Everyone is worried about growing concentration in U.S. markets. President Joe Biden’s July 2021 executive order on competition begins with the assertion that “excessive market concentration threatens basic economic liberties, democratic accountability, and the welfare of workers, farmers, small businesses, startups, and consumers.” No word on the threat of concentration to baby puppies, but the takeaway is clear. Concentration is everywhere, and it’s bad.
On the academic side, Ufuk Akcigit and Sina Ates have an interesting paper on “ten facts”—worrisome facts, in my reading—about business dynamism. Fact No. 1: “Market concentration has risen.” Can’t get higher than No. 1, last time I checked.
Unlike most people commenting on concentration, I don’t see any reason to see high or rising concentration itself as a bad thing (although it may be a sign of problems). One key takeaway from industrial organization is that high concentration tells us nothing about levels of competition and so has no direct normative implication. I bring this up all the time (see 1, 2, 3, 4).
So without worrying about whether rising concentration is a good or bad thing, this post asks, “is rising concentration a thing?” Is there any there there? Where is it rising? For what measures? Just the facts, ma’am.
How to Measure Concentration
I will focus here primarily on product-market concentration and save labor-market concentration for a later post. The following is a brief literature review. I do not cover every paper. If I missed an important one, tell me in the comments.
There are two steps to calculating concentration. First, define the market. In empirical work, a market usually includes the product sold or the input bought (e.g., apples) and a relevant geographic region (United States). With those two bits of information decided, we have a “market” (apples sold in the United States).
Once we have defined the relevant market, we need a measure of concentration within that market. The most straightforward measure to use is to look at the use-concentration ratio of some number of firms. If you see “CR4,” it refers to the percentage of total sales in the market is by the four largest firms? One problem with this measure is that CR4 ignores everything about the fifth largest and smaller firms.
The other option used to quantify concentration is called the Herfindahl-Hirschman index (HHI), which is a number between 0 and 10,000 (or 0 and 1, if it is normalized), with 10,000 meaning all of the sales go to one firm and 0 being the limit as many firms each have smaller and smaller shares. The benefit of the HHI is that it uses information on the whole distribution of firms, not just the top few.
The Biggest Companies
With those preliminaries out of the way, let’s start with concentration among the biggest firms over the longest time-period and work our way to more granular data.
When people think of “corporate concentration,” they think of the giant companies like Standard Oil, Ford, Walmart, and Google. People maybe even picture a guy with a monocle, that sort of thing.
How much of total U.S. sales go to the biggest firms? How has that changed over time? These questions are the focus of Spencer Y. Kwon, Yueran Ma, and Kaspar Zimmermann’s (2022) “100 Years of Rising Corporate Concentration.”
Spoiler alert: they find rising corporate concentration. But what does that mean?
They look at the concentration of assets and sales concentrated among the largest 1% and 0.1% of businesses. For sales, due to data limitations, they need to use net income (excluding firms with negative net income) for the first half and receipts (sales) for the second half.
In 1920, the top 1% of firms had about 60% of total sales. Now, that number is above 80%. For the top 0.1%, the number rose from about 35% to 65%. Asset concentration (blue below) is even more striking, rising to almost 100% for the top 1% of firms.
Is this just mechanical from the definitions? That was my first concern. Suppose you have a bunch of small firms enter that have no effect on the economy. Everyone starts a Substack that makes no money. 🤔 This mechanically bumps big firms in the top 1.1% into the top 1% and raises the share. The authors had thought about this more than my 2 minutes of reading, so they did something simple.
The simple comparison is to limit the economy to just the top 10% of firms. What share goes to the top 1%? In that world, when small firms enter, there is still a bump from the top 1.1% to 1%, but there is also a bump from 10.1% to 10%. Both the numerator and denominator of the ratio are mechanically increasing. That doesn’t perfectly solve the issue, since the bump to the 1.1% firm is, by definition, bigger than the bump from the 10.1% firm, but it’s a quick comparison. Still, we see a similar rise in the top 1%.
Big companies are getting bigger, even relatively.
I’m not sure how much weight to put on this paper for thinking about concentration trends. It’s an interesting paper, and that’s why I started with it. But I’m very hesitant to think of “all goods and services in the United States” as a relevant market for any policy question, especially antitrust-type questions, which is where we see the most talk about concentration. But if you’re interested in corporate concentration influencing politics, these numbers may be super relevant.
