Abstract
Hispanic/Latinos are disproportionately represented in the criminal justice system. Using convenience sampling, the present study examined the lifetime and recent offending behavior of Hispanic/Latinos involved in community corrections in Miami, Florida. Descriptive statistics and multivariable logistic regression analyses were conducted. Participants were mostly male (59.7%), less than 40 years old (84.1%), and almost half were of Cuban descent (48.5%). Women were less likely to manufacture or sell drugs than men (AOR=.42, p<.03), and more likely to report recent prostitution (AOR=7.34, p< .001) and stealing from houses or shops (AOR=2.68, p<.01). Central Americans were less likely to report alcohol and drug related offenses than Cubans. Findings suggest that criminality among Hispanic/Latinos may vary by gender and by sub-groups. Prevention programs should be tailored accordingly.
Keywords: Latinos, Hispanic, Criminal Justice, Gender, Ethnic Difference, Offending Behavior
The United States Office of Management and Budgets (OMB) use the term “Hispanic or Latino” to describe those who are from or descended from people from Latin America (including Brazil), Spain, or any Spanish-Speaking Caribbean countries (U.S. Census Bureau, 2018). This would include, for example, people of Mexican, Puerto Rican, Cuban, Central American, and South American origin (U.S. Census Bureau, 2018). For the present study, the term “Hispanic/Latino” will be used to denote this population. Hispanic/Latinos are the largest minority group in the United States (United States Census Bureau, 2016) and from 2015–2016 were the only racial/ethnic category to see increased rates of incarceration in both men (1,043 to 1,092 per 100,000) and women (63 to 67 per 100,000) (Bureau of Justice Statistics, 2016 ).They are disproportionately represented in the criminal justice system, with approximately one in three federal prisoners being Hispanic/Latino (Federal Bureau of Prisons, 2018). They are also more likely to be detained, (Chanin, Rintels, & Drachsler, 2011), disproportionately represented among probation populations (Hill, 2015), and treated unequally within the criminal justice system (Demuth & Steffensmeier, 2004). Of the 4.65 million, or 1 in 53 adults, in community corrections, (Bureau of Justice Statistics, 2016), Hispanic/Latinos represent 13% of all probationers and 16% of all parolees (Bureau of Justice Statistics, 2016). Despite these trends, there is limited data on Hispanic/Latinos involved in the criminal justice system. According to the Urban Institute (2016), within the United States, individual states do not consistently collect data on ethnicity with only 38 states reporting data on Hispanic/Latinos in prison, and only 20 and 18 states report data on Hispanic/Latinos in the parole and probation populations, respectively. According to the Florida Department of Corrections (n.d.), the community corrections population in Florida includes 17.3% Hispanic/Latinos. Although there is data on the types of offenses in general within the community corrections population in Florida, there are no data on types of offenses for only Hispanic/Latinos in community corrections in Florida. In general, studies are needed that focus on the disparities experienced by Hispanic/Latinos in the criminal justice system.
Disparities in incarceration are evident by ethnic group and gender. In states with particularly high ethnic incarceration disparities, Hispanic/Latino men are incarcerated at rates between 4.3 and 3.1 times greater than White offenders (Nellis, 2016). Hispanic/Latinos, especially men, have higher arrest rates in states with a large Hispanic/Latino population (Malavé & Giordano, 2015) such as Florida. Although there are more men than women in the criminal justice system in the U.S., female imprisonment has increased at a 50% higher rate than men since 1980; and Hispanic/Latino women are 1.2 times more likely to be incarcerated than white women (The Sentencing Project, 2015). The literature on Hispanic/Latino women involved in the criminal justice system is scant. The present study attempts to address this gap by examining the gender, age, and Hispanic/Latino subgroup differences in offending behavior among this population.
Gender differences in offending behavior
For decades, studies have shown gender differences in criminal activity and delinquency with males reporting more criminogenic behaviors (Mazerolle, Brame, Paternoster, Piquero, & Dean, 2000; Mears, Ploeger, & Warr, 1998; Shekarkhar & Gibson, 2011). Men are more likely to commit violent crimes and public order offenses, whereas women are more likely to be incarcerated for drug and property offenses (The Sentencing Project, 2015). The spike in recent female incarceration rates is attributed to an increase in drug related behavior (The Sentencing Project, 2015). Among all racial/ethnic groups, the largest gender difference in the decision to incarcerate were found for Hispanic/Latinos, with men more likely to be incarcerated than women (Doerner, 2015). Past research has reported on the interaction between race/ethnicity, gender and age among offenders, and found that Hispanic/Latino male offenders, and especially young Hispanic/Latino males, were more likely to be sentenced to prison as compared to White offenders (Doerner & Demuth, 2010; Franklin, 2018). Another recent study examined gender differences by drug offense and found that drug offenders received longer incarceration sentences than non-drug offenders; and that Hispanic/Latino women were 44% less likely to be incarcerated than Hispanic/Latino men for a drug offense (Doerner, 2015).
However, some studies report no difference in offending behaviors (Mazerolle et al., 2000). Looking at gender differences has become increasingly important as the rate of incarceration of women has increased by 700% between 1980 and 2014 (The Sentencing Project, 2015). Particular attention should be given to Hispanic/Latino women since they are at a higher risk of incarceration than white women (The Sentencing Project, 2015), and only a limited number of studies look at gender differences within Hispanic/Latinos. In fact, most of the research on gender differences among Hispanic/Latinos has focused on teens. For example, one study found that second-generation Hispanic/Latino have higher rates of delinquency in their early teens than males, but this difference converges by around age 15 (Powell, Perreira, & Mullan Harris, 2010). More research is needed on adult Hispanic/Latino women.
Age differences in offending behavior
Age differences in criminal activity has also been reported. Studies on the trajectory of offending behavior shows that as offenders get older, there is a decrease in criminal activity (Bersani, Nieuwbeerta, & Laub, 2009; Doherty & Bersani, 2018). This “age-crime curve” has been shown to be true throughout time and across races (Fabio, Tu, Loeber, & Cohen, 2011). Previous studies also suggest that the earlier the age of onset for criminal activity, the more likely the offending behavior is to continue (Farrington, 1986; Moffitt, 1993). Of arrests made in 2010 for people under 18 years of age, the most frequently reported offenses included: Larceny-theft (281,100 arrests), other (simple) assault (210,200 arrests), drug abuse violation (170,600 arrests), and disorderly conduct (155,900 arrests) (National Center for Juvenile Justice, 2014); young adults are more likely to be involved with drug violations, vandalism, assault charges, larceny-theft, prostitution, disorderly conduct, forgery, fraud, drunk driving and domestic abuse than older adults (FBI, 2012). Individuals above thirty years old are more likely to be involved with larceny-theft, crimes against others, burglary and property damage (FBI: UCR, 2012). While there is evidence that the link between criminality and age exists, there is no clear evidence on how this relationship applies to Hispanic/Latinos. Considering that the Hispanic/Latino population is the youngest minority in the US, with one third of Hispanic/Latinos being under eighteen (Patten, 2016), it is important to understand the criminality patterns of Hispanic/Latinos according to age.
