The Limited Political Implications of Behavioral Economics

A recent post on Marginal Revolution contends that progressives use findings from behavioral economics to support the economic policies they favor, while ignoring the implications that support conservative policies. The short post, originally a comment by blogger and computational biologist Luis Pedro Coelho, is perhaps intentionally controversial, arguing that loss aversion is a case against redistributive policies and social mobility:

"Taking from the higher-incomes to give it to the lower incomes may be negative utility as the higher incomes are valuing their loss at an exaggerated rate (it’s a loss), while the lower income recipients under value it... 

...if your utility function is heavily rank-based (a standard left-wing view) and you accept loss-aversion from the behavioral literature, then social mobility is suspect from an utility point-of-view."

Tyler Cowen made a similar point a few years ago, arguing that "For a given level of income, if some are moving up others are moving down... More upward — and thus downward — relative mobility probably means less aggregate happiness, due to habit formation and frame of reference effects."

I don't think loss aversion, habit formation, and the like make a strong case against (or for) redistribution or social mobility, but I do think Coelho has a point that economists need to watch out for our own confirmation bias when we go pointing out other behavioral biases to support our favorite policies. Simply appealing to behavioral economics, in general, or to loss aversion or any number of documented decision-making biases, rarely makes a strong case for or against broad policy aims or strategies. The reason is best summarized by Wolfgang Pesendorfer in "Behavioral Economics Comes of Age":

Behavioral economics argues that economists ignore important variables that affect behavior. The new variables are typically shown to affect decisions in experimental settings. For economists, the difficulty is that these new variables may be unobservable or even difficult to define in economic settings with economic data. From the perspective of an economist, the unobservable variable amounts to a free parameter in the utility function. Having too many such parameters already, the economist finds it difficult to utilize the experimental finding.

All economic models require making drastic simplifications of reality. Whether they can say anything useful depends on how well they can capture those aspects of reality that are relevant to the question at hand and leave out those that aren't. Behavioral economics has done a good job of pointing out some aspects of reality that standard models leave out, but not always of telling us exactly when these are more relevant than dozens of other aspects of reality we also leave out without second thought. For example, "default bias" seems to be a hugely important factor in retirement savings, so it should definitely be a consideration in the design of very narrow policies regarding 401(K) plan participation, but that does not mean we need to also include it in every macroeconomic model.