Revisiting the role of distributive justice in Tyler’s legitimacy theory



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.


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.


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.


Distributive justice should not be treated as a competing explanation for legitimacy evaluations, but as a concept that contextualizes why procedural justice is important.

This is a preview of subscription content, access via your institution.

Fig. 1


  1. 1.

    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).

  2. 2.

    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).

  3. 3.

    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.

  4. 4.

    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.

  5. 5.

    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.

  6. 6.

    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.

  7. 7.

    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.

  8. 8.

    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.


  1. Adams, J. S. (1965). Inequity in social exchange. Advances in Experimental Social Psychology, 2, 267–299.

    Google Scholar 

  2. Berk, R., Pitkin, E., Brown, L., Buja, A., George, E., & Zhao, L. (2013). Covariance adjustments for the analysis of randomized field experiments. Evaluation Review, 37, 170–196.

    Article  Google Scholar 

  3. Brockner, J., & Wiesenfeld, B. M. (1996). An integrative framework for explaining reactions to decisions: interactive effects of outcomes and procedures. Psychological Bulletin, 120, 189–208.

    Article  Google Scholar 

  4. Brockner, J., Siegel, P. A., Daly, J. P., Tyler, T., & Martin, C. (1997). When trust matters: the moderating effect of outcome favorability. Administrative Science Quarterly, 42, 558–583.

    Article  Google Scholar 

  5. Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21, 230–258.

    Article  Google Scholar 

  6. Finney, S.J. & DiStefano, C. (2013). Nonnormal and cateogircal data in structural equation modeling. In G.R. Hancock & R.O. Mueller (Eds.), Structural Equation Modeling: A Second Course (2nd ed.) (pp. 439–492). Charlotte, NC: Information Age Publishing, Inc.

  7. Folger, R. (1986). Re-thinking equity theory: a referent cognitions model. In H. W. Bierhoff, R. L. Cohen, & J. Greenberg (Eds.), Justice in social relations (pp. 145–162). New York: Plenum Press.

    Google Scholar 

  8. Freedman, D. A. (2008). On regression adjustments to experimental data. Advances in Applied Mathematics, 40, 180–193.

    Article  Google Scholar 

  9. Hamm, J. A., Trinkner, R., & Carr, J. D. (2017). Fair process, trust, and cooperation: moving toward an integrated framework of police legitimacy. Criminal Justice and Behavior, 44, 1183–1212.

    Article  Google Scholar 

  10. Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. New York: The Guilford Press.

    Google Scholar 

  11. Homans, G. C. (1961). Social behavior: its elementary forms. New York: Harcourt, Barce & World, Inc..

    Google Scholar 

  12. Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424–453.

    Article  Google Scholar 

  13. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.

    Article  Google Scholar 

  14. Huq, A. Z., Jackson, J., & Trinkner, R. (2017). Legitimating practices: revisiting the predicates of police legitimacy. The British Journal of Criminology, 57, 1101–1122.

    Google Scholar 

  15. Jackson, J., & Gau, J. M. (2016). Carving up concepts? Differentiating between trust and legitimacy in public attitudes towards legal authority. In E. Shockley, T. Neal, L. PytlikZillig, & B. Bornstein (Eds.), Interdisciplinary perspectives on trust (pp. 49–69). New York: Springer.

    Google Scholar 

  16. Jackson, J., Hough, M., Bradford, B., & Kuha, J. (2015). Empirical legitimacy as two connected psychological states. In G. Meško & J. Taknebe (Eds.), Trust and legitimacy in criminal justice: European perspectives (pp. 137–160). New York: Springer.

    Google Scholar 

  17. Jasso, G. (1978). On the justice of earnings: A new specification of the justice evaluation function. American Journal of Sociology, 83, 1398–1419.

  18. Jasso, G. (1980). A new theory of distributive justice. American Sociological Review, 45, 3–32.

    Article  Google Scholar 

  19. Langton, L., & Durose, M. (2013). Police behavior during traffic and street stops (p. 2011). Washington, DC: Bureau of Justice Statistics.

    Google Scholar 

  20. Lind, E. A. (2001). Fairness heuristic theory: justice judgments as pivotal cognitions in organizational relations. In J. Greenberg & R. Copanzano (Eds.), Advances in organizational justice (pp. 56–88). Stanford: Stanford University Press.

