I was going to comment something to this effect, too. The authors write:
For instance, we find ‘heavy-tailed’ distributions (e.g. log-normal, power law) of scientific citations, startup valuations, income, and media sales. By contrast, a large meta-analysis reports ‘thin-tailed’ (Gaussian) distributions for ex-post performance in less complex jobs such as cook or mail carrier: the top 1% account for 3-3.7% of the total.
But there’s an important difference between these groups – the products involved in the first group are cheaply reproducible (any number of people can read the same papers, invest in the same start-up or read the same articles – I don’t know how to interpret income here) & those in the second group are not (not everyone can use the same cook or mail carrier).
So I propose that the difference there has less to do with the complexity of the jobs & more to do with how reproducible the products involved are.
I think you’re right that complexity at the very least isn’t the only cause/explanation for these differences.
E.g. Aguinis et al. (2016) find that, based on an analysis of a very large number of productivity data sets, the following properties make a heavy-tailed output distribution more likely:
Multiplicity of productivity,
Monopolistic productivity,
Job autonomy,
Job complexity,
No productivity ceiling (I guess your point is a special case of this: if the marginal cost of increasing output becomes too high too soon, there will effectively be a ceiling; but there can also e.g. be ceilings imposed by the output metric we use, such as when a manager gives a productivity rating on a 1-10 scale)
As we explain in the paper, I have some open questions about the statistical approach in that paper. So I currently don’t take their analysis to be that much evidence that this is in fact right. However, they also sound right to me just based on priors and based on theoretical considerations (such as the ones in our section on why we expect heavy-tailed ex-ante performance to be widespread).
In the part you quoted, I wrote “less complex jobs” because the data I’m reporting is from a paper that explicitly distinguishes low-, medium-, and high-complexity jobs, and finds that only the first two types of job potentially have a Gaussian output distribution (this is Hunter et al. 1990). [TBC, I understand that the reader won’t know this, and I do think my current wording is a bit sloppy/bad/will predictably lead to the valid pushback you made.]
One theoretical point in favour of complexity is that complex production often looks like an ‘o-ring’ process, which will create heavy-tailed outcomes.
I was going to comment something to this effect, too. The authors write:
But there’s an important difference between these groups – the products involved in the first group are cheaply reproducible (any number of people can read the same papers, invest in the same start-up or read the same articles – I don’t know how to interpret income here) & those in the second group are not (not everyone can use the same cook or mail carrier).
So I propose that the difference there has less to do with the complexity of the jobs & more to do with how reproducible the products involved are.
I think you’re right that complexity at the very least isn’t the only cause/explanation for these differences.
E.g. Aguinis et al. (2016) find that, based on an analysis of a very large number of productivity data sets, the following properties make a heavy-tailed output distribution more likely:
Multiplicity of productivity,
Monopolistic productivity,
Job autonomy,
Job complexity,
No productivity ceiling (I guess your point is a special case of this: if the marginal cost of increasing output becomes too high too soon, there will effectively be a ceiling; but there can also e.g. be ceilings imposed by the output metric we use, such as when a manager gives a productivity rating on a 1-10 scale)
As we explain in the paper, I have some open questions about the statistical approach in that paper. So I currently don’t take their analysis to be that much evidence that this is in fact right. However, they also sound right to me just based on priors and based on theoretical considerations (such as the ones in our section on why we expect heavy-tailed ex-ante performance to be widespread).
In the part you quoted, I wrote “less complex jobs” because the data I’m reporting is from a paper that explicitly distinguishes low-, medium-, and high-complexity jobs, and finds that only the first two types of job potentially have a Gaussian output distribution (this is Hunter et al. 1990). [TBC, I understand that the reader won’t know this, and I do think my current wording is a bit sloppy/bad/will predictably lead to the valid pushback you made.]
[References in the doc linked in the OP.]
Thanks for the clarification & references!
This is cool.
One theoretical point in favour of complexity is that complex production often looks like an ‘o-ring’ process, which will create heavy-tailed outcomes.