欧洲央行-银行信贷条件向企业的传递-来自关联调查的证据（英）

EUROPEANCENTRALBANK

EUROSYSTEM

WorkingPaperSeries

AnnalisaFerrando,SarahHolton,ConorParleThetransmissionofbankcredit

conditionstofirms-evidencefrom

linkedsurveys

No2975

Disclaimer:ThispapershouldnotbereportedasrepresentingtheviewsoftheEuropeanCentralBank(ECB).TheviewsexpressedarethoseoftheauthorsanddonotnecessarilyreflectthoseoftheECB.

ECBWorkingPaperSeriesNo29751

Abstract

UsinganoveldatasetlinkingfirmleveldatafromtheSurveyonAccesstoFinance

ofEnterprises(SAFE)andbankleveldatafromtheBankLendingSurvey(BLS),weexplorehowchangesincreditstandardspassthroughtofirmsatagranularlevel.Wefindthattightercreditstandardsdecreaseloanavailabilityreportedbyfirms,increasethelikelihoodtheyreportaccesstofinanceastheworstproblemanddecreasetheirinvestment.Aftercontrollingforcountry-sector-timefixedeffectsthatcapturecyclicalmacroeconomicconditions,effectsonlyremainforfirmsthatneedfinance.Moreover,wefindthatamorediversifiedfundingbaseinsulatesfirmsfromthenegativeimpactsoftightercreditstandardsonavailabilityofbankloansandaccesstofinance,althoughthereislittleevidenceofsuchaneffectforinvestment.Effectsareasymmetric,withstrongerimpactsrecordedforatighteningthananeasing.Ourresultsunderscoretheimportanceofdemandconditionswheninterpretingthecreditconditionsandwethusproposeanewindicatorofdemandadjustedcreditstandardsataeuroarealevel,whichcanbeusedtoanalysebroadercreditdynamics.

JELclassification:D22,E22,E52

Keywords:Finance,creditconditions,surveys,firm-bankrelationships.

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Non-technicalsummary

Changesincreditconditionscanaffectfirms’accesstofinanceandtheirbusinessdecisionsandarethereforecloselymonitoredbypolicymakers.Thetransmissionofagivenchangeincreditconditionscandependonthesituationandbehaviourofbothbanksandfirms.Inthispaperweaimtoassessthistransmissionviabothfirmsandbanksbyfocusingontheperceptionsthattheybothhaveonchangesinfinancingconditions.Weuseauniquedatasetlinkingtwosurveys:theECB’sSurveyonAccesstoFinanceofEnterprises(SAFE)thatprovidesinformationfromthefirms’side,andtheBankLendingSurvey(BLS)whichscrutiniseseuroareabanks.Inournovelapproach,weanalysethedirectimpactofchangesincreditsupplyofaspecificbank(intheBLS)ontheavailabilityofloansasperceivedbyfirms(intheSAFE)thatarecustomersofthatbank.

WefocusontheconceptofcreditstandardsasdefinedintheBLS.Thesurveycollectsinformationonchangesincreditstandards,whicharetheinternalguidelinesorloanapprovalcriteriadefinedbyeachbankthatloanofficersshouldapplywhenborrowersapproachthemforabankloan.Fromthesideofthefirms,welook,instead,atthelikelihoodoffirmsinreportingdifficultiestoaccessfinanceandthepass-throughtotheirinvestmentdecisionsofchangesinthecreditstandardsappliedbytheirbank(s).

Inourempiricalanalysiswerelyonasampleof22,799firmsmatchedtobanksin11countriesfortheperiod2010-2022.Wefindthattighter(looser)creditstandardslead(i)toadecrease(increase)infirms’reportedloanavailability,(ii)toanincreased(decreased)likelihoodofafirmreportingaccesstofinanceastheirworstproblemand(iii)tolower(higher)firminvestment.Theseeffectsaresignificantmainlyforthosefirmsthatsignalledanincreasedneedforbankloansduringtheperiodweanalyse.Inaddition,byexploringthetimedevelopments,wefindthatourresultsaremainlydrivenbyperiodsoftightening,withverylittleeffectspresentforperiodsofeasing.

Wealsoprovideevidenceoftheimportanceforfirmsofhavingaccesstoadiversifiedfundingbase.Indeed,amongfirmsneedingabankloan,thosehavingamorediversifiedfundingbasecanmoreeasilycounteracttheeffectofatighteningofbankloansconditions.Ourresultsunderscoretheimportanceofdemandconditionswheninterpretingthecreditconditionsandweproposeanewindicatorofdemandadjustedcreditstandardsataeuroarealevel,whichcanbeusedtoanalysebroadercreditdynamics.

