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Tag Archives: Innovation

Innovations at BLS during the COVID-19 Pandemic

Our work at the Bureau of Labor Statistics is driven by the idea that good measurement leads to better decisions. Good measures of economic and social conditions help public policymakers and private businesses and households assess opportunities and areas for improvement. Measuring these conditions consistently over time helps people who use our data evaluate the impact of public and private decisions.

We also believe we must be completely transparent about the design of our surveys and programs and the methods we use to conduct them. It isn’t enough to publish statistics and expect people simply to trust their quality. We gain this trust by documenting the design and procedures for all our programs in our Handbook of Methods. Our website also explains our policies for ensuring data quality and protecting the confidentiality and privacy of the people and businesses who participate in our surveys and programs. Further, BLS works with the wider U.S. statistical community to ensure and enhance the quality of statistical information.

Good measures are essential in “normal” times, but the global COVID-19 pandemic has made these last few months anything but normal. I am so proud of the work of the career professionals at BLS and our fellow statistical agencies for continuing to produce vital economic statistics. Our entire BLS staff moved to full-time telework in mid-March and didn’t miss a beat. We continue to publish measures of labor market activity, working conditions, price changes, and productivity like BLS has done since its founding in 1884. See our dashboard of key economic indicators in the time of COVID-19.

Publishing these measures hasn’t been easy. The pandemic has raised new questions about how businesses, households, and consumers have changed their behavior. BLS also has had to innovate to find new ways of doing things during the pandemic.

Today I want to tell you about the new data we have been collecting to learn more about the effects of the pandemic. I also want to tell you about some of the ways the BLS staff has innovated to keep producing data that are accurate, objective, relevant, timely, and accessible.

New Data

How businesses have responded to the pandemic

We have collected new data on how U.S. businesses changed their operations and employment from the onset of the pandemic through September 2020. This information, combined with data collected in other BLS surveys, will aid in understanding how businesses responded during the pandemic. Other statistics we have collected and published during the pandemic show changes in employment, job openings and terminations, wages, employer-provided benefits, prices, and more. These new data provide more insights by asking employers directly what they experienced as a result of the pandemic and how they reacted. Data for the Business Response Survey to the Coronavirus Pandemic will be released in early December 2020.

Changes in telework, loss of jobs, and job search

The Current Population Survey is the large monthly survey of U.S. households from which we measure the unemployment rate and other important labor market indicators. We added questions to the survey to help gauge the effects of the pandemic on the labor market. These questions were added in May 2020 and will remain in the survey until further notice. One question asks whether people teleworked or worked from home because of the pandemic.

Percent of employed people who teleworked at some point in the previous 4 weeks because of the COVID-19 pandemic, May through October 2020

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

Other questions ask whether people were unable to work because their employers closed or lost business because of the pandemic; whether they were paid for that missed work; and whether the pandemic prevented them from searching for jobs.

Number of people not in the labor force who did not look for work because of the COVID-19  pandemic, May through October 2020

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

Changes in sick leave plans

We added several questions to the National Compensation Survey to understand the effects of the pandemic on sick leave plans. The questions asked whether private industry establishments changed their leave policies and whether employees used sick leave between March 1 and May 31, 2020.

Receiving and using stimulus payments during the pandemic

BLS is one of several federal agencies that developed questions for the rapid response Household Pulse Survey. The survey is a collaboration among the U.S. Census Bureau, BLS, the U.S. Department of Housing and Urban Development, the National Center for Education Statistics, the National Center for Health Statistics, and the U.S. Department of Agriculture’s Economic Research Service. BLS contributed questions on the receipt and use of Economic Impact Payments and on sources of income used to meet spending needs during the pandemic.

Our staff will continue to publish research on how the pandemic has affected the labor market and markets for goods and services. Check back regularly as we add to this library of research.

Innovations in Data Collection and Training

The COVID-19 pandemic has caused profound changes in the daily lives of Americans. BLS is no exception. As I mentioned earlier, all BLS staff moved to full-time telework in March. The pandemic hasn’t prevented us from continuing to publish high-quality data, but we have had to change some of our data-collection methods and estimation procedures. We will continue to explain those changes so you can understand how they affect the quality of our measures.

