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Topic Archives: Labor Force Characteristics

BLS Now Publishing Monthly Data for American Indians and Alaska Natives

I am pleased to announce that BLS is now publishing monthly labor force estimates for American Indians and Alaska Natives! For years we’ve published a small set of annual labor market estimates for American Indians and Alaska Natives in our yearly report on labor force characteristics by race and ethnicity. And we’ve also combined multiple years of data to do more in-depth analyses. But we haven’t published our key economic metrics—such as the unemployment rate, the employment–population ratio, and the labor force participation rate—on a monthly basis for American Indians and Alaska Natives. Monthly estimates give us timely measures to see how groups are faring in the labor market.

The jobless rate for American Indians and Alaska Natives peaked at 28.6 percent in April 2020 (not seasonally adjusted), early in the COVID-19 pandemic. This was nearly double the seasonally adjusted rate of 14.7 percent for the total population. The higher rate for American Indians and Alaska Natives reflects, in part, the extremely sharp increase at the start of the pandemic in the unemployment rate for service occupations. American Indians and Alaska Natives are considerably more likely to work in service occupations compared with the overall labor force. By contrast, the unemployment rate rose less sharply at the start of the pandemic for management, professional, and related occupations and other occupation groups in which American Indians and Alaska Natives are less represented. The unemployment rate for American Indians and Alaska Natives has declined since April 2020 and was 11.1 percent in January 2022, still much higher than the rate of 4.0 percent for the overall population.

Unemployment rates for American Indians and Alaska Natives and for the total population, January 2003 to January 2022

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

In general, American Indians and Alaska Natives are less likely than the overall population to be employed. In April 2020, the employment–population ratio for American Indians and Alaska Natives declined to 42.4 percent, 8.9 percentage points below the ratio of 51.3 percent for the overall population. Since then, the ratio for American Indians and Alaska Natives has risen and stood at 52.7 percent in January 2022, 7.0 percentage points below the ratio of 59.7 percent for the population overall.

Employment–population ratios for American Indians and Alaska Natives and for the total population, January 2003 to January 2022

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

The measures for American Indians and Alaska Natives tend to be fairly volatile for two main reasons. First, the estimates are based on a small sample size. We survey about 60,000 U.S. households every month, and respondents who identify as American Indians and Alaska Natives make up about 1 percent of the labor force. Because of that small sample size, the month-to-month change in the unemployment rate must be pretty large to be statistically significant—around 3 percentage points.

Second, these data are not seasonally adjusted. Seasonal adjustment is a statistical procedure used to remove the effects of seasonality from data so it’s easier to see underlying trends. But not all data series can be seasonally adjusted; they must pass a battery of diagnostic tests to be fitted to a seasonal adjustment model. So far, we haven’t been able to do that for data for American Indians and Alaska Natives, but we’ll keep evaluating them as we get more data. Because these data aren’t seasonally adjusted, it can be challenging to compare one month to the following month.

We’re also publishing quarterly estimates for American Indians and Alaska Natives for the first time starting this month. Because these estimates are averages of three months of data, they are somewhat less volatile.

Unemployment rates for American Indians and Alaska Natives and for the total population, January 2003 to January 2022
MonthTotalAmerican Indians and Alaska Natives

