Tag Archives: Employment Situation

Labor Day 2017 Fast Facts

Since 1884, ten years before President Grover Cleveland signed the law designating “Labor Day” as the first Monday in September, the U.S. Bureau of Labor Statistics has been providing gold-standard data for and about American workers.

In honor of Labor Day, let’s take a look at some fast facts we’ve compiled that show the current picture of our labor market. 

Working

Working or Looking for Work

  • The civilian labor force participation rate—the share of the population working or looking for work—was 62.9 percent in August. The rate has generally been trending down since the early 2000s, although it has leveled off in recent years.

Not Working

  • The unemployment rate was 4.4 percent in August. The rate has shown little movement in recent months after declining earlier in the year. The last time the unemployment rate was lower was in 2000 and early 2001.
  • In August, there were 1.7 million long-term unemployed (those jobless for 27 weeks or more). This represented 24.7 percent of the unemployed, down from a peak of 45.5 percent in April 2010 but still above the 16-percent share seen in late 2006 and 2007.
  • Among the major worker groups, the unemployment rate for teenagers was 13.6 percent in August, while the rates were 4.1 percent for adult men and 4.0 percent for adult women. The unemployment rate was 7.7 percent for Blacks or African Americans, 5.2 percent for Hispanics or Latinos, 4.0 percent for Asians, and 3.9 percent for Whites. 

Job Openings

Pay and Benefits

  • Average weekly earnings rose by 2.8 percent between July 2016 and July 2017; adjusted for inflation, real average weekly earnings are up 1.1 percent during this period.
  • Paid leave benefits are available to a majority of private industry workers, where the access rates were 68 percent for sick leave, 76 percent for vacation, and 77 percent for holidays in March 2017.
  • Nearly half (49 percent) of private industry workers participated in employer-sponsored medical care benefits in March 2017.

Productivity

  • Labor productivity in nonfarm businesses increased 0.9 percent in the second quarter of 2017. Although productivity is growing at a historically slow pace since the Great Recession, the manufacturing sector recently posted the strongest productivity growth in 21 quarters, growing 2.5 percent in the second quarter of 2017. 

Safety and Health

Education

  • Occupations that typically require a bachelor’s degree for entry made up 21 percent of employment. This educational category includes registered nurses, teachers at the kindergarten through secondary levels, and many management, business and financial operations, computer, and engineering occupations.
  • For 11 of the 15 occupations projected to grow the fastest between 2014 and 2024, some postsecondary education is typically required for entry.

Unionization

Work Stoppages

  • Over the past four decades, major work stoppages (a strike or lockout) declined approximately 90 percent. From 1977 to 1986 there were 1,446 major work stoppages, while in 2007–16, there were 143.

From an American worker’s first job to retirement and everything in between, BLS has a stat for that! Want to learn more? Follow us on Twitter @BLS_gov.

Why This Counts: Job Openings and Labor Turnover Survey

Looking solely at net employment change is similar to looking at the surface of a lake. You’ll see ripples and changes, but there’s a whole lot of activity going on underneath the surface. Using JOLTS data—the Job Openings and Labor Turnover Survey—provides a peek at what’s going on below the surface of net employment change.

The basics

JOLTS is a monthly survey of 16,000 establishments that asks employers to provide information on the number of job openings (as of the last business day of the month) and the total number of hires and separations that occurred throughout the month. By asking for the total number of hires and separations over the entire month, we can get a sense of just how many jobs started and ended within a month. For example, in February 2017 there were 5.3 million hires and 5.1 million separations. That’s approximately the population of Colorado moving in and out of jobs in a single month!

A chart showing trends in the numbers of hires and job separations from 2007 to 2017.

Editor’s note: A text-only version of the graphic is below.

Understanding the churn

You may be familiar with the headline payroll employment number that comes out each month, with information on how many net jobs were gained or lost. However, JOLTS data give us insight on what goes on beyond the monthly employment data. JOLTS data show us just how dynamic the U.S. labor market is and can illuminate which industries have persistent unmet demand for workers.

Movement into and out of jobs is often called “churn.” As the rates of hires and separations climb, this increased “churn” can signal a healthy labor market where workers can move in and out of jobs with relative ease. Similarly, when rates of hires and separations fall, workers may have more difficulty moving from job to job.

JOLTS data can also give us insight into labor market changes before the net employment figures can. In the last recession, the hiring rate started to decelerate before payroll employment slowed.

Further insight into industries

Labor market activity differs by industry. By using the combination of hires and job openings rates, we can explore which industries have persistent low-level demand for workers and which industries may have a high unmet demand for workers. When the openings rate exceeds the hiring rate, the industry has an unmet demand for workers.

Consider jobs in construction, retail trade, and accommodation and food services. There are fewer job openings than hires in these industries, suggesting that employers can easily find workers. Many jobs in these industries require minimal training or experience, which means it is easy to find workers. It may also mean that workers don’t stay with one employer for very long. JOLTS data confirm this. These industries have high churn, with large numbers of hires and large numbers of separations. Trends in hires and separations tend to move together, meaning employers are frequently replacing workers.

