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Article

Violent Drug Markets: Relation between Homicide, Drug Trafficking and Socioeconomic Disadvantages: A Test of Contingent Causation in Pereira, Colombia

by
Williams Gilberto Jiménez-García
1,*,
Wilson Arenas-Valencia
2 and
Natalia Bohorquez-Bedoya
2
1
Facultad de Ciencias Sociales, Universidad de Los Andes, Bogota 111711, Colombia
2
Facultad de Ciencias Empresariales, Universidad Tecnologica de Pereira, Pereira 660003, Colombia
*
Author to whom correspondence should be addressed.
Soc. Sci. 2023, 12(2), 54; https://doi.org/10.3390/socsci12020054
Submission received: 26 October 2022 / Revised: 5 January 2023 / Accepted: 6 January 2023 / Published: 18 January 2023
(This article belongs to the Section Social Policy and Welfare)
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Abstract

:
The drug/violence relationship has been a recurrent topic of interest in criminology and sociology. This interest began with the crack epidemic in the 1980s and continues due to the consolidation and functioning of the cocaine markets of Latin American drug trafficking organizations. We approach the drug/violence relationship in Pereira (Colombia) which is strategic for the global cocaine market and for the Colombian domestic market. This city has an average homicide rate of 38.5 per 100,000 inhabitants (2010–2019). To test the drug/violence relationship, we used Goldstein’s systemic violence theory and Zimring and Hawkins’ contingent causality theory. We analyze the influence of socioeconomic and drug trafficking variables on homicides between the years 2010 and 2019. Our dataset comes from official Colombian government sources. We performed multivariate regression modeling through structural equation modeling with partial least squares (PLS-SEM). Our model was consistent and obtained statistical significance for all years, resulting in a good approximation to the study of the phenomenon. Based on the evidence, we can affirm that there is a relationship among violence/drug trafficking/socioeconomic disadvantages, thus confirming the contingent causation theory.

1. Introduction

The crack epidemic in the United States produced not only an increase in homicides in U.S. cities in the 1980s and 1990s (Goldstein et al. 1997; Grogger and Willis 2000) but also introduced the interest of criminologists in studying the relationship between drugs and lethal crime (Tonry and Wilson 1990). For Latin American criminology, understanding this relationship has also been a challenge (Del Olmo 1999). On the one hand, researchers have sought to increase and accumulate knowledge on these issues (Zaffaroni 2015). On the other hand, they have tried to design technical and political solutions to overcome a serious problem that in Colombia alone, between 1990 and 2020, has claimed the lives of about 150,000 people (Jiménez-García and Alzate-Zuluaga 2022).
Understanding the structural causes of the homicides in cities that are violent facilitates the comprehension of these crimes and the consequent state action. In Colombia and other countries in Central and South America (e.g., Mexico, Venezuela, Brazil) where drug trafficking is present, the number of homicides has been commonly associated with the dynamics of the drug market (Beittel 2018; Kronick 2020; Pécaut 2001). These dynamics include but are not limited to: (1) the convergence of drug production, commercialization, and money laundering segments (De León Beltrán 2014) and (2) different actors: investors, gangs, corrupt public officials, police and civilian population (Kostelnik and Skarbek 2013).
The characteristics and dynamics of lethal violence in Colombia are strongly associated with the presence of criminal organizations that operate at all levels of the drug trafficking process; drug trafficking is a catalyst for homicidal violence in Colombian cities (Jiménez-García and Rentería-Ramos 2020). However, drug trafficking should not be understood as either an isolated trigger or the primary cause of the high rates of homicides in a specific place (Contreras and Hipp 2020). Other conditions, such as the socieconomic disadvantages, which accumulated over time and are experienced by some sectors of the population, that are fundamental to understand this kind of violence (Portella et al. 2019). In Colombia, it is estimated that 70% of annual homicides (2000–2019) are connected to drug trafficking (Fiscalia General de la Nación 2019; Rubio 2005).
We consider that drug trafficking is a contingent cause (Zimring and Hawkins 1997) of homicides in Colombia: that is, when structural conditions have accumulated historically, the relationship between drugs and homicide is influenced by drug trafficking (Ousey and Lee 2002), since it provides elements that facilitate the lethality of violence such as access to weapons (Schatz and Tobias 2021), recruitment of personnel (Ortiz 2017), training of personnel to use lethal violence (Álvarez 2015) and decreased social sanction due to the fear generated in the inhabitants (Pécaut 2002). We consider that drug trafficking can produce lethal violence of the level and magnitude of Colombia if there are pre-existing structural conditions such as socioeconomical disadvantages.
Based on Zimfring and Hawkins’ statement of contingent causality of drug markets and homicides, this paper attempts to extend the understanding of this relationship in two ways. (1) First, our case study is a Colombian city that is key to two segments of the drug production chain in the local Colombian market (heroin and coca paste) and in the global market (cocaine). This city has also presented high homicide rates in a sustained manner for over 20 years, and we (2) test the multivariate relationship through structural equation modeling with partial least squares method (PLS-SEM) (Hair et al. 2017) between socioeconomic disadvantages, drug trafficking and homicides in this city, trying to verify the relationship found in other studies (Contreras and Hipp 2020; Ousey and Lee 2007; Zimring and Hawkins 1997).

