Women in Science, Engineering and Technology:
the “Field Status” Paradox

What can we do to be able to name more female scientists in the next hundred years

First published 10-3-2011


  1. 1.    Why so few? Why so slow?  Why so low?

In spite of notable advances, these questions are still salient decades after having been raised in the 1960’s at the beginning of the contemporary debate over the status of women in science and technology (Rossi, 1965). ‘Why so few?’ refers to the persistence of relatively small numbers of women in many science, engineering and technology (SET) disciplines, an issue that has spread also in emerging related areas, like innovation and entrepreneurship, which have been found to replicate the discriminatory gender patterns identified in academia (Valian, 1999; Etzkowitz, Kemelgor and Uzzi, 2000), albeit to a lesser extent (Ranga et al. 2008). Women’s academic career remains markedly vertically segregated: for example, in the European Union the proportion of female students (55%) and graduates (59%) exceeds that of male students, but men outnumber women among PhD students and graduates, and at the full professor level, women become least represented (from 27% in humanities and social sciences to 7.2% in engineering and technology). In the US, for over 30 years, women accounted for over 30% of PhDs in social sciences and behavioral sciences and over 20% in the life sciences, but at the top research institutions, only 15.4% of the full professors in the social and behavioral sciences and 14.8% in the life sciences are women—and these are the only fields in science and engineering where the proportion of women reaches into the double digits (National Academies of Sciences 2007). Women leave scientific fields disproportionately to men, which is both a waste of human resources and a serious obstacle for the development of sciences and for society as a whole.


‘Why so slow?’ refers to the exceedingly slow pace of transition from inequality to equality in many of these fields, and is inextricably related to ‘Why so low?’, which arises from the most often lower hierarchical positions than men’s in academia or business. ‘Few, slow and low’ thus became the defining line for women’s participation in SET disciplines, due to strong and persisting beliefs that these essential areas for progress and growth operate on meritocratic and universalistic principles, where only the results obtained and the individual’s contribution to knowledge matter, not the gender or the personal characteristics of the scientists who achieved them (Etzkowitz and Kemelgor, 2001). Over time, ‘few’ and ‘slow’ translate into ‘low’ or even worse, ‘invisible’. For example, the story of the ‘women computers’ - a group of female mathematicians who did secret ballistics research for the US Army during WWII, a handful of whom went on to serve as the programmers of ENIAC, the first electronic computer - is little known, and the ‘Top Secret Rosies: The Female Computers of WWII’ documentary, shown at the Computer History Museum in California does a laudable job in bringing it to public attention[1].  An August 2010 poll of public attitudes to women in science commissioned by the UK’s Royal Society revealed that nearly 90% of 18-24 year-olds and two thirds of the British public were unable to name a single famous female scientist, despite a general opinion that scientists were good role models, and an almost unanimous belief (96% of respondents) that men and women were equally well-suited to a career as a scientist (Royal Society 2011). This is not surprising if we think that only 40 of all individual 813 Nobel Prize winners in more than a century, between 1901 and 2010, were women[2].


Female attrition in the higher SET hierarchies is increasingly recognized as a serious threat to the competitiveness of academic and business organizations, and a waste of talent and skills for the society. Several obstacles embedded in the SET culture, generally known as the “glass ceiling” (e.g. hostile macho cultures, isolation, work pressure and obstacles to career advancement), or the recently-proposed “invisible web” (Zakaib, 2011) of social, biological, institutional strands and trade-offs between career and family obligations explain this phenomenon. Also, gender effects in research funding identified in most SET disciplines, where decision-making and peer review continue to be male-dominated, in some cases overwhelmingly so, and recruitment procedures that are often not clear, in particular for peer reviewers, contribute to the dearth of women in SET (European Commission, 2009). Moreover, gender-based double standards in assessing scientific competence and excellence further widen the gender gap (European Commission, 2004). Overt discrimination, invisible barriers, over-crediting men for performing traditional female roles and under-crediting women for performing traditional male roles combine to reduce the participation and advancement of women in SET, creating a Matilda effect (Rossiter, 1993), a reverse “Matthew effect” (Merton, 1968): instead of the more, the more, the less, the less has become typical for women choosing to pursue such careers.

