Tag Archives: New tools

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.

Building a Business? Start Here

You have an idea.

It’s time to get serious about it.

Entrepreneurial drive got you to this point, but now it’s time to chart a plan. For that you need a reliable overview of the factors that can lead to a flourishing business — or work against it.

The U.S. Census Bureau’s Business Builder application is designed to provide small business owners with key data to give them a clear-eyed view of their potential market. This data-mapping tool combines data from the Census Bureau’s American Community Survey, Economic Census, and County Business Patterns, and the U.S. Department of Agriculture’s National Agricultural Statistics Service.

For version 2.6 of the tool, released this month, the Bureau of Labor Statistics has collaborated with the Census Bureau to include data from our Quarterly Census of Employment and Wages (QCEW). QCEW is based on quarterly mandatory reports to the Unemployment Insurance systems in each state, covering more than 95 percent of the jobs in the U.S. economy. It is the most complete and current source of data on employment and wages at a detailed geographic and industry level.

To help illustrate why this tool is so useful, and why the data from the QCEW broadens that usefulness, I’ll make up an example.

Ever since you can remember, your grandmother, who was born and raised just outside of Naples, has fed you a type of pizza full of unusual flavors that has never been equaled in all your travels. As you grew and came into your own as a cook, she entrusted you with her secret knowledge, like a magician passing along her repertoire to a favored protégé.

Ever since, you’ve dreamed of sharing the pleasures of that delicacy with the world, and you’re going to start with a pizzeria somewhere near your home in Olympia, Washington. You may ask yourself: What exactly does the restaurant market look like in Olympia? Who are my potential customers? What kind of wages do they earn?

The Census Business Builder is a good place to start.

Census Business Builder home screen

Here, you can enter the type of establishment you’d like to research, as well as the area where you intend to do business. You find that data are not available for Olympia, but knowing that Olympia is the county seat, you are able to search in Thurston County.

The resulting map provides data on income, education, wages, and perhaps most importantly for you, the number of similar establishments in the area – also known as your competition.

Map of Thurston County, Washington, showing Census Business Builder search results

With the new QCEW data, another crucial batch of information is at your fingertips: more up-to-date establishment counts, employment numbers, and wages. It also provides an important metric known as the location quotient. This measure lets you compare an industry’s employment concentration or wages in your search area with the country as a whole. Will you be able to hire enough staff? What might you need to pay them if you want the best in the business?

Map of Thurston County, Washington, showing Census Business Builder search results with QCEW location quotient

The possibilities advance from this example as far as your entrepreneurial mind wants to take them. It is you, after all, who will transform these numbers into the real-world business that fulfills your vision. Our job as public servants is to give you the most relevant tools to realize that transformation. We’re grateful for the opportunity to collaborate with the Census Bureau to bring you this vital information in this user-friendly format.

The Census Business Builder is updated twice per year using feedback that comes from customers and stakeholders, including small business owners, trade associations and other government agencies. The update also adds QCEW data into the Regional Analyst version of the tool, which is designed for chambers of commerce and regional planning staff who need a broad portrait of the people and businesses in their area. The December release, for example, will add more QCEW features to the Regional Analyst version.

BLS publishes data from the QCEW program every quarter in the County Employment and Wages news release. QCEW data are available through our Open Data Access and the QCEW Databases.

Why This Counts: Measuring Occupational Requirements

You probably know that BLS publishes data and analysis about employment, unemployment, job openings, earnings, productivity, occupational safety and health, and more. But did you know we also publish information about how often workers have to lift objects; the maximum weight they lift or carry; whether they work in extreme heat or cold; and how much training and experience they need for a job? We call these characteristics “occupational requirements.”

What are occupational requirements?

The Occupational Requirements Survey provides information about the requirements of jobs:

  • Physical demands of work, such as keyboarding, reaching overhead, lifting or carrying
  • Environmental conditions, such as extreme heat, exposure to outdoors, proximity to moving parts
  • Education, training, and experience requirements, such as prior work experience, on-the-job training, and license requirements
  • Cognitive and mental requirements, such as interaction with other people, independence of work, and the amount of review

How did BLS get into doing this survey?

