Thoughts on Latest Labor Market Concentration Paper

There is an interesting new paper by Azar, Marinescu, Steinbaum, and Taska on measuring concentration in the U.S. labor market using a dataset of nearly all online US vacancies from Burning Glass Technologies. This is obviously a very important issue and I am glad they are investigating it. However, I am concerned about how the authors define labor markets in this paper.

Specifically, they define labor markets based on USDA ERS commuting zone and 6-digit SOC occupation. So, if I understand this correctly, one labor market would be for Economics Professors (SOC: 25-1063) in the area surrounding Asheville, North Carolina (Commuting Zone 91). If that is correct, I can't help but think this definition of a labor market seems very narrow. To illustrate, I have two questions.

1. Aren't there occupations where using ERS commuting zones is less appropriate for defining the labor market? Economics Professors seems like an obvious example. Asheville doesn't have a local market for economics professors. Instead, if UNC Asheville posted a job for an economist, they would get applicants from all over the country. The authors note that 81% of applications on CareerBuilder.com are within the same commuting zone. However, it isn't obvious how well that result applies to the Burning Glass Technologies dataset (the one they actually use in the paper).
2. How common is it for the same person to apply to jobs in different occupation codes? Again, economists seem like a good example since they often apply to jobs in multiple occupation codes. For example, I have applied for jobs as an Economic Professors (SOC: 25-1063) and as a non-academic Economist (SOC: 19-3011). Are economists unique in this regard? Would a person that currently delivers food (SOC: 53-3031) never consider delivering office mail and packages (SOC: 43-5021)? If people are considering jobs across multiple 6-digit SOCs, maybe they are too narrow for defining labor markets?

To be fair, the authors defend defining labor markets using occupation by looking at labor supply elasticities to make sure they are not too big (they argue an elasticity greater than 2 is an indication the market is defined too narrowly). They note that Marinescu and Wolthoff (2016) (an unpublished working paper) found that, within a 6-digit SOC, the elasticity of applications with respect to posted wages is negative using data from CareerBuilder.com. So, if anything, they argue they are defining labor markets too broadly! However, a negative supply elasticity is a very counterintuitive finding. And it is based on a subsample of applications from a single website (only 20% of ads in the CareerBuilder dataset included wages and it isn't clear if selection bias is an issue). So would it really be enough to dismiss the more intuitive concerns raised in my second question above? I don't know.

Anyways. These are just my initial thoughts. It was a very interesting paper, so I will be eager to see what other people think as it gets passed around the web.

--Update--
Just a note that the authors were kind enough to engage with some of my concerns on twitter. You can follow the thread below (I may upload screenshots instead). In the end, I am still not sure they addressed my concerns. Our conversation revolved mostly around whether my economics market example was appropriate (I also brought up delivery drivers as an example but that seemed to be ignored). Sometimes it seemed like they were saying the economics job market was appropriate and well described by their market definitions. For example, at one point Azar said Yale and Harvard face different markets for economics professors because they occupy two different commuting zone. At other times, it seemed like they were saying that the economics job market was an outlier. For example, Azar and Taska both said at the end of the discussion that the economics profession isn't representative of other professions. In the end we basically had to agree to disagree.

https://twitter.com/dedubyadubya/status/969801949448146945