Tuesday, March 5, 2013

The Value of Knowing the Value of Your Degree

Fellow blogger Laura McLay wrote today about a push in Congress to require someone (apparently states) to report statistics on graduate earnings by college/university and major. (See this report at Inside Higher Ed for more details; tip of the hat to Laura for the link.) Laura raises some excellent points about this pitfalls of this, and I will try not to duplicate her analysis.

Proponents of this sort of disclosure throw the word "transparency" around a fair bit, and in general I'm in favor of transparency (possible exceptions being clothing and curtains). Those of us associated with analytics are unlikely to argue against the provision of data (and, hopefully, some statistical analysis of it). Anyone who has used or taught decision analysis knows that, under typical assumptions, the expected value of imperfect information is nonnegative. In other words, it can't hurt to know. Those "typical assumptions", while mathematically mild, are important, and include the following:
1. we have some ability to assess the general accuracy (or inaccuracy) of the information; and
2. we make rational use of it.
Tips about the stock market, for example, point to the importance of the first assumption. Stock tips are never fully accurate, and stock tips from your halfwit brother-in-law may be chronically inaccurate (which raises the value of the information as long as you realize that -- just do the opposite of what he suggests). Buy recommendations from a generally reliable broker who just happens to have a big fish client trying to dump a chunk of that particular security, though, are problematic because you cannot assess even their approximate accuracy.

The second issue -- can we make rational use of the information (and will we) -- is one reason doctors are not always supportive of genetic testing and sometimes even follow-up tests for marginal positive results. Will information that the patient's risk of some relatively unlikely or slowly progressing condition unduly depress the patient, cause the patient to embark on expensive, invasive and/or risky tests or procedures, or otherwise push the patient to do something that might not be entirely reasonable?

So my first reaction to the notion of making information available to potential college students about career prospects (placement rates, starting salaries, salaries five years out etc.) as they relate to the nature and source of the college degree is positive: more information is better. My second reaction is that it needs to be information, not just data, meaning that someone reliable (knows analytics) and trustworthy (not out to recruit students) needs to process the data and translate it into actionable knowledge. Moreover, it needs to be communicated to prospective students in a way that lets them understand both the implications and the limitations of the information. So we need statisticians or analytics professionals involved, and we need communications professionals involved.

I'll end with a few specific comments:
• As Laura mentions, the analysis should at minimum provide ranges and not just averages. Those of us with analytics training are only too aware of the Flaw of Averages.
• Salaries are one way to look at the value of a degree. Break-even analysis (the time required to earn enough to pay off the cost of the degree, include lost earnings for the time spent in college) is another, but it is trickier to compute.
• Some nontrivial statistical modeling may be required to account for various factors other than school and major that might influence earning power. For example, some schools have an explicit pre-med major, while at some schools pre-med students major in chemistry, biology or biochemistry, and at some schools they major in something unrelated. When I was an undergraduate, the student living next door to me was a pre-med who majored in English. If that were true across the board (and I have no idea if it was, but at minimum it was not an anomaly), then our English majors probably out-earned English majors at schools with explicit pre-med majors.
• There is more to a career than salary, and that needs to be conveyed to consumers of this information. Before the markets tanked in '07-'08, finance majors hired by Wall Street trading firms enjoyed rather high salaries (higher than what finance majors earned in corporate finance positions, and certainly higher than many other majors). They also "enjoyed" a high cost of living, ungodly work hours and high stress. My impression is that aggressive personalities tended to fare better than less aggressive ones. So that high salary figure for finance majors at schools that fed the Wall Street mill needed to be tempered by an understanding of those other factors.
• Laura mentions the effect of time. Widespread dissemination of salary data might lead to gluts in the better-paying fields, driving down salaries in those fields. At the same time, demographic, economic or technological trends might augur for higher salaries down the road in fields that recently have not paid that well. (I'd mention gerontology, but someone might read something personal into it.)
• There are risk factors involved in the decision to attend college, the choice of the college to attend, and the choice of major. Major A might pay more after graduation than major B, but if majoring in A makes it likely you will fail to graduate and B is safer (given your particular skill set and inclinations), maybe B is really the better deal.

While ranges of salaries would be helpful, the median salary for a particular degree tells us much of what we need to know. If sociology majors earn a median starting salary of $30K, accounting majors of$50K and engineering majors of $70K, then it is quite obvious that engineers and accountants will have an easier time paying off student loans, buying homes, investing for retirement, etc.$20K extra x 40 years = \$800,000 (ignoring interest, inflation, dividends.) This is not rocket science.

The flaw of averages is itself flawed. There is no reason to think that a drunk will even attempt to walk down the center line, for example. He'll probably get hit before he even gets there.

For the most part, it really isn't a college's concern what any particular student's professional school goals are. The student does not need to get permission from the college to apply to or be accepted by med schools. It is the med schools which decide which applicants to accept, based on grades, MCAT scores, personal statements and the college-level courses that the *med schools* consider to be requirements. Theoretically, a student can major in basket weaving and he might still be accepted if he does well on the admissions requirements of the med schools.

It is best left to the student to decide if the high pressure world of finance is to his liking. That some college graduates might not turn out to have the temperament needed for finance is no reason to suppress the information about salaries. Even some low-paying careers can be quite stressful.

If students flock to what are perceived to be higher paying fields, so be it. Salaries might drop and an equilibrium will be reached. Or salaries might NOT drop. You don't know.

1. On your first point, the higher the median salary, the shorter the median payback time, assuming equal educational costs. Schools are starting to charge premium tuition, lab fees, computing fees etc. for some majors. I don't know that they add up enough to make a meaningful difference in payback time, but it's worth considering.

You're right that the student's post-graduate plans are his/her business, not the college's, but my point was that post-graduate salary data for major X at school A might not be directly comparable to data for the same major at school B.

I agree with your last point. There's a concern about whether the salaries drop precipitously while students are enrolled, though. You might sign up for a major in great demand at the start of your freshman year but not by the time you're close to graduating. That's not a reason to suppress demand or salary data; it's just a caveat to the consumer of the data.

2. -The governor of Florida has recently suggested lower tuitions for STEM majors at state-run colleges and universities. Student loans have probably led to a profusion of unmarketable non-STEM graduates, but it isn't clear if many of those students would switch to STEM under the Governor's plan. A vastly lower interest rate or the possibility of loan forgiveness for STEM majors might be a compromise, but that would probably require changes at the federal level.

-I would expect that Ivy League and "Public Ivy" graduates would make more money, all else being equal, but those make up a relatively small proportion of the total.

-The problem of oversupply might disappear if there were more factors at work that would compel employers to hire people who were not a perfect fit and then train them in-house.

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