Most efforts for change have focused on women’s recruitment rather than retention and advancement, due to unrealized expectations that upward mobility in professional hierarchies would occur naturally once entry was assured. However, reality contradicted expectation: women in SET careers are lost at every educational transition (the so-called ‘leaky pipeline’ phenomenon). Up to 52% of highly qualified women in SET may quit their jobs at a critical “fight-or-flight” moment in their career, producing massive labor shortages in SET fields (Hewlett et al., 2008). It thus comes as no surprise that disproportionate numbers of women remain in low-level positions both in academia and in business, even after their presence has made itself felt for many years, inhibiting generational change. This conundrum is especially evident in computer science, which remains largely a man’s world, in spite of some notable exceptions. For example, at the Massachusetts Institute of Technology women make up 51% of science undergraduates and 35% of its engineering undergraduates (National Academies of Sciences, 2007), a vast improvement over just a few decades ago, yet few women hold senior professorships in most computer science departments and fewer receive venture capital investments for software firms.  The Oscar-nominated Social Network captures the reality of computer science’s misogynous world: excepting a single female intern, women were not invited to play in the Facebook start-up game.  At the founding meet, the Zuckerberg character assigns positions to his male mates. One of the two women in the room asks. “What can we do?” He says, “Nothing.”


  1. 2.    The “field status paradox”

Even when women have demonstrated scientific or technical aptitude, this has not translated into a proportionate rise into high-status roles. We argue that one key reason for women’s under-representation in high-status SET positions is the “field status paradox”: when the status of a field is low, women will be found in large numbers; as the status increases, the number of women declines. The reverse situation is also possible: when the status and pay declines, women are allowed into the field to fill the vacancies created by men’s departure to more financially-rewarding jobs. Either way women lose.


A key example of the “field status paradox” is classical genetics in the early 20th century. During the early years of the fruit fly investigation at Columbia University, Thomas Hunt Morgan’s team included many women collaborators. As the importance of the investigation was recognized in the scientific community, the field status rose and more men came into the field, at the expense of women’s presence (Kohler, 1994). A similar process was observed in computer science. In the 1940s, women were the very first programmers, or ‘coders’ - a low-status skilled work for women in a division of labor giving the highest skilled work to the high-status male scientists. Some of the women coders pursued professional computing careers and became successful programmers, and even leaders in the programming profession[3]. However, their prominence in programming started to wane in the 1950s, as business applications were surpassing scientific applications. Programmers become increasingly sought after in the emerging computer manufacturing industry that was seeking to meet the expanding needs for business applications. Increasing numbers of males entered the programming profession, soon exceeding the number of female coders who had become programmers. Programming quickly became primarily a man's job. The academic technology transfer profession may be in an incipient phase of this transition. As a relatively low-status field in the US a few decades ago, it was heavily populated by women who attained leadership positions. More recently, more men have entered the field, often encouraged by women who viewed their presence as a sign that the prestige of the profession was increasing. There are signs that women may hold their own in this new field, thus possibly breaking the heretofore seemingly inexorable link between field status and gender, e.g. the election of a woman as president elect of the Association of University Technology Managers (AUTM), after a string of male presidents in recent years.


The implication of this relationship is that merely getting an increasing numbers of women in a field does not mean that the problem is solved. Indeed, more women entering a field may be the result of a specific feature of the field that makes it less attractive to men, rather than a sign of increasing attractiveness of work conditions for women. An example is the experience in academic computer science in Mexico.  As salaries stayed low, men left for industry and women were allowed into academic jobs (Etzkowitz, Kemelgor and Uzzi, 2000). Similarly, the Biological Sciences Division at Lund University in Sweden in the early 20002 saw a great increase in the number of women going into dentistry, at a time when the science of dentistry was declining in status in the country[4]. The very indicator of a solution can also be an indicator of a continuing problem.


