Blog 2: Why Course-Based Economic Models are Game Changers!

December 2, 2019

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My first blog (October 2019) described how academic resourcing models allow institutional leaders and faculty to reshape their institutions through better resource management.  This one takes a deeper dive into the new breed of inter

nal economic models—which describe the activities, costs, revenues, and margins associated with teaching and other university activities.  Unlike previous generations of university resource management models, these are sufficiently robust and detailed to help provosts, deans, chairs, and faculty make both strategic and routine academic decisions.

The new models are based on the enrollments and teaching activities for individual courses, not on aggregate data for departments or other entities.  Data include the program registrations and credit hours for different kinds of students, whether the teaching mode is online or face-to-face (F2F), the frequency and duration of class meetings, class size(s), and the type of instructor(s) typically assigned.  The course-level data can easily be rolled up to department level (and then to schools and the university as a whole).  However, one can not allocate downward without losing essential detail.

But why are these data so important?  First, course-level models measure the economics of degree and certificate programs much more accurately than do department-level models.  Consider a laboratory science program like chemistry, for example.  Chemistry courses are likely to be expensive, but to fulfill degree requirements one must take many courses outside the major—most of which are less expensive than chemistry.  Assuming everything to be in-major gives a highly distorted picture of the program’s costs, revenues, and margins.  Yet this is exactly the assumption made when the chemistry department’s average cost per credit hour is assigned to, say, the whole Bachelor of Science in chemistry.  The new models produce data for each course in the curriculum and then roll them up to programs in proportion to the courses that registrants actually take.

The following table illustrates the calculated disparities in margin for a small bachelor’s degree program in engineering physics.  The university loses a substantial amount of money on the 65 credit hours taught within Physics and Engineering but makes it back on the out-of-major courses—especially those in humanities-related subjects.  A decision to cut back Engineering Physics because it is too costly would not be justified by the facts.  However, that is the way the school would likely go if, absent a course-level economic model, it attributed the large losses per credit hour for Physics and Engineering to the whole program.

Distribution of Student Credit Hours (SCH) and Margin by Broad Field,for an Engineering Physics Program

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Course-level models also provide data on teaching mode, class size, adjunct faculty utilization, and other elements of activity.  Chapter 4 of my Reengineering the University: How to be Mission Centered, Market Smart, and Margin Conscious (John’s Hopkins University Press, 2016) described why these variables are important for academic decision-making.  One example involved the English Department at a flagship state university, where reductions in cost per credit hour were welcomed enthusiastically by the central administration—until the causes were identified as substantially increased class sizes and adjunct teaching coupled with changes like multiple-choice exams and reductions in the number of writing assignments.  My conclusion was, and remains, that reliance on cost and other economic data by themselves can be dangerous unless they are accompanied by activity data to provide insight about what’s actually happening on the ground.

The class size data also focus attention on the important questions of course capacity and incremental cost. For example, provosts, deans, and program heads frequently must estimate the consequences of shifting student demand or enrollment targets.  The natural approach is to use the course’s historical average cost per credit hour when it’s available, but this will be seriously misleading in some circumstances.

The following “cost structure” figure shows why.  The cost for F2F teaching follows the step function shown in the figure.  Starting from point A, for example, small increments to enrollment incur only the small variable costs of grading and personal out-of-class contact.  Gauging the distances in the figure shows that stopping just short of the maximum permissible section size (i.e., at point B) will reduce the average cost per enrollment by nearly a factor two.  (Average costs are depicted by the dotted lines between the operating points and the origin.)  Adding additional enrollments will incur the fixed cost of adding another section. This will boost the average cost again, as depicted for point C.  The step function in cost becomes less important as the number of class sections in the course increases, but it is highly significant for the many small courses that characterize some majors.

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Cost Structure for Teaching a F2F Course

I am convinced that future generations of department chairs and staff members will focus routinely on class sizes in relation to capacity.  This will require deeper thought about the meaning of capacity in relation to subject matter and pedagogy than has been common in academe, but this, too, will be a good thing.  The new course-based activity and cost models will make this possible.  The result will be better control of the departments’ teaching schedules and a stretching of departmental dollars.

A new breed of predictive models based on course-level analysis extends this thinking to the larger question of forecasting the consequences of changes in a program’s demand or enrollment target.  These models trace the envisioned enrollment change through all the courses taken by students registered in the program, with each course being analyzed in relation to its current average and maximum class sizes.  The models also calculate the incremental revenue for each student type expected to be involved in the change.  This enables the decisions of provosts, deans, and program heads to be rooted in accurate predictions for incremental margins—the financial gains or losses to be expected as a consequence of their actions.  This, by itself, is a game-changing result.  We shall see in Blog 3 why taking these financial consequences into account is essential for furthering a college or university’s academic mission.

William F Massy

Author:  Resource Management for Colleges and Universities

Gray Associates

Gray Associates, Inc. provides the best available data, software, and facilitated processes to help higher education institutions make high-stakes decisions regarding academic programs, pricing, and locations.

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