Topic Archives: Why This Counts

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

Projected Occupational Openings: Where Do They Come From?

Toward the beginning of each school year, BLS issues a new set of Employment Projections, looking at projected growth and decline in occupations over the next decade. These estimates are important for understanding structural changes in the workforce over time. But to identify opportunities for new workers, we need to look beyond occupational growth and decline, to a concept we call “occupational openings.”

Occupational openings are the sum of the following:

  • Projected job growth (or decline)
  • Occupational separations — workers leaving an occupation, which includes:
    • Labor force exits — workers who leave the labor force entirely, perhaps to retire
    • Occupational transfers — workers who leave one occupation and transfer to a different occupation.

This video explains the concept of occupational openings further.

BLS publishes the projected number of occupational openings for over 800 occupations. Not surprisingly, some of the largest occupations in the country have some of the largest number of openings. For example, certain food service jobs, which include fast food workers, are projected to have nearly 800,000 openings per year over the next decade. I guess this isn’t a surprise in an occupation with over 3.7 million workers.

But when we delve into the information on occupational openings a little further, more stories emerge. Some related occupations have very different patterns of openings. And some occupations have similar levels of openings for different reasons. Let’s take a look at a few examples.

In 2018, there were over 800,000 lawyers in the U.S., and a projected 45,000 annual openings for lawyers, about 5.5 percent of employment. At the same time, there were fewer than half the number of paralegals and legal assistants (325,000), with projected annual openings around 40,000 per year – 12.4 percent of employment. These two related occupations had similar numbers of projected openings, but those openings represented different proportions of current employment. Such differences reflect required education, demographics, compensation, and other variables. Lawyers tend to have professional degrees that are specialized for that occupation and are therefore more closely tied to their occupation than paralegals, who have more diverse educational backgrounds. You can find out more about how worker characteristics affect these numbers in the Monthly Labor Review.

Now let’s look at the sources of occupational openings. In this first example, we compare two occupational groups: installation, maintenance, and repair occupations and healthcare support occupations. These are broad categories that include a number of different individual occupations.

Average annual occupational openings for installation, maintenance, and repair occupations and healthcare support occupations, 2018–28

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

In this example, both occupational groups have projected annual openings of a little over 600,000 per year, yet they come from different sources. Two-thirds of the openings among installation occupations result from workers leaving to go to other occupations; in contrast, just under half the openings among healthcare support occupations are from people moving to other occupations. Looking at projected job growth, BLS projects that healthcare support occupations, the fastest growing occupational group, will add more than three times as many new jobs as installation occupations, annually over the next decade (78,520 versus 23,320).

Now let’s look at two individual occupations — web developers and court, municipal, and license clerks. These are very different jobs, but both are projected to have about 15,000 annual openings over the next decade. Here, too, occupational openings come from very different places, as this chart shows:

Average annual occupational openings for web developers and court, municipal, and license clerks, 2018–28

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

In this case, around 67 percent of openings for web developer jobs come from workers transferring to other jobs, compared with only 49 percent transfers for clerks. But a greater share of clerks are exiting the labor force. Once again, differences are due to a variety of factors, although the age of workers is a significant factor in this case — web developers have a median age of 38.3, while clerks tend to be older, with a median age of 49.1. Younger workers are more likely to transfer occupations, while older workers are more likely to exit the labor force, as for retirement.

So what does all this really mean? If nothing else, you can see that the thousands of individual data elements available through the BLS Employment Projections program tell a thousand different stories, and more. Whether large or small, growing or declining, there’s information about hundreds of occupations that can be helpful to students looking for careers, counselors helping those students and others, workers wanting to change jobs, employers thinking about their future, policymakers considering where to put job training resources, and on and on. These examples just scratch the surface of what BLS Employment Projections information can tell us. Take a look for yourself.

