Tag Archives: Baby boomers

Let’s Celebrate the Productive U.S. Workforce

Earlier this month our nation celebrated Labor Day. We celebrate Labor Day for many good reasons, but one of the best is to appreciate, even for just one day, how amazingly productive our nation’s workforce is. As we shop online or in stores, we rarely stop to think about the skills and effort it takes to produce our goods and services. Let’s take a moment to celebrate that productivity and the progress we have seen in the last few years.

Indeed, productivity of labor is at the heart of the American economy. How much workers produce for each hour they labor and how efficiently they use resources determines the pace of economic growth and the volume of goods that supply everyone (workers included) with the products and services that shape our daily lives. Growing productivity means that our standard of living very likely is improving.

Our workers are very productive. On average, each U.S. worker produced goods and services worth $129,755 last year. That’s compared with the next largest world economies: Germany at $99,377; the United Kingdom at $93,226; Japan at $78,615; China at $32,553; and India at $19,555.

Despite our great reliance on rising productivity to attain the good things of life, academics and researchers still marvel at the mysteries that surround the subject. What drives productivity change? What are the key factors behind these international differences in output per worker?

For example, does the quality of labor alone determine the rate of productivity growth? It is certainly a component of what drives labor productivity, although some countries have high educational and training levels but low productivity per worker. Labor quality has been steadily rising in the United States, but we don’t know the impact on productivity as the baby boomers retire and are replaced.

What is the right mix of labor and technology needed for changing the productivity growth rate? How can we measure the value of the dignity of work, or the personal and social value that work yields? And, what is the role of technical knowledge and product design in determining the productivity of labor?

Then there’s the mysterious role of innovation. Economists think they know that invention and scientific breakthroughs can make massive changes to productivity. However, which innovations transform productivity, and have all the low-lying fruits of productivity enhancement already been harvested?

Despite our strong international showing, analysts who watch these data may be a tad bit concerned with the sluggishness in U.S. productivity growth over the past 10 years. Since 2011, the rate of growth in labor productivity has slowed to one-third of the pace shown between 2000 and 2008, despite acceleration in the past 2 years. Even when we broaden the concept of productivity to include the output attributable to the combination of labor and other productive factors (also known as multifactor productivity), the rate of growth is still one-third of the pace it was in the first decade of this century.

Even with a subsidence in the growth rate, it is worth noting that both labor input and output are on the rise. Since the start of the current business cycle expansion in 2009, the rate of growth in labor input has been five times what it was prior to the Great Recession during the previous expansion.

Output has also grown steadily, but at a slower rate than hours. Because labor productivity is the quotient of output divided by hours, productivity can slow even when both components are rising. The relationship between the relative growth of output and hours is one of the many features that makes productivity both challenging and fascinating to study.

The Bureau of Labor Statistics engages with an extensive network of researchers in and out of the academic community whose mission is, like ours, to better understand and measure the productivity of the U.S. labor force. Labor productivity is an amazing subject because it incorporates so many facets of the nation’s economy into one statistic. By peeling back layers and looking at the details behind the summary number, we can gain valuable insight on the hours and output of our nation’s workforce. We will continue to produce and provide context for these valuable statistics that help tell the story of America’s workers.

That said, we should never lose sight of the big picture. America’s workers lead the world in their capacity to create the goods and services that define our economy and improve our lives. And that, certainly, is something great to celebrate!

How Are Our Older Workers Doing?

May is Older Americans Month. Who are we calling old?

  • The Bureau of Labor Statistics, for one. Next month we will celebrate our 135th birthday. Now that’s old! And we’ve been providing gold-standard information the entire time.
  • Today we are focusing on people age 65 and older.

In honor of Older Americans Month, let’s examine some fast facts about older workers. Many of these facts look over the last 30 years.


  • For workers age 65 and older, employment tripled from 1988 to 2018, while employment among younger workers grew by about a third.
  • Between 1988 and 2018, employment growth for women age 65 and older outpaced that for men.
  • Among people age 75 and older, the number of employed people nearly quadrupled, increasing from 461,000 in 1988 to 1.8 million in 2018.

