BLS publishes employment data for every industry under the sun. If you are looking for employment in shoe stores, we have it. What about bowling alleys or laundromats? We have those too.
But what is an industry? BLS classifies industry employment according to the North American Industry Classification System (NAICS). Each industry has its own NAICS code number.
NAICS uses a production-oriented framework to group establishments into industries based on the activity in which they are primarily engaged. In other words, establishments that do similar things are classified together. The first two digits of a NAICS code correspond to an economic sector, such as construction or manufacturing. Each subsequent digit provides progressively more detail.
Let’s take the oil and gas industry as an example. If we want to know how many people are employed in that industry, we would look at four 6-digit NAICS codes within sector 21 (mining, quarrying, and oil and gas extraction). Specifically, we’re interested in the NAICS codes in the table below. Since the first two digits all start with 21, we can say they all belong to sector 21.
Oil and gas industry
Crude petroleum extraction
Natural gas extraction
Drilling oil and gas wells
Support activities for oil and gas operations
So now we should be able to get total employment in the oil and gas industry, right? Well, let’s take a look.
The 2019 average annual U.S. employment in these industries combined was about 472,000. But wait! You might think that figure is too low. While it captures people who work in extraction, well drilling, and support for oil and gas operations, what about people who work in industries related to the oil and gas industry? You have now stumbled upon one thing NAICS is not designed to do directly: capture an entire industry’s supply and output chain. But what if you are interested in employment across that industry’s supply and output chain?
Let’s continue with the oil and gas example. If you think about all of the activities in the oil and gas industry, they run the gamut from construction to transportation to retail. For example, workers build oil drilling platforms, refine the oil into gasoline and other products after extraction, operate and maintain the pipelines that carry the oil and gas products closer to the end user, and run the gas stations. With that in mind, you can group industries to capture more of the oil and gas industry’s input and output chain. Such a grouping might look like this:
Oil and gas supply and output chain
Transportation and warehousing
Pipeline transportation of crude oil
Transportation and warehousing
Pipeline transportation of natural gas
Transportation and warehousing
Pipeline transportation of refined petroleum products
Transportation and warehousing
All other pipeline transportation
Natural gas distribution
Oil and gas pipeline and related structures construction
Petroleum bulk stations and terminals
Petroleum and petroleum products merchant wholesalers (except bulk stations and terminals)
Gasoline stations with convenience stores
Other gasoline stations
Professional and technical services
Geophysical surveying and mapping services
By adding total employment for the oil and gas industry group and the oil and gas supply and output chain group, you get a 2019 annual average total employment for the oil and gas and related industries of just over 2 million.
Oil and gas, 2019 average employment
Oil and gas industry
Oil and gas supply and output chain
As we have seen in this example, you can use BLS data to build measures of employment in sectors like those related to the oil and gas industry. If you experiment on your own, you will realize there is no official guide for creating these groupings of industry sectors. It may even be difficult to identify all the sectors or subsectors you should include.
BLS employment data by industry are very powerful, and you can use them to paint a picture of employment across an entire supply chain. When using these data, be mindful of which NAICS industry sectors are included in the definition of, say, the oil and gas supply and output chain. As we have seen in this example, two perspectives about what makes up that industry can result in a difference of more than 1.5 million workers.
In a previous edition of Commissioner’s Corner, we described seasonal adjustment, the process BLS and many others use to smooth out increases and decreases in data series that occur around the same time each year. Seasonal adjustment allows us to focus on the underlying trends in the data. Seasonal adjustment works well when seasonal patterns are pretty consistent from year to year. But what about when there are large shocks to the economy, such as natural disasters and the massive effects of the COVID-19 pandemic and resulting business closures and stay-at-home orders? Today we’ll look at how BLS addressed this issue.
