Tag Archives: Methodology

Projected Occupational Openings: Where Do They Come From?

Toward the beginning of each school year, BLS issues a new set of Employment Projections, looking at projected growth and decline in occupations over the next decade. These estimates are important for understanding structural changes in the workforce over time. But to identify opportunities for new workers, we need to look beyond occupational growth and decline, to a concept we call “occupational openings.”

Occupational openings are the sum of the following:

  • Projected job growth (or decline)
  • Occupational separations — workers leaving an occupation, which includes:
    • Labor force exits — workers who leave the labor force entirely, perhaps to retire
    • Occupational transfers — workers who leave one occupation and transfer to a different occupation.

This video explains the concept of occupational openings further.

BLS publishes the projected number of occupational openings for over 800 occupations. Not surprisingly, some of the largest occupations in the country have some of the largest number of openings. For example, certain food service jobs, which include fast food workers, are projected to have nearly 800,000 openings per year over the next decade. I guess this isn’t a surprise in an occupation with over 3.7 million workers.

But when we delve into the information on occupational openings a little further, more stories emerge. Some related occupations have very different patterns of openings. And some occupations have similar levels of openings for different reasons. Let’s take a look at a few examples.

In 2018, there were over 800,000 lawyers in the U.S., and a projected 45,000 annual openings for lawyers, about 5.5 percent of employment. At the same time, there were fewer than half the number of paralegals and legal assistants (325,000), with projected annual openings around 40,000 per year – 12.4 percent of employment. These two related occupations had similar numbers of projected openings, but those openings represented different proportions of current employment. Such differences reflect required education, demographics, compensation, and other variables. Lawyers tend to have professional degrees that are specialized for that occupation and are therefore more closely tied to their occupation than paralegals, who have more diverse educational backgrounds. You can find out more about how worker characteristics affect these numbers in the Monthly Labor Review.

Now let’s look at the sources of occupational openings. In this first example, we compare two occupational groups: installation, maintenance, and repair occupations and healthcare support occupations. These are broad categories that include a number of different individual occupations.

Average annual occupational openings for installation, maintenance, and repair occupations and healthcare support occupations, 2018–28

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

In this example, both occupational groups have projected annual openings of a little over 600,000 per year, yet they come from different sources. Two-thirds of the openings among installation occupations result from workers leaving to go to other occupations; in contrast, just under half the openings among healthcare support occupations are from people moving to other occupations. Looking at projected job growth, BLS projects that healthcare support occupations, the fastest growing occupational group, will add more than three times as many new jobs as installation occupations, annually over the next decade (78,520 versus 23,320).

Now let’s look at two individual occupations — web developers and court, municipal, and license clerks. These are very different jobs, but both are projected to have about 15,000 annual openings over the next decade. Here, too, occupational openings come from very different places, as this chart shows:

Average annual occupational openings for web developers and court, municipal, and license clerks, 2018–28

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

In this case, around 67 percent of openings for web developer jobs come from workers transferring to other jobs, compared with only 49 percent transfers for clerks. But a greater share of clerks are exiting the labor force. Once again, differences are due to a variety of factors, although the age of workers is a significant factor in this case — web developers have a median age of 38.3, while clerks tend to be older, with a median age of 49.1. Younger workers are more likely to transfer occupations, while older workers are more likely to exit the labor force, as for retirement.

So what does all this really mean? If nothing else, you can see that the thousands of individual data elements available through the BLS Employment Projections program tell a thousand different stories, and more. Whether large or small, growing or declining, there’s information about hundreds of occupations that can be helpful to students looking for careers, counselors helping those students and others, workers wanting to change jobs, employers thinking about their future, policymakers considering where to put job training resources, and on and on. These examples just scratch the surface of what BLS Employment Projections information can tell us. Take a look for yourself.

Average annual occupational openings, 2018–28
OccupationEmployment growthExitsTransfers

Installation, maintenance, and repair occupations

23,320195,700413,900

Healthcare support occupations

78,520235,500299,600
Average annual occupational openings, 2018–28
OccupationEmployment growthExitsTransfers

Web developers

2,0902,90010,100

Court, municipal, and license clerks

6707,0007,300

BLS Learns from Civic Digital Fellows

In the few months that I’ve had the pleasure of occupying the Commissioner’s seat at the Bureau of Labor Statistics, it’s been clear that I’m surrounded by a smart, dedicated, and innovative staff who collect and publish high-quality information while working to improve our products and services to meet the needs of customers today and tomorrow. And soon after I arrived, we added to that high-quality staff by welcoming a cadre of Civic Digital Fellows to join us for the summer.

