Topic Archives: Inflation and Prices

Increasing Commuting Costs?

With Earth Day approaching, we have been wondering about increased costs for commuting to work. At BLS, we don’t have environmental cost statistics, but we do have worker costs.

Some employees don’t have to commute — they are able to work from home.

  • In 2015, the share of employed persons who did some or all of their work from home on days they worked was 24 percent. This is up from 19 percent in 2003.

An image showing someone working at home.

 

But a large number of the workforce still travels to and from a physical workplace, day in and day out. If you do need to trek into work, over the last 10 years, changes in consumer prices for a couple modes of commuting follow.

If you go by car:

First you need a vehicle.

  • New cars: Up 6 percent

Next you need to fuel it.

  • Gasoline: Down 7 percent

But before you can put it on the road…

  • State motor vehicle registration and license fees: Up 27 percent
  • Motor vehicle insurance: Up 56 percent

And you may have to pay for parking once you get to work.

  • Parking and other fees: Up 38 percent

An image showing cars in rush hour traffic in an urban area.

Those in an urban area may have another option to driving:

  • Intracity transportation (bus, rail): Up 35 percent

And one last option:

  • Human-powered commuting (walking to work): No increase!

We hope these data help you make wise decisions on your commuting choices. If nothing else, you may decide to set up a car pool — to help pay for parking!

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.

Some Interesting Numbers about the Oscars

The annual Academy Awards ceremony was held Sunday, February 26, to recognize excellence in cinematic achievements in the U.S. film industry. Impress your friends with these facts we’ve gathered about the Oscars and the motion picture business.

This year’s Oscar for Best Picture went to La La Land Moonlight.

  • Not all actors reach the top, but lots are trying: Actors in the U.S. can be found coast to coast with a total employment of 50,570. Almost one-third, or about 14,560, work in the greater Los Angeles metro area alone. Employment of actors is projected to grow 10 percent from 2014 to 2024, faster than the average for all occupations.

Walt Disney is the most Oscar-nominated person ever with 59 nominations.

  • Walt may be gone, but his legacy lives on: Today there are 30,240 multimedia artists and animators employed in the U.S. California employs about a third (10,110) with half of those in the greater Los Angeles area (5,830). Employment of multimedia artists and animators is projected to grow 6 percent from 2014 to 2024, about as fast as the average for all occupations.

Since 1945, the accounting firm Price Waterhouse (now called PricewaterhouseCoopers) has tabulated the Oscar ballots to ensure the secrecy of the results.

  • There are a total of 1,226,910 accountants in the United States, and California again has the largest employment with 144,540. Employment of accountants and auditors is projected to grow 11 percent from 2014 to 2024.

Oscar weekend is a boon to the beauty industry: Before walking down the red carpet, many use the services of a hairstylist – and house calls reportedly start at $500.

  • Nationwide, 348,010 hairstylists are employed. The five states with the most are California (26,340), New York (25,420), Pennsylvania (24,210), Florida (23,840) and Texas (22,050). The metropolitan area with the most hairstylists is New York-Jersey City-White Plains, NY-NJ, with 20,790. Employment of barbers, hairdressers, and cosmetologists is projected to grow 10 percent from 2014 to 2024.

The Academy of Motion Picture Arts and Sciences identified 336 feature films eligible for the 2016 Academy Awards.

The first Academy Awards ceremony was on May 16, 1929, at the Roosevelt Hotel’s Blossom Room with 270 attendees. The price of admission was $5, which included a broiled chicken dinner.

The Oscar statuette is 13.5 inches tall and weighs 8.5 pounds. A New York foundry casts them in bronze before they receive a 24-karat gold finish.

  • Workers who make these kinds of items are part of a small industry, known as “other nonferrous foundries, excluding die-casting,” with only 12,372 employees nationwide. About half are employed in three states: Michigan, Oregon and Ohio. Employment in the foundries industry is projected to decrease by about 17 percent from 2014 to 2024.

After the Oscars ceremony, you may be inspired to go to a movie. But did you know how much these prices have changed over the last 10 years?

