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Inflation’s dual cores

According to the Bureau of Labor Statistics, U.S. core inflation (i.e., excluding food and energy) is about 1.75%. Overall inflation measures combine the prices of both goods and services, but these two categories do not always behave in the same way. The graph above shows annual changes in the consumer price index for core services (purple) and core commodities (red). For about three years after the end of the recession, prices for goods and services changed at about equal rates. But the inflation environment has become a bit more complex in recent years: In 2012, growth in commodities prices began to slow and eventually turned negative. In contrast, prices for services have continued to grow at close to 2.5%.

How this graph was created: Add the two series listed below and use the “Graph Settings” option to set “Graph type” to “Bar.” Make sure that “Stacking” is listed as “None.” Then set “Units” to “Percent Change from Year Ago” for each series. Change “Frequency” to “Quarterly” and “Aggregation Method” to “End of Period.”

Suggested by Ian Tarr.

View on FRED, series used in this post: CUSR0000SACL1E, CUSR0000SASLE

The changing distribution of house sales

How does the distribution of house sales change over time? FRED includes median and mean sales price data for single-family homes, which can give us a hint. The difference between the two series increases when a proportionally greater number of more-expensive houses are sold, for example. The graph above shows the two series, and it is difficult to see any changes in the difference between them: The lines look parallel through booms, busts, and seasonal fluctuations. (The higher prices in the summer and lower ones in the winter may come from both actual seasonal price fluctuations for equivalent houses and from the changing composition of the sold houses.) The graph below shows the ratio of median to mean prices. We see, for example, that the ratio went down during the recent crisis and has more than recovered since. This movement is consistent with proportionally more high-end single-family houses being sold (or fewer low-ends ones), with a reversal around 2011.

How these graphs were created: For the first graph, go to the National Association of Realtors (under sources) and look for the Existing Home Sales release. Select the median series and then the mean series and add them to a graph with a click. Note that you can select single-family homes or all homes (including co-ops and condos), which will show very similar results. For the second graph, remove the mean series, but then add it back by choosing “Modify existing series 1.” Then use “Create Your Own Data Transformation” with formula a/b.

Suggested by Christian Zimmermann

View on FRED, series used in this post: HSFAVGUSM052N, HSFMEDUSM052N

The real minimum wage

Every few years or so, Congress revisits the federal minimum wage. While most of the discussion is about the nominal wage, the real purchasing power of the minimum wage has some interesting trends of its own. Using series from FRED, we can see these trends in action. The graph features the nominal minimum wage (green line) and the minimum wage adjusted for the price level (blue line). You’ll notice the green line tends to rise in steps, the result of Congress’s periodic decisions to raise the minimum wage. But because the wage is not indexed to inflation—and the past half century has largely been inflationary—occasional increases in the minimum wage tend to be eroded by subsequent increases in the price level. We can see this in the zigzag pattern of the blue line. In fact, although the nominal minimum wage is at a historical high, the real minimum wage today is the same as what it was in 2008, 1999, 1992, 1986, and 1950.

How this graph was created: Using the “Add Data Series” and “Modify Existing Series” options, add “Federal Minimum Wage for Nonfarm Workers” as the first series (“a”) and “Consumer Price Index for All Urban Consumers: All Items” as the second series (“b”) to “Data Series 1.” For both, set “Units” to indices and enter “2015-05-01” for the “Observation Date.” In the “Formula” box under “Create your own data transformation,” enter “100*(a/b).” Next, re-add the first series, but as “Data Series 2.” Finally, create a trend line under “Add Data Series,” set its start date to “1947-01-01,” and set its start and end values to “100.” Change colors as needed to distinguish the three lines.

Suggested by Ian Tarr.

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

Is Inflation Running Hot or Cold?

One popular measure of the price level is the consumer price index (CPI), which measures the average change over time in the prices paid by urban consumers for a market basket of goods and services. This index can be broken down into smaller component indexes, each representing a different subset of goods and services. So changes in the aggregate price level can be traced back to changes in the price levels of the underlying components. As described in a recent Economic Synopses essay, we have developed a “heat map” that visually represents CPI data in FRED: specifically, the relative inflation levels of various CPI components over the past 10 years. The heat map shown here lists the components in order according to their weight in the overall index as of July 17, 2015.

2015 July 20 FRED Blog post heat map x800

How this heat map was created: We used the FRED Add-In for Microsoft Excel (view instructions for installing the Add-In here) to download the FRED data: year-over-year percent change in each CPI component index over the past 10 years. We normalized each value by subtracting the series mean and dividing by its standard deviation calculated over the past 10 years to take into account differences in long-term trends and volatility across series. Each colored box in the heat map corresponds to the normalized inflation value for a given CPI component for a particular month. Blue represents an inflation value below the long-term trend of the index, and red represents an inflation value above the long-term trend. The darker the color, the greater the difference between that particular inflation value and the long-run average for the component index in terms of standard deviations.

Because we’re comparing series against their long-run averages, it’s possible for a “blue” series to have a higher inflation rate than a “red” series. For example, for June 2015, owners’ equivalent rent is red, with an inflation value of 2.95 percent; water, sewer, and trash is blue, and yet has a higher inflation value of 4.65 percent. The reason is that the June 2015 owners’ equivalent rent inflation is above its 10-year average of 2.16 percent; and the June 2015 water, sewer, and trash inflation is below its 10-year average of 5.11 percent.

An Excel file containing a version of this heat map can be found here, and anyone who downloads the FRED Excel Add-In has the ability to easily update the heat map when new data are released. Simply select the tab containing the raw data and press the “Update Data” button from the FRED Excel Add-in. (More instructions and details are provided in the Excel file.)

Suggested by Joseph T. McGillicuddy and Lowell R. Ricketts.


Wage stickiness

Unemployment has been a fixture in the news since 2008, but relatively little has been said about wages. So how have wages changed as the U.S. has weathered the Great Recession and the spike in unemployment? Most people would expect that wages have decreased, but data in FRED offer a different perspective. The graph above shows two time series from the Bureau of Labor Statistics: unemployment (red line) and private industry wages and salaries (green line) from the employment cost index. Note that even when unemployment rapidly doubled, the green wages line continued to rise (albeit at a reduced rate). In other words, as the economy contracted and employers sought to cut costs, they almost exclusively opted to lay off workers rather than negotiate for lower wages. This phenomenon is known as downward nominal wage rigidity: During macroeconomic shocks such as recessions, wages remain “sticky.” Of course, it’s possible that inflation is cutting real wages even if nominal wages aren’t changing. However, when we adjust the wages data for inflation in the graph below (blue line), the pattern remains similar. Although real wages posted a slight decline several years after the recession hit, it pales in comparison to six years of elevated unemployment.

How these graphs were created: For the first graph, search for and add the unemployment series (left y-axis) and the total wages series (right y-axis). For the second graph, add the wages series (a) and the consumer price index (b) as parts of a single data series. Do this using the “Modify Existing Series” option within “Add Data Series.” Set the units for both (a) and (b) to “Index” with the observation date equal to 2007-11-01. Then, in the “Create your own data transformation” option, enter “(a/b)*100” in the formula box and apply the transformation. For the trend line, choose “Trend Line” under “Add Data Series” and set the start date to 2007-10-01. Set both the start and end values to 100.

Suggested by Ian Tarr.

View on FRED, series used in this post: CPALTT01USM661S, ECIWAG, UNRATE

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