Digital inequality as a factor of transformation of the social structure in the context of the digitalization of Russian society

Introduction

Over the past two decades digitalization has evolved from a characteristic of the technological environment into one of the key drivers of social dynamics. In Russian society this process unfolds alongside the expansion of state digital infrastructure, the growth of the platform economy and the persistence of stable territorial, age and status differences. Under these conditions access to digital technologies ceases to be an exclusively technical matter and becomes an element of the social structure: it shapes opportunities for education, employment, healthcare, interaction with the state and participation in public communication. For this reason, the evolution of social and humanitarian knowledge in a digitalizing society cannot be adequately discussed without an analysis of how the structure of social inequality itself is changing.

The relevance of the study stems from the fact that digitalization policies focused primarily on coverage indicators tend to conceal qualitative differences in the ways various social groups use digital resources. Russian research shows that closing the access gap does not automatically equalize life chances [5, p. 112]. Moreover, as an increasing share of social interaction moves into digital environments, the position of groups that already possess substantial economic, cultural and social capital is further strengthened [11, p. 74].

The object of the study is the social structure of Russian society under digitalization; its subject is the mechanism linking the level of digital inclusion to an individual's position within the system of social inequality. The aim of the article is to substantiate the treatment of digital inequality as a relatively autonomous, yet embedded, dimension of stratification and to identify its principal social consequences.

Four tasks are set in order to achieve this aim: first, to systematize theoretical approaches to the definition and measurement of digital inequality; second, to compare available statistical evidence on the differentiation of digital inclusion; third, to describe the mechanisms through which inequality of access turns into inequality of opportunity; and fourth, to formulate policy directions capable of mitigating the disparities identified.

The methodological framework combines Giddens's theory of structuration [7, p. 88], Castells's and Bell's conceptions of the network and post-industrial society [2, p. 30; 4, p. 22], and Russian work on the sociological diagnosis of social structure [8, p. 156; 11, p. 41]. The empirical base consists of official statistics from Rosstat and the statistical digests of the Higher School of Economics [1; 10], materials of the International Telecommunication Union [9] and findings of published sociological studies on the dynamics of digital inequality [5, p. 108-120; 12, p. 43-62]. The methods applied are secondary analysis of statistical data, comparative analysis and theoretical systematization. The choice of research strategy is motivated by the need to combine a description of distributions with an explanation of the social mechanisms generating them, which corresponds to the classical logic of sociological inquiry [13, p. 33].

The hypothesis is that, as infrastructure approaches saturation, digital inequality shifts from the first level (access) to the second and third levels (skills and actual outcomes), so that its reproduction is explained decreasingly by technical and increasingly by properly social factors.

1. Theoretical and methodological approaches to digital inequality

The category of the digital divide was initially used to describe a simple dichotomy: access versus no access. Such an approach naturally dominated the period of mass diffusion of personal computers and household Internet connections, when the share of users served as the principal indicator. Yet the classics of post-industrial and information society theory had already demonstrated that technological shifts affect not only the sectoral structure of employment but the entire system of social positions. Bell linked the transition to a post-industrial society with the growing role of theoretical knowledge and with changing criteria of status advancement [2, p. 30-34]. Castells developed this line by describing the network architecture of modern society, in which inclusion in information flows becomes a condition of access to power and resources while disconnection becomes a new source of alienation [4, p. 22-27]. In a later work he emphasizes that communication networks do not simply transmit messages but configure relations of trust and influence [3, p. 42].

Contemporary sociological literature predominantly relies on a three-level model of digital inequality. The first level comprises differences in material and technical access: the availability of devices and the quality and cost of connectivity. The second concerns differences in skills, motivation and patterns of use. The third covers the actual life chances and outcomes that individuals are able to derive from digital inclusion [5, p. 110-113]. Methodologically it is essential that moving to higher levels of analysis does not cancel out the lower ones: the levels overlap and their effects accumulate. Consequently, growth in the average coverage rate does not by itself indicate a reduction of inequality.

From the standpoint of structuration theory, digital technologies should be treated not as a force external to society but as a set of resources and rules that social actors use unevenly and reproduce in everyday practices [7, p. 88-91]. This perspective avoids technological determinism: inequality is generated not by technology but by the unequal capacity to deploy it within pre-existing institutional conditions. A similar logic is found in Russian scholarship: Tikhonova stresses that the social structure is not reducible to the distribution of income but includes stable status positions, unequal access to resources and differences in the capacity to convert them [11, p. 41-47]. Gorshkov, analyzing the state of Russian society, draws attention to the interconnection between structural inequalities, social well-being and the level of trust [8, p. 156-160].

