This study utilizes descriptive statistics and regression analysis to evaluate trends in and relationships between tuition price, institutional aid, enrollment, and tuition revenue and their determination of the net revenue generated by colleges and universities. In doing so, it defines how much institutions generate in net revenue utilizing a new metric, net revenue generation rate (NRGR). This allows a new way of thinking about the relationship between the listed tuition price, the investment in aid, and the resultant gain or loss incurred by institutions due to pricing and aiding strategies. Additionally, it explores NRGR in the context of various tuition prices and institutional types over an extended period of time, as no other previous study has done. Publics institutions with higher tuition prices generate higher NRGR's. The opposite is found for private institutions. However as price increases, NRGR decreases. Larger enrollments relate to higher NRGR's, however increases in enrollment negatively influence NRGR for public institutions and positively influence private institutions' NRGR. Baccalaureate, Doctoral, and institutions of higher selectivity produce the largest net revenue per student, yet do so at the lowest NRGR's.

This study also introduces the first assessment of marginal NRGR as a means of directly measuring the impact of increasing tuition price on aid and how much institutions make from an increase in tuition. As institutions increase tuition price, institutional aid increases, decreasing the amount of incremental revenue generated from the change in tuition price. This behavior is most clear for private institutions and varies by institutional type.

This study also introduces a number of theoretical explanations for pricing and aiding behaviors and their potential effects on the net revenue they generate. This includes a commitment to meeting student financial need as well as attempts to maximize quality and net revenue. Finally, this study provides the first comprehensive use of IPEDS data to address these questions. In doing so, it provides significant gains in the methodology and application of this data set for use in answering questions about tuition price, institutional aid, and net revenue generation across a broad array of institutional types over extended periods of time.