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1Introduction

Creditconditionsareakeychannelthroughwhichmonetarypolicyaffectstheeconomy(BernankeandGertler,1995).Achangeinmonetarypolicycaninduceasimultaneouschangeinbothcreditsupplyanddemandandthismakesitchallengingtopreciselyestimatetheeffectofachangeincreditsupplyonbanklending.Forthisreason,varioustechniquesandmoregranulardatahavebeenusedtomorecleanlyidentifycreditsupplyeffectsandhaveestablishedthatchangesincreditsupplysignificantlyaffectfirms’accesstofinanceandconsequentlytheirinvestmentbehaviour(AmitiandWeinstein(2018)).Moreover,thetransmissionofachangeincreditstandards-forinstance,followingachangeinmonetarypolicy-willcruciallydependalsoontheconditionofbothbanksandborrowers(Jiménezetal.(2012)andAltavillaetal.(2021)).Forinstance,heterogeneityinborroweroutcomesfollowinganaggregatechangeincreditconditionscouldpotentiallystemfromhowbanksbehaveandtransmittheshockorhowfirmcharacteristicsaccentuateormitigatetheshock.Therefore,granularinformationoncreditsupplybyindividualbanksandtheimpactonindividualfirmsiscrucialincapturinghowaggregatecreditstandardsultimatelyaffectfirms’accesstofinanceandinvestment.

Inthispaper,weuseanovelandgranulardatasetonbanksandbusinessestoexploretheeffectsofchangesincreditstandardsonfirms.Theuniquedatasetlinksindividualfirms’responsesfromtheECB’sSurveyonAccesstoFinanceofEnterprises(SAFE)andindividualbanks’responsestotheBankLendingSurvey(iBLS).Itallowsforaquantificationoftheimpactofchangesinbanks’creditstandards,asmeasuredbybankresponsestotheBLS,onfirms’reportedavailabilityofloans,onaccesstofinancebeingtheirworstproblemandonthepass-throughtotheirinvestmentdecisions,asmeasuredintheSAFE.Bylinkingthetwosurveysatagranularlevel,wecanexplorenotjustthedirectimpactofchangesincontemporaneouscreditstandards,butalsotheaccumulationofchangesovermultipleperiods.Thiscombinesthestrengthsofthetwosurveyswell.WhiletheSAFEisusefulforexploringcontemporaneouschanges,itismoredifficulttoassessthecumulativeimpactofcreditconstraintsasfirmsmayappearonmultipleoccasionsbuttheydonotnecessarilytakepartinconsecutivesurveys.However,usingBLScreditsupplyvariablesallowsforthecalculationofabankspecificloansupplyeffectthatwecanfollowovertime.Second,theSAFEcontainsusefuldataonfirmspecificconditions(likedemand)andoutcomes(likeinvestment)thatcanbeexploredatamicrolevel.Similarly,whileindividualbank-levelresponsesfromtheBLSareinformativefor

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thedemandaspecificbankfaces,informationfromtheSAFEsurveyallowsforassessinghow

heterogeneityindemandcaninteractwithcreditsupplyconditions.Overall,combiningtheserichmicrolevelsurveydatacanallowforanin-depthexplorationofthechannelsofcreditconditionsandhowbanksandfirmscanleadtovariationinhowashocktocreditconditionspropagates.

First,wefindthat,asexpected,tighter(looser)creditstandardslead(a)toadecrease(increase)infirmlevelreportedloanavailability,(b)toanincreased(decreased)likelihoodofreportingaccesstofinanceastheworstproblemfacingtheirfirmand(c)tolower(higher)investment.Yetwhencountry-sector-timefixedeffectsareincludedinadditiontofirmlevelfixedeffects,theeffectsofcreditstandardsonfirms’accesstofinanceandinvestmentbecomeinsignificant,implyingthatbanks’creditsupplydecisionsandfirms’investmentdecisionsaredominatedbysectoralandcyclicalconsiderations.However,whenafirmhasanincreaseddemandforcredit,itissignificantlymorelikelytoreportcreditconstraintsandlowerinvestmentwhenfacedwithtightercreditstandards,evenwhencomparedtootherfirmsinthesamesector,countryandtimeperiod.Thissuggeststhat,inadditiontosectoralandcyclicalfactors,itiscrucialtoconsiderindividualfirms’creditdemandconditionswhenassessingtheimpactofachangeincreditsupplyonfirmoutcomes.

