The concept of microaggression provides a framework for a deeper understanding of variation in the career paths of men and women (Gressgård, 2014). While traditional notions of discrimination often refer to the treatment of groups, microaggression targets individuals and may therefore speak more directly to the experiences of particular members of minority groups as they navigate their personal career advancement paths.
Microaggression can play itself out through mechanisms such as expressions of doubt about the competence of an individual, assumptions of a lower rank than one actually has, unusually careful monitoring that notices and strengthens small mistakes, and an assumption that an individual member of a minority group carries all the features of the group of which they are identified as a member (Puwar, 2004).
Studying the experience of the individual through the lens of microaggression promises to cast new light on issues that remain in the shadows when our analysis of the experience of women and minorities is informed exclusively by the implications of antidiscrimination laws and the use of quotas (Gressgård, 2014).
Towards an enhanced understanding of quotas and discrimination
Gressgård (2014) alleges that the systematic use of quotas fails to assert any impact for change on the discriminatory mechanisms found in the workplace and furthermore that quotas as a tool actually cement the status quo in general and the myth of meritocracy in particular. I argue below that these claims are too strong.
While the study of microaggression can undoubtably add to our understanding of workplace dynamics, there is nonetheless much more to be gleaned from a focus on the effects of quotas and the nature of the mechanisms that require them. In fact, an enhanced understanding of quotas and their context might contribute to eliminating them as a source of microaggression.
Academics hold tightly to the view that progress in our system is meritocratic. Hiring, decisions about article publication, citation of the work of our peers, the awarding of research funds, raises, promotions and more are determined, we believe, rationally, as a result of the objective evaluation of clearly stated requirements for advancement. An increasing body of research, however, makes it clear that equally qualified men and women are viewed differently when hiring, that women have less access than men to positions of prominence in article authorship, that citation patterns reflect the sex of the author, that prestigious funding agencies have systems which set the bar lower for men than for women, and that the CVs of men and women are evaluated differently for promotion (Vernos, 2012; Donald, 2013; European Research Council, 2012; Maliniak, Powers and Walter, 2013; West, Jacquet, King, Correll and Bergstrom, 2013; Ministry of Science and Innovation, 2011; Wenneras and Wold, 1997).
Increasingly, scientific evidence demonstrates that the sex of an individual can reliably predict some aspects of their career experience, i.e. there is evidence of systematic discrimination against women. This reflects in some abstract sense the mechanisms of the workplace, but more concretely it reflects the attitudes of individuals who have the power to make decisions on the kinds of issues already noted.
Uncovering the attitudes of those who make decisions relevant for career advancement becomes more interesting as we increasingly understand that the operative attitudes are often grounded in implicit bias. Stereotypes bombard all of us, and these in turn lead to the formation of implicit biases. Implicit biases are attitudes that we do not realize we hold; some even define them as attitudes that are not available to us through the process of introspection (Banaji and Greenwald, 2013). A typical example of an attitude captured in an implicit bias is the belief that it is more natural for men to be in positions of leadership than women, or that it is more natural for women to be involved in childcare than men. Frustratingly, these attitudes seem to be held by many of us, even as we insist on the contrary. One way to gain insight is by taking an Implicit Attitude Test, such as the online variants at Project Implicit.
Another strategy for uncovering implicit bias is to examine the behavior of subjects in experimental settings as they carry out simulations of the workplace experiences which seem to treat men and women differently. One of the most famous recent examples, also cited by Gressgård, is that of Moss-Racusin, Dovidio, Brescoll, Graham Handelsman (2012). In that study, male and female professors were asked to review an application for a beginning level academic position. Upon review of the application materials, they were asked to evaluate the applicant on four different matters: could the person be hired, what should their salary be, could they receive mentoring, and how likable were they. In fact, all subjects evaluated exactly the same file, except that half of the time the name of the file was “John” and half of the time, it was “Jennifer.” John was hired more than Jennifer, he was offered a higher salary, and he was offered more mentoring. The only thing Jennifer had going for her was that they liked her better.
One of the most important results of this study is that male and female professors alike preferred to hire John over Jennifer. And because this is a simulation, the explanation for the difference does not reside in the applications but in those reviewing the applications. The authors themselves put it like this, “The fact that faculty members’ bias was independent of their gender, scientific discipline, age, and tenure status suggests that it is likely unintentional, generated from widespread cultural stereotypes rather than a conscious intention to harm women” (p. 16477).
A recent study with a different methodology focused on implicit biases about the abilities of men and women in math as a contribution to a better understanding of the challenges associated with getting more women into STEM subjects (science, technology, engineering, mathematics) (Rueben, Spaienza and Zingales, 2014). In this study, a hiring process was also simulated, this time with instructions to hire a candidate who would successfully do a job involving solving math problems. The details of the methodology are unimportant here, but the result is that men are more likely than women to be hired, even when past performance on the relevant type of math skills is provided to the employers. Furthermore, the study showed that under some circumstances, when a less qualified person is hired over a more well qualified one, men are favored as much as 90% of the time!
This study again shows the role of bias in hiring, and both studies suggest that implicit bias is crucial to understanding the different career experiences of men and women.
Given mounting evidence for implicit bias as a discrimination-causing mechanism, organizations must grapple with strategies for responding, including the introduction of tools that can counter the effects of implicit bias. Gressgård (2014:25) argues that quotas not only fail to confront the mechanisms of discrimination but indeed that they contribute to maintaining the myth of meritocracy. While I agree that quotas do not repair the causes of discrimination, it seems to me that they can highlight the presence of such causal factors.
