Open Access Senior Thesis
Bachelor of Arts
© 2011 Michael Zaytsev
College admission has become increasingly competitive in the internet era. This is especially true for the highest caliber of students and institutions. College admission is a process filled with asymmetric information. One of the biggest asymmetries occurs when schools admit students not knowing whether or not students will actually enroll. This uncertainty is economically costly to schools. As national rankings become more and more influential, schools are more sensitive to their rank and the statistics that determine them. One of these is yield, the percentage of admitted students who enroll. This paper examines data on admitted students to Claremont McKenna College and uses a probit regression to predict their enrollment decision. By successfully predicting enrollment decisions schools can eliminate some information asymmetry and therefore raise their yield.
Zaytsev, Michael, "Predicting Enrollment Decisions of Students Admitted to Claremont McKenna College" (2011). CMC Senior Theses. 107.