Second,usinginformationonfirms’financingstructurefromtheSAFE,wefindevidencethatadiversifiedfundingbasemitigatestheimpactofacredittightening.Havingamorevariedfundingbasecancounteracttheeffectofatighteningforthosefirmswhohaveincreasedneedforbankloans,indicatingthatdifferentiatedsourcesoffundscanhelpsmoothcreditconstraintscomingfromatighterbankcreditconditions.

Third,wefindtheeffectsfromchangesincreditconditionsareasymmetric,asthestrongesteffectsonaccesstofinanceandinvestmentarereportedbyfirmsduringperiodsoftightening,whileperiodsofeasingsuggestmorelimitedeffects.

Inaddition,wefindthattighter(looser)creditstandardsmainlypassthroughtoinvestmentthroughalower(higher)probabilityofincreasinginvestment,ratherthanahigher(lower)probabilityofdecreasinginvestment.Wequantifythattheimpactofaoneunitincreaseinthetightnessofcreditstandardsdecreasestheprobabilityofincreasinginvestmentby0.08pointsafteroneperiod,upto0.13pointsafter3periodsoftightening,illustratingthecombinationofbothalaggingandcumulativeeffect.Forloanavailability,theeffectsofatightening(easing)ofcreditstandardsarestrongerforincreasing(decreasing)theprobabilityofreportingdeclining

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loanavailability(0.03pointsafteroneperiod,risingto0.10afterthreeperiods),butitalsohassomesignificantnegative(positive)impactontheprobabilityofafirmreportingincreasing(decreasing)loanavailability.Bothoftheseeffectsarestrongrelativetotheoverallproportionoffirmsthatreportincreasesordecreaseseachperiod.

Finally,leaningonthefirm-bankmicrolevelinformation,weproposeanewmeasureofdemandadjustedcreditstandardsthatcanprovideadeeperunderstandingofthedynamicsofcreditconditionsintheeuroarea.Ourresultsshowthat,whendemandishigher,theeffectsofchangesincreditstandardsarestronger.Therefore,adjustingcreditsupplymeasuresbyconsideringalsodemandconditionscanbemoreinformativethanlookingpurelyatcreditstandardsalone.Whencomparingourdemandadjustedcreditstandardstoanunadjustedseries,wefindthatduringthesovereigndebtcrisisunadjustedcreditstandardswouldhaveshownarelativelymorebenignpicturecomparedtotheadjustedseries.Moreover,duringthetighteningperiodin2022,theadjustedseriessignalsthatcreditconditionswerelesstightoncedemandisaccountedfor.Thisnewindicatorcanbeausefuladditionalmetrictoenhanceourunderstandingofthepass-throughofcreditstandardstotherealeconomy.

Tothebestofourknowledge,thisisthefirstpaperthatexplicitlylinkstherepliesofthetwosurveysatagranularlevel.Wedosobymatchingfirm-bankleveloutcomesatahalf-yearlybasis.Inthiswaywecancontrolforbothindividualbankloansupply(fromBLS)andfirmdemand(fromSAFE)toexploretheinteractionbetweenthetwofactorsandderiveameasureofdemandadjustedcreditstandards.

Thispapercontributestotheliteraturethatusessurvey-basedinformationtoexplorethefactorsthatimpactfirms’accesstofinance.ThisispossiblethankstothewidearrayoffirmspecificinformationavailablewithintheSAFE.OurresultsparticularlyspeaktothestudyofOngenaetal.(2012)thatshowedthatfirmswithalternativesourcesoffinancearelesslikelytoneedbankcredit.Wefindthatfirmswithamorediversifiedfundingbasearelessaffectedbyabankbasedtighteningofcreditstandardsincaseswheretheyneedcredit.Additionally,ourfindingsconfirmalsothatfirmandcountry-levelcharacteristicsarerelevantdeterminantsforaccesstofinanceasinBecketal.(2005).Thepaperalsoaddstotheliteraturethatassessestheinformationcontentofbanklendingsurveystoexaminebothcreditconditionsandtheirimpactontheeconomy(LownandMorgan(2006),deBondtetal.(2010),DelGiovaneetal.(2011)andAltavillaetal.(2019)).Thisliteraturetypicallyusesloanflowstoanalysebanks’surveyresponses.Inourcase,theuseoffirmsurveydataallowsustodirectlyassesstheimpactof

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changesincreditsupplyonfirms’financingsituations,asfirmsreportonchangesintheircreditdemand,intheirperceivedsupplyconditionsandeconomicoutcomes.Moreover,theSAFEsurveyallowsustobuildalargesampleofresponsesovertimeinacleanfashion,buildingonpreviousworktoassessqualitativelymeasuredoutcomesandresponsesoffirms.

