Women are under-represented in Science, Technology, Engineering, and Mathematics (STEM) academic programs and professional roles, and some question whether this is a a result of personal preference, implicit bias, institutional barriers, or other factors,
To investigate, Columbia University’s Ernesto Reuben, Paola Sapienza of Northwestern University, and University of Chicago’s Luigi Zingales developed an experimental job market.
Both male and female candidates demonstrated equal skill in performing an arithmetic task, yet both female and male “hiring managers” were twice as like to hire comparable male candidates – even when the hiring managers earned less by hiring less qualified males.
*Even when participants had a financial incentive to choose the candidate with the greatest task-relevant skills, they chose less-qualified male candidates.
Reuben and team also found that when candidates were asked to report their performance on the task-related achievement test, men exaggerated their performance with “honest overconfidence.”
In contrast, women generally underreported their accomplishments, found University of Wisconsin’s Sylvia Beyer.
This gender-based bias in hiring decisions was reduced, but not eliminated when candidates’ previous performance was provided by a third party.
Some candidates were directed to report expected future performance based on initial math task performance, then the “employer” made the hiring decision.
Other candidates provided no estimate, but Reuben’s team reported candidates’ past performance to the “hiring managers.”
In other studies, “employers” had no information on each “candidate’s” previous performance, but met each applicant in person before making a hiring decision.
After the hiring managers’ choice, candidates reported expected future performance, or Reuben’s team provided candidates’ past performance to the “hiring manager.”
Volunteers then completed the Implicit Association Test (IAT), developed by University of Washington’s Anthony Greenwald, Debbie McGhee, and Jordan Schwartz, to elicit unconscious stereotypes of gender, competencies, and occupations.
When the candidates reported their expected performance and the “hiring manager” chose a candidate with a lower score than other contenders, 90% of the selected but underperforming candidates were male.
As a result, “hiring managers” who selected less qualified male candidates sacrificed 5-7% of their own compensation for biased selections.
Reuben and colleagues, with Pedro Rey-Biel of Universitat Autònoma de Barcelona previously demonstrated that this preference for underperforming candidates was explained by the persuasive impact of men’s significantly exaggerated statements (usually by at least 30%) about past and future performance and by scores on the Implicit Association Test.
Hyperbole is apparently effective for male candidates in job interviews when the “hiring manager” scores high on the IAT.
However, this embellishment strategy is ineffective for women, as Reuben and team demonstrated: In another study, women were still selected 33% less than expected even when they showcased their accomplishments.
Women’s overt self-promotion may provoke “backlash” against those who behave in counter-stereotypic ways.
This research suggests the prevalence of implicit biases against hiring women to perform science, technology, engineering, and mathematics (STEM) functions, and male candidates’ tendency to embellish past performance and boast about future potential accomplishments.
As a result, women are selected less frequently for roles in STEM careers, continuing their under representation in these fields.
Even if women do not exaggerate past accomplishments and future potential, this research implies that they should ensure that they communicate and reinforce the full range of skills.
“Real life” hiring managers can overcome implicit hiring biases through awareness and “proper information processing” by focusing on validated performance data, and comparing candidates of the same gender with each other..
-*What strategies have you seen mitigate the influence of implicit bias influence in hiring decisions?
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