【欧洲央行】FDI、引力和聚集：用部门级FDI数据重新审视距离弹性-2026

EUROPEANCENTRALBANK

EUROSYSTEM

NicolaRebmann,KonstantinM.Wacker

WorkingPaperSeries

FDI,gravity,andaggregation:

revisitingthedistanceelasticitywithsector-levelFDIdata

No3245

Disclaimer:ThispapershouldnotbereportedasrepresentingtheviewsoftheEuropeanCentralBank(ECB).TheviewsexpressedarethoseoftheauthorsanddonotnecessarilyreflectthoseoftheECB.

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ABSTRACT

Thispaperre-examinesforeigndirectinvestmentmotivesinthe‘FDIgravity’model(KleinertandToubal,2010),focusingontheroleofdistance.Moreprecisely,weinvestigatewhether

aggregateandpooledgravitymodelsforFDIobscurerelevantheterogeneitiesacrosssectors.ThisispossiblethroughthenovelMREIDdataset,whichprovidesuswithFDIdataatthe2-

digitNAICSlevelfor184countriesovertheperiod2010to2020.Ourresultsrevealthat

aggregateandpooledmodelsmasksignificantsectorheterogeneitiesintwoaspects:(i)intheimportanceofhorizontalversusverticalFDImotives,and(ii)inthedistanceelasticity.

DistanceisnegativelycorrelatedwithFDIonanaggregatelevel,whichisrobusttomultiple

econometricspecifications,butexhibitssignificantsectorheterogeneity.Ouranalysissuggeststhepresenceofcomplexsector-specificcomponentsthatcannoteasilybeexplainedwith

standardeconomicrationales.

JELclassification:F21,F23,C33

Keywords:Gravitymodel,foreigndirectinvestment,sectorheterogeneity

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Non-technicalsummary

Foreigndirectinvestment(FDI)isarguablythemostimportanttypeofcross-countrycapitalflowsandmostlyreflectsinvestmentofmultinationalfirms.Itiswidely

perceivedtopromoteeconomicgrowth,employmentandproductivityinhost

countries,whichiswhymanycountriesactivelyaimtoattractFDI.Understanding

whatdrivesFDIishencecriticaltofacilitatesuchpolicyefforts.Furthermore,for

centralbanks,understandingtheinvestmentmotivesofmultinationalfirmsiscrucialforassessinginternationalfinanciallinkages,particularlybecauseFDIcanreflect

externaldependencies,suchasstrategicacquisitionsinsecurity-relevantsectorsorconcentratedforeignownership.

PreviousempiricalstudiesofFDImotiveswerelimitedtoaggregatecross-countryanalysisortofirm-levelanalysisforonecountry.ThiscanmaskdifferencesinFDImotivesacrosssectors:whatdrivesFDIintextilemanufacturingneednotmattertothesameextentforFDIinagricultureorthefinancialindustry.Analysesusing

aggregateddatathusriskmaskingopposingforcesacrosssectorsandmayleadtoincompleteorevenmisleadinginterpretationsofglobalinvestmentpatterns.AnoveldatasetonFDIacross25sectorsin184countriesovertheperiod2010to2020

allowsustouncoversector-specificFDImotives.Werelyonabilateralgravity

equation,whichrelatesbilateralFDIstockstotheeconomicsizesofcountries,thedistancebetweenthem,andotherfactorsinfluencingcross-borderinvestment

decisions.

WepayparticularattentiontotheroleofgeographicdistanceinexplainingFDI,becauseitcanrevealwhatdrivesfirmstobecomemultinational:

•IfFDIofmultinationalfirmsisdrivenbymarket-seekingobjectives,distanceshouldbepositivelycorrelatedwithFDI:thefurtherawaycustomersare,themoredifficultitbecomestoservethemthroughexportsandestablishingalocalsubsidiarythroughFDIbecomesaviablealternative.

