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In October 2009, in the wake of the Great Recession, the U.S. unemployment rate peaked at 10%. This economy-wide number is useful but masks important regional patterns. To reveal a more detailed picture, we use GeoFRED to look back at county-level unemployment in October 2009.

In this map, counties are divided into three types, according to their unemployment rates:

a rate above the economy-wide peak of 10%a rate between 4% (the current rate as of January 2019) and 10%and a rate below 4%.

Counties in the first group, with rates above 10%, were concentrated on the West Coast and in the Midwest and South Atlantic regions. Counties in the second group, with below-average rates, include other parts of the West and a significant portion of counties in the Northeast (e.g., Wyoming, New York, and Massachusetts).

Counties in the third group, with unemployment below 4%, are concentrated in a

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For Valentine’s Day, we embrace the subject of unemployment. Specifically, this graph shows the unemployment rate for 2 of the 3,000+ U.S. counties for which FRED has data: Love County in Oklahoma and Loving County in Texas. Besides their affectionate names, what’s so special about them?

Loving County has a rather unusual unemployment rate: It can stay at 0% for several months in a row or it can shoot up to 16%. This is what happens when a county has only 134 residents. Also, the BLS gathers unemployment numbers from surveys, so only some residents of this county were reached. The results, therefore, are (1) very volatile due to small samples and (2) more frequently off the mark.

Love County is an agriculture-centric county of a more traditional size: 10,000 residents. Such counties have unemployment rates that are below the national average and more stable, and this holds true here. The only

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This graph shows U.S. dollars in circulation per capita—in other words, how much physical cash is held outside the Federal Reserve for each person living in the U.S. As of the November 2018 observation, that amount is $6,575. We checked, and no one on the FRED Blog team is holding that much cash right now. We assume not many of our readers would hold that much cash. So, who is holding it? Part of it may be lost. Much of it is held by domestic businesses and governments. And then there are all those dollars held abroad. In some countries, the dollar is valued over the local currency for its stability and low inflation rate. In fact, the following countries have adopted the U.S. dollar as legal tender and abolished their own currency: British Virgin Islands, Caribbean Netherlands, East Timor, Ecuador, El Salvador, Marshall Islands, Federated States of Micronesia, Palau,

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This FRED graph plots quarterly foreign direct investment (FDI) flows into the U.S. as a percent of GDP. And what is FDI? It’s the flow of capital across borders when a firm owns a company in another country. But it’s more than simply owning stock in a foreign company: It implies that the investor is directly involved in the foreign company’s day-to-day operations.

FDI is beneficial to job creation and a country’s growth. In the U.S., it began to pick up after 1975 and spiked in the late-1990s and early 2000s, corresponding with the tech bubble. During recessions, which are represented in the graph by shaded bars, FDI systematically falls. Since the Great Recession, average FDI flows have been higher than in previous decades, ranging from 1% to 2% of GDP each quarter.

How this graph was created: Search for and select “Rest of the world; foreign direct investment in U.S.; asset,

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In 1922, the Federal Reserve Board began offering its industrial production index, with data starting in January 1919—which means we now have 100 years of data!

This series has been extremely useful in helping us gauge the state of the economy: At first, industrial production was basically the only data series available before the computation of GDP; and the data are published more frequently and quickly than GDP data. The disadvantage is that industrial production doesn’t encompass the entire U.S. economy. In fact, it has encompassed less and less as the economy has matured into primarily a service economy.

For more about the history of the industrial production index, see the Federal Reserve Board press release and the Federal Release Bulletin on FRASER, which contains the first set of data.

How this graph was created: Search for “industrial production” or click on “industrial production” on the FRED homepage.

Suggested by Christian Zimmermann.

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How competitive are countries’ banking markets? It’s a complicated question that requires some thought and effort to answer: First, there are several ways to measure competition. One way is to measure the concentration of market shares. But this method isn’t perfect because there may still be substantial competition in a market with few players. Another way is to look at markups—that is, the difference between the price charged and the marginal cost (the cost of one additional produced item). The problem here is that it’s difficult to directly observe those costs, so the costs need to be inferred.

Thankfully, FRED has data that already have the measurements built in: World Bank economists Asli Demirguc-Kunt and Maria Soledad Martinez Peria devised a way to measure banking competition, and the World Bank has published these so-called Lerner indices for a few countries and a few years. The graph above includes competition indices for

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How much economic risk are businesses facing these days? FRED can help us consider what the market is telling us about the level of risk by showing us the yields of various types of corporate bonds. Moody’s business is, in part, to classify corporate bonds according to their risk, and bonds within a rating should have the same level of risk through time. The yields of these bonds may still change, though, because they’re driven mostly by a risk premium (which is different in each risk rating) and the supply and demand of funds. An easy way to properly measure the latter is to take the difference between the yields of two risk ratings. In the graph, we’ve done this for the ratings Aaa and Baa. What remains is the excess risk of Baa over Aaa bonds. Indeed, as the economy becomes riskier, lower-rated bonds will become riskier more quickly

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Economists are always looking for ways to better understand and predict the business cycle. Studying capacity utilization can help. Capacity is the maximum volume of productive resources that can be used by firms to produce goods. Capacity utilization is how much of that available capacity is actually being used to produce goods. The capacity index tries to measure the utilization rate of the available productive capacity in different sectors. It sheds light on how much more output firms could produce without adding additional capital stock (structures, machinery, etc.) to the economy. As we can see in the graph, capacity utilization is very volatile in general, especially for the manufacturing sectors, declining sharply during recessions. In particular, capacity utilization for durable-goods manufacturing drops more than for nondurable-goods manufacturing. However, the mining sector and utility sector tend to have a significantly higher capacity utilization rate on average than the manufacturing sectors. In

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The graph above shows real GDP growth for two countries, Japan and the United States. It’s pretty clear the U.S. growth rate has consistently been higher than Japan’s. (Recently, there have been only a few quarters where Japan has higher growth.) But can we really compare these two growth rates? One important difference between the two countries is that the U.S. population is growing while Japan’s is stagnant, if not declining.

The second graph shows GDP growth adjusted by the working age population—that is, the growth rate of GDP less the growth rate of the working age population. Now the story is different: Japan performs better than the U.S. in most quarters.

How does Japan do it? One way is through increasing its labor force participation, which the third graph shows. More women in Japan have joined the labor force, and more older people are staying in the labor force. This latter

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Obviously, workers move from job to job over time and across sectors of the economy. FRED has some convenient release tables you can use to create a graph like the one above, which shows the rate of voluntary turnover (quits) for workers in four sectors: accommodation and food services, retail trade, manufacturing, and government. It’s striking that the ranking of these sectors doesn’t change despite variations in their levels of employment over time.
The consistency of these and other sectors becomes even more striking once you strip out the seasonal adjustments, as in the graph below, created with another convenient release table. In fact, seasonal variation seems to be stronger than variation caused by the business cycle. For example, people quit more when the unemployment rate is lower.
If we look closely, we can see some details: It’s remarkable that, on a regular basis, monthly quits in accommodation and food services represents about 5% of that workforce.

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