All posts by BLS Commissioner

Why This Counts: Maximizing Our Data Using the Consumer Expenditure Survey

Almost all BLS statistical programs are based on information respondents voluntarily give us. We want to squeeze as much information as we can out of the data respondents generously provide. Limiting respondent burden while producing gold-standard data is central to our mission.

Let’s take a look at how one program, the Consumer Expenditure (CE) Survey, squeezes every last drop of information from the data to provide you, our customers, with more relevant information.

What is the Consumer Expenditure Survey?

The CE survey is a nationwide household survey that shows how U.S. consumers spend their money. It collects information from America’s families on their buying habits (expenditures), income, and household characteristics (age, sex, race, education, and so forth). For example, we publish what percentage of consumers bought bacon or ice cream and how much they spent on average.

A little back story: The first nationwide expenditure survey began in 1888. BLS was founded in 1884, so the CE Survey is one of our first surveys! It wasn’t until 1980 that we began publishing CE data each year, however. A 2010 article, The Consumer Expenditure Survey—30 Years as a Continuous Survey, provides more historical information.

How is the CE program doing more with what we have?

We’ll briefly look at four different areas, starting with the most recent improvements:

  • Limited state data
  • Higher-income data
  • Generational data
  • Estimating taxes

Limited State Data – Starting with New Jersey

  • Regarding geographical information, the CE survey is designed to produce national statistics. Enough sample data are available to produce estimates for census regions and for a few metropolitan areas.
  • Up to now, however, we did not produce state data. The CE program recently published state weights for New Jersey, which will allow for valid survey estimates at the state level for the first time.
  • State-level weights are available for states with a sample size that is large enough and meet other sampling conditions.
  • Right now, the state-level weighting is experimental. We provide state-level weights to data users to gauge interest and usefulness.

 Higher-Income Table

  • We evaluated the income ranges of the published tables and found that over time more and more households were earning more, and the top income range had not increased to keep pace. To provide greater detail, we divided the existing top income range of “$150,000 and over” into two new ranges: “$150,000 to $199,999” and “$200,000 and over.” We integrated these changes into the 2014 annual “Income before taxes” research table, allowing more robust analysis for our data users.
  • In addition, we added four new experimental cross-tabulated tables on income without the need for additional information from our respondents.

Generational Table

Grouping respondent information by age cohort can be helpful, since a person’s age can help to predict differences in buying attitudes and behaviors. The CE program has collected age data for years, but never grouped the data into generational cohorts before. A Pew Research Center report defines five generations for people born between these dates:

  • Millennial Generation: 1981 or later
  • Generation X: 1965 to 1980
  • Baby Boomers: 1946 to 1964
  • Silent Generation: 1928 to 1945
  • Greatest Generation: 1927 or earlier

The 2016 annual generational table shows our most recent age information for the “reference person” or the person identified as owning or renting the home included in the CE Survey. In 2016 we wrote a short article on Spending Habits by Generation, including a video, which used 2015 data. We’ve updated the chart using 2016 data:

A chart showing consumer spending patterns by generation in 2016.

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

Estimating Taxes

CE respondents used to provide federal and state income tax information as part of the survey. These questions were difficult for respondents to answer.

Starting in 2013, the CE program estimated federal and state tax information using the TaxSim model from the National Bureau of Economic Research and removed the tax questions from the survey. As a result, the quality and consistency of the data increased, and we have reduced respondent burden!

If you have any questions or want more information, our staff of experts is always around to help! Please feel free to contact us.

This is just one example of how we at BLS are always looking for ways to maximize our value while being ever mindful of the costs—and one of those important costs is the burden our data collection efforts place on our respondents. Maximizing our data means providing gold-standard data to the public while reducing the burden on our respondents—a true win-win!

