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All posts by BLS Commissioner

Measuring Consumer Prices for New Vehicles More Accurately

In May 2020, we announced a new research index designed to improve the way we measure consumer price changes for new vehicles. Part of the Consumer Price Index (CPI) program, the research index uses transaction data on vehicle purchases. BLS obtains the data from J.D. Power. After carefully studying the data, we now plan to implement the new data source into the official CPI, effective with the April 2022 release, which we will publish on May 11, 2022. We also will update Measuring Price Change in the CPI: New vehicles factsheet at that time.

The research index departs from the traditional survey methods and data sources we have used in the CPI. The traditional methods sample vehicle dealers and the makes, models, and features of the vehicles they sell. That’s why we took a very deliberate approach before we incorporated the new data into the official CPI. We discuss details of our methods and research in “A New Vehicles Transaction Price Index: Offsetting the Effects of Price Discrimination and Product Cycle Bias with a Year-Over-Year Index.”

Leading up to March 2020, the movements of the research index for new vehicles were similar to the official index for new vehicles. Since March 2020, the indexes began to diverge, with the research index showing faster price increases that the official index did not reflect.

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

Since the COVID-19 pandemic began in March 2020, the economy experienced significant disruption. This is especially true of the automobile industry, which saw fluctuations in the supply chain, employment, and consumer demand. Combining these shifts in the economy with the changes in methods represented by the research index made it challenging to assess our results.

While the research index and the official new vehicles index maintained similar trends, the recent divergence between them provided an opportunity to assess the robustness of the two approaches.

We weighed several factors in deciding whether to incorporate the J.D Power data on new vehicle prices into the official CPI. The new data include records of the prices paid during hundreds of thousands of transactions each month. That dwarfs the roughly 500 prices collected using traditional CPI methods. The larger dataset allows us to estimate price changes more precisely. As a result, the research index has a much lower standard error than the official new vehicles index.

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

Because the research data reflect actual transactions, the shift in consumer preference from cars to other types of vehicles, such as trucks, is built into the data. This differs from the official index, which has maintained a roughly equal weight between cars and trucks.

In addition to the quantitative evaluations, BLS continued to ask for feedback on the research index through our website and by consulting with other statistical agencies. We received positive feedback and no major concerns, and we remain confident the research index is statistically sound. For these reasons, we have decided to incorporate the new data source and methods for new vehicles in the official CPI.

In some ways, the past 2 years have been an unprecedented time for statistical measurement, but in other ways business at BLS has continued as usual. When the COVID-19 pandemic began in March 2020, BLS ceased in-person data collection for the CPI and other programs. We collected more data online, by telephone, and through video. While the pandemic affected data collection, we continue to publish data on schedule. We also continue to assess our methods and seek ways to improve the quality of our data. Improving our methods for collecting price data for new vehicles is another step forward in innovating and improving the CPI.

Comparison of research index and official index for new vehicle prices
MonthResearch indexOfficial index

Jan 2018

100.000100.000

Feb 2018

100.02699.871

Mar 2018

99.48199.817

Apr 2018

99.84299.370

May 2018

99.58399.560

Jun 2018

99.49999.705

Jul 2018

99.84199.681

Aug 2018

99.94299.424

Sep 2018

99.91799.129

Oct 2018

99.88699.043

Nov 2018

100.07299.204

Dec 2018

99.47199.409

Jan 2019

99.969100.043

Feb 2019

100.295100.157

Mar 2019

100.182100.539

Apr 2019

100.487100.574

May 2019

100.659100.452

Jun 2019

100.362100.287

Jul 2019

100.484100.027

Aug 2019

100.08999.633

Sep 2019

100.20399.224

Oct 2019

100.44399.136

Nov 2019

99.96599.138

Dec 2019

99.91299.472

Jan 2020

100.486100.175

Feb 2020

100.843100.549

Mar 2020

101.301100.087

Apr 2020

102.431100.008

May 2020

102.842100.154

Jun 2020

103.653100.076

Jul 2020

104.047100.549

Aug 2020

104.142100.284

Sep 2020

103.895100.249

Oct 2020

103.841100.653

Nov 2020

103.977100.726

Dec 2020

103.781101.425

Jan 2021

104.818101.620

Feb 2021

105.652101.714

Mar 2021

106.308101.582

Apr 2021

107.156101.971

May 2021

111.189103.502

Jun 2021

113.656105.341

Jul 2021

114.795106.944

Aug 2021

115.205107.930

Sep 2021

115.942109.013

Oct 2021

118.107110.566

Nov 2021

118.980111.915

Dec 2021

120.336113.373

Jan 2022

121.230114.005

Feb 2022

122.481114.308
Comparison of 12-month standard errors for the research index and official index for new vehicle prices
MonthResearch indexOfficial index

