8
Social and Digital Inequality
Introduction: does digital inequality reduce or reinforce social inequality?
What is the relation between digital inequality and social inequality in general? This is the main question to be tackled in this chapter. When computers and the Internet arrived in the 1980s and 1990s, historical expectations were very positive and optimistic (examples are given in Naisbitt 1982; Rheingold 1993; Negroponte 1995; and Dyson 1997). The Internet would distribute knowledge and information in society easily, freely and cheaply. The educational opportunities were thought to be tremendous. The Internet would connect everybody far better than the telephone. People could find and create their own media content and not depend only on the mass media. Vertical hierarchies would be transformed into horizontal networks. Clearly, the dominant perspective was that digital media would reduce inequality and the scarcity of knowledge and information.
After the Internet bubble burst after the millennium, critical perspectives became more negative and pessimistic. One frame of reference was the problem of the digital divide. We have already seen that this problem is extremely complex and that it is unlikely to disappear.
The relation between digital media use and social inequality is suggested by the following three statements:
- digital media use reduces social inequality;
- digital media use makes no difference for social inequality;
- digital media use increases social inequality.
Actually, the word ‘use’ in these statements should read ‘appropriation’, following the sequence in this book – motivation, access, skills and usage – but ‘use’ sounds better. From the perspective of the digital divide, the third statement (increasing social inequality) seems obvious and is supported by the evidence and arguments of the previous chapters. However, we should not take it for granted. In chapter 7 we saw that there are both positive and negative outcomes of digital media use. Once the positive outcomes exceed the negative outcomes for every user, the result could be a reduction of social inequality in absolute terms: people formerly excluded from a particular domain could now be included. People with disabilities who are housebound can now receive services online.
I will now list some obvious arguments for all three statements. In the remainder of this chapter these relatively superficial arguments will be examined through more in-depth theoretical counter-arguments.
Digital media use reduces social inequality
There are a number of strong arguments to support the statement that digital media use reduces social inequality. A majority of people in the world are currently motivated and have a positive attitude towards digital media, which is quite a change from the situation in the 1980s and 1990s. In the developed countries, another majority of users now have physical access to digital media such as the Internet, and the developing countries are following suit. The most elementary operational and formal digital skills are being mastered by a large majority of users, and the frequency of use and the number of Internet applications has multiplied in the last fifteen years.
A second strong argument is that the enormous digital gaps in gender that were observed in the 1980s and 1990s have been closed in most parts of the world, while those in age are now reducing. The gap of levels of education has become less pronounced, although people of low education make different use of the Internet than people with higher education.
Chapter 7 showed that an overwhelming proportion of people are benefiting from the positive outcomes of Internet use in all important domains of society: economic, political-civic, social, cultural and personal. As a result, many would endorse the statement that the Internet, despite all criticisms, is a good thing for individuals and for society (as shown in the American survey by Smith and Olmstead 2018). Simultaneously, the negative outcomes are experienced primarily by those having best access and most frequent use of the digital media.
Another argument in support of digital media reducing social inequality is that, over time, all users benefit from the reduced costs of hardware, software and (some) services. Since the 1990s, the prices of computers, mobile phones and Internet connections have reduced considerably. The same goes for operational systems, office software and all kinds of smartphone apps. Internet provider services, music subscriptions and data bundles for mobile communication have also become cheaper, at least in terms of capacities offered.
The final argument is that using digital media offers low-cost and accessible information, most of which is paid for by advertising, so is free at the point of use. It is also more accessible than traditional sources; formerly, people had to go to a library, an agency or somewhere similar to find information, often in laborious ways. Today, Internet search engines are faster and much more intelligible.
Digital media use makes no difference for social inequality
The second statement, which observes digital inequality simply as a reflection of existing social inequality, also offers some strong arguments. The main one is that perceived inequalities are seen in traditional media use too. If someone has a literacy problem, it will handicap them in dealing with print media as well as with digital media. There are more similarities than differences between the two (see the balance in Van Dijk and van Deursen 2014).
A second argument is that people’s basic personal and positional characteristics are the same in the digital and the offline world. Properties of age (stages of life) and gender are reflected everywhere. People with disabilities have problems in both worlds. Those lacking cognitive and emotional intelligence and technical ability meet problems in every context. Individuals with poor or low-paid jobs will not get a better job online and might end up with monotonous work such as data entry. Using digital media for schoolwork will not necessarily lead to better marks or a higher level of education. Poor living, working and schooling conditions in a developing country will be reproduced in the online world of that country.
