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Labor Day 2020 Fast Facts

I have been Commissioner of Labor Statistics for about a year and a half now, and what a time it has been! BLS has faced many challenges throughout its history, but none quite like those from the COVID-19 pandemic. All of our staff moved to full-time telework March 16, and I am so proud of how well they have worked under trying circumstances. In a very short time—days, not weeks—we had to change our data collection processes to eliminate in-person collection and move to a combination of telephone, internet, and video. We recognize how challenging it is for our survey respondents to provide data during the pandemic, and I am very grateful for their cooperation. Response rates have dipped a bit in some programs, but the quality of our samples remains strong across the board. Despite all of the challenges, BLS has been able to produce all of our economic reports without interruption.

The pandemic has taught us there’s an unlimited appetite for data. The U.S. statistical system is working to satisfy that appetite. At BLS, we strive for more and better data to understand the hardships caused by the pandemic. Starting in May we added new questions to our monthly survey of households. The questions ask whether people teleworked or worked from home because of the pandemic; whether people were unable to work because their employers closed or lost business; whether they were paid for that missed work; and whether the pandemic prevented job-seeking activities. We continue to gather new data from those questions.

We collaborated with our partners at other U.S. statistical agencies to find out how many people received payments from the Coronavirus Aid, Relief, and Economic Security (CARES) Act, signed into law on March 27, 2020. For those who received payments, we asked how they used them.

Soon we will have new data about how businesses have responded to the pandemic. These data are from a brand new survey that seeks to identify changes to business operations, employment, workforce flexibilities, and benefits as a result of the pandemic.

These are just a few examples of how our data collection has responded to the pandemic. Good data are essential for identifying problems, guiding policymakers, and gauging whether and how fast conditions improve for workers, jobseekers, families, and businesses.

Labor Day is a good time to reflect on where we are. Despite these difficult times, I hope you are able to enjoy the long holiday weekend. Take a moment to look at some fast facts we’ve compiled on the current picture of our labor market.

Working

Our monthly payroll survey shows that employment had been increasing through February 2020. With March came the pandemic and the job losses related to it. We lost more than 22 million jobs in March and April and then regained about 48 percent of them in May, June, July, and August.

The employment–population ratio was 56.5 percent in August. This ratio is the number of people employed as a percent of the population age 16 and older. The ratio was 61.1 percent in February.

There were 7.6 million people working part time for economic reasons in August 2020. These are people who would have preferred full-time employment but were working part time because their hours had been reduced or they were unable to find full-time jobs. This number was down from 10.9 million in April. The number was 4.3 million in February.

Not Working

The unemployment rate reached 14.7 percent in April 2020. That was the highest rate, and the largest over-the-month increase, in the history of the data back to January 1948. The rate has fallen since then, reaching 8.4 percent in August. The rate was 3.5 percent back in February, the lowest since 1969.

We have noted the challenges of measuring unemployment during this pandemic. The rates we have seen since March likely understate unemployment, but the trend is clear. The rate rose sharply in March and even more sharply in April and has trended down since April.

Among the major worker groups in August 2020, the unemployment rate was 8.4 percent for adult women and 8.0 percent for adult men. The rate for teenagers was 16.1 percent. The unemployment rate was 13.0 percent for Blacks or African Americans, 10.7 percent for Asians, 10.5 percent for Hispanics or Latinos, and 7.3 percent for Whites.

Job Openings

On the last business day of June 2020, the number of nonfarm job openings was 5.9 million. That was a decline of 18 percent from June 2019.

The ratio of unemployed people per job opening was 3.0 in June 2020. Since the most recent peak of 4.6 in April 2020, the ratio of unemployed people per job opening declined in May and June. In February 2020, there was 0.8 unemployment person per job opening.

Pay and Benefits

Civilian compensation (wage and benefit) costs increased 2.7 percent in June 2020 from a year earlier. After adjusting for inflation, real compensation costs rose 2.1 percent over the year.

Paid leave benefits are available to most private industry workers. The access rates in March 2019 were 73 percent for sick leave, 79 percent for vacation, and 79 percent for holidays.

In March 2019, civilian workers with employer-provided medical plans paid 20 percent of the cost of medical care premiums for single coverage and 33 percent for family coverage.

Productivity

Labor productivity—output per hour worked—in the U.S. nonfarm business sector grew 2.8 percent from the second quarter of 2019 to the second quarter of 2020. That increase reflects large pandemic-related declines in output (−11.2 percent) and hours worked (−13.6 percent).

