Mental Illness and its Link to Illegal Street Market Activity: Prevalence,Risk Factors,and How They Compare to Violence

Abstract

Given the focus on research assessing violence among people with mental illness, other forms of deviance such as illegal street market offending have been relatively ignored. As such, the prevalence and risk factors for illegal street market offending among those with mental disorders is unknown. Utilizing the MacArthur Risk Assessment Study, the prevalence of illegal street market offending among this population is assessed along with the risk factors for engaging in this type of behavior. These factors are investigated for their generality in predicting violent offending to see if there are unique risk factors associated with illegal street market offending. Results indicate that factors related to money, factors related to substance usage, and general factors related to offending are significantly associated with illegal street market offending. Theoretical implications and future research are discussed.

Keywords

mental illness deviant behavior illegal street market offending fencing goods selling drugs

Much of the research examining criminality and mental health has focused on violent behavior among people with mental illness. In doing so, researchers have identified prevalence rates and risk factors associated with violent behavior among this population (Arseneault et al., 2000; Elbogen & Johnson, 2009; Estroff et al., 1994; Hiday, 1997; Link et al., 1999; Monahan et al., 2001; Mulvey, 1994; Silver & Teasdale, 2005; Silver, 2006; Swartz et al., 1998; Teasdale, 2009). More specifically, scholarship has shown that people with mental disorders are more likely than their non-disordered counterparts to commit acts of violence; however, numerous studies note that this is a modest association (Elbogen & Johnson, 2009; Monahan, 1992; Monahan et al., 2001; Mulvey, 1994; Swanson et al., 1990).

In examining factors that influence violent behavior among this population, several situational and dispositional risk factors have been identified. For example, factors related to having a mental illness such as comorbidity (Bubier & Drabick, 2009; Monahan et al., 2001; Steadman et al., 1998; Swanson et al., 1996), psychopathy (Douglas et al., 1999; Hemphill et al., 1998; Monahan et al., 2001; Skeem & Mulvey, 2001), and symptomology (Link et al., 1999; Link et al., 1998; Teasdale et al., 2006) have been implicated as significant predictors of violence among people with mental illness. Other research has identified that the factors that increase risk for violence for people with mental illness are similar to those that increase risk for others. Risk factors such as strain/stress (Link et al., 2016; Silver & Teasdale, 2005; Steadman & Ribner, 1982), substance abuse (Appelbaum et al., 2000; Elbogen & Johnson, 2009; Fazel et al., 2009; Steadman et al., 1998; Swartz et al., 1998), impulsivity (Bonta et al., 1998; Douglas & Skeem, 2005; Grisso et al., 2000; Monahan et al., 2001), and engaging in prior forms of criminal behavior and violence (Monahan et al., 2001) share some commonality with the general population.

Despite this knowledge regarding the extent to which people with mental illness engage in violence and the factors related to their violent offending, what is unknown is the extent to which people with mental illness also participate in other forms of criminality and what factors predict this involvement. More specifically, one type of criminality unexplored in the mental health literature is engagement in illegal street market activity, such as fencing goods or selling drugs. Although unknown, we suspect that people with mental disorders also engage in these types of offending and there may be unique risk factors associated with this type of criminality.

There are several reasons people with mental illness may engage in this specific form of non-violent offending that includes illegal street market activities such as fencing goods, running numbers, or selling drugs. First, scholars have documented that people with mental illness may encounter limited access to legitimate employment opportunities (Burns et al., 2007; Draine et al., 2002; Modini et al., 2016; Mueser et al., 2001). In fact, previous studies have documented that 95% of those with serious mental disorders are unemployed (Mueser et al., 2001). More recently, researchers have found that only 22% of people with serious mental illness were employed (Waghorn et al., 2012). This lack of formal employment opportunities may lead people with mental illness to engage in nonconventional strategies involving the illegal street market economy as a mean to supplement their income.

Second, a large proportion of the homeless population suffers from a mental illness (Fazel et al., 2008; Folsom & Jeste, 2002; Folsom et al., 2005; Fischer & Breakey, 1991; Nishio et al., 2017; Sullivan et al., 2000). In fact, prior studies have found that between one-fourth to one-third of people who are homeless also has a serious mental illness (Folsom & Jeste, 2002; Fischer & Breakey, 1991; Sullivan et al., 2000). As prior scholars have found, people who are homeless may turn to nonconventional survival strategies that often involve the street economy (Ferguson et al., 2011). Such strategies include participating in panhandling, selling stolen goods, or dealing drugs (Adlaf & Zdanowicz, 1999; Ferguson et al., 2011; Greene et al., 1999; O’Grady & Gaetz, 2004). Given that a large proportion of homeless individuals also have a mental illness, and homeless people may engage in the street market economy as a means of survival, it is possible that people with mental illness may engage in illegal street market activities for the same reasons – as means to survive.

