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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

Making It Easier to Find Data on Pay and Benefits

We love data at the U.S. Bureau of Labor Statistics. We have lots of data about the labor market and economy, but we sometimes wish we had more. For example, we believe workers, businesses, and public policymakers would benefit if we had up-to-date information on employer-provided training. I recently wrote about the challenges of collecting good data on electronically mediated work, or what many people call “gig” work. I know many of you could make your own list of data you wish BLS had. One topic for which we have no shortage of data is pay and benefits. In fact, we have a dozen surveys or programs that provide information on compensation. We have so much data on compensation that it can be hard to decide which source is best for a particular purpose.

Where can you get pay data on the age, sex, or race of workers? Where should you go if you want pay data for teachers, nurses, accountants, or other occupations? What about if you want occupational pay data for a specific metro area? Or if you want occupational pay data for women and men separately? What if you want information on workers who receive medical insurance from their employers? Where can you find information on employers’ costs for employee benefits? Here’s a short video to get you started.

But wait, there’s more! To make it easier to figure out which source is right for your needs, we now have an interactive guide to all BLS data on pay, benefits, wages, earnings, and all the other terms we use to describe compensation. Let me explain what I mean by “interactive.” The guide lists 12 sources of compensation data and 32 key details about those data sources. 12 x 32 = a LOT of information! Having so much information in one place can feel overwhelming, so we created some features to let you choose what you want to see.

For example, the guide limits the display to three data sources at a time, rather than all 12. You can choose which sources you want to learn about from the menus at the top of the guide.Snippet of interactive guide on BLS compensation data.

If you want to learn about one of the 32 key details across all 12 data sources, just press or click that characteristic in the left column. For example, if you choose “Measures available by occupation?” a new window will open on your screen to describe the pay data available from each source on workers’ occupations.

There are links near the bottom of the guide to help you find where to go if you want even more information about each data source.

Check out our overview of statistics on pay and benefits. The first paragraph on that page has a link to the interactive guide. We often like to say, “We’ve got a stat for that!” When it comes to pay and benefits, we have lots of stats for that. Let us know how you like this new interactive guide.

Why This Counts: Breaking Down Multifactor Productivity

Productivity measures tell us how much better we are at using available resources today compared to years past. All of us probably think about our own productivity levels every day, either in the workplace or at home. I find my own productivity is best in the morning, right after that first cup of coffee!

On a larger scale, here at the U.S. Bureau of Labor Statistics, we produce two types of productivity measures: labor productivity and multifactor productivity, which we will call “MFP” for short. An earlier Why This Counts blog post focused on labor productivity and its impact on our lives. In this blog we will focus on why MFP measures matter to you.

Why do we need two types of productivity measures?

Labor productivity compares the amount of goods and services produced—what we call output—to the number of labor hours used to produce those goods and services.

Multifactor productivity differs from labor productivity by comparing output not just to hours worked, but to a combination of inputs.

What are these combined inputs?

For any given industry, the combined inputs include labor, capital, energy, materials, and purchased services. MFP tells us how much more output can be produced without increasing any of these inputs. The more efficiently an industry uses its combination of inputs to create output, the faster MFP will grow. MFP gives us a broader understanding of how we are all able to do more with less.

Does MFP tell us anything about the impact of technology?

It does. But we cannot untangle the impact of technology from other factors. MFP describes the growth in output that is not a result of using more of the inputs that we can measure. In other words, MFP represents what is left, the sources of growth that we cannot measure. These include not just technology improvements but also changes in factors such as management practices and the scale or organization of production. Put simply, MFP uses what we do know to learn more about what we want to know.

What can MFP tell us about labor productivity?

Labor productivity goes up when output grows faster than hours. But what exactly causes output to grow faster than hours? Labor productivity can grow because workers have more capital or other inputs or their job skills have improved. Labor productivity also may grow because technology has advanced, management practices have improved, or there have been returns to scale or other unmeasured influences on production. MFP statistics help us capture these influences and measure their impact on labor productivity growth.

How are MFP statistics used?

We can identify the sources of economic growth by comparing MFP with the inputs of production. This is true for individual industries and the nation as a whole.

For example, a lot has been written about the decline of manufacturing in the United States. MFP increased between 1992 and 2004 by an average of 2.0 percent per year. In contrast, MFP declined from 2004 through 2016 by an average of 0.3 percent per year. A recently published article uses detailed industry data to analyze sources of this productivity slowdown.

MFP is a valuable tool for exploring historical growth patterns, setting policies, and charting the potential for future economic growth. Businesses, industry analysts, and government policymakers use MFP statistics to make better decisions.

Where can I go to learn more?

Check out the most recent annual news release to see the data firsthand!

If you have a specific question, you might find it answered in our Frequently Asked Questions. Or you can always contact MFP staff through email or call (202) 691-5606.

Just like your own productivity at work and at home, the productivity growth of our nation can lead to improvements in the standard of living and the economic well-being of the country. Productivity is an important economic indicator that is often overlooked. We hope this blog has helped you to learn more about the value of the MFP!

Why This Counts: What is the Producer Price Index and How Does It Impact Me?

The Producer Price Index (PPI) – sounds familiar, but what is it exactly? Didn’t it used to be called the Wholesale Price Index? It is related to the Consumer Price Index, but how? How does the PPI impact me?

Lots of questions! In this short primer we will provide brief answers and links for more information. Note, if you are an economist, this blog is NOT for you. It’s an introduction for everyone else!

Video: Introduction to the Producer Price Index

Before we go any further – what is an index? (You said this was a primer!)

An index is like a ruler. It is a way of measuring the change of just about anything. Producer price indexes measure the average change in prices for goods, services, or construction products sold as they leave the producer.

