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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

View on FRED, series used in this post: JTSHIR, JTSLDR, JTSQUR

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 nominal GDP/M2 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

View on FRED, series used in this post: CPIAUCSL, M2V

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

View on FRED, series used in this post: M04005US16980M280NNBR, M04099US000NYM297NNBR, M04135US000NYM287NNBR, M0426AUS000NYM292NNBR, M0441AUS000NYM275NNBR, M0482AUS16980M267NNBR

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


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