•Conversely,ifFDIofmultinationalfirmsisdrivenbyefficiency-seeking

objectives,distanceshouldbenegativelycorrelatedwithFDI:firmswillaimtooffshoreproductiontolow-wagecountriesbutthefurtherawaytheyare,themoreexpensiveitbecomestosliceuptheirvaluechainduetointra-firmtrade

costs.

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Therelativeimportanceofthoseaspects,andwhatadditionalaspectsbeyondtradecostsphysicaldistancecaptures,willdifferacrosssectors.Forexample,thereis

morepotentialtosliceupvaluechainsinmanufacturingthaninpersonalservices.

OurresultssuggestthataggregateanalysesofFDIdatafailtoclearlyidentifymarket-seekingandefficiency-seekingFDImotivesinthedataastheymashuprelevant

differencesinFDImotivesacrosssectorsthatcangointooppositedirections.In

particular,thedistanceelasticityofFDIestimatedinanaggregategravitymodel

maskssector-specificdistanceeffects,becausetheeconomicconceptscapturedbygeographicdistance–suchastradecosts,informationfrictions,orcoordination

needs–mattertoverydifferentdegreesacrosssectors.Wealsoshowthatnosimpleeconomicrationalecanexplainsector-specificdifferencesinthedistanceeffecton

FDI.

Fromanacademicperspective,thisimpliesthataggregateanalysesmay

misrepresenttheunderlyingdriversofglobalinvestmentflowsandmorerefinedstudiesofFDImotivesatthesectorlevelareessentialforacorrectinterpretation.Froma

policyperspective,thefindingshighlightthatsuccessfulstrategiestoattractFDImustbesector-specific:WhatworksforattractingFDIinacountrythatisspecializedintextileproduction,forexample,maynotworkforacountrythatisspecializedinprovidingfinancialservices.

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1.Introduction

Foreigndirectinvestment(FDI)isakeycomponentofinternationalcapitalflows.Asof2022,FDIstocksrepresented29.9%ofglobalcross-borderliabilitiesand30.5%ofcross-borderassets(LaneandMilesi-Ferretti,2018).FDIischieflycharacterisedbytheintentofaninvestor,usuallya

multinationalenterprise(MNE),toacquiresignificant,activeownershipinaforeignaffiliate

(OrganisationforEconomicCo-operationandDevelopment,2020).Uncoveringthemotivesthat

driveFDIishencerelevanttounderstandfinancialglobalisationandwhyfirmschooseFDIinsteadofotherformsofmarketinteraction,suchasexportingorarms-lengthoffshoring.Acomprehensiveempiricalliterature,towhichourpapercontributes,hasthereforeestimatedthoseFDIdeterminants(e.g.Davies,2008;BlonigenandPiger,2014;SchneiderandWacker,2022).

TheFDIliteraturetraditionallydistinguishesbetweenamarket-seeking,horizontalFDImotive

(HFDI)andanefficiency-seeking,verticalFDImotive(VFDI);seeDaviesandMarkusen(2021),YeapleandAntràs(2014),andsection2.1.HFDIinvolvesfirmsthatreplicateproductionabroadtosubstituteexportsandreducetradecosts.Incontrast,VFDIfragmentsproductioninternationally,whichinvolvestradecoststhatareweighedagainstproductioncostreductionsfromexploiting

countries’competitiveadvantages(Markusen,2013).KleinertandToubal(2010;‘KT’hereafter)haveshownthatitispossibletobuildamicro-foundedgravitymodelforbothFDImotives,wheredifferentparametersgiveevidenceofprevailingmotivesinthedata.1

DistanceplaysadecisiveroleforFDImotivesbecausetradecostsincreasewithgeographical

distance.InthecaseofVFDI,greaterdistancehenceraisesintra-firmtradecostsandmakes

productionfragmentation(“offshoring”)lessattractive,suggestinganegativedistanceelasticity.Incontrast,the‘proximity-concentrationtrade-off’(PCT;Brainard,1997)predictsapositiveeffectofdistanceonHFDI:thehigherthetransportcosts,thegreateristheincentivetoavoidthemthroughHFDI.WhileempiricalstudiessuggestthatFDIispredominantlyhorizontalinnature(see

