Papers in Evolutionary Economic Geography # 19econ.geo.uu.nl/peeg/peeg1907.pdf · 2019-02-11 · 1...

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Papers in Evolutionary Economic Geography

# 19.07

Cluster externalities, firm capabilities, and the recessionary shock: How the macro-to-micro-transition shapes firm performance during stable times and

times of crisis

Christian Hundt, Linus Holtermann, Jonas Steeger, and Johannes Bersch

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Clusterexternalities,firmcapabilities,andtherecessionaryshock:Howthe

macro-to-micro-transitionshapesfirmperformanceduringstabletimesand

timesofcrisis

ByChristianHundt1,LinusHoltermann2,JonasSteeger3,andJohannesBersch4

1ThünenInstituteofRuralStudies,Bundesallee64,38116Braunschweig,Germany

2GeographyDepartment,RuhrUniversityofBochum,Universitätsstraße150,44801Bochum,Germany

3DataScienceDepartment,NordantechSolutions,200955Hamburg,Germany

4CentreforEuropeanEconomicResearch(ZEW),L7-1,68161Mannheim,Germany

Abstract

In this paper, we examine themacro-to-micro-transition of cluster externalities to firms andhow it is affected by the macroeconomic instability caused by the recessionary shock of2008/2009.Usingdata from16,166manufacturing andbusiness services firmsnested in390Germanregions,weemploywithin-firmregressiontechniquestoestimatetheimpactofcross-levelinteractionsbetweenfirm-andcluster-leveldeterminantsonphase-relateddifferencesinfirmperformancebetweenapre-crisis(2004-2007)andacrisisperiod(2009-2011).

Theempiricalresultsvalidatetheexistenceofamacro-to-micro-transitionthatevolvesbestinthe caseof broad firm-level capabilities andvariety-driven externalities. Furthermore, the re-sultsindicatethatthetransitionstronglydependsonthemacroeconomiccycle.Whilethetransi-tion particularly benefits from a stablemacroeconomic environment (2004-2007), itsmecha-nisms are interruptedwhen being exposed to economic turmoil (2009-2011). Yet, the crisis-inducedinterruptionofthetransitionismainlyrestrictedtothenationalrecessionin2009.Assoon as themacroeconomic pressure diminishes (2010-2011),we observe a reversion of thetransmissionmechanismstothepre-crisislevel.

Ourstudycontributestotheexistingliteraturebycorroboratingpreviousfindingsthattheeco-nomic performance of firms depends on aworkingmacro-to-micro transition of external re-sources,whichpresupposessufficientclusterexternalitiesandadequatefirm-levelcombinativecapabilities. Incontrasttopreviousstudiesonthistopic,thetransitionmechanismisnotmod-eledastime-invariant.Instead,itiscoupledtotheprevailingmacroeconomicregime.

Keywords:Macro-to-micro-transition,combinativecapabilities,agglomerationeconomies,clus-ter-levelexternalities,unrelatedvariety, relatedvariety,macroeconomicregimes,GreatReces-sion,economicresilience

JEL:C33,R11,R58

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1 Introduction........................................................................................................................................................................3

2 Theoreticalbackground................................................................................................................................................4

2.1 Transitionofexternalitiesfromregionstofirms .............................................................. 4

2.2 Firmperformance,clusterexternalities,anddifferentmacroeconomicregimes ............ 6

3 Econometricmodel..........................................................................................................................................................8

4 Datasources,samplingstrategy,andvariables................................................................................................9

4.1 Datacollection ................................................................................................................... 9

4.2 Identificationandsamplingstrategy ................................................................................. 9

4.3 Dependentvariables ........................................................................................................ 10

4.4 Firm-levelvariables ......................................................................................................... 12

4.5 Regional-levelvariables................................................................................................... 12

5 Empiricalevidenceforaregime-specificmacro-to-microtransition................................................14

5.1 Ontheinterplayofcombinativecapabilitiesandclusterexternalities .......................... 14

5.2 Addressingpotentialsampleselectionandotherbiases ................................................ 17

5.3 Decompositionofvariety:theimpactofrelatedvarietyandunrelatedvariety............. 19

5.4 Alternativecrisisdefinition ............................................................................................. 21

6 Conclusion.........................................................................................................................................................................23

References...................................................................................................................................................................................25

Appendices..................................................................................................................................................................................30

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1 Introduction

Thespatialdimensionhasalwaysbeenregardedasanimportantkeytotheexplanationofeco-nomicgrowth,developmentandcompetitiveness,whichholdstrueespeciallyinthefieldofeco-nomic geography(Myrdal, 1957;Hirschman,1958; Scott, 1988;Porter, 1998; Saxenian,1996;Storper,1995;Asheim,1996;Maskell,2001).AnothersurgehasarisenjustrecentlyinresponsetotheGreatRecessionof2008/2009thatwidely(re-)openedthescientificdebateoneconomicresilienceandput,amongotherthings,aparticularfocusontheroleofregionsandtheirsocio-economicendowments.Whileinthebeginning,researchonregionaleconomicresiliencewasforthemostpartdirectedatsettingupconceptualfoundations(Pendalletal.,2010;BoschmaandMartin, 2010; Hassink, 2010; Pike et al., 2010; Simmie andMartin, 2010; Martin and Sunley,2015),recentresearchputsastrongeremphasisonempiricalinvestigations.Studiesofthelattergroup, in turn, frequently explore the impact of regionaldeterminants that eitherpromoteorhampereconomicresilience(foracomprehensiveoverviewseeHoltermannandHundt,2018).Atthesametime,however,thescopeofthesestudiesistypicallyrestrictedtotheregionallevelaloneanditdoesnotaccountfortheheterogeneityatfirm-level,respectively.Suchapproachesare problematic insofar as they reduce resilience to a more or less monolithic phenomenonwhilstsubjectthemselvestotheriskofecologicalfallacies(Coleman,1991).

Inordertoovercomethislimitation,wearegoingtoshifttheresearchfocusfrom‘regionsthatcontainfirms’to‘firmsthatarenestedwithinregions’asweagreewithMartin(2012)thatfirms–andnotregions–aretheactualagentsofeconomicresilience.Asaconsequence,weexamineregionaldeterminantsnotaccordingtotheireffectsonregionspersebutaccordingtotheirim-pactontheeconomicperformanceoffirmsthroughamacro-to-microtransitionofregionalag-glomerationeconomies.Ofcourse,thebasicideaofourapproachisnotentirelynew.Infact,theimpact of agglomeration economies on firm-level performance has already been subject to anumberof studies inbothbusinessand regional economics.Nonetheless, thepresent stateofresearch remains fractional since, among other things, the complex nexus of the region-firm-relationshiphasnotbeensatisfactorilyresolvedyet.Businessstudies,ontheonehand,normallyfocus on firm-related assets that help to internalize externalitieswhile they fail at specifyingboth typeand transmissionof theseexternalities (Tsai,2001;Girma,2005;Kostopoulosetal.,2011).Regionalstudies,ontheotherhand,usuallyconcentrateonthecharacteristicsandinflu-encingpatternsofagglomerationeconomiesattheaggregatelevelwhereasneitherfirm-relatedcapacitiesnor the linkbetween regions and firms is sufficiently included (seedeGroot et al.,2009andMeloetal.,2009forcomprehensiveoverviews).

Only recently, however, regional economists (vanOort et al., 2012; Brunow and Blien, 2015;Smitetal.,2015)havestartedtofillthisgapbyinterpretingregionallynestedfirmsasbeingonthe receiving endof agglomeration (dis-)economies.What is stillmissing, though, is a furtherlinkagebetweenthemacro-to-micro transmissionanddynamicchanges in theeconomicenvi-ronment.Whilstpriorresearchscrutinizestherelationshipatagivenpointintime,thetemporaldimensionandhenceimpacteconomicturbulencesremainunknown.Thisisnoteworthy,sinceturbulences,especiallyiftheyemergeassuddenshocks,areverylikelytoaffectthepatternsofhowexternalitiesaretransmittedfromregionstofirmsandthustakeinfluenceontheeconomic

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performanceof firms.Therefore, time-relatedchangesareanother importantmissingpiece inthejigsawofthefirm-region-relationship.

Inresponsetotheaforementioneddeficits,wedevelopourpaperalongthreeguidinginsights.First,weexplorethemacro-to-microtransitionfromtheperspectiveofthefirmemphasizingitscombinative capabilities as a precondition to internalize regional externalities in the form of tangible resources and knowledge spillovers. Second, we understand the transmission ofknowledgespilloversfromregionstofirmsasaninterlinkedprocesswhichiswhywechooseacross-level regression approach in order to empirically validate themicro-macro-relation be-tween firmsandtheirrespectiveregionalenvironment including theknowledgespilloversbe-tween them. Third andmost important,we integrate time-related changes in the firm’s envi-ronmentaswedistinguishbetweentwomacroeconomicregimes,onerepresentingaperiodofmacroeconomic stabilityand theother aperiodofmacroeconomic instability. In thisway,welinkthemacro-to-microtransitionofexternalitieswiththenotionofresilienceaskinghowfirm-level determinants and regional interactions shape firm performance during macroeconomicstabilityandinstability.

The remainder of our paper is structured as follows. Section 2 explores the theoretical back-ground,whilesection3elucidatesthecomponentsandstructureoftheeconometricmodel.Sec-tion4presents thedatasourcesappliedand introduces thedependentandexplanatoryvaria-bles.TheempiricalresultsarereportedanddiscussedinSection5.Section6concludes.

2 Theoreticalbackground

Inordertonarrowtheresearchgapsasidentifiedabove,weinterlinkthespatialandtemporaldimensionofeconomicactionswhileanalyzingtheaccordingmechanismsfromthemicroper-spectiveofthefirm.Wepursuethisobjectiveintwoconsecutivesteps.First,wesubstantiatethespatialhierarchybetween firmsandregionsandcarveout itsrelevance foreconomic interac-tions(seesection2.1).Second,wedemonstratethatregionalexternalitiesarelikelytoexposedifferingimpactsonfirmperformanceovertimedependingonwhethertheprevailingmacroe-conomicregimeischaracterizedeitherbystabilityorinstability(seesection2.2).

2.1 Transitionofexternalitiesfromregionstofirms

Weexplicitlyagreewiththenotionofa ‘macro-to-microtransition’accordingtowhich‘there-giongenerateseconomicopportunitiesandconstraintsforfirmslocatedinthatregionthroughagglomerationeconomiesandagglomerationdiseconomies’(vanOortetal.,2012:9).Wethere-byacceptthesuppositionthatagglomerationeffectsdonotimpacttheregionaleconomydirect-lybutonly indirectly, i.e. throughtheirmoderatingeffecton firmperformance(forsimilarar-gumentsseeAcsandArmington,2004andMartinetal.,2011).Hence, inlinkingthefirmtotheregionallevelwetaketwoimportantaspectsofeconomicrealityintoaccount.Ontheonehand,ourapproachrecognizesthatfirmsarenotisolatedfromtheirgeographicallysurroundingset-tingbutnestedwithinspecificregionaleconomiccontexts.Ontheotherhand,itdisclosespoten-tialmechanismsofamacro-to-micro transitionsinceregionallynestedor ‘embedded’ (Grano-

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vetter,1973) firmscanpossiblyenlargetheirscopeofeconomicactionsby internalizingaddi-tional economic resources that are external to the firm but internal to the region. However,whether a firmactually succeeds in internalizing regional externalities, at least twoprecondi-tionsmustbemet, namely thepresence of agglomeration economies in the respective regionandthefirm’scapabilitytomakeuseofthem.

Therelevanceofregionalexternalitiesprimarilyarisesfromthelimitationoffirm-relatedassetsastheycanprovidecomplementarygrowth-relevantresourceswhichhelpfirmstoexpandtheirscopeofeconomicactions.Whichkindofgrowth-relevantresourcesareavailableinaparticularcase,dependsonthespecifictypeofagglomerationeconomiesthatarepredominantinthere-spectiveregion(Frenkenetal.,2007).Referringtothis,regionaleconomicliteraturecommonlydistinguishes between localization economies that are based on sector-specific specialization,Jacobs’ externalities that arise from cross-sectoral variety, and urbanization economies thatoriginate from sector-independent density (Frenken et al., 2007; Boschma and Iammarino,2009; vanOort,2013). In thispaper,we concentrateon externalities stemming from regionaleconomicspecializationandregionaleconomicvariety.Ineithercase,ofcourse,firmscanonlyutilize(orbeimpairedby)specificexternalitiesiftheyarebothlocatedintherespectiveregionandincludedintherespectivesector(fortheoperationalizationofspecialization-andvariety-driven externalities see section 4.5). Hereinafter, the term ‘cluster-specific’ (externalities) isusedtodescribesector-specificagglomerationforcesofspecializationandvarietythatdifferbe-tweenregions.

