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Assessing the effects of gender stereotype in STEM in a Brazilian university

Author

Listed:
  • Camila Alvarenga
  • Cicero Braga

Abstract

Purpose - In Brazil, over 4.7 million women enrolled in university in the year 2017. However, Brazilian women have been consistently overrepresented in humanities and care majors and underrepresented in science, technology, engineering and mathematics (STEM). Given that observed gender differences in math-intensive fields have lasting effects on gender inequality in the labor market, and that observed gender variations do not necessarily associate with differences in innate ability, in this paper we explore the paths of societal gender bias and gender differences in a Brazilian university. Design/methodology/approach - We conduct a social experiment at a University in Southeastern Brazil, applying the gender-STEM Implicit Association Test. Findings - We found that women in STEM are less likely to show gender-STEM implicit stereotypes, compared to women in humanities. The results indicate a negative correlation between implicit gender stereotyping and the choice of math-intensive majors by women. Originality/value - The stereotype-congruent results are indicative of the gender bias in Brazilian society, and suggest that stereotypes created at early stages in life are directly related to future outcomes that reinforce gender disparities in Brazil, which can be observed in career choices.

Suggested Citation

  • Camila Alvarenga & Cicero Braga, 2024. "Assessing the effects of gender stereotype in STEM in a Brazilian university," EconomiA, Emerald Group Publishing Limited, vol. 25(1), pages 74-91, March.
  • Handle: RePEc:eme:econpp:econ-05-2022-0025
    DOI: 10.1108/ECON-05-2022-0025
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    Cited by:

    1. Muhammad Qasim Rana & Angela Lee & José Fernando Rodrigues Bezerra & Guilherme Hissa Villas Boas, 2024. "Empowerment and Sustainability: Investigating Barriers to Women’s Transition from Higher Education to Empowerment in Brazil," Societies, MDPI, vol. 14(11), pages 1-19, November.

    More about this item

    Keywords

    Gender gap; Gender bias; Experiment; STEM; University; Tertiary education; J16; C93; A23;
    All these keywords.

    JEL classification:

    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • A23 - General Economics and Teaching - - Economic Education and Teaching of Economics - - - Graduate

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