All posts by BLS Commissioner

Update on the Misclassification that Affected the Unemployment Rate

How hard can it be to figure out whether a person is employed or unemployed? Turns out, it can be hard. When BLS put out the employment and unemployment numbers for March, April, and May 2020, we also provided information about misclassification of some people. I want to spend some time to explain this issue, how it affected the data, and how we are addressing it.

In the monthly Current Population Survey of U.S. households, people age 16 and older are placed into one of three categories:

  • Employed — they worked at least one hour “for pay or profit” during the past week.
  • Unemployed — they did not work but actively looked for work during the past 4 weeks OR they were on temporary layoff and expect to return to work.
  • Not in the labor force — everyone else (including students, retirees, those who have given up their job search, and others).

Again, how hard can this be? It starts to get tricky when we talk to people who say they have a steady job but did not work any hours during the past week. In normal times, this might include people on vacation, home sick, or on jury duty. And we would continue to count them as employed. But during the COVID-19 pandemic, the collapse of labor markets created challenges the likes of which BLS has never encountered. People who reported zero hours of work offered such explanations as “I work at a sports arena and everything is postponed” or “the restaurant I work at is closed.” These people should be counted as unemployed on temporary layoff. As it turns out, a large number of people—we estimate about 4.9 million in May—were misclassified.

With the onset of the COVID-19 pandemic, the unemployment rate—at a 50-year low of 3.5 percent in February—rose sharply to 4.4 percent in March and to 14.7 percent in April, before easing to 13.3 percent in May. Despite the stark difference from February, we believe the unemployment rate likely was higher than reported in March, April, and May. As stated in our Employment Situation news releases for each of those months, some people in the Current Population Survey (also known as the CPS or household survey) were classified as employed but probably should have been classified as unemployed.

How did the misclassification happen?

We uncovered the misclassification because we saw a sharp rise in the number of people who were employed but were absent from their jobs for the entire reference week for “other reasons.” The misclassification hinges on how survey interviewers record answers to a question on why people who had a job were absent from work the previous week.

According to special pandemic-related interviewer instructions for this question, answers from people who said they were absent because of pandemic-related business closures should have been recorded as “on layoff (temporary or indefinite).” Instead, many of these answers were recorded as “other reasons.” Recording these answers as “on layoff (temporary or indefinite)” ensures that people are asked the follow-up questions needed to classify them as unemployed. It does not necessarily mean they would be classified as unemployed on temporary layoff, but I’ll get into that in a moment.

When interviewers record a response of “other reasons” to this question, they also add a few words describing that other reason. BLS reviewed these descriptions to better understand the large increase in the number of people absent from work for “other reasons.” Our analysis suggests this group of people included many who were on layoff because of the pandemic. They would have been classified as unemployed on temporary layoff had their answers been recorded correctly.

What are BLS and the Census Bureau doing to address the misclassification?

BLS and our partners at the U.S. Census Bureau take misclassification very seriously. We’re taking more steps to fix this problem. (The Census Bureau is responsible for collecting the household survey data, and BLS is responsible for analyzing and publishing the labor market data from the survey.) Both agencies are continuing to investigate why the misclassification occurred.

Before the March data collection, we anticipated some issues with certain questions in the survey because of the unprecedented nature of this national crisis. As a result, interviewers received special instructions on how to answer the temporarily absent question if a person said they had a job but did not work because of the pandemic. Nevertheless, we determined that not all of the responses to this question in March were coded according to the special instructions. Therefore, before the April data collection, all interviewers received an email that included instructions with more detailed examples, along with a reference table to help them code responses to this question. However, the misclassification was still evident in the April data. Before the May data collection, every field supervisor had a conference call with the interviewers they manage. In these conference calls, the supervisors reviewed the detailed instructions, provided examples to clarify the instructions, and answered interviewers’ questions.

Although we noticed some improvement for May, the misclassification persisted. Therefore, we have taken more steps to correct the problem. Before the June collection, the Census Bureau provided more training to review the guidance to the interviewers. The interviewers also received extra training aids. The electronic survey questionnaire also now has new special instructions that will be more accessible during survey interviews.

Why doesn’t BLS adjust the unemployment rate to account for the misclassification?

As I explained above, we know some workers classified as absent from work for “other reasons” are misclassified. People have asked why we just don’t reclassify these people from employed to unemployed. The answer is there is no easy correction we could have made. Changing a person’s labor force classification would involve more than changing the response to the question about why people were absent from their jobs.

Although we believe many responses to the question on why people were absent from their jobs appear to have been incorrectly recorded, we do not have enough information to reclassify each person’s labor force status. To begin with, we don’t know the exact information provided by the person responding to the survey. We know the brief descriptions included in the “other reasons” category often appear to go against the guidance provided to the survey interviewers. But we don’t have all of the information the respondent might have provided during the interview.

Also, we don’t know the answers to the questions respondents would have been asked if their answers to the question on the reason not at work had been coded differently. This is because people whose answers were recorded as absent from work for “other reasons” were not asked the follow-up questions needed to determine whether they should be classified as unemployed. Specifically, we don’t know whether they expected to be recalled to work and whether they could return to work if recalled. Therefore, shifting people’s answers from “other reasons” to “on layoff (temporary or indefinite)” would not have been enough to change their classification from employed to unemployed. We would have had to assume how they would have responded to the follow-up questions. Had we changed answers based on wrong assumptions, we would have introduced more error.

In addition, our usual practice is to accept data from the household survey as recorded. In the 80-year history of the household survey, we do not know of any actions taken on an ad hoc basis to change respondents’ answers to the labor force questions. Any ad hoc adjustment we could have made would have relied on assumptions instead of data. If BLS were to make ad hoc changes, it could also appear we were manipulating the data. That’s something we’ll never do.

How much did the misclassification affect the unemployment rate?

