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Rationales and mechanisms for revitalizing US manufacturing R&D strategies

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Abstract

The race to economic superiority is increasingly occurring on a global scale. Competitors from different countries are employing new types of growth strategies in attempts to win that race. The United States cannot, therefore, continue to rely on outdated economic growth strategies, which include an inability to understand the complexity of the typical industrial technology and the synergies among tiers in high-tech supply chains. In this context, a detailed rationale is provided for maintaining a viable domestic technology-based manufacturing capability. In the United States, the still dominant neoclassical economic philosophy is at best ambivalent on the issue of whether a technology-based economy should attempt to remain competitive in manufacturing or let this sector continue to offshore in response to trends in comparative advantage, as revealed through shifts in relative prices. The paper argues that the neoclassical view is inaccurate and that a new innovation model is required to guide economic growth policy. Specifically, the paper provides (1) a rationale for why an advanced economy such as the United States needs a manufacturing sector; (2) examples of the process of deterioration of competitive positions for individual industries and, more important, entire high-tech supply chains; (3) an explanation of the inadequacy of current economic models for rationalizing needed new policy strategies; and (4) a new economic framework for determining both policy mechanisms and targets for those mechanisms, with emphasis on the systems nature of modern technologies and the consequent requirement for public–private innovation ecosystems to develop and deliver these technologies. Several targets are suggested for major policy mechanisms.

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Notes

  1. Source: Thomson Reuters’ Derwent World Patents Index.

  2. These R&D intensities are for company-funded R&D. Adding externally supplied R&D funding (largely from the US government) increases the ratios slightly. For example, using “total R&D performed” as the metric raised the US manufacturing sector’s R&D intensity in 2007 to 4.1%.

  3. American Small Manufacturers Coalition, Next Generation Manufacturing Study (June 2009) http://www.smallmanufacturers.org/picts/NGM-Overview-and-Findings.pdf.

  4. The Census Bureau uses approximately 22,000 product codes to collect trade data. 500 of them are labeled as “advanced technology products” and a separate trade balance has been computed for this subgroup since 1988. The ATP balance was positive every year until 2002, when it turned negative. The deficit has grown every year since 2002 and provides one of several alarming indicators of the declining competitiveness of US manufacturing.

  5. Federal Reserve Board foreign exchange releases. In the period 2000–2008, the dollar declined 24.4% against an index of major foreign currencies (16.9% against all currencies).

  6. Sources: National Science Foundation’s Science and Engineering Indicators 2006 and 2008 and Research & Development in Industry 2007. Between 1999 and 2007, foreign R&D funded by US manufacturing firms grew 191% and their funded R&D performed domestically grew 67%.

  7. See also Hira (2009).

  8. See Atkinson and Audretsch (2008) for a review of the differences between these two bodies of economic thought.

  9. Congressional Budget Office, “Factors Underlying the Decline in Manufacturing Employment Since 2000” (December 23, 2008).

  10. Government data are not sufficiently disaggregated (in particular, to the industry level) to allow all desirable comparisons. For example, Machinery (NAICS code 333) is a large and diversified group of industries. Most of them are low- to moderate-R&D intensive. A few, however, such as semiconductor equipment (code 333295), are R&D intensive and produce high value-added products.

  11. Bhidé points out that the 1930s had the highest productivity growth of any decade in the twentieth century. Technologies developed in the 1920s but not widely adopted then were rapidly diffused in the 1930s in response to corporate desperation to remain viable in the face of falling demand. Similarly, the severe recession of the early 1980s and the onset of significant foreign competition led to rapid diffusion of the PC and other information technologies, as well as concerted efforts to revitalize high-tech industries, in particular, semiconductors.

  12. R&D intensity is conventionally defined as R&D divided by GDP for the entire economy and R&D divided by sales for companies, industries, and sectors.

  13. See McCormack (2009) for this and other examples of declining US manufacturing industries.

  14. Source: Semiconductor Industry Association (SIA). Ironically, a National Research Council Report (Securing the Future, 2001) pointed out that the US electronics industry in 2001—the beginning of the second time period in Table 2—was the largest US manufacturing industry in terms of sales and that the US semiconductor industry (a portion of the electronics industry group) had the highest value added in 1999 of any US manufacturing industry.

  15. See http://www.itif.org/index.php?id=226.

  16. Author’s estimate. No consensus definition of the “high-tech sector” exists. It is defined here as including 4-digit NAICS industries that have an R&D intensity (R&D divided by net sales) greater than 5%. Unfortunately, the Bureau of Economic Analysis does not calculate value added at the 4-digit industry level (they do so only for 3-digit industry groups and above). Thus, only a rough estimate can be made. However, even if the value added were used for the 3-digit groups in which these R&D-intensive industries are classified (a significant overestimate because these groups contain low and moderate R&D-intensive industries), the total contribution to GDP would still only be 12.5%.

  17. Bureau of Labor Statistics, “Production Workers: Hourly compensation costs in US dollars in manufacturing, 34 countries or areas and selected economic groups, 1973–2007,” March 2009. (ftp://ftp.bls.gov/pub/special.requests/ForeignLabor/ichccpwsuppt02.txt.

