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Federal College Rankings: The pitfalls of a magical regression model

Dec 30 2014

Far and away the most interesting idea of the new government college ratings emerges toward the end of the report. It doesn’t quite square the circle of competing constituencies for the rankings I worries about in my last post, but it gets close. Lots of weight is placed on a single magic model that will predict outcomes regardless of all the confounding factors they raise (differing pay by gender, sex, possibly even degree composition). As an inveterate modeler and data hound, I can see the appeal here. The federal government has far better data than US News and World Report, in the guise of the student loan repayment forms; this data will enable all sorts of useful studies on the effects of everything from home-schooling to early-marriage. I don’t know that anyone is using it yet for the sort of studies it makes possible (do you?), but it sounds like they’re opening the vault just for these college ranking purposes.

The challenges raised to the rankings in the report are formidable. Whether you think they can work depends on how much faith you have in the model. I think it’s likely to be dicey for two reasons: it’s hard to define “success” based on the data we have, and there are potentially disastrous downsides to the mix of variables that will be used as inputs.

I should say, by the way that a lot of details about the model is unclear; it looks for the moment like the standard economist’s trick of throwing every variable they have into a linear regression and hoping for the best. But there are interesting possibilities here. Is it going to be a true multilevel model allowing variable coefficients to vary by school? That would let us know if, hypothetically, Ole Miss tends to depress the post-graduation performance of African American students while being a great place for whites, or if Harvard Business School adds more value for men than for women.That would open up the prospect of truly personal college rankings. It might also enable all sorts of Title IX suits. But given the data and the ease of doing this kind of thing, I suspect someone will at least run it in their testing phase: it might be worth getting some subpoenas ready.

How will the rankings define success? It seems to be through a combination of several factors including graduation rate, cost, loan debt, and income years after graduation. None of these are especially new, and all have their problems. (Spelled out in the preliminary report). College presidents like Drew Faust are right to worry that the particular variables being chosen constitute a bit of a federal nudge to train students vocationally and for the short term, which is directly contrary to the mission of much higher education. We don’t yet know exactly how these statistics might be gamed, but it seems likely that colleges may put comparatively too much emphasis to helping students find a job by a certain date. I believe that one key component in my university, Northeastern’s, quick ascent in the rankings was convincing the magazine not to use 4-year graduation rate as a major component, since the typical Northeastern student takes five years to graduate with two 6-month job placements. A federal ranking will likely inadvertently punish schools that deviate from the norm even when there are good reasons.

But where things really get dicey are in the factors that are used to *offset* student success. I previously worried that rankings which used earnings measures would punish schools or disciplines with many women. A massive regression model will eliminate that concern, but will produce really strange and uncertain results.

As the coefficients vary, individual schools will shoot up and down in the rankings based on their demographic profiles. This is likely to be a quite unstable ranking from year to year, which is an unreservedly bad thing–it will encourage deans to make radical shifts and pinpoints turns on the basis of the slightest evidence. This holds promise only for the sort of people who use the word “disrupt” too much.

Most dispiriting about the tone of the report for me is the implicit optimism that they have the data to solve all of the problems of varying group performance. Even if they solve race and gender, a whole slew of other factors will persist. I joked on Twitter that the “moneyball” dean should summarily reject short people and twins from admission, since their lifetime earnings tend to be lower; although that’s obviously foolish, plenty of universities and groups will suffer under a regression-based ranking.

For instance, schools with a large Asian-American population will be expected by the model to perform extremely well. But it’s well known that certain Asian demographic groups don’t share in the benefits. These subgroups tend to be geographically concentrated, so it’s a fair bet that the University of Minnesota, say, will appear to be doing a much worse job than it actually is because the model expects its Hmong-American students to perform as well as UCSD’s Chinese-American ones.

There are also obvious questions about what to include in the model. It won’t include, for instance, a for-profit/not-for-profit flag, because if students attending for-profit schools do worse, that should reflect poorly on them. But should there be a public/private flag? This is less clear. Perhaps thorniest of all is the issue of accounting for degree composition. People training to be nurses make more than people training to be clergy. But the report vacillates, for understandable reasons, on whether degree mix should be included in the model. Most politicians, President Obama included, tend to like the idea that a model like this might give an extra nudge to schools to eliminate their art history major. (His stereotype, not mine).

But that’s the real problem. Including all these factors is a double-edged sword: although it means that the rankings will be more fair to socially disadvantaged groups and schools serving those populations, it also brings a new form of gaming into play. Although people love to complain about the distortionary effects of the US News ranking, the effects are relatively innocuous compared to what they could be. Sure, it’s absurd that Princeton probably deliberately accepts underqualified applicants to improve its yield or that default enrollment caps at Northeastern are 19 and 49 students to fall just short of the minimum class sizes: but those effects aren’t especially pernicious. US News doesn’t include all that many variables in its list, so really evil discriminatory practices in gaming the rankings aren’t as common as they could be.

If this federal ranking is adopted, it could unleash all sorts of new problems precisely because the data being used is so much richer. Depending on the exact calibration of the model, it may quickly become apparent that it makes sense to discriminate against or reward all sorts of classes (wealthy African Americans). And depending on what isn’t included, it may be obviously beneficial to start shuttering or discouraging enrollments in the liberal arts. It will take the actual introduction of the ranking for all the ingenuity of the managerial class to be deployed to reveal the ways that they can be gamed.