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Why This Counts: What is the Producer Price Index and How Does It Impact Me?

The Producer Price Index (PPI) – sounds familiar, but what is it exactly? Didn’t it used to be called the Wholesale Price Index? It is related to the Consumer Price Index, but how? How does the PPI impact me?

Lots of questions! In this short primer we will provide brief answers and links for more information. Note, if you are an economist, this blog is NOT for you. It’s an introduction for everyone else!

Video: Introduction to the Producer Price Index

Before we go any further – what is an index? (You said this was a primer!)

An index is like a ruler. It is a way of measuring the change of just about anything. Producer price indexes measure the average change in prices for goods, services, or construction products sold as they leave the producer.

Here is an example of how an index works:

  • Suppose we created an index to track the price of a gallon of gasoline.
  • When we start tracking, gasoline costs $2.00 a gallon.
  • The starting index value is 100.0.
  • When gasoline rises to $2.50, our index goes to 125.0, which reflects a 25-percent increase in the price of gasoline.
  • If gasoline then drops to $2.25, the index goes to 112.5. The $0.25 decline in price reflects a 10-percent decrease in the price of gasoline from when the price was $2.50.

If you are a gasoline dealer, you might find a gasoline index useful. Instead of driving around every day to write down the prices of each competitor’s gasoline and averaging them together, the index can provide the data for you. (Question #5 in the PPI Frequently Asked Questions explains how to interpret an index.)

PPI is called a “family” of indexes. There are more than 10,000 indexes for individual products we release each month in over 500 industries. That is one big family!

OK, so PPI has lots of data – but what kind of data?

PPI produces three main types of price indexes: industry indexes, commodity indexes, and final demand-intermediate demand (FD-ID) indexes.

An industry refers to groups of companies that are related based on their primary business activities, such as the auto industry. The PPI measures the changes in prices received for the industry’s output sold outside the industry.

  • PPI publishes about 535 industry price indexes and another 500 indexes for groupings of industries.
  • By using the North American Industry Classification System (NAICS) index codes, data users can compare PPI industry-based information with other economic programs, including productivity, production, employment, wages, and earnings.

The commodity classification of the PPI organizes products by type of product, regardless of the industry of production. For example, the commodity index for steel wire uses pricing information from the industries for iron and steel mills and for steel wire drawing.

  • PPI publishes more than 3,700 commodity price indexes for goods and about 800 for services.
  • This classification system is unique to the PPI and does not match any other standard coding structure.

We also have more information on the differences between the industry and commodity classification systems.

The FD-ID classification of the PPI organizes groupings of commodities by the type of buyer. For example, the PPI for final demand measures price change in all goods, services, and construction products sold as personal consumption, capital investment, export, or to government. As a second example, the PPI for services for intermediate demand measures price change for services sold to business as inputs to production.

  • PPI publishes more than 300 FD-ID indexes.
  • This FD-ID classification system is unique to the PPI and does not match any other standard coding structure.

Now let’s go back to the beginning

  • 1902: Wholesale Price Index program begins, which makes it one of the oldest continuous set of federal statistics. The Wholesale Price Index captures the prices producers receive for their output. In contrast, the Consumer Price Index captures the prices consumers pay for their purchases.
  • 1978: BLS renames the program as the Producer Price Index to more accurately reflect that prices are collected from producers, rather than wholesalers.
  • PPI also shifts emphasis from a commodity index framework to a stage of processing index framework. This minimized the multiple counting that can occur when the price for a specific commodity and the inputs to produce that commodity are included in the same total index. For example, think of gasoline and crude petroleum both included in an all-commodities index.
  • 1985: PPI starts expanding its coverage of the economy to include services and nonresidential construction. As of January 2018, about 71 percent of services and 31 percent of construction are covered.
  • 2014: PPI introduces the Final Demand-Intermediate Demand system.
  • The “headline” number for PPI is called the PPI for Final Demand. It measures price changes for goods, services, and construction sold for personal consumption, capital investment, government purchases, and exports. We also produce a series of PPIs for Intermediate Demand, which measure price change for business purchases, excluding capital investment.
  • Let me give you an example: Within the PPI category for loan services, we have separate indexes for consumer loans and business loans. The commodity index for consumer loans is included in the final demand index and the commodity index for business loans is mostly in an intermediate demand index.
  • The Frequently Asked Question on the PPI for Final Demand provides even more information on this new way of measuring the PPI. The blog, Understanding What the PPI Measures, may also be helpful.
  • We also have an article that explains how the PPI for final demand compares with other government prices indexes, such as the CPI.

