About

I am an Assistant Professor in the Department of Economics at the W. P. Carey School of Business at Arizona State University.

I am an Applied Economist working on Empirical IO/Market Design, Education Economics, and Labor Economics. I broadly study market design policies in practice in the context of matching markets. My current research focuses on how dynamic considerations and imperfect information can affect the design of matching markets, change the performance of assignment mechanisms used in many countries worldwide, and impact agents' outcomes.

Research

Working Papers

Dynamic College Admissions, with I. Ríos.
[See paper (new draft!)] [See electronic companion]
Reject and Resubmit (resubmitted!), Econometrica.
Education Market Design Structural Surveys
Dynamic College Admissions
College Application Mistakes and the Design of Information Policies at Scale, with I. Ríos, A. Fabre, and C. Neilson.
[See paper (new draft!)]
Revise and Resubmit (2nd round, Minor Revision), Journal of Political Economy.
Education Information RCT
College Application Mistakes
Physicians’ Occupational Licensing and the Quantity-Quality Trade-Off, with J. P. Atal, P. Muñoz, and C. Otero.
[See paper] [Try Simulation]
Health Labor Quasi-Experimental Semi-Structural
Physicians Licensing
Do “short-list” students report truthfully? Strategic behavior in the Chilean college admissions problem, with I. Ríos.
[See paper]
Structural Market Design Methods
Short-list students

Selected Publications

Improving the Chilean College Admissions System, with R. Cominetti, I. Ríos and G. Parra. First place, Doing Good with Good OR - Student Paper Competition (2018).
[See paper]
Operations Research, 2021.
Market Design Policy Implementation
Chilean College Admissions

Work In-Progress (Active)

The Role of Study Habits on Academic Performance, with A. Affonso, A. Aucejo, and S. El Khoury.
Hybrid Dutch auctions and Toxic bonds, with N. Bozzo, T. Mylovanov, and R. Vohra.

Other Publications

Effect of Including High-School Grades Rank in the Admission Process to Chilean Universities, with A. Mizala and I. Ríos.
[See paper]
Pensamiento Educativo, 52 (1), 95–118, 2015.
Education Policy Simulation
High-School Grades Rank
Hunter-gatherers maintain assortativity in cooperation despite high-levels of residential change and mixing, with K. Smith, I. Mabulla, C. Apicella.
[See paper]
Current Biology, 2018.
Evolution Field Data
Hunter-gatherers

Other Working Papers

College admissions problem with ties and flexible quotas, with R. Cominetti, I. Ríos and G. Parra.
[See paper]
Market Design Applied Theory
College admissions with ties

Code & Projects

My recent open source activity and contributions.

AI_workshop

ASU Workshop: Git, GitHub, and VS Code - Agentic AI for Project Management and Research Productivity. Complete reproducible research template with data analysis, LaTeX documents, and Makefile automation.

[View Details]
AI Agents Productivity
Jur-AI-ssic Park Approach

Education

Ph.D. in Economics

2015-2021 University of Pennsylvania

Master in Management and Public Policies

November 2013 University of Chile

B.A in Industrial Engineering

December 2011 University of Chile

Dynamic College Admissions

We study the relevance of incorporating dynamic incentives and eliciting private information about students' preferences to improve their welfare and downstream outcomes in centralized assignment mechanisms. Using administrative data and two nationwide surveys, we identify two behavioral channels that largely explain students' dynamic decisions: (i) initial mismatches and (ii) learning. Based on these facts, we build and estimate a structural model of students' college progression in the presence of a centralized admission system, allowing students to learn about their preferences and abilities over time and reapply to the system. We use the estimated model to analyze the impact of changing the information environment and the assignment mechanism and reapplication rules on the efficiency of the system. Our counterfactual results show that policies that front-load learning can help to reduce switches, while providing score bonuses that elicit information on students' cardinal preferences and leverage dynamic incentives can significantly increase retention and students' overall welfare.

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College Application Mistakes and the Design of Information Policies at Scale

We study whether large-scale information interventions can improve college applicant outcomes. Using nationwide surveys, we document widespread information frictions and the prevalence of application mistakes. To address these frictions, we conducted a field experiment that provided applicants with personalized information on admission chances and program characteristics. The intervention increased assignment probabilities for previously unmatched students by 44% and increased placement into higher-ranked programs by 20%. Following these results and with the collaboration of policymakers, we successfully scaled up the policy nationwide. Our findings suggest that personalized information policies implemented at scale can effectively reduce application mistakes and improve student outcomes.

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Physicians’ Occupational Licensing and the Quantity-Quality Trade-Off

Occupational licensing is a widespread quality regulation that increases the quality of labor but reduces its quantity. We provide a framework to empirically quantify this trade-off and apply it to physician licensing, where both quality and access to care are critical concerns. Using quasi-exogenous variation driven mostly by a recent and unprecedented migration of physicians to Chile, we show that more physicians improve access and patient outcomes in tertiary care, including mortality. We also find that lower quality—as measured by physician performance on the licensing exam—worsens patient outcomes. Building on these findings, we evaluate the implications of locally changing the stringency of the current licensing policy.

