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Topic Archives: Job Openings, Hires, and Separations

Spend Thanksgiving Day with BLS!

Thanksgiving is right around the corner. As we start to think about how we will celebrate, it might be hard to imagine the ties between BLS statistics and celebrating Thanksgiving. So, here’s a short tour of a typical Thanksgiving Day as seen through a few BLS statistics. Enjoy!

9:00 a.m. Put the turkey in the oven

All good chefs know the key to a successful Thanksgiving feast is to get the turkey in the oven bright and early. Whether you are roasting your turkey or firing up a deep fryer in the driveway, you will have to pay more for the fuel. The Consumer Price Index for household energy was pretty stable through 2019 and the first half of 2020 but then started a steady rise in September 2020.

Consumer Price Index for household energy, 2019–21

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

10:00 a.m. Watch the Macy’s Thanksgiving Day parade

The COVID-19 pandemic has caused ups and downs in the labor market, much like the impact of a windy day for the famous balloons in Macy’s Thanksgiving Day Parade. Keeping with the department store theme, employment in department stores plunged 25.3 percent in April 2020 but then rose 14.1 percent June 2020. These gyrations were more dramatic than the broader retail trade sector.

Monthly percent change in employment in retail trade and department stores, 2019–21

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

2:00 p.m. Scope out Black Friday deals

After watching the parade, it’s time to plan our Black Friday shopping! As consumers, we are always trying to get more for less. In the retail trade industry, it turns out they are doing just that. The industry has produced more output with steady or decreasing hours worked. The result is a corresponding increase in labor productivity. Now, only if we could prepare a bigger Thanksgiving feast in less time!

Indexes for labor productivity, hours worked, and output in retail trade, 2007–20

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

4:00 p.m. Play touch football

We need to make some room of the feast we are about to enjoy, so we assemble willing participants and play some touch football in the yard. The American Time Use Survey is the best source of information on how Americans spend their time each day. In this case, let’s compare how much time people spend playing sports versus how much time they spend watching sports on TV. We’ll look only at time spent in these activities on weekend days and holidays. The survey does not have details on what people watch on TV, but we can assume some time reported here is spent watching sports.

Average hours spent watching TV and playing sports, weekend days and holidays, 2019

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

We can see that Americans, on average, easily spend more time watching TV—3.36 hours—than playing sports—0.34 hours. But what is more interesting is that, on average, those who watch TV watch about 24 percent more than the overall population. However, those who play sports play, on average, nearly 6 times as many hours as the average for the population.

6:00 p.m. Thanksgiving feast

No matter what is on your dinner table this Thanksgiving, chances are it will cost more than previous years. All six major grocery store food groups in the Consumer Price Index for food at home continued to rise sharply in October 2021. Even if you decide to order out, it will set you back a bit more this year. Both full-service meals and limited services meals rose nearly 1 percent in October 2021.

Consumer Price Indexes for food at home and food away from home, 2018–21

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

7:00 p.m. Watch football

Now that we’ve finished our delicious feast, it’s a time-honored tradition to watch a bit of football on TV. If you are buying a new TV for this holiday, you can expect to pay a bit more. After years of steady declines, import prices for television and video receivers have reversed trend in 2021, much like a wide receiver changing direction to find an opening and catch a game-winning touchdown pass!

Import price index for television and video receivers, 2011–21

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

9:00 p.m. Say goodbye

It’s hard to say goodbye to your friends and family. In the United States, however, the Job Openings and Labor Turnover Survey is showing that workers are saying goodbye to their employers more often these days. The number of quits has been rising steadily since the shock of the pandemic affected layoffs and discharges in early 2020. (It’s only a coincidence that the layoffs line in the chart below looks like the outline of a pilgrim’s hat.)

Quits, layoffs and discharges, and other job separations, 2019–21

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

Now we’ve come to the end of our Thanksgiving feast of BLS data. Our hunger for the premier statistics on the U.S. labor force, prices, and productivity, has been satisfied, and we can rest easily knowing there’s a stat for that!

Consumer Price Index for household energy
MonthIndex

Jan 2019

100.000

Feb 2019

99.662

Mar 2019

100.046

Apr 2019

99.952

May 2019

99.679

Jun 2019

99.258

Jul 2019

99.415

Aug 2019

99.253

Sep 2019

99.033

Oct 2019

99.756

Nov 2019

99.890

Dec 2019

99.716

Jan 2020

99.666

Feb 2020

99.355

Mar 2020

98.812

Apr 2020

98.492

May 2020

98.278

Jun 2020

98.501

Jul 2020

98.542

Aug 2020

98.478

Sep 2020

99.590

Oct 2020

100.103

Nov 2020

101.043

Dec 2020

101.377

Jan 2021

101.299

Feb 2021

102.681

Mar 2021

103.436

Apr 2021

104.748

May 2021

105.512

Jun 2021

105.840

Jul 2021

106.664

Aug 2021

107.833

Sep 2021

109.273

Oct 2021

112.872
Monthly percent change in employment in retail trade and department stores
MonthRetail tradeDepartment stores

Jan 2019

-0.1%0.2%

Feb 2019

-0.2-1.4

Mar 2019

-0.1-0.6

Apr 2019

-0.1-0.9

May 2019

-0.1-0.5

Jun 2019

-0.1-0.7

Jul 2019

0.0-0.9

Aug 2019

-0.1-1.4

Sep 2019

0.10.3

Oct 2019

0.20.0

Nov 2019

-0.20.4

Dec 2019

0.3-0.4

Jan 2020

-0.1-2.7

Feb 2020

0.00.3

Mar 2020

-0.8-0.6

Apr 2020

-14.5-25.3

May 2020

3.16.7

Jun 2020

6.314.1

Jul 2020

1.74.3

Aug 2020

1.72.3

Sep 2020

0.2-0.8

Oct 2020

0.70.2

Nov 2020

0.00.7

Dec 2020

0.2-0.6

Jan 2021

0.1-0.3

Feb 2021

0.10.5

Mar 2021

0.30.1

Apr 2021

-0.10.2

May 2021

0.40.9

Jun 2021

0.61.3

Jul 2021

0.00.3

Aug 2021

0.1-0.5

Sep 2021

0.40.5

Oct 2021

0.2-0.2
Indexes for labor productivity, hours worked, and output in retail trade
YearLabor productivityHours workedOutput

2007

100.000100.000100.000

2008

97.76597.65895.475

2009

98.29492.03290.461

2010

100.69492.66793.310

2011

101.39794.68696.008

2012

103.65595.67399.170

2013

108.08095.212102.905

2014

109.91997.268106.916

2015

113.48698.821112.148

2016

118.52598.636116.908

2017

120.71999.896120.593

2018

124.39399.783124.123

2019

130.36098.139127.934

2020

140.39294.650132.880
Average hours spent watching TV and playing sports, weekend days and holidays, 2019
ActivityHours

Watching TV (average of population)

3.36

Watching TV (average of those who watched TV)

4.17

Playing sports (average of population)

0.34

Playing sports (average of those who played sports)

