Abstract

Maritime crime refers to conduct which is perpetrated wholly or partly at sea and is prohibited under applicable national and international law. Unfortunately, it also represents an understudied sector of crime and security. Crimes including maritime piracy and armed robbery; illegal, unreported, and unregulated (IUU) fishing; terrorism; and illegal drug, arms, wildlife, and human trafficking occur across maritime zones and pose serious threats to international trade, national security, and maritime security. Notably, these crimes often do not occur in isolation. Previous research has documented the convergence of these crimes within the terrestrial space but is thus far lacking a similar analysis as it pertains to the maritime space, which accounts for one of the most commonly used trafficking modes. Therefore, using data from the Stable Seas database, we aim to understand how different types of maritime crimes geographically converge and what this intersection means for policy and practice. Specifically, data on maritime piracy and armed robbery; mixed migration; and maritime arms, cannabis, coca, opiate, synthetic drug, and wildlife trades are analyzed to (1) establish rankings of maritime crime importance; (2) develop typologies of maritime crime convergence; and (3) integrate predictor variables to understand why certain regions tend to experience more abundant crime than others. Given data availability and policy relevancy, analyses focused on coastal countries of Africa, Middle East, and the Indo-Pacific.

Keywords: Maritime crime, migration, piracy, narcotics, wildlife, arms

Citation: Sosnowski, M., Petrossian, G., Nunphong, T., and Piza, E. (2024). Crimes at sea: Exploring the nexus of maritime crimes across global EEZs. Marine Policy. https://doi.org/10.1016/j.marpol.2024.106161

Introduction

The term “maritime crime” refers to a variety of criminal behaviors committed at sea or in maritime zones, such as territorial seas, archipelagic waters, countries’ Exclusive Economic Zones (EEZs), and international waters (McLaughlin, 2017). These criminal activities include maritime piracy and armed robbery; illegal, unreported, and unregulated (IUU) fishing; maritime terrorism; illegal ocean dumping; and illicit trafficking, namely drug trafficking and human trafficking (Bellamy, 2020; Papastavridis, 2014) among others. It is also important to recognize that there is no fixed boundary between what happens at sea and on land, i.e. maritime crimes can 'start' at sea but 'continue' on land. For example, the cycle of illegal fishing from illegal catches at sea then continues to the landing of the illicitly caught fish to further selling them to market (McLaughlin, 2017). Previous research on maritime crime – or those illicit activities that are taking place at sea – has sought to identify the concentrations of crimes that occur in maritime landscapes across the globe using a range of quantitative and spatial techniques. The current study will build upon this existing work to understand the convergence of these crimes across the EEZs of coastal countries.

Maritime crimes, such as the aforementioned, are serious crimes, threatening the safety, security, and livelihoods of coastal nations, as well as those inland, given the impact of these crimes beyond that of coastal nations. These crimes heavily affect developing countries, destabilizing the already fragile state of the functioning of their economies and governments. Despite the importance of studying the convergence of these crimes, few studies have examined their prevalence within coastal countries’ territorial waters and the factors associated with them. Analyzing and studying where the criminal activities take place are essential and beneficial to national and international maritime security organizations (Arias & Pressey, 2016). There is need for more data-driven studies that can inform policy to combat these crimes. The overall goal of this study, therefore, is to understand how different types of maritime crimes overlap spatially (i.e., geographically) and what this overlap means for policy and practice for the countries in Africa, Middle East, and the Indo-Pacific (the selection of which was guided by data availability). Using data produced by Stable Seas on various types of maritime crimes as well as potential indicators of the risks of these crime occurring within the EEZs of 71 coastal countries, this research seeks to answer three distinct questions: (a) can maritime crimes be ranked in terms of their degree of importance?; (b) what typologies can be developed to better understand the geographic convergence of these crimes?; and (c) what factors are associated with the risk of these crimes occurring within the territorial waters of the coastal countries?

The Maritime Legal Framework: What Constitutes a Crime at Sea?

To understand the inclusion of maritime crimes for this study, it is critical to provide background information on the international maritime legal framework that guides what constitutes a crime at sea. The United Nations Office of Drugs and Crime (UNODC) Global Maritime Crime Programme denotes four primary reasons for establishing and applying law at sea. First, the idea of a lawless space, such as the maritime space, is against the interest of the states, therefore, parties must be held accountable for their conduct wherever that conduct takes place. Further, state interests often interact at sea, such as in cases where ships of different states collide or a national from one state harms a national from another state aboard a ship. Third, there must be a framework established for handling jurisdictional crossovers. Because the seas are divided into various zones in which they have different, and sometimes competing sets of rights, powers, and obligations, it is necessary to establish rules that clearly allocate and describe these rights. And, lastly, because the seas are used as a means of transport, and the exploitation of some common resources is considered to be a right available to all states, rules must be established to define the governance of shared interest and rights (United Nations 2020).

The extensive legal framework that has emerged in response to these four reasons regulates maritime conduct. This includes a range of treaties and customary international laws. The primary regulatory mechanism, which the UNODC has coined the “centerpiece” of this framework, is the United Nations Law of the Sea (UNCLOS) (United Nations Convention on the Law of the Sea 1982). UNCLOS addresses a range of topics, from establishing maritime zones to describing the rights and obligations that exist within these zones, and details provisions on piracy, boarding and pursuing vessels, managing the exploitation of resources outside of the management reach of individual coastal states, and sets up dispute resolution mechanisms.

