Topic Archives: Inflation and Prices

Improving How We Measure Prices for New Vehicles

We have a guest blogger for this edition of Commissioner’s Corner. Brendan Williams is an economist in the Office of Prices and Living Conditions at the U.S. Bureau of Labor Statistics.

For nearly as long as cars and trucks have been sold, the BLS Consumer Price Index (CPI) has tracked changes in the prices consumers pay for new vehicles. Our traditional method of determining the change in vehicle prices is to survey dealers and collect estimated prices for models with a specific set of features. For example, a Brand X 8-cylinder two-door sports coupe with a sunroof. We recently debuted a research index for new vehicles based on a large dataset of prices actually paid, which we call “transaction” prices. This is just one of many efforts currently underway in the CPI (and throughout BLS) to identify and introduce new sources of data into our statistical measures. As you are about to learn, a lot goes into introducing these new measures.

We purchased the new data for new vehicles from J.D. Power. The new dataset includes records of the prices paid during hundreds of thousands of transactions every month—far more than the roughly 2,000 vehicle prices in the CPI sample. The larger dataset provides more precise measures of price change.

But it’s not as simple as plugging the new data into the monthly CPI. We found that applying current CPI methods to the transaction data produced a biased index. So we had to make some changes. We combined an estimate of the long-run trend in new vehicle prices with a measure of high-frequency fluctuations in the market. The long-run trend is based on the year-over-year price change between a vehicle in the current month and the same vehicle in the prior model year 12 months ago; we get these values from the J.D. Power data. The high-frequency fluctuation is extracted from a monthly index based on current methods used in the CPI.

The research index includes all types of new vehicles—cars, SUVs, and trucks. And since the data reflect actual transactions, the shift in consumer preference from cars to other types of vehicles is reflected in the data. This differs from the currently published CPI, which has maintained a roughly equal weight between cars and trucks.

The new vehicles research index performs very similarly to the published index. From December 2007 to March 2020, the research index (untaxed) increased 8.2 percent, while the official new vehicles index (which is taxed) increased 7.7 percent. Looking under the hood, the research truck index is also similar to its published index. The difference in the car indexes is larger, with the official index showing a 5.2-percent increase, while the research index shows only a 1.5-percent increase.

Chart showing trends in research and official price indexes for new vehicles, 2007 to 2020

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

While the new vehicle indexes look similar, the research index has a much lower standard error, which means there is less variation in the data. The research index had a 12-month standard error of 0.11, compared to the 0.43 standard error in the new vehicles index.

This research index is just one of many ways BLS is innovating the CPI and all our measures. For more information on BLS efforts to use new sources of data in the CPI, see “Big Data in the U.S. Consumer Price Index: Experiences & Plans.” Details of the methods and other aspects of research are in, “A New Vehicles Transaction Price Index: Offsetting the Effects of Price Discrimination and Product Cycle Bias with a Year-Over-Year Index.”

We are asking for your feedback about whether to use this research index or the current index. We specifically want to know whether you think this proposal improves our methods and data sources. Please tell us what you think about the research new vehicles data by emailing cpixnv@bls.gov. You can send other CPI-related questions to cpi_info@bls.gov.

Research and official price indexes for new vehicles
MonthResearch index, trucks untaxedOfficial index, trucks untaxedResearch index, all vehicles untaxedOfficial index, all vehicles untaxedResearch index, cars untaxedOfficial index, cars untaxed

