Department of Labor Logo United States Department of Labor
Dot gov

The .gov means it's official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Topic Archives: Consumer Spending

Celebrating World Statistics Day 2020

At the Bureau of Labor Statistics, we always enjoy a good celebration. We just finished recognizing Hispanic Heritage Month. We are currently learning how best to protect our online lives during National Cybersecurity Awareness Month. We even track the number of paid holidays available to workers through the National Compensation Survey. Today I want to focus on a celebration that happens once every 5 years — World Statistics Day. While there may not be parades, special meals, or department store sales to honor this day, we at BLS and our colleagues worldwide take time out on October 20, 2020, to recognize the importance of providing accurate, timely, and objective statistics that form the cornerstone of good decisions.

United Nations logo for World Statistics Day 2020

World Statistics Day, organized under the guidance of the United Nations Statistical Commission, was first celebrated in October 2010. This year, the third such event, focuses on “connecting the world with data we can trust.” At BLS, the trustworthy nature of our data and processes has been a hallmark of our work since our founding in 1884. Our first Commissioner, Carroll Wright, described our work then as “conducting judicious investigations and the fearless publication of results.” That credo guides us to this day. As the only noncareer employee in the agency, I am surrounded by a dedicated staff of data experts  whose singular mission is to produce the highest-quality data, without regard to policy or politics. BLS and other statistical agencies throughout the federal government strictly follow Statistical Policy Directives that ensure we produce data that meet precise technical standards and make them available equally to all. For nearly 100 years, we have regularly updated our Handbook of Methods to provide details on data concepts, collection and processing methods, and limitations. Transparency remains a hallmark of our work.

The United States has a decentralized statistical system, with numerous agencies large and small spread throughout the federal government. Despite this decentralization, the agencies work together to improve statistical methods and follow centralized statistical guidance. This partnership was recently strengthened by the Foundations for Evidence-Based Policymaking Act of 2018, which reinforced how the statistical agencies protect the confidentiality of businesses and households that provide data. The Act also designated heads of statistical agencies, like myself, as Statistical Officials for their respective Departments. In my case, my BLS colleagues and I advise other Department of Labor agencies on statistical concepts and processes, while continuing to stay clear of policy discussions and decisions.

World Statistics Day is a global event, so this is a good time to share some examples where BLS participates in statistical activities around the world:

  • We have regular contact with colleagues at statistical organizations around the world. Just recently, I participated in a very long-distance video conference on improvements to the Consumer Price Index. For me, it was 6:00 a.m., and I made sure I had a mug of coffee handy; for my colleagues in Australia, it was 6:00 p.m., and I’m certain their mug had coffee as well.
  • We have a well-established training program for international visitors, focusing on our processes and methods. We hold training sessions at BLS headquarters (or at least we did before the pandemic), we send experts to other countries, and we are exploring virtual training. We are eager to share our expertise and long history.
  • We participate in international panels and study groups, such as those organized by the United Nations, the Organization for Economic Cooperation and Development, and others, with topics ranging from measuring the gig economy to use of social media.
  • We provide BLS data to international databases, highlighting employment, price, productivity and related information to compare with other countries.

And that’s just a taste of how BLS fits into the World of Statistics. As Commissioner, I’ve had the honor to represent the United States in conferences and meetings across the globe. The BLS staff and I also hold regular conversations with statistical officials worldwide. In a recent conversation with colleagues in the United Kingdom, we were eager to learn about each other’s changes in the ways we provide data and analyses to our customers. These interactions expand everyone’s knowledge and keep the worldwide statistical system moving forward.

To celebrate World Statistics Day, I asked some BLS cheerleaders if they would join me in a video message about the importance of quality statistical data. Here’s what they had to say:

In closing, let’s all raise a toast to World Statistics Day, the availability of high-quality and impartial data, and the dedicated staff worldwide who provide new information and analysis every day.

Happy World Statistics Day!

How Much Does a Cup of Coffee Cost? It’s Complicated

We have a guest blogger for this edition of Commissioner’s Corner. Rob Cage is the Assistant Commissioner for Consumer Prices and Price Indexes at the U.S. Bureau of Labor Statistics.

The pandemic has changed my morning routine. Before the outbreak of the COVID-19 pandemic and full-time telework at BLS, two things motivated me each morning.

Person holding mobile phone and ordering coffee on an app.

