Even though five new criminal markets and one new criminal actor type have been included in the 2023 iteration, the criminality–resilience dynamics remained consistent with the 2021 Index findings. As the data shows, criminality and resilience are negatively correlated (−0.44, compared to −0.45 in 2021). Therefore, the less resilient a state is to the threat of organized crime, the higher the likelihood of it experiencing criminality, and vice versa: higher resilience equates to lower pervasiveness of criminal markets and actors. Yet, at −0.44, the relationship is not very strong (see Figure 6.1, which shows how the countries are dispersed across the criminality–resilience matrix).

The reason is that the relationship between resilience and criminality is not as linear as intuition might suggest, and there are several factors determining the dynamics between criminality and resilience. Indeed, there are a growing number of countries that deviate from the pattern that logic would support, as can be seen in Figure 6.1. But why do we observe such anomalies? To answer that, we need to look at the two categories in the vulnerability matrix that include countries that deviate from the linear criminality–resilience relationship.

This includes countries that fall into the low crime–low resilience category, as well as some of the countries that fall into the high crime–high resilience category. The low crime–low resilience category comprises the largest number of countries, with 71. Given their comparatively low levels of crime, with an average score of less than 5.50, some of these countries may have very few pervasive criminal markets, or none at all.

Taking Oceania as a good example to illustrate these dynamics, the continent boasts the lowest overall criminality score by a significant margin (3.23 compared to the global score of 5.03). Barring Australia and New Zealand, most island states lie at a great distance from major trafficking flows, which would partly explain their comparably lower criminality scores. Due to these lower levels of vulnerability to criminality, some Pacific island states have just not had the stimulus to establish resilience measures in the form of anti-organized crime regulatory or institutional frameworks. For countries like these, the low resilience–low criminality ranking is not so much an anomaly, but rather a function of how they have been largely bypassed by organized crime flows, and are therefore not incentivized to adopt resilience measures.

Africa is another good example. The large majority of countries on the continent fall within the low resilience band (48 out of 54), and most of them (27) do not experience high criminality. While environmental and geographic factors almost certainly play into the low criminality scores for these countries, there are also economic conditions that have constrained the development of a large number of pervasive criminal markets and influential criminal actors. At the same time, economic factors – such as low income and high poverty – hamper states’ abilities to develop and enforce effective anti-organized crime measures.

These factors help explain how the relationship between criminality and resilience is even less pronounced in these regions than it is globally. The correlations for both Oceania and Africa are not statistically significant.

Besides the considerations outlined above, another circumstance that should be factored in when analyzing the relationship between criminality and resilience is the thematic composition of the Index, i.e. the criminal markets the Index covers. It is arguable that the ambiguous relationship between criminality and resilience could be explained by the nature of specific criminal markets. Indeed, as was the case in the first iteration of the Index, the entire drugs-based category bears little correlation to overall resilience. The correlation between resilience on the one hand and synthetic drugs trafficking, heroin trafficking and the illicit cannabis trade is negative, but not statistically significant. Interestingly, however, repeating the outcome of the 2021 analysis, cocaine continues to exhibit a positive, albeit weak, relationship to overall resilience (0.19). This admittedly non-significant relationship between the two suggests that, as with the other three drug markets, the pervasiveness of the cocaine trade is fairly independent of how resilient a country is in general. The positive direction of the correlation underscores, as in the previous iteration of the Index, that wealthy and generally more highly resilient countries are slightly more likely to be impacted by this illicit trade, as they are predominantly classified as consumer markets.

A similar relationship is modelled by one of the criminal markets newly incorporated into this edition of the Index: cyber-dependent crimes, a market that is also positively correlated with overall resilience (0.31). The results for cyber-dependent crimes also suggest that countries with high levels of resilience to organized crime are just as likely to have a well-developed cyber-dependent crimes market as countries with low levels of resilience. One explanation would be that wealthy, developed states, which tend to be more resilient, are a target for cyber-dependent offences. That would also explain the comparably higher correlation between ‘economic regulatory capacity’ and cyber-dependent crime (0.38) – countries that have well-established business environments, free to operate from traditional organized crime, have allowed citizens to accumulate wealth, which makes them an attractive target. It may therefore be that wealthy nations experience higher victimization rates of cyber-dependent offences. However, those contexts, where essential resilience structures, namely regulatory frameworks and their implementation mechanisms, lag behind, are also likely to be increasingly impacted by such threats, as they cannot keep up with the speed with which this form of criminality is developing.

The criminal markets subcomponent, however, only partially explains the dynamics behind the criminality–resilience relationship. As already outlined, the criminality component also features a second subcomponent: criminal actors. This aims to measure the influence of different criminal group typologies as well as their impact. Numbers show that the overall criminal actors scores are a better predictor of the state of resilience in a given country than criminal markets. The relationship is a moderately negative one (−0.49), which is to say that as criminal actor scores rise, resilience tends to decline. The same relationship is observed between criminal markets and resilience, though it is somewhat weaker at −0.35.

