Tag Archives: Statistics awareness

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

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 table 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
DateNew York-Newark-Jersey City, NY-NJ-PADallas-Fort Worth-Arlington, TXChicago-Naperville-Elgin, IL-IN-WILos Angeles-Long Beach-Anaheim, CAUnited States

Jan 2007

1.81.41.81.61.6

Feb 2007

1.81.41.91.61.6

Mar 2007

1.71.31.81.61.6

Apr 2007

1.61.11.61.51.4

May 2007

1.51.01.51.51.4

Jun 2007

1.51.11.61.41.4

Jul 2007

1.61.21.81.51.5

Aug 2007

1.71.21.91.51.5

Sep 2007

1.71.21.81.71.5

Oct 2007

1.61.11.71.81.5

Nov 2007

1.61.21.81.91.5

Dec 2007

1.71.21.91.91.6

Jan 2008

1.91.32.02.11.7

Feb 2008

2.11.32.22.21.8

Mar 2008

2.21.32.42.21.9

Apr 2008

2.11.22.22.01.8

May 2008

2.01.22.22.11.8

Jun 2008

1.91.22.42.51.9

Jul 2008

2.21.43.03.02.2

Aug 2008

2.31.73.13.52.4

Sep 2008

2.41.83.13.62.5

Oct 2008

2.51.93.04.02.6

Nov 2008

2.82.13.44.62.9

Dec 2008

3.12.43.95.93.3

Jan 2009

3.62.94.86.74.0

Feb 2009

4.43.25.57.34.6

Mar 2009

5.03.56.47.45.1

Apr 2009

5.03.66.77.35.3

May 2009

5.14.06.87.15.4

Jun 2009

5.14.67.26.85.6

Jul 2009

5.25.37.77.46.0

Aug 2009

5.15.67.97.76.2

Sep 2009

5.45.88.08.06.1

Oct 2009

5.95.37.87.85.8

Nov 2009

6.75.68.68.25.9

Dec 2009

7.15.69.58.36.2

Jan 2010

7.06.310.88.36.2

Feb 2010

7.06.410.37.56.2

Mar 2010

7.05.98.87.15.9

Apr 2010

6.55.17.86.75.4

May 2010

5.94.86.66.54.9

Jun 2010

5.05.16.26.74.8

Jul 2010

4.85.26.07.14.9

Aug 2010

4.75.46.27.54.9

Sep 2010

4.94.76.17.54.7

Oct 2010

4.64.25.27.14.5

Nov 2010

4.84.05.07.14.5

Dec 2010

5.34.15.17.14.6

Jan 2011

6.04.35.57.15.0

Feb 2011

6.14.25.76.84.9

Mar 2011

5.54.05.36.34.6

Apr 2011

5.03.85.15.84.2

May 2011

4.63.64.65.84.1

Jun 2011

4.53.74.55.64.0

Jul 2011

4.63.84.65.94.1

Aug 2011

4.53.84.96.04.0

Sep 2011

4.43.44.65.83.8

Oct 2011

4.23.14.25.63.6

Nov 2011

4.03.04.25.33.6

Dec 2011

4.23.04.85.53.7

Jan 2012

4.63.05.25.93.7

Feb 2012

5.33.04.86.23.7

Mar 2012

5.12.84.25.73.5

Apr 2012

4.22.43.65.03.3

May 2012

3.92.13.44.83.1

Jun 2012

3.92.13.64.63.1

Jul 2012

4.22.33.95.33.3

Aug 2012

4.02.43.95.23.4

Sep 2012

3.82.43.55.53.2

Oct 2012

3.72.13.25.03.0

Nov 2012

3.92.03.44.83.0

Dec 2012

4.02.03.75.13.2

Jan 2013

4.22.24.35.43.4

Feb 2013

4.22.14.15.43.4

Mar 2013

4.02.03.94.93.2

Apr 2013

3.51.93.54.12.9

May 2013

3.21.93.53.82.7

Jun 2013

3.22.13.63.62.7

Jul 2013

3.42.33.83.82.9

Aug 2013

3.42.33.73.92.9

Sep 2013

3.42.13.64.02.8

Oct 2013

3.22.03.33.92.5

Nov 2013

3.22.03.24.12.5

Dec 2013

3.22.03.34.22.6

Jan 2014

3.42.03.54.22.7

Feb 2014

3.41.93.53.72.7

Mar 2014

3.21.93.33.32.6

Apr 2014

2.81.72.62.82.2

May 2014

2.51.62.12.82.0

Jun 2014

2.41.62.02.81.9

Jul 2014

2.61.62.13.12.0

Aug 2014

2.61.62.12.91.9

Sep 2014

2.51.62.02.91.9

Oct 2014

2.31.51.92.71.7

Nov 2014

2.41.51.92.91.8

Dec 2014

2.51.31.92.91.8

Jan 2015

2.61.32.02.91.9

Feb 2015

2.51.31.92.61.8

Mar 2015

2.41.31.82.41.7

Apr 2015

2.21.21.62.21.6

May 2015

2.01.11.52.21.5

Jun 2015

1.91.01.62.31.5

Jul 2015

1.91.01.62.31.5

Aug 2015

1.90.91.62.31.5

Sep 2015

1.80.91.52.31.4

Oct 2015

1.70.81.52.01.3

Nov 2015

1.60.81.52.01.3

Dec 2015

1.60.81.51.91.4

Jan 2016

1.70.91.61.91.4

Feb 2016

1.80.81.61.71.5

Mar 2016

1.70.81.61.61.4

Apr 2016

1.60.71.61.61.3

May 2016

1.40.71.41.61.2

Jun 2016

1.40.81.41.61.3

Jul 2016

1.40.91.41.81.3

Aug 2016

1.50.91.51.91.4

Sep 2016

1.40.91.51.91.3

Oct 2016

1.30.91.51.71.3

Nov 2016

1.30.91.41.71.3

Dec 2016

1.30.91.51.71.3

Jan 2017

1.41.01.71.81.4

Feb 2017

1.51.01.71.81.4

Mar 2017

1.51.01.51.81.4

Apr 2017

1.30.91.41.61.2

May 2017

1.30.91.21.51.1

Jun 2017

1.31.01.21.41.1

Jul 2017

1.31.11.21.51.1

Aug 2017

1.41.11.21.61.1

Sep 2017

1.41.01.11.51.1

Oct 2017

1.31.01.01.31.0

Nov 2017

1.21.01.01.31.0

Dec 2017

1.21.01.11.31.0

Jan 2018

1.21.11.31.31.1

Feb 2018

1.21.11.31.21.1

Mar 2018

1.21.11.21.21.1

Apr 2018

1.11.01.11.11.0

May 2018

1.01.00.91.00.9

Jun 2018

1.00.90.81.10.9

Jul 2018

1.00.80.81.10.9

Aug 2018

1.00.80.91.20.9

Sep 2018

0.90.80.91.20.8

Oct 2018

0.90.80.81.10.8

Nov 2018

0.80.80.81.20.8

Dec 2018

0.90.70.81.30.8

Jan 2019

1.00.80.91.40.9

Feb 2019

1.10.81.01.40.9

Mar 2019

1.10.80.91.40.9

Apr 2019

1.00.70.91.10.8

May 2019

0.80.70.81.00.8

Jun 2019

0.80.60.80.90.8

Jul 2019

0.80.70.81.00.8

Aug 2019

0.80.80.91.10.9

Sep 2019

0.80.80.91.20.8

Oct 2019

0.80.80.81.10.8

Nov 2019

0.80.80.71.00.8

Dec 2019

0.80.80.71.00.8

How Many Unemployed People? Comparing Survey Data and Unemployment Insurance Counts

More than 37 million people filed for unemployment insurance benefits in the 10 weeks from the week ending March 21 to the week ending May 23. The unemployment rate in April was 14.7 percent. Or was it 19.5 percent? There were 23 million people counted as unemployed in mid-April, and 18 million people received unemployment insurance (UI) benefits at that time. How can all of these things be true? What’s the real story?

Back in October, I set the record straight on how counts of people receiving unemployment insurance benefits differ from how BLS measures unemployment. These two measures offer distinct but related measures of trends in joblessness, some of which I will explore here. I will focus only on data from states’ regular UI programs, but other programs exist as well. Here’s the bottom line: When all is said and done, the two measures track each other very closely.