At the industry level, which is closer to an antitrust market but still not one, they find similar trends. The paper’s website (yes, the paper has a website. Your papers don’t?) has a simple display of the industry-level trends. They match the aggregate change, but the timing differs.
Industry-Level Concentration Trends, Public Firms
Moving down from big to small, we can start asking about publicly traded firms. These tend to be larger firms, but the category doesn’t capture all firms and is biased, as I’ve pointed out before.
Grullon, Larkin, and Michaely (2019) look at the average HHI at the 3-digit NAICS level (for example, oil and gas is “a market”). Below is the plot of the (sales-weighted) average HHI for publicly traded firms. It dropped in the 80s and early 90s, rose rapidly in the late 90s and early 2000s, and has slowly risen since. I’d say “concentration is rising” is the takeaway.
The average hides how the distribution has changed. For antitrust, we may care whether a few industries have seen a large increase in concentration or all industries have seen a small increase.
One issue with NAICS is that it was designed to lump firms together from a producer’s perspective, not the consumer’s perspective. We will say more about that below.
Another issue in Compustat is that we only have industry at the firm level, not the establishment level. For example, every 3M office or plant gets labeled as “Miscellaneous Manufactured Commodities” and doesn’t separate out the plants that make tape (like my hometown) from those that make surgical gear.
But firms are increasingly doing wider and wider business. That may not matter if you’re worried about political corruption from concentration. But if you’re thinking about markets, it seems problematic that, in Compustat, all of Amazon’s web services (cloud servers) revenue gets lumped into NAICS 454 “Nonstore Retailers,” since that’s Amazon’s firm-level designation.
Hoberg and Phillips (2022) try to account for this increasing “scope” of businesses. They make an adjustment to allow a firm to exist in multiple industries. After making this correction, they find a falling average HHI.
Industry-Level Concentration Trends, All Firms
Why stick to just publicly traded firms? That could be especially problematic since we know that the set of public firms is different from private firms, and the differences have changed over time. Public firms compete with private firms and so are in the same market for many questions.
And we have data on public and private firms. Well, I don’t. I’m stuck with Compustat data. But big names have the data.
Autor, Dorn, Katz, Patterson, and Van Reenen (2020), in their famous “superstar firms” paper, have U.S. Census panel data at the firm and establishment level, covering six major sectors: manufacturing, retail trade, wholesale trade, services, utilities and transportation, and finance. They focus on the share of the top 4 (CR4) or the top 20 (CR20) firms, both in terms of sales and employment. Every series, besides employment in manufacturing, has seen an increase. In retail, there has been nearly a doubling of the sales share to the top 4 firms.
I guess that settles it. Three major papers show the same trend. It’s settled… If only economic trends were so simple.
What About Narrower Product Markets?
For antitrust cases, we define markets slightly differently. We don’t use NAICS codes, since they are designed to lump together similar producers, not similar products. We also don’t use the six “major industries” in the Census, since those are also too large to be meaningful for antitrust. Instead, the product level is much smaller.
Luckily, Benkard, Yurukoglu, and Zhang (2021) construct concentration measures that are intended to capture consumption-based product markets. They have respondent-level data from the annual “Survey of the American Consumer” available from MRI Simmons, a market-research firm. The survey asks specific questions about which brands consumers buy.
They define markets into 457 product-market categories, separated into 29 locations. Product “markets” are then aggregated into “sectors.” Another interesting feature is that they know the ownership of different products, even if the brand name is different. Ownership is what matters for antitrust.
They find falling concentration at the market level (the narrowest product), both at the local and the national level. At the sector level (which aggregates markets), there is a slight increase.
If you focus on industries with an HHI above 2500, the level that is considered “highly concentrated” in the U.S. Horizontal Merger Guidelines, the “highly concentrated” fell from 48% in 1994 to 39% in 2019. I’m not sure how seriously to take this threshold, since the merger guidelines take a different approach to defining markets. Overall, the authors say, “we find no evidence that market power (sic) has been getting worse over time in any broad-based way.”
Is the United States a Market?
Markets are local
Benkard, Yurukoglu, and Zhang make an important point about location. In what situations is the United States the appropriate geographic region? The U.S. housing market is not a meaningful market. If my job and family are in Minnesota, I’m not considering buying a house in California. Those are different markets.