Analysis has demonstrated a weak correlation between resilience and individual criminal actor types globally, with the exception of state-embedded actors, which, in line with the 2021 analysis, continues to exhibit a strong negative relationship (−0.79). It is therefore tenable to suggest that at the centre of the strong interrelationship between criminal actors and resilience lies the state-embedded actors typology. Furthermore, while the affiliation between resilience and individual actor types might differ depending on regional context, state-embedded actors are tied to resilience levels to a considerable degree on all five continents, especially Europe, where the correlation between the two indicators is very strong at −0.90. Mirroring the 2021 results, the current conclusions serve to reiterate how crucial of a deterrent the presence of criminals acting from within the state apparatus is to the development of adequate and functional strategies and institutions to curb organized crime.

Crime convergence

As observed earlier in the report, financial crimes topped the list of the most pervasive markets. Despite their global prevalence, however, the presence of a financial crimes market is not the strongest predictor of overall criminality, with a correlation coefficient of 0.71. In fact, extortion and protection racketeering, and arms trafficking are most strongly correlated to criminality at 0.79, followed by human trafficking (0.78), human smuggling (0.75) and non-renewable resource crimes (0.73).

At the opposite end of the spectrum, as was the case in 2021, the cocaine trade remains weakly correlated to overall criminality (0.31). Similarly, although cyber-dependent crimes have reportedly increased over the past couple of years, this criminal market has the second lowest criminality correlation coefficient (0.45).

The transnational reach of these flows has largely been facilitated by globalization. A number of studies have argued that globalization and advancements in information technology have tremendously benefited organized crime groups, allowing criminals to expand their operations, and at the same time diversify their criminal activities.¹ Thus, there is an increasing convergence between criminal markets, or in other words an overlap between criminal markets as well as the criminal groups that are involved in them.

The Index has identified a number of examples of crime convergence, most notably between human trafficking and human smuggling, where the correlation coefficient is estimated at 0.79, the highest correlation between any two criminal markets. From region to region, this relationship varies in strength from 0.87 and 0.84 in the Americas and Asia, respectively, to 0.79 in Europe and 0.62 in Oceania. The likely reason behind the strong link between the two is the fact that the lines between human trafficking and human smuggling are blurred in many geographies. What may start out as smuggling of individuals seeking to move elsewhere may develop into human trafficking, with people being exploited en route to their destination or after arriving there, being forced to repay incurred debt or left with no means to fend for themselves and falling victim to various forms of exploitation.

Besides its close relationship with human smuggling, globally speaking, human trafficking correlates moderately with half of all the other criminal markets, including the new markets added for this second Index iteration, barring cyber-dependent crimes. Despite the possible overlap between human trafficking and other markets, it is important to acknowledge that correlation does not equal causation. In other words, there is not necessarily a direct link between markets that would explain the apparent relationship.

The second highest correlation coefficient remains the one between flora and fauna crimes (0.72), only a slight change compared to the 2021 Index (0.71). And, as argued previously, the explanation is likely to be that there is a geographic overlap in source countries for both. In other words, source countries that have a higher biodiversity of fauna are also likely to have extensive forest cover or a high diversity of coveted flora species. In spite of the strong correlation, flora and fauna markets are only weakly correlated to other criminal markets, exceptions being the non-renewable resource crimes market and, surprisingly, trade in counterfeit goods. It seems logical that the moderate correlation between flora and non-renewable resources markets (0.52), as well as between fauna and non-renewable resources (0.45), is a degree of convergence between environmental crime markets.

Cyber-dependent crimes are weakly correlated with almost all other criminal markets, and the results from the analysis suggest that correlation coefficients range from 0.22 (cyber-dependent vs fauna crimes) to 0.47 (cyber-dependent vs financial crimes). The relationship with the synthetic drugs trade is the main outlier (see Figure 6.2).

There has been a growing global trend over the past years towards online purchasing of synthetic drugs (classified as the cyber-enabled synthetic drugs trade under the Index definitions). Such user behaviour might point to a well-developed cyber environment, which would, in turn, presumably imply a heightened risk of cyber-dependent criminality. A recent illicit practice that exemplifies this emerging trend is the widespread use of cryptocurrency as the preferred method of payment for some of the transnational organized crime groups involved in the synthetic drug trade, particularly fentanyl and its chemical precursor. Continental results lend further credence to the hypothesis. The correlation between synthetic drugs and cyber-dependent crimes is strong and statistically significant across all regions, especially Europe (0.77) and the Americas (0.74). The only exception is Africa, where the relationship is moderate (0.42) – perhaps a consequence of the absence of a well-developed cyber infrastructure and a cyber-dependent crimes market as a result. But while cybercrime is not among the highest scoring markets, the narratives that underpin country scores indicate the breakneck speed at which the market has grown over the past couple of years. Given the speed at which cybercrime has been growing, it will be interesting to see how the relationship evolves in the next Index iteration.