The number of people filing for UI benefits reached record levels in recent weeks as a result of the COVID-19 pandemic and efforts to contain it. The UI claims numbers don’t come from BLS but rather from our colleagues at the U.S. Department of Labor’s Employment and Training Administration. Their count of people receiving UI benefits hit its highest level ever, nearly 23 million (not seasonally adjusted), for the week ending May 9. Their separate count of people filing new UI claims hit a record high of more than 6 million people in early April.

UI Continued Claimed versus Total Unemployed

First, the contrasts. The Employment and Training Administration publishes weekly counts of UI claims. The UI claims data include both initial claims and continued claims.

  • Initial claims: A count of the new claims people filed to request UI benefits. These claims won’t necessarily all be approved if, for example, a state UI program determines the person isn’t eligible to receive benefits.
  • Continued claims: A count of claims for those who have already filed initial claims and who have experienced a week of unemployment. These people then file a continued claim to receive benefits for that week of unemployment. Continued claims are also called insured unemployment.

Interviewers for the BLS Current Population Survey contact households once a month to ask questions about employment, job search, and other labor market topics for the week containing the 12th of the month. The monthly labor market survey counts people as unemployed if they meet all of these conditions:

  • They are not employed.
  • They could have taken a job if one had been offered.
  • They had made at least one specific, active effort to find employment in the last 4 weeks OR were on temporary layoff.

People counted in the survey as unemployed may or may not be eligible for UI benefits.

Counts of continued UI claims track pretty well with our survey measures of unemployment. The two measures run mostly parallel but at different levels over time. The chart below shows some history through the reference week of the survey data for April 2020.

Continued unemployment insurance claims and total unemployed, 1994–2020, not seasonally adjusted

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

The gap between the two measures shows how the survey captures millions of unemployed people who do not receive UI benefits. This gap partly results from states’ eligibility requirements for their UI programs.

UI Continued Claims versus Job Losers

Our monthly labor market survey lets us see more detail about the characteristics of people who are unemployed. One characteristic is the reason for a person’s unemployment.

  • Some people are labor force entrants or reentrants if they did not have a job immediately before starting their job search.
  • Others quit or leave their job voluntarily and are job leavers.
  • The rest become unemployed by losing their job in one of the following ways:
    • Being permanently laid off
    • Being temporarily laid off
    • Completing a temporary job

People who become unemployed after losing their jobs are job losers. Job losers are more likely to be eligible for UI benefits. Data for this group more closely track the continued claims data.

Continued unemployment insurance claims and unemployed job losers, 1994–2020, not seasonally adjusted

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

To narrow the gap even more, we know the time limits all regular UI programs have for receiving benefits. These limits vary by state, but states rarely offer more than 26 weeks of benefits in their regular program. Our survey estimates of job losers unemployed 26 weeks or fewer track more closely with UI continued claims.

Continued unemployment insurance claims and unemployed job losers who were unemployed 26 weeks or fewer, 1994–2020, not seasonally adjusted

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

Current Trends

Let’s focus on these two measures since last fall. We can see they track even more closely through the April survey reference week.

Continued unemployment insurance claims and unemployed job losers who were unemployed 26 weeks or fewer, November 2019 to May 2020, not seasonally adjusted

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

Labor market data from BLS have always been essential for understanding our economy. Good data—from many sources—are even more essential now, both for guiding the public health response to the COVID-19 pandemic and for tracking its economic impact and recovery. The labor market impacts from the pandemic have been so massive and happened so quickly that policymakers, businesses, and workers have wanted data in almost real time. Our monthly surveys weren’t designed to provide data quite that rapidly, but combining data from multiple sources and understanding how those measures track one another can provide more insight than any single source. We’ve been thinking a lot in recent years about how to complement our survey data with high-frequency information from other sources. I’ve written about some of those efforts and will continue to do so. This pandemic will sharpen our focus on innovating to provide gold standard data for the public good.

