Real-Time Inequality

Real-TimeInequality∗

Thomas Blanchet (ParisSchoolofEconomics)

Emmanuel Saez (UCBerkeleyandNBER)

Gabriel Zucman (UCBerkeleyandNBER)

November23,2023

Abstract

ThispaperconstructsmonthlydistributionsofnationalincomefortheUnitedStates.We developamethodologytodisaggregateannualdistributionsandprojectmonthlychanges inatimelymannerbycombininghigh-frequencypublicdatasources.Thisallowsus toestimategrowthbysocialgroupsassoonasquarterlymacroeconomicgrowthnumbersarereleased,andtotrackthedistributionalimpactsofgovernmentpoliciesduring andintheaftermathofrecessionsinrealtime.Wetestandvalidateourmethodologybyimplementingitretrospectivelybackto1976.Estimatesareavailableat https: //realtimeinequality.org andareregularlyupdatedwithnewreleasesofthenational accounts.

JEL Codes:E01,H2,H5,J3.

∗ ThomasBlanchet:thomas.blanchet@wid.world;EmmanuelSaez:saez@econ.berkeley.edu;GabrielZucman: zucman@berkeley.edu.WethankAkcanBalkir,AnandBharadwaj,andJamesFengforoutstandingresearch assistance,andHeatherBoushey,DennisFixler,MarinaGindelsky,DamonJones,RobertKornfeld,GregLeiserson,DannyYagan,andnumerousconferenceparticipantsforhelpfulcommentsandreactions.Weacknowledge financialsupportfromtheCenterforEquitableGrowthatUCBerkeley,theCarnegieFoundation,NSFgrant SES-1559014,theStoneFoundation,andtheEuropeanResearchCouncil.Thispaperissupplementedbyawebsite, https://realtimeinequality.org,withregularlyupdatedinequalityanddistributionalgrowthestimates anddetailedvisualizations.Allourdataandprogramsarealsopostedonlineatthisaddress.

1Introduction

Amajorgapinglobaleconomicstatisticsisthelackoftimelyinformationonthedistribution ofaggregateincome.Thankstoasophisticatedsystemofnationalaccountsstatistics,macroeconomicdataarepublishedalmostinrealtime.IntheUnitedStates,estimatesofquarterly grossdomesticproductarereleasedlessthanamonthaftertheendofeachquarter;monthly aggregateincomewithinamonth.Thesefiguresareavitalinputfortheanalysisofthebusiness cycleandtheconductofmonetaryandfiscalpolicy.Buttheyarenotdisaggregatedbyincome level,makingithardtotrackhigh-frequencychangesintheeconomicconditionsofthedifferent socialgroups.Thisgaplimitstheabilityofpolicymakerstodesigneffectivemonetaryandfiscal policiesincrisissituationandintheaftermathofrecessions.Italsorestrictstheabilityof economiststostudytheeffectsofthesepoliciesandtotesttheoriesofthebusinesscyclefor whichinfra-annualdistributionaldataarenecessary.

OurpaperattemptstoaddressthisgapbycreatingmonthlydistributionsofnationalincomefortheUnitedStates,thusputtingdistributionalstatisticsonanequalfootingwith macroeconomicstatistics.Weproposeamethodologytocombinetheinformationcontained inhigh-frequencypublicdatasources,includingmonthlyhouseholdandemploymentsurveys, quarterlycensusesofemploymentandwages,andmonthlyandquarterlynationalaccounts series.Theresultofthiscombinationisasetofharmonizedmonthlymicro-files,available at https://realtimeinequality.org,inwhichanobservationisasyntheticadult(obtained bystatisticallymatchingpublicmicro-data)andvariablesincludeincomeanditscomponents. Thesevariablesadduptotheirrespectivenationalaccountstotalsandtheirdistributionsare consistentwiththoseobservedintherawinputdata.

Thesefilesunifyinformationcurrentlyscatteredindisparatedatasources,allowingfora morecomprehensiveanalysisofthebusinesscycle.Forinstance,anemergingbodyofevidence usinghouseholdsurveysshowsacompressioninthewagedistributionfollowingtheheightofthe Covid-19pandemic(e.g.,Autor,DubeandMcGrew,2023).Meanwhilethereisalsoevidence fromSocialSecuritydataofasignificantincreaseintheconcentrationofwagesearnedbythetop 1%,agroupnotwellcapturedbyhouseholdsurveys(e.g.,GouldandKandra,2022).Because thesourcesarescattered,wecurrentlylackacomprehensiveassessmentofoveralltrendsin wageinequalityinthepost-Covidperiod.Ourworkproposesamethodologytocombineall theexistingevidenceinaunifiedframework.Ahistoricalprecedentisthenationalaccounts themselves,whichwerecreatedinthecontextoftheGreatDepressiontounifydisparatebusiness surveysintoaconsistentandcomprehensivesystem.

Themainstepsofourmethodologycanbesummarizedasfollows.Westartwiththe annualdistributionalnationalaccountsmicro-filesofPiketty,SaezandZucman(2018),which allocate100%ofannualnationalincome,householdwealth,andmanycomponentsofthese macroeconomicaggregatesusingprimarilyindividualtaxdata.Wethenstatisticallymatch thesefilestoCurrentPopulationSurveyandSurveyofConsumerFinancemicro-datausing optimaltransportmatchingmethods.Toourknowledgethisisthefirsttimesucha“one-to-one” statisticalmatchisconducted.Thisallowsustobringgender,race,andeducationvariables— whicharemissingintaxdata—intothePiketty,SaezandZucman(2018)distributionalnational accounts,thusallowingustoproducethefirststatisticsonthedistributionofnationalincome bygender,race,andeducationalattainment.

Wethenmovetohigherfrequencyandestimatemonthlystatisticsinthreesteps.First, takingmovingaveragesofcurrentandadjacent-yearannualmicro-data,orusingthelatestfile (2021)from2021onwards,wecreatemonthlyfilesbyrescalingeachcomponentofnationalincometoitsmonthlyseasonally-adjustedaggregatevalue.Second,weincorporatehigh-frequency changesinthewagedistributionusingmonthlysurveymicro-dataandtabulationsofmonthly andquarterlysurveysandadministrativerecords.BuildingontheimportantworkofLee(2020), weshowthatpublictabulationsoftheQuarterlyCensusofEmploymentandWagesby6-digits NAICSindustry × county × ownershipsector(publicvs.private)canbeusedtopredictchanges inwageinequalityremarkablywell,includingatthetopofthedistribution.Forexample,the shareofwagesearnedinthetop1%ofindustries × countieswiththehighestaveragewage(e.g., securitiesbrokerageinNewYorkcounty;InternetpublishinginSantaClaracounty)isstrongly correlatedwiththeshareofwagesearnedbythetop1%workers.Thisallowsustoproject changesinwageswithinthetop10%ofthedistributionreliably.Forthebottom90%,whichis wellcoveredbyhouseholdsurveys,weestimatereal-timewagelevelsbyaveragingpredictions fromtabulatedemploymentsurveysandfromthemonthlyCurrentPopulationSurvey.

Third,wemodelchangesinothercomponentsofpretaxandposttaxnationalincome.For businessandcapitalincome,weaccountforchangesintheaggregatevalueofeachcomponent (rentalincome,corporateprofits,etc.)andassumethatwithin-componentdistributionsare unchangedintheshortterm.Forgovernmenttransfers,wemodelthedistributionofnew programsusingprogramparameters,eligibilityrules,andpublicsources.

Wetestandvalidateourmethodologybyapplyingitretrospectivelybackto1976.Comparingpredictedtoobservedincomechanges,wefindthatwecorrectlyanticipatewhetherincome isgrowingorfallingabout80%–90%ofthetimeforthedifferentincomegroups.Weprovide

quantificationofthebiasandnoiseofourprojections,whichforallincomeconcepts(e.g.,pretax vs.posttax)andincomegroupsarefoundtobelimited.Ourmethodologydeliversaccurate predictionsduringandintheimmediateaftermathofrecessions,whenreal-timeestimatesare mostvaluablefromapolicyperspective.

Theintuitionforwhyourmethodologygenerallydeliversreliableresultsisthefollowing. About30%ofnationalincomeiscapitalincome.Becausewealthisastockvariable,theconcentrationofthevariouscomponentsofcapitalincomeisslow-movingathighfrequency.The impactofcapitalincomeontotalincomeinequalityismostlydrivenbychangesinthesizeof thedifferentcomponentsofaggregatecapitalincome—suchascorporateprofitsandhousing rents—overthebusinesscycle,changeswhicharecapturedbyourmethodology.Forlaborincome,whichaccountsforabout70%ofnationalincome,short-termchangesinthedistribution canbelarge,asunemploymentspikesinrecessions.Butincontrasttocapitalincome,forlabor incomewedonotassumestabledistributions:wecapturehigh-frequencydistributionalchanges thankstoourcombinationofhouseholdandemploymentsurveys.

Becauseourmethodologyonlyusespublicdata,itcaneasilybereplicated,tested,andextended.Lookingforward,itcouldbeenrichedbycombiningadministrativedatasetswithingovernmentagenciesorbyincorporatingadditionaldatasources,suchasprivatesectorinformation (Chettyetal.,2023).Weviewourpaperasconstructingaprototypeofreal-timedistributions combiningallcurrentlypubliclyavailabledatasources—aprototypethatcouldberefinedusing additionaldataandeventuallyincorporatedintoofficialnationalaccountstatistics.1

Therestofthispaperisorganizedasfollows.InSection2werelateourworktothe literature.Sections3andSections4detailourmethodology.Section5providesvalidation tests.InSection6westudythemonth-to-monthdynamicsofincomeinequalityduringthe Covid-19pandemicandinitsaftermath,andcontrastitwiththeGreatRecessionof2008–2009.WediscussracialinequalityinSection7andconcludeinSection8.

2RelatedLiterature

2.1PreviousAttemptsatEstimatingInequalityataHighFrequency Therehasbeenandthereareongoingeffortstoprovidetimelyestimatesofinequalityinthe UnitedStates.

1 TheFederalReservehaspublishedDistributionalFinancialAccountssince2019,distributingaggregate householdwealthquarterly(Battyetal.,2019).Forincome,theBureauofEconomicAnalysis(BEA)distributes annualpersonalincomeandhasexploredthefeasibilityofhigher-frequencystatistics(Fixler,Gindelsky,and Kornfeld,2021).WehavegreatlybenefitedfromdiscussionswiththeFederalReserveandBEAteams.

TheFederalReserveBankofAtlantamaintainsamonthlywagegrowthtracker,constructed usingmicrodatafromtheCurrentPopulationSurveyfollowingamethodologydevelopedin Dalyetal.(2011).2 Thetrackerreportsthemedianpercentchangeinthehourlywageof employedindividualsobserved12monthsapart.Breakdownsby,e.g.,wagequartiles,gender, occupation,andcensusdivisionsareshown.Althoughausefultool,thiswagetrackerhas somelimitations.First,itdoesnotaccountfornon-workers,hencethestatisticsdonotmap ontooverallincomeinequality.Second,thedataaretop-codedat $150,000inannualwage, roughlythe95th percentileofthewagedistribution.Theymissthedynamicofincomeinthe top5%,agroupthatearnsaboutaquarterofallwages.IncontrasttotheAtlantawagegrowth tracker,ourstatisticsincludenon-workers,topearners,andallotherformsofincomebeyond wageincome(e.g.,capitalincomeandtransfers),makingitpossibletodistributeallofnational incomeandtodecomposeitsgrowth.

Since2019,theFederalReservehaspublishedDistributionalFinancialAccounts(DFA),distributingaggregatehouseholdwealthatthequarterlyfrequency(Battyetal.,2019).Following SaezandZucman(2016),theDFAallocatetheofficialFederalReserveFinancialAccountstotals acrossthepopulation.IncontrasttoSaezandZucman(2016)whoprimarilyrelyonindividual incometaxdataandthecapitalizationmethodforthisallocation,theFederalReserveusesthe SurveyofConsumerFinances,atriennalsurveyofabout6,000families.Inthispaper,although ourfocusisprimarilyonincome,wealsoconstructreal-timeestimatesofwealthinequality. AsdetailedinSection6.4,theevolutionofwealthinequalityweobtainisconsistentwiththe DFAestimates.Ourvalue-addedistocapturethetopofthedistributionallthewaytothetop 0.01%,toprovidelongertimeseries(backto1976,whiletheDFAstartsin1989),tohavemore distributionalinformationattheannualfrequency(duetotheannualnatureoftaxdata,as opposedtothetriennalnatureoftheSurveyofConsumerFinances),andtoprovidecurrent-day estimatesofwealthinequality(updateddailyon https://realtimeinequality.org),based ondailychangesinstockmarketindices.

Recently,Fixler,Gindelsky,andKornfeld(2021)buildontheannualdistributionalpersonalincomestatisticscreatedbytheBureauofEconomicAnalysis(Fixleretal.,2017)to explorethefeasibilityofaquarterlydistributionofpersonalincome.ThemainmethodologicaldifferencewithourworkisthatFixler,GindelskyandKornfeld(2021)donotattempt toprojectchangesindistributionswithincomponents,butsimplyrescaletheannualpersonal incometotalscomponentbycomponenttomatchthecorrespondingquarterlytotals.Asthey

2 See https://www.atlantafed.org/chcs/wage-growth-tracker

show(andasweconfirminSection5.3below),thismethodologyproducesreasonableresults inyearsofnormalgrowthbutsignificantlyunderestimatesinequalityduringrecessions.Akey contributionofourworkistodemonstratethatamoresophisticatedmethodology—projecting changesinthedistributionoflaborincomeusinghigh-frequencyhouseholdandemployment surveys—overcomesthisissue.Thereareanumberofadditionalmethodologicaldifferences betweenthetwoprojects.IncontrasttoFixler,GindelskyandKornfeld(2021)whodistribute personalincome,wedistributenationalincome,theaggregateusedtocomputemacroeconomic growth.3 Westartfromannualestimateswhicharelargelybasedonindividualtaxreturndata, whileFixler,GindelskyandKornfeld(2021)relyprimarilyontheCurrentPopulationSurvey, makingithardertoprovideestimateswithinthetop10%.Thesedifferencesnotwithstanding, bothprojectssharethesameobjectiveofcreatingtimelyinequalitystatisticsconsistentwith thenationalaccounts.OurworkwasinspiredbydiscussionswithstaffoftheBureauofEconomicAnalysisandtheongoingdialoguebetweenacademicsandresearcherswithingovernment agenciesisinourviewhighlyvaluable.

2.2ImpactsoftheCovid-19PandemiconInequality

OurworkalsorelatestotheliteratureontheimpactoftheCovid-19pandemiconinequality, recentlysurveyedinStantcheva(2022).Theliteratureemphasizestheequalizingeffectsof governmentinterventioninhigh-incomecountries,whilesuggestingseveralchannelsthrough whichthepandemicmayeventuallywideneconomicdisparities.IntheUScontext,Parolinet al.(2022)usethemonthlyCurrentPopulationSurveyandtheSurveyofIncomeandProgram Participationtoproducemonthlypovertyratesinrealtime.Severalstudiesattempttoanalyze inequalityinrealtimeincountriesotherthantheUnitedStates.Forexample,Aspachsetal. (2020)andBounieetal.(2020)usemicro-levelbankaccountdatatoconstructhigh-frequency distributionaldataforSpainandFrancerespectively.

Relativetothisbodyofwork,ourmaincontributionistoprovideageneralmethodologythat canbeappliedtoallbusinesscyclesandcouldbeimplementedthroughouttheworld.Themain featureofourmethodologyisitscomprehensivecharacter(capturing100%ofnationalincome), timeliness(estimatesareavailableonlinewithinamonth),andgranularity(withestimates availablefromthebottom50%tothetop0.01%forpretaxincome,posttaxincome,disposable income,andwealth).

3 SeeSaezandZucman(2020)foradiscussionofthedifferencesbetweenthesetwoconceptsandtheirimplications.

3RescalingtoMatchMonthlyIncomeAggregates

Therearetwomainstepsinourmethodology.First,werescaleexistingannualincomedistributionstomatchmonthlymacroeconomicincometotals,incomecomponentbycomponent. Secondandmostimportantly,weincorporateinformationonchangesinthedistributionof incomewithinkeycomponents,mostnotablywageincome.InthisSectionwedefineandconstructourmonthlyaggregatesandexplainhowwerescaleannualdistributionstomatchthese aggregates,beforeturningtochangesindistributionswithincomponentsinSection4.

3.1DefinitionofIncome

Ourgoalistoestimatethemonthlyandquarterlydistributionsoftheincomeconceptsstudiedin Piketty,SaezandZucman(2018)andinthedistributionalnationalaccountsliterature(Blanchet etal.,2021):factor,pretax,posttax,anddisposableincome.4 Factorincomeistheincome earnedfromlaborandcapital,beforeanytaxandgovernmentspendingandbeforetheoperation ofthepensionsystem.Pretaxincomeisfactorincomeaftertheoperationofthepension system(publicandprivate),disabilityinsurance,andunemploymentinsurance.Contributions topensions(includingSocialSecuritytaxes)andtounemploymentanddisabilityinsuranceare removed,whilethecorrespondingbenefitsareadded.Pretaxincomethusinparticularcaptures theeffectofexpandedunemploymentinsuranceduringtheCovid-19pandemic.Posttaxincome ispretaxincomeminusalltaxes(otherthanSocialSecuritytaxes,alreadysubtractedfrom pretaxincome),plusallgovernmenttransfers(otherthanSocialSecurityandunemployment benefits,alreadyincludedinpretaxincome)andthegovernmentdeficit.

Factor,pretax,andposttaxincomealladduptonationalincome.Nationalincomeisthe mostcomprehensiveandharmonizednotionofincome:itincludesallincomethataccruesto residentindividuals,nomatterthelegalnatureoftheintermediariesthroughwhichthisincome isearned.Incontrasttopersonalincome,nationalincomeisnotaffectedbybusinessdecisionsto operateascorporationsvs.non-corporatebusinessessuchaspartnerships,adecisioninfluenced bythetaxsystem.Thisfeatureofnationalincomemaximizescomparabilityovertime.National incomeiscomputedfollowinginternationally-agreedmethods,maximizingcomparabilityacross countries.Last,itiscloselyrelatedtoGDP,theaggregatemostoftenusedtocomputeeconomic growth:NationalincomeisGDPminuscapitaldepreciationplusnetincomereceivedfrom abroad.Sincecapitaldepreciationandnetforeignincomeaccountforarelativelysmallfraction

4 DetaileddefinitionsarepresentedPiketty,SaezandZucman(2018)andSaezandZucman(2020)intheUS context,andinAlvaredoetal.(2016)andBlanchetetal.(2021)intheinternationalcontext.

ofGDP,thegrowthofnationalincomeisconceptuallyclosetothegrowthofGDP.5 Ourfocuson nationalincomeisinlinewithrecommendationsmadebytheCommissionontheMeasurement ofEconomicPerformanceandSocialProgress(Stiglitz,SenandFitoussi,2009).

