We are probably aware of reports such as “Why So Few?: Women in Science, Technology, Engineering, and Mathematics” by the American Association of University Women
and studies such as these:
- Even when math skills were identical, both men and women were twice a likely to hire a man for a job that required math (Reuben, Sapienza, & Zingales, 2014) .
- In academic laboratories in elite universities, male (but not female) scientists employed fewer female than male graduate students and post docs (Sheltzer & Smith, 2014) .
- A double-blind randomized study gave science faculty at research-intensive universities application materials of a fictitious student randomly assigned a male or female name, and found that both male and female faculty rated themale applicant as significantly more competent and hirable than the femalewith identical application materials (Moss-Racusin, Dovidio, Brescoll, Graham & Handelsman, 2012) .
What has been less studied is the way issues of bias impact women of color. A recent report called Double Jeopardy? Gender Bias Against Women of Color in Science by the Tools for Change Program at UC Hastings College of the Law identifies four issues facing women in the workplace that have been well-documented since the 1980s:
1 . Prove-It-Again. Women often have to provide more evidence of competence than men in order to be seen as equally competent.
2 . The Tightrope. Women often find themselves walking a tightrope between being seen as too feminine to be competent—or too masculine to be likable.
3 . The Maternal Wall. By far the most damaging form of gender bias is triggered by motherhood. Results in strong assumptions that women lose their work commitment and competence after they have children, as well as stereotyping that penalizes mothers who remain indisputably committed.
4 . Tug of War. Sometimes gender bias against women fuels conflict among women.
The report describes how these issues impact women of color. Their findings are based on 60 interviews with women of color in which the interviewer described the patterns above and asked women scientists, “Does any of that sound familiar?” The report notes that “100% of the sixty scientists interviewed for this study reported encountering one or more of these patterns of gender bias.”
In addition to the interviews, researchers conducted an online survey with women from the Association for Women in Science (AWIS) by sending emails to AWIS members (not only women of color). Five-hundred and fifty-seven scientists responded to the online survey.
Major findings document how the experience of gender bias often differs for women of different racial groups (in this study grouped as Black, Asian-American, Latina and White):
- Prove-It-Again is more common for Black women than for the other three groups of women.
- The stereotype that Asians are good at science appears to help Asian-American women with students—but not colleagues .
- Asian-American scientists were more likely than other women to report workplace pressures to fulfill traditionally feminine roles—and pushback if they didn’t.
- Latinas who behave assertively risk being seen as “angry” or “emotional”—and they shoulder large loads of office housework for both colleagues and students.
- Black women are allowed more leeway than other groups of women to behave in dominant ways—so long as they aren’t seen as “angry Black women.”
- The Maternal Wall affects mothers of all races .
- Tug of War is a complex issue, and especially impacts working relationships between scientists and staff.
- Attributions differ. Black women tended to attribute Prove-It-Again bias to race rather than gender . All groups of women tended to attribute Tightrope and Maternal Wall bias to gender, although race remained more salient for Black women.
The report includes a four-step iterative approach to organizational change to interrupt gender bias, called Metrics-Based Bias Interrupters (Williams, 2014) .
1) Identify how gender bias is playing out, if at all, in basic business systems (recruiting, assignments, evaluations, etc.).
2) Develop objective metrics to measure bias.
3) Implement a bias interrupter to interrupt the bias.
4) See whether the relevant metric improves and, if it doesn’t, strengthen or modify the intervention.
Have you experienced interventions that have interrupted or reduced practices of gender bias in your workplace?
Please share examples of interventions that worked (or did not) in the comments. In the coming years it will be interesting to see what happens with the Intel $300 million diversity initiative, which seems to be taking steps similar to the Bias Interrupters.
Thanks to SP who made me aware of this study!