We don’t know the exact extent of this misclassification. To figure out what the unemployment rate might have been if there were no misclassification, we have to make some assumptions. These assumptions involve deciding (1) how many people in the “other reasons” category actually were misclassified, (2) how many people who were misclassified expected to be recalled, and (3) how many people who were misclassified were available to return to work.

In the material that accompanied our Employment Situation news releases for March, April, and May, we provided an estimate of the potential size of the misclassification and its impact on the unemployment rate. Here we assumed all of the increase in the number of employed people who were not at work for “other reasons,” when compared with the average for recent years, was due solely to misclassification. We also assumed all of these people expected to be recalled and were available to return to work.

For example, there were 5.4 million workers with a job but not at work who were included in the “other reasons” category in May 2020. That was about 4.9 million higher than the average for May 2016–19. If we assume this 4.9 million increase was entirely due to misclassification and all of these misclassified workers expected to be recalled and were available for work, the unemployment rate for May would have been 16.4 percent. (For more information about this, see items 12 and 13 in our note for May. We made similar calculations for March and April.)

These broad assumptions represent the upper bound of our estimate of misclassification. These assumptions result in the largest number of people being classified as unemployed and the largest increase in the unemployment rate. However, these assumptions probably overstate the size of the misclassification. It is unlikely that everyone who was misclassified expected to be recalled and was available to return to work. It is also unlikely that all of the increase in the number of employed people not at work for “other reasons” was due to misclassification. People may be correctly classified in the “other reasons” category. For example, someone who owns a business (and does not have another job) is classified as employed in the household survey. Business owners who are absent from work due to labor market downturns (or in this case, pandemic-related business closures) should be classified as employed but absent from work for “other reasons.”

Regardless of the assumptions we might make about misclassification, the trend in the unemployment rate over the period in question is the same; the rate increased in March and April and eased in May. BLS will continue to investigate the issue, attempting both to ensure that data are correctly recorded in future months and to provide more information about the effect of misclassification on the unemployment rate.

New State and Metropolitan Area Data from the Job Openings and Labor Turnover Survey

Soon after I became Commissioner, the top-notch BLS staff shared with me their vision to expand the Job Openings and Labor Turnover Survey (JOLTS). The JOLTS program publishes data each month on the number and rate of job openings, hires, and separations (broken out by quits, layoffs and discharges, and other separations). These data are available at the national level and for the four large geographic regions—Northeast, Midwest, South, and West.

That left a major data gap on labor demand, hires, and separations for states and metropolitan areas. BLS provides data on labor supply for states and metro areas each month from the Local Area Unemployment Statistics program. We also provide data on employment change in states and metro areas each month from the Current Employment Statistics survey. Employment change is the net effect of hires and separations, but it doesn’t show the underlying flow of job creation and destruction. Having better, timelier state and metro JOLTS data would provide a quicker signal about whether labor demand is accelerating or weakening in local economies.

About 2 months after the staff briefed me, the JOLTS program published experimental state estimates for the first time on May 24, 2019. We have been updating those estimates on a quarterly basis since then. We use a statistical model to help us produce the most current state estimates. We then improve those estimates during an annual benchmark process by taking advantage of data available from the Quarterly Census of Employment and Wages. The JOLTS program is well on its way to moving these state estimates into its official, monthly data stream. Look for that to happen in the second half of 2021!

The President’s proposed budget for fiscal year 2021 includes three improvements to the JOLTS program.

  • Expand the sample to support direct sample-based estimates for each state.
  • Accelerate the review and publication of the estimates.
  • Add questions to provide more information about job openings, hires, and separations.

If funded, this proposal would allow BLS to improve the data quality available from the current JOLTS state estimates. It also would let us add very broad industry detail for each state and more industry detail at the national level.

The proposed larger sample size may also let us produce model-assisted JOLTS estimates for many metro areas. To demonstrate this potential, the JOLTS team produced a one-time set of research estimates for the 18 largest metropolitan statistical areas, those with 1.5 million or more employees. These research estimates show the potential for data that would be available regularly with a larger JOLTS sample. I encourage you to explore this exciting new research series and let us know what you think.

Number of unemployed per job opening in the United States and four large metropolitan areas, 2007–19

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

This is just one example of the excellent work I see at BLS every day. The BLS staff are consummate professionals who continue to do outstanding work even in the most trying of times. The entire BLS staff has been teleworking now for several months due to COVID-19, and every program continues to produce high quality data on schedule! Even in these extraordinary circumstances, BLS professionals continue to innovate and find ways to improve quality and develop new gold standard data products to help the policymakers, businesses, and the public make better-informed decisions.

Number of unemployed per job opening in the United States and four large metropolitan areas
DateNew York-Newark-Jersey City, NY-NJ-PADallas-Fort Worth-Arlington, TXChicago-Naperville-Elgin, IL-IN-WILos Angeles-Long Beach-Anaheim, CAUnited States