Factorincome—thesumofincomefromlaborandcapital,thetwofactorsofproduction— naturallylendsitselftodecompositionsofeconomicgrowth.Pretaxincomeandposttaxincome includeincomewhichissocializedthroughsocialinsuranceandthetax-and-transfersystem.At theindividuallevel,thegrowthofpretaxandposttaxincomethusreflectsbothoutputgrowth andchangesintransfers.Comparingthegrowthofposttaxincometothegrowthoffactor incomeprovidesacomprehensiveviewoftheextenttowhichtaxesandgovernmentspending equalizegrowthacrossthedistribution.

Wealsoconsiderafourthincomeconcept,disposableincome.Itisequaltopretaxincome minusalltaxes,plusallcashandquasi-cashtransfers.Disposableincomecapturestheincome individualshaveattheirdisposaltoconsumeprivategoodsandtosave.Incontrasttoposttax income,disposableincomeexcludesin-kindtransferssuchasMedicareandMedicaid,collective consumptionexpenditures,andthegovernmentdeficit.Disposableincomedoesnotaddupto nationalincomeandthuscannotbeusedtodecomposegrowth.Itis,however,ausefulconcept tostudythedistributionalimpactsofstabilizationpoliciesduringeconomiccrises.6

3.2ConstructionofMonthlyIncomeAggregates

Toconstructaggregatemonthlyfactor,pretax,disposable,andposttaxincomeandtheircomponents,weusethemonthlyandquarterlynationalaccountspublishedbytheBureauofEconomic Analysis.Westartfromthemostdetailedcomponentsofpersonalincome(publishedmonthly) anddomesticproductandincome(publishedquarterly)available.Allthemonthlyandquarterlyaggregatesusedinthispaperareseasonally-adjustedandexpressedinrealdollarsusing thenationalincomepricedeflator.

Fourremarksabouttheconstructionofouraggregatesareinorder.First,factorincomeis estimatedusingtheincomeapproachoftheUSnationalincomeandproductaccounts,notthe

5 Conceptually,GDPandgrossdomesticincomeGDI(fromwhichnationalincomeisderivedbysubtracting depreciationandaddingnetforeignincome)areidentical,butinpracticetheyareestimatedusinglargely independentsourcesintheUnitedStatesandhencetheirgrowthcandiverge;seeSection3.2below.

6 Inperiodsofcrisis,posttaxincome—whichincludesgovernmentspendingotherthancashtransfersbutadds backthegovernmentdeficit—canbelowerthandisposableincome.Thiswasthecaseinthesecondquarterof 2020,duetothemassivefederaldeficitsinducedbytheeconomicresponsetotheCovidpandemic.Disposable incomehastwoadvantagesrelativetoposttaxincomeinthiscontext.First,itdoesnotrequireonetomake (necessarilydebatable)assumptionsaboutwhobearstheburdenofthegovernmentdeficit.Second,itismore directlyinformativeoftheconsumptionpossibilitiesofhouseholdsandoftheextenttowhichgovernmentpolicies managetosmooththemoverthebusinesscycle.

productapproach.Asiswellknown,thereisastatisticaldiscrepancybetweengrossdomestic income(GDI)andgrossdomesticproduct(GDP)intheUnitedStates(e.g.,Fixler,deFranciscoandKanal,2021).Wedonotallocatethestatisticaldiscrepancy.Ourestimatesmatch incomegrowth,notproductgrowth;wheneverthestatisticaldiscrepancyisnotzero,wedo notexactlycaptureGDPgrowth.7 Second,corporateprofitsandGDIareonlyavailableone monthafterthepublicationofthefirstestimateofGDP.8 Asaresult,nationalincomeandour estimatesofquarterlygrowthbysocialgrouparepublishedonemonthaftertheinitialestimate ofGDP.Third,inadditiontobeingpublishedquarterlyinthecontextofGDPstatistics,most componentsoffactorincome—suchascompensationofemployees,proprietors’income,and rentalincome,butnotcorporateprofits—aswellasgovernmenttransfersarealsopublished monthlyaspartofpersonalincome,aboutfourweeksaftertheendofeachmonth.Thisallows ustoestimatecertainmonthlystatisticswithinamonth,suchasfactoranddisposableincome growthforthebottom50%(wherecorporateprofitsarenegligible)andthedistributionoflaborincome.Fourth,forthecomponentsofincomethatareonlyavailablequarterlybutnot monthly—e.g.,forfactorincome,corporateprofits;forpostttaxincome,collectivegovernment expenditure—wedisaggregatethequarterlyserieswhentheybecomeavailableusingDenton’s (1971)method,followingtheInternationalMonetaryFund(2017)recommendationstocompile high-frequencynationalaccounts.

3.3FromAnnualtoMonthlyMicro-Files

Justasmonthlyandquarterlynationalaccountsdataareseasonallyadjustedandannualized (i.e.,presentedinlevelsequivalenttoafullyear),ourmonthlyandquarterlydistributionaldata areseasonallyadjustedandannualizedsothattheyarealsodirectlycomparableinleveltoannualinequalityestimates.Seasonaladjustmentisimportantbecausesomeformsofincomeare highlyseasonal(e.g.,mostbonusesarepaidonceayearinJanuary).Annualizationmeansthat weestimatethedistributionofwhatannualincomewouldbeifseasonally-adjustedmonthly

7 AppendixFigureA1comparesthegrowthofGDItothegrowthofGDP.Bothtrackeachothercloselybut notperfectly.ThestatisticaldiscrepancyhasbeensignificantduringtherecoveryfromtheCovid-19recession, duringwhichGDIhasrecoveredfasterthanGDP.TheotherreasonwhywedonotexactlymatchGDPgrowth isthefactthatnetforeignincome(includedinnationalincomebutnotinGDI)anddepreciation(includedin GDIbutnotinnationalincome)cangrowatdifferentratesthanGDI.

8 ThefirstestimateofquarterlyGDPisavailableneartheendofthefirstmonthaftereachquarter.Asecond estimateisreleasedaboutamonthafter,andathirdandfinalestimateaboutamonthafterthesecondestimate, i.e.aboutthreemonthsaftertheendofthequarter.RelativetoGDP,quarterlyGDIisproducedwithaalag ofanadditionalmonth(2monthsforthefourthquarter)asitrequiresestimatingcorporateprofits,whichis doneusingthedetailed(butnotquiteastimely)CensusBureauQuarterlyFinancialReport(USCensus,2022).

incometotalsandtheirdistributionsremainedstableover12months.9 Concretely,seasonal adjustmentmeansthataJanuarybonusisspreadoutoverthe12monthsoftheyear;annualizationmeansthatifbonusesdoublefromoneJanuarytothenext,thisdoublingisspreadout smoothlyover12months.

Anotherwaytomeasureinequalityatahighfrequencywouldbetoestimatetheinequality ofactualmonthlyincome.Becauseofincomemobility(e.g.,losingorstartingajobinthe middleofayear),thisapproachwouldleadtomoreinequalityatthemonthlyfrequencythan attheannualfrequency.Bycontrast,ourprocedurewhichannualizesincomemakesinequality statisticscomparableathighvs.lowfrequency.10

Ourstartingpointtoestimatemonthlydistributionsistheannualdistributionalnational accountssyntheticmicro-dataofPiketty,SaezandZucman(2018).ThesefilescombineIRStax micro-data,surveys,andnationalaccountsdatatoconstructannualdistributionsofincomeand wealthconsistentwithnationalaccountsaggregates.Sincetheirfirstpublication,aliteraturehas developedtotestassumptions,conductrobustnesstests,developimprovements,andmaximize comparabilitywithothercountrieswheresimilarmethodsarefollowed(Blanchetetal.,2021). Thecurrentfiles,updatedinSaezandZucman(2020b),incorporatetheresultsofthisbodyof work.Anewfileiscreatedeachyearwhenthemostrecenttaxstatisticsandannualnational accountsbecomeavailable.Thelastannualmicro-fileisfortheyear2021.Thisfilecurrently servesasourbaselinefor2021andonwardsestimations.

Toconverttheseannualfilestothemonthlyfrequency,wenormalizethepopulationandthe distributionofeachincomecomponenttoone.Wethencreatemonthlyversionsoftheannual filesmixingsamplesfromtwoadjacentyearswithunequalweights.Specifically,tocreatea filecorrespondingtomonth m inyear y,wecombinethemicro-datafortheyear y withits weightsmultipliedby m/12withthemicro-datafortheyear y 1withitsweightsmultiplied by1 m/12.Therefore,eachmonthlyfileisamovingaverageoftheyearlyfilesoverthelast twelvemonths.Thisproceduresmoothesoutshort-run,year-specific,mean-revertingvariations, whicharenotinformativeofthedistributionforagivenmonth,andwouldotherwiseintroduce discontinuitiesinthemonthlyseries.Likeintheannualmicro-files,eachobservationinthe monthlymicro-filesrepresentsanadultindividual,definedasanindividualaged20ormore.

Wethenrescalethecomponentsoffactor,pretax,posttax,anddisposableincomesothat

9 Quarterlyincomeiscomputedastheaverageofmonthlyincomeoverthethreemonthsofthequarter.

10 Thereisnomicro-dataintheUnitedStatesallowingonetotrackthelongitudinalevolutionofhousehold incomemonthaftermonthorquarterafterquarter(Fixler,GindelskyandKornfeld,2021).Ourapproachthat focusesonmonthlyandquarterlydistributionsofannualizedincomesdoesnotrequirelongitudinaldataand usefullyby-passesthisissue.

theyadduptotheirseasonally-adjustedmonthlytotalvalue,componentbycomponentandat themostgranularlevelpossible.Specifically,forfactorincomewerescalewagesandsalaries, supplementstowagesandsalaries,proprietor’sincome,rentalincome,corporateprofits,interest income,productiontaxes,productionsubsidies,non-mortgageinterestpayments,andgovernmentinterestpaymentstotheirrespectivemonthlytotals.11 Forpretaxincomeweadditionally rescaleprivatepensioncontributions,SocialSecuritytaxes,contributionstounemployment insurance,privatepensionbenefits,SocialSecuritybenefits,andunemploymentinsurancebenefits;fordisposableincome,Medicaretaxes,directtaxes,theestatetax,veteranbenefits,and othercashbenefits;andforposttaxincome,Medicare,Medicaid,otherin-kindtransfers,collectiveexpenditures,andthegovernmentdeficit.Componentsofhouseholdwealtharesimilarly rescaledtotheirend-of-monthvalues,asdetailedinAppendixB.

Becausethevariouscomponentsofaggregateincomedonotgrowatthesameratefromone monthtoanother,themereactofrescalingtomatchmonthlytotalschangesthedistribution ofincome.Fromthesecondtothethirdquarterof2020,forexample,corporateprofitsgrew bycloseto25%,muchfasterthanwages.Sinceprofitsaremoreconcentratedthanwages,this pushestowardsahighertop1%pretaxincomeshare.Rescalingtomatchaggregates,however, isnotsufficienttoaccuratelycapturehigh-frequencychangesininequality,especiallyduring recessions.Withpubliclyavailabledataitispossibletodomore,namelytoprojectchangesin distributionswithinkeycomponents—ataskwenowturnto.

4IncorporatingChangesWithinIncomeComponents

Themostimportantstepofourmethodologyinvolvesincorporatinginformationonthemonthto-monthevolutionofthedistributionoflaborincome,whichaccountsforabout70%ofnationalincome.Weestimatebothchangesintheextensivemargin(numberofemployedvs. non-employedindividuals,includingrecipientsofunemploymentinsurancebenefits)andinthe intensivemargin(changesinthewagedistribution).Weestimatethesechangesbycellsofrace × education × gender × 5-yearagegroup × maritalstatus.Thisrequiresbringinginrace, education,andagevariablesintothePiketty,SaezandZucman(2018)annualdistributional nationalaccountsfiles.WedosobystatisticallymatchingthesefilestotheMarchCPSandthe SurveyofConsumerofFinances.ThisSectionbeginsbydescribingthisstatisticalmatching, beforeturningtohowweincorporatechangesintheextensivemarginandintensivemargin,

11 AppendixAprovidesadetailedmappingoftheseNationalIncomeandProductAccountsconceptstothe variablesusedinourmicro-files.

andinthedistributionofgovernmenttransfers.

4.1One-to-OneStatisticalMatchingtoSurveyData

Method. Considertwodatasets: A (sometimesreferredtoasthebasefile,theannualdistributionalnationalaccountsmicro-filesinourcase)and B (sometimesreferredtoasthesupplementalfile,e.g.,theMarchCPS).Assume A and B havecommonvariables,denotedby X (e.g., incomeanditscomponents).Theremainingvariablesaredenotedby Y infile A and Z infile B

Thegoalistobringthe Z variables(e.g.,education)intofile A.Theoptimalwaytodosoisto implementaconstrainedstatisticalmatch(e.g.,Rodgers1984),inwhicheachobservationfrom B ismatched“one-to-one”withanobservationin A,whileminimizingthesumofdistances overthe X variablesbetweenmatchedobservations.Thisconstrainedstatisticalmatchcanbe implementedusingoptimaltransportmethods.

Formally,assumethetwodatasetsareofsize n and m respectively,andthatobservations ineachdatasetshaveweights u =(u1,u2,...,un)and v =(v1,v2,...,vm).Withoutlossof generality,assumeweightssumtoone.Denoteby Dij thedistancebetweenobservation i inthe firstdatasetand j inthesecondoverthe X variables(inourapplicationweusethe L1 norm,i.e., thesumoftheabsolutevaluesofthedifferences).TheoptimaltransportmapΓ ∈ Rn×m which matchesobservations“one-to-one”whileminimizingthesumofdistancesbetweenmatched observationisthesolutionofthefollowinglinearprogrammingproblem:

Themainappealofconstrainedstatisticalmatchingisthatthemultivariatedistributionsof allthe Z variablesarepreservedinthematcheddataset.Inthatsense,thereisnolessofdistributionalinformation,incontrasttoothermatchingprocedures.12 Themainpracticalobstacle toimplementingconstrainedstatisticalmatchessofarhasbeencomputationalrequirements. FindingtheoptimalΓinvolvessolvingforalarge-scalelinearprogrammingproblem:ifthe twodatasetstobematchedeachhave100,000observations(whichistheorderofmagnitude inourcase)thenthematrixofpairwisedistanceshas10billionentries.Thankstorecently developedimplementationsofoptimaltransportalgorithms,solvingthistypeofproblemhas becomedoableinareasonableamountoftime.

12 One-sidedmatchingdoesnotrespectthedistributionsseenintheseconddataset.One-sidedmatching withoutreplacementisinefficientasmatchqualitybecomesverypoorforthelastobservationsmatched.

Implementation. Tostatisticallymatchthedistributionalnationalaccountsmicro-filesto surveydata,westartbysupplementingtheMarchCPSwithgroupquarterobservationsfrom theAmericanCommunitySurvey:individualslivingincorrectionalfacilities,nursinghomes, collegedormitories,etc.,whoarenotsampledbytheMarchCPS.Thisallowsustocapture theentirepopulationofUSresidents,asinourdistributionalmicro-files,whichisimportant tocaptureincomedynamicsatthebottomofthedistribution.Wethenstatisticallymatchthe augmentedMarchCPStoourannualmicro-filesoverthefollowing X variables(observedin bothdatasets):wageincome,pensionincome,businessincome,interest,dividendandrents, SocialSecuritybenefits,welfarebenefits,andgovernmenttransfersotherthanSocialSecurity andwelfarebenefits.Webringinthefollowing Z variables:race,education,age.Thematchis doneatthehouseholdlevel,separatelyformarriedvs.singles,individualsagedmorevs.less than65,andemployedvs.unemployedindividuals.

Wesimilarlymatchourmicro-filestotheSurveyofConsumerFinances(SCF),atriennal surveyofabout6,500familiesthatover-sampleswealthyhouseholds.WeannualizetheSCF bytakingmovingaveragesofthetwoclosestwavesoftheSCFandmatchittoourmicro-files overwageincome,pensionandSocialSecurityincome,businessincome,interestanddividend income,capitalgains,financialandbusinessassets,housingassets,anddebts.Thisallowsusto bringsocio-economiccharacteristicsforhigh-incomehouseholds,whicharenotwellcoveredin theMarchCPS.Weusethesocio-economicvariablestransportedfromtheSCFforhouseholds inthetop5%oftheincomeorwealthdistribution,andthosetransportedfromtheMarchCPS forthebottom95%.

Valueandlimitation. Thekeyvalueofourone-to-onestatisticalmatchistojointogether severalmicro-databases(includingindividualtaxdata,theCPS,andtheSCF)inasinglefile. Variablesthatarecommonacrossdatabasesexistinversionscorrespondingtoeachdataset (e.g.,SCFnetwealthandnetwealthestimatedfromtaxdata)andareclosetoeachother recordbyrecordthankstothematchingprocedure.Thismakesitstraightforwardtoswitch fromonedatabasetoanotherasneeded.Forexample,researcherswhoareusedtoworking withtheCPScanprimarilyfocusonCPSvariablesandreplacethemwithtax-datavariables whenevertheanalysisconcernsthetopofthedistribution.

However,weemphasizethatthismatcheddatabasecannotprovidereliableinformation aboutthejointdistributionofvariablesthatarenotjointlyincludedinatleastoneofthe originaldatabase.Forexample,becauseneitherthepublic-useindividualtaxdata,northe

CPSortheSCFprovidescomprehensiveinformationonthejointdistributionofincomeand state,13 wecannotanalyzepatternsinincomegrowthbystate × socialgroup.Asanother example,becausetheonlyinformationonthejointdistributionofwealthandracecomesfrom theSCFandtheSCFisnoisyabovethetop1%(duetosmallsamplesizes),itisnotpossible tousethematcheddatasettoobtainreliableestimatesoftheracialcompositionofwealth abovethe99th percentile.Thelimitationsofthesourcefilescarryovertothematchedfileand knowledgeoftheselimitationsiscriticaltomakeaninformedusedofthematcheddatabase.

