1. Innovation Measurement
Keith Smith
Imperial College London/TIK Oslo

2. Why do we need data?
Economy-wide data enables a structural, generalisable view to emerge
It allows us to explore the properties of a system as a whole
It helps us to identify where the really relevant questions are

3. The background issues Historically, 3 sources of data: R&D Patents
Bibliometric
Each has more or less serious problems as innovation indicators

4. Problems with existing indicators
All have problems with their conceptual and definitional bases
Two are by-products of legal or institutional processes – patent law or academic publishing conventions
None focus directly on innovation

5. Research and Development Data
Collected by survey, procedures formalised in OECD ‘Frascati Manual’ (1968)
Collects data on expenditure on R&D, personnel employed (in FTEs), types of research (basic, strategic, applied, experimental), object (by field)
Monitored by OECD NESTI working party

6. R&D Indicators The most common indicator: ‘R&D Intensity’
R&D Intensity = R&D/GDP or R&D/GVA ratio
Countries and firms can be ranked using this ratio
It is often used as a policy target (Norway – target to reach OECD average for R&D/GDP; EU target ‘to reach 3%’)

7. Problems with R&D intensity indicator
The overall indicator reflects not only R&D effort but also the industrial structure of the country
If the country is heavily based on low R&D industries, then the aggregate indicator will be low even if the country is relatively R&D intensive – so the aggregate intensity indicator is misleading as in terms of country efforts (Norway has low R&D/GDP even though it is relatively high in many industries)

8. R&D and high tech sectors
The OECD uses R&D to distinguish between technology intensity of industries
High tech= >4% R&D/GVA ratio
Medium tech = between 1 and 4 %
Low tech = <1%
But this only indicates R&D performance, it does not reflect use of science, non-R&D inputs, technology flows etc. By this criterion food is a low tech sector, when actually it is strongly science using.

9. Patents
A patent is a grant of monopoly use of a discovery, usually for a period of 17 years
The discovery must be an advance in the state of the art, and non-obvious
Problems: patents are only rarely taken into use. Their economic value usually varies enormously. Very few firms patent. Research shows that patenting is not a strong method of appropriation.

10. Bibliometric data
Data on scientific publication and citations (publications from ‘World of Science’, citations from Science Citation Index)
Widely collected and widely available by field
‘High Impact’ publications are in the top 1 percent of highly cited publications
Can map relative national performance, filed changes, international collaboration
Can indicate surprising changes in world patterns

11. Innovation indicators
Emerged in 1980s as researcher-driven exercises in France, Germany, USA, Italy, Scandinavia
Development of OECD ‘Innovation manual’ (the ‘Oslo Manual’) in early 1990s
First Community Innovation Survey 1992

12. The Community Innovation Survey
Covers:
Direct outputs of innovation – sales from new and technologically changed products
Inputs – R&D, design, marketing, training, acquisition of licences etc
Collaboration – partners and locations
Sources of information
Incentives and Obstacles

13. CIS Now implemented six times, currently every two years
Funded and overseen by European Commission (Eurostat in Luxembourg)
Frequently revised by R&D and Innovation working party – covers sampling and collection methodologies
Also collected in Canada, Australia, China, India, Brazil, Russia, South Africa.

14. Main CIS results – what did we learn
Innovation drives growth – the CDM model
Much weaker role of R&D than expected
Pervasiveness of innovation – especially in ‘low tech’ sectors
Asymmetry in innovation performance
Central role of collaboration
Characteristics of highly innovating firms (distributed across all sectors)

15. Publications using CIS

16. Publications and versions of CIS