Abstract
Groups of firms often achieve a competitive advantage through the formation of geo-industrial clusters. Although many exemplary clusters are the subjects of case studies, systematic approaches to identify and analyze the hierarchical structure of geo-industrial clusters at the global scale are scarce. In this work, we use LinkedIn’s employment history data from more than 500 million users over 25 years to construct a labor flow network of over 4 million firms across the world, from which we reveal hierarchical structure by applying network community detection. We show that the resulting geo-industrial clusters exhibit a stronger association between the influx of educated workers and financial performance, compared to traditional aggregation units. Furthermore, our analysis of the skills of educated workers reveals richer insights into the relationship between the labor flow of educated workers and productivity growth. We argue that geo-industrial clusters defined by labor flow provide useful insights into the growth of the economy.
Highlights
Groups of firms often achieve a competitive advantage through the formation of geoindustrial clusters
By proposing an organic way to identify geo-industrial clusters from a labor flow network, we reveal the hierarchical organization of geo-industrial clusters across multiple scales in the global economy and argue that examining their interconnected hierarchical structure is a critical step towards understanding their role in broader economic contexts
We show that the structure of this global labor flow network reveals the multi-scale hierarchical organization of geoindustrial clusters, which constitute a natural, emergent unit of analysis for the global economy
Summary
Groups of firms often achieve a competitive advantage through the formation of geoindustrial clusters. Even though modern telecommunication technologies allow remote collaboration and many companies are no longer restrained by physical supply chains, numerous and conspicuous geo-industrial clusters concentrate within small geographical areas Such geographical agglomeration of interconnected firms, or Clusters[1,2], is a key conceptual framework for policymakers and business economists, from global organizations such as the OECD3 and the World Bank[4,5], to regional development agencies in national governments[6]. Labor flow provides crucial clues to the identification of geo-industrial clusters[15,16] To map these geo-industrial clusters we leverage LinkedIn’s data set, which documents the professional demographics and employment histories of >500 million individuals between 1990 and 2015. We show that the structure of this global labor flow network reveals the multi-scale hierarchical organization of geoindustrial clusters, which constitute a natural, emergent unit of analysis for the global economy
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