Anobviouslysuperiorwaytoconstructaunifieddatabasewouldbetoproceedtoexact matchesacrossadministrativesourcesandsurveys,ascanbedoneforresearchpurposesin, e.g.,Scandinaviancountries.However,thisisnotyetfullyfeasibleintheUnitedStateseven withingovernmentagencies(see,e.g.,Chettyetal.,2010)andcertainlynotusingpublicdata. Furthermore,anysuchmatchcouldnotbeproducedquicklyandthuswouldnothelpwiththe productionreal-timeinequalityestimates.Thestatisticalmatchweimplementleveragesthe strengthofUSpublicdata,whicharerichbutscattered.

Testofthematchingprocedure. Wetestandvalidatethequalityofourmatchingprocedurebycomparingthedemographiccompositionofincomeandwealthbyincomeandwealth groupinthesurveymicro-dataandthematchedfile.Tounderstandthelogicofthetest,considerthecaseofgenderandwageearnings.TheCPSprovidesgoodinformationongender compositionbyannualwageearningsdeciles(i.e.,thefractionofindividualsineachdecileof theearningsdistributionwhoarewomen).Wecancheckthatthisgendercompositionispreservedwhenusingtheannualwageearningsvariablecomingfromthetaxdata(asopposedto theCPS)inthematchedfile.Thiswillnaturallybethecaseifthematchedobservationshave similarearningsintheCPSandthetaxdata.14

Figure1reportstheresultsofthistest.Thefigurecomparesdemographiccomposition alongonekeyincome(orwealth)variableofinterest,asmeasuredintheoriginalsurveydata vs.inthematchedfiletaxdata.Panels(a)and(b)considergenderandracialcompositionby annualwageearningsmeasuredintheCPSvs.taxdata.Inbothcases,thesampleincludes thefullpopulationofworking-ageindividuals(aged20–64),includingindividualswithnowage earnings.Panel(c)considersracialcompositionbyannualtotalincomeintheCPS,theSCF, andthematchedfile.Forthepurposeofthisexercise,totalincomeisthesumofwages,

13 Thestatevariableisnotavailablefortopearnersinthepublic-usetaxfiles.

14 Bycontrast,thistestcouldfailifthestatisticalmatchisnotexcellentonthewageearningsvariable,e.g., becausethesamplesmatchedaretoosmalltofindclosematches,orbecausethematchingprocedureputshigh weightonothervariables.

pensions,SocialSecuritybenefits,businessincome,interest,dividends,andrents.Last,panel (d)considersracialcompositionbywealthintheSCFvs.thematchedfile.Inbothpanels(c) and(d),weconsidertheentireCPS(andSCF)samples.Allfourpanelsshowthatdemographic compositionsareverycloseintheoriginalsurveydataandthematchedfile.Thereasonforthis successisthatourone-to-onematchingprocedurepreservesranksintheincomeandwealth distributionwell,asillustratedinAppendixFigureA2inthecaseofwageearnings.

4.2ChangesinEmployment

Tocapturehigh-frequencychangesinthedistributionoflaborincome,thefirststepofour methodologyinvolvesadjustingemploymentstatusatthemicro-leveleachmonth.Todosowe computeemploymentratesbyrace × education × gender × 5-yearagegroup × maritalstatus cellsinthemonthlyCPS,andusethesetabulationstoimputeemploymentratesbycellsinour monthlydistributionalnationalaccountsmicro-files.Weproceedinfoursteps.

Estimationofaggregateemploymentrate First,weestimatethenumberofworkerseach monthusingtheBLSmonthlyreleaseofnon-farmemploymentatthenationallevel.Sincethe numberofemployedpeopleinagivenyearismechanicallyhigherthaninagivenmonth,we adjustmonthlyemploymentnumberstomakethemcommensurablewithyearlyestimatesfrom SocialSecuritytaxdata.Wedosousingthestrongandconsistentlinearrelationshipobserved betweentheBLSandtheSocialSecuritynumbersovertime.AppendixFigureA3depictsthe rawmonthlyemploymentratesfromtheBLS,theannualemploymentratesfromtheSocial Securitytaxdata,andthemonthlyemploymentratesadjustedtomatchannualrates(inall casesfortheworking-agepopulation,age20–64).Inrecentdecades,rawmonthlyemployment ratesarearound75%whileadjustedmonthlyemploymentrates(thattrackannuallevels)are 10percentagepointshigher,around85%(becauseofpart-yearworkers).

EstimationoftheaggregatenumberofUIrecipients Second,weestimatethenumber ofunemploymentinsurance(UI)recipientseachmonth.Todoso,weusetheDepartmentof Labor’sweeklypublicationofunemploymentclaims.Weaggregatethisdatabymonthand adjustitforseasonalvariationsusingtheX11procedure(Shiskinetal.,1967).Sincethe numberofUIrecipientsinagivenweekismechanicallylowerthaninagivenyear,weadjust thenumberofUIclaimsbyaconstantcoefficienttomatchtheannuallevelsrecordedinthe taxdata(consistentwithourgoalofconstructingmonthlydistributionsofannualizedincome).

Estimationoflaborforcestatusbyindividualcharacteristic Third,usingthemonthly CPS,weestimatemonthlyseriesoflaborforcestatusbyrace × education × gender × 5-year agegroup × maritalstatus.IneachmonthlyCPSdataset,werunalogisticregressionof (i)employmentstatusand(ii)unemploymentstatusagainstrace,education,ageby5-year group,andmaritalstatusinteractedwithgender.Weusethepredictionfromtheseregressions toestimateemploymentandunemploymentratesbycell.Weadjustthisdataforseasonal variationsusingtheX11procedure(Shiskinetal.,1967).

AdjustmentofemploymentandUIrecipientsinthemonthlymicrofiles. Last,inour monthlymicrofiles,weadjustatthemarginwhethersomeone(i)isemployedand(ii)receives UIbenefitsbasedontheinformationcollectedinthethreeprecedingsteps.Theprocedure, describedindetailinAppendixC.1,reproducesrelativechangesinlaborforcestatusbyrace × education × gender × 5-yearagegroup × maritalstatus,whilealsomatchingtheaggregate levelsofemploymentandnumberofUIbenefitrecipients.

4.3ChangesintheDistributionofEarnings

Thesecondstepofourmethodologytocapturehigh-frequencychangesinthedistributionof laborincomeinvolvesestimatingchangesinthedistributionofwagesatthemonthlyfrequency. Wedothisbycombiningalltheavailableevidenceonthisissue:monthlyandquarterlyemploymentsurveysandthemonthlyCPS.

Predictingwageinequalityfromtabulatedemploymentsurveys. Wefirstestimate wageinequalitymonthlyusingthetimelyemploymentcensusesandestablishmentsurveysofthe BureauofLaborStatistics.TheQuarterlyCensusofEmploymentandWages(QCEW)provides employmentandwagestatisticsforabout95%ofemployees,basedonstateandfederalunemploymentinsuranceadministrativerecords.Atthemonthlyfrequency,theCurrentEmployment Statistics(CES)surveyprovidessimilarinformationbasedonarepresentativesampleofabout 144,000businessesandgovernmentagencies.BoththeQCEWandtheCESarepublishedin theformoftabulationsbyindustries × geographicalareas,upto6-digitsNAICSindustry × county × typeofownership(i.e.,publicorprivate)inthecaseoftheQCEW.Althoughtheunderlyingindividual-levelmicro-dataarenotpubliclyavailable,valuableinformationaboutthe distributionofwages,includingatthetop,canberetrievedfromthesegranulartabulations.

BuildingonLee(2020),weconstructquarterlywageincomedistributionsusingtheQCEW data.TheideaistousetheQCEWasifitwereamicro-leveldataset,treatingeach6-digits-

NAICS-industry × county × type-of-ownershipcellasanobservationwhoseweightistheemploymentcountandwhosevalueistheaveragewage.EachwaveoftheQCEWcontainsabout amillionsuchobservationsinrecentyears,muchmorethanatypicalwagesurvey.Weremove outliers,definedascellswhosewageislessthanhalfofafull-timeminimumwagejob.We thenestimatetheaveragewagebypercentile,afterimplementingthreeadjustmentsdescribed below.AsdetailedinSection5below,ourproceduredeliversremarkablyaccuratepredictions oftrendsinwageinequality.

AdjustmentstotheQCEWdata. TheadjustmentsweapplytotheQCEWdataarethe following.WefirstconverttheQCEWwagedatafromquarterlytomonthly.Employment countsarereportedmonthlyintheQCEW,butwageearningsonlyquarterly.Thisisnota significantissuesincewagesaresticky,sochangesinthewagedistributionintheshortrunare drivenbychangesintherelativeemploymentoflow-wageandhigh-wageworkersratherthan bychangesintheirrespectivesalaries.WerunthewagedataintheQCEWthroughamoving averageofthelasttwelvemonthstogetsmoothmonthlywagesandtogetridoftheseasonality inthewagedata(due,forexample,toend-of-yearbonuses).15

Second,becausetheQCEWdataisaggregatedincells,itunderstatesthelevelofinequality betweenindividualworkers.ThisisillustratedonFigure2.Intheannualtaxmicro-data,the top1%wageshare(amongindividualswithpositivewages)increasesfrom6%inthelate1970s toabout12%inthe2000s.Intherawbutde-seasonalizedQCEWdata,thetop1%wageshare islower.Wefixthisdiscrepancybyimplementingasimpleadjustmenttothemonthlyseries. Specifically,weregressthetaxdatarealwageagainsttheQCEWrealwageforeachpercentile, andusethepredictionfromtheseregressionsasourmonthlyestimateforeachpercentile.This procedureworkswellbecausetherelationshipbetweentheaveragewageofagivenpercentilein theQCEWdataandthetaxdataisstronglylinear.Importantly,thiscorrectiondoesnotvary withtimeandthusdoesnotweakenthepredictingpoweroftheQCEW.Figure2illustrates thiscorrectioninthecaseofthetop1%share,whichistypicallyhardtocapturewithnon-tax data.Adjustingtherawde-seasonalizedQCEWwageserieswithamultiplierandlevelshift (constantovertheperiod)generatestheadjustedQCEWseriesdepictedinblue.Thisadjusted seriesalmostperfectlyalignswiththeactualtop1%wageshareobservedinthemicrotaxdata inbothlevelsandtrendsovertheentireperiod.

15 Wheneverthisprocedureintroducesmissingvalues,weimputethembackbyregressinglogwagesoncounty, time,industry,andtype-of-ownershipfixedeffects.Remainingseasonalvariations(introducedbyseasonalityin employmentnumbers)iscorrectedbyrunningtheaveragewageforeachpercentilethroughtheX11procedure.

Third,wesupplementtheQCEWwiththeCurrentEmploymentStatistics(CES)surveyto gettimelyestimates.TheQCEWispublishedwithalagofonetotwoquarters.TheCESis releasedeverymonth,albeitwithacoarserlevelofaggregation:about19,000monthlyseries coveringupto300industriesand450areas,comparedtoaboutamillionseriesintheQCEW. WematcheachQCEWcelltothreeCESseries.ThefirstmatchesthelocationoftheQCEW cellaspreciselyaspossible,thesecondmatchesitatthestatelevel,andthethirdatthenational level.Becausethereisatrade-offintheCESbetweenthelevelofgeographicalandindustry disaggregation,usingthesethreeseriesallowsustoextractasmuchinformationaspossible. Weaveragethetrendfromthesethreeseriesineachcellandusethisaveragetrendtoextend theQCEWdatainthemostrecentquarters.AppendixFigureA4depictsthequalityofthis projectionbycomparingbottom50%andtop10%wagesharesestimatedusingtherawQCEW datavs.projectedusingtheCESupto6monthsforward(indashedlines).Bydefinition, theprojectionmatchestheQCEWlevelinthelastmonthofeachquarter,andthenprojects forwardoverthenexttwoquarters.Overall,theCESprojectioncomesclosetotheQCEW, althoughthefitisabitweakerforthetop10%thanforthebottom50%.

WageinequalityinthemonthlyCPS. Inadditiontoestimatinghigh-frequencychangesin thewagedistributionusingtheQCEW,wealsoestimatechangesinaveragewagesbypercentile usingtheweeklyearningsvariableofthemonthlyCPS.Ourfinalestimatesofaveragewageby percentileinagivenmonthisobtainedastheaverageoftheCPSandQCEWpredictions.For thebottom80%ofthewagedistribution,QCEWandCPSpredictionsareweightedequally.

TheweightontheCPSpredictionthenlinearlyfallsto0aswemovetothe90th percentile,and is0abovethe90th percentilewheretheCPSisnotinformativebecauseoftop-coding.Oncewe haveourfinalestimateofwagesbypercentile,thedistributionwithinpercentilesisinterpolated usinggeneralizedParetointerpolationmethods(Blanchetetal.,2022).

Adjustmentofwagelevelsinthemonthlymicrofiles. Weusetheresultingmarginal wageincomedistributiontoupdateourmonthlymicro-filesasfollows.InthemonthlyCPS,we computetheaveragerankinthewagedistributionofeachrace × education × gender × age × maritalstatuscell.Wealsocomputetheseaverageranksinthe12precedingmonths.Inour monthlymicro-files,weadjusttheaveragerankbycelltoreplicatetheevolutionseeninthe CPS.Oncetheranksareadjusted,weassigneachindividualobservationthewagecorresponding tohisorheradjustedrank.AppendixC.2providescompletedetails.

4.4DistributionofNewGovernmentTransfers

Inthelaststepofourmethodology,wemodelthedistributionofnewgovernmenttransfers. WesimulatethekeycomponentsofthegovernmentresponsetotheCovidcrisis:thePaycheck ProtectionProgram,Covidreliefpayments,andexpandedrefundabletaxcredits.

ThePaycheckProtectionProgramwasaloanprogramdesignedtokeepsmallbusinesses afloat,representingabout $1,000billion,or5%ofnationalincome.Thegovernmentforgave mostoftheseloans,assumingcompanieskepttheiremployeesandwagesstable.Following Autoretal.(2022),wedistribute70%oftheprogram’sexpenditurestobusinessownersandthe remaining30%towageearners.Weconstructanovelestimateoftheprogram’sdistributional effectfortheincidenceonwages.Weusethepubliclyavailabledataoneachloan,whichwe matchtotheQCEWdatabasedonthedateoftheloans,theindustry,andthelocationofthe business.Wemanagetomatchabout5,700,000loansto5,500,000QCEWcells.Weestimate bothanextensivemargin(fractionoftheworkforcecovered)andanintensivemargin(fraction ofthewagebillcovered)foreachpercentileofthelaborincomedistribution,whichweuseto simulatetheeffectofthePaycheckProtectionProgramonworkers.

ThethreewavesofCovidreliefpayments(“economicimpactpayments”)areallocatedbased onprogramrulesusingtaxableincomeasreportedintheupdatedPiketty,SaezandZucman (2018)files.Finally,weallocatetheexpandedrefundabletaxcredits(childtaxcreditandearned incometaxcredit)basedonincomeandeligibilityusingourmicro-data.16

4.5SummaryofAlltheSourcesUsedandTimingofRelease

Table1summarizesallthesourcesusedtoconstructourreal-timeestimates,includingfrequency ofpublicationandtimingofavailability.Ourapproachonlyreliesonpublicdatasources. AppendixDdescribesthestructureoftheprogramsusedtoconstructourreal-timeestimates. Theprogramsareavailableonlineat https://realtimeinequality.org,makingitpossible forresearcherstoassessandimproveanyaspectofthemethodology.

5ValidationTests

Sinceweapplyourmethodologybackto1976(thefirstyearoftheQCEW),wehavealarge numberofmonthlymicro-filesthatcanbeusedtotesttheaccuracyofourapproach.

16 Refundabletaxcreditscorrespondingtoincomesearnedinyear t aregenerallypaidoutinyear t +1and countedastransfersinyear t +1inthenationalaccounts,aconventionwefollow.In2021,however,halfofthe expandedchildtaxcreditwaspaidmonthly(inthelast6monthsof2021)andisassignedto2021(not2022).

5.1WageDistributionPrediction

Webeginbyexamininghowwellourmonthlywageinequalityseriesmatchtheactualdistributionofwageincome—byfarthelargestcomponentofnationalincome—overthe1976–2021 period.Figure3comparesthewagedistributionintheupdatedPiketty,SaezandZucman (2018)micro-files,whicharebasedonpublictaxmicro-files,tothedistributionconstructedin thispaperusingtheQCEWandtheCPS.Eachpaneldepictstheshareoftotalwageincome earnedbyaspecificgroup(bottom50%,middle40%,next9%,andtop1%)amongadult individualswithpositivewageincome.

Ourmonthlyestimatestracktheannualtax-data-basedstatisticswellforallgroups,includingforthetop1%whichisnotmeasuredwellintraditionalhouseholdsurveys.Thetrendin risingwageconcentrationisaccuratelycapturedbyourcombinationofQCEWandCPSdata. Asintheannualtax-data-basedstatistics,thetop1%gains6.5pointsbetweenthelate1970s and2021andthenext9%gains3points.Themiddle40%loses8pointsandthebottom50% loses1.5point.Short-runvariationsareusuallyaccuratelypredictedaswell.Theseresultsare consistentwiththeanalysisofLee(2020)whofirstusedtheQCEWtocreatequarterlywage distributionsforthemacroeconomicanalysisofthebusinesscycle.

5.2VolatilityofCapitalIncome

Next,weprovidesupportforourtreatmentofcapitalincomebycomparingthevolatilityof aggregatecapitalincomecomponentsandthevolatilityoftheirdistributions.Recallthatfor capitalincome(corporateprofits,rentalincome,proprietors’income,interest),toformournext year(s)projectionweassumethatwithin-componentdistributionsarestable.Forexample,if thetop10%earns70%ofaggregatecorporateprofitsin t,weprojectthatthetop10%still earns70%ofcorporateprofitsin t +1.Ifcorporateprofitsshrinkin t +1,individualsinthe top10%ofthetotalincomedistributionaremoreaffectedthanindividualsinthebottom90% andallelseequal,inequalityfalls.Ifaggregatecorporateprofitssoar,inequalityincreases.

Toassessthemeritsofthisprocedure,AppendixFigureA5comparesthesizeofcapital incomecomponents(asmeasuredbytheirshareofnationalincome)andtheconcentrationof theseincomecomponents(asmeasuredbythesharegoingtothetop10%ofthepretaxincome distribution).Allseriesarenormalizedto100in1976tocontrastvolatilities.Thetopleft panelreportsresultsforcorporateprofits,thelargestformofcapitalincome,andshowsthat aggregateprofitsarehighlyvolatile.Duringorjustbeforerecessions,itiscommonfortheshare ofprofitsinnationalincometofallby10%–20%(e.g.,1980,2000,2008).Theshareofprofits

earnedbythetop10%exhibitscomparativelylittleyear-to-yearvariation.Thesameconclusion holdsforothercomponentsofcapitalincome,asreportedintheotherthreepanels.Because changesinaggregatesswampshort-termchangesindistributions,movementsinmacroeconomic aggregatescapturethebulkofthecontributionofcapitalincometochangesininequality.

