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Fields Changed

Registration

Field Before After
Study Withdrawn No
Intervention Completion Date June 30, 2021
Data Collection Complete Yes
Final Sample Size: Number of Clusters (Unit of Randomization) 2086
Was attrition correlated with treatment status? No
Final Sample Size: Total Number of Observations 2086
Final Sample Size (or Number of Clusters) by Treatment Arms 716 in the Neutral treatment, 684 in the Unfair treatment, 686 in the Feedback treatment
Is there a restricted access data set available on request? No
Program Files No
Data Collection Completion Date June 30, 2021
Is data available for public use? No
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Papers

Field Before After
Paper Abstract How do men and women differ in their persistence after experiencing failure in a competitive environment? We tackle this question by combining a large online experiment (N=2,086) with machine learning. We find that when losing is unequivocally due to merit, both men and women exhibit a significant decrease in subsequent tournament entry. However, when the prior tournament is unfair, i.e., a loss is no longer necessarily based on merit, women are more discouraged than men. These results suggest that transparent meritocratic criteria may play a key role in preventing women from falling behind after experiencing a loss.
Paper Citation July 2023 IZA DP No. 16324: Does Unfairness Hurt Women? The Effects of Losing Unfair Competitions Stefano Piasenti, Marica Valente, Roel van Veldhuizen, Gregor Pfeifer
Paper URL https://www.iza.org/publications/dp/16324/does-unfairness-hurt-women-the-effects-of-losing-unfair-competitions
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