Trust occupies a special place in this theoretical frame. Fukuyama treats trust as a resource determining the scale of voluntary cooperation and the effectiveness of social institutions [6, p. 56]. In digital environments this resource is mediated by platform rules, ratings and algorithmic filtering: trust is addressed less to a particular person or institution than to a technical system whose logic is opaque to the user. Social capital is thereby transformed – it retains the properties of a network resource but is distributed along new, algorithmically defined lines.

It is also methodologically important to distinguish digital inequality from digital exclusion. The former describes the distribution of positions within the space of access, skills and outcomes; the latter denotes a process whereby certain groups are pushed out of full participation in key social institutions. It follows that measuring only the share of Internet users cannot reveal the true scale of the problem [12, p. 51]. The levels identified above and their sociological interpretation are summarized in table 1.

Table 1

Levels of digital inequality and their sociological interpretation

2. Data and methods

The study is analytical in character and relies on the secondary use of previously published data. This choice is dictated by the task of comparing heterogeneous sources – official statistics, international comparisons and sociological surveys – without an independent field data collection. The information base comprises the statistical digests on the digital economy issued by the Higher School of Economics [1], regional socio-economic indicators published by Rosstat [10], the annual digital development report of the International Telecommunication Union [9], and studies of the dynamics of digital inequality in Russia [5, p. 108-120; 12, p. 43-62].

Sources were selected for comparability of aggregation level: the indicators refer to households, regions or broad socio-demographic groups and predominantly cover the period 2016–2024. Theoretical propositions are used to interpret the disparities identified rather than to substitute for empirical evidence. One limitation must be stated at the outset: official statistics record mainly the fact and quality of access, whereas skills and outcomes are measured by sample surveys whose question wordings are not always comparable. The comparisons below should therefore be read as an analytical generalization rather than as a strict statistical estimate of associations.

The analysis proceeded in three steps: systematizing indicators by level of digital inequality; identifying the social axes along which the largest disparities occur (settlement, age, education, income); and interpreting these disparities in terms of stratification theory. As Yadov emphasizes, a sound research strategy requires not merely describing distributions but moving toward explanation with due regard for the meaningful contexts of action [13, p. 33-38]; this logic determines the structure of the present section and of the argument that follows.

3. The digital divide in the social structure: empirical manifestations

By 2024 the penetration of Internet access among Russian households had reached a high level overall, yet interregional differentiation persists. According to Rosstat regional data, the share of households with Internet access across the constituent entities of the Russian Federation ranges from 78.1% to 99.4% [10, p. 361]. A gap of more than twenty percentage points shows that the infrastructural stage of digitalization cannot be regarded as universally complete: even with a high national average, there remain territories where basic connectivity is still an issue. Notably, these differences cannot be reduced to a purely geographical factor; to a considerable extent they reproduce the broader unevenness of regional socio-economic development.

The international context confirms the persistence of the urban-rural divide. According to ITU estimates, in 2024 83% of urban dwellers worldwide used the Internet compared with only 48% of the rural population, while the ratio between the two figures has remained virtually unchanged at about 1.7 for four consecutive years [9, p. 8]. Technological progress by itself, in other words, does not narrow this gradient. This matters for Russia because settlement type, together with age and education, remains one of the strongest predictors of digital inclusion.

The age gradient proves even more resilient. Analyzing the dynamics of digital inequality in Russia, Volchenko shows that differences between age cohorts narrow considerably more slowly than the total number of users grows, and that older groups frequently find themselves in a situation of mediated access, in which digital operations are performed on their behalf by relatives or service staff [12, p. 54-57]. Formally counted as users, such people lack autonomy in the digital environment and therefore remain vulnerable whenever service procedures change. In terms of the three-level model, this is a rupture between the first and the second level.

Education determines not so much the fact of being connected as the range of practices. As Dobrinskaya and Martynenko note, with comparable access, groups differ in their ability to use technology for education, job search, interaction with government and the production of their own content [5, p. 116-118]. This is precisely where the third level of inequality is formed: one and the same technical resource is converted into different life chances. Finally, economic capital continues to matter: the quality of devices, connection speed and the ability to renew equipment regularly depend directly on household income [1, p. 45-48].

An analytical typology of population groups by the character of digital inclusion is presented in Table 2. It is constructed on the basis of sources [1; 5, p. 108-120; 9; 10; 12, p. 43-62] as an interpretive scheme and does not claim to be a strict quantitative assessment.

Table 2

Population groups by the character of digital inclusion (analytical generalization)

Comparing table 1 and table 2 reveals the crucial shift: at the first level inequality is measured by a binary variable, whereas at the second and third levels it takes the form of a continuous gradient closely tied to conventional axes of stratification. Rising coverage and deepening social inequality may therefore proceed simultaneously without contradicting each other.

