Latino Nativity Variations Link to Street Violence in Drug Markets
Abstract
The Latino paradox is defined as “Latinos do[ing] much better on various social indicators, including violence, than blacks and apparently even whites, given relatively high levels of disadvantage.” We do not know, however, if the Latino paradox is masquerading what is known as criminal social capital. This study defined geographic drug markets with drug sales crime data in Philadelphia. Multilevel negative binomial models showed census block group street violence levels varied significantly across drug markets. Although each additional 100 native-born Latinos was associated with expected street violent crime counts 8% lower, each additional 100 foreign-born Latinos was associated with expected street violent crime counts 28% lower, controlling for nearby street violence and structural predictors.
Introduction
This study is the first in the series of investigations in trying to determine the differences in drug market violence levels by examining Latino nativity1 variations. The main hypothesis of this study is that foreign-born Latinos are less likely to commit street violent crime in drug markets compared with those of native-born Latinos as they are more likely to sell drugs in a wholesale quantity to a selected few individuals. Compared with native-born Latinos, foreign-born Latinos are more likely to have a direct access to a large amount of drugs from a drug source country, which enable them to sell drugs in a wholesale quantity (see Natarajan, 2006; Natarajan & Hough, 2000). In this article, this process is defined as having strong criminal social capital. On the contrary, native-born Latinos, who lack strong criminal social capital, are more likely to remain as street-level drug dealers and sell drugs in a small amount around street corners. It is well documented in drug market studies that street-level drug dealing is positively related to violent crime (Goldstein, 1985; Jacques & Wright, 2008). Although the Latino variables in this study, analyzed as foreign-born and native-born Latinos, do not account for intra-ethnic variations, they should reflect how variations in the strength of criminal social capital affect violence levels in drug markets.
The Existing Findings on Latino Immigrants and Crime
The Latino immigrant population has been growing rapidly in the United States. Statistical findings showed that they are the largest minority group, comprising approximately 16% of the total U.S. population (Ennis, Rios-Vargas, & Albert, 2011; Jennings, Zgoba, Piquero, & Reingle, 2013; Lopez & Light, 2009). For over a decade or so, researchers examined the relationship between Latino immigrants and crime and found an inverse relationship, particularly concerning homicide (Chavez & Griffiths, 2009; Harris & Feldmeyer, 2013; Lee & Martinez, 1998; Lee, Martinez, & Rosenfeld, 2001; Ousey & Kubrin, 2014; Sampson, 2008, 2012; Wright & Rodriguez, 2012). Studies indicated Latino immigrants do not cause disorder or reduce social cohesion, but create new economic opportunities in disadvantaged neighborhoods, improve the quality of social network, and build additional community-level institutions (Nielsen & Martinez, 2009; Sampson, 2008; Shihadeh & Winters, 2010; Stowell & Martinez, 2009; Velez, 2009). Martinez and Lee (2000) stated Latino immigrants appear to be less influenced by criminogenic conditions in their neighborhoods compared with nonimmigrant residents. Furthermore, studies showed strong social capital among Latino immigrants worked in a way that helped each other to better integrate into the mainstream society, which in turn decreased the violent crime rates (Portes & Rumbaut, 2006; Portes & Zhou, 1993; Ramey, 2013; Waters & Jiménez, 2005).
In addition to these findings, lower crime rates in Latino immigrant communities might be associated with the lower level of residential segregation faced by Latino immigrants compared with other minority groups, particularly African Americans. Charles (2003) indicated two residential segregation models: spatial assimilation and place stratification. “The spatial assimilation model posits that objective differences in socioeconomic status and acculturation [e.g., English language fluency] across racial/ethnic groups are primarily responsible for residential segregation . . . The place stratification model emphasizes the persistence of prejudice and discrimination” (Charles, 2003, p. 170). Both residential segregation models asserted that race is an important factor, and that residential segregation inhibits social and residential mobility of disadvantaged groups including Latino immigrants (Charles, 2003); however, studies on residential segregation showed Latino immigrants are less likely to face residential segregation compared with African Americans in addition to showing a positive relationship between acculturation and residential mobility (Charles, 2003; Denton & Massey, 1988; Logan & Alba, 1995). Charles (2003) also stated that spatial assimilation model is more consistent with residential segregation of Latino immigrants whereas place stratification model is more consistent with residential segregation of African Americans. Based on the existing findings on the relationship between Latino immigrants and crime, it is reasonable to expect that Latino immigrants are less likely to commit street violent crime than African Americans, even in disadvantaged communities, as they are more likely to have strong social capital and family ties, less likely to face residential discrimination, and have a better chance of achieving social and residential mobility as they become more acculturated to the United States.
