Race and Digital Inequalities: Policy Implications
Posted: 31 Mar 2017 Last revised: 31 Dec 2018
Date Written: March 31, 2017
Objectives: As Internet use rises in the United States, Pew Research Center reports that only 12% of Americans were not online in 2016. Those offline are disproportionately from groups with lower incomes, lower educational qualifications, as well as from rural communities and communities of color. Distressed urban neighborhoods are disproportionately digitally excluded. In Detroit, for example, 63% of households with incomes below $35,000 did not have Internet access at home in 2015. And while any Internet access can be deemed better than none, only 73% of Americans had access to broadband at home. The proportion of households with broadband is considerably higher among White Americans (78%) than Hispanics (58%) or African Americans (65%), as well as among households with higher incomes and higher educational qualifications. In this paper, we will examine digital inequalities and intersectionality using secondary survey data and primary qualitative data. While race has previously been investigated as one of many factors that have an impact on digital inequalities, intersectionality has rarely been the focus of digital inequality studies.
Methods: To shed light on this topic, we will analyze secondary survey data from the National Telecommunications and Information Administration (NTIA) and the Pew Research Center to provide a broad overview of digital inequalities in the US. We will also conduct an analysis of the NTIA county level data (Form 477) for a number of US cities. In addition, we will use primary qualitative data from a recent study of community-based organizations, across these same cities, that are working to promote digital inclusion, as case studies that highlight the intersectionality of digital divides. In addition to socio-economic factors — such as age, education, and income — digital inequalities are also highly impacted by race. This characteristic is significantly more pronounced in the US than in other highly developed countries and should be investigated to further understand how race intersects with other identities.
Novelty: According to a recent report from the Free Press, race is a strong factor explaining digital inequalities, even when controlling for income and education. At the same time, systemic discrimination in the US means that non-White communities, particularly in urban areas, are more likely to have lower incomes and lower educational qualifications, which exacerbates inequalities, both online and offline. The combination of quantitative and qualitative data will enable a deeper analysis of the intersectionality of these factors.
Relevance: As digital inequalities persist--especially in distressed urban areas — the intersectionality of race, gender, income, education, and other factors is under-researched in digital inequality studies. Results from this study will have an impact on potential policies that aim to tackle these inequalities. Policies that work for more affluent and predominantly white rural areas, for example, are unlikely to have a positive impact in distressed urban areas. Analyzing the intersectionality of digital inequalities will enable the formulation of policy recommendations that are tailored to specific contexts in which these inequalities occur.
Keywords: digital inequality, digital divide, intersectionality, race, mixed-methods
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