Abstract
The location of the local network of firms impacts, positively or negatively, their economic performance. The interactions between different sectors in a territory are still not easily observable. We test the complexity of the economic structure at a local level, given the availability of data at a very granular scale. This could greatly assist in observing sectors or/and locations that play a dominant role in the regional economy. Thus, in order to interpret the economic structure of a territory, we used cluster-based analysis. The analysis helps in evaluating the interconnections among sectors that constitute a cluster. A novel method of describing the territorial economic structure is presented by applying Social Network Analysis (SNA) within cluster-based analysis to characterize the importance of both location and economic interconnections. In this study, we focus on the industrial agglomerations in Calabria, Italy, to underpin the potential of the region’s industries by using social networking analysis metrics. This research put forward new interpretations of SNA metrics that describe regional economic compositions. Our findings reveal that territorial social networks are a potential instrument for understanding interactions in regional systems and economic clusters and might help in highlighting local industrial potentials. We believe that this study’s results could be considered as the initial steps for a pioneer data-driven place-based structural analysis model.
Highlights
The economy is a system of individuals and enterprises bound together in markets, policies, laws, public services, and regulations [1,2]
Studies on economic complexity led to interdependences between the level of income dictated by the complexity of their productive structures and sustained growth, “indicating that development efforts should focus on generating conditions that would allow complexity to emerge in order to generate growth and prosperity” [3]
We focus on territorial social networking analysis, as the cluster l dimension to the network to identify and anticipate locations’ impact on the industry
Summary
The economy is a system of individuals and enterprises bound together in markets, policies, laws, public services, and regulations [1,2]. The economic complexity theory and methods have acquired interest within a broader perspective on the global system, whilst sustainability and social inequalities cast light on uncertainties for the future [4,5]. These complex systems contain unexpected properties and often respond in a nonlinear manner to shocks or changes [2]. The systems should self-organize, learn, and adapt to shocks to direct this complexity towards new sustainable trajectories [6]; in other words, systems should become “resilient” [1,4,7,8]. We argue that, in order to achieve sustainability and resilience, the system can no longer be “locked into” a particular trajectory of economic development [9,10]. It is argued that new technological pathways that deviate from past practices and attempt to deploy new technologies should be implemented [2]
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