  1. 3.    What can be done?

The ‘field status paradox’ is a reflection of the long-standing and persisting gender divide in science and technology, further combined with (and complicated by) organizational culture, selection and promotion criteria, social stereotypes deeply embedded in individuals’ minds starting from an early age, poor or lack of a gender awareness culture. Therefore, actions for change should focus on each of these levels. Specific actions and supporting structures to promote women role models, monitor gender equality and encouraging research on this area are most needed, as the denial of or lack of interest in gender equality appears to be one of the main sources of imbalance in a large number of countries. At institutional level, changing recruitment, retention and assessment of faculty to be more transparent and objective, providing equal support for men and women faculty at every stage, including in mentoring, peer review and research funding, gender monitoring and regular publishing of funding statistics, differentiated by discipline and research instrument, are just a few measures that are essential for improving women’s status. At social level, a change in taken-for-granted social norms and negative stereotypes about women that can hinder their performance, depress their self-assessments of ability, and bias the evaluations made of them by key decision makers, funneling them away from degrees and careers in male-dominated SET subjects is also much needed (Correll, 2011). Most importantly, the relationship between family and work creates a series of interrelated dilemmas for women in SET that must be addressed in a comprehensive fashion.


These are key issues that we need to reflect on and change if we want more people to be able to name more female scientists in the next hundred years. 




Correll, S. (2011), “How Gender Stereotypes Influence Emerging Career Aspirations”. Clayman Institute for Gender Research, Stanford University. (last accessed 22 February 2011).


Etzkowitz, H. and C. Kemelgor (2001), ‘Gender Inequality in Science: A Universal Condition?’, Minerva 39 (2): 239-257.

Etzkowitz, H., C. Kemelgor and B. Uzzi (2000), Athena Unbound: The Advancement of Women in Science and Technology, Cambridge University Press.


European Commission (2004), ‘Gender and Excellence in the Making’.


European Commission (2009), Gender Challenge in Research Funding. Assessing the European National Scenes.


Hewlett, S.A., C. B. Luce, L. J. Servon, L. Sherbin, P. Shiller, E. Sosnovich and K. Sumberg (2008), ‘The Athena Factor: Reversing the Brain Drain in Science, Engineering and Technology’, Harvard Business Review, June 2008.


Kohler, R. E. (1994), Lords of the Fly: "Drosophila" Genetics and the Experimental Life, Chicago: University of Chicago Press.


Merton, R.K., (1968), ‘The Matthew Effect in science’, Science 159, 56–63.


National Academies of Sciences (2007), Beyond Bias and Barriers: Fulfilling the Potential of Women in Academic Science and Engineering (last accessed 21 Feb 2011).


Ranga, M. et al. (2008), ‘Gender Patterns in Technology Transfer: Social innovation in the making?’, Research Global, 4-5.


Rossi, A. (1965). ‘Women in Science: Why So Few?’. Science 148: 1196-1203.


Rossiter, M. W. (1993), ‘The Matthew Matilda Effect in Science’, Social Studies of Science 23 (2): 325-341.


Royal Society (2011), ‘Scientists trump popstars as role models for girls’, 26 August 2010 (last accessed: 24 February 2011).


Valian, V. (1999). Why so Slow? The Advancement of Women. Cambridge: MIT Press.

Zakaib, G. D. (2011), ‘Science gender gap probed’, Nature 470, 153, 7 February 2011.

[3]            For example, Grace Hopper and Betty Holberton of UNIVAC and Ida Rhodes and Gertrude Blanche of the National Bureau of Standards.

[4]            Personal communication of the Dean of Biology Sciences to Henry Etzkowitz.