Third, research has indicated that substance abuse disproportionately affects people with mental illness, with people with mental illness engaging in higher rates of alcohol and drug use than the general population (Gregg et al., 2007; Reiger et al., 1990). Research has also established that people who use drugs also commonly engage in illegal income-generating activities such as dealing drugs or engaging in sex trade work (Benson et al., 1992; DeBeck et al., 2007; Nurco et al., 1985). Considering that people with mental illness have high rates of substance abuse, and engaging in substance abuse often leads to illegal income-generating activities, it is plausible that people with mental illness will also engage in such behaviors.

Finally, it is notable that three risk factors (i.e., homelessness, unemployment, and substance abuse) associated with mental illness often co-occur, which increases the likelihood of engaging in illegal street market activities. That is, prior research has found considerable overlap between homelessness, mental illness, and alcohol/drug dependence (Fazel et al., 2008). Further, job skills and employment opportunities are often neglected for people experiencing these circumstances (Tyler & Johnson, 2006). Because of the lack of employment opportunities, increased risk for homelessness, and increased likelihood to engage in substance abuse, it is likely that people with mental illness are at heightened risk to engage in illegal street market activities. Importantly, participating in illegal street market activities can further perpetrate the cycle of homelessness, drug abuse, and illegal activities, which may lead to exclusion from the labor force (Baron, 2004; Baron & Hartnagel, 1997).

In addition to these risk factors, there are other factors that may increase the likelihood that a person engages in illegal street market activities. In general, the literature examining non-violent offending suggests that there are three key domains of risk factors that predict non-violent offending. These domains include factors related to money, substance usage (as already discussed) and general factors related to offending. For example, scholars have found that factors related to money, such as the perceived need for cash (Copes & Vieraitis, 2009; Feeney, 1986; Hochstetler, 2001; Jacobs & Wright, 1999; Katz, 1991; Shover & Honaker, 1992; Topalli, 2005) significantly influences forms of non-violent offending among the general population. General factors related to offending such as street culture (Akerstrom, 1985; Anderson, 1994; McCarthy & Hagan, 1992) and the pursuit of the never-ending party (Brezina et al., 2009; Jacobs & Wright, 1999; Shover & Honaker, 1992; Topalli, 2005; Wright et al., 2006) have also been identified as motivations to engage in non-violent offenses.

Although people with mental illness may engage in illegal street market activities, there has yet to be research examining this phenomenon. Additionally, a comparison of the prevalence of engagement in illegal street market activities to violent offending and whether the risk factors are similar across offending types has not been previously examined. This omission is an important oversight as identifying prevalence rates and potentially unique risk factors associated with criminality have important intervention implications. That is, if people with mental illness engage in high rates of non-violent offending, such as illegal street market activities, and there are unique risk factors associated with such behaviors, then interventions should be designed to specifically target these behaviors and risk factors. It would also mean that researchers should consider evaluating engagement in various illegal behaviors in addition to violence.

For these reasons, the purpose of the current study is three-fold. First, we assess the extent to which people with mental illness engage in illegal street market activities, and compare this to involvement in violent offending. Second, we examine what risk factors are significantly associated with illegal street market activities. Lastly, we analyze if these risk factors are significantly associated with both engagement in illegal street market activities and violent offending among people with mental illness.

Methods

Data and Sample

Data were drawn from the MacArthur Violence Risk Assessment study (i.e., MacRisk study) (Monahan et al., 2001). MacRisk is a longitudinal study of post-release psychiatric patients from inpatient facilities in three cities—Kansas City, MO, Pittsburgh, PA, and Worcester, MA. A stratified random sample of all eligible psychiatric hospital admissions within the three sites was conducted (Monahan et al., 2001). To be eligible for the study, participants had to be between the ages of 18 and 40, White or African American (except for the Worcester site, which included Hispanics), and English-speaking. Further, to participate in the study participants must have a diagnosis of a major mental disorder including schizophrenia, schizophreniform, schizoaffective, depression, dysthymia, mania, brief reactive psychosis, delusional disorder, alcohol or other drug abuse or dependence, or a personality disorder (Monahan et al., 2001, pp. 150–151). Eligible participants were civil admissions who had been hospitalized for a median number of 10 days, but fewer than 21 days (Monahan et al., 2001). Eligible participants were stratified by age, race, and gender, resulting in a random sample of eligible patients within each stratum.

