  Groups of firms often achieve a competitive advantage through the formation
of geo-industrial clusters. Although many exemplary clusters, such as Hollywood
or Silicon Valley, have been frequently studied, systematic approaches to
identify and analyze the hierarchical structure of the geo-industrial clusters
at the global scale are rare. In this work, we use LinkedIn's employment
histories of more than 500 million users over 25 years to construct a labor
flow network of over 4 million firms across the world and apply a recursive
network community detection algorithm to reveal the hierarchical structure of
geo-industrial clusters. We show that the resulting geo-industrial clusters
exhibit a stronger association between the influx of educated-workers and
financial performance, compared to existing aggregation units. Furthermore, our
additional analysis of the skill sets of educated-workers supplements the
relationship between the labor flow of educated-workers and productivity
growth. We argue that geo-industrial clusters defined by labor flow provide
better insights into the growth and the decline of the economy than other
common economic units.
