Tomaz Cajner et al., Tracking Labor Market Developments during the COVID-19 Pandemic: A Preliminary Assessment (2020)
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Federal Reserve Board Finance and Economics Discussion Series working paper 2020-030, Tracking Labor Market Developments during the COVID-19 Pandemic: A Preliminary Assessment, by Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas and Christopher Kurz, first drafted April 15, 2020. Arguing that traditional official statistics cannot measure developments that evolve over weeks, the authors build weekly payroll employment series from ADP microdata available essentially in real time. They estimate cumulative losses in paid employment through April 4 at 18 million, with about 13 million paid jobs lost in the two weeks between March 14 and March 28, against fewer than 9 million private payroll jobs lost in the entire Great Recession. Leisure and hospitality is the most affected sector, losing or furloughing about 30 percent of employment.
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Finance and Economics Discussion Series
Divisions of Research & Statistics and Monetary Affairs
Federal Reserve Board, Washington, D.C.
Tracking Labor Market Developments during the COVID-19
Pandemic: A Preliminary Assessment
Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian
Hamins-Puertolas, Christopher Kurz
2020-030
Please cite this paper as:
Cajner, Tomaz, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and
Christopher Kurz (2020). “Tracking Labor Market Developments during the COVID-
19 Pandemic: A Preliminary Assessment,” Finance and Economics Discussion Se-
ries 2020-030. Washington: Board of Governors of the Federal Reserve System,
https://doi.org/10.17016/FEDS.2020.030.
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.
Tracking Labor Market Developments during the
COVID-19 Pandemic: A Preliminary Assessment
Tomaz Cajner Leland D. Crane Ryan A. Decker
Adrian Hamins-Puertolas Christopher Kurz∗
First draft: April 15, 2020
Abstract
Many traditional official statistics are not suitable for measuring high-frequency developments that
evolve over the course of weeks, not months. In this paper, we track the labor market effects of the
COVID-19 pandemic with weekly payroll employment series based on microdata from ADP. These
data are available essentially in real-time, and allow us to track both aggregate and industry effects.
Cumulative losses in paid employment through April 4 are currently estimated at 18 million; just dur-
ing the two weeks between March 14 and March 28 the U.S. economy lost about 13 million paid jobs.
For comparison, during the entire Great Recession less than 9 million private payroll employment
jobs were lost. In the current crisis, the most affected sector is leisure and hospitality, which has so far
lost or furloughed about 30 percent of employment, or roughly 4 million jobs.
Keywords: labor market, economic measurement, big data.
JEL Classification: J2, J11, C53, C55, C81.
∗ All authors are at the Federal Reserve Board of Governors. We thank ADP for access to and help with the
payroll microdata that underlie the work described by this paper. In particular, this work would not have been
possible without the support of Matt Levin, Ahu Yildirmaz, and Sinem Buber. The analysis and conclusions set
forth here are those of the authors and do not indicate concurrence by other members of the research staff or the
Board of Governors.
1 Introduction
The COVID-19 pandemic, and the associated social distancing measures, have deeply affected
the U.S. labor market. The unprecedented speed of deterioration in labor market conditions
calls for higher frequency indicators than are currently available in traditional statistical data
such as the BLS Current Employment Statistics (CES) and the Current Population Survey (CPS).
And while the weekly initial claims for unemployment insurance collected by the Department
of Labor provide information to help understand labor market developments, claims only pro-
vide a partial picture because they capture only job destruction and not decreased hiring.1
In this paper, we build on our extensive previous work in which we constructed employ-
ment measures from the ADP payroll microdata; see Cajner et al. (2018), Cajner et al. (2019a),
Cajner et al. (2019b), and Cajner et al. (2020). In particular, we use two measures of employment:
i) active employment, which corresponds to the number of individuals active in the payroll sys-
tem regardless of whether they were paid or not in a given pay period; and ii) paid employment,
which corresponds to the number of individuals issued a paycheck in a given pay period.2 The
distinction between active and paid employment is especially useful in the context of labor
market developments during the COVID-19 episode. More precisely, changes in active em-
ployment should reflect only (permanent) layoffs, but not (temporary) furloughs. On the other
hand, changes in paid employment should capture both layoffs and unpaid furloughs. In other
words, our active employment series should be similar in concept to CPS employment, while
our paid employment series should be analogous to CES employment.3 We construct both
types of employment series at the weekly frequency, which allows us to track the dynamics of
employment losses occurring since the onset of the COVID-19 crisis.
Importantly, in previous work we found that ADP data are reasonably representative of
1 Additionally, not all workers that lose a job apply for unemployment insurance and the severe processing delays
of initial claims that occurred in the second half of March 2020 imply some further challenges when using initial
claims data to infer current labor market conditions.
2 Active employees include wage earners with no hours in the pay period, workers on unpaid leave, and the
like. Paid employees include any wage or salary workers issued regular paychecks during the pay period as well
as bonus checks and payroll corrections.
