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Digital Inequality and Low-Income Households

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Fall 2016   


Digital Inequality and Low-Income Households


      • Research on digital inequality has shifted toward frameworks that consider multiple dimensions and levels, including social supports and other neighborhood-level factors.
      • Low-income households have lower rates of in-home Internet connectivity compared with higher-income groups. Connectivity rates are particularly low among HUD-assisted renter households, who are also more likely to depend exclusively on smartphones and other handheld devices to access the Internet in the home.
      • Low-income households are most likely to cite affordability constraints as a substantial barrier to in-home broadband adoption. Eighty percent of respondents to the 2015–2016 ConnectHome baseline survey who lacked Internet access at home cited Internet costs as one reason they lacked in-home Internet access, and 37 percent cited device costs.

A bar graph shows percentage of U.S. households in 2013 with high-speed Internet subscription at home by income for 2013.
Source: Thom File and Camille Ryan. 2014. “Computer and Internet Use in the United States: 2013,”American Community Survey Reports, U.S. Census Bureau, 3.

As information, services, and resources increasingly move online, digital inequality has come to both reflect and contribute to other persistent forms of social inequality.1 Disparate access to the Internet and digital devices corresponds closely with longstanding inequalities in income, education, race and ethnicity, age, immigration status, and geography (see “Community Development and the Digital Divide").2 At the same time, the negative consequences of being underconnected are growing, and researchers and policymakers are increasingly concerned that underconnection is fueling other socioeconomic disparities.3 Indeed, Internet access, and particularly broadband Internet access, has become an important tool for taking full advantage of opportunities in education, employment, health, social services, and the production and dissemination of knowledge and digital content.4 Yet those who are most in need of social services are often least able to get online to access those services,5 and low-income children — who are four times less likely to have access to broadband at home than their middle- and upper-income counterparts6 — are particularly vulnerable to the long-term detrimental effects of constrained access to technology-enriched education.7 These trends suggest that digital access will play an increasingly central role in socioeconomic inclusion.

Building on the idea that digital inclusion is an important part of broader efforts to create strong, inclusive communities and improve opportunities and quality of life for all Americans, this article offers a series of frameworks, points of reference, and data for developing strategies to address current relationships between low-income housing and digital inequality.

Digital Inequality Frameworks

Dominant approaches to thinking about and measuring digital inequality have evolved since the commercialization of the Internet in the mid-1990s. Early concerns about digital inclusion highlighted a “digital divide” between those who did and did not have access to new forms of information technology. Studies rooted in this framework sought to identify gaps in access to the Internet and computers by income, geography, age, education, and other types of inequality,8 both within and between countries.9 As digital penetration in the United States has increased, however — growing from 1 in 4 U.S. families having Internet service at home around 2000 to nearly 3 in 4 by 201210 — additional relevant dimensions of digital inequality have emerged.11 Although the presence or absence of Internet access remains an important dimension of digital inequality, the concept of a binary digital divide, which highlights absolute inequalities between the included and excluded, does not account for the fact that many technological inequalities are relative, continually shifting as new technologies emerge.12 As a result, the concept of digital inequality has evolved in two key directions over recent years to focus on the complex ways in which digital access varies.

Multidimensional Digital Inequality. One key way in which digital inequality frameworks shifted was by focusing on the multiple dimensions of digital inequality, highlighting how access to, and the use of, digital technologies varies even among people with formal access to the Internet. This multidimensional approach draws attention to five key aspects of digital inequality, each of which shapes Internet use as well as returns to use.13

First, multidimensional approaches to digital inequality focus on variations in equipment, or the technology people use to access the Internet. This aspect of digital inequality includes the extent to which households have computers, software, and connections that allow them to effectively engage with online content.14 The advent of always-on broadband connections has given rise to qualitatively different kinds of Internet use that involve more time online, a greater variety of activities, and the creation of new content.15 Similarly, smartphones and desktop and laptop computers offer different kinds of mobility and ease in accessing educational, employment, health, and social service opportunities.16 As a result, procuring household access to Internet connections with acceptable speed and reliability, as well as to devices capable of handling a variety of computing activities, is an important component of addressing digital inequality.

