Across the Universe: Policy Support for Employment and Revenue in the Pandemic Recession (2020-099)
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Finance and Economics Discussion Series paper 2020-099 from the Federal Reserve Board, by Ryan A. Decker, Robert J. Kurtzman, Byron F. Lutz and Christopher J. Nekarda, dated May 27, 2021. The abstract says the authors use data from 14 government sources to estimate U.S. economic activity by sector, legal form of organization and firm size. They relate four direct lending programs to those classes: the Paycheck Protection Program, the Main Street Lending Program, the Corporate Credit Facilities and the Municipal Lending Facilities. The classes targeted by the programs account for 97 percent of total U.S. employment, though entity-specific financial criteria limited coverage within specific programs.
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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 Re-
cession,” Finance and Economics Discussion Series 2020-099r1. Washington: Board of Gov-
ernors 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
Using data from 14 government sources, we develop comprehensive estimates of U.S. eco-
nomic activity by sector, legal form of organization, and firm size to characterize how four
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
majority of the economic universe.
JEL codes: C83, E20, E58
Keywords: employment, activity estimates, direct lending programs, Paycheck Protection
Program, PPP, Main Street, Corporate Credit Facilities, alternative data
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 of the Federal Reserve System. We thank Andreas Lehnert for invaluable
discussions and continued support of this project, and we thank Tim Dore, Elizabeth Handwerker, Javier Miranda,
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; the Cajner et al. (2020) project was made possible by ADP and Matt Levin, Ahu Yildirmaz, and Sinem
Buber. We thank Ray Sandza, Adam Liem, and Homebase for providing Homebase data and technical support. U.S.
Census Bureau staff graciously provided several special data tabulations and fielded many questions from us.
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 government-
mandated 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
action of provisioning direct assistance to firms and government entities across nearly the entire
universe of economic activity, including categories of businesses that are not typically the focus
of direct lending programs.
A key challenge for research and analysis of these policies is measuring the economic uni-
verse they target. We present estimates of economic activity in the United States that we par-
tition 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 ac-
counts, railroad regulators, and other sources. We then assess how four government direct
lending programs — the Paycheck Protection Program (PPP), the Main Street Lending Program
(“Main Street”), the Corporate Credit Facilities (CCFs), and the Municipal Lending Facilities
(Muni LF) — relate to our activity estimates.
While tabulating the universe of economic activity may seem a trivial task, it is not. Indeed,
this kind of descriptive exercise is rarely done. In particular, no single product of U.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
unique in that we can map to the four direct lending programs we consider while still capturing
nearly the entirety of economic activity.
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,
the classes targeted include most economic activity, ignoring entity-specific financial criteria that
reduce effective program coverage. This response is substantially broader than that mounted in
response to the Great Recession.
A number of alternative data sources on business activity, such as those compiled by pri-
vate companies, have gained prominence during the Pandemic Recession because they provide
timely insights that are not available from official data sources. Unlike official statistics, how-
ever, alternative data generally come with concerns about coverage and representativeness. We
provide critical context for users of several alternative data sources — in particular ADP, Com-
pustat, 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
Page 1 of 54
alternative data cover substantially smaller portions of the economy than either the Bureau of
Labor Statistics (BLS) data or our universe estimates.
2 Overview of U.S. economic activity
We construct measures of the number of entities, employment, annual payroll, and gross re-
ceipts 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. Quantify-
ing 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
nonemployer businesses, and each level of government). We thus combine data from a variety
of official sources. Our accounting captures virtually all economic activity, with the only excep-
tions 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
(for example, we use BLS Quarterly Census of Employment and Wages (QCEW) data to translate
2017 values to 2019 estimates); the appendix provides detail on our sources and methods.
Table 1 presents our estimates of the universe of economic activity and lists all data sources.
Within the private sector, businesses are divided into categories by a combination of number of
employees and annual revenue:
• Small firms are defined as those with fewer than 500 employees, regardless of revenue,
unless otherwise noted;
• Medium firms are defined as those with at least 500 employees, but fewer than 15,000 em-
ployees or less than $5 billion in annual gross receipts;
• Large firms are defined as those with at least 15,000 employees and $5 billion or more
in annual receipts.1
We separate out private activity by size class at for-profit and nonprofit private businesses. Al-
though 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 in-
formation on nonemployers. Last, we separate out government activity across federal, state,
and local governments.
1. Cutoffs for activity below 15,000 employees or $5 billion in receipts are not available in our source data; the
appendix describes how these are estimated.
Page 2 of 54
Table 1. Activity Measures at U.S. Businesses and Governments, 2019
Annual Annual
Firms or Employment payroll receipts
Class or program entities (millions) (billions) (billions)
By class*
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. Armed forces 2.0 163
18. State 51 5.5 479 1,515
19. Local 90,075 14.3 1,176 1,399
By program*
20. PPP 33,801,428 94.4 3,441 16,477
21. Main Street 93,517 40.3 2,594 14,483
22. CCFs 496 24.8 1,678 11,695
23. Muni LF 90,126 19.8 1,655 2,914
Sources: Annual Survey of Public Employment and Payroll, Bureau of Transportation Statistics, County Business
Patterns, Census of Agriculture, Census of Governments, Current Employment Statistics, Department of Defense
Active Duty Master File, Nonemployer Statistics, national income and product accounts, Office of Management
and Budget Historical Tables, Railroad Retirement Board, State & Local Government Finance Historical Datasets
and Tables, Surface Transportation Board, Statistics of U.S. Businesses, and authors’ calculations. See the ap-
pendix for detail on data construction.
Notes: Blue shading indicates covered primarily by the PPP. Orange shading indicates covered primarily by Main
Street. Green shading indicates covered primarily by the CCFs. Yellow shading indicates covered by the Muni LF.
For state and local governments, receipts refers to own-source general and utility revenue.
* Totals by program will not match the sum of shaded rows by class because nearly all medium and large for-profit
firms in the accommodation and food services sector (NAICS 72) are eligible for the PPP.
Page 3 of 54
In the Census Bureau data underlying the bulk of table 1, a private sector firm is defined
based on operational control or ownership; both our firm counts and our firm size classes reflect
this definition. However, we emphasize that, in some cases, other firm definitions may be used
to determine lending program eligibility, such as definitions based on tax identifiers which may
allow multiple subsidiaries of a firm to access programs independently. In this respect, our
tabulations of firm counts may understate the number of entities qualifying for programs, even
within the private sector.2
For-profit private businesses represent most non-government economic activity, as compared
with nonprofits, farms, and nonemployers. There are 160 million employees at private busi-
nesses, 115 million of which are at for-profit businesses. Although entity and, to some extent,
employment counts are skewed toward small firms, annual payroll and receipts are more evenly
distributed across size classes.
Nonprofit businesses account for less than 10 percent of employment in most sectors; that
said, they constitute a larger share of activity in certain sectors of the economy. Among employer
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, and 28 percent in arts, entertainment, and recreation (which includes museums).
The United States has about 2 million farms (including ranches) employing 2.3 million hired
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 Ad-
ministration (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
overstate farming firms) or that also have activity in other sectors (such that they appear in the
firm counts elsewhere in table 1). These possibilities would not necessarily result in misclassi-
fication of employment, payroll, or receipts, since these measures are categorized at the farm
2. Firms are notoriously difficult to define and count due to sometimes complicated structures of ownership and
control. Moreover, many large firms have operations in multiple industries and may operate under any number of tax
identifiers under which they might apply for government programs. There is likely some double counting of activity
between farms and other classes, as many farms are nonemployers or may be subsidiaries of firms with activity in
other industries. Thus, our firm-count estimates should be treated with greater uncertainty than the other activity
measures. We outline our methodology for estimating firm counts in the appendix.
