Topic Archives: Why This Counts

Most Dangerous Jobs?

TV shows like Dangerous Jobs, Deadliest Job Interview, Ax Men, and Deadliest Catch vividly portray some of the most dangerous jobs people have. Here at the Bureau of Labor Statistics we produce data about dangers in the workplace, or workplace injuries, illnesses, and fatalities.

Our list of occupations with high fatal injury rates (on page 19) is often used externally as a list of the “most dangerous” jobs. However, at BLS we strongly believe there is no one measure that tells which job is the most dangerous. Why is that?

A graphic showing the 3 occupations with the highest death rates.

For starters, there is no universal definition of “dangerous” or “hazardous.” There are many other elements that factor into any definition of a “dangerous job,” such as the likelihood of incurring a nonfatal injury, the potential severity of that nonfatal injury, the safety precautions necessary to perform the job, and the physical and mental demands of the job.

It’s also difficult to accurately measure fatal injury rates for occupations with fewer workers.

BLS has certain minimum thresholds that must be met for a fatal injury rate to be published. So, fatal injury rates are not calculated for many occupations that have a relatively small number of fatal work injuries and employment.

A graphic showing the 3 occupations with the highest number of deaths.

Take the occupation elephant trainer*, for instance. Because few workers are employed as elephant trainers, a small number of fatal injuries to elephant trainers would make the fatal injury rate extremely high for a single year, despite their low number of deaths. On the other hand, in most years, this occupation incurs no deaths, rendering their fatality rate 0 and ranking them among the least at risk for incurring a fatal injury.

BLS provides the data to help people, from policymakers to businesses and workers, better understand hazards in the workplace. However, we can only talk about what our data show, such as the number of deaths and fatal injury rates of different occupations. We have to leave it to others to analyze or rank the danger of particular jobs.

*“Elephant trainer” is a hypothetical occupational classification. The classification BLS uses groups these workers with either “artists and performers” or “animal caretakers,” both of which include many more people than just elephant trainers.

Why This Counts: How the Consumer Price Index Affects You

Editor’s note: The following has been cross-posted from the U.S. Department of Labor blog. The writer is Steve Reed, an economist at the U.S. Bureau of Labor Statistics.

Every month, Debi Bertram, an economic assistant in our Philadelphia region, checks the price of milk at a local grocery store. She also goes to several stores to check the prices of items such as toothpaste, sports equipment, and appliances. You may not know Debi—or any of the men and women who collect data for the Bureau of Labor Statistics—but their findings have a real impact on your life.

Among other things, the data are used for making changes in the federal income tax structure and providing cost-of-living wage adjustments for millions of American workers. Additionally, the president, Congress, and the Federal Reserve Board use trends in the data to inform fiscal and monetary policies.

How does it work? BLS data collectors visit or call thousands of locations across the country, from grocery stores to doctors’ offices, to get the prices of about 80,000 different items every month. The data help BLS compile the Consumer Price Index, which measures the average change over time in prices consumers pay for a market basket of goods and services. It is the key measure of consumer inflation in the U.S. economy.

Just got paid

Person's Hand Giving CheckIt’s very possible the CPI helps determine how big your paycheck is. Many employers use the CPI, formally or informally, to decide how much of a cost-of-living raise to give employees. Additionally, many states index their minimum wage by the overall CPI increase. The CPI helps determine how much comes out of your paycheck too, as the IRS uses it to adjust tax bracket thresholds. And many states use CPI data to calculate and adjust workers’ compensation payments.

 

The check’s in the mail

Woman inserting letter into a mailboxMailing a birthday card? The CPI helps determine how much it costs. The Postal Regulatory Commission uses CPI data in the decision about price increases for stamps and postal fees.

 

 

 

 

Back to school

Smiling student eating her lunch.The U.S. Department of Agriculture’s Food and Nutrition Services uses CPI data to determine the annual payments and rate adjustments for the National School Lunch and School Breakfast Programs. The CPI is also consulted to adjust thresholds for eligibility to these programs.

 

 

 

 

Got to pay the rent

Hand holding money with a house in the background.The CPI may even affect where you live. Many landlords tie rent changes to CPI increases; in some cities rent increases for some properties cannot exceed the increase in the CPI. The CPI may also come into play if you want to rent government facilities; the CPI for rent is used to adjust fees for using federal facilities.

You can find out more about how the CPI affects your economic life from the CPI webpage.

Why This Counts: Job Openings and Labor Turnover Survey

Looking solely at net employment change is similar to looking at the surface of a lake. You’ll see ripples and changes, but there’s a whole lot of activity going on underneath the surface. Using JOLTS data—the Job Openings and Labor Turnover Survey—provides a peek at what’s going on below the surface of net employment change.

The basics

JOLTS is a monthly survey of 16,000 establishments that asks employers to provide information on the number of job openings (as of the last business day of the month) and the total number of hires and separations that occurred throughout the month. By asking for the total number of hires and separations over the entire month, we can get a sense of just how many jobs started and ended within a month. For example, in February 2017 there were 5.3 million hires and 5.1 million separations. That’s approximately the population of Colorado moving in and out of jobs in a single month!