Digital media use increases social inequality
The third statement argues that two types of inequality – absolute and relative – reinforce or increase existing social inequality. Absolute inequality means that people are excluded from or included in particular domains of society. When they are forced to use digital channels for which they have no access or skills, they will be excluded in absolute terms. They will not find a job without using the obligatory online job application; they will not be able to attend an event when all tickets are sold online; and they might not manage to obtain a particular social or public benefit without using online public or government services. While such functions are not yet universal in most countries, this is the trend. Increasingly, governments and businesses expect that people without the relevant access and skills can get support from others. In this way their position as second-class citizens, consumers and workers is further emphasized.
Relative inequality is more important for this third statement. This means that some people are participating in and benefiting more or earlier than others in all domains of society in the context of access, skills and use of digital media. Existing social inequality is reinforced because the material, mental, social, cultural and temporal resources of these individuals are improving while the resources of those without access and skills stay at the same level or are reduced.
A second set of arguments backing this statement claims that the problems in the four phases of technology appropriation are not dissipating; some are actually growing. Motivations and attitudes for digital media use have become more positive overall, but in chapter 3 we saw that large differences in motivation and attitude among users remain. Allegedly, physical access problems are disappearing in the developed countries, though material access problems and unequal possession of devices and connections remain (chapter 4). Inequality in digital skills, especially content-related skills, is growing as increasingly complex applications are offered (chapter 5). The frequency and diversity of digital media use is expanding, but diversity may lead to a structural and persistent gap between those using primarily capital-enhancing applications and those more interested in entertainment or less advanced communication and commerce (chapter 6).
The last set of arguments backing this statement concern the presumed declining costs and the level of accessibility in using digital media. It is questionable whether the prices for hardware, software and services are in fact declining. Adding all costs together, they might actually be increasing, though the investment compensates for the expense of other things now available online. For example, subscriptions for newspapers on the Internet are often cheaper than buying the printed versions. However, the numbers of devices, programs and subscriptions for digital media are multiplying. It is impossible to compile a full balance sheet of today’s digital media household and business expenses.
Free online content is sometimes of low quality. Such content is paid for by often intrusive advertising and sometimes leads to a loss of privacy and security. The quality of online content is shrinking in this age of disinformation. Traditional media at least tried to provide news and information that was more or less reliable, while digital media provide both quality and poor sources of information and news. One of the risks concerning inequality is that, while some people are happy to pay for quality newspapers and information, others are satisfied with free news abstracts and low-quality information.
Clearly, there is more support in this book so far for the third statement than for the first two, despite the good arguments for these. So, to make my case, we have to dig deeper. The numerous demographics discussed in this book are too superficial. We need to look for more abstract concepts of stratification and social class to explain social and digital inequality. We should also pay attention to the changing societal and technological context.
These theoretical concepts will be discussed in the next section. Our societies are increasingly so-called information and network societies. In the information society, people lacking essential information are exposed to absolute inequality. In a network society, people in the best positions and with the best relationships benefit more. This means relative inequality.
We will then deal with the most important domains of social inequality: economic, social-network, cultural and personal. What are the trends in these domains without considering digital inequality? Is economic inequality rising in the world? We will link these domains of social inequality with the resources discussed in this book – material, mental, social, cultural and temporal resources – and then relate these domains to digital inequality.
In another section these separate domains of inequality will be combined as they are actually found in contemporary society. Nowadays people tend to live in separate worlds: they increasingly live, marry and have children with people of their own social standing. They reside in their own communities, homogeneous neighbourhoods and ghettos. In the digital world they locate themselves in ‘hotspots’, ‘echo chambers’ and ‘filter bubbles’. Is segregation really happening in a network society that has the potential to connect everybody? If so, is digital inequality encouraging this?
I next sketch the evolution of the digital technology to come and what it means for the digital divide. So far we have dealt with ‘simple’ digital hardware, software and networks. The future is for the Internet of Things, artificial intelligence, big data and virtual or augmented reality. This technological complex will bring new challenges for digital media users. Some observers foresee a future of extreme inequality and an even wider digital divide.
The main questions of this book will be answered in the concluding section. Will the use of digital media reduce or reinforce social inequality, or does it make no difference? Is the digital divide here to stay or will it disappear?
Social inequality and digital divides
The context: the information and network society
To understand the workings of the digital divides discussed in this book we have to consider some basic characteristics of contemporary society. Although these abstract characteristics are very important, they are not reflected and discussed in the theory and model of the resources and appropriation theory framing this book. First, we are now living in a society in which the production of information seems to have become even more important than the production of material goods. Data comprise the raw material of information, while information is an interpretation of data. An information society is a society in which the information intensity of all activities has become extremely high (van Dijk 2005: 134).
