All posts by BLS Commissioner

You Paid HOW MUCH for a Ticket to the Game?

I hear there is a big game coming up soon, and ticket prices are pretty high (and I’m not talking about the Washington Nationals’ Opening Day). So I reached out to the BLS experts on the Consumer Price Index (CPI) to learn a little more about how we handle the change in the price of sporting event tickets. We hear the face value of a ticket to a certain high-profile game is X, but then we hear the cost on the secondary market is Y (often several times X). But what if the game features a major-market team or a high-profile player, versus a game with less well-known teams or players? What if heavy rains are predicted for an outdoor game, or the halftime entertainer has recently encountered some social-media scandal? How can the CPI account for such differences when determining the rate of change in the price of a ticket?

What I learned

It turns out that BLS has experts in the price of sporting event tickets, and the experts know lots of answers—and know more questions we could explore. BLS even has detailed instructions for determining the price of tickets to sporting events—17 pages of instructions!

Sporting events are a subset of the CPI item category “Admissions,” which also includes movies and concerts. Here’s a chart showing the annual rate of change in the price of admission to sporting events over the past 20 years.

Over-the-year percent change in the Consumer Price Index for admission to sporting events, 1999–2019

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

Constant quality

A basic principle behind the CPI is what’s known as the matched model. That is, we want to determine the price of comparable (matched) items from one time period to the next. The fancy CPI term for this is constant quality. This means we have to carefully identify the characteristics of the item we choose to study, with the goal of keeping those characteristics constant each time we determine the price.

The source of tickets included in the CPI sample can include both individual sports franchises (such as the Mudville nine) and ticket resellers. In all cases we want to determine the price of a ticket based on a variety of price-determining factors that we hope to hold constant over time. For example:

  • Admission type, such as adult versus child, but also individual tickets versus season tickets or a multigame ticket plan. When we follow the price of season tickets, we do so during a limited time of the year. For example, we might follow the price of baseball season tickets in the fall, when they are typically marketed, and then again the next fall.
  • Seat type, such as box seats versus those in the nosebleed section. Ideally, we want to follow the price for the same seat (or same section) from one period to the next.
  • Other factors, such as weekday versus weekend games or day versus night games.

We include professional, college, and high school sporting events in our sample. We see much less variation in the price of high school games than in the upper ranks. And we include both preseason (exhibition) and regular season games.

To get back to the original question, what about playoff games? We do not follow the price of tickets for most professional sports playoff games because the venue is not guaranteed to host a playoff game every year. To be included in the CPI, the requirement is for the venue to be constant—for example, certain college bowl games held in the same venue every year.

Are there other price-determining characteristics that can be tracked? One that CPI uses when appropriate is the type of opponent. Some ticket prices vary by type of opponent, such as a premium game against a conference opponent or traditional rival versus a nonpremium game. Such a distinction may be difficult to identify, however.

Quality adjustment

While we take care to select items that can be tracked over time, sometimes the price-determining characteristics change. Suppose the partial season ticket package that we first identified included 20 games, but the following year a comparable package included only 18 games. We consider this to be a quality change, something we don’t want to include when comparing prices.

We have a couple options for handling quality change. We can drop the item from collection and replace it with a new item. We determine the price of the new item and then track the change in that price going forward. This may be done for tickets to sporting events; if we can’t determine the price of a comparable ticket to one we examined previously, the item may be dropped from the index and replaced.

Alternatively, we can make a quality adjustment to account for changes in quality. In the case of the season ticket package where the number of games changes but all else is the same, we may be able to use simple math to make the old and new prices comparable.

For other items that change frequently, such as apparel and electronics, quality adjustment can be more complex. If you are a price index methodology geek, read the following to learn more about quality adjustment. Or, since we’ve carefully indented it, you can easily skip it.

The CPI uses hedonic quality adjustment, the practice of decomposing an item into its constituent characteristics, obtaining estimates of the value of the utility derived from each characteristic, and using those value estimates to adjust prices when the quality of a good changes. The CPI obtains the value estimates used to adjust prices through the statistical technique known as regression analysis. Hedonic regression models are estimated to determine the value of the utility derived from each of the characteristics that jointly constitute an item.

