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Spend Thanksgiving Day with BLS!

Thanksgiving is right around the corner. As we start to think about how we will celebrate, it might be hard to imagine the ties between BLS statistics and celebrating Thanksgiving. So, here’s a short tour of a typical Thanksgiving Day as seen through a few BLS statistics. Enjoy!

9:00 a.m. Put the turkey in the oven

All good chefs know the key to a successful Thanksgiving feast is to get the turkey in the oven bright and early. Whether you are roasting your turkey or firing up a deep fryer in the driveway, you will have to pay more for the fuel. The Consumer Price Index for household energy was pretty stable through 2019 and the first half of 2020 but then started a steady rise in September 2020.

Consumer Price Index for household energy, 2019–21

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

10:00 a.m. Watch the Macy’s Thanksgiving Day parade

The COVID-19 pandemic has caused ups and downs in the labor market, much like the impact of a windy day for the famous balloons in Macy’s Thanksgiving Day Parade. Keeping with the department store theme, employment in department stores plunged 25.3 percent in April 2020 but then rose 14.1 percent June 2020. These gyrations were more dramatic than the broader retail trade sector.

Monthly percent change in employment in retail trade and department stores, 2019–21

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

2:00 p.m. Scope out Black Friday deals

After watching the parade, it’s time to plan our Black Friday shopping! As consumers, we are always trying to get more for less. In the retail trade industry, it turns out they are doing just that. The industry has produced more output with steady or decreasing hours worked. The result is a corresponding increase in labor productivity. Now, only if we could prepare a bigger Thanksgiving feast in less time!

Indexes for labor productivity, hours worked, and output in retail trade, 2007–20

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

4:00 p.m. Play touch football

We need to make some room of the feast we are about to enjoy, so we assemble willing participants and play some touch football in the yard. The American Time Use Survey is the best source of information on how Americans spend their time each day. In this case, let’s compare how much time people spend playing sports versus how much time they spend watching sports on TV. We’ll look only at time spent in these activities on weekend days and holidays. The survey does not have details on what people watch on TV, but we can assume some time reported here is spent watching sports.

Average hours spent watching TV and playing sports, weekend days and holidays, 2019

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

We can see that Americans, on average, easily spend more time watching TV—3.36 hours—than playing sports—0.34 hours. But what is more interesting is that, on average, those who watch TV watch about 24 percent more than the overall population. However, those who play sports play, on average, nearly 6 times as many hours as the average for the population.

6:00 p.m. Thanksgiving feast

No matter what is on your dinner table this Thanksgiving, chances are it will cost more than previous years. All six major grocery store food groups in the Consumer Price Index for food at home continued to rise sharply in October 2021. Even if you decide to order out, it will set you back a bit more this year. Both full-service meals and limited services meals rose nearly 1 percent in October 2021.

Consumer Price Indexes for food at home and food away from home, 2018–21

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

7:00 p.m. Watch football

Now that we’ve finished our delicious feast, it’s a time-honored tradition to watch a bit of football on TV. If you are buying a new TV for this holiday, you can expect to pay a bit more. After years of steady declines, import prices for television and video receivers have reversed trend in 2021, much like a wide receiver changing direction to find an opening and catch a game-winning touchdown pass!

Import price index for television and video receivers, 2011–21

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

9:00 p.m. Say goodbye

It’s hard to say goodbye to your friends and family. In the United States, however, the Job Openings and Labor Turnover Survey is showing that workers are saying goodbye to their employers more often these days. The number of quits has been rising steadily since the shock of the pandemic affected layoffs and discharges in early 2020. (It’s only a coincidence that the layoffs line in the chart below looks like the outline of a pilgrim’s hat.)

Quits, layoffs and discharges, and other job separations, 2019–21

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

Now we’ve come to the end of our Thanksgiving feast of BLS data. Our hunger for the premier statistics on the U.S. labor force, prices, and productivity, has been satisfied, and we can rest easily knowing there’s a stat for that!

