Topic Archives: Productivity

New State Data on Labor Productivity and Job Openings and Labor Turnover

While international trade has become increasingly important to our economy over the past 60 years, U.S. households and businesses continue to rely primarily on local markets for most goods and services. The products we create come from all over our country. Workers, businesses, and policymakers care deeply about the economy in our own backyards. That’s why BLS recently began publishing new data on labor productivity by state and, separately, on job openings and labor turnover by state.

State labor productivity

Our measures of labor productivity for states are still experimental, meaning we’re still assessing them and considering ways to improve them. These measures cover the private nonfarm sector for all 50 states and the District of Columbia from 2007 to 2017. They show that labor productivity growth varies a lot from state to state. From 2007 to 2017, labor productivity changes ranged from a gain of 3.1 percent per year in North Dakota to a loss of 0.7 percent per year in Louisiana. In 2017, labor productivity grew fastest in Montana (2.0 percent), West Virginia (1.9 percent), California (1.8 percent), and Hawaii (1.7 percent). You can get the complete dataset from our state labor productivity page.

U.S. map showing productivity growth in the private nonfarm sector in each state from 2007 to 2017

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

We construct these state measures from data published by several BLS programs and by our colleagues at the Bureau of Economic Analysis. A recent Monthly Labor Review article, “BLS publishes experimental state-level labor productivity measures,” explains the data and the methods for putting them all together. The article also highlights how you might use these new state data. We’re happy to have your feedback on these new measures. Just send us an email.

State job openings and labor turnover

We also have new data on job openings, hiring, and separations by state. Data from the Job Openings and Labor Turnover Survey are widely used by economic policymakers and others who want to understand the job flows that lead to net changes in employment. We have these data back to December 2000 and update them every month for the nation and the four broad census regions. Now we have them for all states and the District of Columbia too. These state estimates are available from February 2001 through December 2018 for the total nonfarm sector.

Many of you have told us you want more geographic details about job openings and turnover. To make sense of data on job openings, for example, it helps to know where the jobs are. The survey sample size is designed to estimate job openings and turnover for major industries only at the national and regional levels. For several years we have researched ways to produce model-assisted estimates for states. As with the state productivity data, these estimates are experimental. We plan to update the state estimates each quarter while we assess your feedback on the models and the usefulness of the data. We encourage you to send us your comments.

But wait, there’s more! We’ve updated the BLS Local Data App!

In previous blog posts, we’ve told you about our mobile app for customers who want to know more about local labor markets. This app now includes employment and wage data for detailed industries and occupations. (It doesn’t yet have the new data on state productivity, job openings, and turnover.)

Interested in local data for a particular industry or occupation? The latest version allows you to quickly search or use the built-in industry and occupational lists. Want to know which industry employs the most workers in your area or which occupation pays the highest? The updated app allows you to sort the employment and wage data across groups of industries and occupations. You can still find data on unemployment rates and total employment. You also can find your state, metro area, or county by searching for a zip code or using your device’s current location.

These new data and features result from the continued partnership between BLS and the U.S. Department of Labor’s Office of the Chief Information Officer. Be on the lookout for more new features to be added in future releases.

Download the BLS Local Data app from the App Store or Google Play today!

Annual percent change in labor productivity in the private nonfarm sector, 2007–17
State Annual percent change
North Dakota 3.1
California 1.7
Oregon 1.7
Washington 1.7
Colorado 1.6
Oklahoma 1.6
Maryland 1.5
Montana 1.5
Pennsylvania 1.5
Massachusetts 1.4
New Mexico 1.4
Vermont 1.4
Idaho 1.3
Kansas 1.3
Nebraska 1.1
New Hampshire 1.1
South Carolina 1.1
Tennessee 1.1
Texas 1.1
West Virginia 1.1
Alabama 1.0
Hawaii 1.0
Kentucky 1.0
Minnesota 1.0
New York 1.0
Rhode Island 1.0
South Dakota 1.0
Virginia 1.0
Georgia 0.9
Arkansas 0.8
Missouri 0.8
Ohio 0.8
Utah 0.8
Illinois 0.7
North Carolina 0.7
Delaware 0.6
Florida 0.6
Iowa 0.6
Indiana 0.5
Mississippi 0.5
New Jersey 0.5
Wisconsin 0.5
Alaska 0.4
Arizona 0.4
District of Columbia 0.4
Michigan 0.4
Maine 0.3
Nevada 0.3
Wyoming 0.1
Connecticut -0.5
Louisiana -0.7

Wage Information Yesterday, Today, and Tomorrow

On April 16, BLS reported that median weekly earnings for full-time wage and salary workers rose 2.7 percent over the year.

