Job Market Paper
This paper shows that the within-country spatial distribution of colleges largely contributes to spatial inequalities. Using data on the universe of college applicants and programs in France, I document that higher education options are unevenly distributed across space while students' demand is highly sensitive to geographic proximity. This creates inequalities in access to higher education across space, feeding gaps in educational attainment and spatial skill sorting. To quantify these effects, I build a dynamic model linking equilibrium sorting on the higher education market and location choices of entry-level workers. I show that students' and programs' preferences can be identified and estimated from data on choices and equilibrium outcomes. One-third of regional gaps in educational attainment are explained by the interaction of the uneven distribution of colleges and mobility frictions. Eliminating the latter, however, generates a trade-off, as it benefits students from low-opportunity areas but accelerates their migration to higher education hubs, magnifying regional inequalities. Low-opportunity areas could halt this brain drain and outsource the education of their local labor force by tying mobility scholarships and incentives to return.
Robustness of Two-Way Fixed Effects Estimators to Heterogeneous Treatment Effects, TSE Working Paper, September 2022, revised June 2023
This paper provides necessary and sufficient conditions for the Two-Way Fixed Effects (TWFE) estimator to be robust to heterogeneous treatment effects. I decompose the TWFE estimator to show that it is a weighted sum of five different types of two-by-two comparisons, with positive weights. I show that parallel trends assumptions on either the untreated or treated potential outcomes must hold for each comparison to identify the Average Treatment Effect (ATE) of the group switching treatment status, when the effect of the treatment is contemporaneous. Both parallel trends assumptions are thus necessary and sufficient for the TWFE estimator to weigh each ATE positively, when allowing treatment effects to be heterogeneous across groups and periods. I further provide sufficient conditions under which the TWFE estimator remains valid even in the presence of dynamic treatment effects. Finally, I show how to exploit all available comparisons to build unbiased estimators of the ATT and ATE.
College Admission Mechanisms and the Opportunity Cost of Time, with Olivier De Groote, Margaux Luflade and Arnaud Maurel
College admission platforms aim at achieving a balance between avoiding congestion and 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 and its distributional consequences in a setting in which waiting costs can be heterogeneous across socio-economic groups. To do so, we use administrative data on rank-ordered lists and waiting decisions from the French college admission system to estimate a dynamic model of application decisions. We find that waiting costs are a key determinant of the timing of students’ acceptance decisions and of their final assignment. Nevertheless, we find substantial, but unequal, welfare gains from using a multi-round system.
Application Mistakes and Information Frictions in College Admissions, with Tomas Larroucau, Christopher Neilson and Ignacio Rios
We investigate the prevalence and relevance of application mistakes in a seemingly strategyproof centralized college admissions system. Using data from Chile, we identify a common mistake: applying to programs without meeting all requirements. We find that changes in admission requirements over time increase admissibility mistakes. To address these issues, we design and implement a large-scale information policy, providing personalized information on admission probabilities to students. Results from a randomized controlled trial show that warning messages about listed programs significantly reduce application mistakes and improve outcomes. In collaboration with policymakers, we implement the policy at scale and show that on-the-fly information about programs’ cutoff scores has a causal effect on reducing students’ biases, application mistakes, and improving students’ outcomes. We find that changes in outcomes are primarily driven by changes in beliefs over admission probabilities at the bottom of their preference orders, reducing the incidence of biases on students’ applications.
Work in Progress
Incomplete Information and the Complexity of Centralized College Application Processes (Draft available upon request)
Centralized application systems have been argued to have the potential to help close the socioeconomic gap in access to higher education, by making information about the college application process transparent and easily accessible. Yet, exploiting data from the Chilean centralized admission mechanism, I show that 26% of the students submit an invalid application, suggesting that misinformation about the rules of the system is widespread. Taking this feature into account, I build a structural model of students' application decisions in order to study the impact of being misinformed on a student's admission outcome. I simulate the rank-ordered lists submitted by students in a counterfactual scenario where they would have been aware of the requirements for an application to be valid. I find that more than 80% of previously unassigned uninformed students would have been assigned under this scenario. More than half of these students belong to one of the first three deciles of the income distribution, indicating that the complexity of the application process disproportionately harms disadvantaged students.