Markusen,2013;DaviesandMarkusen,2021),gravityestimatesofFDIusuallyfindanegative

distanceeffectonFDI.ThisgivesrisetoaparadoxpointedoutbyNeary(2009):highertradecostsshouldencourageHFDIandifthebulkofFDIishorizontal,FDIshouldincreasewithdistance,notdecrease.

OneleadingexplanationforthisparadoxisthatactualFDIisdrivenbycomplexintegration

strategieswhichdonotfitneatlyintoeitherthehorizontalorverticalcategories(Neary,2009;

BadingerandEgger2010).Forexample,HFDImayrequireintermediateinputsfromthehome

country(KT,2010).Anotherkeyexplanationisthatdistanceraisesfixedsetupandinformation

costs(KT,2010),asalsopointedoutbytheinternationalfinanceliterature(e.g.Portesetal.,2001;DaudeandFratzscher,2008).Moreover,integrationcostsassociatedwithFDIincreasewith

geographicaldistanceifitiscorrelatedwithculturalandinstitutionaldistance(Aleksynskaand

1OthertheoreticalfoundationsforagravitymodelofFDIarepossible.See,forexample,HeadandRies(2008).

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Havrylchyk,2013;Beugelsdijketal.,2017;vanHoornandMaseland,2016).Thishighlightsthatgeographicaldistancecapturesseveralaspectsbeyondtradecosts.

Therelativeimportanceofthefactorscapturedbydistanceislikelytodifferacrosssectors.Forexample,theimportanceofdistance-relatedsetupcostsincreasewithsectors’fixedentrycostsandintegrationcostsaremorerelevantincontract-intensivesectors(NunnandTrefler,2013;

EggerandPfaffermayr2004;OttavianoandTurrini2007).McCann(2011)pointsoutthatmarketproximityisparticularlyimportantinhigh-valueknowledge-intensiveactivitiesandsectorsthat

involvemovementofpeople.Forthesamereason,FDIinthewholesaleandretailsectorareacaseofexport-supportingFDI(Krautheim,2013).

Againstthisbackground,ourpaperinvestigatestowhatextentaggregate,asopposedtosector-

level,data2maskFDImotivesinbilateralanalysisofFDIdeterminants,withafocusonthedistanceelasticity.Inparticular,weinvestigatetowhatextentdistanceisnegativelyassociatedwithbilateralFDIpositionsandhowrobustthisresultisacrosseconometricspecificationsandsectors.Wefindaclearlynegativedistanceeffectinaggregateandpooleddataacrossvariousspecificationsbut

considerableheterogeneityacrosssectors,withsomesectorsexhibitingpositivepointestimatesforthedistanceelasticity(seeFigure1).Sinceaccountingforsectorheterogeneityappearsimportant,thisleadsustotheresearchquestionwhatplausibleeconomicfactorscanexplainthesector

heterogeneityinFDI’sdistanceelasticity?

Figure1/Sector-specificdistanceelasticities

2IntheNAICSclassification,‘sectors’refersto1-or2-digitcategories(‘industry’representsmoregranularclassifications).

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Aback-of-the-envelopeexampleillustrateswhysuchsector-specificheterogeneityinthedistanceelasticityofFDIcanbehighlyproblematic.ConsiderBangladesh’sFDIinMalaysia:basedon

aggregateempiricalresultspresentedlater,increasingthebilateraldistancebyswitchingthe

sourcecountryfromBangladeshtoIsraelwould,onaverage,reducetotalassetsinvestedin

Malaysianaffiliatesby46.28%.However,forbothBangladeshandIsrael,theirFDIinMalaysiaishighlyconcentratedinthewarehouseandstoragesector,accountingfor100%and87%oftotalassets,respectively.AgravitymodelestimatedforthisspecificsectorshowsthatincreasingthedistancefromBangladesh-MalaysiatoIsrael-Malaysiawouldactuallyincreasetotalassetsby

87.18%,onaverage.