Jan 2019

0.110.61

Feb 2019

0.100.58

Mar 2019

0.110.42

Apr 2019

0.110.43

May 2019

0.130.43

Jun 2019

0.120.46

Jul 2019

0.140.43

Aug 2019

0.130.42

Sep 2019

0.140.42

Oct 2019

0.120.45

Nov 2019

0.160.46

Dec 2019

0.160.40

Jan 2020

0.150.41

Feb 2020

0.150.38

Mar 2020

0.140.33

Apr 2020

0.150.36

May 2020

0.130.40

Jun 2020

0.130.36

Jul 2020

0.150.48

Aug 2020

0.140.47

Sep 2020

0.130.60

Oct 2020

0.130.64

Nov 2020

0.120.65

Dec 2020

0.120.62

Jan 2021

0.130.62

Feb 2021

0.130.66

Mar 2021

0.120.67

Apr 2021

0.110.60

May 2021

0.120.59

Jun 2021

0.130.63

Jul 2021

0.130.61

Aug 2021

0.130.69

Sep 2021

0.130.74

Oct 2021

0.160.72

Nov 2021

0.150.55

Dec 2021

0.150.69

Jan 2022

0.140.67

Feb 2022

0.140.68

How Timing and World Events Affect Price Statistics

Rising prices have certainly been in the news lately, and we have received a lot of questions about BLS price statistics. Some questions, however, are “evergreen.” Even in times of moderate price changes, BLS staff often hear that the Consumer Price Index (CPI) doesn’t reflect an individual’s experience. We address this concern and a wide range of other issues in our Questions and Answers about the CPI:

Q. Whose buying habits does the CPI reflect?

A. The CPI does not necessarily measure your own experience with price change. It is important to understand that BLS bases the market baskets and pricing procedures for the CPI-U and CPI-W populations on the experience of the relevant average household, not of any specific family or individual. For example, if you spend a larger-than-average share of your budget on medical expenses, and medical care costs are increasing more rapidly than the cost of other items in the CPI market basket, your personal rate of inflation may exceed the increase in the CPI. Conversely, if you heat your home with solar energy, and fuel prices are rising more rapidly than other items, you may experience less inflation than the general population does. A national average reflects millions of individual price experiences; it seldom mirrors a particular consumer’s experience.

Beyond the differences in individual spending habits, price statistics are affected by a variety of factors, including world events and the timing of price data collection. To explore these factors, we will look beyond the CPI to all BLS price indexes. We’ll focus on the price of oil and related items. Let’s start with a reminder of what is included in the BLS family of price indexes and look at how oil-related prices changed in March.

  • The Consumer Price Index measures the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.
    • The CPI for gasoline (all types) rose 18.3 percent in March and 48.0 percent over the last 12 months.
    • The CPI for energy rose 11.0 percent in March and 32.0 percent over the last 12 months.
  • The Producer Price Index (PPI) measures the average change over time in the selling prices domestic producers receive for their output.
    • The PPI for crude petroleum rose 7.2 percent in March and 62.2 percent over the last 12 months.
    • The PPI for petroleum refineries rose 17.0 percent in March and 62.1 percent over the last 12 months.
    • The PPI for fuels and lubricants retailing rose 22.7 percent in March and 40.0 percent over the last 12 months.
  • The Import and Export Price Indexes show changes in prices of nonmilitary goods and services traded between the United States and the rest of the world.
    • The Import Price Index for crude petroleum rose 15.6 percent in March and 62.0 percent over the last 12 months.
    • The Export Price Index for crude petroleum rose 19.1 percent in March. (This is a new measure, and we haven’t yet tracked it over 12 months.)

National or international events, whether started by Mother Nature or human action, affect the prices businesses and consumers pay for goods and services. We’ve seen this in the past with weather disruptions, such as hurricanes along the Gulf Coast that shut down oil drilling and refining. Current prices may be influenced by the war in Ukraine, the embargo on Russian oil, and other events around the world.

We can see the influence of these events in price changes throughout the production and distribution of oil-related goods and services. BLS estimates the changes in the prices that domestic producers receive through the PPI; this includes petroleum-related industries such as drillers and refiners and the margins on gasoline station sales. Gasoline retailers make money on the margins of their sales—the difference between how much they pay for the fuel they buy from wholesalers and the prices they receive from consumers. Margins for gas stations typically decline when oil prices increase. To learn more, see “As crude oil plunges, retail gasoline margins spike, then retreat.”

Some domestic producers import oil rather than purchase it domestically, and the Import Price Index reflects changes in prices they pay. Some domestic producers also export petroleum-related products, which is captured in Export Price Indexes. Ultimately, consumers purchase gasoline, home heating oil, and other petroleum-based products, and often producers pass price changes on to consumers. Thus, an increase in oil prices can result in higher costs at the pump, more expensive airline fares, and price increases for goods transported by trucks. The CPI reflects these higher prices consumers may face.

The price of oil and related products can change rapidly, adding to the challenges of collecting and publishing timely price statistics. Ideally, BLS would collect prices throughout the month for all goods and services in all price indexes. While that is a long-term goal, it is not simple to implement. Currently, BLS identifies the official “pricing date” for each index, as follows:

  • We collect prices for the CPI throughout the month, with each outlet (such as a gas station) assigned one of three pricing periods, which roughly correspond to the first 10 days, second 10 days, and third 10 days of the month. Once established, prices are updated each month during the same pricing period.
  • We collect prices for most items in the PPI as of the Tuesday of the week containing the thirteenth day of the month. This is the case for the petroleum-related items. (Some items in the PPI have prices collected throughout the month.)
  • We obtain import price data for petroleum from the U.S. Department of Energy. We obtain export price data for petroleum from secondary source market prices. These data represent a weighted average of imported and exported oil throughout the month.

Let’s look at the price of oil over the past few months and how the BLS pricing dates might affect the price indexes.