Jan 2003

5.8%10.0%

Feb 2003

5.914.2

Mar 2003

5.913.1

Apr 2003

6.010.8

May 2003

6.111.8

Jun 2003

6.39.7

Jul 2003

6.28.8

Aug 2003

6.17.9

Sep 2003

6.18.4

Oct 2003

6.09.5

Nov 2003

5.812.7

Dec 2003

5.78.2

Jan 2004

5.710.9

Feb 2004

5.612.2

Mar 2004

5.811.7

Apr 2004

5.69.6

May 2004

5.610.4

Jun 2004

5.68.2

Jul 2004

5.58.2

Aug 2004

5.49.3

Sep 2004

5.47.8

Oct 2004

5.58.0

Nov 2004

5.49.2

Dec 2004

5.410.2

Jan 2005

5.313.5

Feb 2005

5.411.3

Mar 2005

5.211.1

Apr 2005

5.210.6

May 2005

5.110.0

Jun 2005

5.08.3

Jul 2005

5.08.8

Aug 2005

4.99.0

Sep 2005

5.07.0

Oct 2005

5.07.6

Nov 2005

5.06.8

Dec 2005

4.98.0

Jan 2006

4.79.2

Feb 2006

4.89.1

Mar 2006

4.77.0

Apr 2006

4.77.5

May 2006

4.67.6

Jun 2006

4.68.3

Jul 2006

4.78.9

Aug 2006

4.77.7

Sep 2006

4.57.2

Oct 2006

4.47.0

Nov 2006

4.59.0

Dec 2006

4.46.5

Jan 2007

4.69.9

Feb 2007

4.510.9

Mar 2007

4.49.4

Apr 2007

4.55.8

May 2007

4.46.2

Jun 2007

4.67.2

Jul 2007

4.79.9

Aug 2007

4.69.9

Sep 2007

4.76.9

Oct 2007

4.77.7

Nov 2007

4.76.4

Dec 2007

5.07.8

Jan 2008

5.07.4

Feb 2008

4.910.6

Mar 2008

5.19.2

Apr 2008

5.010.4

May 2008

5.48.6

Jun 2008

5.611.3

Jul 2008

5.813.3

Aug 2008

6.112.3

Sep 2008

6.19.7

Oct 2008

6.58.0

Nov 2008

6.87.9

Dec 2008

7.310.3

Jan 2009

7.812.3

Feb 2009

8.313.1

Mar 2009

8.79.8

Apr 2009

9.08.7

May 2009

9.413.0

Jun 2009

9.513.7

Jul 2009

9.518.3

Aug 2009

9.615.1

Sep 2009

9.815.0

Oct 2009

10.013.9

Nov 2009

9.913.1

Dec 2009

9.914.9

Jan 2010

9.816.4

Feb 2010

9.814.4

Mar 2010

9.917.9

Apr 2010

9.912.7

May 2010

9.613.2

Jun 2010

9.414.3

Jul 2010

9.416.4

Aug 2010

9.517.8

Sep 2010

9.514.4

Oct 2010

9.415.4

Nov 2010

9.813.9

Dec 2010

9.314.8

Jan 2011

9.118.4

Feb 2011

9.014.8

Mar 2011

9.012.1

Apr 2011

9.110.2

May 2011

9.012.6

Jun 2011

9.115.3

Jul 2011

9.014.7

Aug 2011

9.014.4

Sep 2011

9.016.1

Oct 2011

8.815.4

Nov 2011

8.617.2

Dec 2011

8.513.7

Jan 2012

8.311.7

Feb 2012

8.314.2

Mar 2012

8.214.1

Apr 2012

8.212.8

May 2012

8.29.4

Jun 2012

8.212.2

Jul 2012

8.211.8

Aug 2012

8.111.9

Sep 2012

7.811.4

Oct 2012

7.811.8

Nov 2012

7.712.5

Dec 2012

7.913.4

Jan 2013

8.013.0

Feb 2013

7.710.5

Mar 2013

