Oecd smart specialization project feedback on the complete project status may 10 11 2012 p aris
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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 project feedback on the complete project status may 10 11 2012 p aris

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 project step 1 constructing the baseline quantitative baseline profiles

OECD Smart Specialization ProjectStep 1: Constructing the BaselineQuantitative Baseline Profiles


Structure of the baseline presentation

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


Oecd smart specialization project feedback on the complete project status may 10 11 2012 p aris

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)


  • Oecd smart specialization project feedback on the complete project status may 10 11 2012 p aris

    • 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

    Specialisation indicators deployed for data on scientific research


    Measures of national and regional specialisation

    Measures of national and regional specialisation


    Oecd smart specialization project feedback on the complete project status may 10 11 2012 p aris

    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

    Specialisation indicators deployed for data on technology


    Measures of national and regional specialisation1

    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 specialisation2

    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 specialisation3

    Measures of national and regional specialisation:


    Economic specialisation indicators

    Economic specialisation indicators


    Measures of national and regional specialisation4

    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 specialisation5

    Measures of national and regional specialisation:


    Results per country region

    Results per country / region


    Oecd smart specialization project feedback on the complete project status may 10 11 2012 p aris

    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

    Austria

    Scientific profile according to the Activity Index

    Data source: Thomson Reuters Web of Knowledge


    Lower austria scientific profile according to the activity index

    Lower AustriaScientific profile (accordingto the Activity Index)

    Data source: Thomson Reuters Web of Knowledge


    Lower austria scientific profile according to the activity index1

    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 austria scientific profile according to the activity index2

    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 austria scientific profile

    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 austria technology profile

    Lower AustriaTechnology profile:


    Austria1

    Austria


    Lower austria

    Lower Austria


    Lower austria1

    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


    Austria2

    Austria

    Data source: OECD


    Lower austria2

    Lower Austria

    Data source: OECD


    Lower austria3

    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


    Austria3

    Austria

    Scientific profile according to the Activity Index

    Data source: Thomson Reuters Web of Knowledge


    Upper austria scientific profile according to the activity index

    Upper AustriaScientific profile (accordingto the Activity Index)

    Data source: Thomson Reuters Web of Knowledge


    Upper austria scientific profile according to the activity index1

    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 austria scientific profile according to the activity index2

    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 austria scientific profile

    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 austria technological profile

    Upper AustriaTechnological profile:


    Austria4

    Austria


    Upper austria

    Upper Austria


    Upper austria1

    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.


    Austria5

    Austria

    Data source: OECD


    Upper austria2

    Upper Austria

    Data source: OECD


    Upper austria3

    Upper Austria

    Observations, economic profile

    • Top 3 highest and lowest specialisations

    • Highlights

      • Specialisations and under-specialisations are relatively stable over time


    General observations

    General observations


    What do we see

    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 see1

    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 see2

    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:Lessons from the baseline analysis


    Case studies for flanders

    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 flanders1

    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.)


    Nano electronics for health impact ex1

    Nano-electronics for Health – Impact (ex.)


    Case studies for flanders2

    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

    Step 2: The governance template


    Focal points 1

    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

    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


    Oecd smart specialization project feedback on the complete project status may 10 11 2012 p aris

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


    Process steps to be taken further

    Process steps to be taken further …


    Time line

    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

    Thank you!Further discussion …


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