Daily price per barrel of West Texas Intermediate Crude, January to March 2022

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

The chart shows the volatility of the oil prices, particularly in March. When the February CPI was released on March 10, West Texas Intermediate Crude Oil prices had already soared from $96 per barrel on the last day of February to over $123 two days before the CPI release. While consumers were feeling the pinch at the pump, this steep rise was not reflected in the February CPI data. Similarly, both the February and March PPI price dates (February 15 and March 15) missed the large run-up in oil prices in the first week of March. The Import Price Index, Export Price Index, and CPI did include the highest prices seen in early March, however.

BLS price indexes represent averages—average selections of goods and services, average weights, and typically average time periods. Over time, these indexes provide an accurate view of price change throughout the economy. But during periods of rapidly changing world events, and corresponding rapid changes in the price of individual commodities (and oil in particular), the index pricing periods may miss unusual highs and lows.

Daily price per barrel of West Texas Intermediate Crude, January to March 2022
DateDollars per barrel

Jan 3

$75.99

Jan 4

77.00

Jan 5

77.83

Jan 6

79.47

Jan 7

79.00

Jan 10

78.11

Jan 11

81.17

Jan 12

82.51

Jan 13

81.97

Jan 14

83.82

Jan 18

85.42

Jan 19

86.84

Jan 20

86.29

Jan 21

85.16

Jan 24

84.48

Jan 25

86.61

Jan 26

88.33

Jan 27

87.61

Jan 28

87.67

Jan 31

89.16

Feb 1

88.22

Feb 2

88.16

Feb 3

90.17

Feb 4

92.27

Feb 7

91.25

Feb 8

89.32

Feb 9

89.57

Feb 10

89.83

Feb 11

93.10

Feb 14

95.52

Feb 15

92.07

Feb 16

93.83

Feb 17

91.78

Feb 18

91.26

Feb 22

92.11

Feb 23

92.14

Feb 24

92.77

Feb 25

91.68

Feb 28

96.13

Mar 1

103.66

Mar 2

110.74

Mar 3

107.69

Mar 4

115.77

Mar 7

119.26

Mar 8

123.64

Mar 9

108.81

Mar 10

105.93

Mar 11

109.31

Mar 14

103.22

Mar 15

96.42

Mar 16

94.85

Mar 17

102.97

Mar 18

104.69

Mar 21

112.14

Mar 22

111.03

Mar 23

114.89

Mar 24

114.20

Mar 25

116.20

Mar 28

107.55

Mar 29

104.25

Mar 30

107.81

Mar 31

100.53

Has the Labor Market Recovered from the COVID-19 Pandemic?

I recently had the pleasure of speaking at the National Association for Business Economics Policy Conference, regarding labor market recovery. Today I’m sharing some highlights from that talk, updated to include the latest BLS data.

There continues to be widespread interest in how well U.S. labor markets are recovering from the massive job losses at the start of the COVID-19 pandemic. Not only does this interest involve counting the number of jobs, but it also includes shifts in labor skills and changes in compensation levels. Some parts of the labor market may emerge from a recession very differently from how they entered. This difference could be in skills required, compensation, changes in the workplace, or a worker’s use of capital. Thus, we always should consider the possibility that recovery in a labor market will differ from what we had before the economic shock. In other words, we should be ready to be surprised.

What does “labor market recovery” mean? According to the National Bureau of Economic Research, the pandemic-related recession lasted just 2 months in the first half of 2020. But we know resumption of work varied considerably over the past 2 years, with some industries maintaining or even expanding employment in the early months of the pandemic, while others continue a slower return to pre-pandemic work levels.

Let’s now step through some ways to understand labor market recovery. Many observers would define recovery as a return to the employment levels before the recession, or, in our case, before the economic collapse and slower economic activity as the pandemic continued. We measure this definition of recovery by the number of jobs below and above the February 2020 levels. Based on our most recent data releases, total nonfarm employment, as measured by the BLS Current Employment Statistics survey, has recovered 93 percent of the jobs lost in March and April 2020. This chart shows the change in employment since February 2020 for major industry groups. It’s easy to see industries with large gains and industries that have not yet recovered their previous employment level.

Change in jobs in each industry in March 2022 above or below the levels of February 2020

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

However, this straightforward definition of recovery does not account for the growth in the civilian population (16 years and older) over the past 2 years, which constitutes the base of employment and potential employment. The U.S. population age 16 and older grew by around 3.8 million between February 2020 and March 2022. Thus, we could define recovery as total jobs from February 2020 plus additional employment to account for population growth.

Of course, that does not account for one of the principal labor market characteristics of the past 2 years: The number of workers who left the labor force and never returned. Looking at data from the Current Population Survey, we learn that many of those people who are not in the labor force indicate that they want a job but are not looking. That number rose by nearly 5 million at the beginning of the pandemic but has declined significantly since then. In fact, it was still 741,000 higher in March 2022 than in February of 2020. And we still have nearly a million people who say they are not looking for work now because of the pandemic.

People not in labor force who say they want a job now, January 2006 to March 2022

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

In addition to these straightforward concepts of recovery, our colleagues at the Bureau of Economic Analysis report on our nation’s output of goods and services, called Gross Domestic Product (GDP). Nominal GDP was $4.5 trillion higher in the fourth quarter of 2021 than in the depths of the recession in the second quarter of 2020; after adjusting for inflation, real GDP was $2.5 trillion higher. Both nominal and real GDP were also higher in the fourth quarter of 2021 than in the quarters before the pandemic and recession. The recovery in output implies that the current labor force is enough to support more GDP than we had in the pre-pandemic economy.

Likewise, the BLS index of total private sector labor hours is 99.9 percent recovered from its February 2020 level.

