Introduction
Can colleges and universities increase their tuition rates continuously, despite the suffering they cause students? This issue can be and has been tackled by economics – specifically, we can use the tools offered by the study of price elasticity of demand to see what colleges take into consideration before raising their prices.
Let us assume that, in an attempt to raise more revenue, New York State University increases its tuition rates. Will this necessarily result in more income for the university? Not always. As we know, revenue depends on price and quantity of the demanded good or service. In the case of NYSU or any other school:
Revenue = Tuition Price x Number of Enrolled Students
If tuition price increases, the number of enrolled students may decrease as fewer students should be able to afford NYSU. Some students will prefer a nearby competitor, such as a Community College, or may forfeit higher education altogether. It is easy to grasp the qualitative part of demand (if one increases prices, there will be reduced demand). However, the quantitative side is more subtle. Mankiw (2012) stated it is simple to establish the “direction in which quantity demanded moves, but not the size of the change. To measure how much consumers respond to changes in these variables, economists use the concept of elasticity” (Mankiw, 2012, p. 90).
We can posit an example in which a 15% escalation in tuition at NYSU will reduce pupil enrollment by 20%. Applying the revenue formula, the effect will be: 115% x 80% = 92%. This is equivalent to an 8% reduction in total revenue, despite the 15% tuition hike. Following the Mankiw (2012) definition of elasticity, for a 15% increase in tuition for New York State Univeristy, the price elasticity of demand has a negative effect – the decrease in enrollment is more significant than the tuition increase.
In the introduction, we saw that Arizona schools have experienced a 70% growth in tuition in five years. If the price elasticity of demand had a negative effect, we would expect a decrease in total revenue – as the schools are investing in infrastructure to cater for higher-income students, this does not seem to be the case. Another question is asked: Under what conditions will school revenue increase, decrease, or stay unchanged?
Acemoglu, Laibson, & List (2016, p. 132) show us the formula for the price elasticity of demand:
Applying to our NYSU example, (-20%)/(15%) = - 1.33. This is the price elasticity of demand, indicating us that for each one percent of tuition boost in NYSU, there will be a 1.33 percent decrease in the number of enrolled students. This result conforms to the Law of Demand since a price increase causes a decrease in quantity demanded, coeteris paribus.
More importantly, the price elasticity of demand is greater than one: if tuitions were raised by 10%, the price elasticity of demand of -1.33 would indicate a decrease of 13.3% of enrolled students. A price elasticity of demand greater than one is said to be elastic (Acemoglu, Laibson, & List, 2016, p. 134). As a result, the forfeited revenue from lower enrollment more than offsets the effects of greater tuition rates. Since demand is elastic (elasticity > 1), NYSU revenues fall after a tuition increase.
The last possible case is the one of inelastic demand (elasticity < 1), where the NYSU tuition hike offsets the drop in enrollment. For example, if the price elasticity of demand was -0.8, a 10% increase in tuition would cause an 8% drop in enrollment. Using the revenue formula:
New revenue = 110% x 92% = 101.2%.
As we can see, the price increase of 10% would generate a 1.2% increase in revenue.
The constant increases in tuition rates in the Introduction examples are likely indicative of a relatively inelastic demand. Arizonan universities were able to increase tuition rates by an average 70%, and display increasing costs with pools, luxury apartments, and similar items. The only way they can have increasing costs and tuition is if the price elasticity of demand for higher education is less than one. In the next section, we will summarize a sample of the current literature on the subject.
Selected literature review on price (tuition) elasticity of demand for higher education
In this section, we will discuss two academic articles related to the subject. The first was written by G. Kaul, referring to the University of Minnesota, and is titled Will a Proposed $2,000 Tuition Hike Hurt the U's Ability to Recruit Students from out of State? The next was written by three authors (Byrd, J., Roufagalas, J., & Mixon, P.) and is named Tuition Sensitivity in Online Education.
Kaul (2016) debates the possible effects of a $2,000 tuition increase at the University of Minnesota. The author’s calculations indicate that the proposed hike would make an estimated $13.2 million in new tuition revenue. From the previous discussion, we can infer that the demand for University of Minnesota education is price inelastic, as the tuition increase generates additional revenue. Naturally, these are projected numbers: effective demand response will only be known post fact.
Kaul (2016) reports some of the controversies: students say the price increase could prevent out-of-state students from attending University of Minnesota. The author questions this statement and declares that is an issue about price elasticity of demand, which measures the sensitivity of consumers to fluctuations in price. Kaul (2016) also says that demand elasticity depends on some factors, including the availability of substitutes for the good or service, the percentage of income the good or service takes, its alleged necessity, among others. The author finishes the exposition declaring most research reports have shown that demand is usually price inelastic (particularly at public colleges), pointing out that as tuition increases, education demand falls at a slower pace (Kaul, 2016).