Safety and Health

In 2018, there were 5,250 fatal workplace injuries. That was a 2-percent increase from 2017 and was the highest number of fatal work injuries in a decade. It was, however, below the numbers of workplace deaths in the 1990s, when over 6,000 fatalities occurred per year.

There were about 2.8 million nonfatal workplace injuries and illnesses reported in 2018 by private industry employers. This resulted in an incidence rate of 2.8 cases per 100 full-time workers in 2018. The rate is down from 9.2 cases per 100 full-time workers in 1976.

Unionization

The union membership rate—the percent of wage and salary workers who were members of unions—was 10.3 percent in 2019, down by 0.2 percentage point from 2018. In 1983, the first year for which comparable union data are available, the union membership rate was 20.1 percent.

Total employer compensation costs for private-industry union workers were $48.57 and for nonunion workers $34.16 per employee hour worked in March 2020. The cost of benefits accounted for 40.5 percent of total compensation (or $19.65) for union workers and 28.4 percent (or $9.71) for nonunion workers.

Looking to the Future

We released our latest set of long-term employment projections September 1. We project employment to grow by 6.0 million jobs from 2019 to 2029. That is an annual growth rate of 0.4 percent, slower than the 2009–19 annual growth rate of 1.3 percent. The healthcare and social assistance sector is projected to add the most new jobs, and 6 of the 10 fastest growing occupations are related to healthcare. These projections do not include impacts of the COVID-19 pandemic and response efforts. We develop the projections using models based on historical data. The historical data for this set of projections cover the period through 2019, so all input data precede the pandemic. We will continue to examine the effects of the pandemic as we update our projections next year and the years that follow.

From an American worker’s first job to retirement and everything in between, BLS has a stat for that! Want to learn more? Follow us on Twitter @BLS_gov.

New Measures of How Widespread Employment Changes Are across States and Metro Areas

BLS recently began publishing a new set of measures on employment changes in states and metropolitan areas. For decades we have published monthly estimates of employment, hours, and earnings for each state and metro area. Our new measure summarizes how widespread employment increases or decreases are across all states or metro areas. We call this measure a diffusion index.

What’s a diffusion index? Let me explain how we create the measure.

Let’s say we’re creating a diffusion index for the 50 states and the District of Columbia. We start by assigning each state and D.C. a value depending on whether its employment decreased, stayed the same, or increased over the period we’re looking at.

  • The assigned value is 0 if employment decreased.
  • The assigned value is 50 if employment stayed the same.
  • The assigned value is 100 if employment increased.

The diffusion index is the average of those 51 values. To create a diffusion index for metro areas, we assign values of 0, 50, or 100 for each of 388 metro areas and then average those values. We calculate diffusion indexes for employment changes over 1 month, 3 months, 6 months, and 12 months.

Now that we understand the simple arithmetic for calculating diffusion indexes, what do they mean? An index greater than 50 means more states or metro areas had increasing employment over the period. An index below 50 means more states or metro areas had decreasing employment. At the extremes, an index of 0 means employment fell in all states or metro areas; an index of 100 means employment rose in all of them. A diffusion index of 50 doesn’t necessarily mean 50 percent of the states or areas had increasing employment and the other 50 percent had decreasing employment. It just means the same number of states or areas had increases and decreases, with any of the other states or areas having no change.

The chart below shows 3-month diffusion indexes for all states and metro areas. You can see how all states and nearly all metro areas had job losses during the worst of the 2007–09 recession. We see it again more recently with the downturn associated with the COVID-19 pandemic.

3-month diffusion indexes for all states and all metropolitan areas, 2007–20

Editor’s note: Data for this chart are available in the table below.

Diffusion indexes aren’t a new analytical tool. We publish other diffusion indexes using national employment data that summarize how employment change is dispersed across industries. The Federal Reserve Bank of Philadelphia publishes diffusion indexes using a variety of data. The new BLS diffusion indexes summarize how employment is changing across geographic areas to give us another perspective of the labor market.

Keep a look out for the new data. We update the indexes each month in our public database.

