我国P2P贷款与银行贷款的动态因果关系

北京理工大学珠海学院2020届本科生毕业论文我国P2P贷款与银行贷款的动态因果关系我国P2P贷款与银行贷款的动态因果关系摘要本文检验的是在2014年1月至2019年12月期间，中国8个主要省份及直辖市的个人对个人贷款(Peer-to-PeerLoans简称P2PL)与银行贷款(BankLoans简称BL)之间的动态因果关系。这中国8个主要的省份及直辖市分别是：广东、北京、浙江、上海、江苏、山东、湖北和四川。在研究过程中，我们先使用ADF/PP-KPSS联合检验法检验相关数据的平稳性。考虑到不同省份及直辖市之间可能存在一定的相关性和异质性，因此我们使用的方法是多变量面板格兰杰因果检定法。实证结果显示，在不同的地区之间，P2PL和BL存在相关性和异质性，两者之间的关系不能一概而论。其中，在江苏和湖北两个地区，是支持P2PL主导影响BL的假设成立，而在浙江和上海地区，则是支持BL领先主导P2PL的假设成立。除此之外，我们发现在山东地区，P2PL和BL之间是相互影响的，具有双向格兰杰因果关系。同时，实证结果还显示，广东、北京和四川这三个地区的P2PL和BL之间互不敏感。在这八个省份和直辖市的金融相关部门了解和预测市场状况的时候，这些实证结果可以提供一定的参考。关键词：个人对个人贷款；银行贷款；Bootstrap多变量面板格兰杰因果；中国

TheCausalRelationshipbetweenP2PloansandbankloansinChinaAbstractThepurposeofthispaperistestingthecausalrelationshipbetweenbankloans(BL)andpeer-to-peerloans(P2PL)ineightmajorareasofChinabetweenJanuary2014andDecember2019.TheseeightmajorChineseareasincludeGuangdong,Beijing,Zhejiang,Shanghai,Jiangsu,Shandong,HubeiandSichuan.Intheresearchprocess,weuseunitroottest(ADF/PPandKPSS)totestthestationaryofthedata.AmultivariatepanelGrangercausalitytestisusedbecausethedependencyandheterogeneityacrossareasmightexist.Theempiricalresultsshowthatthecross-sectionaldependencyandheterogeneityacrosstheseregionsprovideevidencethattherelationshipbetweenP2PLandBLcannotbegeneralized.Inthreeoftheseeightmajorareas(Jiangsu,ShandongandHubei),theP2PLleadinghypothesisrelationshipsupportsevidence,whiletheevidenceissupportedfortheBLleadinghypothesisinthreeoftheseeightmajorareas(Zhejiang,ShanghaiandShandong).Atthesametime,aninteractivecausalrelationshipbetweenBLandP2PLisfoundinShandong.Inaddition,theresultintheareassuchasGuangdong,BeijingandSichuansupportstheneutralityhypothesis.ThesefindingsimplythateachregionshoulddevelopitsownP2PLpolicies.Theycangivethegovernmentsoftheseareasimportantpolicyimplicationsaswell.Keywords:P2PL;BL;BootstrapmultivariatepanelGrangercausality;China目录一、引言 .IntroductionWhyarethepeer-to-peerlending(P2PL)andbanklending(BL)marketinteresting?Asanewfinancialintermediarybetweenborrowersandlenders,P2Plendingreferstounsecuredlendingbetweenlendersandborrowersthroughonlineplatformswithouttheintermediationoffinancialinstitutions.TheP2PLsystemisfavoredbyborrowersandinvestorsduetoitsdeclaredlowlendinginterestratesandfastliquidity.OverthepastyearswiththespreadofInternettechnologyandthewidelyuseofthelargedatabases,onlineP2PLasafinancialinnovationimplicationhasdevelopedquicklyworldwide.OnlineP2Plendinghasrecentlyemergedasausefulfinancingalternativewhereindividualscanborrowandlendmoneydirectlythroughanonlinetradingplatformwithoutthehelpofinstitutionalintermediariessuchasbanks.ManyresearchershavestudiedthefactorsaffectingtheP2Plendingplatform.Forexample,Chenetal.(2013)studiedthattherewassignificantgenderdiscriminationinP2PlendingmarketinChina,showingthatfemaleborrowerswerelesslikelytobefundedthanmaleones,buttheirdefaultrateswerelower.What’smore,LuandZhang(2018)proposedthatplatformstrength,profitability,riskcontrol,liquidityandtransparencycouldpredicttheprobabilityoftheplatformbecomingproblematic.TocomparetheloanstructureoftheP2PlendingmarketwiththeloanstructureoftraditionalChinesefinancialinstitutions(banks),thispapercollectedtheloanstructureofChina’sfivelargestbanksbetween2014and2019.Thecumulativeloanscaleofthefivelargestbankswas527,648.762billionChineseyuan(76,399.97billionUSD),wherecorporateloansandadvancesaccumulated268,646.977billionChineseyuan(38,898.26billionUSD),accountingfor51%.Thecumulativepersonalloanandadvancewere123,107.939billionChineseyuan(17,825.20billionUSD),accountingfor23%,whileindividualhousingloanaccumulated91,210.47Chinesebillionyuan(13,206.66billionUSD),accountingfor17%.Therefore,thecorporateloanisatypeofloaninChina’sbankswhilepersonalcreditistheothertypeofloaninChina’sP2Plendingmarkets.HowisthenexusrelationshipbetweenP2PLandBLmarkets?TherehasalreadybeeninterestintherelationshipbetweenP2PLandBLeventhoughthetheoreticalgroundofsuchrelationshipisnotexplicitlydefined.Duetotheincreasingpopularityofnetworks,P2PLhasbeendevelopingveryrapidly,particularlyinChina.However,someChinesebanks,includingtheBankofChina,AgriculturalBankofChina,IndustrialandCommercialBankofChinaandtheChinaConstructionBank,havelaunchedtheirownP2PLplatforms.Bytheendof2019,thesebankshadformallyestablishedmorethan30P2PLplatforms.P2PLisrecognizedtohaveapositiveeffectonlong-runBLthroughdifferentchannels.Overthepastseveraldecades,theP2PLdestinationsworldwidehaveexperiencedinflowsofdifferentcapitalastheresultofincreaseindemandforloanservices,particularlyforbothuseandinvestmentpurposes.TheP2PLisperceivedasincreasingoverallfinancialactivities,andtheincreaseintheseactivitiesisgenerallyconsidereddesirable;namely,thepositiveimpactofP2PLonfinancialactivitiesisfrequentlydescribed.Also,P2PLisrecognizedtohaveapositiveeffectonshort-term,mid-termorlong-termfinancialandeconomicgrowththroughdifferentchannels.First,P2PLisanewformofloanoriginatinginthecreditmarket.ThistypeofloanmarketisdesignedtocomplementtraditionalBLtomeetthesmallloanneedsofindividualsandsmallandmediumenterprises.Second,theP2PLplatformcanquicklyassessandassignriskgradesthroughtheuseofinformationtechnologyinsteadofrelyingondelegatedmonitoringwithbanksasintermediaries.Finally,lendershavetheopportunitytohandlethefinancialandpersonalinformationprovidedbytheborrowersanddirectlyofferaloanthatmeetstheirinvestmentcriteria.Asaresult,lenderscanreceivethemoneytheyneedonP2PLforashortperiodoftimewhichisrelativetoBL.However,itwouldbeinterestingandusefultodeterminewhetherthereisacomplementaryrelationshipbetweenBLandP2PLplatformsorthereisacompetitiverelationshipbetweenthem.ThereareseveralreasonswhytheexperienceoftheChineseP2PLindustryisofinterest.First,afterapproximately10yearsofrapiddevelopment,thecumulativenumberofP2PLplatformshadreached30,000bySeptember2019.TheP2PLturnoverexceeded3trillionyuan,reaching4trillionyuanin2018,withayear-on-yeargrowthof358.87%.Inaddition,thehistoricalcumulativeturnoverreached3.63trillionyuanaccordingtotheP2PLindustryexpressinChinain2018.Second,thefinancialactivitiesofP2PLandBLhavebecomewidelydiversifiedowingtovarioussocio-economic,economicgeographicalandpoliticalfactorsinChina’sregions.GiventhediversifiedstructureofthefinancialactivitiesofregionswithinwhichtheChineseP2PLindustryoperates,thisindustryservesasanidealcasestudytotestwhetherP2PLhasthesamecausalrelationshipwithfinancialactivitiesinregionsthatsignificantlydifferintheirdegreesofP2PLdependence.Third,mostpreviousstudieshaveconcentratedononespecificcountryorregion.Becauseofdifferentmethodsanddataperiods,itisdifficulttocomparetheresultsandfindingsofthesestudies.Asmentionedabove,therefore,thisstudyadoptspaneldataofmultipleregionstofillinthegap.ThispaperalsoemploysrecentlydevelopedmultivariatepanelGrangercausalitymethodstooffercompatiblefindingsinChina’seightmajorregions.Therefore,thequestioninthispaperishowthenexusrelationshipisbetweenP2PLandBLinChina’slendingindustries.ThepurposeofthispaperistoinvestigatethecausalrelationshipbetweenP2PLandBLinChina’seightmajorregionsusingtheregionalcausalitytestdevelopedbyKónya(2006)todeterminethedynamicandcausalrelationshipbetweenP2PLandBL.Thisprocedurewillundoubtedlyallowthespecificeffectsofregionstobemorereadilyuncovered.ThispaperattemptstoexaminewhetherthereisacausalrelationshipbetweengrowthinP2PLandBLusingabootstrapmultivariatepanelGrangercausalitytestthroughasampleofChina’seightmajorregionsovertheperiodfrom2014M01to2019M12.ThisisthefirststudytoemployabootstrapmultivariatepanelGrangercausalitytesttoexaminetherelationshipbetweenP2PLandBLinChina’seightmajorregions.ThisworkisexpectedtofillthegapintheexistingliteratureongrowthintheP2PLandBL.Therestofthispaperisorganizedasfollows.Section2brieflyreviewstherelatedliterature.Section3providesanoverviewofdatacollectionandmethodology.Section4presentstheempiricalresults.Section5showstherobustnesscheckfromthisstudywhiletheconclusionsandresearchlimitationaregiveninSection6.2.LiteraturereviewThedevelopmentofP2Plendinghasattractedtheattentionoftheacademiccommunity.IthaslongbeenrecognizedthatP2PLhasanimpactonBL(e.g.Belleflammeetal.,2015;Colomboetal.,2015;CummingandJohan,2016;CummingandZhang,2016;Dorfleitneretal.,2016;Hotaetal.,2015;Iyeretal.,2016;Linetal.,2013;Liuetal.,2015).Severalresearchers(Culkinetal.,2016;Duarteetal.,2012;Guoetal.,2016;Kankanhallietal.,2016;Klievinketal.,2017;LoureiroandGonzalez,2015;Mildetal.,2015;Thuanetal.,2016)haveinvestigatedtherelationshipbetweenP2PLandBL.Basedonthepastliterature,P2PLfocusesonthebehavioroftradersandtheriskoftradinginthismarket.Asfortheriskoftrading,theresearchersfocusonthefactorsthataffectcreditriskintheP2PLmarketandP2PLriskregulations.Lenderscanevaluateone-thirdofcreditriskusingbothsoftandhardinformationaboutborrowers(Iyeretal.,2009).Inthestudyofthebehavioroftraders,theresearchersmainlydiscussherdingbehaviors.Becauseoftheriskofinformationasymmetry,tradersexhibitherdingbehaviorsinP2PL(LeeandLee,2012).Forexample,ChaffeeandRapp(2012)describedthatP2PLwasriskyandthatitwasthereforenecessarytobuildanevolvingregulatoryregimeforanevolvingindustry.Herzensteinetal.(2008)foundthatstrategicherdingbehaviorcontributedtoP2PLbiddersindividuallyandcollectively.Usingamultinomiallogitmarketsharemodel,LeeandLee(2012)findstrongevidenceofherdinganditsdiminishingmarginaleffectsasbiddingadvances.Lin(2009)providesanintroductiontoP2Plendingandpresentsapreliminarydiscussionofthecreditandriskofonlineloans.Withinthecontextoftheimpactsofsocialnetworksonbehavior,whichwasahottopicatthattime,Linetal.(2013)investigatedtheeffectsofsocialrelationsonP2Plending.Emekteretal.(2015)pointoutthatthecreditclassificationofborrowersplaysanimportantroleinloandefaultandthatcharginghigherinterestratestohigh-riskborrowerscannotcompensatetheriskofloss.Chenetal.(2018)andXuetal.