A theoretical approachonhow tomodel the transmissionof regional externalities to firms isprovidedbyvanOort(2013)withreferencetoMcCannandFolta(2011).VanOort(2013)sug-gestsapplyingtheknowledge-basedviewofthefirm(Grant,1996and2002)inordertocapturetheheterogeneityoffirms’capabilitiestoaccessandutilizeagglomerationeconomies.Thebasicideaissimplebutconvincing:‘Agglomeratedfirmscanrealizethepotentialbenefitsoflocationin an agglomeration only to the extent that they are capable of using and commercializingknowledgefromco-locatedfirmsincombinationwiththeirownknowledgeassetstocreateval-ue’(vanOort2013:9).Such‘combinativecapabilities’ containa)thefirm’sexistingknowledgebase, b) the number of its localized connections and c) its ‘organizing principles’ (Kogut andZander,1992).Whatismore,allthreeassetsareassumedtoincreasethe‘combinativecapabili-ties’ofthefirm.AsexplainedbyvanOort(2013),alargeexistingknowledgebase(a)isexpectedto improve the firm’s ability to assess, access, and internalize externally available knowledge.Similarly,ahighnumberoflocalizedconnections(b)isthoughttofacilitatethefirm’sactiveandpurposefulcollaborationwithotherfirmstoobtain,exchange,andmutuallydevelopresources.Finally,‘organizingprinciples’(c),definedasthefirm’sabilitytocoordinatedifferentpartsoftheorganizationandtransferknowledgeamongthem,areassumedtoincreasethefirm’sefficiencyininternalorganizationandthusmaketheresourcefulapplicationofregionalexternalitiesmorelikely.

As for thispaper,weaddanother specification to this conceptandassumeadual functionofcombinativecapabilitiesthatmanifestsitself inatwo-fold-impactonfirmperformance.Onthe

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onehand,whileemphasizingtheir ‘combinative’character,capabilitiescanhaveanindirectef-fectas faras theypotentiallybroadenthe firm’spossibilities tomakeuseofregionallyboundexternalities.Theseindirecteffectsarethemainfocusofourstudy.Ontheotherhand,though,combinativecapabilitiescanalsodirectlyaugmenttheeconomicperformanceofthefirmastheexistingknowledgebase,thenumberofitslocalizedconnectionsaswellasthe‘organizingprin-ciples’ofthefirm(KogutandZander,1992)arelikelytobegrowth-stimulatingirrespectiveoftheinternalizationofadditionalresourcesstemmingfromregionalagglomerationeconomies.Toconclude,weexpecttheimpactofbroadfirm-levelcapabilitiestobepositiveineitherway:thebroaderthecapabilities,thebetterceterisparibustheeconomicperformanceofthefirm.

2.2 Firmperformance,clusterexternalities,anddifferentmacroeconomicregimes

Notonlyare firmswilling toaccess thoseagglomeration externalities fromwhich they expectthe most effective and efficient support. We further assume that firms, in case they areconfrontedwith abrupt changes in theirmacroeconomic environment,are likely toalter theirgrowthstrategiesandaccordinglywillseekfordifferent,moresuitableexternalities.Theextenttowhichfirmswillsuccessfullyadapttoshock-inducedmarketfluctuationsinturndependsontheir ‘dynamic capabilities’which Teece et al. (1997: 516) define as the facility to ‘integrate,build,andreconfigure [its] internalandexternalresources’.Henceforth, ‘dynamiccapabilities’canbebestunderstoodasadynamicversionofcombinativecapabilities.Throughaddingady-namiccomponenttoourstudy,wealsotakeintoaccountawidelyacceptedinsight inregionalresearchaccordingtowhichagglomerationeconomiesonlocalizedeconomicgrowthgenerallydifferacrosssectors,space,andtime(RosenthalandStrange,2004;VanOort,2007;deGrootetal.,2009;Meloetal.,2009;BeaudryandSchiffauerova,2009;Puga,2010).Weexpectthatfirmsseektoaccessother,morefittingexternalitiesifsuddenchangesintheirmacroeconomicenvi-ronmentrequireso.Apartfromthisverybasicassumption,phase-specificimpactsofspecializa-tion-resp.variety-drivenexternalitiesonfirmperformancesarehardtopredict.Instead,differ-entscenariosseemequallyplausible.

Tostartwith,webrieflydiscusspossiblemacro-to-micro transitionsoriginating fromlocaliza-tioneconomiesashighlightedbyMarshall(fromastaticperspective)andMarshall,ArrowandRomer(fromadynamicperspective)respectively(Marshall,1920;Arrow,1962;Romer,1986).MARexternalitiesarise fromindustrialspecializationofadefinedgeographicareaandare,byourdefinition,availableonlytofirmsthatoperatewithinthisspecializedsector(seesection4.5for a comprehensive operationalization). Provided this requirement ismet,MAR externalitiescanhelp firmsnot only toparticipate in specialized factormarkets, infrastructureor suppliernetworks,theyalsotendtopromoteincrementalinnovationandprocessinnovationviaregionalknowledgespillovers,thankstothetacittransmissionofinformationacrossagents(Glaeseretal., 1992; Henderson et al., 1995). At least two distinct outcomes of the interaction betweenbroadcombinativecapabilitiesandMARexternalitiesareconceivable.Inthefirstscenario,firmswithbroadcapabilitiesmightderive thegreatestbenefit fromMARexternalities inperiodsofmacroeconomic stability.A simple explanation couldbe that firmsmake thebestuseof theirpositionswithinhighlyspecializedandramifiedclustersonlyifthecluster-relatednetworksandbusinesslinksarenotdisturbedbyshock-inducedturbulences.Besides, ittakesacriticalmass

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beforespecializedresourcesarelikelytogenerateanaddedvalueforthefirmsinvolvedandacriticalmassinturntakesastableeconomicenvironmenttoevolvebest(Porter,1998;MaskellandMalberg, 1999). In the second scenario, firms with broad capabilities might particularlyprofit fromMARexternalities in case the environment isspecifically volatile.The idea is that,duringandimmediatelyfollowingasharpeconomicdownturn,firmswithalreadyhighcapabili-tiescommand,comparedto lesscapable firms,ahighernumberandmorestablemarketrela-tions inandoutsideof thecluster. In thisway, theyensure themselvessufficientaccess tore-gionalresources,whilefirmswithlessercapabilities,onthecontrary,mightbecutofffromsup-plier-costumer-relationshipsmoreeasily.Thequestionofwhether the firstor thesecondsce-nariowillprevailcannotbeansweredonameretheoreticalbaseandrequiresdetailedempiricalexamination.

Inanextstep,wetakeashortlookattheinterplayofbroadfirmcapabilitiesandexternalitiesstemmingfromvariety.Thelatterare,byourdefinition,availableonlytofirmsthatbelongtoasectorthatatthesametimeischaracterizedbyahighdegreeofintra-sectoralvarietyatthesub-ordinate(digit)level(s)(seesection4.5foracomprehensiveoperationalization).Fromaregionalperspective, this measure corresponds with the notion of related variety as introduced byFrenkenetal.(2007).Fromafirmperspective,however,thereasoningisverysimilar.Thesoledifferenceisthattherelevantvarietyoccurswithinspecificsectors(thusintra-sectoralvariety),notwithintheentireregion.Ineithercase,variety-basedexternalitiesarerootedinaportfolioofindustriesthatareinterconnectedthroughsharedorcomplementarycompetences(BoschmaandIammarino,2009).Firmscanprofitfromsuchinterconnectionsastheygenerallyfacilitatethefirm’saccesstoresourcesthatareneitherredundantnortoodissimilartobecomeintegratedinto theirownportfolios.Themorediversified theregionalsectorof theembedded firms, theeasier theiraccess to interconnected resources.The economic advantageof such firms is thatthey havemoreoptions torecombine theirownknowledgebasewithknowledge inputs fromother firms of the same intra-sectoral complex. Ideally, this constellation leads to spillover-drivenknowledgefertilizationandenablesfirmstoobtainacompetitiveadvantagebymakingquickerandbetterprogressinthedevelopmentofnewproductsand/ortechnologies(Lazzeret-tietal.,2011;HolmandØstergaard,2013).

Similartothecaseofspecialization,theinterchangebetweenbroadcombinativecapabilitiesandexternalitiesstemmingfromvarietyallowsfortwoplausible,yetdifferentscenarios.Ontheonehand,firmswithbroadcapabilitiesmightbestutilizevariety-drivenexternalitieswhenthemac-roeconomic environment is stable. The underlying assumption is that the adoption of relatedtechnologies and, equally important, the incorporation of related production factors are chal-lengingproceduresthatrequireahighlevelofplanningsecuritythatismostlikelyguaranteedinperiodsofstability.Ontheotherhand,though,variety-basedexternalitiescouldturnouttobeparticularlybeneficialintimesofcrisis.Thisscenarioreflectsthenotionthatagreatervarietyofaccessibleresourcesalsopromotesflexibilitythat,oncefirmsareexposedtoasuddenpressuretoact,becomesaveryessentialtoolintermsofdefendingandgainingmarketshares.Heretoo,theoryalonecannotforetellwhetherthefirstorthesecondscenariowillprevail.Instead,athor-oughempiricalexaminationismandatory.

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3 Econometricmodel

Ourgoalistoexaminetheinterplayofcombinativecapabilitiesandcluster-specificexternalitiesanditsimpactonfirmperformance.Toinvestigatethismatter,weemploycross-levelinterac-tionsof firm features and regional externalities in addition to firm-level controls.Wealso in-cluderegionalandsectoralindicatorstoreducetheomittedvariablebias.Changesinthemacro-economicenvironmentaremodeledthroughtwodifferentregimes.Thefirstregimerangesfrom2004to2007andrepresentsaperiodofrelativeoverallstability(pre-crisis).Sharplyinterrupt-edby theGreatRecessionof2008-2009,however, it is followedbyaperiodof instability thatspansfrom2009to2011(crisis).1Ourreferencemodelreadsasfollows:

!"#$%#&'()"*+, = .+ + 0, + 123*+,,5 × 7+,,589 + 1:3*+,,5 + 1;7+,,589 + <%(=#%>?*+,,5 + @*+, (1)

whereAstandsforfirm,Bforsector,#forregion,and=fortheperiodinwhichtheexplanatoryvariablesofinterestaremeasured.!"#$%#&'()"*+,isthegrowthoftheperformanceindicatormeasuredasthechangeofthelogarithmizedindicatorbetweenthebeginningandtheendoftherespective period.23*+,,5 denotes proxy variables for the combinative capacity of a firm and7+,,589representscluster-specificexternalities.<%(=#%>?*+,,5standsforadditionalcontrolvaria-blesatfirm-level..+isasectorfixedeffectforsectorBand0, isaregionfixedeffectforregion#.Toavoidendogeneityproblems,wemeasuretheexplanatoryvariablesatthebeginningofoursampleperiod,hence= = 2003.FollowingHenderson(2003)andKnobenetal.(2015),weas-sumethatspatialexternalitiesrequiretimetobeaccessibleforfirmsanduseaone-yearlag(? =1)tomeasurecluster-levelexternalities.3

Next to the potential moderation of cluster-level effects through firm characteristics, we aremainlyinterestedinthedifferenceinfirmperformancebetweenthecrisisandpre-crisisperiod.For thispurpose,wemodify specification (1) anduse thedifference inperformancebetweencrisisandpre-crisisasdependentvariable.4Thisleadsustotheformulaofmodel(2):

1 AsproposedbyKroszneretal.(2007),weseparatethepre-crisisperiodfromthecrisisperiodbyoneyear,becausetheyearpriortothecrisisonset,inthisspecificinstance2008,cannotbeclearlycategorizedaseitherstableorunstable. 2Notethattheperformanceindicatoratthebeginningofacertainperiodisequivalenttothevalueoftheindicator at the end of the previous year.For example, to gauge the performance during the pre-crisisperiod,wecomputethedifferenceofthelogarithmizedindicatorattheendof2007andtheendof2003.Incomparisontoannualpaneldatamodels,thedistributionofthedependentvariableislesssusceptibletooutliersandzero-inflationduetotheuseofthegeometricmean.Ifperformanceindicators,e.g.salesoremployment,aremeasuredthroughannualgrowthrates,thegrowthtrajectoriesoffirmsareoftendomi-natedbyoutlieryearswithlargeincrementsasthemajorityofyearsshoweithernoneoronlyverysmallchanges.Inourset-up,wemitigatethisproblemthroughtemporalaggregation.Itisnoteworthy,however,thatthisadvantagecomesattheexpenseof informationloss.FollowingthecritiqueofBarro(1997)onfixedeffectspanelmodelwithannualdata thatpurely relyon timeseries informationand thusneglectconditionalvariablesthatareslowlymoving,suchastheregionalexternalitiesofinterest,weconsideritnecessarytomodeltheeffectsofthelatterasdescribedbyEquation(1)and(2). 3 Weexperimentwithalternativetimelags(? = 0, ? = 2, ? = 3).Duetothestationarynatureoftheclus-ter-levelexternalities,resultsarevirtuallythesame.Moreover,weconductrobustnessteststhatemploypre-crisisdataoffirm-levelandregional-levelindicatorstoverifythereliabilityofourestimationresults. 4 Thisapproachisverycommoninthecontextoffirm-levelmicrodataandcanbefoundinseveralempiri-cal studies, accordingly: Rajan and Zingales (1998), Claessens and Leaven (2003), Fisman and Love(2003),CetorelliandStrahan(2006),Kroszneretal.(2007),andClaessensetal.(2012).

