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A recent letter to the editor of the New York Times, in response to an article titled “How Single Motherhood Hurts Kids“:

As the product of a single-mother family, I take issue with the broad generalizations in your article. To those single mothers who work diligently to provide good homes for their children, the headline itself is an affront. The assumption is that single motherhood hurts children and this is how that happens.

In fact, this article is really about single, low-income, multipartner mothers, many of whom are teenagers. That is only a segment of the broad range of mothers who are single.

Setting aside the claim that single motherhood hurts kids, which I am in no way qualified to assess, I wanted to make an important point for consumers of social science results with this post. My point consists of two related sub-points, viz.

1. The vast majority of findings in the quantitative social sciences are true on average.
2. By definition, there is a whole distribution around that average, which means that the effect might be negative for some, zero for others, and positive for still others.

In other words, if I tell you that having a college degree increases your annual income by $30,000 on average, it is likely that the along the distribution of the average treatment effect of going to college on annual income, there are some people for whom the effect is negative — they went to college, but they ended up with a worse job than they would’ve had otherwise gotten had they accumulated four years of experience instead — and there are some people for whom the effect is more on the order of $100,000 per year.

Does the author of the letter to the editor quoted above make the mistake of assuming that average effects are uniformly distributed? Not really, because he recognizes that if you condition on certain things (high-income, adult single-mothers who have few partners), the result is likely to disappear.

And it’s not just the general, non-quantitative lay readers of the New York Times who make the mistake of assuming a uniform distribution of treatment effects. Every once in a while, I will get a comment in a referee report that goes something like “You find that an increase in food prices increases the number of food riots, but there are cases where food gets so expensive that people eat too little and, as a consequence, they are too weak to riot” (this is a fictitious example, by the way). To which your response should be: “Well, yeah.”

The whole point of running regressions, in most cases, is to know what happens on average. To be sure, there is a certain movement toward characterizing heterogeneous treatment effects in applied microeconomics, but doing so is difficult, because causally identifying average effects is difficult enough as is, and causally identifying heterogeneous effects is even more difficult.

[M]y own experience is that preparing for an economics exam has more to do with preparing for a musical performance than with preparing for an exam in the humanities. You cannot expect to play a piece of music perfectly the first time you sight-read your way through it. Rather, you will have to do a number of very rough sight-readings of it before you can identify weaknesses in your playing. When you have identified those weaknesses, you will isolate those measures that are the most difficult and work on them until you can play them seamlessly at the right tempo. Once that’s done, you will play the whole piece so as to make sure you can integrate those difficult measures into the easier material to make the whole piece of music flow evenly.

It’s that time of the semester once again, when students are preparing for their first wave of midterm exams. In my own APEC3001 — Consumers, Producers, and Markets course, my students have their first of two midterm next week, on Thursday.

As such, I just sent them the handout I had written a while back about how to do well in econ your econ courses. I thought I would share it again with the world at large; you can find here (link opens a .pdf document).

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.