Camagni, Roberto; Capello, Roberta
Conference Paper
Regional innovation patterns and the EU regional
policy reform: towards smart innovation policies
52nd Congress of the European Regional Science Association: "Regions in Motion -
Breaking the Path", 21-25 August 2012, Bratislava, Slovakia
Provided in Cooperation with:
European Regional Science Association (ERSA)
Suggested Citation: Camagni, Roberto; Capello, Roberta (2012) : Regional innovation patterns
and the EU regional policy reform: towards smart innovation policies, 52nd Congress of the
European Regional Science Association: "Regions in Motion - Breaking the Path", 21-25 August
2012, Bratislava, Slovakia, European Regional Science Association (ERSA), Louvain-la-Neuve
This Version is available at:
http://hdl.handle.net/10419/120488
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Regional Innovation
Patterns and the EU
Regional Policy Reform:
Towards Smart
Innovation Policies
Roberto Camagni and Roberta Capello,
Politecnico di Milano
Paper presented at the 52° ERSA Conference in Bratislava, 21-24
August 2012
Submitted to Growth and Change
2
Regional Innovation Patterns and the EU Regional Policy Reform:
Towards Smart Innovation Policies
Roberto Camagni and Roberta Capello
Politecnico di Milano
Abstract
The present debate on regional policy design to fit the Europe 2020 Agenda calls for additional reflections on the way
sectoral policies, like innovation policies, can be translated appropriately into a regional setting. The paper enters the
debate on smart specialization strategies by stressing the need to overcome the simplistic dichotomy between core and
periphery in the Union, between an advanced ‘research area’ (the core) and a ‘co-application area’ of general purpose
technologies to local technological specificities (the periphery). The geography of innovation is much more complex
than a simple core-periphery model, and the logical pathway towards innovation is much more complex than the linear
model of R&D-invention-innovation direct link: the innovation patterns are differentiated among regions, according to
their regional context conditions. The identification of specific ‘innovation patterns’ is necessary to design ‘smart
innovation’ policies. The paper presents a critic to the smart specialization debate, suggests a new taxonomy of
European innovative regions based on their innovation patterns, and proposes innovation policies for each regional
mode of innovation.
1. Introduction
In the recent EU regional policy debate, two main documents captured the interest of experts: the
EU Report Europe 2020 (European Commission, 2010a), which presents the general context in
which Europe will act in the next decade, and the Barca Report to Commissioner for Regional
Policies, Danuta Hubner (Barca, 2009), paving the way towards a reformed regional policy. The
first Report proposes a strategy based on three pillars – namely, smart, sustainable and inclusive
growth.
1
The second report discusses and proposes a new process of EU Regional Policy Reform,
launched in preparation of the new programming period 2014-20; in particular, the rationale,
economic justification, conditionality, process design and delivery style of regional policy itself are
discussed, supplying wide material for institutional and political decision.
At the cross-yard of these two streams of reflections, an interesting policy debate was launched,
related in particular to the ‘smart growth’ pillar, stressing the need to conceptually integrate the
tasks put forward by Europe 2020 Report and the new cohesion policy reform into a common
framework. On the one hand, Europe 2020 is seen as lacking a more explicit territorial dimension, a
way through which to engage all potential and dispersed actors to contribute to the Agenda with
their decision processes, in a bottom-up way (Camagni, 2011). On the other hand, the EU policy
reform should be conceptualized in a way to be able to contribute to the achievement of the three
pillars (smart, sustainable and inclusive growth) of Europe 2020 Agenda; in particular, the latter
might become the occasion for re-launching a knowledge-intensive growth model for Europe on a
regional base, supplying operational answers to the request of one of its ‘flagship initiatives’,
namely ‘Innovation Union’.
The EU official document Regional Policy Contributing to Smart Growth in Europe (EC, 2010b) is
a first official move in this direction, calling for the need to identify sectors and technological
1
These pillars may look relatively autonomous, touching the challenges of the knowledge society, of the environment
and of the equitable society, but in fact are integrated with each other and “mutually reinforcing”. Sustainable growth is
pursued not just per se, but as a possible driver for “resource efficiency” and consequently “competitiveness”; inclusive
growth is requested for the sake of social equity but also as a means for the “acquisition of skills”, social cohesion and
social capital.
3
domains on which regional policies should be tailored to promote local innovation processes in
these specialization fields. The document fully subscribes to the ‘smart specialization’ (SmSp)
strategy suggested by the ‘Knowledge for Growth’ expert group advising to former European
Commissioner for Research, Janez Potocnik (Foray, 2009; Foray and David, 2009), advocating for
a consistent matching between investments in knowledge and human capital and the present
industrial and technological “vocations” and competences of territories. “Strategies have to consider
the heterogeneity of research and technology specialization patterns” (Giannitsis, 2009, p. 1).