Other international laws related to the sea include the Convention for the Suppression of Unlawful Acts at Sea (SUA), which is primarily concerned with dangers to ships and navigations (for example, by criminalizing the transport of terrorists and biological, chemical, and nuclear weapons); the Convention on the Conservation and Management of Highly Migratory Fish Stocks in the Western and Central Pacific Ocean (2000), the objective of which is to ensure the long-term conservation and sustainable use of highly migratory fish in the namesake regions; the United Nations Convention against Transnational Organized Crime, which applies to both land and sea and is supplemented by the Protocol against the Smuggling of Migrants by Land, Sea and Air; the Convention on the International Trade in Endangered Species of Wild Fauna and Flora of 1973 (CITES), which seeks to regulate certain species found at sea, but also, given that much trade relies on the maritime transport sector, applies to trade by sea; United Nations Convention against Illicit Traffic in Narcotic Drugs and Psychotropic Substances of 1988, which regulates the trafficking of drugs by sea; and the Programme of Action to Prevent, Combat, and Eradicate the Illicit Trade in Small Arms and Light Weapons in All Its Aspects (2001), which regulates arms trafficking; and a range of other bilateral and regional initiatives. While the UN’s Global Maritime Crime Programme exclusively discusses the relevance of these aforementioned treaties (listed with relevant targets in Table 1), other maritime treaties do exist. These include (but are not limited to) the International Maritime Organization’s (IMO) International Convention for the Prevention of Pollution from Ships (MARPOL), the IMO’s International Convention for the Safety of Life at Sea (SOLAS), the International Labor Organization’s (ILO) Maritime Labor Convention, and the Food and Agriculture Organization’s (FAO) Agreement on Port State Measures (PSMA), which targets IUU fishing.

Based on the identified legal frameworks, key maritime crimes were identified for inclusion in the study (see Table 1). For the purpose of this paper, and due to data availability constraints, only crimes deriving from the UN Global Maritime Crime Programme will be assessed. Certain of these frameworks, such as the Fish Stocks Agreement cover crimes included in other frameworks like the PSMA. The identified crimes include piracy and armed robbery; illegal, unreported, and unregulated (IUU) fishing; terrorism; and illegal drug, arms, wildlife, and human trafficking that occur across maritime zones. All these crimes pose serious threats to international trade, national security, and maritime security.

Table 1. Assessed International Legal Frameworks Pertaining to Global Maritime Crime and Relevant Targets

The Key Maritime Crimes

Piracy and Armed Robbery. Maritime piracy and armed robbery at sea are recognized as primary international concerns (Chalk & Hansen, 2012). Since the 1980s, a surge of piracy – specifically in the Straits of Malacca, off the coast of Somalia, the Gulf of Guinea, and the Sulu and Celeb Seas – raised international attention to piracy and armed robbery (Bueger & Edmunds, 2020). In 1992, the International Chamber of Commerce's (ICC) International Maritime Bureau Piracy Reporting Centre was established to provide an online piracy report and maps of piracy and armed robbery incidents (Joubert, 2020). Even though the problem of piracy and armed robbery incidents remains underreported, according to the IMB, the rate of such crimes have dramatically increased by more than 10 percent during the first nine months of 2020 due to the pressure from COVID-19, especially in the areas off the West African coast (The Maritime Executive, 2020; Chalk, 2008; Drew, 2020). The IMB has since reported a concerning increase in incidents of piracy and armed robbery against ships as of 2023 (International Maritime Bureau 2024; IMB Piracy Reporting Centre 2024)(International Maritime Bureau 2024; IMB Piracy Reporting Centre 2024). IMB further reports that violence against crews remains on the rise, and the Regional Cooperation Agreement on Combating Piracy and Armed Robbery against Ships in Asia (ReCAAP) cautioned ships to remain on guard at all times and increase their safety (The ICC, 2021; The Maritime Executive, 2021). Evidently, the fluctuation of piracy attacks might be influenced by a variety of factors ranging from corruption to poverty—which are the root causes of piracy—and still poses a major threat at the global level (Graf, 2019).

Illegal, Unreported, and Unregulated (IUU) Fishing. Another major maritime crime problem is illegal, unreported, and unregulated (IUU) fishing. IUU fishing refers to any fishing activities that are conducted by vessels from countries participating in fishery organizations in violation of national and international laws and regulations (Papastavridis, 2014; Sumaila et al., 2006; Petrossian, 2019). IUU fishing occurs across a variety of maritime zones—both in international waters and within national jurisdictions—contributing to widespread overfishing and posing great challenges to marine ecosystems and food security (Agnew et al., 2009; Mackay et al., 2020). According to the Food and Agriculture Organization (FAO) (as cited in Lindley et al., 2019), approximately twenty-six million tons of fish are illegally caught around the world, accounting for more than fifteen percent of global fishing. Overall, illegal catches contribute to a global economic loss of around twenty-six to fifty billion dollars (Orlowski, 2020). Illegal fishing poses a significant threat as it accounts for about twenty percent of the world's reported catch, and nearly thirty percent of all illegal caught fish come from overfishing (Agnew et al., 2009; Arias & Pressey, 2016). Overfishing also threatens the ecosystem and endangered species of fish, as well as the socio-economic stability of fishing communities worldwide (Arias & Pressey, 2016; Sumaila et al., 2020).

Trafficking in Drugs, Humans, and Wildlife. Piracy and illegal fishing are associated with other types of criminal activities, such as drug, human, and wildlife trafficking. Global drug trafficking is valued at roughly $400 billion per year. The most trafficked drugs include cocaine, opium, and cannabis (Fritch, 2009; Papastavridis, 2014; (“World Drug Report” 2023). Known drug trafficking hotspots—where fishing vessel involvement has been routinely reported—exist in the Caribbean, North Atlantic, and Southeast Asia (Belhabib et al., 2020). The fishing industry also intersects with human trafficking, which constitutes the third most profitable transnational organized crime (Stanslas, 2010). Human trafficking victims are typically immigrants from poor countries seeking employment and a better life, and that makes them easy prey for the criminal element of the fishing industry (Mileski et al., 2020; Stanslas, 2010). Aside from undocumented workers, people seeking asylum may become victims of human smuggling as they aim to avoid persecution in their countries of origin (Ventrella, 2015).

