Journal of Economic Behavior and Organization ARTICLE IN PRESS

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JID: JEBO [m3Gsc; December 21, 2018;18:30 ]

Journal of Economic Behavior and Organization xxx (xxxx) xxx

ContentslistsavailableatScienceDirect

Journal

of

Economic

Behavior

and

Organization

journalhomepage:www.elsevier.com/locate/jebo

Financial

dependence

and

growth:

The

role

of

input-Output

linkages

R

Alessia

Lo

Turco

a,∗

_,

_Daniela

_Maggioni

b

_,

_Alberto

_Zazzaro

c

a Department of Economics and Social Sciences, Università Politecnica delle Marche, Piazzale Martelli 8, Ancona 60121, Italy b Department of Economics, Università Ca’ Foscari Venezia, Cannaregio 873, Fondamenta San Giobbe, Venezia 30121, Italy c Department of Economics and Statistics, CSEF and MoFiR, Università di Napoli Federico II, Via Cintia 21, Napoli 80126, Italy

a

r

t

i

c

l

e

i

n

f

o

Article history: Received 29 December 2017 Revised 17 September 2018 Accepted 27 November 2018 Available online xxx JEL classiﬁcation: O1 G1 D57 Keywords: Manufacturing growth Financial development

Upstream and downstream linkages

a

b

s

t

r

a

c

t

We widen the understanding of the finance-growth nexus by accounting for the indirect effect of financial development through input-output (IO) linkages in determining the growth of industries across countries. If financial development is expected to promote dis- proportionately more the growth of industrial sectors that are more in need of external finance, it also favours more the industries that are linked by IO relations to more financially dependent industries. We explore this new channel in a sample of countries at different development stages over the period 1995–2007. Our results highlight that financial development, besides easing the growth of industries highly dependent on external finance, also fosters the growth of industries strongly linked to highly financially dependent upstream industries. Moreover, the indirect effect - propagated through IO linkages - of finance has a higher and non-negligible role compared to the direct effect and its omission leads to a biased and underestimated perception of the role of finance for industries’ growth.

1. Introduction

Ageneralconsensus inthegrowthliterature existsonthepositiverepercussionsthata welldevelopedfinancialsector hason aggregate output (Levine,2005).1 In particular,banking industry is pivotalto channel financial resources towards moreinnovativefirmsandsectorswhichareplaguedbyasymmetric informationproblems,andwhoseinvestment expen-dituresarestronglyconstrainedbytheavailabilityofexternalfinance.Inthisview,therewouldbealinkbetweenfinancial developmentandthegrowthofanon-financial industrySgoing throughthesupportthat banksprovidedirectlytofirms inthat sectorforseizinggrowthopportunitiesandrespondingtoglobalshocks(Becketal.,2000;Fisman andLove,2007;

RajanandZingales,1998).

R We thank participants of the conference “Finance and Growth in the Aftermath of the Crisis” and seminar participants at the University of Messina, the University of Trento and the Univesrity of Florence. We are particularly grateful to Giovanni Busetta, Matteo Lanzafame, Fabio Pieri, Giorgio Ricchiuti and Chiara Tomasi for their helpful comments and suggestions. Finally, we thank Alex Borisov for providing us the COMPUSTAT data useful for the calculation of the financial dependence indicators and Luc Laeven for sharing the do file with the calculation of the financial dependence indicator for the 80s and the 90s.

∗ _{Corresponding author.}

E-mail addresses: a.loturco@univpm.it (A. Lo Turco), daniela.maggioni@unive.it (D. Maggioni), alberto.zazzaro@unina.it (A. Zazzaro).

1 As long as banks and ﬁnancial markets reach a hypertrophic size compared to the rest of the economy ( _{Arcand et al., 2015; Manganelli and Popov,} 2013; Rousseau and Watchel, 2011; Tobin, 1984 ) or the ﬁnancial sector grows at a very high pace ( Cecchetti and Kharroubi, 2015; Ductor and Grechyna, 2015 ).

https://doi.org/10.1016/j.jebo.2018.11.024

Pleasecitethisarticleas:A.LoTurco,D.MaggioniandA.Zazzaro,Financial dependenceandgrowth:Theroleof input-Outputlinkages,JournalofEconomicBehaviorandOrganization,https://doi.org/10.1016/j.jebo.2018.11.024

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2 A. Lo Turco, D. Maggioni and A. Zazzaro / Journal of Economic Behavior and Organization xxx (xxxx) xxx

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However,quantitative macroeconomicmodels andempiricalstudieshaveclearlydocumentedthatcreditsupplyshocks propagateintheeconomythroughthenetworkofIOlinkagesacrossﬁrmsandindustries(AlfaroandGarcia-Santana,2017;

BigioandLa’O,2016;Dewachteretal., 2017).Thismeansthatﬁnancialdevelopmentcanbeexpectedtofoster thegrowth

ofanon-financialsectorSalsoindirectly,throughthesupporttoinvestmentsoffinanciallydependentfirmsindownstream andupstream sectors,which buyoutputsfromandsupplyinputstosector S.Inthispaperwe analyzetherole of input-output (IO) linkagesin transmittingandamplifying the effectsof financialdevelopmenton the growthofindustrial sec-tors. Morespecifically, weexplore thisconjectureby extending thecross-countryindustry approachinitially proposed by

RajanandZingales(1998)toincludeIOlinkagesamongsectors.2

TheideathatIOlinkagesareattheheartoftheprocessofeconomicdevelopmenthasalongtraditionintheeconomic literature, dating back to Scitovsky (1954), Fleming (1955)and Hirschman (1958). In a nutshell, the development of an industry activates sizablepositive effectson ﬁrmsin other industries,whichprovide inputsto theformerone (backward linkageeffects)anduseitsoutputsasinputsintheproductionprocess(forwardlinkageeffects).

Recently, literature has refocused the attention on the importance of IO linkages for productivityimprovements and economic growth.Ciccone(2002)develops amodel ofindustrialisation inwhich,consistently withempirical regularities, increasing-returntechnologiesarehighlyintensiveintheuseofintermediateinputsandareadoptedthroughoutthechain ofintermediateinputs.Inthiscontext,theintroductionofnewindustrialtechnologiesgeneratealargeincreaseinaggregate productivityandincome,evenifthe productivityimprovementsatthe ﬁrmlevel aresmall.By thesametoken, however, minorfrictionsinIOlinkages(due,forexample,toimperfectionsinﬁnancialmarkets)canalsobeexpectedtocausegreat differences in productivityand incomelevels across countries. This view is corroborated by Acemoglu etal. (2007) and

Jones(2011).Theformershowthatgreatercontractualincompletenessleadstotheadoptionoflessadvancedtechnologies,

andthatthiseffectismorepronouncedwhenthereisstrongcomplementarityamongintermediateinputs.Jones(2011) ex-plores the role of input linkages and complementarity and shows that frictions along the production chain can sharply reduceaggregateoutput.

Ontheempirical side,BartelmeandGorodnichenko(2015) atthecountryleveldocumentthe “Hirschmanconjecture”, thatthestrengthofIOlinkagesispositivelyassociatedtooutputperworkerandtotalfactorproductivity.3 Inthesamevein,

Fadingeretal.(2016)ﬁndthatamulti-sectormodelwithIOlinkagesexplainscross-countryincomedifferencesmuchbetter

than a modelabstracting fromIO linkages.4 Inaddition, a numberofstudies have showntherole of IO linkages among industrialsectorsingeneratingaggregateﬂuctuations,ﬁndingthatthechainofinput-outputrelationscontributestospread out andamplifytheeffects ofidiosyncraticindividual orsectoralshocksover theentireeconomy(Acemogluetal.,2012;

Carvalho,2014;DiGiovannietal.,2014).Inparticular,Acemogluetal.(2016)ﬁndthatintheUnitedStatesindustries’value

added,employmentandlaborproductivityrespondmoretoindirectsupplyanddemandshocksaffectingtheIOchainthan todirectshockshittingthesameindustry.Moreover,whentheydistinguishbetweenupstreamanddownstreamIOlinkages, theyshowthatdemandshockspropagateupwardstoinput-supplyingindustries,whilesupplyshockspropagatedownwards tocustomerindustries.5

RelativelyunexploredarethefactorsthatcontributetoexplaintheaggregateimpactofIOlinkages,andinparticularthe role ofﬁnancial markets in thepropagationof shocksin thepresence ofinter-sectoral IO linkages.As partial exceptions,

Acemoglu et al. (2009) document that countriescharacterised by highcontracting costs and low ﬁnancial development

areconcentrated inindustrieswherefirmstendtobemoreverticallyintegrated, relyinglessonIOlinkages. Furthermore, withineachindustry,theyshowthatfinancial developmenthelpsfirmstocircumventcontractingcostsbyprovidingthem withfinancialresourcesnecessarytogrowinsizeandverticallyintegrateactivities. BigioandLa’O(2016)introduce finan-cialfrictionsinamulti-sectornetworkframework à laAcemogluetal.(2012).Theycalibratethetheoreticalmodeltothe IO structureof theU.S. economyduringthe 2007–2008GreatRecession,andshow that IO linkagesamplify theeffect of financial shocks on aggregate output by a factor betweentwo and six, relative to the case ofan hypotheticalindustrial structure withnointeractions across sectors. Finally,Alfaro andGarcia-Santana(2017)andDewachter etal.(2017),using very detailedfirm leveldataon SpanishandBelgianfirms respectively,findthat individual credit supplyshocksstrongly propagatetootherfirmsinthevaluechainbyaffectingtheircapitalinvestments,exportsalesandoutput.