Average annual occupational openings, 2018–28
OccupationEmployment growthExitsTransfers

Installation, maintenance, and repair occupations

23,320195,700413,900

Healthcare support occupations

78,520235,500299,600
Average annual occupational openings, 2018–28
OccupationEmployment growthExitsTransfers

Web developers

2,0902,90010,100

Court, municipal, and license clerks

6707,0007,300

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

New Data on Balancing Family Needs with Work

Among the many challenges for today’s families is the balance between caregiving and the demands of working outside the home. Some workers are even sandwiched between the need to provide both childcare and eldercare. New information from the Bureau of Labor Statistics shows that about two out of three employees have paid time off available to meet these needs.

Interest among federal, state, and local policymakers in paid time off and other job flexibilities motivated the U.S. Department of Labor’s Women’s Bureau to sponsor an extra set of questions in the American Time Use Survey. The 2017–18 Leave and Job Flexibilities Module gives us data on the characteristics of wage and salary workers who have access to paid and unpaid leave in their jobs. The module also asked questions about workers who work at home and whether they have flexible work schedules. We also know more about workers who do not have access to leave and job flexibilities. Because we collected the data directly from workers, we could ask them about their experiences, such as the reasons they take leave, or don’t take it even when they need to, and why they work at home.

We now know that 66 percent of U.S. wage and salary workers were able to take paid leave from their jobs in 2017–18. Workers were most often able to use paid leave for a vacation and if they were sick or needed medical care. One area of interest is about people who provide unpaid eldercare. The survey showed that 64 percent of eldercare providers who were employed were able to use paid leave to provide elder caregiving. Another 28 percent of these caregivers were not able to take paid leave for this reason, and 8 percent didn’t know if their employer would allow them to use paid leave to provide eldercare.

Percent of workers with access to paid leave who could use it for the following reasons, 2017–18

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

We also have learned that 36 million workers (25 percent) sometimes worked at home, and they did so for different reasons. Twenty-four percent worked at home because of a personal preference, 23 percent did so to catch up on work, 22 percent worked at home to coordinate their work schedule with personal or family needs, and 16 percent did so because their job required it. Among those who sometimes worked at home, men and women had different reasons for doing so. Women were more likely than men to work at home to finish or catch up on work and to coordinate their work schedule with personal or family needs. Men were more likely than women to work at home because of a personal preference.

Percent of workers who work at home by main reason, 2017–18

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

We published these results and more in two recent news releases. One news release focused on workers’ access to leave, their use of leave, and an unmet need for leave. The second focused on workers’ job flexibilities and work schedules.

These releases present data on:

  • Access to paid and unpaid time off
  • Use of paid and unpaid time off
  • Needing to take leave from a job but deciding not to take it
  • Flexible work hours
  • Knowing work schedule in advance
  • Working from home

The releases provide information by:

  • Gender
  • Age
  • Race
  • Hispanic or Latino ethnicity
  • Educational attainment
  • Full- or part-time status
  • Earnings

We also have data files that allow researchers to analyze the data and gain even more insights. Following the policies of BLS and the U.S. Census Bureau to protect the privacy of survey respondents, these data files do not have any information that could identify individual participants.

Percent of workers with access to paid leave who could use it for the following reasons, 2017–18
ReasonYesNoDon’t know

Vacation

95%5%0%

Own illness or medical care

9461

Illness or medical care of another family member

78166

Birth or adoption of a child

76159

Errands or personal reasons

70282

Childcare, other than for illness

65314

Eldercare

64288

Note: The estimates for “childcare, other than for illness” are for workers who were parents of household children under age 18. The estimates for “eldercare” are only for workers who were eldercare providers.

Percent of workers who work at home by main reason, 2017–18
ReasonTotalMenWomen

Personal preference

24%27%21%

Finish or catch up on work

232126

Coordinate work schedule with personal or family needs

222025

Job requires working at home

161616

Reduce commuting time or expense

9109

Weather

443

Other

221

New Data on Employment and Wages in U.S. Establishments with Foreign Ownership

Did you know that U.S. establishments at least partially owned by foreign companies employed 5.5 million U.S. workers in 2012? That was 5.0 percent of U.S. private-sector employment. The U.S. Bureau of Labor Statistics recently partnered with the Bureau of Economic Analysis to produce new data on foreign direct investment in the United States. These two agencies created a new, richer dataset on employment, wages, and occupations in U.S. establishments that have at least one foreign owner.

