Prestigious Award for BLS and U.S. Census Bureau Researchers

There are so many things I love about being Commissioner of Labor Statistics. The part of the job I enjoy the most is working every day with so many talented, dedicated, hard-working people. I am especially pleased when BLS staff members receive recognition for their good work. We recently celebrated one of those occasions.

Thesia Garner and Kathleen Short holding their Roger Herriot Award certificates.

Thesia Garner and Kathleen Short

Thesia Garner of the Office of Prices and Living Conditions and Kathleen Short of the U.S. Census Bureau received the 2016 Roger Herriot Award for Innovation in Federal Statistics at the 2016 Joint Statistical Meetings. The award recognizes the important and extensive research Thesia and Kathleen have done together over more than 20 years to develop better measures of poverty in the United States. Their most recent work focused on producing the Supplemental Poverty Measure. This measure provides insight about the effects of public policies and programs on reducing poverty. Herriot Award winners are chosen by a committee of the American Statistical Association and the Washington Statistical Society. Please join me in congratulating Thesia and Kathleen for this recognition and for their research into improving the ways we measure economic hardship.

Why This Counts: Productivity and Its Impact on Our Lives

How can we achieve a higher standard of living? One way might simply be to work more, trading some free time for more income. Although working more will increase how much we can produce and purchase, are we better off? Not necessarily. Only if we increase our efficiency—by producing more goods and services without increasing the number of hours we work—can we be sure to increase our standard of living.

That’s why BLS produces labor productivity statistics every quarter that tell us how well we are improving our economic efficiency. These measures compare the amount of goods and services we produce with the number of hours we work. How can we can improve labor productivity? There are many ways. We can use more and newer machinery and equipment. We can develop new technologies that streamline production. We can improve organization and communication in the workplace and manage people more effectively. Or, we can increase worker skills through education or job training.

So, how much has U.S. labor productivity improved over the years? Compared to 1947, we now produce 330 percent more goods and services per hour of work. On average, thanks to advances in technology, education, management, and so on, you can do in 15 minutes what your grandparents or great grandparents needed more than an hour to do in 1947. This is a substantial increase, and we can see it in the many improvements in living standards since World War II.

Productivity growth in recent years hasn’t been as strong, however. It may seem surprising, given all the new technologies and products in recent years, but we are now living through one of the lowest productivity-growth periods ever recorded. Since the Great Recession of 2007–09 began in the fourth quarter of 2007, labor productivity has grown just 1.0 percent per year. That is less than half the long-term average rate of 2.2 percent since 1947. Although the U.S. economy has been experiencing slow productivity growth since 2007, some industries have been doing well. For instance, the wireless telecommunication carrier industry has had annual labor productivity growth of over 15.0 percent since the beginning of the Great Recession.

Labor productivity growth in the nonfarm business sector is lower in the current business cycle than during any of the previous ten business cycles. Chart 1 shows average annual labor productivity growth during business cycles since World War II.

Chart 1. Average annual percent change in labor productivity in the nonfarm business sector during business cycles

Multifactor productivity—which accounts for the use of machinery, equipment, and other capital, in addition to labor—has also increased more slowly over the current business cycle; it has grown 0.4 percent per year during the 2007–15 period, compared to its long-term rate of 0.9 percent per year since 1987.

Historically, productivity growth has led to gains in compensation for workers, greater profits for firms, and more tax revenue for governments. Compensation, which includes pay and benefits, has not always risen as fast as productivity, however. (See chart 2.) The difference between labor productivity gains and real hourly compensation growth is often called the “wage gap.” Real hourly compensation growth tracked labor productivity growth more closely before the 1970s. Since then, growth in real hourly compensation has lagged behind gains in productivity, widening the gap considerably. Since the start of the Great Recession in the fourth quarter of 2007, real hourly compensation has grown by only 0.6 percent per year; that’s less than half the long-term average of 1.6 percent per year.