Consumer Price Index for household energy
MonthIndex

Jan 2019

100.000

Feb 2019

99.662

Mar 2019

100.046

Apr 2019

99.952

May 2019

99.679

Jun 2019

99.258

Jul 2019

99.415

Aug 2019

99.253

Sep 2019

99.033

Oct 2019

99.756

Nov 2019

99.890

Dec 2019

99.716

Jan 2020

99.666

Feb 2020

99.355

Mar 2020

98.812

Apr 2020

98.492

May 2020

98.278

Jun 2020

98.501

Jul 2020

98.542

Aug 2020

98.478

Sep 2020

99.590

Oct 2020

100.103

Nov 2020

101.043

Dec 2020

101.377

Jan 2021

101.299

Feb 2021

102.681

Mar 2021

103.436

Apr 2021

104.748

May 2021

105.512

Jun 2021

105.840

Jul 2021

106.664

Aug 2021

107.833

Sep 2021

109.273

Oct 2021

112.872
Monthly percent change in employment in retail trade and department stores
MonthRetail tradeDepartment stores

Jan 2019

-0.1%0.2%

Feb 2019

-0.2-1.4

Mar 2019

-0.1-0.6

Apr 2019

-0.1-0.9

May 2019

-0.1-0.5

Jun 2019

-0.1-0.7

Jul 2019

0.0-0.9

Aug 2019

-0.1-1.4

Sep 2019

0.10.3

Oct 2019

0.20.0

Nov 2019

-0.20.4

Dec 2019

0.3-0.4

Jan 2020

-0.1-2.7

Feb 2020

0.00.3

Mar 2020

-0.8-0.6

Apr 2020

-14.5-25.3

May 2020

3.16.7

Jun 2020

6.314.1

Jul 2020

1.74.3

Aug 2020

1.72.3

Sep 2020

0.2-0.8

Oct 2020

0.70.2

Nov 2020

0.00.7

Dec 2020

0.2-0.6

Jan 2021

0.1-0.3

Feb 2021

0.10.5

Mar 2021

0.30.1

Apr 2021

-0.10.2

May 2021

0.40.9

Jun 2021

0.61.3

Jul 2021

0.00.3

Aug 2021

0.1-0.5

Sep 2021

0.40.5

Oct 2021

0.2-0.2
Indexes for labor productivity, hours worked, and output in retail trade
YearLabor productivityHours workedOutput

2007

100.000100.000100.000

2008

97.76597.65895.475

2009

98.29492.03290.461

2010

100.69492.66793.310

2011

101.39794.68696.008

2012

103.65595.67399.170

2013

108.08095.212102.905

2014

109.91997.268106.916

2015

113.48698.821112.148

2016

118.52598.636116.908

2017

120.71999.896120.593

2018

124.39399.783124.123

2019

130.36098.139127.934

2020

140.39294.650132.880
Average hours spent watching TV and playing sports, weekend days and holidays, 2019
ActivityHours

Watching TV (average of population)

3.36

Watching TV (average of those who watched TV)

4.17

Playing sports (average of population)

0.34

Playing sports (average of those who played sports)