1.94
Consumer Price Indexes for food at home and food away from home
MonthFood at homeFood away from home

Jan 2018

100.000100.000

Feb 2018

99.793100.243

Mar 2018

99.780100.352

Apr 2018

100.026100.594

May 2018

99.779100.929

Jun 2018

99.865101.113

Jul 2018

100.127101.229

Aug 2018

100.198101.421

Sep 2018

100.252101.645

Oct 2018

100.046101.738

Nov 2018

100.259102.029

Dec 2018

100.554102.437

Jan 2019

100.683102.789

Feb 2019

101.014103.153

Mar 2019

101.163103.342

Apr 2019

100.716103.676

May 2019

100.913103.894

Jun 2019

100.718104.232

Jul 2019

100.716104.443

Aug 2019

100.654104.669

Sep 2019

100.902104.940

Oct 2019

101.124105.139

Nov 2019

101.324105.310

Dec 2019

101.331105.611

Jan 2020

101.440106.000

Feb 2020

101.851106.236

Mar 2020

102.220106.395

Apr 2020

104.775106.550

May 2020

105.718106.942

Jun 2020

106.309107.496

Jul 2020

105.343108.002

Aug 2020

105.322108.309

Sep 2020

105.051108.911

Oct 2020

105.177109.210

Nov 2020

105.012109.342

Dec 2020

105.335109.751

Jan 2021

105.203110.122

Feb 2021

105.474110.180

Mar 2021

105.587110.311

Apr 2021

106.047110.649

May 2021

106.423111.258

Jun 2021

107.309112.047

Jul 2021

108.031112.923

Aug 2021

108.431113.405

Sep 2021

109.779114.013

Oct 2021

110.841114.965
Import price index for television and video receivers
MonthIndex

Jan 2011

100.000

Feb 2011

100.173

Mar 2011

100.173

Apr 2011

99.136

May 2011

98.964

Jun 2011

97.409

Jul 2011

97.064

Aug 2011

96.373

Sep 2011

95.855

Oct 2011

94.991

Nov 2011

93.092

Dec 2011

94.128

Jan 2012

94.819

Feb 2012

94.473

Mar 2012

93.955

Apr 2012

92.573

May 2012

92.573

Jun 2012

92.401

Jul 2012

92.401

Aug 2012

92.573

Sep 2012

92.228

Oct 2012

92.573

Nov 2012

90.155

Dec 2012

90.155

Jan 2013

89.810

Feb 2013

89.637

Mar 2013

88.256

Apr 2013

88.083

May 2013

87.910

Jun 2013

87.910

Jul 2013

87.392

Aug 2013

87.219

Sep 2013

85.838

Oct 2013

85.492

Nov 2013

85.492

Dec 2013

85.492

Jan 2014

85.320

Feb 2014

85.320

Mar 2014

85.147

Apr 2014

84.801

May 2014

84.283

Jun 2014

84.111

Jul 2014

83.074

Aug 2014

82.902

Sep 2014

83.074

Oct 2014

81.865

Nov 2014

81.865

Dec 2014

81.347

Jan 2015

79.965

Feb 2015

79.965

Mar 2015

79.965

Apr 2015

79.965

May 2015

79.620

Jun 2015

79.620

Jul 2015

79.620

Aug 2015

79.620

Sep 2015

79.620

Oct 2015

79.447

Nov 2015

79.275

Dec 2015

78.929

Jan 2016

78.756

Feb 2016

77.547

Mar 2016

77.375

Apr 2016

77.029

May 2016

76.857

Jun 2016

77.029

Jul 2016

76.857

Aug 2016

76.684

Sep 2016

76.684

Oct 2016

76.684

Nov 2016

76.684

Dec 2016

76.684

Jan 2017

76.166

Feb 2017

76.166

Mar 2017

75.820

Apr 2017

75.993

May 2017

75.993

Jun 2017

75.993

Jul 2017

75.993

Aug 2017

75.993

Sep 2017

75.820

Oct 2017

75.475

Nov 2017

75.302

Dec 2017

75.130

Jan 2018

75.130

Feb 2018

75.302

Mar 2018

74.784

Apr 2018

74.439

May 2018

74.266

Jun 2018

73.575

Jul 2018

72.884

Aug 2018

72.884

Sep 2018

72.712

Oct 2018

72.539

Nov 2018

72.366

Dec 2018

72.021

Jan 2019

71.330

Feb 2019

70.812

Mar 2019

70.466

Apr 2019

70.466

May 2019

70.294

Jun 2019

69.948

Jul 2019

69.775

Aug 2019

69.603

Sep 2019

69.603

Oct 2019

69.430

Nov 2019

69.085

Dec 2019

68.912

Jan 2020

69.430

Feb 2020

68.048

Mar 2020

67.358

Apr 2020

66.839

May 2020

66.667

Jun 2020

66.667

Jul 2020

66.494

Aug 2020

66.494

Sep 2020

66.321

Oct 2020

66.667

Nov 2020

67.358

Dec 2020

68.048

Jan 2021

68.739

Feb 2021

68.739

Mar 2021

68.566

Apr 2021

69.775

May 2021

70.639

Jun 2021

70.812

Jul 2021

73.402

Aug 2021

73.402

Sep 2021

74.439

Oct 2021

74.784
Quits, layoffs and discharges, and other job separations
MonthQuitsLayoffs and dischargesOther separations

Jan 2019

3,521,0001,689,000301,000

Feb 2019

3,543,0001,769,000353,000

Mar 2019

3,524,0001,721,000331,000

Apr 2019

3,494,0001,954,000313,000

May 2019

3,487,0001,776,000307,000

Jun 2019

3,527,0001,771,000316,000

Jul 2019

3,627,0001,826,000344,000

Aug 2019

3,591,0001,825,000306,000

Sep 2019

3,449,0001,982,000345,000

Oct 2019

3,414,0001,793,000359,000

Nov 2019

3,482,0001,788,000374,000

Dec 2019

3,487,0001,952,000354,000

Jan 2020

3,568,0001,788,000358,000

Feb 2020

3,430,0001,953,000332,000

Mar 2020

2,902,00013,046,000360,000

Apr 2020

2,107,0009,307,000368,000

May 2020

2,206,0002,096,000316,000

Jun 2020

2,646,0002,204,000331,000

Jul 2020

3,182,0001,845,000365,000

Aug 2020

2,987,0001,573,000342,000

Sep 2020

3,307,0001,555,000373,000

Oct 2020

3,352,0001,728,000347,000

Nov 2020

3,296,0002,123,000325,000

Dec 2020

3,407,0001,823,000352,000

Jan 2021

3,306,0001,724,000294,000

Feb 2021

3,383,0001,723,000323,000

Mar 2021

3,568,0001,525,000343,000

Apr 2021

3,992,0001,450,000360,000

May 2021

3,630,0001,353,000347,000

Jun 2021

3,870,0001,354,000389,000

Jul 2021

4,028,0001,423,000341,000

Aug 2021

4,270,0001,385,000378,000

Sep 2021

4,434,0001,375,000410,000

Making Sense of Job Openings and Other Labor Market Measures

The current “supply” of labor gets a lot of attention. That concept refers to the number of people working or looking for work. Our monthly Employment Situation report is where policymakers and the general public learn how that supply has changed. BLS also examines the current “demand” for labor with monthly information on filled jobs and job openings. Readers find those estimate in the BLS Job Openings and Labor Turnover Survey (JOLTS). JOLTS defines job openings as all positions that are open, but not filled, on the last business day of the month. A job is “open” only if it meets all of these conditions:

  • A specific position exists and there is work available for that position.
  • The job could start within 30 days.
  • There is active recruiting for workers from outside the establishment.

There were 9.2 million job openings in May 2021, the same record-high level first reached in April. The May job opening rate also was the same as April’s record high; 6.0 percent of all currently available positions were unfilled. This rate is the number of job openings divided by the sum of current employment plus job openings. You can think of it as a measure of capacity or the rate of current unmet demand for labor.

Job openings rate, total nonfarm, December 2000 to May 2021

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

This spike in openings was sudden by historical standards. It came just one year after an equally sudden drop, which bottomed out in April 2020. In contrast, openings fell more gradually during the 2007–09 recession, then grew even more gradually during the subsequent recovery. The labor market movements during the COVID-19 pandemic have been far more abrupt than those in earlier business cycles.

An abundance of job openings usually signals a “tight” labor market; the demand for labor exceeds the supply at the offered wage. For workers, this may mean it is relatively easy to find a desirable job, assuming they possess the skills employers are seeking. In contrast, employers must compete to hire well-qualified workers.

High unemployment usually signals a “loose” labor market, in which many applicants compete for a limited number of openings; the supply of labor exceeds the demand. Unemployment—the number of workers who lack but seek jobs—stood at 9.5 million in June 2021. That was, down from its pandemic peak of 23 million in April 2020 but still well above its level of less than 6 million before the pandemic. Millions more have left the labor force during the pandemic, and many of them have not returned. These people are not counted as unemployed because they are not actively looking for work. However, we know that 6.4 million of those not in the labor force indicate they want a job now, and 1.6 million say they are not currently searching because of pandemic-related reasons. Some of these people might be willing to consider offers and might add more “looseness” to the labor market.

Comparing the number of job openings to the number of unemployed people provides one measure of the current job market. In May 2021, there was just one unemployed person per job opening—a ratio usually associated with a tight labor market.

Number of unemployed per job opening, December 2000 to May 2021

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

So, with openings at an all-time high, and unemployment still elevated, is the labor market tight or loose? The answer is complicated. It also can feel different depending on each worker’s and employer’s circumstances. The answer also differs when you look beyond the national data to uncover differing stories by industry or geography.

As the COVID-19 pandemic subsides and many restaurants and other businesses return to normal operations, some employers are finding it hard to hire enough workers quickly. Some economists are unsure whether recent, temporary increases in the availability and generosity of unemployment insurance have influenced some unemployed workers’ interest in taking jobs. At the same time, the lingering effects of the pandemic probably kept some potential workers from entering or reentering the labor force, especially those with school-aged children whose schools were still closed, and those lacking childcare options. These factors could also affect employers’ ability to hire.

We should also remember that not all job applicants come from the ranks of the unemployed. Many are changing jobs or entering (or reentering) the labor force. The recent abundance of job openings may be increasing workers’ likelihood to change jobs. Just as openings reached a new high in April 2021, so did quits, at 4.0 million. Unlike openings, however, quits edged down a bit in May.

Job openings, hires, and quit rates, total nonfarm, December 2000 to May 2021

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

Another factor could be mismatches between the open jobs and the jobseekers. In June 2021, about 15 percent of unemployed people were seeking part-time work. We don’t know how many of the openings were part-time. Since February of this year, the share of unemployed workers who were unemployed 27 weeks or longer has remained above 40 percent, a level last seen in 2012 and roughly twice the 2019 level. Historically, those unemployed longer are slower to connect with new jobs and more likely to stop looking. It is also possible that some workers’ job preferences changed, at least temporarily, as the pandemic changed the perceived risks and other characteristics of many jobs.

