Topic Archives: Why This Counts

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

 

Why the unemployment rate still matters

Just like your body, the economy is a superbly complex system. When you visit doctors or other healthcare providers, they routinely take several measurements — height, weight, blood pressure, and temperature. Tracking these vital signs over time can lead you and your healthcare providers to seek further tests. Yet, even when your healthcare providers need more information, they continue to take the basic measurements.

In much the same way, the government routinely measures the health of the economy. Here at BLS, we specialize in tracking labor market activity, working conditions, productivity, and price changes. One of our most important measures is the national unemployment rate. Since it is measured the same way each month, year after year, changes in the rate can be an important signal of changes in the labor market and economy.

We realize, of course, that the unemployment rate doesn’t tell the full story. It isn’t meant to. Much like your temperature is a necessary measurement, its usefulness increases when viewed with other measures. When we release the unemployment rate each month, we also publish five alternative measures of labor underutilization to help assess labor market conditions from several perspectives.

Chart showing trends in alternative measures of labor underutilization.

In addition, the source for the unemployment data, the Current Population Survey, provides a wealth of information about workers, jobseekers, and people who aren’t working or looking for work. For example, we also get information about trends in labor force participation, a topic that has received much public attention in recent years. BLS releases thousands of other measures monthly, quarterly, and annually, depending on the topic.

For example, if you want to know how adult Black men are performing in the labor market, we have a stat for that. Ditto for people with a less than high school education or veterans with service-connected disabilities.

And if you want to know how employers are doing (say, how many job openings they’ve posted and how many workers have been fired or quit their jobs in the past month), check out our Job Openings and Labor Turnover Survey.

Want to know what is happening in your local area? Not a problem. Each month BLS releases state employment and unemployment data and metropolitan area data too.

We invite you to visit our website or contact one of our expert economists next time you have a question about the health of labor market—or your favorite economic “symptom.”

Why This Counts: Productivity and Its Impact on Our Lives

How can we achieve a higher standard of living? One way might simply be to work more, trading some free time for more income. Although working more will increase how much we can produce and purchase, are we better off? Not necessarily. Only if we increase our efficiency—by producing more goods and services without increasing the number of hours we work—can we be sure to increase our standard of living.

That’s why BLS produces labor productivity statistics every quarter that tell us how well we are improving our economic efficiency. These measures compare the amount of goods and services we produce with the number of hours we work. How can we can improve labor productivity? There are many ways. We can use more and newer machinery and equipment. We can develop new technologies that streamline production. We can improve organization and communication in the workplace and manage people more effectively. Or, we can increase worker skills through education or job training.

So, how much has U.S. labor productivity improved over the years? Compared to 1947, we now produce 330 percent more goods and services per hour of work. On average, thanks to advances in technology, education, management, and so on, you can do in 15 minutes what your grandparents or great grandparents needed more than an hour to do in 1947. This is a substantial increase, and we can see it in the many improvements in living standards since World War II.

Productivity growth in recent years hasn’t been as strong, however. It may seem surprising, given all the new technologies and products in recent years, but we are now living through one of the lowest productivity-growth periods ever recorded. Since the Great Recession of 2007–09 began in the fourth quarter of 2007, labor productivity has grown just 1.0 percent per year. That is less than half the long-term average rate of 2.2 percent since 1947. Although the U.S. economy has been experiencing slow productivity growth since 2007, some industries have been doing well. For instance, the wireless telecommunication carrier industry has had annual labor productivity growth of over 15.0 percent since the beginning of the Great Recession.

Labor productivity growth in the nonfarm business sector is lower in the current business cycle than during any of the previous ten business cycles. Chart 1 shows average annual labor productivity growth during business cycles since World War II.

Chart 1. Average annual percent change in labor productivity in the nonfarm business sector during business cycles

Multifactor productivity—which accounts for the use of machinery, equipment, and other capital, in addition to labor—has also increased more slowly over the current business cycle; it has grown 0.4 percent per year during the 2007–15 period, compared to its long-term rate of 0.9 percent per year since 1987.

