Greg Mankiw‘s latest post discusses the potential effects of increasing the minimum wage. He refers to a recent paper by Lee and Saez that argues that increasing the minimum wage can be optimal (for everyone not just those receiving a higher income). But according to Mankiw, the argument of Lee and Saez is based on a model that uses an implausible assumption that drives all the results. Given how unrealistic the assumption is we should dismiss the results.
I do not want to go into the details of the particular assumption of the Lee and Saez paper and how plausible it is (I have no views on it), but let me comment on the reliance of economic models on implausible assumptions.
All models rely on assumptions and economic models are known (and made fun of) for relying on very strong assumptions about rationality, perfect information,… Many of these assumptions are unrealistic but they are justified as a way to set a benchmark model around which one is then allowed to model deviations from the assumptions. The strategy of setting up a benchmark model might sound like a reasonable one as we need to start with a tractable view of the world before one can get “closer” to the complexities of the real world.
But the problem is that the model becomes (or has become) the reference in a way that sets a high burden of proof for any deviations from it. If you think individuals are not rational, go ahead and model their behavior but you should do it in a way that is realistic and backed by data (good luck). If you want to allow for government spending to be productive, you can do it but you better have perfect econometric results that prove that returns to government investment is indeed high. Given that experiments are not possible in economics, it will always be very difficult to produce supporting evidence for some of these assumptions that is not controversial. So what do we do? We claim that we do not know enough about the real world, we claim ignorance, and that would be ok except that we do not stay quiet when we are asked for our opinion. We go back to the results of our benchmark model and if someone asks us about a relevant policy question we use them to justify our answer. Do we care about the fact that the assumptions of that model have never been proven and have no connection to reality? No, we don’t.
This subtle (or not so subtle) bias in economic analysis is my biggest source of frustration with my profession. Not being able to predict crisis, the stock market or exchange rates does not bother me, it is just a reflection of the limits of our knowledge and I can live with it. But using the same naive predictions of models that refer to a fictitious world as the reference and only moving away from them when someone produces an unquestionable piece of empirical evidence is in my mind the true cost of our profession to society.