Despite the abovementioned information, it is plausible to argue that the Latino paradox might be the outcome of underreporting of crime. Studies have indicated immigrants may not report crime when it occurs for many reasons, such as lack of fluency in English language, unfamiliarity with the crime reporting process, and the fear of encountering police (Davis & Erez, 1998; Menjívar & Bejarano, 2004). A recent study on immigrants and crime reporting, however, indicated victims’ individual characteristics such as immigration status did not result in underreporting of crime (National Institute of Justice, 2011). Therefore, the concern of crime underreporting by Latino immigrants appears to be negligible.
Is the Latino Paradox a Fallacy? Latinos and Drug Crime
Irrespective to a large body of research findings which indicated an inverse relationship between Latino immigrants and crime, the Latino immigrants make up a large portion in the U.S. criminal justice system. Approximately 50% of federally sentenced inmates are Hispanic or Latino (Light, Lopez, & Gonzalez-Barrera, 2014; Lopez & Light, 2009; Reedt & Reimer, 2012), and over 46% of federally convicted drug violators were Hispanic (Reedt & Reimer, 2012). With respect to Latinos and drug crime, the National Drug Intelligence Center (NDIC) (2010) stated that Mexican-based drug trafficking organizations (DTOs) control the majority of narcotics operations in the United States. The Philadelphia/Camden High Intensity Drug Trafficking Area (HIDTA) task force reported similar findings regarding Mexican-based DTOs. NDIC (2011, p.1) indicated Mexican-based DTOs import a large amount narcotics from the U.S.–Mexico border, resulting in “more stable and consistent availability of these [cocaine, heroin, marijuana, and methamphetamine] drugs” in its area of responsibility. In addition, Mexican-based DTOs’ various methods of drug smuggling and trafficking from the Southwest border regions to the areas around Philadelphia have substantially increased the threat level.
What Causes the Variations in Drug Market Violence Levels?
The existing literature on drug market indicated violent crime is clearly associated with illegal narcotics activities (Braga et al., 1999; Eck & Wartell, 1996; Lum, 2011; Rengert, 1989; Rengert, Chakravorty, Bole, & Henderson, 2000; Taniguchi, Rengert, & McCord, 2009). Studies showed violent crime is strongly associated with drug activities related to distribution and control of geographic drug markets (Goldstein, 1985; Rasmussen & Benson, 1994; Taniguchi, Ratcliffe, & Taylor, 2011). Increased enforcement efforts by police and private security revealed limited or no effect on decreasing crime rates in drug hot spots (Frogner, Andershed, Lindberg, & Johansson, 2013; Lawton, Taylor, & Luongo, 2005; Mazerolle, Soole, & Rombouts, 2007). A study by Taniguchi et al. (2009) reported policing initiatives do not appear to have any effect on crime reduction or deterrence because of the large amount of profit that offenders obtain from illicit drug sales. Some studies on offender-based interventions, such as pulling levers in drug markets, have shown mixed results in reducing violent crime rates but only during the intervention period (Corsaro & Brunson, 2013; Corsaro, Brunson, & McGarrell, 2013; Corsaro, Hunt, Hipple, & McGarrell, 2012).
Reuter and MacCoun (1992) developed four different types of neighborhood-level drug markets—local, import, export, and public—based on geographic locations and travel patterns of where drug dealers and buyers converge for transactions. Local markets were characterized as places where community residents serve as both drug dealers and customers. Export markets were characterized as places where community residents are selling drugs to nonresidents. Import markets were characterized as places where community residents are purchasing drugs from nonresidents. Finally, public markets were characterized as places where both drug dealers and customers converge at public spaces, such as “parks, train or bus stations, or schools,” for transactions. Based on the characteristics of drug markets, Reuter and MacCoun (1992) hypothesized local markets would more likely to show the lowest level of drug market–related violence whereas both import and public markets would more likely to show a higher level of drug market–related violence with export markets fall somewhere in between.
L. T. Johnson (2016) tested Reuter and MacCoun’s (1992) drug market typology model by examining the violent crime counts in Philadelphia drug markets and found some support for the model. L. T. Johnson (2016) found a significant relationship between the total violent crime and assault incident counts and proposed four different types of drug markets. In addition, the results showed an increase in the expected violent crime counts as drug dealers and buyers travel a longer distance for a drug transaction. Although indicators of drug offenders’ travel distance have shown some validity in understanding drug market violence level, they are not easily available to all researchers. Therefore, it is important to consider other critical factors, such as criminal social capital, in drug market violence research.
