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Reflecting on Our Recent Price Data User Conference

I have repeatedly seen during my time as Commissioner of Labor Statistics how driven and conscientious BLS employees are. This is especially true of how they relate to our customers.

At the core of our agency’s mission is a responsibility to our customers. BLS strives to meet the needs of a diverse set of customers for accurate, objective, relevant, timely, and accessible information. At the same time, we need to keep pace with a rapidly changing economy. Our data must reflect world events, such as the COVID-19 pandemic.

How do we meet this responsibility to our customers and keep them informed? How do we stay informed about what our customers face and what they need? For years, BLS Regional Offices have sponsored data user conferences to address these questions. These conferences have always been successful forums with broad representation from our data users.

I have participated in many such conferences virtually and in person. I recently had the pleasure of a different kind of virtual BLS data user conference, the price index users conference.

How did it come about? The staff of our Office of Prices and Living Conditions saw the success of our regional events and wanted to interact directly with our customers. What better way to communicate with price index users and get their feedback!

Especially now, with so much attention focused on inflation, customers want to know the pandemic’s effects on not only our survey results, but also on our survey methods, participation, and data quality.

This conference featured presentations by program experts from the Consumer Price Index, Producer Price Index, and U.S. Import and Export Price Indexes. There was plenty of technical detail that researchers, financial journalists, finance professionals, and other participants welcomed. The conference covered alternative data collection methods, medical care, quality adjustment, the impact of COVID-19, and other topics.

Beyond the technical detail, this event featured a listening session. This session went beyond the usual questions and answers to provide a forum for a robust exchange between our sophisticated data users and our experts. Everyone was in the same “room” and could participate in this discussion about methods, customer needs, COVID-19 effects, and future plans.

We at BLS benefit from this type of open exchange, and we thank all who attended for enhancing the 2-day event. We also owe a big thank you to all of our respondents for their survey participation throughout a very challenging time. When you agree to share your company’s information with BLS, you help ensure that we can continue to provide quality data. Survey participants are our bedrock, the foundation for good information about our economy. We cannot succeed in our mission for the American people, let alone our customers, without your help.

We look forward to your participation at our next event!

BLS at the Olympics

When you find yourself in a 16-day marathon on the sofa shouting “U-S-A, U-S-A” at every swimmer, weightlifter, and beach volleyball player, you may not see the relationship to the U.S. Bureau of Labor Statistics. But as you sprint through the pages of our website or add your likes to Twitter, you’ll begin to see how BLS has a stat for that.

Olympic symbol with five interlocking rings and BLS emblem

Uneven bars

As we head into the gymnastics venue, we notice one of the women’s apparatus reminds us of how we measure productivity. We use two factors to compute labor productivity—output and hours worked. Over the past decade, the “bars” for output and hours worked aren’t quite parallel, but they are definitely uneven; output grew a little faster than hours, leading to rising productivity.  The COVID-19 pandemic resulted in sharp drops in both output and hours, leaving productivity to maintain its steady climb. BLS productivity staff stick the landing by providing a series of quarterly charts to let you vault into all the details.

Labor productivity (output per hour), output, and hours worked indexes, nonfarm business, 2012 to 2021

Editor’s note: Data for this chart are available in our interactive chart packages.

Decathlon

You may not have to run, jump, and throw, but the fastest growing occupations from our annual employment projections represent a diversity of skills. A decathlon has 10 events, but we have so much Olympic spirit we want to show you the 12 fastest growing occupations. Half of these jobs are in the healthcare field, while a couple involve alternative forms of energy. And, of course, BLS is pleased to see statisticians and data scientists and mathematical science occupations make the list. While the “World’s Greatest Athlete” is decided at the track and field venue, our Employment Projections staff goes the extra mile (1,500 meters, actually) to identify where the jobs will be in the future.

Fastest growing occupations, projected, 2019–29

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

Swimming 4×100 medley relay

At the natatorium, we are here to witness one of the premier events of the Olympic Games, the swimming 4×100 medley relay. Four price indexes will each take a lap to demonstrate how they work together to provide a complete inflation picture. In the leadoff position is the Import Price Index, which rose 11.2 percent from June 2020 to June 2021—with fuel prices being one of the largest drivers. After touching the wall first, imports made way for the Producer Price Index, which rose 7.3 percent for the year ending in June. Price increases for a variety of goods drove this gain. The third leg belonged to the Export Price Index, which rose 16.8 percent over the past year, the largest gain among the quartet. Agricultural products were among the largest contributors to the increase in export prices. In the anchor position was the Consumer Price Index, freestyling with a 5.4-percent increase over the year, leading BLS to the gold medal. Among the largest increases over the past year were consumer prices for gasoline and for used cars and trucks.

