Fed Feds Across Universe 2020 099
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A Federal Reserve Board Finance and Economics Discussion Series working paper, FEDS 2020-099, titled Across the Universe: Policy Support for Employment and Revenue in the Pandemic Recession, by Ryan A. Decker, Robert J. Kurtzman, Byron F. Lutz and Christopher J. Nekarda, dated November 30, 2020. Using data from 14 government sources, the authors estimate 2019 U.S. economic activity by sector, legal form of organization and firm size. They relate those estimates to four direct lending programs: the Paycheck Protection Program, the Main Street Lending Program, the Corporate Credit Facilities and the Municipal Lending Facilities. The abstract states that the classes targeted account for 97 percent of total U.S. employment. Table 1 lists firm counts, employment, payroll and receipts, and the paper compares its estimates with ADP, Compustat, Homebase and Dun & Bradstreet data.
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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 (2020).
“Across the Universe: Policy Support for Employment and Revenue in the Pandemic Reces-
sion,” Finance and Economics Discussion Series 2020-099. Washington: Board of Governors
of the Federal Reserve System, https://doi.org/10.17016/FEDS.2020.099.
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
November 30, 2020
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— re-
late 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 limit coverage within specific programs. These programs notionally
cover a far larger universe than what was targeted by analogous Great Recession-era lend-
ing policies. 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. 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.
To assess the scale of the policy response, we present estimates of economic activity in the
United States that we partition by sector, legal form of organization, and firm size. 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.
In particular, 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
alternative data cover substantially smaller portions of the economy than either the Bureau of
Labor Statistics (BLS) data or our universe estimates.
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2 Overview of U.S. economic activity
We construct measures of the number of entities, their employment, annual payroll, and gross
receipts by sector, legal form of organization, and firm size for the U.S. economy in 2019, with
a focus on the criteria that are relevant for the four lending programs we consider. 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 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. 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;
• 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 revenue.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 many readers may be accustomed to seeing the nonfarm economy in isolation. We also
include information on nonemployers. Last, we separate out government activity across federal,
state, and local governments.
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
1. Cutoffs for activity below 15,000 employees or $5 billion in receipts are not available our source data; the
appendix describes how these are estimated.
Page 3 of 31
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 33,895,440 159.9 7,792 42,656
2. For-profit 5,653,982 114.9 6,538 38,468
3. Small 5,636,548 55.3 2,737 13,765
4. Medium 16,718 34.5 2,033 9,850
5. Large 716 25.0 1,768 14,852
6. Nonprofit 439,064 17.0 828 2,447
7. Small 436,215 7.4 267 887
8. Medium 2,775 7.4 391 1,040
9. Large 74 2.3 170 521
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 25.8 394 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 33,801,420 94.5 3,498 16,456
21. Main Street 93,279 39.3 2,383 10,916
22. CCFs 740 26.2 1,911 15,285
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 4 of 31
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 employment and entity
counts are skewed toward small firms, their annual payroll and receipts are more evenly dis-
tributed across size classes.
Nonprofit businesses account for less than 5 percent of employment in most sectors; that
said, they constitute a larger share of activity in certain sectors of the economy. Medium and
large nonprofits account for 86 percent of employment in educational services, 57 percent in
health care and social assistance, 37 percent in other services (which includes religious organi-
zations), and 10 percent in arts, entertainment, and recreation (which includes museums).3
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).4 Farms with less than $1 million in revenue are eligible for Small Business Administration
(SBA) programs such as the PPP, so we include them in the small size class, with the remain-
ing farms included in the medium size class.5 Farms are not directly comparable to firms; some
farms may be owned by larger firms that own other farms (such that farm counts overstate farm-
ing 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 misclassification of em-
ployment, payroll, or receipts, since these measures are categorized at the farm 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 million. In short,
measurement of farms is based upon different concepts from measurement of the rest of the
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. For small firms, nonprofit activity across sectors differs: Nonprofits account for 66 percent of employment
in educational services, 61 percent in utilities, 50 percent in other services, 33 percent in arts, entertainment, and
recreation, and 28 percent in health care and social assistance.
4. 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; farms report roughly 2 million unpaid workers and 8 million contract workers
(some of the latter may also appear in nonemployer data).
