The FRED® Blog

Quits and layoffs

Consider times when employment has declined. What are the causes? An employment decline can come from fewer hires, more layoffs, and people quitting their jobs. But these factors can interact in complex ways. Indeed, the Job Openings and Labor Turnover release from the Bureau of Labor Statistics shows that hires went down and layoffs went up during the past two recessions. But quits went down, not up; in fact, the decrease in quits partially counteracted the impact the other two factors had on employment, even to the point of entirely canceling the increase in layoffs. This makes perfect sense: The incentive to quit a job is lower when there are fewer opportunities.

The graph also highlights that layoffs came back down quickly after the most recent recession to the lowest levels in this sample. So, the sluggishness of hiring is to blame for the slow recovery in the labor market.

How this graph was created: Go to the Job Openings and Labor Turnover release, select the three series (Rate, Seasonally Adjusted), and click on “add to graph.”

Suggested by Christian Zimmermann

When population drops

The population of a country can decrease for various reasons: fewer people are born, more people die, or migration out of the country is large enough to counter the usual population growth. Japan currently shows no population growth because of their balance between fertility and mortality. For the countries shown in the graph here, the story is mostly demographic and thus economic. Bulgaria, Moldova, and Romania are poor countries from Eastern Europe. In the early 1990s, it became possible to emigrate from these countries to look for better opportunities. Many residents chose to do so, and this trend continues to this day. For Greece and Portugal, the story is different. For example, the economic turmoil in recent years prompted a sufficient number of locals to leave for jobs elsewhere, which also led to a reduction in population. The data sample available in FRED shows that this happened twice before in Portugal.

How this graph was created: Search for total population and the respective countries. Convert units to “Percent Change from Year Ago” (if frequency is not annual) or “Percent Change.”

Suggested by Christian Zimmermann

M2 velocity and inflation

It is quite common to see arguments that if M2 velocity (the M2/nominal GDP ratio) is low, it must be that inflation is high. While M2 velocity is currently at historical lows, inflation is clearly not high. Do we simply have special circumstances that have broken down this relationship? Is there such a relationship in the first place? Let us look at the data:

Eyeballing the graph, we see no clear relationship between these variables. There is a better alternative than line graphs to eyeball correlations, though: scatter plots. For each quarter, CPI inflation is plotted on one axis (horizontal) and M2 velocity is plotted on the other (vertical):

Not much of a relationship can be found here. If anything, there is a slight upward slope, indicating that higher M2 velocity is associated with higher inflation, although this would not be statistically significant.

How these graphs were created: Search for M2 velocity, then add CPI. Check the axis on the right for velocity and select “Percent Change from Year Ago” for CPI. This gives you the first graph. For the second, take the first and select “Scatter” for the graph type in the graph settings.

Suggested by Christian Zimmermann

Finding old inflation data

A recent FRED Blog post showed that individual products provide an incomplete understanding of overall inflation, but sometimes individual products are all you have. For example, before 1913 there was no official CPI (and the CPI wasn’t even seasonally adjusted until 1948). But specific prices from the past do exist. The NBER Macrohistory Database gathers a variety of historical sources, including newspapers, to create data series on prices. The graph shows some of these series. Again, it becomes pretty clear pretty quickly that tracking these individual prices doesn’t allow for a well-defined picture of the evolution of the general price level. You need to compose an index with a broad base of products for that.

The NBER Macrohistory Database does have a few price indexes, including one for wholesale prices that uses the series shown in this graph and one for general prices that is cobbled together from available sources, including wage data. The quality and scope of this slice of economic history certainly don’t match the standards of the current CPI.

How this graph was created: Search for and select the NBER Macrohistory Database, select the tag “price” in the left bar, and choose the various series you want to see. It may require searching more than a screenful to find the series used in this graph.

Suggested by Christian Zimmermann

The speed of Internet adoption

FRED recently added Internet usage data from the World Bank. The Internet was initially available only to the richest households who could afford both a computer and the connection. It has democratized considerably since, although the poorest still cannot afford it. The Internet was invented in the U.S., so it’s no surprise that its use became widespread in this country before it did elsewhere. The graph shows, however, that other countries have been catching up and even overtaking the U.S. It also shows that China and India are developing rapidly. At some point in the future, the Internet will be like refrigerators and televisions: Everyone will have access to it, except those who purposefully abstain from it.

How this graph was created: Search for “Internet” and the country name to find the series and then add it to the graph.

Suggested by Christian Zimmermann

CPI component volatility

Most people recognize the CPI (consumer price index) as a common measure of U.S. inflation. But the CPI sometimes seems at odds with the personal experiences of some consumers, who often point out that particular goods have become more expensive than the CPI seems to imply. This incongruity occurs mostly because the CPI is an index that covers many products; the variations in prices are averaged out when forming the aggregate CPI. Case in point: We show here how price fluctuations increase as the range of products narrows. The graph shows the inflation rate for the CPI covering all items (blue line), which is quite stable. But compare this with energy prices (red line), which fluctuate wildly. Narrow down energy prices to just gasoline (green line) and you find even more volatility. CPI data even include particular types of gasoline for particular regions, which display even more volatility (purple line). It is true that the volatility of energy prices is most stark, but similar trends do appear for other categories as well.