  • Admission to movies, theaters and concerts is up 21 percent, carbonated drinks are up 19 percent, and candy and chewing gum are up 28 percent. We don’t track popcorn — sorry!

Editor’s note: Oscar-specific facts are from the official Oscars website, unless another source is provided.

Innovating for the Future

Erica L. Groshen was the 14th Commissioner of Labor Statistics. She served from January 2013 to January 2017. This is her final post for Commissioner’s Corner.

Image of former BLS Commissioner Erica L. Groshen

It didn’t take long after I became Commissioner of Labor Statistics in January 2013 for me to appreciate the skill, dedication, and innovation of the staff that works here. Whether they’re doing sampling, data collection, estimation, or dissemination; whether they’re the IT professionals or the statisticians or the HR staff; whether they’re the newest employees who are so tech-savvy or the more senior employees who hold a wealth of institutional knowledge. To a person they are phenomenal. I am honored to have had the pleasure of leading them — and letting them lead me — during the past 4 years.

 

I have had many opportunities to observe and encourage innovation during my tenure at the U.S. Bureau of Labor Statistics, from listening tours to senior staff conferences to regional office visits to discussions with a wide variety of stakeholders. From these efforts, we have identified several activities that will help us develop and implement the next generation of labor statistics. These days, we call these efforts a variety of names, such as “modernization” and “reengineering.” But, in truth, they just continue the impressive progress that has been the hallmark of BLS for the past 133 years.

In my final Commissioner’s Corner post, I want to tell you a little about some of our current reengineering efforts.

One of the things we do best at BLS is data collection, largely because we are always looking for ways to improve. Recent efforts include identifying alternative data sources, expanding electronic collection, and “scraping” information directly from the Internet. These efforts can expand the information we provide, lessen the burden we place on employers and households that provide data, and maybe even save some money to provide taxpayers the best value for their data dollar.

These efforts are not new. One source of alternative data we’ve used for many years comes from state unemployment insurance filings, which identify nearly every employer in the country. We tabulate these data but also use them as the source of our sample of employers for certain surveys and as a benchmark of detailed employment by industry. We also use information from private sources and from administrative sources, like vital statistics. Our latest efforts involve examining techniques to combine data across multiple sources, including mixing survey and nonsurvey data.

We want to give employers the opportunity to leverage the electronic data they already keep so it’s easier to respond to our surveys. These efforts include allowing employers to provide electronic information in multiple formats; identifying a single source of electronic data from employers, reducing the number of locations and number of requests made to multiple sites of the same organization; and working with employers to allow BLS to access their data directly from the Internet. We rely on good corporate citizens to supply the information that we use to produce important economic data. Making data collection easier is a win-win.

The innovation doesn’t stop at collection. We are using electronic text analysis systems extensively to streamline some of our data-processing activities. Much of the information we collect is in the form of text, such as a description of an industry or occupation, details about a workplace injury, or summaries of employee benefit plans. Transforming text into a classification system for tabulation and publication used to be a manual task. BLS has begun to transform this task through the use of machine-learning techniques, where computers learn by reviewing greater and greater amounts of information, resulting in accurate classification. As we expand our skills in this area and find more uses for these techniques, the benefits include accurate and consistent data and greater opportunities for our staff to use their brainpower to focus on new, unique, and unusual situations.

We are also modernizing our outputs, producing more with the information we have. For example, we have begun several matching projects, combining data from two or more sources to produce new information. One example is new information on nonprofit organizations. By linking our employment data with nonprofit status obtained from the Internal Revenue Service, we now have employment data separately for the for-profit and nonprofit sectors. And we took that effort one step further and produced compensation information for these sectors as well. Look for more output from these matching efforts in the future.

Finally, we’ve made great strides in how we present our information, including expanded graphics and video. And we are not stopping there. Each year we are expanding the number of data releases that include a companion graphics package. We are developing prototypes of a new generation of data releases, with more graphics and links to data series. And we have more videos to come.

My 4 years as Commissioner of Labor Statistics have flown by. I’m excited to see so many innovations begin, thrive, and foster additional innovations. I have no doubt that the culture of innovation at BLS will continue. As my term comes to an end, I know now more than ever that the skill, dedication, and creativity of the BLS staff will lead this agency to even greater advances in the years to come.