(2015)expandtheirhorizonstoChinaandexploretheimpactofborrowers’socialcapitalonloanoutcomes.UsingaSouthKoreanplatformasanexample,LeeandLee(2012)examinetheherdingbehaviorofP2Plendersandusealongerperiodofstudy.ZhangandChen(2017)identifytheexistenceofbothrationalandirrationalherdingintheP2Pmarket.Severalstudies(Clemonsetal.,2017;Granadosetal.,2010;Lietal.,2014;Yumetal.,2012)evaluatethedecisionsoflendersunderdifferentlevelsofinformationtransparencythroughbigdataanalysis.Felleretal.(2017)findthatthereismorecomplexityandheterogeneityinparticipantbehavior.Guoetal.(2016)designaninstance-basedcreditriskassessmentmodelthatcaneffectivelyimproveinvestmentperformancecomparedtoexistingmethodsinP2PL.Intermsoftheriskoftrading,Guoetal.(2016)focusonthefactorsthataffectcreditriskintheP2PLmarketandriskregulation.Lenderscanevaluateone-thirdofcreditriskusingbothsoftandhardinformationaboutborrowers(Iyeretal.,2009).Zhangetal.(2017)applythebootstrappanelcausalityapproach,indicatingthatthecausalitybetweenbankloansandpeer-to-peerloansvariesacrossregionswithdifferentconditionsinChina.Althoughthebootstrappanelcausalityapproachcanbeappliedtoanindependentvariable,therearemorethantwoargumentsintherealrelationshipbetweenthevariables,indicatingthatthestatisticalresultsaremisleading.Wangetal.(2018)examinetheensemblemixturerandomforestandconsiderthatitprovidesbetterandmeaningfuloutputforpredictionofChineseP2Ploandefault.Xiaetal.(2017)proposeacost-sensitivemodeltodistinguishpotentialdefaultriskmoreaccurately.However,othersources,suchastheriskoftradingandthecreditclassificationofborrowersandP2PLriskregulation,haveundoubtedlyattractedalotofattentiontothemostrecentfinancialactivity.Forexample,Everett(2015)pointedoutonlineP2PlendingasonlinesociallendingandemployedaseriesofprobitmodelstotesttheimpactsofpersonalconnectionsondefaultriskandinterestrateswithinonlinesociallendingcommunitiesusingProsperdata.FreedmanandJin(2008)andWeissetal.(2010)showedthatsocialnetworksinP2Plendingcouldalleviateinformationasymmetryeventhoughdifferentresultsexisted.Brill(2010)presentedthatP2PLmightdirectlycompetewithtraditionalbanklinesofcredit.TheonlineP2PLmarketprimarilyprovidesmicrocreditstosmallbusinessesandindividualsthatoftenhavedifficultiesinobtainingloansfrombanks.Verstein(2011)proposesapreferableregulatoryschemedesignedtopreserveanddisciplineP2Plending’sinnovativemixofsocialfinance,micro-lendinganddisintermediation.Forexample,somestudiesnotedthattheborrower’ssoftinformationincludingsocialtiescouldbenefitbothborrowersandlenders(FreedomandJin,2014;Linetal.,2013),andthattheborrower’shardinformationincludingdemographiccharacteristicsandfinancialstrengthhadaninfluenceonthelikelihoodoffundingsuccess(Herzensteinetal.,2008).AsummaryofliteraturereviewispresentedinTable1.Table1.ASummaryofLiteratureReviewAuthor(s)Country/RegionPeriodVariableMethodConclusionZhangetal.(2017)China2014-2016P2Plendingbalances,averageP2Plendingrates,short-termbenchmarklendingratesandM2PanelSmoothTransitionRegression(PSTR)modelsThereisanonlineardynamicrelationshipbetweenP2Plendingbalancesanddomesticbankloanbalances.Also,therearetwothresholdvaluesandthreeregimes.LeeandLee(2012)SouthKorea2009-2010InformationoflenderchoiceandauctionavailableonpopmusicfundingMultinomiallogitmarket-sharemodelsHerdinganditsdiminishingmarginaleffectareconsideredasbiddingadvances.Liang(2019)China2015Regions,loanamount,interestrates,loanterms,creditratings,creditgrades,ages,gender,maritalstatus,educationallevels,income,worktime,house,houseloans,cars,carloans,jobcertification,creditreportcertification,identitycertificationandincomecertificationBinarylogisticregressionmodelsTheimpactofregionaldifferenceissignificantandtheborrowerfromnorthernChinaismorelikelytofundsuccessfully.However,theimpactofregionaldifferenceonthedefaultrateisinsignificant,andtheeconomic,financialandeducationaldevelopmentlevelsinregionshaveasignificantimpactonthesuccessrateofborrowing.Zhangetal.