Topic Archives: Industries

The Challenges of Seasonal Adjustment during the COVID-19 Pandemic

December 21, 2020BLS Commissioner

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 ta ble below.

BLS seasonally adjusts data in several of its monthly and quarterly news releases.

National Employment and Unemployment (monthly)
State Employment and Unemployment (monthly)
Metro Employment and Unemployment (monthly)
Job Openings and Labor Turnover (monthly)
Consumer Price Index (monthly)
Producer Price Indexes (monthly)
Employment Cost Index (quarterly)
Labor Productivity and Costs (quarterly)
Business Employment Dynamics (quarterly)

Two Approaches to Seasonal Adjustment

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.

Approach #1

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.

Approach #2

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.

BLS does not produce the weekly data on unemployment insurance. We do, however, compute the seasonal adjustment factors used by the Department of Labor’s Employment and Training Administration for their Unemployment Insurance Weekly Claims data. As we recommended, the Employment and Training Administration recently switched from using multiplicative to additive seasonal adjustments.

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.

Construction employment, 2015–19
Month	Seasonally adjusted	Not seasonally adjusted
Jan 2015	6,320,000	5,953,000
Feb 2015	6,361,000	5,962,000
Mar 2015	6,334,000	6,051,000
Apr 2015	6,392,000	6,300,000
May 2015	6,427,000	6,491,000
Jun 2015	6,441,000	6,633,000
Jul 2015	6,472,000	6,718,000
Aug 2015	6,490,000	6,754,000
Sep 2015	6,508,000	6,704,000
Oct 2015	6,547,000	6,740,000
Nov 2015	6,598,000	6,685,000
Dec 2015	6,630,000	6,542,000
Jan 2016	6,620,000	6,252,000
Feb 2016	6,650,000	6,256,000
Mar 2016	6,680,000	6,402,000
Apr 2016	6,701,000	6,614,000
May 2016	6,691,000	6,758,000
Jun 2016	6,702,000	6,913,000
Jul 2016	6,736,000	6,989,000
Aug 2016	6,737,000	6,997,000
Sep 2016	6,768,000	6,971,000
Oct 2016	6,798,000	6,981,000
Nov 2016	6,819,000	6,903,000
Dec 2016	6,821,000	6,700,000
Jan 2017	6,847,000	6,459,000
Feb 2017	6,889,000	6,527,000
Mar 2017	6,909,000	6,634,000
Apr 2017	6,916,000	6,820,000
May 2017	6,928,000	6,998,000
Jun 2017	6,955,000	7,169,000
Jul 2017	6,960,000	7,212,000
Aug 2017	6,990,000	7,248,000
Sep 2017	7,004,000	7,201,000
Oct 2017	7,027,000	7,208,000
Nov 2017	7,066,000	7,147,000
Dec 2017	7,093,000	7,004,000
Jan 2018	7,114,000	6,729,000
Feb 2018	7,200,000	6,840,000
Mar 2018	7,205,000	6,933,000
Apr 2018	7,223,000	7,129,000
May 2018	7,266,000	7,336,000
Jun 2018	7,282,000	7,497,000
Jul 2018	7,304,000	7,554,000
Aug 2018	7,335,000	7,586,000
Sep 2018	7,355,000	7,535,000
Oct 2018	7,378,000	7,557,000
Nov 2018	7,376,000	7,454,000
Dec 2018	7,402,000	7,311,000
Jan 2019	7,452,000	7,069,000
Feb 2019	7,423,000	7,062,000
Mar 2019	7,443,000	7,170,000
Apr 2019	7,469,000	7,377,000
May 2019	7,478,000	7,540,000
Jun 2019	7,497,000	7,699,000
Jul 2019	7,504,000	7,753,000
Aug 2019	7,508,000	7,760,000
Sep 2019	7,524,000	7,700,000
Oct 2019	7,541,000	7,720,000
Nov 2019	7,539,000	7,609,000
Dec 2019	7,555,000	7,447,000

Tags: COVID-19, Hurricanes, Pandemic, Seasonal adjustment, Wildfires

Celebrating World Statistics Day 2020

October 20, 2020BLS Commissioner

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.

Happy World Statistics Day!

