Across the Universe: Policy Support for Employment and Revenue in the Pandemic Recession

Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C. Across the Universe: Policy Support for Employment and Revenue in the Pandemic Recession Ryan A. Decker, Robert J. Kurtzman, Byron F. Lutz, and Christopher J. Nekarda 2020-099 Please cite this paper as: Decker, Ryan A., Robert J. Kurtzman, Byron F. Lutz, and Christopher J. Nekarda (2021). “Across the Universe: Policy Support for Employment and Revenue in the Pandemic Recession,” FinanceandEconomicsDiscussionSeries2020-099r1. Washington: BoardofGovernors of the Federal Reserve System, https://doi.org/10.17016/FEDS.2020.099r1. NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. References in publications to the Finance and Economics Discussion Series (other than acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.

Across the Universe: Policy Support for Employment and Revenue in the Pandemic Recession Ryan A. Decker, Robert J. Kurtzman, Byron F. Lutz, and Christopher J. Nekarda Board of Governors of the Federal Reserve System May 27, 2021 Abstract Usingdatafrom14governmentsources,wedevelopcomprehensiveestimatesofU.S.economicactivitybysector,legalformoforganization,andfirmsizetocharacterizehowfour government direct lending programs—the Paycheck Protection Program, the Main Street Lending Program, the Corporate Credit Facilities, and the Municipal Lending Facilities— related to these classes of economic activity in the United States. The classes targeted by these programs are vast—accounting for 97 percent of total U.S. employment—though entity-specific financial criteria limited coverage within specific programs. We relate our estimates to those from timely alternative data sources, which do not typically cover the majorityoftheeconomicuniverse. JELcodes: C83,E20,E58 Keywords: employment, activity estimates, direct lending programs, Paycheck Protection Program,PPP,MainStreet,CorporateCreditFacilities,alternativedata Theanalysisandconclusionssetfortharethoseoftheauthorsanddonotindicateconcurrencebyothermembersof theresearchstaffortheBoardofGovernorsoftheFederalReserveSystem. WethankAndreasLehnertforinvaluable discussionsandcontinuedsupportofthisproject,andwethankTimDore,ElizabethHandwerker,JavierMiranda, Dipak Subedi, and ASSA 2021 participants for helpful comments. We are also grateful to the authors of Cajner et al. (2020) for sharing certain ADP-based tabulations and, in particular, Adrian Hamins-Puertolas for technical assistance; theCajneretal.(2020)projectwasmadepossiblebyADPandMattLevin,AhuYildirmaz,andSinem Buber. WethankRaySandza,AdamLiem,andHomebaseforprovidingHomebasedataandtechnicalsupport. U.S. CensusBureaustaffgraciouslyprovidedseveralspecialdatatabulationsandfieldedmanyquestionsfromus.

1 Introduction The Pandemic Recession of 2020 has been unprecedented in its speed and severity. Firms across sectors and size classes experienced massive reductions in revenue due to governmentmandated activity restrictions and behavioral changes arising from health concerns (Cajner et al., 2020; Chetty et al., 2020). To mitigate the impact of the shock on the economy and maintain financial stability, the U.S. government and Federal Reserve took the unprecedented actionofprovisioningdirectassistancetofirmsandgovernmententitiesacrossnearlytheentire universeofeconomicactivity,includingcategoriesofbusinessesthatarenottypicallythefocus ofdirectlendingprograms. A key challenge for research and analysis of these policies is measuring the economic universe they target. We present estimates of economic activity in the United States that we partition by sector, legal form of organization, and firm size. Our estimates draw primarily on U.S. Census Bureau sources but also on data from the Department of Agriculture, national accounts, railroad regulators, and other sources. We then assess how four government direct lendingprograms—thePaycheckProtectionProgram(PPP),theMainStreetLendingProgram (“Main Street”), the Corporate Credit Facilities (CCFs), and the Municipal Lending Facilities (MuniLF)—relatetoouractivityestimates. Whiletabulatingtheuniverseofeconomicactivitymayseematrivialtask,itisnot. Indeed, thiskindofdescriptiveexerciseisrarelydone. Inparticular,nosingleproductofU.S.statistical agencies is able to answer the question of how much economic activity falls within the scope of each direct lending program initiated during 2020. Our partitioning of the economy is thus uniqueinthatwecanmaptothefourdirectlendingprogramsweconsiderwhilestillcapturing nearlytheentiretyofeconomicactivity. We draw on our universe data to illustrate the vast scale and scope of the economic policy response to the Pandemic Recession. The four direct-lending programs we study notionally cover the entirety of private-sector jobs as well as nearly all government employment; that is, theclassestargetedincludemosteconomicactivity,ignoringentity-specificfinancialcriteriathat reduceeffectiveprogramcoverage. Thisresponseissubstantiallybroaderthanthatmountedin responsetotheGreatRecession. A number of alternative data sources on business activity, such as those compiled by privatecompanies,havegainedprominenceduringthePandemicRecessionbecausetheyprovide timely insights that are not available from official data sources. Unlike official statistics, however,alternativedatagenerallycomewithconcernsaboutcoverageandrepresentativeness. We provide critical context for users of several alternative data sources—in particular ADP, Compustat, Homebase, and Dun & Bradstreet—by comparing their estimates of economic activity with our universe estimates. We show that their timeliness comes at a cost of coverage; the Page1of54

alternative data cover substantially smaller portions of the economy than either the Bureau of LaborStatistics(BLS)dataorouruniverseestimates. 2 Overview of U.S. economic activity We construct measures of the number of entities, employment, annual payroll, and gross receipts by sector, legal form of organization, and firm size for the U.S. economy in 2019, with a focus on the criteria that are relevant for the four lending programs we consider. Quantifying the universe of economic activity is a considerable undertaking, as no single data source covers all areas of the economy (e.g., nonfarm and farm businesses, railroads, employer and nonemployerbusinesses, andeachlevelofgovernment). Wethuscombinedatafromavariety ofofficialsources. Ouraccountingcapturesvirtuallyalleconomicactivity,withtheonlyexceptions being informal activity, private households, certain financial entities without employees, and businesses owned by Tribal governments. Our main data sources are the Census Bureau’s Statistics of U.S. Businesses (SUSB) and Census of Governments, but our tabulations require many other sources as well. All told, to compile our universe estimates we use data from 14 separate sources along with a handful of others necessary for temporal and other adjustments (forexample,weuseBLSQuarterlyCensusofEmploymentandWages(QCEW)datatotranslate 2017valuesto2019estimates);theappendixprovidesdetailonoursourcesandmethods. Table1presentsourestimatesoftheuniverseofeconomicactivityandlistsalldatasources. Withintheprivatesector,businessesaredividedintocategoriesbyacombinationofnumberof employeesandannualrevenue: • Small firms are defined as those with fewer than 500 employees, regardless of revenue, unlessotherwisenoted; • Mediumfirmsaredefinedasthosewithatleast500employees,butfewerthan15,000employeesorlessthan$5billioninannualgrossreceipts; • Large firms are defined as those with at least 15,000 employees and $5 billion or more inannualreceipts.1 We separate out private activity by size class at for-profit and nonprofit private businesses. Although farms are typically for-profit businesses, we provide their activity statistics separately since readers are accustomed to seeing the nonfarm economy in isolation. We also include information on nonemployers. Last, we separate out government activity across federal, state, andlocalgovernments. 1. Cutoffsforactivitybelow15,000employeesor$5billioninreceiptsarenotavailableinoursourcedata;the appendixdescribeshowtheseareestimated. Page2of54

Table 1. Activity Measures at U.S. Businesses and Governments, 2019 Annual Annual Firmsor Employment payroll receipts Classorprogram entities (millions) (billions) (billions) Byclass* 1. Private 33,895,441 159.6 7,714 42,656 2. For-profit 5,654,239 114.7 6,517 38,468 3. Small 5,636,791 55.2 2,729 13,765 4. Medium 16,979 35.7 2,230 13,342 5. Large 469 23.7 1,558 11,361 6. Nonprofit 438,808 16.9 828 2,447 7. Small 435,976 7.3 267 887 8. Medium 2,796 7.5 418 1,158 9. Large 36 2.1 142 403 10. Farms 2,023,619 2.3 33 377 11. Small 1,947,453 1.3 10 118 12. Medium 76,166 1.0 23 259 13. Nonemployers 25,778,775 25.8 336 1,364 14. Government 90,127 24.5 2,120 6,378 15. Federal 1 4.8 466 3,464 16. Civilian 2.8 302 17. Armedforces 2.0 163 18. State 51 5.5 479 1,515 19. Local 90,075 14.3 1,176 1,399 Byprogram* 20. PPP 33,801,428 94.4 3,441 16,477 21. MainStreet 93,517 40.3 2,594 14,483 22. CCFs 496 24.8 1,678 11,695 23. MuniLF 90,126 19.8 1,655 2,914 Sources: Annual Survey of Public Employment and Payroll, Bureau of Transportation Statistics, County Business Patterns,CensusofAgriculture,CensusofGovernments,CurrentEmploymentStatistics,DepartmentofDefense ActiveDutyMasterFile,NonemployerStatistics,nationalincomeandproductaccounts,OfficeofManagement andBudgetHistoricalTables,RailroadRetirementBoard,State&LocalGovernmentFinanceHistoricalDatasets and Tables, Surface Transportation Board, Statistics of U.S. Businesses, and authors’ calculations. See the appendixfordetailondataconstruction. Notes: BlueshadingindicatescoveredprimarilybythePPP.OrangeshadingindicatescoveredprimarilybyMain Street. GreenshadingindicatescoveredprimarilybytheCCFs. YellowshadingindicatescoveredbytheMuniLF. Forstateandlocalgovernments,receiptsreferstoown-sourcegeneralandutilityrevenue. *Totalsbyprogramwillnotmatchthesumofshadedrowsbyclassbecausenearlyallmediumandlargefor-profit firmsintheaccommodationandfoodservicessector(NAICS72)areeligibleforthePPP. Page3of54