A chart showing hires rates and job separations rates in construction, retail trade, and accommodation and food service from 2007 to 2017.

Editor’s note: A text-only version of the graphic is below.

In contrast, many jobs in health care and financial activities require more training and experience, suggesting it may be more difficult to find qualified workers. In these industries, job openings are greater than hires—employers are always looking for qualified workers. These industries also exhibit low churn, stemming from low numbers of hires and separations as a share of industry employment. This suggests workers remain with their employers for longer periods of time.

The professional and business services industry presents an unusual case, perhaps because of the diverse set of occupations within the industry. Included in this industry are many professional service workers, such as those in computer service and engineering firms. But the industry also includes temporary help supply firms and building services, such as janitorial and landscaping firms. Until recently, the industry as a whole had traditionally had more hires than job openings, suggesting an ease in attracting labor. This may be due in part to the number of lower-skilled jobs in this industry. But several times over the past year, job openings have exceeded hires, suggesting that employers need qualified workers. Perhaps this reflects the higher-skilled jobs in this industry. This recent trend bears watching.

A chart showing hires rates and job separations rates in financial activities, health care, and professional and business services from 2007 to 2017.

Editor’s note: A text-only version of the graphic is below.

Jobs in government and education exhibit both low hiring and low job openings rates. These lower rates indicate that few workers are needed in these industries—workers may tend to stay in these jobs for long periods of time.

For more info on JOLTS, see www.bls.gov/jlt. For more in-depth information on the interaction between job openings and hires, see Charlotte Oslund’s article, “Which industries need workers? Exploring differences in labor market activity.”

 

Number of hires and separations, February 2007 to February 2017, seasonally adjusted
Month Hires Separations
Feb 2007 5,202,000 5,094,000
Mar 2007 5,380,000 5,123,000
Apr 2007 5,158,000 5,138,000
May 2007 5,268,000 5,080,000
Jun 2007 5,187,000 5,065,000
Jul 2007 5,075,000 5,118,000
Aug 2007 5,106,000 5,105,000
Sep 2007 5,145,000 5,031,000
Oct 2007 5,227,000 5,129,000
Nov 2007 5,162,000 5,031,000
Dec 2007 4,968,000 4,926,000
Jan 2008 4,868,000 5,005,000
Feb 2008 4,943,000 5,010,000
Mar 2008 4,766,000 4,762,000
Apr 2008 4,875,000 5,121,000
May 2008 4,602,000 4,728,000
Jun 2008 4,751,000 4,900,000
Jul 2008 4,471,000 4,713,000
Aug 2008 4,522,000 4,815,000
Sep 2008 4,316,000 4,751,000
Oct 2008 4,454,000 4,895,000
Nov 2008 3,954,000 4,605,000
Dec 2008 4,218,000 4,814,000
Jan 2009 4,158,000 4,974,000
Feb 2009 4,011,000 4,674,000
Mar 2009 3,730,000 4,536,000
Apr 2009 3,853,000 4,655,000
May 2009 3,793,000 4,146,000
Jun 2009 3,675,000 4,192,000
Jul 2009 3,854,000 4,297,000
Aug 2009 3,744,000 4,060,000
Sep 2009 3,859,000 4,084,000
Oct 2009 3,767,000 3,951,000
Nov 2009 3,992,000 3,873,000
Dec 2009 3,806,000 3,989,000
Jan 2010 3,880,000 3,894,000
Feb 2010 3,781,000 3,830,000
Mar 2010 4,182,000 3,949,000
Apr 2010 4,082,000 3,892,000
May 2010 4,376,000 3,831,000
Jun 2010 4,064,000 4,223,000
Jul 2010 4,116,000 4,278,000
Aug 2010 3,910,000 4,009,000
Sep 2010 3,978,000 4,026,000
Oct 2010 4,061,000 3,784,000
Nov 2010 4,101,000 3,843,000
Dec 2010 4,155,000 4,026,000
Jan 2011 3,910,000 3,908,000
Feb 2011 4,061,000 3,838,000
Mar 2011 4,291,000 3,980,000
Apr 2011 4,218,000 3,924,000
May 2011 4,116,000 4,035,000
Jun 2011 4,297,000 4,094,000
Jul 2011 4,139,000 4,082,000
Aug 2011 4,168,000 4,120,000
Sep 2011 4,320,000 4,115,000
Oct 2011 4,239,000 4,011,000
Nov 2011 4,244,000 4,001,000
Dec 2011 4,234,000 3,994,000
Jan 2012 4,292,000 4,010,000
Feb 2012 4,419,000 4,175,000
Mar 2012 4,465,000 4,134,000
Apr 2012 4,299,000 4,260,000
May 2012 4,445,000 4,336,000
Jun 2012 4,432,000 4,367,000
Jul 2012 4,269,000 4,138,000
Aug 2012 4,447,000 4,360,000
Sep 2012 4,238,000 4,059,000
Oct 2012 4,299,000 4,194,000
Nov 2012 4,393,000 4,171,000
Dec 2012 4,360,000 4,038,000
Jan 2013 4,422,000 4,297,000
Feb 2013 4,509,000 4,156,000
Mar 2013 4,293,000 4,113,000
Apr 2013 4,533,000 4,376,000
May 2013 4,572,000 4,363,000
Jun 2013 4,409,000 4,267,000
Jul 2013 4,529,000 4,384,000
Aug 2013 4,732,000 4,517,000
Sep 2013 4,681,000 4,537,000
Oct 2013 4,444,000 4,288,000
Nov 2013 4,588,000 4,268,000
Dec 2013 4,500,000 4,335,000
Jan 2014 4,615,000 4,443,000
Feb 2014 4,627,000 4,436,000
Mar 2014 4,758,000 4,452,000
Apr 2014 4,812,000 4,518,000
May 2014 4,796,000 4,565,000
Jun 2014 4,817,000 4,552,000
Jul 2014 5,001,000 4,784,000
Aug 2014 4,839,000 4,627,000
Sep 2014 5,078,000 4,882,000
Oct 2014 5,118,000 4,927,000
Nov 2014 5,027,000 4,633,000
Dec 2014 5,165,000 4,789,000
Jan 2015 5,027,000 4,843,000
Feb 2015 4,991,000 4,705,000
Mar 2015 5,090,000 4,986,000
Apr 2015 5,095,000 4,906,000
May 2015 5,143,000 4,812,000
Jun 2015 5,162,000 5,011,000
Jul 2015 5,136,000 4,849,000
Aug 2015 5,129,000 4,958,000
Sep 2015 5,150,000 5,067,000
Oct 2015 5,304,000 4,983,000
Nov 2015 5,323,000 5,003,000
Dec 2015 5,504,000 5,223,000
Jan 2016 5,117,000 5,033,000
Feb 2016 5,447,000 5,183,000
Mar 2016 5,297,000 5,040,000
Apr 2016 5,038,000 4,962,000
May 2016 5,153,000 5,101,000
Jun 2016 5,176,000 4,940,000
Jul 2016 5,328,000 5,001,000
Aug 2016 5,288,000 5,059,000
Sep 2016 5,179,000 4,942,000
Oct 2016 5,200,000 5,041,000
Nov 2016 5,263,000 5,075,000
Dec 2016 5,303,000 5,084,000
Jan 2017 5,424,000 5,247,000
Feb 2017 5,314,000 5,071,000

