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Field
Intervention (Hidden)
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Before
Each team is composed of three members: one male team leader and two predictors, one male and one female. However, the gender or any other demographic information of any team member is not explicitly disclosed to anyone within the team. Instead, participants are assigned random numbers and avatars. These avatars are randomly assigned as either male or female to the two predictors in each team. Consequently, one-fourth of the time, predictors are allocated avatars corresponding to their actual gender, while in two-fourth of cases, one predictor is assigned an avatar of the incorrect gender. In the remaining one-fourth of instances, both predictors receive avatars representing the incorrect gender. These avatars are visible solely to the team leader and remain concealed from the predictors, who can only see the random number assigned to the team members and to the team. The team leader, however, is consistently assigned a male icon, aligning with their actual gender. This approach suggests to the leader that the assigned avatar gender of the predictors corresponds to their true gender.
The random allocation of gendered avatars serves as our primary experimental intervention, allowing us to assess whether leaders evaluate individuals based on these assigned genders inferred through the avatars. We refer to this as our Baseline treatment (Treatment T1), which helps us understand whether team leaders exhibit bias in their replacement decisions against female predictors when individual predictor performances are not visible. This experimental manipulation is similar, in spirit, to several other studies that examine the presence of gender bias by varying names on resumes or in emails, and has emerged as a response to challenges in causally identifying discrimination using naturally occurring data. In our case, we vary avatars that appear male or female but do not use any names. Use of avatars to signal gender has been used in several prior studies to induce a particular gender. This intervention helps us understand how a random assignment of gender, controlling for actual performance and team performance, influences team leaders' decisions, providing clearer evidence of biases based on stereotypes and personal tastes.
The experiment will include two additional treatments: Performance Information Treatment (T2) and Reward for Retention Treatment (T3). Treatment T2 aims to determine if providing team leaders with partial visibility into individual predictor performance reduces bias in their replacement decisions, helping to identify whether the discrimination is statistical or taste-based. Leaders will receive performance data for 60% of of the predicted outcomes on a match day, with the remaining 40% performance still ambiguous. Treatment T3 investigates whether incentivizing team leaders to retain their team members promotes fairer treatment of employees and reduces biased decision-making against female avatars, even when individual predictor performance is not visible.
Across all treatments, the primary outcome measured is the team leader's decision to replace or retain existing predictors. The scoring rules will remain consistent across all treatments and teams; however, the visibility of individual performance varies between T1 and T2, and the personal reward for the leader to retain the existing team varies between T1 and T3. Team leaders (or teams) will be randomly assigned to the three treatments (T1, T2, and T3) in a between-subject design, where each team will participate in only one of the three treatments across their four rounds (match days) of surveys. This setup ensures that each team leader is assigned to a single treatment, while each predictor will be part of two treatments. Predictors will not predict match outcomes separately for each of their team leaders, who belong to different treatment arms. They will not be informed about the different treatments, as the treatments only affect the decision-making of the team leaders. Each predictor will be informed about one treatment and will predict the outcomes for the leader assigned to that treatment, unaware that the leader in the other team they are part of is assigned to a different treatment.
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After
Each team is composed of three members: one male team leader and two predictors, one male and one female. However, the gender or any other demographic information of any team member is not explicitly disclosed to anyone within the team. Instead, participants are assigned random numbers and avatars. These avatars are randomly assigned as either male or female to the two predictors in each team. Consequently, one-fourth of the time, predictors are allocated avatars corresponding to their actual gender, while in two-fourth of cases, one predictor is assigned an avatar of the incorrect gender. In the remaining one-fourth of instances, both predictors receive avatars representing the incorrect gender. These avatars are visible solely to the team leader and remain concealed from the predictors, who can only see the random number assigned to the team members and to the team. The team leader, however, is consistently assigned a male icon, aligning with their actual gender. This approach suggests to the leader that the assigned avatar gender of the predictors corresponds to their true gender. Predictors are, however, informed explicitly that their team comprises of a male leader, a female predictor and a male predictor.
The random allocation of gendered avatars serves as our primary experimental intervention, allowing us to assess whether leaders evaluate individuals based on these assigned genders inferred through the avatars. We refer to this as our Baseline treatment (Treatment T1), which helps us understand whether team leaders exhibit bias in their replacement decisions against female predictors when individual predictor performances are not visible. This experimental manipulation is similar, in spirit, to several other studies that examine the presence of gender bias by varying names on resumes or in emails, and has emerged as a response to challenges in causally identifying discrimination using naturally occurring data. In our case, we vary avatars that appear male or female but do not use any names. Use of avatars to signal gender has been used in several prior studies to induce a particular gender. This intervention helps us understand how a random assignment of gender, controlling for actual performance and team performance, influences team leaders' decisions, providing clearer evidence of biases based on stereotypes and personal tastes.
The experiment will include two additional treatments: Performance Information Treatment (T2) and Reward for Retention Treatment (T3). Treatment T2 aims to determine if providing team leaders with partial visibility into individual predictor performance reduces bias in their replacement decisions, helping to identify whether the discrimination is statistical or taste-based. Leaders will receive performance data for 60% of of the predicted outcomes on a match day, with the remaining 40% performance still ambiguous. Treatment T3 investigates whether incentivizing team leaders to retain their team members promotes fairer treatment of employees and reduces biased decision-making against female avatars, even when individual predictor performance is not visible.
Across all treatments, the primary outcome measured is the team leader's decision to replace or retain existing predictors. The scoring rules will remain consistent across all treatments and teams; however, the visibility of individual performance varies between T1 and T2, and the personal reward for the leader to retain the existing team varies between T1 and T3. Team leaders (or teams) will be randomly assigned to the three treatments (T1, T2, and T3) in a between-subject design, where each team will participate in only one of the three treatments across their four rounds (match days) of surveys. This setup ensures that each team leader is assigned to a single treatment. Due to our primary focus on leaders' decision, we assign each predictor to more than one teams (which can increase decrease depending on replace decision or if a predictor from another team became unresponsive to our study). Each predictor will overwhelmingly be a part of one treatment, but may belong to more than one if such a requirement arises. Male predictors are lesser in number than female predictors, so we assign male predictors to many more teams than female predictors to ensure that each team comprises of one male and one female predictors. Predictors will not predict match outcomes separately for each of their team leaders. They will not be informed about the different treatments, as the treatments only affect the decision-making of the team leaders. Each predictor will be informed about one treatment and will predict the outcomes for the leader assigned to that treatment, unaware that the leader in the other team they are part of may be assigned to a different treatment.
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