How this graph was created: Search for the various series and add them to a graph. Change each series to “Percent Change from Year Ago” and adjust the sample to eliminate the years where only the all-items CPI was available.

Suggested by Christian Zimmermann

Measuring risk

How much risk is in the economy? It depends what you mean by risk. If you want to know, roughly, the level of risk that businesses cannot honor their debts, then you want to look at the risk premium. The risk premium quantifies the differences in the yields of bonds in different risk categories. This risk premium is not constant through time: It changes as the overall economy goes through its ups and downs that impact businesses. Here we look at the difference in yields between Moody’s Aaa and Baa corporate bonds. It is clearly visible that much risk was present in the 1920s, 1930s, and 1940s and also in several of the recent recessions, in particular the previous one. Lately, this risk has largely been vanishing, as corroborated by the St Louis Fed Financial Stress Index©.

How this graph was created: Search for “Moody’s” and select the Aaa rate at a monthly frequency (for a longer sample). Then you add the Baa series and apply transformation “b-a.”

Suggested by Christian Zimmermann

Negative interest rates

On June 11, 2014, the European Central Bank broke new ground by lowering one of its key policy rates below zero. That is, the rate in question (the rate on the deposit facility available overnight to European banks) is now negative. While a policy rate can in principle be set at any level, it is more difficult to think about a market interest rate that would be negative. Indeed, one would always earn a higher return by simply holding on to one’s money rather than depositing it for a negative return. Yet, the graph shows two rates—one in Switzerland and one in Denmark—that are in negative territory.

What’s special about Switzerland and (more recently) Denmark? The Swiss franc has a reputation as a very stable currency and hence acts as a refuge currency when trouble is brewing elsewhere. Given that Switzerland is a small economy, when the Swiss franc is high in demand worldwide, investors are willing to accept negative rates if they think their own local currencies may depreciate. This happened when the fixed exchange rate regime of Bretton Woods was in jeopardy, later when European currencies were volatile, and recently when the European Monetary Union went through some pains and few non-euro currency options were available. One of those currencies was the Danish krone, which then also found itself in the role of a refuge currency.

How this graph was created: Search for “immediate rates,” select the relevant countries, and click on the “Add to graph” button.

Suggested by Christian Zimmermann

Output volatility in small and large countries

The best investment advice is to diversify your asset portfolio because it reduces the volatility and risk of the portfolio. The same applies to the economic performance of countries. The better diversified they are in terms of sectors, the less they suffer from large economic fluctuations. (This concept applies when all other factors are equal, of course; we have recently seen that emerging economies suffer from large fluctuations.) So, how to illustrate the benefit of diversification? One way is to contrast a large country such as the U.S., which covers virtually every imaginable sector, with smaller countries whose size limits the number of industries they can have. The graph shows per-capital real GDP growth for the U.S. (thick black line) and for three countries whose combined population amounts to about 3.5% of the U.S. population. It is quite easy to see that U.S. GDP growth fluctuates less.

How this graph was created: Search for “Constant GDP per capita” for the various countries and add those series to the graph. Transform each series to “Percentage change” and emphasize the line for the U.S. so it stands out (in this case, it is thicker and black).

Suggested by Christian Zimmermann

Spurious correlation

Relationships between macroeconomic time series are not usually straightforward enough to establish with a simple graph. The problem is that almost all time series tend to grow in the long term as an economy grows. So, any measure in nominal terms will grow even more, since inflation rates are almost always positive. Because time series can exhibit a common trend, it becomes difficult to interpret whether there is a relationship between them beyond that common trend. We call this spurious correlation. There are various ways one can isolate the common trend, and we show some here using M2 and total federal debt. Above, with just the raw series, all we can see is that they both tend to increase in the long run at roughly the same rates.

In the second graph, we simply take growth rates of both series. Now the trend is gone, and it is much more difficult to argue that there is some correlation here, positive or negative. (Remember also that correlation does not mean causation: Even if we saw some relationship, we wouldn’t be able to tell whether one series is affected by the other. That requires more substantial statistical analysis.)

In the third graph, we remove the trend in another way: by dividing each series by another series that also has this trend. In this case, we take nominal GDP: GDP because it measures the size of the economy, and nominal because both M2 and the federal debt are measured in nominal terms. The picture of the two ratios now looks different, but it is still difficult to claim that there is a systematic relationship between them. Looking only at the first graph, one would not have concluded that.

How these graphs were created: Search for “M2″ and “federal debt” to find the series: Be sure one of the series has its y-axis on the right. For the second graph, select “Percent change from year ago” for both series. For the third graph, change units to levels and add “Gross Domestic Product” to “M2″ and apply the transformation “a/b”; then replace federal debt with the debt/GDP ratio available in the database (or create that ratio yourself).

Suggested by Christian Zimmermann

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