Participation in the Labor Force

  • The labor force participation rate for older workers has been rising steadily since the late 1990s. Participation rates for younger age groups either declined or flattened over this period.

Chart showing labor force participation rates for people age 55 and older from 1988 to 2018

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

Employment Projections

  • The total labor force is projected to increase by 6.6 percent from 2016 to 2026, while the number of workers age 65 and older is predicted to rise by 57.6 percent.
  • By 2026, workers age 65 and older are expected to account for 8.6 percent of the total labor force, up from 5.8 percent in 2016.
  • The labor force participation rate of people age 65 and older is projected to increase from 19.3 percent in 2016 to 21.8 percent in 2026. This contrasts with the overall labor force participation rate, which is expected to decrease from 62.8 percent to 61.0 percent.

Work Schedules

  • Over the past 20 years, the number of older workers on full‐time work schedules grew two and a half times faster than the number working part time.
  • Full‐timers now account for a majority among older workers—61 percent in 2018, up from 46 percent in 1998.


  • In 1998, median weekly earnings of older full‐time employees were 77 percent of the median for workers age 16 and up. In 2018, older workers earned 7 percent more than the median for all workers.


  • In 1998, 1 in 5 older workers had less than a high school education. By 2018, fewer than 1 in 10 older workers had less than a high school diploma.
  • The percentage of older workers with a college degree grew from 26 percent in 1998 to 42 percent in 2018.

Safety and Health

  • While fatal occupational injuries to all workers declined 17 percent from 1992 to 2017, workers age 65 and older incurred 66 percent more fatal work injuries in 2017 (775) than they did in 1992 (467).
  • Workers age 65 and older had a fatality rate that was nearly three times the rate for all workers in 2017.

Chart showing fatal injury rates by age from 2013 to 2017

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

Want to know more? These statistical programs contributed data to this blog:

From an American worker’s first job to retirement and everything in between, BLS has a stat for that!

Labor force participation rates by age, 1988–2018 annual averages
Year 55–64 65–69 70–74 75 and older
1988 54.6 20.1 10.9 4.2
1989 55.5 20.8 11.2 4.3
1990 55.9 21.0 11.3 4.3
1991 55.5 20.6 10.9 4.4
1992 56.2 20.6 11.1 4.5
1993 56.4 20.3 10.9 4.3
1994 56.8 21.9 11.8 5.4
1995 57.2 21.8 12.5 4.7
1996 57.9 21.9 12.5 4.7
1997 58.9 22.5 12.6 4.8
1998 59.3 22.5 12.5 4.7
1999 59.3 23.0 13.1 5.1
2000 59.2 24.5 13.5 5.3
2001 60.4 24.7 14.1 5.2
2002 61.9 26.1 14.0 5.1
2003 62.4 27.4 14.6 5.8
2004 62.3 27.7 15.3 6.1
2005 62.9 28.3 16.3 6.4
2006 63.7 29.0 17.0 6.4
2007 63.8 29.7 17.2 6.8
2008 64.5 30.7 17.8 7.3
2009 64.9 31.1 18.4 7.3
2010 64.9 31.5 18.0 7.4
2011 64.3 32.1 18.8 7.5
2012 64.5 32.1 19.5 7.6
2013 64.4 32.2 19.2 7.9
2014 64.1 31.6 18.9 8.0
2015 63.9 32.1 18.6 8.2
2016 64.1 32.2 19.2 8.4
2017 64.5 32.3 19.7 8.3
2018 65.0 33.0 19.5 8.7
Rate of fatal work injuries per 100,000 full-time equivalent workers by age
Year All workers 18 to 19 years 20 to 24 years 25 to 34 years 35 to 44 years 45 to 54 years 55 to 64 years 65 years and over
2013 3.3 2.6 2.2 2.5 2.8 3.4 4.1 9.2
2014 3.4 2.0 2.3 2.4 2.8 3.6 4.3 10.7
2015 3.4 2.1 2.7 2.3 2.7 3.5 4.3 9.4
2016 3.6 1.9 2.4 2.5 3.1 3.5 4.7 9.6
2017 3.5 2.6 2.2 2.5 2.9 3.3 4.6 10.3

Why This Counts: What Does the Future Hold for the Workforce?

