Promoting regional growth and innovation: relatedness, revealed comparative advantage and the product space

A multi-sector model of relatedness, growth and industry clustering

“…This would be particularly clear in an expanded framework in which λ M differs by varying degrees for every pair of industries, but the degrees of relatedness here are sufficient to intuitively see these regional growth characteristics. In this way, the model captures the implications of the product space (Hidalgo et al, 2007) for regional economies (Cicerone et al, 2019). The model also captures many of the principles of regional development policies (McCann & Ortega-Argilés, 2015).…”

“…Meanwhile, following (Teece et al, 1994), other types of frameworks use coherence measures of co-occurrence as weightings for knowledge flows (Rocchetta & Mina, 2019). Furthermore, regional applications of the Hausmann-Hidalgo types of complexity measures (Daboín et al, 2019;Escobari et al, 2019;Mealy & Coyle, 2019) use network-centrality models based on the conditional probabilities of specialisation to weight these knowledge spillover values, although these latter models need to be adjusted using bootstrapping techniques to make them meaningful at the regional level (Cicerone et al, 2019). Delgado et al (2016) use similar relatedness metrics to define types of clusters that alternatively can be used to assign firms as related-industry members of each cluster.…”

“…In particular, the relatedness dimensions share some common features such that even though the sectors may differ, to some extent they also overlap in terms of common knowledge characteristics or capabilities. Moreover, these relatedness features also cut across simple explanations of growth based on regional sectoral structure, in that while the distinction between sectoral specialisation and diversity appears to play little role in explaining regional growth (Melo et al, 2009;Beaudry & Schiffauerova, 2009;de Groot et al, 2016), relatedness between local firms and industries appears to be almost always evident in local growth processes (Cicerone et al, 2019). Secondly, in all countries there are successful clusters of economic activity, and especially knowledge-intensive activities, which are located well away from the core highly urbanised areas, and which seem to thrive even without significant scale or proximity effects.…”

“…These types of proximity mechanisms also give technologically related firms an incentive to co-locate in clusters, but the relatedness feature is missing from spatial endogneous growth models despite providing a key mechanism for explaining differences in rates of regional innovation. Complex network relationships are incompatible with orthodox endogenous growth modelling techniques, so much of the empirical research on growth and proximity relies on defining metrics that describe the position of a product or region within the network space (Hausmann & Hidalgo, 2011;Cicerone et al, 2019). Alternatively, this article shows how the technological and spatial proximity features of the relatedness literature can be incorporated into orthodox endogenous growth theory in a relatively straightforward manner.…”

Detecting the intellectual pathway of resilience thinking in urban and regional studies: A critical reflection on resilience literature

“…MAI And CHAn specialization versus diversification, the role of network, such as related variety (Cainelli, Ganau, & Modica, 2019) and unrelated variety (Lee, 2017), connectedness and centrality (Cicerone, McCann, & Venhorst, 2019) in shaping regional economic resilience have been discussed in the recent literature.…”