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Innovations at BLS during the COVID-19 Pandemic

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.

New Data

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.

Percent of employed people who teleworked at some point in the previous 4 weeks because of the COVID-19 pandemic, May through October 2020

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.

Number of people not in the labor force who did not look for work because of the COVID-19  pandemic, May through October 2020

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

Changes in sick leave plans

We added several questions to the National Compensation Survey to understand the effects of the pandemic on sick leave plans. The questions asked whether private industry establishments changed their leave policies and whether employees used sick leave between March 1 and May 31, 2020.

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
MonthPercent

May 2020

35.4%

Jun 2020

31.3

Jul 2020

26.4

Aug 2020

24.3

Sep 2020

22.7

Oct 2020

21.2
Number of people not in the labor force who did not look for work because of the COVID-19 pandemic
MonthNumber not in the labor force

May 2020

9,740,000

Jun 2020

7,043,000

Jul 2020

6,454,000

Aug 2020

5,200,000

Sep 2020

4,499,000

Oct 2020

3,563,000

Celebrating World Statistics Day 2020

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.

United Nations logo for World Statistics Day 2020

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.

Happy World Statistics Day!

Increasing Access and Opportunity: Using Quality Data to Inform Evidence-Based Policy

We have a guest blogger for this edition of Commissioner’s Corner. Savi Swick is a Supervisory Research Analyst in the U.S. Department of Labor’s Office of Disability Employment Policy.

Symbols representing different types of disabilities

During National Disability Employment Awareness Month each October, there is much to celebrate and reflect upon. It’s a time to reaffirm our commitment to increasing access and opportunity for workers with disabilities and honor the value and talent they add to America’s workforce and economy. And this year’s celebration is doubly essential, marking both 75 years of this observance and helping bring to a close the yearlong commemoration of the 30th anniversary of the Americans with Disabilities Act.

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.

How Much Does a Cup of Coffee Cost? It’s Complicated

We have a guest blogger for this edition of Commissioner’s Corner. Rob Cage is the Assistant Commissioner for Consumer Prices and Price Indexes at the U.S. Bureau of Labor Statistics.

The pandemic has changed my morning routine. Before the outbreak of the COVID-19 pandemic and full-time telework at BLS, two things motivated me each morning.

Person holding mobile phone and ordering coffee on an app.

First, I was always on a mission to minimize my commute to work. I would do things each night so I wouldn’t waste time in the morning. Things like shaving, setting out clothes, and preparing the next day’s lunch. I timed my alarm to go off to allow just enough time to shower, suit up, grab that sandwich, and catch my commuter train as it rolled into the station.

The second thing I needed to start each work day was a strong, fresh, hot cup of joe—actually more like two or three cups. Not one of those fancy drinks with mocha, caramel, steamed milk, or anything like that. Ordinary drip-brewed, filtered coffee. Medium to dark roast and like Betty MacDonald, my coffee had to be “…so strong it snarled as it lurched out of the pot.” Then I add some cream (and by cream, I mean half-and-half), but no sugar. But by far the most important element of the drink: temperature. I like coffee precisely at a certain temperature. If it’s too hot, you taste nothing but a scalded tongue. If it’s too cold, you’re met with an overwhelming sense of disappointment. In that ideal temperate zone, you are jolted alive with a satisfying sip of silky cocoa and nutty fragranced bliss.

Through trial and error, I eventually unearthed a way to satisfy both of these morning habits efficiently: getting my coffee along the commute. Brewing the coffee at home took too much time, and I’d drink most of it on the train, arriving at my desk empty handed. Getting my first cup after I arrived was also uneconomical since I’d have to backtrack to get it. The simple solution? Find a place to get the coffee along the way, and preferably as close to my office as possible. This way, the temperature of the drink was in that sweet spot as I turned on my computer.