Another profitable transnational crime is wildlife trafficking, which includes the trade in animals, animal products, and plants that are illegally acquired and sold to global markets, particularly in Asia and Southeast Asia (Petrossian, Pires, and van Uhm 2016; Sosnowski and Moreto 2021)(Petrossian, Pires, and van Uhm 2016; Sosnowski and Moreto 2021). Wildlife trafficking is one of the foremost illicit trades (Joubert, 2021). According to the United Nations Office on Drugs and Crime’s World Wildlife Seizures (World WISE) database, more than 180,000 wildlife and wildlife products were seized globally in 2020, and illegal wildlife trade is worth between $7 billion to $23 billion a year (Joubert, 2021; Wrigley & Duthie, 2020), excluding fish and timber. One of the key factors that contributes to the rise in illicit wildlife trade is corruption, both individual and structural (Zavagli, 2021; Wyatt et al., 2017).

Crime Convergence

A key component involves the exploration of the geographic convergence of these key crimes in the maritime space. Geographic convergence is defined as the occurrence of two or more organized crime activities commonly taking place in the same physical location (Anagnostou and Doberstein 2022). It should be noted that geographic convergence is one form of convergence, while many others exist, as defined and classified by various scholars. Illegal activities at sea are complicated and generally interrelated, hence posing great challenges to maritime security, the global economy, the environment, and human rights. Statistically, two thirds of the criminal networks engaged in narcotics trading were also involved in wildlife trading (Feltham, 2021). Recent research has demonstrated the complexity of the linkage between drug trafficking and wildlife trades (UNODC 2024; Wildlife Justice Commission 2023)(UNODC 2024; Wildlife Justice Commission 2023). The patterns of smuggling commodities have been classified, in one example, as camouflage convergence (legal wildlife is used to conceal illegal drugs in shared shipments), combined contraband (illegal wildlife and illegal drugs in shared shipments), and multiple trade lines (illegal wildlife and illegal drug businesses and routes are controlled by the same network) (van Uhm et al., 2021). Research by Earth League International and the John Jay College of Criminal Justice (Earth League International and John Jay College of Criminal Justice 2023) also created typologies based on field investigations revealing wildlife crime convergence with other crime types. Additionally, wildlife commodities that are typically smuggled alongside illicit drugs and humans include tiger parts, ivory, live reptiles, and rhino horns (Feltham, 2021).

The wildlife trade is also associated with other types of crime such as fraud, money laundering, corruption, and other enabling crimes. According to Feltham (2021), other types of crime that most commonly converge with illegal wildlife trading incidents from 2004 to 2019 were corruption (53%), illicit drugs (14%), fraud (13%), firearms (9%), money laundering (6%), and other (5%). The advancement of technology and international trade influence organized crime groups to establish more sophisticated modus operandi of crime convergence (INTERPOL, 2015). However, organized crime groups, particularly those involved in wildlife trafficking, may not have full control over the operations. According to Moreto and van Uhm (2021), depending on the country, organized crime groups play various roles when engaging in the wildlife trade. For example, in Uganda, organized criminal networks play an important role in facilitating transportation to countries of destination while, in China, organized crime groups engage more directly in outsourcing of illegal hunting, transporting products into China, and protecting wildlife trades.

Furthermore, illegal fishing has been associated with various forms of human rights abuses that take place aboard these vessels. Many workers on these fishing vessels are commonly forced laborers who are recruited from developing countries and are inhumanely treated, often being subjected to physical and verbal abuse (Petrossian, 2019). These fishing vessels are also being covertly used for human smuggling, drug trafficking, and wildlife trafficking (Galani, 2020; Mackay et al., 2020), complicating crime fighting efforts and security measures in many nations.

Empirical Literature on the Spatial Patterns of Maritime Crimes

Given the spatial focus of this research, this section reviews the existing empirical literature on the spatial patterns of various maritime crimes with the purpose of identifying what is known about these patterns, what gaps remain, and how the current proposed research will fill these gaps. There is a large volume of published literature using quantitative methods to analyze the spatial patterns of various maritime crimes and their hotspots, some of which has provided select insights into their overlap.

Combatting maritime piracy has proved challenging for maritime security organizations for as long as ships have gone to sea (Nincic, 2009). Marchione and Johnson (2013) conducted a study examining the spatio-temporal patterns of maritime piracy using time series analysis and methods. They found that the Gulf of Aden, the Gulf of Guinea, and the Bay of Bengal were particularly affected by maritime piracy (Marchione & Johnson, 2013). In a similar vein, Nnadi et al. (2016) examined maritime piracy and armed robbery in the Gulf of Guinea maritime domain. Their research sought to determine what countries among the fifteen Gulf of Guinea countries including nine coastal zones of in the six coastal states of Lagos, Rivers, Bayelsa, Ondo, Akwa-Ibom, and Cross River in Nigeria had the highest number of piracy and armed robbery attacks. The results showed that the highest number of attacks in the Gulf of Guinea region occurred in Nigeria, which Marchione and Johnson (2013) point out as a piracy attack hotspot, with a total of 204 attacks over the past 14 years. Additionally, according to the ICC IMB (2021), the Gulf of Guinea remains the world’s prime piracy hotspot nowadays.

