美联储-美国高增长企业：主要趋势和新数据机遇 High-Growth Firms in the United States Key Trends and New Data Opportunities

FinanceandEconomicsDiscussionSeries

FederalReserveBoard,Washington,D.C.

ISSN1936-2854(Print)

ISSN2767-3898(Online)

High-GrowthFirmsintheUnitedStates:KeyTrendsandNewDataOpportunities

J.DanielKim,JoonkyuChoi,NathanGoldschlag,JohnHaltiwanger

2024-074

Pleasecitethispaperas:

Kim,J.Daniel,JoonkyuChoi,NathanGoldschlag,andJohnHaltiwanger(2024).“High-GrowthFirmsintheUnitedStates:KeyTrendsandNewDataOpportunities,”FinanceandEconomicsDiscussionSeries2024-074.Washington:BoardofGovernorsoftheFederalReserveSystem,

/10.17016/FEDS.2024.074

.

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

High-GrowthFirmsintheUnitedStates:

KeyTrendsandNewDataOpportunities*

J.DanielKim†JoonkyuChoi‡NathanGoldschlag§

JohnHaltiwanger¶September10,2024

Abstract

UsingadministrativedatafromtheU.S.CensusBureau,weintroduceanewpublic-usedatabasethattracksactivitiesacrossfirmgrowthdistributionsovertime.Withthesenewdata,weuncoverseveralkeytrendsforhigh-growthfirms—criticalen-ginesofinnovationandeconomicgrowth.First,theshareoffirmsthatarehigh-growthhassteadilydecreasedoverthepastfourdecades,drivennotonlybyfallingratesofentrepreneurshipbutalsolanguishinggrowthamongexistingfirms.Sec-ond,thisdeclineisparticularlypronouncedamongyoungandsmallfirms,whiletheshareofhigh-growthfirmshasbeenrelativelystableamonglargeandoldfirms.Wealsofindrichvariationacrossstatesandsectors.Tofacilitatefutureresearch,wehighlighthowthesedatacanbeusedtoaddressvariousresearchquestions.

JELCodes:L11,L25,L26,O30,O40

Keywords:OrganizationalGrowth,Entrepreneurship,High-GrowthFirms,Busi-nessDynamism,PubliclyAvailableDataset

*AnyopinionsandconclusionsexpressedhereinarethoseoftheauthoranddonotrepresenttheviewsoftheU.S.CensusBureau,theFederalReserveBoardofGovernorsoritsstaff.TheCensusBureauhasensuredappropriateaccessanduseofconfidentialdataandhasreviewedtheseresultsfordisclosureavoidanceprotection.DRBApprovalNumber(s):CBDRB-FY24-0168.DMSProjectNumber7083300.WethankSeanWang,CherylGrim,andparticipantsattheU.S.CensusBureauCenterforEconomicStudiesseminar,andparticipantsattheCRIWNBERSummerInstitutesessionforhelpfulcommentsandsuggestions.

†TheWhartonSchool,UniversityofPennsylvania‡FederalReserveBoardofGovernors

§U.S.CensusBureau

¶UniversityofMaryland

1

1Introduction

SincePenrose’s(1959)seminalworkonthegrowthoffirms,extantliteratureinbothmanagementandeconomicshasofferedvarioustheoreticalaccountsofwhereandhowhigh-growthfirmsemerge—rangingfromexternalfactorssuchasenvironmentalcon-ditionsandcompetition(e.g.,

CarrollandHannan

1989;

BaumandMezias

1993;

Sterk

etal.

2021;

Sivadasanetal.