OK, back to things we all understand, like how the price of tickets to sporting events might vary. Could we consider making other quality adjustments for ticket prices? There are a variety of features that might be part of that equation, such as:

  • The quality of the opponent. (Is their record 12-3 or 3-12?)
  • The quality of the home team. A team having a poor season might have declining attendance as the season progresses, perhaps lowering ticket prices. A contender, conversely, may see rising prices as the season progresses.
  • The weather on game day.

BLS actually has some index theorists working right now to investigate whether it is possible to develop a hedonic model to address some of these issues. But that’s for another today. For now, let’s just enjoy the game.

Before I go, let’s look at one more question you may be wondering about: How do price trends for admission to sporting events over the past 20 years compare to prices for all items? If you guessed that it’s more expensive to attend a sporting event, you’re right. Since 1999, prices for admission to sporting events have grown more than twice as fast as overall consumer prices. See the chart below.

Consumer Price Indexes for admission to sporting events and all items, 1999–2019

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

Over-the-year percent change in the Consumer Price Index for admission to sporting events
Month12-month percent changeTrend line

Dec 1999

7.3%6.1%

Jan 2000

6.56.2

Feb 2000

5.36.2

Mar 2000

5.96.3

Apr 2000

5.16.3

May 2000

4.76.2

Jun 2000

3.85.9

Jul 2000

5.25.6

Aug 2000

6.75.7

Sep 2000

6.75.7

Oct 2000

5.85.6

Nov 2000

8.96.0

Dec 2000

5.85.9

Jan 2001

5.85.8

Feb 2001

6.65.9

Mar 2001

6.05.9

Apr 2001

9.06.3

May 2001

8.96.6

Jun 2001

6.66.8

Jul 2001

5.56.9

Aug 2001

5.56.8

Sep 2001

6.86.8

Oct 2001

6.86.9

Nov 2001

6.86.7

Dec 2001

6.06.7

Jan 2002

5.86.7

Feb 2002

4.66.5

Mar 2002

5.66.5

Apr 2002

3.36.0

May 2002

2.95.5

Jun 2002

3.95.3

Jul 2002

4.75.2

Aug 2002

4.95.2

Sep 2002

4.04.9

Oct 2002

5.14.8

Nov 2002

3.34.5

Dec 2002

4.04.3

Jan 2003

4.44.2

Feb 2003

5.54.3

Mar 2003

3.24.1

Apr 2003

1.54.0

May 2003

3.44.0

Jun 2003

2.33.9

Jul 2003

0.83.5

Aug 2003

0.13.1

Sep 2003

0.72.9

Oct 2003

-0.62.4

Nov 2003

0.52.2

Dec 2003

0.71.9

Jan 2004

3.01.8

Feb 2004

2.91.5

Mar 2004

4.01.6

Apr 2004

5.01.9

May 2004

3.21.9

Jun 2004

3.52.0

Jul 2004

4.82.3

Aug 2004

5.02.7

Sep 2004

4.83.1

Oct 2004

5.83.6

Nov 2004

7.14.2

Dec 2004

6.94.7

Jan 2005

4.84.8

Feb 2005

4.24.9

Mar 2005

4.75.0

Apr 2005

4.34.9

May 2005

5.25.1

Jun 2005

8.55.5

Jul 2005

7.55.7

Aug 2005

7.45.9

Sep 2005

7.36.1

Oct 2005

7.76.3

Nov 2005

6.36.2

Dec 2005

6.46.2

Jan 2006

5.56.3

Feb 2006

5.96.4

Mar 2006

5.96.5

Apr 2006

1.76.3

May 2006

3.56.1

Jun 2006

2.75.7

Jul 2006

2.85.3

Aug 2006

3.14.9

Sep 2006

2.94.5

Oct 2006

3.14.2

Nov 2006

4.04.0

Dec 2006

3.73.7

Jan 2007

4.73.7

Feb 2007

4.13.5

Mar 2007

4.33.4

Apr 2007

9.84.1

May 2007

7.24.4

Jun 2007

4.34.5

Jul 2007

3.94.6

Aug 2007

3.24.6

Sep 2007

4.44.7

