Full text of Monthly Labor Review : June 2020
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June 2020 Demographics, earnings, and family characteristics of workers in sectors initially affected by COVID-19 shutdowns In the initial weeks of the coronavirus disease 2019 (COVID-19) pandemic, employment in several industries was especially vulnerable because of shutdown policies imposed by states, as well as a drop in demand as people engaged in social distancing. This article looks at the demographic characteristics of workers in the initially highly exposed industries, as well as the characteristics and earnings of families with workers in these industries. The article also uses recent Current Population Survey data to look at how various demographic groups have fared in the early weeks of the COVID-19 pandemic between February and April. In the initial weeks of the COVID-19 pandemic, employment in several industries was especially vulnerable because of shutdown policies imposed by states, as well as a drop in demand as people engaged in social distancing. As a general rule, these were industries that were not deemed essential and that provide goods and services requiring considerable interaction between workers and customers. A recent article by Matthew Dey and Mark A. Loewenstein, Matthew Dey dey.matthew@bls.gov Matthew Dey is a research economist in the Office of Employment and Unemployment Statistics, U.S. Bureau of Labor Statistics. Mark A. Loewenstein loewenstein.mark@bls.gov published in the April 2020 Monthly Labor Review,[1] using a taxonomy developed by Joseph Vavra to identify vulnerable industries, provides estimates of the number of jobs and the wages paid in these vulnerable industries of Mark A. Loewenstein is a senior research economist in the Office of Employment and Unemployment Statistics, U.S. Bureau of Labor Statistics. the economy.[2] A key finding of that article is that, in 2019, about 20 percent of all employees worked in these highly exposed industries. Furthermore, occupations with lower wages are more common in the highly exposed sector than elsewhere in the economy. (Throughout this article, we 1 David S. Piccone Jr piccone.david@bls.gov David S. Piccone Jr is a statistician in the Office of Employment and Unemployment Statistics, U.S. Bureau of Labor Statistics. U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW characterize the economy as being made up of two sectors, the highly exposed sector and the not highly exposed sector.) The effects of the pandemic have now become widespread, and employment losses have not been confined to Anne E. Polivka polivka.anne@bls.gov Anne E. Polivka is a supervisory research economist in the Office of Employment and Unemployment Statistics, U.S. Bureau of Labor Statistics. businesses in industries that were forced to shut down. Furthermore, some localities and states have now begun to lift stay-at-home orders and businesses in vulnerable industries have begun to reopen. However, a number of unknowns exist: Will customers return when industries that have been shut down are reopened? Will workers return? Will further breakouts occur that result in industries again being shut down? In the state of these uncertainties, industries initially identified as vulnerable may continue to face difficulties. This article expands on the earlier analysis by looking at the demographic characteristics of workers in the highly exposed industries. We also look at family earnings and other family characteristics. In the final section of this article, we briefly examine Current Population Survey (CPS) April 2020 estimates to gauge how employment in the highly exposed sector and elsewhere was affected at the start of the pandemic. The estimates indicate that, between February and April, employment losses have been especially severe in the highly exposed industries. Note: Analysis on more recent CPS estimates is available at https://www.bls.gov/ers/update-ondemographics-earnings-and-family-characteristics-of-workers-in-sectors-initially-affected-by-covid-19shutdowns.htm. Data The Dey and Loewenstein’s April 2020 article uses establishment data from the U.S. Bureau of Labor Statistics (BLS) Quarterly Census of Employment and Wages and Occupational Employment Statistics (OES) programs. In this current article, we use household data from the CPS. Conducted by the U.S. Census Bureau for the BLS, the CPS is a monthly survey of approximately 60,000 households. The CPS provides a comprehensive body of data on the labor force status of individuals (employed, unemployed, or not in the labor force), hours of work, and other demographic and labor force characteristics. In addition, for one-fourth of the monthly sample, information about the earnings of wage and salary workers is collected.[3] The estimates in this article are obtained by averaging the data across months in 2019, with use of only a quarter of the sample for which earnings information was collected. The current article uses the same industry classification scheme as that used in the initial article.[4] (The list of census industries used for this analysis can be found in the appendix, table A-1.) Industries in the highly exposed sector include “Restaurants and Bars, Travel and Transportation, Entertainment (e.g., casinos and amusement parks), Personal Services (e.g., dentists, daycare providers, barbers), other sensitive Retail (e.g., department stores and car dealers), and sensitive Manufacturing (e.g., aircraft and car manufacturing).”[5] Using CPS data, we 2 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW find 27.5 million workers, or 19.4 percent of U.S. employment in 2019 (similar to the OES estimates), were in these highly exposed industries. Demographic characteristics and wages of workers in the highly exposed sector Employment estimates for various demographic groups are presented in table 1 and in tables A-2 and A-3 of the appendix. The figures and the ensuing discussion in this section are based on the estimates in these tables. Table 1. 2019 annual Current Population Survey worker counts and wage estimates for key demographics of all workers Figure 1 depicts the fraction of workers by race, gender, and Hispanic ethnicity in the highly exposed industries and in the rest of the economy. One sees that the racial composition of the two sectors is quite similar, with a slight overrepresentation of minorities in the highly exposed sector. One also sees that the gender composition between the two sectors is similar, with a slight overrepresentation of women. (However, tables A-2 and A-3 show a gender imbalance among younger workers. In the highly exposed sector, 40 percent of those ages 16 to 24 are women and only 35 percent are men.) Other demographic differences are more pronounced. As figure 1 shows, Hispanics are overrepresented in the highly exposed sector. Twenty-three percent of Hispanic workers are employed in the highly exposed sector. The corresponding estimate for non-Hispanics is 18.6 percent. 3 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Age, marital status, and education attainment differences of workers between the two sectors are even starker. Figure 2 shows the age composition of workers in the highly exposed industries and the rest of the economy. Note that workers under the age of 25 make up 25.9 percent of employment in the highly exposed industries and only 10.3 percent of employment in the remaining industries. This, in turn, implies that 37.9 percent of workers under age 25 are in the highly exposed sector even though this sector accounts for a little less than 20 percent of overall employment. Marital status estimates for workers age 25 and older appear in figure 3. A disproportionate share of workers in the highly exposed sector is never married. Workers age 25 and older make up 20.7 percent of never-married workers employed in the highly exposed sector, while the estimate for married workers is 14.6 percent.[6] 4 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW The highly exposed sector is also disproportionately composed of workers with lower educational attainment. As shown in figure 4, at lower levels of education, the share of workers age 25 and older in the highly exposed sector exceeds the share in the other sector, whereas the pattern is reversed at higher levels of education. Thus, as can be seen from table A-3, the higher the level of education, the lower the share of workers in the highly exposed sector. This share is 24.3 percent for workers age 25 and older without a high school degree and 22.4 percent for workers age 25 and older with just a high school degree. The share drops to 19.1 percent for workers with some college or an associate’s degree, 12.7 percent for workers with a bachelor’s degree, and 6.2 percent for workers with an advanced degree. 5 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW As illustrated in figures 5 and 6, the highly exposed industries have more hourly workers and part-time workers. While 71.7 percent of workers are paid hourly in the highly exposed industries, 54.9 percent of workers are paid hourly in the remaining industries. Approximately one-third of workers in the highly exposed sector usually worked part-time hours—less than 35 hours per week. In the rest of the economy, this estimate is 18.5 percent. 6 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 7 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Wages are considerably lower in the highly exposed sector than elsewhere.[7] As noted earlier, the highly exposed sector has more part-time workers than the other sector, and as can be seen in table 1, part-time workers earn less than full-time workers do.[8] In addition, as shown in figure 7, the wages of part-time and full-time workers in the highly exposed sector are both lower than the wages of workers with similar working arrangements elsewhere. The median hourly wage of part-time workers in the highly exposed sector is $11.80, compared with $15.00 elsewhere. The median wage of full-time workers is $17.00 in the highly exposed sector, compared with $23.00 for the other sector. 8 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW The lower wages in the highly exposed sector translate into lower earnings. Median usual weekly earnings of parttime workers in the highly exposed sector are $260.00, compared with $386.00 elsewhere. Median usual weekly earnings of full-time workers are $700.00 in the highly exposed sector and $961.53 in the rest of the economy. Of course, averaged over all workers, earnings in the highly exposed sector are also lower because of the much higher proportion of part-time workers. Family characteristics of workers in the highly exposed sector From table 1, one sees that approximately three-fourths of workers in the highly exposed sector live with other family members.[9] (The proportion is the same for workers in the other sector.) Within these families, workers both contribute to overall family earnings and are able to receive support from other family members who also work. Looking at the composition of these families and the proportions of family earnings that come from the highly exposed sector yields insights into how vulnerable workers may be to possible shutdowns in response to the COVID-19 pandemic. Workers in the highly exposed sector disproportionately come from single-parent families. From table 1, one sees that approximately 26.0 percent of workers from single-parent families are employed in the highly exposed sector. In contrast, about 18.0 percent of workers from married families with children, 19.0 percent of workers in families with no children, and 20.0 percent of workers living alone or with nonrelatives are employed in the highly exposed sector. Figure 8 shows the percentage of workers in the highly exposed and not highly exposed sectors who are living alone or with nonrelatives and the type of family they live in if they are in a family. Note that approximately 25.0 percent of workers in the highly exposed sector do not live in a family and another 11.4 percent live in a 9 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW single-parent family. Taken together, these percentages suggest that if workers were unable to work in the highly exposed sector, as many as 36.0 percent of them would be unable to draw on earnings from other family members in the household. For workers who do live in families, the highly exposed sector disproportionately employs workers from families with low earnings.[10] Figure 9 shows in each family earnings quintile the proportion of workers who are employed in the highly exposed and not highly exposed sectors. From the numbers in table 1, one sees that 27.6 percent of workers whose family earnings (not adjusted for the size of a person’s family) are in the bottom quintile are employed in the highly exposed sector. For workers whose family earnings are in the second quintile, this estimate is 22.2 percent, and it is 19.9 percent for workers whose family income is in the third quintile. The percentage falls to 17.1 percent and 14.7 percent for workers whose family earnings are in the fourth and top quintiles. The finding that the highly exposed sector disproportionately employs workers from families with low earnings further illustrates that even workers in the highly exposed sector who live in families may only be able to obtain minimal financial support from other family members should they lose their jobs. 10 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Characteristics of families with workers in the highly exposed sector The analysis in the previous section has been conducted from the perspective of the worker and of how much support workers who lose their jobs may expect to receive from other family members in their household. A related question concerns the financial support that workers employed in the highly exposed sector provide to their families. For families with at least one employed family member, the estimates in table 2 show that a little more than 26 percent have at least one worker who is employed in the highly exposed sector and about half of these families have children. For one to assess the vulnerability of these families for each quintile of the family earnings distribution, table 3 shows the percentage of family earnings that stem from employment in the highly exposed sector. Table 2. 2019 annual Current Population Survey family estimates, by percent of family earnings from workers in highly exposed sectors and type of family Table 3. 2019 annual Current Population Survey family counts, by percent of family earnings from workers in highly exposed sectors and family earnings quintiles Overall, the estimates in table 3 indicate that 10.9 percent of families receive 100.0 percent of their earnings from workers in the highly exposed sector. The estimates also suggest that families with the lowest earnings depend heavily on employment in the highly exposed sector. To illustrate, figure 10 shows the percentage of families in each earnings quintile that receive all of their earnings from the highly exposed sector. Almost 46.0 percent of families in the bottom quintile receive all of their earnings from the highly exposed sector. For families in the 11 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW second quintile, this percentage is 24.2 percent. The percentage of families in the middle quintile is 14.7 percent. This percentage drops to 8.8 percent and 6.5 percent for families in the fourth and top quintiles, respectively. Families with children are similarly vulnerable as families overall. Of families with children, 11.4 percent had 100.0 percent of their earnings coming from workers in the highly exposed sector.[11] In many instances, children in families in which 100.0 percent of the earnings are from the highly exposed sector live in single-parent households. For each family type (married families with children, single-parent families with children, and families with no children), table 2 shows the percentage of family earnings that stem from employment in the highly exposed sector. The data in the table show that single-parent families are especially vulnerable to shutdowns in the highly exposed sector. Of these families, 19.0 percent obtain 100.0 percent of their family’s earnings from workers in the highly exposed sector. In contrast, 8.3 percent of married families with children and 10.5 percent of married families with no children receive all of their earnings from the highly exposed sector.[12] Figure 11 shows the breakdown of full-exposure cases by family type—47.5 percent are families with no children, 25.3 percent are single-parent families, and 27.3 percent are married families with children. 12 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Early effects of the pandemic The recently released CPS April 2020 estimates, which are summarized in table 4, confirm the vulnerability of workers in the highly exposed sector.