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 |
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 |