Hispanic/Latino subgroup differences and offending behavior
The term Hispanic/Latino encompasses a diverse group of people, who are heterogeneous in terms of nativity, immigration status, language, and acculturation which are important factors to understand when creating culturally tailored interventions (Gonzalez Burchard et al., 2005; Ibañez et al., 2016; Schwartz, Montgomery, & Briones, 2006). Most studies on Hispanic/Latino subgroups look at acculturation and compare US born versus foreign-born Hispanic/Latinos. US born Hispanic/Latinos are more likely to report violent offending behaviors (Shekarkhar & Gibson, 2011), to be involved in lifetime and recent crime, to use illicit drugs (Ibañez et al., 2016; Ibañez et al., 2017; Mancini, Salas-Wright, & Vaughn, 2015), and to be arrested for miscellaneous offenses or property crime (Ibañez et al., 2016) compared to foreign-born Hispanic/Latinos.
Fewer studies have examined Hispanic/Latinos by region or country of origin. However, one study found that Central Americans are more likely to be arrested for violent and domestic crimes than other Hispanic/Latino subgroups (Gil-Rivas, Anglin, & Annon, 1997); and another study found that second generation Cubans on government assistance are more likely than second generation Mexicans to engage in crime (Ortiz & Telles, 2012). Increased insight on the differences among Hispanic/Latinos is essential in creating culturally competent services in reaching out to Hispanic/Latino subpopulations that may be criminal justice involved (Ibañez, et al., 2016).
The present study
The present study is an exploratory, descriptive study that examines a variety of specific offenses, both violent and non-violent offenses, and comparing offending behavior by gender, age, and Hispanic/Latino subgroups (i.e., Cuban, Puerto Rican, Central American, South American) among a sample of Hispanic/Latinos involved in community corrections in Miami, Florida. Findings will help in the development of age, gender, and culturally appropriate interventions. The findings in this study will help better target services for the Hispanic/Latino criminal justice population, which is often neglected in prevention interventions (Ibañez et al., 2016). The research questions are the following: 1) what offenses are most common among Hispanic/Latino criminal justice clients by gender, age, and Hispanic/Latino subgroups; and 2) are there any differences in offending behavior by gender, age, or Hispanic/Latino subgroups?
Methods
Participants
Participants were involved in community corrections and were recruited from local jail diversion and court-mandated substance abuse service programs in the Miami-Dade County area. Recruitment was done via flyers posted in waiting rooms, word of mouth, research staff presentations, and through referrals from the community agency staff. Eligibility criteria included the following: must self-identify as Hispanic/Latino, age 18–49, report heterosexual preference or behavior, currently or recently involved in the criminal justice system (within the last 3 months); and current or recent drug use (within the last 3 months). Potential participants were screened for eligibility either by phone or in person at one of the community agencies. During the brief screening process, the research staff would explain the study, emphasize that all information was confidential, and then proceed to ask a few questions to determine eligibility.
Measures
Demographic information was collected on age, gender (male=1, female=2), ‘what is your ethnicity?’ (1=Cuban, 2=Puerto Rican, 3=Central American, 4=South American, 5=Other), country of origin (“where are you from originally?”), education level (e.g., did not attend high school=1 to Professional degree = 8), and income level (“less than $20,000” = 1 to “over $100,000 = 7). Crime history data were collected using the Criminal Justice Drug Abuse Treatment Studies (CJ-DATS) assessment tool (Prendergast, n.d.). The crime history tool asks the participants whether they have ever been involved in 24 different types of criminal activity regardless of arrest (e.g., “have you ever been involved in public intoxication”, “have you ever been involved in possession with intent to sell”). Recent criminal activity was assessed by asking participants if they had been involved in these same 24 criminal activities in the last 90 days. Except for the demographic variables, all variables were dichotomous (yes/no).
Procedures
Once deemed eligible, participants were scheduled for a baseline interview. Baseline interviews were conducted at either the research field office, at one of the study community partner agencies, or a private location convenient to the participant. At the interview, trained, bilingual research staff provided participants with a detailed explanation of the study, and an informed consent form to sign. Interviews lasted approximately 90 minutes and were administered using the computer assisted personal interviewing (CAPI) method. At the end of the baseline interview, participants were provided a $30 monetary incentive for their participation. This study was approved by the Institutional Review Boards (IRB) of the University of Delaware and Florida International University.
Data Analysis Plan
Data were analyzed using SAS (v9.4; SAS Institute Inc., Cary, NC). Descriptive characteristics were used to report sample characteristics and crime frequencies. Univariate logistic regression models were conducted to assess the odds of each demographic variable on committing different types of crimes. Demographic variables were then analyzed together in a multivariate logistic regression analysis.
Results
Demographics
A total of 200 participants were interviewed in this study. Country of origin varied with 70.0% of the sample from the U.S., 11.0% from Cuba, 5.5% from Puerto Rico, 4.0% from Colombia, 2.0% from Nicaragua, 1.5% from Honduras, 0.5% from Guatemala, 0.5% from the Dominican Republic, and 5% identifying as other. As shown in Table 1., the sample was comprised of a majority male (60.0%), 18–28 year old (49.0%), Cuban (48.5%), Never married (75.0%), white (52.5%), high school educated (33.5%), unemployed (55.0%), and making less than $20,000 per year (60.5%) participants. Twenty-three participants were not included in yearly income as they did not respond. Descriptive statistics for each lifetime and recent criminal activity is shown in Table 2.
Table 1.