[13] In April, the economic shocks due to the pandemic were clearly not confined to the highly exposed sector but were felt throughout the entire economy. Employment disruptions were widespread throughout the entire labor market. Between February and April, overall employment (not seasonally adjusted) fell by 15.6 percent. However, the reduction in employment was especially severe in what we have identified as the initially highly exposed sector. The CPS estimates indicate that, between February and April, employment in the highly exposed sector fell by 38.2 percent, compared with 10.5 percent elsewhere. The same is true of the unemployment rate. Overall, the unemployment rate (not seasonally adjusted) increased from 3.8 percent to 14.4 percent between February and April. However, the unemployment rate in the highly exposed sector increased from 4.5 percent to 34.1 percent. Elsewhere, the unemployment rate increased from 3.6 percent to 10.3 percent. Table 4. February 2020 to April 2020 CPS worker counts for key demographics for all workers, highly exposed workers and not highly exposed workers The employment disruptions during the first few months of the pandemic have been spread unevenly among the demographic groups. A thorough analysis of how the pandemic affected all the various demographic groups is 13 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW beyond the scope of this article. Here, we simply highlight how several groups have fared. To this point in time, women have been more affected than men have. Throughout the economy as a whole, female employment has fallen by 17.9 percent, whereas male employment has dropped by 13.5 percent (see table 4). Female employment in the highly exposed sector fell by a whopping 43.3 percent. Hispanics also suffered severe losses in employment. Hispanic employment fell by 20.1 percent overall and by 42.2 percent in the highly exposed sector. Young workers are another group that experienced a large fall in employment. Employment of workers ages 16–24 fell by 31.3 percent overall and by 48.1 percent in the more highly exposed sector. Less educated workers are another group suffering a larger-than-average fall in employment. Employment of individuals 25 and older with less than a high school diploma fell by 20.7 percent and those with a high school diploma, but no college, declined by 20.8 percent overall and by 35.1 percent and 40.4 percent, respectively, in the highly exposed sector. Finally, looking at the experience of the various family types, one sees from table 4 that employment of workers in single-parent families fell by 24.3 percent in the economy as a whole. In the highly exposed sector, employment declined by 47.4 percent. Employment of individuals who are not living with a family member fell by 19.3 percent in the economy as a whole and by 42.8 percent in the highly exposed sector. Conclusion To combat the COVID-19 pandemic, industries that are most prone to being shut down are disproportionately composed of workers who are younger, are unmarried, and have less education. Workers in the highly exposed sector are more likely to be in part-time jobs and generally have lower wages and total earnings than do workers in other parts of the economy. Workers in the highly exposed sector disproportionately belong to single-parent families or do not live in a family. When workers in the highly exposed sector do live with other family members, family earnings are often toward the bottom of the earnings distribution. Families with workers in the highly exposed sector are particularly vulnerable to industry shutdowns. A substantial percentage of families receive all of their earnings from family members working in the highly exposed sector. This percentage is particularly high for families whose earnings are at the bottom end of the earnings distribution. Almost 46 percent of families in the bottom quintile of the earnings distribution receive all of their earnings from the highly exposed sector. For families in the second quintile, this number is 24.2 percent. Families with children are similarly vulnerable to shutdowns as are families overall, but single-parent families with children are especially vulnerable—about 19 percent of single-parent families obtain 100 percent of their family’s earnings from workers in the highly exposed sector. The recently released CPS April 2020 estimates confirm the vulnerability of workers in the highly exposed sector. Employment losses were widespread but were especially severe in the highly exposed sector. Furthermore, the reductions in employment were spread unevenly among various demographic groups. In some demographic groups, employment decreased substantially overall and especially sharply in the more highly exposed sector. Particularly hard-hit groups include, Hispanics, younger workers, and workers with less education level. Workers who are not family members and workers in single-parent families also experienced a large fall in employment and an increase in unemployment. 14 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Appendix: Lists of highly exposed industries and Current Population Survey employment and wage estimates by demographic categories and sector Table A-1. List of highly exposed census industries Table A-2. 2019 annual Current Population Survey worker counts and wage estimates for key demographics of workers 16 to 24 years old Table A-3. 2019 annual Current Population Survey worker counts and wage estimates for key demographics of workers 25 years old and older Tables Table 1. 2019 annual Current Population Survey worker counts and wage estimates for key demographics of all workers Workers in highly exposed sectors Workers not in highly exposed sectors Percent of category in Demographic Category Number of workers None Gender Race Age Hispanic ethnicity Marital status Educational attainment All workers Men Women White only Black only Asian only All other 16 to 24 years old 25 to 54 years old 55 to 64 years old 65+ years old Hispanic NonHispanic Married Never married Other marital status Less than a high school diploma Median Median Percent hourly weekly wage earnings Number of workers Median Median Percent hourly weekly wage earnings highly exposed sectors 27,512,307 14,108,189 13,404,118 20,672,218 3,600,997 1,980,547 1,258,546 100.0 51.3 48.7 75.1 13.1 7.2 4.6 $15.00 16.17 13.50 15.00 13.86 15.38 14.00 $560.00 650.00 480.00 570.00 500.00 600.00 500.00 114,039,962 59,172,580 54,867,382 88,317,777 14,605,046 7,300,556 3,816,583 100.0 51.9 48.1 77.4 12.8 6.4 3.3 $21.50 23.56 19.65 22.00 17.67 28.85 18.75 $865.38 1,000.00 769.00 900.00 711.53 1,154.00 738.46 19.4 19.3 19.6 19.0 19.8 21.3 24.8 7,134,436 25.9 11.35 315.00 11,711,911 10.3 13.50 480.00 37.9 15,564,728 56.6 16.88 668.00 76,144,803 66.8 23.00 953.84 17.0 3,475,087 12.6 18.00 709.00 19,690,633 17.3 24.00 961.53 15.0 1,338,056 4.9 15.00 480.00 6,492,615 5.7 20.00 720.00 17.1 5,861,321 21.3 13.70 500.00 19,533,372 17.1 17.00 680.00 23.1 21,650,986 78.7 15.00 576.92 94,506,590 82.9 22.59 923.07 18.6 10,910,070 39.7 18.00 720.00 62,882,158 55.1 24.59 1,000.00 14.8 13,184,014 47.9 13.00 440.00 35,274,848 30.9 17.31 680.00 27.2 3,418,224 12.4 16.00 615.38 15,882,956 13.9 20.48 840.00 17.7 3,741,271 13.6 11.00 326.92 7,603,357 6.7 13.50 520.00 33.0 See footnotes at end of table. 15 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 1. 2019 annual Current Population Survey worker counts and wage estimates for key demographics of all workers Workers in highly exposed sectors Workers not in highly exposed sectors Percent of category in Demographic Category Number of workers Hourly worker status Full- or parttime status Family status Family earnings quintile High school diploma, no college Some college or associate’s degree Bachelor’s degree only Advanced degree Nonhourly worker Hourly worker Worked fulltime hours Worked part-time hours Not living with a family member Family member Not living with a family member Lowest quintile: less than 34,321 Second quintile: 34,321 to 59,539 Middle quintile: 59,540 to 89,959 Forth quintile: 89,960 to 137,019 Top quintile: more than 137,020 Median Median Percent hourly weekly wage earnings Number of workers Median Median Percent hourly weekly wage earnings highly exposed sectors 9,192,224 33.4 14.06 534.00 27,707,986 24.3 17.00 680.00 24.9 8,854,747 32.2 15.00 540.00 30,399,521 26.7 18.75 750.00 22.6 4,498,094 16.3 22.01 923.00 29,934,233 26.2 28.27 1,153.00 13.1 1,225,971 4.5 33.65 1,346.15 18,394,866 16.1 36.05 1,461.53 6.2 7,795,148 28.3 24.04 1,000.00 51,448,807 45.1 29.91 1,250.00 13.2 19,717,159 71.7 13.25 480.00 62,591,155 54.9 17.00 664.61 24.0 18,619,643 67.7 17.00 700.00 92,903,573 81.5 23.00 961.53 16.7 8,892,664 32.3 11.80 260.00 21,136,389 18.5 15.00 387.00 29.6 6,789,356 24.7 15.63 600.00 27,349,806 24.0 21.15 865.38 19.9 20,722,951 75.3 15.00 540.00 86,690,156 76.0 21.63 865.38 19.3 6,789,356 24.7 15.63 600.00 27,349,806 24.0 21.15 865.38 19.9 3,931,100 14.3 11.50 350.00 10,328,297 9.1 12.50 420.00 27.6 3,967,332 14.4 13.83 500.00 13,882,507 12.2 17.78 715.00 22.2 4,464,770 16.2 15.00 600.00 17,971,413 15.8 19.75 800.00 19.9 4,308,153 15.7 17.81 702.69 20,940,813 18.4 25.00 1,045.00 17.1 4,051,597 14.7 21.67 920.00 23,567,126 20.7 38.46 1,682.69 14.7 See footnotes at end of table. 16 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 1. 2019 annual Current Population Survey worker counts and wage estimates for key demographics of all workers Workers in highly exposed sectors Workers not in highly exposed sectors Percent of category in Demographic Category Number of workers Family type Not living with a family member No children family Singleparent family Married with children family Median Median Percent hourly weekly wage earnings Number of workers Median Median Percent hourly weekly wage highly exposed earnings sectors 6,789,356 24.7 15.63 600.00 27,349,806 24.0 21.15 865.38 19.9 10,392,597 37.8 15.00 560.00 44,109,378 38.7 21.00 850.00 19.1 3,122,716 11.4 12.50 442.30 8,978,055 7.9 17.00 670.00 25.8 7,207,638 26.2 15.00 560.00 33,602,724 29.5 24.00 976.00 17.7 Source: Authors’ calculations based on U.S. Bureau of Labor Statistics Current Population Survey 2019 annual data. Table 2. 2019 annual Current Population Survey family estimates, by percent of family earnings from workers in highly exposed sectors and type of family Percent of No children family Single-parent family Married with children family Overall Number of Column Row Number of Column Row family earnings from workers in highly Number of Column Row Number of Column Row families percent percent families percent percent families percent percent families percent percent exposed sectors 0 >0 and <25 >25 and <50 >50 and <75 >75 and <100 100 Total 23,118,971 1,443,308 1,872,857 1,348,542 73.6 4.6 6.0 4.3 345,547 1.1 3,286,454 31,415,679 10.5 100.0 49.6 6,579,595 46.9 328,655 53.6 335,906 54.6 164,603 48.4 71.4 3.6 3.6 1.8 53,864 0.6 47.5 1,750,179 49.6 9,212,801 19.0 100.0 14.1 16,907,323 10.7 1,302,945 9.6 1,282,344 6.7 957,731 7.5 74.6 5.8 5.7 4.2 314,841 1.4 25.3 1,888,419 14.6 22,653,604 8.3 100.0 Note: Children are under 18 years old. Source: Authors’ calculations based on U.S. Bureau of Labor Statistics Current Population Survey 2019 annual data. 17 36.3 46,605,889 42.4 3,074,908 36.7 3,491,107 38.8 2,470,876 73.6 4.9 5.5 3.9 100.0 100.0 100.0 100.0 714,252 1.1 100.0 27.3 6,925,052 35.8 63,282,084 10.9 100.0 100.0 100.0 44.1 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 3. 2019 annual Current Population Survey family counts, by percent of family earnings from workers in highly quintiles Percent of Lowest quintile less than Second quintile $34,321 to Middle quintile $59,540 to Fourth quintile $89,960 to family $34,321 $59,539 $89,959 $137,019 Top earnings from workers in highly Number of families exposed Col Row Number of percent percent families Col Row Number of percent percent families Col Row Number of percent percent families Col Row Numbe percent percent famil sectors 0 >0 and <25 >25 and <50 >50 and <75 >75 and <100 100 Total 9,148,927 78,697 162,783 114,679 72.0 0.6 1.3 0.9 19.6 2.6 4.7 4.6 9,390,697 346,443 653,132 465,719 74.5 2.7 5.2 3.7 20.1 11.3 18.7 18.8 9,230,531 621,945 1,062,718 688,196 72.3 4.9 8.3 5.4 19.8 20.2 30.4 27.9 9,225,686 847,635 969,227 723,279 73.5 6.8 7.7 5.8 19.8 27.6 27.8 29.3 9,610 1,180 643 479 39,367 0.3 5.5 77,323 0.6 10.8 142,506 1.1 20.0 169,978 1.4 23.8 285 3,165,526 12,709,979 24.9 100.0 45.7 1,676,345 20.1 12,609,658 13.3 100.0 14.7 611,147 20.2 12,546,953 4.9 100.0 24.2 1,020,890 8.0 19.9 12,766,787 100.00 8.8 451 19.8 12,648 Note: Col = column. Source: Authors’ calculations based on U.S. Bureau of Labor Statistics Current Population Survey 2019 annual data. Table 4. February 2020 to April 2020 CPS worker counts for key demographics for all workers, highly exposed workers and not highly exposed workers Employment Unemployment rate February Worker type Demographic Category February March April to April employment employment employment percent difference All workers None Gender Race Age Hispanic ethnicity All workers Men Women White only Black only Asian only All other 16 to 24 years old 25 to 54 years old 55 to 64 years old 65+ years old Hispanic NonHispanic 158,017,404 155,167,192 133,325,808 83,047,264 81,793,960 71,810,038 74,970,140 73,373,232 61,515,770 122,668,581 120,660,190 104,082,574 19,529,751 19,017,683 16,248,270 10,327,393 10,058,336 8,475,618 5,491,680 5,430,982 4,519,345 19,081,546 February March April unemployment unemployment unemployme rate rate rate –15.6 –13.5 –17.9 –15.2 –16.8 –17.9 –17.7 3.8 4.1 3.4 3.4 6.3 2.5 6.2 4.5 4.8 4.2 4.1 7.0 4.1 6.9 14 13 15 13 16 14 20 18,059,739 13,112,044 –31.3 8.0 10.0 26 101,151,803 100,141,056 87,909,549 –13.1 3.3 3.9 12 26,939,383 26,706,244 23,609,245 –12.4 2.6 3.4 12 10,844,671 10,260,153 8,694,971 –19.8 3.2 3.7 15 28,311,217 27,531,184 22,625,491 –20.1 4.8 6.3 18 129,706,187 127,636,009 110,700,317 –14.7 3.6 4.2 13 See footnotes at end of table. 18 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 4. February 2020 to April 2020 CPS worker counts for key demographics for all workers, highly exposed workers and not highly exposed workers Employment Unemployment rate February Worker type Demographic Category February March April to April employment employment employment percent difference Marital status (25+ years old) Educational attainment (25+ years old) Family type Highly exposed workers None Gender Race Age Married Never married Other marital status Less than a high school diploma High school diploma, no college Some college or associate’s degree Bachelor’s degree only Advanced degree Not living with a family member No children family Singleparent family Married with children family All highly exposed workers Men Women White only Black only Asian only All other 16 to 24 years old February March April unemployment unemployment unemployme rate rate rate 83,815,233 83,628,130 74,819,947 –10.7 2.2 2.7 10 33,986,850 33,024,188 27,839,427 –18.1 4.8 5.5 16 21,133,775 20,455,135 17,554,390 –16.9 4.1 5.1 14 8,670,067 8,439,022 6,872,495 –20.7 7.2 8.1 20 34,793,442 33,460,473 27,556,750 –20.8 4.1 4.8 17 36,061,032 35,803,359 30,633,663 –15.1 3.3 3.9 14 36,792,009 36,686,256 33,549,993 –8.8 2.2 2.5 9 22,619,308 22,718,343 21,600,863 –4.5 1.6 2.3 6 37,390,088 35,715,717 30,179,966 –19.3 3.8 4.8 14 62,524,760 61,588,954 53,220,784 –14.9 3.8 4.3 15 12,676,955 12,298,149 9,591,655 –24.3 6.4 7.3 19 45,425,601 45,564,372 40,333,402 –11.2 3.0 3.8 12 29,113,690 28,660,646 17,982,024 –38.2 4.5 6.5 34 14,927,838 14,185,851 22,154,774 3,568,795 2,121,172 1,268,949 15,130,943 13,529,703 21,781,872 3,528,243 2,108,080 1,242,451 9,942,024 8,040,000 14,010,204 2,094,194 1,231,265 646,361 –33.4 –43.3 –36.8 –41.3 –42.0 –49.1 4.2 4.9 3.9 8.9 1.8 6.4 6.0 7.1 5.7 11.3 6.6 6.3 30 38 32 38 35 45 6,821,739 6,557,727 3,537,243 –48.1 7.2 11.3 41 See footnotes at end of table. 19 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 4. February 2020 to April 2020 CPS worker counts for key demographics for all workers, highly exposed workers and not highly exposed workers Employment Unemployment rate February Worker type Demographic Category February March April to April employment employment employment percent difference Hispanic ethnicity Marital status (25+ years old) Educational attainment (25+ years old) Family type 25 to 54 years old 55 to 64 years old 65+ years old Hispanic NonHispanic Married Never married Other marital status Less than a high school diploma High school diploma, no college Some college or associate’s degree Bachelor’s degree only Advanced degree Not living with a family member No children family Singleparent family Married with children family February March April unemployment unemployment unemployme rate rate rate 16,630,771 16,275,118 10,637,505 –36.0 4.0 5.2 32 4,016,878 4,117,047 2,613,718 –34.9 2.9 4.6 32 1,644,301 1,710,753 1,193,558 –27.4 2.6 3.7 27 6,210,105 6,103,419 3,588,418 –42.2 4.8 8.4 38 22,903,584 22,557,227 14,393,605 –37.2 4.5 6.0 33 12,119,940 11,881,614 8,193,025 –32.4 2.4 4.0 28 6,694,549 6,637,646 4,011,576 –40.1 5.7 6.0 36 3,477,462 3,583,659 2,240,180 –35.6 4.3 6.3 34 1,716,042 1,852,118 1,113,106 –35.1 4.8 6.7 36 7,331,270 7,042,372 4,371,212 –40.4 4.2 4.9 35 6,794,829 6,796,208 4,365,846 –35.7 3.5 5.2 33 4,916,510 4,858,730 3,401,036 –30.8 3.0 4.5 27 1,533,299 1,553,491 1,193,581 –22.2 2.8 4.0 20 7,238,650 6,967,738 4,140,208 –42.8 4.1 5.9 34 11,285,791 11,293,478 7,360,872 –34.8 4.8 6.2 34 2,954,695 2,864,840 1,554,376 –47.4 7.2 9.2 39 7,634,554 7,534,589 4,926,567 –35.5 3.6 6.4 31 See footnotes at end of table. 20 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 4. February 2020 to April 2020 CPS worker counts for key demographics for all workers, highly exposed workers and not highly exposed workers Employment Unemployment rate February Worker type Demographic Category February March April to April employment employment employment percent difference Not highly exposed workers None Gender Race Age Hispanic ethnicity Marital status (25+ years old) Educational attainment (25+ years old) Family type All not highly 128,903,714 126,506,547 115,343,785 exposed workers Men 68,119,426 66,663,017 61,868,015 Women 60,784,288 59,843,529 53,475,770 White only 100,513,807 98,878,318 90,072,371 Black only 15,960,955 15,489,441 14,154,076 Asian only 8,206,221 7,950,256 7,244,354 All other 4,222,731 4,188,531 3,872,984 16 to 24 12,259,806 11,502,012 9,574,801 years old 25 to 54 84,521,032 83,865,938 77,272,043 years old 55 to 64 22,922,506 22,589,196 20,995,527 years old 65+ years 9,200,370 8,549,400 7,501,413 old Hispanic 22,101,112 21,427,765 19,037,073 Non106,802,603 105,078,782 96,306,712 Hispanic Married 71,695,294 71,746,516 66,626,922 Never 27,292,301 26,386,542 23,827,851 married Other marital 17,656,313 16,871,476 15,314,210 status Less than a high 6,954,025 6,586,904 5,759,389 school diploma High school 27,462,172 26,418,101 23,185,538 diploma, no college Some college or 29,266,203 29,007,151 26,267,817 associate’s degree Bachelor’s degree 31,875,499 31,827,526 30,148,957 only Advanced 21,086,009 21,164,853 20,407,282 degree Not living with a 30,151,439 28,747,979 26,039,758 family member See footnotes at end of table. 