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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


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