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∆!"#$%#&'()" = .+ + 0, + 123*+,,5 × 7+,,589 + 1:3*+,,5 + 1;7+,,589 + <%(=#%>?*+,,5 + @*+, (2)

where∆!"#$%#&'()" isourmeasureof thecrisis-inducedchanges in firm-levelperformance(∆!"#$%#&'()" = !"#$%#&'()"*+,,H,*9*9 −!"#$%#&'()"*+,,K,L8H,*9*9). Using within-firm dif-ferencesinperformancehastheadvantageofcontrollingformanyfirmcharacteristics,suchasdifferencesinprofitabilitybeforethecrisisand(unobserved)time-sluggishfactorsthatexplaindifferencesingrowthpotentialsbetweenfirms(Kroszneretal.,2007).Furthermore,thesectorfixedeffectscontrolforthegeneralseverityofthecrisisineachsectorandtheregionfixedef-fectsabsorballregionalinfluencesthatarecommontoallfirmswithinaregion,suchasurbani-zationeconomies.Weapplyheteroscedasticity-robuststandarderrorsclusteredsimultaneouslyby sector and region, allowing for arbitrary correlation of firms within a sector or a region(Cameronetal.,2011).

4 Datasources,samplingstrategy,andvariables

4.1 Datacollection

Totestourmodels,weusedatafromtheMannheimEnterprisePanel(MannheimerUnterneh-menspanel–MUP)oftheCentreforEuropeanEconomicResearch(ZEW).TheMUPisbasedonthefirmdatapooloftheCreditreforme.V.,Germany’slargestcreditratingagency,andprovidesoneofthemostcomprehensivedatabasesofcompaniesinGermany,secondonlytotheofficialbusinessregister(whichisnotaccessibletothepublic).Attheendof2013,theMUPcontainedinformationon7.7millionfirms.ComparisonsoftheactivestockoffirmsintheMUPwiththeBusinessRegisteroftheFederalStatisticalOfficeindicatethattheMUPgivesbyandlargearep-resentativepictureof thecorporate landscape inGermany(Berschetal.,2014).The firmdataaremergedwithadditionalinformationfromtheCreditreforme.V.oncreditrating,ownershipstructureandfirm´sbankaccount.

Detaileddataonregionalindustrystructurearerequiredforouranalysisasweintendtocalcu-late indicesof regional specialization and regional variety. Sincenoofficial statisticsprovidesdetailedinformationontheindustrystructureattheregionallevel(WZ2008digitclassfourorfive),theindicatorsofregionalagglomerationeconomiesmustbecomputedbymeansofanal-ternativeapproach.Onecommonmethodproposestheappropriationofarepresentativesampleofregionallylocatedfirmsassuchdatacanbeusedtocalculatesharesofspecificsectorsandtotherebycreateadetailedandreliablerepresentationoftheregionalindustrystructure(Seditaetal.,2015).Forthispurpose,weagainmakeuseoftheMUPbecausefirmlocation,firmindustryclass,andthenumberoffull-timeemployeesarewelldocumentedinthedatasource.Thispro-videsuswiththeuniqueopportunitytogenerateindicatorsthatcapturetheeconomicstructureofregionsatanylevelofthe WZ2008industryclassification.FollowingSeditaetal.(2015),wechooseemploymentdatatoderivevariablesfortheregionalindicators.

4.2 Identificationandsamplingstrategy

Toassesstheperformanceofanestablishmentintimesofeconomicturmoilisoftenproblematicas itentails theriskofreceivingbiased indicators.Forexample, theperformanceofanestab-

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lishmentduring a crisis-ridden business yearmight be additionally influenced by the perfor-manceofthefirmtowhichtheestablishmentbelongs.Intimesoffinancialpressureorliquidityconstraints it cannotbe ruledout that firms cutback employment insomeof their establish-mentsnotduetobadperformanceintheseeconomicentitiesbutbecauseit isimposedbytheoverridingfirmstrategy.Otherpossibilitiesofexercisinginfluenceareinternalprofittransfersoreventheshutdownofcompanydivisionsasaresultoflong-termstrategicinterests.Forthisreason,weemployfirmsinsteadofestablishmentsaseconomicunitsofouranalysis.Wehavetoexclude, however, multi-regional firms from our sample as their data cannot be properly at-tachedtotherespectiveregionsandthuswouldnotallowustodrawpreciseconclusionsontheeffectsofagglomerationforces.

Withregardtotheindustrialfocus,werestrictouranalysistomanufacturingandbusinessser-viceswhileweexcluderetailandcustomer-relatedservices.Thereasonforthisisthatthelatterpredominantly follow the spatial distribution of the population and are therefore unlikely toexhibit specialization- resp. variety-driven patterns of geographical concentration thatwe in-tendtostudy.Furthermore, it isnecessary toadjust theempiricalset-up forextraordinaryef-fectsonfirmgrowth.WethereforeexcludeallfirmsthatarepartofM&Atransactionsortakeo-vers,becausetheseeventscanbesupposedtobethemainfactorofgrowthinsubsequentyears.As the internalizationofregionalexternalitiesrequiresembeddedness in thesectoral-regionalsystem,wealsoconstrainthesampletofirmsthatwereactiveonthemarketatleastfiveyearsbeforethesampleperiodstarts.Moreover,wedeleteallfirmswithlessthanfivefull-timeem-ployees in 2003, because the combinative capabilities of such enterprises are considered toosmall tomake effective use of cluster-related externalities. To remove small and young firmsfrom the sample is in linewith thewell-established empirical finding thatmature companiestend to benefit more strongly from specialized sectoral environments and the associated ag-glomerationforces(Keilbach,2000;AudretschandThurik,2000;BoschmaandFrenken,2011).Toeventuallyensureavalidcomparisonoffirmperformancesbetweeneconomicallystableandinstabletimes,wearecompelledtorestrictoursampletofirmsthatareactiveoverthespanofbothmacroeconomicperiods.

4.3 Dependentvariables

Weselecttwodependentvariablestomeasurefirmperformanceinbothmacroeconomicphasesthatwe aim to compare: sales growth and growth of full-time equivalent employment (FTE).Bothindicatorsarewinsorizedatthe1%leveltoreducetheimpactofoutliers.Salesgrowthcanberegardedasamarket-orientatedresilienceindicatorbecausebenefitsthatarisefromfinan-cialreservesandassetsofafirmaretypicallylimitedtostabilizesalesintheshort-run.Thus,thesalesgrowthratecapturesthedirectshort-termconsequencesofthe2008demandshockandisof special interest at themanagement level.Asa complementarymeasure,we followBaptistaand Swann (1998) resp. Chodorow-Reich (2014) and utilize employment rate as a proxy togaugeliquidity.Itisrelatedtothecrisisinsofaraswhilefinancialdistressdevelops,layoffsareused as quick fixes to secure liquidity (Gittell et al., 2006). By using employment growth,wehenceexamineadaptivechangesthatmanagersmayapplytoeasefinancialconstraintswiththegoalofimprovingtheoperatingandnetprofitandultimatelytheperformanceofthefirm.Since

11

downsizingthelaborpooldepletesrelationalreserveswhichmaylimitafirm’sfuturedevelop-mentpotential(Gittelletal.,2006),weassumethatlayoffsareameasureoflastresorttocopewith crisis-driven constraints.Therefore, employment growth canbe regardedas a resilienceindicatorthatinparticularattenuatesthelong-termconsequencesoftherecessionaryshockandshouldalsoconcernregionalpolicymakers.

Figure1:Densitydistributionsoffirm-levelperformanceindicators.

Figure1plotsthedensitydistributionsofthefirmperformanceindicators.5Asitcanbeseen,thepatternsforsalesgrowthandFTEgrowthconsiderablydiffer.Asforsalesgrowth,thelefttailsincreaseovertimewhiletherighttailsdecreasewhichindicatesariseintheshareofrelativelypoorlyperformingfirmsduringthecrisisyears(2009-2011).ThedistributionsofFTEgrowth,onthecontrary,aremorestable,suggestingonlyminorcrisis-inducedchanges.FTEgrowthis,especiallyinthecrisisperiod,concentratedatzero,whichimplies,ingeneral,alowvarianceinthis indicatorand that themajorityof firmsdonotalter theirworkforceduring thecrisis.AnimportantreasonforthislowfluctuationinFTEgrowthcanbefoundinthegovernmentsupportofshort-timeworkingthatenablesfirmstomaintaintheirworkforceregardlessofthedropindemandand the resulting cutbackon labor input (MöllerandOrmerod, 2017;Pudelko et al.,2018).Sinceallfirmsinoursamplecaninprinciplebenefitfromthisgovernmentintervention(EuropeanFoundationfortheImprovementofLivingandWorkingConditions,2010),wearguethat,despitetheunderestimationofthetruecrisis-effectonFTEgrowth,thephase-relatedcom-

5 AppendixA.1providessummarystatisticsforthefirmperformancemeasuresbefore(2004-2007)andduringthecrisis(2009-2011andalternatedefinitions).

12

parison of firm performances remains unbiased.6 Overall, Figure 1 reveals a wide dispersionacross firms in performance.Whilemany firms’ performances weakened during the turmoil,therewere also firms that increased theirperformance in spite of the crisis. These variationswouldallowustoconductmeaningfulempiricalanalyses.

4.4 Firm-levelvariables

AsestablishedbyKogutandZander (1992), combinative capabilities consist of three comple-mentaryelements:(a)the‘existingknowledgebase’,(b)the‘numberoflocalizedconnections’,and (c) the ‘organizing principles’ of the firm. As for this study, we measure the ‘existingknowledge base’ by means of human capital while the ‘organizing principles’ are modeledthrough the sizeof the firm.Unfortunately,wehave todrop the ‘numberof localized connec-tions’becauseappropriatedataisnotavailable.Asanalternative,wearguethatfirmsizealsocaptures some aspects originating from the firm´s cooperation networks, since it can be as-sumedthatlargerfirmsmaintainahighernumberofintra-regionalco-operationsthansmallerfirms. The firm’s human capital ismeasured by relating thenumber of employeeswith an atleast tertiaryschoolqualification to theentireamountofemployees.Thenatural logarithmofthenumberoffull-timeemployeesisusedtogaugethesizeofthefirm.

