This paper tests for employer learning about worker ability and quantifies the role of learning in improving the allocation of talent in the labor market for computer scientists. We match the job history of over 40,000 Ph.D. computer scientists (CS) with publications and patent applications that signal their research ability. Workers who publish at CS conferences are twice as likely to move into a top tech firm in the next year as similar coworkers without a paper. Higher-quality papers are often filed as patent applications, but the fact of filing remains private information at the incumbent employer for 18 months. Authors of such papers experience a delayed increase in inter-firm and upward mobility. Without employer learning from on-the-job research, the innovation output of early career computer scientists would drop by 16%, 30% of which is due to less efficient sorting between firms and 70% due to greater misallocation within firms.

This paper investigates the role of firms in discovering new inventors who apply for a patent for the first time. Using employer-employee data from the Italian Social Security Institute matched with patent applications from 1987 to 2009, we identify more than one hundred thousand potential inventors, who either apply for a patent on the job or are predicted to ever invent based on observable characteristics. We find substantial heterogeneity in the discovery of new inventors across firms. Younger potential inventors are much less likely to start applying for patents at a lower-wage firm. The gap between low-wage and high-wage firms in patenting disappears, however, among established inventors with prior patent applications. Further, there is on average a 3-8 log point increase in the annual wage when a worker files her first patent application. We interpret the empirical findings through a model that combines employer learning with incentive contract. When firm investment and worker effort are substitutable, less productive firms would rely more on wage incentive to increase innovation, consistent with our finding that lower-wage firms set a higher wage return to patenting despite limited job mobility among inventors.

This paper measures gender bias in what people say about women versus men in an anonymous online professional forum. I study the content of posts that refer to each gender, and the transitions in the topics of discussion that occur between consecutive posts in a thread once attention turns to one gender or the other. I find that discussions about women tend to highlight their personal characteristics (such as physical appearance or family circumstances) rather than their professional accomplishments. Posts about women are also more likely to lead to deviations from professional topics than posts about men. I interpret these findings through a model that highlights posters’ incentives to boost their own identities relative to the underrepresented out-group in a profession.

This paper examines the existence of an unwelcoming or stereotypical culture using evidence on how women and men are portrayed in anonymous discussions on the Economics Job Market Rumors forum (EJMR). I use a Lasso-Logistic model to measure gendered language in EJMR postings, identifying the words that are most strongly associated with discussions about one gender or the other. I find that the words most predictive of a post about a woman are typically about physical appearance or personal information, whereas those most predictive of a post about a man tend to focus on academic or professional characteristics.

This paper documents gender differences in life-cycle returns to social skills and math skills in the labor market. Using the National Longitudinal Survey of Youth 1979 data, I test for whether women and men sort into occupations that match with their pre-market skills, and whether there are increasing returns to skills as employers learn about workers’ abilities over time. Workers with higher social skills choose occupations that put higher emphasis on job interactions, but this sorting effect is stronger for men than for women and the gap is widening over the life-cycle. Math skills are also positively correlated with social characteristics of an occupation such as leadership activities, and there is a significant gender gap in sorting by math skills. I then follow the employer learning literature to estimate the returns to each skill and the growth of returns with experience. Returns to social skills and math skills grow at a faster rate for men than for women, suggesting differential speed of employer learning. However, the initial of return to a female worker’s math skills is significantly higher such that on average women enjoy higher returns to math skills in the first 10-15 years of their career. These findings reflect gender differences in both workers’ occupational sorting and employers’ belief updating process, and suggest a higher return to investing in skills that counter beliefs about gender stereotypes.