3 Active employment may also be affected by delays in payroll system maintenance. For example, if workers
are permanently separated, some might not be automatically removed from payroll systems. That said, in the
previous work cited above we found that ADP active employment is better than paid employment for forecasting
CES employment.
1
U.S. businesses, with a wide range of coverage across business size, industry, and geography.
In this respect, ADP data are unique among private sector data sources currently being used to
track labor markets during the COVID-19 crisis. Moreover, our main ADP indexes are based on
ADP data aggregated with size-by-industry weights derived from the BLS Quarterly Census
of Employment and Wages (QCEW), so the data are able to provide a good measure of the U.S.
private sector. We also emphasize that our ADP-based employment measures are distinct from
those published jointly by ADP and Moody’s in the monthly National Employment Report
(NER).
We find that, among paycheck-to-paycheck continuing businesses, active employment has
cumulatively declined by a bit more than 6 million between February 15 and April 4; recall
that this decline captures laid off workers who were removed from the payroll system. On the
other hand, paid employment has cumulatively declined by about 18 million during the same
period. Most severe job losses were experienced in the second half of March; our estimates
suggest that the U.S. economy lost about 13 million jobs between March 15 and March 28. For
comparison, during the entire Great Recession (between December 2007 and February 2010)
8.8 million jobs were lost. Our estimates of employment losses are also comparable to those
in other recent papers: Brynjolfsson et al. (2020) estimate that about 16 million Americans lost
jobs through April 5, while Coibion, Gorodnichenko and Weber (2020) estimate that about 20
million jobs were lost by April 8.
Our estimate of employment losses is based on the sample of PACs that can be longitu-
dinally matched over consecutive pay periods, which implies we do not account for lower
business entry and higher business exit. In some preliminary work, we find that reduced en-
try and elevated exit could potentially increase cumulative employment losses, perhaps by as
much as 5 to 10 million.4
4 Moreover, recently released weekly data on new business applications from the Census Bureau show a sharp
decline; see Haltiwanger (2020).
2
2 Methodology for Constructing Weekly Employment Indexes
The weekly employment data used in this paper come from the company ADP, which processes
payrolls for about 20 percent of total U.S. private employment.5 The microdata are at the level
of Payroll Account Controls (PAC), which often correspond to business establishments (but
may sometimes correspond to firms) as defined by the Census Bureau and the BLS. Each PAC
updates their record at the end of each pay period. The record consists of the date payroll
was processed, employment information for the pay period, and many time-invariant PAC
characteristics (such as an anonymized PAC identifier, NAICS industry code, zip code, etc.).6
PAC records include both the number of individuals employed (“active” employees) and the
number of individuals issued a paycheck in a given pay period (“paid” employees). Active
employees include wage earners with no hours in the pay period, workers on unpaid leave,
and the like. Paid employees include any wage or salary workers issued regular paychecks
during the pay period as well as those issued bonus checks and payroll corrections.
The data begin in July 1999, but are available at the weekly frequency only since July 2009.
As argued in Cajner et al. (2018), ADP payroll data appear to be quite representative of the
U.S. economy, though the data somewhat overrepresent the manufacturing sector and large
businesses, as compared to the QCEW universe of establishments. We address these issues by
reweighting the data as explained below.
The process of transforming the raw data into usable aggregate series is complex, and we
refer the interested reader to Cajner et al. (2018) for details. In short, we calculate the weighted
average growth of employment at PACs appearing in the data for two consecutive weeks. We
build a weekly time series of employment for each PAC, estimating employment at the PAC
each Saturday. Technically, the employment concept is PAC employment for the pay period
that includes the Saturday in question, as we cannot observe within-pay period changes. Lack-
ing any information on events within a pay period, we assume that businesses adjust their
employment discretely at the beginning of each pay period and that employment is constant
within the pay period. This assumption is consistent with the typical practice of human re-
5 Other papers that use ADP data include Cho (2018) and Grigsby, Hurst and Yildirmaz (2019).
6 When accessing the microdata, we follow a number of procedures to ensure confidentiality. Business names are
not present in the data we access.
3
source departments, according to which job start dates often coincide with the beginning of
pay period. The restriction to “continuers” allows us to abstract from changes in the size of
ADP’s client base. Growth rates are weighted by PAC employment and further weighted for
representativeness by size and industry (treating PACs as establishments for weighting pur-
poses). We use QCEW employment counts by establishment size and two-digit NAICS as the
target population. Formally, let w j,t be the ratio of QCEW employment in a size-industry cell
j to ADP employment in cell j in week t, let C ( j) be the set of ADP businesses in cell j, let ei,t
ei,t −ei,t−1
be the employment of the i’th business, and let gi,t = ei,t−1 be the weekly growth rate of
business i.7 Aggregate growth is estimated as:
J
∑ j=1 w j,t−1 ∑i∈C( j) ei,t−1 gi,t
gt = J
. (1)
∑ j=1 w j,t−1 ∑i∈C( j) ei,t−1
Cumulating the weekly growth rates yields a weekly index level for employment. We
benchmark the data annually to QCEW employment levels and use a forward benchmarking
projection akin to the CES birth and death model. While we believe benchmarking is impor-
tant, since the QCEW (when available) represent the most complete and accurate estimate of
employment, the raw ADP data align well with official sources even before benchmarking. The
top panel of Figure 1 compares the monthly change in employment in the unbenchmarked ADP-
FRB series to the (QCEW-benchmarked) CES series through February 2020. The series track
each other closely, indicating that both are picking up the same underlying signal (i.e., true U.S.
