Salary Data FAQ

Where does your salary data come from?

We take data provided by PayScale and data from the Department of Education and feed it into our own proprietary statistical models to estimate what we think the average salaries are for graduates of each major at a college and that college as a whole.

What salary data is included?

All data used to produce this analysis was collected by PayScale from those who successfully completed PayScale’s employee survey.

Total Cash Compensation (TCC): Combines base annual salary or hourly wage, bonuses, profit sharing, tips, commissions, overtime, and other forms of cash earnings, as applicable.

TCC does not include equity (stock) compensation, which can be a significant portion of pay for some executive and high-tech jobs. In addition, salary does not include cash value of retirement benefits, or value of other non-cash benefits (e.g. healthcare).

All Pay figures are TCC.

Starting vs. Mid-Career Salaries: Starting salary figures include those workers with 0-5 years of experience. Mid-Career salary figures include those workers with 10 or more years of experience.

Bachelor’s Only: Only employees who possess a bachelor’s degree and no higher degrees are included. This means bachelor’s degree graduates who go on to earn a master’s degree, M.B.A., M.D., J.D., Ph.D., or other advanced degrees are not included.

For some highly selective schools, graduates with degrees higher than a bachelor’s degree can represent a significant fraction of all graduates.

U.S. Only: All reports are for graduates of schools from the United States who work in the United States. This sample does not include U.S. territories, such as Puerto Rico or Guam.

Full-Time, Civilian Employees Only: Only graduates who are employed full-time, not on active military duty and paid with either an hourly wage or an annual salary are included.

Self-employed, project-based, and contract employees are not included. For example, project-based graphic designers and architects, and nearly all small business owners and novelists, are not included.

Note: The alumni sample considered for the military schools (e.g., The United States Air Force Academy) only includes those who are currently in the civilian labor force and does not include alumni who are active service members.

Is the salary data you show directly from PayScale?

Yes and No. We get salary data for many schools by major from PayScale. While PayScale provides salary data for many of the majors at each school, there are many where they did not have enough people completing their survey to report official numbers.

To account for this, we feed the original PayScale data into our own advanced statistical models along with other factors and come up with estimated salaries for most majors at a college. So, the PayScale data is an important factor used in calculating our salary estimates, but we do not actually show direct PayScale data on the site or use it in our rankings. This goes for schools and majors where PayScale did not report data to us, as well as schools and majors where they did report data to us. All data shown are estimates based on our own statistical models.

Why is salary data ‘estimated’? What does that mean?

The only way to get 100% accurate salary data would be to collect it from every single person, throughout the world, that graduated from a college in the past 30+ years. That data would then need to be updated every time his or her employer changed and there was in increase or decrease in salary. Salary analysis sites use estimates because there is no current source that reports what was just described, nor is there likely to be one in the relatively near future.

The United States Internal Revenue Service might be the only organization that is even close to being able to capture such information, but they would be missing the large number of international college graduates. And though they know the salary of most people in the U.S., they don’t collect information on where anyone went to college. Thus, the role estimating plays in any analysis of graduate salary information.

Is Payscale data estimated as well?

PayScale data, while being one of the best , if not the best, sources of raw salary data available, is also estimated based on information gathered from the people that reported salary information to them. They extrapolate this information using statistical analysis to estimate what they expect salaries will be for the population as a whole.

In fact, a significant number of the statistics you hear quoted in the media, from the government and various research studies, etc., are also estimates based on a sampling of the population. This is often necessary due to the prohibitive cost in time and resources it would take to get the true data on 100% of the population.

Estimates are extremely useful in determining the earnings for a school and/or major. PayScale is extremely diligent about reporting only the data that is “statistically significant” -- meaning enough people reported earnings for that school and/or major for PayScale to feel comfortable saying that the average earnings for a particular group of people is representative of the overall average. They are indeed the leaders in this space, which is why we, and many others, have partnered with them.

How do your estimates differ from PayScale's?

The biggest difference between our estimates and PayScale’s relates to how we fill in estimates for “missing” data. By “missing” data, we mean majors at a college where PayScale did not feel they had enough data to provide an estimate of their own.

To account for this, we have employed a number of advanced statistical techniques to predict the salaries for many of those majors.

We will not detail the exact, proprietary techniques we use, as that is some of the secret sauce behind our prediction algorithms, but we will give a rough outline about how these work, philosophically.

  • The higher the amount of confidence (statistical significance) PayScale has in the data they provided to us for a particular major at a school, the closer our estimate is to the original number provided by PayScale.
  • The lower the amount of confidence (statistical significance) PayScale had in the data they provided to us for a particular major at a school, including cases where they had no data, the closer our estimates are to the following:
    • Average earnings for that major as a whole across all schools.
    • Average earnings boost for that school as a whole (based on the data reported for other majors at that school).

This is probably best illustrated by using the following example:

Let’s pretend we don’t have earnings data for Electrical Engineering at School ABC. Chances are pretty good that the earnings for graduates at this school are going to fall within the general range of earnings for graduates in Electrical Engineering at other colleges, as opposed to another unrelated major like Philosophy or Nursing. By analyzing just those that graduated with Electrical Engineering degrees, that narrows the data quite a bit.

