Women of Color in STEM Face Double Jeopardy, New Study Finds

Feature image via Shutterstock.

A study released this month by University of California Hastings’ Tools For Change conducted in-depth interviews with 60 women of color working in STEM and found that 100% of them had encountered patterns of gender bias. That’s right. All of them. The particular ways in which that discrimination manifested across racial groups, however, varied widely. Although WOC live these experiences every day, “Double Jeopardy? Gender Bias Against Women of Color in Science” is one of the few studies to formally delve into this topic.

According to the report:

Women of color face “double jeopardy” because they encounter race as well as gender bias. This study explores how the experience of gender bias differs by race. We use the interviews of women of color in science and a survey that quantifies the experiences of White, Black, Asian-Americans, and Latina women in STEM fields to document the little-explored differences between the experiences of White women and women of color, and between different groups of women of color.

Among the interview subjects (all recruited through the Association for Women in Science), there was an even distribution of Latina, Asian, and Black women. A further 557 female scientists (including white women) responded to an online survey.

Double Jeopardy? Gender Bias Against Women of Color in Science

Some key findings:

  • Black women were hardest hit when it came to “prove-it-again” discrimination, in which women have to provide more evidence of competence than men in order to be able to be seen as equally competent. This type of bias was reported by 77% of Black women, 65% of Latinas, 64% of Asian women, and 63% of white women.
  • Asian women experienced increased workplace pressure to fulfill traditionally feminine roles (such as office mother or dutiful daughter), and also reported more backlash if they didn’t. Fully 61% reported pushback for assertiveness. In comparison, 53% of white women, 50% of Black women and 47% of Latinas reported the same.
  • Latinas shouldered large loads of “office housework” (such as making coffee or doing administrative work typically performed by support personnel), and reported being perceived as “angry” or “emotional” by colleagues when they behaved assertively.
  • Women of all races were impacted by the “Maternal Wall,” which refers to the assumption that that women lose their work commitment and competence after they have children.
  • Female scientists without children also reported being disadvantaged in a variety of ways, including being expected to work longer hours to make up for the schedules of colleagues who did have children.
"You go ahead and tell them my 'work-life balance' is none of their business, sweetie." Via Shutterstock.

“You go ahead and tell them my ‘work-life balance’ is none of their business, sweetie. There’s no reason to put my personal life under the microscope if they aren’t doing the same to the men.” Via Shutterstock.

Women of color from all backgrounds reported that they had to specifically confront negative racial stereotypes. Latinas and Black women often reported being mistaken for janitors — something the researchers had never heard in interviews with white women. Asian women reported facing the “forever foreign” assumption, wherein they were assumed to be from another country. Many also discussed tokenism (the idea that there’s only room for one woman) as fuel for “tug of war” conflict among women, where individuals sometimes deploy racial privilege to cushion the effect of gender bias.

As described in the report, the current body of social psychological work on gender bias in STEM has disappointingly (but not unsurprisingly) focused almost exclusively on the experiences of white women. In practical terms, what this means is that racialized women systematically receive inadequate support. Lack of research renders women of color’s experiences invisible; thus, support groups cannot adequately prepare for their unique needs. Women of color are isolated, essentially left to fend for themselves.

Latinas and Black women often reported being mistaken for janitors — something the researchers had never heard in interviews with white women.

Following their results of the study was a thorough overview of the “Metrics-Driven Bias Interrupters” model developed by study author Joan C. Williams, a law professor and Director of the Center for WorkLife Law. The model gives STEM employers a tool to assess bias in everyday situations and address it in real time. According to the report, the four steps are:

  1. Assess. Using interviews or focus groups, investigate whether, and how, subtle bias is playing out in your institution in hiring, Rank and Tenure processes, compensation, and elsewhere. Where bias is suspected, identify an objective metric that will measure whether bias exists.
  2. Implement A Bias Interrupter. Put in place a Bias Interrupter.
  3. Measure. Measure to see if the intervention interrupted the bias effectively enough so that the metric improved.
  4. Ratchet Up If Necessary. If the metric did not show improvement, strengthen or modify the Interrupters until it does.

