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June 2021

As the COVID-19 pandemic affects the nation,
hires and turnover reach record highs in 2020
Data from the Job Openings and Labor Turnover Survey
(JOLTS) highlight the effects of the coronavirus disease
2019 (COVID-19) pandemic and the results of efforts to
mitigate its spread in 2020. With the challenges of the
pandemic, many of the JOLTS data elements experienced
shocks early in the year before returning to previous trends.
In fact, many of the data elements experienced series
highs. For example, the hires level reached a series high of
8.3 million in May 2020, bouncing back from a depressed
level of 3.9 million in April 2020. The total separations level,
also referred as turnover, reached a series high of 16.3
million in March 2020, boosted largely by a spike in layoffs
and discharges.
The Job Openings and Labor Turnover Survey (JOLTS)

Larry Akinyooye
akinyooye.larry@bls.gov

data show that job openings, hires, and total separations
experienced large movements early in 2020 in the wake of
an economic recession because of the coronavirus disease
2019 (COVID-19) pandemic.1 After the initial economic
downturn, many of the JOLTS data series started to return
to prepandemic levels. This article reviews the JOLTS data
for 2020 at the total nonfarm, industry, and regional levels.2
(For definitions of JOLTS terms, see the box that follows.)

Larry Akinyooye is an economist in the Office of
Employment and Unemployment Statistics, U.S.
Bureau of Labor Statistics.
Eric Nezamis
nezamis.eric@bls.gov
Eric Nezamis is an economist in the Office of
Employment and Unemployment Statistics, U.S.
Bureau of Labor Statistics.

Definitions of JOLTS terms*
Job Openings
Job openings include all positions that are open on the last business day of the reference month. A job is
open only if it meets the following three conditions: (1) A specific position exists and there is work available
for that position; the position can be full time or part time, and it can be permanent, short term, or seasonal;
(2) the job could start within 30 days, whether or not the employer can find a suitable candidate during that
time; and (3) the employer is actively recruiting workers from outside the establishment to fill the position;
active recruiting means that the establishment is taking steps to fill a position and may include advertising in

1

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

newspapers, on television, or on the radio; posting internet notices, posting “help wanted” signs, networking
or making “word-of-mouth” announcements; accepting applications; interviewing candidates; contacting
employment agencies; or soliciting employees at job fairs, state or local employment offices, or similar
sources.
Excluded are positions open only to internal transfers, promotions or demotions, or recalls from layoffs. Also
excluded are openings for positions with start dates more than 30 days in the future, positions for which
employees have been hired but the employees have not yet reported for work, and positions to be filled by
employees of temporary help agencies, employee leasing companies, outside contractors, or consultants.
Hires
Hires include all additions to the payroll during the entire reference month, including newly hired and rehired
employees; full-time and part-time employees; permanent, short-term, and seasonal employees; employees
who were recalled to a job at the location following a layoff (formal suspension from pay status) lasting more
than 7 days; on-call or intermittent employees who returned to work after having been formally separated;
workers who were hired and separated during the month; and transfers from other locations.
Excluded are transfers or promotions within the reporting location, employees returning from a strike, and
employees of temporary help agencies, employee leasing companies, outside contractors, or consultants.
Separations
Separations include all separations from the payroll during the entire reference month and are reported by
type of separation: quits, layoffs and discharges, and other separations. Quits include employees who left
voluntarily, except for retirements or transfers to other locations. Layoffs and discharges include involuntary
separations initiated by the employer, including layoffs with no intent to rehire; layoffs (formal suspensions
from pay status) lasting or expected to last more than 7 days; discharges resulting from mergers,
downsizing, or closings; firings or other discharges for cause; terminations of permanent or short-term
employees; and terminations of seasonal employees (whether or not they are expected to return the next
season). Other separations include retirements, transfers to other locations, separations due to employee
disability, and deaths.
Excluded are transfers within the same location, employees on strike, and employees of temporary help
agencies, employee leasing companies, outside contractors, or consultants.
*From U.S. Bureau of Labor Statistics, Handbook of Methods, "Job Openings and Labor Turnover Survey:
Concepts," https://www.bls.gov/opub/hom/jlt/concepts.htm.

Job openings
The job openings level is a procyclical measure of labor demand; the number of job openings tends to increase
during economic expansion and decrease during an economic contraction.3 A larger number of job openings

2

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

generally indicates that employers need additional workers, which is a sign of a demand for labor and confidence
in the economy. Job openings and employment are closely linked and tend to rise and fall together. Also notable in
this context is that the number of employees on nonfarm payrolls is considered a Principal Federal Economic
Indicator; more particularly, it is frequently cited as a coincident economic indicator.4
Job openings fell sharply in March 2020 by 17.1 percent. In April, job openings fell further to a level of 4.6 million
on the last business day of the month. As lockdown efforts lifted throughout the country, job openings showed a
slow recovery by the close of 2020, although still below levels seen in 2019. Comparing December 2019 and
December 2020, job openings declined by 0.1 percent.5 The small decline in job openings signals a drop in the
demand for labor from December 2019 to December 2020. (See table 1.) The volatility of job openings from
December 2019 to December 2020 correlates with the economic recession as a result of the COVID-19 pandemic.
Despite over-the-year declines in job openings, they are still at a higher level compared with historical levels.
Table 1. Change in level and percentage of job openings, by industry and region, not seasonally adjusted,
December 2018–December 2020 (level in thousands)

Level by month and year
Industry and region

Change,

Change,

December

December

2018 to

2019 to

December

December

2019

2020

December 2018 December 2019 December 2020 Level Percent Level Percent
Total nonfarm
Industry
Total private
Mining and logging
Construction
Manufacturing
Durable goods
Nondurable goods
Trade, transportation, and utilities
Wholesale trade
Retail trade
Transportation, warehousing, and
utilities
Information
Financial activities
Finance and insurance
Real estate and rental and leasing
Professional and business services
Education and health services
Educational services
Healthcare and social assistance
Leisure and hospitality
Arts, entertainment, and recreation
Accommodation and food services

6,749

6,039

6,032

-710

-10.5

-7

-0.1

6,124
23
293
441
297
144
1,269
163
801

5,323
12
210
349
206
144
1,063
165
631

5,422
14
211
444
253
191
1,086
159
682

-801
-11
-83
-92
-91
0
-206
2
-170

-13.1
-47.8
-28.3
-20.9
-30.6
0.0
-16.2
1.2
-21.2

99
2
1
95
47
47
23
-6
51

1.9
16.7
0.5
27.2
22.8
32.6
2.2
-3.6
8.1

305

266

245

-39

-12.8

-21

-7.9

132
341
278
63
1,196
1,251
93
1,159
907
97
810

145
311
221
90
1,071
1,164
101
1,064
749
114
635

107
279
220
59
1,327
1,190
75
1,115
569
36
533

13
-30
-57
27
-125
-87
8
-95
-158
17
-175

9.8 -38
-8.8 -32
-20.5
-1
42.9 -31
-10.5 256
-7.0
26
8.6 -26
-8.2
51
-17.4 -180
17.5 -78
-21.6 -102

-26.2
-10.3
-0.5
-34.4
23.9
2.2
-25.7
4.8
-24.0
-68.4
-16.1

See footnotes at end of table.

3

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Table 1. Change in level and percentage of job openings, by industry and region, not seasonally adjusted,
December 2018–December 2020 (level in thousands)

Level by month and year
Industry and region

Change,

Change,

December

December

2018 to

2019 to

December

December

2019

2020

December 2018 December 2019 December 2020 Level Percent Level Percent
Other services
Government
Federal
State and local
Education
Excluding education
Region
Northeast
South
Midwest
West

271
624
105
519
220
299

249
716
88
628
216
412

195
610
89
521
196
325

-22
92
-17
109
-4
113

-8.1 -54
14.7 -106
-16.2
1
21.0 -107
-1.8 -20
37.8 -87

-21.7
-14.8
1.1
-17.0
-9.3
-21.1

1,123
2,544
1,598
1,484

1,061
2,263
1,253
1,461

1,029
2,394
1,294
1,315

-62
-281
-345
-23

-5.5 -32
-11.0 131
-21.6
41
-1.5 -146

-3.0
5.8
3.3
-10.0

Note: Details may not sum to totals because of rounding.
Source: U.S. Bureau of Labor Statistics.

Job openings by industry
During 2020, the monthly job openings level for 5 of the 19 industries reached series highs. The top industries
highlight the employer industries during the COVID-19 pandemic that could seamlessly shift employees to work
remotely or employer industries that are considered essential services that needed to remain open. The top three
industries with the most job openings were professional and business services, at 1.5 million in December 2020;
healthcare and social assistance, at 1.3 million in October 2020; and state and local government education, at
270,000 in January 2020. (See table 2.)
Table 2. Monthly series highs, by industry and region, seasonally adjusted, 2020
Industry and region
Industry
Nondurable goods
Professional and business services
Healthcare and social assistance
Arts, entertainment, and recreation
State and local government education
Construction
Durable goods
Nondurable goods
Wholesale trade

Industry and region data element

Job openings
Job openings
Job openings
Job openings
Job openings
Hires
Hires
Hires
Hires

See footnotes at end of table.

4

Month

November
December
October
January
January
May
May
May
May

Level

263,000
1.5 million
1.3 million
158,000
270,000
724,000
370,000
269,000
228,000

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Table 2. Monthly series highs, by industry and region, seasonally adjusted, 2020
Industry and region
Retail trade
Transportation, warehousing, and utilities
Real estate and rental and leasing
Professional and business services
Healthcare and social assistance
Accommodation and food services
Other services
Mining and logging
Construction
Durable goods
Nondurable goods
Wholesale trade
Retail trade
Transportation, warehousing, and utilities
Information
Real estate and rental and leasing
Professional and business services
Educational services
Healthcare and social assistance
Arts, entertainment, and recreation
Accommodation and food services
Other services
State and local government education
State and local government, excluding education
Retail trade
Transportation, warehousing, and utilities
Healthcare and social assistance
State and local government education
Mining and logging
Construction
Durable goods
Nondurable goods
Wholesale trade
Retail trade
Transportation, warehousing, and utilities
Information
Real estate and rental and leasing
Professional and business services
Educational services
Healthcare and social assistance
Arts, entertainment, and recreation
Accommodation and food services
Other services
State and local government education
State and local government, excluding education
Professional and business services
State and local government education

Industry and region data element
Hires
Hires
Hires
Hires
Hires
Hires
Hires
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations
Quits
Quits
Quits
Quits
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Other separations
Other separations

See footnotes at end of table.

5

Month

Level

May
November
July
August
May
June
May
April
April
April
March
March
March
March
March
March
March
March
March
March
March
March
March
April
January
December
October
July
April
April
April
March
April
March
March
March
April
March
March
March
March
March
March
March
April
April
July

1.0 million
421,000
131,000
1.4 million
1.1 million
1.9 million
646,000
68,000
820,000
542,000
358,000
335,000
1.8 million
602,000
246,000
203,000
1.9 million
371,000
1.7 million
665,000
5.0 million
965,000
294,000
248,000
594,000
170,000
467,000
147,000
61,000
713,000
483,000
277,000
257,000
1.4 million
443,000
196,000
176,000
1.3 million
320,000
1.2 million
618,000
4.6 million
895,000
137,000
151,000
114,000
52,000

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Table 2. Monthly series highs, by industry and region, seasonally adjusted, 2020
Industry and region
Region
West
Northeast
South
Midwest
West
Northeast
South
Midwest
West
Northeast
South
Midwest
West

Industry and region data element

Job openings
Hires
Hires
Hires
Hires
Total separations
Total separations
Total separations
Total separations
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges

Month

January
June
May
May
May
March
March
March
March
March
March
March
March

Level

1.7 million
1.4 million
2.9 million
1.9 million
2.2 million
3.3 million
5.4 million
3.8 million
3.8 million
2.8 million
4.1 million
3.1 million
3.1 million

Source: U.S. Bureau of Labor Statistics.

Monthly job openings increased over the year from December 2019 to December 2020 in 8 of the 19 industry
groups for which data are published. The largest over-the-year increases in job openings occurred in nondurable
goods manufacturing (+32.6 percent); professional and business services (+23.9 percent); and durable goods
manufacturing (+22.8 percent). Eleven of the nineteen industries showed job opening declines from December
2019 to December 2020. Industries with the largest declines were arts, entertainment, and recreation (−68.4
percent); real estate and rental and leasing (−34.4 percent); and information (−26.2 percent). (See table 1.)

Job openings by region
The West region was the only region to experience a monthly series high in job openings, with a level of 1.7 million
in January 2020. (See table 2.) Two of the four census regions experienced increases in the number of job
openings from December 2019 to December 2020. The South region had an increase of 5.8 percent and the
Midwest region increased by 3.3 percent. The West region had the largest over-the-year decline in job openings at
10.0 percent. The Northeast region also experienced a decline of 3.0 percent. (See table 1.)

Job openings and unemployment
One way to analyze job openings and unemployment is to consider the number of unemployed people per job
opening. The number of unemployed people per job opening is the ratio of unemployed people, as published by
the Current Population Survey, to the number of job openings. To calculate this ratio, we divide the number of
unemployed people by the number of job openings. Unemployment and job openings levels generally share an
inverse relationship. That is, when the economy enters a period of expansion, the number of unemployed people
tends to fall or remain at a low level. During economic expansions, job openings tend to increase or remain high,
causing the unemployed people per job opening ratio to decrease. The opposite occurs when the economy enters
periods of economic contraction—unemployment increases and job openings decrease, leading to a higher
unemployed people per job openings ratio that helps describe the slack in the labor market.6

6

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

In January and February of 2020, the unemployed people per job openings ratio was 0.8—indicating there were
more openings than unemployed jobseekers. This had made 27 consecutive months that the ratio was at or below
1.0—dating back to December 2017. The current recession that began in February 2020 helped end the streak.
The unemployed per job openings ratio then increased to 1.2 in March 2020 and peaked in April 2020 at 5.0. In the
latter part of the year, the ratio began to decline steadily to 1.6 in October 2020, were it remained through
December 2020. From the pre-COVID months to December, there was less demand by employers for job
openings than supply of unemployed jobseekers. (See chart 1.) The Great Recession began in December 2007,
with an unemployed people per job opening ratio of 1.7. The ratio peaked at 6.5 in July 2009, 1 month after the
recession officially ended—an increase of 282 percent.7 Though this current recession did not reach the same
ratio high as during the Great Recession, the sudden rise represented a much larger (525) percentage change in
the ratio.

Hires
Hires, like job openings, are considered a procyclical measurement. Hires showed a similar trend to job openings
in 2020, with sharp declines in March and April 2020. The declines were offset by large gains in May, with hires
reaching a series high that month and later hires declining to pre-COVID levels. Specifically, hires declined in
March by 847,000 and continued to plunge in April by 1.2 million as the country reacted to the COVID-19
pandemic and took steps to contain it. In May 2020, hiring rebounded (+4.3 million) reaching 8.3 million. The total

7

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

number of annual hires increased to a level of 73.1 million in 2020 (+4.4 percent), making it the 11th consecutive
year that the annual hires level has increased. (See table 3.)
Table 3. Change in level and percentage of annual hires, by industry and region, not seasonally adjusted,
December 2018–December 2020 (levels in thousand)

Level by year

Change, December

Change, December

2018 to December

2019 to December

2019

2020

Industry and region
2018
Total
Industry
Total private
Mining and logging
Construction
Manufacturing
Durable goods
Nondurable goods
Trade, transportation, and utilities
Wholesale trade
Retail trade
Transportation, warehousing, and utilities
Information
Financial activities
Finance and insurance
Real estate and rental and leasing
Professional and business services
Education and health services
Educational services
Healthcare and social assistance
Leisure and hospitality
Arts, entertainment, and recreation
Accommodation and food services
Other services
Government
Federal
State and local
Education
Excluding education
Region
Northeast
South
Midwest
West

2019

2020

Level

Percent

Level

Percent

68,596 69,984 73,094

1,388

2.0

3,110

4.4

64,284
447
4,526
4,391
2,511
1,879
13,673
1,755
9,026
2,894
1,089
2,500
1,636
864
13,749
8,516
1,160
7,352
12,798
2,208
10,587
2,596
4,311
419
3,891
2,013
1,879

65,564
303
4,987
4,053
2,272
1,781
13,902
1,775
9,032
3,095
1,133
2,656
1,682
973
13,790
8,665
1,166
7,499
13,464
1,992
11,470
2,609
4,420
501
3,918
2,037
1,882

68,899
246
5,022
4,819
2,746
2,073
15,306
1,843
9,836
3,629
977
2,704
1,690
1,013
13,362
9,288
1,081
8,207
13,952
1,646
12,308
3,223
4,193
907
3,286
1,647
1,641

1,280
-144
461
-338
-239
-98
229
20
6
201
44
156
46
109
41
149
6
147
666
-216
883
13
109
82
27
24
3

2.0
-32.2
10.2
-7.7
-9.5
-5.2
1.7
1.1
0.1
6.9
4.0
6.2
2.8
12.6
0.3
1.7
0.5
2.0
5.2
-9.8
8.3
0.5
2.5
19.6
0.7
1.2
0.2

3,335
-57
35
766
474
292
1,404
68
804
534
-156
48
8
40
-428
623
-85
708
488
-346
838
614
-227
406
-632
-390
-241

5.1
-18.8
0.7
18.9
20.9
16.4
10.1
3.8
8.9
17.3
-13.8
1.8
0.5
4.1
-3.1
7.2
-7.3
9.4
3.6
-17.4
7.3
23.5
-5.1
81.0
-16.1
-19.1
-12.8

10,496
27,315
15,193
15,590

10,864
28,278
14,895
15,946

12,037
27,803
15,904
17,350

368
963
-298
356

3.5
3.5
-2.0
2.3

1,173
-475
1,009
1,404

10.8
-1.7
6.8
8.8

Note: Details may not sum to totals because of rounding.
Source: U.S. Bureau of Labor Statistics.

8

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Hires by industry
Annual hires increased in 12 of 19 industries in 2020 and decreased in 7 industries. The largest percentage
increases in the annual hires levels were in federal government (+81.0 percent). The increase was primarily driven
by the 2020 Decennial Census and the need to hire additional canvas employees at the U.S. Census Bureau.8
Other services (+23.5 percent), and durable goods manufacturing (+20.9 percent) also increased. The largest
percentage decreases in hires occurred in state and local government education (−19.1 percent); mining and
logging (−18.8 percent); and arts, entertainment, and recreation (−17.4 percent). (See table 3.) As the employers
around the United States opened up from the COVID-19 restrictions, four industries experienced annual series
highs for the level of hires in 2020. The four industries were accommodation and food services (12.3 million);
healthcare and social assistance (8.2 million); transportation, warehousing, and utilities (3.6 million); and other
services (3.2 million). (See table 4.) To note, these four industries also reached annual series highs for total
separations, which was attributed to COVID-19 pandemic.
Table 4. Annual series highs by industry and region, not seasonally adjusted, 2020, (levels in thousand)
Industry and region

Industry and region data element

Industry
Transportation, warehousing and Utilities
Healthcare and social assistance
Accommodation and food services
Other Services
Healthcare and social assistance
State and local government education
Wholesale trade
Retail trade
Transportation, warehousing, and utilities
Information
Real estate and rental and leasing
Professional and business services
Educational services
Healthcare and social assistance
Arts, entertainment, and recreation
Accommodation and food services
Other services
State and local government education
Transportation, warehousing, and utilities
State and local government education
Wholesale trade
Retail trade
Transportation, warehousing, and utilities
Real estate and rental and leasing
Professional and business services
Education and health services
Educational services
Arts, entertainment, and recreation
Accommodation and food services
Other services

Hires
Hires
Hires
Hires
Quits
Quits
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Other separations
Other separations
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations
Total separations

See footnotes at end of table.

9

Level

3,629
8,207
12,308
3,223
4,892
1,270
1,019
4,410
1,767
663
617
6,458
914
3,573
1,724
8,818
2,450
851
226
438
2,109
10,356
3,626
1,098
14,016
1,466
8,939
2,305
15,087
3,637

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Table 4. Annual series highs by industry and region, not seasonally adjusted, 2020, (levels in thousand)
Industry and region

Industry and region data element

State and local government education
State and local government, excluding education
Region
Northeast
Midwest
West
Northeast
South
Midwest
West
Northeast
South
Midwest
West

Level

Total separations
Total separations

2,556
1,909

Hires
Hires
Hires
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Total separations
Total separations
Total separations
Total separations

12,037
15,904
17,350
8,244
13,329
9,136
10,309
13,914
30,119
18,095
19,370

Source: U.S. Bureau of Labor Statistics.

In 2020, 11 industries experienced seasonally adjusted monthly series highs. Of the 11 industries, 7 industries saw
series highs in May 2020. The industries reaching the highest level were healthcare and social assistance (1.1
million), retail trade (1.0 million), and construction (724,000). Three industries saw series highs throughout the
summer months. Those industries were accommodation and foods services in June 2020 (1.9 million), real estate
and rental and leasing in July 2020 (131,000), and professional and business services in August 2020 (1.4 million).
Transportation, warehousing, and utilities experienced a series hires high in November 2020 (421,000). (See table
2.)
Six industries experienced monthly series lows in hires—all in April 2020. These industries include accommodation
and food services (395,000), construction (201,000), state and local government, excluding education (79,000),
real estate and rental and leasing (41,000), information (37,000), and arts, entertainment, and recreation (31,000).
(See table 5.) Out of the six industries, only construction experienced a monthly series high in hires 1month later in
May 2020.
Table 5. Monthly series lows by industry and region, seasonally adjusted, 2020
Industry and region
Industry
Construction
Information
Real estate and rental and leasing
Arts, entertainment, and recreation
Accommodation and food services
State and local government, excluding education
Arts, entertainment, and recreation
Other services
Arts, entertainment, and recreation

Industry and region data element

Hires
Hires
Hires
Hires
Hires
Hires
Total separations
Total separations
Quits

See footnotes at end of table.

10

Month

April
April
April
April
April
April
August
August
August

Level

201,000
37,000
41,000
31,000
395,000
79,000
44,000
86,000
11,000

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Table 5. Monthly series lows by industry and region, seasonally adjusted, 2020
Industry and region
Other services
Wholesale trade
Arts, entertainment, and recreation
Other services
State and local government, excluding education
Retail trade
Educational and services
Other services
Region
Northeast
South

Industry and region data element

Month

Level

Quits
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Layoffs and discharges
Other separations
Other separations
Other separations

May
September
August
August
August
May
June and July
June

36,000
20,000
31,000
15,000
9,000
11,000
1,000
1,000

Hires
Layoffs and discharges

April
September

558,000
502,000

Source: U.S. Bureau of Labor Statistics.

Hires by region
The Northeast region had the highest percentage increase in annual hires in 2020, rising 10.8 percent. Annual
hires also increased in the West region (+8.8 percent) and Midwest region (+6.8 percent). The South region was
the only region to experience a decline in annual hires in 2020 (−1.7 percent). In 2019, both the Northeast and the
South regions increased by 3.5 percent in annual hires, followed by the West region at 2.3 percent. In 2019, the
Midwest region was the only region to experience a decline of 2.0 percent in annual hires. (See table 3.) In 2020,
all four regions experienced series high in monthly hires. Of the four regions, the South (2.9 million), West (2.2
million) and Midwest (1.9 million) experienced their series high in monthly hires in May 2020. The Northeast region
followed in June 2020 with a series high in monthly hires at 1.4 million. (See table 2.)

Hires and job openings
Leading up to the COVID-19 recession, the trend occurring was that job openings were outpacing hires; which
signaled an increase demand for labor. In January 2020, job openings were at a level of 7.2 million. The decline in
job openings in March 2020 was likely related to the fact that job openings are a stock measure, meant to capture
job openings activity on the last business day of the month. Mid-March is also when many of the COVID-19
lockdown orders began. JOLTS hires and the Current Employment Statistics employment figures experienced
declines in March; however, the larger decline in those measures was not experienced until April 2020. Job
openings experienced their largest decline in April 2020, with a 1.2 million decrease. The declines were offset by
immediate gains in both job openings and hires, with hires reaching a series high of 8.3 million in May 2020. The
large spike and series high caused hires to briefly outpace job openings in May and June 2020. Hires have since
declined and returned to the levels seen around the fall and winter of 2016. Job openings have returned to levels
close to that of January 2020, with a difference of a little over 400,000 job openings. (See chart 2.)

11

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Total separations
In 2020, the COVID-19 pandemic affected both employees and employers as measured by JOLTS total
separations data. The year began flat in January and February 2020, with 5.7 million total separations before the
effect of the COVID-19 pandemic shutdowns took place in March 2020. In March 2020, total separations soared to
an all-time series high at 16.3 million; however, over the next 2 months, total separations quickly reset back to preCOVID-19 levels. JOLTS annual data show that the annual number of total separations reached its highest level in
series history. It increased 20 percent from December 2019 to December 2020, rising from 68.0 million to 81.5
million. (See table 7.) The level of total separations has grown annually for 10 consecutive years, with the most
recent year increase largely due to the influence of the COVID-19 pandemic.
Table 6. Change in level and percentage of annual total separations, by industry and region, not
seasonally adjusted, December 2018–December 2020 (levels in thousand)
Change, December Change, December
Level by year

2018 to December

2019 to December

2019

2020

Industry and region
2018

See footnotes at end of table.

12

2019

2020

Level

Percent

Level

Percent

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Table 6. Change in level and percentage of annual total separations, by industry and region, not
seasonally adjusted, December 2018–December 2020 (levels in thousand)
Change, December Change, December
Level by year

2018 to December

2019 to December

2019

2020

Industry and region
2018
Total
Industry
Total private
Mining and logging
Construction
Manufacturing
Durable goods
Nondurable goods
Trade, transportation, and utilities
Wholesale trade
Retail trade
Transportation, warehousing, and utilities
Information
Financial activities
Finance and insurance
Real estate and rental and leasing
Professional and business services
Education and health services
Educational services
Healthcare and social assistance
Leisure and hospitality
Arts, entertainment, and recreation
Accommodation and food services
Other services
Government
Federal
State and local
Education
Excluding education
Region
Northeast
South
Midwest
West

2019

2020

Level

Percent

Level

Percent

66,201 67,993 81,493

1,792

2.7

13,500

19.9

62,062
394
4,216
4,124
2,291
1,832
13,508
1,715
9,159
2,635
1,057
2,336
1,532
803
13,293
8,030
1,126
6,904
12,542
2,108
10,434
2,565
4,135
401
3,735
1,928
1,807

63,785
353
4,867
4,048
2,300
1,747
13,707
1,746
9,119
2,843
1,103
2,491
1,584
906
13,509
8,062
1,115
6,947
13,105
1,937
11,170
2,541
4,206
468
3,736
1,924
1,813

76,187
336
4,985
5,391
3,166
2,223
16,093
2,109
10,356
3,626
1,207
2,728
1,630
1,098
14,016
10,406
1,466
8,939
17,389
2,305
15,087
3,637
5,306
844
4,463
2,556
1,909

1,723
-41
651
-76
9
-85
199
31
-40
208
46
155
52
103
216
32
-11
43
563
-171
736
-24
71
67
1
-4
6

2.8
-10.4
15.4
-1.8
0.4
-4.6
1.5
1.8
-0.4
7.9
4.4
6.6
3.4
12.8
1.6
0.4
-1.0
0.6
4.5
-8.1
7.1
-0.9
1.7
16.7
0.0
-0.2
0.3

12,402
-17
118
1,343
866
476
2,386
363
1,237
783
104
237
46
192
507
2,344
351
1,992
4,284
368
3,917
1,096
1,100
376
727
632
96

19.4
-4.8
2.4
33.2
37.7
27.2
17.4
20.8
13.6
27.5
9.4
9.5
2.9
21.2
3.8
29.1
31.5
28.7
32.7
19.0
35.1
43.1
26.2
80.3
19.5
32.8
5.3

10,087
26,298
14,622
15,194

10,389
27,007
14,396
16,197

13,914
30,119
18,095
19,370

302
709
-226
1,003

3.0
2.7
-1.5
6.6

3,525
3,112
3,699
3,173

33.9
11.5
25.7
19.6

Note: Details may not sum to totals because of rounding.
Source: U.S. Bureau of Labor Statistics.

Total separations include quits, layoffs and discharges, and other separations. Each of these data elements has its
own unique trend and cyclical movements that were affected by the COVID-19 pandemic. Quits are procyclical,
which means that the number of quits typically rises when the economy expands and declines when the economy
contracts. In normal economic conditions, a higher level, of quits generally indicates workers are willing to leave

13

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

their current employment and are confident in future job prospects. However, during the 2020 COVID-19
pandemic, there were additional factors employee considered such as widespread employee layoffs and business
closings, health reasons, and dependent care. This is clear in 2020 with the COVID-19 pandemic negatively
influencing the U.S. economy as employees were less willing to leave their current job for a new one. Quits began
the year relatively flat, averaging 3.5 million quits in January and February 2020 before declining over the next 2
months to the lowest 2020 level of 2.1 million in April. Over the remaining portion of the year, the quits level
gradually increased and by reached pre-COVID levels by December 2020. The annual quits level fell from 42.1
million in 2019 to 36.3 million in 2020 (−13.8 percent). Layoffs and discharges are countercyclical, which means
that the number typically rises during economic contractions and falls during economic expansions. In 2020,
JOLTS layoffs and discharges data reveal that, at the start of the year, the levels were flat over the first 2 months.
Then as the economic recession progressed because of the COVID-19 pandemic, layoffs and discharges spiked
to a series high in March 2020. Throughout the spring, the number of layoffs and discharges declined to preCOVID-19 levels and remained relatively constant for the rest of the year. Annual layoffs and discharges grew to a
series high in 2020 because of the economic recession caused by the COVID-19 pandemic. In 2020, monthly
other separations remained relatively constant throughout the year; however, when combined for the year, other
separations were higher by 4.1 percent.
Chart 3 shows the relationship of the three components to total separations by displaying the percentage of total
separations attributed to each type of separation. Quits as a percentage of total separations decreased to 44.6
percent in 2020, the lowest share since 2010. Layoffs and discharges as a percentage of total separations
increased to 50.3 percent in 2020, the largest share since 2009. Other separations as a percentage of total
separations decreased to 5.1 percent in 2020, the lowest percentage in series history. (See chart 3.)

14

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

The number of annual quits declined over the year, from 42.1 million to 36.3 million (–13.8 percent). (See table 7.)
The annual quits level decline in 2020 comes after 10 consecutive years of increases in quits. Annual layoffs and
discharges increased notably over the year, from 21.9 million in 2019 to 41.0 million in 2020, an increase of 87.7
percent. (See table 8.) The annual level of other separations increased, from 4.0 million in 2019 to 4.2 million in
2020, an increase of 4.1 percent. (See table 9.)
Table 7. Change in level and percentage of annual quits, by industry and region, not seasonally adjusted,
December 2018–December 2020 (levels in thousand)

Level

Change, December

Change, December

2018 to December

2019 to December

2019

2020

Industry and region
2018
Total
Industry
Total private
Mining and logging
Construction
Manufacturing
Durable goods

2019

2020

Level

Percent

Level

Percent

40,329 42,142 36,318

1,813

4.5

-5,824

-13.8

38,173 39,916 33,883
245
179
108
2,059 2,084 1,614
2,504 2,490 2,356
1,378 1,396 1,274

1,743
-66
25
-14
18

4.6
-26.9
1.2
-0.6
1.3

-6,033
-71
-470
-134
-122

-15.1
-39.7
-22.6
-5.4
-8.7

See footnotes at end of table.

15

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Table 7. Change in level and percentage of annual quits, by industry and region, not seasonally adjusted,
December 2018–December 2020 (levels in thousand)
Change, December

Change, December

2018 to December

2019 to December

2019

2020

Level
Industry and region

Nondurable goods
Trade, transportation, and utilities
Wholesale trade
Retail trade
Transportation, warehousing, and utilities
Information
Financial activities
Finance and insurance
Real estate and rental and leasing
Professional and business services
Education and health services
Educational services
Healthcare and social assistance
Leisure and hospitality
Arts, entertainment, and recreation
Accommodation and food services
Other services
Government
Federal
State and local
Education
Excluding education
Region
Northeast
South
Midwest
West

2018

2019

2020

Level

Percent

Level

Percent

1,127
8,500
1,069
5,959
1,474
566
1,406
856
550
7,561
5,374
582
4,795
8,441
918
7,523
1,514
2,159
183
1,974
1,044
933

1,094
8,907
1,029
6,234
1,645
554
1,547
1,005
545
7,767
5,532
648
4,885
9,220
936
8,286
1,634
2,226
208
2,016
1,091
927

1,082
8,259
1,008
5,613
1,637
480
1,307
896
414
6,740
5,386
492
4,892
6,544
553
5,993
1,087
2,434
246
2,190
1,270
921

-33
407
-40
275
171
-12
141
149
-5
206
158
66
90
779
18
763
120
67
25
42
47
-6

-2.9
4.8
-3.7
4.6
11.6
-2.1
10.0
17.4
-0.9
2.7
2.9
11.3
1.9
9.2
2.0
10.1
7.9
3.1
13.7
2.1
4.5
-0.6

-12
-648
-21
-621
-8
-74
-240
-109
-131
-1,027
-146
-156
7
-2,676
-383
-2,293
-547
208
38
174
179
-6

-1.1
-7.3
-2.0
-10.0
-0.5
-13.4
-15.5
-10.8
-24.0
-13.2
-2.6
-24.1
0.1
-29.0
-40.9
-27.7
-33.5
9.3
18.3
8.6
16.4
-0.6

5,386 5,700 4,992
16,466 17,255 15,249
8,989 9,187 8,097
9,488 10,000 7,981

314
789
198
512

5.8
4.8
2.2
5.4

-708
-2,006
-1,090
-2,019

-12.4
-11.6
-11.9
-20.2

Note: Details may not sum to totals because of rounding.
Source: U.S. Bureau of Labor Statistics.

Table 8. Change in level and percentage of annual layoffs and discharges by industry and region, not
seasonally adjusted, December 2018–Decembber 2020 (levels in thousand)
Change, December Change, December
Level

2018 to December

2019 to December

2019

2020

Industry and region
2018
Total

2019

2020

21,804 21,851 41,018

See footnotes at end of table.

