This data commentary offers a critical review of the evidence currently available to understand the nature of digital exclusion in the UK. We find:
The main official surveys providing evidence of digital exclusion are too focused on a simplistic binary conception of the digital divide as either ‘being connected’ or ‘offline’ and do not sufficiently explore the problems faced by ‘limited users’.
The digital inequalities literature and our qualitative research highlights the barriers that ‘limited users’ may face in accessing increasingly digitalised job-markets and essential services, such as data poverty; a reliance on smartphones for complex tasks; limited access to shared devices; and relying on others to perform digital tasks on their behalf among other barriers.
New methodologies using a range of proxy indicators, or based on measuring data poverty, have been piloted by some London boroughs and offer additional insights into the links between digital and social inequalities but also rely on official statistics and are unable to account for barriers faced by ‘limited users’.
Official surveys should be updated to improve understanding of the barriers faced by ‘limited’ or disadvantaged users of digital tools and services, alongside investment in qualitative research.
Given how much of our lives have moved online in the 2 years since the start of the UK Lockdown, it is easy to assume that everyone is now connected. The latest figures suggest that digital exclusion in the UK has reduced over the last year of the pandemic. Data from the Office of National Statistics show that in 2020 more than 95% of adults in the UK were online. However, this same research shows that 2.6 million people were still offline, impacting access to education and health during the pandemic. Digital exclusion is clearly still a critical issue.
The oft-cited overall figure of 2.6 million people being digitally excluded in the UK seems large, but this underplays the full extent of the problem. The problem is that once people are counted as ‘internet users’ they are no longer considered to be digitally excluded in many surveys and so factors that restrict their use, and questions of digital competence are seldom explored. However, amongst UK ‘internet-users’ many cannot perform basic digital tasks like turning on a digital device (11%) or opening up an app (13%). Other barriers that some ‘internet users’ face (see table in section 2) include: a reliance on smartphones, rather than computers to complete complex (e.g. form-filling) tasks; being unable to afford sufficient data to apply for jobs or benefits or participate in other online activities; and reliance on others to perform digital tasks on their behalf. If such barriers were included in categorisations and surveys of digital exclusion, then figures to represent this would likely be much higher.
2 Measuring digital exclusion; beyond binaries of access to understand barriers
Survey data has, to date, been the key source of evidence used to measure and understand digital exclusion. As part of our research for the Digit’s Research Theme 4 ‘Connecting the Disconnected’ programme we have been looking at the most widely used surveys for measuring digital exclusion and inclusion in the UK, US and Europe. This data commentary reflects on what we have found in the UK context. In the UK, two key sources of data are the OFCOM Adult’s Media Use and Attitudes survey and data from the Office for National Statistics (click to expand sections below).
2.1 Key digital inclusion surveys in the UK and what they measure
The UK’s Office of National Statistics’ (ONS) Internet Access – Households and Individuals survey counts individuals as ‘internet users’ if they have used the internet once in the last three months. As mentioned, using this figure, 95% of adults in the UK are ‘internet users’. However, the most granular level of internet use data the ONS publishes is ‘daily or almost everyday’. Once this figured is considered, the number of ‘internet users’ in the UK falls to 89%.
Surprisingly, although the ONS questionnaire has separate responses for ‘daily’ and ‘almost every day’, they publish them jointly—as a sum—rather than separately. Because respondent level data is not made available, we are unable to determine the proportion of adults using the internet every day in the UK, but we can infer it is likely to be below 89%. Moreover, the ONS questionnaire includes a response option for individuals using the internet more than once a day, but this figure is not published.
The ONS does not seem to collect much data regarding barriers for both ‘non-internet users’ and ‘internet users’. The ONS has conducted its survey annually since 2011 and findings from the data are publicly available through data tables. However, the data itself is not made publicly available. The ONS survey is done over the phone and its main limitation is its small sample size (2,754 in 2018) which limits its generalisability.
OFCOM’s adults’ media use and attitudes questionnaire does not count individuals as ‘internet users’ based on them having used the internet during a specific timeframe (e.g. in the last month, 3 months or year). Instead, respondents are counted as ‘internet users’ if they report having used any device to go online over an unspecified amount of time including: smartphones, tablets, computers (PC, Laptop), gaming consoles, smart TVs, wearable technology, smart speakers (e.g. Alexa), and others. OFCOM’s questionnaire also includes a question regarding how many hours users spend online per week. 90% of users reported going online on at least one device.