Importantly, these are macro correlations, the analysis of which, despite looking at some regional dynamics, largely ignores local context. To better understand the convergence between different criminal markets and the subsequent dynamics in order to conceptualize and implement adequate counter-organized crime measures and initiatives, it is critical to assess local regional conditions – an ambitious undertaking that would hopefully be aided by the successive Index iterations.

Who is doing what?

Although criminal networks are not the highest ranked actor types overall, their reach and impact are felt all over the world. This particular criminal actor type is central to the functioning of transnational organized crime flows, regardless of the commodity that is trafficked. This assertion is supported by the fact that criminal networks demonstrate the highest correlation with the overall criminal markets score of all five actor types (0.78). Put in simpler terms, criminal networks continue to be the common denominator across many illicit economies.

The state-embedded actor category is the second best predictor of the presence of a developed criminal market environment in a given country, with a pairwise correlation of 0.64. This is hardly a surprise, given the pervasiveness of state-embedded actors and their various roles at different institutional levels as both perpetrators and facilitators of criminality.

State-embedded actors are strongly associated with the arms trafficking and human trafficking markets (both at 0.66). In line with the analysis in the previous iteration of the Index, the results of the 2023 Index show that 12 of the 15 countries ranking highest on the arms trafficking market experience some form of fragility – such as civil or open wars, unrest or coups – or are known suppliers of arms to conflict areas in breach of embargoes. In these instances, illicit arms flows either happen with the knowledge of the state or are an unwritten state policy. Ukraine has become a key example, where arms are being illegally imported by the Russian state into conflict-torn Ukraine. Iran is another notable case, where arms trafficking is a key element of the country’s regional geopolitical strategy, supplying weapons to other states in Western Asia and North Africa, as well as to Russia and Afghanistan. It is thus evident how state-embedded actors are able to attain a dominant position in a market such as arms trafficking. The arms trade is also among those markets where the state is the sole authority able to exert an oversight over the legal economy. This would arguably allow for opportunities for corruption or direct involvement in the illicit arms trade.

Other groups are also known to exhibit strong relationships with specific criminal markets. Mafia-style groups are one such instance. Although they are the lowest scoring actor type globally and not even moderately associated with most markets, mafia-style groups do form a strong pairwise correlation with the arms trafficking (0.53), and with extortion and protection racketeering (0.75), which is the highest correlation coefficient observed among all actor–market relationships. These results might arguably speak to the violent nature of gangs, syndicates and mafia-style groups in general, and reaffirm their role as primary perpetrator of extortion and protection rackets.

There is also an apparent link between private sector actors and financial crimes, estimated at 0.64. This provides crucial empirical evidence of the tangible role of private entities – companies and individuals – as facilitators and perpetrators of financial crime, assessed as the most pervasive criminal market globally. This should flag the importance of better regulatory frameworks to curb or inhibit the involvement of the private sector in organized crime.

Resilience

Following the adverse effects of the pandemic on the global state of affairs, the world emerged divided. Climate emergencies, political and economic turmoil, an open war in Europe: the ramifications of these only contribute to a more fractured world. The way forward seems to place international cooperation as the main prong of the solution to global issues, including the increased scope and scale of organized crime. What does the data tell us though?

‘International cooperation’ scored the highest among all the resilience indicators (5.87) and has improved the most since the first iteration of the Index, climbing by 0.19 points. Why is it then that the overall criminality score also went up? The ‘international cooperation’ indicator refers to not only the structures and processes of interaction and policymaking, but also the concrete implementation of measures. It is very likely that political talk and ‘on paper’ measures push the score upwards, whereas implementation is weaker. Furthermore, results indicate that higher levels of international cooperation are not a good predictor of criminality levels, with the pairwise correlation between the two indicators estimated at −0.28, which is the lowest correlation of criminality with any of the resilience building blocks.

It is evident therefore that international cooperation would do little to curb organized crime if no follow-up mechanisms to evaluate implementation are designed. The push towards better state accountability should, however, come from different vectors, non-state actors being one. The past couple of years, particularly in the wake of the pandemic, have seen even more restrictions on free speech and a shrinking environment where non-state actors can freely work, as evidenced by the Index results. In this challenging context, it is essential that the role of civil society actors in countering organized crime is recognized for its value and active steps are taken towards bridging the gap between state and non-state actors in their anti-organized crime efforts. The strong relationship between ‘non-state actors’ and ‘political leadership and governance’ (0.78) as well as between ‘non-state actors’ and ‘government transparency and accountability’ (0.83) only lend further credence to this observation.