payroll growth.) The bottom panel of Figure 1 shows the more recent evolution of monthly es-
timates, including the sharp declines in March 2020.8 Note that the ADP-FRB monthly series is
calculated as the change in the monthly average level of employment, whereas the CES calcu-
lates differences between pay periods including the 12th of the month. As such, the monthly
ADP-FRB series is more influenced by the last half of March, which helps explain the larger
decline we see in that series.
Returning to the weekly data, the last step is to seasonally adjust the series using the meth-
ods of Cleveland and Scott (2007), which combine a fixed coefficient regression with locally
7 For weighting, we use March QCEW employment values for each year.
8 In the bottom panel we use our preferred, benchmarked version of the ADP-FRB series.
4
400
200
Thousands of Jobs
0
-200
-400
-600
CES
-800 ADP-FRB active employment,
not benchmarked
-1000
Jan2006 Jan2008 Jan2010 Jan2012 Jan2014 Jan2016 Jan2018 Jan2020
500
0
Thousands of Jobs
-500
-1000
-1500
CES
-2000 ADP-FRB active employment,
benchmarked
-2500
Jan2019 Mar2019 May2019 Jul2019 Sep2019 Nov2019 Jan2020 Mar2020
Source: CES, ADP, authors’ calculations.
Figure 1: Monthly Change in Private Payroll Employment
weighted regressions on trigonometric functions. Note that the Cleveland and Scott approach
was employed by the BLS to seasonally adjust the weekly unemployment claims release.9
Since the primary focus of this paper is in weekly data, it is worth noting the distribution of
pay frequencies in the ADP data. As of March 2017, 22 percent of PACs were issuing paychecks
weekly, 46 percent biweekly, 21 percent semi-monthly, and 11 percent monthly (in terms of
employment, these shares are 23 percent, 55 percent, 18 percent, and 4 percent, respectively).
9 For the weekly seasonal adjustment, we specifically control for holiday weeks, including Thanksgiving, Memo-
rial Day, Labor Day, New Years, Christmas, and July 4th. We also account for strong employment weeks leading up
to holidays and the seasonal employment related to Christmas. Special thanks to Charlie Gilbert for his assistance
with seasonal adjustment.
5
These fractions are not far from what the BLS reports.10
3 Measuring Employment Losses during COVID-19 Pandemic
Figure 2 summarizes our main aggregate results by showing cumulative employment changes
since February 15 2020, weekly employment changes, and initial claims for unemployment
insurance, which serve as a comparison point. As the BLS March employment report indi-
cated, significant payroll employment losses had already occurred in the first half of March
(the BLS CES employment numbers refer to the pay period including March 12), and our ADP-
FRB employment numbers are consistent with an early March decline. Employment losses
then sharply accelerated in the second half of March; our ADP-FRB paid employment num-
ber suggests that an additional 13 million jobs were lost between March 15 and March 28. For
comparison, during the entire Great Recession (between December 2007 and February 2010)
8.8 million jobs were lost.
Our estimate of job losses between March 15 and March 28 is higher than the sum of initial
unemployment insurance claims filed during those two weeks. One likely explanation is that
our estimates of employment losses also include reduced hiring, while initial claims predomi-
nantly capture job destruction (workers fired or furloughed). Additionally, initial claims lag job
losses, some workers never file a claim, and unemployment offices were facing substantial pro-
cessing delays in the second half of March. We currently see some stabilization of employment
losses in the week ending April 4, although it is possible that the estimate for that week will
get revised downward when more data become available (revision properties are described in
the next subsection).
10 See BLS (2019) "Length of pay periods in the Current Employment Statistics survey."
6
Cumulative Job Loss since 15 Feb 2020
Weekly, SA Millions of Jobs
2 2
0 0
-2 -2
-4 -4
-6 -6
-8 -8
-10 -10
-12 -12
-14 ADP-FRB Active Employment -14
-16 ADP-FRB Paid Employment -16
-18 BLS CES -18
-20 -20
15 22 29 7 14 21 28 4
Feb. Mar. Apr.
ADP FRB First Differences
Weekly, SA Millions of Jobs
2 2
0 0
-2 -2
-4 -4
-6 -6
ADP-FRB Active Employment
-8 ADP-FRB Paid Employment -8
-10 -10
15 22 29 7 14 21 28 4
Feb. Mar. Apr.