Oct 2007

4.74.9

Nov 2007

4.84.9

Dec 2007

4.75.0

Jan 2008

4.35.0

Feb 2008

4.65.0

Mar 2008

4.85.1

Apr 2008

4.64.6

May 2008

4.94.4

Jun 2008

5.44.5

Jul 2008

5.74.7

Aug 2008

7.65.0

Sep 2008

7.25.3

Oct 2008

5.55.3

Nov 2008

5.25.4

Dec 2008

5.75.5

Jan 2009

5.65.6

Feb 2009

5.15.6

Mar 2009

4.45.6

Apr 2009

1.65.3

May 2009

1.05.0

Jun 2009

2.34.7

Jul 2009

2.44.5

Aug 2009

2.34.0

Sep 2009

1.33.5

Oct 2009

1.23.2

Nov 2009

1.12.8

Dec 2009

1.02.4

Jan 2010

0.12.0

Feb 2010

0.91.6

Mar 2010

1.31.4

Apr 2010

3.71.6

May 2010

4.61.9

Jun 2010

3.92.0

Jul 2010

3.22.1

Aug 2010

2.12.0

Sep 2010

2.02.1

Oct 2010

1.72.1

Nov 2010

1.42.2

Dec 2010

0.82.1

Jan 2011

2.32.3

Feb 2011

1.62.4

Mar 2011

1.12.4

Apr 2011

0.12.1

May 2011

-0.21.7

Jun 2011

-1.41.2

Jul 2011

-2.00.8

Aug 2011

-1.30.5

Sep 2011

-0.80.3

Oct 2011

0.70.2

Nov 2011

-0.80.0

Dec 2011

-0.6-0.1

Jan 2012

1.5-0.2

Feb 2012

0.7-0.3

Mar 2012

1.3-0.2

Apr 2012

1.9-0.1

May 2012

2.40.1

Jun 2012

4.50.6

Jul 2012

5.21.2

Aug 2012

4.41.7

Sep 2012

3.72.1

Oct 2012

3.12.3

Nov 2012

4.62.7

Dec 2012

6.23.3

Jan 2013

3.23.4

Feb 2013

3.83.7

Mar 2013

3.03.8

Apr 2013

2.13.9

May 2013

2.73.9

Jun 2013

1.23.6

Jul 2013

1.63.3

Aug 2013

2.63.2

Sep 2013

3.53.1

Oct 2013

2.53.1

Nov 2013

3.03.0

Dec 2013

2.32.6

Jan 2014

3.22.6

Feb 2014

4.72.7

Mar 2014

3.52.7

Apr 2014

4.52.9

May 2014

2.62.9

Jun 2014

3.23.1

Jul 2014

3.73.3

Aug 2014

1.73.2

Sep 2014

1.33.0

Oct 2014

2.63.0

Nov 2014

2.23.0

Dec 2014

2.73.0

Jan 2015

3.63.0

Feb 2015

4.53.0

Mar 2015

7.03.3

Apr 2015

6.43.5

May 2015

9.14.0

Jun 2015

8.84.5

Jul 2015

7.24.8

Aug 2015

9.55.4

Sep 2015

7.86.0

Oct 2015

8.86.5

Nov 2015

8.17.0

Dec 2015

5.07.2

Jan 2016

8.87.6

Feb 2016

6.07.7

Mar 2016

0.07.1

Apr 2016

0.96.7

May 2016

-0.55.9

Jun 2016

2.35.3

Jul 2016

5.85.2

Aug 2016

3.94.7

Sep 2016

6.94.7

Oct 2016

4.44.3

Nov 2016

-0.33.6

Dec 2016

2.53.4

Jan 2017

2.62.9

Feb 2017

5.52.8

Mar 2017

8.23.5

Apr 2017

7.04.0

May 2017

4.94.5

Jun 2017

0.34.3

Jul 2017

-0.53.8

Aug 2017

-1.03.4

Sep 2017

-2.02.6

Oct 2017

-1.72.1

Nov 2017

2.52.4

Dec 2017

0.92.2

Jan 2018

-2.41.8

Feb 2018

-1.71.2

Mar 2018

-3.00.3

Apr 2018

-2.4-0.5

May 2018

1.2-0.8

Jun 2018

5.4-0.4

Jul 2018

3.4-0.1

Aug 2018

3.10.3

Sep 2018

3.70.8

Oct 2018

2.71.1

Nov 2018

3.61.2

Dec 2018

9.21.9

Jan 2019

7.02.7

Feb 2019

-3.42.5

Mar 2019

5.23.2

Apr 2019

7.84.1

May 2019

2.24.2

Jun 2019

-0.83.6

Jul 2019

-1.23.3

Aug 2019

0.83.1

Sep 2019

-1.32.7

Oct 2019

1.32.5

Nov 2019

5.32.7

Dec 2019

1.92.1
Consumer Price Indexes for admission to sporting events and all items
MonthAdmission to sporting eventsAll items

Dec 1999

100.000100.000

Jan 2000

100.177100.297

Feb 2000

100.177100.891

Mar 2000

101.065101.723

Apr 2000

101.686101.783

May 2000

101.952101.901

Jun 2000

103.993102.436

Jul 2000

105.679102.674

Aug 2000

106.034102.674

Sep 2000

105.324103.209

Oct 2000

105.058103.387

Nov 2000

105.413103.446

Dec 2000

105.768103.387

Jan 2001

106.034104.040

