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

1. OECD Smart Specialization ProjectFeedback on the complete ProjectMay 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. Project OutlineA refresher overview

3. Objective • This (pilot) project aims at identifying good practices in policy development, methodologies and selection criteria for designing and assessing smart specialisation strategies

4. Expected outputs • Indicator-based specialisation profiles of the countries and regions involved in the project, as a tool for strategic monitoring • Strategic governance profiles of the countries involved in the project, allowing countries to benchmark their capacity for managing the 'discovery' of smart specialisations, and their policies to promote smart specialisation strategies • One or two case studies per country, enabling an in-depth analysis of real-life experience in policies and governance mechanisms for developing and building smart specialisation strategies • A final report with • Insights on good methodological practices for designing and monitoring smart specialisation strategies • A self-assessment tool for ‘upgrading’ existing strategies

5. Project design • Learning loop over 16 months: a ‘discovery process’ for advanced policy development • Baseline: present specializations and governance capabilities • Step 1: construction of specialization profiles consisting of quantitative indicators for the countries involved in the project • Step 2: the development of a template for the presentation of qualitative information on governance mechanisms and policies supporting smart specialization • Step 3: the construction of strategic governance profiles for the countries involved in the project • Beyond the baseline: strategy development as discovery process • Step 4: one or two case studies per country involved in the project: • Representing real-life experiences in developing and building smart specialization strategies in clusters, in order to deepen the understanding of governance mechanisms, policies and methods for strategy development for future ‘smart’ specialization • With a focus on the interaction between national and regional policy levels to support smart specialization

6. Step 1: Indicator-based specialisation profiles(deadline: December 2011) • To develop a standard model for assessing specialisations along the innovation trajectory, by means of presently available databases: • Possible indicators: • Inputs (education, investment in R&D and innovation) • Outputs (scientific publications, citations and patents) • Economic activities (employment, value-added, exports) • Critical points include the choice of the categories (such as economic sectors, scientific disciplines, technology domains, …) and the way the different category types can be cross-linked to one another (e.g. as nodes in an innovation trajectory) • To draw a comparative picture on the relative specialisation of ± 10 pilot countries and regions • Multi-level approach: • Focus on the relation between national specialisation profiles for the participating countries, and regional profiles that can identify the clusters of specialisation in the case-studies • Include cross-border regional profiles • Specialisation profiles can be constructed for any administrative region, going to NUTS 3 level

7. Step 2: Strategic governance profiles: template (deadline: December 2011) • Strategic intent and leadership of dominant actors, such as leading companies or research institutes • Priority setting that is taking place (both explicitly and implicitly) • Strategic governance of cluster policies • Existence of specific governance capabilities (e.g. foresight) • Actions for appropriate framework conditions (including ‘quality of life’ and “sustainable growth & development” in urban environments) • Possible legal mechanisms that are deployed in support of cluster policies • Policy learning cycles • …

8. Step 3: Strategic governance profiles: data and policy learning (deadline: June 2012) • Countries and regions involved in the project will fill out the template, using: • Data in existing policy monitoring instruments (e.g. ERAWATCH, RIM, etc.) • Own information • Benchmarking profiles and policy learning on bottlenecks • Facilitated trough OECD STI Platform

9. Step 4: Case-studies (deadline: June 2012) • One or two case-studies per country involved in the project • Presenting real-life experiences in developing and building smart specialisation strategies in clusters and at the regional level, each in their specific country setting • Including different types of strategies: • Retooling or modernizing existing specialisations with new knowledge inputs • Transforming existing specialisations into new (smart, inclusive and sustainable) growth regimes • Diversification into new specialisations • Foundation of new specialisations from new knowledge creation • Some specific points of interest: • Cross-border clusters (functional regions) and international networks of cluster nodes • Combination of top-down and bottom-up management mechanisms required for acceleration of economic restructuring towards a new growth regime driven by ‘smart’ specialisations • Role of flagship companies and institutes to ‘brand’ a region to attract focused investments • Alignment between the different levels of regional, national and international governance • Role of shared Foresight and the use of early warning technology watch • Framework: • Cases can be developed according to an ‘smart specialisation strategy matrix’ articulating the regional competence fields (technology platforms) with the global societal and economic challenges (new markets and value chains)