7.512.7

Apr 2013

7.610.9

May 2013

7.59.9

Jun 2013

7.512.8

Jul 2013

7.314.3

Aug 2013

7.213.8

Sep 2013

7.213.1

Oct 2013

7.213.9

Nov 2013

6.914.5

Dec 2013

6.713.5

Jan 2014

6.612.4

Feb 2014

6.712.6

Mar 2014

6.713.0

Apr 2014

6.211.0

May 2014

6.311.1

Jun 2014

6.110.8

Jul 2014

6.212.7

Aug 2014

6.111.0

Sep 2014

5.99.4

Oct 2014

5.712.3

Nov 2014

5.810.7

Dec 2014

5.69.1

Jan 2015

5.711.8

Feb 2015

5.511.2

Mar 2015

5.410.7

Apr 2015

5.49.5

May 2015

5.68.4

Jun 2015

5.310.8

Jul 2015

5.211.6

Aug 2015

5.19.4

Sep 2015

5.07.9

Oct 2015

5.08.5

Nov 2015

5.110.3

Dec 2015

5.08.8

Jan 2016

4.89.9

Feb 2016

4.99.3

Mar 2016

5.09.9

Apr 2016

5.18.0

May 2016

4.89.2

Jun 2016

4.99.5

Jul 2016

4.88.2

Aug 2016

4.99.7

Sep 2016

5.09.9

Oct 2016

4.96.6

Nov 2016

4.78.6

Dec 2016

4.78.2

Jan 2017

4.78.6

Feb 2017

4.67.4

Mar 2017

4.49.8

Apr 2017

4.48.3

May 2017

4.48.8

Jun 2017

4.39.1

Jul 2017

4.37.6

Aug 2017

4.47.4

Sep 2017

4.36.5

Oct 2017

4.26.8

Nov 2017

4.26.2

Dec 2017

4.17.8

Jan 2018

4.09.7

Feb 2018

4.17.9

Mar 2018

4.08.8

Apr 2018

4.07.5

May 2018

3.87.0

Jun 2018

4.06.5

Jul 2018

3.85.1

Aug 2018

3.84.3

Sep 2018

3.75.4

Oct 2018

3.86.1

Nov 2018

3.85.2

Dec 2018

3.96.3

Jan 2019

4.06.9

Feb 2019

3.86.4

Mar 2019

3.86.1

Apr 2019

3.66.1

May 2019

3.65.5

Jun 2019

3.66.7

Jul 2019

3.76.4

Aug 2019

3.74.9

Sep 2019

3.55.3

Oct 2019

3.66.0

Nov 2019

3.66.8

Dec 2019

3.65.7

Jan 2020

3.57.3

Feb 2020

3.57.5

Mar 2020

4.47.4

Apr 2020

14.728.6

May 2020

13.218.0

Jun 2020

11.013.1

Jul 2020

10.213.0

Aug 2020

8.410.3

Sep 2020

7.96.7

Oct 2020

6.910.8

Nov 2020

6.78.6

Dec 2020

6.710.0

Jan 2021

6.410.1

Feb 2021

6.212.1

Mar 2021

6.07.9

Apr 2021

6.09.2

May 2021

5.89.5

Jun 2021

5.97.7

Jul 2021

5.47.2

Aug 2021

5.28.5

Sep 2021

4.75.9

Oct 2021

4.65.7

Nov 2021

4.26.7

Dec 2021

3.97.9

Jan 2022

4.011.1

Note: Monthly data for American Indians and Alaska Natives are not seasonally adjusted. The total unemployment rate is seasonally adjusted.

Employment–population ratios for American Indians and Alaska Natives and for the total population, January 2003 to January 2022
MonthTotalAmerican Indians and Alaska Natives