You or someone you know may be deciding on a career, whether just starting out in the workforce or looking to change jobs. If so, you may have questions about potential careers. BLS employment projections and our Occupational Outlook Handbook can help answer them.

“Our team highlights the Occupational Outlook Handbook in their workshops and in their individual coaching sessions with students as a key resource for them to explore, expand, and understand all of their options. Our team also uses the information to assess job trends so we can help students prepare for the job market of the future.” — George Washington University, Center for Career Services

Even if you aren’t looking for a career change, you may be interested in a broader picture of the future of the U.S. economy and workforce. You can find this information, and much more, from the Employment Projections program.

What’s a projection and how do you make one?

A projection is an estimate of future conditions or trends based on a study of past and present trends.

Every 2 years, the Employment Projections program publishes 10-year projections of national employment by industry and occupation based on analysis of historical and current economic data. The purpose is to offer some insight into questions about the future growth or decline of industries and occupations.

We use historical and current BLS data primarily from the Current Population Survey, the Current Employment Statistics survey, and the Occupational Employment Statistics survey. You can see an overview of our six-step projections process.

BLS is working toward releasing the projections each year, rather than every 2 years.

See our video on “Understanding BLS Employment Projections.”

What are some data highlights for the 2016–26 projections?

The most recent labor force projections tell us about the impact of the aging of the population.

  • As the large baby-boom generation (those born between 1946 and 1964) grows older, the overall labor force participation rate is projected to be lower than in previous decades. The labor force participation rate is the share of people working or looking for work. We project the rate to be 61.0 percent in 2026, compared with 62.8 percent in 2016 and 66.2 percent in 2006. This is because older people have lower labor force participation rates than younger age groups.
  • The 55-and-older age group is projected to make up nearly one-quarter of the labor force in 2026, up from 22.4 percent in 2016 and 16.8 percent in 2006.
  • The share held by the youngest age group—ages 16 to 24—is projected to continue to decline as they focus on their education.

Percent distribution of the labor force by age in 1996, 2006, 2016, and projected 2026

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

We can view employment projections in terms of the change in the number of jobs and as a percent change. The projected percent change represents how fast an occupation or industry is projected to grow.

  • The chart below includes the top ten fastest-growing occupations from 2016 to 2026.
  • Five of the occupations are related to healthcare, which makes sense with a population that is growing older.
  • The top two fastest-growing occupations install, repair, and maintain solar panels and wind turbines. These two occupations are small in numbers but are both projected to double in size over the decade, reflecting the current interest in alternative forms of energy.
  • The remaining occupations fall into what is known as STEM (science, technology, engineering, and math).

Fastest-growing occupations, projected, 2016–26

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

What other information can you get from the Employment Projections program?

BLS also provides information on the education and training path for occupations. What education do people usually need to enter an occupation? Does the occupation typically need work experience in a related occupation? Is specific on-the-job training typically needed? BLS provides this information for every detailed occupation for which we publish projections. We describe the typical path to entry in the base year of the projections. This education and training information, with the occupational projections and wages, form the basis of the Occupational Outlook Handbook.

“It [the Occupational Outlook Handbook] is a great jumping off point. I use it to go more in depth with students. We look at what the career entails, and which fields really appeal to them.” — Gail Grand, College Counselor, Westlake Village, California

What is the Occupational Outlook Handbook?

The Occupational Outlook Handbook has been around for nearly 70 years, and it is a trusted (and free!) source of career information. It incorporates BLS data and lots of other information about careers, along with tools to find the information you need. Another publication, Career Outlook, is published throughout the year and provides practical information about careers for students, career counselors, jobseekers, and others planning careers.