16

Level
47

Percent
0.2

Level
19,167

Percent
87.7

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Table 8. Change in level and percentage of annual layoffs and discharges by industry and region, not
seasonally adjusted, December 2018–Decembber 2020 (levels in thousand)
Change, December Change, December
Level

2018 to December

2019 to December

2019

2020

Industry and region
2018
Industry
Total private
Mining and logging
Construction
Manufacturing
Durable goods
Nondurable goods
Trade, transportation, and utilities
Wholesale trade
Retail trade
Transportation, warehousing, and utilities
Information
Financial activities
Finance and insurance
Real estate and rental and leasing
Professional and business services
Education and health services
Educational services
Healthcare and social assistance
Leisure and hospitality
Arts, entertainment, and recreation
Accommodation and food services
Other services
Government
Federal
State and local
Education
Excluding education
Region
Northeast
South
Midwest
West

2019

2020

20,550 20,607 39,081
128
151
206
2,002
2,583
3,216
1,370
1,315
2,752
752
750
1,727
619
566
1,025
4,174
4,055
7,198
500
614
1,019
2,658
2,420
4,410
1,013
1,021
1,767
412
463
663
637
639
1,107
417
320
489
219
320
617
4,991
5,041
6,458
2,102
2,037
4,486
479
402
914
1,626
1,633
3,573
3,800
3,551 10,541
1,147
967
1,724
2,654
2,586
8,818
942
769
2,450
1,253
1,244
1,937
90
123
443
1,167
1,123
1,496
600
543
851
565
577
647
3,930
8,353
4,788
4,733

Note: Details may not sum to totals because of rounding.
Source: U.S. Bureau of Labor Statistics.

17

3,968
8,244
8,248 13,329
4,419
9,136
5,217 10,309

Level

Percent

Level

Percent

57
23
581
-55
-2
-53
-119
114
-238
8
51
2
-97
101
50
-65
-77
7
-249
-180
-68
-173
-9
33
-44
-57
12

0.3
18.0
29.0
-4.0
-0.3
-8.6
-2.9
22.8
-9.0
0.8
12.4
0.3
-23.3
46.1
1.0
-3.1
-16.1
0.4
-6.6
-15.7
-2.6
-18.4
-0.7
36.7
-3.8
-9.5
2.1

18,474
55
633
1,437
977
459
3,143
405
1,990
746
200
468
169
297
1,417
2,449
512
1,940
6,990
757
6,232
1,681
693
320
373
308
70

89.6
36.4
24.5
109.3
130.3
81.1
77.5
66.0
82.2
73.1
43.2
73.2
52.8
92.8
28.1
120.2
127.4
118.8
196.8
78.3
241.0
218.6
55.7
260.2
33.2
56.7
12.1

38
-105
-369
484

1.0
-1.3
-7.7
10.2

4,276
5,081
4,717
5,092

107.8
61.6
106.7
97.6

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Table 9. Change in level and percentage of annual other separations, by industry and region, not
seasonally adjusted, December 2018–December 2020 (levels in thousand)
Change, December Change, December
Level

2018 to December

2019 to December

2019

2020

Industry and region

Total
Industry
Total private
Mining and logging
Construction
Manufacturing
Durable goods
Nondurable goods
Trade, transportation, and utilities
Wholesale trade
Retail trade
Transportation, warehousing, and utilities
Information
Financial activities
Finance and insurance
Real estate and rental and leasing
Professional and business services
Education and health services
Educational services
Healthcare and social assistance
Leisure and hospitality
Arts, entertainment, and recreation
Accommodation and food services
Other services
Government
Federal
State and local
Education
Excluding education
Region
Northeast
South
Midwest
West

2018

2019

2020

4,066

3,997

4,159

-69

-1.7

162

4.1

3,339
21
155
246
161
88
835
146
543
147
80
294
256
37
745
554
68
484
303
42
259
115
724
127
595
284
310

3,262
17
201
244
154
89
745
102
466
178
86
305
260
44
698
493
65
428
333
36
296
141
736
140
596
287
309

3,226
19
155
283
166
117
636
80
330
226
63
313
245
68
817
532
58
474
304
29
274
101
934
156
777
438
340

-77
-4
46
-2
-7
1
-90
-44
-77
31
6
11
4
7
-47
-61
-3
-56
30
-6
37
26
12
13
1
3
-1

-2.3
-19.0
29.7
-0.8
-4.3
1.1
-10.8
-30.1
-14.2
21.1
7.5
3.7
1.6
18.9
-6.3
-11.0
-4.4
-11.6
9.9
-14.3
14.3
22.6
1.7
10.2
0.2
1.1
-0.3

-36
2
-46
39
12
28
-109
-22
-136
48
-23
8
-15
24
119
39
-7
46
-29
-7
-22
-40
198
16
181
151
31

-1.1
11.8
-22.9
16.0
7.8
31.5
-14.6
-21.6
-29.2
27.0
-26.7
2.6
-5.8
54.5
17.0
7.9
-10.8
10.7
-8.7
-19.4
-7.4
-28.4
26.9
11.4
30.4
52.6
10.0

769
1,480
844
970

722
1,503
794
977

668
1,549
867
1,077

-47
23
-50
7

-6.1
1.6
-5.9
0.7

-54
46
73
100

-7.5
3.1
9.2
10.2

Note: Details may not sum to totals because of rounding.
Source: U.S. Bureau of Labor Statistics.

Components of separations by industry

18

Level

Percent

Level

Percent

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

As mentioned previously, separations are the total number of employees separated from their employer at any
time during the reference month. Separations consist of quits, layoffs and discharges, and other separations. This
section discusses what happened in 2020 with the components of separations by industry.

Quits
Quits include employees who left their job voluntarily, excluding retirements or transfers to other locations, which
are counted as other separations. In 2020, the number of annual quits grew in 3 of 19 industries, while the
remaining 16 industries had fewer quits. The largest percentage increases in annual quits levels were in federal
government (+18.3 percent), followed by state and local government education (+16.4 percent), and healthcare
and social assistance (+0.1 percent). Of note, the increased quits in federal government were primarily driven by
changes in employment needed for the 2020 Decennial Census. The largest percentage decreases in annual quits
levels were in arts, entertainment, and recreation (−40.9 percent), followed by mining and logging (−39.7 percent),
and other services (−33.5 percent). (See table 7.)
Two of nineteen industries reached a series high for the annual level of quits in 2020. These two industries are
healthcare and social assistance, at 4.9 million, and state and local government education, at 1.3 million. (See
table 4.) Four of nineteen industries reached monthly seasonally adjusted series highs for quits in 2020. Those
industries were retail trade, at 594,000 in January; healthcare and social assistance, at 467,000 in October:
transportation, warehousing, and utilities, at 170,000 in December: and state and local government education, at
147,000 in July. (See table 2.) Two industries reached monthly seasonally adjusted series lows for quits in 2020:
arts, entertainment, and recreation, at 11,000 in August, and other services, at 36,000 in May. (See table 5.)

Layoffs and discharges
As defined earlier, layoffs and discharges include involuntary separations initiated by the employer, including
layoffs with no intent to rehire. In 2020, annual layoffs and discharges increased in all 19 industries. The largest
percentage increases in annual layoffs and discharges were in federal government (+260.2 percent),
accommodation and food services (+241.0 percent), and other services (+218.6 percent). The industries with the
lowest percentage increases in annual layoffs and discharges were in state and local government, excluding
education (+12.1 percent), construction (+24.5 percent), and professional and business services (+28.1 percent).
(See table 7.) Notably, the increase in federal government layoffs and discharges was primarily driven by the 2020
Decennial Census concluding; the widespread increases among other industries were mainly due to the economic
recession caused by the COVID-19 pandemic and the efforts to contain it.
Twelve of nineteen industries reached a series high for the annual level of layoffs and discharges. The largest
series highs industries were in accommodation and food services, at 8.8 million; professional and business
services, at 6.5 million; and retail trade, at 4.4 million. (See table 4.) There were no annual series lows in layoffs
and discharges. In 2020, 17 industries reached a series high for monthly layoffs and discharges. The largest series
highs among the industries were in accommodation and food services, at 4.6 million in March; retail trade, at 1.4
million in March; and professional and business services, at 1.3 million in March. (See table 2.) As a measure of
the effect of the COVID-19 pandemic, all 17 industries monthly series highs occurred in March or April. For
monthly layoffs and discharges, four industries reached a series low in 2020. The three lowest monthly layoffs and

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U.S. BUREAU OF LABOR STATISTICS

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discharges were in state and local government, excluding education, at 9,000 in August; other services, at 15,000
in August; and wholesale trade, at 20,000 in September. (See table 5.)

Other separations
In 2020, annual other separations increased in 10 of 19 industries, with 9 industries having fewer annual other
separations than in the previous year. The largest percentage increase in annual other separations were in real
estate and rental and leasing (+54.5 percent), state and local government education (+52.6 percent), and
nondurable goods manufacturing (+31.5 percent). The industries with the largest percentage declines in annual
other separations were in retail trade (−29.2 percent), other services (−28.4 percent), and information (−26.7
percent). (See table 8.) Two of nineteen industries reached a series high for the annual level of other separations:
state and local government education, and transportation, warehousing, and utilities at 438,000 and 226,000,
respectively. Two of nineteen industries reached series lows in the annual level of other separations: wholesale
trade and retail trade at 80,000 and 330,000, respectively. (See table 4.) There were two monthly seasonally
adjusted series highs in other separations: professional and business services at 114,000 in April, and state and
local government education at 52,000 in July. Three of nineteen industries had monthly seasonally adjusted series
lows in other separations: educational services at 1,000 in June and July, other services at 1,000 in June, and
retail trade at 11,000 in May. (See table 2 and 5.)

Components of separations by region
The U.S. regions were affected by the COVID-19 pandemic at different rates and levels. This section describes the
differences with the components of separations among the regions in 2020.

Northeast region
In 2020, the Northeast region had an annual level of 13.9 million total separations, an increase of 33.9 percent and
the highest increase of all the regions. The Northeast region quits level decreased to 5.0 million (−12.4 percent),
the lowest level of the regions. For layoffs and discharges, the Northeast region rose notably to 8.2 million, the
largest percentage (+107.7 percent) increase of the four regions. The Northeast region other separations level
declined to 668,000, the only percentage (−7.5 percent) decline of the four regions.

South region
In the South region, the annual level of total separations rose to 30.1 million, the lowest percentage (+11.5 percent)
increase of the regions. Within total separations, the quits level fell to 15.3 million for the South region, the largest
percentage decline (−11.6 percent) of the regions. The South region layoffs and discharges level rose to 13.3
million, the lowest percentage increase (+61.6 percent) of the regions, and the other separations level rose to 1.5
million, increasing over the year (+3.1 percent).

Midwest region
In the Midwest region, the annual total separations level rose to 18.1 million (+25.7 percent). Within total
separations, there were 8.1 million (−11.9 percent) quits in the Midwest region and 9.1 million (+106.7 percent)
layoffs and discharges. Other separations rose to 867,000 (+ 9.2 percent).

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West region
The West region had an annual total separations level increase of 19.4 million (+19.6 percent). Within total
separations in the West region, the quits level decreased to 8.0 million, the largest percentage (−20.2 percent)
decrease among the regions. The layoffs and discharges level rose to 10.3 million (+97.6 percent), and the other
separations level rose to 1.1 million, the largest percentage increase (+10.2 percent) of the regions. (See tables 6,
7, 8, and 9.)

Separations for the regions
With annual total separations and layoffs and discharges, all four regions saw series highs in 2020. (See table 4.)
The Northeast region reached a series low with other separations. All four regions reached a monthly series high
for total separation in March 2020. The Northeast region total separations level reached a monthly series high of
3.3 million, the South region total separations level reached a monthly series high of 5.4 million, and the Midwest
and West regions both reached a monthly series high of 3.8 million. All four regions reached a monthly series high
for layoffs and discharges in March 2020. The Northeast region layoffs and discharges level reached a series high
of 2.8 million, the South region total separations level reached a series high of 4.1 million, and the Midwest and
West regions both reached a series high of 3.1 million. None of the four regions reached monthly series highs for
quits and other separations. (See table 2 and 4.)
The South region was the only region that reached a monthly series low for layoffs and discharges, with a level of
502,000 in September 2020. No region reached a series low in total separations, quits, and other separations.
(See tables 5.)
An analysis of each region by the components as a percentage of total separations illustrates the different
characteristics of the JOLTS data at the regional level. The Northeast region had the smallest percentage of quits
within total separations, at 35.9 percent in 2020. The South region experienced the highest percentage of quits, at
50.6 percent. In 2020, the Northeast region had the largest percentage of layoff and discharges within total
separations, at 59.2 percent. The South region had the lowest percentage of layoffs and discharges, at 44.3
percent. The West region had the highest percentage of other separations, at 5.6 percent, while the Northeast and
Midwest regions had the lowest percentage, at 4.8 percent. (See chart 4.)

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Quits compared with layoffs and discharges
Over the period from July 2011 to February 2020, there were 104 consecutive months in which the monthly quits
level exceeded the monthly layoffs and discharges level. During this period, the gap between the level of quits and
the level of layoffs and discharges continued to widen. This growing gap is attributable to a persistent rise in the
number of quits while the number of layoffs and discharges remained flat. However, with the onset of the
pandemic, this persistent trend of quits exceeding layoffs and discharges reversed suddenly. In March 2020,
layoffs and discharges exceeded quits by 10.1 million. This large gap was caused by a sharp increase in layoffs
and discharges and a substantial decline in quits. In April 2020, the gap narrowed slightly to 7.2 million. In May, the
pattern of quits exceeding layoffs and discharges returned to pre-COVID pandemic levels and remained over the
rest of the year. (See chart 5.)

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Summary
JOLTS 2020 data reflect the effects, efforts to mitigate, and the recovery of the U.S. labor market caused by the
COVID-19 pandemic. Many establishments either closed or operated on a limited capacity because of the
COVID-19 pandemic. At the start of 2020, the job openings level trended higher until the COVID-19 pandemic
affected the U.S. economy in March 2020. Although job openings declined early in the year, job openings slowly
trended up by the end of the year. In 2020, the number of hires reached a series high after the notable declines in
March and April; however, by the end of 2020, the number of hires decreased to pre-COVID-19 levels. The number
of total separations also reached a series high in 2020. This was mainly attributed to the large rise in layoff and
discharges, which also rose to a series high. Total separations gradually trended back toward pre-COVID-19 levels
later in the year.
SUGGESTED CITATION

Larry Akinyooye and Eric Nezamis, "As the COVID-19 pandemic affects the nation, hires and turnover reach
record highs in 2020," Monthly Labor Review, U.S. Bureau of Labor Statistics, June 2021, https://doi.org/10.21916/
mlr.2021.11.
NOTES

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U.S. BUREAU OF LABOR STATISTICS

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1 JOLTS produces monthly data on job openings, hires, quits, layoffs and discharges, and other separations from a sample of
approximately 21,000 establishments. This sample consists of establishments from all 50 states, the District of Columbia, and all
nonfarm industries as classified by the North American Industry Classification System (NAICS). The JOLTS sample allows publication
of data by four census regions and by select two-digit NAICS codes. For more information on the program’s concepts and
methodology, see “Job Openings and Labor Turnover Survey: Handbook of Methods (Washington, DC: U.S. Bureau of Labor
Statistics, July 13, 2020), https://www.bls.gov/opub/hom/jlt/home.htm. See also the JOLTS page on the BLS website, at https://
www.bls.gov/jlt/. All annual data are not seasonally adjusted, and all monthly data are seasonally adjusted. Over-the-year changes are
calculated from December of the previous year through December of the reference year.
2 JOLTS estimates are produced by region for the Northeast, the South, the Midwest, and the West.
3 According to the finance and investment education website Investopedia, procyclical “refers to a condition of a positive correlation
between the value of a good, a service, or an economic indicator and the overall state of the economy. In other words, the value of the
good, service, or indicator tends to move in the same direction as the economy, growing when the economy grows and declining
when the economy declines.” For more information, see Akhilesh Ganti, “Procyclic,” Investopedia, updated March 4, 2021, http://
www.investopedia.com/terms/p/procyclical.asp.
4 For more information, see “What Principal Federal Economic Indicators (PFEIs) are published by the U.S. Bureau of Labor
Statistics?” News Room—Frequently Asked Questions (U.S. Bureau of Labor Statistics, December 29, 2016), https://www.bls.gov/
newsroom/faqs.htm. For more on the background, see “Statistical Policy Directive No. 3: Compilation, Release, and Evaluation of
Principal Federal Economic Indicators,” Federal Register, vol. 50, no. 186, September 25, 1985. For more on the concepts of leading,
coincident, and lagging economic indicators, see “Description of components” (The Conference Board, February 6, 2012), https://
www.conference-board.org/data/bci/index.cfm?id=2160.
5 BLS considers job openings a stock measure and does not produce job openings annual totals.
6 Countercyclical is a condition of negative correlation in which the value of the good, service or indicator moves “in the opposite
direction of the overall economic cycle: rising when the economy is weakening, and falling when the economy is strengthening.” For
more information, see InvestorWords, http://www.investorwords.com/1166/countercyclical.html.
7 The National Bureau of Economic Research is the official arbiter of the beginning and ending dates of U.S. business cycle
expansions and contractions. For more information, see “U.S. Business cycle dating” (Cambridge, MA: National Bureau of Economic
Research, September 20, 2010), http://www.nber.org/cycles/.
8 The large increase in annual hires for the federal government was largely the result of the hiring of temporary Census 2020 workers
in late summer 2019.

RELATED CONTENT

Related Articles
Job openings, hires, and quits set record highs in 2019, Monthly Labor Review, June 2020.
Job openings, hires, and quits reach historic highs in 2018, Monthly Labor Review, July 2019.
Job openings and labor turnover trends for Metropolitan Statistical Areas in 2019, Beyond the Numbers, September 2020.

24

June 2021

Unemployment rises in 2020, as the country
battles the COVID-19 pandemic
Total civilian employment fell by 8.8 million over the year, as
the COVID-19 pandemic brought the economic expansion
to a sudden halt, taking a tremendous toll on the U.S. labor
market. The unemployment rate increased in 2020, surging
to 13.0 percent in the second quarter of the year before
easing to 6.7 percent in the fourth quarter. Although some
people were able to work at home, the numbers of
unemployed on temporary layoff, those working part time
for economic reasons, and those unemployed for 27 or
more weeks increased sharply over the year.
A decade-long economic expansion ended early in 2020, as
the coronavirus disease 2019 (COVID-19) pandemic and
efforts to contain it led businesses to suspend operations or
close, resulting in a record number of temporary layoffs.
The pandemic also prevented many people from looking for
work. For the first 2 months of 2020, the economic
expansion continued, reaching 128 months, or 42 quarters.
This was the longest economic expansion on record before
millions of jobs were lost because of the pandemic.1

Sean M. Smith
smith.sean@bls.gov
Sean M. Smith is an economist in the Office of
Employment and Unemployment Statistics, U.S.
Bureau of Labor Statistics.
Roxanna Edwards
edwards.roxanna@bls.gov

Total civilian employment, as measured by the Current
Population Survey (CPS), fell by 21.0 million from the fourth
quarter of 2019 to the second quarter of 2020, while the
unemployment rate more than tripled, from 3.6 percent to
13.0 percent. This was the highest quarterly average
unemployment rate in the history of the CPS.2 (See the box
that follows for more information about the CPS, as well as

Roxanna Edwards is an economist in the Office
of Employment and Unemployment Statistics,
U.S. Bureau of Labor Statistics.
Hao C. Duong
duong.hao@bls.gov
Hao C. Duong is an economist in the Office of
Employment and Unemployment Statistics, U.S.
Bureau of Labor Statistics.

the Current Employment Statistics survey.)

The CPS and the CES

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The U.S. Bureau of Labor Statistics (BLS) produces two monthly employment series obtained from two
different surveys: the estimate of total nonfarm jobs, derived from the Current Employment Statistics (CES)
survey, also called the establishment or payroll survey; and the estimate of total civilian employment,
derived from the Current Population Survey (CPS), also called the household survey. The two surveys use
different definitions of employment, as well as different survey and estimation methods. The CES survey is a
survey of employers that provides a measure of the number of payroll jobs in nonfarm industries. The CPS
is a survey of households that provides a measure of employed people ages 16 years and older in the
civilian noninstitutional population.
Employment estimates from the CPS provide information about workers in both the agricultural and
nonagricultural sectors and in all types of work arrangements: workers with wage and salary jobs (including
employment in a private household), those who are self-employed, and those doing unpaid work for at least
15 hours per week in a business or farm operated by a family member. CES payroll employment estimates
are restricted to nonagricultural wage and salary jobs and exclude private household workers. As a result,
employment estimates from the CPS are higher than those from the CES survey. In the CPS, however,
workers who hold multiple jobs (referred to as “multiple jobholders”) are counted only once, regardless of
how many jobs these workers held during the survey reference period. By contrast, because the CES
survey counts the number of jobs rather than the number of people, each nonfarm job is counted separately,
even when two or more jobs are held by the same person.
The reference periods for the surveys also differ. In the CPS, the reference period is generally the calendar
week that includes the 12th day of the month. In the CES survey, employers report the number of workers
on their payrolls for the pay period that includes the 12th of the month. Because pay periods vary in length
among employers and may be longer than 1 week, the CES employment estimates can reflect longer
reference periods.
For more information on the two monthly employment measures, see “Employment from the BLS household
and payroll surveys: summary of recent trends” (U.S. Bureau of Labor Statistics, February 5, 2021),
www.bls.gov/web/empsit/ces_cps_trends.htm.

However, late in the second quarter, the labor market began a slow recovery that continued for the rest of the year.
The unemployment rate fell to 8.8 percent in the third quarter and to 6.7 percent in the fourth quarter. This was still
3.1 percentage points higher than a year earlier and reflected the 10.8 million people who were unemployed in the
fourth quarter of 2020, which was 4.9 million more than at the end of 2019.3
Total employment, as measured by the CPS, rose by 8.6 million in the third quarter of 2020 and by 3.6 million in
the fourth quarter. At the end of the year, total employment averaged 149.8 million, 8.8 million (or 5.5 percent) less
than in the fourth quarter of 2019. The employment–population ratio (the percentage of the population ages 16 and
older who are employed) averaged 57.4 percent in the fourth quarter, down by 3.6 percentage points over the year.

2

U.S. BUREAU OF LABOR STATISTICS

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The labor force participation rate (the percentage of the population ages 16 and older who are either employed or
actively seeking employment) averaged 61.5 percent, down by 1.7 percentage points over the year.4
This article highlights important developments in key labor market measures from the CPS during 2020, both
overall and for various demographic groups. New questions added to the CPS beginning in May 2020 provide data
on the number of people who teleworked, were unable to work, or were unable to look for work because of the
pandemic. The article also examines usual weekly earnings and labor force status flows in 2020, as well as the
employment situations of veterans, people with a disability, and the foreign born.

Employment declined by a record amount in 2020
Although the labor market remained quite strong early in 2020, employment fell sharply in the spring with the onset
of the COVID-19 pandemic. Employment growth in the second half of the year led to a recovery of about half of
these employment losses. Employment averaged 149.8 million in the fourth quarter of 2020, down 8.8 million from
a year earlier.
Much of the employment decline early in the pandemic occurred among part-time workers. Part-time workers
accounted for 29 percent of the employment decline from the fourth quarter of 2019 to the second quarter of 2020,
well above their prepandemic share of employment, at 17 percent. However, part-time workers made up 37
percent of the employment gain from the second quarter to the fourth quarter of 2020. In the fourth quarter of
2020, part-time employment was down by about 6 percent from a year earlier, matching the decline among fulltime workers, which was also down by about 6 percent over the year.
The employment–population ratio decreased in 2020. The ratio dropped to 52.9 percent in the second quarter of
2020, which is the lowest quarterly average for this measure in the history of the CPS. The employment–
population ratio improved in the second half of 2020, increasing to 57.4 percent in the fourth quarter of 2020, but it
was still 3.6 percentage points lower than it had been a year earlier. Following a similar pattern, the labor force
participation rate fell sharply in 2020—at 60.8 percent in the second quarter, it was at its lowest level since 1973. In
the second half of 2020, the rate showed some recovery, rising to 61.5 percent in the fourth quarter, but it
remained 1.7 percentage points below the rate from a year earlier. (See table 1 and chart 1.)
Table 1. Employment status of the civilian noninstitutional population ages 16 years and older, by gender,
race, and Hispanic or Latino ethnicity, quarterly averages, seasonally adjusted, 2019–20 (levels in
thousands)
2020
Characteristic

Total, 16 years and older
Civilian labor force
Participation rate
Employed
Full-time workers
Part-time workers

Fourth quarter,
2019

164,435
63.2
158,544
131,462
27,019

Change, fourth quarter 2019 to
First

Second

Third

Fourth

quarter

quarter

quarter

quarter

163,875
63.1
157,642
130,160
27,299

158,158
60.8
137,565
116,711
21,020

See footnotes at end of table.

3

160,327
61.5
146,199
121,664
24,634

160,607
61.5
149,769
124,209
25,476

fourth quarter 2020

–3,828
–1.7
–8,775
–7,253
–1,543

U.S. BUREAU OF LABOR STATISTICS

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Table 1. Employment status of the civilian noninstitutional population ages 16 years and older, by gender,
race, and Hispanic or Latino ethnicity, quarterly averages, seasonally adjusted, 2019–20 (levels in
thousands)
2020
Characteristic

Employment–
population ratio
Unemployed
Unemployment rate
Men, 16 years and older
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate
Women, 16 years and older
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate
White
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate
Black or African American
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate
Asian
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed

Fourth quarter,
2019

Change, fourth quarter 2019 to
First

Second

Third

Fourth

quarter

quarter

quarter

quarter

fourth quarter 2020

61.0

60.7

52.9

56.1

57.4

–3.6

5,891
3.6

6,232
3.8

20,594
13.0

14,128
8.8

10,838
6.7

4,947
3.1

86,996
69.2
83,873

86,660
69.0
83,355

83,879
66.7
73,748

85,001
67.4
77,711

85,277
67.5
79,428

–1,719
–1.7
–4,445

66.7

66.4

58.6

61.7

62.9

–3.8

3,123
3.6

3,305
3.8

10,132
12.1

7,291
8.6

5,849
6.9

2,726
3.3

77,439
57.7
74,670

77,214
57.6
74,287

74,279
55.3
63,817

75,326
56.0
68,488

75,330
55.9
70,341

–2,109
–1.8
–4,329

55.6

55.4

47.5

50.9

52.2

–3.4

2,768
3.6

2,927
3.8

10,462
14.1

6,838
9.1

4,989
6.6

2,221
3.0

127,141
63.2
123,050

126,647
63.0
122,416

122,617
61.0
107,702

124,154
61.6
114,408

124,306
61.6
116,838

–2,835
–1.6
–6,212

61.1

60.9

53.5

56.8

57.9

–3.2

4,091
3.2

4,232
3.3

14,915
12.2

9,746
7.8

7,468
6.0

3,377
2.8

20,787
62.6
19,575

20,770
62.5
19,462

19,788
59.4
16,570

20,040
60.0
17,423

20,114
60.1
18,034

–673
–2.5
–1,541

59.0

58.6

49.8

52.2

53.9

–5.1

1,211
5.8

1,308
6.3

3,217
16.3

2,617
13.1

2,080
10.3

869
4.5

10,605
64.4
10,322

10,445
63.9
10,109

10,029
61.1
8,584

10,511
63.5
9,411

10,338
62.4
9,643

–267
–2.0
–679

62.6

61.9

52.3

56.8

58.2

–4.4

283

335

1,445

1,099

696

413

See footnotes at end of table.

4

U.S. BUREAU OF LABOR STATISTICS

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Table 1. Employment status of the civilian noninstitutional population ages 16 years and older, by gender,
race, and Hispanic or Latino ethnicity, quarterly averages, seasonally adjusted, 2019–20 (levels in
thousands)
2020
Characteristic

Unemployment rate
Hispanic or Latino ethnicity
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate

Fourth quarter,
2019

Change, fourth quarter 2019 to
First

Second

Third

Fourth

quarter

quarter

quarter

quarter

fourth quarter 2020

2.7

3.2

14.4

10.5

6.7

4.0

29,538
67.3
28,286

29,618
67.6
28,163

28,319
64.3
23,517

28,777
65.0
25,558

29,153
65.4
26,569

–385
–1.9
–1,717

64.4

64.3

53.4

57.7

59.6

–4.8

1,251
4.2

1,455
4.9

4,802
17.0

3,219
11.2

2,584
8.9

1,333
4.7

Note: Employed full-time workers are people who usually work 35 hours or more per week. Employed part-time workers are people who usually work less than
35 hours per week. Seasonally adjusted data for full-time and part-time workers will not necessarily add to totals because of the independent seasonal
adjustment of the series. Estimates for the above race groups (White, Black or African American, and Asian) do not sum to totals because data are not
presented for all races. People whose ethnicity is identified as Hispanic or Latino may be of any race. Updated population controls are introduced annually
with the release of January data.
Source: U.S. Bureau of Labor Statistics, Current Population Survey.

5

U.S. BUREAU OF LABOR STATISTICS

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The unemployment rate peaked above levels seen in the Great
Recession
The number of unemployed people was 10.8 million in the fourth quarter of 2020, an increase of 4.9 million from a
year earlier. In the second quarter of 2020, after the onset of the pandemic, the number of unemployed averaged
20.6 million, much higher than the peak reached in the aftermath of the Great Recession, when unemployment hit
15.2 million in the fourth quarter of 2009.5 The unemployment rate also spiked in the second quarter of 2020 and,
at 13.0 percent, was the highest quarterly average ever recorded in the history of the series, which goes back to
1948. (See the box that follows for more information about the effects of the pandemic on CPS estimates.) Despite
rapid declines in the second half of the year, the unemployment rate averaged 6.7 percent in the fourth quarter of
2020, which is nearly twice what it had been in the fourth quarter of 2019. (See chart 2.)

6

U.S. BUREAU OF LABOR STATISTICS

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Effects of the COVID-19 pandemic on CPS estimates
Misclassification of some people in the labor force
In 2020, many people were not able to work as businesses closed or reduced hours because of the
COVID-19 pandemic. Depending on the responses to the CPS, some of these people may or may not have
been classified as unemployed. People who did not work but said that they have a job are asked for the
reason they did not work. Those who missed work because of vacation, illness, parental leave, or bad
weather are classified as employed. In March 2020, some people who said they had jobs but did not work
during the week prior to the survey cited the pandemic as the reason they did not work. The Census Bureau
interviewers were given guidance that people who had jobs but did not work because they were under
quarantine or self-isolating because of health concerns should be counted in the “own illness, injury, or
medical problem” category. Those who were not ill or under quarantine and did not work “because of the
coronavirus” should be classified as unemployed, on layoff (temporary or indefinite). (People on temporary
layoff do not need to look for work to be classified as unemployed.)

7

U.S. BUREAU OF LABOR STATISTICS

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Both BLS and the Census Bureau found that, despite this guidance, some people who were not working
because of the coronavirus were recorded as having a job and not working for “other reasons.” Starting in
March 2020, BLS began producing an estimate of what the unemployment rate would have been if those
with a job but not at work for “other reasons” (over and above the typical level) had been counted among the
unemployed on temporary layoff.
This estimate of misclassification requires some assumptions. First, BLS assumed that all of the increase in
the number of employed people not at work for “other reasons” was solely due to misclassification. Second,
BLS assumed that these people expected to be recalled and were available to return to work. These
assumptions represent an upper bound and likely overstate the degree of misclassification. Business
owners who do not have another job and are not at work because of pandemic-related closures or cutbacks
are correctly recorded as employed and absent from work for “other reasons.”
After adjusting for misclassification using this methodology, BLS estimated that the unemployment rate for
March 2020 would have been 5.3 percent, 0.9 percentage point higher than the official seasonally adjusted
unemployment rate for that month. For April 2020, the same methodology showed that the unemployment
rate would have been 19.5 percent, compared with the official seasonally adjusted rate of 14.7 percent. In
response to the misclassification, the Census Bureau increased training for interviewers and reviewed
responses for those who were recorded as employed and not at work for “other reasons.” Thus,
misclassification was highest in the early months of the pandemic and was considerably lower later in the
year. In December, the unemployment rate would have been 7.3 percent, compared with the official estimate
of 6.7 percent. For more information about the misclassification, see “Impact of the coronavirus (COVID-19)
pandemic on The Employment Situation for December 2020,” https://www.bls.gov/covid19/employmentsituation-covid19-faq-december-2020.htm.
Response rates
The household survey is conducted by the Census Bureau and normally includes both in-person and
telephone interviews, with the majority of interviews conducted by telephone. Households are in the CPS
sample for a total of 8 months (4 months in a row, followed by an 8-month break, followed by another 4
months in the survey), meaning that interviewers attempt to interview someone in the household in each of
those 8 months. Generally, households entering the sample for their first month are interviewed through a
personal visit, and households in their fifth month also often receive a personal visit. Interviews for other
months are generally conducted by telephone.
For the safety of both interviewers and respondents, in-person interviews were suspended on March 20,
2020. Additionally, the two Census Bureau call centers that assist with telephone interviewing were closed.
Starting in July, interviewers resumed conducting some in-person interviews on a limited basis in certain
areas of the country, and the call centers also resumed activity on a limited basis. Restrictions gradually
eased, and by November, interviewers in nearly all areas of the country conducted in-person interviews,
though only after first attempting to reach households by telephone.

8

U.S. BUREAU OF LABOR STATISTICS

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The response rate for the household survey was 73 percent in March 2020, and it reached a low for the year
of 65 percent in June. The response rate began to improve in July, and it was between 77 and 80 percent
for September through December. For the 12 months ending in February 2020, the response rate averaged
83 percent. For CPS response rates by month, see https://data.bls.gov/timeseries/LNU09300000. Although
the response rate was adversely affected by pandemic-related issues, BLS was still able to obtain estimates
that met its standards for accuracy and reliability.

The sharpest rise in unemployment occurred in service occupations
In 2020, the unemployment rate increased for all five major occupational categories.6 (Data are annual averages.)
The jobless rate for service occupations had the sharpest increase, rising by 8.6 percentage points over the year
to reach 13.0 percent in 2020. Production, transportation, and material moving occupations had the second-largest
increase, rising by 5.9 percentage points over the year to 10.2 percent in 2020. The pandemic and efforts to
contain it had a substantial impact on these occupations. Within service occupations, food preparation and serving
related occupations and personal care and service occupations were the most affected, with jobless rates that
were nearly 4 times higher than they were in 2019. The jobless rates for natural resources, construction, and
maintenance occupations (8.9 percent); sales and office occupations (8.0 percent); and management,
professional, and related occupations (4.5 percent) also rose sharply from 2019 to 2020. (See table 2.)
Table 2. Unemployment rates, by occupational group and gender, annual averages, not seasonally
adjusted, 2019–20
Total
Occupational group
2019 2020
Management, professional, and related
occupations
Management, business, and
financial operations occupations
Professional and related
occupations
Service occupations
Health care support occupations
Protective service occupations
Food preparation and serving
related occupations
Building and grounds cleaning
and maintenance occupations
Personal care and service
occupations
Sales and office occupations
Sales and related occupations

Men

Change, 2019 to
2020

2019 2020

Women

Change, 2019 to
2020

2019 2020

Change, 2019 to
2020

2.0

4.5

2.5 1.8

4.2

2.4

2.1

4.9

2.8

1.8

4.1

2.3 1.7

3.8

2.1

2.0

4.4

2.4

2.1

4.9

2.8 2.0

4.6

2.6

2.2

5.1

2.9

4.4 13.0
3.1 7.3
2.9 5.1

8.6 4.8 12.6
4.2 2.8 7.5
2.2 2.3 3.9

7.8
4.7
1.6

4.2 13.3
3.2 7.3
4.8 8.7

9.1
4.1
3.9

5.5 19.6

14.1 6.1 20.8

14.7

5.0 18.5

13.5

9.4

3.8

4.4 13.1

8.7

12.1 4.4 17.5

13.1

3.7 15.5

11.8

3.7
4.0

3.8 8.5
4.7 10.8

4.7
6.1

5.1 10.9

5.8 5.6

3.9 16.0
3.7
3.8

8.0
8.8

4.3 3.5
5.0 2.9

See footnotes at end of table.