5. 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, from which we infer that few farms would have more than $5 billion in receipts. 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.
Page 5 of 31
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.6 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 $400 billion in payroll equivalent last year; 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 51 state governments and the federal government, there are more
than 90,000 local government entities, including special purpose entities such as transit author-
ities 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 significant fractions of employees at large firms. In appendix B we
show versions of this figure based on firm counts, payroll, and receipts.
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
6. 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 each rental property in a sep-
arate nonemployer business). Moreover, nonemployer businesses include “side gigs” of workers with other income.
We discuss nonemployer versus self-employment distinctions further below.
Page 6 of 31
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.
classes — for example, although some medium businesses are 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 since small firms are generally eligible.
For example, all blue-shaded rows in the upper panel correspond to the PPP.7 We assign all state
and local government entities to the Muni LF, even though some of these organizations (e.g.,
public hospitals) may also be eligible for the nonprofit Main Street facilities.
It is important to note that firms of any size in the accommodation and food services sector
that have an establishment with fewer than 500 employees are also eligible for the PPP.8 We
7. PPP eligibility is determined by firm size, not establishment size, with the exception of establishments in
NAICS 72; see section A.1.4 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).
8. We interpret the PPP eligibility criteria to imply that all firms with fewer than 500 employees are eligible, though
some additional firms meeting standard industry-specific SBA “small business” definitions are eligible as well, and
certain smaller firms are excluded. Tracking special industry-specific “small business” definitions is infeasible 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
Page 7 of 31
include activity of medium for-profit firms and farms as the target population for the Main Street
program (line 21). Although smaller firms are 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 21.9 Similarly, there is no minimum size criterion for CCFs, but
we have identified the target population as only the large for-profit firms.10
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 — are included in
the PPP. Medium for-profit and nonprofit businesses (as well as many small ones) are targeted
by Main Street. Large businesses and nonprofits, which typically have access to the corporate
bond or syndicated loan markets, are covered by the CCFs.11 State and local governments, as
well as their enterprises, are able to access the Muni LF, albeit with a potential intermediate
step.12
Given its focus on small firms, the PPPs portion of the business universe includes the vast
majority of firms; even if nonemployers and farms are excluded, the PPP still covers more than
99 percent of firms. However, in terms of economic activity, the other lending programs are
similarly important to the PPP. Main Street is targeted at less than 1 percent of the number
of firms as the PPP but these firms account 40 to 60 percent of the employment, payroll, and
revenue of PPP firms (though we emphasize that a large share of the Main Street firm count is
from medium-sized farms). The 750 large firms we assign to the CCFs collectively account for
nearly the same amount of annual receipts 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 100 percent of firms or entities fall into business categories targeted by policy,
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).
9. Main Street has eligibility criteria that may limit take-up, especially among small firms. In particular, there are
loan-size minimums and leverage limits that vary by facility. As of October 30, 2020, the minimum loan size was
$100,000 (Federal Reserve Board, 2020a).
10. Among other eligibility criteria, firms need 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.
11. Nonprofits have access to the CCFs (Federal Reserve Bank of New York, 2020).
12. State and local government entities directly eligible for the Muni LF include 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 also satisfy minimum credit rating requirements. Gov-
ernment entities not directly eligible for the Muni LF are, in principal, indirectly eligible for the facility as any directly
eligible participant may use 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).
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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 69 11 0
4. Construction 87 12 2 0
5. Manufacturing 45 33 23 0
6. Wholesale trade 59 21 20 0
7. Retail trade 43 19 38 0
8. Transportation and warehousing 53 21 26 0
9. Information 34 28 37 0
10. Finance and insurance 37 26 37 0
11. Real estate and rental and leasing 86 10 5 0
12. Professional, scientific, and technical services 70 18 13 0
13. Management of companies and enterprises 12 61 27 0
14. Administrative and waste services 42 40 18 0
15. Educational services 54 39 8 0
16. Health care and social assistance 51 37 12 0
17. Arts, entertainment, and recreation 75 23 2 0
18. Accommodation and food services 94 4 1 0
19. Other services 91 8 1 0
20. Public administration 0 0 0 81
Sources: See table 1.
