Monday, January 9, 2017

Watch This Before Making Tenure Decisions: The Curse of the Top 5

I started this blog for graduate students, but I'm hoping that by now some tenured professors who vote on tenure cases have a look every now and then. This one is for you guys.

Tenure or no tenure, what is one of our favorite topics of conversation? That new AER/QJE/JPE/etc. paper that shouldn't have been published there because it's not so good. Or that other paper (usually our own or our friend's) that for sure should've been published in a top 5 but was desk rejected in five minutes. Yes, this is all fun and good chatter at lunch--actually, I think it's great to be talking about papers period--but when it comes time for tenure decisions, this stuff is really, really important. These decisions have a huge impact on our colleagues' lives as well as the future of our departments. For this reason, I'm really happy to see important economists talking about these issues in a public way.

Have a look at this webcast of a panel discussion at the recent AEA meetings called, "Publishing and Promotion in Economics: The Curse of the Top Five." James Heckman, George Akerlof, Angus Deaton, Drew Fudenberg, and Lars Hansen talk about the problems arising when departments place too much emphasis on Top 5's when making tenure and hiring decisions. Many excellent points were brought up. I was especially impressed by the notion that different Top 5's are relatively more influential in different fields within economics. Some suggestions were made for improving things, but what I got out of the talk was mostly that this is a difficult problem. What is the alternative to relying on publications for making tenure decisions? Yes, we could theoretically read the papers, but even if we understood all of our colleagues' papers perfectly, it's very difficult to have a sense for the contribution of the paper in its subfield, especially given how specialized economics has become. Should we rely more on algorithms? Maybe, but we don't have much by way of data for new researchers. What to do? I don't know, but at the very least, we should be thinking about these things very carefully when making decisions.


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