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Latina Fortitude in the Face of Disadvantage: Exploring the Conditioning Effects of Ethnic Identity and Gendered Ethnic Identity on Latina Offending

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Abstract

Recent literature has investigated if socialization and identity protect against the criminogenic effects of strainful experiences for African Americans. Here Latinas are brought to the forefront. This study not only investigates if a positive ethnic identity increases fortitude against strainful events, but if its effects are further influenced by gender socialization. Results reveal ethnic identity increases resilience against the criminogenic effects of vicarious victimization and acculturation and gendered ethnic identity protects against direct victimization. This study reinforces the need for further investigation into cultural explanations for within group differences in criminogenic outcomes.

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Notes

  1. In the present work, “Latina”, “Latino”, and “Latinx” refers to those whom classify themselves as a person of Cuban, Mexican, Puerto Rican, Central American, South American, or other Spanish cultural origin (Masuoka 2008; Scharrón-Del Río and Aja 2015; U.S. Census Bureau 2017).

  2. The word Color, and other racial ethnic group categories, are capitalized out of respect and recognition that it is often an important component of one’s identity (see Potter 2015).

  3. The following series of analyses utilize the third wave of the PHDCN cohort study along with the structural variables from the community survey (Earls et al. 1997, 2007a).

  4. Or anyone with self-identified origins in Mexico, Central and South America, and the Spanish-speaking Caribbean were classified as Latinx.

  5. One question included in the acculturation strain factor asking, “To what degree are your home customs American or ethnic?” with responses ranging from 1 = very ethnic to 4 = very American (reverse coded from original), was missing 44.2% of its responses. The next highest missingness rate was for income at 14.4%. No other questions included in the final dataset were missing more than 7% of their response (i.e., four of the six negative affect questions), with most questions missing none or less than 1% of responses.

  6. Multiple imputation involves imputing missing values several times, analyzing across the imputed values, and pooling the results (van Buuren 2012). The present analyses draw from five imputed datasets, the default in SPSS.

  7. Third wave data were collected between 2000 and 2002, and the present subsample involves cohorts originally aged 9, 12, 15, and 18 in 1995. The present analyses are limited to the third wave because it is the only time point when all the theoretical variables were administered. For more information on the data structure of the PHDCN see Earls et al. (2005, 2006b, 2007a) and Sampson et al. (1997).

  8. Respondents were asked, “In the last year have you…” and provided a yes or no response to the following behaviors: carried a weapon; purposefully damaged or destroyed property that did not belong to you; purposely set fire to a house, building or vacant lot; entered or broken into a building to steal something; snatched someone’s purse or wallet or picked someone’s pocket; stolen from a car; knowingly bought or sold stolen goods; stolen a car or motorcycle to keep or sell; sold marijuana or pot; sold cocaine or crack; sold heroin; hit someone you live with with the idea of hurting them; hit someone you did not live with with the idea of hurting them; attacked someone with a weapon; used a weapon or force to get money or things from people; thrown objects at people; chased someone to scare or hurt them; shot someone; shot at someone; been in a gang fight in which someone was hurt or threatened with harm; threatened to physically hurt someone; had or tried to have sexual relations with someone against their will.

  9. While interpersonal violence and child abuse are significant forms of victimization greatly experienced by Latinas (Breiding et al. 2014; Ulibarri et al. 2009), the present data do not allow for the identification of one’s assailant, and therefore determination of the relationship between the participant and their victimizer. Thus, interpersonal violence and child abuse are likely imbedded in the present measure, but cannot be currently separated from other forms of victimization.

  10. Principal components analysis was used to create a measure of acculturation strain. Factor loadings are as follows: English proficiency = 0.665; English preference = 0.666; U.S. nativity = 0.607; U.S. citizenship = 0.775; and home customs = 0.751. Such methodology was deemed necessary given the variation in the unit of measurement between the component questions.

  11. The Youth Self Report Protocol was developed by Achenbach System of Empirically Based Assessment, and more information of the psychometric development, including which items measure which emotional traits, may be found at aseba.org.

  12. Measures of socioeconomic status (SES) are meant to capture individual’s access to social and economic resources. Income is a common proxy and is a stronger predictor of associated outcomes (e.g., health) than other SES indicators such as education and occupation (Duncan et al. 2002). In the present analysis, income is based on primary caregiver income for respondents in cohorts 9 and 12 and self-reported income for respondents in cohorts 15 and 18.

