Organized Crime Research Highlights Number 7

Organized Crime Research Highlights Number 7 PDF Version (303 KB)

Table of contents

• Alliances and Conflicts in Street Gangs
• Solving Gang Homicides
• Counting Drug Users
• Regulating Illicit Markets
• Economic Cost of Organized Crime
• Reducing Offending Opportunities

Alliances and Conflicts in Street Gangs

Independent street gangs often self-identify based on symbolic associations. Street gangs tend to get along best with gangs of other ethnicities.

This study applies an analytical framework using social network analysis to review inter-gang relations in Montreal. The study used data drawn from the experiences of incarcerated juvenile gang members. The researchers describe the core features of Montreal gangs and examine the structure of social relations among these gangs.

Regarding youth street gangs, different allegiances are likely to lead to animosity and conflict to validate a gang's existence and solidify a group's cohesion. Conflicting and violent relationships have been seen at the core of gang history and remain part of the underlying culture of the gang phenomenon. Recent research has highlighted the ongoing, polarized conflict throughout North American street gang culture that often emerges between local gangs that are associated, or identify with the Los Angeles-based Bloods and the Crips street gangs.

Descormiers and Morselli identify three factors why street gang settings are more structurally imbalanced than is expected by popular opinion. First, street gangs are generally not highly organized. Second, gangs tend to lack cohesion, both between members of a gang and between gangs. Third, individual actions demonstrate the gang's general instability, with individual gang members often acting on their own initiative.

The study used focus group interviews with incarcerated gang members. These sessions had 20 male gang members between the ages of 14 and 18, who were detained under the Youth Criminal Justice Act of Canada in a low security youth detention centre. Four focus groups composed of four or more gang members of diverse gang affiliations were created. Each focus group went through two sessions that lasted about two hours.

The first session was in three stages. First, each participant completed a short questionnaire on himself and his gang. Second, each participant identified the geographical territory of the gang on a City of Montreal map. Choice was offered using the coalitions used on the street: blue (the Crips), red (the Bloods) or black (neither coalition). Third, a network matrix was created based on information provided by each participant. Participants were then asked to comment on whether there were either positive or negative interactions with another gang, which was qualified as to whether it was based on interpersonal or inter-gang conflict. The second session was used to validate the network matrix.

The 20 participants belonged to 15 distinct gangs that were active in Montreal. The focus group interviews identified 20 other gangs. The City of Montreal was divided into three sectors: the Downtown area, the East and West areas. Neither Blood- or Crip-identifying gangs were represented in the Downtown sector, but were represented in both the East and West sectors. The majority of gangs were affiliated either with the Bloods or the Crips.

Based on participants' responses to questions about allegiance-based conflicts, the Crips were the most targeted by other gangs. Most alliances were between gangs of the same coalition, and most conflicts were with gangs of the rival coalition. However, “alliances and conflicts do not always follow the orientation prescribed by the traditional Bloods versus Crips conception” (307). There were conflicts within coalitions due to personal motives of individuals. Violence was also used against members in an attempt to maintain the group reputation of a coalition. There are opportunistic alliances between enemies despite the polarized conception of coalitions of Bloods and Crips. The label of Blood or Crip were largely symbolic labels indicating a likely pattern of association between gangs and gang members, and were not a reflection of participation in a wider hierarchical gang structure or formal relationship. Interestingly, street gangs that are drawn from different ethnicities tended to get along better with each other than gangs of the same ethnic background. “The effort to coexist regardless of pre-established rivalries was emphasized throughout the focus group interviews” (310). In Montreal, stereotypical indicators of social cohesion, such as shared gang colours or ethnic make-up, cannot simply be used to predict the use of violence or the emergence of conflicts or alliances, amongst street gangs.

Solving Gang Homicides

Less evidence is available to solve gang-related homicides, so these cases demand additional police resources.