“The Handbook has been an effective tool during our strategic planning process at the Foundation. We’ve used the data to design an investment strategy that will focus on linking opportunity youth with promising careers in the region. OOH enabled us to sync up resource allocation with program development.” — Kristopher Smith, Foundation for the Mid-South

Want to know about projections for your state or local area?

While BLS makes projections at the national level, each state makes projections for states and local areas. Find information on state projections at Projections Central.

Want more Employment Projections information?

Check out the latest news release. Head to the Frequently Asked Questions to learn more. Or contact the information folks by phone, (202) 691-5700, or email.

Changing jobs or starting a new career is a big decision. Use these gold-standard BLS data to help you make smart decisions, which could help you for years to come. Don’t be a buggy whip maker when everyone is riding in a self-driving car—or a rocket ship!

Percent distribution of the labor force by age
Year 16 to 24 25 to 34 35 to 44 45 to 54 55 and older
1996 15.8% 25.3% 27.3% 19.7% 11.9%
2006 14.8 21.5 23.7 23.2 16.8
2016 13.3 22.3 20.6 21.3 22.5
Projected 2026 11.7 22.1 22.2 19.2 24.8
Fastest-growing occupations, projected, 2016–26
Occupation Percent change
Solar photovoltaic installers 104.9%
Wind turbine service technicians 96.3
Home health aides 47.3
Personal care aides 38.6
Physician assistants 37.3
Nurse practitioners 36.1
Statisticians 33.8
Physical therapist assistants 31.0
Software developers, applications 30.7
Mathematicians 29.7

Why This Counts: Tracking Workers over Time

In many ways, BLS is very much about the now. For example, two of our major statistical programs are the Current Employment Statistics and the Current Population Survey. But to understand the U.S. labor market, we also need a longer-term focus.

The National Longitudinal Surveys (NLS) program provides information about the long-term workings of the economy.

What is a “longitudinal survey”?

A longitudinal survey interviews the same sample of people over time. At each interview, the surveys ask people about their lives and changes since their prior interview. With this information we create histories that allow researchers to answer questions about long-term labor market outcomes. For example, how many jobs do people hold over their lifetimes? How do earnings grow at different stages of workers’ careers? How do events that happened when a person was in high school affect labor market success as an adult?

How does the NLS work?

The NLS program is more than 50 years old, and today we have two active cohorts, or nationally representative samples of people, whom we interview every year or two:

  • The National Longitudinal Survey of Youth 1979 (NLSY79) consists of people born from 1957 to 1964, who were ages 14 to 22 when first interviewed in 1979.
    • The NLSY79 cohort has been interviewed 27 times since the late 1970s.
    • The children of the women in this sample (captured in the NLSY79 Children and Young Adults survey) have been assessed and interviewed 16 times since 1986.
  • The National Longitudinal Survey of Youth 1997 (NLSY97) consists of people born in the years 1980 to 1984, who were ages 12 to 17 when first interviewed in 1997.
    • The NLSY97 cohort has been interviewed 17 times.

These surveys are voluntary, and what a commitment our participants have shown! A big “thank you” to our respondents for their help!

What information is available from NLS?

By gathering detailed labor market information over time, researchers can create measures that are not available in other surveys.

One measure is the number of jobs held across various ages. The chart that follows is from the most recent NLSY79 news release.

  • The chart shows the cumulative number of jobs held from ages 18 to 50.
  • People born from 1957 to 1964 held an average of 11.9 jobs from ages 18 to 50. From ages 18 to 24 these baby boomers held an average of 5.5 jobs. The number steadily fell over time until these baby boomers held an average of just 0.8 job from ages 45 to 50.
  • The decline in the slope of the curves shows that workers change jobs more often when they are younger.

Cumulative number of jobs held from ages 18 to 50, by sex and age

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

The decline in the number of jobs held over time is also true for the NLSY97 cohort.

A second measure available from the surveys is the percentage of weeks worked over various ages. Let’s look at data from the most recent NLSY97 news release.