Average Weekly Initial Claims
Weekly, SA Millions of Claims
7 7
UI Claims
6 6
5 5
4 4
3 3
2 2
1 1
0 0
15 22 29 7 14 21 28 4
Feb. Mar. Apr.
Note: ADP-FRB figures refer to the pay periods including each Saturday, UI claims refer to the week ending on each
Saturday.
Source: ADP, authors’ calculations.
Figure 2: Labor Market Developments since February 15 2020
7
3.1 Evolution of Real-Time Estimates
When calculating weekly employment estimates, the most recent observation incorporates data
from PACs with weekly pay frequency and approximately half of the PACs with biweekly pay
frequency. As additional PACs report payroll, our employment estimates for the more recent
weeks revise. Our real time estimation procedure attempts to account for the not-yet-available
data from nonreporting PACs by using the historical relationship between the raw “first print”
estimate and the final estimate, obtained when data from all PACs have arrived. While this
procedure works reasonably well during normal times, the current sharp movements in em-
ployment represent additional challenges for the real-time estimation of employment, since the
forecasting part of the estimation might work less well than usual.11
Figure 3 shows the evolution of real-time estimates for active and paid employment since
the beginning of March. These data indicate that initial employment estimates have been gen-
erally somewhat too high, revising down when more data became available. That said, after
the second read, revisions appear to be relatively small. Thus, it seems likely that the cur-
rent estimate of employment during the week including April 4 will get revised (most likely
downward), but estimates before that week are likely to remain little revised.
11 Indeed, our real-time adjustment to the most recent week of data in hand has performed worse during the
COVID-19 crisis than at any point since 2005. However, errors in the real-time forecasts do not persist, since the
adjustment is replaced by reporting PACs.
8
ADP-FRB Active Employment
Cumulative Job Loss since 15 Feb 2020
Weekly, SA Millions of Jobs
1 1
0 0
-1 -1
-2 -2
-3 -3
-4 -4
-5 -5
March 7 March 28
-6 March 14 April 4 -6
March 21
-7 -7
-8 -8
15 22 29 7 14 21 28 4
Feb. Mar. Apr.
ADP-FRB Paid Employment
Cumulative Job Loss since 15 Feb 2020
Weekly, SA Millions of Jobs
2 2
0 0
-2 -2
-4 -4
-6 -6
-8 -8
-10 -10
-12 -12
-14 March 7 March 28 -14
-16 March 14 April 4 -16
March 21
-18 -18
-20 -20
15 22 29 7 14 21 28 4
Feb. Mar. Apr.
Note: ADP-FRB figures refer to the pay periods including each Saturday.
Source: ADP, authors’ calculations.
Figure 3: Real-Time Estimates of Payroll Employment since February 15 2020
3.2 Sector Level Data
Since ADP microdata also contain information about NAICS industry of PACs, we construct
estimates of active and paid employment at the “supersector” level.12 These estimates are
presented in Table 1 and Figures 4-7.13
In absolute terms, the most notable cumulative drops in paid employment were in leisure
and hospitality (almost 4 million); trade, transportation, and utilities (almost 2.5 million); and
professional and business services (1.3 million). Large paid employment losses were also
12 Our ADP supersector definitions correspond to the broad supersector divisions reported in CES releases.
13 Note that supersector estimates do not necessarily add up to topline estimates, since the topline estimate is
constructed independently by using aggregated data only.
9
Percent change in employment Ability to telework
Active emp Paid emp (share, in percent)
Mining and logging -2.9 -2.9 ND
Construction -2.2 -9.3 17.2
Manufacturing -2.1 -5.5 30.3
Trade, transportation, and utilities -4.1 -9.4 15.8
Information Services -2.6 -4.7 53.3
Financial Services -1.3 -1.9 57.4
Professional and Business Services -2.4 -6.6 53.4
Education and Health Services +0.7 -4.5 25.9
Leisure and Hospitality -10.5 -30.5 8.8
Other Services -2.7 -9.9 27.7
Table 1: Cumulative Employment Changes and Ability to Telework
recorded in education and health (1 million); manufacturing (0.7 million); and construction
(0.65 million). In relative terms, the largest employment losses were in leisure and hospital-
ity (30.5 percent), other services (9.9 percent), trade, transportation, and utilities (9.4 percent),
and construction (9.3 percent). Table 1 also reports general ability to telework as estimated by
the BLS.14 It appears that the largest employment losses occurred in sectors with low general
ability to telework.
14 See, https://www.bls.gov/news.release/pdf/flex2.pdf. For similar estimates, see also Dingel and Neiman
(2020).