Annual consumer spending by generation of reference person, 2016
Item Millennials, 1981 to now Generation X, 1965 to 1980 Baby Boomers, 1946 to 1964 Silent Generation, 1928 to 1945 Greatest Generation, 1927 or earlier
Food at home $3,370 $4,830 $4,224 $3,450 $2,023
Food away from home 2,946 4,040 3,100 2,042 1,095
Housing 16,959 22,669 18,917 14,417 17,858
Apparel and services 1,753 2,577 1,602 920 615
Transportation 8,426 10,545 9,762 5,952 3,142
Healthcare 2,473 4,492 5,492 6,197 5,263
Entertainment 2,311 3,613 3,144 2,114 1,223
All other spending 10,338 15,766 14,963 6,671 4,125

BLS Celebrates Read Across America Day

BLS celebrates the National Education Association’s Read Across America Day on March 2. Not by coincidence, it is also the birthday of the well-known author Dr. Seuss.

In the words of the famous author, “The more that you read, the more things you will know. The more that you learn, the more places you’ll go.”

BLS data show that reading is every bit as important as Dr. Seuss claimed. Only 2.5 percent of workers do not need to read or write on their job, according the Occupational Requirements Survey. However, the American Time Use Survey finds that only about 20 percent of people read for personal interest on an average day.

In honor of Dr. Seuss and Read Across America Day, how about taking some time to learn what else BLS data tell us about reading?

“Fill your house with stacks of books, in all the crannies and all the nooks.” –Dr. Seuss

Consumers spent $15,268,000,000 on reading in 2016, according to the Consumer Expenditure Surveys. On average, households (technically referred to as consumer units) spent $118 on reading. So, of the Whos down in Whoville, which Whos are reading?

  • Households in the West region spent an average of $171 on reading. Those in the Midwest averaged $121, while households in the Northeast and South regions averaged just under $100.
  • Married couples without children spent an average of $174 on reading for their household; those with children spent $123. The households of single parents with children under 18 spent an average of $41.
  • Generationally, when the reference person was a baby boomer (born between 1946 and 1964), the household spent an average of $130 on reading. That compares with an average of $64 spent by households of millennials (those born in 1981 or later).

The Consumer Price Index gives us information about changes in the prices of the goods and services we buy. For example, prices for eggs (white or brown, but not green) increased 11.6 percent in 2017, and prices for ham were up 2.7 percent.

  • Prices for recreational books decreased 3.2 percent in 2017 and were 7.7 percent lower than in 2007.
  • Costs for newspapers and magazines declined 1.1 percent in 2017, but were 37.5 percent higher than a decade ago.
  • Prices for educational books and supplies decreased 1.8 percent in 2017, but were 58.3 percent higher than in 2007.

“I can read in red. I can read in blue. I can read in pickle color too.” –Dr. Seuss

According to the American Time Use Survey, the share of women who spent time reading for personal interest was larger than the share of men. In addition, women were slightly more likely than men to spend time reading to and with children in the household (excluding education- and health-related reading).

  • Seventeen percent of men and 21.8 percent of women spent time reading for personal interest on an average day. On the days they read, men and women spent an average of around an hour and a half participating in this activity.
  • On an average day, 13.4 percent of fathers and 18.5 percent of mothers spent time reading to and with their young children. On days they engaged in this activity, it accounted for about a half an hour of time for both fathers and mothers.

“You’re never too old, too wacky, too wild, to pick up a book and read to a child.” –Dr. Seuss

Do you want to spend more time with Thing 1 and Thing 2? How about a fox in socks or a cat in a hat? Library workers get to do all of that!

Librarians, library technicians and clerical library assistants spend all day with books. Librarians earn the highest wages of the three and also require higher levels of education and work experience, according to the Occupational Employment Statistics and the Occupational Requirements Surveys.