The list of firm-level controls includesadummyvariable indicating if the founder isactive infirm´sstrategicmanagement,thenumberofpatentapplicationsperFTE(averageoflastthreeyears), the age cycle (age and age squared), and the legal structure (lone founder, limited,listed).7 Each of the aforementioned indicators is againmeasured by their values in the year2003,soitisensuredthattheyarepre-determinedwithrespecttothecrisis.Anotherkeyroleinexplainingvarianceinperformancecanbeassignedtothesectoralaffiliationofthefirmasfirmsinagivenindustryclasscanbesubstantiallyaffectedby,amongstotherthings,industry-specificcapitalandassetstructures,exportdependencies,andvulnerabilities.Toeffectivelycontrolforsucheffects,weincludesectorfixedeffectsatthetwo-digitleveloftheWZ2008industryclassi-fication.

4.5 Regional-levelvariables

Inthisstudy,weexaminethe interactionbetweenfirmcharacteristicsandspatialexternalitiesthat originate from regional economic specialization and from regional economic variety (seesection2.2).Bothformsofexternalitiesare,bydefinition,availabletoalllocalfirmswithinthesameindustrywhichgivesthemaregion-andsector-specificcharacter.Forthisreason,wetermtheregionalagglomerationeconomiesofinterestascluster-specific(sectorwithinregion).Tech-nically,weassigneachfirmtoitstwo-digitindustrysectoraswellastotheNUTS-3region(no-menclatureinGermany:‘Kreise’and‘KreisfreieStädte’)inwhichthefirmislocated.Thechoiceofarelativelydetailedlevelofspatialaggregationismotivatedbythestrongdistancedecaythat

6 Itisconceivablethatsomefirmsmightbeabletoreapgreaterbenefitsfromtheshort-timeworkingpro-gramthanotherfirms.Asubstantialportionoftheseheterogeneouseffects,however,iscancelledoutbysectorfixedeffects,sizeofthefirm,andlegalstructurethatareincludedascontrolsinourmodels.7 SummarystatisticsforthedependentvariablesandkeyexplanatoryvariablesaredisplayedinAppendixA.2reports,whilethecorrelationamongvariablesisreportedinAppendixA.3.

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canbeobserved inknowledgediffusion and spilloversof spatial externalities (Audretsch andKeilbach,2007;Baldwinetal.,2010).Furthermore,theapplicationofsmallerarealunitsallowsustodepictahigherdegreeofregionalvariationintheeconomicstructure.

Aswealsodecomposevariety into furthersub-groups,weexaminea totalof fourtypesofag-glomerationeconomies.InadditiontoMARexternalities,theseincludeexternalitiesarisingfromwithin-clustervariety(V),fromunrelatedwithin-clustervariety(UV),andfromrelatedwithin-cluster variety(RV), respectively.Our first focus is on specialization-drivenMARexternalitiesthatarisefromthegeographicalconcentrationoffirmsbelongingtothesameindustry.Follow-ingHenderson(2003),wearguethatthenumberoffirmswithinaclusterisasuitableproxyforknowledgetransmission.Withthis,weassumethateachlocalfirm,ratherthaneachlocalem-ployee, experiments with the choice of suppliers, input factors, costumers etc. which is whyfirms can be considered central agents in the exchange of knowledge and other relevant re-sourceswithinthecluster.Inconsequence,wemodelMARexternalitiesthroughthe‘size-effect’oftheclusterthatweinturncomputeasthenaturallogarithmofthenumberoffirmsinthere-gionalsector.

Second,wequantifyexternalitiesstemmingfromwithin-clustervariety(V)bywhichwecaptureregions-specificformsofintra-sectoraldiversity.Thismeasureisbasedonthevarietyspecifica-tionofFujitaandOgawa(1982),whichbearsresemblancewith theentropy functionsused ininformationtheory.Forourpurposeit isnecessarytocalculatethevarietyforeverytwo-digitclass(sector)ineachregion,becausefirmsareassignedtoindustriesbymeansoftwo-digitlev-els.

M5NO8P*Q*5 = ∑ S*T*U2 >%V: W

2

KXY (3)

whereS*denotestheshareoffive-digitsectorsAineachtwo-digitsectorofinterest.Notethatinourcase,thefive-digitsharesS*arethenumberofemployeesinafive-digitsectorAdividedbythesumofemployees in therespective two-digitsectorandnot in therespectiveregionas inFrenkenetal.(2007).8

Still,theaforementionedmeasurelacksdetailedinformationaboutthespecificnatureof intra-sectoraldiversity.Thisiswhywe,inathirdstep,decomposetheoverallwithin-clustervarietyintounrelated(within-cluster)variety(UV)andrelated(within-cluster)variety(RV).9Unrelatedvariety, forone thing, reflects theextent towhicha two-digitcluster isdiversifiedindifferenttypesofactivityat thesubordinate three-digit level.Relatedvariety, incomparison,displaysameasureofrelateddiversificationwithinagiventhree-digitsectorandthuscapturesaparticu-larlyhighdegreeoftechnologicalproximitywithinanalreadyinterconnectedtwo-digitsectoral 8 ComparedtoFrenkenetal.(2007),achangeinthedepthofdigit-levelisnecessarybecausewearepri-marilyinterestedincluster-specificexternalities.SinceEquation(3)measuresthewithin-clustervariety,it is identicaltoEquation(4)inFrenkenetal.(2007).Thesumofthevarietiesofthetwo-digitsectors,weightedbythetwo-digitshareswithintheregion,resultsintherelatedvarietyatregionallevel.9 Theoverallvarietyofaclusteristhesumof itsrelatedvarietyandunrelatedvariety.Themeasureofoverallvarietyshowshighcorrelationwithothervariety/diversificationindicatorsthatareoftenusedinliterature,suchas1-Hirschman-Herfindahl-Index(Z = 0.9751).

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complex(seesection2.2).Theextenttowhichthesetwoformsofvarietycontributetotheeco-nomicsuccessoffirmsisanotherfocusofourempiricalanalysis(section5.3).Bothvariablesarecomputed followingFrenkenet al. (2007), butwith a change to thedepthof useddigit-level.Henceforth,foreachtwo-digitclassintheregion,unrelatedvarietyiscomputedasthesumofthree-digitentropy,whilerelatedvarietyismeasuredasaweightedsumofentropyatthefive-digitlevel(subcategory)withintherespectivethree-digitclass(categories):

_M5NO8P*Q*5 = ∑ !Q`QU2 >%V: a

2

bcd (4)

eM5NO8P*Q*5 = ∑ !Q`QU2 fQ (5)

with:

fQ = ∑KX

bc*∈hc >%V: a

2

KX/bcd (6)

!Q = ∑ S**∈hc (7)wherejQ is the three-digitsectorwhichcontains thecorresponding five-digitsectorsA,whereV = 1,… , l.!Q denotes three-digit shares (categories) that canbe derived by summing up allfive-digitsharesS*(subcategories)ofjQ .

Inadditiontothecluster-specificagglomerationvariables,weusetheaveragefirmsizeinaclus-tertocontrolforthedegreeofcompetition:smalleraveragefirmsizeindicateshighercompeti-tivepressure(Smitetal.,2015).Furthermore,theregionfixedeffectscontrolforallfactorsthatare common for all firms in one region, such as the level of urbanization or regional policymeasures.

5 Empiricalevidenceforaregime-specificmacro-to-microtransition

5.1 Ontheinterplayofcombinativecapabilitiesandclusterexternalities

Inafirststep,weestimatemodel(1)separatelyforboththepre-crisis(2004-2007)andthecri-sisperiod(2009-2011).Table1summarizestheresultsforsalesgrowthincolumns(1)and(2)andforFTEgrowthincolumns(4)and(5),respectively.10Withregardtothepre-crisisperiodwefindthat,onaverage,firmswithhighercombinativecapabilitiesgrowfasterinsalesoncon-ditionthattheyarelocatedinclusterswithamorediversifiedstructureofeconomicactivities(column(1)).Infact,overallvariety(V)significantlyinteractswithbothfirmsizeandfirm’sex-istingknowledgebasewhichpointstowardsanexistingtransmissionchannelbetweencluster-levelexternalitiesandfirmlevelcapabilities.Thefindingisinlinewithourbasictheoreticalar-gument (see section 2.1): the broader the combinative capabilities arising from size andknowledgebase,theeasiertheinternalizationofcluster-drivenexternalitiesandthemorebene-ficialtheirrelatedeconomicimpact.Furthermore,ourestimationresultssuggestthatthetrans-missionislikelyto fail if thecombinativecapabilitiesare toosmall.As the insignificantcoeffi-

10Astheregressionmodelsrevealsimilarresultsforbothvariablestomeasurefirmperformance(albeitwithsmallereffectsizesincaseofFTEgrowth),welimitourdiscussiontothevariantusing‘salesgrowth’thatislessaffectedbygovernmentalinterventions(seesection4.3).

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cientsofthegrand-meancenteredcluster-levelvariablesimply,firm-levelcapabilitieshavetobeatasufficientminimumtoreapsignificantgrowtheffectsfromtheprevailingclusterexternali-ties.11Thisgrowth-enhancingmechanism,however, onlyapplies tovariety-basedexternalitieswhileittendstobenegative,thoughnotstatisticallysignificant,inthecaseofMARexternalities.Apossibleexplanationmightbethatintimesofstablegrowth(2004-2007)theregionalavaila-bility of specialized resources declines due to intense inter-firm competition. Assuming thatlargefirmsshowahigherdemandforproductionfactorsthansmallerfirms,atleastinabsolutenumbers,thefirst-mentionedmightexperiencethecompetition-drivenshortageofregionalre-sourcesearlier.Thesamemighthappentofirmswithabroadknowledgebasethatparticularlydependonhigh-quality,thusalreadyrelativelyscarcelocationfactors.

In a second step, we employ the phase-related within-firm differences in performance asdependentvariable(seemodel(2)insection3).Theestimatedcoefficientsincolumn(3)thusrepresentthedifferencebetweentherespectivecoefficientsofthecrisisandthepre-crisismod-el incolumn(2)and(1), respectively.Asalready indicatedbythepre-crisismodel,thephase-relatedcomparisonrevealsoppositecross-levelmechanismsforvariety-andMAR-basedexter-nalities.Asforvariety-drivenforces,thecrisisvs.pre-crisisreductioningrowthrateisgreaterforfirmswithbroadcombinativecapabilities–modeledthroughsizeandknowledgebase–thatare at the same time located indiversified clusters.This canbe explainedby a crisis-inducedinterruption of the hitherto workingmacro-to-micro transmission channels: those firms thatparticularlybenefitfromcluster-specificexternalitiesduringstabletimesarenolongerabletomakeuseof themin timesofeconomic turmoil(asdocumentedby thesmalland insignificantcross-leveleffectsincolumn(2)),whichiswhytheyexperienceamorepronouncedreductioningrowthratethanotherfirms.Apparently,thespecificbenefitsofahighoverallvariety,suchasthe facilitated exchangeof relatedknowledge, do not assist firmswithdeveloping short-termmeasurestoresponsetocrisis-drivenchallenges.

With respect toMAR externalities,we find an opposing relationship, namely, that firmswithbothbroadcombinativecapabilitiesandalocationinaspecializedclusteraremorecapableofwithstandinga shock-inducedeconomicdownturn.One explanation couldbe that these firmspossessstrongermarketpowerandbenefitfromahighernumberandmorestablemarketrela-tionswithin(andoutsideof)thecluster.Smallerfirms,ontheotherhand,mightbecutofffromsupplier-costumer-relationshipsmoreeasily(JüttnerandMaklan,2011).Also,skilledemployeesmightpreferbiggercompaniesbecausetheyareassumedtoprovidehigherjobsecurity(Win-ter-Ebmer,2007).

11 Due to grand-mean centering, coefficients of cluster-level variables report themarginal effect of thedeterminantwhenfirmsizeandknowledgebasearebothattheirsamplemean.Thepositivebutinsignifi-cantcoefficientofvarietyincolumn(1)indicatesthatgrowtheffectsstemmingfromadiversifiedclusterarenotsignificantforfirmswithaverageknowledgebaseandsize.However,growtheffectsbecomesig-nificantoncecombinativecapabilitiesareslightlyabovethesampleaverage.Detailedresultsareavailableuponrequest.

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Table1:Firmperformance,combinativecapacities,andcluster-specificexternalities

Notes: Columns (1)-(3) show estimation results for firm performance measured by sales growth and columns (4)-(6) show results for analogous regressions with FTE growth. The dependent variable in columns (1) and (4) is the firm performance in the pre-crisis period. The dependent variable in regressions (2) and (5) is firm performance during crisis years. The dependent variable in columns (3) and (6) is the difference in firm performance between crisis period and pre-crisis period. Sector and region fixed effects and additional controls are included but not reported. All metric explanatory variables are grand mean centered such that the coefficients of non-interaction terms can be interpreted as the marginal effects evaluated when all variables are at the sample mean. Heteroskedasticity-consistent standard errors clustered by region and sector are reported in parentheses. Statistical significance level: 1 % ***, 5 % **, 10 % *.

