Abstract
Objectives
Tyler’s theory of legitimacy identified procedural justice and distributive justice as antecedents of legitimacy, but placed distributive justice in a relatively minor position compared with procedural justice. This has led to researchers paying less attention to distributive justice in the development of theory, despite consistent findings that distributive justice is important to a number of outcomes for criminal justice authorities. This report uses uncertainty management theory to revisit Tyler’s legitimacy model and gain a more nuanced understanding of distributive justice.
Methods
The proposed model is tested using a series of latent variable analyses conducted on a sample of 2169 adults and a factorial vignette design. The vignette design randomly manipulates outcome favorability and officer behavior during a hypothetical traffic stop. Multiple indicator multiple cause (MIMIC) models are then utilized to test the impact of these manipulations on perceptions of procedural justice and distributive justice. This is followed by a structural equation model that tests the relationships between procedural justice, distributive justice, and legitimacy.
Results
Officer behavior is a primary predictor of both procedural justice and distributive justice. Furthermore, the results demonstrate that distributive justice judgments are shaped by perceptions of procedural justice. Accordingly, distributive justice mediates the relationship between procedural justice and legitimacy.
Conclusions
Distributive justice should not be treated as a competing explanation for legitimacy evaluations, but as a concept that contextualizes why procedural justice is important.
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Notes
Only about one-quarter of all US residents have contact with the police in a year and less than half of those are involuntary (e.g., police-initiated traffic stops, suspected of a crime; Langton and Durose 2013).
Sociological studies of social networks have demonstrated considerable homogeneity in individuals’ social networks. That is, individuals tend to form relationships with others like them, while relationships with dissimilar individuals are more likely to dissolve over time (McPherson et al. 2001).
As referenced earlier, holding the offense constant means the equation specified by Adams (1965) can be simplified to Person A’s outcome = Person B’s outcome.
Outcome favorability was chosen as a manipulation because (1) it falls more in line with a traditional perspective on distributive justice—that distributive justice is related to the outcome delivered—and (2) outcome fairness, as demonstrated in the review of Adams, Jasso, and Markovsky’s theories, is impacted by a number of variables. Thus, manipulating outcome fairness would have resulted in a much more complex research design that would have compromised the interpretability of the results. Additionally, keeping the offense constant across vignette conditions gives additional evidence that perceptions of outcome fairness should be directly related to changes in the outcome itself.
There is considerable debate in the literature regarding the components of legitimacy (see Huq et al. 2017; Jackson and Gau 2016; Jackson et al. 2015; Tankebe 2013). As a result, multiple dimensions of legitimacy were measured and included in the model to account for potential differences by measurement strategy. Note, however, that the normative alignment measure relates specifically to the incident, where other legitimacy measures are more broadly focuses. Still, all legitimacy measures were assessed after the subject had read the vignette. The components of legitimacy are assessed independently and not combined into a single legitimacy construct because the measurement model did not fit when combined into a single construct.
While the measures to be utilized in this study did not provide any indications of univariate non-normality, the robust estimator is still appropriate given the potential for multivariate non-normality. If multivariate non-normality is present, the estimator will be able to adjust for it. If there is no non-normality in the data, the estimator will reduce down to provide the same estimates that would be seen using the traditional maximum likelihood estimator.
Hu and Bentler (1999) note that using a single-index to assess model fit is problematic because indices are able to detect different aspects of model misfit (e.g., structural fit compared with measurement fit). As such, it is recommended that scholars use the SRMR—due to its unique advantages for detecting fit—and at least one other fit index.
Note, however, that uncertainty was not directly measured. Rather, the competing theoretical hypotheses of traditional distributive justice theories and UMT were tested and UMT hypotheses were supported.
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Acknowledgments
The author would like to thank Justin Nix, Scott Wolfe, and the anonymous reviewers for their comments on earlier drafts of this work. All errors remain the authors' responsibility.
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This study was reviewed and approved by the Institutional Review Boards at the University of South Carolina and Florida State University.
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McLean, K. Revisiting the role of distributive justice in Tyler’s legitimacy theory. J Exp Criminol 16, 335–346 (2020). https://doi.org/10.1007/s11292-019-09370-5
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DOI: https://doi.org/10.1007/s11292-019-09370-5