Within academia, men are tenured at higher rates than women are in most quantitative fields, including economics. Researchers have attempted to identify the source of this disparity but find that nearly 30% of the gap remains unexplained even after controlling for family commitments and differences in productivity. Using data from academic economists' CVs, I test whether coauthored and solo-authored publications matter differently for tenure for men and women. While solo-authored papers send a clear signal about one's ability, coauthored papers are noisy in that they do not provide specific information about each contributor's skills. I find that men are tenured at roughly the same rate regardless of whether they coauthor or solo-author. Women, however, suffer a significant penalty when they coauthor. The results hold after controlling for the total number of papers published, quality of papers, field of study, tenure institution, tenure year, and the number of years it took an individual to go up for tenure. The result is most pronounced for women coauthoring with only men and is less pronounced the more women there are on a paper, suggesting that some gender bias is at play. I present a model in which bias enters when workers collaborate and test its predictions in the data.
Does a confidence gap exist between men and women who made it to the very top of their careers? Using data from a select group of economists working in top U.S. universities, we find that women are still less confident than men along two margins. First, when asked about their level of agreement on survey questions about the economy, women are less likely to give “extreme” answers in which they strongly agree or disagree. Second, women are less confident in the accuracy of their answer. The results persist after controlling for the year the PhD was granted, the PhD awarding institution, the current institution, and the number of solo and co-authored publications up to the point of tenure. We provide suggestive evidence that the confidence gap is driven by women being less confident when asked questions that are outside their field of expertise.
Stereotypes lead people to over-react to information that confirms their priors and under-react to information that goes against their priors. I test a model of stereotyping using data on police officer assaults. I match data from the New York City Stop, Question, and Frisk program to data on police deaths to test whether police officers have different responses to shootings depending on the shooter's race. I find that when an officer is shot by a white civilian, there is no change in frisk, arrest, or use of force patterns. However, when an officer is shot by a black civilian, frisking and use of force against black civilians increases dramatically. I test alternative explanations such as rational updating, retaliation, and demographic segregation.
There is evidence that, in some contexts, income shocks cause conflict. The literature demonstrating this relationship uses rainfall shocks to instrument for income shocks, arguing that in agriculturally-dependent regions, negative rain shocks lower income which incites violence. This identification strategy relies on the assumption that rainfall shocks affect conflict only through their impacts on income. This paper evaluates this exclusion restriction in the context of religious conflict in India. Using data on dam construction, I identify districts that are downstream from irrigation dams and show that income in these areas is much less sensitive to rainfall fluctuations. However, rain shocks remain equally strong predictors of riot incidence in these districts. I explore other channels through which rainfall might affect conflict.