First, a little background on seasonal adjustment. Here’s an example similar to one we have used before, looking at employment in the construction industry. Construction employment varies throughout the year, mostly because of weather. As the chart shows in the “not seasonally adjusted” line, construction adds jobs in the spring and throughout the summer before it starts to lose jobs when the weather turns colder. The large seasonal fluctuations make it hard to see the overall employment trend in the industry. That makes it harder to study other factors that affect the trend, like changes in consumer demand or interest rates. After seasonal adjustment, the construction industry grew by 1.2 million jobs from the beginning of 2015 to the end of 2019.
Editor’s note: Data for this chart are available in the table below.
BLS seasonally adjusts data in several of its monthly and quarterly news releases.
BLS uses one of two approaches to seasonally adjust data in these releases—projected factors or concurrent seasonal adjustment. When we project seasonal adjustment factors, we only use historical data in the models. That means we calculate factors in advance, so they are not influenced by the most recent trends. Concurrent seasonal adjustment uses all the data available, including the most recent month or quarter. As a result, the factors are influenced by recent changes.
Regardless of whether the factors are projected or concurrent, the seasonal adjustment models can be additive or multiplicative. We’ll explain more about that below. The COVID-19 pandemic affected the seasonal adjustment process in different ways depending on how the seasonal factors are calculated.
The Consumer Price Index, Producer Price Indexes, and Employment Cost Index use the projected-factor approach and calculate seasonal factors once a year. BLS staff estimated the 2020 seasonal factors at the beginning of 2020 and have used them throughout the year. When new factors for 2021 and revised historical factors are calculated, BLS will examine the effects of the pandemic on the seasonal adjustment models.
We use a concurrent process to calculate the seasonal factors each month for nonfarm employment estimates for the nation, states, and metro areas, unemployment and labor force estimates for the nation, states, and metro areas, and job openings and labor turnover estimates. Each quarter, BLS also uses a similar concurrent process to calculate seasonal factors for productivity measures and business employment dynamics. This helps create the best seasonal factors when seasonality may shift over time. For example, think of schools letting out for summer a little earlier than they usually do each year, or the changing nature of delivery services because of online shopping. Using the most recent data to calculate seasonal factors helps pick up these changes to seasonality faster than the forecasted method. The risk of using the concurrent process is that it may attribute some of the movement in the estimates to a changing seasonal pattern when it really resulted from a nonseasonal event. BLS also annually examines and revises the historical seasonal factors even if the factors were originally calculated using concurrent adjustment. As the saying goes, hindsight is 20/20.
Before the COVID-19 pandemic, the concurrent seasonal adjustment models required limited real-time intervention. Examples of potential reasons for intervention include major events like hurricanes. The COVID-19 pandemic is unusual in its severity and duration, so significant intervention was needed.
BLS intervened in several ways to create the highest quality, real-time seasonal factors. The tool we use most often is called outlier detection. We consider outliers not to represent a normal or typical seasonal movement. When we label an observation as an outlier, we don’t use it to inform the seasonal adjustment model. Since economic activity is still being heavily influenced by COVID-19 and efforts to contain it, BLS has detected more outliers. When this happens, concurrent models behave more like projected-factor models because the most recent data are not used to create seasonal factors.
The Local Area Unemployment Statistics program uses another type of intervention, a technique call a level shift. It is used when there is a sudden change in the level of a data series. In this case, level shifts were used over a series of months.
Additive versus Multiplicative Models
As noted earlier, all BLS programs review their seasonal adjustment models each year. One of the steps during this process is to select a model—either additive or multiplicative. We use an additive model when seasonal movements are stable over time regardless of the level of the series. A multiplicative model is better to use when seasonal movements become larger as the series itself increases—that is, the seasonality is proportional to the level of the series. That means a sudden large change in the level of a series, such as the large increase in the number of unemployed people in April 2020, will be accompanied by a proportionally large seasonal effect. BLS did not want this to occur. When there are large shifts in a measure, multiplicative seasonal adjustment factors can result in adjusting too much or too little. In these cases, additive seasonal adjustment factors usually reflect seasonal movements more accurately and have smaller revisions.