In its third year, the Civic Digital Fellowship program was designed by college students for college students who wanted to put their data science skills to use helping federal agencies solve problems, introduce innovations, and modernize functions. This year, the program brought 55 fellows to DC and placed them in 6 agencies – Census Bureau, Citizenship and Immigration Service, General Services Administration, Health and Human Services, National Institutes of Health, and BLS. From their website:

Civic Digital Fellowship logo describing the program as "A first-of-its-kind technology, data science, and design internship program for innovative students to solve pressing problems in federal agencies."

BLS hosted 9 Civic Digital Fellows for summer 2019. Here are some of their activities.

  • Classification of data is a big job at BLS. Almost all of our statistics are grouped by some classification system, such as industry, occupation, product code, or type of workplace injury. Often the source data for this information is unstructured text, which must then be translated into codes. This can be a tedious, manual task, but not for Civic Digital Fellows. Andres worked on a machine learning project that took employer files and classified detailed product names (such as cereal, meat, and milk from a grocery store) into categories used in the Producer Price Index. Vinesh took employer payroll listings with very specific job titles and identified occupational classifications used in the Occupational Employment Statistics program. And Michell used machine learning to translate purchases recorded by households in the Consumer Expenditure Diary Survey into codes for specific goods and services.
  • We are always looking to improve the experience of customers who use BLS information, and the Civic Digital Fellows provided a leg up on some of those activities. Daniel used R and Python to create a dashboard that pulled together customer experience information, including phone calls and emails, internet page views, social media comments, and responses to satisfaction surveys. Olivia used natural language processing to develop a text generation application to automatically write text for BLS news releases. Her system expands on previous efforts by identifying and describing trends in data over time.
  • BLS staff spend a lot of time reviewing data before the information ends up being published. While such review is more automated than in the past, the Civic Digital Fellows showed us some techniques that can revolutionize the process. Avena used Random Forest techniques to help determine which individual prices collected for the Consumer Price Index may need additional review.
  • Finally, BLS is always on the lookout for additional sources of data, to provide new products and services, improve quality, or reduce burden on respondents (employers and households). Christina experimented with unit value data to determine the effect on export price movements in the International Price Program. Somya and Rebecca worked on separate projects that both used external data sources to improve and expand autocoding within the Occupational Requirements Survey. Somya looked at data from a private vendor to help classify jobs, while Rebecca looked at data from a government source to help classify work tasks.

The Civic Digital Fellows who worked at BLS in summer 2019

Our cadre of fellows has completed their work at BLS, with some entering grad school and the working world. But they left a lasting legacy. They’ve gotten some publicity for their efforts. Following their well-attended “demo day” in the lobby at BLS headquarters, some of their presentations and computer programs are available to the world on GitHub.

I think what most impressed me about this impressive bunch of fellows was the way they grasped the issues facing BLS and focused their work on making improvements. I will paraphrase one fellow who said “I don’t want to just do machine learning. I want to apply my skills to solve a problem.” Another heaped praise on BLS supervisors for “letting her run” with a project with few constraints. We are following up on all of the summer projects and have plans for further research and implementation.

We ended the summer by providing the fellows with some information about federal job opportunities. I have no doubt that these bright young minds will have many opportunities, but I also saw an interest in putting their skills to work on real issues facing government agencies like BLS. I look forward to seeing them shine, whether at BLS or wherever they end up. I know they will be successful.

And, we are already making plans to host another group of Civic Digital Fellows next summer.

What is “Benchmarking” of Bureau of Labor Statistics Employment Data?

BLS has released the “preliminary benchmark” information for the Current Employment Statistics (CES) survey, the source of monthly information on jobs.

You know what a bench is

Image of a park bench

and you know what a mark is,

Image of a checkmark

but what pray tell is a benchmark? And what does this preliminary benchmark tell us?

So as not to bury the lead, I’ll let you know that this year’s preliminary estimate of the benchmark revision is a bit bigger than it has been in the last few years. Our preliminary estimate indicates a downward adjustment to March 2019 total nonfarm employment of 501,000. Still, that estimated revision is only -0.3 percent of nonfarm employment. In most years our monthly employment survey has done a good job at estimating the total number of payroll jobs. More details on that below. This year our survey estimates are off more than we would like. Our goal is to provide estimates that are excellent and not just good or pretty good, and that’s why we benchmark the survey data each year.