10
Mining and Logging Mining and Logging
Cumulative Job Loss since 15 Feb 2020 ADP FRB First Differences
Weekly, SA Millions of Jobs Weekly, SA Millions of Jobs
0.02 0.02 0.02 0.02
0.01 0.01 0.01 0.01
0.00 0.00 0.00 0.00
-0.01 -0.01 -0.01 -0.01
-0.02 -0.02 -0.02 -0.02
-0.03 ADP-FRB Active Employment -0.03 -0.03 ADP-FRB Active Employment -0.03
ADP-FRB Paid Employment ADP-FRB Paid Employment
-0.04 -0.04 -0.04 -0.04
-0.05 -0.05 -0.05 -0.05
15 22 29 7 14 21 28 4 15 22 29 7 14 21 28 4
Feb. Mar. Apr. Feb. Mar. Apr.
Construction Construction
Cumulative Job Loss since 15 Feb 2020 ADP FRB First Differences
Weekly, SA Millions of Jobs Weekly, SA Millions of Jobs
0.0 0.0 0.0 0.0
-0.1 -0.1 -0.1 -0.1
-0.2 -0.2 -0.2 -0.2
-0.3 -0.3 -0.3 -0.3
-0.4 -0.4 -0.4 -0.4
-0.5 -0.5 -0.5 -0.5
ADP-FRB Active Employment ADP-FRB Active Employment
-0.6 ADP-FRB Paid Employment -0.6 -0.6 ADP-FRB Paid Employment -0.6
-0.7 -0.7 -0.7 -0.7
-0.8 -0.8 -0.8 -0.8
15 22 29 7 14 21 28 4 15 22 29 7 14 21 28 4
Feb. Mar. Apr. Feb. Mar. Apr.
Manufacturing Manufacturing
Cumulative Job Loss since 15 Feb 2020 ADP FRB First Differences
Weekly, SA Millions of Jobs Weekly, SA Millions of Jobs
0.0 0.0 0.0 0.0
-0.1 -0.1 -0.1 -0.1
-0.2 -0.2 -0.2 -0.2
-0.3 -0.3 -0.3 -0.3
-0.4 -0.4 -0.4 -0.4
-0.5 -0.5 -0.5 -0.5
ADP-FRB Active Employment ADP-FRB Active Employment
-0.6 ADP-FRB Paid Employment -0.6 -0.6 ADP-FRB Paid Employment -0.6
-0.7 -0.7 -0.7 -0.7
-0.8 -0.8 -0.8 -0.8
15 22 29 7 14 21 28 4 15 22 29 7 14 21 28 4
Feb. Mar. Apr. Feb. Mar. Apr.
Note: The most recent week of the sector series is not corrected for incomplete reporting of biweekly, semimonthly,
and monthly payers.
Source: ADP, authors’ calculations.
Figure 4: Sector Payroll Employment since February 15 2020
11
Trade, Trans. and Util Trade, Trans. and Util
Cumulative Job Loss since 15 Feb 2020 ADP FRB First Differences
Weekly, SA Millions of Jobs Weekly, SA Millions of Jobs
0.50 0.50 0.2 0.2
0.25 0.25
0.00 0.00 -0.0 -0.0
-0.25 -0.25
-0.2 -0.2
-0.50 -0.50
-0.75 -0.75 -0.4 -0.4
-1.00 -1.00
-1.25 -1.25 -0.6 -0.6
-1.50 -1.50
ADP-FRB Active Employment -0.8 ADP-FRB Active Employment -0.8
-1.75 ADP-FRB Paid Employment
-1.75 ADP-FRB Paid Employment
-2.00 -2.00 -1.0 -1.0
-2.25 -2.25
-2.50 -2.50 -1.2 -1.2
15 22 29 7 14 21 28 4 15 22 29 7 14 21 28 4
Feb. Mar. Apr. Feb. Mar. Apr.
Information Services Information Services
Cumulative Job Loss since 15 Feb 2020 ADP FRB First Differences
Weekly, SA Millions of Jobs Weekly, SA Millions of Jobs
0.10 0.10 0.10 0.10
0.05 0.05
0.05 0.05
-0.00 -0.00
-0.00 -0.00
-0.05 -0.05
-0.10 -0.10 -0.05 -0.05
-0.15 -0.15
-0.10 -0.10
ADP-FRB Active Employment ADP-FRB Active Employment
-0.20 -0.20
ADP-FRB Paid Employment ADP-FRB Paid Employment
-0.15 -0.15
-0.25 -0.25
-0.30 -0.30 -0.20 -0.20
15 22 29 7 14 21 28 4 15 22 29 7 14 21 28 4
Feb. Mar. Apr. Feb. Mar. Apr.
Financial Services Financial Services
Cumulative Job Loss since 15 Feb 2020 ADP FRB First Differences
Weekly, SA Millions of Jobs Weekly, SA Millions of Jobs
0.20 0.20
0.05 0.05 0.15 0.15
0.00 0.00 0.10 0.10
-0.05 -0.05
0.05 0.05
-0.10 -0.10
-0.00 -0.00
-0.15 -0.15
-0.05 -0.05
-0.20 -0.20
ADP-FRB Active Employment ADP-FRB Active Employment
-0.25 -0.25 -0.10 -0.10
ADP-FRB Paid Employment ADP-FRB Paid Employment
-0.30 -0.30 -0.15 -0.15
-0.35 -0.35 -0.20 -0.20
15 22 29 7 14 21 28 4 15 22 29 7 14 21 28 4
Feb. Mar. Apr. Feb. Mar. Apr.