The relationship between piracy and other types of maritime crimes – such as IUU fishing – has also been investigated. Denton and Harris (2019) used a generalized adaptive model to test whether reported and unreported fish catch correlated with the number of pirate attacks focusing on eighteen countries in the Gulf of Guinea. The findings of this study suggest that IUU fishing was associated with piracy; an increase in reported IUU fishing contributes to the rise in piracy. These results were similar to those of Desai and Shambaugh (2021), who conducted a spatial analysis to determine the association between frequency of piracy attacks and particular locations that also had a higher risk for illegal fishing. The researchers found that piracy attacks in certain areas are linked to the high volume of bycatch and IUU fishing activity in international waters and EEZs (Desai & Shambaugh, 2021).

In addition to the existing literature on maritime piracy, there is a large volume of published studies analyzing the extent of IUU fishing. Sumaila et al. (2006) gathered data on IUU fishing incidents around the globe observing that IUU fishing activities are geographically widespread; however, most reported IUU fishing were in EEZs of the countries where the laws and regulations were exceedingly violated (Sumaila et al., 2006). A more recent study highlights that in areas near known ports of convenience, the presence of high-value fish, as well as, known illegally caught species, have the highest concentration of IUU fishing activities (Petrossian, 2018). Previous research has, as well, emphasized a positive relationship between illegal fishing activities and the number of commercial fish species, lack of monitoring, control, and surveillance, as well as access to known ports of convenience (Petrossian, 2015).

Additionally, regions with high occurrence of IUU fishing are frequently associated with transshipment (Kroodsma et al., 2017). Boerder et al. (2018) explored the global hotspots of transshipment at sea (i.e., where a fishing vessel typically offloads its catch to a refrigerated cargo ship in order to stay out at sea for longer periods of time), one of the utmost challenges in global fisheries. The researchers collected data from the Automatic Identification System (AIS), a vessel tracking and anti-collision system, to analyze spatial patterns of transshipments and vessel types worldwide. Evidence from this study suggested that sixty-five percent of all transshipments occurred within EEZs off coasts of Russia and West Africa (Boerder et al., 2018) which was consistent with previous research findings into global patterns of transshipment behavior (Miller et al. 2018). The researchers used AIS data to detect where fishing vessels encountered transshipment vessels and to determine where loitering events of transshipment vessels occurred. The study showed that the Russian Far East and the Barents Sea, located outside the EEZs of South America, within the EEZs of African nations, and across the Equatorial Pacific, had the highest event densities (Miller et al., 2018). Evidently, areas within EEZs where majority of illegal fishing took place recorded a high volume of transshipments.

Research Produced by the Stable Seas

Establishing maritime security measures is crucial in the prevention of criminal activities at sea. A considerable amount of literature has been published by Stable Seas on the topic. These studies have focused on nine maritime security indices, namely: (1) international cooperation, (2) rule of law, (3) maritime enforcement, (4) coastal welfare, (5) blue economy, (6) fisheries, (7) piracy and armed robbery, (8) illicit trade, and (9) maritime mixed migration (“The Index” n.d.). Amongst their studies is an examination of maritime governance which analyzed the relationship between rule of law and coastal welfare and illicit maritime activities in the Sulu and Celebes Seas (between the Philippines and Indonesia) (Amling et al., 2019). The researchers examined various types of crimes, such as piracy and armed robbery, IUU fishing, human trafficking, and wildlife trafficking, as well as maritime governance themes. Their results indicated that piracy and armed robbery, as well as other illicit activities, remained concentrated in the Sulu and Celebes Seas as opposed to other countries in Southeast Asia, where piracy and armed robbery were generally on a decline. The findings also showed that strong international cooperation between governments can improve maritime security and prevent illicit activities at sea (Amling et al., 2019). A similar method was used to study crimes in Somali waters. The results indicated that poor governance and weak economic conditions in the Somali region enabled it to become a hotspot for illicit transnational crimes ranging from human traffickers to pirates (Bell et al., 2019). Okafor-Yarwood et al. (2020) also explore the relationship between maritime governance themes and security challenges in the Gulf of Guinea region. The findings show that countries in the Gulf of Guinea region have developed in terms of maritime security. However, underdeveloped coastal welfare in littoral states, corruption, and onshore security threats still undermine maritime stability.

A series of studies on nine maritime security issues in several areas indicates that the global ocean still faces significant challenges regarding maritime security. The study found that in the Western Indian Ocean region, maritime security indices are interrelated. For instance, dwindling fisheries and coastal welfare indices can help strengthen criminal networks operating in coastal areas (Stable Seas, 2020). The Indian Ocean region has also been examined across nine issue areas (i.e., maritime security index). The study suggests that one of nine maritime security indices generally has an impact across all other indicators. The impact generally presents diverse challenges to the Indian Ocean Rim Association (IORA) to combat crime overseas. For instance, weakening rule of law and coastal welfare enable a wide range of illicit maritime activities in the Indian Ocean (Stable Seas, 2021). Similar results were found in the study of addressing maritime security in Southeast Asia. The Association of Southeast Asian Nation (ASEAN) indicates that they face various challenges tackling maritime crimes. Corruption and poor judicial integrity invalidate the rule of law, hence affecting all others maritime security indices (Stable Seas, 2020a). Additionally, in West and Central Africa, the report found that rule of law (i.e., the Yaoundé Code) is a key to securing the maritime space as the Yaoundé Code provides a useful framework to coordinate on maritime security issues (Stable Seas, 2020b).

Although extensive research has been conducted on various maritime crime hotspots and, the correlation between maritime security indices in particular areas, no single study exists which adequately addresses the convergence of these various crime in the maritime domain. Further study of the nexus of maritime crimes aims to fill those gaps by exploring the patterns of such convergence and the concentration of maritime crimes across EEZs globally. Furthermore, supposing that the concentration is identified, the study will ultimately lead to a better understanding of why there is a concentration in some areas.