A chart showing trends in the numbers of hires and job separations from 2007 to 2017.

Editor’s note: A text-only version of the graphic is below.

Understanding the churn

You may be familiar with the headline payroll employment number that comes out each month, with information on how many net jobs were gained or lost. However, JOLTS data give us insight on what goes on beyond the monthly employment data. JOLTS data show us just how dynamic the U.S. labor market is and can illuminate which industries have persistent unmet demand for workers.

Movement into and out of jobs is often called “churn.” As the rates of hires and separations climb, this increased “churn” can signal a healthy labor market where workers can move in and out of jobs with relative ease. Similarly, when rates of hires and separations fall, workers may have more difficulty moving from job to job.

JOLTS data can also give us insight into labor market changes before the net employment figures can. In the last recession, the hiring rate started to decelerate before payroll employment slowed.

Further insight into industries

Labor market activity differs by industry. By using the combination of hires and job openings rates, we can explore which industries have persistent low-level demand for workers and which industries may have a high unmet demand for workers. When the openings rate exceeds the hiring rate, the industry has an unmet demand for workers.

Consider jobs in construction, retail trade, and accommodation and food services. There are fewer job openings than hires in these industries, suggesting that employers can easily find workers. Many jobs in these industries require minimal training or experience, which means it is easy to find workers. It may also mean that workers don’t stay with one employer for very long. JOLTS data confirm this. These industries have high churn, with large numbers of hires and large numbers of separations. Trends in hires and separations tend to move together, meaning employers are frequently replacing workers.

A chart showing hires rates and job separations rates in construction, retail trade, and accommodation and food service from 2007 to 2017.

Editor’s note: A text-only version of the graphic is below.

In contrast, many jobs in health care and financial activities require more training and experience, suggesting it may be more difficult to find qualified workers. In these industries, job openings are greater than hires—employers are always looking for qualified workers. These industries also exhibit low churn, stemming from low numbers of hires and separations as a share of industry employment. This suggests workers remain with their employers for longer periods of time.

The professional and business services industry presents an unusual case, perhaps because of the diverse set of occupations within the industry. Included in this industry are many professional service workers, such as those in computer service and engineering firms. But the industry also includes temporary help supply firms and building services, such as janitorial and landscaping firms. Until recently, the industry as a whole had traditionally had more hires than job openings, suggesting an ease in attracting labor. This may be due in part to the number of lower-skilled jobs in this industry. But several times over the past year, job openings have exceeded hires, suggesting that employers need qualified workers. Perhaps this reflects the higher-skilled jobs in this industry. This recent trend bears watching.

A chart showing hires rates and job separations rates in financial activities, health care, and professional and business services from 2007 to 2017.

Editor’s note: A text-only version of the graphic is below.

Jobs in government and education exhibit both low hiring and low job openings rates. These lower rates indicate that few workers are needed in these industries—workers may tend to stay in these jobs for long periods of time.

For more info on JOLTS, see www.bls.gov/jlt. For more in-depth information on the interaction between job openings and hires, see Charlotte Oslund’s article, “Which industries need workers? Exploring differences in labor market activity.”

 