3. In addition to formal hired workers, farms also rely heavily on unpaid labor (e.g., family) and contract workers
which we do not include in table 1; in the 2017 Census of Agriculture, farms reported roughly 2 million unpaid
workers and 8 million contract workers (some of the latter may also appear in nonemployer data).
4. Farm size criteria are complicated, but the $1 million receipts cutoff is a standard SBA “small business” criterion
that we can easily measure in Census of Agriculture data. The upper size category in the Census of Agriculture is
$5 million in annual receipts; in discussions with Department of Agriculture staff, we determined that the existence
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, which means their firm counts are represented elsewhere on the table. That said, the farm employment,
payroll, and receipts of such large firms would be misclassified as medium in the farms category on table 1.
Page 4 of 54
or establishment level; but farming activity may be misclassified in terms of our size categories
if, for example, a large number of small farms are owned by a firm with revenue above $1 mil-
lion. In short, measurement of farms is based upon different concepts from measurement of the
rest of the business universe, which suggests caution should be exercised when inferring farm
eligibility for lending programs.
Nonemployers are businesses that produce goods or services but do not have formal em-
ployees. 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 busi-
nesses — 26 million — are nonemployer businesses; however, these businesses account for only
$1.4 trillion in annual receipts, which is equivalent to about 4 percent of for-profit employer
revenue.5 Nevertheless, self-employment is an important source of income for millions of Amer-
icans. 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 ap-
pendix for details.
The government activity figures on table 1 include nearly all government activity, includ-
ing the Postal Service, the armed forces, and government-owned businesses in many industries.
State and local governments employ nearly 20 million workers, far more than the federal gov-
ernment. 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
entities such as transit authorities and public hospitals.
In figure 1, we present sector-level employment decompositions mimicking those on table 1
(sectors roughly correspond to 2-digit NAICS codes). The largest sector is public administra-
tion, which has nearly 25 million employees, most of which are concentrated in state and local
governments. Eight other sectors have close to or more than 10 million employees. Of these
other sectors, employment is highest in the health care and social assistance sector, followed by
retail trade. These two sectors demonstrate the substantial heterogeneity in size and legal form
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 em-
ployment in nonprofits and a significant fraction of employment at large firms. In appendix C
we show versions of this figure based on firm counts, payroll, and receipts.
5. Counts of nonemployer businesses should not be thought of as counts of self-employed persons. Many business
owners control multiple nonemployer entities (for example, some landlords may own multiple rental properties in
separate nonemployer businesses). Moreover, nonemployer businesses include “side gigs” of workers with other
income. We discuss nonemployer versus self-employment distinctions further below.
Page 5 of 54
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: See table 1.
Notes: Transparent bars indicate classes that are not covered by one of the four direct lending programs we consider.
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— for example, although some medium businesses were ineligible for Main Street due to
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. For example, all blue-shaded rows in the upper panel correspond to the PPP.6 We assign
all state and local government entities to the Muni LF, even though some of these organizations
(e.g., public hospitals) were also eligible for the nonprofit Main Street facilities.
6. PPP eligibility was determined by firm size, not establishment size, with the exception of establishments in
NAICS 72; see section B.2 in the appendix. SBA documentation states, “For purposes of the determining the number
of employees of an applicant to the Paycheck Protection Program, the applicant is considered together with its
affiliates. . . . Concerns and entities are affiliates of each other when one controls or has the power to control the
other, or a third party or parties controls or has the power to control both.” Affiliation is defined in a manner similar
to the Census Bureau definition of firms; see U.S. Small Business Administration (2020).
Page 6 of 54
It is important to note that firms of any size in the accommodation and food services sector
that had an establishment with fewer than 500 employees were also eligible for the PPP.7 We
include activity of medium for-profit firms and farms as the target population for the Main Street
program (line 21). Although smaller firms were also eligible for Main Street, we do not include
them on line 19 for simplicity; to obtain an extreme upper bound on Main Street eligibility
simply combine lines 20 and 21.8 Similarly, there was no minimum size criterion for CCFs, but
we have identified the target population as only the large for-profit firms.9
Most of the activity in the top panel of table 1 is covered by one of the programs listed on the
bottom panel. Small organizations of any kind — for-profit and nonprofit — were included in
the PPP. Medium for-profit and nonprofit businesses (as well as many small ones) were targeted
by Main Street. Large businesses and nonprofits, which typically have access to the corporate
bond or syndicated loan markets, were covered by the CCFs.10 State and local governments, 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 — were similarly important to the PPP. Main Street was targeted at less than 1 percent
7. We interpret the PPP eligibility criteria to imply that all firms with fewer than 500 employees were eligible,
though some additional firms meeting standard industry-specific SBA “small business” definitions were eligible as
well, and certain smaller firms were excluded. Tracking special industry-specific “small business” definitions is in-
feasible for our analysis, so we focus on the simple 500-employee firm-size cutoff, thereby potentially modestly
understating the activity that is eligible for the PPP. In addition, we include the eligible portions of medium- and
large-firm activity in NAICS 72 in the PPP line on table 1. We estimate that the vast majority of NAICS 72 is eligible
for the PPP: More than 99 percent of establishments in that sector have fewer than 500 employees, and these account
for about 94 percent of employment and payroll in the sector; see the appendix for details. The SBA published the
full list of criteria making firms ineligible in the Interim Final Rule (Federal Register, 2020).
8. Main Street had eligibility criteria that could have limited take-up, especially among small firms. In particular,
loan-size minimums and leverage limits varied by facility. In October 2020, the minimum loan size across facilities
was changed to $100,000 (Federal Reserve Board, 2020a) and remained at that level until the program ceased
making new loans in January 2021.
9. Among other eligibility criteria, firms needed to be rated above investment grade as of March 22, 2020 (Federal
Reserve Bank of New York, 2020). Our examination of eligible firms that meet the ratings criteria suggest that almost
all firms meeting these criteria are large. That said, we note that large firms are rare, representing only a tiny fraction
of all firms, so there may be many firms outside the “large” category that may be in scope for the CCFs. We also note
that in 2019 there were roughly 3,500 publicly traded firms in Compustat, most of which are in our medium size
category.
10. According to the Federal Reserve Bank of New York’s FAQs for the CCFs (Federal Reserve Bank of New York,
2020), nonprofits had access to the CCFs.
11. State and local government entities directly eligible for the Muni LF included all U.S. states, the District of
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. In order to be directly eligible, governments must have also been able to satisfy minimum credit rating
requirements. Government entities not directly eligible for the Muni LF were, in principal, indirectly eligible for the
facility as any directly eligible participant may have used the proceeds from Muni LF loans to “purchase similar notes
issued by, or otherwise to assist, political subdivisions and other governmental entities of the relevant State, City, or
County.” See Federal Reserve Board (2020b).
Page 7 of 54
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, table 1 reveals the striking comprehensiveness of the pandemic lending facility policy
response. Nearly all firms or entities fall into business categories targeted by policy, accounting
for 97 percent of employment, 95 percent of payroll, and 93 percent of receipts. Policy coverage
includes the entire private sector and a large portion of government entities, notionally omitting
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
criteria for Main Street programs may have been ineligible due to leverage requirements.
Because the effects of the Pandemic Recession have been unevenly distributed across indus-
tries, we also explore program coverage by sector. Table 2 reports the share of sector employ-
ment that is at firms targeted by each program.12 We include nonemployers on table 2, all of
which we assume were eligible for the PPP (and each of which accounts for one employee).
The distribution of sector activity across programs varies widely, with significant implica-
tions for how the programs affected different sectors. Since PPP eligibility requirements were
minimal (aside from size-based criteria) and many PPP loans were actually grants, sectors with
heavy concentration of activity in PPP–eligible firms (such as accommodation and food services
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.