Feb 2001

106.832104.456

Mar 2001

107.098104.694

Apr 2001

110.825105.110

May 2001

111.003105.585

Jun 2001

110.825105.764

Jul 2001

111.535105.466

Aug 2001

111.890105.466

Sep 2001

112.511105.942

Oct 2001

112.156105.585

Nov 2001

112.600105.407

Dec 2001

112.156104.991

Jan 2002

112.156105.229

Feb 2002

111.713105.645

Mar 2002

113.043106.239

Apr 2002

114.463106.833

May 2002

114.197106.833

Jun 2002

115.173106.892

Jul 2002

116.770107.011

Aug 2002

117.391107.368

Sep 2002

117.036107.546

Oct 2002

117.924107.724

Nov 2002

116.327107.724

Dec 2002

116.593107.487

Jan 2003

117.125107.962

Feb 2003

117.835108.794

Mar 2003

116.681109.447

Apr 2003

116.149109.210

May 2003

118.101109.031

Jun 2003

117.835109.150

Jul 2003

117.657109.269

Aug 2003

117.480109.685

Sep 2003

117.835110.042

Oct 2003

117.214109.923

Nov 2003

116.948109.626

Dec 2003

117.391109.507

Jan 2004

120.586110.042

Feb 2004

121.207110.636

Mar 2004

121.384111.349

Apr 2004

121.917111.705

May 2004

121.828112.359

Jun 2004

121.917112.715

Jul 2004

123.336112.537

Aug 2004

123.336112.597

Sep 2004

123.514112.834

Oct 2004

124.046113.428

Nov 2004

125.288113.488

Dec 2004

125.466113.072

Jan 2005

126.353113.310

Feb 2005

126.353113.963

Mar 2005

127.063114.854

Apr 2005

127.152115.627

May 2005

128.217115.508

Jun 2005

132.298115.567

Jul 2005

132.564116.102

Aug 2005

132.476116.696

Sep 2005

132.476118.122

Oct 2005

133.629118.360

Nov 2005

133.185117.409

Dec 2005

133.452116.934

Jan 2006

133.363117.825

Feb 2006

133.807118.063

Mar 2006

134.516118.717

Apr 2006

129.281119.727

May 2006

132.653120.321

Jun 2006

135.936120.559

Jul 2006

136.291120.915

Aug 2006

136.646121.153

Sep 2006

136.291120.559

Oct 2006

137.799119.905

Nov 2006

138.509119.727

Dec 2006

138.421119.905

Jan 2007

139.608120.271

Feb 2007

139.238120.914

Mar 2007

140.280122.015

Apr 2007

142.013122.808

May 2007

142.248123.559

Jun 2007

141.714123.798

Jul 2007

141.659123.766

Aug 2007

141.075123.540

Sep 2007

142.327123.880

Oct 2007

144.292124.145

Nov 2007

145.193124.882

Dec 2007

144.960124.799

Jan 2008

145.623125.419

Feb 2008

145.642125.783

Mar 2008

147.063126.873

Apr 2008

148.610127.643

May 2008

149.190128.718

Jun 2008

149.368130.015

Jul 2008

149.803130.698

Aug 2008

151.776130.176

Sep 2008

152.563129.996

Oct 2008

152.175128.683

Nov 2008

152.741126.218

Dec 2008

153.213124.913

Jan 2009

153.806125.456

Feb 2009

153.136126.080

Mar 2009

153.481126.387

Apr 2009

150.956126.702

May 2009

150.700127.068

Jun 2009

152.768128.160

Jul 2009

153.338127.957

Aug 2009

155.325128.244

Sep 2009

154.484128.324

Oct 2009

153.990128.447

Nov 2009

154.461128.538

Dec 2009

154.737128.312

Jan 2010

153.909128.750

Feb 2010

154.500128.783

Mar 2010

155.536129.311

Apr 2010

156.522129.536

May 2010

157.687129.636

Jun 2010

158.697129.510

Jul 2010

158.177129.537

Aug 2010

158.556129.716

Sep 2010

157.627129.791

Oct 2010

156.669129.953

Nov 2010

156.575130.008

Dec 2010

156.002130.231

Jan 2011

157.438130.851

Feb 2011

156.972131.497

Mar 2011

157.272132.779

Apr 2011

156.734133.634

May 2011

157.336134.263

Jun 2011

156.415134.119

Jul 2011

154.968134.238

Aug 2011

156.483134.608

Sep 2011

156.339134.812

Oct 2011

157.745134.534

Nov 2011

155.304134.421

Dec 2011

155.073134.089

Jan 2012

159.771134.679

Feb 2012

158.120135.272

Mar 2012

159.240136.299

Apr 2012

159.785136.711

May 2012

161.083136.551

Jun 2012

163.382136.351

Jul 2012

163.088136.128

Aug 2012

163.300136.886

Sep 2012

162.162137.497

Oct 2012

162.679137.443

Nov 2012

162.489136.792

Dec 2012

164.639136.424

Jan 2013

164.948136.827

Feb 2013

164.125137.948

Mar 2013

163.967138.308

Apr 2013

163.133138.165

May 2013

165.370138.411

Jun 2013

165.374138.743

Jul 2013

165.667138.797

Aug 2013

167.568138.964

Sep 2013

167.903139.126

Oct 2013

166.722138.768

Nov 2013

167.327138.484

Dec 2013

168.464138.472

Jan 2014

170.172138.988

Feb 2014

171.804139.501

Mar 2014

169.716140.400

Apr 2014

170.497140.863

May 2014

169.610141.355

Jun 2014

170.730141.618

Jul 2014

171.731141.563

Aug 2014

170.453141.326

Sep 2014

170.089141.433

Oct 2014

171.031141.077

Nov 2014

171.055140.316

Dec 2014

173.089139.520

Jan 2015

176.239138.863

Feb 2015

179.553139.466

Mar 2015

181.596140.296

Apr 2015

181.493140.582

May 2015

185.010141.298

Jun 2015

185.770141.793

Jul 2015

184.019141.803

Aug 2015

186.659141.602

Sep 2015

183.286141.381

Oct 2015

186.038141.318

Nov 2015

184.849141.020

Dec 2015

181.771140.538

Jan 2016

191.665140.770

Feb 2016

190.403140.886

Mar 2016

181.666141.493

Apr 2016

183.061142.163

May 2016

184.011142.739

Jun 2016

190.112143.207

Jul 2016

194.719142.976

Aug 2016

193.903143.107

Sep 2016

195.903143.451

Oct 2016

194.214143.630

Nov 2016

184.257143.406

Dec 2016

186.386143.453

Jan 2017

196.600144.289

Feb 2017

200.824144.743

Mar 2017

196.593144.861

Apr 2017

195.966145.291

May 2017

193.042145.415

Jun 2017

190.618145.547

Jul 2017

193.815145.446

Aug 2017

192.050145.882

Sep 2017

191.916146.654

Oct 2017

190.941146.561

Nov 2017

188.826146.565

Dec 2017

187.982146.479

Jan 2018

191.956147.277

Feb 2018

197.325147.945

Mar 2018

190.654148.279

Apr 2018

191.194148.869

May 2018

195.298149.488

Jun 2018

200.874149.726

Jul 2018

200.350149.736

Aug 2018

198.094149.819

Sep 2018

199.063149.993

Oct 2018

196.009150.258

Nov 2018

195.664149.755

Dec 2018

205.223149.277

Jan 2019

205.476149.561

Feb 2019

190.615150.194

Mar 2019

200.619151.041

Apr 2019

206.087151.841

May 2019

199.602152.164

Jun 2019

199.217152.194

Jul 2019

197.878152.449

Aug 2019

199.659152.441

Sep 2019

196.463152.560

Oct 2019

198.633152.909

Nov 2019

206.044152.827

Dec 2019

209.195152.688

Ensuring Security and Fairness in the Release of Economic Statistics

The U.S. Bureau of Labor Statistics is the gold standard of accurate, objective, relevant, timely, and accessible statistical data, and I am committed to keeping it that way. As Commissioner, it is my obligation to do everything possible to protect the integrity of our data and to make sure everyone has equitable access to these data.