5.3RetrospectiveValidation

Lastandmostimportantly,weretrospectivelycheckwhetherourmethodologycombiningprioryearannualmicro-fileswithcurrent-yearhigh-frequencydatasourcesprovidesaccurateestimatesofcurrent-yeardistributions.Thistestincorporatesallformsofincome,asopposedto wagesorcomponentsofcapitalincomeonly.

Methodology. Foreachyear t from1975to2019(or2018),westartfromtheyear t updated annualmicro-filesofPiketty,SaezandZucman(2018)andimplementourreal-timemethodology toagethesedataintoayear t +1(oryear t +2)simulatedannualmicro-dataset—usingthe samesources(high-frequencynationalaccountsaggregates,householdandemploymentsurveys, etc.)andassumptionsasinourreal-timemethodology.Usingthesesimulatedmicro-datafor year t +1or t +2,wecancomputeanydistributionalstatisticsandcomparethemwiththe statisticscomingoutoftheactualannualmicro-data.Themostdirectlyrelevantstatisticfor ourpurposesisrealincomegrowth.Foreachgroupofthepopulation,wecomputeactualincome growthusingtheannualdistributionalnationalaccountmicro-filesforbothyears t and t +1 (or t +2),andpredictedincomegrowthratesusingtheactualannualmicro-filesforyear t but thesimulatedmicro-dataforyear t +1(or t +2).The t +2simulationsarerelevantgiventhat ourreal-timeinequalityestimatesforyear t aretypicallybasedonannualmicro-filesforyear t 2,duetothenearly2-yeardelayintheavailabilityoftaxdata.

Results. Figure4comparesactualandpredictedrealincomegrowthratesforthebottom 50%(toppanels)andthemiddle40%(bottompanels)ofthefactorincomedistribution,with incomeequallysplitamongmarriedspouses.Leftpanelsshowactualvs.predictedgrowthfrom year t to t +1andrightpanelsfrom t to t +2.Inallcasesthedotsarecloselyscatteredaround the45-degreeline,showingthatourpredictionsarehighlyinformativeoftheactualgrowthin bottom50%andmiddle40%incomes.Forexample,wecorrectlypredictwhetherbottom50% isrisingorfalling82%ofthetimeoneyearforwardand91%ofthetime2yearsforward.17 One

17 The2-yearforwardpredictionperformsbetterbythatmetricbecausethebottom50%oftenhaslittleannual incomegrowthduringoursampleperiod,sothatinafewcasespredictedgrowthisslightlynegativewhenactual

canconditiononcertainactualgrowthratestovisuallyascertainhowwellourmethodology performsindifferentcontexts.Forinstanceifoneconditionsonactualgrowthbelow-2.5%(or below-5%inthe2-yearaheadgraph),typicallycorrespondingtorecessionyears,thecorrelation betweenactualandpredictedgrowthremainsveryhigh.Figure5repeatsthisanalysisfortop 1%incomesandnext9%incomes.Thedotsagainalignwellwiththe45-degreeline.We correctlypredictwhethertop1%incomesarerisingorfalling89%ofthetimeoneyearforward and93%ofthetime2yearsforward.

Onecaveatwhenconsideringthetopofthedistributionisthatpredictingshort-rungrowth duringtaxreformscanbechallenging.Thelargesterrorsareobservedin1987and1988,when predictedgrowth—thoughstronglypositive—issignificantlylowerthanobservedgrowth.As shownbyFigure3,inthoseyearstheQCEWpredictslargegainsinthetop1%wagesharebut failstocapturethefullmagnitudeoftheriseinthistopshare.Onepossibleinterpretationis thattheTaxReformActof1986,whichreducedthetopmarginalincometaxratefrom50%in 1986to28%in1988,ledtoanimmediateandacross-the-boardincreaseintop-endwagesand bonusesforexecutiveswithinindustries × counties(inadditiontogainsinspecifichigh-paying industries × counties,byconstructioncapturedbyourmethodology).Morebroadly,itcan bechallengingtopredictthegrowthoftopincomesinyearsofsignificanttaxreforms,which inadditiontorealresponsescangenerateavoidanceresponses,suchasinter-temporalincome shifting.AsshownbyFigure5,thepredictionerrorsintop1%growthareconcentratedduring taxreformyears(althoughnotalltaxreformyearsleadtoerrors).Innon-tax-reformyearsour methodologydeliversaccuratepredictionsforthetop1%.

Rescalingvs.adjustmentsofdistributions. Tobetterunderstandwhichaspectsofour methodologymattertogenerateaccuratepredictions,AppendixFigureA6reportssimilarfiguresofactualvs.predictedgrowthratesbutusingasimplifiedpredictionmethodologythatonly rescalesmacroeconomicaggregates(followingSection3)withoutincorporatinganychangesin thedistributionoflaborincome.Thesimplifiedmethodologyperformswellinyearsofnormal growth,butdeliverssignificantlyworseresultsthanourfullmethodologyforthebottom50% duringrecessions,whenitsignificantlyover-estimatesgrowth.Thisfindingechoestheresultsof Fixler,Gindelsky,andKornfeld(2021)discussedinSection2.1andshowsthatadjustingthelaborincomedistributioniscriticaltoprojectinequalityduringrecessions.Oncethedistribution oflaborincomeisadjusted,ourmethodologyaccuratelypredictsgrowthduringdownturns. growthisbarelypositive(orvice-versa),aproblemthatattenuateswhenoneconsidersgrowthover2years.

Goodnessoffitsummary. Tosummarizetheperformanceofourapproach,Table2reports detailedstatisticsforgoodnessoffitandnoise.Foreachgroup(bottom50%,middle40%, next9%,andtop1%)andincomeconcept(andwealth),wecomputethefractionofyearsin whichwecorrectlypredictwhetherincome(orwealth)isgrowingorfalling,themeandifference betweenpredictedandobserved1-yeargrowth,thestandarderrorofthismean,andtheroot meansquareerror.Forreferencewealsoreportthestandarddeviationofactual1-yeargrowth rates.Weconsiderthreesamplesofyears:all44yearsfrom1976to2019,the34yearswhich arenottaxreformyears,andthe12yearscorrespondingtorecessionsandtheirimmediate aftermath.AppendixTableA1reportsthesamestatisticsforgrowthover2years.Anumber ofresultsareworthnoting.

First,thegoodfitandlimitednoiseobtainedforfactorincome(Figures4and5)extendsto otherincomeconcepts—pretax,disposable,andposttax—andtowealth.Acrossconceptsand samplesofyears,wecorrectlypredictthesignofgrowtharound90%ofthetime.18 Biasin annualgrowthislimitedtoafewtenthofapercentagepoint,whichisreasonableconsidering thesamplesizesinvolved.Thereislittlebiasforthetop1%eventhoughthemethodology doesnotrelyonanytaxdata.Standarderrorsfortop1%predictionsfallwhenexcluding tax-reformyears.Forothergroups(whereavoidancepossibilitiesandincentivestoavoidare morelimited),includingtaxreformsdoesnotmakeadifference.Second,theseresultscarry overtorecessions:ourmethodologyispredictiveofincomedynamicsduringdownturnsandthe ensuingrecoveries.Weunder-estimatedisposableandposttaxincomegrowthforthebottom 50%duringpastrecessions,perhapsbecausewedonotattempttoincorporatethecreationof newgovernmenttransfersduringpastrecessions(e.g., $600individualtaxcreditsin2008)in oursimulatedmicro-files.

6InequalityDuringtheCovid-19Pandemic

ThisSectionusesourreal-timeestimatestoanalyzethemonth-to-monthdynamicsofincome andwealthduringtheCovid-19pandemicandinitsaftermath.Westartbystudyingthedynamicofincomeandwagesbeforegovernmentintervention,thenmovetodisposableincome, beforeturningtowealthinequality.Unlessotherwisenoted,allthestatisticswereportare for“equal-splitadults,”definedasindividualadultswithincomeandwealthequallysplitbetweenmarriedspouses.On https://realtimeinequality.org,wealsoreportstatisticsatthe 18 Asnotedinfootnote17forfactorincome,predictionsareslightlyworseforthebottom50%becausegrowth isoftenclosetozeroforthatgroupinthosedecades.Ifoneconsidersgrowthover2years,wecorrectlypredict thesignofgrowthforthebottom50%about90%ofthetimejustlikeforothergroups(AppendixTableA1).

householdlevel,whereahouseholdisataxunitasdefinedbythetaxcode,i.e.,eitherasingle personaged20oraboveoramarriedcouple,inbothcaseswithchildrendependentsifany.All growthnumbersareadjustedforinflationusingtheofficialnationalincomedeflator.Thesame deflatorisusedforallgroupsofthepopulation.

6.1TheDynamicofFactorIncomeDuringtheCovid-19Recession Dynamicacrosstheincomedistribution. TheCovid-19pandemicledtohighlyheterogenousdeclinesinincomes.BetweenFebruary2020(thelastmonthbeforetherecession)andApril 2020(itstrough),annualizedrealnationalincomeperadultfell15%.Butthefallwasmuch largerfortheworking-class(-33%forthebottom50%),duetothedeclineinemployment.It wasalsostrongerthanaverageforthetoponepercent(-19%)duetothecollapseofbusiness profits,akeysourceofincomeatthetop.Thecrisisaffectedthemiddleclassandupper-middle classrelativelyless,becauseindividualsinthesegroupsweremorelikelytoremainemployed. Therecoverywasalsoheterogenous:topgroupsrecoveredfasterthantheworkingclass.For thetop1%,therecoverytookoneyear,whileittooktwoyearsforthebottom50%.Becausethe bottom50%washitthehardestandrecoveredlast,thepandemicexacerbatedfactorincome inequality.Theshareoffactorincomeearnedbythetop1%was19.5%inDecember2021,its highestlevelinthepost-WorldWarIIera.

ComparingtheCovid-19andGreatRecessionrecessionsandrecoveries. Itiswell knownthatintheaggregate,therecoveryfromtheCovid-19crisis(18months)wasmuchfaster thantherecoveryfromtheGreatRecession(4yearsandamonth).Ourreal-timeestimates allowustomovebeyondaggregatesandcomparerecoverypatternsfortheworking-class.To doso,Figure6bfocusesontheworking-agepopulation(tocontrolforpopulationaginginthe 2010s,duringthelongrecoveryoftheGreatRecession)andnormalizesincometo100inthe monthprecedingeachrecession.Twomainresultsemerge.

First,intheaftermathoftheGreatRecessionithadtakenastaggering8yearsand1months forthebottom50%oftheworking-agepopulationtorecoveritspre-crisisrealfactorincome level.From2008to2012,aperiodduringwhichtheeconomyreboundedandcrosseditspre-crisis outputlevel,thebottom50%ofworking-ageadultsexperiencedvirtuallynogrowth.Income startedgrowingin2013butslowly,sothatitonlyexceededitsDecember2007levelinJanuary 2016.TheslowrecoveryoftheworkingclassisarobustfeatureoftheGreatRecession.19 Itis

19 ThetopfiscalincomesharesofPikettyandSaez(2003),whichhavebeenusedtostudythefractionof growthaccruingtotopincomegroups(seeSaez,2008,andsubsequentupdates),alsorevealedit.

notanartifactofpopulationaging(sincewerestricttotheworking-agepopulation),butrather reflectsthestagnationofwagesatthebottomofthedistribution(detailedinSection6.2below).

Secondandbycontrast,realfactorincomeforthebottom50%reboundedmorequickly aftertheCovidrecession.BythetimeaverageincomehadrecoveredfromtheGreatRecession, averageincomeforthebottom50%wasstill10%belowitspre-crisisincomelevelandstillfour yearsawayfromafullrecovery.BythetimeaverageincomehadrecoveredfromtheCovid-19 crisis,bycontrast,thebottom50%wasonly4%belowitspre-crisisincomelevelandwasgrowing fast.TheseresultsillustratethefactthatagiventrajectoryofGDPgrowthiscompatiblewith widelydifferentmarketincomedynamicsfortheworkingclass,highlightingtheusefulnessof timelyanddisaggregatedgrowthstatistics.

6.2WageGrowthAfterRecessions:Covid-19vs.GreatRecession

Intheaftermathofthepandemic,theunemploymentratereachedhistoricallylowlevels.Who benefitedmostfromthetightlabormarket?Toshedlightonthisissuewecanuseourmicro-files tostudythemonth-to-monthdynamicsoflaborincome.ThemainfindingisthatthepostCovidperiodwascharacterizedbysignificantwagegrowthatthebottomofthedistribution—a breakfromthetrendprevailingsincetheearly1980s—butalsoattheverytop,withthemiddle ofthedistributionexperiencingnogains.

Methodologytostudylaborincomeinequality. Toestablishtheseresults,weanalyze changesinlaborincomeinequalityintheworking-agepopulation(includingnon-workers)and computegrowthratesoflaborincomesbypercentilesoflaborincome.20 Ourgoalisnotto characterizewagegrowthforagivenworkerortofixthecompositionoftheworkforce;rather, wewanttodescribetheevolutionofthedistributionoflaborincomeascomprehensivelyas possible.Thegrowthstatisticsbypercentilewecomputecapturetheeffectofchangesinboth employmentandwages.Ourmeasureoflaborincomeincludeswages,supplementstowages andsalaries(suchashealthinsuranceandretirementbenefits)andthelaborincomeofselfemployedindividuals(definedas70%ofself-employmentincome),beforeanytaxordeduction forpensioncontributions.Conceptually,itcorrespondstothetotalcost,foremployers,of employingaworker.Ouranalysis,asalwaysinthispaper,iscross-sectionalinnature:wedo

20 Tobetterconnecttothelaboreconomicsliteratureandbecausewehavegoodmeasuresofindividualwages, fortheanalysisoflaborincomeinequalitywefocusonindividualizedincomeseries,i.e.,wedonotsplitincome equallybetweenmarriedspouses.

notfollowindividualsovertime.21

Toprovidethecontextrequiredtointerpretchangesinthedistributionoflaborincome, Figure7adepictstherawmonthlyemployment-to-working-agepopulationratiofromBLSfrom January2019toSeptember2022.Thisratewas78%beforetheCovid-19pandemic,fellto68% atthetroughoftherecession,andbySeptember2022hadreturnedtoitspre-Covidlevel.22

ThismeansthatalthoughthecompositionoftheworkforcemaynotbethesameinSeptember 2022asinJanuary2019,acomparisonoftheleveloflaborincomeinthesetwomonthsisnot confoundedbychangesinthelevelofemployment,whichfacilitatestheinterpretationoflabor incomegrowth.

Thedynamicoflaborincomeinequalityin2019–2022.

Figure7cdepictstheevolution ofaveragelaborincomebylaborincomegroupfromJanuary2019toSeptember2022.Since thebottomquartileoftheworking-agepopulationismostlyunemployed,wefocusonthenext threequartiles.Wealsoreportincomeforthetop1%,whichearnsasizablefractionoftotal laborincomeandcannotbestudiedwithavailablehouseholdsurveys.Averagelaborincome ineachgroupisnormalizedto100inJanuary2019.WecanseethatfromJanuary2019to September2022,reallaborincomegrewsignificantlyinthesecondquartile,thegroupwiththe lowest-wageworkers.Becausethesamenumberofadultswasemployedinbothmonths,this growthdoesnotreflectajobs(i.e.,quantity)effect.Itreflectsthefactthatlow-payingjobspaid moreinSeptember2022thaninJanuary2019,byabout10%inrealterms.Incomegrewmuch lessforthethirdandfourthquartiles:inthesegroupsrealaveragelaborincomeincreasedbya mere3%overthisperiod.Therelativelystronggrowthatthebottomillustratestheequalizing effectsoftightlabormarkets.

Wagesalsogrewevenfasterattheverytop.Averagelaborincomeforthetop1%grew around15%betweenJanuary2019andSeptember2022.Thetop1%grewespeciallyfastinthe firsthalfof2021,beforeplateauinginthesecondsemesterandfallingslightlyinthefirstthree quartersof2022.Thus,althoughwageinequalityfellfrom2019toSeptember2022withinthe bottom99%,theshareoflaborincomeearnedbythetop1%rosesignificantly.

21 Webuildonalonglongtraditioninlaboreconomicsstudyingchangesincross-sectionalwageinequality; see,e.g.,KatzandMurphy(1992).Themaindifferenceisthatourstatisticsincorporatetheentireworking-age populationincludingnon-workers(asopposedtoworkersonly).Thisapproachallowsonetocomprehensively capturehowgovernmentpoliciesaffectthelabormarket,includingchangesintheextensivemargin.Withour micro-files,itisalsopossibletofocusonemployedindividualsonly.Weviewbothapproachesascomplementary.

22 Employmentratesinourmicro-filesarehigherbyabout10percentagepointsthroughoutbecauseourmicrofilesmatchannualemploymentrates,whileFigure7areportsrawactualmonthlyemploymentrates(whichare mechanicallylower);seeappendixFigureA3andSection4.2foradiscussion.

Laborincomeinequality:Covidvs.GreatRecession. Thedynamicsoflaborincome inequalityobservedduringandaftertheCovidpandemiccontrastsharplywiththoseobserved duringandaftertheGreatRecession,asdepictedinFigure7.Panel(b)ofthatfigureshows thatittookalmost10yearsfortheemploymentratetogetbacktoitspre-GreatRecessionlevel. Panel(d)showsthatsecondquartilelaborincomesfellthemostduringGreatRecession(as observedduringtheCovid-19pandemic),but—insharpcontrasttotheCovid-19recession— continuedtofallinrealtermsuntilthebeginningof2012,eventhoughtheemploymentrate hadalreadystartedtorecover.Secondquartilesreallaborincomesdidnotrecovertheirreal pre-GreatRecessionlevelsuntil2016.

Finally,Figure8depictsreallaborincomegrowthratesbyvingtilesofthelaborincome distributionabovethe25th percentile,fromtheeveoftheCovidrecession(February2020)to December2022(whichhasanemploymentrateequaltothatofFebruary2020),andfromthe eveoftheGreatRecession(December2007)toMay2017(themonthwhentheemployment raterecovereditsDecember2007level).Inbothcaseswecaptureafullemploymentcycleand laborincomegrowthstatisticsarenotconfoundedbychangesinaggregateemployment.