2. Theoretical Background and Review of Research

2.1. Drug Trafficking and Systemic Violence

The idea of a relationship between drugs and crime dates back 100 years (Inciardi 1981), but it became famous among criminologists thanks to the increase in homicides in North American cities suffering from the crack epidemic. Although there are many scholarly papers, most of them have been conducted in American cities, and several investigations produced contrasting results. Even today, it is difficult to clearly establish the relationship between drugs and crime (Berg and Rengifo 2009; Blumstein 1995).
One of the primary references in the study of this drug–violence relationship is Paul Goldstein Goldstein (1985), whose tripartite conceptual model has been widely tested and debated by academics worldwide (Jacques and Wright 2011; Ousey and Lee 2007; Sarrica 2008). Goldstein’s conceptual model explains the connection between drugs and violence through three ways: the psychopharmacological, the economically compulsive, and the systemic (Goldstein 1985).
In this study, we focus on the third model: the systemic. Systemic violence is “traditionally aggressive patterns of interaction within the system of drug distribution and use” (Goldstein 1985, p. 497). This type of violence arises from the demands of working or doing business in an illicit market where the actors cannot resort to the legal system to resolve their conflicts or to promote their product (Goldstein et al. 1997). From this perspective, we understand drug-related violence as the product of efforts to sustain the informal social control carried out by the actors involved in the drug market, and the distrust in the agents of social–formal control (authorities) to manage the disputes generated in the transactions/relationships between illegal and/or violent actors. Goldstein (1985) also provided examples of systemic violence, which include but are not limited to: territorial disputes, sanctions to internal and external regulatory codes of the drug market, elimination of competition, punishments for disobedience, restructuring of the power structures of criminal organizations, and response to state force.
Many studies followed the theory of systemic violence (Goldstein 1985). For example, Blumstein (1995) determined that the increase in homicide rates in the 1980s in the United States was caused by the recruitment of young people into the drug markets, especially associated to crack cocaine. Another study that approached violence from the perspective of justice and drug pricing explained how judicial proceedings affected the drug market (supply/demand) and stimulated violence in places where the drug market is more cohesive (Jacques and Wright 2011).
A study conducted with incarcerated women in Appalachian Kentucky provided a test of the utility of Goldstein’s tripartite conceptual framework. Different clusters of drug–violence linkages were examined and classified within each category of violence. The results showed that the three forms of violence described in the conceptual framework were significant in predicting violence in this group of women. To date, this is the first study to report on the relationship between systemic violence victimization in rural prison communities (Victor and Staton 2022).

2.2. Contingent Causation Models

Systemic violence models are useful in highly violent contexts. But not all drug markets are violent (Jacques and Wright 2011). There are drug markets that seek to inhibit violence to ensure successful transactions and are therefore peaceful (MacCoun and Reuter 2001). Zimring and Hawkins, aware that the drug/violence relationship did not always produce contexts of high lethality (e.g., European markets) (Paoli 2004; Reuter and Paoli 2020), developed an alternative hypothesis which they called contingent causation (Zimring and Hawkins 1997).
Contingent causation refers to the fact that the relationship between drugs and violence depends on the social context in which the illegal activity takes place. Thus, for example, in the United States, there are more favorable conditions for drug markets to be more violent and more lethal compared to other markets in industrialized countries. Zimring and Hawkins Zimring and Hawkins (1997) considered that the drug–violence relationship is spurious and that it can only be understood by measuring the influence of social variables in the spatio-temporal context in which this relationship developed, indicating that “the creation and expansion of illegal markets will produce additional homicides when social circumstances conducive to lethal violence already exist” (Zimring and Hawkins 1997, p. 153). Ousey and Lee (2002) examined the contingent causation hypothesis and studied the effects of heroin and cocaine trafficking on violence in 122 U.S. cities. This study reported that drug proliferation increased the likelihood of homicides. They found that the drug–violence relationship was stronger in cities with greater city-level resource deprivation. This relationship found that cities with higher homicide rates were those with large drug markets and problems for part of the population to access resources.
Contingent causation theory has also been used to understand the longitudinal relationship between drug activity and crime rates (Contreras and Hipp 2020). A study in Miami-Dade County, Florida, found that drug activity increased rates of wage violence and acquisitive crime in low-income neighborhoods (Contreras and Hipp 2020). Another study investigated the influence of the racial variable on the drug/violence relationship, finding that understanding the socio-historical context of race and institutional racism is critical to understanding the drug/violence connection (Johnson and Bennett 2017).
The studies that involve the relationship between drugs/violence and that make use of the theories of Goldstein (1985) and Zimring and Hawkins (1997) have been applied to contexts of the global north and not to contexts of the global south such as Colombia. With the condition that Colombia, unlike North America and Europe, not only has consumer markets but is also the main exporter of cocaine, so in Colombia, there is a double condition: a domestic drug market (coca paste, heroin and cocaine) that is violent and operates mainly in Colombian cities and a global drug market that is highly violent and operates in the cocaine production centers, in the routes that connect these production centers with the export ports, and in the cities that provide all the logistics for the drug produced in Colombia to reach consumers around the world.