Those sectors with substantial activity in the CCFs category, such as information or finance and
insurance, benefited only to the extent that their firms had access to corporate bond markets
and were rated as investment-grade prior to March 22, 2020.13
4 Direct lending programs during the Great Recession
The economic policy response to the recent pandemic by the Congress and Administration and
the Federal Reserve has been unprecedented in its nature and scope. As in the Pandemic Re-
cession, in response to the Great Recession of 2007–09, the Federal Open Market Committee
lowered the federal funds rate to its effective lower bound, and pursued additional policies such
12. Because industry is determined at the establishment level, large firms may have employees in multiple sec-
tors. Importantly, however, firm size, which determines the classification by lending program, is determined at the
economy-wide level.
13. Four sectors have effectively zero employment at firms in the CCFs category. Agriculture, forestry, fishing and
hunting does have large firms, but their employment is within rounding error of zero percent of sector employment.
We estimate that there are no firms with at least $5 billion in receipts in either educational services or other services.
Page 8 of 54
Table 2. Share of Employment Covered by Program
Main Muni
Sector PPP Street CCFs LF
1. Agriculture, forestry, fishing and hunting 62 38 0 0
2. Mining, quarrying, and oil and gas extraction 49 40 11 0
3. Utilities 19 48 33 0
4. Construction 87 11 2 0
5. Manufacturing 45 37 18 0
6. Wholesale trade 59 26 16 0
7. Retail trade 43 17 41 0
8. Transportation and warehousing 53 22 25 0
9. Information 34 29 36 0
10. Finance and insurance 37 29 34 0
11. Real estate and rental and leasing 86 11 3 0
12. Professional, scientific, and technical services 70 20 10 0
13. Management of companies and enterprises 12 52 36 0
14. Administrative and waste services 42 43 15 0
15. Educational services 54 46 0 0
16. Health care and social assistance 51 36 13 0
17. Arts, entertainment, and recreation 75 23 2 0
18. Accommodation and food services 95 4 1 0
19. Other services 91 9 0 0
20. Public administration 0 0 0 81
Sources: See table 1.
as forward guidance and large-scale asset purchases of U.S. Treasuries and agency mortgage-
backed securities. These responses were considered extraordinary at the time (Mishkin and
White, 2016). Among other policy responses to improve financial stability, the Federal Reserve
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,
Main Street, and the Muni LF. Moreover, most Federal government lending was targeted at the
financial system and toward households, though emergency loans were granted to a few firms
in the auto industry experiencing financial distress (see Blinder and Zandi, 2015; Goolsbee and
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
specific industries, such as airlines, which we have omitted from our broader discussion.
Page 9 of 54
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
In this section, we compare our universe estimates to comparable tabulations from the BLS and
several prominent alternative data sources on business activity.
5.1 The BLS business universe
The BLS maintains a register of businesses that is almost entirely independent of the Census Bu-
reau sources that underlie most of our main analysis. The main BLS business universe product
is the QCEW, which covers the universe of business establishments known to state (and federal)
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
delay of roughly two quarters.15 Separately, the BLS also publishes the Current Population Sur-
vey (CPS), which provides a monthly measure of employment based on a survey of households
(thereby avoiding the industry and organizational scope restrictions that characterize business-
based 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
more consistent with the business-based jobs counts in our universe estimates, the CES, and the
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 em-
ployment estimates, while the fourth and fifth columns report relative establishment counts and
annual payroll. For example, the first column indicates that CES nonfarm employment equals
97 percent of employer jobs from table 1; the rows for farm employment and self 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 unemploy-
ment system, which does not cover all businesses (the largest omitted group being nonprofit
businesses that do not participate in state unemployment systems).16
15. The primary reasons we used the SUSB data for our main analysis are that SUSB has more detail on firm
size along with more consistent definitions of firm concepts than the QCEW and that SUSB contains information on
firms’ receipts.
16. The scope of the CES is broader than the scope of the QCEW by about 3 percent; the difference between the two
data products reflects activity that is not subject to unemployment insurance coverage, including many nonprofits
Page 10 of 54
Table 3. Comparison of Activity Measures with the BLS Business Universe
Percent of corresponding measure from table 1
Annual
Employment Estab. payroll
Class CES QCEW CPS QCEW QCEW
1. Wage and salary 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. Self employed 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
table 1.
Notes: Empty cells indicate data for comparison are not available.
CPS coverage is more comprehensive in terms of scope — the survey includes nonfarm and
farm workers as well as self employed individuals (which we compare to the nonemployer tabu-
lations from row 13 of table 1); however, coverage within categories appears more limited than
other sources.17 Possible reasons for lower counts in the CPS than in the CES among employer
businesses include the CPS exclusion of workers in institutions (e.g., prisons) or on active mili-
tary duty who might work in unemployment insurance-covered establishments, workers below
the age of 16, and foreign commuters (i.e., members of foreign households that work in U.S. es-
tablishments); additionally, job-to-job transitions within the CES reference week can raise CES
estimates relative to those in the CPS, and the jobs of workers with more than two jobs are not all
counted in our estimates. Note also that employment in the CPS is not directly benchmarked, as
it is in the CES.18 Bowler and Morisi (2006) gives a thorough discussion of differences between
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 differ-
and religious organizations, railroads, and various other smaller categories. The CES is benchmarked annually to the
QCEW with supplemental information from the RRB (the same source we use for railroad data in table 1) and the
CBP (which is based on the Census Bureau’s Business Register underlying the SUSB–based estimates on table 1); see
Bureau of Labor Statistics (2020b) for detail. Our estimate that QCEW covers about 95 percent of the employment
universe is broadly consistent with the BLS estimate noted in Bureau of Labor Statistics (2020a).
17. Doniger and Kay (2021) use CPS data to study the effects of the PPP on local employment by firm size; an
advantage of using CPS data for this purpose is the inclusion of nonemployers. The authors find that PPP had positive
effects overall but that delays in funding disbursement were costly in terms of jobs, particularly at the smallest firms.
18. Every year the CPS’s population estimates are adjusted using information from the Decennial Census and the
American Community Survey. This adjustment is done at a disaggregated demographic level but is not based on
labor force (e.g., employment) status.
Page 11 of 54
ing classification across the sources (the Census of Agriculture data underlying our universe
estimates likely include many establishments whose primary activity is not farming).
The low count of self-employed individuals in the CPS relative to the nonemployer estimate
from table 1 partly reflects differences in concepts; indeed, Bowler and Morisi (2006) explicitly
caution against direct comparisons of CPS self-employment and nonemployer business counts.19
The businesses owned by self-employed individuals may appear in employer categories in ta-
ble 1; more importantly for explaining the relatively low CPS count, many nonemployer busi-
nesses are likely owned by CPS respondents who report being wage and salary workers.20
Notably, establishment counts in the QCEW are higher than the counts we find in our uni-
verse 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, Cen-
sus 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
dynamics statistics (BDS), which uses the same Census Bureau source data but counts only firms
with positive employment in March, reports 5,252,110 firms. A third product, the BLS’s Business
Employment Dynamics (BED), reports 5,189,000 firms for 2017 under a similar criterion to the
BDS but with firm identifier concepts and industry scope that differ from Census Bureau sources
(see Handwerker and Mason, 2013, for an exploration of firm identification in BLS data).
Establishment counts aside, the BLS sources are generally below our universe estimates
presented in table 1, consistent with our goal of describing the entire business universe.
5.2 Alternative data sources
A number of researchers have turned to alternative data sources on business activity during the
Pandemic Recession, because they provide timely insights into economic activity that are not
available from official data sources. While some of these data sources provide timely indicators
19. “The concepts and definitions used to create each of these data series are so different, however, that it is
difficult to make comparisons between the two.” Bowler and Morisi (2006), p. 35.
20. Abraham et al. (2018) link BLS household microdata with Internal Revenue Service (IRS) nonemployer busi-
ness data and find a large number of households do not self-report as self-employed in BLS data but do have business
income, a gap that has risen over time; the authors explore a number of other dimensions of self-employment activity
in BLS and Census Bureau data.