Baseline characteristics of the sample (N=201)
| N | % | |
|---|---|---|
| Gender | ||
| Male | 120 | 60.0 |
| Female | 80 | 40.0 |
| Age | ||
| 18–28 | 98 | 49.0 |
| 29–39 | 69 | 34.5 |
| ≥40 | 33 | 16.5 |
| Latino Sub-group | ||
| Cuban | 97 | 48.5 |
| Puerto Rican | 27 | 13.5 |
| Central American | 19 | 9.5 |
| South American | 18 | 9.0 |
| Other | 39 | 19.5 |
| Marital Status | ||
| Never married | 150 | 75.0 |
| Legally married or living as married | 12 | 6.0 |
| Separated | 13 | 6.5 |
| Divorced | 24 | 12.0 |
| Widowed | 1 | 0.01 |
| Race | ||
| White | 105 | 52.5 |
| Black/African American | 11 | 5.5 |
| Other | 84 | 42.0 |
| Level of education | ||
| <high school | 45 | 22.5 |
| High school (GED) | 67 | 33.5 |
| some college | 51 | 25.5 |
| ≥Associates Degree | 37 | 18.5 |
| Employment status in the last 90 days | ||
| Employed full time (35+ hours per week) | 52 | 26.0 |
| Employed part time | 28 | 14.0 |
| Unemployed | 110 | 55.0 |
| Other | 10 | 5.0 |
| Yearly Income* | ||
| Less than 20,000 | 107 | 60.5 |
| 20,000– 29,999 | 26 | 14.7 |
| 30,000–39,999 | 9 | 5.0 |
| 40,000–49,999 | 8 | 4.5 |
| ≥50,000 | 27 | 15.3 |
Note: 23 Missing Data
Table 2.
The overall life time (ever) and the last 90 days (current) involvement in crimes by type of crime among Latino community corrections clients in Miami, Florida, 2014–2015 (N = 201)
| Crime type | Response | Ever involved |
Currently involved |
||
|---|---|---|---|---|---|
| n | % | n | % | ||
| Public intoxication from alcohol or drugs | No | 50 | 25.0 | 68 | 34.0 |
| Yes | 150 | 75.0 | 132 | 66.0 | |
| Driving under influence of alcohol or drugs | No | 56 | 28.0 | 89 | 44.5 |
| Yes | 144 | 72.0 | 111 | 55.5 | |
| Use or possession of illegal drugs or drug paraphernalia | No | 7 | 3.5 | 12 | 6.0 |
| Yes | 193 | 96.5 | 188 | 94.0 | |
| Manufacturing or selling drugs or paraphernalia | No | 86 | 43.0 | 122 | 61.0 |
| Yes | 114 | 57.0 | 78 | 39.0 | |
| Forgery/Fraud(bad checks, credit card fraud) | No | 148 | 74.0 | 166 | 83.0 |
| Yes | 52 | 26.0 | 34 | 17.0 | |
| Fencing (buying or selling stolen property) | No | 142 | 71.0 | 157 | 78.5 |
| Yes | 58 | 29.0 | 43 | 21.5 | |
| Illegal gambling (running numbers) | No | 170 | 85.0 | 184 | 92.0 |
| Yes | 30 | 15.0 | 16 | 8.0 | |
| Prostitution or pimping | No | 165 | 82.5 | 173 | 86.5 |
| Yes | 35 | 17.5 | 27 | 13.5 | |
| Stealing | No | 63 | 31.5 | 122 | 61.0 |
| Yes | 137 | 68.5 | 78 | 39.0 | |
| Assault/aggravated assault/batterya | No | 140 | 70.0 | 168 | 84.0 |
| Yes | 60 | 30.0 | 32 | 16.0 | |
| Weapons offenses | No | 166 | 83.0 | 182 | 91.0 |
| Yes | 34 | 17.0 | 18 | 9.0 | |
| Vandalism/property damage/tagging | No | 154 | 77.0 | 177 | 88.5 |
| Yes | 46 | 23.0 | 23 | 11.5 | |
| Probation/parole violations | No | 127 | 63.5 | 170 | 85.0 |
| Yes | 73 | 36.5 | 30 | 15.0 | |
Gender Correlates
In the crude analysis of correlates to drug and alcohol associated crimes, it was found that female sex had lower odds of manufacturing or selling drugs or paraphernalia in comparison to male sex (COR=0.48; p=0.016). In the adjusted analysis, female sex remained to have lower odds of involvement with manufacturing or selling drugs or paraphernalia in comparison to male sex (AOR=0.416; p=0.028).
In the crude analysis of correlates to non-drug and alcohol associated crimes, we found that female sex had significantly lower odds of illegal gambling and involvement in weapon offenses (COR=0.19; p=0.033, COR=0.27; p=0.046, respectively), but higher odds of prostitution or pimping and stealing from houses or shops (COR=8.72; p<0.001, COR=2.36; p=0.004, respectively) in comparison to men. In the adjusted analysis of correlates to non-drug and alcohol associated crimes, we found that female sex remained having lower odds of illegal gambling and involvement in weapon offenses (AOR=0.13; p=0.040, AOR=0.16; p=0.027, respectively), and higher odds of prostitution or pimping and stealing from houses or shops (AOR=8.72; p=0.003, AOR=2.68; p=0.012, respectively). Additionally, in the adjusted analysis, we found that females had lower odds of fencing and involvement with vandalism/property damage/tagging (AOR=0.36; p=0.033, AOR=0.25; p=0.040, respectively).
Hispanic/Latino Sub-group Correlates
In the crude analysis of correlates to drug and alcohol associated crimes, we found that Central American ethnicity had lower odds of involvement in public intoxication from alcohol or drugs (COR=0.37; p=0.049) in comparison to Cuban ethnicity. In the adjusted analysis of correlates to drug and alcohol associated crimes, it was found that Central American ethnicity remained to have lower odds of involvement in public intoxication from alcohol or drugs (AOR=0.29; p=0.034) in comparison to Cuban ethnicity. Uncovered in the adjusted analysis, Central American ethnicity had lower odds of involvement with manufacturing or selling drugs or paraphernalia and involvement with use or possession of illegal drugs (AOR=0.14; p=0.015, AOR=0.10; p=0.024, respectively) in comparison to Cuban ethnicity.
In the crude analysis of correlates to non-drug and alcohol associated crimes, we found that Puerto Rican ethnicity had higher odds of involvement in weapon offenses (COR=3.67; p=0.032) in comparison to Cuban ethnicity. However, in the adjusted analysis the difference between Puerto Rican and Cuban ethnicity in regard to involvement in weapon offenses became non-significant (AOR=4.61; p=0.081).