Therestofthepaperproceedsasfollows.Section2introducesourdatasources,section3showsourempiricalresults,section4introducesdemandadjustedcreditstandardsandsection5concludes.

2Data

Ourempiricalmethodologyreliesonseveraldatasources,thekeytwobeingtheSurveyonAccesstoFinanceforEnterprises(SAFE)andBankLendingSurvey(BLS)datasets.Acrucialinnovationofthisstudyisthatthesetwosurveysarethenmatchedatfirm-banklevelusingtheinformationonthenamesofthemainbanksasreportedbySAFEfirmsintheBvDOrbisdatabase.Webrieflydescribetheattributesofeachdataset.

2.1SAFE

TheSAFEhasbeenconductedonbehalfoftheEuropeanCentralBankandtheEuropeanCommissionsince2009.SAFEgathersfirm-levelinformationaboutthefinancialsituationandthefinancingneedsandaccesstofinanceintheeuroarea.Thesurveyisconductedathalfyearlyintervalswithquestionsexaminingthepreviousandnextsixmonths.FirmsinthesamplearerandomlyselectedfromtheDun-Bradstreetdatabase.Thesampleisstratifiedbyfirm-sizeclass,economicactivity,andcountry.Thesamplesizeforeacheconomicactivityischosentoguaranteerepresentationacrossthefourlargestindustries:manufacturing,construction,trade,andservices.Also,thesamplesizesareselectedbasedonrepresentationatthecountrylevel.ThefirsttwosurveyswereconductedwiththeperiodsofinterestbeingJanuary-JuneandJuly-December,butsincethefirstsurveyin2010,thesurveyconcentratesontheperiodsApril-SeptemberandOctober-March.Mostfirmswithinthesurveyaresmallandmedium-sizedenterprises(SMEs)andarethusmostlyreliantonfinancingfrombanksrelativetolargeenterprises.Thus,thesurveyishighlyappropriateforanalysingtheimpactofchangesinbankcreditstandardsortermsandconditions.

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2.2BLS/iBLS

TheBLShasbeenconductedbytheECBsince2003andasksapanelofeuroareabanksaboutchangesintheirlendingconditions.Thesurveyisconductedquarterlywithkeyquestionsfocusingonchangesincreditstandards,termsandconditions,rejectionratesandthedemandforloanssetbyorfacedbyeachbank.Questionsareeitherbackwardlookingoverthepreviousthreemonthsorforwardlookingoverthenextthreemonths.

ThispaperusesaproprietarymicroleveldatasetofindividualbankresponsestotheBLSsurvey,callediBLS.TheiBLSallowsustoexploitbanklevelheterogeneitybeyondthecountrylevelheterogeneitythatcanbeseenintheaggregatedcountry-leveldataset.ThedatasetcomprisesindividuallevelresponsestotheBLSquestionnairefrom120banksacross15euroareacountries.1

2.3Thematchingprocess-SAFE-BvDOrbis

ThedatasetusedinthispaperinvolvesmatchingtheBLSandSAFEindividuallevelrepliesbyusinganECBproprietarydatasetthatlinksSAFEfirmstothefinancialstatementsasprovidedbyBureauVanDijk(BvD)Orbisdatabase.Inaddition,BvDOrbisprovidesalistofmainbanksassociatedtothefirmsinthedatabase.ThislistisdirectlycompiledbyBvDusingacombinationoffirmregistriesanddirectinterviewswithfirmrepresentatives.Itshouldbenotedthatfirmscouldreportmorethanonebank.Weexploitthisfulluniverseofbankswithinourmatchingprocess,inordertomatchallpossiblebankstoeachindividualSAFEfirm.Thiscontrastswithpriorapproachesthatmadeanassumptionthatbankswereorderedintermsoftheirimportancetoeachindividualfirm(seeforexampleCorbisieroandFaccia(2020),Ferrandoetal.(2019)andKalemli-Özcanetal.(2022)).

