Reveal or Conceal? Employer Learning in the
Labor Market for Computer Scientists
The efficient allocation of labor relies on the identification of talent. When employee output is not publicly observable, employers have an incentive to take advantage of private information, potentially leading to the misallocation of labor among firms. This paper provides empirical evidence of employer learning and quantifies the impact of learning on job mobility and innovation outputs in the labor market for computer science (CS) Ph.D.’s. CS conference proceedings provide public information on research effort by existing CS workers. Among papers authored by researchers from industry, about one-quarter can be matched to a contemporaneous patent application – an indicator of a more valuable innovation. Yet the fact of the application remains private information at the incumbent employer for 18 months. Consistent with public learning, researchers with a new paper have higher inter-firm mobility rates than do coworkers without a paper. Initially, authors of papers with a matched patent are less likely to move than authors without a patent application. But once the patent application becomes public, their mobility rates cross over. Authors of papers with a matched patent are also 35% more likely to move to a top tech firm. These patterns confirm the predictions of a model in which incumbent firms have initially private information on more productive researchers. Structural estimates of the model suggest that if papers and patents were disclosed simultaneously, high-ability workers would sort more quickly to high-productivity firms. The implied increase in allocative efficiency would increase innovation outputs by about 5%.