3

Theeffectsfromtheaggregateestimateandthewarehouseandstorage-specificestimatethereforehaveoppositesignsandthelatteristwiceaslargeastheformerinabsoluteterms.Relyingonaggregategravitymodelscanthusbehighlymisleading.

Ourempiricalanalysisbuildsonthereduced-form‘gravityforFDI’modelbyKT(2010)andthenovelMultinationalRevenue,Employment,andInvestmentDatabase(MREID)byAhmadetal.

(2025).TheMREIDoffersFDIdatawithextensivegeographicalandsectoralcoverage,spanningbothdevelopinganddevelopedcountriesaswellas25sectorsatthegranular2-digitNorth

AmericanIndustryClassificationSystem(NAICS)level.ItthusprovidesauniqueopportunitytoassessthequestionofwhatmotivesdriveFDIusingsector-leveldata.

ToaddresssectorheterogeneityinFDIdeterminants,weestimatetheFDIgravitymodelat

threelevelsofaggregation.Initially,weaggregatethesectoraldatauptothecountrylevel.This

providesabenchmark,asitresembleswhattherelevantempiricalliteraturehasdone.Second,weestimateapooledsectoralmodel,wherethedataareincludedatsectorlevel,butparametersareconstrainedtohomogeneityacrosssectors.Andthird,weestimatethemodelseparatelyforeachtwo-digitsector,whichallowsregressors,parametersandfixedeffects(FEs)tovarybysector

(whichisalsothebasisforFigure1).Thisthree-tierapproachallowsustocompareaggregateandpooledestimatestoidentifypotentialaggregationbias,andtoexaminethefullsectorheterogeneityinthedistanceelasticity.

ThekeyfindingofourpaperisthataggregateorpooledgravitymodelsforFDIfailtoclearly

distinguishbetweenHFDIandVFDImotivesinthedata.Theestimatedparametersforinvestmentmotives,andparticularlythedistanceelasticity,exhibitsubstantialsectorheterogeneity.This

suggeststhatuncoveringFDImotivesrequiressector-specificgravitymodels,ratherthanaggregateorpooledmodels.TheheterogeneityinFDI’sdistanceelasticityacrosssectorsthatwedocumentinourpaperlikelystemsfromsector-specificFDImotivesandfromthefactthatgeographicaldistancecapturesavarietyofconceptsbeyondtransportcosts.Yet,wealsodocumentthatanaggregate

3ThecalculationisbasedonthedistancecoefficientsfromthepooledsectoralmodelinTable2,col.(4),andfromagravitymodelestimatedonlyforthewarehouseandstoragesectorinTableB11,col.(12)inAppendixB.

MovingfromBangladesh(distance:2,613km)toIsraelasthesourcecountry(7,774km)increasesthebilateraldistanceby197.5%.Forsuchlargechangesinx,theformula:((1+1.975)β−1)∙100followingKaplan(2023:8.1.4)andBenoit(2011)isusedtoobtaintheexactpercentagechangeintotalassetsforanincreaseindistanceby197.5%.

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negativedistanceeffectisrobusttotheinclusionofrichmultiplicativefixedeffectandtotheinclusionofotherdistancecomponents,includinggeopoliticaldistance.