Chart 2. Labor productivity and real hourly compensation in the nonfarm business sector, 1947–2015

Measures of gross domestic product and employment tell us how the U.S. economy is doing in producing goods and services and creating jobs. Measures of productivity link what our economy produces and the labor and capital used to produce it. Labor productivity is an important statistic to track because gains in productivity are essential to improving our lives and the well-being of our nation. That’s what Nobel Prize-winning economist Paul Krugman meant when he noted, “Productivity isn’t everything, but in the long-run it’s almost everything.”

You can stay up to date on productivity trends and other economic news by signing up for our email alerts or following us on Twitter.

Why This Counts: Working Together to Keep Workers Safe on the Road

As summer begins, many of us start thinking about vacation travel. Whenever my family and I go somewhere in a car, I usually don’t think of it as risky. Indeed, over the past couple of decades, traffic safety has improved markedly. Beginning in 2011, traffic incidents were no longer the leading cause of death from injury in the United States, according to the National Center for Health Statistics. Despite this progress, BLS data show that transportation incidents continue to be the leading cause of fatal work-related injuries in the United States.

As with so many other risks, we need good data to reduce work-related traffic deaths. Today I’ll highlight a new multi-agency project that links existing datasets to produce rich new insights to help keep employees safer on the road.

fatal-work-injuries-2014

Safety professionals have long considered the BLS Census of Fatal Occupational Injuries to be the most complete, accurate, and well-documented count of all types of fatal work injuries. We use a broad range of documents to identify fatal injuries and verify they are work related. We can identify work-related cases that may not be obvious. One example is a person traveling for work but not in a work vehicle. Another example is a commute to work in which the person was also running a work-related errand along the way. For all of these cases, we also collect information on the nature of the injury and the demographic and employment characteristics of the person who died.

The National Highway Traffic Safety Administration is another great source of traffic safety data. Their Fatality Analysis Recording System (FARS) has rich detail on crashes. FARS captures complete data for all vehicles involved in a crash and their occupants. The BLS data, by comparison, only include the vehicle of the person who died and the vehicle or other object it crashed into. The FARS data tell us more about the risks involved in the incident, including road conditions, use of safety equipment, and even driver behavior such as cell phone use.

While research with both datasets has helped to improve traffic safety, neither dataset has complete detail. Over the last several years, BLS has been collaborating with the National Highway Traffic Safety Administration and the National Institute for Occupational Safety and Health to merge the data.

The combined dataset provides the accident detail of FARS with the BLS information on the people who died and their jobs. For 2010, researchers matched 91 percent of the 1,044 roadway death cases from the BLS data to a FARS case. BLS researchers will continue to work with their colleagues in the other agencies to analyze the data and gain new safety insights.

The research team published an article recently in Accident Analysis and Prevention to explain how they matched the data from the two sources. The team also has begun a second article to analyze 3 years of the combined data. This project has given us the most detailed and complete look at fatal work-related traffic crashes in the United States. We are excited to gain these new insights into traffic safety. It makes me proud to see top-notch researchers from different agencies work together to understand and solve some of our nation’s most challenging problems. It’s another example of how we strive to use your data dollars more effectively to produce gold-standard information.

Entrepreneurship facts: Announcing new research data on job creation and destruction by firm age and size

I’m delighted to announce that we now have new research data on job gains and losses by firm age and size across industries and states.

For many years, policymakers, economists, and others have debated whether small or large firms create more jobs. Our Business Employment Dynamics program, which measures gross job gains and losses to help us understand net employment changes, informs that debate with data on firm size. A related question is whether startups or older establishments create more jobs. Again, BLS has a stat for that. We have data on employment and business survival rates by the age of the establishment.

While it’s useful to know the age of an establishment—that is, a single location of a business—for some questions, we need to know the age of the firm. A firm may include several or even many establishments. To understand entrepreneurship in particular, we want to know how both the age and size of firms affect job gains, job losses, and employment growth.

