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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

View on FRED, series used in this post: CPIAPPSL, CPIAUCSL, CUSR0000SERE03, CUUR0000SEFP01, CUUR0000SEGB02, CUUR0000SETA01

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.

View on FRED, series used in this post: CPIAUCSL, CUSR0000SEEE01, CUUR0000SEED, CUUR0000SEEE

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 these graphs were 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

View on FRED, series used in this post: OEHRENWBSHNO


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