3-month diffusion indexes for all states and all metropolitan areas
MonthAll statesAll metropolitan areas

Jan 2007

96.177.7

Feb 2007

84.372.2

Mar 2007

84.372.6

Apr 2007

74.559.1

May 2007

86.365.3

Jun 2007

69.661.2

Jul 2007

78.467.7

Aug 2007

74.561.2

Sep 2007

56.951.3

Oct 2007

62.753.2

Nov 2007

79.459.1

Dec 2007

80.463.0

Jan 2008

81.465.3

Feb 2008

78.464.0

Mar 2008

52.050.6

Apr 2008

41.236.2

May 2008

25.529.8

Jun 2008

23.535.4

Jul 2008

16.733.9

Aug 2008

16.729.8

Sep 2008

15.723.6

Oct 2008

7.817.8

Nov 2008

7.811.9

Dec 2008

3.910.1

Jan 2009

2.04.3

Feb 2009

2.03.9

Mar 2009

0.04.1

Apr 2009

0.03.6

May 2009

2.04.9

Jun 2009

3.911.7

Jul 2009

8.814.6

Aug 2009

4.914.0

Sep 2009

5.920.0

Oct 2009

16.728.0

Nov 2009

21.638.5

Dec 2009

19.638.1

Jan 2010

26.538.8

Feb 2010

18.636.0

Mar 2010

70.656.3

Apr 2010

94.174.6

May 2010

100.086.6

Jun 2010

98.079.0

Jul 2010

85.364.3

Aug 2010

35.342.4

Sep 2010

39.244.7

Oct 2010

70.663.7

Nov 2010

74.564.8

Dec 2010

88.273.7

Jan 2011

62.757.0

Feb 2011

76.561.9

Mar 2011

87.367.9

Apr 2011

98.075.5

May 2011

90.267.0

Jun 2011

80.456.4

Jul 2011

83.366.8

Aug 2011

82.472.3

Sep 2011

98.081.8

Oct 2011

82.464.8

Nov 2011

96.166.8

Dec 2011

82.463.1

Jan 2012

88.276.7

Feb 2012

97.178.9

Mar 2012

98.083.1

Apr 2012

96.175.9

May 2012

94.170.0

Jun 2012

70.658.1

Jul 2012

74.557.0

Aug 2012

77.564.4

Sep 2012

86.367.5

Oct 2012

97.176.7

Nov 2012

93.175.4

Dec 2012

90.273.7

Jan 2013

88.270.2

Feb 2013

94.179.5

Mar 2013

99.075.9

Apr 2013

87.375.0

May 2013

82.468.7

Jun 2013

82.468.4

Jul 2013

81.470.6

Aug 2013

94.176.4

Sep 2013

92.277.8

Oct 2013

90.277.8

Nov 2013

94.174.7

Dec 2013

81.473.2

Jan 2014

88.268.7

Feb 2014

80.466.5

Mar 2014

86.373.6

Apr 2014

96.182.0

May 2014

98.083.4

Jun 2014

96.183.5

Jul 2014

96.174.2

Aug 2014

92.274.5

Sep 2014

90.277.2

Oct 2014

98.079.9

Nov 2014

96.179.8

Dec 2014

98.081.8

Jan 2015

93.180.0

Feb 2015

84.374.5

Mar 2015

64.762.5

Apr 2015

74.565.1

May 2015

84.377.1

Jun 2015

84.378.2

Jul 2015

92.284.1

Aug 2015

80.474.5

Sep 2015

86.374.1

Oct 2015

88.275.9

Nov 2015

88.274.6

Dec 2015

88.273.2

Jan 2016

75.571.1

Feb 2016

81.472.4

Mar 2016

78.469.5

Apr 2016

86.377.1

May 2016

72.567.3

Jun 2016

55.957.7

Jul 2016

84.371.3

Aug 2016

86.376.2

Sep 2016

94.185.1

Oct 2016

68.666.9

Nov 2016

82.473.6

Dec 2016

78.464.7

Jan 2017

84.370.0

Feb 2017

79.468.9

Mar 2017

98.076.5

Apr 2017

88.272.0

May 2017

78.468.4

Jun 2017

91.269.6

Jul 2017

80.471.6

Aug 2017

91.275.6

Sep 2017

76.560.8

Oct 2017

80.473.8

Nov 2017

84.370.7

Dec 2017

91.273.8

Jan 2018

90.274.2

Feb 2018

96.180.2

Mar 2018

96.180.9

Apr 2018

86.372.9

May 2018

82.473.6

Jun 2018

94.176.7

Jul 2018

91.281.3

Aug 2018

94.177.2

Sep 2018

82.468.4

Oct 2018

94.172.8

Nov 2018

92.272.3

Dec 2018

88.267.9

Jan 2019

89.279.4

Feb 2019

84.373.3

Mar 2019

82.474.9

Apr 2019

61.856.4

May 2019

64.758.5

Jun 2019

66.755.4

Jul 2019

74.560.8

Aug 2019

80.467.1

Sep 2019

79.466.4

Oct 2019

70.660.3

Nov 2019

68.663.7

Dec 2019

74.567.9

Jan 2020

87.375.9

Feb 2020

86.371.8

Mar 2020

5.929.0

Apr 2020

0.00.0

May 2020

0.00.3

Jun 2020[p]

0.00.6

[p] preliminary

New Recommendations on Improving Data on Contingent and Alternative Work Arrangements

The workplace is changing. We have seen more evidence of that in recent months as workplaces have adapted to the COVID-19 pandemic. Even before the pandemic, many of us wanted to learn more about telework, flexible work hours, and independent contracting. We also wanted to know more about intermittent or short-term work found through mobile devices, unpredictable work schedules, and other employment relationships we might not think of as traditional. It’s our job at BLS to keep up with these new work relationships and figure out how to measure them.

