The SCHEV analysis on Return on Investment (ROI) starts with constructing a lifetime wage projection model using person-level wage data spanning 25 years, following the steps below:
1. All wage data from 9596 to 1920 are adjusted to 2023 value.
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 9596 to 1920. 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 1920 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 1819 Bachelor’s graduating cohort as the study population, students with valid wage data in 2023, and Bachelor’s degree as their highest level of degree, are included in the analysis. Assuming they will continue to work until age 67 when reaching 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 the meantime, 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 the steps below:
1. Census Bureau’s ACS 1-year estimates of wages for high school (HS) graduates in Virginia from 2010 to 2023 are adjusted to 2023 value.
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 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 value.
The projected Bachelor’s lifetime wage represents the total gain from earning a Bachelor’s degree. The sum of projected HS level lifetime wage and total cost of Bachelor’s degree represents the total loss from earning a Bachelor’s degree. Contrasting total gain and total loss, our ROI estimates are two-fold: net lifetime difference and ROI ratio, using the following formulae:
Net Lifetime Difference = Projected Bachelor’s Lifetime Wage - Projected HS Lifetime Wage - Cumulative Student Loan - Net Price
ROI Ratio = Projected Bachelor’s Lifetime Wage / (Projected HS Lifetime Wage + Cumulative Student Loan + Net Price)
A positive Net Lifetime Difference shows a net gain from earning a Bachelor’s degree, the same as an ROI ratio larger than 1.