdoesnotaffectsubjectivelymeasuredfirm performance.Usingafirm-leveldatasetforJapansimilartothat usedinthisstudy,Bernardetal.(2014)andBelderbosetal.(2015) examinehowfirms’productivityisaffectedbytheirbuyersand suppliers.

However,therearetworemainingissuesintheliterature.First, theexistingstudieshavenotfocusedonhowafirm’sdirect suppli-ersandclientsareconnectedwithotherfirms.Knowledgediffusion toaparticularfirmfromitssupplychainpartnersmaybe influ-encedbywhetherthosepartnersareconnectedwithoneanother and/orwithwhomtheyareconnected.Forexample,theamount ofknowledgethatdiffusestoafirmfromitssuppliersmayvary dependingonwhetherthesuppliersareinthesameclosedfirm grouporareconnectedwithdifferenttypesoffirms.However,the previousliteratureignoressuchdetailedcharacteristicsofwhole supplychainnetworksintheeconomyandinsteadidentifiessupply chainrelationsonlythroughfirms’engagementintrade(Kimura and Kiyota, 2006; Lööf and Andersson, 2010; Van Biesebroeck, 2005), firms’subjectiveperceptions (Crespiet al.,2008a;Flynn etal.,2010), input-outputtablesattheindustrylevel (Javorcik, 2004;PiermartiniandRubínová,2014),or–atbest–firms’direct supplychainpartners(Belderbosetal.,2015;Bernardetal.,2014; Isakssonetal.,2016).

Theliteratureonsocialnetworkshasemphasizedthe impor-tanceofconsideringtheoverallstructureofnetworks(Granovetter, 2005).Forexample,Burt(1992)findsthatactorswhocreate bridg-inglinksbetweenotherwisedisconnectedgroupsofactors–or acrossstructural holes – have superioraccess todiverse infor-mation. Thisfinding is related to theargument of Granovetter (1973)that weakties torelativelyless frequentlymetpartners areinstrumentalforaccessingnewinformationbecausesuchlinks frequentlyextendbeyondtheimmediatecircleofdensely inter-connectedstrongtiesamongsimilarpartnerswithsimilarshared information.Inotherwords,networkdensitymaypreventactive knowledgediffusionduetoknowledgeoverlapsandredundancy amongpartners.

However,structuralholes and weakties maynotalwaysbe thekey to knowledge diffusion.Other studieshave foundthat densenetworkswithinanorganizationinwhichactorsareclosely connectedwithoneanotherbutarenotclosely connectedwith outsiders canpromote knowledge diffusion.The positive effect ofdensenetworksmostlikelyemergesinthesestudiesbecause actorsinthesenetworksknowoneanotherwelland thustrust newknowledgefromeachother(Ahuja,2000;Phelps,2010).

This study adoptsmethods fromsocial network analysisto examine howthe structure oftheentire supply chain network affectstheknowledgediffusionmanifestedininnovationsand pro-ductivityincreasesusingalargefirm-levelpaneldatasetforthe Japanesemanufacturingsectorthatcoversmostfirmswithinthe countryandmajorbuyer-supplierrelations.Followingthe litera-ture,wetestforknowledgediffusionbyestimatingwhetherthe

structureofsupplychainnetworkspositivelyaffectsproductivity andinnovationcapability,asmeasuredbysalesperworkerandthe numberofregisteredpatentsrespectively.

Morespecifically,weinvestigatehowthedensityofafirm’sego network–orhowfrequentlyitssupplychainpartnerstransactwith oneanother–affectsitsperformance,whichisthefirsttimethis subjecthasbeenaddressedintheliteratureonknowledge diffu-sionthroughsupply chainnetworks.Theeffectsofegonetwork densityhavebeenstudiedbyAhuja(2000)andPhelps(2010)in researchcollaborationnetworksbutnotinsupplychainnetworks. Egonetworkdensitymayhavebothpositiveandnegativeeffectson knowledgediffusion,aswearguedabove.Therefore,theneteffect ofthenetworkdensityshouldbeempiricallyexamined.

Thesecondremainingissueintheliteratureistheroleof geo-graphicdistanceinknowledgediffusion.Intheirseminalpapers, JaffeandTrajtenberg(1999)andJaffeetal.(1993)foundthat geo-graphicdistancehasnegativeeffectsonthedegreeofknowledge andinformationdiffusion.Knowledgediffusionfromneighboring partnersmaybeeasierthanknowledgediffusionfromdistant part-nersbecauseoflowertransportationcosts(Marshall,1890).Ithas beenshownthatsupplychaintiesandresearchcollaborationties aremorelikelytobecreatedbetweenneighboringfirms(Crescenzi etal.,2016;Nakajimaetal.,2012).However,geographic proxim-itymayhaveanegativeimpactoninnovationbecauseneighboring partnersaremorelikelytobesimilartothefirmandtooneanother andthustobecharacterizedbysimilarknowledge,asarguedby Boschma(2005).Inotherwords,moreknowledgeand intermedi-ateproductsthatarenewtothefirmareavailablefromthefirm’s distantpartnersthanfromitsneighbors.Therefore,theneteffect ofdistancefromnetworkpartnersonfirmperformanceisnot par-ticularlyclear.

Thisstudyincorporatesthetwoissuesandexamineswhether andhowknowledgediffusesthroughsupplychainnetworksusing alargefirm-leveldatasetthatcontainsdetailedinformationonthe majortransactionpartnersof800,000firmsinJapan.Ourempirical estimationemploysadynamicpanelmodel,assumingthatsupply chainties andfirmperformanceinteractwithoneanotherover time.Inthisframework,wecanincorporatecausalitybetweenfirm performanceandcharacteristicsofsupplychainnetworksinboth directionsandhencecanalleviatepossiblebiasesinestimations oftheeffectofnetworksonperformancethatareduetoreverse causality.

Ourfindingssuggestthatthegeographicproximityofsupply chainpartnersandthedensityofsupplychainnetworkstendto reducethebenefitsofknowledgediffusion,whichismostlikely duetoknowledgeredundancyinsuchnetworks.Therefore,this studyemphasizestheimportanceofdiversenetworkpartnersin knowledgediffusion.

2. Conceptualframework

2.1. Channelsofknowledgediffusionthroughsupplychain networks

Supplychaintiescanimprovefirmperformancethroughthe diffusionofknowledgeinthefollowingthreeways.First,clients frequentlyprovidenewknowledgeandtechnologyforproduction and marketinformationtotheirsupplierstoimprove the qual-ityandreducethepriceofthegoodstheypurchase.Forexample, DyerandNobeoka(2000)showthatToyotafrequentlyorganizes associationsofitssuppliersinwhichitprovidesvaluable techni-calandmanagerialassistancetosuppliers.EganandMody(1992) showthataUSshoeimportersentItalianskilledartisansto Tai-waneseshoemanufacturerstoprovidethemtechnicalassistance. Throughsuchtechnicalassistance,buyers’knowledgediffusesto

suppliers.Second,buyers’productionislargerwhentheyutilizea largervarietyofinputsfromtheirsuppliers,astypicallydescribed bytheproductionfunctiondevelopedbyDixitandStiglitz(1977) thatassumesaconstantelasticityofsubstitutionamonginputs.In otherwords,suppliers’knowledgeisembodiedintheirproducts anddiffuses–or,followingBaldwinandLopez-Gonzalez(2014), is“lent” –totheirclients.Finally,supply chain networksoften facilitateresearchcollaboration,asUesugi(2015)shows.For exam-ple,Toyotaoftenconductsresearchanddevelopmentactivitiesfor partsandcomponentswithitssuppliers.Insomecases,suppliers andclientsmaynotconductresearchcollaborationexplicitly,but theymayexchangeideasforproductivityimprovementand prod-uctdevelopment.Throughresearchcollaborationandknowledge exchangesassociatedwithsupplychainties,suppliersandbuyers exchangetheirknowledgewitheachotherandthusimprovetheir knowledgeandtechnology.

2.2. Densityofnetworksandstrengthofties

Theeffectsofsupplychaintiesmayvarydependingonthe struc-tureofnetworksandthecharacteristicsoftheties.Inparticular, thedensityofnetworks(howdenselyactorsinanetworkare con-nectedwithone another)and thestrengthof ties (howclosely anactorisconnectedtoanotheractor)maypositivelyor nega-tivelypromoteknowledgediffusion.Ontheonehand,itmightbe expectedthatdensenetworksandstrongtiesfacilitateknowledge diffusionbecausethesenetworkcharacteristicscanfostershared normsandexplicitknowledge-sharinginstitutions(Ahuja,2000). UsingdatafromleadingchemicalfirmsintheUnitedStates,Ahuja (2000)findsthatwhenafirm’sresearchcollaborationnetworkis dense,thefirmgeneratesmorepatents.Centola(2010)alsoshows inhissocialexperimentontheInternetthatmembersofanonline healthforumaremorelikelytoadoptnewhealthbehaviorfrom informationprovidedbyamemberwhenmoremembersinthe forumknowoneanother.Centola(2010)interpretsthesefindings asshowingthatreinforcementfrommultipleinformantspromotes diffusionandadoptionofbehaviors.