21 February March April unemployment unemployment unemployme rate rate rate –10.5 3.6 4.1 10 –9.2 –12.0 –10.4 –11.3 –11.7 –8.3 4.1 3.1 3.3 5.6 2.7 6.1 4.5 3.6 3.7 6.0 3.4 7.1 9 10 9 11 9 14 –21.9 8.4 9.2 19 –8.6 3.2 3.6 9 –8.4 2.6 3.2 9 –18.5 3.3 3.7 13 –13.9 4.9 5.7 13 –9.8 3.4 3.8 9 –7.1 2.2 2.5 8 –12.7 4.6 5.3 11 –13.3 4.1 4.8 11 –17.2 7.7 8.5 16 –15.6 4.1 4.8 12 –10.2 3.2 3.6 10 –5.4 2.0 2.2 6 –3.2 1.5 2.2 5 –13.6 3.8 4.6 9 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 4. February 2020 to April 2020 CPS worker counts for key demographics for all workers, highly exposed workers and not highly exposed workers Employment Unemployment rate February Worker type Demographic Category February March April to April employment employment employment percent difference No children family Singleparent family Married with children family February March April unemployment unemployment unemployme rate rate rate 51,238,969 50,295,476 45,859,912 –10.5 3.6 3.9 10 9,722,260 9,433,309 8,037,279 –17.3 6.1 6.8 14 37,791,047 38,029,783 35,406,835 –6.3 2.9 3.3 8 Source: Authors’ calculations based on U.S. Bureau of Labor Statistics Current Population Survey February 2020, March 2020, and April 2020 basic monthly data. Table A-1. List of highly exposed census industries Highly exposed sector Restaurants and bars Travel and transportation Personal services Entertainment Other sensitive retail Census industrial classification system Census industry codes 8680 8690 Restaurants and other food services Drinking places, alcoholic beverages 6070 Air transportation 6190 8660 7980 8970 8980 8990 9090 Taxi and limousine service Traveler accommodation Offices of dentists Barber shops Beauty salons Nail salons and other personal care services Other personal services Independent artists, performing arts, spectator sports, and related industries Bowling centers Other amusement, gambling, and recreation industries Automobile dealers Other motor vehicle dealers Furniture and home furnishings stores Clothing stores Shoe stores Jewelry, luggage, and leather goods stores Sporting goods, and hobby and toy stores Sewing, needlework, and piece goods stores Musical instrument and supplies stores Book stores and news dealers 8560 8580 8590 4670 4680 4770 5170 5180 5190 5275 5280 5295 5370 See footnotes at end of table. 22 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table A-1. List of highly exposed census industries Highly exposed sector Census industrial classification system Census industry codes Sensitive manufacturing 5380 5470 5480 5570 5580 5690 7170 7180 4390 4690 5680 Department stores and discount stores Retail florists Office supplies and stationery stores Gift, novelty, and souvenir shops Miscellaneous retail stores Other direct selling establishments Video tape and disk rental Other consumer goods rental Apparel, piece goods, and notions merchant wholesalers Automotive parts, accessories, and tire stores Fuel dealers 3470 Household appliance manufacturing 3570 3580 3590 3680 3895 Motor vehicles and motor vehicle equipment manufacturing Aircraft and parts manufacturing Aerospace product and parts manufacturing Ship and boat building Furniture and related product manufacturing Sporting and athletic goods; and doll, toy, and game manufacturing Miscellaneous manufacturing, n.e.c. Motor vehicle and motor vehicle parts and supplies merchant wholesalers Furniture and home furnishing merchant wholesalers Recyclable material merchant wholesalers Miscellaneous durable goods merchant wholesalers Fabric mills, except knitting mills Knitting fabric mills and apparel knitting mills Carpet and rug mills Textile product mills, except carpet and rug Cut and sew apparel manufacturing 3970 3980 4070 4080 4280 4290 1480 1670 1570 1590 1680 Note: n.e.c. = not elsewhere classified. Source: Authors’ calculations based on U.S. Bureau of Labor Statistics Current Population Survey 2019 annual data. Table A-2. 2019 annual Current Population Survey worker counts and wage estimates for key demographics of workers 16 to 24 years old Workers in highly exposed sectors Workers not in highly exposed sectors Percent of category in Demographic Category Number of workers None All workers 16 to 24 years old 7,134,436 Median Median Percent hourly weekly wage 100.0 $11.35 earnings $315.00 See footnotes at end of table. 23 Number of workers 11,711,911 Median Median Percent hourly weekly wage 100.0 $13.50 earnings $480.00 highly exposed sectors 37.9 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table A-2. 2019 annual Current Population Survey worker counts and wage estimates for key demographics of workers 16 to 24 years old Workers in highly exposed sectors Workers not in highly exposed sectors Percent of category in Demographic Category Number of workers Gender Race Hispanic ethnicity Marital status Educational attainment Hourly worker status Full- or parttime status Family status Family earnings quintile Men Women White only Black only Asian only All other Hispanic NonHispanic Married Never married Other marital status Less than a high school diploma High school diploma, no college Some college or associate’s degree Bachelor’s degree only Advanced degree Nonhourly worker Hourly worker Worked fulltime hours Worked parttime hours Not living with a family member Family member Not living with a family member Lowest quintile: less than 34,321 Median Median Percent hourly weekly wage earnings Number of workers Median Median Percent hourly weekly wage earnings highly exposed sectors 3,323,717 3,810,719 5,443,063 933,669 296,171 461,533 1,650,645 46.6 53.4 76.3 13.1 4.2 6.5 23.1 11.93 11.00 11.50 11.00 12.00 11.25 12.00 341.25 300.00 312.50 315.00 300.00 322.50 336.00 6,115,444 5,596,467 8,999,044 1,542,351 582,502 588,014 2,528,419 52.2 47.8 76.8 13.2 5.0 5.0 21.6 14.25 12.90 13.75 12.12 15.00 13.50 13.50 520.00 420.00 480.00 440.00 480.00 480.00 480.00 35.2 40.5 37.7 37.7 33.7 44.0 39.5 5,483,791 76.9 11.00 300.00 9,183,493 78.4 13.50 480.00 37.4 322,787 4.5 12.50 480.00 1,027,860 8.8 15.00 600.00 23.9 6,738,401 94.4 11.25 300.00 10,509,777 89.7 13.25 461.53 39.1 73,248 1.0 11.00 360.00 174,275 1.5 12.71 500.00 29.6 1,721,972 24.1 10.00 175.00 1,306,296 11.2 10.25 225.00 56.9 2,222,071 31.1 12.00 400.00 3,629,245 31.0 13.00 480.00 38.0 2,717,944 38.1 12.00 325.00 4,410,433 37.7 13.00 401.20 38.1 452,339 6.3 15.00 556.00 2,169,400 18.5 19.23 769.00 17.3 20,110 0.3 16.50 660.00 196,539 1.7 24.23 980.76 9.3 563,316 7.9 15.00 560.00 2,255,884 19.3 19.23 761.53 20.0 6,571,120 92.1 11.00 300.00 9,456,027 80.7 13.00 440.00 41.0 2,976,745 41.7 13.00 500.00 7,338,709 62.7 15.00 600.00 28.9 4,157,691 58.3 10.50 200.00 4,373,203 37.3 11.50 230.76 48.7 1,518,315 21.3 12.90 450.00 3,338,566 28.5 15.00 600.00 31.3 5,616,121 78.7 11.00 285.00 8,373,345 71.5 13.00 438.00 40.1 1,518,315 21.3 12.90 450.00 3,338,566 28.5 15.00 600.00 31.3 1,022,135 14.3 10.38 240.00 1,291,445 11.0 11.26 310.00 44.2 See footnotes at end of table. 24 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table A-2. 2019 annual Current Population Survey worker counts and wage estimates for key demographics of workers 16 to 24 years old Workers in highly exposed sectors Workers not in highly exposed sectors Percent of category in Demographic Category Number of workers Family type Second quintile: 34,321 to 59,539 Middle quintile: 59,540 to 89,959 Fourth quintile: 89,960 to 137,019 Top quintile: more than 137,020 Not living with a family member No children family Singleparent family Married with children family Median Median Percent hourly weekly wage earnings Number of workers Median Median Percent hourly weekly wage earnings highly exposed sectors 1,005,098 14.1 11.00 300.00 1,399,368 11.9 12.00 400.00 41.8 1,123,970 15.8 11.00 303.00 1,808,978 15.4 13.00 480.00 38.3 1,184,837 16.6 11.50 300.00 1,875,480 16.0 13.13 460.00 38.7 1,280,081 17.9 11.52 290.00 1,998,075 17.1 14.90 500.00 39.0 1,518,315 21.3 12.90 450.00 3,338,566 28.5 15.00 600.00 31.3 2,590,592 36.3 11.76 336.00 4,634,972 39.6 13.50 480.00 35.9 1,100,037 15.4 10.60 275.00 1,298,240 11.1 12.00 415.38 45.9 1,925,492 27.0 10.50 220.00 2,440,133 20.8 12.00 378.00 44.1 Source: Authors’ calculations based on U.S. Bureau of Labor Statistics Current Population Survey 2019 annual data. Table A-3. 2019 annual Current Population Survey worker counts and wage estimates for key demographics of workers 25 years old and older Workers in highly exposed sectors Workers not in the highly exposed sectors Percent of category in Demographic Category Number of workers None Gender Race All workers 25+ years old Men Women White only Black only Asian only All other Median Median Percent hourly weekly wage earnings Number of workers Median Median Percent hourly weekly wage earnings highly exposed sectors 20,377,871 100.0 $17.00 $666.00 102,328,051 100.0 $23.00 $942.30 16.6 10,784,472 9,593,399 15,229,155 2,667,328 1,684,375 797,013 52.9 47.1 74.7 13.1 8.3 3.9 18.89 15.00 17.31 15.00 17.00 15.86 776.15 560.00 680.00 600.00 673.00 600.00 53,057,136 49,270,915 79,318,733 13,062,695 6,718,054 3,228,569 51.9 48.1 77.5 12.8 6.6 3.2 25.00 20.50 23.40 18.45 31.00 20.00 1,057.69 804.80 961.00 760.00 1,250.00 800.00 16.9 16.3 16.1 17.0 20.0 19.8 See footnotes at end of table. 25 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table A-3. 2019 annual Current Population Survey worker counts and wage estimates for key demographics of workers 25 years old and older Workers in highly exposed sectors Workers not in the highly exposed sectors Percent of category in Demographic Category Number of workers Hispanic ethnicity Marital status Educational attainment Hourly worker status Full- or parttime status Family status Family earnings quintile Hispanic NonHispanic Married Never married Other marital Status Less than a high school diploma High school diploma, no college Some college or associate’s degree Bachelor’s degree only Advanced degree Nonhourly worker Hourly worker Worked fulltime hours Worked part-time hours Not living with a family member Family member Not living with a family member Lowest quintile: less than 34,321 Second quintile: 34,321 to 59,539 Median Median Percent hourly weekly wage earnings Number of workers Median Median Percent hourly weekly wage earnings highly exposed sectors 4,210,676 20.7 15.00 570.00 17,004,953 16.6 18.00 720.00 19.8 16,167,195 79.3 17.75 700.00 85,323,098 83.4 24.04 992.30 15.9 10,587,282 52.0 18.10 722.40 61,854,299 60.4 25.00 1,000.00 14.6 6,445,613 31.6 15.15 600.00 24,765,071 24.2 20.00 800.00 20.7 3,344,976 16.4 16.12 620.00 15,708,681 15.4 20.73 841.50 17.6 2,019,298 9.9 13.00 480.00 6,297,062 6.2 14.42 560.00 24.3 6,970,154 34.2 15.00 600.00 24,078,742 23.5 18.00 720.00 22.4 6,136,804 30.1 17.00 660.00 25,989,088 25.4 20.00 800.00 19.1 4,045,755 19.9 23.79 961.53 27,764,833 27.1 28.85 1,180.00 12.7 1,205,861 5.9 34.00 1,384.00 18,198,327 17.8 36.06 1,480.00 6.2 7,231,832 35.5 25.00 1,057.00 49,192,923 48.1 30.77 1,269.23 12.8 13,146,040 64.5 15.00 560.00 53,135,128 51.9 18.00 716.00 19.8 15,642,898 76.8 18.00 760.00 85,564,864 83.6 24.03 1,000.00 15.5 4,734,974 23.2 13.00 337.50 16,763,187 16.4 17.00 467.07 22.0 5,271,041 25.9 17.00 673.00 24,011,240 23.5 22.22 923.07 18.0 15,106,831 74.1 16.96 660.00 78,316,811 76.5 23.00 950.00 16.2 5,271,041 25.9 17.00 673.00 24,011,240 23.5 22.22 923.07 18.0 2,908,966 14.3 12.00 384.61 9,036,852 8.8 13.00 437.00 24.4 2,962,233 14.5 15.00 600.00 12,483,139 12.2 18.00 730.76 19.2 See footnotes at end of table. 26 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table A-3. 2019 annual Current Population Survey worker counts and wage estimates for key demographics of workers 25 years old and older Workers in highly exposed sectors Workers not in the highly exposed sectors Percent of category in Demographic Category Number of workers Family type Middle quintile: 59,540 to 89,959 Fourth quintile: 89,960 to 137,019 Top quintile: more than 137,020 Not living with a family member No children family Singleparent family Married with children family Median Median Percent hourly weekly wage earnings Number of workers Median Median Percent hourly weekly wage earnings highly exposed sectors 3,340,799 16.4 17.00 682.50 16,162,436 15.8 20.36 840.00 17.1 3,123,316 15.3 22.00 923.07 19,065,334 18.6 26.43 1,111.00 14.1 2,771,516 13.6 33.65 1,461.00 21,569,051 21.1 41.67 1,800.00 11.4 5,271,041 25.9 17.00 673.00 24,011,240 23.5 22.22 923.07 18.0 7,802,005 38.3 16.83 670.00 39,474,406 38.6 22.50 920.00 16.5 2,022,679 9.9 14.42 538.46 7,679,815 7.5 18.00 720.00 20.8 5,282,147 25.9 18.25 720.00 31,162,591 30.5 25.00 1,038.46 14.5 Source: Authors’ calculations based on U.S. Bureau of Labor Statistics Current Population Survey 2019 annual data. SUGGESTED CITATION 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, U.S. Bureau of Labor Statistics, June 2020, https://doi.org/10.21916/mlr.2020.11. NOTES 1 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, https://www.bls.gov/opub/mlr/2020/article/ covid-19-shutdowns.htm. 2 Joseph S. Vavra, “Shutdown sectors represent large share of all U.S. employment” (Chicago, IL: Becker Friedman Institute for Economics at the University of Chicago, March 31, 2020), https://bfi.uchicago.edu/insight/blog/key-economic-facts-about-covid-19/. 3 Earnings information is not collected for the self-employed. 4 While the Quarterly Census of Employment and Wages and Occupational Employment Statistics (OES) surveys use the North American Industry Classification System (NAICS) industry classification, the Current Population Survey (CPS) uses census industry codes. A few situations exist in which differences between the NAICS and census industry definitions made it necessary to add or 27 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW subtract industries identified as exposed in our initial article. As noted in the text, the list of Census industries used for this analysis can be found in the appendix, table A-1. 5 Vavra, “Shutdown sectors represent large share of all U.S. employment.” As noted in the initial article, one could quibble about certain industries, but we are convinced that reasonable modifications to the list are likely to have relatively minor effects on our overall findings. 6 These particular numbers do not appear in figure 3. Figure 3 depicts the demographic composition of the highly exposed and not highly exposed sectors. The numbers in the text refer to the sectoral composition of workers in a particular demographic category. 7 In our earlier article, we looked at occupational wages by using the OES and showed that lower paying occupations are more heavily represented in the exposed sector. In this article, using the CPS data, we look at the wages of individual workers by using the CPS data. 8 To obtain a more comprehensive picture, we have constructed an hourly wage for all workers (BLS only does this for hourly workers). We also have chosen to calculate usual median weekly earnings for part-time workers and full-time workers. BLS press releases report usual weekly earnings for only full-time workers. 9 For this analysis, families are defined as two or more individuals living together who are related by birth, marriage, or adoption. All related individuals in a household are considered one family, even if several generations of a family are living together (for example, a divorced mother living with her adult son and his infant daughter would be one family). The estimates for families with children refer to children under the age of 18. Individuals who are living alone or in a household with completely unrelated people (for example, a group of unrelated people sharing a house) are classified as not living with a family member. Unmarried partners and same-sex spouses also are not classified as living with a family member. 10 Earnings are what individuals receive from being employed. Although typically the largest component, earnings are just one component of family income. Other potential sources of family income include payments from government programs such as social security and public assistance programs, rental income, and dividend payments. 11 Children are those age 18 and under who are sons, daughters, stepchildren, or adopted children living in the household. Nieces, nephews, grandchildren, other related and unrelated children, and children not living in the household are not included as children. 12 However, the exposure of single-parent families does not vary much by the number of children in the household. For single-parent households with one child, 19.5 percent of families obtain all the family’s earnings from workers in the highly exposed sector, while 18.5 percent of single-parent families with two or more children do. 13 The definition of the exposed sector is admittedly subjective. With current CPS data, we can identify industries that are potentially misclassified. To this end, we deem an industry in the exposed sector as potentially misclassified if employment grew or decreased less than 15 percent from February to April. In addition, we deem an industry in the not highly exposed sector as potentially misclassified if employment decreased by 30 or more percent from February to April. Given these definitions, we find that only 6.2 percent of highly exposed sector employment and 5.5 percent of not highly exposed sector employment are potentially in misclassified industries. 