Inathirdstep,weassesswhethertheimpactthatfirm-andcluster-leveldeterminantsexhibitonchangesinfirmperformancebetweenphases,i.e.inacrisisvs.pre-crisiscomparison,is‘eco-nomicallysignificant’.Forthispurpose,wecalculatethedifferenceinpredictedcrisis(vs.pre-crisis)effectsonfirmperformance,whenfirm-and/orcluster-levelvariablesareincreasedfromthe first quartile to the thirdquartile.We conduct threedifferent scenarios: the simultaneousincrease of all firm- and cluster-level determinants (a) and the increase of (selected) factorsfromeitherthefirm-(b)orthecluster-level(c).Table2showstheresults.Inscenario(a)wherecluster-specificexternalitiesandcombinativecapabilitiesare increasedsimultaneously,theef-fectiseconomicallyrelevantforsalesgrowth:onaverage,afirmatthethirdquartileofcombi-nativecapabilitiesand located inaclusterat the thirdquartileofcluster-specificexternalitiesexperiences a 3.6 percentage points stronger decline of annual growth rate in sales betweencrisisandpre-crisisperiodsthanafirmatthefirstquartileofcombinativecapabilitiesandlocat-edinaclusteratthefirstquartileofcluster-specificexternalities.Thisisalargeeffectcomparedwithanoverallmeandeclineof6.7percentagepointsinsalesgrowthbetweenthetwoperiods.Incontrast,however,theanalogouseffectisnegligibleforFTEgrowth.Accordingly,wefindonlysmallandstatisticallyinsignificanteffectssizesrelatingtoFTEgrowthwhenincreasinganyofthefactorsofinterestfromthefirsttothethirdquartile.Again,thismightbeexplainedbythefact that theGermanlabormarkethasproventobehighlyresistanttowardstherecessionaryshockof2008/2009(seesection4.3).Overall,theresultscorroborateourpreviousfindingsac-cordingtowhich thereductioninsalesgrowthrateacross the twoperiodsishigher for firms

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

Pre-crisis Crisis Pre-crisis Crisis(2004-2007) (2009-2011) (2004-2007) (2009-2011)

Firm-level

Size 0.0180 *** 0.0018 -0.0163 *** -0.0081 *** 0.0006 0.0075 ***(0.0032) (0.0022) (0.0032) (0.0023) (0.0015) (0.0020)

Knowledge base 0.0210 *** 0.0053 -0.0157 * 0.0074 * 0.0029 -0.0044(0.0079) (0.0042) (0.0082) (0.0041) (0.0021) (0.0042)

Cluster-level

Variety (V) 0.0043 -0.0022 -0.0066 -0.0034 -0.0012 0.0021(0.0048) (0.0029) (0.0064) (0.0033) (0.0016) (0.0033)

MAR 0.0051 0.0038 -0.0013 0.0043 -0.0009 -0.0053 **(0.0046) (0.0026) (0.0061) (0.0027) (0.0016) (0.0025)

Cross-Level Moderations

Knowledge base * Variety (V) 0.0200 *** 0.0015 -0.0185 *** 0.0136 *** 0.0005 -0.0131 ***(0.0045) (0.0033) (0.0061) (0.0037) (0.0029) (0.0039)

Size * Variety (V) 0.0078 ** -0.0014 -0.0092 *** 0.0014 -0.0019 * -0.0034 **(0.0038) (0.0023) (0.0034) (0.0020) (0.0016) (0.0015)

Knowledge base * MAR -0.0052 0.0030 0.0082 * -0.0027 * 0.0019 0.0046 **(0.0037) (0.0023) (0.0049) (0.0016) (0.0016) (0.0023)

Size * MAR -0.0040 0.0016 0.0055 ** -0.0017 0.0006 0.0023 *(0.0026) (0.0014) (0.0023) (0.0013) (0.0007) (0.0012)

Observations 16,166 16,166 16,166 16,166 16,166 16,166F-test (p-value) 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 ***Adjusted R-Squared 0.0563 0.0154 0.0358 0.0273 0.0175 0.0157

Crisisvs. pre-crisis

Sales growth


FTE growth

17

thatareendowedwithbroadcombinativecapabilitieswhilstbeingnestedindiversifiedorspe-cialized clusters.This is another indication that the formerlyworking transmissionof cluster-relatedresourcestofirmsissharplyinterruptedbythecrisis.Whenlookingattheeffectsizesofspecific determinants,we find that firm-specific factors (b)aremore important for the cross-levelinterplaythancluster-specificexternalities(c).Inotherwords:Awell-workingmacro-to-micro transmission predominantly depends on a sufficient amount of firm-level capabilitieswhilstrequiringastablemacroeconomicenvironment.

Table2:Economicsizeofcrisiseffects

Notes: Prediction of crisis impact when values of determinants are increased from the first quartile to the third quartile (while holding the remaining determinants constant at the sample mean). Calculations are based on estimations reported in columns (3) and (6) of Table 1. Statistical significance level: 1 % ***, 5 % **, 10 % *.

Toensurethereliabilityofourestimationresults,weconductaseriesofrobustnesschecksthatusethedifferenceinperformancebetweencrisisandpre-crisisasdependentvariable(i.e.model(2),seeAppendixA.4).Theresultsarerobusttovariousmodelspecificationsasweemploymi-cro-levelsalesdatainsteadofemploymentdatatocalculatetheregionalvariables,measuretheexplanatory variables one year before crisis outbreak instead at the beginning of the sample,replacetheregionfixedeffectsandsectorfixedeffectsbyregion×sector(cluster)fixedeffects,and,eventually,addpatentsperemployeeasfurtherfirm-levelcontrolthatpotentiallymoder-ates the impact of cluster externalities. For the lattermodel specification,wedonot findanystatisticallysignificant interactioneffectsofpatentsperemployeewhile the coefficientsoftheotherinteractiontermsremainstable.Moreover,wecannotdetectstatisticallysignificantdevia-tionsintheimpactsoftesteddeterminantsbetweenmanufacturingfirmsandbusinessservicefirms (seeAppendixA.5). Finally,wedonot find any statistically significantnon-linearities inoursampleaswetestforpotentialnon-lineareffectsoffirm-leveldeterminantsasproposedbyKnobenetal.(2015)(seeAppendixA.6).

5.2 Addressingpotentialsampleselectionandotherbiases

Oursampleoffirmsismostlikelysufferingfromabuild-insurvivorshipbias,becausefirmsthatexperiencedthebiggestdeclineinsalesorFTEgrowthmayhaveexitedthesampleduetobank-ruptcy,merger, or liquidation. Thus,we perform aHeckman selectionmodel to control for apossiblesurvivorshipbias.Intheselectionequation,weincludeallexplanatoryvariablesfromtheoutcomeequation(whichareidenticaltotheexplanatoryvariablesinmodel(2)),aswellasacreditratingindexforeachfirm.ThecreditratingindexisdevelopedbyCreditreforme.V.to

Sales growth FTE growth

(a) Firm-level & Cluster-level -0.0364 *** 0.0008

(b) Firm-level Size & Knowledge base -0.0274 *** 0.0068 Size -0.0195 *** 0.0090 Knowledge base -0.0079 * -0.0022

(c) Cluster-level Variety (V) & MAR -0.0098 -0.0065 Variety (V) -0.0076 0.0024 MAR -0.0022 -0.0089

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evaluatethecreditworthinessoffirms,wherebyhigherindexvaluesindicatelesscreditworthi-nessandthemaximumscoreof600representsinsolvency.Incolumn(1)and(4)ofTable3,wereport the results of theHeckman selectionmodel for thedifference in firmperformancebe-tweenthecrisisandpre-crisisperiod.Theselectionequationsconfirmthatfirmswithahigherscoreinthecreditratingindexweremorelikelynotincludedinoursample.Aftercontrollingforthisselectionbiasoriginatingfromtherestrictiontofirmsthatareactiveovertheentiresampleperiod,thecoefficientsoftheoutcomeequationsareinlinewithresultsofourbaselinemodels(see Table 1). If at all, the discrepancy between estimation results for sales growth and FTEgrowthisreducedduetotheHeckmancorrectionprocedure.

Anotherbias inour samplemayarise from theunevendistributionof firms across regions.Adominance effect of denselypopulatedurban regionswith anabove-averagenumberof firmsmightpotentiallygoverntheestimationoutcome.Therefore,werunaweightedregression,us-ingthesamespecificationasinmodel(2),withtheweightsequaltotheinverseofthesquarerootof thenumberof firms foreachregion.Thisweightingschemereduces thedominanceofregionsthatpossessahugestockoffirmsintheestimationresults.Asitcanbeseenfromcol-umn(2)and(5),theoutcomesoftheweightedregressionslargelyconfirmourbaselineresults.ExceptionsaretheinteractiontermsoffirmsizeandMARexternalitieswhichenterpositive,asinourbaselineset-up,butlackstatisticalsignificanceintheweightedregressionmodels.Duetothedown-weightingofurbanregionsthatcontainmoreoften large firmsandlargeclusters inoursample,theinterdependencybetweenfirmsizeandclustersizebecomeslessdistinct.

Finally,wetestwhetherthegrowthperformanceoffirmsisadditionallyinfluencedbysystemat-icalcross-borderspilloversfromneighboringregions.Wedosotoaddressthemodifiableareaproblemthatmayarisefromtheusageofadministrativedistrictsinouranalysis(OpenshawandTaylor,1979).Ourbaselineset-upisconstructedinawaythatspilloversoriginatingfromclus-ter-specificexternalitiesarerestrictedtothehomeregion,whichmightproducebiasedresults.Therefore,we additionally include the average of cluster-specific externalities of neighboringregions in ourmodel to assess the impact of larger distance knowledge-spillovers. Followingspatialeconometrictradition,weadoptthe‘queencontiguitymatrix’asspatialweightsintheso-calledspatiallagapproach,assumingthatregionswithacommonboundaryareneighbors(An-selin1988).Theestimationresultsarereported incolumn(3)and(6).Mostof thespatial lagcoefficientsshowthesamesignastheirhomeregioncounterparts,butallarestatisticallyinsig-nificant.These findingsareconsistentwithmanyempiricalstudies thatconcludeastrongdis-tancedecayandalimitedeffectiverangeofknowledgediffusionandspilloversfromspatialex-ternalities (e.g.AudretschandKeilbach,2007;Baldwinetal.,2010).Hence,weargue that theapplicationofadministrativedistrictsisappropriateforourempiricalanalysis.

