Conference Agenda

Various states have considered a range of policies to address broadband affordability. New York enacted its Affordable Broadband Act, requiring that providers offer eligible households a prescribed broadband plan at state-set prices—$15 or $20, depending on the plan’s specifics [1]. In California, regulators have imposed and proposed conditions on some providers to ensure affordable service for low-income consumers and at monopoly-served locations [2, 3]. Virginia’s Joint Commission on Technology and Science (JCOTS)—a state agency charged with developing policy recommendations for the legislature—recommended that the state require that providers offer a $30 broadband plan [4]. Some states also offer subsidies to help low-income consumers pay for broadband access [5].

Hence, to ensure affordable broadband access, policymakers typically balance three policy levers: (1) an affordability threshold; (2) a price cap, if any, for broadband service; and (3) a subsidy budget to close the gap between prices and the affordability threshold.

Consider, for example, the Affordable Connectivity Program (ACP). ACP offered a $30 subsidy to eligible low-income households [6]. As ACP launched, the White House announced that several providers had agreed to offer $30 plans to qualifying households [6, 7]. The agreement, combined with the public subsidy, thus made broadband free for many ACP-eligible consumers. ACP’s budget was dictated by these three features—the price, the subsidy, and the desire to make broadband free for eligible households. Though Congress lost the will to sustain this investment, the example suggests that states might design sustainable affordability policies that link affordability targets, pricing constraints, and subsidy commitments. How much would it cost a state to make broadband free for all? What if the state were to impose a price cap first? And what if the state asked consumers to pay—but no more than 2% of their disposable monthly income?

This paper presents an analytical framework that models broadband affordability policies as a constrained optimization problem defined by three inputs: a subsidy budget; a price cap; and an affordability threshold. We show that the relationship among these inputs forms a strictly convex system, where fixing one parameter determines the other two. Policymakers can specify a subsidy budget and compute the rate cap required to achieve a coverage target, or alternatively fix an affordability threshold and determine the subsidy needed to ensure broadband affordability with no price controls. By framing broadband affordability as a convex optimization problem, the framework provides a systematic method for designing and evaluating affordability policies.

We operationalize this framework by integrating it with income data from the American Community Survey (ACS) [8], as well as street-address-level broadband pricing data collected using the latest iteration of the Broadband-Plan Querying Tool, BQT+ [9]. We begin with the FCC’s affordability benchmark—2% of a household’s disposable monthly income—to define an affordability threshold on a per-census-block-group basis [10]. BQT+ adds visibility into broadband plans available to consumers on a per-street-address basis—data unavailable in existing public datasets.

We combine this data with our framework to show how policymakers can design sustainable affordability policies across three jurisdictions: Virginia; California; and New York. In Virginia, in collaboration with JCOTS, we show that a broadband plan priced at $30 per month would be affordable for 93% of the population without any added subsidy investment. An additional $30 subsidy for households with a disposable income under $18,000 would close the affordability gap completely. Conversely, with no policy intervention, over 40% of Virginia’s households presently lack access to broadband plans that satisfy the FCC’s affordability threshold. Hence, JCOTS recommended that Virginia both mandate a 100 Mbps broadband plan priced at $30 per month, and that it enact a subsidy for qualifying households to cover the gap. [4] A different affordability target, or a different price cap, would require a different subsidy investment. We apply the same framework to California and New York, demonstrating the applicability of this optimization framework across jurisdictions with different parameters.

This work thus provides a practical framework for broadband policy design. Beyond the written work, we use our framework and data to implement an interactive tool that allows policymakers to specify the three parameters we have identified to compute population coverage, affordability gaps, and required subsidy investments. By framing broadband affordability as a convex optimization problem grounded in address-level pricing data and granular income data, we can help policymakers implement empirically grounded policies. And as initiatives, such as the Broadband Equity, Access, and Deployment (BEAD) Program, expand broadband access nationwide, such tools are critical to ensuring that broadband availability translates into broadband affordability.