The following article is an excerpt from Start, Stop or Grow? A Data-Informed Approach to Academic Program Evaluation and Management
Understanding program economics is a vital part of fixing an academic program portfolio and running a healthy institution. Usually, the goal is not profit. The goal is to generate funds that the college can re-invest to advance its mission, strategy, and quality of instruction. A healthy program portfolio is a web of cross-subsidies from stronger programs to weaker mission-critical programs, growing programs that need funding, and new program launches that will increase enrollment and margins.
Program economics should focus on the revenue, cost, and margin that a program decision is likely to change–this is what we call direct instructional margin. The analysis will include metrics that vary with short-term changes in student headcount or faculty instructional time. The analysis will assign revenue, cost, and margin to sections, courses, and programs; it will exclude overhead.
Conceptually, we assign a pro-rata portion of a student’s revenue (tuition and fees net of institutional scholarships) to the courses and sections in which the student is enrolled. We then assign a pro-rata portion of a faculty member’s wages and benefits to the courses they teach. We add in the other direct expenses that may be associated with a course, such as consumables used in labs. The pro-rata revenues and costs of the course then follow each student into their program.
Once you have calculated a professor’s instructional cost, there is a question: should it be assigned to the courses they teach or averaged across all the professors in the department? There is an argument that faculty assignments to courses are somewhat arbitrary. Within a department, a senior professor may teach a large introductory session one semester and an adjunct may teach it the next. If faculty are randomly switched from course to course, the average departmental cost for an instructor is a better metric than the actual cost of the instructor who happened to teach a course.
In practice, course assignments may not be random. More senior and expensive professors may be more likely to get assigned higher-level small courses and independent studies. This combination of small class size and high-cost professors can drive the cost per student credit hour through the roof (thousands of dollars per SCH is common). Using average cost per instructor would mask this issue. On the other hand, if you are estimating the cost of adding or deleting a program or course, the average may be more useful, since the effect on staffing may be uncertain. Of course, a good system could allow you to toggle between the average and actual costs as needed.
More data is needed to calculate direct instructional cost by section, course, and program. The data includes faculty IDs (which should be hashed) and the courses taught by each faculty member. Payroll should be able to provide salaries and benefits by faculty ID. You should also collect non-wage costs that vary with the number of students taught, such as lab supplies for biology. In some cases, these charges are substantial – pilot training programs may use over $100,000 a year in gasoline. We consider capital investments, such as buildings and labs, as overhead and do not include them in direct instructional costs.
Once you have all the instructional costs, you can use course credit hours (CCH) to assign the costs to programs. As with revenue, you simply divide the cost of each faculty member by the total number of CCHs they teach, to get their cost per CCH taught. Each course credit hour a faculty member teaches is then assigned the faculty member’s cost per CCH.
Robert G. Atkins created Start, Stop or Grow? A Data-Informed Approach to Academic Program Evaluation and Management for anyone who seeks a proven system for making better academic program decisions at their college, university or institution of higher education or adult learning. Click here to order your copy through Amazon.