Aleks Yankelevich’s First Blog Post (Chipotle, Market Definition, and Digital Inequality)

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.

Primary takeaways

Digital inequality shows larger impacts on youth academic performance as compared to time spent on screens.
Digital skills play a significant role in mediating unstructured online engagement (social media use, playing video games, browsing the web) and youth academic, social, and psychosocial development.
Unstructured online engagement and face-to-face social interaction are complementary and continuously interact to create and enhance youth capital outcomes.

A familiar story: concerns of screen time

Today’s discussions of adolescent well-being have coalesced around a clear narrative: teenagers spend too much time online, and their academic performance, mental health, and social lives are deteriorating as a result. A steady stream of academic papers, books, and op-eds, alongside a growing number of policy proposals––school phone bans, age-gated social media use, restrictive screen-time limits––rest on the same underlying claim, aligning with a contemporary, digitized version of the displacement hypothesis:

Screen time, particularly the unstructured, free-time spent on social media, gaming, watching video content, or browsing the web, is said to displace the productive face-to-face activities that build adolescents into capable adults.

The implied and often practiced solution is restriction. In response, this dissertation tested this claim directly, and placed it within the broader context of adolescence.

Across three years, I followed 653 Michigan adolescents from early through late adolescence: in grades 8 or 9 (survey one, 2019) to grades 11 or 12 (survey two, 2022). Notably, these students, studied over time, were part of a broader pooled sample of 5,825 students across the same eighteen highschools. The study window captured the year before and the year after the peak of the COVID-19 pandemic and related lockdown orders, functioning as an unprecedented stress test for theories of adolescent social, academic, and digital life and, importantly, as a benchmark to compare the effects of pandemic-related change and inequality to those effects from screen time alone.

Across four studies of adolescents, consisting of six cross-sectional and longitudinal analyses, findings are not consistent with the displacement narrative, nor the broader concerns about the time youth spend on screens.

Findings are, however, consistent with something the current public and (most) academic discussions have largely overlooked or ignored: the gaps and inequalities that determine whether adolescents can access and use the internet meaningfully in the first place.

What the displacement hypothesis overlooks

Displacement and related research and policy concerning the time young people spend online assumes a “zero-sum” model of adolescent day-to-day time. An hour online is an hour not spent studying, reading, sleeping, or interacting face-to-face (i.e., time spent on more productive or developmentally “better” activity).

Indeed, this makes sense logically. However, as an empirical claim, this model requires time spent online to behave differently from all other ways adolescents allocate time; it must produce uniquely negative outcomes and be inherently harmful across digital contexts, rather than the typical mix of trade-offs corresponding to, and often overlooked among any other social or developmental context.

Yet, online time does not differ from other youth activity. Instead, I find it has a mix of pros, cons, and even some “uniquely digital” benefits which youth utilize for social and academic gains. When I compared unstructured digital media use against traditional face-to-face interaction and activities, both produced similar patterns: some negative associations with academic outcomes, some null, and some positive.

Trade-offs within traditional face-to-face activity (for example, social time with friends and family, or time spent in after-school extracurriculars) are treated as ordinary developmental experiences that must be experienced for the betterment of development. The identical trade-offs involving digital time tend to be overlooked or ignored, and online engagement is perceived as altogether harmful.

A growing body of evidence, including this dissertation, do not support that distinction. Indeed, the developmental context is routinely misread, leaving out the context of the experiences and time spent on digital, as well as face-to-face activities, interactions, existing inequalities, and changes inherent to development. As such, I proposed a novel framework to understand these contexts:

Digital capital exchange

Rather than treating screen time as a unified harm, this dissertation advances an “exchange”-based framework, grounded in James Coleman’s theories of youth capital and digital inequality scholarship, particularly following Eszter Hargittai, Jan van Dijk, and Alexander van Deursen (see this list of all dissertation references for full works).

The core proposition is that adolescents’ online engagement is not an alternative to developmental activity but another, albiet modern domain through which young people accumulate and mobilize online resources––particularly digital skills––that work alongside existing social networks and experiences to be exchanged for human capital (measured as: academic achievement, aspirations, STEM interest) and social capital (peer networks, community participation, extracurricular involvement).

Online time is not the mechanism; instead, it is digital skills that I find to be the most vital component in youth capital exchange and enhancement. Unstructured online engagement contributes to online skills; those skills, accumulated and mobilized alongside existing peer, family, and community networks, translate into the outcomes researchers and parents care about, i.e., academic achievement, aspirations, and face-to-face interaction and social networks.