Theoretical Refinement: Criminal Social Capital and Illicit Drug Activities
Loughran, Nguyen, Piquero, and Fagan (2013) stated criminal social capital serves an important function for individuals to gain criminal capital “because of the informal social nature of most criminal enterprises” (p. 930). In addition, criminal social capital is more “relia[ble]” in building criminal capital since individuals cannot learn necessary skills to succeed in criminal enterprises through legitimate ways, and “informal economy is dependent on social ties, trust, and mutual obligations for effective functioning” (Loughran et al., 2013, p. 930). Based on the criminal capital model of Loughran et al. (2013), in a broad sense, criminal social capital could be referred to individuals’ or criminal organizations’ possessions of or ties to criminal networks and criminal resources. In drug trafficking and distribution, criminal networks could be interpreted as having ties to foreign DTOs who can manufacture and transport a large amount of drugs across the U.S. border in addition to having ties to U.S.-based street gangs or independent drug distributors who can sell a large amount of drugs (see Natarajan & Hough, 2000; Schmidt, 2012; Williams, 1998). On the contrary, criminal resources could be interpreted as having material possessions, such as financial proceeds from illicit drug activities, and a direct access to wholesale quantities of drugs.
Within this criminal social capital framework, it is expected that foreign-born Latinos would have stronger criminal social capital compared with native-born Latinos in the United States. Foreign-born Latinos are more likely to have a direct transnational criminal network through a personal or family kinship (see Murji, 2007; Natarajan & Hough, 2000; Reuter, 2000). In addition, foreign-born Latinos are more likely to have a better understanding and acceptance of cultural values of their country of origin. Therefore, it is reasonable to assume that foreign-born Latinos would better maintain their criminal networks located in their country of origin compared with native-born Latinos, once established (see Friman, 2004; Kleemans & de Poot, 2008; Ruggiero & Khan, 2006). As a result, foreign-born Latinos are more likely to easily gain access to criminal networks and criminal resources that could give them a higher status in latent criminal hierarchy compared with native-born Latinos. As their criminal status elevate, they will become more like businesspersons by developing a strong criminal enterprise which eliminates the necessity to commit street-level violence to generate economic gain (see Calderoni, 2012). On the contrary, native-born Latinos’ lack of strong criminal social capital places them at the lower end of criminal hierarchy. Deficiencies in criminal networks and criminal resources would more likely bar those individuals from elevating their criminal status, and therefore would remain street-level drug dealers. Based on this model, the researcher hypothesizes that foreign-born Latinos’ stronger criminal social capital would give them more opportunities and means to transport and distribute a large amount of high-quality drugs directly from the source, which will generate a higher amount of criminal capital while decreasing the price of drugs for street purchase.
Data and Method
This study used crime frequencies of homicide, robbery, aggravated assault, and violation of Uniform Firearms Act, covering January 1, 2011 through December 31, 2014. In addition, drug sales crime frequencies, covering January 1, 2011 through December 31, 2013, were used to create geographic drug markets. All crime incidents were recorded by the Philadelphia Police Department (PPD). Except race and ethnicity variables, all structural variables used in this study were retrieved at the Philadelphia census block group level. Race and ethnicity variables were unavailable at the census block group level due to the possibility of statistical disclosure (see Taylor, 1994). Therefore, these variables were retrieved at the Philadelphia census tract level. As all retrieved structural and demographic predictors were nested within geographic drug markets, multilevel models (MLMs) were used for this study (Raudenbush & Bryk, 2002). The Level 2 and Level 1 predictors were placed in MLMs based on from which geographic level they were retrieved (i.e., census block group or census tract; see Sorg & Taylor, 2011; Taylor, Ratcliffe, & Perenzin, 2015). Philadelphia was chosen as the study location as it is “[the] fifth largest city in the country with a population of about 1.5 million people” (Taylor et al., 2015, p. 9); thus, choosing Philadelphia as the study location will enhance the chances of capturing important underlying dynamics of urban drug markets.
Level 2 Unit
Philadelphia drug markets
The Level 2 unit of this study was geographic drug markets in Philadelphia, PA. The geographic drug markets were generated using nearest neighbor hierarchical clustering (Nnh) method (D’Andrade, 1978; Everett, 1974; Levine, 2006) based on the locations where drug sales incidents occurred between January 1, 2011 and December 31, 2013. All drug sales incidents were recorded by the PPD. The PPD’s uniform crime report data showed a total of 11,213 drug sales incidents during the 3-year period. A total of 27 drug sales incidents were dropped from the Nnh analysis, however, because they were incorrectly geocoded. As a result, a total of 11,186 drug sales incidents were analyzed for this study.
