Growing up, my parents, brother, and I usually avoided restaurants. For my parents, this was initially out of necessity; as Soviet refugees, they did not have the financial means to eat out. However, even having achieved a modicum of success, my parents are not generally in the habit of frequenting restaurants, having perhaps out of a lifetime habit, developed a taste for home cooking. Restaurants are exclusively for special occasions.
Thus, having never eaten at a Chipotle Mexican Grill, they were sufficiently impressed by the restaurant’s façade to wish to eat there, but only when the grand occasion merits such an extravagant excursion. Their two sons were informed as such. Naturally, my brother and I (perhaps spoiled as we are) jumped at the chance to poke fun at our parents for placing Chipotle on a pedestal. This is, after all, a restaurant chain that is victim to some serious defecation humor, not Eleven Madison Park.
For a number of months, my parents were subjected to text messages and Facebook or Instagram posts with visuals of me or my brother outside various Chipotle restaurants, posing next to Chipotle ads, and in one instance, wearing a Chipotle t-shirt (I have no idea how that shirt found its way into my wardrobe). My parents responded, saying things like (and I could not make this up), “I wish someone would take us to that dream place.”
However, recently, my mother sent a group text directing the family to a news report about dozens of confirmed E.Coli cases related to Chipotle (even the FDA got involved) and asking for alternative dining suggestions. The text responses, in order, were as follows:
Me: California Tortilla
My Wife: Taco Bell
My Brother: Sushi
My Mother: Eating In (with picture of latest home cooked meal)
My Brother’s Girlfriend: Bacon
How does a reasonable individual interpret this chain of responses? As an economist with some regulatory and antitrust experience, I found the answer obvious. I sent the following group text (modified for concision): “Has anyone noticed that this text conversation has turned into the classic antitrust debate about appropriate market definition, with each subsequent family member suggesting a broader market?”
Surprisingly, no one else had noticed, but I was asked to unpack my statement a little bit (my mom sent a text that read: “English please.”).
The U.S. Department of Justice and the Federal Trade Commission’s Horizontal Merger Guidelines stipulate that market definition serves two roles in identifying potential competitive concerns. First, market definition helps specify the line of commerce (product) and section of the country (geography) in which a competitive concern arises. Second, market definition allows the Agencies to identify market participants and measure market shares and concentration.
As the Agencies point out, market definition focuses solely on demand substitution factors, i.e., on customer’s ability and willingness to substitute away from one product to another in response to a price increase or a corresponding non-price change (in the case of Chipotle, an E.Coli outbreak might qualify as a reduction in quality). Customers generally face a range of potential substitutes, some closer than others. Defining a market broadly to include relatively distant substitutes can lead to misleading market shares. As such, the Agencies may seek to define markets to be sufficiently narrow as to capture the relative competitive significance between substitute products. For some precision with this regard, I refer the reader to Section 4.1.1 of the Guidelines.
As for the group texts above, the reader can now infer how market definition was broadened by each subsequent family member. To reiterate:
Me: California Tortilla (Mexican food in a similar quality dining establishment to Chipotle.)
My Wife: Taco Bell (Mexican . . . inspired . . . dining out, generally.)
My Brother: Sushi (Dining out, generally.)
My Mother: Eating In (Dining, generally.)
My Brother’s Girlfriend: Bacon (Eating.)
Why is market definition relevant to the Quello Center at Michigan State University? As the Center’s website suggests, the Center seeks to stimulate and inform debate on media, communication and information policy for our digital age. One area where market definition plays a role with this regard is within the Quello Center’s broad interest in research about digital inequality.
Digital inequality represents a social inequality with regard to access to or use of the Internet, or more broadly, information and communication technologies (ICTs). Digital inequalities can arise as a result of individualistic factors (income, age and other demographics) or contextual ones (competition where a particular consumer is most likely to rely on ICTs). Market definition is most readily observed in the latter.
For instance, consider the market for fixed broadband Internet. An immediate question that arises is the appropriate geographic market definition. If we rule out individuals’ ability to procure fixed broadband Internet at local hotspots (e.g., libraries, coffee shops) from the relevant market definition, then the relevant geographic market appears to be the home. This is unfortunately a major burden for researchers attempting to assess the state of fixed broadband competition and its potential impact on digital inequality because most market level data in use is at a much more aggregated level than the home. The problem is that when an aggregated market, say a zip code, contains multiple competitors, it is unclear how many of these competitors actually compete in the same home.