View Paper

Do “short-list” students report truthfully?

We analyze the application process in the Chilean College Admissions problem. Students can submit up to 10 preferences, but most students do not fill their entire application list ("short-list"). Even though students face no incentives to misreport, we find evidence of strategic behavior as students tend to omit programs for which their admission probabilities are too low. To rationalize this behavior, we construct a portfolio problem where students maximize their expected utility given their preferences and beliefs over admission probabilities. We adapt the estimation procedure proposed by Agarwal and Somaini (2018) to solve a large portfolio problem. To simplify this task, we show that it is sufficient to compare a ROL with only a subset of ROLs ("one-shot swaps") to ensure its optimality without running into the curse of dimensionality. To better identify the model, we exploit a unique exogenous variation on the admission weights over time. We find that assuming truth-telling leads to biased results. Specifically, when students only include programs if it is strictly profitable to do so, assuming truth-telling underestimates how preferred selective programs are and overstates the value of being unassigned and the degree of preference heterogeneity in the system. Ignoring the constraint on the length of the list can also result in biased estimates, even if the proportion of constrained ROLs is relatively small. Our estimation results strongly suggest that "short-list" students should not be interpreted as truth-tellers, even in a seemingly strategy-proof environment. Finally, we apply our estimation method to estimate students' preferences for programs and majors in Chile and find strong differences in preferences regarding students' gender and scores.

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Improving the Chilean College Admissions System

In this paper we present the design and implementation of a new system to solve the Chilean college admissions problem. We develop an algorithm that obtains all stable allocations when preferences are not strict and when all tied students in the last seat of a program (if any) must be allocated, and we used this algorithm to determine which mechanism was used to perform the allocation. In addition, we propose a new method to incorporate the affirmative action that is part of the system and correct the inefficiencies that arise from having double-assigned students. By unifying the regular admission with the affirmative action, we have improved the allocation of approximately 3% of students every year since 2016. From a theoretical standpoint, we introduce a new concept of stability and we show that some desired properties, such as strategy-proofness and monotonicity, cannot be guaranteed under flexible quotas. Nevertheless, we show that the mechanism is strategy-proof in the large, and therefore truthful reporting is approximately optimal.

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Effect of Including High-School Grades Rank in the Admission Process to Chilean Universities

This paper analyses the effect of including high school grade rankings as a new factor in the admission process to Chilean universities. The paper evaluates the impact of different weighting strategies of the high school grade ranking and identifies socioeconomic and gender characteristics of the students who were benefited and harmed by the inclusion of this new factor. Starting with the weightings of the different factors considered in the Admission Process for 2012, we simulate, using a selection algorithm, alternative weightings for high school grade rankings in the 2013 Admission Process. We also evaluate the effect of the actual increase in the weighting of grade ranking in the 2014 admission process. Even though the impact on students' entrance and exit from the selection list is rather small, the introduction of the high school grade ranking into the admission process has an effect on the composition of the students selected, producing greater socioeconomic and gender equality.

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Hunter-gatherers maintain assortativity in cooperation despite high-levels of residential change and mixing.

Widespread cooperation is a defining feature of human societies from hunter-gatherer bands to nation states , but explaining its evolution remains a challenge. Although positive assortment of cooperators is recognized as a basic requirement for the evolution of cooperation, the mechanisms governing assortment are debated. Moreover, the social structure of modern huntergatherers, characterized by high mobility, residential mixing, and low genetic relatedness, undermines assortment and adds to the puzzle of how cooperation evolved. Here, we analyze four years of data (2010, 2013, 2014, 2016) tracking residence and levels of cooperation elicited from a public goods game in Hadza hunter-gatherers of Tanzania. Data were collected from 56 camps, comprising 383 unique individuals, 137 of whom we have data for two or more years. Despite significant residential mixing, we observe a robust pattern of assortment that is necessary for cooperation to evolve; in every year, Hadza camps exhibit high between-camp and low within-camp variation in cooperation. We find little evidence that cooperative behavior within individuals is stable over time or that similarity in cooperation between dyads predicts their future cohabitation. Both sets of findings are inconsistent with models that assume stable cooperative and selfish types, including partner choice models. Consistent with social norms, culture, and reciprocity theories, the strongest predictor of an individual’s level of cooperation is the mean cooperation of their current campmates. These findings underscore the adaptive nature of human cooperation—particularly its responsiveness to social contexts—as a feature that is important in generating the assortment necessary for cooperation to evolve.

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College admissions problem with ties and flexible quotas.

We study an extension of the classical college admission problem where applicants have strict preferences but careers may include ties in their preference lists. We present an algorithm which enables us to find stable assignments without breaking ties rules, but considering flexible quotas. We investigate the properties of this algorithm -- stability, optimality -- and we show that the resulting algorithm is neither monotone nor strategy-proof. The mechanism is used to solve real instances of the Chilean college admission problem. Among our results, we show that the welfare of students is increased if flexible quotas and a student-optimal assignment are combined. Finally, we argue why such assignment may be desirable in the Chilean context.

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