Inthispaper, wedepart fromthisbusiness cycleperspectiveandforthe firsttime, tothe bestofour knowledge,we investigatewhetherandtowhatextent,inalongrunperspective,theimpactoffinancialdevelopmentonthegrowthrateof anindustrySisamplifiedbyIOlinkagesconnectingthatindustrytootherindustrieswhichareinneedofexternalfinance. Ourintuitionissimpleandextendsthesameargumentadvancedby RajanandZingales (1998)forfinancialdependent sectors to the whole IO chain. If financial intermediaries mitigate asymmetric information problems that hamper firms’

2 The cross-country industry approach and indicators in _{Rajan and Zingales (1998)}_{have been widely used in the ﬁnance-growth literature. Among others,} see: Cetorelli and Gambera (2001) , Beck and Levine (20 02) , Fisman and Love (20 03, 20 07) , Kroszner et al. (20 07) , Pagano and Pica (2012) .

3 “If we had homogeneous input-output statistics for all countries, it would certainly be instructive to rank countries according to the proportion of intersectoral transactions to total output; it is likely that this ranking would exhibit a close correlation with both income per capita and with the percentage of the population occupied in manufacturing.” ( Hirschman, 1958 , p.109).

4 At the ﬁrm level, the importance of IO linkages has been explored quite extensively in the literature on productivity spillovers from multinational enterprises to domestic ﬁrms ( Javorcik, 2004 ).

5 A similar pattern is documented at the ﬁrm level by _{Barrot and Sauvagnat (2016)}_{who show that sales growth and market value of US ﬁrms suffer} from idiosyncratic shocks, related to natural disasters, hitting their suppliers.

Please citethisarticleas:A.LoTurco,D.MaggioniandA.Zazzaro, Financialdependenceandgrowth:The roleof input-Outputlinkages,JournalofEconomicBehaviorandOrganization,https://doi.org/10.1016/j.jebo.2018.11.024

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A. Lo Turco, D. Maggioni and A. Zazzaro / Journal of Economic Behavior and Organization xxx (xxxx) xxx 3

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accesstocredit,sectorsbuyingfromandsellingtohighlyfinanciallydependentsectorsshouldgrowatadisproportionately fasterpaceincountrieswithawelldevelopedandfunctioningfinancialsector.Toillustratethispoint,consideranumberof sectorsconnectedbyIOlinkages.Eachsectorproducesanoutputwhichisusedindownstreamindustriesasaninput,and buyinputsfromupstreamsectors. Iffinancialdevelopmentallowsfirmsina sectorS₁along thisIOchaintohavealarger accesstocredit,capitalaccumulationinthissectorincreases,aswellasproductivity.Thispossiblycausesanincreaseinthe demandofinputsbyS₁producedbyfirmsinanupstreamsectorS₂,andadecreaseinthepriceofoutputinS₁usedasan inputbyfirmsinadownstreamsectorS3.Asaresult,theoutputofS2 andS3 increase,aswell astheirinvestment oppor-tunities.Iffirmsinthesesectorsarefinanciallydependent,thegrowthopportunitiesproducedbythehigherinvestmentsin sectorS₁canbe betterexploitedwheretheycanrely onawell developedfinancialsector. Beyondthesefirstorder inter-connections,theincreaseininvestmentsandproductivityinsectorsS₂andS₃maycreate,inturn,opportunitiesofgrowth intheirupstreamanddownstreamsectorsthatadevelopedfinancialsectorallowstoactuallytake,andsoon.6 Inthisway, theimpactoffinancialdevelopmentonthegrowthofan industrySreflectsthefinancialsupportthatbanksprovidealong thewhole IOchainlinking thisindustry toother industries,andit isthegreater themorefinancial dependent arethese industries.

Ourempiricalanalysisconsidersasampleofcountriesatdifferentdevelopmentstagesovertheperiod1995–2007. Fol-lowingRajanandZingales (1998)andKroszner etal.(2007),themeasure offinancialdependencevariesby sectorandis calculatedonthebasisofU.S.firms’cumulatedcapitalexpendituresandcashflowsovertheperiod1990–2007.IOlinkages, instead,refertothefirstyear-1995-ofoursampleandareretrievedfromtheOECDIOdatabase. Foreveryindustry,we distinguishbetweenupstream linkages withindustriessupplying intermediate goods anddownstream linkageswith cus-tomerindustries.Then,wecomputetwoindicatorsreflectingtheoverallfinancialdependenceofupstreamanddownstream industrieswherethefinancialdependenceofeach upstream/downstreamsectorisweightedbyits shareintheindustry’s totalpurchases/sales.Inthebaselinemodel,financialdevelopmentisproxiedbythestandardratioofdomesticcreditover GDP(WDI,2015).

Wemodelthegrowthofrealvalue addedofan industry asdependent ontheinteractions betweenacountry’s finan-cialdevelopmentandthefinancial dependenceofthesameindustry andofits upstreamanddownstreamindustries.Our resultsconfirm thefindingbyRajanandZingales(1998)ofapositivecontributionoffinancial developmenttothegrowth offinanciallydependentsectors.However,wealsofindthat,quantitatively,thedirecteffectoffinancialdevelopmentonthe industry’sgrowthrateissmallerthantheindirecteffectthatfinancialdevelopmentexertsbyrelaxingfinancialconstraints ofthesectorslinked totheindustry by IOrelations. Morespecifically,we findthat thelattereffectsoperatethrough the financialsupportthatadevelopedfinancialsector warrantstoupstreamindustriessellingintermediateinputs.Thesustain ofwelldevelopedfinancialintermediariestodownstreamsectors,instead,isnotsignificantlyassociatedwiththegrowthof supplyingsectors.Thisisconsistent withthehypothesisthat,ifwe focustheanalysisonintermediategoods,thepositive role offinancial developmentfor theinvestments of financiallydependent firms inupstream sectors fostersproductivity improvements(Becketal.,2000),thusreducing thepricesoftheirgoodsandopeningnewopportunitiesofinvestmentfor downstreamcustomer sectors(Acemogluetal., 2016;Hirschman, 1958).The search forthe channelsbehindthebaseline evidencecorroboratesthisinterpretation.Bycontrast,thebenefitsoffinancialdevelopmentarenotsignificantlytransmitted acrosssectorsbyitspotentialeffectsonthedemandforintermediateinputs.

Ourresults are robust to several checks.In order to account for the potential endogeneity of financial development, weimplementaninstrumental variable(IV)approachbasedonthecloserelationbetweenthequality ofa country’slegal systemanditsfinancialdevelopment(Becketal.,2003;LaPortaetal.,1998).Furthermore,weusealternativemeasuresof financialdevelopment,industry’sgrowth,financialdependenceandIOlinkages.Also,weexplorecompetingandpotentially confoundingfactorswhich couldaffectthegrowthofindustrieswithin acountry.Moreprecisely, weconsiderthe impact ofcountries’ developmentstage,asaproxyoftheextentofmaturityofacountry’sindustries,humancapitalendowment, asa further alternative growth determinantof more skillintensive industries, andforeign direct investment (FDI) asan additionalsourceoffinanceinrecipienteconomies,especiallyforcapitalintensiveindustries.Wealsoconfirmourbaseline evidenceontheoriginalsampleusedbyRajanandZingales(1998).

In our country-industry framework, we further inspect the existence ofsome potential heterogeneity in the finance-growthnexus. We, first, test whetherthe documented non-linearityof the nexus is alsovalid forfinancial development workingthroughIOlinkages(Arcandetal.,2015;CecchettiandKharroubi,2012;Demirguc-Kuntetal.,2013;Easterlyetal., 2000).Finally,weseparatelytesttheroleoffinanceinthe90s,whenarelevantnumberofbankingcrisesoccurred,andin the2000s,duringtheproductivityslowdowndecade.

Therestofthepaperisorganisedasfollows.Thenext Section presentstheempiricalmodel,whileSection 3presents the data anddescribes the computation ofﬁnancial dependence fora sector, for its upstream and downstream sectors.

Section4showstheresultsfromtheestimationoftheempiricalmodel,allthearray ofrobustnesschecksandthetestfor

non-linearityintheﬁnance-growthnexusthroughIOlinkages.Finally,Section6concludesthework.

6 While in the empirical analysis we focus on the ﬁrst-order linkages, as a robustness we will also test the importance of higher order interconnections by means of the Leontief inverse matrix.