AsurprisingresultofourpaperisthatnocleareconomicrationalecanexplainasignificantdegreeofvariationinFDI’sdistanceelasticityacrosssectors.ThisisalsoillustratedinFigure1,whichplotsthesector-specificdistanceelasticitiesagainstameasureforupstreamnessinglobalvaluechains

(GVCs),showingnoclearassociation(seesection5fordetails).Likewise,wefindnorelevant

correlationbetweensector-specificdistanceelasticitiesandsectorclassificationsintohigh-skillvs.

low-skill(redvs.blueinFigure1),goods-producingvs.service-producing(differentmarkersinFigure1),andsector-specifictradecosts—noneofthosevariablesexplainsmorethan3%ofthevariationinthedistanceelasticityacrosssectors.Atbest,allofthemtogetherexplain5%.TheNeary(2009)

paradoxhencedoesnotseemtovanishoncewetakeasector-levelperspective.

ThefindingsofourpapercontributetothecomprehensiveliteratureonFDIdeterminantsandlinkstoaliteratureonaggregationbiasesintrade(ImbsandMejean,2015)anditsworkhorsegravity

model(Borchertetal.,2022;Fontagnéetal.,2022;Schreiber,2022;Breinlichetal.,2024).In

particular,Breinlichetal.(2024)haveshownthatforelasticitiesthatvaryatthesectorlevelPPMLestimationusingaggregatedatawillapproximatelyrecoveraweightedaverageofsector-level

elasticitiesifthevariablesassociatedwiththiselasticitydonotvaryatthesectorlevel,asisthe

casefordistance.ThisaveragingishighlyproblematicforFDIdeterminantsbecausewemaynotbeinterestedonlyinthequantitativemagnitudeofanelasticity,asisthecasefortradecosts

(whicharealwaysnegativelyassociatedwithtrade),butalsoinitsqualitativeimplicationforwhatmotivepredominantlydrivesFDI.Incaseslikedistance,theelasticitiescanbepositiveornegative,dependingonwhetherasectorispredominantlyhorizontalorvertical.Theaboveback-of-the-

envelopeexampleofBangladesh’sFDIinMalaysiashowsthataggregationmayleadtoerroneouspredictionsofthedirectionhowFDIisassociatedtodistance.

Toadvancetowardsagenuinelysectoralgravitymodel,ourkeytakeawayistoplacemoreemphasisontheory:withthegrowingavailabilityofhigh-quality,granularandgloballycomprehensivedata,

theoreticalmodelsareneededtoaccuratelyidentifysector-specificeffectsandaccommodateFDI

motivesbeyondthehorizontal-verticaldichotomy.Thesemodelsshouldincreasinglyinclude

regressorsatsectorlevel,wheretheeffectsofinterestmanifest.Inparticular,theoreticalmodels

shouldaccountforthevariouscomponentscapturedbydistance.Whathaspreviouslybeenframedasanempiricalpuzzle–wheretheory-predictedeffectsdonotappearinthedata–shouldinsteadbereframedasatheoreticalmismatch.

Theremainderofthispaperisorganizedasfollows.Wefirstreviewtherelatedliterature,witha

focusonmotivesforHFDIvsVFDI,whytheymayvaryatthesectorlevel,andhowtheylinktothegravityequation.Section3spellsoutourempiricalgravityspecificationandhowwebringittothedata.Section4presentsourmainempiricalresultsandrobustnesschecks;section5additionallyexplorespossiblereasonsforsectorheterogeneityinthedistanceelasticity.Section6concludes.

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2.Theoreticalbackgroundandrelatedliterature

2.1HorizontalversusverticalmotivesforFDI

ThetheoreticalFDIliteraturetraditionallydistinguishestwomotivesofFDI,eachwithdistinct

predictionsonhowdistanceaffectsFDI(seeTableA1inAppendixA).Market-seeking,orHFDI,

formallyintroducedbyMarkusen(1984)andextendedwithfirmheterogeneityinHelpmanetal.

