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:
- we have some ability to assess the general accuracy (or inaccuracy) of the information; and
- we make rational use of it.
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.