21. See Becker et al. (2005) for discussion of differences between business data in BLS sources and the Census
Bureau sources underlying most of table 1; discrepancies between these sources are well known. Barnatchez, Crane
and Decker (2017) report time series patterns of employment and establishment counts in BLS versus Census sources
for 1998–2014; the employment discrepancy is roughly stable, but for establishment counts a positive gap between
BLS and Census data opened in the early 2000s and has expanded since then. The rising discrepancy appears to be
driven largely by small establishments and may reflect, in part, movement between the employer and nonemployer
universes.
Page 12 of 54
Table 4. Comparison of Activity Measures across Alternative Data Sources
Percent of corresponding universe measure
Employment Firms Estab.
Compu- Home- D&B/ D&B/ D&B/
Class ADP stat base NETS NETS NETS
1. Private nonfarm 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. (data for February 2020), Compustat North America (data for 2019), Homebase (data for February
2020), National Establishment Time Series (NETS) (data for 2014).
Notes: ADP, Compustat, and Homebase are expressed as a percent of nonfarm private employer universe from
table 1. D&B/NETS are expressed as a percent of nonfarm private employer plus nonemployer universe. Empty
cells indicate data for comparison are not available. “–” indicates a value below 1 percent. ADP values are
rounded to nearest 5 percentage points for confidentiality.
of economic activity, they also face limitations in terms of coverage and representativeness. Ta-
ble 4 compares activity measures from several sources with our universe estimates from table 1.
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) study business death during 2020. A key strength of ADP data is its significant coverage
across business size and industry, making the data potentially appropriate for studying the entire
firm size distribution described in table 1— particularly through the use of sampling weights (see
Cajner et al., 2018, for an exploration of ADP’s representativeness).22 The significant coverage
and the high-frequency nature of the data make ADP data well suited for studying business and
employment dynamics during the pandemic.
5.2.2 Compustat
Perhaps the most popular source of business microdata is Compustat, which provides firm-level
information from balance sheets, income statements and statements of cash flows.23 These data
22. We use total active employment (i.e., number of workers in payroll databases) aggregated by parent company
identifier in the payroll database used by Cajner et al. (2018). The parent company identifier differs from the control
unit identifier used in that paper, which is focused on establishment characteristics. We define firm size bins using
active employment.
23. We access these data from S&P Global, Compustat North America, via Wharton Research Data Services.
Page 13 of 54
are highly useful due to the range of information available for firms over several decades. Com-
pustat data are limited to publicly traded firms; therefore, in terms of the breakdown provided
on table 1, Compustat data are best suited to studying the CCFs and Main Street.24 This can
be seen in the second column of table 4, where coverage of medium and, especially, large firms
is substantial while coverage of small firms is negligible.25 Notably, however, Cororaton and
Rosen (2020) study publicly traded firms that received PPP assistance (of which there were 273
at the time their paper was written).
5.2.3 Small business data
Homebase is a provider of time clock services for small businesses; the rich microdata provided
by Homebase have been used in key studies of the early Pandemic Recession period (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
QCEW universe data. In short, early 2020 Homebase data include about 500,000 (hourly) em-
ployees at about 60,000 establishments, almost all with fewer than 50 employees and most with
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 Kur-
mann, Lalé and Ta (2020), the industries with the strongest Homebase coverage are retail trade
(NAICS 44–45) where 0.5 percent of small firm employment is covered; arts, entertainment, and
recreation (NAICS 71) with 0.8 percent; and accommodation and food services (NAICS 72) with
1.9 percent. Businesses in these industries were particularly vulnerable to social distancing, so
Homebase has been a valuable resource for studying effects of the pandemic on small businesses
(Dvorkin, 2020).
Given the concentration in smaller establishments (and firms), businesses found in Home-
base data are most relevant for the PPP— though Homebase businesses represent the small end
of the distribution of PPP–eligible firms — and may lack the financial resources to benefit from
Main Street programs. Granja et al. (2020) use Homebase data (among other sources) to study
the effects of PPP on small business employment growth and argue that PPP disbursements were
poorly targeted geographically.
Another popular source of data on small business experiences in 2020 is Womply, an aggre-
gator of credit card transactions that provides analytical services. Small businesses are defined
based on SBA criteria, which correspond roughly — but imperfectly — with our simplified ap-
proach in table 1. Womply data are used and described by Chetty et al. (2020). To our knowl-
edge, the representativeness and coverage properties of Womply data have not been thoroughly
24. The effects of the CCFs can also be studied with bond market data or other market data (e.g., Bordo and Duca,
2020).
25. We use 2019 Compustat data. See the appendix for a discussion of these calculations, which rely on insights
from Dinlersoz et al. (2018).
Page 14 of 54
explored, though presumably the coverage is focused primarily on businesses that sell to con-
sumers.
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
business economy.
5.2.4 D&B/NETS
Dun & Bradstreet (D&B), a business marketing company, maintains a list of U.S. establishments
intended to be comprehensive and inclusive of both employers and nonemployers. D&B data
have been widely used by researchers in the past, often in the form of the National Establishment
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
adjust the data to be comparable to our universe estimates in the appendix.
In principal, D&B/NETS data would be the primary private sector alternative to the Census
Bureau and the BLS for describing the business universe, with a large sample of establishment-
level data on both employment and revenue along with firm identifiers. D&B/NETS are also the
only data sources shown on table 4 that include nonemployers. The specific coverage properties
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
differing firm identifier definitions, but the overall discrepancies have not been well explained.
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, and revenue data are almost entirely imputed such that a study of the entire universe
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, so the type of high-frequency analyses most useful for studying the 2020 pandemic are
not feasible in these data (a challenge faced by Hubbard and Strain, 2020). However, the data
may be useful for obtaining information about specific firms participating in federal programs.
26. Comparing table 4 with table 3 suggests that D&B/NETS discrepancies with BLS universe data are even
larger, both in terms of employment and establishment counts. One possible explanation for excess employment in
D&B/NETS is the inclusion of informal (e.g., family) workers.
Page 15 of 54
5.2.5 Other alternative data sources
While we have listed the primary alternative data sources that are likely to be used for studying
Pandemic Recession programs, a number of other sources have been used as well. Most notably,
Chetty et al. (2020) employ data from Kronos and Paychex, combined with a D&B weighting
scheme, to study the PPP; these data sources are less well understood than the ones we describe
above, but the authors explore representativeness explicitly. Chetty et al. (2020) note that, like
ADP, these sources are high frequency and have coverage across the business size distribution,
though ADP is a larger sample (and each of Kronos, Paychex, and ADP exclude nonemployers).
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”); address questions of representativeness (with sampling weights if necessary), frequency,
and imputation; and account for excluded portions of the universe when aggregating estimated
policy effects.
6 Conclusions
Our estimates of the U.S. economic universe are nearly exhaustive, omitting only a small hand-
ful of business types. Assigning this economic activity to pandemic-related policies reveals a
striking fact about the pandemic response: almost every job is associated with firms or entities
meeting notional eligibility criteria for a direct lending program. This implies that the domi-
nant limitations on program coverage existed within entity categories defined by legal form and
business size; for example, large for-profit businesses met basic qualifications for the CCFs but
may nevertheless have lacked the ability to issue bonds, and medium-sized businesses met basic
Main Street qualifications but may have been ineligible due to leverage criteria.
The direct lending policy response to the Pandemic Recession was substantially broader than
that during the Great Recession, when such lending was largely limited to the financial sector
and automakers. Nevertheless, some caution is warranted when considering the policy support,
as our mapping of the support provided by specific programs to areas of the economy is not
exact. For example, we assign firms to the PPP based on a 500-employee threshold even though
some firms with greater than 500 employees were eligible for the PPP under industry-specific
SBA criteria (though we do include all eligible activity in the accommodation and food services
sector). Moreover, some small businesses may have been able to use a loan funded by Main
Street rather than relying exclusively on the PPP.