In the Census Bureau data underlying the bulk of table 1, a private sector firm is defined basedonoperationalcontrolorownership;bothourfirmcountsandourfirmsizeclassesreflect thisdefinition. However,weemphasizethat,insomecases,otherfirmdefinitionsmaybeused todeterminelendingprogrameligibility,suchasdefinitionsbasedontaxidentifierswhichmay allow multiple subsidiaries of a firm to access programs independently. In this respect, our tabulationsoffirmcountsmayunderstatethenumberofentitiesqualifyingforprograms,even withintheprivatesector.2 For-profitprivatebusinessesrepresentmostnon-governmenteconomicactivity,ascompared with nonprofits, farms, and nonemployers. There are 160 million employees at private businesses, 115 million of which are at for-profit businesses. Although entity and, to some extent, employmentcountsareskewedtowardsmallfirms,annualpayrollandreceiptsaremoreevenly distributedacrosssizeclasses. Nonprofit businesses account for less than 10 percent of employment in most sectors; that said,theyconstitutealargershareofactivityincertainsectorsoftheeconomy. Amongemployer firms, nonprofits account for 78 percent of employment in educational services, 48 percent in other services (which includes religious organizations), 42 percent in health care and social assistance,and28percentinarts,entertainment,andrecreation(whichincludesmuseums). TheUnitedStateshasabout2millionfarms(includingranches)employing2.3millionhired workers (farms are classified in North American industrial classification system (NAICS) 111 and 112).3 Farms with less than $1 million in revenue were eligible for Small Business Administration (SBA) programs such as the PPP, so we include them in the small size class, with the remaining farms included in the medium size class.4 Farms are not directly comparable to firms; some farms may be owned by larger firms that own other farms (such that farm counts overstatefarmingfirms)orthatalsohaveactivityinothersectors(suchthattheyappearinthe firm counts elsewhere in table 1). These possibilities would not necessarily result in misclassification of employment, payroll, or receipts, since these measures are categorized at the farm 2. Firmsarenotoriouslydifficulttodefineandcountduetosometimescomplicatedstructuresofownershipand control. Moreover,manylargefirmshaveoperationsinmultipleindustriesandmayoperateunderanynumberoftax identifiersunderwhichtheymightapplyforgovernmentprograms. Thereislikelysomedoublecountingofactivity betweenfarmsandotherclasses,asmanyfarmsarenonemployersormaybesubsidiariesoffirmswithactivityin otherindustries. Thus,ourfirm-countestimatesshouldbetreatedwithgreateruncertaintythantheotheractivity measures. Weoutlineourmethodologyforestimatingfirmcountsintheappendix. 3. Inadditiontoformalhiredworkers,farmsalsorelyheavilyonunpaidlabor(e.g.,family)andcontractworkers which we do not include in table 1; in the 2017 Census of Agriculture, farms reported roughly 2 million unpaid workersand8millioncontractworkers(someofthelattermayalsoappearinnonemployerdata). 4. Farmsizecriteriaarecomplicated,butthe$1millionreceiptscutoffisastandardSBA“smallbusiness”criterion thatwecaneasilymeasureinCensusofAgriculturedata. TheuppersizecategoryintheCensusofAgricultureis $5millioninannualreceipts;indiscussionswithDepartmentofAgriculturestaff,wedeterminedthattheexistence of farms with at least $5 billion receipts is extremely unlikely. While there are likely some large firms that own establishments engaged in farming activities, firms of that size are likely to also have establishments in nonfarm activities,whichmeanstheirfirmcountsarerepresentedelsewhereonthetable. Thatsaid,thefarmemployment, payroll,andreceiptsofsuchlargefirmswouldbemisclassifiedasmediuminthefarmscategoryontable1. Page4of54

orestablishmentlevel;butfarmingactivitymaybemisclassifiedintermsofoursizecategories if,forexample,alargenumberofsmallfarmsareownedbyafirmwithrevenueabove$1million. Inshort,measurementoffarmsisbasedupondifferentconceptsfrommeasurementofthe rest of the business universe, which suggests caution should be exercised when inferring farm eligibilityforlendingprograms. Nonemployers are businesses that produce goods or services but do not have formal employees. This includes self-employed individuals who do not employ others as well as other businesses with no employees, such as owners of rental properties. The vast majority of businesses—26million—arenonemployerbusinesses;however,thesebusinessesaccountforonly $1.4 trillion in annual receipts, which is equivalent to about 4 percent of for-profit employer revenue.5 Nevertheless,self-employmentisanimportantsourceofincomeformillionsofAmericans. While nonemployer businesses do not technically pay wages and salaries, we estimate these businesses generated more than $300 billion in payroll equivalent in 2019; see the appendixfordetails. The government activity figures on table 1 include nearly all government activity, includingthePostalService,thearmedforces,andgovernment-ownedbusinessesinmanyindustries. State and local governments employ nearly 20 million workers, far more than the federal government. In addition to the 50 state governments, the District of Columbia, and the federal government, there are more than 90,000 local government entities, including special purpose entitiessuchastransitauthoritiesandpublichospitals. Infigure1,wepresentsector-levelemploymentdecompositionsmimickingthoseontable1 (sectors roughly correspond to 2-digit NAICS codes). The largest sector is public administration,whichhasnearly25millionemployees,mostofwhichareconcentratedinstateandlocal governments. Eight other sectors have close to or more than 10 million employees. Of these othersectors,employmentishighestinthehealthcareandsocialassistancesector,followedby retailtrade. Thesetwosectorsdemonstratethesubstantialheterogeneityinsizeandlegalform of organization in the economy: Health care and social assistance has a significant fraction of its employment in nonprofits and at medium and small firms, while retail trade has little employment in nonprofits and a significant fraction of employment at large firms. In appendix C weshowversionsofthisfigurebasedonfirmcounts,payroll,andreceipts. 5. Countsofnonemployerbusinessesshouldnotbethoughtofascountsofself-employedpersons. Manybusiness ownerscontrolmultiplenonemployerentities(forexample,somelandlordsmayownmultiplerentalpropertiesin separate nonemployer businesses). Moreover, nonemployer businesses include “side gigs” of workers with other income. Wediscussnonemployerversusself-employmentdistinctionsfurtherbelow. Page5of54

Figure 1. Employment at U.S. Businesses and Governments, 2019, by Sector and Class Agriculture, forestry, fishing and hunting Mining, quarrying, and oil and gas extraction Utilities Construction Manufacturing Wholesale trade Retail trade Transportation and warehousing Information Finance and insurance Real estate and rental and leasing Professional, scientific, and technical services Management of companies and enterprises Administrative and waste services Educational services Health care and social assistance Arts, entertainment, and recreation Accommodation and food services Other services Public administration 0 2 4 6 8 10 12 14 16 18 20 22 24 26 Million Private, for-profit, small Private, nonprofit, medium Private, nonemployer Govt., local Private, for-profit, medium Private, nonprofit, large Govt., federal, civilian Private, for-profit, large Private, farm, small Govt., federal, armed forces Private, nonprofit, small Private, farm, medium Govt., state Sources: Seetable1. Notes:Transparentbarsindicateclassesthatarenotcoveredbyoneofthefourdirectlendingprogramsweconsider. 3 Direct lending programs during the Pandemic Recession The lower panel of table 1 reports tabulations of the activity targeted by each program, and the color shading of these lines can be used to identify specific classes in the upper panel of the table that are targeted by a specific program. Our categorization of program targets is based only on our size and legal form classifications, abstracting from eligibility rules within classes—forexample,althoughsomemediumbusinesseswereineligibleforMainStreetdueto the program’s leverage requirements, they are nonetheless categorized here as being targeted by Main Street. We assign all small firm activity to the PPP because small firms were generally eligible. Forexample,allblue-shadedrowsintheupperpanelcorrespondtothePPP.6Weassign allstateandlocalgovernmententitiestotheMuniLF,eventhoughsomeoftheseorganizations (e.g.,publichospitals)werealsoeligibleforthenonprofitMainStreetfacilities. 6. PPP eligibility was determined by firm size, not establishment size, with the exception of establishments in NAICS72;seesectionB.2intheappendix. SBAdocumentationstates,“Forpurposesofthedeterminingthenumber of employees of an applicant to the Paycheck Protection Program, the applicant is considered together with its affiliates.... Concernsandentitiesareaffiliatesofeachotherwhenonecontrolsorhasthepowertocontrolthe other,orathirdpartyorpartiescontrolsorhasthepowertocontrolboth.” Affiliationisdefinedinamannersimilar totheCensusBureaudefinitionoffirms;seeU.S.SmallBusinessAdministration(2020). Page6of54

Itisimportanttonotethatfirmsofanysizeintheaccommodationandfoodservicessector that had an establishment with fewer than 500 employees were also eligible for the PPP.7 We includeactivityofmediumfor-profitfirmsandfarmsasthetargetpopulationfortheMainStreet program(line21). AlthoughsmallerfirmswerealsoeligibleforMainStreet,wedonotinclude them on line 19 for simplicity; to obtain an extreme upper bound on Main Street eligibility simplycombinelines20and21.8 Similarly,therewasnominimumsizecriterionforCCFs,but wehaveidentifiedthetargetpopulationasonlythelargefor-profitfirms.9 Mostoftheactivityinthetoppaneloftable1iscoveredbyoneoftheprogramslistedonthe bottom panel. Small organizations of any kind—for-profit and nonprofit—were included in thePPP.Mediumfor-profitandnonprofitbusinesses(aswellasmanysmallones)weretargeted by Main Street. Large businesses and nonprofits, which typically have access to the corporate bondorsyndicatedloanmarkets,werecoveredbytheCCFs.10 Stateandlocalgovernments,as well as their enterprises, were able to access the Muni LF, albeit with a potential intermediate step.11 Given its focus on small firms, the PPP’s portion of the business universe included the vast majority of firms. However, in terms of economic activity, the other lending programs—taken together—weresimilarlyimportanttothePPP.MainStreetwastargetedatlessthan1percent 7. WeinterpretthePPPeligibilitycriteriatoimplythatallfirmswithfewerthan500employeeswereeligible, thoughsomeadditionalfirmsmeetingstandardindustry-specificSBA“smallbusiness”definitionswereeligibleas well,andcertainsmallerfirmswereexcluded. Trackingspecialindustry-specific“smallbusiness”definitionsisinfeasible for our analysis, so we focus on the simple 500-employee firm-size cutoff, thereby potentially modestly understatingtheactivitythatiseligibleforthePPP.Inaddition, weincludetheeligibleportionsofmedium-and large-firmactivityinNAICS72inthePPPlineontable1. WeestimatethatthevastmajorityofNAICS72iseligible forthePPP:Morethan99percentofestablishmentsinthatsectorhavefewerthan500employees,andtheseaccount forabout94percentofemploymentandpayrollinthesector;seetheappendixfordetails. TheSBApublishedthe fulllistofcriteriamakingfirmsineligibleintheInterimFinalRule(FederalRegister,2020). 8. MainStreethadeligibilitycriteriathatcouldhavelimitedtake-up,especiallyamongsmallfirms. Inparticular, loan-sizeminimumsandleveragelimitsvariedbyfacility. InOctober2020,theminimumloansizeacrossfacilities was changed to $100,000 (Federal Reserve Board, 2020a) and remained at that level until the program ceased makingnewloansinJanuary2021. 9. Amongothereligibilitycriteria,firmsneededtoberatedaboveinvestmentgradeasofMarch22,2020(Federal ReserveBankofNewYork,2020). Ourexaminationofeligiblefirmsthatmeettheratingscriteriasuggestthatalmost allfirmsmeetingthesecriteriaarelarge. Thatsaid,wenotethatlargefirmsarerare,representingonlyatinyfraction ofallfirms,sotheremaybemanyfirmsoutsidethe“large”categorythatmaybeinscopefortheCCFs. Wealsonote thatin2019therewereroughly3,500publiclytradedfirmsinCompustat,mostofwhichareinourmediumsize category. 10. AccordingtotheFederalReserveBankofNewYork’sFAQsfortheCCFs(FederalReserveBankofNewYork, 2020),nonprofitshadaccesstotheCCFs. 11. Stateandlocalgovernmententitiesdirectly eligiblefortheMuniLFincludedallU.S.states, theDistrictof Columbia, counties with a population of at least 500,000 residents, cities with a population of at least 250,000 residents, certain multistate entities, and revenue bond issuers and cities and counties designated by their state governors. Inordertobedirectlyeligible,governmentsmusthavealsobeenabletosatisfyminimumcreditrating requirements. GovernmententitiesnotdirectlyeligiblefortheMuniLFwere,inprincipal,indirectlyeligibleforthe facilityasanydirectlyeligibleparticipantmayhaveusedtheproceedsfromMuniLFloansto“purchasesimilarnotes issuedby,orotherwisetoassist,politicalsubdivisionsandothergovernmentalentitiesoftherelevantState,City,or County.” SeeFederalReserveBoard(2020b). Page7of54

