Update on the 2016 President’s Budget for the Bureau of Labor Statistics

The links below provide an overview of the 2016 budget request for the Bureau of Labor Statistics:

While the 2016 budget process continues, the Office of Management and Budget has recently expressed concerns about the level of funding being considered by the Senate:

  • Office of Management and Budget Letter to Senate Appropriators (July 9, 2015)
    • Relevant BLS Text on Page 5: “The bill also includes numerous other problematic reductions that would undermine priorities ranging from generation of critical economic data to Medicare program administration. For example, the bill significantly underfunds the Bureau of Labor Statistics (BLS). BLS produces vital data that support U.S. economic growth, including valuable and highly visible sources of data on the Nation’s economy and workforce that are regularly used by the Federal Reserve; Federal, State, and local government programs; businesses; jobseekers; and many other decision-makers. Under the bill BLS would have difficulty continuing to operate its core programs and would need to permanently eliminate surveys as a result.”

The links below are to the most recent information available on the Labor, Health and Human Services, Education, and Related Agencies Appropriation for 2016:

BLS will provide periodic updates on the 2016 budget as the process continues.

Why This Counts: How BLS data support the goals of the Americans with Disabilities Act

This July marks the 25th anniversary of the Americans with Disabilities Act (ADA). The ADA prohibits discrimination against people with disabilities. Its goal is to ensure that people with disabilities have equal opportunities in employment, government services, transportation, and other aspects of their lives. The law prohibits employment discrimination and requires employers to make reasonable accommodations for workers with disabilities.

Today I want to tell you about the role BLS data play in supporting the goals of the ADA—one of our nation’s proudest civil rights triumphs.

The renowned physicist Lord Kelvin wrote more than a century ago, “If you cannot measure it, you cannot improve it.” When the ADA became law, we had no reliable measure of the number of people with disabilities who were working or seeking work. To track our nation’s progress, we needed statistics.

In 1998, President Clinton signed Executive Order 13078 to reduce barriers to employment for people with disabilities. The order required BLS, the U.S. Census Bureau, and other agencies to “design and implement a statistically reliable and accurate method to measure the employment rate of adults with disabilities.” The order also required us to publish employment data on people with disabilities as often as possible. BLS joined with other federal agencies and academic researchers to develop a short set of survey questions to identify people with disabilities.

Extensive research and testing showed the challenges of counting the number of people with disabilities using only a few survey questions. The team persevered, however, and in June 2008, we added six new questions to the Current Population Survey (CPS) to identify people with disabilities. The CPS is the monthly survey of about 60,000 households that we use to measure the U.S. labor force and unemployment rate. Adding these questions allowed us for the first time to track the employment status of people with disabilities. You can learn more in our Frequently Asked Questions about the disability data we collect.

The charts below show a few of the facts we have learned about the labor force status of people with a disability.

In 2014, there were about 29 million people age 16 and older with a disability in the civilian population outside of institutions. (Institutions include skilled-nursing facilities, in-patient hospice facilities, psychiatric hospitals, and prisons.) That was 11.8 percent of the total. We call this the disability rate, the percentage of people in a group who have a disability. Older people are more likely than younger people to have a disability.

chart1

About 8 in 10 people with a disability were not in the labor force in 2014, compared with about 3 in 10 of those with no disability. Many of those with a disability are age 65 and older; older people are less likely to participate in the labor force than people in younger age groups.

chart2

The employment–population ratio—the number employed as a percentage of the population—for people with a disability is much lower than for those with no disability. This is the case across all major race and ethnicity groups.

chart3

The unemployment rate for people with a disability was 12.5 percent in 2014, about twice the rate of 5.9 percent for those with no disability. (The unemployed are people who did not have a job, were available for work, and were actively looking for a job in the 4 weeks preceding the survey.)

chart4

BLS publishes monthly data on people with a disability in Table A-6 of the Employment Situation news release. In addition, we have published an annual news release on the labor force characteristics of people with a disability since 2009.

Many dedicated economists, statisticians, and survey methodologists inside and outside the U.S. government conducted the research to develop questions about disability. Terry McMenamin was one of the key contributors in developing the first questions about people with a disability. Terry was also instrumental in developing, testing, and fielding questions in the May 2012 CPS that gathered even more data on people with a disability. Those questions asked about the labor market problems confronting people with a disability. Tragically, Terry passed away in an automobile accident last fall. Terry was passionate about his work in collecting high-quality labor market data on people with a disability. He was a valued member of the BLS family, and all who worked with him miss him greatly.

BLS is committed to providing essential economic information to support public and private decision making. As Commissioner, I am proud of the work by BLS and others not only in developing measures about disability, but also in supporting the ADA’s goal to promote fairness in labor practices for all Americans.

How does BLS deal with uncertainty in our measures?