A system of affirmative action in which employers are instructed to hire a women when a male and female applicant are found to be equally well qualified does, I would claim, entail a confrontation of the myth of meritocracy. Such a requirement acknowledges that some decisions must be made without a merit-based process. In particular, it fantasizes a situation in which the merits of the applicants are exactly equal and therefore unusable for selecting one over the other. At the same time, it acknowledges that choices can be made in this situation, and therefore de facto recognizes the use of non-merit-based criteria for selection. In this sense, meritocracy is confronted, not maintained.
Furthermore, the implementation of such an affirmative action measure strongly implies that the criteria standardly used to resolve these cases in which merit is not relevant are biased, favoring men over women, and that this urge must be countered, by now forcing the selection of the woman over the man.
Although I argue that affirmative action of this type does in fact confront the notion of a meritocracy, discussions of this particular kind of measure refers to situations that are so idealized that they constitute little more than a philosophical frivolity.
A much more fundamental sense in which quotas clash with the notion of meritocracy is related to the familiar article of faith stating that stronger varieties of quotas — hiring a woman no matter what, or hiring a female applicant if she is only marginally worse than a male applicant — necessarily entail a compromise of quality. Recent research suggests that this is not a straightforward claim.
Specifically, two recent articles in Science magazine explore various aspects of affirmative action. In a simulation testing the effects of four different kinds of affirmative action measures, researchers found that announcing the potential use of affirmative action to achieve gender balance in a resulting group actually attracted women with higher qualifications to participate in the process than otherwise. In fact, in several of the simulations, the potential use of affirmative action attracted such a more highly qualified pool of female applicants that it was not necessary to employ the affirmative action measure. The group selection process itself gave the desired gender composition without any post hoc intervention. (Balafoutas and Sutter, 2012; Villeval, 2012)
The details of that study are too elaborate to discuss here, but they provide solid evidence that it is a logical error to posit a necessary implication between the use of quotas and the reduction of quality among those selected by a process. By establishing this, we can see at least two subtle but real confrontations with the myth of meritocracy. Gressgård notes that meritocracy becomes a myth when there are institutionalized power structures that impede equal treatment. The research on quotas demonstrates that the way in which jobs are advertised impacts men and women differently and that it affects the gender composition of the applicant pool in ways that do not simply reflect the qualifications of individuals but which instead favor men. Secondly, the research shows that the overall quality of the pool of selected (e.g. hired) individuals can in some circumstances be elevated by the use of quotas, which confronts the belief that the status quo provides results of the highest quality.
I find Gressgård’s presentation of microaggression to be important and a potentially useful source for achieving greater understanding of workplace dynamics. But I disagree with her claims that quotas support the myth of meritocracy and that they fail to either confront or bring into focus the mechanisms of discrimination. While the study of microaggression will surely bring us further, I am convinced that deeper research into the nature and effects of quotas will continue to shed light on the complexities of workplace dynamics and will raise issues which, if properly addressed, will lead to greater fairness and increased quality.
Balafoutas, Loukas and Matthis Sutter. 2012. Affirmative action policies promote women and do not harm efficiency in the laboratory. Science. February 3, 2012: 335, 579-582.
Banaji, Mahzarin R. and Anthony G. Greenwald. 2013. Blindspot: Hidden Biases of Good People. New York: Delacorte Press.
Donald, Athene. 2013. Gender issues in European academic science. Occam’s typewriter. http://occamstypewriter.org/athenedonald/2013/06/21/gender-issues-in-european-academic-science/
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Gressgård, Randi. 2014. Å stange hodet i veggen: Mikroaggresjon i akademia. Nytt Norsk Tidsskrift 01/2014, s. 17-29.
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Ministry of Science and Innovation. 2011. White Paper on the position of women in science in Spain. http://www.idi.mineco.gob.es/stfls/MICINN/Ministerio/FICHEROS/UMYC/WhitePaper_Interactive.pdf
Moss-Racusin, Corinne A., John F. Dovidio, Victoria L. Brescoll, Mark J. Graham and Jo Handelsman (2012). «Science faculty’s subtle gender biases favor male students». PNAS, Early edition, 21 August: 1–6. Hentet fra: www.pnas.org/cgi/doi/10.1073/pnas.1211286109.
Project Implicit. https://implicit.harvard.edu/implicit/
Puwar, Nirmal. (2004) Space invaders: Race, gender and bodies out of place. London: Berg.
Rueben, Ernesto, Paola Spaienza, and Luigi Zingales. 2014. How stereotypes impair women’s careers in science. PNAS Early Edition. http://www.pnas.org/content/early/2014/03/05/1314788111.abstract
Vernos, Isabelle. 2013. Research management: quotas are questionable. Nature. 495, 39.
Villeval, Marie Claire. 2012. Ready, Steady, Compete. Science. February 3, 2012: 335, 544-545.
Wenneras, Christine and Agnes Wold. 1997. Nepotism and sexism in peer-review. Nature 387, pp. 341-343.
West, Jevin D., Jennifer Jacquet, Molly M. King, Shelly J. Correll and Carl T. Bergstrom. 2013. The role of gender in scholarly authorship. PLoS ONE 8(7): e66212. doi: 10.1371/journal.pone.0066212