What is benchmarking and why do we do it?

The CES is a monthly survey of approximately 142,000 businesses and government agencies composed of approximately 689,000 individual worksites. As with all sample-based surveys, CES estimates are subject to sampling error. This means that while we work hard to ensure those 689,000 worksites represent all 10 million worksites in the country, sometimes our sample may not perfectly reflect all worksites. So the monthly CES estimates aren’t exactly the same as if we had counted employment from all 10 million worksites each month. To fix this problem, we “benchmark” the CES data to an actual count of all employees, information that’s only available several months after the initial CES data are published.

In essence, we produce employment information really quickly from a sample of employers, then anchor that information to a complete count of employment once a year.

The primary source of the CES sample is the BLS Quarterly Census of Employment and Wages (QCEW) program, which collects employment and wage data from states’ unemployment insurance tax systems. This is also the main source of the complete count of employment used in the benchmark process. QCEW data are typically available about 5 months after the end of each quarter.

Each year, we re-anchor the sample-based employment estimates to these full population counts for March of the prior year. This process—which we call benchmarking—improves the accuracy of the CES data. That’s because the population counts are not subject to the sampling and modeling errors that may occur with the CES monthly estimates. Since the CES data are re-anchored to March of the last year, CES estimates are typically revised from April of the year prior up to the March benchmark. Then estimates from the benchmark forward to December are revised to reflect the new March employment level.

We will publish the final benchmark revision in February 2020 and will incorporate revisions to data from April 2018 to December 2019. (Thus, we’re not showing a 2019 number in graph and table below). On August 21, BLS released a first look at what this revision will be—what we call the “preliminary benchmark.” This preliminary benchmark gives us an idea of what the revised nonfarm employment estimates for March 2019 will be.

The size of the national benchmark revision is a measure of the accuracy of the CES estimates, and we take pride that these revisions are typically small.

Chart showing differences in nonfarm employment after benchmarking, 2009–18

For total employment nationwide, the absolute annual benchmark revision has averaged about 0.2 percent over the past decade, with a range from −0.7 percent to +0.3 percent.

The following table shows the total payroll employment estimated from the CES before and after the benchmark over the past 10 years. For example, pre-benchmark employment for 2018 was 147.4 million; post-benchmark employment was also 147.4 million.

Nonfarm employment estimates before and after benchmarking, March 2009–March 2018
Year Level before benchmark Level after benchmark Difference Percent difference
2009 132,077,000 131,175,000 -902,000 –0.7
2010 128,958,000 128,584,000 -374,000 –0.3
2011 129,899,000 130,061,000 162,000 0.1
2012 132,081,000 132,505,000 424,000 0.3
2013 134,570,000 134,917,000 347,000 0.3
2014 137,147,000 137,214,000 67,000 <0.05
2015 140,298,000 140,099,000 -199,000 –0.1
2016 142,895,000 142,814,000 -81,000 –0.1
2017 144,940,000 145,078,000 138,000 0.1
2018 147,384,000 147,368,000 -16,000 <-0.05

The 2019 preliminary benchmark revision is following the same pattern, with an estimated difference of -0.3 percent. We provide this first look at the benchmark revision to give data users a sense of what we are seeing in the data. The final benchmark may be a little different—could be higher, could be lower. But based on recent experience, we are confident the benchmark released next February will show only a moderate difference from what we’ve been publishing each month and will validate the accuracy of our monthly CES estimates.

Want to know more? See our Current Employment Statistics webpage, send us an email, or call (202) 691-6555.

Why This Counts: Measuring Occupational Requirements

You probably know that BLS publishes data and analysis about employment, unemployment, job openings, earnings, productivity, occupational safety and health, and more. But did you know we also publish information about how often workers have to lift objects; the maximum weight they lift or carry; whether they work in extreme heat or cold; and how much training and experience they need for a job? We call these characteristics “occupational requirements.”

What are occupational requirements?

The Occupational Requirements Survey provides information about the requirements of jobs:

  • Physical demands of work, such as keyboarding, reaching overhead, lifting or carrying
  • Environmental conditions, such as extreme heat, exposure to outdoors, proximity to moving parts
  • Education, training, and experience requirements, such as prior work experience, on-the-job training, and license requirements
  • Cognitive and mental requirements, such as interaction with other people, independence of work, and the amount of review

How did BLS get into doing this survey?