Percent change in BLS price indexes, June 2020 to June 2021

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

Greco-Roman wrestling

We bypassed the freestyle wrestling venue to watch Greco-Roman wrestling. The difference between freestyle and Greco-Roman wrestling is that freestyle wrestlers can use their legs for both defensive and offensive moves, but Greco-Roman forbids any holds below the waist. Our Survey of Occupational Injuries and Illnesses reports on the part of the body where workplace injuries occur, and, just like Greco-Roman, many of those occur above the waist.

Among workplace injuries that resulted in time away from work, nearly two out of three affected parts of the body above the waist, with the greatest number related to the upper extremities (shoulder, arm, hand, and wrist).

Number of workplace injuries and illnesses requiring days away from work, by part of body, 2019

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

Among the most prevalent injuries to the upper extremities were sprains, strains, punctures, cuts, and burns.

Beach volleyball

This popular sport takes place out on the sandy beaches, with two athletes on each side battling for the gold. Let’s look at some popular beach volleyball spots around the United States and pair them with the unemployment rates by state and metropolitan area. Florida serves up the lowest unemployment rate among the four states we have selected, at 5.7 percent (not seasonally adjusted) in June. Miami had an unemployment rate of 6.2 percent in June—the lowest among the metro areas chosen. Receiving the serve, Hawaii’s rate stood at a 7.9 percent. They bumped it to their teammate Illinois, which also had a rate of 7.9 percent. California reached a little higher, with a rate of 8.0 percent.

Unemployment rates in selected beach volleyball states and metropolitan areas, June 2021, not seasonally adjusted

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

BLS heads to Tokyo

Just as the United States exports its athletes to Japan for the Olympic Games, the two countries are regular trading partners. The BLS International Price Program provides a monthly look at inflation for U.S. imports and exports. Among the data available are price changes based on where the imports come from and where the exports go. And yes, this includes data for Japan. While we’ve seen increases in many inflation measures in recent months, the data show more modest increases in prices of U.S. imports from Japan. Not so for U.S. exports to Japan, which increased 15.8 percent from June 2020 to June 2021. No, this does not represent the price of exporting our athletes; it mostly relates to sharp increases in the price of agricultural exports.

Percent change in U.S. import and export prices, June 2020 to June 2021

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

Whether it’s weightlifting or dressage or the new sports climbing activities, BLS is cheering on the U.S. Olympians and Paralympians in Japan. At the same time, we’ll still be keeping to our data release schedule. Find out what’s available from BLS during August and September and be sure to follow BLS on Twitter.

Fastest growing occupations, projected, 2019–29
OccupationProjected percent change

Wind turbine service technicians

60.7%

Nurse practitioners

52.4

Solar photovoltaic installers

50.5

Occupational therapy assistants

34.6

Statisticians

34.6

Home health and personal care aides

33.7

Physical therapist assistants

32.6

Medical and health services managers

31.5

Physician assistants

31.3

Information security analysts

31.2

Data scientists and mathematical science occupations, all other

30.9

Derrick operators, oil and gas

30.5
Percent change in BLS price indexes, June 2020 to June 2021
Price indexPercent change

Import Price Index

11.2%

Producer Price Index

7.3

Export Price Index

16.8

Consumer Price Index

5.4
Number of workplace injuries and illnesses requiring days away from work, by part of body, 2019
Part of bodyNumber

Upper extremities (shoulder, arm, hand, wrist)

284,860

Lower extremities (knee, ankle, foot)

216,850

Trunk

187,130

Multiple body parts

82,650

Head

79,620

Body systems

15,150

Neck

11,600

All other body parts

10,360
Unemployment rates in selected beach volleyball states and metropolitan areas, June 2021, not seasonally adjusted
State or metropolitan areaRate

States

Florida

5.7%

Hawaii

7.9

Illinois

7.9

California

8.0

Metropolitan areas

Miami

6.2

Honolulu

7.1

Chicago

8.5

Los Angeles

9.5
Percent change in U.S. import and export prices, June 2020 to June 2021
Price indexAll countriesJapan

Import prices

11.2%1.8%

Export prices

16.815.8

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

Employment Trends of Asians and Native Hawaiians and Other Pacific Islanders

May is Asian-Pacific American Heritage Month, so let’s take a closer look at national employment statistics for Asians and for Native Hawaiians and Other Pacific Islanders (NHPIs). We’ll focus on how the COVID-19 pandemic affected the labor market for these groups.

