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What’s the story behind who’s working?

Disaggregating EPOP by race and gender

Back in 2016, a FRED Blog post discussed the volatility of the labor market for people of different races based on the employment-to-population ratio (EPOP). The Bureau of Labor Statistics defines this measure as “the number of employed people as a percentage of the civilian noninstitutional population.” So EPOP is basically the percentage of adults who are employed.

The EPOPs for Hispanic and Black Americans have increased at roughly the same rates since the Great Recession of 2007-09, while the growth rate for White Americans has leveled off. This change occurred in spite of the decrease in employment from the Great Recession, which hit Hispanics and Blacks harder than Whites, judging by the steepness and level of decreases between 2008 and 2010. In fact, the EPOP for Hispanics has again risen above the ratio for Whites, which first happened in January 2000.

The next graph shows EPOPs according to both race and gender: It appears that the gap between Black and White overall is mostly due to the gap between Black men and White men. The EPOP for Black women has been higher than the EPOP for White women since the fourth quarter of 2014. A recent working paper from the Levy Institute at Bard College indicates that the changes in EPOP are due to increases in labor force participation for Blacks and the aging/retiring of White Baby Boomers.

The EPOP is by no means a comprehensive measure of well-being or fairness in the labor market. For example, St. Louis Fed Review articles discuss the significant gaps in wages and homeownership rates between Black and White Americans, and a stratification economics approach explores the enduring racial wealth gap. And there’s also no EPOP data for smaller racial and ethnic groups, such as Asians and Native Americans. But the EPOP does present interesting trend data about employment and demographic changes that can be useful for research.

How this graph was created: From the employment situation release table, select the series you want according to race and gender and click “Add to Graph.” For the second graph, the women’s employment-population ratio line is a different shade of the color for the men’s employment-to-population ratio line. This can be adjusted with the “Edit Graph” panel’s “Format” tools. The data range selected is 1972-01-01 to present.

Suggested by Darren Chang and Christian Zimmermann.

View on FRED, series used in this post: LNS12300003, LNS12300006, LNS12300009, LNS12300028, LNS12300029, LNS12300032, LNU02300031

Are firms too attached to bonds?

The evolution of corporate debt securities

If you asked FRED how much the U.S. non-financial sector has in outstanding corporate debt securities (i.e., “bonds”), FRED would answer, “Nearly $6.24 trillion, which is over 30% of GDP, which is the highest it has been since the early 1950s.”

Non-financial corporate debt in the form of securities has grown about 6% on average year over year almost every quarter since 2014. Policymakers have recently voiced concerns about excessive borrowing by the corporate sector.

The graph above shows outstanding corporate debt securities as a share of GDP for four countries: the U.S., Japan, the U.K., and China. The ratio of corporate debt securities to GDP is higher in the U.S. than any of the other nations. Japan’s corporate debt-to-GDP ratio in 2017 was around 14%, the U.K. had a ratio of around 20%, and China had a ratio of around 22%. The ratio has increased substantially since the early 2000s, signaling the development and deepening of financial markets.

Now, this comparison provides an incomplete picture of total corporate debt, as corporations can borrow through securities as well as through loans from banks and other institutions. And countries differ in their borrowing traditions: Some prefer to rely on banks, others on security markets.

How this graph was created: Search for and select the series “Amount Outstanding of Total Debt Securities in Non-Financial Corporations Sector, All Maturities, Residence of Issuer in United States” and click “Add to Graph.” From the “Edit Graph” panel’s “Edit Line 1” tab, aggregate the data by choosing “Annual” in the “Modify frequency” dropdown. Then use the “Customize data” option to search for and add the series “Gross Domestic Product for United States, Current U.S. Dollars” to the same graph. Then, in the formula bar, type in a*10^6*100/b to adjust for the units of the first series and obtain corporate debt as a percentage of GDP. Repeat the above steps for the U.K, Japan, and China (using these countries’ respective GDPs).