Here is an example of how an index works:

  • Suppose we created an index to track the price of a gallon of gasoline.
  • When we start tracking, gasoline costs $2.00 a gallon.
  • The starting index value is 100.0.
  • When gasoline rises to $2.50, our index goes to 125.0, which reflects a 25-percent increase in the price of gasoline.
  • If gasoline then drops to $2.25, the index goes to 112.5. The $0.25 decline in price reflects a 10-percent decrease in the price of gasoline from when the price was $2.50.

If you are a gasoline dealer, you might find a gasoline index useful. Instead of driving around every day to write down the prices of each competitor’s gasoline and averaging them together, the index can provide the data for you. (Question #5 in the PPI Frequently Asked Questions explains how to interpret an index.)

PPI is called a “family” of indexes. There are more than 10,000 indexes for individual products we release each month in over 500 industries. That is one big family!

OK, so PPI has lots of data – but what kind of data?

PPI produces three main types of price indexes: industry indexes, commodity indexes, and final demand-intermediate demand (FD-ID) indexes.

An industry refers to groups of companies that are related based on their primary business activities, such as the auto industry. The PPI measures the changes in prices received for the industry’s output sold outside the industry.

  • PPI publishes about 535 industry price indexes and another 500 indexes for groupings of industries.
  • By using the North American Industry Classification System (NAICS) index codes, data users can compare PPI industry-based information with other economic programs, including productivity, production, employment, wages, and earnings.

The commodity classification of the PPI organizes products by type of product, regardless of the industry of production. For example, the commodity index for steel wire uses pricing information from the industries for iron and steel mills and for steel wire drawing.

  • PPI publishes more than 3,700 commodity price indexes for goods and about 800 for services.
  • This classification system is unique to the PPI and does not match any other standard coding structure.

We also have more information on the differences between the industry and commodity classification systems.

The FD-ID classification of the PPI organizes groupings of commodities by the type of buyer. For example, the PPI for final demand measures price change in all goods, services, and construction products sold as personal consumption, capital investment, export, or to government. As a second example, the PPI for services for intermediate demand measures price change for services sold to business as inputs to production.

  • PPI publishes more than 300 FD-ID indexes.
  • This FD-ID classification system is unique to the PPI and does not match any other standard coding structure.

Now let’s go back to the beginning

  • 1902: Wholesale Price Index program begins, which makes it one of the oldest continuous set of federal statistics. The Wholesale Price Index captures the prices producers receive for their output. In contrast, the Consumer Price Index captures the prices consumers pay for their purchases.
  • 1978: BLS renames the program as the Producer Price Index to more accurately reflect that prices are collected from producers, rather than wholesalers.
  • PPI also shifts emphasis from a commodity index framework to a stage of processing index framework. This minimized the multiple counting that can occur when the price for a specific commodity and the inputs to produce that commodity are included in the same total index. For example, think of gasoline and crude petroleum both included in an all-commodities index.
  • 1985: PPI starts expanding its coverage of the economy to include services and nonresidential construction. As of January 2018, about 71 percent of services and 31 percent of construction are covered.
  • 2014: PPI introduces the Final Demand-Intermediate Demand system.
  • The “headline” number for PPI is called the PPI for Final Demand. It measures price changes for goods, services, and construction sold for personal consumption, capital investment, government purchases, and exports. We also produce a series of PPIs for Intermediate Demand, which measure price change for business purchases, excluding capital investment.
  • Let me give you an example: Within the PPI category for loan services, we have separate indexes for consumer loans and business loans. The commodity index for consumer loans is included in the final demand index and the commodity index for business loans is mostly in an intermediate demand index.
  • The Frequently Asked Question on the PPI for Final Demand provides even more information on this new way of measuring the PPI. The blog, Understanding What the PPI Measures, may also be helpful.
  • We also have an article that explains how the PPI for final demand compares with other government prices indexes, such as the CPI.

Why is the PPI important?

To me?

  • Inflation is the higher costs of goods and services. Low inflation may be good for the economy as it increases consumer spending while boosting corporate profits and stocks.
  • A change in producer prices may be a leading indicator of consumers paying more or less. Higher producer prices may mean consumers will pay more when they buy, whereas lower producer prices may mean consumers will pay less to retailers. For example, if the PPI gasoline index increases, you may see an increase soon at the pump!

To others (which may impact me!)?

  • Policymakers, such as the Federal Reserve, Congress, and federal agencies regularly watch the PPI when making fiscal and monetary policies, such as setting interest rates for consumers and businesses.
  • Business people use the PPI in deciding price strategies, as they measure price changes in inputs for their goods and services. For example, a company considering a price increase can use PPI data to compare the growth rate of their own prices with those in their industry.
  • Business people adjust purchase and sales contracts worth trillions of dollars by using the PPI family of indexes. These contracts typically specify dollar amounts to be paid at some point in the future. For example, a long-term contract for bread may be escalated for changes in wheat prices by applying the percent change in the PPI for wheat to the contracted price for bread.

Video: How the Producer Price Index is Used for Contract Adjustment

PPI is a voluntary survey completed by thousands of businesses nationwide every month. BLS carefully constructs survey samples to keep the number of contacts to a minimum, making every business, large and small, critical to the accuracy of the data. We thank you, our faithful respondents! Without you, BLS could not produce gold-standard PPI data.

Finally, check out the most recent monthly PPI release to get all the latest numbers. Head to the PPI Frequently Asked Questions to learn more. Or contact the PPI information folks at (202) 691-7705 or ppi-info@bls.gov.

Want to learn more about BLS price programs? See these blogs:

 

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