2.3. Socioeconomic Disadvantages as a Factor Conditioning the Drug Market–Homicide Relationship

Several studies have analyzed/studied the association between homicide rates and the levels of resource deprivation (Jiménez-García et al. 2021; Ousey and Lee 2002; Vinasco-Martínez 2019). Many of these studies were based on either the absolute deprivation theory or the concentrated disadvantage perspectives (Garthe et al. 2018; Parker and Reckdenwald 2008). There are five types of social disadvantages: poverty, unemployment, income inequality, and single-parent households (Ousey and Lee 2002, p. 78). Johnson and Bennett (2017) add to these factors the racial element.
Socioeconomic disadvantages are those social and economic conditions that negatively affect the performance of communities, households and individuals. They correspond to lower access (knowledge and/or availability) and management capabilities to the resources and opportunities that society provides for the development of its members (Rodríguez Vignoli 2000).
Social disadvantages historically accumulate by large sectors in the population and limit their access to: (1) services that allow them to have a life with dignity, such as work, education, and health, and (2) basic goods such as adequate housing and functional modes of transportation.
The impact of the accumulation of socioeconomic disadvantages on lethal violence has been discussed in several studies (Bowles et al. 2006; McIlwaine and Moser 2001; Pratt and Cullen 2005). These discussions focused on questioning whether the social disadvantages generate a loop of violence that cyclically reproduces acts of violence in certain sectors of the population (Sampson 2009).
The link between lethal violence and socioeconomic disadvantages is closely associated with concepts such as poverty (Papachristos et al. 2013; Pratt and Cullen 2005). The poverty–violence relationship has been widely questioned because it can criminalize poverty (Gustafson 2009; Herring et al. 2020), especially in poor countries such as those in the southern hemisphere, where poverty is more widespread than in the global north (Moser and Shrader 1999).
One way to address the relationship between socioeconomic disadvantage and violence without criminalizing poverty is through concepts such as poverty traps (Azariadis and Stachurski 2005). From this conceptual framework, lethal violence intensifies in a territory where the spiral of poverty forces people to be poor and limits their ability to develop stable economic and political projects. The people living in poverty are more vulnerable to events they cannot control; for example, violence and the impossibility of exiting the cycle of poverty, which increasingly reinforce and feed violence and organized crime.
In Latin America, this relationship between socioeconomic disadvantages/violence has been studied from the theoretical framework of social vulnerability (Bergman and Kessler 2008). Manzano et al. (2020) designed a vulnerability index to find a relationship between households with a high level of social disadvantages and the experience of victimization in poor neighborhoods of South America. The variables tested for vulnerability were unemployment, informal work and inequality (Jiménez-García et al. 2021), and similar findings to other studies were found (Berg and Rengifo 2009; Wo 2018). The findings of this study indicated that low-income households have less capacity to solve problems of violence and are prone to being victims of criminal organizations when (1) DTOs recruit minors for their organizations (Springer 2012) or (2) DTOs impose a territorial control to establish illegal taxes (Alzate-Zuluaga and Jiménez-García 2021).

2.4. The Application of Structural Equation Modeling (SEM) in Studies of Violence and Crime

SEM is a multivariate analysis technique whose aim is to test theoretical constructs from observed data (Martínez and Fierro 2018) through the search for a quantitative model that maximizes the variance explained (Hair et al. 2017, p. 104) and that allows the analysis of causality in models with multiple variables and effects among them, such as the relationships of the variables of a phenomenon as complex as violence in a territory associated with drug trafficking. This method has gained acceptance in the social sciences for its flexibility and robustness in the analysis of complex phenomena (Jöreskog 1993; Kline 2011).
Studies that have applied the SEM technique have examined the relationship between drug trafficking, drug use and violence and have had consistent results indicating that simultaneous relationships between drug trafficking and criminality are clearly demonstrable (Roman et al. 2020; Speckart and Anglin 1986). Another study has found positive associations between the social disruption of a neighborhood and the rates of violent crime (Boggess and Maskaly 2014). Another study verified that the socio-cultural context increases crime by reducing the limitations against it and increasing the motivations for it (Zhang et al. 2012). Finally, a study that examined the relationship between poverty, social control and homicides found that violence, as measured by the homicide rate, is the product of a combination of poverty and other adverse social conditions as well as weak formal social control mechanisms (Ouimet et al. 2018).