Because of the unusual data patterns beginning in March 2020, both the Current Population Survey, which we use to measure unemployment and the labor force, and the Job Openings and Labor Turnover Survey switched from multiplicative to additive seasonal models for most series and did not wait until the typical yearend model review.
Our quarterly Labor Productivity and Costs news release uses input data from the Bureau of Economic Analysis, the U.S. Census Bureau, and several BLS programs. Most of the input data are already seasonally adjusted by the source agencies or programs. The productivity program only seasonally adjusts monthly Current Population Survey data on employment and hours worked for about ten percent of workers, mostly the self-employed, who are not included in the monthly data from the Current Employment Statistics survey on nonfarm employment and hours. The productivity program detected outliers in some of the data beginning at the start of the COVID-19 pandemic in March 2020 and accounted for them in the estimates.
Science and Art
Seasonal adjustment of economic data is a scientific process that involves complex math. But seasonal adjustment also involves some art in addition to science. The art comes in when we use our judgment about outliers in the data or when we decide whether an additive or multiplicative model more closely reflects seasonal variation in economic measures. The art also comes in when we recognize how complicated the world is. During 2020 we have experienced not just a global pandemic but also massive wildfires in several western states, a historic number of hurricanes that made landfall, and other notable events that affect economic activity. Did our seasonal adjustment models properly account for all of these events? I can say we have tried our best with the information we have available. As we gather more data for 2020 and future years, we will continue to examine how we can improve our models to help us distinguish longer-term trends from the seasonal variation in economic activity.
Acknowledgment: Many BLS staff members helped make the technical details in this blog easier to understand, and they all have my gratitude. Three who were especially helpful were Richard Tiller, Thomas Evans, and Brian Monsell.
Our work at the Bureau of Labor Statistics is driven by the idea that good measurement leads to better decisions. Good measures of economic and social conditions help public policymakers and private businesses and households assess opportunities and areas for improvement. Measuring these conditions consistently over time helps people who use our data evaluate the impact of public and private decisions.
We also believe we must be completely transparent about the design of our surveys and programs and the methods we use to conduct them. It isn’t enough to publish statistics and expect people simply to trust their quality. We gain this trust by documenting the design and procedures for all our programs in our Handbook of Methods. Our website also explains our policies for ensuring data quality and protecting the confidentiality and privacy of the people and businesses who participate in our surveys and programs. Further, BLS works with the wider U.S. statistical community to ensure and enhance the quality of statistical information.
Good measures are essential in “normal” times, but the global COVID-19 pandemic has made these last few months anything but normal. I am so proud of the work of the career professionals at BLS and our fellow statistical agencies for continuing to produce vital economic statistics. Our entire BLS staff moved to full-time telework in mid-March and didn’t miss a beat. We continue to publish measures of labor market activity, working conditions, price changes, and productivity like BLS has done since its founding in 1884. See our dashboard of key economic indicators in the time of COVID-19.
Publishing these measures hasn’t been easy. The pandemic has raised new questions about how businesses, households, and consumers have changed their behavior. BLS also has had to innovate to find new ways of doing things during the pandemic.
Today I want to tell you about the new data we have been collecting to learn more about the effects of the pandemic. I also want to tell you about some of the ways the BLS staff has innovated to keep producing data that are accurate, objective, relevant, timely, and accessible.
How businesses have responded to the pandemic
We have collected new data on how U.S. businesses changed their operations and employment from the onset of the pandemic through September 2020. This information, combined with data collected in other BLS surveys, will aid in understanding how businesses responded during the pandemic. Other statistics we have collected and published during the pandemic show changes in employment, job openings and terminations, wages, employer-provided benefits, prices, and more. These new data provide more insights by asking employers directly what they experienced as a result of the pandemic and how they reacted. Data for the Business Response Survey to the Coronavirus Pandemic will be released in early December 2020.