Jan 2003

62.5%57.9%

Feb 2003

62.556.4

Mar 2003

62.457.8

Apr 2003

62.458.1

May 2003

62.358.9

Jun 2003

62.360.9

Jul 2003

62.159.9

Aug 2003

62.158.1

Sep 2003

62.055.1

Oct 2003

62.155.4

Nov 2003

62.354.7

Dec 2003

62.258.7

Jan 2004

62.356.0

Feb 2004

62.356.9

Mar 2004

62.257.0

Apr 2004

62.357.9

May 2004

62.359.2

Jun 2004

62.461.5

Jul 2004

62.562.4

Aug 2004

62.459.6

Sep 2004

62.355.2

Oct 2004

62.355.5

Nov 2004

62.554.9

Dec 2004

62.455.8

Jan 2005

62.453.6

Feb 2005

62.454.1

Mar 2005

62.456.3

Apr 2005

62.759.2

May 2005

62.856.7

Jun 2005

62.760.1

Jul 2005

62.860.1

Aug 2005

62.957.5

Sep 2005

62.859.4

Oct 2005

62.858.6

Nov 2005

62.757.7

Dec 2005

62.857.4

Jan 2006

62.955.6

Feb 2006

63.057.7

Mar 2006

63.157.1

Apr 2006

63.056.1

May 2006

63.158.5

Jun 2006

63.158.7

Jul 2006

63.058.7

Aug 2006

63.159.6

Sep 2006

63.158.8

Oct 2006

63.360.7

Nov 2006

63.357.3

Dec 2006

63.458.4

Jan 2007

63.354.5

Feb 2007

63.356.9

Mar 2007

63.359.7

Apr 2007

63.061.6

May 2007

63.060.2

Jun 2007

63.058.2

Jul 2007

62.956.9

Aug 2007

62.758.0

Sep 2007

62.959.1

Oct 2007

62.757.7

Nov 2007

62.957.6

Dec 2007

62.756.7

Jan 2008

62.956.9

Feb 2008

62.857.7

Mar 2008

62.759.3

Apr 2008

62.758.1

May 2008

62.556.8

Jun 2008

62.457.8

Jul 2008

62.254.3

Aug 2008

62.053.1

Sep 2008

61.957.5

Oct 2008

61.759.3

Nov 2008

61.459.2

Dec 2008

61.058.8

Jan 2009

60.656.5

Feb 2009

60.352.9

Mar 2009

59.954.4

Apr 2009

59.853.5

May 2009

59.650.6

Jun 2009

59.450.1

Jul 2009

59.347.5

Aug 2009

59.149.5

Sep 2009

58.749.6

Oct 2009

58.550.6

Nov 2009

58.649.1

Dec 2009

58.349.0

Jan 2010

58.548.3

Feb 2010

58.550.3

Mar 2010

58.550.6

Apr 2010

58.751.4

May 2010

58.650.2

Jun 2010

58.548.6

Jul 2010

58.546.3

Aug 2010

58.647.1

Sep 2010

58.548.2

Oct 2010

58.348.4

Nov 2010

58.248.8

Dec 2010

58.349.1

Jan 2011

58.350.6

Feb 2011

58.450.4

Mar 2011

58.453.6

Apr 2011

58.453.9

May 2011

58.351.2

Jun 2011

58.249.2

Jul 2011

58.249.9

Aug 2011

58.350.7

Sep 2011

58.449.6

Oct 2011

58.449.0

Nov 2011

58.648.5

Dec 2011

58.650.0

Jan 2012

58.450.8

Feb 2012

58.550.4

Mar 2012

58.551.2

Apr 2012

58.451.1

May 2012

58.553.4

Jun 2012

58.652.7

Jul 2012

58.553.3

Aug 2012

58.451.6

Sep 2012

58.753.4

Oct 2012

58.853.5

Nov 2012

58.752.0

Dec 2012

58.751.3

Jan 2013

58.648.4

Feb 2013

58.652.4

Mar 2013

58.552.1

Apr 2013

58.653.2

May 2013

58.654.0

Jun 2013

58.652.4

Jul 2013

58.751.3

Aug 2013

58.750.4

Sep 2013

58.752.4

Oct 2013

58.351.9

Nov 2013

58.651.3

Dec 2013

58.750.2

Jan 2014

58.849.9

Feb 2014

58.751.3

Mar 2014

58.952.4

Apr 2014

58.951.5

May 2014

58.954.3

Jun 2014

59.054.1

Jul 2014

59.054.2

Aug 2014

59.055.2

Sep 2014

59.154.9

Oct 2014

59.356.0

Nov 2014

59.255.1

Dec 2014

59.357.3

Jan 2015

59.356.0

Feb 2015

59.254.1

Mar 2015

59.255.0

Apr 2015

59.352.2

May 2015

59.453.4

Jun 2015

59.452.7

Jul 2015

59.354.1

Aug 2015

59.456.0

Sep 2015

59.257.6

Oct 2015

59.356.3

Nov 2015

59.453.3

Dec 2015

59.654.4

Jan 2016

59.753.6

Feb 2016

59.855.2

Mar 2016

59.855.1

Apr 2016

59.753.9

May 2016

59.754.0

Jun 2016

59.753.9

Jul 2016

59.854.7

Aug 2016

59.855.2

Sep 2016

59.758.4

Oct 2016

59.759.0

Nov 2016

59.758.0

Dec 2016

59.757.2

Jan 2017

59.954.9

Feb 2017

60.057.6

Mar 2017

60.254.7

Apr 2017

60.253.7

May 2017

60.153.7

Jun 2017

60.155.2

Jul 2017

60.257.1

Aug 2017

60.155.9

Sep 2017

60.457.3

Oct 2017

60.157.0

Nov 2017

60.154.9

Dec 2017

60.154.4

Jan 2018

60.251.9

Feb 2018

60.453.3

Mar 2018

60.454.6

Apr 2018

60.455.0

May 2018

60.556.4

Jun 2018

60.457.7

Jul 2018

60.558.5

Aug 2018

60.358.1

Sep 2018

60.456.1

Oct 2018

60.555.6

Nov 2018

60.555.4

Dec 2018

60.655.0

Jan 2019

60.654.2

Feb 2019

60.856.6

Mar 2019

60.757.7

Apr 2019

60.657.1

May 2019

60.656.9

Jun 2019

60.757.6

Jul 2019

60.858.0

Aug 2019

60.858.8

Sep 2019

60.957.0

Oct 2019

60.956.3

Nov 2019

61.058.3

Dec 2019

61.056.8

Jan 2020

61.155.9

Feb 2020

61.254.2

Mar 2020

59.954.3

Apr 2020

51.342.4

May 2020

52.848.4

Jun 2020

54.752.9

Jul 2020

55.252.5

Aug 2020

56.553.7

Sep 2020

56.655.8

Oct 2020

57.452.8

Nov 2020

57.453.3

Dec 2020

57.452.3

Jan 2021

57.551.3

Feb 2021

57.653.6

Mar 2021

57.855.2

Apr 2021

57.954.1

May 2021

58.056.5

Jun 2021

58.057.8

Jul 2021

58.458.0

Aug 2021

58.554.3

Sep 2021

58.856.7

Oct 2021

58.956.8

Nov 2021

59.356.6

Dec 2021

59.555.6

Jan 2022

59.752.7

