Key Takeaways

Contents

Key Takeaways 1

Introduction. 2

The Clout Index 2

Application to a Single Industry 7

Application to All National Power Industries 10

Policy Implications 15

Conclusion. 15

Endnotes 16

Introduction

We live in an age of “weaponized interdependence,” wherein China is working to gain advantage in a wide range of advanced industries in which it can employ economic coercion, including leveraging chokepoints. Case in point: Last year, the United States backed off Biden-era export controls of semiconductors and jet engines to China in return for China loosening restrictions on rare earth minerals to the United States. As Secretary of State Marco Rubio admitted, “[O]ur industrial capacity is deeply dependent on a number of potential adversary nation-states, including China, who can hold it over our head.”[1] And China is not averse to doing so, as it recently banned the export of some dual-use technologies to Japan in response to Japan’s statement that a Chinese invasion of Taiwan would represent an “existential threat.”[2]

Not all industries can be used for coercive power. China has a near monopoly on Christmas tree tinsel, but no one cares. However, we do care about its strength in active pharmaceutical ingredients, since the United States cannot be without drugs.

This underscores the importance of “power” industries—defense, dual-use, and enabling industries—that are responsible for the wellspring of a nation’s power. (See box 1.) In recent work, the Information Technology and Innovation Foundation (ITIF) identified 216 national power industries—representing approximately 22 percent of the 970 six-digit industries in the North American Industry Classification System (NAICS)—and compared each with respect to several economic indicators including value of shipments, employment, capital expenditures, value added, etc.[3]

In this report, we add to this body of work by introducing a clout index (CI), a measure that combines comparative advantage that the United States has in particular sectors with its relative dependency on China for imports compared with all other nations.

We apply it first to a single industry—machine tools—and then to all national power industries. We present the results, noting those industries at the top and the bottom, and then suggest a few other applications. Finally, we describe certain policy implications and recommend that the federal government establish a statistical monitoring system for power industries.

The Clout Index

We start with a premise: The techno-economic clout of a nation is a function of its domestic production capabilities in key industries and its relative dependence on the production capabilities of its key geopolitical rivals.

To assess domestic production capabilities, we employ a long-established metric known as revealed comparative advantage (RCA), which is the share of a nation’s exports of a certain good divided by the world exports of that good as a share of total world exports.[4] When RCA exceeds 1, the world signals that it prefers to purchase that good from the exporting country, thereby suggesting world-class production capability.[5] The higher the value of RCA, the greater the capability. For example, if U.S. airplane exports represent 6 percent of all U.S. exports and world exports of airplanes are 3 percent of all world exports, then the U.S. RCA in the airplane industry would be 2. If U.S. exports of airplanes were 1.5 percent of all U.S. exports—or half the global figure—then the U.S. RCA would be 0.5.

One measure of U.S. dependence on imports for a particular good is revealed comparative dependence (RCD), which is the share of a nation’s imports of a good divided by the share of world imports represented by that good.[8] When RCD exceeds 1, a nation purchases more of that good from other nations than the global average. The higher the value of RCD, the greater the dependence on imports. For example, if the U.S. imports of telecom equipment represent 2 percent of all U.S. imports, and telecom equipment represents 1 percent of all world imports, then the U.S. RCD would be 2.

We can take this even further and look at dependence on a particular nation, in this case, China. When U.S. RCD for a good exceeds China’s global trade share for all goods, then the United States is dependent on China for that good. In our telecom example, if telecom imports from China represent 0.8 percent of U.S. imports, then U.S. RCD (from China) would be 0.8. Because this number exceeds China’s world total trade share (which was 0.13 in 2023), we can say that the United States is more dependent on China than it is on other nations for telecom equipment, on average.

The CI is a whole number that reflects both RCA and RCD. The quadrant chart in figure 2 illustrates how it is assigned. Note that it does not depend simply on the underlying quadrant. This is because both RCA and RCD are continuous variables, and there is no practical difference between an RCA of 0.99 and 1.01, even though the demarcation of superior production capabilities falls in between these two numbers. A similar situation arises with RCD. Therefore, we assign a CI of 1 to 5 depending on the combination of RCA and RCD. When the CI is low (4 or 5), a power industry is underperforming and has higher-than-average dependence on China, which the country could exploit. When the CI is high (1 or 2), a power industry is performing well (it exhibits world-class capabilities) and much less domestic vulnerability arises for China to exploit.

A CI of 3 reflects two kinds of risk. We differentiate by assigning either a 3+ or 3-. The former indicates RCA > 1 but is not CI = 1 or CI = 2. For example, pharmaceutical preparations (pills, tablets, intravenous solutions) and pressed or blown glass industries are 3+. The latter indicates RCA < 1 but is not CI = 4 or CI = 5. For example, industries such as steel foundries, power boilers, and heat exchangers and machine tools are 3-.