Hires rates and job openings rates in selected industries, February 2007 to February 2017, seasonally adjusted
Month Construction hires rate Construction job openings rate Retail trade hires rate Retail trade job openings rate Accommodation and food services hires rate Accommodation and food services job openings rate
Feb 2007 4.2 3.5 5.1 2.8 7.2 4.1
Mar 2007 6.1 2.6 5.0 2.7 6.9 4.4
Apr 2007 5.0 2.8 4.7 2.6 7.3 4.0
May 2007 5.3 2.7 4.9 2.3 6.9 4.4
Jun 2007 5.6 2.2 4.6 2.8 7.0 4.4
Jul 2007 5.2 2.6 4.6 2.8 6.9 4.5
Aug 2007 5.3 2.1 4.7 2.5 6.8 4.8
Sep 2007 5.1 1.6 4.9 2.6 6.7 4.7
Oct 2007 5.3 1.6 5.0 2.3 6.9 4.6
Nov 2007 5.0 1.1 5.2 2.7 6.6 4.4
Dec 2007 5.0 1.3 4.8 2.5 6.7 4.5
Jan 2008 5.0 1.7 4.5 2.4 6.3 4.3
Feb 2008 5.1 1.5 4.6 2.3 7.0 4.0
Mar 2008 5.4 1.3 4.4 2.4 6.2 4.0
Apr 2008 5.2 1.6 4.4 2.5 6.3 4.0
May 2008 4.8 2.3 3.9 2.4 6.4 3.9
Jun 2008 5.3 1.6 4.5 1.9 6.1 3.7
Jul 2008 4.9 1.7 4.4 2.4 6.0 3.4
Aug 2008 5.6 1.2 4.4 2.3 5.9 2.8
Sep 2008 4.7 1.7 4.1 1.7 5.9 3.1
Oct 2008 5.4 1.0 4.2 2.4 5.8 2.9
Nov 2008 4.9 0.7 3.8 2.3 5.3 2.5
Dec 2008 5.1 0.7 4.2 1.9 5.2 2.4
Jan 2009 5.4 0.6 3.7 2.2 5.2 1.8
Feb 2009 5.1 1.0 3.6 2.0 5.4 2.5
Mar 2009 4.9 0.7 3.6 1.7 5.0 2.2
Apr 2009 5.3 0.4 3.9 1.4 4.9 2.2
May 2009 5.4 0.7 3.8 2.1 5.3 2.1
Jun 2009 4.3 0.9 3.4 1.8 4.9 2.1
Jul 2009 5.5 1.0 3.4 1.1 4.7 1.8
Aug 2009 4.3 1.0 3.7 1.6 4.8 1.6
Sep 2009 5.5 1.1 3.8 1.9 4.6 2.3
Oct 2009 5.4 1.0 3.4 1.3 4.5 2.0
Nov 2009 5.5 0.8 3.6 1.6 5.0 2.1
Dec 2009 6.0 1.0 3.7 1.7 4.8 2.1
Jan 2010 5.6 0.9 3.8 1.6 4.9 2.2
Feb 2010 5.0 1.1 3.8 1.9 4.7 2.0
Mar 2010 7.4 1.4 4.5 2.3 5.0 1.7
Apr 2010 6.6 1.8 3.7 1.8 4.9 2.1
May 2010 5.5 1.5 3.7 1.8 4.8 2.1
Jun 2010 5.0 1.5 3.9 1.8 4.7 2.0
Jul 2010 6.2 2.1 4.0 1.7 4.9 2.3
Aug 2010 5.9 0.9 3.7 1.6 4.8 2.7
Sep 2010 5.8 1.3 3.9 1.5 5.0 2.1
Oct 2010 6.4 1.2 3.9 1.7 4.9 2.4
Nov 2010 6.2 1.2 3.9 1.8 4.8 2.3
Dec 2010 6.8 0.6 3.4 2.0 4.7 2.3
Jan 2011 5.1 1.1 3.9 1.9 4.7 2.5
Feb 2011 6.3 0.8 3.9 1.8 4.8 3.0
Mar 2011 6.9 1.2 4.0 1.8 5.5 2.8
Apr 2011 6.7 2.2 4.0 2.2 5.1 2.4
May 2011 6.8 2.1 3.9 2.1 4.7 2.5
Jun 2011 6.8 1.2 4.0 2.3 5.3 2.6
Jul 2011 6.2 1.6 4.0 2.3 5.3 2.1
Aug 2011 5.9 1.7 3.6 2.2 5.2 2.8
Sep 2011 6.7 1.5 3.9 2.2 5.3 3.0