In the information society, information is both a primary and a positional good or asset. Primary goods are so essential for survival and self-respect that they cannot be exchanged for other goods (Sen 1985). They are not only the means of sustaining life, or life chances, particular rights and freedoms, but they also serve as basic information for individuals to survive in society. Primary goods of information are expressed by the basic knowledge of how our complex society works: the markets of labour and exchange, housing, transport, education and health care. In an information society, those who are completely illiterate are not able to live without help.
Having or lacking primary goods means absolute (in)equality. The relationship with the digital divide here is that, when essential information for living in a particular society moves online, primary goods of information can be lost for those without digital access or skills. Meanwhile, the quality and ease of use of essential information offline may become less good. For example, it might be that a systematic comparison of the best hospital for the treatment you need can be found more easily on a health care site than by consulting your doctor.
Positional goods are goods that people value because of their limited supply and because they convey a high relative standing within society. Thus information as a positional good means that particular information is not readily available to all. Some people in society and the economy have more opportunity of accessing this information than others (Hirsch 1976). Teleworking at home has not made a breakthrough in society partly because some crucial information is still obtained by talking to colleagues in the workplace, if only during a coffee break. A clear example of a positional good is the prior knowledge of insider traders: those who work in an investment company are in a better position to make decisions on the stock market than outsiders. Despite the move towards information being online, offline positions remain decisive when scarce or unique information is required.
Lacking positional goods means relative (in)equality, and such goods are becoming more important in the information society. They also mark the digital divides of skills and usage. For example, there are work roles that involve a range of tasks, from entering data in spreadsheets and databases through designing and applying decision support systems with artificial intelligence. The latter tasks define the data and the information to be used and the former simply apply it.
However, the most important reason why information as a positional good is becoming more important is because the information society has also become a network society (van Dijk [1991] 2001, [1999] 2012; Castells 1996; Barney 2004). The infrastructure of a modern network society has social and media networks at every level: individual, group/organizational and societal (van Dijk [1991] 2001, [1999] 2012). This can be compared to a mass society (a society with an infrastructure of ‘masses’ – organic groups, organizations and communities in which people are living). In an individualized network society, an individual has to be strong to acquire and keep their position; in a mass society an individual can be taken along by the group or community (‘mass’) to which they belong. The network society tends to become more unequal than the mass society (van Dijk [1999] 2012).
The network society is marked by relative inequality because the positions and relations in its social and media networks are distributed unequally. This is the case both for offline social networks and for online media networks, the focus of this book. Traditional social networks and new digital media networks are integrated, and in this way positions of social networks are reflected and reinforced by positions in digital media networks. When social and media links are combined, the strength of their links will be different, leading to a structure of power in a tripartite society (see figure 8.1).
In the network society you have an ‘information elite’ – a minority of units or people with strong social and media network relations. They have the best access opportunities and skills and the most frequent and varied digital media use, while the majority of the population, even in the developed countries, enjoy lesser social and media network relations. Access, skills and usage are also more limited. Finally, the unconnected and excluded from the network society have only traditional social network relations which, in turn, are less frequent and dense than those of the majority.
Figure 8.1. Tripartite participation in the network society
Source: Van Dijk (2005: 179).
The positions, links and resources of the information elite tend to become stronger than the positions, links and resources of the majority in the network society. This is a case of the so-called Matthew effect (Merton 1968) – the name comes from the Gospel of St Matthew (13: 12): ‘For whosoever hath, to him shall be given, and he shall have more abundance.’ The popular adage is ‘the rich are getting richer’. The scientific backing for this trend is the statistical regularity of the power law, which says that a relative change in one quantity results in a proportional relative change in another; in this context, then, a few units of a network already having many links acquire even more, while most units retain only a few links.
Social classes and digital divides
The positional categories of labour, education and social-networking connections and their contexts in nations/regions and households have already been described in the previous chapters. In fact they are clustered in social classes. This is an important analytic distinction in social science, part of stratification theory that attempts to explain the social strata of society. There are many concepts and theories of social class, but most of them come from the classical Marxian or Weberian tradition or from modern sociology, with scholars ranging from critical sociology (Bourdieu) to mainstream sociology (Goldthorpe). See the summaries of class approaches in Wright (2005). The Marxian tradition (summarized in Draper 1978) looks mainly at the positions of production relations: capital versus labour. Weber ([1922] 1978) focuses on market relations and the life chances of individuals, while Bourdieu (1987) distinguished various kinds of capital as resources: economic, social and cultural. Finally, mainstream sociology (e.g. Goldthorpe et al. 1987) analysed primarily labour market positions with occupations and status.
