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Field
Last Published
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Before
May 30, 2024 03:22 AM
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After
June 03, 2024 02:24 PM
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Field
Intervention (Public)
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Before
Our study has three groups of agents: economic Experts, experts’ Employers, and Customers. We are interested in the behavior and beliefs of Customers and Employers.
Customers` participate in an RCT, and are tasked to make a prediction of the inflation rate for 2024. The Customer who makes the most accurate prediction (as determined by the comparison of the prediction to the official actual inflation rate in May -- a number made official by US govt agencies in mid June 2024) wins a bonus of $200. Customers therefore have an incentive to make the best possible prediction.
To help them successfully predict the inflation rate, Customers can buy advice from economic Experts, consisting of the Expert's prediction of the inflation rate in May 2024. Each Customer is randomly paired with either a man or a woman Expert, from whom they can buy advice. This advice consists of a prediction of the inflation rate in May made by the economic Expert. Economic Experts are identical in their qualifications, but differ in gender. Customers' WTP for advice is elicited in a Becker-DeGroot-Marschak (BDM) auction. This design allows us to examine whether Customers do exhibit a gender bias, i.e., differ in their WTP for advice from a man vs a women Expert, even though the Experts have identical qualifications.
Experts only role in our study is to make a prediction of the inflation rate for May 2024 that gets provided to the Customer they are paired with, should that Customer win the BDM auction.
Employers’ task is to hire an Expert. They can choose between hiring either a woman or a man Expert, with identical credentials. The Employer has an incentive to hire the Expert that they believe will generate the highest WTP from Customers -- the Employers’ payoff depends on Customers’ willingness to pay (WTP) for the Expert’s service (advice). We elicit Employers' incentivized beliefs about Customers' WTP for advice from a man and a woman Expert. We also elicit Employers' own beliefs about the value of Expert advice from a man vs a woman Expert. This design allows us to examine if Employers believe that Customers are biased against women Experts, and therefore choose to hire a male Expert, even if they themselves do not exhibit any gender bias.
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After
Our study has three groups of agents: economic Experts, experts’ Employers, and Customers. We are interested in the behavior and beliefs of Customers and Employers.
Customers` participate in an RCT, and are tasked to make a prediction of the inflation rate for 2024. The Customer who makes the most accurate prediction (as determined by the comparison of the prediction to the official actual inflation rate in May -- a number made official by US govt agencies in mid June 2024) wins a bonus of $200. Customers therefore have an incentive to make the best possible prediction.
To help them successfully predict the inflation rate, Customers can buy advice from economic Experts, consisting of the Expert's prediction of the inflation rate in May 2024. Each Customer is randomly paired with either a man or a woman Expert, from whom they can buy advice. This advice consists of a prediction of the inflation rate in May made by the economic Expert. Economic Experts are identical in their qualifications, but differ in gender. Customers' WTP for advice is elicited in a Becker-DeGroot-Marschak (BDM) auction. This design allows us to examine whether Customers do exhibit a gender bias, i.e., differ in their WTP for advice from a man vs a women Expert, even though the Experts have identical qualifications.
Experts only role in our study is to make a prediction of the inflation rate for May 2024 that gets provided to the Customer they are paired with, should that Customer win the BDM auction.
Employers’ task is to hire an Expert. They can choose between hiring either a woman or a man Expert, with identical credentials. The Employer has an incentive to hire the Expert that they believe will generate the highest WTP from Customers -- the Employers’ payoff depends on Customers’ willingness to pay (WTP) for the Expert’s service (advice). We elicit Employers' incentivized beliefs about Customers' WTP for advice from a man and a woman Expert. We also elicit Employers' own beliefs about the value of Expert advice from a man vs a woman Expert. This design allows us to examine if Employers believe that Customers are biased against women Experts, and therefore choose to hire a male Expert, even if they themselves do not exhibit any gender bias.
We will also separately collect data from Employers where we correct for their biased beliefs, i.e., inform them about the Customers' actual WTP for advice from a man vs a woman Expert. This enables us to examine whether correcting for biased beliefs of any gender bias amongst their Customers changes their willingness to hire male and female Experts.
