for publication

Men, Women, and Science 1

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Running head: MEN, WOMEN, AND SCIENCE

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Men, Women, and Science

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Why the Differences and What Should Be Done?

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Steve Stewart-Williams

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School of Psychology, University of Nottingham Malaysia Campus, Semenyih

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Lewis G. Halsey

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Department of Life Sciences, University of Roehampton, London

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The authors would like to thank Volker Behrends, Tom Reeve, and Martha

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Villegas-Montes for their perceptive comments on an early draft of this paper.

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Correspondence concerning this article should be addressed to Steve Stewart-

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Williams at the School of Psychology, University of Nottingham Malaysia Campus.

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Email: [email protected]

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Word count: 20,500


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Abstract

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It is a well-known and widely lamented fact that men outnumber women in a number

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of fields in STEM, including physics, mathematics, and computer science. The most

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common explanations for the gender gaps are discrimination and social norms, and

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the most common policy prescriptions are targeted at these ostensible causes.

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However, a great deal of evidence in the behavioral sciences suggests that

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discrimination and social norms are only part of the story. Other plausible contributors

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include relatively large mean sex differences in career and lifestyle preferences, and

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relatively small mean differences in cognitive aptitudes – some favoring males, others

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favoring females – which are associated with progressively larger differences the

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further above the mean one looks. A more complete picture of the causes of the

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unequal sex ratios in STEM may productively inform policy debates, and is likely to

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improve women’s situation across the STEM fields.

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Public Significance Statements A review of the literature suggests that gender gaps in STEM fields have many causes, including average sex differences in preferences and cognitive specialties. Anti-female discrimination, though it demonstrably exists, is probably not the primary cause of STEM gender gaps in modern Western nations. Policy prescriptions founded on the notion of pervasive anti-female bias are unlikely to have positive effects.

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KEYWORDS: Discrimination; Equality; Gender; Sex Differences; STEM.


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Men, Women, and Science

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Why the Differences and What Should Be Done?

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Never has the issue of gender disparities been as widely discussed, or as

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bitterly contested, as it has been in recent years. From the Oscars to the political

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podium, from TV shows to the workplace, disparities are identified and debate

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inevitably ensues. In the occupational realm, one of the primary focuses of this debate

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has been the differential representation of men and women in STEM (science,

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technology, engineering, and math).1 This was epitomized by the infamous “Google

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memo,” in which then-Google employee James Damore (2017) questioned the extent

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to which observed gender disparities in STEM are a product of discrimination. The

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memo, and Damore’s subsequent dismissal from Google, provoked a great deal of

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discussion and debate about the nature and nurture of human sex differences.

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Unfortunately, much of this debate was decidedly inaccurate in its presentation of the

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scientific research on the topic. This is the case even of our most prestigious scientific

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journals; Nature, for instance, ran an article claiming that sex differences in

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occupational outcomes were due entirely to discrimination (Chachra, 2017). No

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doubt, discrimination is part of the explanation. However, a great deal of research

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suggests that other factors contribute as well to the relative dearth of women in

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STEM, and that these factors quite possibly play a larger role than discrimination

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does. In this article, we survey the various lines of evidence bearing on this issue.

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Strictly speaking, the issue is not STEM per se, but rather STEM fields that have a strong spatial or mathematical component. According to Ceci, Ginther, Kahn, and Williams (2014), the STEM fields should be divided into GEEMP fields (geoscience, engineering, economics, mathematics/computer science, and the physical sciences) and LPS fields (life science, psychology, and social science). Men outnumber women in GEEMP fields, but women are at parity with men, or even sometimes outnumber them, in LPS fields.


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We divide the task into six parts. First, we look at research suggesting that

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men and women differ, on average, in their career and lifestyle preferences, and that

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these differences are due in part to biological causes. Second, we consider the

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possibility that men and women differ, on average, in certain cognitive aptitudes –

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that men, for instance, score somewhat higher on tests of spatial ability whereas

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women score somewhat higher on verbal tests. Third, we look at the controversial

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suggestion that men are more variable than women in cognitive ability, such that there

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are more men at the top of the ability distribution, and more men as well at the

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bottom. Fourth, we look at the issue of gender discrimination in STEM, and the extent

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to which this explains why more men than women end up in certain fields. Fifth, we

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look some of the major policy prescriptions aimed at shrinking the STEM gender gap

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and redressing the gender imbalances. And sixth, we consider whether the appropriate

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aim of such interventions is a 50:50 sex ratio in every STEM field or simply a level

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playing field. In other words, should the ultimate goal be equality of outcome or

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equality of opportunity?