Multidimensional approaches also emphasize variations in the autonomy of Internet use. Autonomy includes whether users access the Internet from work or home, whether their use is monitored, their frequency of use, whether they must compete with others for time and access, and the extent to which their use is circumscribed by filters or other constraints.17 Attention to how autonomy shapes digital experiences underscores the relevance of in-home Internet access; the heightened control over the environment and usage frequency associated with in-home access tends to provide the greatest opportunities for learning, increasing earnings, and participating in the production of digital content.18 Having Internet at home also allows families to access Internet from a private — and therefore safe — space, particularly in contexts where safety is a concern.19

Multidimensional perspectives of digital inequality also address variations in the level of skill that people bring to their Internet use. Skill encompasses users’ digital literacy, “their capacity to respond pragmatically and intuitively to online challenges and opportunities,” and their ability to master new technologies and mobilize information resources to meet everyday goals and concerns. 20,21 Those with higher levels of digital skill typically incorporate more technology into their learning, exhibit more confidence in online engagements, are less hesitant about finding trusted information online, and are better able to take advantage of emerging technologies.22 Studies have suggested that inequalities in skill levels are larger than inequalities in physical access to the Internet, that skill gaps have grown even as gaps in physical access have closed,23 and that most newcomers to the Internet would need assistance to go online.24

Variation in the level of social support on which Internet users can draw constitutes a fourth dimension of digital inequality. Such support can include formal technical assistance, technical assistance from friends and family, and emotional reinforcement from friends and family.25 This dimension involves fostering institutional and social networks that can support effective digital connectivity.

Finally, a multidimensional perspective emphasizes variations in the purposes for which people use technology. This dimension involves the ways in which people use the Internet to increase their economic productivity and their political and social capital.26 This realm can also include inequalities in the creation of digital content; although the Internet has the potential to be an egalitarian public sphere, differences in control over digital tools and usage of online information can contribute to digital production gaps.27

Multilevel Digital Inequalities. In addition to highlighting multiple dimensions of digital inequality, digital inequality frameworks have also paid increasing attention to how social dynamics at different levels of society influence Internet access and use. This multilevel perspective builds on earlier digital inequality literature that focused on individual-level characteristics, behaviors, and outcomes, to also consider how family, community, neighborhood, and network factors contribute to digital inequalities.28 Studies of the influence of local environments on people’s willingness to adopt the Internet and related technologies29 have highlighted two key approaches to thinking about the multi-level dynamics of digital inequality.

A table shows rates of in-home high-speed Internet subscription and device ownership among U. S. households by HUD subsidy status in 2013.
1 Does not include those who use the Internet without a paid subscription. High-speed Internet indicates that a household has Internet service other than dial-up.
2 Includes households that own or use a desktop, laptop, netbook, or notebook computer at their home.
3 Includes households that own or use only a handheld computer, smart mobile phone, or other handheld wireless computer at their home.
Source: U.S. Census Bureau and U.S. Department of Housing and Urban Development. “2014 American Community Survey and HUD Administrative Data (PIC, TRACS, HUD-951).”

The first approach focuses on place-based influences, including neighborhood level effects on digital access and the roles that communities play in shaping digital behaviors.30 Local digital and social infrastructures can influence how residents engage with digital resources, including through affecting: the local cost, speed, and availability of Internet connectivity and devices; the available opportunities for training and support that facilitate meaningful digital connectivity; and the involvement of community partners and digital-inclusion organizations as part of broader citywide and regional digital initiatives.31 Spaces such as libraries and community organizations can provide access to in-person support, classes and workshops, and social contexts that encourage the development of hands-on digital skills.32 Factors such as segregation and concentrated poverty can also create disparities in Internet access and use even in areas where broadband networks are available.33 Ultimately, examining these place-based influences can help clarify the ways in which community-based organizations and support structures help people gain meaningful access to technology.34

A second approach to thinking about multilevel digital inequality focuses on the effects of social networks on digital access. This approach emphasizes the role that human-to-human interactions play in shaping digital adoption, situating broadband use within broader communications networks and social resources.35 This social networks framework suggests that people’s social relationships influence the value they place on Internet adoption. For example, the price that people are willing to pay for Internet access tends to rise as more people in one’s social network start using it. These dynamics, particularly within networks consisting of people of similar status, can increase inequality by significantly reducing adoption rates in less privileged groups.36 The concept of network dynamics encourages new thinking about how coordinated efforts to bring social networks online might foster heightened digital engagement among disadvantaged populations over time.