Index of total weekly hours of all employees, private sector, May 2007 to March 2022

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

Still another way to think about labor market recovery is to measure the return of demand for labor. Labor demand is tricky because it is driven by so many factors in the product and service markets. That said, some recent evidence is instructive. In February 2022, the Job Openings and Labor Turnover Survey reported 11.3 million job openings, which is near a historical high. Hires stood at 6.7 million, and separations at 6.1 million. That’s a hires rate of 4.4 percent, little changed from the prior 12 months. In short, demand is high and rising, but hires remain relatively flat and at a normal level.

Job openings, hires, and separations rates, total nonfarm, January 2019 to February 2022

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

Finally, there’s the issue of labor force participation, the percentage of people age 16 and older who either are working or have looked for work in the past 4 weeks. The latest rate is 62.4 percent in March 2022, up from a low of 60.2 percent in April 2020. However, the rate stood at 63.4 percent in January and February of 2020.

For people ages 25 to 54, the March 2022 labor force participation rate for men was 88.7 percent, compared with 89.3 percent in January 2020. For women, the March 2022 rate was 76.5 percent, down from 76.9 percent in January 2020. Both rates, but particularly the rate for women, were buffeted by the waves of infections and the closing of schools and daycare facilities.

Labor force participation rates of people ages 25 to 54, January 2019 to March 2022

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

Let me conclude with a few observations. First, we see total work hours returning to their pre-pandemic level and GDP increasing. Labor force participation continues to lag its pre-pandemic rate. The recovery in output suggests the lagging labor force participation may result from demographic and other social factors, and not just economic conditions.

Change in jobs in each industry in March 2022 above or below the levels of February 2020
IndustryEmployment change

Professional and business services

723,000

Transportation and warehousing

607,500

Retail trade

278,300

Financial activities

41,000

Information

26,000

Construction

4,000

Utilities

-10,000

Mining and logging

-86,000

Wholesale trade

-103,900

Manufacturing

-128,000

Other services

-291,000

Education and health services

-456,000

Government

-710,000

Leisure and hospitality

-1,474,000
People not in labor force who say they want a job now
MonthWant a job now