Ontheotherhand,networkdensityandstrongtiesmayweaken thebenefits of knowledge diffusion through networks because thesecharacteristicsmight translateintoredundant knowledge (Burt,1992).Whenafirmisconnectedonlytopartnerswithwhich italreadysharesthesameknowledge,thefirmcannotlearnmuch fromthesepartners(BerliantandFujita,2011).Therefore,the ben-efitsofnetworkscanbemaximizedwhenfirmsareconnectedwith differenttypesofpartners sothat partners’technology, knowl-edge,and informationaccessare diversified.The importanceof diversifiednetworksinknowledgecreationanddiffusionhasbeen emphasizedin the social network literature.For example,Burt (2004)findsthatameasureofthediversityofworkers’networks inafirmispositivelyrelatedtosalaryandtheprobabilityof pro-motion,arguingthat thestructuralholes that connectdifferent groupsfacilitateknowledgediffusion.Withrespecttojob seek-ers,Granovetter(1973)findsthatpersonsthatjobseekersmeet lessoftenaremoreimportantinformationsources,thus emphasiz-ingthestrengthofweakties.Theimportanceofnetworkdiversity isalsoconfirmedbyBeugelsdijkandSmulders(2004),McFadyen andCannella(2004),andPerry-Smith(2006),whofindthatstrong tieswithtrustworthypartnersordensenetworkswithinthe com-munityororganizationmaynotenhanceeconomicperformance withoutlinkstoothercommunities.

InthecaseofsupplychainsintheJapaneseautomobile indus-try,DyerandNobeoka(2000)arguethatkeiretsu,adensenetwork inwhicheachmajorassemblersuchasToyotaisfirmlytiedtoa groupofsuppliers,improvestheproductivityofbothassemblers andsuppliers.However,AhmadjianandLincoln(2001)claimthat

thebenefitsofkeiretsuhaverecentlydeterioratedduetothechange initsstructure.

Thus,whethernetworkdensityandthestrengthofties posi-tivelyornegativelyaffectsknowledgediffusionmaydependonthe situation(Phelpsetal.,2012).Withthisinmind,thisstudyfocuses onsupplychainnetworksandempiricallyexamineshownetwork densityaffectssupplychainproductivityandinnovativecapability throughknowledgediffusion.

2.3. Geographicdiversity

The argument in the previous subsection emphasizes the importanceof diversity among network partners in knowledge diffusion.Thegeographiclocationofnetworkpartners is gener-allyacknowledgedtogenerateknowledgediversity.Wepresume thatneighboringfirmssharemoreknowledgeandinformationin commonwithoneanotherthanwithdistantfirms.Therefore,we expectthatmorenewknowledgeandinformationdiffusefrom dis-tantsupply chainpartnersthan fromneighboring partners.The underlyingideaisanalogoustothe“learning-by-exporting”and “learning-by-importing”hypothesesusedtoexplainhowexporters and importers canimprove their productivityby learning new knowledge and technology from foreign countries. The former hypothesiswassupportedbyBlalockandGertler(2004),Kimura and Kiyota (2006), and Van Biesebroeck (2005),whereas other studies,suchasCleridesetal.(1998),donotsupportit.Themixed resultsmayoccurbecausetheeffectsofexportingon productiv-ityareheterogeneousanddependonthecharacteristicsoffirms and destination countries, as Lileeva and Trefler (2007) show. Thelearning-by-importinghypothesisissupportedbyAmitiand Konings(2007)andKasaharaandRodrigue(2008).Althoughnot supportedbyVogelandWagner(2010)forGermany,itisstill the-oreticallypossiblethatfirmsindevelopedcountriescanimprove productivitybyusingproductsofdistantfirms.

Althoughknowledgediffusionfromdistanttransactionpartners isempiricallydemonstrated tosomeextent,creatingand main-tainingtieswithdistantpartnersismorecostlythandoingsowith neighboringpartnersbecauseoftransportationcosts.Infact,the lowtransportationcostoftransactionswithneighboring suppli-ersisoneofthemajorfactorsforindustrialagglomeration(Fujita andThisse,2013;Marshall,1890).Jaffeetal.(1993)andJaffeand Trajtenberg(1999)usedataonpatentcitationsandfindevidence thatgeographicdistancenegativelyaffectsthedegreeof knowl-edgediffusion.

Therefore,itisunclearwhethertieswithneighboringor dis-tant partners are more beneficial to firm performance through knowledgediffusion.BellandZaheer(2007)usedatafrom Cana-dian mutual fundcompaniesand find that proximitycan have apositiveorinsignificanteffectonknowledgeflows.Thispaper examineshowneighboringanddistantsupplychainpartnersaffect theproductivityandinnovativecapabilityoffirmsdifferently, dis-tinguishingamongpartnerswithinthesameprefectureandoutside oftheprefecture.

2.4. Interactionsbetweenstrongtiesandweakties

Finally, there may be complementarity between strong ties withinthecommunity and weakties withoutsiders, asPhelps (2010),Rost(2011), andTiwana (2008)find.For example,Rost (2011)examinesnetworksamonginventorsintheGerman auto-mobileindustryandfindsthatstrongtieswithregularcollaborators promoteinnovationandthatweaktieswithunfamiliarresearchers leverage the effects of strong ties. This evidence implies that newknowledgeobtainedfromoutsiderscandisseminate effec-tivelywithinacommunitywhencommunitymembersaredensely connected, confirming the importance of knowledge diversity

indiffusion.Thisstudyalsoteststhecomplementarity between strongtieswithinthecommunityandweaktieswithoutsiders, particularlyassumingthatnetworkdensityisassociatedwiththe strengthoftiesandthatdistantpartnersareoutsidersequipped withnewknowledge.

3. Data

3.1. Datasources

Thedatasetusedinthisstudyismostlybasedondatacollected byTokyoShokoResearch(TSR),oneofthetwomajorcorporate researchcompaniesinJapan.TheTSRdatacontaincorporate infor-mation,suchasfirmlocation,sales,andthenumberofemployees, inadditiontoinformationonupto24suppliersofmaterialand intermediatesandupto24clientsofproductsforeachfirm.The informationonsuppliersandclientscanbemergedwiththe cor-porate information datato establishthecharacteristics of each supplierandclient.Althoughtheupperlimitofthenumberof sup-pliersandclients(24)isclearlytoosmallformanylargefirms,it nonethelessallowsmostsupplychainnetworkstobecapturedby consideringsupplier-clientrelationsfrombothdirections.

ThisstudyutilizesdatalicensedfromTSRtotheResearch Insti-tuteofEconomy,TradeandIndustry(RIETI)in2006and2012to constructa paneldataset.ThenumberoffirmsintheTSRdata licensedin2006and2012is803,531and1,109,549,respectively, whereasthenumberofsupplier-clientties in2006and2012is 3,783,623and5,106,081,respectively.

TSRhasbeencollectinginformationfromallfirmsinJapan rec-ognizedbyTSRthroughouttheyeartoselltheinformationtoother firms.Hence,thetimeofdatacollectionvariesacrossfirms.Inthe datalicensedin2006(the2006data),informationon67%offirms wascollectedin2005,28%in2004,andfivepercentinotheryears. Inthedatalicensedin2012(the2012data),informationon12%of firmswascollectedin2012,69%in2011,and18%inearlieryears. Amongthe2006data,weutilizeonlydatacollectedin2004or2005 toavoidoldinformation.Amongthe2012data,weutilizeonlydata collectedafterApril2011becausetheGreatEastJapanearthquake onMarch11,2011resultedinchangesinthesuppliersandclientsof manyfirms,includingthoseoutsideofthedirectlyimpactedareas. Using theTSR data,we identifythe suppliersand clientsof eachfirmandtheirlocationsandcharacteristics.Japanisregionally dividedinto47prefectures,whicharethebasicunitsof admin-istrationandjurisdiction.Wecountthenumberofsuppliersand clientswithinandoutsideofthesameprefectureforeachfirm.It shouldbenotedthatbecausetheTSRdataareatthefirmlevel, supplier-clientrelationshipsattheestablishmentlevelcannotbe identified.Therefore,thesampleinourestimationsincludesonly single-establishmentfirmstoclarifythegeographiccharacteristics ofsupplier-clientrelationships,althoughtransactiontiesare iden-tifiedusingallfirms,includingsingle-andmultiple-establishment firms.Inotherwords,wedropmultiple-establishmentfirmsfrom thesampleforouranalysis,butweutilizethesupplychaintiesof single-establishmentfirmswithmultiple-establishmentfirmsin theanalysis.

Atthisjuncture,oneproblemremains.Consider,forexample, thatsingle-establishmentfirmAinprefectureXhasatransaction withabranchoffirmBinthesameprefecturewhose headquar-tersareinprefectureY.OurdataidentifythetransactionoffirmA withBasthatwithafirmoutsideoftheprefecture,althoughitis indeedatiewithafirmwithinthesameprefecture.However,thisis acceptablebecausewedistinguishbetweentransactionswithinthe sameprefectureandthoseacrossprefecturestoexaminetheroleof knowledgediversity.ThetransactionoffirmAwithabranchinthe sameprefectureoffirmBwhoseheadquartersareinadifferent

pre-fecturemayberegardedasatransactionacrossprefecturesbecause thebranchoffirmBmaysharetheknowledgeofitsheadquarters. TheTSR datainclude data forboth manufacturingand non-manufacturing firms. However, because the relation between supply chain networks and firm performance maybe different acrosssectors,thisstudyfocusesonfirmsinthemanufacturing sectortohighlighttheeffectofsupplychainsforpartsand compo-nentsonfirmperformance.Wefurtherrestrictthestudytofirms whoseinformationisavailableinboth2006and2012data.Thus, thenumberoffirmsinoursampleforestimationsis36,814.

WemergedtheTSRdatawithdataforthenumberofregistered patentsforeachfirmtakenfromtheInstituteofIntellectual Prop-ertyPatentDatabase(GotoandMotohashi,2007).Thisdatabase coversallpatentsregisteredbytheJapanPatentOfficefrom1963 to2013.Wemergethetwodatasetsusingthenamesandaddresses offirms.Addressesinthetwodatasetscannotbestraightforwardly matchedusingtheirlinguisticcharactersbecausethesameaddress maybeexpressedindifferentcharactersduetoabbreviationsor different mixtures of Japanese, Chinese,and Roman characters. Therefore,weutilizetheCSVAddressMatchingServiceofthe Cen-terforSpatialInformationScience,theUniversityofTokyotounify expressionsofaddresses.Usingtheunifiedaddressesatthe town-shiplevel,wematchaddressesbetweentheTSRandthepatent data.