3. Materials and Methods

3.1. Data

We test our prepositions using data for a Colombian City, Pereira. This city has 500,000 inhabitants (República de Colombia. DANE 2022). It is a commercial and productive node that is connected to the so-called Colombian golden triangle—Cali, Medellin and Bogota—and is also connected to the ports of the Pacific and the Colombian Caribbean. Pereira stands out for being the Colombian coffee capital thanks to its production and commercialization in the world coffee market (Zuluaga 2013).
Pereira was selected because of its relevance in the global drug trade characterized by its geostrategic location regarding the structures of (1) drug production centers (García 2022; Jiménez-García et al. 2021); (2) logistics to supply the national and international drug market (Cortés et al. 2012); and (3) money laundering (Ruíz et al. 2020).
We obtained the data from three sources: (1) the criminal statistics (2010–2019) administered by the Colombian National Police (CNP) using the SIEDCO application (Rodríguez 2008). The data from the SIEDCO application was related to homicide rates, capture rates, and drug seizure rates for different drugs (i.e., cocaine, heroin, and marijuana). We kept in mind that police datasets may have bias introduced by police decision-making processes (Papachristos et al. 2013); (2) the system used by the Colombian government to identify potential beneficiaries of social programs (SISBEN), which is operated by the National Planning Department of Colombia (DNP) (Fresneda and Martínez 2000). The data from the SISBEN was related to the rates of low-income households; (3) the social statistics administered by the secretariat of social development of the Mayor’s Office of Pereira, in which we could collect further information about the households and the families that are likely to benefit from social programs. This database allowed us to collect data related to the rates of mother-headed single-parent households.
Data collected were number of homicides, amount of cocaine–heroin–basuco seized, number of people arrested for drug possession, number of socioeconomically disadvantaged households, and number of people in the country arrested for possession of drugs.
We select these data because of their availability and the way the competent authorities collected and processed them. The data set has limitations such as being subject to the systematic error of the agent, person or employee who enters the data on topics such as: geographic location, age, gender, and schedules. The validity of these data is guaranteed by the scaling of the information at the national level by other information operators such as the National Administrative Department of Statistics.

3.2. Sample

We used convenience sampling (Etikan et al. 2016). Our sample was composed of 31 Territorial Administrative Units (TAU) from Pereira city. Overall, 19 TAU are in urban areas and 12 are in rural areas (Figure 1). For each TAU, data were collected from the last 10 years (2010–2019). Although small-sized samples have drawn criticism (Marcoulides and Saunders 2006), current studies state that SEM produces high levels of statistical power and favorable convergence performance, even with small samples (Benitez et al. 2020; Cassel et al. 1999; Hair et al. 2021).

3.3. Variables

The study variables are shown in Table 1.

3.4. Statistical Methods

To estimate the connection between homicide (violence) and drug trafficking and socioeconomic disadvantages in the TAU studied, we used structural equation modeling with partial least squares method (PLS-SEM) (Hair et al. 2017, 2021; Kline 2011). This technique allowed us to simultaneously examine a series of dependency relationships using multivariate techniques such as regression and factor analysis (Kahn 2006). SEM has gained acceptance in the social sciences for its flexibility and robustness in the analysis of complex phenomena (Martínez and Fierro 2018).
The model planned is reflective, since the defined latent variables are “reality” and the observed variables are a sample of indicators or manifestations of that “reality” (Martínez and Fierro 2018). We show the structure of the model in Figure 2.

3.5. Model Assessment

Once the model parameters were estimated with the software, we evaluated the quality of the results. The evaluation comprised a model fit assessment, a measurement model assessment, and a structural model assessment (Benitez et al. 2020; Hair et al. 2017, 2021; Martínez and Fierro 2018).
For model fit assessment, we used the standardized root mean square residual (SRMR) metric, with a recommended value of less than 0.08 (Benitez et al. 2020; Ruíz et al. 2014). This assessment determines if the predicted values of the covariances adequately reproduced the sample covariances to determine if the model is correct, and it serves as an approximation to the real phenomenon, thus specifying its predictive power (Cupani 2012).
For measurement model assessment, we used various indices (Benitez et al. 2020; Garson 2016; Hair et al. 2017) which evaluate:
  • Internal consistency: Composite Reliability (CR);
  • Convergent validity: Average Variance Extracted (AVE);
  • Discriminant validity: Heterotrait–Monotrait (HTMT);
  • Reliability Indicator: External loads and their significance.
For structural model assessment, we used the determination coefficient R 2 , the effect sizes f 2 , and the sizes and significance level of the trajectory coefficients (Benitez et al. 2020; Martínez and Fierro 2018; Ruíz et al. 2014).

4. Results

In Pereira during the years 2010–2019, 1628 homicides were committed, averaging 38.9 homicides per 100,000 people, which is higher than the Colombian national average (28.5 homicides per 100,000 people) (Jiménez-García and Alzate-Zuluaga 2022). Overall, 11,164 people were captured in Pereira for crimes related to drug trafficking (230.2 captures per 100,000 people). In addition, 62,099,734 grams of drug were seized in Pereira (averaging 2,332,372 gr per 100,000 people) from which marijuana was the most seized drug (2,298,491 gr per 100,000 people), followed by cocaine (10,275 gr per 100,000 people) and heroin (1059 gr per 100,000 people) (Table 2).
Furthermore, 28.9% of households in Pereira reported socioeconomic vulnerability and received state aid, which showed an average rate of 28,917.7 low-income household per 100,000 population. From these low-income households, 19.3% were mother-headed or single-parent households, which shows a rate of 19,340.4 mother-headed single-parent households per 100,000 population (Table 2).