Continued unemployment insurance claims and Current Population Survey measures of unemployed, not seasonally adjusted
DateCurrent Population Survey unemployedCurrent Population Survey unemployed job losersCurrent Population Survey unemployed job losers who were unemployed 26 weeks or fewerUnemployment insurance continued claims under state programs

1/15/1994

9,492,0005,215,0004,204,0003,142,036

2/12/1994

9,262,0004,925,0003,968,0003,529,168

3/12/1994

8,874,0004,522,0003,577,0003,255,495

4/16/1994

8,078,0003,832,0002,959,0002,780,268

5/14/1994

7,656,0003,319,0002,520,0002,558,790

6/18/1994

8,251,0003,459,0002,722,0002,399,131

7/16/1994

8,281,0003,701,0002,919,0002,616,255

8/13/1994

7,868,0003,565,0002,832,0002,382,494

9/17/1994

7,379,0003,206,0002,543,0002,095,343

10/15/1994

7,155,0003,168,0002,557,0002,119,751

11/12/1994

6,973,0003,366,0002,759,0002,246,077

12/10/1994

6,690,0003,514,0002,947,0002,501,945

1/14/1995

8,101,0004,350,0003,697,0003,045,968

2/18/1995

7,685,0003,923,0003,288,0003,011,244

3/18/1995

7,480,0003,718,0003,112,0002,872,790

4/15/1995

7,378,0003,479,0002,817,0002,578,113

5/13/1995

7,185,0003,275,0002,697,0002,362,466

6/17/1995

7,727,0003,160,0002,615,0002,321,295

7/15/1995

7,892,0003,470,0002,899,0002,643,997

8/12/1995

7,457,0003,331,0002,823,0002,363,074

9/16/1995

7,167,0003,017,0002,492,0002,110,587

10/14/1995

6,884,0003,104,0002,588,0002,154,971

11/18/1995

7,024,0003,355,0002,851,0002,095,335

12/9/1995

6,872,0003,533,0003,065,0002,596,696

1/13/1996

8,270,0004,425,0003,884,0003,211,081

2/17/1996

7,858,0004,099,0003,517,0003,234,481

3/16/1996

7,700,0003,849,0003,248,0003,086,902

4/13/1996

7,124,0003,610,0002,903,0002,753,405

5/18/1996

7,166,0003,164,0002,643,0002,314,322

6/15/1996

7,377,0003,116,0002,570,0002,285,632

7/13/1996

7,693,0003,323,0002,717,0002,494,226

8/17/1996

6,868,0002,932,0002,451,0002,219,197

9/14/1996

6,700,0002,812,0002,375,0002,007,009

10/12/1996

6,577,0002,757,0002,320,0001,910,925

11/16/1996

6,816,0003,126,0002,677,0002,169,112

12/7/1996

6,680,0003,230,0002,805,0002,406,303

1/18/1997

7,933,0004,027,0003,561,0002,975,311

2/15/1997

7,647,0003,659,0003,158,0002,903,673

3/15/1997

7,399,0003,493,0003,026,0002,719,345

4/12/1997

6,551,0003,050,0002,593,0002,392,620

5/17/1997

6,398,0002,696,0002,318,0002,039,498

6/14/1997

7,094,0002,878,0002,457,0002,008,106

7/12/1997

6,981,0002,895,0002,405,0002,273,294

8/16/1997

6,594,0002,859,0002,374,0002,047,159

9/13/1997

6,403,0002,616,0002,190,0001,797,836

10/18/1997

5,995,0002,525,0002,105,0001,761,841

11/15/1997

5,914,0002,698,0002,319,0001,977,179

12/13/1997

5,957,0003,051,0002,663,0002,251,072

1/17/1998

7,069,0003,556,0003,136,0002,726,043

2/14/1998

6,804,0003,254,0002,868,0002,660,864

3/14/1998

6,816,0003,311,0002,906,0002,590,407

4/18/1998

5,643,0002,647,0002,262,0002,181,018

5/16/1998

5,764,0002,517,0002,196,0001,895,102

6/13/1998

6,534,0002,628,0002,316,0001,908,179

7/18/1998

6,567,0002,847,0002,499,0002,277,800

8/15/1998

6,173,0002,715,0002,365,0001,987,304

9/12/1998

6,039,0002,534,0002,169,0001,805,455

10/17/1998

5,831,0002,426,0002,109,0001,735,477

11/14/1998

5,711,0002,587,0002,299,0001,944,590

12/12/1998

5,565,0002,849,0002,537,0002,232,312

1/16/1999

6,604,0003,394,0003,095,0002,808,153

2/13/1999

6,563,0003,151,0002,859,0002,669,301

3/13/1999

6,119,0002,888,0002,597,0002,581,727

4/17/1999

5,688,0002,633,0002,360,0002,219,359

5/15/1999

5,507,0002,362,0002,107,0002,016,349

6/12/1999

6,271,0002,495,0002,204,0001,963,530

7/17/1999

6,319,0002,729,0002,422,0002,181,103

8/14/1999

5,826,0002,559,0002,251,0001,978,309

9/18/1999

5,661,0002,299,0002,039,0001,728,476

10/16/1999

5,372,0002,162,0001,935,0001,705,790

11/13/1999

5,380,0002,340,0002,092,0001,828,872

12/11/1999

5,245,0002,451,0002,193,0002,094,337

1/15/2000

6,316,0003,134,0002,808,0002,531,224

2/12/2000

6,284,0003,066,0002,771,0002,604,156

3/18/2000

6,069,0002,802,0002,535,0002,277,154

4/15/2000

5,212,0002,259,0002,027,0001,975,507

5/13/2000

5,460,0002,196,0001,957,0001,783,386

6/17/2000

5,959,0002,303,0002,080,0001,812,319

7/15/2000

6,028,0002,508,0002,273,0002,107,129

8/12/2000

5,863,0002,570,0002,317,0001,933,774

9/16/2000

5,359,0002,284,0002,010,0001,709,044

10/14/2000

5,153,0002,105,0001,857,0001,735,297

11/18/2000

5,336,0002,355,0002,108,0001,822,245

12/9/2000

5,264,0002,618,0002,376,0002,261,776

1/13/2001

6,647,0003,449,0003,162,0002,787,024

2/17/2001

6,523,0003,343,0003,061,0002,954,857

3/17/2001

6,509,0003,379,0003,047,0002,932,361

4/14/2001

6,004,0003,027,0002,697,0002,772,097

5/12/2001

5,901,0002,839,0002,578,0002,554,830

6/16/2001

6,816,0003,136,0002,833,0002,634,433

7/14/2001

6,858,0003,372,0003,060,0003,053,451

8/18/2001

7,017,0003,379,0003,004,0002,793,540

9/15/2001

6,766,0003,294,0002,961,0002,630,082

10/13/2001

7,175,0003,753,0003,356,0002,888,718

11/17/2001

7,617,0004,252,0003,738,0003,105,348

12/8/2001

7,773,0004,485,0003,937,0003,604,679

1/12/2002

9,051,0005,449,0004,757,0004,234,835

2/16/2002

8,823,0005,105,0004,457,0004,206,538

3/16/2002

8,776,0004,861,0004,145,0004,078,226

4/13/2002

8,255,0004,550,0003,744,0003,731,669

5/18/2002

7,969,0004,180,0003,293,0003,314,004

6/15/2002

8,758,0004,429,0003,547,0003,248,721

7/13/2002

8,693,0004,607,0003,673,0003,518,751

8/17/2002

8,271,0004,427,0003,545,0003,195,935

9/14/2002

7,790,0004,123,0003,170,0002,947,854

10/12/2002

7,769,0004,151,0003,181,0002,912,625

11/16/2002

8,170,0004,555,0003,521,0003,205,969

12/7/2002

8,209,0004,849,0003,718,0003,481,337

1/18/2003

9,395,0005,641,0004,564,0004,011,764

2/15/2003

9,260,0005,487,0004,336,0004,042,069

3/15/2003

9,018,0005,150,0004,047,0004,009,388

4/12/2003

8,501,0004,716,0003,526,0003,693,322

5/17/2003

8,500,0004,589,0003,475,0003,341,816

6/14/2003

9,649,0004,775,0003,654,0003,334,821

7/12/2003

9,319,0004,958,0003,842,0003,635,324

8/16/2003

8,830,0004,789,0003,715,0003,278,613

9/13/2003

8,436,0004,500,0003,363,0002,985,665

10/18/2003

8,169,0004,319,0003,206,0002,944,236

11/15/2003

8,269,0004,505,0003,401,0003,090,089

12/13/2003

7,945,0004,629,0003,598,0003,338,180

1/17/2004

9,144,0005,195,0004,063,0003,754,598

2/14/2004

8,770,0004,888,0003,838,0003,690,774

3/13/2004

8,834,0004,920,0003,765,0003,466,491

4/17/2004

7,837,0004,253,0003,255,0002,994,939

5/15/2004

7,792,0003,778,0002,845,0002,690,913

6/12/2004

8,616,0003,930,0003,010,0002,685,110

7/17/2004

8,518,0004,233,0003,372,0002,892,369

8/14/2004

7,940,0003,809,0002,982,0002,639,474

9/18/2004

7,545,0003,644,0002,853,0002,342,492

10/16/2004

7,531,0003,653,0002,832,0002,348,403

11/13/2004

7,665,0003,898,0003,103,0002,481,768

12/11/2004

7,599,0004,166,0003,320,0002,781,151

1/15/2005

8,444,0004,771,0003,905,0003,269,319

2/12/2005

8,549,0004,461,0003,624,0003,200,271

3/12/2005

7,986,0004,067,0003,289,0003,044,727

4/16/2005

7,335,0003,559,0002,787,0002,565,759

5/14/2005

7,287,0003,265,0002,606,0002,347,511

6/18/2005

7,870,0003,482,0002,883,0002,354,977

7/16/2005

7,839,0003,618,0003,034,0002,581,153

8/13/2005

7,327,0003,297,0002,705,0002,361,634

9/17/2005

7,259,0003,373,0002,775,0002,293,043

10/15/2005

6,964,0003,162,0002,535,0002,377,075

11/12/2005

7,271,0003,329,0002,687,0002,515,835

12/10/2005

6,956,0003,622,0003,000,0002,659,503

1/14/2006

7,608,0003,990,0003,419,0003,010,836

2/18/2006

7,692,0003,846,0003,233,0002,865,435

3/18/2006

7,255,0003,707,0003,108,0002,712,772

4/15/2006

6,804,0003,426,0002,771,0002,430,217

5/13/2006

6,655,0003,152,0002,546,0002,183,176

6/17/2006

7,341,0003,222,0002,718,0002,177,172

7/15/2006

7,602,0003,374,0002,820,0002,450,260

8/12/2006

7,086,0003,132,0002,585,0002,283,575

9/16/2006

6,625,0002,878,0002,366,0002,022,552

10/14/2006

6,272,0002,724,0002,247,0002,077,157

11/11/2006

6,576,0003,025,0002,555,0002,231,475

12/9/2006

6,491,0003,374,0002,928,0002,536,673

1/13/2007

7,649,0004,127,0003,668,0002,887,810

2/17/2007

7,400,0003,942,0003,425,0003,037,700

3/17/2007

6,913,0003,487,0002,987,0002,788,224

4/14/2007

6,532,0003,249,0002,724,0002,598,802

5/12/2007

6,486,0003,070,0002,591,0002,293,089

6/16/2007

7,295,0003,241,0002,752,0002,241,672

7/14/2007

7,556,0003,730,0003,157,0002,548,427

8/18/2007

7,088,0003,472,0002,899,0002,335,412

9/15/2007

6,952,0003,208,0002,645,0002,128,411

10/13/2007

6,773,0003,259,0002,668,0002,143,999

11/10/2007

6,917,0003,382,0002,814,0002,260,475

12/8/2007

7,371,0004,013,0003,396,0002,665,956

1/12/2008

8,221,0004,608,0003,951,0003,242,075

2/16/2008

7,953,0004,471,0003,805,0003,265,157

3/15/2008

8,027,0004,555,0003,922,0003,220,809

4/12/2008

7,287,0003,931,0003,212,0003,018,445

5/17/2008

8,076,0003,949,0003,204,0002,759,158

6/14/2008

8,933,0004,201,0003,451,0002,801,895

7/12/2008

9,433,0004,562,0003,782,0003,097,770

8/16/2008

9,479,0004,735,0003,855,0003,112,252

9/13/2008

9,199,0004,699,0003,655,0002,957,202

10/18/2008

