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Psychology Politics · 2 April 2013 ·

Gender biases on the job market in academia: There are already quotas, but they are implicit

The issue of supporting quotas for supporting women in board room positions and academia are highly contested: Is it really fair that women receive extra support through guaranteed quotas during the application process? Does this not bias the employment process, whereas less qualified personnel was employed? In a blog post over at the LSE’s Engenderings, Linnea Sandström has made a case in favour of quotas, and I want to present some research here that suggests that there is already an implicit selection process favouring one sex over the other—only that it works in favour of men, and that this imbalance is not just attributable to life style choices. Rather, there is a bias direct against female applicants, even when the information about the applicant is the same. Therefore, quotas are about balancing an already existing bias, rather than introducing a new one.

A recent study by Moss-Racusin and colleagues has added onto the already extensive literature on this subject, with a very neat and simple design that illustrates just how pervasive the problem is. They used the same ostensive application to apply for a position as lab manager for a sample of biology, chemistry, and physics professors across the US. All evaluators received the very same application materials, however half of them with a male name, the other half with a female name. Both names had been pre-tested for likability and recognizeability, and received similar scores. Yet, despite showing exactly the same information, male applicants were more likely to be invited for an interview.

This suggests that the under-representation of women is not just explicable by life style choices, but that there is a bias against female applicants. An alternative account might suggest that it is in fact previous experience of the professors with female applicants being generally less suitable for the position, for whatever reason. However, this view is just as discriminatory, since high skilled women will stand less of a chance than they deserve, whereas low skilled men have higher chances compared to their female peers.

This isn’t the first study of its kind. For example, Uhlmann and Cohen (2005) have found that students at Yale University, who had been asked to rate the suitability of a male and a female candidate for the position as chief constable in a police department, adjusted the criteria for the job so that it matched the male applicant. The authors asked participants first to rate profiles of two applicants, and decide who may be more suitable for the position, one being a tough, streetwise applicant, the other thoughtful and educated. When the male applicant was streetwise, participants indicated that they thought that the job required a candidate that was streetwise, too. But when the female candidate was streetwise, participants suggested that the applicant with the better education may be better suited for the job. Uhland and Cohen poignantly note:

…participants who exhibited the most pro-male bias in their hiring criteria also proved the most confident in the objectivity of their decision. They, perhaps, felt that they had chosen the right man for the job, when in fact they had chosen the right job criteria for the man. (Uhlmann & Cohen, 2005, p. 479)

In addition to this, Uhlmann and Cohen were able to tease apart the effect they observed: When the job was stereotypically female, in their experiment they tested a women studies professor, participants were more willing to hire a female applicant, adopting the appropriate hiring criteria according to the gender of the applicant: Participants either favoured the activist or the academics, but overall preferred the woman. I’m not too happy with their choice of using a women’s studies professor, there may be other, linguistic biases that make participants prefer a woman for this job. But in the wider context of the study, it is likely that the results still hold. Furthermore, the key point of this study is that participants adjusted the job’s criteria according to the applicant’s sex, rather than using the job’s criteria to pick the right applicant.

Lastly, Uhlmann and Cohen tested what would happen if participants committed to the criteria before revealing the gender of the potential applicant. In this instance, participants did indeed stick to the criteria, rather than the applicant’s gender. This validates their treatment effect on the one hand, whilst also showing a way out of the dilemma: If clear criteria are defined for job openings, and applications are reviewed anonymously, then we can reduce the effect of stereotype discrimination. Note however that this only applies for discrimination based on the same application portfolio. Discrimination due to the different life style decisions available to men and women would not be taken into account: Thus, if a female applicant lacks in certain criteria due to an earlier pregnancy, or lower involvement in career-relevant social networks, this would not be rectified through anonymous reviews.

It is noteworthy that Uhlmann and Cohen only tested university students, and we might object that people working in HR departments would not exemplify these biases. (Even though many of them will themselves take on leadership positions.) Therefore, what’s nice about Moss-Racusin et al.‘s study is that they conducted their study using real world job applications, not just students rating them. Both studies complement each other well, and integrate into an already large set of literature on gender biases show that we’re still far away from offering equal opportunities to all sexes. Just having a female name can make you less likely to get a job.

