大型科技公司与货币政策的信贷渠道

BISWorkingPapers No1088 Bigtechsandthecreditchannelofmonetarypolicy byFiorellaDeFiore,LeonardoGambacortaandCristinaManea MonetaryandEconomicDepartment April2023 JELclassification:E44,E51,E52,G21,G23. Keywords:BigTechs,monetarypolicy,creditfrictions. BISWorkingPapersarewrittenbymembersoftheMonetaryandEconomicDepartmentoftheBankforInternationalSettlements,andfromtimetotimebyothereconomists,andarepublishedbytheBank.Thepapersareonsubjectsoftopicalinterestandaretechnicalincharacter.TheviewsexpressedinthemarethoseoftheirauthorsandnotnecessarilytheviewsoftheBIS. ThispublicationisavailableontheBISwebsite(www.bis.org). ©BankforInternationalSettlements2023.Allrightsreserved.Briefexcerptsmaybereproducedortranslatedprovidedthesourceisstated. ISSN1020-0959(print) ISSN1682-7678(online) BigTechsandtheCreditChannelofMonetaryPolicy F.DeFiore∗L.Gambacorta†C.Manea‡§March9,2023 Abstract Wedocumentsomestylizedfactsonbigtechcreditandrationalizethemthroughthelensofamodelwherebigtechsfacilitatematchingonthee-commerceplatformandextendloans.Thebigtechreinforcescreditrepaymentwiththethreatofexclusionfromtheplatform,whilebankcreditissecuredagainstcollateral.Ourmodelsuggeststhat:(i)ariseinbigtechs’matchingefficiencyincreasesthevalueforfirmsoftradingontheplatformandtheavailabilityofbigtechcredit;(ii)bigtechcreditmitigatestheinitialresponseofoutputtoamonetaryshock,whileincreasingitspersistence;(iii)theefficiencygainsgeneratedbybigtechsarelimitedbythedistortionaryfeescollectedfromusers. JELclassification:E44,E51,E52,G21,G23 Keywords:BigTechs,monetarypolicy,creditfrictions ∗BankforInternationalSettlementsandCEPR.Email:Fiorella.DeFiore@bis.org. †BankforInternationalSettlementsandCEPR.Email:Leonardo.Gambacorta@bis.org. ‡BankforInternationalSettlements.Email:Cristina.Manea@bis.org. §ThisresearchprojectwaspartiallycompletedwhileC.ManeawasworkingfortheresearchcenteroftheDeutsche Bundesbank.TheviewsexpressedinthispaperareourownandshouldnotbeinterpretedasreflectingtheviewsoftheBankforInternationalSettlements,theDeutscheBundesbank,ortheEurosystem.WethankK.Adam,F.Alvarez, J.Gal´ı,R.ReisandH.Uhligforusefuldiscussionswhiledevelopingtheanalyticalframeworkofthisproject,toF.SmetsandM.Bussi`erefordiscussingourpaperattheCEBRAandtheAnnualSNBMonetaryEconomicsConference,aswellasforusefulcommentstoJ.Benchimol,F.Boissay,B.Bundick,P.Cavallino,K.Dogra,M.Gertler,A.Glover, M.Hoffmann,T.Holden,D.Lee,V.Lewis,M.Lombardi,G.Lombardo,Y.Ma,J.Matschke,L.Melosi,E.Mertens, T.Mertens,E.Moench,M.Rottner,J.Sim,M.Spiegel,I.Vetlov,L.ZhengandseminarandconferenceparticipantsattheBankforInternationalSettlements,DeutscheBundesbank,CentralBankofIsrael’sVIMACROseminarseries,KansasCityFed,BSESummerForum,IFRMP,PadovaMacro,RAD,CEBRA,OsloMacroconference,ASSA2023,andTinbergenInstitute.WearegratefultoG.CornelliandA.Maurinforexcellentresearchassistance,andtoV.Shreetiforsharingdataonbigtechs’fees. 1Introduction LargetechnologyfirmssuchasAlibaba,Amazon,FacebookorMercadoLibre(bigtechs)haverecentlyventuredinfinancialmarketsbyprovidingloanstovendorsontheire-commerceplatforms.Bigtechcredithasrapidlyexpandedovertherecentyears,reachingvolumesofUSD530billionin2019,upfromonlyaroundUSD11billionin2013.Thepaceofincreaseinbigtechcreditcanbeexpectedtoexceedthatofbankcreditinsomecountries.Forinstance,during2020-21,bigtechcreditinChinarecordedanaverageannualgrowthrateof37%,comparedto13%forbankcredit. Thesechangesinfinancialintermediationcanshapethetransmissionofmonetarypolicyinnotableways.Thebusinessmodelofbigtechsreliesonthecollectionanduseofvasttrovesofdataratherthancollateraltosolveagencyproblemsbetweenlendersandborrowers.Creditscoringgeneratedusingmachinelearningandbigdataareabletoidentifyfirms’creditworthinesswithmoreprecisionthantraditionalcreditbureauratings(Frostetal.(2020)).Moreover,thethreatofbeingbannedfromthee-commerceplatformorevenofhavingone’sreputationtarnishedservesasanextra-legalbuthighlyeffectivemeansofcontractenforcementforbigtechcompanies(Gambacorta,KhalilandParigi(2022)).Thecrucialroleofdatainthecreditscoringprocessandthethreatofexclusionfromthebigtechecosystemreducetheneedforfirmstopledgecollateralinloancontracts.Thisexplainswhybigtechcreditisuncorrelatedwithrealestatevalues,butitishighlycorrelatedwithfirm-specificcharacteristics,suchastransactionvolumesonthebigteche-commerceplatform(Gambacortaetal.(2022)).Astheshareofbigtechcreditrises,monetarypolicywillaffectcreditsupplylessviaassetprices(thetraditional”physicalcollateralchannel”`alaKiyotakiandMoore(1997)),andmoreviarepaymentincentivecompatibilityconstraintswithinBigTechs’ecosystems(thenovel”networkcollateralchannel”thatwehighlight). Ourpaperaimstoshedsomelightontheeffectsofbigtechs’entryintofinanceonthemacroeconomyandonmonetarypolicytransmission.Wedevelopamodelthatcanreplicatetwokeyempiricalfactsaboutbigtechs.First,usingmacrodataforChinaandtheUS,andextendingpreviousevidencebasedonChinesemicrodata,weshowthatbigtechcreditdoesnotreacttochangesinassetpricesandlocaleconomicconditions,unlikebankcredit.Second,weuselocalprojectionstoshedl