Anaïs Fabre's PhD thesis, June 12th, 2024

June 12, 2024 Research

Anaïs Fabre will attend her thesis on Wednesday 12 June 2024 at 2:00pm, Auditorium 5.

Title: "Essays on Higher Education Access and Econometrics"

Supervisors: Thierry MAGNAC and Olivier DE GROOTE

To attend the conference, please contact the secretariat Christelle Fotso Tatchum

Memberships are:

  • Thierry Magnac: Professor of Economics, TSE-UT Capitole, Directeur de thèse
  • Olivier de Groote: Assistant professor, TSE-UT Capitole, Co-directeur de thèse
  • Michela TINCANI: Associate professor,  University College London, Rapporteure
  • Peter ARCIDIACONO: Professor of Economics, Duke University, Rapporteur
  • Christian BELZIL: Senior Researcher, CNRS/ENSAE-CREST, Examinateur


This thesis is composed of four chapters that have two main goals: (i) broaden our understanding of the determinants of inequalities in access to higher education, and (ii) expand and deepen our understanding of available econometric tools.

Chapter 1, `The Geography of Higher Education and Spatial Inequalities’, uses rich administrative data to document that the spatial distribution of higher education options is highly uneven, while students' demand for programs is elastic to their geographic proximity. Leveraging policy changes, I show that mitigating barriers to mobility would allow students to apply farther away from their home location, but could generate a long-lasting brain drain toward higher education hubs. I build and estimate a dynamic model encompassing these channels, linking equilibrium sorting on the higher education market and location choices of entry-level workers. I find that the interaction of the uneven distribution of colleges and mobility frictions accounts for one-third of regional gaps in educational attainment. Eliminating mobility frictions, however, generates a trade-off, as it benefits students from low-opportunity areas but accelerates their migration to higher education hubs, magnifying regional inequalities. Policies tying mobility scholarships and incentives to return would resolve this tension.

Chapters 2 and 3 explore additional drivers of inequalities in access to higher education across students. Chapter 2, `Opportunity Costs of Time and the Design of College Admission Mechanisms', co-authored with Olivier De Groote, Margaux Luflade and Arnaud Maurel, documents how the design of college admission platforms impacts students' enrollment outcomes. We explore the welfare and distributional effects of using a sequential assignment procedure to match students to programs. Such mechanisms, commonly used around the world, create 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. Building and estimating a dynamic model of application and enrollment, we find that waiting costs are a key determinant of the timing of students’ acceptance decisions and of their final assignment. We find substantial, but unequal, welfare gains from using a multi-round system.

Chapter 3, `Incomplete Information and the Complexity of Centralized College Application Processes', focuses on information frictions in college admission procedures. Using data from the Chilean centralized admission mechanism, I document that 26% of college applicants submit an invalid application, suggesting that misinformation about the rules of the system is widespread. To study the impact of being misinformed on students' admission outcomes, I build and estimate a model of application decisions under incomplete information. 80% of unassigned uninformed students would have been assigned in a counterfactual scenario where information frictions are eliminated. Results indicate that the complexity of the application process disproportionately harms disadvantaged students.

Finally, Chapter 4, `Robustness of Two-Way Fixed Effects Estimators to Heterogeneous Treatment Effects', aims at deepening economists' understanding of the widely used Two-Way Fixed Effects (TWFE) estimator. It provides necessary and sufficient conditions for this estimator to be robust to heterogeneous treatment effects. I decompose the estimator to show that it is a weighted sum of five types of difference-in-differences, with positive weights. 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.