Topic Archives: Industries

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

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

New State and Metropolitan Area Data from the Job Openings and Labor Turnover Survey

June 16, 2020BLS Commissioner

Soon after I became Commissioner, the top-notch BLS staff shared with me their vision to expand the Job Openings and Labor Turnover Survey (JOLTS). The JOLTS program publishes data each month on the number and rate of job openings, hires, and separations (broken out by quits, layoffs and discharges, and other separations). These data are available at the national level and for the four large geographic regions—Northeast, Midwest, South, and West.

That left a major data gap on labor demand, hires, and separations for states and metropolitan areas. BLS provides data on labor supply for states and metro areas each month from the Local Area Unemployment Statistics program. We also provide data on employment change in states and metro areas each month from the Current Employment Statistics survey. Employment change is the net effect of hires and separations, but it doesn’t show the underlying flow of job creation and destruction. Having better, timelier state and metro JOLTS data would provide a quicker signal about whether labor demand is accelerating or weakening in local economies.

About 2 months after the staff briefed me, the JOLTS program published experimental state estimates for the first time on May 24, 2019. We have been updating those estimates on a quarterly basis since then. We use a statistical model to help us produce the most current state estimates. We then improve those estimates during an annual benchmark process by taking advantage of data available from the Quarterly Census of Employment and Wages. The JOLTS program is well on its way to moving these state estimates into its official, monthly data stream. Look for that to happen in the second half of 2021!

The President’s proposed budget for fiscal year 2021 includes three improvements to the JOLTS program.

Expand the sample to support direct sample-based estimates for each state.
Accelerate the review and publication of the estimates.
Add questions to provide more information about job openings, hires, and separations.

If funded, this proposal would allow BLS to improve the data quality available from the current JOLTS state estimates. It also would let us add very broad industry detail for each state and more industry detail at the national level.

The proposed larger sample size may also let us produce model-assisted JOLTS estimates for many metro areas. To demonstrate this potential, the JOLTS team produced a one-time set of research estimates for the 18 largest metropolitan statistical areas, those with 1.5 million or more employees. These research estimates show the potential for data that would be available regularly with a larger JOLTS sample. I encourage you to explore this exciting new research series and let us know what you think.

Number of unemployed per job opening in the United States and four large metropolitan areas, 2007–19

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

This is just one example of the excellent work I see at BLS every day. The BLS staff are consummate professionals who continue to do outstanding work even in the most trying of times. The entire BLS staff has been teleworking now for several months due to COVID-19, and every program continues to produce high quality data on schedule! Even in these extraordinary circumstances, BLS professionals continue to innovate and find ways to improve quality and develop new gold standard data products to help the policymakers, businesses, and the public make better-informed decisions.

