使用Robo Advice改善家庭債務(wù)管理 Improving Household Debt Management with Robo-Advice

NBERWORKINGPAPERSERIESMPROVINGHOUSEHOLDDEBTMANAGEMENTWITHROBOADVICEaChakrenCroxsonancescoDAcuntoathanReuterAlbertoGRossiJonathanM.ShawWorkingPaper30616http//papers/w30616NATIONALBUREAUOFECONOMICRESEARCHCambridgeMA8November2022IdaChakandKarenCroxsonareattheFinancialConductAuthorityoftheUnitedKingdom(FCA).FrancescoD’AcuntoandAlbertoRossiareatGeorgetownUniversity.JonathanReuterisatBostonCollegeandNationalBureauofEconomicResearch.JonathanShawisattheFCAandInstituteforFiscalStudies.OurexperimentaldesignwaspreregisteredintheAEARCTregistry(AEARCTR-0006447)andimplementedbytheFCA.WethanktheFCAforfundingthisresearch.WethankChrisBurkeandMauraFeddersenforhelpdesigningandimplementingtheexperiment.TheviewsexpressedinthispaperarethoseoftheauthorsandshouldnotbeattributedtotheFinancialConduitAuthority.TheviewsexpressedhereinarethoseoftheauthorsanddonotnecessarilyreflecttheviewsoftheNationalBureauofEconomicResearch.NBERworkingpapersarecirculatedfordiscussionandcommentpurposes.Theyhavenotbeenpeer-reviewedorbeensubjecttothereviewbytheNBERBoardofDirectorsthataccompaniesofficialNBERpublications.?2022byIdaChak,KarenCroxson,FrancescoD’Acunto,JonathanReuter,AlbertoG.Rossi,andJonathanM.Shaw.Allrightsreserved.Shortsectionsoftext,nottoexceedtwoparagraphs,maybequotedwithoutexplicitpermissionprovidedthatfullcredit,including?notice,isgiventothesource.ArandomizedcontrolledtrialsregistryentryisArandomizedcontrolledtrialsregistryentryisavailableathttps///trials/6447/IdaChak,KarenCroxson,FrancescoD’Acunto,JonathanReuter,AlbertoG.Rossi,andJonathanM.ShawNBERWorkingPaperNo.30616November2022JELNoD,D91,G51,G53STRACTPoordebt-managementskillslowerfinancialsecurityandwealthaccumulation.Becauseoptimalsolutionstocreditrepaymentproblemsdependonneitherriskpreferencesnorbeliefs,loanrepaymentisaprimeapplicationforrobo-advising.Vulnerablehouseholds,though,tendtodistrustnewtechnologiesandoverridesuggestionsthatdonotalignwithingrainedheuristics,suchasmatchingtheminimumpaymentonacreditcardbalance.Loweradoptionratesbythesegroupsmightincreaseratherthanreducewealthinequalities.Toassessthesetrade-offs,wedesignandimplementanRCTinwhichrobo-adviceforborrowerrepaymentdecisionsisofferedtoasetofrepresentativeUKconsumers.Theavailabilityoffreerobo-advicesignificantlyimprovesaverageloanrepaymentchoices.Whentheirwillingnesstopayiselicited,manysubjectsreportvalueslargerthanthemonetarybenefitsofthetool,perhapsduetolowercognitiveandpsychologicalcostsdecision-makersfacewhenmakingassistedchoices.Non-adoptersandoverridersreportlowertrustinalgorithmsattheendoftheexperiment.Wefindnoevidenceoflearningfromrobo-advice,whichbarelyimprovessubsequentunassistedchoices,evenwhenpairedwithexplicittips.Infact,robo-adviceusagecrowdsoutlearning-by-doing,whichishighestforthosewhomakeallchoicesunassisted.aChakaChaknancialConductAuthorityndeavourSquareLondonE201JNUnitedKingdomIda-chak@renCroxsonnancialConductAuthorityndeavourSquareLondonE201JNUnitedKingdomkaren.croxson@.uklSchoolofManagementstonCollegeBFultonHallChestnutHill,MA02467andNBERreuterj@AlbertoGRossiDonoughSchoolofBusinessrgetownUniversityWashingtonDC0057ancescoDAcuntoMcDonoughSchoolofBusinessGeorgetownUniversityWashington,DC20057francesco.dacunto@AdataappendixMcDonoughSchoolofBusinessGeorgetownUniversityWashington,DC20057francesco.dacunto@Adataappendixisavailableat/data-appendix/w30616nitedKingdomndInstituteForFiscalStudiesJonathanShawfcaorguk1Theoriginsandconsequencesofgrowingwealthinequalityareamongthemostdebatedissuesintoday’ssociety(Keister(2000);Zucman(2019)).Alargeliteraturelinksthelackofwealthaccumulationbylow-incomehouseholds(Campbell,Ramadorai,andRanish(2019))tolowsavingratesand?nancialreturnsdueto?nancialilliteracyandcostlyaccesstoadvice(LusardiandMitchell(2014),ChalmersandReuter(2020)).Di?erencesinwealthaccumulationalsodependonhowmuchdebthouseholdsaccumulateandhow(in)e?cientlytheymanageit,whichisespeciallyrelevantforvulnerablehouseholds,whotendtodisplaylow?nancialliteracy(Lusardi,Schneider,andTufano(2011);Drexler,Fischer,andSchoar(2014))andmisunderstandtheimplicationsofannualpercentagerates(APRs),minimumpayments,andlatefees(Agarwal,Chomsisengphet,andLiu(2010);AgarwalandMazumder(2013)).Heuristicsandbiasesinloanrepaymentcanleadtodebtspiralsandfurtherreducewealth(Agarwaletal.(2008);Argyle,Nadauld,andPalmer(2020);Gathergoodetal.(2019);LusardiandTufano(2015);Ponce,Seira,andZamarripa(2017)).Althoughweunderstandmanyoftheseheuristicsandbiases(Zinman(2015);AgarwalandZhang(2015);StangoandZinman(2021)),thequestionofhowtoe?ectivelyande?cientlyminimizetheirimpactremainsopen.Forinstance,whilemanycountrieshaveimposeddisclosurerequirementsonlenders,itisunclearthatborrowerswithlowlevelsof?nancialliteracyareabletoprocessthetechnicalinformationembeddedindisclosures(Adamsetal.(2021);BertrandandMorse(2011);Navarro-Martinezetal.(2011)).2Financialliteracyprogramscanimproveoutcomes(Buetal.(2020)),buttheabilitytoscalethemupislimitedbythe?nancialandcognitivecostsfacedbythosewhodeliverclassesandthosewhoattendthem.Inthispaper,weimplementarandomizedcontroltrial(RCT)intheUnitedKingdomtoassessthetake-upande?ectivenessofanalternativemeanstoimprovehouseholds’loanrepaymentchoices:robo-advice(D’Acunto,Prabhala,andRossi(2019);D’AcuntoandRossi(2021b)).3Inprinciple,loanrepaymentisanaturalsettingforrobo-advicebecause,unlikeassetallocationdecisions,theoptimalchoicedependsonneitherriskpreferencesnorbeliefsaboutexpectedreturnsandcorrelations.Moreover,incontrasttohumanadviceanddebtcounseling,2Agarwaletal.(2015)showthatregulationcanimproveloanrepaymentbylimitingcreditcardfees.3ThetrialwaspreregisteredintheAEARCTregistry(AEARCTR-0006447)andimplementedbytheFinancialConductAuthority(FCA)oftheUnitedKingdom.2robo-advisingisaneasy-to-scaletechnologythatcanbedeliveredcheaplythroughpersonaldevices,makingitaviableresourceforlow-incomeandvulnerablehouseholds(D’AcuntoandRossi(2022,2021a)).Atthesametime,algorithmicaversion,whichisespeciallycommonamongtheleastsophisticatedconsumers(NiszczotaandKasz′as(2020)),mightlimitdemandforrobo-advicetoolsamongthedemographicgroupsthatwouldmostbene?tthemost,therebypotentiallyincreasingwealthinequalitiesratherthanreducingthem.InourRCT,subjectsallocatefundsacrossloansinninereal-lifeloanrepaymentscenarios.Eachscenarioincludesloansthatvarywithrespecttointerestrates,balances,andlevelsofdi?culty.Theexperimentincludesthreephases,eachconsistingofthreerandomlychosenscenarios.Inthepre-interventionphase,weassessthemistakessubjectsmakewhenmanagingmultipleloansindependently.Thisphasecon?rmsearlierevidencethatloanrepaymentmistakesarecommonandhavesizableeconomicconsequencesondebtors’(experimental)wealth:theaveragepercentageofsavingsforgonerelativetotheoptimalchoicesis21.9%,whichtranslatesinto29basispointsofinterestandfeespermonthandcompoundsto3.55%higherpaymentperyear.FortheaverageUSfamilywith$6,270increditcarddebtaroundthetimeofourRCT,4thesemistakeswouldresultinanoverpaymentof$222.42peryear.Themagnitudesincreaseto37basispointspermonthand$249.37whenwefocusonthehardversionsoftheproblems.Consistentwithintuitionandearlierresearch(Lusardi(2012)),higher?nancialliteracy,numeracy,andpatiencereducetheextentofmistakes.Intheinterventionphase,weintroducerobo-adviceforloanrepayment.Subjectsworkonthreenewallocationscenariosafterbeingrandomlyassignedtooneof?veexperimentalarms:acontrolgroupforwhomthetaskisidenticaltothepre-interventionphase;groupsforwhomrobo-adviceisavailableforfreewithorwithoutexplicittipsaboutthe?nancialprinciplesbehindoptimalstrategies(“robo-advicewitheducation”);andgroupsforwhomitcanbeobtainedforafeewithorwithouteducation.Inallfourtreatmentarms,subjectsaretoldthemaximuminterestandfeesthattheycouldsavebyusingtherobo-advisor.Inthetreatmentswithpaidrobo-advice,weelicitsubjects’willingnesstopay(WTP)fortherobo-advicetoolinanincentive-compatible4Source:FederalReserveSurveyofConsumerFinances(Link)3way.5Thepost-interventionphasemirrorsthepre-interventionphase,inwhichsubjectssolvethreeloanmanagementproblemswithoutaccesstorobo-advice.Subjectsreceivenofeedbackaboutoptimalsolutionsaftertheirchoices,whichareusedtodeterminetheirincentivepayments.The?veexperimentalarmsallowustodeterminewhether(andbyhowmuch)exposuretorobo-adviceimprovesloanrepayment,aswellasaseriesofrelatedquestions:(i)whichformsofrobo-adviceareinthegreatestdemand;(ii)howmuchsubjectsarewillingtopaytoobtainrobo-advice;and(iii)whichsubjectsoverridetherobo-advisor’srecommendations.Thepost-interventionphaserevealswhetherexposuretooptimalallocationsbytherobo-advisor,withorwithouteducation,helpsborrowerslearntheoptimalrulesforloanrepaymentor,instead,whetherrobo-adviceneedstobecontinuallyprovidedtoimproverepaymentdecisions.We?ndthatsubjectsexposedtofreerobo-adviceimprovetheirrepaymentstrategiessigni?cantlyrelativetothecontrolgroup.Becauseloanrepaymentproblemshaveunambiguousex-antesolutions,anysubjectwhoimplementstherecommendedrepaymentstrategywillminimizeinterestandlatefees.Asaresult,theTreatmentontheTreated(TOT)e?ect—thee?ectofrobo-advisingonthesubjectswhoseekit—islarge.Theaveragepre-interventionlosses(21.9%)declineby19.6percentagepoints.Forgonesavingsdonotcompletelydropto0%because5.7%oftreatedsubjectschoosetooverridetherobo-advisordespiteactivelyseekingit.Inthecross-section,theTOTe?ectsareheterogeneousanddisproportionallybene?tsubjectswithlow?nancialliteracyandnumeracy,suggestingthatrobo-advisingcanleveltheplaying?eldinhouseholddebtmanagement.Next,weassesstheIntentiontoTreat(ITT)e?ect—thedi?erencebetweentheloanrepaymentperformanceofsubjectsexposedtorobo-advisingandthecontrolgroup,irrespectiveofwhetherthetoolisused.TheITTe?ectaccountsforthepossibilitythatborrowerswhoareo?eredrobo-advicemightdecidetonotuseit,perhapsbecausetheydonottrustalgorithms.Despiteremainingeconomicallyandstatisticallysigni?cant,theITTestimateis-14.6percentagepoints—about25%lessthantheTOTestimates.Relatedly,subjectsdeclinefreerobo-adviceinabout25%oftheproblemsforwhichtheyareo?eredit.5AswedescribeinSection1.1.,thefeewasdrawnrandomlyfromauniformdistributionaftersubjectsprovidedtheirWTPtoelicitWTPinanincentive-compatiblefashion.Thismethoddi?ersfromelicitingthedemandforrobo-advisingafterannouncingapre-determinedfeeandimpliesthatmanyrobo-advisingseekersdidnotreceiverobo-advisinginthepaidtreatmentarms.4Consumerswillbene?tlessfrompaidrobo-advicebecausethebest-caseoutcomeswitchesfromnosavingsforgonetothepositivefeepaid.Adoptingrobo-advisingisthusonlybene?cialinconsumption-utilitytermsifthefeethatconsumerspayislowerthanthesavingstheywouldhaveforgoneabsentthetool.Interestingly,we?ndthatsubjects’WTPis,onaverage,higherthanthemonetarybene?tstheyobtainfromit,whichmayre?ectsubjects’pessimisticassessmentoftheirperformancewhenunassistedorawillingnesstopaytomoretoavoidthecognitiveandpsychologicalcostsofmakingchoicesontheirown.Fromapolicyperspective,itwouldbeoptimalifdemandforrobo-adviceweregreatestamongtheless?nanciallyandnumericallyskilled,whomakecostliermistakesinthepre-interventionphase.And,indeed,demandforrobo-adviceisinverselyrelatedto?nancialandnumericalliteracy.Everythingelseequal,itisalsoinverselyrelatedtocon?denceinone’sskillsandpositivelyrelatedtotrustinrobo-advice.6FinanciallyliteratesubjectshavealowerWTP,whilemenandmoretrustfulsubjectsarewillingtopaymore.(Low)trustinalgorithmsisstronglycorrelatedwithoverridingrobo-advice,whichisneveroptimalinoursetting,andthedesiretointeractwithahumanadvisor.Finally,weaskwhetherrobo-advicehelpssubjectstolearnaboutoptimalloanrepaymentor,instead,limitslearning-by-doingbyreducingtheirexperiencesolvingloanmanagementproblems.Whilewe?ndbetterpost-interventionperformanceacrossallofthetreatmentarms,thelargestdi?erenceisforthecontrolgroup,whohadtoworkthroughmoreproblemswithoutassistancebeforereachingthepost-interventionphase.Wedetectneitherlearningbyimitationnorfromtheeducationaltipsbundledwithrobo-advice.1ExperimentalDesignandProcedureInthissection,wedescribethedesignoftheexperimentaltasksandtheexperimentalprocedure,includingtherecruitmentofsubjects,theircharacteristics,andthesequenceofactionsintheiment6Weelicitedtrustaftertheexperimenttoavoidprimingsubjectsbeforeassessingrobo-demand.Beingexposedtorobo-advisingduringtheexperimentmighta?ecttrust.Wecannotsayifsubjects’pre-determinedtrustincreasestheirWTPorifhigherWTPandhencemorelikelyexposureduringtheRCTincreasesreportedtrust.51.1.ExperimentalDesignToimplementourexperimentaldesign,wecreated27loanrepaymentproblems,whichwereportamongtheexperimentalmaterialsintheOnlineAppendix.Ineachproblem,subjectsreceivedanamountofmoneydenotedinpoundsthattheywereaskedtoallocatefullyacrossmultipleloanstominimizethesummonthlyinterestpaymentsor,inproblemsthatincludeminimumpaymentamounts,tominimizethesumofmonthlyinterestpaymentsandlatefees.We?rstdesignednineproblemsthatdi?eredbasedonthenumberofloans,themixtureofAPRs,andloanbalances,whichwelabelAtoI.7Wethendesignedthreealternativeversionsofeachproblem:easy,medium,andhighdi?culty.TheeasyversionfocusesonloanamountsandAPRs;themediumandharddi?cultyversionsintroduceminimumpayments,whichtriggerlatefeeswhenleftunpaid.Themostdi?cultversionmimicsreal-worldscenariosbyintroducingadditionalinformationnotneededtocalculateoptimalrepaymentamounts.WereportthethreeversionsofasampleprobleminFigureA.1intheOnlineAppendix.Theexperimentconsistedofthreeorderedphases,executedsequentiallywithinthesamesession.Wesummarizethephasesandthesequenceofsubjects’actionsinFigure1.Overthecourseoftheexperiment,loanrepaymentproblemsAthroughIarepresentedtoeachsubjectinrandomorder.Withineachofthethreephases,subjectsarerandomlypresentedwithoneeasyproblem,onemediumproblem,andonehardproblem.Inthepre-interventionphase,eachsubjectsolvedthreeproblemswithoutanyexternalinputsorinformationbeyondthatrequiredtodescribetheproblem.Subjectsdidnotreceiveanyfeedbackonwhetherorhowtheinterestandlatefeesassociatedwiththeirrepaymentstrategydi?eredfromtheoptimalrepaymentstrategy.Theexperimentalconditionswereadministeredintheinterventionphase.Subjectswererandomlyassignedtooneof?veexperimentalarms:(i)acontrolgroupforwhomthetaskwasidenticaltothepre-interventionphase(group1);(ii)agroupinwhichrobo-advicewasavailableforfree(group2);(iii)agroupinwhichrobo-advicewasavailableforfreeandincludedan7IntheUK,theAPRisane?ectiveannualinterestrate.AccordingtotheFCA:“ifyouborrowed￡100andtheloanAPRis56%,afterayear,youwouldpayback￡156intotal.”Therefore,tocalculatethemonthlyinterestpaymentforagivenloan,weneedtocalculatermonthly=(1+APR)(1/12)?1.6explanationoftherobo-advisor’sproposedallocation(“robo-advicewitheducation”;group3);(iv)agroupinwhichrobo-advicecouldbeobtainedforafee(group4);and,(v)agroupinwhichrobo-advisingwitheducationcouldbeobtainedforafee(group5).Inallfourtreatmentarms,subjectsaretoldthemaximuminterestandfeesthattheycouldsavebyusingtherobo-advisor(i.e.,themaximumpossibleinterestandfeesminustheminimumpossibleinterestandfees).Theinterventionphaseintroducedthreefeaturesabsentinthepre-interventionphase:accesstorobo-advice,educationaboutthestrategiesproposedbytherobo-advisor,andelicitationofsubjects’willingnesstopayfortherobo-advisingtool.Robo-adviceconsistedofanautomatedproposalforallocatingtheamountsubjectshadattheirdisposalacrossthemultipleloansintheproblem.Therobo-advisorallocationcoincidedwiththeoptimalallocation,whichminimizedtheinterestandfeesthatsubjectshadtopayontheirportfolioofloansoverthenextmonth.Notethatthisallocationcanbecomputedwithoutriskanduncertainty.Forexample,intheeasyversionoftrialE,thesubjecthas1,000poundstoallocatebetweenthreeloans:a1,040.55balancewithanAPRof22.5%,a466.74balancewithanAPRof45.9%,anda879.04balancewithanAPRof49.5%.Intheabsenceofminimumpayments,therobo-advisorwouldallocate879.04totheloancharging49.5%,thusextinguishingtheloansubjecttothehigherAPR,andtheremaining120.96totheloancharging45.9%.Thisallocationresultsinthebest-casemonthlyinterestpaymentof28.81pounds.(AllocatingeverythingtotheloanwithanAPRof22.5%wouldhaveresultedintheworst-casemonthlyinterestpaymentof45.57pounds.)Subjectswhochosetoreceivefreerobo-advice(withintreatmentgroups2and3)andsubjectswhochosetoreceivepaidrobo-adviceandwhosewillingnesstopay(WTP)exceededthecostofthetool(withintreatmentgroups4and5)foundthesuggestedbest-caseallocationautomatically?lledinontheirproblemscreen.Subjectsweretoldthattherobo-advisor’sallocationswereoptimal,butthattheywerefreetoacceptormodifythisallocationbeforemovingtothenextscreen.Educationconsistedofbriefexplanationsofthestrategiesproposedbytherobo-advisor.WereportexamplesoftheexplanationsamongtheexperimentalmaterialsintheOnlineAppendix.Forinstance,intheeasyversionofproblemEdescribedabove,subjectswereexposedtotheeducationstepsreportedinFigureA.2.Subjectswhoacceptedfreerobo-advisingtreatment7witheducation(group3)andsubjectswhotookrobo-advisingwitheducationforafeeandwhosewillingnesstopayexceededthecostofthetool(group5)wereshowntheeducationaltextbeforetheallocationproposedbytherobo-advisingtoolappearedontheirscreen.Inthetreatmentswithpaidrobo-advice,weelicitedWTPinanincentive-compatiblefashion.Ratherthanbeingaskedwhethertheywerewillingtopayapre-speci?edfee,subjectswhosoughtpaidrobo-advicewereaskedtostatetheirWTPforaccesstotherobo-advicetool,usingasliderthatrangedfrom0poundstothemaximumpossiblesavingsininterestandfeesfortheirloanrepaymentproblem.Subjectsweretold:“Youshouldrespondtruthfully—afteryousayhowmuchyouarewillingtopay,theactualpriceoftheassistantispickedrandomly.Ifwhatyousaidyou’rewillingtopayishigherthantherandomprice,you’llbuytheautomatedassistanceatthatprice.Ifwhatyousaidyou’rewillingtopayislowerthantherandomprice,youwon’tbuytheautomatedassistanceandwillpaynothing.”Subjectswerenottoldhowtherandompricewouldbechosen.Foreachsubject-problempair,wedrewthepricerandomlyfromtheuniformdistributionrangingfromzerotothemaximumpossiblesavingsininterestandfees.Ifthesubjects’reportedwillingnesstopaywashigherthanthisprice,subjectsobtainedtherobo-advisingtool.Otherwise,theydidnot.Ourpricesettingmechanismisclearlynotcomparabletothewaypriceswouldbesetin?eldapplications,whichwouldbeapre-setfeecommontoanyinteresteduser.Ouraim,though,istoelicitWTPinanincentivedfashionratherthanassessinghowmanyconsumerswouldacceptrobo-adviceinthe?eldbasedondi?erentpotentialprices.Yet,knowingthetruthfulWTPofeachsubjectinformsusonwhowouldtakerobo-adviceforeachpotentialpricethatwasattachedtothisserviceinthe?eld.Overall,only36.6%ofthesubjectswhosoughtpaidrobo-adviceactuallyreceivedit.ThisisbecausetheaverageWTP(scaledbythemaximumpossiblesavings)was34.7%whiletheaveragerandomprice(scaledbythemaximumpotentialCrucially,subjectscouldseetheproblembeforereportingtheirwillingnesstopayfortherobo-advisingtool,whichisarealisticfeaturesince,inthe?eld,borrowerscanseekadvicebasedontheperceiveddi?cultyofmanagingtheirloanportfolio.Moreover,becausesubjectsshouldhavechosentheirwillingnesstopaybasedontheexpecteddi?erencebetweenthesavingsthey8couldhavemadeontheirownandthemaximumsavingstheycouldhavemadewhenusingtherobo-advisingtool(whichtheproblemtoldthemexplicitly),observingtheloanrepaymentproblemandassessingitscomplexitywasimportantforthesubjectstomakeaninformedinferenceabouttheirwillingnesstopay.Thepost-interventionphasefollowedthesamedesignasthepre-interventionphase.Subjectshadtosolvethreeproblemsofdi?erentdi?cultylevelswithoutaccesstorobo-advice.Beforetheendofthesurvey,subjectsansweredafewquestionsinadebrie?ngsurvey,whichwediscussinmoredetaillater.1.2.ExperimentalProcedureandSubjectPoolWepre-registeredtheexperimentaldesignandprocedure,includingdetailsaboutsubjectrecruitmentandtargetsamplesizes,intheAEARCTRegistry(trialID:AEARCTR-0006447).8SubjectrecruitmentaimedtoobtainaUKnationallyrepresentativesampleofadultsaged18andabove.9Thetargetsamplesizewas4,500subjects;basedontheFCA’sbudgetandtimelineforcompletingtheexperimentphase,thedeliveredsamplesizewas3,423.Recruitmentwasrunbyathird-partyproviderthatengagedinquotacontrolonseveraldemographiccharacteristicstomaintainthesample’srepresentativeness.TheexperimentaldatawerecollectedinSummer2020intheformofasurveyadministeredthroughtheonlineplatformQualtrics.Inadditiontorecordingsubjects’choicesinthesurveytasks,therecruiterprovideduswiththedemographiccharacteristicsthatwecontrolforintheanalysis.Therandomizedassignmentofsubjectsacrossexperimentalarmsshouldensurethatobservableandunobservablecharacteristicsareuncorrelatedwiththeassignmentrule.Afteraccessingandsigningtheconsentform,subjectsreadtheinstructionsdescribingthetaskofsolvingaloanrepaymentproblem.Theinstructionsspeci?edthatthemainscopeoftheresearchwastounderstandhowpeoplemanagetheirloans.Theinstructionsbrie?y