美联储-自动提高信用额度和消费者福利 Automated Credit Limit Increases and Consumer Welfare

FinanceandEconomicsDiscussionSeries

FederalReserveBoard,Washington,D.C.

ISSN1936-2854(Print)

ISSN2767-3898(Online)

AutomatedCreditLimitIncreasesandConsumerWelfare

VitalyM.Bord,AgnesKovacs,andPatrickMoran

2025-088

Pleasecitethispaperas:

Bord,VitalyM.,AgnesKovacs,andPatrickMoran(2025).“AutomatedCreditLimitIncreasesandConsumerWelfare,”FinanceandEconomicsDiscussionSe-ries2025-088.Washington:BoardofGovernorsoftheFederalReserveSystem,

/10.17016/FEDS.2025.088

.

NOTE:StafworkingpapersintheFinanceandEconomicsDiscussionSeries(FEDS)arepreliminarymaterialscirculatedtostimulatediscussionandcriticalcomment.TheanalysisandconclusionssetfortharethoseoftheauthorsanddonotindicateconcurrencebyothermembersoftheresearchstafortheBoardofGovernors.ReferencesinpublicationstotheFinanceandEconomicsDiscussionSeries(otherthanacknowledgement)shouldbeclearedwiththeauthor(s)toprotectthetentativecharacterofthesepapers.

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AutomatedCreditLimitIncreasesandConsumerWelfare*

VitalyM.BordtAgnesKovacs‡PatrickMoran§

September17,2025

Clickformostrecentversion

Abstract

IntheUnitedStates,creditcardcompaniesfrequentlyusemachinelearningalgo-rithmstoproactivelyraisecreditlimitsforborrowers.Incontrast,anincreasingnumberofcountrieshavebeguntoprohibitcreditlimitincreasesinitiatedbybanksratherthanconsumers.Inthispaper,weexploitdetailedregulatorymicrodatatoexaminetheextenttowhichbank-initiatedcreditlimitincreasesaredirectedtowardsindividualswithrevolvingdebt.Wethendevelopamodelthatcapturesthecostsandbenefitsofregulatingproactivecreditlimitincreases,whichweusetoquantifytheirimportanceandevaluatetheimplicationsforhouseholdwell-being.

*ThispaperwaspreparedfortheCarnegie-Rochester-NYUConferenceSeriesonPublicPolicy.TheviewsexpressedinthispaperaresolelythoseoftheauthorsanddonotrepresenttheviewsoftheFederalReserveBoardortheFederalReserveSystem.CordBarnesprovidedexcellentresearchassistance.

tFederalReserveBoard(

vitaly.bord@

)

‡King’sCollegeLondon,UniversityofManchester,andIFS(

agnes.kovacs@kcl.ac.uk

)

§FederalReserveBoard,IFS,andCEBI(

patrick.e.donnellymoran@

)

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1Introduction

Asalgorithmicdecision-makingreshapesconsumerfinance,acriticaltensionhasemergedbetweentheefficiencyofautomatedcreditdecisionsandtheprotectionofvulnerableconsumers.Inthecreditcardmarket,limitincreasesareaparticularlyimportantbutunderstudiedsourceofcredit,affectingmorethan12%ofaccountseachyear.Countriesdifferintheirapproachtoregulatinglimitincreases.Forexample,intheUnitedStates,theoverwhelmingmajorityoftheseincreasesareimplementedautomaticallybylendersusingproprietarymodelsratherthanrequestedbyconsumers.Bycontrast,reflectingconcernsaboutindebtednessandconsumerprotection,severalcountrieshaverestrictedbanks’abilitytoraisecreditlimits:forinstance,theUKnowprohibitslimitincreasesforborrowerswhohavebeeninpersistentrevolvingdebt,whileCanadaprohibitsbank-initiatedcreditlimitincreaseswithoutconsumers’consent.