In short, research on digital inequalities has shifted over the past several decades from frameworks focused on capturing inequalities between the connected and unconnected to more nuanced frameworks that consider digital inequalities along multiple dimensions and at multiple levels of society. These new frameworks call for strategies that address multiple aspects of digital inequality, including affordable devices and broadband access, digital literacy training, and publicly accessible computing centers with helpful staff and support.37

Digital Inequality and Low-Income Housing Trends

HUD-assisted households include populations that tend to face digital disadvantages, such as families earning less than $25,000 per year, individuals without a high school degree, and minorities.38 HUD-assisted housing also serves both urban and rural populations; school-aged youth and the elderly; people with disabilities; and households facing a range of institutional, organizational, and social contexts. Although assisted housing providers are well positioned to address many of the central challenges that shape digital inequality today, relatively little research has examined specific associations between low-income housing and Internet access. This section reviews recent data detailing the relationship between low-income housing and digital inequality.

Internet Connectivity Trends. One dimension of digital inequality focuses on Internet connectivity, defined here as in-home adoption of high-speed Internet. Connectivity disparities — by both income and geography — align in important ways with low-income housing patterns.

Household income is strongly associated with in-home Internet connectivity levels, with low-income households being less connected than higher-income households.39 Although 67 percent of all U.S. adults aged 18 and older had broadband Internet access at home in 2015, this rate was 41 percent among adults with a household income below $20,000 and 90 percent among adults with a household income of more than $100,000. Evidence also suggests that the gap between low- and high-income households with a broadband connection at home may have increased slightly in recent years; while the rate of households with at-home broadband who earn less than $20,000 per year dropped by 5 percent (from 46% to 41%) between 2013 and 2015, the rate for households earning more than $100,000 dropped by only 3 percent (from 93% to 90%) during the same period. As a result, modest declines in broadband adoption from 2013 to 2015 were concentrated among low- to middle-income households.40 Highlighting the relevance of income for digital inequality, even after accounting for age, a 90-year-old in the top quartile of income was more likely to have an in-home Internet connection in 2013 than a person of any age in the bottom quartile of the income distribution.41

Place-based characteristics are also associated with disparities in rates of in-home Internet connectivity. Broadband continues to be less available in rural areas than in urban areas, particularly at higher speeds. Although most areas have Internet service at speeds of at least 10 Mbps today, and almost all areas offer dial-up Internet access, the presence of infrastructure capable of supporting broadband speeds of more than 25 Mbps, including fiber-optic technology, is still divided along urban/ rural lines. Many rural areas have only one Internet service provider, and some rural areas have access to only satellite and cellular modem service or have no broadband availability at all.42 Other place-based dynamics complicate the urban/rural divide; broadband availability is associated not only with population density but with a community’s proximity to a major urban area. As a result, small-town residents tend to have less broadband availability than ex-urbanites despite living in much more densely populated areas.43 At the same time, disparities in urban and rural broadband access are less severe than they once were;44 recent investments in broadband infrastructure have made fast 4G wireless broadband available to more than 98 percent of Americans.45

A clustered bar graph shows device ownership among U.S. households by income in 2013.
Source: Thom File and Camille Ryan. 2014. “Computer and Internet Use in the United States: 2013,” American Community Survey Reports, U.S. Census Bureau, 3, 9.

Although broadband availability may be higher than before, evidence of disparities in place-based broadband adoption persists, and broad urban/rural divides are less instructive in understanding these dynamics. Substantial variation in adoption rates, Internet quality, and connection speeds exists within cities and is correlated with household income.46 Examples from several cities suggest that income can be more important than population density in explaining Internet adoption rates in certain areas.47 An analysis of Chicago found that neighborhood-level factors such as segregation and concentrated poverty influenced access to in-home Internet connections,48 and qualitative work has suggested that Internet adoption may be more limited for residents of low-income urban areas: Internet service providers may not offer strong coverage of some low-income housing areas or may charge high installation fees to initiate service in unserved buildings or neighborhoods.49 Figure 1 draws on 2013 American Community Survey (ACS) data to show how home high-speed Internet service in the United States varies by household income.