Tags: Confidentiality, International, Quality, Security, World Statistics Day

A Closer Look at Recent Employment Trends

September 17, 2020BLS Commissioner

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 12^th 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 ta ble 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 ta ble 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 ta ble 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 ta ble 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
Industry	Percent 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
Industry	Percent 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
Industry	April	May	June	July	August
Offices of physicians	89.2	91.5	94.1	95.2	96.2
Offices of dentists	43.8	69.2	89.0	93.9	96.1
Hospitals	97.7	97.0	97.1	97.6	97.8
Nursing and residential care facilities	96.4	94.9	94.3	93.7	93.2

Percent of February 2020 employment level in months after February, selected retail industries
Industry	April	May	June	July	August
Electronics and appliance stores	89.8	74.7	80.2	86.2	90.5
Building material and garden supply stores	97.3	101.8	104.3	105.1	106.1
Food and beverage stores	98.6	100.4	101.7	101.0	101.2
Clothing and clothing accessories stores	38.2	44.5	62.4	70.3	71.1
Department stores	75.2	79.4	90.0	94.5	97.5
General merchandise stores, including warehouse stores	104.6	106.2	109.0	105.8	110.1

Topics: Employment | Geographic Information | Industries | Survey Respondents

Tags: COVID-19, Employment Situation, Pandemic

New Measures of How Widespread Employment Changes Are across States and Metro Areas

August 19, 2020BLS Commissioner

BLS recently began publishing a new set of measures on employment changes in states and metropolitan areas. For decades we have published monthly estimates of employment, hours, and earnings for each state and metro area. Our new measure summarizes how widespread employment increases or decreases are across all states or metro areas. We call this measure a diffusion index.

What’s a diffusion index? Let me explain how we create the measure.

Let’s say we’re creating a diffusion index for the 50 states and the District of Columbia. We start by assigning each state and D.C. a value depending on whether its employment decreased, stayed the same, or increased over the period we’re looking at.

The assigned value is 0 if employment decreased.
The assigned value is 50 if employment stayed the same.
The assigned value is 100 if employment increased.

The diffusion index is the average of those 51 values. To create a diffusion index for metro areas, we assign values of 0, 50, or 100 for each of 388 metro areas and then average those values. We calculate diffusion indexes for employment changes over 1 month, 3 months, 6 months, and 12 months.

Now that we understand the simple arithmetic for calculating diffusion indexes, what do they mean? An index greater than 50 means more states or metro areas had increasing employment over the period. An index below 50 means more states or metro areas had decreasing employment. At the extremes, an index of 0 means employment fell in all states or metro areas; an index of 100 means employment rose in all of them. A diffusion index of 50 doesn’t necessarily mean 50 percent of the states or areas had increasing employment and the other 50 percent had decreasing employment. It just means the same number of states or areas had increases and decreases, with any of the other states or areas having no change.

The chart below shows 3-month diffusion indexes for all states and metro areas. You can see how all states and nearly all metro areas had job losses during the worst of the 2007–09 recession. We see it again more recently with the downturn associated with the COVID-19 pandemic.

3-month diffusion indexes for all states and all metropolitan areas, 2007–20

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

Diffusion indexes aren’t a new analytical tool. We publish other diffusion indexes using national employment data that summarize how employment change is dispersed across industries. The Federal Reserve Bank of Philadelphia publishes diffusion indexes using a variety of data. The new BLS diffusion indexes summarize how employment is changing across geographic areas to give us another perspective of the labor market.

Keep a look out for the new data. We update the indexes each month in our public database.