  • Nearly 50 percent of librarian jobs required a bachelor’s degree, and another 42 percent required a master’s degree in 2017. High school diplomas were more common for library techs (42 percent) and clerical library assistants (80 percent).
  • The average annual wage for librarians in 2016 was $59,870. Library technicians averaged $34,780 and clerical library assistants, $27,450.
  • Lifting books is a big job. On a scale from sedentary to very heavy, a medium level of strength was required for about 57 percent of librarian jobs and 71 percent of clerical library assistant jobs.

“There’s no limit to how much you’ll know, depending how far beyond zebra you go.” –Dr. Seuss

So, how will you celebrate Read Across America Day—in a boat, with a goat, in the rain, on a train, in a box, with a fox, in a house, with a mouse? Don’t forget the green eggs and ham! And remember, “You can find magic wherever you look, sit back and relax, all you need is a book.” –Dr. Seuss

Do You Understand Your Local Economy?

The national unemployment rate may make the headline news every month, but many folks are most interested in understanding their own local economy.

BLS has a stat for that (really MANY statistics for that)! In fact, BLS data were highlighted in a webinar focusing on local data sponsored by the Association for Public Data Users, the American Statistical Association, and the Congressional Management Foundation.

Dr. Martin (Marty) Romitti, a Senior Fellow at the Center for Regional Economic Competitiveness, presented a webinar called “Understanding Your Congressional District’s Economy and Workforce Using Federal Statistical Data.” Though geared to Congressional staff, the information is applicable to anyone interested in knowing more about their local economy.

By using an extended example of the Napa, California, metropolitan area (where we immediately think, “Wine Country!”), Dr. Romitti finds some interesting information that may shatter some of your preconceived notions of that region.

He does this by answering 10 questions — 5 about “our people,” where he uses U.S. Census Bureau data and 5 about “our economy,” where he uses BLS data.

We are going to focus on the BLS portion (run time 31:12)* of the webinar. The five questions Dr. Romitti poses about our economy are:

  1. How healthy is my economy now?
  2. How many unemployed people live in my area?
  3. What are the largest employing industries?
  4. Which industries pay most to workers?
  5. What are our economic strengths?

Below are some steps and tips if you want to access the same information as Dr. Romitti on Note that he uses Internet Explorer; use a different browser and your screen will look different.

Dr. Romitti uses two BLS tools; we have included the path and links to pages as appropriate:

  • To answer Questions 1 and 2: Economy at a Glance -> California -> Napa (Dr. Romitti suggests clicking on the maps.)
    • Tips:
    • For context, suggest you compare your area data to your state numbers. Beware: Your state unemployment rate is seasonally adjusted, while your area data are not.
    • Also, for context, you may want to look at the data over time, such as the last 10 years. Just remember the “Great Recession” occurred starting in late 2007.
  • To answer Questions 3, 4, and 5: BLS Data Tools -> Employment -> Quarterly -> State and County Employment and Wages -> Tables

By following these instructions, you can uncover the same information as Dr. Romitti. We believe Dr. Romitti does a good job of explaining how to answer questions related to local economic data in under an hour!

But wait, there’s more! Let me offer two more resources in your quest for local data:

  1. Are you familiar with our Economic Summaries? These summaries present a sampling of economic information for the area covered, such as unemployment, employment, wages, prices, spending, and benefits. For example, take a look at San Francisco. If you are looking for something quick and easy, you might find what you need in one of these summaries.
  2. The Economic Summaries are produced by the BLS regional information offices. The BLS regional office staff stand ready to assist you with questions about your local economy.

*The taped webinar starts with a musical interlude and some brief introductions. The real action starts at the following run-time intervals:

Run Time                    Presentation Topic   

6:46                             Introduction by Dr. Romitti

11:30                           About our people (Census Bureau data)

31:12                           About our economy (BLS data) begins

52:36                           Regional Economic Accounts (Bureau of Economic Analysis data)

58:53                           Conclusion

60:00                           End

Recalibrating the Jobs Thermometer

The U.S. Bureau of Labor Statistics is responsible for measuring labor market activity. Each month BLS releases some of the most up-to-date measures of economic health in The Employment Situation, often called “the jobs report.” We also release the Commissioner’s statement each month at the same time as the jobs report.