28 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW RELATED CONTENT Related Articles 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. How did employment fare a decade after its 2008 peak? Monthly Labor Review, October 2018. Healthcare jobs and the Great Recession, Monthly Labor Review, June 2018. Related Subjects Labor force Labor market Earnings and wages Unemployment industry 29 Employment Part time work Family issues U.S. BUREAU OF LABOR STATISTICS HOME ARCHIVES FOR AUTHORS ABOUT Search MLR GO BEYOND BLS JU N E 2020 Income “Crow”? Maya B. Brandon Income segregation is often considered a result of the rising marks of income inequality shown racially and economically within and between social classes. In “Income segregation: up or down, and for whom?” (National Bureau of Economic Research, Working Paper 27045, April 2020), authors John R. Logan, Andrew Foster, Hongwei Xu, and Wenquan Zhang report that “rising income segregation has been brought into question by the observation that post-2000 estimates are upwardly biased due to a reduction in the sample sizes on which they are based.” Fueled by job loss, foreclosure, heightened mortgage requirements, and declining asset values, income segregation, or the separating of people into different communities and neighborhoods based on income level, is on the rise in the United States. The segregation can be seen in the composition of neighborhoods, social groups, and class. Although attempts have been made to measure the effects of income inequality in residential communities across the United States, they lacked consistency. Methods of measuring income inequality and segregation are topics gaining more traction and attention in the statistical community. As incomes and opportunities of people and families increase, particularly those of minorities, they are expected to “seek more advantaged neighborhoods.” This expectation does not apply clearly to Black families but more readily applies to Hispanic families. More factors affect the residential and social mobility of families than only increases in income. Higher income can influence neighborhood composition, both racially and economically; however, it is not the sole factor of composition. Logan et al. point out that studies have shown that most of the “socioeconomic residential sorting seen in the last forty years occurred in the 1980s and 2000s.” The authors, while recognizing that income segregation of some families rose in the 1980s 1990s, conclude that the segregation of Black and Hispanic families was not generally higher than that of White families. They further conclude that income segregation is mostly proven by the separation seen in Hispanic families between the bottom 90 percent and top 10 percent. Sources of data and income segregation indicators, modifications in the collection methods of public data, bias inherent to smaller sample sizes, and changes in income distribution across racial and familial lines have all contributed to inflated estimates of income segregation. Logan, Foster, Xu, and Zhang pose that “rather than focusing on why income segregation seems to be rising in parallel with growing income inequality, scholars need to give more attention to why it may not.” Census data have long been used to measure changes among and between demography, geography, and economics. Although the base premises are partly true, further techniques for researching the data, collecting the data, and using the collected data are necessary to quantify social factors into measurable units for calculation. The quantifying of social, typically considered immeasurable, factors is needed to develop more accurate and effective measures of income inequality and subsequent segregation. Scholars are now tasked to effectively use available data sources to reflect the nature of reality, remove the bias included in smaller samples, and more accurately calculate multivariate studies to explore the nuances between race, geography, class, and income. 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 June 2020 Ability to work from home: evidence from two surveys and implications for the labor market in the COVID-19 pandemic This article examines the relationship between workers’ ability to work at home, as captured in job characteristics measured by the Occupational Information Network, and the actual incidence of working at home, as measured by the American Time Use Survey and the National Longitudinal Survey of Youth 1979. For occupations in which telework is feasible, the article also estimates the proportion of workers who actually teleworked for a substantial amount of time prior to the coronavirus disease 2019 (COVID-19) pandemic. The article concludes by examining recent (April 2020) employment estimates from the Current Population Survey, aiming to gauge how the initial employment effects of the pandemic differed between occupations in which telework is feasible and occupations in which it is not. In an attempt to contain the coronavirus disease 2019 (COVID-19) pandemic, states and localities across the country have adopted “social distancing” measures, closing businesses and enacting stay-at-home orders. Many workers are now working remotely. Although teleworking had been on the rise even before the pandemic,1 it has now increased substantially, with more people working at home whenever possible. A recent article by Erik Brynjolfsson et al. estimates that 31 percent of workers who were employed in early March had switched to working at home of April.2 by the first week Even when stay-at-home orders are relaxed, many workers may continue working at home until the pandemic is fully contained. Of course, many jobs cannot be performed remotely and Matthew Dey dey.matthew@bls.gov Matthew Dey is a research economist in the Office of Employment and Unemployment Statistics, U.S. Bureau of Labor Statistics. Harley Frazis frazis.harley@bls.gov Harley Frazis is a research economist in the Office of Employment and Unemployment Statistics, U.S. Bureau of Labor Statistics. Mark A. Loewenstein loewenstein.mark@bls.gov Mark A. Loewenstein is a senior research economist in the Office of Employment and Unemployment Statistics, U.S. Bureau of Labor Statistics. require that workers be physically present at their worksites. Data on job characteristics provided by the Occupational Information Network (O*NET), together with 1 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW occupational employment estimates from the Occupational Employment Statistics (OES) survey, make it possible to estimate the number of jobs that can and cannot be performed remotely.3 O*NET contains occupation-level measures not only of the knowledge and skills required by Hugette Sun sun.hugette@bls.gov Hugette Sun is a research economist in the Office of Employment and Unemployment Statistics, U.S. Bureau of Labor Statistics. an occupation, but also on how and where the work associated with that occupation is carried out. Information captured in the O*NET categories “work context” and “general work activities” is especially helpful for determining whether a job cannot be done at home. Examples of jobs that one would expect to be unsuitable for telework are jobs that involve operating equipment or interacting face to face with the public. Using O*NET and OES data, for instance, Jonathan I. Dingel and Brent Neiman estimate that 63 percent of U.S. jobs require significant onsite presence and that the remaining 37 percent can be performed entirely at home.4 Simon Mongey, Laura Pilossoph, and Alex Weinberg provide evidence that information on working at home in the American Time Use Survey (ATUS) is consistent with the type of O*NET measures constructed by Dingel and Neiman.5 In a supplement to the 2017–18 ATUS, workers were asked whether they could work at home.6 Averaging the responses to this question across individuals, Mongey, Pilossoph, and Weinberg estimate the proportion of workers in broad (two-digit census) occupations who can work at home. In addition, averaging O*NET-based estimates for more detailed occupations, they obtain an O*NET-based measure of the inability to work at home across two-digit occupations. Comparing the two measures, the authors find that, as predicted, the measures are inversely correlated. In this article, we take a closer look at the relationship between the ability to work at home, as captured in job characteristics measured by O*NET, and the actual incidence of working at home, as measured by two U.S. Bureau of Labor Statistics surveys—the ATUS and the National Longitudinal Survey of Youth 1979 (NLSY79). Rather than comparing broader occupational averages of the incidence of working at home and the ability to work at home, we analyze behavior at the individual level. This approach allows us to (1) determine the incidence of classification errors (that is, the incidence of working at home in detailed occupations that would otherwise seem to preclude working at home) and (2) examine takeup rates (that is, the proportions of workers in detailed occupations who can work from home and actually spend a significant amount of time doing so). Working at home in response to the pandemic is more likely to increase in occupations in which teleworking is feasible and the takeup rate is relatively low. In the final section of the article, we examine recent (April 2020) employment estimates from the Current Population Survey (CPS), aiming to gauge how the initial employment effects of the pandemic differed between occupations in which telework is feasible and occupations in which it is not. Is the O*NET-based telework feasibility measure consistent with observed telework behavior in the ATUS and the NLSY79? Because the questions in the ATUS and the NLSY79 differ, it is difficult to construct perfectly comparable definitions of teleworkers in the two surveys. To avoid this difficulty, we formulate a plausible definition for each survey and then examine the degree to which the survey results conforming to that definition are consistent with the O*NET measure. For the ATUS, our definition is based on whether workers who worked entirely at home on 2 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW some days received pay for some of their time. For the NLSY79, our definition is based on the number of hours that respondents worked at home. The ATUS is a single-day time-diary survey administered to a sample of individuals in households that have recently completed their participation in the CPS, the main labor force survey for the United States. The information on working at home used here is from the 2017–18 Leave and Job Flexibilities Module of the ATUS. Administered to every respondent who was a wage or salary worker, this module has a sample size of 10,071. We classify workers as telecommuters if, in response to questions about working at home, they replied that they (1) were able to and did work at home, (2) worked entirely at home on some days, and (3) were paid for at least some of the hours they worked at home. The ATUS also provides information on other variables that may be related to working at home. These variables include a worker’s education level, age, gender, race, ethnicity, and marital status; the presence of children in the household; the worker’s job status (full or part time); and the size of the metropolitan area in which the worker resides.7 Following the methodology of Dingel and Neiman, we classify occupations on the basis of their telework feasibility and then merge this information with data from the ATUS.8 The results are summarized in table A-1 of the appendix. As indicated in the first data column of the table, approximately 54 percent of workers in the ATUS sample (1) are in occupations in which working at home is not feasible (according to the O*NET-based telework feasibility measure) and (2) did not telework. As shown in the second data column, about 2 percent of workers in the sample worked at home despite being in occupations in which telework is not feasible. Dividing the latter percentage by the percentage of workers for which working at home is predicted to be infeasible yields a relatively low classification error rate of about 4 percent. This result provides strong support for the O*NET-based measure, whose ruling out of telework for occupations in which working at home is deemed infeasible is correct about 96 percent of the time. As shown in the third data column of table A-1, about 33 percent of workers in the ATUS sample (1) are in occupations in which working at home is feasible (according to the O*NET-based telework feasibility measure) and (2) did not telework. As seen in the fourth data column, the percentage of those who are in occupations in which telework is feasible and who did telework is about 11 percent. Dividing this percentage by the percentage of workers for which working at home is predicted to be feasible yields an estimated takeup rate of about 25 percent. Table 1 shows estimates for the ability-to-telework rate, the classification error rate, and the takeup rate. The entries in the table’s first data column provide ability-to-telework rates by various worker characteristics. One sees that workers with less education tend to be in jobs in which working at home is not feasible, as is the case for workers who are younger than 25, not married, or Hispanic. Teleworking is also less feasible in part-time jobs and in jobs found in nonmetropolitan areas. Working at home is generally feasible in management, professional, and administrative support jobs, but not in most service, construction, transportation, and production jobs. Similarly, while telework feasibility is high in the information, financial activities, professional and business services, and public administration industries, it is low in the leisure and hospitality, agriculture, and construction industries. 3 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 1. Telework statistics, by demographic, occupational, industry, and job-task characteristics, ATUS and NLSY79 (in percent) ATUS Category All Educational attainment Less than a high school diploma High school diploma, no college Some college or associate's degree Bachelor's degree and higher Age 15 to 24 years 25 to 54 years 55 years and older Comparable NLS age range (51–59) Presence of children No children Children Job status Full time Part time Gender Men Women Maritial status Not married Married Race or ethnicity Non-Hispanic White Black Hispanic Occupations Management, business, and financial occupations Professional and related occupations Service occupations Sales and related occupations Office and administrative support occupations Farming, fishing, and forestry occupations Construction and extraction occupations Installation, maintenance, and repair occupations Production occupations Transportation and material moving occupations NLSY79 Ability-to- Classification Takeup Ability-to- Classification Takeup telework rate error rate rate telework rate error rate rate 43.6 3.9 24.7 44.8 5.6 21.6 10.7 24.5 36.4 67.5 0.4 1.4 3.0 10.8 7.7 11.3 16.3 31.4 17.0 30.3 42.5 70.5 4.4 4.0 5.0 11.3 3.7 12.8 18.2 28.7 23.7 46.7 48.1 46.6 0.0 5.0 4.7 5.1 11.5 27.8 20.1 22.2 — — — — — — — — — — — — 44.7 42.0 3.9 4.0 23.5 26.6 44.0 50.1 4.8 11.4 20.5 28.4 47.2 28.7 4.6 1.9 25.8 17.1 46.8 32.2 5.9 4.0 22.0 18.3 40.0 47.6 3.5 4.4 27.8 21.9 38.8 51.5 5.7 5.5 25.5 18.4 34.4 50.2 2.3 5.4 21.1 26.5 39.3 47.7 5.0 5.9 18.7 22.9 48.7 39.5 28.9 5.2 2.8 1.5 26.4 24.2 14.4 46.9 33.5 39.0 6.0 3.9 4.9 22.8 16.0 12.8 86.6 13.6 29.7 86.5 22.0 23.4 64.4 7.9 31.9 8.2 2.0 4.3 28.1 7.0 29.2 64.3 13.4 30.1 7.7 4.2 8.4 28.5 6.3 36.4 59.2 5.9 10.4 61.5 4.6 7.7 0.0 0.9 — 0.0 0.0 — 0.0 2.6 — 0.0 4.0 — 1.0 1.2 0.0 3.9 3.0 0.0 0.4 1.7 0.0 3.9 3.9 0.0 0.3 1.1 0.0 1.3 2.0 0.0 See footnotes at end of table. 4 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 1. Telework statistics, by demographic, occupational, industry, and job-task characteristics, ATUS and NLSY79 (in percent) ATUS Category Industries Agriculture, forestry, fishing, and hunting Mining, quarrying, and oil and gas extraction Construction Manufacturing Wholesale and retail trade Transportation and utilities Information Financial activities Professional and business services Education and health services Leisure and hospitality Other services Public administration Industry missing Area Nonmetropolitan area Metropolitan area, unknown size Metropolitan area, 100,000–250,000 Metropolitan area, 250,000–500,000 Metropolitan area, 500,000–1,000,000 Metropolitan area, 1,000,000– 2,500,000 Metropolitan area, 2,500,000– 5,000,000 Metropolitan area, 5,000,000+ Time on physical tasks Almost all More than half Less than half Almost none Time on repetitive tasks Almost all More than half Less than half Almost none Time on managing or supervising Almost all Half or more Less than half NLSY79 Ability-to- Classification Takeup Ability-to- Classification Takeup telework rate error rate rate telework rate error rate rate 8.3 3.0 20.4 16.0 29.7 25.3 55.9 28.0 26.3 15.0 0.0 52.6 17.3 36.4 26.9 25.4 71.2 77.9 69.9 48.9 13.0 31.0 65.2 — 2.6 4.6 2.1 1.8 4.2 17.2 9.0 3.7 0.9 7.1 7.3 — 13.0 31.6 19.3 22.2 36.9 29.6 40.8 15.8 12.7 14.0 16.5 — 21.8 36.6 29.3 26.4 77.3 75.3 68.5 49.7 20.5 55.5 54.9 50.2 6.3 2.7 2.4 2.3 16.8 11.2 10.1 6.1 5.3 13.7 3.5 12.3 10.5 16.5 22.8 13.8 37.3 27.3 30.1 19.2 19.9 19.0 13.7 30.4 31.8 39.6 40.4 40.1 42.4 1.5 4.5 2.5 3.8 4.8 10.8 17.2 28.1 13.7 21.6 — — — — — — — — — — — — — — — 44.8 4.5 25.4 — — — 49.5 6.0 31.0 — — — 48.8 4.0 PDII task measures 29.5 — — — — — — — — — — — — — — — 16.6 31.3 54.1 74.3 3.1 5.6 7.0 12.7 9.8 13.2 20.3 26.0 — — — — — — — — — — — — 27.1 36.4 51.6 59.4 4.2 3.8 6.3 8.6 13.1 13.0 20.0 28.9 — — — — — — — — — 53.1 52.2 44.0 6.7 7.3 5.8 19.8 24.1 21.3 See footnotes at end of table. 5 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 1. Telework statistics, by demographic, occupational, industry, and job-task characteristics, ATUS and NLSY79 (in percent) ATUS Category Almost none Solve problems of 30+ minutes 1+/day 1+/week 1+/month Never Use high school+ math 1+/day 1+/week 1+/month Never Longest document typically read at job < 1 page 2–5 pages 6–10 pages 11–25 pages 25+ pages Never Frequency of personal contact with people other than coworkers or supervisors A lot A moderate amount A little None at all Frequency of personal contact with customers or clients A lot Some None at all Frequency of personal contact with suppliers or contractors A lot Some None at all Frequency of personal contact with students or trainees A lot Some None at all Frequency of personal contact with patients A lot Some None at all NLSY79 Ability-to- Classification Takeup Ability-to- Classification Takeup telework rate error rate rate telework rate error rate rate — — — 40.7 4.9 21.9 — — — — — — — — — — — — 55.5 44.5 36.1 24.3 7.7 5.7 5.1 2.5 26.8 18.4 12.7 12.6 — — — — — — — — — — — — 46.7 46.8 52.3 42.9 4.3 9.6 10.6 4.5 26.2 24.9 22.6 20.1 — — — — — — — — — — — — — — — — — — 27.4 50.1 55.9 60.3 68.6 14.8 3.2 6.1 4.1 11.9 11.7 5.9 12.6 19.9 25.0 29.5 26.0 15.7 — — — — — — — — — — — — 40.7 51.8 49.4 42.8 5.3 7.1 6.2 4.2 21.3 20.7 19.1 28.8 — — — — — — — — — 36.8 54.3 47.9 5.7 6.7 4.4 18.7 20.8 25.9 — — — — — — — — — 44.8 47.0 42.7 6.7 6.7 4.3 17.4 20.8 23.6 — — — — — — — — — 54.6 42.7 42.8 5.7 3.9 6.7 22.3 20.2 22.3 — — — — — — — — — 24.3 49.2 47.2 5.0 4.6 5.7 10.6 27.9 22.0 See footnotes at end of table. 