Methods

Data

Stable Seas is a transnational non-profit initiative for actionable maritime security and governance research. The goal of Stable Seas is to facilitate research to assist efforts to counter the dynamics of threats to peace at sea, namely by producing and analyzing The Stable Seas Maritime Security Index. This Index is an effort to “measure and map nine issues central to achieving sustainable maritime security and maritime governance” (Bell and Glaser 2020). These nine issues include: (1) international cooperation; (2) rule of law; (3) maritime enforcement; (4) coastal welfare; (5) blue economy; (6) fisheries; (7) piracy and armed robbery at sea; (8) illicit trades; and (9) maritime mixed migration. Each of these issues has an “issue-area score” which is calculated from a number of relevant “indicators” which are further comprised of “components” and “subcomponents”. The calculation of each score is detailed in the Index Codebook1. Scores are developed from a mix of original research, information from stakeholders, and secondary sources combining qualitative and quantitative data.

Data was extracted from the online platform for 15 variables, ranging from “issue-area scores” through “subcomponents”, due to their individual relevancy to the current study. These included eight (8) crimes and seven (7) predictors. Stable Seas included: (1) piracy and armed robbery; (2) maritime arms trade; (3) maritime cannabis trade; (4) maritime coca trade; (5) maritime opiates trade; (6) maritime synthetic drugs trade; (7) maritime wildlife products trade; and (8) maritime mixed migration. The predictor variables were both macro- and micro-level. Macro-level variables included broader country-level indicators, namely international cooperation, rule of law, and fisheries management. Micro-level variables were specific/situational to the country’s EEZ or ports, and included coastal welfare, maritime enforcement, and port infrastructure indicators. Table 2 contains all variables extracted from Stable Seas for this study. Variables were made available for 71 coastal countries listed in Appendix 1. These countries fell within Africa, the Middle East, and the Indo-Pacific as the maps found within the analysis will illuminate.

Table 2. Variables Extracted from the Stable Seas Maritime Security Index

Prior to analysis, outcome/dependent variables were dichotomized for the purpose of the conjunctive analysis and subsequent logistic regression analysis. These included: Piracy and Armed Robbery at Sea Score, Maritime Arms Trade Score, Maritime Wildlife Trade Score, and Maritime Mixed Migration Score. An Illicit Narcotics Trade Score was also created and dichotomized into 0 and 1 from a cumulative of cannabis, coca, opiates, and synthetics scores that were individually coded into 0 and 1. The original variables scoring 0-4 were coded as seen in the table below (Table 3). Piracy and Armed Robbery at Sea Score and Maritime Mixed Migration Score were recoded slightly differently because these variables were scored 0-100. The break between “high” and “low” for these two variables was developed from the creation of “natural breaks” in the distribution. For Piracy and Armed Robbery at Sea, the base of the third natural break was at 38.24, so any score between 0 to 38.24 was recoded to 1 (high risk) and all other values were recoded to 0 (low risk). As for Maritime Mixed Migration, 54.82 was the base of the third natural break, so any score between 0 to 54.822743 were recoded to 1 (high risk) and all other values were recoded to 0 (low risk). 

Table 3. Original and Dichotomized Coding

Analytical Strategy

Several analytical methods were used to examine the data gathered for this research. First, to visualize the distribution of the different maritime crimes analyzed in this study, we conducted a multivariate cluster analysis in the ArcGIS Pro software (Figures 1 & 2). The software’s multivariate clustering tool uses a k-mean clustering algorithm to classify units into aggregate
“clusters” in which all included units are more similar to each other than units assigned to alternate clusters (Piza and Baughman 2021). In the current study, units (i.e. countries) are classified according to their observed levels across 9 types of maritime crimes: piracy and armed robbery; arms trade; cannabis trade; cocaine trade; opiate trade; synthetic drug trade; wildlife trade; and mixed migration. This allowed us to determine (a) the geographic distribution of these crimes across the 71 coastal countries, and (b) to what extent these countries show overlap in the maritime crimes occurring in their waters.

Second, this study includes a conjunctive analysis of case configuration (CCCA), which is an analytical technique that can be used, broadly speaking, for three goals: (a) to establish all possible combinations of predictor variables if used to make predictions; (b) to determine how cases are distributed among the combinations of attributes; and (c) to determine the “relative distribution of particular categories of the outcome variable across these configurations” (Miethe et al, 2008, p. 229). For the purposes of this research, CCCA was used for the third goal, i.e., to determine how the outcome variables cluster together and compare all observed combinations of configurations across a “truth-table” or data matrix. This configuration matrix also allowed us to determine the dominant case configurations, which are those most commonly shared by a set of cases in the sample. Additionally, this analytical approach also allowed us to determine the potential for spatial overlap of high-risk EEZs across various types of maritime crimes (Connealy & Piza, 2019).

Lastly, logistic regression models were built to examine the risk indicators that best predict why the coastal countries experience the various types of maritime crimes (including maritime piracy, wildlife trafficking, maritime mixed migration, drug trafficking, and arms trafficking).

Results

Descriptive Results

Dependent (Outcome) Variables. A total of nine outcome variables have been examined in this research (Table 4). Piracy and Armed Robbery (Par_Final) was measured on a scale of 0-100, where a score of “0” indicating that the states had at least 25 such incidents reported and 100 that had no piracy events reported within 1,000 kilometers of their EEZ boundary. Therefore, a higher score on this variable indicated a better performance. With a mean of 75.55 (SD 30.64) it is clear that the states examined in this research were not, overall, affected by the piracy and armed robbery incidents.