But these gender biases extend even further. A real world analysis of conference data reveals that even in an academic field that consists largely of female researchers, conferences organised by male scientists show a considerable male bias. This is the outcome of a study by Isbell and colleagues, who have found this pattern in the field of Anthropology. Isbell and colleagues studied presentations on primate behaviour and ecology during 21 annual meetings of the American Journal of Physical Anthropology for their gender ratio. Apart from reasons of convenience (this is one of the author’s main field of research), they chose this particular section of anthropology as it has more female than male researchers. They found that more women prepare posters than give talks, although the latter are more prestigious. For men this was the other way around. Of course, this could have also to do with a self-selecting bias. Hence the study also compared participation in self-submitted presentations with invited symposia. Furthermore they took into account the gender of the symposiums’ organisers. They found that for virtually all kinds of invited symposia the gender balance is approximately balanced at the rate of presentations, with one exception: When the symposia’s organisers were male, female participation dropped below 30%.

Lastly, the issue of identity-based discrimination is not limited to the gender of the applicant, but can also relate to other factors of the identity, such as the ethnic background of the applicant. For example, a study by Kaas and Manger using a similar design to the study by Moss-Racousin and colleagues, found that on the German labour market, applicants with Turkish sounding names are less likely to be invited for an interview compared to applicants with a German sounding name. However, adding information on the applicant’s personality makes it possible to reduce this effect. They write:

The field experiment shows that an application with a German-sounding name is on average 14% more likely to receive a callback. Discrimination is more pronounced among smaller firms: firms with less than 50 employees give ‘Dennis’ and ‘Tobias’ about 24% more callbacks than ‘Fatih’ and ‘Serkan’. We also find evidence that a reasonable fraction of the differential treatment can be attributed to statistical discrimination: while there is almost no difference in callback probabilities for the application that is equipped with personality information (37.4% with a German name vs. 36.9% with a Turkish name), the absence of such information in the other application gives rise to significant differences in callback probabilities (41.8% with a German and 32.5% with a Turkish name).

So we have seen that there is a large set of evidence that suggests that there is sex-based discrimination on the labour market. I have summarised three studies that taken together suggest that (a) there is a bias in selecting applicants based on their gender, rather than their merit (Uhlmann & Cohen), (b) that even when the credentials were the same, and there is no a priori reason to take the gender of an applicant into account, men are preferred (Moss-Racousin et al.) and that even in an academic field dominated by women, men tend to select women for the more prestigious presentations (Isbell et al). All this supports the notion that there is an implicit bias against women, even when their qualifications are the same as men’s. Furthermore, these biases also extend to other areas of identity, e.g. the bias against ethnic minorities that Kaas and Manger have found.

There are different means to circumvent these issues; one being the introduction of anonymous applications, which would also benefit applicants with foreign-sounding names. But on a larger scale, I think explicit quotas can help to introduce a little more fairness into the application process, by counterbalancing the selection bias favouring male applicants. In this, they are arguably more like a sledge hammer, rather than a precision tool. But sometimes you do need a sledge hammer to get the job done.


Isbell, L., Young, T., & Harcourt, A. (2012). Stag parties linger: Continued gender bias in a female-rich scientific discipline. PLOS ONE, 7(11), e49682. doi: 10.1371/journal.pone.0049682

Kaas, L., & Manger, C. (2012). Ethnic discrimination in Germany’s labour market: A field experiment. German Economic Review, 13(1), 1–20. doi: 10.1111/j.1468-0475.2011.00538.x

Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J., & Handelsman, J. (2012). Science faculty’s subtle gender biases favor male students. Proceedings of the National Academy of Sciences, 109(41), 16474-16479. doi: 10.1073/pnas.1211286109

Uhlmann, E. L., & Cohen, G. L. (2005). Constructed criteria: Redefining merit to justify discrimination. Psychological Science, 16(6), 474-480. doi: 10.1111/j.0956-7976.2005.01559.x

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