Suggested by Asha Bharadwaj and Miguel de Faria e Castro.

View on FRED, series used in this post: MKTGDPCNA646NWDB, MKTGDPGBA646NWDB, MKTGDPJPA646NWDB, MKTGDPUSA646NWDB, TDSAMRIAONCCN, TDSAMRIAONCGB, TDSAMRIAONCJP, TDSAMRIAONCUS

It sure looks like hosting the 2019 World Cup boosted France’s construction sector…

A real-world example of how to avoid confirmation bias

The 2019 FIFA Women’s World Cup just ended on July 7. The host nation, France, provided nine venues across the country to hold the matches. The FRED Blog team and the St. Louis Fed in general loves using real-world situations to showcase applications of economics, and sports is a fairly popular real-world situation. (Also check out these essays on stadium subsidies, the Olympics, and France’s World Cup win in 1998.) One might expect that a large sporting event such as the World Cup would give an economic boon to the host, but let’s test that expectation by looking at data in FRED. Specifically, let’s see if the literal build-up to this World Cup affected construction in France.

One could easily assume that building stadiums to host the World Cup would cause a spike in construction. And the first graph does show that construction in France has gradually risen since the prior Women’s World Cup in 2015, which was held in Canada. At first glance, then, one might conclude that construction increased because France needed to expand its sports and tourism infrastructure ahead of hosting the World Cup.

But, before making sweeping claims about the causal effect of the World Cup on construction, we should be thoughtful about the theory. First, did France actually construct new stadiums? The short answer is no. Eight of the nine stadiums that hosted matches this year were built before France even learned it would host the World Cup in 2019. The lone exception is Parc Olympique Lyonnais, which opened in 2016. Although this venue hosted the World Cup Final, in which the U.S. defeated the Netherlands 2-0, it was not built specifically for this event. The stadium is better known as the home of Olympique Lyonnais, one of France’s most successful football (soccer) clubs.

Another key sanity check is to see whether the same result holds in similar periods. If it’s true that construction increases leading up to a major sporting event, then we’d expect to see the same trend before the 2016 Men’s European Championship, the 1998 Men’s World Cup, and the 1992 Olympics, all of which were hosted in France. However, when we expand the graph to a larger time window (shown below, with these events marked in red), the hypothesis doesn’t seem to hold. Rather, we see a decrease in construction leading up to both the 2016 Euro and the 1998 World Cup and an upward trend prior to the 1992 Olympics that seems to continue over too long of a time to truly be the result of hosting. Looking again at the period from 2015 to 2019, it seems more likely that there are other factors at play—beyond this year’s World Cup—driving changes in construction.

So what’s the takeaway here? It’s not likely that hosting the World Cup caused an uptick in construction in France. But there’s an important lesson about confirmation bias to consider: It was easy for us to theorize that a few years of increasing construction would precede the World Cup. It was easy to find a short time window of data in which we could see this expected effect. So it was easy to assume we proved our hypothesis by theorizing about the effects and finding evidence to support the claim. But as we saw, the longer-term time series showed something different. And that should serve as a warning against simply finding a small sample of data to support a theory and asserting it as truth. Rather, in making claims like this one, we should consider as many confounding factors as possible before being convinced. In other words, we should be our own biggest skeptics.

How these graphs were created: Search for “France construction,” select the quarterly series for “Production of Total Construction in France,” and click “Add to Graph.” For the first chart, use either the date boxes above the graph or the timeline below it to set the start date to June 2015. For the second graph, either set the start date to January 1960 using the same method or click the “Max” button above the graph. To add the vertical lines marking the major events, go to the “Edit Graph” panel and use the “Add Line” tab to create user-defined lines. Try different date ranges to see how the choice of time sample affects the appearance of the graph.

Suggested by Darren Chang, Matthew Kaye, Andrew Spewak, and Christian Zimmermann.

View on FRED, series used in this post: FRAPROCONQISMEI


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