Policymakersfaceanimportantquestion:towhatextentshouldtheyregulatealgo-rithmicdecision-makingincreditmarkets,particularlywhenbanksincreasecreditlimitsautomatically?Ontheonehand,automaticcreditlimitincreasescanbebeneficial,astheyrelaxcreditconstraintsandgivehouseholdsgreaterflexibilitytosmoothconsump-tionoveradverseshocks.Ontheotherhand,suchincreasesmayalsobedetrimental.Mostofabank’screditcardprofitscomefromconsumerswhocarrypersistentdebt(

Adamsetal.

,

2022

),creatingincentivesforbankstodirectlimitincreasestowardtheseindividuals.Ifsomeconsumersstrugglewithself-control,additionalcreditmayleadtogreaterindebtedness(

Laibson

,

1997

;

GulandPesendorfer

,

2004

).Indeed,empiricalevi-denceshowsthatconsumersborrowmoreaftercreditlimitincreases,evenwhentheydidnotrequesttheincreaseandarenotobservablyconstrained(

GrossandSouleles

,

2002

).

Inthispaper,weexploitregulatorydataoncreditcardlendingtoinvestigatewhore-ceivesbank-initiatedcreditlimitincreases,thendevelopaquantitativemodeltoevaluatethecostsandbenefitsofallowingbankstoproactivelyraisecreditlimits.Wemakethreemainsetsofcontributions.First,inthedata,wepresentseveralnewstylizedfactsabouttheimportanceoflimitincreasesincreditcardlendingstrategies,theroleofproactivebank-initiatedincreasesthatarenotrequestedbyconsumers,andtheirrelationshiptorevolvingdebt.Second,weexaminetheextenttowhichbank-initiatedcreditlimitin-creasesaredirectedtowardsindividualswithrevolvingdebt,andconsistentwithexistingliterature,documentthatdebtrisesafterlimitincreases.Third,wedevelopamodelofhouseholdbehaviorthatallowsustoperformthefirstquantitativeanalysisofthepositiveandnormativeimplicationsofrestrictingbank-initiatedcreditlimitsincreases,examiningboththeUKapproachofprohibitingincreasesforrevolvingborrowersandtheCanadianapproachofrequiringexplicitconsumerconsent.Overall,webelieveourresultshaveimportantimplicationsforconsumerprotectionandtheemergingfieldof

3

algorithmicregulationinconsumerfinance.Indeed,whilemanyaspectsofcreditcardmarketsareregulated,thereislittleoversightofthefactorslenderscanusetoproactivelyraisecreditlimits.

OurempiricalanalysisutilizesregulatorydataoncreditcardlendingfromFederalReserve’sCapitalAssessmentsandStressTestingReports(Y-14)filedbylargecreditcardissuers,coveringmorethan70%oftheU.S.creditcardmarket.Thisdatasetisuniquelysuitedtoourpurposebecauseitallowsustodifferentiatebetweenbank-andconsumer-initiatedcreditlimitincreases,somethingwhichisimpossibletoobserveinmostotherdatasets,suchascreditbureaudata.TheY-14datacontainmonthly,account-levelinformationaboutgranularaspectsofcreditusageincludingbalances,purchasevolumes,financecharges,andfees.

WebeginbycatalogingseveralnewstylizedfactsabouttheroleandprevalenceoflimitincreasesintheU.S.creditcardmarket.Weshowthatlimitincreasesareanimportantsourceofconsumercredit,withhalfasmuchadditionalavailablecreditcomingfromlimitincreasesasfromnewaccountoriginations.Limitincreasesarerelativelymoreimportantforlowercreditscoreborrowers,aslendersoftenfollowa“lowandgrow”strategyontheseaccounts—withlowlimitsatorigination,followedbyincreasesdependingonborrowerbehavior.Theshareofrevolvingbalances—thepartoftotalbalancesthatarecarriedfromthepreviousmonthratherthanpaidoffandthusaccrueinterest—madepossiblebylimitincreasesissimilarlyhigherforlow-creditscoreborrowers.