Data collection began in 1992, with enrollment of new patients continuing until 1994. Follow-up interviews were conducted every 10 weeks for a year, resulting in five waves of follow-up data ending the study in 1995 (for additional information on methodology see Monahan et al., 2001; Steadman et al., 1998; Teasdale, 2009).

After multiple imputations, the total analytic sample includes 1,062 people with mental disorders.¹ As shown in Table 1, 40% of the sample engaged in illegal street market offending and approximately 33% engaged in violent offending. The average age of the sample was approximately 30 years of age, and the sample was primarily White (70%) and male (58%). Approximately 40% of the sample had a primary diagnosis of major depression, 17% schizophrenia spectrum disorders, 13% bipolar spectrum disorders, 6% substance abuse/dependence disorder, and 2% had a primary diagnosis of a personality disorder.

Table 1.

Descriptive Statistics (n = 1,062).

	Mean or %	SE or N	Min-Max
Illegal street market offending	40.02%	425	0-1
Violent offending	33.21%	353	0-1
Factors related to money
Gambling	14.15%	150	0-1
Careless spending	46.21%	491	0-1
Difficulty w/ money	1.106	.034	0-3
Employment	30.91%	328	0-1
Other sources of income	51.94%	552	0-1
Factors related to substance usage
Alcohol use	62.89%	668	0-1
Drug use	34.21%	363	0-1
Factor related to homelessness
Homelessness	11.71%	124	0-1
Factors related to mental health
Symptomology	32.124	.335	−6.34-74.40
PD Schizophrenia	17.23%	183	0-1
PD Depression	40.49%	430	0-1
PD Bipolar	12.52%	133	0-1
PD Substance abuse/dependence	5.74%	61	0-1
PD Personality	1.98%	21	0-1
General factors related to offending
Impulsivity	57.76	.560	8-107
Personal reaction scale	.014	.036	−3.57-5.18
Victimization	38.67%	509	0-1
Violence at wave 1	39.70%	422	0-1
Control variables
Age	29.74	.192	18-40
White	69.68%	740	0-1
Male	58.00%	621	0-1
SES	64.34	.439	6-108

Note. SES = socioeconomic status.

Measures

Dependent variables

Illegal street market offending

To assess engagement in illegal street markets, the following question, “have you ever been involved in illegal/illicit activities such as buying/selling stolen property, selling drugs, running numbers, etc.?” is used. Notably, this question was only asked in wave two. Response options included yes (1) and no (0).

Violent offending

To assess violent offending, several sources of information from wave two were utilized including follow-up interviews with the participants, interviews with collateral informants, and official records (Monahan et al., 2001). Specifically, participants were asked if they engaged in any of the following behaviors including (1) pushing, grabbing, or shoving, (2) kicking, biting, or chocking, (3) slapping, (4) throwing an object, (5) hitting with a first or object, (6) sexual assault, (7) threatening with a weapon in hand, or (8) using a weapon (Monahan et al., 2001). Other aggressive acts included incidents of battery that did not result in an injury, but excluded verbal threats. A violent offending measure was created utilizing both violent behaviors and other aggressive acts, which is consistent with other researchers (see Harris & Teasdale, 2021; Skeem & Mulvey, 2001). If a participant engaged in at least one of the aforementioned behaviors, they were scored as 1 and scored as a 0 if they had not engaged in any of the violent behaviors at wave two.

Independent variables

To assess factors that could influence illegal street market and violent offending behaviors, several independent variables were included in the analyses. Because of our interest in illegal street market activity, we include variables that may be specific to this type of offending such as factors related to money like gambling or careless spending habits, factors related to substance usage such as drug and alcohol usage, and factors related to homelessness. In addition, we also include measures related to mental illness, such as symptomology and diagnostic category. Finally, we include measures related more generally to offending such as violent victimization or impulsivity.

Factors Related to Illegal Street Market Activity

Factors related to money

Because spending money that one cannot afford may lead to involvement in illegal street market behaviors to acquire necessary funds, several variables related to money utilization are included. In addition, if people have access to money either through employment or other sources, they may be less likely to engage in illegal street markets. As such, measures of this access are also included.

Gambling

To assess if a participant engaged in problematic gambling behaviors or not, the question, “have you gambled more money than you could afford in the past 2 years?” is used. Responses included yes (1) or no (0). Because the measure of gambling was only administered during the second follow-up interview, the variable from wave two is used.