GiventhecharacteristicsofthebankvariableintheBvDOrbisdataset,weneedtoassumethatbank-firmrelationshipsareunchangedthroughoutoursample.Thisassumptionisnotsostrongasitmaylookprimafacie,asingeneraltheserelationshipstendtobestickyovertime.ThisisbackedbyevidenceseeninKalemli-Özcanetal.(2022)andGiannettiandOngena(2012),whofindthatbycomparingmultiplevintagesoftheOrbis-Amadeusbank-firmmatches,thatthereareverylimitedchangesovertime.Moreover,thisisevenlesslikelytobeanissuein

1BanksinCyprus,Greece,MaltaandSloveniaarenotincludedintheiBLSsample.Ourfinalmatchedsampleisfurtherlimitedbydataavailabilityonthenamesoffirms’banks,asisshowninTable4.

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ourcaseinthatwedonotrestrictourselvestoonlyfocusingonasinglebankforagivenfirm,andinsteaduseallforwhomtheyhavearelationship.

ThedataprovidedintheBvDOrbisdatasetcomeinuncleanedformat,withonlytherawtextnamesofbanks.Assuch,thematchingprocessbetweenSAFEandBLSisnecessarilyamulti-stepautomatedandmanualapproach.Asafirststep,firmsforwhomtheirbanks’namesmatchpreciselythelegalnameofaBLSbankareautomaticallymappedtothatbank.Regardingtheremaindingones,manualmatchingisundertaken,witheachuniqueentryassessedandmatchedtoaBLSbankifamatchexists.Suchanapproachispreferabletoafuzzymatchingapproach,asinmanycaseseithercolloquialnamesareusedforbanksorindividualbankshavesimilarnames.Thisbothmaximisesthesizeofourmatchedsetandminimisesourrisksoffalsepositives.

2.4Datasetcharacteristics

Ourinitialdatasetincludes29,768firms,ofwhich22,799arematchedtoatleastonebank.Ofthese,13,703(60.1%)arematchedtoonesinglebank,whiletherestarematchedtomorethanonebankfromtheBLSsample,varyingfromatotalof2matchesto19forthreecases.ThedistributionoftheseisshowninTable1.

Regardingthepanelstructureofthedataset,thetotalnumberoffirmsbrokendownbytheirnumberofoccurrencesisshowninTable2whilethetotalnumberofobservationsperwaveisshowninTable3.Ascanbeseenthemajorityoffirmsappearmorethanonceinthesurvey,allowingustousefirmfixedeffects.Regardingthetimedistribution,surveyroundspriortotheeleventhwave(September2014)arerelativelyunderrepresented,withthefirstthreeroundshavingparticularlylownumbers.ThisisjusttheresultofthegreatercoverageoffirmsintheanonymisedSAFE-BvDOrbisdatasetfromthispointonwards.

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Table1.Distributionoftotalnumberofbanksforeachfirm

NumberofBLSMatches

TotalMatches

Percentage

1

13703

60.10

2

3700

16.23

3

1736

7.61

4

1024

4.49

5

579

2.54

6

551

2.42

7

407

1.79

8

286

1.25

9

229

1.00

10-14

501

2.21

15-19

83

0.36

Total

22799

100

Table2.Distributiionofthenumberofroundseachfirmappearsinthesurvey

Appearancesindataset

Numberoffirms

Percentageofdataset

1

7854

34.45

2

4578

20.08

3

3019

13.24

4

1990

8.73

5

1809

7.93

6

970

4.25

7

1231

5.40

8

534

2.34

9

302

1.32

10-14

472

2.07

15-22

40

0.18

Total

22799

100

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Table3.Totalobservationsbywave

Wavenumber

Totalobservations

Wavenumber

Totalobservations

1

39

15

3621

2

145

16

3901

3

182

17

3637

4

602

18

3713

5

749

19

3579

6

987

20

3686

7

1411

21

3536

8

1706

22

3456

9

2226

23

3552

10

2023

24

3403

11

3603

25

3424

12

3922

26

3526

13

3628

27

2520

14

3658

Wehaveobservationsfrom11euroareacountries,limited,ononeside,bycoverageintheiBLSdataset,and,ontheotherside,bycoverageinthefirm-bankmatchingdataintheBvDOrbisdatabase.ThedistributionofthenumberoffirmsbycountryisshowninTable4,alongsidetheirrelativematchingrates.Ingeneral,thelargercountrieshaveahighernumberofobservations,withGermany,SpainandFrancemakingupthelargestproportionofoursample.Regardingmatching,weseethatinAustriaandGermanytherelativematchingrateislower,partlyreflectingthehighnumberofbankswithinthosecountries,whilecountrieswithamoreconcentratedbankingsystem(likeSpain)haveahighermatchingrate.