Importantly,theoverwhelmingmajorityoflimitincreasesintheU.S.areproactivelyimplementedbybanksratherthanrequestedbyconsumers.Creditcardlendershaveextensiveamountsofdataontheirexistingcustomers,whichtheycanminewithmachinelearningandartificialintelligencealgorithmstoascertainthemostprofitablecustomerstowhomtogivelimitincreases.Consistentwiththeiruseofalgorithms,weshowthatbanksthatmoreoftenreferto‘artificialintelligence’or‘machinelearning’intheirannualfilingstendtosupportalargershareofrevolvingbalanceswithlimitincreases,ratherthanthecreditlimitgrantedatorigination.

Next,buildingonthesestylizedfacts,weexaminetheextenttowhichcreditcardutilizationiscorrelatedwiththelikelihoodofreceivingalimitincrease.Wedistinguishbetweentwodifferenttypesofcreditcardutilization:revolvingutilization,whichreflectsdebtthatiscarriedfrompriormonths,andtransactingutilization,whichreflectsnewpurchases.Whilebothrevolvingutilizationandtransactingutilizationarepositivelycorrelatedwithalimitincrease,wefindthatthelikelihoodofreceivingalimitincreasevariesacrossthetwotypesofutilization.Morespecifically,thecorrelationwithrevolvingutilizationfollowsaninverse-Ushapewiththehighestprobabilityofreceivingalimitincreaseoccurringformoderatelevelsofutilization.Bycontrast,thecorrelationwith

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transactingutilizationfollowsalogarithmicgrowthpattern:theprobabilityofalimitincreaseriseswithutilizationuntilaboutautilizationof0.3,andabovethatlevel,uti-lizationdoesnotappeartovarywiththeprobabilityofanincrease.Wefindthatthesepatternsvarysomewhatwithcreditscore,likelyreflectingunderlyingdifferencesinrisk.

Weconcludetheempiricalresultswithasimpleeventstudyexaminingwhathappenstoutilizationafteralimitincrease.Ourfindingsareconsistentwiththeresultsestablishedintheliterature:inthemonthsafteralimitincrease,utilizationreboundstopre-increaselevels,asconsumerincreasetheirrevolvingdebt.Notably,thiseffectoccurseveninaccountsthatarenotneartheircreditlimitandsoareunlikelytobeliquidityconstrained.Thisisconsistentwiththepresenceofself-controlissues,whichalsopredictsthathighercreditlimitsmayleadtogreaterborrowing.

Afterpresentingtheempiricalresults,wedevelopamodelthatallowsustoperformthefirstquantitativeanalysisofnovelreal-worldpoliciesthatrestrictbank-initiatedcreditlimitincreases.Inthemodel,householdsmakeconsumption,saving,andborrowingdecisionsoverthelife-cyclewhilefacedwithuninsurableincomeandemploymentrisk.Weallowforheterogeneouspreferencesfollowing

Nakajima

(

2017

),allowingfortwotypesofhouseholdswithandwithoutself-controlissues`ala

GulandPesendorfer

(

2001

,

2004

).Weassumehiddeninformationonhouseholdtype,suchthatcreditcardcompaniescannotmakelendingdecisionsbasedontype,butonlybasedonobservedconsumerbehavior.Inthemodel,thecreditcardcompanyinitiallyoffersasimilarproducttoallconsumers,butwiththeoptionoftailoringcreditlimitslater.WecalibratethecreditlimitincreasefunctionusingY-14data,theninternallyestimatethepreferenceparameterstomatchaggregatestatisticsontheU.S.creditcardmarket.

Theabovemodelcapturestwoopposingeffectsfromallowingproactivelimitincreasesbasedonmachinelearningalgorithms.Accordingtotraditionaltheory,consumersbenefitfromadditionalcredit,asitgivesthemgreaterflexibilitytosmoothconsumptionandself-insureagainstrisk.Atthesametime,ifbankalgorithmsimplicitlytargetconsumerswithself-controlissues,itmayleadtogreatertemptationandincreasedborrowing,whichmaybedetrimentaltoconsumersevenifitisprofitabletobanks.Takentogether,ourmodelcapturesthebenefitofrelaxedconstraints,aswellasthepotentialriskofgivingtoomuchcredittoconsumerswithbehavioralbiases(seee.g.