Why is the PPI important?

To me?

  • Inflation is the higher costs of goods and services. Low inflation may be good for the economy as it increases consumer spending while boosting corporate profits and stocks.
  • A change in producer prices may be a leading indicator of consumers paying more or less. Higher producer prices may mean consumers will pay more when they buy, whereas lower producer prices may mean consumers will pay less to retailers. For example, if the PPI gasoline index increases, you may see an increase soon at the pump!

To others (which may impact me!)?

  • Policymakers, such as the Federal Reserve, Congress, and federal agencies regularly watch the PPI when making fiscal and monetary policies, such as setting interest rates for consumers and businesses.
  • Business people use the PPI in deciding price strategies, as they measure price changes in inputs for their goods and services. For example, a company considering a price increase can use PPI data to compare the growth rate of their own prices with those in their industry.
  • Business people adjust purchase and sales contracts worth trillions of dollars by using the PPI family of indexes. These contracts typically specify dollar amounts to be paid at some point in the future. For example, a long-term contract for bread may be escalated for changes in wheat prices by applying the percent change in the PPI for wheat to the contracted price for bread.

Video: How the Producer Price Index is Used for Contract Adjustment

PPI is a voluntary survey completed by thousands of businesses nationwide every month. BLS carefully constructs survey samples to keep the number of contacts to a minimum, making every business, large and small, critical to the accuracy of the data. We thank you, our faithful respondents! Without you, BLS could not produce gold-standard PPI data.

Finally, check out the most recent monthly PPI release to get all the latest numbers. Head to the PPI Frequently Asked Questions to learn more. Or contact the PPI information folks at (202) 691-7705 or ppi-info@bls.gov.

Want to learn more about BLS price programs? See these blogs:

 

Why This Counts: Maximizing Our Data Using the Consumer Expenditure Survey

Almost all BLS statistical programs are based on information respondents voluntarily give us. We want to squeeze as much information as we can out of the data respondents generously provide. Limiting respondent burden while producing gold-standard data is central to our mission.

Let’s take a look at how one program, the Consumer Expenditure (CE) Survey, squeezes every last drop of information from the data to provide you, our customers, with more relevant information.

What is the Consumer Expenditure Survey?

The CE survey is a nationwide household survey that shows how U.S. consumers spend their money. It collects information from America’s families on their buying habits (expenditures), income, and household characteristics (age, sex, race, education, and so forth). For example, we publish what percentage of consumers bought bacon or ice cream and how much they spent on average.

A little back story: The first nationwide expenditure survey began in 1888. BLS was founded in 1884, so the CE Survey is one of our first surveys! It wasn’t until 1980 that we began publishing CE data each year, however. A 2010 article, The Consumer Expenditure Survey—30 Years as a Continuous Survey, provides more historical information.

How is the CE program doing more with what we have?

We’ll briefly look at four different areas, starting with the most recent improvements:

  • Limited state data
  • Higher-income data
  • Generational data
  • Estimating taxes

Limited State Data – Starting with New Jersey

  • Regarding geographical information, the CE survey is designed to produce national statistics. Enough sample data are available to produce estimates for census regions and for a few metropolitan areas.
  • Up to now, however, we did not produce state data. The CE program recently published state weights for New Jersey, which will allow for valid survey estimates at the state level for the first time.
  • State-level weights are available for states with a sample size that is large enough and meet other sampling conditions.
  • Right now, the state-level weighting is experimental. We provide state-level weights to data users to gauge interest and usefulness.