Careless spending

The question, “have you been unable to buy necessities because you spent so much on unnecessary things in the past 2 years?” is used to assess careless spending behaviors. Responses included yes (1) and no (0). Similar to the measure of gambling, the measure of careless spending was also only administered during the second follow-up interview. As such, wave two is utilized for this measure.

Difficulty with money

To measure difficult in managing money, participants were asked during wave one, “how much difficulty do you usually have (or would have) managing your money by yourself (i.e., tasks such as keeping track of expenses, paying bills, or making money last until the end of the month)?”. Responses include (0) none, (1) some, (2) a lot, or (3) unable to do so. The referent group is unable to manage money.

Employment

As hypothesized above, it is possible that unemployment may lead a person to engage in illegal street market activities. To test this assertion, the question, “are you working (for pay) outside of your home now?” is used from wave one. If a participant is employed, they are scored as a 1. If the participant is unemployed, they are scored as a 0.

Other sources of income

To assess if the respondent has additional sources of income, the question, “do you have other sources of income, like child support, welfare, social security, etc.” is used from wave one. Responses include yes (1) or no (0).

Factors related to substance usage

As established in previous research, engaging in substance usage behaviors is significantly associated with illegal street market behaviors. To account for substance use, two measures from wave one assessing substance abuse are included in the analyses. First, a drug use measure is used. Specifically, participants are asked, “since [reference date], have you used any street drugs, even if it was just one time?”. Responses include yes (1) or no (0). Second, an alcohol use measure is also included in the analyses. The question, “since [reference date], have you had any alcoholic drinks?”. Respondents who have engaged in alcohol use are scored as a 1, and respondents who have not engaged in alcohol use are scored as a 0.

Factors related to homelessness

Because people who are homeless may engage in illegal street market behaviors, a homelessness measure is included from wave one. Participants were asked, “have you lived on the streets or in a shelter since the reference period?”. If a participant has lived on the streets since the reference period, they were scored as a 1 and scored as a 0 if they have not.

Factors related to mental health

Beyond these risk factors that have been identified as important within the general population, it is also possible that there are risk factors specific to mental health that predict engaging in illegal street market activities. The literature on violence perpetration suggests that symptomology or a diagnosis of substance abuse/dependence disorder is related to violence; thus, these factors may also be relevant for engagement in illegal street market activities. It is possible that a person who is experiencing heightened symptomology or is diagnosed with substance abuse/dependence disorder may be at risk to become homeless, which may lead to illegal street market activities. Another possibility is that these factors lead individuals to self-medicate, which could create a need for money. Because of these possibilities, two mental health variables are included in the analysis from wave one: symptomology and diagnostic category. To assess symptomology, the Brief Psychiatric Ratings Scale (BPRS) is used. The BPRS was administered through clinically trained interviewers in which the participants were rated at each follow-up wave on a number of negative symptoms. Examples include symptomology related to anxiety, grandiosity, or hostility. The BPRS consists of 18 items, each rated on a 7-point ordinal scale ranging from 1 (mild) to 7 (very severe) (Monahan et al., 2001). To measure symptomology, the sum of all of the BPRS items was taken at wave one, where higher scores reflect greater severity of the symptomology. To measure diagnostic category, diagnoses for participants were obtained during the baseline interview by a trained clinical interviewer. Utilizing definitions consistent with the DSM-III-R, there are five primary diagnostic categories including (1) major depression, (2) schizophrenia spectrum disorders, (3) manic spectrum disorders (including bipolar disorder), (4) personality disorders, and (5) substance abuse/dependence (Monahan et al., 2001). To account for diagnostic category, a series of dummy variables were created. The omitted reference category is major depression disorder.

