The FRED® Blog

Oil prices and business fixed investment in structures

Most economists believe lower oil prices are positive for the economy: They lead to lower gasoline and diesel prices, which tend to reduce headline inflation, which increases consumer purchasing power. Lower oil prices also tend to reduce operating expenses for transportation firms, such as airlines, trucking, and delivery services. The sharp drop in crude oil prices since mid-June 2014 is generally expected to produce positive (if temporary) economic effects. Lower oil prices generally don’t benefit energy producers, but the vast majority of households, firms, and organizations are net consumers, not net producers; so, lower prices still tend to bring net benefits.

One way the effects of lower oil prices reveal themselves is through mining activity. (More precisely, “real private nonresidential fixed investment in mining exploration, shafts, and wells.”) In 2013, fixed private investment in mining activity was about 5 percent of total fixed private investment and only 0.8 percent of real GDP. Still, since the third quarter of 2009, mining activity has increased at a 17.1 percent annual rate—much faster than the 5.5 percent rate of gain in total fixed private investment.

As the graph shows, mining activity (which includes drilling) is positively correlated with crude oil prices. When oil prices rise, this activity increases and so does investment in it. When oil prices fall, this activity slows and investment in it falls.

How this graph was created: Search for mining investment to find the first series, then add “Crude oil prices WTI” for the second. Limit the sample to start in 1999.

Suggested by Kevin Kliesen

Of sticky and flexible prices

The consumer price index (CPI) is composed of many prices with wildly different characteristics. One dimension in which they can differ is how frequently they change. Everybody is aware that gasoline prices can change daily. Other prices may not even change every year, such as administrative fees. To highlight the difference between these extremes, the Federal Reserve Bank of Atlanta produces separate indices for goods that have flexible prices on the one hand and sticky prices on the other hand. The graph above clearly shows that flexible prices have a much wilder ride. The sticky price index is informative even if doesn’t move much, though. Indeed, it can reflect longer trends in inflation, and these are the ones everyone cares more about.

How this graph was created: Go to the Sticky price CPI source, select the sticky and flexible consumer price indices (percent change from year ago), and add them to the graph.

Suggested by Christian Zimmermann.

How fast has the unemployment rate declined?

One way to compare recessions is to compare their unemployment rates, and the graph above includes the civilian unemployment rate for the four most recent business cycles. In this case, index values are used to show how the rate for each cycle changed in comparison with the highest rate that occurred in that cycle. (The graph shows each cycle’s unemployment rate relative to the highest rate in that cycle, which has an index value of 100.) None of the four rates seem to stand out; they all follow a similar path downward. But we know that the last cycle’s unemployment rate went higher than any of the others. So, that must mean the most recent unemployment rate declined faster in absolute terms (the actual percentage unemployment rate) because it hit a higher point than any of the other rates but still had a relative decline similar to the other rates.

How this graph was created: Find the “Civilian Unemployment Rate,” then select “Index (Scale value to 100 for chose period)” under Units. Then choose the data to match the highest unemployment rate in the previous cycle. Finally, check “Display integer periods” with values 0 and +60. Add the civilian unemployment rate three more times to the graph (it is preselected) while including the different dates that correspond to the highest value in each of these three earlier cycles.

Suggested by Christian Zimmermann

Bank failures

The previous recession was clearly associated with substantial problems in the financial sectors. As the graph shows, there has been a significant number of bank failures, as recorded by the Federal Deposit Insurance Corporation (FDIC), which is responsible for managing the closure process and insuring depositors. The number of failures, however, is nowhere near the peak around 1989, the time of the savings and loan crisis. The recession around that time involved different financial problems and thankfully was much less deep than the previous recession.

How this graph was created: Search for “bank failures” and then change the graph type to “Area” under graph settings in the graph tab.

Suggested by Christian Zimmermann

What’s the “normal” unemployment rate?

As the U.S. unemployment rate inches down, it seems reasonable to ask when it will be back to normal. One measure of “normal” is the natural rate of unemployment, sometimes referred to as NAIRU, published by the Congressional Budget Office. This measure is meant to contain all relevant information except for cyclical factors in the unemployment rate. Thus, when there is no difference between the NAIRU and the standard unemployment rate, the standard unemployment rate should be back to normal. Note that the natural rate is calculated, not measured, and thus is subject to the assumptions made. Some of those assumptions relate to whether structural factors should be taken into account. This question led (temporarily) to two different natural rates during the previous recession.