Note: Monthly data for American Indians and Alaska Natives are not seasonally adjusted. The total employment–population ratio is seasonally adjusted.

What Have You Been Looking for on the BLS Website?

In 2021, the BLS public website welcomed nearly 29 million users, who viewed just over 158 million pages. Wow, that’s a lot of data! It shows the extensive and growing interest in information about our economy. Let’s take a quick look back over the past year. What are the topics of interest? We see clear trends and a few surprises.

From its humble beginnings more than a quarter century ago, www.bls.gov has become the primary way we make the latest BLS data and analysis available to the public.

BLS website homepage, September 1995
First edition of the BLS website, 1995

Today, thousands of users get their first glimpse of the latest economic data through the website or through email alerts and tweets that link to the website. National economic news on employment, inflation, productivity, and other topics is first available on the website, with about 150 national releases each year. Not to be outdone, BLS regional office staff around the country last year posted nearly 1,000 regional and local news releases on the website.

And you came to check out those data—all 29 million of you.

Here’s a look at the five subject homepages that saw the greatest increase in page views from 2020 to 2021. You’ll note that all are timely topics.

  • The Business Response Survey to the Coronavirus Pandemic was a special data collection effort. Information from this survey was first available late in 2020, so the 166-percent increase in page views in 2021 is not surprising, especially given the great interest in all COVID-19 information. Results from a second round of this survey, with updated questions, will be available February 9, 2022.
  • Information from the Consumer Price Index also had more than a 100-percent increase in page views from 2020 to 2021, 106 percent increase to be exact. This is not a surprise, given the significant rise in prices recently.
  • Interest in inflation throughout the supply chain also led to a 60-percent increase in page views for Producer Price Indexes data.
  • BLS has been collecting data on Work Stoppages (strikes and lockouts) for many years, but interest in these data grew in 2021, perhaps because of several high-profile stoppages. There was a 25-percent increase in page views for these data.
  • Rounding out the top five was an 18-percent increase in page views for Job Openings and Labor Turnover Survey data. With record numbers of job openings and heightened interest in churn in the labor force, these data have garnered much attention recently. We also began publishing a news release on state data in 2021 to meet the growing need for geographic information on job openings and labor turnover.

Turning to analytical data, some of the most viewed pages were those focusing on fast growing industries, inflation at both the consumer and producer level, and the impact of COVID-19 on many aspects of the economy, such as unemployment and food prices. But viewers were also attracted to some unique topics:

  • The most read Commissioner’s Corner blog was about the 17-year cycle of cicadas, with a look at economic trends during past cicada invasions.
A cicada
A group of friends and family watching a football game on TV

We welcome our 29 million website visitors and encourage you to check back regularly. Your interests drive our commitment to provide timely research on relevant topics. There’s new content every business day, so you never know what new research may be right around the corner in 2022. It will all be at www.bls.gov. See you there!

BLS website homepage in 2022
BLS website homepage in 2022

It’s a Small Statistical World

BLS is one of several U.S. statistical agencies that follow consistent policies and share best practices. These agencies also frequently work with their statistical counterparts around the world to develop standards, share information, troubleshoot issues, and improve the quality of available data. At BLS, our Division of International Technical Cooperation coordinates these activities. The division helps to strengthen statistical development by organizing seminars, consultations, and meetings for international visitors with BLS staff. The division also provides BLS input on global statistical initiatives. Without missing a beat, most of these activities moved to virtual platforms during the COVID-19 pandemic. Despite some time-zone challenges, which often lead to early morning or late-night video meetings, BLS continues to play an active role on the world stage.

