  This paper develops a new data-driven approach to characterizing latent
worker skill and job task heterogeneity by applying an empirical tool from
network theory to large-scale Brazilian administrative data on worker--job
matching. We microfound this tool using a standard equilibrium model of workers
matching with jobs according to comparative advantage. Our classifications
identify important dimensions of worker and job heterogeneity that standard
classifications based on occupations and sectors miss. The equilibrium model
based on our classifications more accurately predicts wage changes in response
to the 2016 Olympics than a model based on occupations and sectors.
Additionally, for a large simulated shock to demand for workers, we show that
reduced form estimates of the effects of labor market shock exposure on
workers' earnings are nearly 4 times larger when workers and jobs are
classified using our classifications as opposed to occupations and sectors.
