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Tag Archives: COVID-19

It’s a Small Statistical World

BLS is one of several U.S. statistical agencies that follow consistent policies and share best practices. These agencies also frequently work with their statistical counterparts around the world to develop standards, share information, troubleshoot issues, and improve the quality of available data. At BLS, our Division of International Technical Cooperation coordinates these activities. The division helps to strengthen statistical development by organizing seminars, consultations, and meetings for international visitors with BLS staff. The division also provides BLS input on global statistical initiatives. Without missing a beat, most of these activities moved to virtual platforms during the COVID-19 pandemic. Despite some time-zone challenges, which often lead to early morning or late-night video meetings, BLS continues to play an active role on the world stage.

World map

Today I’m highlighting some recent international engagements, which have included our colleagues from Australia, Canada, France, Greece, Italy, Mexico, South Korea, and the United Kingdom. These events are often mutually beneficial, as they provide opportunities for BLS staff to learn more about the experiences of our international counterparts.

  • BLS staff met with a former Australian Bureau of Statistics official who was working with the U.K. Statistics Authority and the U.K. Office for National Statistics to research best practices in implementing international statistical standards. They discussed the international comparability of domestic industry and product classifications, data quality and publishing, and the independence of statistical organizations.
  • Staff from the Australian Bureau of Statistics are planning to revise their household expenditure survey. They turned to BLS experts, who shared their insights and experiences in improving our Consumer Expenditure Surveys.
  • Staff from the Statistical Division at the United Nations asked BLS to comment on issues surrounding the classification of business functions; household income, consumption, and wealth; and unpaid household service work. Input from staff in multiple offices will inform the BLS response to this request.
  • BLS staff, our counterparts in Canada and Mexico, and colleagues from across Europe and Asia discussed data ethics in a meeting organized by the Centre for Applied Data Ethics at the U.K. Statistics Authority. Country representatives summarized how their organizations assess ethical considerations when producing official statistics. The U.K. Statistics Authority identified the following ethical considerations as being especially important:
Public Good: The use of data has clear benefits for users and serves the public good.
Confidentiality, Data Security: The data subject's identity (whether person or organisation) is protected, information is kept confidential and secure, and the issue of consent is considered appropriately.
Methods and Quality: The risks and limits of new technologies are considered and there is sufficient human oversight so that methods employed are consistent with recognised standards of integrity and quality.
Legal Compliance: Data used and methods employed are consistent with legal requirements.
Public Views and Engagement: The views of the public are considered in light of the data used and the perceived benefits of the research.
Transparency: The access, use and sharing of data is transparent, and is communicated clearly and accessibly to the public.

From its founding, BLS has understood the importance of these issues. Our written policies and strategic plans reflect these principles. They also are reflected in the Foundations for Evidence-Based Policymaking Act and the newly formed Scientific Integrity Task Force, which includes BLS staff among its members.

And that’s just some of what we did this summer! BLS has a longstanding reputation for providing expert training and guidance and participating in international statistical forums. We also provide BLS data to the International Labour Organization and the Organisation for Economic Cooperation and Development, among others. These organizations often feature BLS statistics in their databases. Since its inception, BLS has provided technical assistance to our international counterparts, starting with our first Commissioner, Carroll Wright, who directed BLS staff to advise foreign governments establishing statistical agencies. Commissioner Wright was also a member of several international statistical associations, a tradition that continues today. Currently, BLS staff participate in many international expert groups, including the Voorburg Group on Service Statistics, the Wiesbaden Group on Business Registers, and the International Conference of Labor Statisticians. These groups provide BLS staff with opportunities to discuss topics of common interest, to propose and learn about innovative solutions to data measurement issues, and to influence discussions about important economic concepts.

BLS began providing technical assistance in earnest in the late 1940s as part of the U.S. government’s European Economic Recovery Program. BLS staff planned and conducted productivity studies and helped European governments establish their own economic statistics. Similar efforts continue today for our colleagues around the world, many of whom have participated in our international training programs. While we have temporarily halted in-person training programs because of the pandemic, our staff plan to provide more training modules virtually in response to the popularity of these programs. Over the last 10 years, BLS has provided training or other technical assistance to over 1,700 seminar participants and other visitors from 95 countries. More recently, the International Monetary Fund has asked BLS to provide training on Producer Price Indexes and Import and Export Price Indexes to our colleagues abroad.

I am incredibly grateful to all the subject matter experts throughout BLS who provide invaluable assistance with these activities and help maintain our excellent reputation in the international statistical community. We look forward to your continued support as BLS strengthens important international relationships, virtually for now, and hopefully in person soon.

The Latest on Improving the Accuracy of the Consumer Price Index

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

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

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

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

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

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

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

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

Reflecting on Our Recent Price Data User Conference

I have repeatedly seen during my time as Commissioner of Labor Statistics how driven and conscientious BLS employees are. This is especially true of how they relate to our customers.

At the core of our agency’s mission is a responsibility to our customers. BLS strives to meet the needs of a diverse set of customers for accurate, objective, relevant, timely, and accessible information. At the same time, we need to keep pace with a rapidly changing economy. Our data must reflect world events, such as the COVID-19 pandemic.

How do we meet this responsibility to our customers and keep them informed? How do we stay informed about what our customers face and what they need? For years, BLS Regional Offices have sponsored data user conferences to address these questions. These conferences have always been successful forums with broad representation from our data users.

I have participated in many such conferences virtually and in person. I recently had the pleasure of a different kind of virtual BLS data user conference, the price index users conference.

How did it come about? The staff of our Office of Prices and Living Conditions saw the success of our regional events and wanted to interact directly with our customers. What better way to communicate with price index users and get their feedback!

Especially now, with so much attention focused on inflation, customers want to know the pandemic’s effects on not only our survey results, but also on our survey methods, participation, and data quality.

This conference featured presentations by program experts from the Consumer Price Index, Producer Price Index, and U.S. Import and Export Price Indexes. There was plenty of technical detail that researchers, financial journalists, finance professionals, and other participants welcomed. The conference covered alternative data collection methods, medical care, quality adjustment, the impact of COVID-19, and other topics.

Beyond the technical detail, this event featured a listening session. This session went beyond the usual questions and answers to provide a forum for a robust exchange between our sophisticated data users and our experts. Everyone was in the same “room” and could participate in this discussion about methods, customer needs, COVID-19 effects, and future plans.