Number of unemployed per job opening in the United States and four large metropolitan areas
Date	New York-Newark-Jersey City, NY-NJ-PA	Dallas-Fort Worth-Arlington, TX	Chicago-Naperville-Elgin, IL-IN-WI	Los Angeles-Long Beach-Anaheim, CA	United States
Jan 2007	1.8	1.4	1.8	1.6	1.6
Feb 2007	1.8	1.4	1.9	1.6	1.6
Mar 2007	1.7	1.3	1.8	1.6	1.6
Apr 2007	1.6	1.1	1.6	1.5	1.4
May 2007	1.5	1.0	1.5	1.5	1.4
Jun 2007	1.5	1.1	1.6	1.4	1.4
Jul 2007	1.6	1.2	1.8	1.5	1.5
Aug 2007	1.7	1.2	1.9	1.5	1.5
Sep 2007	1.7	1.2	1.8	1.7	1.5
Oct 2007	1.6	1.1	1.7	1.8	1.5
Nov 2007	1.6	1.2	1.8	1.9	1.5
Dec 2007	1.7	1.2	1.9	1.9	1.6
Jan 2008	1.9	1.3	2.0	2.1	1.7
Feb 2008	2.1	1.3	2.2	2.2	1.8
Mar 2008	2.2	1.3	2.4	2.2	1.9
Apr 2008	2.1	1.2	2.2	2.0	1.8
May 2008	2.0	1.2	2.2	2.1	1.8
Jun 2008	1.9	1.2	2.4	2.5	1.9
Jul 2008	2.2	1.4	3.0	3.0	2.2
Aug 2008	2.3	1.7	3.1	3.5	2.4
Sep 2008	2.4	1.8	3.1	3.6	2.5
Oct 2008	2.5	1.9	3.0	4.0	2.6
Nov 2008	2.8	2.1	3.4	4.6	2.9
Dec 2008	3.1	2.4	3.9	5.9	3.3
Jan 2009	3.6	2.9	4.8	6.7	4.0
Feb 2009	4.4	3.2	5.5	7.3	4.6
Mar 2009	5.0	3.5	6.4	7.4	5.1
Apr 2009	5.0	3.6	6.7	7.3	5.3
May 2009	5.1	4.0	6.8	7.1	5.4
Jun 2009	5.1	4.6	7.2	6.8	5.6
Jul 2009	5.2	5.3	7.7	7.4	6.0
Aug 2009	5.1	5.6	7.9	7.7	6.2
Sep 2009	5.4	5.8	8.0	8.0	6.1
Oct 2009	5.9	5.3	7.8	7.8	5.8
Nov 2009	6.7	5.6	8.6	8.2	5.9
Dec 2009	7.1	5.6	9.5	8.3	6.2
Jan 2010	7.0	6.3	10.8	8.3	6.2
Feb 2010	7.0	6.4	10.3	7.5	6.2
Mar 2010	7.0	5.9	8.8	7.1	5.9
Apr 2010	6.5	5.1	7.8	6.7	5.4
May 2010	5.9	4.8	6.6	6.5	4.9
Jun 2010	5.0	5.1	6.2	6.7	4.8
Jul 2010	4.8	5.2	6.0	7.1	4.9
Aug 2010	4.7	5.4	6.2	7.5	4.9
Sep 2010	4.9	4.7	6.1	7.5	4.7
Oct 2010	4.6	4.2	5.2	7.1	4.5
Nov 2010	4.8	4.0	5.0	7.1	4.5
Dec 2010	5.3	4.1	5.1	7.1	4.6
Jan 2011	6.0	4.3	5.5	7.1	5.0
Feb 2011	6.1	4.2	5.7	6.8	4.9
Mar 2011	5.5	4.0	5.3	6.3	4.6
Apr 2011	5.0	3.8	5.1	5.8	4.2
May 2011	4.6	3.6	4.6	5.8	4.1
Jun 2011	4.5	3.7	4.5	5.6	4.0
Jul 2011	4.6	3.8	4.6	5.9	4.1
Aug 2011	4.5	3.8	4.9	6.0	4.0
Sep 2011	4.4	3.4	4.6	5.8	3.8
Oct 2011	4.2	3.1	4.2	5.6	3.6
Nov 2011	4.0	3.0	4.2	5.3	3.6
Dec 2011	4.2	3.0	4.8	5.5	3.7
Jan 2012	4.6	3.0	5.2	5.9	3.7
Feb 2012	5.3	3.0	4.8	6.2	3.7
Mar 2012	5.1	2.8	4.2	5.7	3.5
Apr 2012	4.2	2.4	3.6	5.0	3.3
May 2012	3.9	2.1	3.4	4.8	3.1
Jun 2012	3.9	2.1	3.6	4.6	3.1
Jul 2012	4.2	2.3	3.9	5.3	3.3
Aug 2012	4.0	2.4	3.9	5.2	3.4
Sep 2012	3.8	2.4	3.5	5.5	3.2
Oct 2012	3.7	2.1	3.2	5.0	3.0
Nov 2012	3.9	2.0	3.4	4.8	3.0
Dec 2012	4.0	2.0	3.7	5.1	3.2
Jan 2013	4.2	2.2	4.3	5.4	3.4
Feb 2013	4.2	2.1	4.1	5.4	3.4
