Peer effects, health behaviors and adolescents

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.


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

Child obesity, Latin America and a good reminder

A few weeks ago, Adam Ozimek and I of Modeled Behavior had a discussion in the comments section here about the soda ban in New York City and the debate around paternalism. When I was slow to respond, we continued over email, just proof that you’re never really going to end a debate with an economist.

Adam was kind enough to send me a link to a piece in the Atlantic, which I thought did a much better job of summing up the arguments against the soda ban and paternalism in general, which I had, up to that point, not seen as convincingly articulated. What I liked about the argument is that it alluded to culture and how creating laws that are both nonsensical and devoid of cultural understanding and social norms makes for really bad law. And this I can totally get behind.

With that in mind, I spent much of last week searching for recent programs in the developing world for adolescent girls. The scope of this new project is rather wide and includes programs aimed at increasing political and community participation by girls, delaying marriage and sexual debut, improving education, health status, and bargaining power, decreasing HIV and violence against women, and so much more. I was thumbing through websites on health and violence and found the program Agente F, partially sponsored by Telefonica, one of the major cell carriers in Latin America. It’s intended to teach kids about healthy eating habits and avoid obesity, which, apparently, is fast becoming a problem in Latin America. I didn’t know. I thought we were still dealing with hunger and poverty, but apparently I’m behind the times. I have been unable to ascertain how widely this program is used, or whether anyone has actually played the game, but it’s interesting in that it has a lot of institutional support, at any rate.

I consider myself somewhat adept at Latin American cultures, and some more than others, having lived and spent time in many Latin American countries. I tell people “buen provecho” when they’re eating and can sing happy birthday in Spanish, Portuguese and Venezuelan (it’s a different song). I know where it’s appropriate to wait in line and where you’ll never get your coffee if you don’t hustle your way to the counter. I can talk to you a little bit about Catholics and saints and am sure to take a shower immediately if I get wet in the rain (RIP, Tomas.). I’m not a native, by any means, and I surely make mistakes, but it’s not a completely foreign world to me.

So I was struck by how many of the questions on the Agente F game I was unable to answer. Not just the ones about how many bones are in the body or how many muscles. Those, I guessed on and mostly did fine. One question in particular asked what should you do to ensure a good night’s sleep? I said exercise, but the answer was take a cold shower before going to bed. I see the logic. Your body needs to cool down before going to bed, and it’s often hot in many Latin American countries, which can make it difficult to sleep, but I thought it was a very odd answer.

A few questions were in this vein. The answers seemed totally foreign to me and reminded me how important cultural context is in creating programs and legislation with the policy goals of influencing behavior and actions. Despite my experience living in Latin America, I’m not a native. I have no idea whether taking a cold shower before bed would sound like a reasonable thing to a Mexican or a Colombian; maybe it’s totally within the realm of reason. Heck, maybe it’s within the realm of reason for natives of the United States and I’ve totally missed the boat. Regardless, culture is an important element to take into consideration when designing programs and laws.

Vaccines–or the lack thereof

A recently released study in Pediatrics shows that more than 1 in 10 children don’t receive their vaccinations as scheduled by their doctor, and likely as scheduled by the American Academy of Pediatrics. There are indicators that race and class have some bearing on whether parents follow the recommended schedule, but also a strong sense that the decision not to follow a schedule is often made before birth of the child. This may seem unsurprising to some, as often parents discuss and establish how they are going to raise a child before it’s brought into the world, but from an economic standpoint, kind of flies in the face of treating a vaccination as an investment. I am careful, in my research, to include controls for things like current medical insurance or medicaid assistance when it comes to measuring a similar outcome. As economists, it makes sense to assume that the marginal decision of taking the child to the doctor at any given scheduled checkup is subject to financial constraints. But if those decisions are made before the child is even born, then perhaps marginal analysis isn’t the correct way of approaching the problem.