One step toward equitable access and data security is coming soon; on March 1, 2020, the U.S. Department of Labor (DOL) will eliminate all electronics from the lock-up facility where we allow members of the media to review economic releases and prepare news stories before the official release of the data. We are changing the procedures to better protect our statistical information from premature disclosure and to ensure fairness in providing our information to the public.

For many years the news media have helped BLS and the Employment and Training Administration (ETA) inform the public about our data. Since the mid-1980s, BLS and ETA have provided prerelease data access to news organizations under strict embargoes, known as “lock-ups.” We have provided this early access consistent with federal Statistical Policy Directives of the Office of Management and Budget. BLS uses the lock-up for several major releases each month, including the Employment Situation and Consumer Price Index. ETA uses the lock-up for the Unemployment Insurance Weekly Claims data. These economic data have significant commercial value and may affect the movement of commodity and financial markets upon release.

Because of technological advancements, the current lock-up procedure creates an unfair competitive advantage for lock-up participants who provide BLS data to trading companies. Today, the internet permits anyone in the world to obtain economic releases for themselves directly from the BLS or DOL websites. However, unlike media organizations with computer access in the current lock-up, others who use the data do not have up to 30 minutes before the official release to process the data. Their postings about the data may lag behind those released directly from the lock-up at official publication time, 8:30 a.m. Eastern. High-speed algorithmic trading technology now gives a notable competitive advantage to market participants who have even a few microseconds head start. To eliminate this advantage and further protect our data from inadvertent or purposeful prerelease, no computers or any other electronic devices will be allowed in the lock-up.

In recent years, BLS and ETA have devoted significant resources to introducing improved technologies that strengthen our infrastructure and ensure data are posted to the BLS or DOL websites immediately following the official release time.

We at BLS and ETA are committed to the principle of a level playing field—our data must be made available to all users at the same time. We are equally committed to protecting our data. We are now positioned to continue helping the media produce accurate stories about the data, while also ensuring that all parties, including the media, businesses, and the general public, will have equitable and timely access to our most sensitive data.

You can find more details about these changes in our notice to lock-up participants. We also have a set of questions and answers about the changes to the lock-up procedures.

Planning BLS Strategy for 2025 and Beyond

The start of the New Year seems like an appropriate time to share the new BLS Strategic Plan, which is designed to provide a roadmap for BLS over the next 5 years and beyond. Today, I want to tell you a little bit about how we developed this plan and then highlight some of its content.

We have a lot of resources to guide us in crafting the strategic plan. Consider:

  • As an agency of the U.S. Department of Labor, BLS provides statistical guidance and support to the department and its agencies. As noted in the Department of Labor’s Strategic Plan, BLS provides sound and impartial information about the economy for decision making.
  • As part of the decentralized U.S. statistical system, BLS works with its sister statistical agencies to share ideas, coordinate common activities, and improve operations.
  • We adhere to various laws, regulations, and policies to ensure that we provide accurate, objective, relevant, timely, and accessible information. Of particular note is the Foundations for Evidence-Based Policymaking Act of 2018, which reaffirms the confidentiality of statistical information and encourages cooperation and efficiencies across the statistical system.

Using all these inputs, BLS senior staff spent the last year looking both inward and outward to refine our mission and vision, and to identify broad strategies and individual goals and objectives for the coming years. We considered our strengths and weaknesses, looked for opportunities and identified threats, and refined a laundry list of ideas into a concise yet comprehensive plan.

It starts with our mission statement:

The Bureau of Labor Statistics measures labor market activity, working conditions, price changes, and productivity in the U.S. economy to support public and private decision making.

We then present the values and principles that guide us in fulfilling that mission, including:

  • Independence from partisan interests
  • Consideration of the needs of a diverse set of customers
  • Confidentiality of our data providers
  • Innovation
  • Stewardship of our staff and our resources

The plan includes five strategies, the first of which is to produce objective statistics and analysis, the core work of our agency. While always striving to improve, we must never lose focus on the hundreds of new data releases we produce each year.

The remaining strategies focus on how we do our work, and how we improve upon that work. Strategy 2 is about making improvements in the information we provide and what techniques we use to produce that information. Strategy 3 is about our source data, with special focus not only on traditional survey respondents but also on alternative data sources. Strategy 4 focuses on managing the resources that allow us to do our work, including our people, our funding, and our infrastructure. Finally, Strategy 5 is about you—our customers who come to us for information. We strive to let you have a seamless customer experience today, and we look for ways to make that experience even better tomorrow.

