Social network analysis - Analytic procedure

4.4.2.1. Categorisation for social network analysis

Exploration of the associations among interdependent individuals/groups in social and geographic space is performed on the basis of several key concepts. For instance, node refers to subjects or sample elements that are not mutually independent. Determining how central a node in the network is can give information about how important role it plays in the network (Borgatti and Evereett, 2006). The institutions have been conceptualized as nodes with an aim to explore the identified institutional network. Visualisation of the network of state institutions is provided in Chapter IV (see Graph V.1 and Graph V.2).

Relationships among nodes are valued in line with the nature of social ties (tie strength). These ties between nodes represent edges, which may be directed (in single direction) or undirected (free), weighted or un-weighted. In weighted edges, the connection includes a measure of strength (Hanneman and Riddle, 2005; Wasserman and Faust, 1994). Moreover, the concept of cliques is used to determine what kind of ties or exchange patterns might be performed in the network, as well as bridges between subgroups which play an essential role in recruitment of resources and information exchange (Hanneman, 2001).

The nature and strength of these inter-relations is calculated by specific measures. Degree centrality represents a measure of the extent of connections an individual has with other nodes.

It is particularly useful in determining the level of activity of network members (Xu et al., 2004).

This measure may be prone to bias, since it can be affected by the amount and source of information collected about particular individuals (e.g. law enforcement investigation).

Betweenness centrality measures “the extent to which a particular node lies between other nodes in a network or group” (Borgatti, Everett, and Johnson, 2013; Xu et al., 2004), that is, it calculates

128 the number of contacts within a network associated with a particular node. Research indicates that high betweenness centrality may imply that a particular individual plays an important role in the network (gatekeeper, broker, responsible for the flow of information, drugs). This concept is applied to determine the role of a specific institution in the network of state institutions.

Betweenness centrality can assist in identifying actors who have a key role in the network, even though they are not so visible when measured by the degree centrality. The significance of this measure lies in its ability to capture the role of particular actors who often represent key channels of data. Freeman’s (1979) betweenness centrality measures positional importance by estimating the probability that a given actor lies between pairs of others in the network. Actors with high betweenness centrality scores (referred to as “brokers”) can have rapid and significant influence on the entire network, either facilitating the flow or exchange or inhibiting progress by act ing as a bottleneck (Wasserman and Faust, 1994). These individuals tend to be seen as leaders by others in the network (Mullen, Johnson and Salas, 1991). Closeness centrality measures proximity among nodes in the network. It reflects direct and indirect connections among the network nodes and is manly utilized by law enforcement agencies (Borgatti and Everett, 2006; Xu et al., 2004).

Eigenvector Centrality measures influence of a node based on the number of links it has to other nodes within the network, but also takes into account how well connected a particular node is, and how many links and connections it has. This measure has the potential to identify nodes with influence over the whole network and not only those directly connected to it. Network cohesion is assessed by distance measures and density coefficients. A distance measure indicates the average number of ties between all accessible members of the network. The density measure (coefficients range from 0 to 1) indicates the degree of interconnectedness or organization of a group. Finally, the network's diameter indicates the maximum path length (number of steps) between the two actors in the network.

4.4.2.2. Specifying the network questions

As discussed in Chapter III, the concept of state capture relevant for this research refers to the inferences of links of state institutions with organised crime, mutual interests and corruption at various/all levels of state governance leading to decreased effectiveness of state actions aimed at

129 suppressing organised crime. Social network analysis allows for the examination of group structure in terms of consistent patterns of interactions between individuals, as well as the elucidation of subgroups between network nodes. This feature of social network analysis is valuable for this research, given that it conceptualizes the network as a group of institutions (nodes) and aims to analyse the links between them in relation to identified problems in functioning. Hence it aims to analyse the institutional network, identify gaps and communities, the dynamics across the network, and draw conclusions with regard to potential state capture.

With an aim to respond to the main research question, the two research sub-questions were additionally analysed by using Social network analysis. These questions are the following:

RQ3. What are the structural holes that enable organised crime networks to engage in drug trafficking in Serbia?

RQ4. Do, and if so, how do these structural holes indicate a phenomenon of state capture?

In order to implement social network analysis, it was necessary to develop specific network questions that would assist in responding to these two research questions. This was performed in order to decide what will social network analysis measure. The following network questions were developed:

1. In order to see is the state response network sufficiently connected:

-What are the basic characteristics of the network in terms of density and connectivity?

-What are the average distance and diameter of the network?

2. In order to see which issues i.e. ‘structural holes’ are central to the network and to which institutions they are associated?

-Which institutions are central per different metrics?

-Which institutions are the core of the network?

3. In order to see which institution may appear as brokers:

-Do some institutions represent bridges in the network?