Model Assessment

We performed three types of model evaluation: model fit evaluation, measurement model evaluation and structural model evaluation (Benitez et al. 2020; Garson 2016; Hair et al. 2017; Ruíz et al. 2014). Table 3 shows the standardized root mean square residual (SRMR) for the saturated model and for the estimated model. Most years showed a value lower than 0.08, which indicates (following the discussions explained around this metric) an acceptable overall fit of the models in the corresponding years.
We evaluated the internal consistency of the model with the CR indicator (Table 4). Latent variables Drug Trafficking and Socioeconomic Disadvantages had values greater than 0.7, which was a value recommended in other research for confirmatory studies (Garson 2016). In many years (2011–2019), the CR was greater than 0.9, which makes us wonder whether the indicators are just small variations of the same measure.
Despite the promising results, we want to highlight that the indicators of Socioeconomic Disadvantages, SISB and SFHH, are taken from different databases, national and municipal, respectively; the nature of what each indicator measures allows us to say that although the correlation between the variables is high, they are not the same measure for the construct of social disadvantage. SISB is the classification of potential beneficiaries of social assistance by the Colombian state according to their social vulnerability conditions, while SFHH represent the rate of households in which the mother is the head of household and there is an absence of male parental control.
As for the latent variable Drug Trafficking, the indicators are different measures for the construct, since they are different drugs (coca, heroin, marijuana) (Cortés et al. 2012). The latent variables Capture and Violence have only one indicator, so CR, AVE and loadings are equal to 1 and the latter have a p = 0 value (Table 4).
The AVE for all years is greater than 0.5 (Table 4), which suggests that there is empirical evidence for convergent validity (Hair et al. 2017), i.e., that each latent variable (Drug Trafficking and Socioeconomic Disadvantages) explains for our case much more than half of the variance of its indicators (Garson 2016).
All but one (SCOC year 2017) of the external loads (loading) are greater than 0.707 (Table 4), which indicates that over 50% of the indicator variance is explained by the latent variable, all of which have acceptable levels of significance, suggesting that the measures are reliable (Hair et al. 2017).
Heterotrait–monotrait (HTMT) measures for all construct pairs (Drug Trafficking–Captures; Socioeconomic Disadvantages–Captures; Socioeconomic Disadvantages–Drug Trafficking; Violence–Capture; Violence–Drug Trafficking; Violence–Socioeconomic Disadvantages) were less than 0.7, indicating that they are statistically different (Benitez et al. 2020).
Finally, we estimated path coefficients (Path). Path values were all positive and are shown in Table 5 with their respective significance level reached as well as the effect sizes f2. For the Socioeconomic Disadvantage → Violence relationship, the path coefficients are between 0.203 and 0.724, with seven of them being statistically different from zero. For the Drug Trafficking → Capture relationship, the range for path varies between 0.435 and 0.812. Only the lowest path coefficient, corresponding to the year 2013, is not statistically different from zero. For the Capture → Violence relationship, the path coefficients range from 0.076 to 0.380, with four of them statistically different from zero, resulting in the theoretical relationship having less evidence to be confirmed with the sample data.
Table 5 also shows the f2 effect sizes of the relationships between the constructs. The model showed a high effect for the relationship Socioeconomic Disadvantage → Violence for six years, a medium effect for two years, and a low effect for two years. No effect was less than 0.020, showing that social disadvantage has a substantial effect on violence expressed in homicides for the years studied. For the relationship Drug Trafficking → Violence, there was 8 years that show a high effect and two years with a medium effect. For the relationship Capture → Violence, no year showed a high effect, four years show a medium effect, three years show a low effect and three of the years show no substantial effect on this relationship.
Table 5 shows the R2 of the different years for the dependent variables Capture (between 0.118–0.659) and Violence (between 0.059–0.602). Since this is a model that presents relationships that have been little studied, the R2 found are a good approach to the study of the phenomenon.

5. Disscusion

This study examined the connections between homicides, drug trafficking, and socioeconomic disadvantages in Pereira, Colombia, which have a strategical role in an internal and global market of cocaine. Two theoretical frameworks informed this study. Goldstein’s framekwork informed the conceptualization of the drugs/violence victimization (Goldstein 1985). Zimring’s framework informed the influence of socioeconomic disadvantages in the drugs/violence relationship (Zimring and Hawkins 1997). The theoretical frameworks and findings of this study are contextualized within macro-political factors such as the global anti-drug strategy (Zaffaroni 2008, 2015) or Colombian anti-drug policies that have been characterized by very violent repressive securitized models, which are responsible for the violence (Arias and Goldstein 2010; Franz 2016; Thoumi 2012).
Empirical evidence supports our findings (Grogger and Willis 2000; Jacques and Wright 2011; Ouimet et al. 2018; Victor and Staton 2022) and the theoretical application of the model (Goldstein 1985; Ousey and Lee 2002; Zimring and Hawkins 1997). As Jacques and Wright (2011) noted, creating more specific knowledge that measures the drug/violence relationship is important. We think it is essential, especially in contexts such as those occurring in Latin America where global networks of the global cocaine trade coexist with domestic markets that increasingly demand more cocaine and its derivatives. While more research is necessary, the current findings may provide a basis for further adaptations of the theoretical understanding of the drug/violence nexus.