  • The chart below shows the percent of weeks worked from ages 18 to 30, by educational attainment and sex.
  • Women with less than a high school diploma were employed an average of 40 percent of weeks from ages 18 to 30. Men with less than a high school diploma were employed 64 percent of weeks.
  • Among people with a bachelor’s degree and higher, women were employed an average of 80 percent of weeks, while men were employed 78 percent of weeks.

Percent of weeks employed from ages 18 to 30, by educational attainment and sex

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

Who uses the NLS?

The main users of these data are researchers in academia, think tanks, and government. They use the surveys to examine how life experiences are connected. For example, how do early life events (schooling, employment during one’s teens, parental divorce) affect adult outcomes (employment, income, family stability)?

“Studies using the NLS cover a staggeringly broad array of topics. Looking through them, I was startled to realize how much of what we know about the labor market is only knowable because of the NLS.” — Janet Currie, Henry Putnam Professor of Economics and Public Affairs, Princeton University

Researchers value the surveys’ combination of large samples, long histories, and range of topics. These features allow researchers to study our economy and society from a rare and complex perspective.

Researchers have used the data in thousands of journal articles, working papers, Ph.D. dissertations, and books that shape theory and knowledge in economics, sociology, education, psychology, health sciences, and other fields.

You can find information about more than 8,000 studies in the NLS Bibliography. Looking at journal articles published in 2018, I found these studies using NLS data:

  • Racial and Ethnic Variation in the Relationship between Student Loan Debt and the Transition to First Birth
  • The Impact of Childhood Neighborhood Disadvantage on Adult Joblessness and Income
  • The Effect of an Early Career Recession on Schooling and Lifetime Welfare
  • The Early Origins of Birth Order Differences in Children’s Outcomes and Parental Behavior
  • Earnings Dynamics: The Role of Education Throughout a Worker’s Career

“[From the NLS] I learned that we cannot understand why adults have such diverse employment and earnings trajectories without going back to their youth to understand the skill and background differences that shaped how they transitioned into adulthood.” — Derek Neal, Professor of Economics, University of Chicago

How can I get more information?

The data are free to the public and provided online with search and extraction tools and detailed documentation.

If you have a specific question, you might find it answered in our Frequently Asked Questions. Or you can always contact the staff by email or phone at 202-691-7410.

If you care about the long view—how peoples’ careers evolve over time, how people fare after job loss, how childbirth affects women’s careers, and so on—the National Longitudinal Surveys may be just what you need! Check out these gold-standard data!

Cumulative number of jobs held from ages 18 to 50, by sex and age
Age Men Women
18 1.6 1.5
19 2.4 2.3
20 3.1 2.9
21 3.8 3.5
22 4.5 4.2
23 5.1 4.7
24 5.7 5.3
25 6.2 5.7
26 6.7 6.2
27 7.2 6.6
28 7.6 7.0
29 8.0 7.3
30 8.3 7.6
31 8.6 7.9
32 8.9 8.2
33 9.2 8.5
34 9.5 8.8
35 9.7 9.0
36 10.0 9.3
37 10.2 9.5
38 10.4 9.8
39 10.5 10.0
40 10.7 10.1
41 10.9 10.3
42 11.0 10.5
43 11.2 10.6
44 11.4 10.8
45 11.5 11.0
46 11.6 11.1
47 11.7 11.3
48 11.9 11.4
49 12.0 11.5
50 12.1 11.6
Percent of weeks employed from ages 18 to 30, by educational attainment and sex
Education Men Women
Less than a high school diploma 63.5% 40.3%
High school graduates, no college 75.5 64.4
Some college or associate degree 79.4 72.0
Bachelor’s degree and higher 78.4 80.1

Why This Counts: Maximizing Our Data Using the Consumer Expenditure Survey

Almost all BLS statistical programs are based on information respondents voluntarily give us. We want to squeeze as much information as we can out of the data respondents generously provide. Limiting respondent burden while producing gold-standard data is central to our mission.

Let’s take a look at how one program, the Consumer Expenditure (CE) Survey, squeezes every last drop of information from the data to provide you, our customers, with more relevant information.

