This paper studies the design of higher education policies targeted at improving the recruitment of public servants. I leverage the introduction of a policy in Chile that aimed to increase teacher quality by combining financial incentives and admission standards. Exploiting a sharp assignment rule for financial incentives eligibility, I estimate that, at the threshold, enrollment of high performing students at teacher colleges increased by 42%. For low-income students, two thirds of the increase is due to switching away from non-enrollment. The policy led to a 0.25SD increase in the scores at the college entrance exam, which translated into a 0.11SD increase in Teacher Value Added (TVA). I embed the reduced-form results into a demand and supply model of higher education that incorporates a novel method for solving discrete-continuous games in large markets. Alternative combinations of incentives and admission standards lead to increases of up to 6.6% in the test scores of students enrolled at teacher colleges, and up to 20% in TVA. Targeting the policy to low-income students yields further gains in TVA at no additional cost. To achieve similar gains, the expected wages of graduates from teacher colleges would need to increase by approximately 35%, highlighting the cost-effectiveness of the policy.

This paper examines the effects of Tuition-Free Higher Education (TFHE) policies on student enrollment, persistence, and academic performance, with a focus on Chile’s 2016 Gratuidad reform, which granted free higher education to students from the lowest five income deciles. Using a difference-in-differences design, we find that the policy raised enrollment among eligible students by around 10 percentage points, with the response concentrated among lower-ability students previously excluded from public funding. Among students whose enrollment decision was already made before the reform, dropout fell by up to 2.5 percentage points and on-time graduation rose by 1.8 percentage points. For post-reform cohorts, aggregate completion rates are essentially unchanged despite a leftward shift in the ability distribution of entrants. To disentangle selection from behavioral responses, we develop and estimate a dynamic discrete-choice model of enrollment, program choice, and academic effort. We find no evidence that the removal of performance requirements weakened effort among infra-marginal students. Instead, the composition of new entrants induced by the reform, who exhibit higher fixed and marginal costs, accounts for most of the post-reform progression patterns. TFHE thus expanded access without compromising aggregate educational quality.

This paper studies the roles of financial constraints and information frictions on enrollment and progression in higher education. We use Chilean administrative data, allowing us to link students to their parents and their high school peers. Our empirical strategy exploits the massive entry of private universities during the 1980s to instrument parental educational achievement, panel data methods to estimate peer influence, and the staggered rollout of Free College from 2016 that generated exogenous variation to out-of-pocket fees. Results show that parental exposure to higher education increases enrollment through Short Cycle Programs, older peers enhance enrollment and match quality, and subsidies increase university access.

While small-scale educational interventions provide valuable insights into effective investments, large-scale education programs may generate substantial general equilibrium (GE) effects that alter their ultimate impact on welfare and inequality. This paper analyzes the consequences of a large-scale college enrollment expansion in Chile, explicitly accounting for GE effects in both education and labor markets, to understand how firms adjust their demand for skill-biased labor in response to increased college graduate supply. The model enables us to decompose observed wage changes into individual returns to education and GE effects, revealing how firm responses shape education decisions and ultimately affect the distribution of earnings. By incorporating both household education choices and firm production decisions, this framework provides a comprehensive assessment of how large-scale education expansions affect welfare and distributional outcomes.

This project develops scalable, low-cost strategies to enhance early warning systems aimed at preventing school dropout in low- and middle-income countries. Despite the growing adoption of data-driven platforms that predict students' dropout risk and provide targeted guidance to school staff, real-world impact remains limited by low user engagement and minimal improvements in outcomes. We propose an integrated approach that strengthens both the targeting of at-risk students and the engagement of key stakeholders. By systematically combining school principals’ expert beliefs about students with machine learning predictions, we harness local knowledge to improve the accuracy of dropout risk assessments. Simultaneously, we foster parental involvement by inviting families of at-risk students to structured meetings with school leadership, sharing clear risk information and practical guidance to support continued enrollment. At-risk students are also offered tailored tutoring in foundational skills by trained educators.

We study the impact of a policy change in an online labor market requiring disclosure of projects’ experience requirements, which prompted roughly half of employers to stop revealing their budgets. Exploiting detailed data from the universe of auctions in PeoplePerHour—one of the largest online labor markets—we document that employers who conceal budgets after the policy systematically pay higher winning fees across all project types. Bid variance increases, indicating greater strategic uncertainty for workers. Worker win probabilities remain largely unaffected by the disclosure change, though we observe stronger price-based selection for low-value projects and more pronounced firm advantage in mid-value segments. To interpret these empirical patterns and evaluate counterfactual information policies, we build a structural beauty-contest auction model. Our framework explicitly captures the strategic interplay between employers and heterogeneous workers under incomplete information, enabling us to simulate the welfare and market impacts of alternative disclosure policies. The results highlight the multifaceted consequences of market design for both sides of online labor platforms, especially when buyers’ strategic concealment of information is endogenous.