Topic Archives: Industries

New State and Metropolitan Area Data from the Job Openings and Labor Turnover Survey

Soon after I became Commissioner, the top-notch BLS staff shared with me their vision to expand the Job Openings and Labor Turnover Survey (JOLTS). The JOLTS program publishes data each month on the number and rate of job openings, hires, and separations (broken out by quits, layoffs and discharges, and other separations). These data are available at the national level and for the four large geographic regions—Northeast, Midwest, South, and West.

That left a major data gap on labor demand, hires, and separations for states and metropolitan areas. BLS provides data on labor supply for states and metro areas each month from the Local Area Unemployment Statistics program. We also provide data on employment change in states and metro areas each month from the Current Employment Statistics survey. Employment change is the net effect of hires and separations, but it doesn’t show the underlying flow of job creation and destruction. Having better, timelier state and metro JOLTS data would provide a quicker signal about whether labor demand is accelerating or weakening in local economies.

About 2 months after the staff briefed me, the JOLTS program published experimental state estimates for the first time on May 24, 2019. We have been updating those estimates on a quarterly basis since then. We use a statistical model to help us produce the most current state estimates. We then improve those estimates during an annual benchmark process by taking advantage of data available from the Quarterly Census of Employment and Wages. The JOLTS program is well on its way to moving these state estimates into its official, monthly data stream. Look for that to happen in the second half of 2021!

The President’s proposed budget for fiscal year 2021 includes three improvements to the JOLTS program.

  • Expand the sample to support direct sample-based estimates for each state.
  • Accelerate the review and publication of the estimates.
  • Add questions to provide more information about job openings, hires, and separations.

If funded, this proposal would allow BLS to improve the data quality available from the current JOLTS state estimates. It also would let us add very broad industry detail for each state and more industry detail at the national level.

The proposed larger sample size may also let us produce model-assisted JOLTS estimates for many metro areas. To demonstrate this potential, the JOLTS team produced a one-time set of research estimates for the 18 largest metropolitan statistical areas, those with 1.5 million or more employees. These research estimates show the potential for data that would be available regularly with a larger JOLTS sample. I encourage you to explore this exciting new research series and let us know what you think.

Number of unemployed per job opening in the United States and four large metropolitan areas, 2007–19

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

This is just one example of the excellent work I see at BLS every day. The BLS staff are consummate professionals who continue to do outstanding work even in the most trying of times. The entire BLS staff has been teleworking now for several months due to COVID-19, and every program continues to produce high quality data on schedule! Even in these extraordinary circumstances, BLS professionals continue to innovate and find ways to improve quality and develop new gold standard data products to help the policymakers, businesses, and the public make better-informed decisions.

Number of unemployed per job opening in the United States and four large metropolitan areas
DateNew York-Newark-Jersey City, NY-NJ-PADallas-Fort Worth-Arlington, TXChicago-Naperville-Elgin, IL-IN-WILos Angeles-Long Beach-Anaheim, CAUnited States