We at BLS benefit from this type of open exchange, and we thank all who attended for enhancing the 2-day event. We also owe a big thank you to all of our respondents for their survey participation throughout a very challenging time. When you agree to share your company’s information with BLS, you help ensure that we can continue to provide quality data. Survey participants are our bedrock, the foundation for good information about our economy. We cannot succeed in our mission for the American people, let alone our customers, without your help.

We look forward to your participation at our next event!

BLS at the Olympics

When you find yourself in a 16-day marathon on the sofa shouting “U-S-A, U-S-A” at every swimmer, weightlifter, and beach volleyball player, you may not see the relationship to the U.S. Bureau of Labor Statistics. But as you sprint through the pages of our website or add your likes to Twitter, you’ll begin to see how BLS has a stat for that.

Olympic symbol with five interlocking rings and BLS emblem

Uneven bars

As we head into the gymnastics venue, we notice one of the women’s apparatus reminds us of how we measure productivity. We use two factors to compute labor productivity—output and hours worked. Over the past decade, the “bars” for output and hours worked aren’t quite parallel, but they are definitely uneven; output grew a little faster than hours, leading to rising productivity.  The COVID-19 pandemic resulted in sharp drops in both output and hours, leaving productivity to maintain its steady climb. BLS productivity staff stick the landing by providing a series of quarterly charts to let you vault into all the details.

Labor productivity (output per hour), output, and hours worked indexes, nonfarm business, 2012 to 2021

Editor’s note: Data for this chart are available in our interactive chart packages.

Decathlon

You may not have to run, jump, and throw, but the fastest growing occupations from our annual employment projections represent a diversity of skills. A decathlon has 10 events, but we have so much Olympic spirit we want to show you the 12 fastest growing occupations. Half of these jobs are in the healthcare field, while a couple involve alternative forms of energy. And, of course, BLS is pleased to see statisticians and data scientists and mathematical science occupations make the list. While the “World’s Greatest Athlete” is decided at the track and field venue, our Employment Projections staff goes the extra mile (1,500 meters, actually) to identify where the jobs will be in the future.

Fastest growing occupations, projected, 2019–29

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

Swimming 4×100 medley relay

At the natatorium, we are here to witness one of the premier events of the Olympic Games, the swimming 4×100 medley relay. Four price indexes will each take a lap to demonstrate how they work together to provide a complete inflation picture. In the leadoff position is the Import Price Index, which rose 11.2 percent from June 2020 to June 2021—with fuel prices being one of the largest drivers. After touching the wall first, imports made way for the Producer Price Index, which rose 7.3 percent for the year ending in June. Price increases for a variety of goods drove this gain. The third leg belonged to the Export Price Index, which rose 16.8 percent over the past year, the largest gain among the quartet. Agricultural products were among the largest contributors to the increase in export prices. In the anchor position was the Consumer Price Index, freestyling with a 5.4-percent increase over the year, leading BLS to the gold medal. Among the largest increases over the past year were consumer prices for gasoline and for used cars and trucks.

Percent change in BLS price indexes, June 2020 to June 2021

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

Greco-Roman wrestling

We bypassed the freestyle wrestling venue to watch Greco-Roman wrestling. The difference between freestyle and Greco-Roman wrestling is that freestyle wrestlers can use their legs for both defensive and offensive moves, but Greco-Roman forbids any holds below the waist. Our Survey of Occupational Injuries and Illnesses reports on the part of the body where workplace injuries occur, and, just like Greco-Roman, many of those occur above the waist.

Among workplace injuries that resulted in time away from work, nearly two out of three affected parts of the body above the waist, with the greatest number related to the upper extremities (shoulder, arm, hand, and wrist).

Number of workplace injuries and illnesses requiring days away from work, by part of body, 2019

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

Among the most prevalent injuries to the upper extremities were sprains, strains, punctures, cuts, and burns.

Beach volleyball

This popular sport takes place out on the sandy beaches, with two athletes on each side battling for the gold. Let’s look at some popular beach volleyball spots around the United States and pair them with the unemployment rates by state and metropolitan area. Florida serves up the lowest unemployment rate among the four states we have selected, at 5.7 percent (not seasonally adjusted) in June. Miami had an unemployment rate of 6.2 percent in June—the lowest among the metro areas chosen. Receiving the serve, Hawaii’s rate stood at a 7.9 percent. They bumped it to their teammate Illinois, which also had a rate of 7.9 percent. California reached a little higher, with a rate of 8.0 percent.

Unemployment rates in selected beach volleyball states and metropolitan areas, June 2021, not seasonally adjusted

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

BLS heads to Tokyo

Just as the United States exports its athletes to Japan for the Olympic Games, the two countries are regular trading partners. The BLS International Price Program provides a monthly look at inflation for U.S. imports and exports. Among the data available are price changes based on where the imports come from and where the exports go. And yes, this includes data for Japan. While we’ve seen increases in many inflation measures in recent months, the data show more modest increases in prices of U.S. imports from Japan. Not so for U.S. exports to Japan, which increased 15.8 percent from June 2020 to June 2021. No, this does not represent the price of exporting our athletes; it mostly relates to sharp increases in the price of agricultural exports.

Percent change in U.S. import and export prices, June 2020 to June 2021

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

Whether it’s weightlifting or dressage or the new sports climbing activities, BLS is cheering on the U.S. Olympians and Paralympians in Japan. At the same time, we’ll still be keeping to our data release schedule. Find out what’s available from BLS during August and September and be sure to follow BLS on Twitter.

