Experimental disease-based price indexes now available

I am extremely pleased to announce that BLS has released a new data product, experimental disease-based price indexes.

These indexes will give data users better ongoing information about the evolution of the nation’s healthcare system. Because healthcare is such a large part of our economy, it is incredibly important we produce timely, accurate, and reliable medical statistics.

Currently, all federal statistical agencies report healthcare data by what’s called the “medical goods and services categories.” However, this approach doesn’t tell us one important thing: Which diseases have the greatest effect on healthcare spending over time?

After identifying the diseases that affect spending the most, we can drill down to learn the reasons for their growth. With disease-based price indexes, we can break down the growth into categories, such as the parts that come from inflation, population growth, growth in disease prevalence, and real per capita output growth. We can’t do any of this with our traditional medical goods and services categories, such as physician services, pharmaceuticals, and hospitals.

The Bureau of Economic Analysis now reports spending by disease in their national healthcare satellite accounts, so we also need disease-based price indexes to adjust for inflation. Disease-based price indexes measure healthcare inflation differently and capture the effects of innovations that our traditional medical goods and services price indexes do not. For example, better surgical procedures have enabled doctors to perform many types of surgery using a less expensive outpatient setting, instead of an expensive inpatient hospital. The disease-based price indexes allow us to measure the effect of this shift.

I am incredibly proud of the team who produced these experimental disease-based price indexes. These indexes fulfill our BLS mission to continuously improve our products and provide timely, accurate, and relevant data to our users. These indexes also come at no additional cost because of the team’s innovative use of existing data, such as the freely available Medical Expenditure Panel Survey. We at BLS strive every day to provide the best value for your taxpayer dollars, and this is a shining example of that effort!

We also could not provide gold-standard data such as these indexes without the help of our survey respondents. I deeply appreciate all the medical providers who voluntarily participate in our survey. Their cooperation is essential for generating our medical price indexes and ensures our healthcare data are accurate.

Disease-based price indexes are still in their infancy, and we have much to learn, including the best way to incorporate them into overall price indexes. That’s why we describe them as experimental. We hope data users will find them helpful. We invite you to share your thoughts and ideas to help us continue to develop these indexes. And, as Commissioner, I hope you can see why I am so proud of this key contribution BLS has made to ensuring better medical statistics now and in the future.

Why This Counts: New data on employee benefits in 2015

Be honest now. What do you do with all those booklets you get that describe your employee benefits in detail? I suspect most of us add them to our “reading pile,” along with the latest magazines or inserts from the electric bill. Of course, you may no longer do this physically, since these days a lot of information about employee benefits comes to us electronically, perhaps on our employer’s internal website. Either way, the result is largely the same—you probably add the link to your online viewing pile with the latest cat-playing-piano video. Here at BLS, we actually read those documents and websites on employee benefits for you and use them to help everyone understand the benefits coverage of American workers.

As a result, last week BLS released data on the percentage of workers covered by employee benefits in 2015. This report is part of an annual series about benefits. The newest data show that 50 percent of private industry workers participated in a medical care benefit plan through their work, 49 percent had a retirement plan, and 61 percent had paid sick leave available.


A strength of these data is the detail about the workplaces that provide benefits and the workers who receive them. For example, paid sick leave benefits vary based on the number of workers at the employer’s location: 80 percent of private industry workers had paid sick leave in the largest establishments (500 workers or more), while 49 percent had paid sick leave in the smallest establishments (fewer than 50 workers). Looking at retirement benefits, 12 percent of private industry workers in the group with the lowest 10 percent of wages ($9.00 or less per hour) participated in a retirement plan through their employer. That compares with 78 percent of workers in the group with the highest 10 percent of wages ($43.27 or more per hour).

A recent edition of The Economics Daily features visualizations about how retirement and medical care benefits vary by occupation.

The new report also shows how the cost of medical care benefits is divided between employee and employer. This year, employees in private industry paid, on average, 22 percent of the cost for single coverage and 32 percent of the cost for family coverage; employers picked up the remaining cost. Later this year, more detail will be available on the cost that employees and employers paid for these benefits.

Back to those plan booklets. At BLS, we cull through the details to report on plan features and the value of benefits. Earlier this year BLS released those details for 2014 health and retirement plans in private industry. Among the findings are that one in three workers covered by a medical care plan was in a high-deductible plan, with a median individual deductible of $2,000 per year. Among 401(k) plans, 41 percent of participants were in plans that automatically enroll new employees into the plan, often with a default contribution of 3 percent of earnings. Plan booklets also showed that 401(k) plans commonly match 50 percent of the first 6 percent of earnings that workers contribute.

Employee benefits are a key part of compensation—just over 30 percent, on average. Benefits protect employees and families in the face of medical, financial, and other concerns. So, even though reading the details of those plans may not be high on your to-do list, rest assured that BLS is keeping on top of these issues for you and reporting the details throughout the year.

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.


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.


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.


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.)


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.