Changes in telework, loss of jobs, and job search
The Current Population Survey is the large monthly survey of U.S. households from which we measure the unemployment rate and other important labor market indicators. We added questions to the survey to help gauge the effects of the pandemic on the labor market. These questions were added in May 2020 and will remain in the survey until further notice. One question asks whether people teleworked or worked from home because of the pandemic.
Editor’s note: Data for this chart are available in the table below.
Other questions ask whether people were unable to work because their employers closed or lost business because of the pandemic; whether they were paid for that missed work; and whether the pandemic prevented them from searching for jobs.
Editor’s note: Data for this chart are available in the table below.
Receiving and using stimulus payments during the pandemic
BLS is one of several federal agencies that developed questions for the rapid response Household Pulse Survey. The survey is a collaboration among the U.S. Census Bureau, BLS, the U.S. Department of Housing and Urban Development, the National Center for Education Statistics, the National Center for Health Statistics, and the U.S. Department of Agriculture’s Economic Research Service. BLS contributed questions on the receipt and use of Economic Impact Payments and on sources of income used to meet spending needs during the pandemic.
Our staff will continue to publish research on how the pandemic has affected the labor market and markets for goods and services. Check back regularly as we add to this library of research.
Innovations in Data Collection and Training
The COVID-19 pandemic has caused profound changes in the daily lives of Americans. BLS is no exception. As I mentioned earlier, all BLS staff moved to full-time telework in March. The pandemic hasn’t prevented us from continuing to publish high-quality data, but we have had to change some of our data-collection methods and estimation procedures. We will continue to explain those changes so you can understand how they affect the quality of our measures.
Our survey respondents are the heart of everything we do at BLS. Without their generous and voluntary cooperation, we would not be able to publish high-quality data for public and private decision making. Respondents have businesses and households to run, and a pandemic is a challenging time to ask for their help. The data-collection staffs at BLS, the U.S. Census Bureau, and our state partners form great relationships with survey respondents. We must continue to protect the health of data collectors while also training them in a rapidly changing environment. Let me highlight a few of the innovative changes we have made during the pandemic that focus on our relationships with respondents and how we train data collectors.
Using videoconferencing technology for data collection
Several of our surveys have started using videoconferencing tools to speak with respondents and collect data from them. Some of the surveys that now use this technology include the National Compensation Survey, the Occupational Requirements Survey, and the Producer Price Index. Many of our surveys previously relied on interviewers visiting businesses or households to collect data. We suspended all in-person data collection in March to protect the health of data collectors and respondents, so we had to find other ways to collect data. Many of our surveys also use telephone and internet to collect data, but those modes aren’t always ideal for every kind of data. We often need to develop personal relationships with respondents to gain their trust and cooperation and ensure high-quality data. Videoconferencing helps us accomplish what we often can’t do with phones or web survey forms.
The Occupational Requirements Survey is one that has begun using videoconferencing in data collection. The survey provides information about the physical demands; environmental conditions; education, training, and experience; and cognitive and mental requirements for jobs in the U.S. economy. Collecting data for this survey often requires visual aids, hand gestures, and other nonverbal information to understand job characteristics. It often helps to watch jobs as they are performed at a worksite, but that’s not an option during the pandemic. Videoconferencing is the next best alternative.
Many of our data collectors and respondents have mentioned how helpful videoconferencing is for developing a rapport and for sharing screens and other visual information. Videoconferencing also helps us reduce travel and lodging costs, so we likely will continue to rely on videoconferencing at least partly even after the pandemic.
Using videoconferencing technology for training and mentoring
Many of our surveys are complex and require considerable ongoing training for data collectors. For example, before the pandemic, our Consumer Price Index Commodities and Services (C&S) survey involved in-person training at our Washington, DC, headquarters. There were two classroom training courses: a 2-week introductory course and a 1-week advanced course. Each course was followed by on-the-job training held in our regional offices. Even before the pandemic, we were developing videoconference training. The pandemic caused us to accelerate these plans. We now provide C&S survey training through video collaboration tools. We also integrate on-the-job training throughout the classes.