This survey is one of our newest statistical programs; we first published data on December 1, 2016.

The Social Security Administration asked us to help them obtain accurate and current data to use in their disability programs. They are developing an Occupational Information System, which will use data from the Occupational Requirements Survey. That means the survey is crucial for Social Security to manage their disability programs fairly and efficiently.

How can I use occupational requirements information?

Users of Occupational Requirements Survey data include:

  • Researchers exploring occupational change
  • Jobseekers and students
  • Government agencies evaluating skill gaps
  • People with disabilities and their advocates

Let’s discuss a couple of examples to show you what I mean.

Educational requirements

You may want to know the minimum formal education requirements for jobs. The survey has a stat for that! In 2018, a high school diploma was required for jobs covering 40.7 percent of workers, while 17.9 percent had a bachelor’s degree requirement. The chart below shows the percent of jobs by minimum education requirement.

Percent of jobs with a minimum education requirement, 2018

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

We have more information on education, training, and experience. The 2018 news release showed that on-the-job training was required for about 77 percent of workers, and the average duration was 34 days.

We also have information on preparation time, which includes minimum formal education, training, and work experience a typical worker needs to perform a job. Preparation time between 4 hours and 1 month was required for 31.5 percent of workers.

Environmental Conditions

Is the noise level at your workplace closer to a library (quiet) or a rock concert (very loud)? For some jobseekers, understanding the noise level and other environmental conditions might be extremely important as they evaluate job options. The chart below provides examples of the noise intensity in different occupations.

Percent of jobs with noise intensity level requirements, selected occupations, 2018

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

Examples of work environments with different noise intensity levels include:

  • Quiet: private office, a golf course, or art museum
  • Moderate: department stores, business office, or fast food restaurant
  • Loud: manufacturing plant, atop large earth moving equipment, or jobs next to the highway
  • Very loud: rock concert venues, working with jack hammers, or rocket testing areas

How do we collect job requirement data?

To collect job requirement data, our field economists ask business owners, human resource professionals, worker safety officers, and supervisors to collect requirements of work. Field economists do not use paper or online questionnaires to collect these data; instead, they rely on a conversational interviews and descriptive documents, such as task lists, to collect information on occupational requirements.

How are we improving the survey?

Survey scope: Since it began, we have continued to refine the survey to improve its accuracy. In the third year of collection, we redefined the survey scope to focus on critical job functions—that is, the reason the job exists.

Survey content: Beginning with the current sample in collection, we added questions about cognitive and mental requirements. The Social Security Administration asked for this change so we can provide information on the requirements for workers to adapt to changes in the pace of work, solve problems, and interact with others.

Sample: The survey sample is collected over a 5-year period. That will provide the large amount of data necessary to publish information about detailed occupations. We have revised the sampling process to ensure we collect information about less common occupations.

Website: We recently improved the web layout to make it easier for users to find the data they want.

Where is more information?

We have data for occupational groups and occupations through the Occupational Profiles. All data are available through the public data tools. For concepts, methods, and history of the survey see the Handbook of Methods or visit our homepage.

Let us know if you have questions or comments about occupational requirements:

  • Email
  • Phone: (202) 691-6199

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

Percent of jobs with a minimum education requirement, 2018
Education requirement Percent
No minimum education requirement 31.5%
High school diploma 40.7
Associate’s degree 3.8
Associate’s vocational degree 2.1
Bachelor’s degree 17.9
Master’s degree 2.3
Professional degree 0.9
Doctorate degree 0.5
Percent of jobs with noise intensity level requirements, selected occupations, 2018
Occupation Quiet Moderate Loud
Bus and truck mechanics and diesel engine specialists 49.0% 51.0%
Computer programmers 60.1
Construction laborers 48.6 51.4
Electricians 49.0 51.0
Highway maintenance workers 46.2 53.8
Home health aides 54.1 45.9
Library technicians 56.0
Medical transcriptionists 68.7
Paralegals and legal assistants 66.5 33.5
Welders, cutters, and welder fitters 48.2 50.9

New State Data on Labor Productivity and Job Openings and Labor Turnover

While international trade has become increasingly important to our economy over the past 60 years, U.S. households and businesses continue to rely primarily on local markets for most goods and services. The products we create come from all over our country. Workers, businesses, and policymakers care deeply about the economy in our own backyards. That’s why BLS recently began publishing new data on labor productivity by state and, separately, on job openings and labor turnover by state.