9,469,0005,138,0004,006,0003,188,153

11/15/2008

10,015,0005,746,0004,609,0003,714,261

12/13/2008

10,999,0006,878,0005,483,0004,531,208

1/17/2009

13,009,0008,633,0007,041,0005,647,319

2/14/2009

13,699,0009,098,0007,380,0006,031,637

3/14/2009

13,895,0009,315,0007,347,0006,354,009

4/18/2009

13,248,0008,687,0006,347,0006,237,658

5/16/2009

13,973,0008,930,0006,531,0006,049,295

6/13/2009

15,095,0009,194,0006,567,0006,012,730

7/18/2009

15,201,0009,447,0006,275,0005,989,877

8/15/2009

14,823,0009,316,0005,955,0005,578,533

9/12/2009

14,538,0009,170,0005,520,0005,131,447

10/17/2009

14,547,0009,176,0005,482,0004,893,301

11/14/2009

14,407,0009,130,0005,237,0004,996,155

12/12/2009

14,740,0009,822,0005,881,0005,262,045

1/16/2010

16,147,00010,574,0006,302,0005,538,244

2/13/2010

15,991,00010,664,0006,347,0005,465,212

3/13/2010

15,678,00010,311,0005,880,0005,270,644

4/17/2010

14,609,0009,110,0004,607,0004,715,968

5/15/2010

14,369,0008,812,0004,484,0004,333,973

6/12/2010

14,885,0008,769,0004,606,0004,226,459

7/17/2010

15,137,0008,964,0004,695,0004,471,386

8/14/2010

14,759,0008,894,0004,724,0004,138,097

9/18/2010

14,140,0008,651,0004,526,0003,737,930

10/16/2010

13,903,0008,331,0004,306,0003,697,842

11/13/2010

14,282,0008,926,0004,787,0003,804,696

12/11/2010

13,997,0008,995,0004,986,0004,119,344

1/15/2011

14,937,0009,520,0005,545,0004,552,936

2/12/2011

14,542,0009,212,0005,403,0004,521,733

3/12/2011

14,060,0008,841,0004,874,0004,215,458

4/16/2011

13,237,0007,958,0004,276,0003,726,578

5/14/2011

13,421,0007,885,0004,051,0003,492,720

6/18/2011

14,409,0007,940,0004,315,0003,454,731

7/16/2011

14,428,0008,107,0004,441,0003,695,537

8/13/2011

14,008,0007,897,0004,395,0003,497,400

9/17/2011

13,520,0007,636,0003,849,0003,150,942

10/15/2011

13,102,0007,390,0003,911,0003,141,911

11/12/2011

12,613,0007,201,0003,813,0003,323,025

12/10/2011

12,692,0007,691,0004,392,0003,571,487

1/14/2012

13,541,0008,234,0004,977,0004,019,589

2/18/2012

13,430,0007,866,0004,783,0003,834,179

3/17/2012

12,904,0007,415,0004,394,0003,650,071

4/14/2012

11,910,0006,555,0003,580,0003,377,436

5/12/2012

12,271,0006,607,0003,601,0003,079,181

6/16/2012

13,184,0006,927,0003,973,0003,069,545

7/14/2012

13,400,0007,151,0004,248,0003,288,629

8/18/2012

12,696,0006,820,0004,003,0003,068,519

9/15/2012

11,742,0006,161,0003,497,0002,796,675

10/13/2012

11,741,0006,125,0003,428,0002,772,151

11/10/2012

11,404,0006,069,0003,520,0002,902,343

12/8/2012

11,844,0006,592,0004,086,0003,203,819

1/12/2013

13,181,0007,575,0005,046,0003,661,355

2/16/2013

12,500,0007,130,0004,468,0003,483,983

3/16/2013

11,815,0006,638,0004,024,0003,345,945

4/13/2013

11,014,0006,079,0003,642,0003,049,657

5/18/2013

11,302,0005,751,0003,437,0002,752,679

6/15/2013

12,248,0005,939,0003,794,0002,745,766

7/13/2013

12,083,0005,934,0003,771,0002,995,510

8/17/2013

11,462,0005,856,0003,677,0002,772,037

9/14/2013

10,885,0005,470,0003,409,0002,412,302

10/12/2013

10,773,0005,649,0003,657,0002,418,279

11/9/2013

10,271,0005,400,0003,371,0002,514,678

12/7/2013

9,984,0005,460,0003,492,0002,838,295

1/18/2014

10,855,0006,152,0004,163,0003,334,697

2/15/2014

10,893,0006,024,0004,089,0003,329,510

3/15/2014

10,537,0005,779,0003,853,0003,096,231

4/12/2014

9,079,0004,972,0003,100,0002,721,859

5/17/2014

9,443,0004,613,0002,948,0002,421,319

6/14/2014

9,893,0004,670,0003,239,0002,372,393

7/12/2014

10,307,0004,867,0003,359,0002,518,959

8/16/2014

9,787,0004,750,0003,442,0002,363,077

9/13/2014

8,962,0004,176,0002,774,0002,076,867

10/18/2014

8,680,0003,995,0002,682,0002,046,031

11/15/2014

8,630,0004,182,0002,954,0002,158,767

12/13/2014

8,331,0004,355,0003,083,0002,445,747

1/17/2015

9,498,0004,912,0003,601,0002,750,868

2/14/2015

9,095,0004,721,0003,514,0002,720,615

3/14/2015

8,682,0004,503,0003,259,0002,674,331

4/18/2015

7,966,0003,977,0002,789,0002,251,252

5/16/2015

8,370,0003,962,0002,850,0002,047,456

6/13/2015

8,638,0003,951,0003,029,0002,066,476

7/18/2015

8,805,0004,204,0003,207,0002,217,720

8/15/2015

8,162,0003,987,0002,968,0002,124,998

9/12/2015

7,628,0003,509,0002,647,0001,903,085

10/17/2015

7,597,0003,576,0002,678,0001,825,692

11/14/2015

7,573,0003,633,0002,815,0001,970,435

12/12/2015

7,542,0003,820,0002,964,0002,255,937

1/16/2016

8,309,0004,287,0003,357,0002,624,638

2/13/2016

8,219,0004,244,0003,299,0002,582,311

3/12/2016

8,116,0004,149,0003,123,0002,461,697

4/16/2016

7,413,0003,716,0002,761,0002,126,849

5/14/2016