Number of hires and separations, February 2007 to February 2017, seasonally adjusted
Month Hires Separations
Feb 2007 5,202,000 5,094,000
Mar 2007 5,380,000 5,123,000
Apr 2007 5,158,000 5,138,000
May 2007 5,268,000 5,080,000
Jun 2007 5,187,000 5,065,000
Jul 2007 5,075,000 5,118,000
Aug 2007 5,106,000 5,105,000
Sep 2007 5,145,000 5,031,000
Oct 2007 5,227,000 5,129,000
Nov 2007 5,162,000 5,031,000
Dec 2007 4,968,000 4,926,000
Jan 2008 4,868,000 5,005,000
Feb 2008 4,943,000 5,010,000
Mar 2008 4,766,000 4,762,000
Apr 2008 4,875,000 5,121,000
May 2008 4,602,000 4,728,000
Jun 2008 4,751,000 4,900,000
Jul 2008 4,471,000 4,713,000
Aug 2008 4,522,000 4,815,000
Sep 2008 4,316,000 4,751,000
Oct 2008 4,454,000 4,895,000
Nov 2008 3,954,000 4,605,000
Dec 2008 4,218,000 4,814,000
Jan 2009 4,158,000 4,974,000
Feb 2009 4,011,000 4,674,000
Mar 2009 3,730,000 4,536,000
Apr 2009 3,853,000 4,655,000
May 2009 3,793,000 4,146,000
Jun 2009 3,675,000 4,192,000
Jul 2009 3,854,000 4,297,000
Aug 2009 3,744,000 4,060,000
Sep 2009 3,859,000 4,084,000
Oct 2009 3,767,000 3,951,000
Nov 2009 3,992,000 3,873,000
Dec 2009 3,806,000 3,989,000
Jan 2010 3,880,000 3,894,000
Feb 2010 3,781,000 3,830,000
Mar 2010 4,182,000 3,949,000
Apr 2010 4,082,000 3,892,000
May 2010 4,376,000 3,831,000
Jun 2010 4,064,000 4,223,000
Jul 2010 4,116,000 4,278,000
Aug 2010 3,910,000 4,009,000
Sep 2010 3,978,000 4,026,000
Oct 2010 4,061,000 3,784,000
Nov 2010 4,101,000 3,843,000
Dec 2010 4,155,000 4,026,000
Jan 2011 3,910,000 3,908,000
Feb 2011 4,061,000 3,838,000
Mar 2011 4,291,000 3,980,000
Apr 2011 4,218,000 3,924,000
May 2011 4,116,000 4,035,000
Jun 2011 4,297,000 4,094,000
Jul 2011 4,139,000 4,082,000
Aug 2011 4,168,000 4,120,000
Sep 2011 4,320,000 4,115,000
Oct 2011 4,239,000 4,011,000
Nov 2011 4,244,000 4,001,000
Dec 2011 4,234,000 3,994,000
Jan 2012 4,292,000 4,010,000
Feb 2012 4,419,000 4,175,000
Mar 2012 4,465,000 4,134,000
Apr 2012 4,299,000 4,260,000
May 2012 4,445,000 4,336,000
Jun 2012 4,432,000 4,367,000
Jul 2012 4,269,000 4,138,000
Aug 2012 4,447,000 4,360,000
Sep 2012 4,238,000 4,059,000
Oct 2012 4,299,000 4,194,000
Nov 2012 4,393,000 4,171,000
Dec 2012 4,360,000 4,038,000
Jan 2013 4,422,000 4,297,000
Feb 2013 4,509,000 4,156,000
Mar 2013 4,293,000 4,113,000
Apr 2013 4,533,000 4,376,000
May 2013 4,572,000 4,363,000
Jun 2013 4,409,000 4,267,000
Jul 2013 4,529,000 4,384,000
Aug 2013 4,732,000 4,517,000
Sep 2013 4,681,000 4,537,000
Oct 2013 4,444,000 4,288,000
Nov 2013 4,588,000 4,268,000
Dec 2013 4,500,000 4,335,000
Jan 2014 4,615,000 4,443,000
Feb 2014 4,627,000 4,436,000
Mar 2014 4,758,000 4,452,000
Apr 2014 4,812,000 4,518,000
May 2014 4,796,000 4,565,000
Jun 2014 4,817,000 4,552,000
Jul 2014 5,001,000 4,784,000
Aug 2014 4,839,000 4,627,000
Sep 2014 5,078,000 4,882,000
Oct 2014 5,118,000 4,927,000
Nov 2014 5,027,000 4,633,000
Dec 2014 5,165,000 4,789,000
Jan 2015 5,027,000 4,843,000
Feb 2015 4,991,000 4,705,000
Mar 2015 5,090,000 4,986,000
Apr 2015 5,095,000 4,906,000
May 2015 5,143,000 4,812,000
Jun 2015 5,162,000 5,011,000
Jul 2015 5,136,000 4,849,000
Aug 2015 5,129,000 4,958,000
Sep 2015 5,150,000 5,067,000
Oct 2015 5,304,000 4,983,000
Nov 2015 5,323,000 5,003,000
Dec 2015 5,504,000 5,223,000
Jan 2016 5,117,000 5,033,000
Feb 2016 5,447,000 5,183,000
Mar 2016 5,297,000 5,040,000
Apr 2016 5,038,000 4,962,000
May 2016 5,153,000 5,101,000
Jun 2016 5,176,000 4,940,000
Jul 2016 5,328,000 5,001,000
Aug 2016 5,288,000 5,059,000
Sep 2016 5,179,000 4,942,000
Oct 2016 5,200,000 5,041,000
Nov 2016 5,263,000 5,075,000
Dec 2016 5,303,000 5,084,000
Jan 2017 5,424,000 5,247,000
Feb 2017 5,314,000 5,071,000

