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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 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
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 • 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 • 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 • 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 • 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 • 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.
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.
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 • 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 Covers: • 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
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.
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)