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Table3:Heckmanselectionmodel,weightedregressions,andspatiallagofclusterexternalities

Notes: Dependent variable is the difference in firm performance between crisis period and pre-crisis period. Columns (1) and (4) show estimation results of Heckman selection model, columns (2) and (5) show results of weighted regressions, columns (3) and (6) show results of model specification that includes the cluster-specific externalities of neighboring regions as additional variable (spatial lag: Queen). Sector and region fixed effects and additional controls are included but not reported. All metric explanatory variables are grand mean centered such that the coefficients of non-interaction terms can be interpreted as the marginal effects evaluated when all variables are at the sample mean. Heteroskedasticity-consistent standard errors clustered by region and sector are reported in parentheses. Statis-tical significance level: 1 % ***, 5 % **, 10 % *. 5.3 Decompositionofvariety:theimpactofrelatedvarietyandunrelatedvariety

Wenowtakeacloserlookattheroleofcluster-levelvarietybydecomposingtheoverallvariety(V)intorelatedvariety(RV)andunrelatedvariety(UV)(seesection2.2resp.4.5).Itisnotewor-thythattheoverallwithin-clustervariety(V)alreadycontainsrelatednessbetweenfirmactivi-ties,sinceallfirmsthatbelongtoaclusterarepartofthesametwo-digitindustrysector.Table4showstheestimationresults.Theyare,overall,consistentwithourbaselineresults(seeTable1).12Withrespecttotheroleofvariety,theresultssuggestthatunrelatedvarietywithinaclusteristhemaindriverofthegrowthenhancingeffectstemmingfromtheinterplayofwithin-clustervarietyandbroadcombinativecapabilitiesduringthepre-crisisperiod.Incontrast,wefindnostatisticallysignificantgrowtheffectsofrelatedvarietyatcluster-levelduringstablemacroeco-

12 Regardingthesimilar,yetlessdistinctresultsforFTEgrowth,weagainfocusonthemodelsusing‘salesgrowth’asdependentvariable.

Crisis vs. pre-crisis

(2) (3) (5) (6)

Weighted Spatial lag Weighted Spatial lag

Firm-level

Credit rating -0.0052 *** -0.0052 ***(0.0004) (0.0004)

Size -0.0385 -0.0163 *** -0.0164 *** -0.0161 *** -0.0375 0.0074 *** 0.0073 *** 0.0076 ***(0.0252) (0.0032) (0.0036) (0.0032) (0.0248) (0.0020) (0.0021) (0.0020)

Knowledge base 0.0619 -0.0155 * -0.0135 * -0.0158 * 0.0569 -0.0042 -0.0047 -0.0046(0.0524) (0.0081) (0.0082) (0.0081) (0.0516) (0.0042) (0.0047) (0.0043)

Cluster-level

Variety (V) -0.0061 -0.0066 -0.0022 -0.0057 0.0459 0.0023 0.0017 0.0027(0.0443) (0.0064) (0.0080) (0.0074) (0.0424) (0.0033) (0.0034) (0.0036)

MAR -0.0232 -0.0013 0.0024 -0.0006 0.0186 -0.0051 ** -0.0049 -0.0064 **(0.0425) (0.0061) (0.0063) (0.0068) (0.0402) (0.0024) (0.0031) (0.0026)

Spatial lag: Variety (V) -0.0045 -0.0025(0.0084) (0.0033)

Spatial lag: MAR -0.0009 0.0024(0.0053) (0.0021)


Knowledge base * Variety (V) -0.0032 -0.0185 *** -0.0199 *** -0.0117 * -0.0021 -0.0132 *** -0.0134 *** -0.0158 ***(0.0617) (0.0061) (0.0070) (0.0068) (0.0606) (0.0039) (0.0042) (0.0050)

Size * Variety (V) -0.0280 -0.0093 *** -0.0065 * -0.0094 ** 0.0024 -0.0033 ** -0.0037 ** -0.0008(0.0285) (0.0033) (0.0035) (0.0042) (0.0282) (0.0015) (0.0017) (0.0023)

Knowledge base * MAR 0.0353 0.0083 * 0.0136 ** 0.0040 0.0144 0.0047 ** 0.0053 ** 0.0074 ***(0.0352) (0.0049) (0.0054) (0.0052) (0.0335) (0.0022) (0.0026) (0.0026)

Size * MAR 0.0110 0.0056 ** 0.0035 0.0041 * 0.0288 0.0023 ** 0.0024 0.0027 *(0.0179) (0.0023) (0.0028) (0.0022) (0.0177) (0.0012) (0.0015) (0.0014)

Spatial lag: Knowledge base * Variety (V) -0.0107 0.0059(0.0075) (0.0069)

Spatial lag: Size * Variety (V) -0.0004 -0.0041(0.0035) (0.0032)

Spatial lag: Knowledge base * MAR 0.0106 0.0050(0.0080) (0.0037)

Spatial lag: Size * MAR 0.0032 0.0005(0.0037) (0.0015)



Outcome Equation

Selection Equation

Outcome Equation

(1)

Heckman Selection

(4)

Heckman SelectionSelection Equation

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nomictimes(seecolumn(1)).Itisnotable,however,thattheinteractiontermbetweenrelatedvarietyandfirm-levelknowledgehasanegativesignandthuspointsintoanoppositedirectionthan any other variety-measure. These results are consistent with the findings of Boschma(2005),Nooteboom et al. (2007),Mameli etal. (2012),andCrespoetal. (2014) that ahigheraccumulationofrelatedactivitiesinasectormightresultintoomuchcognitiveproximity,whichleadstolock-ineffects,redundantknowledgecreation,andscarcecontributiontotheenhance-mentofexistingknowledge.

Table4:Decompositionofcluster-levelvariety

Notes: Columns (1)-(3) show estimation results for firm performance measured by sales growth and columns (4)-(6) show results for analogous regressions with FTE growth as performance indicator. The dependent variable in columns (1) and (4) is the firm performance in the pre-crisis period. The dependent variable in regressions (2) and (5) is firm performance during crisis years. The dependent variable in columns (3) and (6) is the difference in firm performance between crisis period and pre-crisis period. Sector and region fixed effects and additional controls are included but not reported. All metric explanatory variables are grand mean centered such that the coefficients of non-interaction terms can be interpreted as the marginal effects evaluated when all variables are at the sample mean. Heteroskedasticity-consistent standard errors clustered by region and sector are reported in parentheses. Statistical significance level: 1 % ***, 5 % **, 10 % *.

While thegrowth-decreasinginteractionofrelatedvarietyandknowledgebaseonlybeginstotakeshapeduringthepre-crisisperiod,itdoublesintermsofeffectsizewhilstbecomingstatis-ticallysignificantintimesofeconomicturmoil.Oneexplanationmightbethatfirmswithahighamountofinternalknowledgethatareatthesametimelocatedinhighlyrelatedclusterssufferfromatightintegrationinaninterconnectednetworkofsector-specificspilloverswhichleadstoahigher exposure andvulnerability to shock transmission. In contrast, the interplayof firm’sknowledgebaseandunrelatedvarietymaintainsitspositiveimpactonfirmperformanceduringcrisisyears,albeittheeffectsizeismuchlowerthanduringpre-crisisyears.Seemingly,agreatervarietyofaccessibleresources, ifnot toorelated,promotes flexibility thathelps firmstocope

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

Pre-crisis Crisis Pre-crisis Crisis(2004-2007) (2009-2011) (2004-2007) (2009-2011)

Firm-level

Size 0.0179 *** 0.0018 -0.0161 *** -0.0081 *** 0.0006 0.0075 ***(0.0032) (0.0022) (0.0031) (0.0023) (0.0015) (0.0020)

Knowledge base 0.0222 *** 0.0061 * -0.0161 ** 0.0081 ** 0.0031 -0.0050(0.0070) (0.0035) (0.0081) (0.0033) (0.0021) (0.0036)

Cluster-level

Related Variety (RV) -0.0020 -0.0076 -0.0056 -0.0071 * -0.0029 0.0042(0.0064) (0.0051) (0.0088) (0.0041) (0.0026) (0.0045)

Unrelated Variety (UV) 0.0091 0.0019 -0.0072 0.0005 0.0000 0.0005(0.0069) (0.0036) (0.0092) (0.0039) (0.0016) (0.0042)

MAR 0.0060 0.0045 -0.0016 0.0049 * 0.0008 -0.0057 **(0.0045) (0.0027) (0.0061) (0.0029) (0.0017) (0.0026)


Knowledge base * Related Variety (RV) -0.0083 -0.0153 *** -0.0070 -0.0033 -0.0032 0.0001(0.0083) (0.0058) (0.0102) (0.0054) (0.0043) (0.0076)

Size * Related Variety (RV) 0.0037 0.0017 -0.0019 0.0009 -0.0026 -0.0035(0.0053) (0.0052) (0.0065) (0.0031) (0.0028) (0.0033)

Knowledge base * Unrelated Variety (UV) 0.0332 *** 0.0096 ** -0.0236 *** 0.0216 *** 0.0023 -0.0193 ***(0.0053) (0.0039) (0.0062) (0.0044) (0.0027) (0.0042)

Size * Unrelated Variety (UV) 0.0091 ** -0.0032 -0.0123 *** 0.0013 -0.0017 -0.0030(0.0041) (0.0025) (0.0038) (0.0026) (0.0014) (0.0022)

Knowledge base * MAR -0.0024 0.0047 ** 0.0071 -0.0009 0.0023 0.0033(0.0043) (0.0024) (0.0053) (0.0023) (0.0017) (0.0028)

Size * MAR -0.0036 0.0013 0.0049 ** -0.0017 0.0006 0.0023 *(0.0027) (0.0015) (0.0024) (0.0013) (0.0008) (0.0012)




FTE growthSales growth

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withunexpectedchallenges.Overall,ourfindingsimplythatunrelatedvariety(UV)isthepivotalcomponentofvarietyinthecontextoffirmresilienceasitlargelydeterminesthemodeofactionof overall variety(V) asdisplayed inTable1.However,we like to reiterate that in thispaperunrelatedvarietyismeasuredfromafirmperspectiveandinfactcorrespondswiththenotionofrelatedvarietyattheregionallevelasintroducedbyFrenkenetal.(2007).

5.4 Alternativecrisisdefinition

Wefurtherexaminewhether theresultsareaffectedbyalternativecrisisdefinitions.Sincees-tablishingtheendofacrisisismoredifficultthandeterminingitsstartingpoint(Claessensetal.,2001;HoltermannandHundt,2018),were-estimateourbaselinemodelinTable1withanal-ternativecrisiswindow,increasingthelengthofcrisisbyoneyear.Overall,thechangeincrisisduration does not alter our general findings (see Appendix A.7). Nevertheless, an interestinginsightof thisexercise isthe factthatdifferences ingrowtheffectsof the testeddeterminantsbetweencrisisandpre-crisisdiminishifthecrisisperiodisextended.Thismotivatesustofur-thersubdividethecrisisperiod(2009-2011)intothenationalrecession(2009)andthesubse-quentnationalrecovery(2010-2011).Then,wecalculatesalesgrowthandTFEgrowthforthenewlydefinedperiodsandemploythemasdependentvariablesinare-estimationofmodel(1)andmodel(2).TheaccordingresultsarepresentedinTable5.

Most importantly, the subdivision of the crisis into a recession (2009) anda recovery period(2010-2011)revealsthatphase-specificdifferencesinthemacro-to-microtransitionarealmostentirely driven by the actual recession in 2009. This becomes evident when comparing thecross-levelinteractionstermsofthe‘crisis’(column(2),Table1)andthe‘recession’model(col-umn(2),Table5)asthelattershowssimilarbutthenmuchmoredistinctresultstermsofeffectsizeandstatisticalsignificance,atleastinthecaseofsalesgrowth.Consequently,thecross-levelmoderationofcombinativecapabilitiesandMARexternalitiesisfosteringfirmgrowthinasta-tisticallymeaningfulwayonlyinthe‘recession’,butnotinthe(longer) ‘crisis’scenario.Hence,the presumedmarket power of larger andmore knowledge-intensive firms (see section 5.1)seemsparticularlyhelpfulduringtheimmediateeconomicdownturn.

Similarly,thepreviouslyinsignificant(andsmaller)interactiontermbetweenfirmsizeandvari-ety gains statistical significance when the ‘recession’ model is applied. As additional calcula-tions13show,thiscanbeexplainedbytherelatedcomponentofoverallvarietythat, inturn,isassumedtoamplifyshocktransmissionandthustendstoincreasetheshock-sensitivityofmoreintegratedlargeandknowledge-intensefirms(seesection5.3).

13DuetolackofspacetheresultsarenotreportedinTable5.Theyare,however,availableuponrequest.

