In a word, “yes.” Consider the following laboratory and real-world evidence:
- Although women hold nearly 50% of the jobs in this country, they hold only 25% of all STEM jobs (Beede et al., 2011).
- When evaluated for a graduate-assistant position in a science lab, female applicants were rated less competent, less hirable, and were offered less mentoring and less money than male applicants (Moss-Racusin et al., 2012).
- Women in occupations that use quantitative methods are less likely to be hired for a mathematical task, even if they provide information about their past performance and mathematical ability (Rueben, Sapienza, and Zingales, 2014).
- Women also face higher attrition rates than men, with more than half of female scientists quitting their jobs midcareer (Hewlett et al., 2008). Even within specialized, high-tech careers, 41% of female workers compared to 17% of males leave their jobs (McClelland & Holland, 2015).
- Although more than fifty years have passed since the equal pay act was signed, women still earn only approximately 75% of what their male peers earn in male-dominated fields, like quantitative methods (Aarons, 2008).
So, why are women in quantitative work disadvantaged? In later posts, we will review structural and institutional factors that disadvantage females in the workplace. But, for now, we have chosen three psychological explanations as a place to start the discussion. Below, we briefly define role incongruity, stereotype threat, and implicit bias. We conclude by suggesting some research on each topic, in case you want to learn more.
What is it? Role incongruity refers to a situation in which a person performing well at a task, typically attributed to the opposite gender, is perceived as being in violation of a gender-prescribed norm (see Heilman et al. 2004). For example, in a leadership position, a male may be seen as “authoritative” whereas a woman may be perceived as “aggressive” or “bossy” and therefore less competent than a male.
How does it apply to female quantitative researchers? Because gender norms associate “male” with mathematical and scientific abilities, when female quantitative researchers engage in tasks demonstrating these abilities they violate this gender-specific norm. This violation may lead to consequences such as being perceived as less capable than male counterparts, being offered fewer opportunities in quantitative domains, receiving lower wages, and a experiencing a decreased likelihood of promotion. Moreover, when women move into leadership positions (as quantitative methodologists) they may suffer the consequences of violating an additional culturally held belief that suggests that men are better leaders than women.
What is it? Stereotype threat refers to a situation in which an individual is aware of a negative stereotype associated with her or his social identity, fears confirming that negative stereotype, and then exhibits degraded performance in the threatened domain through a variety of physiological and psychological stress responses. For example, if an individual consciously (or unconsciously) feels her performance will confirm a negative stereotype (such as girls are bad at math), her performance in a task related to that stereotype may be attenuated. In an experiment conducted by Davies and colleagues (2002), when subjects viewed gender-stereotypic commercials that activated the stereotype of, “women are less competent at math than men,” women:
- avoided mathematical test items in favor of verbal items
- indicated less interest in education/vocational options that conflicted with stereotypic expectations
- demonstrated lower performance on tests of mathematic aptitude
How does it apply to female quantitative researchers? In response to culturally pervasive ideas about women, female quantitative researchers, may consciously, or unconsciously “fear” confirming the negative stereotype that women are not as skilled in scientific endeavors as men. The combination of the awareness of the stereotype and the “fear” of confirming it may lead to women to have less confidence in their ability to tackle quantitative tasks than equally competent men, decreased engagement in discussions about quantitative methods, and retracted efforts to continue movement in career pathways that requires use of their quantitative skills.
What is it? Implicit bias refers to thoughts and feelings about an individual (or groups of individuals) that we are not aware of. They are stronger predictors of behavior than consciously held beliefs and they are stronger predictors of behavior than demonstrated aptitude (Banaji & Greenwald, 2016).
How does it apply to female quantitative researchers? Implicit biases may lead both men and women to favor male applicants for jobs, even when females are equally qualified (Moss-Racusin, et al., 2012). Furthermore, implicit bias may also downgrade perceptions of female accomplishments, which could lead to lower likelihood of women winning competitive grants, being published, and being cited (Bornmann, Mutz, & Daniel, 2007; Knobloch-Westerwick, Glynn & Huge, 2013). Over time, lower rates of hiring and less professional recognition can result in lower wages for women and can also lead women to leave their profession midcareer.
Want to learn more?
Check out these citations to learn more about role incongruity:
Check out these citations to learn more about stereotype threat:
Steele, C. Whistling Vivaldi: How stereotypes affect us and what we can do (issues of our time). 2011. W. W. Norton & Company, Inc.
Davies, P.G., Spencer, S.J., Quinn, D.M., & Gerhardstein, R. (2002). Consuming Images: How Television Commercials that Elicit Stereotype Threat Can Restrain Women Academically and Professionally.
Check out these citations to learn more about implicit bias:
Knobloch-Westerwick, S., Glynn, C. J., & Huge, M. (2013). The Matilda effect in science communication: an experiment on gender bias in publication quality perceptions and collaboration interest. Science Communication, 35(5), 603-625.
Check out these citations and websites to learn more about implicit bias: