This is a joke that I heard many times and once on a big stage at the 2014 annual meeting of the Verein für Socialpolitik where some supposedly important person from a supposedly important central bank (if I recall correctly) used it as a criticism of current economic methodology (as this person understood it) and generalizing it to mean it as a criticism of any economic methodology that uses math (if I understood this person correctly).

The joke goes like this. A police officer patrols the city at night and finds a perhaps slightly inebriated person apparently looking for something under the dim light of a street lamp. The police officer approaches said person and inquires: “Are you looking for something?” The perhaps slightly inebriated person responds: “Yes, I am looking for my keys.” “Where did you lose them?” the police officer asks. To which the perhaps slightly inebriated person answers: “Over there.” loosely waving at a bunch of bushes in the distance. “Why aren’t you looking for your keys over there then?” the police officer wonders out loud. “Well I only have light here” the slightly inebriated person replies.

How does this apply to research methodology in economics? Think of the slightly inebriated person in the joke as your economic researcher (now you see why this person had to be slightly inebriated). Think of the keys as the answer to a research question and think of the light as the research methodology that the economic researcher applies. The economic researcher is thus just as unlikely to find the right answer using their methodology as the slightly inebriated person is to find their keys.

I like this joke because it does ring true. I am sure it is a valid criticism of thousands of research articles in economics every year. But I do not come to the same conclusion that I believe the supposedly important person from a supposedly important central bank came to which is that, if I understood correctly, we should abandon serious mathematical modelling in economics (in favor, I believe this person indicated, of large scale simulation studies with intricately interwoven agents who all behave mechanically according to some simple heuristic). A brief aside: I do not mind if some people try simulation as a means of getting to an answer. I did not intend to here write a criticism of simulation as a methodology, although I am not overly optimistic of its usefulness. Simulation, as I see it, can only ever provide a fairly dim light. But it could on occasion be in just the right place to find the beginnings of an answer. But I believe that when we find a mathematical model to be unhelpful we should not abandon math completely but we should try and find or develop a more appropriate math to deal with the situation.

The more math we have at our disposal the more stats we have at our disposal the more light we have and the more likely will it be that we will find answers to our economic problems.

A final note, perhaps, and again very much only my opinion: the more brilliantly creative and intuitive you are the less you may need to know the tools. But then again who is brilliant?

“The more math we have at our disposal the more stats we have at our disposal the more light we have and the more likely will it be that we will find answers to our economic problems.”

I don’t fully agree. I can certainly think of examples of math-heavy research papers where I felt that the math only served to demonstrate the author’s ability to do math and didn’t actually add to our understanding of the issue at hand. In my experiance discussions about mathematical details of an economic model can sometimes crowd out more important discussions about the plausbility of, or empirical evidence for, the model. I sometimes worry that econ education puts too much emphasis on math and too little on understanding the economic principles that the math is supposed to express or illustrate. (Perhaps not in Graz, though.)

So I would say yes, math is good, but only to the extent it actually produces more light than heat. (Same for stats.) Or to put it in slighlty more “mathy” terms: math doesn’t go into an economist’s utility function, but it does go into his or her production function. (Let the reader judge if this particular instance of mathiness has produced light on net.)

I completely agree that there are many papers that are motivated by math and not by the economic problem. I have also seen many(!) papers in which the math used is actually (in my opinion at least) completely unhelpful for our understanding of the economic problem. I just think that they are using the wrong maths (or even model) then. How do you choose the right math? How do you choose the right model to think about the economic problem? Of course, there are many cases, where the current state of understanding of an economic problem is such that it is necessary or at least more fruitful to gather more evidence, to engage in new experiments, or to simply look at a problem from an entirely different angle. In the end, however, when we are trying to make sense of what we found, we will probably write down a now hopefully appropriate model, which we then have to analyse with the appropriate maths.

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