Dec 2007

100.0100.0100.0100.0100.0100.0

Jan 2008

99.9100.299.6100.199.2100.0

Feb 2008

100.199.999.899.799.599.7

Mar 2008

100.899.3100.299.399.699.5

Apr 2008

99.998.799.698.999.399.2

May 2008

99.698.199.698.599.699.1

Jun 2008

100.197.7100.898.4101.599.2

Jul 2008

98.797.1100.098.3101.499.6

Aug 2008

96.395.898.397.6100.799.3

Sep 2008

95.794.797.996.9100.599.0

Oct 2008

95.894.797.896.8100.398.9

Nov 2008

95.294.797.296.999.999.0

Dec 2008

94.094.795.996.898.598.9

Jan 2009

94.095.595.797.597.899.5

Feb 2009

95.296.796.498.298.199.7

Mar 2009

95.297.496.398.597.899.7

Apr 2009

96.697.897.498.798.699.8

May 2009

96.898.197.698.998.699.9

Jun 2009

97.098.697.499.397.9100.1

Jul 2009

96.698.996.699.696.9100.3

Aug 2009

96.997.797.098.197.498.7

Sep 2009

99.098.099.498.5100.199.0

Oct 2009

98.899.899.3100.4100.0101.1

Nov 2009

99.2100.799.5101.6100.0102.5

Dec 2009

99.3100.999.2101.699.3102.5

Jan 2010

99.3101.199.2101.599.3102.1

Feb 2010

99.8101.499.5101.699.4102.1

Mar 2010

100.4101.4100.2101.4100.2101.7

Apr 2010

100.9101.2100.7101.198.3101.3

May 2010

101.0100.8100.8100.8100.7101.1

Jun 2010

101.3100.6100.9100.6100.7101.0

Jul 2010

101.5100.5101.1100.598.2100.8

Aug 2010

101.7100.5101.2100.3100.6100.6

Sep 2010

101.7100.7100.9100.5100.0100.8

Oct 2010

102.3101.0101.2100.999.7101.1

Nov 2010

102.5101.5101.2101.199.4101.2

Dec 2010

102.3101.9100.8101.498.9101.3

Jan 2011

102.4102.4100.8101.798.7101.3

Feb 2011

102.7103.3101.1102.699.2102.4

Mar 2011

103.7103.8102.0103.199.9102.9

Apr 2011

104.3104.0103.0103.5101.4103.5

May 2011

104.7104.3103.8104.3102.7104.7

Jun 2011

104.6104.3103.8104.7103.1105.5

Jul 2011

104.4104.0103.7104.5103.1105.4

Aug 2011

104.3103.7103.6104.1103.2105.1

Sep 2011

104.1103.6103.5104.1103.4105.2

Oct 2011

104.2103.8103.5104.3103.1105.2

Nov 2011

104.3104.1103.4104.4102.6105.2

Dec 2011

104.4104.3103.5104.6102.5105.3

Jan 2012

105.0105.0103.9105.0102.7105.4

Feb 2012

105.1105.9104.0105.6102.8105.8

Mar 2012

105.4106.0104.5105.6103.5105.7

Apr 2012

105.7106.1104.8105.7103.8105.9

May 2012

105.2105.8104.4105.7103.5105.9

Jun 2012

105.4105.8104.5105.6103.5105.9

Jul 2012

105.1105.5104.1105.3103.1105.5

Aug 2012

105.0105.5104.1105.2103.1105.4

Sep 2012

105.2105.6104.3105.2103.3105.3

Oct 2012

105.3105.8104.5105.4103.7105.4

Nov 2012

105.6106.2104.6105.9103.4106.1

Dec 2012

105.7106.5104.5106.2103.0106.4

Jan 2013

105.7107.1104.6106.7103.1106.8

Feb 2013

106.3107.2105.1106.8103.5106.8

Mar 2013

106.4107.4105.2106.8103.6106.8

Apr 2013

106.7107.7105.5107.0103.8106.8

May 2013

106.8107.6105.5106.8103.8106.6

Jun 2013

106.4107.8105.1106.9103.3106.4

Jul 2013

106.4107.6105.0106.6103.2106.1

Aug 2013

106.4107.3105.0106.3103.2105.8

Sep 2013

106.3107.6104.9106.4102.9105.8

Oct 2013

106.5107.6105.1106.5103.2105.7

Nov 2013

106.7107.8105.1106.6103.0105.8

Dec 2013

106.4108.0104.6106.7102.0105.9

Jan 2014

106.5108.1104.6106.7101.8106.0

Feb 2014

107.1108.6105.2107.1102.3106.3

Mar 2014

107.3108.6105.3107.1102.4106.2

Apr 2014

107.8109.0105.7107.4102.6106.4

May 2014

108.1108.9105.8107.3102.4106.4

Jun 2014

107.9108.4105.5106.9101.8106.0

Jul 2014

108.2108.6105.7106.9101.9105.9

Aug 2014

108.6108.7105.9106.7101.7105.4

