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Insights from the Field

Teacher Implicit Bias Widens Math Gender Gap, Hurting Girls' Confidence and Track Choices


implicit bias
IAT
math achievement
teacher effects
tracking
Teaching and Learning
Q.J. Econ.
5 Stata files
2 Datasets
Dataverse
Implicit Stereotypes: Evidence from Teachers' Gender Bias was authored by Michela Carlana. It was published by Oxford in Q.J. Econ. in 2019.

📊 How teacher stereotypes were measured and linked to students

Exposure to teacher stereotypes was measured using the Gender-Science Implicit Association Test (IAT) and linked to student achievement on standardized tests. The math gender gap is defined here as the score of boys minus the score of girls on those standardized tests. Analysis compares students assigned to math teachers with stronger versus weaker gender-science stereotypes.

🔍 Key findings

  • Assignment to math teachers who show stronger gender-science stereotypes increases the math gender gap (boys' score minus girls' score).
  • Girls assigned to more gender-biased math teachers underperform on math tests relative to comparable peers.
  • Those girls also tend to self-select into less demanding high schools by following teachers' track recommendations.
  • These effects are at least partially driven by lower self-confidence in math ability among girls exposed to gender-biased teachers.
  • The net result is impaired test performance for girls, causing them to fall short of their full potential.
  • No statistically significant effects on student outcomes are detected for literature teacher stereotypes.

🧭 Why this matters

Findings show that teachers' implicit gender-science stereotypes can shape both short-term performance and longer-term educational trajectories through confidence and tracking decisions. This identifies teacher-held implicit bias as a mechanism that contributes to persistent gender gaps in math achievement and course-taking.

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Quarterly Journal of Economics
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