3.2. Keyvariablesforestimation

Weexaminewhetherknowledgediffusesthroughsupplychain networks by estimatingthe effects of variables for the charac-teristicsofnetworksonmeasuresofproductivityandinnovative capability.Tomeasurethediversityofknowledgeofnetwork part-ners,we usethenumberof suppliersand clients,whichvaries substantiallyacrossfirms.Fig.1showsthecumulativedistribution functionforthenumberofsuppliers(theleftfigure)andclients (right)forsingle-establishmentfirmsinthemanufacturingsector inthe2006data.Bothfiguresshowthatthemedianofthenumber ofsuppliersandclientsissmall(two),althoughitisextremelylarge forsomefirms.Thispower-lawnaturehasbeenfoundinprevious studies,includingBernardetal.(2014).

We furtherdistinguishbetweenneighboring partners(those withinthesameprefecture)anddistantpartners(thoseoutside oftheprefecture),assumingthattheknowledgeofdistant part-nersismorediversified.Althoughourdatasetincludesthedetailed addressofeachfirm,wedonotutilizethedistancebetween sup-plychainpartnerstoexaminetheeffectsofdistancetopartners butsimplydistinguishamongpartnerswithinandoutsideofthe sameprefecturetosimplifytheanalysis.1_{Thissimplificationmay} bejustifiablebecausedifferentprefecturesreflect differencesin industrial, social, and cultural backgrounds in addition to geo-graphicdistance,allofwhichleadtoknowledgediversity.Such border effects are also foundin theliterature on international knowledgediffusion(Griffithetal.,2011).

Fig.2demonstratestherelationshipbetweenthenumbersof suppliersinsideandoutsideofthesameprefecture.Thereisaweak positiverelationbetweenthetwovariablesbecausethecorrelation coefficientbetweenthetwois0.20.However,itshouldbe empha-sizedthatmanyfirmsareconfinedtoaregionallyclosednetwork becausetheyhavenosuppliersoutsideoftheprefecture.By

con-1_{Belderbosetal.(2015)utilizethefullinformationofthedistancebyestimating}

anon-linearmodelinwhichtheeffectsofknowledgediffusiondecayexponentially withdistance.

Fig.1.DistributionoftheNumberofSuppliersandClients.Notes:Thisfigureisbasedonallpossibleobservationsinthemanufacturingsectorinthedatalicensedin2006.

Fig.2.SupplierswithinthePrefectureandoutsideofthePrefecture.

Notes:Thisfigureisbasedonallpossibleobservationsinthemanufacturingsectorinthedatalicensedin2006.

trast,therearemanyotherswhosesuppliersaremostlyinother provinces.2

Firmsoccasionallychangetheirsuppliersandclients,andthe numberofsuppliersandclientscanthuschangeovertime.Fig.3 showsthedistributionofthechangeinthenumberofsuppliersin total(thetopfigure),inthesameprefecture(middle),andoutsideof theprefecture(bottom).Approximatelyone-thirdoffirmsdidnot changethenumberoftheirsuppliersfrom2006to2012,whereas someincreasedthisnumberandasmallernumberdecreasedit.As aresult,themeanofthechangeinthetotalnumberofsuppliersis

2 _{Twelvepercentoffirmsinthesamplehavenosupplierreportedinthedata.We}

includedtheminthesubsequentestimations.

0.99.Whenwefocusonthenumberofclientsratherthansuppliers, wefindsimilarcharacteristics.

Anotherkeyvariableforthenetworkstructureisthedensityof eachfirm’segonetwork,whichismeasuredbytheratioofthe num-berofactualtiesamongeachfirm’ssupplychainpartners(both suppliersand clients)tothenumber of allpossibleties among them.Forexample,whenafirmhasthreesupplychainpartners ofwhichtwoalsotransactwithoneanother,thedensitymeasure is1/3=0.333.Whenafirmhasonlyonesupplychainpartner,we definethedensityaszero.Ifthedensitymeasureislarge,weassume thatthediversityofknowledgeamongsupplychainpartnersislow butthatthestrengthoftiesishigh.Thisassumptionistestedbelow inSection3.3.AsshowninTable1,themeanofthenetworkdensity is0.26.

Fig.3. ChangesintheNumberofSuppliersfrom2006to2012.

Note:Thisfigureshowstheheatmapoftechnologicaldiversity.Eachrowshowsthemeasureofthetechnologyclassesinaparticularprefecture,definedbyequation(1)in thetext.Thetechnologyclassesarelistedinalphabeticalorder.Thelargernumberindicatesmoreinnovationsinthetechnologyclassintheprefecturethanotherprefectures.

Table1

SummaryStatistics.

Variables Mean S.D. Min. Max.

Numberofsuppliersinthesameprefecture 1.92 2.58 0 204

Addedoneandlogged 0.82 0.68 0 5.32

Numberofsuppliersoutsideoftheprefecture 1.57 2.72 0 406

Addedoneandlogged 0.69 0.67 0 6.01

Numberofclientsinthesameprefecture 2.34 3.52 0 111

Addedoneandlogged 0.91 0.73 0 4.72

Numberofclientsoutsideoftheprefecture 2.06 3.44 0 426

Addedoneandlogged 0.81 0.75 0 6.06

Densityofegonetwork 0.26 0.20 0 1

Salesperworker(thousandyen) 24,278 60,871 21 5,155,000

Logged 9.73 0.77 3.06 15.46

Sales(thousandyen) 462,435 4,148,635 64 633,799,000

Logged 11.96 1.31 4.17 20.27

Numberofregisteredpatents 0.06 1.18 0 144

Firmage 41 21 0 138

Note:Thenumberofobservationsis73,628(observationsfortwoyearsforeachof36,814firms).Thedensityofafirm’segonetworkisdefinedastheratiooftheactual numberoftiesbetweenthefirm’ssupplychainpartnerstothenumberofallpossibletiesbetweenthem.

Ourproductivitymeasureissalesperworker.Obviously,value addedperworkerandTFParebettermeasuresofproductivity,but becausetheTSRdatadonotincludevalueadded,thecostsof inter-mediategoods,ortheamountofcapitalstock,werelyonsalesper worker.Themeanofsalesperworkerandsalesis24millionyen and462millionyen,respectively(Table1).Aswehaveexplained above,wefocusonfirmswithonlyoneestablishmentforwhich themeanofsalesperworkerandsalesissmallerthanthemean forallmanufacturingfirms(36millionyenand1458millionyen, respectively).

Onematerialproblemwithusingsalesperworker asa pro-ductivitymeasureisthatitovervaluesproductivityforfirmsthat purchaseintermediategoodsfromotherfirms.Thus,inthe con-textofthisstudy,salesperworkerarelikelytobehigherwhen firmsareconnectedwithmoresuppliersandthusprocuremore intermediategoodsfromsuppliers.Regardlessofthepresenceof knowledgediffusionthroughsupplychainnetworks,apositive cor-relationbetweenthenumber of suppliersandourproductivity measuremayberealized.However,thisproblemdoesnotarisein

thisstudybecauseweemployadynamicpanelestimationmethod, asexplainedindetailbelow,whichessentiallyestimatestheeffects ofpastnetworkcharacteristicsonthechangeinsalespercapita ratherthanthecontemporaneousrelationbetweenournetwork variablesandtheproductivitymeasure.

Ourmeasureofinnovativecapabilityisthenumber of regis-teredpatents.Becausetheminimumlengthbetweenthetwodata periodsfortheTSRdataisfiveyears,wedefineasthenumberof registeredpatentsforyeartthetotalnumberofpatentsappliedfor inyearsfromt–4totandregisteredby2013.3_Themeanofthe numberofregisteredpatentsisquitesmallat0.06,asitiszerofor 97.7%offirm-yearobservations.

3_{Theaveragelengthoftimefromapplicationtoregistrationis11monthsinfiscal}

year2013(JapanPatentOffice,2014).Therefore,thepatentdataupto2013usedin thisstudyshouldcovermostregisteredpatentsthatwereappliedforin2011and 2012,thesecondperiodoftheTSRdata.

3.3. Networkstructureandknowledgediversity

Asexplainedintheprevioussection,wepresumeinthe con-ceptualframework(1)thatknowledgeofdenselyinterconnected firmsismoreredundantthanthediversityofknowledgeacquired byfirmsintopologicallydistantpartsoftheeconomicnetworksand (2)thatfirmslocatedindifferentregionsaremorelikelytohave accesstodiversetypesofknowledgecomparedwith geographi-callysimilarfirmsinthesameprefectures.Thetwopresumptions aresupportedbyourdata.