5.1. Drug/Violence Nexus

Overall, the drug–violence relationship was significant and produced moderately significant correlations (homicides related to drug seizures and captures of drug traffickers). This finding allows us to infer that drug trafficking influences (although we do not yet know the magnitude of this influence) the homicide dynamics of the city studied.
The influence of drug trafficking in the homicide dynamics in Colombian cities (with internal consumption markets and participation in global markets) is caused by situations such as the diversity of armed actors and DTO (Domínguez 2010), the strategic location of the city with respect to the cocaine production chain (Jiménez-García et al. 2021), the capacity to corrupt the authorities (Thoumi 1999), the capacity to defend strategic territories (Kostelnik and Skarbek 2013) and the weak social sanction produced in the Colombian population that has become accustomed to it (Thoumi 2005).
In addition, from Goldstein’s perspective of systemic violence, homicides in the city studied are related to the paraphernalia of drugs: the illegal nature of the domestic cocaine market, the inability to resort to the Colombian justice system to settle disputes between market players, and the sociability that exists between drug traffickers, consumers and inhabitants of a geographical space (Arias 2019; Espejo-Duarte 2021; Kenney 2007).
Although the model evaluated showed significance, we know that there are limitations in this study. For example, we do not know what influence the territorial scale used had when correlating all the variables. Nor do we know about the real influence of criminal actors on homicide rates and much less about the influence of the Colombian armed forces or the impact of international cooperation aid with its drug war strategies.

5.2. Socioeconomic Disadvantages/Violence Nexus

In general, the relationship between socioeconomic disadvantages and violence was significant and produced significant correlations (homicides related to households with limited income and single-parent female headship). This finding allows us to draw the inference that, in places with a high flow of drug trafficking, structural socioeconomic conditions drive homicide rates. This fact also supports Zimring and Hawkins (1997) that the drug/violence relationship is based on contingent causation in which socioeconomic variables are important to better understand the relationship.
The influence of socioeconomic conditions in violent places where drug trafficking operates is related to aspects such as low socioeconomic income (Manzano et al. 2020), the labor market, precarious access to goods and services (Jiménez-García et al. 2021), the geographic isolation of some city dwellers (Pina and Poom 2019), the concentration of wealth (Operti 2018) and the lack of state presence in large geographic sectors of the cities (Alzate-Zuluaga and Jiménez-García 2021).
Adverse socioeconomic conditions and the accumulation of precariousness (Alzate-Zuluaga and Jiménez-García 2021) are a breeding ground for drug trafficking organizations to recruit personnel for criminal activities. In cities such as Pereira, DTO are a legitimate source of employment (Thoumi 2006). Many families, given the unfavorable conditions of formal employment, obtain economic income by performing illegal activities of all kinds for the DTO. These conditions allow the DTO to acquire services at very low cost and with little risk to the authorities, since if the employee is caught, he or she can easily be replaced by another person in need of economic income.
Regarding the configuration of households, it must be said that single-parent households, especially those headed by women, are more exposed to violence (Jiménez-García et al. 2021). There are several reasons for this: (1) because the female heads of household must go out to work and their children are left alone at home and can be co-opted by criminal organizations; (2) because in very poor households, working for the DTOs becomes a possibility to meet basic needs; (3) because in cases of being victims of violence, these households usually do not have the necessary resources to face an adversity or calamity (Manzano et al. 2020).
Although the model was consistent in evaluating this socioeconomic disadvantages/violence relationship, we know that our study is limited because we were not able to evaluate other indicators such as educational levels, collective facilities, access to goods and services, territorial control, institutional trust, access to justice, living conditions, legal cynicism, social control, and wealth distribution, among others.

5.3. About PLS-SEM Uses

Our study was able to test a multivariate model combining variables that explain violence in a specific territory (Pereira, Colombia). The PLS method for applying the SEM technique showed consistency in the various years evaluated and for the TAUs studied. PLS-SEM has the statistical power to perform complex social problems such as the one studied (Victor and Staton 2022), even in small samples such as the one used in this study (Hair et al. 2021).
The model in general allowed us to see the influence and nexus between the latent variables studied and found significant correlations. This allows us to continue testing this model in other Colombian cities, perhaps with more socioeconomic variables and with more drug trafficking variables. To improve the model, we should consider including new variables that measure drug trafficking, such as the size of the drug market. Although we know that determining the size of the cocaine market is difficult and dangerous, we think that it can give us light to better understand this evaluated relationship.
Other limitations should be mentioned. This study used an analysis of existing data, so we had to use proxy measures, which is why the operationalization of the variables was not as precise as when using primary data. Future research may consider the development of temporal and spatial order, new datasets and qualitative variables to further explain these relationships.

