Kahan and Targeted Incentives
Kahan argues that, because of the multipile equilibria and hetergoneity of actors in collective action problems, maximum cooperation “probably requires that reciprocity dynamics be supplemented with appropriately tailored incentives, most likely in the form of penalties aimed specifically at persistent free-riders.” (p. 9).
However, some sticky policy issues may have so many intertwined moving parts that it is impossible to segregate and target “dedicated free-riders.” Any external incentives put into play then risk also hitting more reciprocal-minded actors and crowding out reciprocal norms.
So, what to do when no single policy prescription is “smart” enough to hit only one set of actors within a given collective action disposition but not the others? I suggest two alternatives. One is the default option: offer no external incentive and let the reciprocity norms settle on an equilibrium. The second is to shoot for the middle – i.e., design an incentive to push “neutral reciprocators” to cooperate. They represent the middle, and perhaps largest, block in Kahan’s model of heterogeneity. (Figure 3, p. 8). Incentives designed for this group will not be strong to push towards 100% cooperation because of more dedicated free-riders, and they may crowd out some of the reciprocal motivations of more natural cooperators. But overall, an effective design likely will settle on an equilibrium north of 50% — perhaps 60-80% cooperation.
Ultimately, choosing between these two alternatives will depend on the natural default’s equilibrium point and a host of other factors, such as the degree of monitoring available, as in Jolls’s examples.
nbramble
February 5, 2008 @ 11:43 am
Right. And I would want to elaborate more on the many troubles in organizing “penalties aimed specifically at persistent free-riders” (Kahan p. 9).
First, it’s not just that free-riders are hard to segregate, it’s that it’s pretty unhelpful in the first place to conceptualize free-riding as a discrete activity engaged in by discrete bad actors. Consider the times when you’ve free-ridden in the past: ripping a friend’s CD rather than buying it yourself; skimming class reading on the assumption that someone else will have done it more thoroughly and will be able to carry on a convincing conversation with the professor; paying less than the full amount at a group dinner when others have overpaid; movie-hopping from one theatre to another; and so on. Some of these activities are less ethical than others, but what they have in common is that they represent one point on a spectrum of behaviors, and that it is easy to see how they might shade into either more or less ethical action based on the context in which they occurred. Thus attempting to *target* these behaviors will likely overshoot or undershoot the intended scope of deterrence, and in the process will chill a fair amount of on-the-margin cooperation and sharing that we take to be socially beneficial.
It’s also worth considering how penalties can perversely empower those doing the penalizing (*cough*, RIAA, MPAA, *cough*), and how we don’t really want to live in a Panopticon where our every action is monitored and linked to some kind of reputation evaluation mechanism.
I’ll try to expand more on this later and talk about how we might be better off with communication mechanisms (see Camerer & Fehr p. 69, citing Sally 1995) than punishment mechanisms.