Topic Archives: U.S. Statistical System

Why This Counts: What Types of Jobs Are in the U.S. Labor Market?

Ever wonder how many accountants there are in the United States? Or how much an occupational therapist gets paid? Or maybe you already have a job, but you’re thinking about working somewhere new. What areas or industries have the highest pay for your occupation?

We have the answers to these questions, plus much, much more!

The Occupational Employment Statistics (OES) survey publishes hundreds of thousands of estimates for employment and wages covering around 800 detailed occupations in 600 areas spanning all 50 states, the District of Columbia, and three territories: Guam, Puerto Rico, and the U.S. Virgin Islands.

That sounds impressive, but what does it mean? It means you can see employment and wages for occupations where you live or in the type of business where you work. OES provides specific information on the types of jobs found in each industry or area and their wages.

OES building blocks: occupation and industry

Before we dive into the deep end with data, let’s wade in a little by clarifying some terms. In our everyday lives, occupation and industry may be interchangeable, but in fact occupation refers to the worker and industry refers to the employer.

Occupation refers to what people do and the jobs people have. BLS uses the Standard Occupational Classification system to code workers into more than 800 different occupations based on their job duties. This system is the standard used by federal agencies to classify workers into occupations.

Industry refers to the types of businesses where people work. BLS uses the North American Industry Classification System to code business establishments into industries based on what they produce or sell. This also is the standard used by federal agencies to classify business establishments into industries.

Because we use these federally mandated coding structures, data users can easily compare OES data with other federal statistical programs.

Why does OES data count?

For this blog post, we will only focus on national level data. We’re saving state and area data for a later post. Let’s take a closer look at the occupational data for the United States and in certain industries.

People count on OES data to see employment by occupation

Did you know that the largest occupation in the United States is retail salespersons? This chart shows the 10 largest occupations, which together account for more than one in five jobs in the United States.

Employment in the largest occupations, May 2017

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

According to May 2017 OES data, there were 4.4 million retail salespersons in the United States, accounting for 3 percent of all jobs. The largest three occupations combined account for 8 percent of all U.S. jobs and also include cashiers and combined food preparation and serving workers (each 3.6 million).

We also have data on some of the smallest occupations in the country, such as geographers, watch repairers, astronomers, fabric menders, and mine shuttle car operators. Each of these occupations has fewer than 5,000 jobs.

People count on OES data for wages by occupation

Eight of the 10 largest occupations in the United States had below-average wages. Retail salespersons ($27,460), combined food preparation and serving workers ($21,230), and cashiers ($22,130) had annual mean wages significantly below the average for all occupations of $50,620.

Registered nurses ($73,550) and general and operations managers ($123,460) were the largest occupations with above-average wages.

Annual mean wages for the largest occupations, May 2017

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

People count on OES data to compare occupations

Occupational employment and wage information is useful to students and schools making investments in education. They can see which fields have the best prospects for getting a job with good wages.

The pairs of related occupations in the table below show wages are generally higher for the occupation with more education and training requirements. In many cases employment is higher in the occupation with more education or training, and in some cases employment is lower.

 Median wage and employment data by select occupations, May 2017

Occupation Median hourly wage Employment
Mechanical Drafters $26.50 58,190
Mechanical Engineers $41.29 291,290
Cooks, Restaurant $12.10 1,276,510
Chefs and Head Cooks $22.09 131,430
Shampooers $9.77 13,330
Hairdressers, Hairstylists, and Cosmetologists $11.95 351,910
Retail Salespersons $11.16 4,442,090
First-Line Supervisors of Retail Sales Workers $18.54 1,200,180
Bookkeeping, Accounting, and Auditing Clerks $18.87 1,532,340
Accountants and Auditors $33.34 1,241,000
Dental Assistants $18.09 337,160
Dental Hygienists $35.61 211,600
Light Truck or Delivery Services Drivers $15.12 877,670
Heavy and Tractor-Trailer Truck Drivers $20.42 1,748,140

People count on OES data to see the types of jobs in each industry

OES data can complement other BLS data by showing the different types of jobs in each industry. For example, healthcare and social assistance is one of the largest industries in the United States. OES data show the types of jobs in this industry. This chart shows the 10 largest occupations in the health care and social assistance industry.

Largest occupations in health care and social assistance, May 2017

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

Although many of the largest occupations in health care and social assistance are concentrated in this industry, some of the largest occupations in this sector, such as childcare workers, general office clerks, and receptionists and information clerks, can be found in many other industries as well. Jobseekers or workers wanting to increase their wage can use OES data to see which industries pay more by occupation.