of the number of firms as the PPP but these firms have about 40 percent of the employment and almost 90 percent of the receipts of PPP firms. The 500 large firms we assign to the CCFs collectively have receipts over two-thirds as large as the millions of small firms covered by the PPP. Thus,table1revealsthestrikingcomprehensivenessofthepandemiclendingfacilitypolicy response. Nearlyallfirmsorentitiesfallintobusinesscategoriestargetedbypolicy,accounting for97percentofemployment,95percentofpayroll,and93percentofreceipts. Policycoverage includestheentireprivatesectorandalargeportionofgovernmententities,notionallyomitting only the federal government itself. This implies that most limitations on program coverage existed within firm or entity categories; for example, many firms that met size and legal form criteriaforMainStreetprogramsmayhavebeenineligibleduetoleveragerequirements. BecausetheeffectsofthePandemicRecessionhavebeenunevenlydistributedacrossindustries, we also explore program coverage by sector. Table 2 reports the share of sector employment that is at firms targeted by each program.12 We include nonemployers on table 2, all of whichweassumewereeligibleforthePPP(andeachofwhichaccountsforoneemployee). The distribution of sector activity across programs varies widely, with significant implications for how the programs affected different sectors. Since PPP eligibility requirements were minimal(asidefromsize-basedcriteria)andmanyPPPloanswereactuallygrants,sectorswith heavyconcentrationofactivityinPPP–eligiblefirms(suchasaccommodationandfoodservices or other services) were potentially able to benefit disproportionately from the economy-wide pandemic response. For sectors with significant activity in the Main Street category (such as utilities), policy benefits depended heavily on how well firms in those sectors met Main Street requirements on firm leverage and minimum loan sizes, as well as the costs of program loans. ThosesectorswithsubstantialactivityintheCCFscategory,suchasinformationorfinanceand insurance, benefited only to the extent that their firms had access to corporate bond markets andwereratedasinvestment-gradepriortoMarch22,2020.13 4 Direct lending programs during the Great Recession TheeconomicpolicyresponsetotherecentpandemicbytheCongressandAdministrationand the Federal Reserve has been unprecedented in its nature and scope. As in the Pandemic Recession, in response to the Great Recession of 2007–09, the Federal Open Market Committee loweredthefederalfundsratetoitseffectivelowerbound,andpursuedadditionalpoliciessuch 12. Becauseindustryisdeterminedattheestablishmentlevel,largefirmsmayhaveemployeesinmultiplesectors. Importantly,however,firmsize,whichdeterminestheclassificationbylendingprogram,isdeterminedatthe economy-widelevel. 13. FoursectorshaveeffectivelyzeroemploymentatfirmsintheCCFscategory. Agriculture,forestry,fishingand huntingdoeshavelargefirms,buttheiremploymentiswithinroundingerrorofzeropercentofsectoremployment. Weestimatethattherearenofirmswithatleast$5billioninreceiptsineithereducationalservicesorotherservices. Page8of54

Table 2. Share of Employment Covered by Program Main Muni Sector PPP Street CCFs LF 1. Agriculture,forestry,fishingandhunting 62 38 0 0 2. Mining,quarrying,andoilandgasextraction 49 40 11 0 3. Utilities 19 48 33 0 4. Construction 87 11 2 0 5. Manufacturing 45 37 18 0 6. Wholesaletrade 59 26 16 0 7. Retailtrade 43 17 41 0 8. Transportationandwarehousing 53 22 25 0 9. Information 34 29 36 0 10. Financeandinsurance 37 29 34 0 11. Realestateandrentalandleasing 86 11 3 0 12. Professional,scientific,andtechnicalservices 70 20 10 0 13. Managementofcompaniesandenterprises 12 52 36 0 14. Administrativeandwasteservices 42 43 15 0 15. Educationalservices 54 46 0 0 16. Healthcareandsocialassistance 51 36 13 0 17. Arts,entertainment,andrecreation 75 23 2 0 18. Accommodationandfoodservices 95 4 1 0 19. Otherservices 91 9 0 0 20. Publicadministration 0 0 0 81 Sources: Seetable1. as forward guidance and large-scale asset purchases of U.S. Treasuries and agency mortgagebacked securities. These responses were considered extraordinary at the time (Mishkin and White,2016). Amongotherpolicyresponsestoimprovefinancialstability,theFederalReserve also established facilities to improve market functioning and financial conditions, in particular in short-term funding markets, as it did in response to the Pandemic Recession. However, the Federal Reserve did not purchase longer-term corporate bonds of, or make longer-term loans directly to, any nonfinancial firms or state and local governments as it did through the CCFs, MainStreet, andtheMuniLF.Moreover, mostFederalgovernmentlendingwastargetedatthe financial system and toward households, though emergency loans were granted to a few firms intheautoindustryexperiencingfinancialdistress(seeBlinderandZandi,2015;Goolsbeeand Krueger, 2015).14 Digler (2020) describes some of the programs intended to increase lending to small businesses through the SBA during and in the aftermath of the Great Recession. The 14. Importantly, even in the Pandemic Recession, the Congress and Administration created programs to assist specificindustries,suchasairlines,whichwehaveomittedfromourbroaderdiscussion. Page9of54

appropriated sum of these small business lending programs is order of magnitudes lower than the nearly $1 trillion in mostly forgivable loans appropriated through the three rounds of the PPP. 5 Comparing our universe estimates to other sources Inthissection,wecompareouruniverseestimatestocomparabletabulationsfromtheBLSand severalprominentalternativedatasourcesonbusinessactivity. 5.1 The BLS business universe TheBLSmaintainsaregisterofbusinessesthatisalmostentirelyindependentoftheCensusBureausourcesthatunderliemostofourmainanalysis. ThemainBLSbusinessuniverseproduct istheQCEW,whichcoverstheuniverseofbusinessestablishmentsknowntostate(andfederal) unemployment insurance systems. These data provide most of the annual benchmark used for adjusting the popular monthly payroll survey, the Current Employment Statistics (CES). A key advantage of the QCEW relative to the SUSB is its timeliness: The QCEW is released with a delayofroughlytwoquarters.15 Separately,theBLSalsopublishestheCurrentPopulationSurvey (CPS), which provides a monthly measure of employment based on a survey of households (therebyavoidingtheindustryandorganizationalscoperestrictionsthatcharacterizebusinessbased data). Importantly, workers who hold multiple jobs are only counted once in official CPS tallies; we use information on first and second jobs from the March 2019 CPS microdata to create a count of jobs (see Bowler and Morisi, 2006). This adjustment renders CPS counts moreconsistentwiththebusiness-basedjobscountsinouruniverseestimates,theCES,andthe QCEW. On table 3, we tabulate BLS-based job counts and express the resulting totals as a percent of corresponding universe estimates from table 1. The first three columns report relative employmentestimates,whilethefourthandfifthcolumnsreportrelativeestablishmentcountsand annual payroll. For example, the first column indicates that CES nonfarm employment equals 97percent ofemployer jobsfromtable 1; therows forfarm employmentandself employment are left blank since those jobs are outside of the CES scope. QCEW coverage of the nonfarm universe is just slightly smaller than CES reflecting its administrative sources in the unemployment system, which does not cover all businesses (the largest omitted group being nonprofit businessesthatdonotparticipateinstateunemploymentsystems).16 15. The primary reasons we used the SUSB data for our main analysis are that SUSB has more detail on firm sizealongwithmoreconsistentdefinitionsoffirmconceptsthantheQCEWandthatSUSBcontainsinformationon firms’receipts. 16.ThescopeoftheCESisbroaderthanthescopeoftheQCEWbyabout3percent;thedifferencebetweenthetwo dataproductsreflectsactivitythatisnotsubjecttounemploymentinsurancecoverage,includingmanynonprofits Page10of54

Table 3. Comparison of Activity Measures with the BLS Business Universe Percentofcorrespondingmeasurefromtable1 Annual Employment Estab. payroll Class CES QCEW CPS QCEW QCEW 1. Wageandsalary 91 2. Nonfarm 97 95 92 122 94 3. Private 96 94 91 118 101 4. Government 101 100 95 68 5. Farm 33 47 4 92 6. Selfemployed 62 Sources: Authors’ calculations using Bureau of Labor Statistics data from the Current Employment Statistics, the Quarterly Census of Employment and Wages, and the Current Population Survey, as well as the sources from table1. Notes: Emptycellsindicatedataforcomparisonarenotavailable. CPS coverage is more comprehensive in terms of scope—the survey includes nonfarm and farmworkersaswellasselfemployedindividuals(whichwecomparetothenonemployertabulationsfromrow13oftable1);however,coveragewithincategoriesappearsmorelimitedthan othersources.17 PossiblereasonsforlowercountsintheCPSthanintheCESamongemployer businessesincludetheCPSexclusionofworkersininstitutions(e.g.,prisons)oronactivemilitarydutywhomightworkinunemploymentinsurance-coveredestablishments,workersbelow theageof16,andforeigncommuters(i.e.,membersofforeignhouseholdsthatworkinU.S.establishments); additionally, job-to-job transitions within the CES reference week can raise CES estimatesrelativetothoseintheCPS,andthejobsofworkerswithmorethantwojobsarenotall countedinourestimates. NotealsothatemploymentintheCPSisnotdirectlybenchmarked,as itisintheCES.18 BowlerandMorisi(2006)givesathoroughdiscussionofdifferencesbetween the CPS and the CES even within the intersection of their intended scopes. Farm employment creates additional measurement challenges when comparing CPS with table 1, such as differandreligiousorganizations,railroads,andvariousothersmallercategories. TheCESisbenchmarkedannuallytothe QCEWwithsupplementalinformationfromtheRRB(thesamesourceweuseforrailroaddataintable1)andthe CBP(whichisbasedontheCensusBureau’sBusinessRegisterunderlyingtheSUSB–basedestimatesontable1);see BureauofLaborStatistics(2020b)fordetail. OurestimatethatQCEWcoversabout95percentoftheemployment universeisbroadlyconsistentwiththeBLSestimatenotedinBureauofLaborStatistics(2020a). 17. DonigerandKay(2021)useCPSdatatostudytheeffectsofthePPPonlocalemploymentbyfirmsize; an advantageofusingCPSdataforthispurposeistheinclusionofnonemployers. TheauthorsfindthatPPPhadpositive effectsoverallbutthatdelaysinfundingdisbursementwerecostlyintermsofjobs,particularlyatthesmallestfirms. 18. EveryyeartheCPS’spopulationestimatesareadjustedusinginformationfromtheDecennialCensusandthe American Community Survey. This adjustment is done at a disaggregated demographic level but is not based on laborforce(e.g.,employment)status. Page11of54