(2017)China,TaiwanandtheUSA2014-2016MonthlyP2PloansandbankloansAbootstrappanelcausalityanalysisthatconsidersbothcross-dependencyandheterogeneityacrosscities.AunidirectionalGrangercausalityrunningfromP2PloanstobankloansforBeijing,Shanghai,ZhejiangandShandong;thefeedbackbetweenP2PloansandbankloadsforJiangsuonlyandindependenceforotherthreeregions.Taoetal.(2017)ChinaandtheUK2013-2015Creditprofilesandfinancialinformation,informationdescribingspecificfeaturesoflisting/loans,demographicinformation,listingtypes,andothercontrolvariables.RegressionanalysisTheuniqueofflineprocessinChineseP2Ponlinelendingplatformsexertsasignificantinfluenceonlendingdecision.Chenetal.(2018)China2015Loanamount,borrowingrates,loanperiods,duration,numbersofbiddersandautomaticbidsindicators,numbersofautomaticbids,gender,ages,photoindicators,timesoffundingsuccess/failureandborrow/lendercreditPaneldataregressionHerdingbehaviorexistsinonlineP2Plending.Chenetal.(2020a)China,HongKong,ItalyandtheAsianDevelopmentBank2011-2015Borrower’sID,borrowingamount,interestratesandterms,correspondingbiddingandpaymentrecordsSocialnetworkmodelsThelenderswhoareatthenetworkingcenternotonlyinvestlargeramountbutalsoinvestmoreswiftlythantheirpeers,whiletheborrowerswhoareatthenetworkingcenterareabletoborrowatlowerinterestratesandwithhighersuccessratesandarelesslikelytodefault.Linetal.(2017)China2015Borrower’sregions,genders,maritalstatus,childrenstatus,educationallevels,monthlyincome,companysizes,workingyears,ages,amountofloan,periodsofrepayment,monthlypayment,debt-to-incomeratios,delinquencyhistoryanddefaultstatusNonparametrictestsandbinarylogisticregressionmodels.Genders,ages,maritalstatus,educationallevels,workingyears,companysizes,monthlypayment,amountofloan,debt-to-incomeratiosanddelinquencyhistoryplayasignificantroleinloandefault.Loetal.(2019)China2009-2014Totalamountofbadloan,borrowinginterestratesofsuccessfulloans,totalborrowingamount,amountofsuccessfulborrowingandhowmanytimesbankers/non-bankersborrow,biddingtimes,successfulbiddingtimesandtotalbiddingamountofsuccessfulloanTime-seriesvariablesRe-intermediationintheformofbankersbenefitsborrowers,investorsandP2Pplatformsbyincreasingborrowinginterestratesofsuccessfulloans.Songetal.(2018)China2016Averagefundingtime,averageloaninterestrates,numbersofrequestsandlenders,totallendingvolume,borrowingbalanceofperborrower,averagematurityofloans,duebalance,dispersityandliquiditytransparencyTwostagemodelsAverageperformanceefficiencyoftheplatformsthatislocatedinnon-firsttiercitiesishigherthanthatinfirsttiercities,andtheleadingbigplatformsaregoodatmanagingtherisk.Lietal.(2018)Macau2012Successfullyfundedandfailedtoreceivefunds,borrower’sscores,loanamount,loaninterestratesandloantermsLogitandregressionmodeltestsInformationdisclosuredoesincreasetheprobabilitythatloanlistingwillbesuccessfullyfundedbyaround10%onaverage,andvoluntarilyverifiableinformationdisclosurehelpstodecreasetheequilibriuminterestratebyaround0.20%onaverage.Chenetal.