There are a number of factors that contribute to police successfully solving homicides, including: the amount of time required to investigate the crime; the presence of witnesses and their cooperation; the type of weapon used; and the relationship between offender and victim.

The rate at which crimes are solved is called “the clearance rate.” Researchers have argued that there might be differences in clearance rates because police value certain types of victims more than others, the situational factors surrounding a crime might differ, of the physical evidence left behind, differentials in police access to particular types of investigative resources, or due to investigative procedures, analytic techniques, police service demographics or political variables. What has been “a consistent finding in this body of research is that gang related homicides have a much lower clearance rate” than other homicides (3). Certain features of gang-related homicides have been used to explain this lower clearance rate, including: the higher likelihood of using a gun as a weapon; that the lethal attack often occurs at a greater distance; that the relationship between victim and offender is more impersonal; that witnesses of the crime and associates of the offender do not provide information to police; that motivations for homicide can be collective rather than individual; and that these barriers add up to less evidence being immediately available to solve cases, resulting in them taking longer to solve.

In 2003, the Royal Canadian Mounted Police (RCMP) and various municipal police services in British Columbia established the Integrated Homicide Investigative Team (IHIT). The group investigates homicides in the Lower Mainland of British Columbia. Their typical homicide clearance rate has been 20 to 25% lower than the Canadian national average of 57%. Since clearance rate is an important measure of performance, this study was launched to investigate the reason for the discrepancy.

The researchers had IHIT detectives randomly select half of 280 cases that IHIT had investigated between 2003 and 2010. These 140 cases were coded using 95 variables, including: “victim and suspect characteristics, such as time of day, location, and method of homicide; investigative characteristics, including time frames; investigative outcomes; and post-arrest judicial elements” (5). The anonymized data was analyzed by the researchers.

Of the 140 cases, half (n=71) of the cases were gang-related (as defined by nationally accepted definition used by the Canadian Centre for Justice Statistics on the Homicide Survey). Of these gang-related homicides, there were 49 suspects involved with 26 cleared homicides and 45 unsolved homicides. For the 69 non-gang-related homicides, there were 59 suspects for 55 cleared cases and 14 unsolved cases.

About 70% of the gang-related homicides had “firearm” as the cause of death, while 49% of non-gang-related homicide involved the “use of a firearm.” For gang-related homicides almost all victims were male (98%) compared to about half (54%) of non-gang-related homicides. Half of the victims of gang-related homicides were themselves gang members or affiliates of gangs, but this was the case in less than 1% of non-gang-related homicides. Suspects in gang-related homicides were more than twice as likely to have a criminal record for violence or drug charges, as well as being twice as likely to be a prolific offender. “Investigators were able to identify a chargeable suspect on the same day the homicide occurred just 12% of the time compared to 55% for non-gang-related cases” (11).

Another potential difference between gang and non-gang-related homicides are the nature and extent of police resources applied to the case and the degree to which particular resources contribute to solving a homicide. Hypothetically, since gang related homicides are considered more difficult to solve, these homicides would be more likely to involve the request for and the use of specific resources. In this study, five types of resources were analysed: wiretaps; surveillance; undercover operations; interview teams, and the use of police agents” (8). The researchers' analysis found that “there was clearly less readily available evidence in the case of cleared gang-related homicides” (8). These resources were identified by investigators as important to both gang- and non-gang-related homicides. They were also identified as being more important in unsolved gang-related homicides. Still, “the added resources were only applied in a fraction of the instances where they were deemed appropriate to apply, and the discrepancy [was] greater in gang-related cases than in non-gang related cases” (9).

IHIT's caseload is typically made up of 50% gang-related homicides, while outside of British Columbia 15% of homicides were gang-related during the study period. IHIT had nearly identical clearance rates for non gang-related homicides (78% versus 80%). The researchers note that “it should not be surprising that IHIT's overall gang related homicide clearance rate (38 per cent) is lower than the national average gang related homicide clearance rate (50 per cent): (10).