Several other surveys have adopted a similar training approach as the Consumer Price Index. Our data-collection staffs also increasingly use videoconferencing for mentoring and to share ideas about how to make the data-collection experience better for data collectors and respondents.
A final note
Before I conclude, I want to share some sad news about one of the people who played an indispensable leadership role in developing the new survey questions and innovative data-collection and training methods. Jennifer Edgar, our Associate Commissioner for Survey Methods Research, died November 8 in a tragic fall in her home. She leaves behind her husband and two young children, her parents, and her sister. Moreover, she leaves hundreds of BLS colleagues and many more throughout the statistical community and beyond, who will grieve the loss of an exceptionally gifted friend and professional whose great promise was cut suddenly and tragically short. Jennifer was using her considerable energies to move BLS forward. Her passing is a huge blow to her family, loved ones, and the entire statistical community. We are working on ways to ensure Jennifer’s memory and passion is forever present at BLS.
Percent of employed people who teleworked at some point in the previous 4 weeks because of the COVID-19 pandemic
Number of people not in the labor force who did not look for work because of the COVID-19 pandemic
At the Bureau of Labor Statistics, we always enjoy a good celebration. We just finished recognizing Hispanic Heritage Month. We are currently learning how best to protect our online lives during National Cybersecurity Awareness Month. We even track the number of paid holidays available to workers through the National Compensation Survey. Today I want to focus on a celebration that happens once every 5 years — World Statistics Day. While there may not be parades, special meals, or department store sales to honor this day, we at BLS and our colleagues worldwide take time out on October 20, 2020, to recognize the importance of providing accurate, timely, and objective statistics that form the cornerstone of good decisions.
World Statistics Day, organized under the guidance of the United Nations Statistical Commission, was first celebrated in October 2010. This year, the third such event, focuses on “connecting the world with data we can trust.” At BLS, the trustworthy nature of our data and processes has been a hallmark of our work since our founding in 1884. Our first Commissioner, Carroll Wright, described our work then as “conducting judicious investigations and the fearless publication of results.” That credo guides us to this day. As the only noncareer employee in the agency, I am surrounded by a dedicated staff of data experts whose singular mission is to produce the highest-quality data, without regard to policy or politics. BLS and other statistical agencies throughout the federal government strictly follow Statistical Policy Directives that ensure we produce data that meet precise technical standards and make them available equally to all. For nearly 100 years, we have regularly updated our Handbook of Methods to provide details on data concepts, collection and processing methods, and limitations. Transparency remains a hallmark of our work.
The United States has a decentralized statistical system, with numerous agencies large and small spread throughout the federal government. Despite this decentralization, the agencies work together to improve statistical methods and follow centralized statistical guidance. This partnership was recently strengthened by the Foundations for Evidence-Based Policymaking Act of 2018, which reinforced how the statistical agencies protect the confidentiality of businesses and households that provide data. The Act also designated heads of statistical agencies, like myself, as Statistical Officials for their respective Departments. In my case, my BLS colleagues and I advise other Department of Labor agencies on statistical concepts and processes, while continuing to stay clear of policy discussions and decisions.
World Statistics Day is a global event, so this is a good time to share some examples where BLS participates in statistical activities around the world:
We have regular contact with colleagues at statistical organizations around the world. Just recently, I participated in a very long-distance video conference on improvements to the Consumer Price Index. For me, it was 6:00 a.m., and I made sure I had a mug of coffee handy; for my colleagues in Australia, it was 6:00 p.m., and I’m certain their mug had coffee as well.
We have a well-established training program for international visitors, focusing on our processes and methods. We hold training sessions at BLS headquarters (or at least we did before the pandemic), we send experts to other countries, and we are exploring virtual training. We are eager to share our expertise and long history.