The Nnh analysis delineated geographic areas where a higher level of drug sales incidents occurred by generating spatial polygons (i.e., Nnh clusters). To generate Nnh clusters, nearest neighbor analysis (NNA) was conducted. “The NNA measures the distance of each point to its nearest neighbor, determines the mean distance between neighbors, and compares the mean distance to what would have been expected in a random nearest neighbor distribution” (Levine, 2007, p. 38). In other words, any point that has a shorter distance with its nearest neighbor than the expected random nearest neighbor distance formed clusters until there cannot be any more clusters (L. T. Johnson & Ratcliffe, 2013; Levine, 2007). The Nnh analysis generated 186 first-order, 18 second-order, and 2 third-order Nnh clusters. The second-order Nnh clusters were chosen as drug markets for this study, as these clusters most closely resembled the actual geographic areas where a higher level of drug sales incidents occurred in Philadelphia (see S. D. Johnson et al., 2007).
The 18 second-order Nnh clusters generated for this study were spatially aggregated to the Philadelphia census block groups that overlap them, and each of these overlapping census block group was assigned a unique drug market identification number. A subsequent analysis revealed six census block groups had more than one Nnh clusters overlapping them. Therefore, a synthetic drug market was generated for those six census block groups and assigned a different unique drug market identification number. Creating a synthetic drug market for those six census block groups allowed all census block groups to “retain statistical independence” (Haberman, Groff, & Taylor, 2013, p. 169). As a result, a total of 19 second-order Nnh clusters (i.e., spatial polygons) were used in this study as drug markets.
Level 2 Independent Variable
Percentage of a drug market in a census block group
The Level 2 independent variable was the percentage of a drug market polygon overlapping a census block group. The spatial aggregation identified a total of 199 census block groups that overlap drug market polygons. This variable controlled for the effect that the size of drug markets may have on street violent crime counts. The percentage of a drug market in a census block group ranged from 0.0005553% to 100%, with a mean and a standard deviation of 42.08 and 37.02, respectively (see Table 1).
| n | Minimum | Maximum | M | SD | |
|---|---|---|---|---|---|
| Street violent crime counts | 199 | 3.000 | 97.000 | 36.462 | 17.840 |
| Foreign-born Latinos | 199 | 0.000 | 2.011 | 0.298 | 0.454 |
| Native-born Latinos | 199 | 0.000 | 10.440 | 1.694 | 2.216 |
| Drug market size | 199 | 0.001 | 100.000 | 42.082 | 37.017 |
| Socioeconomic status index | 199 | −2.147 | 0.727 | −0.920 | 0.591 |
| Residential stability index | 199 | −3.062 | 1.214 | −0.185 | 0.717 |
| Female-headed households | 199 | 0.000 | 2.180 | 0.699 | 0.489 |
| Total population African American males | 199 | 0.178 | 10.592 | 2.989 | 1.860 |
| Total population (exposure variable) | 199 | 2.640 | 25.630 | 10.704 | 4.625 |
| Spatially lagged crime rate | 199 | 1.478 | 6.760 | 3.739 | 0.970 |
Level 1 Unit
Philadelphia census block groups
The Level 1 unit of this study was 199 Philadelphia census block groups overlapping drug market polygons. Each census block group in this study was assigned to just one drug market polygon and unique drug market identification number so that the researchers could avoid counting census block groups multiple times in the analysis (see Table 1).
Level 1 Independent Variable
Compositional variables and nearby violence
All demographic and structural variables used in this study were retrieved from the American Community Survey (ACS) of the U.S. Census Bureau. The ACS annually collects statistical census data at both census block group and census tract level (ACS, 2012; Taylor et al., 2015). The ACS publishes three estimates of 1-year estimates, which incorporates 12 months data for places with 65,000 or more in total population; 3-year estimates, which incorporates 36 months data for places with 20,000 or more in total population; and 5-year estimates, which incorporates 60 months data for all places in the United States (ACS, 2013). This study used Philadelphia ACS data, covering years 2008 through 2012, which was downloaded through ACS Alchemist software program. The ACS Alchemist software program is designed to organize and retrieve specific U.S. census data up to 100 variables for spatial analysis (Azavea, 2012). Socioeconomic index (SES index) and residential stability index were generated by following the method used by Taylor et al. (2015). In addition, spatially lagged street violent crime rate per 100 persons variable was generated and included in the analysis upon identifying the presence of spatial autocorrelation of street violent crime counts across drug markets.
SES index
The SES index was generated by using the following census variables: (a) percent households’ income less than US$20,000—reversed, and (b) percent households’ income greater than US$50,000. These variables were combined and standardized to generate the SES index (Cronbach’s α = .87; see Table 1).