I recently spoke in Pittsburgh at the 2015 Policy Summit on Housing, Human Capital, and Inequality. The Federal Reserve Banks of Cleveland, Philadelphia, and Richmond sponsored this event. I spoke on a panel with Professor Charles Manski of Northwestern University and Jeffrey Kling of the Congressional Budget Office about measuring uncertainty in federal statistics. You can watch the full discussion below.

When I speak to groups around the country or write in the Commissioner’s Corner, I always discuss the importance of having good information to make good decisions. Federal, state, and local policymakers use information from BLS, and so do private businesses, nonprofit organizations, and households. But how do the users of our data and analyses know they can rely on BLS information? Our users shouldn’t simply have blind faith. After all, households, businesses, and governments make decisions based on our data, and those decisions can involve a lot of money. Users of statistics need to understand that all measures have limitations. Data are a tool. Just like screwdrivers or spatulas, data have specific uses and different levels of precision. Data users need to choose the right tools for their purpose and use them correctly. Our goal is to measure the true state of the economy, but data users must recognize that all measures of the truth come with some uncertainty.

So what are the sources of uncertainty in our measures? One source is what we call sampling error. Most statistics we publish at BLS come from sample surveys. Sampling error is the uncertainty that results by chance because we collect the information from a sample instead of the full population. Even though we select our samples carefully using scientific methods, the characteristics of a sample still may differ from those of the population. We rely on sample surveys because it is far too expensive to ask questions of all workers or all businesses every time we need new information about the labor market and economy. Fortunately, statisticians have developed tools to measure sampling error. We publish these measures on our website. For example, you can see whether the most recent monthly changes in our measures of the labor force, employment, and unemployment are statistically significant. If we want to reduce sampling error, we can increase the size of our samples. Larger samples cost more money, but our measures of sampling error can help us decide whether the benefit of reducing that source of uncertainty is worth the cost.

Other types of uncertainty are harder to measure. For example, some people and businesses choose not to respond to our surveys. If those who don’t respond have different characteristics from those who respond, it could bias our measures. Even when people and businesses agree to participate in a survey, they might not answer every question or their answers might not be accurate. It’s hard to measure the effects of these challenges in collecting information about the economy. We try to minimize the sources of uncertainty, however. For example, we try to design our surveys to make it easier for people and businesses to respond. We show people and businesses how they benefit from responding. We test our survey questionnaires carefully to make sure they are clear and easy to answer. We seek out other sources of information to supplement our surveys, using what many people call “big data.”

Most of all, we communicate with our data users about the strengths and limitations of our data and the methods we use to compile them. We’re always looking for better, clearer ways to explain our data, and I welcome you to share your ideas.

Why This Counts: How much time do Americans spend working and engaging in other activities outside of work?

The U.S. Bureau of Labor Statistics is best known for our monthly job and inflation reports, but we also publish data on many other topics. My blog series, “Why This Counts,” explains why we conduct our surveys and how people can use BLS data at work and home. I hope this series will help bring BLS data to life—to transform a set of abstract statistics into a snapshot of life in America.

“Time is money,” said Benjamin Franklin in Advice to a Young Tradesman, Written by an Old One. One of the most precious resources each of us has is our time. Indeed, how we use our time can tell you a lot about our priorities, opportunities, and constraints.

Today I am happy to announce our annual news release and 2014 data for the American Time Use Survey (ATUS)—and that we’ve made it easier to compare this year’s results to previous years.

Unlike other BLS surveys that track employment, wages, and prices, the ATUS tracks a less conventional economic resource that we never have enough of: time. The survey compiles data on how much time Americans spend doing paid work, unpaid household work (such as taking care of children or doing household chores), and all the other activities that compose a typical day. With this information, economists, sociologists, and other researchers can examine the different time choices and tradeoffs that people make every day.

ATUS data are behind many of the facts you often see in the news, such as:

  • At what times of day do workers with different occupations work? (25% of workers in protective service occupations, such as firefighters and police, are at work at midnight.)
  • How many hours of sleep do people get at different ages? (For teenagers, 9.8 hours!)
  • How many hours per day do employed parents spend caring for their children? (See chart below.)

time-use-blog-chart

How do we get this key information? The ATUS is the only nationally representative, continuous survey that provides estimates of how, where, and with whom Americans spend their time. In 2014, more than 11,500 respondents completed the survey. The results represent the national population by gender, age, race and ethnicity, and other characteristics. We select the ATUS sample from households that have already responded to another BLS survey, the Current Population Survey. This enriches ATUS data because we already know the demographic characteristics and household composition and don’t have to ask for that information again. Instead, most of the ATUS interview involves asking respondents to report a time-use “diary” for the previous day.