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 exist within firm or entity categories; for example, many firms that meet size
and legal form criteria for Main Street programs may be 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.13 Importantly, we include nonemployers on
table 2, all of which we assume to be eligible for the PPP (and each of which accounts for one
employee).
13. 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.
Page 9 of 31
The distribution of sector activity across programs varies widely, with significant implica-
tions for how the programs might affect different sectors. Since PPP eligibility requirements
are minimal (aside from size-based criteria) and many PPP loans will be forgiven, sectors with
heavy concentration of activity in PPP–eligible firms (such as accommodation and food services
or other services) may benefit disproportionately from the economy-wide pandemic response.
For sectors with significant activity in the Main Street category (such as utilities or manage-
ment), policy benefits will depend heavily on how well firms in those sectors meet 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, benefit only to the extent that their firms have access to corporate bond markets and
were rated as investment-grade prior to March 22, 2020.
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
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 (Bernanke, 2018).
Among other policy responses to improve financial stability, the Federal Reserve also established
facilities to improve market functioning, in particular in short-term funding markets, as it has
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 has 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 dur-
ing and in the aftermath of the Great Recession. The appropriated sum of these small business
lending programs is an order of magnitude lower than the approximately $670 billion in mostly
forgivable loans appropriated through the PPP.
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 10 of 31
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 their relative timeliness: The QCEW is released
with a delay of roughly two quarters.15 Separately, the BLS also publishes the Current Popu-
lation Survey (CPS), which provides a monthly measure of employment based on a survey of
households (thereby avoiding the industry and organizational scope restrictions that character-
ize 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 uni-
verse is just slightly smaller than CES reflecting its administrative sources in the unemployment
system, which does not cover all businesses (for example, railroads are excluded from QCEW
but not the CES or our universe estimates).16
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-
15. The primary reasons we used the SUSB data for our main analysis are (a) SUSB has more detail on firm size
along with more consistent definitions of firm concepts, and (b) SUSB contains revenue information.
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
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 estimates on table 1). See
Bureau of Labor Statistics (2020) for detail.
Page 11 of 31
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 94 92 122 94
3. Private 96 93 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.
lations from row 13 of table 1); however, coverage within categories appears more limited than
other sources. 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 benchmarked, as
it in the CES.17 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-
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.18
The businesses owned by self-employed individuals may appear in employer categories in ta-
17. Every year the CPS’s population estimates are benchmarked 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.
18. “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.
Page 12 of 31
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.19
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.20 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 alternative data sources on business activity have risen to prominence during the
Pandemic Recession, because they provide timely insights into economic activity that are not
available from official data sources. While some of these sources provide timely indicators of
economic activity, they also face limitations in terms of coverage and representativeness. Table 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.
19. 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.
20. 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 13 of 31
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 22 108 72 72
2. Small 25 – 1 119 72 72
3. Medium 30 16 85 103 69
4. Large 10 82 111 85 80
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.
(2020) study business death during 2020. A key strength of ADP data is 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).21 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.22 These data
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 CCFs and Main Street facilities.23 This
can be seen in the second column of table 4, where coverage of medium and large firms is sub-
stantial while coverage of small firms is negligible.24 Notably, however, Cororaton and Rosen
21. 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.
22. We access these data from S&P Global, Compustat North America, via Wharton Research Data Services.
23. CCFs are likely to also be widely studied without firm-level data, focusing instead on bond market data and
the like (e.g., Bordo and Duca, 2020).
24. 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 31
(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., Bar-
tik et al., 2020) and in more recent work on business death (Crane et al., 2020). 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)
employees 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 ta-
ble 4, Homebase covers only 1 percent of small firm employment. Based on tabulations from
Kurmann, Lalé and Ta (2020), the industries with the strongest Homebase coverage are re-
tail trade (NAICS 44–45) where 0.5 percent of small firm employment is covered; arts, enter-
tainment, 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 so-
cial 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 Homebase 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.
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
explored, though presumably the coverage is focused primarily on businesses that sell to con-
sumers.
We also note that the Bureau of Economic Analysis is currently developing new small busi-
ness 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
Page 15 of 31
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.
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 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 establishments than our estimates of the private sector universe such
that it is unclear what set of businesses are actually covered by the data.25 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
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 presumably exclude
nonemployers). Other sources may be in use of which we are not aware. Our analysis above
25. 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.
