Why so few women in scientific careers?


There are about 8 billion humans in 2022, 50% of them women. The latter, although as numerous as men, are under-represented in scientific fields.

Also, among the 956 Nobel laureates, are there only 60 women , or 6%: would the differences between women and men be such as to justify such a disparity?

Natural differences?

The first difference is chromosomal. The human being has 23 pairs of chromosomes, the last pair being different according to sex: 2 X chromosomes for women, and an X chromosome and a Y chromosome for men. This chromosomal difference accounts for that of the genital organs, which can be differentiated from birth in more than 99% of cases. To this genetic difference is added another: that of gender.

Gender is a social norm that defines the way in which we, according to our sex, should behave (ways of speaking, sitting, walking, dancing, etc.). Norms vary: in the 17th century  in France, wealthy men wore shoes with heels, reflecting their high social position. In Europe today, with the exception of the Scots, few men wear skirts. But, in Asia, the skirt is very widespread among men. These variations in time and space show that, in the expression of gender characteristics, what counts is not the sex of the person, but, above all, the social and cultural context.

Gender is also defined by stereotypes about skills and aptitudes, often considered innate, which we will see largely explain why women are so little present in science.

We know that, from an early age, the environment of boys and girls differs according to these stereotypes. However, when entering CP, girls are better than boys in French and as good as boys in maths. However, at university, there are only 22% female mathematicians . What happened to make almost all women turn away from mathematics? A set of phenomena that will act both on women themselves, stereotypes, but also on teachers, recruiters and parents, gender bias.

The power of stereotypes

Stereotypes are character traits, abilities that will be arbitrarily attributed to a group of people. If they have no scientific basis, they will nevertheless influence the way individuals behave.

Also, the girls will they integrate, very early, the idea according to which they are not made for maths. These gender stereotypes are not recent. During the Renaissance, a dark period for equality between women and men, women were excluded from the cultural, economic and political domains. Then in the Age of Enlightenment, female names in intellectual and artistic professions (author, painter, poetess, doctor, etc.) were suppressed by the French Academy, legitimizing the absence of women in these professions.

However, researchers from the University of Aix-Marseille tested the mathematical skills of children of both sexes aged 12, separated into two groups. In one, the instruction given to the children was that they were going to do a geometry test. In the other, the instruction was that they were going to do a drawing test. In the “geometry test” group, the boys performed better than the girls, while in the “drawing test” group, it was the girls. While the test is the same, girls perform worse when told they are taking a geometry test. It is therefore the mention of geometry that constitutes an obstacle, and not differences in ability, since, in the “drawing test” instruction, they are better than the boys.

This is the stereotype effect: performance drops are observed in situations where individuals fear confirming a negative stereotype attributed to the group to which they belong. We talk about the threat of the stereotype. While the stereotype itself has no biological basis (at the cerebral level, the brains of two men have as many differences as the brains of a man and a woman ), it induces in those who are the target of behavior that conforms to it: women will be less sure of themselves, and feel less legitimate in disciplines whose stereotypes exclude them, such as maths, and sciences in general.

Stereotypes will also induce biases in those who will teach, judge, evaluate and recruit. A study has shown that for the same CV, we will judge a candidate (boy’s first name) more competent than a candidate (girl’s first name), and we will offer her a better salary. This is called gender bias: people are treated differently, not because of their skills or qualities, but because of their gender.

The exclusion of women from scientific careers and its mechanisms

Gender inequality, observed at the start of scientific studies, is amplified throughout the career. Although their number is increasing, women are still a minority among teacher-researchers , all disciplines combined (40% in 2021), but more pronounced in science (at the same date, 34% lecturers and 19% science and technology teachers).

This erosion is described and analyzed in the documentary Picture a scientist .

Since, as pointed out above, women are endowed with the same abilities as men, would they have a lesser appetite for science?

It is significant to note the significant variations from one country to another in the share of women in science courses. And, apparently paradoxically, they are all the more excluded as the country is egalitarian. Indeed, women who manage to study in countries where they have to fight to access it have already made a transgressive choice, so that their disciplinary orientation is freer. We can see that these variations are explained by the context and, as mentioned above, not by recourse to natural differences according to gender. In countries where women’s access to studies is not in question, the stereotype plays into the choice of disciplines. It also has an overall impact on test scores, through the mechanism known as stereotype threat described above.

Also, in the major scientific schools in France, is the percentage of women very low, especially at the ENS-PSL (École Normale Supérieure) as described in the study: Girls + science = an insoluble equation ? . The analysis of school reports, according to social origin and gender, is valuable. It shows that gendered appreciations, of which we are not necessarily aware, are commonplace. Specific teacher training is therefore desirable to limit these biases.

This phenomenon is not limited to studies. The behavior of promotion juries at the CNRS has been analyzed by I. Régner: it is not the implicit bias that is responsible for the inequality in terms of the promotion of women, but its non-recognition.

Why act and how to do it?

It is a question of working for greater equity on an individual and social scale, but also for greater efficiency: in academic research, but also in industry and in education, several studies have shown that mixed groups (gender, social origin, etc.) perform better.

We must therefore take advantage of this observation on a global scale. It is a question, faced with the scientific challenges with which we are confronted, of not losing 50% of the brains.

It is therefore necessary to inform and convince of the deleterious effects of gender biases which are more commonly widespread than is generally believed. One way to realize this is to carry out an implicit association test  : we then measure the strength of this bias in the difficulty, via the slowness, of associating “man” and “literature”, “woman” and “science “.

A perverse effect must be mentioned: if the representation in university bodies is equal, which is desirable, we nevertheless observe effects of exhaustion on the careers of women. Indeed, insofar as, in particular for rank A (teachers), the pool remains unequal, women are individually overstretched for collective tasks, which are not particularly rewarding for their careers. The result is ultimately contrary to the objective of fairness sought.

It would be better to worry about the foundations, that is to say the conditions of access to university and research careers. Incentive measures could be considered to promote the recruitment of young women: reception funding which would be added to that already existing, awarding of a thesis grant within two years of taking office, etc. Measures also justified by inequalities in terms of the biological clock. And above all, in order to objectify these questions of gender bias, we need collections of gendered data, on careers, working conditions: Nancy Hopkins in the documentary Picture a scientist shows that the laboratory surfaces allocated to female professors were , at MIT, much lower than those granted to professors!

In short, the developments, even if they go in the right direction, remain very slow. A recent study by the Ministry estimates that, if we maintain the current pace, gender equality within the ESR, although a priority enshrined in the law, will only be achieved in 2068. There is an urgent need to ‘to act !

Author Bios: Clotilde Policar is Professor, Director of Science Studies at ENS and Charlotte Jacquemot is a Researcher in Cognitive Sciences, Director of the department of Cognitive Studies both at the École Normale Supérieure (ENS) – PSL