7,207,0003,322,0002,480,0001,982,730

6/18/2016

8,144,0003,677,0002,855,0001,975,334

7/16/2016

8,267,0003,869,0003,001,0002,109,038

8/13/2016

7,996,0003,787,0002,918,0002,030,018

9/17/2016

7,658,0003,536,0002,660,0001,728,317

10/15/2016

7,447,0003,352,0002,551,0001,710,066

11/12/2016

7,066,0003,271,0002,490,0001,828,034

12/10/2016

7,170,0003,668,0002,917,0002,071,781

1/14/2017

8,149,0004,361,0003,481,0002,437,106

2/18/2017

7,887,0004,184,0003,335,0002,364,751

3/18/2017

7,284,0003,812,0002,990,0002,256,527

4/15/2017

6,555,0003,369,0002,561,0001,984,675

5/13/2017

6,572,0003,017,0002,317,0001,757,086

6/17/2017

7,250,0003,359,0002,698,0001,780,061

7/15/2017

7,441,0003,519,0002,822,0001,936,985

8/12/2017

7,287,0003,536,0002,866,0001,851,667

9/16/2017

6,556,0002,992,0002,314,0001,611,895

10/14/2017

6,242,0002,859,0002,252,0001,572,784

11/11/2017

6,286,0002,907,0002,289,0001,672,980

12/9/2017

6,278,0003,298,0002,715,0001,909,886

1/13/2018

7,189,0003,891,0003,259,0002,242,438

2/17/2018

7,091,0003,716,0003,069,0002,226,157

3/17/2018

6,671,0003,375,0002,799,0002,082,891

4/14/2018

5,932,0002,805,0002,272,0001,841,572

5/12/2018

5,756,0002,493,0002,004,0001,584,129

6/16/2018

6,812,0003,022,0002,511,0001,564,998

7/14/2018

6,730,0003,164,0002,551,0001,738,468

8/18/2018

6,370,0002,885,0002,291,0001,605,843

9/15/2018

5,766,0002,474,0001,912,0001,396,832

10/13/2018

5,771,0002,510,0001,958,0001,353,628

11/10/2018

5,650,0002,598,0002,107,0001,429,209

12/8/2018

6,029,0002,947,0002,443,0001,703,504

1/12/2019

7,140,0003,791,0003,291,0002,070,444

2/16/2019

6,625,0003,300,0002,760,0002,107,108

3/16/2019

6,382,0003,098,0002,513,0001,990,542

4/13/2019

5,387,0002,484,0001,984,0001,686,671

5/18/2019

5,503,0002,281,0001,783,0001,491,921

6/15/2019

6,292,0002,703,0002,196,0001,538,052

7/13/2019

6,556,0002,986,0002,483,0001,673,714

8/17/2019

6,203,0002,906,0002,418,0001,594,845

9/14/2019

5,465,0002,227,0001,711,0001,379,722

10/12/2019

5,510,0002,340,0001,889,0001,366,544

11/9/2019

5,441,0002,561,0002,123,0001,439,799

12/7/2019

5,503,0002,752,0002,361,0001,707,456

1/18/2020

6,504,0003,267,0002,808,0002,053,978

2/15/2020

6,218,0003,151,0002,712,0002,036,213

3/14/2020

7,370,0004,441,0003,907,0002,055,283

4/18/2020

22,504,00020,384,00019,953,00017,601,283
Continued unemployment insurance claims and unemployed job losers who were unemployed 26 weeks or fewer, not seasonally adjusted
DateCurrent Population Survey unemployed job losers who were unemployed 26 weeks or fewerUnemployment insurance continued claims under state programs

11/2/2019

1,428,992

11/9/2019

2,123,0001,439,799

11/16/2019

1,523,691

11/23/2019

1,488,691

11/30/2019

1,733,682

12/7/2019

2,361,0001,707,456

12/14/2019

1,780,860

12/21/2019

1,760,439

12/28/2019

2,124,746

1/4/2020

2,229,673

1/11/2020

2,114,161

1/18/2020

2,808,0002,053,978

1/25/2020

2,125,243

2/1/2020

2,060,389

2/8/2020

2,073,658

2/15/2020

2,712,0002,036,213

2/22/2020

2,079,249

2/29/2020

2,032,792

3/7/2020

1,954,265

3/14/2020

3,907,0002,055,283

3/21/2020

3,391,238

3/28/2020

8,107,677

4/4/2020

12,356,980

4/11/2020

16,138,295

4/18/2020

19,953,00017,601,283

4/25/2020

21,576,373

5/2/2020

20,733,760

5/9/2020

22,637,743

5/16/2020

18,855,114

How Have We Improved the Consumer Price Index? Let Me Count the Ways

Soon after I became Commissioner, the top-notch BLS staff was briefing me on the many programs and details that make up BLS. I asked the staff working on the Consumer Price Index (CPI) if they could list for me some of the improvements that have occurred over the past few years. It has been nearly a quarter century since the Boskin Commission studied the CPI and recommended enhancements. I knew many of these enhancements had been implemented, along with other improvements. But I was shocked to see my staff come back with an 8-page, detailed listing of 77 substantive improvements that have been implemented since 1996.

You may have thought a price index that has been around since 1913 is happy to rest on its laurels. Well, you’d be wrong. There are improvements to the CPI going on all the time. As I reviewed the list, I noticed a number of improvements involve the introduction of new or changed goods and services, such as cell phones or streaming services. I also noticed improvements in how we handle product changes, as I wrote recently in a blog about quality adjustment. But these topics only scratch the surface.