Residential stability index
The residential stability index was generated by using the following census variables: (a) percent owner occupied housing units, (b) percent occupied housing units with current residents moved in before 2005, and (c) percent occupied housing units with current residents moved in before 2000. These variables were combined and standardized to generate the residential stability index (Cronbach’s α = .85; see Table 1).
Race, ethnicity, and female-headed households
The total population counts of African American males, foreign-born Latinos, native-born Latinos, and female-headed households with children with no husband were included as predictors. The ACS contains one additional Latino variable, native Latinos who were born outside of the United States. There were two issues associated with this variable. First, it is difficult to determine to which group this variable belongs (i.e., foreign-born Latinos or native-born Latinos). Second, this variable had a multicollinearity problem with other Latino variables. Therefore, in an effort to minimize any statistical error, this variable was excluded in the analysis. All demographic predictors were divided by 100 persons (see Table 1).
Spatially lagged street violent crime rate
As the dependent variable of this study was street violent crime counts in drug markets, an analysis was conducted to check for spatial autocorrelation. The analysis showed a higher level of spatial autocorrelation of street violent crime counts across all drug markets (Moran’s I = 28.90; pseudo p value < .01). To mitigate the effect of spatial concentration of street violent crime counts in drug markets, a spatial lag variable was created and included in the analysis as a predictor. The spatial lag variable was generated using first-order Queen’s contiguity method (see Andresen, 2011) at the census block group level, incorporating all Philadelphia census block groups that had a total population greater than 25 persons. Once generated, the variable was converted into street violent crime rate per 100 persons (see Table 1).
Total population as the exposure variable
The total population of 199 census block groups overlapping with drug market polygons was used as the exposure variable as the analysis used generalized count model (King, 1988; Maddala, 1983). As the dependent variable of this study was crime count, it was important to include total population as the exposure variable as there may be a positive relationship between the total population and crime count. Variation influence factor (VIF) of all predictors showed that the mean VIF value was 1.81 and the highest VIF value was 2.94. These VIF values were below the recommended value of 4 (see Taylor, 1994).
Dependent Variable
Street violent crime counts
The aggregated total count of four different types of street violent crime (homicide, robbery, assault, and violation of Uniform Firearms Act) that occurred in Level 1 unit between January 1, 2011 and December 31, 2014 was used as the outcome variable for this study (see Table 1; Lawton et al., 2005; Wyant, Taylor, Ratcliffe, & Wood, 2012). The PPD recorded all street violent crime incidents by crime type, and all incidents were geocoded based on where the incidents occurred.
Results
The initial null model (see Table 2) showed that there was a significant (p < .01) variation across drug markets in the expected street violent crime counts, controlling for total population per 100 persons as the exposure variable. The ANCOVA MLMs, which examined the fixed effect by controlling for foreign-born and native-born Latinos in separate models with total population per 100 persons as the exposure variable, showed that the significant variation in the expected street violent crime counts across drug markets remained (p < .01; see Tables 2 and 3).
| ANOVA | Model 1a | Model 1b | Model 1c | Model 1d | Model 1e | Model 1f | Model 1g | |
|---|---|---|---|---|---|---|---|---|
| IRR (SE) | IRR (SE) | IRR (SE) | IRR (SE) | IRR (SE) | IRR (SE) | IRR (SE) | ||
| Fixed effects | ||||||||