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

11. 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

12. Introduction

13. Introduction to baseline data: the road ahead … • Using robust, existing data sources with benchmark potential: • WoS • Patent databases (EPO, USPTO, PCT) • CIS & R&D surveys • (Regional) economic data (employment, added value, export, …) • Using robust indicators such as: • Activity index • Relative specialisation index • Salton cosine measures • Robust classification systems --- that may differ though between science (journal classification), technology (patent classification) and economic data (sector classification) • Using those indicators: • Longitudinally and across consistent time periods • Focusing on relative advantages and disadvantages of countries and regions

14. 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)

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

16. Specialisation indicators deployed for data on scientific research

17. Measures of national and regional specialisation

18. Properties of the Activity Index: • AI may take values in the range [0, ]. • Its neutral value is 1. • AI= 0 indicates a completely idle research field. • AI< 1 indicates a lower-than-average activity. • AI> 1 a higher-than-average activity. AI reflects a certain internal balance among the fields: • AI> 1 values in some fields is always balanced by AI < 1 in others.

19. The successful application of this index strongly depends on the underlying subject classification system, notably on its granularity. If a multi-level hierarchical scheme is used, then AI allows for zooming in on the broader fields. The Budapest–Leuven classification scheme (Glänzel et al., 2003) is used in this project. This scheme is hierarchically structured and comprises the 12 major fields, 60 sub-fields and 170 disciplines in the sciences. The disciplines are identical with the JCR Subject Categories of Thomson Reuters.

20. The science classification scheme comprises the following 12 major fields: A: Agriculture & Environment Z: Biology B: Biosciences R: Biomedical research I: Clinical & Experimental Medicine I (General & Internal Medicine) M: Clinical & Experimental Medicine II (Non-Internal Medicine Specialties) N: Neuroscience & Behaviour C: Chemistry P: Physics G: Geosciences & Space Sciences E: Engineering H: Mathematics

21. The scheme allows for zooming in on each level, for instance:

22. The basic idea of the method applied here • Those major fields are identified, where the highest relative specialisation is observed. • Then the same indicator is used to zoom in on these major fields in order to identify outstanding relative activity in discipline at the lowest hierarchical level within the selected major field. • In addition, those subjects are selected, which are not sub-disciplines in high-activity research fields, but reflect considerablespecialisation within the corresponding main area. • Special attention is paid to increasing specialisation. • Underlying the data and indicators, lead institutions can be identified (not yet reported here).

23. 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

24. Specialisation indicators deployed for data on technology

25. 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). • In the current presentation, we focus on EPO application data; USPTO grant data (only on country level) and WO application data will be reported in the full report (the USPTO results run parallel to the EPO results, though).

26. 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

27. Measures of national and regional specialisation:

28. Economic specialisation indicators

29. 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)

30. Measures of national and regional specialisation:

31. Results per country / region

32. 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.).

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

34. Australia Specialisation within the science fields with the highest relative activity (AI values are given in chronological order) geosciences & space sciences (G) oceanography (AI=1.35; 1.45; 1.60) geography (AI=1.24; 1.04; 1.47) mineralogy (AI=1.88; 2.05; 1.70) clinical and experimental medicine II (non-internal medicine specialties) nursing (AI=1.43; 1.38; 2.02) rehabilitation (AI=1.51; 1.86; 2.12) health care sciences & services (AI=1.34; 1.35; 1.84) psychiatry (AI=1.33; 1.35; 1.56) emergency medicine (AI=0.76; 0.74; 1.34) gerontology (AI=0.93; 0.98; 1.51) health policy & services (AI=0.93; 0.81; 1.54) neuroscience & behavior psychology (AI=1.37; 1.51; 1.45) substance abuse (AI=1.77; 1.97; 1.77) Data source: Thomson Reuters Web of Knowledge