On April 30, BLS reported that the Employment Cost Index for wages of private industry workers rose 3.0 percent over the year.

On May 2, BLS reported that hourly compensation in the nonfarm business sector rose 2.5 percent over the year.

On May 3, BLS reported that average hourly earnings for private industry workers rose 3.2 percent over the year.

What’s going on here? Why so much wage information? And which one is RIGHT?

At BLS, we get questions like this all the time, and the answer is usually “it depends.” There is no one answer that fits every question on wages; there are just different answers depending on what you want to measure. People come to BLS looking for all kinds of answers, and we want to provide as much information as we can. Thus, we have many measures of wages (and other forms of compensation) — a dozen, to be exact.

Do you want to know about wages for an industry? An occupation? By location? For men and women? Based on education? Adjusted for inflation? Including benefits? How wages relate to spending patterns? How wages relate to worker productivity? BLS has it all, and more.

We have so much wage information that even we get confused. So we developed a tool to make the dozen wage series a little easier to understand. It’s an interactive guide that lists all 12 data sources and 32 key details about each of those sources, like how often it is available.

I can hear you now — that’s 384 pieces of information (12 x 32). I’m just looking for one piece of information, not almost 400. And how do you fit all that information on one page, anyway?

The interactive guide limits the display to 3 sources at a time — you pick the sources you want to see.

A table showing 3 BLS sources of compensation information and data characteristics available from those sources.

Or you can pick one characteristic, like “measures available by occupation” and get an answer for all 12 data sources.

A table showing the occupational information available from several BLS data sources on compensation.

This tool is on the BLS beta site. We want you to give it a try and provide feedback. Check it out and leave us a comment. Want to know even more? Watch this video that helps make sense of BLS wage information.

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!

The Griswold Family Vacation through the Lens of BLS Data

We have a guest blogger for this edition of Commissioner’s Corner. Joy Langston is a budget analyst at the U.S. Bureau of Labor Statistics. She enjoys watching classic movies when she’s not working.

As summer wraps up, let’s slow the transition into cooler weather to explore the dream American summer vacation of the Griswold family. America first met the Griswolds in the cult classic National Lampoon’s Vacation. We’ll relive their vacation through the lens of our gold-standard data. Clark Griswold, the easygoing and optimistic patriarch of the family, wants a fun vacation with his wife, Ellen, and adolescent son and daughter, Rusty and Audrey, before the kids grow up. For the past 15 years, Clark has worked as a food scientist creating “new and better food additives.” Data from the 2017 Employee Benefits Survey show that after 10 years of service, full-time workers like Clark receive on average 18 days of vacation, or almost 4 weeks.

Since he has the time, Clark decides to lead the family on a cross-country expedition from the Chicago suburbs to Walley World — “America’s Favorite Family Fun Park” in Southern California. Ellen agrees to the destination but wants to fly, as it will be less of a hassle. However, data from the Consumer Expenditure Surveys suggest driving may not be a bad idea. The average amount a household spent on vacations was $2,076 in 2017, with $684 for transportation costs, so flying from Chicago to Southern California was likely not in the Griswolds’ budget. To jumpstart this trip, Clark ordered the new “Antarctic Blue Super Sports Wagon with the Rally Fun Pack” from the local car dealership. He is scammed into buying the far less appealing, but now iconic, metallic pea, wood grained trimmed station wagon instead. Nevertheless, Clark is determined to make this the best family vacation ever.

