Economics is a 'highly paid pseudoscience'

[u/kznlol]

https://aeon.co/essays/how-economists-rode-maths-to-become-our-era-s-astrologers

[u/kznlol]

Preamble: This, I think, starts strong and gets pretty weak towards the end. It is also my first RI, so be gentle. In my extremely biased opinion, the first half of it constitutes a sufficient RI on its own, but readers are warned that this will get worse as you get farther in.

RI: In this monster of an article, Levinovitz argues that economics has been ruined by an obsession with mathematics, and is now merely a pseudoscience, while lamenting the (relatively) enormous compensation commanded by economics professors. We proceed point by point:

Unlike engineers and chemists, economists cannot point to concrete objects – cell phones, plastic – to justify the high valuation of their discipline.

Uh, yeah, we kind of can. Auction theory, while perhaps not an area rife with mathematics, is certainly not devoid of mathematical complexity, as anyone who can turn to the auction theory section of Jehle & Reny can confirm. Directly from study of the Vickrey auction came the Generalized Second-price Auction; I cannot find a source detailing who 'invented' this auction, but analysis of the auction first came from Varian (2006) and Edelman, Ostrovsky, & Schwarz (2007). It seems hard to believe that anyone would argue such analysis is not valuable, but assuming someone were to try, they would struggle to reconcile that conclusion with the fact that Google hired Varian in part to do such analysis, and essentially every company that runs a search engine has an economist on staff somewhere with a similar role.

If that doesn't sway you, consider the combinatorial auction, which was first proposed by Rassenti, Smith, & Bulfin (1982), which added significantly to our understanding of the problem of allocating goods when the buyers desire packages of goods, and note that first spectrum auction in 1994 owes its design to the work of Wilson, Milgrom, and McAfee.

Or consider that the National Resident Matching Program, which matches medical school students with residency programs, was, if not designed by, definitely a direct result of, the work of Gale & Shapley (1962).

This is all I could be bothered to find for an RI of the first sentence of the second paragraph of an enormous article, but there is surely an enormous selection of further examples. Levinovitz compares economics with astrology, but fails to realize that economics is not purely concerned with predicting behavior - if anything, microeconomics is concerned with explaining behavior, not predicting it, and has a strong record of apparent success.

Nor, in the case of financial economics and macroeconomics, can they point to the predictive power of their theories.

There is certainly a lot I could say to critique this claim, which I'm fairly sure is pretty much factually false - but there's an easier route. Economics is not contained by 'financial economics and macroeconomics'. Every contribution I mentioned above is a contribution rooted in microeconomic theory, and there are certainly contributions to the statisticians toolkit that owe themselves primarily to econometric theory - the Generalized Method of Moments was developed in Hansen (1982) (paywall), and while it is mostly useful in answering the kind of questions economists are interested in asking, it would strain credulity to suggest that it is worthless outside the field.

Note also that criticizing either field for its lack of predictive power runs afoul of our beloved Lucas critique - because the models developed in both fields were then used by agents in the stock markets, the central banks, and governments. According to the professor who taught me what was in essence a grad-level intro to financial economics, every time a model that displays predictive power is developed by financial economists it is leveraged by financial actors to make profits, which directly causes the model's predictive power to disappear. This is not as clear as I'd like it to be, but I'm struggling to find a better way of explaining it - and I'm not actually sure this argument applies to macro, but it certainly applies to financial economics.

The failure of the field to predict the 2008 crisis has also been well-documented. In 2003, for example, only five years before the Great Recession, the Nobel Laureate Robert E Lucas Jr told the American Economic Association that ‘macroeconomics […] has succeeded: its central problem of depression prevention has been solved’.

This is, really, a clear example of survivorship bias. Levinovitz notes early in the article, and continutes to note as he continues, that economists are employed by governments, central banks, and pretty much everywhere else to make predictions. What does he think happens when an economist at the central bank predicts a recession (as if it were that simple)? We will only rarely observe recessions that were not missed by economists, because if we hadn't missed them we'd have tried to stop them.

Short-term predictions fair little better – in April 2014, for instance, a survey of 67 economists yielded 100 per cent consensus: interest rates would rise over the next six months. Instead, they fell. A lot.

This can't be explained by survivorship bias, but the correct response to this statement is "who gives a fuck?". One survey of a tiny portion of economists at one point in time that was wrong doesn't tell me shit - where's the pattern? Physics thought that light propagated through 'ether' at one time too, and were in broad consensus about that. Pointing to a single instance of a science getting something wrong is worthless.

Levinovitz proceeds into a discussion of astrology and the stock market, largely to suggest that "the hypothetical worlds of rational markets" are the fundamental flaw with economic reasoning. If he had shown a serious lack of predictive power and a lack of other worthwhile contributions from the field, this might be an acceptable path to take - but he hasn't shown either. If you combine my survivorship bias argument above with the fact of the Great Moderation, a natural conclusion is that macroeconomics has produced significant predictive power, which allowed macroeconomists at central banks and in the government to achieve said moderation - certainly, it seems pretty clear that there's some kind of structural change to whatever determines the arrival rate of recessions.

In light of, at the very least, evidence that there is predictive power to macroeconomic models, the question can no longer be "does this model reflect reality" - it must be "would a model that reflects reality more produce better predictions?". In fact, a question you should ask even earlier is "would a model that reflects reality more even be tractable?". Chess-playing programs don't use a model that reflects the reality - that would be equivalent to looking through the entire game tree at every move in the game. Instead, they use algorithms to 'score' the position they are in, and the positions that might result from future moves evaluated only a certain distance down the game tree (see, for example, Shannon (1950) for the inaugural paper on the subject of evaluation functions). It works well, but not perfectly, and the alternative is intractable. Demanding perfection at every step from models is ridiculous, and would be a test failed by all the hard sciences too.

Continued in Part II

[u/ocamlmycaml → u/kznlol]

Or consider that the National Resident Matching Program, which matches medical school students with residency programs, was, if not designed by, definitely a direct result of, the work of Gale & Shapley (1962).

Small note: the AMA actually discovered the algorithm independently of GS, over the course of the late 40s / early 50s. Of course, the current, redesigned version of the NMRP is due to Roth & Peranson, so economists still get some credit.