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2. Empiricalmodel

Ourmain testing hypothesis is that industrial sectorsthat are linked in the IO chain toindustries that are more de-pendentonexternal financegrowatrelativelyhigherratesincountrieswhosefinancialsector ismoredeveloped.Totest thishypothesisweneedamodelofindustrygrowthwheretheeffectoffinancialdevelopmentisheterogeneousacross sec-torsaccordingtothefinancialdependenceoftheirupstreamsuppliersanddownstreambuyers.Therefore,weestimatethe followingempiricalmodel:

growth ict/τ=

α

+

β

share ic τ+

γ

0 ED i× FD cτ+

γ

1 ED iDownstream× FD cτ+

γ

2 ED U pstream

i × FD cτ+

λ

i+

μ

c+

ic (1) wheregrowthict/ τ istheaverageannualgrowthofrealvalue addedofsector iincountryc recordedinthetimespan be-tween t and

τ

,wheret₌2007and

τ

₌1995.In theright handside ofthe equation, ourcoeﬃcientsof interestare

γ

₀,

γ

1 and

γ

2 whicharemeanttoidentifytheimpactoffinancialdevelopmentaccordingto sectori’s own,downstream and upstreamfinancialdependence,respectively.Tothispurpose,weinteracteachfinancialdependenceindicator-namelyEDi,

EDDownstream

i andED

U pstream

i -by thedegreeofﬁnancialdevelopmentincountryc intheinitialperiod

τ

,FDcτ.Eq.(1) in-cludesthefullsetofindustry,

λ

i,andcountry,

μ

cﬁxedeffectstocontrolforanyobservableandunobservablecharacteristic varying atthe country orindustry level,and theshare ofsector i in countryc’s totalmanufacturing value added atthe initialperiod

τ

(share_ic_τ)tocontrolfortheinitialconditionandthepresenceofaconvergenceeffect.

Therefore,ourempiricalspeciﬁcationcloselyparallelsthatofRajan andZingales(1998),themain differencebeingthe inclusionofthetwointeractiontermsEDDownstream

ic × FDcτ andED U pstream

ic × FDcτ,whichaimatcapturingtheroleofﬁnance inenhancingthegrowthofindustriesbyremovingfrictionsfrominter-sectoralIOlinkages.

Table A1 and Table A2 in Appendix A describe the sample composition by sector and country, respectively, while

Tables A3 and A4 show descriptive statistics of the variables included in our empirical model andpairwise correlations

betweenthem,respectively.Intheremainder ofthepaper,OLSwillbe ourbaselineestimator.Neverthelesswewillprove thatourresultsarerobusttotheadoptionofanIVestimator.

3. Dataandmeasurementissues

3.1. Industryleveldata

Data oncountries’ value added by industryare retrieved fromtheUNIDO Industrial StatisticsDatabase fortheperiod 1995–2007atthe2-digitleveloftheISICrevision3.7 Databyindustryhavebeenslightlyre-aggregatedinordertomatch theclassificationsystemoftheIOtablesshowninTableA1.FromtheUNIDOdatabasewecalculatediscreteannualgrowth rates of value added, deflatedby means of the consumer price index retrieved fromthe Penn World Tables 8.1. In our baselinespecification,wewinsoriseannualgrowthratesatthe1stand99thpercentilesofthedistributionandthencalculate averagegrowthratesacrossthe1996–2007periodforallthecountry-sectorpairsinoursample.8

3.2. Externalﬁnancialdependence

3.2.1. Measuringindustryexternaldependence

Following RajanandZingales (1998),we measureindustry financial dependenceasthe medianamountofexternal fi-nance used by the U.S. companies in each industry.The underlying hypothesis is that, asthe U.S. financial markets are almostperfectandfrictionless,firmsintheU.S.industriesdonotsufferfromfinancialconstraints.Asaresult,theamount ofexternalfundsthat largefirmsintheU.S.demandreflectsthe“technological” dependence oftheircapitalexpenditures onexternalsourcesoffinancing,due,forexample,tothescaleofinvestmentprojects,theirgestationperiodand informa-tionopaqueness.Furthermore,itisassumedthatthe“technological” needsofexternalfinancearecommonacrosscountries, suchthatthefinancialdependenceofthesectoriisthesameonethatweobserveintheUnitedStatesineverycountry.

WeusetheCOMPUSTATdatabaseanddefine firms’external dependenceascapitalexpendituresminus cashflow from operationsdividedbycapitalexpenditures.9 AsinRajanandZingales(1998),inordertosmooth temporalfluctuationsand reducetheeffectofoutliers,weaggregatethefirm’suseofexternalfinanceoverthe1990–2007timespan-whichcovers our sampleperiod - and divide it by the sumof capital expenditure over the sameperiod. Then, we take the industry median ofthese firm level aggregate ratios asour measure of sectoral financial dependence,EDi. COMPUSTATrecords a firm’sindustryinUSSICatthe4-digitwhich,atthe2-digit,hasbroaddirectcorrespondencewiththe3-digitISICrevision

7 The availability of cross-country IO tables for 1995 has driven the choice of the initial sample year. We have, therefore, neglected years before 1995 in order to limit endogeneity issues related to the use of IO linkages from a later period to explain growth of preceding years. Also, we decided to exclude year 2008 from the analysis due to the outburst of the economic downturn in that year.

8 However, it is worth noting that our results are robust to alternative cleaning procedures and persist when growth rates are not cleaned at all and/or are calculated as average annual logarithmic differences.

9 We measure capital expenditures as the corresponding COMPUSTAT variable (COMPUSTAT # 128), while we define cash flow as the sum of cash flow from operations (COMPUSTAT # 110) plus decreases in inventories, decreases in receivables, and increases in payables. For cash flow statements with format code 7, cash flow is constructed as the sum of items # 123, 125, 126, 106, 213, 217.

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Fig. 1. External dependence - 1990–2007. Comparison with ED’s measures from the literature. Notes: The y axis refers to the measures of external de- pendence for the 1980s and for 1980–1990 from Rajan and Zingales (1998) and Kroszner et al. (2007) respectively. The x axis measures our external dependence indicator concerning the 1990–2007 period and is based on own calculations on COMPUSTAT data. Each dot in the Figure corresponds to an industry according to the deﬁnition of Rajan and Zingales (1998) and Kroszner et al. (2007) .

2 classification. Thus, we first match SIC COMPUSTATdata with ISIC Rev. 3 through the official ISIC Rev. 2- ISIC Rev. 3 availablefromRAMONwebsiteandwe,then,calculatethemedianoftheratiosacrossfirmsbysector.

WhiletheoriginalmeasurebyRajanandZingales(1998)referstothe80sandisavailableforthemanufacturingsectors only, we extend it toall sectorsin the economy in orderto compute the financial dependenceof upstream and down-stream sectors and, as our empirical analysis concerns manufacturing sectors’ growth over the 1990s to the 2000s, we updatethefinancialdependencemeasureby focusingonthesameperiod.Tocheck thevalidityofourmeasure,we com-putethefinancialdependenceindicatorforthespecificmanufacturingsectorsconsideredbyRajanandZingales(1998)and

Kroszneretal.(2007),andobtainarankcorrelationindexwiththeiroriginalindicatorsequalto0.56and0.81(anda

sim-plecorrelationof0.8and0.96),respectively.Also,inFig.1wereportascatterplotcontrastingourmeasure(x-axis)against theother twomeasures fromtheliterature, whereeachdot correspondsto anindustry,clearlyconﬁrmingthatthe three measuresallbearasimilarpieceofinformationandsectorordering.10

Inorder to test our basicpremise that external dependencein the U.S.is a good proxy foran industry’s technologi-cal needforexternal finance outsidethe UnitedStates, we calculatethe weightedaveragefinancial dependenceforeach countrybymultiplying anindustry’sfinancialdependencebytheindustry’s contributiontocountries’valueaddedin1995

(RajanandZingales,1998).WethenregressdomesticcredittoGDP-ourbaselineﬁnancialdevelopmentindicator-against

thisweightedaverageﬁnancialdependenceforthe39countriesinthesampleandweﬁndastrongandpositivecorrelation betweenthetwovariablesin1995(

β

=2.27,t=3.43).Thissupportstheassumptionthattheexternalfinancedependence ofindustrialsectorsintheUnitedStatescapturestheindustry“technological” needofexternalfinanceandisagoodproxy forexternalfinancingusedbythesameindustryinothercountries.

3.2.2. Measuringexternaldependenceofdownstreambuyersandupstreamsuppliers

In orderto compute external dependence for downstream and upstream sectors withrespect to each industry i,we retrieveinput-output linkages fromOECDIO Tables. The latterare available fora large numberofcountriesfor theyear 1995and,foreachindustrywithin acountry,theyprovideinformationonitspurchasesfromandsalestoanyother sector intheeconomy.TableA6inAppendixAreportsthelistofcountriesforwhichtheIOtablesareavailableandthatweuse inourworkforthecomputationofaveragedownstreamandupstreamsectors’ﬁnancialdependence.

