Innovation and the Knowledge Economy: KIT Project
RTPI: ESPON in a Welsh Context; City Hall, Cardiff 24 September, 2012
Selyf Morgan: Cardiff University
Knowledge, Innovation, and Territory (KIT) An ESPON project: 2010‐ 2012 Partner institutions: Lead Partner: Politecnico di Milano (Prof. Roberta Capello) Partners: CRENOs, University of Cagliari, Italy AQR, University of Barcelona, Spain LSE, United Kingdom University of Bratislava, Slovakia Cardiff University, United Kingdom
Knowledge Innovation and Territory (KIT) Context Upcoming scientific and technological powers outside the European territory attracting R&D investments. New opportunities arise but Europe’s ability to sustain a competitive edge in knowledge and innovation questioned
General Aim Investigate strategic approaches to innovation that are consistent with the overall reforms of EU Cohesion Policy. To achieve this aim, KIT relates to “smart specialization” strategies by overcoming the simplistic dichotomy between centre/ core and periphery
KIT ‐ current state, patterns, and potentials of regions wrt knowledge economy and innovation; identify new development opportunities from innovation for Europe and its territories.
Knowledge Innovation and Territory (KIT) The project set out to: Show spatial trends in the knowledge economy Identify pathways towards innovation and modernization Measure the impact of different forms or patterns of innovation on regional economic performance Propose innovation policy actions http://www.espon.eu/main/Menu_Projects/Menu_AppliedResearch/kit.html
I: Knowledge Economy Region A definition of “knowledge economy” based on a multidimensional approach. A knowledge economy region can be identified as a region specialised in high‐tech sectors, and/ or in scientific functions and /or is capable of obtaining knowledge from other economies through cooperation and networking.
...........but moves away from the idea that •Knowledge generation equates R&D •Knowledge economy is a synonym for a scientific R&D based economy •R&D investments are the only/correct innovation policy measure to support a knowledge economy
II: Knowledge Economy Regions Measured at regional level through •presence of high‐tech manufacturing and service sectors •the presence of scientific activities (human capital and research activities) •the capacity of a region to cooperate with other regions 3 Knowledge Economy Region Types (not exclusive) ‘Technologically Advanced Regions’ (Sectoral Basis) •hosting ‘science‐based’ or high‐tech manufacturing and service sectors •Geographically highly concentrated; some peripheral regions a major role ‘Scientific Regions’ (Functional Basis) •Higher than average values both in research activity and in levels of human capital hosting large and well‐known scientific institutions ‘Knowledge Networking Regions’ (Networking Basis) •Access external sources of knowledge; facilitating interactive learning and interaction in innovation •a relational paradigm identifying cognitive capability to manage information in order to identify and solve problems (transform information and inventions into innovation and productivity increases)
Typology of Knowledge Economy Regions •Most of the technologically‐advanced regions are also networking regions •In general, scientific regions are also networking regions (knowledge accumulation inside a region also requires networking activity) •A number of regions are networking types only •A number of European regions, mainly in Eastern countries and in the Southern peripheral countries are below the EU average in each respect...... ........in most of European regions the knowledge economy is still in its infancy
Innovation Driven Economy Innovation‐driven economy has the ability to transform knowledge and inventions into increases in innovation and productivity KIT: Measuring penetration of an innovation‐driven economy into a regional system using indicators of different types of innovations Community Innovation Scoreboard and robust KIT methodology to estimate regional CIS data where it is unavailable Produces territorial patterns of innovation across European Regions: different combinations of context conditions and modes of performing different phases of the innovation process
Innovation Driven Economy Patterns Differentiated spatial patterns depending on type of innovation Product innovation •Characterised by strong spatial concentration •Core carried out in German, Scandinavian, Swiss, UK regions •Also concentration within countries: capital regions ‐higher innovation rates
Process innovation •Higher than average values in southern Europe – Cyprus, Spain, France, Greece, Italy, Malta, Portugal, •Low variance – a more evenly distributed practice