  18. From the BLS establishment survey (http://www.bls.gov/schedule/archives/empsit_nr.htm#2000), non farm employment was unchanged in this decade (130.3 million in January 2000 compared to 130.8 million in October 2009). Employment in the manufacturing sector declined in the same time interval from 18.4 million to 11.7 million.

  19. A 2003 Boston Consulting Group study, Innovation to Cash, estimated that the cost of taking new products to market doubled over the previous 10 years and the innovation failure rate appeared to be in the 60–85% range. Christensen (1997) estimated the failure rate to be 80–90%. A more recent study by the Boston Consulting Group (Innovation 2009) found significant dissatisfaction with innovation RoI among corporate managers globally. Dissatisfaction was particularly high among North American companies (58%).

  20. See van Opstal (2009) for an excellent characterization of the increasing speed and complexity of technological change.

  21. See “Nanotechnology Is Not Quite Ready for Prime Time,” Manufacturing & Technology News, April 4, 2006.

  22. More functionally referred to as the “risk spike”. See Tassey (2005a, b, 2008b).

  23. See http://www.nist.gov/director/prog-ofc/report02-3.pdf.

  24. See http://www.nist.gov/director/planning/strategicplanning.htm.

  25. The segmentation (horizontal disintegration) of domestic R&D and manufacturing and the subsequent loss of the entire tier is the first part of the process off shoring high-tech supply chains. See Pisano and Shih (2009) for a number of examples.

  26. For an example of support for the fabless strategy, see Doraiswamy (2006).

  27. From several sources. See Dewey and LeBoeuf LLP (2009, p. 14).

  28. Chinese semiconductor consumption in 2007 was $88 billion (34% of the world’s total consumption of $256 billion). In keeping with the process of convergence, domestic production is being stimulated by the large domestic consumption of chips. China’s domestic production of semiconductors increased from 2% of the world’s total in 2000 to 9% in 2007. Source: Semiconductor Industry Association and PricewaterhouseCoopers.

  29. The federal government’s first program to support regional innovation clusters is a $50 million FY2010 budget request for the Department of Commerce’s Economic Development Administration.

  30. MEMS technology has already produced new higher-performance products such as accelerometers for automobile airbags, tiny nozzles for ink jet printers, and projectors for high-end video displays. Continued commercialization of MEMS technology has been forecast by some analysts to produce a second semiconductor revolution that will drive growth in the US economy for decades to come.

  31. The phenomenon of co-location is not just about the provision of complementary technology assets and the efficient transfer of technical information but also includes benefits from institutional integration (Hermans et al. 2008).

  32. Since the mid-1990s, the number of new drugs approved per year has declined, while the cost of taking a drug candidate from discovery through FDA approval has increased. This means that from a portfolio perspective, a successful drug must cover not only the growing total cost the portfolio R&D, but also the costs of an increasing number of failures.

  33. See http://cnse.albany.edu/ContentManager/index.cfm?Step=Display&ContentID=172.

  34. For example, semiconductor manufacturers now create multi-function “systems on a chip” that can yield greater innovation impact than traditional single-function chips. However, doing so requires close interaction with downstream electronic product companies and other users to define R&D objectives.

  35. Compact Power Inc. is a subsidiary of South Korea’s LG Chem. Korea is where much of the current world’s supply of advanced automobile batteries is located.

  36. President’s Council of Advisors on Science and Technology (PCAST), The National Nanotechnology Initiative: Second Assessment and Recommendations of the National Nanotechnology Advisory Panel. Washington, DC: April 2008. The global nanotechnology R&D estimate is from Lux Research.

  37. Even including NIH funding, federal S&T research has declined in real dollar terms since 2004.

  38. One can ask why do not existing domestic supply chains remain in place, especially with the increased need for collaboration due to backward distribution of R&D. As explained in the sections on technology life cycles, a particular tier can move offshore in a sequence of steps (first manufacturing and then R&D), especially as a technology matures. Once established in a another economy, more attractive investment incentives and superior overall innovation infrastructure in that economy can lead to indigenous tiers evolving above and/or below the original transplanted tier (for example, the emergence of semiconductor design firms in Taiwan after the establishment of chip manufacturing capability).

  39. See Zhang et al. (2009). Clusters promote more interactions among research institutions (universities and government laboratories), innovators, their suppliers, venture capitalists, and other public-sector infrastructure. Such clusters increase both cooperation and competition, resulting in a much more effective innovation ecosystem. They also increase the efficiency of R&D through localized knowledge spillovers.

  40. Lawrence Summers, talk to the National Academy of Sciences’ Science, Technology, and Economic Policy (STEP) Board, October 2008.

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Acknowledgments

The author is indebted to Albert Jones of NIST’s Manufacturing Engineering Laboratory for very helpful comments on draft text. Also, thanks go to Daryl Hatano of the Semiconductor Industry Association for providing data and SIA analysis relating to semiconductor technology and to Taffy Kingscott of IBM for helpful insights into global manufacturing and service strategies.

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Correspondence to Gregory Tassey.

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Tassey, G. Rationales and mechanisms for revitalizing US manufacturing R&D strategies. J Technol Transf 35, 283–333 (2010). https://doi.org/10.1007/s10961-009-9150-2

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