Most of the attention focuses on the headline numbers—how many jobs were added (or lost) that month and did the unemployment rate change? However, with the release of January data each February, we make some yearly updates to improve the accuracy of the numbers. In our survey of households, which is the source for the unemployment rate and other measures, we update the U.S. population totals to reflect the latest information about births, deaths, and international migration. In our survey of nonfarm establishments, which is the source of the jobs count, we make our annual benchmark revisions. Today I’m going to focus on the establishment data.

Each month, the establishment program surveys a sample of businesses and governments around the country. The survey asks how many people worked or received pay for the pay period that included the 12th of the month. While the establishment sample is large, covering about one-third of all nonfarm jobs, the employment changes reported each month are still subject to revisions. Monthly revisions result from more establishments reporting their numbers or correcting previous reports, and from updated information about seasonal employment patterns.

The establishment survey also benefits from another source of data, the Quarterly Census of Employment and Wages. That is a nearly complete count of all establishments, although it is available with a delay of about 6 months. A full count of employment helps us in several ways. We use the data to measure the error associated with the establishment survey. This way data users don’t have to guess how accurate the monthly employment data are. We have a stat for that! In case you are wondering, the data are very accurate. Annual benchmark revisions (which I will explain in a moment) have averaged only 0.3 percent in absolute terms over the past 10 years.

Besides measuring error, once a year we realign the sample-based estimates with the full count of employment. We call this “benchmarking.” This realignment makes sure the employment levels do not stray too far from the “truth” over time. (For several reasons that I won’t go into right now, the establishment survey employment totals will not exactly equal employment totals from the full counts. If you really want to know the details, you can read more about benchmarking, but remember I tried to spare you.)

During this annual benchmarking, we also introduce other changes to the survey. Sometimes we update the industry classification, like we will this year. We also use new information to update the statistical model that accounts for business births and deaths. We review the establishment sample for size, coverage, and response rates, and we may drop some series if the data quality doesn’t meet our standards. We also update the models and information used in seasonal adjustment.

As you can see, there’s a lot going on during this annual updating. All establishment data, including employment, hours, and earnings, are subject to adjustment. I hope this brief explanation and the material we have on our website help to make everything more transparent and easier to understand.

The same basic benchmarking that occurs nationally also happens for the state and local employment estimates. Want to know more? Visit their homepage. If you still have questions, call or email us. We are here to help.

Data Privacy Day is Every Day at BLS

There are many commemorative days, weeks, and months, but Data Privacy Day on January 28 is one that we here at BLS live every day of the year.

If this is the first time you’re hearing about it, Data Privacy Day is an international effort to “create awareness about the importance of:

  • respecting privacy,
  • safeguarding data and
  • enabling trust.”

These three phrases are central to everything we do at BLS—but don’t take my word for it! Instead, let’s hear from some of our staff members about what data privacy means in their day-to-day work lives.

I chatted with staff members from three key areas at the Bureau:

  • Collection — our field economists collect data from respondents.
  • Systems — our computer specialists protect the IT infrastructure where we keep the data.
  • Analysis — our economists analyze the data, prepare products, and explain the data to our customers.

Now, let’s meet the staff.

Richard Regotti

Richard Regotti

My name is Richard Regotti, Field Economist in the BLS Chicago Regional Office, Cleveland Area Office. I have proudly served the public in this position for 12 years. As a Field Economist I am responsible for collecting data and developing positive relationships and securing cooperation from survey respondents for the Producer Price Index and the International Price Indexes.







Jess Mitchell

Jess Mitchell

My name is Jess Mitchell and I have been an Information Security Specialist in the Bureau’s national office since 2013. I started with BLS in 1999. Currently, I am the Computer Security Incident Response Team Lead, so I, along with my team members, investigate, analyze and report on computer security incidents as well as the impact or potential impact of cyber threats and vulnerabilities to BLS systems and data.