First, I was always on a mission to minimize my commute to work. I would do things each night so I wouldn’t waste time in the morning. Things like shaving, setting out clothes, and preparing the next day’s lunch. I timed my alarm to go off to allow just enough time to shower, suit up, grab that sandwich, and catch my commuter train as it rolled into the station.

The second thing I needed to start each work day was a strong, fresh, hot cup of joe—actually more like two or three cups. Not one of those fancy drinks with mocha, caramel, steamed milk, or anything like that. Ordinary drip-brewed, filtered coffee. Medium to dark roast and like Betty MacDonald, my coffee had to be “…so strong it snarled as it lurched out of the pot.” Then I add some cream (and by cream, I mean half-and-half), but no sugar. But by far the most important element of the drink: temperature. I like coffee precisely at a certain temperature. If it’s too hot, you taste nothing but a scalded tongue. If it’s too cold, you’re met with an overwhelming sense of disappointment. In that ideal temperate zone, you are jolted alive with a satisfying sip of silky cocoa and nutty fragranced bliss.

Through trial and error, I eventually unearthed a way to satisfy both of these morning habits efficiently: getting my coffee along the commute. Brewing the coffee at home took too much time, and I’d drink most of it on the train, arriving at my desk empty handed. Getting my first cup after I arrived was also uneconomical since I’d have to backtrack to get it. The simple solution? Find a place to get the coffee along the way, and preferably as close to my office as possible. This way, the temperature of the drink was in that sweet spot as I turned on my computer.

With four different coffee shops located along my route in Washington’s Union Station, one would think I could easily achieve this. But no, I’m foiled by impatience. According to a 2014 Journal of Consumer Behavior study, the time before ordering has the greatest influence on how customers perceive waiting times and service quality. A customer who has to wait 10 minutes in line before ordering will feel more dissatisfied than a customer waiting 10 minutes after ordering, even if the total wait time is the same. I couldn’t agree more. Queues at Union Station during the morning rush were just too long and unpredictable to meet my needs. I didn’t have the patience to wait behind customers pondering through a long order recital: Quad Grande nonfat extra hot caramel macchiato upside down, please. I needed my expeditiously stated, two-word order quickly. Luckily, the employee cafeteria in my building—conveniently located just off the lobby—had self-serve coffee. No competing commuters. No preorder queue. No postorder queue. Only a payment queue. I had found my routine: a 55-minute total commute, landing at my desk with strong, hot coffee in hand.

Then one day, I bumped into a coworker on the train. As we walked through Union Station and approached the maze of coffee shops with the insufferable queues, she stopped in front of one; took two steps to the left, scanned the drinks on top of a cart, found one with her name on it, picked it up, and met me back in stride. Amazed, I asked her how she pulled off this sensational stunt. She had placed her order on the coffee shop’s mobile app, of course. That was her routine. Curious but unconvinced, I asked her if she was concerned the coffee would be too cold by the time she picked it up. Through trial and error, she had figured out that if she placed her order on the app as the train rolled out of the L’Enfant Plaza stop, her drink would typically be hot and ready as she passed the cart. Could this be coffee-ordering nirvana? Guaranteed no-wait service, with guaranteed handoff at perfect temperature? Surely this improvement in the quality of the purchasing experience would cost more, which was my next question. And the astonishing answer: the coffee was the same price! I immediately downloaded the app, copied her process, and shaved three minutes off my morning routine. An equilibrium commute down to 52 minutes, about a 5-percent improvement!

Which brings me to how this tortured story relates to the business of BLS and specifically the measurement of the cost of living and consumer inflation. If the cost of my preferred cup of coffee was identical ($2.45 before sales tax) whether I stood in line to get it or not, then surely I would be better off by ordering on the app. Doing that resulted in a 5-percent time savings on my commute—an attribute of purchasing coffee that was critically important to me. In other words, the app-ordered coffee represented a higher-quality product, even though the price was the same. Using the federal minimum wage rate of $7.25\hour (or 12 cents a minute), an estimate of the time savings is 3 minutes x $0.12 = $0.36. One could say $0.36 is a reasonable estimate of the difference in quality. So what is the correct measure of price change between these two choices?

