Exploring the Value of Public Investment in “Generative” Fiber Infrastructure

In an earlier post I described the BTOP Comprehensive Community Infrastructure (CCI) program as a “very good investment of public funds.” My reasons were twofold, the first one being that it expanded the availability of high-speed connectivity in underserved areas, including more than 42,000 miles of new and 24,000 miles of upgraded fiber infrastructure. The second was that research by ASR Analytics suggests that the CCI program accomplished this expansion in a way that addresses both forms of economic harm claimed by advocates on both sides of the special access regulation debate. As a result, I suggested “that the federal government consider expanding its CCI investment in geographic areas that the FCC’s special access data collection project indicates still face a lack of competitive options and an abundance of excess-profit-extracting prices in the special access market.”

In five related posts, I considered a number of issues and perspectives that inform this policy suggestion, including the following:

the challenge of addressing two dueling forms of economic harm associated with special access: 1) the underinvestment discussed in a paper by Economists Inc. principal Hal Singer and; 2) the harms associated with excess-profit-extracting prices considered in a paper by Consumer Federation of America Director of Research Mark Cooper;
the public policy relevance of Modern Monetary Theory (MMT), which explains that the federal government has a lot more flexibility than commonly believed to fund resource-mobilizing and infrastructure-strengthening investments, even if this investment increases the federal deficit/debt;
Brett Frischmann’s demand-side analysis of Internet access as an infrastructure resource supporting diverse and expansive—albeit difficult to predict, quantify and internally monetize—social value, and how the realization of that value is best supported via a non-discriminatory access regime;
the ownership framework developed by Marjorie Kelly, which identifies key characteristics that distinguish “generative” from “extractive” ownership, and how this framework applies to the telecom sector, with particular focus on AT&T as an example of extractive ownership;
the application of Kelly’s framework to the last mile access market, including research questions worthy of further study in this politically-charged area.

In this post I’m going to:

review how BTOP’s middle mile fiber projects are examples of publicly-funded generative infrastructure that can help address the economic harms claimed by both Singer and Cooper, and encourage the variety of direct and indirect social benefits considered in ASR Analytics’ BTOP evaluation study;
sketch the outlines of a research project that can: 1) update and expand upon the ASR study of the social value of BTOP fiber deployments and; 2) help guide public policy related to the special access market and, in particular, the potential role of public investment in bringing fiber connectivity to underserved areas.

I’ll start with an excerpt from an earlier post:

According to Table 7 on pg. 15 of ASR’s final report, the total amount (including both federal grants and matching funds) budgeted for 109 CCI projects was $3.9 billion. The table also indicates that, at the time the study was done, these projects had connected 21,240 CAIs, at a budgeted cost of $184,141 per CAI. Assuming federal grants paid for 80% of this total cost, the average federal grant amount per CAI would be in the neighborhood of $147,300.

Table 13 on pg. 34 of the report shows the changes in subscription speeds and pricing experienced by the 86 CAI locations providing this information to ASR. The table shows very large increases in speed and, depending on the category of CAI, dramatic 94-96% average reductions in per-Mbps pricing. Table 14 on pg. 36 uses these reported changes in speed and price to extrapolate CAI cost savings from switching to CCI-provided fiber connections. Averaged across all CAI categories, the per-CAI annual savings amounted to $236,151.

This means that, in just one year, the average CAI saved 28% more in operating costs ($236,151) than the total capital cost ($184,141) required to connect it to a CCI fiber network, and 60% more than the federal government’s share of that investment ($147,300). Based only on these direct social costs and benefits, I’d consider this a good investment of public funds.

But these direct cost savings to CAIs were not the only impacts of the federally supported BTOP fiber deployment program that were considered by ASR. It also estimated economic benefits driven by increased broadband availability in areas newly reached by the BTOP fiber networks. Using matched pair county-level analysis, ASR found that CCI-impacted counties achieved broadband penetration two percentage points higher than control counties. Based on this, ASR derived estimates of economic benefits of the $3.9 billion in CCI network investments using a number of widely accepted economic impact models. These impacts included:

In terms of GDP, ASR estimated that “BTOP infrastructure spending could be expected to yield $5.7 to $21.0 billion in increased output annually using results from Czernich et al. (2011) and LECG Ltd. (2009) as the bases for extrapolation, respectively.”
In terms of employment, ASR estimated that “the additional broadband infrastructure provided by BTOP could be expected to create more than 22,000 long-term jobs and generate $1.1 billion in additional household income each year based on a model developed by Kolko (2010), and 6,900 long-term jobs per year for at least five years, and a $328 million increase in household income for each year of increased employment, based on the work of Gillett et al. (2006).
Based on an Allen Consulting Group finding that the value of broadband Internet access to the average American household is about 3.4% of average household income, ASR estimated the value of broadband to new subscribers of in CCI-impacted areas to be $2.6 billion per year.

These findings of the ASR Analytics study suggest that:

The total capital cost (including 80% covered by a federal grant, plus matching funds), for constructing open-access fiber networks in underserved areas can be more than offset by just the first year of direct cost savings for Community Anchor Institutions connected to the network.
The indirect economic benefits of such public investments, including increases in GDP, jobs and household income are also likely to exceed the cost of construction within the first year of operation.