World map

Today I’m highlighting some recent international engagements, which have included our colleagues from Australia, Canada, France, Greece, Italy, Mexico, South Korea, and the United Kingdom. These events are often mutually beneficial, as they provide opportunities for BLS staff to learn more about the experiences of our international counterparts.

  • BLS staff met with a former Australian Bureau of Statistics official who was working with the U.K. Statistics Authority and the U.K. Office for National Statistics to research best practices in implementing international statistical standards. They discussed the international comparability of domestic industry and product classifications, data quality and publishing, and the independence of statistical organizations.
  • Staff from the Australian Bureau of Statistics are planning to revise their household expenditure survey. They turned to BLS experts, who shared their insights and experiences in improving our Consumer Expenditure Surveys.
  • Staff from the Statistical Division at the United Nations asked BLS to comment on issues surrounding the classification of business functions; household income, consumption, and wealth; and unpaid household service work. Input from staff in multiple offices will inform the BLS response to this request.
  • BLS staff, our counterparts in Canada and Mexico, and colleagues from across Europe and Asia discussed data ethics in a meeting organized by the Centre for Applied Data Ethics at the U.K. Statistics Authority. Country representatives summarized how their organizations assess ethical considerations when producing official statistics. The U.K. Statistics Authority identified the following ethical considerations as being especially important:
Public Good: The use of data has clear benefits for users and serves the public good.
Confidentiality, Data Security: The data subject's identity (whether person or organisation) is protected, information is kept confidential and secure, and the issue of consent is considered appropriately.
Methods and Quality: The risks and limits of new technologies are considered and there is sufficient human oversight so that methods employed are consistent with recognised standards of integrity and quality.
Legal Compliance: Data used and methods employed are consistent with legal requirements.
Public Views and Engagement: The views of the public are considered in light of the data used and the perceived benefits of the research.
Transparency: The access, use and sharing of data is transparent, and is communicated clearly and accessibly to the public.

From its founding, BLS has understood the importance of these issues. Our written policies and strategic plans reflect these principles. They also are reflected in the Foundations for Evidence-Based Policymaking Act and the newly formed Scientific Integrity Task Force, which includes BLS staff among its members.

And that’s just some of what we did this summer! BLS has a longstanding reputation for providing expert training and guidance and participating in international statistical forums. We also provide BLS data to the International Labour Organization and the Organisation for Economic Cooperation and Development, among others. These organizations often feature BLS statistics in their databases. Since its inception, BLS has provided technical assistance to our international counterparts, starting with our first Commissioner, Carroll Wright, who directed BLS staff to advise foreign governments establishing statistical agencies. Commissioner Wright was also a member of several international statistical associations, a tradition that continues today. Currently, BLS staff participate in many international expert groups, including the Voorburg Group on Service Statistics, the Wiesbaden Group on Business Registers, and the International Conference of Labor Statisticians. These groups provide BLS staff with opportunities to discuss topics of common interest, to propose and learn about innovative solutions to data measurement issues, and to influence discussions about important economic concepts.

BLS began providing technical assistance in earnest in the late 1940s as part of the U.S. government’s European Economic Recovery Program. BLS staff planned and conducted productivity studies and helped European governments establish their own economic statistics. Similar efforts continue today for our colleagues around the world, many of whom have participated in our international training programs. While we have temporarily halted in-person training programs because of the pandemic, our staff plan to provide more training modules virtually in response to the popularity of these programs. Over the last 10 years, BLS has provided training or other technical assistance to over 1,700 seminar participants and other visitors from 95 countries. More recently, the International Monetary Fund has asked BLS to provide training on Producer Price Indexes and Import and Export Price Indexes to our colleagues abroad.

I am incredibly grateful to all the subject matter experts throughout BLS who provide invaluable assistance with these activities and help maintain our excellent reputation in the international statistical community. We look forward to your continued support as BLS strengthens important international relationships, virtually for now, and hopefully in person soon.

A Labor Day Look at How American Workers Have Changed over 40 Years

Forty years ago, teenagers ages 16 to 19 made up 8 percent of all U.S. workers. By 2019, that share fell to just 3 percent. With fewer teenagers working, the face of American labor looks much different today than it did when the Bee Gees ruled the American pop charts.

Happy Labor Day! The U.S. workforce has been changing over many generations. It’s been changing with respect to the work people do. For example, an increasing share of workers is engaged in service or technology work, while a decreasing share is engaged in factory or farm work. My focus today, however, is on the people who do the work.