With four different coffee shops located along my route in Washington’s Union Station, one would think I could easily achieve this. But no, I’m foiled by impatience. According to a 2014 Journal of Consumer Behavior study, the time before ordering has the greatest influence on how customers perceive waiting times and service quality. A customer who has to wait 10 minutes in line before ordering will feel more dissatisfied than a customer waiting 10 minutes after ordering, even if the total wait time is the same. I couldn’t agree more. Queues at Union Station during the morning rush were just too long and unpredictable to meet my needs. I didn’t have the patience to wait behind customers pondering through a long order recital: Quad Grande nonfat extra hot caramel macchiato upside down, please. I needed my expeditiously stated, two-word order quickly. Luckily, the employee cafeteria in my building—conveniently located just off the lobby—had self-serve coffee. No competing commuters. No preorder queue. No postorder queue. Only a payment queue. I had found my routine: a 55-minute total commute, landing at my desk with strong, hot coffee in hand.

Then one day, I bumped into a coworker on the train. As we walked through Union Station and approached the maze of coffee shops with the insufferable queues, she stopped in front of one; took two steps to the left, scanned the drinks on top of a cart, found one with her name on it, picked it up, and met me back in stride. Amazed, I asked her how she pulled off this sensational stunt. She had placed her order on the coffee shop’s mobile app, of course. That was her routine. Curious but unconvinced, I asked her if she was concerned the coffee would be too cold by the time she picked it up. Through trial and error, she had figured out that if she placed her order on the app as the train rolled out of the L’Enfant Plaza stop, her drink would typically be hot and ready as she passed the cart. Could this be coffee-ordering nirvana? Guaranteed no-wait service, with guaranteed handoff at perfect temperature? Surely this improvement in the quality of the purchasing experience would cost more, which was my next question. And the astonishing answer: the coffee was the same price! I immediately downloaded the app, copied her process, and shaved three minutes off my morning routine. An equilibrium commute down to 52 minutes, about a 5-percent improvement!

Which brings me to how this tortured story relates to the business of BLS and specifically the measurement of the cost of living and consumer inflation. If the cost of my preferred cup of coffee was identical ($2.45 before sales tax) whether I stood in line to get it or not, then surely I would be better off by ordering on the app. Doing that resulted in a 5-percent time savings on my commute—an attribute of purchasing coffee that was critically important to me. In other words, the app-ordered coffee represented a higher-quality product, even though the price was the same. Using the federal minimum wage rate of $7.25\hour (or 12 cents a minute), an estimate of the time savings is 3 minutes x $0.12 = $0.36. One could say $0.36 is a reasonable estimate of the difference in quality. So what is the correct measure of price change between these two choices?

ApproachWalk-up purchaseApp purchasePrice changeNote

Ignore purchase time

$2.45 $2.45 0%No change in price

Add purchase time

$2.81 $2.45 -13%Deflation

Assume purchase time is built into market price, and adjust prices to reflect zero purchase time

$2.09 $2.45 17%Inflation

This is the million dollar question in consumer price index measurement, and the answer depends on how a unique consumer good—in this case a prepared cup of coffee—is defined. In the price index literature, the buzzword is homogeneity. To measure inflation accurately, goods that are homogenous must be identified and grouped together for proper treatment. This is at the core of getting the CPI right. Homogenous is defined as “of the same kind, alike; consisting of parts all of the same kind.” In CPI jargon, the component “parts” of a unique item in the sample are called “attributes.” So what are the attributes that define a cup of coffee? We could consider a list of attributes that most baristas might say are important, like size, bean variety, country of origin, blend, roast, freshness, or caffeine content; and a couple you already know that are important to me: temperature and queue time.

How many of these attributes do we explicitly control for in the CPI as obvious, overt, and separate variables used in scientifically selecting a sample of coffee drinks from quick service establishments, for use in calculating the index each month? You might be surprised by the answer: none! How, then, do we capture constant-quality price change for prepared coffee drinks accurately in the CPI?