“There is simply less evidence and information in gang related homicide cases. This results in the need for additional resources to adequately investigate these cases” (11).

The authors argue that “it would appear that additional resources should be used when requested by investigators, especially in gang-related homicides” (12).

The researchers caution that findings of this study may not make direct comparisons with police services outside of the Lower Mainland of British Columbia appropriate, without first accounting for an “understanding of the nuances of homicide units in other jurisdictions and the factors that affect their operation” (13).

Counting Drug Users

Estimating the size of a hidden population from an anonymous snowball sample is possible. New York City has about 12,229 meth users.

Researching “hidden populations,” such as criminals, gang members or the illicit drug markets managed by organize crime groups usually relies on a single source or combination of sources of data, such as arrests or hospital admissions. However, it is mostly unclear how accurately these sources reflect the overall population. Therefore, the estimates of the “hidden population” are somewhat problematic. Researchers are becoming more creative with the application of indirect, network-based research methods when it comes to statistically studying “hidden populations.”

Two of the most popular methods previously used to estimate the size of the market for illicit substances are “Network Scale-Up Method” (NSUM) and “Capture-Recapture Method” using Respondent-Driven Sampling (RDS). NSUM assumes that the people's networks are representative of the general population in which they live. Once the network is identified and the activities inside the network are observed, inferences are then made about the trends in the general population. When it comes to populations that are engaged in illicit activities, however, the assumption about representativity of the general population becomes problematic because the people who are engaged in these activities are likely to keep their activities secret due to their illicit nature.

A “Capture-Recapture Method” is intended to estimate the size of an unknown population through successive samples of identical research subjects. A statistical formula, called “Lincoln-Peterson formula” is used to estimate the size of the population when a quorum of matching samples is achieved. When used on its own, the “Capture-Recapture Method” produces population estimates by observing the number of criminals who were caught for a certain type of crime, released, and then re-caught. The population of criminals is then inferred from the number of criminals who were re-caught. “Capture-Recapture Method” is often mixed with a Respondent-Driven Sampling (RDS) strategy, which is a novel strategy for the creation of a representative sample that begins with a convenience snowball sample. The main limitation of this method is that if the initial sampling procedure was incorrect (i.e., not reflecting the population under study), the subsequent samplings will be skewed thus affecting the overall estimates.

To overcome the issues associated with these two popular methods, the authors of this study developed a network-based RDS version of the “Capture-Recapture Method” to undertake a quantitative analysis of New York City's methamphetamine-using population. The main idea behind the method is to develop estimates of a hidden population from a small sample of methamphetamine users in New York city who are willing to share their “network” of other known users, while maintaining anonymity of both the individuals in the initial sample and their contacts.

The researchers gathered an initial sample of 37 methamphetamine users, or RDS “seeds,” through an ad on craigslist. The respondents to the ad were asked to identify meth users in their mobile phone directory; an additional 95 contacts of users were identified. Information was collected on approximate height, approximate weight, hair colour, eye colour, gender, and race/ethnicity. The researchers also recorded and coded the last three digits of respondents' and their contacts' phone numbers through a procedure they called “telefunken.” The coding involved the following: each of the three digits was coded as odd or even, and high if it was above 4.5 or low if it was below 4.5. For example, digits 123 were coded as odd-even-odd-low-low-low. Basic personal characteristics of respondents were also recorded and lumped together with the codified last three digits of phone numbers to create a unique code for each respondent. These unique codes were later used for matching procedures while maintaining respondents' anonymity.

To follow the “Capture-Recapture” principles when using the method to estimate the size of a population, the initial “seeds” were considered as the “capture” and the identified contacts as “recapture assay.” A number of “seeds” were discovered in the “recapture assay” through the matching procedure The researchers then used a statistical procedure to infer an overall estimate of methamphetamine users in New York City from the discoveries of “seeds” in the “recapture assay.” The estimated number of meth users ended up being 12,229, with a range 8,253 – 23,750. The authors further mention that even though measures were taken to correct for various issues associated with the statistical procedures used, such as the possibility of false matches and a relatively small sample size, the range of estimates could be as high as 30,756 and as low as 7,689.

