Leonard Bocquet

How fast do labor markets adjust to technology shocks? This paper introduces a novel network framework to analyze how the structure of skill frictions shapes labor reallocation dynamics. Using expert data, I construct an occupation network where links capture feasible transitions based on shared skills. This network is sparse and clustered, with a few critical “bridge occupations” connecting otherwise separate clusters of occupations. Leveraging French administrative data, I show that workers transitioning through these bridges reach higher-wage, lower-unemployment occupations. Next, I develop a job-search model embedded in this occupation network and find that bridge occupations disproportionately influence overall reallocation speed. Quantitatively, the model predicts that robot adoption induces a slow reallocation process—lasting around ten years—and reduces welfare gains by 40%, an order of magnitude higher than previous estimates. Policies targeting bridge occupations significantly accelerate reallocation, outperforming interventions focused on expanding sectors. These findings highlight the crucial role of occupation networks in shaping labor market adjustments and provide new insights for policy design.