Jan 2007

1.81.41.81.61.6

Feb 2007

1.81.41.91.61.6

Mar 2007

1.71.31.81.61.6

Apr 2007

1.61.11.61.51.4

May 2007

1.51.01.51.51.4

Jun 2007

1.51.11.61.41.4

Jul 2007

1.61.21.81.51.5

Aug 2007

1.71.21.91.51.5

Sep 2007

1.71.21.81.71.5

Oct 2007

1.61.11.71.81.5

Nov 2007

1.61.21.81.91.5

Dec 2007

1.71.21.91.91.6

Jan 2008

1.91.32.02.11.7

Feb 2008

2.11.32.22.21.8

Mar 2008

2.21.32.42.21.9

Apr 2008

2.11.22.22.01.8

May 2008

2.01.22.22.11.8

Jun 2008

1.91.22.42.51.9

Jul 2008

2.21.43.03.02.2

Aug 2008

2.31.73.13.52.4

Sep 2008

2.41.83.13.62.5

Oct 2008

2.51.93.04.02.6

Nov 2008

2.82.13.44.62.9

Dec 2008

3.12.43.95.93.3

Jan 2009

3.62.94.86.74.0

Feb 2009

4.43.25.57.34.6

Mar 2009

5.03.56.47.45.1

Apr 2009

5.03.66.77.35.3

May 2009

5.14.06.87.15.4

Jun 2009

5.14.67.26.85.6

Jul 2009

5.25.37.77.46.0

Aug 2009

5.15.67.97.76.2

Sep 2009

5.45.88.08.06.1

Oct 2009

5.95.37.87.85.8

Nov 2009

6.75.68.68.25.9

Dec 2009

7.15.69.58.36.2

Jan 2010

7.06.310.88.36.2

Feb 2010

7.06.410.37.56.2

Mar 2010

7.05.98.87.15.9

Apr 2010

6.55.17.86.75.4

May 2010

5.94.86.66.54.9

Jun 2010

5.05.16.26.74.8

Jul 2010

4.85.26.07.14.9

Aug 2010

4.75.46.27.54.9

Sep 2010

4.94.76.17.54.7

Oct 2010

4.64.25.27.14.5

Nov 2010

4.84.05.07.14.5

Dec 2010

5.34.15.17.14.6

Jan 2011

6.04.35.57.15.0

Feb 2011

6.14.25.76.84.9

Mar 2011

5.54.05.36.34.6

Apr 2011

5.03.85.15.84.2

May 2011

4.63.64.65.84.1

Jun 2011

4.53.74.55.64.0

Jul 2011

4.63.84.65.94.1

Aug 2011

4.53.84.96.04.0

Sep 2011

4.43.44.65.83.8

Oct 2011

4.23.14.25.63.6

Nov 2011

4.03.04.25.33.6

Dec 2011

4.23.04.85.53.7

Jan 2012

4.63.05.25.93.7

Feb 2012

5.33.04.86.23.7

Mar 2012

5.12.84.25.73.5

Apr 2012

4.22.43.65.03.3

May 2012

3.92.13.44.83.1

Jun 2012

3.92.13.64.63.1

Jul 2012

4.22.33.95.33.3

Aug 2012

4.02.43.95.23.4

Sep 2012

3.82.43.55.53.2

Oct 2012

3.72.13.25.03.0

Nov 2012

3.92.03.44.83.0

Dec 2012

4.02.03.75.13.2

Jan 2013

4.22.24.35.43.4

Feb 2013

4.22.14.15.43.4

Mar 2013

4.02.03.94.93.2

Apr 2013

3.51.93.54.12.9

May 2013

3.21.93.53.82.7

Jun 2013

3.22.13.63.62.7

Jul 2013

3.42.33.83.82.9

Aug 2013

3.42.33.73.92.9

Sep 2013

3.42.13.64.02.8

Oct 2013

3.22.03.33.92.5

Nov 2013

3.22.03.24.12.5

Dec 2013

3.22.03.34.22.6

Jan 2014

3.42.03.54.22.7

Feb 2014

3.41.93.53.72.7

Mar 2014

3.21.93.33.32.6

Apr 2014

2.81.72.62.82.2

May 2014

2.51.62.12.82.0

Jun 2014

2.41.62.02.81.9

Jul 2014

2.61.62.13.12.0

Aug 2014

2.61.62.12.91.9

Sep 2014

2.51.62.02.91.9

Oct 2014

2.31.51.92.71.7

Nov 2014

2.41.51.92.91.8

Dec 2014

2.51.31.92.91.8

Jan 2015

2.61.32.02.91.9

Feb 2015

2.51.31.92.61.8

Mar 2015

2.41.31.82.41.7

Apr 2015

2.21.21.62.21.6

May 2015

2.01.11.52.21.5

Jun 2015

1.91.01.62.31.5

Jul 2015

1.91.01.62.31.5

Aug 2015

1.90.91.62.31.5

Sep 2015

1.80.91.52.31.4

Oct 2015

1.70.81.52.01.3

Nov 2015

1.60.81.52.01.3

Dec 2015

1.60.81.51.91.4

Jan 2016

1.70.91.61.91.4

Feb 2016

1.80.81.61.71.5

Mar 2016

1.70.81.61.61.4

Apr 2016

1.60.71.61.61.3

May 2016

1.40.71.41.61.2

Jun 2016

1.40.81.41.61.3

Jul 2016

1.40.91.41.81.3

Aug 2016

1.50.91.51.91.4

Sep 2016

1.40.91.51.91.3

Oct 2016

1.30.91.51.71.3

Nov 2016

1.30.91.41.71.3

Dec 2016

1.30.91.51.71.3

Jan 2017

1.41.01.71.81.4

Feb 2017

1.51.01.71.81.4

Mar 2017

1.51.01.51.81.4

Apr 2017

1.30.91.41.61.2

May 2017

1.30.91.21.51.1

Jun 2017

1.31.01.21.41.1

Jul 2017

1.31.11.21.51.1

Aug 2017

1.41.11.21.61.1

Sep 2017

1.41.01.11.51.1

Oct 2017

1.31.01.01.31.0

Nov 2017

1.21.01.01.31.0

Dec 2017

1.21.01.11.31.0

Jan 2018

1.21.11.31.31.1

Feb 2018

1.21.11.31.21.1

Mar 2018

1.21.11.21.21.1

Apr 2018

1.11.01.11.11.0

May 2018

1.01.00.91.00.9

Jun 2018

1.00.90.81.10.9

Jul 2018

1.00.80.81.10.9

Aug 2018

1.00.80.91.20.9

Sep 2018

0.90.80.91.20.8

Oct 2018

0.90.80.81.10.8

Nov 2018

0.80.80.81.20.8

Dec 2018

0.90.70.81.30.8

Jan 2019

1.00.80.91.40.9

Feb 2019

1.10.81.01.40.9

Mar 2019

1.10.80.91.40.9

Apr 2019

1.00.70.91.10.8

May 2019

0.80.70.81.00.8

Jun 2019

0.80.60.80.90.8

Jul 2019

0.80.70.81.00.8

Aug 2019

0.80.80.91.10.9

Sep 2019

0.80.80.91.20.8

Oct 2019

0.80.80.81.10.8

Nov 2019

0.80.80.71.00.8

Dec 2019

0.80.80.71.00.8

How Many Unemployed People? Comparing Survey Data and Unemployment Insurance Counts

