Narrative | Discussion on Adding Return on Investment to Program Productivity Reports

The purposes of this item are to facilitate Council discussion of: (i) additional staff work toward a draft return-on-nvestment (ROI) measure, derived from Virginia-specific data, for potential inclusion in the next iteration of Council’s program-productivity policy; and (ii) a first-draft mock-up of a revised policy, reflective of prior discussions and input by Council’s Academic Affairs Committee.

Background Information/Summary of Major Elements:

As articulated in the Code of Virginia (§ 23.1-203.6), Council’s sixth statutory duty is to:

Review and require the discontinuance of any undergraduate or graduate academic program that is presently offered by any public institution of higher education when the Council determines that such academic program is (i) nonproductive in terms of the number of degrees granted, the number of students served by the program, the program's effectiveness, and budgetary considerations or (ii) supported by state funds and unnecessarily duplicative of academic programs offered at other public institutions of higher education.

To guide public institutions and SCHEV staff in the administration of this duty, Council has adopted policies and procedures, the most recent of which were enacted in October 2019 as the “Virginia Public Higher Education Policy on Program Productivity,” which is accessible here.

At Council’s July 2025 meeting, the Academic Affairs Committee requested from staff a model for measuring the return on investment (ROI) of degree programs at public institutions. Staff presented an outline and draft of such a measure at the September and October 2025 meetings; see the September Minutes here and the October discussion item on page 28 here (draft October Minutes appear in this Agenda Book).

The draft ROI measure presented on the next page (Material #1) reflects input from Council at its October 2025 meeting, consultation by staff with external experts, and additional subsequent modification by staff.

A summary and description of potential policy modifications appears behind the ROI measure (as Material #2). A draft program-productivity policy (Material #3) incorporates return-on-investment as a factor that a public institution would be required to address when seeking to justify the continuation of a degree program that has been flagged for review due to low enrollment and degree productivity. These draft policy modifications are in accord with Academic Affairs Committee discussions at the July 2025 and September 2025 Council meetings, and with discussions with public-institution chief academic officers in September 2025 and November 2025.

Materials Provided:

• #1: Update on Return on Investment (ROI) Measure
• #2: Policy Modifications
• #3: Draft Policy: “Degree Program Productivity and Viability at Public Institutions: Policies and Procedures”

Financial Impact:

Discussion and implementation of this item entail no financial impacts for the agency. For institutions, closures of degree programs are intended to contribute to improved efficiencies by reducing resources dedicated to low-productivity, low-need programs, and by reducing unnecessary duplication.

Relationship to Goals of The Virginia Plan for Higher Education:

Council’s consideration of this agenda item supports all three goals – student readiness; institutional responsiveness; and postsecondary relevance – as outlined in Developing Tomorrow’s Talent: The Virginia Plan for Higher Education.

Timetable for Further Review/Action:

Dependent upon Council’s feedback to the information herein, staff can prepare a final version of the program-productivity policy for Council action at the March meeting or a subsequent meeting.

Resolution:

No action recommended at this meeting.

MATERIALS #1: Update on Return on Investment (ROI) Measure

Methodology

Staff analysis of Return on Investment (ROI) starts with constructing a lifetime wage projection model using person-level wage data spanning 25 years, following these four steps:

1. Wage data of graduates from 1995-96 to 2019-20 are adjusted to 2023 values, by each year post-completion for which wages are available, for at least three quarters of the calendar year.
2. To minimize the impact of random and short-term fluctuations, seven-year moving averages are calculated using median wages per program level and area of instructional program (i.e., two-digit CIP code).
3. A linear regression model is constructed for each program area at each program level based on historic performance of wages from 1995-96 to 2019-20. For each program level, we limit records to only students whose highest degree earned is at the same level. For example, students who have earned both Bachelor’s and Master’s degrees are not included in the construction of the model for Bachelor’s level wage projection.
4. Assuming wages will continue to change following the same pattern beyond 2019-20 in each program area and at each program level, we project lifetime wage income for students individually using their 4th year postgraduation wages as an anchor.

Using the 2018-19 Bachelor’s graduating cohort as the study population: Students with valid wage data in 2023, and a Bachelor’s degree as their highest level of degree, are included in the analysis. Assuming they will continue to work until age 67 when they reach full retirement, we apply the regression model to each student and calculate their lifetime wage income starting in 2020, until the year they turn 67. In other words, students have varying numbers of working years based on their ages in the degree award year.

In parallel, we construct an alternative lifetime wage income projection model for the same population in a scenario where the students never pursued any Bachelor’s degrees, following these three steps:

1. Census Bureau’s ACS 5-year estimates of wages for high school (HS) graduates in Virginia from 2010 to 2023 are adjusted to 2023 values.
2. Linear regression coefficients are calculated based on the above ACS estimates. HS level wages beyond 2023 are projected based on the historic pattern of change in the past 14 years.
3. Assuming each student included in the analysis would enter the workforce upon HS graduation, earning HS level wage each year up to age 67, we calculate their HS lifetime wage income.

In addition, the total cost of a Bachelor’s degree is calculated at the student level as the sum of cumulative student loan and net price charged to each student (i.e., institutional budget minus all gift aid in every semester enrolled in a Bachelor’s program). Net prices are adjusted to 2023 equivalents.

• The projected Bachelor’s lifetime wage represents the total gain from earning a Bachelor’s degree.
• The sum of projected high school graduate-level lifetime wages represents the counterfactual comparison.
• The total cost of earning a Bachelor’s degree and years delayed entering the workforce full-time combined represent the real and opportunity costs of  earning a Bachelor’s degree.

We then contrast the total gain and total loss, with ROI estimates are represented in two different calculations:

• Net Lifetime Difference
  • Net Lifetime Difference=[Projected Bachelor’s Lifetime Wages]-[Projected HS Lifetime Wages]-[ Student Debt]-[Net Price]
• ROI ratio
  • ROI ratio=[Projected Bachelor’s Lifetime Wages]/([Projected HS Lifetime Wage]+[Student Debt]+[Net Price])

A positive Net Lifetime Difference shows a net gain from earning a Bachelor’s degree, the same as an ROI ratio greater than one.

Findings

To ensure data representativeness, we examined programs with at least 3 wage data points available in 2023, and the amount of wage data represents at least 10% of the 2018-19 graduating cohort. Extremely small programs may be excluded as a result. Of 577 Bachelor’s level academic programs at public institutions, 56 (10%) have 50% or more graduates suffering a net loss from earning a Bachelor’s degree. When looking at program level average ROI estimates, 53 (9%) of them have negative returns. 244 (42%) programs generate on average $1 million or more (in 2023 value) in net gain for their graduates.

Programs with the highest ROI estimates are typically in the areas of computer science and engineering (Table 1), with graduates gaining more than 2.5 times the total loss from earning a Bachelor’s. Programs in arts and letters tend to produce lower ROI estimates (Table 2). Similar programs in different institutions often perform differently.

Figure 1: Screenshot of ROI Dashboard while under development.