(2020b)China2015-2019Inflowrates,ages,privatebanks,states,assignment,trusteeship,guarantee,cityranks1,2andothers,return,termcapitalandShiborRegressionmodelsBothcapitalandoperationalstructuredesignofplatformshasasignificantimpactontheplatform’snetcashinflowrates.Gaoetal.(2018)Taiwan2017-2019Non-controllableinputsandundesirableinputs/outputsApplicationofanimprovedversionofthemodifiedslacks-basedmeasurethataccommodatesnon-controllableinputs,andundesirableinputsandoutputsunderatwo-dimensionalgrowthandoperatingefficiencyparadigm.TherearecontradictionsbetweentwotypesofefficiencyinP2Pplatforms,andlistedcompaniesandplatformswithventurecapitalinvestment,andplatformsfundedbystate-ownedcapitalexhibithighergrowthefficiency,whileplatformswithfinancialgroupinvolvementanddiversifiedownershipshowincreasedoperatingefficiency.ChengandGuo(2019)ChinaandtheUK-Potentialincome,possibilityofsuccessofresearchanddesign(R&D),interestrates,andpotentialincomeofR&DBasicmodelassumptions:onlyexistingriskfromtheoperatorofplatformsTheratioofinstitutionalinvestorsoverretailinvestors,theintermediaryfeepaidfortheplatformandtheprobabilityofbeingarrestedfortheplatformarefactorsthatcaninfluencetheriskofP2Pplatforms;andthehigherthedegreeoftheriskaversionofinvestors,thehighertheleveloftheriskofP2Plendingplatforms.Zhangetal.(2019)China2014-2016P2Plendingbalancessettingasthresholdvariables,averageP2Plendingrates,short-termbenchmarklendingratesandM2ascontrolvariablesPSTRmodelsTherearetwothresholdvaluesandthreeregimes.Inregimes1and2,theP2Plendingbalanceissmall.TheP2PlendingbalanceandaverageP2Plendingratesexertapositiveimpactondomesticbankloanbalances,andshort-termbenchmarklendingratesexertanegativeimpactondomesticbankloanbalances.(TheCollationofthisstudy)Fromtheperspectivesofbothcreditandinvestment,P2PfinancehasbecomeacrucialpartoftheChinesefinancialsystemthatcannotbeignored.However,thestudiesrelatedtoP2Plendingdonotsufficientlyremaincomprehensive.Theexistingliteraturefocusesontheriskcontrolofplatformsandbehavioralmodelsofborrowersorlendersandmainlycomprisesindividualcasestudies.ThisstudyhasexceededindividualcasestudiesbyexaminingP2Pfinanceasanewlyemergingindustry.Also,thispapercontributestotheexistingliteratureinseveralways.First,tothebestofourknowledge,thisworkcontributestothestudiesofemergingP2PLandBLmarkets.WhilefewexistingstudiesfocusontherelationshipproblemsbetweenP2PLandBL,thispaperstressesthatpreviousstudieshavefocusedononeorafewregions.ThisworkcollectsthedataforChina’seightmajorregions.Second,thisisthefirstattempttoexplorethecausalnexusbetweenP2PLandBLinChina’seightmajorregionsusingtheequationsystemformultivariatepanelGrangercausalityanalysistooptimizeandsupplementpreviousresearch.Thispaperhasseveralaspectswhicharedifferentfrompreviousstudies.Thecontributionsofthisworkarethreefold.First,thisstudyutilizesanewdatasetwhichcomesfromalargeP2PLplatforminChina(i.e.WangDaiZhiJia)whichbroadensthestudyofcreditriskanalysis.Incontrast,mostpreviousresearchhasutilizedthedatafromP2PlendingplatformsintheUSA(suchasProsperandtheLendingClub).Second,thisstudyusestwocontrolvariablesforotherimportantvariablesthat,ifomitted,wouldcausetheestimatedcoefficientstobebiased.Therefore,thisstudyadoptstheP2PLandBLinterestratesascontrolvariablesoneandtworespectively.Third,theresultofthispapercanprovideacomprehensiveanddetailedviewoftheP2PLandBLdevelopmentlinkinChina’seightmajorregions.ItwouldbeinterestingandusefultodeterminewhetherthereisacausallinkbetweenbanksandP2Ploansinthelongrun.3.Introductiontodataselectionandresearchmethods3.1DataselectionThedataforChina’seightmajorregionswerecollectedfromWangDaiZhiJia.WangDaiZhiJiaisthefirstandthelargestthird-partyonlinelendinginformationplatforminChina.Hereisthewebsiteforthedataavailableat:http://\hwww.wdzi.\hcom.WangDaiZhiJiaiscurrentlythemostauthoritativeportalsiteoftheP2PlendingindustryinChina.Foundedin2010,WangDaiZhiJiawasoneoftheearliestP2PonlinelendingplatformsinChina.Afteryearsofdevelopment,WangDaiZhiJiaservesmorethanfivemillionusersandthebusinessincludes35provincesinChina.TheWangDaiZhiJiaplatformalwaysinsistsondispersedloansinasmallamount,whichbringsgreatexperiencesforborrowers,lendersandtheplatformitself.BasedonthedatafromWangDaiZhiJia,bytheendof2018,thecumulativeturnoverofWangDaiZhiJiaexceeded100.4billionChineseyuan;thetransactionvolumeinDecember2018was10.5billionChineseyuan.Moreover,asoneofthetop100ChineseInternetcompaniesreleasedbytheInternetSocietyofChinaandMinistryofIndustryandInformationTechnologyin2015and2016,WangDaiZhiJiawasawardedtheAAAratinginChina’sonlineloanevaluationsystems(composedoftheChineseAcademyofSocialSciencesandtheChinaInstituteofFinanceandCapitalMarkets)forfourconsecutivequarters.ThestatisticalresultsdisplaythatWangDaiZhiJiaisalwaysanoutstandingonlinelendingplatforminChina’sonlineP2Plendingplatformratings.Owningtosignificantdifferencesineconomicdevelopmentlevels,consumptionconceptsandvaluesinChina’sregionsandthesegeographicaldifferencesaffectP2Plendingbehavior(Pengetal.,2016).Guoetal..WangDaiZhiJiaisthefirstandthelargestthird-partyonlinelendinginformationplatforminChina.Hereisthewebsiteforthedataavailableat:http://\hwww.wdzi.\hcomInthisstudythedatasetemployedincludedtheperiodfrom2014M01to2019M12forChina’seightmajorregions(i.e.Beijing,Shanghai,Jiangsu,Zhejiang,Shandong,Hubei,GuangdongandSichuan).ThesampleperiodwasdecidedpurelybydataavailabilityonthemeasurefortheP2PLactivities.ThispapereliminatestheeffectofinflationonP2PLandBL,whicharetransformedintonaturallogarithmspriortotheeconometricanalysis.TheuseddataincludedBLtomeasuretheperformanceoftheBLactivitiesandP2PLtomeasuretheperformanceoftheP2PLactivitiesforeachregion.Besides,thisworkusedcontrolvariablesforotherimportantvariablesthat,ifomitted,wouldcausetheestimatedcoefficientstobebiased.Therefore,thisstudyadoptedtheP2PLandBLinterestratesascontrolvariablesoneandtworespectively.Consequently,thisworkorganizesthepaneldatafortheempiricalpurpose.PriortothemultivariatepanelGrangercausalityanalysisresults,thisworkappliedthreeconventionalunitroottests,namely,ADF,PPandKPSStests.TheseresultsarereportedinTable2.Table2.UnitRootTests(ADF,PPandKPSS)forChina’sEightMajorRegionsCountriesLevelsFirstDifferenceADF(k)PP(k)KPSS(k)ADF(k)PP(k)KPSS(k)Beijing:P2PL-3.701(0)-3.692(1)0.708[6]**-3.939(1)***-7.120(3)***0.896[4]BL-2.224(5)-1.808(1)1.935[6]***-8.725(4)***-17.445(8)***0.327[5]P2PLIR-2.783(0)-7.647(6)0.779[6]***-10.279(0)***-10.133(1)***0.445[7]BLIR-2.139(0)-1.999(4)0.743[6]***-9.225(0)***-10.927(8)***0.083[10]Shanghai:P2PL-2.2