Moreindetail,foreachcountry intheOECDIO database,we compute theﬁnancialdependenceofindustry i’s down-streamsectorsastheweightedaverageofﬁnancialdependenceofdownstreamsectors, wheretheweightsarerepresented bydownstreamsectors’sharesintotalsectori’ssales.Namely:

E D Downstream ic = j = i∈ JE D j× salesi jc sales ic (2)

10 _{Table A5}_in_{Appendix A}_{shows the values of ﬁnancial dependence by sector according to the three measures.}

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Similarly, we compute financial dependenceof industry i’s upstream sectors asthe weightedaverage of financial de-pendenceofupstreamsectors,wheretheweightsarerepresentedbyupstreamsectors’sharesintotalsectori’spurchases. Namely: E D U pstream_ic = j = i∈ JE D j× purchasesi jc purchases ic (3) Inordertogetauniquemeasureoffinancialdependenceofupstreamanddownstreamsectorsbyindustry,inthe base-lineempiricalmodelweusetheaveragevalueofEDU pstream_ic andEDDownstream

ic acrossNcountries11 : ED U pstream_i = N c=1 ED U pstreamic N (4) ED Downstream i = N c=1 ED Downstreamic N (5)

The useoftheaveragevalue offinancial dependenceofupstream anddownstream sectorsacrosscountriesinsteadof countrylevelmeasuresrestsontwoconsiderations.First,wewanttomodeltheimpactofotherindustries’financial depen-denceinaconsistentwaywithrespectto theimpactofasector’sownfinancial dependence,asthiseasesthereadability andinterpretationofourresults,aswellastheassessmentofthemagnitudeofthefinanceeffectthatfollowsthestrategy adoptedinRajanandZingales(1998).Second,asfinancialdependencerecordedineachcountrymaysufferfromfinancial constraints,inasimilarway,thecountrylevelIOsharesareexpectedtobeaffectedbycountryspecific-technological, in-stitutional,etc.-constraints.Ideally,wewouldliketoexploitupstreamanddownstreamfinancialdependencebuiltonthe basisofIOsharesthatreflecttheoptimaluseofeachinputintheoutputproductionintheabsenceofanyconstraint.Due tothealmostperfectandfrictionlessU.S.financialmarkets,weexpectthatthefinancialdependencerecordedforindustries intheU.Sreflectstheoptimaluseoffinancebythesameindustriesinothercountries.Nonetheless,itisnotstraightforward toidentifyasinglecountrywhereIOsharesarenotaffectedbyanyexistingconstraintandthat,then,couldbeconsidered asbenchmark.We,thus,optedfortheuseoftheaveragevalueofupstreamanddownstreamfinancialdependenceacross countriesasourbenchmark.Intherobustnesschecks,however,wealsousethecountry-varyingEDU pstream_ic andEDDownstream

ic indicators,wecalculateIOsharesfroman aggregateworldIOtableand, ﬁnally,we usethemeasuresbasedontheU.S.IO Tablesasanexternalbenchmarkforallcountries.

Inordertoavoidredundancyinthemeasurementofupstreamanddownstreamindustries’financialdependence,inthe baseline model we considerthe IO linkagesof amanufacturing sector withthe whole setof industriesbutthe financial sector. Inthe robustness checkswe willshow that our insights are not affected by the inclusion of the financial sector amongtheIOlinkagesinindicators(2)and(3).

3.3. Financialdevelopment

We measure financial development by the ratio of domestic private credit over GDP (WDI, 2015), the most widely adoptedindicatorintheempiricalliteratureonfinanceandgrowth(Levine,2005).Intherobustnesschecks,wewill docu-mentthatourbaselineresultsarerobusttotheadoptionofseveralotherproxiesoffinancialdepthgatheredfromtheWorld Bank Global FinancialIndicators Databaseorto alternativewaysofmeasuring countries’ financialdevelopmentrelatedto capitalisationandaccountingstandardsavailableintheRajanandZingales’sdatabase.

4. Finance,IOlinkagesandgrowth

4.1. Baselineresults

Table1showsbaselineestimationresultsfrommodel(1)onthe1996–2007cross-sectionofcountry-sectorgrowthrates.

Financialdevelopmentnotonlyfavoursdirectlythegrowthofafinanciallydependentsectori,butitalsomattersindirectly throughthesupporttofinanciallydependentupstreamindustriessupplyinginputstosectori.Morespecifically,incolumns [2]-[4]financialdevelopmentseemstopromoteeconomicgrowthofsectorsthroughbothoutputandinputlinkages. How-ever,whenindustryandcountrydummiesareincludedinthemodel(columns[5]-[8])financedoesnotplayanysignificant roleonthevalueaddedgrowththroughoutputlinkageswithfinancialdependentdownstreamsectors,whiletheimportance ofinputlinkagesinpropagatingthebeneficialeffectsoffinancialdevelopmentpersists.12

11 It is worth mentioning that from the initial list of 61 economies we exclude two oil countries - Brunei and Saudi Arabia. We further exclude Luxem- bourg as its IO shares were rather different from the rest of the other economies, and Japan which presents outlier observations for the ratio of domestic credit over GDP. Nevertheless, results are robust to the inclusion of these countries both in the calculation of the upstream and downstream ﬁnancial dependence measures and in the estimation sample.

12 In _{Table A9}_in_{Appendix A}_{we show that our results are broadly confirmed when we aggregate U.S. firm financial dependence by industry, rather than} taking the industry median across firms, and use this aggregate industry level indicator to compute financial dependence of upstream and downstream Please citethisarticleas:A.LoTurco,D.MaggioniandA.Zazzaro, Financialdependenceandgrowth:The roleof input-Outputlinkages,JournalofEconomicBehaviorandOrganization,https://doi.org/10.1016/j.jebo.2018.11.024

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Table 1 Baseline Evidence. OLS IV [1] [2] [3] [4] [5] [6] [7] [8] [9] shareicτ −0.239 ∗∗∗ −0.261 ∗∗∗ −0.266 ∗∗∗ −0.259 ∗∗∗ −0.183 ∗∗∗ −0.176 ∗∗∗ −0.166 ∗∗∗ −0.178 ∗∗∗ −0.193 ∗∗∗ [0.035] [0.036] [0.037] [0.037] [0.034] [0.033] [0.033] [0.034] [0.034] EDi∗FDcτ 0.111 ∗∗∗ 0.080 ∗∗∗ 0.051 0.104 ∗∗ 0.190 ∗∗∗ [0.020] [0.023] [0.045] [0.047] [0.068] EDDownstream i × F D cτ 0.538 ∗∗∗ 0.105 −0.035 −0.168 −0.422 [0.114] [0.142] [0.236] [0.234] [0.315] EDU pstreami × F D cτ 0.241 ∗∗∗ 0.147 ∗∗∗ 0.227 ∗∗ 0.321 ∗∗∗ 0.324 ∗∗ [0.047] [0.048] [0.107] [0.111] [0.151] Constant 0.130 ∗∗∗ _0.126∗∗∗ _0.129∗∗∗ _0.140∗∗∗ _0.195∗∗∗ _0.186∗∗∗ _0.178∗∗∗ _0.194∗∗∗ [0.007] [0.007] [0.007] [0.008] [0.073] [0.072] [0.067] [0.068] Observations 503 503 503 503 503 503 503 503 503 R-squared 0.115 0.097 0.097 0.129 0.61 0.609 0.613 0.617 0.617 Fixed Effects Country N N N N Y Y Y Y Y Industry N N N N Y Y Y Y Y Hansen J 16.18 P-Value 0.18 1 st Stage F tests EDi × FD cτ 18.6 EDDownstream i × F D cτ 33.47 EDU pstream_i × F D cτ 27.64

∗_{Significant at 10% level;}∗∗_{significant at 5% level;}∗∗∗_{significant at 1% level. Robust standard errors in brackets. The dependent variable is the} average annual growth of real value added of sector i in country c recorded in the time span between t = 2007 and τ= 1995 .

Incolumn[9]ofTable1,inordertoaccountforreversecausalityissuesand,moregenerally,forthepotential endogene-ityoffinancialdevelopment, weadoptanIVapproachbyfollowingextantliterature whichhighlightsasignificant impact ofacountry’slegalsystemonthedevelopmentofdomesticcapitalmarketsandfinancialindustry(Becketal.,2003;2000;

LaPortaetal.,1998).Therefore,weuselegaloriginsfromLaPortaetal.(2008)andtheruleoflawindexfromtheWorld

BankWorldwideGovernmentIndex(WGI)asinstrumentsforfinancialdevelopment.TheHansentestfailstorejectthe va-lidityoftheover-identifyingexclusionrestrictions,whilethelowerpartoftheTableshowssatisfactory valuesforthefirst stageF teststatistics.13 The IVestimatorconfirmsourresults,andthemagnitudeoftheeffects offinancialdevelopment, eithermediatedby thesector’s ownfinancialdependenceorbythefinancialdependenceofupstreamindustries,doesnot sensiblychange.

Inorderto assesstheeconomic magnitudeofthe estimatedeffects,we considercoefficientsfromthe specificationin column[8].SimilarlytoRajanandZingales(1998),wetakethecountriesatthe25thand75thpercentilesofthedistribution oftheratioofdomesticcreditoverGDP-MexicoandItaly, respectively-andthesectorsatthe25thand75thpercentiles ofthedistributionoftheEDiindicator-metalproducts andchemicals,respectively-andcalculatethedifferentialgrowth rateofchemicalscomparedtometal productsinItaly comparedtoMexicoexplainedby thetwosignificant factorsunder analysis.TheestimateonthecoefficientofEDi× FDcτpredictsthatthechemicalindustryshouldgrow1.45percentagepoints fasterthan Metalproducts in Italythan in Mexico.Beyondthe direct effectoffinancial development, the effectworking through upstream linkages would deliver a growth advantage to the Italian chemical industry over the sector of metal productsinItalythatis2.16percentagepointshigherthanthegrowthdifferentialbetweenchemicalsandmetalproductsin Mexico.Consideringthatinoursampletheaveragegrowthrateofmanufacturingsectorsisaround9.5%,thebeneficialeffect offinancialdevelopmenttriggeredbyinputlinkagesturnstobeparticularlyrelevant.Moreover,thefinancialdependenceof industries’suppliershasahigherimportanceindrivingthepositiveroleoffinancialdevelopmentongrowth.