Our survey respondents are the heart of everything we do at BLS. Without their generous and voluntary cooperation, we would not be able to publish high-quality data for public and private decision making. Respondents have businesses and households to run, and a pandemic is a challenging time to ask for their help. The data-collection staffs at BLS, the U.S. Census Bureau, and our state partners form great relationships with survey respondents. We must continue to protect the health of data collectors while also training them in a rapidly changing environment. Let me highlight a few of the innovative changes we have made during the pandemic that focus on our relationships with respondents and how we train data collectors.

Using videoconferencing technology for data collection

Several of our surveys have started using videoconferencing tools to speak with respondents and collect data from them. Some of the surveys that now use this technology include the National Compensation Survey, the Occupational Requirements Survey, and the Producer Price Index. Many of our surveys previously relied on interviewers visiting businesses or households to collect data. We suspended all in-person data collection in March to protect the health of data collectors and respondents, so we had to find other ways to collect data. Many of our surveys also use telephone and internet to collect data, but those modes aren’t always ideal for every kind of data. We often need to develop personal relationships with respondents to gain their trust and cooperation and ensure high-quality data. Videoconferencing helps us accomplish what we often can’t do with phones or web survey forms.

The Occupational Requirements Survey is one that has begun using videoconferencing in data collection. The survey provides information about the physical demands; environmental conditions; education, training, and experience; and cognitive and mental requirements for jobs in the U.S. economy. Collecting data for this survey often requires visual aids, hand gestures, and other nonverbal information to understand job characteristics. It often helps to watch jobs as they are performed at a worksite, but that’s not an option during the pandemic. Videoconferencing is the next best alternative.

Many of our data collectors and respondents have mentioned how helpful videoconferencing is for developing a rapport and for sharing screens and other visual information. Videoconferencing also helps us reduce travel and lodging costs, so we likely will continue to rely on videoconferencing at least partly even after the pandemic.

Using videoconferencing technology for training and mentoring

Many of our surveys are complex and require considerable ongoing training for data collectors. For example, before the pandemic, our Consumer Price Index Commodities and Services (C&S) survey involved in-person training at our Washington, DC, headquarters. There were two classroom training courses: a 2-week introductory course and a 1-week advanced course. Each course was followed by on-the-job training held in our regional offices. Even before the pandemic, we were developing videoconference training. The pandemic caused us to accelerate these plans. We now provide C&S survey training through video collaboration tools. We also integrate on-the-job training throughout the classes.

Several other surveys have adopted a similar training approach as the Consumer Price Index. Our data-collection staffs also increasingly use videoconferencing for mentoring and to share ideas about how to make the data-collection experience better for data collectors and respondents.

A final note

Before I conclude, I want to share some sad news about one of the people who played an indispensable leadership role in developing the new survey questions and innovative data-collection and training methods. Jennifer Edgar, our Associate Commissioner for Survey Methods Research, died November 8 in a tragic fall in her home. She leaves behind her husband and two young children, her parents, and her sister. Moreover, she leaves hundreds of BLS colleagues and many more throughout the statistical community and beyond, who will grieve the loss of an exceptionally gifted friend and professional whose great promise was cut suddenly and tragically short. Jennifer was using her considerable energies to move BLS forward. Her passing is a huge blow to her family, loved ones, and the entire statistical community. We are working on ways to ensure Jennifer’s memory and passion is forever present at BLS.

Percent of employed people who teleworked at some point in the previous 4 weeks because of the COVID-19 pandemic
MonthPercent

May 2020

35.4%

Jun 2020

31.3

Jul 2020

26.4

Aug 2020

24.3

Sep 2020

22.7

Oct 2020

21.2
Number of people not in the labor force who did not look for work because of the COVID-19 pandemic
MonthNumber not in the labor force

May 2020

9,740,000

Jun 2020

7,043,000

Jul 2020

6,454,000

Aug 2020

5,200,000

Sep 2020

4,499,000

Oct 2020

3,563,000

State Productivity: A BLS Production

We have a guest blogger for this edition of Commissioner’s Corner. Jennifer Price is an economist in the Office of Productivity and Technology at the U.S. Bureau of Labor Statistics. She enjoys watching theatrical performances when she’s not working.

