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The importance of imports

Import tariffs, imports of production inputs, and domestic investment

U.S. trade policy continues to change, with rising tariffs on imports of capital goods and intermediate inputs from China and other countries. But how important are these types of imports for the U.S. economy, especially compared with total U.S. imports? As usual, FRED can help answer our question: The graph above plots the share of capital and intermediate inputs in aggregate U.S. imports over the period 1999-2019.

As the graph shows, the share is not small. In fact, it’s the majority of total imports, ranging from 46% to 61% over this period, with an average well above 50%. Because these imports play an important role for the domestic production of U.S. goods, one would expect that raising tariffs on these goods would have a negative impact on domestic production.

Again, FRED sheds some light on the question: The graph below shows that imported capital goods make up a substantial fraction of aggregate investment, ranging from a bit under 12% to almost 18% for 1999-2019. In particular, the share of imported capital goods in gross fixed capital formation has been growing over the past two decades: Between the 2001 recession and the Great Recession, it was in the 12% to 14% range; after the Great Recession, the values were largely above 16%.

These specific imports comprise a significant portion of both total U.S. imports and domestic investment, which suggests that the ongoing changes to U.S. trade policy might have a negative impact on firms that rely on these capital goods and inputs to conduct their productive activities. In particular, tariffs on capital goods might negatively affect aggregate U.S. investment and, thus, aggregate output.

How these graphs were created: For the first, search for and select “Imports of Goods: General Merchandise: Capital goods except automotive” and click “Add to Graph.” From the “Edit Graph” panel, under “Customize Data,” select another series to combine with the existing series. Search for and select “Imports of Goods: General Merchandise: Industrial Supplies and materials” and click “Add.” This series is now labelled as series “(b)” in the “Edit Graph” panel. Repeat this procedure to add the series: “Imports of Goods: General Merchandise.” Now use formula (a + b)/c*100.

For the second, start with the same search, then add another series by searching for “Gross Fixed Capital Formation in the United States” under “Customize Data” and clicking “Add.” The units of the two series are different, so to normalize we need to multiply the Imports of Capital Goods by one million. So, use formula (a*1000000)/b*100.

Suggested by Matthew Famiglietti and Fernando Leibovici.

View on FRED, series used in this post: IEAMGC, IEAMGI, IEAMGM, USAGFCFQDSMEI

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


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