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Does Procedural Fairness Influence Evaluations of Government Efforts to Combat Gender-Based Violence? Evidence from Brazil

Published online by Cambridge University Press:  31 July 2023

Helen Rabello Kras*
Affiliation:
Regis University, Denver, CO, USA
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Abstract

Does information about the way victims of gender-based violence (GBV) are treated by the police influence evaluations of government policies to combat gender-based violence? I theorize that because most citizens have incomplete information about such policies, information about procedural fairness should be given more weight when forming evaluations of the government’s performance in this domain. Using original experiments embedded in public opinion surveys collected from Brazil, I find that information about procedural unfairness powerfully predicts more critical evaluations of GBV laws and the government’s performance in helping victims. In addition, these critical opinions influence bystander intervention attitudes. Mediation analysis confirms that views of procedural unfairness are critical in explaining these effects. The implications of the findings for the implementation of specialized services are discussed in the results and conclusion.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the Women, Gender, and Politics Research Section of the American Political Science Association
Figure 0

Table 1. Description of treatment manipulations

Figure 1

Table 2. Effects of procedural fairness on opinions about the state’s action on GBV

Figure 2

Figure 1. Predicted probability of agreeing or disagreeing with government supports and laws protect items across conditions. Panel A is based on Model 1 and Panel B is based on Model 3, Table 2. Treatment groups are as follows (from left to right): positive distributive fairness, negative distributive fairness, negative distributive/positive procedural, positive distributive/positive procedural, and positive distributive/ negative procedural. Figures A5 and A6 of the appendix present results for all options in the Likert-scale (1–7) across all treatment groups.

Figure 3

Figure 2. Based on Model 6, Table 2. Predicted probabilities, 87% CIs.

Figure 4

Figure 3. Results of mediation analysis. Dashed arrows represent statistically significant paths. Full results are reported in Table A6 in the appendix. Control variables are included in the models.

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