One of our many challenges in developing this strategic plan was to ensure all BLS staff see themselves in the strategies, goals, and objectives. We also want all BLS stakeholders—data providers, data users, researchers, policymakers, and more—to see their unique perspectives addressed. We hope you will take a few minutes to review the BLS Strategic Plan and let us know if we’ve met this challenge. Feel free to leave a comment below.

Why This Counts: Measuring Industry Productivity

At BLS, productivity is the economic statistic that describes the efficiency of production. The productivity statistics you hear about most often in the news are for the entire U.S. economy. But there’s more to the productivity story than just the overall numbers. The economy is made up of hundreds of industries, and each one works in a different way. Productivity data for each industry help us understand how specific types of production have changed over time. Let’s look at a few specific industries to see how labor productivity data can enhance our understanding of their unique production systems.

General Freight Trucking: Technological Innovations

Economic conditions in the general freight trucking industry closely mirror the health of the overall economy. During the 2007–09 recession, both output and hours worked fell dramatically in trucking. Because employment and spending were down nationwide, there was less demand for the transportation of all kinds of goods. After the recession ended, output and hours in trucking picked back up. Output reached prerecession levels by 2014, but in 2018 hours worked were still slightly below their 2007 level.

Dividing output by hours worked yields labor productivity. Because output in trucking has grown faster than hours during the recovery from the recession, labor productivity has increased. This helps us understand the nature of operations in general freight trucking. Innovative technologies such as communications systems, mapping software, and truck-based sensors and monitors known as “telematics” have improved transportation efficiency. These systems allow deliveries to be planned more efficiently with fewer delays, allowing more freight to be delivered without an equivalent increase in worker hours.

General freight trucking, average yearly percent change in output, hours worked, and productivity from 2007 to 2018

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

Travel Agencies: Digital Transformation

Another industry that has changed the way it operates is travel agencies. Since 2000, output has increased substantially, while hours fell from 2000 to 2010 and have increased only slightly since then. The major transformation for travel agencies has been the Internet. Online tools have allowed clients to make travel reservations with far less help from workers. This increase in efficiency is reflected in the industry’s labor productivity, which has more than tripled from 2000 to 2017.

Travel agencies, average yearly percent change in output, hours worked, and productivity from 2000 to 2017

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

Supermarkets: Incremental Change

Changes in other industries have been more subtle. Supermarkets are a particularly competitive industry, and firms employ a large number of workers to maintain high levels of customer service. Managing inventories, stocking shelves, checking out merchandise, and staffing specialty stations are all tasks that supermarkets continue to need. But even in supermarkets, productivity has been increasing since 2009, as output has grown faster than worker hours. To continue growing sales with lower costs, many firms in this industry have relied more on labor-saving technology, such as self-checkout machines. This technology increases efficiency by allowing supermarkets to process more transactions with less help from workers.

Supermarkets, average yearly percent change in output, hours worked, and productivity from 2009 to 2018

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

Cut and Sew Apparel Manufacturing: Establishment Turnover

Productivity declines also can show the changing nature of work. Cut and sew apparel manufacturing has seen much of its production move outside the United States. In 2018, U.S. apparel manufacturers produced less than 15 percent of the output they produced in 1997. Although worker hours also have declined, they have not dropped as much as output, leading to a decline in labor productivity. This indicates a shift over time in the nature of the average apparel manufacturer. While many large establishments moved overseas in search of cheaper labor, the remaining domestic apparel manufacturing establishments are on average smaller and more specialized, requiring more labor-intensive work.

Cut and sew apparel manufacturing, average yearly percent change in output, hours worked, and productivity from 1997 to 2018

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

To Learn More

BLS industry productivity data help us study the efficiencies of economic activities. Historical trends in productivity provide an important window into each industry’s working conditions, competitiveness, contribution to the economy, and potential for future growth. These data are used by investors, business leaders, jobseekers, researchers, and government decision makers. We have annual labor productivity measures for over 275 detailed industries.

To dive into the data for yourself, check out the BLS webpages on labor productivity. You also can see productivity data in a brand new way using our industry productivity viewer! Even more specialized industry data are on our webpages for hospitals, construction industries, elementary and secondary schools, and urban transit systems. We also have a recent article on productivity in grocery stores.