Measuring Uncertainty in the Producer Price Index

Our mission at the U.S. Bureau of Labor Statistics is to publish information about the labor market and economy. We always seek to improve our methods and provide the most accurate data in a cost-effective manner. All statistics, however, come with some uncertainty. Last year I wrote about how we deal with uncertainty in our measures. Today let’s talk about how we recently have improved our uncertainty measures in the Producer Price Index.

You may think it’s odd that an agency that tells the public what we know also works hard to explain what we don’t know. It may seem like we’re airing our dirty laundry, but that’s not how we see it. At BLS, one of our core values is to be transparent about our methods. Not only don’t we consider the laundry dirty, but we believe that airing it—that is, giving you more information about the strengths and the limitations of our data—is central to our mission. It’s part of our responsibility to give you information you can use to make better decisions.

The Producer Price Index (PPI) program measures the average change over time in the prices U.S. businesses receive for the goods they produce and the services they provide. BLS started publishing the PPI 126 years ago, making it one of our oldest measures. In 2014, the PPI expanded its coverage to provide a broader view of price change for goods, services, and construction. The PPI for final demand measures price change for goods and services sold for personal consumption, capital investment, government, and export. The PPI for intermediate demand tracks price change for goods, services, and construction products sold to businesses.

The PPI for final demand was unchanged in October 2016 and was up 0.8 percent over the last 12 months. But these figures are subject to sampling error. What’s that? It’s the uncertainty that results when we collect data from a sample of prices, rather than gathering prices from each of the millions of transactions that occur every day. For the PPI, we collect about 93,500 prices every month. A different sample of prices might give us different estimates of price change. Fortunately, we have tools to measure this sampling error. Most BLS programs collect data from sample surveys because it is far too expensive and would overburden businesses and workers to send all our surveys to everyone. Instead, we select samples carefully using scientific methods. These sampling methods work well, but they can’t avoid the possibility that the characteristics of a sample may differ from those of the population. We provide estimates of this sampling error by publishing variance estimates with the data. We recently released the first-ever variance estimates for the PPI.

If you aren’t into math, skip the next paragraph.

The measure of variance we use for the PPI is called a standard error. We use the standard error to calculate what statisticians call a confidence interval around the estimate. For example, the 1-month median absolute percent change in the PPI for final demand in 2015 was 0.30 percent. The standard error of that median was 0.11 percent. We can use these two numbers to calculate a confidence interval. In this example, we will use what we call a 95-percent confidence interval. To calculate that confidence interval, we take the estimated median price change of 0.30 percent, plus and minus two times the standard error of 0.11 percent. This gives us a confidence interval between 0.08 percent and 0.52 percent. We call this a 95-percent confidence interval because, if we were to choose 100 different samples of producer prices, the median price change would be between 0.08 percent and 0.52 percent in 95 of those samples.

Chart showing median 1-month changes in Producer Price Indexes in 2015 and the 95-percent confidence intervals around those changes.

OK, if you don’t like math, you can come back now. The chart above shows estimates of 1-month PPI changes (the red dots) each surrounded by its sampling uncertainty (the blue bars). If the blue bar crosses the 0.0 percent line, it means the change is not significantly different from zero.

Variance estimates are just one way BLS evaluates and explains the quality of our data and our methods. We have published information about our methods almost since our beginnings in 1884. Carroll Wright, the first BLS Commissioner, insisted on the “fearless publication of the facts.” We believe the fearless publication of the facts means not just explaining our measures and methods in highly technical terms. We want our measures and the uncertainty around them to be understood by a wide range of people, not just those who have advanced degrees in economics or statistics. We continue to seek clearer ways to explain uncertainty. One way is a new chart we are publishing on the monthly changes in nonfarm employment. In the future, we hope to publish more charts like this and simpler explanations of our methods. If you have ideas on how we can explain our data and methods more clearly, please share them with us below.

BLS data are the gold standard of economic statistics. But even gold bars have marks to indicate their impurities. Similarly, we at BLS don’t hide our impurities. We want you to understand the strengths and limitations of our data so you can use that knowledge to make good decisions.