26. Importantly, the latest NETS data to which we have access are from 2014, which are what we use in the
table 4 calculations. A more direct comparison with NETS data is beyond the scope of this paper. In particular, it
would be better to compare 2014 NETS data to 2014 data in our main universe files; this has already been done by
Barnatchez, Crane and Decker (2017), who find similar discrepancies to those reported on table 4. Moreover, the
literature cited above documents extensive imputation concentrated in the recent years of each NETS release, so the
value of matching years for comparison purposes is likely limited.
Page 16 of 31
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 dominant
limitations on program coverage exist within entity categories defined by legal form and busi-
ness size; for example, large for-profit businesses meet basic qualifications for the CCFs but may
nevertheless lack ability to issue bonds, and medium-sized businesses meet basic Main Street
qualifications but may be 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 are 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 be 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
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
Page 17 of 31
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.
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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.
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 are included in the SUSB universe, 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.
Since SUSB data date from 2017, we adjust all activity measures to 2019 estimates. For
establishment counts, employment, and payroll, we apply the sector-level growth rates of es-
tablishment 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
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 22 of 31
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 author-
ities—central bank, federally-chartered savings institutions, federally-chartered credit unions,
hospitals, gambling industries, and casino hotels. In discussions with Census Bureau staff, we
determined that most government-owned businesses present in SUSB are also counted in the
Census of Governments and Annual Survey of Public Employment and Payroll figures underlying
our measures of government activity. Therefore, we drop government-owned businesses from
SUSB–based tabulations. This leads to a small amount of undercounting of activity, however,
because certain government-owned businesses are not included in the Census of Governments
and Annual Survey of Public Employment and Payroll figures. The main omissions are those
businesses owned by Tribal governments as well as some banks and credit unions (except the
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 (very dated)
U.S. Treasury report suggests that gaming and gambling hotel businesses represent a signifi-
cant — 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 Administration, 2007).
A.1.3 Estimating Main Street size cut-offs in SUSB
For firms to quality for the Main Street programs they must have fewer than 15,000 employees
or less than $5 billion in annual revenue. SUSB tabulations do not include cut-offs at 15,000 em-
ployees or $5 billion in receipts, reporting only a category of firms with greater than $2.5 billion
in revenue or categories bounded by 9,999 employees and 19,999 employees. We estimate the
share of activity accounted for by firms with less than $5 billion in revenue and, separately,
firms with fewer than 15,000 employees by estimating the cumulative density function (CDF)
for each NAICS sector from available size cut-offs (and we obtain the highest revenue values in
each sector from Compustat data on publicly traded firms, where we multiply revenue by 0.79
consistent with Dinlersoz et al. (2018), on the implicit assumption that the largest firm in each
sector is publicly traded.
While we could, in principle, estimate the entire CDF using SUSB and Compustat data and
a statistical distribution assumption, in practice we find this to be excessively difficult. For
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.
Page 23 of 31
example, when fitting these distributions with a lognormal density assumption, it is difficult
to obtain a tight fit across the size distribution such that the researcher must choose which
areas to fit most closely.29 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. SUSB data provide
the firm size distribution in terms of both employment and revenue by NAICS sector; the only
missing ingredient for CDF estimation is the size of the top firm in each sector. We assume
that the largest firm in each sector is publicly traded and obtain each firm and its employment
and revenue from Compustat (scaling each according to the rules of thumb found by Dinlersoz
et al., 2018).30 We estimate the employment-based and revenue-based CDFs separately, with a
focus on tightly fitting the CDF around the Main Street cut-offs. 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 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 form and a logit form. For each of firms, employment, payroll, and revenue CDFs, we
select either the linear quadratic or the logit form based on root mean square error.
With estimated sector-level CDF curves (in terms of employment size) in hand, we identify
the share of firms, employment, payroll, and revenue associated with firms with fewer than
15,000 employees. To estimate the share of activity at firms with less than $5 billion in rev-
enue, we use the same methodology on SUSB revenue size categories, omitting categories be-
low $100 million in revenue. We then have sector-level estimates of (1) the share of firms,
employment, payroll, and revenue that is at firms with fewer than 15,000 employees, and (2)
the share of firms, employment, payroll, and revenue that is at firms with less than $5 billion
in revenue. We then identify the top of the “medium” size category by sector based on which
cut-off — 15,000 employees or $5 billion in revenue — is higher (consistent with Main Street
criteria).