I recently had the pleasure of attending a high school play. The cast was composed of a male and female lead and at least a dozen supporting actors. The program listed the performers and acknowledged many other students, parents, teachers, and administrators. They all played some important role to bring the play to life—lighting, sound, painting props, sewing costumes, creating promotional materials, selling tickets, working concessions. All of these pieces came together harmoniously to make the performance a success.

Setting the Stage: New Measures of State Productivity

We can view the health of the nation’s economy through the same lens. Our diversified economy is made up of lead performers and supporting roles in the form of industries. Some industries contribute more heavily to growth in output or productivity, playing the star role. Other industries are supporting characters, contributing to a smaller, but necessary, share of growth. Our productivity program recently published a webpage that examines how industries contribute to the nation’s private business output and productivity growth.

We also can examine these roles geographically. Until recently, BLS productivity measures were only produced at the national level. Last June, BLS published experimental measures of state labor productivity for the private nonfarm business sector. These measures, which cover the period from 2007 to 2017, will help us learn more about productivity growth in each state and how each state contributes to national productivity trends.

Measuring productivity for all states allows us to credit the role played by each state, not just the total performance of the national economy or region. Just as each person, no matter how small their role, was necessary for the success of the school play, each state contributes to how we evaluate national or regional productivity. When we examine the contribution of each state to total productivity trends, we find that, like actors, no two states perform identically. Similar individual growth rates may have different impacts on the productivity of the nation or region. By analyzing state productivity trends over the long term, we learn more about regional business cycles, regional income inequality, and the role of local regulations and taxes on growth.

From 2007 to 2017, labor productivity changes ranged from a gain of 3.1 percent per year in North Dakota to a loss of 0.7 percent per year in Louisiana.

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

We estimate each state’s annual contribution to national or regional productivity growth by multiplying the state’s productivity growth rate by its average share of total current dollar national or regional output. The economic size of each state influences its contribution to national and regional estimates. From 2007 to 2017, California was our lead performer, with the largest contribution to national productivity growth. The state’s productivity grew 1.7 percent per year on average, and its large economy means it contributed more than one-fifth of the 1.0-percent growth in national labor productivity.

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

Supporting actors included Texas and New York. Making a cameo appearance was North Dakota; despite having the largest productivity growth rate, it ranked 28th in terms of its contribution to national productivity growth. Stars in each region included Illinois (Midwest), New York (Northeast), Texas (South), and California (West). Understudies—those states with the largest growth rates—were North Dakota (Midwest), Pennsylvania (Northeast), and Oklahoma (South). Oregon and Washington shared this role out West.

Second Act

For now, our new measures cover the private nonfarm sector for all 50 states and the District of Columbia from 2007 to 2017. These measures include output per hour, output, hours, unit labor costs, hourly compensation, and real hourly compensation. Our measures of labor productivity for states are experimental, meaning we’re still assessing them and considering ways to improve them. In the second act, we will be looking into producing state-level measures for more detailed sectors and industries.

For an encore performance, check out our state labor productivity page. We’d love to hear your feedback! Email comments to productivity@bls.gov.

Annual percent change in labor productivity in the private nonfarm sector, 2007–17
StateAnnual percent change