Jan 2006

4,964,000

Feb 2006

4,901,000

Mar 2006

4,918,000

Apr 2006

4,719,000

May 2006

4,635,000

Jun 2006

4,726,000

Jul 2006

4,862,000

Aug 2006

4,951,000

Sep 2006

4,666,000

Oct 2006

4,868,000

Nov 2006

4,818,000

Dec 2006

4,390,000

Jan 2007

4,506,000

Feb 2007

4,706,000

Mar 2007

4,565,000

Apr 2007

4,794,000

May 2007

4,968,000

Jun 2007

4,857,000

Jul 2007

4,737,000

Aug 2007

4,827,000

Sep 2007

4,750,000

Oct 2007

4,352,000

Nov 2007

4,648,000

Dec 2007

4,657,000

Jan 2008

4,846,000

Feb 2008

4,739,000

Mar 2008

4,718,000

Apr 2008

4,733,000

May 2008

4,851,000

Jun 2008

4,929,000

Jul 2008

5,023,000

Aug 2008

4,922,000

Sep 2008

5,153,000

Oct 2008

5,094,000

Nov 2008

5,421,000

Dec 2008

5,431,000

Jan 2009

5,708,000

Feb 2009

5,617,000

Mar 2009

5,807,000

Apr 2009

5,927,000

May 2009

5,986,000

Jun 2009

5,908,000

Jul 2009

6,003,000

Aug 2009

5,649,000

Sep 2009

5,949,000

Oct 2009

6,002,000

Nov 2009

5,998,000

Dec 2009

6,186,000

Jan 2010

5,942,000

Feb 2010

6,098,000

Mar 2010

5,993,000

Apr 2010

5,913,000

May 2010

5,824,000

Jun 2010

5,909,000

Jul 2010

5,895,000

Aug 2010

6,037,000

Sep 2010

6,270,000

Oct 2010

6,289,000

Nov 2010

6,182,000

Dec 2010

6,431,000

Jan 2011

6,472,000

Feb 2011

6,390,000

Mar 2011

6,527,000

Apr 2011

6,537,000

May 2011

6,289,000

Jun 2011

6,519,000

Jul 2011

6,513,000

Aug 2011

6,463,000

Sep 2011

6,262,000

Oct 2011

6,384,000

Nov 2011

6,538,000

Dec 2011

6,323,000

Jan 2012

6,343,000

Feb 2012

6,335,000

Mar 2012

6,302,000

Apr 2012

6,426,000

May 2012

6,309,000

Jun 2012

6,564,000

Jul 2012

6,516,000

Aug 2012

7,011,000

Sep 2012

6,817,000

Oct 2012

6,551,000

Nov 2012

6,833,000

Dec 2012

6,728,000

Jan 2013

6,637,000

Feb 2013

6,772,000

Mar 2013

6,670,000

Apr 2013

6,428,000

May 2013

6,726,000

Jun 2013

6,614,000

Jul 2013

6,526,000

Aug 2013

6,284,000

Sep 2013

6,119,000

Oct 2013

6,024,000

Nov 2013

5,754,000

Dec 2013

6,126,000

Jan 2014

6,360,000

Feb 2014

6,011,000

Mar 2014

6,174,000

Apr 2014

6,207,000

May 2014

6,553,000

Jun 2014

6,207,000

Jul 2014

6,264,000

Aug 2014

6,376,000

Sep 2014

6,326,000

Oct 2014

6,431,000

Nov 2014

6,558,000

Dec 2014

6,406,000

Jan 2015

6,300,000

Feb 2015

6,503,000

Mar 2015

6,355,000

Apr 2015

6,221,000

May 2015

6,051,000

Jun 2015

6,130,000

Jul 2015

6,097,000

Aug 2015

5,890,000

Sep 2015

5,868,000

Oct 2015

5,997,000

Nov 2015

5,649,000

Dec 2015

5,909,000

Jan 2016

6,006,000

Feb 2016

5,927,000

Mar 2016

5,730,000

Apr 2016

5,812,000

May 2016

5,962,000

Jun 2016

5,590,000

Jul 2016

5,906,000

Aug 2016

5,752,000

Sep 2016

6,017,000

Oct 2016

5,948,000

Nov 2016

5,864,000

Dec 2016

5,668,000

Jan 2017

5,758,000

Feb 2017

5,653,000

Mar 2017

5,758,000

Apr 2017

5,708,000

May 2017

5,465,000

Jun 2017

5,277,000

Jul 2017

5,425,000

Aug 2017

5,734,000

Sep 2017

5,637,000

Oct 2017

5,293,000

Nov 2017

5,219,000

Dec 2017

5,275,000

Jan 2018

5,191,000

Feb 2018

5,169,000

Mar 2018

5,044,000

Apr 2018

5,163,000

May 2018

5,188,000

Jun 2018

5,204,000

Jul 2018

5,195,000

Aug 2018

5,413,000

Sep 2018

5,288,000

Oct 2018

5,408,000

Nov 2018

5,398,000

Dec 2018

5,320,000

Jan 2019

5,262,000

Feb 2019

5,216,000

Mar 2019

5,136,000

Apr 2019

5,107,000

May 2019

4,994,000

Jun 2019

5,272,000

Jul 2019

4,999,000

Aug 2019

5,212,000

Sep 2019

4,852,000

Oct 2019

4,778,000

Nov 2019

4,849,000

Dec 2019

4,839,000

Jan 2020

4,937,000

Feb 2020

4,996,000

Mar 2020

5,462,000

Apr 2020

9,921,000

May 2020

8,916,000

Jun 2020

8,182,000

Jul 2020

7,712,000

Aug 2020

7,070,000

Sep 2020

7,194,000

Oct 2020

6,685,000

Nov 2020

7,120,000

Dec 2020

7,277,000

Jan 2021

6,956,000

Feb 2021

6,923,000

Mar 2021

6,822,000

Apr 2021

6,628,000

May 2021

6,583,000

Jun 2021

6,422,000

Jul 2021

6,529,000

Aug 2021

5,701,000

Sep 2021

5,918,000

Oct 2021

5,935,000

Nov 2021

5,819,000

Dec 2021

5,713,000

Jan 2022

5,704,000

Feb 2022

5,355,000

Mar 2022

5,737,000
Index of total weekly hours of all employees, private sector, May 2007 to March 2022
MonthIndex

