peer effects – ERIN K. FLETCHER

As a first-time job market candidate, the annual ASSA meetings every January are stressful and busy and kind of terrible, but as I’ve gone more and more, I’ve realized they’re kind of awesome. My two favorite events are the CSWEP mentoring breakfast and the CU reception, but everywhere you go, you’re running into people you want to have a conversation with, people you haven’t seen in a year or more, people who want to ask you something or share something exciting. I spent most of the weekend hearing about cool papers, having great conversations about economics, and seeing people I care about. I’m a big fan, turns out.

Even if it’s 0 degrees F and we’re all tromping around in the snow that the city won’t clear.

But I digress. One of the other events I was excited for this time around was the T. Schulz memorial lecture put on by the Agricultural and Applied Economics Association. I like ag economists.

The lecture was given by Michael Kremer of Harvard. It wasn’t a traditional lecture in the sense there wasn’t much talk of big ideas or themes. He really just presented a new paper, which was a bit disappointing, but, taken at face value, ultimately interesting.

The paper was trying to ascertain the extent to which asset-collateralized debt would be successful in an experimental setting in East Africa (yes, likely a community that has seen plenty of these interventions). Most of the debt we take on in the US is asset collateralized, if you don’t pay your car loan, they take your car, for instance, but it’s not like that in many other parts of the world. Collateral for loans, especially small loans, often comes in the form of guarantees from family or neighbors, or some cash reserve itself, or sometimes none at all. So, asking whether individuals saw these loan as different is an interesting question if someone is trying to institute them.

Perhaps the most important result is that people were paying back their loans, and not only paying them back, but paying them back early, which Kremer attributed to debt aversion.

As Kremer started in on his preliminary results, the first things I heard were not his interpretation, but rather whispers from all sides around me.

“Neighborhood effects.”

“Peer effects.”

“Why should we think debt aversion is driving this behavior?” There seemed to be a consensus, at least in my part of the audience, that individuals were paying back their debts not because they disliked having debt, per se, but that they thought it made them look bad in the eyes of their neighbors. Some of the first questions following the lecture pertained to the interpretation of the observations.

Two ideas immediately came to my mind during this exchange. The first has to do with quantum physics and how when we observe something, we change it. The second is that many of the whispers around me could be re-interpreted as a discussion of social norms. In the peer effects interpretation, borrowers could see their peers repaying and thus be more likely to repay. And in the social norms sense, borrowers could perceive that having debt is not seen well by the community and thus be more likely to repay. It seems that much of the debate could have been settled by a survey question or two regarding attitudes about debt, social norms around debt, and the perception of debt aversion on a community level. “What percentage of people in this community pay their debts on time?” or “How are people who don’t pay their debts treated in this community?” Or something like that.

It strikes me that the language economists and other social scientists use to explain similar phenomena are often very different. Also, it seems that Kremer could have fairly quickly disabused his critics of their notions had he conducted at least a little surveying on debt aversion and social norms.

Some months ago I was at a conference, listening to a presentation on breastfeeding initiation and the presenter cited a paper by Fletcher. My first and second thoughts were, “how did that person get my breastfeeding paper?” and then “I didn’t say that in my paper.” Thanks to my trusty smartphone, I went searching for the paper, thinking perhaps my Gettysburg colleague, Jean Fletcher, had actually written it (a source of endless confusion for students, believe me), but found instead that it was Jason Fletcher, at Yale’s School of Public Health. Since then, I’ve run into a number of his papers and today, one came out in the NBER Working Paper series (gated), a paper on adolescent health behaviors and network effects with Stephen L. Ross.

The paper seeks to identify the effect that adolescents’ peers’ choices have on an individual’s health. If that sounds complicated, you’re not alone. Basically, the idea is that we want to know how strongly a child’s friend’s choices affect the child’s choices. The problem of how to causally identify this effect has plagued researchers for some time. In particular, the issue is that ideally, we would want to observe one student’s choices in different peer groups. But even if we can identify an exogenous change in peer groups (or in peer groups’ choices, but most likely through a change in peer group), the change in peer group is generally coupled with a dramatic change in environment as well. For instance, Fletcher and Ross cite one paper that shows that children who move from high-poverty areas to lower poverty areas experience better outcomes. Clearly, their peer group changes because the kids in one area have access to different activities, different stimuli, etc, but also the general environment changes. Mothers of these children report reduced stress, for example, which in and of itself has been shown to improve outcomes for children (or more precisely, children in high-stress living situations have worse outcomes–memory is failing me at the moment, I’ll update when I recall a relevant paper). So, when the environment changes and the peer group changes, it’s difficult to separate out the effects.

Using Add Health, which is a really cool survey instrument, by the way, the authors identify the effect by arguing that there is rather little variation in cohorts within a grade, but friend groups that look similar (on characteristics observable to the researcher)

At any rate, I think it’s a pretty neat identification strategy. It rests on some pretty strong assumptions, primarily that when groups cluster on observable characteristics, they’re unobservable characteristics are also similar, but dissimilar on the characteristics that influence health behaviors. This assumption is a bit problematic, I think, but I’m resolving it in my head by thinking of the insertion of one student with a particular tendency to smoke (his older sister does it, perhaps?) into a peer group in 9th grade, while a similarly made-up peer group in 10th grade doesn’t receive that idiosyncratic shock. Thus, the two groups look pretty similar, but by virtue of being in different grades, they have exposure to different kids and thus end up with different health behaviors.

Neat, no?

One concern I do have, though, is the idea that these friend groups are really that separate. I’m not very familiar with the way Add Health identifies friend groups, but I seem to recall some issues arising for researchers given a) the definition changing, and b) there being a limit on the number of friends that could be identified. From my own experience (clearly the most relevant), there was also a lot of grade mixing of friends in high school, even more so in dating. Sports, off periods, electives, and activities all gave way to friends in classes above and below. I grant that I went to a rather unique high school (billed as a sort of mini college campus), but it seems like it might be even more pronounced in a small schools. The assumptions of separation might be easier to make with middle schoolers, although incidence of averse health behaviors are going to be lower there and perhaps harder to identify.

Sources:

Jason M. Fletcher and Stephen L. Ross. Estimating the Effects of Friendship Networks on Health Behaviors of Adolescents. NBER Working Paper 18253. July 2012.
Kling, J.R., J.B. Liebman, & L. Katz. (2007). Experimental Analysis of Neighborhood Effects. Econometrica 75(1): 83-119.

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