Full text of Monthly Labor Review : June 2021
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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 19 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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). 20 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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.) 21 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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.) 22 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 23 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 1 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 MONTHLY LABOR REVIEW 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 MONTHLY LABOR REVIEW 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 MONTHLY LABOR REVIEW 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 MONTHLY LABOR REVIEW 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 MONTHLY LABOR REVIEW 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 MONTHLY LABOR REVIEW 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 MONTHLY LABOR REVIEW 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 6.9 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 MONTHLY LABOR REVIEW 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 MONTHLY LABOR REVIEW 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 MONTHLY LABOR REVIEW 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 MONTHLY LABOR REVIEW 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. 18 U.S. BUREAU OF LABOR STATISTICS 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.) 19 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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.) 22 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 MONTHLY LABOR REVIEW 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 28 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 29 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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.) 30 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 31 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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.) 32 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 MONTHLY LABOR REVIEW 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 MONTHLY LABOR REVIEW 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 35 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 MONTHLY LABOR REVIEW 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 MONTHLY LABOR REVIEW 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.) 38 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 39 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 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. 41 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 42 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 43 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. RELATED CONTENT Related Articles 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 Review, April 2021 Employment projections in a pandemic environment, Monthly Labor Review, February 2021 Employment recovery in the wake of the COVID-19 pandemic, Monthly Labor Review, December 2020 Job market remains tight in 2019, as the unemployment rate falls to its lowest level since 1969, Monthly Labor Review, April 2020 Related Subjects Race and ethnicity Employment Unemployment Current population survey COVID-19 Recession Self-employment Labor force Foreign born Expansions Long-term unemployed Earnings and wages Women 45 Men Asian Hispanic 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 MONTHLY LABOR REVIEW 2 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 3 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 4 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 5 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 6 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 7 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 8 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 9 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 10 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 11 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 12 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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.) 13 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 14 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 15 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 16 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 17 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 18 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 19 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 20 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 21 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 22 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 23 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 24 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 25 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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, 26 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 27 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 28 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 29 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 30 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 31 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 32 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 33 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 34 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 35 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 36 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 37 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 38 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 39 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 40 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 41 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 42 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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/. 43 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 44 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 45 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 46 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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: 47 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 48 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. RELATED CONTENT Related Articles Employment projections in a pandemic environment, Monthly Labor Review, February 2021. Employment recovery in the wake of the COVID-19 pandemic, Monthly Labor Review, December 2020. Employment expansion continued in 2019, but growth slowed in several industries, Monthly Labor Review, April 2020. Employment growth accelerates in 2018, extending a lengthy expansion, Monthly Labor Review, May 2019. Related Subjects Labor dynamics safety and health Hours of work Labor market Earnings and wages COVID-19 Job creation Women 49 Recession Employment Industry studies Social issues Worker 6/29/2021 The commercialization of academic discovery: a look at startup formation : Monthly Labor Review: U.S. Bureau of Labor Statistics U.S. BUREAU OF LABOR STATISTICS Bureau of Labor Statistics Publications Monthly Labor Review HOME ARCHIVES FOR AUTHORS ABOUT SUBSCRIBE Search MLR GO BEYOND BLS JU N E 2021 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. Download PDF » U.S. BUREAU OF LABOR STATISTICS Division of Information and Marketing Services PSB Suite 2850 2 Massachusetts Avenue NE Washington, DC 20212-0001 Telephone:1-202-691-5200 Federal Relay Service:1-800-877-8340 www.bls.gov/OPUB Contact Us https://www.bls.gov/opub/mlr/2021/beyond-bls/the-commercialization-of-academic-discovery-a-look-at-startup-formation.htm 1/1 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, 1 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 2 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 3 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 1 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 2 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 3 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 4 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 5 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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- 6 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 7 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 8 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 9 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 10 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 11 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 12 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 13 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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