In this book I will try to merge these concepts in a general image and analysis of social class in order to relate this broadly to the digital divide. I have maintained the popular distinction of upper class, middle class and working class. To differentiate this distinction further, traditional and new middle and working classes are introduced. The new middle class of professionals and the new working class of flexible service workers are the fastest growing social classes in contemporary society, while the traditional middle classes and working classes are shrinking. Finally, an underclass, or precariat, is added to the spectrum (Standing 2011). Members of this class have the lowest paid and most insecure jobs; they might also be permanently unemployed and/or have no home.
My distinction comprises six social classes merging the social class perspectives of Marx, Weber, Bourdieu and Goldthorpe. They are the upper class (the bourgeoisie, capitalists or the old elite), the new middle class (the new professionals or the new elite), the traditional middle class of shopkeepers, farmers, doctors and teachers, the traditional working class of industrial, construction, transportation and care workers, the new working class of flexible service workers, more or less skilled and largely doing manual work, and, finally, the underclass or precariat. This six-class distinction is a combination of economic classes such as the bourgeoisie, the working class and independent farmers or shopkeepers (Marx), status distinctions of ‘up’, ‘middle’ and ‘under’ or ‘elite’ (Weber and Bourdieu’s cultural capital) and occupations such as industrial or service workers and teachers (Goldthorpe).
In the developed countries roughly less than one-third of the population is part of the upper class and the new middle class, one-third belongs to the traditional middle and working class, and more than one-third comprises the new working class and the underclass. In developing countries the traditional middle class (farmers and shopkeepers, often poor), together with the traditional working class and the underclass, is still much larger than that in developed countries. The characteristics of these six classes are stratified according to high, medium and low levels (see table 8.1).
Table 8.1. Estimated levels of characteristics of social classes in developed countries
The usual characteristics of social class (property, income, labour position, education and forms of capital or life chances) are compared to the characteristics of digital access and digital skills as discussed in this book. When the estimations are justified, the parallel of social and digital inequality is striking. The upper class and the new middle class have the best digital access and skills. The new working class and the underclass have low levels of access and skills. The traditional middle class and working class show medium levels of digital access and skills. This distribution is supported by the survey data discussed in the earlier chapters under the characteristics of digital media (non-)users: income, education and labour position together with social and cultural capital.
The distribution conforms with similar social class comparisons inspiring my scheme – the Great British Class Survey organized by the BBC (Savage et al. 2013: 230) and a Dutch survey (Boelhouwer et al. 2014: 292) – though their class labels are different. The result is also similar to the comparison of social class and digital media use by Yates et al. (2015), although these authors used the UK National Readership Survey classification of social grade (based primarily on occupations). Their survey also found that, as far as the Internet was concerned, the lowest social classes (skilled, semi-skilled and unskilled manual workers, casual and lowest grade workers, and people living on social benefits or pensions) revealed low and limited use, less varied use, and less information-seeking behaviour. However, these classes were much more frequent users of social media (see also Yates and Lockley 2018).
To lend more support to this distribution, I will now briefly summarize the current trends of inequality in the most important domains of society and relate these trends to digital inequality.
Economic inequality
Most economists agree that economic inequality has increased in almost every part of the world in the last thirty to forty years. Although fastgrowing emerging economies such as China, India and Brazil have created a relatively wealthy middle class and the prosperity of the whole population has increased, relative inequality inside these economies has also grown (Milanovic 2016). The range between the very rich and the very poor in China is now much larger than during Mao’s time (Shambaugh 2016). Both absolute and relative inequality have increased in the Western developed countries, where the incomes of the lowest classes (the traditional and new working class and the underclass), taking inflation into account, have in fact remained at much the same level for forty years. Recently the incomes of the middle class have also fallen; all productivity growth has gone to the upper class (Stiglitz 2013), mostly as a result of their steep accumulation of property or capital (Piketty 2014).
One of the reasons why the majority of the population, at least in the developed world, could not afford to acquire what were then relatively expensive computers and Internet connections in the 1980s and 1990s was because the level of their income and property had stagnated. Only once prices had gone down and digital media had become a necessity did access and use multiply fast. However, inequality of material access is still a problem even in the richest countries (see chapter 4). In the developing countries, the cost of all digital media is still so high that access and use remain a problem.
People with high levels of economic capital (income and property), and mostly also highly educated and having a professional career or a modern business, benefit more from the positive economic outcomes of digital media use (see chapter 7). They are part of the upper class of capital owners or the new middle class of professionals who gain the advantages of cheaper consumer goods and services (Helsper et al. 2015; van Deursen and Helsper 2018) and through acquiring capital goods and services. Because members of the upper class have not only capital but also the advanced financial and digital skills – sometimes hired – necessary to buy and sell on the world’s stock exchanges and financial markets, they manage to accumulate capital (Piketty 2014). The upper and new middle class also profit from employment relations online (Helsper et al. 2015): for them, a specialized social-networking site such as LinkedIn is much more powerful than a general one such as Facebook, so popular among the rest of society.