Oct 2011 5.9 1.3 3.8 2.3 5.2 3.0
Nov 2011 5.6 1.1 3.8 2.1 5.4 3.0
Dec 2011 5.6 0.8 3.4 2.2 5.3 3.2
Jan 2012 5.8 1.4 3.9 2.4 5.4 3.2
Feb 2012 6.0 1.0 3.8 2.3 5.1 2.9
Mar 2012 5.4 1.6 3.9 2.5 5.8 3.1
Apr 2012 5.2 2.1 3.9 2.2 5.2 3.2
May 2012 5.8 1.5 3.9 2.3 5.3 3.2
Jun 2012 6.3 1.7 3.9 2.2 5.2 3.4
Jul 2012 6.4 1.4 3.8 2.1 5.3 3.3
Aug 2012 6.0 2.0 4.1 2.4 5.5 3.0
Sep 2012 6.2 1.4 4.0 2.4 5.3 2.7
Oct 2012 5.6 1.8 4.0 2.5 5.4 3.2
Nov 2012 6.9 1.2 3.9 3.0 5.2 3.5
Dec 2012 5.4 1.1 3.9 2.6 5.4 3.5
Jan 2013 5.8 2.0 4.0 2.7 5.6 3.4
Feb 2013 6.5 1.9 4.2 2.6 5.5 3.5
Mar 2013 6.1 1.8 3.7 2.7 5.5 3.5
Apr 2013 5.1 2.3 4.1 2.9 6.1 3.3
May 2013 5.7 2.1 4.2 3.1 5.4 3.3
Jun 2013 5.6 2.3 4.1 3.8 5.4 3.5
Jul 2013 5.3 2.0 4.1 3.0 5.4 3.7
Aug 2013 5.1 2.1 4.6 2.9 5.3 3.6
Sep 2013 5.4 1.9 4.4 3.1 5.5 3.8
Oct 2013 5.5 2.1 4.5 2.8 5.5 3.5
Nov 2013 5.1 1.7 4.7 2.8 5.3 3.7
Dec 2013 4.7 1.4 4.8 2.7 5.3 4.0
Jan 2014 4.8 2.1 4.1 2.7 5.6 3.9
Feb 2014 4.4 1.7 4.7 2.9 5.8 4.0
Mar 2014 4.4 2.0 4.6 3.2 5.6 4.2
Apr 2014 5.0 2.1 4.9 3.4 5.6 4.4
May 2014 5.3 2.4 5.0 2.8 5.8 4.9
Jun 2014 4.5 2.7 4.9 3.0 5.9 4.8
Jul 2014 6.4 2.5 5.0 2.8 5.7 4.0
Aug 2014 5.3 2.3 4.6 3.3 5.5 4.7
Sep 2014 4.8 1.7 4.6 3.0 5.9 4.6
Oct 2014 5.1 2.3 5.0 3.1 6.0 4.9
Nov 2014 5.1 1.7 5.0 3.2 6.1 4.3
Dec 2014 6.3 1.5 5.0 3.4 6.3 4.6
Jan 2015 5.7 2.2 4.9 3.2 5.8 5.2
Feb 2015 5.1 2.4 4.5 3.3 5.8 4.9
Mar 2015 4.9 2.6 4.9 3.2 6.0 4.7
Apr 2015 5.3 2.6 4.7 3.4 6.2 5.0
May 2015 4.9 2.6 5.0 3.5 6.1 4.8
Jun 2015 5.2 2.3 4.9 3.4 6.2 4.4
Jul 2015 4.7 2.3 4.9 3.8 6.3 5.2
Aug 2015 5.0 2.4 4.7 3.7 6.5 4.8
Sep 2015 5.3 1.6 4.6 4.0 6.5 4.7
Oct 2015 5.0 2.0 4.7 3.5 6.5 5.0
Nov 2015 5.2 1.3 4.9 3.2 6.6 4.8
Dec 2015 4.7 1.9 4.9 3.2 6.9 4.8
Jan 2016 4.4 2.3 4.8 3.9 5.9 4.8
Feb 2016 5.2 2.8 5.3 3.7 6.8 5.1
Mar 2016 5.3 3.0 4.7 3.7 6.4 5.1
Apr 2016 5.0 2.7 4.3 3.6 6.1 4.9
May 2016 4.7 2.7 4.4 3.6 6.3 4.8
Jun 2016 4.2 2.5 4.5 3.7 6.2 4.6
Jul 2016 5.0 3.4 4.5 3.8 6.3 4.6
Aug 2016 5.1 2.7 4.6 3.7 6.3 4.8
Sep 2016 4.7 3.4 4.8 3.8 6.0 4.6
Oct 2016 5.1 2.8 4.7 3.9 6.1 4.5
Nov 2016 5.0 2.6 4.2 3.9 6.7 4.7
Dec 2016 5.9 2.0 4.2 3.9 6.4 4.5
Jan 2017 5.7 2.0 4.3 3.5 6.4 4.6
Feb 2017 5.4 2.4 4.8 3.3 6.2 5.0