6 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Note: ATUS = American Time Use Survey, NLSY79 = National Longitudinal Survey of Youth 1979, NLS = National Longitudinal Surveys, PDII = Princeton Data Improvement Initiative, O*NET = Occupational Information Network. Source: Authors' calculations using the 2017–18 Leave and Job Flexibilities Module of the ATUS, the most recent interview (2016–17) of the 1979 cohort of the NLSY79, and job-content data provided by O*NET. The NLSY79 is a second source of data on hours worked at home. It is a survey of 12,686 individuals who were ages 14 to 21 in 1979. These individuals were interviewed annually from 1979 to 1994, and every 2 years after that. We use information from the most recent NLSY79 interview (round 27), which was conducted from October 2016 through November 2017, when respondents were ages 51 to 59. The sample used here is restricted to respondents who provided full information on their education, gender, race, wages, hours worked at home, occupation, and job tasks. The resultant sample size is 4,293. For the NLSY79, our telework measure is derived from individual responses to a question about the number of hours per week respondents usually worked at home while at their current or most recent employer. Some workers in the NLSY79 work at home just a few hours a week, and, for our present purposes, it is not useful to designate them as teleworkers. During a pandemic, teleworking is a realistic alternative to working onsite only if individuals can work at home on a nearly full-time basis or at least for a considerable number of hours. In the ATUS, we address this issue by classifying workers as teleworkers only if they worked entirely at home on some days. In the NLSY79, we classify workers as teleworkers only if they usually worked at home at least 8 hours a week, which roughly corresponds to working at home for a full day.9 As shown below, with this restriction, the NLSY79 data look very similar to the ATUS data. Like the ATUS, the NLSY79 has information on a worker’s age, gender, race, ethnicity, and marital status; the presence of children in the household; and the worker’s job status (full or part time). As with the ATUS, we merge the O*NET-based telework feasibility measure with data from the NLSY79.10 The results are summarized in table A-2 of the appendix. The estimates for the ability-to-telework rate, the classification error rate, and the takeup rate are presented in table 1. As shown in the table’s fourth data column, approximately 45 percent of workers in the NLSY79 sample are in occupations in which working at home is feasible. The classification error rate, shown in the fifth data column, is about 6 percent, just a tad higher than the rate for workers of comparable age in the ATUS. Looking at other entries in the fourth data column of table 1, one sees estimates that are quite similar to those obtained from the ATUS. Workers with less education are concentrated in jobs in which working from home is generally not feasible. Black, Hispanic, male, unmarried, and part-time workers also are more likely to be in jobs in which teleworking is not feasible. Working at home is generally feasible in management, professional, and administrative support jobs, but not in most service, sales, farming, construction, and transportation jobs. Similar to the occupation results, the industry results obtained from the NLSY79 largely mirror those obtained from the ATUS. Round 27 of the NLSY79 also added variables based on individual responses to questions about the nature of a worker’s job duties. Looking at these variables, which are similar to those in O*NET, suggests that lower skilled jobs with repetitive tasks are typically jobs in which telework is not feasible (according to the O*NET criteria). The same is true for jobs that require physical tasks or contact with patients and, to a lesser extent, for jobs that involve personal contact with customers.11 7 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Takeup rates in the ATUS and the NLSY79 As shown in appendix table A-1, about 44 percent of workers in the ATUS sample are in jobs in which telework is feasible. However, because only about 11 percent of workers in the sample (1) are in jobs in which telework is feasible and (2) did work at home, the takeup rate is only about 25 percent. As seen in the third data column of table 1, the takeup rate is higher for more educated workers, workers in fulltime jobs, and men, and it is lower for Hispanics. Examined by age group, the takeup rate is the highest for workers ages 25 to 54 and the lowest for workers younger than 25. Workers in larger metropolitan areas have a higher takeup rate, as do workers in management, professional, and sales occupations. Similarly, the industry estimates indicate higher takeup rates in the information industry and the professional and business services industry. The takeup rate is quite low in service occupations and office and administrative support occupations. Turning to the NLSY79 and looking at the sixth data column in table 1, one sees that the overall takeup rate is a little less than 22 percent, comparable to the rate for workers of similar age in the ATUS. The other entries in the column show basic patterns similar to those in the ATUS. The takeup rate is lower for Hispanics and workers with less education. It is higher for men and people with children in the household. The takeup rate is very low in service occupations and office and administrative support occupations, and it is higher in jobs that involve more complex cognitive tasks such as frequent problem solving and reading long documents. The most striking feature of the takeup rate estimates is that they are so low. As noted earlier, the overall takeup rate is 25 percent in the ATUS sample and 22 percent in the NLSY79 sample, whose respondents are older, on average. Even for the groups with the highest takeup rates, these rates generally top out at around 30 percent. However, both anecdotal reports and the evidence provided by Brynjolfsson et al. indicate that, in response to the COVID-19 pandemic, takeup rates are much higher than this percentage.12 Two factors determine the takeup rate: the employers’ willingness to let workers telework and the workers’ desire to work at home when they are offered the opportunity.13 There are several possible reasons why employers might be reluctant to let their workers telework. Working from home may require costly investments in computers or improved internet access. Alternatively, employers might see telework as a job perk given only to the most deserving workers. Likewise, employers might be concerned about difficulties in monitoring the behavior of employees working at home. (There are reports that employers are now increasing their use of surveillance software to monitor the work habits of their teleworking employees.14) As mentioned earlier, the ATUS asks workers not only whether they work at home, but also whether they can work at home. Workers may interpret the latter question as being primarily about the employer’s telework policies. An affirmative response would then indicate that a formal agreement or an informal understanding with the employer allows workers to work at home.15 Across the entire ATUS sample, 45 percent of workers who can telework actually do so under our definition. Although this percentage is almost double the takeup rate (as we have measured it), it still indicates that, for whatever reason, a majority of workers choose not to telework when given the opportunity. It is possible that many workers miss the social interactions at the workplace, forfeiting the timesaving benefits of telework.16 Implications for the labor market in the COVID-19 pandemic 8 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW The COVID-19 pandemic has led to widespread employment losses as businesses have closed, stay-at-home orders have been enacted, and workers and customers have made efforts to avoid close interactions with others. Teleworking has enabled some workers to continue working while maintaining social distancing. Table 2 presents CPS estimates of the change in employment and unemployment between February and April 2020. Separate estimates are presented for workers in occupations in which the O*NET-based telework feasibility measure predicts that working at home is feasible. All estimates shown are not seasonally adjusted.17 The CPS estimates indicate that, overall, employment fell by 16 percent from February to April, and the unemployment rate increased by 11 percentage points. However, employment fell by 21 percent in occupations in which telework is not feasible, compared with 8 percent in occupations in which telework is feasible. Over the same period, the unemployment rate increased by 14 percentage points in occupations in which telework is not feasible, but only by 6 percentage points in occupations in which telework is feasible. In a recent article published in the June 2020 Monthly Labor Review, Matthew Dey et al. use a taxonomy developed by Joseph S. Vavra to identify vulnerable industries at the beginning of the COVID-19 pandemic.18 The authors show that while job losses were widespread throughout the economy from February to March, they were especially severe in these initially vulnerable, or highly exposed, industries. Table 2 breaks down employment and unemployment estimates for the highly exposed industries and for the remainder of the economy. In the highly exposed industries, workers in occupations in which working at home is not feasible were especially hard hit by the pandemic. For these occupations, employment fell by 42 percent between February and April, and the unemployment rate rose by 32 percentage points. By comparison, in occupations also located in the highly exposed industries but in which working at home is feasible, employment fell by a still substantial but smaller 22 percent, and the unemployment rate increased by 18 percentage points. In February, only 15 percent of employment in the highly exposed industries was in occupations in which telework is feasible. As a result, the overall fall in employment in these industries was very large (39 percent) and not far off from the reduction in employment in jobs in which working at home is not feasible. Table 2. Changes in CPS employment and unemployment statistics, by ability to telework and industry exposure, February–April 2020 Percent change in employment Percentage-point change in unemployment rate Telework status Unable to telework Able to telework Total Total Exposed industries Nonexposed industries Total Exposed industries Nonexposed industries -21.2 -41.5 -14.6 14.3 32.3 8.7 -7.7 -15.9 -22.1 -38.6 -6.7 6.2 -11.1 11.0 18.1 30.1 5.4 7.2 Source: Authors' calculations based on February–April 2020 Current Population Survey (CPS) data and O*NET job-content data. The percent reduction in employment in the remaining industries was substantial, but not nearly as large as that in the highly exposed industries. However, the same pattern holds here as in the highly exposed industries: the percent reduction in employment and the increase in the unemployment rate were substantially smaller in occupations in which it is possible to work at home. Specifically, in occupations in which telework is not feasible, employment fell by 15 percent between February and April, and the unemployment rate rose by 9 percentage points. By comparison, in occupations in which telework is feasible, employment fell by 7 percent over the same 9 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW period, and the unemployment rate increased by 5 percentage points. In February, 44 percent of employment in the less highly exposed industries was in occupations in which telework is feasible, which moderated both the overall reduction in employment and the increase in unemployment in those industries. Table 3 presents CPS estimates of employment and unemployment, by major industry.19 As indicated by the final entries in the table’s second and third data columns, across the entire economy, employment fell by 16 percent from February to April, and the unemployment rate increased by 11 percentage points. Examining the entries in the first three data columns, one sees that, for the most part, industries in which a higher proportion of workers can telework have a smaller reduction in employment and a smaller increase in unemployment. An even stronger relationship between employment loss and the ability to telework is evident in the remaining columns of the table. The fourth and fifth data columns show that, in every industry except agriculture, workers in occupations in which telework is feasible have a smaller percent decline in employment. In some industries, this difference is very large. For example, in information, employment fell by 37 percent in occupations in which telework is not feasible, but only by 2 percent in occupations in which telework is feasible. In the category of other services, the corresponding numbers are 36 percent and 8 percent. The table’s seventh and eight data columns, which break down the change in industry unemployment rates by the ability to work at home, tell the same story. In every industry, unemployment increased by a smaller amount for workers who are in occupations in which telework is feasible. Table 3. Industry statistics Percent Labor market outcomes Percent change in Percentage-point change in employment (February–April unemployment rate ( 2020) February– April 2020) share of Industry employed able to telework (April 2020) Percent change in employment ( February– April 2020) Financial activities Information Professional and business services Public administration Education and health services Manufacturing Mining, quarrying, and oil and gas extraction Other services Transportation and utilities Percentage-point change in unemployment rate ( February– Able to telework Not able to Difference telework Able to telework Not able to Difference telework April 2020) 81.1 -6.1 3.7 -5.8 -7.2 1.4 2.8 7.2 -4.4 80.4 -11.8 9.3 -2.1 -37.3 35.2 5.8 21.1 -15.3 71.6 -9.6 5.5 -6.4 -16.8 10.4 3.5 10.0 -6.5 57.0 -3.8 3.4 -1.5 -6.7 5.1 3.2 3.8 -0.6 47.9 -13.9 9.4 -12.5 -15.2 2.8 8.8 9.9 -1.1 41.0 -13.7 9.2 -3.9 -19.5 15.5 4.3 12.3 -8.0 40.3 -14.9 4.2 5.5 -24.8 30.3 4.2 5.1 -0.8 39.9 -27.2 19.4 -8.4 -35.9 27.5 10.6 24.3 -13.6 32.7 -10.9 8.7 4.7 -16.9 21.6 4.9 10.4 -5.5 See footnotes at end of table. 10 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 3. Industry statistics Percent Labor market outcomes Percent change in Percentage-point change in employment (February–April unemployment rate ( 2020) February– April 2020) share of Industry employed able to telework (April 2020) Percent change in employment ( February– April 2020) Wholesale and retail trade Construction Leisure and hospitality Agriculture, forestry, fishing, and hunting Total Percentage-point change in unemployment rate ( February– Able to telework Not able to Difference telework Able to telework Not able to Difference telework April 2020) 26.5 -16.4 12.6 -9.4 -18.6 9.2 7.6 14.2 -6.6 20.7 -16.6 10.2 -11.9 -17.8 5.8 5.1 11.3 -6.2 20.3 -42.0 32.1 -25.5 -45.1 19.6 22.9 34.1 -11.2 8.1 -1.2 -1.7 -4.3 -1.0 -3.3 -5.9 -1.3 -4.5 45.8 -15.6 10.8 -7.9 -21.2 13.3 6.2 14.3 -8.1 Source: Authors' calculations based on Februrary–April 2020 Current Population Survey data and O*NET job-content data. Conclusion Our analysis of merged O*NET–ATUS data and merged O*NET–NLSY79 data indicates that about 45 percent of U.S. employment is in occupations in which telework is feasible. However, a much lower percentage of workers actually worked at home prior to the COVID-19 pandemic. Specifically, only a little more than 10 percent of workers in the ATUS spent any paid workday working only at home, and a similar percentage in the NLSY79 usually spent more than 8 hours a week working at home. Thus, according to both surveys, the implied takeup rate —that is, the percentage of workers who were in occupations in which telework is technologically feasible and who actually worked at home—was quite low prior to the pandemic. According to the ATUS, the takeup rate was about 25 percent. In the NLSY79, with its sample of older workers, the takeup rate was about 22 percent. Many workers have begun working at home in response to the pandemic. CPS estimates indicate that, from February to April, the drop in employment in occupations in which telework is feasible was considerably smaller than the drop in employment in occupations in which telework is not feasible. This differential effect exists both within and across major industries, and it is likely to persist throughout the pandemic. The extent to which working patterns will be permanently affected by the pandemic is an open question. One might speculate that the takeup rate will increase permanently as workers and employers become more comfortable with telework arrangements. Appendix 11 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table A-1. Percentage of workers in telework status categories in the ATUS, by demographic, occupational, and industry characteristics Telework status category Category Unable to telework Unable to telework Able to telework and and did not telework All Educational attainment Less than a high school diploma High school diploma, no college Some college or associate's degree Bachelor's degree and higher Age 15 to 24 years 25 to 54 years 55 years and older Comparable NLS age range (51–59) Presence of children No children Children Job status Full time Part time Gender Men Women Marital status Not married Married Area Nonmetropolitan area Metropolitan area, unknown size Metropolitan area, 100,000– 250,000 Metropolitan area, 250,000– 500,000 Metropolitan area, 500,000– 1,000,000 Metropolitan area, 1,000,000– 2,500,000 Metropolitan area, 2,500,000– 5,000,000 Metropolitan area, 5,000,000+ Race or ethnicity Non-Hispanic White Black Hispanic Occupations and did telework did not telework Able to telework and did telework 54.2 2.2 32.8 10.8 88.9 74.4 0.4 1.1 9.9 21.8 0.8 2.8 61.7 1.9 30.5 5.9 29.0 3.5 46.3 21.2 76.3 50.6 49.4 0.0 2.6 2.5 21.0 33.8 38.5 2.7 13.0 9.7 50.7 2.7 36.3 10.3 53.2 55.7 2.2 2.3 34.2 30.8 10.5 11.2 50.4 70.0 2.4 1.4 35.0 23.8 12.2 4.9 57.9 50.1 2.1 2.3 28.9 37.1 11.1 10.4 64.0 47.1 1.5 2.7 27.2 36.9 7.3 13.3 67.1 57.6 1.0 2.7 28.4 32.8 3.4 6.8 58.1 1.5 29.0 11.3 57.6 2.3 34.7 5.5 54.9 2.8 33.2 9.2 52.7 2.5 33.4 11.4 47.5 3.0 34.2 15.4 49.2 2.0 34.4 14.4 48.6 58.8 70.0 2.7 1.7 1.1 35.8 29.9 24.8 12.9 9.6 4.2 See footnotes at end of table. 