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György Csepeli and Gábor Csere
Inequalities and Networks in Society
1. Paradigm Change in Quantitative Sociology
The technological innovations of the Information Age have radically changed the functioning
of society on both the life-world and systemic levels. The degree and pace of change clearly
imply that change has enormous reserves. Over time no element of man’s natural and socially
constructed reality will be left unaffected by the radical effects of the technology for creating,
transmitting and storing information.
The culmination of these effects is the information society, which can be hardly understood
through the exploration methods developed before the Information Age. Empirical sociology
was created in the twentieth century in response to the ideological and sociopolitical
challenges of the industrial society. The critical potential of sociology drew on the concept of
a perfect society. Since the social reality experienced had never met this concept, knowledge
gained through sociological theories and methods about society could serve as fertile soil for
plans to enhance society and for concepts born under the sign of modernizing reforms.
Whether politicians got to implementing these plans and reforms or dropped them, sociology
had never been able to keep up with the processes occurring within society. The image which
sociologists developed about society based on their research work could never be utilized.
Quantitative sociological research relied on the collection and analysis of data through
samples which were generated according to principles described by mathematical statistics.
The different samples were based on a universe with varying status over time, making it
difficult to account for the time dimension which is essential to understand the functioning of
society. Moreover, data processing took a long time, and thus a research report could easily be
published in a journal of history rather than a sociological review.
Modernist presumptions implied normative and universal rules which were thought to be
applicable to the largest possible populations, whose examination seemed to require only
operationalizing and measuring a relatively small number of independent and dependent
variables which were held a priori relevant. The sociologist created order in the universe of
measured variables through applying multivariate means of mathematical statistics.
Quantitative sociology responds properly to the challenges of social changes triggered by the
information society if it attempts to utilize in its investigations the same technological changes
which brought forth the information society. As information conditions are changing, no area
within the operation of society will remain where generated information are not retained
intactly. Data generated in the course of various social operations are continuously placed in
data storages and can be retrieved for the purposes of comprehensive as well as sample-based
analyses. The time-span between data generation and analysis will be considerably shorter,
while changes occurring over time will also be retained and ready for analysis. All of the
above changes are based on the possibilities offered by digital information management
technology, which also allow researchers to have an immediate and interactive contact with
the people who entrust a survey to them.
When defining the paradigm of the new quantitative sociology, it is reasonable to start from
the change in the nature of databases and our attitudes to these databases. However, a change
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in the empirical foundations can result a complete paradigm change only if theoretical starting
points are also altered. Access to digital databases for research purposes is aptly called “data
mining”. Theoretically, there is no a priori truth, which the analyzer has to prove in order to
justify his critical notions. The regularities sought can be applied to a great number of smaller
populations. The relationship between dependent and independent variables develops
dynamically. Order is a status of chaos, which is fragile and completely unintelligible.
Consequently, the information provided by the new quantitative sociological paradigm is not
aimed at politicians, intellectuals and popular leaders but decision-makers in the spheres of
public policy and business, who are interested in running efficiently the organizations under
their management.
2. A Methodological Experiment: Network Analysis through a Telecommunications Database
In this article we will not attempt to describe the variety of effects that IT devices have on
society. We aim only to apply the new paradigm of quantitative sociology, in the course of
which we will try to describe a new empirical model of social stratification.
In the context of the information society, the possession of information and network capital
gained importance. They are latent forms of capital which can be examined only through their
communication aspects rather than directly. Telecommunications companies, such as Matáv,
possess an asset of enormous value: the matrix of their customers’ social network, which
allows us to discover the real, multidimensional structure of society.
The inputs for this new model of social stratification were drawn from call records archived in
the data files for calls initiated and received by the whole population of Matáv’s residential
customers over its telephone network in March 2004, which the company provided for us
retaining their anonymity. We tried to sort the immense pool of data generated from the traffic
in March through a clustering procedure. A program was applied which could, without any
intervention, group calls based on reciprocity into sets of customers who have “strong” ties
with each other, while customer who typically did not call each other were excluded from
these sets. In the course of this grouping process the program cut up the whole customer base
at minimum points where ties were the weakest, and in turn divided the resulted sets again
and again. The strength of a tie between two customers were measured through the sum of
money spent on calls by the two customers, but the duration and number of calls could also be
used as a measure.
Clustering was carried out in a hierarchical fashion, from top down. Cutting up into sets was
repeated until the number of customers in each did not exceed 100. The dimension number of
the call-graph, which originally equaled the number of customers initiating or receiving calls,
was reduced through the Singular Value Decomposition procedure into a 3-15 dimensional
space, which was clustered through a simple K-means algorithm.
Experience gained so far suggests that customers who call each other are extremely similar, i.
e., they tend to have contacts with customers who are similar to them: they usually live close
to each other, often have the same line type and subscription package, and spend more or less
the same amount of money on telecommunications. When mapping this social network with
the procedure above, we found homogeneous groups, which means that customers can be
sorted into a set of homogeneous groups through the interpretation of their networks.
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Line-based telephone is not an individual device, since measurable traffic reflects the
combined activities of household members, and thus our database helps examine networks on
the household rather than the individual level. Keeping contact through telephone lines is only
one of several contact forms which develop between the members of a society. Therefore the
database itself does not allow us to conclude how the social network explored through
telephone use relates to the “full” or “real” network. However, we can get closer to this
vigorously functioning “real” network within society, whose members communicate face-toface, via the Internet, mobile phones as well as telephones, if we also include other databases
(of mobile phone, Internet and e-mail use) into our analysis. Obviously, the network outlined
on the basis of usage data for a wide spectrum of communication devices will be more
complex than the one we present below.