2024)toorganizationaltraitsincludingroutines,manage

-rialquality,andtechnologicalinnovation(e.g.,

LucasJr

1978;

NelsonandWinter

1982;

AcsandAudretsch

1987;

EisenhardtandSchoonhoven

1990)

.Inspiteofthedifferentdisciplinaryroots,acommonthemeinthislargebodyofresearchisthatfirmgrowthcriticallydependsontheageoftheorganization(forreview,see

Khaire

2010

and

Coad

2024)

.Inparticular,recentstudieshaveshownthatyoungfirmsexhibitbothhighdis-

persionandpositiverightskewnessingrowthrates(Deckeretal.,

2016;

Haltiwanger

etal.,

2016;

GuzmanandStern,

2020),highlightingthecentralroleofentrepreneurship

foreconomicdynamismandhigh-growthactivity.Atthesametime,othersdocumentadeclineinthepaceofentrepreneurshipandgrowthratesamongyoungfirms,aswellas

anincreasinglydominantroleoflargeincumbentfirms(Autoretal.,

2020;

Haltiwanger,

2022)

.Whiletheheterogeneousnatureoffirmgrowthisanincreasinglyactiveareaofresearch,onebarriertostudyinghigh-growthfirms—especiallyamongyoungandsmallorganizations—isthelimitedaccesstolarge-scaledata.

Nationallyrepresentativeadministrativemicrodata,suchastheCensusBureau’sLongitudinalBusinessDatabase(LBD),havebeenvaluableforadvancingourunder-

standingoforganizationsandhowtheygrow(NagarajandTranchero

2023;

Sterketal.

2021;

Sivadasanetal.

2024)

.However,gainingaccesstosuchdatacanbedifficult.InthecaseofconfidentialCensusmicrodata,accessislimitedtoapprovedprojectsviatheFederalStatisticalResearchDataCenters(FSRDCs).AlternativedatasourcessuchasNationalEstablishmentTimeSeries(NETS)orCompustatintroducesampleselection

2

andmeasurementchallenges

.1

Meanwhile,theOrganisationforEconomicCo-operationandDevelopment(OECD)hascollectedandmadeavailableaggregatedataonfirmdynamics—withanemphasisonhigh-growthfirmsgiventheirrelevanceforpolicy—bypartneringwithnationalstatisticalagencies.Todate,nosuchdataareavailablefortheU.S.

WeaddressthesegapsbyintroducingtheBusinessDynamicsStatisticsofhigh-growthfirms(BDS-HG)tables,whichoffersarich,public-usesourceofinformationonfirmgrowthintheU.S.AlthoughtheBDS-HGtablesprovidecategory-levelstatis-tics,theyareverygranular.Forexample,thestatisticsbydetailedindustriesprovidesmorethan114thousandobservationsofgrowthratebinby4-digitNAICSclassifica-tion.Thesenewlyavailablepublic-usestatisticsopenupnewresearchopportunitiesbyprovidingvariationbystateandindustry.Wedescribesomeofthepotentialresearchavenuesenabledbythesedataingreaterdetailbelow.Additionally,theBDS-HGtablesallowresearcherstovalidateanalysesconductedusingotherdatasetsormeasures.WeregardtheBDS-HGdataasacomplementratherthanasubstituteforcomprehensiveadministrativemicrodataonfirmssuchastheLBD.Thelatterremainimportantsourcesforfirm-levelanalysis.However,theBDS-HGprovidesnovelfirm-basedgrowthratedistributionstatisticsatagranularlevelinthepublicdomainthatcanbeusedforavarietyofresearchpurposes.

Inthispaper,wedescribetheBDS-HGtabulations,whichprovidetheannualstockandflowoffirms,establishments,andemploymentacrossthefirmgrowthratedistri-bution.Thesenewpublic-usedatatablesleverageconfidentialadministrativedatafromtheU.S.CensusBureaucoveringallnon-farmemployerbusinessesintheU.S.between1977and2021.TheBDS-HGtablesprovidetabulationsbybothwithin-yeargrowthratepercentilegroupsandabsolutegrowthrates.TheBDS-HGdatatabulatesfirms,estab-

lishments,andemploymentbyfirmgrowthatthenational-levelandbystate,detailed

1Wediscusssomeofthesechallengesingreaterdetailbelow.

3

industry(upto4-digitNAICS),firmsize,andfirmagecategorieswithseveralmulti-way

tables(e.g,.firmgrowthbyfirmageandfirmsize)

.2

TheBDS-HGdatawillbeupdatedonannualbasis.Accesstothesenewdataalongwithadditionaldocumentationcanbefoundat

/programs-surveys/ces/data/public-use-data/ex

perimental-bds/bds-high-growth.html.

