Behavioral Bias in School Choice: Theory and Empirics

Title: Behavioral Bias in School Choice: Theory and Empirics

Abstract: A fundamental component in the theoretical school choice literature is the subset selection problem a student faces in deciding which schools to apply to. Recent models consider a student who is unsure of their strength as an applicant and can only apply to at most k schools of varying selectiveness, and therefore must diversify their limited portfolio in order to optimize the best school they get into. However, experience suggests that students are additionally influenced by crucial behavioral biases based on reputational effects: they experience a subjective reputational benefit when admitted to a selective school, and a subjective loss based on disappointment when rejected.

Guided by behavioral economics work on loss aversion, we propose a behavioral model by which a student balances these subjective effects with the quality of the school they eventually attend, changing their choices in interesting and dramatic ways (EC 2024, https://arxiv.org/abs/2403.04616). In particular, where a rational applicant spreads their applications evenly across the spectrum of selectiveness, a biased student applies very sparsely to highly selective schools and instead primarily targets less selective schools. Simultaneously, our model is also rich enough to cover a range of ways that biased students cope with fear of rejection, including occasionally applying to schools that are too selective. As a result of these “mistakes,” biased students obtain a quantitatively lower expected admissions outcome.

In the second half of the talk, I will touch on empirical evidence of such behavioral biases from the NYC High School Match. Using data from the DOE, we document disparities in undermatching -- that is, matching to a less selective school than a student could have been admitted to -- and investigate common "student-side" sources of these disparities (e.g. informational and strategic barriers), with the ultimate goal of designing helpful interventions.

Bio: Emily Ryu is a fourth year PhD student in CS at Cornell University advised by Eva Tardos and Jon Kleinberg, and supported by an NSF Graduate Research Fellowship. She is broadly interested in algorithmic game theory and mechanism design, especially involving agents with behavioral and cognitive biases. In her free time, she spends large amounts of time holding large cups of coffee or her large cat (or frequently both).