General factors related to offending

Prior scholarship has found that certain general factors influence forms of offending. To account for these factors, several general measures related to offending are included from wave. First, a measure accounting for impulsivity is included. To measure impulsivity, the Barratt Impulsiveness Scale (BIS; Barratt, 1959) is used. MacRisk uses the 11th revision of the BIS and contains 30 questions that ask the participant how often they engage in impulsive acts (measured on a likert scale; 1 = rarely/never through 4 = almost always/always) (Monahan et al., 2001). Examples include “I find it hard to sit still for long periods of time,” or, “I say things without thinking”. The total impulsivity score is used for the analyses (Cronbach’s alpha = .76). Secondly, a personal reaction scale is also included. Briefly, the personal reaction scale is a portion of the personal reaction inventory included in MacRisk (Monahan et al., 2001) that measures the participants’ reactions to others in relation to irritation, jealousy, and interpersonal interactions. To create the personal reaction subscale, an exploratory factor analysis (EFA) was conducted. EFA results identified a single latent construct consisting of five items assessing a person’s feelings and reactions toward others (Cronbach’s alpha = .80).² Examples of items included in the personal reaction scale include, “sometimes I try to get even rather than forgive and forget”, or “sometimes I am jealous of other people’s good fortune”. Responses include false (0) and true (1). To create the personal reaction scale, the five items were standardized and the mean was taken. Finally, the measure violent victimization is also included as a general factor that is related to offending. Specifically, participants were asked if they have experienced eight violent victimization behaviors ranging from being pushed, grabbed, or shoved to having a weapon used against them (Monahan et al., 2001). If a participant experienced a violent victimization event, they were scored as a 1 and scored as a 0 if they had not experienced any victimization.

Control variables

Age

A continuous measure of participants’ age in years at admission is included as a control variable.

Race

Race is also included as a control variable. Specifically, participants who are White are coded as (1) and participants who are not White are coded as (0) (which includes Black and Hispanic participants).³

Gender

A measure of gender is included (males coded 1; females coded 0).

Socioeconomic status

Based on Hollingshead and Redlich’s (1958) operationalization of socioeconomic status (SES), SES is included as a control variable. More specifically, the Hollingshead’s Index of Social Position reflects scores created by the individual’s address, occupation, and number of years of school the participant had completed (Hollingshead & Redlich, 1958, p. 37). Each factor is given a scaled score and then multiplied by a factor weight. The three factors are then summed together to create a socioeconomic index. MacRisk’s SES measure reflects a continuous measure of the combined score of educational attainment and occupational status of the participants (Monahan et al., 2001). Higher scores reflect lower socioeconomic status.⁴

Analysis

Due to the sampling technique utilized by MacRisk, which includes interviewing the participants every 10 weeks for a year, there is an issue of missing data due to subject attrition. To account for this, multiple imputations, is utilized. Multiple imputations estimate missing values based on each of the participant’s previous observed values (Schafer & Graham, 2002). Because previous researchers have suggested that 40 imputed data sets can remove noise from statistical summaries (Graham et al., 2007), 40 imputed data sets were used and pooled for analysis. Multivariate logistic regression models were estimated on the pooled data to examine the factors related to engagement in illegal street market activities and violent offending (Hosmer & Lemeshow, 2000).

Results

Table 2 presents the findings from the two logistic regression models examining the relationship between factors related to money, substance usage, homelessness, and general factors related to offending on illegal street market offending and violent offending, holding constant demographic controls. As can be seen in Model 1 of Table 2, factors related to money, factors related to substance usage, and general factors related to offending are all significantly related to illegal street market offending. More specifically, among people who gamble, the odds of engaging in illegal street market offending increases by 67.3%. Furthermore, for people who engage in careless spending habits, the odds of engaging in illegal street market offending doubles (OR: 2.079). One factor related to substance usage is also significant. Among people who use drugs, the odds of engaging in illegal street market offending doubles (OR: 2.170). Finally, both impulsivity (OR: 1.018) and the personal reaction scale (OR: 1.289) significantly increases the odds of engaging in illegal street market offending. Lastly, two control variables were significantly associated with this type of offending including race and gender. In fact, the odds of engaging in illegal street market offending significantly decreases among those who are White compared to those who are Non-White (OR: .518) and significantly increases among males compared to females (OR: 1.639).

Table 2.

Multivariate Logistic Regression Predicting Illegal Street Market and Violent Offending (n = 1,062).