Figure 2: The CI matrix of revealed comparative advantage and dependence

The CI for an industry is determined by combining RCA and RCD for each traded good associated with that industry.[9] We employ the U.S. International Trade Commission (ITC) commodity translation tool to identify Harmonized Tariff Schedule (HTS) codes of traded goods associated with the six-digit NAICS for each national power industry.[10] We then truncate the 10-digit HTS codes (particular to the United States) to 6-digit HTS codes (used by all nations) to allow the gathering of international trade data (using Comtrade) sufficient to estimate RCA and RCD.[11]

▪ We assign a CI of “1” where RCA > 2 and RCD < 0.05.

▪ A CI of “2” is where CI is not “1” and where RCA > 1.2 and RCD < 0.1.

▪ A CI of “3” is simply where CI is not “1,” “2,” “4,” or “5.”

▪ A “3+” is a “3” when RCA > 1 and a “3-” is a “3” when RCA < 1.

▪ A CI of “4” is where CI is not “5” and where RCA < 0.8 and RCD > 0.15.

▪ A CI of “5” is where RCA < 0.4 and RCD > 0.25.

In employing this approach, we have made two methodological choices. First, we exclude from the CI any traded good where missing trade-flow data prevent an estimation of both RCA and RCD. For example, if there are 10 goods (i.e., 10 HTS codes) associated with an industry, and 1 of the goods contains missing information on U.S. exports, we exclude that good because RCA cannot be estimated.[12] Second, we base the CI on the mean value of both RCA and RCD for all traded goods associated with an industry; that is, we give equal weight to different goods produced by a single industry.[13]

One drawback to this methodology is that we do not account for goods that are made in China and then rerouted through another nation (e.g., Vietnam) before being exported to the United States, a practice that has drawn increased attention after the first Trump administration imposed higher tariffs on Chinese goods. To the extent that such practices occur, our methodology understates RCD.

Application to a Single Industry

With the CI, one can see how clout changes over time for any industry. Figure 3 and figure 4 show RCA and RCD for all 41 categories of traded goods associated with the U.S. machine tool industry over a 15-year period, 2007–2022. The scatterplot is shown on a quadrant matrix chart. Each quadrant represents a combination of domestic advantage and dependence on China. For example, clout is highest in the upper-left quadrant, when domestic advantage is high and dependence on China is low. Conversely, clout is lowest in the bottom-right quadrant, when domestic advantage is low and dependence on China is high. In both figures, outliers are constrained to a maximum value for visual effect. These are shown on the upper and right-hand borders.

In 2007 (figure 3), most industries were in the bottom left quadrant, which indicates that the United States was dependent on other trading partners except China for these goods.

Figure 3: U.S. machine tool industry, 2007

By 2022 (figure 4), the U.S. preferences for machine tools from China (RCD) increased, as shown by the larger number of sub-industries in the upper-right and lower-right quadrants of these two charts. World preferences for U.S.-made machine tools (RCA) declined (leaving fewer subindustries in the left-hand quadrants). The overall CI (3-) remained the same.

Figure 4: U.S. machine tool industry, 2022

Over the period of interest, the mean value of both RCA and RCD changed. The mean RCA dropped from 0.80 to 0.75 and the mean RCD nearly doubled from 0.05 to 0.09. The net result was a decrease in clout, but not enough to change the overall CI. In both years, CI was 3-.

Instead of looking at just the mean value of RCA or RCD, one could look for outliers (i.e., goods whose exports or imports changed the most over time). Table 1 lists those goods where RCA changed the most between 2007 and 2022. The United States gained the most ground in producing lathes for removing metal (HTS 845899) and lost the most ground in producing shaping/slotting machines (HTS 846120). For those seeking to understand U.S. capabilities, exploring the reasons behind these large changes might be a worthwhile exercise. Similarly, one could look at machine tools where RCD changed the most. Such outliers (in RCA or RCD) could serve as critical case studies to link specific factors (e.g., policies) to changes in manufacturing capabilities.

Table 1: Largest changes in domestic capabilities, machine tools, 2007–2022

Instead of choosing the starting or ending year of this period, we could examine all 15 years. Such an approach would reveal swings in clout that would not otherwise be noticeable. We could also select two different years if we want to explore the impact of an important event or policy. For example, China joined the WTO in 2001, and choosing the year 2000 to compare with a recent year would likely reveal, for many industries, a significant rise in RCD associated with the well-documented offshoring trend of the early 2000s.[14]

Application to All National Power Industries

To compare CI across industries, we estimate RCA, RCD, and then CI for all 216 national power industries previously identified by ITIF.[15] Our methodology requires us to exclude industries that are service industries and industries for which missing data on trade flows excludes estimation of RCA or RCD. Using the most recent year for which complete trade data is available (2023), we estimate CI for 177 industries, as shown in figure 5. As before, we constrain outliers (appearing on the upper and right-hand borders of the figure) for visual effect.

Figure 5: Vulnerability of U.S. national power industries, 2023

As figure 5 shows, the CI for U.S. power industries spanned the gamut, reflecting a relatively normal distribution with a CI of 3 being by far the most common among industries. A small subset of industries represented considerable U.S. strength (RCA values exceeding 2), and a larger but still small subset of other industries represented U.S. dependence on China (RCD exceeding 0.3).