Note: The most recent week of the sector series is not corrected for incomplete reporting of biweekly, semimonthly,
and monthly payers.
Source: ADP, authors’ calculations.
Figure 5: Sector Payroll Employment since February 15 2020 (continued)
12
Prof. & Bus. Services Prof. & Bus. Services
Cumulative Job Loss since 15 Feb 2020 ADP FRB First Differences
Weekly, SA Millions of Jobs Weekly, SA Millions of Jobs
0.3 0.3
0.2 0.2
0.0 0.0
0.1 0.1
-0.2 -0.2 0.0 0.0
-0.4 -0.4 -0.1 -0.1
-0.2 -0.2
-0.6 -0.6
-0.3 -0.3
-0.8 -0.8 -0.4 -0.4
-1.0 ADP-FRB Active Employment
-1.0 -0.5 ADP-FRB Active Employment -0.5
ADP-FRB Paid Employment ADP-FRB Paid Employment
-0.6 -0.6
-1.2 -1.2
-0.7 -0.7
-1.4 -1.4 -0.8 -0.8
15 22 29 7 14 21 28 4 15 22 29 7 14 21 28 4
Feb. Mar. Apr. Feb. Mar. Apr.
Edu. & Health Edu. & Health
Cumulative Job Loss since 15 Feb 2020 ADP FRB First Differences
Weekly, SA Millions of Jobs Weekly, SA Millions of Jobs
0.3 0.3
0.2 0.2 0.2 0.2
0.1 0.1
0.0 0.0
0.0 0.0
-0.2 -0.2
-0.1 -0.1
-0.4 -0.4 -0.2 -0.2
-0.6 -0.6 -0.3 -0.3
-0.4 -0.4
-0.8 -0.8
ADP-FRB Active Employment -0.5 ADP-FRB Active Employment -0.5
-1.0 ADP-FRB Paid Employment -1.0 ADP-FRB Paid Employment
-0.6 -0.6
-1.2 -1.2 -0.7 -0.7
-1.4 -1.4 -0.8 -0.8
15 22 29 7 14 21 28 4 15 22 29 7 14 21 28 4
Feb. Mar. Apr. Feb. Mar. Apr.
Leisure + Hosp. Leisure + Hosp.
Cumulative Job Loss since 15 Feb 2020 ADP FRB First Differences
Weekly, SA Millions of Jobs Weekly, SA Millions of Jobs
0.0 0.0 0.00 0.00
-0.5 -0.5
-0.25 -0.25
-1.0 -1.0
-1.5 -1.5 -0.50 -0.50
-2.0 -2.0 -0.75 -0.75
-2.5 -2.5 -1.00 -1.00
-3.0 -3.0
-1.25 -1.25
-3.5 ADP-FRB Active Employment
-3.5 ADP-FRB Active Employment
ADP-FRB Paid Employment -1.50 ADP-FRB Paid Employment -1.50
-4.0 -4.0
-4.5 -4.5 -1.75 -1.75
-5.0 -5.0 -2.00 -2.00
15 22 29 7 14 21 28 4 15 22 29 7 14 21 28 4
Feb. Mar. Apr. Feb. Mar. Apr.
Note: The most recent week of the sector series is not corrected for incomplete reporting of biweekly, semimonthly,
and monthly payers.
Source: ADP, authors’ calculations.
Figure 6: Sector Payroll Employment since February 15 2020 (continued)
13
Other Services Other Services
Cumulative Job Loss since 15 Feb 2020 ADP FRB First Differences
Weekly, SA Millions of Jobs Weekly, SA Millions of Jobs
0.1 0.1
0.0 0.0
-0.0 -0.0
-0.1 -0.1
-0.2 -0.2 -0.1 -0.1
-0.3 -0.3
-0.2 -0.2
-0.4 -0.4
-0.5 -0.5 -0.3 -0.3
ADP-FRB Active Employment ADP-FRB Active Employment
-0.6 ADP-FRB Paid Employment -0.6 ADP-FRB Paid Employment
-0.4 -0.4
-0.7 -0.7
-0.8 -0.8 -0.5 -0.5
15 22 29 7 14 21 28 4 15 22 29 7 14 21 28 4
Feb. Mar. Apr. Feb. Mar. Apr.
Note: The most recent week of the sector series is not corrected for incomplete reporting of biweekly, semimonthly,
and monthly payers.
Source: ADP, authors’ calculations.
