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

1. 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)

2. OECD Smart Specialization ProjectStep 1: Constructing the BaselineQuantitative Baseline Profiles

3. 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

4. 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)

5. South Korea • Jeolla (KR04) • Spain • Pais Vasco (ES21) • Andalusia (ES61) • Murcia (ES62) • Turkey • East Marmara (TR42) • UK • West Midlands (UKG)

6. Specialisation indicators deployed for data on scientific research

7. Measures of national and regional specialisation

8. 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

9. Specialisation indicators deployed for data on technology

10. 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!

11. 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

12. Measures of national and regional specialisation:

13. Economic specialisation indicators

14. 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)

15. Measures of national and regional specialisation:

16. Results per country / region

17. 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.

18. Austria Scientific profile according to the Activity Index Data source: Thomson Reuters Web of Knowledge

19. Lower AustriaScientific profile (accordingto the Activity Index) Data source: Thomson Reuters Web of Knowledge

20. 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

21. 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

22. 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

23. Lower AustriaTechnology profile:

24. Austria

25. Lower Austria

26. 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

27. Austria Data source: OECD

28. Lower Austria Data source: OECD

29. 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

30. Austria Scientific profile according to the Activity Index Data source: Thomson Reuters Web of Knowledge

31. Upper AustriaScientific profile (accordingto the Activity Index) Data source: Thomson Reuters Web of Knowledge

32. 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

33. 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

34. 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

35. Upper AustriaTechnological profile:

36. Austria

37. Upper Austria

38. 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.

39. Austria Data source: OECD

40. Upper Austria Data source: OECD

41. Upper Austria Observations, economic profile • Top 3 highest and lowest specialisations • Highlights • Specialisations and under-specialisations are relatively stable over time

42. General observations

43. 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

44. 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

45. 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

46. Case-studies for Flanders:Lessons from the baseline analysis

47. 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

48. 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?

49. Nano-electronics for Health – Impact (ex.)

50. Nano-electronics for Health – Impact (ex.)