This paper analyzes the impact of student loans for higher education on enrollment, dropout decisions, and earnings. We investigate the massive State Guaranteed Loan (SGL) program implemented in Chile in 2006. Our empirical analysis is based on the estimation of a sequential schooling decision model with unobserved heterogeneity. We supplement this model with labor market outcomes. The model is estimated using rich longitudinal data generated from administrative records.Our findings show that the SGL program increased the probability of enrollment and reduced the probability of dropping out from tertiary education: SGL reduced the first year dropout rate by 6.8% for students enrolled in five-year colleges and by 64.3% for those enrolled in institutions offering twoor four-year degrees. Moreover, we document that the SGL program has been more effective in reducing the probability of dropping out for low-skilled individuals from low-income families. When analyzing labor market outcomes, we find that SGL beneficiaries have lower wages (up to 6.4% less) than those who did not "benefit'' from the program. We attribute this negative result to the design of the SGL program, which has incentivized higher education institutions to retain students at the expense of not securing the quality of education
This paper proposes a novel matching estimator where neighbors used and weights are endogenously determined by optimizing a covariate balancing criterion. The estimator is based on finding, for each unit that needs to be matched, sets of observations such that a convex combination of them has the same covariate values as the unit needing matching or with minimized distance between them. We implement the proposed estimator with data from the National Supported Work Demonstration, finding outstanding performance in terms of covariate balance. Monte Carlo evidence shows that our estimator performs well in designs previously used in the literature.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.