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This past weekend, New York Times columnist Nicholas Kristof caused an uproar among academic bloggers when he published an op-ed titled “Professors, We Need You!,” in which he decried a supposed generalized lack of public engagement among academics. The response from those academics who are on social media was “Just because you don’t read us doesn’t mean we’re not here.”

I don’t want to add to my publicly engaged colleagues’ outrage regarding this last Kristof crisis beyond the fact that in my job, my social media engagement (insofar as it relates to my research and teaching, of course) counts as “outreach,” which is a distinct portion of our annual review, so maybe Kristof should look to land grant institutions for solace: Just on my part of the University of Minnesota campus, my colleague Jonathan Foley finds time to be publicly engaged, even though I’m sure being director of the Institute on the Environment (on top of his own research, teaching, and other committee responsibilities) keeps him very busy.

I did want to comment, however, on how this should not have surprised anyone in light of past experience.

In econometrics, goodness-of-fit measures tell us what percentage of the variation in a dependent variable is explained by the explanatory variables. If you’ve ever taken a statistics class, you are almost surely familiar with the R-square measure. In a regression of, say the logarithm of wage on age, gender, and education level, the R-square is simply the fraction of the total variation in wage that is explained by variation in age, gender, and education level.

Given the foregoing, you’d think R-square is a great measure, right? I mean, it tells you how much of the variation in Y all of your X‘s explain! Yeah, no… R-square is actually not all that interesting, because you can thrown in any variable on the right-hand side — for example, the color of one’s underwear in the log wage regression above — and R-square can only increase, because there is bound to be a (spurious) correlation between the color of one’s underwear and one’s wage. Even the adjusted R-square, which corrects for how many variables there are in X, isn’t that great, since that correction is somewhat arbitrary.

I began a three-year term as associate editor over at Food Policy at the end of last year, which means that I handle submissions in my areas of expertise, deciding which manuscripts get reviewed and which ones get desk rejected, selecting reviewers for those manuscripts that do get reviewed, and so on.

Once again, I wanted to feature a few articles from the latest issue of the journal. There is nothing special about those articles beyond the fact that I thought they would be of interest to readers of this blog. Those are also regular articles–there is an entire special section of this latest issue dedicated to impact assessment in agricultural research, which you should check out.