Jan 2007

1.81.41.81.61.6

Feb 2007

1.81.41.91.61.6

Mar 2007

1.71.31.81.61.6

Apr 2007

1.61.11.61.51.4

May 2007

1.51.01.51.51.4

Jun 2007

1.51.11.61.41.4

Jul 2007

1.61.21.81.51.5

Aug 2007

1.71.21.91.51.5

Sep 2007

1.71.21.81.71.5

Oct 2007

1.61.11.71.81.5

Nov 2007

1.61.21.81.91.5

Dec 2007

1.71.21.91.91.6

Jan 2008

1.91.32.02.11.7

Feb 2008

2.11.32.22.21.8

Mar 2008

2.21.32.42.21.9

Apr 2008

2.11.22.22.01.8

May 2008

2.01.22.22.11.8

Jun 2008

1.91.22.42.51.9

Jul 2008

2.21.43.03.02.2

Aug 2008

2.31.73.13.52.4

Sep 2008

2.41.83.13.62.5

Oct 2008

2.51.93.04.02.6

Nov 2008

2.82.13.44.62.9

Dec 2008

3.12.43.95.93.3

Jan 2009

3.62.94.86.74.0

Feb 2009

4.43.25.57.34.6

Mar 2009

5.03.56.47.45.1

Apr 2009

5.03.66.77.35.3

May 2009

5.14.06.87.15.4

Jun 2009

5.14.67.26.85.6

Jul 2009

5.25.37.77.46.0

Aug 2009

5.15.67.97.76.2

Sep 2009

5.45.88.08.06.1

Oct 2009

5.95.37.87.85.8

Nov 2009

6.75.68.68.25.9

Dec 2009

7.15.69.58.36.2

Jan 2010

7.06.310.88.36.2

Feb 2010

7.06.410.37.56.2

Mar 2010

7.05.98.87.15.9

Apr 2010

6.55.17.86.75.4

May 2010

5.94.86.66.54.9

Jun 2010

5.05.16.26.74.8

Jul 2010

4.85.26.07.14.9

Aug 2010

4.75.46.27.54.9

Sep 2010

4.94.76.17.54.7

Oct 2010

4.64.25.27.14.5

Nov 2010

4.84.05.07.14.5

Dec 2010

5.34.15.17.14.6

Jan 2011

6.04.35.57.15.0

Feb 2011

6.14.25.76.84.9

Mar 2011

5.54.05.36.34.6

Apr 2011

5.03.85.15.84.2

May 2011

4.63.64.65.84.1

Jun 2011

4.53.74.55.64.0

Jul 2011

4.63.84.65.94.1

Aug 2011

4.53.84.96.04.0

Sep 2011

4.43.44.65.83.8

Oct 2011

4.23.14.25.63.6

Nov 2011

4.03.04.25.33.6

Dec 2011

4.23.04.85.53.7

Jan 2012

4.63.05.25.93.7

Feb 2012

5.33.04.86.23.7

Mar 2012

5.12.84.25.73.5

Apr 2012

4.22.43.65.03.3

May 2012

3.92.13.44.83.1

Jun 2012

3.92.13.64.63.1

Jul 2012

4.22.33.95.33.3

Aug 2012

4.02.43.95.23.4

Sep 2012

3.82.43.55.53.2

Oct 2012

3.72.13.25.03.0

Nov 2012

3.92.03.44.83.0

Dec 2012

4.02.03.75.13.2

Jan 2013

4.22.24.35.43.4

Feb 2013

4.22.14.15.43.4

Mar 2013

4.02.03.94.93.2

Apr 2013

3.51.93.54.12.9

May 2013

3.21.93.53.82.7

Jun 2013

3.22.13.63.62.7

Jul 2013

3.42.33.83.82.9

Aug 2013

3.42.33.73.92.9

Sep 2013

3.42.13.64.02.8

Oct 2013

3.22.03.33.92.5

Nov 2013

3.22.03.24.12.5

Dec 2013

3.22.03.34.22.6

Jan 2014

3.42.03.54.22.7

Feb 2014

3.41.93.53.72.7

Mar 2014

3.21.93.33.32.6

Apr 2014

2.81.72.62.82.2

May 2014

2.51.62.12.82.0

Jun 2014

2.41.62.02.81.9

Jul 2014

2.61.62.13.12.0

Aug 2014

2.61.62.12.91.9

Sep 2014

2.51.62.02.91.9

Oct 2014

2.31.51.92.71.7

Nov 2014

2.41.51.92.91.8

Dec 2014

2.51.31.92.91.8

Jan 2015

2.61.32.02.91.9

Feb 2015

2.51.31.92.61.8

Mar 2015

2.41.31.82.41.7

Apr 2015

2.21.21.62.21.6

May 2015

2.01.11.52.21.5

Jun 2015

1.91.01.62.31.5

Jul 2015

1.91.01.62.31.5

Aug 2015

1.90.91.62.31.5

Sep 2015

1.80.91.52.31.4

Oct 2015

1.70.81.52.01.3

Nov 2015

1.60.81.52.01.3

Dec 2015

1.60.81.51.91.4

Jan 2016

1.70.91.61.91.4

Feb 2016

1.80.81.61.71.5

Mar 2016

1.70.81.61.61.4

Apr 2016

1.60.71.61.61.3

May 2016

1.40.71.41.61.2

Jun 2016

1.40.81.41.61.3

Jul 2016

1.40.91.41.81.3

Aug 2016

1.50.91.51.91.4

Sep 2016

1.40.91.51.91.3

Oct 2016

1.30.91.51.71.3

Nov 2016

1.30.91.41.71.3

Dec 2016

1.30.91.51.71.3

Jan 2017

1.41.01.71.81.4

Feb 2017

1.51.01.71.81.4

Mar 2017

1.51.01.51.81.4

Apr 2017

1.30.91.41.61.2

May 2017

1.30.91.21.51.1

Jun 2017

1.31.01.21.41.1

Jul 2017

1.31.11.21.51.1

Aug 2017

1.41.11.21.61.1

Sep 2017

1.41.01.11.51.1

Oct 2017

1.31.01.01.31.0

Nov 2017

1.21.01.01.31.0

Dec 2017

1.21.01.11.31.0

Jan 2018

1.21.11.31.31.1

Feb 2018

1.21.11.31.21.1

Mar 2018

1.21.11.21.21.1

Apr 2018

1.11.01.11.11.0

May 2018

1.01.00.91.00.9

Jun 2018

1.00.90.81.10.9

Jul 2018

1.00.80.81.10.9

Aug 2018

1.00.80.91.20.9

Sep 2018

0.90.80.91.20.8

Oct 2018

0.90.80.81.10.8

Nov 2018

0.80.80.81.20.8

Dec 2018

0.90.70.81.30.8

Jan 2019

1.00.80.91.40.9

Feb 2019

1.10.81.01.40.9

Mar 2019

1.10.80.91.40.9

Apr 2019

1.00.70.91.10.8

May 2019

0.80.70.81.00.8

Jun 2019

0.80.60.80.90.8

Jul 2019

0.80.70.81.00.8

Aug 2019

0.80.80.91.10.9

Sep 2019

0.80.80.91.20.8

Oct 2019

0.80.80.81.10.8

Nov 2019

0.80.80.71.00.8

Dec 2019

0.80.80.71.00.8

Paid Leave Benefits When You Are Unable to Work

Many American workers have lost jobs or had their work hours reduced as a result of the COVID-19 pandemic and response efforts. Many other workers still have jobs, but their work environment probably has changed since March. It’s reasonable to assume more people are working from home now than the 29 percent we reported who could work at home in 2017–18. At BLS we are still working to provide you with the latest economic data and analysis, but nearly all of us are now working from home, instead of in our offices.