With these new data we can answer many interesting questions, including:

  • How much do older firms contribute to job growth? Firms 10 years or older created 800,000 jobs, or 29 percent of the total 2.7 million net employment gain in the year ending March 2015. See the chart below.
  • How much do startup firms contribute to job growth? In the year ending March 2015, startup firms—firms less than 1 year old—created 1.7 million jobs or 60 percent of total employment growth. More than half these jobs were from firms with fewer than 10 employees.
  • How does the age or size of the firm affect the rate of business closures? In 2015, 788,000 establishments closed. Of these, 55 percent were from firms 10 years or older; 16 percent were from firms 5 to 9 years old; and 28 percent were from firms less than 4 years old. Of the establishments that closed from March 2014 to March 2015, 91,000 of them, or 12 percent of the total, had 500 or more employees.
  • Which firm-age group accounted for most job losses during the last two recessions? Firms 10 years or older lost the most jobs during both recessions. Again, see the chart below.

net-job-changes-by-firm-age

The new research data measure annual gross job gains and gross job losses by firm age and size from March of one year to March of the next. We get the data on firms from the Quarterly Census of Employment and Wages by linking individual establishments over time. Besides firm age and size, we also measure establishment age and size. We have two methods to examine size. One method compares the current size of firms or establishments with the size at the beginning of the year (the base-sizing method). The other method compares the current size with the average size over the year (the average-sizing method).

I really want to know how you like these new data and what we can do to make them more useful. I invite you to explore the data and share your comments. Your feedback will help us develop the dataset and possibly move it into our regular production. Please write your comments below, or you can email the Business Employment Dynamics staff.

Why This Counts: New Timely Data on Professional Certifications and Licenses

To data nerds like us at BLS and many of our data users, there’s little more satisfying than releasing an important set of new, needed information. Today is such a day!

When applying for a job, people often point to their education to show they have the necessary skills to do the job. But many jobs also require professional certifications or licenses. While BLS has published statistics on labor force status by level of education for a long time, nondegree credentials, such as professional certifications or licenses, have received less attention in national surveys. That is, until today—BLS now has a stat for that, too!

Professional certifications and licenses are nondegree credentials that show a person has the skill or knowledge needed to do a specific job. These include credentials like commercial driver’s licenses, teaching licenses, medical licenses, information technology certifications, and many others. They are important. Just take a moment to think about yourself and your family members. Chances are that many of you have such credentials. Indeed, three of my four siblings do: as an accountant, a nurse, and a hairdresser.

To learn more about who has professional certifications and licenses and how they fare in the labor market, we’ve added new questions to the Current Population Survey. That’s the monthly survey of about 60,000 households that we use to measure the U.S. labor force and unemployment rate.

From these new data, we find that 25.5 percent of employed people held a currently active certification or license in 2015.

These credentials are more common in certain occupations. For example, a high proportion of workers in the healthcare field had certifications or licenses.

We also find that employed people with a college degree are more likely to have professional certifications and licenses than workers with less formal education.

percent-with-certification-or-license

Additionally, these new data show that having a certification or license is associated with higher earnings among people with similar levels of education. For example, among workers age 25 and older with some college or an associate degree, people who held a certification or license earned a median of $825 per week. That was 11 percent higher than the earnings of people who did not have these credentials ($742).

earnings-for-certification-or-license

Looking ahead, collecting these data regularly will allow us to see whether the percentage of people with certifications and licenses changes over time. We will also be able to track measures of labor market success for people who hold a certification or license, compared with people who don’t hold these credentials. These new data and analyses will help guide key personal, business, and policy decisions about professional credentials.

The questions in the survey are based on the work of the Interagency Working Group on Expanded Measures of Enrollment and Attainment. This group played an important role in developing concepts and survey questions to measure alternative credentials.

BLS is committed to providing essential labor market information to support public and private decision making. As Commissioner, I am proud of the work done by BLS and others to shed light on this aspect of the labor market. Don’t forget to check out all of our new data on professional certifications and licenses.