3-month diffusion indexes for all states and all metropolitan areas
Month	All states	All metropolitan areas
Jan 2007	96.1	77.7
Feb 2007	84.3	72.2
Mar 2007	84.3	72.6
Apr 2007	74.5	59.1
May 2007	86.3	65.3
Jun 2007	69.6	61.2
Jul 2007	78.4	67.7
Aug 2007	74.5	61.2
Sep 2007	56.9	51.3
Oct 2007	62.7	53.2
Nov 2007	79.4	59.1
Dec 2007	80.4	63.0
Jan 2008	81.4	65.3
Feb 2008	78.4	64.0
Mar 2008	52.0	50.6
Apr 2008	41.2	36.2
May 2008	25.5	29.8
Jun 2008	23.5	35.4
Jul 2008	16.7	33.9
Aug 2008	16.7	29.8
Sep 2008	15.7	23.6
Oct 2008	7.8	17.8
Nov 2008	7.8	11.9
Dec 2008	3.9	10.1
Jan 2009	2.0	4.3
Feb 2009	2.0	3.9
Mar 2009	0.0	4.1
Apr 2009	0.0	3.6
May 2009	2.0	4.9
Jun 2009	3.9	11.7
Jul 2009	8.8	14.6
Aug 2009	4.9	14.0
Sep 2009	5.9	20.0
Oct 2009	16.7	28.0
Nov 2009	21.6	38.5
Dec 2009	19.6	38.1
Jan 2010	26.5	38.8
Feb 2010	18.6	36.0
Mar 2010	70.6	56.3
Apr 2010	94.1	74.6
May 2010	100.0	86.6
Jun 2010	98.0	79.0
Jul 2010	85.3	64.3
Aug 2010	35.3	42.4
Sep 2010	39.2	44.7
Oct 2010	70.6	63.7
Nov 2010	74.5	64.8
Dec 2010	88.2	73.7
Jan 2011	62.7	57.0
Feb 2011	76.5	61.9
Mar 2011	87.3	67.9
Apr 2011	98.0	75.5
May 2011	90.2	67.0
Jun 2011	80.4	56.4
Jul 2011	83.3	66.8
Aug 2011	82.4	72.3
Sep 2011	98.0	81.8
Oct 2011	82.4	64.8
Nov 2011	96.1	66.8
Dec 2011	82.4	63.1
Jan 2012	88.2	76.7
Feb 2012	97.1	78.9
Mar 2012	98.0	83.1
Apr 2012	96.1	75.9
May 2012	94.1	70.0
Jun 2012	70.6	58.1
Jul 2012	74.5	57.0
Aug 2012	77.5	64.4
Sep 2012	86.3	67.5
Oct 2012	97.1	76.7
Nov 2012	93.1	75.4
Dec 2012	90.2	73.7
Jan 2013	88.2	70.2
Feb 2013	94.1	79.5
Mar 2013	99.0	75.9
Apr 2013	87.3	75.0
May 2013	82.4	68.7
Jun 2013	82.4	68.4
Jul 2013	81.4	70.6
Aug 2013	94.1	76.4
Sep 2013	92.2	77.8
Oct 2013	90.2	77.8
Nov 2013	94.1	74.7
Dec 2013	81.4	73.2
Jan 2014	88.2	68.7
Feb 2014	80.4	66.5
Mar 2014	86.3	73.6
Apr 2014	96.1	82.0
May 2014	98.0	83.4
Jun 2014	96.1	83.5
Jul 2014	96.1	74.2
Aug 2014	92.2	74.5
Sep 2014	90.2	77.2
Oct 2014	98.0	79.9
Nov 2014	96.1	79.8
Dec 2014	98.0	81.8
Jan 2015	93.1	80.0
Feb 2015	84.3	74.5
Mar 2015	64.7	62.5
Apr 2015	74.5	65.1
May 2015	84.3	77.1
Jun 2015	84.3	78.2
Jul 2015	92.2	84.1
Aug 2015	80.4	74.5
Sep 2015	86.3	74.1
Oct 2015	88.2	75.9
Nov 2015	88.2	74.6
Dec 2015	88.2	73.2
Jan 2016	75.5	71.1
Feb 2016	81.4	72.4
Mar 2016	78.4	69.5
Apr 2016	86.3	77.1
May 2016	72.5	67.3
Jun 2016	55.9	57.7
Jul 2016	84.3	71.3
Aug 2016	86.3	76.2
Sep 2016	94.1	85.1
Oct 2016	68.6	66.9
Nov 2016	82.4	73.6
Dec 2016	78.4	64.7
Jan 2017	84.3	70.0
Feb 2017	79.4	68.9
Mar 2017	98.0	76.5
Apr 2017	88.2	72.0
May 2017	78.4	68.4
Jun 2017	91.2	69.6
Jul 2017	80.4	71.6
Aug 2017	91.2	75.6
Sep 2017	76.5	60.8
Oct 2017	80.4	73.8
Nov 2017	84.3	70.7
Dec 2017	91.2	73.8
Jan 2018	90.2	74.2
Feb 2018	96.1	80.2
Mar 2018	96.1	80.9
Apr 2018	86.3	72.9
May 2018	82.4	73.6
Jun 2018	94.1	76.7
Jul 2018	91.2	81.3
Aug 2018	94.1	77.2
Sep 2018	82.4	68.4
Oct 2018	94.1	72.8
Nov 2018	92.2	72.3
Dec 2018	88.2	67.9
Jan 2019	89.2	79.4
Feb 2019	84.3	73.3
Mar 2019	82.4	74.9
Apr 2019	61.8	56.4
May 2019	64.7	58.5
Jun 2019	66.7	55.4
Jul 2019	74.5	60.8
Aug 2019	80.4	67.1
Sep 2019	79.4	66.4
Oct 2019	70.6	60.3
Nov 2019	68.6	63.7
Dec 2019	74.5	67.9
Jan 2020	87.3	75.9
Feb 2020	86.3	71.8
Mar 2020	5.9	29.0
Apr 2020	0.0	0.0
May 2020	0.0	0.3
Jun 2020^[p]	0.0	0.6
^[p] preliminary