4. From inequality of access to inequality of opportunity: social consequences

As digital technologies move from the sphere of leisure into institutional interaction, the nature of inequality changes. Three interrelated mechanisms can be distinguished through which the digital divide is reproduced within the social structure.

The first is institutional. Once medical appointments, school communication, benefit applications and appeals to public bodies move online, the skill of using services becomes a de facto condition for exercising a social right. Where alternative channels have been withdrawn, a lack of skills is equivalent to restricted institutional access even though the service is formally universal. In this sense the share of Internet users ceases to be an adequate measure of inclusion: what matters is whether non-digital channels are preserved.

The second is market-based. Platform employment, algorithmic screening of résumés, digital ratings and reputational metrics create a new line of differentiation in the labour market. Access to opportunities is increasingly determined not only by qualifications but by the capacity to fit into an algorithmically managed system of matching and evaluation. Castells emphasizes that power in the network society is concentrated among those who control the configuration of networks and the rules of communication [3, p. 42-48]; for an individual worker this translates into dependence on opaque ranking criteria.

The third is communicative and symbolic. Algorithmic filtering of content alters the structure of the public sphere and, with it, the grounds of trust. As noted above, trust is a central component of social capital [6, p. 116], yet in digital environments it is addressed simultaneously to an institution, a platform and other users. For groups with low digital literacy this creates a double vulnerability: they are both less protected against manipulative practices and less able to convert network ties into tangible resources.

All three mechanisms have cumulative effects. An advantage obtained at an early stage facilitates the mastery of new services, which in turn widens access to education and income; a lag, conversely, becomes entrenched and self-reproducing. This logic corresponds to the well-known Matthew effect and is consistent with findings on the dynamics of digital inequality in Russia [12, p. 58]. The hypothesis advanced above is thus supported: as infrastructural tasks are resolved, properly social factors become decisive.

It would nevertheless be wrong to interpret digitalization exclusively as a polarizing force. The spread of mobile access lowers communication costs for previously isolated territorial groups, while distance formats in education and medicine partly compensate for the shortage of specialists in small settlements. What is decisive is not the introduction of technology as such but whether it is accompanied by targeted support, the retention of offline channels and transparency of algorithmic procedures. In other words, the social effect of digitalization is determined by institutional design.

5. Policy directions for mitigating digital inequality

The analysis suggests several mutually reinforcing directions. Above all, a transition is needed from a policy of average coverage to one of minimum quality guarantees: infrastructure planning should be guided not by the national mean but by the regions with the lowest values of availability and connection speed [10, p. 362]. This changes the very logic of reporting – success is measured by a narrowing spread rather than by growth in the average alone.

The second direction concerns lifelong development of digital skills. The most effective delivery channels are institutions already embedded in everyday proximity – libraries, social service centres and educational organizations – since these are precisely the ones accessible to groups least likely to take online courses. For older groups the priority should be not technical training as such but the formation of autonomy: the ability to perform standard operations without an intermediary [12, p. 58].

The third direction is the normative consolidation of the principle "digital by default, but not digital only". Preserving offline channels for socially significant services should be treated not as a temporary concession but as a permanent element of social infrastructure. This is particularly important for procedures affecting citizens' rights: appointments, applications, certificates and benefit payments.

The fourth direction is the development of monitoring at the third level of inequality. Access statistics must be complemented by regular surveys of practices and outcomes: which tasks people actually solve with digital services, which barriers they encounter and what savings of time and resources connectivity produces [1, p. 48]. Without such data, formal inclusion cannot be distinguished from real inclusion. Finally, the transparency of algorithmic decisions in employment, lending and the allocation of social benefits deserves separate attention: the requirement of explainable criteria should be regarded as an element of social protection [3, p. 51].

Conclusion

Three main conclusions follow from the study. First, digital inequality in contemporary Russian society is not exhausted by differences in access to equipment and networks: its most significant manifestations concern the unequal capacity to convert connectivity into skills and skills into life chances. Second, the digital divide does not replace conventional axes of stratification but is superimposed upon them, amplifying settlement-related, age-related, educational and property-based differences; it therefore acts as a factor of transformation of the social structure rather than as a mere technological reflection of it. Third, the social consequences of digitalization are not technologically predetermined and depend on institutional choices – the preservation of non-digital channels, targeted development of skills and the transparency of algorithmic procedures.

The hypothesis of a shift of inequality from the first level to the second and third is broadly confirmed: with a high average level of coverage, stable gradients persist which can no longer be explained by infrastructural causes and must be explained by social ones. A promising line for further research is the construction of a composite digital inclusion index suitable for interregional comparison and for incorporation into models of social stratification, together with an analysis of the transformation of trust and social capital under conditions of platform-mediated interaction. In our view, this is one of the most productive lines along which social and humanitarian knowledge evolves in a digitalizing society.