Finally, with many people on the sidelines of the labor market, and job openings at record high levels, employers may look to increase wages to entice potential employees back into the market. The BLS monthly measure of wage trends, average hourly earnings, has been heavily influenced by large employment shifts since the pandemic began. When employment dropped sharply in the spring of 2020, average wages increased, mainly because lower-paid workers were more likely to be out of work. Now that many businesses are reopening, some evidence of wage increases can be seen by focusing on the leisure and hospitality industry. From February 2020, just before the pandemic began, to June 2021, average hourly earnings for this industry rose 3.1 percent, after adjusting for inflation. Data from the Employment Cost Index, which are not influenced by employment shifts, show wages and salaries in the leisure and hospitality industry increasing 1.6 percent, after adjusting for inflation, for the year ending March 2021.

Percent change since February 2020 in real (inflation-adjusted) average hourly earnings

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

While some employers might find it hard to hire workers quickly, there is a lot of hiring going on. Consider the leisure and hospitality industry, which includes restaurants. In May, a whopping 9.0 percent of positions were open. But the hiring rate was even higher—9.3 percent, far above levels before the pandemic.

Job openings and hires rates, leisure and hospitality, December 2000 to May 2021

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

The labor market cannot be characterized with a single number. Over time, people change jobs, look for jobs, or leave the labor market entirely. These dynamics can be complicated, as they certainly were during the COVID-19 pandemic. This discussion covers just some of the many measures BLS reports to illuminate labor market conditions. For more analysis of JOLTS data, check out recent articles in the Monthly Labor Review and Beyond the Numbers.