Our tabulations highlight the challenges faced by statistical agencies seeking to measure the
economy. Taken together, the various data sources we use illustrate steep trade-offs between
timeliness and detail; for example, SUSB data provide rich detail on firm size and revenue, but
these data are only available with a lag measured in years (and the revenue data only appear
Page 16 of 54
in semi-decadal Economic Census years). We therefore rely heavily upon the QCEW to adjust
2017 SUSB values to 2019 estimates; QCEW data are more timely (being released with a lag of
just two quarters) but lack revenue and firm size detail (the BED, a close cousin of QCEW, has
firm size tabulations, but they do not reach the larger size classes we study). Universal compre-
hensiveness 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 Re-
tirement Board, 2020), some nonprofits are excluded from the BLS lists due to laws governing
unemployment insurance, farm data are the purview of the Department of Agriculture and mea-
sured with concepts and definitions that are unique to the industry, and the nonemployer and
self-employment universes are inherently difficult to consistently measure across agencies. The
steep trade-offs faced by the statistical agencies ultimately arise from the source data upon which
they must rely, some of which (e.g., IRS data) are generated based on concepts and timelines
designed for purposes other than optimal measurement.
More broadly, measurement of the economy depends heavily on the taxonomic framework
available to those collecting data, with basic concepts around business objectives, industry, and
location having significant implications for how the economy is measured. Alternative private
sector data provide advantages in terms of timeliness and are often immune to the intricate legal
constraints that govern statistical agency coverage and definitions, but such data come with sub-
stantial limitations in terms of quality and representativeness; ultimately, there is no substitute
for scientifically produced statistics. The statistical agencies navigate these various trade-offs
with impressive skill, ultimately producing statistics that are remarkably consistent even when
based on differing source data (see table 3). Our main contribution is to combine data sources
based on their respective advantages to paint a comprehensive picture of the business universe;
achieving such comprehensiveness is an important goal, since crafting and evaluating business-
facing policies must first begin with accurate measurement of the entire business universe.
Appendix
A Data sources
Our estimates combine various data sources to cover virtually the entire universe of U.S. eco-
nomic activity. Our tabulations exclude only informal activities, private households, certain
financial entities (pension, health, welfare, and vacation funds and trusts, estates, and agency
accounts), and business entities owned by Tribal governments.
Page 17 of 54
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, annual payroll, and annual receipts by firm size, where a firm is a collection of op-
erating business locations (“establishments”) under unified ownership or operational control.27
SUSB data cover the universe of employer business establishments excluding farms (NAICS 111
and 112), railroads (NAICS 482), private households, and public administration (NAICS 92).
Certain government-owned businesses (i.e., outside of NAICS 92) are included in the SUSB uni-
verse, though, as discussed in section A.1.2, we drop these. Nonemployer businesses — those
businesses without formal W-2 employees — are excluded from SUSB.
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-profit businesses (including corporations, partnerships, and sole proprietorships), 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
LFO shares from this group to both the “medium” and the “large” size categories on table 1. If,
for example, nonprofits are less likely than for-profit businesses to be large, we overstate non-
profit activity in the large category and understate nonprofit activity in the medium category.
Since SUSB data are for 2017, we adjust all activity measures to 2019 estimates. For es-
tablishment counts, employment, and payroll, we apply the sector-level growth rates of estab-
lishment counts, employment, and payroll in QCEW from 2017:Q1 to 2019:Q1 (we apply these
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
counts in 2017:Q1 from BED data, which are available only at the economy-wide level (i.e., not
by sector). We obtain sector-level firm count growth rates with a two-step process: we begin
by adjusting firm counts using the growth rate of establishment counts at the sector level (from
QCEW, 2017 to 2019); we then revise these sector growth rates to ensure that sector-level firm
count growth rates are consistent with the aggregate firm count growth obtained from BED (i.e.,
we multiply sector-level establishment count growth rates by the ratio of the economy-wide firm
count growth rate to the economy-wide establishment count growth rate). In other words, we
assume that firm count growth is distributed across sectors in the same way that establishment
count growth is distributed across sectors. We adjust revenue using the ratio of national income
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
from the possibility of individual firm movements across size classes; essentially we assume that
the proportional distribution of activity across firm size classes is unchanged.
A.1.2 Government-owned businesses in SUSB
As just noted, SUSB data include government-owned businesses in certain industries: wholesale
liquor establishments, retail liquor stores, tobacco stores, book publishers, monetary authorities–
27. The SUSB data are available on the Census Bureau website at https://www.census.gov/data/tables/
2017/econ/susb/2017-susb-annual.html.
Page 18 of 54
central bank, federally-chartered savings institutions, federally-chartered credit unions, hospi-
tals, gambling industries, and casino hotels. In discussions with Census Bureau staff, we deter-
mined that most government-owned businesses present in SUSB are also counted in the Cen-
sus 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
Bank of North Dakota, which is noted in U.S. Census Bureau, 2006).28
The omission of businesses owned by Tribal governments is unfortunate and, to our knowl-
edge, represents an area of economic activity that is particularly difficult to quantify. These
businesses are included in published SUSB totals but are not separated from other government-
owned businesses in any recent public tabulations of which we are aware. A (dated) U.S. Trea-
sury 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
of federal tax returns of, and about 79 percent of tax dollars paid by, Tribe-owned businesses in
2004, but other businesses are also significant contributors, such as agriculture, fishing, gasoline
stations, smoke shops, restaurants, and banks (Treasury Inspector General for Tax Administra-
tion, 2007).
A.1.3 Railroads
As noted above, SUSB excludes railroads (NAICS 482), so we estimate railroad activity as fol-
lows. We obtain total railroad employment and payroll data from the Railroad Retirement Board
(RRB), which also provides railroad data for CES.29 We use March 2019 employment figures (to
be consistent with QCEW–adjusted estimates used elsewhere), but payroll data for 2019 are not
yet available. To be consistent with our SUSB methodology, we use 2017 payroll data, which we
adjust to 2019 values using 2017–19 payroll-per-worker growth for NAICS 48–49 (transporta-
tion and warehousing) from QCEW; in other words, we assume that payroll per railroad worker
grew at the same pace as payroll per worker in the transportation and warehousing sector more
broadly.
We distribute employment and payroll across firm size classes as follows. Employment data
for Class I railroads, most of which have more than 15,000 employees and more than $5 billion
in revenue, are available from the Surface Transportation Board (STB); and data for Amtrak
are available from the Bureau of Transportation Statistics.30 We construct employment of large
railroad firms as the sum of employment for the large Class I firms. The difference between this
sum and the totals obtained from RRB represents the activity of medium and small firms; we
28. We confirmed these exclusions from the Census of Government with Census Bureau staff. We could not find
them spelled out in Census of Government technical documentation.
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 -
Santa Fe, CSX Transportation, CN/Grand Trunk Corporation, Kansas City Southern, Norfolk Southern, Soo Line, and
Union Pacific. We classify CN/Grand Trunk Corporation, Kansas City Southern, and Soo Line as medium firms in our
taxonomy, while the remaining Class I railroads are classified as large firms.
Page 19 of 54
share out this residual employment using corresponding shares for NAICS 48–49 from SUSB.31
We then assign payroll to size classes by sharing out total payroll with the assumption that
payroll per worker is distributed across railroad firm size classes in the same manner as payroll
per worker is distributed across firm size classes for NAICS 48–49 as a whole.
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
are medium along with Amtrak, which is a medium firm as well.