1.94
Consumer Price Indexes for food at home and food away from home
MonthFood at homeFood away from home

Jan 2018

100.000100.000

Feb 2018

99.793100.243

Mar 2018

99.780100.352

Apr 2018

100.026100.594

May 2018

99.779100.929

Jun 2018

99.865101.113

Jul 2018

100.127101.229

Aug 2018

100.198101.421

Sep 2018

100.252101.645

Oct 2018

100.046101.738

Nov 2018

100.259102.029

Dec 2018

100.554102.437

Jan 2019

100.683102.789

Feb 2019

101.014103.153

Mar 2019

101.163103.342

Apr 2019

100.716103.676

May 2019

100.913103.894

Jun 2019

100.718104.232

Jul 2019

100.716104.443

Aug 2019

100.654104.669

Sep 2019

100.902104.940

Oct 2019

101.124105.139

Nov 2019

101.324105.310

Dec 2019

101.331105.611

Jan 2020

101.440106.000

Feb 2020

101.851106.236

Mar 2020

102.220106.395

Apr 2020

104.775106.550

May 2020

105.718106.942

Jun 2020

106.309107.496

Jul 2020

105.343108.002

Aug 2020

105.322108.309

Sep 2020

105.051108.911

Oct 2020

105.177109.210

Nov 2020

105.012109.342

Dec 2020

105.335109.751

Jan 2021

105.203110.122

Feb 2021

105.474110.180

Mar 2021

105.587110.311

Apr 2021

106.047110.649

May 2021

106.423111.258

Jun 2021

107.309112.047

Jul 2021

108.031112.923

Aug 2021

108.431113.405

Sep 2021

109.779114.013

Oct 2021

110.841114.965
Import price index for television and video receivers
MonthIndex

Jan 2011

100.000

Feb 2011

100.173

Mar 2011

100.173

Apr 2011

99.136

May 2011

98.964

Jun 2011

97.409

Jul 2011

97.064

Aug 2011

96.373

Sep 2011

95.855

Oct 2011

94.991

Nov 2011

93.092

Dec 2011

94.128

Jan 2012

94.819

Feb 2012

94.473

Mar 2012

93.955

Apr 2012

92.573

May 2012

92.573

Jun 2012

92.401

Jul 2012

92.401

Aug 2012

92.573

Sep 2012

92.228

Oct 2012

92.573

Nov 2012

90.155

Dec 2012

90.155

Jan 2013

89.810

Feb 2013

89.637

Mar 2013

88.256

Apr 2013

88.083

May 2013

87.910

Jun 2013

87.910

Jul 2013

87.392

Aug 2013

87.219

Sep 2013

85.838

Oct 2013

85.492

Nov 2013

85.492

Dec 2013

85.492

Jan 2014

85.320

Feb 2014

85.320

Mar 2014

85.147

Apr 2014

84.801

May 2014

84.283

Jun 2014

84.111

Jul 2014

83.074

Aug 2014

82.902

Sep 2014

83.074

Oct 2014

81.865

Nov 2014

81.865

Dec 2014

81.347

Jan 2015

79.965

Feb 2015

79.965

Mar 2015

79.965

Apr 2015

79.965

May 2015

79.620

Jun 2015

79.620

Jul 2015

79.620

Aug 2015

79.620

Sep 2015

79.620

Oct 2015

79.447

Nov 2015

79.275

Dec 2015

78.929

Jan 2016

78.756

Feb 2016

77.547

Mar 2016

77.375

Apr 2016

77.029

May 2016

76.857

Jun 2016

77.029

Jul 2016

76.857

Aug 2016

76.684

Sep 2016

76.684

Oct 2016

76.684

Nov 2016

76.684

Dec 2016

76.684

Jan 2017

76.166

Feb 2017

76.166

Mar 2017

75.820

Apr 2017

75.993

May 2017

75.993

Jun 2017

75.993

Jul 2017

75.993

Aug 2017

75.993

Sep 2017

75.820

Oct 2017

75.475

Nov 2017

75.302

Dec 2017

75.130

Jan 2018

75.130

Feb 2018

75.302

Mar 2018

74.784

Apr 2018

74.439

May 2018

74.266

Jun 2018

73.575

Jul 2018

72.884

Aug 2018

72.884

Sep 2018

72.712

Oct 2018

72.539

Nov 2018

72.366

Dec 2018

72.021

Jan 2019

71.330

Feb 2019

70.812

Mar 2019

70.466

Apr 2019

70.466

May 2019

70.294

Jun 2019

69.948

Jul 2019

69.775

Aug 2019

69.603

Sep 2019

69.603

Oct 2019

69.430

Nov 2019

69.085

Dec 2019

68.912

Jan 2020

69.430

Feb 2020

68.048

Mar 2020

67.358

Apr 2020

66.839

May 2020

66.667

Jun 2020

66.667

Jul 2020

66.494

Aug 2020

66.494

Sep 2020

66.321

Oct 2020

66.667

Nov 2020

67.358

Dec 2020

68.048

Jan 2021

68.739

Feb 2021

68.739

Mar 2021

68.566

Apr 2021

69.775

May 2021

70.639

Jun 2021

70.812

Jul 2021

73.402

Aug 2021

73.402

Sep 2021

74.439

Oct 2021

74.784
Quits, layoffs and discharges, and other job separations
MonthQuitsLayoffs and dischargesOther separations

Jan 2019

3,521,0001,689,000301,000

Feb 2019

3,543,0001,769,000353,000

Mar 2019

3,524,0001,721,000331,000

Apr 2019

3,494,0001,954,000313,000

May 2019

3,487,0001,776,000307,000

Jun 2019

3,527,0001,771,000316,000

Jul 2019

3,627,0001,826,000344,000

Aug 2019

3,591,0001,825,000306,000

Sep 2019

3,449,0001,982,000345,000

Oct 2019

3,414,0001,793,000359,000

Nov 2019

3,482,0001,788,000374,000

Dec 2019

3,487,0001,952,000354,000

Jan 2020

3,568,0001,788,000358,000

Feb 2020

3,430,0001,953,000332,000

Mar 2020

2,902,00013,046,000360,000

Apr 2020

2,107,0009,307,000368,000

May 2020

2,206,0002,096,000316,000

Jun 2020

2,646,0002,204,000331,000

Jul 2020

3,182,0001,845,000365,000

Aug 2020

2,987,0001,573,000342,000

Sep 2020

3,307,0001,555,000373,000

Oct 2020

3,352,0001,728,000347,000

Nov 2020

3,296,0002,123,000325,000

Dec 2020

3,407,0001,823,000352,000

Jan 2021

3,306,0001,724,000294,000

Feb 2021

3,383,0001,723,000323,000

Mar 2021

3,568,0001,525,000343,000

Apr 2021

3,992,0001,450,000360,000

May 2021

3,630,0001,353,000347,000

Jun 2021

3,870,0001,354,000389,000

Jul 2021

4,028,0001,423,000341,000

Aug 2021

4,270,0001,385,000378,000

Sep 2021

4,434,0001,375,000410,000

Expanding BLS Data on Total Factor Productivity

Our data on multifactor productivity are getting a makeover. You’ll get the same great data but with a new name, “total factor productivity.” Why change the name if it’s the same data? To reach you! More web searches seek total factor productivity than multifactor productivity. That’s probably because most other countries, including our major trading partners, call it total factor productivity. We want to make it easier to find us and stop having to answer how multifactor productivity differs from total factor productivity. They’re the same thing!

Besides the name change, we will expand our annual release of trends in total factor productivity for manufacturing to include not only manufacturing, but all the major industries in the private sector. With this addition, total factor productivity measures for all private major industries of the economy will be available in our news release and the BLS database.

Back to Basics

For those new to productivity data, let’s back up a bit. What is productivity and why should we care about it?