ApproachWalk-up purchaseApp purchasePrice changeNote

Ignore purchase time

$2.45 $2.45 0%No change in price

Add purchase time

$2.81 $2.45 -13%Deflation

Assume purchase time is built into market price, and adjust prices to reflect zero purchase time

$2.09 $2.45 17%Inflation

This is the million dollar question in consumer price index measurement, and the answer depends on how a unique consumer good—in this case a prepared cup of coffee—is defined. In the price index literature, the buzzword is homogeneity. To measure inflation accurately, goods that are homogenous must be identified and grouped together for proper treatment. This is at the core of getting the CPI right. Homogenous is defined as “of the same kind, alike; consisting of parts all of the same kind.” In CPI jargon, the component “parts” of a unique item in the sample are called “attributes.” So what are the attributes that define a cup of coffee? We could consider a list of attributes that most baristas might say are important, like size, bean variety, country of origin, blend, roast, freshness, or caffeine content; and a couple you already know that are important to me: temperature and queue time.

How many of these attributes do we explicitly control for in the CPI as obvious, overt, and separate variables used in scientifically selecting a sample of coffee drinks from quick service establishments, for use in calculating the index each month? You might be surprised by the answer: none! How, then, do we capture constant-quality price change for prepared coffee drinks accurately in the CPI?

We implicitly account for all of these characteristics one way or another. The CPI uses the matched-model approach to index measurement. We select a sample of 100,000+ unique, well-specified, strictly homogenous goods and services for the sample. Then we compare the price of each unique sampled item to the price of the exact same item in subsequent months. The key, of course, is defining and selecting the unique items. Generally speaking, sample selection has two major components: selection of the establishments (for example, a coffee shop) and then selection of a unique item (for example, 16-ounce dark roast drip coffee) at the selected establishments. Limited budget requires BLS to take a sample rather than a census of all goods and services consumers purchase. Thus, we group unique products into broadly homogeneous categories so the selected products can accurately reflect price change for unsampled items in those categories. We bundle prepared coffee from quick service establishments into the elementary category “limited service meals and snacks.” Comparatively, this is one of the more broadly defined components in the CPI basket. With a variety of different food and beverage items eligible for the sample, there are simply too many attributes to consider as separate selection steps to create the sample of unique items. Instead, we base the selection largely on the descriptions of different items listed on the menu. This is how we would distinguish an ordinary brewed coffee drink from other coffee drinks, such as a latte and cappuccino.

Any attribute expressly identified in the description of the menu item becomes a characteristic defining the unique item. For example, “12-ounce Cup of Organic Single Origin Light Roast Coffee” and “12-ounce Cup of Organic Classic Blend Medium Roast Coffee” may be two different menu items at a coffee shop. By rule, they are treated as distinct, unique, separate products for CPI sample selection. Then each month, CPI data collectors meticulously capture the price of the exact same product. If any of the characteristics change, that would trigger a quality review. Suppose medium roast was no longer available. A decision would have to be made to substitute the most comparable item to the originally selected item. Then a commodity analyst in the national office would have to decide if the new item was comparable to the old item. For example, is there a difference in quality between the light roast and the medium roast? Obviously, consumer taste and preferences are idiosyncratic, and the difference in quality of light roast and medium roast is a function of individual preference. But to the average consumer, perhaps not. In fact, prices tend not to vary by roast type. So in this situation, the analyst might judge medium and light to be comparable, and the price of the light will be matched to the previous price of the medium and used in the index. However, if a single-origin coffee was selected, a different outcome might result, especially if the price of the single-origin coffee was considerably different from a previously selected blend coffee, with all other characteristics being the same. Then a decision would need to be made as to how much of the difference was a quality difference (single origin versus blend), and how much was pure price change.

But what about the other factors that are not expressly identified in the description of the menu item, like temperature, freshness, and queue time? These are ostensibly identified, and held constant month after month, by the selection of the establishment. The outlet itself is associated with many attributes of product quality which are not observed. Over time, customers come to expect a certain level of service or product quality within each specific store, or at specific locations of chain stores. So, by controlling for the outlet, we are effectively able to hold constant these unobservable attributes.

Now that I am teleworking, my morning habits are out of equilibrium. My commute time is drastically shorter, reduced to the time it takes me to walk from my bedroom to the guest room, which has been hastily converted into a home office. My problem is the coffee. I haven’t figured out the roast, or the precise coffee-to-water ratio for the perfect strength; I don’t like spending time grinding whole bean, so I substitute ground coffee instead. My barista tells me that’s a quality decrease.

I’d say I am better off commute-timing wise but worse off coffee wise. A push. All in all, I can’t wait to return to on-premises work, mostly for that reliable cup of java.

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.