More than 37 million people filed for unemployment insurance benefits in the 10 weeks from the week ending March 21 to the week ending May 23. The unemployment rate in April was 14.7 percent. Or was it 19.5 percent? There were 23 million people counted as unemployed in mid-April, and 18 million people received unemployment insurance (UI) benefits at that time. How can all of these things be true? What’s the real story?

Back in October, I set the record straight on how counts of people receiving unemployment insurance benefits differ from how BLS measures unemployment. These two measures offer distinct but related measures of trends in joblessness, some of which I will explore here. I will focus only on data from states’ regular UI programs, but other programs exist as well. Here’s the bottom line: When all is said and done, the two measures track each other very closely.

The number of people filing for UI benefits reached record levels in recent weeks as a result of the COVID-19 pandemic and efforts to contain it. The UI claims numbers don’t come from BLS but rather from our colleagues at the U.S. Department of Labor’s Employment and Training Administration. Their count of people receiving UI benefits hit its highest level ever, nearly 23 million (not seasonally adjusted), for the week ending May 9. Their separate count of people filing new UI claims hit a record high of more than 6 million people in early April.

UI Continued Claimed versus Total Unemployed

First, the contrasts. The Employment and Training Administration publishes weekly counts of UI claims. The UI claims data include both initial claims and continued claims.

  • Initial claims: A count of the new claims people filed to request UI benefits. These claims won’t necessarily all be approved if, for example, a state UI program determines the person isn’t eligible to receive benefits.
  • Continued claims: A count of claims for those who have already filed initial claims and who have experienced a week of unemployment. These people then file a continued claim to receive benefits for that week of unemployment. Continued claims are also called insured unemployment.

Interviewers for the BLS Current Population Survey contact households once a month to ask questions about employment, job search, and other labor market topics for the week containing the 12th of the month. The monthly labor market survey counts people as unemployed if they meet all of these conditions:

  • They are not employed.
  • They could have taken a job if one had been offered.
  • They had made at least one specific, active effort to find employment in the last 4 weeks OR were on temporary layoff.

People counted in the survey as unemployed may or may not be eligible for UI benefits.

Counts of continued UI claims track pretty well with our survey measures of unemployment. The two measures run mostly parallel but at different levels over time. The chart below shows some history through the reference week of the survey data for April 2020.

Continued unemployment insurance claims and total unemployed, 1994–2020, not seasonally adjusted

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

The gap between the two measures shows how the survey captures millions of unemployed people who do not receive UI benefits. This gap partly results from states’ eligibility requirements for their UI programs.

UI Continued Claims versus Job Losers

Our monthly labor market survey lets us see more detail about the characteristics of people who are unemployed. One characteristic is the reason for a person’s unemployment.

  • Some people are labor force entrants or reentrants if they did not have a job immediately before starting their job search.
  • Others quit or leave their job voluntarily and are job leavers.
  • The rest become unemployed by losing their job in one of the following ways:
    • Being permanently laid off
    • Being temporarily laid off
    • Completing a temporary job

People who become unemployed after losing their jobs are job losers. Job losers are more likely to be eligible for UI benefits. Data for this group more closely track the continued claims data.

Continued unemployment insurance claims and unemployed job losers, 1994–2020, not seasonally adjusted

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

To narrow the gap even more, we know the time limits all regular UI programs have for receiving benefits. These limits vary by state, but states rarely offer more than 26 weeks of benefits in their regular program. Our survey estimates of job losers unemployed 26 weeks or fewer track more closely with UI continued claims.

Continued unemployment insurance claims and unemployed job losers who were unemployed 26 weeks or fewer, 1994–2020, not seasonally adjusted

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

Current Trends

Let’s focus on these two measures since last fall. We can see they track even more closely through the April survey reference week.

Continued unemployment insurance claims and unemployed job losers who were unemployed 26 weeks or fewer, November 2019 to May 2020, not seasonally adjusted

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

Labor market data from BLS have always been essential for understanding our economy. Good data—from many sources—are even more essential now, both for guiding the public health response to the COVID-19 pandemic and for tracking its economic impact and recovery. The labor market impacts from the pandemic have been so massive and happened so quickly that policymakers, businesses, and workers have wanted data in almost real time. Our monthly surveys weren’t designed to provide data quite that rapidly, but combining data from multiple sources and understanding how those measures track one another can provide more insight than any single source. We’ve been thinking a lot in recent years about how to complement our survey data with high-frequency information from other sources. I’ve written about some of those efforts and will continue to do so. This pandemic will sharpen our focus on innovating to provide gold standard data for the public good.