Marketing and organisational innovation Quality improvements, reduction in environmental damage, energy consumption, new markets, reduce labour costs, materials required for production, conformance to regulations Concentration in EU 15 particular German and Austrian regions, but distribution quite even across Europe
Innovation Driven Economy Patterns Social innovation •Proxied by penetration rate of broadband network •Evident as whole countries rather than internal differentiation, although capital regions c.f. other regions in same country •High values in Nordic countries and Netherlands
Green innovation •Green innovative technologies (OECD definition) •Scandinavian countries, UK
Knowledge /Innovation Driven Economy Links Data at the regional level show a discrepancy between knowledge economy and innovation As expected: Knowledge economy regions show a higher innovation performance compared to those regions that do not exhibit a Knowledge Economy And would expect: the regions with the highest R&D and scientific activities would be most innovative But: KIT empirical results show instead that scientific regions, although registering a high innovation rate, are not significantly more innovative than TAR or networking regions And: only a few regions show a pattern of innovation that goes directly from R&D to innovation
Knowledge /Innovation Driven Economy Links Different modes of performing innovation exist A region can innovate by: •exploiting the knowledge that it produces •using knowledge coming from outside the region •imitating innovation that is produced elsewhere A region ‘adopts’ one of these modes of innovation according to its local context/ conditions The empirical results report that five groups of regions can be identified on the basis of their patterns of innovation, namely (map 1):
Reykjavik !
Canarias !
Guadeloupe !
Martinique
Réunion
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Helsinki !
Tallinn
Oslo
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Stockholm
Guyane
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Riga !
Madeira !
København
Vilnius
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Minsk !
Dublin !
Acores !
Warszawa
Berlin
Amsterdam
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Kyiv !
London !
Bruxelles/Brussel !
Praha
This map does not necessarily reflect the opinion of the ESPON Monitoring Committee
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Luxembourg !
Paris !
Kishinev
WienBratislava ! ! Budapest
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Bern !
Vaduz !
Ljubljana !
Zagreb
Bucuresti
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Beograd !
Sarajevo !
Sofiya !
Podgorica !
Skopje
Ankara
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Roma Madrid
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Tirana
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Lisboa !
Athina !
Nicosia !
El-Jazair !
Tounis !
Valletta !
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© Politecnico di Milano, ESPON KIT Project, 2012
260
520
km
Regional level: NUTS2 Source: Own elaboration, 2012 Origin of data: EUROSTAT, 2012 © EuroGeographics Association for administrative boundaries
Legend No data Imitative innovation area Smart and creative diversification area Smart technological application area Applied science area European science-based area
Knowledge /Innovation Driven Economy Links The European science‐based area (pattern 1a): •strong knowledge and innovation producing regions •specialized in general purpose technologies (i.e.GPTs) ‐a high generality and originality of the science‐based knowledge •a high accessibility to knowledge from regions with a similar knowledge base •R&D endowment is high in these regions. Applied science area (pattern 1b): •strong knowledge producing regions characterized by applied science • high accessibility to knowledge from regions with a similar knowledge base Smart technological application area (pattern 2a): •high product innovation rate •more limited degree of local applied science •high creativity ‐ translates external basic and applied science knowledge into innovation • R&D endowment similar to 1b
Knowledge /Innovation Driven Economy Links Smart and creative diversification area (pattern 2b): •low degree of local applied knowledge sources •some internal innovation capacity •high degree of local competences ‐not negligible innovation activities carried out & rely upon tacit knowledge embedded into human capital •strongly endowed with characteristics such as creativity that help to absorb knowledge and to adapt it to local innovation needs.
