R&D and Innovation Statistics, challenges of interpretation
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R&D and Innovation Statistics, challenges of interpretation



Presentation for Elsievier workshop for librarians regarding the use of indicators in research policy development.

Presentation for Elsievier workshop for librarians regarding the use of indicators in research policy development.



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R&D and Innovation Statistics, challenges of interpretation R&D and Innovation Statistics, challenges of interpretation Presentation Transcript

  • A story about research policy and numbers On research assessment Per M. Koch Director for Analysis and Strategic Development The Research Council of Norway 6th Elsevier Nordic Librarian Forum, Helsinki, Nov 6th 2008
  • Research and innovation policy assessment for librarians
    • Universities, colleges and research institutes are policy organizations in their own right
    • Politicians, civil servants, industrialists and NGO staff ask for such information
  • A lesson in how to wake up a policy maker
  • Lesson 1: Give her a simple story that anybody can understand
  • The linear model works perfectly in a policy setting Research and technological development in universities, RTOs and companies gives birth to an idea and relevant new knowledge Companies make use of these ideas in the development of new products and processes The company brings the new product to the market
  • Why?
    • It is a good story:
      • The unselfish scientist striving for the common good
      • It is simple: Give us the money and we will give you the results
      • By black boxing the whole process it communicates easily.
        • You don’t need to know how the computer works to make use of it
        • It works well with many of the traditional macroeconomists
  • The systemic model of learning and innovation is not easily reduced to a one minute sound bite Knowledge of customer and market needs In-house learning market pull New or improved products, processes or services User input Marked knowledge Tacit knowledge Acquired technology Literature Conferences and fairs New employees Commissioned R&D In-house R&D
    • Rogers 1995
    • Klein
    • Havelock/Przybylinski
    • … because social reality is very complex
  • Lesson number 2: Policy makers are number fetishists
  • The policy game is based on indicators
    • Numbers make things true
      • There are limits to how much info policy makers can absorb, and they have to make a leap of faith
      • Since Newton, mathematics is understood as the best tool for describing the world. Numbers give legitimacy
      • Economists are powerful in this field, and their science ideal is physics
  • The number game part 2
    • 2. You can choose the numbers that proves your point
      • (and disregard the rest)
      • Compare government budget meeting memos from various ministries
    • 3. You can use the lack of indicators to kill a competitor
      • “ It has not been documented that this policy has an effect…”
  • The numbers game part 3
    • Politicians and policy makers know the system. To get something done, you need a concrete commitment, and guess what?
      • You need a number!
    3% objective
  • Policy makers are not stupid
    • But they know that they will have to make decisions based on less that perfect information.
    • They have to persuade!
    • Numbers make you look good.
    • There is a whole advisory industry that thrives on numbers.
      • Experts inside government
      • Consultants and researchers
  • Cynical? Not me!
    • This is the setting in which indicators are used.
    • There is no point in denying it.
    • There remains more than enough policy-makers and politicians that are genuinely interested in how the world really works!
  • How to Thomas Kuhn STI indicators
    • As long as the indicators do not diverge to much from “the facts on the ground” a “story” might survive.
    • When the epicycles become too complex the story becomes a political burden instead of a useful tool.
    • Then there is a need for a new “story” – a new interpretation of the numbers.
  • The Norwegian puzzle, a case study
  • The Norwegian Paradox Low R&D Investements Extreme wealth creation 1,9% 3.5% 2.4% 3.9% 1.5% R&D/GDP 2005 7,7% 110 FI 7,9% 3,9% 7,0% 3.5% Unem-ployment 2006 100 122 115 172 GDP per capita 2005 EU25 DK SE NO
  • The Swedish and Norwegian paradoxes
    • Sweden
      • Huge investments in R&D and high tech companies do not lead to more innovation, productivity or profit
    • Norway
      • Seemingly low R&D investments
      • Nevertheless the richest and most productive country in Europe
  • EU Innovation Scoreboard indicates a poor innovation capacity Kilde: EU Innovation Scoreboard, 2004 Summary Innovation Index, 2004 EUs innovasjonsindeks 2004 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9
  • But Norway has an extremely high productivity EU 25 Finland EU 15 USA Luxem- burg Norway with oil Sweden Mainland Norway Denmark
    • There seems, in fact, to be no clear correlation between national R&D investments and economic growth.
  • So now what do we do?
    • We are convinced R&D in a narrow sense and learning in a broad sense explains growth.
    • But where is the convincing story that makes sense to our policy audience?