9

7.2
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U.S. BUREAU OF LABOR STATISTICS

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Table 2. Unemployment rates, by occupational group and gender, annual averages, not seasonally
adjusted, 2019–20
Total
Occupational group
2019 2020
Office and administrative support
occupations
Natural resources, construction, and
maintenance occupations
Farming, fishing, and forestry
occupations
Construction and extraction
occupations
Installation, maintenance, and
repair occupations
Production, transportation, and material
moving occupations
Production occupations
Transportation and material
moving occupations

Men

Change, 2019 to
2020

2019 2020

Women

Change, 2019 to
2020

2019 2020

Change, 2019 to
2020

3.6

7.3

3.7 4.3

7.9

3.6

3.3

7.1

3.8

4.7

8.9

4.2 4.4

8.6

4.2

9.0 12.5

3.5

9.6 10.3

0.7 7.7

8.4

0.7 14.8 15.7

0.9

5.2 10.1

4.9 5.1 10.0

4.9

6.1 10.6

4.5

2.6

6.4

3.8 2.5

6.2

3.7

3.7 10.9

7.2

4.3 10.2

5.9 4.2

9.8

5.6

4.9 11.6

6.7

3.9

5.1 3.7

8.5

4.8

4.5 10.0

5.5

6.4 4.5 10.6

6.1

5.4 13.2

7.8

9.0

4.7 11.1

Note: The unemployed are classified by occupation according to their last job, which may or may not be similar to the job they are currently looking for.
Updated population controls are introduced annually with the release of January data. Effective with January 2020 data, occupations reflect the introduction of
the 2018 Census occupational classification system into the Current Population Survey, or household survey. This classification system is derived from the
2018 Standard Occupational Classification (SOC). No historical data have been revised. Data for 2020 are not strictly comparable with earlier years.
Source: U.S. Bureau of Labor Statistics, Current Population Survey.

Unemployment rates rose more for women than for men in four of the five major occupational categories in 2020.
The unemployment rate for women in service occupations increased by 9.1 percentage points over the year,
reaching 13.3 percent, compared with an increase of 7.8 percentage points in the rate for men, whose rate rose to
12.6 percent in 2020. Within this occupational group, women accounted for 57.0 percent of employment in 2020.
(See table 3.) (These data are annual averages.) Over the same period, in production, transportation, and material
moving occupations, the jobless rate increased by 6.7 percentage points for women and by 5.6 percentage points
for men, reaching 11.6 percent and 9.8 percent, respectively. In sales and office occupations, the unemployment
rate increased by 4.7 percentage points for women and 3.7 percentage points for men, to 8.5 percent and 7.2
percent, respectively. In management, professional, and related occupations, the unemployment rate increased by
2.8 percentage points for women and 2.4 percentage points for men, rising to 4.9 percent for women and 4.2
percent for men. Lastly, for natural resources, construction, and maintenance occupations, the unemployment rate
increased more for men (4.2 percentage points, to 8.6 percent) than for women (3.5 percentage points, to 12.5
percent).

Women were disproportionately affected by the pandemic-related
recession, especially in the early stages

10

U.S. BUREAU OF LABOR STATISTICS

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From the fourth quarter of 2019 to the second quarter of 2020, the number of employed women decreased by 14.5
percent, compared with a 12.1-percent decrease for men. Over the same period, the unemployment rate for
women increased by 10.5 percentage points, to 14.1 percent, while the rate for men rose by 8.5 percentage points,
to 12.1 percent. From the second quarter to the fourth quarter of 2020, however, the number of employed women
increased by 10.2 percent, compared with a 7.7-percent increase in the number of employed men. The
unemployment rate for women decreased by 7.5 percentage points, compared with a 5.2-percentage-point
decrease for men. (See table 1.)
As these measures suggest, the employment situation deteriorated considerably more for women than for men
during the early part of the pandemic—from the fourth quarter of 2019 to the second quarter of 2020; it then
improved somewhat more for women than for men between the second quarter and the end of the year. This
pattern reflects employment changes in 2020 that were particularly acute for people in food services and serving
related occupations, an occupational group in which women make up slightly more than half of employment, and
personal care and service occupations, in which women represented more than three-fourths of employment. (See
table 3.) Women are also more likely than men to usually work part time—that is, less than 35 hours per week—
and part-time employment declined more sharply than full-time employment in the early stages of the pandemic.
Women accounted for about three-fifths of part-time workers in 2020.
Table 3. Employed people, by occupational group, gender, race, Hispanic or Latino ethnicity, and age,
annual averages, not seasonally adjusted, 2020
Percent of total employed
Occupational group

Total employed

Black or

(in thousands) Women White

African

Asian

American
Total, 16 years and older
Management,
professional, and related
occupations
Management, business,
and financial operations
occupations
Management
occupations
Professional and related
occupations
Service occupations
Healthcare support
occupations
Protective service
occupations
Food preparation and
serving related
occupations
Building and grounds
cleaning and
maintenance
occupations

Hispanic
or Latino

Ages 16 Ages 25

Ages 55

to 24

to 54

years and

years

years

older

147,795

46.8 78.0

12.1

6.4

17.6

11.6

64.5

23.9

63,644

51.7 78.7

9.7

8.6

10.4

5.6

69.9

24.6

27,143

44.6 81.7

8.8

6.7

10.9

3.8

68.6

27.6

18,564

40.4 83.4

8.0

5.8

10.7

3.1

67.5

29.4

36,502

57.0 76.5

10.5 10.1

10.1

6.9

70.8

22.3

22,853

57.0 72.9

17.0

5.6

25.0

21.3

57.9

20.8

4,790

85.3 64.1

25.3

6.2

20.2

14.9

61.4

23.7

3,024

23.6 74.5

19.4

2.5

15.9

10.2

70.2

19.6

6,556

54.4 74.8

13.9

6.4

27.3

39.4

47.1

13.4

5,084

40.3 78.2

14.2

3.1

37.9

10.8

60.5

28.6

See footnotes at end of table.

11

U.S. BUREAU OF LABOR STATISTICS

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Table 3. Employed people, by occupational group, gender, race, Hispanic or Latino ethnicity, and age,
annual averages, not seasonally adjusted, 2020
Percent of total employed
Occupational group

Total employed

Black or

(in thousands) Women White

African

Asian

American
Personal care and
service occupations
Sales and office
occupations
Sales and related
occupations
Office and administrative
support occupations
Natural resources,
construction, and
maintenance
occupations
Farming, fishing, and
forestry occupations
Construction and
extraction occupations
Installation,
maintenance, and repair
occupations
Production,
transportation, and
material moving
occupations
Production occupations
Transportation and
material moving
occupations

Hispanic
or Latino

Ages 16 Ages 25

Ages 55

to 24

to 54

years and

years

years

older

3,399

77.0 72.8

13.2 10.1

16.1

20.9

59.0

20.2

29,726

61.3 78.7

12.5

5.1

17.3

15.6

58.6

25.8

14,168

48.7 80.2

10.6

5.5

17.1

18.9

56.1

25.0

15,558

72.7 77.4

14.3

4.7

17.4

12.6

60.9

26.4

13,357

5.6 86.7

7.5

2.1

31.1

11.3

67.3

21.5

1,045

24.1 90.0

4.3

1.6

43.0

18.5

57.3

24.3

7,710

4.0 87.8

7.0

1.6

35.7

11.2

68.9

19.9

4,602

4.1 84.0

9.1

3.1

20.8

9.7

66.7

23.5

18,215

23.7 74.6

16.7

4.8

23.8

14.5

61.6

23.9

7,590

28.3 77.8

13.1

5.6

23.6

10.8

65.0

24.1

10,625

20.5 72.3

19.4

4.2

23.9

17.1

59.1

23.8

Note: Estimates for the above race groups (White, Black or African American, and Asian) do not sum to totals because data are not presented for all races.
People whose ethnicity is identified as Hispanic or Latino may be of any race. Updated population controls are introduced annually with the release of January
data. Effective with January 2020 data, occupations reflect the introduction of the 2018 Census occupational classification system, derived from the 2018
Standard Occupational Classification (SOC). No historical data have been revised. Data for 2020 are not strictly comparable with earlier years.
Source: U.S. Bureau of Labor Statistics, Current Population Survey.

At the end of 2020, labor market conditions for both women and men were weaker than they were a year earlier.
The unemployment rate for women was 6.6 percent in the fourth quarter of 2020, up 3.0 percentage points from
the fourth quarter of 2019; the rate for men was up 3.3 percentage points over this period, averaging 6.9 percent in
the fourth quarter of 2020. Employment was down 5.8 percent for women between the fourth quarter of 2019 and
the fourth quarter of 2020, compared with a decline of 5.3 percent for men. The labor force participation rate also
was down more for women than for men over the year, with declines of 1.8 percentage points and 1.7 percentage
points, respectively; the fourth-quarter rate was 55.9 percent for women and 67.5 percent for men. (See table 1.)

12

U.S. BUREAU OF LABOR STATISTICS

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Looking at a broader array of economic indicators sheds even more light on the labor market difficulties that
women encountered in 2020. For example, according to data from the American Time Use Survey, women are
more likely than men to provide childcare and to perform household activities such as housework, food preparation
and cleanup, and household management on a given day; women are also more likely than men to be unpaid
eldercare providers.7 Mothers of school-age children with no other working-age adults in the home suffered
disproportionate declines in employment.8 Any adverse impact on their employment situation, especially when
compounded by the shift of many schools to distance learning during the pandemic and the temporary closure of
many childcare facilities, meant that many women were forced to juggle an unprecedented array of pandemicrelated challenges.

Blacks, Asians, and Hispanics were more adversely affected than
Whites by the pandemic-induced recession
In 2020, employment fell sharply for all race and ethnicity groups, as evidenced by declines in the employment–
population ratios for Whites, Blacks, Asians, and Hispanics.9 Improvements in the second half of the year were not
substantial enough to make up for the steep drops that occurred in the second quarter. However, some groups
were affected more than others. Although the ratio for Whites decreased by 3.2 percentage points over the year, to
57.9 percent, the declines in the employment–population ratios for Blacks, Hispanics, and Asians were more
pronounced. The ratio for Blacks decreased to 53.9 percent in the fourth quarter of 2020, a loss of 5.1 percentage
points over the year. The employment–population ratios for Hispanics and Asians also fell sharply during 2020,
with the ratio for Hispanics decreasing by 4.8 percentage points, to 59.6 percent, and the ratio for Asians
decreasing by 4.4 percentage points, to 58.2 percent. (See table 1.)
Similarly, the labor force participation rates decreased over the year for Whites, Blacks, Asians, and Hispanics.
Higher participation in the second half of the year fell short of making up for the steep drops in the second quarter.
The participation rate for Whites decreased by 1.6 percentage points over the year, to 61.6 percent in the fourth
quarter of 2020, but declines in labor force participation among the other major race and ethnicity groups were
larger. The rate for Blacks decreased by 2.5 percentage points over the year, to 60.1 percent in the fourth quarter
of 2020. The rate for Asians decreased by 2.0 percentage points, to 62.4 percent. The rate for Hispanics
decreased by 1.9 percentage points, to 65.4 percent in the fourth quarter of 2020. (See table 1.)
Among the major race and ethnicity groups, jobless rates at the end of 2020 were much higher than in the fourth
quarter of 2019. For each of the groups, some improvement in the second half of the year was not sufficient to
bring the rates back to their prepandemic levels. The unemployment rate for Blacks, at 10.3 percent in the fourth
quarter of 2020, increased by 4.5 percentage points over the year. The jobless rate for Asians more than doubled,
increasing by 4.0 percentage points over the year, to 6.7 percent. The rate for Hispanics increased by 4.7
percentage points, to 8.9 percent. The unemployment rate for Whites, at 6.0 percent, increased by 2.8 percentage
points over the year. (See table 1 and chart 3.)

13

U.S. BUREAU OF LABOR STATISTICS

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Roughly 1 in 4 young workers were unemployed in the second
quarter of 2020
In terms of the increase in the unemployment rate, the labor market disruption in the early months of the pandemic
was greatest among younger workers. For people ages 16 to 24, for example, the unemployment rate jumped to
24.2 percent in the second quarter of 2020, an increase of 15.9 percentage points from the fourth quarter of 2019.
By the fourth quarter of 2020, the unemployment rate for people ages 16 to 24 was back down to 12.0 percent,
albeit still 3.7 percentage points higher than it was at the end of 2019. This is largely a reflection of younger
workers being more likely than older workers to be employed in food preparation and serving related occupations,
an occupational group hit particularly hard at the onset of the pandemic. Younger workers are also more likely to
be employed part time and, as previously mentioned, employment declined more sharply among part-time workers
in the early stages of the recession. (See table 4.)
Employment for people ages 16 to 24 fell by 4.9 million, or 25.1 percent, from the fourth quarter of 2019 to the
second quarter of 2020. Employment for this age group rebounded by 3.7 million from the second quarter to the
fourth quarter of 2020, but the level was still down by 1.1 million over the year. The employment–population ratio

14

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

was 48.7 percent in the fourth quarter of 2020, 2.7 percentage points lower than it was a year earlier. Much of the
employment decline occurred among those ages 20 to 24. (See table 4.)
The unemployment rate for people in the prime working age of 25 to 54 was 6.1 percent in the fourth quarter of
2020, up 3.1 percentage points over the year. The unemployment rate increased for both prime-working-age
women and men. In the fourth quarter, the unemployment rate was 6.0 percent for women and 6.2 percent for
men, with increases over the year of 2.8 percentage points and 3.2 percentage points, respectively. (See table 4.)
Table 4. Employment status of the civilian noninstitutional population ages 16 years and older, by age and
gender, quarterly averages, seasonally adjusted, 2019-20 (levels in thousands)
2020
Characteristic

Total, 16 to 24 years
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate
Total, 16 to 19 years
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate
Total, 20 to 24 years
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate
Total, 25 to 54 years
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate
Men, 25 to 54 years
Civilian labor force
Participation rate

Fourth quarter,
2019

Change, fourth quarter 2019 to
First

Second

Third

Fourth

quarter

quarter

quarter

quarter

fourth quarter 2020

21,135
56.0
19,382

20,972
55.9
19,142

19,162
51.1
14,522

19,958
53.3
16,854

20,736
55.3
18,250

–399
-0.7
–1,132

51.4

51.0

38.7

45.0

48.7

–2.7

1,754
8.3

1,831
8.7

4,639
24.2

3,105
15.6

2,486
12.0

732
3.7

5,968
35.8
5,210

5,964
35.9
5,206

5,356
32.3
3,853

5,636
34.1
4,665

5,925
35.8
5,056

–43
0.0
–154

31.2

31.3

23.3

28.2

30.6

–0.6

758
12.7

759
12.7

1,504
28.1

970
17.2

869
14.7

111
2.0

15,167
72.1
14,172

15,008
71.8
13,936

13,806
66.0
10,670

14,323
68.5
12,188

14,811
70.8
13,194

–356
–1.3
–978

67.4

66.7

51.0

58.3

63.1

–4.3

996
6.6

1,072
7.1

3,136
22.7

2,134
14.9

1,617
10.9

621
4.3

104,727
82.9
101,533

104,275
82.8
100,938

101,632
80.6
90,102

102,387
81.2
94,294

102,223
81.0
95,987

–2504
–1.9
–5,546

80.3

80.1

71.5

74.8

76.1

–4.2

3,194
3.0

3,337
3.2

11,530
11.3

8,092
7.9

6,236
6.1

3,042
3.1

55,628
89.2

55,378
89.1

54,192
87.1

54,600
87.7

54,528
87.6

–1,100
–1.6

See footnotes at end of table.

15

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Table 4. Employment status of the civilian noninstitutional population ages 16 years and older, by age and
gender, quarterly averages, seasonally adjusted, 2019-20 (levels in thousands)
2020
Characteristic

Employed
Employment–
population ratio
Unemployed
Unemployment rate
Women, 25 to 54 years
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate
Total, 55 years and older
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate
Men, 55 years and older
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate
Women, 55 years and older
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate

Fourth quarter,
2019

Change, fourth quarter 2019 to
First

Second

Third

Fourth

quarter

quarter

quarter

quarter

fourth quarter 2020

53,983

53,625

48,406

50,387

51,137

–2,846

86.5

86.3

77.8

81.0

82.1

–4.4

1,645
3.0

1,753
3.2

5,786
10.7

4,213
7.7

3,391
6.2

1,746
3.2

49,099
76.7
47,550

48,897
76.6
47,313

47,440
74.3
41,696

47,786
74.8
43,907

47,695
74.7
44,850

–1,404
–2.0
–2,700

74.3

74.2

65.3

68.8

70.2

–4.1

1,548
3.2

1,584
3.2

5,744
12.1

3,879
8.1

2,845
6.0

1,297
2.8

38,676
40.3
37,676

38,543
40.1
37,453

37,294
38.6
32,935

38,037
39.2
35,126

37,744
38.7
35,568

–932
–1.6
–2,108

39.3

38.9

34.1

36.2

36.5

–2.8

1,000
2.6

1,091
2.8

4,359
11.7

2,911
7.7

2,176
5.8

1,176
3.2

20,635
46.4
20,119

20,613
46.2
19,991

19,963
44.6
17,892

20,253
45.0
18,816

20,184
44.7
19,047

–451
–1.7
–1,072

45.3

44.8

40.0

41.8

42.1

–3.2

516
2.5

622
3.0

2,071
10.4

1,437
7.1

1,138
5.6

622
3.1

18,037
35.0
17,557

17,937
34.8
17,462

17,330
33.5
15,043

17,780
34.2
16,310

17,557
33.6
16,521

–480
–1.4
–1,036

34.1

33.9

29.0

31.4

31.6

–2.5

481
2.7

475
2.6

2,287
13.2

1,471
8.3

1,035
5.9

554
3.2

Note: Updated population controls are introduced annually with the release of January data.
Source: U.S. Bureau of Labor Statistics, Current Population Survey.

Among people ages 25 to 54, the number of employed fell by 11.4 million from the fourth quarter of 2019 to the
second quarter of 2020, but it increased by 5.9 million from the second quarter to the fourth quarter of 2020. The
employment–population ratio fell by 4.2 percentage points over the year, averaging 76.1 percent in the fourth

16

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

quarter of 2020. Among workers ages 55 and older, the unemployment rate, at 5.8 percent in the fourth quarter of
2020, increased by 3.2 percentage points over the year. The jobless rates for men and women in this age group
showed similar increases, 3.1 percentage points and 3.2 percentage points, respectively, over the year. The
employment–population ratio for older workers, at 36.5 percent in the fourth quarter of 2020, declined by 2.8
percentage points over the year.10 This ratio decreased by 3.2 percentage points over the year for men, to 42.1
percent, and by 2.5 percentage points for women, to 31.6 percent. (See table 4.)

The unemployment rate rose markedly for those with less education
Among workers ages 25 and older, jobless rates across all education levels spiked to their highest point ever
following the onset of the pandemic in the second quarter of 2020 (these data series began in 1992).11
Unemployment rates for people with less than a high school diploma and for high school graduates reached 19.0
percent and 14.9 percent, respectively, in the second quarter of 2020. For those with some college or an associate
degree, and those with a bachelor’s degree and higher, jobless rates in the second quarter were 13.0 percent and
7.5 percent, respectively. (See table 5.)
Although these measures improved after the second quarter, they remained about twice as high in the fourth
quarter of 2020, as compared with a year earlier. The jobless rate for people with less than a high school diploma
was 9.6 percent in the fourth quarter of 2020, 4.1 percentage points higher than a year earlier. The unemployment
rate for high school graduates with no college degree (7.9 percent) and for those with some college or an
associate degree (6.4 percent) increased by a similar amount over the year (by 4.2 percentage points and 3.5
percentage points, respectively). The rate for those with a bachelor’s degree and higher increased by 2.1
percentage points over the year, reaching 4.1 percent in the fourth quarter of 2020. As has historically been the
case, jobless rates for those with higher levels of education remained well below the rates for those with less
formal education. (See table 5 and chart 4.)
Table 5. Employment status of the civilian noninstitutional population ages 25 years and older, by
educational attainment, quarterly averages, seasonally adjusted, 2019–20 (levels in thousands)
2020
Characteristic

Fourth quarter,
2019

Less than a high school diploma
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate
High school graduates, no college
Civilian labor force
Participation rate
Employed

Change, fourth quarter 2019 to
First

Second

Third

Fourth

quarter

quarter

quarter

quarter

fourth quarter 2020

9,726
46.3
9,194

9,513
46.4
8,934

8,458
42.9
6,849

8,543
44.5
7,454

9,197
45.6
8,310

–529
–0.7
–884

43.8

43.5

34.7

38.8

41.2

–2.6

533
5.5

580
6.1

1,609
19.0

1,089
12.7

887
9.6

354
4.1

36,164
58.0
34,814

35,821
58.0
34,449

33,519
54.9
28,517

34,434
55.2
31,042

35,189
55.6
32,413

–975
–2.4
–2,401

See footnotes at end of table.

17

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Table 5. Employment status of the civilian noninstitutional population ages 25 years and older, by
educational attainment, quarterly averages, seasonally adjusted, 2019–20 (levels in thousands)
2020
Characteristic

Fourth quarter,
2019

Employment–
population ratio
Unemployed
Unemployment rate
Some college or associate degree
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate
Bachelor's degree and higher
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate

Change, fourth quarter 2019 to
First

Second

Third

Fourth

quarter

quarter

quarter

quarter

fourth quarter 2020

55.9

55.8

46.7

49.8

51.2

–4.7

1,351
3.7

1,372
3.8

5,002
14.9

3,392
9.9

2,776
7.9

1,425
4.2

37,483
64.7
36,392

37,236
64.5
36,066

36,298
63.2
31,578

36,376
63.8
33,226

35,694
62.4
33,408

–1,789
–2.3
–2,984

62.8

62.5

55.0

58.3

58.4

–4.4

1,090
2.9

1,170
3.1

4,720
13.0

3,150
8.7

2,285
6.4

1,195
3.5

59,856
73.8
58,666

60,211
73.3
58,936

60,782
72.1
56,196

61,162
72.3
57,753

59,696
72.0
57,267

–160
–1.8
–1,399

72.4

71.8

66.6

68.3

69.1

–3.3

1,190
2.0

1,276
2.1

4,587
7.5

3,409
5.6

2,429
4.1

1,239
2.1

Note: Updated population controls are introduced annually with the release of January data.
Source: U.S. Bureau of Labor Statistics, Current Population Survey.

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MONTHLY LABOR REVIEW

The number of unemployed on temporary layoff surged to
unprecedented levels
Unemployed people are grouped by their reasons for unemployment. People are unemployed because they either
(1) were on temporary layoff, permanently lost their job, or completed a temporary job (referred to as job losers);
(2) voluntarily left their job (job leavers); (3) reentered the labor force (reentrants); or (4) entered the labor force for
the first time (new entrants).
The number of job losers rose to an unprecedented level during the pandemic. By comparison, following the Great
Recession, the number of unemployed job losers peaked at 9.8 million in the fourth quarter of 2009. It then steadily
declined throughout the record-long economic expansion, bottoming out at 2.7 million in the fourth quarter of 2019.
By the second quarter of 2020, however, the number of unemployed people who lost their job surged to 17.7
million, the highest quarterly average in the history of the data series. Virtually all of this increase consisted of
people on temporary layoff, rising from 799,000 in the fourth quarter of 2019 to 14.7 million in the second quarter
of 2020, the highest level on record.12 (See table 6.)

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U.S. BUREAU OF LABOR STATISTICS

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The number of unemployed job losers declined after the second quarter of 2020, as the number of people on
temporary layoff declined sharply. In the fourth quarter of 2020, out of the 7.5 million unemployed people who had
lost their job, about 40 percent were on temporary layoff, down from 83 percent in the second quarter. Although the
number of people on temporary layoff decreased from the second quarter of 2020 to the fourth quarter, the number
of people not on temporary layoff (permanent job losers, and those who completed temporary jobs) increased in
2020. The number of people who permanently lost their jobs, at 3.6 million in the fourth quarter of 2020, increased
by 2.2 million over the year.
The number of unemployed reentrants—people who previously worked but had been out of the labor force before
they began their job search—increased by 357,000 over the year, to 2.1 million in the fourth quarter. The number
of job leavers (people who voluntarily left their job) edged down by 73,000 over the year, reaching 735,000 in the
fourth quarter of 2020. The number of new entrants, at 529,000 in the fourth quarter of 2020, was little changed
from the prior year. (See table 6 and chart 5.)
Table 6. Unemployed people, by reason and duration of unemployment, quarterly averages, seasonally
adjusted, 2019–20 (levels in thousands)
2020
Reason and duration

Reason for unemployment
Job losers and people who
completed temporary jobs
On temporary layoff
Not on temporary layoff
Permanent job losers
people who completed
temporary jobs
Job leavers
Reentrants
New entrants
Percent distribution
Job losers and people who
completed temporary jobs
On temporary layoff
Not on temporary layoff
Job leavers
Reentrants
New entrants
Duration of unemployment
Less than 5 weeks
5 to 14 weeks
15 weeks or longer
15 to 26 weeks
27 weeks or longer
Average (mean) duration in weeks
Median duration, in weeks

Change,

Fourth quarter,
2019

First

Second

Third

Fourth

fourth quarter 2019 to

quarter

quarter

quarter

quarter

fourth quarter 2020

2,742

3,153

17,738

10,726

7,454

4,712

799
1,943
1,322

1,146
2,007
1,373

14,650
3,088
2,397

6,676
4,050
3,277

3,011
4,443
3,569

2,212
2,500
2,247

621

634

691

773

874

253

808
1,721
587

770
1,800
533

567
1,818
506

661
2,180
532

735
2,078
529

–73
357
–58

46.8

50.4

86.0

76.1

69.0

22.2

13.6
33.2
13.8
29.4
10.0

18.3
32.1
12.3
28.8
8.5

71.0
15.0
2.7
8.8
2.5

47.3
28.7
4.7
15.5
3.8

27.9
41.2
6.8
19.2
4.9

14.3
8.0
–7.0
–10.2
–5.1

2,030
1,735
2,081
876
1,205
20.8
9.2

2,526
1,763
1,981
827
1,154
19.9
8.1

7,003
11,114
2,411
1,222
1,188
10.6
7.6

2,684
3,714
7,813
5,986
1,827
19.4
16.5

2,618
2,323
5,839
2,033
3,807
22.6
18.2

588
588
3,758
1,157
2,602
1.8
9.0

See footnotes at end of table.

20

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Table 6. Unemployed people, by reason and duration of unemployment, quarterly averages, seasonally
adjusted, 2019–20 (levels in thousands)
2020
Reason and duration

Percent distribution
Less than 5 weeks
5 to 14 weeks
15 weeks or longer
15 to 26 weeks
27 weeks or longer

Change,

Fourth quarter,
First

Second

Third

Fourth

fourth quarter 2019 to

quarter

quarter

quarter

quarter

fourth quarter 2020

2019

34.7
29.7
35.6
15.0
20.6

40.3
28.1
31.6
13.2
18.4

34.1
54.1
11.7
6.0
5.8

Note: Updated population controls are introduced annually with the release of January data.
Source: U.S. Bureau of Labor Statistics, Current Population Survey.

21

18.9
26.1
55.0
42.1
12.9

24.3
21.5
54.2
18.9
35.3

–10.4
-8.2
18.6
3.9
14.7

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

The duration of unemployment changed markedly after the initial
pandemic shock
The evolving effects of the pandemic on the labor market in 2020 were also evident in the changing estimates of
the duration of unemployment during the year. For instance, as unemployment surged following the onset of the
pandemic, there was an increase in the number of people who were newly unemployed—that is, those
unemployed for less than 5 weeks. Those who were unemployed for less than 5 weeks accounted for 34.7 percent
of the total unemployed in the last quarter of 2019; that figure increased to 40.3 percent in the first quarter of 2020,
when the effects of the pandemic first became noticeable. After peaking early in the second quarter, the share of
the unemployed who were unemployed for less than 5 weeks began to decrease and the number of those
unemployed for 5 to 14 weeks began to increase.
The share of those unemployed for 5 to 14 weeks rose from 29.7 percent in the fourth quarter of 2019 to 54.1
percent in the second quarter of 2020, before it began to decline. As the year progressed, the initial surge in
unemployment continued to move through the longer duration categories. By the third quarter of 2020, the number
of people who were unemployed for 15 to 26 weeks represented the largest share of the total unemployed, at 42.1
percent.
Because of the large and rapid influx of newly unemployed people, the long-term unemployed—those looking for
work for 27 weeks or more—initially accounted for a declining share of the total unemployed, representing only 5.8
percent of the total unemployed in the second quarter of 2020, the smallest share since 1970.13 However, by the
fourth quarter of 2020, the number of people who were long-term unemployed had increased to 3.8 million, which
was more than triple the prepandemic level of 1.2 million and represented 35.3 percent of the total unemployed.
(See chart 6.)

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U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

The number of involuntary part-time workers increased over the year
People who work part time for economic reasons, often referred to as involuntary part-time workers, worked less
than 35 hours per week but would have preferred full-time employment.14 They mainly worked a reduced number
of hours because of unfavorable business conditions (slack work) or their inability to find full-time work. Involuntary
part-time workers are often described as underemployed. (See chart 7.)

23

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

The number of involuntary part-time workers increased by 2.2 million over the year, averaging 6.5 million in the
fourth quarter of 2020, which represented 4.3 percent of total employment, compared with 2.7 percent of
employment the previous year. This measure of the underemployed reached an all-time high of 10.2 million in the
second quarter of 2020, with essentially all of the increase occurring among those working part time because of
slack work. The number of people who could only find part-time work declined by 156,000 over the year, dropping
to 1.1 million in 2020.
Before the pandemic, men and women each accounted for about half of involuntary part-time workers, but men
made up slightly more than half of the underemployed at the end of 2020. The number of men who worked part
time for economic reasons increased by 1.2 million, or 57 percent, from the fourth quarter of 2019 to the fourth
quarter of 2020, ending the year at 3.3 million. Over the same period, the number of women working part time for
economic reasons increased by 1.0 million, or 50 percent, to 3.0 million. (These data are not seasonally adjusted.)

The number of self-employed workers declined in 2020

24

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

The number of self-employed workers whose businesses were unincorporated fell from 9.6 million in the fourth
quarter of 2019 to 8.6 million in the second quarter of 2020, a 10-percent decline. By the fourth quarter of 2020,
employment for this group, at 9.5 million, had nearly recovered to the prepandemic level. In the fourth quarter of
2020, there were 6.1 million self-employed workers whose businesses were incorporated (data are not seasonally
adjusted), 267,000 less than a year earlier.

The unemployment rate for veterans nearly doubled over the year
There were 18.3 million veterans ages 18 and older in the civilian noninstitutional population in the fourth quarter of
2020. Veterans who served during World War II, the Korean War, and the Vietnam era account for the largest
share of the veteran population, at 6.7 million, followed by veterans who served during Gulf War era II (4.5 million)
and Gulf War era I (3.1 million). Four million veterans served on active duty during “other service periods,” mainly
between the Korean War and the Vietnam era and between the Vietnam era and Gulf War era II.15 Among
veterans, women accounted for 10 percent of the total veteran population in the fourth quarter of 2020.
In the fourth quarter of 2020, the unemployment rate for veterans was 5.7 percent (not seasonally adjusted), up by
2.6 percentage points over the year. The unemployment rate for nonveterans, at 6.5 percent in the fourth quarter,
increased by 3.3 percentage points over the year. Among the youngest veterans, the jobless rate for Gulf War-era
II veterans (those who served from September 2001 to the present), at 6.1 percent in the fourth quarter of 2020,
increased by 2.3 percentage points from a year earlier. The unemployment rate for male veterans, at 5.9 percent,
increased by 3.0 percentage points over the year, while the jobless rate for female veterans, at 4.7 percent,
changed little over the same period. (See table 7.)
Table 7. Employment status of people ages 18 years and older, by veteran status, period of service, and
gender, quarterly averages, not seasonally adjusted, 2019–20 (levels in thousands)
Total
Change,

Employment status,
veteran status, and
period of service

Men
Change,

Fourth

quarter,

quarter, quarter 2019 quarter, quarter, quarter 2019 quarter, quarter, quarter 2019
2020

to fourth

Fourth

2019

Fourth

2020

quarter 2020
Veterans, 18 years and older
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate
Gulf War-era II veterans
Civilian labor force
Participation rate
Employed

fourth

Change,

Fourth

2019

fourth

Women

to fourth

Fourth

2019

Fourth

2020

quarter 2020

fourth

to fourth
quarter 2020

9,187
49.2
8,905

8,721
47.6
8,222

–466
–1.6
–683

8,094
48.2
7,859

7,607
46.4
7,161

–487
–1.8
–698

1,092
57.8
1,046

1,114
58.4
1,062

22
0.6
16

47.7

44.9

–2.8

46.8

43.6

–3.2

55.3

55.6

0.3

282
3.1

499
5.7

217
2.6

236
2.9

446
5.9

210
3.0

46
4.2

52
4.7

6
0.5

3,464
79.2
3,333

3,502
77.4
3,289

38
–1.8
–44

2,942
81.7
2,847

2,960
79.2
2,773

18
–2.5
–74

522
67.5
486

541
68.4
516

19
0.9
30

See footnotes at end of table.