| Foreign-born Latinos | 0.782*** | 0.754*** | 0.739*** | 0.739*** | 0.760*** | 0.704*** | 0.722*** | |
| (0.0690) | (0.0644) | (0.0623) | (0.0626) | (0.0652) | (0.0651) | (0.0652) | ||
| Drug market size | 1.003*** | 1.003*** | 1.003*** | 1.003*** | 1.002*** | 1.002** | ||
| (0.000907) | (0.000907) | (0.000910) | (0.000905) | (0.000905) | (0.000904) | |||
| SES index | 0.884** | 0.885** | 0.873** | 0.888** | 0.899* | |||
| (0.0517) | (0.0522) | (0.0515) | (0.0525) | (0.0525) | ||||
| Residential stability index | 0.997 | 0.997 | 1.008 | 1.007 | ||||
| (0.0454) | (0.0451) | (0.0455) | (0.0452) | |||||
| Female-headed households | 0.896* | 0.984 | 0.992 | |||||
| (0.0582) | (0.0782) | (0.0781) | ||||||
| Total population African American males | 0.953** | 0.952** | ||||||
| (0.0222) | (0.0218) | |||||||
| Spatially lagged crime rate | 1.085** | |||||||
| (0.0403) | ||||||||
| Total population (exposure) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Constant | 3.597*** | 3.819*** | 3.453*** | 3.151*** | 3.150*** | 3.341*** | 3.813*** | 2.857*** |
| (0.181) | (0.208) | (0.209) | (0.230) | (0.231) | (0.270) | (0.394) | (0.470) | |
| Random effects | ||||||||
| Drug market variance mean | 1.021 | 1.021 | 1.021 | 1.019 | 1.019 | 1.019 | 1.020 | 1.011 |
| (0.0169) | (0.0150) | (0.0136) | (0.0127) | (0.0127) | (0.0128) | (0.0129) | (0.00997) | |
| ln alpha | 0.172*** | 0.164*** | 0.154*** | 0.151*** | 0.151*** | 0.148*** | 0.144*** | 0.144*** |
| (0.0209) | (0.0199) | (0.0188) | (0.0185) | (0.0185) | (0.0183) | (0.0179) | (0.0179) | |
| BIC | 1672.512 | 1670.266 | 1664.845 | 1665.795 | 1671.084 | 1673.537 | 1674.734 | 1675.319 |
| Observations | 199 | 199 | 199 | 199 | 199 | 199 | 199 | 199 |
| Number of groups | 19 | 19 | 19 | 19 | 19 | 19 | 19 | 19 |
Note. Standard error exponentiated form in parentheses. SES = socioeconomic status; BIC = Bayesian information criterion; IRR = incidence rate ratio.
*
p < .1. **p < .05. ***p < .01.
| ANOVA | Model 2a | Model 2b | Model 2c | Model 2d | Model 2e | Model 2f | Model 2g | |
|---|---|---|---|---|---|---|---|---|
| IRR (SE) | IRR (SE) | IRR (SE) | IRR (SE) | IRR (SE) | IRR (SE) | IRR (SE) | ||
| Fixed effects | ||||||||
| Native-born Latinos | 0.937*** | 0.933*** | 0.928*** | 0.928*** | 0.935*** | 0.918*** | 0.920*** | |
| (0.0200) | (0.0186) | (0.0179) | (0.0180) | (0.0188) | (0.0196) | (0.0179) | ||
| Drug market size | 1.003*** | 1.003*** | 1.003*** | 1.003*** | 1.002** | 1.002** | ||
| (0.000898) | (0.000893) | (0.000897) | (0.000895) | (0.000894) | (0.000886) | |||
| SES index | 0.873** | 0.874** | 0.867** | 0.882** | 0.891** | |||
| (0.0514) | (0.0517) | (0.0513) | (0.0521) | (0.0511) | ||||
| Residential stability index | 0.997 | 0.997 | 1.009 | 1.008 | ||||
| (0.0450) | (0.0449) | (0.0451) | (0.0444) | |||||
| Female-headed households | 0.922 | 1.029 | 1.053 | |||||
| (0.0613) | (0.0843) | (0.0861) | ||||||
| Total population African American males | 0.949** | 0.942*** | ||||||
| (0.0221) | (0.0216) | |||||||
| Spatially lagged crime rate | 1.113*** | |||||||
| (0.0442) | ||||||||
| Total population (exposure) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Constant | 3.597*** | 3.879*** | 3.519*** | 3.191*** | 3.189*** | 3.323*** | 3.842*** | 2.651*** |
| (0.181) | (0.239) | (0.230) | (0.241) | (0.242) | (0.274) | (0.402) | (0.438) | |
| Random effects | ||||||||
| Drug market variance mean | 1.021 | 1.034 | 1.030 | 1.025 | 1.025 | 1.024 | 1.026 | 1.006 |
| (0.0169) | (0.0228) | (0.0191) | (0.0169) | (0.0169) | (0.0166) | (0.0176) | (0.0104) | |
| ln alpha | 0.172*** | 0.158*** | 0.149*** | 0.146*** | 0.146*** | 0.145*** | 0.140*** | 0.142*** |
| (0.0209) | (0.0196) | (0.0186) | (0.0183) | (0.0183) | (0.0182) | (0.0178) | (0.0182) | |
| BIC | 1672.512 | 1667.941 | 1662.861 | 1662.861 | 1668.216 | 1672.022 | 1672.4 | 1670.433 |
| Observations | 199 | 199 | 199 | 199 | 199 | 199 | 199 | 199 |
| Number of groups | 19 | 19 | 19 | 19 | 19 | 19 | 19 | 19 |
Note. Standard error exponentiated form in parentheses. SES = socioeconomic status; BIC = Bayesian information criterion; IRR = incidence rate ratio.
