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Topic Archives: Earnings and Wages

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

Brood X Cicadas over the History of BLS Data

For readers around several Eastern and Midwest states, you likely know that “Brood X” is the name of the cohort of 17-year cicadas that have made their appearance known, and heard, starting in mid-May 2021. According to the U.S. Forest Service, scientists have been studying cicadas for a couple of centuries, and there are historical reports going back centuries. In fact, in The Iliad, Homer speaks of two elder sages who were “… too old to fight, but they were fluent orators, and sat on the tower like cicadas that chirrup delicately from the boughs of some high tree in a wood.”

A cicada

We can’t find any record of President Chester A. Arthur (who signed the law to create BLS) nor Carroll Wright (the first BLS Commissioner) speaking of cicadas, but is it a coincidence that Brood X appeared just one year after BLS was founded in 1884? Since BLS has lived through 9 appearances of Brood X, let’s take a look at what we reported during those years.

Year of BLS founding in 1884 and Brood X appearances in 1885, 1902, 1919, 1936, 1953, 1970, 1987, 2004, and 2021

Brood X of 1919 was the first to encounter the BLS Consumer Price Index, which provides information back to 1913. Using the CPI Inflation Calculator, you can look at how buying power has changed over time. As the chart below shows, Brood X from 1919 could spend $6.31 and have buying power equal to their great, great, great, great grandchildren spending $100 today. The 1936 cicadas were affected by the Great Depression, with increasing buying power because of deflation. The 1987 cicadas were affected by high inflation rates that occurred after their 1970 ancestors disappeared.

Purchasing power of $100 in January 2021 compared with January of other Brood X years

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

While BLS has reported on the number of workers on employer payrolls since before the 1919 cicadas came out of the ground, consistent data for all states were not available until the late 1940s. That was in time for the 1953 cicadas, which witnessed about 43 million jobs on private nonfarm payrolls. The 1953 cicadas saw about 45 percent of private sector employment in good-producing industries — mining, construction, and manufacturing. Interestingly, cicadas from the groups that followed saw little change in the number of jobs on good-producing payrolls. The peak number in Brood X years was 23 million in 1987. Goods-producing employment in 2021 is just over 20 million. In contrast, payrolls of service-providing industries have soared over the same period, from nearly 24 million in 1953 to just over 100 million today. These service-providing industries include trade, transportation, financial activities, education and health services, restaurants and other hospitality businesses, and many more. The 2021 cicadas have seen that 83 percent of payroll employment is in service-providing industries.

Private-sector employment in goods-producing and service-providing industries, January of Brood X years

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

BLS has been studying productivity since before Brood X of 1902 emerged. The first such study was of “Hand and Machine Labor” in 1898. Consistent measures of labor productivity in the nonfarm business sector date from 1947, in time for the 1953 cicadas. Over the more than 70-year history of these data, the percent change from the previous quarter, at an annual rate, has been negative about 20 percent of the time (based on the first quarter of each year). But perhaps the sound and fury of Brood X has some influence, as 2 out of 5 (40 percent) of the cicada-year changes have been negative. Yes, it’s a small sample, but let’s not discount the cicada effect.

Annualized percent change in nonfarm business sector labor productivity in the first quarter of Brood X years, compared with the previous quarter

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

Finally, our flying friends have studied BLS data on pay and know about all our measures of worker pay. The longest consistent series on pay began in 1964, in time for the 1970 cicadas to track average hourly earnings for production and nonsupervisory employees. The 1987 cicadas saw pay nearly triple from that of their parents, and future generations saw continued increases as well.

Average hourly earnings of production and nonsupervisory employees in the private sector, January of Brood X years

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

BLS is looking forward to providing the latest in labor statistics in 2038, when the children of the 2021 cicadas check out the latest on www.bls.gov. In the meantime, maybe they’ll follow us on Twitter.