In 2018, we released data collected in 2017 about people in contingent and alternative work arrangements. Contingent workers are people who do not expect their jobs to last or who report their jobs are temporary. Alternative work arrangements include independent contractors, on-call workers, temporary help agency workers, and workers provided by contract firms. We also published data in 2018 about electronically mediated work. All of these data reflect the rapidly changing workplace.

Those reports received a lot of attention, but policymakers, employers, researchers, and others told us they want to know more about these nontraditional workers. We need to understand people in jobs that often involve doing short-term tasks, such as ridesharing or data-entry services. Our 2017 survey included a few questions about these arrangements, but this work can be complex and varied. That makes it hard to measure nontraditional work arrangements with just a few questions.

To effectively analyze these hard-to-measure work arrangement, BLS sought out experts on nontraditional work. In 2019, we contracted with the Committee on National Statistics to explore what we should measure if we had the funding to collect and publish more data about these workers. We asked the committee not to recommend changes to the main Current Population Survey, the large monthly survey of U.S. households from which we measure the unemployment rate and other important labor market measures. The committee had free rein, however, to recommend topics we should examine in any future edition of the Contingent Worker Supplement to the Current Population Survey. We also asked the committee to recommend changes to the survey design and methods of data collection if we were to conduct the supplement again.

The Committee on National Statistics is a federally supported independent organization whose mission is to improve the statistical methods and information that guide public policies. The committee moved quickly to form a group of experts on the relevant topics. I asked these experts to review the Contingent Worker Supplement and consider other sources of information on nontraditional work arrangements. The group was impressive and included a former BLS Commissioner, a former Administrator of the U.S. Department of Labor Wage and Hour Division, and several experts in economics and survey methods. They all volunteered their time to help us improve the Contingent Worker Supplement.

The group held public meetings and a workshop, hearing from experts, data users, and policymakers to understand what data would be the most valuable. At the end of their year-long review, they produced a report with specific recommendations in July of 2020 about measurement objectives and data collection.

BLS thanks the Committee on National Statistics and the expert panel for the time and effort they put into the report. Their recommendations thoughtfully balanced the desire to measure everything about this important topic with the limited time and information survey respondents can give us. In the coming months, we will study the report. It will guide us as we consider how to update the Contingent Worker Supplement to reflect the variety of work arrangements in the U.S. labor market.

Update on the Misclassification that Affected the Unemployment Rate

How hard can it be to figure out whether a person is employed or unemployed? Turns out, it can be hard. When BLS put out the employment and unemployment numbers for March, April, and May 2020, we also provided information about misclassification of some people. I want to spend some time to explain this issue, how it affected the data, and how we are addressing it.

In the monthly Current Population Survey of U.S. households, people age 16 and older are placed into one of three categories:

  • Employed — they worked at least one hour “for pay or profit” during the past week.
  • Unemployed — they did not work but actively looked for work during the past 4 weeks OR they were on temporary layoff and expect to return to work.
  • Not in the labor force — everyone else (including students, retirees, those who have given up their job search, and others).

