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Table 4 SNA studies of human and social capital within drug trafficking groups

From: Drug supply networks: a systematic review of the organizational structure of illicit drug trade

Sources from systematic review Main data source Market focus Focusa Group type Drug market Links Nodes Measures Findings
Bright et al. (2014b) Courts—2 related seed cases (prosecution evidence) Australia CS-GF Independent Meth Illicit co-activity 128 SC = degree & betweenness
HC = resources, tangible (drugs, money, premises, equipment, precursors) & intangible (information, skills, labor) for production & distribution
Dif. of means
 Welch test of equality of means, Temhanes post hot test find significant SC covariation with HC
 High SC and high resources both tangible & intangible or tangible
Bright et al. (2012), Bright et al. (2014b) Courts—11 related judge sentencing comments Australia CS-GF Independent Meth Illicit co-activity 35 SC = degree (both studies), betweenness, & closeness
HC both studies = 7 operational/manufacturing roles (manager, clan lab, resource provider, specialist skills, worker, corrupt official, wholesale dealer) for production & distribution
Size of largest component & disruption function metric used to assess simulation
Descriptive analysis
 Appears scale-free (few people with highly central positions)
 Several high SC people also had high HC; some variation (Bright et al. 2012)
Disruption analysis:
 4 disruption strategies (random, degree, weighted HC, combined degree and weighted HC
 Sig. scale-free/fits power law function
 Simulation degree targeting and mixed strategy performs best by shrinking largest component and maximum disruption function (Bright et al. 2014b)
Calderoni (2012) Courts—2 court orders (wiretaps/surveillance) Italy (activity extending to South America, Spain, & the Netherlands) CS-DC Mafia (2 groups) Cocaine Communication about illicit co-activity 39 & 48 (subnet. of 2 ± contacts, 5 ± communications) SC = degree, betweenness & density
HC = 6 roles in distribution chain (supplier, trafficker, buyer, courier, support & retailer) & operational status based on conversations (e.g., giving orders, express satisfaction, request information)
Descriptive analysis
 As a group traffickers are higher on all SC measures for both networks
 Operational status varied, one group medium highest on SC measures, other high status high on density and degree
 Avg. scores driven by key figure in each network
 Drug activity might be different structurally from other criminal enterprise of the groups
Calderoni (2014) Courts—2 to 3 judgments & invest. evidence Italy (activity extending to South America, Spain, & the Netherlands) CS-DC mafia (2 groups) Cocaine Communication about illicit co-activity 61 & 73 (subnet. of 2 ±) SC = degree, betweenness & clustering coefficient
HC = operational roles (trafficker, buyer, courier, & support reclassified to trafficker vs. non-trafficker) and status (communications analysis) and formal rank (boss, member, & non-member)
Descriptive analysis & dif. of means
 Traffickers, bosses, and medium status actor groups have higher betweenness, degree but lower clustering
 38–40% high status not members of mafia group, highest single boss in each group had highest betweenness followed by another boss and member (3 per group)
 Traffickers and bosses had highest average status
Duijn et al. (2014) Police—all intel. & co-arrests Netherlands Population Assortment Cannabis Illicit co-activity & co-arrests 793 (cannabis cultivation subset) SC = degree & betweenness
HC = 21 activities/roles crime scriptsb to identify targets; assess change in efficiency and density to assess impact of removals (production & distribution)
Disruption analysis change in efficiency and density
 5 disruption strategies (random, degree, betweenness, HC, & HC degree) and 3 recovery mechanisms (random, preference by distance, and preference by degree)
 Networks become more efficient after attacks & HC based attacks increase density (exposure)
 Some roles more visible and thus, more vulnerable (e.g., coordinator & international trade)
 Disruption is a long term effort to counter increased resilience
Framis (2014) Police—investigations Spain (1 group extends to Colombia, Brazil & Uruguay; 1 group extends to Mexico; 2 Spain only) CS-GF Independent (4 groups) Multiple Illicit co-activity & co-arrests 58, 69, 62 & 23 SC = degree & betweenness
HC = operational roles (wholesaler, importer, retailer, or money launderers) and status (leaders, coordinators, operational, or money movers)
Descriptive analysis
 Low levels of centralization
