Essays on Student Loans and Returns to Skill

Abstract

This thesis consists of three studies, which explore topics related to labor economics. Chapters 2 and 3 examine the returns on student loans and student loan repayment policy, respectively. Chapter 4 examines the returns to skill and the evolution of skills at older ages. In Chapter 2 (co-authored with Lance Lochner), we study rates of return on government student loans in Canada using novel administrative data from the Canada Student Loans Program. We exploit rich information on personal characteristics, loan amounts, field of study, and institution of attendance to explain differences in rates of return across different types of borrowers. We find that field of study is a particularly important determinant of rates of return, explaining 60-70% of the variation in predicted returns across borrowers, while institution differences explain only about 10% of the variation. We also show that if private lenders were to cream-skim borrowers with predicted returns above 10% (5%), the average return would fall from the current -5% to -6.4% (-9.4%), raising the cost of the government student loan program and adverse selection concerns. In Chapter 3, I study the effects of introducing an income-based student loan repayment (IBR) plan when considering labor market risks and the insurance provided by parents. I develop a dynamic life-cycle model with endogenous parents-to-children transfers, together with children's education, borrowing, repayment, and labor supply decisions. After estimating the model using the National Longitudinal Survey of Youth 1997, I quantify the impacts of introducing an IBR while keeping the government budget constant. IBR crowds out savings and parental transfers as it provides more insurance to borrowers. Interestingly, a weak labor supply response to IBR suggests that moral hazard is not a concern. Further, the college enrollment rate increases, and the largest gains are for low-income and low-ability families. Finally, aggregate welfare increases with relatively low-income families benefiting the most. In Chapter 4 (co-authored with Lance Lochner, Youngmin Park, and Youngki Shin), we show that repeated cross-section data with multiple skill measures (one continuous and repeated) available each period are sufficient to nonparametrically identify the evolution of skill returns and cross-sectional skill distributions. With panel data and the same available measurements, the dynamics of skills can also be identified. Our identification strategy motivates a multi-step nonparametric estimation strategy. We further show that if any continuous repeated measurement is shown to be linear in skills, a much simpler GMM estimator can be used. Using Health and Retirement Survey data on men ages 52+ from 1996-2016, we show that one of the available (continuous and repeated) skill measures (word recall) is linear in skills and implement our GMM estimation approach. Our estimates suggest that the returns to skill were fairly stable from the mid-1990s to the Great Recession and rising thereafter. We document considerable differences in skills and lifecycle skill profiles over ages 52-70 across cohorts, with more recent cohorts possessing lower skills in their mid-50s but experiencing much weaker skill declines with age. We also document skill differences by education and race, which are stable across ages and explain roughly one-third and one-half, respectively, of the corresponding differences in wages.