jfe息技术与贷款人竞争

Xavier Vivesa,∗,Zhiqiang Yeb aIESE Business School, Av. de Pearson, 21, Barcelona, 08034, SpainbSchool of Economics, Zhejiang University, 866 Yuhangtang Rd, Hangzhou, 310058, China A R T I C L EI N F O A B S T R A C T We study how information technology (IT) affects lender competition, entrepreneurs’ investment, and welfarein a spatial model. The effects of an IT improvement depend on whether it weakens the influence of lender–borrower distance on monitoring costs. If it does, it has a hump-shaped effect on entrepreneurs’ investment andsocial welfare. If not, competition intensity does not vary, improving lender profits, entrepreneurs’ investment,and social welfare. When entrepreneurs’ moral hazard problem is severe, IT-induced competition is more likelyto reduce investment and welfare. We also find that lenders’ price discrimination is not welfare-optimal. Ourresults are consistent with received empirical work on lending to SMEs. Dataset link:Information Technology and Lender Competition (Original data) JEL classification:G21G23I31 Keywords:CreditMonitoringFinTechPrice discriminationMoral hazardRegulation toprice discriminate.The COVID-19 pandemichas accelerated thisdigitalizationprocess and fostered remote loan operations and thedevelopmentanddiffusionofITinthecreditmarket(Carlettietal.,2020). 1.Introduction Thebankingindustryisundergoingadigitalrevolution.Agrow-ingnumberoffinancialtechnology(FinTech)companiesandBigTechplatformsareengagingintraditionalbankingbusinessesusingtheirin-novativeinformationandautomationtechnologies.1Incumbentbanksarealsomovingfromrelianceonphysicalbranchestoadoptinginfor-mationtechnology(IT)andBigDatainresponsetotheavailabilityoftechnology and to changes in consumer expectations of service,whicharetwomaindriversofdigitaldisruption(FSB,2019).Suchatransformationspursthebankingsector’sincreasinginvestmentinIT,allowingfinancialintermediariestoofferpersonalizedservicesand Howdothedevelopmentanddiffusionofinformationtechnologyaffectlendingcompetition?WhatarethewelfareimplicationsofITprogress?Inparticular,doesthetypeofITmatterforcompetitionandwelfare?Isthereawelfarelossfrompricediscrimination?Toanswerthosequestions,we build a model of spatial competition in whichlenderscompetetoprovideentrepreneurswithloans.Lendersinourmodelrefertoinstitutionsthatcanprovideloansinthecreditmar-ket,includingcommercialbanks,shadowbanks,fintechs,orBigTech Themodelhastwoimportantingredients:First,lendermonitoringmattersforwelfaresinceitenablesentrepreneurswithmoralhazardproblemstoobtaincreditandinvest.Second,lenderscannot credi-blycommittomonitoringeffortexantesincetheycanadjustcreditavailabilitylater(afterobservingentrepreneurs’privatebenefits). platforms.Ourmodelwillhelptoilluminatethefollowingempiricalresults: •BusinesslendingbybankswithbetterITadoptionislessaffectedbythedistancebetweenbanksandtheirborrowers(Ahnertetal.,2024).•Borrowerswithbetteraccesstobankfinancingrequestloansatlowerinterestratesonafintech’splatform(Butleretal.,2017).Abankwillchargeitsborrowershigherloanratesiftheborrowersgetgeographicallyclosertothebankor/andfartherawayfromcompetingbanks(Herpferetal.,2022).•Increasedbank/branchindustryspecialization(e.g.,inexport/SME)lendingcurtailsbankcompetition(Paravisinietal.,2023;Duquerroyetal.,2022).Broadbandinternetimplementationin-tensifiesbank competition and reduces banks’loan prices(D’Andreaetal.,2021).•Banks with superior IT adoption have higher loan growth(Dadoukisetal.,2021andBranzolietal.,2024).Entrepreneur-shipis stronger in US counties that are more exposed to IT-intensivebanks(Ahnertetal.,2024).•Therelationshipbetweenbankcompetitionandbankcreditsup-plyishump-shaped(DiPattiandDell’Ariccia,2004). Wedistinguish two types of information technology:(a)infor-mationcollection/processingtechnology(IT-basicforshort)and(b)distancefriction-reducingtechnology(IT-distanceforshort).Improve-mentsinthetwotypesofITgeneratedifferentoutcomes.Specifically,animprovementinIT-basiclowersevenlythecostsofmonitoringen-trepreneursindifferentlocations.Suchanimprovementinthelendingsectordoesnotaffectlenders’relativecostadvantageindifferentloca-tions–forexample,byimprovingtheabilitytocollectmorevaluabledataand process them with better computer hardware or informa-tionmanagement software(e.g.,desktop applications).In contrast,improvingIT-distancereducesthenegativeeffectoflender–borrowerdistanceonmonitoringcosts.Suchanimprovementlowersmoresignif-icantlythecostsofmonitoringentrepreneurslocatedfartheraway.Forexample,betterinternetconnectivityandcommunicationtechnology(e.g.,videoconferencing)reducethephysicaldistancefriction.5Theimprovementinremotelearningdevices,searchengines,andartificialintelligence(AI)makesiteasiertoextendexpertise,therebyreduc-ingthe expertise distance friction.Big Data and machine learningtechniquesmayimprovebothIT-basicandIT-distance.6Underthe set-up described,we study how information technol- ThelendingmarketismodeledasalinearcityàlaHotelling(1929)wheretwolenderslocatedatthetwo