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Sex Differences in Preferences and Priorities

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To begin with, we examine arguably the most important non-bias explanation

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for sex differences in STEM representation: the idea that men and women differ – on

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average – in certain career-relevant preferences. Specifically, we look at sex

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differences in occupational preferences and sex differences in life priorities. After

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that, we consider the extent to which these differences are products of nature or

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nurture, and we look at the evolutionary pressures that might have helped to shape

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them.


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Occupational Preferences

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A large literature in psychology shows that men and women differ, on

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average, in the kinds of occupations that interest them (Morris, 2016). One of the most

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important recent papers on this topic was a comprehensive meta-analysis by Su,

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Rounds, and Armstrong (2009). The paper focused on two main areas: preferences for

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general types of occupations (e.g., those focused on people vs. things) and preferences

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for specific careers (e.g., engineering vs. mathematics). In both cases, the authors

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found substantial sex differences that plausibly help to explain observed STEM

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gender gaps.

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Starting with general occupational types, by far the largest sex difference was

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that for interest in “people jobs” vs. “things jobs.” People jobs are jobs that center on

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interacting with other human beings; things jobs are jobs that center on working with

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objects, machines, or abstract rules. Members of both sexes can be found at every

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point on the people vs. things continuum; however, more men than women exhibit a

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preference for things jobs, whereas more women than men exhibit a preference for

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people jobs. Averaging across studies, Su et al. (2009) found an effect size of d = .93

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for the people vs. things sex difference. This is notably larger than most human sex

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differences (Lippa, 2009; Stewart-Williams & Thomas, 2013), and indeed than most

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effects in psychology (Eagly, 1995). To get an intuitive sense of the magnitude of the

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difference, if one were to pick pairs of people at random, one man and one woman,

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the man would be more things-oriented than the woman around 75% of the time.

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The people vs. things sex difference immediately suggests an explanation – or

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rather a partial explanation – for the fact that men outnumber women in fields such as

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physics, engineering, and mathematics, whereas women are at parity with or even

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outnumber men in fields such as psychology and the social sciences: The former


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fields are of interest to more men than women, and the latter more women than men,

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and people tend to gravitate to fields that are of greater interest to them. This is not

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merely a plausible-sounding speculation; Yang and Barth (2015) have shown that

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interest in things vs. people accounts for much (although not all) of the variance in

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people’s occupational outcomes.

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Research looking at preferences for specific occupations leads to a similar

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conclusion. As Su et al. (2009) report, males on average express considerably more

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interest than females in engineering (d = 1.11), and somewhat more interest in science

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and mathematics (d = .36 and .34, respectively). These differences are present by

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early adolescence and closely match the observed numbers of men and women

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working in the fields in question. Su et al. (2009) point out that, if we assume for the

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sake of argument that people working in a given field tend to come from the 25% of

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people most interested in that field, sex differences in occupational interests would

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account for the entirety of the engineering gender gap and much of the gap in science

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and mathematics. In short, sex differences in occupational preferences are far from

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trivial, and plausibly make a substantial contribution to observed occupational gender

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gaps.

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Life Priorities

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Gender gaps in STEM – and especially the higher echelons of STEM fields –

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may also be shaped in part by average sex differences in life priorities. As with

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occupational preferences, people vary a lot in their life priorities, and the full range of

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priorities can be found within each sex. Nonetheless, some priorities are more

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common among men than women, and others among women than men (Hakim, 2005;

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Schwartz & Rubel, 2005). One longitudinal study, for instance, found that, among

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adults identified as mathematically gifted in early adolescence, the average man


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reported placing more importance on career success and income, whereas the average

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woman reported placing more importance on work-life balance and making time for

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one’s family and friends (Benbow, Lubinski, Shea, & Eftekhari-Sanjani, 2000;

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Lubinski, Benbow, & Kell, 2014). These differences were particularly pronounced

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among people with children, largely because women’s priorities changed after they

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became mothers (Ferriman, Lubinski, & Benbow, 2009). Moreover, sex differences in

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self-reported priorities were evident in real-world behaviour. For example, as

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Lubinski et al. (2014) observed, over the course of the last fifteen years, the men in

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their sample had worked an average of 51 hours per week, whereas the women had

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worked an average of 40.

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Of course, sex differences in lifestyle preferences do not explain why the sex

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ratio is so much more male-biased in math-intensive STEM fields than in most others.

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Still, the differences do plausibly help to explain the fact that, in STEM and

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elsewhere, men outnumber women among the minority in the higher echelons: Rising

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to the top is a priority for fewer women than men, and thus fewer women than men

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are willing to make the sacrifices required to achieve that goal. To be clear, some

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women are willing to make those sacrifices, and the majority of men are not.