We implicitly account for all of these characteristics one way or another. The CPI uses the matched-model approach to index measurement. We select a sample of 100,000+ unique, well-specified, strictly homogenous goods and services for the sample. Then we compare the price of each unique sampled item to the price of the exact same item in subsequent months. The key, of course, is defining and selecting the unique items. Generally speaking, sample selection has two major components: selection of the establishments (for example, a coffee shop) and then selection of a unique item (for example, 16-ounce dark roast drip coffee) at the selected establishments. Limited budget requires BLS to take a sample rather than a census of all goods and services consumers purchase. Thus, we group unique products into broadly homogeneous categories so the selected products can accurately reflect price change for unsampled items in those categories. We bundle prepared coffee from quick service establishments into the elementary category “limited service meals and snacks.” Comparatively, this is one of the more broadly defined components in the CPI basket. With a variety of different food and beverage items eligible for the sample, there are simply too many attributes to consider as separate selection steps to create the sample of unique items. Instead, we base the selection largely on the descriptions of different items listed on the menu. This is how we would distinguish an ordinary brewed coffee drink from other coffee drinks, such as a latte and cappuccino.

Any attribute expressly identified in the description of the menu item becomes a characteristic defining the unique item. For example, “12-ounce Cup of Organic Single Origin Light Roast Coffee” and “12-ounce Cup of Organic Classic Blend Medium Roast Coffee” may be two different menu items at a coffee shop. By rule, they are treated as distinct, unique, separate products for CPI sample selection. Then each month, CPI data collectors meticulously capture the price of the exact same product. If any of the characteristics change, that would trigger a quality review. Suppose medium roast was no longer available. A decision would have to be made to substitute the most comparable item to the originally selected item. Then a commodity analyst in the national office would have to decide if the new item was comparable to the old item. For example, is there a difference in quality between the light roast and the medium roast? Obviously, consumer taste and preferences are idiosyncratic, and the difference in quality of light roast and medium roast is a function of individual preference. But to the average consumer, perhaps not. In fact, prices tend not to vary by roast type. So in this situation, the analyst might judge medium and light to be comparable, and the price of the light will be matched to the previous price of the medium and used in the index. However, if a single-origin coffee was selected, a different outcome might result, especially if the price of the single-origin coffee was considerably different from a previously selected blend coffee, with all other characteristics being the same. Then a decision would need to be made as to how much of the difference was a quality difference (single origin versus blend), and how much was pure price change.

But what about the other factors that are not expressly identified in the description of the menu item, like temperature, freshness, and queue time? These are ostensibly identified, and held constant month after month, by the selection of the establishment. The outlet itself is associated with many attributes of product quality which are not observed. Over time, customers come to expect a certain level of service or product quality within each specific store, or at specific locations of chain stores. So, by controlling for the outlet, we are effectively able to hold constant these unobservable attributes.

Now that I am teleworking, my morning habits are out of equilibrium. My commute time is drastically shorter, reduced to the time it takes me to walk from my bedroom to the guest room, which has been hastily converted into a home office. My problem is the coffee. I haven’t figured out the roast, or the precise coffee-to-water ratio for the perfect strength; I don’t like spending time grinding whole bean, so I substitute ground coffee instead. My barista tells me that’s a quality decrease.

I’d say I am better off commute-timing wise but worse off coffee wise. A push. All in all, I can’t wait to return to on-premises work, mostly for that reliable cup of java.

A Closer Look at Recent Employment Trends

BLS has closely tracked the upheaval in the U.S. job market in recent months, most notably through the monthly “payroll jobs” data. These data, from the Current Employment Statistics survey, provide detail on the change in employment in each industry. We count jobs by asking thousands of employers every month the number of employees on their payroll for the pay period that includes the 12th of the month. For August, we reported that employers added 1.4 million jobs. Today I want to scratch beneath that surface and examine recent employment trends in several industries.

But before I go on, let me take a moment to thank all those businesses that respond voluntarily to our request for information every month. With so much going on, responding to a BLS survey may not be your highest priority. Yet, you continue to come through every month, and for that we extend our sincere thanks.

Using February 2020 as our starting point, let’s look at the job losses that occurred through April. From the nearly 152 million jobs recorded in February, we lost just over 22 million by the end of April. That’s a drop of 14.5 percent in total nonfarm employment. But that decline varied across industries. The leisure and hospitality industry, including restaurants, hotels, and amusements, saw the largest percentage decline, down 49.3 percent from February. Other industries saw percentage declines similar to the overall total, such as retail trade (decline of 15.2 percent) and construction (decline of 14.2 percent). And some industries experienced small declines, such as financial activities (decline of 3.2 percent). These differences stem from many factors, including stay-at-home orders, the need for workers in essential industries, the ability for some work to be done remotely, and on and on.