Again, how hard can this be? It starts to get tricky when we talk to people who say they have a steady job but did not work any hours during the past week. In normal times, this might include people on vacation, home sick, or on jury duty. And we would continue to count them as employed. But during the COVID-19 pandemic, the collapse of labor markets created challenges the likes of which BLS has never encountered. People who reported zero hours of work offered such explanations as “I work at a sports arena and everything is postponed” or “the restaurant I work at is closed.” These people should be counted as unemployed on temporary layoff. As it turns out, a large number of people—we estimate about 4.9 million in May—were misclassified.

With the onset of the COVID-19 pandemic, the unemployment rate—at a 50-year low of 3.5 percent in February—rose sharply to 4.4 percent in March and to 14.7 percent in April, before easing to 13.3 percent in May. Despite the stark difference from February, we believe the unemployment rate likely was higher than reported in March, April, and May. As stated in our Employment Situation news releases for each of those months, some people in the Current Population Survey (also known as the CPS or household survey) were classified as employed but probably should have been classified as unemployed.

How did the misclassification happen?

We uncovered the misclassification because we saw a sharp rise in the number of people who were employed but were absent from their jobs for the entire reference week for “other reasons.” The misclassification hinges on how survey interviewers record answers to a question on why people who had a job were absent from work the previous week.

According to special pandemic-related interviewer instructions for this question, answers from people who said they were absent because of pandemic-related business closures should have been recorded as “on layoff (temporary or indefinite).” Instead, many of these answers were recorded as “other reasons.” Recording these answers as “on layoff (temporary or indefinite)” ensures that people are asked the follow-up questions needed to classify them as unemployed. It does not necessarily mean they would be classified as unemployed on temporary layoff, but I’ll get into that in a moment.

When interviewers record a response of “other reasons” to this question, they also add a few words describing that other reason. BLS reviewed these descriptions to better understand the large increase in the number of people absent from work for “other reasons.” Our analysis suggests this group of people included many who were on layoff because of the pandemic. They would have been classified as unemployed on temporary layoff had their answers been recorded correctly.

What are BLS and the Census Bureau doing to address the misclassification?

BLS and our partners at the U.S. Census Bureau take misclassification very seriously. We’re taking more steps to fix this problem. (The Census Bureau is responsible for collecting the household survey data, and BLS is responsible for analyzing and publishing the labor market data from the survey.) Both agencies are continuing to investigate why the misclassification occurred.

Before the March data collection, we anticipated some issues with certain questions in the survey because of the unprecedented nature of this national crisis. As a result, interviewers received special instructions on how to answer the temporarily absent question if a person said they had a job but did not work because of the pandemic. Nevertheless, we determined that not all of the responses to this question in March were coded according to the special instructions. Therefore, before the April data collection, all interviewers received an email that included instructions with more detailed examples, along with a reference table to help them code responses to this question. However, the misclassification was still evident in the April data. Before the May data collection, every field supervisor had a conference call with the interviewers they manage. In these conference calls, the supervisors reviewed the detailed instructions, provided examples to clarify the instructions, and answered interviewers’ questions.

Although we noticed some improvement for May, the misclassification persisted. Therefore, we have taken more steps to correct the problem. Before the June collection, the Census Bureau provided more training to review the guidance to the interviewers. The interviewers also received extra training aids. The electronic survey questionnaire also now has new special instructions that will be more accessible during survey interviews.

Why doesn’t BLS adjust the unemployment rate to account for the misclassification?

As I explained above, we know some workers classified as absent from work for “other reasons” are misclassified. People have asked why we just don’t reclassify these people from employed to unemployed. The answer is there is no easy correction we could have made. Changing a person’s labor force classification would involve more than changing the response to the question about why people were absent from their jobs.

Although we believe many responses to the question on why people were absent from their jobs appear to have been incorrectly recorded, we do not have enough information to reclassify each person’s labor force status. To begin with, we don’t know the exact information provided by the person responding to the survey. We know the brief descriptions included in the “other reasons” category often appear to go against the guidance provided to the survey interviewers. But we don’t have all of the information the respondent might have provided during the interview.

