Via Alex Tabarrok at Marginal Revolution – OK, who am I fooling, via LinkedIn when it said that schools I’d attended were highly ranked – LinkedIn has put out university rankings based on career outcomes. They’ve done this in eight fields: accounting professionals, designers, finance professionals, investment bankers, marketers, media professionals, software developers, and software developers at startups. Here’s the blog post by Navneet Kapur this explains the rankings. Essentially, employer A is assumed to be more attractive than employer B if more people leave jobs at B to go to A than vice versa, and employers with less employee turnover are assumed to be more attractive. A school performs better on these rankings if its students work for better employers.
This is basically revealed preference ranking for employers, although LinkedIn hasn’t made public the rankings of employers, only the rankings of schools. Some years ago there was a revealed preference ranking for schools1 that was based on admissions data – school X is better than school Y if people who were admitted to both choose one over the other. It would be interesting to see a comparable ranking for employers, although that would be a much harder statistical problem. If I get into school X and school Y, the offers are roughly comparable; if I get job offers from employer A and employer B, the offers may not be. In addition, since general employment doesn’t have some centralized calendar, multiple offers are less likely – I would guess that most people in job searches in most fields take the first “good enough” offer that comes.
One idea that I’ve thought about on and off for a while — although it hasn’t crossed my mind nearly as frequently since leaving academia — is if one could construct a ranking of graduate departments in a field by exploiting the fact that such departments are both educators and employers. A good academic department is one where the students end up working in good departments. Of course this is deeply academia-centric, and one would want to take into account where students who leave academia after their PhD end up as well. But if we ignore that and only look at the tip of the iceberg, it wouldn’t be too hard to put together the data set, at least in a field like mathematics – university web sites have lists of faculty, and the mathematics genealogy project can give where they graduated from.
1. Christopher N. Avery & Mark E. Glickman & Caroline M. Hoxby & Andrew Metrick, 2013. “A Revealed Preference Ranking of U.S. Colleges and Universities,” The Quarterly Journal of Economics, Oxford University Press, vol. 128(1), pages 425-467.
God plays dice 
I’m pretty sure somebody mapped out a network like that for some social science. No idea where to find it though.
How do they correct for company size? All other things being equal, wouldn’t more people get jobs at the bigger company, because they have numerically more jobs than a smaller company? A similar argument for school size.
How do they correct for people who start somewhere and never leave?
Since we don’t know the details, how can we tell when the system is being gamed? Pay for rankings?
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Michael,
I had an undergraduate student do her honors thesis on just this. She looked at ranking PhD granting math departments by where their graduates have jobs. She gathered data by hand (to get the tenure track info) and got the AMS data for for first job after graduation. In her case she used variations on the PageRank algorithm and the various rankings she obtained were quite interesting. If you’re interested, let me know and I’ll send you a copy of her thesis.
Jon Kujawa