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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


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