    Google Scholar 

  21. Lind, E. A., & Tyler, T. R. (1988). The social psychology of procedural justice. New York: Plenum Press.

    Book  Google Scholar 

  22. Lind, E. A., Kulik, C. T., Ambrose, M., & de Vera Park, M. V. (1993). Individual and corporate dispute resolution: using procedural fairness as a decision heuristic. Administrative Science Quarterly, 38, 224–251.

    Article  Google Scholar 

  23. Lind, E. A., Kray, L., & Thompson, L. (2001). Primacy effects in justice judgments: testing predictions from fairness heuristic theory. Organizational Behavior and Human Decision Processes, 85, 189–210.

    Article  Google Scholar 

  24. Markovsky, B. (1985). Toward a multilevel distributive justice theory. American Sociological Review, 50, 822–839.

    Article  Google Scholar 

  25. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: homophily in social networks. Annual Review of Sociology, 27, 415–444.

    Article  Google Scholar 

  26. Qin, X., Ren, R., Zhang, Z.-X., & Johnson, R. E. (2015). Fairness heuristics and substitutability effects: inferring the fairness of outcomes, procedures, and interpersonal treatment when employees lack clear information. Journal of Applied Psychology, 100, 749–766.

    Article  Google Scholar 

  27. Reisig, M. D., Bratton, J., & Gertz, M. G. (2007). The construct validity and refinement of process-based policing measures. Criminal Justice and Behavior, 34, 1005–1028.

    Article  Google Scholar 

  28. Sunshine, J., & Tyler, T. R. (2003). The role of procedural justice and legitimacy in shaping public support for policing. Law and Society Review, 37, 513–548.

    Article  Google Scholar 

  29. Tankebe, J. (2013). Viewing things differently: the dimensions of public perceptions of police legitimacy. Criminology, 51, 103–135.

    Article  Google Scholar 

  30. Thibaut, J., & Walker, L. (1978). A theory of procedure. California Law Review, 66, 541–566.

    Article  Google Scholar 

  31. Thompson, M. S., & Green, S. B. (2013). Evaluating between-group differences in latent variable means. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: a second course (pp. 163–218). Charlotte: Information Age Publishing, Inc.

    Google Scholar 

  32. Tyler, T. R. (1990). Why people obey the law. New Haven: Yale University Press.

    Google Scholar 

  33. Tyler, T. R., & Huo, Y. J. (2002). Trust in the law: encouraging public cooperation with the police and courts. New York: Russell Sage Foundation.

    Google Scholar 

  34. Tyler, T. R., & Lind, E. A. (1992). A relational model of authority in groups. Advances in Experimental Social Psychology, 48, 72–81.

    Google Scholar 

  35. Tyler, T. R., & Wakslak, C. J. (2004). Profiling and police legitimacy: procedural justice, attributions of motive, and acceptance of police authority. Criminology, 42, 253–281.

    Article  Google Scholar 

  36. Van den Bos, K., & Lind, E. A. (2002). Uncertainty management by means of fairness judgments. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 34, pp. 1–60). Boston: Elsevier.

    Google Scholar 

  37. Van den Bos, K., Lind, E. A., Vermunt, R., & Wilke, H. A. M. (1997a). How do I judge my outcome when I do not know the outcome of others? The psychology of the fair process effect. Journal of Personality and Social Psychology, 72, 1034–1046.

    Article  Google Scholar 

  38. Van den Bos, K., Vermunt, R., & Wilke, H. A. M. (1997b). Procedural and distributive justice: what is fair depends more on what comes first than on what comes next. Journal of Personality and Social Psychology, 72, 95–104.

    Article  Google Scholar 

  39. Wolfe, S. E., Nix, J., Kaminski, R. J., & Rojek, J. (2016). Is the effect of procedural justice on police legitimacy invariant? Testing the generality of procedural justice and competing antecedents of legitimacy. Journal of Quantitative Criminology, 32, 253–282.

    Article  Google Scholar 

  40. Zhao, X., Lynch, J. G., Jr., & Chen, Q. (2010). Reconsidering Baron and Kenny: myths and truths about mediation analysis. Journal of Consumer Research, 37, 197–206.

    Article  Google Scholar 

Download references


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.

Author information



Corresponding author

Correspondence to Kyle McLean.

Ethics declarations


This study was reviewed and approved by the Institutional Review Boards at the University of South Carolina and Florida State University.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material


(DOCX 19 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

McLean, K. Revisiting the role of distributive justice in Tyler’s legitimacy theory. J Exp Criminol 16, 335–346 (2020).

Download citation


  • Procedural justice
  • Distributive justice
  • Legitimacy
  • Policing