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Explaining Gender Differences in Confidence and Overconfidence in Math

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  • Seo-Young Cho

    (University of Marburg)

Abstract

This paper investigates empirically how and why men and women are different in their confidence levels. In the analysis, confidence is disentangled into two dimensions: confidence in correct math knowledge and overconfidence in false knowledge. Using the data of the PISA test in math, the findings highlight that math abilities have different effects on boys and girls. Overall, math abilities increase confidence and decrease overconfidence. However, the positive effect on confidence is smaller for girls, and the negative effect on overconfidence is larger for them. This gender-asymmetric effect implies that well-performing girls are more constrained from gaining confident attitudes through their abilities, compared to well-performing boys. The empirical evidence further indicates that the gender-asymmetric effect of abilities can be explained by gender socialization that undermines women’s achievements and limit their opportunities.

Suggested Citation

  • Seo-Young Cho, 2017. "Explaining Gender Differences in Confidence and Overconfidence in Math," MAGKS Papers on Economics 201701, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    • Handle: RePEc:mar:magkse:201701
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    File URL: https://www.uni-marburg.de/fb02/makro/forschung/magkspapers/paper_2017/01-2017_cho.pdf
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    References listed on IDEAS

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    Cited by:

    1. Teija Keränen & Heidi Enwald, 2023. "Everyday Energy Information Literacy and Attitudes towards Energy-Related Decisions: Gender Differences among Finns," Resources, MDPI, vol. 12(6), pages 1-16, June.

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    More about this item

    Keywords

    gender differences in confidence and overconfidence; gender gaps in math; genderasymmetric effects of ability; gender equality; gender socialization;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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