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How to gauge the world’s banking markets? Look to the World Bank

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 the U.S., Canada, Switzerland, and the U.K., where smaller numbers indicate more competition. It seems that bank competition in the U.S. is relatively stable, with a slight trend toward less competition. The U.K. seems to endure wild swings, with a similar trend. Smaller countries such as Canada and Switzerland don’t necessarily have less competition due to their smaller market sizes. In fact, the Swiss banking market seems to be even more cut-throat since the Great Recession, likely because some interest rates are negative. The opposite happened in Canada, which hasn’t been affected as much by the previous financial crisis.

How this graph was created: Search for “Lerner index,” select the chosen countries, and click “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: DDOI04CAA066NWDB, DDOI04CHA066NWDB, DDOI04GBA066NWDB, DDOI04USA066NWDB

Risky business?

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 than higher-rated bonds. The graph shows that risk today (at the date of this writing) is basically historically normal, if a little elevated compared with previous years. However, it’s nowhere near as risky as it was during the Great Depression, the early 1980s, or the Great Recession.

How this graph was created: Search for and select “Moody’s Baa” and click “Add to Graph.” From the “Edit Graph” panel, add a series by searching for “Moody’s Aaa” and apply formula a-b.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: AAA, BAA

Capacity utilization

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 fact, capacity utilization in the manufacturing sectors often starts to decline just before a recession starts; so, the manufacturing sector’s capacity utilization could be a useful leading indicator of a downturn.

How this graph was created: Search for and select “Total Industry Capacity Utilization (TCU).” Then, from the “Edit Graph” panel, use the “Add Line” feature to search for and select the rest of the series, clicking “Add data series” for each.

Suggested by Brian Reinbold and Yi Wen.

View on FRED, series used in this post: CAPUTLG21S, CAPUTLG2211A2S, CAPUTLGMFDS, CAPUTLGMFNS, MCUMFN, TCU

Japan’s anti-retirement miracle

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 point is especially important for Japan, which has one of the oldest populations (if not the oldest) in the world.

How these graphs were created: For the first graph, search for Japan real GDP, select the series, and click on “Add to Graph.” From the “Edit Graph” panel, add a line by searching for “real GDP,” select as units “percent change from previous year,” and click “Apply to all.” For the second graph, take the first and for each line add a series by searching for “working age population 15-64,” choose units “percent change from previous year,” and apply formula a-b. For the third graph, search for “employment rate 15-64” and the two series should be among your top choices. For all graphs, adjust the sample period to start when both series are available.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: GDPC1, JPNRGDPEXP, LFWA64TTJPM647S, LFWA64TTUSM647S, LREM64TTJPA156N, LREM64TTUSM156S

Which workers quit more?

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 represent about 5% of that workforce. And, in both graphs, the government sector consistently has the lowest quit rate. Given the right circumstances, of course, even consistent patterns can change.

How these graphs were created: Go to the release tables noted above, select the series you want displayed, and click “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: JTS3000QUR, JTS4400QUR, JTS7200QUR, JTS9000QUR, JTU3000QUR, JTU4000QUR, JTU510099QUR, JTU7200QUR, JTU9000QUR


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