This paper is a contribution in the same direction. It enters the debate on smart specialization
strategies by stressing the need to overcome the simplistic dichotomy between core and periphery in
the Union, between an advanced ‘research area’ (the core) and a ‘co-application area’ of general
purpose technologies (the periphery) - present in the original but also in subsequent contributions. A
slightly more complex but similar taxonomy was also proposed by OECD, pointing out a threefold
partitioning – ‘knowledge regions’, ‘industrial production zones’ and ‘non-S&T driven regions’
(OECD, 2010, 2011). The geography of innovation is much more complex than a simple core-
periphery model: the capacity to pass from knowledge to innovation and from innovation to
regional growth is different among regions, and the identification of specific ‘innovation patterns’
(Capello, 2012) is essential to build targeted normative strategies, well beyond what is proposed by
the smart specialization model. Regional ‘innovation patterns’ may be found empirically in the way
knowledge and innovation are developed inside the single regions according to the nature of their
traditional knowledge base and productive specificities, and/or are captured from other regions via
cooperation, scientists and professionals mobility, market procurement and trans-regional
investments.
In this paper ‘smart innovation policies’ are advocated. They are defined as those policies able to
increase the innovation capability of an area and to enhance local expertise in knowledge
production and use, acting on local specificities and on the characteristics, strengths and weaknesses
of already established innovation patterns in each region.
The two key concepts of ‘embeddedness’ and ‘connectedness’ – put forward in the recent debate on
SmSp – are starting concepts around which smart innovation policies could be designed: policies
have to be embedded in the local reality, in local assets and strategic design capabilities, and have to
guarantee the achievement of external knowledge through strong and virtuous linkages with the
external world (McCann and Ortega-Argilés, 2011). However, this is not enough: a ‘smart
innovation’ strategy goes a step forward, taking into consideration the R&D element but adapting
the two concepts of ‘embeddedness’ and ‘connectedness’ to the specificities of each ‘pattern of
innovation’. Smart innovation policies look for targeted interventions - appropriate for each single
territorial innovation pattern - with the aim to reinforce regional innovation process, to enhance the
virtuous aspects that characterize each pattern, and to upgrade and diversify the local specialization
into related technological fields (ESPON, 2012).
2
The paper is organized as follows. The debate on smart specialization is illustrated in sect. 2
together with a reflection on its acceptability in a regional policy context. The need for the
identification of territorial elements supporting innovation patterns to build a sound and efficient
regional taxonomy of innovative regions is presented in sect. 3. The new workable conceptual
framework on which regional innovation policies should be developed is built in sect. 4. Smart
innovation policies are then presented (sect. 5), leading to some concluding remarks (sect. 6).
2
Most of the ideas presented in this work were elaborated by the authors within the ESPON KIT Project. For the final
report of KIT, see http://www.espon.eu/main/Menu_Projects/Menu_AppliedResearch/kit.html.
4
2. The smart specialization debate: embeddedness and connectedness
The smart specialization approach was developed with the aim to find an explanation – and a
consequent rational strategy – for the large R&D gap between Europe and some key trading
partners. The most straightforward reason for the knowledge gap was outlined in the smaller share
of European economy composed of high-tech, R&D intensive sectors. A second reason of the gap
was pointed out in the spatial dispersion of the limited R&D efforts, generating insufficient critical
mass and investment duplications, inefficient resource allocation, consequent weak learning
processes (Pontikakis et al., 2009).
On the basis of this diagnosis, a rational and concrete proposal was put forward by the “Knowledge
for Growth” expert group. It advocates differentiated policies for ‘core’ and ‘periphery’ regions, the
former able to host laboratories and research activities on general purpose technologies (GPT), the
latter oriented towards the identification of their ‘knowledge domain’ in which to specialize and
towards co-operation with external R&T providers (‘co-application of innovation’) (Foray et al.,
2009; Foray, 2009; Giannitsis, 2009).
The advantages of such a strategy are strongly underlined in the smart specialization debate,
namely:
the possibility to achieve at the same time a “polarization” and a “distribution” of research
activities in space. GPT research activities would achieve the critical mass of financial and
human resources necessary to their efficient development, reinforcing the idea of a
European Research Area (ERA); peripheral areas would not be penalized, taking advantage
of financial resources to support the application of technological advances to their specific
specialization fields;
the achievement of a more productive use of the potentials of each region – defined in terms
of traditional competence and skills, tacit knowledge and specific innovation processes - that
would be reinforced by investments in human capital and research able to match each
region’s innovation profile;
the development of cumulative learning in advanced R&D activities and the consequent
exploitation of increases in R&D productivity;
the creation of synergic effects between GPT and co-applications, thus increasing the size of
GPT markets and the returns on R&D investment, enlarging at the same time the potential
for technological adoption, adaptation and diffusion.
An important caveat is stressed concerning the achievements of the above mentioned advantages:
the SmSp approach makes the strong assumption that an area is able to discover new specialization
fields inside its ‘knowledge domain’, i.e. well defined innovation niches on the basis of its present
competences and human capital endowment, in which it can hope to excel in the future also thanks
to synergetic policy support (Pontikakis et al., 2009). Some members of the group are explicit in
this sense: “the concept of smart specialization (…) assumes that there are criteria to judge which
specializations, and consequently which policy targets are smart” (Giannitsis, 2009, p.4). In other
words, a consistent matching between investments in knowledge and human capital and the present
territorial ‘vocations’ represents a difficult and crucial challenge, inpinging on a creative and by no
means mechanistic decision process.
On this particular aspect, the SmSp argument is very clear: the search and discovery process around
the traditional specialization has to be a bottom-up process, in which local entrepreneurs are
identified as the leading actors, being the main knowledge and creativity keepers, interested in
efficiently exploiting existing cognitive resources and driving their re-orientation towards new