Building on the ASR Analytics evaluation study

As noted above, ASR’s BTOP evaluation study used matched pair analysis of CCI-impacted counties to compare their growth in broadband availability to that of counties that were comparable on key control variables. ASR used NTIA availability data for multiple time periods to measure and compare these changes in availability (for more details see Appendix D of the ASR final BTOP evaluation report).

As discussed above, ASR found that, on average, the increase in broadband availability for CCI-impacted counties was two percentage points higher than in control counties, using the then-current broadband speed threshold of 3Mbps downstream service. ASR then used this differential to estimate and extrapolate economic impact variables (e.g., GDP, job growth, income) using the broadband impact models referenced above.

In light of ASR’s well-documented research and its promising though preliminary findings, an effort to update and expand on the strong foundation it and NTIA have built strikes me as timely, especially with special access policy questions getting focused attention from the FCC. More specifically, what I’d propose is to:

1. Use updated FCC availability data to explore how the matched-county broadband availability differential has evolved over a longer period of time.

2. Examine this broadband availability differential using speed thresholds higher than the 3 Mbps downstream level used by ASR, including the FCC’s current threshold of 25 Mbps downstream and 3 Mbps upstream.

3. For counties for which data is available, add to the matched pair comparison an analysis of broadband adoption data derived from the Census Bureau’s American Community Survey (ACS). Beginning with 2013 data, this data is being released annually for geographies with populations greater than 65,000, and should be available for virtually all counties on a blended five-year basis starting in 2017.

4. Examine and compare actual county-level economic indicators (e.g., County Business Patterns and other datasets available from the Census Bureau and other sources) for the matched pair counties. The goal here would be to explore the extent to which the economic impacts predicted by the models used by ASR actually occurred and/or whether there were other impacts suggested by these economic indicators.

5. Where notably large variations are found among the matched pair differentials in broadband availability and/or penetration, and/or in actual economic impact variables, explore potential reasons for these differentials based on qualitative and/or quantitative analysis of CCI projects and CCI-impacted counties exhibiting these large variations. The goal here would be to extract additional insights and lessons learned regarding how CCI networks can best deliver social value, as well as the contextual factors impacting how effective different approaches are in achieving that value.

Factors to be considered in #5 might include the ownership and management models employed by CCI grant recipients, the specific approach they take to providing “open access” to their fiber networks, as well as other policies and strategies they employ in relation to wholesale and last mile providers, CAIs, local community development programs, and local economic, demographic and institutional factors.

For example, the specifics of how CCI grantees approached the BTOP program’s open access requirement have not been uniform, as explained on pages 28-29 of ASR’s final evaluation report:

To help expand service within unserved and underserved areas…[e]ach of the grantees in the evaluation study sample implemented at least one strategy, and in many cases a combination of strategies, to ensure open access to the BTOP-funded network by third-party service providers. For example, the research and education network and the healthcare network in Arkansas established a partnership to deploy new and upgraded fiber and colocation facilities. Merit Network in Michigan offered indefeasible right-of-use agreements to private third- party service providers. MassTech fostered competition by helping CAIs compare services and prices offered by third-party providers that use the BTOP-funded network.

Similarly, different CCI grantees adopted different usage and pricing policies to support positive impacts of their network investments. For example, as described on pages 3-4 of its case study of Merit Network, a Michigan-based CCI grantee owned by member institutions of higher learning, ASR explained that:

The Merit network connects institutions of higher learning and facilitates collaboration by allowing them to freely connect to other institutions on the network, or access on-net services at speeds up to 1 Gbps. This allows institutions to collaborate on research, and to cut costs by sharing services, including hosting. Merit provides some content over this network as well, including Internet2. These services give faculty, staff, and students fast and reliable access to educational and research opportunities…The free on-net services provide incentive for CAIs to create wide area networks (WAN) using Merit fiber…[and] cost-savings [and greater efficiency] for any CAI organization with multiple locations.

Merit is an example of the Research and Education Network (REN) category of CCI grantee. Owned by member universities, it exhibits a range of generative characteristics, including support for training, collaboration and feedback among its user community, including:

[Regular] opportunities for Members to learn from each other and share best practices in the networking arena. Forums include the Michigan Information Technology Executive (MITE) Forum, Merit Joint Technical Staff (MJTS), Networking Summit, Bring Your Own Device (BYOD) Summit, and the Merit Member Conference (MMC).

The Merit Advisory Council (MAC) has a direct voice to our Board of Directors and leadership through which feedback and recommendations are provided.

The Merit Services Innovation Group enables Members to provide suggestions and feedback regarding current and future services.

Merit facilitates collaboration between Members and regularly contributes staff and resources to educational and research activities.

Professional Learning events are tailored to the needs of our Members and are offered at reduced cost.

One starting point for considering the impacts of differential policies, structures, strategies and programs among CCI grantees would be a careful review of the dozen CCI case studies conducted by ASR. Another would be using the expanded matched pair county analysis described above to identify differences in the availability, penetration and economic impact variables across CCI projects.

In my view, a research project along these general lines would: 1) help maximize the ongoing value provided by existing CCI projects; 2) provide valuable guidance for consideration of future programs designed to build on the success of and lessons learned from the BTOP program and; 3) shed light on policy debates and options related to special access and perhaps other communication and infrastructure policy 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: policy public infrastructure research opportunities special access

Exploring the Value of Public Investment in “Generative” Fiber Infrastructure