The RRB also provides a listing of all covered railroad firms, which we use to obtain a 2018
firm count; given employment trends in the sector, we assume the firm count was constant from
2018 to 2019 (Amtrak is not in this RRB listing, so we add it). We assign four of these firms to
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
passenger railroad firms.
A.1.4 Farms
Both farms (NAICS 111, crop production) and ranches (NAICS 112, livestock production) are
omitted from most Census and BLS data products. We obtain farm data from the 2017 Census of
Agriculture produced by the Department of Agriculture. We use the number of farms to indicate
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,
omitting the large categories of contract workers and unpaid workers to be consistent with the
methodology used by other statistical agencies (note that some contract workers may appear in
the nonemployer data). Our farm payroll measure is labor costs for hired workers. Receipts data
are also available in the Census of Agriculture. We adjust farm counts, employment, and pay-
roll to 2019 levels using biennial Department of Agriculture survey data (National Agricultural
Statistics Services, 2021), and we adjust receipts using NIPA gross output data for the agricul-
tural sector. Note that our farm data include both employers (i.e., farms with hired workers)
and nonemployers (i.e., farms with no hired workers); Census of Agriculture data suggest that
roughly three-fourths of farms have no formally hired workers.
Importantly, the Census of Agriculture counts as a farm any business producing at least
$1,000 worth of crops or livestock, regardless of whether farming is the primary activity of that
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. We define small railroads as those with fewer than 1,500 employees, consistent with SBA criteria.
32. It is difficult to determine how many firms belong in the medium category. Various internet sources indicate
that there are roughly 20 Class II railroads; we adopt this figure then add Amtrak and the four Class I railroads that
are medium to produce an estimate of 25 medium railroad firms.
33. See https://www.nass.usda.gov/Publications/AgCensus/2017/Full_Report/Volume_1,
_Chapter_1_US/usintro.pdf
Page 20 of 54
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
may not be counted in any of our data sources, a more general measurement lacuna described
by Dunn and Hueth (2017).
A.2 Nonemployers
Nonemployers are those businesses that sell goods or services but do not have formal W-2 em-
ployees as recognized by the Social Security Administration. For example, ride-sharing drivers
and freelance journalists are likely to count as nonemployer businesses for statistical purposes.
The Census Bureau’s Nonemployer Statistics (NES) report the number and receipts of nonem-
ployer businesses based on IRS data. The Bureau first drops businesses with negligible sales,
typically defined at $1,000 but varying by industry (the threshold is just $1 in construction), 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
each nonemployer establishment is its own firm. We also use the establishment count as an “em-
ployment” count under the assumption that each nonemployer has one business owner working
at the business, though it is important to note that we do not include nonemployed business
owners in employment counts for employer businesses. Similarly, though nonemployers do not
have payroll, owners of nonemployer businesses qualified for PPP assistance to cover their own
compensation; to estimate nonemployer “payroll” we obtain sector-level ratios of payroll to re-
ceipts among employer firms with less than $5 million in annual revenue (SUSB data), then we
apply these ratios to NES receipts figures.34
Our nonemployer data are for 2017; we adjust these to 2019 values using the growth rate
of unincorporated self-employment in the CPS. We note that measurement of the nonemployer
(and self-employed) universe is difficult, with diverging estimates between Census Bureau and
BLS data sources (Abraham et al., 2018).
A.3 Government
Data on government economic activity is obtained from a number of sources. Federal civilian
payroll is obtained from the NIPA and is equal to federal nondefense compensation plus federal
civilian defense compensation; federal civilian employment is from the CES; federal revenues
are from the OMB Historical Tables, Table 2.1. Armed forces employment is obtained from the
Department of Defense Active Duty Master File and is the sum of active duty military, National
Guard, and armed forces reserve employment. Armed forces payroll is obtained from the NIPA
and is equal to federal military compensation. State and local government own source general
and utility revenue comes from the 2017 State & Local Government Finance Historical Datasets
and Tables, and state and local employment and payrolls come from the Individual Unit File of
34. We use the payroll-to-receipts ratio for firms with less than $5 million in receipts because more than 99 percent
of nonemployer businesses have receipts below $5 million. Choosing a lower size cut-off would typically result in a
higher ratio, though the ratio is fairly stable in the $100,000 to $5 million range (between 30 and 27 percent among
all sectors combined in 2017). An alternative would be to look only at employers with receipts below $100,000,
which have a payroll-to-receipts ratio of about 39 percent; nonemployers in this revenue category account for about
89 percent of all nonemployers, but they are likely to account for a far smaller share of total nonemployer receipts.
Page 21 of 54
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
payroll figures, we inflate these figures by the inverse of the percent of total compensation from
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. For state and local employment, we inflate by the ratio of March 2019 to March
2017 CES employment.
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
bulk of our main universe estimates (Bureau of Labor Statistics, 2019). QCEW is based on state
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.
The industry scope of QCEW is slightly different from the Census Bureau’s universe coverage.
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 establish-
ments 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
activities corresponding with multiple industries appear as multiple establishments in QCEW (if
separate payroll records are kept and available).
A.4.2 CES
Current Employment Statistics (CES) is the workhorse monthly BLS payroll survey (Bureau of
Labor Statistics, 2020b). While the monthly CES estimate is derived from a sample, rather than
the universe of businesses, the series is benchmarked annually to reflect the total nonfarm busi-
ness universe as measured by QCEW, supplemented with data on industries that are out of scope
for QCEW. In particular, data on railroads (NAICS 482) are taken from the Railroad Retirement
Board, and data on nonprofits without unemployment insurance coverage are eventually taken
from County Business Patterns (CBP) (Bureau of Labor Statistics, 2020b). The annual bench-
mark 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
includes government establishments (except the military and certain national security agencies).
Page 22 of 54
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
sources that are based on surveys or censuses of businesses. The CPS measures employment of
workers at any kind of business, including self-employed individuals, agricultural workers, and
unpaid family workers who are excluded from the CES, QCEW, SUSB, and CBP. Importantly,
workers who hold multiple jobs are only counted once in official CPS tallies, even though each
job would, in theory, be counted separately in the business surveys. We use information on first
and second jobs from the March 2019 CPS microdata to create a count of jobs for the purposes
of table 3.
A.4.4 Compustat
Compustat is a widely used panel data set containing balance sheet, cash flow and income state-
ment data for public firms. We access these data from S&P Global, Compustat North America,
via Wharton Research Data Services.35 For our analysis, we clean the data as follows. We drop
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
performing these steps, we identify the year of the observations and drop observations that are
duplicates for a given firm (gvkey) for a given year. Finally, we scale the employment and sales
values by 0.75 and 0.79, respectively, following Dinlersoz et al. (2018) who find that Compustat
employment figures appear to overstate firms’ 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), and establishment (dunsnumber) counts. To be comparable to the nonfarm private
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 out-
side NAICS 92 because they cannot be identified. We do not perform additional cleaning steps
common in the literature (e.g., Barnatchez, Crane and Decker, 2017), which would further re-
duce firm and establishment counts. We render NETS data equivalent to our universe estimates
by first adjusting firm size cut-offs to be equivalent to 2017 SUSB firm size cut-offs. We adjust
employment cutoffs using CES employment growth for 2014–17; the 500-employee and 15,000-
employee cutoffs in 2017 translate to 470-employee and 14,085-employee cut-offs in 2014. We
adjust the receipts cut-off using nominal GDP growth for 2014–17; the $5 billion cut-off in 2017
translates to $4.48 billion in 2014. After tabulating employment and establishment counts by
firm size, we adjust these quantities to 2019 equivalent using our usual method of applying CES
employment growth rates and QCEW establishment count growth rates.
35. https://wrds-www.wharton.upenn.edu/pages/about/data-vendors/sp-global-market-
intelligence/
Page 23 of 54
Figure B1. CDF of Employment (all sectors)
(a) By firm employment size (b) By firm receipt size
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 2 3 4 5 6 0.0 0 1 2 3 4 5
10 10 10 10 10 10 10 10 10 10 10
Firm size, employment (log scale) Firm size, receipts (millions, log scale)
Sources: See table 1.