TheGreatRecessionandensuingrecoverywerecharacterizedbymodestgainsinthemiddle ofthelaborincomedistribution—andastagnationatthebottomandatthetop.TheCovid cycleisthemirrorimage:largeearningsgainsatthebottomandtopofthedistribution—and lossesinthemiddle.Moreprecisely,duringtheCovidcycle,laborincomegrewatannualrates ofabout2%–3%atthebottom.Bycontrast,betweenDecember2007andMay2017,average laborincomeforthesecondquartileoftheworking-agepopulationgrewonly0.2%ayear.By thetimetheemploymentratehadrecovereditspre-GreatRecessionlevel(inMay2017),average earningsforlow-wageworkerswerebarelyhigherthanadecadebefore.Growthwasstronger fromthe75th tothe95th percentileduringtheGreatRecession,whilethesegroupsexperienced reallossesduringtheCovidcycle.Finally,thetop1%grewfastduringCovidwhileitstagnated from2007—apeakyearfortoplaborincomes,whichincludestockoptions—to2017.

6.3TheEffectsofGovernmentIntervention

Governmentinterventionduringrecessionsaffectthelevelanddistributionofdisposableincome, sometimesmassively.In2021,averagerealdisposableincomeperadultintheUnitedStates wasabout10%higherthanin2019duetolargegovernmentdeficits.

Thebottom50%mostbenefittedfromtheincreaseingovernmentspending.Afteraccountingfortaxesandcashandquasi-cashtransfers,averagedisposableincomeforthebottom50%

wasnearly20%higherin2021thanin2019.Figure9showsastep-by-stepdecompositionofthis evolution.Tofacilitatetheinterpretationoftheresults,wefocusontheworking-agepopulation (aged20to64)andwealwaysrankbyfactorincomesothatallfiguresforagivenmonthrefer tothesamegroup.23 Thefigurerevealstherelativeimportanceofthedifferentgovernment programsenactedduringthepandemic.

Intheearlymonthsofthecrisis,thePaycheckProtectionProgramliftedincomes.But availableevidenceontheincidenceoftheprogram(Autoretal.,2022)impliesthattheeffect islimited:byourestimates,thePaycheckProtectionProgramincreasedtheaveragemonthly incomeofthebottom50%ofworking-ageadultsbyabout $100.Itreplacedaboutafifth ofthedeclineinfactorincomethatoccurredinthefirstmonthsofthecrisisforthisgroup (fromabout $2,000inFebruary2020toabout $1,500inAprilandMay2020).Unemployment insurance,whichwasexpandedduringthecrisis,hadmuchlargereffects,liftingaveragebottom 50%monthlyincomebyabout $800inMay,June,andJuly2020,andbyupto $400amonth throughtothesummerof2021.

ThethreewavesofCovid-reliefpayments(April2020,January2021,andMarch2021)had massivebuttemporaryeffectsonmonthlyincome.Disposablemonthlyincomeforthebottom 50%peakedinMarch2021followingthethirdpayment,toreach $4,000—twiceasmuchas beforethepandemic($2,000).Forthebottom50%,disposableincomethatmonthwastwice aslargeasfactorincome(about $2,000).Thisgapbetweendisposableandfactorincomewas historicallyhigh:disposableincomeisusuallyclosetofactorincomeforthebottom50%(that is,thisgroupusuallypaysaboutasmuchintaxesasitreceivesincashandquasi-cashtransfers).

Bythefallof2021,disposablemonthlyincomeforthebottom50%haddeclinedto $2,400.The mainreasonwhydisposableincomewashigherfortheworkingclassintheendof2021than beforethepandemicwastheexpandedchildtaxcreditandtheexpandedearnedincometax creditforadultswithchildren.24 Inthebeginningof2022,disposableincomefellastheexpanded taxcreditsexpired.Theonlyreasonwhyitremainedhigherthanpre-Covid(byabout10%) isthatfactorincomewashigher—drivenbytherealwagegainsdocumentedabove.Insum, governmentprogramsenactedduringthepandemicledtoadramaticandunprecedented—but short-lived—improvementsinlivingstandardsforthelowerhalfoftheincomedistribution.25

23 AppendixFigureA7showsdisposableincomeinthefulladultpopulation(i.e.,notrestrictingtoworking-age adults)rankingbyfactorincome;theresultsaresimilar.

24 Asinthenationalaccounts,refundabletaxcredits—i.e.,cashtransfersadministeredthroughthetax system—arecategorizedascashtransfers(notnegativetaxes);thusthechildtaxcreditandtheearnedincometaxcreditsshowupas“regularcashtransfers”inFigure9.

25 AppendixFigureA8showsthatthesameconclusionholdstruewhenlookingattotalposttaxincome,i.e., includingMedicaidandMedicare,otherin-kindgovernmentspending,collectiveconsumptionexpenditures,and

6.4ChangesinWealthConcentration

Last,westudytheeffectoftheCovid-19crisisonwealthinequality.Weproducemonthly anddailyestimatesofwealthlevelsacrossthedistribution.Monthlyestimatesareobtained byrescalingquarterlyFinancialAccountsaggregatestotheirmonthlyvalueusingrealestate andequityindices,anddailyestimatesbyupdatingequityvaluesusingdailystockindices.26 Thismakesitpossibletotrackchangesinwealthinequalityduringperiodsofturmoilinasset markets,whichisvaluabletosimulatewealtheffectsonconsumptioninrealtimeandtoimprove theanalysisandmanagementofthebusinesscycle.

Figure10showsthemonthlydynamicofwealthacrossthedistributionfromJuly2019to March2023,withprojectionstotheJuly1st 2023usingourdailymethodology.Twodistinct phasescanbeobserved.First,untiltheendof2021,wealthgrewstronglyforallgroupsand wealthconcentrationrose.Fromtheendof2019totheendof2021,averagerealwealthper adultgrew26%,primarilyduetotheriseinassetprices,bothinhousingandequitymarkets. Forthetop1%theincreasewas31%andforthetop0.01%itreached34%.Theshareofwealth ownedbythetop0.1%adultsincreased1.2pointfromtheendof2019totheendof2021,to reach18.8%—thehighestlevelrecordedinthepost-WordWarIIera.Wealthgainswerethen partlyerasedbythedeclineinstockpricesin2022.

Ourfindingsonthedynamicsofwealthinequalityareconsistentwithotherexistingevidence. TheFederalReserveDistributionalFinancialAccounts(DFA)showsimilarpatterns,bothfor theCovid-19crisisandoverthelongrun.Ifanything,theDFAsuggestanevenstrongerincrease inwealthconcentrationsince1989.Fromthethirdquarterof1989(thestartoftheDFA)to thesecondquarterof2022,thetop1%wealthsharegrew8.6pointsaccordingtotheFederal Reserve(vs.+5.7inourseriesoverthesameperiodoftime);thenext9%lost-0.4point(-0.6 inourseries);themiddle40%lost-7.4points(-4.3inourseries);thebottom50%lost-0.6 point(-0.7inourseries).Giventhatthesetwoseriesrelyondifferentdistributionalsources(a triennalsurveyofabout6,000familiesinthecaseoftheDFA,annualtaxdatainourcase),this similaritysuggeststhattheriseofwealthconcentrationintheUnitedStatessincethe1980sis highlyrobust.27 InboththeDFAandourseries,thetop1%wealthsharewasatitshighest thegovernmentdeficit.

26 ForhousingwealthweusethequarterlyCase-Schillerindex,projectedtothemostrecentmonthwith theZillowHomeValueindex.Forequities,weusetheWilshire5000TotalMarketIndex,themostextensive representativeindexofUSpubliccompanies.Wealsoadjustthewealthofthetop400dailytomatchthe real-timeestimatespublishedby Forbes.SeeAppendixBforcompletedetails.

27 AsdetailedinSaezandZucman(2020),topwealthshares,althoughtheyexhibitthesametrend,arelower throughoutinlevelintheDFAthaninSaezandZucman(2016);e.g.,thetop1%wealthsharerisesfrom22.5% in1989Q3to31.1%in2022Q2intheDFA(vs.28.6%to34.3%inourseries).ThisisbecauseincontrasttoSaez

recordedlevelattheendof2021andfellby1–2percentagepointsinthefirsthalfof2022.

7RacialEconomicDisparities

Thestatisticalmatchbetweensurveyandtaxdataimplementedinthispaperallowsusto studythereal-timedynamicsofracialincomedisparities,inparticularwhetherBlackand whitehouseholdsrecoveredatthesamepaceaftertheCovid-19recession.

7.1AComprehensiveMeasureoftheBlack-whiteIncomeGap

ToprovidecontextfortheanalysisoftheCovid-19pandemic,westartbydescribingthemediumrundynamicsoftheBlack-whiteincomegap.Althoughalargeliteraturestudiesracialincome disparities(e.g.,BayerandCharles,2018;Chettyetal.,2020;DerenoncourtandMontialoux, 2021),todatethereisnoestimateofhowaveragenationalincome—thebroadestnotionof income—differsforBlackvs.whiteAmericans.Duetodatalimitations(thelackofinformation onraceintaxdataandthepoorcoverageofcapitalincomeinhouseholdsurveys,inparticular), mostexistingstatisticsfocusonearningsorsomemeasureofdisposableincome.Ourapproach, bycontrast,allowsusforthefirsttimetoprovideacomprehensivemeasureoftheBlack-white incomegap.Concretely,in2021averagenationalincomeperadultintheUnitedStateswas around $79,000;withourfileswecanask:Howdoesthisnumberdifferacrossracialgroups?

Figure11showstheaveragepretaxnationalincomeofBlackadultsrelativetowhiteadults. OnaverageBlackAmericansearnhalfofwhatwhiteAmericansdo: $48,000in2021vs. $95,000. ThisgapissignificantlylargerthantheBlack-whiteearningsgapthatisthetraditionalfocus oftheliterature.AsFigure11shows,BlackAmericans(includingnonworkers)aged20to64 onaverageearn65%ofwhatworking-agewhiteAmericansdo.28 TheBlack-whitenational incomegapisevenlargerbecauseracialdisparitiesincapitalincomearelargerthandisparities inlaborincome:onaverageBlackadultsearnonlyabout20%oftheaveragecapitalincome ofwhiteadults.Thisgapitselfprimarilyreflectsthemajordisparitiesinwealthreported inFigure11andrecentlystudiedin,e.g.,Derenoncourtetal.(2022).29 Propertyownership andZucman(2016)andthispaper,theDFAincludeunfundeddefinedbenefitpensionsandvehiclesinwealth, bothofwhicharerelativelyequallydistributed.Oncethesamedefinitionofwealthisused,thelevel,trend, andcompositionoftopwealthsharesarenearlyidenticalinthetwoprojects(see,e.g.,SaezandZucman,2020, Figure1).

28 Restrictingtoworkers,theBlack-whiteearningsgapislower(Blackworkersearnabout75%ofwhatwhite workersdo).AsshownbyBayerandCharles(2018),takingnonworkersintoaccountiscriticaltoanalyzethe dynamicsoftheBlack-whiteearningsgap,duetodifferentialtrendsinemployment.

29 TheBlack-whitewealthgapdisplayedinFigure11isclosetotheoneintheFederalReserveDistributional FinancialAccounts,themostcomparablestatistics.In2019theaveragewealthofBlackhouseholdsis22%of

remainsmuchmoreunequalthanlabormarketincomesandthisinequalityisakeycontributorto thepersistentracialincomedisparitiesthatcharacterizetheUnitedStates.30 Capitalincomeis evenmoreunequallydistributedthanwealthbecauseofdifferencesinyields,inturncomingfrom differencesinassetcomposition.Relativetowhitehouseholds,agreaterfractionofthewealth ofBlackhouseholdsisinrelativelylow-yieldassets,namelyhousingandpensions.Business assetsandcorporateequity,whichhaveahigheryield,aremoreconcentratedamongwhite households.

Figure11alsoshowsthattherehasbeennoreductionintheBlack-whiteincomegapsince thelate1980s.Ifanything,racialdisparitiesareslightlyhigherin2022thanin1989.Inequality increasedfrom1989to2013;itthenstartedfallinginthemid-2010s,inbothcasesdrivenby changesinlaborincomedisparities.Therecentdeclineininequalitywasnotenoughtooffset thepreviousincrease,sothattheaveragepretaxincomeofBlackadultsrelativetowhiteadults remainslowerin2022thanin1989.

7.2RacialDisparitiesOvertheBusinessCycle

Turningtotherecentdynamic,Figure12contrastsincomegrowthatthequarterlyfrequency duringtheGreatRecessionanditsaftermathandduringtheCovidcrisis.DuringtheGreat Recession,theaverageincomeofBlackpeopleexperiencedaprolongeddeclineof4years.In thethirdquarterof2011,itwas8%lowerthanontheeveoftheGreatRecession,whileaverage incomeforwhiteworking-ageadultshadalreadyfullyrecovered.Thisyearcorrespondstothe peakinBlack-whiteincomedisparitiesoverthelastthreedecades.AverageBlackincomethen startedrecovering,firstatthesamepaceasforwhites,thenfasterafter2014,leadingtoa declineintheBlack-whiteincomegapthatcontinueduntiltheCovidpandemichit.Thetrend forHispanicsmirrorstheoneseenforBlacks,exceptthatgrowthwasevenstrongerfrom2014.

DuringCovid,racialdisparitieswerelesspronouncedthanduringtheGreatRecession.The collapseinincomeinthesecondandthirdquarterof2020wasbroadlysimilarforthedifferent racialgroups.Theythenrecoveredatroughlythesamepace:bythefirstquarterof2021all theaveragewealthofwhitehouseholdsintheDFAvs.25%inFigure11.Thedifferenceisduetotheunitof observation(householdsinDFAvs.adultindividualsinFigure11).Becauseofdifferencesinhouseholdsize, racialwealthdisparitiesareslightlylargeratthehouseholdlevel.Usingourmicrofilesonecanalsostudyracial wealthdisparitiesatthehouseholdlevel;resultsareidenticaltotheDFA.IntheSurveyofConsumerFinances (used,e.g.,byDerenoncourtetal.,2022),racialwealthdisparitiesarehigherthanintheDFAandourseries becauseofthedifferentwealthtotals(inparticularthehighertotalforprivatebusinesswealth,whoseownership isconcentratedamongwhitehouseholds).Trendsaresimilarinallseries.

30 Similarly,FigureA9showthateventhoughBlackindividualsaccountfor12%oftheentireadultpopulation, theyaccountforlessthan8%ofthetop10%ofthewagedistributionand4.5%ofthetop10%ofthewealth distribution.

hadrecoveredtheirpre-crisispretaxincomelevel.Wethusagainseeanillustrationofthefact thattheCovid-19recoverywasmuchmoreequalthantheGreatRecessionrecovery.31

8Conclusion

Macroeconomicgrowthstatisticsarenotnecessarilyinformativeofhowincomegrowsformost socialgroups.Yetgovernmentstatisticscurrentlyavailablegloballydonotmakeitpossibleto knowwhobenefitsfromeconomicgrowthinatimelymanner.Ourpaperattemptstoaddress thisgapintheUnitedStatesbycreatingmonthlyincomedistributions,availableshortlyafter thepublicationofofficialhigh-frequencynationalaccountsaggregates.Ourmethodology,which weretrospectivelytestandvalidatebackto1976,combinesallpubliclyavailablehigh-frequency datainaunifiedframework.

Real-timedistributionalgrowthstatisticscouldplayacriticalroleinguidingstabilization policiesduringandintheaftermathofrecessions.Forexample,followingarecession,theycould beusedtoestimate“distributionaloutputgaps,”thatistheextenttowhichincomeremains belowitspre-recessionlevelortrendforthebottom50%ofthedistribution,thenext40%,and thetop10%.Sinceourfilesincorporatealltaxesandgovernmenttransfers,theycouldbeused tostudywhetherfiscalpolicyenactedduringacrisismitigatesincomelossesfortheworking classonamonth-to-monthbasis.

Thisprojectonlyusespubliclyavailabledatasetsandourprogramsareavailableonlineat https://realtimeinequality.org,makingitpossibleforinteresteduserstoexamineallthe aspectsofourapproachandrefineit.Althoughourestimationprocedureappearstodeliver reliableresults,ourestimatescouldbeimprovedbycomplementingthedataweusewithrealtimeprivatesectordata(Chettyetal.,2023),byleveraginginternaladministrativedata,orby collectingnewadministrativedata.

Anumberofpotentialdataimprovementsareworthhighlighting.First,high-frequencynationalaccountstotalsare—likeallimportanteconomicstatistics—stillaworkinprogressand couldberefined.Itwouldbevaluabletodevelopprocessestoremovethestatisticaldiscrepancy betweenGDIandGDP,i.e.,tosystematicallyreconciletheincomeandexpenditureapproach. ThiswouldallowBEAtoproduceasingleunifiedestimateofquarterlygrowth,asmanycountriesdo.Progressinthatdirectioncouldbefacilitatedbyamoresystematicexploitationof high-frequencydataoncorporateprofits,suchaslistedcompanies’quarterlyearningsstate-

31 AppendixFigureA11reportssimilarcomparisonsofaverageincomeformenvs.womeninthetwodownturns andrecoveries.

ments.32 Thenationalaccountscouldalsobeimprovedbyreportingseparateprofitsestimates forpublicvs.privatecompanies(orC-corporationsvs.S-corporations).Becauseprofitsofprivatecompaniestendtobemoreconcentratedtowardsthetopoftheincomedistribution,this additionalbreakdownwouldimprovetheaccuracyofdistributionalestimates.33 Last,governmentagenciescouldproduceadditionalhigh-frequencyinequalityestimates.Mostimportantly, theBureauofLaborStatisticscouldcomputeaquarterlyindividual-levelwagedistributionusingtheadministrativeunemploymentinsurancemicro-datathatunderlytheQCEW.Weview ourreal-timestatisticsasaprototypewhichwehopewillberefined,enriched,andeventually incorporatedintoofficialnationalaccountstatistics.

32 PubliccompaniesmustsubmitquarterlyearningstotheSecuritiesandExchangeCommissionwithin40 days.Ifthisdelaywasreducedtolessthan30days,thesedatacouldbeusedbyBEAasakeyinputtoform anestimateofcorporateprofitswithinamonthoftheendofeachquarter.Inturn,thiswouldmakeitpossible forBEAtosimultaneouslyestimategrowthfromboththeincomeandexpenditureapproach,makingiteasier tointegratedatafromthesetwoapproachesintoasinglenumber.

33 Krakoweretal.(2021)presentprototypeNIPAestimatesofprofitsforS-corporations,butthereareno quarterlyestimatestodate.TheCensusBureauQuarterlyFinancialReport—whichserveasakeyinputfor theestimationofquarterlyprofitsbyBEA—couldincludetabulationsforpublicvs.privatefirmsseparately.

