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. The reaction of students to the increasing tuitions and other expenses might be a driving force on the demand side for higher education. If the students believe that higher education’s high costs will not create a value equal to or less than its cost, they might prefer getting these degrees. Otherwise, they might stop attending higher degrees.
Consider a situation that New York State University is increasing tuition rates to increase the profit of university. The university management assumes that the students will continue their education, and the university might attract new students though they need to pay relatively more now. Will this necessarily result in more income for the university? The answer is ambiguous. As known, revenue depends on price, quantity of the demanded good or service, and the reaction of customers. 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 or even though some students can afford paying higher tuitions, they will consider alternatives to this university. Some students might prefer a nearby competitor like a near Community College, or may forfeit higher education. 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). Therefore, the formula given above for revenue needs a new part of probability. Some students might change their decisions after the tuition increase. Therefore, the university might lose some of its students, or the new potential students might look for alternatives. This depends on students' demand price elasticity. If it is high enough for this university,
As known, elasticity is a calculus of change. Simply it is a rate of change between change in demanded quantity and change in price. Price elasticity of demand gives us information about the sensitivity of customers to the price change of good or service. If price elasticity is above 1 or higher, then this customer is sensitive to the changes in prices, and he might If it is 1, then the customer changes his demand as the same change rate in price. If it is less than 1, then the customer give up from his demand by a smaller than the change rate in price. If the price elasticity of demand is larger than 1, then these customers are very sensitive to the changes in price, and they react strongly. In this case, increasing price might cause a loss for universities because many customers will check alternatives instead of attending this university. In this case, the university will lose a lot of students, and subsequently, the revenue increase the university expects might not occur.
In the introduction, we saw that Arizona schools had experienced a 70% growth in tuition in five years. If the price elasticity of demand is bigger than 1, then we would expect a decrease in total revenue – as the schools are investing in infrastructure to cater for higher-income students might attract people from the high-income class; however, the university should be able to attract people from other income groups to maximize its profit. The main question is as follows: “Under what conditions will school revenue increase, decrease, or stay unchanged?”
We can develop a relatively better understanding of this situation by using an example. 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, ceteris 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. The Arizonan universities can increase their profits by increasing tuition rates if only if the price elasticity of demand for higher education is less than one (inelastic) in Arizona. 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). Subsequently, this university can increase its profit by raising tuition rates.
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 run 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 elasticity of demand 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. Offering online education might be a new strategy for maximizing profits for universities. From the point view of microeconomics, differentiating education services for different students with different price elasticity of demand might increase their profits.
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. When making a decision of changing prices for maximizing profits, the price elasticity of demand is a good indicator for decision makers.
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