I’m not going to describe every CPI enhancement that has taken place over the past 24 years; you can find much more detail on the CPI webpage. But I will whet your appetite by highlighting a few categories of improvements.

Keeping the CPI market basket up to date

The goal of the CPI is to track the change in the prices consumers pay for a representative market basket of goods and services. Let’s look at the what and where of that market basket.

  • What we collect — goods and services. We collect prices for a market basket of goods and services, designed to represent what consumers are buying. In January 2002, we switched from updating that market basket every 10 years to every 2 years, providing a better representation of current spending patterns.
  • What we collect — housing. We track the cost of housing by a separate sample of housing units. In 2010, we increased that sample to improve accuracy. In 2016 we began rotating that sample every 6 years. Previously, the housing sample was only rotated when new geographic areas were introduced after the U.S. census.
  • Where we collect — outlets. We collect prices from stores and businesses that are chosen based on where consumers shop and buy goods and services. In January 1998, we switched from updating this sample of “outlets” every 5 years to every 4 years. And in January 2020, we switched the source used to determine those outlets to the Consumer Expenditure Survey, which is also the source of our spending information for the market basket of goods and services people buy. Previously we used a separate survey of households to identify outlets.
  • Where we collect — geography. We collect prices for goods and services in selected geographic areas, designed to represent all urban areas of the United States. In January 2018, we updated the geographic areas, designed to represent current population trends. We last updated these areas in 1998.

Collecting CPI information

According to folklore, CPI data collection was accomplished by staff who dressed up in high fashion, the ladies in fancy hats and white gloves and the gentlemen in the finest haberdashery, who then went shopping to determine the latest prices. That’s not how it happens. CPI staff are not paid to “shop” to collect prices. We use trained experts who are skilled at gaining cooperation from many different types of businesses; ensuring they are obtaining price information for goods and services that are consistent from one month to the next or making appropriate adjustments; and gathering information from thousands of outlets about hundreds of thousands of goods and services over a short data-collection period. Let’s look at how the data-collection process has improved:

  • When we collect — In June 2005, the CPI switched from a collection period that spanned the first 15–18 days of the month to collection across the entire month. This provides more representative data, especially for items that frequently vary in price within the same month.
  • How we collect — In January 1998, the CPI began using computerized data-collection tools, which automate certain math functions and screen for errors or inconsistencies. We continue to upgrade our processes; CPI data-collection staff recently began using a new generation of tablet computers.
  • Alternative collection — Not all price information comes from traditional collection with stores. Some information comes from websites, corporate data files, third parties that combine data from different sources, and more. In fact, the CPI and other BLS programs are focused on identifying even more alternative collection methods in the coming years.

Calculating the CPI

Once we collect the prices on all these goods and services, we need to calculate an index. In simple terms, we find the difference between the price in month 1 and the price in month 2, and express that difference as a rate of change from month 1. We publish rates of change and also express current prices as an index, which is equal to 100 in a base period.

Many factors and decisions go into combining data for an item and then combining data for all items into the published CPI. We’ve improved those calculations in several ways over the past few years.

  • Geometric mean — In January 1999, the CPI switched the formula for calculating price changes at the component item level from an arithmetic mean to a geometric mean. This allows the overall index to capture substitutions consumers make across specific products within a component item category when the prices of those products change relative to one another. With the geometric mean formula, BLS does not assume consumers substitute hamburgers for steak, which are in different component categories. The formula only captures substitution within a component category, such as among types of steak.
  • More decimal places — In January 2007, the CPI began publishing index numbers to 3 decimal places, which improved consistency between published index numbers and rates of change.

New information available to the public

While the CPI has been around for over a century, we have added a number of new indexes over time, to provide a variety of inflation figures. Here are some of the newest products in the CPI family:

  • Research (Retroactive) series — The CPI Research Series incorporates many of the improvements that came out of the Boskin Commission. The series provides a pretty consistent way to measure price changes from 1978 up to the most recent full year.
  • Chained CPI series – The Chained CPI uses an alternative formula that applies spending data in consecutive months to reflect any substitution that consumers make across component item categories in response to changes in relative prices. For example, this index would capture consumer substitution of hamburger for steak. This measure is designed to come closer to a “cost-of-living” index than other BLS measures. The series was first produced in 2002.
  • Elderly research series – The CPI for the Elderly reweights the component CPI data based on the spending patterns of elderly households. This series, mandated by Congress, began in 1988. In 2008, we extended the series retrospectively back to 1982.

As I have mentioned in the past, we are always working to improve the CPI. We recently contracted with the Committee on National Statistics, part of the National Academy of Sciences, to provide guidance on a variety of issues. I’ll use this space to report on the Committee’s work, as well as other improvements underway in the CPI.

State Productivity: A BLS Production

We have a guest blogger for this edition of Commissioner’s Corner. Jennifer Price is an economist in the Office of Productivity and Technology at the U.S. Bureau of Labor Statistics. She enjoys watching theatrical performances when she’s not working.

I recently had the pleasure of attending a high school play. The cast was composed of a male and female lead and at least a dozen supporting actors. The program listed the performers and acknowledged many other students, parents, teachers, and administrators. They all played some important role to bring the play to life—lighting, sound, painting props, sewing costumes, creating promotional materials, selling tickets, working concessions. All of these pieces came together harmoniously to make the performance a success.

Setting the Stage: New Measures of State Productivity

We can view the health of the nation’s economy through the same lens. Our diversified economy is made up of lead performers and supporting roles in the form of industries. Some industries contribute more heavily to growth in output or productivity, playing the star role. Other industries are supporting characters, contributing to a smaller, but necessary, share of growth. Our productivity program recently published a webpage that examines how industries contribute to the nation’s private business output and productivity growth.

We also can examine these roles geographically. Until recently, BLS productivity measures were only produced at the national level. Last June, BLS published experimental measures of state labor productivity for the private nonfarm business sector. These measures, which cover the period from 2007 to 2017, will help us learn more about productivity growth in each state and how each state contributes to national productivity trends.

