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Tag Archives: Labor market trends

What is “Benchmarking” of Bureau of Labor Statistics Employment Data?

BLS has released the “preliminary benchmark” information for the Current Employment Statistics (CES) survey, the source of monthly information on jobs.

You know what a bench is

Image of a park bench

and you know what a mark is,

Image of a checkmark

but what pray tell is a benchmark? And what does this preliminary benchmark tell us?

So as not to bury the lead, I’ll let you know that this year’s preliminary estimate of the benchmark revision is a bit bigger than it has been in the last few years. Our preliminary estimate indicates a downward adjustment to March 2019 total nonfarm employment of 501,000. Still, that estimated revision is only -0.3 percent of nonfarm employment. In most years our monthly employment survey has done a good job at estimating the total number of payroll jobs. More details on that below. This year our survey estimates are off more than we would like. Our goal is to provide estimates that are excellent and not just good or pretty good, and that’s why we benchmark the survey data each year.

What is benchmarking and why do we do it?

The CES is a monthly survey of approximately 142,000 businesses and government agencies composed of approximately 689,000 individual worksites. As with all sample-based surveys, CES estimates are subject to sampling error. This means that while we work hard to ensure those 689,000 worksites represent all 10 million worksites in the country, sometimes our sample may not perfectly reflect all worksites. So the monthly CES estimates aren’t exactly the same as if we had counted employment from all 10 million worksites each month. To fix this problem, we “benchmark” the CES data to an actual count of all employees, information that’s only available several months after the initial CES data are published.

In essence, we produce employment information really quickly from a sample of employers, then anchor that information to a complete count of employment once a year.

The primary source of the CES sample is the BLS Quarterly Census of Employment and Wages (QCEW) program, which collects employment and wage data from states’ unemployment insurance tax systems. This is also the main source of the complete count of employment used in the benchmark process. QCEW data are typically available about 5 months after the end of each quarter.

Each year, we re-anchor the sample-based employment estimates to these full population counts for March of the prior year. This process—which we call benchmarking—improves the accuracy of the CES data. That’s because the population counts are not subject to the sampling and modeling errors that may occur with the CES monthly estimates. Since the CES data are re-anchored to March of the last year, CES estimates are typically revised from April of the year prior up to the March benchmark. Then estimates from the benchmark forward to December are revised to reflect the new March employment level.

We will publish the final benchmark revision in February 2020 and will incorporate revisions to data from April 2018 to December 2019. (Thus, we’re not showing a 2019 number in graph and table below). On August 21, BLS released a first look at what this revision will be—what we call the “preliminary benchmark.” This preliminary benchmark gives us an idea of what the revised nonfarm employment estimates for March 2019 will be.

The size of the national benchmark revision is a measure of the accuracy of the CES estimates, and we take pride that these revisions are typically small.

Chart showing differences in nonfarm employment after benchmarking, 2009–18

For total employment nationwide, the absolute annual benchmark revision has averaged about 0.2 percent over the past decade, with a range from −0.7 percent to +0.3 percent.

The following table shows the total payroll employment estimated from the CES before and after the benchmark over the past 10 years. For example, pre-benchmark employment for 2018 was 147.4 million; post-benchmark employment was also 147.4 million.

Nonfarm employment estimates before and after benchmarking, March 2009–March 2018
Year Level before benchmark Level after benchmark Difference Percent difference
2009 132,077,000 131,175,000 -902,000 –0.7
2010 128,958,000 128,584,000 -374,000 –0.3
2011 129,899,000 130,061,000 162,000 0.1
2012 132,081,000 132,505,000 424,000 0.3
2013 134,570,000 134,917,000 347,000 0.3
2014 137,147,000 137,214,000 67,000 <0.05
2015 140,298,000 140,099,000 -199,000 –0.1
2016 142,895,000 142,814,000 -81,000 –0.1
2017 144,940,000 145,078,000 138,000 0.1
2018 147,384,000 147,368,000 -16,000 <-0.05

The 2019 preliminary benchmark revision is following the same pattern, with an estimated difference of -0.3 percent. We provide this first look at the benchmark revision to give data users a sense of what we are seeing in the data. The final benchmark may be a little different—could be higher, could be lower. But based on recent experience, we are confident the benchmark released next February will show only a moderate difference from what we’ve been publishing each month and will validate the accuracy of our monthly CES estimates.

Want to know more? See our Current Employment Statistics webpage, send us an email, or call (202) 691-6555.