Hires rates and job openings rates in selected industries, February 2007 to February 2017, seasonally adjusted
Month Construction hires rate Construction job openings rate Retail trade hires rate Retail trade job openings rate Accommodation and food services hires rate Accommodation and food services job openings rate
Feb 2007 4.2 3.5 5.1 2.8 7.2 4.1
Mar 2007 6.1 2.6 5.0 2.7 6.9 4.4
Apr 2007 5.0 2.8 4.7 2.6 7.3 4.0
May 2007 5.3 2.7 4.9 2.3 6.9 4.4
Jun 2007 5.6 2.2 4.6 2.8 7.0 4.4
Jul 2007 5.2 2.6 4.6 2.8 6.9 4.5
Aug 2007 5.3 2.1 4.7 2.5 6.8 4.8
Sep 2007 5.1 1.6 4.9 2.6 6.7 4.7
Oct 2007 5.3 1.6 5.0 2.3 6.9 4.6
Nov 2007 5.0 1.1 5.2 2.7 6.6 4.4
Dec 2007 5.0 1.3 4.8 2.5 6.7 4.5
Jan 2008 5.0 1.7 4.5 2.4 6.3 4.3
Feb 2008 5.1 1.5 4.6 2.3 7.0 4.0
Mar 2008 5.4 1.3 4.4 2.4 6.2 4.0
Apr 2008 5.2 1.6 4.4 2.5 6.3 4.0
May 2008 4.8 2.3 3.9 2.4 6.4 3.9
Jun 2008 5.3 1.6 4.5 1.9 6.1 3.7
Jul 2008 4.9 1.7 4.4 2.4 6.0 3.4
Aug 2008 5.6 1.2 4.4 2.3 5.9 2.8
Sep 2008 4.7 1.7 4.1 1.7 5.9 3.1
Oct 2008 5.4 1.0 4.2 2.4 5.8 2.9
Nov 2008 4.9 0.7 3.8 2.3 5.3 2.5
Dec 2008 5.1 0.7 4.2 1.9 5.2 2.4
Jan 2009 5.4 0.6 3.7 2.2 5.2 1.8
Feb 2009 5.1 1.0 3.6 2.0 5.4 2.5
Mar 2009 4.9 0.7 3.6 1.7 5.0 2.2
Apr 2009 5.3 0.4 3.9 1.4 4.9 2.2
May 2009 5.4 0.7 3.8 2.1 5.3 2.1
Jun 2009 4.3 0.9 3.4 1.8 4.9 2.1
Jul 2009 5.5 1.0 3.4 1.1 4.7 1.8
Aug 2009 4.3 1.0 3.7 1.6 4.8 1.6
Sep 2009 5.5 1.1 3.8 1.9 4.6 2.3
Oct 2009 5.4 1.0 3.4 1.3 4.5 2.0
Nov 2009 5.5 0.8 3.6 1.6 5.0 2.1
Dec 2009 6.0 1.0 3.7 1.7 4.8 2.1
Jan 2010 5.6 0.9 3.8 1.6 4.9 2.2
Feb 2010 5.0 1.1 3.8 1.9 4.7 2.0
Mar 2010 7.4 1.4 4.5 2.3 5.0 1.7
Apr 2010 6.6 1.8 3.7 1.8 4.9 2.1
May 2010 5.5 1.5 3.7 1.8 4.8 2.1
Jun 2010 5.0 1.5 3.9 1.8 4.7 2.0
Jul 2010 6.2 2.1 4.0 1.7 4.9 2.3
Aug 2010 5.9 0.9 3.7 1.6 4.8 2.7
Sep 2010 5.8 1.3 3.9 1.5 5.0 2.1
Oct 2010 6.4 1.2 3.9 1.7 4.9 2.4
Nov 2010 6.2 1.2 3.9 1.8 4.8 2.3
Dec 2010 6.8 0.6 3.4 2.0 4.7 2.3
Jan 2011 5.1 1.1 3.9 1.9 4.7 2.5
Feb 2011 6.3 0.8 3.9 1.8 4.8 3.0
Mar 2011 6.9 1.2 4.0 1.8 5.5 2.8
Apr 2011 6.7 2.2 4.0 2.2 5.1 2.4
May 2011 6.8 2.1 3.9 2.1 4.7 2.5
Jun 2011 6.8 1.2 4.0 2.3 5.3 2.6
Jul 2011 6.2 1.6 4.0 2.3 5.3 2.1
Aug 2011 5.9 1.7 3.6 2.2 5.2 2.8
Sep 2011 6.7 1.5 3.9 2.2 5.3 3.0
Oct 2011 5.9 1.3 3.8 2.3 5.2 3.0
Nov 2011 5.6 1.1 3.8 2.1 5.4 3.0
Dec 2011 5.6 0.8 3.4 2.2 5.3 3.2
Jan 2012 5.8 1.4 3.9 2.4 5.4 3.2
Feb 2012 6.0 1.0 3.8 2.3 5.1 2.9
Mar 2012 5.4 1.6 3.9 2.5 5.8 3.1
Apr 2012 5.2 2.1 3.9 2.2 5.2 3.2
May 2012 5.8 1.5 3.9 2.3 5.3 3.2
Jun 2012 6.3 1.7 3.9 2.2 5.2 3.4
Jul 2012 6.4 1.4 3.8 2.1 5.3 3.3
Aug 2012 6.0 2.0 4.1 2.4 5.5 3.0
Sep 2012 6.2 1.4 4.0 2.4 5.3 2.7
Oct 2012 5.6 1.8 4.0 2.5 5.4 3.2
Nov 2012 6.9 1.2 3.9 3.0 5.2 3.5
Dec 2012 5.4 1.1 3.9 2.6 5.4 3.5
Jan 2013 5.8 2.0 4.0 2.7 5.6 3.4
Feb 2013 6.5 1.9 4.2 2.6 5.5 3.5
Mar 2013 6.1 1.8 3.7 2.7 5.5 3.5
Apr 2013 5.1 2.3 4.1 2.9 6.1 3.3
May 2013 5.7 2.1 4.2 3.1 5.4 3.3
Jun 2013 5.6 2.3 4.1 3.8 5.4 3.5
Jul 2013 5.3 2.0 4.1 3.0 5.4 3.7
Aug 2013 5.1 2.1 4.6 2.9 5.3 3.6
Sep 2013 5.4 1.9 4.4 3.1 5.5 3.8
Oct 2013 5.5 2.1 4.5 2.8 5.5 3.5
Nov 2013 5.1 1.7 4.7 2.8 5.3 3.7
Dec 2013 4.7 1.4 4.8 2.7 5.3 4.0
Jan 2014 4.8 2.1 4.1 2.7 5.6 3.9
Feb 2014 4.4 1.7 4.7 2.9 5.8 4.0
Mar 2014 4.4 2.0 4.6 3.2 5.6 4.2
Apr 2014 5.0 2.1 4.9 3.4 5.6 4.4
May 2014 5.3 2.4 5.0 2.8 5.8 4.9
Jun 2014 4.5 2.7 4.9 3.0 5.9 4.8
Jul 2014 6.4 2.5 5.0 2.8 5.7 4.0
Aug 2014 5.3 2.3 4.6 3.3 5.5 4.7
Sep 2014 4.8 1.7 4.6 3.0 5.9 4.6
Oct 2014 5.1 2.3 5.0 3.1 6.0 4.9
Nov 2014 5.1 1.7 5.0 3.2 6.1 4.3
Dec 2014 6.3 1.5 5.0 3.4 6.3 4.6
Jan 2015 5.7 2.2 4.9 3.2 5.8 5.2
Feb 2015 5.1 2.4 4.5 3.3 5.8 4.9
Mar 2015 4.9 2.6 4.9 3.2 6.0 4.7
Apr 2015 5.3 2.6 4.7 3.4 6.2 5.0
May 2015 4.9 2.6 5.0 3.5 6.1 4.8
Jun 2015 5.2 2.3 4.9 3.4 6.2 4.4
Jul 2015 4.7 2.3 4.9 3.8 6.3 5.2
Aug 2015 5.0 2.4 4.7 3.7 6.5 4.8
Sep 2015 5.3 1.6 4.6 4.0 6.5 4.7
Oct 2015 5.0 2.0 4.7 3.5 6.5 5.0
Nov 2015 5.2 1.3 4.9 3.2 6.6 4.8
Dec 2015 4.7 1.9 4.9 3.2 6.9 4.8
Jan 2016 4.4 2.3 4.8 3.9 5.9 4.8
Feb 2016 5.2 2.8 5.3 3.7 6.8 5.1
Mar 2016 5.3 3.0 4.7 3.7 6.4 5.1
Apr 2016 5.0 2.7 4.3 3.6 6.1 4.9
May 2016 4.7 2.7 4.4 3.6 6.3 4.8
Jun 2016 4.2 2.5 4.5 3.7 6.2 4.6
Jul 2016 5.0 3.4 4.5 3.8 6.3 4.6
Aug 2016 5.1 2.7 4.6 3.7 6.3 4.8
Sep 2016 4.7 3.4 4.8 3.8 6.0 4.6
Oct 2016 5.1 2.8 4.7 3.9 6.1 4.5
Nov 2016 5.0 2.6 4.2 3.9 6.7 4.7
Dec 2016 5.9 2.0 4.2 3.9 6.4 4.5
Jan 2017 5.7 2.0 4.3 3.5 6.4 4.6
Feb 2017 5.4 2.4 4.8 3.3 6.2 5.0