	Model 1: Predicting illegal street market offending at wave 2				Model 2: Predicting violent offending at wave 2
	b	se	OR	CI	b	se	OR	CI
Factors related to money
Gambling	.515*	.248	1.673	1.028-2.722	.360	.252	1.434	.872-2.355
Careless spending	.732***	.186	2.079	1.441-2.999	−.086	.195	.917	.625-1.345
Difficulty w/ money (no difficulty)	−.378	.342	.686	.350-1.343	.098	.352	1.103	.552-2.204
Difficulty w/ money (some difficulty)	−.287	.315	.751	.404-1.395	.352	.310	1.384	.753-2.546
Difficulty w/ money (a lot of difficulty)	−.516^†	.304	.597	.329-1.083	.154	.330	1.127	.923-1.378
Employment	.131	.251	1.140	.696-1.866	.255	.273	1.291	.753-2.212
Other sources of income	.066	.188	1.068	.737-1.547	.184	.190	1.197	.827-1.746
Factors related to substance usage
Alcohol use	−.126	.183	.882	.615-1.264	.180	.212	1.197	.789-1.817
Drug use	.775***	.197	2.170	1.473-3.195	.116	.212	1.1222	.739-1.705
Factor related to homelessness
Homelessness	.444^†	.259	1.558	.936-2.5593	−.770*	.337	.463	.238-.901
Factors related to mental health
Symptomology	−.010	.010	.990	.970-1.011	.008	.010	1.008	.987-1.029
PD Schizophrenia¹	−.226	.247	.798	.492-1.295	−.596*	.268	.551	.326-.933
PD Bipolar¹	.071	.272	1.074	.629-1.834	−.376	.273	.686	.401-1.174
PD Substance abuse/dependence¹	.550	.345	1.734	.881-3.414	−.351	.356	.704	.350-1.417
PD Personality¹	.420	.528	1.522	.541-4.283	−.299	.559	.742	.248-2.221
General factors related to offending
Impulsivity	.018**	.006	1.018	1.006-1.030	.006	.006	1.006	.995-1.018
Personal reaction Scale	.254**	.092	1.289	1.075-1.546	.120	.102	1.127	.923-1.378
Victimization	.159	.205	1.173	.784-1.754	.824***	.185	2.280	1.586-3.277
Violence at wave 1	.340^†	.195	1.405	.958-2.060
Non-violent offending					.483**	.187	1.620	1.121-2.341
Control variables
Age	.001	.013	1.001	.976-1.027	−.026^†	.014	.974	.948-1.001
White	−.658***	.180	.518	.364-.737	−.439*	.190	.645	.444-.936
Male	.494**	.163	1.639	1.191-2.256	−.117	.186	.890	.618-1.282
SES	−.002	.009	.998	.980-1.016	.018^†	.010	1.018	.998-1.039

Note. SES = socioeconomic status; ¹PD Depression is the referent group.

^†p <.10. *p < .05. **p < .01. ***p <.001.

Model 2 of Table 2 presents the findings for factors related to violent offending. Interestingly, factors related to money and substance usage are not significantly associated with violent offending. Rather, homelessness, mental health factors, and general factors related to offending such as experiencing violent victimization, and engaging in illegal street market offending predicts violent offending. In particular, among people who are homeless, the odds of engaging in violent offending decreases (OR: .463). Further, one mental health factor was related to violent offending. More specifically, the odds of engaging in violent offending decreased among those with a diagnosis of schizophrenia compared to those diagnosed with depression (OR: .551). Finally, among people who experienced a violent victimization event, the odds of engaging in violent offending more than doubles (OR: 2.280) and among people who engage in illegal street market offending, the odds of engaging in violent offending increases by 62%. Lastly, one control variable was significantly associated with violent offending including race. Specifically, the odds of engaging in violent offending decreased among people who were White compared to Non-White individuals (OR: .645).

Discussion

The role of mental illness in violent outcomes has been well-established. What has yet to be fully explored are other forms of criminality including illegal street market activities such as fencing goods or selling drugs. Further, the rate of involvement in illegal street market activities, as well as predictors related to this form of criminality has not been determined. Our research explores this gap and contributes at least five main findings.

First, we demonstrate that people with mental illness engage in illegal street market offending at slightly higher rates than violent offending. In fact, among our sample, 40% of people with mental disorders engaged in illegal street market offending compared to 33% of people who committed acts of violence, which is a statistically significant difference (p < .001). The finding that the rate of involvement is actually higher for illegal street market offending than violent offending suggests the need to expand and explore other forms of criminality in which people with mental illness may engage.

Second, there are unique predictors, including factors related to money, factors related to substance usage, and general factors that only correlated with illegal street market offending. Specifically, the odds of engaging in illegal street market activities such as fencing stolen goods or selling drugs doubles among people with mental illness who engage in careless spending practices. Further, among people who gamble, the odds of engaging in the illegal street market economy increase by approximately 67%. As hypothesized earlier, because people with mental illness may lack formal means of obtaining money (Burns et al., 2007; Draine et al., 2002; Modini et al., 2016; Mueser et al., 2001), it may be necessary for this population to engage in nonconventional means to supplement their income such as fencing stolen goods or selling drugs.