12 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table A-1. Percentage of workers in telework status categories in the ATUS, by demographic, occupational, and industry characteristics Telework status category Category Unable to telework Unable to telework Able to telework and and did not telework Management, business, and financial Professional and related Service Sales and related Office and administrative support Farming, fishing, and forestry Construction and extraction Installation, maintenance, and repair Production Transportation and material moving Industries Agriculture, forestry, fishing, and hunting Mining, quarrying, and oil and gas extraction Construction Manufacturing Wholesale and retail trade Transportation and utilities Information Financial activities Professional and business services Education and health services Leisure and hospitality Other services Public administration and did telework did not telework Able to telework and did telework 11.6 1.8 60.9 25.7 32.7 90.2 65.2 2.9 1.9 2.9 46.3 7.3 22.6 18.1 0.6 9.3 38.4 2.4 53.1 6.1 99.1 97.4 0.9 2.6 0.0 0.0 0.0 0.0 97.9 1.2 1.0 0.0 97.9 1.7 0.4 0.0 98.6 1.1 0.3 0.0 89.0 2.8 6.6 1.7 31.8 12.4 41.2 14.7 80.5 60.7 71.6 73.3 27.6 18.3 2.2 2.9 1.6 1.4 1.2 3.8 15.1 24.9 21.7 19.7 45.0 54.8 2.3 11.5 5.2 5.6 26.3 23.0 27.4 2.7 41.4 28.5 49.2 86.2 64.1 32.3 1.9 0.7 4.9 2.5 41.2 11.4 26.6 54.5 7.7 1.7 4.4 10.8 Note: NLS = National Longitudinal Surveys. Source: Authors' calculations using the 2017–18 Leave and Job Flexibilities Module of the American Time Use Survey (ATUS). Table A-2. Percentage of workers in telework status categories in the NLSY79, by demographic, occupational, industry, and job-task characteristics Telework status category Category All Unable to telework and Unable to telework did not telework and did telework 52.2 3.1 See footnotes at end of table. 13 Able to telework and Able to telework and did not telework 35.1 did telework 9.7 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table A-2. Percentage of workers in telework status categories in the NLSY79, by demographic, occupational, industry, and job-task characteristics Telework status category Category Educational attainment Less than a high school diploma High school diploma, no college Some college or associate's degree Bachelor's degree and higher Gender Men Women Race or ethnicity Non-Hispanic White Black Hispanic Marital status Not married Married Presence of children No children Children Job status Full time Part time Occupations Management, business, and financial Professional and related Service Sales and related Office and administrative support Farming, fishing, and forestry Construction and extraction Installation, maintenance, and repair Production Transportation and material moving Industries No industry reported Unable to telework and Unable to telework did not telework and did telework Able to telework and Able to telework and did not telework did telework 79.3 3.6 16.4 0.6 66.9 2.8 26.4 3.9 54.6 2.9 34.8 7.7 26.2 3.4 50.3 20.2 57.8 45.9 3.5 2.7 28.9 42.0 9.9 9.5 49.9 64.0 58.1 3.2 2.6 3.0 36.2 28.1 34.0 10.7 5.4 5.0 57.7 49.2 3.0 3.1 32.0 36.8 7.3 10.9 53.4 44.2 2.7 5.7 35.0 35.9 9.0 14.3 50.1 65.1 3.1 2.7 36.5 26.3 10.3 5.9 10.5 3.0 66.3 20.2 32.9 83.0 64.0 2.7 3.6 5.9 46.0 12.5 19.2 18.3 0.9 11.0 36.8 1.8 56.8 4.7 100.0 0.0 0.0 0.0 96.0 4.0 0.0 0.0 93.2 2.9 3.9 0.0 92.4 3.7 3.9 0.0 98.7 2.0 1.3 0.0 43.7 6.1 34.9 15.3 See footnotes at end of table. 14 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table A-2. Percentage of workers in telework status categories in the NLSY79, by demographic, occupational, industry, and job-task characteristics Telework status category Category Agriculture, forestry, fishing, and hunting Mining, quarrying, and oil and gas extraction Construction Manufacturing Wholesale and retail trade Transportation and utilities Information Financial activities Professional and business services Education and health services Leisure and hospitality Other services Public administration Time on physical tasks Almost all More than half Less than half Almost none Time on repetitive tasks Almost all More than half Less than half Almost none Time on managing or supervising Almost all Half or more Less than half Almost none Solve problems of 30+ minutes 1+/day 1+/week 1+/month Never Use high school+ math 1+/day 1+/week 1+/month Never Unable to telework and Unable to telework did not telework and did telework Able to telework and Able to telework and did not telework did telework 59.1 24.9 12.0 4.1 85.0 0.0 7.1 7.9 73.3 61.7 69.0 71.9 18.9 22.0 5.0 1.7 1.7 1.7 3.8 2.8 19.5 30.6 22.6 22.8 48.5 54.7 2.3 6.0 6.7 3.7 28.8 20.5 28.3 3.2 47.8 20.6 47.3 3.1 40.2 9.5 75.3 38.4 43.5 PDII task measures 4.2 6.1 1.6 16.4 45.0 47.4 4.1 10.5 7.5 80.9 64.8 42.7 22.4 2.6 3.9 3.2 3.3 15.0 27.2 43.1 55.0 1.6 4.1 11.0 19.3 69.9 61.1 45.4 37.1 3.0 2.4 3.1 3.5 23.6 31.7 41.2 42.2 3.5 4.8 10.3 17.2 43.7 44.3 52.8 56.4 3.1 3.5 3.2 2.9 42.6 39.6 34.6 31.7 10.5 12.6 9.4 8.9 41.0 52.3 60.7 73.9 3.4 3.2 3.3 1.9 40.6 36.3 31.5 21.2 14.9 8.2 4.6 3.1 51.0 48.1 42.7 54.5 2.3 5.1 5.0 2.6 34.5 35.2 40.5 34.3 12.3 11.7 11.8 8.6 See footnotes at end of table. 15 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table A-2. Percentage of workers in telework status categories in the NLSY79, by demographic, occupational, industry, and job-task characteristics Telework status category Category Longest document typically read at job < 1 page 2–5 pages 6–10 pages 11–25 pages 25+ pages Never Frequency of personal contact with people other than coworkers or supervisors A lot A moderate amount A little None at all Frequency of personal contact with customers or clients A lot Some None at all Frequency of personal contact with suppliers or contractors A lot Some None at all Frequency of personal contact with students or trainees A lot Some None at all Frequency of personal contact with patients A lot Some None at all Unable to telework and Unable to telework did not telework and did telework Able to telework and Able to telework and did not telework did telework 70.3 46.9 42.3 35.0 27.7 80.2 2.3 3.1 1.8 4.7 3.7 5.0 23.9 40.1 42.0 42.5 50.7 12.5 3.5 10.0 14.0 17.8 17.9 2.3 56.2 44.7 47.5 54.8 3.1 3.4 3.1 2.4 32.0 41.1 40.0 30.5 8.7 10.7 9.4 12.3 59.7 42.6 49.8 3.6 3.1 2.3 29.9 43.0 35.5 6.9 11.3 12.4 51.5 49.4 54.8 3.7 3.6 2.5 37.0 37.2 32.6 7.8 9.8 10.1 42.8 55.0 53.4 2.6 2.2 3.9 42.5 34.1 33.3 12.2 8.6 9.6 71.9 48.5 49.7 3.8 2.3 3.0 21.7 35.4 36.9 2.6 13.7 10.4 Note: NLSY79 = National Longitudinal Survey of Youth 1979, PDII = Princeton Data Improvement Initiative. Source: Authors' calculations using the most recent interview (2016–17) of the 1979 cohort of the NLSY79. ACKNOWLEDGMENTS: We are grateful to Dave Piccone for his help with the recent CPS estimates. We thank Dori Allard, Rachel Krantz-Kent, Joe Piacentini, and Bill Wiatrowski for their helpful comments. SUGGESTED CITATION 16 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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, U.S. Bureau of Labor Statistics, June 2020, https://doi.org/10.21916/mlr.2020.14. NOTES 1 Analyzing diary information in the American Time Use Survey (ATUS), Rachel Krantz-Kent finds that, from 2003–07 to 2013–17, workers in management, professional, and related occupations increased their time working at home. (See Krantz-Kent, “Where did workers perform their jobs in the early 21st century?” Monthly Labor Review, July 2019, https://doi.org/10.21916/mlr.2019.16.) The increased work at home documented by Krantz-Kent could possibly involve tasks done at home during a workday spent primarily at the workplace. However, as reported by Global Workforce Analytics and Flexjobs, the American Community Survey shows that the number of workers who worked at home at least half the time increased by 115 percent from 2005 to 2017. (See 2017 state of telecommuting in the U.S. employee workforce (Global Workforce Analytics and Flexjobs, 2017).) According to Lexico.com, teleworking is defined as “the action or practice of working from home, making use of the Internet, email, and the telephone” (https:// www.lexico.com/en/definition/teleworking). Most of the increase in work at home presumably involves teleworking. In this article, the terms teleworking and working at home are used interchangeably. 2 Erik Brynjolfsson, John J. Horton, Adam Ozimek, Daniel Rock, Garima Sharma, and Hong Yi Tu Ye, “COVID-19 and remote work: an early look at US data,” Working Paper 27344 (Cambridge, MA: National Bureau of Economic Research, April 2020), https:// www.nber.org/papers/w27344. 3 O*NET data are produced under the sponsorship of the U.S. Department of Labor’s Employment and Training Administration. Initially, the information in the O*NET database was collected by occupational analysts. Over time, this information has been updated through surveys of both occupational experts and each occupation’s worker population. The OES survey is a U.S. Bureau of Labor Statistics survey that measures, by geography and industry, occupational employment and wages in the United States. 4 Jonathan I. Dingel and Brent Neiman, “How many jobs can be done at home?” white paper (Chicago, IL: Becker Friedman Institute for Economics at the University of Chicago, April 2020), https://bfi.uchicago.edu/wp-content/uploads/BFI_WhitePaper_Dingel_Neiman_3.2020.pdf. 5 Simon Mongey, Laura Pilossoph, and Alex Weinberg, “Which workers bear the burden of social distancing policies?” Working Paper 27085 (Cambridge, MA: National Bureau of Economic Research, May 2020), https://www.nber.org/papers/w27085. 6 An examination of the ATUS data suggests that the percentage of workers who indicate they can work at home is somewhat higher than the percentage of workers who work at home, but the former appears to be significantly lower than the percentage of workers who are working at home in response to the pandemic. We suspect that workers in the ATUS indicate they can work at home if a formal agreement or an informal understanding with their employer allows them to work at home, rather than whether or not teleworking is technologically feasible given the nature of the job. 7 In a recent article, Harley Frazis analyzes the relationship between teleworking and the ATUS variables. (See Frazis, “Who telecommutes? Where is the time saved spent?” Working Paper 523 (U.S. Bureau of Labor Statistics, April 2020), https:// www.bls.gov/osmr/research-papers/2020/pdf/ec200050.pdf.) Our current analysis differs from that of Frazis in that we use O*NET information to determine whether working at home is technologically feasible. A recent article by Rose Woods pictorially depicts some of the relationships between teleworking and the ATUS variables. (See Woods, “Job flexibilities and work schedules in 2017–18,” Spotlight on Statistics (U.S. Bureau of Labor Statistics, April 2020), https://www.bls.gov/spotlight/2020/job-flexibilities-and-workschedules/home.htm.) 8 Occupations in O*NET are based on an extended version of the Standard Occupational Classification (SOC) system structure. The ATUS uses a slightly aggregated version of the SOC-based 2010 occupation codes. There are many cases in which multiple O*NET occupations map to a single ATUS occupation. In these cases, we first average the O*NET estimates at the ATUS occupation level and then apply Dingel and Neiman’s (“How many jobs can be done at home?”) definition for telework feasibility. 17 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 9 As noted earlier, our NLSY79 and ATUS definitions of teleworkers do not fully correspond to each other. In the NLSY79, some individuals who usually work at home 8 hours a week may never work full days at home, in which case they would not be counted as teleworkers under the ATUS definition. And there are workers we classify as teleworkers in the ATUS who report teleworking less than once a week in response to a question about the frequency of teleworking. These workers would likely not be counted as teleworkers under the NLSY79 definition. 10 The NLSY79 uses 2002 census occupation codes. There are many cases in which multiple O*NET occupations map to a single NLSY79 occupation. In these cases, we first average the O*NET responses and then apply the Dingel and Neiman’s (“How many jobs can be done at home?”) definition for telework feasibility. 11 In the NLSY79 questionnaire, “physical tasks” are defined broadly as “standing, handling objects, operating machinery or vehicles, or making or fixing things with your hands.” Since occupations with a high O*NET value for any of these characteristics are classified as jobs in which teleworking is not feasible, it is not surprising that jobs that NLSY79 respondents identify as physical fall into this category. The same observation applies to jobs that workers in the NLSY79 identify as requiring extensive personal contact, because occupations with a high O*NET value for dealing with the public are classified as jobs in which teleworking is not feasible. We had hoped that the NLSY79 variables on the time spent on physical tasks and on the frequency of personal contacts would be helpful in ascertaining whether or not teleworking in an occupation is feasible, but these variables did not improve the predictive performance of the O*NET measure. 12 Brynjolfsson et al., “COVID-19 and remote work.” 13 Another possible explanation for the low estimated takeup rates is that job variations within an occupation result in some error in our measure of whether a worker is in a job in which working at home is technically feasible. 14 See Drew Harwell, “Managers turn to surveillance software, always-on webcams to ensure employees are (really) working from home,” The Washington Post, April 30, 2020, https://www.washingtonpost.com/technology/2020/04/30/work-from-home-surveillance/. 15 Formal telework agreements are common in the federal government, but rare in the private sector. According to National Compensation Survey estimates, flexible workplace agreements covered only 7 percent of private sector workers in 2019. 16 See Alana Semuels, “The coronavirus is making us see that it’s hard to make remote work actually work,” Time, March 13, 2020, https://time.com/5801882/coronavirus-spatial-remote-work/. 17 After our article was written, Dimitris Papanikolaou and Lawrence D. W. Schmidt published a working paper that uses ATUS information on whether workers work from home, measuring the extent to which workers in an industry can telework. Similarly to us, the authors find that, during the early stage of the COVID-19 pandemic, employment fell by a greater amount in industries in which fewer workers were working from home prior to the pandemic. See Papanikolaou and Schmidt, “Working remotely and the supplyside impact of Covid-19,” Working Paper 27330 (Cambridge, MA: National Bureau of Economic Research, June 2020), https:// www.nber.org/papers/w27330. 18 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, https://doi.org/ 10.21916/mlr.2020.11; and Joseph S. Vavra, “Shutdown sectors represent large share of all U.S. employment” (Chicago, IL: Becker Friedman Institute for Economics at the University of Chicago, March 31, 2020), https://bfi.uchicago.edu/insight/blog/key-economicfacts-about-covid-19/. The highly exposed industries identified by Vavra are “Restaurants and Bars, Travel and Transportation, Entertainment (e.g., casinos and amusement parks), Personal Services (e.g., dentists, daycare providers, barbers), other sensitive Retail (e.g., department stores and car dealers), and sensitive Manufacturing (e.g., aircraft and car manufacturing).” 19 Our estimates differ slightly from published CPS estimates because of such things as the treatment of missing industry codes. RELATED CONTENT 18 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Related Articles Demographics, earnings, and family characteristics of workers in sectors initially affected by COVID-19 shutdowns, Monthly Labor Review, June 2020. 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. Related Subjects Current population survey Health Employment Time use Technology Home-based work Unemployment Industry and Occupational studies 19 Flexiplace Demographics Workplace Safety and June 2020 Job openings, hires, and quits set record highs in 2019 Data from the Job Openings and Labor Turnover Survey show that the labor market continued to be strong throughout most of 2019, with job openings, hires, total separations, and quits reaching their highest monthly levels since these data series began in December 2000. The job openings level reached 7.5 million in January 2019; the hires level reached 6.0 million in April 2019; the separations level reached 5.8 million in April, July, and December 2019; and the quits level reached 3.6 million in July 2019. The annual hires level increased from 68.6 million in 2018 to 70.0 million in 2019, which is a series high since 2001, the first full year of data. The annual total separations level increased from 66.2 million in 2018 to 67.9 million in 2019, another series high since 2001. Within total separations, annual quits rose from 40.3 million in 2018 to 42.1 million in Montgomery McCarthy mccarthy.mongtomery@bls.gov 2019, which also was a series high. The number of layoffs and discharges—another component of total separations— edged down from 21.8 million in 2018 to 21.7 million in 2019. The annual number of other separations declined slightly over the year, from 4.1 million in 2018 to 4.0 million in 2019. Montgomery McCarthy is an economist in the Office of Employment and Unemployment Statistics, U.S. Bureau of Labor Statistics. Larry Akinyooye akinyooye.larry@bls.gov Larry Akinyooye is an economist in the Office of Employment and Unemployment Statistics, U.S. Bureau of Labor Statistics. The Job Openings and Labor Turnover Survey (JOLTS) data continued to show signs of a strong labor market in 2019, as job openings, hires, and total separations generally trended upward for total nonfarm and total private throughout the year.