Also, we don’t know the answers to the questions respondents would have been asked if their answers to the question on the reason not at work had been coded differently. This is because people whose answers were recorded as absent from work for “other reasons” were not asked the follow-up questions needed to determine whether they should be classified as unemployed. Specifically, we don’t know whether they expected to be recalled to work and whether they could return to work if recalled. Therefore, shifting people’s answers from “other reasons” to “on layoff (temporary or indefinite)” would not have been enough to change their classification from employed to unemployed. We would have had to assume how they would have responded to the follow-up questions. Had we changed answers based on wrong assumptions, we would have introduced more error.

In addition, our usual practice is to accept data from the household survey as recorded. In the 80-year history of the household survey, we do not know of any actions taken on an ad hoc basis to change respondents’ answers to the labor force questions. Any ad hoc adjustment we could have made would have relied on assumptions instead of data. If BLS were to make ad hoc changes, it could also appear we were manipulating the data. That’s something we’ll never do.

How much did the misclassification affect the unemployment rate?

We don’t know the exact extent of this misclassification. To figure out what the unemployment rate might have been if there were no misclassification, we have to make some assumptions. These assumptions involve deciding (1) how many people in the “other reasons” category actually were misclassified, (2) how many people who were misclassified expected to be recalled, and (3) how many people who were misclassified were available to return to work.

In the material that accompanied our Employment Situation news releases for March, April, and May, we provided an estimate of the potential size of the misclassification and its impact on the unemployment rate. Here we assumed all of the increase in the number of employed people who were not at work for “other reasons,” when compared with the average for recent years, was due solely to misclassification. We also assumed all of these people expected to be recalled and were available to return to work.

For example, there were 5.4 million workers with a job but not at work who were included in the “other reasons” category in May 2020. That was about 4.9 million higher than the average for May 2016–19. If we assume this 4.9 million increase was entirely due to misclassification and all of these misclassified workers expected to be recalled and were available for work, the unemployment rate for May would have been 16.4 percent. (For more information about this, see items 12 and 13 in our note for May. We made similar calculations for March and April.)

These broad assumptions represent the upper bound of our estimate of misclassification. These assumptions result in the largest number of people being classified as unemployed and the largest increase in the unemployment rate. However, these assumptions probably overstate the size of the misclassification. It is unlikely that everyone who was misclassified expected to be recalled and was available to return to work. It is also unlikely that all of the increase in the number of employed people not at work for “other reasons” was due to misclassification. People may be correctly classified in the “other reasons” category. For example, someone who owns a business (and does not have another job) is classified as employed in the household survey. Business owners who are absent from work due to labor market downturns (or in this case, pandemic-related business closures) should be classified as employed but absent from work for “other reasons.”

Regardless of the assumptions we might make about misclassification, the trend in the unemployment rate over the period in question is the same; the rate increased in March and April and eased in May. BLS will continue to investigate the issue, attempting both to ensure that data are correctly recorded in future months and to provide more information about the effect of misclassification on the unemployment rate.

New State and Metropolitan Area Data from the Job Openings and Labor Turnover Survey

Soon after I became Commissioner, the top-notch BLS staff shared with me their vision to expand the Job Openings and Labor Turnover Survey (JOLTS). The JOLTS program publishes data each month on the number and rate of job openings, hires, and separations (broken out by quits, layoffs and discharges, and other separations). These data are available at the national level and for the four large geographic regions—Northeast, Midwest, South, and West.

That left a major data gap on labor demand, hires, and separations for states and metropolitan areas. BLS provides data on labor supply for states and metro areas each month from the Local Area Unemployment Statistics program. We also provide data on employment change in states and metro areas each month from the Current Employment Statistics survey. Employment change is the net effect of hires and separations, but it doesn’t show the underlying flow of job creation and destruction. Having better, timelier state and metro JOLTS data would provide a quicker signal about whether labor demand is accelerating or weakening in local economies.

About 2 months after the staff briefed me, the JOLTS program published experimental state estimates for the first time on May 24, 2019. We have been updating those estimates on a quarterly basis since then. We use a statistical model to help us produce the most current state estimates. We then improve those estimates during an annual benchmark process by taking advantage of data available from the Quarterly Census of Employment and Wages. The JOLTS program is well on its way to moving these state estimates into its official, monthly data stream. Look for that to happen in the second half of 2021!

The President’s proposed budget for fiscal year 2021 includes three improvements to the JOLTS program.

  • Expand the sample to support direct sample-based estimates for each state.
  • Accelerate the review and publication of the estimates.
  • Add questions to provide more information about job openings, hires, and separations.