Fastest growing occupations, projected, 2019–29
OccupationProjected percent change

Wind turbine service technicians

60.7%

Nurse practitioners

52.4

Solar photovoltaic installers

50.5

Occupational therapy assistants

34.6

Statisticians

34.6

Home health and personal care aides

33.7

Physical therapist assistants

32.6

Medical and health services managers

31.5

Physician assistants

31.3

Information security analysts

31.2

Data scientists and mathematical science occupations, all other

30.9

Derrick operators, oil and gas

30.5
Percent change in BLS price indexes, June 2020 to June 2021
Price indexPercent change

Import Price Index

11.2%

Producer Price Index

7.3

Export Price Index

16.8

Consumer Price Index

5.4
Number of workplace injuries and illnesses requiring days away from work, by part of body, 2019
Part of bodyNumber

Upper extremities (shoulder, arm, hand, wrist)

284,860

Lower extremities (knee, ankle, foot)

216,850

Trunk

187,130

Multiple body parts

82,650

Head

79,620

Body systems

15,150

Neck

11,600

All other body parts

10,360
Unemployment rates in selected beach volleyball states and metropolitan areas, June 2021, not seasonally adjusted
State or metropolitan areaRate

States

Florida

5.7%

Hawaii

7.9

Illinois

7.9

California

8.0

Metropolitan areas

Miami

6.2

Honolulu

7.1

Chicago

8.5

Los Angeles

9.5
Percent change in U.S. import and export prices, June 2020 to June 2021
Price indexAll countriesJapan

Import prices

11.2%1.8%

Export prices

16.815.8

Making Sense of Job Openings and Other Labor Market Measures

The current “supply” of labor gets a lot of attention. That concept refers to the number of people working or looking for work. Our monthly Employment Situation report is where policymakers and the general public learn how that supply has changed. BLS also examines the current “demand” for labor with monthly information on filled jobs and job openings. Readers find those estimate in the BLS Job Openings and Labor Turnover Survey (JOLTS). JOLTS defines job openings as all positions that are open, but not filled, on the last business day of the month. A job is “open” only if it meets all of these conditions:

  • A specific position exists and there is work available for that position.
  • The job could start within 30 days.
  • There is active recruiting for workers from outside the establishment.

There were 9.2 million job openings in May 2021, the same record-high level first reached in April. The May job opening rate also was the same as April’s record high; 6.0 percent of all currently available positions were unfilled. This rate is the number of job openings divided by the sum of current employment plus job openings. You can think of it as a measure of capacity or the rate of current unmet demand for labor.

Job openings rate, total nonfarm, December 2000 to May 2021

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

This spike in openings was sudden by historical standards. It came just one year after an equally sudden drop, which bottomed out in April 2020. In contrast, openings fell more gradually during the 2007–09 recession, then grew even more gradually during the subsequent recovery. The labor market movements during the COVID-19 pandemic have been far more abrupt than those in earlier business cycles.

An abundance of job openings usually signals a “tight” labor market; the demand for labor exceeds the supply at the offered wage. For workers, this may mean it is relatively easy to find a desirable job, assuming they possess the skills employers are seeking. In contrast, employers must compete to hire well-qualified workers.

High unemployment usually signals a “loose” labor market, in which many applicants compete for a limited number of openings; the supply of labor exceeds the demand. Unemployment—the number of workers who lack but seek jobs—stood at 9.5 million in June 2021. That was, down from its pandemic peak of 23 million in April 2020 but still well above its level of less than 6 million before the pandemic. Millions more have left the labor force during the pandemic, and many of them have not returned. These people are not counted as unemployed because they are not actively looking for work. However, we know that 6.4 million of those not in the labor force indicate they want a job now, and 1.6 million say they are not currently searching because of pandemic-related reasons. Some of these people might be willing to consider offers and might add more “looseness” to the labor market.

Comparing the number of job openings to the number of unemployed people provides one measure of the current job market. In May 2021, there was just one unemployed person per job opening—a ratio usually associated with a tight labor market.

Number of unemployed per job opening, December 2000 to May 2021

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

So, with openings at an all-time high, and unemployment still elevated, is the labor market tight or loose? The answer is complicated. It also can feel different depending on each worker’s and employer’s circumstances. The answer also differs when you look beyond the national data to uncover differing stories by industry or geography.

As the COVID-19 pandemic subsides and many restaurants and other businesses return to normal operations, some employers are finding it hard to hire enough workers quickly. Some economists are unsure whether recent, temporary increases in the availability and generosity of unemployment insurance have influenced some unemployed workers’ interest in taking jobs. At the same time, the lingering effects of the pandemic probably kept some potential workers from entering or reentering the labor force, especially those with school-aged children whose schools were still closed, and those lacking childcare options. These factors could also affect employers’ ability to hire.

We should also remember that not all job applicants come from the ranks of the unemployed. Many are changing jobs or entering (or reentering) the labor force. The recent abundance of job openings may be increasing workers’ likelihood to change jobs. Just as openings reached a new high in April 2021, so did quits, at 4.0 million. Unlike openings, however, quits edged down a bit in May.

Job openings, hires, and quit rates, total nonfarm, December 2000 to May 2021

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

Another factor could be mismatches between the open jobs and the jobseekers. In June 2021, about 15 percent of unemployed people were seeking part-time work. We don’t know how many of the openings were part-time. Since February of this year, the share of unemployed workers who were unemployed 27 weeks or longer has remained above 40 percent, a level last seen in 2012 and roughly twice the 2019 level. Historically, those unemployed longer are slower to connect with new jobs and more likely to stop looking. It is also possible that some workers’ job preferences changed, at least temporarily, as the pandemic changed the perceived risks and other characteristics of many jobs.

Finally, with many people on the sidelines of the labor market, and job openings at record high levels, employers may look to increase wages to entice potential employees back into the market. The BLS monthly measure of wage trends, average hourly earnings, has been heavily influenced by large employment shifts since the pandemic began. When employment dropped sharply in the spring of 2020, average wages increased, mainly because lower-paid workers were more likely to be out of work. Now that many businesses are reopening, some evidence of wage increases can be seen by focusing on the leisure and hospitality industry. From February 2020, just before the pandemic began, to June 2021, average hourly earnings for this industry rose 3.1 percent, after adjusting for inflation. Data from the Employment Cost Index, which are not influenced by employment shifts, show wages and salaries in the leisure and hospitality industry increasing 1.6 percent, after adjusting for inflation, for the year ending March 2021.

Percent change since February 2020 in real (inflation-adjusted) average hourly earnings

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

While some employers might find it hard to hire workers quickly, there is a lot of hiring going on. Consider the leisure and hospitality industry, which includes restaurants. In May, a whopping 9.0 percent of positions were open. But the hiring rate was even higher—9.3 percent, far above levels before the pandemic.

Job openings and hires rates, leisure and hospitality, December 2000 to May 2021

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

The labor market cannot be characterized with a single number. Over time, people change jobs, look for jobs, or leave the labor market entirely. These dynamics can be complicated, as they certainly were during the COVID-19 pandemic. This discussion covers just some of the many measures BLS reports to illuminate labor market conditions. For more analysis of JOLTS data, check out recent articles in the Monthly Labor Review and Beyond the Numbers.

