Economists pretending to know
March 2, 2015
from Lars Syll
We are storytellers, operating much of the time in worlds of make believe. We do not find that the realm of imagination and ideas is an alternative to, or retreat from, practical reality. On the contrary, it is the only way we have found to think seriously about reality. In a way, there is nothing more to this method than maintaining the conviction … that imagination and ideas matter … there is no practical alternative”
Robert Lucas (1988) What Economists Do
Sounds great, doesn’t it? And here’s an example of the outcome of that serious think about reality …
In summary, it does not appear possible, even in principle, to classify individual unemployed people as either voluntarily or involuntarily unemployed depending on the characteristics of the decision problems they face. One cannot, even conceptually, arrive at a usable definition of full employment as a state in which no involuntary unemployment exists.
The difficulties are not the measurement error problems which necessarily arise in applied economics. They arise because the “thing” to be measured does not exist.
A perverse intellectual hierarchy
February 28, 2015
In the sense that there now exists in the economics profession an implicit and perverse intellectual hierarchy which is premised on the understanding that the less of what you do is related to the real world, the cleverer you are. So, if you are really clever, you would do mathematical modelling of a kind that has nothing to do with the real world. You would do something on the Turing machine [a theoretical computing device] or on information cascade or some such thing. If you are a little less clever, you would do econometrics, and if you are not even that clever, you would work on monetary policy or development economics. And, if you are not even that good, you would do economic history. But if you are the worst, you would go around factories interviewing managers. So, the leadership of the profession is moving towards abstraction for the sake of abstraction.
This has resulted in the shutting down of courses such as the history of economics, history of economic thought, philosophy of economics and other such fields. Basically, teaching economics has become like one of the other trades, like becoming a plumber or a bricklayer, as if it is about providing students with a set of skills which they can apply. There is no encouragement of critical thinking or teaching of real-world issues.
How to get published in ‘top’ economics journals
from Lars Syll
By the early 1980s it was already common knowledge among people I hung out with that the only way to get non-crazy macroeconomics published was to wrap sensible assumptions about output and employment in something else, something that involved rational expectations and intertemporal stuff and made the paper respectable. And yes, that was conscious knowledge, which shaped the kinds of papers we wrote.
More or less says it all, doesn’t it?
And for those of us who do not want to play according these sickening hypocritical rules — well, here’s one good alternative.
Econom(etr)ic fictions masquerading as rigorous science
from Lars Syll
In econometrics one often gets the feeling that many of its practitioners think of it as a kind of automatic inferential machine: input data and out comes casual knowledge. This is like pulling a rabbit from a hat. Great — but first you have to put the rabbit in the hat. And this is where assumptions come in to the picture.
As social scientists — and economists — we have to confront the all-important question of how to handle uncertainty and randomness. Should we define randomness with probability? If we do, we have to accept that to speak of randomness we also have to presuppose the existence of nomological probability machines, since probabilities cannot be spoken of – and actually, to be strict, do not at all exist – without specifying such system-contexts.
Accepting a domain of probability theory and a sample space of “infinite populations” — which is legion in modern econometrics — also implies that judgments are made on the basis of observations that are actually never made! Infinitely repeated trials or samplings never take place in the real world. So that cannot be a sound inductive basis for a science with aspirations of explaining real-world socio-economic processes, structures or events. It’s not tenable.
In his great book Statistical Models and Causal Inference: A Dialogue with the Social Sciences David Freedman touched on this fundamental problem, arising when you try to apply statistical models outside overly simple nomological machines like coin tossing and roulette wheels:
Lurking behind the typical regression model will be found a host of such assumptions; without them, legitimate inferences cannot be drawn from the model. There are statistical procedures for testing some of these assumptions. However, the tests often lack the power to detect substantial failures. Furthermore, model testing may become circular; breakdowns in assumptions are detected, and the model is redefined to accommodate. In short, hiding the problems can become a major goal of model building.
Using models to make predictions of the future, or the results of interventions, would be a valuable corrective. Testing the model on a variety of data sets – rather than fitting refinements over and over again to the same data set – might be a good second-best … Built into the equation is a model for non-discriminatory behavior: the coefficient d vanishes. If the company discriminates, that part of the model cannot be validated at all.
