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The rise of the service economy

One constant throughout economic history is that, as an economy develops, its service sector keeps growing. The graph shows that this is certainly true for the United States. It divides nonfarm payrolls into three categories: government (at all levels), goods-producing industries (mining, manufacturing, construction…), and service-providing industries. Although government is roughly constant, services have far surpassed goods.

Is this bad? Of course not. The standard of living has clearly improved since 1939, when the graph starts. Indeed, goods can now be produced with fewer people—thanks to technological progress and automation…and perhaps also automatization. This transformation allows the economy to direct more of the labor force to enhancing our lives in other ways, such as tourism and entertainment, advanced health care, and anything related to the Internet, all of which are services that were either nonexistent or luxuries in 1939.

How this graph was created: Using the nonfarm payrolls by industry sector release table from the establishment survey, check the series and click “Add to Graph.” From the “Edit Graph” panel, open the “Format” tab and select graph type “Area” and “Stacked.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CES0800000001, USGOOD, USGOVT

What’s up (or down) with the yield curve?

Analyzing the new most-popular series in FRED

For as long as we can remember, the most popular series in FRED has been the consumer price index (CPI). Well, not anymore. Recently, the series describing the difference between the 10-year and 2-year Treasury constant maturity rates became the most popular. Why this sudden interest? It has to do with the concept of the yield curve: Under normal circumstances, long-term interest rates are higher than short-term interest rates (when annualized), principally because the long term is usually perceived as riskier and so long-term debt demands a higher return. Again, normally, if you plot the interest rates at different maturities, you get an upward-sloping (yield) curve. But if for some reason the short term becomes unusually risky, the curve (or portions of it) may become downward sloping. And why is that important? The graph makes it clear that this kind of yield curve inversion has been associated with impending recessions. (See the gray vertical bars.) As the yield curve gets close to such a situation, there’s going to be a lot of interest in it.

How this graph was created: From the FRED homepage, open the tab “Popular Series,” click on the first one (at the time of this writing, anyway), and expand the sample to the maximum.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: T10Y2Y

If they drive, they will park (Or if they park, they will drive?)

Correlation does not always equal causation

This graph shows that the more people drive, the more they park and generate revenue for parking lot and garage operators. While there’s clearly a correlation between these two indicators, it isn’t clear that there’s a straightforward causality between them. In fact, a third indicator may be affecting the other two: the number of cars in use, the size of the road network, economic activity in general, commuting distance… Or maybe it’s a combination of all or some of these. This ambiguity is what makes statistical analysis much more complex than simply looking at correlations in a graph. FRED helps you stay rigorous by allowing you to download data into your favorite statistical software, either with a download from FRED itself (for example, via the “Download Data” link below the graph) or natively from the software of your choice. For starters, you can use this published data list.

How this graph was created: Search for and select “parking lot revenue” and click on “Add to Graph.” From the “Edit Graph” menu, search for “GDP deflator” in the “Customize data” section and add the series, applying formula a/b. Then from the “Add Line” tab, search for and add “vehicle miles.” Finally, from the “Format” tab, place the y-axis of the second line on the right side.

How this data list was created. For starters, you need to (create and) log on to a FRED account. Then, from any account page, click on “Add new” and select “Data list.” Give it a name. Then search for the series, check the series you want, and click on “Add to data list.” Repeat until satisfied. You can make the data list public and will be required to give it a public name.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: GDPDEF, REVEF81293TAXABL, TRFVOLUSM227NFWA


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