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Measuring inflation trends

Why use different inflation measures for policy analysis?

Congress has instructed the Federal Reserve to pursue monetary policies that promote maximum employment and price stability. The Federal Open Market Committee (FOMC) has determined that “inflation at the rate of 2 percent, as measured by the annual change in the price index for personal consumption expenditures [PCE], is most consistent over the longer run with the Federal Reserve’s statutory mandate” for price stability. As of March 2018, the year-over-year percent change in the PCE was 2.01 percent, or just 1 basis point above the FOMC’s 2 percent target. However, inflation was substantially lower over much of the past year—as low as 1.40 percent in July 2017—and economists were uncertain whether the low readings reflected temporary factors that would soon dissipate or an underlying inflation rate that was below the level consistent with price stability.

Because the inflation rate measured by the headline PCE tends to be volatile from month to month, many observers monitor other measures, such as the PCE excluding food and energy prices (“core PCE”), to gauge underlying inflation trends. The near-term growth in core PCE is among the economic variables that the FOMC includes in its quarterly Summary of Economic Projections. As of March 2018, the year-over-year growth in core PCE was 1.88 percent. Some critics argue that this measure of inflation is “rotten,” however, because it arbitrarily excludes particular categories of goods whose prices affect the cost of living.

An alternative, and somewhat less arbitrary, measure of underlying inflation trends is based on the mean of changes in the prices of the individual goods and services that make up the price index after dropping items with exceptionally large or exceptionally small price changes in a given month. For example, the Federal Reserve Bank of Dallas calculates a trimmed mean PCE inflation measure designed to hew closely to the trend in overall PCE inflation. By omitting price changes for goods and services having the largest or smallest price movements in a given month, extreme values have less impact on the measured inflation rate, which arguably is a better measure of underlying inflation trends than the traditional core measure.

The graph shows the data at the time of this writing: It plots the headline, core, and Dallas Fed trimmed mean PCE inflation rates, measured as percent changes over the past 12 months, for the past year. Whereas the headline PCE inflation rate increased from 1.73 percent in February to 2.01 percent in March, and the core rate rose from 1.57 percent to 1.88 percent, the trimmed mean rose only from 1.71 percent to 1.77 percent. Hence, in contrast with the headline and core measures, the trimmed mean indicates little, if any, change in underlying inflation pressures in recent months, suggesting that low inflation readings might be more reflective of underlying trends than temporary special factors.

How this graph was created: Search for “PCE,” check the three series, and click on “Add to Graph.” From the “Edit Graph” menu, change the units to “Percent Change from Year Ago.” Change the frequency to “Monthly” and the starting date to “2017-03-01.”

Suggested by David Wheelock.

View on FRED, series used in this post: PCEPI, PCEPILFE, PCETRIM12M159SFRBDAL

Spectacular recoveries

Comparing the strongest economic recoveries in recent U.S. history

In a previous post, we discussed how the economic recoveries from recessions are longer and slower than the downturns that lead to those recessions. Today, we compare the sizes of recoveries across the economic history of the United States. In the graph above, which shows the unemployment rate, the current recovery is clearly remarkable: The drop from unemployment’s high point of 10% down to 3.9% (at the time of this writing) is 6.1 percentage points. The only recovery that comes close (in the time period shown here) is the 1983-89 recovery, with a 5.8-percentage-point decrease (10.8% to 5%). However, this sample is limited to only the ten recoveries since 1948.

To go back farther, we need to use different data. Thank goodness FRED has some historical data compiled by the NBER on the unemployment rate! The NBER methods for compiling the data shown below aren’t entirely comparable to the BLS methods for the data shown above. In fact, the NBER series is a composite of three different series. But as long as we acknowledge which data sets we’re looking at, we should be able to make some generally fair comparisons. Here, the recoveries from the Great Depression stand out: First, the unemployment rate topped at 25.6% and then dropped to 11%. A 14.6-percentage-point drop. The second recovery went from 20% to 0.2%. A 19.8-percentage-point drop!

So, although the most recent recovery seems remarkable after WWII, it’s small compared with the recoveries before WWII. Even if in some sense we’re comparing apples and oranges, the oranges are a lot bigger.

How these graphs were created: Search for “unemployment rate” and choose each series individually: civilian unemployment rate (monthly, seasonally adjusted, starting in January 1948) and unemployment rate for United States (monthly, seasonally adjusted, starting in April 1929).

Suggested by George Fortier and Christian Zimmermann.

View on FRED, series used in this post: M0892AUSM156SNBR, UNRATE

Human capital around the globe

How to measure the human input in GDP

What does it take to produce stuff? On a basic level, you need means of production: raw materials, land, some machinery, some structure, and humans. Gross domestic product (GDP) is the measure of all the stuff produced in a country (in each quarter or each year) and depends heavily on the means of production. Today, we look at the human component. How much humans influence GDP depends on essentially two things: how much they work and how good they are at working. The latter is obviously a little difficult to measure, especially if you want to look at the input angle. We can measure output, which is labor productivity—that is, how much each unit of work produces. To measure human efficiency at working, though, economists use the concept of human capital, which is in some ways parallel to physical capital (machinery and structures) in the production process. When working, humans use their education, work experience, and intelligence to try to do more within the same amount of time. Human capital is a measure of all this.

When comparing nations, a good proxy for human capital is the average numbers of years of schooling. Economists have long used the Barro and Lee dataset and its revisions (see here and also here). But just adding up years of school doesn’t seem quite right. Indeed, the impact of 3rd grade in Nepal is likely not the same as 11th grade in Canada or 1st grade in Nigeria. To adjust this measure, economists use the returns (in earnings) to each additional year of education as measured by George Psacharopoulos. Once you apply those returns, which are different for every year of schooling and every country, to the average number of years of schooling in each country, you get the human capital index that is shown in the map above.

How this map was created: The original post referenced an interactive map from our now discontinued GeoFRED site. The revised post provides a replacement map from FRED’s new mapping tool. To create FRED maps, go to the data series page in question and look for the green “VIEW MAP” button at the top right of the graph. See this post for instructions to edit a FRED map. Only series with a green map button can be mapped.

Suggested by Christian Zimmermann.



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