If funded, this proposal would allow BLS to improve the data quality available from the current JOLTS state estimates. It also would let us add very broad industry detail for each state and more industry detail at the national level.

The proposed larger sample size may also let us produce model-assisted JOLTS estimates for many metro areas. To demonstrate this potential, the JOLTS team produced a one-time set of research estimates for the 18 largest metropolitan statistical areas, those with 1.5 million or more employees. These research estimates show the potential for data that would be available regularly with a larger JOLTS sample. I encourage you to explore this exciting new research series and let us know what you think.

Number of unemployed per job opening in the United States and four large metropolitan areas, 2007–19

Editor’s note: Data for this chart are available in the table below.

This is just one example of the excellent work I see at BLS every day. The BLS staff are consummate professionals who continue to do outstanding work even in the most trying of times. The entire BLS staff has been teleworking now for several months due to COVID-19, and every program continues to produce high quality data on schedule! Even in these extraordinary circumstances, BLS professionals continue to innovate and find ways to improve quality and develop new gold standard data products to help the policymakers, businesses, and the public make better-informed decisions.

Number of unemployed per job opening in the United States and four large metropolitan areas
DateNew York-Newark-Jersey City, NY-NJ-PADallas-Fort Worth-Arlington, TXChicago-Naperville-Elgin, IL-IN-WILos Angeles-Long Beach-Anaheim, CAUnited States