Similarly, the Maritime Mixed Migration (MMM_Final) variable was measured on a scale of 0-100, where higher scores on the scale represented stronger legal protections and mechanisms in place to deal with maritime migration. In other words, higher scores represented better performance. A mean of 60 (SD 13.66), therefore, indicates that overall, countries performed slightly above average when handling maritime migration.

The Maritime Arms Trade (IT_Arms_M) variable was based on the ranking of the countries by expert evaluators on the prevalence of illegal transfer of weapons and ammunition across country borders. The variable was coded from 0-4, with the scores ranging from “no credible evidence” to “nearly universally recognized evidence” of the prevalence of the trade in the country. Therefore, higher scores on this variable indicated severity of the trade. With a mean of 1.03 (SD 1.09), the maritime arms trade seems to be of low incidence.

All the outcome variables related to Maritime Drug Trade, including Maritime Canabis Trade (IT_Can_M); Maritime Coca Trade (IT_Coc_M); Maritime Opiates Trade (IT_Opi_M); and Maritime Synthetic Drugs Trade (IT_Syn_M), were coded similar to the Maritime Arms Trade varianble, with the codes ranging between 0-4 that indicate the severity/prevalence of the trade related to a given country. The mean scores for these variables ranged between 1.18 (IT_Syn_M) and 1.61 (IT_Coc_M), indicating relatively low prevalence of these trades withing the 71 countries examined. The IT_Drug variable’s score was derived by using the combination of all the other drug trade related measures, with a score of “0” indicating none of these trades occurred, and “1” indicating that the countries had at least one of these illicit drug trade-related issues. With a mean score of 0.82 (SD 0.39), it is evident that all the countries examined had at least one illicit drug-trade related problem.

Independent (Predictor) Variables. A total of six predictor variables will be used in subsequent logistic regression analyses to explain the variations in the prevalence of the different types of maritime crimes in the 71 countries examined. These include International Cooperation Score (IC_Final); Rule of Law (RL_Final); Maritime Enforcement (ME_Final); Coastal Welfare (CW_Final), and Port Services and Quality Indicator (Be_Trans_2). All these variables were measured on a scale of 0-100, with the higher score indicating better performance. On all these indicators, the countries performed above average, with some performance scores being significantly stronger than others. Countries overall were ranked relatively highly on their performance on BE_Trans_2, with an average score of 88.13 (SD 14.80). The countries also ranked relatively high on the IC_Final score scoring, on average, 76.82 (SD 18.70) on this indicator. Lastly, the Fisheries Score (FI_Final) variable, which is measured in terms of various indicators related to fisheries governance, was measured on a scale of 0-100, with the higher score indicating stronger governance mechanisms in place. On average, countries scored 55.83 (SD 14.24).

Table 4. Descriptive Results for Dependent and Independent Variables

Multivariate Clustering of Maritime Crimes

Figure 1 shows the clustering of the maritime crimes in terms of the commonalities the countries share when all maritime crime types are considered. Figure 2 (boxplot) provides additional information in terms of the high-low values pertaining these concentrations. The countries in the striped, green cluster, which include some of the countries in the Western African coastline, the Middle East, some minor islands in the Southern and Indian Oceans, and Australia, exhibit relatively low concentrations of the illegal maritime trade in arms, drugs, and wildlife, however they have higher concentrations of maritime mixed migration and piracy. Cluster 2 (indicated in orange), which includes countries in West and Southeast Africa, Asia, and Oceania, demonstrated high concentrations in illegal maritime trade in wildlife, drugs, and arms, but lower scores on maritime mixed migration and piracy. Appendix A contains a full list of countries that are associated with each cluster.

Figure 1. Maritime Crime Multivariate Clustering at EEZ Zones

Figure 2. Boxplot of Maritime Crime Multivariate Clustering

Conjunctive Analysis of Case Configurations

The conjunctive analysis was performed for the purpose of identifying not only the relative importance of the different types of maritime crimes in terms of their prevalence, but also identifying the combinations of the types of maritime crimes most prevalent within the 71 countries examined – i.e., to establish any typologies for classification purposes. The results indicate that in 20.83% of the cases, countries dealt with the problem of maritime narcotics trade, making this the most prevalent maritime crime. About 11% of the countries have a combination of maritime narcotics trade and maritime mixed migration issues to deal with. A total of 36% of the countries seem to have a problem of 3 or 4 combinations of various maritime crimes, with an additional 2.78% of the countries having to deal with all five maritime crime problems. Only 9.72% of the countries were determined as having no significant problem related to maritime crimes (Table 5).

We found that narcotics trade was the most prevalent maritime crime problem in the analyzed dataset (with a sum score of 12), followed by maritime trade in wildlife products (with a total score of 10), followed by maritime arms trade and maritime mixed migration, which shared similar weights in terms of their prevalence/importance (i.e., sum scores of “7”). Maritime piracy overall emerged as the maritime crime of lesser concern in light of the other maritime crimes examined.2

Table 5. Conjunctive Analysis of Case Configurations Data Matrix

The conjunctive analysis also allowed for the development of geographic convergence typologies based off the diversity of crimes reported to take place in each EEZ (see column “Type” in Table 5). Given that five maritime crime types were analyzed, there are six corresponding types – scored zero through five. Countries with a score of zero (9.72%) had no crimes reported in their EEZs. Of the 71 countries, 23.61% had a score of one, reporting one crime type; this only occurred for narcotics trade and mixed migration. 22.22% were scored three, presenting three crime types; 13.88% scored four; and 2.78% scored a five, presenting all five crime types in their EEZ. These typologies are mapped in Figure 3.

