The FRED® Blog

The real minimum wage

Posted on July 23, 2015

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?

Posted on July 20, 2015

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.

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

View on FRED, series used in this post: CPIAPPNS, CPIAUCNS, CPIEDUNS, CPIENGNS, CPILFENS, CPIRECNS, CPIUFDNS, CUUR0000SAF116, CUUR0000SAG1, CUUR0000SAH3, CUUR0000SAM1, CUUR0000SAM2, CUUR0000SAS4, CUUR0000SEGA, CUUR0000SEHA, CUUR0000SEHB, CUUR0000SEHC, CUUR0000SEHG, CUUR0000SETA01, CUUR0000SETA02

Wage stickiness

Posted on July 16, 2015

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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