[1] This article reviews the JOLTS data for 2019 at the total nonfarm, industry, and region levels.[2] (For definitions of JOLTS terms, see the box that follows.) Definitions of JOLTS terms* 1 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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 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,” p. 2, https://www.bls.gov/opub/hom/pdf/homch18.pdf. 2 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Job openings The job openings level is a procyclical measure of demand; the number of job openings tends to increase during economic expansions and decrease during economic contractions.[3] A larger number of job openings 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, payroll employment has frequently been cited as a coincident economic indicator.[4] Monthly data show that job openings reached a data series high of 7.5 million in January 2019, indicating that the demand side of the labor force continued to show signs of strength. However, since the 2019 series high, job openings have trended downward, returning to early 2018 levels. Over the year, job openings fell from a December 2018 level of 6.7 million to a December 2019 level of 6.0 million, a 10.8-percent decrease.[5] (See table 1.) However, even with this decrease, job openings were still robust, compared with historical levels. Table 1. Change in level and percentage of job openings, by industry and region, not seasonally adjusted, December 2018–December 2019 (levels in thousands) Level by month and year Industry and region Change, Change, December December 2017 to 2018 to December December 2018 2019 December 2017 December 2018 December 2019 Level Percent Level Percent Industry Total nonfarm 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 5,638 5,108 20 180 381 233 148 1,260 208 834 6,699 6,106 23 291 441 297 144 1,265 164 791 5,974 5,282 13 216 360 222 138 1,045 168 633 1,061 998 3 111 60 64 –4 5 –44 –43 18.8 19.5 15.0 61.7 15.7 27.5 –2.7 0.4 –21.2 –5.2 –725 –824 –10 –75 –81 –75 –6 –220 4 –158 –10.8 –13.5 –43.5 –25.8 –18.4 –25.3 –4.2 –17.4 2.4 –20.0 218 310 244 92 42.2 –66 –21.3 118 353 269 84 806 1,087 86 1,000 711 129 340 279 61 1,198 1,238 92 1,146 907 146 306 222 84 1,069 1,147 106 1,041 744 11 –13 10 –23 392 151 6 146 196 9.3 –3.7 3.7 –27.4 48.6 13.9 7.0 14.6 27.6 17 –34 –57 23 –129 –91 14 –105 –163 13.2 –10.0 –20.4 37.7 –10.8 –7.4 15.2 –9.2 –18.0 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 2019 (levels in thousands) Level by month and year Industry and region Change, Change, December December 2017 to 2018 to December December 2018 2019 December 2017 December 2018 December 2019 Level Percent Level Percent Arts, entertainment, and recreation Accommodation and food services Other services Government Federal State and local State and local education State and local, excluding education Region Northeast South Midwest West 62 649 191 530 89 442 139 96 810 275 593 98 495 202 98 646 236 691 88 603 211 34 161 84 63 9 53 63 54.8 24.8 44.0 11.9 10.1 12.0 45.3 2 –164 –39 98 –10 108 9 2.1 –20.2 –14.2 16.5 –10.2 21.8 4.5 302 293 393 –9 –3.0 100 34.1 1,024 1,994 1,325 1,295 1,114 2,525 1,586 1,473 1,055 2,245 1,255 1,418 90 531 261 178 8.8 26.6 19.7 13.7 –59 –280 –331 –55 –5.3 –11.1 –20.9 –3.7 Note: Details may not sum to totals because of rounding. Source: U.S. Bureau of Labor Statistics, Job Openings and Labor Turnover Survey. Job openings by industry During 2019, the monthly job openings level for eight industries reached series highs. The top three industries with the most job openings were healthcare and social assistance, at 1.3 million in March; accommodation and food services, at 1.0 million in January; and construction, at 430,000 in April. (See table 2.) Table 2. Monthly data series highs, by industry and region, seasonally adjusted, 2019 Industry and region Industry Mining and logging Construction Wholesale trade Educational services Healthcare and social assistance Accommodation and food services State and local government education State and local government, excluding education Healthcare and social assistance Accommodation and food services Industry and region data element Job openings Job openings Job openings Job openings Job openings Job openings Job openings Job openings Hires Hires See footnotes at end of table. 4 Month July April January November March January October August July June Level 40,000 430,000 279,000 146,000 1,300,000 1,000,000 234,000 409,000 655,000 992,000 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 2. Monthly data series highs, by industry and region, seasonally adjusted, 2019 Industry and region Retail trade Transportation, warehousing, and utilities Professional and business services Educational services Arts, entertainment, and recreation Accommodation and food services Other services State and local government education Region Northeast South West South Northeast South West Industry and region data element Month Level Quits Quits Quits Quits Quits Quits Quits Quits November December March December November February October January 577,000 150,000 697,000 66,000 86,000 714,000 183,000 100,000 Job openings Job openings Job openings Hires Quits Quits Quits August October January July August February December 1,300,000 2,800,000 1,800,000 2,400,000 535,000 1,500,000 854,000 Source: U.S. Bureau of Labor Statistics, Job Openings and Labor Turnover Survey. Monthly job openings were up over the year from December 2018 to December 2019 in 7 of the 19 groups of industries for which data are published.[6] The largest over-the-year increases in job openings occurred in real estate and rental and leasing (+37.7 percent), state and local government, excluding education (+34.1 percent), and educational services (+15.2 percent). Industries with the largest declines over the year include mining and logging (−43.5 percent), construction (−25.8 percent), and durable goods manufacturing (−25.3 percent). (See table 1.) Job openings by region Three out of the four regions reached monthly series highs for job openings in 2019. In the South, the number of job openings reached a high of 2.8 million in October 2019. In the West, job openings reached a high of 1.8 million in January 2019; and in the Northeast, there were a record number of job openings, at 1.3 million in August 2019. (See table 2.) All four census regions experienced over-the-year declines in job openings from December 2018 to December 2019. The largest regional downward trend was in the Midwest, at 20.9 percent. Job openings fell in the South by 11.1 percent, followed by the Northeast (−5.3 percent) and the West (−3.7 percent). (See table 1.) Job openings and unemployment One way to analyze job openings and unemployment is to consider the number of unemployed persons per job opening. The number of unemployed persons per job opening is the ratio of unemployed persons, as published by the Current Population Survey (CPS), to the number of job openings. To calculate this ratio, divide the number of unemployed by the number of job openings. Unemployment and job openings levels generally move in opposite directions. That is, when the economy is strong, the number of unemployed is low and the number of job openings is high, causing the ratio to decrease. The opposite occurs when the economy weakens—unemployment increases and job openings decrease, leading to a higher ratio. Because of this countercyclical behavior, the ratio 5 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW of the number of unemployed persons per job opening provides a metric that helps describe the slack or tightness in the labor market.[7] When the “Great Recession” began in December 2007, the number of unemployed persons per job opening was 1.7.[8] The ratio peaked at 6.4 unemployed persons per job opening in July 2009, the month after the recession ended. In 2018, the ratio of unemployed persons per job opening went below 1.0 for the first time. For 22 consecutive months—from March 2018 to December 2019—the ratio of unemployed persons per job opening was below 1.0. Within the year (2019), the ratio fell to a series low of 0.8 from March through October. (See figure 1.) Hires Like job openings, hires are a procyclical measure. The hires level has increased each year since the end of the 2007–09 recession, in June 2009. The 2019 monthly level for hires rose to a series high of 6.0 million in April. The total annual hires level has risen for 10 consecutive years; it increased from 68.6 million in 2018 to 69.9 million in 2019, or 2.0 percent. (See table 3.) 6 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 3. Change in level and percentage of annual hires, by industry and region, not seasonally adjusted, 2017–19 (levels in thousands) Change, 2017 to Change, 2018 to Level by year 2018 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 State and local education State and local, excluding education Region Northeast South Midwest West 2019 2017 2018 2019 Level Percent Level Percent 65,638 68,594 69,943 2,956 4.5 1,349 2.0 61,502 374 4,585 3,985 2,238 1,748 12,642 1,656 8,479 2,507 1,018 2,530 1,657 874 13,430 8,007 1,141 6,867 12,236 2,048 10,188 2,687 4,138 380 3,757 1,820 1,936 64,286 449 4,524 4,390 2,512 1,879 13,682 1,756 9,032 2,895 1,088 2,501 1,636 864 13,747 8,509 1,159 7,350 12,797 2,211 10,587 2,598 4,310 420 3,889 2,013 1,874 65,567 319 4,981 4,081 2,297 1,783 13,870 1,806 9,088 2,976 1,123 2,649 1,672 977 13,860 8,689 1,209 7,480 13,388 1,979 11,408 2,603 4,376 506 3,868 1,993 1,877 2,784 75 –61 405 274 131 1,040 100 553 388 70 -29 –21 –10 317 502 18 483 561 163 399 –89 172 40 132 193 –62 4.5 20.1 –1.3 10.2 12.2 7.5 8.2 6.0 6.5 15.5 6.9 –1.1 –1.3 –1.1 2.4 6.3 1.6 7.0 4.6 8.0 3.9 –3.3 4.2 10.5 3.5 10.6 –3.2 1,281 –130 457 –309 –215 –96 188 50 56 81 35 148 36 113 113 180 50 130 591 –232 821 5 66 86 –21 –20 3 2.0 –29.0 10.1 –7.0 –8.6 –5.1 1.4 2.8 0.6 2.8 3.2 5.9 2.2 13.1 0.8 2.1 4.3 1.8 4.6 –10.5 7.8 0.2 1.5 20.5 –0.5 –1.0 0.2 10,486 25,898 14,340 14,909 10,496 27,315 15,192 15,592 11,000 28,094 14,972 15,876 10 1,417 852 683 0.1 5.5 5.9 4.6 504 779 –220 284 4.8 2.9 –1.4 1.8 Note: Details may not sum to totals because of rounding. Source: U.S. Bureau of Labor Statistics, Job Openings and Labor Turnover Survey. Hires by industry Annual hires rose in 14 of 19 industries in 2019 and fell in 5 industries. The largest percentage increases in annual hires levels in 2019 were in federal government (+20.5 percent), real estate and rental and leasing (+13.1 percent), and construction (+10.1 percent).[9] The largest percentage declines in hires occurred in mining and logging 7 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW (−29.0 percent); arts, entertainment, and recreation (−10.5 percent); and durable goods manufacturing (−8.6 percent). (See table 3.) There were 5 industries that had annual series highs for the number of hires in 2019. The top 3 industries in terms of hires are professional and business services, accommodation and food services, and healthcare and social assistance. (See table 4.) Table 4. Annual data series highs, by industry and region, not seasonally adjusted, 2019 (levels in thousands) Industry and region Industry Transportation, warehousing, and utilities Professional and business services Educational services Healthcare and social assistance Accommodation and food services Retail trade Transportation, warehousing, and utilities 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 Region Northeast South West Northeast South Midwest West Industry and region data element Level Hires Hires Hires Hires Hires Quits Quits Quits Quits Quits Quits Quits Quits Quits 2,976 13,860 1,209 7,480 11,408 6,238 1,639 7,782 640 4,901 942 8,239 1,621 1,103 Hires Hires Hires Quits Quits Quits Quits 11,000 28,094 15,876 5,778 17,158 9,245 9,931 Source: U.S. Bureau of Labor Statistics, Job Openings and Labor Turnover Survey. Monthly seasonally adjusted hires reached series highs in two industries during 2019: accommodation and food services, at 992,000 in June, and healthcare and social assistance, at 655,000 in July. (See table 2.) Hires by region The Northeast region had the highest percentage increase in annual hires in 2019, rising 4.8 percent. Annual hires also increased in the South (+2.9 percent) and West (+1.8 percent), while they declined in the Midwest (−1.4 percent). In 2018, the Midwest had the highest percentage increase in annual hires, at 5.9 percent, while the Northeast had the lowest percentage increase in annual hires, at 0.1 percent. (See table 3.) The South, Northeast, and West regions had series highs in the number of annual hires in 2019. In July 2019, the South experienced a series high of 2.4 million hires based on its monthly seasonally adjusted level. (See table 2.) 8 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Hires and job openings Following steady growth in the number of job openings after the end of the 2007–09 recession in June 2009, job openings started to increase rapidly in early 2014. Hires also increased after the recession, but at a slower pace than job openings. The monthly number of total nonfarm hires has exceeded the number of job openings for most of the history of the JOLTS series. In January 2015, however, job openings began to exceed hires, which was not expected, because hires is a full-month (or flow) measure whereas job openings is a 1-day, end-of-month snapshot (or stock) measure. When job openings exceed hires, it may suggest that employers have unmet demand for workers. For 60 consecutive months—from January 2015 to December 2019—job openings exceeded hires. The last time that the number of hires exceeded the number of job openings was in December 2014. (See figure 2.) Total separations The annual number of total separations increased 2.5 percent from 2018 to 2019, rising from 66.2 million to 67.9 million. (See table 5.) Total separations—also known as turnover—has risen annually for 9 consecutive years. 9 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 5. Change in level and percentage of annual total separations, by industry and region, not seasonally adjusted, 2017–19 (levels in thousands) Level by year Change, 2017 to 2018 Change, 2018 to 2019 Industry and region 2017 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 State and local education State and local, excluding education Region Northeast South Midwest West 2018 2019 Level 63,497 66,199 67,856 2,702 Percent Level Percent 4.3 1,657 2.5 2.5 –12.0 15.2 –2.5 –0.6 –4.7 1.4 1.6 –0.5 8.0 4.1 7.5 4.4 13.4 1.5 0.1 –2.5 0.6 4.1 –7.8 6.5 –1.4 1.9 16.3 0.2 0.5 0.1 59,429 327 4,278 3,813 2,116 1,695 12,512 1,625 8,540 2,352 1,014 2,381 1,576 806 13,024 7,558 1,068 6,487 11,910 1,969 9,941 2,609 4,068 401 3,666 1,782 1,885 62,058 393 4,215 4,123 2,291 1,830 13,501 1,714 9,154 2,630 1,057 2,334 1,530 804 13,294 8,034 1,129 6,906 12,547 2,108 10,438 2,561 4,138 400 3,739 1,928 1,810 63,640 2,629 346 66 4,855 -63 4,021 310 2,277 175 1,744 135 13,685 989 1,741 89 9,106 614 2,840 278 1,100 43 2,508 –47 1,597 –46 912 –2 13,488 270 8,046 476 1,101 61 6,945 419 13,064 637 1,943 139 11,120 497 2,525 –48 4,216 70 465 –1 3,748 73 1,937 146 1,811 –75 4.4 1,582 20.2 –47 –1.5 640 8.1 –102 8.3 –14 8.0 –86 7.9 184 5.5 27 7.2 –48 11.8 210 4.2 43 –2.0 174 –2.9 67 –0.2 108 2.1 194 6.3 12 5.7 –28 6.5 39 5.3 517 7.1 -165 5.0 682 –1.8 –36 1.7 78 –0.2 65 2.0 9 8.2 9 –4.0 1 10,303 25,125 13,832 14,233 10,086 26,299 14,621 15,191 10,511 -217 26,781 1,174 14,493 789 16,072 958 –2.1 4.7 5.7 6.7 425 482 –128 881 4.2 1.8 –0.9 5.8 Note: Details may not sum to totals because of rounding. Source: U.S. Bureau of Labor Statistics, Job Openings and Labor Turnover Survey. Total separations include quits, layoffs and discharges, and other separations. Each of these data elements has its own unique trend and cyclical movements. Quits are procyclical, which means that the number of quits typically rises when the economy expands and falls when the economy contracts. Layoffs and discharges are countercyclical, which means that their numbers typically rise during economic contractions and fall during economic expansions. The other separations data element remains relatively constant over time. Figure 3 shows this relationship by displaying the percentage of total separations attributed to each type of separation. Quits as a 10 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW percentage of total separations have been increasing since 2009, whereas layoffs and discharges as a percentage of total separations have been decreasing since 2009. The number of annual quits rose over the year, from 40.3 million to 42.1 million. (See table 6.) Table 6. Change in level and percentage of annual quits, by industry and region, not seasonally adjusted, 2017–19 (levels in thousands) Change, 2017 to Change, 2018 to Level by year 2018 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 2017 2018 2019 37,708 40,331 42,113 2,623 7.0 1,782 4.4 35,682 172 1,852 2,292 1,261 1,033 7,882 1,020 5,616 1,244 38,174 247 2,058 2,506 1,378 1,127 8,497 1,067 5,958 1,473 39,878 177 2,082 2,475 1,380 1,093 8,897 1,022 6,238 1,639 2,492 75 206 214 117 94 615 47 342 229 7.0 43.6 11.1 9.3 9.3 9.1 7.8 4.6 6.1 18.4 1,704 -70 24 -31 2 –34 400 –45 280 166 4.5 –28.3 1.2 –1.2 0.1 –3.0 4.7 –4.2 4.7 11.3 See footnotes at end of table. 