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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
Month
Research index
Official index
Jan 2018
100.000
100.000
Feb 2018
100.026
99.871
Mar 2018
99.481
99.817
Apr 2018
99.842
99.370
May 2018
99.583
99.560
Jun 2018
99.499
99.705
Jul 2018
99.841
99.681
Aug 2018
99.942
99.424
Sep 2018
99.917
99.129
Oct 2018
99.886
99.043
Nov 2018
100.072
99.204
Dec 2018
99.471
99.409
Jan 2019
99.969
100.043
Feb 2019
100.295
100.157
Mar 2019
100.182
100.539
Apr 2019
100.487
100.574
May 2019
100.659
100.452
Jun 2019
100.362
100.287
Jul 2019
100.484
100.027
Aug 2019
100.089
99.633
Sep 2019
100.203
99.224
Oct 2019
100.443
99.136
Nov 2019
99.965
99.138
Dec 2019
99.912
99.472
Jan 2020
100.486
100.175
Feb 2020
100.843
100.549
Mar 2020
101.301
100.087
Apr 2020
102.431
100.008
May 2020
102.842
100.154
Jun 2020
103.653
100.076
Jul 2020
104.047
100.549
Aug 2020
104.142
100.284
Sep 2020
103.895
100.249
Oct 2020
103.841
100.653
Nov 2020
103.977
100.726
Dec 2020
103.781
101.425
Jan 2021
104.818
101.620
Feb 2021
105.652
101.714
Mar 2021
106.308
101.582
Apr 2021
107.156
101.971
May 2021
111.189
103.502
Jun 2021
113.656
105.341
Jul 2021
114.795
106.944
Aug 2021
115.205
107.930
Sep 2021
115.942
109.013
Oct 2021
118.107
110.566
Nov 2021
118.980
111.915
Dec 2021
120.336
113.373
Jan 2022
121.230
114.005
Feb 2022
122.481
114.308
Comparison of 12-month standard errors for the research index and official index for new vehicle prices
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.
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
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.
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.
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.
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.
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.
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
Industry
Employment 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
Month
Want 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
Month
Index
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
Month
Job openings rate
Hires rate
Separations rate
Jan 2019
4.7%
3.8%
3.7%
Feb 2019
4.5
3.8
3.8
Mar 2019
4.6
3.8
3.7
Apr 2019
4.6
4.0
3.8
May 2019
4.6
3.8
3.7
Jun 2019
4.5
3.8
3.7
Jul 2019
4.5
3.9
3.9
Aug 2019
4.5
3.9
3.7
Sep 2019
4.5
3.9
3.8
Oct 2019
4.7
3.8
3.7
Nov 2019
4.4
3.9
3.7
Dec 2019
4.3
3.9
3.8
Jan 2020
4.5
3.9
3.8
Feb 2020
4.4
4.0
3.8
Mar 2020
3.8
3.5
10.8
Apr 2020
3.5
3.1
8.9
May 2020
3.9
6.1
3.6
Jun 2020
4.2
5.4
3.8
Jul 2020
4.5
4.5
3.7
Aug 2020
4.3
4.3
3.4
Sep 2020
4.4
4.2
3.6
Oct 2020
4.6
4.3
3.7
Nov 2020
4.6
4.1
4.0
Dec 2020
4.6
4.0
4.0
Jan 2021
4.8
4.0
3.6
Feb 2021
5.2
4.2
3.8
Mar 2021
5.5
4.3
3.8
Apr 2021
6.0
4.2
4.0
May 2021
6.2
4.2
3.8
Jun 2021
6.3
4.4
4.0
Jul 2021
6.9
4.5
4.0
Aug 2021
6.7
4.3
4.0
Sep 2021
6.8
4.4
4.1
Oct 2021
7.0
4.4
4.0
Nov 2021
6.8
4.5
4.2
Dec 2021
7.1
4.3
4.1
Jan 2022
7.0
4.3
4.0
Feb 2022
7.0
4.4
4.1
Labor force participation rates of people ages 25 to 54, January 2019 to March 2022
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.
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.
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.
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
Year
Total employed
Women employed
Percent of total employed that are women
2011
18,902,000
14,836,000
78.5%
2012
19,405,000
15,209,000
78.4
2013
19,562,000
15,343,000
78.4
2014
19,577,000
15,379,000
78.6
2015
20,077,000
15,752,000
78.5
2016
20,589,000
16,212,000
78.7
2017
20,720,000
16,271,000
78.5
2018
21,133,000
16,558,000
78.4
2019
21,701,000
16,959,000
78.1
2020
20,736,000
16,141,000
77.8
2021
21,204,000
16,446,000
77.6
Average hours per day mothers with children in the household spent caring for and helping household members, May to December 2020
Employment status
Youngest child under age 18
Youngest child under age 6
Total
2.10
3.25
Not employed
2.95
3.88
Employed
1.64
2.81
Employed full time
1.47
2.65
Employed part time
2.12
3.18
Average hours per day spent providing secondary childcare, mothers and fathers of children under age 13, May to December, 2019 and 2020
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.
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.
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.
United States Department of Labor