North Dakota

3.1

California

1.7

Oregon

1.7

Washington

1.7

Colorado

1.6

Oklahoma

1.6

Maryland

1.5

Montana

1.5

Pennsylvania

1.5

Massachusetts

1.4

New Mexico

1.4

Vermont

1.4

Idaho

1.3

Kansas

1.3

Nebraska

1.1

New Hampshire

1.1

South Carolina

1.1

Tennessee

1.1

Texas

1.1

West Virginia

1.1

Alabama

1.0

Hawaii

1.0

Kentucky

1.0

Minnesota

1.0

New York

1.0

Rhode Island

1.0

South Dakota

1.0

Virginia

1.0

Georgia

0.9

Arkansas

0.8

Missouri

0.8

Ohio

0.8

Utah

0.8

Illinois

0.7

North Carolina

0.7

Delaware

0.6

Florida

0.6

Iowa

0.6

Indiana

0.5

Mississippi

0.5

New Jersey

0.5

Wisconsin

0.5

Alaska

0.4

Arizona

0.4

District of Columbia

0.4

Michigan

0.4

Maine

0.3

Nevada

0.3

Wyoming

0.1

Connecticut

-0.5

Louisiana

-0.7
States with the largest contributions to national labor productivity, average annual percent change, 2007–17
StateState contribution to U.S. labor productivity

California

0.22

Texas

0.10

New York

0.08

Pennsylvania

0.06

Washington

0.04

Massachusetts

0.04

Illinois

0.03

Improving the Accuracy of the Consumer Price Index

We are in the “hot stove” months of the baseball year, when teams make trades and other decisions to improve their prospects for next season. Even the best teams, like the World Champion Washington Nationals, can’t rest on their laurels. In much the same way the Nationals continue to tinker with a good thing to make it better, we constantly work to improve our gold standard products, including the Consumer Price Index (CPI). There’s a lot going on with the CPI these days, and we’ll use this blog and other publications to share the latest information. You’ll read about how we reflect changes in consumer spending patterns, (including new goods), how we’re using other rich sources of data on prices and spending, how we’re accounting for changes in the quality of goods and services, and much more. So let’s get started.

The CPI is designed to measure the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. The CPI is used to determine annual cost-of-living allowances for Social Security beneficiaries. The CPI also is used to adjust the federal income tax system for inflation and as the yardstick for U.S. Treasury inflation-indexed bonds. These are just a few of the many uses of the CPI.

The CPI dates back to 1912, when the Washington baseball team was called the Senators and Walter Johnson ruled the mound. Throughout the history of the CPI, there has been debate about the concepts the CPI should measure and whether it might overstate or understate changes in consumer living costs. The CPI has undergone methodological changes both in response to these discussions and to reflect the changing economic environment. If we hadn’t made these changes, transportation, medical care, recreation, and other goods and services would still be combined into one “miscellaneous” category. Taking the long view, we can track major shifts in consumer inflation for more than a century.

Chart showing 12-month percent change in the Consumer Price Index for All Urban Consumers (CPI-U), 1914 to 2019

Editor’s note: Data for this chart are available in our database at data.bls.gov/timeseries/CUUR0000SA0

In the 1960s, a committee commissioned by Congress recommended that BLS move the CPI closer toward a cost-of-living measure. We responded to those recommendations by creating the CPI for all urban consumers (CPI-U). The former index for urban wage earners was relabeled as the CPI-W. Today, the CPI-U represents the spending patterns of about 93 percent of the population, while the CPI-W represents the spending patterns of about 29 percent.

Here are a few more recent milestones in the history of the CPI:

  • In 1988, following direction from Congress, BLS began calculating the CPI for Americans age 62 and older—called the CPI-E—as an experimental index.
  • In the early 1990s, Congress directed another study of the CPI, popularly referred to as the Boskin Commission. This commission estimated the CPI was overstating the rise in the cost of living and recommended changes in the way the CPI is designed and estimated.
  • In response, BLS sponsored a project in 2002 with the National Academy of Sciences, Committee on National Statistics (CNSTAT) to investigate conceptual, measurement, and other statistical issues in the development of cost-of-living indexes. At this point, we have adopted completely, partially, or experimentally almost all of the CNSTAT recommendations. This includes developing and publishing the Chained CPI, which broadly accounts for consumer substitution of goods and services.

But we can’t stop researching and improving. Today, consumers buy goods and services that weren’t even known a decade ago. And we buy things in many different ways, including from the living room sofa. The growth of e-commerce has created enormous opportunities, but also challenges, for measuring inflation. We continue to work on improvements in response to these developments, and we will talk more about them in future blogs and other publications. In addition, we recently sponsored another CNSTAT panel to investigate three key methodological issues for the CPI:

  1. How best to incorporate data on transactions?
  2. How best to integrate other data sources in the indexes for health insurance, owner-occupied housing, and durable goods?
  3. How to lessen certain types of substitution bias, such as when consumers purchase chicken when the price of steak increases? (Our methods already do a good job accounting for shifts between more similar items, such as between steak and ground beef.)