Karen Kosanovich

Karen Kosanovich

My name is Karen Kosanovich, Economist, and I have spent the past 19 years working with unemployment data from the Current Population Survey, and 25 years total at BLS. I develop analyses, such as The Employment Situation, and talk to our customers about the data.







Question 1. One of our core BLS values is the confidentiality of data: All respondent data are completely confidential and used for statistical purposes only. How does this impact you in your daily work?

Richard: On a daily basis I am asking producers and service providers to voluntarily provide very sensitive company information. Even after identifying myself as a representative of our Federal Government, some respondents are not comfortable with agreeing to provide us their confidential information for use in our statistical output. By focusing on the mission of the BLS and the legal protections that are in place to safeguard survey data, I am able to function on the front line as a data collector.

Jess: This core value of data confidentiality helps me to focus on the importance of protecting the confidentiality of BLS data when my team members and I are investigating threats. The importance of BLS data underscores the importance of our daily work to keep BLS data and data respondent information confidential.

Karen: I don’t have access to information about specific people who respond to our survey. All personally identifying information is stripped away before the statistical information is given to an economist like me to analyze. For my colleagues and me, confidentiality means protecting our estimates from being distributed in advance of the official release of the unemployment rate at 8:30 a.m. on the day we publish our data.

Question 2. Does adherence to this core value create any challenges for you in your work? How have you overcome those challenges?

Richard: Adherence to complete confidentiality, supported by the fact that the data are used for statistical purposes only, presents no challenge to me; this core value is a selling point and something I make sure all potential survey participants are aware of prior to providing any data to the BLS.

Jess: Adherence to the core BLS value of data confidentiality does create a challenge when we need to engage our office in an incident or threat investigation; we must be very diligent not to share Confidential Information Protection and Statistical Efficiency Act (CIPSEA) information.

Karen: Our procedures for working with embargoed (prerelease) information are so ingrained in my work routine that I don’t notice any challenges from them. The people I work with all have the same responsibility and a strong commitment to public service, so it is easy for us to keep vigilant.

Question 3. If you could make a statement to the American people about why they should trust BLS with their information, what would that be?

Richard: BLS is not a compliance or regulatory agency in any way. We are only concerned with providing accurate, timely, relevant, and unbiased data that reports on the health and well-being of our economy. Your information contributes to the validity of BLS data.

Jess: The confidentiality of BLS data is always at the root of my office’s work, and I see the same focus on data privacy and confidentiality and diligence toward the safeguarding of CIPSEA data throughout the entire culture of BLS.

Karen: Although I don’t have names and personal details of specific unemployed people who respond to our survey, my colleagues and I are very mindful of the importance of representing the experience of all Americans when we produce our estimates. The data we publish are not just numbers, but tell the story of real people. It can be very stressful to be unemployed, and those who have been looking for work for a very long time face significant challenges in the labor market. We take our jobs, and our mission, very seriously.

And now the rules:

Of course, we don’t work in a vacuum. Like any other organization, we have rules that we live under.

BLS makes a pledge of confidentiality to its respondents that data collected are used for statistical purposes only. The pledge is covered by CIPSEA, which makes it a felony to disclose or release the information for either nonstatistical purposes (for example, regulatory or law-enforcement purposes) or to unauthorized persons. In addition, the Office of Management and Budget has Statistical Policy Directives (3 and 4) that govern BLS news releases to ensure they meet specific accuracy, timeliness, and accountability standards.

On January 28, and every day, we hope you will take steps to protect your own privacy and the privacy of others. Here at BLS we will continue to educate and raise awareness about respecting privacy and safeguarding data. It is core to our mission and central to our staff values. Without the trust these actions produce among the American people, we could not do our work in providing gold-standard data for and about America’s workers.

Thank you for your trust and happy Data Privacy Day!