Continued unemployment insurance claims and Current Population Survey measures of unemployed, not seasonally adjusted
DateCurrent Population Survey unemployedCurrent Population Survey unemployed job losersCurrent Population Survey unemployed job losers who were unemployed 26 weeks or fewerUnemployment insurance continued claims under state programs

1/15/1994

9,492,0005,215,0004,204,0003,142,036

2/12/1994

9,262,0004,925,0003,968,0003,529,168

3/12/1994

8,874,0004,522,0003,577,0003,255,495

4/16/1994

8,078,0003,832,0002,959,0002,780,268

5/14/1994

7,656,0003,319,0002,520,0002,558,790

6/18/1994

8,251,0003,459,0002,722,0002,399,131

7/16/1994

8,281,0003,701,0002,919,0002,616,255

8/13/1994

7,868,0003,565,0002,832,0002,382,494

9/17/1994

7,379,0003,206,0002,543,0002,095,343

10/15/1994

7,155,0003,168,0002,557,0002,119,751

11/12/1994

6,973,0003,366,0002,759,0002,246,077

12/10/1994

6,690,0003,514,0002,947,0002,501,945

1/14/1995

8,101,0004,350,0003,697,0003,045,968

2/18/1995

7,685,0003,923,0003,288,0003,011,244

3/18/1995

7,480,0003,718,0003,112,0002,872,790

4/15/1995

7,378,0003,479,0002,817,0002,578,113

5/13/1995

7,185,0003,275,0002,697,0002,362,466

6/17/1995

7,727,0003,160,0002,615,0002,321,295

7/15/1995

7,892,0003,470,0002,899,0002,643,997

8/12/1995

7,457,0003,331,0002,823,0002,363,074

9/16/1995

7,167,0003,017,0002,492,0002,110,587

10/14/1995

6,884,0003,104,0002,588,0002,154,971

11/18/1995

7,024,0003,355,0002,851,0002,095,335

12/9/1995

6,872,0003,533,0003,065,0002,596,696

1/13/1996

8,270,0004,425,0003,884,0003,211,081

2/17/1996

7,858,0004,099,0003,517,0003,234,481

3/16/1996

7,700,0003,849,0003,248,0003,086,902

4/13/1996

7,124,0003,610,0002,903,0002,753,405

5/18/1996

7,166,0003,164,0002,643,0002,314,322

6/15/1996

7,377,0003,116,0002,570,0002,285,632

7/13/1996

7,693,0003,323,0002,717,0002,494,226

8/17/1996

6,868,0002,932,0002,451,0002,219,197

9/14/1996

6,700,0002,812,0002,375,0002,007,009

10/12/1996

6,577,0002,757,0002,320,0001,910,925

11/16/1996

6,816,0003,126,0002,677,0002,169,112

12/7/1996

6,680,0003,230,0002,805,0002,406,303

1/18/1997

7,933,0004,027,0003,561,0002,975,311

2/15/1997

7,647,0003,659,0003,158,0002,903,673

3/15/1997

7,399,0003,493,0003,026,0002,719,345

4/12/1997

6,551,0003,050,0002,593,0002,392,620

5/17/1997

6,398,0002,696,0002,318,0002,039,498

6/14/1997

7,094,0002,878,0002,457,0002,008,106

7/12/1997

6,981,0002,895,0002,405,0002,273,294

8/16/1997

6,594,0002,859,0002,374,0002,047,159

9/13/1997

6,403,0002,616,0002,190,0001,797,836

10/18/1997

5,995,0002,525,0002,105,0001,761,841

11/15/1997

5,914,0002,698,0002,319,0001,977,179

12/13/1997

5,957,0003,051,0002,663,0002,251,072

1/17/1998

7,069,0003,556,0003,136,0002,726,043

2/14/1998

6,804,0003,254,0002,868,0002,660,864

3/14/1998

6,816,0003,311,0002,906,0002,590,407

4/18/1998

5,643,0002,647,0002,262,0002,181,018

5/16/1998

5,764,0002,517,0002,196,0001,895,102

6/13/1998

6,534,0002,628,0002,316,0001,908,179

7/18/1998

6,567,0002,847,0002,499,0002,277,800

8/15/1998

6,173,0002,715,0002,365,0001,987,304

9/12/1998

6,039,0002,534,0002,169,0001,805,455

10/17/1998

5,831,0002,426,0002,109,0001,735,477

11/14/1998

5,711,0002,587,0002,299,0001,944,590

12/12/1998

5,565,0002,849,0002,537,0002,232,312

1/16/1999

6,604,0003,394,0003,095,0002,808,153

2/13/1999

6,563,0003,151,0002,859,0002,669,301

3/13/1999

6,119,0002,888,0002,597,0002,581,727

4/17/1999

5,688,0002,633,0002,360,0002,219,359

5/15/1999

5,507,0002,362,0002,107,0002,016,349

6/12/1999

6,271,0002,495,0002,204,0001,963,530

7/17/1999

6,319,0002,729,0002,422,0002,181,103

8/14/1999

5,826,0002,559,0002,251,0001,978,309

9/18/1999

5,661,0002,299,0002,039,0001,728,476

10/16/1999

5,372,0002,162,0001,935,0001,705,790

11/13/1999

5,380,0002,340,0002,092,0001,828,872

12/11/1999

5,245,0002,451,0002,193,0002,094,337

1/15/2000

6,316,0003,134,0002,808,0002,531,224

2/12/2000

6,284,0003,066,0002,771,0002,604,156

3/18/2000

6,069,0002,802,0002,535,0002,277,154

4/15/2000

5,212,0002,259,0002,027,0001,975,507

5/13/2000

5,460,0002,196,0001,957,0001,783,386

6/17/2000

5,959,0002,303,0002,080,0001,812,319

7/15/2000

6,028,0002,508,0002,273,0002,107,129

8/12/2000