The major contribution of this creative approach to estimating the size of hidden populations is perhaps not the estimate itself, which the authors admit could still use some statistical polishing. Rather, the contribution is in the idea that the data on which the estimates are based is not dependent on external sources such as number of arrests or hospital admissions. Furthermore, the anonymity of a sensitive population is maintained throughout the project. Overall, this promising method is capable of producing statistically-sound estimates of a hard-to-reach population engaged in illicit activities from a small sample size, all while maintaining anonymity. While such estimates could be created using other, more traditional methods like intelligence data, the method presented in this paper is an inexpensive and anonymous approach to what could otherwise be a lengthy and costly effort.

Regulating Illicit Markets

Decriminalizing and regulating illicit markets can reduce crime. However, the markets need to be carefully studied and understood before such attempts takes place.

The example of the Netherlands, which has a long history of regulating formerly illicit markets such as gambling, prostitution, and drugs, shows that decriminalization and regulation of such markets can contribute to a significant reduction of crime. Still, given the often inflexible nature of regulations, criminal elements and organized crime may be quick to re-enter the markets and look for business opportunities.

When decriminalizing illicit markets, a regulatory system would need to account for the negative side effects associated with decriminalization. While it might be simple in theory, it is very difficult to regulate formerly illicit markets since the markets cannot always be perfectly understood by the regulating body. Further, the regulatory system is not, and cannot be, perfectly designed to quickly respond to evolving consumer demands or the emergence of new products and innovative techniques which occurs when the structure of the market radically changes.

In any society that features consumer demand for products and services that are deemed illicit, governments and law enforcement authorities will encounter difficulties in dealing with these markets providing such products or services. Criminalizing such markets often leads to a presence of black markets. Further, by directing efforts at dealing with the criminalized activities and black markets, law enforcement authorities have fewer resources to direct towards other priorities. Decriminalizing certain activities would ultimately lead to reducing opportunities for criminals and freeing up resources for law enforcement.

There are several risks that accompany decriminalization or legalization of previously illicit activities. First, it may lead to an expansion of the market, as legalized products or services may attract new customers who previously did not use it due to the stigma, danger, restricted availability, or higher price caused by the illegality. Second, honest operators of the newly-legalized services could either be lacking the necessary experience or not wish to be associated with the previously-stigmatized service, thus leaving the window open for experienced criminals to operate the services. Finally, authorities are sometimes unable to support the balance of price and quality of the legalized products. Once again, experienced criminals could be competing with legitimate providers by offering cheaper or better quality products and services, which, in a way, defeats the purpose of the legalization.

To illustrate the impact of decriminalization of certain activities, the author discusses two examples from the Netherlands: the gambling industry and the soft drug market. The gambling industry in the Netherlands was illegal until 1948. It was slowly decriminalized over four decades. Studies show that the decriminalization of gambling activities, such as casino gaming, bingo, lotto and sports betting, reduced the harmful impact of the illicit markets, but at the same time left place for illicit operators to function. That, the author argues, could have been partially prevented if a system was in place to screen the applicants for operation licences. It took a lot of time and effort on behalf of law authorities to screen applicants, revoke licences, and even buy out gambling establishments.

Another unintended consequence of the legalization of gambling was a rise in gambling addiction, particularly among young males. The addiction was dealt with by removing electronic gambling machines from places accessible to youth, as well as restricting youth in gambling establishments.