*
p < .1. **p < .05. ***p < .01.
Full Model: Controlling for Compositional Differences
The effect size of both foreign-born and native-born Latino variables were examined separately in different models, controlling for compositional differences and the exposure variable (see Tables 2 and 3; see Sampson, Morenoff, & Gannon-Rowley, 2002). The main purpose of these tests was to see whether the effect size of the Latino variables change when both Level 1 and Level 2 predictors were included in the models. The sequence of Level 1 and Level 2 predictors included in both models was as follows: (a) Percentage of a drug market overlapping a census block group, (b) SES index, (c) Residential stability index, (d) Female-headed household with children with no husband per 100 persons, (e) African American males per 100 persons, and (f) Spatially lagged street violent crime rate per 100 persons. The sequence was determined based on at which census level (i.e., census block group or census tract) the variables were retrieved.
Foreign-born Latino models
The MLM negative binomial analysis showed approximately 22% decrease in the expected street violent crime counts for each additional 100 foreign-born Latinos in drug markets (p < .01), controlling only for total population per 100 persons as the exposure variable. The full MLM negative binomial analysis, which included both Level 2 and Level 1 predictors, showed approximately 28% decrease in the expected street violent crime counts for each additional 100 foreign-born Latinos in drug markets (p < .01). In addition, the full model showed 1% increase in the percentage of a drug market in a census block group increased the expected street violent crime counts by 0.2% (p < .05). Moreover, one unit increase in the spatially lagged street violent crime rates per 100 persons increased the expected street violent crime counts by 8.5% (p < .05). Each additional 100 African American males in drug markets, however, decreased the expected street violent crime counts by 4.8% (p < .05; see Table 2).
Native-born Latino models
The MLM negative binomial analysis showed 6.3% decrease in the expected street violent crime counts for each additional 100 native-born Latinos in drug markets (p < .01), controlling only for total population per 100 persons as the exposure variable. The full MLM negative binomial analysis, which included both Level 2 and Level 1 predictors, showed 8% decrease in the expected street violent crime counts for each additional 100 native-born Latinos in drug markets (p < .01). In addition, the full model showed 1% increase in the percentage of a drug market in a census block group increased the expected street violent crime counts by 0.2% (p < .05). Moreover, one unit increase in the spatially lagged street violent crime rates per 100 persons increased the expected street violent crime counts by approximately 11% (p < .01). On the contrary, one unit increase in SES index decreased the expected street violent crime counts by 11% (p < .05), and each additional 100 African American males in drug markets decreased the expected street violent crime counts by 5.8% (p < .01; see Table 3).
The findings from both foreign-born and native-born Latino models showed that the compositional differences did not have a substantial impact in the expected street violent crime counts, perhaps suggesting the presence or the strength of the Latino paradox (see Sampson, 2012); however, this view should be carefully examined as the overall context of the findings are more in line with the criminal social capital argument. As hypothesized in this article, the strength of criminal social capital affects individual’s criminal status, and individuals with strong criminal social capital are less likely to commit street violent crime. If the Latino paradox was the main reason for the inverse relationship between Latinos and the expected street violent crime counts, then it is highly likely that the direction of the relationship between African American males and the expected street violent crime counts would be the other way around as evidenced by many of the existing literature on drug markets (see Beckett, Nyrop, & Pfingst, 2006; Beckett, Nyrop, Pfingst, & Bowen, 2005; Parker & Maggard, 2005).
Inter- and Intra-Race and Ethnicity Segregation
In addition to the abovementioned findings from the MLM analyses, this study probed further to study the race and ethnicity dynamics in drug markets. Kendall’s Tau (ktau) correlation coefficients indicated the correlation between the foreign-born Latino variable and the native-born Latino variable was .57, and the correlation between the foreign-born Latino variable and the African American male variable was –.37. In addition, the correlation between the native-born Latino variable and the African American male variable was –.33 (descriptive table not provided). These results showed that even within drug markets, which accounted for a small portion of the city of Philadelphia (see Figure 1), there were a high level of residential segregation between Latinos and African Americans. These findings could be viewed as supporting evidence to the argument of this study that foreign-born Latinos, who have strong criminal social capital, might have self-selected themselves to not to reside by native-born Latinos, who have weak criminal social capital, as individuals with weak criminal social capital are more likely to commit street violent crime, and a higher level of street violent crime attracts a higher level of attention from law enforcement.
Figure 1. 1st and 2nd order Nnh clusters of drug sales incidents in Philadelphia, PA.
Note. Nnh = nearest neighbor hierarchical clustering.