Hires rates and job openings rates in selected industries, February 2007 to February 2017, seasonally adjusted
Month Financial activities hires rate Financial activities job openings rate Professional and business services hires rate Professional and business services job openings rate Health care and social assistance hires rate Health care and social assistance job openings rate
Feb 2007 3.0 3.0 5.5 3.9 2.9 4.0
Mar 2007 3.4 3.9 5.5 4.3 3.0 4.2
Apr 2007 2.7 2.8 5.0 4.6 2.9 4.3
May 2007 3.4 3.3 5.4 4.1 3.1 4.4
Jun 2007 3.0 3.2 4.9 4.1 3.0 4.5
Jul 2007 2.9 3.4 5.1 3.7 2.8 3.9
Aug 2007 3.1 3.6 5.1 4.0 3.0 4.3
Sep 2007 3.0 3.3 5.1 4.0 2.9 4.8
Oct 2007 3.0 3.3 5.4 4.1 3.0 4.1
Nov 2007 2.8 2.8 5.4 4.0 3.0 4.2
Dec 2007 2.9 3.2 5.1 4.1 2.7 4.2
Jan 2008 2.9 3.8 4.8 4.0 3.0 4.0
Feb 2008 2.9 2.7 4.7 4.0 3.2 4.5
Mar 2008 2.6 3.0 4.5 4.1 3.1 4.3
Apr 2008 2.8 2.7 5.1 4.1 3.1 4.1
May 2008 2.4 2.4 4.5 3.4 2.9 4.1
Jun 2008 2.7 2.3 5.3 4.0 2.7 4.0
Jul 2008 2.5 2.6 4.4 3.6 2.8 3.9
Aug 2008 2.6 2.6 4.5 3.5 2.8 3.7
Sep 2008 2.6 2.3 4.3 3.4 2.7 3.4
Oct 2008 2.2 2.0 4.5 3.2 2.9 3.4
Nov 2008 2.5 2.4 4.2 3.0 2.6 3.3
Dec 2008 2.0 2.4 4.8 3.2 2.7 3.3
Jan 2009 2.4 2.3 4.4 3.1 2.8 3.1
Feb 2009 2.2 2.5 4.3 3.1 2.8 3.0
Mar 2009 2.3 2.3 3.6 2.5 2.6 2.8
Apr 2009 1.7 1.6 4.0 2.4 2.5 2.8
May 2009 2.0 2.2 4.0 2.4 2.4 2.9
Jun 2009 2.0 1.9 3.9 2.4 2.6 2.8
Jul 2009 2.4 1.7 4.2 2.6 2.6 2.9
Aug 2009 2.2 1.6 3.8 2.1 2.8 2.8
Sep 2009 1.9 2.2 4.2 2.6 2.8 3.1
Oct 2009 2.3 2.0 4.2 2.2 2.5 2.9
Nov 2009 1.8 2.1 5.1 2.5 2.6 2.8
Dec 2009 2.3 1.7 4.1 2.6 2.5 2.9
Jan 2010 2.2 2.1 4.5 2.4 2.3 3.2
Feb 2010 2.1 1.9 4.4 2.3 2.4 2.8
Mar 2010 1.9 2.0 4.4 2.5 2.5 2.6
Apr 2010 2.3 2.8 4.7 3.0 2.5 2.7
May 2010 2.3 2.7 4.7 3.4 2.4 2.6
Jun 2010 2.4 2.6 5.0 2.8 2.6 2.5
Jul 2010 2.1 2.8 4.8 3.2 2.7 2.7
Aug 2010 2.0 3.1 4.7 3.5 2.4 2.4
Sep 2010 2.2 2.9 4.5 3.3 2.6 2.7
Oct 2010 2.2 3.1 4.5 3.5 2.4 3.1
Nov 2010 2.0 3.2 4.7 3.7 2.5 2.8
Dec 2010 2.4 2.5 5.4 3.4 2.5 2.8
Jan 2011 2.0 2.7 4.8 2.7 2.1 2.6
Feb 2011 1.9 2.7 5.0 3.4 2.3 2.8
Mar 2011 2.1 2.5 5.3 3.4 2.3 3.0
Apr 2011 1.7 3.1 5.1 3.2 2.4 3.0
May 2011 2.0 2.5 5.1 3.3 2.4 3.0
Jun 2011 2.1 2.7 4.8 3.5 2.6 3.1
Jul 2011 2.1 2.9 4.8 4.3 2.4 3.1
Aug 2011 2.0 2.3 5.1 3.3 2.5 3.1