This headline 90% figure quickly becomes questionable once more granular frequency of use stats are considered. 12% of adults reported using the internet less than once a week and 3% reported using it only 1 – 2 hours a week. Only 73% of respondents reported using the internet around an hour a day (at least 6 hours a week) or more in 2021. The questionnaire includes a separate list of questions for internet ‘users’ and ‘non-users’. Non-users are given predetermined options for reasons they are non-users and incentives that may convince them to go online. However, ‘internet-users’ are asked few questions regarding any barriers that they encounter. OFCOM has conducted this survey annually since 2011 and the data is available publicly in an open format. Findings are also published in data table reports with some findings available in an interactive webpage. The most recent media use and attitudes included a sample of 3,015 adults and data was collected through a combination of online and postal surveys. In-home person interviews have been conducted in the past, but were paused due to COVID. In fact, Ofcom warns that their most recent survey, which found a steep reduction in the number of households without internet access from 11% in 2020 to just 6% in 2021, ‘should be seen as indicative only’ due to the enforced methodology change.
2.2 Beyond binary conceptions of inclusion and exclusion
As the details above show, we have found that people are counted as internet users in the UK if they’ve used the internet once in the last three months (ONS) or if they have used any device to go online (Ofcom). This reflects outdated conceptions of the digital divide as a binary – the notion that internet access is merely about having access to a device and/or the Internet or not – and does not take barriers of use into consideration. The table below summarises the top-level digital inclusion measures and more granular inclusion data collected by both surveys.
There is now a significant body of research showing that digital divides go beyond a binary understanding of digital inequalities. The barriers that prevent people from either going online or making effective use of digital tools have been extensively researched and are linked to predictive demographic factors of class, age, race, gender, education levels and disability. It is important to note that because the demographic factors are merely predictive, not all people belonging to these demographic groups will be offline or face any additional barriers and it is also possible that people belonging to none of these groups could also be offline or face additional barriers once online. Barriers reported include a lack of digital skills, motivation, reliance on ‘proxies’ to help get online, or using broken devices. Research has also revealed that there are different types of users of digital technologies, and that inequalities in access are best understood in terms of these categories and other social variables. ‘Limited internet users‘, for example, typically just use the internet for entertainment and social media, and are predominantly from marginalized socio-economic backgrounds (Yates et al., 2020). The ONS and OfCom surveys need to be brought up to date to better reflect these additional factors for capturing digital exclusion.
Moreover, although measuring internet-use time and activities can help advance understanding of the extent of usage, it doesn’t provide us with any indication of the reasons for low usage. Answering these questions will require additional collection of data about the barriers that ‘limited users’ may encounter once they go online. Understanding these barriers is necessary now so that targeted interventions can be identified to ensure that they are not (at least partially) excluded by increased digitalisation, or included in ways that disadvantage them.
The table below summarises some demographic predictors of digital inequality and some of the barriers ‘internet users’ may encounter after gaining access. As can be seen, surveys are currently doing a decent job at disaggregating data along predictor demographic groups, especially regarding frequency of use amongst different groups. However, surveys could do a better job at incorporating questions—and publishing findings—about barriers that users face once online.
3 Why it matters
Given the rapid digitalisation of many aspects of life during the pandemic, there is an urgent need for policy makers to have an accurate understanding of who is able to participate digitally in society and who is not. Without this data there is a risk of increasing inequalities and leaving people behind. This has led to the development of new methodologies for measuring digital exclusion and an increased policy and advocacy focus on data poverty – the challenges people face in affording sufficient connectivity to perform everyday activities online as a key factor. Our ongoing research within Digit suggests that neither measuring digital inequalities in binaries, nor the current ‘limited user’ framing is sufficient to help us understand how and why users may have lower levels of access and what actions may be taken to tackle digital inequalities.
In our Digit research on job-seeking and access to benefits during the pandemic, we explored more granular questions about barriers. This was helpful to better understand gradations or types of exclusion. For example, due to the challenges of explaining digital processes over the phone to people without any familiarity with digital technology, one NGO employee in NYC talked about how they had to support ‘internet users’ during the pandemic;
“…since they can’t do it by themselves and don’t know how to use emails, I ask if they have a nephew or somebody they can trust? Because the only other way is they give me everything and I do it myself, but they may not want to share sensitive information, so we ask if they have somebody in high school or a friend or somebody with them and then we would just help them do it”
Although data that disaggregates digital inequality by gender, age or socio-economic group is useful in providing a clearer picture of who is online, it still fails to illuminate internet-use barriers (such as those reported in the table above) that specific groups may face. Whilst disaggregated digital exclusion data is very useful, current approaches risk resulting into one-size-fits-all digital inclusion policies that do not account for the specific experiences of different ‘limited’ users.
