Abstract: In recent years, explicit bias against women in Science, Technology, Engineering and Math (STEM) is disappearing but gender discrimination is still prevalent. We assessed the gender-biased behavior and related explicit and implicit stereotypes of 93 math teachers to identify the psychological origins of such discrimination. We asked the teachers to grade math exam papers and assess the students’ capabilities while manipulating the perceived gender of the students to capture gender-biased grading and assessment behavior. We also measured the teachers’ implicit and explicit stereotypes regarding math, gender, and talent. We found that implicit, but not explicit, gender stereotypes correlated with grading and assessment behavior. We also found that participants who underestimated their own implicit stereotypes engaged in more pro-male discrimination compared to those who overestimated or accurately estimated them. Reducing implicit gender stereotypes and exposing individuals to their own implicit biases may be beneficial in promoting gender equality in STEM fields.
Saturday, September 19, 2020
93 math teachers grading exam papers: those who underestimated their own implicit stereotypes engaged in more pro-male discrimination compared to those who overestimated or accurately estimated them
On the Origins of Gender-Biased Behavior: The Role of Explicit and Implicit Stereotypes. Eliana Avitzour, Adi Choen, Daphna Joel, Victor Lavy. NBER Working Paper No. 27818, September 2020. https://www.nber.org/papers/w27818
Abstract: In recent years, explicit bias against women in Science, Technology, Engineering and Math (STEM) is disappearing but gender discrimination is still prevalent. We assessed the gender-biased behavior and related explicit and implicit stereotypes of 93 math teachers to identify the psychological origins of such discrimination. We asked the teachers to grade math exam papers and assess the students’ capabilities while manipulating the perceived gender of the students to capture gender-biased grading and assessment behavior. We also measured the teachers’ implicit and explicit stereotypes regarding math, gender, and talent. We found that implicit, but not explicit, gender stereotypes correlated with grading and assessment behavior. We also found that participants who underestimated their own implicit stereotypes engaged in more pro-male discrimination compared to those who overestimated or accurately estimated them. Reducing implicit gender stereotypes and exposing individuals to their own implicit biases may be beneficial in promoting gender equality in STEM fields.
Abstract: In recent years, explicit bias against women in Science, Technology, Engineering and Math (STEM) is disappearing but gender discrimination is still prevalent. We assessed the gender-biased behavior and related explicit and implicit stereotypes of 93 math teachers to identify the psychological origins of such discrimination. We asked the teachers to grade math exam papers and assess the students’ capabilities while manipulating the perceived gender of the students to capture gender-biased grading and assessment behavior. We also measured the teachers’ implicit and explicit stereotypes regarding math, gender, and talent. We found that implicit, but not explicit, gender stereotypes correlated with grading and assessment behavior. We also found that participants who underestimated their own implicit stereotypes engaged in more pro-male discrimination compared to those who overestimated or accurately estimated them. Reducing implicit gender stereotypes and exposing individuals to their own implicit biases may be beneficial in promoting gender equality in STEM fields.
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