James Demore raised a number of significant issues about biases in our thinking and how they influence the efforts towards achieving diversity and inclusion. In the Google’s Ideological Echo Chamber , the most auspicious claims that Demore makes are that there are fundamental differences between men and women that are scientifically factual, and not socially constructed. These factors include the response of men and women towards things and people, the differences in expression of emotions and stress tolerance. In these assertions, Demore claims that men and women are basically different as men prefer to place more attention on things, while women place more emphasis on people. Similarly, men are more aggressive and thus are more likely to speak up about issues like salary raises more than women, and women express different levels of neurotics. Demore further suggests that these innate differences define the career choices that men and women make. Particularly, he states that women are less represented in the fields of leadership and technology because (in context to Google work environment) “More men may like coding because it requires systemizing and even within [software engineering] SWEs, comparatively more women work on front end, which deals with both people and aesthetics.” I agree with these assertions and I will explain in further details why this statement defines how men and women make career choices. In my analysis, I hope that it remains clear that when I refer to gender disparity, I do not refer to sexism.
Women and men, as Demore states, are naturally (biologically) different, and these biological predispositions have made them approach life decisions differently, and particularly, career decisions. There is abundant evidence to prove that men and women are more oriented towards certain jobs differently, even though, both men and women can do the types of jobs that the other gender is doing. Correll (2001) calls it “the differential occupational distribution of men and women” (pg. 1691). Sociologists have attributed the segregation within the labor force with respect to gender to the biological construction of the two sexes.
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The decision making for career choices begin as early as the times that both sexes are in their high school studies. Demore uses the example of coding (in the context, coding means writing of computer programs or building computer languages) to refer to a broader context, Science, Technology, Engineering and Math generally referred to as STEM subjects. McIlwee and Robinson (1992) noted that the ratio of girls to boys dwindles as the number of people in the quantitative professions rise, often the ratio being favorable to men. This trend is confirmed to begin at the highschool level, where boys are more likely to enroll in advanced level math and science classes than girls (Roberta, 2018). This cascades up to the graduation level, where the ratio of graduates in physical science degrees by men and women, in 2016, for example was 18.4% to 3.5% respectively. Furthermore, as the trend continues, in the US, it has been noted that of all the engineers in 2017, only 8% are female, and also, only 9% of the physicists are female (Roberta, 2018). The technicality of math and physics is also highly evident in computer technology, as only 26% of the advanced computer engineers are female (Roberta, 2018). From the choice of the career path, right from highschool to graduation and thence, to job selection, men evidently make bolder decisions to pursue math and technology related courses, compared to men. Thus, the assertion by Demore that “More men may like coding” is entirely true, based on the evidence since the time of McIlwee and Robinson to date.
However, the choices made by men and women which lead men towards more tech and more math while women are led towards more social sciences do not mean that the aptitude of men on math and tech are better than that of men. In fact, Hyde et al. (1990) confirmed early enough that there is no discernible difference between men and women mental aptitude for math, thus doubting the biological superiority theories. Correll (2001) claims that “cultural beliefs about gender and mathematics differentially influence the movement of males and females along educational and career paths leading to careers in science, math, and engineering” (pg. 1695). In the context of cultural beliefs about gender, stereotypes largely inform these beliefs. For instance, math is viewed to be masculine. For this reason, even the women (girls) who make decisions at the college level to pursue math related majors are likely to switch to other social sciences oriented courses without being able to “explain precisely what had discouraged them” (Correll, 2001, 1696, Seymor and Hewitt, 1997). The switching of courses confirms that due to the stereotypes, females who choose math may later view their choice as being inconsistence with their gender orientation. These stereotypes thus are propagated further, forming a belief that men must do math and they look better while doing math, and women are otherwise awkward in the same context. The stereotypes are not only perpetrated by the students and their perceptions, but are also influenced by the guardians of these children, alongside the teachers’ expectations. Corell confirms that parents have a different expectation for their female children in math, as do teachers, when they grade the students.
Demore also suggests that even though women may be found on the tech field, they are more likely to be at the other end, where aesthetics and people form a major part of their job requirements. He (Demore) states that “…comparatively more women work on front end [of the tech division], which deals with both people and aesthetics.” Demore thus confirms that Google, as an employer, displays bias in the selection of the division in which the employers are placed. This trend is confirmed by Correll (2001) who asserts that employers also play a major role in determining the career path of men and women. As Demore states, men and women working in tech fields are placed in different divisions. It is a fair assumption that the men and women working within the same division in a company like Google, are similarly qualified, even if not exactly similar, their areas of major must be within the same field. From this perspective, it shows that employers, Google included make biased decisions, placing gender in the front of making a decision of placement position for similarly qualified men and women (Demore, 2017). The same trends are evident in pay rates, as well as other bonuses, including the frequency of promotion, among other things (Roberta, 2018).
I therefore agree with Demore that, “More men may like coding because it requires systemizing and even within [software engineering] SWEs, comparatively more women work on front end, which deals with both people and aesthetics.” From the above discussion, though not exhaustive, it glimpses on the factors that determine the choices that men and women make for their career and also, how stereotypes on gender roles and social expectation define these choices. It is also evident that employers are biased in the placement of men and women in work stations, placing women in service of people, thus forcing them towards enhancing social relations at work, while men are preferred at the technical end, where there is minimum interaction with people.
References
Demore, James. (2017) Google’s Ideological Echo Chamber p. (1-8).
Hyde, Jane Shibley, Elizabeth Fennema, and Susan J. Lamon. 1990. “Gender Differences in Mathematics Performance: A Meta-Analysis.” Psychological Bulletin 107 (2): 139–55.
McIlwee, Judith S., and J. Gregg Robinson. 1992. Women in Engineering: Gender, Power and Workplace Culture. Albany: State University of New York Press.
Roberta Rincon. (2018, Sept. 11). SWE Research Update: Women in Engineering By the Numbers . Retrieved from Society of Women Engineers: https://alltogether.swe.org/2018/09/swe-research-update-women-in-engineering-by-the-numbers/
Shelley J. Correll. (2001). Gender and the Career Choice Process: The Role of Biased Self‐Assessments––. American Journal of Sociology, 106 (6), 1691-1730.
Seymour, Elaine, and Nancy M. Hewitt. 1997. Talking about Leaving: Why Undergraduates Leave the Sciences. Bolder, Colo.: Westview Press.