Livshits

,

2020

).

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Webeginbyanalyzingthepropertiesofthebaselinemodel,whereconsumersreceivecreditlimitincreasesbasedontheempiricalevidencefromtheY-14data.Throughthelensofourmodel,wefindthatmostcreditlimitincreasesgotowardsconsumerswithself-controlissues,sincetheseborrowersaremorelikelytomaintainrevolvingbalances.Asaresult,manylimitincreasesaredetrimentalfromtheperspectiveoftheconsumer.

1Wefocusonself-controlissuesduetotheirwell-documentedimportanceincreditcardborrowing(

MeierandSprenger

,

2010

;

Gathergood

,

2012

;

KuchlerandPagel

,

2021

).

5

Thatsaid,wefindimportantheterogeneityregardingwhichlimitincreasesarebeneficial:whilecustomerswithzeroorlowutilizationalmostalwaysbenefitfromgreateraccesstocredit,customerswithmoderateorhighutilizationarefrequentlyharmedbyproactivelimitincreases,astheseconsumersaremorelikelytosufferfromself-controlissues.

Usingtheabovemodel,weanalyzethepositiveandnormativeimplicationsofalter-nativeregulationsthatrestrictbanksfromraisingcreditlimitsincertainsituations.Inourfirstcounterfactualexercise,weevaluateapolicythatprohibitsbanksfromraisingthecreditlimitsofrevolvingborrowers,motivatedbyarecentpolicyintheUK.Inourmodel,wefindthatthispolicyresultsinalargedecreaseintheshareofcreditlimitincreasesgoingtowardsconsumerswithself-controlissues.Asaresultoffewerlimitincreases,thedebt-to-incomeratiodeclinesbyroughly2percentagepoints,evenastheaverageutilizationrateincreasesslightlyduetoreducedcredit.Turningtowardswelfare,wefindthatprohibitinglimitincreasestorevolvingborrowersimproveswelfareby1.1%intermsofconsumptionequivalentvariation.Whileagentswithoutself-controlissuesareslightlyharmedbyreducedaccesstocredit,thesecostsaresmallcomparedtothebenefitsofreducedtemptationandlowerinterestexpenditurebyagentswithself-controlissues.Finally,wefindthatwhenthefirmisabletore-optimizeitscreditlimitincreasefunctioninresponsetothenewpolicyrestriction,thepolicyhelpstoshiftcreditlimitincreasesfromagentswithself-controlissuestoagentswithoutsuchissues,andthusthecounterfactualpolicycontinuestoimproveoverallwell-being.

Thesecondcounterfactualpolicyweanalyzerestrictsbanksfromincreasingcreditlimitswithoutconsumerconsent,inspiredbyrecentregulationsinCanada,Singapore,andNewZealand,whichwillbeimplementedacrossEUmemberstatesin2026.Thispolicyshowssimilarpositiveandnormativeimplicationsasprohibitinglimitincreasesforrevolvers,undertheassumptionthatconsumersarefullyawareoftheirself-controlissues.Thatsaid,wefindmeaningfuldifferencesifwealterourassumptionsabouttheshareofsophisticatedversusna.Requiringconsumerconsentimproveswelfarewhenconsumersaresophisticated,whereasprohibitinglimitincreasesforrevolvingborrowersremainseffectiveevenifallhouseholdsarena.

Takingstock,ourresultshaveimportantpolicyimplicationsforalgorithmicregulationincreditcardmarkets.WhiletheU.S.hasextensiveregulationsoncreditrejectionsandlimitdecreases,thereisanotablelackofregulationsurroundingproactivebank-initiatedlimitincreases.