Productivity is a measure of economic performance, often touted as the engine of a nation’s economic growth. Productivity compares the output of goods and services with the inputs used to produce them. The difference in growth rates between these two amounts—the unexplained portion—equals productivity growth. Productivity tells us how good we are at using the inputs to create the output.

Productivity growth is important because, in the long run, it accounts for a third of the growth in a nation’s output. This growth supports increased wages, profits, public sector revenue, and global competitiveness. There are two types of productivity measures produced by BLS, labor productivity and total factor productivity. They are similar, as you can see in the chart below, but they have key differences.

Labor and total factor productivity, annualized percent change, private nonfarm business, 2010–19

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

Much of the limelight goes to labor productivity, output per hour worked, which measures how many hours it takes to produce the goods and services in our economy. This measure is pretty simple to interpret and apply. If you used the same number of hours but produced more goods and services than last year, the economy became more efficient and labor productivity increased. Labor productivity increased because something besides worker hours (which we know stayed the same) contributed to the increased output. That something is productivity—the growth we didn’t account for in the calculation. With labor productivity, we only account for one input—hours worked. All the other inputs to production, like capital investment and materials, get lumped together into the unknown efficiency gains that can result from changes in technology or how work is organized.

This is where total factor productivity comes in. As the name suggests, total factor productivity measures more than just labor as an input to producing goods and services. It puts more “knowns” into the equation to help us pinpoint a more detailed story of why productivity is changing. For an industry, total factor productivity measures the output produced by a combined set of inputs: capital, labor, energy, materials, and purchased services. Total factor productivity tells us how much more output can be produced without increasing any of these inputs. The more efficiently an industry uses its inputs to create output, the faster total factor productivity will grow.

Total factor productivity gives us great insight into what drives economic growth. Is it the industries’ choice of capital investment? Better or more skilled labor? Or is it a change to the other factors of production, such as energy expenditures, materials consumed, or services purchased, or more efficient use of these inputs? The more detail with which we measure an industry, the more we can learn how these choices contribute to growth in this industry and ultimately our economy.

Let’s recap what we know:

Total factor productivity = output ÷ (combined inputs of capital, labor, energy, materials, and services)

And if we rearrange this equation and transform it to growth over time, we can see that increasing total factor productivity is a way to increase our nation’s output growth.

Output growth = total factor productivity growth + combined inputs growth

More is More

Previously, the annual release on Multifactor Productivity Trends in Manufacturing brought you information on the manufacturing sector and its 19 detailed industries. The manufacturing sector has often been a pioneer of technological development that drives productivity growth and is thus an important sector of the nation’s economy. You can see just how big of a role it has played in productivity growth in The Economics Daily.

And now we are providing a more complete picture. Not only will you get the first comprehensive look into what the COVID-19 pandemic in 2020 meant for labor, capital, and more, but we also will include all major industries and not just manufacturing. The chart below gives a taste of the expanded information that we will now include in the reimagined release with a new name. For example, we can see that in 2019, the information industry had strong output growth (third highest), stemming mostly from combined inputs growth and total factor productivity growth (those things that are harder to measure).

Percent change in total factor productivity, combined inputs, and output, by major private industry, 2019

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

Connecting Total Factor Productivity to Labor Productivity

We can use total factor productivity and combined inputs for more than just an explanation of output growth. These measures give us is a way to break down the growth of labor productivity. We are the Bureau of Labor Statistics after all, so using our data to understand the growth in efficiency of the nation’s workforce is important.

We can express labor productivity growth as the sum of the growth of six components:

  • Total factor productivity
  • Contribution of capital intensity
  • Contribution of labor composition (shifts in the age, education, and gender composition of the workforce)
  • Contribution of energy
  • Contribution of materials
  • Contribution of purchased services

The contribution of each input is the ratio of the services provided by that input to hours worked. When we look at the contribution of each input, we can measure the effect of increasing the use of that input on an industry’s labor productivity.

The chart below shows sources of labor productivity in 2019 for each industry. The information industry had the second largest increase in labor productivity, rising 5.9 percent. That increase was driven by an increase in capital of 2.8 percent and total factor productivity growth of 1.5 percent. Knowing what drives productivity helps businesses make better decisions and pass those efficiencies on to workers and customers.

Sources of labor productivity change (in percentage points) by major private industry, 2019

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

We will release new annual data for Total Factor Productivity in 2020 on November 18, 2021, at 10:00 a.m. Eastern Time. More detailed industry data are also available. For more information on productivity check, out our Productivity page. Want to help us improve our productivity data and publications? Please fill out our 10-minute survey by November 16, 2021.