CNSTAT will convene an expert panel and hold a workshop. Both the panel kickoff and the workshop will be open to the public and will be announced in advance on the BLS website. The panel will then spend about a year in internal discussions and preparing a written report for our consideration.

We expect the CNSTAT report in May of 2021—new ideas, to go with the start of a new baseball season. I’ll be back to blog about the results, so be sure to check back here.

Meet Our New Science and Technology Fellow at BLS

Samantha Tyner
Samantha Tyner

Seeing that we are the U.S. Bureau of LABOR Statistics, we go the extra mile to attract the highest quality labor to accomplish our mission. This includes over 2,000 permanent staff scattered around the country. We also partner with state employees on several BLS programs, and we work with contractors and others to get the job done. Further, we look for opportunities to bring in specialized talent to help with some projects, such as the Civic Digital Fellows who joined us this past summer. Today I want to recognize the first-ever Science and Technology Policy Fellow to spend time at BLS — Samantha Tyner.

The Science & Technology Policy Fellowship is a program of the American Association for the Advancement of Science (AAAS). To understand this program in a nutshell, let me quote directly from their website:

“AAAS Science & Technology Policy Fellowships (STPF) provide opportunities to outstanding scientists and engineers to learn first-hand about policymaking and contribute their knowledge and analytical skills in the policy realm. Fellows serve yearlong assignments in the federal government and represent a broad range of backgrounds, disciplines, and career stages. Each year, STPF adds to a growing corps over 3,000 strong of policy-savvy leaders working across academia, government, nonprofits, and industry to serve the nation and citizens around the world.”

This is the first year BLS has worked with AAAS to bring on a Science and Technology Fellow. We are so fortunate that Samantha (Sam) Tyner started in September and will be with us over the next year. Sam, one of about 200 fellows in the current class, earned her Ph.D. in statistics from Iowa State University and was most recently a postdoctoral researcher at the Center for Statistics and Applications in Forensic Evidence. She is working in the BLS Office of Survey Methods Research (OSMR), focusing on interactive data visualization, text mining, and effective communications to wider audiences.

Let’s find out a little bit about Sam and her fellowship. I asked her what drew her to the federal government. She said she knew pretty early on in graduate school that she didn’t want to go the traditional professor route. She also wasn’t particularly interested in working in one of those internet giants, where the statistics are interesting but the focus is on getting people to click more. She wanted to find ways to use her statistical skills to solve real world problems, and government seemed like a good place for that.

Her first impressions of BLS have been positive. “It’s like hanging out with a bunch of professors, but the staff in OSMR is much more laid back.” One of her current projects involves text mining of BLS mentions on Twitter — what are people saying about us. We’ll use this research to learn how we can better serve our customers.

Another project involves BLS data from the Quarterly Census of Employment and Wages. There is so much data each quarter, down to the county level. She is developing an R Shiny app that will graph these data and allow users to do quick searches. I got to see a quick demo — impressive work after only 2 months on the job.

She is an expert in data visualization, so I asked her what she thinks of some of the charts that BLS produces. I think she was a bit reluctant to criticize, but the comment “you do have a lot of bar charts” was very telling. She describes her goal as to “take a sad chart and make it better.” We certainly welcome her guidance and look forward to producing fewer sad charts in the future.

Beyond all the work Sam is doing at BLS, she also provides posts on the AAAS blog, focusing on some practical aspects of her research. A recent blog taps into her expertise on data visualization. She writes about a problem that can sometimes occur when charts provide too much information. We hope we are not making this mistake with BLS charts.

I’m glad that Samantha has gotten a good start to her Fellowship. We are planning to take full advantage of her research and skills to improve BLS products. I asked her what will make this year a success. Her response — a job offer. Maybe at BLS, or at one of many government agencies where she can use her skills. She will be an asset anywhere she goes.