The past reasons women are not well represented in so many STEM industry segments roll off the tongue with the top two being they are not encouraged in school and personal choice. The truth is qualified women and women of color are discouraged from STEM careers through a variety of biases.
— By Debra Jenkins
Asians, especially dragon ladies, are good at science." "Women are not as proficient as men in math." "Women give up their careers to have children, so why invest resources in them." "More women will have STEM careers as the next generation of STEM professionals are finally encouraged to study STEM subjects as girls."
The list of such statements could go on and on as to the stereotypes used to explain away the slow progress in attracting and retaining women in STEM careers. Bias comes in many shapes and forms. There are plenty of women who could pursue STEM careers, but they are discouraged from doing so in a variety of subtle and not-so-subtle ways. Even when they find employment, many leave their positions to either find jobs in non-STEM fields or to find tech jobs in industries where women have traditionally held most of the jobs, like healthcare.
The past responses to the question of why more women, especially women of color, are not in STEM careers include there are not enough women in the pipeline yet or women are choosing work-life balance over time-consuming career paths.
The reality may be quite different based on several studies over the last few years that need more attention. These studies found bias is the ruling factor in whether women land and stay in STEM positions.
Patterns of Bias
A group of researchers from the Center for WorkLife Law at the University of California's Hastings College of the Law investigated racial and gender biases in STEM professions. They interviewed 60 female scientists of color and surveyed 557 female scientists of all races. Research team members Katherine W. Phillips, Erika V. Hall and Joan C. Williams explored the reasons more women are not in science jobs. Their results presented a picture of hiring practices and workplace conditions that are riddled with bias. Bias not only keeps women in general out of STEM, but researchers also learned that bias plays out differently for women of different races and ethnicities.
The Phillips, Hall, Williams study found four bias patterns that apply to all women and a fifth one that applies to black women.
Pattern one is "Prove-it-Again," referring to the fact female scientists must prove themselves over and over again because people assume they cannot handle the work. Three-fourths of black women said they experienced this pattern.
The second pattern was called "The Tightrope," in which women are expected to be feminine while behaving in masculine ways. In this pattern, 40.9 percent of the Asian-Americans indicated they are pressured to play a traditionally feminine role. Ironically, 53 percent of all the women in the study said they experienced backlash for displaying masculine behaviors. Black and Latina women were particularly at risk of being considered angry when they demonstrated direct, assertive behavior.
In the third pattern, "The Maternal Wall," professional women with children often find opportunities disappearing and their competence and commitment questioned. The biased assumption is that women cannot be good scientists and good mothers.
The fourth pattern is "Tug-of-War." Older women who had to overcome discrimination in their young careers tend to distance themselves from other women, creating generational conflict.
The fifth pattern applies mostly to black and Latina women. Called "Isolation," it is a pattern in which women feel that socially engaging with colleagues will negatively impact perceptions of their competence. Minority women are often not invited to gatherings on the faulty assumption they will not feel comfortable. There is the problem of a lack of authenticity, too. Many minority female scientists are not able to bring their whole selves to work, meaning they keep their personal lives hidden out of fear personal information will be used against them.
Good at Math and Science
Gender bias is expressed in so many different ways, depending on the diversity status of the woman. For decades, people have stereotyped Asians as good at science and math. Despite the stereotyping, Asian-Americans, especially women, were subjected to the "prove-it-again" bias, forcing them to provide evidence of competence over and over again.
One Asian-American woman said they are expected to be perfect. Asian-Americans are held to a higher standard, expected to act in a certain way, seen as “worker bees” and discouraged from taking maternity leave.
The impact of the biases is that it is more difficult for Asian women (and men) to meet the impossible standards, and they are the least likely to become managers and executives.
The Pew Research Center also found similar evidence of bias and that discrimination is a major reason more women are not working in STEM. The women experience pay inequity, workplace discrimination and sexual harassment.
Forty-eight percent of women in the Pew Research project said it is discrimination in hiring and promotions that keeps women out of STEM jobs. The women who are in STEM jobs reported the most common forms of discrimination are pay inequality, being treated as incompetent because of gender, experiencing repeated small slights, and getting less senior leader support than men in the same jobs get. Fifty-three percent of the women considered sexual harassment a problem in their industry.
This is a good sampling of what research projects are learning about women in the STEM industries. Until the discrimination ends, women are not likely to make great inroads in STEM careers. It is exhausting to continually have to prove competence and equality.
One suggestion to overcome the barriers that women face is to establish metrics, measuring frequently, and to hold leaders accountable. If half the women in an organization say they feel intimidated or harassed or are failing to get equal pay for equal work or promotional opportunities, there is clearly bias at play.
The only reliable way to uncover the many forms of bias is through metrics because ingrained gender bias can be hard to prove.