4 Towards better measures of digital exclusion
In the remainder of the commentary, we explore two emerging methodologies for capturing digital exclusion. We argue that both have considerable potential for illuminating the nature of inequalities in digital access. First, we look at research which provides spatial mapping of digital exclusion in London Boroughs. This uses many familiar demographic variables as a basis to understand inequalities, but also incorporates less widely used indicators, many of which are available through locally specific datasets. These can be used to build better proxies for understanding digital exclusion. Secondly, we consider broader indicators of data poverty and suggest that these can provide valuable insight into the reasons for inequalities in digital access. However, as we argue in the conclusion, these methodologies combine official ONS and Ofcom digital access data with other official data. This limits their ability to paint a clear and useful image of the problem because these sources don’t measure barriers faced by those labelled as ‘internet users’.
4.1 Mapping digital exclusion with richer demographic data
During the pandemic, five London Boroughs collaborated with the London Office of Technology & Innovation (LOTI) to develop an interactive demographic map of user needs to tackle digital exclusion in London. This was undertaken with the aim of providing a more detailed prediction of digital inequalities in particular localities, and creating tailored interventions that meet specific citizen and community needs. The resultant map uses several publicly available datasets and shows common community demographics and characteristics that have been identified through research as key factors or proxy indicators to the propensity for digital exclusion.
Figure 1: Illustrative LOTI Map showing geographies with high prevalence of internet speeds below 30 mbps, older residents, low income families, unemployed people and people living with disabilities. Please access link for colour key.
Groups included in these demographics are older people, low-income families, members of minority ethnic communities – especially low-income migrant households, and unemployed people with disabilities or other vulnerabilities such as mental health issue. Small and micro businesses – who may struggle to get their businesses online, or conversely in seeking and obtaining skilled workforce locally as they grow, are also identified as being more likely to experience digital exclusion.
An advantage of this type of mapping is that boroughs can leverage data they might hold about their specific localities to make these insights richer. For example, the Low Income Family Tracker (LIFT) has information on Pension-aged residents, Disability, and Residents with one or more children. LOTI also make the limitations of this approach clear in their toolkit; these indices act as proxies to aspects and characteristics that have been shown to be related—and thus predictors—to potential digital exclusion barriers.
This is undoubtedly a rich and useful resource for local authorities and voluntary sector organisations who previously had to rely on national/regional level datasets. However, this use of proxy indicators – extrapolating based on existing demographics – still only tells you who is likely to be offline but not why. It doesn’t shed light on the many barriers that people face in effective internet use. For many people, these barriers take the form of being reliant on cheap smartphones for internet connectivity, as we found in our Digit research. One of our interviewees reported:
“…when I ask them if they have a computer at home, most of the time they don’t, they just have a smartphone and sometimes a smartphone that can’t do any smart things like taking a picture clearly or basic things. Sometimes they have an old phone that can’t do most of the things that you need them to do.”
A lack of data on barriers for current (or limited) users makes the work of tackling digital exclusion even more challenging, as policy makers lack insight into the particular challenges certain groups face, and thus what specific tailored interventions may be needed to address their needs.
4.2 Understanding the impact of data poverty on digital exclusion
One barrier to digital use is affordability; what has been described as ‘data poverty’. Since the start of the pandemic this has come under increased scrutiny with the launch of the APPG on Data Poverty and the launch of the Data Poverty Lab, by the Good Things Foundation and Nominet. Research by NESTA in Scotland and Wales found a dearth of studies or reports about the affordability or sufficiency of broadband or mobile data for households or individuals; “Despite a comprehensive search, and locating studies and reports at a global, international, national and local level, none answered the question of how many people could not afford to go online, nor who might struggle to afford sufficient access to broadband or mobile data”. They argue for a definition of data poverty which is; “those individuals, households or communities who cannot afford sufficient, private and secure mobile or broadband data to meet their essential needs”. The authors outline a range of policy responses including ‘zero-rating’: providing free access to essential sites. This approach has also been championed by the ClickZero campaign. There are signs that affordability may soon become an even more significant barrier given recently announced home and mobile broadband price increases by the major internet service providers in the UK. A more recent Ofcom survey showed that that only 1.2% of eligible households (those receiving universal credit) take up social tariffs, inexpensive internet plans offered to low-income households. 84% of social tariff eligible households were unaware that they exist.
To help understand this issue further we recently supported Barking and Dagenham Council in carrying out some small-scale qualitative research on data poverty in the Borough. Of the 50 people interviewed, 25 couldn’t get monthly phone contracts because of their personal circumstance (e.g., age, temporary housing or poor credit), and 35 couldn’t afford as much internet access data as they needed, despite being reliant on connectivity for job seeking and claiming benefits. For those residents who were on Pay as You Go phones, most had to wait to top up data when they ran out – some for as long as a month. These residents were also facing a range of other issues including being house-bound, mental health issues or having learning difficulties.
This highlights the multi-faceted impacts of affordability and data-poverty on digital exclusion, which merit closer scrutiny and inclusion and investigation in surveys. Consideration of affordability in surveys is needed, but as decades of digital inequalities literature shows, affordability provides only a single and partial explanation of limited usage.