Labor and total factor productivity, annualized percent change, private nonfarm business, 2010–19
YearLabor ProductivityTotal Factor Productivity

2010

3.4%2.7%

2011

-0.1-0.1

2012

0.80.7

2013

0.50.1

2014

0.80.6

2015

1.61.1

2016

0.4-0.4

2017

1.20.5

2018

1.50.9

2019

1.80.7
Percent change in total factor productivity, combined inputs, and output, by major private industry, 2019
IndustryOutputCombined inputsTotal Factor Productivity

Mining

6.7%2.0%4.6%

Management of companies

6.32.33.9

Information

5.74.21.5

Professional and technical services

3.72.21.5

Administrative and waste services

3.52.41.1

Real estate and rental and leasing

2.82.40.4

Accommodation and food services

2.82.10.7

Retail trade

2.30.51.7

Health care and social assistance

2.31.70.7

Transportation and warehousing

2.12.10.0

Arts, entertainment, and recreation

2.10.51.5

Finance and insurance

1.93.0-1.1

Agriculture, forestry, fishing, and hunting

0.50.8-0.3

Educational services

0.10.6-0.5

Construction

-0.70.7-1.4

Other services, except government

-0.8-2.21.3

Utilities

-1.1-4.63.6

Nondurable manufacturing

-1.60.3-1.9

Manufacturing

-1.70.3-2.0

Durable manufacturing

-1.80.1-1.9

Wholesale trade

-2.10.0-2.1
Sources of labor productivity change (in percentage points) by major private industry, 2019
IndustryServices intensityMaterials intensityEnergy intensityLabor compositionCapital intensityTotal Factor Productivity

Management of companies

1.9-0.10.00.50.03.9

Information

1.40.10.00.12.81.5

Mining

1.1-0.7-0.10.5-0.64.6

Arts, entertainment, and recreation

1.70.00.0-0.21.11.5

Administrative and waste services

1.80.30.00.30.31.1

Retail trade

1.70.0-0.10.00.51.7

Accommodation and food services

0.8-0.1-0.20.20.00.7

Finance and insurance

1.60.00.00.10.9-1.1

Professional and technical services

-0.1-0.20.0-0.10.31.5

Health care and social assistance

0.1-0.3-0.10.20.10.7

Real estate and rental and leasing

0.5-0.1-0.20.00.00.4

Utilities

-1.40.2-3.30.01.63.6

Other services, except government

-0.8-0.3-0.10.00.11.3

Agriculture, forestry, fishing, and hunting

0.00.4-0.30.1-0.3-0.3

Nondurable manufacturing

0.3-0.1-0.10.10.6-1.9

Manufacturing

0.00.1-0.20.00.5-2.0

Durable manufacturing

-0.20.1-0.10.00.4-1.9

Transportation and warehousing

-1.20.3-1.0-0.3-0.10.0

Wholesale trade

-1.00.0-0.10.10.7-2.1

Construction

-0.3-1.3-0.20.00.1-1.4

Educational services

-2.20.2-0.4-0.1-0.1-2.2

BLS Data in More Than a Century of Pictures

BLS was established in 1884, and some of our programs date back nearly that far. We have more than a century of statistics on prices, employment, wages, productivity, and more. But even in those early days, we realized that pages full of numbers can be a little dull. We frequently use pictures to tell the stories behind those numbers and help readers see the important points more quickly. Let’s look at over a century of BLS price statistics, in five charts.

The first chart, which looks hand drawn, was originally presented as part of the Department of Labor’s exhibit at the Century of Progress International Exposition in Chicago in 1933, also known as the Chicago World’s Fair.

Poster for Century of Progress International Exposition in Chicago in 1933

The chart below depicts changes in the cost of living from 1913 to 1932, based on the BLS Consumer Price Index. Here we see market baskets (with legs) rising during World War I, then declining and holding steady during the roaring 1920s, and declining as the nation entered the Great Depression.

Chart showing changes in cost of living from 1913 to 1932, based on the Consumer Price Index

Source: What Are Labor Statistics For? A Series of Pictorial Charts, Bulletin of the United States Bureau of Labor Statistics, No. 599, published in 1933.

The next chart, again looking hand drawn – this time perhaps with a ruler – compares wholesale prices (what we now call the Producer Price Index) in the years leading up to the United States entering World War I and World War II. It comes from the first of two BLS bulletins on Wartime Prices. The increase in the wholesale price for all commodities was nearly twice as great in the earlier period, reflecting large differences in the price change for such commodities as fuel and chemicals.

Chart showing percent changes in wholesale prices for commodities in World War 1 and World War 2

Source: Wartime Prices, Bulletin of the United States Bureau of Labor Statistics, No. 749, published in 1944.

Now, let’s move forward about 20 years. BLS published a chart book in 1963 focusing on price changes over the prior decade. The chart book presented both consumer and wholesale prices for the nation, along with consumer price trends in the 12 largest U.S. cities. The chart shown here, perhaps produced on an early computer, tracks the change in prices for all consumer items, and separately for various categories. Prices for durable commodities, such as appliances and furniture, declined in the early part of the period and later rebounded, resulting in virtually no price change over the decade. In contrast, the price of services, such as shelter, transportation, and medical care, rose steadily throughout the period.

Chart showing changes in consumer prices, 1953 to 1962

Source: Prices: A Chartbook, 1953–62, Bulletin of the United States Bureau of Labor Statistics, No. 1351, published in 1963.