25

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Table 7. Employment status of people ages 18 years and older, by veteran status, period of service, and
gender, quarterly averages, not seasonally adjusted, 2019–20 (levels in thousands)
Total
Change,

Employment status,
veteran status, and
period of service

Men
Change,

Fourth

quarter,

quarter, quarter 2019 quarter, quarter, quarter 2019 quarter, quarter, quarter 2019
2020

to fourth

Fourth

2019

Fourth

2020

quarter 2020
Employment–
76.2
72.7
–3.5
population ratio
Unemployed
130
213
83
Unemployment rate
3.8
6.1
2.3
Gulf War-era I veterans
Civilian labor force
2,305
2,257
–48
Participation rate
74.7
73.2
–1.5
Employed
2,241
2,148
–93
Employment–
72.7
69.7
–3.0
population ratio
Unemployed
64
109
45
Unemployment rate
2.8
4.8
2.0
World War II, Korean War, and Vietnam-era veterans
Civilian labor force
1,459
1,167
–292
Participation rate
20.7
17.5
–3.2
Employed
1,417
1,111
–306
Employment–
20.1
16.6
–3.5
population ratio
Unemployed
42
56
14
Unemployment rate
2.9
4.8
1.9
Veterans of other service periods
Civilian labor force
1,959
1,795
–164
Participation rate
47.1
44.5
–2.6
Employed
1,914
1,675
–239
Employment–
46.0
41.5
–4.5
population ratio
Unemployed
46
121
75
Unemployment rate
2.3
6.7
4.4
Nonveterans, 18 years and older
Civilian labor force
153,028 149,779
–3,249
Participation rate
65.9
64.0
–1.9
Employed
148,080 140,099
–7,981
Employment–
63.7
59.9
–3.8
population ratio
Unemployed
4,948
9,680
4,732
Unemployment rate
3.2
6.5
3.3

fourth

Change,

Fourth

2019

fourth

Women

to fourth

Fourth

2019

Fourth

2020

fourth

to fourth

quarter 2020

quarter 2020

79.1

74.2

–4.9

62.9

65.2

2.3

95
3.2

187
6.3

92
3.1

35
6.8

26
4.7

–9
–2.1

1,984
75.8
1,925

1,933
74.4
1,837

–51
–1.4
–88

321
68.7
316

324
67.0
310

3
–1.7
–6

73.6

70.7

–2.9

67.6

64.1

–3.5

59
3.0

95
4.9

36
1.9

5
1.6

14
4.3

9
2.7

1,412
20.7
1,370

1,135
17.6
1,080

–277
–3.1
–290

47
19.2
47

33
14.0
31

–14
–5.2
–16

20.1

16.8

–3.3

19.1

13.5

–5.6

42
2.9

55
4.8

13
1.9

0
–

1
–

1
–

1,757
46.8
1,717

1,579
43.5
1,470

–178
–3.3
–247

202
49.9
197

216
54.0
204

14
4.1
7

45.7

40.5

–5.2

48.6

51.1

2.5

41
2.3

109
6.9

68
4.6

5
2.5

12
5.4

7
2.9

77,656
74.3
75,085

76,442
72.5
71,356

–1,214
–1.8
–3,729

75,371
58.9
72,995

73,337
57.1
68,743

–2,034
–1.8
–4,252

71.9

67.7

–4.2

57.1

53.5

–3.6

2,571
3.3

5,086
6.7

2,515
3.4

2,377
3.2

4,594
6.3

2,217
3.1

See footnotes at end of table.

26

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Note: Veterans are men and women who previously served on active duty in the U.S. Armed Forces and were not on active duty at the time of the survey.
Nonveterans never served on active duty in the U.S. Armed Forces. Veterans could have served anywhere in the world during these periods of service: Gulf
War era II (September 2001–present), Gulf War era I (August 1990–August 2001), Vietnam era (August 1964–April 1975), Korean War (July 1950–January
1955), World War II (December 1941–December 1946), and other service periods (all other periods). Veterans are only counted in one period of service: their
most recent wartime period. Veterans who served in both a wartime period and any other service period are classified in the wartime period. Dash indicates no
data available, data that do not meet publication criteria, or a base that is less than 60,000.
Source: U.S. Bureau of Labor Statistics, Current Population Survey.

In the fourth quarter of 2020, the labor force participation rate for veterans was 47.6 percent, while the rate for
nonveterans was 64.0 percent. The participation rate for veterans declined by 1.6 percentage points over the year,
and the rate for nonveterans declined by 1.9 percentage points over the same period. Labor force participation
rates—for both veterans and nonveterans—tend to be lower for older people than they are for people of prime
working age. For instance, the labor force participation rate for those who served during World War II, the Korean
War, and the Vietnam era—who are all over the age of 60 and accounted for 36 percent of the veteran population
—was 17.5 percent in the fourth quarter of 2020, down by 3.2 percentage points over the year. By contrast, Gulf
War-era II veterans—who tend to be younger—had a much higher participation rate, 77.4 percent, which was little
changed from a year earlier. (See table 7.)

The unemployment rate for people with a disability increased to a
double-digit level
Many people experienced challenging labor market conditions in 2020, including those with a disability. The
unemployment rate for people with a disability, at 11.5 percent in the last quarter of 2020, remained much higher
than the rate for people without a disability (6.3 percent). (Data are not seasonally adjusted.) The rate for those
with a disability increased by 4.6 percentage points in 2020, compared with an increase of 3.1 percentage points
for those without a disability.
Among the 29.9 million people ages 16 years and older with a disability in the fourth quarter of 2020, 6.1 million, or
20.3 percent, participated in the labor force. By contrast, the participation rate for people without disability was 66.8
percent. The lower rate for people with a disability reflects, in part, the older age profile of those with a disability;
older people, regardless of disability status, are less likely to be in the labor force. About half of all people with a
disability were ages 65 and over, nearly 3 times the share of those with no disability. (See table 8.)
Table 8. Employment status of the civilian noninstitutional population, by gender, age, and disability
status, quarterly averages, not seasonally adjusted, 2019–20
People with a disability
Employment status,
gender, and age

Total, 16 years and older
Civilian labor force
Participation rate

People with no disability

Fourth

Fourth

Change, fourth quarter

quarter,

quarter,

2019 to fourth quarter

2019

2020

2020

6,256
20.6

6,078
20.3

–178
–0.3

See footnotes at end of table.

27

Fourth

Fourth

quarter, 2019 quarter, 2020

158,067
68.8

154,434
66.8

Change, fourth quarter
2019 to fourth quarter
2020

–3,633
–2.0

U.S. BUREAU OF LABOR STATISTICS

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Table 8. Employment status of the civilian noninstitutional population, by gender, age, and disability
status, quarterly averages, not seasonally adjusted, 2019–20
People with a disability
Employment status,
gender, and age

Employed
Employment–
population ratio
Unemployed
Unemployment rate
Men, 16 to 64 years
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate
Women, 16 to 64 years
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate
Total, 65 years and older
Civilian labor force
Participation rate
Employed
Employment–
population ratio
Unemployed
Unemployment rate

People with no disability

Fourth

Fourth

Change, fourth quarter

quarter,

quarter,

2019 to fourth quarter

2019

2020

2020

Fourth

Fourth

quarter, 2019 quarter, 2020

Change, fourth quarter
2019 to fourth quarter
2020

5,824

5,381

–443

153,015

144,702

–8,313

19.2

18.0

–1.2

66.6

62.6

–4.0

432
6.9

697
11.5

265
4.6

5,052
3.2

9,732
6.3

4,680
3.1

2,730
36.0
2,525

2,651
35.0
2,342

–79
–1.0
–183

77,958
82.8
75,382

76,445
81.5
71,392

–1,513
–1.3
–3,990

33.3

30.9

–2.4

80.1

76.1

–4.0

205
7.5

310
11.7

105
4.2

2,576
3.3

5,053
6.6

2,477
3.3

2,306
30.8
2,125

2,344
31.7
2,037

38
0.9
–88

70,322
72.4
68,090

68,389
70.5
64,221

–1,933
–1.9
–3,869

28.4

27.5

–0.9

70.1

66.2

–3.9

181
7.9

307
13.1

126
5.2

2,232
3.2

4,168
6.1

1,936
2.9

1,219
8.0
1,173

1,083
7.3
1,003

–136
–0.7
–170

9,787
25.6
9,543

9,600
23.7
9,089

–187
–1.9
–454

7.7

6.7

–1.0

24.9

22.5

–2.4

46
3.8

80
7.4

34
3.6

244
2.5

511
5.3

267
2.8

Note: A person with a disability has at least one of the following conditions: is deaf or has serious difficulty hearing; is blind or has serious difficulty seeing even
when wearing glasses; has serious difficulty concentrating, remembering, or making decisions because of a physical, mental, or emotional condition; has
serious difficulty walking or climbing stairs; has difficulty dressing or bathing; or has difficulty doing errands alone such as visiting a doctor's office or shopping
because of a physical, mental, or emotional condition. Updated population controls are introduced annually with the release of January data.
Source: U.S. Bureau of Labor Statistics, Current Population Survey.

The foreign-born unemployment rate increased more than the nativeborn rate in 2020
The foreign born accounted for 17.0 percent of the U.S. civilian labor force ages 16 years and older in the fourth
quarter of 2020, down from 17.2 percent a year earlier.16 Foreign-born people saw a larger increase in their
unemployment rate in 2020 (up 4.4 percentage points, to 7.2 percent) than did native-born people (up 2.8

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percentage points, to 6.3 percent). (Data are not seasonally adjusted.) Foreign-born workers were more likely to
be employed in service occupations and production, transportation, and material moving occupations; as noted
previously, these occupations had the largest over-the-year increases in unemployment rates.17 The employment–
population ratio for the foreign born decreased by 4.8 percentage points over the year, dropping to 59.6 percent,
while the ratio for the native born decreased by 3.3 percentage points to reach 57.1 percent. (See table 9.)
Table 9. Employment status of the foreign- and native-born populations, by gender, quarterly averages,
not seasonally adjusted, 2019–20 (levels in thousands)
Total

Men
Change,

Employment status

Fourth

Fourth

and nativity

quarter,

quarter,

2019

2020

fourth

Change,
Fourth

quarter 2019 quarter,
to fourth

2019

Fourth

fourth

Change,
Fourth

quarter, quarter 2019 quarter,
2020

quarter 2020
Foreign born, 16 years and older
Civilian labor
28,207
force
Participation rate
66.3
Employed
27,420
Employment64.4
population ratio
Unemployed
787
Unemployment
2.8
rate
Native born, 16 years and older
Civilian labor
136,116
force
Participation rate
62.6
Employed
131,418
Employment60.4
population ratio
Unemployed
4,698
Unemployment
3.5
rate

Women

to fourth

2019

Fourth

fourth

quarter, quarter 2019
2020

quarter 2020

to fourth
quarter 2020

27,314

–893

16,247

15,692

–555

12,249

11,623

–626

64.2
25,340

–2.1
–2,080

77.9
15,782

76.8
14,712

–1.1
–1,070

55.0
11,795

52.6
10,628

–2.4
–1,167

59.6

–4.8

75.7

72.0

–3.7

53.0

48.1

–4.9

1,974

1,187

464

979

515

454

995

541

7.2

4.4

2.9

6.2

3.3

3.7

8.6

4.9

133,198

–2,918

69,724

69,322

–402

64,413

63,876

–537

60.9
124,743

–1.7
–6,675

66.9
67,059

65.5
64,634

–1.4
–2,425

57.9
62,179

56.7
60,109

–1.2
–2,070

57.1

–3.3

64.3

61.1

–3.2

55.9

53.3

–2.6

8,455

3,757

2,664

4,688

2,024

2,234

3,767

1,533

6.3

2.8

3.8

6.8

3.0

3.5

5.9

2.4

Note: The foreign born are those residing in the United States who were not U.S. citizens at birth. That is, they were born outside of the United States or one
of its outlying areas, such as Puerto Rico or Guam, to parents who were not U.S. citizens. This group includes legally admitted immigrants, refugees, students,
temporary workers, and undocumented immigrants. The survey data, however, do not separately identify the number of people in these different categories.
The native born are people who were born in the United States or one of its outlying areas, such as Puerto Rico or Guam, or who were born abroad of at least
one parent who was a U.S. citizen.
Source: U.S. Bureau of Labor Statistics, Current Population Survey.

Although labor force participation declined for both the native born and the foreign born in 2020, foreign-born
people continued to have a higher labor force participation rate than native-born people. The labor force
participation rate for the foreign born declined by 2.1 percentage points in 2020, to 64.2 percent, while the rate for
the native born decreased by 1.7 percentage points, to 60.9 percent.

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The number of people not in the labor force increased by 4.9 million
People who are not employed or unemployed are classified as not in the labor force.18 The total number of people
not in the labor force increased by 4.9 million over the year to reach 100.5 million at the end of 2020. Although
most people who are not in the labor force do not want a job, the number of people who do want a job but had not
sought employment in the 4 weeks preceding the survey increased by 2.2 million over the year, reaching 7.0
million in the fourth quarter of 2020. (See table 10.)
Table 10. Number of people not in the labor force, quarterly averages, seasonally adjusted, 2019–20 (in
thousands)
2020
Category

Total not in the labor
force
People who
currently want a
job
Marginally
attached to the
labor force[1]
Discouraged
workers[2]

Fourth
quarter, 2019

Change, fourth quarter 2019
First quarter,

Second

Third quarter,

Fourth

2020

quarter, 2020

2020

quarter, 2020

to fourth quarter 2020

95,581

95,755

101,891

100,231

100,473

4,892

4,834

5,149

8,990

7,304

7,047

2,213

1,252

1,401

2,382

1,990

2,076

824

308

424

635

601

637

329

[1] The marginally attached refer to people who want a job, have searched for work during the prior 12 months, and were available to take a job during the

reference week but had not looked for work in the 4 weeks prior to the survey.
[2] Discouraged workers include people who did not actively look for work in the 4 weeks prior to the survey for reasons such as they thought that no work is

available, they could not find work, they lack schooling or training, the employer thinks they are too young or old, and other types of discrimination.
Source: U.S. Bureau of Labor Statistics, Current Population Survey.

People who were not in the labor force were considered marginally attached to the labor force if they wanted a job,
were available for work, and had looked for work in the prior 12 months (but not in the 4 weeks before the survey).
In the fourth quarter of 2020, 2.1 million people were marginally attached to the labor force, an increase of 824,000
from a year earlier. (See chart 8.)

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A subset of the marginally attached are discouraged workers—people not currently looking for work because they
are discouraged over their job prospects.19 In the fourth quarter of 2020, the number of discouraged workers, at
637,000, was about twice the number from a year earlier.

All six measures of labor underutilization increased in 2020
Each of the six measures of labor underutilization increased in 2020. In the third quarter of 2020, U-1, at 4.9
percent, reached its highest level since the fourth quarter of 2011, before it decreased to 3.6 percent in the fourth
quarter of 2020. (See the box that follows for more information about the six measures of labor underutilization.)
Because U-1 is a measure of people who were unemployed for 15 weeks or longer, the rate remained low in the
early days of the pandemic; the U-1 rate increased later in the year, as many unemployed people were not recalled
to work as they originally expected, while others lost their jobs permanently and did not find new work, despite
searching for work for 3 months or longer.

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Alternative measures of labor underutilization
Six alternative measures of labor underutilization have been available on a monthly basis from the Current
Population Survey for the United States as a whole since 1994. The official unemployment rate (U-3 in the
six alternative measures) includes all jobless people who are available to take a job and have actively
sought work in the past 4 weeks (as well as people on temporary layoff). The other measures encompass
concepts both narrower (U-1 and U-2) and broader (U-4, U-5, and U-6) than the official unemployment rate.
The six measures are defined as follows:
• U-1: people unemployed 15 weeks or longer, as a percent of the civilian labor force;
• U-2: job losers and people who completed temporary jobs, as a percent of the civilian labor force;
• U-3: total unemployed, as a percent of the civilian labor force (this is the definition used for the official
unemployment rate);
• U-4: total unemployed plus discouraged workers, as a percent of the civilian labor force plus
discouraged workers;
• U-5: total unemployed, plus discouraged workers, plus all other marginally attached workers, as a
percent of the civilian labor force plus all marginally attached workers;
• U-6: total unemployed, plus all marginally attached workers, plus total employed part time for
economic reasons, as a percent of the civilian labor force plus all marginally attached workers.
Discouraged workers (included in the U-4, U-5, and U-6 measures) are people who are not in the labor
force, want and are available for work, and had looked for a job sometime in the prior 12 months. They are
not counted as unemployed because they had not searched for work in the 4 weeks preceding the survey.
Discouraged workers are not currently looking for work specifically because they believe that no jobs are
available for them or there are none for which they are qualified. The group of people who are marginally
attached to the labor force (included in the U-5 and U-6 measures) includes discouraged workers. The
criteria for the marginally attached are the same as for discouraged workers, with the exception that any
reason can be cited for their lack of job search in the 4 weeks prior to the survey. People at work part time
for economic reasons (included in the U-6 measure) are those working less than 35 hours per week who
want to work full time, are available to do so, and give an economic reason (their hours had been cut back
or they were unable to find a full-time job) for working part time. These individuals are sometimes referred to
as involuntary part-time workers.

The other five measures of labor underutilization (U2 to U6) reached their highest levels since 1994 in the second
quarter of 2020.20 Each of the five rates have fallen since their peak in the second quarter of 2020; however, the
rates in the fourth quarter of 2020 were still well above those of a year earlier. (See chart 9.)

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Unemployed people were more likely to remain unemployed than they
were before the pandemic
In the CPS, for any given month, a person can be classified in one of three labor force categories: employed,
unemployed, or not in the labor force. A person’s labor force status can change or remain the same from month to
month. For example, an unemployed person could remain unemployed, find employment, or leave the labor force.
In 2020, 21.7 million people, or 8.4 percent of the population ages 16 and older, changed their labor force status in
an average month. This represents the highest annual rate of change in labor market status since 1990, the first
year for which comparable data are available.
The CPS data on labor force flows provide additional insights into changes in the unemployment rate.21 In
December 2020, 54.6 percent of the unemployed remained unemployed in the following month. (Data are
seasonally adjusted 3-month moving averages.) This was higher than the percentage a year earlier, when 48.6
percent remained unemployed. Among the unemployed, 24.8 percent found employment and 20.6 percent left the

33

U.S. BUREAU OF LABOR STATISTICS

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labor force in December 2020. These measures are down from 27.3 percent and 24.1 percent, respectively, from a
year earlier. (See chart 10.)

New pandemic-related questions were added to the CPS in May 2020
In May 2020, the CPS included new questions to measure the impact of the COVID-19 pandemic on the labor
market.22 The questions gathered information on whether people teleworked because of the pandemic, whether
they were unable to work because their business closed or lost business because of the pandemic, whether they
received pay for the time they were unable to work, and whether they were unable to look for work because of the
pandemic.
In May 2020, shortly after the onset of the pandemic, 35.4 percent of employed people had teleworked or worked
from home at any time during the 4 weeks prior to the survey because of the pandemic.23 The share of the
employed who teleworked trended down during the rest of the year and had dropped to 23.7 percent by December
2020.
People with higher levels of educational attainment were more likely to telework because of the pandemic than
those with less formal education. In December 2020, among workers ages 25 and older, 52.0 percent of people
with an advanced degree and 37.5 percent of those with only a bachelor’s degree had teleworked in the 4 weeks

34

U.S. BUREAU OF LABOR STATISTICS

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prior to the survey because of the pandemic.24 By contrast, only 3.2 percent of people with less than a high school
diploma had teleworked in the prior 4 weeks because of the pandemic. (See table 11.)
Table 11. Employed people who teleworked or worked at home for pay at any time in the 4 weeks prior to
the survey because of the COVID-19 pandemic, by selected characteristics, December 2020 (levels in
thousands)
People who teleworked
because of the COVID-19
Characteristic

Total

pandemic

employed
Total

Total, 25 years and
older
Less than a high
school diploma
High school
graduates, no
college
Some college or
associate degree
Bachelor's degree
and higher
Bachelor's degree
only
Advanced degree

Percent distribution

Percent of total

Total

People who teleworked because of the

employed

employed

COVID-19 pandemic

131,817

33,663

25.5

100.0

100.0

8,288

264

3.2

6.3

0.8

32,006

2,738

8.6

24.3

8.1

33,538

5,677

16.9

25.4

16.9

57,985

24,983

43.1

44.0

74.2

35,675

13,372

37.5

27.1

39.7

22,309

11,611

52.0

16.9

34.5

Note: Data for people who teleworked because of the COVID-19 pandemic refer to those who teleworked or worked at home specifically because of the
COVID-19 pandemic and do not include people whose telework was unrelated to the pandemic, such as those who worked entirely from home before the
pandemic. The data are not seasonally adjusted.
Source: U.S. Bureau of Labor Statistics, Current Population Survey.

As noted previously, people working in food preparation and serving related occupations were among those most
affected by the pandemic—this occupation had the highest unemployment rate (19.6 percent) among the major
occupational groups in 2020.25 People in these occupations tend to be younger—39.4 percent of workers in food
preparation and serving related occupations were ages 16 to 24, compared with 11.6 percent for all occupations.
(Data are 2020 annual averages.) Although some workers could continue doing their jobs remotely, telework was
not a viable option for many restaurant workers, and this was reflected in the data on pandemic-related telework.
In December 2020, only 2.8 percent of workers in food preparation and serving related occupations had
teleworked.
This pattern was also evident in the age breakdown of pandemic-related telework.26 In December 2020, 10.3
percent of workers ages 16 to 24 had teleworked in the 4 weeks prior to the survey because of the pandemic,
compared with 26.7 percent of workers ages 25 to 54 and 22.3 percent of workers ages 55 and over.
In May 2020, 49.8 million people (19.2 percent of the population) reported that they could not work at some point
during the 4 weeks prior to the survey because their employer closed or lost business as a result of the pandemic.
This measure includes people whose hours had been reduced and those who were unemployed or not in the labor

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force. By December, the number of people unable to work because of the pandemic had decreased to 15.8 million,
or 6.1 percent of the population. (Data are not seasonally adjusted.)
People who could not work because of the pandemic were asked if they had received any pay from their employer
for hours they did not work in the 4 weeks prior to the survey. In May 2020, 17.6 percent of those unable to work
because of the pandemic received pay. This estimate was lower later in the year—12.8 percent in December.
People who were not in the labor force were asked if the pandemic had prevented them from looking for work in
the previous 4 weeks. In May 2020, 9.7 million people were prevented from looking for work because of the
pandemic. In December, less than half as many (4.6 million) were prevented from looking for work. This group
included 2.2 million who currently wanted a job; if they had looked for work and were available to take a job, they
would have been counted among the unemployed. (See table 12.)
Table 12. Percent of people who teleworked, were prevented from working, were paid for hours not
worked, and who did not look for work, not seasonally adjusted, May to December, 2020
Month

Month teleworked[1]

May
June
July
August
September
October
November
December

Prevented from working[2]

35.4
31.3
26.4
24.3
22.7
21.2
21.8
23.7

Paid for hours not worked[3]

19.2
15.5
12.0
9.3
7.4
5.8
5.7
6.1

Did not look for work[4]

17.6
15.4
12.6
11.6
10.3
11.7
13.7
12.8

9.5
7.1
6.5
5.2
4.5
3.6
3.9
4.5

[1] These are people who teleworked or worked from home because of the COVID-19 pandemic in the 4 weeks prior to the survey. The question was asked of

employed people. People whose telework was not related to the pandemic are not included.
[2] These are people who were unable to work during the 4 weeks prior to the survey because their employer closed or lost business because of the

COVID-19 pandemic.
[3] These are people who received pay from their employer for hours not worked in the 4 weeks prior to the survey. The question was asked of people who

were unable to work because of the COVID-19 pandemic.
[4] People who were prevented from looking for work within the last 4 weeks because of the COVID-19 pandemic. The question was asked of people who

were not in the labor force.
Source: U.S. Bureau of Labor Statistics, Current Population Survey.

Pandemic-related job losses made it difficult to gauge earnings
growth
In 2020, most economic indicators showed the impact of the pandemic, and earnings were no exception. However,
changes in median weekly earnings during the year must be interpreted with caution.27 There was an unusually
large increase in median weekly earnings in the second quarter of 2020, but that reflected the precipitous declines
in employment among lower paid workers (who were disproportionately affected by job loss related to the
pandemic), compared with higher-paid workers.28 When lower paid workers lost their jobs, they dropped out of the
distribution of earnings, and this put upward pressure on the median (the midpoint of the earnings distribution).
Earnings data for the third and fourth quarters of the year continued to be affected by the uneven pace of the
resumption of labor market activity. This large and abrupt shift in the earnings distribution during the year led to an
36

U.S. BUREAU OF LABOR STATISTICS

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increase in earnings in 2020; however, the underlying rate of growth in workers’ earnings is difficult to discern
because of the sudden and dramatic shift in the earnings distribution.29
Median usual weekly earnings of full-time wage and salary workers were $984 in 2020. (Data are annual averages
and are in current dollars.) Although pandemic-related job losses made it difficult to gauge the trend growth in
earnings, the earnings profile in 2020 in terms of major demographic and other characteristics mirrored those of
recent years. Women’s median weekly earnings in 2020 were $891, or 82.3 percent of men’s weekly earnings
($1,082). The women’s-to-men’s earnings ratio has remained between 80 to 83 percent since 2004. (See table 13
and chart 11.)
Table 13. Median usual weekly earnings of full-time wage and salary workers, by selected characteristics,
annual averages, 2019–20
Current dollars
Characteristic
2019
Total, 16 years and older
CPI-U (1982–84 = 100)
Men
Women
White
Men
Women
Black or African American
Men
Women
Asian
Men
Women
Hispanic or Latino ethnicity
Men
Women
Total, 25 years and older
Less than a high school diploma
High school graduate, no college
Some college or associate degree
Bachelor's degree or higher

$917
255.66
$1,007
821
945
1,036
840
735
769
704
1,174
1,336
1,025
706
747
642
969
592
746
856
1,367

Source: U.S. Bureau of Labor Statistics, Current Population Survey.

37

2020
$984
258.81
$1,082
891
1,003
1,110
905
794
830
764
1,310
1,447
1,143
758
797
705
1,029
619
781
903
1,421

Percent change, 2019–20
7.3
1.2
7.4
8.5
6.1
7.1
7.7
8.0
7.9
8.5
11.6
8.3
11.5
7.4
6.7
9.8
6.2
4.6
4.7
5.5
4.0

U.S. BUREAU OF LABOR STATISTICS

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Earnings also continued to vary by age and gender, exhibiting the same basic patterns as in recent years. For both
men and women, earnings were lowest for those ages 16 to 24, followed by 25- to 34-year-olds. Earnings of those
ages 35 to 64 ranged from $1,205 to $1,260 for men and $955 to $978 for women. The women’s-to-men’s
earnings ratio was higher among younger workers than among older workers. For example, the ratio was 94.7
percent for 16- to 24-year-olds, compared with 77.5 percent among 45- to 54-year-olds. (See chart 12.)

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In 2020, median weekly earnings among the major race and ethnicity groups continued to be higher for Asians
($1,310) and Whites ($1,003) than for Blacks ($794) and Hispanics ($758). The women’s-to-men’s earnings ratio
varied by race and ethnicity. White women earned 81.5 percent as much as their male counterparts, compared
with 92.0 percent for Black women, 79.0 percent for Asian women, and 88.5 percent for Hispanic women.
Earnings are positively correlated with educational attainment.30 Among full-time wage and salary workers ages 25
and older, workers with a bachelor’s degree and higher had median weekly earnings of $1,421. Those with some
college or an associate degree had weekly earnings of $903, and earnings for high school graduates (no college)
were $781. Workers with less than a high school diploma had the lowest weekly earnings, at $619.
Among the major occupational groups, people employed full time in management, professional, and related
occupations had the highest median weekly earnings—$1,578 for men and $1,164 for women. As has historically
been the case, men ($704) and women ($574) employed in service occupations earned the least in 2020. (See
table 14.)
Table 14. Median usual weekly earnings of full-time wage and salary workers, by occupation and gender,
annual averages, 2020
Occupation and gender

Number of workers (in thousands) Median weekly earnings

See footnotes at end of table.

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Table 14. Median usual weekly earnings of full-time wage and salary workers, by occupation and gender,
annual averages, 2020
Occupation and gender

Number of workers (in thousands) Median weekly earnings

Total, 16 years and older
Management, professional, and related occupations
Management, business, and financial operations
occupations
Professional and related occupations
Service occupations
Sales and office occupations
Sales and related occupations
Office and administrative support occupations
Natural resources, construction, and maintenance
occupations
Farming, fishing, and forestry occupations
Construction and extraction occupations
Installation, maintenance, and repair occupations
Production, transportation, and material moving
occupations
Production occupations
Transportation and material moving occupations
Men, 16 years and older
Management, professional, and related occupations
Management, business, and financial operations
occupations
Professional and related occupations
Service occupations
Sales and office occupations
Sales and related occupations
Office and administrative support occupations
Natural resources, construction, and maintenance
occupations
Farming, fishing, and forestry occupations
Construction and extraction occupations
Installation, maintenance, and repair occupations
Production, transportation, and material moving
occupations
Production occupations
Transportation and material moving occupations
Women, 16 years and older
Management, professional, and related occupations
Management, business, and financial operations
occupations
Professional and related occupations
Service occupations
Sales and office occupations
Sales and related occupations
Office and administrative support occupations
Natural resources, construction, and maintenance
occupations
See footnotes at end of table.

40

110,387
50,023

$984
1,356

20,811

1,461

29,213
13,771
21,165
8,958
12,207

1,270
621
809
880
781

10,690

905

787
5,826
4,077

589
906
984

14,738

746

6,820
7,917
60,911
24,090

775
719
1,082
1,578

11,082

1,667

13,008
6,740
8,435
4,991
3,445

1,532
704
956
1,046
868

10,152

917

600
5,635
3,917

608
910
991

11,494

796

5,055
6,439
49,476
25,933

841
759
891
1,164

9,729

1,274

16,204
7,032
12,729
3,967
8,762

1,121
574
746
715
756

538

682

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Table 14. Median usual weekly earnings of full-time wage and salary workers, by occupation and gender,
annual averages, 2020
Occupation and gender

Number of workers (in thousands) Median weekly earnings

Farming, fishing, and forestry occupations
Construction and extraction occupations
Installation, maintenance, and repair occupations
Production, transportation, and material moving
occupations
Production occupations
Transportation and material moving occupations

187
191
160

528
796
801

3,243

614

1,765
1,478

630
600

Note: Updated population controls are introduced annually with the release of January data.
Source: U.S. Bureau of Labor Statistics, Current Population Survey.

Summary
The national unemployment rate reached 13.0 percent in the second quarter of 2020, as the economic expansion
ended early in 2020 and the nation fell into recession because of the COVID-19 pandemic. As the nation struggled
to reopen its economy fully, the jobless rate fell to 6.7 percent in the fourth quarter of 2020; even with the decline,
the rate was almost twice as high as it was a year earlier. At the end of the year, the number of people on
temporary layoff, as well as permanent job losers and people unemployed for 27 weeks or longer, were also much
higher than they were a year earlier. The number of employed people, at 149.8 million in the fourth quarter of
2020, fell by 8.8 million over the year, as improvements in the third and fourth quarters did not make up for the
employment losses in the second quarter. The labor force participation rate fell by 1.7 percentage points over the
year, reaching 61.5 percent in the fourth quarter of 2020, with the rate for women declining somewhat more
sharply.
SUGGESTED CITATION

Sean M. Smith, Roxanna Edwards, and Hao C. Duong, "Unemployment rises in 2020, as the country battles the
COVID-19 pandemic," Monthly Labor Review, U.S. Bureau of Labor Statistics, June 2021, https://doi.org/
10.21916/mlr.2021.12.
NOTES
1 The Business Cycle Dating Committee of the National Bureau of Economic Research (NBER) is the official arbiter of the beginning
and ending dates of recessions and expansions in the United States. According to NBER, the most recent expansion began in June
2009 and ended in February 2020. Or, in terms of quarters, the expansion began in the second quarter of 2009 and ended in the
fourth quarter of 2019. For the quarterly analysis in this article, the NBER-designated quarterly dates are used. According to NBER,
the “trough” of a recession marks the beginning of an expansion, and the “peak” of an expansion marks the beginning of a recession.
Therefore, the economic expansion that ended in February 2020 lasted for 128 months or 42 quarters, surpassing the economic
expansion of March (first quarter) 1991 to March (first quarter) 2001, which lasted for 120 months (or 40 quarters) and had been the
longest expansion on record. An endpoint for the recession that began in February 2020 has not yet been determined. For further
analysis of the U.S. labor market during the Great Recession and the decade that followed, see Evan Cunningham, “Great
Recession, great recovery? Trends from the Current Population Survey,” Monthly Labor Review, April 2018, www.bls.gov/opub/mlr/
2018/article/great-recession-great-recovery.htm.

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2 The forerunner to the Current Population Survey (CPS), known as the Sample Survey of Unemployment, was initiated in 1940 by
the Work Projects Administration. The survey was transferred to the U.S. Census Bureau in 1942 and became widely known as the
“Current Population Survey” in 1948. Historical comparisons in this article use data for 1948 and later years; the data are seasonally
adjusted and are for people 16 years and older. For more on the history of the CPS, see “Current Population Survey,” Handbook of
Methods (U.S. Bureau of Labor Statistics, 2018), pp. 19–21, https://www.bls.gov/opub/hom/cps/pdf/cps.pdf; see also, Megan Dunn,
Steven E. Haugen, and Janie-Lynn Kang; “The Current Population Survey—tracking unemployment in the United States for over 75
years,” Monthly Labor Review, January 2018, https://www.bls.gov/opub/mlr/2018/article/the-current-population-survey-trackingunemployment.htm.
3 In the CPS, unemployed people are defined as those ages 16 years and older who were not employed during the survey reference
week, had actively searched for work during the 4 weeks prior to the survey, and were available for work. People who were on
temporary layoff and available for work are counted as unemployed and do not have to have searched for work during the reference
period.
4 Although data from the CPS are published monthly, the data analyzed in this article are seasonally adjusted quarterly averages,
and, unless otherwise noted, all over-the-year changes compare data from the fourth quarter of 2019 to the fourth quarter of 2020.
5 The Great Recession began in December 2007, or the fourth quarter of 2007, and ended in June 2009, or the second quarter of
2009, as determined by the National Bureau of Economic Research (NBER). For more information about U.S. business cycle
expansions and contractions, see https://www.nber.org/research/data/us-business-cycle-expansions-and-contractions.
6 Beginning with data for January 2020, the Current Population Survey (CPS) has classified occupations according to the Census
2018 occupational classification system, which is derived from the 2018 Standard Occupational Classification (SOC) system. The
2018 SOC system replaced the earlier 2010 Census occupational classification based on the 2010 SOC system, which was used in
the CPS from January 2011 through December 2019. As a result of this change, CPS occupational data from January 2020 and later
are not comparable with occupational data from earlier years. Although the names of the broad- and intermediate-level occupational
groups in the 2018 SOC system remained the same, some detailed occupations were reclassified between the broader groups, which
substantially affects data comparability over time. For example, within sales and office occupations, the office and administrative
support occupations group is now smaller in scope. (The titles of the groups were unchanged.) Stock clerks and order fillers, which
employed 1.5 million people in 2019, moved out of the broad group office and administrative support occupations and into
transportation and material moving occupations. Similarly, computer operators, which employed 72,000 people in 2019, moved out of
office and administrative support occupations and into computer and mathematical occupations. In addition, within production,
transportation, and material moving occupations, the transportation and material moving occupations group is now larger in scope
because it includes stock clerks and order fillers. Finally, some detailed occupations were reclassified but remained in the same broad
occupation category—within service occupations, for example, personal care aides, which employed 1.5 million people in 2019,
moved from personal care and service occupations to healthcare support occupations. For more information, see “Industry and
Occupation Classification” (U.S. Census Bureau, October 2020), https://www.census.gov/programs-surveys/cps/technicaldocumentation/methodology/industry-and-occupation-classification.html.
7 For more information on the percentage of women and men who take care of children and perform other household activities on a
given day, see American Time Use Survey—2019 Results, USDL-20-1275 (U.S. Department of Labor, June 25, 2020), www.bls.gov/
news.release/pdf/atus.pdf. For estimates of unpaid eldercare providers, see Unpaid Eldercare in the United States—2017–18: Data
from the American Time Use Survey, USDL-19-2051 (U.S. Department of Labor, November 22, 2019), www.bls.gov/news.release/pdf/
elcare.pdf.
8 For more information, see Misty L. Heggeness, Jason Fields, Yazmin A. Garcia Trejo, and Anthony Schulzetenberg, “Tracking job
losses for mothers of school-age children during a health crisis,” (U.S. Census Bureau, March 3, 2021), https://www.census.gov/
library/stories/2021/03/moms-work-and-the-pandemic.html.
9 People whose ethnicity is identified as Hispanic or Latino may be of any race. In the CPS, about 90 percent of people of Hispanic or
Latino ethnicity are classified as White.

