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OECD Smart Specialization Project Feedback on the complete Project – STATUS May 10-11, 2012 --- P aris. ECOOM KU Leuven & EWI W. Glänzel , B. Thijs (ECOOM) J. Callaert , M. du Plessis (ECOOM) P. Andries (ECOOM) K. Debackere (ECOOM) J. Larosse (EWI) N. Geerts (EWI).

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OECD Smart Specialization Project Feedback on the complete Project – STATUS May 10-11, 2012 --- P aris

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OECD Smart Specialization ProjectFeedback on the complete Project – STATUSMay 10-11, 2012 --- Paris

ECOOM KU Leuven & EWI

W. Glänzel, B. Thijs (ECOOM)

J. Callaert, M. du Plessis (ECOOM)

P. Andries (ECOOM)

K. Debackere (ECOOM)

J. Larosse (EWI)

N. Geerts (EWI)


OECD Smart Specialization ProjectStep 1: Constructing the BaselineQuantitative Baseline Profiles


Structure of the baseline presentation

  • Introduction

  • Specialisation in scientific research

  • Specialisation in technology

  • Economic specialisation

  • First results

    • Specialisation of countries and regions

  • Case-study for Flanders

  • First conclusions

  • Further steps and future tasks


Data and indicators are determined for the following eleven countries and fourteen regions:

  • Australia

  • Austria

    • Lower Austria (AT12)

    • Upper Austria (AT31)

  • Belgium

    • Flanders (BE2)

  • Finland

    • Etela-Suomi (FI18)

  • Germany

    • Berlin (DE3)

    • Brandenburg (DE4)

  • Netherlands

    • South Netherlands (NL4)

  • Poland

    • Malopolska (PL21)


    • South Korea

      • Jeolla (KR04)

    • Spain

      • Pais Vasco (ES21)

      • Andalusia (ES61)

      • Murcia (ES62)

  • Turkey

    • East Marmara (TR42)

  • UK

    • West Midlands (UKG)


  • Specialisation indicators deployed for data on scientific research


    Measures of national and regional specialisation


    Data sources:

    • Data of Thomson Reuters’ Web of Science (WoS) are used.

    • Only original research work and review articles were extracted from the database.

    • A full counting scheme was applied to country, region and institutional assignment.

    • The observation period comprises 13 years and is subdivided into the following sub-periods:

      • 1998–2002

      • 2003–2006

      • 2007–2010


    Specialisation indicators deployed for data on technology


    Measures of national and regional specialisation:

    • Technological specialisation is studied using patent-based indicators, broken down by:

      • Country / Region (based on applicant addresses)

      • Technology domain (Fraunhofer classification into 35 domains)

      • Application years (1998-2001; 2002-2005; 2006-2009)

      • Patent system: EPO – USPTO - PCT

    • Full counting schemes are used for allocation to countries, regions and technology domains.

    • Data source: PATSTAT database (EPO Worldwide Patent Statistical Database, version October 2011).

    • Focus on EPO + PCT application data; USPTO grant data (only on country level). EPO & PCT lead to similar results!


    Measures of national and regional specialisation:

    • Relative specialization indicators are typically used:

      • RTAij = (Pij/SiPij)/(SjPji/SijPij)

        • with P the number of patents

        • with i = country or region grouping variableand j = patent IPC-class grouping (technological domain or industrial sector)

        • value of 1 = benchmark group average

        • various mapping possibilities (RCA - RTA or RTA over different periods, …) exist


    Measures of national and regional specialisation:


    Economic specialisation indicators


    Measures of national and regional specialisation:

    • National economic specialisation is usually studied using export data or production output, broken down by NACE sector.

    • However, data not available at the regional level.

    • Most appropriate available data are OECD’s regional labour market statistics:

      • Available for selection of countries and regions

      • Aggregated in 32 industries (not all industries represented)


    Measures of national and regional specialisation:


    Results per country / region


    Presentation of results:

    • Results are organised by countries and – within individual countries – by regions.

    • Results consistently presented for three considered time periods (1998–2001 / 2002–2005 / 2006–2009).

    • Research and technology specialisation are presented separately.

    • Research specialisation:

      • By major fields with high specialisation

      • By disciplines within fields of high activity

      • By disciplines with high specialisation in other fields

    • Technological specialisation:

      • Evolution (1998-2009) of the number of patents per million inhabitants (EPO patents) for the top 10 technological domains in each country

      • Radar plots of the RTAN values for the 35 Fraunhofer technological sectors (EPO patents)

    • Economic specialisation:

      • Radar plots of the RCAN values for 32 industries

    • Striking observations are summarised.

    • NOTE: underlying those results is a wealth of rich data that are not reported in this presentation but that are available (e.g. lead institutions, etc.) per country/region.