Figure 3. Mapped Maritime Crime Typologies

Logistic Regressions

Table 6. Logistic Regression Results: Piracy and Armed Robbery at Sea

Table 6 shows the results of the logistic regression model examining maritime piracy and armed robbery as the outcome variables. The main covariates in the model include international cooperation, rule of law, maritime enforcement, coastal welfare, port services and quality, and fisheries score. Given the predictor variables included various measures, these were standardized by using their z-scores in order to make the variable interpretations easily comparable. The spatial lag variable was used as a control variable, as pre-analysis diagnostics indicated statistically significant autocorrelation between the piracy incidents within the study area (Moran’s I Index = .64, z = 11.85, p<.001). Overall, based on the Pseudo R² value, which measures effect size, the model can be classified as ‘strong’. However, when controlling for all variables in the model, maritime enforcement and coastal welfare emerged as the two variables that statistically significantly contributing to the model. One standard deviation increase in the maritime enforcement score decreased the log odds of maritime piracy and armed robbery by about 29 times, when controlling for the effect of all the other variables in the model8. One standard deviation increase in coastal welfare score was associated with an 87% decrease in the log odds of maritime piracy, when controlling for all other variables in the model.

Table 7. Logistic Regression Results: Arms Trade – Maritime

Table 7 shows the results of the logistic regression model examining maritime arms trade as the outcome variable. Like the previous model, the main covariates in the model include international cooperation, rule of law, maritime enforcement, coastal welfare, port services and quality indicators, and fisheries score, all of which were standardizes for the ease of interpretation. The spatial lag variable was used as a control variable, as pre-analysis diagnostics indicated statistically significant autocorrelation between the maritime arms trade incidents within the 71 countries (Moran’s I Index = .13, z = 2.50, p<.01). The overall model, based on the Pseudo R² value, can be classified as “moderately strong”. When controlling for all the variables in the model, coastal welfare emerged as the variables that statistically significantly contributed to the model. One standard deviation increase in coastal welfare score was associated with a 74% decrease in the log odds of maritime arms trade, when controlling for the effect of all the other variables in the model.

Table 8. Logistic Regression Results: Drug Trafficking – Maritime, Combined

Table 8 indicates the results of the logistic regression model examining illegal maritime drug trade as the outcome variable, which incorporates the scores international maritime cannabis trade, international maritime coca trade, international maritime opiates trade and the international maritime trade in synthetic drugs. The main covariates in the model include international cooperation, rule of law, maritime enforcement, coastal welfare, port services and quality indicators, and fisheries score. The spatial lag variable was used as a control variable, as pre-analysis diagnostics indicated statistically significant autocorrelation between the illicit maritime drugs trade incidents within the 71 countries (Moran’s I Index = .25, z = 4.85, p<.001). Based on the Pseudo R² value, the overall model can be classified as “moderately strong”. When controlling for all variables in the model, three variables emerged as marginally significant (at the p<.10 level), which include rule of law, maritime enforcement, and port services and quality indicator. One standard deviation increase in rule of law was associated with the odds of drug trade increasing by 3 time, when holding all other variables constant. One standard deviation increase in the coastal welfare score was associated with a 49% decrease in drug trade; while one standard deviation increase in port services and quality indicators increased the log of odds of maritime drug trade by 109%, when controlling for all variables in the model.

Table 9. Logistic Regression Results: Wildlife Trade – Maritime

Table 9 shows the results of the logistic regression model examining the international maritime trade in wildlife as the outcome variable. All the covariates used in previous models were also used in the model listed in Table 8. The spatial lag variable was used as a control variable, as pre-analysis diagnostics indicated statistically significant autocorrelation between the incidents of maritime trade in wildlife within the 71 countries (Moran’s I Index = .44, z = 8.18, p<.001). When all variables are considered, the overall model can be considered “moderately strong”. Two variables emerged as significantly (or marginally significantly, i.e., at p<.10 level) contributing to the model: port services and quality indicators, and fisheries governance indicator. One standard deviation increase in port services and quality indicators score increased the odds of maritime wildlife trade by almost 8 times, and one standard deviation increase in fisheries governance was associated with a 58% decrease in maritime trade in wildlife, when controlling for all other variables in the model.

Table 10. Logistic Regression Results: Mixed Migration – Maritime

Lastly, Table 10 shows the results of the logistic regression model examining maritime mixed migration as the outcome variable. The main covariates in the model remained the same as for the previous models. The spatial lag variable was used as a control variable, as pre-analysis diagnostics indicated statistically significant autocorrelation between the maritime migration incidents within the study area (Moran’s I Index = .14, z = 2.69, p<.01). The overall model, based on the Pseudo R² value, can be classified as “moderately strong”. When controlling for all variables in the model, rule of law emerged as the single most important variable contributing significantly to the model. One standard deviation increase in the rule of law score was associated with a 90% decrease in maritime mixed migration, when holding all other variables constant.

In summary, the logistic regression models revealed several important findings across the different maritime crimes and covariate predictors examined. Poor coastal welfare emerged as one of the most important predictors of increased piracy and armed robbery; arms trade; and illegal drug trade. Lack of maritime enforcement was one of the core predictors of piracy and armed robbery in the maritime domain; while port quality and services were core to facilitating illegal drug trade and wildlife trafficking. In addition to port quality and services, poor fisheries governance was also associated with high wildlife trafficking. Lastly, rule of law emerged as the single most important predictor of mixed migration via the maritime domain.