Figure 7: Sector Payroll Employment since February 15 2020 (continued)
14
3.3 The Distribution of Employment Change among Small Businesses
Small businesses (typically defined as those with fewer than 500 employees) have been a par-
ticular focus of policymakers, but they are especially difficult to study due to data limitations.
We next use the ADP microdata to characterize the distribution of employment changes among
small businesses as the U.S. entered the COVID-19 crisis. A particular advantage of the ADP
data, as we note elsewhere, is the ability to distinguish between “active” employees (i.e., em-
ployees existing on payroll records, regardless of whether they received pay in a given pay
period) and “paid” employees (i.e., employees who received pay in a given pay period). The
difference between these measures may shed some light on the permanence of employment
contractions—complete removal of employees from payroll records may reflect permanent job
destruction, while a decline in paid employment could reflect both permanent and temporary
layoffs.
ADP data afford a rich characterization of the distribution of employment changes among
small business units, both in terms of active and paid employees.15 Figure 8 reports the distri-
bution of cumulative employment changes from February 15 through March 28 among ADP
businesses operating in both periods16 . For each initial employment size class (defined based
on active employment), we report percentiles of employment change. The top panel corre-
sponds to changes in active employment, while the bottom panel refers to changes in paid
employment. The figure reveals striking patterns.
We focus first on active employment, which shows markedly smaller declines than paid
employment. Among the smallest initial size class (1-9 employees), every decile shows zero
cumulative change in active employment; this may be due to “integer problems” (i.e., for very
small business units, even a single employee layoff reflects a substantial change in productive
capacity), or it may reflect selection issues in which businesses of this size that come under
stress must exit entirely rather than shedding employment (in which case they do not appear
15 Here we again emphasize that ADP payroll units may not map directly to either firm or establishment concepts
used in official data; in this section; we treat payroll units as “small businesses” with the usual caveat that some of
them may actually be small establishments of larger firms.
16 For this figure, we use raw data without seasonal adjustment for simplicity. Moreover, the calculations for this
figure are performed directly on the microdata, without additional weighting and index machinery used elsewhere
in this paper.
15
Cumulative Growth Rate by Initial Business Size
ADP-FRB Active Employment
Percentile by Size Growth Rate
10 10
5 5
0 0
-5 -5
-10 Initial Business Size -10
1-9 10 - 19 20 - 49
50 - 99 100 - 249 250 - 499
-15 -15
-20 -20
10 20 30 40 50 60 70 80 90
Percentile
Growth Rate Deciles by Initial Business Size
ADP-FRB Paid Employment
Percentile by Size Growth Rate
20 20
10 10
0 0
-10 -10
-20 -20
-30 -30
-40 -40
-50 -50
-60 Initial Business Size -60
-70 1-9 10 - 19 20 - 49 -70
50 - 99 100 - 249 250 - 499
-80 -80
-90 -90
-100 -100
10 20 30 40 50 60 70 80 90
Percentile
Source: ADP, authors’ calculations.
Figure 8: Distribution of Cumulative Employment Changes from February 15 to March 28
in these calculations).
Within the next initial size class (10-19 employees), however, the 10th percentile business
saw an active employment decline of more than 10 percent over this period, though the 90th
percentile business saw gains of almost 10 percent. Roughly speaking, aside from the smallest
(1-9) size category, absolute employment changes in either direction were smallest among the
largest businesses; the interdecile range for the largest size class (250-499) is about 12 percent-
age points, while the range for the 10-19 class was more than 19 percentage points. It is also
striking that many businesses saw substantial employment gains over this period, even though
the figure is limited to businesses traditionally thought of as “small” (i.e., fewer than 500 em-
16
ployees). The median business in every size class saw zero change in active employment, with
positive gains among many above-median businesses.
We next turn to the bottom panel of Figure 8, which illustrates the employment change
distribution in terms of paid employment. Here was see substantially larger moves. The 10th
percentile business within every size class saw declines of at least 30 percent, with the largest
class shown (250-499) seeing a decline of almost 80 percent. Even the smallest business size
class (1-9) saw substantial declines among many businesses. This panel also reveals extremely
wide dispersion in outcomes; the interdecile range of paid employment changes varies from 50
percentage points among the smallest businesses to over 90 percentage points for the 250-499
size class. Of course, “integer problems” may be playing a role among the smallest businesses.
Interestingly, Bartik et al. (2020) (using rich Homebase data on small business hourly em-
ployment and hours) find that declines in hours worked between January and late March are
driven almost entirely by business shutdowns (i.e., zero hours worked in a week) and hours
reductions among retained employees, with minimal contribution from layoffs of hourly em-
ployees.17 Figure 8 is not markedly inconsistent with this while adding considerable color.
