Elastic or Inelastic, That is the Question - Monday Musings

By Don Gray on May 07, 2012

Just as a compass is a tool that assists you in determining which direction is north, elasticity is a tool that assists you in determining how students are likely to respond to changes in grants.

Tags: data, financial aid

What is price elasticity?If you work in the Admissions or Financial Aid office of your institution you’ve probably heard a hundred different students say, “If you could just find a way to give me a couple thousand extra dollars in scholarships, then I’d enroll.” Or perhaps something like this, “University X is offering me $16,000 in grants and you’re only offering me $15,000? Isn’t there anything you can do to match their offer?” Students and their families have become savvy consumers. Some are facing financial hardships, others are just looking to get the best deal possible, and most aren’t afraid to ask for more money. The big questions for college administrators are, “Should we offer these students a little more money?” “Will it increase our enrollments in an appreciable way?” “How will it impact net tuition revenue?”

The answers lie in the elasticities.

In economics, the price elasticity of demand is a measure of the responsiveness of the quantity of a good or service demanded to a change in its price. In practical terms, an enrollment manager can view elasticity as the ratio of the percent change in enrollment over the percent change in average grants offered. Let’s look at an example:

In 2011, Example University had 500 enrolled freshmen with an average grant of $10,000. In 2012, the University increased their average grant by $2,000 per student for an average grant of $12,000. This resulted in a freshmen class of 525 students. Enrollment increased by 25 students on a base of 500 students which is a percent change of 5%. Average grants offered increased by $2,000 on a base of $10,000 which is a percent change of 20%. If we divide .05 by .20, we arrive at Example University’s price elasticity of demand of .25. Elasticity values of less than 1 are considered relatively inelastic and values greater than 1 are considered relatively elastic. In the aggregate then, Example University’s admit pool is relatively inelastic. When an institution’s admit pool is inelastic in the aggregate, increasing grant offers decreases net tuition revenue; conversely when an institution’s admit pool is elastic in the aggregate, increasing grant offers increases net tuition revenue (NTR). In the case of Example University with a relatively inelastic admit pool, increases in grant expenditures are outpacing increases in enrollment and subsequently NTR declines.

At S&K, a standard part of our strategic financial aid consulting is to calculate the elasticity of each admitted student in our econometric models. We use these elasticities to generate aggregate reports analyzing the percent of the admit pool that is relatively elastic/inelastic by various subpopulations. This guides our development of alternate packaging strategies. In our above illustration, Example University’s admit pool was relatively inelastic in the aggregate, so across the board increases in financial aid would not be in their best interest, if increasing net tuition revenue is one of their goals. However, an analysis of subpopulations may show that a particular subpopulation, e.g., communication majors, is relatively elastic. If that is the case, a modest increase in departmental scholarships for communication majors would generate additional net tuition revenue.

Revisiting our initial concern about whether or not we should offer students/families more money, the answer lies at least in part in the elasticity of your admit pool. Just as a compass is a tool that assists you in determining which direction is north, elasticity is a tool that assists you in determining how students are likely to respond to changes in grants.

Image © iStockphoto.

Don GrayAbout the author: Don Gray served as a Research Analyst and IT Manager for Scannell & Kurz from 2005 through April of 2013. His responsibilities included analyzing and reporting on client data, building predictive models, and maintaining the FAST and SKORE software environments.

Don has a B.A. in Mathematics from Cedarville University and is working on a Masters degree in Theology at Dallas Theological Seminary.

Connect with Don on LinkedIn.

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