Reveal or Conceal? Employer Learning in the Labor Market for Computer Scientists

About this event

How does employer learning affect the allocation of talent in the market for research scientists? I study this question using the job histories of 40,000 Ph.D.'s in computer science (CS) matched to their scientific publications and patent applications. Authorship of a CS conference proceeding doubles the probability that a researcher moves to one of the top tech firms in the following year, controlling for her origin firm and experience, implying a strong role for public learning in the matching process between more productive workers and more productive firms. Many higher-quality papers are accompanied by a related patent application, but the application is private information for 18 months. Authors of such papers are somewhat less likely to move up the firm ladder in the following year, but are more likely to end up at a top firm within three years, as predicted by a model of employer wage setting with asymmetric information. I estimate a structural version of the model and find that if employers did not learn about workers from post-PhD research, there would be 16% fewer scientific publications by early-career computer scientists. Disclosing patent applications one year faster would increase innovation by 1%, driven by a faster rate of positive assortative matching.

This event will take place in SAL 1.04, 1st Floor Conference Room, Sir Arthur Lewis Building, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH.

The building is labelled SAL on the map. Enter the building via Lincoln's Inn Fields.