Discussion

While many of the 71 countries examined had relatively low prevalence of these crimes within their territorial waters (see Table 5), only less than 10% of these countries were free of these maritime crimes, leaving about 90% of the countries to deal with at least one such crime. Maritime illicit drug trade emerged as the most prevalent maritime crime compared to others, reflecting the overall global trade in drugs patterns identified in past literature (Belhabib, Le Billon, and Wrathall 2020; UNODC n.d.)(Belhabib, Le Billon, and Wrathall 2020; UNODC n.d.). Building upon prior research – such as that conducted by the UNODC (UNODC 2014) – that has ranked human trafficking and arms trafficking as the second and third most profitable illicit trades, respectively, and wildlife crime as the fourth, the analyses in this study revealed that maritime illegal trade in wildlife is, in fact, the second largest trade lagging slightly behind the maritime illegal trade in drugs, when examined in terms of prominence. This finding can possibly be explained in several ways. One, while wildlife crime is not a new phenomenon, the significant impacts that are associated with this crime (including safety and security; health; environmental, among others) have brought increased attention to the problem in the past decade, which, in turn, may have prompted the governments, international organizations and global actors to take a closer look at its prevalence. This increased attention has subsequently led to a more accurate understanding of the problem’s prevalence. Another explanation could be the increased globalization that has led to the easier means of the flow of the goods globally, which subsequently allowed criminal organizations to diversify their operations to include the illegal trade in wildlife. Wildlife crime is also increasingly recognized as a known low risk, high reward crime, which facilitates individuals and organizations to engage (Sosnowski and Moreto 2021).

The current research provided new insights into geographic convergence through the lens of complexity. Whereas the previous studies – such as those by van Uhm et al. (2021), Earth League International and the John Jay College of Criminal Justice (Earth League International and John Jay College of Criminal Justice 2023), and the (Wildlife Justice Commission 2023) – developed typologies based on the ways in which wildlife crime, for instance, converged with other types of trafficking, thereby creating unique profiles of convergence, the current study examined convergence at the macro-level, looking at the diversity of maritime crime reported within each EEZ. Given the five major categories of maritime crime analyzed in the study, we outline six levels of complexity – zero through five – to shed light on the diversity of crimes occuring within a country’s EEZ. These levels correspond to how many of the five crime types were reported per EEZ.

The predictor variables included in the current study involved both macro- and micro-level indicators, ranging from the broader rule of law, fisheries management, and international cooperation variables to the narrower (more situational) variables of coastal welfare, maritime enforcement, and port infrastructure indicators. Taken collectively, the situational, micro-level indicators were among the strongest predictors of the various maritime crimes examined, while among the macro-level variables, ‘rule of law’ was most important when dealing with maritime migration and maritime illegal trade in drugs only, and fisheries management indicators showing relevant importance when assessing the variance in the international trade in wildlife. This finding is important for several reasons. First, regardless of the governments’ commitments to and ratifications of international treaties and their demonstrated willingness for international cooperation, the bottom line is that the actual concrete efforts invested in specific measures geared toward addressing the maritime crimes within their territorial waters prevail. Subsequently, motivated criminals that intend to commit any of these maritime crimes are not likely to weigh the costs of the countries’ willingness to commit to international regulatory mechanisms against the benefits of committing the crimes (broadly defined in more abstract terms), but rather the costs of being caught due to these countries’ concrete “on-the-ground” prevention measures in place (i.e., in more concrete terms) against the risks of engaging in these crimes. This finding can lead to one important conclusion: international cooperation and rule of law measures will be most effective when these are translated into more concrete steps designed to prevent maritime crimes.

Another interesting finding was the positive relationship between the prevalence of the illicit maritime drug trade, maritime wildlife trafficking and the strength of port infrastructure. The countries that had more problems with illegal drug and wildlife trafficking via maritime routes were those that had strong port infrastructure in place. This finding is similar to Petrossian et al. (2015) who examined the extent to which the ports as “risky facilities” and their infrastructure were associated with the risk of illegal fishing vessels visiting these ports to offload their catch. Their findings indicated that ports that had stronger infrastructure in place were more vulnerable to illegal landings of fish, thus leading to the conclusion that criminals choose ports strategically to help them facilitate their offloading of the illegal goods. This is not completely surprising given the fact that these illegal maritime trades do not generally involve small quantities but are rather more large-scale and would require the “tools” necessary to facilitate the offloading process.

The small number of countries available for analyses in this research prevented us from building more nuanced analyses. Almost all predictor variables, except for the port quality and services variables, were the indices composed by Stable Seas, which comprised about 10-15 different sub-indicators. With more data becoming available for additional coastal countries, future research can break down the analysis at the sub-indicator levels in order to identify more nuances. Meanwhile, as this research revealed that micro-level indicators are stronger predictors of maritime crimes, it is recommended that governments invest more energy, resources, and time into devising concrete, situational approaches to deal with maritime crimes prevalent within their territorial waters.

Conclusions

Several key conclusions can be drawn from the current study. Aligning with the research questions, first, yes, we can rank maritime crimes in terms of their degree of importance. Illicit maritime drug trade emerged as the most prominent maritime crime problem across the analyzed EEZs, followed by maritime wildlife trafficking. Second, typologies can be created based on the diversity of maritime crimes reported within a given maritime space to inform law enforcements to the crime prevention needs and potential policy interventions. Lastly, such prevention and policy interventions can and should be informed by both situational, micro-level indicators of risk as well as macro-factors. Specifically, situational, micro-level indicators (such as such as poor coastal welfare, lack of maritime law enforcement, port quality) were among the strongest predictors of the various maritime crimes examined. International cooperation and rule of law measures will be most effective when these are translated into more concrete steps designed to prevent maritime crimes.

Author Contributions

MS conceptualized the paper, wrote the introduction and data sections, advised analysis, and edited drafts. GP sourced data, advised analysis, wrote the results and discussion, and edited drafts. TN wrote the literature review, cleaned and prepared all data, and edited drafts. EP ran the analyses and edited drafts.

Appendix

Appendix A: List of Exclusive Economic Zones Associated with Clusters 1 and 2

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