Sep 2011 2.0 2.2 5.2 4.1 2.3 3.0
Oct 2011 2.2 2.9 5.0 3.4 2.4 3.2
Nov 2011 2.2 2.0 4.9 3.0 2.5 3.3
Dec 2011 2.2 2.4 4.9 4.2 2.4 3.2
Jan 2012 2.1 3.0 4.6 4.4 2.6 3.4
Feb 2012 2.2 2.5 5.4 3.5 2.8 3.5
Mar 2012 2.3 2.9 5.2 4.4 2.6 3.5
Apr 2012 2.4 2.7 4.8 3.3 2.4 3.5
May 2012 2.3 3.0 5.2 3.7 2.7 3.5
Jun 2012 2.3 2.8 5.4 3.9 2.6 3.9
Jul 2012 2.2 3.1 4.8 3.7 2.5 3.3
Aug 2012 2.5 3.2 4.8 4.0 2.5 3.3
Sep 2012 2.6 3.5 4.7 3.3 2.4 3.6
Oct 2012 2.4 3.3 4.7 3.5 2.5 3.5
Nov 2012 2.8 2.9 4.9 3.2 2.5 3.5
Dec 2012 2.2 3.2 4.7 3.3 2.6 3.5
Jan 2013 2.7 2.9 4.9 3.7 2.6 3.0
Feb 2013 2.9 4.4 4.6 4.0 2.6 3.5
Mar 2013 2.3 3.4 4.6 3.7 2.6 3.5
Apr 2013 2.4 3.5 4.9 3.6 2.8 3.6
May 2013 2.7 3.8 4.9 3.3 2.7 3.4
Jun 2013 2.4 3.8 5.2 3.3 2.4 3.4
Jul 2013 2.7 3.9 5.3 3.1 2.6 3.3
Aug 2013 2.7 3.3 5.5 3.6 2.7 3.6
Sep 2013 2.8 3.0 5.2 3.6 2.7 3.2
Oct 2013 2.4 3.0 4.6 4.1 2.5 3.2
Nov 2013 2.3 2.7 5.2 3.6 2.5 3.2
Dec 2013 2.2 2.9 4.9 3.7 2.5 2.9
Jan 2014 2.0 2.8 5.2 3.4 2.7 3.4
Feb 2014 2.2 2.9 5.2 4.1 2.5 3.5
Mar 2014 2.5 3.0 5.3 3.6 2.7 3.6
Apr 2014 2.3 3.2 5.1 4.2 2.8 3.5
May 2014 2.4 3.5 4.9 4.1 2.6 3.9
Jun 2014 2.3 3.8 5.1 4.2 2.6 3.8
Jul 2014 2.4 3.6 5.3 4.3 2.8 4.1
Aug 2014 2.7 3.9 5.5 4.7 2.5 4.5
Sep 2014 2.6 3.1 5.8 4.3 2.9 4.1
Oct 2014 2.2 3.9 5.6 4.7 2.9 4.4
Nov 2014 2.8 3.3 5.1 5.1 2.8 3.8
Dec 2014 2.8 3.2 5.1 5.0 2.9 4.4
Jan 2015 2.5 3.6 5.2 4.6 2.8 4.3
Feb 2015 2.0 4.1 5.3 4.6 2.9 4.4
Mar 2015 2.4 3.2 5.4 5.0 2.8 4.1
Apr 2015 2.6 4.5 5.3 5.5 2.9 4.9
May 2015 2.4 3.7 5.4 5.4 2.8 4.5
Jun 2015 2.5 3.3 5.3 5.6 2.7 4.6
Jul 2015 2.3 4.5 5.1 5.5 2.9 5.2
Aug 2015 2.2 4.0 5.1 5.2 2.8 4.9
Sep 2015 2.4 3.6 5.2 5.5 2.9 5.0
Oct 2015 2.5 3.8 5.5 5.4 3.0 4.8
Nov 2015 2.6 4.1 5.4 5.6 3.0 5.0
Dec 2015 2.6 4.5 5.9 5.3 3.0 4.9
Jan 2016 2.6 4.0 5.6 5.4 2.7 5.3
Feb 2016 2.9 4.0 5.4 5.3 2.9 4.7
Mar 2016 2.7 3.7 5.4 6.1 2.9 4.8
Apr 2016 2.3 3.9 5.5 4.7 2.6 4.8
May 2016 2.2 3.4 5.5 5.7 2.8 4.8
Jun 2016 2.4 3.5 5.0 4.9 2.9 5.0
Jul 2016 2.2 3.7 6.0 5.9 2.9 4.9
Aug 2016 2.3 3.8 5.5 4.9 2.9 4.8
Sep 2016 2.1 3.9 5.5 5.3 2.7 4.9
Oct 2016 2.0 3.7 5.4 5.1 2.9 5.2
Nov 2016 2.1 3.7 5.3 4.9 3.0 5.2
Dec 2016 2.3 4.1 5.6 4.6 2.9 5.2
Jan 2017 2.6 4.4 5.5 4.9 2.9 5.2
Feb 2017 2.2 4.2 5.2 4.7 2.8 5.6