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

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

State labor productivity

Our measures of labor productivity for states are still experimental, meaning we’re still assessing them and considering ways to improve them. These measures cover the private nonfarm sector for all 50 states and the District of Columbia from 2007 to 2017. They show that labor productivity growth varies a lot from state to state. From 2007 to 2017, labor productivity changes ranged from a gain of 3.1 percent per year in North Dakota to a loss of 0.7 percent per year in Louisiana. In 2017, labor productivity grew fastest in Montana (2.0 percent), West Virginia (1.9 percent), California (1.8 percent), and Hawaii (1.7 percent). You can get the complete dataset from our state labor productivity page.

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

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

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

State job openings and labor turnover

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

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

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

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

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

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

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

Annual percent change in labor productivity in the private nonfarm sector, 2007–17
State Annual percent change
North Dakota 3.1
California 1.7
Oregon 1.7
Washington 1.7
Colorado 1.6
Oklahoma 1.6
Maryland 1.5
Montana 1.5
Pennsylvania 1.5
Massachusetts 1.4
New Mexico 1.4
Vermont 1.4
Idaho 1.3
Kansas 1.3
Nebraska 1.1
New Hampshire 1.1
South Carolina 1.1
Tennessee 1.1
Texas 1.1
West Virginia 1.1
Alabama 1.0
Hawaii 1.0
Kentucky 1.0
Minnesota 1.0
New York 1.0
Rhode Island 1.0
South Dakota 1.0
Virginia 1.0
Georgia 0.9
Arkansas 0.8
Missouri 0.8
Ohio 0.8
Utah 0.8
Illinois 0.7
North Carolina 0.7
Delaware 0.6
Florida 0.6
Iowa 0.6
Indiana 0.5
Mississippi 0.5
New Jersey 0.5
Wisconsin 0.5
Alaska 0.4
Arizona 0.4
District of Columbia 0.4
Michigan 0.4
Maine 0.3
Nevada 0.3
Wyoming 0.1
Connecticut -0.5
Louisiana -0.7

Why This Counts: 10 Million U.S. Establishments for the First Time

In the third quarter of 2018, the number of establishments in the U.S. economy reached 10 million. This milestone is based on data from the Quarterly Census of Employment and Wages (QCEW), which uses administrative records to identify the number of establishments in our economy.

What is the QCEW?

The QCEW compiles quarterly reports of the Unemployment Insurance systems in each state, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands. Every business with an employee, other than the owner, must register with the state where it has a business location. We call these locations establishments.

 Behind the number

Behind the 10 million establishments are some interesting facts about our labor market.

First, the U.S. labor market is dynamic

Roughly 200,000 establishments are new each quarter. That adds up to almost 800,000 new establishments each year. If we consider that we first hit the 9 million mark in third quarter 2007, you may wonder why we didn’t reach 10 million sooner.

Some establishments continue for long periods, while others close. In 2018, more than half of all private sector establishments were 10 years or older. And while the number of establishments grows in most years, during a recession fewer establishments open and more close. Openings and closings that mostly offset one another result in a pretty stable rate of change in the total number of establishments over time.

Line chart showing the number of establishments each quarter from 2001 to 2018

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

Second, most establishments are small

If we define small establishments as having fewer than 500 employees, 99.8 percent of all establishments in the U.S. economy are small. These employ 82.6 percent of workers and pay 73.5 percent of all wages, including bonuses.

Less than one-tenth of 1 percent of establishments have 1,000 employees or more, yet these establishment have an outsized impact. In first quarter 2018 they accounted for 11.0 percent of employment and 17.8 percent of total wages, far higher than their representation in our economy.

Chart showing the share of establishments, employment, and total wages by establishment size in the first quarter of 2018

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

Slicing the data a different way, we find that 62.5 percent of all establishments in first quarter 2018 had fewer than 5 employees. In fact, the five industries with the most establishments are all dominated by small establishments.

Services for the elderly and disabled is the industry with both the highest number of establishments and the highest number of small establishments. In first quarter 2018, it had 742,364 private establishments, and 97.1 percent of them had fewer than 5 employees. The industry office of physicians, except mental health ranks fifth in the U.S. economy in terms of the number of establishments, and 52.5 percent of them had fewer than 5 employees.

An interesting story about physicians’ offices

The real value of the QCEW data is in its fine level of detail. Drilling down, we find that in first quarter 2018 Los Angeles County had 10,360 establishments in offices of physicians, except mental health, ranking first in the U.S. economy.

