Among the many reasons students seek a postsecondary education, the promise of economic mobility is front and center. In fact, the majority say higher earning potential is a primary reason they enrolled. We designed the Student Access and Earnings Classification to study whether institutions are making good on this fundamental promise.
To be clear, we believe that earnings cannot and should not be perceived as the sole indicator of institutional success – but we also believe it is a measure that should not be ignored. In our approach, we wanted to provide more context and nuance into how we analyze earnings, and we wanted to do something different than others are offering.
This blog is the second in a two-part series that explores both dimensions of the Student Access and Earnings Classification. You can read the blog on access here.
Why do we measure earnings?
In 2022, when the Carnegie Foundation and the American Council on Education first partnered on updates to the Carnegie Classifications, our initial premise was to measure social and economic mobility. We wanted to understand which institutions were helping students who had historically been underserved or who were from lower-income backgrounds and propelling them into higher earning careers, and we wanted to organize (i.e., classify) the sector accordingly to pull out trends and identify positive outliers.
The classification we ultimately created, the Student Access and Earnings Classification, doesn’t measure the value of the degree or colleges’ return on investment. Instead, it offers a perspective on whether institutions are ensuring their students are competitive in their respective job markets based on their local economies and demographics. This gives us insight into where schools are succeeding or falling short, while also giving a new tool to compare, collaborate, and improve.
Which students are included in the earnings measurement?
The Student Access and Earnings Classification uses data provided by the U.S. Department of Education’s College Scorecard, specifically the salaries of former Title IV undergraduate students (meaning undergrads who received a federal student loan loan, a Pell Grant, and/or were on work study) eight years after they enrolled. We compare their data to the earnings of 22 to 40-year-olds in their area with the same demographic profile who hold at least a high school diploma.
We use this earnings data because federal aid records provide the most comprehensive and consistent data source that exists. In addition, Title IV students are of particular interest because they are supported by public dollars and generally have more financial need than non-Title IV students – so they would particularly benefit from the economic mobility that could come with a college degree.
This specific earnings figure also includes a fuller picture of the student experience, including those who didn’t complete a degree and those who transferred. Including these students ensures institutions are not accountable for their completers only, and it gives schools like community colleges credit for the ultimate outcome a student achieves. It also allows us to capture the potential economic mobility of other learning experiences, like earning a non-degree credential or having a work-based learning opportunity that led to a job, even if a student did not complete a degree. Because this figure captures what students were earning eight years after enrollment, students have had some time make progress on their education pathway and enter the workforce, but we can capture both students who took four years to complete as well as those who took seven.
While the earnings data includes those who had W2 or 1099 self-employment earnings, those who are working in the gig economy and/or earning income through other channels (e.g., Zelle/Venmo or cryptocurrency) may be either excluded or potentially have their earnings undercounted. For former students who are still enrolled in college (including graduate school), who are not working, or who are not working in the organized workforce (such as a full-time parent), they are omitted from the data and do not count against the school.
How do we ensure fairness in our methodology?
We take several steps to make the methodology as fair and comparable as possible.
First, we use median earnings, not the mean, so results are not distorted by a small number of unusually high earners. For example, if most graduates from one school become nurses and one becomes a billionaire entrepreneur, the mean would give a misleading picture of typical outcomes.
We also compare institutions to similar types of colleges and universities (Institutional Classification) in our default approach. Schools with distinct academic missions are grouped accordingly, so an engineering school is not evaluated against an art school.
To provide a more contextualized view of outcomes, we account for ways race, ethnicity, and geography shape earnings. Someone working in a small town in South Dakota will likely earn less than someone working in midtown Manhattan, and our methodology reflects those differences by customizing the comparison value for each institution based on their student profile and setting expectations accordingly.
But we also know no methodology can capture every factor that shapes earnings. Instead, this serves as a baseline for understanding how a typical student is faring compared to their peers in their area, and we invite users to think of it as a starting point for that analysis.
How are institutions classified based on their earnings score?
The earnings measure places institutions into low, medium, and higher earnings categories based on how their former students’ earnings compare to the local labor market.
The benchmark is whether the median former student out-earns 22 to 40-year-olds in their area who have at least a high school diploma. That places them into Medium Earnings. Institutions earn a Higher Earnings designation when former students’ earnings are at least 25% (2-year schools) or 50% (4-year schools) above that benchmark.
Simply put, if students can outperform their local labor market, that signals the institutions they attend are setting them up for financial success.
What have we learned from the Student Access and Earnings Classification so far?
Big picture, the earnings measurement shows that the vast majority of higher education institutions across the country are providing reliable economic pathways for their students. At 90% of four-year institutions and at two-thirds of associate colleges, students’ earnings eight years after enrollment are at least equal to those of their peers in their area if not substantially higher.
Overall, we think the Student Access and Earnings Classification prompts interesting questions. How well are students transitioning into the workforce? Are institutions offering high-quality programs aligned with employer needs? Are students gaining access to paid work-based learning, completing their degrees, and getting the guidance they need to pursue paths to economic mobility? How do other measures like cost or completion intersect with our data?
We also know there are places in the data where the methodology may not tell the whole story. For example, institutions located in larger, urban areas typically have former students competing against a highly educated population, where there’s a higher cost of living. This prompts questions about the best way to capture mobility for students in those areas. In rural communities, by contrast, the value of a college degree is especially visible. For example, our data shows that community colleges in rural areas often are classified as Medium or Higher Earnings, suggesting their students are going onto earn wages above and beyond what their peers are able to do and inviting questions about partnerships with local employers and high schools that may be leading to those results.
We have also seen that institution-level earnings don’t always reflect the differences across academic programs. A college’s overall earnings measure may look very different when broken down by field of study. That’s why earnings data is most useful when institutions use it to ask questions, identify patterns, and learn from one another.
As institutions, policymakers, and researchers work to ensure higher education remains a powerful engine for economic mobility, these early insights tell us we have colleges and universities that are already excelling in this area. There also are pockets of the sector – some known and some less discussed – where it seems like certain credential types or certain majors may not have as much of an economic pay-off. But there are also places where we see institutions bucking the trend or outpacing their peers and equipping their students for the reality of today’s labor market. That is really encouraging.
We plan to release the next update of the Student Access and Earnings Classification in 2028 and will continue to refine the methodology to factor in more nuance and geographical considerations as we are able. We’ll share more about those updates as we get closer to release.