Both the approaches we outline above – of measuring data poverty and of using proxy indicators – are a useful contribution to increasingly important debates about the links between digital and social inequalities. But these approaches still fail to address why access and use is limited. Our research suggests multiple barriers leading to low and non-use, including affordability issues, slow internet speeds, intermittent access, sharing devices, use of subpar devices, being a ‘mobile only’ internet user, lack of digital skills, lack of content in your own language, fear of being scammed online, and a reliance on proxies or public internet in libraries and community centres.
Given these insights, we think it’s vital that questions about these barriers are included in digital inclusion surveys. What is counted is also what counts. Outdated approaches impact how digital access is framed by government and how it can go about tackling it. It also has implications for how others speak about digital inclusion as figures are recycled by academics and activists working on issues related to digital access. We would encourage more qualitative research on these issues, so that we can unpack the specific challenges faced by different communities. Employers, local authorities, and service providers in health and education need this data to ensure that the most marginalised communities are not left behind by digital service delivery. But beyond this, we need to interrogate the links between poverty and digital exclusion, as recognised by demands for internet access to be organised as a 21st century necessity to fully participate in society with connectivity free at the point of use. Digital inequality is a multifaceted, moving target, and needs to be considered as an increasingly important axis of inequality in an increasingly digitised world.
ONS and Ofcom should draw on this analysis to add questions related to barriers to increased internet use. A new ‘disadvantaged internet user’ figure should be published, to include users who face barriers after going online.
More qualitative research is also needed to understand the nature of the barriers that ‘limited users’ face once accessing the internet. Official data inclusion surveys should update their questionnaires periodically based on findings from such research.
Recent UK government Levelling Up initiatives focus on infrastructure and internet speeds (eg ‘project gigabit’). While this is important, government policies must also address the other barriers that may hinder ‘limited users’.
Civil society, activists, media, government, and academics in the UK should continue to be sceptical about the ability of digital inclusion stats to fully capture or explain the nature of digital exclusion until recommendations 1 and 2 are implemented.
Addendum – 12 April 2022
Ofcom published its updated Adults’ Media Use and Attitudes (AMUA) report shortly after this data commentary was published. We were pleased to see that it has already taken some steps towards improving measures of digital exclusion, with a recent review of all its digital exclusion research.
Importantly, Ofcom have acknowledged that digital exclusion is a multi-faceted issue, to be captured by measures that go beyond ‘internet access’. However, we urge Ofcom and the ONS to go further in two key ways, which we set out below.
1 Improving counts of how many people in the UK are digitally excluded
Unfortunately, we still don’t have an overall figure for the number of digitally excluded people in the UK. The section of Ofcom’s AMUA report titled ‘How many people in the UK are digitally excluded?’ fails to provide an overall figure for the number of people who are digitally excluded by a deficit of skills, confidence, affordability and other factors. Instead, statistics are presented separately for each barrier. Ofcom has also acknowledged and highlighted further barriers uncovered by the Communications Consumer Panel (CCP), many of which significantly overlap with the barriers covered in this commentary. However, no commitments were made to begin capturing data related to these barriers.
We recommend Ofcom begin to collect data for the barriers covered in this commentary and those highlighted by the CCP. We also recommend that Ofcom create a new single figure for ‘partially’ digitally excluded or disadvantaged internet users in the UK, which counts anyone facing barriers, as mentioned above, once online.
2 Internet access is not the same as digital inclusion
Secondly, Ofcom still opens its summary of digital inclusion with the figure of 6% of households not having internet access. It may be unintended, but the focus on this figure obscures the extent of digital exclusion and the further barriers internet users may face once they have access. This has implications for how Ofcom’s survey findings get recycled by academia, civil society, and the media, who tend only to cite the figure that 94% of households have internet access figure as evidence of widespread digital inclusion.
We therefore recommend Ofcom provide a figure for the number of individuals who are able to access the internet without facing any additional barriers as the headline statistic relevant to understanding the extent of digital exclusion.
As we have argued, what is counted is also what counts. Policymakers need this data to understand the problem they are trying to solve. Employers, local authorities, and service providers in health and education need this data to ensure that the most marginalised communities are not left behind by digital service delivery. Digital inequality is a multifaceted, moving target, and needs to be considered as an increasingly important axis of inequality in an increasingly digitised world.
Kevin Hernandez is a Research Fellow in Digit’s Research Theme 4: Reconnecting the Disconnected and a Research Officer in the digital and technology cluster at the Institute of Development Studies.
Dr Becky Faith is Co-Lead of Digit Research Theme 4: Reconnecting the Disconnected and a Research Fellow and Co-Leader of the Digital and Technology cluster at the Institute of Development Studies.