Tag Archives: BLS staff

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.

Greetings and a Meditation on Alan Krueger

William W. Beach became the 15th Commissioner of Labor Statistics in March 2019.

I am a little late with my first blog, but I’m sure readers can appreciate what it means to start this job as Commissioner of Labor Statistics on a week that ends in the publication of the Employment Situation report.

Every moment of my first week at BLS has been highlighted by the unfailing grace and cheerfulness of the career staff.

I felt very strongly that my first blog as BLS Commissioner should be about the late Alan Krueger’s pioneering work, particularly as it relates to both the Department of Labor and the Bureau of Labor Statistics.

A Meditation on Alan Krueger
(1960 – 2019)

I have been thinking a lot about Alan Krueger since his passing on March 16. Thinking about the loss, of course: the shock of losing such a penetrating mind, such a courageous scholar. And thinking about the insights and breakthroughs he could yet have made: at 58, Alan Krueger was striding strongly.

The past three weeks have seen a steady flow of recollections in the popular and professional press. Let me recommend two highly accessible pieces: Ben Casselman and Jim Tankersley’s New York Times essay and Larry Summers’s deeply thoughtful recollection in the Washington Post. There are more out there and more to come.

I’m writing today to remind us of Professor Krueger’s close ties to our daily work. He, indeed, connected in so many ways. First, he was a consummate though sometimes reluctant government economist. Dr. Krueger served as the Department of Labor’s chief economist from 1994 to 1995, returned to the federal government service in 2009 as an assistant secretary in the Treasury Department from 2009 through 2010, and finally served on President Barack Obama’s Council of Economic Advisers from 2011 through 2013.

This service record as a government economist, as important as it is, is not Professor Krueger’s deepest tie to BLS. Rather, and second, he stood out among peers for his leadership as an empirical economist. Starting with his celebrated study of the economic effects of the minimum wage in 1994, when he and David Card pioneered the use of natural experiments in policy analysis, to his recent pathbreaking work on the opioid crisis, Alan Krueger made important contributions to our understanding of work and public policy through innovative use of data.

This is what ties him most to us, in my view. His sometimes controversial conclusions to one side, Professor Krueger looked at the world when he wrote. That may seem an obvious posture for any economist, but too often analysts look elsewhere: for instance, they wrap themselves in strictly theoretical work or confine their own work to the research channels that others have dredged. While theory and replication are essential parts of our profession, they cannot substitute for an active curiosity about the real world and how it is changing. Unless you’re looking out into the world, you may never see the amazing, new developments there that could inspire you to grow beyond the current limits of your economic understanding.

It will take time to define Alan Krueger’s legacy in economics and public policy, but this much is already clear: he left a strong marker of what it means to be a labor economist and a public servant, and he showed two generations of labor researchers that the most fruitful laboratory for economic science is the swirling, crazy world outside our office doors.

Passing the Baton to the New BLS Commissioner

I am pleased to announce that Dr. William Beach has been confirmed by the Senate and sworn in as the fifteenth Commissioner of Labor Statistics.

A highlight of my time as Acting Commissioner was being able to share this blog with you, our customers. This forum allowed me to provide updates on program improvements and concrete examples of how BLS data can help everyone make smart decisions. I hope you check back here often to hear more updates from Commissioner Beach. I learned that this blog is a great vehicle for communicating to you in an informal, but hopefully informative, way. I want to thank the BLS staff who helped keep the blog fresh — without them, it would not have been nearly as interesting!

Finally, I want to use my last post as Acting Commissioner to sincerely and publicly thank all BLS staff. They work tirelessly day in and day out to ensure we provide gold-standard data to the American people. They also share their technical expertise in terms that even I can understand!

I know that all of you join me in extending a warm welcome to Dr. Beach.

Thank you for your continued support,

Bill Wiatrowski

Reaching out to Stakeholders—and Steakholders—in Philadelphia

The U.S. Bureau of Labor Statistics has staff around the country who serve several critical roles:

  • Contacting employers and households to collect the vital economic information published by BLS
  • Working with partners in the states who also collect and review economic data
  • Analyzing and publishing regional, state, and local data and providing information to a wide variety of stakeholders

To expand the network of local stakeholders who are familiar with and use BLS data to help make good decisions, the BLS regional offices sponsor periodic Data User Conferences. The BLS office in Philadelphia recently held such an event, hosted by the Federal Reserve Bank of Philadelphia.

These Data User Conferences typically bring together experts from several broad topic areas. In Philadelphia, participants heard about trends in productivity measures; a mash-up of information on a single occupation—truck drivers—that shows the range of data available (pay and benefits, occupational requirements, and workplace safety); and an analysis of declines in labor force participation.

Typically, these events provide a mix of national and local data and try to include some timely local information. The Philadelphia conference included references to the recent Super Bowl victory by the Philadelphia Eagles and showed how to use the Consumer Price Index inflation calculator to compare buying power between 1960 (the last time the Eagles won the NFL Championship) and today.

We also tried to develop a cheesesteak index, a Philadelphia staple. Using data from the February 2018 Consumer Price Index, we can find the change in the price of cheesesteak ingredients over the past year.

Ingredient Change in Consumer Price Index, February 2017 to February 2018
White bread 2.5 percent decrease
Beef and veal 2.1 percent increase
Fresh vegetables 2.1 percent increase
Cheese and related products 0.8 percent decrease

Image of a Philadelphia cheesesteak

These data are for the nation as a whole and are available monthly. Consumer price data are also available for many metropolitan areas, including Philadelphia. These local data are typically available every other month and do not provide as much detail as the national data.

While the Data User Conferences focus on providing information, we also remind attendees the information is only available thanks to the voluntary cooperation of employers and households. The people who attend the conferences can help us produce gold standard data by cooperating with our data-collection efforts. In return we remind them we always have “live” economists available in their local BLS information office to answer questions by phone or email or help them find data quickly.

Although yet another Nor’easter storm was approaching, the recent Philadelphia Data User Conference included an enthusiastic audience who asked good questions and left with a greater understanding of BLS statistics. The next stop on the Data User Conference tour is Atlanta, later this year. Keep an eye on the BLS Southeast Regional Office webpage for more information.

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