Examining merged 2014 ACS and HUD administrative data offers insight into the relationship between housing and in-home Internet access. These data indicate that connectivity rates among HUD-assisted households are very low; only 43 percent of HUD-assisted renters subscribed to high-speed Internet service at home compared with 69 percent of unassisted renters and 80 percent of owners (table 1). The connectivity rate for HUD-assisted renters is even lower than the rate for all U.S. households earning less than $25,000 per year (43% and 47%, respectively),
a finding that suggests that HUD-assisted renters are among the nation’s most disconnected households.50

Another source of insight into connectivity in low-income housing is baseline survey data from the ConnectHome pilot program, HUD’s initiative to extend affordable broadband access, technical training, digital literacy programs, and devices to HUD-assisted households in 28 ConnectHome pilot communities across the nation. The survey collected data on in-home Internet access in 22 of these communities in 2015 and 2016.51 These data include information about levels of Internet access, the types of Internet connections available, the types of devices used to connect to the Internet, the reasons for any lack of Internet access, the existence of previous Internet access, awareness of the ConnectHome program, and the receipt of free or low-cost Internet through ConnectHome.52 These data found that 34 percent of surveyed households have a high-speed Internet subscription in addition to a desktop computer, laptop computer, or tablet at home. Another 35 percent of surveyed households are underconnected; these households may have access to the Internet only through a smartphone device and with a smartphone data plan, or they may rely on another combination of devices and connection types, such as a tablet with a data plan only, or a high-speed Internet connection with only a smartphone device. Finally, 31 percent of households have no Internet access at home.53

Device Trends. Another dimension of digital inequality focuses on access to Internet-enabled devices at home, as households can only take full advantage of Internet access if they have devices that enable them to effectively connect to the Internet and its content. Although desktop and laptop computers offer households important access to tools, information, and skill-building opportunities,54 they can be prohibitively expensive for many families. On the other hand, smartphones offer advantages such as mobile connectivity,55 but being limited to smartphone-only Internet access is associated with data cap limits, risk of service cancellations or suspensions due to financial constraints, and difficulty performing essential tasks such as applying for jobs or writing papers on a smartphone’s small screen.56

Device access is a substantial barrier to in-home Internet use for many low-income households. People from higher-income households are more likely to own a computer than those from lower-income households.57 At the same time, a much higher percentage of lower-income households rely solely on smartphones for Internet access compared with more affluent households (fig. 2).58 In 2015, 21 percent of adults with an annual household income below $20,000 had a smartphone but no broadband at home, compared with 6 percent of adults with a household income above $100,000.59 Evidence also suggests that the gap between low- and high-income households with smartphone-only access may have increased slightly in recent years; between 2013 and 2015, the percentage of adults with smartphone only access in households with annual incomes below $20,000 increased from 13 percent to 21 percent, while the percentage of adults with smartphone only access in households with incomes above $100,000 grew only from 4 percent to 6 percent.60

Device ownership also presents a substantial barrier to in-home Internet use for HUD-assisted households (table 1). Only 44 percent of HUD-assisted renters own a desktop, laptop, netbook, or notebook computer.61 This rate is much lower than the national average of more than 78 percent and lower than even the 54 percent of households earning less than $25,000 per year that own a desktop, laptop, netbook, or notebook computer.62 Among HUD-assisted renters, computer access is particularly limited for public housing and multifamily households, with only 36 percent of HUD-assisted multifamily households owning a desktop, laptop, netbook, or notebook computer. HUD-assisted households are also more likely to be smartphone-only users; 14.1 percent of HUD-assisted households access the Internet only through smartphones or other handheld computers compared to 6.5 percent of total U.S. households.63 High rates of dependence on smartphones are found across voucher, public housing, and multifamily households. Together, these trends further suggest that HUD-assisted renters are among the most disconnected households in the country.64

Data from the 2015–2016 ConnectHome baseline survey indicate that, of the 69 percent of HUD-assisted ConnectHome households with some Internet access in the home (including by smartphone), only 65 percent have a desktop or laptop computer or a tablet in their home, meaning that 35 percent of the ConnectHome households that have some Internet access in the home lack access to a device that can fully take advantage of connectivity. At the same time, about three-quarters of HUD-assisted ConnectHome households with some Internet access at home use a smartphone to access the Internet.65

Barriers to Obtaining Home Broadband Internet Service. According to a 2015 Pew Research Center survey, 43 percent of all U.S. adults age 18 and older cited cost as the most important reason for not having home broadband service; 33 percent cited the monthly subscription cost as the main barrier, and 10 percent stated that a computer was too expensive. Additionally, 12 percent of nonadopters stated that their smartphone was sufficient, 10 percent responded that they had other options to get online outside the home, and 5 percent stated that Internet service was either unavailable or insufficient.66 Other studies of households without home broadband access have cited similar rationales, including lack of relevance, usability obstacles, limited availability, device access, and price.67

