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The data behind the fear of yield curve inversions

FRED can help us make sense of the recent discussions about an inverted yield curve. But first, some definitions to get us started: The yield curve is the difference (or spread) between the yield on the 10-year Treasury bond and the yield on a shorter-term Treasury bond—for example, the 3-month or the 1-year. The yield curve is flattening if short-term rates are increasing relative to long-term rates, which is what's been happening lately. The yield curve is inverted if short-term rates exceed long-term rates, making the spread negative. Inverted yield curves have historically been reliable predictors of impending recessions, which is why people are paying so much attention to the yield curve now. This FRED graph effectively illustrates that every recession since 1957 has been preceded by a yield curve inversion. (Note that the lag between the inversion and a recession varies: With the 10-year and 1-year yields, the lag is between 8 and 19 months, with an average of about 13 months.) A common interpretation is that the yield curve measures investors’ expectations of economic growth in the current period compared with economic growth in the future. According to this interpretation, a yield curve inversion implies that investors expect current economic growth to exceed future economic growth, indicating a recession is likely. Of course, some question the strength of the relationship between U.S. yield curves and recessions. The graph shows that, in 1965, the yield curve inverted but a recession didn't closely follow. So, although yield curve inversions are good predictors of recessions, they're not perfectly correlated and the exact relationship isn't completely understood. In December 2013, the spread between long and short rates was very close to 3 percent. In September 2018, the spread was 0.44 percent for the 10-year and 1-year yields and 0.87 percent for the 10-year and 3-month yields. If the yield curve were to continue its downward trend from its previous high in December 2013, the yield curve would invert in August 2019 (using the 10-year and 1-year yields). Historically, this would predict a recession sometime in 2020. As the yield curve flattens, we can expect economists and financial markets will closely monitor its level and make many predictions about whether and when a recession will follow. How this graph was created: On the FRED homepage under the search box, use the “Browse data by..." option to search under "Category." From there, select “Interest Rates” under “Money, Banking, & Finance.” Select “Treasury Constant Maturity.” Find and select the monthly “10-Year Treasury Constant Maturity Rate” series. From the “Edit Graph” menu, use the “Customize data” section to search for “1-Year Treasury Constant Maturity Rate” and select the option with “Monthly, Percent, Not Seasonally Adjusted” and add to the graph. The latter series is labeled as series “b.” Under “Customize data,” type a-b into “Formula” box and select “Apply.” Now select “Add Line” and follow this same process using “3-Month Treasury Bill: Secondary Market Rate” as the “b” series. Suggested by Matthew Famiglietti and Carlos Garriga.
View on FRED, series used in this post: GS1, GS10, TB3MS

Unemployment rates by occupation Layoffs are more likely for some jobs

This FRED graph shows the unemployment rates for various occupations. What's striking is that, over the 18-year sample period, the ordering hardly changes. Of course, the magnitude of unemployment responds to what's happening in the overall economy. But management occupations and professionals, for example, always have the lowest unemployment rates by quite a margin. Mining, agriculture, construction, and maintenance have the highest unemployment rates, whether the economy is in a boom or a recession, with manufacturing and other production occupations a close second. These two are particularly affected by recessions. Sales, office, and service occupations fall in the middle. Obviously, what happens in specific labor markets correlates with what happens in the sector at large: For example, construction workers typically work in the construction sector. But this correlation isn't absolute—a prime example being that managers are sprinkled across all sectors. This data picture shows that choosing which occupation to work in can be more important than which sector to work in. The Current Population Survey has more detailed data that can add to this perspective. How this graph was created: Start from the Current Population Survey, click on the table with employment and unemployment by occupation (A-13), select the relevant series, and click "Add to Graph." Change the order of the series legend to match the order in the graph by clicking "Edit Graph," opening the format tab, and moving the series up or down. (This last step can be slow.) Suggested by Christian Zimmermann.
View on FRED, series used in this post: LNU04032215, LNU04032218, LNU04032219, LNU04032222, LNU04032226

When economies just don’t grow Some countries suffer from long-term economic stagnation

In general, economies grow. They do this by accumulating capital (machinery, structures, infrastructure), increasing higher education, and making technological progress. Sometimes they shrink for a time because of recessions, but the general trend is for economies to move upward. Yet, there are a few countries that have stagnated or even shrunk over the longer run. The graph shows five of these countries. (By the way, we use real GDP per capita so that population growth and inflation don't muddy our measurement of economic performance.) Two of these are poor countries that just don't seem to be making any progress. The worst case is Madagascar, which has suffered from endemic mismanagement since its independence. The other is Zimbabwe, which had impressive growth in the late 1960s and early 1970s that was reversed in the past two decades by mismanagement that culminated in extreme hyperinflation. Two other countries have a very different history. Brunei is a small southeast Asian nation rich in oil and natural gas. It has been wealthy since its independence, but the decline of oil prices as well as lower production have had an adverse impact on the economy. Equatorial Guinea benefited from the discovery of oil in the 1990s, leading to a spectacular boom that allowed it to shoot past its former colonial power, Spain. But here, again, the lower price of oil has shrunk the economy substantially in subsequent years. The final case is Ukraine, which had a few very rough years after the fall of the Berlin Wall, as did all economies in the former Soviet Union and Eastern Bloc. But unlike its neighbors, it's still far from returning to the level it started from, at least in part because of chronic corruption and political upheaval at home and with neighboring countries. How this graph was created: Search for "constant GDP per capita Madagascar" and click on the series. To add the other lines, click on "Edit Graph," open the "Add Line" panel, and search for the next series. Add the series and repeat until satisfied. Finally, as the range of values for the series is quite wide, open the "Format" panel and put the y-axis to right for some of them (in our case, Brunei and Equatorial Guinea). Suggested by Christian Zimmermann.
View on FRED, series used in this post: NYGDPPCAPKDBRN, NYGDPPCAPKDGNQ, NYGDPPCAPKDMDG, NYGDPPCAPKDUKR, NYGDPPCAPKDZWE


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