So how do we define foreign direct investment anyway? In the simplest sense, it is when a U.S. establishment has an owner from another country with at least a 10-percent stake. We consider any establishment that does not meet this threshold as domestically owned. The new data are more detailed than any data previously available on foreign direct investment in the United States. This first set of data is for 2012, but the agencies plan to work together to produce more recent data soon.

Nearly two-thirds of jobs in establishments with foreign ownership had European ownership (3.5 million jobs). The United Kingdom accounted for 874,000 of these jobs. Asia accounted for 17 percent (936,000 jobs) of jobs in U.S. establishments with foreign ownership. Canada accounted for 12 percent (671,000 jobs). The remaining world regions together accounted for less than 8 percent.

Now let’s look at how employment in establishments with foreign ownership breaks down within the United States. The map below shows the percent of private employment in establishments with foreign ownership in each state. South Carolina had the largest share of private employment in establishments with foreign ownership, 8.0 percent. Other states with large shares include New Hampshire, Michigan, Connecticut, New Jersey, and Indiana.

Map showing  each state's percent of private employment in establishments with foreign ownership, 2012

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

Each state’s percent of employment in establishments with foreign ownership depends in part on the industry mix in the state. The chart below shows the percent of each industry’s employment in establishments with foreign ownership. In mining, quarrying, and oil and gas extraction, 14.7 percent of employment is in establishments with foreign ownership. A large share of employment in Alaska is in this industry. Alaska’s share of employment in establishments with foreign ownership, 5.7 percent, is above the national average. Alaska’s vast energy resources may play a role in its share of employment in establishments with foreign ownership.

About 13.2 percent of all employees in manufacturing work in establishments with foreign ownership. Michigan has a large share of employment in manufacturing, and also a large share of employment in establishments with foreign ownership.

Chart showing percent of private employment in establishments with foreign ownership, by industry, 2012

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

Now let’s turn from employment to wages. The map below shows how wages in establishments with foreign ownership compare with wages in domestically owned establishments across the country. We make this comparison by calculating the ratio of what workers make in average wages in establishments with foreign ownership compared to the average wage in domestically owned establishments. Wage ratios greater than one mean the average for establishments with foreign ownership is higher than for domestically owned establishments. The U.S. wage ratio in 2012 was 1.57, and every state had a wage ratio greater than one. The highest wage ratio was in New York, at 1.98. At the other end of the spectrum, Vermont had a wage ratio of 1.05.

Map showing each state's ratio of average wages in establishments with foreign ownership to domestically owned establishments, 2012

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

Does this mean every establishment with foreign ownership pays higher wages than domestically owned establishments? Let’s analyze wage ratios by industry. We see that the health care and social assistance industry had a wage ratio of 0.86 in 2012. All other major industry groups had wage ratios of 1.00 or higher. The finance and insurance industry had a wage ratio of 1.82.

Want to know more about these data? See our Spotlight on Statistics, “A look at employment and wages in U.S. establishments with foreign ownership.”

Chart showing ratio of average wages in establishments with foreign ownership to domestically owned establishments, by industry, 2012

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

BLS and the Bureau of Economic Analysis hope to continue this interagency collaboration. Our goal is to merge and analyze more recent data from both agencies. When agencies work together to produce new datasets with little increase in cost to the public, all data users benefit. Producing accurate, objective, relevant, timely, and accessible products is the BLS mission. This collaboration to produce new relevant data allows us to improve our service to the American people.