Ice Cream versus Bacon

Editor’s note: The following has been cross-posted from the U.S. Department of Labor blog. The writer is Steve Henderson. When not relaxing with a bowl of ice cream, Steve is a supervisory economist at the U.S. Bureau of Labor Statistics. He’s spent half of his government career working on the Consumer Price Index and half on the Consumer Expenditure Survey.

How much did you spend on ice cream last year? According to the BLS Consumer Expenditure Survey, the average U.S. household spent around $54. But why does BLS need to know that?

Let’s take a deep dive into that ice cream. That’s just one of thousands of data we collect to calculate the Consumer Price Index, a monthly assessment of price changes for goods and services in the United States. The CPI has separate inflation indexes for just about everything people purchase. For example, the CPI has an index for “Bacon and related products,” and lots of other itemized food categories, including “Ice cream and related products.”

(Curious about what else we measure? Here’s the CPI’s online table generator tool. You can drill down to the most detailed CPI categories in step 2. Note: You’ll need to enable Java to see the chart.)

Why so many indexes? The CPI needs to carefully track how the prices of food, and just about everything else, change because not every item’s price goes up or down at the same rate. For example, bacon has increased in price almost 32 percent over the past 10 years, while ice cream went up 21 percent over the same time period.

A graphic showing trends in ice cream prices and bacon prices from 2007 to 2017.

Looking at how prices have moved over the last year, bacon is slightly less expensive than it was in January 2016, while the price of ice cream has gone up slightly. This information is helpful for families looking to see where their food budget money went, as well as researchers investigating changing food prices and other indicators of inflation.

Most importantly, the CPI needs to know how much the average U.S. household spends on both of those two food items in order to measure the impact different inflation rates have on total inflation. If everybody spent the same number of dollars on ice cream as they do on bacon, then you could just use a simple average of the two inflation rates to get a total. Here is where BLS’s Consumer Expenditure Survey comes in. It measures, in great detail, all the different goods and services consumers purchase in a year, and passes these numbers to the CPI to form a “market basket” — that is, a list of everything people buy and what percentage of their total spending goes to each item.