Job openings rate, total nonfarm, December 2000 to May 2021
MonthRate

Dec 2000

3.7%

Jan 2001

3.8

Feb 2001

3.7

Mar 2001

3.5

Apr 2001

3.4

May 2001

3.2

Jun 2001

3.2

Jul 2001

3.3

Aug 2001

3.0

Sep 2001

3.0

Oct 2001

2.7

Nov 2001

2.8

Dec 2001

2.7

Jan 2002

2.7

Feb 2002

2.6

Mar 2002

2.7

Apr 2002

2.6

May 2002

2.6

Jun 2002

2.5

Jul 2002

2.5

Aug 2002

2.6

Sep 2002

2.5

Oct 2002

2.6

Nov 2002

2.6

Dec 2002

2.4

Jan 2003

2.6

Feb 2003

2.4

Mar 2003

2.3

Apr 2003

2.3

May 2003

2.5

Jun 2003

2.5

Jul 2003

2.2

Aug 2003

2.4

Sep 2003

2.3

Oct 2003

2.5

Nov 2003

2.5

Dec 2003

2.5

Jan 2004

2.6

Feb 2004

2.6

Mar 2004

2.6

Apr 2004

2.6

May 2004

2.7

Jun 2004

2.5

Jul 2004

2.8

Aug 2004

2.6

Sep 2004

2.8

Oct 2004

2.9

Nov 2004

2.6

Dec 2004

3.0

Jan 2005

2.8

Feb 2005

2.9

Mar 2005

2.9

Apr 2005

3.0

May 2005

2.8

Jun 2005

2.9

Jul 2005

3.1

Aug 2005

3.0

Sep 2005

3.1

Oct 2005

3.0

Nov 2005

3.1

Dec 2005

3.1

Jan 2006

3.1

Feb 2006

3.1

Mar 2006

3.4

Apr 2006

3.4

May 2006

3.2

Jun 2006

3.3

Jul 2006

3.1

Aug 2006

3.3

Sep 2006

3.3

Oct 2006

3.2

Nov 2006

3.3

Dec 2006

3.3

Jan 2007

3.3

Feb 2007

3.3

Mar 2007

3.5

Apr 2007

3.3

May 2007

3.3

Jun 2007

3.4

Jul 2007

3.2

Aug 2007

3.2

Sep 2007

3.3

Oct 2007

3.2

Nov 2007

3.3

Dec 2007

3.2

Jan 2008

3.2

Feb 2008

3.0

Mar 2008

3.0

Apr 2008

2.8

May 2008

3.0

Jun 2008

2.7

Jul 2008

2.7

Aug 2008

2.6

Sep 2008

2.3

Oct 2008

2.4

Nov 2008

2.3

Dec 2008

2.3

Jan 2009

2.0

Feb 2009

2.1

Mar 2009

1.9

Apr 2009

1.7

May 2009

1.9

Jun 2009

1.9

Jul 2009

1.7

Aug 2009

1.8

Sep 2009

1.9

Oct 2009

1.8

Nov 2009

1.9

Dec 2009

1.9

Jan 2010

2.1

Feb 2010

2.0

Mar 2010

2.0

Apr 2010

2.4

May 2010

2.2

Jun 2010

2.1

Jul 2010

2.3

Aug 2010

2.2

Sep 2010

2.2

Oct 2010

2.4

Nov 2010

2.4

Dec 2010

2.3

Jan 2011

2.3

Feb 2011

2.4

Mar 2011

2.4

Apr 2011

2.4

May 2011

2.4

Jun 2011

2.6

Jul 2011

2.7

Aug 2011

2.5

Sep 2011

2.8

Oct 2011

2.7

Nov 2011

2.6

Dec 2011

2.8

Jan 2012

2.8

Feb 2012

2.6

Mar 2012

2.9

Apr 2012

2.8

May 2012

2.8

Jun 2012

2.8

Jul 2012

2.7

Aug 2012

2.8

Sep 2012

2.8

Oct 2012

2.7

Nov 2012

2.8

Dec 2012

2.9

Jan 2013

2.8

Feb 2013

2.9

Mar 2013

2.9

Apr 2013

2.9

May 2013

3.0

Jun 2013

3.0

Jul 2013

2.8

Aug 2013

2.9

Sep 2013

2.9

Oct 2013

3.0

Nov 2013

2.9

Dec 2013

2.9

Jan 2014

2.9

Feb 2014

3.1

Mar 2014

3.1

Apr 2014

3.2

May 2014

3.3

Jun 2014

3.5

Jul 2014

3.4

Aug 2014

3.7

Sep 2014

3.4

Oct 2014

3.5

Nov 2014

3.3

Dec 2014

3.5

Jan 2015

3.7

Feb 2015

3.7

Mar 2015

3.6

Apr 2015

3.8

May 2015

3.8

Jun 2015

3.6

Jul 2015

4.1

Aug 2015

3.7

Sep 2015

3.7

Oct 2015

3.9

Nov 2015

3.8

Dec 2015

3.9

Jan 2016

4.0

Feb 2016

3.9

Mar 2016

4.1

Apr 2016

3.9

May 2016

3.9

Jun 2016

3.8

Jul 2016

4.0

Aug 2016

3.8

Sep 2016

3.9

Oct 2016

3.7

Nov 2016

4.0

Dec 2016

3.9

Jan 2017

3.7

Feb 2017

3.9

Mar 2017

3.8

Apr 2017

4.0

May 2017

3.8

Jun 2017

4.1

Jul 2017

4.1

Aug 2017

4.1

Sep 2017

4.1

Oct 2017

4.2

Nov 2017

4.1

Dec 2017

4.1

Jan 2018

4.3

Feb 2018

4.3

Mar 2018

4.4

Apr 2018

4.4

May 2018

4.5

Jun 2018

4.7

Jul 2018

4.6

Aug 2018

4.6

Sep 2018

4.7

Oct 2018

4.7

Nov 2018

4.8

Dec 2018

4.7

Jan 2019

4.7

Feb 2019

4.5

Mar 2019

4.7

Apr 2019

4.6

May 2019

4.6

Jun 2019

4.5

Jul 2019

4.5

Aug 2019

4.5

Sep 2019

4.5

Oct 2019

4.6

Nov 2019

4.4

Dec 2019

4.2

Jan 2020

4.5

Feb 2020

4.4

Mar 2020

3.7

Apr 2020

3.4

May 2020

3.9

Jun 2020

4.2

Jul 2020

4.6

Aug 2020

4.4

Sep 2020

4.5

Oct 2020

4.6

Nov 2020

4.5

Dec 2020

4.5

Jan 2021

4.7

Feb 2021

5.0

Mar 2021

5.4

Apr 2021

6.0

May 2021

6.0
Number of unemployed per job opening, December 2000 to May 2021