5,863,0002,570,0002,317,0001,933,774

9/16/2000

5,359,0002,284,0002,010,0001,709,044

10/14/2000

5,153,0002,105,0001,857,0001,735,297

11/18/2000

5,336,0002,355,0002,108,0001,822,245

12/9/2000

5,264,0002,618,0002,376,0002,261,776

1/13/2001

6,647,0003,449,0003,162,0002,787,024

2/17/2001

6,523,0003,343,0003,061,0002,954,857

3/17/2001

6,509,0003,379,0003,047,0002,932,361

4/14/2001

6,004,0003,027,0002,697,0002,772,097

5/12/2001

5,901,0002,839,0002,578,0002,554,830

6/16/2001

6,816,0003,136,0002,833,0002,634,433

7/14/2001

6,858,0003,372,0003,060,0003,053,451

8/18/2001

7,017,0003,379,0003,004,0002,793,540

9/15/2001

6,766,0003,294,0002,961,0002,630,082

10/13/2001

7,175,0003,753,0003,356,0002,888,718

11/17/2001

7,617,0004,252,0003,738,0003,105,348

12/8/2001

7,773,0004,485,0003,937,0003,604,679

1/12/2002

9,051,0005,449,0004,757,0004,234,835

2/16/2002

8,823,0005,105,0004,457,0004,206,538

3/16/2002

8,776,0004,861,0004,145,0004,078,226

4/13/2002

8,255,0004,550,0003,744,0003,731,669

5/18/2002

7,969,0004,180,0003,293,0003,314,004

6/15/2002

8,758,0004,429,0003,547,0003,248,721

7/13/2002

8,693,0004,607,0003,673,0003,518,751

8/17/2002

8,271,0004,427,0003,545,0003,195,935

9/14/2002

7,790,0004,123,0003,170,0002,947,854

10/12/2002

7,769,0004,151,0003,181,0002,912,625

11/16/2002

8,170,0004,555,0003,521,0003,205,969

12/7/2002

8,209,0004,849,0003,718,0003,481,337

1/18/2003

9,395,0005,641,0004,564,0004,011,764

2/15/2003

9,260,0005,487,0004,336,0004,042,069

3/15/2003

9,018,0005,150,0004,047,0004,009,388

4/12/2003

8,501,0004,716,0003,526,0003,693,322

5/17/2003

8,500,0004,589,0003,475,0003,341,816

6/14/2003

9,649,0004,775,0003,654,0003,334,821

7/12/2003

9,319,0004,958,0003,842,0003,635,324

8/16/2003

8,830,0004,789,0003,715,0003,278,613

9/13/2003

8,436,0004,500,0003,363,0002,985,665

10/18/2003

8,169,0004,319,0003,206,0002,944,236

11/15/2003

8,269,0004,505,0003,401,0003,090,089

12/13/2003

7,945,0004,629,0003,598,0003,338,180

1/17/2004

9,144,0005,195,0004,063,0003,754,598

2/14/2004

8,770,0004,888,0003,838,0003,690,774

3/13/2004

8,834,0004,920,0003,765,0003,466,491

4/17/2004

7,837,0004,253,0003,255,0002,994,939

5/15/2004

7,792,0003,778,0002,845,0002,690,913

6/12/2004

8,616,0003,930,0003,010,0002,685,110

7/17/2004

8,518,0004,233,0003,372,0002,892,369

8/14/2004

7,940,0003,809,0002,982,0002,639,474

9/18/2004

7,545,0003,644,0002,853,0002,342,492

10/16/2004

7,531,0003,653,0002,832,0002,348,403

11/13/2004

7,665,0003,898,0003,103,0002,481,768

12/11/2004

7,599,0004,166,0003,320,0002,781,151

1/15/2005

8,444,0004,771,0003,905,0003,269,319

2/12/2005

8,549,0004,461,0003,624,0003,200,271

3/12/2005

7,986,0004,067,0003,289,0003,044,727

4/16/2005

7,335,0003,559,0002,787,0002,565,759

5/14/2005

7,287,0003,265,0002,606,0002,347,511

6/18/2005

7,870,0003,482,0002,883,0002,354,977

7/16/2005

7,839,0003,618,0003,034,0002,581,153

8/13/2005

7,327,0003,297,0002,705,0002,361,634

9/17/2005

7,259,0003,373,0002,775,0002,293,043

10/15/2005

6,964,0003,162,0002,535,0002,377,075

11/12/2005

7,271,0003,329,0002,687,0002,515,835

12/10/2005

6,956,0003,622,0003,000,0002,659,503

1/14/2006

7,608,0003,990,0003,419,0003,010,836

2/18/2006

7,692,0003,846,0003,233,0002,865,435

3/18/2006

7,255,0003,707,0003,108,0002,712,772

4/15/2006

6,804,0003,426,0002,771,0002,430,217

5/13/2006

6,655,0003,152,0002,546,0002,183,176

6/17/2006

7,341,0003,222,0002,718,0002,177,172

7/15/2006

7,602,0003,374,0002,820,0002,450,260

8/12/2006

7,086,0003,132,0002,585,0002,283,575

9/16/2006

6,625,0002,878,0002,366,0002,022,552

10/14/2006

6,272,0002,724,0002,247,0002,077,157

11/11/2006

6,576,0003,025,0002,555,0002,231,475

12/9/2006

6,491,0003,374,0002,928,0002,536,673

1/13/2007

7,649,0004,127,0003,668,0002,887,810

2/17/2007

7,400,0003,942,0003,425,0003,037,700

3/17/2007

6,913,0003,487,0002,987,0002,788,224

4/14/2007

6,532,0003,249,0002,724,0002,598,802

5/12/2007

6,486,0003,070,0002,591,0002,293,089

6/16/2007

7,295,0003,241,0002,752,0002,241,672

7/14/2007

7,556,0003,730,0003,157,0002,548,427

8/18/2007

7,088,0003,472,0002,899,0002,335,412

9/15/2007

6,952,0003,208,0002,645,0002,128,411

10/13/2007

6,773,0003,259,0002,668,0002,143,999

