The Un#ballogetic World of Wireless Ads

I belong to that rare breed of human that enjoys commercials. As a social scientist with an interest in the impact of advertisement on consumer behavior, I often find myself, possibly to the chagrin of my wife (though she has not complained), assessing commercials out loud. Are they informative? Are they persuasive or attempt simply to elicit attention to the good in the ad? Might they unintentionally lead to brand confusion? Most importantly, are they funny?

Thus, having also spent some time among wireless regulators, I cannot help but comment on the recent spate of wireless attack ads perpetuated by three of the U.S. nationwide mobile wireless providers. The initial culprit this time around was Verizon Wireless, which determined that balls were a good method to represent relative mobile wireless performance among the nationwide competitors. Shortly thereafter, Sprint aired a commercial using bigger balls while T-Mobile brought in Steve Harvey to demand that Verizon #Ballagize.

There are myriad takeaways that can be had from these commercials. First, at least on the face of it, the nationwide mobile wireless providers appear to be fiercely competitive with one another. It would be interesting to look at advertising to sales ratios for this industry relative to that of other industries in the U.S., though at the time of writing of this blog, I did not have access to such data (Ad Age appears to be a convenient source). Moreover, the content of the commercials suggests that although price continues to be an important factor (Sprint did not veer away from its “half-off” theme in its ball commercial), quality competition that allows competitors to differentiate their product (and in doing so, justify higher prices) remains paramount.

Unfortunately, as a consumer, it is difficult for me to properly assess what these commercials say about wireless quality. There are a number of points at play here.

The relative comparisons are vague: When Sprint says that it delivers faster download speeds than the other nationwide providers, what does that mean? When I zoom into the aforementioned Sprint commercial at the 10 second mark, the bottom of the screen shows, “Claim based on Sprint’s analysis of average LTE download speeds using Nielsen NMP data (Oct. thru Dec. 2015). NMP data captures real consumer usage and performance for downloads of all file sizes greater than 150kb. Actual speeds may vary by location and device capability.” As a consumer who spends most of his time in East Lansing, MI, I am not particularly well informed by a nationwide average. Moreover, I know nothing about the statistical validity of the data (though here I am willing to give Nielsen the benefit of the doubt). Moreover, I would be interested to know when Sprint states that it delivers faster download speeds, how much faster they are (in absolute terms) relative to the next fastest competitor.
The small print is too small: Verizon took flak from its competitors for using outdated data in its commercial. This is a valid claim. Verizon’s small print (13 second mark in its commercial) states that RootMetrics data is based on the 1^st half of 2015. But unless I am actually analyzing these commercials as I am here, and viewing them side by side, it is difficult for me to make the comparison.
The mobile wireless providers constantly question one another’s credibility, and this is likely to make me less willing to believe that they are indeed credible. Ricky Gervais explains this much better than I do: Ricky Gervais on speed, coverage, and network comparisons.

Alas, how is a consumer supposed to assess wireless providers? An obvious source is Consumer Reports, but my sense, without paying for a subscription, is that these largely depend on expert reviews and not necessarily data analysis (someone correct me if I am wrong). Another if one is not in the habit of paying for information about rival firms is the FCC. The FCC’s Wireless Telecommunications Bureau publishes an “Annual Report and Analysis of Competitive Market Conditions with Respect to Mobile Wireless.” The most recent, Eighteenth Report, contains a lengthy section on industry metrics with a focus on coverage (see Section III) as well as a section on service quality (see Section VI.C). The latter section focuses on nationwide average speed according to FCC Speed Test data as well as on data from private sources Ookla, RootMetrics (yes, the one mentioned in those commercials), and CalSPEED (for California only). If you are interested, be sure to check out the Appendix, which has a wealth of additional data. For those who don’t want to read through a massive pdf file, there is also a set of Quick Facts containing some of the aforementioned data.

However, what I think is lacking is speed data at a granular level. When analyzing transactions or assessing competition, the FCC does so at a level that is far more granular than the state, and rightly so, as consumers do not generally make purchasing decision across an entire state, needless to say, the nation as a whole. This is because service where consumers are likely to be present for the majority of their time is a major concern when deciding on wireless quality. In a previous blog post I mentioned that the FCC releases granular fixed broadband data, but unfortunately, as far as I am aware, this is still not the case for wireless, particularly with regard to individual carrier speed data.

The FCC Speed Test App provides the FCC with such data. The Android version which I have on my phone provides nifty statistics about download and upload speed as well as latency and packet loss, with the option to parse the data according to mobile or WiFi. My monthly mobile only data for the past month showed a download speed above 30 Mbps. Go Verizon! My Wifi average was more than double that. Go SpartenNet! Yet, my observation does not allow me to compare data across providers in East Lansing and my current contract happens to expire in a couple of weeks. The problem is that in a place like East Lansing and particularly so in more rural areas of the United States, not enough people have downloaded the FCC Speed Test App and I doubt that the FCC would be willing to report firm level data at a level deemed not to have statistical validity.

For all I know, the entire East Lansing sample consists of my twice or so daily automatic tests that if aggregated to a quarter of a year make up less than 200 observations for Verizon Wireless. Whether this is sufficient for a statistically significant sample depends on the dispersion in speed observations for a non-parametric measure such as a median speed and also on the assumed distribution for mean speeds. I encourage people to try this app out. The more people who download it, the more likely that the FCC will have sufficient data to be comfortable enough to report it at a level that will make it reliable as a decision making tool. Perhaps then, the FCC will also redesign the app to also report competitor speeds for the relevant geographic area.

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: Advertising Commercials FCC Mobile Wireless Speed Data Sprint T-Mobile Verizon Wireless Wireless Competition

The Un#ballogetic World of Wireless Ads