The top paying industries for receptionists and information clerks include utilities ($34,780), construction ($31,070), and manufacturing ($30,900), in addition to health care and social assistance ($30,840). According to the May 2017 OES estimates, the national average annual wage for receptionists and information clerks was $29,640.

Industries with the highest annual mean wages for receptionists and information clerks, May 2017

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

Who uses OES data?

Employers frequently use OES data for their industry. Business startups and entrepreneurs use the data to help determine typical staffing needs and expenses for businesses similar to theirs. Established businesses use occupational wage distributions to ensure they remain competitive and retain and attract good workers. In addition, OES data are used by students, jobseekers, and career advisors to help with career planning.

You may also encounter OES data in other places, because the data are used by a number of other federal agencies. The BLS Employment Projections program uses industry staffing patterns and wages from OES to produce estimates of future job growth. The U.S. Department of Labor Office of Foreign Labor Certification uses OES data to set prevailing wages for visa applicants. The Bureau of Economic Analysis uses OES wages to estimate social security receipts. The Centers for Medicare and Medicaid Services use the data to set reimbursement rates for health care providers. These are just a few of the ways OES data are used by other government programs and agencies.

 Want to know more?

You can further explore all of the reasons why OES data count at the OES homepage. Read the latest OES news release, get answers to frequently asked questions and check out our maps. Also, contact the OES information staff with questions by email or call (202) 691-6569.

Use these gold-standard data to learn more about your current occupation or to find out about new ones. Whatever your occupational employment question, “We have a stat for that!”

Employment in the largest occupations, May 2017
Occupation Employment
Retail salespersons 4,442,090
Combined food preparation and serving workers, including fast food 3,576,220
Cashiers 3,564,920
Office clerks, general 2,967,620
Registered nurses 2,906,840
Customer service representatives 2,767,790
Laborers and freight, stock, and material movers, hand 2,711,320
Waiters and waitresses 2,584,220
Secretaries and administrative assistants, except legal, medical, and executive 2,254,820
General and operations managers 2,212,200
Annual mean wages for the largest occupations, May 2017
Occupation Annual mean wage
General and operations managers $123,460
Registered nurses 73,550
All Occupations 50,620
Secretaries and administrative assistants, except legal, medical, and executive 36,920
Customer service representatives 35,650
Office clerks, general 33,910
Laborers and freight, stock, and material movers, hand 29,690
Retail salespersons 27,460
Waiters and waitresses 25,280
Cashiers 22,130
Combined food preparation and serving workers, including fast food 21,230
Largest occupations in health care and social assistance, May 2017
Occupation Employment
Registered nurses 2,557,530
Personal care aides 1,944,270
Nursing assistants 1,344,390
Home health aides 783,910
Medical assistants 614,180
Licensed practical and licensed vocational nurses 608,080
Medical secretaries 539,680
Receptionists and information clerks 478,800
Office clerks, general 364,060
Childcare workers 330,090
Industries with the highest annual mean wages for receptionists and information clerks, May 2017
Industry Annual mean wage
Utilities $34,780
Management of companies and enterprises 31,970
Finance and insurance 31,180
Transportation and warehousing 31,110
Wholesale trade 31,080
Construction 31,070
Manufacturing 30,900
Health care and social assistance 30,840
Federal, state, and local government, excluding state and local schools and hospitals and the U.S. Postal Service 30,710
Mining, quarrying, and oil and gas extraction 30,710
Topics: Earnings and Wages | Employment | Employment Projections | Geographic Information | Industries | Occupations | Students (K-12) | U.S. Statistical System | Why This Counts
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New Measures of Prices for Global Trade

Shipping containers sitting on a dock at a port.How do prices for U.S. manufacturing exports compare to prices for goods manufactured abroad? How has the balance of export and import prices between the United States and Mexico changed over time? BLS has new measures to answer these and other questions on the competitiveness of U.S. production. We have published data on import and export price indexes since 1973. Since then we have made many improvements to the data we provide. Our latest improvements are the locality of destination export price indexes and the U.S. terms of trade indexes.

What are the locality of destination indexes?

Each locality of destination index measures price changes in dollars for U.S. goods exported to another country, region, or group of countries. These include major U.S. trade partners like China and the European Union. The indexes are available for all goods and for manufacturing and nonmanufacturing goods industries for some localities. The locality of destination indexes are a counterpart to the locality of origin import price indexes, which we have published since 1990. The locality of origin indexes let us examine price trends for goods imported from other countries, regions, and groups of countries.

