The Internet as “Spillover-Rich” Infrastructure

A key source that informs my perspective on special access policy—and telecom policy in general—is Brett Frischmann’s 2012 book, Infrastructure: The Social Value of Shared Resources.  Selected chapters from the book can be found here, and Frischmann provides a good overview of the book, including its Internet-related sections, in this talk at Harvard’s Berkman Center).  Frischmann is a professor and co-Director of the Intellectual Property and Information Law program at Cardozo Law School in New York City.

On page five of the book, Frischmann notes that “most economists recognize that infrastructure resources are important to society precisely because infrastructure resources give rise to large social gains.”  But he also notes that infrastructure’s “externalities are sufficiently difficult to observe or measure quantitatively, much less capture in economic transactions, and the benefits may be diffuse and sufficiently small in magnitude to escape the attention of individual beneficiaries.”  On page 6 he cites the “comedy of the commons” concept developed by Carol Rose which, he explains, “arises where open access to a resource leads to scale returns—greater social value with greater use of the resource.”

The book includes a chapter focused specifically on the Internet, which Frischmann describes on page 345 as “a spillover-rich environment because of the basic user capabilities it provides and the incredibly wide variety of user activities that generate and share public and social goods.”

But, as with other infrastructure, it is not easy to quantify the net value of Internet-supported social goods, or even identify what all of those social goods are.  As Frischmann explains on pg. 347, Internet connectivity involves “a very high degree of social value uncertainty.”

 It is impossible to predict with any degree of confidence who or what will be the sources of social value in the future.  Accordingly, there is no reason to defer to private firms in this context. First, there is no reason to believe that firms are better informed, capable of maximizing social value, or likely to resist the pressure to discriminate, prioritize, or optimize the infrastructure based on foreseeable and appropriable private returns. Second, there is no reason to trust that markets will correct misallocations…[F]irms may be strongly biased in their estimation of the future market value to favor services that they currently offer or expect to offer, sponsor, or otherwise control, and to disfavor those that they do not.

One recent example of the tendency of private access network owners with market power to “discriminate, prioritize, or optimize the infrastructure based on…appropriable private returns” is Comcast’s recently launched Stream TV service.  The element of discrimination is clear in that Stream TV, unlike competing services like Netflix, does not count against the data caps that Comcast customers are or will be subject to.

On page 346 Frischmann points to two demand-driven problems that need to be considered by Internet-related policies:

 The Internet infrastructure is a mixed infrastructure, and as such, it faces the two types of demand-driven problems discussed throughout this book: First, it faces concerns about undersupply and underuse of infrastructure to produce infrastructure-dependent public and social goods, which leads to underproduction of those goods. Second, it faces concerns that infrastructure development may be skewed in socially undesirable directions. For example, if private infrastructure owners prematurely optimize infrastructure for uses that they expect will maximize their private returns, and in doing so choose a path that forecloses production of various public or social goods that would yield greater net social returns, the social option value of the Internet is reduced. This latter concern may involve dynamic shifts in the nature of the Internet infrastructure, such as optimizing networks in a manner that shifts from mixed infrastructure toward commercial infrastructure.

In key respects, these two problems correspond to the social harms of primary concern to Singer (undersupply by companies subject to regulation) and Cooper (actions by private infrastructure owners to “maximize their financial returns” in ways “that foreclose production of various public or social goods that would yield greater net social returns”).

Frischmann’s analysis of infrastructure and Internet dynamics leads him to conclude that the abundant but difficult to predict and quantify social benefits of the Internet are best supported via commons management.  On page 7 he describes commons management as “the situation in which a resource is accessible to all members of a community on nondiscriminatory terms, meaning terms that do not depend on the users’ identity or intended use.”  This, he says, “can be implemented through a variety of public and private institutions, including open access, common property, and other resource management or governance regimes.

Frischmann acknowledges that “grouping ‘open access’ and ‘commons’ under the ‘commons management’ umbrella will be troublesome to some property scholars:”

Open access typically implies no ownership or property rights. No entity possesses the right to exclude others from the resource; all who want access can get access, typically for free. Commons typically involves some form of communal ownership (community property rights, public property rights, joint ownership rights), such that members of the relevant community obtain access “under rules that may range from ‘anything goes’ to quite crisply articulated formal rules that are effectively enforced” and nonmembers can be excluded.

 There are at least three dimensions of distinction between open access and commons as traditionally understood: first, ownership (none vs. communal/group); second, the definition of community (public at large vs. a more narrowly defined and circumscribed group with some boundary between members and nonmembers); and third, the degree of exclusion (none vs. exclusion of nonmembers). These distinctions are important, especially for understanding different institutions and how social arrangements operate at different scales.

In two other posts (see here and here) I consider key issues related to infrastructure ownership in more detail, from the perspective of the “generative” vs. “extractive” ownership framework developed by Marjorie Kelly in her book, Owning our Future.

 

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.

The Internet as “Spillover-Rich” Infrastructure