Jan 2007

1.81.41.81.61.6

Feb 2007

1.81.41.91.61.6

Mar 2007

1.71.31.81.61.6

Apr 2007

1.61.11.61.51.4

May 2007

1.51.01.51.51.4

Jun 2007

1.51.11.61.41.4

Jul 2007

1.61.21.81.51.5

Aug 2007

1.71.21.91.51.5

Sep 2007

1.71.21.81.71.5

Oct 2007

1.61.11.71.81.5

Nov 2007

1.61.21.81.91.5

Dec 2007

1.71.21.91.91.6

Jan 2008

1.91.32.02.11.7

Feb 2008

2.11.32.22.21.8

Mar 2008

2.21.32.42.21.9

Apr 2008

2.11.22.22.01.8

May 2008

2.01.22.22.11.8

Jun 2008

1.91.22.42.51.9

Jul 2008

2.21.43.03.02.2

Aug 2008

2.31.73.13.52.4

Sep 2008

2.41.83.13.62.5

Oct 2008

2.51.93.04.02.6

Nov 2008

2.82.13.44.62.9

Dec 2008

3.12.43.95.93.3

Jan 2009

3.62.94.86.74.0

Feb 2009

4.43.25.57.34.6

Mar 2009

5.03.56.47.45.1

Apr 2009

5.03.66.77.35.3

May 2009

5.14.06.87.15.4

Jun 2009

5.14.67.26.85.6

Jul 2009

5.25.37.77.46.0

Aug 2009

5.15.67.97.76.2

Sep 2009

5.45.88.08.06.1

Oct 2009

5.95.37.87.85.8

Nov 2009

6.75.68.68.25.9

Dec 2009

7.15.69.58.36.2

Jan 2010

7.06.310.88.36.2

Feb 2010

7.06.410.37.56.2

Mar 2010

7.05.98.87.15.9

Apr 2010

6.55.17.86.75.4

May 2010

5.94.86.66.54.9

Jun 2010

5.05.16.26.74.8

Jul 2010

4.85.26.07.14.9

Aug 2010

4.75.46.27.54.9

Sep 2010

4.94.76.17.54.7

Oct 2010

4.64.25.27.14.5

Nov 2010

4.84.05.07.14.5

Dec 2010

5.34.15.17.14.6

Jan 2011

6.04.35.57.15.0

Feb 2011

6.14.25.76.84.9

Mar 2011

5.54.05.36.34.6

Apr 2011

5.03.85.15.84.2

May 2011

4.63.64.65.84.1

Jun 2011

4.53.74.55.64.0

Jul 2011

4.63.84.65.94.1

Aug 2011

4.53.84.96.04.0

Sep 2011

4.43.44.65.83.8

Oct 2011

4.23.14.25.63.6

Nov 2011

4.03.04.25.33.6

Dec 2011

4.23.04.85.53.7

Jan 2012

4.63.05.25.93.7

Feb 2012

5.33.04.86.23.7

Mar 2012

5.12.84.25.73.5

Apr 2012

4.22.43.65.03.3

May 2012

3.92.13.44.83.1

Jun 2012

3.92.13.64.63.1

Jul 2012

4.22.33.95.33.3

Aug 2012

4.02.43.95.23.4

Sep 2012

3.82.43.55.53.2

Oct 2012

3.72.13.25.03.0

Nov 2012

3.92.03.44.83.0

Dec 2012

4.02.03.75.13.2

Jan 2013

4.22.24.35.43.4

Feb 2013

4.22.14.15.43.4

Mar 2013

4.02.03.94.93.2

Apr 2013

3.51.93.54.12.9

May 2013

3.21.93.53.82.7

Jun 2013

3.22.13.63.62.7

Jul 2013

3.42.33.83.82.9

Aug 2013

3.42.33.73.92.9

Sep 2013

3.42.13.64.02.8

Oct 2013

3.22.03.33.92.5

Nov 2013

3.22.03.24.12.5

Dec 2013

3.22.03.34.22.6

Jan 2014

3.42.03.54.22.7

Feb 2014

3.41.93.53.72.7

Mar 2014

3.21.93.33.32.6

Apr 2014

2.81.72.62.82.2

May 2014

2.51.62.12.82.0

Jun 2014

2.41.62.02.81.9

Jul 2014

2.61.62.13.12.0

Aug 2014

2.61.62.12.91.9

Sep 2014

2.51.62.02.91.9

Oct 2014

2.31.51.92.71.7

Nov 2014

2.41.51.92.91.8

Dec 2014

2.51.31.92.91.8

Jan 2015

2.61.32.02.91.9

Feb 2015

2.51.31.92.61.8

Mar 2015

2.41.31.82.41.7

Apr 2015

2.21.21.62.21.6

May 2015

2.01.11.52.21.5

Jun 2015

1.91.01.62.31.5

Jul 2015

1.91.01.62.31.5

Aug 2015

1.90.91.62.31.5

Sep 2015

1.80.91.52.31.4

Oct 2015

1.70.81.52.01.3

Nov 2015

1.60.81.52.01.3

Dec 2015

1.60.81.51.91.4

Jan 2016

1.70.91.61.91.4

Feb 2016

1.80.81.61.71.5

Mar 2016

1.70.81.61.61.4

Apr 2016

1.60.71.61.61.3

May 2016

1.40.71.41.61.2

Jun 2016

1.40.81.41.61.3

Jul 2016

1.40.91.41.81.3

Aug 2016

1.50.91.51.91.4

Sep 2016

1.40.91.51.91.3

Oct 2016

1.30.91.51.71.3

Nov 2016

1.30.91.41.71.3

Dec 2016

1.30.91.51.71.3

Jan 2017

1.41.01.71.81.4

Feb 2017

1.51.01.71.81.4

Mar 2017

1.51.01.51.81.4

Apr 2017

1.30.91.41.61.2

May 2017

1.30.91.21.51.1

Jun 2017

1.31.01.21.41.1

Jul 2017

1.31.11.21.51.1

Aug 2017

1.41.11.21.61.1

Sep 2017

1.41.01.11.51.1

Oct 2017

1.31.01.01.31.0

Nov 2017

1.21.01.01.31.0

Dec 2017

1.21.01.11.31.0

Jan 2018

1.21.11.31.31.1

Feb 2018

1.21.11.31.21.1

Mar 2018

1.21.11.21.21.1

Apr 2018

1.11.01.11.11.0

May 2018

1.01.00.91.00.9

Jun 2018

1.00.90.81.10.9

Jul 2018

1.00.80.81.10.9

Aug 2018

1.00.80.91.20.9

Sep 2018

0.90.80.91.20.8

Oct 2018

0.90.80.81.10.8

Nov 2018

0.80.80.81.20.8

Dec 2018

0.90.70.81.30.8

Jan 2019

1.00.80.91.40.9

Feb 2019

1.10.81.01.40.9

Mar 2019

1.10.80.91.40.9

Apr 2019

1.00.70.91.10.8

May 2019

0.80.70.81.00.8

Jun 2019

0.80.60.80.90.8

Jul 2019

0.80.70.81.00.8

Aug 2019

0.80.80.91.10.9

Sep 2019

0.80.80.91.20.8

Oct 2019

0.80.80.81.10.8

Nov 2019

0.80.80.71.00.8

Dec 2019

0.80.80.71.00.8