Average yearly percent change in output, hours worked, and productivity in selected industries
IndustryOutputHours workedProductivity

General freight trucking, 2007 to 2018

1.0%-0.1%1.2%

Travel agencies, 2000 to 2017

4.8-3.08.1

Supermarkets, 2009 to 2018

1.90.71.2

Cut and sew apparel manufacturing, 1997 to 2018

-9.4-7.5-2.1

Improving the Accuracy of the Consumer Price Index

We are in the “hot stove” months of the baseball year, when teams make trades and other decisions to improve their prospects for next season. Even the best teams, like the World Champion Washington Nationals, can’t rest on their laurels. In much the same way the Nationals continue to tinker with a good thing to make it better, we constantly work to improve our gold standard products, including the Consumer Price Index (CPI). There’s a lot going on with the CPI these days, and we’ll use this blog and other publications to share the latest information. You’ll read about how we reflect changes in consumer spending patterns, (including new goods), how we’re using other rich sources of data on prices and spending, how we’re accounting for changes in the quality of goods and services, and much more. So let’s get started.

The CPI is designed to measure the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. The CPI is used to determine annual cost-of-living allowances for Social Security beneficiaries. The CPI also is used to adjust the federal income tax system for inflation and as the yardstick for U.S. Treasury inflation-indexed bonds. These are just a few of the many uses of the CPI.

The CPI dates back to 1912, when the Washington baseball team was called the Senators and Walter Johnson ruled the mound. Throughout the history of the CPI, there has been debate about the concepts the CPI should measure and whether it might overstate or understate changes in consumer living costs. The CPI has undergone methodological changes both in response to these discussions and to reflect the changing economic environment. If we hadn’t made these changes, transportation, medical care, recreation, and other goods and services would still be combined into one “miscellaneous” category. Taking the long view, we can track major shifts in consumer inflation for more than a century.

Chart showing 12-month percent change in the Consumer Price Index for All Urban Consumers (CPI-U), 1914 to 2019

Editor’s note: Data for this chart are available in our database at data.bls.gov/timeseries/CUUR0000SA0

In the 1960s, a committee commissioned by Congress recommended that BLS move the CPI closer toward a cost-of-living measure. We responded to those recommendations by creating the CPI for all urban consumers (CPI-U). The former index for urban wage earners was relabeled as the CPI-W. Today, the CPI-U represents the spending patterns of about 93 percent of the population, while the CPI-W represents the spending patterns of about 29 percent.

Here are a few more recent milestones in the history of the CPI:

  • In 1988, following direction from Congress, BLS began calculating the CPI for Americans age 62 and older—called the CPI-E—as an experimental index.
  • In the early 1990s, Congress directed another study of the CPI, popularly referred to as the Boskin Commission. This commission estimated the CPI was overstating the rise in the cost of living and recommended changes in the way the CPI is designed and estimated.
  • In response, BLS sponsored a project in 2002 with the National Academy of Sciences, Committee on National Statistics (CNSTAT) to investigate conceptual, measurement, and other statistical issues in the development of cost-of-living indexes. At this point, we have adopted completely, partially, or experimentally almost all of the CNSTAT recommendations. This includes developing and publishing the Chained CPI, which broadly accounts for consumer substitution of goods and services.

But we can’t stop researching and improving. Today, consumers buy goods and services that weren’t even known a decade ago. And we buy things in many different ways, including from the living room sofa. The growth of e-commerce has created enormous opportunities, but also challenges, for measuring inflation. We continue to work on improvements in response to these developments, and we will talk more about them in future blogs and other publications. In addition, we recently sponsored another CNSTAT panel to investigate three key methodological issues for the CPI:

  1. How best to incorporate data on transactions?
  2. How best to integrate other data sources in the indexes for health insurance, owner-occupied housing, and durable goods?
  3. How to lessen certain types of substitution bias, such as when consumers purchase chicken when the price of steak increases? (Our methods already do a good job accounting for shifts between more similar items, such as between steak and ground beef.)

CNSTAT will convene an expert panel and hold a workshop. Both the panel kickoff and the workshop will be open to the public and will be announced in advance on the BLS website. The panel will then spend about a year in internal discussions and preparing a written report for our consideration.

We expect the CNSTAT report in May of 2021—new ideas, to go with the start of a new baseball season. I’ll be back to blog about the results, so be sure to check back here.