6. Conclusions

In this study, we reviewed the theories and evidence relating to homicide nexus with drug trafficking and socioeconomic disadvantages. We demonstrate the usefulness of using the theoretical frameworks of Goldstein (1985) and Zimring and Hawkins (1997) to analyze the behavior of the variables studied in a Colombian city strategic for the Colombian domestic and global cocaine market. Future research that includes other strategic cities for cocaine commercialization could explore the relationship between lethal violence and other criminal markets.
Based on the evidence collected and the theoretical and statistical models evaluated, we can affirm that there is a relationship between violence/drug trafficking/socioeconomic disadvantages and that it is within the framework of contingent causation (Zimring and Hawkins 1997). Understanding this connection would allow us to improve strategies to reduce homicides (not only for Colombian cities), to combat drug trafficking in cities and to prioritize attention to social problems.
Improving the limitations of the present study, this model can be replicated in other cities with similar conditions to the one studied and compared with other cities that do not have the juxtaposition of markets, such as cities with consumer markets.

Author Contributions

W.G.J.-G. is the main researcher and obtained funding for the study. W.G.J.-G. and N.B.-B. coordinated the data collection. W.A.-V., together with N.B.-B. performed the data analysis. W.G.J.-G. drafted the manuscript with important contributions from N.B.-B. and W.A.-V. All authors read and approved the final manuscript.

Funding

This research was funded by Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS), Conv. 848-2019.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Restrictions apply to the availability of these data. Data was obtained from National Police of Colombia and, local government of Pereira. You can request the information with a formal request to the main author: w.jimenezgarcia@uniandes.edu.co.

Acknowledgments

We are very thankful (1) To John A. Jiménez-García (Concordia University) for Statistical consulting, (2) Gloria Giraldo for providing the socioeconomic information of the TAU; (3) To the National Police of Colombia for providing their data.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
PLS-SEMPartial Least Squares—Structural Equation Modelling
CNPColombian National Police
TAUTerritorial Administrative Units
HOMIHomicides
SMARMarijuana Seizures
SCOCCocaine Seizures
SHERCocaine Seizures
CAPTCaptured for drugs
SFHHSingle-Parent Female-Headed Households
SISBSISBEN
DTODrug Trafficking Organization
CRComposite Reliability
AVEAverage Variance Extracted
HTMTHeterotrait–Monotrait