What’s great about this model is that rather than promoting actions that involve self-monitoring (which is where well-meaning organizations land as they try to “fix the problem” of getting more women in STEM), it shifts the focus to systematic change. For example, on the issue of “office housework,” this model doesn’t tell individual women to stop planning office parties, scheduling meetings, ordering supplies, taking notes, doing other admin tasks, or cleaning (if those are things that they want to do). Rather, this model would suggest that departments:

  1. Use interviews or focus groups to identify the kinds of “office housework” that exist in a particular workplace.
  2. Take action by assigning an admin to plan the parties, establish a rule that each professor order their own equipment, or address whatever specific issues were identified in the first step.
  3. Follow up with additional data collection to see whether the “housework” is now more evenly distributed.
  4. If not, take further action by establishing clearer rules or assigning specific tasks.

At the end, the report also provided employers with guidelines for best practice throughout recruiting, hiring, tenure and promotions processes. Although these approaches could benefit a wide variety of fields, they’re particularly well-suited (and necessary) for white male-dominated STEM fields, which are often resistant to your typical HR-led sensitivity trainings. A better approach in these fields is to provide evidence-based trainings and structured guidelines that clearly communicate specific patterns of discrimination and bias to look out for.

The full report can be viewed on UC Hastings’ website.

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Laura Mandanas

Laura Mandanas is a Filipina American living in Boston. By day, she works as an industrial engineer. By night, she is beautiful and terrible as the morn, treacherous as the seas, stronger than the foundations of the Earth. All shall love her and despair. Follow her: @LauraMWrites.

Laura has written 210 articles for us.


  1. i love that the word ratchet was used seriously in this article

    also laura thank you so much for writing about this it is so important! and depressing. and important.

  2. This is great science reporting, and I love how you discuss their model (which has clear, actionable advice) for changing office dynamics.

    Hearing about these experiences is upsetting, but being able to talk about them objectively and from a place that seeks solutions is very important.

  3. I must of have skipped it, but I didn’t see anywhere mention the stereotype that Asians are disciplined and have excellent math & science skills. I take that’s not prevalent among women in the STEM field?

    • Oh, that’s actually in the report! I just didn’t include it in my summary points. The gist was that it actually doesn’t help them much, because the stereotype of women being bad at science was stronger.

      • I don’t think the study mentions this, but just from my own personal experience, as an Asian woman who was once in a STEM field and left because it got to be too much: the Asian stereotype and the woman stereotype kind worked together to create a really toxic environment.

        Basically, people would come to me with the expectation that I would be intelligent/skilled above and beyond my actual academic level because of my Asian-ness, and when I failed to live up to that, their demeanor towards me would change. That’s when I would start seeing the snide looks and getting pushed out of group projects and all the things that other women in STEM fields experience. I would guess that I’m not the only Asian woman in STEM who’s experienced being set to impossible standards and then having their failure to meet them be attributed to their gender.

        • Ugh, I’m so sorry. I haven’t experienced that exactly, but I agree — the “model minority” myth has never helped me (Filipina + engineer) either. It just feels like people want so badly for us to confirm the stereotypes they have in their heads. And when there are competing expectations, they’re hunting for any ‘hint’ from us about which stereotype to slot us into.

    • There was a very interesting comment (page 16) from an Asian American scientist where she expresses that “Her strategy
      was to make sure she was seen as an Asian in STEM
      rather than a woman in STEM: “I’m more acceptable,
      if you will, as an Asian woman scientist rather than a
      woman scientist.”

  4. Thank you! I am looking for similar on women in engineering – has anyone seen that? (this study focused on science, and for influencing the suits it would be better to have it be engineering).

    • I wish! This area is really under researched, and I haven’t seen anything like it specifically for engineering. The report did cite an earlier study on the idea of double jeopardy for WOC, though — maybe check that out, and see if anyone else cited that paper? I’d love to hear if you find anything.

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