Sep 2014

108.4108.7105.6106.7101.3105.4

Oct 2014

108.7109.0105.9107.1101.5105.7

Nov 2014

108.5109.2105.5107.2100.8105.9

Dec 2014

108.3109.4105.1107.2100.0105.8

Jan 2015

109.0109.3105.8107.2100.9105.8

Feb 2015

109.2109.9106.0107.8101.0106.4

Mar 2015

109.4110.2106.2108.0101.1106.5

Apr 2015

109.8110.5106.6108.2101.6106.5

May 2015

109.7110.6106.4108.2101.3106.5

Jun 2015

109.9110.5106.5108.2101.3106.5

Jul 2015

109.7110.2106.2107.7100.9105.9

Aug 2015

110.0109.8106.3107.3100.5105.5

Sep 2015

110.5109.8106.7107.2100.6105.3

Oct 2015

110.5109.8106.6107.2100.4105.2

Nov 2015

110.6110.2106.5107.499.9105.2

Dec 2015

111.0110.1106.9107.4100.4105.3

Jan 2016

111.5110.6107.3107.9100.7105.8

Feb 2016

111.8111.2107.7108.5101.2106.4

Mar 2016

112.0111.4107.8108.5101.1106.2

Apr 2016

112.2111.2108.0108.2101.3105.8

May 2016

111.9111.0107.6108.0100.7105.6

Jun 2016

111.9110.8107.4107.7100.1105.2

Jul 2016

111.1110.7106.8107.7100.0105.0

Aug 2016

111.8110.3107.3107.499.8104.7

Sep 2016

111.5110.3106.9107.299.5104.6

Oct 2016

111.3110.6106.7107.599.1104.9

Nov 2016

110.9110.6106.4107.699.0105.0

Dec 2016

111.1110.9106.5107.898.8105.1

Jan 2017

112.0111.9107.4108.999.8106.3

Feb 2017

111.8111.9107.3109.0100.0106.5

Mar 2017

112.1111.7107.3108.799.5106.0

Apr 2017

112.1111.7107.3108.699.3105.9

May 2017

111.9111.6107.1108.399.2105.5

Jun 2017

112.0111.1107.1107.899.1104.9

Jul 2017

111.9110.4106.9107.098.4103.9

Aug 2017

111.8110.2106.6106.697.9103.4

Sep 2017

111.4109.8106.3106.197.6102.8

Oct 2017

111.5109.7106.5106.097.9102.7

Nov 2017

112.0109.9106.8106.497.4103.2

Dec 2017

111.4110.7106.3107.297.9104.0

Jan 2018

111.9111.0106.9107.698.7104.4

Feb 2018

111.8110.8106.9107.498.9104.2

Mar 2018

111.2110.8106.3107.498.3104.2

Apr 2018

111.4110.3106.7106.999.3103.7

May 2018

111.1110.5106.4107.198.8104.1

Jun 2018

110.9110.6106.3107.299.1104.2

Jul 2018

111.3110.5106.7107.299.4104.3

Aug 2018

111.4110.2106.8106.999.5104.0

Sep 2018

111.3109.8106.8106.699.8103.9

Oct 2018

111.2109.6106.8106.5100.0103.9

Nov 2018

111.5109.8107.0106.799.9104.1

Dec 2018

110.7110.0106.3106.999.6104.2

Jan 2019

111.3110.8106.8107.6100.0104.8

Feb 2019

111.7111.0107.2107.7100.2104.9

Mar 2019

111.6111.5107.1108.199.9105.2

Apr 2019

112.0111.5107.4108.2100.1105.2

May 2019

112.2111.3107.6108.0100.3105.2

Jun 2019

111.7111.0107.2107.9100.6105.2

Jul 2019

111.9110.7107.4107.6100.6104.9

Aug 2019

111.5110.3106.9107.2100.2104.6

Sep 2019

111.6109.9107.1106.7100.1104.1

Oct 2019

111.9109.8107.3106.6100.3104.1

Nov 2019

111.3109.9106.8106.6100.0104.1

Dec 2019

111.2110.4106.8107.099.8104.3

Jan 2020

111.8111.0107.4107.7100.4105.1

Feb 2020

112.2111.4107.7108.2101.0105.7

Mar 2020

112.7110.9108.2107.7101.5105.2

When Worlds Converge: Statistics Agencies Learning from Each Other during the Pandemic

We never know when our worlds are going to converge. I have used this blog to tell you about how BLS operations are continuing—and changing—due to the COVID-19 pandemic. I also plan to tell you about our international activities and will continue writing about the BLS Consumer Price Index (CPI) and other programs. Today, all three of these topics converge into one.