Toillustratethepotentialuseandpromiseofthesenewdata,webeginbycharacterizingthechangingnatureofthefirmemploymentgrowthdistributioninthepastfewdecadesandtheoriginsofhigh-growthfirms.

Severalstrikingpatternsemerge.First,whiletheregenerallyhasbeenanincreaseinthenumberoffirmsintheU.S.inthepastfourdecades,theshareoffirmsthatarehigh-growthhassteadilydecreasedfrom18%in1985to12%in2015.Thisdeclineisdrivenbybothfallingsharesofnewfirmsandlanguishinggrowthamongexistingfirms.Intandem,theU.S.economyhaswitnessedasubstantialriseinfirmswithvirtuallyzeroemploymentchangefrom32%to40%overthesame30-yearperiod.Second,weexploretheevolvingsourcesofhigh-growthfirms.Withrespecttofirmsizeandage,wefindthatthedeclineintheshareofhigh-growthfirmsisparticularlysevereforyoungandsmallfirms.Incontrast,matureandlargefirmsdonotexhibitsubstantialchangesinhigh-growthactivityovertime.Intermsofindustry,wefindthatallsectors—especiallyconstructionandmanufacturing—haveshownageneraldeclineintheshareofhigh-growthfirms.Interestingly,afewsectorssuchasInformation(e.g.,software,mediastreaming,computinginfrastructure)haveshownamodestreboundbeginningin2010.However,allsectorsremainfarbelowtheirsharesofhigh-growthfirmsfromearlierdecades.Wealsofindmeaningfulgeographicvariationinhigh-growthfirmactivity.Comparedtothebaselineshareofallfirms,14statesandDCare“overrepresented”intheirshareofhigh-growthfirms.Amongthem,Florida,California,andTexasdispropor-

2TheFall2024releasewillincludemoregranularstatisticsatthesector×statelevel,whichwouldenableresearcherstomakeuseofsector-orstate-specificshocksinadifference-in-differencesanalyticframework.

4

tionatelycontributetohigh-growthfirmsevenafteraccountingfortheirrelativelylargebaselineshareofallfirms.

OurinitialresultshighlighttherichpotentialoftheBDS-HGdataforfutureresearch.Tofurtherdemonstratethispoint,wediscussvariousdomainsforfutureresearchrelatedtofirmgrowth—suchasdiversification,labormarketfrictions,andinnovation—andhowtheBDS-HGdatacanhelpinadvancingeachlineofwork.OurhopeisthattheBDS-HGdatawillhelpenableahostofnewresearchquestionsunderlyinghigh-growthfirms.

Thepaperproceedsasfollows.Section

2

describestheinputdataandthemethod-ologyforcomputingfirmgrowthratesalongwithsomedescriptivestatisticsaboutthepropertiesofthefirmgrowthratedistribution.Section

3

providesapreviewoftheBDS-HGdata,describingthecharacteristicsofhigh-growthfirms.Sections

4

outlinesspecificusecasesoftheBDS-HGforrelevantfutureresearchinstrategicmanagementandeconomics.Section

5

concludes.

2DataandMethodology

TheBDS-HGtablesarederivedfromtheLongitudinalBusinessDatabase(LBD),theframeofallnon-farmemployerbusinessesintheU.S.

(JarminandMiranda,

2002;

Chow

etal.,

2021)

.3

Currently,theLBDcoversyears1976to2021.TheLBDprovidesinfor-mationattheestablishment-levelsuchasemployment,payroll,industry,age,andfirm

3Severalalternativedatasourcesarecurrentlyavailable,buttheyfacesomeconstraints.Forinstance,researcherscommonlyusedataonventurecapital-financedstartups(e.g.,VentureSource)andpubliccom-panies(e.g.,Compustat)tostudyhigh-growthfirms.However,akeylimitationisthatthesesamplesarehighlyselectedonsuccessfulcompaniesthatmanagetoraisefundingorreachanIPO.Incontrast,establishment-leveldatafromNETSprovideamorecomprehensivecoverage.However,thisdatasetisknowntobelimitedinitslongitudinalcoverageespeciallyforstartups;forexample,

CraneandDecker

(2019)estimatethat90%ofyoungfirms’employmentrecordsareimputed

.Similarly,statebusinessregis-

trationrecordsprovidelarge-scaledataonpotentialhigh-growthfirmsatbirth(GuzmanandStern

2020),

butlongitudinalinformation(e.g.,employmentgrowth)isnotavailable.