 Higher-Income Table

  • We evaluated the income ranges of the published tables and found that over time more and more households were earning more, and the top income range had not increased to keep pace. To provide greater detail, we divided the existing top income range of “$150,000 and over” into two new ranges: “$150,000 to $199,999” and “$200,000 and over.” We integrated these changes into the 2014 annual “Income before taxes” research table, allowing more robust analysis for our data users.
  • In addition, we added four new experimental cross-tabulated tables on income without the need for additional information from our respondents.

Generational Table

Grouping respondent information by age cohort can be helpful, since a person’s age can help to predict differences in buying attitudes and behaviors. The CE program has collected age data for years, but never grouped the data into generational cohorts before. A Pew Research Center report defines five generations for people born between these dates:

  • Millennial Generation: 1981 or later
  • Generation X: 1965 to 1980
  • Baby Boomers: 1946 to 1964
  • Silent Generation: 1928 to 1945
  • Greatest Generation: 1927 or earlier

The 2016 annual generational table shows our most recent age information for the “reference person” or the person identified as owning or renting the home included in the CE Survey. In 2016 we wrote a short article on Spending Habits by Generation, including a video, which used 2015 data. We’ve updated the chart using 2016 data:

A chart showing consumer spending patterns by generation in 2016.

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

Estimating Taxes

CE respondents used to provide federal and state income tax information as part of the survey. These questions were difficult for respondents to answer.

Starting in 2013, the CE program estimated federal and state tax information using the TaxSim model from the National Bureau of Economic Research and removed the tax questions from the survey. As a result, the quality and consistency of the data increased, and we have reduced respondent burden!

If you have any questions or want more information, our staff of experts is always around to help! Please feel free to contact us.

This is just one example of how we at BLS are always looking for ways to maximize our value while being ever mindful of the costs—and one of those important costs is the burden our data collection efforts place on our respondents. Maximizing our data means providing gold-standard data to the public while reducing the burden on our respondents—a true win-win!

Annual consumer spending by generation of reference person, 2016
Item Millennials, 1981 to now Generation X, 1965 to 1980 Baby Boomers, 1946 to 1964 Silent Generation, 1928 to 1945 Greatest Generation, 1927 or earlier
Food at home $3,370 $4,830 $4,224 $3,450 $2,023
Food away from home 2,946 4,040 3,100 2,042 1,095
Housing 16,959 22,669 18,917 14,417 17,858
Apparel and services 1,753 2,577 1,602 920 615
Transportation 8,426 10,545 9,762 5,952 3,142
Healthcare 2,473 4,492 5,492 6,197 5,263
Entertainment 2,311 3,613 3,144 2,114 1,223
All other spending 10,338 15,766 14,963 6,671 4,125

Do You Understand Your Local Economy?

The national unemployment rate may make the headline news every month, but many folks are most interested in understanding their own local economy.

BLS has a stat for that (really MANY statistics for that)! In fact, BLS data were highlighted in a webinar focusing on local data sponsored by the Association for Public Data Users, the American Statistical Association, and the Congressional Management Foundation.

Dr. Martin (Marty) Romitti, a Senior Fellow at the Center for Regional Economic Competitiveness, presented a webinar called “Understanding Your Congressional District’s Economy and Workforce Using Federal Statistical Data.” Though geared to Congressional staff, the information is applicable to anyone interested in knowing more about their local economy.

By using an extended example of the Napa, California, metropolitan area (where we immediately think, “Wine Country!”), Dr. Romitti finds some interesting information that may shatter some of your preconceived notions of that region.

He does this by answering 10 questions — 5 about “our people,” where he uses U.S. Census Bureau data and 5 about “our economy,” where he uses BLS data.

