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When Criminal Coping is Likely: An Examination of Conditioning Effects in General Strain Theory

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Abstract

Objectives

This paper addresses a central problem in general strain theory (GST): the mixed results regarding those factors said to condition the effect of strains on crime. We test Agnew’s (Deviant Behav 34(8):653–670, 2013) assertion that a criminal response to strain is likely only when individuals score high on several factors that increase the propensity for criminal coping or possess markers that indicate a strong propensity for criminal coping.

Methods

We use survey data from nearly 6000 juveniles from across the United States to examine whether the effect of criminogenic strains across several domains—perceptions of police, school environment, and victimization—on crime are conditioned by: (1) respondents’ criminal propensity and (2) gang membership. To the best of our knowledge, this is the first criminological study to employ an analytical framework that simultaneously considers nonlinear (i.e., curvilinear) dynamics, non-additive (i.e., interactive) effects, and non-normally distributed dependent variables. This approach has the advantage of properly differentiating nonlinear and non-additive dimensions and therefore significantly improving our understanding of conditioning effects.

Results

We find considerable support for Agnew’s (2013) postulation about conditioning effects and GST. Criminal behavior is more likely among those with a strong overall propensity for criminal coping and among gang members. Furthermore, we discover that the conditioning effects are, themselves, nonlinear. That is, the effect of criminal propensity on moderating the relationship between our three measures of strain and delinquency varies across the range of the criminal propensity index. Our models that simultaneously consider both the non-additive and nonlinear relationship between strains, criminal propensity, and criminal offending better fit the data than models that consider these dimensions separately. These results hold whether examining a composite measure of criminal activity or, alternatively, three separate subscales indexing violent, property, and drug offenses.

Conclusion

Our study advances GST and the crime literature by identifying the types of strained individuals most likely to engage in criminal coping. Additionally, the analytical framework we adopt serves as a model for the correct measurement and interpretation of conditioning effects for criminological data, which almost invariably violate the assumptions of the linear regression model. Parametric interactions are the most commonly investigated type of interactions, but other kinds of interactions are also plausible and may reveal conditional relationships that are either overlooked or understated when analysts adopt a fully parametric framework. We demonstrate the utility of expressly modeling both the nonlinear effects of component variables in an interaction and the nonlinear nature of the conditioning effect.

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Notes

  1. Our constituent variables exhibit significant variability independent of their association with one another (e.g., the average bivariate correlation is \(\rho = 0.22\)), thereby reducing concern in this area.

  2. For a critical assessment of the cultural value systems hypothesis, see Tamis-LeMonda et al. (2008).

  3. Although Craig et al. focus on respondents who already committed serious offenses, thereby increasing the likelihood of containing significant numbers of juveniles at high risk of offending, the parental consent requirement, coupled with the use of face-to-face interviews for data collection, may have potentially impacted the candor of the respondents.

  4. Notable limitations of the study design have been discussed elsewhere—e.g., exclusion of private schools, truants, sick, and tardy students (Esbensen et al. 2009).

  5. Sex is typically included as a risk factor in delinquency research because it remains highly correlated with delinquency—especially serious delinquency—even after controlling for a wide range of confounding variables. In our analyses, we also examined models where sex was used as separate control variable. The results were substantively identical to models that included sex as a risk factor.

  6. Some software programs include routines that will perform the correction calculations for conditional first differences and their associated standard errors when examining conditional effects on the natural scale of the outcome variable. We used Stata 14 and its associate “margins” command to perform our calculations.

  7. The results in Table V are not directly comparable to Table III because the analysis of the gang interactions did not take into account the overall propensity for criminal coping, since gang membership is viewed as a rough surrogate for this propensity.

  8. For the model including all three nonparametric interaction terms, AIC = 19552, whereas the model including all three parametric interaction terms, AIC = 19584. A likelihood ratio comparison of the semiparametric and (nested) parametric model also reveals that the semiparametric model provides a superior fit to the data: \((\chi^{2} \left( 6 \right) = 44.18, p < 0.001)\).

  9. Violent Offenses: Victimization-Risk (semiparametric AIC = 10047; parametric AIC = 10100); Police Strain-Risk (semiparametric AIC = 10091; parametric AIC = 10124); School Strain-Risk (semiparametric AIC = 10092; parametric AIC = 10132); Victimization-Gang (semiparametric AIC = 12251; parametric AIC = 12300); Police Strain-Gang (semiparametric AIC = 12284; parametric AIC = 12342); School Strain-Gang (semiparametric AIC = 12293; parametric AIC = 12362).

  10. Property Offenses: Victimization-Risk (semiparametric AIC = 10794; parametric AIC = 10815); Police Strain-Risk (semiparametric AIC = 10816; parametric AIC = 10827); School Strain-Risk (semiparametric AIC = 10820; parametric AIC = 10835); Victimization-Gang (semiparametric AIC = 13010; parametric AIC = 13031); Police Strain-Gang (semiparametric AIC = 13024; parametric AIC = 13030); School Strain-Gang (semiparametric AIC = 13038; parametric AIC = 13048).

  11. Drug Offenses: Victimization-Risk (semiparametric AIC = 6798; parametric AIC = 6808); Police Strain-Risk (semiparametric AIC = 6788; parametric AIC = 6797); School Strain-Risk (semiparametric AIC = 6801; parametric AIC = 6811); Victimization-Gang (semiparametric AIC = 8391; parametric AIC = 8409); Police Strain-Gang (semiparametric AIC = 8399; parametric AIC = 8413); School Strain-Gang (semiparametric AIC = 8409; parametric AIC = 8428).

  12. While the measure of gang membership used in this study did condition the effect of strains on crime, it did not function as well as the overall propensity measure. It may be possible, however, to further refine this measure, taking account of such things as the length and centrality of gang membership.

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Acknowledgements

We would like to thank the editorial staff, especially Badi Hasisi, and anonymous reviewers for their helpful comments.

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Authors and Affiliations

  1. School of Law, University of California-Los Angeles, Los Angeles, CA, 90095, USA

    Sherod Thaxton

  2. Department of Sociology, Emory University, Atlanta, GA, 30322, USA

    Robert Agnew

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  1. Sherod Thaxton
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Correspondence to Sherod Thaxton.

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Thaxton, S., Agnew, R. When Criminal Coping is Likely: An Examination of Conditioning Effects in General Strain Theory. J Quant Criminol 34, 887–920 (2018). https://doi.org/10.1007/s10940-017-9358-5

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