2

Althoughwedonotobservetheexactalgorithmsusedbybankstodeterminetheiroptimalpolicyforproactivelyraisingcreditlimits,ourempiricalanalysis

2Lendershavetofollowtwomaintypesofregulationsaroundcreditlimitchanges:whendecreasingacreditlimitorrejectinganapplicationtoincreaseacreditlimit,lendersneedstoprovideareasonundertheEqualCreditOpportunityAct;andwhenincreasinglimits,theyneedtoabideby“ability-to-pay”rulesandonlymakeloansthatborrowerscanreasonablypaygiventheirincomeandotherobligations,although

FulfordandStavins

(

2025

)findthattheserulesaregenerallynon-binding.

6

demonstratesarevealedpreferenceforgivingadditionalcredittorevolvingborrowers.Inourcalibratedmodel,thisimpliesthatcreditincreasesaredisproportionatelyallocatedtoconsumerswithself-controlissues,raisingconcernsaboutexploitativecontractinginthespiritof

HeidhuesandK˝oszegi

(

2010

).Takentogether,weconcludethatifsomehouse-holdshaveself-controlissues,therearestrongconsumerprotectionreasonstoregulatethealgorithmsusedbybankstoproactivelytoraisecreditlimits.

RelatedLiterature.Ourpapercontributestoseveralstrandsofliterature.First,anextensiveliteraturehasexaminedhowcreditlimitsaffectconsumptionandborrowinginthecreditcardmarket(

Agarwaletal.

,

2023

,

2017

;

FulfordandSchuh

,

2023

;

Grossand

Souleles

,

2002

).Thesepapersgenerallyfindthatborrowersaltertheirspendingwithchangesintheircreditlimiteveniftheyareunconstrainedandhaveutilizationbelowtheircreditlimit.Mostofthesestudiesdonotestablishacausalinterpretationtothesefindings,withtheexceptionof

Aydin

(

2022

)whousesafieldexperimenttoidentifyacausaleffectand

Chavaetal.

(

2023

)whoexaminetheeffectsofcreditlimitchangesarisingfrombankfundingshocks.Acausaleffectisgenerallydifficulttoidentify,asautomatedchangestocreditlimitsmayberesponsestoborroweractions.

Kovrijnykh

etal.

(

2023

)findthatlendersincreaselimitsonborrowers’existingcreditcardaccountswhenborrowersopennewcreditcards,consistentwiththenewaccountsrevealingpositiveinformationabouttheborrowers’creditworthiness.

3

Weaddtothisliteraturebyexaminingtheaccountandborrowercharacteristicsthathelppredictlimitincreases.Toourknowledge,theonlypreviouspaperthathasfocusedonaccount-leveldriversis

FulfordandStavins

(

2025

),whoexaminehowtheability-to-payrulesimplementedaspartofthe2009CreditCardAccountabilityResponsibilityandDisclosure(CARD)Actaffectedlenders’grantingofcreditlimitincreases.Theyfindthatalthoughmostincomeupdatesarefollowedbylimitincreases,lendersoftenincreaselimitswithoutreceivinganyupdatesaboutborrowerincome.Weaddtothisliteraturebybeingthefirsttoevaluatetheimportanceofutilizationandrevolvingbehaviorindrivingbank-initiatedlimitincreases,andtodocumentthedifferentialrolesofbank-andconsumer-initiatedlimitincreases.

Second,ouranalysisofthewelfareeffectsofrestrictingbank-initiatedcreditexpansioncontributestotheliteratureonconsumerprotectionregulationforconsumerswithbiasedornonstandardpreferences.

HeidhuesandK˝oszegi

(

2010

)showthatwhensomeborrowershavepresent-biaspreferences,itiswelfare-improvingtobanlargepenaltiesfordeferringsmallrepayments,and

HeidhuesandKo…szegi

(

2015

)arguethatinthecreditcardmarket,thewelfarecostsoflenderstakingadvantageofconsumers’misunderstandingsmaybelarge.Althoughregulationmayhelppreventover-borrowing,itmayneverthelessbe

3Inaddition,

Yin

(

2022

)showsthatborrowersinferbothhighergrowthinfuturepersonalincomeandandhighermacroeconomicgrowthfromlender-initiatedcreditlimitincreases.

7

welfarereducingforirrationalborrowers(

Exleretal.