Clearly, digital inequality reinforces relative inequality in the economic domain. For the social classes at the highest end of the spectrum, use of digital media is literally capital-enhancing.
Social network inequality
The total volume of social capital (the number of social contacts) in contemporary society is expanding overall. This is a consequence of a network society with growing individualization and mobility and where people live, work and spend leisure time separately (van Dijk [1999] 2012). The Internet, and especially social media, has given a boost to new and existing relationships (Ellison et al. 2011; Wang and Wellman 2010; Rainie and Wellman 2012; Poushter et al. 2018). The quality of such relationships with friends, colleagues and acquaintances is another matter, however, as some of them have become less intensive or complex and more diluted or superficial (Mesch and Talmud 2011).
However, the distribution of this growing social capital has also become more relatively unequal. People with high incomes or education and a higher profession – in general, the upper and new middle class – have a much larger network than people with low income or education and lower occupations – in general, the traditional and new working class and the underclass (see Poushter et al. 2018 for the US, Savage et al. 2013 in the UK, and Boelhouwer et al. 2014 for the Netherlands). See also the assumptions of table 8.1 and figure 8.1. The disparity is not so much among core or strong ties (family and friends) as among significant weak ties (colleagues, acquaintances, customers and contacts online whom one never meets in person).
Significant weak ties matter in a network society, as Granovetter (1983) explained in his article ‘The strength of weak ties’. They offer valuable resources which are not found by people with fewer weak ties. In general, social networks provide access to valuable resources (Tilly 1998; DiMaggio and Garip 2012). Networks show network effects that exacerbate inequality in the adoption of beneficial practices (the positive outcomes of chapter 7). Network effects are of three kinds: network externalities, social learning and normative influence (DiMaggio and Garip 2012). With network externalities, the early members of a network profit both first and the most when others join in; with social learning, the larger your network, the more support you can obtain and the more you can learn from others; and, with normative influence, the larger your network, the more contacts you have available to ask for advice before taking decisions.
These network effects can also be related to social class. Tilly (1998: 10) has explained that powerful, connected people command resources from which they draw significant returns by exploiting outsiders who have been excluded from the full value of the network. These powerful connected people are hoarding opportunities in the network because they have greater access to resources that are valuable, renewable and a subject of monopoly and social support.
The rise of digital media has reinforced the unequal distribution of social capital and all these network effects. The results are the unequal social resources discussed as causes for all four phases of the digital divide: motivation, physical access, digital skills and usage.
Cultural inequality
Just as in the economic domain growth occurs worldwide and in the social domain the number of contacts increases, in the cultural domain cultural production and consumption are rising. This is a side effect of the growing diversity in postmodern society (see chapter 6). Inequality exists in the cultural domain, though it is less visible, since diversity, rather than exclusion, has become the principle of cultural distinction today (Ferrant 2018). In the past, those in the higher social classes distinguished themselves through ‘high-brow’ culture, in comparison to the masses, with their popular, ‘lowbrow’ culture (Bourdieu [1979] 2010).
Today, all social classes have become omnivorous in creating and consuming culture (Peterson 1992), so that ‘even the snobbiest of snobs’ (Coulangeon 2015) among the upper and middle classes enjoy popular culture (Ferrant 2018). However, the distinction is not that some classes are more omnivorous than others (Warde et al. 2007) but that some types of culture are peculiar to specific classes. While the upper and middle classes frequently enjoy classical music, art and literature, a large proportion of the working and underclasses show little interest in such matters but prefer popular music or literature, videos, TV programmes and games or watching sports.
Surveys in the UK (Bennett et al. 2009; Savage et al. 2013) have shown that the higher classes appreciate both classical and popular culture, while the lower classes focus more on popular culture (see also table 8.1). This is reflected in digital media use, where the higher classes benefit from all kinds of online culture, while the lower classes limit themselves to video, television, film, pictures, music, sports and games. Witte and Mannon (2010: 114) conclude, in analysing American surveys, that ‘the intensive and extensive nature of Internet use among the well-off and well-educated suggests an elite lifestyle from which the poor and uneducated are marginalized.’
Inequality of personal development
The personal domain has become the principal domain of life for individuals in a late-modern or postmodern society. According to Giddens (1991), this importance is revealed in the rise of self-identity as a ‘reflective project’. Whereas, before modernity, certain decisions were made largely by the community and the family, individuals are now constantly confronted with lifestyle choices, whether in the field of food, health, education, work, leisure, social and family relationships, children or their own personal well-being.
