Putting more women in the pipeline
At primary and secondary school age, boys and girls perform equally well in scientific subjects. However, fewer women than men choose to take “STEM” (science, technology, engineering and mathematics) subjects at degree level. Even more women are ‘missing’ from scientific jobs – an effect known as the ‘leaky pipeline’.
In my own field of psychology there is a much higher ratio of females to males at undergraduate level. The proportions even out at graduate level and then reverse at the level of lecturer and professor.
It is unclear why women tend to drop out before they reach the higher levels in a scientific career. Some argue that there is no barrier against women entering STEM careers; the difference in proportions is simply because women have different interests. For example, they may want to work in a social setting, which science is not often associated with.
On the face of it, this is a plausible argument. However, research as well as anecdotal evidence suggests that there are still gender biases which pervade society and prevent women from progressing. As long as these barriers exist, it cannot be said that men and women have equal opportunities.
One important finding is that negative stereotypes about women’s abilities can actually demoralise and discourage women, thus leading to poorer performance. This is called ‘stereotype threat’. A classic study found that women performed worse than men on a maths test when they were explicitly told that women tended not to do well. This effect was not found when the participants were given no such information.
Other studies suggest that the gender biases that exist today are more subtle than they used to be. In a study published last year, researchers investigated these subtle biases in the fields of physics, chemistry and biology.
The authors sent out identical resumes to professors in American universities, and asked the recipients to rate the candidate on various scales: competence, hireability, likability and how willing the professor would be to mentor the student. There was only one difference: half of the fictional applicants were named “John”, the other half “Jennifer”.
The results were surprising. The respondents rated John higher on all scales apart from likability. This result was found regardless of the respondent’s age, sex, area of specialization or level of seniority. Furthermore, John was offered an average starting salary of $30,238, versus $26,508 for Jennifer.
It is particularly interesting that there was no difference in the way male or females responded in this study. It seems that everyone is equally guilty of gender bias.
Role models are hugely important for giving women the confidence to pursue a career in science. I am very lucky to have such a role model in my PhD supervisor, Anna Franklin. I am also grateful to be writing this blog with two talented female scientists. The future is looking up for women in science, but we need to tackle the problems rather than deny them.