,

2024

).Alongtheselines,

Jolls

andSunstein

(

2005

)arguethatthelawcanhelpde-biasconsumersbynudgingthemtowardstherationaloutcome,ratherthanbyenactingregulation.Ontheempiricalside,

BertrandandMorse

(

2011

)performafieldexperimentandfindthatadditionalinformationdisclosuresaboutpaydayloansreducecustomers’borrowing.

Stangoand

Zinman

(

2011

)showthatweakerenforcementofTruthinLendingActdisclosureswidensthegapinpricesbetweenmore-andless-biasedconsumers.

Gathergood

(

2012

)showsthatself-controlissuesarestronglycorrelatedwithover-indebtednessintheUKcreditcardmarket.Relativetotheexistingliterature,wearethefirsttodocumentthatbank-initiatedcreditlimitincreasesareoftentargetedtowardsrevolvingborrowers,andthefirsttoanalyzetherecently-implementedreal-worldpoliciesthatrestrictthisbehavior.

Third,wecontributetoanemergentliteratureontheregulationofalgorithmicdeci-sionmaking(seee.g.

Blattneretal.

,

2021

).Whilebanksreleaselittlepublicinformationontheirapproachtoproactivecreditlimitincreases,manycreditcardcompaniesuselarge-scaleexperimentstoevaluateandoptimizetheprofitabilityofcreditlimitadjust-mentfunctions(

Botella

,

2022

).Thisapproachcloselyresemblesreinforcementlearning,whereabankadoptsapolicyruleforcreditlimitincreases,whichtheyoptimizebasedontheobservedrewardstofollowingthatpolicyversusoccasionallydeviatingfromthatpolicy,asdescribedby

SuttonandBarto

(

2018

).Indeed,recentresearchshowsthatamachinelearningalgorithmsbank-initiatedcreditlimitincreasesmayoutperformotherapproaches.

4

Wecontributetothisliteraturebyexamininghowcreditcardcompaniesgivelimitincreasesbasedonobservedborrowingbehaviorandthepotentialimplicationsforhouseholdwell-being.

Finally,wecontributetotheliteratureontherevolvingandtransacting,orconve-nience,functionsofcreditcards.

AdamsandBord

(

2020

)stresstheimportanceofsepa-ratingaccountsbytheircreditcardusagetounderstandcreditmarketdynamicsduringtheCOVID-19pandemic.

GrodzickiandKoulayev

(

2021

),

FulfordandSchuh

(

2023

),and

LeeandMaxted

(

2023

)showthatrevolverstatusishighlypersistent.

Adamsetal.

(

2022

)emphasizethatrevolversaretheprimarydriverofcreditcardlenders’income,within-terestincomecomprisingthevastmajorityoflenders’revenuesandprofits.Whileotherpaperssuchas

Agarwaletal.

(

2017

)havediscussedprofitabilityinthecontextofcreditlimitincreases,wefocusondisentanglingtherolesthattherevolvingandtransactingutilizationsplayindrivinglimitincreases.

Therestofthepaperisorganizedasfollows.Section2introducesthedataandes-tablishesseveralmotivatingstylizedfactsontheimportanceofproactivelimitincreases,theprevalenceof“low-and-grow”strategies,andtheirroleinmakingpossiblehigherag-

4See(

Alfonso-Snchezetal.

,

2024

)forananalysisoftheoutperformanceoftheDoubleQ-Learning

algorithm

8

gregaterevolvingdebt.Section3evaluatestherelationshipbetweenrevolvingutilizationandlimitincreaseattheloanlevel.Buildingonthesefindings,section4presentsthequantitativemodelandsection5discussesthemainmodelresultsandcounterfactuals.Section6concludesanddiscussesdirectionsforfurtherresearch.

2Dataandstylizedfacts

Inthissection,wefirstdiscusstheregulatorydataweuseintheempiricalpartsofthepaper.Next,wepresentseveralnewstylizedfactsontheprevalenceandimportanceofcreditcardlimitincreases.Finally,weturntoanoverviewoftheregulatoryframeworkregardinglimitincreases.

