innovation measurement keith smith imperial college london/tik oslo
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Innovation Measurement
Keith SmithImperial College London/TIK Oslo
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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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The background issues
Historically, 3 sources of data:• R&D• Patents• Bibliometric
Each has more or less serious problems as innovation indicators
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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
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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
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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%’)
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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)
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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.
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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.
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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
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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
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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
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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.
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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)
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Publications using CIS
1994 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
0
50
100
150
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250
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350
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0
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4 9 14 20 27 34 5081
100140
165 182211
261
316
366
427
Academic papers (in English) using Community Innovation Survey data (1994-2011)
Cumulative ...
Publication year
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of
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Publications and versions of CIS
0
5
10
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35
40
1994 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011Nu
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Publication year
Use of each CIS version over time
CIS 1
CIS 2
CIS 3
CIS 4
CIS 2006
CIS 2008