11/10/2007

6,917,0003,382,0002,814,0002,260,475

12/8/2007

7,371,0004,013,0003,396,0002,665,956

1/12/2008

8,221,0004,608,0003,951,0003,242,075

2/16/2008

7,953,0004,471,0003,805,0003,265,157

3/15/2008

8,027,0004,555,0003,922,0003,220,809

4/12/2008

7,287,0003,931,0003,212,0003,018,445

5/17/2008

8,076,0003,949,0003,204,0002,759,158

6/14/2008

8,933,0004,201,0003,451,0002,801,895

7/12/2008

9,433,0004,562,0003,782,0003,097,770

8/16/2008

9,479,0004,735,0003,855,0003,112,252

9/13/2008

9,199,0004,699,0003,655,0002,957,202

10/18/2008

9,469,0005,138,0004,006,0003,188,153

11/15/2008

10,015,0005,746,0004,609,0003,714,261

12/13/2008

10,999,0006,878,0005,483,0004,531,208

1/17/2009

13,009,0008,633,0007,041,0005,647,319

2/14/2009

13,699,0009,098,0007,380,0006,031,637

3/14/2009

13,895,0009,315,0007,347,0006,354,009

4/18/2009

13,248,0008,687,0006,347,0006,237,658

5/16/2009

13,973,0008,930,0006,531,0006,049,295

6/13/2009

15,095,0009,194,0006,567,0006,012,730

7/18/2009

15,201,0009,447,0006,275,0005,989,877

8/15/2009

14,823,0009,316,0005,955,0005,578,533

9/12/2009

14,538,0009,170,0005,520,0005,131,447

10/17/2009

14,547,0009,176,0005,482,0004,893,301

11/14/2009

14,407,0009,130,0005,237,0004,996,155

12/12/2009

14,740,0009,822,0005,881,0005,262,045

1/16/2010

16,147,00010,574,0006,302,0005,538,244

2/13/2010

15,991,00010,664,0006,347,0005,465,212

3/13/2010

15,678,00010,311,0005,880,0005,270,644

4/17/2010

14,609,0009,110,0004,607,0004,715,968

5/15/2010

14,369,0008,812,0004,484,0004,333,973

6/12/2010

14,885,0008,769,0004,606,0004,226,459

7/17/2010

15,137,0008,964,0004,695,0004,471,386

8/14/2010

14,759,0008,894,0004,724,0004,138,097

9/18/2010

14,140,0008,651,0004,526,0003,737,930

10/16/2010

13,903,0008,331,0004,306,0003,697,842

11/13/2010

14,282,0008,926,0004,787,0003,804,696

12/11/2010

13,997,0008,995,0004,986,0004,119,344

1/15/2011

14,937,0009,520,0005,545,0004,552,936

2/12/2011

14,542,0009,212,0005,403,0004,521,733

3/12/2011

14,060,0008,841,0004,874,0004,215,458

4/16/2011

13,237,0007,958,0004,276,0003,726,578

5/14/2011

13,421,0007,885,0004,051,0003,492,720

6/18/2011

14,409,0007,940,0004,315,0003,454,731

7/16/2011

14,428,0008,107,0004,441,0003,695,537

8/13/2011

14,008,0007,897,0004,395,0003,497,400

9/17/2011

13,520,0007,636,0003,849,0003,150,942

10/15/2011

13,102,0007,390,0003,911,0003,141,911

11/12/2011

12,613,0007,201,0003,813,0003,323,025

12/10/2011

12,692,0007,691,0004,392,0003,571,487

1/14/2012

13,541,0008,234,0004,977,0004,019,589

2/18/2012

13,430,0007,866,0004,783,0003,834,179

3/17/2012

12,904,0007,415,0004,394,0003,650,071

4/14/2012

11,910,0006,555,0003,580,0003,377,436

5/12/2012

12,271,0006,607,0003,601,0003,079,181

6/16/2012

13,184,0006,927,0003,973,0003,069,545

7/14/2012

13,400,0007,151,0004,248,0003,288,629

8/18/2012

12,696,0006,820,0004,003,0003,068,519

9/15/2012

11,742,0006,161,0003,497,0002,796,675

10/13/2012

11,741,0006,125,0003,428,0002,772,151

11/10/2012

11,404,0006,069,0003,520,0002,902,343

12/8/2012

11,844,0006,592,0004,086,0003,203,819

1/12/2013

13,181,0007,575,0005,046,0003,661,355

2/16/2013

12,500,0007,130,0004,468,0003,483,983

3/16/2013

11,815,0006,638,0004,024,0003,345,945

4/13/2013

11,014,0006,079,0003,642,0003,049,657

5/18/2013

11,302,0005,751,0003,437,0002,752,679

6/15/2013

12,248,0005,939,0003,794,0002,745,766

7/13/2013

12,083,0005,934,0003,771,0002,995,510

8/17/2013

11,462,0005,856,0003,677,0002,772,037

9/14/2013

10,885,0005,470,0003,409,0002,412,302

10/12/2013

10,773,0005,649,0003,657,0002,418,279

11/9/2013

10,271,0005,400,0003,371,0002,514,678

12/7/2013

9,984,0005,460,0003,492,0002,838,295

1/18/2014

10,855,0006,152,0004,163,0003,334,697

2/15/2014

10,893,0006,024,0004,089,0003,329,510

3/15/2014

10,537,0005,779,0003,853,0003,096,231

4/12/2014

9,079,0004,972,0003,100,0002,721,859

5/17/2014

9,443,0004,613,0002,948,0002,421,319

6/14/2014

9,893,0004,670,0003,239,0002,372,393

7/12/2014

10,307,0004,867,0003,359,0002,518,959

8/16/2014

9,787,0004,750,0003,442,0002,363,077

9/13/2014

8,962,0004,176,0002,774,0002,076,867

10/18/2014

8,680,0003,995,0002,682,0002,046,031

11/15/2014

8,630,0004,182,0002,954,0002,158,767

