Lars Jörgen Pålsson Syll is a Swedish economist who is a Professor of Social Studies and Associate professor of Economic History at Malmö University College.
Although prepared to admit that our empirical research procedures may be based on some very shaky assumptions, [some thoughtful scholars see] no point in saying much about this unless superior alternatives are presented. I understand this concern … Nevertheless, a hard look at the skeletons in the closet is beneficial, especially when there is a propensity to keep the door locked. Nothing is gained by avoiding that which the discipline must face up to sooner or later. If a current procedure appears to be patently wrong, I have not hesitated to indicate this, even if the alternatives remain to develop.
Like Stanley Lieberson, those of us in the economics community who are impolite enough to dare to question the preferred methods and models applied in mainstream economics and econometrics are as a rule met with disapproval. But although people seem to get very agitated and upset by the critique, defenders of “received theory” always say that the critique is “nothing new”, that they have always been “well aware” of the problem, “what Syll points out, we all know; there is nothing new in it; the real issue is to find out the alternative,” and so on, and so on.
So, for the benefit of all mindless practitioners of mainstream economics and econometrics — and who don’t want to be disturbed in their doings, eminent mathematical statistician David Freedman put together a very practical list of vacuous responses to criticism that can be freely used to save your peace of mind:
We know all that. Nothing is perfect … The assumptions are reasonable. The assumptions don’t matter. The assumptions are conservative. You can’t prove the assumptions are wrong. The biases will cancel. We can model the biases. We’re only doing what everybody else does. Now we use more sophisticated techniques. If we don’t do it, someone else will. What would you do? The decision-maker has to be better off with us than without us … The models aren’t totally useless. You have to do the best you can with the data. You have to make assumptions in order to make progress. You have to give the models the benefit of the doubt. Where’s the harm?