  13. The original PHDCN investigators constructed the factor scores currently used from ten items from the 1990 U.S. Census. The percentage of African Americans, juveniles, unemployed, female-headed households, individuals living below the poverty line, and people on public assistance in a neighborhood cluster were combined to gauge concentrated disadvantage. Residential stability comes from the percentage of individuals who have resided in the same home since 1985 and percentage of those who own their home. The percentage of Latinxs and the percentage of other foreign-born individuals measure immigrant concentration. Original items used to create the given factor scores were eliminated from the restricted data presently utilized and obtain from the Interuniversity Consortium for Political and Social Research. For further discussion of the measures see Earls et al. (2007a) and Sampson et al. (1997).

  14. Ethnic heterogeneity was originally labeled “immigrant concentration,” but presently relabeled because it captures those that identify as Latinx as well as foreign-born individuals. Additionally, research finds immigrants are significantly less likely to engage in violent crime, especially if they reside in communities with high concentrations of immigrants (Feldmeyer and Steffensmeier 2009). This measure, therefore, is presently included for traditional theoretical consistency (Sampson et al. 1997; Shaw and McKay 1942) as well as a conservative measure control for the complex relationship between ethnicity and place.

  15. Table 4 reveals the present sample distribution across the neighborhood clusters, and Table 5 provides the correlation matrix and descriptive statistics for the present variables, both located in the “Appendix”.

  16. The present outcome variable contains a high number of zeroes, and the variance (s2 = 0.555) greatly exceeds the mean (\( \bar{x} = 0.277 \)) violating the assumptions of a Poisson distribution. The Stata14 command “countfit” was used to compare the model residuals between negative binomial regression and zero-inflated negative binomial regression to determine which distribution best fits the present sample. While the predicted outcomes are similar, comparisons of model fit statistics suggest negative binomial distribution significantly fits the present data better. Countfit outcomes available upon request. .

  17. VIF were all below 5.0; available upon request.

  18. For GST, negative emotions are a primary causal link between strain and crime. Agnew (1992, 2006) argues anger energizes individuals to action while depression inspires retreatist behaviors. Yet, research often finds negative emotions, particularly anger, directly affect criminogenic outcomes in addition to strain (e.g., Brezina 2010; Mazerolle et al. 2003; Roberton et al. 2015). For theoretical consistency and to provide a conservative assessment of the mediating effects of negative emotions (Baron and Kenny 1986), ordinary least squares regressions were used to assess the direct effects of victimization, discrimination, and acculturation on anger and depression. Vicarious victimization is significantly associated with anger, but no other significant effects were revealed for the present sample. These results are available upon request. While a thorough assessment of the mediating effects of negative emotions are warranted for a full test of GST, that is not the primary aim of the present study. While theoretically grounded in GST, the present study seeks to highlight the conditioning effects of ethnic and gendered ethnic identity within a strain-based model. Further investigation into the mediating effects of negative emotions for Latinas is warranted in future research, but is beyond the present scope.

  19. “margins” is the command; see Buis (2010) for full discussion.

  20. Low values are one standard deviation below the mean and high values are one standard deviation above the mean.

  21. Only the multiplicative effects of high and low ethnic and gendered ethnic identity on high levels of significant strains are presently presented, but all effects are available upon request.

  22. Marginal effects are calculated by taking the multiplicative effect (presented in Table 7) of high vicarious victimization and low ethnic identity and subtracting it from the multiplicative effect of high vicarious victimization and high ethnic identity (Buis 2010). This is repeated for each significant interaction pair. The significance level is determined by calculating the z-score and determining the p value (Buis 2010; Paternoster et al. 1998).

  23. I.e., subordination to others, silencing self to maintain harmony, being the family pillar, and being a spiritual pillar; though, the latter are somewhat controlled for with the inclusion of familism, religion, and Catholicism in the present study.

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  1. Department of Criminology and Criminal Justice, African American Studies Program, University of South Carolina, 109 Currell College, 1305 Greene Street, Columbia, SC, 29208, USA

    Deena Isom Scott

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Appendix

Appendix

See Tables 4 and 5.

Table 4 Ethnic and racial composition by socioeconomic status (SES) strata: distribution of 447 Latina youth across the neighbourhood clusters (NC) in the PHDCN data
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Table 5 Correlation matrix and descriptives
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Scott, D.I. Latina Fortitude in the Face of Disadvantage: Exploring the Conditioning Effects of Ethnic Identity and Gendered Ethnic Identity on Latina Offending. Crit Crim 26, 49–73 (2018). https://doi.org/10.1007/s10612-017-9380-9

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