Research
Research
Information Bias and Selection of Female Professors
Media Coverage: State Press
Abstract: Individuals frequently rely on signals produced by others, such as ratings, rankings, or informal evaluations, when forming beliefs. While these signals can be informative, they often embed information bias. In the context of higher education, one such bias arises along gender lines: female professors tend to receive lower ratings than their male counterparts, even when performance is comparable. Using novel survey data collected from one of the largest public universities in the U.S., this paper examines how students interpret and act upon these biased signals when selecting instructors. I show that while students do value better rated professors, they are largely unaware of the underlying gender bias in evaluations. To better understand student decision-making and belief formation, I develop a Bayesian updating model in which students form enrollment choices based on signals that systematically disadvantage women. I find that this process leads to statistical discrimination: conditional on the same rating, students expect lower performance from female professors than from male professors. Structural estimates reveal that this misperception results in suboptimal enrollment choices, leading students to forgo utility. To address this, I implement a randomized informational intervention that corrects students’ beliefs about the presence of gender bias in evaluation signals. The treatment increases students’ willingness to pay for female instructors, shifts enrollment behavior, and eliminates rating gaps in evaluations. These findings illustrate that a light-touch informational interventions can meaningfully improve decision-making and promote equity.
Assessing the Heterogeneous Effects of Remedial Education (with Esteban Aucejo)
Journal of Human Capital
Abstract: Using data from a large U.S. public university, we analyze the effect of remedial math education on student outcomes with a regression discontinuity approach. Students with different math preparation levels are placed in remedial courses of varying difficulty. Our study finds that remediation increases graduation rates by 22.5 percentage points for students with lower math skills in math-intensive majors. However, students with stronger math backgrounds in advanced remedial classes do not see improved graduation outcomes. Additionally, implementing an adaptive learning remedial program increased passing rates by 9.5 percentage points, leading to a 4.5 percentage point rise in five-year graduation probability.
Assessing the Role of Study Habits on Students' Beliefs and Academic Performance (with Agustina Affonso Peyre, Esteban Aucejo, and Tomas Larroucau)