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 licences etc 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)

Publications using CIS

Publications and versions of CIS