2.1Data

OurdatacomefromFormFRY-14MoftheFederalReserve’sCapitalAssessmentsandStressTestingReport(henceforth,Y-14)fortheJanuary2014toDecember2024timeperiod.Thesedataprovidemonthly,account-levelinformationonallcreditcardsissuedbylargestress-testedbankswithmorethan§100Binassets.The26banksinthedatasetduringoursampleperiodcomprise,inaggregate,morethan70%ofcreditcardbalancesintheU.S..Becauseofthelargesizeofthedataset,forouranalysis,weusea0.5%sampleofaccounts,consistingofmorethan150millionobservationscorrespondingtomorethan3.6uniqueactivecreditcards.

Thedataincludedetailedinformationonthecharacteristicsandusageofeachcreditcardaccount,includingcreditlimit,balances,utilization,totalpurchases,financecharges,anddelinquency.Thedataalsoincludemeasuresoftheborrower’screditscore,whichareavailablebothupdatedandatorigination.Self-reportedincomeisavailableatoriginationformostborrowers.Until2020,lendersalsoreportedupdatedincomewheneveritwasrefreshedbytheborrower;however,thisinformationisnotavailablesince2020(see

FulfordandStavins

(

2025

)formoredetails).

TheY-14datasethastwoadvantagesovercreditbureaudata,whichisoftenusedtoexamineconsumerfinancialhealthandbehavior.First,becausethedataincludeinformationonbalancesandactualpayments,itispossibletoidentifyrevolvers—accountsthatutilizethecreditfeatureofacreditcardandcarrybalancesmonthtomonthonwhichtheypayinterest.Itisimportanttoidentifyrevolversbecauseinterestcomprisesthevastmajorityoftheprofitabilityofcreditcardportfolios,andsorevolversareacreditcardlender’smostprofitablecustomers(

Adamsetal.

,

2022

).Second,thedataallowustoidentifycreditlimitchangesandwhethertheywereinitiatedbythelenderortheborrower.Toourknowledge,thisistheonlycurrently-availabledatasetthatidentifiesthesourceof

9

achangeincreditlimit,thusallowingustostudybank-initiatedlimitchangesseparatelyfromconsumer-initiatedones.

OnedifferencebetweentheY-14dataandmore-commonly-usedcreditbureaudataisthattheY-14datasetisattheaccountlevel.Althoughweareabletoexaminesomeborrowercharacteristicssuchascreditscore,weareunabletolinktotheborrower’sotherloansorevenothercreditcards.Afurtherdiscussionofthedataandsummarystatisticsispresentedinsection

A.2

oftheAppendix.

2.2Motivatingstylizedfactsaboutcreditcardlimitincreases

Creditcardlimitincreasesareanunderstudiedaspectoftheprovisionofcreditincon-sumercreditmarkets.Inthissubsection,wepresentavarietyofaggregatestylizedfactsthatmotivatethemodelwepresentinsection

4

.

Fact1.Limitincreasesareanimportantsourceofcredit.

Figure

1

,PanelA,plotsthetotalcreditlimit—thatis,theamountofavailablecredit—comingfromnewlyoriginatedcardsandfromcreditcardlimitincreaseseachquarter.PanelBplotsthetotalnumberofcreditcardsthatareissuedeachquarterandthetotalnumberofcardsthatundergolimitincreases.

$B

PanelA.Credit

80

60

40

20

0

2014q12017q12020q12023q1

M

PanelB.Numberofcards

20

15

10

5

2014q12017q12020q12023q1

Limitincreases---Neworiginations

Figure1:Creditcardlimitincreasesandnewissuance

Asthefiguresshow,duringthepost-pandemicperiod,creditcardlimitincreasesresultinmorethan§40Bofadditionalavailablecrediteachquarter,whichisalmost60%oftheapproximately§70Bofavailablecreditthatarisesfromnewcardissuance.Priortothepandemic,creditlimitincreaseswereabout§30B,orabouthalfofnewissuance.Inaddition,thetotalnumberofcardsthatundergoacreditlimitincreaseeac