Asafurtherexercise,wetake twosectorswhichhavean oppositepositionintherankingofEDiandEDU pstreami .14 Ma-chinery,whichisatthebottomoftheEDU pstream_i rankingandrecordsatoppositionforEDi,andFoodandBeverages,which recordsatop positionintherankingofEDU pstream_i andabottompositionforED_i.Accordingtoourcomputation,Foodand

sectors. It is also worth mentioning that the above evidence of a positive effect of financial development stemming from upstream sectors is confirmed when we calculate upstream and downstream effects by means of the inverse Leontief purchases and sales matrices elements and, therefore, consider indirect IO effects, that is upstream and downstream higher order effects, spilling from suppliers of a sector’s supplier and from buyers of a sector’s buyer and so on. Results are shown in Table A7 in Appendix A for the baseline model including all upstream and downstream sectors. In the paper we have decided to analyse the first tier effect because, when the elements of the Leontief matrices are considered, the own sector effect is not easily identifiable out of the upstream and downstream effects, which, moreover, turn to be highly correlated with the own sector effect. More details on the calculation of inverse Leontief purchases and sales matrices and on the identification of direct and indirect IO effects are available in Acemoglu et al. (2016) .

13 _{Table A10}_in_{Appendix A}_{shows complete ﬁrst stage results and, at the bottom, reports satisfactory values also for partial R-squares.}

14 Indeed, the rank correlation between the two measures is equal to −0.34 - the simple correlation is equal to −0.36 - and this corroborates the view that neglecting the indirect effect through a sector’s interconnections with suppliers delivers an underestimated and biased picture of the role of ﬁnance for industry growth.

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Beverages should grow2.72 percentagepoints fasterthan Machineryin Italy -the country atthe 75thpercentile ofthe financialdevelopmentdistribution-thaninMexico-thecountryatthe25thpercentileofthefinancialdevelopment distri-bution-thankstotheimpactoffinancialdevelopmentonitsupstreamsectors, butitshouldgrow2.16percentagepoints slower dueto the direct effect offinancial development. In thiscase, the indirect effect morethan counterbalances the directone,thusrevealingtheimportanceofthepotentialbiasduetoneglectingtheIOlinkages.

Insum,ourresultssuggesttheimportanceofnetworkeffectsamongindustriesintheanalysisofthegrowth-enhancing role of finance.The understanding ofthe industrial growthprocess andthe contribution offinance cannot disregardthe considerationoftheIO linkages.Thelatter,indeed,are notonlystatistically,buteveneconomicallysignificant forthe ex-planationofthefinance-growthnexus.Inthisrespect,ourfindingssupporttherecentstrandofliteraturewhichhighlights hownetwork effectsareessential tounderstandthe propagationandamplificationofa widevariety ofshocks,from nat-uraldisasters (Barrotand Sauvagnat,2016) toincreased competition(Acemogluetal., 2016) tofinancial crisis andcredit supplyshock(Acemogluetal., 2012;AlfaroandGarcia-Santana, 2017;BigioandLa’O,2016;Dewachteretal., 2017).More specifically,we corroboratetheview thatIO linkagesamplifytherole offinance ineconomies.Theinterplaybetweenthe financialdependenceofinterrelatedsectorsandfinancialdevelopmentmaygeneratesupplysideproductivityshockswhich, asmodeledbyAcemogluetal.(2016),propagatefromupstreaminputproviderstodownstreambuyers.

4.2. Robustness

WetestthesensitivityofourbaselineOLSresultsfromcolumn[5]ofTable1toanumberofchecks.InTable2weshow that resultsare robustwhen incolumn[1]weuseraw growthrateswithoutapplyinganycleaning procedureandwhen we adoptcontinuousgrowthrates,whetherwe winsorisethem -column[2]-ornot -column[3]. Resultsholdwhenin column[4]we clusterstandard errorsby countryto accountforpotential correlation withincountriesandacross sectors thatcouldbeinduced byourIObasedfinancialdependencemeasures,whenwere-includethefinancialsectoramongthe IO linkagesin column[5],when we replacethevalues ofEDby industry withtheindustry rankingin termsoffinancial dependenceincolumn[6],andwhenincolumns[7]and[8]werecalculatefinancialdependenceondifferentsub-periods. Furthermore,resultsareunchangedwhen,ratherthantakingaveragesofdownstreamandupstreamfinancialdependence, weaverageIOsharesacrosscountriesincolumn[9],useIO linkagesfromtheU.S.IOTableonlyincolumn[10]andwhen we usecountry-varying upstream anddownstream financial dependencemeasures in column[11].In thelatter case, we includethenoninteractedmeasures ofthecountry-sectorspecificupstreamanddownstreamfinancialdependenceinthe modelspecification.In orderto overcomethe veryhighcorrelation betweenIO basedfinancialdependencemeasures and their respective interactions withfinancial developmentinthe baseline cross-section sample, we run thespecificationof column[11]onannualdatawiththeinclusionofsector-yearandcountry-yearfixedeffectsand,forcomparison,incolumn [12]wereporttheestimationofourbaselinemodelwithannualdata.Thenumberofobservationsishigherinthiscase,as wedonot haveinformationonIOlinkagesforallofthecountriesintheUNIDOdatabase.15 Theimportanceofupstream linkagesisconfirmedinallcases.

Furthermore,inTable3weusealternativemeasuresoffinancialdevelopmentandfocusonalldomesticcreditdelivered byfinancialinstitutionsincolumn[1]anddomesticcreditdeliveredbybankstotheprivatesectorincolumn[2].16 Whether weadoptabroaderdefinitionoffinancialdevelopment-astheoneincolumn[1]-oranarrower-astheonefromcolumn [2]-baselineresultsareconfirmed.Also,thesameoccurswhenincolumn[3]wemodifyoursamplecompositioninorder toexcludeBICcountries-thatisBrazil,IndiaandChina-thatintheperiodofouranalysishaveexperiencedunprecedented growthratesinandoutthemanufacturingsector.

Asadditionalrobustnesschecks,weinspectthepossibilitythatourbaselineevidenceisaspurious onepossiblydriven by the omissionofrelevant factorsin theempirical modelspecification. The highergrowth ofvalue added infinancially dependent sectorscould be fosteredby other sources of comparative advantage other than theavailability of a well de-velopedfinancialsector. Hence, incolumn[4]and[5]wefollowRajan andZingales(1998)andincludetheinteraction of financial dependencewiththe levelofcountries’ percapita GDP(WDI2015)andtheaverage yearsofschoolingof work-ing agepopulation availablefromthe BarroandLee’sEducational AttainmentDataset(BarroandLee, 2013), respectively. First,industries’financial dependenceintheUnitedStatescould dependonthedifferentindustrial maturitystage of sec-tors andthis, in turn,is strictly relatedto countries’ developmentstages.Hence, by introducing the interaction between financialdependenceandpercapitaGDPweaimatcapturinganyindustrygrowthsourcewhichmaysimplybedrivenby a country’s developmentstage. Second, a higherfinancial dependence ofan industry could be highlycorrelated withits humancapitalintensityand,bythesametoken,higherfinancialdevelopmentcouldbehighlycorrelatedwithhuman cap-italendowment.Hence, ashighlyskillintensivesectorsareexpectedtogrowmorewhereahigherendowmentofhuman capitalis available, wetest whetherthe estimatedeffectoffinancial developmentis notspurious andactually driven by theomissionofacontrolforhumancapital.Forthisreason,weincludetheinteractionofahumancapitalproxywiththe

15 Botswana, Ecuador, Ethiopia, Iran, Jordan, Kenya Macao, Morocco and Mauritius are dropped from the sample of column [11]. It is worth mentioning that baseline results hold when upstream and downstream ﬁnancial dependence measures are calculated as averages among the countries available in the UNIDO database only. Results are not shown for the sake of brevity, but they are available upon request.

16 In _{Table A8}_{in the Appendix we show that our results are unchanged when we adopt further indicators of ﬁnancial depth available from the World} Bank Global Financial Indicators Database.

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JID: JEBO [m3Gsc; Decemb e r 21, 2018;18:30 ] Table 2 Robustness I.