We participate in international panels and study groups, such as those organized by the United Nations, the Organization for Economic Cooperation and Development, and others, with topics ranging from measuring the gig economy to use of social media.
We provide BLS data to international databases, highlighting employment, price, productivity and related information to compare with other countries.
And that’s just a taste of how BLS fits into the World of Statistics. As Commissioner, I’ve had the honor to represent the United States in conferences and meetings across the globe. The BLS staff and I also hold regular conversations with statistical officials worldwide. In a recent conversation with colleagues in the United Kingdom, we were eager to learn about each other’s changes in the ways we provide data and analyses to our customers. These interactions expand everyone’s knowledge and keep the worldwide statistical system moving forward.
To celebrate World Statistics Day, I asked some BLS cheerleaders if they would join me in a video message about the importance of quality statistical data. Here’s what they had to say:
In closing, let’s all raise a toast to World Statistics Day, the availability of high-quality and impartial data, and the dedicated staff worldwide who provide new information and analysis every day.
For researchers like me, it’s also an opportunity to celebrate the importance of data. As a research analyst in the U.S. Department of Labor’s Office of Disability Employment Policy, my thoughts turn to how credible, consistent data are key to delivering on the promise of disability inclusion inherent in the Americans with Disabilities Act, today and into the future.
That’s why we partnered with BLS in 2008 to add six disability-related questions to the monthly Current Population Survey, the official source for estimates on U.S. labor force participation, employment, and unemployment. As a result, monthly data on the employment status of people with disabilities were released for the first time in January 2009—and have been every month since.
In addition, we collaborated with BLS and the U.S. Department of Labor’s Chief Evaluation Office to gather additional data through supplements to the Current Population Survey in May 2012 and July 2019. Through these supplements, we gleaned critical information on barriers to employment, prior work experience, career and financial assistance, requested changes to the workplace, and other related topics from respondents with disabilities.
Today, these data provide reliable, accurate information to a range of stakeholders on a topic of critical importance to America’s families and communities. Most significantly, they help facilitate evidence-based policymaking at the national, state, and local levels.
What exactly is evidence-based policymaking? It’s the simple notion that public policy should be informed by established, objective evidence. While that may seem obvious in principle, the reality is that, absent such evidence, policymakers often make decisions based on assumptions derived from anecdotal evidence, which can be subjective. This can lead to inefficient use of public resources and poor outcomes.
That’s because—and this is what often fascinates data geeks like me—things are not always as they seem. Often, data reveal that what we assume to be true, in fact, may not be true, or at least not the whole truth. This is especially the case for complex, multifaceted issues, such as the employment of people with disabilities.
Increasing access and opportunity requires us to first understand what the barriers to access are and where the opportunities exist. It also requires us to anticipate changes and identify intersections. For example, data from May 2012 and July 2019 supplements pinpointed a lack of transportation as an ongoing barrier to work for many people with disabilities. As a result, the Office of Disability Employment Policy, in partnership with the U.S. Department of Transportation and the U.S. Access Board, is engaging disability advocates and private industry to promote more accessible transportation options—especially inclusive autonomous vehicles that can help people with disabilities get to work.
Of course, the employment landscape has shifted this year due to the COVID-19 pandemic. The changes it has brought to our workforce and economy compel us to consider what questions we need to ask now to ensure we can meet the needs of workers with disabilities in the years ahead. Already, BLS data are helping us detect trends, especially in the context of different occupations.
In any climate—whether the historically robust economy before the pandemic or one recovering in the wake of unprecedented challenges—quality data helps us serve America’s 15 million working-age people with disabilities better. They also help us deliver on the spirit of the bipartisan 2018 Foundations for Evidence-Based Policymaking Act. Going forward, with continued support from BLS, the Chief Evaluation Office, and Department of Labor’s Chief Data Officer, we will continue to develop and implement data-driven policies and programs that meet the needs of America’s workers with disabilities, every month of every year.