ing classification across the sources (the Census of Agriculture data underlying our universe estimateslikelyincludemanyestablishmentswhoseprimaryactivityisnotfarming). Thelowcountofself-employedindividualsintheCPSrelativetothenonemployerestimate fromtable1partlyreflectsdifferencesinconcepts;indeed,BowlerandMorisi(2006)explicitly cautionagainstdirectcomparisonsofCPSself-employmentandnonemployerbusinesscounts.19 The businesses owned by self-employed individuals may appear in employer categories in table 1; more importantly for explaining the relatively low CPS count, many nonemployer businessesarelikelyownedbyCPSrespondentswhoreportbeingwageandsalaryworkers.20 Notably, establishment counts in the QCEW are higher than the counts we find in our universe estimates (we do not explicitly report our establishment counts on table 1). That QCEW employment counts are lower than, while QCEW establishment counts are higher than, Census Bureau counts is a known issue.21 Firm counts also differ somewhat between official data sources (not shown on table 3). For example, for 2017 the SUSB, which counts all firms with positive payroll any time in the year, reports 5,996,900 firms; for the same year the business dynamicsstatistics(BDS),whichusesthesameCensusBureausourcedatabutcountsonlyfirms withpositiveemploymentinMarch,reports5,252,110firms. Athirdproduct,theBLS’sBusiness EmploymentDynamics(BED),reports5,189,000firmsfor2017underasimilarcriteriontothe BDSbutwithfirmidentifierconceptsandindustryscopethatdifferfromCensusBureausources (seeHandwerkerandMason,2013,foranexplorationoffirmidentificationinBLSdata). Establishment counts aside, the BLS sources are generally below our universe estimates presentedintable1,consistentwithourgoalofdescribingtheentirebusinessuniverse. 5.2 Alternative data sources Anumberofresearchershaveturnedtoalternativedatasourcesonbusinessactivityduringthe Pandemic Recession, because they provide timely insights into economic activity that are not availablefromofficialdatasources. Whilesomeofthesedatasourcesprovidetimelyindicators 19. “The concepts and definitions used to create each of these data series are so different, however, that it is difficulttomakecomparisonsbetweenthetwo.” BowlerandMorisi(2006),p.35. 20. Abrahametal.(2018)linkBLShouseholdmicrodatawithInternalRevenueService(IRS)nonemployerbusinessdataandfindalargenumberofhouseholdsdonotself-reportasself-employedinBLSdatabutdohavebusiness income,agapthathasrisenovertime;theauthorsexploreanumberofotherdimensionsofself-employmentactivity inBLSandCensusBureaudata. 21. SeeBeckeretal.(2005)fordiscussionofdifferencesbetweenbusinessdatainBLSsourcesandtheCensus Bureausourcesunderlyingmostoftable1;discrepanciesbetweenthesesourcesarewellknown. Barnatchez,Crane andDecker(2017)reporttimeseriespatternsofemploymentandestablishmentcountsinBLSversusCensussources for1998–2014;theemploymentdiscrepancyisroughlystable,butforestablishmentcountsapositivegapbetween BLSandCensusdataopenedintheearly2000sandhasexpandedsincethen. Therisingdiscrepancyappearstobe drivenlargelybysmallestablishmentsandmayreflect,inpart,movementbetweentheemployerandnonemployer universes. Page12of54

Table 4. Comparison of Activity Measures across Alternative Data Sources Percentofcorrespondinguniversemeasure Employment Firms Estab. Compu- Home- D&B/ D&B/ D&B/ Class ADP stat base NETS NETS NETS 1. Privatenonfarm 20 22 120 76 78 2. Small 25 – 1 130 76 78 3. Medium 30 15 119 115 103 4. Large 10 87 84 54 49 Sources:ADP,Inc.(dataforFebruary2020),CompustatNorthAmerica(datafor2019),Homebase(dataforFebruary 2020),NationalEstablishmentTimeSeries(NETS)(datafor2014). Notes: ADP, Compustat, and Homebase are expressed as a percent of nonfarm private employer universe from table1. D&B/NETSareexpressedasapercentofnonfarmprivateemployerplusnonemployeruniverse. Empty cells indicate data for comparison are not available. “–” indicates a value below 1 percent. ADP values are roundedtonearest5percentagepointsforconfidentiality. ofeconomicactivity,theyalsofacelimitationsintermsofcoverageandrepresentativeness. Table4comparesactivitymeasuresfromseveralsourceswithouruniverseestimatesfromtable1. 5.2.1 ADP ADP, Inc. is the country’s largest payroll processor, accounting for roughly one-fifth of private payrolls (see the first column of table 4). ADP data have been used for tracking the economy during the pandemic: Cajner et al. (2020) study employment, wages, and business shutdown during 2020, Autor et al. (2020) study the effects of the PPP on employment, and Crane et al. (2021)studybusinessdeathduring2020. AkeystrengthofADPdataisitssignificantcoverage acrossbusinesssizeandindustry,makingthedatapotentiallyappropriateforstudyingtheentire firmsizedistributiondescribedintable1—particularlythroughtheuseofsamplingweights(see Cajneretal.,2018,foranexplorationofADP’srepresentativeness).22 Thesignificantcoverage andthehigh-frequencynatureofthedatamakeADPdatawellsuitedforstudyingbusinessand employmentdynamicsduringthepandemic. 5.2.2 Compustat PerhapsthemostpopularsourceofbusinessmicrodataisCompustat,whichprovidesfirm-level informationfrombalancesheets,incomestatementsandstatementsofcashflows.23 Thesedata 22. Weusetotalactiveemployment(i.e.,numberofworkersinpayrolldatabases)aggregatedbyparentcompany identifierinthepayrolldatabaseusedbyCajneretal.(2018). Theparentcompanyidentifierdiffersfromthecontrol unitidentifierusedinthatpaper,whichisfocusedonestablishmentcharacteristics. Wedefinefirmsizebinsusing activeemployment. 23. WeaccessthesedatafromS&PGlobal,CompustatNorthAmerica,viaWhartonResearchDataServices. Page13of54

arehighlyusefulduetotherangeofinformationavailableforfirmsoverseveraldecades. Compustatdataarelimitedtopubliclytradedfirms;therefore,intermsofthebreakdownprovided on table 1, Compustat data are best suited to studying the CCFs and Main Street.24 This can beseeninthesecondcolumnoftable4,wherecoverageofmediumand,especially,largefirms is substantial while coverage of small firms is negligible.25 Notably, however, Cororaton and Rosen(2020)studypubliclytradedfirmsthatreceivedPPPassistance(ofwhichtherewere273 atthetimetheirpaperwaswritten). 5.2.3 Small business data Homebaseisaprovideroftimeclockservicesforsmallbusinesses;therichmicrodataprovided byHomebasehavebeenusedinkeystudiesoftheearlyPandemicRecessionperiod(e.g.,Bartik et al., 2020) and in more recent work on business death (Crane et al., 2021). Kurmann, Lalé and Ta (2020) describe Homebase data in detail, including industry and size comparisons to QCEWuniversedata. Inshort,early2020Homebasedataincludeabout500,000(hourly)employeesatabout60,000establishments,almostallwithfewerthan50employeesandmostwith fewer than 20 employees, concentrated in local service sector activities. As shown on table 4, Homebase covers only 1 percent of small firm employment. Based on tabulations from Kurmann,LaléandTa(2020),theindustrieswiththestrongestHomebasecoverageareretailtrade (NAICS44–45)where0.5percentofsmallfirmemploymentiscovered;arts,entertainment,and recreation(NAICS71)with0.8percent;andaccommodationandfoodservices(NAICS72)with 1.9percent. Businessesin theseindustries wereparticularly vulnerableto socialdistancing, so Homebasehasbeenavaluableresourceforstudyingeffectsofthepandemiconsmallbusinesses (Dvorkin,2020). Given the concentration in smaller establishments (and firms), businesses found in HomebasedataaremostrelevantforthePPP—thoughHomebasebusinessesrepresentthesmallend of the distribution of PPP–eligible firms—and may lack the financial resources to benefit from MainStreetprograms. Granjaetal.(2020)useHomebasedata(amongothersources)tostudy theeffectsofPPPonsmallbusinessemploymentgrowthandarguethatPPPdisbursementswere poorlytargetedgeographically. Anotherpopularsourceofdataonsmallbusinessexperiencesin2020isWomply,anaggregatorofcreditcardtransactionsthatprovidesanalyticalservices. Smallbusinessesaredefined based on SBA criteria, which correspond roughly—but imperfectly—with our simplified approach in table 1. Womply data are used and described by Chetty et al. (2020). To our knowledge,therepresentativenessandcoveragepropertiesofWomplydatahavenotbeenthoroughly 24. TheeffectsoftheCCFscanalsobestudiedwithbondmarketdataorothermarketdata(e.g.,BordoandDuca, 2020). 25. Weuse2019Compustatdata. Seetheappendixforadiscussionofthesecalculations,whichrelyoninsights fromDinlersozetal.(2018). Page14of54

explored, though presumably the coverage is focused primarily on businesses that sell to consumers. Last, we want to note that the Bureau of Economic Analysis is currently developing new small business GDP accounts based on Census Bureau and other data. Highfill et al. (2020) describes these efforts and summarizes previous efforts to comprehensively measure the small businesseconomy. 5.2.4 D&B/NETS Dun&Bradstreet(D&B),abusinessmarketingcompany,maintainsalistofU.S.establishments intended to be comprehensive and inclusive of both employers and nonemployers. D&B data havebeenwidelyusedbyresearchersinthepast,oftenintheformoftheNationalEstablishment Time Series (NETS). NETS is a product of Walls & Associates and focuses on the integrity of longitudinal linkages in D&B data. We have NETS data ending in 2014; we detail how we adjustthedatatobecomparabletoouruniverseestimatesintheappendix. Inprincipal,D&B/NETSdatawouldbetheprimaryprivatesectoralternativetotheCensus BureauandtheBLSfordescribingthebusinessuniverse,withalargesampleofestablishmentleveldataonbothemploymentandrevenuealongwithfirmidentifiers. D&B/NETSarealsothe onlydatasourcesshownontable4thatincludenonemployers. Thespecificcoverageproperties of these data are difficult to determine, however. As shown on table 4, D&B/NETS data have somewhat more employment but far fewer firms and establishments than our estimates of the private sector universe such that it is unclear what set of businesses are actually covered by the data.26 Discrepancies in the distribution of activity across firm size class can arise from differingfirmidentifierdefinitions,buttheoveralldiscrepancieshavenotbeenwellexplained. Importantly, however, the biggest challenges to using D&B/NETS for studying policy have to do with data quality among covered businesses. D&B/NETS employment data are frequently imputed,andrevenuedataarealmostentirelyimputedsuchthatastudyoftheentireuniverse is not feasible (Barnatchez, Crane and Decker, 2017; Crane and Decker, 2020). Moreover, Crane and Decker (2020) show that D&B/NETS data are not well suited to studying business dynamics,sothetypeofhigh-frequencyanalysesmostusefulforstudyingthe2020pandemicare notfeasibleinthesedata(achallengefacedbyHubbardandStrain,2020). However,thedata maybeusefulforobtaininginformationaboutspecificfirmsparticipatinginfederalprograms. 26. Comparing table 4 with table 3 suggests that D&B/NETS discrepancies with BLS universe data are even larger,bothintermsofemploymentandestablishmentcounts. Onepossibleexplanationforexcessemploymentin D&B/NETSistheinclusionofinformal(e.g.,family)workers. Page15of54