Historically, productivity growth has led to gains in compensation for workers, greater profits for firms, and more tax revenue for governments. Compensation, which includes pay and benefits, has not always risen as fast as productivity, however. (See chart 2.) The difference between labor productivity gains and real hourly compensation growth is often called the “wage gap.” Real hourly compensation growth tracked labor productivity growth more closely before the 1970s. Since then, growth in real hourly compensation has lagged behind gains in productivity, widening the gap considerably. Since the start of the Great Recession in the fourth quarter of 2007, real hourly compensation has grown by only 0.6 percent per year; that’s less than half the long-term average of 1.6 percent per year.

Chart 2. Labor productivity and real hourly compensation in the nonfarm business sector, 1947–2015

Measures of gross domestic product and employment tell us how the U.S. economy is doing in producing goods and services and creating jobs. Measures of productivity link what our economy produces and the labor and capital used to produce it. Labor productivity is an important statistic to track because gains in productivity are essential to improving our lives and the well-being of our nation. That’s what Nobel Prize-winning economist Paul Krugman meant when he noted, “Productivity isn’t everything, but in the long-run it’s almost everything.”

You can stay up to date on productivity trends and other economic news by signing up for our email alerts or following us on Twitter.

Why This Counts: Working Together to Keep Workers Safe on the Road

As summer begins, many of us start thinking about vacation travel. Whenever my family and I go somewhere in a car, I usually don’t think of it as risky. Indeed, over the past couple of decades, traffic safety has improved markedly. Beginning in 2011, traffic incidents were no longer the leading cause of death from injury in the United States, according to the National Center for Health Statistics. Despite this progress, BLS data show that transportation incidents continue to be the leading cause of fatal work-related injuries in the United States.

As with so many other risks, we need good data to reduce work-related traffic deaths. Today I’ll highlight a new multi-agency project that links existing datasets to produce rich new insights to help keep employees safer on the road.

fatal-work-injuries-2014

Safety professionals have long considered the BLS Census of Fatal Occupational Injuries to be the most complete, accurate, and well-documented count of all types of fatal work injuries. We use a broad range of documents to identify fatal injuries and verify they are work related. We can identify work-related cases that may not be obvious. One example is a person traveling for work but not in a work vehicle. Another example is a commute to work in which the person was also running a work-related errand along the way. For all of these cases, we also collect information on the nature of the injury and the demographic and employment characteristics of the person who died.

The National Highway Traffic Safety Administration is another great source of traffic safety data. Their Fatality Analysis Recording System (FARS) has rich detail on crashes. FARS captures complete data for all vehicles involved in a crash and their occupants. The BLS data, by comparison, only include the vehicle of the person who died and the vehicle or other object it crashed into. The FARS data tell us more about the risks involved in the incident, including road conditions, use of safety equipment, and even driver behavior such as cell phone use.

While research with both datasets has helped to improve traffic safety, neither dataset has complete detail. Over the last several years, BLS has been collaborating with the National Highway Traffic Safety Administration and the National Institute for Occupational Safety and Health to merge the data.

The combined dataset provides the accident detail of FARS with the BLS information on the people who died and their jobs. For 2010, researchers matched 91 percent of the 1,044 roadway death cases from the BLS data to a FARS case. BLS researchers will continue to work with their colleagues in the other agencies to analyze the data and gain new safety insights.

The research team published an article recently in Accident Analysis and Prevention to explain how they matched the data from the two sources. The team also has begun a second article to analyze 3 years of the combined data. This project has given us the most detailed and complete look at fatal work-related traffic crashes in the United States. We are excited to gain these new insights into traffic safety. It makes me proud to see top-notch researchers from different agencies work together to understand and solve some of our nation’s most challenging problems. It’s another example of how we strive to use your data dollars more effectively to produce gold-standard information.