Industrial districts/clusters and smart specialisation policies

The literature on clusters and industrial districts has been growing at an unprecedented pace in the last two decades. While the origin of the notion of industrial district is older and can be attributed to the important work of Marshall (1920), the term “cluster” was introduced by Porter (1990; 1998) in the 1990s, to characterize the emergence in space (clustering) of specific types of specialized agglomerations, where specialized firms and institutions co-evolve and interact (Belussi, 1996).
A better general theoretical understanding of the elements representing the constituency of the “model” was developed by numerous contributions at the intersection between economic geography and management studies (Becattini, 1990; Saxenian, 1994; Prouder and St John, 1996; Asheim, 1996; Markusen, 1996; Gordon and McCann, 2000; Belussi, 2006; Maskell and Kebir, 2006; Asheim et al., 2011).
Numerous authors also focused their attention on the granularity of the concept, articulating their analysis on various aspects of industrial districts and clusters, studying the growth-factors linked to the elements which form this specific pattern of local development (Becattini et al., 2009): a) the presence of external economies or externalities (Breschi and Lissoni, 2001), b) the process of knowledge creation and diffusion (Belussi, and Gottardi, 2000, Belussi and Pilotti, 2002), c) new firm entry and start-ups (Baptista and Swann, 1998; Stuart and Sorenson 2003; Feldman and Braunerhjelm, 2006), d) learning and capability formation (Amin and Wilkinson, 1999), e) skills transmission and labor market specialization (Sorenson and Audia, 2000), and f) the emergence of indigenous specialized suppliers (Hervas-Oliver, et al., 2017).
Another important issue discussed in the literature concerns the evolution of clusters during time. Belussi and Sedita (2009) have adopted the perspective of multiple path dependencies, based on an empirical analysis of Italian cases. The authors highlight that clusters may share some commonalities as regards the factors that underpin their emergence and take-off, but subsequently they give rise to a variety of developments, depending on knowledge variety, innovation intensity, local firm leadership, and external conditions. Other theoretical contributions (Martin and Sunley, 2011; Ter Wal and Boschma, 2011) have suggested the existence of more deterministic cluster trajectories (allowing only a possible adaption) across different stages over time (with time as an irreversible factor) such as emergence, growth, maturity, decline or renewal (for a review see Bergman, 2007). Thus, cluster specialization leads to higher synergies among firms but too much similarity bears the so-called “cluster paradox”: the risks of decreasing returns, uniformity, drop of innovativeness, and at the end, lock-in (Martin and Sunley, 2006; Menzel and Fornahl, 2010; Audretsch and Feldman, 1996).
A broad distinction can be made between industry-driven explanations of cluster growth (Ter Wal and Boschma, 2011) and place-based explanations.
The former explains the emergence of the clusters as deriving from knowledge discontinuity and the introduction of breakthrough innovations. During the first stage of experimentation and when knowledge is not much codified but grows in a cumulative way, agent proximity and spin-offs create favorable business conditions. Thus, one can observe high levels of industry concentration in clusters. In the maturity phase other firms are created at a global scale in dispersed places, and clusters lose their shape. This picture is clearly significant in the case of high-tech sectors (Menzel and Fornahl, 2010).
The latter reflect a cluster-specific view and suggest that clusters can gr