Mar 2013	4.0	2.0	3.9	4.9	3.2
Apr 2013	3.5	1.9	3.5	4.1	2.9
May 2013	3.2	1.9	3.5	3.8	2.7
Jun 2013	3.2	2.1	3.6	3.6	2.7
Jul 2013	3.4	2.3	3.8	3.8	2.9
Aug 2013	3.4	2.3	3.7	3.9	2.9
Sep 2013	3.4	2.1	3.6	4.0	2.8
Oct 2013	3.2	2.0	3.3	3.9	2.5
Nov 2013	3.2	2.0	3.2	4.1	2.5
Dec 2013	3.2	2.0	3.3	4.2	2.6
Jan 2014	3.4	2.0	3.5	4.2	2.7
Feb 2014	3.4	1.9	3.5	3.7	2.7
Mar 2014	3.2	1.9	3.3	3.3	2.6
Apr 2014	2.8	1.7	2.6	2.8	2.2
May 2014	2.5	1.6	2.1	2.8	2.0
Jun 2014	2.4	1.6	2.0	2.8	1.9
Jul 2014	2.6	1.6	2.1	3.1	2.0
Aug 2014	2.6	1.6	2.1	2.9	1.9
Sep 2014	2.5	1.6	2.0	2.9	1.9
Oct 2014	2.3	1.5	1.9	2.7	1.7
Nov 2014	2.4	1.5	1.9	2.9	1.8
Dec 2014	2.5	1.3	1.9	2.9	1.8
Jan 2015	2.6	1.3	2.0	2.9	1.9
Feb 2015	2.5	1.3	1.9	2.6	1.8
Mar 2015	2.4	1.3	1.8	2.4	1.7
Apr 2015	2.2	1.2	1.6	2.2	1.6
May 2015	2.0	1.1	1.5	2.2	1.5
Jun 2015	1.9	1.0	1.6	2.3	1.5
Jul 2015	1.9	1.0	1.6	2.3	1.5
Aug 2015	1.9	0.9	1.6	2.3	1.5
Sep 2015	1.8	0.9	1.5	2.3	1.4
Oct 2015	1.7	0.8	1.5	2.0	1.3
Nov 2015	1.6	0.8	1.5	2.0	1.3
Dec 2015	1.6	0.8	1.5	1.9	1.4
Jan 2016	1.7	0.9	1.6	1.9	1.4
Feb 2016	1.8	0.8	1.6	1.7	1.5
Mar 2016	1.7	0.8	1.6	1.6	1.4
Apr 2016	1.6	0.7	1.6	1.6	1.3
May 2016	1.4	0.7	1.4	1.6	1.2
Jun 2016	1.4	0.8	1.4	1.6	1.3
Jul 2016	1.4	0.9	1.4	1.8	1.3
Aug 2016	1.5	0.9	1.5	1.9	1.4
Sep 2016	1.4	0.9	1.5	1.9	1.3
Oct 2016	1.3	0.9	1.5	1.7	1.3
Nov 2016	1.3	0.9	1.4	1.7	1.3
Dec 2016	1.3	0.9	1.5	1.7	1.3
Jan 2017	1.4	1.0	1.7	1.8	1.4
Feb 2017	1.5	1.0	1.7	1.8	1.4
Mar 2017	1.5	1.0	1.5	1.8	1.4
Apr 2017	1.3	0.9	1.4	1.6	1.2
May 2017	1.3	0.9	1.2	1.5	1.1
Jun 2017	1.3	1.0	1.2	1.4	1.1
Jul 2017	1.3	1.1	1.2	1.5	1.1
Aug 2017	1.4	1.1	1.2	1.6	1.1
Sep 2017	1.4	1.0	1.1	1.5	1.1
Oct 2017	1.3	1.0	1.0	1.3	1.0
Nov 2017	1.2	1.0	1.0	1.3	1.0
Dec 2017	1.2	1.0	1.1	1.3	1.0
Jan 2018	1.2	1.1	1.3	1.3	1.1
Feb 2018	1.2	1.1	1.3	1.2	1.1
Mar 2018	1.2	1.1	1.2	1.2	1.1
Apr 2018	1.1	1.0	1.1	1.1	1.0
May 2018	1.0	1.0	0.9	1.0	0.9
Jun 2018	1.0	0.9	0.8	1.1	0.9
Jul 2018	1.0	0.8	0.8	1.1	0.9
Aug 2018	1.0	0.8	0.9	1.2	0.9
Sep 2018	0.9	0.8	0.9	1.2	0.8
Oct 2018	0.9	0.8	0.8	1.1	0.8
Nov 2018	0.8	0.8	0.8	1.2	0.8
Dec 2018	0.9	0.7	0.8	1.3	0.8
Jan 2019	1.0	0.8	0.9	1.4	0.9
Feb 2019	1.1	0.8	1.0	1.4	0.9
Mar 2019	1.1	0.8	0.9	1.4	0.9
Apr 2019	1.0	0.7	0.9	1.1	0.8
May 2019	0.8	0.7	0.8	1.0	0.8
Jun 2019	0.8	0.6	0.8	0.9	0.8
Jul 2019	0.8	0.7	0.8	1.0	0.8
Aug 2019	0.8	0.8	0.9	1.1	0.9
Sep 2019	0.8	0.8	0.9	1.2	0.8
Oct 2019	0.8	0.8	0.8	1.1	0.8
Nov 2019	0.8	0.8	0.7	1.0	0.8
Dec 2019	0.8	0.8	0.7	1.0	0.8