State labor productivity

Our measures of labor productivity for states are still experimental, meaning we’re still assessing them and considering ways to improve them. These measures cover the private nonfarm sector for all 50 states and the District of Columbia from 2007 to 2017. They show that labor productivity growth varies a lot from state to state. 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. In 2017, labor productivity grew fastest in Montana (2.0 percent), West Virginia (1.9 percent), California (1.8 percent), and Hawaii (1.7 percent). You can get the complete dataset from our state labor productivity page.

U.S. map showing productivity growth in the private nonfarm sector in each state from 2007 to 2017

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

We construct these state measures from data published by several BLS programs and by our colleagues at the Bureau of Economic Analysis. A recent Monthly Labor Review article, “BLS publishes experimental state-level labor productivity measures,” explains the data and the methods for putting them all together. The article also highlights how you might use these new state data. We’re happy to have your feedback on these new measures. Just send us an email.

State job openings and labor turnover

We also have new data on job openings, hiring, and separations by state. Data from the Job Openings and Labor Turnover Survey are widely used by economic policymakers and others who want to understand the job flows that lead to net changes in employment. We have these data back to December 2000 and update them every month for the nation and the four broad census regions. Now we have them for all states and the District of Columbia too. These state estimates are available from February 2001 through December 2018 for the total nonfarm sector.

Many of you have told us you want more geographic details about job openings and turnover. To make sense of data on job openings, for example, it helps to know where the jobs are. The survey sample size is designed to estimate job openings and turnover for major industries only at the national and regional levels. For several years we have researched ways to produce model-assisted estimates for states. As with the state productivity data, these estimates are experimental. We plan to update the state estimates each quarter while we assess your feedback on the models and the usefulness of the data. We encourage you to send us your comments.

But wait, there’s more! We’ve updated the BLS Local Data App!

In previous blog posts, we’ve told you about our mobile app for customers who want to know more about local labor markets. This app now includes employment and wage data for detailed industries and occupations. (It doesn’t yet have the new data on state productivity, job openings, and turnover.)

Interested in local data for a particular industry or occupation? The latest version allows you to quickly search or use the built-in industry and occupational lists. Want to know which industry employs the most workers in your area or which occupation pays the highest? The updated app allows you to sort the employment and wage data across groups of industries and occupations. You can still find data on unemployment rates and total employment. You also can find your state, metro area, or county by searching for a zip code or using your device’s current location.

These new data and features result from the continued partnership between BLS and the U.S. Department of Labor’s Office of the Chief Information Officer. Be on the lookout for more new features to be added in future releases.

Download the BLS Local Data app from the App Store or Google Play today!

Annual percent change in labor productivity in the private nonfarm sector, 2007–17
State Annual 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

Improved Mapping Tool for Local Area Unemployment Statistics

We publish thousands of unemployment rates each month for states, metro areas, and counties. That can make them hard to follow, but we just upgraded our mapping tool to make it easy. Instead of wading through all those numbers, just check out the latest maps for what you need. We have rebuilt the tool using a more modern and versatile mapping technology. That will make it easier to update with future geographic changes. We have improved several features of the tool:

  • We have added tooltips to help you identify each area and its data. Just hover over an area on the map to see its information.
  • In the tab for state data that are not seasonally adjusted, you can choose a state and pull up a map of that state’s county data for the same period.
  • The metro area tab has returned and reflects the areas currently used by the Local Area Unemployment Statistics program.
  • You can choose the dates, states, areas, and measures you want to see.
  • You can select the key data ranges to highlight all areas in the same group. (Click or press the range a second time to deselect.)
  • The map space is larger and framed.
  • Use the arrow in the lower right corner of the map space to print the map image or export it to .PNG, .JPEG, and .SVG formats.

Missouri map showing counties and their unemployment rates

We hope these improved maps make finding data for your state and local area easier. Let us know what you think.