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10 See Amara Omeokwe, “Pandemic accelerates retirements, threatening economic growth,” Wall Street Journal, March 28, 2021,
https://www.wsj.com/articles/pandemic-accelerates-retirements-threatening-economic-growth-11616940000?
mod=hp_major_pos2#cxrecs_s.
11 Since 1992, educational attainment in the CPS refers to the highest diploma or degree obtained. Prior to 1992, educational
attainment referred to the number of years of school completed. The pre-1992 educational attainment categories are not strictly
comparable with the current categories.
12 The CPS collects data on the different reasons that people are unemployed, including being on temporary layoff. Unemployed
people on temporary layoff are those who (1) said they were laid off or were not at work during the survey reference week because of
layoff (temporary or indefinite) or slack work or business conditions, (2) have been given a date to return or expect to be recalled
within the next 6 months, and (3) could have returned to work if they had been recalled (except for those who had a temporary illness
that prevented them from returning to work). Unlike other unemployed people, those on temporary layoff do not need to be actively
looking for work to be classified as unemployed. Pay status is not part of the criteria for being classified as unemployed on temporary
layoff. People absent from work because of temporary layoff are classified as unemployed on temporary layoff, whether or not they
were paid during the time they were off work.
13 For more information about duration of unemployment during 2020, see “36.9 percent of unemployed jobless 27 weeks or more as
pandemic continues, November 2020,” The Economics Daily (U.S. Bureau of Labor Statistics, December 9, 2020), www.bls.gov/opub/
ted/2020/36-point-9-percent-of-unemployed-jobless-27-weeks-or-more-as-pandemic-continues-november-2020.htm.
14 BLS produces measures of people at work part time for economic and noneconomic reasons from the CPS. People at work part
time for economic reasons, also referred to as involuntary part-time workers, include those who gave an economic reason when
asked why they worked 1 to 34 hours during the reference week (the week including the 12th of the month). Economic reasons
include the following: slack work, unfavorable business conditions, inability to find full-time work, and seasonal declines in demand.
People who usually work part time and were at work part time during the reference week must indicate that they wanted and were
available for full-time work to be classified as part time for economic reasons.
15 In the CPS, veterans are defined as men and women 18 years and older who previously served on active duty in the U.S. Armed
Forces and who were civilians at the time the survey was conducted. Veterans are categorized as having served in the following
periods of service: (1) Gulf War era II (September 2001 to the present), (2) Gulf War era I (August 1990 to August 2001), (3) Vietnam
era (August 1964 to April 1975), (4) Korean War (July 1950 to January 1955), (5) World War II (December 1941 to December 1946),
and (6) other service periods (all other periods). Veterans who served in more than one wartime period are classified into only the
most recent one. Veterans who served in both a wartime period and any other service period are classified only in the wartime period.
16 The foreign born are people who reside in the United States but were not U.S. citizens at birth. Specifically, they were born outside
the country (or outside one of its outlying areas, such as Puerto Rico or Guam), and neither parent was a U.S. citizen. The foreign
born include legally admitted immigrants; refugees; temporary residents, such as students and temporary workers; and
undocumented immigrants.
17 Foreign-Born Workers: Labor Force Characteristics—2020, USDL 21-0905 (U.S. Department of Labor, May 18, 2021), https://
www.bls.gov/news.release/archives/forbrn_05182021.pdf.
18 For additional information, see Steven F. Hipple, “People who are not in the labor force: why aren’t they working?” Beyond the
Numbers, December 2015, www.bls.gov/opub/btn/volume-4/people-who-are-not-in-the-labor-force-why-arent-they-working.htm.
19 Discouraged workers may indicate that no jobs are available for them; they lack education, training, or experience needed to find a
job; or they believe they face some type of discrimination, such as being too young or too old.
20 The alternative measures of labor underutilization were introduced in 1994. U-3, the total number of people unemployed as a
percentage of the labor force, is the official unemployment rate. For more information on the alternative measures of labor
underutilization, see table A–15 in The Employment Situation news release, https://www.bls.gov/news.release/empsit.nr0.htm.

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21 For more information on this topic, see Harley Frazis, “Employed workers leaving the labor force: an analysis of recent trends,”
Monthly Labor Review, May 2017, https://www.bls.gov/opub/mlr/2017/article/employed-workers-leaving-the-labor-force-an-analysisof-recent-trends.htm; Randy E. Ilg and Eleni Theodossiou, “Job search of the unemployed by duration of unemployment,” Monthly
Labor Review, March 2012, https://www.bls.gov/opub/mlr/2012/03/art3full.pdf; and “Research series on labor force status flows from
the Current Population Survey,” available at www.bls.gov/cps/cps_flows.htm.
22 These data are not seasonally adjusted and are available as monthly estimates. For more information, see www.bls.gov/cps/
effects-of-the-coronavirus-covid-19-pandemic.htm.
23 People did not have to telework for the entire time that they worked to be counted among those who telework. People whose
telework was not related to the pandemic, such as those who worked entirely from home before the pandemic, are not included in this
measure.
24 Data on people ages 25 and older who teleworked because of the pandemic, by educational attainment, are available at
www.bls.gov/cps/effects-of-the-coronavirus-covid-19-pandemic.htm#table1.
25 See Matthew Dey and Mark A. Loewenstein, “How many workers are employed in sectors directly affected by COVID-19
shutdowns, where do they work, and how much do they earn?” Monthly Labor Review, April 2020, www.bls.gov/opub/mlr/2020/article/
covid-19-shutdowns.htm.
26 See Matthew Dey, Harley Frazis, Mark A. Loewenstein, and Hugette Sun, “Ability to work from home: evidence from two surveys
and implications for the labor market in the COVID-19 pandemic,” Monthly Labor Review, June 2020, www.bls.gov/opub/mlr/2020/
article/ability-to-work-from-home.htm.
27 Data are annual averages and are in current dollars. The CPS data on earnings represent earnings before taxes and other
deductions and include any overtime pay, commissions, or tips typically received. For multiple jobholders, only earnings received at
their main job are included. Earnings reported on a nonweekly basis are converted to a weekly equivalent. The term “usual” reflects
each survey respondent’s understanding of the term. If the respondent asks for a definition of “usual,” interviewers are instructed to
define the term as more than half the weeks worked during the past 4 or 5 months. Wage and salary workers are defined as those
who receive wages, salaries, commissions, tips, payment in kind, or piece rates. This definition includes both public- and privatesector employees but excludes all self-employed people, regardless of whether their business is incorporated or unincorporated.
Earnings comparisons made in this article are on a broad level and do not control for many factors that can be important in explaining
earnings differences, such as job skills and responsibilities, work experience, and specialization. Finally, full-time workers are those
who usually work 35 hours or more per week at their main job.
28 See Matthew Dey, Mark A. Loewenstein, David S. Piccone Jr, and Anne E. Polivka, “Demographics, earnings, and family
characteristics of workers in sectors initially affected by COVID-19 shutdowns,” Monthly Labor Review, June 2020, www.bls.gov/opub/
mlr/2020/article/demographics-earnings-and-family-characteristics-of-workers-in-sectors-initially-affected-by-covid-19-shutdowns.htm.
29 For more information on this issue, see Erin E. Crust, Mary C. Daly, and Bart Hobijn, “The illusion of wage growth,” FRBSF
Economic Letter, August 31, 2020, www.frbsf.org/economic-research/publications/economic-letter/2020/august/illusion-of-wagegrowth/.
30 For further discussion about the benefits of college education, see Jaison R. Abel and Richard Deitz, “Do the benefits of college
still outweigh the costs?” Current Issues in Economics and Finance, vol. 20, no. 3, 2014, www.newyorkfed.org/medialibrary/media/
research/current_issues/ci20-3.pdf.

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44

U.S. BUREAU OF LABOR STATISTICS

MONTHLY LABOR REVIEW

Estimating state and local employment in recent disasters—from Hurricane Harvey to the COVID-19 pandemic, Monthly Labor
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45

Men

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Black

June 2021

COVID-19 ends longest employment recovery and
expansion in CES history, causing unprecedented
job losses in 2020
In March and April 2020, the longest employment recovery
and expansion in U.S. history abruptly ended, with total
nonfarm employment falling sharply because of the
coronavirus disease 2019 (COVID-19) pandemic and the
efforts to contain it. Job losses were historic and
widespread. Although state and local government
restrictions on businesses and individuals began to ease
somewhat after April 2020, total nonfarm employment
ended the year 10.0 million below its February peak.
According to data from the U.S. Bureau of Labor Statistics
(BLS) Current Employment Statistics (CES) survey,
nonfarm payroll employment in the United States declined
by 9.4 million in 2020,1 the largest calendar-year decline in
the history of the CES employment series.2 (See chart 1.)
As with virtually all economic activity in 2020, this decline
was due to the coronavirus disease 2019 (COVID-19)
pandemic, including pandemic-driven social and behavioral
changes and government restrictions on business activity.3
While the job losses were widespread, they were greatest
in industries that involve people (employees, customers, or
both) coming in close contact. The leisure and hospitality
industry suffered the greatest job losses, but every major

Ryan Ansell
cesinfo@bls.gov
Ryan Ansell is an economist in the Office of
Employment and Unemployment Statistics, U.S.
Bureau of Labor Statistics.
John P. Mullins
cesinfo@bls.gov
John P. Mullins is an economist in the Office of
Employment and Unemployment Statistics, U.S.
Bureau of Labor Statistics.

industry lost jobs over the year. (See charts 2 and 3.)
Although 2020 can certainly be characterized as a year of
extreme job loss, it also saw several months of recovery
with historic job growth. In addition, the pandemic affected each industry differently, resulting in considerable
variability in employment impacts.

1

U.S. BUREAU OF LABOR STATISTICS

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2

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MONTHLY LABOR REVIEW

3

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This article details the historic employment declines of 2020, specifically within the context of the COVID-19
pandemic. It also touches upon nonemployment data from the CES survey, including hours, earnings, and
diffusion indexes, which measure the breadth of employment change across industries. The focus is on
employment changes after the February 2020 peak in nonfarm payroll employment.

Historical context and employment trends
By the end of 2019, payroll employment in the United States had been growing steadily for over 9 years, marking
the longest recovery and expansion in CES history. This growth continued into the start of 2020, with January and
February adding a combined 604,000 jobs. At about the same time, however, news of a rapidly spreading disease
was growing dire. After the first disease cases were identified in early January, their number grew rapidly.4 On
January 21, the U.S. Centers for Disease Control and Prevention (CDC) announced the first U.S. case.5 On March
11, the day the World Health Organization designated COVID-19 as a pandemic, the count of new daily cases in
the United States stood at 312.6 This number would grow to the thousands within a week and exceed 22,000 by
the end of the month.7

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In March 2020, state and local governments began imposing restrictions in response to the pandemic, including
stay-at-home orders, social distancing requirements, travel restrictions, school closures, and capacity limitations
on, or complete closures of, some businesses.8 These restrictions varied from state to state and from industry to
industry, as did the rigorousness of their enforcement. However, combined with a public reluctance to engage in
activities requiring close human contact, the restrictions led to immediate job losses.9 In March, nonfarm
employment fell by 1.7 million, a loss only surpassed by a 1945 employment decline that came as the country
demobilized after World War II.10 Unsurprisingly, February was designated as the month in which overall economic
activity peaked, marking the beginning of an economic recession.11 In April, employment plummeted, dropping by
20.7 million—the largest decline in the history of the CES employment series, which originated in 1939.12 Then,
however, a job recovery began, with nonfarm employment growing by 2.8 million in May and by 4.8 million in June.
In fact, May, June, July, and August brought the four largest monthly job gains in CES history. As COVID-19 cases
surged back later in 2020 and as most state and local governments tightened restrictions in response, employment
gains grew progressively smaller, and the year ended with a December employment loss.
The job loss of the 2020 recession is particularly stark when compared with losses in the previous four
recessions.13 (See chart 4.) After reaching a peak in February 2020, employment fell by a combined 22.4 million in
March and April, a decline of 15 percent. By contrast, in the previous four employment downturns since 1981, job
losses averaged 3 percent, with the largest decline (6 percent) occurring during the Great Recession of 2007–09.

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U.S. BUREAU OF LABOR STATISTICS

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Employment of women, average hourly earnings, and average weekly
hours
Besides driving large movements in employment, the pandemic led to substantial volatility in three other CES data
series: employment of women, average weekly hours, and average hourly earnings. In all three cases, large
swings in the data were due mainly to the industry mix of employment change.

Employment of women
In 2020, employment of women fell by 5.2 million. In March and April, women’s nonfarm employment declined by
12.2 million, accounting for 55 percent of the total employment decline over this time. (See table 1.) This
disproportionate decline in women employment is attributable to two factors. The first is that pandemic-related job
losses were concentrated in industries with large shares of women workers. In March and April, the leisure and
hospitality industry, in which women made up 53 percent of total employment, lost 8.2 million jobs, an employment
decline of 49 percent. Also hard hit was the private education and health services industry, whose total
employment, made up mostly of women (77 percent), fell by 2.8 million, or 12 percent. Other industries in which
women constituted a large share of total employment, such as government (58 percent), other services (53
percent), retail trade (50 percent), and professional and business services (46 percent), all saw employment
declines of more than a million in March and April. Industries with relatively small shares of women employees
generally experienced smaller job declines over the year. The second factor responsible for employment declines
among women is that, within industry groups with large proportions of women employees, job losses were
disproportionately concentrated among those employees. In education and health services, for example,
employment declines among women accounted for 84 percent of total employment declines in March and April.
And in leisure and hospitality—the industry with the greatest overall employment declines—women accounted for
54 percent of job losses in those 2 months. From April to December, women’s employment across industry groups
grew at about the same pace as did total employment.
Table 1. Employment of women, by industry, seasonally adjusted

All employees,
Industry

February 2020
(thousands)

Total nonfarm
Total private
Mining and
logging
Construction
Manufacturing
Wholesale trade
Retail trade
Transportation
and
warehousing

Women employees

Change in all

Change in women

as a percent of all

employees,

employees,

employees,

February– April

February– April

February 2020

2020 (thousands)

2020 (thousands)

Change in women
employees as a percent
of change in all
employees, February–
April 2020

152,523
129,688

50.0
48.7

-22,362
-21,353

-12,205
-11,575

54.6
54.2

690

12.9

-68

-1

1.5

7,648
12,799
5,895
15,610

13.0
28.7
30.0
49.6

-1,113
-1,385
-409
-2,375

-112
-461
-157
-1,423

10.1
33.3
38.4
59.9

5,823

26.1

-575

-222

38.5

See footnotes at end of table.

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U.S. BUREAU OF LABOR STATISTICS

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Table 1. Employment of women, by industry, seasonally adjusted

All employees,
Industry

February 2020

Women employees

Change in all

Change in women

as a percent of all

employees,

employees,

employees,

February– April

February– April

February 2020

2020 (thousands)

2020 (thousands)

(thousands)

Utilities
Information
Financial
activities
Professional
and business
services
Education and
health services
Leisure and
hospitality
Other services
Government

Change in women
employees as a percent
of change in all
employees, February–
April 2020

547
2,914

24.4
39.6

-4
-281

-1
-105

12.8
37.4

8,875

56.5

-279

-136

48.7

21,469

45.9

-2,387

-1,198

50.2

24,565

77.4

-2,843

-2,379

83.7

16,915

53.2

-8,224

-4,459

54.2

5,937
22,835

53.4
57.8

-1,410
-1,009

-923
-630

65.5
62.4

Source: U.S. Bureau of Labor Statistics, Current Employment Statistics survey.

Earnings
In 2020, average hourly earnings of all private sector employees rose by $1.55, or 5.5 percent, and average hourly
earnings of production and nonsupervisory employees rose by $1.31, or 5.5 percent. In both dollar and percent
terms, these are the largest annual changes in CES history.14 Over-the-year changes, however, mask volatility in
the monthly data. For example, in April, which saw the largest over-the-month earnings change during the year, the
hourly earnings of all private sector employees rose by $1.33, or 4.6 percent, and the earnings of private sector
production and nonsupervisory employees rose by $1.01, or 4.2 percent. These changes were largely dictated by
pandemic-related employment losses.
Employment declines were the greatest in industries with low earnings. (See table 2.) The leisure and hospitality
industry, whose February average hourly earnings ($16.90) were the lowest of any industry, accounted for 39
percent of the February–April decline in total private employment. Retail trade, which had the second-lowest
February earnings ($20.18) accounted for an additional 11 percent of job losses over the same period. Job losses
in industries whose workers have higher earnings, such as information and financial activities, made up a relatively
small share of the employment decline in March and April. Because overall private earnings in the CES program
are calculated as an average weighted by employment share and by industry, the removal of workers with lower
earnings drove up the total private average.15 In other words, the loss of jobs in industries with low earnings
pushes up average hourly earnings at the total private sector level.

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Table 2. Employment change from February to April 2020, and February 2020 all-employee average hourly
earnings, by industry, seasonally adjusted
Industry

Employment change, February–April 2020 (thousands) Average hourly earnings, February 2020

Mining and logging
Construction
Manufacturing
Wholesale trade
Retail trade
Transportation and
warehousing
Utilities
Information
Financial activities
Professional and
business services
Education and health
services
Leisure and hospitality
Other services

-68
-1,113
-1,385
-409
-2,375

$34.41
31.36
28.23
31.81
20.18

-575

25.05

-4
-281
-279

42.42
42.95
36.85

-2,387

34.43

-2,843

27.90

-8,224
-1,410

16.90
25.59

Source: U.S. Bureau of Labor Statistics, Current Employment Statistics survey.

There is also evidence that job losses were concentrated among workers with low earnings. As businesses tried to
adapt to the pandemic-driven economic slowdown, newer and lower earning workers were let go, with supervisors
and experienced (and therefore higher paid) workers being relied upon to maintain business operations.16 This
resulted in increases in the average earnings of workers, and these increases aggregated to the topside level.

Hours
In 2020, average weekly hours of all private sector employees rose by 0.4 hour, to 34.7 hours, and average weekly
hours of production and nonsupervisory employees rose by 0.7 hour, to 34.2 hours. The annual change for all
private sector employees was the largest since 2010, when average weekly hours grew by the same amount. The
annual change for production and nonsupervisory employees was the largest in CES history. In May, which saw
the largest over-the-month change during the year, weekly hours of all private sector employees grew by 0.5 hour,
while hours of private sector production and nonsupervisory employees grew by 0.6 hour. These changes, like
those for earnings, were largely dictated by pandemic-related employment changes. While employees in most
industries saw an increase in their workweeks in May, the employment changes, especially those in industries with
shorter workweeks, complicate monthly comparisons of average weekly hours.

Diffusion indexes
CES uses diffusion indexes as a supplementary analysis tool to measure how widespread employment changes
are across industries. Rather than measuring the magnitude of employment change (i.e., the number of jobs
gained or lost over time), diffusion indexes measure how many industries added or lost jobs over 1, 3, 6, and 12
months. An index value above 50 indicates that, over the relevant period, more industries are adding jobs than

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losing them, whereas a value below 50 indicates that more industries are losing jobs than adding them. CES
produces diffusion indexes of employment for the total private sector and for manufacturing.
Diffusion indexes show that pandemic-related job losses were spread across most industries. In March 2020, the
1-month diffusion index for total private employment fell by 42.6 points, to 12.5, and in April, it fell a further 8.0
points, to 4.5—the lowest reading in the 30-year history of the time series. (See chart 5.) In March, job losses
occurred in 221 of the 257 industries included in the total private diffusion index, and in April, 244 industries
experienced job losses. The 1-month manufacturing index fell by 28.0 points, to 15.3, in March, and it fell a further
12.0 points, to 3.3, in April. As was the case with the index for total private employment, the April reading for
manufacturing was the lowest in the history of the data series. In March, 61 of 75 manufacturing industries shed
jobs, and in April, 72 manufacturing component industries lost jobs.

From April to May, the total private index quickly surged back to 63.2, and the index for manufacturing rose to 69.3,
reflecting the beginning of widespread job gains. For the rest of 2020, the 1-month total private index remained
above 50, indicating a preponderance of job-gaining industries each month, and the 1-month manufacturing index
was above 50 in all but 1 month between April and December. The 12-month diffusion index, however, paints a
somewhat darker picture.17 In December, the 12-month total private index stood at 14.6 and the manufacturing
index stood at 11.3, indicating that, in 2020, job-losing industries outnumbered job-gaining industries. From
December 2019 to December 2020, 219 of 257 total private industries lost jobs, with only 37 industries seeing job
gains; in manufacturing, 66 of 75 industries shed jobs over this period, with only 8 seeing employment increases.

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Detailed industry analysis
This section discusses CES employment changes by industry.

Leisure and hospitality
After reaching a peak in February 2020, employment in leisure and hospitality fell by 8.2 million in March and April.
(See chart 6.) By the end of the year, 4.4 million of these jobs, or 54 percent, had been recovered. Leisure and
hospitality’s overall job loss for 2020 was 3.7 million—the largest of any major industry group.

It is unsurprising that leisure and hospitality industries were seriously affected by the pandemic. These industries
include businesses such as restaurants and bars, hotels, and sports and entertainment venues. What made this
industry group so susceptible to pandemic impacts is that the activities of many of its businesses involve close
gatherings of people. Restaurant and bar patrons dine and drink with other patrons, and theatergoers sit next to
other theatergoers. In addition, leisure and hospitality industries, especially hotels, are tightly linked to travel, an
activity that was seriously curtailed by fears of COVID-19 transmission. With individuals traveling less and staying
home more, hotel occupancy rates plummeted.
Within leisure and hospitality, the accommodation and food services industry lost 6.9 million jobs in the 2 months
following its February employment peak. Most of this decline occurred in the industry’s largest component, food
services and drinking places.18 (See chart 7.) Restaurants and bars faced tight restrictions early in the
pandemic.19 These restrictions, combined with customer reticence to visit such establishments, led to sudden and

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severe declines in restaurant sales, which fell by 49 percent from February to April.20 Employment in the industry
fell just as fast over this period, declining by 6.0 million, or 49 percent.

Job losses in food services and drinking places were greatest in full-service restaurants, in which patrons place
orders and are served while seated. (See chart 8.) These types of restaurants faced wariness by customers, as
well as government restrictions that imposed capacity limitations or limited sales to takeout, curbside pickup, or
delivery. Employment in full-service restaurants fell by a combined 3.7 million in March and April, accounting for 70
percent of job losses in restaurants and other eating places over these 2 months. As restrictions began to ease in
the late spring and summer months, full-service restaurants added 1.9 million jobs in May and June.21 Growth
continued at a lower rate through October, with the industry ending the year with employment declines in
November and December. These late-year job losses coincided with a resurgence in the number of COVID-19
cases and a corresponding resumption of state and local government restrictions on restaurants.22 By the end of
the year, 63 percent of the jobs lost in March and April had been recovered, but employment in full-service
restaurants was still 1.4 million below its February peak.

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Employment in limited-service restaurants also fell sharply in March and April, declining by 1.1 million. Although
this decline—at 23 percent—was certainly large, it was much less steep than the 65-percent loss suffered by fullservice restaurants. The relatively greater resilience of limited-service restaurants is largely a byproduct of their
general business model. These restaurants, which include pizza and sandwich shops, takeout eating places, and
fast-food and similar restaurants, had in place many systems and practices that benefitted them during the
pandemic. Limited-service restaurants rely less on in-person dining and more on takeout, drive-through service,
and delivery, and therefore posed less risk to customers. Most fast-food chains fared relatively well, because many
of them have drive-through windows, which allow for social distancing.23 By the end of 2020, limited-service
restaurants had recovered 75 percent of their March and April job losses.
Other food service industries also experienced pandemic-related job losses. March and April saw combined job
losses of 415,000 in snack and nonalcoholic beverage bars, which include businesses such as donut, bagel, and
coffee shops. By the end of the year, 77 percent of these jobs had been recovered. Employment in cafeterias, grill
buffets, and buffets fell by 94,000 in March and April. School and university closures eliminated much of the need
for cafeteria service, and concerns about the health risks associated with dining at buffets led to their closures in
much of the country.24 About a quarter of cafeteria and buffet employment had been recovered by the end of 2020.
Employment in the accommodation industry fell by 1.0 million in March, April, and May, as travel restrictions hit this
industry especially hard. The United States restricted travel for residents of many countries, and some states
imposed traveler quarantine restrictions or prohibitions on domestic travel.25 Both pleasure and business travel—
key revenue sources for the accommodation industry—fell in response to these restrictions, as well as out of
concern for employee safety.26 According to one source, hotel occupancy rates in April 2020 fell to 25 percent, a

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decline of 64 percent from April 2019.27 Along with these low occupancy rates came job losses. Occupancy rates
recovered slightly throughout the remainder of the year, but they ended the year 32 percent below 2019 levels.28
By the end of the year, accommodation had recovered only 33 percent of its pandemic-related job losses.
Within leisure and hospitality, the arts, entertainment, and recreation industry lost 1.3 million jobs in March and
April. This decline was concentrated in amusements, gambling, and recreation, which lost 1.0 million jobs. This
industry includes casinos, amusement parks, bowling centers and fitness clubs, and similar businesses. These
industry components faced the same combination of customer reluctance and state and local government
restrictions as did other public-facing industries, such as performing arts and spectator sports. Employment in this
industry fell by 259,000 in March, April, and May, finishing the year 204,000 below its January 2020 level.

Government
Government employment declined by 1.3 million in 2020, a drop of 6 percent. (See chart 9.) The largest job losses
occurred in March, April, and May, when employment fell by a combined 1.5 million. Driving these losses were
employment declines in local government education, which lost 749,000 jobs over the same period, and local
government (excluding education), which lost 514,000 jobs. (See chart 10.)

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In early 2020, education officials started to discuss closing schools, as fears of spreading COVID-19 to children
through exposure in classrooms, lunchrooms, and playgrounds gripped the nation. Eventually, most states
recommended the closure of school buildings for the rest of the 2019–20 academic year, and by the end of March,
all U.S. public school buildings were closed.29 This either ended the school year early or moved students to virtual
learning. Along with these closures came layoffs.30 In March, April, and May, employment in local government
education fell by 749,000. In June, July, and August, there were fewer layoffs than usual because workers had
already been laid off as a result of the pandemic, and this led to employment gains after seasonal adjustment.
Local government education continued to lose jobs for the remainder of the year, shedding 346,000 jobs from
August to December, largely because of fewer hires being brought on than usual for the beginning of the 2020–21
school year. By the end of 2020, the industry had recovered only 6 percent of the jobs lost from February to May.
State government education behaved similarly to local government education, losing 255,000 jobs in March, April,
and May; adding 28,000 jobs in June, July, and August; and losing 125,000 jobs in September, October,
November, and December. Again, seasonally adjusted job gains over the summer reflected layoffs that had
already occurred in the spring, and job losses in the fall reflected reduced hiring for the 2020–21 school year.
However, state government education did not recover any of the jobs lost in March, April, and May.31
The education industries ended the year quite differently than they started it, with some state and local schools
reopening fully, some adopting a hybrid model of in-person and virtual learning, and some remaining fully virtual.32
Over the year, federal government employment grew by 50,000, with monthly movements largely driven by the
hiring and laying off of temporary workers tied to conducting the 2020 decennial census. Activities related to the
census were supposed to peak in May, but they were delayed for several months, peaking in August instead, as
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the U.S. Census Bureau modified normal activities in order to follow pandemic guidelines. These modifications
included changing when and how temporary workers would follow up with residents who failed to file their census
forms on time. Over the year, hiring tied to the census accounted for 280,000 jobs between December 2019 and
August 2020, but by the end of the year, 285,000 jobs were lost because of layoffs. As a result, monthly data on
federal government employment were skewed by changes to the pool of temporary census workers. For example,
the federal government added 256,000 jobs in August, and decennial census workers accounted for 238,000 of
those gains. In all, excluding temporary workers tied to the 2020 census suggests that the federal government was
one of the few industries that were not affected by the pandemic, adding an average of 4,000 jobs per month in
2020, up from 3,000 jobs added per month in 2019.

Education and health services
Employment in education and health services fell by 1.2 million in 2020. After peaking in February, employment in
this industry dropped by 218,000 in March and 2.6 million in April. Prior to these losses, the largest decline in the
industry had been a loss of 48,000 jobs in September 1989, and there had not been a single monthly job loss
since September 2013.
Job losses were widespread across component industries, but the largest declines occurred in healthcare,[33]
whose employment fell by a combined 1.6 million in March and April. (See chart 11.) This loss was concentrated in
ambulatory healthcare services, whose employment fell by 1.4 million. (See chart 12.) This industry includes
offices of physicians, dentists, home healthcare providers, and other healthcare practitioners. On March 18, the
Centers for Medicare and Medicaid Services—the federal agency that administers Medicare—recommended that
elective and nonemergency procedures be deferred in order to preserve protective equipment for pandemicrelated care.34 Some state governments issued similar guidance, and others specifically prohibited nonemergency
care.35 In response to these recommendations and restrictions, and to protect themselves and their employees,
healthcare practitioners began to cancel nonemergency, elective medical procedures. In addition, state-issued
stay-at-home orders further kept patients away from medical offices. Together, these factors led to steep declines
in office visits, and these declines were followed by job cuts.36 In April and May, however, 30 states relaxed
guidance or began eliminating restrictions on elective and nonemergency care.37 Patient visits to ambulatory care
facilities began to rebound and, according to one survey, had recovered to prepandemic levels by October.38
Employment also began to grow, with ambulatory healthcare services recovering 1.2 million, or 87 percent, of the
jobs lost in March and April by the end of 2020. Offices of dentists had an especially strong rebound and, by the
end of the year, recovered 99 percent of the 555,000 jobs lost in the spring. Facing many of the same conditions
as ambulatory healthcare services, hospitals lost 165,000 jobs in March, April, and May, and the industry’s
employment ended the year 63,000 below its February peak.39

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Employment in social assistance declined by 701,000 in the 2 months following its peak in February 2020. The
largest declines occurred in child daycare services,[40] which lost 373,000 jobs. Daycare centers were subject to a
variety of state and local closure orders, capacity restrictions, and newly instituted protective equipment
requirements.41 In addition, some states approved certain daycare centers to open, but allowed them to provide
services only to frontline workers.42 These measures led to enrollment declines that one source pegged at twothirds.43 At the same time, daycare providers faced increased costs from modifying their facilities to prevent
COVID-19 transmission, and by July, 18 percent of childcare centers were closed.44 By year’s end, child daycare
services had recovered 54 percent of pandemic-related job losses.
Employment in individual and family services fell by 251,000 in March and April. Businesses in this industry
provide nonmedical, nonresidential social assistance to children, young people, the elderly, people with disabilities,
and all other individuals and families. Social-distancing requirements and funding shortfalls may have eliminated or
hampered the operation of facilities such as drug-counseling centers, youth centers, and food banks.45 State and
local governments, facing falling revenue, cut back on payments to providers of individual and family services, and
these providers responded with job cuts. By year’s end, this industry had recovered 52 percent of the jobs lost in
March and April.
Employment in nursing and residential care facilities fell by 271,000 in 2020. Although these job losses occurred
throughout the year, they were greatest in April and May, at 179,000. This industry has long faced a shortage of
qualified workers and high turnover, and, with the onset of the pandemic, nursing home staff faced the new and
stressful prospect of becoming infected with COVID-19 at work. One study suggests that the prospect of infection
was a reasonable fear, with nursing home employees representing one of the most dangerous occupations during

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the pandemic.46 In addition, as of May, a third of all deaths from COVID-19 involved residents and workers at longterm care facilities.47 These factors, along with layoffs, led to 2020 employment declines in all nursing and
residential care industries. No component industry within nursing and residential care facilities recovered any of
the jobs lost in March and April 2020.
Private educational services employment declined by 533,000 in the 3 months following its peak in January 2020.
Within educational services, colleges and universities experienced eight monthly declines in 2020, losing 254,000
jobs by the end of the year. With campuses largely closed, and with enrollment declining, these institutions turned
to layoffs to cut expenses.48 The months of May, June, July, and August saw fewer layoffs than usual because
workers had already been laid off as a result of the pandemic, and this led to employment gains in educational
services (after seasonal adjustment). Educational services employment then continued to fall for the rest of the
year, declining by 140,000 from August to December, largely because of fewer hires being brought on than normal
for the beginning of the 2020–21 school year. By the end of 2020, educational services had recovered just 13
percent of the jobs lost in February, March, and April.49
By the end of 2020, education and health services had recovered 54 percent of the 2.8 million jobs lost in March
and April.

Professional and business services
Employment in professional and business services fell by 860,000 in 2020, with the industry losing 2.4 million jobs
in March and April. (See chart 13.) Temporary help services, a component industry of administrative and waste
services, accounted for 1.0 million of these job losses. (See chart 14.) Establishments in this industry supply
temporary workers to business clients. Employment in temporary help services is a leading economic indicator,
because, in tight business conditions, employers tend to stop using temporary help employees before laying off
their own employees. In March and April, when state and local government pandemic-related restrictions led to
mass business closures, the need for temporary help services declined, prompting employment declines.
According to a survey conducted by the American Staffing Association, the pandemic was the primary reason for
employment declines in temporary and contract staff.50 However, when restrictions eased and businesses began
to reopen, many of these workers were rehired as businesses added telework options or optimized resources by
adopting hybrid schedules.51 By the end of the year, temporary help services had recovered 68 percent of the jobs
lost in March and April.

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Services to buildings and dwellings lost 273,000 jobs over March and April. Establishments in this industry provide
services that include the exterior cleaning of buildings, swimming pool cleaning, housekeeping and washroom
sanitation services, and drain and gutter cleaning. Like temporary help services, services to buildings and
dwellings experienced employment declines and gains closely tied to the implementation and easing of pandemicrelated restrictions. By the end of the year, the industry had recovered 77 percent of the jobs lost in March and
April.
Elsewhere in professional and business services, professional and technical services lost 545,000 jobs in March
and April. Within professional and technical services, accounting and bookkeeping services lost 61,000 jobs,
architectural and engineering services lost 69,000 jobs, computer systems design and related services lost 68,000
jobs, and management and technical consulting services lost 98,000 jobs.52 These industries contain
establishments providing technical services within their respective industry areas. By the end of 2020, about twothirds of the jobs lost in professional and technical services had been recovered. The share of recovered jobs in
major professional and technical services component industries was 49 percent in accounting and bookkeeping
services, 70 percent in architectural and engineering services, 59 percent in computer systems design and related
services, and 79 percent in management and technical consulting services.
Employment in management of companies and enterprises fell by 96,000 in March and April. This industry
contains establishments engaged in influencing management decisions or managing the companies or enterprises
that assume organizational planning and decision-making roles. Given the widespread employment declines
across industries, it is unsurprising that management of companies and enterprises experienced strong declines;
with so many business closures—and with employees either being laid off or staying at home—the need for the

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industry’s management services inevitably declined. By the end of 2020, the industry had recovered only 23
percent of the jobs lost in March and April. Employment in all professional and business services recovered 61
percent of the jobs lost over the same period.

