Topic Archives: U.S. Statistical System

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

How We Collect Data When People Don’t Answer the Phone

I was asked recently how the U.S. Bureau of Labor Statistics can collect data these days when no one answers the telephone. A legitimate question and one we grapple with all the time. I had two answers – one related to data collection methods and one related to sources of data. I will elaborate here about both.

Beige wall phone with rotary dial

But first, do you remember the days before caller ID, when everyone answered the phone? If you were at home, the rotary phone, permanently attached to the kitchen wall, always rang during dinner.

If you were in the office, the phone probably had a row of clear plastic buttons at the bottom that would light up and flash. In either case, who was on the other end of the phone was a mystery until you answered. In those days, your friendly BLS caller could easily get through to you and ask for information.

Vintage office phone with rows of buttons

Fast forward to today’s world of smart phones and other mobile devices. Nobody talks on the phone anymore. Many phone calls are nuisances. A call from BLS might show up as Unknown Number, U.S. Government, or U.S. Department of Labor on your caller ID, or identified as potential spam. With the spread of “spoofing,” many people do not answer calls from numbers they don’t recognize. How do we get around these issues?

Data Collection

At BLS, we consider data collection as much an art as a science. Sure, our staff needs to be well-versed in the information they are collecting. But they also need to be salespersons, able to convince busy people to spend a few minutes answering key questions. Part of that art is making a connection. There are old-fashioned ways that still work, such as sending a letter or showing up at the door. And there are more modern techniques, such as email and text. We are nothing if not persistent.

Our data-collection techniques have been called “High Touch, High Tech.” We start by building a relationship—the High Touch step. BLS has a wide range of information that people and businesses can use to help make informed decisions. We can help you access that information, and we love to see survey respondents use BLS data they helped us produce. In return, we ask for some information from you. There’s where High Tech comes in. We continue to add flexibility to our data-collection toolkit. You can provide information in person, on paper, or on the phone. You also can email information or an encrypted file. Or you can access our online portal anytime and anywhere to provide information or upload a data file. We need your information, and we want to make providing that information as easy as possible.

For example, this chart shows the number of employer self-reports that we’ve received through our online portal over the past several years. Internet data collection has really taken off.

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

Another data-collection strategy we use is asking businesses to allow us to get the information we need from their website. This might involve web scraping data or using an Application Programming Interface (API). We have had success showing businesses that we can get what we need from their website, often eliminating the need for them to compile data.

Alternative Data

Beyond these data-collection strategies, we are expanding efforts to get information from alternative sources, lessening our need to contact businesses and households. Some BLS programs, such as Local Area Unemployment Statistics, the Quarterly Census of Employment and Wages, and Productivity Studies, rely heavily on administrative data and information from other surveys. In these cases, there is little need to contact businesses or people directly.

Other BLS programs, such as the Consumer Price Index (CPI) and the Employment Cost Index (ECI), need to capture timely information. But there are alternatives that can complement direct data collection. The CPI, for example, has produced an experimental price index for new vehicles based on a file of vehicle purchase transactions provided by J.D. Power. Using information from sources like that may eventually lessen the need to have BLS employees contact vehicle dealerships. The ECI found that it was easier to capture employer premiums for unemployment insurance from state tax records than to ask employers.

Alternative data come in many forms, from government records, data aggregators, scanners, crowdsourcing, corporate data files, and many more. BLS is investing heavily in alternative data-collection techniques and alternative data sources. The High Touch and High Tech approach we use every day in our data-collection operations helps us to maximize data quality and minimize respondent burden and cost.

The telephone may go the way of the dinosaur, but that’s not stopping us from using every tool at our disposal to continue to produce gold standard data to inform your decisions.

Number of transactions with BLS internet data collection
YearNumber of transactions

2004

105,145

2005

148,754

2006

219,923

2007

534,555

2008

972,605

2009

1,544,795

2010

1,909,410

2011

2,322,540

2012

2,769,694

2013

3,236,376

2014

3,288,665

2015

3,554,639

2016

4,013,415

2017

4,513,297

2018

4,685,414

2019

4,868,939

Ensuring Security and Fairness in the Release of Economic Statistics

The U.S. Bureau of Labor Statistics is the gold standard of accurate, objective, relevant, timely, and accessible statistical data, and I am committed to keeping it that way. As Commissioner, it is my obligation to do everything possible to protect the integrity of our data and to make sure everyone has equitable access to these data.

