About

I am an applied microeconomist working on the economics of education and labor economics. My research combines original surveys, field experiments and structural models to understand agents’ choices and their consequences under imperfect information or externalities.

I received my PhD in Economics from Arizona State University and am currently Assistant Professor in the Department of Economics at Universidad ORT Uruguay.

• Economics of Education
• Labor Economics
• Applied Econometrics

Research

Working Papers

• The Role of Information Frictions in Study Habit Choice and Academic Performance with E. Aucejo, S. El Khoury and T. Larroucau
  Abstract

  We study whether information frictions about the relative effectiveness of study methods distort how college students allocate effort. Standard models of learning typically treat effort as a single input, with outcomes determined by its level rather than its composition. We relax this assumption and study effort as an allocation across methods with heterogeneous effectiveness. Combining panel surveys, administrative records, digital activity logs, and a randomized information intervention with over 2,000 undergraduates, we ask whether imperfect information about relative effectiveness leads to systematic misallocation and quantify the resulting consequences for performance. At baseline, students hold strikingly different beliefs about the effectiveness of common study methods, and many devote substantial time to passive strategies, such as rereading, despite evidence that active retrieval is more effective. These choices explain lower performance and larger errors in self-assessment. To distinguish biased beliefs from efficient heterogeneity, we randomize treated students into a general-feedback arm or a personalized-feedback arm that benchmarks their habits against peers and quantifies the gains from reallocation. General feedback has little effect. Personalized feedback widens the perceived effectiveness gap between active and passive methods by 42 percent, shifts time toward more effective practices, and raises subsequent exam scores by 0.05–0.08 standard deviations. To quantify the mechanisms linking belief updating to behavior, we develop and estimate a dynamic model of multidimensional study effort with learning-by-doing and convex allocation costs. We find that, for students with the weakest baseline beliefs, the personalized treatment is equivalent to a 16.5 percent reduction in the cost of adopting more effective methods. Together, these results identify information frictions about the effectiveness of multidimensional effort as a relevant determinant of academic performance.
• Overconfidence and College Applications: Biased Beliefs or Private Information?
  Abstract

  This paper studies the influence of overconfidence on college application behaviors and the subsequent implications for assignment outcomes within Chile’s centralized admission system. Using novel survey data, I construct an overconfidence measure based on students’ overestimation of their university admission exam scores. Combining the survey data with administrative records, I show that overconfident students have greater aspirations, evidenced by their higher likelihood of applying to college and opting for more selective programs than their non-overconfident counterparts. However, conditional on applying, this ambitious behavior backfires. Overconfident students are less likely to be assigned to a program in their application list than non-overconfident students with similar scores. Two main mechanisms drive the differences in application behavior: overconfident students overestimate their admission probabilities more than non-overconfident students and expect higher outcomes in case of enrollment. In addition, I study how preferences and beliefs are revised after students receive their actual scores. Overconfident students react to the lower grades revising the selectiveness of their preferences downward, mainly driven by a reduction in their expectations about admission probabilities.

Work in Progress

• Hybrid Higher Education: Attendance Modes and Peer Effects with S. Acerenza
• Navigating College: Personalized Information and Student Success with E. Aucejo, S. El Khoury, J. C. Fruehwirth and M. Troop
  Abstract

  This project evaluates whether personalized feedback can improve student well-being and academic performance in college. We conduct a randomized controlled trial with 2,800 undergraduates at Arizona State University. At the start of the semester, students complete a survey that measures challenges across four domains of college life: study habits, academic performance, social integration, and financial stress. Based on these scores, students are randomly assigned to treatment or control. Treated students receive personalized emails highlighting their weakest domain, comparative feedback relative to peers, and targeted video resources designed to address their primary area of concern. Outcomes are measured through follow-up surveys and academic records, focusing on exam performance. The study aims to assess whether tailored, low-cost interventions can reduce barriers to student success and improve persistence in higher education.
• AI Study Habits Chatbot and Student Classroom Outcomes with E. Aucejo and S. El Khoury
  Abstract

  This study evaluates whether AI-powered academic coaching can improve college students’ study habits and classroom performance. We implement a randomized controlled trial with 1,500 undergraduate students enrolled in intermediate economics courses at Arizona State University. After completing a baseline survey on study strategies and academic expectations, students are randomly assigned to one of three groups: a control group, a group receiving personalized written feedback on their study habits, and a group receiving the same feedback plus access to a custom-designed AI chatbot. The chatbot provides tailored academic support by interpreting study scores, generating personalized study plans, answering freeform questions, and processing course materials in-session. Follow-up survey data and administrative academic records will be used to measure effects on beliefs about study habits, time allocation, attitudes toward AI, and academic outcomes.
• Enhancing Active Learning in Microeconomics: The Implementation of Interactive Educational Bots with E. Aucejo, M. Carbajal Vega, S. El Khoury and P. Ramirez De La Viña
  Abstract

  We evaluate the impact of AI-powered instructional bots on student learning and study behaviors in introductory microeconomics courses at Arizona State University. These conversational bots, aligned with core topics such as demand, supply, and elasticity, provide tailored explanations, walk students through practice problems, and offer real-time feedback. In a randomized controlled trial, students are assigned either to a treatment group that receives access to the bots or to a control group that relies solely on standard course materials. The intervention integrates the bots into course assignments, weekly study reminders, and pre-exam reviews. Outcomes are measured using academic performance scores, changes in beliefs and study habits, engagement patterns, and bot usage data. By testing whether AI tutors can substitute for or complement traditional instruction, this study provides evidence on the scalability and effectiveness of AI tools in higher education.

Publications

• The Slow Climb for Women with Children: Central Banking Evidence with Fernando Borraz · Journal of Labor Research, 47, article 12, 2026 · doi.org/10.1007/s12122-026-09390-x
  Abstract

  This study examines gender differences in pay scale progression and career advancement within a rigid internal labor market: the Central Bank of Uruguay (BCU). The institution is characterized by three key features: hierarchical pay scales, promotion-based wage growth, and competitive internal contests. These features substantially limit the role of discretionary pay-setting and external mobility. Using confidential personnel records spanning 19 years, we show that substantial gender gaps emerge only among employees with children under 18. While promotion rates between women and men are similar on average, mothers experience a significantly slower accumulation of promotions early in their careers. This “slow climb” through the internal hierarchy translates into persistent differences in pay scale attainment over time, whereas gender differences among childless employees are negligible. To address concerns related to the endogeneity of fertility, we implement a control-function approach exploiting variation induced by a major national reproductive healthcare reform, and complement this strategy with lagged fertility specifications. The resulting evidence should be interpreted cautiously. Within the BCU, it is consistent with internal promotion systems amplifying parenthood-related constraints into persistent gender inequality. Because our evidence comes from a single institution, we do not extrapolate these estimates beyond the BCU.

Education

• PhD in EconomicsArizona State University 2021–2026
• MA in EconomicsArizona State University 2023
• MA in Economics Universidad Alberto Hurtado (ILADES) and Georgetown University, dual degree 2018
• BA in EconomicsUniversidad ORT Uruguay 2015