{ inclusiveness }

  • What's different between female STEM workers and those in other professions?

    Many studies of women in STEM use men as a referent group to women: how do women compare to men in CS with regard to retention, attitudes, discrimination, etc? While there’s certainly benefit to using men as a referent group (and it’s far, far better than no referent group at all), there’s a threat to validity that we tend to overlook when studying women in CS: how much of what we see is an artifact of CS culture versus that of our wider society? **
    Triangulation using different referent groups is a good way to get around this issue. I’ve talked before about differences between women in CS vs. other STEM fields, differences between women in CS between different cultures, and differences over time/generations. But in every one of these posts, I’ve really only looked at scientists.

    Glass et al’s “What’s So Special about STEM? A Comparison of Women’s Retention in STEM and Professional Occupations“ addresses another angle: what’s different for women in STEM vs. women in other professional occupations? After all, women are more likely than men to leave other professional occupations such as business, medicine and law [1]. And in all these fields, substantial problems remain at the top: women may make up a substantial proportion of workers, but a tiny minority of those running the show.

    The Glass et al Paper

    To make the comparison of STEM women and non-STEM women, the Glass et al paper uses longitudinal data from the National Longitudinal Survey of Youth 1979. The longitudinal approach is a strength of the paper. A weakness, however, is that the women participating are a single generational cohort who entered the workforce in the late 80s/90s: “second generation” per my previous post.

    Overall, Glass et al found that women in STEM jobs had more in common with women in non-STEM professional jobs – and that “few differences in job characteristics emerge” overall. This is a rather important finding – it means that if we work carefully, we can often generalize findings about women in the general workforce to women in the STEM workforce.

    I say “carefully” since there were a few differences that they found. Here’s what’s unique to STEM women:

    1. Women who are married to fellow STEM workers are nearly 100% more likely to stay in their STEM jobs than women married to non-STEM workers.2. A higher education does not increase a STEM woman’s likelihood of staying in a STEM career. In other occupations, such as medicine or law, the more advanced degrees a woman has, the more likely she’ll stay in the field. Glass et al attribute to this to the type of work done by those with MSc/PhDs: the more education you need to do the job, the more likely it’ll be isolating and in a “noxious” work environment for women.
    2. Unlike non-STEM women who leave their jobs to stay at home, when STEM women leave their jobs, they overwhelmingly do so to fill non-STEM jobs, rather than to stay at home permanently. Switching to management explains almost a quarter of these job departures.Those three differences aside, everything they looked at turned out to be the same for both STEM women and non-STEM women.

    Similarities between female STEM and non-STEM workers

    For both women in STEM jobs and women in professional non-STEM jobs, the following things are positively correlated with the retention of women in the workplace: Higher pay, job commitment, higher reported job satisfaction, longer time working in that career, and the presence of parental leave.

    Sociologists have documented the “Work-Family Narrative“ – the cultural narrative that women leave (or struggle with) their jobs because they can’t balance work and family. They’ve similarly documented that the majority of workplace interventions to improve the status of women focus on this narrative.

    Yet, what Glass et al found is that primary propellant of women out of the workforce – both STEM and non-STEM – is not childcare. Nor is it lack of confidence or lack of training – or lack of “leaning in”.

    The primary propellants are dissatisfaction with pay and promotion prospects. There’s a ton of sociology papers out there finding similar results. Childcare might be the catalyst for acting on that dissatisfaction, but it’s not the underlying cause.

    This dissatisfaction is linked to a number of sources of inequality, such as being left out of the “boys networks”, subconscious biases against women, open prejudice about the competence of women, and sexual harassment. Correspondence studies of women in STEM and other professional domains have consistently found that women are less likely to be thought worth of a promotion as an equally qualified man, less worthy of a higher salary, and less likable overall. And there’s evidence that men in our society are promoted based on potential – while women are promoted based on past accomplishments. This sort of unintentional, de facto discrimination is not unique to STEM.

    The Work-Family Narrative as a Social Defense

    The paper I linked to about the “Work-Family Narrative” – by Padravic and Ely – presents a rather compelling argument that the reason that people focus on this narrative is because it is an unconscious social defense. The Work-Family Narrative gives a way of thinking about the problems facing women in the professional workplace that doesn’t involve coming to terms with discrimination and systematic problems in the workforce.

