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Tag Archives: CPI

Spend Thanksgiving Day with BLS!

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

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

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

Consumer Price Index for household energy, 2019–21

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

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

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

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

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

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

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

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

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

4:00 p.m. Play touch football

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

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

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

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

6:00 p.m. Thanksgiving feast

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

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

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

7:00 p.m. Watch football

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

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

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

9:00 p.m. Say goodbye

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

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

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

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

Consumer Price Index for household energy
MonthIndex

Jan 2019

100.000

Feb 2019

99.662

Mar 2019

100.046

Apr 2019

99.952

May 2019

99.679

Jun 2019

99.258

Jul 2019

99.415

Aug 2019

99.253

Sep 2019

99.033

Oct 2019

99.756

Nov 2019

99.890

Dec 2019

99.716

Jan 2020

99.666

Feb 2020

99.355

Mar 2020

98.812

Apr 2020

98.492

May 2020

98.278

Jun 2020

98.501

Jul 2020

98.542

Aug 2020

98.478

Sep 2020

99.590

Oct 2020

100.103

Nov 2020

101.043

Dec 2020

101.377

Jan 2021

101.299

Feb 2021

102.681

Mar 2021

103.436

Apr 2021

104.748

May 2021

105.512

Jun 2021

105.840

Jul 2021

106.664

Aug 2021

107.833

Sep 2021

109.273

Oct 2021

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

Jan 2019

-0.1%0.2%

Feb 2019

-0.2-1.4

Mar 2019

-0.1-0.6

Apr 2019

-0.1-0.9

May 2019

-0.1-0.5

Jun 2019

-0.1-0.7

Jul 2019

0.0-0.9

Aug 2019

-0.1-1.4

Sep 2019

0.10.3

Oct 2019

0.20.0

Nov 2019

-0.20.4

Dec 2019

0.3-0.4

Jan 2020

-0.1-2.7

Feb 2020

0.00.3

Mar 2020

-0.8-0.6

Apr 2020

-14.5-25.3

May 2020

3.16.7

Jun 2020

6.314.1

Jul 2020

1.74.3

Aug 2020

1.72.3

Sep 2020

0.2-0.8

Oct 2020

0.70.2

Nov 2020

0.00.7

Dec 2020

0.2-0.6

Jan 2021

0.1-0.3

Feb 2021

0.10.5

Mar 2021

0.30.1

Apr 2021

-0.10.2

May 2021

0.40.9

Jun 2021

0.61.3

Jul 2021

0.00.3

Aug 2021

0.1-0.5

Sep 2021

0.40.5

Oct 2021

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

2007

100.000100.000100.000

2008

97.76597.65895.475

2009

98.29492.03290.461

2010

100.69492.66793.310

2011

101.39794.68696.008

2012

103.65595.67399.170

2013

108.08095.212102.905

2014

109.91997.268106.916

2015

113.48698.821112.148

2016

118.52598.636116.908

2017

120.71999.896120.593

2018

124.39399.783124.123

2019

130.36098.139127.934

2020

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

Watching TV (average of population)

3.36

Watching TV (average of those who watched TV)

4.17

Playing sports (average of population)

0.34

Playing sports (average of those who played sports)

