Medical School Admission and Residence of Applicants: Empirical Bayes Estimates of Logit Coffficients

Abstract

Using data on all applicants to U.S. medical schools in 1975, we analyzed how an applicant’s characteristics affect the probability of his admission to medical school. Specifically, separate logit regressions for minority and majority applicants are performed to estimate this probability as a function of the applicant’s academic attributes (admission test scores, grade point averages, etc.) and of his non-academic attributes (state of residence, age, etc.). The coefficients of the state of residence dummy variables in the logit equation are estimated by discriminant analysis and then modified by empirical Bayes methods to give more accurate estimates of the state of residence effects. These modified estimates show that state of residence has a much larger effect for majority applicants than for minority applicants. An exploratory regression analysis indicates that legal residents of states with high ratios of medical school places to population are more likely to be admitted to a medical school.