Given enough motivation, QRPs, biased reviewing and time, one can build an entire literature of studies proving anything. There’s plenty of all of these to prove left-wing ideological beliefs (and libertarian in economics). However, it is much harder to QRP large N datasets to give preferred results. So, what do large scale studies show about sex, race etc. biases in hiring, grading etc.? Here’s an attempt at a collection. Ping me anywhere if you know of any more.
Discrimination against women is seen as one of the possible causes behind their underrepresentation in certain STEM (science, technology, engineering, and mathematics) subjects. We show that this is not the case for the competitive exams used to recruit almost all French secondary and postsecondary teachers and professors. Comparisons of oral non–gender-blind tests with written gender-blind tests for about 100,000 individuals observed in 11 different fields over the period 2006–2013 reveal a bias in favor of women that is strongly increasing with the extent of a field’s male-domination. This bias turns from 3 to 5 percentile ranks for men in literature and foreign languages to about 10 percentile ranks for women in math, physics, or philosophy. These findings have implications for the debate over what interventions are appropriate to increase the representation of women in fields in which they are currently underrepresented.
National randomized experiments and validation studies were conducted on 873 tenure-track faculty (439 male, 434 female) from biology, engineering, economics, and psychology at 371 universities/colleges from 50 US states and the District of Columbia. In the main experiment, 363 faculty members evaluated narrative summaries describing hypothetical female and male applicants for tenure-track assistant professorships who shared the same lifestyle (e.g., single without children, married with children). Applicants’ profiles were systematically varied to disguise identically rated scholarship; profiles were counterbalanced by gender across faculty to enable between-faculty comparisons of hiring preferences for identically qualified women versus men. Results revealed a 2:1 preference for women by faculty of both genders across both math-intensive and non–math-intensive fields, with the single exception of male economists, who showed no gender preference. Results were replicated using weighted analyses to control for national sample characteristics. In follow-up experiments, 144 faculty evaluated competing applicants with differing lifestyles (e.g., divorced mother vs. married father), and 204 faculty compared same-gender candidates with children, but differing in whether they took 1-y-parental leaves in graduate school. Women preferred divorced mothers to married fathers; men preferred mothers who took leaves to mothers who did not. In two validation studies, 35 engineering faculty provided rankings using full curricula vitae instead of narratives, and 127 faculty rated one applicant rather than choosing from a mixed-gender group; the same preference for women was shown by faculty of both genders. These results suggest it is a propitious time for women launching careers in academic science. Messages to the contrary may discourage women from applying for STEM (science, technology, engineering, mathematics) tenure-track assistant professorships.
In characteristic spin language:
This study assessed whether women and minorities are discriminated against in the early stages of the recruitment process for senior positions in the APS, while also testing the impact of implementing a ‘blind’ or de-identified approach to reviewing candidates. Over 2,100 public servants from 14 agencies participated in the trial 1 . They completed an exercise in which they shortlisted applicants for a hypothetical senior role in their agency. Participants were randomly assigned to receive application materials for candidates in standard form or in de-identified form (with information about candidate gender, race and ethnicity removed). We found that the public servants engaged in positive (not negative) discrimination towards female and minority candidates:
• Participants were 2.9% more likely to shortlist female candidates and 3.2% less likely to shortlist male applicants when they were identifiable, compared with when they were de-identified.
• Minority males were 5.8% more likely to be shortlisted and minority females were 8.6% more likely to be shortlisted when identifiable compared to when applications were de-identified.
• The positive discrimination was strongest for Indigenous female candidates who were 22.2% more likely to be shortlisted when identifiable compared to when the applications were de-identified.
Interestingly, male reviewers displayed markedly more positive discrimination in favour of minority candidates than did female counterparts, and reviewers aged 40+ displayed much stronger affirmative action in favour for both women and minorities than did younger ones. Overall, the results indicate the need for caution when moving towards ’blind’ recruitment processes in the Australian Public Service, as de-identification may frustrate efforts aimed at promoting diversity 2 .
Meta-analysis of juror decision making studies finds that Whites show no own-group bias, but Blacks do.