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There is a persistent belief in American higher education that what you study matters more than where you study it. The logic is intuitive: a nursing degree is a nursing degree, an engineering degree is an engineering degree. The credential opens the door, and the labor market takes it from there.

The College Scorecard’s field-of-study data tells a more complicated story. For the first time, the federal government publishes median earnings for specific majors at specific institutions—not just what computer science graduates earn on average, but what computer science graduates from Harvard earn versus computer science graduates from a regional state university. The differences are staggering, and they are not distributed randomly across fields.

We analyzed 24,271 bachelor’s-level program records spanning 393 fields of study at nearly 2,900 institutions. The central finding: in some fields, the institution you attend barely moves the needle on earnings. In others, it is the single most important variable in your financial future. Understanding which fields fall into which category is one of the most consequential pieces of information a prospective student can have.

The Variance Map: Where School Choice Reshapes Earnings

To measure how much institutional choice matters within a given field, we computed the 10th-to-90th percentile earnings spread for every major offered at 50 or more institutions. This range captures the practical difference between attending a low-earning and high-earning school for the same degree—stripping out the extreme outliers while still reflecting the breadth of outcomes a student might realistically encounter.

The results divide cleanly into two worlds. In one, choosing the right school can mean a $40,000 to $60,000 difference in median earnings four years after graduation. In the other, the spread collapses to $13,000 or less—a range narrow enough that the choice of institution becomes almost secondary to the choice of major itself.

Computer Science leads the variance chart with a 90/10 spread of nearly $61,000. A CS graduate at the 10th-percentile institution earns about $69,000, while one at the 90th earns roughly $129,000—nearly double. Computer Engineering ($44,600 spread), Economics ($37,000), and Finance ($33,300) follow close behind. These are fields where institutional prestige, alumni networks, geographic proximity to industry hubs, and recruiting pipelines amplify or compress outcomes far beyond what the degree title alone would predict.

At the other end of the spectrum, Teacher Education shows a spread of just $14,400. Animal Sciences, Dietetics, and Human Services cluster below $14,000. These are fields governed by licensing requirements, unionized salary schedules, or relatively uniform labor markets where a credential from any accredited institution opens essentially the same set of doors at essentially the same compensation level.

In Computer Science, the gap between a 10th-percentile and 90th-percentile school is $61,000. In Teacher Education, it’s $14,400. Same decision, radically different stakes.

Case Study: 261 Computer Science Programs, One Degree

To understand what drives this variance at the field level, computer science offers the most vivid case study. The Scorecard contains four-year earnings data for 261 bachelor’s programs in CS. The distribution is not a bell curve. It is a long, steep ramp stretching from under $26,000 to above $256,000—a ratio of nearly ten to one for the exact same degree classification.

Figure 2: The Computer Science Earnings Ladder

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At the top sit the expected names: Harvard ($256,500), Carnegie Mellon ($247,600), Brown ($218,500), Stanford ($201,000), and MIT ($199,800). These programs feed directly into Silicon Valley, Wall Street quant desks, and the upper tier of the technology industry. Their graduates enter a labor market that pays a premium not just for the CS credential but for the specific institutional stamp that accompanies it.

But the chart’s most important story is in the middle. The median CS program produces four-year earnings of roughly $90,200. The 25th percentile sits around $76,200, and the 75th at $106,200. That $30,000 interquartile range represents the difference between a typical state university CS program and a well-regarded one—a spread that compounds significantly over a career.

At the bottom of the distribution, the picture is sobering. CS graduates from CUNY York College earn a median of about $50,700 four years out; those from Fayetteville State University earn $48,200. Several for-profit institutions cluster below $55,000. These are earnings that, while above the national median for all workers, represent a fraction of what the same degree commands at schools just one or two tiers above. For a student choosing between a CS program at a strong public flagship and one at a minimally resourced regional campus, the data suggests the decision could be worth $30,000 or more per year within half a decade.

The Pell Dimension: Does Background Compound the School Effect?

The field-of-study data includes a cut that previous installments in this series have shown to be essential: earnings disaggregated by Pell Grant receipt. Comparing Pell and non-Pell graduates within the same field reveals whether low-income students experience the same returns to their degree as their wealthier classmates—or whether family background introduces a second layer of earnings inequality on top of institutional choice.