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Table5:Firmperformance,combinativecapacity,andcluster-specificexternalities:separatingnationalrecessionandrecoveryyears

Notes: Columns (1)-(5) show estimation results for firm performance measured by sales growth and columns (6)-(10) show results for analogous regressions with sales per FTE growth as performance indicator. The dependent variable in columns (1) and (6) is the firm performance in the pre-crisis period. The dependent variable in regressions (2), (4), (7), and (9) is firm performance during the crisis for alternative definitions of crisis years: national recession period (2009) and national recovery period (2010-2011). The dependent variable in columns (3), (5), (8), and (10) is the difference in firm performance between alternative definitions of crisis period and pre-crisis period. Sector and region fixed effects and additional controls are included but not reported. All metric explanatory varia-bles are grand mean centered such that the coefficients of non-interaction terms can be interpreted as the marginal effects evaluated when all variables are at the sample mean. Heteroskedasticity-consistent standard errors clustered by region and sector are reported in parentheses. Statistical significance level: 1 % ***, 5 % **, 10 % *.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Pre-crisis Recession Recovery Pre-crisis Recession Recovery

(2004-2007) (2009) (2010-2011) (2004-2007) (2009) (2010-2011)

Firm-level

Size 0.0180 *** -0.0095 *** -0.0275 *** 0.0113 *** -0.0068 ** -0.0081 *** -0.0003 0.0078 *** -0.0003 0.0078 ***(0.0032) (0.0018) (0.0035) (0.0020) (0.0031) (0.0023) (0.0008) (0.0018) (0.0011) (0.0023)

Knowledge base 0.0210 *** 0.0040 -0.0170 ** 0.0013 -0.0196 ** 0.0074 * -0.0007 -0.0080 ** 0.0036 * -0.0037(0.0079) (0.0026) (0.0076) (0.0039) (0.0088) (0.0041) (0.0014) (0.0038) (0.0019) (0.0046)

Cluster-level

Variety (V) 0.0043 -0.0062 *** -0.0105 ** 0.0040 -0.0004 -0.0034 -0.0006 0.0028 -0.0007 0.0027(0.0048) (0.0015) (0.0053) (0.0028) (0.0061) (0.0033) (0.0007) (0.0033) (0.0014) (0.0033)

MAR 0.0051 0.0002 -0.0049 0.0036 -0.0015 0.0043 0.0005 -0.0038 -0.0014 -0.0058 **(0.0046) (0.0015) (0.0052) (0.0025) (0.0057) (0.0027) (0.0006) (0.0028) (0.0016) (0.0025)


Knowledge base * Variety (V) 0.0200 *** -0.0011 -0.0211 *** 0.0026 -0.0174 *** 0.0136 *** -0.0016 -0.0152 *** 0.0021 -0.0115 ***(0.0045) (0.0018) (0.0053) (0.0030) (0.0055) (0.0037) (0.0015) (0.0033) (0.0023) (0.0041)

Size * Variety (V) 0.0078 ** -0.0066 *** -0.0144 *** 0.0052 ** -0.0026 0.0014 -0.0002 -0.0017 -0.0017 -0.0031(0.0038) (0.0014) (0.0040) (0.0022) (0.0033) (0.0020) (0.0010) (0.0014) (0.0011) (0.0023)

Knowledge base * MAR -0.0052 0.0025 ** 0.0077 * 0.0005 0.0057 -0.0027 * 0.0022 *** 0.0049 *** -0.0003 0.0024(0.0037) (0.0013) (0.0042) (0.0022) (0.0046) (0.0016) (0.0008) (0.0016) (0.0012) (0.0022)

Size * MAR -0.0040 0.0037 *** 0.0077 *** -0.0022 0.0018 -0.0017 -0.0001 0.0016 0.0007 0.0024 *(0.0026) (0.0007) (0.0025) (0.0023) (0.0025) (0.0013) (0.0004) (0.0012) (0.0006) (0.0013)

Observations 16,166 16,166 16,166 16,166 16,166 16,166 16,166 16,166 16,166 16,166F-test (p-value) 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 ***Adjusted R-Squared 0.0563 0.0422 0.0694 0.0224 0.0252 0.0273 0.0093 0.0232 0.0113 0.01463

Recovery(2010-2011)vs. pre-crisis

Sales growth

Recession(2009)vs. pre-crisis

FTE growth

Recession(2009)vs. pre-crisis

Recovery(2010-2011)vs. pre-crisis

23

Studying the changes in firm performance in recovery vs. pre-crisis comparison,we find thatmostofthecross-levelinteractions,withtheexceptionoftheinterplaybetweenknowledgebaseandvariety,lackstatisticalsignificance(seecolumn(5)).Thisobservationsuggeststhat,fromastatisticalpointofview,thebasicmechanismsofthemacro-to-microtransitionareforthemostparts not different between pre-crisis period (2004-2007) and recovery phase (2010-2011).Clearphase-specificdifferences,however,canbedetectedwithregardtothecombinativecapa-bilitiesatfirm-levelwheretheimprintoftherecessionyearisstillvisibleintherecoveryphaseandleadstostatisticallysignificantcoefficientsinthedirectrecoveryvs.pre-crisiscomparison(alsocolumn(5)).Butevenhere, tendenciesofapre-crisisreversionare recognizableasbothcoefficientsreturntopositivesignsand–inthecaseoffirmsize–regainstatisticalsignificancewhenbeingcalculatedfortherecoveryperiod(seecolumn(4)).

We thus can conclude that, overall, the pre-crisismechanisms, after being interrupted by thenationaldownturnin2009,areatleastmoderately, ifnotlargelyrestoredoncethemacroeco-nomicpressurediminishes.Thesefindingssuggestthatthetesteddeterminantsandcross-leveltransmission channels fostering firmgrowthare to a certain extent robust against exogenousshockswhiletheobjectionthatnoneofthefactorsregainsitspre-crisisimpactlevel(compari-sonoftheeffectsizesincolumn(1)and(4))canbequalifiedbytheshortdurationoftherecov-eryperiodinourset-up.

6 Conclusion

In thispaper,wemodel themacro-to-micro transitionofexternalities to firms throughthe in-terplay between combinative capabilities and localization resp. variety-driven economies.WethenexaminetheimpactofthisinterplayonfirmperformanceinGermanyandexploretowhatextentit isaffectedbychangesinthemacroeconomicenvironment.Forthispurpose,wecom-binetwodifferentstrandsofliterature:regionalscienceliteratureonagglomerationeconomiesandtheconceptofregionaleconomicresilience.Toensureareliablerepresentationoftheen-trepreneurial landscape in Germany and the sector-specific regional environments in whichfirmsarenested,wemergemicro-levelemploymentdatafromtheMannheimerUnternehmensPanel (MUP) and data from Creditreform e.V. to compute the regional sector-structure at thefive-digitleveloftheWZ2008industryclassification.Thisuniquedatasetallowsustolinkfirmperformance to cluster-specific externalitiesand to compare the effects of cross-level interac-tions(macro-to-microtransition)betweenregimesofmacroeconomicstabilityandinstability.

Employingwithin-firmregressiontechniques,weestimatetheimpactofcross-levelinteractionsbetweenfirm-andcluster-leveldeterminantsonphase-relateddifferencesinfirmperformancebetween the pre-crisis (2004-2007) and the crisis period (2009-2011). Firm performance iscapturedbytwomeasures:salesgrowthandFTEgrowth.Ourestimatesarebasedonasampleof16,166firmsfromthemanufacturingandbusinessservicesectorthatarenestedinatotalof390regions.Overall,wefindthreeprimaryresults.First, firmsthatpossessahigherdegreeofcombinativecapabilitiesarebetterabletogaingrowth-stimulatingimpulsesfromthe(unrelat-ed) within-cluster variety of economic activities. The interactions between firm-level compe-

24

tencesandcluster-specificexternalitiesprovetobebothstatisticallysignificantandeconomical-lyrelevant.Hence,ourresultsindeedpointtowardstheexistenceofamacro-to-microtransitionthatevolvesbest,however,inastablemacroeconomicenvironment(2004-2007).Anotherfind-ingisthatthepresenceofcluster-specificexternalitiesalonedoesnotfosterfirmperformance;rather,firmsrequireaminimumamountofcombinativecapabilitiestointernalizetheprevailingexternalities.Ourresultscorroboratepreviousfindingsintheliteratureaccordingtowhichin-fluences of regional externalities affect firm performance heterogeneously as the capacity tointernalize growth enhancing stimuli from the cluster-level environment depends on firm-inherentcharacteristics(e.g.Knobenetal.,2015).Second,theformerlyworkingtransmissionofvariety-driven externalities is supposedly interrupted by the crisis as we find no significantcross-level interactions during the period of macroeconomic instability (2009-2011). Whencomparing both macroeconomic regimes, we find that the crisis vs. pre-crisis reduction ingrowthrateismorepronouncedforfirmsthatbenefitfromcluster-levelexternalitiesduringthepre-crisisperiod,becauseexternalgrowthinputsstemmingfromthecluster-levelenvironmentarenolongerusable.Third, thecrisis-induced interruptionof themacro-to-micro transition ismainlyrestrictedtothenationalrecessionin2009.Assoonasthemacroeconomicpressuredi-minishes,weobserveareversionoftheeconomiceffectsoriginatingfromtheinterplayofcom-binative capabilities and cluster externalities to the pre-crisis level. It is conceivable that thequickrestorationofthetransmissionchannelsistosomeextentinfluencedbythefastrecoveryofthenationaleconomyasawholethatinturnbenefitedfromanti-cyclicalstimulusmeasuresandthere-strengtheningof internationaldemandfor long-term investmentgoods(see, for in-stance, Pudelko et al., 2018). For this reason, future studiesmight compare firm-level resultsfromotherEuropeancountries toour findings,because thenational institutionalsettingmostlikelyplaysanimportantroleinshapingtheeconomiceffectsofthemacro-to-microtransitionindifferentmacroeconomicregimes.Likewise,asourstudyisfocusedonabankingcrisis,itwouldbefruitfultoinvestigatetheimpactofothertypesofeconomicdistresses,suchascurrencycri-sesortradeshocks.

Tosummarize,weinterpretourresultsasevidenceconsistentwiththeexistenceofamacro-to-micromechanisminoursampleofGermanfirms.Incontrasttopreviousstudiesonthistopic,thistransitionmechanismisnotmodeledastime-invariant.Instead,itiscoupledtotheprevail-ingmacroeconomicregime.Theresultsarerobusttocontrollingforseveralpotentialsamplingbiasesandemployingvariousmodelspecifications.Onceagain,however,itisworthmentioningthatoursampleisrestrictedtofirmsthatsurvivetheshock,thusallfirmsarecharacterizedbyaminimumdegreeofresilience.

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30

Appendices

Appendix A.1: Summary statistics of firm performance indicators

Appendix A.2: Summary statistics of key explanatory variables

Appendix A.3: Correlation table of key explanatory variables

Variable Period Min. 1. Qu. Median Mean 3. Qu. Max. SD

a) Sales growth 2004-2007 -0.5856 0.0000 0.0465 0.1135 0.2624 0.9338 0.2714b) Sales growth 2009-2011 -0.5223 -0.0375 0.0000 0.0458 0.1252 0.8597 0.2176c) Sales growth 2009 -0.3581 -0.0127 0.0000 -0.0121 0.0000 0.3449 0.0977d) Sales growth 2010-2011 -0.0441 0.0000 0.0000 0.0580 0.1236 0.6581 0.1778Difference in sales growth b) minus a) -1.1570 -0.2662 -0.0275 -0.0677 0.1054 1.2340 0.3418Difference in sales growth c) minus a) -1.0840 -0.2763 -0.0487 -0.1158 0.2247 0.8018 0.2916Difference in sales growth d) minus a) -1.1190 -0.2231 -0.0121 -0.0556 0.1054 1.0560 0.3079

e) FTE growth 2004-2007 -0.4055 0.0000 0.0000 0.0350 0.1133 0.5390 0.1671f) FTE growth 2009-2011 -0.3087 0.0000 0.0000 0.0273 0.0606 0.4383 0.1263g) FTE growth 2009 -0.1671 0.0000 0.0000 0.0039 0.0000 0.2036 0.0478h) FTE growth 2010-2011 -0.2231 0.0000 0.0000 0.0208 0.0000 0.3460 0.0928Difference in FTE growth f) minus e) -0.6230 -0.1178 0.0000 -0.0077 0.1002 0.5875 0.1990Difference in FTE growth g) minus e) -0.5777 -0.1054 0.0000 -0.0261 0.0408 0.4603 0.1724Difference in FTE growth h) minus e) -0.5960 -0.1054 0.0000 -0.0151 0.0816 0.5270 0.1839

Variable Min. 1. Qu. Median Mean 3. Qu. Max. SD

Firm levelEntrepreneur 0.00 1.00 1.00 0.89 1.00 1.00 0.32Patent applications per FTE 0.00 0.00 0.00 0.00 0.00 0.29 0.01Age 5.00 11.00 18.00 26.91 32.00 191.00 25.42Number of FTE 4.50 8.00 13.50 24.08 27.00 248.00 28.99Share of tertiary workforce (%) 0.00 0.00 0.00 0.30 0.67 1.00 0.42

