Gender Diversity in the Tech Industry — What does the literature say?

If you work in the tech industry, and you haven’t been so buried in backlog, code, project requirements, or one of the other myriad ancillary duties that you haven’t been able to check tech blogs or social media, you’ve still probably heard about the Google Diversity Manifesto that has been taking the industry by storm. As with any hot button issue, the response by the community has been overwhelming with support on both sides. Opinions and alternate facts run rampant, as they do with most culturally charged issues.

I like a good debate as much as the next person, but I dislike when debates are sorely lacking in actual data to substantiate presented arguments. The purpose of this article to present some information that inspires some critical thinking about the subject of diversity; specifically, gender diversity in the U.S. tech industry. The following discussion is based on about 18 months of reviewing the subject of gender diversity in the tech industry for a doctoral dissertation. It is intended to provide a starting point for interested parties. Before you weigh into a strenuous debate regarding the subject, I recommend that you peruse my sources list (there’s over 100), do a little reading, and formulate your own opinion.

Is there really a gender diversity issue?

According to the U.S. Department of Labor (2016), the number of women in the workplace was 73,510 compared to 83,620 men in 2015, or approximately forty-six percent of the workforce. Despite the fact that women comprise almost half of the overall workforce, a 2013 study showed that only twenty-six percent of computing industry workers are women (NCWIT, 2014). According to the study, this percentage had decreased from thirty-five percent in 1990 — the only STEM discipline to decrease. This disparity is not only alarming, but it also begs the question of what factors contribute to the skewed numbers and whether or not those factors could equally affect other industries.

(Corbett and Hill, 2015)

One of the numerous contributing factors to the feeling of gender inequality is that of fair compensation. According to the American Association of University Women (AAUW) (2014), in 2012 women made twenty-three percent less than their male counterparts for the performing the same work. The AAUW research indicates that job choice does play a factor in pay equality, as traditionally male-associated professions, such as computing, tend to have higher pay. This fact poses the interesting question of why more women don’t pursue careers in tech if the pay is so much better?

Why should I care?

Quite simply put, there is a shortage of qualified people in the U.S. to support the growing number of tech jobs. Charette (2013) argues that there may be a shortage in key IT occupations, such as data scientists and cloud computing engineers. The issue is not one of job availability; rather it is indicative of an underlying cultural phenomenon that has become an impediment for women choosing to pursue a career in tech. Additionally, there is much literature that discusses the benefits of having a diverse workforce (Derven, 2014; DuBow, 2013; Nelson, 2014).

(NCWIT, 2014)

Why is there a shortage of women seeking tech careers?

There is no shortage of literature on why more women aren’t seeking careers in IT. Many theories have been presented, but the two major theories that have emerged are the leaky pipeline theory and the hostile workplace theory.

The leaky pipeline theory. The leaky pipeline theory uses the analogy of a leaking pipe to represent the loss of female candidates for tech jobs. Schimpf, Andronicos, and Joyce (2015) argue that the fact that women only account for thirty percent of computer science (CS) degrees is indicative of the much larger, systemic problem of how women interact with computers in their formative years. Schimpf et al. argue that the lack of exposure of girls to technology and the corresponding lack of interest of women in pursuing degrees in computer science is attributing to the low numbers of female candidates for tech jobs.

Also persistent in the literature is the issue of self-alignment with the perceived stereotype of a CS student and computing professional. Identity alignment acts as a catalyst for interest in both pursuing CS degrees and pursuit and retention of female computing professionals (Alvarado & Judson, 2014; Beyer, 2014; Bock, Taylor, Phillips, & Sun, 2013; Cundiff & Vescio, 2016; Lewis, Anderson, & Yasuhara, 2016). Coupling these issues along with conscious and unconscious biases regarding the technical proficiency of female computing professionals (Anita Borg Institute, 2013) all form a barrier to creating a gender-balanced industry, or at least reflect a female-to-male ratio that is consistent with the overall workforce.

(NCWIT, 2014)

The hostile workplace theory. Karpowitz, Mendleberg, and Shaker (2012) argue that according to their “gender role” hypothesis (p. 534) women have lowered status and are less participatory when they represent the minority population of a group. The greater management community should be cognizant to this phenomenon because according to Taylor (1914) of equal importance to employer success, is employee success.

Once in the workplace, women report a more hostile, or at least less accepting, environment than their male counterparts. Informal peer groups are more likely to exclude women new to the workplace, with larger companies displaying more exclusionary behavior than smaller business and start-ups. While both men and women reported difficulties maintaining a work-life balance (especially in large companies), more women than men reported difficulty (Level Playing Field Institute, 2011). Holtzblatt, Balakrishnan, Effner, Rhodes, and Tuan (2016) indicate that workplace environment has a higher influence on retention despite the fact that initial thoughts put feelings of familial responsibility as the key factor in the exodus of women from computing.