One step toward equitable access and data security is coming soon; on March 1, 2020, the U.S. Department of Labor (DOL) will eliminate all electronics from the lock-up facility where we allow members of the media to review economic releases and prepare news stories before the official release of the data. We are changing the procedures to better protect our statistical information from premature disclosure and to ensure fairness in providing our information to the public.

For many years the news media have helped BLS and the Employment and Training Administration (ETA) inform the public about our data. Since the mid-1980s, BLS and ETA have provided prerelease data access to news organizations under strict embargoes, known as “lock-ups.” We have provided this early access consistent with federal Statistical Policy Directives of the Office of Management and Budget. BLS uses the lock-up for several major releases each month, including the Employment Situation and Consumer Price Index. ETA uses the lock-up for the Unemployment Insurance Weekly Claims data. These economic data have significant commercial value and may affect the movement of commodity and financial markets upon release.

Because of technological advancements, the current lock-up procedure creates an unfair competitive advantage for lock-up participants who provide BLS data to trading companies. Today, the internet permits anyone in the world to obtain economic releases for themselves directly from the BLS or DOL websites. However, unlike media organizations with computer access in the current lock-up, others who use the data do not have up to 30 minutes before the official release to process the data. Their postings about the data may lag behind those released directly from the lock-up at official publication time, 8:30 a.m. Eastern. High-speed algorithmic trading technology now gives a notable competitive advantage to market participants who have even a few microseconds head start. To eliminate this advantage and further protect our data from inadvertent or purposeful prerelease, no computers or any other electronic devices will be allowed in the lock-up.

In recent years, BLS and ETA have devoted significant resources to introducing improved technologies that strengthen our infrastructure and ensure data are posted to the BLS or DOL websites immediately following the official release time.

We at BLS and ETA are committed to the principle of a level playing field—our data must be made available to all users at the same time. We are equally committed to protecting our data. We are now positioned to continue helping the media produce accurate stories about the data, while also ensuring that all parties, including the media, businesses, and the general public, will have equitable and timely access to our most sensitive data.

You can find more details about these changes in our notice to lock-up participants. We also have a set of questions and answers about the changes to the lock-up procedures.

Planning BLS Strategy for 2025 and Beyond

The start of the New Year seems like an appropriate time to share the new BLS Strategic Plan, which is designed to provide a roadmap for BLS over the next 5 years and beyond. Today, I want to tell you a little bit about how we developed this plan and then highlight some of its content.

We have a lot of resources to guide us in crafting the strategic plan. Consider:

  • As an agency of the U.S. Department of Labor, BLS provides statistical guidance and support to the department and its agencies. As noted in the Department of Labor’s Strategic Plan, BLS provides sound and impartial information about the economy for decision making.
  • As part of the decentralized U.S. statistical system, BLS works with its sister statistical agencies to share ideas, coordinate common activities, and improve operations.
  • We adhere to various laws, regulations, and policies to ensure that we provide accurate, objective, relevant, timely, and accessible information. Of particular note is the Foundations for Evidence-Based Policymaking Act of 2018, which reaffirms the confidentiality of statistical information and encourages cooperation and efficiencies across the statistical system.

Using all these inputs, BLS senior staff spent the last year looking both inward and outward to refine our mission and vision, and to identify broad strategies and individual goals and objectives for the coming years. We considered our strengths and weaknesses, looked for opportunities and identified threats, and refined a laundry list of ideas into a concise yet comprehensive plan.

It starts with our mission statement:

The Bureau of Labor Statistics measures labor market activity, working conditions, price changes, and productivity in the U.S. economy to support public and private decision making.

We then present the values and principles that guide us in fulfilling that mission, including:

  • Independence from partisan interests
  • Consideration of the needs of a diverse set of customers
  • Confidentiality of our data providers
  • Innovation
  • Stewardship of our staff and our resources

The plan includes five strategies, the first of which is to produce objective statistics and analysis, the core work of our agency. While always striving to improve, we must never lose focus on the hundreds of new data releases we produce each year.

The remaining strategies focus on how we do our work, and how we improve upon that work. Strategy 2 is about making improvements in the information we provide and what techniques we use to produce that information. Strategy 3 is about our source data, with special focus not only on traditional survey respondents but also on alternative data sources. Strategy 4 focuses on managing the resources that allow us to do our work, including our people, our funding, and our infrastructure. Finally, Strategy 5 is about you—our customers who come to us for information. We strive to let you have a seamless customer experience today, and we look for ways to make that experience even better tomorrow.

