Monday, March 26, 2018

Thoughts on Another Labor Market Concentration Paper

Efraim Benmelech et al (2018) released an NBER working paper last month that looked at the impact of labor market concentration on wages. Specifically, they estimate a series of models using panel data on on actual wages at the establishment level from the U.S. Census Bureau and estimates of local-labor concentration. The paper is well worth a read, but I am still skeptical about labor market concentration being a huge policy problem. Here are three questions that came to mind as I read the paper that kept me from being fully convinced.
  1. Are the authors defining local-labor markets correctly? My first concern with the paper is the way the authors define local-labor markets. Specifically, they define labor markets by county and 3 or 4 digit SIC industry code. So, for example, there is a market for labor in the "Paper Mill" industry (SIC 2621) in Haywood County, North Carolina (FIPS 37087). And this market is separate from the labor market of other industries. Just speaking from personal experience, this definition seems very narrow to me. Wouldn't a paper mill in Haywood County actually be employing people based on their skills and not which industry they worked in?  For example, my mother was hired at the Champion paper mill in Haywood County after working in the Ingles grocery store bakery. They didn't hire her because she had paper mill experience, they hired her because they needed unskilled labor.
  2. Are they capturing the effect of higher market concentration on wages or the effect of lower labor demand? The authors measure local-labor market concentration using an Herfindahl-Hirschman Index (HHI) for each county-industry (see page 3 and page 9).The authors find that wages fall when the HHI for a county labor market increases. If HHI only increased because of firms merging, then it seems obvious that wages must be falling because the market is becoming more concentrated and firms have more bargaining power.However, this is not the only reason that the HHI increases. As the authors note on page 24, the HHI might also increase if a firm closes and leaves fewer firms in the market. That seems like a very different story to me. If a firm closes, that doesn't just mean the market is becoming more concentrated, it also means demand for labor has likely fallen. So how do we know rising HHI isn't just mostly serving as a proxy for falling labor demand? How often is HHI rising due to firms exiting a market? The authors don't seem to address this concern at all in their paper. 
  3. Do their results really suggest labor market concentration had a large effect on wages? Ignore my previous two questions. Suppose the authors are correctly defining the labor market and are successfully capturing the effect of higher concentration on wages. What do their results actually tell us? They find that a 1 standard deviation increase in HHI lowers wages by as much 1.7% when defining labor markets using 3-digit SIC codes or 2.1% when using 4-digit SIC codes (see pages 10-14). But, in this context, raising HHI by 1 standard deviation is huge! The average HHI when defining labor markets using 3-digit SIC codes is 0.545. An increase of 1 standard deviation (0.35) would mean increasing HHI to 0.895! To put that in context, the max value for the HHI is 1. That means taking a county from the "average" level of concentration to near pure monopsony will only lower wages by at most 2% (maybe less). That's not nothing, but that seems surprisingly low given such a dramatic increase in concentration. 
So, overall, I found the paper interesting but unconvincing. But I could be missing something. If anyone has answers to my questions above, I'd be happy to hear them. 

Friday, March 2, 2018

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