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

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Innovation measurement

Innovation Measurement

Keith Smith

Imperial College London/TIK Oslo

Why do we need data
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

The background issues
The background issues

Historically, 3 sources of data:

  • R&D

  • Patents

  • Bibliometric

    Each has more or less serious problems as innovation indicators

Problems with existing indicators
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

Research and development data
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

R d indicators
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%’)

Problems with r d intensity indicator
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)

R d and high tech sectors
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.


  • 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.

Bibliometric data
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

Innovation indicators
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

The community innovation survey
The Community Innovation Survey


  • Direct outputs of innovation – sales from new and technologically changed products

  • Inputs – R&D, design, marketing, training, acquisition of licencesetc

  • Collaboration – partners and locations

  • Sources of information

  • Incentives and Obstacles

Innovation measurement

  • 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.

Main cis results what did we learn
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)