Qualitative research examining organized crime groups, with an emphasis on drug trafficking activity, finds varying group structure within a loosely connected trade network. While these assessments are useful, there is little consistency in how authors operationalize organizational forms (as noted by Dorn et al. 2005), in part because these analyses are unable to map the actual structure of the group. Deepening our understanding of how illicit drug markets operate is pivotal to designing effective policy and crime control strategies. If structure varies, perhaps by market niche, drug trafficked or group characteristics, then we must tailor crime control efforts so they target the vulnerabilities of specific types of operations. Structure matters.
To standardize our descriptions of criminal group structures and begin the process of verifying the suppositions generated by qualitative research, scholars are turning to social network analysis (SNA). Capitalizing on a suite of empirical tools—theory, metrics, and analytics—crime scientists use SNA to document the interdependence among actors involved in drug trafficking. Rather than describing a group in general terms using researcher generated typologies, SNA studies use common metrics to characterize group structure, pinpoint specific actors and groups that control key market activities, i.e., importing drugs, laundering proceeds, etc., and identify individuals positioned to reestablish trade activity when central figures are removed. Thus, SNA provides an opportunity to re-examine what we think we know about market structure from a fresh perspective.
Examining SNA research of drug trafficking organizations, this systematic review of 34 published studies, describing 55 trade networks, is the first to synthesize what we currently know about the structure of illicit drug trade. We begin with a brief overview of landmark qualitative research and describe how SNA can contribute to the study of the organizational structure of crime groups. Then, we outline our document search protocol, and detail our methods. The results are partitioned into two sections. First, we examine network structure and find evidence confirming the idea that groups and drug markets are loosely organized and that groups have identifiable central figures. Second, we consider the relative importance of social capital (e.g. network position) and human capital (e.g. access to resources), confirming a correlation between social and human capital and that network vulnerabilities, representing key persons, are identifiable. We conclude with a discussion of the implications these findings have for crime control policy and provide direction for future research to facilitate meta-analyses and improve cross-network comparison.
Structure of drug trafficking groups
Group structure
Contrary to media inspired conceptualizations of organized crime, qualitative research investigating the configuration of drug trafficking organizations finds varying group structures within a loosely connected trade network (for a review of some of this literature see Natarajan and Hough 2000). While a thorough review of the field is beyond the scope of the present study, a number of seminal research projects inform hypotheses about the structure of groups involved in illicit drug markets.
Adler (1985) showed early on that organized crime groups tend to operate similar to legitimate business. Using ethnographic methods, she revealed that drug trafficking operations are loosely structured, often involving informal agreements among market participants. Arguing that market structure is to some extent dependent upon the source of the drug handled; the specialized importation of foreign drugs requires fewer people and less formal structure than domestic drug production. Domestic drug production is also more likely to mimic a legitimate organization due to local competition.
Interviewing 40 incarcerated high-level cocaine and marijuana traffickers, Reuter and Haaga (1989) discovered that their networks typically take the form of small partnerships consisting of temporary and dynamic coalitions of dealers. Acknowledging methodological limitations associated with the sample, Reuter and Haaga make several key observations about the markers of ‘success’ in the industry that are of relevance to the present study. (1) There are few barriers to getting involved in the market; namely, access to capital, effort, luck, and use of violence are not required for success. (2) Traffickers are not limited to working regionally—the market is national. In the wholesale market, experience and the willingness to make and take opportunities limits involvement. (3) Large or long lasting networks exist, but they are not required for success in high-level drug operations.
To this point, Eck and Gersh (2000) examined 620 cases gathered from federal, state, and local drug investigations in the Washington-Baltimore High Drug Trafficking area (W/B HIDTA) from 1995 to 1997. The results show that 60.4% of cases involved individuals or actors conspiring with a loose-knit association. Further, of the 39.1% involved in some form of a criminal organization, most (66.7%) comprised groups of less than 21 people. After studying operations in greater detail (e.g., communications patterns, transactions, and security), the authors conclude that drug trafficking more closely resembled a cottage industry of small, somewhat temporary sets of people, and that there were few instances of large, hierarchically-organized distribution networks.
Qualitative studies of drug operations trafficking crack, cocaine, and heroin throughout the 1990s and 2000s found similar results. For instance, Dorn et al. (2005) reviewed upper-level drug trafficking literature, concluding that drug traffickers are diverse and driven by different motivations. These differences are reflected in group structure and vulnerability: business criminals motivated by profit are more likely to have a durable core with several connections to different groups and individuals than ideologically motivated offenders (Dorn et al. 2005). In his interviews with Colombian, drug cartel informants, Kenney (2007) shows that trafficking networks are flexible and react to opportunities and constraints by expanding and contracting in size and reach. Research by Spapens (2010, 2011) also supports these findings. He shows that drug market monopolies are rare and difficult to maintain. He highlights the differences between legitimate and criminal markets, focusing on the need for trust in illicit business.
