Be careful what you wish for. Last February 2nd I [Sumner] started this blog with very low expectations… I knew I wasn’t a good writer . . . And I was also pretty sure that the content was not of much interest to anyone.
Now my biggest problem is time–I spend 6 to 10 hours a day on the blog, seven days a week. Several hours are spent responding to reader comments and the rest is spent writing long-winded posts and checking other economics blogs. . . .
I [Sumner] don’t think much of the official methodology in macroeconomics. Many of my fellow economists seem to have a Popperian view of the social sciences. You develop a model. You go out and get some data. And then you try to refute the model with some sort of regression analysis. . . .
My problem with this view is that it doesn’t reflect the way macro and finance actually work. Instead the models are often data-driven. Journals want to publish positive results, not negative. So thousands of macroeconomists keep running tests until they find a “statistically significant” VAR model, or a statistically significant “anomaly” in the EMH. Unfortunately, because the statistical testing is often used to generate the models, and determine which get published, the tests of statistical significance are meaningless.
I’m not trying to be a nihilist here, or a Luddite who wants to go back to the era before computers. I do regressions in my research, and find them very useful. But I don’t consider the results of a statistical regression to be a test of a model, rather they represent a piece of descriptive statistics, like a graph, which may or may not usefully supplement a more complex argument that relies on many different methods . . .
I [Sumner] like Rorty’s pragmatism; his view that scientific models don’t literally correspond to reality, or mirror reality. Rorty says that one should look for models that are “coherent,” that help us to make sense of a wide variety of facts. . . .
Interesting, especially given my own veneration of Popper (or, at least the ideal version of Popper as defined in Lakatos’s writings). Sumner is writing about macroeconomics, which I know nothing about. In any case, I should probably read something by Rorty. (I’ve read the name “Rorty” before–I’m pretty sure he’s a philosopher and I think his first name is “Richard,” but that’s all I know about him.)
Sumner also writes:
I suppose it wasn’t a smart career move to spend so much time on the blog. If I had ignored my commenters I could have had my manuscript revised by now. . . . And I really don’t get any support from Bentley, as far as I know the higher ups don’t even know I have a blog. So I just did 2500 hours of uncompensated labor.
I agree with Sethi that Sumner’s post is interesting and captures much of the blogging experience. But I don’t agree with that last bit about it being a bad career move. Or perhaps Sumner was kidding? (It’s notoriously difficult to convey intonation in typed speech.) What exactly is the marginal value of his having a manuscript revised? It’s not like Bentley would be compensating him for that either, right? For someone like Sumner (or, for that matter, Alex Tabarrok or Tyler Cowen or my Columbia colleague Peter Woit), blogging would seem to be an excellent career move, both by giving them and their ideas much wider exposure than they otherwise would’ve had, and also (as Sumner himself notes) by being a convenient way to generate many thousands of words that can be later reworked into a book. This is particularly true of Sumner (more than Tabarrok or Cowen or, for that matter, me) because he tends to write long posts on common themes. (Rajiv Sethi, too, might be able to put together a book or some coherent articles by tying together his recent blog entries.)
Blogging and careers, blogging and careers . . . is blogging ever really bad for an academic career? I don’t know. I imagine that some academics spend lots of time on blogs that nobody reads, and that could definitely be bad for their careers in an opportunity-cost sort of way. Others such as Steven Levitt or Dan Ariely blog in an often-interesting but sometimes careless sort of way. This might be bad for their careers, but quite possibly they’ve reached a level of fame in which this sort of thing can’t really hurt them anymore. And this is fine; such researchers can make useful contributions with their speculations and let the Gelmans and Fungs of the world clean up after them. We each have our role in this food web. (Personally I think I’m as careful in everything I blog as in my published research–take this one however you want!–and I welcome blogging as a way to put ideas out there and often get useful criticism. My impression is that Sumner and Sethi feel the same way, but authors who have reached the bestseller level probably just don’t have the time to read their blog comments.)
And then of course there are the many many bloggers, academic and otherwise, whose work I assume I would’ve encountered much more rarely were they not blogging.
The other issue that Sethi touches on in is the role of blogging in economic discourse. Which brings us to the (“reverse causal”) question of why there are so many prominent academic bloggers from economics (also sociology and law, it appears) but not so many in political science or psychology or, for that matter, statistics.
I guess the last one of these is easy enough to answer: there aren’t so many statisticians out there, most of them don’t seem to really enjoy writing, and statistics isn’t particularly newsworthy. I had a conversation about this the other day after writing something for Physics Today. Physics Today is the monthly magazine of the American Physical Society, and it’s fun to read. It was a pleasure to write for it. But could there be Statistics Today? It wouldn’t be so easy! In physics there’s news every month, exciting new experiments, potential path-breaking theories, and the like. Somebody somewhere is building a microscope that can look inside a quark, and somebody else is figuring out how to generalize Heisenberg’s uncertainty principle to account for this. Meanwhile, in statistics, there’s . . . a new efficient estimator for Poisson regression? News about the Census? No, when statisticians try to be entertaining, they typically end up writing about statistical errors made by non-statisticians. (Oops, I’ve done that too!). This can be fun now and then, but you can’t make a monthly magazine out of it.