    Padravic and Ely also argue that this narrative similarly allows people to keep their cultural stereotypes in tact: women are the caregivers, men are the workers – and so women have a hard time in the workforce because they must balance their position as caregiver. I’ve noted before that our brains are wired to keep cultural stereotypes in tact.

    Discrimination is an ugly thing to talk about. I don’t blame people for shying away from it. But it needs to be tackled to change the numbers of women in the workforce – whether it be STEM or other fields. And it’s important to compare STEM to the rest of society – we need to know what’s a STEM problem and what’s even more systematic.

  • Why are there more women in some STEM fields than in others?

    Why is it that there are more women in biology than there are in computer science in North America? Women in the biomedical fields are now earning more than 50% of undergraduate degrees in the US [1].

    Biology, like computer science, was once stereotyped as masculine. Medicine continues to be stereotyped as masculine, especially fields such as surgery. Why has biology attracted so many more women than computer science?

    To answer this question, I’ll be synthesizing the findings of Cheryan’s “Understanding the Paradox in Math-Related Fields: Why Do Some Gender Gaps Remain While Others Do Not?” [2], Cohoon’s “Women in CS and Biology” [3], and Carter’s “Why students with an apparent aptitude for computer science don’t choose to major in computer science” [4].

    Between these three papers, four themes emerge for why women choose one STEM field over another:

    1. Exposure to the field
    2. Expected value of the major
    3. Lack of prejudice in the scientific culture
    4. Prospects of raising a family in that scientific culture


    The ultimate finding of Carter’s “Why students with an apparent aptitude for computer science don’t choose to major in computer science” is that students simply didn’t know what CS is, or had misconceptions of the field [4].

    Most undergraduates in North America never have to take any CS, and never saw any in high school. The big boon to biology enrollment is that biology is a course that pretty much everybody has to take in k-12. As we saw in comparing female representation in STEM between cultures, compulsory schooling plays a role in getting women into STEM.

    But high school science isn’t the only way to expose young men and women to science. Women are better represented in astronomy and the earth sciences than they are in computer science, and neither of those fields are well-represented in k-12. Exposure can come from museums; television programmes and other documentaries; popular science books, magazines and blogs; public lectures; and science camps. Computer science does comparatively little public outreach.

    Early exposure is also important. In Carter’s study, numerous students – disproportionately female – would only discover CS near the end of their degrees – too late to major or minor in the field. Multiple points of entry to CS majors, and multidisciplinary programmes, are hence recommended to increase female participation in CS [5].

    Exposure at an early age also is useful. Girls who are given hands-on exposure to computers at an early age are more likely to wind up in CS [6]. Girls whose mothers are confident around computers are more likely to be confident around computers [6]. Girls who come from academic families are more likely to wind up in CS [6].

    Finally, exposure is important for overcoming stereotypes about CS. Cheryan compared giving women descriptions of computer science as being a nerdy discipline – versus descriptions of computer science “not being like that” [7]. Women were statistically significantly more interested in computer science when given a non-stereotypic description of computer science.

    Expected Value

    Expectancy-value theory is one of the numerous theories out there used to model how undergraduates choose their majors. In a nutshell: undergrads are more likely to choose majors that they expect to align with their values and beliefs.

    Cheryan argues that women are choosing biology over CS because they see in as more fulfilling: there is the promise of intellectual challenge combined with the promise of benefiting society [2].

    But it’s not that simple – not all women have the same values, beliefs, and backgrounds. Margolis and Fisher found that women from racial minorities and international students who came to the US to study were motivated by the financial stability promised by a CS career [8]. And biology careers tend to appear more stable to undergraduates: biology faculty almost never turn over, whereas CS faculty will leave academia to go to industry. Maintaining a stable faculty in a department is good for gender representation [3].

    Actual Openness

    Another finding of Cohoon’s is that biology professors have better opinions of female students than CS professors do [3]. Biology professors also spend more time mentoring students than do CS professors [3].