Discussion
This study examined the differences in drug market violence level through the refined criminal social capital model. The results of the analyses revealed that there were significant Latino nativity variations in the expected street violent crime counts in geographic drug markets. The ANOVA MLM negative binomial analysis showed approximately 22% and 6% decrease in the expected street violent crime counts per each additional 100 foreign-born Latinos and each additional 100 native-born Latinos in drug markets, respectively. The ANCOVA MLM negative binomial analysis showed approximately 28% and 8% decrease in the expected street violent crime counts per each additional 100 foreign-born Latinos and each additional 100 native-born Latinos in drug markets, respectively. These results revealed that the Latino nativity status was strongly related to the drug market violence level. In addition, the results showed each additional 100 native-born Latinos and each additional 100 African American males have similar effect sizes in the expected street violent crime counts in drug markets.
It is important to note that the existing literature on MLM suggests researchers to abide by “30/30” rule, recommending the sample size should be at least 30 groups and 30 individuals nested within each groups for accurate estimates and standard errors (Hox, 1998, p. 5; Kreft, 1996); however, this is not a set rule, and modifications can be made depending on the study’s focus (Centre for Multilevel Modelling, 2011; Hox, 1998). In this study, the Level 2 unit was treated as random effects and all analyses were conducted at the Level 1 unit. In addition, although this study had only 19 groups for Level 2 unit, a large sample size of individuals nested within each group was sufficient enough to generate reliable estimates of individual-level effect (Bryan & Jenkins, 2016).
The findings of this study added additional insights to the existing literature on drug market and criminal capital. The proposed theoretical refinement of criminal social capital and its implications were arguably supported as the results of this study showed how the Latino nativity status differently affects drug market violence level. If the variations in the strength of criminal social capital, driven by Latino nativity status, are directly related to the stabilization of drug markets and drug market violence, then future drug market study could empirically test the magnitude of impact that transnational drug trafficking and distribution activities have on local drug markets. In addition, the ktau analysis results, which showed a moderate level of residential segregation between foreign-born Latinos and native-born Latinos, might be indicating that there is intra-ethnicity residential segregation within drug markets driven by the strength of criminal social capital.
The effect sizes of African American males per 100 persons which showed 5% decrease in the expected street violent crime counts in both foreign-born and native-born Latino models were contradictory to many previous findings (see Beckett et al., 2006; Beckett et al., 2005; Parker & Maggard, 2005). Although not tested in this study, there are two possible explanations for these outcomes. First, as it is assumed that both African American males and native-born Latinos have weak or no criminal social capital, the effect sizes of these variables in the expected street violent crime counts were similar to one another regardless of the direction of association. Second, the strength of unobserved drug market dynamics or social factors related to concentrated disadvantage might have neutralized the influence which African American males might have had in the drug market violence level. These possible explanations should be further investigated by future drug market research.
From policy and theoretical standpoint, the findings of this study could lead to new ways of combating drug and violent crime as well as enhancing our current understanding of drug market dynamics. The results of this study showed that factors such as the size of geographic drug markets and street violent crime rates in nearby areas increased the expected street violent crime counts. Therefore, spatial and statistical analysis driven examinations of drug hot spots could generate a better crime-fighting strategy for law enforcement agencies that could increase the impact of diffusion of crime control benefit (see Clarke & Weisburd, 1994; Weisburd, Groff, & Yang, 2012). Moreover, based on the theoretical points and findings of this study, drug market studies geared toward focusing on demographic changes and population shifts in drug markets, particularly Latino immigrants, could reveal how these changes affect drug and violent crime. Finally, in-depth ethnographic research on the link between Latino nativity variations and drug or violent crime could provide valuable information on latent factors which affect drug market dynamics, such as the interconnectedness and interactional processes of criminal social networks.
This study extended previous drug market research by introducing a new way of analyzing and understanding drug market dynamics and the variations in drug market violence level. In general, the existing studies indicated that Latino immigrants bring numerous positive benefits to their communities, including lowering violence level. Based on the findings of this study, however, some of these previously stated claims regarding Latino immigrants may need additional examination through a different theoretical frame, such as criminal social capital.
Acknowledgments
The author would like to thank Professor Ralph Taylor for his guidance, constructive comments on earlier drafts, and invaluable contributions to this study.
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.
Footnote
1. In this study, the term Latino nativity status was used to distinguish foreign-born and native-born (i.e., U.S.-born) Latinos to mitigate potential semantic ambiguity entailed to race/ethnicity-related terminologies. For additional information, see U.S. Census Bureau’s Glossary.
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Biographies
Jung Y. Cho is a PhD student of criminal justice at Temple University. His research interests include drug markets and violence, social ecology of crime, and geography of crime.
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