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What a wonderful world Tracking global GDP

Here at the FRED Blog, we often describe the economies of individual countries, but what about the world as a whole? That calculation is more complicated than you may think. It’s not sufficient to just add up all the economies’ GDPs. First, they’re all denominated in different currencies, so you need to convert them into a common currency. Second, determining what exchange rate to use isn’t straightforward: Some are set at levels that don’t correspond to market forces, and even market forces may deviate significantly from purchasing power parity across countries. Thank goodness for the World Bank, which does all these calculations for us. Now, they do make some assumptions that people may or may not agree with; but if you’re looking for the long-run picture, these assumptions shouldn’t matter much.

We offer such a picture in the FRED graph above. We still had to modify it a bit, as the World Bank series is in current U.S. dollars. To remove the impact of inflation, we divided the figures by the implicit deflator for U.S. GDP. (This is also a debatable choice, as one would ideally use a deflator for world GDP. But no such deflator is available.)

The graph shows several phases: We see steady growth until about 2002. Then we see a stronger growth spurt. And then, since 2011, we see noticeably slower growth. While this data series is based on every economy in the world, it’s most influenced by the large developed economies. Some of the larger but less-developed economies, such as China and India, have grown a lot. The growth of the latter has slowed down a bit recently, and there’s talk of secular stagnation for the former, stemming from slower productivity growth and declines in the working-age population. Return to FRED in, say, ten years to see how things have evolved, in particular whether the other developing countries have become more significant forces in driving world economic growth.

How this graph was created: Search for “world GDP,” select the series with the longest sample period, and click “Add to Graph.” From the “Edit Graph” panel, add a series by searching for “GDP deflator” and selecting the implicit GDP deflator for the U.S. Apply formula a/b.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: GDPDEF, NYGDPMKTPCDWLD

Employer contributions The steep climb of supplemental wage benefits

Employees get paid, and their compensation can be divided into (a) wages and salaries and (b) supplements to wages and salaries. The Bureau of Economic Analysis defines supplements to wages and salaries as “employer contributions for employee pension and insurance funds and employer contributions for government social insurance.” This FRED graph shows the fraction of supplements to wages and salaries in total employee compensation from 1950 to 2017. (Note: A past FRED Blog post also discusses compensation, including non-wage benefits.)

We can see a clear trend here: The fraction of these supplements has increased consistently, from less than 8% of total compensation in 1950 to 18% in 1990. Since 1990, though, the series seems fairly stable, hovering between 17% and 20%. The Social Security tax rate exhibits a similar pattern and, thus, may be one of the causes behind this phenomenon. (Specifically, the Social Security tax rate was 1.5% in 1950, was about 5% in the mid-1970s, and has been 6.2% since 1990.)

How this graph was created: Search for and select “Gross Domestic Income: Compensation of Employees Paid: Wages and Salaries.” From the “Edit Graph” panel’s “Modify Frequency” option, choose “Annual.” Next, select the time frame option and change the start date to “1950-01-01.” From the “Edit Graph” panel’s “Customize Data” section, type “Compensation of Employees: Supplements to Wages and Salaries” and select this option to add a second series. In the formula box, enter b/(a+b) and click “Apply” to divide supplemental compensation by total compensation.

Suggested by Makenzie Peake and Guillaume Vandenbroucke.

Want to learn more about employee compensation?

  • Income for the top 1% has risen mainly because of higher executive compensation, esp. in the form of stocks, options, and bonuses.
  • Remember the 2018 teacher strikes in West Virginia, Oklahoma, Kentucky, Arizona, and Colorado? Wages there are lower than average, but so is the cost of living.
View on FRED, series used in this post: A038RC1Q027SBEA, A4102C1Q027SBEA

The usual suspects (behind U.S. trade deficits): China, Canada, Mexico, Japan, and Germany A long-term lineup of U.S. trading partners

According to economic theory, countries should export goods in which they have a comparative advantage in production and import those in which they don’t. For several years, the U.S. has been the number 1 importer and the number 2 exporter in the world. But the U.S. has recently imposed tariffs on imports from several foreign nations, citing the growing U.S. trade deficit as a main reason. So let’s use FRED to examine the overall picture of the U.S. trade deficit and the trade balance with its largest trading partners.

The first graph shows net U.S. exports, defined as the difference between total exports and total imports, divided by GDP. This net exports-to-GDP ratio has been negative since the late 1970s, when the U.S. started running a continual trade deficit. One explanation involves important sources of income the U.S. receives from abroad, as explained in a past FRED Blog post. This flow of foreign income allows the U.S. economy to consume more than it produces.

Exploring this and other theories in detail is beyond the scope of this post, but this persistent trade deficit over the past 40 or so years does lead to interesting questions involving the U.S.’s trading partners. For instance, is the trade deficit driven mostly by trade with one particular country?

The second graph plots the difference between exports and imports as a share of GDP with respect to the U.S.’s five largest trading partners: China, Canada, Mexico, Japan, and Germany. We can see right away that there’s a significant difference between the U.S. trade deficit with China and the U.S. trade deficits with the other countries. It’s also interesting to note that, in the 1990s, the largest share of the trade deficit originated from trade with Japan. But since China’s entry to the WTO in late 2001, the largest share is China’s. We also see that the U.S. had roughly balanced trade with Mexico in the early 1990s; but around 1994, coinciding with the implementation of NAFTA, the trade pattern changed and a noticeable deficit with Mexico emerged.

Now, is having persistently large trade deficits a bad thing? The answer to this question is not straightforward. There are several forces affecting the direction of trade with different countries, and a substantial amount of research in economics is dedicated to answering this question.

How these graphs were created: For the first graph, search for and select “Net Exports of Goods and Services, Billions of Dollars.” From the “Edit Graph” panel, add a second series to the graph: “Gross Domestic Product, Billions of Dollars.” In the formula box, type a*100/b. For the second graph, search for and select “U.S. Exports of Goods by F.A.S. Basis to China, Mainland (EXPCH).” From the “Edit Graph” panel, add a second series to the graph: “U.S. Imports of Goods by Customs Basis from Germany.” Then add the “Gross Domestic Product, Billions of Dollars” series again. In the formula box, type (a-b)*100/(c*1000). Then use the “Add Line” feature to repeat the above steps for the other countries (Canada, Mexico, Japan, and Germany).

Suggested by Asha Bharadwaj and Maximiliano Dvorkin.

View on FRED, series used in this post: A019RC1A027NBEA, EXPCA, EXPCH, EXPGE, EXPJP, EXPMX, GDPA, IMPCA, IMPCH, IMPGE, IMPJP, IMPMX

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


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