In service to the rest of us, ATUS respondents voluntarily and carefully report every activity they did for the 24-hour period beginning at 4 a.m. on the day before the interview and ending at 4 a.m. on the day of the interview. So, dear reader, each of us owes the ATUS participants a huge thank you for their public service!

Economists from the U.S. Census Bureau and BLS put these responses together in a way that prevents anyone from identifying individual respondents. BLS then makes the data available to the public in the form of public-use data files, tables, charts, and news releases.

People often ask us how these estimates have changed over time. It’s now much easier for you to examine trends in time use because we have added ATUS historical data to the Labstat database on our BLS website. You can view thousands of ATUS time series going back to 2003, the first year of data collection. These data have always been available through the ATUS news release archive and tables. We are pleased to present them now in a more accessible and convenient format.

There are two big benefits to having ATUS data available from the Labstat database. First, all the historical data are now available in one place. You no longer have to combine data from different Excel or PDF documents to create a time series. Second, all estimates in Labstat have been created using the same statistical weighting method, which has been consistent since 2006 but changed slightly each year before that. This allows you to directly compare estimates over time.

We’ve selected a few data series that we think will be of particular interest. You can view these “Top Picks” at http://data.bls.gov/cgi-bin/surveymost?tu

Some examples are:

  • The percent of employed people who did some or all of their work from home on days they worked
  • The average hours per day mothers and fathers spent providing childcare, by age of their youngest child
  • The average hours per day spent in leisure, including watching TV, and socializing, on weekdays and weekend days

Many more data series are available through the Historical Tables extraction tool. These tables allow you to select one or many estimates from ATUS tables, specify a time range, and retrieve the historical data. I hope you will explore this new tool.

For help working with ATUS data and for more information on the survey, please visit the ATUS Labstat Tips webpage and the ATUS homepage.

Visualizing BLS Data to Improve Understanding

If a picture is worth a thousand words, what’s the value of a striking, cool chart or map of some BLS data? At the U.S. Bureau of Labor Statistics, we’re always thinking of better ways to help our users understand the information we produce. The global economy is complex, and the statistics to explain the economy can be complex too.

Data visualizations are one tool we use to present our data more clearly. What are data visualizations? They are any method of presenting numerical information visually—most commonly through charts and maps. Good data visualizations can improve understanding for all types of audiences, from students of all ages to experts with advanced degrees in economics, statistics, or other fields.

In recent years we’ve done more to include data visualizations in nearly all our publications. We have designed two of our publications to showcase data visualizations. One is The Economics Daily—or TED, as we call it. We publish a new edition of TED every business day, and we’ve done that since 1998. Each edition of TED typically includes a chart or map, sometimes two, with a few words to explain the data in the visualization.

Another publication geared toward data visualizations is Spotlight on Statistics. Spotlight tells a longer, more detailed story about a topic through a series of visualizations presented in a slideshow format. As with TED, Spotlight includes brief written analysis to explain more about the data.

Even our publications that feature mostly written analysis often include visualizations to tell a more complete story. Our flagship research journal, the Monthly Labor Review, has evolved a lot over its 100 years of publication to serve readers better; that evolution includes more and better data visualizations. Beyond the Numbers and BLS Reports often include visualizations as well.

We take pride in crafting our words carefully, but good data visualizations can complement the words. For example, during and after the Great Recession, the monthly Employment Situation news release has discussed the historically high levels of long-term unemployment. The number of long-term unemployed—those jobless 27 weeks or longer—has remained high years after the recession ended in June 2009. It’s one thing to read about long-term unemployment, but a good chart can tell the story even more clearly. long-term-unemployment

For an even broader perspective, we have a Spotlight on Statistics that examines long-term unemployment more fully.

Not only have we presented more data visualizations in recent years, but our visualizations also have gotten more sophisticated. A basic image can present information effectively. Take this simple map that shows the proportion of each state’s population age 16 or older that had a job in 2014. state-employment-population ratios

Now check out the interactive version of this map that we published in the March 9, 2015, edition of TED. When you hover over each state, more information pops up to show the state’s employment–population ratio in 2014 and how much it changed from 2013. When you hover over the items in the map legend, the states in each category light up more brightly to help you see the states with similar employment–population ratios. When you click on each state, you go to a webpage that provides even more information about the state’s labor market. Interactive features in our charts and maps give you the power to choose what information you want to see.

If you like the interactive features in our charts and maps, I think you’ll love the animation in some of our visualizations. Animation adds a time dimension to our data to let you see how measures change. For a great example of animation, see a TED we published last year that shows state unemployment rates before, during, and after the Great Recession.

The BLS website will feature even more data visualizations soon. Watch this space to learn more about them.

We share many of our data visualizations on Twitter, so follow us @BLS_gov. You also can sign up to receive email alerts for TED, Spotlight on Statistics, and our other publications.

And if you have created a great visualization of BLS data, please share it with us and the readers of this blog!