Job openings rate, total nonfarm, December 2000 to May 2021
MonthRate

Dec 2000

3.7%

Jan 2001

3.8

Feb 2001

3.7

Mar 2001

3.5

Apr 2001

3.4

May 2001

3.2

Jun 2001

3.2

Jul 2001

3.3

Aug 2001

3.0

Sep 2001

3.0

Oct 2001

2.7

Nov 2001

2.8

Dec 2001

2.7

Jan 2002

2.7

Feb 2002

2.6

Mar 2002

2.7

Apr 2002

2.6

May 2002

2.6

Jun 2002

2.5

Jul 2002

2.5

Aug 2002

2.6

Sep 2002

2.5

Oct 2002

2.6

Nov 2002

2.6

Dec 2002

2.4

Jan 2003

2.6

Feb 2003

2.4

Mar 2003

2.3

Apr 2003

2.3

May 2003

2.5

Jun 2003

2.5

Jul 2003

2.2

Aug 2003

2.4

Sep 2003

2.3

Oct 2003

2.5

Nov 2003

2.5

Dec 2003

2.5

Jan 2004

2.6

Feb 2004

2.6

Mar 2004

2.6

Apr 2004

2.6

May 2004

2.7

Jun 2004

2.5

Jul 2004

2.8

Aug 2004

2.6

Sep 2004

2.8

Oct 2004

2.9

Nov 2004

2.6

Dec 2004

3.0

Jan 2005

2.8

Feb 2005

2.9

Mar 2005

2.9

Apr 2005

3.0

May 2005

2.8

Jun 2005

2.9

Jul 2005

3.1

Aug 2005

3.0

Sep 2005

3.1

Oct 2005

3.0

Nov 2005

3.1

Dec 2005

3.1

Jan 2006

3.1

Feb 2006

3.1

Mar 2006

3.4

Apr 2006

3.4

May 2006

3.2

Jun 2006

3.3

Jul 2006

3.1

Aug 2006

3.3

Sep 2006

3.3

Oct 2006

3.2

Nov 2006

3.3

Dec 2006

3.3

Jan 2007

3.3

Feb 2007

3.3

Mar 2007

3.5

Apr 2007

3.3

May 2007

3.3

Jun 2007

3.4

Jul 2007

3.2

Aug 2007

3.2

Sep 2007

3.3

Oct 2007

3.2

Nov 2007

3.3

Dec 2007

3.2

Jan 2008

3.2

Feb 2008

3.0

Mar 2008

3.0

Apr 2008

2.8

May 2008

3.0

Jun 2008

2.7

Jul 2008

2.7

Aug 2008

2.6

Sep 2008

2.3

Oct 2008

2.4

Nov 2008

2.3

Dec 2008

2.3

Jan 2009

2.0

Feb 2009

2.1

Mar 2009

1.9

Apr 2009

1.7

May 2009

1.9

Jun 2009

1.9

Jul 2009

1.7

Aug 2009

1.8

Sep 2009

1.9

Oct 2009

1.8

Nov 2009

1.9

Dec 2009

1.9

Jan 2010

2.1

Feb 2010

2.0

Mar 2010

2.0

Apr 2010

2.4

May 2010

2.2

Jun 2010

2.1

Jul 2010

2.3

Aug 2010

2.2

Sep 2010

2.2

Oct 2010

2.4

Nov 2010

2.4

Dec 2010

2.3

Jan 2011

2.3

Feb 2011

2.4

Mar 2011

2.4

Apr 2011

2.4

May 2011

2.4

Jun 2011

2.6

Jul 2011

2.7

Aug 2011

2.5

Sep 2011

2.8

Oct 2011

2.7

Nov 2011

2.6

Dec 2011

2.8

Jan 2012

2.8

Feb 2012

2.6

Mar 2012

2.9

Apr 2012

2.8

May 2012

2.8

Jun 2012

2.8

Jul 2012

2.7

Aug 2012

2.8

Sep 2012

2.8

Oct 2012

2.7

Nov 2012

2.8

Dec 2012

2.9

Jan 2013

2.8

Feb 2013

2.9

Mar 2013

2.9

Apr 2013

2.9

May 2013

3.0

Jun 2013

3.0

Jul 2013

2.8

Aug 2013

2.9

Sep 2013

2.9

Oct 2013

3.0

Nov 2013

2.9

Dec 2013

2.9

Jan 2014

2.9

Feb 2014

3.1

Mar 2014

3.1

Apr 2014

3.2

May 2014

3.3

Jun 2014

3.5

Jul 2014

3.4

Aug 2014

3.7

Sep 2014

3.4

Oct 2014

3.5

Nov 2014

3.3

Dec 2014

3.5

Jan 2015

3.7

Feb 2015

3.7

Mar 2015

3.6

Apr 2015

3.8

May 2015

3.8

Jun 2015

3.6

Jul 2015

4.1

Aug 2015

3.7

Sep 2015

3.7

Oct 2015

3.9

Nov 2015

3.8

Dec 2015

3.9

Jan 2016

4.0

Feb 2016

3.9

Mar 2016

4.1

Apr 2016

3.9

May 2016

3.9

Jun 2016

3.8

Jul 2016

4.0

Aug 2016

3.8

Sep 2016

3.9

Oct 2016

3.7

Nov 2016

4.0

Dec 2016

3.9

Jan 2017

3.7

Feb 2017

3.9

Mar 2017

3.8

Apr 2017

4.0

May 2017

3.8

Jun 2017

4.1

Jul 2017

4.1

Aug 2017

4.1

Sep 2017

4.1

Oct 2017

4.2

Nov 2017

4.1

Dec 2017

4.1

Jan 2018

4.3

Feb 2018

4.3

Mar 2018

4.4

Apr 2018

4.4

May 2018

4.5

Jun 2018

4.7

Jul 2018

4.6

Aug 2018

4.6

Sep 2018

4.7

Oct 2018

4.7

Nov 2018

4.8

Dec 2018

4.7

Jan 2019

4.7

Feb 2019

4.5

Mar 2019

4.7

Apr 2019

4.6

May 2019

4.6

Jun 2019

4.5

Jul 2019

4.5

Aug 2019

4.5

Sep 2019

4.5

Oct 2019

4.6

Nov 2019

4.4

Dec 2019

4.2

Jan 2020

4.5

Feb 2020

4.4

Mar 2020

3.7

Apr 2020

3.4

May 2020

3.9

Jun 2020

4.2

Jul 2020

4.6

Aug 2020

4.4

Sep 2020

4.5

Oct 2020

4.6

Nov 2020

4.5

Dec 2020

4.5

Jan 2021

4.7

Feb 2021

5.0

Mar 2021

5.4

Apr 2021

6.0

May 2021

6.0
Number of unemployed per job opening, December 2000 to May 2021
MonthRatio

Dec 2000

1.1

Jan 2001

1.2

Feb 2001

1.2

Mar 2001

1.3

Apr 2001

1.4

May 2001

1.4

Jun 2001

1.5

Jul 2001

1.5

Aug 2001

1.8

Sep 2001

1.8

Oct 2001

2.1

Nov 2001

2.1

Dec 2001

2.2

Jan 2002

2.2

Feb 2002

2.4

Mar 2002

2.3

Apr 2002

2.5

May 2002

2.4

Jun 2002

2.5

Jul 2002

2.5

Aug 2002

2.4

Sep 2002

2.5

Oct 2002

2.4

Nov 2002

2.4

Dec 2002

2.7

Jan 2003

2.5

Feb 2003

2.7

Mar 2003

2.8

Apr 2003

2.8

May 2003

2.7

Jun 2003

2.7

Jul 2003

3.0

Aug 2003

2.8

Sep 2003

2.9

Oct 2003

2.6

Nov 2003

2.6

Dec 2003

2.4

Jan 2004

2.4

Feb 2004

2.3

Mar 2004

2.4

Apr 2004

2.3

May 2004

2.2

Jun 2004

2.5

Jul 2004

2.1

Aug 2004

2.3

Sep 2004

2.1

Oct 2004

2.0

Nov 2004

2.3

Dec 2004

1.9

Jan 2005

2.0

Feb 2005

2.0

Mar 2005

1.9

Apr 2005

1.8

May 2005

2.0

Jun 2005

1.9

Jul 2005

1.7

Aug 2005

1.8

Sep 2005

1.7

Oct 2005

1.8

Nov 2005

1.8

Dec 2005

1.7

Jan 2006

1.6

Feb 2006

1.7

Mar 2006

1.5

Apr 2006

1.5

May 2006

1.6

Jun 2006

1.5

Jul 2006

1.6

Aug 2006

1.5

Sep 2006

1.4

Oct 2006

1.5

Nov 2006

1.5

Dec 2006

1.5

Jan 2007

1.5

Feb 2007

1.5

Mar 2007

1.4

Apr 2007

1.5

May 2007

1.5

Jun 2007

1.4

Jul 2007

1.6

Aug 2007

1.6

Sep 2007

1.5

Oct 2007

1.6

Nov 2007

1.6

Dec 2007

1.7

Jan 2008

1.7

Feb 2008

1.8

Mar 2008

1.9

Apr 2008

1.9

May 2008

2.0

Jun 2008

2.2

Jul 2008

2.4

Aug 2008

2.6

Sep 2008

2.9

Oct 2008

3.0

Nov 2008

3.3

Dec 2008

3.6

Jan 2009

4.4

Feb 2009

4.5

Mar 2009

5.3

Apr 2009

6.0

May 2009

5.7

Jun 2009

5.9

Jul 2009

6.5

Aug 2009

6.3

Sep 2009

6.0

Oct 2009

6.4

Nov 2009

6.1

Dec 2009

5.9

Jan 2010

5.3

Feb 2010

5.7

Mar 2010

5.7

Apr 2010

4.9

May 2010

5.0

Jun 2010

5.2

Jul 2010

4.7

Aug 2010

4.9

Sep 2010

5.0

Oct 2010

4.5

Nov 2010

4.7

Dec 2010

4.7

Jan 2011

4.5

Feb 2011

4.3

Mar 2011

4.2

Apr 2011

4.3

May 2011

4.4

Jun 2011

4.0

Jul 2011

3.8

Aug 2011

4.2

Sep 2011

3.7

Oct 2011

3.8

Nov 2011

3.7

Dec 2011

3.5

Jan 2012

3.3

Feb 2012

3.5

Mar 2012

3.2

Apr 2012

3.3

May 2012

3.3

Jun 2012

3.2

Jul 2012

3.4

Aug 2012

3.3

Sep 2012

3.1

Oct 2012

3.2

Nov 2012

3.1

Dec 2012

3.1

Jan 2013

3.2

Feb 2013

3.0

Mar 2013

2.9

Apr 2013

2.9

May 2013

2.8

Jun 2013

2.8

Jul 2013

2.9

Aug 2013

2.8

Sep 2013

2.7

Oct 2013

2.6

Nov 2013

2.6

Dec 2013

2.5

Jan 2014

2.5

Feb 2014

2.4

Mar 2014

2.4

Apr 2014

2.1

May 2014

2.1

Jun 2014

1.9

Jul 2014

2.0

Aug 2014

1.8

Sep 2014

1.9

Oct 2014

1.8

Nov 2014

1.9

Dec 2014

1.7

Jan 2015

1.7

Feb 2015

1.6

Mar 2015

1.6

Apr 2015

1.5

May 2015

1.6

Jun 2015

1.6

Jul 2015

1.3

Aug 2015

1.5

Sep 2015

1.4

Oct 2015

1.4

Nov 2015

1.4

Dec 2015

1.4

Jan 2016

1.3

Feb 2016

1.3

Mar 2016

1.3

Apr 2016

1.4

May 2016

1.3

Jun 2016

1.3

Jul 2016

1.3

Aug 2016

1.4

Sep 2016

1.4

Oct 2016

1.4

Nov 2016

1.3

Dec 2016

1.3

Jan 2017

1.3

Feb 2017

1.2

Mar 2017

1.2

Apr 2017

1.2

May 2017

1.2

Jun 2017

1.1

Jul 2017

1.1

Aug 2017

1.1

Sep 2017

1.1

Oct 2017

1.0

Nov 2017

1.1

Dec 2017

1.0

Jan 2018

1.0

Feb 2018

1.0

Mar 2018

1.0

Apr 2018

0.9

May 2018

0.9

Jun 2018

0.9

Jul 2018

0.9

Aug 2018

0.9

Sep 2018

0.8

Oct 2018

0.8

Nov 2018

0.8

Dec 2018

0.9

Jan 2019

0.9

Feb 2019

0.9

Mar 2019

0.8

Apr 2019

0.8

May 2019

0.8

Jun 2019

0.8

Jul 2019

0.8

Aug 2019

0.8

Sep 2019

0.8

Oct 2019

0.8

Nov 2019

0.9

Dec 2019

0.9

Jan 2020

0.8

Feb 2020

0.8

Mar 2020

1.2

Apr 2020

5.0

May 2020

3.9

Jun 2020

2.9

Jul 2020

2.4

Aug 2020

2.1

Sep 2020

1.9

Oct 2020

1.6

Nov 2020

1.6

Dec 2020

1.6

Jan 2021

1.4

Feb 2021

1.3

Mar 2021

1.2

Apr 2021

1.1

May 2021

1.0
Job openings, hires, and quit rates, total nonfarm, December 2000 to May 2021
MonthJob openings rateHires rateQuits rate