The emergence of online casinos that are accessible to anyone anywhere where the Internet is available further complicated the regulations of the gambling market in the Netherlands. Since online casinos are almost always operated from countries or territories with a rather lenient gambling and financial regulation, they fall outside of the Dutch legal system. It is practically impossible for law authorities to deal with money laundering or other illicit activities that may be associated with online gambling. International efforts have thus far had minimal success, despite several strong international legislations being put in place.

Another example of the impact of decriminalization in the Netherlands is that of the “soft drug” market, such as marijuana and hashish. Two committees were commissioned to study the impact of drugs on society in the early 1970's. The first concluded that the use of marijuana is limited to sub-cultures and that its criminalization carries mostly moral rather than objective connotations. The committee further suggested that a policy of suppression would create a negative spiral effect on society. The second committee stated that it would be ineffective to punish drug users. What followed is a slow process of decriminalization for possession of marijuana and hashish, whereby the products would still be illegal but law enforcement authorities would not prosecute possession, use, or sale of small consumer-size amounts of these drugs. At the same time, no regulatory measures were taken when it came to the drug supply.

A rather large number of shops (1,500 by 1990) selling cannabis products mushroomed across the Netherlands in the ensuing decades. Analysis showed that just like with the gambling industry, criminals and organized crime elements were running a lot of illegal shops alongside the legal ones. By 1992, the Dutch government introduced a licencing scheme and the screening of applicants to remedy the situation.

To address the gap in regulation of the supply of cannabis, home growing of the drug was permitted starting in the mid-90's.

However, it did not take long before organized crime recognized the potential of growing and trafficking cannabis to other European nations; the abandonment of border control inside the Schengen zone only made the trafficking easier. Foreign drug tourism also began thriving, where tourists or drug dealers from outside of the Netherlands would drive to a Dutch border town, load their vehicle with cannabis, and drive back to their home country without any enforcement.

The options for the government to respond to these unintended consequences of decriminalizing soft drugs are as following: a) further decriminalization, which is unlikely due to political concerns; b) “sealing the border” and restricting the sale to Dutch residents only, which could further boost illicit markets; and c) increased law enforcement efforts, which, as past research shows, can have a limited success.

Decriminalization and regulation of illicit markets could be a potential response option for battling the criminals who profit from such markets. However, the markets need to be very well studied and understood prior to the design of the regulation policy; the policy needs to be carefully crafted to take into account the unintended consequences of decriminalization. The policy further needs to be flexible enough to quickly respond to the evolving nature of the products and the market's supply and demand.

The discussion in this paper highlights the importance of research that needs to take place when governments consider the option of regulating or decriminalizing certain products or activities. The existing or future market needs to be carefully studies in terms of constraints on supply and demand; the demographic portrait of both the potential suppliers and consumers, and their behaviour; price and purity of the products; and models of demand curve.

Economic Cost of Organized Crime

The presence of organized crime reduces economic growth and increases homicides.

The origins of mafia in southern Italian regions, such as Sicily and Camorra, can be traced back to the unification of Italy in 1861. The creation of mafia groups was a response to a societal demand for an informal and stable structure of governance. On the other hand, the regions of Apulia and Basilicata did not experience much organized crime activity until the early 1970's, when mafia elements began to take root. The most important explanations for the establishment of mafia in the early 1970's in Apulia and Basilicata are the lucrative business of tobacco smuggling, and the diversion of public procurement funds that were flowing to the regions following a destructive earthquake in 1980. Apulia and Basilicata were among the most economically affluent Italian regions prior to the early 1970's, but subsequently became the two regions with the lowest growth rate shortly afterwards.

The economic impact of crime on society has been studied for a few decades, but little attention has been paid to the study of the impact of organized crime specifically. The traditional methods of estimating the cost of crime, namely “Monetary Cost Accounting;” “Contingent Valuation Surveys;” and “Willingness-to-Pay Measures” have not addressed the issue of the economic cost of organized crime to society.