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(a)GenderbyWageEarnings

(b)RacebyWageEarnings

(c)RacebyIncome

(d)RacebyWealth

Notes:Thisfigurecomparesthedemographiccompositionalongonekeyincomeorwealthvariableofinterest intheoriginalsurveydatavs.thesamekeyincomeorwealthvariablefromthetaxdatainourstatistically matcheddataset.Panel(a)considersgendercompositionbyannualwageearningsbracketsintheCPSvs. matcheddata.Panel(b)considersracialcompositionbyannualwageearningsbrackets.Inbothpanels(a)and (b),thesampleincludeszeros(individualswithnowageearnings)butonlyincludesworking-ageindividuals (20–64).Panel(a)usesindividualearnings,whilepanel(b)dividesearningsequallyamongmarriedspouses. Panel(c)considersracecompositionbyannualtotalincomebracketintheCPS,theSCF,andmatcheddata. Forthepurposeofthatexercise,totalincomeisthesumofwages,pensions,SocialSecuritybenefits,business income,interest,dividends,andrents.Panel(d)considersthesameracecompositionbywealthbracketinthe SCFvs.matcheddata.Inbothpanels(c)and(d),weconsidertheentireCPS(and/orSCF)samples.

Figure2:Top1%WageShare:AdjustingtheQCEW

Notes:Thisfiguredepictsthetop1%wageshare(amongindividualswithpositivewages)intheannualtaxdata(inredcircles)andintherawbut de-seasonalizedQCEWdata(orangeseriesfortheoldQCEWbasedonSICindustrycodes,andgreenseriesforthemodernQCEWbasedonmoregranular NAICSindustrycodes).Thetop1%wageshareissubstantiallylowerintherawQCEWbecausethedataisaggregatedbycounty×industrycells,butthe trendsaresimilar.AdjustingtheQCEWserieswithamultiplierpluslevelshiftconstantovertheperiod(basedonatimeseriesregressionapproachas describedinthetext)generatestheblueadjustedQCEWseriesthatalmostperfectlyalignswiththetaxdatabothinlevelsandtrendsoverthefullperiod.

Figure3:WageDistributions:TaxDatavs.QCEW&CPSBasedEstimates

Notes:ThisfigurecomparesthewagedistributionsintheannualdistributionalnationalaccountsofPiketty,SaezandZucman(2018,updated),which arebasedonpublictaxmicro-data,andthoseobtainedinourmonthlymicro-filesusingtheQCEWandtheCPSasdescribedinSection4.3.Eachpanel depictstheshareoftotalwageincomeearnedbyaspecificgroup(bottom50%,middle40%,next9%,andtop1%)ofindividualswithpositivewageincome. Wagesareindividualized(theyarenotequallysplitbetweenmarriedspouses).ThemonthlyseriesestimatedfromtheQCEWandCPStracktheannual micro-datacloselyforallgroups,includingthetop1%whichisnotmeasuredwellintraditionalsurveydata.

Figure4:Actualvs.PredictedGrowthattheBottom

(c)Middle40%,1year

• Regularyear Taxreformyear

Notes:Thisfigurecomparespredictedtoactualgrowthinaveragerealfactorincomeperadult(withincome equallysplitamongmarriedspouses)forthebottom50%(toppanels)andthenext40%(bottompanels). Growthiscomputedfromyear t to t +1(leftpanels)andfrom t to t +2(rightpanels)foreachyear t from1975 to2018(2017intherightpanels).Actualgrowthisobtainedusingtheannualdistributionalnationalaccount micro-dataforbothyears t and t +1(or t +2).Predictedgrowthisobtainedusingtheannualmicro-datafor year t buttheprojectedmicro-datausingourmethodologyfor t +1(or t +2).Yearsofsignificanttaxreforms (whichcangenerateincomeshifting)areshowninred.Years t +1(or t +2)withsignificantpredictionerrorsare labelled.Overall,thedotsalignwellwiththe45-degreelinedepictedonthegraphs:ourmethodologyaccurately predictsgrowthatthebottomandinthemiddleofthedistribution.

Figure5:Actualvs.PredictedGrowthattheTop

• Regularyear Taxreformyear

Notes:Thisfigurecomparespredictedtoactualgrowthinaveragerealfactorincomeperadult(withincome equallysplitamongmarriedspouses)forthetop1%(toppanels)andthenext9%(bottompanels).Growthis computedfromyear t to t +1(leftpanels)andfrom t to t +2(rightpanels)foreachyear t from1975to2018 (2017intherightpanels).Actualgrowthisobtainedusingtheannualdistributionalnationalaccountmicro-data forbothyears t and t +1(or t +2).Predictedgrowthisobtainedusingtheannualmicro-dataforyear t but theprojectedmicro-datausingourmethodologyfor t +1(or t +2).Yearsofsignificanttaxreforms(whichcan generateincomeshifting)areshowninred.Years t +1(or t +2)withsignificantpredictionerrorsarelabelled. Overall,thedotsalignwellwiththe45-degreelinedepictedonthegraphs:ourmethodologyaccuratelypredicts thegrowthoftopincomes.

Figure6:FactorIncome:Covid-19vs.GreatRecession

(a)RealFactorIncomeAroundtheCovid-19Pandemic

(b)IncomeDynamics:Covid-19vs.GreatRecession

Months after recession started

Notes:Panel(a)showsthemonthlydynamicofrealfactorincomeperadult(withincomeequallysplitamong marriedspouses)aroundtheCovid-19pandemic.Thepandemicledtothestrongestincomedeclinesforthe bottom50%andtoalesserextentforthetoponepercent.ByOctober2021allgroupshadrecoveredtheir pre-crisisincomelevel.Panel(b)comparesthegrowthofrealfactorincomeperworking-ageadult(withincome equallysplitamongmarriedspouses)onaverageandforthebottom50%oftheworkingagepopulationduring theGreatRecessionandtheCovid-19recession.Werestricttotheworking-agepopulation(20to64)tocontrol forpopulationaginginthe2010s.Incomeisnormalizedto100inDecember2007fortheGreatRecessionand February2020fortheCovid-19recession,correspondingtothemonthimmediatelyprecedingeachrecession. Thex-axiscountsthenumberofmonthssincethestartofeachrecession.

RealLaborIncomebyGroup

Notes:Thetoppanelsdepicttheevolutionofthemonthlyemploymenttoworking-agepopulationratio,defined asseasonally-adjustednonfarmemployment(fromtheBLSCurrentEmploymentStatisticsestablishmentsurvey) dividedbythenumberofadultsaged20to64duringCovidinpanel(a)andtheGreatRecessioninpanel (b).Thismeasureofemploymentexcludesproprietors,privatehouseholdemployees,unpaidvolunteers,farm employees,andtheunincorporatedself-employed.Thebottompanelsdepicttheaveragereallaborincomefor variousfractilesofthelaborincomedistributionamongadultsaged20to64(includingnon-workers)during Covidinpanel(a)andtheGreatRecessioninpanel(b),withbase100atthestartofeachperiod.Labor incomeisindividualized(i.e.,notequallysplitbetweenmarriedspouses)andincludesallwagesandsalaries, supplementstowagesandsalaries,and70%ofself-employmentincome.

Great recession and recovery (12/2007 to 05/2017)

Figure8: EarningsGrowthAcrosstheDistribution:Covid-19vs.GreatRecession

Notes:Thisfigureshowstheannualizedgrowthrateofreallaborincomebyvingtileoftheworking-agepopulation(withazoomonthetop1%),fromthe eveoftheCovidrecession(February2020)toDecember2022,andfromtheeveoftheGreatRecession(December2007)toMay2017.Inbothcases,we captureafullemploymentcycle(i.e.,May2017isthemonthwhentheemploymentratehadreturnedtoitspre-GreatRecessionlevel;andbyDecember 2022theemploymentratehadreturnedtoitspre-Covidlevel).Laborincomeisindividualized(i.e.,notequallysplitbetweenmarriedspouses)andincludes wages,supplementstowagesandsalaries,and70%ofself-employmentincome.Weincludeallworking-ageadults(aged20to64),includingnon-workers. Thegraphstartsatthe25th percentilesincethebottomquartileofworking-ageadultsismostlyunemployed.Thefigureshowsthatthebottom(and top)ofthelaborincomedistributionexperiencedfastgrowthfromthebeginningof2020toDecember2022,incontrastwiththerecoveryfromtheGreat Recession.

Post-tax disposable income

COVID stimulus checks

Regular cash transfers (net of taxes)

Subsidized pretax national income

Other benefits (net) (pensions & DI, minus contributions)

Unemployment insurance benefits

Subsidized factor national income

Paycheck Protection Program

Factor national income (matching national income)

Notes:Thisfiguredecomposestheaveragerealmonthlyincomeofthebottom50%workingage(20-64)adults fromJuly2019toMarch2023.Individualadultsarerankedbytheirfactorincome,andincomeisequally splitbetweenmarriedspouses.Thefigurerevealstherelativeimportanceofthedifferentgovernmentprograms enactedduringtheCovid-19pandemic,mostimportantlythethreewavesofCovid-reliefpayments(April2020, January2021,andMarch2021),theexpansionofunemploymentinsurance,theexpansionofrefundabletax credits(EITCandchildtaxcredit),andthePaycheckProtectionProgram.Bythebeginningof2022allofthese programshadexpired,andtheonlyreasonwhyaveragebottom50%disposableincomeremainedhigherthan pre-Covid(byabout10%)wasthehigherleveloffactorincome.

Figure10: WealthGrowthDuringandAfterCovid

Notes:Thisfigureshowstheevolutionofaveragerealwealthperadult(withwealthequallysplitbetween marriedspouses)inthemiddle40%ofthewealthdistribution(i.e.,fromthemediantothe90th percentile),the top10%,thetop1%,andthetop0.1%,fromJuly2019toMarch2023(plainline)andprojectedvaluesforJuly 1st ,2023(dottedline)usingourmethodologyforproducingdailyestimates.Theaveragewealthofeachgroup isnormalizedto100inJuly2019.Thefigureshows,e.g.,thatthewealthofthetop0.1%increasedbycloseto 40%(adjustedforinflation)fromJuly2019toDecember2021andthatmoretwothirdsthesegainswereerased in2022.

Figure11:

Notes:ThisfigureshowsBlack-whitedifferencesinincomeandwealth.Theunitofobservationistheindividual adult.Thelaborincomelinerestrictstotheworking-agepopulation(individualsaged20to64);otherseries includetheentireadultpopulation.Theseriespresentedarequarterlyandstartin1989,thefirstyearofthe SurveyofConsumerFinances.

Notes:ThisfigureshowstheevolutionofaveragerealpretaxnationalincomebyracialgroupduringtheCovid recessionanditsaftermath(leftpanel)andtheGreatRecessionanditsaftermath(rightpanel).Incomeis normalizedto100inthequarterprecedingeachrecession.

Figure12:

Table1:OverviewofSources

UsageSourceProducerUsageFrenquencyLagNotes

Public-usetaxdataInternalRevenue Service

SocialSecurityWage Statistics

SocialSecurity Administration

Constructionof annualmicrodata

Intra-annual distribution adjustments

CurrentPopulation Survey(ASEC)

SurveyofConsumer Finances

QuarterlyCensusof Employmentand Wages(QCEW)

CurrentEmployment Statistics(Stateand Area)

CurrentEmployment Statistics(National)

Unemployment InsuranceWeekly Claims

CurrentPopulation Survey(Monthly)

CensusBureau(via IPUMS)

Mainmicrodatasource.Yearly1–2yearsThelastpublic-usemicrofiledatesfrom2014.Forlateryears,we updatethemicrodatausingIRStabulationsofincome.

Complementarydataonthewage incomedistribution.

Integrationofsocio-demographic informationintothetaxdata(bottom 95%).

FederalReserveIntegrationofsocio-demographic informationintothetaxdata(top 5%).

BureauofLabor Statistics(BLS)

BureauofLabor Statistics(BLS)

BureauofLabor Statistics(BLS)

Estimationofthewageincome distribution.

Estimationofthewageincome distribution.

Estimationofthenumberofwage earners.

DepartmentofLaborEstimationofthenumberof unemploymentinsuranceclaims.

CensusBureau(via IPUMS)

Estimationofthewageincome distribution,andtheranksinthe earningsdistribution,andofthe numberofwageearnersbyage, gender,education,raceandmarital status.

Yearly6monthsWeusethewageincomedistributionfromtheSSAbecauseitis betteratcapturinglowwages.

Yearly6monthsWematchtheCPStothetaxdatausingoptimaltransporton detailedincomevariables.

Triennial1yearWematchtheSCFtothetaxdatausingoptimaltransporton detailedincomeandwealthvariables.

Quarterly5monthsThewagedataintheQCEWisquarterlybuttheemployment dataismonthly.ThereforewetreattheQCEWasamonthly dataset.

Monthly3weeksThisdataissimilartotheQCEWbutcoarserandreleasedmore frequently.WematchittotheQCEWtoextrapolatethe evolutionofearningsandemploymentinthemostrecentmonths.

Monthly3weeksWeuseaggregatenonfarmemploymentstatisticstoadjustthe numberofwageearnersmonthly.

Weekly1weekWeaveragetheseriesbymonth,adjustforseasonalvariations, anduseittoadjustthenumberofunemploymentinsurance recipientsbymonth.

Monthly2weeksWeusetheCPStoestimatewageincomeforthebottom90%of thedistributiononly(becauseoftop-coding),andaveragethat estimatewiththeonefromtheQCEW.Forthetop10%weonly relyontheQCEW.

Real-timeBillionairesForbesMagazineWealthforthe400richestAmericans.DailyNoneWeadjusttheoverallwealthofthe400wealthiesthouseholdsto matchthereal-timeForbesestimate.

NationalIncomeand ProductAccounts (NIPA)

Estimationand rescalingtomacro aggregates

BureauofEconomic Analysis(BEA)

Estimationofmonthlyincome aggregates.

FinancialAccountsFederalReserveEstimationofquarterlywealth aggregates.

Wilshire5000Total MarketIndex WilshireAssociates (viaFRED)

Case-ShillerNational HomePriceIndex

Standard&Poor’s (viaFRED)

Disagreggationofthevalueofstocks atthemonthlyfrequency.

Disagreggationofthevalueofhousing atthemonthlyfrequency.

ZillowHomeValue Index ZillowDisagreggationofthevalueofhousing atthemonthlyfrequency.

Monthly/ Quarterly

1monthWhenneeded,wedisagreggatethequarterlyoryearlydataatthe monthlyfrequencyusingDenton’s(1971)method.Withthe exceptionofcorporateprofits,themostimportantincome componentsareavailablemonthly.

Quarterly2monthsWhenneeded,wedisagreggateyearlydataatthequarterly frequencyusingDenton’s(1971)method.Themostimportant wealthcomponentsareavaialblequarterly.

DailyNoneWeusetheindextodisagreggatethequarterlyestimateofstocks valuesatthemonthlylevelusingDenton’s(1971)method.

Monthly2monthsWeusetheindextodisagreggatethequarterlyestimateofhousing valuesatthemonthlylevelusingDenton’s(1971)method.

Monthly2weeksWeusetheZillowindextoextrapolatetheCase-Shillerinthe latestmonths.

Table2:PredictionErrorsforGrowthRatesofIncomeandWealth

ConceptBracket

FactorIncome

PretaxIncome

DisposableIncome

Post-taxIncome

Wealth

AllyearsExcl.taxreformsRecessions

Std.Dev.CorrectsignRMSEBiasStd.Err.CorrectsignRMSEBiasStd.Err.CorrectsignRMSEBiasStd.Err.

Bottom50%4.0pp.82%2.1pp.-0.8pp.2.0pp.85%2.1pp.-0.9pp.1.9pp.92%1.7pp.-0.4pp.1.7pp. Middle40%1.5pp.82%0.9pp.-0.3pp.0.9pp.91%0.7pp.-0.2pp.0.7pp.83%1.2pp.-1.0pp.0.7pp.

Next9%1.8pp.93%1.1pp.-0.7pp.0.9pp.100%1.1pp.-0.8pp.0.8pp.83%1.1pp.-0.6pp.0.9pp.

Top1%6.0pp.89%3.4pp.-0.2pp.3.4pp.91%2.5pp.-0.2pp.2.5pp.92%3.6pp.1.4pp.3.3pp.

Bottom50%3.0pp.75%2.0pp.-1.0pp.1.7pp.76%2.0pp.-1.0pp.1.7pp.83%1.8pp.-1.3pp.1.3pp.

Middle40%1.5pp.80%1.0pp.-0.2pp.0.9pp.88%0.8pp.-0.2pp.0.7pp.83%1.1pp.-0.9pp.0.7pp.

Next9%2.3pp.91%1.2pp.-0.7pp.0.9pp.100%1.1pp.-0.8pp.0.9pp.75%1.0pp.-0.3pp.0.9pp. Top1%6.2pp.89%3.4pp.-0.1pp.3.4pp.91%2.6pp.-0.2pp.2.6pp.92%3.7pp.1.4pp.3.4pp.

Bottom50%2.4pp.70%2.5pp.-1.6pp.1.9pp.68%2.3pp.-1.4pp.1.8pp.75%3.3pp.-2.8pp.1.7pp. Middle40%1.4pp.86%0.9pp.-0.2pp.0.9pp.91%0.8pp.-0.2pp.0.8pp.83%0.8pp.-0.5pp.0.6pp. Next9%2.2pp.91%1.4pp.-0.6pp.1.3pp.88%1.4pp.-0.7pp.1.1pp.100%1.1pp.0.2pp.1.1pp. Top1%6.4pp.89%4.0pp.0.7pp.3.9pp.91%3.2pp.0.6pp.3.1pp.83%4.5pp.2.1pp.3.9pp.

Bottom50%2.4pp.75%1.9pp.-1.2pp.1.5pp.79%1.8pp.-1.0pp.1.5pp.75%2.5pp.-2.0pp.1.4pp. Middle40%1.7pp.93%0.9pp.-0.2pp.0.8pp.97%0.7pp.-0.2pp.0.7pp.92%0.7pp.-0.5pp.0.5pp. Next9%2.6pp.89%1.3pp.-0.6pp.1.1pp.97%1.2pp.-0.7pp.1.0pp.75%0.9pp.0.0pp.0.9pp. Top1%6.7pp.84%3.9pp.0.2pp.3.9pp.88%2.9pp.0.2pp.2.9pp.67%4.1pp.1.4pp.3.8pp.

Middle40%4.9pp.82%1.8pp.0.0pp.1.8pp.88%1.7pp.-0.2pp.1.7pp.75%1.4pp.0.1pp.1.4pp.