Topics: Employment | Geographic Information | Industries | Job Openings, Hires, and Separations | Unemployment

Tags: Hires, Metropolitan areas, Regions and states, Separations, Statistics awareness, Turnover

Paid Leave Benefits When You Are Unable to Work

April 22, 2020BLS Commissioner

Many American workers have lost jobs or had their work hours reduced as a result of the COVID-19 pandemic and response efforts. Many other workers still have jobs, but their work environment probably has changed since March. It’s reasonable to assume more people are working from home now than the 29 percent we reported who could work at home in 2017–18. At BLS we are still working to provide you with the latest economic data and analysis, but nearly all of us are now working from home, instead of in our offices.

Still, there are many jobs that just can’t be done from home. In these challenging times, I know we all are grateful for the healthcare workers who are treating patients who have COVID-19 and other medical conditions. We’re grateful for our emergency responders and for the truck drivers, warehouse workers, delivery workers, and staff in grocery stores, pharmacies, and other retail establishments that provide us with the necessities of daily life. As much as I think of these men and women as superheroes, I know they are humans. Even extraordinary humans can get sick, or they may need to take care of family members who get sick. Let’s look at the leave benefits available to them if they need it.

According to our National Compensation Survey, 73 percent of private industry workers were covered by paid sick leave in 2019. Among state and local government workers, 91 percent were covered by paid sick leave. The availability of sick leave benefits varied by occupation, ranging from 94 percent of managers in private industry to 56 percent of workers in construction and extraction occupations.

The share with paid sick leave also varies by industry, pay level, size of establishment, and other characteristics of jobs and employers. The following chart shows sick leave availability for employers of different sizes.

Percent of workers in private industry with access to paid sick leave by establishment size, March 2019

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

Paid sick leave plans commonly provide a fixed number of days per year. The number of days may vary by the worker’s length of service with the employer. The average in private industry in 2019 was 7 paid sick leave days.

Average number of paid sick leave days per year for workers in private industry, by length of service and establishment size, March 2019

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

About half of workers with such a plan could carry over unused days from year to year.

We recently posted a new fact sheet on paid sick leave that provides even more detail.

In the past few years, some states and cities have mandated that certain employers provide their workers with paid sick leave. We include these mandated plans in our data on paid leave. A Federal law passed in March 2020 requires paid sick leave for certain workers affected by COVID-19.

In addition to paid sick leave, some employers offer a short-term disability insurance plan when employees can’t work because of illness. These plans are sometimes called sickness and accident insurance plans. This was traditionally a blue-collar or union benefit, and it often replaces only a portion of an employee’s pay. In 2019, 42 percent of private industry workers had access to such a benefit. Like sick leave, the availability of short-term disability benefits varies widely across worker groups. Some states provide Temporary Disability Insurance plans that provide similar benefits.

While the National Compensation Survey asks employers what benefits they offer to workers, the American Time Use Survey recently asked workers whether paid leave is available from their employer and whether they used it. In 2017–18, two-thirds of workers had access to paid leave at their jobs. These data include information on age, sex, and other characteristics. For example, younger workers (ages 15–24) and older workers (age 65 and older) were less likely to have access to paid leave than were other workers.

Percent of workers with access to paid leave by age, 2017–18 averages

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

While the survey did not ask workers to classify the type of leave, they were asked the reasons they could take leave. Of those with paid leave available, 94 percent could use it for their own illness or medical care, and 78 percent could use it for the illness or medical care of another family member.

I hope you and your loved ones remain healthy and are able to take care of each other in these challenging times. High-quality data will be vital in the public health response to the COVID-19 pandemic. High-quality data also will be vital for measuring the economic impact of the pandemic and recovery from it. My colleagues at BLS and our fellow U.S. statistical agencies remain on the job to provide you with gold standard data.

Percent of workers in private industry with access to paid sick leave by establishment size, March 2019
Establishment size	Percent
1–49 workers	64%
50–99 workers	68
100–499 workers	80
500 workers or more	89

Average number of paid sick leave days per year for workers in private industry, by length of service and establishment size, March 2019
Length of service	All establishments	1 to 49 workers	50 to 99 workers	100 to 499 workers	500 workers or more
After 1 year	7	6	6	7	8
After 5 years	7	7	6	7	9
After 10 years	7	7	7	7	9
After 20 years	7	7	7	7	9

Percent of workers with access to paid leave by age, 2017–18 averages
Age	Percent
Ages 15–24	35.4%
Ages 25–34	70.3
Ages 35–44	71.7
Ages 45–54	74.4
Ages 55–64	74.2
Age 65 and older	51.7

Tags: COVID-19, Family, Healthcare, Pandemic, Sick leave