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 can-
not 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
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.
A.1.4 PPP adjustments for NAICS 72
As noted in the main text, PPP is available to firms of any size with establishments in the accom-
modation 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 Bureau’s
County Business Patterns (CBP) to calculate the activity of large firms which qualify for PPP un-
der this special criterion. CBP data rely on the same microdata as SUSB (the Business Register)
29. See Kondo, Lewis and Stella (2018) for evidence on the firm and establishment size distribution.
30. No firms in Compustat are in NAICS 55, so we apply the NAICS 54 maximum to NAICS 55.
Page 24 of 31
but provide 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, employ-
ment, and payroll at establishments with fewer than 500 employees in NAICS 72 (from CBP)
then subtract the employment and payroll of firms with fewer than 500 employees (from SUSB)
to obtain the employment and payroll of establishments with fewer than 500 employees that
are controlled by firms with at least 500 employees. We assign the resulting activity to medium-
or large-firm size bins using the share of establishments, employment, and payroll in the two
size classes from SUSB. For the piece of activity assigned to medium-sized firms, we impute firm
counts using the number of firms per establishment in SUSB data for medium-sized firms in
NAICS 72, and we impute receipts using the ratio of receipts to payroll for medium-sized firms
in NAICS 72. We use an analogous method for the activity assigned to large-sized firms.
A.1.5 Railroads
As noted above, SUSB excludes railroads, so we estimate railroad activity as follows. We obtain
total railroad employment and payroll data from the Railroad Retirement Board (RRB), which
also provides railroad data for CES. 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 481, 483, and 484 (air, water, and
truck transportation) from QCEW. Employment and payroll 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.31 We construct employment and payroll of large railroad firms as the
sum of employment and payroll 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 share out this
residual employment and payroll using corresponding shares for NAICS 48–49 from SUSB.32 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.33 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
31. The Class I railroads are Burlington Northern - Santa Fe (BNSF), CSX Transportation, CN/Grand Trunk Corpo-
ration, Kansas City Southern, Norfolk Southern, Soo Line, and Union Pacific. CN/Grand Trunk Corporation, Kansas
City Southern, and Soo Line count as medium-sized firms in our taxonomy, while the remaining Class I railroads are
large.
32. We define small railroads as those with fewer than 1,500 employees, consistent with SBA criteria.
33. 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.
Page 25 of 31
of NAICS 48–49 (transportation and warehousing) data in our main calculations. Our railroad
methodology may omit some passenger railroad firms.
A.1.6 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 Cen-
sus 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, employ-
ment, and payroll to 2019 levels using biennial Department of Agriculture survey data, and we
adjust receipts using NIPA gross output data for the agricultural sector. Note that our farm data
include both employers (i.e., farms with hired workers) 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).34 On the one hand, this means that there may be
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
34. See https://www.nass.usda.gov/Publications/AgCensus/2017/Full_Report/Volume_1,
_Chapter_1_US/usintro.pdf
Page 26 of 31
at the business, though it is important to note that we do not include non-employed business
owners in employment counts for employer businesses. Similarly, though nonemployers do not
have payroll, owners of nonemployer businesses qualify 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.35
Our nonemployer data date from 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
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
35. 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 27 of 31
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, 2020). 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 CBP (Bureau of Labor Statistics, 2020). The annual benchmark is focused on March of a
given year and is available in February of the following year; this makes CES the most timely
official measure of total nonfarm employment. Importantly, CES scope excludes proprietors, the
unincorporated self-employed, and domestic workers but includes government establishments
(except the military and certain national security agencies).
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.36 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
36. https://wrds-www.wharton.upenn.edu/pages/about/data-vendors/sp-global-market-
intelligence/
Page 28 of 31
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. employment.
A.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 government-owned businesses. We do
not perform additional cleaning steps common in the literature (e.g., Barnatchez, Crane and
Decker, 2017), which would further reduce firm and establishment counts.
B Extra material
Figure B1. 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 29 of 31
Figure B2. 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 30 of 31
Figure B3. 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.
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