The COVID-19 pandemic has compelled BLS and statistical agencies worldwide to examine our processes and concepts to ensure the information we collect and publish reflects current conditions. For BLS, this means suspending all in-person data collection and relying on other methods, including telephone, internet, and email. Adding to our toolbox, BLS is now piloting video data collection. To be flexible, we have changed some collection procedures to accommodate current conditions. For example, we are now doing all of our work at home instead of in our offices. We are learning more every day about teleworking more effectively, and we are training our staff as we learn.

Once we collect the data, we are examining how we need to adapt our processing and publication. Will our typical procedures to account for missing data still apply? Will seasonal patterns in the data change due to COVID-19? Will we be able to publish the level of detail our data users have come to expect? These and more are open questions. We will make informed decisions as we learn more about the pandemic’s impact on our data and operations. What I do know is that BLS has a long practice of sharing its procedures and methods, including any changes. We already have extensive information about COVID-19 on the BLS website, and we continue to update that information. We also provide program-specific information with each data release to alert users to any unique circumstances in the data.

Since BLS has long been known for producing gold-standard data, information about our procedures and methods is also of great interest to our international colleagues. In fact, BLS has helped statistical organizations throughout the world with the collection, processing, analysis, publishing, and use of economic and labor statistics for more than 70 years. We provide this assistance primarily by our Division of International Technical Cooperation. They strengthen statistical development by organizing seminars, consultations, and meetings for international visitors with BLS staff. This division also serves as the main point of contact for the many international statistical organizations that compile information, publish comparable statistics worldwide, share concepts and definitions, and work to incorporate improvements and innovations.

A hallmark of our international activities has been onsite seminars at BLS, often attended by a multinational group of statistical experts and those working to become experts. At these seminars, BLS technical staff present details on every aspect of statistical programs, including concept development, sampling, data collection, estimation procedures, publishing, and more. In recent years, funding, travel restrictions, and other limitations have reduced the number of in-person events, replaced to some extent by virtual events. And of course, the current COVID-19 pandemic and related travel restrictions mean all such events are now being held virtually. But they still go on.

Recently, our international operations converged with our COVID-19 response when the International Technical Cooperation staff set up a virtual meeting between BLS staff primarily from our Consumer Price Index program and their counterparts at India’s Ministry of Statistics and Programme Implementation (MOSPI). They met to discuss challenges in producing consumer price data during the ongoing pandemic. The discussion was largely about methodology: what to do with missing prices and how to adjust weights to reflect real-time shifts in spending that consumers are making in response to the pandemic. It is helpful to hear from worldwide colleagues who are facing similar challenges. These issues are unprecedented, and we know the potential solutions for one country may not be ideal for the nuanced conditions in another country.

In India, for instance, commerce has been limited to essential commodities—food, fuel, and medicine. This will likely leave them unable to publish some indexes. While this is unfortunate in the present time, it is fairly straightforward; they can’t publish what they don’t have. It gets more complicated a year from now. What does it mean to have an annual price change when the denominator is missing? The CPI deals with this by having a fairly robust imputation system—basically “borrowing” price change from similar areas and items—but we will be monitoring the situation closely to make sure our assumptions about what is similar remain valid.

One advantage BLS has over MOSPI is that we are able to collect data by telephone, email, or on the web. MOSPI has traditionally only done in-person collection. Both agencies are transitioning to different modes of collection, but we have significantly greater experience.

Sharing information with our international colleagues, about the CPI and other programs, and about our COVID-19 experience, is a key part of the BLS mission. These worlds continue to converge, not just during organized meetings but also on websites and wikis maintained by statistical organizations and through participation in expert groups and conferences. For example, the United Nations Economic Commission for Europe hosts a ”statswiki” that currently has pages dedicated to COVID-19 and Official Statistics. It is a small world after all, and the worldwide social distancing we are all experiencing makes it clear that we are all in this together. And together, BLS and our international colleagues, reacting to COVID-19 and making adjustments to consumer price indexes and other statistics, will continue to provide vital information that tracks changes in the world economy.