The Kaul (2016) article conforms to our studied theory on the price elasticity of demand. Additionally, the author indicates this demand is price inelastic, as we expected, considering the ever-mounting increases in tuition prices. However, Kaul (2016) does not offer us any numbers estimating this elasticity. The next article provides us with such estimate.
Byrd, Roufagalas, & Mixon (2015) wrote an article studying the price elasticity of demand for online higher education. To support their findings, they research academic literature on regular, ‘brick-and-mortar’ education and its tuition elasticity. Their review is extensive, comprising of long time series data, public and private institutions, and various demographics, among others. Their main findings include that tuition for higher education is more inelastic in the short than in the long run. Byrd, Roufagalas, & Mixon (2015) also report that tuition elasticity has changed next to nothing since the 1980s and 1990s. The authors they reviewed noted that alterations in tuition price have a minor impact on enrollment yields.
The empiric results of their study show that “the demand for e-leaming undergraduate credit hours is highly price elastic and the demand for traditional undergraduate credit hours is inelastic” (Byrd, Roufagalas, & Mixon, 2015). The authors posit that such results maintain for nearly 400 American two- and four-year public institutions. “Price elasticity estimates ranged from 4.54 to 4.57 for e-leaming hours and 0.40 and 0.39 for traditional hours” (Byrd, Roufagalas, & Mixon, 2015).
It is interesting to notice that the price elasticity of demand is much higher for online education than for regular undergraduate schooling. This is likely a result of online education perception as a ‘commoditized’ service – for an e-student, there might be no much difference in studying at Online University A or Online University B as the apparent value of their diplomas is similar. Therefore, price becomes of great importance, and any small change in them will result in a massive drop in e-enrollment.
On the other hand, this substitution effect is not perceived in traditional higher education: a price demand elasticity of 0.40 or 0.39 is very low. A 20% increase in prices would result in a mere 8% decline in enrollment! If this statistic is true for Arizona schools mentioned in the Introduction, their tuition increase of 70% caused an enrollment decline of 28%. The revenue effect would be:
1.70 x .72 = 1.224
That is over 20% increase in school revenue. Under these circumstances, it is natural that the Arizona schools can spend more in meals courts and private pools, as related by Vogt (2015).
Final Thoughts and Conclusion
In this paper, we approached the significant issue of tuition increases from the point of view of the price elasticity of demand. There is overwhelming evidence that both undergraduate and graduate tuition rates have increased in the past years. This increase has been much greater than the inflation rate. An author relates that tuition and fees rose from an average $924 in 1976 to $10,600 in 2012 (Williams, 2015, p. 124). This has placed a disproportionate burden upon students and their families, and increased student loans to unusual amounts (Williams, 2015, p. 125).
While the reasons for this increase remain unclear, the mechanism is precise. Universities and colleges raise their tuition rates because the price elasticity of demand for higher education is less than one – it is price inelastic.
One interesting fact is posited by Byrd, Roufagalas, & Mixon (2015), which calculated price elasticity for e-leaming credits in the interval between 4.54 and 4.57. While this may signify that online education is viewed as a commoditized, or even substandard service, the clear implication is that online learning will not increase prices by leaps and bounds as traditional education. Many colleges and universities offer the option to complement traditional credits with online learning. It will be no surprise if indebted families start to favor such institutions, as a good percentage of their tuition costs will be indirectly ‘capped’ by the price-elastic demand of e-learning.
Finally, we can posit that microeconomics offers robust and analytical means to understand facts of the ‘outside world’ such as increasing or decreasing prices, and that elasticity is one of the many tools available in the economist’s box for this task.
References
Acemoglu, D., Laibson, D. I., & List, J. A. (2016). Microeconomics. Boston, Mass.: Pearson
Byrd, J., Roufagalas, J., & Mixon, P. (2015). Tuition Sensitivity in Online Education. Journal
of Economics and Economic Education Research, 16(3), 25. Retrieved from Questia.
Kaul, G. (2016, May 31). Will a Proposed $2,000 Tuition Hike Hurt the U's Ability to
Recruit Students from out of State? MinnPost.com. Retrieved from Questia.
Mankiw, N. G. (2012). Essentials of Economics. Mason, OH: South-Western Cengage
Learning.
Mulig, L. (2015). The High Cost of Graduate School Loans: Lessons in Cost Benefit
Analysis, Budgeting and Payback Periods. Academy of Accounting and Financial
Studies Journal, 19(1), 20. Retrieved from Questia.
Vogt, L. (2015, Fall). News for Educational Workers. Radical Teacher, (103), 80. Retrieved
Williams, J. J. (2015). How to Be an Intellectual: Essays on Criticism, Culture, and the