Age Correlates
There were no significant age group correlates in the analysis of drug and alcohol use crimes; however, in the crude analysis of correlates to non-drug and alcohol associated crimes, we found that participants aged 29–39 had lower odds of involvement in probation/parole violations (COR=0.31; p=0.024) in comparison to participants aged 18–28. In the adjusted model, the correlation between age and involvement in probation/parole violations became non-significant (AOR=0.52; p=0.334), but being aged 40+ emerged as having significantly lower odd of involvement with vandalism/property damage/tagging (AOR=0.06; p=0.020) in comparison to participants aged 18–28.
Race Correlates
In the crude analysis of correlates to drug and alcohol associated crimes, we found that Black Hispanic/Latinos had lower odds of involvement in public intoxication from alcohol or drugs (COR=0.11; p=0.007) in comparison to White Hispanic/Latinos. In the adjusted analysis, it was found that Black Hispanic/Latinos remained to have lower odds of involvement in public intoxication from alcohol or drugs (AOR=0.12; p=0.019) in comparison to White Hispanic/Latinos.
In the crude analysis of correlates to non-drug and alcohol associated crimes race was not seen as a significant variable. However, race became statistically significant in the adjusted models where Black Hispanic/Latinos had higher odds of prostitution or pimping and involvement in probation/parole violations (AOR=7.65; p=0.045, AOR=10.84; p=0.033, respectively) in comparison to White Hispanic/Latinos.
Income Correlates
There were no significant income group correlates in the analysis of drug and alcohol use crimes; however, in the adjusted analysis of correlates to non-drug and alcohol associated crimes, income of $30–39,000 emerged as having significantly higher odds of illegal gambling (AOR=12.68; p=0.038) and lower odds of stealing from houses or shops (AOR=0.09; p=0.049).
Education Correlates
There were no significant education group correlates in the analysis of drug and alcohol use crimes; however, in the crude analysis of correlates to non-drug and alcohol associated crimes, we found having an associate’s degree or higher education had lower odds of stealing from houses or shops and involvement in probation/parole violations (COR=0.15; p<0.001, COR=0.10; p=0.030, respectively) in comparison to less than high school education. In the adjusted analysis, associate’s degree education level remained having lower odds of stealing from houses or shops (AOR=0.14; p=0.004), but lost significance in its relationship with and involvement in probation/parole violations (AOR=0.25; p=0.236) in comparison with less than high school education.
Relationship Correlates
Though non-significant in the crude model, in the adjusted analysis of correlates to drug and alcohol associated crimes, divorced marital status had lower odds of involvement with manufacturing or selling drugs or paraphernalia (AOR=0.21; p=0.030) in comparison to those never married.
In the crude analysis of correlates to non-drug and alcohol associated crimes, we found being divorced had higher odds of involvement in prostitution or pimping in comparison to those never married (COR=1.57; p=0.011). In the adjusted model, being divorced lost its significance and being separated gained significance in that being separated had higher odds of involvement in prostitution or pimping (AOR=8.29; p=0.028).
Discussion
Summary of findings
The present study is one of the few studies to examine specific lifetime and recent offending behavior among criminal justice-involved Hispanic/Latinos by several key demographics including gender, age, and by certain Hispanic/Latino subgroups such as Cuban, Puerto Rican, Central American, and South American. Regarding drug related offenses, women were less likely to sell or manufacture drugs than men. One reason for the difference in selling or manufacturing drugs could be that there may be gender inequality for Hispanic/Latino women in the drug economy. Women may be given opportunities to sell drugs but it is not often nor consistent; and women may not be seen as tough as men, especially African-American and Hispanic/Latino men (Maher & Daly, 1996). Cultural factors such as machismo and traditional gender roles may also prevent Hispanic/Latino women from participating in the drug economy. There is research suggesting that there’s a machismo or masculinity that involves dubious activities to earn income as well as an absence of vulnerability (Daley, 2016) particularly with urban neighborhoods (Trimbur, 2011). Cultural criminology, which emphasizes the meaning of interactions within subcultures, and how a person makes sense of their actions and social environment (Ferrell, Hayward, & Young, 2015), may shed light on women’s roles within the drug economy subculture. In fact, Naegler & Salman (2016) argue for a “feminist cultural criminology”, which suggest that women may find meaning differently than men and deal with oppression in risky defiant actions. In addition to being a source of income, involvement in sex work could be a form of resistance and defiance against the Hispanic/Latino cultural norm of marianismo, the notion that women should be chaste, non-sexual, humble, and passive (Castillo & Cano, 2007), and against a male-dominated drug economy subculture. The finding that Hispanic/Latino women reported more prostitution, and less selling and manufacturing of drugs than men, could be reflective of an oppressive patriarchal society within the illicit drug economy subculture. Immigration status is another factor to consider in the power dynamics for Hispanic/Latino women. Women who are undocumented may prefer a less visible offending behavior such as sex work versus a more outwardly visible behavior such as selling drugs. Future ethnographic studies, using a feminist cultural criminology lens, should examine the meaning of these interactions for women.
Regarding non-drug related offenses, women reported less criminal behavior across several types of offenses, as expected. However, as previously mentioned, they were more likely to report prostitution, as well as stealing from houses or shop. This is congruent with previous literature. For example, one study on inner city female drug users found that over 2/3 of the women reported prostitution and stealing for income (M. Miller & Neaigus, 2002), whereas shoplifting has been seen as the best alternative to other income generating activities for drug users (Caputo & King, 2015). This finding complements the finding that women were less likely to sell drugs. Previous studies have shown that women who sell drugs tend to avoid prostitution and other income generating activities (Sommers, Baskin, & Fagan, 1996). In a more recent study, White female offenders were more likely to be arrested for drug offending behavior because of an addiction whereas Hispanic/Latino female offenders commit drug offenses for financial reasons (Caldwell-Gunes, Silver, Smith, & Norton, 2016). Perhaps they are more likely to commit non-drug related offenses for financial reasons as well. Future studies should include more contextual factors when examining incarceration disparities such as reasons for the offenses and neighborhood context, income and gender inequality.
Interestingly, there were several Hispanic/Latino subgroup differences. Central Americans reported less drug-related criminal behavior than Cubans regarding public intoxication, the use and possession of drugs, and the manufacturing or selling of drugs. Differences between Hispanic/Latino sub-groups have been found in previous studies (Gil-Rivas et al., 1997; H. V. Miller, 2012). This finding could be because Central Americans are more likely to be recent or first-generation immigrants than are Cubans in Miami. Previous studies show that those who have been living longer in the U.S or are U.S. born Hispanic/Latinos are more likely to report offending behaviors (Bersani et al., 2009; Ibañez et al., 2017). To the author’s knowledge, this is the first study comparing Central Americans with Cubans specifically. Future studies should continue to examine subgroups and include a large enough sample to examine differences by countries instead of by regions as was done in the present study (e.g., Honduran, Guatemalan vs. Central America).
Black Hispanic/Latinos were also more likely to report violating probation or parole. Interestingly, this finding is congruent with several recent studies on colorism; that is, prejudice or discrimination because one’s skin tone or color (Burch, 2015; Jones, 2000), among Hispanic/Latinos involved with the criminal justice system. For example, a recent study that found darker skin tones among Hispanic/Latinos was linked to unsuccessful completion of probation (Steinmetz & Koeppel, 2017). Another study found that darker skinned Hispanic/Latinos were stopped and arrested by police more often than lighter skinned Hispanic/Latinos, even after controlling for prior delinquency and other factors (White, 2014). Yet another study found that darker skinned Hispanic/Latinos had higher odds of arrest than lighter skinned Hispanic/Latinos, and this varied by generation status (Alcala & Montoya, 2016). Clearly, more studies are needed to examine the intersectionality of race and ethnicity among Black Hispanic/Latino offenders. Still, the current sample size of Black Hispanic/Latinos was small, and therefore, meaningful inferences should not be made. In addition, no age difference was found in offending behavior, which is not congruent with previous studies that report more criminal activity among younger people (Bersani et al., 2009; Fabio et al., 2011; H. V. Miller, 2012). It is unclear why there was no age difference. However, it could be that the age range was too narrow (18–49) to detect any differences.
Implications
There are several theoretical implications of the study. The findings suggest that future research should examine intersectional criminology as a theoretical approach (Potter, 2013). Intersectional criminology is based on the notion of intersectionality; that is, that there is a multiplicative effect of an individual’s social identities, including race, gender, age, and socioeconomic status (Potter, 2013). For example, a recent study utilized an intersectionality approach to examine age, race, and gender effects on sentencing among a diverse sample; and found a gender and ethnicity effect: that Hispanic males, regardless of age, received harsher sentences compared to their White counterparts; whereas younger women received the most lenient sentences (Steffensmeier, Painter-Davis, & Ulmer, 2017). Our findings suggest that even within the same ethnic group, Hispanic/Latinos, there were significant differences by gender and race on offending behavior. There are other identities that were not explored in the present study such as generational status and country of origin, which can be the focus of future studies. Future research should use large enough samples to examine interactions of social identities among Hispanic/Latinos in order to power appropriate analyses and make meaningful comparisons.
Critical race theory suggests that certain forms of oppression such as sexism, can also influence how identities interact (Delgado & Stefancic, 2012). Traditional gender roles in Hispanic/Latino culture include machismo and marianismo (Faulkner, 2003). Traditional machismo is the ideal of hyper-masculinity, that men need to have sexual prowess, and that they be providers for their household (Scott, 2018). Marianismo can lead to negative emotions (Nuñez, et al., 2016) such as depression (Cespedes & Huey, 2008); however previous studies show that higher levels of marianismo could also serve as a protective factor for risky behavior, such as substance abuse (Sanchez, et al., 2017). Previous studies have suggested that marianismo can also lead to discriminatory stereotypes that influence how criminal justice professional interact with Hispanic/Latino women (Pasko, 2017; Scott, 2018). Building on this notion, Hispanic/Latino women may be experiencing strain differently, and that a gendered general strain theory would be a fruitful theoretical framework to guide future research with Hispanic/Latino women involved in the criminal justice system (Scott, 2018).
The present study has some practical implications. In Weisburd and colleagues’ (2017) assessment of systematic reviews, several types of rehabilitative and diversion programs were listed such as community interventions, sentencing/deterrence programs, correctional interventions, and drug treatment interventions. Correctional interventions refer to programs for offenders within an adult correctional setting and include educational and vocational programs; substance abuse programs, and psychosocial programs (Weisburd, et al., 2017). The present study suggests that community corrections populations may also benefit from correctional interventions especially those that promote economic opportunities for Hispanic/Latino women. Vocational training, job placement, and budgeting/financial planning classes are some examples of correctional interventions that may be helpful in preventing recidivism. However, more research is needed to more fully understand the best rehabilitative programs for Hispanic/Latino offenders.
Limitations
The present study has some limitations worth noting. First, it was a cross-sectional study with a convenience sample of individuals in community corrections; therefore, no causality can be determined, and the findings may not be generalizable to the general population. Second, data was self-report and vulnerable to both social desirability bias as well as recall bias. However, we attempted to limit bias by having a well-trained bilingual and bicultural research staff; and using CAPI data collection procedures. Third, although we ask about country of origin, the sample was too small to compare offending behaviors by nationality nor generational immigration to the US. Future research studies should also include a large enough sample of Hispanic/Latinos that analysis can be conducted between various nationalities. The strength of the study is the uniqueness of the sample – Hispanic/Latinos involved in community corrections – especially Hispanic/Latino women, who are often neglected in the research literature.
Conclusion
In summary, our findings suggest that the types of offenses reported by Hispanic/Latinos involved in community corrections is full of complexities, including the intersection of gender, race, and income inequality. Further studies are needed to better understand this intersection as well as the role that cultural variables may play such as traditional gender roles. More understanding of the unique needs of this population is warranted before any interventions to prevent recidivism can be implemented successfully.
Table 3.
