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A bit of religion in the dismal science

FRED’s main contribution to the “dismal science” of economics is its core economic and monetary data. But FRED recently added some socio-demographic indicators as well. None of these indicators covers religion, per se; but quite a few relate to religion indirectly. A recent search for “religion” in FRED yielded 185 results, and two of those series are highlighted above: (i) real private consumption expenditures dedicated to religion and other social services and (ii) real investment in religious nonresidential structures, which we presume are mostly churches. (Both series are chain-type indexes.) For comparison, we also include real GDP in the graph, with all indexes having a value of 100 in 2009. Religious consumption expenditures (which may have a variable non-religious component) have tracked GDP quite well since 1929, but church building has plummeted over the past ten years. This decline predates the construction industry’s overall decline during the previous recession. Thus, there may be non-economic factors at play here.

How this graph was created: Search for “religion” and narrow the results by clicking on the “nation” tag. You should find the first two series fairly quickly. Again: The “religious” series are chain-type indexes. Select them and click on the “Add to graph button.” Then add the “real GDP” series and change units to “Index (Scale value to 100 for chosen period)” to 2009-01-01.

Suggested by Christian Zimmermann

View on FRED, series used in this post: C309RA3A086NBEA, DSOCRA3A086NBEA, GDPCA

More about comparing oranges

Our previous post was about comparing apples and oranges. This post takes a different approach and searches FRED for just oranges. Most of the results have nothing to do with fruit, but rather are economic indicators for Orange County, California. FRED houses many regional data series, and if you look you’ll see there are seven other Orange Counties in the U.S. We compare four in the graph by looking at their unemployment rates. Indiana’s O.C. stands out, with a high and highly fluctuating rate; it is small and poor, with employment dominated by a large casino and golf resort. (Note: None of these series are seasonally adjusted.) Vermont’s O.C. is a little larger and better diversified, so it has smaller fluctuations. California’s O.C. is the largest and also has small fluctuations. North Carolina’s O.C., home of the University of North Carolina flagship campus, is of special interest, as its unemployment rate jumps up between 1999 and 2000. This could be due to a reclassification or a mass layoff. Maybe a reader knows why…

How this graph was created: Search for “Orange County unemployment,” select the counties you want to graph, and click on “Add to graph” to do so.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CAORAN7URN, INORURN, NCORAN2URN, VTORAN7URN

Comparing apples and oranges

When we talk about apples and oranges, we usually mean objects or concepts that cannot be compared. But FRED is all about comparing many kinds of economic data, and it allows you to place series that represent different concepts from different sources in the same graph. It even allows you to compare apples and oranges. Literally.

The graph above shows the producer price index (PPI) for several varieties of apples. A quick look at the graph reveals two things: First, the lines are not continuous. This very specific product doesn’t have price observations for every month. Second, the deviations across varieties of apples are sometimes large and persistent.

What about oranges? The producer price index also includes information about frozen orange juice, for which there has been a “liquid” market for decades. In the graph below, we compare the PPI for frozen orange juice with the PPI for red delicious apples (which is the apple with the most information available). What’s surprising is that the price of frozen orange juice fluctuates just as much as the price for fresh fruit. But while the price of apples is clearly influenced by seasonal factors, the price for orange juice appears more persistent, especially in recent years.

How these graphs were created: Searching for “apple” gives you a long list of fruit-related series sorted by popularity, but the series we want for this graph are at the bottom of that list. Apparently few people are interested in the price of apples… Searching for “PPI apples” gets you the series you want (plus a few others). Select them, click on the “Add to graph” button, and restrict the sample to the past 10 years. The price indexes have different base years. To make them uniform, we choose “Index (Scale value to 100 for chosen period)” under “Units” and enter 2008-06-01 for all series except the special index. (FRED will choose the closest date if there are missing observations.) Finally, change the color of the special index to black and make the line thicker. For the second graph, search for “PPI orange,” add that series to the graph, and then add the series for red delicious apples.

Suggested by Christian Zimmermann

View on FRED, series used in this post: PCU31141131141117, WPU01110208, WPU01110209, WPU01110211, WPU01110215, WPU01110216, WPUSI01102A

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