Manufacturing
In 2020, manufacturing employment fell by 578,000, a decline of 5 percent. (See chart 15.) At the onset of the
pandemic, in March and April, employment in manufacturing fell by 1.4 million, or 11 percent, despite some
manufacturing facilities being a part of the “Critical Manufacturing Sector,” one of the infrastructure sectors deemed
critical by the Cybersecurity and Infrastructure Security Agency of the U.S. Department of Homeland Security.53

Manufacturing job losses were concentrated in durable goods manufacturing industries, which accounted for 68
percent of the job losses in March and April. Transportation equipment manufacturing was the durable goods
industry with the greatest employment decline, losing 403,000 jobs over March and April. Most of this decline (89
percent) came in motor vehicles and parts manufacturing, which lost 360,000 jobs. This job loss coincided with a
steep drop in motor vehicle production, which fell by 99 percent over the same period, as automakers largely
shuttered factories.54 (See chart 16.) These declines, however, were offset after just a few months, as production
resumed and auto sales surged in late spring.55 In response to these developments, employment in motor vehicles
and parts manufacturing rebounded quickly, and by the end of the year, the industry recovered 285,000, or 79
percent, of the jobs lost because of the pandemic.

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Nondurable goods employment fell by 440,000 in March and April. Although these losses were spread across
component industries, the largest decline occurred in food manufacturing, which lost 111,000 jobs over this time.
The pandemic caused at least 30 meatpacking plants to temporarily close, which resulted in a 40-percent drop in
pork production capacity and a 25-percent drop in beef production capacity, alarming the public about the nation’s
food supply. In addition, news reports stated that tens of thousands of meatpacking workers were contracting
COVID-19, with some dying from the disease, further exacerbating public concerns.56 Employees returned to work
in food manufacturing, as 83,000, or 74 percent, of the jobs lost in March and April were recovered in the industry
by the end of 2020.
Employment declines in nondurable goods manufacturing also occurred in the printing and related support
activities industry, in which pandemic-related economic impacts reduced demand for printing and resulted in a loss
of 78,000 jobs in March and April. Employment in plastics and rubber products manufacturing fell by 70,000 in
March and April, and employment in miscellaneous nondurable goods manufacturing fell by 58,000. Smaller
employment declines were spread throughout the remaining nondurable goods manufacturing industries. After the
initial effects of the pandemic subsided, nondurable goods manufacturing employment rebounded, recovering 64
percent of its pandemic-related job losses by the end of the year. As employees returned to work in both durable
and nondurable goods industries, manufacturing recovered 817,000 jobs, or 59 percent, of the jobs lost in March
and April.

Retail trade

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In 2020, employment in retail trade fell by 459,000, a decline of 3 percent. (See chart 17.) Retail trade had been in
a period of slow employment decline even before the pandemic, losing about 8,000 jobs per month between an
employment peak in January 2017 and February 2020. In March and April, however, retail trade employment
plummeted, falling by 2.4 million, or 15 percent, with losses coming in all component industries. Over the same
period, retail sales declined by 17 percent.57 In terms of both sales and employment, the largest declines were in
industries selling nonessential products whose purchase could easily be deferred. Among these industries were
clothing and clothing accessories stores; motor vehicle and parts dealers; miscellaneous store retailers; sporting
goods, hobby, book, and music stores; and furniture and home furnishings stores. In March and April, the greatest
job losses occurred in clothing and clothing accessories stores, which lost 781,000 jobs, an employment decline of
61 percent. (See chart 18.) Many people, especially those working from home, deferred new clothing purchases,
and, most importantly, clothing and clothing accessories stores were classified as nonessential in state shutdown
orders. Over March and April, these factors led to a decline of 87 percent in retail sales at clothing and clothing
accessories stores.58 (See chart 19.) Shutdown orders were largely lifted by late spring, and although sales
experienced a healthy rebound by year’s end, only 63 percent of the jobs lost in the industry were recovered by
December.

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In March and April, employment in motor vehicle and parts dealers fell by 381,000, or 19 percent. This job loss
coincided with a decline in total vehicle sales, which fell by 47 percent.59 (See chart 20.) Social distancing
upended traditional aspects of selling vehicles, such as in-person test drives and meetings with salespersons in
showrooms. In addition, automobile factory shutdowns limited inventory and led to shortages of new vehicles.
Motor vehicles and parts dealers worked to get customers back in their doors, advertising pandemic-safe sales
floors or allowing fully online vehicle purchases with flexible return policies. As factories reopened, vehicle sales
rebounded because of pent-up demand and an increasing preference for car ownership among urban consumers
desiring social distancing and a pandemic-safe means of transportation.60 By year’s end, vehicle sales had
rebounded by 84 percent, and motor vehicle and parts dealers had recovered 76 percent of the jobs lost in March
and April.61

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The other component industries of retail trade experienced variable job gains from April to December. Furniture
and home furnishings stores recovered 83 percent of the jobs lost in March and April, whereas health and personal
care stores recovered only 35 percent of the jobs lost in those 2 months. Some industries were able to fully
recover their losses and continue to grow. For example, food and beverage stores ended 2020 with a 256-percent
recovery, an unsurprising gain given that grocery stores fall within this industry group. Considered essential during
state and local shutdowns—and helped by consumer avoidance of restaurants in an effort to maintain social
distancing—grocery stores benefitted from considerable demand increases during the pandemic.62 In fact, fearing
scarcity amid COVID-19 lockdowns and panic buying, consumers rushed to grocery stores, causing demand to
exceed supply.63 Items in high demand included meat, eggs, milk, and toilet paper.
Building material and garden supply stores added 159,000 jobs between April and December, more than 6 times
the industry’s modest loss of 25,000 jobs during March and April. In fact, the industry thrived during the pandemic.
Its stores were classified as essential businesses in state and local shutdowns, and they benefitted from a trend
called “nesting,” whereby homebound consumers focus on renovating and upgrading their living spaces. This trend
resulted in substantial revenue gains at large building materials retailers, helping the industry weather the
pandemic.64 Overall, by the end of 2020, retail trade had recovered 81 percent of its March and April job losses.
Much attention has been paid to increases in online retail sales during the pandemic, as consumers shifted to this
shopping method in order to maintain social distancing. Web, catalog, and mail-order retailers are classified in the
electronic shopping and mail-order houses industry, which experienced a small employment decline early in the
pandemic, followed by a robust recovery. It is important to note, however, that much of the employment change
resulting from increased online sales likely is not captured in this industry. As mentioned later in this article,

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establishments engaged in fulfillment activities for web retailers are likely classified in the transportation and
warehousing industry.

Other services
Employment in the other services industry fell by 450,000, or 8 percent, in 2020. After reaching a peak in February,
employment dropped by 1.4 million, or 24 percent, in March and April. (See chart 21.) This loss was heavily
concentrated in the personal and laundry services industry, whose employment fell by 884,000, or 56 percent, in
March and April. These losses occurred primarily in the personal care services industry, which includes businesses
such as barber shops, beauty and nail salons, and tattoo parlors. What characterizes many of these businesses is
that they involve close personal contact with customers. As such, they were among the businesses most hurt by
state shutdown orders, indoor capacity limits, and customer avoidance of close personal contact. In March and
April, employment in barber shops and beauty salons declined by 375,000, or 82 percent, and employment in nail
salons declined by 119,000, or 91 percent. (See chart 22.) Over the same time, employment declined by 122,000,
or 70 percent, in other personal care services, a miscellaneous category that includes everything from tattoo
parlors to massage parlors to ear piercing services. Given its scope, the personal care services industry provided
services that customers seemed to miss the most at the onset of the pandemic. As a result of this consumer need,
component industries in personal care services experienced the most rapid employment recovery within the other
services industry.

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Employment in personal care services began to recover in May, with most component industries seeing job growth
throughout the summer and into the fall. The job gains were tied to reopening guidelines issued by state and local
jurisdictions, and the approaches taken by states and localities varied widely. For example, Oklahoma fully
reopened personal care businesses on April 24, whereas New York City allowed those businesses to reopen at 50percent capacity on July 6.65 However, facing a resurgence of COVID-19 cases and a reinstitution of government
restrictions in the last 2 months of the year, businesses requiring close personal contact lost jobs again because of
renewed closures or capacity limitations. Despite these restrictions, by December, barber shops and beauty salons
had recovered 79 percent of their March and April job losses, nails salons had recovered 92 percent, and other
personal care services had recovered 77 percent.
Membership associations and organizations—the largest industry in other services—lost 279,000 jobs in March
and April, an employment decline of 9 percent. These losses occurred primarily in civic and social organizations,
an industry that includes charitable organizations, business associations, and trade unions. Early pandemic-related
employment declines were concentrated in civic and social organizations, examples of which include fraternal
lodges, social clubs, and ethnic associations. Many of these organizations operate bars and restaurants for their
members and, like establishments in the leisure and hospitality industry, were affected by closure orders and
general public wariness about social gatherings. In March and April, employment in civic and social organizations
fell by 208,000, or 53 percent. The industry’s subsequent employment recovery lagged that of other component
industries in other services. Civic and social organizations often engage in social advocacy, fundraising, and other
similar activities, but their dependence on public assistance and donations makes them vulnerable to economic
downturns. The industry’s employment gains began in May and lasted through October, with the last 2 months of
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the year seeing renewed job losses alongside a resurgence in COVID-19 cases across the United States. By the
end of the year, civic and social organizations had recovered just 38 percent of the jobs lost in March and April.
Employment in the repair and maintenance industry fell by 248,000, or 18 percent, in March and April. Most of
these job losses occurred in the automotive repair and maintenance industry, which lost 191,000 jobs, an
employment decline of 20 percent. The general economic decline associated with the pandemic, including stay-athome orders and business closures, drove down vehicular traffic, reduced the need for repairs related to normal
wear and tear and car accidents, and caused businesses to defer routine maintenance on their vehicles.66
However, these negative impacts proved short lived and did not have a lasting effect on employment. By the end
of 2020, 79 percent of the jobs lost in the automotive repair and maintenance industry had been recovered.
Overall, by the end of the year, the other services industry was able to recover 942,000, or 67 percent, of the jobs
lost in March and April.

Wholesale trade
Employment in wholesale trade fell by 409,000 in March and April, but it was down by 282,000 at the end of the
year. (See chart 23.) The March and April job losses were divided among wholesale trade’s durable and
nondurable goods component industries, which lost 207,000 jobs and 164,000 jobs, respectively. These losses
were spread across more detailed component industries, but the greatest job loss came in the grocery and related
products industry, which lost 65,000 jobs over March and April. The pandemic disrupted supply chains associated
with this industry, resulting in shortages of groceries at retail establishments.67 Supply-chain disruptions also
decreased demand for wholesalers to fulfill orders to certain sectors hit particularly hard by the pandemic, and this
led to employment declines. Even as fears of contracting COVID-19 subsided for some, and as state and local
government restrictions eased, only 23 percent of the jobs lost in the grocery and related products industry were
recovered by the end of the year. In addition, wholesalers were affected by increased sales by businesses directly
to consumers, because these sales bypassed wholesale intermediaries.68

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By the end of 2020, durable goods wholesalers recovered 30 percent of their March and April job losses, while
nondurable goods wholesalers recovered 39 percent. In total, wholesale trade recovered 32 percent of its March
and April job losses by the end of the year.

Information
Information employment declined by 238,000, or 8 percent, in 2020. (See chart 24.) After reaching an employment
peak in February, information industries shed 322,000 jobs through July, an employment decline of 11 percent.
This decline was most heavily concentrated in the motion picture and sound recording industries, which lost
231,000 jobs over this period. This industry group includes businesses that produce and distribute sound
recordings, as well as those which produce, distribute, and exhibit motion pictures and videos. Amid stay-at-home
orders and other state and local restrictions, television and movie production was largely shut down in the early
months of the pandemic. Production was canceled indefinitely for many television shows and movies, and many
major production companies postponed premiere dates of already completed projects.69 However, as state and
local restrictions eased, production resumed, and by the end of the year, motion picture and sound recording
industries—as well as the entire information industry—recovered 21 percent of the jobs lost from February to July.

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Construction
Construction employment fell by 1.1 million in March and April, but by the end of 2020, it had regained all but
248,000 of these jobs. (See chart 25.) Like employment in many other industries, employment in construction was
affected by state and local government efforts to contain COVID-19, as worksites shuttered or substantially
reduced employment across the United States. In March and April, pandemic-driven job declines occurred in every
construction component industry, with the greatest decline coming in specialty trade contractors. Employment in
this industry, which includes establishments involved in such trades as concrete pouring, plumbing, and electrical
work for building construction, fell by 766,000, or 16 percent, between February and April. Construction
employment is largely a function of consumer demand for new buildings, offices, or homes, and this demand was
held back in March and April.70 As the country reopened and demand returned, especially for new homes,
construction recovered 78 percent of the jobs lost in March and April. Of the recovered jobs, 71 percent came in
specialty trade contractors.

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One notable aspect of pandemic-related employment changes in construction was the divergence between the
industry’s residential and nonresidential components during the job recovery that began in May. Although both
components suffered sharp job losses in March and April, residential construction industries experienced an
immediate and sustained turnaround thereafter. By year’s end, residential building construction had more than
recovered the jobs lost in March and April, and residential specialty trade contractors fell just short of a complete
recovery. Nonresidential component industries, on the other hand, saw a job recovery that was far less robust.
(See chart 26.) Construction spending followed these employment trends, with residential construction spending
growing by 30 percent from May to December, after declining 9 percent from February to May, and with
nonresidential construction spending falling by 5 percent from March to December, after being essentially flat in
February and March.71 Other residential construction indicators, such as housing starts and new home sales,
experienced steady growth from April to December after suffering declines in February and March.72 This rebound
matches growth in other housing-related industries, such as real estate and building material and garden supply
stores, which were all helped by the desire of consumers to renovate or improve their homes, or to move to new
homes after spending considerable time in their homes as a result of COVID-19 restrictions. Nonresidential
construction, on the other hand, largely depends on spending by businesses and governments, both of which were
reluctant to invest in new construction projects because of pandemic-related uncertainty.

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Mining and logging
In 2020, mining and logging employment fell by 97,000, or 14 percent. (See chart 27.) Although employment in the
industry had already been on the decline since reaching a peak in January 2019, the pandemic exacerbated the
losses, with 89,000 of the jobs lost in 2020 occurring in March, April, and May. Of those jobs, 70,000 were lost in
the support activities for mining industry, which contains establishments providing mining support, such as site
preparation, on a contract or fee basis. Support activities for mining lost another 15,000 jobs through December.
Employment in mining and logging was likely affected by fluctuations in oil prices, which fell to a low in April and
then recorded gains through December.73 The 2020 employment decline in mining and logging came as the price
of most mining commodities fell.74

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Transportation and warehousing
Transportation and warehousing employment fell by 93,000, or 2 percent, in 2020. (See chart 28.) This decline is
modest in comparison with that in other industry groups, a fact due entirely to employment strength in couriers and
messengers and in warehousing and storage.

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Peaking in February, transportation and warehousing employment changed little in March, but it fell by 583,000 in
April and May. The details of this decline reflect common themes of the pandemic. (See chart 29.) Industries
dependent on individuals being near one another experienced steep employment declines, whereas industries
able to capitalize on people’s desire for social distancing were able to mitigate losses, or even thrive.

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Industries related to travel and commuting lost jobs from travel restrictions, increased telecommuting, school
closures, and people’s reluctance to travel. Between January and May, employment in transit and ground
passenger transportation declined by 193,000, or 39 percent. This industry group includes businesses such as
privately run urban transit systems, intercity and chartered bus services, school bus services, and taxis.75 As
employers increasingly allowed—or required—employees to work from home as much as possible, passenger
traffic in transit systems fell dramatically.76 Additionally, closures of school buildings reduced the need for school
bus services, resulting in the loss of 104,000 jobs, or half of all workers on payrolls in the school and employee bus
transportation industry, between January and June. Later in the year, the reopening of school buildings across the
nation—usually with reduced schedules and limited capacity—led to a partial rebound in employment, with the
industry recovering 44 percent of its job losses by the end of the year.77 From May to December, school and
employee bus transportation accounted for 43,000 of the 62,000 jobs gained in transit and ground passenger
transportation, which recovered 32 percent of its job losses by the end of the year.
Between January and April, air revenue passenger miles fell by 96 percent.78 (See chart 30.) The Coronavirus Aid,
Relief, and Economic Security (CARES) Act, passed in March, brought some relief to the airline industry. To
prevent job loss, the act provided $25 billion in payroll support to passenger airlines in the form of partially

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forgivable loans. In order to receive support, airlines had to agree not to conduct involuntary furloughs of
employees until September 30.79 Despite this aid, air transportation employment fell in April, May, and June,
declining by 135,000, or 26 percent. These losses were likely due to attrition and voluntary employee buyouts.
After briefly rallying in the summer, air transportation employment fell in October (immediately after the CARES Act
funding expired), before trending back up through December. By the end of the year, the industry had recovered
only 11 percent of its pandemic-related job losses. A related transportation industry, support activities for air
transportation, followed largely the same employment trend, losing 51,000 jobs between February and June and
then entering a lackluster job recovery.

In March and April, trucking employment fell by 96,000, or 6 percent. These job losses occurred in both general
freight and specialized freight trucking, but the former suffered greater losses. (See chart 31.) Establishments in
general freight trucking haul a vast array of goods whose transportation requires no specialized equipment. They
also may provide related support services, such as local pickup, local delivery, and sorting and terminal operations.
General freight trucking lost 64,000 jobs in March, April, and May, an employment decline of 6 percent. By the end
of the year, the industry had recovered 57 percent of its pandemic-driven job losses. Employment also fell in
specialized freight trucking, an industry in which businesses haul articles whose transportation is complicated by
item size, weight, and other characteristics and requires the use of specialized equipment, such as dump trucks or
refrigerated trailers. Establishments within this industry also haul used residential or office goods. After seeing its
employment decline by 36,000, or 8 percent, in March and April, specialized freight trucking recovered 45 percent
of its losses by the end of 2020. Overall, as of December, truck transportation had recovered 52 percent of the jobs
lost in March and April.

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The job declines and gains in trucking coincided with changes in shipping volumes. In April, the Cass Freight
Index, a measure of shipping activity, fell by 15 percent.80 Another measure of trucking activity, the For-Hire Truck
Tonnage Index, which measures the gross tonnage of freight, fell by 9 percent that same month.81 These declines
in shipping volume were due to the near-cessation of business activity and disruptions in global supply chains.82
However, they were short lived, and as consumers turned to online shopping, shipping volumes recovered. As a
result, trucking companies began modest rehiring, although the benefits accruing to trucking from online shopping
were offset, to some degree, by declines in shipping to brick-and-mortar retail establishments.
Two transportation and warehousing industries added jobs in 2020. Over the year, couriers and messengers
employment grew by 167,000, or 19 percent, and warehousing and storage employment grew by 133,000, or 10
percent. The employment growth of these two industries largely offset job losses in the other transportation and
warehousing component industries, especially from May to December, when transportation and warehousing
recovered 429,000 jobs, or 74 percent of the jobs lost in April and May. From May to December, couriers and
messengers added 108,000 jobs, and warehousing and storage added 169,000 jobs, accounting for most of
transportation and warehousing’s recovery from pandemic-related losses in other industries.
Couriers and messengers employment saw only one monthly decline in the early days of the pandemic—a loss of
5,000 jobs in April. After that, the industry added an average of 18,000 jobs per month through the end of the year,
well above the average monthly gain of 9,000 jobs experienced in 2019. Job gains in couriers and messengers
were concentrated in the couriers and express delivery services industry, which, after losing 3,000 jobs in
February, grew steadily through November. This industry includes businesses that provide air, surface, or

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combined-mode parcel delivery services. Consumers, eager to observe social distancing and hesitant to shop in
person, increasingly turned to online shopping during the pandemic.83 The e-commerce share of total retail sales
grew from 11 percent in the first quarter of 2020 to a high of 16 percent in the second quarter.84 (See chart 32.)
The relative strength of online shopping is further confirmed by the fact that e-commerce sales grew by 32 percent
in 2020, while overall retail sales grew by only 7 percent. Increases in online shopping drove up shipping volumes
for courier and delivery companies, and they responded with increased hiring.

The warehousing and storage industry also managed to benefit from increases in online sales. Initially, the
pandemic did drive job losses in this industry, which lost 106,000 jobs in April, an employment decline of 8 percent.
However, the industry bounced back, adding jobs in 6 of the 8 months that followed. In December, employment in
warehousing and storage exceeded its March level by 77,000 jobs. Many companies commonly described as
online retailers frequently operate warehouses and fulfillment centers. So, when the volume of online sales rose,
warehousing and storage employment rose with it. And even warehouses not owned by large retailers likely
benefitted from the increased volume of shipments.

Financial activities
The financial activities industry was among the industries whose employment was minimally affected by the
pandemic, losing 58,000 jobs in 2020, an employment decline of 1 percent. (See chart 33.) Even in the early
months of the pandemic, when total nonfarm employment fell by 15 percent in March and April, employment in
financial activities declined by only 3 percent, a drop of 279,000 jobs. Although nearly all component industries in

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financial activities lost jobs over this time, the declines were primarily concentrated in rental and leasing and in real
estate.

The rental and leasing industry includes businesses engaged in renting consumer goods and equipment, as well
as those leasing machinery and equipment for business operations. Employment in this industry fell by 124,000 in
March and April, with about half of this decline coming in automotive equipment rental and leasing, which lost
58,000 jobs over this period and shed an additional 9,000 jobs in May. This employment loss occurred as the car
rental industry suffered from a decline in both business and leisure travel. The loss was exacerbated by declining
used car prices, which made selling used fleet vehicles less lucrative for automobile rental and leasing companies.
One industry publication pegged the annual car rental revenue decline for 2020 at 27 percent.85 Despite this
dismal annual figure, the industry did see improvement in the second half of the year. Increased prices for used
cars helped rental companies strengthen their financial position, and traveler preferences shifted toward
automobile travel, which was perceived as safer than air, rail, and other forms of travel that involve close
gatherings of people and provide limited opportunities for safe social distancing.86 By the end of the year,
employment in automotive equipment rental and leasing had rebounded somewhat, recovering 34 percent of its
pandemic-related job losses. In the larger component, rental and leasing services, 24 percent of pandemic-related
job losses were recovered by the close of 2020.
The real estate industry includes businesses that sell, buy, rent, lease, or manage real estate, both residential and
commercial, for others. Employment in this industry fell by 115,000, or 7 percent, in March and April. These losses
were spread across component industries, but the largest decline was in lessors of real estate, whose employment
fell by 53,000, or 9 percent, over these 2 months. Lessors of real estate’s residential and nonresidential

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components both suffered job losses, having to deal with the problem of tenants being unable to pay rent because
of pandemic-related layoffs or cuts in their workweek.87 Within the residential component, establishments faced
not only tenants struggling to keep up with their rent payments but also eviction prohibitions by various levels of
government, finding themselves in a difficult financial situation. At the federal level, the CARES Act moratorium on
evictions was in effect from March 27 to July 24, covering tenants living in “federally related properties,” a category
estimated to include between 28 and 46 percent of all occupied rental units. On September 4, the CDC issued a
moratorium on evictions, citing public health concerns associated with homelessness. This moratorium was initially
intended to last through the end of 2020, but it has since been extended.88 Although landlords were restrained by
eviction prohibitions, they were helped by federal, state, and local government rental assistance programs, some
of which allowed landlords to apply for benefits on behalf of their tenants.89
Within lessors of real estate’s nonresidential component, lessors faced similar problems. Real estate service
company Coldwell Banker Richard Ellis’s cash payments indexes, which serve as proxies for rent collections and
indicators of tenants’ financial health, declined rapidly in the early days of the pandemic, with the retail sector being
especially hard hit.90 With employees largely working from home, office tenants sometimes demanded
renegotiations of their leases. With few other prospective tenants, landlords often agreed.91 By the end of the year,
lessors of real estate had recovered 48 percent of the jobs lost in March and April.
A related industry, real estate property managers, faced the same business conditions. Lower rent revenue led to
job losses in this industry, which lost a combined 31,000 jobs in March and April, an employment decline primarily
concentrated in the industry’s residential component. By the end of the year, however, employment in the property
management industry had regained 91 percent of these job losses.
Employment in offices of real estate agents declined by 29,000, or 8 percent, in March and April. This industry—in
which businesses buy, sell, or rent real estate on behalf of others—played a part in one of the year’s prominent
economic stories. After declining early in the pandemic, existing home sales recovered by the summer of 2020 and
then expanded rapidly through much of the fall. Along with increasing home sales and prices, employment in
offices of real estate agents rebounded, recovering 88 percent of the March and April job losses by the end of the
year.92
Among other financial activities industries, credit intermediation and related activities lost 34,000 jobs in March and
April, an employment decline of only 1 percent. By the end of the year, 95 percent of these jobs had been
recovered. Examples of businesses in this industry include banks, mortgage companies, and loan brokers. The
nature of work at many of these businesses is well suited to having employees work from home, allowing this
industry to adapt well to the pandemic. Other finance-related industries were similarly suited for remote work and
saw little employment change during the pandemic.
By the end of 2020, the financial activities industry had recovered 64 percent of its March and April job losses.

Utilities
Employment in utilities changed little for the majority of 2020.

Conclusion

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In 2020, the longest nonfarm payroll employment recovery and expansion in U.S. history abruptly ended, with
historic and widespread employment declines occurring in March and April. State and local government restrictions
implemented to slow the spread of COVID-19, coupled with pandemic-avoidance behaviors by the public, led to
these unusually large job losses. Despite recovering quickly as restrictions eased, nonfarm employment still ended
the year 10.0 million below its February peak. Job losses over the year were especially large in leisure and
hospitality, government, education and health services, and professional and business services. None of the major
industry sectors had fully recovered by the end of 2020.
SUGGESTED CITATION

Ryan Ansell and John P. Mullins, "COVID-19 ends longest employment recovery and expansion in CES history,
causing unprecedented job losses in 2020," Monthly Labor Review, U.S. Bureau of Labor Statistics, June 2021,
https://doi.org/10.21916/mlr.2021.13.
NOTES
1 The Current Employment Statistics (CES) program, which provides detailed industry data on employment, hours, and earnings of
workers on nonfarm payrolls, is a monthly survey of about 144,000 businesses and government agencies representing approximately
697,000 individual worksites. For more information on the program’s concepts and methodology, see “Technical notes for the Current
Employment Statistics survey” (U.S. Bureau of Labor Statistics), https://www.bls.gov/web/empsit/cestn.htm. To access CES data, see
“Current Employment Statistics—CES (national)” (U.S. Bureau of Labor Statistics), https://www.bls.gov/ces. The CES data used in
this article are seasonally adjusted unless otherwise noted.
2 The CES employment series goes back to 1939. Prior to 2020, the two next highest annual job losses occurred during the Great
Recession years of 2008 and 2009, when job losses were 3.6 million and 5.1 million, respectively. Job losses in 2020 were greater
than the combined losses for 2008 and 2009.
3 See Maria Nicola, Zaid Alsafi, Catrin Sohrabi, Ahmed Kerwan, Ahmed Al-Jabir, Christos Iosifidis, Maliha Agha, and Riaz Agha, “The
socio-economic implications of the coronavirus pandemic (COVID-19): a review,” International Journal of Surgery, vol. 78, June 2020,
pp. 185–193, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7162753/.
4 See “Listings of WHO’s response to COVID-19” (World Health Organization, June 29, 2020), https://www.who.int/news/item/
29-06-2020-covidtimeline.
5 See “First travel-related case of 2019 novel coronavirus detected in United States” (Centers for Disease Control and Prevention,
January 21, 2020), https://www.cdc.gov/media/releases/2020/p0121-novel-coronavirus-travel-case.html.
6 See “WHO Director-General’s opening remarks at the media briefing on COVID-19—11 March 2020” (World Health Organization,
March 11, 2020), https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefingon-covid-19---11-march-2020.
7 See “Trends in number of COVID-19 cases and deaths in the U.S. reported to CDC, by state/territory” (Centers for Disease Control
and Prevention, updated daily), https://covid.cdc.gov/covid-data-tracker/#trends_dailytrendscases.
8 See “A timeline of COVID-19 developments in 2020,” American Journal of Managed Care, updated January 1, 2021, https://
www.ajmc.com/view/a-timeline-of-covid19-developments-in-2020.
9 According to one study, 27 percent of people polled said they would avoid eating in restaurants. See Mark Brandau, “COVID-19
report 2: fear and response” (Datassential, March 17, 2020), https://mcusercontent.com/45027c46b385d9b28f2d3a6d7/files/
291b3e84-bd28-48a4-9f29-6c5e43d0bc2e/Datassential_Coronavirus_3_17_20_.pdf.

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10 In September 1945, employment fell by 2.0 million, accelerating ongoing job losses. September’s losses were concentrated in
manufacturing and coincided with declines in industrial production. See “Industrial production: total index” (FRED, Federal Reserve
Bank of St. Louis), https://fred.stlouisfed.org/series/INDPRO.
11 The National Bureau of Economic Research (NBER) Business Cycle Dating Committee is the generally recognized arbiter of
business cycle turning points in the United States. The NBER defines recessions as economywide declines in economic activity
lasting more than a few months that can be observed in major economic indicators, such as employment and output. Recession
starting points are business cycle peaks, and recession ending points are business cycle troughs. The length and timing of the NBERdesignated peaks and troughs do not necessarily align with the peaks and troughs in the CES employment series.
12 In both level and percent terms, the employment decline in April 2020 was the largest job change in CES history.
13 In each of the past four recessions, peaks in employment occurred within a month of peaks in the business cycle.
14 Hours and earnings data series for all employees originate in March 2006, and most hours and earnings data series for production
and nonsupervisory employees originate in January 1964. Although production and nonsupervisory employees are defined differently
for certain major industry sectors, they generally exclude workers whose primary duty is to supervise the work of others. In
manufacturing and in mining and logging, production and nonsupervisory employees include only production and related employees.
In construction, production and nonsupervisory employees include only construction employees, and in private service-providing
industries, they include all nonsupervisory employees.
15 For an explanation of the CES aggregation procedures, see “Technical notes for the Current Employment Statistics survey” (U.S.
Bureau of Labor Statistics), https://www.bls.gov/web/empsit/cestn.htm#section6d.
16 See Kim Parker, Rachel Minkin, and Jesse Bennett, “Economic fallout from COVID-19 continues to hit lower income Americans the
hardest” (Washington, DC: Pew Research Center, September 24, 2020), https://www.pewresearch.org/social-trends/2020/09/24/
economic-fallout-from-covid-19-continues-to-hit-lower-income-americans-the-hardest/.
17 The 12-month diffusion index is not seasonally adjusted.
18 According to nonseasonally adjusted annual average data for 2019, food services and drinking places employment made up 73
percent of total leisure and hospitality employment.
19 See Rebecca Klar, “More states close restaurants, entertainment venues amid pandemic,” The Hill, March 16, 2020, https://
thehill.com/policy/healthcare/487776-new-york-new-jersey-connecticut-set-to-close-restaurants-entertainment.
20 In a national survey conducted between April 28 and May 3, 2020, 78 percent of respondents said they would not be comfortable
dining in a restaurant, regardless of government restrictions. See “Washington Post–University of Maryland national poll, April 28–May
3, 2020,” The Washington Post, May 5, 2020, https://www.washingtonpost.com/context/washington-post-university-of-marylandnational-poll-april-28-may-3-2020/9ac3c026-f68c-4733-82a0-daa6862d99b3/?itid=lk_inline_manual_2; and “Retail sales: restaurants
and other eating places” (FRED, Federal Reserve Bank of St. Louis), https://fred.stlouisfed.org/series/MRTSSM7225USN.
21 See Thomas Franck, “Restaurant bookings data show U.S. economy is starting to revive after Covid closures,” CNBC, May 27,
2020, https://www.cnbc.com/2020/05/27/restaurant-data-show-us-economy-is-recovering-after-covid-closures.html.
22 See “Official return to work guidelines for foodservice establishments” (Washington, DC: Restaurant Law Center, National
Restaurant Association), https://restaurantlawcenter.org/archive-official-return-to-work-guidelines-for-foodservice-establishments/.
23 See Jonathan Maze, “Fast food chains are thriving, some more than others,” Restaurant Business, November 30, 2020, https://
www.restaurantbusinessonline.com/financing/fast-food-chains-are-thriving-some-more-others; Heather Haddon, “McDonald’s,
Chipotle and Domino’s are booming during coronavirus while your neighborhood restaurant struggles,” The Wall Street Journal,
October 12, 2020, https://www.wsj.com/articles/mcdonalds-chipotle-and-dominos-are-feasting-during-coronavirus-while-yourneighborhood-restaurant-fasts-11602302431; and David Vanamburg, “Why limited-service chains were better positioned for the
pandemic than full-service restaurants” (American Customer Satisfaction Index, June 30, 2020), https://www.acsimatters.com/
2020/06/30/why-limited-service-chains-were-better-positioned-for-the-pandemic-than-full-service-restaurants/.