With advances in computer software, BLS expanded the use of charts to allow readers to visualize data trends. Such charts became prominent in the BLS flagship publication, the Monthly Labor Review. In an article from 1987, data from the BLS International Price Program track price changes for selected imports.

Chart showing changes in U.S. Import Price Indexes for machinery and transportation equipment and intermediate manufactures, 1982 to 1986

Source: “Import price declines in 1986 reflected reduced oil prices,” Monthly Labor Review, April 1987.

BLS ushered in the age of interactive charts in recent years, making chart packages available with most news releases. In the chart below, readers can track a decade of consumer price changes for all items, and then click on selected categories to compare trends. Want to compare price changes for food at home with food away from home? It’s just a couple of clicks away.

Chart showing 12-month changes in the Consumer Price Index, August 2001 to August 2021

Source: Charts related to the Consumer Price Index news release.

Our charts today are a lot more sophisticated than the hand-drawn charts of the early twentieth century. They may not have amusing cartoon characters like the CPI market basket with legs, but they have interactive features that let you dig into more details about the data or choose the data you want to see. We also have several publications that focus on the visual display of data. Check out The Economics Daily and Spotlight on Statistics!

It’s a Small Statistical World

BLS is one of several U.S. statistical agencies that follow consistent policies and share best practices. These agencies also frequently work with their statistical counterparts around the world to develop standards, share information, troubleshoot issues, and improve the quality of available data. At BLS, our Division of International Technical Cooperation coordinates these activities. The division helps to strengthen statistical development by organizing seminars, consultations, and meetings for international visitors with BLS staff. The division also provides BLS input on global statistical initiatives. Without missing a beat, most of these activities moved to virtual platforms during the COVID-19 pandemic. Despite some time-zone challenges, which often lead to early morning or late-night video meetings, BLS continues to play an active role on the world stage.

World map

Today I’m highlighting some recent international engagements, which have included our colleagues from Australia, Canada, France, Greece, Italy, Mexico, South Korea, and the United Kingdom. These events are often mutually beneficial, as they provide opportunities for BLS staff to learn more about the experiences of our international counterparts.

  • BLS staff met with a former Australian Bureau of Statistics official who was working with the U.K. Statistics Authority and the U.K. Office for National Statistics to research best practices in implementing international statistical standards. They discussed the international comparability of domestic industry and product classifications, data quality and publishing, and the independence of statistical organizations.
  • Staff from the Australian Bureau of Statistics are planning to revise their household expenditure survey. They turned to BLS experts, who shared their insights and experiences in improving our Consumer Expenditure Surveys.
  • Staff from the Statistical Division at the United Nations asked BLS to comment on issues surrounding the classification of business functions; household income, consumption, and wealth; and unpaid household service work. Input from staff in multiple offices will inform the BLS response to this request.
  • BLS staff, our counterparts in Canada and Mexico, and colleagues from across Europe and Asia discussed data ethics in a meeting organized by the Centre for Applied Data Ethics at the U.K. Statistics Authority. Country representatives summarized how their organizations assess ethical considerations when producing official statistics. The U.K. Statistics Authority identified the following ethical considerations as being especially important:
Public Good: The use of data has clear benefits for users and serves the public good.
Confidentiality, Data Security: The data subject's identity (whether person or organisation) is protected, information is kept confidential and secure, and the issue of consent is considered appropriately.
Methods and Quality: The risks and limits of new technologies are considered and there is sufficient human oversight so that methods employed are consistent with recognised standards of integrity and quality.
Legal Compliance: Data used and methods employed are consistent with legal requirements.
Public Views and Engagement: The views of the public are considered in light of the data used and the perceived benefits of the research.
Transparency: The access, use and sharing of data is transparent, and is communicated clearly and accessibly to the public.

From its founding, BLS has understood the importance of these issues. Our written policies and strategic plans reflect these principles. They also are reflected in the Foundations for Evidence-Based Policymaking Act and the newly formed Scientific Integrity Task Force, which includes BLS staff among its members.

And that’s just some of what we did this summer! BLS has a longstanding reputation for providing expert training and guidance and participating in international statistical forums. We also provide BLS data to the International Labour Organization and the Organisation for Economic Cooperation and Development, among others. These organizations often feature BLS statistics in their databases. Since its inception, BLS has provided technical assistance to our international counterparts, starting with our first Commissioner, Carroll Wright, who directed BLS staff to advise foreign governments establishing statistical agencies. Commissioner Wright was also a member of several international statistical associations, a tradition that continues today. Currently, BLS staff participate in many international expert groups, including the Voorburg Group on Service Statistics, the Wiesbaden Group on Business Registers, and the International Conference of Labor Statisticians. These groups provide BLS staff with opportunities to discuss topics of common interest, to propose and learn about innovative solutions to data measurement issues, and to influence discussions about important economic concepts.

