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Increasing Gender Diversity in the It Work Force
Jon Han
It is commonly understood that the IT work force lacks gender diversity. In 1983 women made up approximately 43% of the IT work force according to the U.S. Bureau of Labor Statistics Current Population Survey. By 2008, while the total IT work force had more than doubled, the female percentage had dropped to 26%. In comparison, women represented approximately 46% of administrative, science, and technical workers and approximately 42% of all other occupations. A variety of explanations have been offered to account for the small share of women in IT. But based on our research4,5 we believe choice plays an important role in explaining why there are so few women in IT, and this in turn has important policy implications for what kinds of interventions will be effective in encouraging more women to enter IT.
Encouraging more women and minorities to choose IT careers would help raise the numbers in the field. Beyond this, however, increasing the diversity of IT will produce additional benefits by ensuring that IT professionals have a broad range of experience and interests. As Wulf has argued, "...those differences in experience are the "gene pool" from which creativity springs."6
The dearth of females in IT fields is part of a larger phenomenon of occupational segregation by gender. Explanations for these occupational differences can be grouped under three broad headings: discrimination; differences in ability; and choice. Identifying the reasons so few women enter IT careers is not simply an academic exercise; it also suggests some possible solutions that may help to rectify this situation.
In the past few years a number of pilot efforts have been undertaken to address a variety of perceived obstacles to women's participation in IT. These policy initiatives have focused on a variety of ways the problem of underrepresentation might be addressed. We think these policy proposals must, however, be informed by a clear understanding of the underlying reasons for the limited numbers of women in IT careers.
The KU Professional Worker Career Experience Study
To shed light on how men and women make career choices we conducted four in-depth focus groups with IT professionals in the greater Kansas City area, and then collected detailed information from a sample of over 500 IT and non-IT professionals. Participants in the survey were solicited from employees at several large organizations with offices in the central U.S. and from business school and computer science alumni of a large Midwestern university.
We sought to compare the family backgrounds, work histories, educational experiences, and personality characteristics of IT professionals with those of individuals working in equally demanding careers that required roughly comparable levels of education and skills. This quasi-experimental design allowed us to isolate the reasons for gender-based differences in career choice.
The sample consists of 523 working professionals. The non-IT professionals include accountants, auditors, CEOs, CFOs, presidents, consultants, engineers, managers, administrators, management analysts, scientists, technicians, nurses, and teachers. The IT professionals include application developers, programmers, software engineers, database administrators, systems analysts, Web administrators, and Web developers.
The dearth of females in IT fields is part of a larger phenomenon of occupational segregation by gender.
About three-quarters of the sample (73%) are non-IT professionals, with the remainder being IT professionals. The overall sample is almost evenly divided between men (54%) and women (46%), but consistent with broader national patterns the IT workers were mostly male (68%), while the non-IT professionals were nearly evenly divided between men and women. The average age of participants in our survey was 39 years and they averaged 17 years of formal education (92% held four-year college degrees).
Personality Matters for Career Choice
Vocational psychologists have developed a way of quantifying the personality differences between individuals and how those differences affect the choice of occupation. This line of research began in 1927 when E.K. Strong developed the Strong Vocational Interest Bank (SVIB; now the Strong Interest Inventory, SII). By the 1950s, Holland had augmented Strong's work by introducing six basic occupational interest categories that closely resembled the dimensions found in research on vocational interests using the SVIB.
In 1974, the theories developed by Holland and by Strong were combined to create the Strong Interest Inventory, which is used to measure six general occupational themes (GOT) for both people and jobs, and this approach remains one of the leading tools used by career counselors to match individuals to careers. These six vocational types (RIASEC) are:
- Realistic (R) refers to a person's preference for activities that entail the explicit, ordered, or systematic manipulation of objects, tools, and machines.
- Investigative (I) refers to a person's preference for activities that entail the systematic or creative investigation of physical, biological, and cultural phenomena.
- Artistic (A) refers to a person's preference for activities that are ambiguous, free, non-systematic and that entail the manipulation of materials to create art forms or products.
- Social (S) refers to a person's preference to lead others or for activities that entail the manipulation of others to inform, train, develop, cure, or enlighten.
- Enterprising (E) refers to a person's preference for activities that entail the manipulation of others to attain organization goals or economic gain.
- Conventional (C) refers to a person's preference for activities that entail the explicit, ordered, systematic manipulation of data.