Dec 2000

3.7%4.1%2.2%

Jan 2001

3.84.32.4

Feb 2001

3.74.02.3

Mar 2001

3.54.22.3

Apr 2001

3.43.92.4

May 2001

3.24.12.3

Jun 2001

3.23.92.2

Jul 2001

3.34.02.2

Aug 2001

3.04.02.2

Sep 2001

3.03.82.1

Oct 2001

2.73.92.1

Nov 2001

2.83.72.0

Dec 2001

2.73.72.0

Jan 2002

2.73.72.2

Feb 2002

2.63.72.0

Mar 2002

2.73.61.9

Apr 2002

2.63.82.0

May 2002

2.63.71.9

Jun 2002

2.53.71.9

Jul 2002

2.53.82.0

Aug 2002

2.63.72.0

Sep 2002

2.53.71.9

Oct 2002

2.63.71.9

Nov 2002

2.63.71.8

Dec 2002

2.43.71.9

Jan 2003

2.63.91.9

Feb 2003

2.43.61.9

Mar 2003

2.33.41.8

Apr 2003

2.33.51.8

May 2003

2.53.61.8

Jun 2003

2.53.61.8

Jul 2003

2.23.61.7

Aug 2003

2.43.61.7

Sep 2003

2.33.71.8

Oct 2003

2.53.81.9

Nov 2003

2.53.71.8

Dec 2003

2.53.81.9

Jan 2004

2.63.71.8

Feb 2004

2.63.71.9

Mar 2004

2.64.02.0

Apr 2004

2.63.91.9

May 2004

2.73.81.8

Jun 2004

2.53.82.0

Jul 2004

2.83.72.0

Aug 2004

2.63.82.0

Sep 2004

2.83.81.9

Oct 2004

2.93.91.9

Nov 2004

2.63.92.1

Dec 2004

3.03.92.0

Jan 2005

2.83.92.1

Feb 2005

2.94.02.0

Mar 2005

2.94.02.1

Apr 2005

3.04.02.1

May 2005

2.83.92.1

Jun 2005

2.94.02.1

Jul 2005

3.14.02.0

Aug 2005

3.04.02.2

Sep 2005

3.14.12.3

Oct 2005

3.03.82.1

Nov 2005

3.14.02.1

Dec 2005

3.13.92.1

Jan 2006

3.13.92.2

Feb 2006

3.14.02.2

Mar 2006

3.44.12.2

Apr 2006

3.43.82.0

May 2006

3.24.02.2

Jun 2006

3.34.02.2

Jul 2006

3.14.12.2

Aug 2006

3.33.92.2

Sep 2006

3.33.92.1

Oct 2006

3.23.92.2

Nov 2006

3.34.02.2

Dec 2006

3.33.82.2

Jan 2007

3.33.92.1

Feb 2007

3.33.82.1

Mar 2007

3.54.02.2

Apr 2007

3.33.92.1

May 2007

3.34.02.2

Jun 2007

3.43.82.1

Jul 2007

3.23.82.1

Aug 2007

3.23.92.2

Sep 2007

3.33.91.9

Oct 2007

3.23.92.1

Nov 2007

3.33.72.0

Dec 2007

3.23.72.0

Jan 2008

3.23.72.1

Feb 2008

3.03.72.1

Mar 2008

3.03.61.9

Apr 2008

2.83.62.1

May 2008

3.03.41.9

Jun 2008

2.73.61.9

Jul 2008

2.73.41.8

Aug 2008

2.63.41.8

Sep 2008

2.33.31.8

Oct 2008

2.43.31.7

Nov 2008

2.33.01.6

Dec 2008

2.33.21.5

Jan 2009

2.03.11.5

Feb 2009

2.13.01.5

Mar 2009

1.92.91.4

Apr 2009

1.72.91.3

May 2009

1.92.91.3

Jun 2009

1.92.81.3

Jul 2009

1.73.01.3

Aug 2009

1.82.91.2

Sep 2009

1.93.01.2

Oct 2009

1.83.01.3

Nov 2009

1.93.11.4

Dec 2009

1.93.11.4

Jan 2010

2.13.01.3

Feb 2010

2.03.01.4

Mar 2010

2.03.31.4

Apr 2010

2.43.21.5

May 2010

2.23.41.4

Jun 2010

2.13.11.5

Jul 2010

2.33.21.4

Aug 2010

2.23.11.4

Sep 2010

2.23.11.5

Oct 2010

2.43.21.4

Nov 2010

2.43.21.4

Dec 2010

2.33.31.5

Jan 2011

2.33.11.4

Feb 2011

2.43.21.5

Mar 2011

2.43.41.5

Apr 2011

2.43.31.4

May 2011

2.43.21.5

Jun 2011

2.63.31.5

Jul 2011

2.73.21.5

Aug 2011

2.53.31.5

Sep 2011

2.83.31.5

Oct 2011

2.73.31.5

Nov 2011

2.63.31.5

Dec 2011

2.83.31.5

Jan 2012

2.83.31.5

Feb 2012

2.63.41.6

Mar 2012

2.93.41.6

Apr 2012

2.83.31.6

May 2012

2.83.41.6

Jun 2012

2.83.31.6

Jul 2012

2.73.21.5

Aug 2012

2.83.31.5

Sep 2012

2.83.21.4

Oct 2012

2.73.31.5

Nov 2012

2.83.31.5

Dec 2012

2.93.31.5

Jan 2013

2.83.31.7

Feb 2013

2.93.41.7

Mar 2013

2.93.21.6

Apr 2013

2.93.41.7

May 2013

3.03.41.6

Jun 2013

3.03.31.6

Jul 2013

2.83.31.7

Aug 2013

2.93.51.7

Sep 2013

2.93.51.7

Oct 2013

3.03.31.7

Nov 2013

2.93.41.7

Dec 2013

2.93.41.7

Jan 2014

2.93.41.7

Feb 2014

3.13.41.8

Mar 2014

3.13.51.8

Apr 2014

3.23.51.8

May 2014

3.33.51.8

Jun 2014

3.53.51.8

Jul 2014

3.43.61.9

Aug 2014

3.73.51.8

Sep 2014

3.43.72.0

Oct 2014

3.53.71.9

Nov 2014

3.33.61.9

Dec 2014

3.53.71.8

Jan 2015

3.73.62.0

Feb 2015

3.73.61.9

Mar 2015

3.63.62.0

Apr 2015

3.83.71.9

May 2015

3.83.61.9

Jun 2015

3.63.61.9

Jul 2015

4.13.61.9

Aug 2015

3.73.62.0

Sep 2015

3.73.72.0

Oct 2015

3.93.72.0

Nov 2015

3.83.82.0

Dec 2015

3.93.92.1

Jan 2016

4.03.62.0

Feb 2016

3.93.82.1

Mar 2016

4.13.72.0

Apr 2016

3.93.72.1

May 2016

3.93.62.1

Jun 2016

3.83.72.1

Jul 2016

4.03.82.1

Aug 2016

3.83.72.1

Sep 2016

3.93.72.1

Oct 2016

3.73.62.1

Nov 2016

4.03.72.1

Dec 2016

3.93.72.1

Jan 2017

3.73.82.2

Feb 2017

3.93.72.1

Mar 2017

3.83.72.2

Apr 2017

4.03.62.1

May 2017

3.83.72.1

Jun 2017

4.13.92.2

Jul 2017

4.13.82.1

Aug 2017

4.13.82.1

Sep 2017

4.13.72.2

Oct 2017

4.23.82.2

Nov 2017

4.13.72.1

Dec 2017

4.13.72.2

Jan 2018

4.33.72.1

Feb 2018

4.33.82.2

Mar 2018

4.43.82.2

Apr 2018

4.43.82.3

May 2018

4.53.92.3

Jun 2018

4.73.92.3

Jul 2018

4.63.82.3

Aug 2018

4.63.92.3

Sep 2018

4.73.82.3

Oct 2018

4.73.92.3

Nov 2018

4.83.92.3

Dec 2018

4.73.82.3

Jan 2019

4.73.82.3

Feb 2019

4.53.82.4

Mar 2019

4.73.82.3

Apr 2019

4.64.02.3

May 2019

4.63.82.3

Jun 2019

4.53.82.3

Jul 2019

4.54.02.4

Aug 2019

4.53.92.4

Sep 2019

4.53.92.3

Oct 2019

4.63.82.3

Nov 2019

4.43.82.3

Dec 2019

4.23.92.3

Jan 2020

4.53.92.3

Feb 2020

4.43.92.2

Mar 2020

3.73.41.9

Apr 2020

3.43.01.6

May 2020

3.96.21.7

Jun 2020

4.25.61.9

Jul 2020

4.64.52.3

Aug 2020

4.44.62.1

Sep 2020

4.54.22.3

Oct 2020

4.64.22.4

Nov 2020

4.54.22.3

Dec 2020

4.53.82.4

Jan 2021

4.73.82.3

Feb 2021

5.04.02.4

Mar 2021

5.44.22.5

Apr 2021

6.04.22.8

May 2021

6.04.12.5
Percent change since February 2020 in real (inflation-adjusted) average hourly earnings
MonthTotal privateLeisure and hospitality