While the first few papers above focused on concentration in the United States as a whole or within U.S. companies, is that really the appropriate market? Maybe markets are much more localized, and the trends could be different.
Along comes Rossi-Hansberg, Sarte, and Trachter (2021) with a paper titled “Diverging Trends in National and Local Concentration.” In that paper, they argue that there are, you guessed it, diverging trends in national and local concentration. If we look at concentration at different geographic levels, we get a different story. Their main figure shows that, as we move to smaller geographic regions, concentration goes from rising over time to falling over time.
How is it possible to have such a different story depending on area?
Imagine a world where each town has its own department store. At the national level, concentration is low, but each town has a high concentration. Now Walmart enters the picture and sets up shop in 10,000 towns. That increases national concentration while reducing local concentration, which goes from one store to two. That sort of dynamic seems plausible, and the authors spend a lot of time discussing Walmart.
The paper was really important, because it pushed people to think more carefully about the type of concentration that they wanted to study. Just because data tends to be at the national level doesn’t mean that’s appropriate.
As with all these papers, however, the data source matters. There are a few concerns with the “National Establishment Time Series” (NETS) data used, as outlined in Crane and Decker (2020). Lots of the data is imputed, meaning it was originally missing and then filled in with statistical techniques. Almost every Walmart stores has exactly the median sales to worker ratio. This suggests the data starts with the number of workers and imputes the sales data from there. That’s fine if you are interested in worker concentration, but this paper is about sales.
Instead of relying on NETS data, Smith and Ocampo (2022) have Census data on product-level revenue for all U.S. retail stores between 1992 and 2012. The downside is that it is only retail, but that’s an important sector and can help us make sense of the “Walmart enters town” concentration story.
Unlike Rossi-Hansberg, Sarte, and Trachter, Smith and Ocampo find rising concentration at both the local and national levels. It depends on the exact specification. They find changes in local concentration between -1.5 and 12.6 percentage points. Regardless, the –17 percentage points of Rossi-Hansberg, Sarte, and Trachter is well outside their estimates. To me, that suggests we should be careful with the “declining local concentration” story.
Ultimately, for local stories, data is the limitation. Take all of the data issues at the aggregate level and then try to drill down to the ZIP code or city level. It’s tough. It just doesn’t exist in general, outside of Census data for a few sectors. The other option is to dig into a particular industry, Miller, Osborne, Sheu, and Sileo (2022) study the cement industry. 😱 (They find rising concentration.)
Markets are global
Instead of going more local, what if we go the other way? What makes markets unique in 2022 vs. 1980 is not that they are local but that they are global. Who cares if U.S. manufacturing is more concentrated if U.S. firms now compete in a global market?
The standard approach (used in basically all the papers above) computes market shares based on where the good was manufactured and doesn’t look at where the goods end up. (Compustat data is more of a mess because it includes lots of revenue from foreign establishments of U.S. firms.)
What happens when we look at where goods are ultimately sold? Again, that’s relevant for antitrust. Amiti and Heise (2021) augment the usual Census of Manufacturers with transaction-level import data from the Longitudinal Firm Trade Transactions Database (LFTTD) of the Census Bureau. They see U.S. customs forms. That’s “export-adjusted.”
They then do something similar for imports to come up with “market concentration.” That is their measure of concentration for all firms selling in the U.S., irrespective of where the firm is located. That line is completely flat from 1992-2012.
Again, this is only manufacturing, but it is a striking example of how we need to be careful with our measures of concentration. This seems like a very important correction of concentration for most questions and for many industries. Tech is clearly a global market.
If I step back from all of these results, I think it is safe to say that concentration is rising by most measures. However, there are lots of caveats. In a sector like manufacturing, the relevant global market is not more concentrated. The Rossi-Hansberg, Sarte, and Trachter paper suggests, despite data issues, local concentration could be falling. Again, we need to be careful.
Alex Tabarrok says trust literatures, not papers. What does that imply here?
Take the last paper by Amiti and Heise. Yes, it is only one industry, but in the one industry that we have the import/export correction, the concentration results flip. That leaves me unsure of what is going on.
 There’s often a third step. If we are interested in what is going on in the overall economy, we need to somehow average across different markets. There is sometimes debate about how to average a bunch of HHIs. Let’s not worry too much about that for purposes of this post. Generally, if you’re looking at the concentration of sales, the industries are weighted by sales.