This paper studies the aggregate and distributional consequences of allowing colleges discretion over whom to admit. Two distinctive features of Chile's centralized assignment system allow us to make progress in identifying colleges' objectives. First, the admissions rules chosen by each college-major program are publicly observed. Second, a 2014 reform expanded programs' discretion by giving them the option to increase emphasis on high school class rank. Programs responded differently: many maintained their admissions rules, while those that placed more emphasis on class rank admitted more low-SES and female students. To quantify the importance of discretion beyond this reform, we estimate an equilibrium model of students' preferences for programs, programs' preferences for students, and their interaction in determining graduation. Match effects matter: the same program raises graduation more for some students than for others, allowing colleges to improve aggregate graduation by admitting students based on fit. Exploiting observed rule choices, we infer that programs value graduation but differ in how they trade it off against socioeconomic composition, consistent with their differential response to the reform. In counterfactuals that limit or expand discretion, lotteries reduce segregation but also graduation, while test-only admissions and full discretion increase segregation without raising graduation. The results support delegating some admissions authority to colleges, but with guardrails to ensure alignment with a policymaker's goals.

One way workers can adapt to adverse shocks after entering the labor market is by returning to school later in life, a choice involving substantial opportunity costs in terms of foregone earnings and lost work experience. This paper uses administrative data on earnings and enrollment histories to estimate a dynamic model quantifying these tradeoffs. I first exploit variation from the Great Recession to show that adult workers respond to industry-level shocks by increasing community college enrollment, with heterogeneity across fields of study and industries playing a key role in driving enrollment. The model unpacks what these patterns imply for policies intended to increase worker earnings, such as targeted tuition subsidies. A key contribution of the framework is that it flexibly quantifies multidimensional skills using a data-driven approach, capturing interactions between fields of study and career pathways. A counterfactual tuition subsidy increases enrollment but results in returns that are negative on average, with substantial heterogeneity. Decomposing these returns emphasizes the importance of on-the-job work experience and aligning education with desired career transitions in the design of retraining policies.

Lead pipes are much more likely to contaminate drinking water at both very high (low acidity) and very low (high acidity) levels of pH. We exploit differences in water chemistry across cities with historical lead piping to identify the causal effects of waterborne lead exposure on cognitive ability.  Combining historical piping and pH measurement data with the AFQT scores of the universe of applicants to the US military from 1976 to 1991, we find that childhood exposure to lead pipes in cities with extreme pH levels lowers AFQT scores by at least 1.3 percentiles. By contrast, differences in water's pH levels have no effect where pipes are not made of lead. Lead exposure corresponds to reductions in the present value of lifetime earnings of about $14,000.  Under conservative assumptions, a policy that removes lead service lines in cities with extreme pH would be highly cost effective, with a marginal value of public funds (MVPF) of 2.8.

This paper studies how the choice of college and major is related to earnings in a context where selection bias can potentially confound direct measurement. Three decades of ranked college application and assignment data for the population of college students in Chile are linked to a decade of administrative tax data on earnings, enabling the study of how student choices, college, and major interact to affect earnings. Ranked order application lists, biased OLS regressions, and a multitude of local RD estimates are tied together in a model of college major choice and earnings outcomes. Estimates from the model indicate that student outcomes are quite heterogeneous – match effects explain a large share of the variance in earnings, much of which is on observable dimensions. Students display a weak preference for these earnings gains, which is strongest for mean returns and observable match components. The results suggest that uniform policy recommendations may be misleading in the context of college major choice, but that targeted program expansions can significantly improve student outcomes without requiring fundamental changes to the assignment mechanism.