12/13/2014

8,331,0004,355,0003,083,0002,445,747

1/17/2015

9,498,0004,912,0003,601,0002,750,868

2/14/2015

9,095,0004,721,0003,514,0002,720,615

3/14/2015

8,682,0004,503,0003,259,0002,674,331

4/18/2015

7,966,0003,977,0002,789,0002,251,252

5/16/2015

8,370,0003,962,0002,850,0002,047,456

6/13/2015

8,638,0003,951,0003,029,0002,066,476

7/18/2015

8,805,0004,204,0003,207,0002,217,720

8/15/2015

8,162,0003,987,0002,968,0002,124,998

9/12/2015

7,628,0003,509,0002,647,0001,903,085

10/17/2015

7,597,0003,576,0002,678,0001,825,692

11/14/2015

7,573,0003,633,0002,815,0001,970,435

12/12/2015

7,542,0003,820,0002,964,0002,255,937

1/16/2016

8,309,0004,287,0003,357,0002,624,638

2/13/2016

8,219,0004,244,0003,299,0002,582,311

3/12/2016

8,116,0004,149,0003,123,0002,461,697

4/16/2016

7,413,0003,716,0002,761,0002,126,849

5/14/2016

7,207,0003,322,0002,480,0001,982,730

6/18/2016

8,144,0003,677,0002,855,0001,975,334

7/16/2016

8,267,0003,869,0003,001,0002,109,038

8/13/2016

7,996,0003,787,0002,918,0002,030,018

9/17/2016

7,658,0003,536,0002,660,0001,728,317

10/15/2016

7,447,0003,352,0002,551,0001,710,066

11/12/2016

7,066,0003,271,0002,490,0001,828,034

12/10/2016

7,170,0003,668,0002,917,0002,071,781

1/14/2017

8,149,0004,361,0003,481,0002,437,106

2/18/2017

7,887,0004,184,0003,335,0002,364,751

3/18/2017

7,284,0003,812,0002,990,0002,256,527

4/15/2017

6,555,0003,369,0002,561,0001,984,675

5/13/2017

6,572,0003,017,0002,317,0001,757,086

6/17/2017

7,250,0003,359,0002,698,0001,780,061

7/15/2017

7,441,0003,519,0002,822,0001,936,985

8/12/2017

7,287,0003,536,0002,866,0001,851,667

9/16/2017

6,556,0002,992,0002,314,0001,611,895

10/14/2017

6,242,0002,859,0002,252,0001,572,784

11/11/2017

6,286,0002,907,0002,289,0001,672,980

12/9/2017

6,278,0003,298,0002,715,0001,909,886

1/13/2018

7,189,0003,891,0003,259,0002,242,438

2/17/2018

7,091,0003,716,0003,069,0002,226,157

3/17/2018

6,671,0003,375,0002,799,0002,082,891

4/14/2018

5,932,0002,805,0002,272,0001,841,572

5/12/2018

5,756,0002,493,0002,004,0001,584,129

6/16/2018

6,812,0003,022,0002,511,0001,564,998

7/14/2018

6,730,0003,164,0002,551,0001,738,468

8/18/2018

6,370,0002,885,0002,291,0001,605,843

9/15/2018

5,766,0002,474,0001,912,0001,396,832

10/13/2018

5,771,0002,510,0001,958,0001,353,628

11/10/2018

5,650,0002,598,0002,107,0001,429,209

12/8/2018

6,029,0002,947,0002,443,0001,703,504

1/12/2019

7,140,0003,791,0003,291,0002,070,444

2/16/2019

6,625,0003,300,0002,760,0002,107,108

3/16/2019

6,382,0003,098,0002,513,0001,990,542

4/13/2019

5,387,0002,484,0001,984,0001,686,671

5/18/2019

5,503,0002,281,0001,783,0001,491,921

6/15/2019

6,292,0002,703,0002,196,0001,538,052

7/13/2019

6,556,0002,986,0002,483,0001,673,714

8/17/2019

6,203,0002,906,0002,418,0001,594,845

9/14/2019

5,465,0002,227,0001,711,0001,379,722

10/12/2019

5,510,0002,340,0001,889,0001,366,544

11/9/2019

5,441,0002,561,0002,123,0001,439,799

12/7/2019

5,503,0002,752,0002,361,0001,707,456

1/18/2020

6,504,0003,267,0002,808,0002,053,978

2/15/2020

6,218,0003,151,0002,712,0002,036,213

3/14/2020

7,370,0004,441,0003,907,0002,055,283

4/18/2020

22,504,00020,384,00019,953,00017,601,283
Continued unemployment insurance claims and unemployed job losers who were unemployed 26 weeks or fewer, not seasonally adjusted
DateCurrent Population Survey unemployed job losers who were unemployed 26 weeks or fewerUnemployment insurance continued claims under state programs

11/2/2019

1,428,992

11/9/2019

2,123,0001,439,799

11/16/2019

1,523,691

11/23/2019

1,488,691

11/30/2019

1,733,682

12/7/2019

2,361,0001,707,456

12/14/2019

1,780,860

12/21/2019

1,760,439

12/28/2019

2,124,746

1/4/2020

2,229,673

1/11/2020

2,114,161

1/18/2020

2,808,0002,053,978

1/25/2020

2,125,243

2/1/2020

2,060,389

2/8/2020

2,073,658

2/15/2020

2,712,0002,036,213

2/22/2020

2,079,249

2/29/2020

2,032,792

3/7/2020

1,954,265

3/14/2020

3,907,0002,055,283

3/21/2020

3,391,238

3/28/2020

8,107,677

4/4/2020

12,356,980

4/11/2020

16,138,295

4/18/2020

19,953,00017,601,283

4/25/2020

21,576,373

5/2/2020

20,733,760

5/9/2020

22,637,743

5/16/2020

18,855,114

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.