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

Following large losses through April, many industries gained jobs over the next four months. By August, about 10.6 million jobs were added to employer payrolls. One way to look at these figures is to consider what share of the March/April job loss was “recovered” by the May/June/July/August job gain. Overall, 47.9 percent of the decline was recovered. The retail trade industry restored the greatest percentage of job losses, 72.5 percent, followed by other services (including barbers and salons, 61.2 percent) and construction (60.8 percent). Education and health services recovered 47.6 percent of lost jobs, nearly equal to the overall percentage of jobs recovered, as did manufacturing (47.2 percent). Utilities, mining and logging, and the information industry had fewer jobs in August than in April.

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

While the percentages let you compare industries, digging a little deeper uncovers other interesting stories. For example, three sectors, professional and business services; manufacturing; and transportation and warehousing, each lost between 10 and 11 percent of jobs from February to April 2020. But those losses amounted to vastly different numbers of jobs: 2.3 million in professional and business services; 1.4 million in manufacturing; and 570,000 in transportation and warehousing.

Some detailed industries provide interesting contrasts. Within health care from February to April, hospital employment showed a slight decline while offices of physicians lost about 11 percent of jobs. In contrast, offices of dentists declined by 56 percent, losing more than half a million jobs. As of August, employment had rebounded in most health care industries, with the notable exception of nursing and residential care facilities, which has declined each month since February.

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

Americans were encouraged to stay at home and only venture out for essential items, which is reflected in employment in various retail industries. For example, food and beverage stores showed little employment change from February to August. In contrast, clothing store employment declined by 62 percent through April, and only half of that loss had been recovered by August. Jobs in electronics and appliance stores declined through May and in August stood at about 90 percent of their February total.

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

A reminder that Current Employment Statistics data are updated as new information becomes available. Thus, the July and August data shown here are preliminary and will be revised. Employment data by industry are also available for states and localities.

When looking for trends or comparing industries of different sizes, the comparisons shown here can be helpful. The detailed data are available for you to compare other industries, too. Get the data through the BLS data query system.

Percent decline in payroll employment from February through April 2020, by major industry
IndustryPercent decline

Leisure and hospitality

-49.3

Other services

-23.1

Retail trade

-15.2

Total nonfarm

-14.5

Construction

-14.2

Education and health services

-11.3

Professional and business services

-10.7

Manufacturing

-10.6

Transportation and warehousing

-10.0

Information

-9.8

Mining and logging

-8.5

Wholesale trade

-6.7

Government

-4.3

Financial activities

-3.2

Utilities

-0.7
Percent of payroll employment decline from February to April 2020 that was recovered by August 2020, by major industry
IndustryPercent recovered

Retail trade

72.5

Other services

61.2

Construction

60.8

Leisure and hospitality

50.2

Total nonfarm

47.9

Education and health services

47.6

Manufacturing

47.2

Professional and business services

35.8

Transportation and warehousing

33.2

Financial activities

31.5

Wholesale trade

17.4

Government

14.2

Information

-9.5

Mining and logging

-59.0

Utilities

-86.8
Percent of February 2020 employment level in months after February, selected health care industries
IndustryAprilMayJuneJulyAugust

Offices of physicians

89.291.594.195.296.2

Offices of dentists

43.869.289.093.996.1

Hospitals

97.797.097.197.697.8

Nursing and residential care facilities

96.494.994.393.793.2
Percent of February 2020 employment level in months after February, selected retail industries
IndustryAprilMayJuneJulyAugust

Electronics and appliance stores

89.874.780.286.290.5

Building material and garden supply stores

97.3101.8104.3105.1106.1

Food and beverage stores

98.6100.4101.7101.0101.2

Clothing and clothing accessories stores

38.244.562.470.371.1

Department stores

75.279.490.094.597.5

General merchandise stores, including warehouse stores

104.6106.2109.0105.8110.1