Hires rates and job openings rates in selected industries, February 2007 to February 2017, seasonally adjusted
Month Financial activities hires rate Financial activities job openings rate Professional and business services hires rate Professional and business services job openings rate Health care and social assistance hires rate Health care and social assistance job openings rate
Feb 2007 3.0 3.0 5.5 3.9 2.9 4.0
Mar 2007 3.4 3.9 5.5 4.3 3.0 4.2
Apr 2007 2.7 2.8 5.0 4.6 2.9 4.3
May 2007 3.4 3.3 5.4 4.1 3.1 4.4
Jun 2007 3.0 3.2 4.9 4.1 3.0 4.5
Jul 2007 2.9 3.4 5.1 3.7 2.8 3.9
Aug 2007 3.1 3.6 5.1 4.0 3.0 4.3
Sep 2007 3.0 3.3 5.1 4.0 2.9 4.8
Oct 2007 3.0 3.3 5.4 4.1 3.0 4.1
Nov 2007 2.8 2.8 5.4 4.0 3.0 4.2
Dec 2007 2.9 3.2 5.1 4.1 2.7 4.2
Jan 2008 2.9 3.8 4.8 4.0 3.0 4.0
Feb 2008 2.9 2.7 4.7 4.0 3.2 4.5
Mar 2008 2.6 3.0 4.5 4.1 3.1 4.3
Apr 2008 2.8 2.7 5.1 4.1 3.1 4.1
May 2008 2.4 2.4 4.5 3.4 2.9 4.1
Jun 2008 2.7 2.3 5.3 4.0 2.7 4.0
Jul 2008 2.5 2.6 4.4 3.6 2.8 3.9
Aug 2008 2.6 2.6 4.5 3.5 2.8 3.7
Sep 2008 2.6 2.3 4.3 3.4 2.7 3.4
Oct 2008 2.2 2.0 4.5 3.2 2.9 3.4
Nov 2008 2.5 2.4 4.2 3.0 2.6 3.3
Dec 2008 2.0 2.4 4.8 3.2 2.7 3.3
Jan 2009 2.4 2.3 4.4 3.1 2.8 3.1
Feb 2009 2.2 2.5 4.3 3.1 2.8 3.0
Mar 2009 2.3 2.3 3.6 2.5 2.6 2.8
Apr 2009 1.7 1.6 4.0 2.4 2.5 2.8
May 2009 2.0 2.2 4.0 2.4 2.4 2.9
Jun 2009 2.0 1.9 3.9 2.4 2.6 2.8
Jul 2009 2.4 1.7 4.2 2.6 2.6 2.9
Aug 2009 2.2 1.6 3.8 2.1 2.8 2.8
Sep 2009 1.9 2.2 4.2 2.6 2.8 3.1
Oct 2009 2.3 2.0 4.2 2.2 2.5 2.9
Nov 2009 1.8 2.1 5.1 2.5 2.6 2.8
Dec 2009 2.3 1.7 4.1 2.6 2.5 2.9
Jan 2010 2.2 2.1 4.5 2.4 2.3 3.2
Feb 2010 2.1 1.9 4.4 2.3 2.4 2.8
Mar 2010 1.9 2.0 4.4 2.5 2.5 2.6
Apr 2010 2.3 2.8 4.7 3.0 2.5 2.7
May 2010 2.3 2.7 4.7 3.4 2.4 2.6
Jun 2010 2.4 2.6 5.0 2.8 2.6 2.5
Jul 2010 2.1 2.8 4.8 3.2 2.7 2.7
Aug 2010 2.0 3.1 4.7 3.5 2.4 2.4
Sep 2010 2.2 2.9 4.5 3.3 2.6 2.7
Oct 2010 2.2 3.1 4.5 3.5 2.4 3.1
Nov 2010 2.0 3.2 4.7 3.7 2.5 2.8
Dec 2010 2.4 2.5 5.4 3.4 2.5 2.8
Jan 2011 2.0 2.7 4.8 2.7 2.1 2.6
Feb 2011 1.9 2.7 5.0 3.4 2.3 2.8
Mar 2011 2.1 2.5 5.3 3.4 2.3 3.0
Apr 2011 1.7 3.1 5.1 3.2 2.4 3.0
May 2011 2.0 2.5 5.1 3.3 2.4 3.0
Jun 2011 2.1 2.7 4.8 3.5 2.6 3.1
Jul 2011 2.1 2.9 4.8 4.3 2.4 3.1
Aug 2011 2.0 2.3 5.1 3.3 2.5 3.1
