By Neha Sethi
A few months ago, during a text conversation with a guy, I casually lamented over my struggle with prepping for the quantitative section of my management exams. His response: Aww, ho jaayega. Girls struggling with maths is kinda cute. Now, normally, good sense and experience with everyday sexism would warrant that I cut the conversation short right there, but I chose to engage and call him out on his sexist comment. After all, why must my individual peeve with maths be attributed to women at large? An hour and a heated messaging match debating perceptions about women and maths later, neither of us budged from our respective stances.
A couple of days ago, I woke up to read ICICI Bank Chief Chanda Kochhar’s statement that quantitative reasoning-focused entrance exams keep more women from joining MBA programs. She also questioned, the article said, the need for such a focus in the exams, given that MBA courses are more general-management-oriented. While the latter is an issue that merits deliberation, statements like the former reinforce the erroneous perception that women are not as good as men when it comes to quantitative ability and logical reasoning.
Crucially, Kochhar’s statement, in tacitly correlating quantitative aptitude as inherent to a person’s sex, neglects the sociological reasons behind fewer women applying for and making it to management programs. Do women fare worse than their male counterparts when it comes to quantitative ability? Evidence from a 2014 study conducted by the non-profit organisation Feminist Approach to Technology (FAT) in fact suggests the opposite: girls perform slightly better than boys in maths and science, and despite having an interest in STEM (Science, Technology, Engineering and Mathematics) subjects and good school grades, girls opt out of these subjects at secondary and higher secondary levels.
Importantly, the study provides strong evidence for something feminists have said often: that there are deep-set beliefs around girls’ and women’s inaptitude for STEM subjects, which hinder education and career prospects for them in these fields. (This of course is in addition to other factors such as lack of infrastructure, especially in government schools, favouring the education of the male child, language barriers and so on.) Taking care of household duties is still considered the primary responsibility for women (which is especially true in India and further amplified in socio-economically disadvantaged communities). This affects women’s self-perception about their abilities and colours family members’ opinions about the higher education stream or profession that women should choose. (On a personal note, I remember that growing up, family members would often suggest teaching as a good profession for women, as it is apparently less taxing and allows the woman to balance home and career, as opposed to business, which is far too demanding and chaotic for a woman.) Also, decades of conditioning lead girls and women to believe that they do not have much of an aptitude for STEM subjects or careers, thereby creating a ‘confidence gap’ (Sadker and Sadker, 1994, as quoted in the FAT study).
The dearth of exposure to women role models in influential managerial positions or STEM careers also certainly contributes to these self-perceptions. Young women rarely hear or read much about the contributions of women scientists or leaders, and it has a lasting effect on their ability to dream and aspire for such careers themselves.
We instead hear powerful women leaders like Chanda Kochhar and Indra Nooyi wax eloquent about work-life balance (which somehow never comes up as a question for working men) or why women “can’t have it all”, reminding us yet again that no matter how high up the career echelons a woman climbs, her identity as a wife and mother will always trump her individual or professional identity.
Neha Sethi is an Advocacy and Fundraising Officer at Urja Trust, a Mumbai-based grassroots NGO working with homeless young women. She previously worked as a Research Assistant with the International Center for Research on Women (ICRW) in Delhi.
November 5, 2015 at 9:57 am
Interesting and well written, I’d like to point out however that whether boys actually outperform girls in quantitative aptitude or not is still debatable.
The study referenced was conducted on students across 16 government schools over the 6th to 9th standard. From the 6th to 8th, based on the graphs, boys outperformed girls based on class averages all 3 years and there’s a sudden drastic decline in boys’ performance for the 9th standard. Seems all too ambiguous to be used as a gauge for actual aptitude. Never mind the fact that the issue in question is quantitative aptitude and reasoning at management entrance exam levels.
What I’d like to see is research of how women fare vis-a-vis men in management entrances on average. That might provide clarity as to whether there’s any merit to the argument of boys being better at QA and reasoning.
May 12, 2016 at 9:38 am
Hi. I think there is some merit to Chanda Kochhar’s statement here. I too feel that it is the quantitative aptitude that keeps women from entering prestigious B Schools. The questions on Quants in competitive exams are at a much highre difficulty level, such that only engineering grduates will be able to perform and crack them with ease. However, the gender ratio in engineering schools is extremely skewed and hence this puts women at a disadvantage.
September 8, 2016 at 3:01 pm
PrernaJoshi Speak for yourself, I think it panders to a stereotype. Some people are numerically inclined and some aren’t. Has precious little to do with gender or mathematical training in an engineering school. Also, I think if more girls were given opportunity they would also be allowed to go to engineering school to fix the skewed ratio.
January 5, 2017 at 9:16 pm
Late response but but ….have to:
Chanda is right. Any idea how tough Math is at JEE? I am one of the rare IITians and also a PhD in Math and am appalled that many girls can’t solve Math q’s , including ones exposed to role models and whatnot, likely for reasons you mention or .. what about that they just can’t?! On a statistical sampling level, all biases get removed, you know.