What do the locality of destination indexes tell us?

The locality of destination indexes show how export price movements can vary depending on where U.S. goods are sold. For instance, from August to September 2018, prices for manufacturing exports to Latin America increased 0.3 percent. During the same period, manufacturing export prices to the European Union did not change. Comparing the two price movements, we can conclude market prices for U.S. exports arriving in Latin America increased relative to exports bound for Europe. Identifying these trends allows data users to dig deeper to see how currency exchange rates or shifts in global supply and demand affect price movements across trade partners.

What are the terms of trade indexes?

Each terms of trade index measures the purchasing power of U.S. exports, in terms of imports, for a specific country, region, or group of countries. In other words, the terms of trade index for China provides information on the price for exports to China, and how those export prices compare to prices for imports coming from China. Prices for exports and imports are measured in U.S. dollars, so exchange rates are already taken into account. We calculate the terms of trade index for China by dividing the China export index by the China import index, then multiplying by 100. An increase in the China terms of trade index means prices for exports to China are rising faster than prices for imports from China.

What does a terms of trade index price change mean?

A change in a terms of trade index provides information on the competitiveness of U.S. goods in the global market. Take the previous example, an increase in the China terms of trade index. U.S. producers are receiving higher prices for exported goods, meaning U.S. companies can now afford to purchase more imports. The U.S. terms of trade—or competitiveness—with China have improved. When looking at the trends, remember that the types of goods U.S. businesses export to and import from China are different, and underlying price changes may have different causes.

How broad is the coverage of the terms of trade indexes?

We have terms of trade indexes for each country, region, or group of countries where we publish both a locality of destination export index and a locality of origin import index. These countries include major trading partners:

  • Canada
  • Mexico
  • Germany
  • China
  • Japan

They also include regions or groups of countries:

  • Industrialized Countries (Western Europe, Canada, Japan, Australia, New Zealand, and South Africa)
  • European Union
  • Latin America (Mexico, Central America, South America, and the Caribbean)
  • Pacific Rim (China, Japan, Australia, Brunei, Indonesia, Macao, Malaysia, New Zealand, Papua New Guinea, Philippines, and the Asian Newly Industrialized Countries)

We publish the terms of trade indexes and the locality of destination indexes monthly. Data are available beginning with December 2017.

Why did we develop these new indexes?

The locality of destination and terms of trade indexes come from an ongoing effort to better measure the competitiveness of U.S. goods. We began expanding our measures of competitiveness in 2010 by extending the locality of origin import indexes to more detailed industries. Next we began work on the locality of destination and terms of trade indexes, eventually introducing them in September 2018.

Want to learn more?

Topics: Industries | Inflation and Prices | U.S. Statistical System | Why This Counts
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Making It Easier to Find Data on Pay and Benefits

We love data at the U.S. Bureau of Labor Statistics. We have lots of data about the labor market and economy, but we sometimes wish we had more. For example, we believe workers, businesses, and public policymakers would benefit if we had up-to-date information on employer-provided training. I recently wrote about the challenges of collecting good data on electronically mediated work, or what many people call “gig” work. I know many of you could make your own list of data you wish BLS had. One topic for which we have no shortage of data is pay and benefits. In fact, we have a dozen surveys or programs that provide information on compensation. We have so much data on compensation that it can be hard to decide which source is best for a particular purpose.

Where can you get pay data on the age, sex, or race of workers? Where should you go if you want pay data for teachers, nurses, accountants, or other occupations? What about if you want occupational pay data for a specific metro area? Or if you want occupational pay data for women and men separately? What if you want information on workers who receive medical insurance from their employers? Where can you find information on employers’ costs for employee benefits? Here’s a short video to get you started.

But wait, there’s more! To make it easier to figure out which source is right for your needs, we now have an interactive guide to all BLS data on pay, benefits, wages, earnings, and all the other terms we use to describe compensation. Let me explain what I mean by “interactive.” The guide lists 12 sources of compensation data and 32 key details about those data sources. 12 x 32 = a LOT of information! Having so much information in one place can feel overwhelming, so we created some features to let you choose what you want to see.

For example, the guide limits the display to three data sources at a time, rather than all 12. You can choose which sources you want to learn about from the menus at the top of the guide.Snippet of interactive guide on BLS compensation data.

If you want to learn about one of the 32 key details across all 12 data sources, just press or click that characteristic in the left column. For example, if you choose “Measures available by occupation?” a new window will open on your screen to describe the pay data available from each source on workers’ occupations.