Digital media are very powerful tools to support these areas. Increasingly, people are looking on the Internet to find lifestyle and leisure choices or options, to discover what disease they think they have and its treatment, and to search out food choices and recipes. They are also looking for support to realize their potential, for life coaching and even to overcome loneliness. And they may search for training courses or other kinds of adult education.
The story here is much the same: the personal domain is growing, but so is the inequality in benefits. The opportunities for personal development via the Internet are being taken mostly by people with higher education and incomes and, for some activities, also young people (working on self-identity and spending leisure time) and females (health and food information). See a number of benefits combined in Helsper et al. (2015) and Helsper and van Deursen (2017). Those who are in greatest need of the key benefit, health information and care, are the elderly or disabled and people with low education, who on average have more health problems; but they are using these applications less (see van Deursen and van Dijk 2012; Hale 2013; Robinson et al. 2015). According to a Pew health online report (Fox and Duggan 2013), at that time Americans with college grades used them roughly double more than Americans with high school grades, and young people (age 18–29) more than the elderly (65+), although the information retrieved spurred them equally often to go to a doctor.
A balance sheet
Taking into account these four domains, the conclusion is evident: today digital inequality not only reflects but also tends to reinforce social inequality. Digital media are powerful tools that support people who already have an advantage in a particular domain, while those who are already disadvantaged in certain respects benefit less. The parallel between the social and the digital media characteristics of social class summarized in table 8.1 is striking. In the future, the use and the benefits of digital media may become more equally divided in absolute terms. The question is whether relative inequality will also be reduced.
Separate worlds: the trend of segmentation
So far we have looked at domains of society or life and people’s individual resources, combining them only in the general concept of social class. In fact these domains and resources are related in many ways. Helsper (2012) talks about corresponding fields, and van Deursen et al. (2017) discuss compounded inequality. People are divided not only by social class but also by ethnic, cultural, health (ability), age and gender distinctions; these may be conceived as stratification (high and low) in terms of hierarchy and as segmentation in terms of distance (separation). Social segmentation of society is a strong term, but in fact we can see social categories drifting apart in all domains of society (Bruch and Mare 2008). While there has been much investigation of residential, ethnic, school and occupational segmentation, all domains of life and society are affected, often by the even stronger process of segregation (structurally living and working apart). Figure 8.2 depicts the links between these domains and shows cycles in which people move from segregation in one domain to similar segregation in another domain. This figure was designed to describe what this means for the digital divide.
Figure 8.2. A cycle of segregation in various life domains
While in earlier societies segmentation was derived by birth or descent, in modern society it is achieved by property, work, education and lifestyle. Late-modern or postmodern society created even more distinctions of a cultural kind (Turner 1990; Giddens 1991). Today, the network society links all sorts of abstract social segments or concrete small worlds with strong ties to the rest of society via weak ties (Milgram 1967; van Dijk [1999] 2012). All these distinctions are marked by particular differential resources and types of inequality.
The most important of these distinctions and inequalities today are the divides of social class, especially the level of education, as they characterize all domains of society and life. Starting with the domain of ‘living’ in figure 8.2, we find, on the one hand, people with low education in neighbourhoods that are predominantly poor and, on the other, those who are more highly educated in well-off areas. Today there is a trend of residential segregation in large parts of the world, as the social mix between people is declining (Ostendorf et al. 2001). This applies especially to the poor, who tend to congregate in neighbourhoods with others of similar background (Volker et al. 2014; Hofstra et al. 2017). Moving anticlockwise in figure 8.2, most people start a family and raise children from within their own communities. These children then go to schools in their own neighbourhoods with other children of the same class or ethnic composition, a process of school segregation.
When leaving school and applying for a job, these children of mostly working-class and underclass families are more likely to find employment at a similar level to that of their parents than a job at another, perhaps higher level (occupational segmentation). Later, these children find a home in the same or a similar neighbourhood. In all these domains they will reproduce the same, sometimes unhealthy lifestyle, with such problems as obesity and heart disease, and a low level of lifelong learning in adult education.
This is an extreme picture of segregation with no upward social mobility. At the other extreme we find high or persisting social mobility among upper-class and middle-class people, starting with the domain of work. It is here, and in their friendship network, rather than in the neighbourhood, that such people meet their contacts (Volker et al. 2014). Most likely they will find a partner of the same social class and level of education. They will reproduce their culture in bringing up their children, who will attend good schools (not necessarily in the neighbourhood) with the same kind of children. These children will most likely secure employment away from home, probably in an urban environment, at a level similar to that of their parents. They will move wherever is necessary in order to support their career. They are more likely to have a relatively healthy lifestyle and all kinds of choices and lifelong learning options.
