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%.

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.

Does the rise in open-source software development provide an opportunity for software developers and engineers to signal their ability to potential employers, and is this signaling value higher for workers from less advantaged backgrounds? We answer this question by matching open-source contributions on GitHub to employment outcomes from LinkedIn. We investigate whether workers increase open-source contributions before changing jobs. In particular, we examine whether the effects of this activity on labor market outcomes, such as moving into a higher-paid job, are stronger for workers from less advantaged education and demographic backgrounds.

We use the texts of legal complaints from trade secret litigation to study how firms responded to the enactment of the Defend Trade Secrets Act (DTSA) in 2016. One of the goals of this act was to increase the protection of American firms against international trade secret theft. Within a year of the act's passage, trade secret litigation surged by 33%. However, this increase was predominantly driven by US companies suing employees who transitioned to other domestic firms. We show that the spike in litigation post-DTSA was more pronounced in states with weaker enforceability of non-compete agreements. This evidence suggests that firms might resort to trade secret litigation as an alternative to non-compete clauses. We examine whether a trade secret lawsuit against an employee affects her own job mobility, productivity and business ventures, as well as spillover effects on her former co-workers at the plaintiff.

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.