May 2007

100.0

Jun 2007

100.3

Jul 2007

100.1

Aug 2007

100.0

Sep 2007

100.0

Oct 2007

99.8

Nov 2007

100.1

Dec 2007

100.2

Jan 2008

100.2

Feb 2008

100.1

Mar 2008

100.3

Apr 2008

99.5

May 2008

99.6

Jun 2008

99.5

Jul 2008

99.0

Aug 2008

98.7

Sep 2008

98.1

Oct 2008

97.6

Nov 2008

96.7

Dec 2008

95.6

Jan 2009

95.1

Feb 2009

94.5

Mar 2009

93.3

Apr 2009

92.6

May 2009

92.4

Jun 2009

91.7

Jul 2009

91.8

Aug 2009

91.6

Sep 2009

91.7

Oct 2009

91.2

Nov 2009

91.5

Dec 2009

91.3

Jan 2010

91.9

Feb 2010

91.0

Mar 2010

91.6

Apr 2010

92.1

May 2010

92.2

Jun 2010

92.3

Jul 2010

92.3

Aug 2010

92.7

Sep 2010

93.1

Oct 2010

93.3

Nov 2010

93.1

Dec 2010

93.5

Jan 2011

93.2

Feb 2011

93.7

Mar 2011

93.9

Apr 2011

94.5

May 2011

94.3

Jun 2011

94.5

Jul 2011

94.9

Aug 2011

94.8

Sep 2011

95.3

Oct 2011

95.5

Nov 2011

95.6

Dec 2011

95.8

Jan 2012

96.4

Feb 2012

96.6

Mar 2012

96.6

Apr 2012

96.9

May 2012

96.7

Jun 2012

96.8

Jul 2012

96.9

Aug 2012

97.1

Sep 2012

97.2

Oct 2012

97.4

Nov 2012

97.5

Dec 2012

98.0

Jan 2013

97.9

Feb 2013

98.4

Mar 2013

98.6

Apr 2013

98.4

May 2013

98.9

Jun 2013

99.1

Jul 2013

98.9

Aug 2013

99.4

Sep 2013

99.3

Oct 2013

99.5

Nov 2013

100.0

Dec 2013

99.8

Jan 2014

99.9

Feb 2014

99.8

Mar 2014

100.6

Apr 2014

100.8

May 2014

101.1

Jun 2014

101.3

Jul 2014

101.5

Aug 2014

102.0

Sep 2014

101.9

Oct 2014

102.4

Nov 2014

102.6

Dec 2014

102.9

Jan 2015

102.7

Feb 2015

103.2

Mar 2015

103.0

Apr 2015

103.2

May 2015

103.5

Jun 2015

103.7

Jul 2015

103.9

Aug 2015

104.0

Sep 2015

104.1

Oct 2015

104.7

Nov 2015

104.6

Dec 2015

104.8

Jan 2016

105.2

Feb 2016

104.7

Mar 2016

104.9

Apr 2016

105.1

May 2016

105.1

Jun 2016

105.3

Jul 2016

105.6

Aug 2016

105.4

Sep 2016

105.9

Oct 2016

106.0

Nov 2016

106.2

Dec 2016

106.3

Jan 2017

106.5

Feb 2017

106.3

Mar 2017

106.5

Apr 2017

106.9

May 2017

107.1

Jun 2017

107.3

Jul 2017

107.4

Aug 2017

107.6

Sep 2017

107.4

Oct 2017

107.8

Nov 2017

108.2

Dec 2017

108.4

Jan 2018

108.2

Feb 2018

108.8

Mar 2018

109.0

Apr 2018

109.2

May 2018

109.4

Jun 2018

109.6

Jul 2018

109.6

Aug 2018

109.8

Sep 2018

109.9

Oct 2018

110.0

Nov 2018

109.8

Dec 2018

110.3

Jan 2019

110.5

Feb 2019

110.2

Mar 2019

110.7

Apr 2019

110.6

May 2019

110.6

Jun 2019

110.7

Jul 2019

110.9

Aug 2019

111.0

Sep 2019

111.0

Oct 2019

111.1

Nov 2019

111.0

Dec 2019

111.1

Jan 2020

111.4

Feb 2020

111.9

Mar 2020

109.7

Apr 2020

93.2

May 2020

97.3

Jun 2020

101.0

Jul 2020

102.1

Aug 2020

103.4

Sep 2020

104.6

Oct 2020

105.6

Nov 2020

105.6

Dec 2020

105.2

Jan 2021

106.5

Feb 2021

105.9

Mar 2021

107.4

Apr 2021

107.6

May 2021

107.9

Jun 2021

108.0

Jul 2021

108.6

Aug 2021

108.7

Sep 2021

109.4

Oct 2021

110.0

Nov 2021

110.5

Dec 2021

111.0

Jan 2022

110.8

Feb 2022

111.8

Mar 2022

111.8
Job openings, hires, and separations rates, total nonfarm, January 2019 to February 2022
MonthJob openings rateHires rateSeparations rate

Jan 2019

4.7%3.8%3.7%

Feb 2019

4.53.83.8

Mar 2019

4.63.83.7

Apr 2019

4.64.03.8

May 2019

4.63.83.7

Jun 2019

4.53.83.7

Jul 2019

4.53.93.9

Aug 2019

4.53.93.7

Sep 2019

4.53.93.8

Oct 2019

4.73.83.7

Nov 2019

4.43.93.7

Dec 2019

4.33.93.8

Jan 2020

4.53.93.8

Feb 2020

4.44.03.8

Mar 2020

3.83.510.8

Apr 2020

3.53.18.9

May 2020

3.96.13.6

Jun 2020

4.25.43.8

Jul 2020

4.54.53.7

Aug 2020

4.34.33.4

Sep 2020

4.44.23.6

Oct 2020

4.64.33.7

Nov 2020

4.64.14.0

Dec 2020

4.64.04.0

Jan 2021

4.84.03.6

Feb 2021

5.24.23.8

Mar 2021

5.54.33.8

Apr 2021

6.04.24.0

May 2021

6.24.23.8

Jun 2021

6.34.44.0

Jul 2021

6.94.54.0

Aug 2021

6.74.34.0

Sep 2021

6.84.44.1

Oct 2021

7.04.44.0

Nov 2021

6.84.54.2

Dec 2021

7.14.34.1

Jan 2022

7.04.34.0

Feb 2022

7.04.44.1
Labor force participation rates of people ages 25 to 54, January 2019 to March 2022
MonthTotalMenWomen

Jan 2019

82.5%89.3%75.8%

Feb 2019

82.589.475.8

Mar 2019

82.589.675.5

Apr 2019

82.389.275.5

May 2019

82.288.975.7

Jun 2019

82.288.875.9

Jul 2019

82.188.975.4

Aug 2019

82.689.076.3

Sep 2019

82.789.276.4

Oct 2019

82.889.176.7

Nov 2019

82.889.276.6

Dec 2019

82.989.176.8

Jan 2020

83.189.376.9

Feb 2020

83.089.276.9

Mar 2020

82.589.076.1

Apr 2020

79.986.473.5

May 2020

80.687.274.3

Jun 2020

81.587.875.3

Jul 2020

81.287.575.1

Aug 2020

81.487.974.9

Sep 2020

81.087.774.4

Oct 2020

81.287.874.8

Nov 2020

80.987.374.6

Dec 2020

81.087.474.7

Jan 2021

81.187.674.7

Feb 2021

81.287.674.9

Mar 2021

81.387.675.2

Apr 2021

81.487.975.1

May 2021

81.487.975.0

Jun 2021

81.788.175.4

Jul 2021

81.988.375.6

Aug 2021

81.888.375.4

Sep 2021

81.688.275.3

Oct 2021

81.788.175.4

Nov 2021

81.988.275.7

Dec 2021

81.988.075.9

Jan 2022

82.088.276.0

Feb 2022

82.288.875.8

Mar 2022

82.588.776.5

Celebrating Women’s History Month with BLS Data

The U.S. Congress passed Public Law 100-9 on March 12, 1987, designating March as Women’s History Month. Beginning in 1995, each President has issued annual proclamations designating March as Women’s History Month.