BLS began providing technical assistance in earnest in the late 1940s as part of the U.S. government’s European Economic Recovery Program. BLS staff planned and conducted productivity studies and helped European governments establish their own economic statistics. Similar efforts continue today for our colleagues around the world, many of whom have participated in our international training programs. While we have temporarily halted in-person training programs because of the pandemic, our staff plan to provide more training modules virtually in response to the popularity of these programs. Over the last 10 years, BLS has provided training or other technical assistance to over 1,700 seminar participants and other visitors from 95 countries. More recently, the International Monetary Fund has asked BLS to provide training on Producer Price Indexes and Import and Export Price Indexes to our colleagues abroad.

I am incredibly grateful to all the subject matter experts throughout BLS who provide invaluable assistance with these activities and help maintain our excellent reputation in the international statistical community. We look forward to your continued support as BLS strengthens important international relationships, virtually for now, and hopefully in person soon.

A Labor Day Look at How American Workers Have Changed over 40 Years

Forty years ago, teenagers ages 16 to 19 made up 8 percent of all U.S. workers. By 2019, that share fell to just 3 percent. With fewer teenagers working, the face of American labor looks much different today than it did when the Bee Gees ruled the American pop charts.

Happy Labor Day! The U.S. workforce has been changing over many generations. It’s been changing with respect to the work people do. For example, an increasing share of workers is engaged in service or technology work, while a decreasing share is engaged in factory or farm work. My focus today, however, is on the people who do the work.

Here at BLS, we spend a great deal of time and effort measuring and reporting on employment. How many jobs are there this month? What kind of jobs? But as Labor Day approaches, I’d like to shift the focus away from employment and jobs and toward labor itself. Who are the people holding down the jobs that we count? What is the face of American labor? And how has labor’s profile changed—and yes, it has changed—over a generation or more?

So today I’m not going to say much about what jobs workers hold or what their jobs pay. Instead, I’ll focus on more personal characteristics of the people who hold the jobs—characteristics that are not a function of workers’ jobs, but that are intrinsic to the workers themselves. Is America’s employed population getting older or younger? Are African Americans, Hispanics and Latinos, Asians, and other groups making up an increasing share of employment? And so forth. Call it the “composition” of America’s employed population. To examine this, I’ll be using data from the Current Population Survey, or CPS, which is a large, monthly survey of many thousands of U.S. households.

BLS collects data directly from lots of employers, such as businesses and state and local governments. This data collection is behind our monthly news release about how many jobs were added or lost in the U.S. economy. It gives us a vital, current, and accurate picture of work in America—but not of workers.

To learn more about workers, rather than about just their jobs, we can’t ask their employers. We have to ask workers themselves. BLS partners with the U.S. Census Bureau each month to survey some 60,000 U.S. households about their work and other topics. We can learn at least three important things by surveying workers that we can’t learn by surveying employers. First, we can learn about things like self-employment, multiple jobholding, and “alternative” work arrangements, like so-called “gig” work. Second, we can learn about people who are not currently employed. In fact, BLS uses these data each month to measure how many are “unemployed,” roughly meaning they are actively looking for a job and available to start. Third, and most relevant here, we can learn about people’s personal characteristics—things like their age, race, and marital status, which their employers might not know or might find hard to detail in a BLS survey.

Let’s look at data from the CPS to explore how the personal characteristics of America’s employed population have shifted. I’ll share some of my own favorite nuggets of information, which I think you’ll find interesting. I’ll mostly compare 1979 with 2019—a 40-year span that roughly coincides with two peaks in U.S. employment and economic activity. The comparisons would look similar if we looked at 2020 or today, but I think the long-term trends are better understood “peak to peak” than in comparison to the more recent but very unusual COVID-19 economy. Along the way I’ll link to some BLS resources that go deeper into these topics. Let’s dive in!

Where have the teenagers gone? In 1979, 8 percent of U.S. workers were ages 16 to 19. By 2019, just 3 percent were. Over the same 40-year period, the share that were ages 16 to 24 fell from 23 percent to 12 percent. Two things happened. First, the age composition of the entire population shifted. In 1979, the tail end of the large, post-World War II “baby boom” generation was about 16 years old. The generation that came after this group was smaller, so its share of the workforce was smaller too. Second, young people’s “participation rate”—the share that were working or seeking work—declined. In fact, that rate peaked at 58 percent in 1979, then fell to 34 percent by 2011. This huge change coincided with increases in school enrollment and educational attainment. This example illustrates how two forces combine to reshape the face of American labor: the shifting composition of the working age population, and shifts in participation rates of different groups.

The American workforce has aged. Between 1979 and 2019, the fraction of the employed population that is 65 years old or older grew from 3 percent to 7 percent. The share that is 55 or older grew from 15 percent to 24 percent. Forces behind this trend include the aging of baby boomers (they are mostly 60 or older today), medical and other advances that have extended lives and health, and less physically strenuous jobs. The participation rate story is a little more complicated: As a group, today’s older women always were more likely to work for pay than their mothers or grandmothers were. Participation among older men, in contrast, first ebbed and then rebounded across these 40 years.

