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Gender differences in self-view and desired salaries: A study on online recruitment website users in China

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  • Xiaoqi Zhang
  • Yanqiao Zheng

Abstract

One explanation for the gender pay differences in labor markets is that women propose lower desired salaries. By using an actual job seeking resume database and applying text mining techniques, we are able to observe both the extent of gender differences in desired salaries and job-related self-view. We find gender differences in global self-view favoring females, and in some domain-specific self-view favoring males. Previous findings of disadvantaged groups having levels of self-view at least as high as those of advantaged groups lend credibility to our findings. Moreover, we argue that the differences in global self-view favoring females may be related to the theories of “belief flipping”, since women in our sample of online-recruitment markets are distinct from the general population, with on average 15.2 years of education and 8.99 years of work experience, due to self-selection. In addition, we find that women do propose lower desired salary than men, after controlling for various factors such as human capital, marital status, industries. We further investigate the role of self-view and find it contributes to explain desired salaries, with modest mediator effect but little moderator effect on gender differences in desired salaries.

Suggested Citation

  • Xiaoqi Zhang & Yanqiao Zheng, 2019. "Gender differences in self-view and desired salaries: A study on online recruitment website users in China," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-17, January.
  • Handle: RePEc:plo:pone00:0210072
    DOI: 10.1371/journal.pone.0210072
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    References listed on IDEAS

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    Cited by:

    1. Ana Fernandes & Martin Huber & Giannina Vaccaro, 2021. "Gender differences in wage expectations," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-24, June.
    2. Zheng, Yanqiao & Zhang, Xiaoqi & Zhu, Yu, 2021. "Overeducation, major mismatch, and return to higher education tiers: Evidence from novel data source of a major online recruitment platform in China," China Economic Review, Elsevier, vol. 66(C).
    3. Dai Debao & Ma Yinxia & Zhao Min, 2021. "Analysis of big data job requirements based on K-means text clustering in China," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-14, August.

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