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Field
Abstract
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Before
Stereotypical beliefs about individual skills and traits can have substantial implications for hiring decisions, which subsequently translate into economic externalities. While existing literature provides evidence of racial and gender differences in traits like competitiveness, risk tolerance, and overconfidence, limited research has been conducted to understand how these traits are perceived across different racial and gender groups. This study aims to bridge this gap by investigating beliefs about racial and gender differences in these traits, and examining their relation to decision-making within a hiring context. Data for this research will be collected from a representative sample of 2,400 U.S. residents, aged between 25 and 65, spanning six racial-ethnic-gender (REG) groups. By studying these perceptions and their impact on hiring decisions, we aim to provide a comprehensive understanding of the role of stereotypical beliefs in the labor market and to offer valuable insights for addressing discriminatory practices.
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After
Stereotypical beliefs about individual skills and traits can have substantial implications for hiring decisions, which subsequently translate into economic externalities. While existing literature provides evidence of racial and gender differences in traits like competitiveness and overconfidence, limited research has been conducted to understand how these traits are perceived across different racial and gender groups. This study aims to bridge this gap by investigating beliefs about racial and gender differences in these traits and their relationship with beliefs about earnings and performance, as well as examining their relation to decision-making within a hiring context. The research also investigates the role of a candidate's competitiveness and the ambiguity of their competition choices, with a focus on variations across race and gender. Data for this research will be collected from a stratified sample of 3,600 U.S. residents, aged between 25 and 65, spanning six racial-ethnic-gender (REG) groups. By studying these perceptions and their impact on hiring decisions, we aim to provide a comprehensive understanding of the role of stereotypical beliefs in the labor market and to offer valuable insights for addressing discriminatory practices.
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Trial Start Date
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Before
July 27, 2023
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After
March 04, 2024
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Trial End Date
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Before
July 27, 2024
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After
April 04, 2024
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Last Published
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Before
July 28, 2023 02:06 PM
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After
February 28, 2024 02:48 AM
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Intervention Start Date
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Before
July 27, 2023
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After
March 04, 2024
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Intervention End Date
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Before
July 27, 2024
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After
April 04, 2024
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Primary Outcomes (End Points)
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Before
Prediction for each trait and REG group, prediction's accuracy, amount of times an REG group was chosen by an individual in the hiring task
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After
Prediction for each trait and REG group, prediction's accuracy for each trait and REG group, wage offer for each candidate profile
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Field
Experimental Design (Public)
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Before
Our study comprises two parts. In the first part, participants predict the behavior of subjects from an earlier study (Dariel et al., In progress) by guessing their risk aversion, competitiveness, and overconfidence, as well as performance in a numerical task. Accurate predictions are incentivized with monetary rewards.
In the second part, participants engage in a hiring task, selecting representatives from fifteen pairs covering all combinations of six racial-ethnic-gender (REG) groups. Their payoffs are tied to the performance of their chosen representative. Half of the participants are placed in a treatment where their hiring decision affects the earnings of an individual from the same REG group as their chosen representative, aiming to probe potential biases in hiring. This is also what we refer to as treatment, when it comes to the randomisation.
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After
Our study comprises two parts. In the first part, participants predict the behavior of subjects from an earlier study (Dariel et al., in progress) by guessing their earnings, competitiveness, and overconfidence, as well as their performance in a numerical task. Accurate predictions are incentivized with monetary rewards.
In the second part, participants engage in a hiring task. They are shown six profiles of candidates who will engage in a future task and are told that they need to make a wage offer for each. The profiles differ depending on the treatment. In the treatment conditions, apart from country, gender, race, and age, information about whether or not the candidate has chosen to compete is also included.
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Planned Number of Clusters
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2400
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After
3600 individuals
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Planned Number of Observations
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Before
2400 approx
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After
3600 individuals
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Sample size (or number of clusters) by treatment arms
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Before
1200 control, 1200 treatment
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After
1200 control, 1200 treatment 1, 1200 treatment 2
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