This literature led to the following working hypothesis: while several structures exist, most operations are loosely connected networks that can quickly react to shifting market conditions. What is not clear from this body of work, however, is whether mapped networks exhibit loose connectivity and to what extent this structure pertains to specific, clearly defined groups of actors, and to what extent these patterns characterize general market structure. A key issue in understanding the form and function of a network is to establish membership boundaries, because including peripheral individuals who are not really part of the group can significantly alter how we describe the network. A dense, cohesive group with a single leader will appear to look like a loosely connected set of clusters if people linking groups together are also included. Thus, it is important to consider group structure (within a definable crime group) and market structure (connections between different groups in a distribution chain) independently—it is possible that within group structure can be hierarchical even when the market as a whole exhibits the properties of a loosely connected network. Moreover, with each author developing their own typology of group structure it is difficult to conduct the cross-study comparisons needed to establish general patterns. Standardized metrics are needed to describe the nature and distribution of organizational structures.
Role differentiation
The importance of role differentiation by activity (i.e., fetching precursor drugs versus cooking methamphetamine) or market niche (i.e., cross-border smuggling versus wholesale supplying) also emerges from the review of qualitative research. Variation in organizational structure means that disruption efforts will need to be tailored to the type of operation and the inherent resilience of the group structure. For instance, through extensive interviewing of 296 subjects involved in crack, cocaine, and heroin distributions, Johnson et al. (2000) found evidence of role differentiation in response to police attention: countermoves involved parsing drug market activities into specific tasks, (e.g., separating holders, transporters, deliverers, money counters, versus guards, etc.) in order to be flexible and resilient to crime suppression activities. Their research also uncovered that market niches, such as low-level distribution, dealing, and upper-level distribution, show variation in organizational structure. This suggests that market forces at each level of trade impose unique constraints upon individuals engaged in drug trafficking.
Even within money laundering, a function we generally recognize as a relatively specialized facet of drug trafficking, we find evidence of the varied, and thus flexible, nature of operations (Schneider 2010; Soudijn 2012). For example, studying 31 Dutch cases involving large-scale cocaine importation, Soudijn (2014) discovered that only half of the investigations (14 cases) involved people providing financial services typically associated with money laundering. Contrary to conventional wisdom, however, the study uncovered a wide range of financial activity and financial facilitators were not accountants or lawyers; rather, individuals were involved in either sending money between countries (e.g., smuggling cash and hawala banking) or they participated in activities to give money a legal appearance, such as investing in the legal economy. Though not commented on by Soudijn (2014), this suggests human capital—individual resources and skill sets—influences whether, and in what capacity, someone is involved in drug market activities. Human capital may also differentiate leaders and critical personnel from easy to replace subordinates.
More recently, Natarajan et al. (2015) examined 89 organizations uncovered through major investigations of the Drug Enforcement Administration (50 cases constituted the nation-wide sample) or prosecuted in New York City (39 cases) from 1997 to 2007 with the aim of testing a system of classifying groups along two dimensions—organizational structure and tasks. Most notably, they find that data source impacts structural variation. For instance, when using New York City data, 12.8% of groups have a corporate organizational style and 30.1% were communal businesses, whereas, federal cases tended to involve corporate (54%) or communal businesses (42%). Where corporate organizational style includes a formal hierarchy and division of labor and communal businesses are comprised of members linked by at least one common characteristic, i.e., religion, nationality, neighborhood, or race. Additionally, 41% of New York cases and 62% of federal cases concerned groups involved in multiple niches (e.g., smuggling, wholesale, and regional distribution). Again, having a flexible, informal structure and being involved in a range of activities speaks to the potential impact that the collective resources and individual human capital play in shaping operational structure.
These studies suggest that drug trafficking is comprised of entrepreneurs exploiting their social and human capital. Our second working hypothesis follows from this idea. The hypothesis states, varying structural properties emerge for different types of market involvement and that market leaders and critical personnel (central individuals) are those with the greatest human capital. Soudijn (2014) and Natarajan et al. (2015), however, raise the concern that what we think we know about organizational structure is to a large part, pre-determined by the focus of and resources deployed during investigations, as well as the prosecutorial discretion of attorneys at the local and federal levels. Thus, we may find that variation in the predominance of central individuals is contingent on the scope of the study and source of information.
Network analysis of trafficking networks
While SNA-oriented study of organized crime is relatively new, the material advantage of using network science to study criminal organizations was lauded over two decades ago [see for example Jackson et al. (1996) and Sparrow (1991)]. Because we are still in a relatively nascent stage of development, crime scientists are still working through SNA theory and metrics to identify the most appropriate mechanisms to test our ideas about the structure of crime groups. With this caveat in mind, two themes dominate our efforts to map the structure of illicit drug trafficking.
Criminal network structures
Crime scientists working with SNA have come to view criminal networks differently from other social networks because they operate in hostile environments. For instance, Morselli writes, “Criminal networks are not simply social networks operating in a criminal context. The covert settings that surround them call for specific interactions and relational features within and beyond the network (2009; 8).” With various agents of the criminal justice system working to constrain illicit trade, individuals profiting from criminal enterprise must work in secrecy, under a cloak of invisibility; whereas, legitimate trade activity may organize to maximize the efficiency of operations. This ongoing challenge shapes how the group, and overall market, operates. As stated in our first working hypothesis, qualitative investigations suggest that drug operations are primarily loosely connected networks capable of rapid change in response to shifting market conditions. While direct SNA metrics of these concepts do not exist, we can explore comparable concepts of network density (or sparseness) and centrality, and what this means for operational structure.
