On the trajectory of discrimination: a meta-analysis and forecasting survey capturing 44 years of field experiments on gender and hiring decisions
On the trajectory of discrimination: a meta-analysis and forecasting survey capturing 44 years of field experiments on gender and hiring decisions
A preregistered meta-analysis, including 244 effect sizes from 85 field audits and 361,645 individual job applications, tested for gender bias in hiring practices in female-stereotypical and gender-balanced as well as male-stereotypical jobs from 1976 to 2020. A “red team” of independent experts was recruited to increase the rigor and robustness of our meta-analytic approach. A forecasting survey further examined whether laypeople (n = 499 nationally representative adults) and scientists (n = 312) could predict the results. Forecasters correctly anticipated reductions in discrimination against female candidates over time. However, both scientists and laypeople overestimated the continuation of bias against female candidates. Instead, selection bias in favor of male over female candidates was eliminated and, if anything, slightly reversed in sign starting in 2009 for mixed-gender and male-stereotypical jobs in our sample. Forecasters further failed to anticipate that discrimination against male candidates for stereotypically female jobs would remain stable across the decades.
Field experiments, Gender, discrimination, forecasting, meta-analysis, open science, Discrimination, Open science, Forecasting, Meta-analysis
Schaerer, Michael
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du Plessis, Christilene
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Nguyen, My Hoang Bao
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van Aert, Robbie C.M.
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Tiokhin, Leo
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Lakens, Daniël
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Clemente, Elena Giulia
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Pfeiffer, Thomas
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Dreber, Anna
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Johannesson, Magnus
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Clark, Cory J.
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Uhlmann, Eric Luis
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Dawson, Ian G.J.
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Conway, Paul
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Gender Audits Forecasting Collaboration
10 November 2023
Schaerer, Michael
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du Plessis, Christilene
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Nguyen, My Hoang Bao
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van Aert, Robbie C.M.
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Tiokhin, Leo
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Lakens, Daniël
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Clemente, Elena Giulia
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Pfeiffer, Thomas
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Dreber, Anna
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Johannesson, Magnus
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Clark, Cory J.
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Uhlmann, Eric Luis
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Dawson, Ian G.J.
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Conway, Paul
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Schaerer, Michael, du Plessis, Christilene, Nguyen, My Hoang Bao, van Aert, Robbie C.M., Tiokhin, Leo, Lakens, Daniël, Clemente, Elena Giulia, Pfeiffer, Thomas, Dreber, Anna, Johannesson, Magnus, Clark, Cory J. and Uhlmann, Eric Luis
,
Gender Audits Forecasting Collaboration
(2023)
On the trajectory of discrimination: a meta-analysis and forecasting survey capturing 44 years of field experiments on gender and hiring decisions.
Organizational Behavior and Human Decision Processes, 179, [104280].
(doi:10.1016/j.obhdp.2023.104280).
Abstract
A preregistered meta-analysis, including 244 effect sizes from 85 field audits and 361,645 individual job applications, tested for gender bias in hiring practices in female-stereotypical and gender-balanced as well as male-stereotypical jobs from 1976 to 2020. A “red team” of independent experts was recruited to increase the rigor and robustness of our meta-analytic approach. A forecasting survey further examined whether laypeople (n = 499 nationally representative adults) and scientists (n = 312) could predict the results. Forecasters correctly anticipated reductions in discrimination against female candidates over time. However, both scientists and laypeople overestimated the continuation of bias against female candidates. Instead, selection bias in favor of male over female candidates was eliminated and, if anything, slightly reversed in sign starting in 2009 for mixed-gender and male-stereotypical jobs in our sample. Forecasters further failed to anticipate that discrimination against male candidates for stereotypically female jobs would remain stable across the decades.
Text
The Trajectory of Discrimination - OBHDP - Nov 2023
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More information
Accepted/In Press date: 4 September 2023
e-pub ahead of print date: 10 November 2023
Published date: 10 November 2023
Additional Information:
Publisher Copyright:
© 2023 The Authors
Keywords:
Field experiments, Gender, discrimination, forecasting, meta-analysis, open science, Discrimination, Open science, Forecasting, Meta-analysis
Identifiers
Local EPrints ID: 484323
URI: http://eprints.soton.ac.uk/id/eprint/484323
ISSN: 0749-5978
PURE UUID: 492e348a-33fc-47c0-8af1-8951cc91497b
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Date deposited: 15 Nov 2023 18:09
Last modified: 06 Jun 2024 02:15
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Contributors
Author:
Michael Schaerer
Author:
Christilene du Plessis
Author:
My Hoang Bao Nguyen
Author:
Robbie C.M. van Aert
Author:
Leo Tiokhin
Author:
Daniël Lakens
Author:
Elena Giulia Clemente
Author:
Thomas Pfeiffer
Author:
Anna Dreber
Author:
Magnus Johannesson
Author:
Cory J. Clark
Author:
Eric Luis Uhlmann
Author:
Paul Conway
Corporate Author: Gender Audits Forecasting Collaboration
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