Next9%3.9pp.95%1.2pp.-0.2pp.1.2pp.97%1.1pp.-0.1pp.1.0pp.100%1.4pp.-0.3pp.1.4pp.

Top1%5.8pp.82%2.8pp.-1.5pp.2.4pp.82%2.5pp.-1.4pp.2.1pp.75%2.9pp.-1.4pp.2.5pp.

Notes:Thistablereportsstatisticsforgoodnessoffitandnoiseofour1-yearaheadrealincomeandrealwealthgrowthpredictions.“Std.dev.”isthe standarddeviationofobserved1-yeargrowth.“Correctsign”isthefractionofyearsinwhichwecorrectlypredictwhetherincome(orwealth)isgrowingor falling.“Bias”isthemeandifferencebetweenpredictedandobserved1-yeargrowth,“Std.Err”isthestandarderrorofthismean,and”RMSE”istheroot meansquareerrorcapturingtotalerror(RMSE 2 = bias2 + std.err.2 ).“Allyears”includes44observations(growthrelativetotheprecedingyearin1976, 1977,...,2019).“Allyearsexcludingtaxreforms”includes34observations(itexcludes1987,1988,1991,1992,1993,2001,2003,2012,2013).“Recessions” includes12observations,correspondingtorecessionyears(1980,1981,1982,1990,1991,2001,2008,2009)andtheirimmediateaftermath(1983,1992, 2002,2010).

Appendix(forOnlinePublication)

ALinkBetweenNIPANationalIncomeComponentsand DINAConcepts

Ourmonthlymicro-filesdistributeBEA’shigh-frequencynationalincomeaccounts,starting fromtheannualdistributionalnationalaccountsmicro-filesofPiketty,SaezandZucman(2018). Thesefilesarebasedoninternationallyharmonizedguidelines(Blanchetetal.,2021),which themselvesarebasedontheUNSystemofNationalAccountsanddefinitionsofincomecomponentsthatmaximizeconsistencywithcomponentsofhouseholdwealth.Theconceptsusedby theBureauofEconomicAnalysisfortheUSnationalaccountsarelargelyconsistentwiththe SystemofNationalAccounts,butsometimesslightlydiffer.Toclarifyhowthemainvariable inourmicro-filesrelatetotheheadlineaggregatesoftheofficialUSnationalaccounts,this SectionprovidesamappingofthemaincomponentsofnationalincomeaspublishedbyBEA (henceforthNIPA)intothemaincomponentsoffactornationalincomeinourdistributional nationalaccountsmicro-files(henceforthDINA).

RecallthatnationalincomeaspublishedbyBEA(NIPATable1.12)isdecomposedinto compensationofemployees,proprietors’income,rentalincomeofpersons,corporateprofits, netinterestandmisc.payments,taxesonproductionandimportslesssubsidies,netbusiness transferpayments,andcurrentsurplusofgovernmententerprises.Themaindifferenceswith DINAarethefollowing:

• InDINA,businesstransferpaymentsareallocatedtocorporateprofits(forcorporate transfers),toproprietors’income(fornon-corporatebusinesses’transfers)andtorental income(forhousingtransfers).

• inDINA,thesmallcurrentsurplusofgovernmententerprisesistreatedasataxonproduction.

• InDINA,propertytaxes(businessandrealestate)arenottreatedastaxesonproduction butasdirecttaxes(likewealthtaxeswouldbe),henceallocatedtocorporateprofits(for propertytaxespaidbycorporations),proprietors’income(forpropertytaxespaidby non-corporatebusinesses),andrentalincome(forresidentialpropertytaxes).

• IntheNIPAs,therearevariousimputationsofinterestincome(e.g.,dividendsreceived bylife-insurancecompanies;notionalinterestonunderfundedpensionplans)thatare re-classifiedinDINAforconsistencywithhouseholdwealth.

Asaresultoftheseandotherminorotherreclassificationstoimproveconsistencywithhousehold wealthaggregates,NIPAnationalincomeconceptsmapontoDINAfactorincomeconceptsas follows(NIPATablenumbersandDINAvariablenamesinparenthesis):34

34 Themappingforcompensationofemployees,corporateprofits,netinterest,andtaxesonproductionand importslesssubsidiesisexact.Themappingforproprietors’incomeandrentalincomeisalmostexact(witha discrepancyoflessthan0.1%ofnationalincome).

NIPAcompensationofemployees(Table1.12line2)=DINAcompensationofemployees(flemp)

NIPAproprietors’income(Table1.12line9)=DINAbusinessassetincome(fkbus)

+DINAlaborcomponentofmixedincome(flmil)

–DINAbusinesspropertytaxesallocatedtonon-corporatebusinesses(non-corporatebusiness shareof propbustax)

+NIPArentalincomeincludedinproprietors’income(Table7.4.5line20)

–NIPAnetnon-corporatebusinesstransferspaid(Table1.12line21–Table1.14line10–Table7.4.5line19)

–NIPAroyalties(Table7.9line7)

NIPArentalincomeofpersons(Table1.12line12)=DINAhousingassetincome(fkhou)

-NIPAresidentialpropertytaxes(Table7.4.5line15= proprestax)

-NIPAmortgageinterestpayments(Table7.4.5line18)

-NIPAhousingnetcurrenttransferpayments(Table7.4.5line19)

-NIPArentalincomeincludedinproprietors’income(Table7.4.5line20)

+NIPAtenant-occupiedrentalincomeofnonprofits(Table7.9line14)

+NIPAroyalties(Table7.9line7)

NIPAcorporateprofits(Table1.12line13)=DINAequityassetincome(fkequ)

+DINAequityincomeearnedthroughpensionplans(equityshareof fkpen)

–DINAbusinesspropertytaxesallocatedtocorporations(corporateshareof propbustax)

+NIPAdividendsreceivedbygovernment(Table3.1line14)

+NIPAdividendsreceivedbynonprofits(Table2.9line51)

–NIPAnetcorporatebusinesstransferspaid(Table1.14line10)

–NIPAimputedinterestpaidbycorporationsonunderfundedpensionplans(Table7.12line 192)

–NIPAdividendreceiptsoflife-insurancecompaniesincludedunder“imputedinterestreceived fromlife-insurancecarriers”(partofTable7.11line68,notseparatelyreported).

NIPAnetinterestandmisc.payments(Table1.12line18)=DINAcurrency,deposits,and bondincome(fkfix)

+DINAinterestincomeearnedthroughpensionplans(interestshareof fkpen)

–DINAnon-mortgageinterestpaid(fknmo)

+NIPAmisc.corporatepayments(Table1.14line9–Table7.11line101)

+NIPAimputedinterestpaidbycorporationsonunderfundedpensionplans(Table7.12line 192)

+NIPAdividendreceiptsoflife-insurancecompaniesincludedunder“imputedinterestreceived fromlife-insurancecarriers”(partofTable7.11line68,notseparatelyreported)

+NIPAinterestreceivedbynonprofits(Table2.9line50)

–NIPAnetinterestpaidbygovernment,otherthanimputedforunfundedpensionplans(Table 7.11line107-line50)

NIPAtaxesonproductionandimportslesssubsidies(Table1.12line19–line20)=DINA

salesandexcisetaxes(fkprk + flprl)

+DINAresidentialpropertytaxes(proprestax)

+DINAbusinesspropertytaxes(propbustax)

–DINAsubsidiesonproductionandimports(fksubk + flsubl)

–NIPAcurrentsurplusofgovernmententerprises(Table1.12line25)

BReal-TimeWealthDistributions

Toconstructquarterlywealthdistributions,westartfromthePiketty,SaezandZucman(2018) micro-files,lastupdatedinFebruary2022,35 andre-scalethemaincomponentsofhousehold wealthtotheirend-of-quartervalue,usingthelatestquarterlyreleaseoftheFinancialAccounts.

Thewealthcomponentsweconsiderare,ontheassetside,tenant-occupiedhousing,owneroccupiedhousing,S-corporationequity,C-corporationequity,equityinnon-corporatebusinesses,fixed-incomeassets,pensionassets;andontheliabilityside,tenant-occupiedmortgages, owner-occupiedmortgages,andnon-mortgagedebt.TheaggregatevalueofallofthesecomponentsarepublishedquarterlybytheFederalReserveintheFinancialAccountsoftheUnited States,around70daysaftertheendofeachquarter.FollowingSaezandZucman(2016),our estimatesexcludeunfundedpensions(suchaspromisesoffutureSocialSecuritybenefits),consumerdurables(whicharenotassetsintheSystemofNationalAccountsandthusexcludedfrom wealthinothercountries;seeUnitedNations,2009),andtheassetsandliabilitiesofnon-profit institutionssuchasprivatefoundations.

Wethenassumethatdistributionsarestablewithineachofthesecomponentsfromone quartertotheotherandcomputetheimplieddistributionofwealth.Weusereal-time Forbes datatoadjustthewealthofthetop400taxunitssothatitmatchesthe Forbes estimateat theendofeachquarter.Thusourestimatesofwealthinequalitybyconstructionmatch Forbes attheverytop,liketheannualestimatesconstructedinSaezandZucman(2020b)andthe SCF-yearestimatesofBattyetal.(2019).TheForbesestimatescontainvaluableinformation onhigh-frequencywealthdynamicsatthetop-endbecausetheycombinepublicinformationon ownershipofstockinlistedcompanies(frommandatorySecuritiesandExchangeCommission filings)withdailystockpricechangesforthesecompanies.Closetohalfofthewealthofthe Forbes 400isinpublicequityinrecentyears.Thelimitationsofannual Forbes estimates(lack ofpublicinformationondiversifiedportfoliosandondebts,imperfectinformationonthevalue ofprivatebusinesses)carryovertothereal-time Forbes estimates.

Additionally,weconstructmonthlyanddailywealthdistributionsasfollows.Startingfrom thequarterlywealthtotalsbycomponentsdescribedabove,weusehousingandequityprice indicestoupdatetotalhousingwealthandtotalequitywealthatthemonthlyfrequency.Wedo sousingtheDenton(1971)methodforavailablequarters,andextrapolatingusingtheindexes’ growthrateinthemostrecentmonths,beforethelastquarterbecomesavailable.Thestock marketindexthatweuseistheWilshire5000,acomprehensiveindexofthestockperformance ofpubliclytradedU.S.firms.Forhousingprices,weusetheCase-Shillerindex,extrapolated

35 Themicro-filesareupdatedannually.Adescriptionofeachupdateisavailableat http://gabriel-zucman. eu/usdina,asarecurrentmicro-files,computercode,andtabulationsofkeyfindings.Allvintagereleasesand correspondingcodearealsopublishedatthisaddress.

usingtheZillowhousepriceindexinthemostrecentmonths.Fordailyestimates,wesimply updatetotalequity(includingS-corporationsequity)usingthedailyWilshire5000.Forboth monthlyanddailyestimates,wekeepdistributionsconstantwithincomponentsandrescalethe wealthofthetop400taxunitstomatchreal-time Forbes numbers.

CAdjustmentofEmploymentStatusandWages

C.1AdjustmentofEmploymentandUIRecipients

Inourmonthlymicrofiles,weadjustatthemarginwhethersomeone(i)isemployedand(ii) receivesUIbenefitsbasedontheinformationdescribedinSection4.2,namely(i)Bureauof LaborStatistics(BLS)monthlyreleasesofnon-farmemploymentatthenationallevel,(ii)the DepartmentofLabor’sweeklyunemploymentclaimsstatistics,and(iii)laborforcestatusby race × education × gender × 5-yearagegroup × maritalstatuscellintheCPS.Hereweprovide detailsonouradjustmentprocedure.

Consideranannualmicrofilefordate t,whichwasconstructedasaweightedaverageof year y1 (withwealth1 λ)and y2 (withweight λ,with y2 =2019and λ =1forthemost recentyears)torepresenttheaveragedistributionoverthe12-monthperiod {tmin,...,tmax} Letususetheletter i todenoteagivencellofrace × education × gender × 5-yearagegroup × maritalstatus.Let xit ∈ [0, 1]betheemploymentrateofcell i,andlet nit ∈ [0, 1]bethe relativesizeofcell i,andlet xt ≡ i nitxit betheaggregateemploymentrateinthemicrofile. IntheCPS-basedemploymentseriesconstructedabove,let yit ∈ [0, 1]betheemploymentrate ofcell i attime t.FromtheBLS-basedaggregateemploymentseries,let zt betheaggregate employmentrateattimet.

Let yi ≡ 1 12 tmax t=tmin yit betheaverageemploymentratefromtheCPSseriesovertheperiod notionallycoveredbythesyntheticmicrofile.Let∆yit ≡ yit yi yi(1 yi) betherelativedifference betweenintheseries’valuesatdate t andtheiraverageover {tmin,...,tmax}. 36 Wecalculate thenewemploymentrateforcell i inthemicrofileas xit + xit(1 xit)(∆yit + zt xt γ) where γ = i nitxit(1 xit)∆yit i nitxit(1 xit) isarenormalizationtermthatensuresthatwematchtheaggregate employmentrate.Thismethodhasthedesirablepropertyofreproducingtherelativechangesin employmentbetweencellswhilematchingaggregateemploymentoverallandwithoutmodifying employmentwithinagivencellunlesstheCPS-basedwarrantssuchachange.37

Usingthisnewlycalculatedemploymentrate,weadjustthenumberofemployedindividuals withineachcellasfollows.Iftheemploymentratehasincreased,werandomlyselectnonemployedunitsandmarkthemasemployedtomatchthenewrate.Conversely,iftheemploymentratehasdecreased,werandomlyselectemployedunitsandmarkthemasnon-employed. Ifemploymenthasnotchanged,wedonothing.Wegiveawagetoalltheobservationswe markedasemployed,usingtheproceduredescribedinSection4.3.

Wefollowtheexactsameprocedureasabove,usingtheunemploymentratestoadjustthe

36 Notethatwenormalizebyafactor yi (1 yi )andnot yi togetrelativechangessinceweareworkingwith ratiosbetween0and1.

37 Inrarecaseswheretheadjustedemploymentrateisbelow0orabove1,wetruncateitandrepeatthe procedureuntiltheconstraintissatisfied.

numberofUIrecipients.WhenweneedtoincreasethenumberofUIrecipientswithinacell, weselectinpriorityindividualswhoarenotemployed.

C.2AdjustmentofWageIncome

Toattributeawagetoeveryindividualmarkedasemployedinourmonthlymicrofiles,we combine(i)themarginalwageincomedistributionestimatedbyaveragingQCEWandCPS predictions(asdescribedinSection4.3)with(ii)therankofeachindividualinthewage distribution,whichweadjustasexplainedbelow.

Estimationoftheaveragewagerankbyindividualcharacteristic. Weestimatethe averagewagerankbyrace × education × gender × 5-yearagegroup × maritalstatusin theCPS.Sincethemonthlywagedataistop-codedintheCPS,weuseaninterval-censored regressionmodel.IneachmonthlyCPSfile,weestimatethewagerankofeachobservation(or, fortop-codedobservations,thelowerboundoftherank).Wetransformtheseranksusingthe logisticfunctionandthenrunaninterval-censoredregressionoftherankagainstrace,education, 5-yearagegroups,andmaritalstatusinteractedwithgender,andassumingnormalresiduals. Weusethepredictionfromthatregressiontoconstructmonthlyseriesoftheaveragewagerank foreachcell.WecorrecttheseseriesforseasonalvariationsusingtheX11procedure.

Adjustmentofwageincomeinthemicrofiles. Inthemonthlymicrofiles,(i)weadjust eachobservation’swagerankatthemarginusingtherelativevariationsinwagerankfromthe CPS,andthen(ii)weattributethewageincomecorrespondingtotheirrank.

Consideranannualmicrofilefordate t,whichwasconstructedasaweightedaverageof year y1 (withwealth1 λ)and y2 (withweight λ,with y2 =2019and λ =1forthemost recentyears)torepresenttheaveragedistributionoverthe12-monthperiod {tmin,...,tmax}. Forindividualswithapositivewage,define xikt thewagerankofindividual k incell i atdate t IntheCPS-basedwagerankseriesconstructedabove,let yit ∈ [0, 1]betheaveragewagerank ofcell i attime t.

Let yi ≡ 1 12 tmax t=tmin yit betheaveragewagerankfromtheCPSseriesovertheperiodnotionallycoveredbythesyntheticmicrofile.Define∆yit ≡ yit yi yi(1 yi) betherelativedifferencebetween intheseries’valuesatdate t andtheiraverageover {tmin,...,tmax}. 38

Wecalculatetheanewrankforobservation i,k as xikt + xikt(1 xikt)∆yit.Ifanobservation doesnothaveaninitialrankbecauseitwaspreviouslynon-employed,wedirectlygiveitthe averagewagerankofitscell.39 Thenweinterpolatethemonthlywagedistributionestimated bypercentileusingthegeneralizedParetointerpolation(Blanchetetal.,2022)andattributeto eachobservationthewageincomethatcorrespondstoitsrank.Weadjustrankssimilarlyin theUIbenefitsdistributionanddistributeUIbenefitsbykeepingtheirmarginaldistribution identical.

38 Notethatwenormalizebyafactor yi (1 yi )andnot yi togetrelativechangessinceweareworkingwith ranksbetween0and1.

39 Wesortobservationsaccordingtothenewrank,andre-computetherankfromthisorder,toensurethat thedistributionofranksisuniform.

DStructureofProgramsandFiles

Thesectiondescribestheoverallstructureoftheprogramfilesusedinthispaper.Theprograms (aswellasamoredetailedandup-to-dateversionofthefollowingdescriptionandinstructions) areavailableonlineat https://github.com/thomasblanchet/real-time-inequality.

D.1Descriptionofprograms/code

• Thefolder raw-data containstherawinputdata,primarilyincaseswheredirectdownload/scrapingisnotpossibleornotjustified,orincaseswheredatafilesareheavy(like theQCEW)andthereforedownloadingthemovertheinterneteverytimeisnotdesirable.

• Thefolder work-data containsintermediarydatafilesthatareproducedbythecode.It isdividedintosubfolderscorrespondingtoeachcodefile,andnointermediarydatafileis maybechangedbytwodistinctcodefiles.

• Thefolder graphs containstheallthefigures(andafewtables)generatedbythecode. Itisdividedintosubfolderscorrespondingtoeachcodefile.

• Thefolder programs containsthecodes(exceptthoseperformingtheoptimaltransport).

– Thecodesnamed programs/01-* handletheretrievaloftherawdata,eitherdirectly fromtheinternetorfromthefolder raw-data

– Thecodesnamed programs/02-* handlepreliminarytreatmentsofthedata.