MonthRatio

Dec 2000

1.1

Jan 2001

1.2

Feb 2001

1.2

Mar 2001

1.3

Apr 2001

1.4

May 2001

1.4

Jun 2001

1.5

Jul 2001

1.5

Aug 2001

1.8

Sep 2001

1.8

Oct 2001

2.1

Nov 2001

2.1

Dec 2001

2.2

Jan 2002

2.2

Feb 2002

2.4

Mar 2002

2.3

Apr 2002

2.5

May 2002

2.4

Jun 2002

2.5

Jul 2002

2.5

Aug 2002

2.4

Sep 2002

2.5

Oct 2002

2.4

Nov 2002

2.4

Dec 2002

2.7

Jan 2003

2.5

Feb 2003

2.7

Mar 2003

2.8

Apr 2003

2.8

May 2003

2.7

Jun 2003

2.7

Jul 2003

3.0

Aug 2003

2.8

Sep 2003

2.9

Oct 2003

2.6

Nov 2003

2.6

Dec 2003

2.4

Jan 2004

2.4

Feb 2004

2.3

Mar 2004

2.4

Apr 2004

2.3

May 2004

2.2

Jun 2004

2.5

Jul 2004

2.1

Aug 2004

2.3

Sep 2004

2.1

Oct 2004

2.0

Nov 2004

2.3

Dec 2004

1.9

Jan 2005

2.0

Feb 2005

2.0

Mar 2005

1.9

Apr 2005

1.8

May 2005

2.0

Jun 2005

1.9

Jul 2005

1.7

Aug 2005

1.8

Sep 2005

1.7

Oct 2005

1.8

Nov 2005

1.8

Dec 2005

1.7

Jan 2006

1.6

Feb 2006

1.7

Mar 2006

1.5

Apr 2006

1.5

May 2006

1.6

Jun 2006

1.5

Jul 2006

1.6

Aug 2006

1.5

Sep 2006

1.4

Oct 2006

1.5

Nov 2006

1.5

Dec 2006

1.5

Jan 2007

1.5

Feb 2007

1.5

Mar 2007

1.4

Apr 2007

1.5

May 2007

1.5

Jun 2007

1.4

Jul 2007

1.6

Aug 2007

1.6

Sep 2007

1.5

Oct 2007

1.6

Nov 2007

1.6

Dec 2007

1.7

Jan 2008

1.7

Feb 2008

1.8

Mar 2008

1.9

Apr 2008

1.9

May 2008

2.0

Jun 2008

2.2

Jul 2008

2.4

Aug 2008

2.6

Sep 2008

2.9

Oct 2008

3.0

Nov 2008

3.3

Dec 2008

3.6

Jan 2009

4.4

Feb 2009

4.5

Mar 2009

5.3

Apr 2009

6.0

May 2009

5.7

Jun 2009

5.9

Jul 2009

6.5

Aug 2009

6.3

Sep 2009

6.0

Oct 2009

6.4

Nov 2009

6.1

Dec 2009

5.9

Jan 2010

5.3

Feb 2010

5.7

Mar 2010

5.7

Apr 2010

4.9

May 2010

5.0

Jun 2010

5.2

Jul 2010

4.7

Aug 2010

4.9

Sep 2010

5.0

Oct 2010

4.5

Nov 2010

4.7

Dec 2010

4.7

Jan 2011

4.5

Feb 2011

4.3

Mar 2011

4.2

Apr 2011

4.3

May 2011

4.4

Jun 2011

4.0

Jul 2011

3.8

Aug 2011

4.2

Sep 2011

3.7

Oct 2011

3.8

Nov 2011

3.7

Dec 2011

3.5

Jan 2012

3.3

Feb 2012

3.5

Mar 2012

3.2

Apr 2012

3.3

May 2012

3.3

Jun 2012

3.2

Jul 2012

3.4

Aug 2012

3.3

Sep 2012

3.1

Oct 2012

3.2

Nov 2012

3.1

Dec 2012

3.1

Jan 2013

3.2

Feb 2013

3.0

Mar 2013

2.9

Apr 2013

2.9

May 2013

2.8

Jun 2013

2.8

Jul 2013

2.9

Aug 2013

2.8

Sep 2013

2.7

Oct 2013

2.6

Nov 2013

2.6

Dec 2013

2.5

Jan 2014

2.5

Feb 2014

2.4

Mar 2014

2.4

Apr 2014

2.1

May 2014

2.1

Jun 2014

1.9

Jul 2014

2.0

Aug 2014

1.8

Sep 2014

1.9

Oct 2014

1.8

Nov 2014

1.9

Dec 2014

1.7

Jan 2015

1.7

Feb 2015

1.6

Mar 2015

1.6

Apr 2015

1.5

May 2015

1.6

Jun 2015

1.6

Jul 2015

1.3

Aug 2015

1.5

Sep 2015

1.4

Oct 2015

1.4

Nov 2015

1.4

Dec 2015

1.4

Jan 2016

1.3

Feb 2016

1.3

Mar 2016

1.3

Apr 2016

1.4

May 2016

1.3

Jun 2016

1.3

Jul 2016

1.3

Aug 2016

1.4

Sep 2016

1.4

Oct 2016

1.4

Nov 2016

1.3

Dec 2016

1.3

Jan 2017

1.3

Feb 2017

1.2

Mar 2017

1.2

Apr 2017

1.2

May 2017

1.2

Jun 2017

1.1

Jul 2017

1.1

Aug 2017

1.1

Sep 2017

1.1

Oct 2017

1.0

Nov 2017

1.1

Dec 2017

1.0

Jan 2018

1.0

Feb 2018

1.0

Mar 2018

1.0

Apr 2018

0.9

May 2018

0.9

Jun 2018

0.9

Jul 2018

0.9

Aug 2018

0.9

Sep 2018

0.8

Oct 2018

0.8

Nov 2018

0.8

Dec 2018

0.9

Jan 2019

0.9

Feb 2019

0.9

Mar 2019

0.8

Apr 2019

0.8

May 2019

0.8

Jun 2019

0.8

Jul 2019

0.8

Aug 2019

0.8

Sep 2019

0.8

Oct 2019

0.8

Nov 2019

0.9

Dec 2019

0.9

Jan 2020

0.8

Feb 2020

0.8

Mar 2020

1.2

Apr 2020

5.0

May 2020

3.9

Jun 2020

2.9

Jul 2020

2.4

Aug 2020

2.1

Sep 2020

1.9

Oct 2020

1.6

Nov 2020

1.6

Dec 2020

1.6

Jan 2021

1.4

Feb 2021

1.3

Mar 2021

1.2

Apr 2021

1.1

May 2021

1.0
Job openings, hires, and quit rates, total nonfarm, December 2000 to May 2021
MonthJob openings rateHires rateQuits rate