Percent of private employment in establishments with foreign ownership, 2012
StateEmployment share

National

5.0%

Alabama

5.4

Alaska

5.7

Arizona

3.9

Arkansas

4.5

California

4.2

Colorado

4.6

Connecticut

6.5

Delaware

6.0

District of Columbia

3.4

Florida

3.6

Georgia

5.5

Hawaii

6.0

Idaho

2.9

Illinois

5.1

Indiana

6.4

Iowa

4.0

Kansas

5.7

Kentucky

6.2

Louisiana

3.9

Maine

6.1

Maryland

4.7

Massachusetts

6.3

Michigan

6.6

Minnesota

4.0

Mississippi

3.4

Missouri

4.0

Montana

1.8

Nebraska

3.6

Nevada

3.8

New Hampshire

6.9

New Jersey

6.5

New Mexico

3.0

New York

5.8

North Carolina

6.2

North Dakota

3.8

Ohio

5.3

Oklahoma

3.6

Oregon

3.4

Pennsylvania

5.5

Rhode Island

6.1

South Carolina

8.0

South Dakota

2.1

Tennessee

5.5

Texas

5.3

Utah

4.0

Vermont

3.7

Virginia

5.1

Washington

4.0

West Virginia

4.8

Wisconsin

3.5

Wyoming

3.8
Percent of private employment in establishments with foreign ownership, by industry, 2012
IndustryEmployment share

Mining, quarrying, and oil and gas extraction

14.7%

Manufacturing

13.2

Management of companies and enterprises

9.6

Wholesale trade

9.0

Information

7.8

Finance and insurance

7.5

Utilities

7.3

Transportation and warehousing

6.3

Administrative and waste services

6.0

Professional, scientific, and technical services

5.5

Total private

5.0

Retail trade

4.7

Real estate and rental and leasing

2.2

Construction

1.8

Accommodation and food services

1.6

Other services (except public administration)

1.3

Agriculture, forestry, fishing, and hunting

1.0

Health care and social assistance

0.9

Arts, entertainment, and recreation

0.7

Educational services

0.6
Ratio of average wages in establishments with foreign ownership to domestically owned establishments, 2012
StateWage ratio

National

1.57

Alabama

1.44

Alaska

1.63

Arizona

1.28

Arkansas

1.43

California

1.49

Colorado

1.53

Connecticut

1.53

Delaware

1.78

District of Columbia

1.08

Florida

1.52

Georgia

1.36

Hawaii

1.06

Idaho

1.30

Illinois

1.61

Indiana

1.56

Iowa

1.48

Kansas

1.56

Kentucky

1.36

Louisiana

1.67

Maine

1.26

Maryland

1.28

Massachusetts

1.46

Michigan

1.84

Minnesota

1.50

Mississippi

1.63

Missouri

1.55

Montana

1.63

Nebraska

1.35

Nevada

1.47

New Hampshire

1.39

New Jersey

1.64

New Mexico

1.22

New York

1.98

North Carolina

1.47

North Dakota

1.55

Ohio

1.49

Oklahoma

1.40

Oregon

1.41

Pennsylvania

1.43

Rhode Island

1.31

South Carolina

1.43

South Dakota

1.45

Tennessee

1.42

Texas

1.80

Utah

1.45

Vermont

1.05

Virginia

1.23

Washington

1.40

West Virginia

1.33

Wisconsin

1.38

Wyoming

1.72
Ratio of average wages in establishments with foreign ownership to domestically owned establishments, by industry, 2012
IndustryWage ratio

Finance and insurance

1.82

Construction

1.62

Total private

1.57

Accommodation and food services

1.51

Real estate and rental and leasing

1.50

Arts, entertainment, and recreation

1.45

Other services (except public administration)

1.44

Agriculture, forestry, fishing, and hunting

1.40

Wholesale trade

1.39

Professional, scientific, and technical services

1.39

Mining, quarrying, and oil and gas extraction

1.28

Management of companies and enterprises

1.23

Retail trade

1.20

Educational services

1.19

Manufacturing

1.18

Utilities

1.15

Administrative and waste services

1.13

Information

1.05

Transportation and warehousing

1.00

Health care and social assistance

0.86