KIT Knowledge, Innovation and Territory ESPON 2013 Programme Internal Seminar Crossing Knowledge Frontiers - Serving the Territories November 2010 Liege, Belgium

The project team Lead Partner (LP): BEST, Politecnico di Milano, Italy: Project Coordinator: Prof. Roberta Capello (Full Professor in Regional Economics) Project Manager: Camilla Lenzi (Assistant Professor) Prof. Roberto Camagni (Full Professor in Urban Economics) Ugo Fratesi (Assistant Professor), and Andrea Caragliu (Post-Doc Fellow) Project Partner 2 (PP2): CRENOs, University of Cagliari, Italy: Prof. Raffaele Paci (Full Professor of Applied Economics) Francesco Pigliaru (Full Professor of Economics) and Stefano Usai (Associate Professor of Economics) Alessandra Colombelli (Post-Doc Fellow) Matteo Bellinzas (Research assistant) Project Partner 3 (PP3): AQR, University of Barcelona, Spain: Prof. Rosina Moreno (Full Professor in Applied Economics) Prof. Jordi Suriñach (Full Professor in Applied Economics) Prof. Raúl Ramos (Associate Professor in Applied Economics) Ernest Miguélez (Technical Researcher and PhD student)

The project team Project Partner 4 (PP4): LSE, Great Britain: Dr. Riccardo Crescenzi (Lecturer in Economic Geography) Prof. Andrés Rodríguez-Pose (Professor in Economic Geography) Prof. Michael Storper (Professor in Economic Geography) Project Partner 5 (PP5): University of Bratislava, Slovakia: Prof. Milan Buček (Full Professor in Regional Economics and Policy) Dr. Miroslav Šipikal (Coordinator - Senior Lecturer) Dr. Rudolf Pástor (Researcher) Project Partner 6 (PP6): University of Cardiff, Great Britain: Prof. Phil Cooke (Full Research Professor in Regional Economic Development) Julie Porter (Coordinator – Senior Researcher/Lecturer) Selyf Morgan (Researcher)

General Goal (1) To contribute to the understanding of: -diffusion processes of knowledge and innovation and -the socio-economic impacts of innovation and knowledge in space, by identifying the different “territorial patterns of innovation” in Europe. A territorial pattern of innovation is defined as a combination of context conditions and of specific modes of performing the different phases of the innovation process.

General Goal (2) The general phylosophy of the project is in line with the words of Danuta Hübner (2009): “Innovation is not considered as a linear process that starts with research, eventually leading to development, translated later into growth in the territories that have more capabilities. Instead, it is the product of a policy mix, including several bodies and stakeholders in which the territories, their specificities and conditions are paramount”.

General Goal (3) In our project: -> we do not look for the territorial capabilities that allow territories (in general) to exploit innovation and knowledge; -> instead, we look for territorial specificities (context conditions) that are behind different modes of performing the different phases of the innovation process through the identification of territorial patterns of innovation.

Requirements Requirements for achieving this goal: -a consistent database for the state of the art in innovation and knowledge; -comparison with the EU and national data; -identification of the most important inter-regional spillover mechanisms; -the identification of new development opportunities through innovation for Europe and its territories; -an inventive framework for a scientific answer to the policy questions.

Structure of the project B) Territorial elements explaining spatial trends. Different modes of innovation and knowledge creation and diffusion. A comparison with other regional knowledge economies in more advanced and emerging countries Output: typologies of territorial patterns of innovation WP and 2.5 A) Main spatial trends of innovation and knowledge. (both endogenous knowledge creation and flows from outside) Output: typologies of innovative regions WP 2.1 and 2.2 C) Impact of the different modes of innovation and knowledge on regional performance. Output: typologies of regional performance based on innovation and knowledge WP D) Case studies WP and E) Policy implications for the development of a successful knowledge economy WP 2.6

A) Knowledge Economy and its Spatial Trends (I) Basic idea: knowledge-based economy has not got a unique interpretative paradigm. Different approaches are necessary: A1. Sectoral approach (presence in the region of science-based, high-technology sectors). A2. Functional approach (presence in the region of functions like R&D and high education). A3. Relation-based approach (presence in the region of interactive and collective learning processes).

A) Knowledge Economy and its Spatial Trends (II) Spatial elements matter: -high-technology firms cluster along valleys, corridors, glens and high-tech districts to exploit the innovative atmosphere (technologically advanced regions); -high-education and research functions cluster in space since physical proximity acts as a driver of knowledge (scientific regions); -geographical areas characterised by cognitive proximity (shared behavioural codes, common culture, mutual trust and sense of belonging) show wider collective learning processes (networking regions).