This digital capital framework treats online and in-person contexts as complementary rather than antagonistic, and it situates adolescents’ digital lives within the structural conditions––connectivity quality, device reliability, autonomy of use––that determine whether exchange can occur at all.

Methods (in brief)

Paper-and-pencil surveys were administered to students in classrooms at two time-points: spring 2019 (N=2,876) and spring 2022 (N=2,949), across the same eighteen predominantly rural Michigan schools, grades 8–12. Official, nationally-ranked standardized reading, writing, and math test scores (PSAT 8/9, PSAT 10, SAT; College Board) were then anonymously linked to students’ survey responses with the help of participating districts.

Cross-sectional path analyses modeled pooled and wave-specific samples (pooled N=5,825); two-wave cross-lagged panel models tested reciprocal, longitudinal relationships on the 653 students who completed both surveys. Multi-group analyses of the cross-lagged panel models compared relationships between girls (N=345) and boys (N=308). All longitudinal models included time-invariant socioeconomic covariates as well as time-varying covariates to reduce omitted-variable bias.

Key findings: an overview

To summarize, to the best of my ability, eight chapters across 376 pages, I present two primary findings:

First: digital inequality predicted larger and more consistent declines in human capital than screen time did.

Unreliable home internet and technology maintenance problems––experiencing and/or dealing with broken or outdated devices and software, restrictive school-issued hardware, issues with connecting to or maintaining internet access––decreased youth GPA and standardized test achievement. And, these effect sizes were substantially larger than any negative direct effect from unstructured digital media use.

Across all four empirical studies, digital inequality emerged as the most substantial predictor of academic and developmental decline.

Second: digital skills mediated the relationship between online time and adolescent academic and social outcomes.

Unstructured digital media use, particularly online gaming and web browsing, predicted higher internet and social media skills for adolescents, which in turn predicted stronger academic achievement and self-efficacy (human capital), and social interaction and extracurricular participation (social capital). The positive indirect effect of screen time through skills offset or exceeded any small negative direct effects across several outcomes (supporting our existing peer-reviewed work: Hales & Hampton, 2025, and which you can read more about here).

These exchange processes were amplified when peer and family networks were modeled alongside digital skills, consistent with the premise that online and offline contexts operate together rather than in competition. The effect was not universal: social media skills amplified rather than offset a negative association with consistency of interest, one of the two subscales of grit. The exchange framework describes a contextual and conditional, domain-specific mechanism, not a blanket defense of time spent online.

Implications

If digital inequality, and not screen time, is the primary predictor of adolescent academic and developmental decline, and still warrants concern regarding access quality and experience even with the broader adoption of digital devices across the United States, the current policy emphasis on restriction is pointed at the wrong target. The evidence supports a different set of priorities.

Stable, reliable home (fast) broadband should be treated as an educational prerequisite rather than a consumer amenity. Unreliable connectivity exerted larger downward pressure on human capital than any measure of screen time, and that pressure intensified during the pandemic-era reliance on digital infrastructure. Technology maintenance, device repair, replacement, technical support, and the flexibility to install software and explore the web autonomously, matters as much as initial access, and school-issued devices that restrict autonomous use appear to hinder skill accumulation rather than support it.

Restrictive parental mediation of internet use was negatively associated with grit and self-efficacy at magnitudes comparable to the positive contributions of face-to-face activity. This challenges the assumption that digital restriction functions protectively. Instructive mediation, teaching adolescents to verify information, navigate platforms critically, and mobilize online resources toward meaningful ends, is the posture the data supports.

Finally, the technical skill-building that occurs through gaming, self-directed exploration, and deep web use is skill-building, not wasted time. Closing the persistent gender gap in these domains likely requires legitimizing technical play for girls, rather than restricting it for everyone.

None of the above is an argument that screen time is benign. It is an argument that screen time is the wrong focus, particularly when studied mostly in isolation. Context matters substantially, whether that is time spent on other activities during adolescence, the period of adolescence itself, digital inequality, resources gained from such online use, and how all such factors interact. The factor that predicts whether a given adolescent can convert online engagement into capital outcomes is structural: access, infrastructure, skills, and the autonomy to use them. These factors are distributed unevenly, and its uneven distribution, not hours logged, is what separates adolescents who thrive from those who fall behind.

The full dissertation is available through Michigan State University’s ProQuest archive, or see the embedded full-text PDF below. I’m happy to share papers, preprints, or the underlying framework with anyone interested and working in this area––don’t hesitate to reach out via my contact form. Thanks for reading.

Tags: Broadband Digital Inequality FCC FTC Market Definition Quello Center US DOJ

Aleks Yankelevich’s First Blog Post (Chipotle, Market Definition, and Digital Inequality)