Univariate analysis of recent alcohol and drug use criminal activity and demographic variables
| Demographic Variables | Involved in public intoxication from alcohol or drugs | Driving under influence | Use or possession of illegal drugs | Manufacturing or selling drugs or paraphernalia | ||||
|---|---|---|---|---|---|---|---|---|
| COR | p | COR | p | COR | p | COR | p | |
| Sex | ||||||||
| Male | - | - | - | - | - | - | - | - |
| Female | 0.71 | 0.248 | 1.05 | 0.862 | 0.65 | 0.469 | 0.48 | 0.016 |
| Age | ||||||||
| 18–28 | - | - | - | - | - | - | - | - |
| 29–39 | 0.73 | 0.341 | 1.00 | 0.997 | 1.18 | 0.822 | 0.77 | 0.430 |
| ≥40 | 0.77 | 0.541 | 1.11 | 0.805 | 0.39 | 0.181 | 1.07 | 0.871 |
| Ethnicity | ||||||||
| Cuban | - | - | - | - | - | - | - | - |
| Puerto Rican | 0.81 | 0.654 | 0.90 | 0.807 | * | * | 0.96 | 0.933 |
| Central American | 0.37 | 0.049 | 0.56 | 0.243 | 0.23 | 0.069 | 0.23 | 0.025 |
| South American | 1.06 | 0.925 | 0.49 | 0.173 | 0.73 | 0.785 | 0.96 | 0.943 |
| Other | 0.58 | 0.173 | 0.53 | 0.096 | 0.38 | 0.183 | 0.47 | 0.068 |
| Marital Status | ||||||||
| Never Married | - | - | - | - | - | - | - | - |
| Living as Married | 1.09 | 0.889 | 1.19 | 0.772 | 0.54 | 0.578 | 0.99 | 0.982 |
| Separated | 0.87 | 0.821 | 2.84 | 0.124 | 0.59 | 0.632 | 1.18 | 0.771 |
| Divorced | 1.64 | 0.324 | 1.01 | 0.988 | 0.34 | 0.142 | 0.20 | 0.011 |
| Widowed | * | * | * | * | * | * | * | * |
| Race | ||||||||
| White | - | - | - | - | - | - | - | - |
| Black | 0.11 | 0.007 | 0.70 | 0.578 | 0.40 | 0.428 | 0.16 | 0.082 |
| Other | 1.25 | 0.483 | 1.18 | 0.578 | 0.44 | 0.198 | 1.17 | 0.597 |
| Education | ||||||||
| <High school | - | - | - | - | - | - | - | - |
| High school (GED) | 0.66 | 0.292 | 1.08 | 0.844 | 1.21 | 0.786 | 0.95 | 0.899 |
| Some College | 1.82 | 0.198 | 0.91 | 0.818 | 4.88 | 0.164 | 1.33 | 0.487 |
| ≥Associates Degree | 0.92 | 0.864 | 1.62 | 0.293 | 1.71 | 0.551 | 0.56 | 0.220 |
| Income | ||||||||
| Less than 20,000 | - | - | - | - | - | - | - | - |
| 20,000– 29,999 | 0.88 | 0.779 | 1.25 | 0.614 | * | * | 1.14 | 0.778 |
| 30,000–39,999 | 1.10 | 0.896 | 0.63 | 0.504 | * | * | 0.77 | 0.727 |
| 40,000–49,999 | 3.85 | 0.215 | 0.47 | 0.318 | 0.64 | 0.694 | 1.55 | 0.552 |
| ≥50,000 | 1.10 | 0.832 | 1.86 | 0.181 | 2.39 | 0.419 | 0.77 | 0.572 |
Table 4.
Multivariate analysis of recent alcohol and drug criminal activity and demographic variables.
| Demographic Variables | Involved in public intoxication from alcohol or drugs | Driving under influence | Use or possession of illegal drugs | Manufacturing or selling drugs or paraphernalia | ||||
|---|---|---|---|---|---|---|---|---|
| AOR | p | AOR | p | AOR | p | AOR | p | |
| Sex | ||||||||
| Male | - | - | - | - | - | - | - | - |
| Female | 0.80 | 0.565 | 1.25 | 0.548 | 0.92 | 0.924 | 0.416 | 0.028 |
| Age | ||||||||
| 18–28 | - | - | - | - | - | - | - | - |
| 29–39 | 0.78 | 0.564 | 1.17 | 0.709 | 0.59 | 0.593 | 0.93 | 0.875 |
| ≥40 | 0.49 | 0.184 | 1.03 | 0.961 | 0.28 | 0.216 | 1.14 | 0.802 |
| Ethnicity | ||||||||
| Cuban | - | - | - | - | - | - | - | - |
| Puerto Rican | 0.64 | 0.400 | 0.77 | 0.596 | * | * | 0.90 | 0.837 |
| Central American | 0.29 | 0.034 | 0.55 | 0.290 | 0.10 | 0.024 | 0.14 | 0.015 |
| South American | 0.64 | 0.512 | 0.40 | 0.138 | 1.07 | 0.976 | 1.63 | 0.450 |
| Other | 0.73 | 0.517 | 0.53 | 0.174 | 0.22 | 0.154 | 0.72 | 0.514 |
| Marital Status | ||||||||
| Never Married | - | - | - | - | - | - | - | - |
| Living as Married | 1.40 | 0.642 | 1.00 | 0.997 | 1.22 | 0.879 | 1.01 | 0.992 |
| Separated | 1.44 | 0.611 | 2.20 | 0.295 | 0.76 | 0.842 | 1.81 | 0.399 |
| Divorced | 2.64 | 0.116 | 0.93 | 0.897 | 0.33 | 0.305 | 0.21 | 0.030 |
| Widowed | * | * | * | * | * | * | * | * |
| Race | ||||||||
| White | - | - | - | - | - | - | - | - |
| Black | 0.12 | 0.019 | 0.87 | 0.865 | 0.31 | 0.436 | 0.20 | 0.169 |
| Other | 1.22 | 0.592 | 1.26 | 0.505 | 0.34 | 0.184 | 0.98 | 0.965 |
| Education | ||||||||
| <High school | - | - | - | - | - | - | - | - |
| High school (GED) | 0.65 | 0.384 | 1.31 | 0.556 | 1.01 | 0.996 | 0.72 | 0.504 |
| Some College | 1.56 | 0.426 | 1.21 | 0.703 | 4.17 | 0.300 | 1.06 | 0.911 |
| ≥Associates Degree | 0.79 | 0.695 | 2.11 | 0.181 | 2.14 | 0.605 | 0.33 | 0.068 |
| Income | ||||||||
| Less than 20,000 | - | - | - | - | - | - | - | - |
| 20,000– 29,999 | 0.76 | 0.601 | 1.28 | 0.616 | * | * | 0.89 | 0.826 |
| 30,000–39,999 | 2.30 | 0.372 | 0.53 | 0.420 | * | * | 0.86 | 0.863 |
| 40,000–49,999 | 2.65 | 0.411 | 0.54 | 0.453 | 0.09 | 0.277 | 0.64 | 0.604 |
| ≥50,000 | 0.77 | 0.627 | 1.94 | 0.205 | 1.98 | 0.645 | 0.68 | 0.468 |
Table 5.
