The Higher the Heels, the Lower Your Chances

Walking into your first college lecture hall can be nerve-racking, especially when you’re one of ten girls in a class of thirty-three. My current class, which is only a level up from intro, has five girls in a class of thirty. These gender disparities not only reflect the greater systematic issue of gender inequality, but the individual discouragements women encounter in the STEM fields.

Before I noticed the disproportionate ratios, in the students and faculty in my own classes, I was oblivious to the need for females in STEM. Now, I realize not only the greatness of the disparities, but why they are there. Gender inequality among the science, technology, engineering, and mathematics fields exposes just how influential implicit bias and gender stereotypes are in society.

In STEM, men are employed at twice the rate of women. Almost a fifth of female STEM graduates are unemployed. In 2011, 26% of STEM workers were female. Compared to the 12% of STEM women in the 1970s, a mere 14% increase raises questions. Why aren’t more women pursuing the programs? Why is it that men are employed at a higher rate than women? Some may argue that the reason for the disparities is that men perform inherently better and are more equipped to handle those jobs. Actually, in the STEM fields, both genders perform equally as well on exams.

Maybe men are just entering the field more than women, which would explain the disparities. It is true that fewer women than men intend to major in STEM, but only by a small margin, one that doesn’t fully explain the vast disparities. So why are the gender disparities much greater than this? In engineering, women have a higher likelihood of dropping the program than their counterparts. In the computer sectors of STEM, the disparities are actually increasing as fewer women complete their degrees. When exploring professor’s perceptions and their treatment of students, there was found to be a subtle gender bias towards males in the biological and physical sciences by both males and females. The favoring of men in the fields is apparent, but why is this occurring?

The answer can be summed up into two words: power dynamics. Power, according to Roscingo, is legitimized when the entire population complies and tends to be systematic. This answers the question of why implicit bias remains an issue in STEM. The socialized biases against women, as seen in the patriarchy, result in stereotypes and negative attitudes that influence the way women act, or in this case get hired. Lukes proposes different power structures. The power dynamics among men and women can be seen in the third dimension of power, when desires and beliefs secure consent to the dominant power. A study looking into computer science attributed the gender gaps to social standards and pressures that were created over a lifetime, and not just in college. Here we can see the socialization of gender’s role in disparities.

Not only is gender implicit bias relevant for STEM, but the same dynamics are seen in politics. Female politicians are negatively evaluated due to stereotypes inconsistent with the masculine traits necessary for leadership. However, in instances where an institution’s faculty is predominately female, there are found to be counter stereotype beliefs. So you can see where the need for female role models is in more than just STEM. Males and females alike express far less frequent gender stereotypes when there is a large number of female faculty. When ambitious women are able to see other women in powerful positions, they are able to picture themselves in those same roles and maintain tangible goals.

The gender disparities among STEM, resulting from implicit bias should be a more urgent concern of the United States. For me, the implicit biases became an issue when my parents encouraged me to drop the computer science classes after my brother had majored in the same computer science program. Without greater support for gender equality and a foundational way to raise children without enforcing gender stereotypes, gender disparities will remain an issue.

Initiatives, like NGCP and Obama’s conference on commitments to action on college opportunity, are two examples of work being done to work toward gender equality. Yet, neither initiative is well known or proven successful. I argue for more research into how individuals are raised to maintain biases against women in STEM and what can be done to stand against the systematic inequalities. When an entire society is socialized to intrinsically believe that men are better at certain jobs, there is a problem.

 

To find out your own implicit bias for gender click here