Purchasing power of $100 in January 2021 compared with January of other Brood X years
YearPurchasing power

1919

$6.31

1936

5.28

1953

10.17

1970

14.45

1987

42.51

2004

70.80

2021

100.00
Private-sector employment in goods-producing and service-providing industries, January of Brood X years
YearGoods producingService providing

1953

19,721,00023,629,000

1970

22,726,00035,954,000

1987

23,232,00060,401,000

2004

21,715,00087,516,000

2021

20,221,000100,948,000
Annualized percent change in nonfarm business sector labor productivity in the first quarter of Brood X years, compared with the previous quarter
YearAnnualized percent change

1953

3.5%

1970

1.3

1987

-1.8

2004

-1.3

2021

5.4
Average hourly earnings of production and nonsupervisory employees in the private sector, January of Brood X years
YearAverage hourly earnings

1970

$3.31

1987

9.02

2004

15.51

2021

25.14

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

Celebrating World Statistics Day 2020

At the Bureau of Labor Statistics, we always enjoy a good celebration. We just finished recognizing Hispanic Heritage Month. We are currently learning how best to protect our online lives during National Cybersecurity Awareness Month. We even track the number of paid holidays available to workers through the National Compensation Survey. Today I want to focus on a celebration that happens once every 5 years — World Statistics Day. While there may not be parades, special meals, or department store sales to honor this day, we at BLS and our colleagues worldwide take time out on October 20, 2020, to recognize the importance of providing accurate, timely, and objective statistics that form the cornerstone of good decisions.

United Nations logo for World Statistics Day 2020

World Statistics Day, organized under the guidance of the United Nations Statistical Commission, was first celebrated in October 2010. This year, the third such event, focuses on “connecting the world with data we can trust.” At BLS, the trustworthy nature of our data and processes has been a hallmark of our work since our founding in 1884. Our first Commissioner, Carroll Wright, described our work then as “conducting judicious investigations and the fearless publication of results.” That credo guides us to this day. As the only noncareer employee in the agency, I am surrounded by a dedicated staff of data experts  whose singular mission is to produce the highest-quality data, without regard to policy or politics. BLS and other statistical agencies throughout the federal government strictly follow Statistical Policy Directives that ensure we produce data that meet precise technical standards and make them available equally to all. For nearly 100 years, we have regularly updated our Handbook of Methods to provide details on data concepts, collection and processing methods, and limitations. Transparency remains a hallmark of our work.

The United States has a decentralized statistical system, with numerous agencies large and small spread throughout the federal government. Despite this decentralization, the agencies work together to improve statistical methods and follow centralized statistical guidance. This partnership was recently strengthened by the Foundations for Evidence-Based Policymaking Act of 2018, which reinforced how the statistical agencies protect the confidentiality of businesses and households that provide data. The Act also designated heads of statistical agencies, like myself, as Statistical Officials for their respective Departments. In my case, my BLS colleagues and I advise other Department of Labor agencies on statistical concepts and processes, while continuing to stay clear of policy discussions and decisions.

World Statistics Day is a global event, so this is a good time to share some examples where BLS participates in statistical activities around the world:

  • We have regular contact with colleagues at statistical organizations around the world. Just recently, I participated in a very long-distance video conference on improvements to the Consumer Price Index. For me, it was 6:00 a.m., and I made sure I had a mug of coffee handy; for my colleagues in Australia, it was 6:00 p.m., and I’m certain their mug had coffee as well.
  • We have a well-established training program for international visitors, focusing on our processes and methods. We hold training sessions at BLS headquarters (or at least we did before the pandemic), we send experts to other countries, and we are exploring virtual training. We are eager to share our expertise and long history.
  • We participate in international panels and study groups, such as those organized by the United Nations, the Organization for Economic Cooperation and Development, and others, with topics ranging from measuring the gig economy to use of social media.
  • We provide BLS data to international databases, highlighting employment, price, productivity and related information to compare with other countries.

And that’s just a taste of how BLS fits into the World of Statistics. As Commissioner, I’ve had the honor to represent the United States in conferences and meetings across the globe. The BLS staff and I also hold regular conversations with statistical officials worldwide. In a recent conversation with colleagues in the United Kingdom, we were eager to learn about each other’s changes in the ways we provide data and analyses to our customers. These interactions expand everyone’s knowledge and keep the worldwide statistical system moving forward.

To celebrate World Statistics Day, I asked some BLS cheerleaders if they would join me in a video message about the importance of quality statistical data. Here’s what they had to say:

In closing, let’s all raise a toast to World Statistics Day, the availability of high-quality and impartial data, and the dedicated staff worldwide who provide new information and analysis every day.

Happy World Statistics Day!