Cluster levelVariety (V) 0.00 1.36 1.91 1.95 2.52 3.88 0.84Related variety (RV) 0.00 0.38 0.78 0.81 1.16 2.83 0.54Unrelated variety (UV) 0.00 0.50 1.04 1.12 1.76 2.60 0.72Number of firms 1.00 37.00 84.00 242.50 202.00 7247.00 582.47Average firm size 1.22 6.23 11.86 17.48 21.37 1826.00 24.32

Related UnrelatedVariety Variety

Firm levelEntrepreneur 1Patents per FTE -0.0057 1Age -0.0014 -0.0206 1Number of FTE -0.1930 0.0112 0.0796 1Share of tertiary workforce -0.1272 0.0277 -0.1005 0.1192 1

Cluster levelVariety (V) 0.0033 0.0132 -0.0348 -0.0064 0.0616 1Related variety (RV) -0.0196 0.0101 -0.0733 -0.0236 0.1959 0.5252 1Unrelated variety (UV) 0.0187 0.0077 0.0154 0.0106 -0.0775 0.7619 -0.1510 1Number of firms -0.0343 0.0155 -0.0939 -0.0207 0.1191 0.0809 0.1643 -0.0311 1Average firm size -0.0207 0.0063 0.0363 0.1307 0.0019 -0.0521 -0.1063 0.0203 -0.0902 1

Avg. firmsize

Pat./FTE Age No. FTEEntr. Sh. of tert. workforce

Variety No. Of firms

31

Appendix A.4: Robustness checks of baseline results

Notes: (1): baseline results of differences in sales growth between both macroeconomic phases: specification (3) in Table 1. (2): as in column 1 but using the log of total number of employees instead of firms in the cluster to approximate MAR externalities. (3): as in column 1 but using micro-level sales data instead of employment data to calculate the regional externalities. (4): as in column 1 but all explanatory variables are measured one year prior to crisis outbreak instead at the beginning of sample period. (5): applying cluster-specific fixed effects instead of sector and region fixed effects. (6): additional interaction terms between agglomeration econo-mies at cluster-level and patents per employee at firm-level are included in the model. Columns (7)-(12) report estimation results for analogous models that employ FTE growth as firm performance measure. Sector and region fixed effects and additional controls are included but not reported. All metric explanatory variables are grand mean centered such that the coefficients of non-interaction terms can be interpreted as the marginal effects evaluated when all variables are at sample mean. Heteroskedasticity-consistent standard errors clustered by region and sector are reported in parentheses. Statistical significance level: 1 % ***, 5 % **, 10 % *.


(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Baseline model Baseline model

Firm level

Size -0.0163 *** -0.0165 *** -0.0029 -0.0490 *** -0.0170 *** -0.0163 *** 0.0075 *** 0.0077 *** -0.0032 ** -0.0351 *** 0.0080 *** 0.0076 ***(0.0032) (0.0035) (0.0029) (0.0033) (0.0036) (0.0032) (0.0020) (0.0021) (0.0013) (0.0021) (0.0024) (0.0020)

Knowledge base -0.0157 * -0.0168 ** -0.0194 ** -0.0111 -0.0131 -0.0156 * -0.0044 -0.0049 -0.0020 0.0031 0.0017 -0.0045(0.0082) (0.0082) (0.0080) (0.0074) (0.0102) (0.0082) (0.0042) (0.0042) (0.0040) (0.0040) (0.0065) (0.0041)

Patents per FTE 0.1274 *** 0.1124(0.3471) (0.2204)

Cluster level

Variety (V) -0.0066 -0.0065 -0.0080 -0.0065 -0.0066 0.0021 0.0021 0.0016 0.0020 0.0021(0.0064) (0.0063) (0.0073) (0.0073) (0.0064) (0.0033) (0.0033) (0.0029) (0.0033) (0.0033)

MAR -0.0013 -0.0012 -0.0017 -0.0009 -0.0013 -0.0053 ** -0.0046 ** -0.0051 ** -0.0037 ** -0.0053 **(0.0061) (0.0046) (0.0063) (0.0062) (0.0061) (0.0025) (0.0022) (0.0023) (0.0027) (0.0025)


Knowledge base * Variety (V) -0.0185 *** -0.0165 ** -0.0208 *** -0.0129 ** -0.0148 * -0.0186 *** -0.0131 *** -0.0101 ** -0.0120 *** -0.0025 -0.0195 *** -0.0130 ***(0.0061) (0.0066) (0.0063) (0.0063) (0.0084) (0.0060) (0.0039) (0.0047) (0.0035) (0.0043) (0.0043) (0.0039)

Size * Variety (V) -0.0092 *** -0.0065 * -0.0013 -0.0113 *** -0.0084 ** -0.0094 *** -0.0034 ** -0.0018 -0.0021 ** -0.0045 *** -0.0008 -0.0031 **(0.0034) (0.0036) (0.0039) (0.0036) (0.0037) (0.0033) (0.0015) (0.0015) (0.0010) (0.0017) (0.0015) (0.0015)

Knowledge base * MAR 0.0082 * 0.0054 0.0087 * 0.0014 0.0041 0.0081 * 0.0046 ** 0.0001 0.0040 * 0.0007 0.0016 0.0047 **(0.0049) (0.0042) (0.0048) (0.0047) (0.0068) (0.0049) (0.0023) (0.0028) (0.0021) (0.0021) (0.0039) (0.0023)

Size * MAR 0.0055 ** 0.0013 0.0038 ** 0.0069 *** 0.0071 *** 0.0054 ** 0.0023 * 0.0001 0.0019 *** 0.0020 0.0028 * 0.0023 *(0.0023) (0.0027) (0.0018) (0.0024) (0.0026) (0.0024) (0.0012) (0.0014) (0.0007) (0.0013) (0.0017) (0.0012)

Patents per FTE * Variety 0.0083 -0.0166(0.0279) (0.0140)

Patents per FTE * MAR 0.0129 -0.0025(0.0152) (0.0057)

Observations 16,166 16,166 16,166 16,166 16,166 16,166 16,166 16,166 16,166 16,166 16,166 16,166F-test (p-value) 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 ***Adjusted R-Squared 0.0358 0.0353 0.0341 0.0490 0.0471 0.0357 0.0157 0.0150 0.0149 0.0358 0.0222 0.0158

Cluster FE Patents appl.as moderator


Cluster FE Patents appl.as moderator

MAR:empl.-based

Externalities:sales-based

Expl. Var.: 2007

Expl. Var.: 2007

MAR:empl.-based

Externalities:sales-based

32

AppendixA.5: Heterogeneityacrosssubsamplesoffirms

Notes: Table shows differences in coefficients between subsample of firms and remaining firms: (1) and (3) business service firms vs. manufacturing firms, (2) and (4) firms with less than 50 employees vs. larger firms. Differences between subgroups are estimated via dummy-interaction (dummy varia-ble equal to one for firms that belong to subgroup and zero otherwise). Dependent variable is the difference in firm performance between crisis period and pre-crisis period. Sector and region fixed effects and additional controls are included but not reported. All metric explanatory variables are grand mean centered such that the coefficients of non-interaction terms can be interpreted as the marginal effects evaluated when all variables are at sample mean. Heteroskedasticity-consistent standard errors clustered by region and sector are reported in parentheses. Statistical significance level: 1 % ***, 5 % **, 10 % *.


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

Firm-level

Size 0.0213 *** -0.0265 0.0057 -0.0084

(0.0070) (0.0238) (0.0123) (0.0100)

Knowledge base 0.0115 0.0026 0.0141 0.0156

(0.0274) (0.0204) (0.0253) (0.0119)

Cluster-level

Variety (V) -0.0071 -0.0216 -0.0114 0.0075

(0.0136) (0.0562) (0.0190) (0.0177)

MAR 0.0024 0.0066 0.0039 -0.0170

(0.0059) (0.0240) (0.0051) (0.0140)


Knowledge base * Variety (V) -0.0192 -0.0276 -0.0182 -0.0081

(0.0300) (0.0201) (0.0131) (0.0076)

Size * Variety (V) 0.0090 0.0150 0.0087 -0.0038

(0.0067) (0.0365) (0.0061) (0.0088)

Knowledge base * MAR -0.0072 0.0014 0.0074 -0.0039

(0.0105) (0.0159) (0.0057) (0.0080)

Size * MAR -0.0012 0.0041 -0.0035 0.0094

(0.0041) (0.0142) (0.0026) (0.0079)

Observations 16,166 16,166 16,166 16,166

F-test (p-value) 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 ***

Adjusted R-Squared 0.0361 0.0358 0.0159 0.0149

FTE growth

Small firms(< 50 FTE)

Sales growth

Businessservices

Businessservices

Small firms(< 50 FTE)

33

Appendix A.6: Testing for potential non-linearities

Notes: Sector and region fixed effects and additional controls are included but not reported. All metric explanatory variables are grand mean centered such that the coefficients of non-interaction terms can be interpreted as the marginal effects evaluated when all variables are at sample mean. Heteroskedasticity-consistent standard errors clus-tered by region and sector are reported in parentheses. Statistical significance level: 1 % ***, 5 % **, 10 % *.

Crisis vs. pre-crisis Sales growth FTE growth(1) (2)Non-linear Non-linear

Firm level

Size -1.7070 *** 0.8419 ***(0.3550) (0.2124)

Size sq. 0.1534 -0.3648(0.3682) (0.2319)

Knowledge base -0.8826 ** -0.2586(0.4365) (0.2132)

Knowledge base sq. 0.4275 0.2933(0.3726) (0.2319)

Cluster level

Variety (V) -0.0064 0.0021(0.0063) (0.0034)

MAR -0.0014 -0.0054 **(0.0061) (0.0025)


Knowledge base * Variety (V) -0.9928 *** -0.6822 ***(0.3238) (0.2145)

Knowledge base sq. * Variety (V) 0.1879 -0.2986(0.3153) (0.2399)

Size * Variety (V) -0.9382 ** -0.3905 **(0.3757) (0.1565)

Size sq. * Variety (V) -0.0198 0.0416(0.4816) (0.2490)

Knowledge base * MAR 0.4338 0.2332 *(0.2700) (0.1333)

Knowledge base sq. * MAR -0.0435 0.0081(0.2138) (0.1397)

Size * MAR 0.5947 ** 0.2213(0.2574) (0.1425)

Size sq. * MAR -0.1206 0.0171(0.2344) (0.2019)

Observations 16,166 16,166F-test (p-value) 0.0000 *** 0.0000 ***Adjusted R-Squared 0.0355 0.0158

34

Appendix A.7: Variation of crisis duration

Notes: Sector and region fixed effects and additional controls are included but not reported. All metric explanatory variables are grand mean centered such that the coefficients of non-interaction terms can be interpreted as the marginal effects evaluated when all variables are at sample mean. Heteroskedasticity-consistent standard errors clustered by region and sector are reported in paren-theses. Statistical significance level: 1 % ***, 5 % **, 10 % *.

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

Firm-level

Size -0.0163 *** 0.0006 0.0075 *** 0.0006

(0.0032) (0.0033) (0.0020) (0.0023)

Knowledge base -0.0157 * 0.0029 -0.0044 0.0076

(0.0082) (0.0052) (0.0042) (0.0037)

Cluster-level

Variety (V) -0.0066 -0.0027 0.0021 -0.0023

(0.0064) (0.0041) (0.0033) (0.0019)

MAR -0.0013 0.0010 -0.0053 ** -0.0028

(0.0061) (0.0044) (0.0025) (0.0027)


Knowledge base * Variety (V) -0.0185 *** 0.0059 -0.0131 *** -0.0027

(0.0061) (0.0060) (0.0039) (0.0047)

Size * Variety (V) -0.0092 *** 0.0010 -0.0034 ** -0.0019

(0.0034) (0.0034) (0.0015) (0.0020)

Knowledge base * MAR 0.0082 * 0.0005 0.0046 ** 0.0023

(0.0049) (0.0024) (0.0023) (0.0023)

Size * MAR 0.0055 ** -0.0009 0.0023 * 0.0019

(0.0023) (0.0024) (0.0012) (0.0011)

Observations 16,166 16,166 16,166 16,166

F-test (p-value) 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 ***

Adjusted R-Squared 0.0358 0.0263 0.0157 0.0143

Crisis

(2009-2011)

vs. pre-crisis

Sales growth

Crisis

(2009-2012)

vs. pre-crisis

FTE growth

Crisis

(2009-2012)

vs. pre-crisis

Crisis

(2009-2011)

vs. pre-crisis

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