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Field
Planned Number of Observations
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Before
800
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After
920
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Field
Sample size (or number of clusters) by treatment arms
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Before
360 Customers (they are in turn randomized into being paired with either a man or a woman Expert)
360 Employers
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After
360 Customers (they are in turn randomized into being paired with either a man or a woman Expert)
360 Employers with no information on the actual WTP for advice amongst Customers
200 Employers with information on the actual WTP for advice amongst Customers, including whether Customers exhibit a gender bias
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Field
Intervention (Hidden)
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Before
Our study has three groups of agents: economic Experts, experts’ Employers, and Customers.
Customers` participate in an RCT, and are tasked to make a prediction of the inflation rate for 2024. The Customer who makes the most accurate prediction (as determined by the comparison of the prediction to the official actual inflation rate in May -- a number made official by US govt agencies in mid June 2024) wins a bonus of $200. Customers therefore have an incentive to make the best possible prediction.
To help them successfully predict the inflation rate, Customers can buy advice from economic Experts, consisting of the Expert's prediction of the inflation rate in May 2024. Each Customer is randomly paired with either a man or a woman Expert, from whom they can buy advice. This advice consists of a prediction of the inflation rate in May made by the economic Expert. Economic Experts are identical in their qualifications, but differ in gender. Customers' WTP for advice is elicited in a Becker-DeGroot-Marschak (BDM) auction. This design allows us to examine whether Customers do exhibit a gender bias, i.e., differ in their WTP for advice from a man vs a women Expert, even though the Experts have identical qualifications.
Experts only role in our study is to make a prediction of the inflation rate for May 2024 that gets provided to the Customer they are paired with, should that Customer win the BDM auction.
Employers’ task is to hire an Expert. They can choose between hiring either a woman or a man Expert, with identical credentials. The Employer has an incentive to hire the Expert that they believe will generate the highest WTP from Customers -- the Employers’ payoff depends on Customers’ willingness to pay (WTP) for the Expert’s service (advice). We elicit Employers' incentivized beliefs about Customers' WTP for advice from a man and a woman Expert. We also elicit Employers' own beliefs about the value of Expert advice from a man vs a woman Expert. This design allows us to examine if Employers believe that Customers are biased against women Experts, and therefore choose to hire a male Expert, even if they themselves do not exhibit any gender bias.
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After
Our study has three groups of agents: economic Experts, experts’ Employers, and Customers.
Customers` participate in an RCT, and are tasked to make a prediction of the inflation rate for 2024. The Customer who makes the most accurate prediction (as determined by the comparison of the prediction to the official actual inflation rate in May -- a number made official by US govt agencies in mid June 2024) wins a bonus of $200. Customers therefore have an incentive to make the best possible prediction.
To help them successfully predict the inflation rate, Customers can buy advice from economic Experts, consisting of the Expert's prediction of the inflation rate in May 2024. Each Customer is randomly paired with either a man or a woman Expert, from whom they can buy advice. This advice consists of a prediction of the inflation rate in May made by the economic Expert. Economic Experts are identical in their qualifications, but differ in gender. Customers' WTP for advice is elicited in a Becker-DeGroot-Marschak (BDM) auction. This design allows us to examine whether Customers do exhibit a gender bias, i.e., differ in their WTP for advice from a man vs a women Expert, even though the Experts have identical qualifications.
Experts only role in our study is to make a prediction of the inflation rate for May 2024 that gets provided to the Customer they are paired with, should that Customer win the BDM auction.
Employers’ task is to hire an Expert. They can choose between hiring either a woman or a man Expert, with identical credentials. The Employer has an incentive to hire the Expert that they believe will generate the highest WTP from Customers -- the Employers’ payoff depends on Customers’ willingness to pay (WTP) for the Expert’s service (advice). We elicit Employers' incentivized beliefs about Customers' WTP for advice from a man and a woman Expert. We also elicit Employers' own beliefs about the value of Expert advice from a man vs a woman Expert. This design allows us to examine if Employers believe that Customers are biased against women Experts, and therefore choose to hire a male Expert, even if they themselves do not exhibit any gender bias.
We will also separately collect data from Employers where we correct for their biased beliefs, i.e., inform them about the Customers' actual WTP for advice from a man vs a woman Expert. This enables us to examine whether correcting for biased beliefs of any gender bias amongst their Customers changes their willingness to hire male and female Experts.
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