Continuous Growth Rates S.E. Cluster Re-including Finance EDi Time Span Fin.Dep Average USA Country-Varying

No Cleaning Winsorised No Cleaning Country in IO Linkages Rank 1995–2007 1990–1999 IO Shares IO shares ED Downstream/U pstream

i

Annual Growth Rates

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] shareicτ −0.450 ∗∗∗ −0.067 ∗∗∗ −0.097 ∗∗∗ −0.178 ∗∗∗ −0.175 ∗∗∗ −0.171 ∗∗∗ −0.176 ∗∗∗ −0.169 ∗∗∗ −0.173 ∗∗∗ −0.180 ∗∗∗ −0.279 ∗∗∗ −0.243 ∗∗∗ [0.158] [0.025] [0.029] [0.045] [0.034] [0.034] [0.033] [0.034] [0.033] [0.034] [0.087] [0.048] EDi × FD cτ 0.453 0.056 ∗ 0.088 ∗∗ 0.104 ∗∗ 0.101 ∗∗ 0.003 ∗∗ 0.076 ∗ 0.103 ∗ 0.102 ∗∗ 0.087 0.101 ∗∗ 0.101 ∗∗ [0.292] [0.032] [0.043] [0.050] [0.047] [0.002] [0.039] [0.053] [0.048] [0.055] [0.046] [0.045] EDDownstream i × F D cτ 0.375 0.062 0.106 −0.168 −0.098 0.002 −0.101 −0.025 −0.045 −0.012 −0.098 [0.837] [0.161] [0.186] [0.276] [0.194] [0.002] [0.210] [0.299] [0.181] [0.166] [0.198] EDU pstreami × F D cτ 0.813 ∗ 0.195 ∗∗ 0.182 ∗∗ 0.321 ∗∗ 0.264 ∗∗∗ 0.009 ∗∗ 0.314 ∗∗∗ 0.367 ∗∗∗ 0.301 ∗∗∗ 0.248 ∗∗ 0.264 ∗∗ [0.427] [0.076] [0.084] [0.126] [0.090] [0.004] [0.108] [0.119] [0.103] [0.106] [0.103] EDDownstream ic × F D cτ −0.112 [0.122] EDU pstreamic × F D cτ 0.143 ∗∗ [0.067] EDDownstream ic −0.093 [0.087] EDU pstream_ic 0.275 ∗∗ [0.101] Observations 503 503 503 503 503 503 503 503 503 503 4 86 8 6008 R-squared 0.279 0.532 0.457 0.617 0.617 0.615 0.617 0.618 0.617 0.616 0.422 0.368 Fixed Effects Country Y Y Y Y Y Y Y Y Y Y N N Industry Y Y Y Y Y Y Y Y Y Y N N Country ∗_Year _N _N _N _N _N _N _N _N _N _N _Y _Y Industry ∗_Year _N _N _N _N _N _N _N _N _N _N _Y _Y

∗_{Significant at 10% level;}∗∗_{significant at 5% level;}∗∗∗_{significant at 1% level. Robust standard errors in brackets.}

The dependent variable of columns [1]-[10] is the average annual growth of real value added of sector i in country c recorded in the time span between t = 2007 and τ= 1995 . In columns [11] and [12] the

dependent variable is the yearly growth of real value added of sector i in country c between t and τ= t − 1 observed over the period 1995–2007.

In column [2], we winsorise continuous annual growth rates at the 1% tails of their distribution. In column [6] we compute sectors’ own, downstream and upstream financial dependence on the basis of the ranking of ED i . In column [9] we compute downstream and upstream financial dependence on the basis of the average of IO shares across all the countries included in the OECD IO Tables and listed in Table A6 , while in column [10] we compute them on the basis of the US IO shares from the same OECD IO Tables. In column [11] we exploit country-varying upstream and downstream financial dependence, and, as consequence, we include both these indicators non interacted and their interaction with FD cτ. In columns [11] and [12] we estimate a pooled model on annual growth rates, where the sectoral share of value

added and FD refer to the period t − 1 .

Please cit e this article as: A. Lo T u rco, D. Maggioni and A. Zazzar o , Financial dependence and gr o w th: The ro le of in put-Output linkag es, Journal of Economic Beha vior and Or g a nization, https://doi.or g /1 0.1 0 1 6/j.jebo.20 18 .1 1.02 4

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JID: JEBO [m3Gsc; Decemb e r 21, 2018;18:30 ] Table 3 Robustness II.

Alternative FD c Deﬁnition Restricted Sample Concurring Explanations

By Financial Sec. Banks to Priv. Exclusion of BIC Per Capita GDP Human Capital and FDI

Midpoint Growth Rate

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] shareicτ −0.172 ∗∗∗ −0.175 ∗∗∗ −0.153 ∗∗∗ −0.194 ∗∗∗ −0.180 ∗∗∗ −0.176 ∗∗∗ −0.187 ∗∗∗ −0.195 ∗∗∗ −0.196 ∗∗∗ [0.033] [0.033] [0.035] [0.037] [0.036] [0.044] [0.036] [0.045] [0.046] EDi × FD cτ 0.130 ∗∗∗ 0.102 ∗∗ 0.096 ∗ 0.084 ∗ 0.098 ∗ 0.134 ∗∗ 0.092 ∗ 0.130 ∗∗ 0.119 ∗∗ 0539 0923 [0.048] [0.051] [0.050] [0.051] [0.054] [0.054] [0.049] [0.057] [0.057] [0.585] [0.567] EDDownstream i × F D cτ 0.009 −0.138 −0.079 0.044 0.066 −0.17 −0,01 −0,195 −0,026 2714 3984 [0.202] [0.210] [0.203] [0.211] [0.212] [0.220] [0.236] [0.263] [0.256] [2.553] [2.587] EDU pstream i × F D cτ 0.291 ∗∗∗ 0.289 ∗∗∗ 0.282 ∗∗∗ 0.227 ∗∗ 0.272 ∗∗∗ 0.281 ∗∗∗ 0.286 ∗∗ 0.387 ∗∗∗ 0.346 ∗∗∗ 2.608 ∗ 4.671 ∗∗∗ [0.103] [0.099] [0.095] [0.100] [0.101] [0.094] [0.114] [0.117] [0.118] [1.549] [1.618] EDi × Control cτ 0.006 0.024 0 [0.011] [0.052] [0.008] EDDownstream i × Control cτ −0.063 −0.306 0.018 [0.055] [0.218] [0.028] EDU pstream i × Control cτ 0.009 −0.155 0.014 [0.024] [0.096] [0.014] Ski × HumanCapital cτ 0.187 ∗∗ 0.153 ∗∗ 2.494 ∗∗∗ [0.079] [0.075] [0.946] Capi × FDI cτ 0 0 06 0 0 06 0018 [0.004] [0.004] [0.033] Observations 503 503 463 490 491 492 491 492 480 534 574 R-squared 0.62 0.617 0.588 0.623 0.63 0.613 0624 0609 0622 0,33 0375 Fixed Effects Country Y Y Y Y Y Y Y Y Y Y Y Industry Y Y Y Y Y Y Y Y Y Y Y

The dependent variable of columns [1]–[9] is the average annual growth of real value added of sector i in country c recorded in the time span between t = 2007 and τ= 1995 . In columns [10] and

[11] the dependent variable is the midpoint growth of real value added of sector i in country c recorded between t = 2007 and τ= 1995 .

In columns [1] and [2], the baseline FD indicator is replaced with the ratio of the domestic credit delivered by the ﬁnancial sector over GDP and the credit to private sector from the banking sector over GDP, respectively (WBDI 2015). In columns [3] we exclude Brazil India and China from the estimation sample. In columns [5]–[6], the variable Control is represented by the log of GDP per capita (WBDI 2015), the log of average years of schooling as a proxy for countries’ human capital endowment ( Barro and Lee, 2013 ) and the FDI net inﬂows over GDP (WBDI 2015), respectively. Sk i in column [7] represents the sectoral skill intensity (UNCTAD). Cap i in column [8] represents the sectoral capital intensity (UNCTAD).

Please cit e this article as: A. Lo T u rco, D. Maggioni and A. Zazzar o , Financial dependence and gr o w th: The ro le of in put-Output linkag es, Journal of Economic Beha vior and Or g a nization, https://doi.or g /1 0.1 0 1 6/j.jebo.20 18 .1 1.02 4

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Table 4

Original Rajan&Zingales Database .

