Research on occupational licensing suggests that licenses reduce labor supply and generate a wage premium. Rather than effects on one’s own occupation, I test for the presence of wage spillovers onto other occupations with similar latent skills. Using data from O*NET, I cluster occupations together using Hierarchical Agglomerative Clustering. Leveraging cross-state variation in individual licensing status from the CPS, and using a border discontinuity design on individual ACS microdata, I estimate the labor market spillovers of licenses onto other occupations. I find that a 10 percentage point increase in licensure rates in related occupations reduces individual earnings in one’s own occupation by approximately 2-2.5%. These effects are particularly strong for women, Non-Hispanic black, and foreign-born Hispanic workers. Licensing spillovers shift the composition of workers in related occupations. Contrary to a standard labor supply prediction, overall employment falls in related occupations. Falling earnings combined with falling employment are more in line with the predictions of a monopsony model where licensing reduces the feasibility of outside options and increases search costs.