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24 See Chris Fuhrmeister, “‘Cessation of services’: food service companies lay off hundreds due to lack of college demand,” Atlanta
Business Journal, August 20, 2020, https://www.bizjournals.com/atlanta/news/2020/08/20/coronavirus-layoffs-colleges-foodservicecompanies.html; and Max Matza, “Coronavirus: the slow death of the American all-you-can-eat buffet,” BBC News, July 19, 2020,
https://www.bbc.com/news/world-us-canada-53410931.
25 See Madeline Holcombe, “Here are the states restricting travel from the US,” CNN, March 31, 2020, https://www.cnn.com/
2020/03/31/us/states-travel-restrictions-list/index.html.
26 See Scott McCartney, “The Covid pandemic could cut business travel by 36%—permanently,” The Wall Street Journal, December
1, 2020, https://www.wsj.com/articles/the-covid-pandemic-could-cut-business-travel-by-36permanently-11606830490?
mod=article_inline.
27 See “U.S. COVID-19 travel guidelines,” VisitTheUSA.com, https://www.visittheusa.com/us-covid-19-travel-guidelines; and “STR:
U.S. hotel performance for April 2020” (STR, May 20, 2020), https://str.com/press-release/str-us-hotel-performance-april-2020.
28 See “STR: U.S. hotel performance for December 2020” (STR, January 20, 2021), https://str.com/press-release/str-us-hotelperformance-december-2020.
29 See “The coronavirus spring: the historic closing of U.S. schools (a timeline),” Education Week, July 1, 2020, https://
www.edweek.org/leadership/the-coronavirus-spring-the-historic-closing-of-u-s-schools-a-timeline/2020/07.
30 In the CES survey, all persons employed by an establishment are included in the estimates for the industry into which that
establishment has been classified, regardless of occupation. Therefore, employment estimates for state and local government
education include not only teachers but also administrators and support staff employed by a school system.
31 When state and local government education industries are experiencing large employment changes not tied to the normal
seasonal hiring or laying off of workers, employment gains on a seasonally adjusted basis can be misleading.
32 See Dana Goldstein and Eliza Shapiro, “Many students will be in classrooms only part of the week this fall,” The New York Times,
June 26, 2020, https://www.nytimes.com/2020/06/26/us/coronavirus-schools-reopen-fall.html; and Hristina Byrns, “Reopening schools
amid COVID-19: a mix of in-person attendance, remote learning, and hybrid plans,” USA Today, August 3, 2020, https://
www.usatoday.com/story/money/2020/08/03/every-states-plan-to-reopen-schools-in-the-fall/112599652/.
33 This industry is referred to as “health care” in all official CES news releases, websites, and databases, but it is referred to as
“healthcare” in this article to conform to Government Printing Office publication standards.
34 See “CMS releases recommendations on adult elective surgeries, non-essential medical, surgical, and dental procedures during
COVID-19 response” (Centers for Medicare and Medicaid Services, March 18, 2020), https://www.cms.gov/newsroom/press-releases/
cms-releases-recommendations-adult-elective-surgeries-non-essential-medical-surgical-and-dental.
35 See “State guidance on elective surgeries” (Alexandria, VA: Ambulatory Surgery Center Association, updated April 20, 2020),
https://www.ascassociation.org/asca/resourcecenter/latestnewsresourcecenter/covid-19/covid-19-state.
36 One study estimated a 40-percent decline in outpatient visits after the first week of March 2020; another showed a decline of 60
percent. See Engy Ziedan, Kosali I. Simon, and Coady Wing, “Effects of state COVID-19 closure policy on non-COVID-19 health care
utilization,” Working Paper 27621 (Cambridge, MA: National Bureau of Economic Research, July 2020), https://www.nber.org/system/
files/working_papers/w27621/w27621.pdf; Ateev Mehrotra, Michael Chernew, David Linetsky, Hilary Hatch, and David Cutler, “The
impact of the COVID-19 pandemic on outpatient visits: a rebound emerges,” To The Point (The Commonwealth Fund, May 19, 2020),
https://www.commonwealthfund.org/publications/2020/apr/impact-covid-19-outpatient-visits; and Rita Rubin, “COVID-19’s crushing
effects on medical practices, some of which may not survive,” JAMA, vol. 324, no. 4, pp. 321–323, https://jamanetwork.com/journals/
jama/fullarticle/2767633.
37 See Anuja Vaidya, “30 states resuming elective surgeries,” Becker’s Hospital Review, updated May 15, 2020, https://
www.beckershospitalreview.com/cardiology/11-states-resuming-elective-surgeries.html.

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38 See Ateev Mehrotra, Michael Chernew, David Linetsky, Hilary Hatch, David Cutler, and Eric C. Schneider, “The impact of the
COVID-19 pandemic on outpatient care: visits return to prepandemic levels, but not for all providers and patients” (The
Commonwealth Fund, October 15, 2020), https://www.commonwealthfund.org/publications/2020/oct/impact-covid-19-pandemicoutpatient-care-visits-return-prepandemic-levels.
39 See Alia Paavola, “266 hospitals furloughing workers in response to COVID-19,” Becker’s Hospital Review, updated August 31,
2020, https://www.beckershospitalreview.com/finance/49-hospitals-furloughing-workers-in-response-to-covid-19.html.
40 This industry is referred to as “child day care services” in all official CES news releases, websites, and databases, but it is referred
to as “child daycare services” in this article to conform to Government Printing Office publication standards.
41 See Caroline Kitchener, “20,000 day cares may have closed in the pandemic. What happens when parents go back to work?” The
Lily, February 22, 2021, https://www.thelily.com/20000-day-cares-may-have-closed-in-the-pandemic-what-happens-when-parents-goback-to-work/.
42 See Julie Kashen, “States are stepping up with emergency child care solutions for frontline essential personnel in response to
COVID-19” (The Century Foundation, April 30, 2020), https://tcf.org/content/commentary/states-stepping-emergency-child-caresolutions-frontline-essential-personnel-response-covid-19/.
43 See “Holding on until help comes: a survey reveals child care’s fight to survive” (Washington, DC: National Association for the
Education of Young Children, July 13, 2020), https://www.naeyc.org/sites/default/files/globally-shared/downloads/PDFs/our-work/
public-policy-advocacy/holding_on_until_help_comes.survey_analysis_july_2020.pdf.
44 See ibid.; Ana North, “‘We are on our own’: how the coronavirus pandemic is hurting child care workers,” Vox, updated April 6,
2020, https://www.vox.com/2020/4/4/21203464/coronavirus-child-care-workers-pandemic-unemployment-cares-act; Scott
MacFarlane, Rick Yarborough, and Jeff Piper, “Thousands of local child care centers closed due to COVID-19,” NBC Washington,
September 1, 2020, https://www.nbcwashington.com/investigations/thousands-of-local-child-care-centers-closed-due-tocovid-19/2406310/; and Abby Vesoulis, “COVID-19 has nearly destroyed the childcare industry—and it might be too late to save it,”
Time, September 8, 2020, https://time.com/5886491/covid-childcare-daycare/.
45 See “The need for social workers, during and after the COVID-19 pandemic” (Gwynedd Valley, PA: Gwynedd Mercy University,
June 2, 2020), https://www.gmercyu.edu/news-and-events/news/need-social-workers-during-and-after-covid-19-pandemic.
46 See Tanya Lewis, “Nursing home workers had one of the deadliest jobs of 2020,” Scientific American, February 18, 2021, https://
www.scientificamerican.com/article/nursing-home-workers-had-one-of-the-deadliest-jobs-of-2020/; Jenna Carlesso and Kasturi
Pananjady, “‘They can’t continue’: nursing homes struggle to maintain staffing as COVID cases continue to rise,” CT Mirror,
December 10, 2020, https://ctmirror.org/2020/12/10/they-cant-continue-nursing-homes-struggle-to-maintain-staffing-as-covid-casescontinue-to-rise/; and Will Englund, “In a relentless pandemic, nursing-home workers are worn down and stressed out,” The
Washington Post, December 3, 2020, https://www.washingtonpost.com/business/2020/12/03/nursing-home-burnout/.
47 See Sophie Quinton, “Staffing nursing homes was hard before the pandemic. Now it’s even tougher,” Fierce Healthcare, May 18,
2020, https://www.fiercehealthcare.com/hospitals-health-systems/staffing-nursing-homes-was-hard-before-pandemic-now-it-s-eventougher.
48 See “Spring 2021 enrollment (as of March 25)” (National Student Clearinghouse Research Center, April 29, 2021), https://
nscresearchcenter.org/stay-informed/; “As Covid-19 pummels budgets, colleges are resorting to layoffs and furloughs. Here’s the
latest,” The Chronicle of Higher Education, May 13, 2020, https://www.chronicle.com/article/were-tracking-employees-laid-off-orfurloughed-by-colleges/; and Abigail Johnson Hess, “At least 50,904 college workers have been laid off or furloughed because of
Covid-19,” CNBC Make It, July 2, 2020, https://www.cnbc.com/2020/07/02/218-colleges-have-laid-off-or-furloughed-employees-dueto-covid-19.html.
49 As was the case with government education industries, when educational services are experiencing large employment changes
not tied to the normal seasonal hiring or laying off of workers, employment gains on a seasonally adjusted basis can be misleading.

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50 See “Staffing jobs hit historic low amid pandemic” (Alexandria, VA: American Staffing Association, April 21, 2020), https://
americanstaffing.net/posts/2020/04/21/staffing-jobs-hit-historic-low-amid-pandemic/.
51 See “Staffing jobs tick up in May” (Alexandria, VA: American Staffing Association, May 27, 2000), https://americanstaffing.net/
posts/2020/05/27/staffing-jobs-tick-up-in-may/; and Stephanie Hegarty, “Staffing firms are adapting to the COVID-19 pandemic
economy” (First Advantage, November 9, 2020), https://fadv.com/blog/staffing-firms-are-adapting-to-the-covid-19-pandemiceconomy/.
52 See “Pandemic fallout: US tech sector sheds record number of jobs in April, CompTIA analysis reveals,” Cision PR Newswire, May
8, 2020, https://www.prnewswire.com/news-releases/pandemic-fallout-us-tech-sector-sheds-record-number-of-jobs-in-april-comptiaanalysis-reveals-301055911.html; and Tekla S. Perry, “Tech jobs in the time of COVID,” IEEE Spectrum, May 5, 2020, https://
spectrum.ieee.org/view-from-the-valley/at-work/tech-careers/tech-jobs-in-the-time-of-covid.
53 See “Guidance on the essential critical infrastructure workforce: ensuring community and national resilience in COVID-19
response,” version 3.0 (U.S. Department of Homeland Security, Cybersecurity and Infrastructure Security Agency, April 17, 2020),
https://www.cisa.gov/sites/default/files/publications/
Version_3.0_CISA_Guidance_on_Essential_Critical_Infrastructure_Workers_1.pdf.
54 See “Domestic auto production” (FRED, Federal Reserve Bank of St. Louis), https://fred.stlouisfed.org/series/DAUPSA#0; and
Michael Martinez, Hannah Lutz, and Vince Bond Jr., “Detroit 3 begin to idle North American plants,” Automotive News, March 18,
2020, https://www.autonews.com/automakers-suppliers/detroit-3-begin-idle-north-american-plants.
55 See Neal E. Boudette, “As sales rise, automakers ramp up production,” The New York Times, June 24, 2000, https://
www.nytimes.com/2020/06/24/business/auto-industry-coronavirus-recovery.html.
56 See “Union opposes reopening U.S. meat plants as more workers die,” Reuters, May 8, 2020, https://www.reuters.com/article/ushealth-coronavirus-usa-meat/union-opposes-reopening-u-s-meat-plants-as-more-workers-die-idUSKBN22K2WP.
57 See “Advance retail sales: retail (excluding food services)” (FRED, Federal Reserve Bank of St. Louis), https://fred.stlouisfed.org/
series/RSXFS.
58 See “Advance retail sales: clothing and clothing accessory stores” (FRED, Federal Reserve Bank of St. Louis), https://
fred.stlouisfed.org/series/RSCCAS.
59 See “Total vehicle sales” (FRED, Federal Reserve Bank of St. Louis), https://fred.stlouisfed.org/series/TOTALSA.
60 See “Cars.com: pandemic spurs new ways to buy and sell cars,” Cars.com, July 27, 2020, https://www.cars.com/articles/cars-compandemic-spurs-new-ways-to-buy-and-sell-cars-digitally-424717/.
61 See Nora Naughton, “U.S. auto sales showed signs of recovery in third quarter,” The Wall Street Journal, updated October 1,
2020, https://www.wsj.com/articles/u-s-auto-sales-show-signs-of-recovery-in-third-quarter-11601569086.
62 See Ignacio Felix, Adrian Martin, Vivek Mehta, and Curt Mueller, “Changes in consumer behavior continue to ripple through the US
food and agricultural supply chains. What should companies do now?” (McKinsey & Company, July 2, 2020), https://
www.mckinsey.com/industries/consumer-packaged-goods/our-insights/us-food-supply-chain-disruptions-and-implications-fromcovid-19.
63 See Tahir Islam, Abdul Hameed Pitafi, Vikas Arya, Ying Wang, Naeem Akhtar, Shujaat Mubarik, and Liang Xiaobei, “Panic buying
in the COVID-19 pandemic: a multi-country examination,” Journal of Retailing and Consumer Services, vol. 59, March 2021, https://
www.sciencedirect.com/science/article/abs/pii/S0969698920313655; and Anuradha Varanasi, “Panic buying during the Covid-19
pandemic associated with psychological distress & higher income: study,” Forbes, January 30, 2021, https://www.forbes.com/sites/
anuradhavaranasi/2021/01/30/panic-buying-during-the-covid-19-pandemic-associated-with-psychological-distress--higher-incomestudy/?sh=740a3eed267a.

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64 See Julia Carpenter, “Home-improvement stocks stay the course amid nesting during Covid-19,” The Wall Street Journal, updated
August 21, 2020, https://www.wsj.com/articles/home-improvement-stocks-stay-the-course-amid-nesting-duringcovid-19-11598011211.
65 See “Where states reopened and cases spiked after the U.S. shutdown,” The Washington Post, updated September 11, 2020,
https://www.washingtonpost.com/graphics/2020/national/states-reopening-coronavirus-map/.
66 According to Traffic Volume Trends reports from the U.S. Department of Transportation (https://www.fhwa.dot.gov/
policyinformation/travel_monitoring/tvt.cfm), vehicle miles traveled fell by 19 percent in March and by 27 percent in April, before rising
by 24 percent in May and by 16 percent in June.
67 See Russell Redman, “Pandemic taught grocery supply chain valuable lessons,” Supermarket News, January 29, 2021, https://
www.supermarketnews.com/issues-trends/pandemic-taught-grocery-supply-chain-valuable-lessons.
68 See Gary Drenik, “Growth in direct-to-consumer ecosystem brings new challenges for brands and opportunities for consumers,”
Forbes, November 10, 2020, https://www.forbes.com/sites/garydrenik/2020/11/10/growth-in-direct-to-consumer-ecosystem-bringsnew-challenges-for-brands-and-opportunities-for-consumers/?sh=45b6dbca618d; and “E-commerce, trade and the COVID-19
pandemic,” information note (World Trade Organization, May 4, 2020), https://www.wto.org/english/tratop_e/covid19_e/
ecommerce_report_e.pdf.
69 See “Coronavirus cancellations: every film, TV show, and event affected by the outbreak,” IndieWire, August 11, 2020, https://
www.indiewire.com/feature/coronavirus-cancellations-hollywood-entertainment-covid19-1202215596/.
70 See “Select information on construction industry jobs due to coronavirus (Covid-19) in the United States as of May 2020” (Statista,
May 2020), https://www.statista.com/statistics/1116499/covid-19-us-construction-industry-jobs/.
71 See “Total construction spending: residential in the United States” (FRED, Federal Reserve Bank of St. Louis), https://
fred.stlouisfed.org/series/TLRESCONS; and “Total construction spending: nonresidential in the United States” (FRED, Federal
Reserve Bank of St. Louis), https://fred.stlouisfed.org/series/TLNRESCONS.
72 See “New privately-owned housing units started: total units” (FRED, Federal Reserve Bank of St. Louis), https://fred.stlouisfed.org/
series/HOUST; and “New one family houses sold: United States” (FRED, Federal Reserve Bank of St. Louis), https://
fred.stlouisfed.org/series/HSN1F.
73 See “Spot crude oil price: West Texas Intermediate (WTI)” (FRED, Federal Reserve Bank of St. Louis), https://fred.stlouisfed.org/
series/WTISPLC.
74 See “Lessons from the past: informing the mining industry’s trajectory to the next normal” (McKinsey & Company, July 24, 2020),
https://www.mckinsey.com/industries/metals-and-mining/our-insights/lessons-from-the-past-informing-the-mining-industrys-trajectoryto-the-next-normal.
75 Employees who work as contractors, such as those working for rideshare companies, are not included in CES estimates, which
exclude the self-employed.
76 For examples of transit system traffic declines, see Katherine Shaver, “Metro preparing for possibility of scaling back service if too
many employees call in sick from coronavirus,” The Washington Post, March 12, 2020, https://www.washingtonpost.com/
transportation/2020/03/12/metro-preparing-possibility-scaling-back-service-if-too-many-employees-call-sick-coronavirus/; Erica
Pieschke, “BART cancels extra commute trains after ridership drops nearly 90%,” KRON, March 18, 2020, https://www.kron4.com/
news/bay-area/bart-cancels-extra-commute-trains-after-ridership-drops-nearly-90/; and Laura Bliss, “When the world stops moving,”
Bloomberg, March 19, 2020, https://www.bloomberg.com/news/articles/2020-03-19/the-mobility-impacts-of-coronavirus.
77 Only private employment is included in CES estimates for the transportation and warehousing industry group. For example,
employees of a company contracted to operate a city’s transit system or a county’s school bus network would be classified in the
transportation and warehousing industry. Employees of government agencies providing the same services, however, would be
classified in the government sector. According to a 2013 report from the U.S. Government Accountability Office (GAO), “61 percent of
463 transit agencies responding to GAO’s survey reported they contract out some or all operations and services”; see “Public transit:

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transit agencies’ use of contracting to provide service,” GAO-13-782 (U.S. Government Accountability Office, September 2013),
https://www.gao.gov/assets/660/658171.pdf. In addition, a New York Times article reported that 40 percent of school bus service is
provided by private companies; see Pranshu Verma, “‘End of the line’: school bus industry in crisis because of the coronavirus,” The
New York Times, August 28, 2020, https://www.nytimes.com/2020/08/28/us/coronavirus-school-buses.html.
78 See “Air revenue passenger miles” (FRED, Federal Reserve Bank of St. Louis), https://fred.stlouisfed.org/series/AIRRPMTSID11.
Air revenue passenger miles are a measure of the volume of air passenger transportation. An air revenue passenger mile is equal to
one paying passenger carried 1 mile.
79 See Coronavirus Aid, Relief, and Economic Security Act, Pub. L. No. 116-136, 2020, https://www.congress.gov/bill/116th-congress/
house-bill/748; and Frank S. Murray Jr., Jared B. Rifis, Leah R. Imbrogno, Jamie N. Class, Julia Di Vito, Kaitlyn M. Foley, and Michael
A. Donadio, “The Coronavirus Aid, Relief, and Economic Security Act (‘CARES Act’) is enacted into law” (Foley & Lardner LLP, March
27, 2020), https://www.foley.com/en/insights/publications/2020/03/coronavirus-cares-act-enacted-into-law.
80 The Cass Freight Index, a measure of North American freight volumes and expenditures, “includes all domestic freight modes and
is derived from more than 33 million invoices and more than $26 billion in spend processed by Cass annually on behalf of its client
base of hundreds of large shippers. These companies represent a broad sampling of industries including consumer packaged goods,
food, automotive, chemical, medical/pharma, OEM, retail and heavy equipment.” See “A measure of North American freight
volumes” (Cass Information Systems, Inc.), https://www.cassinfo.com/freight-audit-payment/cass-transportation-indexes/cass-freightindex; and “Cass Freight Index: shipments” (FRED, Federal Reserve Bank of St. Louis), https://fred.stlouisfed.org/series/
FRGSHPUSM649NCIS.
81 Truck tonnage is a measure of the gross tonnage of freight transported by motor carriers in the United States. The estimates cited
here come from the U.S. Bureau of Transportation Statistics (see https://www.transtats.bts.gov/osea/seasonaladjustment/?
PageVar=truck) and are calculated by using data for the For-Hire Truck Tonnage Index of the American Trucking Associations.
82 See “Parcel experts weigh in on FedEx & UPS so far throughout the COVID-19 pandemic,” Logistics Management, June 8, 2020,
https://www.logisticsmgmt.com/article/parcel_experts_weigh_in_on_fedex_ups_so_far_throughout_the_covid_19_pandemi.
83 See “Survey: US consumer sentiment during the coronavirus crisis” (McKinsey & Company, May 13, 2021), https://
www.mckinsey.com/business-functions/marketing-and-sales/our-insights/survey-us-consumer-sentiment-during-the-coronaviruscrisis.
84 See “Table 1. Estimated quarterly U.S. retail sales: total and e-commerce” (U.S. Census Bureau, last revised May 18, 2021),
https://fred.stlouisfed.org/series/AIRRPMTSID11; and “E-commerce retail sales as a percent of total sales” (FRED, Federal Reserve
Bank of St. Louis), https://fred.stlouisfed.org/series/ECOMPCTSA.
85 The U.S. Census Bureau does publish data on rental car company revenues, but it did not publish anything in 2020 because of
sample variability. For the industry source of data on revenue declines, see “U.S. car rental revenue dives 27.4% in 2020,” Auto
Rental News, December 16, 2020, https://www.autorentalnews.com/10132672/u-s-car-rental-revenue-dives-27-4-in-2020.
86 See Nora Naughton, “Covid-19 slammed rental-car firms, then business turned around,” The Wall Street Journal, November 2,
2020, https://www.wsj.com/articles/with-more-americans-hitting-the-road-for-travel-rental-car-companies-revive-11604317493.
87 See Annie Nova, “Millions of Americans may not be able to pay their rent in October. What to do if you’re one of them,” CNBC,
October 2, 2020, https://www.cnbc.com/2020/10/02/millions-of-americans-may-not-be-able-to-pay-rent-in-october.html.
88 See “Federal eviction moratoriums in response to the COVID-19 pandemic,” CRS Insight (Congressional Research Service,
updated March 30, 2021), https://crsreports.congress.gov/product/pdf/IN/IN11516.
89 See “Rental assistance in COVID-19 relief packages” (Washington, DC: National Association of Home Builders), https://
www.nahb.org/advocacy/industry-issues/emergency-preparedness-and-response/coronavirus-preparedness/key-housing-provisionsin-covid-19-relief-package/rental-assistance-in-covid-19-relief-package.

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90 See “COVID & collections: are occupiers paying their bills?” U.S. Market Flash (CBRE, August 10, 2020), https://www.cbre.us/
research-and-reports/US-MarketFlash-COVID-Collections-Are-Occupiers-Paying-Their-Bills.
91 See Shawn Moura, “Working together as a team: negotiating with tenants and leasing space during COVID-19” (Herndon, VA:
Commercial Real Estate Development Association), https://www.naiop.org/en/Research-and-Publications/Reports/Working-Togetheras-a-Team-Negotiating-With-Tenants-and-Leasing-Space-During-COVID-19.
92 CES data exclude the self-employed. Real estate sales agents are generally self-employed workers rather than employees of the
real estate agency with which they are affiliated. See “Licensed real estate agents—real estate tax tips” (Internal Revenue Service),
https://www.irs.gov/businesses/small-businesses-self-employed/licensed-real-estate-agents-real-estate-tax-tips.

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The commercialization of academic discovery: a look at startup formation
Yavor Ivanchev
Startup formation is one of the main pathways through which scientific innovations originating at academic institutions find their way to market. It remains unclear, however,
what factors make this kind of ingenuity transfer more or less likely to occur, and available research on the topic has been scant and inconclusive. In “Revisiting the
entrepreneurial commercialization of academic science: evidence from ‘twin’ discoveries” (National Bureau of Economic Research, Working Paper 28203, December 2020),
Matt Marx and David H. Hsu address this question through a novel empirical approach, reporting results that both challenge and refine previous findings.
Relying on the work of others, the authors identify two sets of factors that may determine whether a discovery birthed in a university lab would make the leap to commercial
application. The first set, emphasized by what Marx and Hsu term the “resource munificence” view of commercialization, revolves around the resource endowments of a
given geographical area, including things such as local availability of venture capital and technical know-how. The second set of factors, which the authors attribute to what
they call the “discovery team composition” view of startup formation, centers on the internal makeup of academic research teams, in particular the entrepreneurial experience
and networking abilities of their members.
While Marx and Hsu do not challenge the logical grounds of these theoretical perspectives, they do identify a major omission in the empirical studies that test their
predictions. Specifically, they observe that neither perspective accounts for potential differences in the intrinsic suitability of various inventions for commercial application—
suitability referred to as “unmeasured latent commercializability” in the study—even though it stands to reason that discoveries in certain areas of academic inquiry would be
markedly more easily to commercialize than others.
To capture this underappreciated quality of academic innovation, the authors adopt a “twin-discovery” research design, whereby startup formation outcomes are compared for
academic breakthroughs with the same commercial potential. Using matched data on scholarly codiscoveries reported in tens of thousands of published academic articles, as
well as information on paper–patent pairs and government disbursements of small-business research grants (measures aiming to capture startup formation and
commercialization), Marx and Hsu report the results of two empirical analyses: one that controls for latent commercializability and another that does not.
The two sets of results are notably different. In the cross-sectional analysis that sidesteps commercial potential, the evidence is largely in line with that reported in earlier
studies, backing both the resource and compositional views of commercialization. By contrast, in the twin-discovery analysis that controls for latent commercializability, the
authors find no support for the resource availability account and strong support for the team composition account. Moreover, their results show that the primary characteristics
of team composition that drive startup formation are entrepreneurial experience and interdisciplinary diversity, and that these factors work independently of project selection
in successful commercialization.
In their concluding remarks, Marx and Hsu caution that their study examines only one channel of scientific commercialization—namely, startup formation initiated by
academic researchers—and that other channels, such as making licensing arrangements with existing companies for product manufacturing, may be affected by a different set
of factors. In addition, the authors recognize that their research design does not rule out possible selection effects in team composition (effects that may influence the
likelihood that a discovery will find its way into a research publication), noting that future studies should look more closely at the antecedents of team formation.
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June 2021

The world at an economic inflection point
The Great Demographic Reversal: Ageing Societies,
Waning Inequality, and an Inflation Revival. By Charles
Goodhart and Manoj Pradhan. Palgrave Macmillan, 2020,
280 pp., $29.99 hardcover.
The last few decades have largely been a time of
remarkable economic growth for the world. This long-term
picture can be difficult to see while we remain hyperfocused on recovering from the economic shock wrought by
the COVID-19 pandemic. However, as we move further into
the second year of the pandemic—and as economic
forecasts in advanced economies grow increasingly
optimistic in light of vaccine rollouts—it may be helpful to
take a step back and seek to understand these long-term
economic trends. In The Great Demographic Reversal:
Ageing Societies, Waning Inequality, and an Inflation
Revival, authors Charles Goodhart and Manoj Pradhan
take a global, long-term view of past and future
macroeconomic developments. They point to rising labor
supply and strong globalization pressures as the key
factors responsible for the global economic growth we have
seen over the last three decades. The authors single out
China as the biggest economic story over this period,
describing it as the foremost champion of globalization.
Looking forward, however, they propose that a future
reversal of labor supply and globalization trends will

Alexis Jones
jones.alexis@bls.gov

likewise reverse the global economic trends of recordbreaking growth, low inflation, low interest rates, and rising
inequality. While the bulk of the book was written in 2019,
before the pandemic hit, the authors believe that the impact

Alexis Jones is an economist in the Office of
Prices and Living Conditions, U.S. Bureau of
Labor Statistics.

of the pandemic will accelerate these developments.
The book’s central argument is that inflation will rise
because of demographic shifts. Declining fertility rates and
rising life expectancy will increase the proportion of the elderly in the populations of advanced economies. This
shift will cause a slowdown in labor force growth, an absolute decline in the labor force of many countries,

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worsening dependency ratios, increasing medical and pension costs, and rising real wages driven up by labor
scarcity. Together, these changes will exert a strong inflationary pressure that future surges in labor productivity or
aggressive policymaking will be unlikely to counter. Goodhart and Pradhan also forecast that, in the absence of a
substantial increase in labor productivity, output growth will slow as a direct result of labor force declines. Although
the effects of demographics on interest rates are harder to project because of many competing forces, the authors
suggest that short-term rates will likely be held below the increase in inflation by central banks, whereas long-term
rates will likely rise above that level, leading to a steeper yield curve. On a more benign note, the authors believe
that income and wealth inequality within countries will fall as a side effect of increasing real wages.
In advanced economies, effective labor supply has more than doubled in the past three decades. Goodhart and
Pradhan cite the rise of China, the post-Cold War reintegration of Eastern Europe into global economic structures,
increasing participation rates among women, and the influx of baby boomers into the labor force as the primary
forces responsible for the large positive labor supply shock. This shock, the largest ever, has significantly reduced
the bargaining power of labor, decreased union membership, and suppressed wage growth among workers within
the bottom 90 percent of the income distribution. The observed wage stagnation for all but the top income earners
has been the primary culprit for inequality, and while inequality across countries has decreased, inequality within
countries has skyrocketed. According to the authors, stagnant wages, coupled with a decrease in prices for
manufactured goods, have been a powerful deflationary force. Even prepandemic expansionary monetary and
fiscal policies that have led to a persistent rise in public sector debt have not generated much inflation, and in
recent years, many central banks have struggled to hit their 2-percent inflation targets.
What will the demographic reversal described in the book look like? Japan is often used as a case study illustrating
the employment implications of population aging, and the authors spend some time examining its experience.
Japan, which has the oldest population in the world, with 28 percent of its people being 65 and over, is the first
country to face a shrinking labor supply because of demographics. The authors give several reasons why the labor
force shrinkage in Japan did not lead to inflation. First, as Japan’s working-age population began to decline in the
mid-1990s, other regional economies were overflowing with cheap and efficient labor. Many Japanese jobs,
particularly those in the manufacturing sector, were moved to China and other countries. Second, cultural norms in
Japan account for a uniquely flat Phillips curve, an economic model that shows an inverse relationship between
unemployment and inflation. The Japanese highly value job security, frowning on job terminations except under
rare and extreme circumstances. At the height of the financial crisis of 2007–08, Japan’s unemployment rate
topped out at 5.5 percent, mainly because the country’s preferred approach to confronting the crisis was through
lowering hours and wages rather than through mass layoffs. Lastly, Japan has seen a large rise in labor force
participation among those ages 55 and over. This is due to both an increased life expectancy and cuts in pension
benefits aimed at reducing the government’s fiscal burden.
Goodhart and Pradhan argue that little of Japan’s experience will be applicable to the West as it ages. Perhaps
most significantly, the authors say that the demographic goalpost has shifted, and it may take two or three
countries like China to support the aging global economy. India and emerging economies in Africa and elsewhere
will not be able to support the aging West, although they will keep growing and may have the populations to do so.
India, which has suffered from internal collisions among political parties and between individual states and the
federal government, will be limited by its lack of administrative capital. Overall, India ranks poorly on measures
such as ease of doing business and contract enforcement, and this reputation creates difficulties in attracting
foreign capital and investment. The country simply lacks the needed infrastructure and administrative

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cohesiveness that have been a key component of China’s success. The population of Africa can rival that of India,
but it is spread over a much larger geographic area and across more than 50 independent countries. Further,
Goodhart and Pradhan note that the labor force participation rates of older people have already been slowly rising
in advanced economies. Different countries have different combinations of pension benefits and participation rates,
and this variation suggests that some countries will have more fiscal flexibility than others. However, raising
retirement ages and cutting pension and welfare programs are deeply unpopular policies that can be difficult to
pass legislatively.
Whether the authors’ projections come to fruition or not, I agree that the effects of the “great demographic reversal”
will be ubiquitous. Government monetary and fiscal policies, healthcare and pension systems, and finance will
need to adapt in the wake of demographic change. Given the lack of a readily available means to absorb the costs
of an aging population and a shrinking workforce, I find the authors’ argument about a future increase in inflation
particularly persuasive. Still, demographic change is only one of many forces that steer major economic indicators
such as inflation and interest rates, and its effect on macroeconomics is not easily quantifiable. Future
technological and productivity developments are perhaps the biggest missing piece of the puzzle. As proposed by
the authors, large advancements in productivity could help mitigate the economic effects of demographic change,
just as slow technological advancement could exacerbate them. I appreciate the global scope of this book and its
emphasis on the complexity and interconnectedness of the global economy. This is the kind of long-term thinking
that economists, policymakers, and others may find beneficial.

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June 2021

Consumer Expenditure Survey Methods
Symposium and Microdata Users’ Workshop, July
21–24, 2020
The Consumer Expenditure Surveys (CE) program collects
expenditure, demographic, and income data from families
and households. The CE program held its annual Survey
Methods Symposium and Microdata Users’ Workshop from
July 21 to 24, 2020, to address CE-related topics in survey
methods research, to provide free training in the structure
and uses of the CE microdata, and to explore possibilities
for collaboration. Economists from the CE program, staff
from other U.S. Bureau of Labor Statistics offices, and
research experts in a variety of fields—including academia,
government, and private industry—gathered virtually to
explore better ways to collect CE data and to learn how to
use the microdata once they are produced. The experience
was unique for presenters and attendees alike in that this
was the first time either event was held online, in whole or
in part.

Geoffrey D. Paulin
paulin.geoffrey@bls.gov
Geoffrey D. Paulin is a senior economist in the
Division of Consumer Expenditure Surveys, U.S.
Bureau of Labor Statistics.

The Consumer Expenditure Surveys (CE) are the most
Parvati Krishnamurty
krishnamurty.parvati@bls.gov

detailed source of data on expenditures, demographics,
and income that the federal government collects directly
from families and households (or, more precisely,
“consumer units”).1 In addition to publishing standard
expenditure tables twice a year, the U.S. Bureau of Labor

Parvati Krishnamurty is a senior economist in the
Division of Consumer Expenditure Surveys, U.S.
Bureau of Labor Statistics.

Statistics (BLS) CE program releases annual microdata on
the CE website from its two component surveys (the
Quarterly Interview Survey and the Diary Survey).2
Researchers use these data in a variety of fields, including academia, government, and various private industry
areas, such as market research.
In July 2006, the CE program office conducted the first in a series of annual workshops in order to achieve three
goals: (1) to help users better understand the structure of the CE microdata; (2) to provide training in the uses of
the surveys; and (3) to promote awareness, through presentations by current users and interactive forums, of the
different ways in which the data are used and thus provide opportunities to explore collaboration. In 2009, the

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workshop expanded from 2 days to 3 days to include presentations from data users not affiliated with BLS. This
expansion allowed users to showcase their experiences with the public use microdata (PUMD) files (https://
www.bls.gov/cex/pumd.htm), to discuss problems and successes using the data, and to seek comment and
guidance from CE program staff in completing their work.
In every year from 2012 onward, a 1-day symposium has preceded the workshop. The purpose of the symposium
is to support the CE Gemini Redesign Project (Gemini Project), a major initiative to redesign the CE (for more
information, go to https://www.bls.gov/cex/geminiproject.htm).
In addition to the CE program staff, workshop speakers have included economists from BLS regional offices and
researchers not affiliated with BLS. Similarly, symposium speakers have included CE program staff, other BLS
national office staff, and speakers from outside BLS. This article describes the 2020 Survey Methods Symposium,
conducted on July 21, 2020, and the 2020 Microdata Users’ Workshop, conducted July 22–24, 2020.
For the first time, in whole or in part, both events were held online, rather than at the BLS national office in
Washington, D.C. The CE program made this decision because of the continuing COVID-19 pandemic. As a result,
to minimize potential disruption due to possible technological failures or other, unanticipatable, problems, both
events were streamlined. For example, the Symposium, which usually features several speakers outside the CE
program, instead consisted of only two presentations, both from CE staff. While the workshop maintained its
tradition of featuring outside (non-CE program) speakers, BLS speakers, which usually include staff from several
programs, were limited to members of one branch (Information and Analysis) of the CE program. The one
exception was an overview of CE data presented by the CE program director.