5

identifiers.Employmentcapturesbothfullandpart-timeemployeeswhoareontheestablishment’spayroll,includingsalariedofficersandexecutivesofcorporations,dur-ingthepayperiodthatincludesMarch12th.Thisalsoincludesemployeesonpaidsickleave,holidays,andvacationsbutexcludesproprietorsandpartnersofunincorporatedbusinesses

.4

Wecomputeyear-to-yeargrowthofestablishmentsandfirmsusingameasurefirstdevelopedby

Törnqvistetal.

(1985),whichhasbecomestandardinthefirmdynamics

literature(Davisetal.,

1996;

Coad,

2024)

.Thisgrowthmeasure,henceforthTVV/DHS,dividesthechangeinemploymentfromt−1totbyaverageemployment.Wediscussthismeasureingreaterdetailbelow.TheTVV/DHSmeasuresharesusefulpropertieswithlogdifferencesandnaturallyaccommodatesentryandexit.Sincethedenominatorcontainstheaveragevalueovertwoyears,thismeasureisalsosymmetricandalleviates

regression-to-the-meaneffects(Haltiwangeretal.,

2013)

.Moreover,TVV/DHSgrowthhasusefulaggregationproperties.Itcanbeflexiblydefinedforaggregationsofes-tablishmentseitherintofirmsorcellsdefinedbyestablishmentorfirmcharacteristics.Aggregatingthisgrowthmeasuretothefirm-levelresultsinameasureof“organic”firmgrowththatabstractsawayfromfirm-levelemploymentchangesduetomergersandac-quisitions(c.f.,

Sivadasanetal.

2024)

.ByconstructionTVV/DHSgrowthisboundedbetween-2(firmsthattransitionfromnon-zerotozeroemploymenti.e.,exit)and2(firmsthattransitionfromnon-zerotozeroemploymenti.e.,entry).

Specifically,establishmenti’sgrowthrate(gi,t)isdefinedas

gi,t=(1)

4TheMarch12threferenceperiodalsoimpliesthatmuchoftheeconomiceffectsoftheCOVID-19pandemicappearinthe2021butnot2020BDStabulations.NoneofthestatesintheU.S.hadamandatoryshelter-in-placeorder(i.e.,lockdown)asofMarch12,2020.ForadditionalinformationaboutthetimingofCensusBusinessdataandtheCOVID-19pandemicsee

Beemetal.

(2022)

.

6

whereEi,tisemploymentatestablishmentiattimetandXi,t=.Firm-levelgrowthisthenthesumofemploymentchangesdividedbythesumofaverageestablishmentemployment,forallestablishmentsiassociatedwithfirmfattimet,asfollows:

Thesumofemploymentchangesweightedbyaverageemploymentisequivalenttotheweightedaverageofestablishment-levelgrowthratesusingtheemploymentshare, ,asweights.

Thereareseveralrelationshipsbetweenfirmsize,age,andgrowththatareimportanttonote.First,theshapeofthegrowthratedistributionbyfirmsizedependscriticallyonwhetheraverage(betweent-1andt)orinitial(t-1)firmsizeisusedtoclassifyfirms

.6

Averagesizewilltendtoallocatemoregrowthandlesscontractiontolargefirmsizebinscomparedtoaninitialsizemeasure.Thisisbecausefirmgrowthexhibitssignificantmeanreversion—firmsthatgrewfromt-2tot-1aremuchmorelikelytocontractfromt-1tot—duetotransitoryshocks.Second,inadditiontotransitoryshocks,firmsexperiencesystematicandpersistentgrowthpatternsoverthefirmage

lifecycle(Deckeretal.,

2016)

.OnlineAppendixAprovidesadditionaldetailsabouttheTVV/DHSmeasureanditsrelationshiptosizeandage.