Table4.Distributionofsamplebycountry

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Matched

Country

TotalFirms

Un-

matched

uniquefirms

infinaldataset

Match-ing

Success

Rate

Total

observa-tionsinsample

Averageobserva-tionsperfirm

Austria

3382

1796

1586

46.90%

4793

3.02

Germany

7354

3365

3989

54.24%

12117

3.04

Estonia

325

1

324

99.69%

607

1.87

Spain

5276

127

5149

97.59%

17418

3.38

France

4272

813

3459

80.97%

10912

3.15

Ireland

2023

100

1923

95.06%

6206

3.23

Lithuania

647

104

543

83.93%

1154

2.13

Luxembourg

259

11

248

95.75%

511

2.06

Latvia

544

255

289

53.13%

557

1.93

Netherlands

3166

217

2949

93.15%

8967

3.04

Portugal

2520

180

2340

92.86%

7193

3.07

TOTAL:

29768

6969

22799

76.59%

70435

3.09

2.5KeyVariables

Forouranalysis,weusemeasuresofcreditsupplyfromtheBLSandassesstheirimpactonfirmlevelresultsfromtheSAFE.Thisexploitsakeyadvantageofourmatcheddataset.SincetheSAFE,byconstruction,isnotabalancedpanelwecannotdelveintothelongerruneffectsofchangesinloansupplyatthefirmlevel.Bycontrast,usingtheinformationfromtheBLSwecanconstructalongrunmeasureofcreditsupplyasabackwardlookingaverage.Fortheanalysiswegobackuptooneandahalfyears(threesurveyroundsinSAFE),alongsidemorecontemporaneousmetrics.

Ourmeasureofcreditsupplyisbasedonthebanks’responsesoncreditstandardsfromtheBLS.Banksareaskedeachquarteraboutchangesintheircreditstandardsappliedtoloanstofirmsoverthepreviousthreemonths.Bankscanrespondwithfivepossibleanswers,each

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reflectingthedirectionandthelevelofintensityofchanges.Thequestionisframedintermsofatighteningoraneasing,withbanksstatingwhether:(withthestandardnumericalvaluesassignedtoeachresponseinbracketsafterwards)"tightenedconsiderably”(1),“tightenedsomewhat”(2),“unchanged”(3),“easedsomewhat”(4)and“easedconsiderably”(5).

AstheBLSisquarterlyandtheSAFEisbiannual(firmsareaskedaboutthechangesovertheprevioussixmonths),weaveragebanks’responsesoverthetwoBLSroundsthatcorrespondtoeachSAFEround.Inaddition,asfirmsareoftenmatchedtomorethanonebank,weconstructafirmlevelmeasureofcreditstandardsbyaveragingtheresponsesofeachbankmatchedtothefirm.Thisgivesusacontinuousscalemeasuringthefulluniverseofcreditstandardsfacedbyeachfirminawave.Wetransformtheresponsestobeonascalebetween-2and+2,with+2beingthestrongesttighteningand-2thestrongesteasingeasing.2

Weareinterestedinboththeimpactofshort-runchangesincreditstandardsbutalsothelongerrunchanges,astakingindividualsurveyroundresponsesalonewillnotaccountfortheimpactofcontinuouschangesincreditstandardsacrossmultipleperiods.Lettingoneperiodcsf,tdenotetheindividualperiodcreditstandardsfacingfirmfattimet.Weconstructthefollowingmeasure:

WeconstructthismeasureforK=1,2,3.Thisprovides3measuresofcreditstandards-K=1issimplyequivalenttooneperiodcst,fandisthesingleperiodchangeincontemporaneouscreditstandards.ForK=2andK=3wemeasurethelongerruncreditstandardsfacingafirm,forperiodsofayearandayearandahalfrespectively.AoneunitincreaseforK=1isequivalenttoasingleunitofincreasedtightening,whileforK=2andK=3,aoneunitincreasecanbeinterpretedasamorelong-runcontinuoustightening.

FromtheSAFE,weareinterestedinassessingpotentialresponsesoffirmstochangesincreditstandards.Wedosobyextractingthreevariablesmeasuring(a)changesintheavailabilityofbankloans(b)wh