Another week, another Friday – as in part one, this occasional series describes fixations, hobbyhorses and other bees in my bonnet. The usual warning applies – argue in the comments if you like, but these are not issues on which I’m susceptible to rationality or persuasion, they’re not opinions, they’re bees.
This particular buzzy little fellow was annoying me the other week because once more they awarded a Nobel Prize (in Economics, but still) at least partly for something I consider to be actually embarrassing. In this case, the practice of carrying out econometrics using an “index” of “institutions” on the left hand side.
Guys, it’s just a number that somebody made up! I mean, I suppose that as an accounting influencer, I have to say that nearly all statistical data is to some extent invented, that coding systems and collection practices are ideological rather than neutral and the creation of “objective” data is often a crucial tool of rhetoric.
But when you’re using the Freedom House Index Of Effective Property Rights[1], and asking “why is France a 3 and Germany a 4? If America is 2 and Cuba is 8 does that mean America is four times better? Could somewhere be 3.5 and if not why not?”, then … all those questions are potential showstoppers from a statistical point of view, but more importantly these are just numbers that somebody made up! At their desk, in the knowledge that they were going to publish them. They might have had a set of criteria and a weighting scheme, they might even have been surprised at some of the conclusions, but fundamentally they, and their boss, knew how the rankings were going to have to look.
Why do people create these numerical indexes rather than just saying which systems they think are best? Usually, because you can then do statistical analysis and demonstrate rigorously that certain desirable characteristics are correlated with economic growth and prosperity. Can you really do that? Of course you bloody can’t. You put the rabbit in the hat, then you took the rabbit out of the hat; the statistical analysis is just your original argument.
Buzz, buzz, buzz. At various points in my career, I used to be responsible for creating “Eurocrisis political risk indicators” and the like, basically because research reports look better with a chart on the front. I was always happy to print them, because it was actually potentially useful to my clients (and to me, comparing back to previous times) to have a time series line that went up and down and roughly summarised my views of the situation. If I was comparing complicated things like tax systems between countries, I’d much rather have a knowledgeable professional’s score out of ten than a list of marginal rates, for example. But that’s all these things are; they’re made up numbers.
[1] It’s not called that, it’s not scaled like that and I don’t care. Mention of FH rather than any other provider of indices shouldn’t be taken as endorsement or its opposite – it was just the one that sprung to mind. In many ways, I have more respect for the more nakedly ideological and half-assed versions of these indicators than the ones which try too hard to be scientific. As my dad used to love to say, “if a job’s not worth doing, it’s not worth doing properly”.
Agree. I wrote a book Models.Behaving.Badly about different ways of knowing the world and somewhere in there I wrote "There are no “raw” data. Choosing what data to collect takes insight; making good sense of the data collected requires the classic methods. We still need a model, a theory, or intuition to find a cause."
All agreed, but is the work that's being rewarded the econometric work or the Thought Leadership of making up a big thesis that people want to believe, that says big things about world history and how things aught to be, and appropriates it for the field of Econ.
My sense is the econometrics is kind of a ritual followed because serious papers are supposed to have that sort of stuff, "in science we test hypotheses empirically!" and the like but no one is basing their evaluation of Acemoglu on the data, it's all on the strength of priors, anecdote, authority, and skill of rhetoric.