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The FRED® Blog

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.

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

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

Public pensions

Some cities and states, such as Detroit and Illinois, are struggling to fund their public-sector employee pensions. These crises may have seemed abrupt, but we can observe some structural causes using FRED. In the graph above, the blue line shows pension benefits paid to public-sector employees; the red and green lines show contributions from employers and employees. Adding the two revenue streams (red and green lines) creates the purple line. Note how the purple revenue line dipped below the blue payouts line in the mid-1990s and never fully rebounded. When the Great Recession hit in 2008, revenues dropped dramatically and payouts continued to rise. The graph below shows the resulting gap between pension payouts and contributions has increased markedly since the past recession.

Elected officials hoped for higher future dividend and interest income to close this gap; but cities and states typically invest pension contributions in very safe assets, which have underperformed. The graph below shows that rates on 10-year Treasuries (the epitome of safe pension fund investments) have reached all-time lows since the past recession. So pension funds are being squeezed from both directions: Payouts rose faster than contributions, and returns on investments fell. Cities and states usually cannot revise or renege on benefits promised to their pensioners because pensions are often rigidly guaranteed in city charters and state constitutions. Elected officials will try to find new ways to shore up pension funds, but Baby Boomer retirements won’t make that task any easier.

How these graphs were created: First graph: Add the first series (benefits) to the graph, then add the second (employer contributions) and third (employee contributions) series to the graph with the “Add Data Series” feature. To create the fourth series, start by re-adding the second series to the graph and then adding the third series by using the “Modify Data Series” option. Then, with the “Create your own transformation” option, add employer (a) and employee (b) contributions by using “a + b” in the formula box. Second graph: Start by adding benefits (a) to the graph, then use the “Modify Data Series” option to add employer (b) and employee (c) contributions as components of one data series. With the “Create your own transformation” option, use “(b + c) – a” in the formula box and change the graph type to “Area.” Third graph: Simply search for and add the “10-Year Treasury Constant Maturity Rate” series to the graph.

Suggested by Ian Tarr.

View on FRED, series used in this post: GS10, S121000A144NBEA, S251100A144NBEA, S251200A144NBEA

The Greek tumble

With the U.S. economy on the mend and the euro area (perhaps) out of crisis mode, it seems as if the worst of the Great Recession has passed. At least in terms of real output. However, while most of the OECD bottomed out during 2008-2010, Greece took much longer to reach its nadir and fell much further. The graph above shows that Greece had lost over 25% of its 2007 GDP by the time it plateaued in 2014—a staggering drop in living standards, especially compared with a decline of 4-5% at most in the U.S. and euro area overall. By 2011, the U.S. had returned to pre-contraction output and Europe was steady, whereas Greece had just entered one of the sharpest periods of its downturn.

The graph below demonstrates how Greece’s relative decline is even starker in historical terms. The lowest point of the recession in the U.S. and euro area occurred in 2009 and pushed those economies back to 2005 levels of output—about four years of lost growth. But the lowest point for Greece was in 2014 and pushed back its economy to 1999 levels of output—about a decade and a half of lost growth.

How these graphs were created: Search for “Gross Domestic Product by Expenditure in Constant Prices: Total Gross Domestic Product for Greece,” and select the “Index 2010=1.00, Seasonally Adjusted” version. Use the “Add Data Series” option to add similarly titled OECD series for the euro area and U.S. Set the units for each series to “Index (Scale value to 100 for chosen period),” and use “2007-12-01” for the first graph and “1995-01-01” for the second graph under “Observation Date.”

Suggested by Ian Tarr.

View on FRED, series used in this post: NAEXKP01EZQ661S, NAEXKP01GRQ661S, NAEXKP01USQ661S

Measuring misery

The mandate of the Federal Reserve calls for stable prices and maximum employment. One way to assess these conditions is to look at the consumer price index inflation rate and the unemployment rate, respectively. It has even become somewhat popular to look at the sum of these two measures, the so-called “misery index,” shown here. Now, you may not consider the “misery” of inflation to be entirely equivalent to the “misery” of unemployment. So, if you believe that a multiplier should apply to one of these two measures, you can use a custom formula to transform the series in the FRED graph.

How this graph was created: On the FRED homepage, you’ll see CPI (among other popular series): Click on that to open the related FRED graph. Add the series “Civilian Unemployment Rate,” making sure to use the “Modify existing data series” option. Then change the units for the first series to “Percent Change from Year Ago” and create your own data transformation with formula a+b or any other formula you find appropriate.

Suggested by Christian Zimmermann

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

Fun fact: vehicle miles traveled

While teaching students, you may find it helpful to locate “fun facts” to call out data that illustrate the topic at hand. (This blog poster had fun reading with her youngest son, who’d point out these facts and read them aloud, starting with the phrase “Fun fact…”) FRED is the perfect tool for highlighting economic facts because it has so many different categories of economic data. For instance, let’s look at transportation. Fun fact: The number of vehicle miles traveled relative to the population old enough to drive has been declining for a decade.

How this graph was created: This FRED graph requires a simple transformation. Find “Vehicle Miles Traveled,” add population to that line, and divide the first series by the second. There are several choices for population: Here we use the “Civilian Noninstitutional Population,” which includes everyone above age 16 who is not in the military or institutionalized.

Suggested by Katrina Stierholz

View on FRED, series used in this post: CNP16OV, TRFVOLUSM227NFWA

Measuring inflation expectations, part II

In the previous blog post, we looked at using survey data to measure inflation expectations. Now we consider market-based measures. The graph shows various measures of the breakeven inflation rate, which is computed as the difference in returns of constant-maturity Treasury bills: one being the traditional bill and the other being the inflation-indexed bill. If we look at 10-year Treasury bills, we can evaluate what the markets think the average yearly inflation rate will be over the next 10 years. With such a long horizon, it makes less sense to compare these expectations to realized inflation. But this graph still includes a segment to signal the Fed’s 2% inflation target announced on January 25, 2012, since the purpose of that announcement was to anchor inflation expectations.

How this graph was created: Search for “breakeven inflation” and many series will be shown. Here, we used those with a monthly frequency. For the segment, choose “Add a Series” but select “Trend line” from the pull-down menu. Once that’s added, change the initial date to “2012-01-25” and use “2” for both start and end values.

Suggested by Christian Zimmermann

View on FRED, series used in this post: T10YIEM, T20YIEM, T30YIEM, T5YIEM, T7YIEM


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