Abstract
Social connections that reach distant places are advantageous for individuals, firms and cities, providing access to new skills and knowledge. However, systematic evidence on how firms build global knowledge access is still lacking. In this paper, we analyse how global work connections relate to differences in the skill composition of employees within companies and local industry clusters. We gather survey data from 10% of workers in a local industry in Sweden, and complement this with digital trace data to map co-worker networks and skill composition. This unique combination of data and features allows us to quantify global connections of employees and measure the degree of skill similarity and skill relatedness to co-workers. We find that workers with extensive local networks typically have skills related to those of others in the region and to those of their co-workers. Workers with more global ties typically bring in less related skills to the region. These results provide new insights into the composition of skills within knowledge-intensive firms by connecting the geography of network contacts to the diversity of skills accessible through them.
Highlights
Understanding the role of networks in the various domains of social and economic life is one of the central challenges for science today (e.g., Borgatti et al 2009; Lazer et al 2009)
We find that the relatedness of skills between those who are connected on LinkedIn does not differ from the relatedness of skills between co-workers
While there is a high degree of similarity and relatedness of skills between co-workers, which is expected given the formation of creative and more productive teams (Becker and Murphy 1992; Brennecke and Rank 2017; Neffke 2019), this is the case concerning social connections reported on LinkedIn
Summary
Understanding the role of networks in the various domains of social and economic life is one of the central challenges for science today (e.g., Borgatti et al 2009; Lazer et al 2009). Social networks are proven to inform us about how information spreads (Bakshy et al 2012; Helbing et al 2015), about the role of social influence on individual behaviour (Aral and Nicolaides 2017; Ugander et al 2012), and the impact of social network structure on the performance of teams (Guimera et al 2005; Vedres 2017), among other things. Research on information flows and innovation, for example, shows that distant ties tend to be most beneficial when combined with cohesive local networks that can efficiently process complex information (Aral 2016; Bathelt et al 2004; Granovetter 1973; Ter Wal et al 2016; Tóth and Lengyel 2019)
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