Here at BLS, we spend a great deal of time and effort measuring and reporting on employment. How many jobs are there this month? What kind of jobs? But as Labor Day approaches, I’d like to shift the focus away from employment and jobs and toward labor itself. Who are the people holding down the jobs that we count? What is the face of American labor? And how has labor’s profile changed—and yes, it has changed—over a generation or more?

So today I’m not going to say much about what jobs workers hold or what their jobs pay. Instead, I’ll focus on more personal characteristics of the people who hold the jobs—characteristics that are not a function of workers’ jobs, but that are intrinsic to the workers themselves. Is America’s employed population getting older or younger? Are African Americans, Hispanics and Latinos, Asians, and other groups making up an increasing share of employment? And so forth. Call it the “composition” of America’s employed population. To examine this, I’ll be using data from the Current Population Survey, or CPS, which is a large, monthly survey of many thousands of U.S. households.

BLS collects data directly from lots of employers, such as businesses and state and local governments. This data collection is behind our monthly news release about how many jobs were added or lost in the U.S. economy. It gives us a vital, current, and accurate picture of work in America—but not of workers.

To learn more about workers, rather than about just their jobs, we can’t ask their employers. We have to ask workers themselves. BLS partners with the U.S. Census Bureau each month to survey some 60,000 U.S. households about their work and other topics. We can learn at least three important things by surveying workers that we can’t learn by surveying employers. First, we can learn about things like self-employment, multiple jobholding, and “alternative” work arrangements, like so-called “gig” work. Second, we can learn about people who are not currently employed. In fact, BLS uses these data each month to measure how many are “unemployed,” roughly meaning they are actively looking for a job and available to start. Third, and most relevant here, we can learn about people’s personal characteristics—things like their age, race, and marital status, which their employers might not know or might find hard to detail in a BLS survey.

Let’s look at data from the CPS to explore how the personal characteristics of America’s employed population have shifted. I’ll share some of my own favorite nuggets of information, which I think you’ll find interesting. I’ll mostly compare 1979 with 2019—a 40-year span that roughly coincides with two peaks in U.S. employment and economic activity. The comparisons would look similar if we looked at 2020 or today, but I think the long-term trends are better understood “peak to peak” than in comparison to the more recent but very unusual COVID-19 economy. Along the way I’ll link to some BLS resources that go deeper into these topics. Let’s dive in!

Where have the teenagers gone? In 1979, 8 percent of U.S. workers were ages 16 to 19. By 2019, just 3 percent were. Over the same 40-year period, the share that were ages 16 to 24 fell from 23 percent to 12 percent. Two things happened. First, the age composition of the entire population shifted. In 1979, the tail end of the large, post-World War II “baby boom” generation was about 16 years old. The generation that came after this group was smaller, so its share of the workforce was smaller too. Second, young people’s “participation rate”—the share that were working or seeking work—declined. In fact, that rate peaked at 58 percent in 1979, then fell to 34 percent by 2011. This huge change coincided with increases in school enrollment and educational attainment. This example illustrates how two forces combine to reshape the face of American labor: the shifting composition of the working age population, and shifts in participation rates of different groups.

The American workforce has aged. Between 1979 and 2019, the fraction of the employed population that is 65 years old or older grew from 3 percent to 7 percent. The share that is 55 or older grew from 15 percent to 24 percent. Forces behind this trend include the aging of baby boomers (they are mostly 60 or older today), medical and other advances that have extended lives and health, and less physically strenuous jobs. The participation rate story is a little more complicated: As a group, today’s older women always were more likely to work for pay than their mothers or grandmothers were. Participation among older men, in contrast, first ebbed and then rebounded across these 40 years.

Percent of employed people by age, 1979–2019 annual averages

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

There are more working women. By 1979, women’s share of the employed population, at 42 percent, had already been growing for some time; it was up from just 28 percent in 1948. It kept growing for about 20 more years, before leveling off at around 47 percent by 2000 and remaining there through 2019.

What’s love got to do with it? Between 1979 and 2019, the trend in marital status was more pronounced than the trend in gender. The fraction of all workers who were unmarried grew from 36 percent to 48 percent. This trend was sharper among men (31 percent to 45 percent) than women (44 percent to 51 percent).

