Smart specialization (S3) occupies a special position in European policymaking. The adoption of the National / Regional Research and Innovation Strategies for Smart Specialisation in 2014 represented a major strategic shift in the European Union’s (EU) cohesion policy. The reform’s objective was not only to increase the policy’s overall effectiveness (e.g. by improving sectoral targeting of funding and creating production synergies), but also to introduce a new way of thinking about local economic development: from a ‘one-size-fits-all’ approach to more place-based intervention; from a top-down to a more bottom-up approach; and from targeting economic convergence between European regions to a multitude of objectives that are better adapted to regions’ individual circumstances and potentials.
Smart specialization has been designed as a policy mechanism that can support EU regions (and countries) to unleash their growth potential by helping them identify and harness their dynamic (and latent) comparative and competitive advantages.
Because Smart Specialisation is a relatively new concept and policy, there is only limited knowledge about its effectiveness and impact. It was introduced at a large scale during the 2014–2020 programming period—and despite some early attempts to assess its impact1—it will take time for a full picture of its effectiveness to emerge. What is more striking, however, are the limited accounts of how the S3 strategies implemented across Europe genuinely reflect the endogenous potential of those regions in which an entrepreneurial discovery process was carried out and for which a smart specialization strategy was subsequently designed. In other words, we lack a full picture of how ‘smart’ smart specialization policies across Europe truly are.
We conducted a comprehensive analysis of the full set of regional S3 strategies currently being implemented in the EU to determine how independent they are from one another and how they are influenced by economic and institutional characteristics, namely the quality of government, economic and technological capacities of regions.
We specifically focused on three related aspects: First, we documented and analysed some key features of S3 strategies, focusing on the prevalence of different economic/scientific domains (priority areas for investment) and policy objectives the smart specialization strategies are based on. Second, we examined how groups of regions cluster together in terms of their economic priorities and, based on these findings, identified distinct clusters of sectoral specializations across the EU. Third, we performed an analysis of the S3 strategies’ key features across space to understand whether the policy approach as a whole has contributed to a ‘smarter’ policy at the aggregate level. In other words, we sought to ascertain whether the ‘smart’ strategies adopted at the local level truly matched the local economic context and whether they can therefore be deemed a suitable approach for mobilizing Europe’s economic potential as a whole.
The figure below depicts the number of economic and scientific domains within each S3 strategy for the period 2014–2020. Economic and scientific domains represent the key investment targets of S3 strategies, i.e. those sectors the region aims to ‘specialize’ in. As evidenced in the figure, some S3 strategies had disproportionally high numbers of economic and scientific domains. We identified over 40 economic sectors and over 80 scientific sectors of specialization in some regions. Hence, our key takeaway is that there has been a ‘proliferation’ in many EU regions of both economic and scientific domains of the S3 strategies.
Number of economic and scientific domains of S3 strategies by region
We examined the clustering of regions according to economic priorities by applying a cluster analysis to the different economic domains with the aim of classify regions into groups of specializations. S3 strategies in Europe are clustered into five distinctive groups, each with a reasonable geographical spread: 1) Food and metal manufacturing; 2) Agri-food and hospitality; 3) ICT and health; 4) Creative and leisure; and 5) Energy and resources. The allocation of territories to these individual clusters reflects their existing specializations reasonably well.
The statistical review of S3 strategies reveals two patterns: on the one hand, regions generally aimed to specialize in ‘relevant’ economic domains, i.e. in domains related to the given region’s existing strengths/specializations; on the other hand, a relative proliferation of specializations (too many regions specializing in too many economic domains) is evident across the EU, producing significant overlaps in specializations across territories.
Finally, the analysis of the underlying drivers of the key characteristics of the regional strategies reveals that S3 strategies were, by and large, loosely connected with the individual regions’ characteristics. Apart from local government quality, the economic and scientific domains as well as the policy objectives included in the strategies did not reflect each region’s intrinsic conditions. More concise and more focused strategies were only implemented in territories with better local government quality, meaning that these territories pursued clearer and less complex strategies with a more realistic and manageable number of priorities.
Rather than reflecting each individual territory’s intrinsic characteristics, S3 strategies, to a large extent, mimic neighbouring territories. It could be described as a ‘copycat’ system that is far more prevalent in regions with a low-quality local government. Countries and regions defined their number of economic and scientific domains and their policy priorities by what their neighbours did, rather than by their own needs and perceived potential. This, to a large extent, accounts for the proliferation of priorities and lack of distinctiveness of individual S3 strategies.
Hence, the question whether smart specialization is truly ‘smart’ remains. Our analysis indicates that how S3 was applied was not ‘smart’ enough. Most S3 strategies include far too many axes of intervention. Limited concurrence with the strengths and specialization of individual territories which the S3 strategies targeted, remains the norm. Further research is necessary to assess the efficiency of the massive S3 policy experiment once the payments linked to the 2014–2020 programming period wrap up. However, the tendency—especially among regions with low-quality local government—to mostly imitate their neighbours and to ensure ‘token compliance’ with EU requirements is likely have resulted in inefficient strategies that have failed to deliver on their promise to mobilize local economic potential and to enhance the level of development and quality of life across Europe as a whole.
Disclaimer: The views expressed in this article are those of the authors based on their experience and on prior research and do not necessarily reflect the views of UNIDO (read more).
- See Iacobucci, D. and Guzzini, E. (2016). Relatedness and connectivity in technological domains: Missing links in S3 design and implementation. European Planning Studies, 24(8), 1511–1526; and McCann, P. and Ortega-Argilés, R. (2016). The early experience of Smart Specialisation implementation in EU Cohesion Policy. European Planning Studies, 24(8), 1407–1427; and Crescenzi, R., de Blasio, G. and Giua, M. (2018). Cohesion Policy incentives for collaborative industrial research: evaluation of a Smart Specialisation forerunner programme. Regional Studies, 1-13; and Gianelle, C., Guzzo, F. and Mieszkowski, K. (2019). Smart Specialisation: what gets lost in translation from concept to practice? Regional Studies, 1-12.