The imitative innovation area (pattern 3): •a low local knowledge and innovation intensity •a relatively high entrepreneurship •high levels of creativity
An endogenous innovation pattern Territorial preconditions for knowledge creation
Knowledge output
Territorial preconditions for innovation
Innovation
Territorial preconditions for innovation adoption
Economic efficiency
REGION J Education, human capital, accessibility, urban externalities
Territorial preconditions and channels for interregional knowledge flows and innovation diffusion
Basic, general purpose knowledge
Territorial receptivity Cognitive proximity Relational capacity
REGION I Education, human capital, accessibility, urban externalities
Basic, general purpose knowledge
Collective learning
Entrepreneurship
Product and process innovation
Best practice governance
Economic efficiency
A creative application pattern Territorial preconditions for knowledge creation
REGION J Education, human capital, accessibility, urban externalities
Knowledge output
Territorial preconditions for innovation
Innovation
Territorial preconditions for innovation adoption
Economic efficiency
Basic, general purpose knowledge Specific, applied knowledge
Territorial creativity Openness to innovation
Territorial preconditions and channels for interregional knowledge flows and innovation diffusion
REGION I Collective learning Specific, applied knowledge
Entrepreneurship
Product and process innovation
Best practice governance
Economic efficiency
An imitative innovation pattern Territorial preconditions for knowledge creation
REGION J Education, human capital, accessibility, urban externalities
Territorial preconditions and channels for interregional knowledge flows and innovation diffusion
Knowledge output
Basic, general purpose knowledge Specific, applied knowledge
Territorial preconditions for innovation
Collective learning
Innovation
Territorial preconditions for innovation adoption
Economic efficiency
Product and process innovation
Entrepreneurship
Territorial attractiveness: FDI
REGION I
Product and process innovation
Best practice governance
Economic efficiency
Policy Consequences The variety of innovation patterns suggests: • a failure of a “one size fits all” policy to innovation such as thematically/regionally neutral and generic R&D incentives On the contrary have to identify: • innovation patterns specific to each area •and their efficiency in generating growth KIT findings: •The maximum return to R&D investments is the right policy goal for regions belonging to the “European science‐based” and the “Applied science” patterns, characterised by a sufficient critical mass of R&D endowment already present in the area. R&D requires a certain critical mass •R&D investment less effective in the regions with the lowest knowledge endowment: a certain degree of knowledge is required to generate new knowledge ‐ human capital investment more important. An efficient combination of both R&D and human capital is important for regional performance
Policy Consequences To benefit from (external) formal knowledge, a region needs to have the capacity to absorb and exploit external knowledge Hence: •Support knowledge diffusion by means of structured and defined channels, such as networks and labour mobility of human capital e.g. supporting inventors’ mobility •Specific policies, to support regions with the provision of organizational and structural assistance reflecting local needs •Smart innovation policies may be defined as those policies able to increase the innovation capability of an area by boosting effectiveness of accumulated knowledge and fostering territorial applications and diversification, on the basis of local specificities and the characteristics of already established innovation patterns in each region.
Summary Key Findings 1. Knowledge Economy has a very differentiated and fragmented spatial pattern • 3 Knowledge Economy region type : TAR; Scientific; Networking • Some regions high specialisation in advanced tech, other as knowledge nodes 2. High number of regions does not belong to any KE type – for many KE is still immature [high number of regions are below European average in high tech specialisation, knowledge creation and knowledge acquisition] 3. Scientific regions – register high in innovation rates but not the most innovative 4. Process innovation rates similar in all types of knowledge regions, and the capacity to access and use external sources of Knowledge diffused over Europe
Key Findings 5. Differentiated innovation spatial pattern depending on type of innovation: 5 innovation patterns identified – may be dependent on institutional, cognitive, cultural elements associated with the types of innovation 6. Regions may also exhibit a shift between different patterns 7. Policy tools differentiated according to local innovation patterns [Since only 33 regions (out of 287) have 3% GDP on R&D‐ should there be a common innovation policy aimed to all regions/ countries or a differentiated aim and set of policy tools at regional level?] 8. Emerging countries improving – Europe will have to compare research activity not only with US but China, India etc, and emerging countries have spatial concentration of R&D – re‐launch of debate about identification of a (core) European Research Area?
Reference http://www.espon.eu/main/Menu_Projects/Menu_AppliedResearch/kit.html