First,toexaminetheformerpresumption,wecheckthe corre-lationbetweensupplychaintiesandthesimilarityofknowledge betweenpairsoffirms.Themeasureofthesimilarityofknowledge betweenfirmsiandjisdefinedastheangleofvectorsofthenumber ofregisteredpatentforthedifferenttechnologyclassesofthetwo firms.However,ifwesimplycalculatetheangles,mostofthe sim-ilaritymeasuresarezerosbecausemostfirmsdonothavemany patents;thus,mostelementsoftechnologicalvectorsarezeros. Therefore,weincorporatetherelatednessbetweentechnologies usingtheweightedangularseparationofBreschietal.(2003).More specifically,letc_ibethevectorthatrepresentsthenumberof reg-isteredpatentsforeachofthe121technologyclassesforfirmi.In addition,letbethematrixthatisconstructedfrompatentcitation dataandrepresentstherelationshipbetweentechnologyclasses. Eachelementshowsthetotalnumberofcitationsbetween tech-nologies.Bothcitingandcitedrelationshipsarecountedequally. Then,thesimilaritymeasurecalculatedbytheweightedangle4_is aij≡ ci˝c

 j



(ci˝ci)∗(cj˝cj)

Fig.4showsthecumulativeprobabilitydistributionsofthe sim-ilaritymeasure forsub-samples offirm pairs withand without supplychainlinksusingasub-sampleoffirmswithanypositive numberofpatentapplicationsduringtheperiodexamined.This figureindicatesthattheknowledgeoffirmpairswithsupplychain tiesismorelikelytobesimilartooneanotherthanthe knowl-edgeofpairswithoutatie.Further,wetestwhethersupplychain tiesandknowledgesimilarityarecorrelatedusingatobit estima-tionwiththesalesofbothfirms,thedifferencebetweentheirsales, andthediversityoftheirknowledgeascontrols.Ourresultsshow apositiveandhighlystatisticallysignificantrelationbetweenthe measureofknowledgesimilarityandthepresenceofsupplychain ties.5_{Whentwofirmsareconnectedwithasupplychaintie,their} measureofknowledgesimilarityislargerbytwicethesizeofits standarddeviationthanotherwise,onaverage. Atthis juncture, wearenotconcernedwithcausality,butthisresultimpliesthat whenfirmsareconnectedthroughsupplychains,theirknowledge ismorelikelytobesimilar.Itisthusfurtherimpliedthatfirmsthat aredenselyconnectedwithoneanothersharesimilarknowledge, aswehavesofarassumed.

Second,toexamine the secondpresumption, we compare a measureoftherevealedcomparativeadvantage(RCA)inthe inno-vationofdifferenttypesoftechnologyacrossprefectures.Applying themethoddevelopedbyBalassa(1965)forRCAinthecontextof

4 _{Weexperimentedwithothersimilaritymeasures,suchastheHornindex(Horn,} 1966),theJaccardindex(Jaccard,1912),andnormalizedEuclideandistance,and weobtainedsimilarpositivecorrelationsbetweensupplychaintiesandeachofthe similaritymeasures.

5 _{Weemployatobitestimationbecausethesimilaritymeasureiszeroformany}

pairs.Wefindthatthecoefficientonthedummyvariableforsupplychainties is0.193anditsstandarderroris0.00183,whereasthestandarddeviationofthe similaritymeasureis0.275.Theseresultscanbeobtainedfromtheauthorsupon request.

Fig.4. KnowledgeDiversificationacrossPrefectures.

Notes:ThisfigureshowstheCDFofthesimilaritymeasureforfirmpairswithout supplychainties(thesolidline)andwithties(thedottedline).

internationaltrade,ourRCAindexfortheinnovationoftechnology classkinprefecturepisdefinedas

RCAkp≡ Ckp/



kCkp



kCkp/



k



pCkp, (1) whereC_kpisthenumberofregisteredpatentsinclasskinprefecture p.Thismeasureindicatestheshareofpatentsinaparticularclassin aparticularprefectureinallpatentsintheprefecture,normalized bytheshareoftheprefectureinallpatentsinJapan.Fig.5shows thismeasureforeachof9prefectures,Miyagi,Tokyo,Kanagawa, Chiba,Aichi,Osaka,Hyogo,Hiroshima,andFukuoka,themajor eco-nomichubsinJapan,illustratingthatthedistributionofinnovations foreach prefecturein termsoftechnologyclassesis not neces-sarilysimilartooneanother.6_{Thisfindingprovidesevidencefor} knowledgediversificationacrossprefectures.

4. Empiricalstrategy

TheconceptualframeworkinSection2showsthatfirms’ sup-plychainnetworksaffectproductivityand innovativecapability throughthediffusionofembodiedanddisembodiedknowledge. However,estimationsoftheeffectsofsupplychainnetworkscan bebiasedbecauseoftheendogeneityofcovariates.Forexample, higherproductivitymayleadtolargersupplychainnetworks, gen-eratingreversecausalitybecausemoreproductivefirmscanmore easilyfindandbefoundbysuppliersandclients.Weemploythe followingestimationmethodstoalleviatepossiblebiasesdueto endogeneity.

4.1. Effectsonproductivity

Aswehaveargued,firms’performanceresults,including pro-ductivityandsupplychainnetworkperformance,areinterlinked withoneanother.Inaddition,firms’performanceresultsand net-worksareaffectedbytheirowndynamics,astherecentliterature onsocialnetworkssuggests(SnijdersandDoreian,2010,2012).To investigatetheinterlinkeddynamicsoffirms’performanceresults andnetworks,weemployadynamicpanelsimultaneousequation modelinwhichoutcomevariablesareassumedtobefunctionsof theirlagsandothercontrolvariables,asfollows:

Yit=˛+ˇY1t−1+ıXit+εit. (2)

6_{Theresultsforallprefecturesarequalitativelythesame;theseresultsare}

Fig.5. KnowledgeDiversificationacrossPrefectures.

Note:Thisfigureshowstheheatmapoftechnologicaldiversity.Eachrowshowsthe measureofthetechnologyclassesinaparticularprefecture,definedbyequation

(1)inthetext.Thetechnologyclassesarelistedinalphabeticalorder.Thelarger numberindicatesmoreinnovationsinthetechnologyclassintheprefecturethan otherprefectures.

whereYitisavectoroffivevariablesthatrepresentfirmi’s

supply-chain networks and performance: the number of suppliers or clientsinthesameprefectureplusoneinlogs,thenumberof suppli-ersorclientsoutsideoftheprefectureplusoneinlogs,thedensity offirmi’segonetwork,salesperworkerinlogs,andsalesinlogs. Totalsalesareincludedinthesetofoutcomevariablestorepresent firmsize.Xitisavectorofcontrolvariablesincludingfirmage,firm

agesquared,anddummiesforindustries,prefectures,andyears ofdatacollection,whileεitisthevectoroferrorterms.Industries

aredefinedatthetwo-digitleveloftheJapanStandardIndustrial Classification.Weallowforcorrelationbetweenerrorterms.Inthis framework,ˇcanbeconsideredasaMarkovmatrix.Weestimate equation(2)separatelyfortieswithsuppliersandclientsbecause thenumbersofsuppliersandclientsarecloselycorrelated with oneanother;hence,incorporatingbothsuppliersandbuyersinone estimationmaycausemulticollinearity.

BecauseEq.(2)canberewrittenas

Y_it−Y_it−1=␣+(␤−1)Y_it−1+␦Xit+␧it (3)

andYisinlogs,wecaninterpretthisequationasestimatingeffects onthegrowthofnetworkandperformancevariables.Inaddition, fixedeffectsthatdetermineYcanbeeliminatedintheestimation. Giventheestimatesof˛,ˇ,andı,wesimulatethemodelto examinewhatwouldoccurifthenumberofsupplierswithinor outsideofthesameprefectureorthenetworkdensityincreased. Thiscomputationalexercisemayleadtodeeperinsightsintothe dynamicsoffirms’performanceandinterlinkednetworksthan esti-mationcoefficients.Inthiscomputationalanalysis,weassumea hypotheticalfirmforwhichindustryandprefecturedummiestake theirmeanvaluesandallothervariablestaketheirmedianvalues. Oneshortcomingofthisempiricalstrategyisnotable.Although thenumberofsupplychainpartnersisalwaysanon-negative inte-ger,Eq.(2)implicitlyassumesthatthelogofthenumberofpartners plusoneiscontinuous.However,incorporatingthislimited depen-dentvariablenatureintothesimultaneousequationframework requiresadditionalassumptionsinthedistributionoferrorterms.

Therefore,weassumethatthelogofthenumberofsuppliersplus oneiscontinuous.

4.2. Effectsoninnovationcapability

Whenwefocusontheeffectsofsupplychainnetworkson inno-vativecapability,wecannotemploytheapproachdescribedabove becausethemeasureofinnovativecapability,thenumberof reg-istered patents,is zerofor98%offirms.Therefore, weestimate atobit modelinwhichthedependentvariableisthelogofthe numberofregisteredpatentsplusone.Itisimportanttotakealog formbecausetheeffectofthenetworkstructureonthenumber ofpatentsmaydiminishasthenumberofpatentsincreases.To alleviateendogeneitybiasesduetoreversecausalityfrom innova-tivecapabilitytonetworks,weemploytheminimumchi-squared estimatorfromNewey(1987)usinglaggednetworkvariablesto instrumentcurrentnetworkvariables.Wedonotemploythefull information maximumlikelihood estimation ofthe tobitmodel becauseitdoesnotconverge,possiblybecauseofthemanydummy variablesforindustriesandprefectures.

5. Results

5.1. Effectsonsalesperworkerandtotalsales

TheresultsoftheestimationofdynamicpanelEq.(2)thatfocus ontieswithsuppliersforthemanufacturingfirmsareshownin Table2andhighlightthefollowingfivenotablefindings.First,the resultsincolumns(4)and(5)highlightdifferencesintheeffects onfirmperformancebetweensupplierswithinthesame prefec-tureandthoseoutsideoftheprefecture.Theeffectofthenumber ofsupplierswithinthesameprefectureonproductivitymeasured bysalesperworkerorfirmsizemeasuredbytotalsalesisnot sta-tisticallysignificantatthe5%level.Onthecontrary,theeffectof thenumberofsuppliersoutsideofthesameprefectureonsales perworkerandsalesispositiveandsignificantatthe1%level.The resultsimplythattieswithneighboringsuppliersarelesslikelyto improveproductivitythroughknowledgediffusion,whereasties withdistantsuppliersaremorelikelytodoso.