5.2.5 Other alternative data sources Whilewehavelistedtheprimaryalternativedatasourcesthatarelikelytobeusedforstudying PandemicRecessionprograms,anumberofothersourceshavebeenusedaswell. Mostnotably, Chetty et al. (2020) employ data from Kronos and Paychex, combined with a D&B weighting scheme,tostudythePPP;thesedatasourcesarelesswellunderstoodthantheoneswedescribe above,buttheauthorsexplorerepresentativenessexplicitly. Chettyetal.(2020)notethat,like ADP, these sources are high frequency and have coverage across the business size distribution, thoughADPisalargersample(andeachofKronos,Paychex,andADPexcludenonemployers). Other sources may be in use of which we are not aware. Our analysis above suggests that researchers should clearly outline the universe targeted by their data (e.g., “small employer firms”);addressquestionsofrepresentativeness(withsamplingweightsifnecessary),frequency, andimputation;andaccountforexcludedportionsoftheuniversewhenaggregatingestimated policyeffects. 6 Conclusions OurestimatesoftheU.S.economicuniversearenearlyexhaustive,omittingonlyasmallhandful of business types. Assigning this economic activity to pandemic-related policies reveals a strikingfactaboutthepandemicresponse: almosteveryjobisassociatedwithfirmsorentities meeting notional eligibility criteria for a direct lending program. This implies that the dominantlimitationsonprogramcoverageexistedwithinentitycategoriesdefinedbylegalformand business size; for example, large for-profit businesses met basic qualifications for the CCFs but mayneverthelesshavelackedtheabilitytoissuebonds,andmedium-sizedbusinessesmetbasic MainStreetqualificationsbutmayhavebeenineligibleduetoleveragecriteria. ThedirectlendingpolicyresponsetothePandemicRecessionwassubstantiallybroaderthan that during the Great Recession, when such lending was largely limited to the financial sector andautomakers. Nevertheless,somecautioniswarrantedwhenconsideringthepolicysupport, as our mapping of the support provided by specific programs to areas of the economy is not exact. Forexample,weassignfirmstothePPPbasedona500-employeethresholdeventhough some firms with greater than 500 employees were eligible for the PPP under industry-specific SBAcriteria(thoughwedoincludealleligibleactivityintheaccommodationandfoodservices sector). Moreover, some small businesses may have been able to use a loan funded by Main StreetratherthanrelyingexclusivelyonthePPP. Ourtabulationshighlightthechallengesfacedbystatisticalagenciesseekingtomeasurethe economy. Taken together, the various data sources we use illustrate steep trade-offs between timelinessanddetail;forexample,SUSBdataproviderichdetailonfirmsizeandrevenue,but these data are only available with a lag measured in years (and the revenue data only appear Page16of54

in semi-decadal Economic Census years). We therefore rely heavily upon the QCEW to adjust 2017SUSBvaluesto2019estimates;QCEWdataaremoretimely(beingreleasedwithalagof just two quarters) but lack revenue and firm size detail (the BED, a close cousin of QCEW, has firmsizetabulations,buttheydonotreachthelargersizeclasseswestudy). Universalcomprehensiveness is also difficult to achieve; for example, railroads are excluded from the business lists at both the Census Bureau and the BLS for idiosyncratic historical reasons (Railroad Retirement Board, 2020), some nonprofits are excluded from the BLS lists due to laws governing unemploymentinsurance,farmdataarethepurviewoftheDepartmentofAgricultureandmeasured with concepts and definitions that are unique to the industry, and the nonemployer and self-employmentuniversesareinherentlydifficulttoconsistentlymeasureacrossagencies. The steeptrade-offsfacedbythestatisticalagenciesultimatelyarisefromthesourcedatauponwhich they must rely, some of which (e.g., IRS data) are generated based on concepts and timelines designedforpurposesotherthanoptimalmeasurement. More broadly, measurement of the economy depends heavily on the taxonomic framework availabletothosecollectingdata,withbasicconceptsaroundbusinessobjectives,industry,and location having significant implications for how the economy is measured. Alternative private sectordataprovideadvantagesintermsoftimelinessandareoftenimmunetotheintricatelegal constraintsthatgovernstatisticalagencycoverageanddefinitions,butsuchdatacomewithsubstantiallimitationsintermsofqualityandrepresentativeness;ultimately,thereisnosubstitute for scientifically produced statistics. The statistical agencies navigate these various trade-offs with impressive skill, ultimately producing statistics that are remarkably consistent even when basedondifferingsourcedata(seetable3). Ourmaincontributionistocombinedatasources basedontheirrespectiveadvantagestopaintacomprehensivepictureofthebusinessuniverse; achievingsuchcomprehensivenessisanimportantgoal,sincecraftingandevaluatingbusinessfacingpoliciesmustfirstbeginwithaccuratemeasurementoftheentirebusinessuniverse. Appendix A Data sources Our estimates combine various data sources to cover virtually the entire universe of U.S. economic activity. Our tabulations exclude only informal activities, private households, certain financial entities (pension, health, welfare, and vacation funds and trusts, estates, and agency accounts),andbusinessentitiesownedbyTribalgovernments. Page17of54

A.1 Employer businesses A.1.1 Statistics of U.S. Businesses Our main data source is the Statistics of U.S. Businesses (SUSB), which reports firm counts, employment,annualpayroll,andannualreceiptsbyfirmsize,whereafirmisacollectionofoperatingbusinesslocations(“establishments”)underunifiedownershiporoperationalcontrol.27 SUSBdatacovertheuniverseofemployerbusinessestablishmentsexcludingfarms(NAICS111 and 112), railroads (NAICS 482), private households, and public administration (NAICS 92). Certaingovernment-ownedbusinesses(i.e.,outsideofNAICS92)areincludedintheSUSBuniverse, though, as discussed in section A.1.2, we drop these. Nonemployer businesses—those businesseswithoutformalW-2employees—areexcludedfromSUSB. The 2017 SUSB data include tabulations by legal form of organization (LFO). We obtain the share of activity, by sector and firm size, associated with each legal form. We observe for-profitbusinesses(includingcorporations,partnerships,andsoleproprietorships),nonprofit businesses, and government-owned businesses. The latter are businesses engaged in regular business activities outside of public administration (e.g., government-owned hospitals). The SUSB LFO data have only one size category for firms with at least 500 employees, so we apply LFOsharesfromthisgrouptoboththe“medium”andthe“large”sizecategoriesontable1. If, for example, nonprofits are less likely than for-profit businesses to be large, we overstate nonprofitactivityinthelargecategoryandunderstatenonprofitactivityinthemediumcategory. Since SUSB data are for 2017, we adjust all activity measures to 2019 estimates. For establishment counts, employment, and payroll, we apply the sector-level growth rates of establishmentcounts,employment,andpayrollinQCEWfrom2017:Q1to2019:Q1(weapplythese growth rates to each firm size class; that is, we assume that activity rose by the same amount for each firm size class). For firm counts, we use the ratio of firm counts in 2019:Q1 to firm countsin2017:Q1fromBEDdata,whichareavailableonlyattheeconomy-widelevel(i.e.,not by sector). We obtain sector-level firm count growth rates with a two-step process: we begin byadjustingfirmcountsusingthegrowthrateofestablishmentcountsatthesectorlevel(from QCEW,2017to2019);wethenrevisethesesectorgrowthratestoensurethatsector-levelfirm countgrowthratesareconsistentwiththeaggregatefirmcountgrowthobtainedfromBED(i.e., wemultiplysector-levelestablishmentcountgrowthratesbytheratiooftheeconomy-widefirm count growth rate to the economy-wide establishment count growth rate). In other words, we assumethatfirmcountgrowthisdistributedacrosssectorsinthesamewaythatestablishment countgrowthisdistributedacrosssectors. Weadjustrevenueusingtheratioofnationalincome and product accounts (NIPA) value added by sector in 2019 to that in 2017. Importantly, our method for “growing” our economic activity estimates from 2017 to 2019 estimates abstracts fromthepossibilityofindividualfirmmovementsacrosssizeclasses;essentiallyweassumethat theproportionaldistributionofactivityacrossfirmsizeclassesisunchanged. A.1.2 Government-owned businesses in SUSB Asjustnoted,SUSBdataincludegovernment-ownedbusinessesincertainindustries: wholesale liquorestablishments,retailliquorstores,tobaccostores,bookpublishers,monetaryauthorities– 27. TheSUSBdataareavailableontheCensusBureauwebsiteathttps://www.census.gov/data/tables/ 2017/econ/susb/2017-susb-annual.html. Page18of54

central bank, federally-chartered savings institutions, federally-chartered credit unions, hospitals,gamblingindustries,andcasinohotels. IndiscussionswithCensusBureaustaff,wedetermined that most government-owned businesses present in SUSB are also counted in the Census of Governments and Annual Survey of Public Employment and Payroll figures underlying our measures of government activity. Therefore, we drop government-owned businesses from SUSB–based tabulations. This leads to a small amount of undercounting of activity, however, because certain government-owned businesses are not included in the Census of Governments and Annual Survey of Public Employment and Payroll figures. The main omissions are those businesses owned by Tribal governments as well as some banks and credit unions (except the BankofNorthDakota,whichisnotedinU.S.CensusBureau,2006).28 TheomissionofbusinessesownedbyTribalgovernmentsisunfortunateand,toourknowledge, represents an area of economic activity that is particularly difficult to quantify. These businessesareincludedinpublishedSUSBtotalsbutarenotseparatedfromothergovernmentownedbusinessesinanyrecentpublictabulationsofwhichweareaware. A(dated)U.S.Treasury report suggests that gaming and gambling hotel businesses represent a significant—and rapidly growing—share of these activities; gaming industries accounted for about 93 percent offederaltaxreturnsof,andabout79percentoftaxdollarspaidby,Tribe-ownedbusinessesin 2004,butotherbusinessesarealsosignificantcontributors,suchasagriculture,fishing,gasoline stations, smoke shops, restaurants, and banks (Treasury Inspector General for Tax Administration,2007). A.1.3 Railroads As noted above, SUSB excludes railroads (NAICS 482), so we estimate railroad activity as follows. WeobtaintotalrailroademploymentandpayrolldatafromtheRailroadRetirementBoard (RRB),whichalsoprovidesrailroaddataforCES.29WeuseMarch2019employmentfigures(to beconsistentwithQCEW–adjustedestimatesusedelsewhere),butpayrolldatafor2019arenot yetavailable. TobeconsistentwithourSUSBmethodology,weuse2017payrolldata,whichwe adjust to 2019 values using 2017–19 payroll-per-worker growth for NAICS 48–49 (transportationandwarehousing)fromQCEW;inotherwords,weassumethatpayrollperrailroadworker grewatthesamepaceaspayrollperworkerinthetransportationandwarehousingsectormore broadly. Wedistributeemploymentandpayrollacrossfirmsizeclassesasfollows. Employmentdata forClassIrailroads,mostofwhichhavemorethan15,000employeesandmorethan$5billion in revenue, are available from the Surface Transportation Board (STB); and data for Amtrak areavailablefromtheBureauofTransportationStatistics.30 Weconstructemploymentoflarge railroadfirmsasthesumofemploymentforthelargeClassIfirms. Thedifferencebetweenthis sum and the totals obtained from RRB represents the activity of medium and small firms; we 28. WeconfirmedtheseexclusionsfromtheCensusofGovernmentwithCensusBureaustaff. Wecouldnotfind themspelledoutinCensusofGovernmenttechnicaldocumentation. 29. https://www.rrb.gov/sites/default/files/2020-06/selectdt.pdf. 30. STB data are from https://www.rrb.gov/node/5129. The Class I railroads are Burlington Northern - SantaFe,CSXTransportation,CN/GrandTrunkCorporation,KansasCitySouthern,NorfolkSouthern,SooLine,and UnionPacific. WeclassifyCN/GrandTrunkCorporation,KansasCitySouthern,andSooLineasmediumfirmsinour taxonomy,whiletheremainingClassIrailroadsareclassifiedaslargefirms. Page19of54