Dec 2000

3.7%4.1%2.2%

Jan 2001

3.84.32.4

Feb 2001

3.74.02.3

Mar 2001

3.54.22.3

Apr 2001

3.43.92.4

May 2001

3.24.12.3

Jun 2001

3.23.92.2

Jul 2001

3.34.02.2

Aug 2001

3.04.02.2

Sep 2001

3.03.82.1

Oct 2001

2.73.92.1

Nov 2001

2.83.72.0

Dec 2001

2.73.72.0

Jan 2002

2.73.72.2

Feb 2002

2.63.72.0

Mar 2002

2.73.61.9

Apr 2002

2.63.82.0

May 2002

2.63.71.9

Jun 2002

2.53.71.9

Jul 2002

2.53.82.0

Aug 2002

2.63.72.0

Sep 2002

2.53.71.9

Oct 2002

2.63.71.9

Nov 2002

2.63.71.8

Dec 2002

2.43.71.9

Jan 2003

2.63.91.9

Feb 2003

2.43.61.9

Mar 2003

2.33.41.8

Apr 2003

2.33.51.8

May 2003

2.53.61.8

Jun 2003

2.53.61.8

Jul 2003

2.23.61.7

Aug 2003

2.43.61.7

Sep 2003

2.33.71.8

Oct 2003

2.53.81.9

Nov 2003

2.53.71.8

Dec 2003

2.53.81.9

Jan 2004

2.63.71.8

Feb 2004

2.63.71.9

Mar 2004

2.64.02.0

Apr 2004

2.63.91.9

May 2004

2.73.81.8

Jun 2004

2.53.82.0

Jul 2004

2.83.72.0

Aug 2004

2.63.82.0

Sep 2004

2.83.81.9

Oct 2004

2.93.91.9

Nov 2004

2.63.92.1

Dec 2004

3.03.92.0

Jan 2005

2.83.92.1

Feb 2005

2.94.02.0

Mar 2005

2.94.02.1

Apr 2005

3.04.02.1

May 2005

2.83.92.1

Jun 2005

2.94.02.1

Jul 2005

3.14.02.0

Aug 2005

3.04.02.2

Sep 2005

3.14.12.3

Oct 2005

3.03.82.1

Nov 2005

3.14.02.1

Dec 2005

3.13.92.1

Jan 2006

3.13.92.2

Feb 2006

3.14.02.2

Mar 2006

3.44.12.2

Apr 2006

3.43.82.0

May 2006

3.24.02.2

Jun 2006

3.34.02.2

Jul 2006

3.14.12.2

Aug 2006

3.33.92.2

Sep 2006

3.33.92.1

Oct 2006

3.23.92.2

Nov 2006

3.34.02.2

Dec 2006

3.33.82.2

Jan 2007

3.33.92.1

Feb 2007

3.33.82.1

Mar 2007

3.54.02.2

Apr 2007

3.33.92.1

May 2007

3.34.02.2

Jun 2007

3.43.82.1

Jul 2007

3.23.82.1

Aug 2007

3.23.92.2

Sep 2007

3.33.91.9

Oct 2007

3.23.92.1

Nov 2007

3.33.72.0

Dec 2007

3.23.72.0

Jan 2008

3.23.72.1

Feb 2008

3.03.72.1

Mar 2008

3.03.61.9

Apr 2008

2.83.62.1

May 2008

3.03.41.9

Jun 2008

2.73.61.9

Jul 2008

2.73.41.8

Aug 2008

2.63.41.8

Sep 2008

2.33.31.8

Oct 2008

2.43.31.7

Nov 2008

2.33.01.6

Dec 2008

2.33.21.5

Jan 2009

2.03.11.5

Feb 2009

2.13.01.5

Mar 2009

1.92.91.4

Apr 2009

1.72.91.3

May 2009

1.92.91.3

Jun 2009

1.92.81.3

Jul 2009

1.73.01.3

Aug 2009

1.82.91.2

Sep 2009

1.93.01.2

Oct 2009

1.83.01.3

Nov 2009

1.93.11.4

Dec 2009

1.93.11.4

Jan 2010

2.13.01.3

Feb 2010

2.03.01.4

Mar 2010

2.03.31.4

Apr 2010

2.43.21.5

May 2010

2.23.41.4

Jun 2010

2.13.11.5

Jul 2010

2.33.21.4

Aug 2010

2.23.11.4

Sep 2010

2.23.11.5

Oct 2010

2.43.21.4

Nov 2010

2.43.21.4

Dec 2010

2.33.31.5

Jan 2011

2.33.11.4

Feb 2011

2.43.21.5

Mar 2011

2.43.41.5

Apr 2011

2.43.31.4

May 2011

2.43.21.5