Sep 2011 2.0 2.2 5.2 4.1 2.3 3.0
Oct 2011 2.2 2.9 5.0 3.4 2.4 3.2
Nov 2011 2.2 2.0 4.9 3.0 2.5 3.3
Dec 2011 2.2 2.4 4.9 4.2 2.4 3.2
Jan 2012 2.1 3.0 4.6 4.4 2.6 3.4
Feb 2012 2.2 2.5 5.4 3.5 2.8 3.5
Mar 2012 2.3 2.9 5.2 4.4 2.6 3.5
Apr 2012 2.4 2.7 4.8 3.3 2.4 3.5
May 2012 2.3 3.0 5.2 3.7 2.7 3.5
Jun 2012 2.3 2.8 5.4 3.9 2.6 3.9
Jul 2012 2.2 3.1 4.8 3.7 2.5 3.3
Aug 2012 2.5 3.2 4.8 4.0 2.5 3.3
Sep 2012 2.6 3.5 4.7 3.3 2.4 3.6
Oct 2012 2.4 3.3 4.7 3.5 2.5 3.5
Nov 2012 2.8 2.9 4.9 3.2 2.5 3.5
Dec 2012 2.2 3.2 4.7 3.3 2.6 3.5
Jan 2013 2.7 2.9 4.9 3.7 2.6 3.0
Feb 2013 2.9 4.4 4.6 4.0 2.6 3.5
Mar 2013 2.3 3.4 4.6 3.7 2.6 3.5
Apr 2013 2.4 3.5 4.9 3.6 2.8 3.6
May 2013 2.7 3.8 4.9 3.3 2.7 3.4
Jun 2013 2.4 3.8 5.2 3.3 2.4 3.4
Jul 2013 2.7 3.9 5.3 3.1 2.6 3.3
Aug 2013 2.7 3.3 5.5 3.6 2.7 3.6
Sep 2013 2.8 3.0 5.2 3.6 2.7 3.2
Oct 2013 2.4 3.0 4.6 4.1 2.5 3.2
Nov 2013 2.3 2.7 5.2 3.6 2.5 3.2
Dec 2013 2.2 2.9 4.9 3.7 2.5 2.9
Jan 2014 2.0 2.8 5.2 3.4 2.7 3.4
Feb 2014 2.2 2.9 5.2 4.1 2.5 3.5
Mar 2014 2.5 3.0 5.3 3.6 2.7 3.6
Apr 2014 2.3 3.2 5.1 4.2 2.8 3.5
May 2014 2.4 3.5 4.9 4.1 2.6 3.9
Jun 2014 2.3 3.8 5.1 4.2 2.6 3.8
Jul 2014 2.4 3.6 5.3 4.3 2.8 4.1
Aug 2014 2.7 3.9 5.5 4.7 2.5 4.5
Sep 2014 2.6 3.1 5.8 4.3 2.9 4.1
Oct 2014 2.2 3.9 5.6 4.7 2.9 4.4
Nov 2014 2.8 3.3 5.1 5.1 2.8 3.8
Dec 2014 2.8 3.2 5.1 5.0 2.9 4.4
Jan 2015 2.5 3.6 5.2 4.6 2.8 4.3
Feb 2015 2.0 4.1 5.3 4.6 2.9 4.4
Mar 2015 2.4 3.2 5.4 5.0 2.8 4.1
Apr 2015 2.6 4.5 5.3 5.5 2.9 4.9
May 2015 2.4 3.7 5.4 5.4 2.8 4.5
Jun 2015 2.5 3.3 5.3 5.6 2.7 4.6
Jul 2015 2.3 4.5 5.1 5.5 2.9 5.2
Aug 2015 2.2 4.0 5.1 5.2 2.8 4.9
Sep 2015 2.4 3.6 5.2 5.5 2.9 5.0
Oct 2015 2.5 3.8 5.5 5.4 3.0 4.8
Nov 2015 2.6 4.1 5.4 5.6 3.0 5.0
Dec 2015 2.6 4.5 5.9 5.3 3.0 4.9
Jan 2016 2.6 4.0 5.6 5.4 2.7 5.3
Feb 2016 2.9 4.0 5.4 5.3 2.9 4.7
Mar 2016 2.7 3.7 5.4 6.1 2.9 4.8
Apr 2016 2.3 3.9 5.5 4.7 2.6 4.8
May 2016 2.2 3.4 5.5 5.7 2.8 4.8
Jun 2016 2.4 3.5 5.0 4.9 2.9 5.0
Jul 2016 2.2 3.7 6.0 5.9 2.9 4.9
Aug 2016 2.3 3.8 5.5 4.9 2.9 4.8
Sep 2016 2.1 3.9 5.5 5.3 2.7 4.9
Oct 2016 2.0 3.7 5.4 5.1 2.9 5.2
Nov 2016 2.1 3.7 5.3 4.9 3.0 5.2
Dec 2016 2.3 4.1 5.6 4.6 2.9 5.2
Jan 2017 2.6 4.4 5.5 4.9 2.9 5.2
Feb 2017 2.2 4.2 5.2 4.7 2.8 5.6