Feb 2020

0.0%0.0%

Mar 2020

1.10.3

Apr 2020

6.57.7

May 2020

5.44.3

Jun 2020

3.51.4

Jul 2020

3.10.2

Aug 2020

3.10.6

Sep 2020

2.90.6

Oct 2020

2.80.6

Nov 2020

3.00.3

Dec 2020

3.80.5

Jan 2021

3.50.6

Feb 2021

3.41.1

Mar 2021

2.71.8

Apr 2021

2.62.5

May 2021

2.42.9

Jun 2021

1.83.1
Job openings and hires rates, leisure and hospitality, December 2000 to May 2021
MonthJob openings rateHires rate

Dec 2000

4.5%7.4%

Jan 2001

5.27.7

Feb 2001

4.87.3

Mar 2001

5.57.8

Apr 2001

4.68.3

May 2001

4.27.6

Jun 2001

3.67.2

Jul 2001

4.67.7

Aug 2001

4.37.2

Sep 2001

4.37.3

Oct 2001

3.06.9

Nov 2001

3.66.8

Dec 2001

3.56.8

Jan 2002

2.96.5

Feb 2002

3.36.9

Mar 2002

3.36.5

Apr 2002

3.16.9

May 2002

3.26.7

Jun 2002

2.86.6

Jul 2002

3.16.7

Aug 2002

3.26.9

Sep 2002

2.86.7

Oct 2002

3.16.5

Nov 2002

3.26.6

Dec 2002

3.06.8

Jan 2003

3.17.0

Feb 2003

2.96.6

Mar 2003

2.86.4

Apr 2003

3.06.5

May 2003

3.47.0

Jun 2003

3.46.7

Jul 2003

2.76.4

Aug 2003

3.16.7

Sep 2003

3.16.8

Oct 2003

3.66.9

Nov 2003

3.46.8

Dec 2003

3.57.1

Jan 2004

3.56.8

Feb 2004

3.66.9

Mar 2004

3.47.3

Apr 2004

3.27.1

May 2004

3.37.2

Jun 2004

3.67.0

Jul 2004

4.07.0

Aug 2004

3.67.0

Sep 2004

4.07.2

Oct 2004

3.76.9

Nov 2004

3.37.0

Dec 2004

3.66.8

Jan 2005

4.17.2

Feb 2005

4.06.9

Mar 2005

4.27.2

Apr 2005

4.77.0

May 2005

4.06.8

Jun 2005

4.37.3

Jul 2005

4.07.2

Aug 2005

3.87.3

Sep 2005

3.67.2

Oct 2005

3.86.8

Nov 2005

3.97.2

Dec 2005

4.47.1

Jan 2006

4.77.2

Feb 2006

4.47.4

Mar 2006

4.17.2

Apr 2006

4.97.1

May 2006

4.07.1

Jun 2006

4.07.2

Jul 2006

4.37.3

Aug 2006

4.26.8

Sep 2006

4.26.6

Oct 2006

4.37.1

Nov 2006

4.47.5

Dec 2006

4.27.0

Jan 2007

3.76.9

Feb 2007

4.06.9

Mar 2007

4.56.8

Apr 2007

4.07.2

May 2007

4.27.0

Jun 2007

4.57.2

Jul 2007

4.56.8

Aug 2007

4.57.0

Sep 2007

4.86.7

Oct 2007

4.36.9

Nov 2007

4.56.7

Dec 2007

4.16.6

Jan 2008

4.16.3

Feb 2008

3.96.8

Mar 2008

4.16.2

Apr 2008

3.96.3

May 2008

3.96.7

Jun 2008

3.45.9

Jul 2008

3.26.0

Aug 2008

3.16.2

Sep 2008

3.05.9

Oct 2008

3.05.8

Nov 2008

2.65.3

Dec 2008

2.65.6

Jan 2009

1.85.4

Feb 2009

2.45.2

Mar 2009

2.04.8

Apr 2009

2.04.7

May 2009

2.25.2

Jun 2009

2.14.8

Jul 2009

1.94.7

Aug 2009

1.55.0

Sep 2009

2.14.8

Oct 2009

2.04.7

Nov 2009

2.15.3

Dec 2009

2.05.0

Jan 2010

2.15.1

Feb 2010

2.04.7

Mar 2010

1.85.2

Apr 2010

2.15.2

May 2010

2.34.9

Jun 2010

2.54.9

Jul 2010

2.45.1

Aug 2010

2.74.9

Sep 2010

2.45.1

Oct 2010

3.15.0

Nov 2010

2.45.0

Dec 2010

2.65.1

Jan 2011

2.74.9

Feb 2011

2.95.1

Mar 2011

2.95.8

Apr 2011

2.45.1

May 2011

2.34.9

Jun 2011

3.05.5

Jul 2011

2.65.4

Aug 2011

2.85.4

Sep 2011

3.15.6

Oct 2011

3.15.5

Nov 2011

3.15.9

Dec 2011

3.25.5

Jan 2012

3.25.7

Feb 2012

2.75.7

Mar 2012

3.26.3

Apr 2012

3.45.5

May 2012

3.25.4

Jun 2012

3.45.3

Jul 2012

3.45.5

Aug 2012

3.05.8

Sep 2012

3.05.2

Oct 2012

3.45.5

Nov 2012

3.55.2

Dec 2012

3.35.8

Jan 2013

3.25.7

Feb 2013

3.65.6

Mar 2013

3.55.7

Apr 2013

3.36.1

May 2013

3.25.7

Jun 2013

3.35.7

Jul 2013

3.45.5

Aug 2013

3.55.4

Sep 2013

3.75.8

Oct 2013

3.65.6

Nov 2013

3.65.5

Dec 2013

3.95.5

Jan 2014

4.05.8

Feb 2014

3.75.9

Mar 2014

3.85.7

Apr 2014

4.35.9

May 2014

4.66.1

Jun 2014

4.46.2

Jul 2014

4.16.0

Aug 2014

4.65.8

Sep 2014

4.66.2

Oct 2014

4.36.0

Nov 2014

4.16.1

Dec 2014

4.56.3

Jan 2015

5.16.1

Feb 2015

4.86.2

Mar 2015

4.66.1

Apr 2015

4.66.3

May 2015

4.46.4

Jun 2015

4.26.1

Jul 2015

4.86.3

Aug 2015

4.46.7

Sep 2015

4.46.7

Oct 2015

4.96.6

Nov 2015

4.76.7

Dec 2015

4.66.8

Jan 2016

4.76.2

Feb 2016

4.76.8

Mar 2016

5.16.6

Apr 2016

4.76.5

May 2016

4.66.6

Jun 2016

4.86.7

Jul 2016

4.66.6

Aug 2016

4.96.6

Sep 2016

4.56.1

Oct 2016

4.66.2

Nov 2016

4.66.7

Dec 2016

4.56.4

Jan 2017

4.46.5

Feb 2017

5.36.4

Mar 2017

4.56.3

Apr 2017

5.06.4

May 2017

5.06.3

Jun 2017

5.06.5

Jul 2017

5.16.3

Aug 2017

5.26.2

Sep 2017

4.56.1

Oct 2017

4.86.5

Nov 2017

5.26.3

Dec 2017

5.26.1

Jan 2018

5.46.3

Feb 2018

5.46.5

Mar 2018

5.46.4

Apr 2018

5.66.5

May 2018

5.66.9

Jun 2018

6.16.4

Jul 2018

5.96.8

Aug 2018

5.86.5

Sep 2018

6.16.4

Oct 2018

5.86.7

Nov 2018

5.86.5

Dec 2018

6.26.3

Jan 2019

6.46.8

Feb 2019

5.76.6

Mar 2019

5.86.7

Apr 2019

5.87.1

May 2019

5.86.6

Jun 2019

5.47.0

Jul 2019

5.56.9

Aug 2019

5.46.9

Sep 2019

5.76.9

Oct 2019

5.66.6

Nov 2019

5.56.5

Dec 2019

5.26.8

Jan 2020

5.26.6

Feb 2020

5.36.5

Mar 2020

3.94.2

Apr 2020

3.84.9

May 2020

6.819.5

Jun 2020

7.017.5

Jul 2020

6.310.6

Aug 2020

6.08.1

Sep 2020

5.98.2

Oct 2020

6.18.5

Nov 2020

5.98.1

Dec 2020

5.45.8

Jan 2021

5.37.1

Feb 2021

6.58.8

Mar 2021

8.08.5

Apr 2021

9.19.5

May 2021

9.09.3

Improving Key Labor Market Estimates during the Pandemic and Beyond

If things were good enough yesterday, why would we change them today? Good enough is OK for folding laundry, cleaning the junk drawer, and raking leaves, but not for official statistics from BLS. We do our best to provide a timely look at the labor market and economy, but we often learn more after we publish those initial data. As a result, we sometimes revise our statistics. That’s mostly a good thing, but there is a fine line between the frequency of revisions and introducing noise and possibly confusion.

I recently wrote about the importance of maintaining and sometimes changing official historical records, using baseball as an example. Today I want to highlight two of our statistical programs: the Job Openings and Labor Turnover Survey (JOLTS) and the Local Area Unemployment Statistics (LAUS) data. We publish monthly statistics from these programs and revise them the following month as more information comes in. In addition to the monthly revisions, we incorporate more information once a year.

The COVID-19 pandemic continues to have a huge impact on our lives. Check out our summary of how the pandemic affected the labor market and economy in 2020. The magnitude of the labor market changes stress tested the JOLTS and LAUS programs. Based on what we observed in real time, and what we know now, we realized we needed to respond to this unusual economic environment. We change our estimating techniques infrequently, but even the best techniques need adjustments to respond to such significant shocks. These adjustments reflect our commitment to continuous improvement.

Changes in Job Openings and Labor Turnover Estimates

The economic conditions caused by the pandemic led us to make two changes to JOLTS procedures. First, we changed the way we handled unusual reports, which we call outliers. In normal times, these outliers may be businesses with unusually large numbers of job separations. This process mutes the outlier impact on the estimates because those outliers are unlikely to represent other businesses. At the start of the pandemic, however, very large increases in separations were followed by very large increases in hires in many businesses. During this period, we adjusted the JOLTS outlier-detection techniques to accept as normal those extreme changes. Under these circumstances, these “outlier” reports did in fact represent many other businesses.

Second, JOLTS uses data from the much larger Current Employment Statistics (CES) sample to adjust estimates of hires and separations to stay in sync with the monthly employment changes. This procedure assumes that, over the long term, the difference between JOLTS hires and separations is close to the CES employment change. This assumption, however, was not appropriate in late March 2020 as people, businesses, and governments tried to contain the spread of COVID-19. The two surveys have different reference periods. The CES reference period is the pay period that includes the 12th of the month, whereas JOLTS estimates of hires and separations cover the entire month. Hires and separations during the latter half of March 2020 were not included in the CES employment change for March but were included in the JOLTS estimates for the month. To accurately capture the timing of this unprecedented event, we stopped aligning the JOLTS estimates of hires and separations with the CES employment change from March through November 2020.

More changes to JOLTS estimates came with the publication of the January 2021 news release. As we do every year, we revised the past 5 years of historical JOLTS data using updated CES employment estimates. We also updated the seasonal adjustment factors and applied them over the past 5 years. In addition, because we stopped using the alignment procedure for most of 2020, the difference between CES and JOLTS estimates had become quite large by December. To preserve the true economic differences between CES and JOLTS but reduce the divergence by the end of 2020, we adjusted estimates of hires and separations for the months in which the alignment procedure was turned off. These adjustments ensure that we report the highest quality data as quickly as we can, while improving accuracy as we learn more information.

Changes in State Labor Force and Unemployment Estimates

We also made real-time changes during the pandemic to the models we use to produce state labor force and unemployment estimates. The primary inputs to the models are from the Current Population Survey (CPS), the source of the monthly national unemployment rate and other labor market measures. Because the CPS sample is not large enough to support state estimates on a monthly basis, we also use CES employment data and counts of continued claims for unemployment insurance to help inform the models. All of these model inputs experienced extreme movements, especially in the early part of the pandemic.

Starting with March 2020, we introduced two monthly adjustments we usually perform only once a year. These adjustments involved closer review and adjustment of outliers from all model inputs and level shifts. We discussed these changes in notes that appeared in the State Employment and Unemployment news releases for March 2020, April 2020, and May 2020.