– Thecodesnamed programs/03-* producethesyntheticmicrofilesandrelatedoutputs.

– Thecodesnamed programs/04-* producethefiguresandtablesusedfortheanalysis.

• Thefolder transport containsthecodeanddataspecificallyrelatedtotheoptimaltransport:itismeanttorunseparatelyfromthemaincodeonacomputingcluster.

D.2LicenseforCode

ThecodeislicensedundertheModifiedBSDLicense.

D.3InstructionsforReplication

• Editthe $root globalin programs/00-setup.do tocorrespondtotheproject’sdirectory.

• Runthefile programs/00-run.do

• Toalsorunthetransport,runprogramsuntil programs/02-export-transport-dina.do andthenexecutethePythoncodeunder transport/transport.py preferablyusing SlurmandtheShellscript transport/transport.sh.Thenresumetheexecutionof programs/00-run.do

Programsareorganizedasfollows:

• programs/01-*

– Thecodesretrievethedatafromtheinternetdirectlytotheextentthatitispossible.

– Unlesstherehasbeenchangesinthestructureofthedata,theyshouldrunwithout anychangeforeachupdate.

–

Insomecases,thedataneedstobemanuallyupdatedinthe raw-data folderateach update.

– Instructionforeachfileinincludedin 00-run.do.

• programs/02-*

– Thecodesprimarilygeneratedatainthe work-data folderthatisusedtogenerate thesyntheticmicrofiles.

• transport

– Thisfolderincludesthedataandcodenecessaryfortheoptimaltransport.

– Thesecodesaremeanttorunonthecomputingcluster.

– Theydonotneedtobeupdatedeverytime(onlywhennewtaxmicrodataisavailable).

– TheCSVdatafilesincludedinthisfolderareproducedbythecodesbefore.

• programs/03-*

– Thecodesinthatfolderproducethesyntheticmicrofiles,includingbacktestingversionsofthemicrofilesthatuseoldertaxdata,andrescalingversionsthatonlyuse informationonmacroaggregates.

– Theglobals $date begin and $date end atthebeginningofthesefilescanbeused togenerateonlythefilesforspecificmonths.Thiscanbeusefulsincenotallthefiles needtobeconstructedforeveryupdate.

– Codesinthatsectionalsoproducetheaggregatedversionofthedatabasebygroup thatisusedforthewebsite http://realtimeinequality.org/.Thesefilesare storedinthefolder website

• programs/04-*

– Usethemicrofilesandrelatedoutputstocreatethetablesandfiguresincludedin thepaper.

D.4DescriptionofMicrofilesandCodebook

Oneoutputofthispaperisasetofharmonizedmonthlymicro-filesinwhichanobservationis asyntheticadult(obtainedbystatisticallymatchingpublicmicro-data)andvariablesinclude income,wealth,andtheircomponents.Thesevariablesadduptotheirrespectivenational accountstotalsandtheirdistributionsareconsistentwiththoseobservedintherawinputdata. Withthesemicro-filesonecanreproducealltheresultsofthepaperandthestatisticsshown at https://realtimeinequality.org exactly.Thefilesareavailablehere.40

ThereisonefilepermonthstartinginJanuary1976.Thefilesareattheadultindividual (aged20andabove)level,sothesumofweights(variable weight)addsuptothetotaladult population,248.9millioninMay2022.Thevariable id identifiesahousehold(asdefinedby thetaxcode,i.e.,eitherasinglepersonaged20oraboveoramarriedcouple,inbothcases withchildrendependentsifany).Incomeisindividualized.Tocomputeourbenchmarkequalsplitadultstatistics,computeaverageincomeorwealthby id.Tocomputestatisticsatthe householdlevel,takethesumofincomeorwealthby id andtheaverageweightby id

Thefilesincludesocio-demographicinformation: age, sex,maritalstatus(married), race, educationalattainment(educ),andthefollowingincomeandwealthvariables:

• princ:factornationalincome

• peinc:pretaxnationalincome

• poinc:posttaxnationalincome

• dispo:disposableincome

• flemp:compensationofemployees

• proprietors:proprietors’income

• rental:rentalincome

• profits:after-taxcorporateprofits

• corptax:corporateincometax

• fkfix:interestincome

• govin:governmentinterestincome

• fknmo:non-mortgageinterestpayments

• prodtax:productiontaxes

• prodsub:productionsubsidies

• contrib:contributionstopensions,disabilityinsurance,&unemployment

• penben:pensionanddisabilityinsurancebenefits

• uiben:unemploymentinsurancebenefits

• taxes:currenttaxesonincomeandwealth

• estatetax:estatetax

• othercontrib:contributionsforgovernmentsocialinsuranceotherthanpension,unemployment,anddisability

• govcontrib:totalcontributionsforgovernmentsocialinsurance

• vet:veterans’benefits

• othcash:othercashbenefits

• medicare:Medicarespending

40 Fulllinkincasehyperlinksbreak: https://www.dropbox.com/home/SaezZucman2014/RealTime/ repository/real-time-inequality/work-data/03-build-monthly-microfiles/microfiles

• medicaid:Medicaidspending

• otherkin:otherin-kindgovernmentspending

• colexp:collectiveconsumptionexpenditure

• covidrelief:covid-19economicimpactpayments

• covidsub:paycheckprotectionprogram

• surplus:surplus/deficitofgovernmentandprivatesocialinsurance

• surplus ss:surplus/deficitofgovernmentsocialinsurance

• prisupenprivate:surplus/deficitofprivatepensionsystem

• prisupgov:primarysurplus/deficitofgovernment

• hweal:netwealth

• housing tenant:tenant-occupiedhousingwealth

• housing owner:owner-occupiedhousingwealth

• equ scorp:equityinS-corporations

• equ nscorp:equityincorporationsotherthanS-corporations

• business:equityinnon-corporatebusinesses

• pensions:pensionwealth

• fixed:currency,deposits,andinterest-bearingassets

• mortgage owner:mortgagesonowner-occupiedhousing

• mortgage tenant:mortgagesontenant-occupiedhousing

• nonmortage:non-mortgagedebt

• top400:dummyifobservationisinthetop400ofhouseholdsbywealth

• acs:dummyifobservationlivesingroupquarters(nursinghomes,dormitories,etc.)

FigureA1:GDPvs.GDIGrowth

Notes:ThisfigurecomparesthequarterlygrowthofrealannualizedGDPtothequarterlygrowthofrealannualizedGDIfrom1947Q2to2022Q1. ObservationscorrespondingtotheCovid-19recessionandrecoveryareshowninredtriangles.Thepointcorrespondingto2020Q2(anoutlierwitharound -35%annualizedGDPgrowthand-35%annualizedGDIgrowth)isomitted.DuringtherecoveryfromCovid-19,GDIhasrecoveredfasterthanGDP.

FigureA2:ComparingWageEarningsRanksintheOneto-OneStatisticalMatch

45deg line mean CPS rank 90% interval

Notes:ThisfigurecomparestheranksinwageearningsbetweenthewageearningsintheDINAdataand thewageearningsintheCPSdataintheone-to-onestatisticallymatcheddataset.Thex-axisistheDINA wageearningspercentileandy-axisistheaveragepercentileintheCPSdata.Thefigureshowaveragesover 1976–2019.Thedatareferstohouseholdearningsandthesampleislimitedtohouseholdsthatearnatleasta full-timeFederalminimumwage.Thefigureshowsthatranksinwageearningsareverycloseimplyingthatthe one-to-onestatisticalmatchrespectsrankswellalongthecrucialwageearningsvariable.

FigureA3:Monthlyvs.AnnualEmploymentRates

Notes:Thisfiguredepicts,fortheworkingagepopulation20-64,therawmonthlyemploymentratesfromtheBLSbasedonthemonthlyCPS(indashed blueline),theannualemploymentratesfromtheSocialSecuritytaxdata(inredcircles),andthemonthlyemploymentratesadjustedtomatchannual rates(insolidblueline).Employmentratesinourmonthlydataisgivenbythebluesolidseries.Quantitatively,inrecentdecades(whenthecatching-up femalelaborforceparticipationtrendstabilizes),rawmonthlyemploymentratesarearound75%whileadjustedmonthlyemploymentrates(thattrack annuallevels)are10pointshigheraround85%becauseofpart-yearworkers.

FigureA4:CapturingMostRecentMonthsUsingCESinsteadofQCEW

(a)Bottom50%WageShare 2019m12

Extrapolation from CES

(b)Top10%WageShare

Notes:ThisfiguredepictsthequalityoftheprojectionusingthemonthlyCurrentEmploymentStatistics(CES)datatoextrapolatetheQCEWdatafor thelast2quarters(whentheQCEWisnotyetavailablebuttheCESis).Theblueseriesdepictsthebottom50%wageshareinpanel(a)andthetop10% wageshareinpanel(b)usingtheraw(de-seasonalized)QCEWdata.Foreachquarter,thefigurealsodepictsindashedlinetheprojectedsharesusingthe CESdatatoextrapolatetheQCEWdataupto6monthsout.Bydefinition,theprojectionmatchestheQCEWlevelinthelastmonthofthequarter,and thenprojectsoutoverthenexttwoquarters.Tocarryouttheprojection,wematcheachQCEWcelltothreeCESseries.Thefirstmatchesthelocationof theQCEWcellaspreciselyaspossible,thesecondmatchesitatthestatelevel,andthethirdatthenationallevel.Weaveragethetrendfromthesethree seriesineachcellandusethisaveragetrendtoextendtheQCEWdatainthemostrecentquarters.Overall,theCESprojectioncomesprettyclosetothe QCEW.Thefitisbetterforthebottom50%sharethanforthetop10%wageshare.

Extrapolation from CES

FigureA5:CapitalIncomeVolatility

(a)CorporateProfits

(b)Interest

(c)RentalIncome

(d)Proprietor’sIncome

Notes:Thisfigurecomparesthevolatilityofthesizeofcapitalincomecomponents(corporateprofits,interest, rentalincome,proprietor’sincome),asmeasuredbytheirshareofnationalincome,andthevolatilityoftheir concentration,asmeasuredbytheshareofeachcomponentgoingtothetop10%ofthepretaxincomedistribution.Forexamplethetopleftpanelshowstheshareofpretaxcorporateprofitsinnationalincomeandthe shareofpretaxcorporateprofitsaccruingtothe10%ofadultsatthetopofthepretaxincomedistribution, from1976to2019.Allseriesaredepictedrelativetoabase100in1976tocomparevolatility.Thefigureshows thatthesizeofcapitalincomecomponentscanbehighlyvolatileathigh-frequencywhiletheirconcentration isslow-moving.Thislendssupporttoourmethodologywhichcaptureshigh-frequencychangesinaggregate capitalincomeandassumesthattheirconcentrationisunchangedintheshortrun.

FigureA6:PredictedGrowth:OurMethodvs.SimplifiedMacroMethod

(a)Bottom50%

Full Methodology Rescaling Only (b)Top1%

• Fullmethodology • Rescalingonly

Notes:Thisfigurecomparesthequalityofourbaselineestimateswiththequalityofsimplerestimatesthatonlyrescalesmacroeconomicaggregateswithout adjustingwithin-componentdistributions.Thisfiguredepictspredictedtoactualgrowthinaveragerealfactorincomeperadult(withincomeequallysplit amongmarriedspouses)fromyear t to t +1forthebottom50%(leftpanel)andthetop1%(rightpanel)foreachyearfrom1976to2019.Actualgrowthis obtainedusingtheannualdistributionalnationalaccountmicro-dataforbothyears t and t +1.Predictedgrowthisobtainedusingtheannualmicro-data foryear t buttheprojectedmicro-datausingourfullmethodologyfor t +1(bluefulldots)orasimplifiedmethodologythatonlyrescalesmacroeconomic aggregateswithoutadjustingwithin-componentdistributions(redemptydots).Thesimplifiedmethodologyperformsworse,especiallyinrecessionsyears forthebottom50%,showingthatadjustingdistributionsiscriticaltoaccuratelyprojectreal-timeinequalityduringrecessions.

FigureA7:RealDisposableIncomeAroundtheCovid-19Pandemic

Notes:ThisfigureshowsthemonthlyevolutionofrealdisposableincomeperadultfromJuly2019toSeptember2022inthefulladultpopulation(not restrictingtoworking-ageadults).Individualadultsarerankedbytheirfactorincome,andincomeisequallysplitbetweenmarriedspouses.Thefigure showsthatforthebottom50%ofthefactorincomedistribution,monthlydisposableincomewasnearlytwiceaslargeinMarch2021asinJuly2019(asa resultofthethirdwaveofEconomicImpactPayments).Bythespringof2022,disposableincomehadreturnedtoitspre-Covidlevelforallgroupsexcept forthebottom50%forwhichitwasabout15%higher.

FigureA8:IncomeoftheBottom50%DuringtheCovidCrisis

Post-tax national income

(matching national income)

Government deficit government spending

Medicaid and Medicare

Post-tax disposable income

COVID stimulus checks

Regular cash transfers taxes)

Subsidized pretax national income

benefits (net)

(pensions & DI, minus contributions)

Unemployment insurance benefits

Subsidized factor national income

Paycheck Protection Program national income

(matching national income)

Notes:Thisfiguredecomposestheaveragerealmonthlyposttaxnationalincomeofthebottom50%oftheworking-agepopulationrankedbyfactorincome fromJuly2019toMarch2023.ThisisthesameFigureasFigure9butaddingMedicaidandMedicare,othergovernmentspending,andthegovernment deficit,soastogoallthewaytoposttaxnationalincome.SeenotestoFigure9.

FigureA9:ShareofBlackadultsintheFullPopulationvs.Top10%

Notes:ThisfigureshowsthefractionofBlackadultsinthefulladultpopulation,inthetop10%ofthewagedistribution,10%ofthepretaxincome distribution,andtop10%ofthewealthdistribution.Theseriesstartin1989,thefirstyearoftheSurveyofConsumerFinances.

(working-age population)

FigureA10:CollegePremium

Notes:Thisfigureshowsdifferencesinincomeandwealthbetweenpeoplewithnocollegeeducationandpeoplewithatleastsomecollegeeducation.The unitofobservationistheindividualadult.Thelaborincomelinerestrictstotheworking-agepopulation(individualsaged20to64);otherseriesinclude theentireadultpopulation.Theseriesstartin1989,thefirstyearoftheSurveyofConsumerFinances.

FigureA11:IncomeDynamicsbyGender:CovidRecessionvs.GreatRecession

2010q3 2013q3 2016q3 Recession (2007-2016)

Notes:ThisfigureshowstheevolutionofaveragepretaxnationalincomebygenderduringtheCovidrecessionanditsaftermath(leftpanel)andtheGreat Recessionanditsaftermath(rightpanel).Incomeisnormalizedto100inthequarterprecedingeachrecession.

ConceptBracket

FactorIncome

PretaxIncome

DisposableIncome

AllyearsExcl.taxreformsRecessions

Std.Dev.CorrectsignRMSEBiasStd.Err.CorrectsignRMSEBiasStd.Err.CorrectsignRMSEBiasStd.Err.

Bottom50%6.6pp.91%2.4pp.-0.7pp.2.3pp.91%2.4pp.-1.0pp.2.2pp.92%1.4pp.-0.4pp.1.4pp.

Middle40%2.3pp.93%1.3pp.-0.2pp.1.3pp.97%1.0pp.-0.1pp.1.0pp.83%1.6pp.-1.2pp.1.0pp. Next9%3.0pp.98%1.2pp.-0.6pp.1.0pp.100%1.1pp.-0.6pp.0.9pp.92%0.9pp.-0.5pp.0.8pp. Top1%9.1pp.93%5.1pp.-1.0pp.5.0pp.94%3.8pp.-1.1pp.3.6pp.83%4.8pp.1.5pp.4.5pp.

Bottom50%4.9pp.88%2.5pp.-1.2pp.2.2pp.88%2.5pp.-1.1pp.2.3pp.83%2.2pp.-1.8pp.1.4pp. Middle40%2.4pp.95%1.3pp.-0.1pp.1.3pp.100%1.1pp.-0.0pp.1.1pp.83%1.4pp.-1.1pp.0.9pp.

Next9%3.7pp.98%1.2pp.-0.4pp.1.1pp.97%1.1pp.-0.5pp.1.0pp.92%0.7pp.0.1pp.0.7pp.

Top1%9.4pp.88%5.2pp.-1.0pp.5.1pp.94%3.8pp.-1.1pp.3.7pp.75%4.9pp.1.5pp.4.6pp.

Bottom50%3.8pp.88%3.5pp.-2.2pp.2.7pp.91%3.1pp.-1.9pp.2.5pp.75%4.8pp.-4.2pp.2.4pp. Middle40%2.2pp.95%1.3pp.-0.2pp.1.2pp.94%1.1pp.-0.2pp.1.1pp.92%1.1pp.-0.5pp.1.0pp. Next9%3.3pp.93%1.6pp.-0.0pp.1.6pp.91%1.4pp.-0.2pp.1.4pp.83%1.5pp.1.2pp.0.9pp. Top1%9.3pp.93%6.4pp.0.3pp.6.3pp.97%4.4pp.-0.0pp.4.4pp.83%6.7pp.1.9pp.6.5pp.

Post-taxIncome

Bottom50%3.8pp.88%2.7pp.-1.6pp.2.2pp.97%2.3pp.-1.2pp.2.0pp.92%3.5pp.-2.9pp.1.9pp. Middle40%2.9pp.93%1.2pp.-0.2pp.1.2pp.97%1.0pp.-0.2pp.1.0pp.83%0.9pp.-0.5pp.0.8pp. Next9%4.1pp.95%1.4pp.-0.2pp.1.4pp.94%1.2pp.-0.4pp.1.2pp.83%1.0pp.0.7pp.0.7pp. Top1%10.0pp.91%6.2pp.-0.4pp.6.2pp.94%4.3pp.-0.7pp.4.3pp.83%6.0pp.1.2pp.5.9pp. Wealth Middle40%8.2pp.88%2.1pp.0.6pp.2.0pp.97%1.8pp.0.1pp.1.8pp.92%1.8pp.0.5pp.1.8pp. Next9%6.6pp.93%1.9pp.0.2pp.1.9pp.97%1.7pp.0.2pp.1.7pp.83%2.3pp.0.1pp.2.3pp. Top1%9.9pp.93%4.7pp.-3.1pp.3.5pp.94%4.1pp.-2.7pp.3.1pp.83%4.5pp.-2.8pp.3.5pp.

Notes:Thistablereportstatisticsforgoodnessoffitandnoiseofour2-yearaheadrealincomeandrealwealthgrowthpredictions.SeenotestoTable2.