Beliefs about gender bias and hiring decisions

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.

2024-05-24

2024-06-28

Employers' beliefs about Customers' WTP for Expert advice from a man vs a woman Expert
Employers' choice to hire a man vs a woman Expert
The difference in Customers' WTP for advice from a man vs a woman Expert
The difference between Customers' actual WTP for advice and Employers' beliefs about their WTP

Employers' beliefs about Customers' WTP for Expert advice from a man vs a woman Expert -- elicited in a BDM auction, and stated as an amount between $0 and $5.
Employers' choice to hire a man vs a woman Expert -- a binary variable that indicates the choice to hire a woman expert or not
The difference in Customers' WTP for advice from a man vs a woman Expert -- stated as an amount between $0 and $5.
The difference between Customers' actual WTP for advice and Employers' beliefs about their WTP -- stated as an amount between $0 and $5.

Customers' and Experts' assessment of the Experts' characteristics (competent, knowledgeable, trustworthy, confident)
Customer's perceived usage of the Expert's advice

Customers' and Experts' assessment of the Experts' characteristics (competent, knowledgeable, trustworthy, confident) -- measured on a Likert scale, 1-7; strongly agree to strongly disagree
Customer's perceived usage of the Expert's advice -- measured as the difference between the inflation rate they think they would have predicted, had they not taken the Expert's advice, and their prediction with the Expert's advice.

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. Our participants get randomized into the role of Customer or Employer.

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.

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. Our participants get randomized into the role of Customer or Employer.

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.

By a computer.

Individual.

No

0

920

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