We are going to focus on the BLS portion (run time 31:12)* of the webinar. The five questions Dr. Romitti poses about our economy are:

  1. How healthy is my economy now?
  2. How many unemployed people live in my area?
  3. What are the largest employing industries?
  4. Which industries pay most to workers?
  5. What are our economic strengths?

Below are some steps and tips if you want to access the same information as Dr. Romitti on www.bls.gov. Note that he uses Internet Explorer; use a different browser and your screen will look different.

Dr. Romitti uses two BLS tools; we have included the path and links to pages as appropriate:

  • To answer Questions 1 and 2: Economy at a Glance -> California -> Napa (Dr. Romitti suggests clicking on the maps.)
    • Tips:
    • For context, suggest you compare your area data to your state numbers. Beware: Your state unemployment rate is seasonally adjusted, while your area data are not.
    • Also, for context, you may want to look at the data over time, such as the last 10 years. Just remember the “Great Recession” occurred starting in late 2007.
  • To answer Questions 3, 4, and 5: BLS Data Tools -> Employment -> Quarterly -> State and County Employment and Wages -> Tables

By following these instructions, you can uncover the same information as Dr. Romitti. We believe Dr. Romitti does a good job of explaining how to answer questions related to local economic data in under an hour!

But wait, there’s more! Let me offer two more resources in your quest for local data:

  1. Are you familiar with our Economic Summaries? These summaries present a sampling of economic information for the area covered, such as unemployment, employment, wages, prices, spending, and benefits. For example, take a look at San Francisco. If you are looking for something quick and easy, you might find what you need in one of these summaries.
  2. The Economic Summaries are produced by the BLS regional information offices. The BLS regional office staff stand ready to assist you with questions about your local economy.

*The taped webinar starts with a musical interlude and some brief introductions. The real action starts at the following run-time intervals:

Run Time                    Presentation Topic   

6:46                             Introduction by Dr. Romitti

11:30                           About our people (Census Bureau data)

31:12                           About our economy (BLS data) begins

52:36                           Regional Economic Accounts (Bureau of Economic Analysis data)

58:53                           Conclusion

60:00                           End

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.

Now on Video: Finding Better Ways to Talk about Data

Our mission at BLS is to help people understand what’s going on in the labor market and the economy. Since our founding in 1884, we’ve aided that understanding by improving our products. We didn’t start with stone tablets, but we have produced mountains of paper and its electronic equivalent in 133 years. Whether it’s news releases or articles or bulletins, most of our output includes text, tables, and, more recently, graphics. Recently we have added another medium to our library, using video to tell stories about our data. We are pleased to introduce you to “Beth’s Bird Houses,” “What if there were only 100 jobs in the United States,” and more, now available on video.

Why video? Video lets us provide a large amount of information in a shorter time. We know you are busy, and we want to use your time wisely. We will always need written words and tables and charts to provide the details of our economic analyses and survey methods, but video helps us provide the main points more quickly. Video is also easy to share through social media, helping us reach more people.

The first video we produced is about our statistics on productivity. Productivity statistics are among the most technically complex data we produce. Despite their complexity, we believe it’s important to understand productivity statistics because productivity directly affects workers’ pay and the nation’s standard of living. We produced a video that explains in about 2 minutes the essential elements of productivity statistics. How’s that for being productive? Check it out and let us know how you enjoy it.

We recently posted two videos about the Employment Cost Index, which measures changes in the costs to employers of worker pay and benefits. One video explains what the Employment Cost Index is. The other video explains how the Employment Cost Index is used.

Want to know more about the different types of jobs workers have in the United States? What about workplace hazards and the safety of America’s workers? We have new videos on those subjects too, and we expect to keep adding to the list to keep you informed. You can see all of our videos on our video page.

Our customers use BLS information to support their private and public decision making. Our mission is to remain relevant to a diverse set of data users regardless of their technical expertise. We believe it’s important not just to tell people what the numbers are but to explain what they mean and where they come from. Video gives us new opportunities to reach a wider audience with our information. As they say in the movies, roll ‘em.