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Abstract
Virtually all scholars recognize that scientific and technological capabilities are becoming increasingly important factors in a nation’s overall power. Unsurprisingly, debates over a possible U.S.–China power transition highlight China’s rise as a science and technology superpower. These discussions overwhelmingly center on national innovation capabilities, reflective of the bias in assessments of scientific and technological capabilities toward the generation of novel advances. This paper argues that these assessments should, instead, place greater weight on a state’s capacity to diffuse, or widely adopt, innovations. Specifically, when there is a significant gap between a rising power’s innovation capacity and its diffusion capacity, relying solely on the former results in misleading appraisals of its potential to sustain economic growth in the long run. I demonstrate this with two historical cases: the U.S. in the Second Industrial Revolution and the Soviet Union in the early postwar period. Lastly, I show that, in contrast to assessments based on innovation capacity, a diffusion-centric approach reveals that China is far from being a science and technology superpower.
Acknowledgements
I am very grateful to Felipe Forero for invaluable research assistance. For helpful comments and input, I thank: Markus Anderljung, Alexis Carlier, Allan Dafoe, Max Daniel, Hiski Haukkala, Yutao Huang, Stephen Kaplan, Alex Lintz, Morgan MacInnes, Karolina Milewicz, Adam Salisbury, Toby Shevlane, Duncan Snidal, participants at the DPhil Workshop at Oxford University, and especially Ben Garfinkel and Remco Zwetsloot.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 International relations scholars are primed to associate diffusion with international diffusion. In this paper, diffusion refers to the internal spread of new S&T advances throughout a country. Thus, diffusion capacity captures a state’s effectiveness in adopting new advances across domestic systems. The source of these new advances can be domestic or international.
2 Beckley (Citation2012) recognizes that ‘the ability to produce and use commercially viable and military relevant innovations’ (p. 67, emphasis mine) is key to technological superiority, and Brooks and Wohlforth (Citation2016) make a distinction between ‘technological inputs’ and ‘technological outputs’ (pp. 22–26). However, nearly all of Beckley’s S&T indicators relate to the ability to produce innovations. While Brooks and Wohlforth note the need to track how R&D inputs translate into outputs, which they measure by article publications and patent filings, I am interested in how these outputs diffuse.
3 Related work on the S&T power of nations makes the same distinction but focuses on the innovation phase. Kennedy (Citation2018, p. 16) and Taylor (Citation2016, p. 28) literature on military innovation tends to employ a broad definition of ‘innovation’ that subsumes the diffusion process. Evangelista (Citation1988, p. 52).
4 Other factors that could boost both innovation and diffusion capacity include relatively open economies, democratic governance, and political decentralization. Even for these variables, it is important to make careful distinctions between effects on innovation and diffusion. For instance, recent scholarship has questioned the connection between decentralization and national innovation rates, arguing that previous work had misidentified decentralization’s boost to diffusion as innovation (Taylor, Citation2016, p. 137).
5 See accompanying dataset.
6 For the U.S. case, alternative factors include trade barriers that protected domestic industries, rapid population growth, and a favorable security environment. Possibly, the U.S. achieved sustained growth because these factors outweighed the influence of the U.S.’s weak innovation capacity. This scenario is unlikely because, as historians have argued, S&T advance was central to U.S. productivity improvements in this period (Mowery & Rosenberg, Citation1993, pp. 31–32). For a summary of alternative factors in the Soviet Union case, including excessive military spending, see Trachtenberg (Citation2018, pp. 84–92).
7 For an analysis of how Britain’s innovation capacity outdistanced its diffusion capacity in this period, see Supplementary Appendix A.
8 See Supplementary Appendix A for more evidence of the U.S.’s relative advantage in diffusion capacity in other technological domains.
9 While some assessments of overall Soviet Union power, especially from the U.S. intelligence community, emphasized Soviet military power, my focus is on Soviet S&T capabilities in the civilian economy.
10 Throughout this paper, especially in the U.S. case study, I employ engineering human capital as an indicator of diffusion capacity. To resolve some of the tension with citing statistics that encompass engineering graduates as indicators of innovation capacity in the Soviet case, I show that this “scientific manpower gap” was mostly framed as a Soviet advantage in scientists and elite researchers. I thank an anonymous reviewer for raising this point.
11 For other indicators of innovation capacity, such as patents, it was difficult to compare the two countries. The Soviet Union developed a distinct system of intellectual property, which awarded inventors with dachas and prizes instead of patents. Gordin, Citation2014.
12 National Science Board (Citation1987, p. 233).
13 The nine areas were automobiles, chemicals, computers, high-voltage electric-power transmission, industrial-process control, iron- and steelmaking, machine tools, military technologies, and rocketry and manned space capsules. Evangelista (Citation1988, pp. 38–62) describes this study as ‘the most careful comparison of the relative levels of Soviet and Western technology.’
14 This section focuses on China’s ability to generate and adopt commercially viable innovations. For analyses of China’s military innovation system, see Cheung (Citation2013) and Walsh (Citation2014).
15 One notable exception is Breznitz and Murphree (Citation2011). They note that ‘policy makers and academics put too much faith in the notion that states and societies must create novel technologies in order to secure long-term growth and enhance national welfare’ (p. 2).
16 An accompanying dataset includes all the articles reviewed. For coding details, see Supplementary Appendix B.
17 Kennedy (Citation2015, p. 284).
18 Moyer and Markle (Citation2017, p. 8). In fact, for periods before 2005, share of global R&D expenditures was the sole indicator of the GPI’s technological dimension of power. Now, a country’s share of global ICT capital stock makes up the other half of its technological power.
19 An accompanying dataset lists the initial and updated versions of the 20 indicators.
20 Because global rankings convey reputational effects, states sometimes try to game these indexes (Kelley & Simmons, Citation2019). This issue is not as relevant for the decomposition exercise because states would not predict the choice of subindexes, and any attempt to game the indicators would apply equally across the subindexes.
21 Supplementary Appendix C contains a detailed explanation of how I sorted the indicators for both the GII and GCI. Possibly, China’s diffusion capacity could benefit from its large population of students in tertiary education. As an additional check, I re-ran the analysis with the tertiary enrollment indicator included in the diffusion capacity subindex. This did not meaningfully affect the main results. Supplementary Appendix C provides further details.
22 This decomposition exercise was undertaken for twelve other countries: Denmark, France, Germany, India, Israel, Japan, Russia, Singapore, South Korea, Sweden, Switzerland, and the United Kingdom. China had the largest difference between its diffusion capacity subindex and innovation capacity subindex. This result holds for the GII in 2014, 2017, and 2020. See Supplementary Figure S1 in Appendix C.
23 I am grateful to an anonymous reviewer for pointing this out. Supplementary Appendix C reports the detailed figures cited in this section.
24 See, for example, Breznitz and Murphree (Citation2011) and de La Bruyère and Picarsic (Citation2020).
25 Allison and Schmidt (Citation2020, p. 10).
26 While this article’s theoretical framework does not focus on variation among technological developments, these ICT-specific measures address one concern about whether the GII decomposition captures a country’s capacity to diffuse the disruptive innovations that are likely to transform the entire economy.
27 In some areas, including measures of ICT access, China’s ranking has declined over the past decade. This is based on figures reported in the 2011 and 2021 GII.
28 Some of these tendencies echo the Soviet Union’s struggles, although China’s diffusion capacity benefits from extensive international linkages, entrepreneurial environment and venture capital sector, and a higher degree of decentralization in the actual implementation of industrial policies.
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Jeffrey Ding
Jeffrey Ding is an Assistant Professor of Political Science at George Washington University.