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
estimating cumulative density functions (CDFs) for each activity measure.
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. To improve our estimates, we augment the SUSB data with the size of the top firm in
each sector, shown by the green circle at the upper-right of each panel.
In the absence of microdata for the entire firm universe, it is not obvious how best to measure
the largest firm in each sector; this observation is not provided with SUSB data but is necessary
for CDF estimation. We assume that the largest firm in each sector is publicly traded and obtain
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
by Dinlersoz et al., 2018).36 A limitation of this method is that it relies upon firm-level industry
36. No firms in Compustat are in NAICS 55, so we apply the NAICS 54 maximum to NAICS 55. We do not apply
the Dinlersoz et al. (2018) rule of thumb in the agriculture sector for reasons of convenience: the top firm we identify
would have fewer than 20,000 employees after the adjustment (but greater than 20,000 before the adjustment), and
Page 24 of 54
codes, whereas industry should actually be defined at the establishment level; the largest firm in
a sector as labeled by Compustat data may not actually be the largest firm with establishments
in that sector.37
We separately estimate the employment-based CDFs (i.e., P(E < e), where e is firm employ-
ment size) and the revenue-based CDFs (i.e., P(R < r) where r is firm revenue size). To estimate
the share of activity at firms with fewer than 15,000 employees, we calculate the empirical CDF
by employment size provided by SUSB data (which includes points at 9,999 and 19,999 employ-
ees) 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) form and a logit form (also quadratic in firm size).38 For each of firm counts, employment,
payroll, and revenue CDFs, we select either the linear quadratic or the logit form based on root
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 rev-
enue 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
same methodology on SUSB revenue size categories, omitting categories below $100 million in
revenue. The orange line on figure B1 shows — for the whole economy — the resulting employ-
ment CDF by firm receipts size, with the purple diamond showing the $5 billion cutoff. We then
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
15,000 employees or $5 billion in annual receipts. In two sectors, education services and other
services, the largest firm has annual receipts below $5 billion, so we assign no activity to the
large class for those sectors.
the procedure is simpler if the top firm has more than 20,000 employees since SUSB data provide a size bin cutoff
at 20,000. We view this discretionary intervention as small relative to the overall measurement error present in our
approach.
37. An alternative method would be to assume that all extremely large firms have establishments in every NAICS
sector, such that the largest firm with activity in each sector is simply the largest firm in the United States. In other
words, perhaps Walmart is the largest firm in every sector. But attaching Walmart to each sector leads to highly
irregular CDFs in some cases, so we judge that our method of using Compustat industry codes to identify top firms
by sector generates less error.
38. While we could, in principle, estimate the entire CDF (i.e., across the entire firm size distribution), in practice
we find this to be excessively difficult. For example, when fitting these distributions with a lognormal density assump-
tion, it is difficult to obtain a tight fit across the size distribution such that the researcher must choose which areas
to fit most closely (see Kondo, Lewis and Stella, 2018, for evidence on the firm and establishment size distribution).
Given these difficulties, we found it more productive to focus on tightly fitting the neighborhood of the distribution
close to the Main Street–based cut-offs at 15,000 employees or $5 billion in revenue with a nonparametric approach.
39. We also estimate the CDFs for establishment counts for each sector.
Page 25 of 54
Figures B2–B21 show results for each NAICS sector. In each figure, panel (a) — the top four
charts — reports CDFs in terms of firm employment size, while panel (b) — the bottom four
charts — reports CDFs in terms of firm receipt size. We show CDFs for each of firm counts (top
left), employment (top right), payroll (bottom left), and receipts (bottom right). Each figure
has the same legend as figure B1: blue circles indicate firm size cutoffs reported in SUSB data,
green circles indicate the size of the largest firm in the sector as inferred from Compustat data,
purple diamonds show Main Street-relevant cutoffs (15,000 employees or $5 billion in receipts),
and orange lines show estimated CDFs.
After allocating activity to size bins using this sector-specific CDF estimation methodology,
we sum employment, payroll, and receipts across sectors to obtain the figures shown on table 1
(we also obtain unreported establishment counts in this manner). However, this method cannot
be used to obtain all-sector firm counts, since in SUSB data firms with establishments in multiple
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, to obtain economy-wide firm counts by firm size categories, we estimate an
economy-wide CDF in terms of firm counts. After all other processing (described in appendix A),
we adjust final firm counts for each size category using the ratio of firms in the two methods
(economy-wide CDF estimation and summation across sector CDF estimations) to adjust totals.
B.2 PPP adjustments for NAICS 72
As noted in the main text, PPP was available to firms of any size with establishments in the ac-
commodation and food services sector (NAICS 72) that have fewer than 500 employees. SUSB
data provide activity measures by firm size but not establishment size. We use the Census Bu-
reau’s CBP for 2017 to calculate the activity of large firms which qualified for PPP under this
special criterion. CBP data rely on the same microdata as SUSB (the Business Register) but pro-
vide tabulations of establishments, employment, and payroll by establishment size (data on firm
counts or receipts are not present in CBP). We obtain counts of establishments, employment,
and payroll at establishments with fewer than 500 employees in NAICS 72 (from 2017 CBP) then
subtract the establishments, employment, and payroll of firms with fewer than 500 employees
(from 2017 SUSB) to obtain the establishments, employment, and payroll among establishments
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 em-
ployees. 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 es-
tablishments, employment, payroll, and receipts in the two size classes from SUSB. Distributing
firm counts is more difficult; we simply assume that all medium and large firms have at least
one establishment with fewer than 500 employees, such that all medium and large firms should
be assigned to the PPP since they each had access to it through at least on establishment (by
assumption).
Page 26 of 54
C Extra material
Figure 1 in the main text shows total employment by sector broken down into firm size and legal
form categories. Figure C1 does the same for total firm counts, illustrating the extreme skewness
of the firm size distribution even within sectors. The vast majority of firms are nonemployers,
and most employer firms are small for-profit enterprises (or, in the case of the agriculture sector,
small farms). Figure C2 reports these decompositions for annual payroll, revealing the markedly
high payroll of the public administration sector relative to other sectors. Finally, figure C3 shows
these decompositions for annual receipts; wholesale trade shows extremely disproportionate
receipts. Importantly, in sectors like wholesale trade, receipts can be high while value added is
much lower.