Percent of employed people by sex and marital status, 1979 and 2019 annual averages

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

Racial and ethnic diversity is changing too. The U.S. population has long been very diverse, shaped by colonization, slavery and emancipation, and migration. Over the last 40 years, workforce diversity has been shaped mostly by immigration and by differences in fertility among racial and ethnic groups. Between 1979 and 2019 the non-White fraction of all U.S. workers grew from 12 percent to 22 percent. The fraction who are Hispanic or Latino (who may be of any race) grew from 5 percent to 18 percent. A note of caution: the survey questions about race and ethnicity changed over the years, and this might skew the measurements a little, but not enough to change the story that non-Whites and Hispanics and Latinos represent a growing share of employed people. The latest survey questions provide lots of detail about diversity today.

Percent of employed people by race and Hispanic or Latino ethnicity, 1979 and 2019 annual averages

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

The more you survey, the more you know. Changes in the CPS and other surveys affect more than just our measures of labor diversity. Of course, we can’t measure everything all the time. Big household surveys can be expensive for taxpayers and burdensome for the thousands of households who answer the long questionnaires. Over time, we may change how we think about who we are and what we do, so survey questions must change as well. New survey questions can inform us better about where we are today, but they can make it harder to compare conditions over time. For example, beginning in 1992, the CPS questions about educational attainment were changed to emphasize degrees earned rather than years of school completed. We know that the fraction of U.S. workers age 25 or older who had a bachelor’s degree or higher grew from 27 percent in 1992 to 42 percent in 2019, but we don’t know for sure what percentage of the workforce was this educated in 1979.

The CPS has its roots in a tough time for American labor. The Great Depression of the 1930s brought mass unemployment to the United States. Back then, there was no sure way to measure the problem or track progress toward recovery. By the end of the 1930s, the U.S. launched the monthly household survey that today we call the CPS. The survey has gone through many changes, but it has measured unemployment each month since then. For economists like me, the history of the CPS is almost as interesting as the history of American labor. If you happen to be an economist or statistician yourself, BLS and the U.S. Census Bureau can tell you all you need to know about this great source of information. But not today – it’s Labor Day! Save the technical stuff for after the celebration.

Percent of employed people by age, 1979–2019 annual averages
YearAges 16–19Ages 20–24Ages 25–54Ages 55–64Age 65 and older

1979

8.2%14.5%62.6%11.7%3.0%

1980

7.814.263.411.73.0

1981

7.214.164.311.52.9

1982

6.613.865.311.52.9

1983

6.313.666.011.22.9

1984

6.113.566.810.92.7

1985

6.013.067.610.72.6

1986

5.912.668.410.42.7

1987

5.912.069.210.22.7

1988

5.911.569.89.92.8

1989

5.811.070.59.82.9

1990

5.511.370.99.42.8

1991

5.011.071.89.32.8

1992

4.810.972.39.32.8

1993

4.810.772.59.22.8

1994

5.010.472.59.13.0

1995

5.110.072.89.22.9

1996

5.19.673.19.32.9

1997

5.19.672.99.52.9

1998

5.49.672.59.82.8

1999

5.49.772.110.02.9

2000

5.39.771.810.23.1

2001

4.99.771.510.73.1

2002

4.69.870.911.53.2

2003

4.39.870.612.13.3

2004

4.29.970.012.43.5

2005

4.29.769.512.93.6

2006

4.39.669.013.43.7

2007

4.09.668.813.83.8

2008

3.89.468.414.34.1

2009

3.59.168.015.04.4

2010

3.19.167.715.64.5

2011

3.19.367.015.94.8

2012

3.19.466.116.35.1

2013

3.19.465.616.55.3

2014

3.19.565.316.75.4

2015

3.29.464.916.85.7

2016

3.39.364.716.95.9

2017

3.39.264.517.06.0

2018

3.39.064.417.16.2

2019

3.39.064.117.16.6

2020

3.28.564.517.26.6
Percent of employed people by sex and marital status, 1979 and 2019 annual averages
Marital status1979, total2019, total1979, men2019, men1979, women2019, women

Married, spouse present

63.6%52.3%69.0%55.0%56.1%49.1%

Never married

24.132.323.332.625.231.9

Widowed, divorced, and separated

12.315.57.712.318.719.0
Percent of employed people by race and Hispanic or Latino ethnicity, 1979 and 2019 annual averages
YearWhiteNot WhiteHispanic or Latino

1979

88.3%11.7%4.8%

2019

77.722.317.6

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