11 Level 2019 Percent Level Percent U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 6. Change in level and percentage of annual quits, by industry and region, not seasonally adjusted, 2017–19 (levels in thousands) Change, 2017 to Change, 2018 to Level by year 2018 Industry and region 2017 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 State and local education State and local, excluding education Region Northeast South Midwest West 2018 2019 Level 2019 Percent Level Percent 521 1,365 909 457 7,458 4,920 576 4,345 7,749 779 6,972 1,470 2,026 177 1,847 926 923 568 1,407 857 549 7,561 5,379 580 4,797 8,444 919 7,524 1,511 2,159 184 1,975 1,043 933 563 1,560 1,014 546 7,782 5,543 640 4,901 9,181 942 8,239 1,621 2,236 206 2,028 1,103 925 47 42 –52 92 103 459 4 452 695 140 552 41 133 7 128 117 10 9.0 3.1 –5.7 20.1 1.4 9.3 0.7 10.4 9.0 18.0 7.9 2.8 6.6 4.0 6.9 12.6 1.1 –5 153 157 -3 221 164 60 104 737 23 715 110 77 22 53 60 -8 –0.9 10.9 18.3 –0.5 2.9 3.0 10.3 2.2 8.7 2.5 9.5 7.3 3.6 12.0 2.7 5.8 –0.9 5,424 15,317 8,116 8,853 5,388 16,467 8,988 9,488 5,778 17,158 9,245 9,931 -36 1,150 872 635 –0.7 7.5 10.7 7.2 390 691 257 443 7.2 4.2 2.9 4.7 Note: Details may not sum to totals because of rounding. Source: U.S. Bureau of Labor Statistics, Job Openings and Labor Turnover Survey. The annual quits level has risen for 10 consecutive years. Annual layoffs and discharges decreased slightly over the year, from 21.8 million in 2018 to 21.7 million in 2019. (See table 7.) Table 7. Change in level and percentage of annual layoffs and discharges, by industry and region, not seasonally adjusted, 2017–19 (levels in thousands) Level by year Change, 2017 to 2018 Industry and region 2017 Total Industry Total private Mining and logging Construction 2018 2019 Level Percent Change, 2018 to 2019 Level Percent 21,608 21,803 21,739 195 0.9 –64 –0.3 20,263 20,544 20,492 128 129 152 2,245 2,002 2,571 281 1 –243 1.4 0.8 –10.8 -52 23 569 –0.3 17.8 28.4 See footnotes at end of table. 12 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 7. Change in level and percentage of annual layoffs and discharges, by industry and region, not seasonally adjusted, 2017–19 (levels in thousands) Level by year Change, 2017 to 2018 Industry and region 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 State and local education State and local, excluding education Region Northeast South Midwest West Level Percent Change, 2018 to 2019 2017 2018 2019 Level Percent 1,253 702 549 3,741 490 2,302 951 396 683 383 303 4,891 2,064 427 1,638 3,847 1,153 2,695 1,012 1,342 120 1,223 562 663 1,371 753 620 4,171 502 2,658 1,012 409 634 417 218 4,989 2,101 480 1,622 3,800 1,146 2,654 938 1,257 89 1,168 601 567 1,305 747 559 4,022 604 2,400 1,019 449 644 323 319 5,012 2,008 399 1,611 3,560 965 2,594 763 1,248 120 1,127 548 580 118 51 71 430 12 356 61 13 –49 34 –85 98 37 53 –16 –47 –7 –41 –74 –85 –31 –55 39 –96 9.4 7.3 12.9 11.5 2.4 15.5 6.4 3.3 –7.2 8.9 –28.1 2.0 1.8 12.4 –1.0 –1.2 –0.6 –1.5 –7.3 –6.3 –25.8 –4.5 6.9 –14.5 –66 –6 –61 –149 102 –258 7 40 10 –94 101 23 –93 –81 –11 –240 –181 –60 –175 –9 31 –41 –53 13 –4.8 –0.8 –9.8 –3.6 20.3 –9.7 0.7 9.8 1.6 –22.5 46.3 0.5 –4.4 –16.9 –0.7 –6.3 –15.8 –2.3 –18.7 –0.7 34.8 –3.5 –8.8 2.3 4,130 8,190 4,869 4,417 3,928 8,352 4,787 4,733 3,989 8,124 4,459 5,171 –202 162 –82 316 –4.9 2.0 –1.7 7.2 61 -228 –328 438 1.6 –2.7 –6.9 9.3 Note: Details may not sum to totals because of rounding. Source: U.S. Bureau of Labor Statistics, Job Openings and Labor Turnover Survey. The annual level of other separations declined slightly, from 4.1 million in 2018 to 4.0 million in 2019. (See table 8.) 13 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Table 8. Change in level and percentage of annual other separations, by industry and region, not seasonally adjusted, 2017–19 (levels in thousands) Level by year Change, 2017 to 2018 Industry and region 2017 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 State and local education State and local, excluding education Region Northeast South Midwest West 2018 2019 Level Percent Change, 2018 to 2019 Level Percent 4,182 4,065 4,002 –117 –2.8 –63 –1.5 3,483 3,342 3,269 25 21 17 181 156 202 270 248 240 155 161 151 114 88 89 892 836 765 115 145 113 622 542 470 155 147 182 95 80 91 333 294 304 283 255 260 48 37 44 677 743 692 570 553 497 66 68 64 505 485 432 312 304 323 42 42 37 271 260 284 127 114 142 698 724 735 104 128 140 593 595 593 294 285 287 300 309 310 –141 –4 –25 –22 6 –26 –56 30 –80 –8 –15 –39 –28 –11 66 –17 2 –20 -8 0 –11 –13 26 24 2 –9 9 –4.0 –16.0 –13.8 –8.1 3.9 –22.8 –6.3 26.1 –12.9 –5.2 –15.8 –11.7 –9.9 –22.9 9.7 –3.0 3.0 –4.0 -2.6 0.0 –4.1 –10.2 3.7 23.1 0.3 –3.1 3.0 –73 –4 46 –8 –10 1 –71 –32 –72 35 11 10 5 7 –51 –56 –4 –53 19 –5 24 28 11 12 –2 2 1 –2.2 –19.0 29.5 –3.2 –6.2 1.1 –8.5 –22.1 –13.3 23.8 13.8 3.4 2.0 18.9 –6.9 –10.1 –5.9 –10.9 6.3 –11.9 9.2 24.6 1.5 9.4 –0.3 0.7 0.3 747 769 746 1,621 1,479 1,496 848 844 790 964 973 972 22 –142 –4 9 2.9 –8.8 –0.5 0.9 –23 17 –54 –1 –3.0 1.1 –6.4 –0.1 Note: Details may not sum to totals because of rounding. Source: U.S. Bureau of Labor Statistics, Job Openings and Labor Turnover Survey. Components of separations by industry 14 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 2019 with the components of separations. Quits Quits include employees who left their job voluntarily, excluding retirements or transfers to other locations, which are counted as other separations. In 2019, the number of annual quits grew in 13 of 19 industries, while 6 industries had fewer quits. The largest percentage increases in annual quits levels in 2019 were in finance and insurance (+18.3 percent), federal government (+12.0 percent), and transportation, warehousing, and utilities (+11.3 percent). After having the largest percentage increase in annual quits in 2018, mining and logging had the largest 2019 annual percentage decrease (−28.3 percent), followed by wholesale trade (−4.2 percent), and nondurable goods manufacturing (−3.0 percent). Nine of 19 industries reached a series high for the annual level of quits. The top 3 of these industries are accommodation and food services, at 8.2 million; professional and business services, at 7.8 million; and retail trade, at 6.2 million. (See table 6.) Eight industries reached monthly seasonally adjusted series highs for quits in 2019: accommodation and food services, at 714,000 in February; professional and business services, at 697,000 in March; and retail trade at 577,000 in November. (See table 2.) Layoffs and discharges In general, layoffs and discharges include involuntary separations initiated by the employer, including layoffs with no intent to rehire. Annual layoffs and discharges dropped in 2019 in 10 of 19 industries, whereas 9 industries had higher layoffs and discharges. The largest percentage declines in annual layoffs and discharges were in finance and insurance (−22.5 percent), other services (−18.7 percent), and educational services (−16.9 percent). After having the largest percentage decrease in annual layoffs and discharges in 2018, real estate and rental and leasing had the largest 2019 annual percentage increase (+46.3 percent), followed by federal government (+34.8 percent),[10] and construction (+28.4 percent). For annual layoffs and discharges, only one industry reached a series low—finance and insurance, at 323,000. (See table 7.) For monthly layoffs and discharges, no industry reached a series high. State and local government, excluding education, was the only industry to reach a series low for the monthly layoffs and discharges level, at 29,000 in December. (See table 4.) Other separations In 2019, annual other separations increased in 11 of 19 industries, with 8 industries having fewer annual other separations than in the previous year. The largest percentage increases in annual other separations include construction (+29.5 percent), other services (+24.6 percent), and transportation, warehousing, and utilities (+23.8 percent). The industries with the largest percentage declines in annual other separations were wholesale trade (−22.1 percent), mining and logging (−19.0 percent), and retail trade (−13.3 percent). No industry reached a series high for the annual level of other separations. Retail trade dropped to an annual series low of 470,000, as did durable goods manufacturing, at 151,000, and mining and logging, at 17,000. (See table 8.) There were no monthly seasonally adjusted series highs in other separations for 2019. (See table 2.) 15 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Components of separations by region In 2019, the Northeast region had an annual level of 10.5 million total separations. Within total separations, the Northeast had 5.8 million quits, 4.0 million layoffs and discharges, and 746,000 other separations. In the South region, the annual level of total separations for 2019 was 26.8 million. Within total separations, the quits level was 17.2 million for the South region, the layoffs and discharges level was 8.1 million, and the other separations level was 1.5 million. In the Midwest region, the annual total separations level was 14.5 million. Within total separations, there were 9.2 million quits in the Midwest region, 4.5 million layoffs and discharges, and 790,000 other separations. In 2019, the West region annual total separations level was 16.1 million. Within total separations in the West region, the quits level was 9.9 million, the layoffs and discharges level was 5.2 million, and the other separations level was 972,000. (See tables 5, 6, 7, and 8.) Three out of the four regions reached monthly series highs for quits in 2019. The South quits level reached a series high of 1.5 million, in February; the West quits level reached a series high of 854,000, in December; and the Northeast quits level reached a series high of 535,000, in August. (See table 2.) No region reached a monthly series high for layoffs and discharges and other separations in 2019. An analysis of each region by the components as a percentage of total separations illustrates the different characteristics of the JOLTS data at the region level. The Northeast region had the smallest percentage of quits within total separations, at 55.1 percent in 2019. The South experienced the highest percentage of quits, at 64.1 percent. In 2019, the Northeast region had the largest percentage of layoff and discharges within total separations, at 37.8 percent. The South region had the lowest percentage of layoffs and discharges, at 30.3 percent. The Northeast had the highest percentage of other separations, at 7.1 percent, while the Midwest region had the lowest percentage, at 5.5 percent. (See figure 4.) 16 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Quits compared with layoffs and discharges Over the period from July 2011 to December 2019, there were 102 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 the number of quits increasing and the number of layoffs and discharges remaining flat. (See figure 5.) 17 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW Summary JOLTS data show that the level of job openings, hires, total separations, and quits in the U.S. labor market rose throughout 2019. The job openings level began the year at its highest level since the data series began in December 2000. Although job openings declined throughout the year, ending at a lower level than in December 2018, the average job openings level in 2019 was higher than the average job openings level in 2018. In 2019, the number of hires continued its strong growth rate throughout the year and reached its highest level since the series began in December 2000. The number of total separations also maintained strong growth in 2019 and reached its highest level since December 2000. Much of the growth in total separations can be attributed to the increase in the number of quits, which also rose to a new high since the series began in December 2000. SUGGESTED CITATION Montgomery McCarthy and Larry Akinyooye, "Job openings, hires, and quits set record highs in 2019," Monthly Labor Review, U.S. Bureau of Labor Statistics, June 2020, https://doi.org/10.21916/mlr.2020.12. NOTES 1 The Job Openings and Labor Turnover Survey (JOLTS) produces monthly data on job openings, hires, quits, layoffs and discharges, and other separations from a sample of approximately 16,000 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 NAICS two-digit sectors. All 18 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW 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. 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, 2015), https://www.bls.gov/opub/hom/pdf/jlt-20130314.pdf. See also the JOLTS page on the BLS website, at https:// www.bls.gov/jlt/. 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, September 9, 2019, 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 payroll employment being a “coincident” economic indicator, see Geoffrey H. Moore, “An introduction to international economic indicators,” in Business Cycles, Inflation, and Forecasting, 2nd ed. (Pensacola, FL: Ballinger Publishing, 1983), pp. 65–92, https://www.nber.org/chapters/c0692.pdf; see p. 70. 5 BLS considers job openings a stock measure and does not produce job openings annual totals. 6 The JOLTS program publishes estimates by seven NAICS supersectors (manufacturing; trade, transportation, and utilities; financial activities; education and health services; leisure and hospitality; government; and state and local government) and for 19 other groups of industries that are within the scope of the JOLTS program; excluded are agriculture and private households. Publicly owned establishments are classified in government. For a complete list of the 19 groups of industries (henceforth referred to as “industries”), see the JOLTS NAICS page at https://www.bls.gov/jlt/jltnaics.htm. 7 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 the definition of "countercyclical" in InvestorWords, at http://www.investorwords.com/1166/countercyclical.html. . 8 The National Bureau of Economic Research (NBER) is the official arbiter of the beginning and ending dates of U.S. business cycle expansions and contractions. The NBER Business Cycle Dating Committee recently determined that a peak in monthly economic activity occurred in the U.S. economy in February 2020, marking an end to the most recent economic expansion and the beginning of a recession. See “Determination of the February 2020 peak in U.S. economic activity” (National Bureau of Economic Research, June 8, 2020), http://www.nber.org/cycles/june2020.html. See also, “U.S. business cycle expansions and contractions” (National Bureau of Economic Research, June 8, 2020), http://www.nber.org/cycles/. 9 The large increase in annual hires for the federal government was largely the result of the hiring of temporary Census 2020 workers in the late summer of 2019. 10 The large increase in annual layoffs and discharges for the federal government was heavily affected by the temporary Census 2020 workers having their positions ended in October 2019. RELATED CONTENT Related Articles Job market remains tight in 2019, as the unemployment rate falls to its lowest level since 1969, Monthly Labor Review, April 2020 Employment expansion continued in 2019, but growth slowed in several industries, Monthly Labor Review, April 2020 19 U.S. BUREAU OF LABOR STATISTICS MONTHLY LABOR REVIEW The cost of layoffs in Unemployment Insurance taxes, Monthly Labor Review, April 2020 Job openings, hires, and quits reach historic highs in 2018, Monthly Labor Review, July 2019 Related Subjects Bureau of labor statistics Expansions Jobseekers 20 June 2020 The number of people who can telework is higher than was estimated Maureen Soyars Hicks March 2020 marked the beginning of a new experiment in the American workplace. Millions of people began working from home in an effort to inhibit the spread of the COVID-19 virus, also known as the novel coronavirus. As more people than ever are skipping daily commutes and holding virtual meetings, a fundamental question arises: how many people can actually perform all of their work duties from home? In their working paper “How many jobs can be done at home?” economists Jonathan I. Dingel and Brent Neiman (National Bureau of Economic Research, Working Paper 26948, April 2020) use data from the Occupational Information Network (O*Net) and U.S. Bureau of Labor Statistics (BLS) to estimate how many jobs in the United States can be performed entirely at home. The authors find that 37 percent of U.S. jobs can be performed entirely at home—a number that greatly exceeds any recent estimate of how many workers telecommute on an average day. According to the 2018 American Time Use Survey, “less than a quarter of all full-time workers work from home on an average day, and even those workers typically spend well less than half of their working hours at home.” Dingel and Neiman determine whether a job should be classified as “feasible for telework” using responses from two O*Net surveys covering “work context” and “generalized work activities.” If a job requires daily “outdoor work,” for example, they determine that it cannot be performed at home. Then, the authors merge their classifications with data from BLS “on the prevalence of each occupation in the aggregate U.S. economy as well as in particular metropolitan statistical areas and 2-digit NAICS industries.” According to Dingel and Neiman, workers in telework-capable occupations typically earn more: the 37 percent of U.S. jobs that can plausibly be performed at home account for 46 percent of all wages. The authors note that findings varied across cities and industries. For example, more than 45 percent of jobs in San Francisco, San Jose, and Washington, DC, can be performed at home, while only 30 percent or less of the jobs in Fort Myers, Grand Rapids, and Las Vegas can be performed at home. The findings also indicate that most jobs in finance, corporate management, and professional and scientific services can plausibly be performed at home, while very few jobs in agriculture, hotels and restaurants, or retail can be. The authors then analyze countries other than the United States and find “a clear positive relationship between income levels and the shares of jobs that can be done from home.” They find that fewer than 25 percent of jobs in Mexico and Turkey can be performed at home, whereas more than 40 percent of jobs in Sweden and the United Kingdom can be. These results suggest that developing economies may face challenges in continuing to work during periods of social distancing during the spread of the COVID-19 virus. 1
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