(2004),involvesfirmsthatestablishaffiliatesabroad,duplicatingactivitiesatthesameproductionstagetodirectlysupplytheforeignmarket.HFDIsubstitutesexporting,thuscircumventingtrade

costs,includingtariffs.Firmsfacea‘proximity-concentrationtrade-off’(PCT;Brainard,1997,

p.520),weighingtradecostsandplant-levelfixedcostsagainstoneanother.TheproximitymotiveprevailsandencouragesHFDIwhentradecosts,typicallyproxiedbygeographicaldistance,are

high.Exportingisfavouredwhenplant-leveleconomiesofscalearelarge,asitallowsforthe

concentrationofproductioninasinglelocation.ThePCThencesuggestsapositivedistanceeffectonHFDIthatcontrastswithatypicalgravityeffect,theempiricalevidenceforwhichisdiscussedinSection0.Thispaperseekstoreconcilethismismatchbetweentheoryandempiricalfindingsby

examiningsector-leveldata.ThemainstudiessupportingHFDIasthedominantFDImotiveincludeBrainard(1997),whowasthefirsttodemonstratethatFDIisdrivenbycountrysimilaritiesrather

thanfactorendowmentdifferences,andMarkusenandMaskus(2002),whoprovideempiricalevidenceforthehorizontal,butnotthevertical,model.

Thesecondmotiveisefficiency-seeking,orVFDI,firstformalisedbyHelpman(1984).VFDIviewstheproductionprocessasverticallyfragmentedintodistinctstageswithdifferentfactorintensities.Forcertainstages,theMNEestablishesnewproductionfacilitiesinaforeignlocationtolower

productioncostsbyexploitingcomparativeadvantagesinfactorendowmentsacrosscountries.ThestandardviewsuggeststhatMNEslocatetheirskill-intensiveheadquartersincountriesthatare

abundantinhigh-skilledlabourandestablishproductionincountriesthatareabundantinlow-

skilledlabour(FukaoandWei,2008).Consequently,VFDIincreaseswithhigh-skilledlabour

abundanceofthehomecountryrelativetothehostcountry.Thehomecountryremainstheprimarydestinationmarket,whileforeignaffiliatesproduceverticallylinkedgoodsandexportthembacktotheparentfirm.Intra-firmtrade,therefore,complementsVFDIandresultsinanunambiguously

negativeeffectofdistanceonVFDI(AlfaroandChen,2018),conditionalonendowment

differences.Thekeytrade-offinVFDIliesbetweenreducinginputcostsandincurringhighertradecosts,alongwithforgoneeconomicsofintegration.SupportforVFDIisprovidedbyCarretal.

(2001)andthemodelrefinementsofBraconieretal.(2005a;2005b)andDavies(2008),whofindstrongsupportfortheverticalcomponentintheknowledge-capitalmodel.AlfaroandCharlton

(2009)supporttheevidenceofaverticalmotiveinFDIbyaccountingforintra-industryVFDI,whereMNEsoffshoreactivitiesclosetothefinalproductionstage.Thisgranularformrequires4-digitleveldataforidentification,whileinter-industryVFDIisvisiblein2-digitleveldata,asitspansmore

distantproductionstages.

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TheoreticalmodelsoftendrawastrictdistinctionbetweenHFDIandVFDI.However,empirically,thisseparationislessclear-cut.FDIincreasinglyreflectscomplexcorporatestrategiesthat

combinemarketaccessandfactorcostmotives(Badinger&Egger,2010;Bricongneetal.,2023),orexport-platformmotives(Ekholmetal.,2007;Yeaple,2003b),whichreflectthemultilateral

natureofFDI.

2.2IdentifyingFDImotivesinthedata

Asthemotiveofinvestmentistypicallynotdisclosedbyinvestors(KoxandRojas-Romagosa,

2020),scholarshavedevelopedex-postapproachestodividedataintoHFDIandVFDI.Mostoftheseapproaches,however,requirefirm-leveldataonthedestination(origin)ofaffiliatesales

(purchases).4Giventhatsuchdataarenotavailableonaglobalscale,amoreflexibleapproachistospecifyanempiricalmodelthatdescribesthedeterminantsofbilateralFDIpositionsandallowsdiscriminationbetweendifferentmotives.

