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This article develops a Digital Capital Index by adopting the definition provided by Ragnedda, who defines Digital Capital as the accumulation of digital competencies and digital technologies, and the model for measuring it developed by Ragnedda and Ruiu. It aims to develop a measure that can be replicated for comparison in different contexts. This article contributes both theoretically and empirically to the literature by (a) consolidating the concept of Digital Capital as a specific capital and (b) empirically measuring it. A Digital Capital Index is developed through an exploratory factor analysis (EFA) and validated with a representative sample survey of 868 UK citizens. The validation procedure shows that the Digital Capital Index is associated with socioeconomic and sociodemographic patterns, such as age, income, educational level and place of residence, while it appears not to be related to gender.

2020 marks the 25th anniversary of the “digital divide.” Although a quarter century has passed, legacy digital inequalities continue, and emergent digital inequalities are proliferating. Many of the initial schisms identified in 1995 are still relevant today. Twenty-five years later, foundational access inequalities continue to separate the digital haves and the digital have-nots within and across countries. In addition, even ubiquitous-access populations are riven with skill inequalities and differentiated usage. Indeed, legacy digital inequalities persist vis-à-vis economic class, gender, sexuality, race and ethnicity, aging, disability, healthcare, education, rural residency, networks, and global geographies. At the same time, emergent forms of inequality now appear alongside legacy inequalities such that notions of digital inequalities must be continually expanded to become more nuanced. We capture the increasingly complex and interrelated nature of digital inequalities by introducing the concept of the “digital inequality stack.” The concept of the digital inequality stack encompasses access to connectivity networks, devices, and software, as well as collective access to network infrastructure. Other layers of the digital inequality stack include differentiated use and consumption, literacies and skills, production and programming, etc. When inequality exists at foundational layers of the digital inequality stack, this often translates into inequalities at higher levels. As we show across these many thematic foci, layers in the digital inequality stack may move in tandem with one another such that all layers of the digital inequality stack reinforce disadvantage.

In this article, we argue that new kinds of risk are emerging with the COVID-19 virus, and that these risks are unequally distributed. As we expose to view, digital inequalities and social inequalities are rendering certain subgroups significantly more vulnerable to exposure to COVID-19. Vulnerable populations bearing disproportionate risks include the social isolated, older adults, penal system subjects, digitally disadvantaged students, gig workers, and last-mile workers. Therefore, we map out the intersection between COVID-19 risk factors and digital inequalities on each of these populations in order to examine how the digitally resourced have additional tools to mitigate some of the risks associated with the pandemic. We shed light on how the ongoing pandemic is deepening key axes of social differentiation, which were previously occluded from view. These newly manifested forms of social differentiation can be conceived along several related dimensions. At their most general and abstract, these risks have to do with the capacity individuals have to control the risk of pathogen exposure. In order to fully manage exposure risk, individuals must control their physical environment to the greatest extent possible in order to prevent contact with potentially compromised physical spaces. In addition, they must control their social interactional environment to the greatest extent possible in order to minimize their contacts with potentially infected individuals. All else equal, those individuals who exercise more control over their exposure risk — on the basis of their control over their physical and social interactional environments — stand a better chance of staying healthy than those individuals who cannot manage exposure risk. Individuals therefore vary in terms of what we call their COVID-19 exposure risk profile (CERPs). CERPs hinge on preexisting forms of social differentiation such as socioeconomic status, as individuals with more economic resources at their disposal can better insulate themselves from exposure risk. Alongside socioeconomic status, one of the key forms of social differentiation connected with CERPs is digital (dis)advantage. Ceteris paribus, individuals who can more effectively digitize key parts of their lives enjoy better CERPs than individuals who cannot digitize these life realms. Therefore we believe that digital inequalities are directly and increasingly related to both life-or-death exposure to COVID-19, as well as excess deaths attributable to the larger conditions generated by the pandemic.

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