Second,thedensityofafirm’segonetwork,definedastheratio ofthenumberofactualtiesamongthefirm’ssupplychain part-nerstothenumberofallpossibletiesamongthem,hasanegative andsignificanteffectonbothproductivityandfirmsize.The neg-ativeeffectoftheego-networkdensityimpliesthatthebenefits fromcloselyrelatedpartnersaresmallerthanthosefrom unre-latedpartners,possiblybecauseknowledgeamongdensenetworks isoverlappedtoalargeextentandnotwelldiversified.

Third,thenumberofsuppliersinthesameprefecturenegatively affectsthenumberofsuppliersoutsideofthesameprefecture,and viceversa.Thisfindingsuggeststhattieswithinandbeyondthe regionaresubstitutesforoneanother,mostlikelyduetothecosts ofcreatingandmaintainingsupplychainties.

Fourth,theeffectoftheego-networkdensityonthenumber ofsupplierswithinandoutsideofthesameprovinceisnegative andsignificant.Thisfindingimpliesthatwhenthesuppliersofa firmtransactwithoneanother,thefirmislikelytolosesomeofits suppliersovertime,possiblytoavoidredundantties.

Finally,wefindthattheeffectofthenumberofsuppliersinthe sameprefectureinthepreviousyearonthesamevariableinthe currentyearispositiveandsignificantbutlessthanone.Thisisalso thecaseforthenumberofsuppliersoutsideoftheprefectureand theego-networkdensity.Thisevidenceimpliesthatshockstothe networkstructure,suchasthenumberofsuppliers,diminishover time.

Table2

TieswithSuppliers,NetworkDensity,andFirmPerformance.

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

#ofsuppliersinthesame prefecture

#ofsuppliersoutsideof thesameprefecture

Densityofnetworksamong directpartners

Salesperworker Sales

#ofsuppliersinthesame prefecture(t-1)

0.684** −0.0542** −0.0366** −0.00630 0.00256

(0.00394) (0.00389) (0.00112) (0.00470) (0.00499)

#ofsuppliersoutsideof thesameprefecture(t-1)

−0.0792** 0.705** −0.0105** 0.0310** 0.0263**

(0.00394) (0.00389) (0.00112) (0.00469) (0.00499)

Densityofegonetwork (t-1)

−0.169** −0.177** 0.729** −0.0270* −0.0681**

(0.0113) (0.0111) (0.00319) (0.0134) (0.0143)

Salesperworker(t-1) −0.0256** −0.00582 −0.00218 0.699** −0.113**

(0.00393) (0.00388) (0.00111) (0.00468) (0.00498)

Sales(t-1) 0.128** 0.112** −0.00414** 0.104** 1.056**

(0.00260) (0.00256) (0.000737) (0.00310) (0.00329)

Observations 36,814 36,814 36,814 36,814 36,814

R-squared 0.619 0.637 0.619 0.610 0.850

Notes:Standarderrorsareinparentheses.*and**signifystatisticalsignificanceatthe5and1%level,respectively.Thenumberofsuppliersofanytypeisaddedoneand logged.Salesperworkerandsalesarealsologged.Thedensityofafirm’segonetworkisdefinedastheratiooftheactualnumberoftiesbetweenthefirm’ssupplychain partnerstothenumberofallpossibletiesbetweenthem.Age,agesquared,industrydummies,prefecturedummies,andsurveyyeardummiesareincluded,buttheresults arenotshownforbrevity.

Table3

TieswithClients,NetworkDensity,andFirmPerformance.

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

#ofclientsinthesame prefecture

#ofclientsoutsideofthe sameprefecture

Densityofnetworksamong directpartners

Salesperworker Sales

#ofclientsinthesame prefecture(t-1)

0.746** −0.0503** −0.0383** 0.00877* 0.00718

(0.00368) (0.00366) (0.000974) (0.00412) (0.00437)

#ofclientsoutsideofthe sameprefecture(t-1)

−0.0735** 0.743** −0.0102** 0.00639 0.0304**

(0.00383) (0.00380) (0.00101) (0.00428) (0.00454)

Densityofegonetwork (t-1)

−0.304** −0.182** 0.723** −0.0301* −0.0646**

(0.0120) (0.0119) (0.00318) (0.0134) (0.0143)

Salesperworker(t-1) −0.00697 −0.0179** −0.000801 0.699** −0.112**

(0.00419) (0.00417) (0.00111) (0.00469) (0.00498)

Sales(t-1) 0.0649** 0.106** −0.00645** 0.106** 1.054**

(0.00270) (0.00269) (0.000715) (0.00302) (0.00321)

Observations 36,814 36,814 36,814 36,814 36,814

R-squared 0.632 0.663 0.623 0.610 0.850

Notes:Standarderrorsareinparentheses.*and**signifystatisticalsignificanceatthe5and1%level,respectively.Thenumberofclientsofanytypeisaddedoneandlogged. Salesperworkerandsalesarealsologged.Thedensityofafirm’segonetworkisdefinedastheratiooftheactualnumberoftiesbetweenthefirm’ssupplychainpartners tothenumberofallpossibletiesbetweenthem.Age,agesquared,industrydummies,prefecturedummies,andsurveyyeardummiesareincluded,buttheresultsarenot shownforbrevity.

Next,wefocusontieswithclientsratherthansuppliersand repeatthesameanalysis;weshowtheresultsinTable3.Thefive majorfindingsfromtheanalysisfocusingonsuppliersapplyto theanalysisusingclients,exceptforone.Thenumberof neigh-boringclientsinthesameprefecturehasapositiveeffectonsales perworker,whereasthenumberofneighboringsuppliershasno significanteffect.Bycontrast,thenumberofdistantclientshasa positiveeffectontotalsales,asdoesthenumberofdistant suppli-ers.Thedifferenceintheeffectsonproductivitybetweentieswith suppliersandclientssuggeststhatknowledgediffusionfrom sup-pliersandclientsisdifferentinnature,aswearguebelowingreater detail.

Toillustratehowchangesinsupplychainnetworksaffectfirm performanceandthestructureofnetworksovertime,wesimulate Eq.(2)usingtheresultspresentedinTables2and3.Inthe simula-tion,wefirstassumethatahypotheticalmedianfirmwithmedian characteristicsintermsofallindependentvariablesincreasesthe numberofsuppliersorclientswithinoroutsideoftheprefectureby 50%.Themedianvalueofthenumberofsuppliersorclientswithin

oroutsideoftheprefectureis1,andweaddonetothenumberof suppliersbeforetakingalog.Therefore,a50%increaseinthe vari-ableimpliesanadditionalonesupplierorclientwithinoroutside oftheprefecture.

Fig.6 illustratesthesimulation resultswhen thenumber of neighboringsuppliersorclientsincreases.On theonehand,the leftfigureindicatesthatwhenthenumberofsupplierswithinthe prefectureincreasesbyashock,thenumberofsuppliersoutside oftheprefecturedeclinesduetosubstitutabilitybetweenthetwo typesofsuppliers.Then,becausetieswithneighboringsuppliers donotcontributetofirmperformancewhereastieswithdistant suppliersdo(Table2),salesperworkerdeclineslightly,andsales donotchangesubstantiallyovertime.Ontheotherhand,theright panelofFig.6showsthatwhenthenumberofneighboringclients increases,salesperworkerimproveovertimeduetothepositive effectofneighboringclientsonproductivity.

Wenowrepeatthesamesimulationassuminganincreaseinthe numberofdistantsuppliersorclientsby50%andshowtheresults inFig.7.Unliketheeffectofneighboringsuppliers,theeffectof

Fig.6. Long-runEffectofanIncreaseintheNumberofSuppliers(left)andClients(right)withintheSamePrefecture.

Notes:Thesefiguresaredrawnfromsimulationsofthefivevariables,thelogofthenumberofnearbysuppliers(right)orclients(left)withinthesameprefectureplusone, thelogofthenumberofdistantsuppliers(right)orclients(left)outsideofthesameprefectureplusone,thedensityofeachfirm’segonetwork(measuredbytheratioof actualtiesamongsupplychainpartnerstoallpossibleties),thelogofsalesperworker,andthelogofsalesofthehypotheticalmedianfirm.Anincreaseinthelogofthe numberofnearbysuppliersorclientswithinthesameprefectureby50%isassumed.Eachlineindicatesthepercentagechange(or10%change,ifindicated)inthevariable assumingthechangeinthenumberofsuppliersorclientscomparedwiththevariablewithoutthechange.

Fig.7. Long-runEffectofanIncreaseintheNumberofSuppliers(left)andClients(right)outsideofthePrefecture.

Notes:Thesefiguresaredrawnfromsimulationsofthefivevariables,thelogofthenumberofnearbysuppliers(right)orclients(left)withinthesameprefectureplusone, thelogofthenumberofdistantsuppliers(right)orclients(left)outsideoftheprefectureplusone,thedensityofeachfirm’segonetwork(measuredbytheratioofactual tiesamongsupplychainpartnerstoallpossibleties),thelogofsalesperworker,andthelogofsalesofthehypotheticalmedianfirm.Anincreaseinthelogofthenumber ofdistantsuppliersorclientsoutsideoftheprefectureby50%isassumed.Eachlineindicatesthepercentagechange(or10%change,ifindicated)inthevariableassuming thechangeinthenumberofsuppliersorclientscomparedwiththevariablewithoutthechange.

distantsuppliersonsalesperworkerandtotalsalesispositiveand significant.Asaresult,in theleftfigureofFig.7,both salesper

workerandtotalsalesimprovesubstantiallyovertimeduetothe expansionoftieswithdistantsuppliers.Theeffectofthenumberof

distantclientsontotalsalesisalsopositiveandsignificant,whereas itseffectonsalespercapitaisinsignificant.Therefore,intheright figureofFig.7,inwhichtieswithdistantclientsexpand,totalsales improvetoagreatextent,whereassalesperworkerimproveonly slightlyinassociationwiththeincreaseinfirmsize.