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Socsci 12 00054 g001 550
Figure 1. Location of Pereira.
Figure 1. Location of Pereira.
Socsci 12 00054 g001
Socsci 12 00054 g002 550
Figure 2. Theoretical Model.
Figure 2. Theoretical Model.
Socsci 12 00054 g002
Table
Table 1. Description of the variables used.
Table 1. Description of the variables used.
Latent VariableObserved VariableCalculation
ViolenceHomicide (HOMI). Rate of homicidesHOMI = ∑ i = i j h o m i c i d e s p o p u l a t i o n · 100.000
Drug TraffikingMarijuana Seizures (SMAR). Rate of drug seized in gramsSMAR = ∑ i = i j M a r i j u a n a S e i z u r e s p o p u l a t i o n · 100.000
Drug TraffikingCocaine Seizures (SCOC). Rate of drug seized in gramsSCOC = ∑ i = i j C o c a i n e S e i z u r e s p o p u l a t i o n · 100.000
Drug TraffikingHeroine Seizures (SHER). Rate of drug seized in gramsSHER = ∑ i = i j H e r o i n e S e i z u r e s p o p u l a t i o n · 100.000
CapturesCaptured for drugs (CAPT). Rate of arrests for all drugsCAPT = ∑ i = i j C a p t u r e s p o p u l a t i o n · 100.000
Socioeconomic DisadvantagesSingle-female-headed-households (SFHH). Single-parent household rateSFHH = ∑ i = i j S i n g l e − p a r e n t f e m a l e − h e a d e d h o u s e h o l d s p o p u l a t i o n · 100.000
Socioeconomic DisadvantagesSISBEN. Rate of low-income households receiving government subsidiesSISB = ∑ i = i j S I S B E N p o p u l a t i o n · 100.000
Table
Table 2. Descriptive statistics (Pereira, 2010–2019).
Table 2. Descriptive statistics (Pereira, 2010–2019).
VariableMError SDSDMinQ1MedianQ3Max
HOMI38.942.3641.560.0014.5128.7349.22356.19
SMAR2,298,491776,48513,671,4240.0011,49341,318145,413133,092,610
SCOC10,725208236,6660635333547360,661
SHER10593926905002019994433
CAPT230.214.3252.30.086.2142.3264.51543.7
SFHH19,34075913,36182111,24717,89924,23385,727
SISB28,972104918,47536919,34127,46732,87195,740
Table
Table 3. Assessment of overall model fit.
Table 3. Assessment of overall model fit.
SRMR2010201120122013201420152016201720182019
Saturated model0.090.04 10.06 10.03 10.03 10.06 10.05 10.01 10.03 10.06 1
Estimated model0.110.06 10.06 10.07 10.05 10.180.07 10.02 10.04 10,08 1
1 These values are found to be less than 0.08, allowing us to accept the value for the assessment of the overall model fit (Benitez et al. 2020; Ruíz et al. 2014).
Table
Table 4. Measurement Model Assessment.
Table 4. Measurement Model Assessment.
Item 5Indicador2010201120122013201420152016201720182019
Drug TraffickingCR0.7290.9830.9530.9950.9780.9720.9231.0000.9990.936
Drug TraffickingAVE0.5750.9660.8720.9900.9360.7650.8000.9990.9980.829
Socioeconomic DisadvantagesCR0.9490.9500.9560.9590.9690.9720.9790.9820.9820.989
Socioeconomic DisadvantagesAVE0.9030.9050.9150.9200.9400.9450.9580.9650.9650.978
LoadingsSCOC40.988 30.844 240.986 20.657 10.797 30.999 20.999 20.900 2
LoadingsSHER0.819 340.975 30.995 20.966 10.962 20.949 21.000 340.869 1
LoadingsSMAR0.687 30.978 30.976 30.995 10.950 30.969 20.931 10.999 20.999 30.961 2
LoadingsSFHH0.959 30.935 30.941 30.935 30.970 30.967 30.9800 30.979 30.975 30.987 3
LoadingsSISB0.942 30.986 30.972 30.983 30.969 30.977 30.978 30.985 30.989 30.991 3
1p < 0.10, two-tailed test; 2 p < 0.05, two-tailed test; 3 p < 0.01, two-tailed test; 4 indicator was suppressed because its external loads were small and therefore not explained by the latent variable; 5 The latent variables Capture and Violence have a single indicator, so RC, AVE and loadings are equal to 1 and the latter have a value: p = 0—p-value = 0.
Table
Table 5. Structural Model Assessment: Path Coefficients, f2 and R2.
Table 5. Structural Model Assessment: Path Coefficients, f2 and R2.
Item 5Indicador2010201120122013201420152016201720182019
Socieconomic Disadvantage → ViolencePath0.573 20.327 10.523 30.2230.724 30.611 30.728 20.577 30.2030.374
Socioeconomic Disadvantage → Violencef20.5430.1310.4330.0521.3120.4281.2860.5060.0510.155
Drug Trafficking → ViolencePath0.737 30.344 20.684 20.4350.556 30.527 40.644 20.812 30.812 30.796 2
Drug Trafficking → Violencef21.2060.1340.8810.2340.4480.3840.7101.9301.9491.725
Capture → ViolencePath0.2700.268 40.326 20.0760.305 20.0800.2140.0730.380 20.058
Capture → Violencef20.1210.0890.1690.1690.2330.0070.1110.0080.1770.004
CaptureR2 40.5430.1180.4680.1890.3090.2770.4150.6590.6590.633
ViolenceR2 40.3970.2000.3700.0590.6020.4380.5880.3460.2160.154
1p < 0.10, two tailed test; 2 p < 0.05, two tailed test; 3 p < 0.01, two tailed test; 4 Weights closer to the absolute 1 reflect the strongest paths. Weights closer to 0 reflect the weaker routes (Benitez et al. 2020; Martínez and Fierro 2018). 5 f2 shows relationships among variables, R2 shows latent variables.
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MDPI and ACS Style

Jiménez-García, W.G.; Arenas-Valencia, W.; Bohorquez-Bedoya, N. Violent Drug Markets: Relation between Homicide, Drug Trafficking and Socioeconomic Disadvantages: A Test of Contingent Causation in Pereira, Colombia. Soc. Sci. 2023, 12, 54. https://doi.org/10.3390/socsci12020054

AMA Style

Jiménez-García WG, Arenas-Valencia W, Bohorquez-Bedoya N. Violent Drug Markets: Relation between Homicide, Drug Trafficking and Socioeconomic Disadvantages: A Test of Contingent Causation in Pereira, Colombia. Social Sciences. 2023; 12(2):54. https://doi.org/10.3390/socsci12020054

Chicago/Turabian Style

Jiménez-García, Williams Gilberto, Wilson Arenas-Valencia, and Natalia Bohorquez-Bedoya. 2023. "Violent Drug Markets: Relation between Homicide, Drug Trafficking and Socioeconomic Disadvantages: A Test of Contingent Causation in Pereira, Colombia" Social Sciences 12, no. 2: 54. https://doi.org/10.3390/socsci12020054

APA Style

Jiménez-García, W. G., Arenas-Valencia, W., & Bohorquez-Bedoya, N. (2023). Violent Drug Markets: Relation between Homicide, Drug Trafficking and Socioeconomic Disadvantages: A Test of Contingent Causation in Pereira, Colombia. Social Sciences, 12(2), 54. https://doi.org/10.3390/socsci12020054

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