The Effect of Loan Debt on Graduation by Department: a Bayesian Hierarchical Approach

Abstract

Using data from three cohorts at the University of Delaware, this study investigates the effects of student loan debt on six-year graduation by department over five years. The effects are estimated from five Bayesian hierarchical models, one model for each year. The Bayesian hierarchical model uses a partial pooling technique to address the over-fitting issue when estimating the effects of loan debt, and this technique is especially beneficial to departments with small enrollments. Similar to the observation that financial aid has different effects by racial and ethnic groups, and socioeconomic groups, findings suggest a pronounced department-level loan debt effect for first-year students that diminishes as students progress through their academic career. These findings suggest that a strategy that considers a students’ academic department when designing a financial aid policy would optimize the efficiency of institutional financial resources. Moreover, universities exploring differential financial aid policies by department should start with randomized trials using first-year students.