  • The Research Council of Norway and Innovation Norway have brainstormed a lot!
    • We are slowly developing a new story that makes sense to us, working with researchers and ministries
    • But the new story brings up new questions with no answers
    • This is not a unique phenomenon. It seems the consequences of a systemic approach to learning and innovation is finally catching up with us.
    (Vinnova of Sweden is also taking part)
  • The main explanation
    • OECD and EIS indicator sets are misused both by the OECD and the EU and national governments.
    • The need for benchmarking leads to the idea of “best practice” and model countries (Finland, US, Ireland and Israel)
    • The heterogeneity of national innovation systems is ignored
    • Model based on old fashioned health care. One treatment fits all.
  • Countries may vary a lot as regards industrial structure
    • Sweden and Finland has some large high tech companies that explain the R&D investment levels
      • Nokia, Ericsson, Volvo, ABB)
    • Norway and Iceland are dominated by traditional industries that do not normally invest much in R&D
      • Fisheries, aquaculture, oil and gas
    • Denmark is somewhere in between
    Note also that the Norwegian GDP is extremely large
  • A nagging suspicion
    • Companies may not fully have grasped the Frascati manual.
    • The understanding of basic concepts like research, (experimental) development and innovation may vary from
      • Industry to industry (e.g. manufacturing vs. services)
      • Company to company (different reporting practices)
      • Country to country (different incentives, e.g. tax)
      • The main problem: Developmental work “That’s just what we do!”
  • Norway EU Innovation Scoreboard
  • We need to shift from a technology push perspective to a vision of learning
  • The 3 percent objective is based on a linear model
    • Increase national investments in R&D
    • Produce more knowledge
    • Apply that knowledge to industrial production
    • Result: innovation
    • and wealth creation
    There is nothing wrong in having increased investments in R&D as a policy objective, but here it is used as a proxy for innovation
  • Focus on other aspects of learning
    • R&D embedded in technology and human capital
    • Design
    • Branding
    • Organisational change
    • Management practices and types of ownership
  • A new focus on research as a learning tool
    • The effects of R&D products on the innovation system
      • Company profits as result of sales of new or improved products, processes or services
      • Spillover effect 1: the new products leads to increasing productivity among customers
      • Spillover effect 2: the new products leads to innovation among customers and suppliers
    • The effects of R&D on learning in the innovation system
      • Research builds competences that can be used to absorb knowledge and technologies developed elsewhere
      • Research may lead to network building
      • University research teaches students how to use the tools of science
  • Do we have indicators for learning?
    • The numbers of students
    • The size of faculty
    • The number of Ph.D’s
    • The size of R&D budgets
    • These are all input-indicators that say nothing about outcome.
  • What about research output indicators?
    • Publications
    • Citations
    • These are important as they are measures of research productivity and quality,
    • But they are not measuring the effects this research has on society
            • Different publication practices in different disciplines
            • Publish or perish hysteria
            • Bottom-up perspective on research
  • Indicators for the interaction with society
    • Patents
    • Licenses
    • Co-publication
    • Popularization and communication
    • Income from commissioned research
    • Collaboration agreements
    • Participation in the EU Framework Programme
    • These are measures of interaction, but not of the effect on society.
  • We have to connect research input to social, economical and environmental output
    • Now research is seen as one of many “deserving causes”, and it lose out if set up against health, poverty or the environment.
    • Have to communicate that research is part of the solution of major social challenges.
    • One example: Connect company R&D investments with accounting data
  • The Nordic advantage: The autonomous workers make the bumblebees fly
    • The Nordic advantage: staff that solves problems independently and finds competences outside the organization.
    • Connected to educational level and research as a learning tool.
    • (Source: Åge Mariussen, NIFU STEP)
  • Concepts like “high tech” and “low tech” makes little sense
  • Understanding innovation in resource based industries
    • Resource-based industries may be knowledge-intensive and profitable, but not R&D intensive
      • Farming, aquaculture fisheries, petroleum and mining
    • The word “high tech” is misleading, as it refers to R&D as a percentage of company turn-over, and not to the company’s use of advanced technology and knowledge
  • Low tech, but knowledge intensive
    • A high tech company is per definition a company that invests much in R&D.