There are links near the bottom of the guide to help you find where to go if you want even more information about each data source.

Check out our overview of statistics on pay and benefits. The first paragraph on that page has a link to the interactive guide. We often like to say, “We’ve got a stat for that!” When it comes to pay and benefits, we have lots of stats for that. Let us know how you like this new interactive guide.

Topics: Benefits and Compensation | Earnings and Wages | Geographic Information | Industries | Occupations | Productivity | U.S. Statistical System | Why This Counts
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New BLS Local Data App Now Available

BLS has partnered with the U.S. Department of Labor’s Office of the Chief Information Officer to develop a new mobile app for iPhones that is now available for free in the App Store! Search “BLS Local Data.”

The BLS Local Data app is ideal for customers who want to know more about local labor markets, such as jobseekers and economic and workforce development professionals. You can search using your current location, a zip code, or a location name to find relevant data quickly without having to navigate through the huge BLS database. With one click, you can find data for states, metro areas, or counties.

Using the BLS API, the app quickly presents local data and national comparisons for unemployment rates, employment, and wages.

In the coming months, look for more features and data in the app. We’re already working on future releases that will include industry and occupation drilldowns and comparisons between local areas.

Check out the app and explore the wealth of local labor market data produced by BLS! And don’t worry, Android users! An Android version of the app will be available in the future.

iPhone screen image for BLS Local Data app

Topics: Earnings and Wages | Employment | Geographic Information | Industries | Occupations | U.S. Statistical System | Unemployment
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Why This Counts: Breaking Down Multifactor Productivity

Productivity measures tell us how much better we are at using available resources today compared to years past. All of us probably think about our own productivity levels every day, either in the workplace or at home. I find my own productivity is best in the morning, right after that first cup of coffee!

On a larger scale, here at the U.S. Bureau of Labor Statistics, we produce two types of productivity measures: labor productivity and multifactor productivity, which we will call “MFP” for short. An earlier Why This Counts blog post focused on labor productivity and its impact on our lives. In this blog we will focus on why MFP measures matter to you.

Why do we need two types of productivity measures?

Labor productivity compares the amount of goods and services produced—what we call output—to the number of labor hours used to produce those goods and services.

Multifactor productivity differs from labor productivity by comparing output not just to hours worked, but to a combination of inputs.

What are these combined inputs?

For any given industry, the combined inputs include labor, capital, energy, materials, and purchased services. MFP tells us how much more output can be produced without increasing any of these inputs. The more efficiently an industry uses its combination of inputs to create output, the faster MFP will grow. MFP gives us a broader understanding of how we are all able to do more with less.

Does MFP tell us anything about the impact of technology?

It does. But we cannot untangle the impact of technology from other factors. MFP describes the growth in output that is not a result of using more of the inputs that we can measure. In other words, MFP represents what is left, the sources of growth that we cannot measure. These include not just technology improvements but also changes in factors such as management practices and the scale or organization of production. Put simply, MFP uses what we do know to learn more about what we want to know.

What can MFP tell us about labor productivity?

Labor productivity goes up when output grows faster than hours. But what exactly causes output to grow faster than hours? Labor productivity can grow because workers have more capital or other inputs or their job skills have improved. Labor productivity also may grow because technology has advanced, management practices have improved, or there have been returns to scale or other unmeasured influences on production. MFP statistics help us capture these influences and measure their impact on labor productivity growth.

How are MFP statistics used?

We can identify the sources of economic growth by comparing MFP with the inputs of production. This is true for individual industries and the nation as a whole.

For example, a lot has been written about the decline of manufacturing in the United States. MFP increased between 1992 and 2004 by an average of 2.0 percent per year. In contrast, MFP declined from 2004 through 2016 by an average of 0.3 percent per year. A recently published article uses detailed industry data to analyze sources of this productivity slowdown.

MFP is a valuable tool for exploring historical growth patterns, setting policies, and charting the potential for future economic growth. Businesses, industry analysts, and government policymakers use MFP statistics to make better decisions.

Where can I go to learn more?

Check out the most recent annual news release to see the data firsthand!

If you have a specific question, you might find it answered in our Frequently Asked Questions. Or you can always contact MFP staff through email or call (202) 691-5606.

Just like your own productivity at work and at home, the productivity growth of our nation can lead to improvements in the standard of living and the economic well-being of the country. Productivity is an important economic indicator that is often overlooked. We hope this blog has helped you to learn more about the value of the MFP!

Topics: Industries | Productivity | Students (K-12) | U.S. Statistical System | Why This Counts
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