Percent of employed people by age, 1979–2019 annual averages

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

There are more working women. By 1979, women’s share of the employed population, at 42 percent, had already been growing for some time; it was up from just 28 percent in 1948. It kept growing for about 20 more years, before leveling off at around 47 percent by 2000 and remaining there through 2019.

What’s love got to do with it? Between 1979 and 2019, the trend in marital status was more pronounced than the trend in gender. The fraction of all workers who were unmarried grew from 36 percent to 48 percent. This trend was sharper among men (31 percent to 45 percent) than women (44 percent to 51 percent).

Percent of employed people by sex and marital status, 1979 and 2019 annual averages

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

Racial and ethnic diversity is changing too. The U.S. population has long been very diverse, shaped by colonization, slavery and emancipation, and migration. Over the last 40 years, workforce diversity has been shaped mostly by immigration and by differences in fertility among racial and ethnic groups. Between 1979 and 2019 the non-White fraction of all U.S. workers grew from 12 percent to 22 percent. The fraction who are Hispanic or Latino (who may be of any race) grew from 5 percent to 18 percent. A note of caution: the survey questions about race and ethnicity changed over the years, and this might skew the measurements a little, but not enough to change the story that non-Whites and Hispanics and Latinos represent a growing share of employed people. The latest survey questions provide lots of detail about diversity today.

Percent of employed people by race and Hispanic or Latino ethnicity, 1979 and 2019 annual averages

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

The more you survey, the more you know. Changes in the CPS and other surveys affect more than just our measures of labor diversity. Of course, we can’t measure everything all the time. Big household surveys can be expensive for taxpayers and burdensome for the thousands of households who answer the long questionnaires. Over time, we may change how we think about who we are and what we do, so survey questions must change as well. New survey questions can inform us better about where we are today, but they can make it harder to compare conditions over time. For example, beginning in 1992, the CPS questions about educational attainment were changed to emphasize degrees earned rather than years of school completed. We know that the fraction of U.S. workers age 25 or older who had a bachelor’s degree or higher grew from 27 percent in 1992 to 42 percent in 2019, but we don’t know for sure what percentage of the workforce was this educated in 1979.

The CPS has its roots in a tough time for American labor. The Great Depression of the 1930s brought mass unemployment to the United States. Back then, there was no sure way to measure the problem or track progress toward recovery. By the end of the 1930s, the U.S. launched the monthly household survey that today we call the CPS. The survey has gone through many changes, but it has measured unemployment each month since then. For economists like me, the history of the CPS is almost as interesting as the history of American labor. If you happen to be an economist or statistician yourself, BLS and the U.S. Census Bureau can tell you all you need to know about this great source of information. But not today – it’s Labor Day! Save the technical stuff for after the celebration.

Percent of employed people by age, 1979–2019 annual averages
YearAges 16–19Ages 20–24Ages 25–54Ages 55–64Age 65 and older

1979

8.2%14.5%62.6%11.7%3.0%

1980

7.814.263.411.73.0

1981

7.214.164.311.52.9

1982

6.613.865.311.52.9

1983

6.313.666.011.22.9

1984

6.113.566.810.92.7

1985

6.013.067.610.72.6

1986

5.912.668.410.42.7

1987

5.912.069.210.22.7

1988

5.911.569.89.92.8

1989

5.811.070.59.82.9

1990

5.511.370.99.42.8

1991

5.011.071.89.32.8

1992

4.810.972.39.32.8

1993

4.810.772.59.22.8

1994

5.010.472.59.13.0

1995

5.110.072.89.22.9

1996

5.19.673.19.32.9

1997

5.19.672.99.52.9

1998

5.49.672.59.82.8

1999

5.49.772.110.02.9

2000

5.39.771.810.23.1

2001

4.99.771.510.73.1

2002

4.69.870.911.53.2

2003

4.39.870.612.13.3

2004

4.29.970.012.43.5

2005

4.29.769.512.93.6

2006

4.39.669.013.43.7

2007

4.09.668.813.83.8

2008

3.89.468.414.34.1

2009

3.59.168.015.04.4

2010

3.19.167.715.64.5

2011

3.19.367.015.94.8

2012

3.19.466.116.35.1

2013

3.19.465.616.55.3

2014

3.19.565.316.75.4

2015

3.29.464.916.85.7

2016

3.39.364.716.95.9

2017

3.39.264.517.06.0

2018

3.39.064.417.16.2

2019

3.39.064.117.16.6

2020

3.28.564.517.26.6
Percent of employed people by sex and marital status, 1979 and 2019 annual averages
Marital status1979, total2019, total1979, men2019, men1979, women2019, women

Married, spouse present

63.6%52.3%69.0%55.0%56.1%49.1%

Never married

24.132.323.332.625.231.9

Widowed, divorced, and separated

12.315.57.712.318.719.0
Percent of employed people by race and Hispanic or Latino ethnicity, 1979 and 2019 annual averages
YearWhiteNot WhiteHispanic or Latino

1979

88.3%11.7%4.8%

2019

77.722.317.6