At the University of Chicago, where I went to graduate school, a quote from Lord Kelvin, was carved in stone at the Social Sciences Research Building: When you cannot measure...your knowledge is meager and unsatisfactory. We graduate students took in this credo like mother’s milk. I came out of Chicago with a tremendous confidence in the power of economics and the ability to quantify that power.
But over the years, I have become increasingly skeptical of the power of statistical techniques to measure causation in complex systems. Edward Leamer’s indictment of modern econometrics, “Let’s take the ‘con’ out of econometrics” is the best known critique of our habits as empirical economists but it has not been taken to heart by the profession.
My thoughts on this issue came to a head with my recent podcast with Ian Ayres on his new book SuperCrunchers. The book is about the power of statistics to improve decision-making. And of course, facts and numbers are crucial for making wise decisions. And there are many examples where statistical analysis helps us in our private and political lives to overcome irrational prejudice or bad ideas.
But in the course of preparing for the interview, I realized in a way I hadn’t before, that how we feel about the reliability of statistical results lines up incredibly neatly with our political and ideological biases.
Regression is cheap so we buy a lot of it. Leamer's point is that this is "faith-based" empirical work. You just keep running the regressions including or excluding this or that, trying this or that specification until you find the result that confirms your worldview before you started the work.
The pragmatists (Peirce and James) and Hayek understood the dangers of rationality and what is essentially fake science.