Jun 2011

2.63.31.5

Jul 2011

2.73.21.5

Aug 2011

2.53.31.5

Sep 2011

2.83.31.5

Oct 2011

2.73.31.5

Nov 2011

2.63.31.5

Dec 2011

2.83.31.5

Jan 2012

2.83.31.5

Feb 2012

2.63.41.6

Mar 2012

2.93.41.6

Apr 2012

2.83.31.6

May 2012

2.83.41.6

Jun 2012

2.83.31.6

Jul 2012

2.73.21.5

Aug 2012

2.83.31.5

Sep 2012

2.83.21.4

Oct 2012

2.73.31.5

Nov 2012

2.83.31.5

Dec 2012

2.93.31.5

Jan 2013

2.83.31.7

Feb 2013

2.93.41.7

Mar 2013

2.93.21.6

Apr 2013

2.93.41.7

May 2013

3.03.41.6

Jun 2013

3.03.31.6

Jul 2013

2.83.31.7

Aug 2013

2.93.51.7

Sep 2013

2.93.51.7

Oct 2013

3.03.31.7

Nov 2013

2.93.41.7

Dec 2013

2.93.41.7

Jan 2014

2.93.41.7

Feb 2014

3.13.41.8

Mar 2014

3.13.51.8

Apr 2014

3.23.51.8

May 2014

3.33.51.8

Jun 2014

3.53.51.8

Jul 2014

3.43.61.9

Aug 2014

3.73.51.8

Sep 2014

3.43.72.0

Oct 2014

3.53.71.9

Nov 2014

3.33.61.9

Dec 2014

3.53.71.8

Jan 2015

3.73.62.0

Feb 2015

3.73.61.9

Mar 2015

3.63.62.0

Apr 2015

3.83.71.9

May 2015

3.83.61.9

Jun 2015

3.63.61.9

Jul 2015

4.13.61.9

Aug 2015

3.73.62.0

Sep 2015

3.73.72.0

Oct 2015

3.93.72.0

Nov 2015

3.83.82.0

Dec 2015

3.93.92.1

Jan 2016

4.03.62.0

Feb 2016

3.93.82.1

Mar 2016

4.13.72.0

Apr 2016

3.93.72.1

May 2016

3.93.62.1

Jun 2016

3.83.72.1

Jul 2016

4.03.82.1

Aug 2016

3.83.72.1

Sep 2016

3.93.72.1

Oct 2016

3.73.62.1

Nov 2016

4.03.72.1

Dec 2016

3.93.72.1

Jan 2017

3.73.82.2

Feb 2017

3.93.72.1

Mar 2017

3.83.72.2

Apr 2017

4.03.62.1

May 2017

3.83.72.1

Jun 2017

4.13.92.2

Jul 2017

4.13.82.1

Aug 2017

4.13.82.1

Sep 2017

4.13.72.2

Oct 2017

4.23.82.2

Nov 2017

4.13.72.1

Dec 2017

4.13.72.2

Jan 2018

4.33.72.1

Feb 2018

4.33.82.2

Mar 2018

4.43.82.2

Apr 2018

4.43.82.3

May 2018

4.53.92.3

Jun 2018

4.73.92.3

Jul 2018

4.63.82.3

Aug 2018

4.63.92.3

Sep 2018

4.73.82.3

Oct 2018

4.73.92.3

Nov 2018

4.83.92.3

Dec 2018

4.73.82.3

Jan 2019

4.73.82.3

Feb 2019

4.53.82.4

Mar 2019

4.73.82.3

Apr 2019

4.64.02.3

May 2019

4.63.82.3

Jun 2019

4.53.82.3

Jul 2019

4.54.02.4

Aug 2019

4.53.92.4

Sep 2019

4.53.92.3

Oct 2019

4.63.82.3

Nov 2019

4.43.82.3

Dec 2019

4.23.92.3

Jan 2020

4.53.92.3

Feb 2020

4.43.92.2

Mar 2020

3.73.41.9

Apr 2020

3.43.01.6

May 2020

3.96.21.7

Jun 2020

4.25.61.9

Jul 2020

4.64.52.3

Aug 2020

4.44.62.1

Sep 2020

4.54.22.3

Oct 2020

4.64.22.4

Nov 2020

4.54.22.3

Dec 2020

4.53.82.4

Jan 2021

4.73.82.3

Feb 2021

5.04.02.4

Mar 2021

5.44.22.5

Apr 2021

6.04.22.8

May 2021

6.04.12.5
Percent change since February 2020 in real (inflation-adjusted) average hourly earnings
MonthTotal privateLeisure and hospitality