In addition to factors related to money, we also found that factors related to substance usage significantly increased the odds of engaging in illegal street market activities. In particular, among people with mental disorders who used drugs, the odds of engaging in illegal street market activities doubles. Given that substance abuse disproportionately affects people with mental illness (Gregg et al., 2007; Reiger et al., 1990), and researchers have found that people who use drugs also commonly engage in illegal income-generating activities (Benson et al., 1992; DeBeck et al., 2007; Nurco et al., 1985), it is not surprising that this sample would engage in high rates of illegal street market activities and this engagement is related to substance usage. Notably, factors related to money and factors related to substance usage have also been key domains of risk factors related to non-violent offending in the general population and can now be extended to illegal street market activities among people with mental illness.

General factors related to offending were also unique predictors of illegal street market behaviors among people with mental illness. More specifically, among people who had higher impulsivity scores and higher scores on the personal reaction scale, the odds of engaging in illegal street market activities significantly increases. Given that impulsivity and low self-control are linked to a host of negative outcomes including violent offending, victimization, or drug abuse within the general population (Agnew et al., 2011; Schreck et al., 2006; Turanovic & Pratt, 2014) and population of people with mental illness (Bonta et al., 1998; Douglas & Skeem, 2005; Grisso et al., 2000; Monahan et al., 2001), it is unsurprising that higher levels of impulsivity would also be linked to engaging in illegal street market offending. That is, because people who have lower levels of impulse control are more likely to engage in riskier behaviors with disregard to consequences related to such behaviors (Schreck, 1999; Pratt & Cullen, 2000), it is possible people with mental disorders engage in illegal street market offending, in part, because of the disregard for consequences related to such behaviors and lack of control to refrain from such acts. Further, given that the personal reaction scale measures how people interact with and respond to others, it makes sense that those who are irritated, resentful, and jealous would engage in behaviors to improve their position, like selling drugs. This relationship may be particularly relevant when considering those who are under fewer social controls, which may be common for people with mental illness.

Third, although we expected homelessness to be related to illegal street market offending among people with mental disorders, we found that its p value (.09) did not meet traditional thresholds for significance. This finding could be due to the low rate of homelessness in our sample. In fact, only approximately 12% of the sample reported that they were homeless. When considering the magnitude of the effect, it does seem that homelessness is an important factor in understanding illegal street market activity. That is, for those who are homeless, the odds of engaging in illegal street market offending increased by approximately 56%. It is possible that with a larger sample and a larger sample of homeless individuals, that the effect of homelessness would be significant at traditional levels of significance. Future research should include measures of homelessness but also ensure that their samples include a range of individuals in a variety of housing situations.

Fourth, although unexpected, none of the factors related to mental health were significantly associated with illegal street market offending. Although we expected that experiencing heightened symptomology would be related to illegal street market offending, we did not find this to be the case. It is possible that symptomology is not related to illegal street market offending because of the nature of the crime. In other words, it may be difficult to run numbers, sell drugs, or buy/sell stolen property if a person is experiencing heightened symptomology. Future research should further examine the relationship between differing types of crimes and their relations to symptomology. Further, it is surprising that people diagnosed with substance abuse/dependence disorder were not facing greater odds of engaging in illegal street market activities. It is notable, however, that only 6% of the sample were diagnosed with substance abuse/dependence disorder. Our findings suggest that it is substance usage rather than diagnosis that is related to engaging in illegal street market offending. Those who have been diagnosed (and treated) may be less likely than others to be using illegal drugs; hence, a diagnosis alone may not reflect the behaviors that precede offending. Given that the MacRisk sample contains patients who are post-release from inpatient treatment, and scholars have found that there are short-term reductions in substance usage after treatment among the substance abuse/dependence diagnostic category (Gossop et al., 2008; Grella et al., 2001), it is likely that the lack of effect related to substance abuse/dependence disorder can be attributed, in part, to receiving treatment.

Fifth, there were few predictors that were significantly associated with violent offending among our sample. More specifically, homelessness was a significant predictor of violent offending, but not in the expected manner. In fact, the odds of engaging in violent offending decreased among those who were homeless. Although some studies have found that there is an increased risk of engaging in violence among people who are homeless and have a mental illness (e.g., Martell, 1991; Swanson et al., 2002), other scholars have found a lack of association between homelessness and violence (e.g., Silver et al., 2011). It is possible that our finding of homelessness reducing violent offending may be related to proximity to attractive targets. In other words, it is possible that being homeless may reduce exposure and opportunity to others who may be attractive targets for violent offending due to their marginal status. As suggested by prior researchers (e.g., Lee & Schreck, 2005), individuals who are homeless are often considered as “outsiders” and excluded from membership in society. Because of this exclusion from society, it is possible that others avoid or fail to interact with people who are homeless, which could then result in a lack of potential targets for violent offending. It is also possible that the relationship between homelessness and violent offending may be linked to certain diagnostic categories or length of homelessness. Future research should examine these nuances in relation to mental illness, homelessness, and violent offending. Further, only one general factor related to offending was significantly correlated with violent offending among people with mental illness. Specifically, as already established in the victim-offender overlap (Broidy et al., 2006; Gottfredson, 1981; Lauritsen & Laub, 2007; Sampson & Lauritsen, 1990; Schreck et al., 2008; Silver et al., 2011), among people who were previously a victim, the odds of engaging in violence more than doubles (OR: 2.28).