Survey methods symposium
The symposium began with a presentation on the Gemini redesign titled “Gemini Redesign: Past, Present, and
Future” by Parvati Krishnamurty from the CE program at BLS. The presentation outlined the original plans for the
redesign and recent modifications made to the redesign plan for implementation. The redesign plan was intended
to be implemented as a whole, but because of budget constraints, the plan will instead be implemented in phases.
Therefore, the plan was modified to move to a phased implementation of key design elements into the CE surveys.
The phased implementation plan is to retain the design elements that have been effective during field tests, which
include a streamlined questionnaire with less expenditure detail, records focus (including a targeted incentive for
record use), online diaries, and token incentives.3 These elements will be implemented directly into the CE
Diary and Interview surveys. Other design elements such as a single sample design, two interview structure, and
two wave design could be tested and implemented in future years, pending changes to requirements or funding
availability. Dr. Krishnamurty provided more detail about the Large Scale Online Diary Feasibility Test (LSF), which
was fielded from October 2019 to March 2020. She also provided a high-level overview of the streamlined
questionnaire design and plans for releasing the new sections of the streamlined questionnaire in three phases
starting in 2023. Dr. Krishnamurty mentioned future enhancements that are being explored by the CE, including
new technologies such as receipt scanning and geolocation, self-administered interviews, adaptive design, split
questionnaire design, single sample design, and gold-standard interviews.
The second presentation by Laura Erhard from the CE program was titled “Going Online: Results from the CE’s
LSF.” The presentation summarized the test design, the online diary design, and some preliminary results from the
first 3 months of unprocessed data. Procedural changes had to be made to the LSF in March 2020 when the
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pandemic made in-person visits impossible, but otherwise fielding went smoothly. The overall response rate was
47.2 percent, and the rate of online diary placement was lower than expected, despite screening respondents for
internet access and frequency of use. Barriers to online diary placement reported by field representatives include
language issues, lack of technological savviness, and lack of connectivity. The LSF included two experiments: an
advance postcard and a token incentive. While there was a small and nonsignificant increase in response rates
from a $5 token incentive, there was a large but nonsignificant increase in response rates from advance postcards
in the preliminary data.4 In general, respondents reported positive experiences with the online diary. One area of
concern was the large number of failed respondent logins to the online diary. Although the online diary has been
used as a contingency measure in the CE Diary Survey during the COVID-19 pandemic, the CE staff is conducting
data analysis of the online diary and will consider its use for production in 2022. Additionally, CE and Census staff
are working on making improvements to the online diary design, protocols, and training based on lessons learned
from the LSF.

Microdata users’ workshop
Meet with an expert: Beginning with the 2017 workshop, the CE organizers have included a feature called the
“Meet with an expert” program. The purpose of the program is to provide an opportunity for attendees to have indepth, one-on-one meetings with members of the CE staff, during which the attendees can ask questions and
receive comments and other guidance about the projects in which they are engaged.5 With the workshop shifting
online this year, the meetings were reimagined. Instead of conducting meetings, attendees met with their experts
by phone, calling a prearranged toll-free number at their appointed times. In addition, several of those who were
waitlisted because of unusually high demand scheduled their meetings for the week following the workshop.
The program has proven beneficial to attendees and to CE staff, who learn more about how researchers are using
the data and about factors related to data, documentation, etc., that can be improved. Despite the differences in
the mode of meeting (i.e., by phone instead of in person), the program was just as successful at the 2020
workshop. During the feedback session, those who participated in this program unanimously praised the
experience both for the content of the meeting and the quality of information received. As a result, the program will
be continued for the 2021 Microdata Users’ Workshop. Attendees are able (and encouraged) to arrange meetings
via the registration form or email.

Day one
The first session of the 2020 workshop consisted of presenters from the CE program. After welcoming remarks by
Scott Curtin, chief of the Branch of Information and Analysis (BIA), Adam Safir provided an overview of the CE,
featuring topics including how the data are collected and published. Economist Bryan Rigg (BIA) then presented
an introduction to the microdata, including how they can be used in research, and the types of documentation
about them available to users. Mr. Curtin completed the session with a description of the data file structure and
variable naming conventions.
Afterward, attendees received their first practical training with the data. In this session, led by senior economist
Aaron Cobet (BIA), they learned basic data manipulation, including how to compute means from the microdata for
consumer units with different characteristics (e.g., by number of children present).6 As expected, the
circumstances of the workshop offered new challenges for this training session and subsequent training sessions:
When conducted in person, members of CE staff circulate among attendees to offer help. In addition, some
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attendees choose to work together on the projects. While these features were obviously not available for the online
workshop, attendees were able to submit questions via an online chat feature or send email to CE staff to receive
an answer directly or arrange a phone call with CE staff.
The afternoon activities included presentations from researchers not affiliated with the CE program. Summaries of
the papers presented by outside researchers are included at the end of this report.
The first speaker, data scientist Aaron R. Williams (Urban Institute, Income and Benefits Policy Center), spoke
about his use of CE microdata to study income and expenditures by low-income families with at least one member
age 50 or older. The work was coauthored with Damir Cosic (Senior Research Associate, Urban Institute, Income
and Benefits Policy Center), who attended the 2019 workshop.7
The second presentation was codelivered by Casey Goldvale (policy analyst) and Vincent Palacios (senior policy
analyst) of the Georgetown Center on Poverty and Inequality. They described their work investigating costs
beyond tuition (e.g., housing) for older (age 25 to 45) college students.
Following this presentation, self-directed practical training resumed with projects, introduced by economist Jimmy
Choi (BIA), involving the integration of data from the Diary and Interview Surveys, a practice used in production of
CE tables, and finding detailed information about education expenditures.8 Attendees also learned how to
integrate results from the Interview and Diary Surveys to match expenditure categories in CE published tables.
After this session, the workshop concluded for the day.9

Day two
To open the second day, Mr. Cobet explained the need to balance confidentiality concerns of respondents with the
usefulness of the data to researchers. Because U.S. Code Title 13 requires confidentiality of response, information
that might identify specific respondents must be removed from the CE data before they are released publicly.
Some identifiers are direct, such as names and addresses. Others are not direct, such as extremely high
expenditures or make and model of automobile(s) owned.
Mr. Cobet explained the methods used to produce the CE microdata files to address these disclosure concerns.
The first method, called topcoding, involves reported values for income or expenditures that exceed a certain
threshold, called the critical value. These top-coded values are replaced by an average of all values exceeding this
critical-value threshold and then flagged as topcoded (or bottom-coded, in the case of large income losses).10 He
also explained recoding, in which data are either made less precise (e.g., if the owned automobile was produced in
1999, the year is replaced with the decade of manufacture [1990s in this example]) or changed in another way
(e.g., state of residence is changed to a nearby state) to preserve both comparability and confidentiality.
Mr. Cobet next explained suppression, in which reported values are removed from the data set. In some cases,
only specific information is suppressed on a record (e.g., details of a specialized mortgage). In other cases, the
entire record is removed (e.g., report of a purchase of an airplane).11 Finally, Mr. Cobet talked about methods to
eliminate reverse engineering, a process through which the user could deduce protected information from other
information provided in the publicly available files.12
Next, Mr. Choi presented a brief description of experimental weights for estimating state-level expenditures with
the use of the CE microdata. He noted that weights for New Jersey, California, Florida, New York, and Texas were
available (https://www.bls.gov/cex/csxresearchtables.htm#stateweights).13 Mr. Choi also presented the criteria

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used by the CE program to assess the feasibility of devising weights for other states (sample size, confidentiality
concerns, and long-term retention of the state under study in the CE sample).
Concluding the session, Dr. Geoffrey Paulin, senior economist in the CE program (BIA), described the correct use
of sample weights in computing consumer unit population estimates. His talk started with an overview of the
computation of the weights.14 Following this, he introduced the procedures needed to get consumer-unitpopulation weighted averages for expenditures; that is, instead of computing mean expenditures from the sample
itself, how to apply weights to estimate mean expenditures for the consumer unit population as a whole.15 Finally,
he noted that the proper use of weights requires a special technique, called balanced repeated replication (BRR).
BRR accounts for sample design effects in order to produce correct estimates of variances for weighted means,
regression parameters, etc. Without BRR, these estimates can be biased or otherwise incorrect when computed
for CE data. Next, he provided an example of BRR he derived from a question that arose during the talk. This led
into a practical training session, instructed by Mr. Curtin, devoted to computing weighted results in two projects:
one related to computing results for collection year estimates and the other for calendar year estimates. The
distinction is that collection year refers to the date on which the respondent reported the expenditures to the
interviewer, while calendar year refers to the period in which the expenditures actually occurred. For example, for a
person participating in the Interview Survey in January 2018 who reports expenditures that occurred during the
final 3 months of 2017 (i.e., October, November, or December), the expenditure collection year is 2018, while the
expenditure calendar year is 2017.
Presentations by non-CE staff researchers continued in a themed session during the afternoon. Each of the
speakers described their work with race and ethnicity variables in the CE microdata. The first presenter, Ziyao
Tian, a Ph.D. candidate in sociology at Princeton University, discussed expenditures on higher education for AsianAmerican families. The second, copresented by Reginald Noël (research economist/data scientist) and Whitney
Hewitt Noël (public health researcher/health equity advocate), both of the Noël Collective, discussed the
intersectionality of sex and race in both income and expenditure patterns. Serving as a moderator of the
discussion, Dr. Paulin briefly described his own work with the Diary Survey to explore food expenditures by race
and ethnicity. He noted the detailed information on geographic origin included in race (Asian) and ethnicity
(Hispanic) categories within the CE data for users interested in studying expenditures by the communities within
these broader groups.16 He also pointed out the benefit of having these characteristics available for each member
of the consumer unit, which he applied to his own research. For example, there is no attempt to identify a “decision
maker” in the consumer unit, so the relationship of race or ethnicity to expenditure patterns is unclear when
members of the consumer unit identify with different races or ethnicities.17
The last session of the day continued practical training. Dr. Paulin described the proper methods for analyzing CE
income data, which are multiply imputed when missing. This presentation led into more self-directed practical
training, in which attendees applied the methods described.

Day three
The final day started with a set of presentations from outside researchers who use CE microdata. The first of these
presenters was Dr. Constantin Burgi, professor of economics at St. Mary’s College of Maryland. Dr. Burgi
discussed his work examining how average consumer expectations differ when reporting households are weighted
by actual expenditures, as opposed to households having equal weight, in computing the average.

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The second speaker, Dr. Ensieh Shojaeddini, a researcher on fellowship at the Environmental Protection Agency,
used CE data in the construction of demand systems to estimate effects of regulation.
The final speakers in this session were Dr. David King, an assistant professor of urban planning at Arizona State
University, Tempe, and Dr. Jonathan Peters, a professor of finance and data analytics at The City University of
New York. The presenters noted several changes in the last decade that affect transportation expenditures for
consumers (the rise of rideshare services, online shopping, and, most recently, the COVID-19 pandemic), and
want to see how these changes will continue to affect these expenditures in the future. They proposed a plan for
studying patterns using CE data and other sources, particularly once the CE data for 2020 are released.
Following a break, Dr. Paulin described work in progress within the CE program to impute data for assets owned
and liabilities owed when the holding, but not specific value, of either is reported. Next, supervisory economist
Brett Creech (BIA) provided a sneak peek of developments for CE publications and microdata. Starting with those
recently implemented, such as the release of free PUMD covering 1980 to 1995 in early 2020,18 he described
changes scheduled or under consideration for future releases.19 Those releases scheduled include new tables
showing expenditures in 2019 at more refined geographic levels (census division in addition to current census
region) and a new column on the generational tables (first published officially to reflect 2016 data) showing
expenditures for the post-Millennial generation (i.e., those born in 1997 or later).20 He also noted the addition of a
new question (July 2018 for the Interview Survey and January 2019 for the Diary Survey) that asks whether
anyone in the consumer unit has previously served in the U.S. military. He stated that tables showing expenditures
by veteran status will be published as soon as sample size permits. In addition, he announced the inclusion of a
special question, starting in June 2020, regarding the receipt and use of the 2020 economic stimulus payments.
Both microdata and published tables will include information collected from the special question.21
To conclude the workshop, David Biagas (BLS) led attendees in a feedback session. During the feedback session,
attendees had the opportunity to provide comments on what they found most (or least) useful about the workshop,
and to make suggestions for future events. Many comments were positive, with attendees liking the progressive
nature of the workshop (i.e., starting with the most basic information about the data collection and file structures
and ending with the most technical topics) and praising the “Meet with an expert” program. Workshop attendees
also provided suggestions on what could be improved. These comments were especially important given the
delivery of the workshop online this year, for the first time ever. Because of the ongoing COVID-19 pandemic, the
workshop will be conducted online again in 2021.

Symposium and workshop of 2021
The next Survey Methods Symposium is scheduled for July 20, 2021, in conjunction with the 16th annual
Microdata Users’ Workshop (July 21–23). Both events will be held online. Although the symposium and
workshop remain free of charge to all participants, advance registration is required (https://data.bls.gov/cgibin/forms/cex-registration). For more information about these and previous events, visit the CE website
(https://www.bls.gov/cex/) and look for the left navigation bar, titled “CE WORKSHOP AND SYMPOSIUM.”
For direct access to this information, the link is https://www.bls.gov/cex/csxannualworkshop.htm. The link to
the combined agendas for the 2020 symposium and workshop (https://www.bls.gov/cex/ce-2020-combined-

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agenda.pdf) is also available on this webpage. Workshop presentations are available in an online zip file
(https://www.bls.gov/cex/ws2020-presentations.zip).

Highlights of workshop presentations
The following are highlights of the papers presented during the workshop, listed in the order of presentation. They
are based on summaries written by the respective authors.
Aaron R. Williams, Data Scientist, Income and Benefits Policy Center (Urban Institute), “Lifetime Income & Costs
of the LI50+” (Interview Survey), day one.
A primary mission of the AARP Foundation is to mitigate, and eventually eliminate, poverty among older
Americans. An important concern for the Foundation in addressing poverty among seniors is to select the
target population that maximizes the impact of their effort. Our report, which focused on households below
250 percent of the Federal Poverty Guideline with at least one member age 50 or older, helped the AARP
Foundation 1) identify demographic groups that are most vulnerable and 2) identify the groups that
represent the biggest share of the vulnerable population. To identify the most vulnerable population—those
in high need—we relied on household spending rather than income because it is measured more
accurately than income and represents a better measure of personal well-being. We selected the bottom
expenditure quartile—25 percent of LI50+ who had the lowest annual expenditures adjusted for household
size—and analyzed the composition of this group and the likelihood of being in high need among the
general population. Through this work, we developed a customized version of the R package library
(cepumd) by Arcenis Rojas, we created a custom mapping of Universal Classification Codes to a custom
hierarchy of grouped expenditures that matched the interests and needs of the AARP Foundation, and we
created a detailed profile of the consumption and income of the LI50+ with extensive data visualization and
tables.
We used a heavily functional approach in R to analyze the data and built a process with version control
that proved useful for this analysis and hopefully future analyses.
Casey Goldvale, Policy Analyst, and Vincent Palacios, Senior Policy Analyst, Georgetown Center on Poverty &
Inequality, “Costs Beyond Tuition: Estimating older college students’ basic needs with Consumer Expenditure
Survey (PUMD) (Interview Survey), day one.
Though estimating the “cost of attendance” is key in determining student financial aid for higher education,
there are no standardized measurement methods and estimates can vary wildly across colleges located
within a few miles of each other. There is also evidence that “cost of attendance” may be severely
underestimated for older students who are more likely to have dependents and be financially independent.
We use the Consumer Expenditure Surveys (CE) to estimate average spending on components of an
adequate living standard among older undergraduate students’ households nationally. Using UCC codes
from MTBI data files, we have adapted FMLI and MEMI samples and variables to be comparable to cost
categories defined in U.S. student financial aid policy and the Census Bureau and BLS basic needs and

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poverty measurement methodologies. We also focus on equity by incorporating race/ethnicity, gender, and
other demographic and geographic characteristics for older students and their households. To ensure
adequate sample sizes, we pooled multiple years of data to increase sample size and adjusted the
sampling weights accordingly. To our knowledge, this is the first time the CE has been used to study the
older student population and is one of few studies beyond Geoffrey Paulin’s 2001 paper using CE
microdata to estimate college students’ cost of living.22
Ziyao Tian, Ph.D. Candidate (Sociology), Princeton University, “How Expensive Is the Battle of Tiger Mothers?
Understanding Race and Class behind the Educational Expenditure of Asian Americans” (Interview Survey), day
two.
Social stratification scholars have been trying to understand the superior academic achievement of Asian
Americans by examining the roles of family socioeconomic status (SES), culture, and the intersection of
the two factors. Yet, the role of expenditure on education as an important mechanism linking social class
and culture remains unexplored. Previous studies demonstrate that superior academic achievement is
partly driven by Asian Americans’ high expectations of education across families of different SES origins. In
other words, family SES has a weaker predicting power of educational expectations for Asian Americans
than for Whites. Beyond this psychological-attitude channel, we use the Consumer Expenditure Surveys
(CE) data from 2009 to 2019 to examine whether Asian Americans’ expenditure on education is also
universally higher and less sensitive to SES. Preliminary results show that Asian Americans, on average
and across SES distribution, spend more dollars, as well as a higher proportion of their spending budget,
on education than their non-Hispanic White counterparts. The difference is primarily a result of Asian
families’ high spending on college tuition. The racial gap in college tuition is more pronounced among
lower-SES families than among higher-SES families. Further explorations of the gap in college tuition
suggest that the difference is mainly driven by more college students from less advantaged Asian families,
rather than a greater tendency to provide stronger college tuition support when having a college student at
home.
Reginald Noël, Research Economist/Data Scientist, and Whitney Hewitt Noël, Public Health Researcher/Health
Equity Advocate, Noël Collective, “Gender Economics, Race, and Intersectionality: Using CE Microdata to examine
inequalities among adult women and men in the U.S. by race and ethnicity, 2016 through 2018
combined” (Interview Survey), day two.
This working paper explores the issues of gender economics, with an intersectional dimension of race and
ethnicity. Comparative analysis from two different datasets, the American Community Survey and the CE,
show similar persistent inequalities in income, stratified by binary sex and race. Specifically, adult men had
higher salaries and wages than adult women. In addition, adult Asian and White populations had higher
salaries and wages than the adult Native, Black, and Hispanic populations. Moreover, the Consumer
Expenditure Interview Survey data allowed for a deep examination of household spending, scarcity,
resource allocation, and consumer patterns among the different cohorts. The data illustrated
socioeconomic inequalities faced by women as compared with men, including the Gender Pension Gap,
Pink Tax (higher prices for goods marketed to women, such as razors, that are actually or nearly identical
to versions marketed to men), health care costs, educational attainment, occupation, and marital status. All

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these factors depicted microeconomic inequalities faced by intersected subpopulations, which disserves
not only these population groups but also the U.S. economy as a whole.
Constantin Burgi, Ph.D., Assistant Professor (Economics), St. Mary’s College of Maryland, “Predicting consumer
expenditure based on the variables available in the Consumer Expectation Survey of the NY Fed” (Interview
Survey), day three.
The aim of this work is to check how the mean household expectations from the New York Fed’s
Consumer Expectations Survey change when households are weighted using consumer expenditure, as in
the Consumer Price Index, instead of equal weights. In order to do so, it is necessary to impute the
consumer expenditure of the households in the Consumer Expectation Survey. Variables that are available
in both the CE and the Consumer Expectations Survey are made comparable and a (weighted) OLS
regression is then used to impute the consumer expenditure. It is found that the consumption-weighted
consumer expectations are around 0.7 percentage points lower than the equally weighted consumer
expectations.
Ensieh Shojaeddini, Ph.D., Oak Ridge Institute for Science and Education fellow at U.S. Environmental Protection
Agency, “Consumer Demand Estimation for Heterogeneous U.S. Households” (Interview Survey), day three.
The specification of the consumer demand system is important for estimating the economy-wide impacts of
environmental regulation. First, it plays a key role in determining the baseline in a dynamic context.
Second, it defines the final good demand curves that help determine the ability to control pollution on the
extensive margin through the output effect. In this role, the specification of consumer demand also helps
determine the share of abatement costs borne by factors or production relative to consumers. Finally, it
plays an important role in determining tax interaction effects.
In computable general equilibrium (CGE) models, household behavior is typically governed by a constant
elasticity of substitution (CES) utility function, though it fails to realistically capture well-known patterns of
consumer behavior. In addition, only a few CGE models econometrically estimate their own elasticities,
which are limited to a representative national-level household. We empirically estimate several flexible
consumer demand systems for the U.S. economy for use in a CGE model with regional and household
income disaggregation. As part of this evaluation, we consider tradeoffs between different specifications
regarding complexity, regularity, the ability to capture cross-price elasticities, Engel curve flexibility, and the
number of commodities that can be reasonably accommodated.
David A. King, Ph.D., Assistant Professor of Urban Planning, Arizona State University, Tempe, Arizona, and
Jonathan Peters, Ph.D., Professor of Finance and Data Analytics, The City University of New York, “Household
Transportation Spending Trends from 2010 to 2020 - Early Indications of the impact of cultural shifts and pandemic
related household activity on transportation spending patterns” (Interview Survey), day three.
The last 10 years have been a time of radical change in household consumption as it relates to
transportation spending. First, we experienced the massive growth in for-hire vehicle services such as
Uber and Lyft that disrupted and transformed traditional taxi services in many cities. Second, we observed
a decline in private vehicle ownership in several cities, with corresponding growth in car sharing services.
Further, the growth in e-bicycles and scooter services, as well as the potential growth for autonomous
vehicles, have made the last decade a time of revolution in the transportation sector. Now, further changes

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are being wrought by the COVID-19 pandemic, reversing many trends in transportation use. Transit
systems reeled from the needs for enhanced sanitation and social distancing, and ridership caps were
instituted on many mass transit systems. Demand for gasoline and diesel collapsed. Online shopping and
at-home consumption skyrocketed. What is still an open question in all of this is, are these changes
temporary and will they reverse when the pandemic moderates, or will they result in a long-term reversal of
the recent trends and usher in a 21st century wave of automobile use and reliance on personal instead of
shared transportation services? These changes have the potential to disrupt many policy initiatives in
terms of infrastructure investment; for example, a shift away from federal funding and a general movement
to local funding sources such as tolls or parking fees.
When available, the authors will utilize new data collected on post-COVID-19 consumption from outside
sources and compare these sources with BLS CE data to examine how household consumption may have
shifted during this period (2010–20). The authors also plan to project what may happen in transportation
consumption over the next five years (2021–25).

Workshop presenters
Staff of the CE program
Choi, Jimmy. Economist, Branch of Information and Analysis, BIA: practical training leader, day one;
presenter, day two.
Cobet, Aaron. Senior Economist, BIA: practical training leader, day one; presenter, day two.
Creech, Brett. Supervisory Economist, Chief, Publications and Tables Production Section, BIA:
presenter, day three.
Curtin, Scott. Supervisory Economist, Chief, BIA: emcee, days one, two, and three; practical training
leader, day two.
Paulin, Geoffrey. Senior Economist, BIA: introducer of speakers, commentator, days one, two, and
three; practical training leader, day two; presenter, days two and three.
Rigg, Bryan. Economist, BIA: presenter, day one.
Safir, Adam. Chief, Division of Consumer Expenditure Surveys: presenter, day one.
Other BLS speakers
Biagas, David. Research Psychologist, Office of Survey Methods Research: feedback coordinator,
day three.
Non-BLS speakers

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Burgi, Dr. Constantin. Assistant Professor of Economics, St. Mary’s College of Maryland, “Predicting
consumer expenditure based on the variables available in the Consumer Expectation Survey of the
NY Fed” (Interview Survey); day three. First-time attendee and presenter (2020).
Goldvale, Casey. Policy Analyst, Georgetown Center on Poverty & Inequality, “Costs Beyond Tuition:
Estimating older college students” basic needs with Consumer Expenditure Survey
(PUMD)” (Interview Survey); day one. Former attendee (2019) and first-time presenter (2020).
King, Dr. David (Ph.D.). Assistant Professor of Urban Planning, Arizona State University (Tempe),
“Household Transportation Spending Trends from 2010 to 2020 - Early Indications of the impact of
cultural shifts and pandemic related household activity on transportation spending
patterns” (Interview Survey); day three. First-time attendee and presenter (2020).
Noël, Reginald. Research Economist/Data Scientist, Noël Collective, “Gender Economics, Race,
and Intersectionality: Using CE Microdata to examine inequalities among adult women and men in
the U.S. by race and ethnicity, 2016 through 2018 combined” (Interview Survey); day two. First-time
attendee and presenter (2020).
Noël, Whitney Hewitt. Public Health Researcher/Health Equity Advocate, copresenter with Reginald
Noël; day two. First-time attendee and presenter (2020).
Palacios, Vincent. Senior Policy Analyst, Georgetown Center on Poverty & Inequality, copresenter
with Casey Goldvale; day one. Former attendee (2019) and first-time presenter (2020).
Peters, Dr. Jonathan (Ph.D.). Professor of Finance and Data Analytics, The City University of New
York, copresenter with David King; day three. Prior presenter (2014, and 2017 through 2019);
returning presenter (2020).
Shojaeddini, Dr. Ensieh (Ph.D.). Oak Ridge Institute for Science and Education fellow at U.S.
Environmental Protection Agency, “Consumer Demand Estimation for Heterogeneous U.S.
Households” (Interview Survey); day three. Former attendee (2019) and first-time presenter (2020).
Tian, Ziyao. Ph.D. Candidate (Sociology), Princeton University, “How Expensive Is the Battle of Tiger
Mothers? Understanding Race and Class behind the Educational Expenditure of Asian
Americans” (Interview Survey); day two. First-time attendee and presenter (2020).
Williams, Aaron R. Data Scientist, Income and Benefits Policy Center (Urban Institute), “Lifetime
Income & Costs of the LI50+” (Interview Survey); day one. First-time attendee and presenter (2020).

SUGGESTED CITATION

Geoffrey D. Paulin and Parvati Krishnamurty, "Consumer Expenditure Survey Methods Symposium and Microdata
Users’ Workshop, July 21–24, 2020," Monthly Labor Review, U.S. Bureau of Labor Statistics, June 2021, https://
doi.org/10.21916/mlr.2021.14.

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NOTES
1 Although a household refers to all people who live together in the same living quarters, “consumer unit” refers to the people living
therein who are a family, or others who share in specific financial arrangements. For example, two roommates living in an apartment
constitute one household. However, if they are financially independent, they each constitute separate consumer units within the
household. Similarly, although families are related by blood, marriage, or legal arrangement, unmarried partners who live together and
pool income to make joint expenditure decisions constitute one consumer unit within the household. For a complete definition, see the
CE glossary at https://www.bls.gov/cex/csxgloss.htm. For more information on households and families, see https://www.census.gov/
glossary/#term_Household and https://www.census.gov/glossary/#term_Familyhousehold.
2 The Quarterly Interview Survey is designed to collect data on expenditures for big-ticket items (e.g., major appliances or
automobiles) and recurring items (e.g., payments for rent, mortgage, or insurance). In the Interview Survey, participants are visited
once every 3 months for four consecutive quarters. In the Diary Survey, on the other hand, participants record expenditures daily for 2
consecutive weeks. This survey is designed to collect expenditures for small-ticket and frequently purchased items, such as detailed
types of food (e.g., white bread, ground beef, butter, or lettuce). The CE microdata for both surveys may be downloaded from the CE
website at https://www.bls.gov/cex/pumd_data.htm.
Data from the Diary and Interview Surveys are published twice a year in various standard tables. One set describes expenditures that
occurred within the calendar year of interest (e.g., January through December 2018 for the most recent set available as of the writing
of this report). The other set provides a midyear update to expenditures, ranging from July of the earlier year to June of the later year
(e.g., July 2017 through June 2018 for the most recent set available as of the writing of this report). The single-year series is available
from 1984 onward. The midyear updates are available from July 2011 to June 2012 onward. Each set includes information on
expenditures by age of reference person, composition of consumer unit, income of consumer unit, and other demographics. For a
complete list, see https://www.bls.gov/cex/tables.htm.
3 Token incentives are being tested in the LSF prior to potential implementation in the CE.
4 Since the symposium, analysis of LSF data from October through February indicates that postcards have no impact on response
rates.
5 Attendees were able to sign up for a meeting by checking a box on their registration forms. They could also sign up via e-mail
throughout the virtual workshop, replacing the option to do so at the registration desk for previous in-person workshops previously.
However, the main benefit—both to attendees and CE staff members—of advance registration was to allow the meetings coordinator
time to find the most appropriate expert, and time for the expert to investigate the question or prepare other information (handouts,
etc.) before the meeting to optimize the quality of the session.
6 The projects in this series built on each other, progressing from basic computation to more complicated use of the data, which
involved finding and merging results from the FMLI, MEMI, and MTBI files. The FMLI files include general characteristics of the
consumer unit (e.g., region of residence, number of members, etc.) and summary variables (e.g., total educational expenditures). The
MEMI files contain information on each individual member of the consumer unit (e.g., each member’s age, race, educational
attainment, etc.). The MTBI files include expenditures for specific educational expenses (e.g., expenditures on “College tuition,”
“Elementary and high school tuition,” “Test preparation, tutoring services,” “School books, supplies, equipment for vocational and
technical schools,” etc.).
7 In an email exchange with the author of this workshop report, Dr. Cosic states, “…my attendance of the 2019 workshop was
instrumental in our successful completion of the project that Aaron presented. I think this is an excellent example of the success of
your workshop.” (email from Damir Cosic to Geoffrey Paulin, August 2, 2020).
8 Specifically, attendees learned how to access the EDA files to ascertain for what type of school or facility (college or university,
elementary through high school, child daycare center, etc.) certain education expenditures were incurred, and whether the
expenditures were for a member of the consumer unit or a gift to someone outside of it.

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9 From 2012 until 2019, the first day of the workshop ended with a networking opportunity, where attendees could meet each other
and informally discuss questions with CE staff. (Prior to 2012, this event was held on the second day of the workshop, to maximize
overlap in attendance between newer and more experienced users.) However, with the delivery of the workshop online, this activity
was unfeasible, due to limitations of the software approved for delivering the workshop.
10 For example, suppose the threshold for a particular income or expenditure is $100. On two records, the reported values exceed
this: $200 on record A and $600 on record B. In this case, the value is topcoded to $400 (the average of $200 and $600) and the
reported amounts are replaced with $400. An additional variable, called a “flag,” is coded to notify the data user that the $400 values
are the result of topcoding, not actual reported values.
11 For details on topcoding and suppression, including specific variables affected and their critical values, see https://www.bls.gov/
cex/pumd_disclosure.htm#Basic.
12 For example, suppose a respondent reports values for two sources of income: (1) wages and salaries and (2) pensions. Further
suppose the following: The reported value for wages and salaries exceeds the critical value, and is therefore replaced by the
topcoded value of $X; the reported value for pension income, $Y, is below the critical value for this income source; and the value for
total income is shown to be $X + $Y + $Z. Because this respondent only has two sources of income reported and pension income is
not topcoded, the reported value for wages and salaries is $X + $Z. To prevent this, total income must be computed after each
individual component has been topcoded as needed. Therefore, in this example, total income is $X + $Y and the actual reported
value of wages and salaries cannot be “reverse engineered.”
13 Weights for the first three states (New Jersey, California, and Florida) are available for 2016 onward; for the latter two (New York
and Texas), they are available for 2017 onward.
14 Traditionally, preceding this talk, a member of the Statistical Methods Division delivers a detailed explanation of the computation of
the weights. However, as noted earlier, the workshop planners cut several detailed presentations due to the uncertainties of the firstever online workshop.
15 For example, suppose the sample consists of two consumer units, one of which represents 10,000 consumer units in the
population (i.e., itself and 9,999 others like it) and another that represents 20,000 consumer units in the population. If the first spent
$150 and the second spent nothing (i.e., $0), the sample mean expenditure is $75. However, the population-weighted mean is $50, or
[($150 x 10,000)+($0 x 20,000)]/(10,000 + 20,000).
16 That is, in addition to asking the respondent the race of each member of the consumer unit, if Asian, the Interview and Diary
Surveys both ask about geographic origin: Chinese, Filipino, Japanese, Korean, Vietnamese, Asian Indian, or other Asian (listed in
the order of appearance in the questionnaire). If the respondent reports that a member is Hispanic, the interviewer asks whether the
member is Mexican, Mexican American, Chicano, Puerto Rican, Cuban, or other Spanish (again, listed in the order of appearance in
the questionnaire).
17 Even if a question were asked about “decision making,” it is not clear that the answer would be meaningful in a “real world”
context. For example, in married couples, it is likely that at least some decisions are made jointly, and in those that are not, it is not
clear who makes the decisions. For example, if only one spouse purchases the groceries, which spouse is it? Furthermore, that
spouse will almost certainly take into account preferences of the other spouse. If the purchasing pattern therefore reflects the tastes
(literally) of both spouses in food consumption, and these tastes are influenced by the different racial or ethnic backgrounds of each
spouse, then the relationship of expenditure to race or ethnicity is diluted within such families. Therefore, comparing consumer units in
which all members share the same race and ethnicity makes the comparisons across racial and ethnic groups much clearer.
18 Available at https://www.bls.gov/cex/pumd_data.htm.

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19 Prior to February 2020, free PUMD were available from 1996 onward. The release of data from 1980 to 1995 allows users to
obtain these data for all years in which CE data were collected on a continual basis. Prior to 1980, they were collected approximately
every 10 years (1972–73, 1960–61, etc.).
20 While a table showing expenditures for the “post-Millennial” generation for July 2018 through June 2019 is available, the 2019
table will be the first standard (i.e., calendar year) table published to feature expenditures for this group.
21 This question is predated by similar questions added regarding earlier stimulus payments. The first was added in response to
payments made in 2001; the second was added in response to payments made in 2008. (See https://www.bls.gov/cex/anthology11/
csxanth5.pdf.) In addition, CE collected information on the special $250 payment made in 2009 to most Social Security recipients and
other eligible persons. (See https://www.bls.gov/opub/btn/archive/how-consumers-used-the-2009-economic-recovery-paymentsof-250.pdf.)
22 See “Expenditures of college-age students and nonstudents,” Monthly Labor Review, July 2001, pp. 46–50, https://www.bls.gov/
opub/mlr/2001/07/ressum1.pdf.

RELATED CONTENT

Related Articles
Consumer Expenditure Survey Methods Symposium and Microdata Users’ Workshop, July 16–19, 2019, Monthly Labor Review, April
2020.
Consumer Expenditure Survey Methods Symposium and Microdata Users’ Workshop, July 17–20, 2018, Monthly Labor Review, May
2019.
Consumer Expenditure Surveys Methods Symposium and Microdata Users’ Workshop, July 18–21, 2017, Monthly Labor Review,
June 2018.

Related Subjects
Survey methods

Expenditures

Sampling

Consumer expenditures

14

Survey procedures