Figure 1 illustrates the difference between dense and sparse operations and introduces two types of central positioning—hubs and brokers [(see Borgatti and Everett 1992, 2006) or for a more information about network centrality and associated metrics visit https://en.wikipedia.org/wiki/Centrality or http://www.faculty.ucr.edu/~hanneman/nettext/C10_Centrality.html]. This begins our discussion of how operational structures may indicate a preference for efficiency or secrecy (security). Note that in this hypothetical example, the circles represent people involved in the manufacture and trafficking of methamphetamine and the arrowheads indicate the flow of communications through the network.
If we look at person 6, denoted by a grey circle, in Fig. 1a, we see their position in the network allows them to exchange information with most others in the network. This information exchange is efficient and may be quick as there are few intermediaries required to reach other group members. In this example, density is high, meaning that most people connect directly to each other. Higher network density positively effects the network’s efficiency, provided messages take direct paths through the network. Arguably, this structure may also increase trust among individuals in the network [see Coleman (1988) and Granovetter (1981) for more details on trust and network closure]. An additional benefit is that with the removal of any individual, the group would continue to function: it is highly resilient to attack because of its high level of interconnectivity. While more efficient and resilient to attack, the structure reduces operational security. This means the network is not “secure” against efforts by law enforcement to uncover information about operations. For example, if we arrest person 6, or anyone else for that matter, they have knowledge of all group members and could implicate everyone in an investigation. Compare this network structure to Fig. 1b; here, we see that if person 6 were to act as an informant, they could only implicate the person they receive information from, person 3, and the person they transmit information to, person 8. The network is relatively secure, because it is sparse and few connections exist among people in the group. The drawback is that rebuilding operations can be lengthy and difficult when crime control efforts remove a centrally placed individual.
Sparse, or loosely connected networks, typically include individuals centrally positioned as hubs and brokers. Individuals with a lot of direct connections (such as person 3 in Fig. 1b), relative to others in the network, are hubs. Theoretically, we consider hubs to have the greatest degree of influence in the network; they can directly share information with more people than anyone else can. Brokerage is a different idea about central positioning—brokerage positions enable someone to control the flow of information between any randomly selected pair of other actors in the network. Returning to Fig. 1a, since any effort to communicate with person 1 or 2 must go through person 3, person 3 is in a better position to broker information within the group. These structural positions offer a strategic advantage for crime control when networks are sparse: disruption efforts that aim to remove central actors, namely hubs and brokers, stand the greatest chance to disrupt network functions.
Social and human capital
Another social network argument is that individuals positioned with ties to unique clusters of people have greater social capital (Burt 1992, 1997). Bridging different groups of people has a strategic advantage; individuals become indispensable to the overall group because they alone “hold” the group together and they ensure that they are the first to hear new information as it passes through the network. When combined with human capital, that is having unique skills or access to resources, a well-equipped bridge has great potential to maximize their success. When applied to organized crime groups and drug markets, we may hypothesize that varying structural properties emerge for different types of market involvement. Owing to the idea that some activities are more important to operations (e.g., money laundering and smuggling) and that, market leaders and critical personnel (central individuals within a group or connecting different groups) are those with the greatest human capital.
As illustrated in Fig. 2a, individuals 3 and 6 have equivalent social capital. They each have efficient connections, meaning they established a single relation with each of three different clusters of people. Since the clusters do not have other connections joining them to the other groups of people, individuals 3 and 6 have unique positions. Sitting between several subgroups, they have the opportunity to reap the most benefit from the information they access from each cluster. This network position presents opportunities to use or act on information first and may serve to enhance the success of persons 3 and 6. In doing so, their actions may enhance the overall success of the entire network. Notably, if we factor for the ability to act on this information, meaning that we consider the individual attributes and resources of each person, we may discover that despite having similar social capital, person 3 (the meth cook as indicated in panel b), has greater human capital, and so, may be better able to use their social capital to their advantage. The argument being, couriers have a less specialized skillset making person 6 easy to replace, whereas with greater individual resources, the meth cook would be harder to substitute. In this scenario, positional advantage is not sufficient; it is only when the information benefit accrued from social position intersects with human capital that material advantages are likely realized.
Present study
Adapting the working hypotheses derived from qualitative research to fit within an SNA framework, we sought to answer two sets of questions.
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1.
Does the SNA literature identify specific network structures common to drug trafficking organizations that are consistent with the findings of qualitative research? If so, is there a difference between group structures and market structures? Moreover, given methodological shortcomings, what strategic implications can we derive from these findings to aid crime control efforts aimed at disrupting drug trade?
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What is the relative importance of social capital (position within the network) and human capital (access to unique resources and skills) in determining who are the critical actors or groups within an illicit drug market? By using such information, do crime control efforts gain an advantage in efforts to disrupt market activity? Do the methodological shortcomings associated with studying criminal networks influence these findings?