BIS-貨幣政策與信用卡消費(fèi)支出（英）-33正式版

BISWorkingPapersNo1064MonetarypolicyandcreditcardspendingbyFrancescoGrigoliandDamianoSandriMonetaryandEconomicDepartmentJanuary2023JELclassification:E21,E52.Keywords:creditcardspending,heterogeneity,monetarypolicy,transmission.BISWorkingPapersarewrittenbymembersoftheMonetaryandEconomicDepartmentoftheBankforInternationalSettlements,andfromtimetotimebyothereconomists,andarepublishedbytheBank.Thepapersareonsubjectsoftopicalinterestandaretechnicalincharacter.TheviewsexpressedinthemarethoseoftheirauthorsandnotnecessarilytheviewsoftheBIS.ThispublicationisavailableontheBISwebsite().? BankforInternationalSettlements2023.Allrightsreserved.Briefexcerptsmaybereproducedortranslatedprovidedthesourceisstated.ISSN1020-0959(print)ISSN1682-7678(online)MonetaryPolicyandCreditCardSpending?FrancescoGrigoli DamianoSandriIMF BISandCEPRDecember12,2022AbstractWeanalyzetheimpactofmonetarypolicyonconsumerspendingusingconfiden-tialcreditcarddata.Beingavailableatdailyfrequency,thesedataimprovetheidenti-ficationofthemonetarytransmissionandallowforamoreprecisecharacterizationofthetransmissionlags.Wefindthatshockstoshort-terminterestratesaffectspendingmuchmorerapidlythanshockstomedium-terminterestrates.Wealsodocumentsignificantasymmetriesintheeffectsofmonetarypolicy.Whileinterestratehikesstronglycurbspending—especiallyifcoupledwithreductionsinstockpricesreflect-ingpuremonetarypolicyshocks—interestratecutsappearunabletoliftspending.Finally,weexploitthedisaggregationofcreditcarddatatoexaminetheheteroge-neouseffectsofmonetarypolicyacrossspendingcategoriesandusers’characteristics.Keywords:creditcardspending,heterogeneity,monetarypolicy,transmissionJELCodes:E21,E52?FrancescoGrigoli,fgrigoli@;DamianoSandri,damiano.sandri@.TheviewsexpressedinthispaperarethoseoftheauthorsanddonotnecessarilyrepresentthoseoftheBIS,theIMF,itsExecutiveBoard,oritsmanagement.WethankFableDataforsharingthecreditcarddatausedintheanalysisandPeterPedroniforveryhelpfuldiscussions.WealsothankparticipantstoseminarsattheIMFandtheBIS.1IntroductionThispaperprovidesthefirstassessmentofthetransmissionofmonetarypolicytocon-sumerspendingusingconfidentialcreditcarddata.Thesedataoffertwokeyadvantagesrelativetotraditionalconsumptionindicatorsusedinthemonetarypolicyliterature.First,creditcarddataareavailableathighfrequency,providinginformationondailytransactions.Thismakesitpossibletoaccuratelymatchthetimingofmonetarypolicyshocksaroundpolicyannouncementswithspendingdata,thusimprovingtheidentifi-cationofthemonetarypolicyeffects.Inexistingstudies,monetarypolicyshockshavetobeaggregatedatthelowerfrequencyoftraditionalconsumptiondata,introducingag-gregationbiasandreducingthenumberofusablemonetarypolicyshockswhenmoreannouncementsoccurwithinthesameaggregationperiod.Thedailyfrequencyofcreditcarddataisalsohelpfultomatchthetimingofothercontrolvariables.Forexample,oureconometricframeworkcontrolsfordailyinformationonCOVID-19casesandthestringencyoflockdownmeasures,withouthavingtoaggregatetheseindicatorsatlowerfrequency.Finally,thehighfrequencyofcreditcarddatamakesitpossibletoexaminethetransmissionlagsofmonetarypolicymoreaccurately,revealingnewinsightsaboutdifferencesinthetransmissionspeedofinterestrateshocksatdifferentmaturities.Second,creditcarddatacanbeusedtotestforvariousformsofpotentialheterogene-ityinthetransmissionofmonetarypolicy.Byprovidingdetailedinformationonindivid-ualtransactions,thesedatacanbeusedtodifferentiatetheimpactofmonetarypolicyonspendingacrossdifferentgoodsandservices.Furthermore,creditcardcompaniescollectdemographicandeconomicinformationabouttheusers,makingitpossibletoexaminepossibleheterogeneityintheimpactofmonetarypolicyacrossdifferentsegmentsofthepopulation.Creditcarddatamayalsoprovideamoreprecisemeasurementofconsumption.Tra-ditionalconsumptiondataconstructedbystatisticalagenciesrelyheavilyonhouseholdsurveyswhicharesubjecttoseverallimitations,suchaslimitedsamplesize,reportingerrors,andtimelagsbetweendifferentsurveywaves.Theseconcernshavebecomemoreacuteinrecentyearsduetodecliningresponseratesinsurveys(Meyer,MokandSulli-van,2015).Byrecordingactualexpendituresinreal-timeandforavastnumberofusers,creditcarddatamaythusovercometheselimitations(Abrahametal.,2022).1OuranalysisusescreditcarddataprovidedbyFableDataforGermanycoveringthe1Adrawbackofcreditcarddataisthattheydonotcoverthefullspectrumofconsumerpurchases,forexamplebig-ticketitemslikecarpurchases.Yetcreditcarddatacorrelateverycloselywithaggregateconsumptiondataasdocumentedlaterinthepaper.2period2017–2021.2FableDatacollectshundredsofmillionsoftransactionsonconsumerspending,coveringmorethan1millionusers.Tocapturemonetarypolicyshocks,werelyonhigh-frequencymovementsinfinancialmarketsduringECBpolicyannouncements,ascompiledbyAltavillaetal.(2019).Weassesstheimpactofmonetarypolicyshocksoncreditcardspendingusinglocalprojections.Wefindthatapositiveinterestrateshock—measuredanasincreaseinthe2-yearyieldduringpolicyannouncements—hasasignificantnegativeimpactonspending.Theeffectstartstomaterializewithalagofabout6monthsandinvolvesareductioninboththenumberofcreditcardtransactionsandintheiraveragespendingamounts.Wealsodoc-umentthatshockstoshort-terminterestratestendtohaveamuchmorerapideffectoncreditcardspending.Onthecontrary,aftercontrollingformovementsin2-yearinterestrates,long-terminterestratesshockshavenosignificanteffectonspending.Theseresultscomplementtheliteratureontherelativeeffectivenessofconventionalversusunconventionalmonetarypolicywhichhasfocusedsofarontheimpactonfinan-cialmarketsgiventhelackofhigh-frequencymacroeconomicdata(Gurkaynak,¨SackandSwanson,2005;KrishnamurthyandVissing-Jorgensen,2011;Gilchrist,Lopez′-SalidoandZakrajsekˇ,2015;Gagnonetal.,2011;Swanson,2021).Ourresultssuggestthatconven-tionalpolicyratechangeshaveamorerapidandtangibleeffectonconsumptionthanunconventionaltools—suchasforwardguidanceandquantitativeeasing—thattendtooperateonlongertermyields.Thesefindingshaveimportantpolicyimplicationsfortheongoingeffortstocurbinflation.Giventheurgentneedtocooldownaggregatedemandbeforeapossiblede-anchoringofinflationexpectations,policyratehikesarepreferabletoquantitativetighteningbecausetheyinvolveshortertransmissionlags.Usingpolicyratestoacceleratemonetarytransmissioncanalsoreducetheriskthatcentralbanksmayendupover-tighteningmonetarypolicybeforeobservingtheeffectsonaggregatedemand.Weextendtheeconometricanalysisalongvariousdimensionstoleveragetheidenti-ficationadvantagesofcreditcarddataandexaminemonetarytransmissioningreaterdetail.Wefirstshowthatmonetarypolicyhashighlyasymmetriceffectsonspend-ing,dependingonthedirectionoftheinterestratemovements.Whilepositiveinter-estrateshockstriggerspendingcontractions,negativeinterestrateshocksareineffectiveinboostingspending.ThesefindingsareconsistentwithrecentworkbasedonUSdata `(TenreyroandThwaites,2016;Angrist,OscarJordaandKuersteiner,2018;BarnichonandMatthes,2018)andhighlighttheneedforfiscalpolicytosupportdemandduringeconomicdownturns.Wethenexaminehowthetransmissionofinterestrateshocksdependsonthecontem-SeeformoreinformationaboutFableData.3poraneousresponseofstockprices.AspointedoutbyCieslakandSchrimpf(2019)andJarocinski′andKaradi(2020),theco-movementbetweeninterestratesandstockpricesduringmonetarypolicyannouncementscanbeusedtodisentangleexogenousshiftsinthemonetarystancefrominformationshocks.Forexample,anexogenousmonetarypol-icytighteningshouldgenerateanincreaseininterestratesanddeclineinequityprices.Onthecontrary,amonetarypolicyannouncementthatprovidespositiveinformationabouttheoutlookshouldtriggeranincreaseinbothinterestratesandequityprices.Consistentwiththisinterpretationoftheco-movementbetweentheshocks,wefindthatpositiveinterestrateshockscoupledwithdeclinesinequitypricesgeneratesignificantspendingcontractions.Consumerspendingtendsinsteadtoincreaseinresponsetopos-itiveinterestrateshocksassociatedwithrisingequityprices.Finally,weexaminepossibleheterogeneouseffectsofmonetarypolicyacrossspend-ingcategoriesandagents’characteristics.Wefindthatmonetarypolicyimpactdifferenteconomicsectorsquiteasymmetrically.Inparticular,whilemonetarytighteningsubstan-tiallyreducesspendingondiscretionarygoods,itboostsspendingonconsumerstaples.Thisispossiblyduetosubstitutioneffects.Forexample,peoplemayrespondtoamon-etarytighteningbyforegoingspendingonrestaurantmealswhileincreasingspendingonat-homefood.Wealsofindevidencethatmonetarytighteningimpactshigh-incomecreditcardusersmorestrongly,possiblyconsistentwiththetheoreticalpredictionthatintertemporalsubstitutioneffectsarestrongeramonglessfinanciallyconstrainedcon-sumers.Monetarypolicydoesnotappearinsteadtohavedifferenteffectsacrossgender,agegroups,andacrosson-lineversusin-personpurchases.Relatedliterature.Thepapercontributestoalargeliteratureontheeffectsofmonetarypolicyonhouseholdconsumption.Earlycontributionswerelimitedtousingmacro-leveldataprovidedbystatisticalagencies(Christiano,EichenbaumandEvans,1999).Inrecentyears,theliteraturehasstartedtoprovidemoregranularevidenceaboutthetransmis-sionchannelsofmonetarypolicyusingindividualleveldatafromhouseholdsurveys.3Forexample,Cloyne,FerreiraandSurico(2020)findthathouseholdswithamortgagerespondtomonetarypolicyshocksmorestronglythanrentersandoutrighthomeowners.Householdsurveydataentail,however,significantlimitations,namelyalowfrequencyofobservation,measurementerrorinself-reportedconsumption,andsmallsamplesize.Toincreasethesampleofanalysis,newstudieshaveleveragedlargeadministrativedatafromgovernmentregistries.UsingincomeandwealthdatafromtaxrecordsinNor-way,Holm,PaulandTischbirek(2021)constructhouseholdexpendituresattheyearly3Thereisalsoaparalleltheoreticalliteraturethatexaminestheheterogeneouseffectsofmonetarypolicyacrosshouseholds(Kaplan,MollandViolante,2018;Auclert,2019).4frequencyandexaminetheirsensitivitytomonetarypolicyshocksdependingonhouse-holds’liquiditypositions.4Theuseofadministrativedataprovidesimportantadvan-tages,suchasuniversalcoverageofthepopulationanddetailedinformationabouthouse-holds’balancesheets.5However,thesetypesofadministrativedataareavailableonlyatlowfrequencyanddonotprovidedirectinformationonconsumptionexpenditureswhichhavetobeinferredusingwealthandincomedata.Therefore,relativetosurveyandadministrativedata,creditcarddatahavetheadvantageofbeingavailableathighfrequencyandbeingaccuratelymeasured.Tocapturemonetarypolicyshocks,thepaperdrawsfromtheliteratureonhigh-frequencyidentification.Thisapproachuncoversmonetarypolicysurprisesbyexamin-ingmovementsinfinancialmarketsduringtightwindowssurroundingpolicyannounce-ments.Moststudieshavefocusedontheimpactoftheseshocksonfinancialmarketvari-ables(Kuttner,2001;CochraneandPiazzesi,2002;BernankeandKuttner,2005;Gurkay¨-nak,SackandSwanson,2005;HansonandStein,2015;Gilchrist,Lopez′-SalidoandZa-krajsekˇ,2015;NakamuraandSteinsson,2018).Yetsomepapershavealsousedhigh-frequencyidentificationtostudytheeffectsonmacroeconomicvariables.Forexam-ple,GertlerandKaradi(2015)aggregatehigh-frequencymonetaryshocksatmonthlyfrequencyandusethemasinstrumentintoaVARtoexaminetheimpactonmedium-termcreditvariablesandindustrialproduction.6Relativetothesepapers,ouranalysisstrengthenstheidentificationofthemonetarypolicytransmissionbybettermatchingthetimingofthemonetarypolicyshockswithdailycreditcardtransactions.Thisalsomakesitpossibletoexaminemonetarytransmissioningreaterdetail,forexamplelookingatdifferencesinthetransmissionlagsassociatedwithdifferentinterestratematurities.Fur-thermore,wecandifferentiatetheeffectsofmonetarypolicyacrossdifferentspendingcategoriesandcreditcardusers’characteristics.ThepaperisalsorelatedtoongoingeffortsintheeconomicprofessiontoharnessthetransformativepotentialofBigData(EinavandLevin,2014).Thevastamountofin-formationcollectedbyprivatefirmsandgovernmentagenciescangreatlyfacilitateeco-nomicmonitoringandallowtotracktheeffectsofeconomicshocksandpolicyactionsatthemicrolevel.TheCOVID-19pandemichasacceleratedworkontheseissues,withsomeresearchersusingcreditcarddatatocapturechangesinspendingpatternsinreal4Andersenetal.(2020b)alsoemploytaxdatatoestimatetheimpactofmonetarypolicyshocksbutfocusontheeffectsonincome,wealth,andcarpurchases.Administrativedatahavealsobeenusedtoexaminetheconsumptionresponsetounemploymentandhealthshocks(Kolsrudetal.,2018;Kolsrud,LandaisandSpinnewijn,2020;LandaisandSpinnewijn,2021).6SeealsoJarocinski′andKaradi(2020),Miranda-AgrippinoandRicco(2021)andAndradeandFerroni(2021).5timeandacrossdifferentgoods(Andersenetal.,2020a;Bounieetal.,2020;Chettyetal.,2020;Hac?oglu?-Hoke,Kanzig¨andSurico,2021).Creditcarddatahavealsobeenusedtoexaminetheresponseofhouseholdconsumptiontounemploymentspells(GanongandNoel,2019;Andersenetal.,2021).Inthispaper,weshowcasetheirabilitytoprovidenovelinsightsaboutmonetarytransmission.Thepaperisorganizedasfollows.Section2describesthecreditcarddatausedintheanalysis.Section3examinestheeffectsofmonetarypolicyonconsumerspending.Sec-tion4differentiatestheeffectsofmonetarypolicyacrossspendingcategoriesandagents’characteristics.Section5concludes.CreditcarddataTheanalysisusesconfidentialcreditcarddataforGermanhouseholdsprovidedbyFableData.ThesamplerangesfromFebruary2017toDecember2021.Thedatareportsthespendingamountsofindividualtransactions,includingthedayofthetransaction,theaccountfromwhichthepaymentismade,aclassificationofthemerchantcategory,andwhetherthetransactiontookplaceinpersonoronline.Foreachcreditcardaccount,weobserveafewcharacteristicsoftheowner,suchgender,agegroup,andanincome-levelindicatorconstructedbyFableData.7Sinceconsumersmayopenandclosecreditcardaccounts,thedataissubjecttosig-nificantconsumergrowthandchurn.Tomitigatethisaspect,FableDatausescriteriabasedonthespendingpatternsofindividualaccountownerstoconstructa“corepanel”ofconsumerswhoseaccountsarelikelytoremainactiveovertime.FableDataprovideduswiththiscorepanelconsistingofabout160milliontransactionsacrossmorethan1millionaccounts.Figure1illustratesafewkeyfeaturesofthedata.Panel1ashowsthespendingdy-namicsovertime.8Beforethepandemic,theyearlygrowthrateofcreditcardspendinghoveredaround10–15percent,partlyreflectingagrowinguseofcreditcardsbyhouse-holds.SpendingcontractedabruptlyattheonsetofthepandemicinMarch2020andreturnedtopositivegrowthinthespringof2021whenhealthconditionsimprovedandthevaccinationcampaignbegan.Panel1aalsoillustratesthegrowthrateinthenumberThisincomeindicatorisconstructedbasedonthepostcodeofwheretheaccountisregistered.Specif-ically,eachpostcodeisclassifiedashigh(low)incomeifmorethan45percentofhouseholdslivingtherefallintothetop(bottom)twoquintilesofthenationaldistributionofdisposableincome.8AspointedoutbyChettyetal.(2020)inthecaseoftheUS,transaction-levelspendingdatatendtobehighlyvolatileacrossdaysoftheweek,weeksofthemonth,publicholidays,andtosomeextentduetoweathervariations.Therefore,wesmoothcreditcardspendingusinga90-daymovingaverageandfocusonyearlygrowthrates.6oftransactions(theextensivemargin)andintheaverageamountofindividualtransac-tions(theintensivemargin).Weseethatthenumberofcreditcardtransactionsgrewatarapidpacebeforethepandemicwhiletheaverageamountpertransactionsdeclined.Thisreflectsthegrowingpenetrationofcreditcardpaymentswhicharebeingusedmorefre-quentlyforsmallerpurchases.Boththenumberoftransactionsandtheaveragespendingpertransactiondeclinedsharplyduringtheacutephasesofthepandemic.Figure1:Creditcarddata,descriptivecharts(a)Year-on-yeargrowthrates(percent) (b)Comparisonwithnationalaccountdata(c)Averagemonthlyspending(euros) (d)Spendingcategories(shares)Notes:Panel1ashowsgrowthratesatdailyfrequency.Theintensiveandextensivemarginsarecomputedasspendingpertransactionandthenumberoftransactions,respectively.Allvariablesarecomputedastheyear-on-yearpercentchangeofthe90-daymovingaverage.Inpanel1b,creditcarddataareaggregatedatquarterlyfrequencytobecomparedwithprivateconsumptiondatafromnationalaccounts.Panel1cshowsthedistributionofaveragemonthlyspendingacrossindividualaccounts,upto2000euros.Thespendingsharesinpanel1darecomputedbysummingthespendinglevelsforeachcategoryacrossalldaysandaccountsandthendividingthembytotalspending.Thedynamicsofcreditcardspendingaretightlycorrelatedwiththethoseoffinal7privateconsumptionfromnationalaccountdata.9Thisisillustratedinpanel1bwherecreditcardspendingisaggregatedatthequarterlyleveltomatchthefrequencyofna-tionalaccounts.Thissuggeststhatcreditcardspendingisrepresentativeofaggregatespendingdynamics.Yetcreditcardspendingmissessomeimportantspendingcategories,notablycarpurchasesandpossiblyexpensivedurablegoods.Theresultsoftheanalysisshouldthusbeinterpretedasbeingmoreindicativeofnon-durableconsumption.10Usingthegranularinformationprovidedbycreditcardtransactiondata,panel1cshowsthedistributionofaveragemonthlyspendingineurosacrossdifferentaccounts.Theaveragemonthlyspendingis357euroswithastandarddeviationof250euros.Thedistributiondisplaysmostofthedensitymassbelow1,000euros.However,therighttailismuchlongerthanpresentedinthechart,withafewaccountsexceedinganaveragespendingof10,000eurospermonth.Thedataalsoprovidedetailedinformationaboutspendingcategorieswhicharecon-structedbyFableDatabasedonthetypeofgoods—forexample,travelexpensesareclassifiedasdiscretionaryspending—andthemerchanttype—forexample,purchasesatgrocerystoresareclassifiedasconsumerstaples.Figure1dillustratestheaveragespend-ingshares.About60percentofspendingisdirectedtodiscretionaryconsumerproducts.Thesecondlargerspendingcategoryincludesconsumerstapleswhichaccountforalmost20percentoftotalspending.TheeffectsofmonetarypolicyoncreditcardspendingToexaminetheimpactonmonetarypolicyoncreditcardspending,weusemonetarypolicyshocksidentifiedviahigh-frequencychangesininterestratesaroundmonetarypolicyannouncements.ThisapproachwaspioneeredbyKuttner(2001)andCochraneandPiazzesi(2002)andhasbeenusedinalargeliteraturereferencedintheintroductionofthepaper.Theidentificationassumptionisthatinterestratechangesinnarrowwin-dowsaroundcentralbanks’announcementsaredrivenbyunanticipatedmonetarypolicydecisions.Acaveattothisapproachisthatinterestratemovementsassociatedwithmon-etarypolicyannouncementsmayalsoincorporateinformationeffectsaboutthestrengthoftheeconomicoutlook.Laterintheanalysis,wewillshowthattheresultsarerobusttousingmorecomplexeconometricspecificationsthatbetterisolatepuremonetarypolicyshocksfrominformationeffects.9Thecorrelationcoefficientisstatisticallysignificantat90percent.10UsingconsumerexpendituredatafortheUSavailableatmonthlyfrequency,Miranda-AgrippinoandRicco(2021)findthattheimpactofmonetarypolicyisconcentratedonnon-durablespending,withnosignificanteffectsondurablepurchases.8Wetakedataonhigh-frequencychangesininterestratesaroundECBmonetaryan-nouncementsfromtheEuroAreaMonetaryPolicyEvent-StudyDatabase(EA-MPD).ThisdatasetiscompiledbyAltavillaetal.(2019)andprovidescomprehensiveinformationaboutmovementsininterestratesatdifferentmaturities.11Sinceduringourperiodofanalysis—from2017to2021—thepolicyrateintheeuroareawaslargelyunchangedat-0.4/-0.5percent,monetarypolicylargelyoperatedviaforwardguidance.Toaccountforthisaspect,wefollowHansonandStein(2015)andGilchrist,Lopez′-SalidoandZakrajsekˇ(2015)anduseshocksto2-yearyieldsratherthantoveryshort-termratesinourbaselineeconometricspecifications.Yetbyexploitingthehighfrequencyofcreditcardspending,wewillalsoexamineshockstootherinterestratematurities.Toexaminetheeffectofinterestrateshocksoncreditcardspending,westartbyesti-matingthefollowinglocalprojectionspecification(Jorda`,2005):PPPP(1)XXXXSt+h?St?1=βphIt?p+γphcasest?p+φphlockdownt?p+θphsupportt?pp=1p=1p=1p=1Pρphst?p+αh+dowh+doyh+εthp=1ThevariableStdenotesthelogofcreditcardspendingattimet.12Thedependentvariableisthusthecumulativelogdifferenceofcreditcardspendingoverthehorizonh=[0,...,365]relativetothevalueatt?1.Themainindependentvariableofinterestistheinterestrateshock,It?p.Theregressionincludesabroadsetofcontrolvariables.AsillustratedinFigure1a,creditcardspendingduringthesampleofanalysiswasheav-ilyinfluencedbytheCOVID-19pandemic.Therefore,wecontrolforseveralvariablesassociatedwiththepandemic,namelythelogofthenumberofCOVID-19infections,cases;anindexcapturingthestrictnessoflockdownrestrictions,stringency;andanindexsummarizingincomesupportanddebtreliefmeasuresprovidedduringthepandemic,11Dataareavailableathttps://www.ecb.europa.eu/pub/pdf/annex/Dataset_EA-MPD.xlsx.ForeachECBpolicyannouncement,theEA-MPDdifferentiatesbetweenmarketmovementssurroundingthepressrelease,thesubsequentpressconference,andovertheentiremonetaryeventwindow—thatisfrombeforethepressconferencetoafterthepressconference.Weusemarketmovementsovertheentirewindowsincehouseholdspendingshouldbeinfluencedbymonetarypolicydecisionsirrespectiveofwhethertheyarecommunicatedduringthepressreleaseorthepressconference.WereferthereadertoAppendix