The latest spending numbers showed that the average dollar amount per year that all U.S. households spent on ice cream was $54.04, while the average amount on bacon was $39.07. That means that ice cream has a greater importance than bacon when tracking inflation, not only in the Henderson household, but in the CPI. In other words, the more people spend on an item, the more inflationary changes to its cost will affect the total inflation rate.

Policymakers, researchers, journalists, government bodies, and others use the CPI to make important decisions that directly affect American citizens. U.S. Census Bureau analysts use CPI data to adjust the official poverty thresholds for inflation, and it’s one of several factors the Federal Reserve Board considers when deciding whether to raise or lower interest rates. Employers may use it to determine whether to give cost-of-living increases, and policymakers use the CPI when considering changes to allotments for things like Social Security, military benefits, or school lunch programs.

I hope this deep dive into ice cream spending helps you understand why the Consumer Expenditure Survey is so detailed.

Why Do We Ask about How People Use Their Time?

Editor’s note: The following has been cross-posted from the U.S. Department of Labor blog. The writer is Rachel Krantz-Kent, an economist at the U.S. Bureau of Labor Statistics.

On any given day, about 80 percent of the population age 15 and up watch television, and they watch for an average of 3 hours 29 minutes.* That’s an interesting piece of trivia, you may be thinking, but why does the Bureau of Labor Statistics need to know that? Without context, TV watching may seem like an odd area of focus — but this is just one of many statistics we collect as part of the American Time Use Survey. And Americans across the country use that information every day to get their jobs done.

The statistics above, for example, may be helpful to those promoting healthy behaviors and products, such as those who work in the health and fitness industries. The data can also be useful to television producers in determining programming.

Unlike other BLS surveys that track employment, wages, and prices, the American Time Use Survey tracks a less conventional, but equally important, economic resource that we never have enough of: time. The survey compiles data on how much time Americans spend doing paid work, unpaid household work (such as taking care of children or doing household chores), and all the other activities that compose a typical day.

Some of these measurements have economic and policy-relevant significance. For example, the time people spend doing unpaid household work has implications for measures of national wealth. Information about eldercare providers and the time they spend providing this care informs lawmakers. Measures of physical activity and social contact shed light on the health and well-being of the population. And information about leisure—how much people have and how they spend it—provides valuable insight into the quality of life in the United States.

All of the data are publically available and used by businesses, government agencies, employers, job seekers, and private individuals to examine the different time choices and tradeoffs that people make every day. Here are some other interesting facts the survey reveals about how Americans spend their time.

Unpaid household work: 66 percent of women prepare food on a given day, compared with 40 percent of men.

Why it’s important: These statistics measure one aspect of women’s and men’s contributions to their families and households and help promote the value of all work people do, whether or not they are paid to perform it. Compared with men, women spend a greater share of their time doing unpaid household work, such as food preparation. Statistics like these can shed light on barriers to equal opportunities for women.

A graphic showing how mothers and fathers spend their time on an average day.Editor’s note: A text-only version of the graphic is below.

Where people work: 38 percent of workers in management, business, and financial operations occupations and 35 percent of those employed in professional and related occupations do some or all of their work at home on days they work. Workers employed in other occupations are less likely to work at home.

Why it’s important: Information like this is important for people starting or changing careers. For those interested in this aspect of job flexibility, or for those who want more separation between their work and home, this information can help them identify occupations that are the right fit and decide which careers to pursue.

Childcare: Parents whose youngest child is under age 6 spend 2 hours 8 minutes per day on average providing childcare as their main activity, compared to 1 hour for parents whose youngest child is between the ages of 6 and 12. (These estimates do not include the time parents spend supervising their children while doing other activities.)

Why it’s important: Parenting can be an intense experience for many reasons, including the time it demands of parents. These statistics provide average measures of the time involved in directly caring for children. The data can be helpful to health and community workers whose work supports parents, as well as employers interested in developing ways to promote work-life balance and staff retention.

Eldercare: 61 percent of unpaid eldercare providers are employed.

Why it’s important: Knowing the characteristics of those who provide unpaid care for aging family, friends, and neighbors can help lawmakers create targeted policies and aid community workers in developing supportive programs.

Transportation: Employed people spend an average of 1 hour 6 minutes driving their vehicles, 7 minutes in the passenger seat, and 8 minutes traveling by another mode of transportation on days they work.

Why it’s important: Knowing how workers travel and the amount of time they spend using different modes of transportation can be useful to a variety of people, including city and transportation planners, land and real estate developers, and designers in the automobile industry.

This is just a snapshot of the information available from the American Time Use Survey, all of which is used by researchers, journalists, educators, sociologists, economists, lawmakers, lawyers, and members of the public. View the data listed above and find out more about how time-use data can be used.

* All data are from the 2014 and 2015 American Time Use Surveys.

Working Parents’ Use of Time

Moms vs. Dads on an Average Day

Based on households with married couples who have children under age 18, in which both spouses work full time, 2011–15.

Dads Moms
+55 minutes more working +28 minutes more on housework
+39 minutes more on sports and leisure +28 minutes more caring for children (more if those children are under 6)
+10 minutes more on lawn & garden care +24 minutes more on food prep & cleanup