Wecategorizehigh-growthfirmsusingtwodistinctbutrelatedmethodsofgroupingfirmsbasedupontheiremploymentgrowthrates.Thefirstispercentile-basedandusesthedistributionofgrowthratesacrossfirmsandthesecondisbasedupongrowthratevalues.Inbothcases,ourmethodologyattemptstomaximizethecomparabilityofthe

BDS-HGtabulationstotheOECD’sDynEmpprogram,whichprovidesinternationally

5Exitingestablishmentswillbeassignedafirmgrowthratebasedupontheirassociatedfirmint-1.

6Averageandinitialfirmsizegroupingscorrespondstofsizeandifsize,respectively,fromtheBDStabulations.

7

comparablemeasuresofbusinessdynamismforanumberofOECDcountries

.7

Forthefirstmethod,weclassifyfirmsbasedontheirpositiononthewithin-yearav-erageemployment-weightedgrowthratedistribution.Employmentweightingisdone,aftercomputingfirmgrowthratesandthesumofaverageemployment(denom)atthefirm-level,sortingfirmswithinayearinascendingorderbytheirgrowthrate,breakingtiesrandomly,computingeachfirm’scumulativeshareofeconomywideaverageem-ployment,thensummingthisshareacrossfirmsinthesortedfile.Firmsthataccountofacumulativeshareoflessthanorequalto10percentoftotalaverageemploymentareassignedtothep1top10bin.

Weclassifyfirmsintofivepercentile-basedemploymentgrowthbins(fempgr_grpct

intheBDS-HGtables):a)p1-p10;b)p11-p25;c)p26-p75;d)p76-p90;e)p91-p99.Byconstruction,thismethodinvolvesgrowthratecut-offsthatvaryovertime.Asthefirmgrowthratedistributioncontractsovertime,thegrowthrateassociatedwiththe90thpercentileoftheemployment-weightedgrowthratedistributionwillchange.Toillus-tratethispoint,Figure

1

showstheaveragegrowthrateassociatedwiththe10th,50th,and90thpercentileoftheemployment-weightedgrowthratedistributionforgroupingsofyears.Consistentwiththefindingsof

Deckeretal.

(2016),wefindthatthegrowthrate

associatedwiththe90thpercentileofthegrowthratedistributionhasfallensignificantly

overtimefrom0.35to0.23fromthelate1980stothe2010s.Lessdramatic,butstillap-parent,istheriseofthegrowthrateassociatedwiththebottom,or10thpercentile,whichrosefrom-0.27to-0.20overthesameperiod.Eventhemedian,or50thpercentile,hascontractedslightlyfallingtowardszero.Thesepatternsoverroughlyfortyyearsimplyanincreasingcompressioninthefirmgrowthratesatboththetopandbottomportions

ofthedistribution,wherebythefastestgrowingfirmsintheeconomyaregrowingless

7NotabledifferencesbetweentheBDS-HGandOECDDynEmpmethodologiesinclude:(1)wedonotrandomlyperturbthegrowthratesofzerogrowthfirms,(2)provideslightlylessdetailaroundthemedianofthewithin-yearemployment-weightedgrowthratedistribution,and(3)wedonotprovidefirmweightedpercentilebins.

8

andthefirmscontractingthemostarecontractingbyless.

Figure1:Firmgrowthratesbypercentileovertime

Source:LBDv202100.

Notes:Figureshowstheaveragefirm-employmentgrowthratesbydecadesamongfirmsclassified

betweenthe9thand11th(labeled10th),49thand51th(labeled50th),andthe89thand91th(labeled90th)percentilesoftheemploymentweigthedgrowthratedistribution.

Motivatedbythetimevaryingnatureofthepercentile-basedmethod,oursecondapproachclassifiesfirmsbasedupontheiremploymentgrowthratevalues.Weclassify

firmsintoninebins(fempgr_grintheBDS-HGtables):a)-2;b)(-2to-0.8];c)(-0.8to-0.2];d)(-0.2to-0.01];e)(-0.01to0.01);f)[0.01to0.2);g)[0.2to0.8);h)[0.8to2);i)2.Bydefiningfixedrangesofemploymentgrowthratesforeachgroup,thistime-invariantapproachallowsustocomparetheabsolutegrowthdynamicsoffirmsovertime.Duringeconomicdownturns,forexample,firmsmaybegrowinglessandcontractingmore,inwhichcaseeconomicactivitywillshiftacrossthegrowthratebins.