The population of nonadopters can be categorized into two groups: those who do not use the Internet at all and those who use the Internet away from home; in 2013, these groups consisted of 15 percent and 9 percent of U.S. adults, respectively.68 Among those who do not use Internet at all, only 19 percent cited device or Internet connection cost as the reason. However, among those who use the Internet away from home — a population that tends, on average, to earn lower incomes — 44 percent cited financial reasons as the main limiting factor.69

Nonadopters can also be classified into two additional groups: never-adopters, who have never had in-home Internet access, and unadopters, who once had in-home Internet access but no longer do.70 In 2013, unadopters accounted for 12 percent of all nonadopting households and were significantly more likely than their never-adopter counterparts to cite cost, the availability of Internet access outside the home, and computer shortcomings as reasons for discontinuing service.71 In the end, price sensitivity is “most prominent among those who have had service in the past, and/or are interested in getting it in the future.”72

Infographic shows baseline Internet access among ConnectHome households.
Graphic reprinted with permission from: U.S. Department of Housing and Urban Development. 2016. “Baseline Internet Access Among ConnectHome Communities: Results From the National Evaluation of ConnectHome,” 2.

Perhaps unsurprisingly, those with the lowest incomes are most likely to cite cost as the main barrier to having broadband access at home.73 A series of studies shows that low-income households tend to recognize the value and relevance of connectivity, and their ability to pay, rather than their willingness to pay, is the main reason for not having home broadband service.74 Among this population, affordability barriers include not only monthly subscription costs but also devices and hidden fees; access to low-cost computers was often just as important to these households as access to low-cost Internet options.75

Cost is also a substantial connectivity obstacle for HUD-assisted households that do not have in-home Internet access. Eighty percent of respondents to the 2015–2016 ConnectHome baseline survey who lacked Internet access at home cited Internet costs as one reason they lacked in-home Internet access, and 37 percent cited device costs. Other reasons cited for lacking in-home Internet access were the ability to use the Internet away from home, lack of interest in using the Internet, being uncomfortable with using computers or the Internet, having difficulty obtaining service, and living in housing that is not wired for service. At the same time, HUD-assisted households have a high incidence of being unadopters; the ConnectHome baseline survey revealed that 35 percent of surveyed households without home Internet access had such access in the past76 compared with 12 percent of all nonadopting households.77

ConnectHome: Confronting Digital Inequality in Low-Income Housing

Because HUD-assisted households have low connectivity rates, limited device access, and other specific barriers to Internet access, HUD-assisted housing offers a promising platform to significantly increase digital inclusion rates and improve residents’ quality of life. HUD’s ConnectHome initiative offers affordable broadband access, devices, technical training, digital literacy programs, educational and workforce related content, and organizational support to families living in HUD-assisted housing.78 ConnectHome is a public-private collaboration that creates a platform for community leaders, local governments, nonprofit organizations, and private sector stakeholders to produce locally tailored solutions for reducing digital inequality.79 The initiative has already made progress toward distributing devices, establishing Internet connections, and providing digital-literacy training in its 28 pilot communities.80 As ConnectHome communities advance their digital inclusion efforts, HUD is evaluating progress, learning about the benefits of expanded in-home Internet access for HUD-assisted residents, and gathering information about what Internet penetration looks like in these low-income households.81

ConnectHome advances digital inclusion in ways that align with current frameworks for thinking about digital inequality. By incorporating connectivity, device access, and digital literacy, as well as opportunities for communities to build coalitions among local organizations, foster social networks, and integrate Internet access with job training and other social programs,82 ConnectHome offers a platform to address digital inequality as a challenge that is both multidimensional and multilevel. Indeed, many of the efforts advanced as part of ConnectHome address inequalities in equipment, autonomy, skill, purpose of use, and support, and provide opportunities to engage with family, community, neighborhood, and network dynamics that can shape digital inclusion.