BLS has been collecting data in its household survey since 2003 on the labor market characteristics of people who identify their race as Asian or NHPI. Looking at historical data, we’ve noticed that the employment–population ratio—the percentage of the population that is employed—is generally higher for Asians and NHPIs than for the U.S. average. The ratio in 2019 was 62.3 percent for Asians and 66.2 percent for NHPIs, well above the national average of 60.8 percent. The greater likelihood of employment among Asians and NHPIs reflects the fact that both groups—particularly NHPIs—are more likely to be ages 25 to 54 than the overall population. People in this age range are more likely to be employed than those in younger and older age groups. Regardless of age, employment declined sharply for Asians and NHPIs in 2020, reflecting the impact of the pandemic.

Employment–population ratio, 2003–20

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

To get a better sense of how Asians and NHPIs fared in the labor market during the pandemic, we compared employment–population ratios for the 12 months before the pandemic with estimates for the 12 months after it started. The following chart shows the decline in the average employment–population ratio for the 12 months ending in February 2021 compared with the 12 months ending in February 2020. The employment–population ratio for NHPIs fell 6.0 percentage points, and the ratio for Asians fell 5.4 percentage points. These compare with a decline of 4.7 percentage points for the overall population.

Percentage point decline in employment–population ratios, first 12 months of COVID-19 pandemic compared with 12 months before pandemic

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

We used the same approach to look at different groups within the Asian population. The employment–population ratio for Asian women declined by 5.7 percentage points from the 12 months before the pandemic to the 12 months following the onset of the pandemic. This was more than the 5.2 percentage point decline for Asian men. Asians age 55 and older had a greater drop in their employment–population ratio (−6.1 percentage points) than did Asians in younger age groups: −5.2 percentage points for Asians ages 25 to 54 and −4.7 percentage points for Asians ages 16 to 24. The decline in the percentage of foreign-born Asians who were employed—6.1 percentage points—was greater than that for native-born Asians (−4.2 percentage points). (Unfortunately, we can’t make these same comparisons for NHPIs because of their small sample size in the survey.)

Percentage point decline in employment–population ratios for Asians, first 12 months of COVID-19 pandemic compared with 12 months before pandemic

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

Asians trace their roots to many different distinct and culturally diverse peoples. Our household survey collects information on seven different Asian groups—Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, and Other Asian. Of these groups, the percentage point decline in the employment–population ratio for the 12 months after the onset of the pandemic was largest for the Vietnamese (−8.8 percentage points). This likely reflects the large number of Vietnamese workers employed in the other services industry. This industry—particularly the nail salon component, in which Vietnamese workers are especially prevalent—lost much of its employment in the early months of the pandemic, when mandatory business closures, stay-at-home orders, and fear of the illness kept many people from engaging in both labor market and consumer activity. By contrast, employment–population ratios for Asian Indians and for Japanese dropped by 3.4 percentage points and 3.0 percentage points, respectively; these two Asian groups are more likely to be employed in industries that lost smaller proportions of employment, such as professional and business services. Notably, workers in this industry were more likely to telework due to the pandemic than workers employed in other services.

Percentage point decline in employment–population ratios for Asian groups, first 12 months of COVID-19 pandemic compared with 12 months before pandemic

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

If you want to learn more about how Asians and NHPIs are faring in the labor market, please check out our data on race and ethnicity.

Employment–population ratio, 2003–20
YearTotalAsiansNative Hawaiians and Other Pacific Islanders

2003

62.3%62.4%63.6%

2004

62.363.067.4

2005

62.763.470.2

2006

63.164.270.6

2007

63.064.369.4

2008

62.264.367.8

2009

59.361.261.8

2010

58.559.960.1

2011

58.460.062.2

2012

58.660.163.0

2013

58.661.262.9

2014

59.060.463.5

2015

59.360.462.8

2016

59.760.965.7

2017

60.161.562.9

2018

60.461.664.9

2019

60.862.366.2

2020

56.857.360.8
Percentage point decline in employment–population ratios, first 12 months of COVID-19 pandemic compared with 12 months before pandemic
GroupPercentage point change

Native Hawaiians and Other Pacific Islanders

-6.0

Asian

-5.4

Total

-4.7
Percentage point decline in employment–population ratios for Asians, first 12 months of COVID-19 pandemic compared with 12 months before pandemic
GroupPercentage point change

Total

-5.4

Men

-5.2

Women

-5.7

Age

Ages 16 to 24

-4.7

Ages 25 to 54

-5.2

Age 55 and older

-6.1

Country of birth

Foreign born

-6.1

Native born

-4.2
Percentage point decline in employment–population ratios for Asian groups, first 12 months of COVID-19 pandemic compared with 12 months before pandemic
GroupPercentage point change

Vietnamese

-8.8

Filipino

-7.6

Korean

-6.7

Other Asian

-5.6

Chinese

-4.9

Asian Indian

-3.4

Japanese

-3.0

Total

-5.4