Domestic Credit/GDP Capitalization Accounting Standards

[1] [2] [3] [4] [5] [6] shareicτ −0.852 ∗∗∗ −0.835 ∗∗∗ −0.873 ∗∗∗ −0.889 ∗∗∗ −0.899 ∗∗∗ −0.634 ∗∗∗ [0.244] [0.247] [0.248] [0.245] [0.249] [0.205] EDi × FD cτ 0.054 ∗ 0.050 ∗ 0.027 ∗ 0.106 ∗∗∗ [0.028] [0.028] [0.016] [0.027] EDDownstream i × F D cτ 0.072 −0.09 −0.005 −0.185 [0.095] [0.102] [0.070] [0.115] EDU pstreami × F D cτ 0.249 ∗∗∗ 0.257 ∗∗∗ 0.128 ∗∗∗ 0.206 ∗ [0.069] [0.071] [0.045] [0.108] Observations 1217 1217 1217 1217 1217 1067 R-squared 0.283 0.28 0.286 0.288 0.288 0.346 Fixed Effects Country Y Y Y Y Y Y Industry Y Y Y Y Y Y

financial dependenceindicators incolumn[5]. Finally,we ascertain that financial developmentfavours industrial growth througha well functioningdomestic creditmarket ratherthan through theincreasedavailability ofexternal financial re-sources,madeavailabletodomesticproducerspossiblythroughForeignDirectInvestments(FDI).Indeed,theperiodofour analysisischaracterisedbyanunprecedentedupsurgeinworldFDIflowingfromhightolowincomeeconomies,especially, andwhichhavecontributedtothedevelopmentofglobalsupplychainsandmanufacturinggrowthinlow andmiddle in-comeeconomies(WorldBank,2017).Hence,incolumn[6]weintroducetheinteractionbetweenourfinancialdependence measuresandtheratioofFDInetinflowsoverGDP(WDI,2015).Inallcases,ourbaselineevidenceisconfirmed.Tofurther excludeanypotentialgrowtheffectfromhumancapitalandFDIwhichcoulddifferacrosssectors,incolumn[7]weinclude theinteraction ofhumancapitalwitha measure ofskillintensityby sector, incolumn[8]weadd the interactionof FDI withameasureofcapitalintensitybysectorandincolumn[9]we includebothinteractions.17 Onceagain,ourresultsare robustandfinancialdevelopmentprovestobe arobustdeterminantofindustrialdevelopmentacrosscountriescompared toothercompetingfactors,amongwhichhumancapitalappearsquiteimportantaswell.Finally,we inspectwhetherthis findingisrobusttotheinclusion ofemerging anddisappearingsectorsinoursample.Tothispurpose,incolumn[10]we substituteourlefthandsidevariablewiththemidpointgrowthrateandwefindthat,differentlyfromtheownsectoreffect, financialdevelopmentmediatedbyupstreamfinancialdependencefavoursentryintonewsectorsand/orhampersexitfrom theoldonesregardlessthecompetingroleofhumancapital,whichturnssignificant,andFDIwhich,instead,doesnotseem tomatter.18 Incolumn[11],we reportthemidpointgrowthmodelestimatesforourbaselinesampleofcountriesandthe evidenceiscorroborated.

AsafinalcheckonthevalidityandeconomicrelevanceofIOlinkagesinthepropagationofthebeneficialeffectsof fi-nancialdevelopmentforindustrialgrowth,wereplicatetheestimationofourmodelontheoriginaldatabaseavailablefrom

RajanandZingales(1998).WecomputeﬁnancialdependencefromtheCOMPUSTATsampleforthe1980stocalculate

finan-cialdependencebyindustryandwefurtherbuildupstreamanddownstreamfinancialdependencemeasuresforallsectors intheU.S. economy,on thebasis ofU.S. IOtablesforthe year1987.19 Then,we calculateandtest their interactionwith countries’financialdevelopmentonRajanandZingales’s sample.Forthesakeofcomparabilitywithourbaselinefindings, incolumns [1]-[4]ofTable 4we first considerthe specificationwithdomestic credit overGDPasfinancial development indicator,whileincolumn[5]and[6]weconsidercountries’capitalisationandaccountingstandardsindicatorsavailablein theoriginaldatabase.Inallcasesweconfirmthatupstreamfinancialdependencematters,asinourbaselinemodelforthe 1995–2007period.

5. Channelsandheterogeneityoftheﬁnance-growthnexus

Afterprovingtherobustnessofourfindings,inthissectionweinvestigatethechannelsthroughwhichfinancial develop-mentfosterindustries’growth.Inaddition,weanalyzewhetherthepositivedirectandindirectaverageeffectsoffinancial developmentonthe industry growthhide heterogeneity inthe finance-growthnexus in two dimensions:the sizeof the financialsectorandthetimeperiodunderanalysis.

17 Sector level indicators are available from UNCTAD at http :// unctad.org / Sections / ditc

t ab / docs / RFII 2 010 E xcel.zip . 18 The midpoint growth rate is calculated as follows: midgrowth

ict/τ= yt−y τ

0 .5 ∗(yt+ yτ) . As a consequence it varies between -2 and 2, taking value -2 for those

country-sector pairs existing in τand disappearing in t and 2 for those country-sector pairs absent in τand existing in t .

19 Note that COMPUSTAT samples and data are updated through the years, so that the 1980s sample at our disposal may differ from the one originally available to Rajan and Zingales (1998) . Also, the lack of cross-country IO Tables for this period led us to use the Unites States as the benchmark economy for IO linkages too.

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Table 5

Inspecting the Channels.

[1] [2] [3] [4] shareicτ −0.168 ∗∗∗ −0.183 ∗∗∗ −0.126 ∗∗∗ −0.137 ∗∗∗ [0.036] [0.035] [0.035] [0.031] CapitalStockGrowth 0.018 ∗∗∗ _0.026∗∗∗ [0.005] [0.006] TFPGrowth 0.025 ∗∗∗ _0.032∗∗∗ [0.004] [0.005] EDi∗FDcτ 0.130 ∗∗ 0.123 ∗∗ 0.074 0.049 [0.057] [0.055] [0.054] [0.050] EDDownstream i × F D cτ −0.076 −0.209 −0.090 −0.289 [0.280] [0.269] [0.274] [0.257] EDU pstream_i × F D cτ 0.313 ∗∗ 0.322 ∗∗∗ 0.260 ∗∗ 0.259 ∗∗ [0.128] [0.124] [0.131] [0.124] Observations 436 436 436 436 R-squared 0.637 0.661 0.687 0.734 Fixed Effects Country Y Y Y Y Industry Y Y Y Y

∗_{Significant at 10% level;}∗∗_{significant at 5% level;}∗∗∗_{significant at 1% level.}

Robust standard errors in brackets. The dependent variable is the average annual growth of real value added of sector i in country c recorded in the time span between t = 2007 and τ= 1995 .

5.1. Inspectingthechannels

Inthe earlycross-countryempirical literatureonthe finance-growthnexus,Becketal.(2000) proved theexistence of a fundamentalrole of financialdevelopment insustaininggrowth by fosteringtotal factorproductivity (TFP)rather than capital accumulation. To inspect the channels through which financial development enhances growth ofindustries with differentlevels offinancial dependenceand withheterogeneousfinancial dependenceofupstream industries,we include capitalandTFPgrowthinourbaselinemodelandinspectifandhowthecoefficientsonourmainrighthandsideregressors’ magnitudeandsignificancechange.Weexpectthat,iftheireffectworksthroughanyofthesetwochannelstheircoefficients’ sizeandsignificanceshouldshrinkorevendisappear.Hence, we,first,calculatethecapitalstockofanindustry bymeans oftheperpetualinventorymethod(BerlemannandWesselhoft,2014)20 and,then,followingBecketal.(2000),wecalculate theindustrylevelTFP.21

Table5showshowthecoeﬃcientsassociatedtoourvariablesofinterestchangewhenweincludethegrowthofcapital

stockandTFPinourbaselinemodel.Itisworthmentioningthat,whenrunningthisexercise,thenumberofobservations dropsfrom503to436,duethelackofdataonthecapitalstockand,consequently,onTFPforsome country-industrypairs inoursample. Forthisreasonwe re-runthe baselinemodelonthissmallersampleandreport thecorrespondingresults incolumn[1]. In columns[2]and[3], wealternatively includethecapital stockandthe TFPgrowth.Wefind thatwhile addingtheformer hardlyaffectsour coefficientsofinterest,theinclusion oftheTFP growthinthemodel totallyabsorbs the significanceofthe interactionbetween countries’financial developmentandan industry’s own financialdependence. Also,we observea mildcontractionofthemagnitudeofcoefficient ontheinteractionbetweenfinancialdevelopmentand thefinancial dependenceofupstreamindustrieswhich,nonetheless,remains significant.Thisevidence,then,corroborates ourinterpretationoffinancialdevelopmentactingasapositiveTFPshockandoflinkagesactingasaneffectivepropagation mechanismofindustryspecificshocks.

Indeed,iffinancial developmentfavours TFP growthdisproportionately moreinmorefinancially dependentindustries, these industries’goods will become cheaper andthis sector specific effect will benefitdownstream sectors that buy in-putsfromthoseindustries.Hence,beyondthedirecteffectoffinancialdevelopment,mainlyworkingthroughTFP growth, downstreamindustriescangrowandexpandtheirscalethankstotheeffectoffinancialdevelopmentonfinancially depen-dent input providers.The persistence ofthesignificance onthe upstream effectis,therefore,consistent withits working asapositivepropagationeffectaffectinganindustry’s growth,beyonditsspecific TFPgrowthand/orcapitalaccumulation (Acemogluetal.,2016).

20 We apply the perpetual inventory method on the basis of investment ﬂows dating back to 1963.

21 Assuming a Cobb-Douglas industry level production function with capital, K , labour, L and Hicks neutral technical progress, A : Y = AK α_L1−α Y L = A K L α

Taking logs, we get TFP as: ln A = ln y −αln k, with y = Y

L , k = KL and α= 1 / 3 .

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Table 6 Non Linearities.