Thus far, most studies of fixed broadband competition have been hampered by the issue of geographic market definition. For instance, Xiao and Orazem (2011) extend Bresnahan and Reiss’s (1991, 1994) classic studies of entry and competition in the market for fixed broadband, albeit at the zip code level. Wallsten and Mallahan (2010) use tract level FCC Form 477 data to test the effects of competition on speeds, penetration, and prices. However, whereas there are approximately 42,000 zip codes and 73,000 census tracts in the United States, there are approximately 124 million households, which implies a fairly large amount of aggregation that can lead researchers to conclude that competition is stronger than it actually is.
Another question that arises is whether fixed broadband is too narrow a product market and if the appropriate market definition is simply broadband, which would include fixed as well as mobile broadband. Thus far, because of data limitations, most studies of wireline-wireless substitution have focused mainly on voice rather than on Internet use (e.g. Macher, Mayo, Ukhaneva, and Woroch, 2015; Thacker and Wilson, 2015) and so do not assess whether mobile has become a medium that can mitigate digital inequality. Prieger (2013) has made some headway into this issue by showing evidence that as late as 2010, mobile and fixed broadband were generally not complementary, and that mobile only broadband subscription was slightly more prevalent in rural areas. However, because of data limitations, Prieger does not estimate a demand system to determine whether fixed and mobile broadband are substitutes or complements as the voice substitution papers above do.
Luckily, NTIA’s State Broadband Initiative (SBI) and more recently, the FCC, have enhanced researchers’ ability to assess competition at a fairly granular level by providing fixed broadband coverage and speed data at the level of the census block. Similarly, new data on Internet usage from the U.S. Census should allow researchers to better tackle the wireline-wireless substitution issue as well. The FCC has also hopped on the speed test bandwagon by collaborating with SamKnows to measure both fixed and mobile broadband quality. In the former case, the FCC periodically releases the raw data and I am optimistic that at some point, mobile broadband quality data will be released as well (readers please correct me if I am glossing over some already publically available granular data on mobile broadband speed and other characteristics).
The Quello Center staff seeks to combine such data, along with other sources, to study broadband competition and its impact on digital inequality. We welcome your feedback and are presently on the lookout for potential collaborators interested in these issues.
For my son and my daughter in law next visit I have a menu list consisting of multiple traditional Russian dishes and many adapted by our family dishes such as lasagna, corn bread, teriyaki salmon. What happen if we skip all listed dishes and go to Chipotle Mexican Grill? What will be our next dream?
P.S. after this year my birthday party in Princeton NJ, I can’t accept any other pizzeria options.
Home cooking over Chipotle any day of the week.
As for the restaurant near Princeton, it was Osteria Procaccini (http://osteriaprocaccini.com/), which I recall as having unimpressive decor, but delicious pizza. However, I am not sure whether this restaurant belongs to the relevant market for Chipotle under the hypothetical monopolist test. On the one hand, there is a Chipotle within 10 minutes drive, on the other, Mexican and Italian food could be considered vastly different product markets. Whereas it is well known that pizza and french fries are nearly perfect substitutes (https://www.youtube.com/watch?v=8yU0TP3B6G8), I really don’t know about diversion between pizza and quesadilla.
You have a promising future in blogging. Great to have a colleague who can see the economics in media and information policy, but also in everyday life.
Fun post! Timely for me as Charter just raised my internet fees, and earlier today I was searching around for alternatives. (There aren’t any, of course…)
If you wanted to investigate competition at the household level, it would be fairly trivial to take 1000 or 10,000 random addresses, and write a program to query the ISPs’ address lookups on their websites to see what is available for each address.
Ben, we welcome collaboration with researchers well versed in Python. Alas, since the release of the NTIA and FCC data, the bottleneck in terms of granularity for any analysis that accounts for both individualistic and contextual factors lies with the U.S. Census Bureau (the spider program you suggest would not include demographic data for said households and the best that the U.S. Census offers for most questions is the census block group). Survey data tends to resolve that issue, however, and if survey participants’ addresses are made available to the researcher, applying the program you suggest to these addresses in place of random addresses could help to resolve some interesting research questions. However, these suggestions may raise potential ethical concerns.
I should add that contrary to popular belief, there are hundreds of ISPs. The bulk of these are relatively small telephone cooperatives that service relatively few households and I’m not sure how easily their websites would fit into a data scrapping program.
Based on my experience working on “verification” efforts in multiple state broadband mapping projects, I’d second Aleks’ comment about a lack of online searchability by address for a good percentage of small ISPs.
What I’d add to that is a strong skepticism as to the accuracy/consistency of online address-level availability queries even for many of the larger ISPs, especially in the rural areas where questions of availability are most likely to arise.
And any speed data available on the sites would be questionable for distance-sensitive technologies like DSL, again, especially in low-density/rural areas.
Very well written! Just recently your brother and I used chipotle sauce flavoring; we sent a picture to the parents because it reminded us of them. Funny how a family joke became an economical equation, looking forward to read more blog posts!