To narrow it down further, we look at how well graduates of School ABC are doing who majored in subjects other than Electrical Engineering. If those graduates are earning more than graduates from other colleges (that we have data for), on a major to major basis, chances are good that earnings for Electrical Engineering grads will be higher as well. From this, we can estimate that the earnings for graduates of Electrical Engineering from School ABC are likely to be higher in the range of what is normal for that major.

Do you calculate estimates for all schools and majors?

No. We only provide estimates for majors that we have enough data on to establish an overall average, and we only provide estimates for schools in which we have enough school-specific data to establish an overall average for that college or university.

When you see “N/A” for a particular school and/or major, these are cases where we did not have enough data to feel comfortable providing an estimate.

How do you estimate salaries for a school as a whole?

Once we have estimated the earnings for each major at a college or university, we then combine all of those estimates and weight them by the number of students at each school to come up with the average earnings for each school weighted by the number of students in each major. We feel this is a better way to calculate the overall average for a school. It removes biases introduced when students from different majors have a tendency to report their earnings more often than others.

When I graduate, will I earn the salary that you show?

You might, but probably not exactly. There are many factors that go into how much you will earn. In addition to the college and major, you select, factors such as your grades, your experience, your connections, your initiative and persistence, the state of the economy and sometimes just “dumb luck” all determine what you might earn.

What we show is an estimated average to give you a rough idea of earnings potential when comparing different majors and/or colleges. You are a unique individual, not an average, and so what you ultimately earn may fall above or below the number we give.

Something else to keep in mind as you daydream about your future earnings:

There have been many studies conducted that show the vast majority of people think of themselves as above average(1). Obviously, that is impossible, as only 50% of people can actually be above the average. Just keep that in the back of your head when you are comparing what you might earn, to what a college might cost, especially if you are using that number to justify taking out large loans to pay for the school.

It is also important to consider the following when estimating whether or not you will fall above or below this average:

  • The ‘Starting Salary’ numbers include earnings reported by people with up to five (5) years of experience in their job. People with five years of experience likely earn more than those who are fresh out of school. So your true starting salary as a freshly minted graduate may be lower than this reported average. Even if you are absolutely amazing, and a truly an ‘above average’ grad, you may initially fall below the average earnings simply due to your lack of experience.
  • These numbers only include those that graduated. The reality is that most students don’t graduate from college, which certainly puts them at a disadvantage earnings-wise. All bets are off if you don’t actually finish, so keep that in mind as well.

Are there cases where your estimations will be off?

Probably. There are exceptions to every rule. An example of this exception might be a major at a school that is truly outstanding when it comes to earnings, while the rest of the school is not. Our methodology might inaccurately reduce the estimated earnings for that particular major at that particular school as a result.

Does this fact make our numbers any less useful? Absolutely not.

The reality is that the best anyone can do is provide a starting point to begin comparing colleges and/or majors. The alternative is to not publish any data or only data on a small subset of majors.

We believe that our statistical estimates, like any estimates, provide value, even without an absolute 100% guarantee of perfection. This type of data analysis is better than leaving the general public completely in the dark, wondering what earnings for a particular school or a particular major might be, or worse, not even consider earnings as a relevant factor in the college decision process.

As with any metric, you need to utilize the information as a guide, not an absolute. Use it to highlight areas for further exploration. If you see a minor difference between two schools and/or majors, you shouldn’t get too hung up on that. Rather, the takeaway should be that they are both relatively similar when it comes to earnings.

On the other hand, if you are considering a particular school and/or major, and you see that their average earnings are significantly lower than another college or university that you are considering, well, that is likely relevant and important for you to be aware of. The difference in earnings figures may not be exact, but such a big difference strongly suggests looking deeper into the earnings potential of that school’s graduates. Speaking directly with the college, asking some tough questions, and doing further research yourself, would be highly recommended.

How should I clarify information with the college? What should I ask?

When you ask a school for more information, try and go beyond just asking them for an average earnings number. Any earnings statistics they give you are likely to be estimates too, as they don’t have exact data on all of their graduates either! Furthermore, it’s not inconceivable to think that some schools might be motivated to bend their calculations to their advantage by making them appear better than they are.

Try to get past the glowing stats offered by the admissions department by asking questions that might help you get an idea of how accurate their numbers might be. Ask questions about the career services they offer, co-op and internship programs, job placement statistics, the companies that send recruiters to the campus, etc. Such questions can often be much more telling about your likelihood of earning a good living once you graduate from that school.

Also, remember to ask all of these questions in the context of the specific major you are considering. Do not ask for generic job placement statistics, and instead, ask for the percentage of students graduating from that major that you are interested in, that actually get jobs in that line of work and with what companies.

If a college or university fails to answer many of these questions to your satisfaction, that itself may be answer enough. It might also suggest that their goals and interests are not in alignment with yours -- assuming you value getting a paying job related to the degree you paid for.

I still have some questions regarding your analysis. Who do I contact?

Please visit where you will see a link in the right-hand column to 'Contact Support'. That will open a support ticket in our system and we can connect you with the appropriate person to answer your questions.


(1) Everyone Thinks They Are Above Average, Tia Ghose, via CBSNews - (1) The Superiority Illusion: Where Everyone is Above Average, Scicurious, ScientificAmerican -