Federal Reserve Economic Data

The FRED® Blog

Posts tagged with: "CPIAUCSL"

View this series on FRED

The rise (and fall?) of the cost of education

Education inflation appears to be converging with general inflation, at least for now

For many years, the cost of education has risen steadily and significantly more than the general level of prices. This trend has led to numerous complaints that education is out of reach; it has also led to a boom in student loans. The graph clearly shows how education inflation (blue line) has been above general inflation (red line) every year since 1994. And, again, quite significantly so. The past few observations, however, exhibit a marked reversal, with one observation even showing CPI inflation higher than education inflation. Does this mean education will become relatively more affordable now? It’s difficult to say from current data, especially since there have been two other episodes, in 2008 and 2011, when the two series converged only to diverge again. Time will tell if this latest development is pomp or circumstance.

How this graph was created: Search for “CPI Education” and create the graph. From the “Edit Graph” section, under “the Add a Line” option, search for and select CPI. Choose units “Percent Change from Year Ago” and click on “Copy to All.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CPIAUCSL, CUUR0000SAE1

Fan your forecasting flame with FREDcast

FRED’s new forecasting game

On January 20th FRED’s newest data gizmo, FREDcast, is coming out of beta. FREDcast is an interactive forecasting game that allows users to enter forecasts for four different economic variables, track their forecast’s accuracy on the scoreboards, and compete with friends and other users in leagues. The game is designed for all levels of users, from high school students to professional forecasters. Just log-in to FREDcast using your FRED account and walk through the prompts to enter your forecasts for each variable. FREDcast forecasts are zero horizon, meaning users forecast economic data for the month (or quarter) in which they are in. For example, from January 1st to January 20th, users submit forecasts for the January unemployment rate, the January consumer price index (CPI), the January payroll employment, and quarter one real gross domestic product (GDP). Forecasts are due by the 20th of each month, and scores are released as the economic data come out. View exact release dates on FRED’s economic calendar.

The four FREDcast series are available in FRED. Below is a graph of each series in the appropriate units for FREDcast forecasts. All series in FREDcast are seasonally adjusted. From top to bottom: Real gross domestic product (GDP) is the only quarterly series, and the units are the percent change from the preceding period at a seasonally adjusted annual rate. Next is the unemployment rate, which is forecast as a monthly rate. Next are the consumer price index (CPI) and payroll employment. The inflation series used in FREDcast is the percent change in the CPI from one year ago, while payroll employment is the level change from the prior month measured in persons.

How these graphs were created: GDP: Search for real gross domestic product, and graph the series with the units “Percent Change from Preceding Period, Quarterly, Seasonally Adjusted Annual Rate.” Set the start date to 2006-07-01, and follow this path: Edit Graph > Format > Graph Type > Bar. Unemployment Rate: Search for unemployment rate, and graph the seasonally adjusted civilian unemployment rate. Set the start date to 2006-12-01. CPI: Search for consumer price index, and graph the series “Consumer Price Index for All Urban Consumers: All Items” with monthly, seasonally adjusted units. Set the start date to 2006-11-01, and follow this path: Edit Graph > Units > Percent Change from Year Ago. Payroll Employment: Search for payroll employment, and graph the series “All Employees: Total Nonfarm Payrolls” in seasonally adjusted units. Set the start date to 2006-12-01, and follow this path: Edit Graph > Units > Change, Thousands of Persons. Last, multiply the series by 1000 to get it in units of persons by entering a*1000 in the formula box and clicking “Apply.”

Suggested by Michael Owyang and Hannah Shell.

View on FRED, series used in this post: A191RL1Q225SBEA, CPIAUCSL, PAYEMS, UNRATE

Price growth at the tails

When policymakers discuss the inflation rate, they’re referring to a measure of the “central tendency” of a distribution of price changes. There are many many many (many) prices in a developed economy. Here in the U.S., for example, we have maybe 20 types of sugar-coated flake-shaped cereal whose prices can change from one month to the next. So, to make the inflation rate meaningful, we must condense this distribution of prices to a measure of, as statisticians would call it, “central tendency.” However, reasonable people can differ on the proper measure because the distribution of price changes has long “tails.”

In short, the tail of a distribution is the part that’s farther away from the average. For example, we see evidence of the tail of the distribution of prices every morning when we pick up a coffee and a newspaper and drive into work: The prices of the first two items, like most other prices, change very slowly; but the price of gasoline fluctuates wildly from day to day. Certain categories—namely, food and energy—have larger swings than most other goods, so some prefer a price measure that looks at all goods except food and energy. This measure is called the “core” CPI. However, food and energy are not the only highly variable goods.

The top graph shows the ratio of mean CPI inflation to median CPI inflation.* The CPI measures inflation by choosing a basket of goods that are prominent among the average consumer’s purchases. Within this basket, the distribution of price changes is usually approximately symmetric, which we see because the ratio of the mean to median is usually about 1. (Actually, it’s slightly less, at about 0.9.) The interesting exception is during the Great Recession period, when commodity prices fell sharply, bringing a strong negative skewness for the first time since the mid-1980s. We can see this by looking at the bottom graph, which plots the ratio of mean core CPI to median CPI. Notice there is no negative spike in this measure of skewness. The Great Recession and its aftermath, however, show large changes in the “third moment.” In this period when the economy seemed to be in tremendous flux, the headline, average CPI moved little. However, the skewness—and the tails of the price distribution—changed quite a bit.

* This is not totally precise, because the change in headline CPI is not exactly the mean change in prices nor is median CPI exactly the median of the change distribution.

How to create these graphs: Top graph: Search for and select “median consumer price index” and “consumer price index for all urban consumers,” selecting “All items” and “Seasonally adjusted.” Chose “Percentage change” for the units in both. In the formula field, apply b/a. Bottom graph: Do the same, but instead of adding the “All items” consumer price index for all urban consumers, select “All Items Less Food and Energy.”

Suggested by David Wiczer.

View on FRED, series used in this post: CPIAUCSL, CPILFESL, MEDCPIM157SFRBCLE


Subscribe to the FRED newsletter


Follow us

Back to Top