# Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters in the world economy

arXiv: https://arxiv.org/abs/1902.04613 · HTML: https://mlsanity.com/paper/1902.04613/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics](https://mlsanity.com/paper/2307.03195/) (2023)
- [Variational Bayes survival analysis for unemployment modelling](https://mlsanity.com/paper/2102.02295/) (2021)
- [Labor Space: A Unifying Representation of the Labor Market via Large Language Models](https://mlsanity.com/paper/2311.06310/) (2023)

## Abstract (arXiv)

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.