The project analyzes how the selected explanatory factors affect tuition and fees by using simple regression models. The main variables of interest include: tuition and fees in year t, GDP in year t from CPI, enrolled students in year t, household debt in year t, interest rate in year t, earnings difference in year t, where t is the time index, in years. The regression derived and used in the empirical investigation is given by: Y(t) = -466.7 + 1.86X1(t) – 3.01X3(t) + 5.26X4(t) + 0.52X6(t) + 0.90X7(t) + e(t). In line with the literature, the regression model shows five significant factors that drive tuition: general inflation; federal government, state, and local government support per enrolled student; household debt as a percentage of GDP; financial sector debt as a percentage of GDP; difference between the mean earnings. Thus, we can divide the right-hand-side into the following groups: general inflation, effects from taxpayer support, effects from leverage, and the earnings difference. The time-series data used all starts at 100. The data represents the most significant factors affecting college tuition and the causes of students accept federal financial aid. Each factor is considered due to how each has the ability to change over time, further explaining how college tuition and federal financial aid change over time.
The model demonstrates that the strongest effect is from the leverage and the second strongest from the taxpayer support. The parameters for the household debt relative to the GDP and the financial sector debt relative to the GDP indicate that leverage in the economy drives tuition. Since the leverage depends on credit markets, tuition and fees depend also on the markets. If households and financial institutions are able to get large loans at low prices, their leverage rises as we have seen during the last 25 years. This credit rise has driven multiple things and tuition is just one example of that. During 1987 – 2017 household debt relative to the GDP rose by 107% and financial sector debt relative to the GDP by 552%. By this model, these changes increased tuition and fees by 848%. On the other hand, when the support per enrolled student increases then tuition and fees fall, i.e., colleges change tuition and fees partly in order to compensate fluctuations in taxpayer support. Between 1987 and 2017 the support per enrolled student rose 246% and, by this model this decreased tuition and fees by 740%. Thus, the support from taxpayers eliminated most of the effects from the leverage.
College inflation is also highly sensitive with respect to the general inflation. During 1987-2017 CPI rose by 218% and, according to the model, this pushed tuition and fees up by 406%. Finally, the earnings difference raises the value of the education and this way also tuition and fees. Between 1987 and 2017 the earnings difference increased by 324% and raised tuition and fees by 292%.
This project used a regression model to explore what affects the rise in college tuition over the period of 1987-2017. In addition, the project assessed the Bennett Hypothesis, which states that the rise in college tuition is due in-part by the rise in federal financial aid offered. The results indicate that the factors of tuition and fees, GDP from CPI, enrolled students, household debt, interest rate, earnings difference in year t, all affect the rise in college tuition. While the Bennett Hypothesis was also considered, it showed to not have as great of an impact on the price of tuition as the other factors did. The correlation between the factors tested and the rise in college tuition gave an R-squared value of 99.7%.
Looking forward, there are two trends: US households and financial institutions are deleveraging, their debt levels with respect to GDP fall and taxpayer support rises and, therefore, the support per enrolled student increases. Both of these trends should decrease college inflation.
Future research should explore how different geographical regions affect college tuition in the area and how much people are willing accept in financial aid moving from the geographical location that they reside to the geographical location that they are attending college. This would allow researchers to develop a better understanding of how tuition prices change based on the permanent residency demographic of students and how financial aid changes from one geographical location to the next.