National power industries with the highest clout (i.e., CI = 1 or 2) are shown in table 2. These industries provide the United States with world-class manufacturing. To the extent they are important to the defense industrial base (e.g., small arms manufacturing, other aircraft parts manufacturing), they are a significant asset to national security.

Of the 177 industries covered in the dataset, just 5 are strong (with a CI score of 1), while 18 are somewhat strong (with a CI score of 2).

Table 2: Power industries with the highest CI scores, 2023

National power industries with the lowest CI scores versus China (i.e., CI = 5 or 4) are shown in figure 6 and listed in table 3. These industries represent a potential risk for the United States. For example, battery storage and electronic computer manufacturing are both critical to national security; low U.S. production capabilities and high U.S. dependence on China present a real vulnerability that China could exploit.

There are also many industries on the verge of a low CI (moving from 3- to 4), among them being synthetic dyes and pigments (325180), food product machinery (333241), other industrial machinery (333249), industrial commercial fans and blowers (334412), totalized fluid meter and counting devices (334514), and power distribution transformers (335311).

As shown in figure 6, of the 177 U.S. power industries, 25 have the lowest CI scores (either 4 or 5), reflecting strategic weakness.

Figure 6: Most vulnerable power industries

Table 3: Economic power industries with the lowest CI scores, 2023

Given China’s rapid advancement, we can expect quite a few 3- industries to drop, over time, to 4 or 5. Examples include NAICS 325180 (other basic inorganic chemicals) and 334412 (bare printed circuit boards).

In a previous report, ITIF documented differences between defense, dual-use, and enabling industries, suggesting that government support for defense industries was a factor in their overall health.[16] We can use RCA and RCD to compare each category of power industry in 2023. The results are shown in table 4. RCA for defense industries was much higher (and RCD lower) than that for dual-use or enabling industries, both of which were close to par in RCA but also reflect relative U.S. dependence on China. However, there is significant uncertainty for the defense power category because we have complete data for just two industries (ammunition and shipbuilding), far fewer than for enabling (57) or dual-use (118) power categories. We exclude three other defense industries (guided missiles, guided missile parts, and armored vehicles) due to missing trade data. Nevertheless, the data align with ITIF’s observation.

Table 4: RCA by Power Category, 2023

Policy Implications

What can the U.S. government do to improve an industry’s CI score? That will be the subject of several forthcoming ITIF reports. But an overarching principle is that industry-specific measures are also needed. Some power industries may be beyond saving because an industrial commons, once eroded, is nearly impossible to bring back. Some power industries are more important than others. And some may be relatively weak yet pose little risk if the underlying goods are adequately produced by core allies.

However, what is clear is that more ongoing analysis of national power industry risk is needed. We developed the CI to fill a void; the federal government doesn’t systematically monitor the capabilities of domestic power industries. But that may be about to change. Congress is considering legislation to establish, by statute, such a monitoring function within the Department of Commerce. The bill, the Promoting Resilient Supply Chains Act, has been introduced and passed in both the Senate (S.257, sponsored by Senator Maria Cantwell, D-WA) and the House of Representatives (H.R. 2444 sponsored by Rep. John James, R-MI).[17] Discussions are underway to resolve differences between the two bills. The legislation focuses on critical industries, critical supply chains, and critical and emerging technologies, with definitions broad enough to include U.S. national power industries and the goods produced by these industries. Ideally, if this effort within Commerce is stood up, it would employ the CI methodology, among others.

Conclusion

The CI is a useful and versatile tool to assess the strengths and weaknesses of U.S. economic power industries. It is most informative when it is combined with other available information as a robustness check and to provide a clearer picture of U.S. strengths and vulnerabilities. For example, a CI score of 5 does not necessarily convey a dire threat to U.S. economic or national security. Additional information—such as whether the goods produced by the industry are critical to national security and the extent to which excess production capacity exists among allied trading partners—would have to be developed before such a conclusion could be drawn.

The CI index can be used to compare a single national industry over time, to compare national industries at the same point in time, or to compare the same industry in two different nations over time. This versatility comes with some drawbacks associated with the use of international trade data: real-time information is not available (complete data is available for all nations after 1.5 or 2 years), the specificity is limited to 6-digit HTS codes (instead of 8- or 10-digit codes), and some trade flows for certain goods are unreported or unavailable.

Congress should enact the “Promoting Resilient Supply Chains Act” because it would create an interagency home for analysis to underpin a stronger and more robust domestic manufacturing sector, which is critical to economic and national security. When implementing this statute, the Commerce Department should include in its definition of critical industries the national power industries identified by ITIF, and in its definition of “critical technologies,” those goods associated with power industries. The CI metric should be employed to evaluate the strength of national power industries and could be used to evaluate the vulnerabilities of U.S. supply chains. The Commerce Department also should establish an ongoing monitoring program focused on China’s production capabilities and track its mercantilist policies that aim to displace the manufacturing share of the United States and other allied nations. It should invite and welcome industry expertise to inform its duties.

Endnotes