Dummy High Squared High Low

Financial Development [1] [2] [3] [4] shareicτ −0.182 ∗∗∗ −0.180 ∗∗∗ −0.268 ∗∗ −0.155 ∗∗∗ [0.034] [0.035] [0.106] [0.037] EDi × FD cτ 0.176 ∗∗ 0.346 ∗∗ −0.088 0.185 ∗∗ [0.087] [0.154] [0.120] [0.092] EDDownstream i × F D cτ −0.485 −0.403 0.266 −0.478 [0.440] [0.884] [0.787] [0.454] EDU pstream i × F D cτ 0.656 ∗∗∗ 1.117 ∗∗∗ 0.078 0.690 ∗∗∗ [0.202] [0.408] [0.417] [0.208] EDi∗FDcτ× D High FD −0.063 [0.055] EDDownstream i × F D cτ× D HighF D 0.273 [0.268] EDU pstream i × F D cτ× D HighF D −0.295 ∗∗ [0.123] EDi × F D 2 cτ −0.189 ∗ [0.097] EDDownstream i × F D 2cτ 0.179 [0.606] EDU pstream i × F D 2 cτ −0.642 ∗∗ [0.283] Observations 503 503 130 373 R-squared 0.621 0.621 0.752 0.571 Fixed Effects Country Y Y Y Y Industry Y Y Y Y

Test βHigh = _βLow _6.58

P-Value 0.01

DHigh FD is a dummy taking value one when FD

cτ is higher than 85% - the upper quartile value - and taking value zero otherwise. Columns [3] and [4] report the estimation of the baseline model for countries with a ﬁnancial development value above and below the 75th percentile, respectively.

5.2.Non-linearities

An increasing number of studies haveprovided evidence of the Tobin’s conjecture that financial developmentis not alwaysbeneficialforeconomicgrowth.RiojaandValev(2004)findthatanincreaseoffinancialdevelopmenthasapositive andstrongimpacton therateofgrowthofcountriesthat areatan intermediate leveloffinancial development,while it hassmall or no effects in countriesat low andhigh levels of financial development. Easterly et al.(2000), Deiddaand

Fattouh(2002),CecchettiandKharroubi (2012),LawandSingh(2014)andArcandetal.(2015),havedocumented thatthe

effectofadditionallendingonGDPgrowthrateandvolatilitybecomenegativewhentheratiobetweenprivate-sectorcredit andGDPexceedsa certain threshold(typicallybetween80–120%).In particularinthe contextofthe Rajan andZingales’

(1998))model,Manganelli andPopov(2013) showthat wherethe ratioofprivate credittoGDPexceeds a60% threshold

ﬁnanciallydependentindustriesgrowlessthanindustrieslessdependentonexternalﬁnance.22

Inthesamevein,inTable6weinspectwhetherthegrowthrateofindustriesdependentonexternalfinanceandlinked tofinanciallydependent upstreamindustriesalways benefitsofthefinancialdevelopmentorwhetherthelatterloses im-portanceafteracertain thresholdandpossiblyharmsrelativelymoretheindustriesinneed(orlinked tosectorsinneed) ofexternal finance.Inorder todo this, webuild a dummytakingvalue one fortheupperquartile ofthe distributionof theratioofdomesticcredit overGDP-domesticcredit overGDPhigherthan85% -andtakingvalue zerootherwiseand interactitwithourfinancialdependenceindicators.Resultsfromcolumn[1]intheTableshowthatthecoefficientsonthe interactionswithbothownandupstreamfinancialdependencearenegativeandthelatterisalsosignificant.

Thisevidenceisconfirmedincolumn[2]when weincludethesquareofthefinancial developmentindicator.Both co-efficientsonEDi× FD2 c andEDU pstreami × FD2 c are statisticallysignificant andnegative, thuscorroboratingtheexistence ofa

22 In a related vein, _{Ductor and Grechyna (2015)}_{find that in countries where the financial sector grows much more rapidly than industrial sectors the} contribution of financial development to real GDP growth is negative, while Cecchetti and Kharroubi (2015) find that higher financial growth unambiguously decreases economic growth and, in particular, those industrial sectors that make greater use of intangible assets and R&D.

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Table 7

Before and after 20 0 0.

Continuous Presence between

1995–2007 1995–1999 or 20 0 0–20 07 1995–1999 20 0 0–20 07 20 0 0–20 07 1995–1999 20 0 0–20 07 20 0 0–20 07 IO 20 0 0 IO 20 0 0 [1] [2] [3] [4] [5] [6] shareicτ −0.168 ∗ −0.146 ∗∗∗ −0.146 ∗∗∗ −0.172 ∗∗ −0.135 ∗∗∗ −0.135 ∗∗∗ [0.096] [0.042] [0.042] [0.075] [0.042] [0.042] EDi∗FDcτ 0.085 0.103 ∗∗∗ 0.102 ∗∗ 0.125 0.063 ∗ 0.063 ∗ [0.107] [0.040] [0.040] [0.077] [0.034] [0.034] EDDownstream i × F D cτ 0.16 −0.219 −0.203 0.216 −0.129 −0.121 [0.488] [0.191] [0.195] [0.376] [0.156] [0.159] EDU pstream i × F D cτ 0.181 0.314 ∗∗∗ 0.317 ∗∗∗ 0.173 0.284 ∗∗∗ 0.290 ∗∗∗ [0.237] [0.093] [0.095] [0.185] [0.079] [0.082] Observations 503 503 503 704 632 632 R-squared 0.384 0.666 0.666 0.361 0.616 0.616

∗_{Significant at 10% level;}∗∗_{significant at 5% level;}∗∗∗_{significant at 1% level. Robust standard errors in brackets.} The dependent variable is the average annual growth of real value added of sector i in country c recorded in the time span between t = 1999 and τ= 1995 in columns [1] and [4] and between t = 2007 and τ= 20 0 0 in the remaining columns.

nonmonotonicdirectandindirecteffectsoffinancialdevelopmentonindustrygrowth.Thethresholdoftheratiobetween privatecreditandGDPabovewhichthecontributionofafurtherexpansionofcredittoindustrygrowthisnegativedepends onthedegreeofindustries’ownandupstreamfinancialdependencevaluesandslightlyvariesacrosssectors,rangingfrom 89%to87%,withhigherthresholdsobservedforsectorswithlowerownfinancialdependencebuthigherupstreamfinancial dependence.Theseratiosfallinthe80–120%rangedocumentedbypreviousstudies.

Finally,werunseparateestimatesforcountriesaboveandbelowthe75thpercentilethreshold(columns[3]and[4]),and wefindthattheevidenceisdrivenbycountriesbelowthethreshold.Onceagain,weconfirmthatafteracertainthreshold level,financemayloseitsbeneficialeffects.

5.3. Bankingcrisesandproductivityslowdown

InthisSectionweexaminewhetherthepositiveaverageeffectsoffinancialdevelopmentonthegrowthratesof indus-triesthataredependentonexternalfinanceandbuyinputsfromfinanciallydependentindustriesholdforthewholeperiod orwhethertherelationshipbetweenfinanceandindustrygrowthvariesaccordingtotheperiodunderanalysis.More specif-ically, we considertwo sub-periods,1995–1999and2000–2007. Ontheone hand,theformer sub-periodis characterised by a highincidenceofcredit-boomepisodes andbankingcrises,23 that havebeenconsidered the reasonbehindthe dis-appearance ofthe positive cross-countryeffects offinancial deepeningon GDPgrowth ratesin the1990s (Rousseau and

Watchel,2011) andthestrongcontractionofﬁnancially dependentindustriesinﬁnancially developedcountries(Kroszner

etal.,2007;PaganoandPica,2012).Ontheotherhand,the2000–2007sub-periodhasbeencharacterisedbyaproductivity

growthslowdown(orevendecline)inmanyadvancedeconomies(Jones,2017).Amongthedeterminantsofthisgeneralised slowdownintheTFP,capitalmisallocationproducedbyfastgrowingfinancialsectorsdisproportionallylendingtofirmswith highcollateral,butnotnecessarilyhighproductivity,seemstohavehadagreatinfluence(Borioetal.,2015;Cecchettiand

Kharroubi,2015;Diasetal.,2015;Gopinathetal.,2017;GortonandOrdoñez,2016).

Table7reportsregressionresults bysub-periods. Incolumns(1)-(3) weconsider thesampleofindustry-countrypairs

which are presentforthe whole 1995–2007period,while incolumns (4)-(6) we enlargethe sampleandconsider those industry-countrypairswhichhaveacontinuouspresenceeitherinthe1995–1999sub-periodorinthe2000–2007one.In columns(1)-(2)and(4)-(5),EDDownstream_and_EDUpstream _are_measured_considering_IO_linkages_at_1995,_while_in_columns₍₃₎ and(6)theIOlinkagesaretheonesprevailingin2000.Consistentwiththebanking-crisishypothesis,wefindthatfinancial developmenthas no significant effecton the industry growthduring theperiod 1995–1999.24 By contrast,in theperiod 2000–2007 the impact of financial developmenton sectoral growthhas been positive and significant, and this effectis associatedbothtotheexternal-financedependenceofthesectorandtotheexternal-financedependenceofinputsuppliers.

23 According to the _{Laeven and Velencia (2012)}_{dating, during the period 1995–1999 there were 34 systemic banking crises in 33 different countries (plus} 7 crises in 1993 and 11 in 1994), while between 20 0 0 and 2007 the crisis episodes were only 4.

24 The same result holds if we consider the period 1990–1999. Results are available upon request.

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