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.

How We Collect Data When People Don’t Answer the Phone

I was asked recently how the U.S. Bureau of Labor Statistics can collect data these days when no one answers the telephone. A legitimate question and one we grapple with all the time. I had two answers – one related to data collection methods and one related to sources of data. I will elaborate here about both.

Beige wall phone with rotary dial

But first, do you remember the days before caller ID, when everyone answered the phone? If you were at home, the rotary phone, permanently attached to the kitchen wall, always rang during dinner.

If you were in the office, the phone probably had a row of clear plastic buttons at the bottom that would light up and flash. In either case, who was on the other end of the phone was a mystery until you answered. In those days, your friendly BLS caller could easily get through to you and ask for information.

Vintage office phone with rows of buttons

Fast forward to today’s world of smart phones and other mobile devices. Nobody talks on the phone anymore. Many phone calls are nuisances. A call from BLS might show up as Unknown Number, U.S. Government, or U.S. Department of Labor on your caller ID, or identified as potential spam. With the spread of “spoofing,” many people do not answer calls from numbers they don’t recognize. How do we get around these issues?

Data Collection

At BLS, we consider data collection as much an art as a science. Sure, our staff needs to be well-versed in the information they are collecting. But they also need to be salespersons, able to convince busy people to spend a few minutes answering key questions. Part of that art is making a connection. There are old-fashioned ways that still work, such as sending a letter or showing up at the door. And there are more modern techniques, such as email and text. We are nothing if not persistent.

Our data-collection techniques have been called “High Touch, High Tech.” We start by building a relationship—the High Touch step. BLS has a wide range of information that people and businesses can use to help make informed decisions. We can help you access that information, and we love to see survey respondents use BLS data they helped us produce. In return, we ask for some information from you. There’s where High Tech comes in. We continue to add flexibility to our data-collection toolkit. You can provide information in person, on paper, or on the phone. You also can email information or an encrypted file. Or you can access our online portal anytime and anywhere to provide information or upload a data file. We need your information, and we want to make providing that information as easy as possible.

For example, this chart shows the number of employer self-reports that we’ve received through our online portal over the past several years. Internet data collection has really taken off.

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

Another data-collection strategy we use is asking businesses to allow us to get the information we need from their website. This might involve web scraping data or using an Application Programming Interface (API). We have had success showing businesses that we can get what we need from their website, often eliminating the need for them to compile data.

Alternative Data

Beyond these data-collection strategies, we are expanding efforts to get information from alternative sources, lessening our need to contact businesses and households. Some BLS programs, such as Local Area Unemployment Statistics, the Quarterly Census of Employment and Wages, and Productivity Studies, rely heavily on administrative data and information from other surveys. In these cases, there is little need to contact businesses or people directly.

Other BLS programs, such as the Consumer Price Index (CPI) and the Employment Cost Index (ECI), need to capture timely information. But there are alternatives that can complement direct data collection. The CPI, for example, has produced an experimental price index for new vehicles based on a file of vehicle purchase transactions provided by J.D. Power. Using information from sources like that may eventually lessen the need to have BLS employees contact vehicle dealerships. The ECI found that it was easier to capture employer premiums for unemployment insurance from state tax records than to ask employers.

Alternative data come in many forms, from government records, data aggregators, scanners, crowdsourcing, corporate data files, and many more. BLS is investing heavily in alternative data-collection techniques and alternative data sources. The High Touch and High Tech approach we use every day in our data-collection operations helps us to maximize data quality and minimize respondent burden and cost.

The telephone may go the way of the dinosaur, but that’s not stopping us from using every tool at our disposal to continue to produce gold standard data to inform your decisions.

Number of transactions with BLS internet data collection
YearNumber of transactions

2004

105,145

2005

148,754

2006

219,923

2007

534,555

2008

972,605

2009

1,544,795

2010

1,909,410

2011

2,322,540

2012

2,769,694

2013

3,236,376

2014

3,288,665

2015

3,554,639

2016

4,013,415

2017

4,513,297

2018

4,685,414

2019

4,868,939

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