What is the Consumer Expenditure Survey?

The CE survey is a nationwide household survey that shows how U.S. consumers spend their money. It collects information from America’s families on their buying habits (expenditures), income, and household characteristics (age, sex, race, education, and so forth). For example, we publish what percentage of consumers bought bacon or ice cream and how much they spent on average.

A little back story: The first nationwide expenditure survey began in 1888. BLS was founded in 1884, so the CE Survey is one of our first surveys! It wasn’t until 1980 that we began publishing CE data each year, however. A 2010 article, The Consumer Expenditure Survey—30 Years as a Continuous Survey, provides more historical information.

How is the CE program doing more with what we have?

We’ll briefly look at four different areas, starting with the most recent improvements:

  • Limited state data
  • Higher-income data
  • Generational data
  • Estimating taxes

Limited State Data – Starting with New Jersey

  • Regarding geographical information, the CE survey is designed to produce national statistics. Enough sample data are available to produce estimates for census regions and for a few metropolitan areas.
  • Up to now, however, we did not produce state data. The CE program recently published state weights for New Jersey, which will allow for valid survey estimates at the state level for the first time.
  • State-level weights are available for states with a sample size that is large enough and meet other sampling conditions.
  • Right now, the state-level weighting is experimental. We provide state-level weights to data users to gauge interest and usefulness.

 Higher-Income Table

  • We evaluated the income ranges of the published tables and found that over time more and more households were earning more, and the top income range had not increased to keep pace. To provide greater detail, we divided the existing top income range of “$150,000 and over” into two new ranges: “$150,000 to $199,999” and “$200,000 and over.” We integrated these changes into the 2014 annual “Income before taxes” research table, allowing more robust analysis for our data users.
  • In addition, we added four new experimental cross-tabulated tables on income without the need for additional information from our respondents.

Generational Table

Grouping respondent information by age cohort can be helpful, since a person’s age can help to predict differences in buying attitudes and behaviors. The CE program has collected age data for years, but never grouped the data into generational cohorts before. A Pew Research Center report defines five generations for people born between these dates:

  • Millennial Generation: 1981 or later
  • Generation X: 1965 to 1980
  • Baby Boomers: 1946 to 1964
  • Silent Generation: 1928 to 1945
  • Greatest Generation: 1927 or earlier

The 2016 annual generational table shows our most recent age information for the “reference person” or the person identified as owning or renting the home included in the CE Survey. In 2016 we wrote a short article on Spending Habits by Generation, including a video, which used 2015 data. We’ve updated the chart using 2016 data:

A chart showing consumer spending patterns by generation in 2016.

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

Estimating Taxes

CE respondents used to provide federal and state income tax information as part of the survey. These questions were difficult for respondents to answer.

Starting in 2013, the CE program estimated federal and state tax information using the TaxSim model from the National Bureau of Economic Research and removed the tax questions from the survey. As a result, the quality and consistency of the data increased, and we have reduced respondent burden!

If you have any questions or want more information, our staff of experts is always around to help! Please feel free to contact us.

This is just one example of how we at BLS are always looking for ways to maximize our value while being ever mindful of the costs—and one of those important costs is the burden our data collection efforts place on our respondents. Maximizing our data means providing gold-standard data to the public while reducing the burden on our respondents—a true win-win!

Annual consumer spending by generation of reference person, 2016
Item Millennials, 1981 to now Generation X, 1965 to 1980 Baby Boomers, 1946 to 1964 Silent Generation, 1928 to 1945 Greatest Generation, 1927 or earlier
Food at home $3,370 $4,830 $4,224 $3,450 $2,023
Food away from home 2,946 4,040 3,100 2,042 1,095
Housing 16,959 22,669 18,917 14,417 17,858
Apparel and services 1,753 2,577 1,602 920 615
Transportation 8,426 10,545 9,762 5,952 3,142
Healthcare 2,473 4,492 5,492 6,197 5,263
Entertainment 2,311 3,613 3,144 2,114 1,223
All other spending 10,338 15,766 14,963 6,671 4,125