Why the Unemployment Rate Still Matters

Just like your body, the economy is a superbly complex system. When you visit doctors or other healthcare providers, they routinely take several measurements — height, weight, blood pressure, and temperature. Tracking these vital signs over time can lead you and your healthcare providers to seek further tests. Yet, even when your healthcare providers need more information, they continue to take the basic measurements.

In much the same way, the government routinely measures the health of the economy. Here at BLS, we specialize in tracking labor market activity, working conditions, productivity, and price changes. One of our most important measures is the national unemployment rate. Since it is measured the same way each month, year after year, changes in the rate can be an important signal of changes in the labor market and economy.

We realize, of course, that the unemployment rate doesn’t tell the full story. It isn’t meant to. Much like your temperature is a necessary measurement, its usefulness increases when viewed with other measures. When we release the unemployment rate each month, we also publish five alternative measures of labor underutilization to help assess labor market conditions from several perspectives.

Chart showing trends in alternative measures of labor underutilization.

In addition, the source for the unemployment data, the Current Population Survey, provides a wealth of information about workers, jobseekers, and people who aren’t working or looking for work. For example, we also get information about trends in labor force participation, a topic that has received much public attention in recent years. BLS releases thousands of other measures monthly, quarterly, and annually, depending on the topic.

For example, if you want to know how adult Black men are performing in the labor market, we have a stat for that. Ditto for people with a less than high school education or veterans with service-connected disabilities.

And if you want to know how employers are doing (say, how many job openings they’ve posted and how many workers have been fired or quit their jobs in the past month), check out our Job Openings and Labor Turnover Survey.

Want to know what is happening in your local area? Not a problem. Each month BLS releases state employment and unemployment data and metropolitan area data too.

We invite you to visit our website or contact one of our expert economists next time you have a question about the health of labor market—or your favorite economic “symptom.”

Seeing Significance: New Chart Showing Confidence Intervals for Nonfarm Employment Changes

Last year I wrote about how we’ve been using more charts and maps to explain our data. In another post I wrote about how BLS deals with uncertainty in our measures and how we explain the strengths and limitations of our data. Today I want to tell you about a new chart that will help you see measures of uncertainty in one of our most closely watched series—nonfarm employment.

We recently fielded a survey to ask data users about our ideas for creating charts that show confidence intervals. A confidence interval is a measure of the uncertainty in our estimates. We asked data users to tell us if confidence intervals would give them useful information. If so, how can we present confidence intervals in a clear, visually appealing way? Based on your responses, we will add a chart showing confidence intervals to the package of interactive graphics we update each month with The Employment Situation report. We will post the new chart when we release the November 2016 national employment numbers on Friday, December 2.

The responses to our survey confirm you want to know more about the limitations of the employment data. The red dots in the chart show the over-the-month employment changes for total nonfarm and the major industries. The blue bars show the 90-percent confidence intervals of the employment changes for each of these groups.

Chart showing nonfarm employment changes for major industries in October 2016 and the confidence intervals for those changes.

What does this chart tell us? The red dot for total nonfarm employment shows a gain of 161,000 jobs in October, as we reported on November 4. That number is an estimate based on our monthly sample survey, rather than a complete count of jobs each month. Different samples of employers might give us different estimates of employment change. We can measure the sampling error, the variation that occurs by chance because we collected the number from a sample of employers instead of all employers. With our measure of sampling error, we can calculate a confidence interval. The blue bar for total nonfarm shows the 90-percent confidence interval ranged from 46,800 to 275,000. We call this a 90-percent confidence interval because, if we were to choose 100 different samples of employers, the October nonfarm employment change would be between 46,800 and 275,000 in 90 of those samples.

We also learn from this chart whether an employment change is statistically significant. A change is statistically significant if the blue bar in the chart does not cross the zero line. For example, the confidence interval for construction includes zero, so we can’t say with confidence that construction employment increased in October. For education and health services, however, the confidence interval does not include zero, so we can say more confidently that employment in the industry rose over the month.