Finally, it should be noted that there is overlap in the types of criminality among people with mental illness. That is, among people who engage in violent acts, the odds of engaging in forms of illegal street market activities significantly increased by 62% and among people who engaged in illegal street market offending, the odds of engaging in violence were marginally increased by approximately 41%. This finding suggests that a proportion of the population who has a mental illness is engaging in multiple forms of deviance, a finding that has yet to be explored. Notably, this is consistent within the general offending literature that suggests offenders often engage in multiple forms of deviance (Gottfredson & Hirschi, 1990; Moffitt et al., 2000; Piquero et al., 2014). As such, future research should further explore the engagement of multiple forms of offending for people with mental illness.

Collectively, our findings have implications regarding prevention and future mental health research. Given that many predictors related to illegal street market activities are malleable, these factors can be targeted in crime prevention efforts. For example, factors regarding money management and substance abuse can be amended through prevention efforts. Promising examples include money management interventions such as Advisor-Teller Money Manager (ATM) money management strategy (Black & Rosen, 2011) or occupational therapy interventions aimed to increase employment, money management, and coping skills (see Thomas et al., 2011 for review). Further, screening instruments that assess impulsivity or interpersonal interactions with others can be administered in clinical or institutional settings. Through the use of these screening tools, clinicians may be able to identify those at risk to engage in illegal street market activities and intervene in ways to reduce this type of behavior.

Given that our findings suggest that people with mental illness may actually engage in higher rates of deviance such as illegal street market activities as compared to violence, our study shows the need to further explore offending types beyond violence. It would be useful for future research to examine other forms of deviance, especially if rates of involvement may actually be higher than for violence. Knowing the risk factors associated with other forms of deviance can help identify targets for change and programs that do so may reap benefits. Further, since our research suggests that there are unique risk factors for involvement in illegal street market activities, prevention and intervention efforts must be tailored with these specific risk factors in mind. Using a one-size fits all approach to reduce involvement in crime generally (including violence) may not be efficacious for reducing illegal street market activity. Being able to reduce engagement in illegal street market activity could provide substantial, tangible benefits. For example, there could be substantial monetary benefits to reducing drug selling insomuch that it would reduce usage. Specifically, scholars have found that depending on the type of drug, social costs related to direct medical costs and indirect costs related to lost current and future earnings are estimated to range from $6.1 billion to $49.3 billion (Caulkins et al., 2002). Given the large social cost associated with certain forms of illegal street market activities, it is imperative to understand and target factors that contribute to these types of behaviors. In doing so, we may be able to reduce the high social cost associated with this form of deviance.

Limitations

Despite our unique findings, this work has some limitations. In regards to measurement, our illegal street market offending measure lumps in several different forms of deviance including fencing stolen goods, running numbers, and selling drugs. Future research should explore other measures of non-violent offending and disaggregate those measures so the extent of and risk factors for specific forms of offending can be investigated. Further, the violent offending measure consisted of eight categories that vary in severity; however, given the low rate of involvement in violence, we are unable to examine counts of violence within the current data. Our data also prohibit us from firmly establishing time order for two of our independent variables: gambling and engaging in forms of careless spending. These measures were only administered at wave two; thus, our findings speak to the relationship between these variables and our offending measures but do not allow for causality to be investigated. Future research should incorporate these factors into a longitudinal analysis to determine if they are predictors of illegal street market offending.

Conclusion

Despite these limitations, our study explores the extent of and factors related to illegal street market offending among people with mental illness. In doing so, we found that people with mental illness engage in higher rates of illegal street market offending than violent offending, and there are unique factors related to illegal street market offending such as factors related to money, substance usage, and general factors related to offending. This investigation into this form of offending suggests the need for continued research into multiple forms of offending rather than the focus on violence.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Michelle N. Harris

Notes

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