Regression models are widely used by social scientists to make causal inferences; such models are now almost a routine way of demonstrating counterfactuals. However, the “demonstrations” generally turn out to depend on a series of untested, even unarticulated, technical assumptions. Under the circumstances, reliance on model outputs may be quite unjustified. Making the ideas of validation somewhat more precise is a serious problem in the philosophy of science. That models should correspond to reality is, after all, a useful but not totally straightforward idea – with some history to it. Developing appropriate models is a serious problem in statistics; testing the connection to the phenomena is even more serious …
In our days, serious arguments have been made from data. Beautiful, delicate theorems have been proved, although the connection with data analysis often remains to be established. And an enormous amount of fiction has been produced, masquerading as rigorous science.
from Peter Radford
Ha-Joon Chang nails it.
But I wish he hadn’t.
You see, I agree with his analysis of the inverse nature of status within the economics profession. As a useful general rule the more notable you are within the profession the less you know about the economy. This is a result of the perverse nature of what economists actually do: they are amongst the very few disciplines — perhaps they are unique — who invent the artifacts that they then seek to explain and study. This relieves them, as you can imagine, from having to engage with the mucky real world.
You might wonder how this came about. It is quite a puzzle isn’t it? All those extremely clever people resolutely avoiding contact with the very substance that their chosen topic of study presents them from outside; averting their eyes from the glare of reality; turning inward as they search for clarity and that song sought after simplicity that so beguiles them.
It’s actually quite dispiriting for anyone who dares imagine that economics has relevance to humanity and its ability to chart a course towards a generally more prosperous world.
So how did this disconnect happen? How is it that the very best are the most ignorant?
Two causes come to mind: math and math. No, I am joking. Only one cause is math. The other is ideology.
Economies are devilishly complicated things. They are full of obstreperous and notoriously difficult subject matter. Notably people. And people, as we all know, do the darnedest things. They, for instance, change their minds and sometimes even contradict themselves — with a straight face too. This makes plotting and explaining their activity very hard. In fact it often makes it impossible. So, if you want to locate general ‘laws’ that govern all that bizarre behavior, you have to ignore it. Or you simply get people out the equation altogether and substitute ‘agents’ in their place. The great advantage that these ‘agents’ have over ‘people’ is that the former are artificial and can thus be trained to act according to rules that can be modeled mathematically.
So, if you want economics to become more mathematically rigorous you are forced to exit reality and enter the realm of the artificial. At least that’s what happened in economics beginning around the 1860’s. Don’t forget that everyone back then was seduced by the thought that everything could be explained through the lens of math. So why not economics?
Besides, the period right before the 1860’s — from 1848 onwards — was one of social turmoil and push back against the rise of industrialization and who was working that angle most effectively? Marx was. Now, Marx may have had his history all wrong, but his critique of capitalism was pretty much on the mark. By this time economics was already deeply concerned with explaining the efficacy of free markets as opposed to the dead hand of government, so the Marxist critique has to be dealt with — at least within the confines of economics. What better way than pretending that the ‘laws’ of economics were natural forces and were not human-made? If those ‘laws’ were, indeed, natural, then mucking around with them à la Marx was a silly delusion.
And once you have launched yourself into the search for natural ‘laws’, what better way to explain them than with math? It makes it extra hard to understand and so acts as a barrier to entry into the domain of higher understanding.
The rest, as they say is history. Once on that trajectory into the world of imagination, and away from reality, each step had, logically, to reinforce the last. It had, in other words, to travel deeper into the imagination. For to do otherwise would be to retreat. The journey was arduous. Along the way alls sorts of sensible ideas had to be jettisoned. The search for imagined purity can claim all sorts of victims, not least the sensibility of its most ardent students. Conversely, in order to rise up within the ranks of economics, students had to demonstrate a dedication and a willingness to believe in magic. The higher reaches of the profession became almost occult. So only those most willing to leave behind the real world, to adhere to the most fantastical beliefs, to tell the least real stories, and to dabble most diligently in the magic itself could become the masters of the profession.
Thus we arrive at the world that Ha-Joon Chang is describing.
My only disagreement is that I would, on reflection, reverse everything. I would borrow from Dante. To enter the highest echelons of economics, is to descend into the deepest error. Thus to any student pondering whether to study economics I say — to paraphrase somewhat — “beware all ye who enter here”.
Which do you want? To lose your mind? Or to study actual economies?
If the latter, think twice about studying what is called economics: because that’s the former.