The majority of people worldwide fall between these two extremes, as segmentation never reaches 100 per cent. However, many present-day sociologists are concerned about the rise of social segmentation and the creation of separate lives accompanying these societal divisions (Coleman 1990; Wrzus et al. 2013; Musterd et al. 2017). They also argue that upward social mobility, so clearly observed since the mid-twentieth century with the rise of higher education, is now stagnating or even declining. Some see polarization between the extremes highlighted above (Gaschet and Gallo 2005; Vuyk 2017).
This has been a long introduction to the main proposition of this section, which is that the use of digital media reflects and reinforces these segmentation processes more than it reduces them. To understand this, one needs to appreciate two main opposing characteristics of digital media networks: they are able to connect everybody in society and they are able to select favoured others (van Dijk [1991] 2001, [1999] 2012) – heterogeneity versus homogeneity. However, in practice, the temptations of selection and homogeneity are dominant because people want to have their views confirmed (against cognitive dissonance; Festinger 1954) and tend to move towards the familiar and to stay in their own group (homogeneity and groupthink; see Sunstein 2008).
In digital media use, selection is the main mechanism supporting the social segregation tendencies discussed in at least four network activities: 1) contacting, 2) producing, consuming and sharing resources, 3) choosing places to live, work and go to school and 4) exchanging opinions. One aspect of contacting is online dating. In most countries today there are popular online dating sites aimed only at people with higher education (Skopek et al. 2010). Clearly, this supports social segregation where relationships and familybuilding are concerned. Having good connections in social-networking sites (a social resource) might lead to new job opportunities (an economic resource). There are also many Internet applications for fine-tuning job selection, finding a course of study, and selecting a place to live. Finally it is easy on the Internet to exchange opinions with people of the same interests and views, a process that is often described pejoratively as visiting ‘echo chambers’ and being stuck in ‘filter bubbles’ (Sunstein 2001; Pariser 2011).
However, just as segregation is never total, people do not only search for and exchange views similar to their own (van Dijk and Hacker 2018). They also browse through unexpected sites and are accidentally exposed to new sources, people they do not know, and opposing views (Brundidge 2010; Lee et al. 2014; Barberá 2015). Deliberately seeking reinforcement of one’s views is the action of a minority (Karlsen et al. 2017).
Intentionally looking for quality information and news online, and paying for the privilege, is an activity undertaken primarily by a minority of people with higher education and income. The majority is tempted to use free news and information sources, often of lower quality. This will be one of the most important manifestations of the digital divide in the future (van Dijk and Hacker 2018).
The evolution of digital technology and inequality
Basic changes are taking place in digital media technology, all of which are capable of increasing or reducing social inequality. Four major directions will change the relations between humans and digital media.
The first direction is the multiplication of digital media. Alongside a growing number of computer types, most traditional media – televisions, cameras and audio equipment – are being manufactured in digital form. There are new types of digital media: wearables, all kinds of meters, virtual and augmented reality devices, and many others. These devices are cheaper than traditional media, and the skills needed to operate them are similar (the same kind of menu operations), thus offering the potential to reduce the digital divide. However, the disadvantages are that all these devices together probably cost more than all the former traditional media combined and that people may have to learn several different operating systems. The multiplication of digital media also means that the process of appropriation recurs several times.
The second direction is the most basic. Up to now, digital divide research has dealt with the ‘simple’ dual interface of humans and devices or connections driven by software – human–computer or human–media interaction. We are now in an era where people work not only with interfaces of hardware and software but also with interfaces of systems. The digital media of the Internet of Things, for instance, are coordinated by systems. Examples are the system for advanced home energy meters and the system of health providers offering patients meters for heart conditions and diabetes or for coaching healthy people with wearables for exercise or sport. Another example is the transport system for safety cars steered by sensors and for self-driving cars.
Users need to know how these systems work and for whom they are providing their data. This is a big problem. Most users – perhaps only 1 or 2 per cent of the entire population on earth – do not even know how the Internet as a system works, and the complicated systems just mentioned are even more difficult to understand and be controlled by the average user. A high level of content-related digital skills, which most people do not have, is required to use the applications of the Internet of Things, where ‘more emphasis [will be] on information skills (selection, interpretation, and quality assessment), communication skills (understanding how devices communicate with other devices and humans and vice versa, and how users communicate with other users in the IoT system), and strategic skills (deciding what data should be collected and how it should be used to gain optimal outcomes)’ (van Deursen and Mossberger 2018: 131).
