Project

Smart Specialization Strategy (S3)

Goal: S3 requires EU regions to identify technological domains in which they are more likely to reach or maintain a competitive advantage, and then focus their investment and innovation policy in those domains.
The research activity in this area deals mainly with the following issues:
- the analysis of the specialization domains chosen by EU regions
- the analysis of relatedness and connectivity between technological domains both within and between regions
- the evaluation of the strategies implemented by EU regions

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Project log

Diego D'Adda
added a research item
The aim of this paper is to evaluate to what extent the implementation of Smart Specialisation Strategy (S3) has changed the allocation of structural funds of Italian regions in the programming period 2014-2020. Given the novelties introduced by this policy, we expect an increase in sectoral and technological concentration of funds, a higher share assigned to research and technological development and a stronger involvement of universities and research centres in the projects funded by regional authorities. In the empirical analysis, we exploit OpenCoesione, an original database that tracks all the projects funded with EU Cohesion Funds in Italy. We compare the characteristics of the projects managed by regional authorities in two consecutive programming periods, namely the one following and the one preceding S3 policy and implementation. Results show that changes between the two programming periods are modest and heterogeneous between regions. Overall, there is no strong evidence of any substantial change in how regions have allocated structural funds.
Diego D'Adda
added a research item
Smart Specialisation Strategy required regional authorities to identify technological domains where to concentrate investment in R&D and innovation. Their choices should originate from an analysis of regional strengths, aiming to identify those domains with the greatest potential for innovation and diversification. This paper aim to assess the degree of relatedness between chosen technological domains. We use patent data to categorize technological domains and the revealed associations methodology to measure their degree of relatedness. We find that the choices made by Italian regions show a significant higher relatedness as compared to a random choice, though with notable exceptions.
Donato Iacobucci
added a project goal
S3 requires EU regions to identify technological domains in which they are more likely to reach or maintain a competitive advantage, and then focus their investment and innovation policy in those domains.
The research activity in this area deals mainly with the following issues:
- the analysis of the specialization domains chosen by EU regions
- the analysis of relatedness and connectivity between technological domains both within and between regions
- the evaluation of the strategies implemented by EU regions
 
Donato Iacobucci
added 2 research items
The aim of this article is to investigate whether and how local agglomeration forces—related and unrelated variety—influence firm diversification. Using a large dataset of 5112 Italian manufacturing business groups for the year 2001, and estimating Tobit models, we show the ‘consistency’ between the patterns of firm diversification and that of the local system in which the firm is located. Specifically, firms located in local systems dominated by unrelated variety are more likely to show unrelated diversification patterns, while firms located in local systems dominated by related variety are more likely to show related diversification patterns. This supports the Evolutionary Economic Geography prediction of firm similarity ‘within’ the same local system, and firm heterogeneity ‘between’ different local systems.
The aim of this paper is to discuss some of the theoretical underpinnings and implementation problems of the RIS3. The analysis is carried out by reviewing the available literature on RIS3 and the proposals of 36 EU regions. The RIS3 approach requires the concentration of R&D efforts in a few domains. It requires a variable mix between research and innovation policy according to the region’s innovative capability. The paper discusses the theoretical and practical issues arising when trying to identify links between technological domains within the same regions and between different regions. The paper also questions the emphasis of the RIS3 on the bottom-up approach given that the design of a ‘strategy’ must necessarily rely, at least at the beginning, on a topdown approach. Suggestions are made on how to improve the design and implementation of the RIS3.
Donato Iacobucci
added 2 research items
European guidelines for the smart specialization strategy (S3) required regions to identify synergies between technological domains within the same region (relatedness) and potential links of the chosen domains with other European regions (connectivity). The aim of this paper is to analyse if and to what extent regions have been able to implement such indications and the methodology adopted. The paper is based on a content analysis of the S3 documents approved by Italian regions. The empirical analysis reveals that only in a few cases regions considered relatedness and connectivity of technological domains. Moreover, the methods adopted by regions to detect potential links between the specialization domains is based more on anecdotal evidence than on the application of theoretically grounded methodologies. The paper suggests that the explanation for this omission is the absence of a consolidated methodology to deal with these issues and proposes some preliminary guidelines to overcome the problem.
The Smart Specialization Strategy requires regions to identify links between technological domains within the same region (relatedness) and between different regions (connectivity). Besides providing a first analysis of technological domains of Italian regions, this paper shows that Italian regions generally neglected the analysis of relatedness and connectivity. We argue that this underestimation is due to the absence of a consolidated methodology to detect such links and to the lack of data and information to carry out this analysis.
Diego D'Adda
added 2 research items
The implementation of the Smart Specialisation Strategy (S3) has required European regions to identify the technological domains in which they show superior innovative capabilities. This choice should promote specialization and facilitate diversification into new sectors. Given that regional specialization shows path dependence, successful diversification can be achieved in domains closely related to the existing knowledge base. The paper provides a first empirical assessment of the coherence between the technological domains chosen by Italian regions and those in which they show actual innovative capabilities, as measured by their patenting activity.
Smart Specialisation Strategy required regional authorities to identify technological domains where to concentrate investment in R&D and innovation. In principle, their choices should originate from an analysis of regional strengths, aiming to identify the domains with the greatest potential for innovation. Given that regional technological specialization shows high path-dependency, a diversification strategy is effective only toward sectors that are closely related to the existing local knowledge base. This work suggests some methodological solutions aimed at facilitating and evaluating regional choices about the technological domains to target, given S3 policy expectations. In particular, we propose methodologies aimed to measure: a) the degree of coherence between the technological domains chosen by regions, which are the targets for their specialisation strategy, and the actual local knowledge base (embeddedness); b) the degree of complementarity of the chosen technological domains (relatedness), so as to maximise the effectiveness of regional innovation and diversification efforts.
Donato Iacobucci
added 2 research items
The smart specialisation strategy (S3) requires the identification in each region of one or more thematic areas where R&D and innovation policy should be focused on to create and sustain a competitive advantage. Not necessarily the chosen areas will belong to the core, general purpose technology that are generally identified as high-tech sectors (ICT, biotech, etc.). For most of the (peripheral) regions the application of the S3 will involve the identification of production domains in which general purpose technology can be applied and adapted. The aim of this paper is to discuss the theoretical underpinning of the S3, focusing the analysis on three concepts: embeddedness, relatedness and connectivity. The analysis is carried out by reviewing the available documents about the definition and implementation of the smart specialisation strategy and the early proposals developed by some European regions. S3 is an important advancement in the design of regional innovation policy. A better clarification of its theoretical basis and implementation problems can improve its effectiveness.
The Smart Specialization Strategy requires regions to identify links between technological domains within the same region (relatedness) and between different regions (connectivity). Besides providing a first analysis of technological domains of Italian regions, this paper shows that Italian regions generally neglected the analysis of relatedness and connectivity. We argue that this underestimation is due to the absence of a consolidated methodology to detect such links and to the lack of data and information to carry out this analysis.