Page 27 of 54
Figure B2. All sectors
(a) By firm employment size
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
3 4 5 6 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.9
.8
.8
.7
.6
.6
.5 3 4 5 6 .4 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
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
2 3 4 5
.5 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8
.8
.6
.6
.4
.4 2 3 4 5 .2 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 28 of 54
Figure B3. NAICS 11 – Agriculture, forestry, fishing and hunting
(a) By firm employment size
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
3 4 5 6
.9 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.98 .98
.96 .96
.94 .94
.92 .92
.9 .9
3 4 5 6 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
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
2 3 4 5 .8 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.95 .95
.9 .9
.85 .85
.8 2 3 4 5 .8 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 29 of 54
Figure B4. NAICS 21 – Mining, quarrying, and oil and gas extraction
(a) By firm employment size
Firm count Employment
Fraction of activity Fraction of activity
1 1
.998 .9
.996 .8
SUSB data
Compustat data
.994 Estimated CDF .7
15,000 employees
.992 .6
3 4 5 6 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.9
.8
.8
.7
.6
.6
.5 3 4 5 6 .4 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
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
2 3 4 5 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8 .8
.6 .6
.4
.4
.2
.2 2 3 4 5 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 30 of 54
Figure B5. NAICS 22 – Utilities
(a) By firm employment size
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 3 4 5 6 .2 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8 .8
.6 .6
.4 .4
.2 3 4 5 6 .2 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
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 2 3 4 5 0 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8 .8
.6 .6
.4 .4
.2 .2
0 2 3 4 5 0 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 31 of 54
Figure B6. NAICS 23 – Construction
(a) By firm employment size
Firm count Employment
Fraction of activity Fraction of activity
1 1
.9999 .98
.9998 .96
.9997 SUSB data .94
Compustat data
.9996 Estimated CDF .92
15,000 employees
.9
.9995 3 4 5 6 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.95 .95
.9 .9
.85 3 4 5 6 .85 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
Firm count Employment
Fraction of activity Fraction of activity
1 1
.95
.999
.9
.998 SUSB data
Compustat data
Estimated CDF .85
$5 billion receipts
.997 .8
2 3 4 5 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.9
.9
.8
.8
.7
.7 2 3 4 5 .6 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 32 of 54
Figure B7. NAICS 31–33 – Manufacturing
(a) By firm employment size
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
3 4 5 6 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.9
.8
.8
.7 .6
.6
.5 3 .4
4 5 6 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
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 2 3 4 5
.4 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8
.8
.6
.6
.4
.4
.2
2 3 4 5 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 33 of 54
Figure B8. NAICS 42 – Wholesale trade
(a) By firm employment size
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
3 4 5 6 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.9
.8
.8
.6
.7
.6 3 4 5 6 .4 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
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 2 3 4 5 .4 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8
.8
.6
.6
.4
.4 .2
2 3 4 5 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 34 of 54
Figure B9. NAICS 44–45 – Retail trade
(a) By firm employment size
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 3 4 5 6
.4 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.9
.8 .8
.7
.6
.6
.5
3 4 5 6 .4 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
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 3 4 5 .2 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8
.8
.6
.6
.4
.4
2 3 4 5 .2 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 35 of 54
Figure B10. NAICS 48–49 – Transportation and warehousing
(a) By firm employment size
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 3 4 5 6 .4 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8 .8
.6 .6
.4 .4 3
3 4 5 6 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
Firm count Employment
Fraction of activity Fraction of activity
1 1
.995 .8
SUSB data
.99 Compustat data .6
Estimated CDF
$5 billion receipts
.4
.985 2 3 4 5 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8 .8
.6 .6
.4 .4
.2 2 3 4 5 .2 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 36 of 54
Figure B11. NAICS 51 – Information
(a) By firm employment size
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 3 4 5 6
.4 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8 .8
.6
.6
.4
.4
3 4 5 6 .2 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
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 3 4 5 .2 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8 .8
.6 .6
.4
.4
.2
.2 2 3 4 5 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 37 of 54
Figure B12. NAICS 52 – Finance and insurance
(a) By firm employment size
Firm count Employment
Fraction of activity Fraction of activity
1 1
.999 .8
.998 SUSB data
Compustat data .6
Estimated CDF
15,000 employees
.997
3 4 5 6 .4 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8
.8
.6
.6
.4
.4
3 4 5 6 .2 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
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 3 4 5 .2 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8
.8
.6
.6
.4
.4
.2
.2 2 3 4 5 0 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 38 of 54
Figure B13. NAICS 53 – Real estate and rental and leasing
(a) By firm employment size
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 3 4 5 6 .75 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.95
.9
.9
.85
.8
.8
.75 3 4 5 6 .7 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
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 2 3 4 5 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.9
.9
.8
.8
.7
.7 .6
.5
.6 2 3 4 5 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 39 of 54
Figure B14. NAICS 54 – Professional, scientific, and technical services
(a) By firm employment size
Firm count Employment
Fraction of activity Fraction of activity
1 1
.9995
.9
.999
SUSB data
Compustat data .8
.9985 Estimated CDF
15,000 employees
.998 3 .7
4 5 6 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.9
.9
.8
.8
.7
.7
.6 3 4 5 6 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
Firm count Employment
Fraction of activity Fraction of activity
1 1
.999 .9
.998
.8
.997 SUSB data
Compustat data
Estimated CDF .7
.996
$5 billion receipts
.995 2 3 4 5
.6 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.9 .9
.8 .8
.7 .7
.6 .6
.5 2 3 4 5 .5 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 40 of 54
Figure B15. NAICS 55 – Management of companies and enterprises
(a) By firm employment size
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 3 4 5 6 .2 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8
.9
.6
.8
.4
.7
.2 3 4 5 6 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
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 2 3 4 5 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8
.8
.6
.6
.4
.4
.2
0 2 3 4 5 .2 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 41 of 54
Figure B16. NAICS 56 – Administrative and waste services
(a) By firm employment size
Firm count Employment
Fraction of activity Fraction of activity
1 1
.999
.8
.998
SUSB data
.997 Compustat data .6
Estimated CDF
15,000 employees
.996
3 4 5 6 .4 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.9
.8
.8
.7
.6
.6
.4 3 4 5 6 .5 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
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 2 3 4 5 .4 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8 .8
.6 .6
.4 .4 2
2 3 4 5 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 42 of 54
Figure B17. NAICS 61 – Educational services
(a) By firm employment size
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
3 4 5 6 .6 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.9 .9
.8
.8
.7
.7
.6
.6
3 4 5 6 .5 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
Sources: See table 1.
Note: There are no firms in this sector with greater than $5 billion in annual receipts.
Page 43 of 54
Figure B18. NAICS 62 – Health care and social assistance
(a) By firm employment size
Firm count Employment
Fraction of activity Fraction of activity
1 1
.9995 .9
.999 .8
SUSB data
Compustat data .7
.9985 Estimated CDF
15,000 employees
.6
.998 3 4 5 6 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.9
.8
.8
.7
.6
.6
.5 .4 3
3 4 5 6 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
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 2 3 4 5 .5 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.8 .8
.6 .6
.4 2 .4
3 4 5 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 44 of 54
Figure B19. NAICS 71 – Arts, entertainment, and recreation
(a) By firm employment size
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 3 4 5 6 .75 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.95
.95
.9
.9
.85
.85
.8
.8 3 4 5 6
.75 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
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
2 3 4 5
.7 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.9 .9
.8
.8
.7
.7
.6
.6
2 3 4 5 .5 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 45 of 54
Figure B20. NAICS 72 – Accommodation and food services
(a) By firm employment size
Firm count Employment
Fraction of activity Fraction of activity
1 1
.9
.9995
SUSB data
Compustat data .8
Estimated CDF
15,000 employees
.999
3 4 5 6 .7 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.9 .9
.8 .8
.7 .7
.6 3 4 5 6 .6 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
(b) By firm receipt size
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
.998 2 .7
3 4 5 2 3 4 5
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.9 .9
.8 .8
.7 .7
.6 2 3 4 5 .6 2 3 4 5
10 10 10 10 10 10 10 10
Firm size, receipts (millions, log scale)
Sources: See table 1.
Page 46 of 54
Figure B21. NAICS 81 – Other services
(a) By firm employment size
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
3 4 5 6 .9 3 4 5 6
10 10 10 10 10 10 10 10
Annual payroll Annual receipts
Fraction of activity Fraction of activity
1 1
.95
.95
.9
.9
.85 3 4 5 6 3 4 5 6
10 10 10 10 10 10 10 10
Firm size, employment (log scale)
Sources: See table 1.
Note: There are no firms in this sector with greater than $5 billion in annual receipts.
Page 47 of 54
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: See table 1.
Notes: Transparent bars indicate classes that are not covered by one of the four direct lending programs we consider.
Page 48 of 54
Figure C2. Annual Payroll 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
0 500 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: See table 1.
Notes: Transparent bars indicate classes that are not covered by one of the four direct lending programs we consider.
Page 49 of 54
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: See table 1.
Notes: Transparent bars indicate classes that are not covered by one of the four direct lending programs we consider.
Page 50 of 54
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