    Biology continues to have issues with prejudice. Women are less likely to be hired than equally competent men [9], will be offered lower salaries [9], and their work is viewed less favourably [10]. But the evidence indicates that biology is still more open to women to female scientists than CS is. And as we saw in the cross-cultural comparison, an unentrenched scientific community is conducive for minorities to enter the community.

    Prospects of Raising a Family

    There’s little evidence that women consciously choose majors based on how friendly the major is with respect to raising a future family. But in a society where women are socialized from a young age to expect to be the primary caretakers of their future offspring, it is not surprising that women are deterred from fields that seem unfriendly to raising a future family.

    The process is more of an accumulation of red flags: The long hours in CS are only one red flag. As Carter found, CS is considered a volatile field without job security [4]. A lack of role models that are actively parenting adds to the notion of family-unfriendliness: they fail to provide evidence that women can have families and be in computer science. The relatability of role models is important: it is counterproductive in this regard to see female professors who have no families and are focused only on science [1].

    The stereotypes about computer scientists are another red flag: computer scientists are seen as unattractive, singularly focused on technology, and asocial. Male computer scientists hence are unattractive as potential partners – and there’s plenty of evidence that humans are subconsciously drawn towards careers that are more conducive to meeting potential partners [11].

    The sad evidence is that a fraction of white women are deterred from STEM because they do not want to be seen as unfeminine or intimidating to future partners [11]. Women who do go into STEM are more likely than non-STEM women to believe that men are unintimidated by their career choice, and they are more likely to have fathers, brothers and boyfriends that support this belief [11].

    Overall, this lines up with what we saw in the cross-cultural comparison: women are more likely to go into STEM in cultures where raising a family is viewed as a communal responsibility.

    [1] Ashcraft, Eger and Friend. “Girls in IT: The Facts”. http://www.ncwit.org/resources/girls-it-facts
    [2] Cheryan. “Understanding the Paradox in Math-Related Fields: Why Do Some Gender Gaps Remain While Others Do Not?” 10.1007/s11199-011-0060-z, Sex Roles 66 (3 2012): 184–190. issn: 0360-0025. http://dx.doi.org/10.1007/s11199-011-0060-z.
    [3] Cohoon. “Women in CS and biology.” SIGCSE Bull. (New York, NY, USA) 34, number 1 (February 2002): 82–86. issn: 0097-8418. doi:10.1145/563517.563370. http://doi.acm.org/10.1145/563517.563370.
    [4] Carter. “Why students with an apparent aptitude for computer science don’t choose to major in computer science.” SIGCSE Bull. (New York, NY, USA) 38, number 1 (March 2006): 27–31. issn: 0097-8418. doi:10.1145/1124706.1121352. http://doi.acm.org/10.1145/1124706.1121352.
    [5] Cohoon. “Recruiting and retaining women in undergraduate computing majors.” SIGCSE Bull. (New York, NY, USA) 34, number 2 (June 2002): 48–52. issn: 0097-8418. doi:10.1145/543812.543829. http://doi.acm.org/10.1145/543812.543829.
    [7] Cheryan, Plaut and Handron. “The Stereotypical Computer Scientist: Gendered Media Representations as a Barrier to Inclusion for Women.” Sex roles (2013): 1–14.
    [8] Margolis and Fisher. Unlocking the clubhouse: Women in computing. MIT press, 2003.
    [9] Moss-Racusin, Dovidio, Brescoll, Graham and Handelsman. “Science
    faculty’s subtle gender biases favor male students.” Proceedings of the National Academy of Sciences 109, number 41 (2012): 16474–16479. doi:10.1073/pnas.1211286109. eprint: http://www.pnas.org/content/
    109/41/16474.full.pdf+html. http://www.pnas.org/content/109/41/16474.abstract.
    [10] Knobloch-Westerwick, Glynn and Huge. “The Matilda Effect in Science Communication: An Experiment on Gender Bias in Publication Quality Perceptions and Collaboration Interest.” Science Communication (2013).
    [11] Hawley. “Perceptions of male models of femininity related to career choice.” Journal of Counseling Psychology 19, number 4 (1972): 308.