Feb 2020

0.0%0.0%

Mar 2020

1.10.3

Apr 2020

6.57.7

May 2020

5.44.3

Jun 2020

3.51.4

Jul 2020

3.10.2

Aug 2020

3.10.6

Sep 2020

2.90.6

Oct 2020

2.80.6

Nov 2020

3.00.3

Dec 2020

3.80.5

Jan 2021

3.50.6

Feb 2021

3.41.1

Mar 2021

2.71.8

Apr 2021

2.62.5

May 2021

2.42.9

Jun 2021

1.83.1
Job openings and hires rates, leisure and hospitality, December 2000 to May 2021
MonthJob openings rateHires rate

Dec 2000

4.5%7.4%

Jan 2001

5.27.7

Feb 2001

4.87.3

Mar 2001

5.57.8

Apr 2001

4.68.3

May 2001

4.27.6

Jun 2001

3.67.2

Jul 2001

4.67.7

Aug 2001

4.37.2

Sep 2001

4.37.3

Oct 2001

3.06.9

Nov 2001

3.66.8

Dec 2001

3.56.8

Jan 2002

2.96.5

Feb 2002

3.36.9

Mar 2002

3.36.5

Apr 2002

3.16.9

May 2002

3.26.7

Jun 2002

2.86.6

Jul 2002

3.16.7

Aug 2002

3.26.9

Sep 2002

2.86.7

Oct 2002

3.16.5

Nov 2002

3.26.6

Dec 2002

3.06.8

Jan 2003

3.17.0

Feb 2003

2.96.6

Mar 2003

2.86.4

Apr 2003

3.06.5

May 2003

3.47.0

Jun 2003

3.46.7

Jul 2003

2.76.4

Aug 2003

3.16.7

Sep 2003

3.16.8

Oct 2003

3.66.9

Nov 2003

3.46.8

Dec 2003

3.57.1

Jan 2004

3.56.8

Feb 2004

3.66.9

Mar 2004

3.47.3

Apr 2004

3.27.1

May 2004

3.37.2

Jun 2004

3.67.0

Jul 2004

4.07.0

Aug 2004

3.67.0

Sep 2004

4.07.2

Oct 2004

3.76.9

Nov 2004

3.37.0

Dec 2004

3.66.8

Jan 2005

4.17.2

Feb 2005

4.06.9

Mar 2005

4.27.2

Apr 2005

4.77.0

May 2005

4.06.8

Jun 2005

4.37.3

Jul 2005

4.07.2

Aug 2005

3.87.3

Sep 2005

3.67.2

Oct 2005

3.86.8

Nov 2005

3.97.2

Dec 2005

4.47.1

Jan 2006

4.77.2

Feb 2006

4.47.4

Mar 2006

4.17.2

Apr 2006

4.97.1

May 2006

4.07.1

Jun 2006

4.07.2

Jul 2006

4.37.3

Aug 2006

4.26.8

Sep 2006

4.26.6

Oct 2006

4.37.1

Nov 2006

4.47.5

Dec 2006

4.27.0

Jan 2007

3.76.9

Feb 2007

4.06.9

Mar 2007

4.56.8

Apr 2007

4.07.2

May 2007

4.27.0

Jun 2007

4.57.2

Jul 2007

4.56.8

Aug 2007

4.57.0

Sep 2007

4.86.7

Oct 2007

4.36.9

Nov 2007

4.56.7

Dec 2007

4.16.6

Jan 2008

4.16.3

Feb 2008

3.96.8

Mar 2008

4.16.2

Apr 2008

3.96.3

May 2008

3.96.7

Jun 2008

3.45.9

Jul 2008

3.26.0

Aug 2008

3.16.2

Sep 2008

3.05.9

Oct 2008

3.05.8

Nov 2008

2.65.3

Dec 2008

2.65.6

Jan 2009

1.85.4

Feb 2009

2.45.2

Mar 2009

2.04.8

Apr 2009

2.04.7

May 2009

2.25.2

Jun 2009

2.14.8

Jul 2009

1.94.7

Aug 2009

1.55.0

Sep 2009

2.14.8

Oct 2009

2.04.7

Nov 2009

2.15.3

Dec 2009

2.05.0

Jan 2010

2.15.1

Feb 2010

2.04.7

Mar 2010

1.85.2

Apr 2010

2.15.2

May 2010

2.34.9

Jun 2010

2.54.9

Jul 2010

2.45.1

Aug 2010

2.74.9

Sep 2010

2.45.1

Oct 2010

3.15.0

Nov 2010

2.45.0

Dec 2010

2.65.1

Jan 2011

2.74.9

Feb 2011

2.95.1

Mar 2011

2.95.8

Apr 2011

2.45.1

May 2011

2.34.9

Jun 2011

3.05.5

Jul 2011

2.65.4

Aug 2011

2.85.4

Sep 2011

3.15.6

Oct 2011

3.15.5

Nov 2011

3.15.9

Dec 2011

3.25.5

Jan 2012

3.25.7

Feb 2012

2.75.7

Mar 2012

3.26.3

Apr 2012

3.45.5

May 2012

3.25.4

Jun 2012

3.45.3

Jul 2012

3.45.5

Aug 2012

3.05.8

Sep 2012

3.05.2

Oct 2012

3.45.5

Nov 2012

3.55.2

Dec 2012

3.35.8

Jan 2013

3.25.7

Feb 2013

3.65.6

Mar 2013

3.55.7

Apr 2013

3.36.1

May 2013

3.25.7

Jun 2013

3.35.7

Jul 2013

3.45.5

Aug 2013

3.55.4

Sep 2013

3.75.8

Oct 2013

3.65.6

Nov 2013

3.65.5

Dec 2013

3.95.5

Jan 2014

4.05.8

Feb 2014

3.75.9

Mar 2014

3.85.7

Apr 2014

4.35.9

May 2014

4.66.1

Jun 2014

4.46.2

Jul 2014

4.16.0

Aug 2014

4.65.8

Sep 2014

4.66.2

Oct 2014

4.36.0

Nov 2014

4.16.1

Dec 2014

4.56.3

Jan 2015

5.16.1

Feb 2015

4.86.2

Mar 2015

4.66.1

Apr 2015

4.66.3

May 2015

4.46.4

Jun 2015

4.26.1

Jul 2015

4.86.3

Aug 2015

4.46.7

Sep 2015

4.46.7

Oct 2015

4.96.6

Nov 2015

4.76.7

Dec 2015

4.66.8

Jan 2016

4.76.2

Feb 2016

4.76.8

Mar 2016

5.16.6

Apr 2016

4.76.5

May 2016

4.66.6

Jun 2016

4.86.7

Jul 2016

4.66.6

Aug 2016

4.96.6

Sep 2016

4.56.1

Oct 2016

4.66.2

Nov 2016

4.66.7

Dec 2016

4.56.4

Jan 2017

4.46.5

Feb 2017

5.36.4

Mar 2017

4.56.3

Apr 2017

5.06.4

May 2017

5.06.3

Jun 2017

5.06.5

Jul 2017

5.16.3

Aug 2017

5.26.2

Sep 2017

4.56.1

Oct 2017

4.86.5

Nov 2017

5.26.3

Dec 2017

5.26.1

Jan 2018

5.46.3

Feb 2018

5.46.5

Mar 2018

5.46.4

Apr 2018

5.66.5

May 2018

5.66.9

Jun 2018

6.16.4

Jul 2018

5.96.8

Aug 2018

5.86.5

Sep 2018

6.16.4

Oct 2018

5.86.7

Nov 2018

5.86.5

Dec 2018

6.26.3

Jan 2019

6.46.8

Feb 2019

5.76.6

Mar 2019

5.86.7

Apr 2019

5.87.1

May 2019

5.86.6

Jun 2019

5.47.0

Jul 2019

5.56.9

Aug 2019

5.46.9

Sep 2019

5.76.9

Oct 2019

5.66.6

Nov 2019

5.56.5

Dec 2019

5.26.8

Jan 2020

5.26.6

Feb 2020

5.36.5

Mar 2020

3.94.2

Apr 2020

3.84.9

May 2020

6.819.5

Jun 2020

7.017.5

Jul 2020

6.310.6

Aug 2020

6.08.1

Sep 2020

5.98.2

Oct 2020

6.18.5

Nov 2020

5.98.1

Dec 2020

5.45.8

Jan 2021

5.37.1

Feb 2021

6.58.8

Mar 2021

8.08.5

Apr 2021

9.19.5

May 2021

9.09.3