Intheanalysesthatfollowwefocusonhigh-growthfirmsusingthegrowthrate-

9

basedtabulations(fempgr_gr).Thisallowsustoanalyzetheabsolutegrowthperfor-manceoffirmsintheU.S.economyovertime.Wedefinehigh-growthfirmswhosefirmgrowthrateis0.8orgreater.Forcomparison,forthepercentile-basedmeasureswedefinefirmsinthetop10percentileofthewithinyeargrowthratedistributionashigh-growth.Continuingfirmsareconsideredhigh-growth,bythisdefinition,iftheirsize

increasedbymorethanapproximately130%year-over-year.8

Inadditiontocontinuers,newandreactivatingfirmswillalsobeconsideredhigh-growthsincetheirgrowthrateismechanicallyequalto2.

Tomotivatethisthresholdforhigh-growthfirms,Figure

2

showsthecountoffirmsacrossdetailedfirmgrowthratebinsinPanelAandthepercentoffirmsPanelB

.9

InPanelA,theheightofeachdarkcircleshowsthecountoffirms(withalogscaley-axis)ateachpointalongthefirmgrowthratedistributiononaveragebetween1978and1982.Thesizeofeachbubblereflectstheshareofaverageemploymentaccountedforbyeachgrowthratebininthoseyears.Thehollowcirclesshowasimilarstatisticfortheyears2016to2021.PanelBshowsthepercentoffirmsacrossthegrowthbinsovertimeratherthanfirmcounts.SeveralpatternsarenotableinFigure

2.

First,inPanelAandBweseethatthemajorityoffirmshavegrowthratesbetween-0.2and0.2,whichroughlycorrespondstoa22%contractionorexpansion

.10

Inparticular,almost29%offirmsinthelate1970shadnearlyzerogrowthrates,whichroseto37%inlate2010s.Therearealsoalargenumberoffirmsthatexit(-2)orenter(2),buttheyaccountformuchlessemploymentthanthosewithinthe-0.2to0.2band.Sincethenumber

8TheTVV/DHSgrowthmeasurecanbetranslatedintopercentdifferencesusingtheimpliedrelation-shipbetweenthetwo.Foragivenxandy,thepercentdifferenceisgivenasgpct=andtheTVV/DHSdifferenceisgtvv/dhs=.Thisimpliesthatgpct=

9ThesebinshavemoredetailsthanareavailableintheBDS-HGtabulations.

10The“kink”inthedistributionat(-1,0.8]and[0.8,1)isdrivenby“lumpiness”inthejointsizeandgrowthratedistribution.Firmswithoneortwoemployeesarequitecommonamongthepopulationoffirms.Ifafirmwithoneemployeeaddstwoadditionalemployeesitsgrowthrateis1,whichisontheexcludededgeofthe[0.8,1)bin.

10

Figure2:DetailedDistributionofFirmGrowthRates,1980vs.2018

(a)LogFirmCounts

(b)PercentofFirms

Source:LBDv202100.

Notes:PanelAshowsaveragecountoffirms(logscaley-axis)acrossgroupingsofyearsbydetailed

growthratebins.NotethatthesebinsaremoredisaggregatethanthoseprovidedintheBDS-HG

tabulations.Solidcirclesrepresentaveragecountsfortheearlierperiod(1978-1982)andhollowcirclesshowaveragecountsforthelaterperiod(2016-2021).PanelBshowstherpercentoffirmsaccountedforbyeachbin.

11

offirmshasrisensignificantlyovertimetheabsolutecountoffirmsthatenterorexit

increasesslightly(PanelA)buttheshareoffirmsthatenterorexithasdeclined(PanelB).Second,thecompressionofthegrowthratedistributionshowninFigure

2

canbeseeninthechangesinthecountsandsharesoffirmsacrossthegrowthratedistribution.Thenumberoffirmsthatwithlittletozerochangeinemploymentrosethemostbetween1980to2018.Thecountoffirmsthatexperiencedsignificantgrowthorcontractiondeclinedsignificantly.