How this graph was created: Search for NAIRU, select both series, and add them to a graph. Then add the civilian unemployment rate. Finally, change the end date to the current date.

Suggested by Christian Zimmermann

International residential prices

FRED recently added a long list of international residential prices from the Bank for International Settlements. The graph above, which offers only a small glimpse of what FRED has to offer, compares the evolution of residential prices for a few countries. Note that the indices are all normalized in 2010, which highlights the large run-up and drop prior to that year in the United States. Similar events also occurred in the other countries, though the effects were much more muted. Surprisingly, Canada seems particularly immune from developments in the United States, except for a temporary drop in 2008.

How this graph was created: Go to the BIS release and select the relevant series. Click on “Add to graph.”

Suggested by Christian Zimmermann

More prices that deviate from the CPI

We recently discussed some CPI categories that do not tend to have rising prices. Those examples were all linked to information technology. Here’s a wide variety of categories where prices can decrease or remain stable for long periods. For example, coffee is subject to wide fluctuations, including steep price drops. Apparel became disconnected from the CPI sometime in the early 1990s and remains largely constant. It is more surprising that cosmetics and musical instruments are also consistently below general inflation or even flat. In the motor vehicles category, some quality improvements only partially affect the overall price of motor vehicles; this is another example, much like computers, of a category that does not closely follow the overall path of the CPI.

How this graph was created: Start with the graph for the CPI, then add the other series. Change the color of the CPI line to black and thicken it to distinguish it from the many other series.

Suggested by Christian Zimmermann

Not all prices increase

It is natural to complain that some prices increase. But don’t forget that prices can also decrease. While there are obvious seasonal fluctuations for some goods (say, agricultural products), other goods have been declining year over year, contributing to a general price inflation that is lower than one may think. The prime example shown here is anything related to information technology. It is no secret that IT devices with a given set of characteristics have continuously fallen in price. Or, to put it differently, a device of the same price year after year will provide much better performance; its price by “unit of performance” must therefore be declining. This graph shows some CPI categories where advances in IT have lead to price decreases. Or at least no price increases. This is not restricted to the IT category, of course. A future blog post will explore more examples on this topic.

How this graph was created: Search for CPI, then add the other series. Because their base years are different, the axis labels get crowded. So, these were removed by unchecking “Axis titles” in the graph settings.

Suggested by Christian Zimmermann.

A clearer picture of housing equity before the crisis

This graph shows housing equity in the United States. The way it’s shown here, housing equity appears to have undergone an extremely unhealthy evolution: rapidly accelerating run-up, sudden and brutal crash, and another rapid run-up. There’s no doubt the housing crash has been significant; after all, housing equity was cut by half. But the alarming run-up shown in this graph is to some degree an optical illusion. Indeed, an increase in the 1950s isn’t equivalent to a same-sized increase in the 2000s because the level of the series was dramatically different. For a clearer picture, we’ll use the natural logarithm of the series.

Now, the run-up around 2000 looks like a normal part of a trend that’s continued for more than half a century. The illusion shown in the top graph can occur whenever a series grows over time. Think of the principal on a savings account that accumulates interest. Soon enough, the effect of compounding interest kicks in and the principal appears to explode, even though it’s still growing at the same interest rate.

How this graph was created: For the first graph, search for the series name. For the second, expand the “Create your own data transformation” option in the graph tab and choose the “Natural Log” transformation.

Suggested by Christian Zimmermann

This recession was different

Most recessions share common characteristics, but not the most recent one. To illustrate this, we use a little known and used feature of FRED: setting a common index value and examining a period before and after that point. In the graph, you see four versions of the same series, civilian unemployment. Each series is centered on a different recession peak date, with a value of 100 for these start dates. The graph also shows data for 60 months before and 80 months after those dates.

The period before the start dates reveals nothing remarkable, but the most recent recession deviates from the other recessions after the start date: The unemployment rate shoots up much higher, and despite a steeper downslope the unemployment rate has yet to reach a value that would be expected from a normal recovery. (By the time 80 months had elapsed from the other recessions’ start dates, the unemployment rates had essentially returned to where they started.)

How this graph was created: Find the “Civilian Unemployment Rate” and modify the units to “Index (Scale value to 100 for chosen period).” For this graph, we use “U.S. Recession Peak” (vs. the “Trough” or another “Observation Date”). The default will be the peak of the most recent recession. Then choose the “Display integer periods instead of dates” option. Choose an interior period range of -60 to 80. Add this unemployment rate series three more times, performing the same manipulations but selecting different recession peaks.

Suggested by Christian Zimmermann


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