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The Symbolic Benefits of Descriptive and Substantive Representation

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Abstract

One of the major challenges in providing quality representation comes from the desire to balance the will of the majority with the needs of political minorities. Of particular importance is whether substantive or descriptive representation are necessary to create symbolic representation and perceptions that government outcomes are fair and legitimate. In this paper, we employ a novel experimental design to investigate how citizens feel about the relative importance of descriptive and substantive representation. Drawing on data from a nationally representative sample and two supplemental experiments, we show that citizens value descriptive representation independently of substantive representation. We also demonstrate that the degree of descriptive representation desired is conditional on the nature of the policy being considered.

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Notes

  1. For notable exceptions, see Tate (2003) and Casellas and Wallace 2015).

  2. This literature traces its roots to the work of Thibaut and Walker (1975)

  3. A finding supported with regard to courts by Scherer and Curry (2010), although their study did not take into account whether this effect would be independent of substantive outcomes.

  4. It is possible that subjects’ evaluations of these rates of descriptive representation depend on the type of community they are thinking of. For example, those living in diverse communities might find 5 black representatives too low, whereas those living in homogeneous white communities might find 5 black representatives too high. To investigate this, we also ran an identical treatment on an MTurk sample that allowed us to identify subjects’ zip codes. Including either actual or perceived percent black in the community as covariates had no effect on our substantive findings.

  5. All data and replication code for this paper can be accessed at http://dx.doi.org/10.7910/DVN/JWXMFE.

  6. There were 23 respondents who self-identified as “Mixed Race,” but they were not required to identify what combination of races they identify as. These respondents were excluded from analysis.

  7. These three measures are all highly correlated and the index has a standardized \(\alpha\) of 0.89 for Study 1, 0.86 for Study 2, and 0.91 for study 3. The index was re-scaled to run from 0 (least fair) to 1 (most fair). Descriptive statistics on the dependent variables can be found in the Appendix (Supplementary material). The substantive results are unchanged when we run the analyses on each dependent variable separately.

  8. Since subjects are randomly assigned to conditions and we have good balance on demographic items across treatment conditions, we are not particularly concerned about potential confounds. To investigate other possible predictors of perceptions of fairness, we also re-ran the results adjusting for several demographic and political covariates. Higher levels of education were associated with higher perceptions of fairness. Being female, black, and possessing a more liberal ideology were associated with lower perceptions of fairness.

  9. Due to the relatively small number of African Americans in our sample, we report 90 % confidence intervals throughout for both white and black subjects. Given the strikingly similar results we find in each of our studies, we are confident that our results are not a statistical artifact due to the relatively small number of African Americans in any one sample. Across all of our samples, the total number of African Americans is 287.

  10. Amazon’s Mechanical Turk is an online system through which workers can complete short tasks for compensation. It has received increasing attention as a source of subjects for social science researchers. For a discussion of the representativeness and validity of MTurk as a data source, see Buhrmester et al. (2011) and Berinsky et al. (2012).

  11. See appendix (Supplementary material) for detailed comparison of CCAP and MTurk data.

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Acknowledgements

The authors would like to thank Bill Bianco, Eileen Braman, Christopher DeSante, Bernard Fraga, Brian Gaines, Jim Kuklinski, Jeff Mondak, Diana Z. O’Brien, and Cara Wong for guidance and feedback on earlier drafts. We would also like to thank attendees at Vanderbilt’s Experimental Methods Speaker Series as well as anonymous reviewers for their helpful comments.

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Authors and Affiliations

  1. Indiana University, 210 Woodburn Hall, 1100 East Seventh Street, Bloomington, IN, 47405, USA

    Matthew Hayes

  2. University of California, Merced, 5200 North Lake Road, Merced, CA, 95343, USA

    Matthew V. Hibbing

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Correspondence to Matthew Hayes.

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Hayes, M., Hibbing, M.V. The Symbolic Benefits of Descriptive and Substantive Representation. Polit Behav 39, 31–50 (2017). https://doi.org/10.1007/s11109-016-9345-9

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