Tofurtherdemonstratethedifferencesbetweenthepercentile-basedandgrowthrate-basedbins,Figure

3

showstheshareoffirmsthatareclassifiedashigh-growthbybothmeasures.PanelAshowsthepercentoffirmsthatareclassifiedashigh-growthusingthegrowthrate-basedmeasures,decomposedintoallfirms(lightgraybars)andjustcon-tinuers(darkgraybars).PanelBdoesthesamebutforthepercentile-basedmeasures.PanelAshowsthatwhereasroughly17%offirmswerehigh-growthin1980,thatsharefellto13%in2020.Incontrast,unsurprisingly,thepercentile-basedbinsshowamuchflatterseriesforbothallhigh-growthfirmsandcontinuerhigh-growthfirms.Asmen-tionedpreviously,thisisdrivenbythefactthatasthegrowthratedistributioncontractssotoodoesthe90thpercentilecutoff.Thetoppercentile-basedbinwillalwayscontainthe10%ofaverageemploymentwiththehighestgrowthratesevenasthegrowthratesofthosefirmsdeclines.Variationintheshareoffirmscoveredbythatbin,then,reflectdifferencesinthesizecompositionofthefastestgrowingfirms.Thepercentile-basedmethodalsocastsawidernetwithmanymorefirmsclassifiedashigh-growththanthe0.8cutoffweuseforthegrowthrate-basedmethod.

Inthenextsectionweprovideapreviewofthesestatistics,highlightinginterestingpatternsinthecompositionofhigh-growthfirmsovertimeintermsoffirmsize,age,industry,andgeography.Allofthefollowingfigurescanbegeneratedusingthenew,publiclyavailableBDS-HGtabulations.

12

Figure3:High-GrowthFirmShares

(a)GrowthRate-basedMethod(b)Percentile-basedMethod

Source:BDS-HG2021.

Notes:Panel(A)showsthepercentoffirmsclassifiedashigh-growthusingthegrowthrate-based

method(≥0.8TVV/DHS)andPanel(B)showsthepercentusingthepercentile-basedmethod(>90thpercentile).Allhigh-growthfirmsasapercentofallfirmsisshowninlightgrayandonlycontinuerhigh-growthfirmsindarkgray.

3TrendsinHigh-GrowthFirmActivity

Anatomyoffirmgrowthovertime

Table

1

showsthecountoffirms,jobcreation,andtotalemploymentassociatedwithallfirmsandtheshareofeachaccountedforbyhigh-growthcontinuingfirms,andhigh-growthentrantsovertime.Alloftheanalysesinthissectionutilizethegrowthrate-basedhighgrowthclassification.Thetotalnumberofnon-farmemployerfirmsrisesfromabout3.6millioninthelate1970storoughly5.3millionby2020.Overthesameperiod,theshareofhigh-growthfirmsfellfromapproximately18.4%to13.1%.Theshareofhigh-growthcontinuersdeclinedbyhalfandhigh-growthentrantsfellbyapproximatelyaquarterovertheperiod.Intermsofemployment,high-growthfirmsaccountforamuchsmallershare,fallingfromabout4%oftotalemploymentinthelate

13

1970stoabout2.2%bytheendoftheperiod.Incontrast,high-growthfirmsaccountforalargebutfallingshareofjobcreation,fromapproximately40.3%inthelate1970sto32.2%inthe2010s

.11

11EntrantsaccountforthebulkofhighgrowthfirmsascanbeseenfromTable

1.

Therefore,withthe

recentsurgeinnewbusinessapplications(Haltiwanger,

2022;

DeckerandHaltiwanger,

2023),theshare

ofhighgrowthfirmsislikelytohaverisensomewhatafterthepandemic.

14

Table1:SummaryStatisticsofHigh-GrowthFirmsintheU.S.

Decade

Firms

Allhigh-growth(%