    Austria

    Scientific profile according to the Activity Index

    Data source: Thomson Reuters Web of Knowledge


    Lower AustriaScientific profile (accordingto the Activity Index)

    Data source: Thomson Reuters Web of Knowledge


    Lower AustriaScientific profile (accordingto the Activity Index)

    Specialisation within the science fields with the highest relative activity (AI values are given in chronological order)

    • Agriculture & Environment (A)

    • Environmental Sciences (AI=1.49; 1.28; 1.62)

    • Environmental Studies (AI=2.27; 1.90; 2.14)

    • Biology (organismic & supraorganismic level) (Z)

    • Ecology (AI=1.20; 1.86; 2.27)

    Data source: Thomson Reuters Web of Knowledge


    Lower AustriaScientific profile (accordingto the Activity Index)

    Subject categories of specialisation outside the ‘focus fields’ with the (AI values are given in chronological order)

    Legend: CO: Biochemical research methods; CU: Biology; HT: Evolutionary Biology; VY: Radiology, Nuclear Medicine & Medical Imaging

    Data source: Thomson Reuters Web of Knowledge


    Lower AustriaScientific profile

    • Striking observations:

    • General trends

      • Low scientific output activities

      • High specialisation Agriculture and Biology but with decreasing AI

    • Highlights

      • In the ‘focus fields’: Specialism in Environmental Sciences and Studies and in Ecology

      • Outside the ‘focus fields’: Specialism in three related fields: Biomedical Research Methods, Biology and Evolutionary Biology. And an increasing specialism in Radiology and medical imaging.

    Data source: Thomson Reuters Web of Knowledge


    Lower AustriaTechnology profile:


    Austria


    Lower Austria


    Lower Austria

    Observations, technology profile

    • Top 3 highest and lowest specialisations

    • Highlights

      • Civil engineering top domain (in terms of patent volume but also specialisation) over whole period, with patent volume peaking around 2005-2006.

      • Specialisation patterns relatively stable over time, but:

        • Increasing level of under-specialisation for Optics, Semiconductors as well as Engines, pumps and turbines

        • A previously outspoken under-specialisation for Analysis of biological materials


    Austria

    Data source: OECD


    Lower Austria

    Data source: OECD


    Lower Austria

    Observations, economic profile

    • Top 3 highest and lowest specialisations

    • Highlights

      • Recent data missing for several sectors

      • Specialisations and under-specialisations appear relatively stable over time


    Austria

    Scientific profile according to the Activity Index

    Data source: Thomson Reuters Web of Knowledge


    Upper AustriaScientific profile (accordingto the Activity Index)

    Data source: Thomson Reuters Web of Knowledge


    Upper AustriaScientific profile (accordingto the Activity Index)

    Specialisation within the science fields with the highest relative activity (AI values are given in chronological order)

    • Mathematics (H)

    • Mathematics, Applied (AI=1.76; 1.87; 1.67)

    • Physics (P)

    • Instruments & Instrumentation (AI=0.70; 1.07; 1.44)

    • Physics, Applied (AI=1.49; 1.59; 1.45)

    • Physics, Mathematical (AI=0.68; 1.20; 1.99)

    • Physics, Condensed Matter (AI=2.17; 1.83; 1.73)

    Data source: Thomson Reuters Web of Knowledge


    Upper AustriaScientific profile (accordingto the Activity Index)

    Subject categories of specialisation outside the ‘focus fields’ with the (AI values are given in chronological order)

    Legend: PZ: Metallurgy and Metallurgical Engineering; QG Material Sciences, Coatings & Films; ZA, Urology & Nephrology

    Data source: Thomson Reuters Web of Knowledge


    Upper AustriaScientific profile

    • Striking observations:

    • General trends

      • Rather low scientific output activities

      • High specialisation in Mathematics (Increasing) and Physics (decreasing)

    • Highlights

      • In the ‘focus fields’: Applied Mathematics and strong growth in Instruments and Instrumentation and mathematical physics. Applied Physics and Condensed Matter are still specialism but declining.

      • Outside the ‘focus fields’: Specialism in three fields: Two in chemistry: Metallurgy; Material Sciences, Coatings and Films and one medical discipline: Urology

    Data source: Thomson Reuters Web of Knowledge


    Upper AustriaTechnological profile:


    Austria


    Upper Austria


    Upper Austria

    Observations, technology profile

    • Top 3 highest and lowest specialisations

    • Highlights

      • High level of technological activity in Machine Tools over the whole period. Since 2006, also high activity levels in Civil Engineering and in Other special machines

      • High activity in Machinery-related fields also visible in the regional specialisation profile (and strong under-specialisation in Communication and IT related fields)

      • RTAN for Microstructure and nano-technology shows strong increase over time  from outspoken under-specialisation at the end of the nineties to modest level of specialisation by 2006-2009.


