1. Scientific knowledge enhances technological innovation but the flow of knowledge and its impact are poorly measured. Incomplete metrics may misguide policymaking. 2. Surveys of scientists and inventors can help understand limitations of existing indicators and provide complementary information, such as what top citations indicate, the role of information cascades, and how significantly inventors utilize scientific knowledge. 3. Collaborative mechanisms like standards setting are important for coordinating R&D and diffusion but their relationship to innovation is not well understood due to lack of data linking standards to patents. Better data collection is needed to measure innovation processes and impacts.

1. Comments on Science and
Innovation policy-making today
Sadao nagaoka
Tokyo Keizai University and RIETI
Economic Advisor to the JPO
OECD Blue Sky III Forum in Ghent, Belgium,
19-21 September 2016
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2. Point 1: Measurement of scientific
knowledge flow and its impact
• Scientific knowledge enhances the technological
opportunities for innovation and furthermore
expands the knowledge stock stimulating further
scientific progress.
• This process and the outcome are still very
poorly measured. Policy incentives based on such
incomplete information may exacerbate rather
than improve the science and innovation
performance, as sharply warned by Minister
Heitor in the context of research assessment
practices and scientific career developments.
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3. Learning directly from scientists and
engineers through surveys and case studies
• Surveys and case studies which allow us to
directly learn from scientists and inventors help
us understand the limitations of the existing
indicators as well as complement them.
• As illustrations,
-What does top 1% in citations mean?
How important is information cascade? (Cite not
because of the paper content but because of the
others’ citations)
Is it a break-through or follow-on?
- How significantly does an inventor report scientific
knowledge they use either as prior art or as tools? 3

4. What does top 1% in citations mean?
Based on survey on Japan scientists supported by the JSPS
(http://www.iir.hit-u.ac.jp/iir-w3/file/WP11-09NagaokaIgamiWalshIjichi.pdf)
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5. How significantly does an inventor report scientific
knowledge they use either as prior art or as tools?
Based on the survey on Japanese inventors supported by the JST (Program of Science for
Redesigning Science, Technology and Innovation Policy )
(http://www.rieti.go.jp/jp/publications/dp/15e105.pdf)
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6. Scientific literature revealed by place in patent
document (literature level)
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7. Point Two: A collaborative mechanism
for innovation
• A collaborative mechanism such as de jure standard
setting is important for innovation in coordinating R&D
and in diffusion.
-a finding from inventor surveys on how significantly
inventions build on standard
• However, the relationship between such standard and
innovation is not well recognized, given the lack of data.
-De jure standard is public good like science literature,
and its linkage with innovation is even less tractable, since
no citation information exists, except for the standards
with the essential patents licensed through pools.
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8. Inventions building on standard
Does this invention utilize or build upon technical standards?
Yes % No % Yes + NO Unknown
ElecEng 132 19.4% 549 80.6% 681 270
Instruments 70 18.7% 305 81.3% 375 172
Chemistry 113 23.3% 371 76.7% 484 264
ProcEng 50 20.3% 196 79.7% 246 121
MechEng 74 19.1% 314 80.9% 388 202
ConsConstr 17 25.4% 50 74.6% 67 36
Total 456 20.3% 1,785 79.7% 2,241 1,065
Based on the survey on Japanese inventors in PAT-VAL II project
(http://www.rieti.go.jp/jp/publications/dp/07j046.pdf, in Japanese)
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9. In concluding
• Innovation process and outcomes are sill poorly measured,
and there is also a danger that knowledge with more
public good nature is less measured, despite its
productivity contribution.
• Given the clear danger that incomplete information can
misguide a policy, there exists a strong need of enhancing
data gathering efforts, despite the impressive progress for
the last decades in data infrastructure, as reflected in the
OECD science and technology indicators.
• We need collaborations with scientists, engineers and
users to understand the knowledge production process
and its impact.
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