This is something I think about a lot. I'm really delighted to see someone write it up formally. I am definitely going to be talking about the piranha theorem from now on.
I will try to write up (my understanding) of the argument better tomorrow, but the essential point is that there cannot be a very large number of independent important causes of the same outcome x, because there is only so much variation in x to be explained.
One nice thing about the paper is that they put a number on this. Basically, if a is a correlation between x and y that represents a genuine causal link, then x must be one of the 1/(a^2) most important influences on y.
Unlike a lot of mathematical results one sees in statistics and econometrics, this is a really strong and general claim. It is literally impossible for - for example - more than 10 independent factors to each explain 30% of the same outcome.
This by itself rules out a lot of strong claims for "nudges". Small causes cannot *systemactically* produce large effects. There are too many possible causes!
They don't say this and might not agree, but to me this points strongly in the direction of narrative as a critical form of validation for causal claims in economics and other social sciences.
The test of a theory is not whether one can isolate variation in x and then show it has some correlation with y, but rather one can take a concrete important evolution of y and give an account of the most important of all the causes of it.
I think there's a strong argument that this is how persuasion works in practice in economics. Theories succeed not by performing better on some econometric test but by offering a more compelling story about some salient historical development.
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This FT article on the divergent fortunes of US-based and European airlines is interesting for several reasons. ft.com/content/695513…
First, it's a reminder that the pandemic (and to a lesser extent the response to the financial crisis) has seen a real reversal of the historic pattern where Western Europe has sen more active, interventionist public sectors and industrial policy.
Support for the airline industry is a nice positive example of policy that limits price increases not by reducing demand, but by maintaining supply.
Glad to see people recognizing that when strong labor markets boost wages, this does not have to mean higher prices. Higher wages can equally well be passed on to faster productivity growth. wsj.com/articles/techn…
What's strange, tho, is how people insist on seeing faster wage growth --> faster productivity growth as a *problem*.
As a matter of logic, if you are concerned either that rising wages will lead to inflation, or that lack of labor is holding back growth, evidence that employers respond to rising wages by taking steps to raise productivity should make you less worried.
Interesting: Because data on consumption basket can't be updated as quickly as prices, changes in consumption patterns distort inflation data. This probably led to exaggerated picture of price falls last year, and exaggerated picture of price rises now. ft.com/content/abad2b…
For example, the big rise in used vehicle prices is incorporated into CPI with a weight based on share of household spending on vehicles pre-pandemic. But less household spending goes to vehicle purchases now, so contribution to overall inflation numbers should have been smaller.
(I have to mention in passing a pet peeve of mine in discussions of price indexes: The constant assumption that the only thing that causes important changes in consumption patterns is relative prices. To be clear, this article doesn't do that.)
This one is more sensible. Does anyone want to make the case that "559,000 more Americans chose to work in May" would be equally reasonable? If not, can we dispense with the idea that labor market outcomes are determined symmetrically by labor supply and demand?
The point that **employment levels are chosen by employers** is so banal and obvious there would be no reason to even mention it, if we weren't hearing so much nonsense about employment being held back by "labor supply constraints".
There are three big things to keep in mind about tomorrow's jobs numbers. First, this stuff is noisy. Wherever the numbers come in, you should not draw strong conclusions from them; whatever the picture is now, it may look very different when the revised numbers come in.
While the revisions this spring weren't that big, they changed the picture in an important way: the initial numbers suggested accelerating growth over December-March, while the revised ones are closer to linear. This is important for interpreting the relatively low April number.
Interesting figure from @dhneilson showing inventory changes over the past year. Makes clear that the initial economic impact of the pandemic was a fall in demand, no supply -- something that for some reason was controversial at the time. neilson.substack.com/p/two-price
Back in spring 2020, I insisted that the economic crisis was a fall in spending, not in potential output. In retrospect, I was right on that, tho - thanks to the extraordinary stimulus - I was wrong that the pandemic would lead to a conventional downturn. rooseveltinstitute.org/2020/03/19/how…