To address equipment inequalities, and because affordability is a significant barrier to access for HUD-assisted residents,83 ConnectHome helps bring free and low-cost Internet and computing devices to HUD-assisted families.84 ConnectHome prioritizes broadband Internet options as well as in-home access to devices that are powerful enough to accommodate a variety of computing and online activities.85 By bringing Internet access directly to the homes of HUD-assisted residents,86 ConnectHome also limits the extent to which long commutes, usage restrictions and monitoring, wait times, and limited hours constrain the learning opportunities associated with autonomous use.87

ConnectHome addresses inequalities in digital skills by promoting affordable digital literacy resources.88 Individual ConnectHome pilot communities have already begun establishing digital literacy trainings, ranging from basic classes on how to set up a computer, create an email address, and browse the Internet safely and securely, to more advanced courses on how to build a computer, code, and provide technical assistance to others.89 These digital literacy trainings also speak to inequalities in purpose of use, or the extent to which digital activities are able to increase economic productivity and political and social capital. 90 Specifically, these digital literacy trainings have covered topics such as employment, health, education, social services, and home safety, and several ConnectHome communities have engaged HUD-assisted residents in advanced digital literacy training, including through the Jobs Plus and Section 3 programs, to provide job training for technology careers, refurbish devices for HUD-assisted households, and develop technical assistance teams for their communities.91

ConnectHome also encourages building regional and local partnerships and engaging local stakeholders,92 which can build social supports for residents. These efforts include developing local collaborations between housing authorities, computing centers, schools, libraries, and nonprofits.93 Various ConnectHome pilot communities have fostered social supports within HUD-assisted housing communities as well, by engaging resident councils in digital inclusion efforts and establishing Internet cafes, technical assistance teams, and social-support spaces for digital participation. 94

Infographic shows reasons for lack of in-home Internet access among unconnected ConnectHome households.
Graphic reprinted with permission from: U.S. Department of Housing and Urban Development. 2016. “Baseline Internet Access Among ConnectHome Communities: Results From the National Evaluation of ConnectHome,” 4.

Finally, ConnectHome supports the development of community-specific implementation plans that account for local needs, stakeholders, and interests.95The program provides communities with strategies to coordinate with government programs such as Choice Neighborhoods and Family Self-Sufficiency initiatives in ways that support local efforts to advance digital access and expand economic, political, and social opportunities for low-income households.96 By encouraging housing authorities to partner with libraries, nonprofits, and local schools to create community-based support networks,97 and by bringing communities and families online together, ConnectHome efforts can also harness the power of social networks to reinforce the value of being online.98

Directions for Future Research

In addition to researching the practical applications of digital inequality frameworks through ConnectHome, opportunities exist for further research into the complex relationships between low-income housing and Internet access. First, researchers should continue analyzing the causal mechanisms through which wide-ranging social inequalities shape digital inequalities, and through which digital inequalities, in turn, affect other kinds of inequality. This area of research involves examining the causes and consequences of digital inequality and the kinds of models that might disrupt cyclical and mutually reinforcing inequalities. Second, more research is needed into how the infrastructure supporting digital access, as well as the market dynamics and processes through which digital resources are developed and disseminated, affect inequality, and how these digital infrastructures could be built in ways that are increasingly inclusive.99 Third, researchers need to consider innovative ways to study and mitigate digital inequality in a world where technologies and patterns of use are constantly changing.100 Finally, researchers should continue examining how different lowincome housing contexts — including rural and urban geographies, different kinds of housing stock, and varied resident needs — affect digital inequality. The ConnectHome effort itself is well-positioned to continue assessing how the initiative’s strategies expand high-speed Internet access; reduce digital inequalities; and create new educational, employment, health, and social-service opportunities.

Continued research on these fronts can guide ongoing efforts to build a digital infrastructure and provide Internet access in ways that are increasingly inclusive. To the extent that digital inequality is both a cause and consequence of other socioeconomic disparities, efforts to increase Internet connectivity, device access, and digital literacy play an important role in stemming cycles of inequality over time.