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

Jan 2018

100.000100.000

Feb 2018

99.793100.243

Mar 2018

99.780100.352

Apr 2018

100.026100.594

May 2018

99.779100.929

Jun 2018

99.865101.113

Jul 2018

100.127101.229

Aug 2018

100.198101.421

Sep 2018

100.252101.645

Oct 2018

100.046101.738

Nov 2018

100.259102.029

Dec 2018

100.554102.437

Jan 2019

100.683102.789

Feb 2019

101.014103.153

Mar 2019

101.163103.342

Apr 2019

100.716103.676

May 2019

100.913103.894

Jun 2019

100.718104.232

Jul 2019

100.716104.443

Aug 2019

100.654104.669

Sep 2019

100.902104.940

Oct 2019

101.124105.139

Nov 2019

101.324105.310

Dec 2019

101.331105.611

Jan 2020

101.440106.000

Feb 2020

101.851106.236

Mar 2020

102.220106.395

Apr 2020

104.775106.550

May 2020

105.718106.942

Jun 2020

106.309107.496

Jul 2020

105.343108.002

Aug 2020

105.322108.309

Sep 2020

105.051108.911

Oct 2020

105.177109.210

Nov 2020

105.012109.342

Dec 2020

105.335109.751

Jan 2021

105.203110.122

Feb 2021

105.474110.180

Mar 2021

105.587110.311

Apr 2021

106.047110.649

May 2021

106.423111.258

Jun 2021

107.309112.047

Jul 2021

108.031112.923

Aug 2021

108.431113.405

Sep 2021

109.779114.013

Oct 2021

110.841114.965
Import price index for television and video receivers
MonthIndex

Jan 2011

100.000

Feb 2011

100.173

Mar 2011

100.173

Apr 2011

99.136

May 2011

98.964

Jun 2011

97.409

Jul 2011

97.064

Aug 2011

96.373

Sep 2011

95.855

Oct 2011

94.991

Nov 2011

93.092

Dec 2011

94.128

Jan 2012

94.819

Feb 2012

94.473

Mar 2012

93.955

Apr 2012

92.573

May 2012

92.573

Jun 2012

92.401

Jul 2012

92.401

Aug 2012

92.573

Sep 2012

92.228

Oct 2012

92.573

Nov 2012

90.155

Dec 2012

90.155

Jan 2013

89.810

Feb 2013

89.637

Mar 2013

88.256

Apr 2013

88.083

May 2013

87.910

Jun 2013

87.910

Jul 2013

87.392

Aug 2013

87.219

Sep 2013

85.838

Oct 2013

85.492

Nov 2013

85.492

Dec 2013

85.492

Jan 2014

85.320

Feb 2014

85.320

Mar 2014

85.147

Apr 2014

84.801

May 2014

84.283

Jun 2014

84.111

Jul 2014

83.074

Aug 2014

82.902

Sep 2014

83.074

Oct 2014

81.865

Nov 2014

81.865

Dec 2014

81.347

Jan 2015

79.965

Feb 2015

79.965

Mar 2015

79.965

Apr 2015

79.965

May 2015

79.620

Jun 2015

79.620

Jul 2015

79.620

Aug 2015

79.620

Sep 2015

79.620

Oct 2015

79.447

Nov 2015

79.275

Dec 2015

78.929

Jan 2016

78.756

Feb 2016

77.547

Mar 2016

77.375

Apr 2016

77.029

May 2016

76.857

Jun 2016

77.029

Jul 2016

76.857

Aug 2016

76.684

Sep 2016

76.684

Oct 2016

76.684

Nov 2016

76.684

Dec 2016

76.684

Jan 2017

76.166

Feb 2017

76.166

Mar 2017

75.820

Apr 2017

75.993

May 2017

75.993

Jun 2017

75.993

Jul 2017

75.993

Aug 2017

75.993

Sep 2017

75.820

Oct 2017

75.475

Nov 2017

75.302

Dec 2017

75.130

Jan 2018

75.130

Feb 2018

75.302

Mar 2018

74.784

Apr 2018

74.439

May 2018

74.266

Jun 2018

73.575

Jul 2018

72.884

Aug 2018

72.884

Sep 2018

72.712

Oct 2018

72.539

Nov 2018

72.366

Dec 2018

72.021

Jan 2019

71.330

Feb 2019

70.812

Mar 2019

70.466

Apr 2019

70.466

May 2019

70.294

Jun 2019

69.948

Jul 2019

69.775

Aug 2019

69.603

Sep 2019

69.603

Oct 2019

69.430

Nov 2019

69.085

Dec 2019

68.912

Jan 2020

69.430

Feb 2020

68.048

Mar 2020

67.358

Apr 2020

66.839

May 2020

66.667

Jun 2020

66.667

Jul 2020

66.494

Aug 2020

66.494

Sep 2020

66.321

Oct 2020

66.667

Nov 2020

67.358

Dec 2020

68.048

Jan 2021

68.739

Feb 2021

68.739

Mar 2021

68.566

Apr 2021

69.775

May 2021

70.639

Jun 2021

70.812

Jul 2021

73.402

Aug 2021

73.402

Sep 2021

74.439

Oct 2021

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

Jan 2019

3,521,0001,689,000301,000

Feb 2019

3,543,0001,769,000353,000

Mar 2019

3,524,0001,721,000331,000

Apr 2019

3,494,0001,954,000313,000

May 2019

3,487,0001,776,000307,000

Jun 2019

3,527,0001,771,000316,000

Jul 2019

3,627,0001,826,000344,000

Aug 2019

3,591,0001,825,000306,000

Sep 2019

3,449,0001,982,000345,000

Oct 2019

3,414,0001,793,000359,000

Nov 2019

3,482,0001,788,000374,000

Dec 2019

3,487,0001,952,000354,000

Jan 2020

3,568,0001,788,000358,000

Feb 2020

3,430,0001,953,000332,000

Mar 2020

2,902,00013,046,000360,000

Apr 2020

2,107,0009,307,000368,000

May 2020

2,206,0002,096,000316,000

Jun 2020

2,646,0002,204,000331,000

Jul 2020

3,182,0001,845,000365,000

Aug 2020

2,987,0001,573,000342,000

Sep 2020

3,307,0001,555,000373,000

Oct 2020

3,352,0001,728,000347,000

Nov 2020

3,296,0002,123,000325,000

Dec 2020

3,407,0001,823,000352,000

Jan 2021

3,306,0001,724,000294,000

Feb 2021

3,383,0001,723,000323,000

Mar 2021

3,568,0001,525,000343,000

Apr 2021

3,992,0001,450,000360,000

May 2021

3,630,0001,353,000347,000

Jun 2021

3,870,0001,354,000389,000

Jul 2021

4,028,0001,423,000341,000

Aug 2021

4,270,0001,385,000378,000

Sep 2021

4,434,0001,375,000410,000

BLS Data in More Than a Century of Pictures

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

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

Poster for Century of Progress International Exposition in Chicago in 1933

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

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

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

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

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

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

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

Chart showing changes in consumer prices, 1953 to 1962

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