4. In order to see the most significant problems:

-What is the largest weight of the relations among the issues?

5. In order to see do institutions share responsibility for specific problems:

130 -Can any communities/clusters grouped around specific issues i.e. ‘structural holes’ be determined?

6. In order to see how is the network connected in regards to other networks:

-Does the network show the characteristics of a small world?

The final network was analysed using methods and metrics for analysing social networks and visualized using the Gephi software package (Bastian, Heymann, and Jacomy, 2009). Gephi was also used to calculate basic metrics. Gephy software package was used due to its advanced features. The UCINET software package is not attainable by individual authors, and it is not particularly user friendly. Where appropriate, the weight variation of metrics was taken into account. For network clustering, the Louvain method was used in the Gephi tool which is based on the maximization of modularity.

4.4.2.3. Coding the data for social network analysis

As explained above, the Content analysis was used to enable interpretation of the findings and identify the key variables for the Social network analysis. This method was implemented as an adequate tool to summarize the findings given the amount of data collected and the diversit y of sources, in particular the written sources.

Gathering complete network information about the state response network, i.e. the network of state institutions responsible for suppressing organised crime and drug trafficking, involved multiple data collection strategies. As described above, data was collected by a comprehensive desk review (official reports, media articles, statistics) and collection of written material from relevant institutions, as well as multiple interviews (semi-structured and in-depth interviews) with state officials, civil servants in relevant institutions, the judiciary and civil society. These multiple sources were analysed in order to gain more in-depth knowledge, map the existing ‘state response’ network developed to fight against drug trafficking in Serbia and assess the results of state action in this regard.

Originally, this implied the mapping of the state response network or the identification of all the institutions responsible for the fight organised crime and drug trafficking in Serbia. The concept

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‘state response network’ was developed for the purpose of this research, as a common label for all the mapped institutions responsible for the fight organised crime and drug trafficking. An overall appraisal of the key institutions and relevant legislative framework was provided in the Chapter III. The institutions of the state response network were coded for the implementation of SNA, as presented in the table below.

Table IV.1. Codes of the institutions in the state response network

132 Furthermore, content analysis implementation enabled the identification of the ‘structural holes’

or issues identified in the functioning of the state response network. The issues are labelled

‘units of analysis’ and explained in detail above (under 3.4.1.2). The units of analysis identified by content analysis were further used to code data for Social network analysis, in order to quantitatively confirm or reject the findings. The following units of analysis were identified:

Table IV.2: Key themes, sub-themes and units of analysis

Key themes Sub-themes Unit of analysis

Stalled

I Stalled transition attributed to the level of organised crime

 Assessments of transition

 Assessments of democratisation

II Stalled EU accession attributed to the level of organised crime

 EU assessments of progress in the fight against organised crime, drug trafficking and corruption

 trial (expertise, relationships in court panel, decision making, judging to benefit of def....)

 identification of financial assets from crime

 sectors vulnerable for corruption

 management of seized assets

 specialisation

133 Serbia as a captured state Spheres of state

capture

The results of the content analysis were used as a starting point for the social network analysis.

Namely, the themes were used as key issues under investigation (to determine relations relevant for these research questions). The identified units of analysis (i.e. the key issues mentioned by the interviewees or in official documents) or structural holes were subsequently used as the key criterion for the development of the ID of issues (label of the issues) for the Social network analysis. Initially, the frequency of units of analysis was used to develop a scale of 1-5 in order to prepare the data sets for performing Social network analysis. The Table Scaling of issues 1 (Annex 4) includes all the scaled units of analysis.

Table IV.3. Scaling of issues 1

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4.4.2.4. Implementing SNA – developing a network based model of a state response network

Based on the initial set of data, a network of institutions was modelled according to social networks. Institutions represent the nodes of the network, and a connection is established if two institutions were involved in addressing a particular problem. The modelled network obtained is non-directional and weighted.

Initially, the network was modelled in accordance with the institutional domain of action (legally prescribed jurisdiction) in relation to a particular unit of analysis i.e. the problems associated with the particular institution. While ‘hard law’ clearly separates the jurisdictions, the ‘soft law’

distributes responsibility between institutions that are connected through direct or indirect links.

This analysis therefore contributes to quantitative determination of critical systematic difficulties and inadequate practices in the state response network fighting organised crime and drug trafficking.

Subsequently, to assess the relations among institutions in the case of particular problems identified, the network metrics were utilised. This was followed by an in-depth analysis of the

135 identified communities with an aim to determine which institutions are responsible for the identified problems. In this way, the map of structural holes was obtained, indicating their significance as well as their locations across the state response network. Further analysis (SNA) demonstrated which particular elements of each of these structural holes are the most significant.

Detailed analysis is provided in Chapter V.