All posts by BLS Commissioner

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.

New BLS Local Data App Now Available

BLS has partnered with the U.S. Department of Labor’s Office of the Chief Information Officer to develop a new mobile app for iPhones that is now available for free in the App Store! Search “BLS Local Data.”

The BLS Local Data app is ideal for customers who want to know more about local labor markets, such as jobseekers and economic and workforce development professionals. You can search using your current location, a zip code, or a location name to find relevant data quickly without having to navigate through the huge BLS database. With one click, you can find data for states, metro areas, or counties.

Using the BLS API, the app quickly presents local data and national comparisons for unemployment rates, employment, and wages.

In the coming months, look for more features and data in the app. We’re already working on future releases that will include industry and occupation drilldowns and comparisons between local areas.

Check out the app and explore the wealth of local labor market data produced by BLS! And don’t worry, Android users! An Android version of the app will be available in the future.

iPhone screen image for BLS Local Data app

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!

Celebrating Our Teachers on World Teachers’ Day!

Teachers of America (and the world), we celebrate you! To commemorate World Teachers’ Day on October 5, I want to share some data about today’s teachers and reflect back on how my own teachers influenced me on my path to become the Acting Commissioner of the Bureau of Labor Statistics. We’ll also include quotes from some amazing teachers on what inspires them to teach.

I love seeing my students grow and the excitement in their eyes when they’re learning. Adrienne Davenport, Preschool teacher, Portland, Oregon

I always enjoyed math class, although college-level calculus proved to be a challenge. One of my favorite teachers taught me both geometry and calculus at Wilbur Cross High School in New Haven, Connecticut. (Home of the Governors!) What I mostly remember was how patient she was with everyone in the class. She wanted everyone to succeed and went out of her way to make everyone feel special. Hers was the last class of the day, and we’d often stay late just to soak up a little more calculus. I guess geek-dom starts early.

Math is something I like and it’s rewarding for me to be able to show students that math isn’t scary and that they’re smart enough to do it. Nikita Midamba, Math teacher, Philadelphia, Pennsylvania

I’m not sure I’d ever heard of economics or statistics back in high school, and I certainly had never heard of the Bureau of Labor Statistics. But I had a good foundation in math, which I put to use every day. I even got pretty good at using a slide rule (kids, you can search for it on the Internet). But that’s a story for another day.

I love teaching for a lot of reasons. Wanting my students to have more access to opportunities in life is what keeps pushing me. Lydia Shelly, High school math teacher, Glendale, Arizona

Oh, economics. I guess I stumbled onto that in college, and was fortunate to have great professors and interesting topics like labor economics, urban economics, economic history, and even Soviet economics. But the one I remember most fondly was “Economics of the Arts,” which explored movies, theater, music, museums, and more. No wonder I came to work in a city brimming with the arts.

I love teaching, especially beginners. When you see students finally connect with a dance move they’ve been trying for weeks, they get so excited. That’s rewarding. Stephanie Yezek-Jolivet, Dance teacher

Enough of me reminiscing. Now let’s get to the facts. I’m happy to report BLS has lots of data about teachers. Table 1 shows employment, wages, and projected growth for a few teacher categories. Links go to the Occupational Outlook Handbook, which provides career information on duties, education and training, pay, and outlook for hundreds of occupations, including, of course, teachers!

Table 1: Employment, projected outlook, and wages for teachers
Occupation Employment, 2016 Employment growth, projected 2016–26 (percent) Employment change, projected 2016–26 Median annual wage, May 2017
Preschool teachers 478,500 10% (Faster than average) 50,100 $28,990
Kindergarten and elementary school teachers 1,565,300 7% (As fast as average) 116,300 $56,900
Middle school teachers 630,300 8% (As fast as average) 47,300 $57,720
High school teachers 1,018,700 8% (As fast as average) 76,800 $59,170
Special education teachers 439,300 8% (As fast as average) 33,300 $58,980
Career and technical education teachers 219,400 4% (Slower than average) 7,700 $55,240
Postsecondary teachers 1,314,400 15% (Much faster than average) 197,800 $76,000
Source: U.S. Bureau of Labor Statistics, Employment Projections program and Occupational Employment Statistics survey.