These changes in 2020 provided a short-term solution for the state models. For the longer term, we respecified the relationships of the model inputs to provide more flexibility when unusual disruptions occur in the labor market. We explain these changes in our “Questions and Answers.”

We implemented the new estimation procedures for model-based areas in early 2021. They were reflected in the estimates published in the Regional and State Unemployment – 2020 Annual Averages news release. We replaced all previously published state data using the new procedures to ensure historically comparable estimates. The recent data revisions also reflect the best available inputs for model estimation. If you are interested in the details, you can read all about them at the LAUS Estimation Methodology page.

The speed with which the JOLTS and LAUS staff researched and implemented these improvements reflects the high quality of the BLS staff and their commitment to producing gold standard data. They make me proud to lead this great agency.

The Challenges of Seasonal Adjustment during the COVID-19 Pandemic

In a previous edition of Commissioner’s Corner, we described seasonal adjustment, the process BLS and many others use to smooth out increases and decreases in data series that occur around the same time each year. Seasonal adjustment allows us to focus on the underlying trends in the data. Seasonal adjustment works well when seasonal patterns are pretty consistent from year to year. But what about when there are large shocks to the economy, such as natural disasters and the massive effects of the COVID-19 pandemic and resulting business closures and stay-at-home orders? Today we’ll look at how BLS addressed this issue.

First, a little background on seasonal adjustment. Here’s an example similar to one we have used before, looking at employment in the construction industry. Construction employment varies throughout the year, mostly because of weather. As the chart shows in the “not seasonally adjusted” line, construction adds jobs in the spring and throughout the summer before it starts to lose jobs when the weather turns colder. The large seasonal fluctuations make it hard to see the overall employment trend in the industry. That makes it harder to study other factors that affect the trend, like changes in consumer demand or interest rates. After seasonal adjustment, the construction industry grew by 1.2 million jobs from the beginning of 2015 to the end of 2019.

Construction employment, 2015–19

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

BLS seasonally adjusts data in several of its monthly and quarterly news releases.

Two Approaches to Seasonal Adjustment

BLS uses one of two approaches to seasonally adjust data in these releases—projected factors or concurrent seasonal adjustment. When we project seasonal adjustment factors, we only use historical data in the models. That means we calculate factors in advance, so they are not influenced by the most recent trends. Concurrent seasonal adjustment uses all the data available, including the most recent month or quarter. As a result, the factors are influenced by recent changes.

Regardless of whether the factors are projected or concurrent, the seasonal adjustment models can be additive or multiplicative. We’ll explain more about that below. The COVID-19 pandemic affected the seasonal adjustment process in different ways depending on how the seasonal factors are calculated.

Approach #1

The Consumer Price Index, Producer Price Indexes, and Employment Cost Index use the projected-factor approach and calculate seasonal factors once a year. BLS staff estimated the 2020 seasonal factors at the beginning of 2020 and have used them throughout the year. When new factors for 2021 and revised historical factors are calculated, BLS will examine the effects of the pandemic on the seasonal adjustment models.

Approach #2

We use a concurrent process to calculate the seasonal factors each month for nonfarm employment estimates for the nation, states, and metro areas, unemployment and labor force estimates for the nation, states, and metro areas, and job openings and labor turnover estimates. Each quarter, BLS also uses a similar concurrent process to calculate seasonal factors for productivity measures and business employment dynamics. This helps create the best seasonal factors when seasonality may shift over time. For example, think of schools letting out for summer a little earlier than they usually do each year, or the changing nature of delivery services because of online shopping. Using the most recent data to calculate seasonal factors helps pick up these changes to seasonality faster than the forecasted method. The risk of using the concurrent process is that it may attribute some of the movement in the estimates to a changing seasonal pattern when it really resulted from a nonseasonal event. BLS also annually examines and revises the historical seasonal factors even if the factors were originally calculated using concurrent adjustment. As the saying goes, hindsight is 20/20.

Before the COVID-19 pandemic, the concurrent seasonal adjustment models required limited real-time intervention. Examples of potential reasons for intervention include major events like hurricanes. The COVID-19 pandemic is unusual in its severity and duration, so significant intervention was needed.

BLS intervened in several ways to create the highest quality, real-time seasonal factors. The tool we use most often is called outlier detection. We consider outliers not to represent a normal or typical seasonal movement. When we label an observation as an outlier, we don’t use it to inform the seasonal adjustment model. Since economic activity is still being heavily influenced by COVID-19 and efforts to contain it, BLS has detected more outliers. When this happens, concurrent models behave more like projected-factor models because the most recent data are not used to create seasonal factors.

The Local Area Unemployment Statistics program uses another type of intervention, a technique call a level shift. It is used when there is a sudden change in the level of a data series. In this case, level shifts were used over a series of months.

Additive versus Multiplicative Models

As noted earlier, all BLS programs review their seasonal adjustment models each year. One of the steps during this process is to select a model—either additive or multiplicative. We use an additive model when seasonal movements are stable over time regardless of the level of the series. A multiplicative model is better to use when seasonal movements become larger as the series itself increases—that is, the seasonality is proportional to the level of the series. That means a sudden large change in the level of a series, such as the large increase in the number of unemployed people in April 2020, will be accompanied by a proportionally large seasonal effect. BLS did not want this to occur. When there are large shifts in a measure, multiplicative seasonal adjustment factors can result in adjusting too much or too little. In these cases, additive seasonal adjustment factors usually reflect seasonal movements more accurately and have smaller revisions.

Because of the unusual data patterns beginning in March 2020, both the Current Population Survey, which we use to measure unemployment and the labor force, and the Job Openings and Labor Turnover Survey switched from multiplicative to additive seasonal models for most series and did not wait until the typical yearend model review.

BLS does not produce the weekly data on unemployment insurance. We do, however, compute the seasonal adjustment factors used by the Department of Labor’s Employment and Training Administration for their Unemployment Insurance Weekly Claims data. As we recommended, the Employment and Training Administration recently switched from using multiplicative to additive seasonal adjustments.

Our quarterly Labor Productivity and Costs news release uses input data from the Bureau of Economic Analysis, the U.S. Census Bureau, and several BLS programs. Most of the input data are already seasonally adjusted by the source agencies or programs. The productivity program only seasonally adjusts monthly Current Population Survey data on employment and hours worked for about ten percent of workers, mostly the self-employed, who are not included in the monthly data from the Current Employment Statistics survey on nonfarm employment and hours. The productivity program detected outliers in some of the data beginning at the start of the COVID-19 pandemic in March 2020 and accounted for them in the estimates.

Science and Art

Seasonal adjustment of economic data is a scientific process that involves complex math. But seasonal adjustment also involves some art in addition to science. The art comes in when we use our judgment about outliers in the data or when we decide whether an additive or multiplicative model more closely reflects seasonal variation in economic measures. The art also comes in when we recognize how complicated the world is. During 2020 we have experienced not just a global pandemic but also massive wildfires in several western states, a historic number of hurricanes that made landfall, and other notable events that affect economic activity. Did our seasonal adjustment models properly account for all of these events? I can say we have tried our best with the information we have available. As we gather more data for 2020 and future years, we will continue to examine how we can improve our models to help us distinguish longer-term trends from the seasonal variation in economic activity.

Acknowledgment: Many BLS staff members helped make the technical details in this blog easier to understand, and they all have my gratitude. Three who were especially helpful were Richard Tiller, Thomas Evans, and Brian Monsell.