    • Norwegian petroleum companies and Swedish and Finish mining companies do not invest much in R&D, but they do make use of advanced technologies) and they do employ highly competent engineers
      • A lot of process innovation
      • Companies make use of other forms of innovation: branding, marketing
      • Little R&D per company, but branches of industry as a whole may invest much (food)
      • Oil- and gas is defined as high tech, but is in fact veeeery advanced
  • Indirect use of research
    • MS Kristian With Refrigeration/containership built by Vaagland Båtbyggeri AS on the North-West Coast of Norway
      • Argon AS has installed the electronics
      • Radar, satellite phone and TV-antenna delivered by ProNav
      • Sonar, logg, radio and electronic map systems delivered by Furuno
      • Gyro compass and autopilot delivered by Simrad
    The advanced technology has been “black-boxed”. You do not need to know how to build a TV to watch the Simpsons.
  • We need to get a better understanding of the role of services
    • The largest part of the economy
    • A very heterogeneous sector
    • A residual factor (what’s left when we leave out manufacturing and food)
    • We need a new categorization
    • Until now: Industrial policies have been focused on manufacturing, even if 70 percent are employed in services
  • Important service sectors
    • B2C services: non-R&D companies, including retail, people care and tourism
    • Retail: Innovation in transport, storage, delivery and customer care
    • Tourism: Innovation in product range, presentation, transportation and marketing
  • Services as partners and knowledge providers for industry
    • Advanced knowledge providers, e.g. R&D intensive B2B ICT companies (Knowledge intensive business services)
    • These companies may compensate for the lack of R&D in others.
    • Low R&D measurements in some industries may be an effect of outsourcing
  • Understanding the effects of public sector innovation
    • Innovation in the private sector is understood as an investment, in the public sector as an expense
    • Innovation in the public sector and the effect on industry
    • Public/private learning arenas
    • Public procurement
    • The effect of social welfare on risk taking and company behavior
    • We have no output indicators!
  • The Copenhagen Manual
    • New Nordic project aimed at developing a statistics manual for public sector innovation
      • Danish Agency for Science, Technology and Innovation, Denmark
      • Danish Centre for Studies in Research and Research Policy, CFA,
      • NIFU STEP, Norway
      • RANNIS, Iceland
      • Innovation Norway, Norway
      • The Research Council of Norway
      • DAMVAD, Denmark
      • Statistics Finland
      • Statistics Norway
      • Statistics Denmark
      • Statistics Sweden
      • In cooperation with:
        • OECD NESTI
        • NESTA UK
  • Company Learning Networks innovation Customers and users Suppliers Policy- institutions Financial Institutions R&D Institutions Consultants Public policy Cultural framework International framework Industrial structure Understanding competence flows in the innovation system
  • The role of competence flows
    • The role of education
    • User-driven innovation
    • Customer/supplier relationships
    • “ Open innovation” and industry collaboration
    • The role of KIBS
    • The role of public sector institutions
    National innovation systems must have porous boundaries. The EU is not competing with the US or Japan. This is not a zero-sum game!
  • Understanding the heterogeneity of innovation systems
    • Norway, Sweden, Iceland, Finland, the Netherlands, Bravaria, Catalonia, Northern Italy and the UK have all successful innovation systems that produces wealth.
    • But they are all totally different from each other.
    • Unique historical trajectories.
    • Unique socio-cultural framework conditions
    • There is no best practice.
    • Finland and Ireland cannot be used as models for other countries!
  • Understanding socio-cultural framework conditions
    • Stable macro-economic framework conditions
      • Disciplined fiscal policy
      • Competition policy encouraging innovation
      • Low taxes
      • An open economy
    • Socio-cultural framework conditions
      • Egalitarian culture with high social mobility
      • High wages for blue collar work gives impetus towards innovation (robots, internet banking)
      • High educational levels brings flexibility and labour mobility
      • An efficient public sector helps industry
      • A trustworthy welfare system reduces risk
      • Political and social stability engender trust
  • Where are we now?
    • The 2008 report on Norwegian innovation tells the new story in full: There is no paradox!
    • Increasing consensus: The Nordic success is competence based.
    • DG Enterprise and DG Research are working on more new nuanced interpretations of indicators
    • White papers in all the Nordic countries try to take the heterogeneity and complexity of innovation into consideration
  • Online R&D statistics and research and innovation policy information
    • Cordis http://cordis.europa.eu/indicators/
    • Pro Inno Europe http://www.proinno-europe.eu
    • Eurostat http://epp.eurostat.ec.europa.eu
    • OECD http://tinyurl.com/6dt2qn
    • Erawatch http://cordis.europa.eu/erawatch/
    • Worldbank: http://www.worldbank.org/kam
    • A Norwegian portal for R&D policy info: www.forinn.no