Career fields are often chosen when a person finds a career that "matches" his or her personality. For example, accountants typically score very high on the Conventional GOT. Accounting jobs typically involve a systematic approach to credits and debits and financial statements. Similarly, computer programmers typically score highly on the Realistic GOT. Programming requires a focus on concrete problem solving to abstract reasoning.
We know from decades of work by vocational psychologists that the occupational themes measured by the SII are not distributed equally between men and women. Men, for example, score higher on Realistic and Investigative themes, while women score higher in Artistic, Social, Enterprising, and Conventional themes.1,2
Our analysis of the survey data we collected indicates that more than two-thirds of the gender difference between IT professions and our control group can be accounted for by differences in the distribution of GOT scores between men and women.4 Based on these figures we estimate that in the absence of systematic gender differences in the distribution of GOT scores the IT work force today would be close to 40% female, rather than the actual figure of 26%.
IT workers in our study had higher scores on the Realistic and Investigative GOT. As discussed previously, fewer women have these types of occupational personalities, preferring occupations higher in the other four GOTs. Women do not view IT professions as artistic, social, enterprising, or conventional so they choose other occupations they feel will better match their personality.
Another recent study, by David Lubinski and Camilla Persson Benbow3 supports our conclusions. Their work found that among a group of mathematically precocious youths who have been followed for up to 20 years women and men make quite different career choices. They note that mathematically talented women are typically endowed with more highly developed verbal-linguistic skills than are men of similar mathematical ability and this versatility encourages different career choices.
Finding Ways to Increase Female Participation in IT
Finding that differences in occupational personality appear to explain much of the gender difference in career choice does not mean it is impossible to increase the number of women entering IT careers. Our discussions with focus group participants indicated there are important differences in how men and women entered IT, and that these offer a number of possible routes through which it may be possible to address current gender imbalances in IT.
Many of our focus group participants felt they had "fallen into" their IT careers, coming into IT by way of another career field. More systematic results from our survey echo this observation. Women in IT were significantly less likely than men or than women in non-IT careers to say their current career choice had been influenced by courses they had taken in high school or their high school teachers.
Focus group participants told us they discovered they had a natural aptitude for IT that led them to their current career field. Only six out of the 16 women in the focus groups actually had computer science degrees, suggesting the importance of maintaining multiple routes into IT professions.
In addition, conversations with the focus group participants emphasized that there are many misconceptions regarding what IT professionals actually do and that many IT jobs actually require occupational personalities that are more common among women. Several focus group participants mentioned they found the reality of their IT jobs to be different from what they had anticipated. These participants observed that their jobs often required them to act as a translator between the end user and the person actually writing the program code, something that made the job more social.
There are many women in other professions with the requisite skills needed to succeed in IT.
Their experiences suggest many IT jobs can be redesigned in ways that are more attractive to women by emphasizing the artistic, social, and conventional dimensions of the tasks they require. There are many women in other professions with the requisite skills needed to succeed in IT. But recruiting them will require careful thought about how job responsibilities are structured and communicated. The benefits of this effort will be a more diverse and creative IT work force.
References
1. Donnay, D.A.C., Morris, M.L, Schaubhut, N.A., and Thompson, R.C. Strong Interest Inventory Manual, Revised Edition. CPP, Inc., Mountain View, CA, 2004.
2. Holland, J.L. Making Vocational Choices: A Theory of Vocational Personalities and Work Environments, Third Edition, Lutz, Psychological Assessment Resources, 1997.
3. Lubinski, D. and Benbow, C.P. Study of mathematically precocious youth after 35 years: Uncovering antecedents for the development of math-science expertise. Perspectives on Psychological Science 1, 4 (Apr. 2006), 316345.
4. Rosenbloom, J. L, Ash, R.A., Coder, L, and Dupont B. Why are there so few women in information technology? Assessing the role of personality in career choices. Journal of Economic Psychology 29, 4 (Apr. 2008), 543554.
5. Rosenbloom, J.L., Ash, R.A., Dupont, B.R., and Coder, L. Examining the obstacles to broadening participation in computing: Evidence from a survey of professional workers. Contemporary Economic Policy (Forthcoming).
6. Wulf, W.A. Diversity in engineering. In Moving Beyond Individual Programs to Systemic Change. Women in Engineering Programs and Advocates Network Member Services, West Lafayette, IN, 1999.
Footnotes
This material is based upon work supported by the National Science Foundation under Grant No. 0204464. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
DOI: http://doi.acm.org/10.1145/1506409.1506417
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