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

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

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

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

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

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

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

The Latest on Improving the Accuracy of the Consumer Price Index

We first reported in December 2019 on the expert panel convened by the National Academy of Sciences, Committee on National Statistics (CNSTAT), to study the U.S. Consumer Price Index (CPI). At the time, we were looking forward to the upcoming baseball season—and we didn’t know yet how different the 2020 season would be. Since then, CNSTAT assembled a panel of experts to tackle some of the biggest issues facing the CPI. The panel is chaired by Daniel Sichel, professor of economics at Wellesley College. Panel members include members of academia and experts at government agencies. Visit the CNSTAT website to review the panel members’ biographies and see the breadth of accomplishments and experiences on this team.

While the future of the baseball season that spring was uncertain, the CNSTAT panel on Improving Cost-of-Living Indexes and Consumer Inflation Statistics in the Digital Age forged ahead. The panel held a public meeting virtually on May 27, 2020, and invited BLS staff and luminaries from across the statistical community to clarify the study’s scope. The discussion centered on how we can harness new sources of data to improve CPI methods and produce accurate, timely, and relevant measures of consumer price change.

The baseball season finally got underway over the summer of 2020, and the CNSTAT panel continued its work. They held closed sessions to discuss the issues and to plan the gathering of information. The panel envisioned a series of public workshops designed to gather information from top experts in the field. Unlike the fate of the 2020 Major League All-Star Game, this gathering of experts would go on—as a series of virtual sessions rather than the typical one- or two-day event.

With the Washington Nationals out of playoff contention, everyone focused on the first workshop session held October 2, 2020. At this session, the panel discussed the challenges of measuring price change for different population groups. The CNSTAT panel added this topic to their scope of work in the May meeting. The panel heard from presenters in academia who use highly granular data and uncover measurement issues when combining information across households. The panel also heard from the United Kingdom’s Office for National Statistics and BLS about each agency’s efforts to improve price measurement for different population groups. Staff from the Bureau of Economic Analysis (BEA) also discussed potential uses for these indexes.

The next two workshop sessions (October 7, 2020 and October 30, 2020) centered on new data sources as an alternative to data from traditional surveys. The panel brought together experts from the Office for National Statistics, Statistics Canada, Statistics Belgium, and the Australian Bureau of Statistics. A benefit of a virtual meeting was the ability to convene people from so many countries without the cost of travel—although it was a challenge to coordinate a meeting over so many time zones. It is both reassuring and enlightening to hear that other countries face similar challenges and opportunities regarding new data sources. To give another perspective, the panel also convened experts from academia and the private sector to review research conducted outside of the statistical agencies. The panel heard about automated data collection efforts and methods to address quality change using new sources of data.

The final sessions (December 15, 2020 and March 31, 2021) tackled housing and medical care, arguably the most difficult areas to measure in the CPI market basket. Measuring the change in the cost of shelter for homeowners is a longstanding challenge. Since the late 1970s, BLS has used an approach called owner’s equivalent rent, which aims to isolate homeowners’ consumption of shelter services from their capital investment in a home. This method has been as hotly debated as baseball’s addition of the designated hitter around the same time. Presenters from BLS, BEA, Statistics Canada, and academia discussed potential improvements to owner’s equivalent rent and alternatives such as a user cost approach (how much it costs a homeowner to own their home).

Measuring price change for medical care services and health insurance is another longstanding challenge. While the panel’s scope is limited to health insurance, any changes to the BLS approach affect the larger scope of measuring price change for medical care services. The panel invited experts in health economics from government, academia, and nonprofits to discuss critical questions about quality change—such as medical care outcomes, utilization rates, and risk premiums.

With the All-Star public sessions now complete, the panel is weighing the information it has gathered. The panel originally planned to deliver its final report around the start of the 2021 baseball season, but the broader scope pushed back their timeline. We now expect the final report to coincide roughly with the beginning of the 2021 World Series. A truly global series of meetings produced a wealth of information for the panel to sift through. As they deliberate, we will enjoy the baseball season and report back on their recommendations in the fall.

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.