College Selection and Major Choice
Paper Session
Sunday, Jan. 4, 2026 10:15 AM - 12:15 PM (EST)
- Chair: Joseph G. Altonji, Yale University
Sequential College Admission Mechanisms and Off-Platform Options
Abstract
College admission platforms aim at achieving a balance between avoiding congestionand allowing for ex-post flexibility in students’ matches. The latter is crucial as the
existence of off-platform options implies that some students will drop out of the platform
in favor of their outside option, freeing up seats in on-platform programs. Sequential
assignment procedures introduce such flexibility, by creating a dynamic trade-off for
students: they can choose to delay their enrollment decision to receive a better offer
later, at the cost of waiting before knowing their final admission outcome. We quantify
this trade-off in a setting in which waiting costs can be heterogeneous. We use rich
administrative data on rank-ordered lists and waiting decisions from the French college
admission system to estimate a dynamic model of application and acceptance decisions.
We find that waiting costs are large, especially for students of low socio-economic status.
Nevertheless, we find large welfare gains for all groups from using a multi-round rather
than a more standard single-round system, as it increases the number of matches and
enables students to enroll in more specialized programs
Heuristic Self-Evaluation in High-Stakes Decisions: Fields of Study and Long-Term Earnings
Abstract
This paper examines how uncertainty about one's ability interacts with cognitive heuristics to shape high-stakes educational choices and career paths. Using administrative data, I implement a regression discontinuity at a round-number threshold on the national university entrance test. Crossing the threshold on the first test sharply increases the likelihood of pursuing high-return STEM degrees, despite conferring no formal admission advantage and often falling below program cutoffs. This behavior is consistent with left-digit bias: students interpret the score's left digit as a meaningful signal, leading them to improve their scores through retesting, apply to, and ultimately enroll in STEM programs. In subsequent years, they are more likely to work in the tech sector, earn higher wages, and reach the top of the income distribution. Leveraging this behavioral discontinuity, I estimate large returns to STEM degrees among students who are uncertain about applying. The findings suggest that heuristic self-evaluation can generate persistent disparities in economic opportunity that are unrelated to actual ability.Reshuffling University Access: Analyzing Grade Distributions in the Absence of Standardized Testing
Abstract
This paper compares grade distributions under standardized and non-standardized university entrance exams. I exploit a natural experiment in the United Kingdom during the COVID-19 pandemic, when teacher-assigned grades replaced in-person standardized A-level exams. I find that top grades were not distributed evenly but varied based on students’ backgrounds, the schools they attended, and the subjects they studied, even after accounting for differences in prior academic achievement. Female students were 6 percentage points more likely to receive top grades than male students, and private schools exhibited the largest increases in top-grade assignments among all school types. Students taking less competitive and less quantitative subjects received top grades 6--8 percentage points more often than their peers. I also find serial correlation in grading patterns across years and within subject groups, with limited spillovers across subject types. These systematic patterns highlight how incorporating non-standardized assessments into university admissions can unevenly benefit students depending on their background, school, and subject choices.JEL Classifications
- I23 - Higher Education; Research Institutions