A1) The sectoral approach: a typology EU average Specialisation in HT services Specialisation in HT manufacturing Technologically Advanced Regions (TAR) HT services HT manufacturing Low tech regions

A1) The sectoral approach Indicators to be collected and computed: 1.Regional specialization in HT manufacturing As measured by employment in HT manufacturing according to Eurostat definition 2.Regional specialization in HT services As measured by employment in knowledge intensive HT services according to Eurostat definition Source: Eurostat

A1) The sectoral approach High-tech manufacturingHigh-tech services

A1) The sectoral approach: a typology

A2) The functional approach: a typology EU average Human capital Research activities Scientific regions Research intensive regions Human capital intensive regions Regions with other specialisations than R&D

A2) The functional approach Indicators to be collected and computed: Research and development Expenditures; Expenditure as share of GDP; Expenditures per capita (1000 inhab.), Personnel in R&D as share of total employment Sources: Eurostat, ISTAT, Institut National de la Statistique et des Études Économiques Patents Number of patents; Patents per capita; Patents per capita percentage variation Source: OECD REGPAT Human capital Share of population with degree (ISCED 5-6) Source: Eurostat Fifth Framework Program Participations; Funding; Funding per capita (Source: CORDIS)

A2) The functional approach: Human capital

A2) The functional approach: R&D expenditures

A2) The functional approach: Patents per capita

A3) The relational approach: a typology Intentional relationship Spatial approach Cooperative neighbouring regions Localised knowledge spillovers regions Formal networking regions Informal networking regions Unintentional relationship A-spatial approach e.g. knowledge spillovers e.g. scientific associationse.g. collaboration in research projects e.g. collaboration in research projects among local actors

A3) The relational approach Possible indicators to be collected and computed: Participations in the 5°FP projects in the neighbouring regions Average funding in the 5°FP in the neighbouring regions Average funding (per capita over total population) in the 5°FP in the neighbouring regions Product+process innovations developed by other regions discounted by distance Number of patent citations on total patents Number of in-migrant and out-migrant inventors on total population Number of co-patents on total patents Sources: OECD - REGPAT, Cordis (Crenos elaboration)

A3) The relational approach: Knowledge spillover regions

A4) Spatial trends of innovation in Europe Indicators to be collected and computed: Innovation Technological innovation Product innovation Process innovation Marketing and/or organisational innovation Adoption Innovation adoption Product innovation adoption Process innovation adoption Source: CIS/EUROSTAT

A) Spatial trends of innovation in Europe Technological innovationProduct innovation

A) Spatial trends of innovation in Europe Process innovationMarketing and org. innovation

A) Spatial trends of innovation in Europe Facing some statistical difficulties at NUTS 2 Official NUTS2 data available in a few countries –Product innovation only and process innovation only available for IT and RO –Product innovation and process innovation available for CH, CZ, DK, PL, UK (NUTS1) RIS data (DG Enterprise, JRC and MERIT) and Regional Innovation Potential (DG Regio) to be checked and validated further. ESPON contact points have already been involved.

A) Social innovation adoption and use broadband penetration rateon-line orders

A) Environmental innovation

A5) Comparison with US, China and India Innovation Patents (source: OECD - REGPAT) R&D (sources: Standard & Poor’s Compustat for US; China Statistical Yearbook on Science and Technology: Ministry of Science and Technology, Govt. of India) Social Filter Education: bachelor’s, graduate or professional degrees Education: college level education Agricultural Labour Force Unemployment Rate Young People Sources: US-Census data; Chinese statistical resources website, National Bureau of Statistics of China; Ministry of Labour, Govt. of India, Central Statistical Organization (CSO), Census of India Structure of the local economy Domestic migration Population density % regional of national GDP Krugman index of specialisation Sources: US-Census data; Chinese statistical resources website, National Bureau of Statistics of China; Ministry of Labour, Govt. of India, Central Statistical Organization (CSO), Census of India

A5) Top 20 performers in US, China and India (patents on population) ChinaIndiaUSA 1 BeijingDelhi San Jose-San Francisco- Oakland, CA 2 ShanghaiHaryana San Diego-Carlsbad-San Marcos, CA 3 GuangdongChandigarh Appleton-Oshkosh- Neenah, WI 4 TianjinMaharashtra Minneapolis-St. Paul-St. Cloud, MN-WI 5 Zhejiang Andhra Pradesh Boston-Worcester- Manchester, MA-NH 6 FujianKarnataka Cincinnati-Middletown- Wilmington, OH-KY-IN 7 JiangsuGoa Rochester-Batavia- Seneca Falls, NY 8 LiaoningGujaratAustin-Round Rock, TX 9 ShandongTamil Nadu Philadelphia-Camden- Vineland, PA-NJ-DE-MD 10 HunanPondicherry Albany-Schenectady- Amsterdam, NY ChinaIndiaUSA 11 Chongqing Himachal Pradesh Reno-Sparks, NV 12 Heilongjiang West Bengal New York-Newark- Bridgeport, NY-NJ-CT-PA 13 SichuanKeralaGainesville, FL 14 ShaanxiPunjab Seattle-Tacoma-Olympia, WA 15 Jilin Uttar Pradesh Boise City-Nampa, ID 16 HainanJharkhand Chicago-Naperville- Michigan City, IL-IN-WI 17 HubeiRajasthan Houston-Baytown- Huntsville, TX 18 Shanxi Madhya Pradesh Hartford-West Hartford- Willimantic, CT 19 Inner Mongolia Jammu & Kashmir Raleigh-Durham-Cary, NC 20 XinjiangOrissaSanta Fe-Espanola, NM