35. Australia Subject Categories of scientific specialisation outside the ‘focus fields’ (according to AI) Legend: GU: ecology; HT: evolutionary biology; JU: fisheries; OU: limnology; PI: marine & freshwater biology; PT: medical informatics; QH: materials science, composites; RE: mineralogy; YQ: transportation Data source: Thomson Reuters Web of Knowledge

36. Australia • Striking observations, scientific profile: • General trends • Increase of relative activity in neuroscience & behaviour; clinical and experimental medicine II (non-internal medicine specialties) • High specialisation in geosciences & space sciences • Decrease of relative activity in mathematics • Highlights • In the ‘focus fields’: Increase of specialisation in nursing; rehabilitation; gerontology; health care sciences & services and related specialties • Outside the ‘focus fields’: Enormous increase of specialisation in transportation and medical informatics

37. Australia Technology profile:

38. Australia

39. Australia • Observations, technology profile: • Top 3 highest and lowest specializations • Highlights • Top domains in terms of patent volume: Pharmaceuticals and Medical technology. • Patent volume ‘peaks’ for Computer technology and Textile & paper machines in 2000. • Textile and paper machines: specialization in 1998-2005; decrease towards under-specialization in recent period 2006-2009. • Other domains stay relatively stable in terms of specialization / under-specialization for the considered time period.

40. Australia • No sectoral OECD employment data found yet on Australia --- TBC.

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

42. Austria Specialisation within the science fields with the highest relative activity (AI values are given in chronological order) • geosciences & space sciences (G) • astronomy & astrophysics (AI=1.25; 1.30; 1.42) • geography, physical (AI=0.91; 0.96; 1.30) • mineralogy (AI=3.38; 2.85; 2.62) • biology (organismic & supraorganismic level) (Z) • mycology (AI=1.60; 2.22; 1.91) • biosciences (general, cellular & subcellular biology; genetics) (B) • evolutionary biology (AI=1.43; 1.69; 1.78) Data source: Thomson Reuters Web of Knowledge

43. Austria Subject Categories of specialisation outside the ‘focus fields’ (according to AI) Legend: AQ: allergy; DS: critical care medicine; EW: computer science, software engineering; EX: computer science, theory & methods; KA: forestry; PJ: materials science, paper & wood; PT: medical informatics; QF: materials science, characterization & testing; RQ: mycology; RX: neuroimaging; RY: nuclear science & technology; WH: rheumatology Data source: Thomson Reuters Web of Knowledge

44. Austria • Striking observations, scientific profile: • General trends • Increase of relative activity in biology (organismic & supraorganismic level); neuroscience & behaviour, and agriculture & environment • High specialisation in biosciences (general, cellular & subcellular biology; genetics) • Highlights • Enormous increase of specialisation in geosciences & space sciences (as field) and materials science, paper & wood (as subject category) • Very high specialisation in mineralogy (in the ‘focus fields’), in allergy; materials science, paper & wood, and medical informatics (outside the ‘focus fields’)

45. AustriaTechnology profile:

46. Austria

47. Austria • Observations, technology profile • Top 3 highest and lowest specialisations • Highlights • Pharmaceuticals and Civil Engineering: top in terms of patent volumes (per capita). • Pharmaceuticals peak around the period 2000-2003, dropping again from 2006 onwards – Translates into specialisation in the same period. • Specialisation patterns for other domains: relatively stable over time … • … except: Analysis of biological materials and Textile and paper machines (both developing towards specialisation after 2005) – and Engines, pumps, turbines (decreasing towards under-specialisation since 2005).

48. Austria Data source: OECD

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

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