 Key individuals for each group were importers or connected to importers (3 groups), transporter and money launderer (1 group), had high betweenness and/or degree centrality
 Some variation by group
Malm and Bichler (2011) Police—all intel. & co-arrests Canada Population Assortment Multiple Illicit co-activity & co-arrests 1696 (main component) SC = degree, betweenness & clustering coefficient
HC = 7 operational roles (production, transport, courier, supply, retail, financial, parasite) & complex (multiple roles)
Descriptive analysis
 Complex positions (transport, & supply) and those in a financial role had highest degree and betweenness centrality with some evidence of scale-free properties (fit power law curve)
 Clustering highest among simple roles [production, parasites feeding of market (grow-op robberies), transport and retail] with evidence of small-world properties
Malm and Bichler (2013) Police—all intel. & co-arrests Canada Population Assortment Multiple Illicit co-activity & co-arrests 916 (102 launderers) HC = betweenness & eigenvector
SC = 4 financial roles (professional launderer, opportunistic launderer, self-launderer, no known money laundering)
Descriptive analysis & dif. of means test (ANOVA)
 Money launderers sig. have higher betweenness than non, but lower eigenvector centrality
 Self-launderers highest betweenness, followed by opportunistic
 Professionals tended not to be members of organized crime groups
 Self-launderers tended to also be involved in smuggling or supply
Morselli (2001) Autobiography—assorted other (surveillance) Arrested in Spain for Canadian market activity (extended to UK, USA, Holland, Pakistan, Philippines, Hong Kong, Thailand, Portugal, & Australia) Egonet-DC Independent Cocaine Illicit co-activity 58 SC = effect size, observed size, & network efficiency during different career phases
(no HC: 1 smuggler’s career examined)
Descriptive analysis
 Building career phase smuggler had low efficiency and effective size, but high observed size, at “attainment” efficiency peaks and observed size drops, the career demise is marked by highest effective and observed size, and decline in efficiency
 Evolving network, not hierarchical DTO
 Atainment phase consignments medium size, early and during downfall more variation in size
Morselli and Giguere (2006) Courts—electronic surveillance Canada
(extending to England, Spain, Italy, Brazil, Paraguay, & Colombia)
CS-DC N.A. Hashish & cocaine Communications about illicit co-activity 110 (82 traffickers) SC = degree centrality, seeds & directionality of contact
HC = 3 operational roles (trafficker, financial non-trafficker, other non-trafficker)
Descriptive analysis
 Traffickers more prevalent seeds but financial non-traffickers more important than traffickers
 Reciprocity of communication similar among groups, except financial non-traffickers had the greatest proportion of non-reciprocated and directed ties
 Some non-traffickers play critical roles in drug distribution- may hold the key to understanding the opportunity structure of a criminal enterprise
Tenti and Morselli (2014) Courts—1 court order (wiretaps & surveillance) Italy (extending to Colombia, Brazil, Spain, & Albania) CS-DC Mafia Multiple Communications about co-offending/illicit co-activity 242 (9 groups & unaffiliated people) SC = degree, betweenness, eigenvector & clustering
HC = 5 operational roles in distribution chain (supply, go-between, import-wholesale, retail activity)
Descriptive analysis & dif. of means (ANOVA)
 Operational role by ethnicity of group involved in distribution- variation found, not all groups in each niche of drug distribution have the same structure;
 At the individual level, people high in degree & betweenness were located in different levels of drug distribution and not concentrated in one group
 Lots of partnership agreements similar to resource-sharing model not hierarchy
 Chain-like structure, low density, with interacting clusters (subsets)
 Different levels of network resilience among groups
  1. CS-GF refers to a case study with a group focus
  2. aCS-DC refers to case study of a distribution chain
  3. bDuijn et al. (2014) used script analysis to generate activities specific to cannabis cultivation. Scripted roles include: financing, coordinator, arranging storage, drying toppings, adding weight to toppings, arranging location for plantation, fake owner of a company or house, arranging fake owners of property, protection of plantation, taking care of plants, growshop owner, international trade, selling to coffee shops, transporting product, controlling cutters, cutting toppings, disposing waste/leftovers, supply of growth necessities, manipulation of electricity supply, building a plantation, and supply cuttings