Topics: Employment | Geographic Information | Industries

Tags: Diffusion index, Metropolitan areas, Regions and states, Statistics awareness

New Recommendations on Improving Data on Contingent and Alternative Work Arrangements

August 10, 2020BLS Commissioner

The workplace is changing. We have seen more evidence of that in recent months as workplaces have adapted to the COVID-19 pandemic. Even before the pandemic, many of us wanted to learn more about telework, flexible work hours, and independent contracting. We also wanted to know more about intermittent or short-term work found through mobile devices, unpredictable work schedules, and other employment relationships we might not think of as traditional. It’s our job at BLS to keep up with these new work relationships and figure out how to measure them.

In 2018, we released data collected in 2017 about people in contingent and alternative work arrangements. Contingent workers are people who do not expect their jobs to last or who report their jobs are temporary. Alternative work arrangements include independent contractors, on-call workers, temporary help agency workers, and workers provided by contract firms. We also published data in 2018 about electronically mediated work. All of these data reflect the rapidly changing workplace.

Those reports received a lot of attention, but policymakers, employers, researchers, and others told us they want to know more about these nontraditional workers. We need to understand people in jobs that often involve doing short-term tasks, such as ridesharing or data-entry services. Our 2017 survey included a few questions about these arrangements, but this work can be complex and varied. That makes it hard to measure nontraditional work arrangements with just a few questions.

To effectively analyze these hard-to-measure work arrangement, BLS sought out experts on nontraditional work. In 2019, we contracted with the Committee on National Statistics to explore what we should measure if we had the funding to collect and publish more data about these workers. We asked the committee not to recommend changes to the main Current Population Survey, the large monthly survey of U.S. households from which we measure the unemployment rate and other important labor market measures. The committee had free rein, however, to recommend topics we should examine in any future edition of the Contingent Worker Supplement to the Current Population Survey. We also asked the committee to recommend changes to the survey design and methods of data collection if we were to conduct the supplement again.

The Committee on National Statistics is a federally supported independent organization whose mission is to improve the statistical methods and information that guide public policies. The committee moved quickly to form a group of experts on the relevant topics. I asked these experts to review the Contingent Worker Supplement and consider other sources of information on nontraditional work arrangements. The group was impressive and included a former BLS Commissioner, a former Administrator of the U.S. Department of Labor Wage and Hour Division, and several experts in economics and survey methods. They all volunteered their time to help us improve the Contingent Worker Supplement.

The group held public meetings and a workshop, hearing from experts, data users, and policymakers to understand what data would be the most valuable. At the end of their year-long review, they produced a report with specific recommendations in July of 2020 about measurement objectives and data collection.

BLS thanks the Committee on National Statistics and the expert panel for the time and effort they put into the report. Their recommendations thoughtfully balanced the desire to measure everything about this important topic with the limited time and information survey respondents can give us. In the coming months, we will study the report. It will guide us as we consider how to update the Contingent Worker Supplement to reflect the variety of work arrangements in the U.S. labor market.

Tags: Committee on National Statistics, Contingent workers, Contract workers, Gig work, Independent contractors, Interviews, Methodology, On-call workers, Temporary workers, Work schedules