Construction employment, 2015–19
MonthSeasonally adjustedNot seasonally adjusted

Jan 2015

6,320,0005,953,000

Feb 2015

6,361,0005,962,000

Mar 2015

6,334,0006,051,000

Apr 2015

6,392,0006,300,000

May 2015

6,427,0006,491,000

Jun 2015

6,441,0006,633,000

Jul 2015

6,472,0006,718,000

Aug 2015

6,490,0006,754,000

Sep 2015

6,508,0006,704,000

Oct 2015

6,547,0006,740,000

Nov 2015

6,598,0006,685,000

Dec 2015

6,630,0006,542,000

Jan 2016

6,620,0006,252,000

Feb 2016

6,650,0006,256,000

Mar 2016

6,680,0006,402,000

Apr 2016

6,701,0006,614,000

May 2016

6,691,0006,758,000

Jun 2016

6,702,0006,913,000

Jul 2016

6,736,0006,989,000

Aug 2016

6,737,0006,997,000

Sep 2016

6,768,0006,971,000

Oct 2016

6,798,0006,981,000

Nov 2016

6,819,0006,903,000

Dec 2016

6,821,0006,700,000

Jan 2017

6,847,0006,459,000

Feb 2017

6,889,0006,527,000

Mar 2017

6,909,0006,634,000

Apr 2017

6,916,0006,820,000

May 2017

6,928,0006,998,000

Jun 2017

6,955,0007,169,000

Jul 2017

6,960,0007,212,000

Aug 2017

6,990,0007,248,000

Sep 2017

7,004,0007,201,000

Oct 2017

7,027,0007,208,000

Nov 2017

7,066,0007,147,000

Dec 2017

7,093,0007,004,000

Jan 2018

7,114,0006,729,000

Feb 2018

7,200,0006,840,000

Mar 2018

7,205,0006,933,000

Apr 2018

7,223,0007,129,000

May 2018

7,266,0007,336,000

Jun 2018

7,282,0007,497,000

Jul 2018

7,304,0007,554,000

Aug 2018

7,335,0007,586,000

Sep 2018

7,355,0007,535,000

Oct 2018

7,378,0007,557,000

Nov 2018

7,376,0007,454,000

Dec 2018

7,402,0007,311,000

Jan 2019

7,452,0007,069,000

Feb 2019

7,423,0007,062,000

Mar 2019

7,443,0007,170,000

Apr 2019

7,469,0007,377,000

May 2019

7,478,0007,540,000

Jun 2019

7,497,0007,699,000

Jul 2019

7,504,0007,753,000

Aug 2019

7,508,0007,760,000

Sep 2019

7,524,0007,700,000

Oct 2019

7,541,0007,720,000

Nov 2019

7,539,0007,609,000

Dec 2019

7,555,0007,447,000

Innovations at BLS during the COVID-19 Pandemic

Our work at the Bureau of Labor Statistics is driven by the idea that good measurement leads to better decisions. Good measures of economic and social conditions help public policymakers and private businesses and households assess opportunities and areas for improvement. Measuring these conditions consistently over time helps people who use our data evaluate the impact of public and private decisions.

We also believe we must be completely transparent about the design of our surveys and programs and the methods we use to conduct them. It isn’t enough to publish statistics and expect people simply to trust their quality. We gain this trust by documenting the design and procedures for all our programs in our Handbook of Methods. Our website also explains our policies for ensuring data quality and protecting the confidentiality and privacy of the people and businesses who participate in our surveys and programs. Further, BLS works with the wider U.S. statistical community to ensure and enhance the quality of statistical information.

Good measures are essential in “normal” times, but the global COVID-19 pandemic has made these last few months anything but normal. I am so proud of the work of the career professionals at BLS and our fellow statistical agencies for continuing to produce vital economic statistics. Our entire BLS staff moved to full-time telework in mid-March and didn’t miss a beat. We continue to publish measures of labor market activity, working conditions, price changes, and productivity like BLS has done since its founding in 1884. See our dashboard of key economic indicators in the time of COVID-19.

Publishing these measures hasn’t been easy. The pandemic has raised new questions about how businesses, households, and consumers have changed their behavior. BLS also has had to innovate to find new ways of doing things during the pandemic.

Today I want to tell you about the new data we have been collecting to learn more about the effects of the pandemic. I also want to tell you about some of the ways the BLS staff has innovated to keep producing data that are accurate, objective, relevant, timely, and accessible.

New Data

How businesses have responded to the pandemic

We have collected new data on how U.S. businesses changed their operations and employment from the onset of the pandemic through September 2020. This information, combined with data collected in other BLS surveys, will aid in understanding how businesses responded during the pandemic. Other statistics we have collected and published during the pandemic show changes in employment, job openings and terminations, wages, employer-provided benefits, prices, and more. These new data provide more insights by asking employers directly what they experienced as a result of the pandemic and how they reacted. Data for the Business Response Survey to the Coronavirus Pandemic will be released in early December 2020.

Changes in telework, loss of jobs, and job search

The Current Population Survey is the large monthly survey of U.S. households from which we measure the unemployment rate and other important labor market indicators. We added questions to the survey to help gauge the effects of the pandemic on the labor market. These questions were added in May 2020 and will remain in the survey until further notice. One question asks whether people teleworked or worked from home because of the pandemic.

Percent of employed people who teleworked at some point in the previous 4 weeks because of the COVID-19 pandemic, May through October 2020

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

Other questions ask whether people were unable to work because their employers closed or lost business because of the pandemic; whether they were paid for that missed work; and whether the pandemic prevented them from searching for jobs.

Number of people not in the labor force who did not look for work because of the COVID-19  pandemic, May through October 2020

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

Changes in sick leave plans

We added several questions to the National Compensation Survey to understand the effects of the pandemic on sick leave plans. The questions asked whether private industry establishments changed their leave policies and whether employees used sick leave between March 1 and May 31, 2020.

Receiving and using stimulus payments during the pandemic

BLS is one of several federal agencies that developed questions for the rapid response Household Pulse Survey. The survey is a collaboration among the U.S. Census Bureau, BLS, the U.S. Department of Housing and Urban Development, the National Center for Education Statistics, the National Center for Health Statistics, and the U.S. Department of Agriculture’s Economic Research Service. BLS contributed questions on the receipt and use of Economic Impact Payments and on sources of income used to meet spending needs during the pandemic.

Our staff will continue to publish research on how the pandemic has affected the labor market and markets for goods and services. Check back regularly as we add to this library of research.

Innovations in Data Collection and Training

The COVID-19 pandemic has caused profound changes in the daily lives of Americans. BLS is no exception. As I mentioned earlier, all BLS staff moved to full-time telework in March. The pandemic hasn’t prevented us from continuing to publish high-quality data, but we have had to change some of our data-collection methods and estimation procedures. We will continue to explain those changes so you can understand how they affect the quality of our measures.

Our survey respondents are the heart of everything we do at BLS. Without their generous and voluntary cooperation, we would not be able to publish high-quality data for public and private decision making. Respondents have businesses and households to run, and a pandemic is a challenging time to ask for their help. The data-collection staffs at BLS, the U.S. Census Bureau, and our state partners form great relationships with survey respondents. We must continue to protect the health of data collectors while also training them in a rapidly changing environment. Let me highlight a few of the innovative changes we have made during the pandemic that focus on our relationships with respondents and how we train data collectors.

Using videoconferencing technology for data collection

Several of our surveys have started using videoconferencing tools to speak with respondents and collect data from them. Some of the surveys that now use this technology include the National Compensation Survey, the Occupational Requirements Survey, and the Producer Price Index. Many of our surveys previously relied on interviewers visiting businesses or households to collect data. We suspended all in-person data collection in March to protect the health of data collectors and respondents, so we had to find other ways to collect data. Many of our surveys also use telephone and internet to collect data, but those modes aren’t always ideal for every kind of data. We often need to develop personal relationships with respondents to gain their trust and cooperation and ensure high-quality data. Videoconferencing helps us accomplish what we often can’t do with phones or web survey forms.

The Occupational Requirements Survey is one that has begun using videoconferencing in data collection. The survey provides information about the physical demands; environmental conditions; education, training, and experience; and cognitive and mental requirements for jobs in the U.S. economy. Collecting data for this survey often requires visual aids, hand gestures, and other nonverbal information to understand job characteristics. It often helps to watch jobs as they are performed at a worksite, but that’s not an option during the pandemic. Videoconferencing is the next best alternative.

Many of our data collectors and respondents have mentioned how helpful videoconferencing is for developing a rapport and for sharing screens and other visual information. Videoconferencing also helps us reduce travel and lodging costs, so we likely will continue to rely on videoconferencing at least partly even after the pandemic.

Using videoconferencing technology for training and mentoring

Many of our surveys are complex and require considerable ongoing training for data collectors. For example, before the pandemic, our Consumer Price Index Commodities and Services (C&S) survey involved in-person training at our Washington, DC, headquarters. There were two classroom training courses: a 2-week introductory course and a 1-week advanced course. Each course was followed by on-the-job training held in our regional offices. Even before the pandemic, we were developing videoconference training. The pandemic caused us to accelerate these plans. We now provide C&S survey training through video collaboration tools. We also integrate on-the-job training throughout the classes.

Several other surveys have adopted a similar training approach as the Consumer Price Index. Our data-collection staffs also increasingly use videoconferencing for mentoring and to share ideas about how to make the data-collection experience better for data collectors and respondents.

A final note

Before I conclude, I want to share some sad news about one of the people who played an indispensable leadership role in developing the new survey questions and innovative data-collection and training methods. Jennifer Edgar, our Associate Commissioner for Survey Methods Research, died November 8 in a tragic fall in her home. She leaves behind her husband and two young children, her parents, and her sister. Moreover, she leaves hundreds of BLS colleagues and many more throughout the statistical community and beyond, who will grieve the loss of an exceptionally gifted friend and professional whose great promise was cut suddenly and tragically short. Jennifer was using her considerable energies to move BLS forward. Her passing is a huge blow to her family, loved ones, and the entire statistical community. We are working on ways to ensure Jennifer’s memory and passion is forever present at BLS.

Percent of employed people who teleworked at some point in the previous 4 weeks because of the COVID-19 pandemic
MonthPercent

May 2020

35.4%

Jun 2020

31.3

Jul 2020

26.4

Aug 2020

24.3

Sep 2020

22.7

Oct 2020

21.2
Number of people not in the labor force who did not look for work because of the COVID-19 pandemic
MonthNumber not in the labor force

May 2020

9,740,000

Jun 2020

7,043,000

Jul 2020

6,454,000

Aug 2020

5,200,000

Sep 2020

4,499,000

Oct 2020

3,563,000