Figure 3: The Pell Gap Within Fields

The results are striking in their inconsistency. In Mechanical Engineering, the Pell/non-Pell gap is effectively zero—$340 on a base of $82,000. In Electrical Engineering, it is $934. In Nursing, it is $642. These are fields where the credential itself drives earnings so strongly that family background barely registers as a factor at the four-year mark.

Compare that to Business Administration, where non-Pell graduates out-earn their Pell counterparts by $9,600—an 18 percent premium. Criminal Justice shows a $7,100 gap (15 percent). Finance comes in at $7,300 (11 percent). In fields that depend more heavily on networking, internship access, geographic sorting, and family connections for career placement, the Pell gap widens considerably. The degree alone does not erase the disadvantage; the ecosystem around it matters.

Computer Science occupies a telling middle ground. The Pell gap for CS is only about $1,350—small in absolute terms but notable given the field’s enormous institutional variance. This suggests that CS’s technical labor market is meritocratic enough to reward skills over social capital, at least in the early career. But the institutional variance data cautions against reading too much into that: the school a Pell student attends for CS still matters enormously, even if Pell status itself is a weak predictor of earnings within any given school.

In Mechanical Engineering, Pell and non-Pell graduates earn virtually the same. In Business Administration, the gap is $9,600. The field you choose determines whether your background follows you into the labor market.

Patterns and Principles: What Explains the Variance?

Three structural factors emerge from the data to explain why institutional choice matters intensely in some fields and barely at all in others.

First, licensing and credentialing regimes compress variance. Fields like nursing, teacher education, and dietetics require graduates to pass standardized examinations and enter regulated labor markets with relatively narrow salary bands. The institution may affect the probability of passing the exam or the speed of job placement, but it cannot dramatically reshape the earnings ceiling that licensure imposes.

Second, proximity to high-variance industries amplifies institutional effects. Computer science, finance, and economics graduates enter labor markets—technology, investment banking, consulting—where the gap between an entry-level position at a top firm and one at a small regional company can be $50,000 or more in starting salary alone. Institutions that feed directly into these elite pipelines deliver dramatically different outcomes than those that do not, even when teaching equivalent technical content.

Third, network effects and brand signaling matter most in ambiguous fields. Business administration is the clearest example. The curriculum at a top-20 business program may not differ dramatically from that at a mid-tier school, but the recruiting relationships, alumni networks, and resume-screening algorithms that favor branded institutions create a feedback loop that separates outcomes by tens of thousands of dollars. In contrast, a mechanical engineering degree signals a specific, verifiable competence that employers can assess independently of the school’s name.

Implications for Students and Families

The practical takeaway for prospective students is a framework, not a ranking. When choosing a field of study, students should ask a deceptively simple question: Is this a field where my school’s identity will follow me into the labor market, or is it one where the credential speaks for itself?

For students pursuing nursing, education, engineering, or other licensed and technically specific professions, the data argues for optimizing on cost, geography, and program quality rather than prestige. The earnings upside of attending a more expensive or more selective institution in these fields is modest at best.

For students considering computer science, business, finance, or economics—fields with wide institutional variance—the calculus shifts. Investing in a stronger program at a higher-ranked school can yield returns that dwarf the additional cost, particularly if the student has the academic profile to access elite recruiting pipelines. But this advice comes with a critical caveat: the data shows medians, not guarantees. A motivated student at a mid-tier CS program who builds a strong portfolio and leverages open-source contributions can and does outperform the median Harvard CS graduate. Institutional brand is a wind at your back, not a destiny.

For low-income students in particular, the Pell gap data suggests targeting fields where background matters least—engineering, nursing, and technically credentialed professions—unless they can access one of the high-variance fields at an institution with a strong track record of launching Pell students into top-tier careers. The field-of-study data makes this calculation possible for the first time.

The Bottom Line: The Degree Is Not the Whole Story

The conventional wisdom that “just pick a good major” oversimplifies a decision that involves at least two dimensions of choice, each with its own magnitude of impact. Major selection sets the baseline. Institutional selection determines the range. And in fields like computer science and business, that range can span more than $100,000 between the top and bottom deciles.

The College Scorecard’s field-of-study data is the first federal dataset to make this reality visible at scale. It should change how guidance counselors advise, how families compare financial aid packages, and how policymakers evaluate which institutions deliver genuine value. Because in the end, the question is not just what you study—it is where you study it, and whether that institution’s particular ecosystem can convert your credential into an outcome that justifies the investment.