The author of this article uses a method called “Synthetic Control Method.”  The method compares the economic structure of regions that are less or not at all affected by organized crime to the regions under examination – Apulia and Basilicata – a few years prior to the introduction of organized crime. In other words, the “Synthetic Control Method” analyses variation in the regions under study over periods of time that have either been exposed to the introduction of organized crime elements, or have not been exposed to mafia at all. The outcome of the analysis is then compared to a weighted average of all other regions. In this study, the outcome results for Apulia and Basilicata are compared to the weighted average of all other Italian regions, except for Sicily, Campania, and Calabria. These three regions were omitted from the analysis because they experience a strong pervasiveness of mafia, as well as because the presence of mafia dates back to the unification of Italy, making pre-treatment observations impossible.

Results of the analysis indicate that compared to the average of regions included in the control group that did not experience much exposure to the mafia, the GDP of Apulia and Basilicata dropped by as much as 16% by the  mid-1970's, when the regions saw a fast and intense exposure to the mafia. Further, the homicide rate increased from zero to three homicides per 100,000 in 1975 and remained very high until 1982.

To demonstrate a causal link between the introduction of mafia in Apulia and Basilicata and the economic decline of the regions, the author notes that private investments in the regions remained stable until the mid-1970's and only declined two years after the beginning of violence in the region. This, the author argues, demonstrates that the economic outlook for the regions did not change before or at the point in time of arrival of the mafia in the region. In other words, the decline in GDP could not have been possible because of a change in the economic outlook for the two regions under study. Further regression analysis, where exogenous factors were controlled, demonstrates that the economic decline of the regions has likely occurred with the introduction and establishment of mafia in the regions.

The winding down of private investments in Apulia and Basilicata was replaced with a significant increase of public procurement investment, which presents new opportunities for organized crime. Mafia is well known to use intimidation, racketeering, and violence to secure access to public funds, which is then re-channelled to the pockets of corrupted politicians, law enforcement, and mafia bosses. Once again, the author attributes the increase in public funding to the presence and activities of the mafia in Apulia and Basilicata.

The present method is one of the first attempts to examine the economic impact of organized crime. It is limited in that it is not capable to examine the impact of organized crime on the economy in greater detail. Further, because of the complex nature of organized crime, not all the exogenous factors can be taken into account. Finally, the analysis was only able to estimate only one of several impacts of the mafia on the economy; other impacts, such as loss of productivity, as well as the social and psychological effects, are not accounted for. Nevertheless, this method is a promising approach to begin the examination of the economic impact of organized crime.

Reducing Offending Opportunities

Opportunity reduction strategies, involving multiple partners and publicized operations, can reduce the harm of organized crime.

The study aims to increase the understanding as to the efficacy of an opportunity reducing approach when attempting to prevent and disrupt organized crime in an operational setting. The study explored the actions of a police service situated in the North of England over a period of three years, establishing how an organized crime group (OCG) is selected and how interventions take place. It also assessed the impact of these interventions using both quantitative and qualitative methodologies.

The authors note that using a multi-agency approach to create a hostile environment for the OCG was a major change from traditional operating procedures. The importance of this change emerged that instead of operating in secrecy, the operation was publicized to the community, partner agencies, and the OCG themselves. Also, changing the purpose from prosecuting OCG members to reducing OCG harm, allowed the informing of others, pooling of information, and making sure that a much wider group contributed to solving the OCG problem.

Each police service in England and Wales identified and analyzed all OCGs within their jurisdiction. Once identified, national criteria were used to assess and rank the actual and potential threat that each OCG generated. The criteria1 included: a) the level of injury to victims or others caused by the OCG; b) the level of harm to the community; c) the level of impact to police/government reputation; d) the criminal capability or capacity of the OCG; e) the geographic extent or ability of the OCG to cross borders (local, regional, national or transnational); and f) the negative economic impact the OCG has on society. The assessment was based on police criminal intelligence from a myriad of sources.

