Urban labor markets are complex systems where occupations are connected through shared skills. When workers lose jobs, their ability to move into related occupations determines how quickly cities recover from economic shocks [1].
At SUNLab we study these networks of occupations and skills to understand the structural resilience of labor markets. Using large-scale employment and skills data, we build job networks that capture how easily workers can transition between occupations [1].
Our research shows that cities with more connected job networks—where occupations share overlapping skills—experience lower unemployment during economic shocks, such as the Great Recession [1].
These networks reveal that economic resilience is not only about the number of jobs in a city, but about the structure of connections between occupations. Workers in occupations that are more embedded in these networks also tend to earn higher wages and have more mobility opportunities [1, 2].
More recently, we extended this framework to study technology-driven labor disruptions, showing how new measures of unemployment risk can reveal the impact of AI and automation across occupations and regions [2, 3].
References
[1] Moro, E., Frank, M. R., Pentland, A., Rutherford, A., Cebrian, M., & Rahwan, I. (2021). Universal resilience patterns in labor markets. Nature Communications, 12, 1972.
[2] Frank, M. R., Autor, D., Bessen, J. E., Brynjolfsson, E., Cebrian, M., Deming, D. J., Feldman, M., Groh, M., Lobo, J., Moro, E., Wang, D., Youn, H., & Rahwan, I. (2019). Toward understanding the impact of artificial intelligence on labor. Proceedings of the National Academy of Sciences, 116, 6531–6539.
[3] Frank, M. R., Ahn, Y.-Y., & Moro, E. (2025). AI exposure predicts unemployment risk: A new approach to technology-driven job loss. PNAS Nexus, 4, pgaf107.