Public Law 100-9 states in part:

“Whereas American women have played and continue to play a critical economic, cultural, and social role in every sphere of our Nation’s life by constituting a significant portion of the labor force working in and outside of the home;”

We at BLS have a lot to say about the critical role women have played in the economic health of our nation, especially their role in the labor market.

Let’s begin with the theme of this year’s Women’s History Month, “Providing healing and promoting hope.” This theme is especially relevant today as women serve on the front lines of the world’s battle against the COVID-19 pandemic. But women have been providing healing and promoting hope since time immemorial. BLS doesn’t have data going back that far, but we have interesting data on women employed in the health care and social assistance industry that highlight the critical importance of women in maintaining the health of our nation.

Women made up 77.6 percent of health care and social assistance employment

In 2021, 16.4 million women were employed in the health care and social assistance industry. This was 77.6 percent of the total 21.2 million workers in the industry. Looking at the component industries that make up health care and social assistance, women accounted for 75.0 percent of total employment in hospitals, 77.4 percent of total employment in health services, except hospitals, and 84.0 percent of total employment in social assistance. Social assistance includes child day care services, vocational rehabilitation services, and services for the elderly and disabled, among other industries.

Women employed in health care and social assistance, 2011 to 2021

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

Women providing care to household members

The excerpt from Public Law 100-9 I shared earlier mentioned activity both inside and outside the home. Data from the American Time Use Survey can shed light on the many ways women provide healing and promote hope, even when it is not directly related to their paid employment.

From May to December 2020, 84.5 percent of women engaged in household activities on a given day. Women who engaged in household activities spent an average of 2.77 hours per day on them as their primary activity.

Almost 25 percent of women also cared for and helped household members on a given day. These women averaged 2.41 hours per day caring for a household member as their primary activity.

Among women who were mothers, the time they spent caring for and helping household members varied depending on the age of the children and the employment status of the parent. Women of all marital and employment statuses averaged 2.1 hours per day caring for household members if their youngest child was under age 18 and 3.25 hours a day if their youngest child was under age 6. The averages were higher if women were not employed: 2.95 hours per day for women with children under age 18 and 3.88 hours for women with children under age 6.

Average hours per day mothers with children in the household spent caring for and helping household members, May to December 2020

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

The COVID-19 pandemic may have had several effects. Mothers of children under age 13 who were employed spent 7.3 hours per day during the pandemic in 2020 providing secondary childcare. Secondary childcare is when parents had at least one child under age 13 in their care while doing activities other than primary childcare. This was up by 1.5 hours per day from 2019. Employed fathers spent about 1 hour more per day providing secondary childcare in 2020 than in 2019.

Mothers and fathers of children under 13 who were not employed spent more time providing secondary childcare than those who were employed. Mothers who were not employed spent 8.7 hours per day providing secondary childcare, and fathers who were not employed spent 8.3 hours in 2020. Both figures are essentially unchanged from 2019.

Average hours per day spent providing secondary childcare, mothers and fathers of children under age 13, May to December, 2019 and 2020

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

This March, we are happy once again to celebrate the women who have made an impact both in the workforce and at home. Now more than ever, the world has come to count on women as healers and caregivers. On behalf of everyone at BLS, I am grateful for all the women who continue this crucial work. Not just this month, but every month.

Women employed in health care and social assistance, 2011 to 2021
YearTotal employedWomen employedPercent of total employed that are women

2011

18,902,00014,836,00078.5%

2012

19,405,00015,209,00078.4

2013

19,562,00015,343,00078.4

2014

19,577,00015,379,00078.6

2015

20,077,00015,752,00078.5

2016

20,589,00016,212,00078.7

2017

20,720,00016,271,00078.5

2018

21,133,00016,558,00078.4

2019

21,701,00016,959,00078.1

2020

20,736,00016,141,00077.8

2021

21,204,00016,446,00077.6
Average hours per day mothers with children in the household spent caring for and helping household members, May to December 2020
Employment statusYoungest child under age 18Youngest child under age 6

Total

2.103.25

Not employed

2.953.88

Employed

1.642.81

Employed full time

1.472.65

Employed part time

2.123.18
Average hours per day spent providing secondary childcare, mothers and fathers of children under age 13, May to December, 2019 and 2020
YearEmployed fathersEmployed mothersNot employed fathersNot employed mothers

2019

4.295.788.298.76

2020

5.247.258.328.66

What’s Going on with the Large Revisions in Seasonally Adjusted Employment Estimates?

When BLS released The Employment Situation for January 2022 on February 4, we reported larger-than-normal revisions to the seasonally adjusted monthly nonfarm employment estimates from the Current Employment Statistics survey. Let’s take a closer look at the seasonal adjustment process and explain why the revisions were unusually large.