I’ve saved the best for last! Time to drill down and look at some local data. Using data from our Occupational Employment Statistics program, let’s look at the Secondary School Teachers page as an example. Scroll down the page and you will see six maps and charts, which include state and metropolitan area data for employment, concentration of jobs and average wages of secondary school teachers. To highlight some of the data:

  • Where is high school teacher employment?
    • Texas has the highest employment of secondary school teachers (113,120) with California coming in second (107,680).
    • Wyoming is the state with the lowest number of high school teachers (1,860) and Vermont has the second lowest number (2,120).
    • New York-Jersey City-White Plains, New York-New Jersey, Metropolitan area has the most employment (42,350).
  • How do wages differ?
    • Average annual wages of secondary school teachers ranged from the lowest in Oklahoma ($41,880) and South Dakota ($41,980) to the highest in Alaska ($85,420) and New York ($83,360).
    • The highest paid area for secondary school teachers is Nassau County-Suffolk County, New York, Metropolitan Division with an average annual wage of $101,110. The lowest paid area for secondary school teachers is Sierra Vista-Douglas, Arizona, at $39,590.
  • Where are the highest and lowest concentrations of secondary school teacher jobs?
    • If you look at the employment per thousand jobs, the state of Missouri has the highest number (9.9 teacher jobs for every 1,000 jobs), with Maine (9.6), Texas (9.5) and Ohio (9.4) close behind.
    • On the low end of the scale are Nevada (4.4 teacher jobs for every 1,000 jobs), Washington (4.5) and the District of Columbia (4.6).

To learn more about teacher data available from the Occupational Employment Statistics program, see Education, Training, and Library Occupation Profiles. For a list of all industries and occupations, see the Create Customized Tables function.

Want more information?

Whatever you do in life, you may have a teacher (or two!) to thank for guiding you on your path. So join with me and say, “Thank you teachers for all you do!”

BLS Measures Electronically Mediated Work

Are you a ride-share driver using a mobile app (like Uber or Lyft) to find customers? Maybe you do household chores or yardwork for others by finding short-term jobs through a website (such as TaskRabbit or Handy) that arranges the payment for your work. Or perhaps you perform online tasks, like taking surveys or adding descriptive keywords to photos or documents through a platform (like Amazon Mechanical Turk or Clickworker). If so, you are an electronically mediated worker. That’s a term BLS uses to identify people who do short jobs or tasks they find through websites or mobile apps that connect them with customers and arrange payment for the tasks. Have you ever wondered how many people do this kind of work?

BLS decided to find out. In the May 2017 Contingent Worker Supplement to the Current Population Survey, we asked people four new questions designed to measure electronically mediated employment.

Measuring electronically mediated work is difficult

After studying respondents’ answers to the new questions and other information we collected about them, we realized the new questions didn’t work as intended. Most people who responded “yes” to the questions clearly had not found their work through a website or app. For example, a vice president of a major bank, a local police officer, and a surgeon at a large hospital all said they had done electronically mediated work on their main job. Many people seemed to think we were asking whether they used a computer or mobile app on their job. That could apply to many jobs that aren’t electronically mediated.

But it wasn’t all for naught. After extensive evaluation, we concluded we could use the other information in the survey about respondents’ jobs to identify and recode erroneous answers. That allowed us to produce meaningful estimates of electronically mediated employment.

So, who does electronically mediated work?

Based on our recoded data, we found that 1.6 million people did electronically mediated work in May 2017. These workers accounted for 1.0 percent of total employment. Compared with workers overall, electronically mediated workers were more likely to be ages 25 to 54 and less likely to be age 55 or older. Electronically mediated workers also were slightly more likely to be Black, and slightly less likely to be White, than workers in general. In addition, electronically mediated workers were more likely than workers overall to work part time (28 percent versus 18 percent).

Workers in the transportation and utilities industry were the most likely to have done electronically mediated work, with 5 percent of workers in this industry having done such work. Self-employed workers were more likely than wage and salary workers to do electronically mediated work (4 percent versus 1 percent).

What’s next?

We currently don’t have plans to collect information on electronically mediated work again. And even if we did, we wouldn’t want to use the same four questions. At the least, we would need to substantially revise the questions so they are easier for people to understand and answer correctly.

Taking a broader look, we are working with the Committee on National Statistics to learn more about what we should measure if we field the survey again. The committee is a federally supported independent organization whose mission is to improve the statistical methods and information on which public policies are based.

How can I get more information?

The data are available on our website, along with an article that details how we developed the questions, evaluated the responses, recoded erroneous answers, and analyzed the final estimates.

If you have a specific question, you might find it in our Frequently Asked Questions. Or you can contact our staff.