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Mitigating gender inequality in women's voices: the role of normative gender-egalitarian messages

Published online by Cambridge University Press:  25 September 2024

Ryo Takahashi*
Affiliation:
Graduate School of Economics, Waseda University, Tokyo, Japan
*
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Abstract

This study empirically examines gender inequality in tolerance for women's opinions and identifies how the provision of normative gender-egalitarian message can mitigate this inequality by conducting online randomized experiments in Japan. In this experiment, I asked the participants to evaluate the agreement score for 10 anonymous statements and implemented two types of random interventions: disclosing the gender of the statement poster and providing normative statement for gender equality. The results of both cross-sectional and panel data analyses showed that people significantly reduced the agreement score for women's opinions compared with men's and non-gender disclosure opinions. Meanwhile, the negative impact of female gender disclosure was neutralized when participants were provided with a normative message.

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Type
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
Copyright © The Author(s), 2024. Published by Cambridge University Press
Figure 0

Table 1. Ten statements presented during the first and second surveys

Figure 1

Figure 1. Experimental design overview. Notes: The two interventions (i.e., the disclosure of poster's gender and the provision of normative message) are illustrated in dash box. Numbers in parentheses indicate the number of observations.

Figure 2

Table 2. Average agreement scores at the individual level by the groups

Figure 3

Table 3. Effect of the gender disclosure and normative message on agreement score

Figure 4

Table 4. Panel data analysis for prefectures with large and small gender gaps

Figure 5

Table 5. Results by participant demographic characteristics and normative message treatment

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