BLS seasonally adjusts data to account for recurring seasonal patterns in a time series. It allows our data users to analyze the underlying trends without the influence of seasonal movements. Seasonal movements might entail hiring extra retail trade workers around the December holidays. Seasonal movements also may entail workers leaving payrolls, such as school bus drivers when the schoolyear ends. These types of movements happen around the same time every year and affect about the same number of workers each year. Because the seasonal movements in nonfarm employment typically don’t change by much year to year, revisions to over-the-month changes from seasonal adjustment are typically small. However, data users may have a hard time seeing seasonal patterns when large events like strikes, hurricanes, or recessions occur. The unprecedented changes brought on by the COVID-19 pandemic make it even more difficult to determine the seasonal patterns.

Every year, during the employment benchmarking process, BLS staff takes a more comprehensive look back at the seasonal adjustment of time series to account for changes in seasonal patterns of nonfarm employment. Months in which large, irregular events occur are treated as outliers, which means that the abnormal change will not be treated as a new seasonal pattern. When we seasonally adjusted the data for 2020 after 2020 had ended, we treated several months individually as outliers. That works well for short-lived events, like strikes or a weather-related event. At that time, with very few observations after the start of the pandemic, that type of individual outlier detection was appropriate.

For example, the economy lost a massive number of jobs in March and April 2020 at the start of the pandemic. If we had not treated those months as outliers, or irregular events, the seasonal adjustment model would have expected massive employment losses as a new seasonal pattern for March and April 2021. That would not have been appropriate because those huge losses in March and April 2020 were not likely to recur. If the model had included the large March and April 2020 employment losses, when large job losses did not occur in March and April 2021, the model would have shown large job gains for those months on a seasonally adjusted basis. That would have distorted the underlying employment trend.

We now have more data observations after the start of the pandemic, and we benefit from the hindsight of our normal annual review of the data, which encompasses the last 5 years of data. We incorporated two additional outlier types that better isolated the initial losses in employment from the pandemic, while simultaneously accounting for new seasonal patterns that we have detected. One new type of outlier accounts for a temporary change in the level of the series. For example, drinking places that serve alcoholic beverages experienced a significant employment decline in April 2020 but have gained jobs since then, even if in fits and starts. In this case, we treated April 2020 as a temporary change outlier, which will adjust the months from April 2020 forward to account for both the large drop and subsequent recovery in employment this industry has experienced. This will better account for this temporary period of readjustment as employment levels recover towards what they were before the pandemic.

Employment in drinking places, alcoholic beverages, January 2020 to December 2021, seasonally adjusted

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

The other new type of outlier accounts for a permanent shift in the level of a series. An example of this is nursing care facilities, which continued to experience employment declines after April 2020. In this case, April 2020 was treated as a level shift outlier, which resulted in subsequent months being adjusted to better reflect the continued lower level of employment.

Employment in nursing care facilities, January 2020 to December 2021, seasonally adjusted

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

Incorporating these additional types of outliers stabilized the normal seasonal patterns and smoothed out the initial large swings in the seasonally adjusted data. These changes resulted in a more accurate payroll employment series and will allow users to better differentiate longer-term trends from seasonal movements.

These changes resulted in some large revisions to our monthly seasonally adjusted data for 2021, although the revisions partly offset each other. For example, total nonfarm employment in November and December 2021 combined is 709,000 higher than previously reported, while employment in June and July 2021 combined is 807,000 lower. The change for all of 2021 is just 217,000 higher than previously reported. Although these revisions are larger than usual, trends in the data emerge more clearly.

Want to really dig into the details of seasonal adjustment for the nonfarm employment data? See our specification files and technical documentation.

Employment in drinking places, alcoholic beverages, January 2020 to December 2021, seasonally adjusted
MonthPreviously publishedRevised

Jan 2020

417,200419,800

Feb 2020

423,700426,400

Mar 2020

381,800383,600

Apr 2020

85,60084,800

May 2020

145,400140,600

Jun 2020

233,800225,300

Jul 2020

238,700226,200

Aug 2020

250,800243,800

Sep 2020

266,200260,000

Oct 2020

289,700278,500

Nov 2020

280,900281,300

Dec 2020

243,000239,700

Jan 2021

248,300248,700

Feb 2021

282,200280,700

Mar 2021

303,900298,300

Apr 2021

311,700309,900

May 2021

318,300322,900

Jun 2021

322,000331,200

Jul 2021

329,700336,800

Aug 2021

329,500342,900

Sep 2021

343,900350,600

Oct 2021

345,500354,200

Nov 2021

347,900365,000

Dec 2021

344,500363,800
Employment in nursing care facilities, January 2020 to December 2021, seasonally adjusted
MonthPreviously publishedRevised

Jan 2020

1,586,5001,585,100

Feb 2020

1,585,7001,584,800

Mar 2020

1,581,6001,581,200

Apr 2020

1,534,3001,537,200

May 2020

1,502,6001,508,000

Jun 2020

1,487,3001,490,000

Jul 2020

1,469,7001,470,600

Aug 2020

1,459,2001,463,300

Sep 2020

1,456,6001,454,500

Oct 2020

1,451,5001,447,800

Nov 2020

1,439,0001,437,200

Dec 2020

1,433,4001,430,900

Jan 2021

1,415,0001,416,700

Feb 2021

1,404,2001,406,300

Mar 2021

1,400,5001,403,600

Apr 2021

1,382,2001,388,700

May 2021

1,375,0001,383,700

Jun 2021

1,371,1001,376,300

Jul 2021

1,371,0001,373,700

Aug 2021

1,365,3001,367,700

Sep 2021

1,349,8001,346,700

Oct 2021

1,359,1001,349,200

Nov 2021

1,350,9001,346,000

Dec 2021

1,345,7001,345,100