shareoutthisresidualemploymentusingcorrespondingsharesforNAICS48–49fromSUSB.31 We then assign payroll to size classes by sharing out total payroll with the assumption that payrollperworkerisdistributedacrossrailroadfirmsizeclassesinthesamemanneraspayroll perworkerisdistributedacrossfirmsizeclassesforNAICS48–49asawhole. We obtain 2019 revenue data for Class I railroads from STB; this provides total revenue for the large railroads (the four Class I railroads that count as large). We impute small and medium railroad revenue using the ratio of revenue per worker for the three Class I firms that aremediumalongwithAmtrak,whichisamediumfirmaswell. TheRRBalsoprovidesalistingofallcoveredrailroadfirms,whichweusetoobtaina2018 firmcount;givenemploymenttrendsinthesector,weassumethefirmcountwasconstantfrom 2018to2019(AmtrakisnotinthisRRBlisting,soweaddit). Weassignfourofthesefirmsto the large category (these are the Class I firms that count as large), and we assign 25 of these firms to the medium category.32 Remaining railroad firms count as small. Finally, we estimate establishment counts using the ratio of establishments to firms, by firm size, from SUSB data for NAICS 48–49. Railroad numbers are included as part of NAICS 48–49 (transportation and warehousing) data in our main calculations. Our railroad methodology likely excludes some passengerrailroadfirms. A.1.4 Farms Both farms (NAICS 111, crop production) and ranches (NAICS 112, livestock production) are omittedfrommostCensusandBLSdataproducts. Weobtainfarmdatafromthe2017Censusof AgricultureproducedbytheDepartmentofAgriculture. Weusethenumberoffarmstoindicate the number of “firms” and establishments, though we emphasize that farm statistical concepts do not map well into Census Bureau firm taxonomy. Moreover, many farms may be owned by firms that also have establishments in nonfarm industries, in which case those firms are also counted in our SUSB tabulations. We use “hired workers” as our farm employment measure, omittingthelargecategoriesofcontractworkersandunpaidworkerstobeconsistentwiththe methodologyusedbyotherstatisticalagencies(notethatsomecontractworkersmayappearin thenonemployerdata). Ourfarmpayrollmeasureislaborcostsforhiredworkers. Receiptsdata are also available in the Census of Agriculture. We adjust farm counts, employment, and payrollto2019levelsusingbiennialDepartmentofAgriculturesurveydata(NationalAgricultural Statistics Services, 2021), and we adjust receipts using NIPA gross output data for the agricultural sector. Note that our farm data include both employers (i.e., farms with hired workers) andnonemployers(i.e., farmswithnohiredworkers); CensusofAgriculturedatasuggestthat roughlythree-fourthsoffarmshavenoformallyhiredworkers. Importantly, the Census of Agriculture counts as a farm any business producing at least $1,000worthofcropsorlivestock,regardlessofwhetherfarmingistheprimaryactivityofthat business location (whereas Census Bureau data assign establishments to industries based on the primary activity of each establishment).33 On the one hand, this means that there may be 31. Wedefinesmallrailroadsasthosewithfewerthan1,500employees,consistentwithSBAcriteria. 32. Itisdifficulttodeterminehowmanyfirmsbelonginthemediumcategory. Variousinternetsourcesindicate thatthereareroughly20ClassIIrailroads;weadoptthisfigurethenaddAmtrakandthefourClassIrailroadsthat aremediumtoproduceanestimateof25mediumrailroadfirms. 33. See https://www.nass.usda.gov/Publications/AgCensus/2017/Full_Report/Volume_1, _Chapter_1_US/usintro.pdf Page20of54

some double counting in table 1; that is, there may be some establishments (and, therefore, firms) classified in nonfarm industries in the SUSB that also appear as farms in the Census of Agriculture. On the other hand, some establishments engaged in agricultural support services maynotbecountedinany ofourdatasources, amoregeneralmeasurementlacunadescribed byDunnandHueth(2017). A.2 Nonemployers Nonemployers are those businesses that sell goods or services but do not have formal W-2 employees as recognized by the Social Security Administration. For example, ride-sharing drivers andfreelancejournalistsarelikelytocountasnonemployerbusinessesforstatisticalpurposes. The Census Bureau’s Nonemployer Statistics (NES) report the number and receipts of nonemployer businesses based on IRS data. The Bureau first drops businesses with negligible sales, typicallydefinedat$1,000butvaryingbyindustry(thethresholdisjust$1inconstruction),as well as businesses that can be identified as out of scope (such as estates and trusts). The NES data report the establishment count, which we use for a firm count under the assumption that eachnonemployerestablishmentisitsownfirm. Wealsousetheestablishmentcountasan“employment”countundertheassumptionthateachnonemployerhasonebusinessownerworking at the business, though it is important to note that we do not include nonemployed business ownersinemploymentcountsforemployerbusinesses. Similarly,thoughnonemployersdonot havepayroll,ownersofnonemployerbusinessesqualifiedforPPPassistancetocovertheirown compensation;toestimatenonemployer“payroll”weobtainsector-levelratiosofpayrolltoreceiptsamongemployerfirmswithlessthan$5millioninannualrevenue(SUSBdata),thenwe applytheseratiostoNESreceiptsfigures.34 Our nonemployer data are for 2017; we adjust these to 2019 values using the growth rate ofunincorporatedself-employmentintheCPS.Wenotethatmeasurementofthenonemployer (andself-employed)universeisdifficult,withdivergingestimatesbetweenCensusBureauand BLSdatasources(Abrahametal.,2018). A.3 Government Data on government economic activity is obtained from a number of sources. Federal civilian payrollisobtainedfromtheNIPAandisequaltofederalnondefensecompensationplusfederal civilian defense compensation; federal civilian employment is from the CES; federal revenues arefromtheOMBHistoricalTables, Table2.1. Armedforcesemploymentisobtainedfromthe DepartmentofDefenseActiveDutyMasterFileandisthesumofactivedutymilitary,National Guard,andarmedforcesreserveemployment. ArmedforcespayrollisobtainedfromtheNIPA andisequaltofederalmilitarycompensation. Stateandlocalgovernmentownsourcegeneral andutilityrevenuecomesfromthe2017State&LocalGovernmentFinanceHistoricalDatasets andTables,andstateandlocalemploymentandpayrollscomefromtheIndividualUnitFileof 34. Weusethepayroll-to-receiptsratioforfirmswithlessthan$5millioninreceiptsbecausemorethan99percent ofnonemployerbusinesseshavereceiptsbelow$5million. Choosingalowersizecut-offwouldtypicallyresultina higherratio,thoughtheratioisfairlystableinthe$100,000to$5millionrange(between30and27percentamong allsectorscombinedin2017). Analternativewouldbetolookonlyatemployerswithreceiptsbelow$100,000, whichhaveapayroll-to-receiptsratioofabout39percent;nonemployersinthisrevenuecategoryaccountforabout 89percentofallnonemployers,buttheyarelikelytoaccountforafarsmallershareoftotalnonemployerreceipts. Page21of54

the 2017 Annual Survey of Public Employment and Payroll. These payroll figures include only wages and salaries. In order to capture benefits, and thus be comparable to the private sector payrollfigures,weinflatethesefiguresbytheinverseofthepercentoftotalcompensationfrom wages. The percent of total compensation from wages is obtained from the Employer Costs for Employee Compensation, Historical Listing, National Compensation Survey. We inflate all 2017 values to 2019 values. For state and local own source general and utility revenue, we inflate by the ratio of 2019:Q1 to 2017:Q1 current tax revenue from the NIPA. For state and local payroll, we inflate by the ratio of 2019:Q1 to 2017:Q1 NIPA state and local government compensation. Forstateandlocalemployment,weinflatebytheratioofMarch2019toMarch 2017CESemployment. A.4 Alternative measures of the business universe A.4.1 QCEW The QCEW is the BLS counterpart to the Census Bureau’s Business Register that underlies the bulkofourmainuniverseestimates(BureauofLaborStatistics,2019). QCEWisbasedonstate unemployment insurance (UI) records and therefore provides an independent measure of the employer universe defined by the UI system. QCEW is a high-quality count of U.S. business establishments. TheindustryscopeofQCEWisslightlydifferentfromtheCensusBureau’suniversecoverage. In addition to excluding many (but not all) farms and all railroads, QCEW also excludes some religious groups (in NAICS 813), some domestic workers (NAICS 814), and some nonprofits; some of these exclusions vary by state. But QCEW includes most government-owned establishments at the local, state, and federal level. For the most part, QCEW defines establishments similarly to Census Bureau data products; however, in some cases establishments engaged in activitiescorrespondingwithmultipleindustriesappearasmultipleestablishmentsinQCEW(if separatepayrollrecordsarekeptandavailable). A.4.2 CES Current Employment Statistics (CES) is the workhorse monthly BLS payroll survey (Bureau of LaborStatistics,2020b). WhilethemonthlyCESestimateisderivedfromasample,ratherthan theuniverseofbusinesses,theseriesisbenchmarkedannuallytoreflectthetotalnonfarmbusinessuniverseasmeasuredbyQCEW,supplementedwithdataonindustriesthatareoutofscope forQCEW.Inparticular,dataonrailroads(NAICS482)aretakenfromtheRailroadRetirement Board,anddataonnonprofitswithoutunemploymentinsurancecoverageareeventuallytaken from County Business Patterns (CBP) (Bureau of Labor Statistics, 2020b). The annual benchmark is focused on March of a given year and is available in February of the following year; this makes CES the most timely official measure of total nonfarm employment. Importantly, CES scope excludes proprietors, the unincorporated self-employed, and domestic workers but includesgovernmentestablishments(exceptthemilitaryandcertainnationalsecurityagencies). Page22of54

A.4.3 CPS The CPS is a monthly survey of roughly 60,000 U.S. households that is published by the BLS (U.S. Census Bureau, 2019). As a household survey, CPS differs substantially from other data sourcesthatarebasedonsurveysorcensusesofbusinesses. TheCPSmeasuresemploymentof workersatanykindofbusiness,includingself-employedindividuals,agriculturalworkers,and unpaid family workers who are excluded from the CES, QCEW, SUSB, and CBP. Importantly, workerswhoholdmultiplejobsareonlycountedonceinofficialCPStallies,eventhougheach jobwould,intheory,becountedseparatelyinthebusinesssurveys. Weuseinformationonfirst andsecondjobsfromtheMarch2019CPSmicrodatatocreateacountofjobsforthepurposes oftable3. A.4.4 Compustat Compustatisawidelyusedpaneldatasetcontainingbalancesheet,cashflowandincomestatement data for public firms. We access these data from S&P Global, Compustat North America, viaWhartonResearchDataServices.35 Forouranalysis,wecleanthedataasfollows. Wedrop firms that do not have a headquarters of “USA” or a currency code of “USD.” We drop firms that have a 2-digit NAICS code of 92 or 99, a 3-digit NAICS code of 325, or do not have a NAICS code. We also drop observations with missing or negative sales or employment. After performingthesesteps,weidentifytheyearoftheobservationsanddropobservationsthatare duplicatesforagivenfirm(gvkey)foragivenyear. Finally,wescaletheemploymentandsales valuesby0.75and0.79,respectively,followingDinlersozetal.(2018)whofindthatCompustat employmentfiguresappeartooverstatefirms’U.S.activity. A.4.5 D&B/NETS We use raw NETS data from 2014 to construct the estimates of employment (emp14), firm (hqduns),andestablishment(dunsnumber)counts. Tobecomparabletothenonfarmprivate sector, we drop from NETS farms (NAICS 111 and 112), public administration (NAICS 92), and the postal service (NAICS 491110), but we keep any government-owned businesses outsideNAICS 92becausethey cannotbe identified. Wedonot performadditionalcleaning steps common in the literature (e.g., Barnatchez, Crane and Decker, 2017), which would further reducefirmandestablishmentcounts. WerenderNETSdataequivalenttoouruniverseestimates by first adjusting firm size cut-offs to be equivalent to 2017 SUSB firm size cut-offs. We adjust employmentcutoffsusingCESemploymentgrowthfor2014–17;the500-employeeand15,000employeecutoffsin2017translateto470-employeeand14,085-employeecut-offsin2014. We adjustthereceiptscut-offusingnominalGDPgrowthfor2014–17;the$5billioncut-offin2017 translates to $4.48 billion in 2014. After tabulating employment and establishment counts by firmsize,weadjustthesequantitiesto2019equivalentusingourusualmethodofapplyingCES employmentgrowthratesandQCEWestablishmentcountgrowthrates. 35. https://wrds-www.wharton.upenn.edu/pages/about/data-vendors/sp-global-marketintelligence/ Page23of54

