https://orcid.org/0000-0002-1683-1787
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ABSTRACT
This paper evaluates to what extent the implementation of Smart Specialisation Strategy (S3) has changed the allocation of Structural Funds of Italian regions in the programming period 2014–20. Given the novelties introduced by this policy, we expect an increase in the 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 European Union 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. The 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.
ACKNOWLEDGEMENTS
The authors thank Ron Boschma and Francesco Prota for providing helpful comments on earlier drafts of this paper. They appreciate all the comments and suggestions received at the 22nd Uddevalla Symposium 2019, AISRE Annual Conference 2019, and c.MET-05 workshop 2019. Finally, they thank the editor, Ugo Fratesi, and three anonymous referees for their comments, which significantly improved the quality of the manuscript.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the authors.
Notes
1. See Fellnhofer (Citation2018) and Mora et al. (Citation2019) for a bibliometric review of the literature, and Hassink and Gong (Citation2019) for a discussion of the main theoretical and implementation issues.
2. We also believe that an empirical assessment of the target choices, as a function of the initial endowment and of the predetermined transformational path, is fundamental. Unfortunately, this is still an almost neglected topic and the few empirical works suggest that regional choices in terms of thematic priorities were very heterogeneous and rather subjective (e.g., D’Adda et al., Citation2019a, Citation2019b; Gianelle et al., Citation2019).
3. ‘The important thing therefore is to insist on this duality between “specialization” (concentration of resources and choice of priorities) and transformation (diversification, transition, modernization …)’ (Foray, Citation2019, p. 7).
4. Indeed, some authors prefer using the acronym RIS3, that is, Research and Innovation Smart Specialization Strategy, to underline the object of the strategy. However, being a regional development strategy, S3 is expected to influence the allocation of other ESIF funds, in particular the European Social Fund (ESF), and of other thematic priorities, in particular: access and use of ICT, competitiveness of SMEs and a low carbon economy.
5. In both programming periods, regions faced some constraints in the choice of thematic areas. Specifically, in the 2007–13 period, at least 60% of the expenditure of regions in the convergence objective and at least 75% of the expenditure of regions in regional competitiveness and employment objective should be assigned to the 11 thematic themes (the first being ‘Research and technological development’). In the 2014–20 programming period the constraint referred to the first four thematic objectives: in developed regions, 80% of total ERDF resources should be allocated to two or more of the four priorities and 20% to the fourth priority; in transition regions, 60% of total ERDF resources should be allocated to two or more of the four priorities and 15% to the fourth priority; and in less developed regions, 50% of total ERDF resources should be allocated to two or more of the four priorities and 12% to the forth priority. Overall, these constrains explain the differences observed between developed and less developed regions in the absolute percentage of funds allocated to R&D. However, they do not explain per se the differences observed between the two programming periods, which are largely dependent on regional choices.
6. This paper is agnostic as to the effectiveness of R&D activity and the role of universities in promoting innovation and the transformation of regional economies. Specifically, we look at these aspects as we believe they are strictly related to the rationale and implementation of S3. However, we acknowledge that some empirical literature on S3 indicates a much stronger need in less developed regions to support non-R&D activities (e.g., Radosevic, Citation2017) and that some recent contributions show that collaborations with universities have offered limited benefits (e.g., Crescenzi et al., Citation2020, have an analysis of the Italian ‘convergence’ regions during the programming period 2007–13).
7. We acknowledge that it would be preferable to analyse the allocation of resources taking into account the whole programming period. However, it would take some more years for data to be available and it would mine the timeliness of our contribution. Besides, looking only at the initial years of the programming periods may be preferable for a practical issue: at the end of the programming period regions are often concerned about spending all the allocated resources more than about how to spend them. For this reason, in the final years they may prefer to use a well-established way of spending rather than trying new different ways.
8. In the two programming periods, the definition of thematic objectives was slightly different. In the 2007–13 period it was ‘Research and technological development (R&TD), innovation and entrepreneurship’; in the programming period 2014–20 it was ‘Strengthening research, technological development and innovation’.
9. In the remaining tables and figures, the following regions are excluded from the analysis: Basilicata, Molise, Sicilia, PA Bolzano, PA Trento and Campania.
10. Having bigger projects is indeed a matter of concentrating resources. This is true as long as the projects are somehow focused in terms of sectors or technologies and not extremely general. Most likely, having fewer projects will concentrate the resources also in terms of the number of recipients. Therefore, we would expect a higher concentration in case of bigger projects, even if in principle a region can achieve high concentration in terms of industries and technologies with numerous smaller projects, if focused.
11. In Italy most of the public research centres are under the CNR (National Research Council) and less spatially distributed than universities.
12. An autoregressive (AR) coefficient, that is, the coefficient of the dependent variable measured in the previous programming period, not significantly different from 1 would mean that there is no evidence of a strong persistence of regional allocation choices, in terms of concentration level.