However, the highest concentration of physicians’ offices is not in Los Angeles, but in Johnson County, Georgia. We use location quotients to measure the concentration of establishments in a geographic area. A location quotient greater than 1 means the industry has a greater concentration of establishments within the county than in the nation. With a location quotient of 5.0, Johnson County has five times the concentration of physicians’ offices than the nation.

Offices of physicians, except mental health, number of establishments and establishment location quotients in selected counties, first quarter 2018
County Number of Establishments Establishment location quotient
Johnson County, Georgia 14 5.0
Greenup County, Kentucky 40 3.2
Boyd County, Kentucky 103 3.1
Angelina County, Texas 107 2.8
Jefferson County, Texas 359 3.0
Los Angeles County, California 10,360 1.0

 

So, who uses the data?

The detail available in QCEW data is important to a range of users. In the private sector, commercial real estate brokers may use the data when deciding the best location for a new business. Small business owners may compare average weekly wages. Large corporations may use local data to develop geographic profiles and market studies.

In the public sector, local and regional economic development agencies use the data for planning and program development. Understanding the type and size of establishments can help them recruit and retain businesses and support workforce development investment. Disaster relief agencies use information on the size of establishments or the concentration in an area to determine risk and track recovery efforts. State governments and academic institutions use the data to study the health of regional economies.

Want to know more?

For this blog, we use private ownership data from first quarter 2018 to explore QCEW establishment size data. You can explore establishment size data using the QCEW Data Viewer.

For even more information, visit our QCEW page.

Number of establishments, 2001–18
Quarter Number of establishments
Q1 2001 7,925,541
Q2 2001 7,958,077
Q3 2001 8,008,006
Q4 2001 8,046,492
Q1 2002 8,042,613
Q2 2002 8,060,770
Q3 2002 8,124,227
Q4 2002 8,179,879
Q1 2003 8,188,261
Q2 2003 8,206,992
Q3 2003 8,239,152
Q4 2003 8,280,956
Q1 2004 8,298,175
Q2 2004 8,305,907
Q3 2004 8,389,106
Q4 2004 8,465,990
Q1 2005 8,478,533
Q2 2005 8,525,655
Q3 2005 8,613,899
Q4 2005 8,666,489
Q1 2006 8,690,719
Q2 2006 8,726,001
Q3 2006 8,816,751
Q4 2006 8,902,635
Q1 2007 8,862,947
Q2 2007 8,936,111
Q3 2007 9,014,197
Q4 2007 9,074,333
Q1 2008 9,028,884
Q2 2008 9,059,689
Q3 2008 9,108,151
Q4 2008 9,131,473
Q1 2009 8,967,310
Q2 2009 8,984,662
Q3 2009 9,020,598
Q4 2009 9,040,216
Q1 2010 8,925,889
Q2 2010 8,962,280
Q3 2010 9,014,193
Q4 2010 9,070,072
Q1 2011 8,989,800
Q2 2011 9,042,922
Q3 2011 9,104,661
Q4 2011 9,153,801
Q1 2012 9,006,016
Q2 2012 9,179,368
Q3 2012 9,128,346
Q4 2012 9,173,740
Q1 2013 9,107,736
Q2 2013 9,178,547
Q3 2013 9,241,547
Q4 2013 9,295,722
Q1 2014 9,288,442
Q2 2014 9,313,909
Q3 2014 9,380,061
Q4 2014 9,463,005
Q1 2015 9,414,823
Q2 2015 9,470,124
Q3 2015 9,561,224
Q4 2015 9,644,927
Q1 2016 9,601,391
Q2 2016 9,677,672
Q3 2016 9,758,568
Q4 2016 9,828,841
Q1 2017 9,718,391
Q2 2017 9,807,791
Q3 2017 9,871,253
Q4 2017 9,942,980
Q1 2018 9,910,520
Q2 2018 9,988,054
Q3 2018 10,065,152
Q4 2018 10,169,140
Percent of establishments, employment, and total wages by establishment size, first quarter 2018
Number of employees in establishment Percent of total establishments Percent of total employment Percent of total wages
1,000 or more 0.1% 11.0% 17.8%
500 to 999 0.1 6.4 8.7
Fewer than 500 99.8 82.6 73.5

Improved Mapping Tool for Local Area Unemployment Statistics

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

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

Missouri map showing counties and their unemployment rates

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

How Are Our Older Workers Doing?

May is Older Americans Month. Who are we calling old?

  • The Bureau of Labor Statistics, for one. Next month we will celebrate our 135th birthday. Now that’s old! And we’ve been providing gold-standard information the entire time.
  • Today we are focusing on people age 65 and older.

In honor of Older Americans Month, let’s examine some fast facts about older workers. Many of these facts look over the last 30 years.