AkeyempiricalmodeltoestimatebilateraldeterminantsofFDIisthegravitymodel,whichpositsthatflowsbetweentwocountriesaredirectlyproportionaltotheireconomicsizes(the‘mass’;

typicallymeasuredbytheirGDPsandweightedbyagravitationalconstant).Frictionscountervailtheattractiveforceofthe‘mass’,withdistanceasthemostprominentproxyofsuchfrictions(Yotovetal.,2016).5Foralongtime,gravityequationsforFDIremainedadhoc(Dorakh,2020),butKT

(2010)madeaseminalcontributionbyestablishingafirsttheoreticalfoundationof‘gravityforFDI’.KT(2010)taketwoproximity-concentrationmodelsofpurelyhorizontalMNEs,aswellasafactor-proportionsmodelofpurelyverticalMNEs,andderivegravityequationsforforeignaffiliatesales(FAS)fromeachmodel.Thekeyoutcomeisthereduced-formgravitymodelpresentedineq.(1)

below,whichaccommodatesbothHFDIandVFDI,markingasignificantadvancefrompreviousFDIgravitymodels,whichwerelimitedtoHFDI.Critically,thehorizontalandverticalFDImotivesimplydifferentpredictionsforthegravityparameters,owingtothedifferenttrade-offsthey

generate.Thus,eq.(1)providestestablepredictionsforthemodelparametersthatallowfor

inferenceastowhetheranHFDIorVFDImotiveisdominantinthedata.ThesepredictionsaresummarisedinTable1.ThegravitymodelforFDIinlog-linearformcanbewrittenas:

yij,t=β1ln(GDpi,t)+β2ln(GDpj,t)+δln(Dij)+θ1SKEDiffij,t+θ2ln(GDpi,t+GDpj,t)+φt+φi(1)+φj+εij,t,

whereyij,tisFDIfromsourcecountryiinhostcountryjinyeart.Thefirstthreeregressorsformthecoreofthegravitymodel:sourceandhostcountryGDPinlogformandbilateraldistanceinlog

form,Dij.Furthermore,KT(2010)includeavariabletocaptureskillendowmentdifferences

4ForUSoutwardFDI,suchdataareusedbyAlfaroandCharlton(2009),whoidentifyVFDIbyusingtheinput-outputlinkagesbetweentheheadquarterandaffiliateindustry,orbyRamondoetal.(2011),whoidentifyVFDIbasedonintra-firmtradedata.

5Initspure,multiplicativeform,thegravitymodeliswrittenasfollows:yij=G(Yotovetal.,2016).

ij

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(SKEDiffij,t)andthesumofGDPs.Theestimatedmodelparameters,whencomparedwiththe

testablepredictionsforHFDIandVFDIsummarisedinTable1,allowfordeterminingwhichmotivepredominatesinthedata.

Table1/PredictionsforthegravityvariablesbasedonKleinertandToubal(2010)

Horizontalmodel

Verticalmodel

Sourcecountrymarketsizeβ1

1

<0

Hostcountrymarketsizeβ2

1

>0

Bilateraldistanceδ

<0?

<0

Skillendowmentdifferenceθ1

0

>0

SumofGDPsθ2

0

1

Intheverticalmodel,thesourcecountry’sGDP,whichreflectsitssupplycapacity,hasanegativeeffectonFDI(β1<0).ThiseffectoccursbecauseVFDIexploitsfactorabundancesinforeign

countriestominimiseproductioncosts.Incontrast,thehostcountry’sGDPhasapositiveeffect

(β2>0).Theverticalmotivestrengthenswithlargerskillendowmentdifferencesbetweensourceandhost(θ1>0)andishinderedbydistance,whichimpedesintra-firmtrade(δ<0).ThesumofGDPsservestopindownmarketdemandandisexpectedtohaveaunitaryelasticity(θ2=1)(KT,2010;Venables,1999).

Inthehorizontalmodel,hostcountr