Weconductthesamesimulationforanincreaseinthedensity ofthehypotheticalmedianfirm’segonetwork.Fig.8showsthat denseregonetworksclearlyleadtolessproductivity,smallerfirm size,andsmallernetworks.

Wefurtherincorporatetheinteractiontermbetweenthe den-sityoftheegonetwork andthenumber ofdistantsuppliersor clientsasacontrolvariabletotestthecomplementaritybetween strongtiesandtieswithoutsiders,asdiscussedaboveinSection 2.Theresultsincolumn(4)ofTables4and5showsthatthe coef-ficientontheinteractiontermbetweenthedensitymeasureand thenumberofdistantpartnersispositiveandsignificantwhich impliesthatthenegativeeffectsofnetworkdensitycanbe allevi-atedwhenfirmsareconnectedwithdistantpartnersandarehence exposedtodiversifiedknowledge.Judgingfromthevaluesofthe coefficientsofdensityandtheinteractionterm,theneteffectof networkdensityispositiveifthelogofthenumberofdistant sup-pliersorclientsplusoneisapproximatelygreaterthanoneorthe numberofsuppliersorclientsisapproximatelygreaterthantwo.

Tochecktherobustnessoftheseresults,weruntwo-stageleast squares(2SLS)estimationsofthelogofsalesperworkerorthe logoftotalsalesonthecurrentnumberofneighboringanddistant suppliersorclientsandothercontrolsandusethelaggednumber ofsuppliersorclientsasinstruments.TheresultsshowninTable6 arevirtuallyidenticaltothebenchmarkresultsincolumns(4)and (5)ofTables4and5.

We also check the possible differences across industries by dividingthesampleintotwo,thefirstformachineryandequipment industries,includingthegeneralmachinery,electricalmachinery, electronics,transportationequipment,and precisionequipment industries,and the second for otherindustries. Because supply chainnetworksinthemachineryandequipmentindustriesinJapan areoftencharacterizedaskeiretsu,inwhichsuppliersandclients arecloselyconnectedwithoneanother(Aoki,1989),theeffectsof supplychainnetworksintheseindustriesmaybedifferentfrom thoseinotherindustries.However,themainresultsfromthetwo sub-samples,whicharenotshownhereintheinterestsofbrevity butareavailableuponrequest,aresimilartothebenchmarkresults fortheentiresample.

5.2. Effectsonthenumberofregisteredpatents

Theestimationresultsoftheeffectsofsuppliersandclientson registeredpatentsfromthetwo-stepTobitestimationofNewey (1987)areshownincolumns(1)–(2)and(3)–(4)ofTable7, respec-tively.Columns(1)and(3)indicatethattheeffectofthenumberof distantsuppliersandclientsoninnovativecapabilityispositiveand significant,whereastheeffectofneighboringsuppliersandclients iseitherinsignificantornegativebutweaklysignificant.Theresult impliesthattieswithdistantpartnersaremoreimportantto inno-vativeactivitiesthantoproductivityimprovementinproduction activities,possiblybecausetheformerrequiresmorediversified knowledgethanthelatter.

Whentheinteractiontermbetweentheego-networkdensity andthenumberofdistantsuppliersisincorporated(columns[2] and[4]ofTable7),thecoefficientontheinteractiontermispositive, whereasthecoefficientonthedensityisnegativeandsignificantat the10%level.Althoughthesignificanceleveloftheresultisquite low,itisnonethelesssimilartotheresultregardingtheeffecton salesperworker.Weinterpretthisevidenceasweaklyindicating thatdensenetworksareharmfultoknowledgediffusionfor

inno-vation,althoughthenegativeeffectcanbealleviatedbytieswith distantsuppliers.

5.3. Discussion

A notable finding from the results delineated above is that salesperworker,ameasureofproductivity,increasesasthe num-berofdistantsuppliersincreases,whereasneighboringsuppliers donotaffectproductivity.Thiscontrastingeffectofneighboring anddistantsuppliersmaybeexplainedbythepositiveeffectof inputvarietiesonproductivityarguedbyDixitandStiglitz(1977). Becauseparts,components,andmaterialsfromdistantsuppliers maybemorediversifiedthanthosefromneighboringsuppliers,ties withdistantsuppliersenhanceproductivitymorethantieswith neighboringsuppliers.

However,theresultsfocusingonclientsarecompletely differ-ent:productivityispositivelyaffectedbyneighboringclientsbut notbydistantclients.Tieswithclientsenhanceproductivitywhen clientsprovidenewinformationorknowledgetotheirsuppliers. InJapan,thisknowledgetransferfromclientstosuppliersis fre-quentlychanneledthroughexplicitknowledgesharinginstitutions, asDyer andNobeoka (2000)illustratein thecase of Toyota.In addition,suppliersandclientsareoftenengagedinresearch col-laborationtodeveloppartsandcomponentsthatmeetthedemand ofsuppliersandareofhighquality.Ourresultimpliesthatsuch knowledgetransferiseasierwhensuppliersandclientsare geo-graphicallyclosertooneanother.

Thestarkcontrastbetweensuppliersandtheirclientsindicates thatdistanceplaysdifferentrolesinknowledgediffusionbetween suppliersandtheirclients.Becauseknowledgefromsuppliersis embodiedintheirintermediateproducts,clientscanbenefitfrom havingdistantsupplierssimplybyusingtheirproductsbecause suchproductsarelikelytobedifferentfromthoseofneighboring suppliers.However,becausethetransferofdisembodied knowl-edgefrom clientstosuppliersthroughtechnical assistanceand research collaboration requires direct communication between them(forexample,intheprovisionoftechnicalsupportby sup-pliers),supplierscanlearnmorefromneighboringsuppliersthan theycanfromdistantsuppliers.Thisresultisconsistentwith previ-ousfindingsinpsychologythatface-to-facecommunicationbuilds trust(BurtandKnez,1995;DasandTeng,1998;Hilletal.,2009; VangenandHuxham,2003;Wilsonetal.,2006).Whenthelevel oftrustamongfirmsishigh,theyaremorelikelytounderstand knowledgedisseminatedfromamemberfirmandtohavefaithin theaccuracyoftheknowledge.

Theresultsregardingtheeffectonthenumberofregistered patents,ameasureofinnovativecapability,aredifferentfromthose onproductivitybecausewefindapositiveeffectfordistant suppli-ersandclientsbutnosignificantlypositiveeffectforneighboring suppliersor clients.Because knowledgefor innovation is more likelytobedisembodiedandtorequireface-to-face communica-tionforitsdiffusion,theresult,whichisdifferentfromthepositive effectsofneighboringclientsonproductivitythrough disembod-iedknowledgediffusion,issurprisingatfirstglance.However,this resultcanbeconvincinglyunderstoodbecauseitimpliesthatthe diversityofknowledgeassociatedwithdistantpartnersismore importanttoknowledgeforinnovationthantoknowledgefor pro-duction.Section3.3andFig.4showsthatknowledgeforinnovation isindeeddiversifiedacrossprefectures,supportingthis interpreta-tion.

Anotherimportantfindingfromourresultsisthatthedensity ofafirm’segonetwork,whichrepresentstheextenttowhicha firm’ssupplychainpartnersareconnectedwithoneanother,has anegativeeffectonallkeyvariables,namely, salesperworker, totalsales,thenumberofregisteredpatents,andtieswith suppli-ersandclients.Section3.3andFig.3showsapositivecorrelation

Fig.8. Long-runEffectofanIncreaseintheDensityofEgoNetwork.

Notes:Thesefiguresaredrawnfromsimulationsofthefivevariables,thelogofthenumberofnearbysuppliers(right)orclients(left)withinthesameprefectureplusone, thelogofthenumberofdistantsuppliers(right)orclients(left)outsideofthesameprefectureplusone,thedensityofeachfirm’segonetwork(measuredbytheratioof actualtiesamongsupplychainpartnerstoallpossibleties),thelogofsalesperworker,andthelogofsales,ofthehypotheticalmedianfirm.Anincreaseinthedensityofthe firm’segonetworkby50%isassumed.Eachlineindicatesthepercentagechange(or10%change,ifindicated)inthevariableassumingthechangeinthenumberofsuppliers orclientscomparedwiththevariablewithoutthechange.

Table4

InteractionbetweenTieswithSuppliersandNetworkDensity.

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

#ofsuppliersinthesame prefecture

#ofsuppliersoutsideof thesameprefecture

Densityofnetworksamong directpartners

Salesperworker Sales

#ofsuppliersinthesame prefecture(t-1)

0.684** −0.0538** −0.0365** −0.00574 0.00240

(0.00395) (0.00390) (0.00112) (0.00471) (0.00500)

#ofsuppliersoutsideof thesameprefecture(t-1)

−0.0774** 0.695** −0.0114** 0.0202** 0.0295**

(0.00578) (0.00571) (0.00164) (0.00689) (0.00732)

Densityofegonetwork (t-1)

−0.165** −0.198** 0.727** −0.0506** −0.0610**

(0.0146) (0.0144) (0.00413) (0.0174) (0.0185)

#ofsuppliersoutsideof thesameprefecture

−0.00748 0.0411* 0.00378 0.0464* −0.0139

*Densityofegonetwork (t-1)

(0.0182) (0.0179) (0.00515) (0.0217) (0.0230)

Salesperworker(t-1) −0.0256** −0.00584 −0.00218 0.699** −0.113**

(0.00393) (0.00388) (0.00111) (0.00468) (0.00498)

Sales(t-1) 0.128** 0.112** −0.00415** 0.104** 1.056**

(0.00260) (0.00256) (0.000737) (0.00310) (0.00329)

Observations 36,814 36,814 36,814 36,814 36,814

R-squared 0.619 0.637 0.619 0.610 0.850

Notes:Standarderrorsareinparentheses.*and**signifystatisticalsignificanceatthe5and1%level,respectively.Thenumberofsuppliersofanytypeisaddedoneand logged.Salesperworkerandsalesarealsologged.Thedensityofafirm’segonetworkisdefinedastheratiooftheactualnumberoftiesbetweenthefirm’ssupplychain partnerstothenumberofallpossibletiesbetweenthem.Age,agesquared,industrydummies,prefecturedummies,andsurveyyeardummiesareincluded,buttheresults arenotshownforbrevity.

betweensupplychaintiesandknowledgesimilarity.Therefore,the negativeeffectofthedensityofnetworksonproductivityand inno-vationcapability,althoughweakforthelatter,impliesthatfirms indensenetworksalreadysharethesameknowledgeandcannot learnsubstantiallyfromoneanother,asaconsequence.However, thisnegativeeffectofdensitycanbealleviatedwhenthefirmis

connectedwithdistantfirmsbecausedistantpartnerscanbring newknowledgethatisunavailableindensenetworks.