Figure B1. CDF of Employment (all sectors) (a)Byfirmemploymentsize (b)Byfirmreceiptsize Fraction of activity Fraction of activity 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 SUSB data SUSB data Compustat data Compustat data 0.2 0.2 Estimated CDF Estimated CDF 15,000-employee cut-off $5 billion-receipt cut-off 0.0 0.0 102 103 104 105 106 100 101 102 103 104 105 Firm size, employment (log scale) Firm size, receipts (millions, log scale) Sources: Seetable1. B Program-specific adjustments to data B.1 Estimating Main Street firm size cut-offs For firms to have qualified for the Main Street programs they needed to have had fewer than 15,000 employees or less than $5 billion in annual revenue in 2019. Alas SUSB tabulations by firm size do not include cut-offs at these values. For firm employment size, SUSB includes a category bounded by 9,999 employees and 19,999 employees, while for firm revenue size, sector-level tabulations are reported only for firms with greater than $2.5 billion in revenue. We estimate the Main Street employment and revenue thresholds for each NAICS sector by estimatingcumulativedensityfunctions(CDFs)foreachactivitymeasure. SUSB data provide the firm size distribution in terms of both employment and revenue by NAICS sector; the blue circles in figure B1 plot the cumulative fraction of employment by firm size from the SUSB tabulations for all sectors. As shown by the purple diamond in panel (a), the 15,000 employee cut-off lies between two firm-size categories in SUSB, whereas in panel (b), the $5 billion receipts cut-off lies entirely inside the “$2.5 billion or higher” receipts size category. Toimproveourestimates,weaugmenttheSUSBdatawiththesizeofthetopfirmin eachsector,shownbythegreencircleattheupper-rightofeachpanel. Intheabsenceofmicrodatafortheentirefirmuniverse,itisnotobvioushowbesttomeasure thelargestfirmineachsector;thisobservationisnotprovidedwithSUSBdatabutisnecessary forCDFestimation. Weassumethatthelargestfirmineachsectorispubliclytradedandobtain each firm and its employment and revenue from Compustat, using Compustat’s industry codes to match top firms with sectors (scaling each firm’s size according to the rules of thumb found byDinlersozetal.,2018).36 Alimitationofthismethodisthatitreliesuponfirm-levelindustry 36. NofirmsinCompustatareinNAICS55,soweapplytheNAICS54maximumtoNAICS55. Wedonotapply theDinlersozetal.(2018)ruleofthumbintheagriculturesectorforreasonsofconvenience:thetopfirmweidentify wouldhavefewerthan20,000employeesaftertheadjustment(butgreaterthan20,000beforetheadjustment),and Page24of54

codes,whereasindustryshouldactuallybedefinedattheestablishmentlevel;thelargestfirmin asectoraslabeledbyCompustatdatamaynotactuallybethelargestfirmwithestablishments inthatsector.37 Weseparatelyestimatetheemployment-basedCDFs(i.e., P(E <e),whereeisfirmemploymentsize)andtherevenue-basedCDFs(i.e.,P(R<r)whererisfirmrevenuesize). Toestimate theshareofactivityatfirmswithfewerthan15,000employees,wecalculatetheempiricalCDF byemploymentsizeprovidedbySUSBdata(whichincludespointsat9,999and19,999employees) by NAICS sector then, omitting categories of firms with fewer than 5,000 employees (to improve fit in the area of focus), we fit the empirical CDF with both a linear quadratic (in firm size)formandalogitform(alsoquadraticinfirmsize).38 Foreachoffirmcounts,employment, payroll,andrevenueCDFs,weselecteitherthelinearquadraticorthelogitformbasedonroot mean square error.39 In practice, we always choose the logit form for the firm CDFs and the quadratic form for all others. (In a few cases, the quadratic specification produces a CDF that is not monotonic in the area of the cut-off, in which case we simply specify a linear function instead.) The orange line on figure B1, panel (a) shows—for the whole economy—the resulting employment CDF by firm employment size. With estimated sector-level CDF curves (in terms of employment size) in hand, we identify the share of firms, employment, payroll, and revenue associated with firms with fewer than 15,000 employees; this is illustrated by the purple diamond. To estimate the share of activity at firms with less than $5 billion in revenue, we use the samemethodologyonSUSBrevenuesizecategories,omittingcategoriesbelow$100millionin revenue. TheorangelineonfigureB1shows—forthewholeeconomy—theresultingemploymentCDFbyfirmreceiptssize,withthepurplediamondshowingthe$5billioncutoff. Wethen have sector-level estimates of (a) the share of firms, employment, payroll, and revenue that is at firms with fewer than 15,000 employees, and (b) the share of firms, employment, payroll, and revenue that is at firms with less than $5 billion in revenue. In line with the Main Street criteria, we identify the top of the medium size class by sector based on the higher of either 15,000employeesor$5billioninannualreceipts. Intwosectors,educationservicesandother services, the largest firm has annual receipts below $5 billion, so we assign no activity to the largeclassforthosesectors. theprocedureissimplerifthetopfirmhasmorethan20,000employeessinceSUSBdataprovideasizebincutoff at20,000. Weviewthisdiscretionaryinterventionassmallrelativetotheoverallmeasurementerrorpresentinour approach. 37. AnalternativemethodwouldbetoassumethatallextremelylargefirmshaveestablishmentsineveryNAICS sector,suchthatthelargestfirmwithactivityineachsectorissimplythelargestfirmintheUnitedStates. Inother words, perhaps Walmart is the largest firm in every sector. But attaching Walmart to each sector leads to highly irregularCDFsinsomecases,sowejudgethatourmethodofusingCompustatindustrycodestoidentifytopfirms bysectorgenerateslesserror. 38. Whilewecould,inprinciple,estimatetheentireCDF(i.e.,acrosstheentirefirmsizedistribution),inpractice wefindthistobeexcessivelydifficult.Forexample,whenfittingthesedistributionswithalognormaldensityassumption,itisdifficulttoobtainatightfitacrossthesizedistributionsuchthattheresearchermustchoosewhichareas tofitmostclosely(seeKondo,LewisandStella,2018,forevidenceonthefirmandestablishmentsizedistribution). Giventhesedifficulties,wefounditmoreproductivetofocusontightlyfittingtheneighborhoodofthedistribution closetotheMainStreet–basedcut-offsat15,000employeesor$5billioninrevenuewithanonparametricapproach. 39. WealsoestimatetheCDFsforestablishmentcountsforeachsector. Page25of54

FiguresB2–B21showresultsforeachNAICSsector. Ineachfigure,panel(a)—thetopfour charts—reports CDFs in terms of firm employment size, while panel (b)—the bottom four charts—reportsCDFsintermsoffirmreceiptsize. WeshowCDFsforeachoffirmcounts(top left), employment (top right), payroll (bottom left), and receipts (bottom right). Each figure hasthesamelegendasfigureB1: bluecirclesindicatefirmsizecutoffsreportedinSUSBdata, greencirclesindicatethesizeofthelargestfirminthesectorasinferredfromCompustatdata, purplediamondsshowMainStreet-relevantcutoffs(15,000employeesor$5billioninreceipts), andorangelinesshowestimatedCDFs. After allocating activity to size bins using this sector-specific CDF estimation methodology, wesumemployment,payroll,andreceiptsacrosssectorstoobtainthefiguresshownontable1 (wealsoobtainunreportedestablishmentcountsinthismanner). However,thismethodcannot beusedtoobtainall-sectorfirmcounts,sinceinSUSBdatafirmswithestablishmentsinmultiple sectors appear in each sector and would therefore be double counted in simple sums across sectors. This problem is particularly salient among larger firms; for example, in 2017 SUSB tabulations, firms with at least 20,000 employees operate in an average of roughly five NAICS sectors. Therefore,toobtaineconomy-widefirmcountsbyfirmsizecategories,weestimatean economy-wideCDFintermsoffirmcounts. Afterallotherprocessing(describedinappendixA), we adjust final firm counts for each size category using the ratio of firms in the two methods (economy-wideCDFestimationandsummationacrosssectorCDFestimations)toadjusttotals. B.2 PPP adjustments for NAICS 72 Asnotedinthemaintext,PPPwasavailabletofirmsofanysizewithestablishmentsintheaccommodationandfoodservicessector(NAICS72)thathavefewerthan500employees. SUSB data provide activity measures by firm size but not establishment size. We use the Census Bureau’s CBP for 2017 to calculate the activity of large firms which qualified for PPP under this specialcriterion. CBPdatarelyonthesamemicrodataasSUSB(theBusinessRegister)butprovidetabulationsofestablishments,employment,andpayrollbyestablishmentsize(dataonfirm counts or receipts are not present in CBP). We obtain counts of establishments, employment, andpayrollatestablishmentswithfewerthan500employeesinNAICS72(from2017CBP)then subtract the establishments, employment, and payroll of firms with fewer than 500 employees (from2017SUSB)toobtaintheestablishments,employment,andpayrollamongestablishments with fewer than 500 employees that are controlled by firms with at least 500 employees. We express the resulting activity in terms of shares of total activity for firms with at least 500 employees. We assume that the share of receipts associated with the relevant establishments is equal to the payroll share; in other words, we assume that the ratio of receipts to payroll is the same for establishments with fewer than 500 employees and larger establishments. We distribute the resulting activity between medium- or large-firm size bins using the share of establishments,employment,payroll,andreceiptsinthetwosizeclassesfromSUSB.Distributing firm counts is more difficult; we simply assume that all medium and large firms have at least oneestablishmentwithfewerthan500employees,suchthatallmediumandlargefirmsshould be assigned to the PPP since they each had access to it through at least on establishment (by assumption). Page26of54

C Extra material Figure1inthemaintextshowstotalemploymentbysectorbrokendownintofirmsizeandlegal formcategories. FigureC1doesthesamefortotalfirmcounts,illustratingtheextremeskewness of the firm size distribution even within sectors. The vast majority of firms are nonemployers, andmostemployerfirmsaresmallfor-profitenterprises(or,inthecaseoftheagriculturesector, smallfarms). FigureC2reportsthesedecompositionsforannualpayroll,revealingthemarkedly highpayrollofthepublicadministrationsectorrelativetoothersectors. Finally,figureC3shows these decompositions for annual receipts; wholesale trade shows extremely disproportionate receipts. Importantly,insectorslikewholesaletrade,receiptscanbehighwhilevalueaddedis muchlower. Page27of54

Figure B2. All sectors (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .9998 .9 .9996 .8 SUSB data .9994 Compustat data .7 Estimated CDF 15,000 employees .9992 .6 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .9 .8 .8 .7 .6 .6 .5 .4 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .999 .9 .8 .998 SUSB data .7 .997 Compustat data Estimated CDF .6 $5 billion receipts .996 .5 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .8 .6 .6 .4 .4 .2 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page28of54