Employment

  • For workers age 65 and older, employment tripled from 1988 to 2018, while employment among younger workers grew by about a third.
  • Between 1988 and 2018, employment growth for women age 65 and older outpaced that for men.
  • Among people age 75 and older, the number of employed people nearly quadrupled, increasing from 461,000 in 1988 to 1.8 million in 2018.

Participation in the Labor Force

  • The labor force participation rate for older workers has been rising steadily since the late 1990s. Participation rates for younger age groups either declined or flattened over this period.

Chart showing labor force participation rates for people age 55 and older from 1988 to 2018

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

Employment Projections

  • The total labor force is projected to increase by 6.6 percent from 2016 to 2026, while the number of workers age 65 and older is predicted to rise by 57.6 percent.
  • By 2026, workers age 65 and older are expected to account for 8.6 percent of the total labor force, up from 5.8 percent in 2016.
  • The labor force participation rate of people age 65 and older is projected to increase from 19.3 percent in 2016 to 21.8 percent in 2026. This contrasts with the overall labor force participation rate, which is expected to decrease from 62.8 percent to 61.0 percent.

Work Schedules

  • Over the past 20 years, the number of older workers on full‐time work schedules grew two and a half times faster than the number working part time.
  • Full‐timers now account for a majority among older workers—61 percent in 2018, up from 46 percent in 1998.

Earnings

  • In 1998, median weekly earnings of older full‐time employees were 77 percent of the median for workers age 16 and up. In 2018, older workers earned 7 percent more than the median for all workers.

Education

  • In 1998, 1 in 5 older workers had less than a high school education. By 2018, fewer than 1 in 10 older workers had less than a high school diploma.
  • The percentage of older workers with a college degree grew from 26 percent in 1998 to 42 percent in 2018.

Safety and Health

  • While fatal occupational injuries to all workers declined 17 percent from 1992 to 2017, workers age 65 and older incurred 66 percent more fatal work injuries in 2017 (775) than they did in 1992 (467).
  • Workers age 65 and older had a fatality rate that was nearly three times the rate for all workers in 2017.

Chart showing fatal injury rates by age from 2013 to 2017

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

Want to know more? These statistical programs contributed data to this blog:

From an American worker’s first job to retirement and everything in between, BLS has a stat for that!

Labor force participation rates by age, 1988–2018 annual averages
Year 55–64 65–69 70–74 75 and older
1988 54.6 20.1 10.9 4.2
1989 55.5 20.8 11.2 4.3
1990 55.9 21.0 11.3 4.3
1991 55.5 20.6 10.9 4.4
1992 56.2 20.6 11.1 4.5
1993 56.4 20.3 10.9 4.3
1994 56.8 21.9 11.8 5.4
1995 57.2 21.8 12.5 4.7
1996 57.9 21.9 12.5 4.7
1997 58.9 22.5 12.6 4.8
1998 59.3 22.5 12.5 4.7
1999 59.3 23.0 13.1 5.1
2000 59.2 24.5 13.5 5.3
2001 60.4 24.7 14.1 5.2
2002 61.9 26.1 14.0 5.1
2003 62.4 27.4 14.6 5.8
2004 62.3 27.7 15.3 6.1
2005 62.9 28.3 16.3 6.4
2006 63.7 29.0 17.0 6.4
2007 63.8 29.7 17.2 6.8
2008 64.5 30.7 17.8 7.3
2009 64.9 31.1 18.4 7.3
2010 64.9 31.5 18.0 7.4
2011 64.3 32.1 18.8 7.5
2012 64.5 32.1 19.5 7.6
2013 64.4 32.2 19.2 7.9
2014 64.1 31.6 18.9 8.0
2015 63.9 32.1 18.6 8.2
2016 64.1 32.2 19.2 8.4
2017 64.5 32.3 19.7 8.3
2018 65.0 33.0 19.5 8.7
Rate of fatal work injuries per 100,000 full-time equivalent workers by age
Year All workers 18 to 19 years 20 to 24 years 25 to 34 years 35 to 44 years 45 to 54 years 55 to 64 years 65 years and over
2013 3.3 2.6 2.2 2.5 2.8 3.4 4.1 9.2
2014 3.4 2.0 2.3 2.4 2.8 3.6 4.3 10.7
2015 3.4 2.1 2.7 2.3 2.7 3.5 4.3 9.4
2016 3.6 1.9 2.4 2.5 3.1 3.5 4.7 9.6
2017 3.5 2.6 2.2 2.5 2.9 3.3 4.6 10.3