All posts by BLS Commissioner

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

Modernizing BLS News Releases for the Next Generation

At BLS we are always trying to refine our products to serve our customers better. Over the years, we have updated several of our publications to be more web-friendly and include more interactive features. One major exception has been news releases. In the past few years we have conducted a great deal of outreach and investigation with our news release readers to understand what would make our releases easier to digest and provide greater context to the data. The outcome of this research is the two news release prototypes we’re presenting.

On our beta site, you can find prototypes for the Consumer Price Index and The Employment Situation news releases. We incorporated interactive charts, downloadable excel tables, and a redesigned technical note (now called “About this release”).

We’d love to hear what you think! Please either drop a comment here, or on our beta site, so we can better refine these prototypes for future news releases.

Did You Know Official Unemployment Estimates Are NOT from Unemployment Insurance Counts?

Editor’s Note: On October 23, 2019, we discovered some errors in the news release we published September 25 on which this blog is based. The news release was reissued with corrected data on November 7, 2019. This blog reflects the corrected data.

As BLS Commissioner, I am keenly aware of how much interest there is in our unemployment figures. It has often seemed to me that people don’t understand how we measure unemployment. I sometimes hear things like, “I’m not getting unemployment insurance benefits, so the BLS unemployment numbers don’t include me.”

I’d like to set the record straight. The unemployment estimates we release each month are completely independent of the unemployment insurance program. We do not use counts of people applying for or receiving benefits to determine the national unemployment rate. In fact, we don’t even ask about unemployment insurance benefits in the monthly survey.

How then do we measure unemployment? Our estimates are based on a nationwide, monthly household survey, known as the Current Population Survey, in which we ask people about their labor market activity in a particular week of the month.

We count people as unemployed if they:

  • Were not employed
  • Could have taken a job if one had been offered
  • Had made at least one specific, active effort to find employment in the last 4 weeks OR were on temporary layoff

The definition of unemployment includes people even if they:

  • Are not eligible for unemployment insurance benefits
  • Have exhausted their benefits
  • Did not apply for benefits

To help us learn more about people who do and do not apply for benefits, the Department of Labor’s Chief Evaluation Office sponsored a special supplement or addition to the Current Population Survey in May and September 2018.

From this survey, we learned that 74 percent of unemployed people who worked in the previous 12 months had not applied for unemployment insurance benefits since their last job. Of the unemployed who did not apply, 3 out of 5 did not apply because they didn’t believe they were eligible to receive benefits. Specifically, they believed they were not eligible because their work was not covered by unemployment insurance, they quit their job, they were terminated for misconduct, they had insufficient past work, or they had previously exhausted their benefits.

Percent distribution of unemployed people who did not apply for unemployment insurance benefits  by the main reason for not applying, 2018

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

Looking further into the characteristics of the 26 percent of people who had applied for benefits, people who were last employed in management, professional, and related jobs were most likely to apply. Those in service jobs were least likely to apply.

Percent of unemployed people who applied for unemployment insurance benefits, by occupation of last job, 2018

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

In 2018, two-thirds of unemployed people who had applied for unemployment insurance benefits since their last job received benefits. The percentage of applicants who had received benefits ranged from 54 percent for those who last worked in production, transportation, and material moving occupations to 71 percent for those in natural resources, construction, and maintenance occupations.

Want to learn more about this topic? We have more data on unemployment insurance benefit applicants, nonapplicants, and recipients on our website.

Percent distribution of unemployed people who did not apply for unemployment insurance benefits by the main reason for not applying, 2018
ReasonPercent of unemployed who had worked in the previous 12 months

Eligibility issues

59.1%

Other reasons for not applying for benefits

24.8

Attitude about or barrier to applying for benefits

11.5

Reason not provided

4.6
Percent of unemployed people who applied for unemployment insurance benefits, by occupation of last job, 2018
Occupation of last jobPercent who applied for benefits

Management, professional, and related

37.6%

Natural resources, construction, and maintenance

29.6

Sales and office

24.6

Production, transportation, and material moving

22.9

Service

15.2

New App for Career Information Now Available

Icon for CareerInfo app

BLS has partnered with the U.S. Department of Labor’s Office of the Chief Information Officer to develop the CareerInfo app that is now available from the Apple App Store and Google Play. CareerInfo presents information from the Occupational Outlook Handbook, the most popular BLS resource for career information.

The CareerInfo app helps you find data and information about employment, pay, job outlook, how to become one, and more for hundreds of detailed occupations. You can browse by occupational groups and titles or search by occupation or keywords. Within occupational groups, the app allows you to sort by occupation title, projected growth, and typical education or median pay.

Future updates will add features that will let you personalize the app by filtering searches and by “liking,” saving, viewing, and comparing favorites.

Check out the new CareerInfo app and explore the occupational information and data produced by BLS. You’ll be glad you did!

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.