Theresultsdescribedabovecontradictthoseofsomeprevious studies.For example,Bernardet al.(2014)and Belderbosetal. (2015) usedtheTSR datafor 2006and founda negativeeffect ofdistancetosupplychainpartnersonproductivity.This contra-dictionis mostlikely becausethetwo studiesdidnotfocus on

Table5

InteractionbetweenTieswithClientsandNetworkDensity.

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

#ofclientsinthesame prefecture

#ofclientsoutsideofthe sameprefecture

Densityofnetworksamong directpartners

Salesperworker Sales

#ofclientsinthesame prefecture(t-1)

0.746** −0.0499** −0.0385** 0.00928* 0.00718

(0.00369) (0.00366) (0.000974) (0.00412) (0.00438)

#ofclientsoutsideofthe sameprefecture(t-1)

−0.0843** 0.731** −0.00329* −0.0104 0.0302**

(0.00574) (0.00570) (0.00152) (0.00642) (0.00682)

Densityofegonetwork (t-1)

−0.330** −0.212** 0.740** −0.0715** −0.0650**

(0.0160) (0.0159) (0.00423) (0.0179) (0.0190)

#ofclientsoutsideofthe sameprefecture

0.0449* 0.0499** −0.0289** 0.0700** 0.000529

*Densityofegonetwork (t-1)

(0.0179) (0.0178) (0.00472) (0.0200) (0.0212)

Salesperworker(t-1) −0.00718 −0.0181** −0.000666 0.699** −0.112**

(0.00420) (0.00417) (0.00111) (0.00469) (0.00498)

Sales(t-1) 0.0650** 0.106** −0.00649** 0.106** 1.054**

(0.00270) (0.00269) (0.000715) (0.00302) (0.00321)

Observations 36,814 36,814 36,814 36,814 36,814

R-squared 0.632 0.663 0.624 0.610 0.850

Notes:Standarderrorsareinparentheses.*and**signifystatisticalsignificanceatthe5and1%level,respectively.Thenumberofclientsofanytypeisaddedoneandlogged. Salesperworkerandsalesarealsologged.Thedensityofafirm’segonetworkisdefinedastheratiooftheactualnumberoftiesbetweenthefirm’ssupplychainpartners tothenumberofallpossibletiesbetweenthem.Age,agesquared,industrydummies,prefecturedummies,andsurveyyeardummiesareincluded,buttheresultsarenot shownforbrevity.

Table6

TieswithSupplyChainPartnersandFirmPerformance:ResultsfromIVEstimations.

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

Salesperworker Sales Salesperworker Sales

#ofsuppliersinthesameprefecture(t) −0.00538 0.00147

(0.00714) (0.00757)

#ofsuppliersoutsideofthesameprefecture(t) 0.0254* 0.0421**

(0.0108) (0.0115)

#ofclientsinthesameprefecture(t) 0.0124* 0.00838

(0.00586) (0.00620)

#ofclientsoutsideofthesameprefecture(t) −0.0160 0.0422**

(0.00951) (0.0101)

Densityofegonetwork(t) −0.0741* −0.0687* −0.107** −0.0712*

(0.0292) (0.0310) (0.0286) (0.0303)

#ofsuppliersoutsideofthesameprefecture(t) 0.0734* −0.0247

*densityofegonetwork(t) (0.0355) (0.0377)

#ofclientsoutsideofthesameprefecture(t) 0.106** −0.00632

*densityofegonetwork(t) (0.0310) (0.0329)

Salesperworker(t-1) 0.698** −0.113** 0.699** −0.111**

(0.00469) (0.00497) (0.00470) (0.00497)

Sales(t-1) 0.0994** 1.051** 0.104** 1.048**

(0.00381) (0.00404) (0.00340) (0.00360)

Observations 36,814 36,814 36,814 36,814

R-squared 0.611 0.851 0.611 0.852

Notes:Standarderrorsareinparentheses.*and**signifystatisticalsignificanceatthe5and1%level,respectively.Thenumberofsuppliersorclientsofanytypeisadded oneandlogged.Salesperworkerandsalesarelogged.Thedensityofafirm’segonetworkisdefinedastheratiooftheactualnumberoftiesbetweenthefirm’ssupplychain partnerstothenumberofallpossibletiesbetweenthem.Age,agesquared,industrydummies,prefecturedummies,andsurveyyeardummiesareincluded,buttheresults arenotshownforbrevity.

single-establishmentfirmsaswedo. Infact,when weincluded multi-establishmentfirmsinouranalysis,wefoundapositiveand negativeeffectforthenumberofsupplierswithinandoutsideof thesameprefecture,respectively,onsalesperworker.Asdiscussed aboveinSection3.1,distancetosupplychainpartnerscannotbe identified clearly when thesepartners have multiple establish-mentsbecausetheTSRdataforsupplychainpartnersareatthe firmratherthantheestablishmentlevel.Therefore,ourfocuson single-establishmentfirmsisjustified.Inaddition,Bernardetal. (2014)andBelderbosetal.(2015)didnotdistinguishbetweenties withsuppliersandclientsorincorporatenetworkdensity,aswe dointhisstudy.

Thenegativeroleofnetwork densityinknowledgediffusion foundinthis studycontrasts withthefindingsof Ahuja(2000) andCentola(2010),whofoundapositiverolefornetwork den-sity.However,ourfindingisconsistentwithmanyotherstudiesin thepreviousliteraturethathavefoundthe“strengthofweakties” (Granovetter,1973), ortheimportanceoftieswithoutsidersin informationdiffusion.Burt(1992,2004)alsoarguesthatstructural holes–actorswhoconnectgroupswithcomplementaryresources orinformation–canpromotetheirperformance.Usingdatafrom universitylaboratories,Perry-Smith(2006)foundthatthenumber ofstrongtiesforeachmemberofalaboratory,asmeasuredbythe subjectivecloseness,duration,andfrequencyofhis/her

relation-Table7

TieswithSuppliersandClientsandInnovation.

Dependentvariable:logofthenumberofpatentsplusone.

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

#ofsuppliersinthesameprefecture(t-1) 0.120 0.146+

(0.0781) (0.0791)

#ofsuppliersoutsideofthesameprefecture(t-1) 0.214** 0.0734

(0.0724) (0.114)

#ofclientsinthesameprefecture(t-1) −0.118+ −0.104

(0.0658) (0.0661)

#ofclientsoutsideofthesameprefecture(t-1) 0.295** 0.228*

(0.0616) (0.0995)

Densityofegonetwork(t-1) −0.116 −0.747+ −0.225 −0.721+

(0.245) (0.424) (0.250) (0.436)

#ofsuppliersoutsideofthesameprefecture(t-1) 0.666+

*densityofegonetwork(t-1) (0.398)

#ofclientsoutsideofthesameprefecture(t-1) 0.338

*densityofegonetwork(t-1) (0.344)

Sales(t-1) 0.233** 0.227** 0.264** 0.269** (0.0608) (0.0612) (0.0578) (0.0581) #ofworkers(t-1) 0.195* 0.201* 0.214** 0.211** (0.0789) (0.0794) (0.0787) (0.0794) #ofpatents(t-1) 1.667** 1.673** 1.650** 1.648** (0.0751) (0.0754) (0.0741) (0.0741) Observations 36,839 36,839 36,839 36,839

Notes:Standarderrorsareinparentheses.+,*,and**signifystatisticalsignificanceatthe10,5,and1%level,respectively.Thenumberofclientsofanytypeisaddedoneand logged.Salesperworkerandsalesarealsologged.Thedensityofafirm’segonetworkisdefinedastheratiooftheactualnumberoftiesbetweenthefirm’ssupplychain partnerstothenumberofallpossibletiesbetweenthem.Age,agesquared,industrydummies,prefecturedummies,andsurveyyeardummiesareincluded,buttheresults arenotshownforbrevity.

ship,hasanegativeeffectonthecreativityoflaboratories,whereas thenumberofweaktieshasapositiveeffect.Furthermore,our find-ingthatthenegativeeffectofnetworkdensitycanbealleviatedby tieswithdistantfirmsisconsistentwithPhelps(2010),Rost(2011), andTiwana(2008),whofoundcomplementaritybetweenstrong tieswithinthecommunityandweaktieswithoutsiders.

Insummary,ourresultsemphasizetheimportanceofthe diver-sityofnetworksinknowledgediffusion.Therefore,wesuggestthat tomaximizethebenefitsofknowledgediffusionthroughsupply chainnetworks,firmsshouldbeconnectedwithoutsiders, includ-ing distant firms and firms unrelated to current suppliers and clients.