Figure 1: Social Networks on Level 1 of the Clustering Routine
Lakossági ügyfélkör
„A” csoport
„B” csoport
„C” csoport
[Residential Customers; Group A, Group B, Group C; Groups of Matáv’s Residential
Customers by Connection Distance (March 2004, sum spent on calls; 1,000,000 – 500,000 –
100,000]
First, the program divided the total of Matáv’s residential customers into three groups. In the
figure above the number of subscribers is indicated by the size of the pie charts which belong
to various localities. Naturally, we had no information about areas outside the Matáv service
domain (LTO), and thus no diagram is shown for such localities. In each locality, the
proportion of customers who belong to the same communication network is marked by the
same color.
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There is no alternative to group membership: the proportion of those who potentially
constitute a communication island (e. g., customers who call each other only) and are not
linked to any of the three customer groups is insignificant.
The sharpest difference from other groups was found for the group whose members live in the
capital or nearby settlements. Of the data recorded by Matáv, Internet use has the greatest
explaining power in terms of statistics: a customer using the Internet is likely to be the
member of Group A, shown in red in Figure 2. This figure also implies that the new medium
(so far) tends to increase rather than reduce existing social inequalities.
Figure 2: The Social Network of People Living in the Capital or Nearby Settlements and
Internet Penetration
[Percentage of Internet Use (Dial-up and ADSL) by Matáv’s Residential Customers]
The boundaries of the two other groups are much less marked. The members of the two
networks of people living in the countryside mingle. The strongest organizing principles of
group membership are, at least on the first level of clustering, topographic proximity and
Internet use (which is, in our assumption, one of the indicators of the level of economic, social
and cultural development for a given settlement).
Due to the hierarchical clustering routine, the program further divided the resultant groups in
the subsequent steps. If we examine these groups, we can conclude that the organizing
principles of group membership change on the deeper levels of clustering: topographic
proximity is replaced by increasingly “softer” characteristics, such as settlement type, account
size, family structure, line type, subscription package, etc.
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In terms of account size, a great difference can be found between the three resultant networks
on the first level of clustering. On he second level of this procedure it seems that increasingly
homogeneous groups are produced by account size, which implies the communicative
segregation of households depending on the account size (and, indirectly, socioeconomic
status). Customers who spend much on telephoning tend to speak to each other, as customers
who spend less do (only less frequently). Regional segregation cuts across status-related
communicative segregation. It is also supported by the result that even when two households
have the same account size, communication via telephone will occur between them only if
they belong to the same “greater” cluster.
Figure 3: Social Network and Account Size on Level 3 of the Clustering Routine
[Capital Hungary, Large Cities, Small Towns, Rural Communities; Average Account Size
(HUF), Number of Calls Between Residential Customers (March 2004); High – Low; Level 1,
Level 2, Level 3]
Work on the deeper and more complex analysis of these data is currently in progress.
However, it is already obvious at this stage that we are discovering a new world of organizing
principles and rules which provide a foundation for the developed networks. This stage of
analysis already implies that membership in major social groups is exclusively determined by
sociodemographic variables, as it is assumed in classical theories of stratification. Although
communication which serves as a basis for group formation is not entirely independent of
social status, it also shows the signs of autonomy. It would be important to know the forces
affecting the kind of communication which facilitates autonomous group formation because it
would allow us to build a more accurate image of how the systemic operations of the
economy, media, culture, politics and ideology penetrate or, in Habermas’ phrase, “colonize”
the life-world.
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3. Prospects
Today we are still at the dawn of the Information Age. This statement is particularly true in
the case of Hungary, where the spread of the new medium, the Internet, is in its second phase,
when the followers of an experimenting avant-garde begin to discover this innovation.
The diffusion of Internet use within society will inevitably accelerate. Particularly, broadband
access constitutes a medium through which all earlier media can reach users. A new world of
a communicating society evolves right before our eyes, which, using the platform of the
Internet, permeates and transforms all segments of social life. The electronic dimension
provides an opportunity for all traditional social functions – education, government, trade,
work, religion, sex, cultural activities, politics, etc. alike. Interpersonal communication will be
the single link capable of updating group membership and identity.
If we want to understand the new patterns of social organization, we have to extend our
investigations to the databases which contain the logs of modern information technology. The
study of society performed in this way opens new perspectives for the application of social
research findings as well as social theory.
Theory and practice will be able to converge eventually.
Literature
Castells, M. 2000. The Rise of Network Society. Blackwell Publishers.
Castells, M. 1997. The Power of Identity (The Information Age). Blackwell Publishers
Csepeli, Gy., and G. Prazsák, 2004. “Paradigm Change in Sociology.” Review of Sociology
10, No. 2 (2004): 1–15.
Dessewffy, T. 2004. Bevezetés a jelenbe. Nemzeti Tankönyvkiadó.
Habermas, J. 1981. Theorie des kommunikativen Handelns. Bde. 1–2. Frankfurt am Main:
Suhrkamp Verlag.
Nyíri, K. 2004. “Enzyklopädisches Wissen im 21. Jahrhundert.” In Vernetztes Wissen:
Philosophie im Zeitalter des Internets. Wien: Passagen Verlag.
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