Chapter 8 | Invention, Knowledge Transfer, and Innovation

Introduction

1 Although indicators are always partial measures of the concepts in which we are interested, this is particularly true with innovation. The output can be intangible and often unique, and although products created by innovation are produced and sold in the market, process and organizational innovations are hard to identify and to distinguish from trivial improvement.

Invention: United States and Comparative Global Trends

1 Figure 8-1 shows 2011 data because that is the most recent year for which these data are available for R&D-performing firms as well as firms that do not perform R&D. For R&D-performing firms, these data are available from NSF’s 2015 BRDIS.

Knowledge Transfer

1 Data on technology transfer metrics such as these are now increasingly available. Nonetheless, the federal technology transfer community has long recognized that counts of patent applications and awards, intellectual property licenses, cooperative research and development agreements, and the like do not usually of themselves provide a reasonable gauge of the downstream outcomes and impacts that eventually result from transfers—many of which involve considerable time and many subsequent developments to reach full fruition. Literature on federal technology transfer success stories is growing, facilitated in part by the annual agency technology transfer performance reporting mandated by the Technology Transfer Commercialization Act of 2000 and through regularly updated reports by technology transfer professional organizations such as the Federal Laboratory Consortium for Technology Transfer (FLC). (For an ongoing, but selective, accounting of federal laboratory technology transfer success stories, organized by the FLC, see the “Success Stories” map in FLC [2017].) Even so, the documentation of these downstream outcomes and impacts remains well short of being complete.

2 Differences in tax policies and protection of intellectual property also likely influence the volume and geographic patterns of global trade in royalties and fees (Gravelle 2010:8; Mutti and Grubert 2007:112).

3 The volume and geographic patterns of U.S. trade in royalties and fees have been influenced by U.S.-based multinational companies transferring their intellectual property to low-tax jurisdictions or their foreign subsidiaries to reduce their U.S. and foreign taxes (Gravelle 2010:8; Mutti and Grubert 2007:112).

Innovation Indicators: United States and Other Major Economies

1 See Bain & Company (2015) and Fung Global Retail and Technology (2017:4–6) for a discussion of the factors in the spike in venture capital financing.

2 Another possibility is that the behavior of venture capital investors changed because fewer opportunities for attractive risky investments were available in the 2000s than in the 1990s.

3 Source: PitchBook, http://pitchbook.com/.

4 Snapchat’s share prices rose more than 40% compared with its initial pricing on its IPO on 2 March 2017, resulting in a market capitalization of $33 billion.

5 Source: PitchBook, http://pitchbook.com/.

6 According to von Hippel (2017) , a user-developed innovation has been developed by the firm or the consumer that expects to benefit from using the product or service, rather than by the firm that expects to benefit from selling the product or service. A free innovation is one created outside of paid work time and not protected against sharing.

7 The rate for the U.S. sample was 5.2% (i.e., 1 in 20 had developed an innovation as defined by the survey).

8 When these technologies become widespread, there are complementarities between technical improvement for the GPTs and innovations in related application sectors that can lead to sustained aggregate economic growth. These gains, however, can take considerable time to emerge and may require significant and costly co-investments. From this perspective, the long process of diffusion of digitally networked GPTs has depressed the MFP growth rate in the near term but can increase it in the future.

9 Branstetter and Sichel (2017) argue that improved measurement of prices for IT products would show multifactor productivity growing more quickly than in official statistics. The topic is not yet settled, including alternate estimates of productivity growth with adjustments for potential mismeasurement. Byrne, Fernald, and Reinsdorf (2016) adjust experimental growth measures for many of the identified issues and find that these adjustments would, overall, make the productivity slowdown worse instead of better.