Still, there are many jobs that just can’t be done from home. In these challenging times, I know we all are grateful for the healthcare workers who are treating patients who have COVID-19 and other medical conditions. We’re grateful for our emergency responders and for the truck drivers, warehouse workers, delivery workers, and staff in grocery stores, pharmacies, and other retail establishments that provide us with the necessities of daily life. As much as I think of these men and women as superheroes, I know they are humans. Even extraordinary humans can get sick, or they may need to take care of family members who get sick. Let’s look at the leave benefits available to them if they need it.

According to our National Compensation Survey, 73 percent of private industry workers were covered by paid sick leave in 2019. Among state and local government workers, 91 percent were covered by paid sick leave. The availability of sick leave benefits varied by occupation, ranging from 94 percent of managers in private industry to 56 percent of workers in construction and extraction occupations.

The share with paid sick leave also varies by industry, pay level, size of establishment, and other characteristics of jobs and employers. The following chart shows sick leave availability for employers of different sizes.

Percent of workers in private industry with access to paid sick leave by establishment size, March 2019

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

Paid sick leave plans commonly provide a fixed number of days per year. The number of days may vary by the worker’s length of service with the employer. The average in private industry in 2019 was 7 paid sick leave days.

Average number of paid sick leave days per year for workers in private industry, by length of service and establishment size, March 2019

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

About half of workers with such a plan could carry over unused days from year to year.

We recently posted a new fact sheet on paid sick leave that provides even more detail.

In the past few years, some states and cities have mandated that certain employers provide their workers with paid sick leave. We include these mandated plans in our data on paid leave. A Federal law passed in March 2020 requires paid sick leave for certain workers affected by COVID-19.

In addition to paid sick leave, some employers offer a short-term disability insurance plan when employees can’t work because of illness. These plans are sometimes called sickness and accident insurance plans. This was traditionally a blue-collar or union benefit, and it often replaces only a portion of an employee’s pay. In 2019, 42 percent of private industry workers had access to such a benefit. Like sick leave, the availability of short-term disability benefits varies widely across worker groups. Some states provide Temporary Disability Insurance plans that provide similar benefits.

While the National Compensation Survey asks employers what benefits they offer to workers, the American Time Use Survey recently asked workers whether paid leave is available from their employer and whether they used it. In 2017–18, two-thirds of workers had access to paid leave at their jobs. These data include information on age, sex, and other characteristics. For example, younger workers (ages 15–24) and older workers (age 65 and older) were less likely to have access to paid leave than were other workers.

Percent of workers with access to paid leave by age, 2017–18 averages

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

While the survey did not ask workers to classify the type of leave, they were asked the reasons they could take leave. Of those with paid leave available, 94 percent could use it for their own illness or medical care, and 78 percent could use it for the illness or medical care of another family member.

I hope you and your loved ones remain healthy and are able to take care of each other in these challenging times. High-quality data will be vital in the public health response to the COVID-19 pandemic. High-quality data also will be vital for measuring the economic impact of the pandemic and recovery from it. My colleagues at BLS and our fellow U.S. statistical agencies remain on the job to provide you with gold standard data.

Percent of workers in private industry with access to paid sick leave by establishment size, March 2019
Establishment sizePercent

1–49 workers

64%

50–99 workers

68

100–499 workers

80

500 workers or more

89
Average number of paid sick leave days per year for workers in private industry, by length of service and establishment size, March 2019
Length of serviceAll establishments 1 to 49 workers50 to 99 workers100 to 499 workers500 workers or more

After 1 year

76678

After 5 years

77679

After 10 years

77779

After 20 years

77779
Percent of workers with access to paid leave by age, 2017–18 averages
AgePercent

Ages 15–24

35.4%

Ages 25–34

70.3

Ages 35–44

71.7

Ages 45–54

74.4

Ages 55–64

74.2

Age 65 and older

51.7

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.

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 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