Univariate analysis of recent non-alcohol/drug criminal activities and demographic variables.
| Demographic Variables | 5Forgery/Fraud (bad checks, credit card fraud) | 6Fencing (buying or selling stolen property) | 7Illegal gambling (running numbers) | 8Prostitution or pimping | 9Stealing from houses or shops | 10Assault/aggravated assault/battery a | 11Involved in weapon offenses | 12Involved in vandalism/property damage/tagging? | 13Involved in probation/parole violations? | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| COR | P | COR | P | COR | P | COR | P | COR | P | COR | P | COR | P | COR | P | COR | P | |
| Sex | ||||||||||||||||||
| Male | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Female | 1.23 | 0.591 | 0.58 | 0.143 | 0.19 | 0.033 | 8.72 | <.001 | 2.36 | 0.004 | 0.88 | 0.753 | 0.273 | 0.046 | 0.38 | 0.065 | 0.50 | 0.111 |
| Age | ||||||||||||||||||
| 18–28 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 29–39 | 1.14 | 0.768 | 1.02 | 0.962 | 0.54 | 0.318 | 1.50 | 0.377 | 0.73 | 0.323 | 0.79 | 0.568 | 0.44 | 0.173 | 0.63 | 0.335 | 0.31 | 0.024 |
| ≥40 | 2.25 | 0.095 | 0.99 | 0.979 | 0.57 | 0.480 | 1.41 | 0.553 | 0.56 | 0.172 | 0.27 | 0.089 | 0.46 | 0.330 | 0.17 | 0.096 | 0.70 | 0.508 |
| Ethnicity | ||||||||||||||||||
| Cuban | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Puerto Rican | 1.10 | 0.856 | 0.34 | 0.100 | 1.22 | 0.776 | 0.21 | 0.140 | 1.30 | 0.554 | 1.15 | 0.804 | 3.67 | 0.032 | 1.78 | 0.330 | 1.56 | 0.410 |
| Central American | 0.45 | 0.315 | 0.51 | 0.318 | * | * | * | * | 0.95 | 0.915 | 0.28 | 0.232 | * | * | 0.92 | 0.918 | 1.03 | 0.971 |
| South American | 0.48 | 0.355 | 0.34 | 0.171 | 0.58 | 0.611 | 0.68 | 0.635 | 0.81 | 0.699 | 0.63 | 0.567 | 0.76 | 0.800 | 1.56 | 0.528 | * | * |
| Other | 0.44 | 0.160 | 0.82 | 0.653 | 0.82 | 0.768 | 1.64 | 0.295 | 1.13 | 0.755 | 1.31 | 0.579 | 1.47 | 0.558 | 0.42 | 0.278 | 0.99 | 0.991 |
| Marital Status | ||||||||||||||||||
| Never Married | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Living as Married | 0.91 | 0.908 | 1.84 | 0.342 | 0.96 | 0.969 | 0.71 | 0.754 | 0.53 | 0.354 | 0.48 | 0.489 | * | * | 1.57 | 0.583 | 0.46 | 0.460 |
| Separated | 0.83 | 0.813 | 0.31 | 0.266 | 0.88 | 0.904 | 3.48 | 0.057 | 1.85 | 0.290 | 1.58 | 0.513 | 1.77 | 0.487 | 0.65 | 0.690 | 0.91 | 0.905 |
| Divorced | 0.65 | 0.511 | 0.97 | 0.956 | 0.46 | 0.462 | 1.57 | 0.011 | 0.95 | 0.913 | 1.05 | 0.934 | 0.88 | 0.875 | 1.12 | 0.868 | 0.46 | 0.306 |
| Widowed | * | * | * | * | * | * | * | * | * | * | * | * | * | * | * | * | * | * |
| Race | ||||||||||||||||||
| White | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Black | 1.15 | 0.865 | 0.34 | 0.312 | 1.65 | 0.658 | 3.71 | 0.057 | 0.86 | 0.815 | 0.56 | 0.588 | 1.40 | 0.764 | * | * | 3.21 | 0.120 |
| Other | 1.13 | 0.762 | 0.92 | 0.814 | 1.98 | 0.213 | 0.78 | 0.585 | 0.92 | 0.790 | 1.21 | 0.629 | 1.89 | 0.217 | 1.74 | 0.218 | 2.01 | 0.099 |
| Education | ||||||||||||||||||
| <High school | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| High school (GED) | 0.87 | 0.781 | 0.71 | 0.437 | 0.64 | 0.465 | 0.42 | 0.090 | 0.82 | 0.615 | 1.57 | 0.375 | 0.93 | 0.911 | 1.09 | 0.893 | 0.61 | 0.326 |
| Some College | 1.10 | 0.850 | 0.76 | 0.551 | 0.55 | 0.385 | 0.34 | 0.062 | 0.57 | 0.174 | 1.01 | 0.986 | 1.07 | 0.920 | 1.49 | 0.515 | 0.75 | 0.575 |
| ≥Associates Degree | 0.23 | 0.071 | 0.22 | 0.026 | * | * | 0.27 | 0.062 | 0.15 | <.001 | 0.31 | 0.161 | * | * | 0.46 | 0.367 | 0.10 | 0.030 |
| Income | ||||||||||||||||||
| Less than 20,000 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 20,000– 29,999 | 0.48 | 0.259 | 0.45 | 0.166 | 3.06 | 0.104 | 0.51 | 0.394 | 0.83 | 0.675 | 0.69 | 0.580 | 1.27 | 0.736 | 0.55 | 0.454 | 1.12 | 0.858 |
| 30,000–39,999 | 1.04 | 0.959 | * | * | 4.81 | 0.083 | 0.77 | 0.809 | 0.47 | 0.096 | * | * | * | * | 0.83 | 0.866 | * | * |
| 40,000–49,999 | 0.52 | 0.552 | 0.35 | 0.336 | 5.61 | 0.060 | 0.88 | 0.905 | 0.44 | 0.331 | 0.76 | 0.800 | 1.39 | 0.771 | 0.95 | 0.962 | 2.04 | 0.407 |
| ≥50,000 | 0.64 | 0.442 | 0.43 | 0.143 | * | * | 0.77 | 0.693 | 0.91 | 0.833 | 1.85 | 0.229 | 0.78 | 0.753 | 0.83 | 0.783 | 0.77 | 0.693 |
Table 6.