However,theseresultsshouldbeviewedwithcautionforthe followingtworeasons.First,theresultsdonotnecessarilymean thathavinganyadditionalsupplychainpartnersoutsideofthe pre-fecturestimulatesproductivityandinnovativecapabilitybecause firms typically choosehigh-productivity partners carefully. Our resultsmaysuggestthatdistantpartnerspromoteknowledge dif-fusionwhentheyarecarefully−notrandomly−chosen.

Second,geographicdiversificationofsupplychainpartnersis costly;wefindthatalargernumberoftieswithdistantsupplychain partnersisassociatedwithasmallernumberoftieswith neighbor-ingpartners.Thissubstitutionbetweenneighboringanddistant partnersismostlikelyduetothecostsofmaintainingsupply-chain ties,whicharealsofoundinPhelpsetal.(2012),amongothers. Therefore,thenetbenefitofthegeographicdiversificationof sup-plychainpartners–oritsbenefitsfromknowledgediffusionless itscreationandmaintenancecosts–remainsunclear.

Nonetheless, the need for cautious interpretation does not alter ourmain conclusion.Distant firms enjoymore diversified knowledgethanneighboringfirms.Thisdiversifiedknowledgecan improvepartner firms’productivityand innovativecapabilities, althoughitmaybecostlytofindsuchvaluablepartnersoutside oftheprefecture.

5.4. Robustnesschecks

Tochecktherobustnessof ourempirical results,we experi-mentedwithseveralalternativespecifications.First,toexamine

theeffectsofgeographyonknowledgediffusion,wedistinguished betweensuppliers/clientswithinandoutsideofthesame prefec-ture.This isbecause thecurrent prefecture bordersare mostly basedonhistoricalregionalbordersandthusreflectdifferencesin cultureandidentity.However,itispossiblethatgeographical dis-tanceaffectsknowledgediffusionmorethanprefecturebordersdo. Therefore,weincorporatedthenumberofsuppliers/clientswithin or outsideof a 50km radiustotest which geographicmeasure ismoreimportant.Tocalculatethedistancebetweentwofirms, weconvertedtheiraddressestogeographiccoordinatesusingthe CSVaddressmatchingserviceoftheCenterforSpatialInformation ScienceoftheUniversityofTokyo.

Second, it is possible that firms in industrial regions, such as Tokyoand Osaka, play a more importantrole in knowledge diffusionthanfirmsinruralregionsdo.Forexample,asFig.4 illus-trates,thetechnicalexpertiseaccessibleintheTokyometropolis isnotlimitedtoanyparticulartechnologicalcategory,asopposed tosmallerprefecturesthattendtohostmorespecialized indus-tries. Therefore,thepositive effectsof distantfirmsmayreflect the effects of firms in major industrial prefectures onfirms in remoteprefectures.Tochecktheeffectoffirmsinindustrialcenters, weincorporatethenumberofsuppliers/clientsinthefourmost populatedprefectures,Tokyo,Kanagawa,Aichi,andOsaka,inthe benchmarkregressions.

Third,positive effects of distant firms maypick up positive effectsofdistantfirmsrelatedthroughcapitalownershipbecause distantpartnersaremorelikelytobeaffiliatedthanneighboring partnersare.Ifthisisthecase,thepositivecorrelationbetweenfirm performanceandtieswithdistantfirmsmaycomefromknowledge diffusionwithinthefirmgroup.Thus,weincorporatethenumber ofaffiliatedsuppliers/clients,whicharedefinedintheTSRdata,in theestimations.

TheresultsfromthesealternativespecificationsinTable8 indi-catesthatthebenchmarkresultsholdandthattheeffectofthe additionalvariablesiseitherinsignificantorsignificantlynegative. Incolumns(4)and(8)ofPanelB,theeffectofaffiliatedpartners isnegative,confirmingourpreviousconclusionthatoverlystrong tiesareharmfultofirmperformance.

Table8

RobustnessChecks.

Panel(A):Effectsonsalesperworkerfromseeminglyunrelatedregressions

(1) (2) (3) (4) (5) (6) (7) (8)

#ofsuppliers #ofclients

Inthesameprefecture −0.00138 −0.00641 −0.00551 −0.00639 −0.00392 0.00915* 0.0142** 0.00881*

(0.0106) (0.00474) (0.00523) (0.00470) (0.00960) (0.00417) (0.00450) (0.00413)

Outsideofthesameprefecture 0.0319** 0.0296** 0.0326** 0.0307** 0.00402 0.0103 0.0196** 0.00640

(0.00503) (0.00856) (0.00677) (0.00469) (0.00457) (0.00806) (0.00614) (0.00428) Within50km −0.00539 0.0139 (0.0104) (0.00952) Beyond50km 0.00165 −0.00478 (0.00883) (0.00823) Inindustrialprefectures −0.00254 −0.0204** (0.00740) (0.00678)

Relatedthroughcapitalownership 0.0660 0.00600

(0.0424) (0.0350)

Panel(B):EffectsonthenumberofpatentsfromIV-tobitestimations

(1) (2) (3) (4) (5) (6) (7) (8)

#ofsuppliers #ofclients

Inthesameprefecture −0.0153 0.128 0.139+ 0.126 0.0124 −0.126+ −0.179** −0.127+

(0.159) (0.0786) (0.0819) (0.0780) (0.134) (0.0662) (0.0687) (0.0661)

Outsideofthesameprefecture 0.184* 0.370* 0.260* 0.218** 0.318** −0.150 0.113 0.283**

(0.0766) (0.162) (0.102) (0.0722) (0.0642) (0.173) (0.0911) (0.0619) Within50km 0.152 −0.147 (0.158) (0.132) Beyond50km −0.188 0.489** (0.167) (0.177) Inindustrialprefectures −0.0755 0.276** (0.113) (0.105)

Relatedthroughcapitalownership −1.254** −0.629*

(0.423) (0.303)

Notes:Standarderrorsareinparentheses.+,*,and**signifystatisticalsignificanceatthe10,5,and1%level,respectively.Thenumberofclientsofanytypeisaddedoneand logged.Theotherindependentvariablesusedinthebenchmarkregressionarealsoincluded,buttheresultsarenotshownforbrevity.

Oneexceptioniscolumn(6)ofPanelBofTable8,whichshowsa positiveeffectofclientsoutsideofthe50kmradiusandan insignif-icanteffectofclientsoutsideoftheprefecture.Thisimpliesthat geographicdistanceismoreimportantthanprefecturebordersin sometypesofknowledgediffusion.However,ourconclusionthat distantfirmscontributetothediffusionofknowledgefor innova-tionmorethanneighboringfirmsdoremainsunchanged.

Theotherexceptioniscolumn(7)ofPanelB,whichshows a positiveeffectofclientsinindustrialcentersandaninsignificant effectofdistantclients.Thisresultimpliesthatthepositiveeffectof distantclientsoninnovativecapacityinthebenchmarkresult indi-catesthatknowledgefromclientsinindustrialcenterspromotes innovation.Thisisprobablybecausefirmsinremoteregionsmay beconnectedtoassemblersinindustrialregionsbutnottobuyers inotherremoteregions.However,intheotherthreespecifications (columns3and7inPanelAandcolumn3inPanelB),partnersin industrialcentersarefoundtohavenopositiveeffectonfirm per-formance,whereasdistantpartnershaveaneffect.Therefore,our conclusionthatthediversityofpartnerfirmspromotesfirm perfor-mancethroughknowledgediffusionshouldstillbevalid,although wemustnotethatinsomecases,accesstoadvancedknowledgein industrialcentersmaybemoreimportantthanaccesstodiversified knowledge.

Finally,thecorrelationbetweenthenumberoftransaction part-nersandsalesmayreflecthigherpricesduetothemonopolypower offirmswithmanyclients.AsRauch(1999)finds,differentiated productsaremorelikelytobetradedlocallythanhomogeneous productsare.Therefore,weaddedtheshareofeach firm’ssales inthetotalsalesatthe3-digitindustryandprefecturelevelasa controlvariable.However,theeffectofthemarketshareonsales andsalesperworkerisinsignificant,whereastheotherresultsare

virtuallythesame.Theresultsarenotshownforbrevitybutare availableuponrequest.

6. Conclusion

This paper examines the effects of the structure of supply chainnetworksonproductivityandinnovationcapabilitythrough knowledge diffusion usingfirm-level panel datafor Japan.The datasetislarge,including800,000toonemillionfirmsperperiod andfourtofivemillionbuyer-supplierties,althoughweultimately focus on a sub-sample of single-establishment manufacturing firms.Thisstudyisthefirst toexaminetheeffectofthe struc-tureoftheentiresupplychainnetworkinalargeeconomy–and notablythedensityofeachfirm’segonetwork –onknowledge diffusion.Wefindthattieswithdistantsuppliersimprove produc-tivity,measuredbysalesperworker,whichismostlikelybecause intermediatesfromdistantfirmsembodydiversifiedknowledge. Tieswithneighboringclientsalsoimproveproductivity,whichis mostlikelybecausethediffusionofdisembodiedknowledgefrom neighboringclientsismoreeffectivethanfromdistantclients.By contrast,tieswithdistantsuppliersandclientsimproveinnovative capability,asmeasuredbythenumberofregisteredpatents,more thanties withneighboring suppliersand clients,which empha-sizestheparticularimportanceofthediversityofknowledgeto innovation.Inaddition,thedensityofafirm’segonetwork,which is measuredbyhow densely itssupply chain partners transact withoneanother,isfoundtohaveanegativeeffecton produc-tivityandinnovativecapability,implyingknowledgeoverlapsand redundancyindenselyconnectedfirms.Overall,ourresults empha-sizetheimportanceofdiversifiedpartnersinknowledgediffusion through supply chain networks, although thenet benefit from