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Field	Before	After
Last Published	May 13, 2022 06:56 AM	May 16, 2022 09:46 AM
Experimental Design (Public)	We run an online experiment to study ways to overcome algorithm aversion. Participants are either in the role of workers or of managers. Workers will either be assigned to the baseline, treatment 1 or treatment 2. Managers will either be assigned to the baseline or the treatment. Workers perform three real-effort tasks: task 1, task 2 and the job task. They must choose whether they prefer hiring decisions between themself and another worker to be made by a participant in the role of a manager or an algorithm. They will get paid if they are hired in one random pair of workers they belong to. They know that the manager will get paid if they hire the best worker at the job task out of a random pair of workers they have made a hiring decision for. In the workers baseline and treatment 1, the managers and the algorithm will base their hiring decision on the genders of the workers and their task 1 and task 2 performances. In the workers' treatment 1, before deciding between the manager and the algorithm, workers are informed of the proportion of men and women who were chosen to be hired by the managers in a managers' session. In the workers' treatment 2, the algorithm will base its hiring decisions on the task 1 and task 2 performances of the workers but not on their gender. Managers must always make 20 hiring decisions between pairs of workers. They can see the gender of the workers and their task 1 and task 2 performances. We elicit for how many of the 20 decisions they believe they have hired the best worker of the pair. They must then decide whether they want to delegate the hiring decisions to the algorithm. If they do, their payoff will depend on the algorithm's hiring decision, not theirs. In the managers' treatment, managers will get a feedback about their over/under or well-calibrated confidence before the decision whether to delegate the hiring decisions to the algorithm.	We run an online experiment to study ways to overcome algorithm aversion. Participants are either in the role of workers or of managers. Workers will either be assigned to the baseline, treatment 1, treatment 2 or treatment 3. Managers will either be assigned to the baseline, treatment 1 or treatment 2. Workers perform three real-effort tasks: task 1, task 2 and the job task. They must choose whether they prefer hiring decisions between themself and another worker to be made by a participant in the role of a manager or an algorithm. They will get paid if they are hired in one random pair of workers they belong to. They know that the manager will get paid if they hire the best worker at the job task out of a random pair of workers they have made a hiring decision for. In the workers baseline and treatments 1 and 3, the managers and the algorithm will base their hiring decision on the genders of the workers and their task 1 and task 2 performances. In the workers' treatment 1, before deciding between the manager and the algorithm, workers are informed of the proportion of men and women who were chosen to be hired by the managers in a managers' session. In the workers' treatment 2, the algorithm will base its hiring decisions on the task 1 and task 2 performances of the workers but not on their gender. In the workers' treatment 3, details will be given about how the algorithm works to decide which workers to hire. Managers must always make 20 hiring decisions between pairs of workers. They can see the gender of the workers and their task 1 and task 2 performances. We elicit for how many of the 20 decisions they believe they have hired the best worker of the pair. They must then decide whether they want to delegate the hiring decisions to the algorithm. If they do, their payoff will depend on the algorithm's hiring decision, not theirs. In the managers' treatment 1, managers will get a feedback about their over/under or well-calibrated confidence before the decision whether to delegate the hiring decisions to the algorithm. In the managers' treatment 2, details are given about how the algorithm works before managers have to decide whether to delegate the hiring decisions to the algorithm.
Randomization Method	The participants will select into one of the treatments (Baseline workers, Treatment 1 workers, Treatment 2 workers, Baseline managers, Treatment managers) randomly.	The participants will select into one of the treatments (Baseline workers, Treatment 1 workers, Treatment 2 workers, Treatment 3 workers, Baseline managers, Treatment 1 managers, Treatment 2 managers) randomly.
Planned Number of Clusters	1250 individuals	1750 individuals
Planned Number of Observations	1250 individuals	1750 individuals
Sample size (or number of clusters) by treatment arms	250 in each of the treatments : Baseline workers, Treatment 1 workers, Treatment 2 workers, Baseline managers, Treatment managers	250 in each of the treatments : Baseline workers, Treatment 1 workers, Treatment 2 workers, Treatment 3 workers, Baseline managers, Treatment 1 managers, Treatment 2 managers.