Exacerbating the problem is an apparent behavioral double standard. Williams (2014) points out the irony that when women display behaviors that are considered key elements of what make men successful — assertiveness, competitiveness, aggressiveness — they are criticized, ostracized, even threatened in today’s computing industry. The gender double standard continues to persist, despite being originally identified in the 1970s (Brenner & Greenhaus, 1979). There has also been research that suggests subconscious bias in the hiring process (Reuben, Sapienza, & Zingales, 2014). Claggett (2016) argues that these types of gender barriers may contribute to the low number of women that attain senior leadership positions in the computing industry, which are critical to correcting the gender imbalance.

What can I do?

The answer is simple, do a little bit of self-reflection and try to become a little more cognizant of implicit bias, or those unconscious biases that are a result of the emotional, mental, cultural, and social interactions that we experience throughout our lives. It is up to the individual to decide whether or not they view this phenomenon as problematic. What can not be argued is the fact that there is most definitely a gender imbalance in the U.S. tech industry.

References

AAUW (Producer). (2014, December 3rd 2106). The Simple Truth About the Gender Pay Gap. Retrieved from http://www.aauw.org/files/2014/03/The-Simple-Truth.pdf

Alvarado, C., & Judson, E. (2014). Using targeted conferences to recruit women into computer science. Communications of the ACM, 57(3), 70–77.

Anita Borg Institute. (2013). Recommendations and Best Practices to Retain Women in Computing. Retrieved from http://anitaborg.org/wp-content/uploads/2013/12/Women_Technologists_Count.pdf

Beyer, S. (2014). Why are women underrepresented in Computer Science? Gender differences in stereotypes, self-efficacy, values, and interests and predictors of future CS course-taking and grades. Computer Science Education, 24(2–3), 153–192.

Bock, S., Taylor, L. J., Phillips, Z., & Sun, W. (2013). Women and minorities in computer science majors: Results on barriers from interviews and a survey. Issues in Information Systems, 14(1), 143–152.

Brenner, O. C., & Greenhaus, J. H. (1979). Managerial status, sex, and selected personality characteristics. Journal of Management, 5(1), 107–113.

Charette, R. N. (2013, September 3rd). Is There a U.S. IT Worker Shortage? IEEE Spectrum. Retrieved from http://spectrum.ieee.org/riskfactor/computing/it/is-there-a-us-it-worker-shortage

Claggett, G. P. (2016). The perception of women contending for first place in the information technology world: A qualitative case study. (10108924 Ph.D.), Capella University, Ann Arbor. Retrieved from https://login.ctu.idm.oclc.org/login?url=http://search.proquest.com/docview/1793942167?accountid=26967 ProQuest Dissertations & Theses Global database.

Corbett, C., & Hill, C. (2015). Solving the Equation: The Variables for Women’s Success in Engineering and Computing Science. Retrieved from Washington, DC: http://www.aauw.org/files/2015/03/Solving-the-Equation-report-nsa.pdf

Cundiff, J. L., & Vescio, T. K. (2016). Gender Stereotypes Influence How People Explain Gender Disparities in the Workplace. Sex roles, 1–13.

Derven, M. (2014). Diversity and inclusion by design: best practices from six global companies. Industrial and Commercial Training, 46(2), 84–91.

DuBow, W. M. (2013). Diversity in Computing: Why It Matters and How Organizations Can Achieve It. Computer, 46(3), 24–29. doi:10.1109/MC.2013.6

Holtzblatt, K., Balakrishnan, A., Effner, T., Rhodes, E., & Tuan, T. (2016). Beyond The Pipeline: Addressing Diversity In High Tech. Paper presented at the Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, Santa Clara, California, USA.

Karpowitz, C. F., Mendelberg, T., & Shaker, L. (2012). Gender inequality in deliberative participation. American Political Science Review, 106(03), 533–547.

Lewis, C. M., Anderson, R. E., & Yasuhara, K. (2016). I Don’t Code All Day: Fitting in Computer Science When the Stereotypes Don’t Fit. Paper presented at the Proceedings of the 2016 ACM Conference on International Computing Education Research.

NCWIT. (2014). Women and Information Technology By the Numbers. In.

Nelson, B. (2014). The data on diversity. Commun. ACM, 57(11), 86–95. doi:10.1145/2597886

Reuben, E., Sapienza, P., & Zingales, L. (2014). How stereotypes impair women’s careers in science. Proceedings of the National Academy of Sciences, 111(12), 4403–4408. doi:10.1073/pnas.1314788111

Schimpf, C., Andronicos, K., & Main, J. (2015). Using life course theory to frame women and girls’ trajectories toward (or away) from computing: Pre high-school through college years. Paper presented at the Frontiers in Education Conference (FIE), 2015. 32614 2015. IEEE.

Taylor, F. W. (1914). The principles of scientific management: Harper.

US Department of Labor. (2016). Women in the Labor Force. Retrieved from https://www.dol.gov/wb/stats/facts_over_time.htm