One of our many challenges in developing this strategic plan was to ensure all BLS staff see themselves in the strategies, goals, and objectives. We also want all BLS stakeholders—data providers, data users, researchers, policymakers, and more—to see their unique perspectives addressed. We hope you will take a few minutes to review the BLS Strategic Plan and let us know if we’ve met this challenge. Feel free to leave a comment below.

Why This Counts: Measuring Industry Productivity

At BLS, productivity is the economic statistic that describes the efficiency of production. The productivity statistics you hear about most often in the news are for the entire U.S. economy. But there’s more to the productivity story than just the overall numbers. The economy is made up of hundreds of industries, and each one works in a different way. Productivity data for each industry help us understand how specific types of production have changed over time. Let’s look at a few specific industries to see how labor productivity data can enhance our understanding of their unique production systems.

General Freight Trucking: Technological Innovations

Economic conditions in the general freight trucking industry closely mirror the health of the overall economy. During the 2007–09 recession, both output and hours worked fell dramatically in trucking. Because employment and spending were down nationwide, there was less demand for the transportation of all kinds of goods. After the recession ended, output and hours in trucking picked back up. Output reached prerecession levels by 2014, but in 2018 hours worked were still slightly below their 2007 level.

Dividing output by hours worked yields labor productivity. Because output in trucking has grown faster than hours during the recovery from the recession, labor productivity has increased. This helps us understand the nature of operations in general freight trucking. Innovative technologies such as communications systems, mapping software, and truck-based sensors and monitors known as “telematics” have improved transportation efficiency. These systems allow deliveries to be planned more efficiently with fewer delays, allowing more freight to be delivered without an equivalent increase in worker hours.

General freight trucking, average yearly percent change in output, hours worked, and productivity from 2007 to 2018

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

Travel Agencies: Digital Transformation

Another industry that has changed the way it operates is travel agencies. Since 2000, output has increased substantially, while hours fell from 2000 to 2010 and have increased only slightly since then. The major transformation for travel agencies has been the Internet. Online tools have allowed clients to make travel reservations with far less help from workers. This increase in efficiency is reflected in the industry’s labor productivity, which has more than tripled from 2000 to 2017.

Travel agencies, average yearly percent change in output, hours worked, and productivity from 2000 to 2017

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

Supermarkets: Incremental Change

Changes in other industries have been more subtle. Supermarkets are a particularly competitive industry, and firms employ a large number of workers to maintain high levels of customer service. Managing inventories, stocking shelves, checking out merchandise, and staffing specialty stations are all tasks that supermarkets continue to need. But even in supermarkets, productivity has been increasing since 2009, as output has grown faster than worker hours. To continue growing sales with lower costs, many firms in this industry have relied more on labor-saving technology, such as self-checkout machines. This technology increases efficiency by allowing supermarkets to process more transactions with less help from workers.

Supermarkets, average yearly percent change in output, hours worked, and productivity from 2009 to 2018

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

Cut and Sew Apparel Manufacturing: Establishment Turnover

Productivity declines also can show the changing nature of work. Cut and sew apparel manufacturing has seen much of its production move outside the United States. In 2018, U.S. apparel manufacturers produced less than 15 percent of the output they produced in 1997. Although worker hours also have declined, they have not dropped as much as output, leading to a decline in labor productivity. This indicates a shift over time in the nature of the average apparel manufacturer. While many large establishments moved overseas in search of cheaper labor, the remaining domestic apparel manufacturing establishments are on average smaller and more specialized, requiring more labor-intensive work.

Cut and sew apparel manufacturing, average yearly percent change in output, hours worked, and productivity from 1997 to 2018

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

To Learn More

BLS industry productivity data help us study the efficiencies of economic activities. Historical trends in productivity provide an important window into each industry’s working conditions, competitiveness, contribution to the economy, and potential for future growth. These data are used by investors, business leaders, jobseekers, researchers, and government decision makers. We have annual labor productivity measures for over 275 detailed industries.

To dive into the data for yourself, check out the BLS webpages on labor productivity. You also can see productivity data in a brand new way using our industry productivity viewer! Even more specialized industry data are on our webpages for hospitals, construction industries, elementary and secondary schools, and urban transit systems. We also have a recent article on productivity in grocery stores.

Average yearly percent change in output, hours worked, and productivity in selected industries
IndustryOutputHours workedProductivity

General freight trucking, 2007 to 2018

1.0%-0.1%1.2%

Travel agencies, 2000 to 2017

4.8-3.08.1

Supermarkets, 2009 to 2018

1.90.71.2

Cut and sew apparel manufacturing, 1997 to 2018

-9.4-7.5-2.1