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.

Understanding how people act during times of increasing unemployment levels is a valuable part of economic development. In our paper, we analyze if a larger share of people decides to enroll in higher education when the unemployment level rises. The study is based on panel data covering 289 Swedish municipalities over the years 2000 to 2021. A fixed effects panel regression is utilized to test for the relationship between higher education enrollment and unemployment. Based on previous research, we expect increased unemployment to have a positive effect on the enrollment in higher education, especially for the female population. The empirical results confirmed our hypothesis that there is a relationship between unemployment and enrollment in higher education and that the relationship is stronger in the female population compared to the male population. The results of this study can help policy makers understand patterns in enrollment decisions and to adjust their policies accordingly.

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