    Austria

    Data source: OECD


    Upper Austria

    Data source: OECD


    Upper Austria

    Observations, economic profile

    • Top 3 highest and lowest specialisations

    • Highlights

      • Specialisations and under-specialisations are relatively stable over time


    General observations


    What do we see?

    • Classification schemes in science – technology –economics are (only) partially convergent, however:

      • The baseline does reveal patterns of specialization at the level of science, technology and economic base that are quite finegrained taking into account the benchmarking needs --- 60 subfields / 170 disciplines (science), 35 Fraunhofer technology categories, 32 economic sectors

      • Observation of the three specialization axes (S-T-E) indicates that subsets of the S-T-E indicator base reveal patterns of alignment and non-alignment


    What do we see?

    • Classification schemes in science – technology –economics are (only) partially convergent, however:

      • Linking S-T-E configurations to 3S typology (radical foundation, transformation, diversification, modernization) is an opportunity

      • S-T-E indicators --- as described & highlighted --- should be used in an interactive, policy learning dialogue and should not be expected to direct a top-down agenda --- the classification methodology allows for such interactive approach, cfr. case studies


    What do we see?

    • Don’t forget:

      • Underlying the spider plots, there is a wealth of individual country/region data that can be made available to the countries/regions participating in the pilot study

      • Data on lead institutions and lead companies can be drawn from those underlying data

      • Alongside the relative positions countries/regions should also take into account their absolute positions in terms of S-T-E economic output


    Case-studies for Flanders:Lessons from the baseline analysis


    Case studies for Flanders

    Nano-electronics (for health)

    • Those subject categories have been chosen in which IMEC has published more that 10%* of its papers each in the period 2000-2009.

      • 45.2% engineering, electrical & electronic

      • 45.0% physics, applied

      • 19.7% physics, condensed matter

      • 19.5% materials science, multidisciplinary

      • 13.2% optics

        ______________

        * Note that multiple assignment is possible


    Case studies for Flanders

    Nano-electronics (for health)

    • In addition, Flemish scientific and technological output in the medical fields (including neurosciences) is high:

      • Above average specialization in clinical research & neuroscience research, as well as in medical informatics & electrical engineering

      • High and increasing RTAN values for biotechnology & pharmaceuticals, microstructure & nanotechnology

    • Hence: there is a strong and diverse basis of knowledge specialization in the area of nanotechnology for health --- but not (yet) translated into or aligned with an existing economic specialization or technology position.

    • Indicative of a “radical foundation” 3S?


    Nano-electronics for Health – Impact (ex.)


    Nano-electronics for Health – Impact (ex.)


    Case studies for Flanders

    • Sustainable chemistry – the case of FISCH:

      • Chemistry is a Belgian rather than just a Flemish specialization

      • Strong technology base in surface technology, macromolecular chemistry and polymers

      • Top economic sector in terms of chemical products and chemical manufacturing

    • Sustainable chemistry can build both on a strong economic and technology base. The scientific base is weaker.

    • Indicative of a “transformative” 3S?


    Step 2: The governance template


    Focal points – 1:

    • Topics:

      • Priority Areas for Research, Technological Innovation and Economic Development

        • Areas

        • Priorities --- Specializations

        • Alignment R --- TI --- ED Priorities

      • Priority Setting Process

        • Methods --- Discovery Processes

        • Involvement

      • Instruments and Budgets to Support Priorities

        • Budget level and allocation

        • Cluster policies

    • Two Template Levels:

      • National

      • Regional


    Focal points – 2:

    • Process:

      • Indentify spokesperson

      • Background information documents: RIM, ERAWATCH …

      • Template Preparation

      • Supportive and Interpretative interview to fill out Template

      • Feedback and corrective comments on filled out Template

      • January --- June 2012

    • Interviews are finished, first experiences available, cfr. clairenauwelaers presentation


    Step 3: the case studies for Flanders,see presentation Johan Van Helleputte (NfH) and Carl Vanderauwera (FISCH)


    Process steps to be taken further …


    Time-line:

    • First Workshop: development of smart specialisation profiles and templateforstrategicgovernance profiles (November 2011)

    • Step 1: Indicator-based specialisation profiles(December 2011)

    • Step 2:Strategicgovernance profiles: template(December 2011)

    • Second Workshop: discussion of first case studyresults(Spring 2012)

    • Step 3: Strategicgovernance profiles: data and policylearning(June 2012)

    • Step 4:Case-studies(June 2012)

    • Final Workshop: discussion of mainlessonsforfinal report (Winter 2012)

    • Step 5:Final report (December 2012)


    Thank you!Further discussion …


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