This survey is one of our newest statistical programs; we first published data on December 1, 2016.

The Social Security Administration asked us to help them obtain accurate and current data to use in their disability programs. They are developing an Occupational Information System, which will use data from the Occupational Requirements Survey. That means the survey is crucial for Social Security to manage their disability programs fairly and efficiently.

How can I use occupational requirements information?

Users of Occupational Requirements Survey data include:

  • Researchers exploring occupational change
  • Jobseekers and students
  • Government agencies evaluating skill gaps
  • People with disabilities and their advocates

Let’s discuss a couple of examples to show you what I mean.

Educational requirements

You may want to know the minimum formal education requirements for jobs. The survey has a stat for that! In 2018, a high school diploma was required for jobs covering 40.7 percent of workers, while 17.9 percent had a bachelor’s degree requirement. The chart below shows the percent of jobs by minimum education requirement.

Percent of jobs with a minimum education requirement, 2018

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

We have more information on education, training, and experience. The 2018 news release showed that on-the-job training was required for about 77 percent of workers, and the average duration was 34 days.

We also have information on preparation time, which includes minimum formal education, training, and work experience a typical worker needs to perform a job. Preparation time between 4 hours and 1 month was required for 31.5 percent of workers.

Environmental Conditions

Is the noise level at your workplace closer to a library (quiet) or a rock concert (very loud)? For some jobseekers, understanding the noise level and other environmental conditions might be extremely important as they evaluate job options. The chart below provides examples of the noise intensity in different occupations.

Percent of jobs with noise intensity level requirements, selected occupations, 2018

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

Examples of work environments with different noise intensity levels include:

  • Quiet: private office, a golf course, or art museum
  • Moderate: department stores, business office, or fast food restaurant
  • Loud: manufacturing plant, atop large earth moving equipment, or jobs next to the highway
  • Very loud: rock concert venues, working with jack hammers, or rocket testing areas

How do we collect job requirement data?

To collect job requirement data, our field economists ask business owners, human resource professionals, worker safety officers, and supervisors to collect requirements of work. Field economists do not use paper or online questionnaires to collect these data; instead, they rely on a conversational interviews and descriptive documents, such as task lists, to collect information on occupational requirements.

How are we improving the survey?

Survey scope: Since it began, we have continued to refine the survey to improve its accuracy. In the third year of collection, we redefined the survey scope to focus on critical job functions—that is, the reason the job exists.

Survey content: Beginning with the current sample in collection, we added questions about cognitive and mental requirements. The Social Security Administration asked for this change so we can provide information on the requirements for workers to adapt to changes in the pace of work, solve problems, and interact with others.

Sample: The survey sample is collected over a 5-year period. That will provide the large amount of data necessary to publish information about detailed occupations. We have revised the sampling process to ensure we collect information about less common occupations.

Website: We recently improved the web layout to make it easier for users to find the data they want.

Where is more information?

We have data for occupational groups and occupations through the Occupational Profiles. All data are available through the public data tools. For concepts, methods, and history of the survey see the Handbook of Methods or visit our homepage.

Let us know if you have questions or comments about occupational requirements:

  • Email
  • Phone: (202) 691-6199

Use these gold-standard data to learn more about your job requirements or to find out about new ones. Whatever your occupational requirements question, “We have a stat for that!”

Percent of jobs with a minimum education requirement, 2018
Education requirement Percent
No minimum education requirement 31.5%
High school diploma 40.7
Associate’s degree 3.8
Associate’s vocational degree 2.1
Bachelor’s degree 17.9
Master’s degree 2.3
Professional degree 0.9
Doctorate degree 0.5
Percent of jobs with noise intensity level requirements, selected occupations, 2018
Occupation Quiet Moderate Loud
Bus and truck mechanics and diesel engine specialists 49.0% 51.0%
Computer programmers 60.1
Construction laborers 48.6 51.4
Electricians 49.0 51.0
Highway maintenance workers 46.2 53.8
Home health aides 54.1 45.9
Library technicians 56.0
Medical transcriptionists 68.7
Paralegals and legal assistants 66.5 33.5
Welders, cutters, and welder fitters 48.2 50.9

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.