New App for Career Information Now Available

Icon for CareerInfo app

BLS has partnered with the U.S. Department of Labor’s Office of the Chief Information Officer to develop the CareerInfo app that is now available from the Apple App Store and Google Play. CareerInfo presents information from the Occupational Outlook Handbook, the most popular BLS resource for career information.

The CareerInfo app helps you find data and information about employment, pay, job outlook, how to become one, and more for hundreds of detailed occupations. You can browse by occupational groups and titles or search by occupation or keywords. Within occupational groups, the app allows you to sort by occupation title, projected growth, and typical education or median pay.

Future updates will add features that will let you personalize the app by filtering searches and by “liking,” saving, viewing, and comparing favorites.

Check out the new CareerInfo app and explore the occupational information and data produced by BLS. You’ll be glad you did!

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.

Let’s Celebrate the Productive U.S. Workforce

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

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

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

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

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

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

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

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

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

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

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

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

Labor Day 2019 Fast Facts

I have been Commissioner of Labor Statistics for 5 months now, and I continue to be amazed by the range and quality of data we publish about the U.S. labor market and the well-being of American workers. As we like to say at BLS, we really do have a stat for that! We won’t rest on what we have done, however. We continue to strive for more data and better data to help workers, jobseekers, students, businesses, and policymakers make informed decisions. Labor Day is a good time to reflect on where we are. This year is the 125th anniversary of celebrating Labor Day as a national holiday. Before you set out to enjoy the long holiday weekend, take a moment to look at some fast facts we’ve compiled on the current picture of our labor market.

Working

Working or Looking for Work

  • The civilian labor force participation rate—the share of the population working or looking for work—was 63.0 percent in July 2019. The rate had trended down from the 2000s through the early 2010s, but it has remained fairly steady since 2014.

Not Working

  • The unemployment rate was 3.7 percent in July. In April and May, the rate hit its lowest point, 3.6 percent, since 1969.
  • In July, there were 1.2 million long-term unemployed (those jobless for 27 weeks or more). This represented 19.2 percent of the unemployed, down from a peak of 45.5 percent in April 2010 but still above the 16-percent share in late 2006.
  • Among the major worker groups, the unemployment rate for teenagers was 12.8 percent in July 2019, while the rates were 3.4 percent for both adult women and adult men. The unemployment rate was 6.0 percent for Blacks or African Americans, 4.5 percent for Hispanics or Latinos, 2.8 percent for Asians, and 3.3 percent for Whites.

Job Openings

Pay and Benefits

  • Average weekly earnings rose by 2.6 percent from July 2018 to July 2019. After adjusting for inflation in consumer prices, real average weekly earnings were up 0.8 percent during this period.
  • Civilian compensation (wage and benefit) costs increased 2.7 percent in June 2019 from a year earlier. After adjusting for inflation, real compensation costs rose 1.1 percent over the year.
  • Paid leave benefits are available to most private industry workers. The access rates in March 2018 were 71 percent for sick leave, 77 percent for vacation, and 78 percent for holidays.
  • About 91 percent of civilian workers with access to paid holidays receive Labor Day as a paid holiday.
  • In March 2018, civilian workers with employer-provided medical plans paid 20 percent of the cost of medical care premiums for single coverage and 32 percent for family coverage.

Productivity

  • Labor productivity—output per hour worked—in the U.S. nonfarm business sector grew 1.8 percent from the second quarter of 2018 to the second quarter of 2019.
  • Some industries had much faster growth in 2018, including electronic shopping and mail-order houses (10.6 percent) and wireless telecommunications carriers (10.1 percent).
  • Multifactor productivity in the private nonfarm business sector rose 1.0 percent in 2018. That growth is 0.2 percentage point higher than the average annual rate of 0.8 percent from 1987 to 2018.

Safety and Health

Unionization

  • The union membership rate—the percent of wage and salary workers who were members of unions—was 10.5 percent in 2018, down by 0.2 percentage point from 2017. In 1983, the first year for which comparable union data are available, the union membership rate was 20.1 percent.

Work Stoppages

  • In the first 7 months of 2019, there have been 307,500 workers involved in major work stoppages that began this year. (Major work stoppages are strikes or lockouts that involve 1,000 or more workers and last one full shift or longer.) For all of 2018, there were 485,200 workers involved in major work stoppages, the largest number since 1986, when about 533,100 workers were involved.
  • There have been 15 work stoppages beginning in 2019. For all of 2018, 20 work stoppages began during the year.

Education

  • Occupations that typically require a bachelor’s degree for entry made up 22 percent of employment in 2018. This educational category includes registered nurses, teachers at the kindergarten through secondary levels, and many management, business and financial operations, computer, and engineering occupations.
  • For 18 of the 30 occupations projected to grow the fastest between 2016 and 2026, some postsecondary education is typically required for entry. Be sure to check out our updated employment projections, covering 2018 to 2028, that we will publish September 4!

From an American worker’s first job to retirement and everything in between, BLS has a stat for that! Want to learn more? Follow us on Twitter @BLS_gov.

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.