These new digital media are directed by artificial intelligence and continually produce big data. Decisions are made not only by users but also automatically by algorithms, so users need to have the strategic skills to follow or overrule these decisions. However, most often people are not informed about the working of these algorithms; nor is it clear who possesses the data and the algorithms processing them – the user, the IoT provider or the system and organizations concerned. If these conditions prevail, the Internet of Things and any other digital media directed by AI and big data will considerably enhance digital and social inequality.
Those individuals who were the first to adopt and use the older digital media, computers and the Internet have also been the first to adopt these new digital media. In a survey in the Netherlands in 2018, people with higher education used the applications of the Internet of Things roughly twice as much as people with lower education. Similarly, young people were far more frequent users than older people (van Deursen et al. 2018).
The third technical direction in the evolution of digital media is miniaturization, which encompasses both external hardware, such as wearables, and implanted devices or chips, where individuals become cyborgs. Here technology becomes quite intimate and may make people more powerful. Smart devices can also boost our intelligence.
The popular historian Y. N. Harari (2016, 2018) has a vision of the future in which super humans are created alongside normal humans. The super humans are the information elite, with not only the best digital skills but also the power to afford, to deal with and to control advanced data worn on or as implants in the body. Normal humans would become superfluous or irrelevant once most jobs are fully or largely automated and robotized. Harari envisages a future of extreme inequality. His pitch-black dystopian vision unfortunately is not some kind of science fiction, and according to my analysis a milder version may become reality. An ever more powerful information elite, consisting of upper-class people and the professional new middle class, with full possession of digital technology and high information and strategic twenty-first-century skills, distancing itself from the rest of the population is not an impossible future.
The fourth direction is the integration or merger of computers and the online world with physical reality and the offline world. This is being realized by the applications of virtual, augmented and mixed reality (mixed reality: an example is a holograph projected on a physical scene). Today these are used in the entertainment, health care and educational sectors. The equipment is rather expensive at present. Moreover, the interfaces are complex, so people need special operational and cognitive skills, as well as high information, communication and strategic skills, to understand and cope with these new applications. As ever, it is people with higher education and incomes and the young who have been the early adopters of this technology.
These four directions are new challenges for society, and all tend to intensify the digital divide and social inequality.
Conclusions
The major conclusion of this chapter is that digital inequality, or the digital divide, tends to reflect and reinforce existing social inequality. This is first of all a matter of relative inequality. Some people and some social classes benefit more and earlier from the outcomes of digital media use than others. Relative inequality matters in a network society where some are able to take greater advantage of resources via relationships than others. In terms of absolute inequality, digital media use may reflect and reinforce existing social inequality when the positive outcomes are reached only by some in society. Absolute inequality matters too in an information society when some cannot find vital information necessary to live and work. When the use of digital media becomes absolutely essential, those without access or elementary digital skills will be excluded.
We have seen that use of digital media makes a difference as far as existing social inequality is concerned. There is a wide spectrum between those who do not use digital media at all and have no skills and those who use it frequently and have superior skills; this range is now wider and more diverse than was that between the unlettered and intellectuals in the past. Similarly, the illiterates did not possess and were not able to use traditional press media while the intellectuals used them frequently and with high skill. Nowadays it is not only a matter of literacy, it is also to do with taking action online (see chapter 6).
Furthermore, today digital media are everywhere, in all spheres of life. Traditional media were concentrated in particular in schools and some workplaces and were important for leisure pursuits. Thus I have argued that the gap in digital media use is more important than the knowledge gap in traditional media. People draw on digital media for much more than just deriving knowledge.
The reflection and reinforcement of digital inequality in existing social inequality is intensified when we look at the background of social class (see table 8.1). The characteristics of digital media are able to reflect and reinforce the characteristics of social class. If these characteristics accumulate in all domains of living, these social classes are going to live in separate worlds.
Regarding the evolution of new digital technologies such as the Internet of Things, decision apps based on artificial intelligence, and virtual or augmented reality, we have seen that there is a greater chance that digital divides and social inequality will increase. This new technology might be easier to operate because more and more decisions are made automatically, but satisfactory use and real benefit might be more difficult to achieve than is the case with older digital technology. Advanced content-related and twenty-first-century digital skills, and at least some knowledge of artificial intelligence and data processing, are required.
The digital divide is here to stay. We have seen that motivation or attitude and physical access have improved, but that the gaps in digital skills and usage are only increasing. At the same time, motivation/attitude and physical access divides have not been closed completely, and certainly not in the developing countries.
Perhaps the most important and evident conclusion of this book is that it is impossible to close the digital divide without reducing other social inequalities. Focusing merely on the problem of the digital divide will lead only to its mitigation. However, the best solution would be to reduce existing social inequality and digital inequality simultaneously. We will now turn to this idea in the final chapter.