The study uses both qualitative and quantitative data and focused on an OCG. The authors obtained their qualitative data by interviewing individuals involved in the operation, specifically seven police officers, three members of external public sector organizations, and five members of the local community. Quantitative data was obtained from national and local police systems that were also used to develop a profile for each OCG member. The data included the offending history both prior to, and two years after, the operation had begun (obtained April 2009 and April 2011).

The study used semi-structured interviews that provided flexibility to explore the complex and sensitive issues associated with the subject. These interviews took place in police and private premises based on the respondent's preference. A content analysis was conducted across the range of responses to identify key recurring themes.

The findings are divided into three sections. The first section explored the characteristics of the OCG selected. The second section described interventions made by the police and partner agencies, and classified them by using the preventative framework in Cornish and Clarke's (1986) account of 'rational choice theory,' which is designed to reduce the rewards, remove the excuses, and increase the effort and risk of committing crime. The final section explored the profile of the OCG two years after starting the operation and supported this with the views expressed by the respondents.

The study showed that community members felt intimidated and fearful of the OCG, which prevented them from reporting OCG-suspicious activity. When law enforcement agencies brought together their intelligence, a much clearer picture regarding OCG criminality emerged. This information was merged with reports from covert sources that showed that OCG had most of the characteristics identified within academic organized crime definitions (van der Heijden 1996), as well as the UK operational definition. The OCG appeared to diversify in different types of crime and presented itself as a close-knit group of entrepreneurs defined by the motivation of making enormous amounts of money through illegal means, such as extortion, organized fraud or illegal waste disposal.

The authors state that the personal behaviours of the OCG members were important as it made OCG members 'visible and vulnerable' to intervention on a range of issues, making a disruption strategy possible. The study found that personal characteristics of the OCG members could emerge in prior- or post-event offences. Such characteristics are also found in their peripheral behaviour coming from their daily lives, such as showing disrespect for the law (ignoring planning permission, committing driving offences), or disrespect for others (making threats, generating noise nuisance).  

All individuals interviewed during the operation felt that the OCG criminality had been reduced. The findings showed that the offending behaviour had not stopped, and started to include offences clearly related to organized crime. Police officers interviewed saw that the true scale of criminality became clear after the operation began due to the increased scrutiny and more effective approach that uncovered the evidence to support the prosecution of OCG activity. Prior to this operation, OCG members were prosecuted for peripheral offences when their behaviour was visible.

This style of policing was seen as a success, since the approach taken was popular with participants and observers. One of the unintended consequences of previous intelligence-led enforcement approaches has been to distance some police officers from the community and the reliance on covert processes of intelligence collection and analysis. The very public approach of this operation allowed the balance of power to be transferred from the OCG to the community. One of the unintended consequences of the public approach appeared to strengthen the police-community relationship, with more active citizens.2

The study highlighted that a large, highly trained, covert investigative team was not always needed to achieve a positive outcome in reducing the effect of organized crime. This project team approach, using both uniformed officers and detective staff, allowed for flexible and diverse disruptive interventions, which could serve as a strong deterrent on offender motivation.

There remain a number of critical questions with this approach. First, is whether this approach is transferable to other policing jurisdictions and whether sustainable? Although this approach used fewer policing resources per year, disruption tactics do rely on police officers and equipment being available over a period of time.3 The authors noted that these “elements appeared key to its success” (410). The authors stated that further empirical research is needed.

The study examined a policing operation that identified and intervened against an OCG by using a more innovative approach. The interventions aligned to disruption appeared to be effective in reducing the capacity and capability of the OCG using fewer police resources than traditional, covert, intelligence-led interventions. The authors concluded that this new approach depended on having effective police leadership to: convince operational staff as to the advantages of using this approach; ensure that tactics applied are lawful and proportional; and, generate strong external partnerships. The authors added that the approach may be less effective for transnational OCGs, which are dispersed over a larger geographic area.

Date modified:

2022-07-27