The median small business shows little movement in either active or paid employment between
mid-February and late March, suggesting minimal layoffs among surviving businesses. But
underlying this median result is considerable distributional variation: a bit less than half of
surviving businesses experienced nontrivial employment declines, and a few businesses expe-
rienced dramatic declines approaching 80 percent. On the other hand, some businesses have
experienced sizable employment gains. That being said, some tension remains between the
results of Bartik et al. (2020) and our results from ADP data in this section and elsewhere in the
paper. Generally speaking, in other sections we have documented substantial declines in both
paid and active employment among continuing businesses, and Figure 8 shows that the half of
businesses showing declines in active employment saw declines that are larger in magnitude
than the gains among the roughly half of businesses that saw gains; and for paid employment,
declines are seen up to the 60th percentile of small businesses. In other words, ADP data do
suggest substantial layoffs among continuing businesses. A possible reason for this discrep-
17 Homebase data track “local” types of businesses with concentration in retail and leisure and hospitality indus-
tries; see Bartik et al. (2020) for details.
17
ancy is that Bartik et al. (2020) observe business shutdowns when zero hourly employees clock
in; it is likely that some businesses are staying “open” with at least a few salaried employees,
so they count as continuing businesses in ADP data despite registering no hourly employment.
Taken together, the various insights from Figure 8 reveal striking heterogeneity in the ex-
periences of small businesses as the U.S. entered the COVID-19 crisis. The relatively muted
moves in active employment (relative to paid employment) might, one would hope, suggest
that many businesses do not see their layoffs as permanent (though it may also reflect record-
keeping inattentiveness), while the extreme declines in paid employment among more than
half of small businesses leave ample room for concern about the state of the small business
economy.
4 Conclusion
In the coming months, the BLS’s monthly labor reports will capture both the depth and breadth
of the current employment losses. Unfortunately, the speed of the recent declines makes it
necessary to turn to nontraditional data, which can provide some higher-frequency insight
about events as they unfold. The responsiveness of policymakers over the past few weeks has
been, in part, engendered by an understanding of how rapidly the COVID-19 pandemic has
afflicted the economy. A broad spectrum of timely and high-frequency economic information
has facilitated this understanding.
Looking forward, in addition to the monthly and weekly employment indexes, we are cur-
rently generating a set of diffusion indexes, measures of entry and exit, size and industry class
measures, and an employment index solely comprised of individuals with weekly paychecks.
This corpus of labor data will not only further our understanding of the rapid deterioration of
the employment situation, but will provide evidence of timing and magnitude of the eventual
rebound in the labor market.
References
Bartik, Alexander W., Marianne Bertrand, Feng Lin, Jesse Rothstein, and Matt Unrath. 2020.
18
“Labor Market Impacts of COVID-19 on Businesses: Update with Homebase Data Through
April 8.” mimeo.
Brynjolfsson, Erik, John Horton, Adam Ozimek, Daniel Rock, Garima Sharma, and Hong
Yi Tu Ye. 2020. “COVID-19 and Remote Work: An Early Look at US Data.” mimeo.
Cajner, Tomaz, Leland Crane, Ryan A. Decker, Adrian Hamins-Puertolas, Christopher Kurz,
and Tyler Radler. 2018. “Using Payroll Processor Microdata to Measure Aggregate Labor
Market Activity.” Board of Governors of the Federal Reserve System (U.S.) FEDS Working
Paper 2018-005.
Cajner, Tomaz, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christo-
pher Kurz. 2019a. “Tracking the Labor Market with "Big Data".” Board of Governors of the
Federal Reserve System (U.S.) FEDS Notes 2019-09-20.
Cajner, Tomaz, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christo-
pher Kurz. 2019b. “Weekly Payroll Employment Data for the United States.” Board of Gov-
ernors of the Federal Reserve System (U.S.) mimeo.
Cajner, Tomaz, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christo-
pher Kurz. 2020. “Improving the Accuracy of Economic Measurement with Multiple Data
Sources: The Case of Payroll Employment Data.” Big Data for 21st Century Economic Statistics.
University of Chicago Press.
Cho, David. 2018. “The Labor Market Effects of Demand Shocks: Firm-Level Evidence from
the Recovery Act.” mimeo.
Cleveland, William P., and Stuart Scott. 2007. “Seasonal Adjustment of Weekly Time Series
with Application to Unemployment Insurance Claims and Steel Production.” Journal of Offi-
cial Statistics, 23(2): 209–221.
Coibion, Olivier, Yuriy Gorodnichenko, and Michael Weber. 2020. “Labor Markets during
the Covid-19 Crisis: A Preliminary View.” mimeo.
19
Dingel, Jonathan I, and Brent Neiman. 2020. “How Many Jobs Can be Done at Home?” Na-
tional Bureau of Economic Research Working Paper 26948.
Grigsby, John, Erik Hurst, and Ahu Yildirmaz. 2019. “Aggregate Nominal Wage Adjustments:
New Evidence from Administrative Payroll Data.” NBER Working Paper 25628.
Haltiwanger, John. 2020. “Applications for New Businesses Contract Sharply in Recent Weeks:
A First Look at the Weekly Business Formation Statistics.” University of Maryland mimeo.
20
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