Measuring productivity for all states allows us to credit the role played by each state, not just the total performance of the national economy or region. Just as each person, no matter how small their role, was necessary for the success of the school play, each state contributes to how we evaluate national or regional productivity. When we examine the contribution of each state to total productivity trends, we find that, like actors, no two states perform identically. Similar individual growth rates may have different impacts on the productivity of the nation or region. By analyzing state productivity trends over the long term, we learn more about regional business cycles, regional income inequality, and the role of local regulations and taxes on growth.

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.

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

We estimate each state’s annual contribution to national or regional productivity growth by multiplying the state’s productivity growth rate by its average share of total current dollar national or regional output. The economic size of each state influences its contribution to national and regional estimates. From 2007 to 2017, California was our lead performer, with the largest contribution to national productivity growth. The state’s productivity grew 1.7 percent per year on average, and its large economy means it contributed more than one-fifth of the 1.0-percent growth in national labor productivity.

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

Supporting actors included Texas and New York. Making a cameo appearance was North Dakota; despite having the largest productivity growth rate, it ranked 28th in terms of its contribution to national productivity growth. Stars in each region included Illinois (Midwest), New York (Northeast), Texas (South), and California (West). Understudies—those states with the largest growth rates—were North Dakota (Midwest), Pennsylvania (Northeast), and Oklahoma (South). Oregon and Washington shared this role out West.

Second Act

For now, our new measures cover the private nonfarm sector for all 50 states and the District of Columbia from 2007 to 2017. These measures include output per hour, output, hours, unit labor costs, hourly compensation, and real hourly compensation. Our measures of labor productivity for states are experimental, meaning we’re still assessing them and considering ways to improve them. In the second act, we will be looking into producing state-level measures for more detailed sectors and industries.

For an encore performance, check out our state labor productivity page. We’d love to hear your feedback! Email comments to productivity@bls.gov.

Annual percent change in labor productivity in the private nonfarm sector, 2007–17
StateAnnual 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
States with the largest contributions to national labor productivity, average annual percent change, 2007–17
StateState contribution to U.S. labor productivity

California

0.22

Texas

0.10

New York

0.08

Pennsylvania

0.06

Washington

0.04

Massachusetts

0.04

Illinois

0.03

Projected Occupational Openings: Where Do They Come From?

Toward the beginning of each school year, BLS issues a new set of Employment Projections, looking at projected growth and decline in occupations over the next decade. These estimates are important for understanding structural changes in the workforce over time. But to identify opportunities for new workers, we need to look beyond occupational growth and decline, to a concept we call “occupational openings.”

Occupational openings are the sum of the following:

  • Projected job growth (or decline)
  • Occupational separations — workers leaving an occupation, which includes:
    • Labor force exits — workers who leave the labor force entirely, perhaps to retire
    • Occupational transfers — workers who leave one occupation and transfer to a different occupation.

This video explains the concept of occupational openings further.

BLS publishes the projected number of occupational openings for over 800 occupations. Not surprisingly, some of the largest occupations in the country have some of the largest number of openings. For example, certain food service jobs, which include fast food workers, are projected to have nearly 800,000 openings per year over the next decade. I guess this isn’t a surprise in an occupation with over 3.7 million workers.

But when we delve into the information on occupational openings a little further, more stories emerge. Some related occupations have very different patterns of openings. And some occupations have similar levels of openings for different reasons. Let’s take a look at a few examples.

In 2018, there were over 800,000 lawyers in the U.S., and a projected 45,000 annual openings for lawyers, about 5.5 percent of employment. At the same time, there were fewer than half the number of paralegals and legal assistants (325,000), with projected annual openings around 40,000 per year – 12.4 percent of employment. These two related occupations had similar numbers of projected openings, but those openings represented different proportions of current employment. Such differences reflect required education, demographics, compensation, and other variables. Lawyers tend to have professional degrees that are specialized for that occupation and are therefore more closely tied to their occupation than paralegals, who have more diverse educational backgrounds. You can find out more about how worker characteristics affect these numbers in the Monthly Labor Review.

Now let’s look at the sources of occupational openings. In this first example, we compare two occupational groups: installation, maintenance, and repair occupations and healthcare support occupations. These are broad categories that include a number of different individual occupations.

Average annual occupational openings for installation, maintenance, and repair occupations and healthcare support occupations, 2018–28

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

In this example, both occupational groups have projected annual openings of a little over 600,000 per year, yet they come from different sources. Two-thirds of the openings among installation occupations result from workers leaving to go to other occupations; in contrast, just under half the openings among healthcare support occupations are from people moving to other occupations. Looking at projected job growth, BLS projects that healthcare support occupations, the fastest growing occupational group, will add more than three times as many new jobs as installation occupations, annually over the next decade (78,520 versus 23,320).

Now let’s look at two individual occupations — web developers and court, municipal, and license clerks. These are very different jobs, but both are projected to have about 15,000 annual openings over the next decade. Here, too, occupational openings come from very different places, as this chart shows:

Average annual occupational openings for web developers and court, municipal, and license clerks, 2018–28

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

In this case, around 67 percent of openings for web developer jobs come from workers transferring to other jobs, compared with only 49 percent transfers for clerks. But a greater share of clerks are exiting the labor force. Once again, differences are due to a variety of factors, although the age of workers is a significant factor in this case — web developers have a median age of 38.3, while clerks tend to be older, with a median age of 49.1. Younger workers are more likely to transfer occupations, while older workers are more likely to exit the labor force, as for retirement.

So what does all this really mean? If nothing else, you can see that the thousands of individual data elements available through the BLS Employment Projections program tell a thousand different stories, and more. Whether large or small, growing or declining, there’s information about hundreds of occupations that can be helpful to students looking for careers, counselors helping those students and others, workers wanting to change jobs, employers thinking about their future, policymakers considering where to put job training resources, and on and on. These examples just scratch the surface of what BLS Employment Projections information can tell us. Take a look for yourself.

Average annual occupational openings, 2018–28
OccupationEmployment growthExitsTransfers

Installation, maintenance, and repair occupations

23,320195,700413,900

Healthcare support occupations

78,520235,500299,600
Average annual occupational openings, 2018–28
OccupationEmployment growthExitsTransfers

Web developers

2,0902,90010,100

Court, municipal, and license clerks

6707,0007,300