Eventually, Ellen gives in to her husband’s enthusiasm and the Griswolds embark on their adventure, but not before stopping for their first tank of gas. You may remember that Clark struggled to find the gas tank, which was ridiculously located under the hood, by the engine, on the passenger’s side. The average household spent $109 in 2017 on gas for out-of-town trips and $1,797 for all uses. In July 2018, the national average price of gas was $2.93 per gallon, according to the Consumer Price Index. Although America has traded in station wagons for SUVs, neither are gas efficient and the Griswolds probably had to fuel up frequently on the 2,460-mile drive.

The family’s first misstep includes taking the wrong exit in St. Louis, Missouri, where they lose a couple of car parts while stopping to ask for directions in a questionable neighborhood. Despite this portrayal of St. Louis, the Occupational Employment Statistics data show this metro area had about 1.4 million jobs in 2017. About 16 percent of them were in office and administrative support occupations, with an average wage of $37,720 per year. Another 10 percent of jobs were in sales and related occupations, and 7 percent were in healthcare practitioners and technical occupations.

Driving through Kansas, they stop in Dodge City to experience life in the Wild West and order drinks in a saloon. According to the Current Employment Statistics survey, stops like these, including historical sites and other historical institutions, provide an average of 69,000 jobs from May to August nationwide.

The Griswolds make it to Coolidge, Kansas, where Ellen’s cousins live. The cousins pressure Clark and Ellen into dropping off cantankerous Aunt Edna — and her equally feisty dog — at her son’s home in Phoenix, Arizona. According to the American Time Use Survey Americans spend an average of 39 minutes a day — or about 237 hours a year — socializing and communicating in person. The survey also shows that Americans spend an average 4 minutes a day caring for and helping nonhousehold adults. The Griswold family gets a concentrated dose of this social activity by adding Aunt Edna to their road trip party.

For lunch, they stop off at rest stop to enjoy some homemade sandwiches. The average American household spent $56 in 2017 on food prepared for out-of-town trips, and $3,365 on food away from home (including fast food establishments and full service restaurants). The Griswolds’ enjoyment is cut short when they realize there is more to their soggy baloney cheese sandwiches than they bargained for. As it turns out, Aunt Edna’s spiteful dog used the picnic basket as a bathroom during the car ride. If you’re driving with a pet and want to avoid this mishap, Kansas has more than 4,600 restaurants and eating places to choose from, according to the Quarterly Census of Employment and Wages.

They spend the night in one of Colorado’s 98 campgrounds in three large, smelly tents. Despite their positive attitudes the next morning, the Griswolds meet with more misfortunes, including being pulled over by a state trooper, Ellen losing her bag with the credit cards, quarrels over their dwindling cash supply, and crashing in the Arizona desert while trying to find a shortcut to the Grand Canyon. After they are rescued and towed to a service station, Clark haggles with the local mechanic, who doubles as the local sheriff, and takes the rest of Clark’s cash. The average American household spent $954 on car maintenance and repairs in 2017, although costs usually are spread throughout the year and not on vacation misadventures.

By the time they drop off Aunt Edna in Phoenix, Ellen and the kids are begging Clark to buy plane tickets to go back home. However, Clark’s enthusiasm hasn’t waned, and he declares this road trip a pilgrimage.

When they finally arrive at Walley World, they discover it is closed for the next two weeks for repairs. Exasperated, Clark demands the security guard open the gates and let the family into the park. After a couple rollercoaster rides, the SWAT team and owner of the park, Roy Walley, arrive. As the police put handcuffs on Clark’s family, Clark begs Roy not to press charges. Clark persuades Roy not only to drop the charges but to allow the family to stay and enjoy all the rides! Americans do love their theme parks. There were nearly 1,000 theme parks in the United States in 2017, with 87 of them in California. These parks provided 185,000 jobs nationwide. This industry increased its labor productivity 13.7 percent in 2017, as theme parks reported higher output while hours worked by employees decreased.

Over the course of their trip, the Griswolds share a number of experiences, many of which either hit a little too close to home, or we hope to never experience for ourselves. After a long and tiresome trip, we hope Ellen finally has her way and Clark doesn’t force the Griswolds to spend another two weeks driving back to Chicago, which would deplete all his vacation days! This classic summer movie shows that BLS really does have a stat for that!