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Abstract
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Gender stereotypes play a crucial role in shaping societal perceptions of individual abilities, often leading to inaccurate beliefs that influence important career and academic decisions. This study investigates whether exposure to peer gender discrimination affects individual preferences for working in environments where strong gender stereotypes prevail. Using a three-part lab experiment conducted at the University of Tennessee, this paper explores how awareness of peer gender biases influences task selection in gender stereotyped tasks. The key outcome of interest is whether revealed gender biases affects participants' likelihood of selecting the male-stereotyped task. Finally, the study investigates two underlying mechanisms driving these choices self-efficacy, where negative stereotypes diminish confidence in male-dominated tasks, and social costs, where individuals avoid environments where they may be perceived as less competent by their peers. By using randomized assignment to reveal peer biases, this study isolates the causal impact of peer discrimination on task selection and provides new insights into how peer-held stereotypes influence career-related decisions.
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After
Gender stereotypes play a crucial role in shaping societal perceptions of individual abilities, often leading to inaccurate beliefs that influence important career and academic decisions. This study investigates whether exposure to peer gender discrimination affects individual preferences for working in environments where strong gender stereotypes prevail. Using a three-part lab experiment conducted online using Prolific, this paper explores how awareness of peer gender biases influences task selection in gender stereotyped tasks. The key outcome of interest is whether revealed gender biases affects participants' likelihood of selecting the male-stereotyped task. Finally, the study investigates two underlying mechanisms driving these choices self-efficacy, where negative stereotypes diminish confidence in male-dominated tasks, and social costs, where individuals avoid environments where they may be perceived as less competent by their peers. By using randomized assignment to reveal peer biases, this study isolates the causal impact of peer discrimination on task selection and provides new insights into how peer-held stereotypes influence career-related decisions.
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Trial End Date
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May 30, 2025
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September 30, 2025
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Last Published
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April 22, 2025 12:22 PM
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June 27, 2025 12:13 PM
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Intervention End Date
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May 30, 2025
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September 30, 2025
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Primary Outcomes (End Points)
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he primary outcome of interest is selection into the math task following receiving the discrimination treatment. This is a binary outcome where individuals have the option to choose math or the facial emotional recognition task. This will also be analyzed across difference in response for men and for women.
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The primary outcome of interest is selection into the math task following receiving the discrimination treatment. This is a binary outcome where individuals have the option to choose math or the facial emotional recognition task. This will also be analyzed across difference in response for men and for women.
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Primary Outcomes (Explanation)
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Difference in self-efficacy- Self-efficacy is measured using incentivized survey questions about the
number of questions that the respondent believes that they got correct in incentivized practice rounds, where
participants complete both the facial emotional recognition questions and math questions.
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After
Difference in self-efficacy- Self-efficacy is measured using incentivized survey questions about the
number of questions that the respondent believes that they got correct in incentivized practice rounds, where
participants complete both the facial emotional recognition questions and math questions.
If groups have an average implicit score that is negative, that is they hold an implicit bias such that they more easily associate women with math these observations will be dropped for the main portion of analysis. These observations will be dropped due to them uncommon and thus will not have the power to separate out the positive bias for women from the negative bias.
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Power calculation: Minimum Detectable Effect Size for Main Outcomes
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Using the power twoproportions command in Stata, the minimum detectable effect size is 0.15 for a sample of 300 with an equal split between treatment and control groups. This will provide a power of .094 at the 95\% level. When comparing effects on men and women separately where the control and treated groups have 75 observations the minimum effect size is approximately .2 power of .87 at the 95% level.
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Using the power twoproportions command in Stata, the minimum detectable effect size is 0.15 for a sample of 300 with an equal split between treatment and control groups. This will provide a power of .094 at the 95\% level. When comparing effects on men and women separately where the control and treated groups have 75 observations the minimum effect size is approximately .2 power of .87 at the 95% level.
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Intervention (Hidden)
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Before
An example of the discrimination information presented to the treatment group is: ”In Experiment 1, you did an Implicit Association Test. An Implicit Association Test (IAT) is a psychological tool used to measure unconscious biases—attitudes or stereotypes people may hold, even if they are not aware of them. It works by asking participants to quickly sort words or images into categories, and the speed and accuracy of their responses reveal hidden associations in their minds. For example, if someone associates certain groups with positive or negative words more quickly, it suggests an implicit bias. On average your teammates showed a strong positive implicit bias toward women participating in math. Having an implicit bias for women and math means unconsciously associating women with strong mathematical abilities while being less likely to associate men with the same skill. An example of this might include assuming female students are more likely to excel in math tasks. ”This signal differs based upon the gender of the respondent and the beliefs of their assigned groups. The discrimination treatment is used to measure how preference to participate in math tasks change when individuals gain information about their peer’s gender biases over the task type. The discrimination signal is constructed using a gender-science implicit association test prior to the start of the choice activities. The IAT produces Cohen’s D which is aggregated to the team level using the leave one out mean method. A threshold of 0.2 is set to indicate that a group has a negative implicit gender bias towards women, and a threshold of -0.2 is set to indicate that a group has a positive implicit gender bias towards women. Further thresholds are set for the level of biases which are slight/moderate/strong holding values (0.2-0.5] (0.5-0.8] (≥0.8) respectively.
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After
An example of the discrimination information presented to the treatment group is: ”In Experiment 1, you did an Implicit Association Test. An Implicit Association Test (IAT) is a psychological tool used to measure unconscious biases—attitudes or stereotypes people may hold, even if they are not aware of them. It works by asking participants to quickly sort words or images into categories, and the speed and accuracy of their responses reveal hidden associations in their minds. For example, if someone associates certain groups with positive or negative words more quickly, it suggests an implicit bias. On average your teammates showed a strong positive implicit bias toward women participating in math. Having an implicit bias for women and math means unconsciously associating women with strong mathematical abilities while being less likely to associate men with the same skill. An example of this might include assuming women are more likely to excel in math tasks. ”This signal differs based upon the gender of the respondent and the beliefs of their assigned groups. The discrimination treatment is used to measure how preference to participate in math tasks change when individuals gain information about their peer’s gender biases over the task type. The discrimination signal is constructed using a gender-science implicit association test prior to the start of the choice activities. The IAT produces Cohen’s D which is aggregated to the team level using the leave one out mean method. A threshold of 0.2 is set to indicate that a group has a negative implicit gender bias towards women, and a threshold of -0.2 is set to indicate that a group has a positive implicit gender bias towards women. Further thresholds are set for the level of biases which are slight/moderate/strong holding values (0.2-0.5] (0.5-0.8] (≥0.8) respectively.
The control group receives information about the first stage of the experiment and information about the average beliefs of past respondents. An example of the information that the control group receives is: "In Experiment 1, you did an Implicit Association Test. An Implicit Association Test (IAT) is a psychological tool used to measure unconscious biases—attitudes or stereotypes people may hold, even if they are not aware of them. It works by asking participants to quickly sort words or images into categories, and the speed and accuracy of their responses reveal hidden
associations in their minds. For example, if someone associates certain groups with positive or negative words more quickly, it suggests an implicit bias."
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