  1. Council of Economic Advisors. 2015. “Mapping the Digital Divide: Issue Brief,” 1; Eszter Hargittai. 2008. “Digital Reproduction of Inequality,” in Social Stratification: Class, Race, and Gender in Sociological Perspective, David Grusky, ed. Boulder, CO: Westview Press, 936.
  2. Council of Economic Advisors; Kathryn Zickuhr. 2013. “Who’s Not Online and Why,” Pew Research Center; The White House. 2015. “Fact Sheet: ConnectHome: Coming Together to Ensure Digital Opportunity for All Americans”; Vikki S. Katz and Carmen Gonzalez. 2015. “Community Variations in Low-Income Latino Families’ Technology Adoption and Integration,” American Behavioral Scientist 60:1, 59–80; Vikki S. Katz and Carmen Gonzalez. 2016. “Toward Meaningful Connectivity: Using Multilevel Communication Research to Reframe Digital Inequality,” Journal of Communication 66:2, 236–49; John B. Horrigan. 2010. “Broadband Adoption and Use in America,” U.S. Federal Communications Commission; Hargittai.
  3. Katz and Gonzalez 2016, 59–60.
  4. Colin Rhinesmith. 2016. “Digital Inclusion and Meaningful Broadband Adoption Initiatives,” Benton Foundation, 7; Paul DiMaggio and Eszter Hargittai. 2001. “From the ‘Digital Divide’ to ‘Digital Inequality’: Studying Internet Use as Penetration Increases,” Princeton University Center for Arts and Cultural Policy Studies Working Paper; Dharma Dailey et al. 2010. “Broadband Adoption in Low-Income Communities,” Social Science Research Council; Jen Schradie. 2011. “The Digital Production Gap: The Digital Divide and Web 2.0 Collide,” Poetics 39:2, 145–68.
  5. Dailey et al., 23.
  6. John B. Horrigan. 2015. “The Numbers Behind the Broadband ‘Homework Gap,’” Pew Research Center.
  7. The White House.
  8. Katz and Gonzalez 2016; DiMaggio and Hargittai; Jan A. V. G. M van Dijk. 2006. “Digital Divide Research, Achievements, and Shortcomings,” Poetics 34: 4–5, 221–35; Hargittai.
  9. Wenhong Chen and Barry Wellman. 2004. “The Global Digital Divide Within and Between Countries,” IT & Society 1:7, 39–45; Van Dijk.
  10. Paul DiMaggio and Filiz Garip. 2014. “When Do Social Networks Increase Inequality?” in Social Stratification: Class, Race, and Gender in Sociological Perspective, David B. Grusky, ed. Boulder, CO: Westview Press, 673.
  11. DiMaggio and Hargittai.
  12. Van Dijk.
  13. Dimaggio and Hargittai, 2.
  14. DiMaggio and Hargittai, 9–10; Hargittai; Shane Greenstein. 2004. “The Economic Geography of Internet Infrastructure in the United States,” in The Handbook of Telecommunications Economics, Volume II, Martin Cave, Sumit Majumdar, and Ingo Vogelsang, eds.
  15. Pew Research Center. 2016. “Three Technology Revolutions.”
  16. John B. Horrigan and Maeve Duggan. 2015. “Home Broadband 2015,” Pew Research Center.
  17. DiMaggio and Hargittai, 9–10.
  18.  DiMaggio and Garip, 679; Schradie, 148; 161.
  19. Katz and Gonzalez 2015, 242–3.
  20. DiMaggio and Hargittai, 8–11; Van Dijk; Hargittai.
  21. Katz and Gonzalez 2016.
  22. John B. Horrigan. 2016. “Digital Readiness  Gap,” Pew Research Center, 2–6.
  23. Van Dijk, 29.
  24. Lee Rainie. 2016. “Digital Divides 2016,” Keynote Address at the Internet Governance Forum.
  25. DiMaggio and Hargittai, 12–3; Hargittai.
  26. DiMaggio and Hargittai; Hargittai; Ezster Hargittai and Amanda Hinnant. 2008. “Digital Inequality: Differences in Young Adults’ Use of the Internet,” Communication Research 35:5, 602–21.
  27. Schradie.
  28. Katz and Gonzalez 2015, 63; Schradie.
  29. Katz and Gonzalez 2015; DiMaggio and Garip.
  30. Karen Mossberger et al. 2012. “Unraveling Different Barriers to Internet Use: Urban Residents and Neighborhood Effects,” Urban Affairs Review 48:6, 773; Katz and Gonzalez 2015.
  31. Katz and Gonzalez 2015; Katz and Gonzalez 2016; Dailey et al 2010; Rhinesmith 2016.
  32. Dailey et al.
  33. Mossberger et al.
  34. Rhinesmith.
  35. Rhinesmith; Dailey et al.
  36. DiMaggio and Garip.
  37. Rhinesmith; J.C. Araque et al. 2013. “Computer Usage and Access in Low-Income Urban Communities,” Computers in Urban Behavior 29:4, 1393–401.
  38. The White House.
  39. The White House; Council of Economic Advisors; Horrigan 2010.
  40. Rainie; Horrigan and Duggan.