Figure B3. NAICS 11 – Agriculture, forestry, fishing and hunting (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .9995 .98 .96 .999 SUSB data .94 .9985 Compustat data Estimated CDF .92 15,000 employees .998 .9 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .98 .98 .96 .96 .94 .94 .92 .92 .9 .9 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .95 .998 .9 SUSB data .996 Compustat data Estimated CDF .85 $5 billion receipts .994 .8 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .95 .95 .9 .9 .85 .85 .8 .8 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page29of54

Figure B4. NAICS 21 – Mining, quarrying, and oil and gas extraction (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .998 .9 .996 .8 SUSB data Compustat data .994 .7 Estimated CDF 15,000 employees .992 .6 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .9 .8 .8 .7 .6 .6 .5 .4 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .99 .8 SUSB data .98 Compustat data .6 Estimated CDF $5 billion receipts .97 .4 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .8 .6 .6 .4 .4 .2 .2 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page30of54

Figure B5. NAICS 22 – Utilities (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .995 .8 .99 .6 SUSB data Compustat data .985 Estimated CDF .4 15,000 employees .98 .2 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .8 .6 .6 .4 .4 .2 .2 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .8 .98 .6 .96 SUSB data .4 Compustat data .94 Estimated CDF .2 $5 billion receipts .92 0 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .8 .6 .6 .4 .4 .2 .2 0 0 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page31of54

Figure B6. NAICS 23 – Construction (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .9999 .98 .9998 .96 .9997 SUSB data .94 Compustat data Estimated CDF .92 .9996 15,000 employees .9 .9995 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .95 .95 .9 .9 .85 .85 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .95 .999 .9 .998 SUSB data Compustat data .85 Estimated CDF $5 billion receipts .997 .8 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .9 .9 .8 .8 .7 .7 .6 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page32of54

Figure B7. NAICS 31–33 – Manufacturing (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .999 .9 .998 .8 .997 SUSB data Compustat data .7 .996 Estimated CDF 15,000 employees .995 .6 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .9 .8 .8 .7 .6 .6 .4 .5 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .995 .8 .99 .985 SUSB data Compustat data .6 Estimated CDF .98 $5 billion receipts .975 .4 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .8 .6 .6 .4 .4 .2 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page33of54

Figure B8. NAICS 42 – Wholesale trade (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .999 .9 .998 SUSB data .8 Compustat data .997 Estimated CDF 15,000 employees .7 .996 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .9 .8 .8 .6 .7 .6 .4 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .99 .8 SUSB data .98 Compustat data .6 Estimated CDF $5 billion receipts .97 .4 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .8 .6 .6 .4 .4 .2 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page34of54

Figure B9. NAICS 44–45 – Retail trade (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .9995 .8 SUSB data .999 Compustat data .6 Estimated CDF 15,000 employees .9985 .4 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .9 .8 .8 .7 .6 .6 .5 .4 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .998 .8 .996 .6 SUSB data Compustat data .994 Estimated CDF .4 $5 billion receipts .992 .2 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .8 .6 .6 .4 .4 .2 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page35of54

Figure B10. NAICS 48–49 – Transportation and warehousing (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .998 .8 SUSB data .996 Compustat data .6 Estimated CDF 15,000 employees .994 .4 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .8 .6 .6 .4 .4 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .995 .8 SUSB data .6 .99 Compustat data Estimated CDF $5 billion receipts .4 .985 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .8 .6 .6 .4 .4 .2 .2 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page36of54

Figure B11. NAICS 51 – Information (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .998 .8 .996 SUSB data Compustat data .6 .994 Estimated CDF 15,000 employees .992 .4 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .8 .6 .6 .4 .4 .2 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .995 .8 .99 .6 SUSB data Compustat data .985 Estimated CDF .4 $5 billion receipts .98 .2 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .8 .6 .6 .4 .4 .2 .2 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page37of54

Figure B12. NAICS 52 – Finance and insurance (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .999 .8 SUSB data .998 Compustat data .6 Estimated CDF 15,000 employees .997 .4 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .8 .6 .6 .4 .4 .2 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .8 .995 .6 SUSB data Compustat data Estimated CDF .4 $5 billion receipts .99 .2 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .8 .6 .6 .4 .4 .2 .2 0 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page38of54

Figure B13. NAICS 53 – Real estate and rental and leasing (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .95 .9995 .9 .999 SUSB data .85 Compustat data .9985 Estimated CDF .8 15,000 employees .998 .75 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .95 .9 .9 .85 .8 .8 .75 .7 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .999 .9 .998 .997 SUSB data .8 Compustat data Estimated CDF .996 $5 billion receipts .7 .995 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .9 .9 .8 .8 .7 .7 .6 .5 .6 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page39of54

Figure B14. NAICS 54 – Professional, scientific, and technical services (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .9995 .9 .999 SUSB data .8 Compustat data .9985 Estimated CDF 15,000 employees .7 .998 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .9 .9 .8 .8 .7 .7 .6 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .999 .9 .998 .8 .997 SUSB data Compustat data .7 Estimated CDF .996 $5 billion receipts .995 .6 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .9 .9 .8 .8 .7 .7 .6 .6 .5 .5 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page40of54

Figure B15. NAICS 55 – Management of companies and enterprises (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .8 .95 .6 SUSB data .9 Compustat data Estimated CDF .4 15,000 employees .85 .2 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .9 .6 .8 .4 .7 .2 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .8 .9 .6 SUSB data .8 Compustat data .4 Estimated CDF $5 billion receipts .2 .7 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .8 .6 .6 .4 .4 .2 0 .2 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page41of54

Figure B16. NAICS 56 – Administrative and waste services (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .999 .8 .998 SUSB data Compustat data .6 .997 Estimated CDF 15,000 employees .996 .4 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .9 .8 .8 .7 .6 .6 .4 .5 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .998 .8 .996 SUSB data Compustat data .6 .994 Estimated CDF $5 billion receipts .992 .4 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .8 .6 .6 .4 .4 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page42of54

Figure B17. NAICS 61 – Educational services (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .999 .9 .998 .8 SUSB data Compustat data .997 Estimated CDF .7 15,000 employees .996 .6 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .9 .9 .8 .8 .7 .7 .6 .6 .5 103 104 105 106 103 104 105 106 Firm size, employment (log scale) Sources: Seetable1. Note: Therearenofirmsinthissectorwithgreaterthan$5billioninannualreceipts. Page43of54

Figure B18. NAICS 62 – Health care and social assistance (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .9995 .9 .8 .999 SUSB data Compustat data .7 .9985 Estimated CDF 15,000 employees .6 .998 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .9 .8 .8 .7 .6 .6 .5 .4 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .9 .999 .8 .998 SUSB data .7 Compustat data .997 Estimated CDF .6 $5 billion receipts .996 .5 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .8 .8 .6 .6 .4 .4 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page44of54

Figure B19. NAICS 71 – Arts, entertainment, and recreation (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .95 .9995 .9 .999 SUSB data .85 Compustat data .9985 Estimated CDF .8 15,000 employees .998 .75 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .95 .95 .9 .9 .85 .85 .8 .8 .75 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .999 .9 .998 SUSB data .997 Compustat data .8 Estimated CDF $5 billion receipts .996 .7 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .9 .9 .8 .8 .7 .7 .6 .6 .5 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page45of54

Figure B20. NAICS 72 – Accommodation and food services (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .9 .9995 SUSB data Compustat data .8 Estimated CDF 15,000 employees .999 .7 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .9 .9 .8 .8 .7 .7 .6 .6 103 104 105 106 103 104 105 106 Firm size, employment (log scale) (b)Byfirmreceiptsize Firm count Employment Fraction of activity Fraction of activity 1 1 .9995 .9 .999 SUSB data Compustat data .8 .9985 Estimated CDF $5 billion receipts .7 .998 102 103 104 105 102 103 104 105 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .9 .9 .8 .8 .7 .7 .6 .6 102 103 104 105 102 103 104 105 Firm size, receipts (millions, log scale) Sources: Seetable1. Page46of54

Figure B21. NAICS 81 – Other services (a)Byfirmemploymentsize Firm count Employment Fraction of activity Fraction of activity 1 1 .9998 .98 .96 .9996 SUSB data .94 .9994 Compustat data Estimated CDF .92 15,000 employees .9992 .9 103 104 105 106 103 104 105 106 Annual payroll Annual receipts Fraction of activity Fraction of activity 1 1 .95 .95 .9 .9 .85 103 104 105 106 103 104 105 106 Firm size, employment (log scale) Sources: Seetable1. Note: Therearenofirmsinthissectorwithgreaterthan$5billioninannualreceipts. Page47of54

Figure C1. Number of U.S. Businesses and Governments, 2019, by Sector and Class Agriculture, forestry, fishing and hunting Mining, quarrying, and oil and gas extraction Utilities Construction Manufacturing Wholesale trade Retail trade Transportation and warehousing Information Finance and insurance Real estate and rental and leasing Professional, scientific, and technical services Management of companies and enterprises Administrative and waste services Educational services Health care and social assistance Arts, entertainment, and recreation Accommodation and food services Other services Public administration 0 1,000 2,000 3,000 4,000 5,000 Thousand Private, for-profit, small Private, nonprofit, medium Private, nonemployer Govt., local Private, for-profit, medium Private, nonprofit, large Govt., federal, civilian Private, for-profit, large Private, farm, small Govt., federal, armed forces Private, nonprofit, small Private, farm, medium Govt., state Sources: Seetable1. Notes:Transparentbarsindicateclassesthatarenotcoveredbyoneofthefourdirectlendingprogramsweconsider. Page48of54

Figure C2. AnnualPayrollofU.S.BusinessesandGovernments,2019,bySectorandClass Agriculture, forestry, fishing and hunting Mining, quarrying, and oil and gas extraction Utilities Construction Manufacturing Wholesale trade Retail trade Transportation and warehousing Information Finance and insurance Real estate and rental and leasing Professional, scientific, and technical services Management of companies and enterprises Administrative and waste services Educational services Health care and social assistance Arts, entertainment, and recreation Accommodation and food services Other services Public administration 0 500 1,000 1,500 2,000 2,500 Billion Private, for-profit, small Private, nonprofit, medium Private, nonemployer Govt., local Private, for-profit, medium Private, nonprofit, large Govt., federal, civilian Private, for-profit, large Private, farm, small Govt., federal, armed forces Private, nonprofit, small Private, farm, medium Govt., state Sources: Seetable1. Notes:Transparentbarsindicateclassesthatarenotcoveredbyoneofthefourdirectlendingprogramsweconsider. Page49of54

Figure C3. Annual receipts of U.S. Businesses and Governments, 2019, by Sector and Class Agriculture, forestry, fishing and hunting Mining, quarrying, and oil and gas extraction Utilities Construction Manufacturing Wholesale trade Retail trade Transportation and warehousing Information Finance and insurance Real estate and rental and leasing Professional, scientific, and technical services Management of companies and enterprises Administrative and waste services Educational services Health care and social assistance Arts, entertainment, and recreation Accommodation and food services Other services Public administration 0 1 2 3 4 5 6 7 8 9 10 Trillion Private, for-profit, small Private, nonprofit, medium Private, nonemployer Govt., local Private, for-profit, medium Private, nonprofit, large Govt., federal, civilian Private, for-profit, large Private, farm, small Govt., federal, armed forces Private, nonprofit, small Private, farm, medium Govt., state Sources: Seetable1. Notes:Transparentbarsindicateclassesthatarenotcoveredbyoneofthefourdirectlendingprogramsweconsider. Page50of54

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