B) Territorial patterns of innovation A territorial pattern of innovation is a combination of context conditions and of specific modes of performing the different phases of the innovation process. Context conditions: Internal generation External attraction Different phases of the innovation process: - from information to knowledge - from knowledge to innovation - from innovation to regional performance of knowledge and innovation

B1) A totally endogenous innovation pattern Tacit knowledge Codified knowledge Collective learning Entrepreneurship Product and process innovation Best practice governance Economic efficiency REGION I Education, human capital, accessibility, urban externalities Territorial preconditions for knowledge creation Knowledge output Territorial preconditions for innovation Innovation Territorial preconditions for innovation adoption Economic efficiency

B2) An endogenous innovation pattern in a dynamic area Territorial preconditions for knowledge creation Knowledge output Territorial preconditions for innovation Innovation Territorial preconditions for innovation adoption Economic efficiency Tacit knowledge Codified knowledge Collective learning Entrepreneurship Product and process innovation Best practice governance Economic efficiency REGION J Territorial accessibility Physical proximity Education, human capital, accessibility, urban externalities REGION I Territorial preconditions for interregional knowledge flows and innovation diffusion

B3) An endogenous innovation pattern in a scientific network Territorial preconditions for knowledge creation Knowledge output Territorial preconditions for innovation Innovation Territorial preconditions for innovation adoption Economic efficiency Tacit knowledge Codified knowledge Collective learning Entrepreneurship Product and process innovation Best practice governance Economic efficiency REGION J REGION I Territorial receptivity Territorial preconditions for interregional knowledge flows and innovation diffusion Tacit knowledge Codified knowledge Territorial relational capital Education, human capital, accessibility, urban externalities

B4) An exogenously driven innovation pattern Territorial creativity Product and process innovation Best practice governance Economic efficiency REGION J Education, human capital, accessibility, urban externalities REGION I Territorial preconditions for interregional knowledge flows and innovation diffusion Territorial preconditions for knowledge creation Knowledge output Territorial preconditions for innovation Innovation Territorial preconditions for innovation adoption Economic efficiency Tacit knowledge Codified knowledge

B5) An imitative pattern of innovation Territorial attractiveness Product and process innovation Best practice governance Economic efficiency REGION J Education, human capital, accessibility, urban externalities REGION I Territorial preconditions for interregional knowledge flows and innovation diffusion Territorial preconditions for knowledge creation Knowledge output Territorial preconditions for innovation Innovation Territorial preconditions for innovation adoption Economic efficiency Tacit knowledge Codified knowledge Collective learning Entrepre neurship Product and process innovation

B6) An integrated innovation pattern Territorial preconditions for knowledge creation Knowledge output Territorial preconditions for innovation Innovation Territorial preconditions for innovation adoption Economic efficiency REGION J Territorial preconditions for interregional knowledge flows and innovation diffusion Education, human capital, accessibility, urban externalities Collective learning Entrepreneurship Product and process innovation Best practice governance Economic efficiency REGION I Education, human capital, accessibility, urban externalities Tacit knowledge Territorial relational capital Territorial receptivity Tacit knowledge Codified knowledge Territorial attractiveness Collective learning Entrepre neurship Product and process innovation Territorial creativity

C) Impact of innovation and knowledge on regional growth This WP will identify: -the role of innovation and knowledge on the performance of different territories; -the return of investments in regional innovation and knowledge in different territories; -the role of knowledge spillovers in the economic performance of different territories.

D) Case studies 2 case studies per PP on regional best practices in knowledge creation 2 case studies per PP on regional best practices in knowledge spillovers Overall 12 case studies 1.Regions selection according to two dichotomies (see the next slide): Concentrated vs diversified Traditional vs advanced 2.Aim of the case studies: - to strengthen the role of territorial elements in knowledge and innovation creation and knowledge spillovers according to the conceptual framework used in the project of territorial pattern of innovation - to highlight the governance elements related to knowledge and innovation diffusion 2.Knowledge spillovers among regions and not only within regions 3.Agreement on the interview protocol, target groups of the planned interviews and selection process of the interviewees (to be provided in the Interim Report)

D) Case studies CONCENTRATED AREASDIVERSIFIED AREAS TRADITIONAL SECTORS Wood processing industry - Banska Bystrica region Automotive - Bratislava Food- Wales Wine – Tuscany Automotive – Piemonte ADVANCED SECTORS Biotechnology – Oxford ICT – Košice ICT – Bratislava Digital Media/TV – Cardiff (Wales) Media – Milan (Lombardy) ICT – Cambridge Arno Valley – High tech (Tuscany)

E) Policy recommendations The aim of the project is to produce policy recommendations on the achievement of a “smart growth” for Europe, intended as an economic growth based on knowledge and innovation. In “EU2020” this priority rejects a “one size fits all approach”. Recommendations in this field have to: be tailored on each “territorial pattern of innovation” be based on specific policy interventions reinforce territorial preconditions that strengthen each innovation pattern in terms of economic performance.