Multivariate analysis of recent non-alcohol/drug criminal activities and demographic variables
| Demographic Variables | 5Forgery/Fraud (bad checks, credit card fraud) | 6Fencing (buying or selling stolen property) | 7Illegal gambling (running numbers) | 8Prostitution or pimping | 9Stealing from houses or shops | 10Assault/aggravated assault/battery a | 11Involved in weapon offenses | 12Involved in vandalism/property damage/tagging? | 13Involved in probation/parole violations? | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AOR | P | AOR | P | AOR | P | AOR | P | AOR | P | AOR | P | AOR | P | AOR | P | AOR | P | |
| Sex | ||||||||||||||||||
| Male | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Female | 1.50 | 0.375 | 0.36 | 0.033 | 0.13 | 0.040 | 7.34 | 0.003 | 2.68 | 0.012 | 0.97 | 0.951 | 0.16 | 0.027 | 0.25 | 0.040 | 0.38 | 0.116 |
| Age | ||||||||||||||||||
| 18–28 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 29–39 | 1.54 | 0.416 | 0.97 | 0.945 | 0.52 | 0.471 | 0.62 | 0.536 | 0.87 | 0.750 | 1.20 | 0.757 | 0.46 | 0.350 | 0.60 | 0.436 | 0.52 | 0.334 |
| ≥40 | 2.93 | 0.074 | 0.45 | 0.213 | 0.57 | 0.576 | 1.31 | 0.755 | 0.48 | 0.176 | 0.18 | 0.059 | 0.16 | 0.097 | 0.06 | 0.020 | 0.80 | 0.745 |
| Ethnicity | ||||||||||||||||||
| Cuban | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Puerto Rican | 1.04 | 0.941 | 0.36 | 0.151 | 0.46 | 0.431 | 0.23 | 0.192 | 1.75 | 0.296 | 1.75 | 0.397 | 4.16 | 0.081 | 2.10 | 0.290 | 1.39 | 0.644 |
| Central American | 0.23 | 0.174 | 0.14 | 0.069 | * | * | * | * | 0.60 | 0.441 | * | * | * | * | 0.34 | 0.342 | 0.80 | 0.797 |
| South American | 0.91 | 0.915 | 0.43 | 0.341 | 0.40 | 0.504 | 1.66 | 0.652 | 1.68 | 0.467 | 1.11 | 0.911 | 0.96 | 0.972 | 0.84 | 0.858 | * | * |
| Other | 0.60 | 0.437 | 0.86 | 0.790 | 0.67 | 0.699 | 0.88 | 0.856 | 1.15 | 0.776 | 1.37 | 0.600 | 1.97 | 0.393 | 0.54 | 0.486 | 0.98 | 0.976 |
| Marital Status | ||||||||||||||||||
| Never Married | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Living as Married | 0.56 | 0.507 | 2.47 | 0.238 | 1.38 | 0.818 | 0.47 | 0.591 | 0.46 | 0.315 | 0.56 | 0.607 | * | * | 2.68 | 0.329 | 0.38 | 0.405 |
| Separated | 0.58 | 0.548 | 0.63 | 0.696 | 1.73 | 0.709 | 8.29 | 0.028 | 2.18 | 0.317 | 2.74 | 0.280 | 6.29 | 0.165 | 1.72 | 0.676 | 1.90 | 0.531 |
| Divorced | 0.39 | 0.198 | 1.46 | 0.568 | 1.82 | 0.645 | 0.58 | 0.523 | 0.78 | 0.659 | 1.05 | 0.950 | 2.37 | 0.392 | 2.53 | 0.331 | 0.34 | 0.338 |
| Widowed | * | * | * | * | * | * | * | * | * | * | * | * | * | * | * | * | * | * |
| Race | ||||||||||||||||||
| White | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| Black | 1.14 | 0.895 | 0.24 | 0.252 | 1.53 | 0.765 | 7.65 | 0.045 | 1.05 | 0.959 | 1.30 | 0.839 | 2.22 | 0.586 | * | * | 10.84 | 0.033 |
| Other | 1.20 | 0.674 | 0.78 | 0.566 | 1.05 | 0.946 | 0.95 | 0.936 | 0.76 | 0.460 | 1.63 | 0.313 | 1.20 | 0.775 | 1.96 | 0.212 | 1.87 | 0.246 |
| Education | ||||||||||||||||||
| <High school | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| High school (GED) | 1.10 | 0.867 | 0.72 | 0.555 | 0.36 | 0.222 | 0.80 | 0.762 | 1.06 | 0.898 | 2.24 | 0.216 | 0.89 | 0.889 | 1.27 | 0.755 | 0.93 | 0.916 |
| Some College | 1.67 | 0.387 | 0.84 | 0.764 | 0.33 | 0.287 | 0.59 | 0.519 | 0.58 | 0.285 | 1.08 | 0.914 | 1.23 | 0.802 | 1.84 | 0.439 | 1.33 | 0.684 |
| ≥Associates Degree | 0.27 | 0.142 | 0.19 | 0.038 | * | * | 0.53 | 0.532 | 0.14 | 0.004 | 0.31 | 0.217 | * | * | 0.22 | 0.212 | 0.25 | 0.236 |
| Income | ||||||||||||||||||
| Less than 20,000 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 20,000– 29,999 | 0.56 | 0.417 | 0.29 | 0.082 | 4.52 | 0.081 | 0.17 | 0.172 | 0.78 | 0.638 | 0.45 | 0.317 | 0.865 | 0.870 | 0.32 | 0.206 | 0.99 | 0.994 |
| 30,000–39,999 | 1.10 | 0.920 | * | * | 12.68 | 0.038 | 0.32 | 0.463 | 0.09 | 0.049 | * | * | * | * | 0.62 | 0.690 | * | * |
| 40,000–49,999 | 0.47 | 0.524 | 0.19 | 0.185 | 5.01 | 0.185 | 1.33 | 0.856 | 0.76 | 0.771 | 1.05 | 0.966 | 0.60 | 0.694 | 0.33 | 0.368 | 2.23 | 0.475 |
| ≥50,000 | 0.98 | 0.971 | 0.30 | 0.075 | * | * | 1.79 | 0.503 | 1.47 | 0.478 | 2.13 | 0.241 | 0.35 | 0.293 | 0.44 | 0.338 | 0.71 | 0.661 |
Acknowledgments
This work was supported by the National Institute on Drug Abuse (NIDA) under Grant R34DA031063
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