  41. Council of Economic Advisors.
  42. Greenstein; Council of Economic Advisors; David Beede and Anne Neville. 2013. “Broadband Availability Beyond the Rural/Urban Divide: Broadband Brief No. 2,” NTIA and Economics and Statistics Administration; Dailey et al.
  43. Beede and Neville.
  44. Greenstein.
  45. The White House.
  46. Council of Economic Advisors.
  47. Ibid.
  48. Mossberger et al.
  49. Dailey et al.
  50. Thom File and Camille Ryan. 2014. “Computer and Internet Use in the United States: 2013 American Community Survey Reports,” U.S. Census Bureau; Calvin Johnson. 2016. “ConnectHome: Demonstrating How to Close the Digital Divide,” U.S. Department of Housing and Urban Development PD&R Edge: Message From PD&R Senior Leadership, March 2016.
  51. Johnson; Insight Policy Research. 2016. “Baseline Memorandum: August 4, 2016.”
  52. Insight Policy Research 2016.
  53. U.S. Department of Housing and Urban Development. 2016. “Baseline Internet Access Among ConnectHome Communities: Results from the National Evaluation of ConnectHome.”
  54. Rhinesmith.
  55. Pew Research Center.
  56. Horrigan and Duggan; Rhinesmith.
  57. Anderson.
  58. File and Ryan.
  59. Horrigan and Duggan; Rainie.
  60. Horrigan and Duggan.
  61. U. S. Census Bureau. “2013 American Community Survey,” U.S. Census Bureau’s American Community Survey Office; U.S. Department of Housing and Urban Development. “HUD Administrative Data (PIC, TRACS, HUD-951).”
  62. File and Ryan.
  63. 2014 American Community Survey and HUD Administrative Data.
  64. Johnson; The White House.
  65. Insight Policy Research 2016; U.S. Department of Housing and Urban Development. 2016. “Baseline Internet Access Among ConnectHome Communities.”
  66. Horrigan and Duggan; Rainie.
  67. Van Dijk; Zickuhr.
  68. Zickuhr.
  69. Ibid.
  70. Dailey et al.; Brian Whitacre and Colin Rhinesmith. 2016. “Broadband Un-Adopters,” Telecommunications Policy 40:1, 1–13.
  71. Whitacre and Rhinesmith.
  72. Horrigan and Duggan, 7.
  73. Horrigan 2010; Rhinesmith.
  74. Dailey et al; Rhinesmith; Horrigan 2010.
  75. Dailey et al; Rhinesmith.
  76. Insight Policy Research 2016; U.S. Department of Housing and Urban Development. 2016. “Baseline Internet Access Among ConnectHome Communities.”
  77. Whitacre and Rhinesmith.
  78. U.S. Department of Housing and Urban Development. 2016. “Comcast and the U.S. Department of Housing and Urban Development (HUD) Announce Pilot Program to Close the Digital Divide for Public Housing Residents in Miami, Nashville, Philadelphia, and Seattle,” 24 March press release.
  79. The White House.
  80. U.S. Department of Housing and Urban Development. 2016. “Baseline Internet Access Among ConnectHome Communities.”
  81. Johnson; U.S. Department of Housing and Urban Development. 2016. “Baseline Internet Access Among ConnectHome Communities.”
  82. The White House; U.S. Department of Housing and Urban Development 2016; U.S. Department of Housing and Urban Development. 2015. “The National ConnectHome Summit: PD&R Expert Convenings Summary Report,” Office of Policy Development and Research, October 2015.
  83. Insight Research Partners 2016; U.S. Department of Housing and Urban Development. 2016. “Baseline Internet Access Among ConnectHome Communities.”
  84. The White House.
  85. U.S. Department of Housing and Urban Development. 2016. “Baseline Internet Access Among ConnectHome Communities.”
  86.  U.S. Department of Housing and Urban Development 2015; Johnson.
  87. Hargittai, 940; Dailey et al.
  88. U.S. Department of Housing and Urban Development. 2016. “Baseline Internet Access Among ConnectHome Communities”; Johnson; The White House.
  89. Interviews with ConnectHome pilot communities.
  90. DiMaggio and Hargittai; Hargittai; Hargittai and Hinnant.
  91. Interviews with ConnectHome pilot communities.
  92. The White House.
  93. U.S. Department of Housing and Urban Development 2015.
  94. Interviews with ConnectHome pilot communities.
  95. U.S. Department of Housing and Urban Development 2015.
  96. Ibid.
  97. Ibid.
  98. DiMaggio and Garip.
  99. DiMaggio and Hargittai; Greenstein.
  100. Van Dijk.


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