The chart here shows the 1-month employment changes. When we begin publishing it on December 2, we will also let you choose charts that show 3-, 6-, and 12-month changes.

Check out the new charts and let us know what you think in the comments below. We’re always looking for better, clearer ways to explain our data, and I welcome you to share your ideas.

The Value and Influence of Labor Statistics in the 21st Century

What’s in the “DNA” of BLS—what were we born with? Not so long ago, as I prepared to become BLS Commissioner, I read the First 100 Years of the Bureau of Labor Statistics. The first chapter describes how BLS was created (in 1884) during a time of severe economic upheaval and industrial unrest. Policymakers of the time realized that a key barrier to peace and shared prosperity was the lack of trustworthy information about the economy. What has struck me ever since is how we can trace some of the distinguishing features of today’s modern BLS directly back to those first days, to the vision of one of our founders. This post links that past to the BLS of today.

Carroll D. Wright, first BLS Commissioner

Commissioner Carroll D. Wright

In 1893, sometime after becoming the first Commissioner of Labor, Carroll D. Wright set forth a mission for the agency. He was a pioneer in the search for truth and a better understanding of labor statistics by the public. In his Value and Influence of Labor Statistics (later published in the 54th Bulletin of the Bureau of Labor), he described our mission as collecting “information upon the subject of labor in the United States, its relation to capital, the hours of labor, and the earnings of laboring men and women, and the means of promoting their material, social, intellectual, and moral prosperity.”

Today, our mission is much the same as it was then. Commissioner Wright established a modern statistical agency long before the Internet made it possible for anyone to access our data and read our publications on demand. These days we say that our mission is “to collect, analyze, and disseminate essential economic information to support public and private decision-making.” While the wording has evolved with the times, the core meaning remains the same. Furthermore, in support of our mission for the past 132 years, BLS has practiced what Commissioner Wright termed “the fearless publication of the facts without regard to the influence those facts may have upon any party’s position or any partisan’s views.”

Wright developed much of the vision and practices that he instilled here while working for the Massachusetts Bureau of Statistics of Labor from 1873 to 1878. There he launched several studies to provide the people of Massachusetts with accurate labor market data. One of the largest studies was to find out the true unemployment level in Massachusetts. At the time, many people believed there were 200,000–300,000 people unemployed in the state and 3,000,000 unemployed in the entire country. Alarmists spread word through newspapers, speeches in Congress, and political resolutions until these figures were widely believed as fact, despite no previous attempt to measure unemployment. Wright’s staff canvassed the state twice to discover if the rumored number was accurate. The Bureau of Statistics of Labor of Massachusetts determined the true number of unemployed in the state was 28,508 skilled and unskilled laborers in June 1878; by November there were fewer than 23,000 unemployed, while the national number could not have been more than 460,000 unemployed. Wright explained that “The figures published by the report were used all over the country, and completely reversed the popular belief relative to the vast number of the alleged unemployed in the country.”

Today, you can see a parallel between Wright’s efforts to learn and classify the number of unemployed workers in Massachusetts and how BLS has expanded its offering to include six alternative measures of unused or underused labor. We call these measures U-1 through U-6. BLS not only calculates these alternative measures nationally, but also for each of the 50 states, the District of Columbia, and two large metro areas. This ensures that the American public, researchers, and policymakers have a wide range of data to understand the health of the labor market and make important decisions.

Also similar is our enduring focus on specific populations in the workforce. Under Wright’s leadership, state Bureaus of Labor investigated the use of child labor and uncovered the “evils it entailed upon the community.” The Bureaus published the number of young children (those under 10 years old) who worked in factories and workshops. Because of these studies, the numbers declined significantly. Time and again, Wright sought out the facts and ensured the American people had the information they needed to make decisions. Wright said, “It is only through rigid, impartial, and fearless investigations that any community can know itself in many directions.”

Today, we continue to seek new and better measures about particular groups in our economy and society. For example, in recent years BLS expanded the scope of the Current Population Survey to include six new questions to identify people with disabilities. These data provide insight into the labor market challenges of people with disabilities. The data aid individuals, nonprofit organizations, employers, and policymakers in making decisions affecting the lives of Americans with disabilities. Our monthly Employment Situation report now includes information about the employment status and labor force participation of the more than 30 million Americans age 16 and older living with a disability.

Our “DNA,” that is, our mission, our vision, and our understanding of the value of the statistics we produce, is as important today as it was in 1884. We continue our determined work to impartially collect, analyze, and publish essential economic information to support private and public decision-making. Today BLS provides a wide variety of information that benefits all Americans. I am certain that Commissioner Wright would be pleased that our reports, charts, and data are far more accessible than he ever could have imagined. Whether you’re exploring a new occupation, starting a business, looking for the change in consumer and producer prices, identifying average wages by occupation, or learning how Americans spend their time, there’s a stat for that. For all these situations and many more, BLS helps Americans make smart decisions in their lives. The cost of providing this valuable information may come out to less than $2 per person each year, but its positive impact remains priceless.