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However, more men than women are willing, and this is plausibly part of the reason

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that the sex ratio at the top is so often male-biased. Note that, according to one large

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US study (N ≈ 4,000), the sex difference in career-mindedness is not a result of

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women thinking that career advancement is impossible for them. The average woman

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views career advancement as just as achievable as the average man, but as less

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desirable (Gino, Wilmuth, & Brooks, 2015).


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The Nature and Nurture of Sex Differences in Occupational Preferences

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Sex differences in occupational preferences suggest one possible reason that

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more men than women go into math-intensive STEM fields. The reason, put simply,

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is that more men than women want to go into these fields. To the extent that this is so,

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it reduces the need to posit workplace discrimination as the root cause of gender

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disparities in STEM. Still, even if occupational preferences explained all of the STEM

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gender gap (which we are certainly not suggesting), this would not imply that

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discrimination played no role. After all, it could be argued that the preference

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differences themselves are products of discriminatory socialization practices and

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sexist cultural norms. No doubt, social factors help to determine which occupations

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people choose or end up in. However, several lines of evidence suggest that sex

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differences in this arena are shaped to an important degree by unlearned biological

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factors.

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First, sex differences in occupational preferences have remained remarkably

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stable throughout the half-century or so that psychologists have measured them, even

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in the face of significant shifts in women’s social roles and place in society (Su et al.,

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2009). In particular, the sex difference in interest in things vs. people seems

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stubbornly resistant to change. One analysis, for instance, found that whereas the

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number of women pursuing high-status professions increased a great deal since the

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1970s, the number pursuing things-related professions remained virtually static

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(Lippa, Preston, & Penner, 2014). Notably, this was the case despite the fact that,

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during that same period, a wide range of initiatives were established to try to entice

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women into those very professions.

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Second, the same sex differences in occupational preferences have been found in every society where psychologists have looked for them. In one large study (N ≈


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200,000), Lippa (2010) found the differences in 53 out of 53 nations: a level of cross-

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cultural unanimity almost unheard of within psychology. Importantly, the gender gap

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in occupational preferences was no larger in nations with higher levels of gender

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inequality, suggesting that gender inequality is not a major determinant of the gap.

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Meanwhile, other research suggests that the gender gap in STEM employment (as

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opposed to STEM-related job preferences) is actually smaller in more gender unequal

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nations, perhaps in part because economic hardship means that people in those nations

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have less scope to act on their personal preferences, and a greater need to place

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financial security above self-fulfillment in choosing a suitable career (Stoet & Geary,

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2018).

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Third, at least some of the relevant sex differences appear in nascent form

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early in the developmental process. The first glimmer of the people vs. things

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difference, for instance, may be evident in the first few days of life. Connellan, Baron-

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Cohen, Wheelwright, Batki, and Ahluwalia (2000) famously presented 102 newborn

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babies with two objects, one after the other: a human face and a mechanical mobile.

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Many babies looked for equal amounts of time at both. However, among those who

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looked for longer at one than the other, more boys than girls looked for longer at the

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mobile (43% vs. 17%), whereas more girls than boys looked for longer at the face

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(36% vs. 25%; although see Spelke, 2005). A similar pattern has been observed in a

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number of nonhuman primates: Newborn female macaques are more attentive to faces

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than are newborn males (Simpson et al., 2016), and female macaques and vervet

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monkeys spend more time playing with toy dolls provided by experimenters than do

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males of their respective species (Alexander & Hines, 2002; Hassett, Siebert, &

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Wallen, 2008).


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Finally, occupational preferences have been convincingly linked to prenatal

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hormonal exposure (Levy & Kimura, 2009). Girls with congenital adrenal

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hyperplasia (or CAH), which involves exposure to high levels of testosterone in the

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womb, tend to have more male-typical career preferences than other girls (Beltz,

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Swanson, & Berenbaum, 2011; Berenbaum & Beltz, 2011). Similarly, in non-clinical

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populations, the average prenatal testosterone levels of workers in a field are

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negatively correlated with the proportion of women working in that field (Manning,

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Reimers, Baron-Cohen, Wheelwright, & Fink, 2010). The correlations between

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prenatal hormones and occupational outcomes tend to be relatively modest; in the

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Manning et al. study, for instance, they range from around .1 to .6. This is presumably

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partly a product of measurement error and partly because prenatal hormones are only

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one influence among many. Nonetheless, in light of the hormonal data, and the other

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data surveyed in this section, it seems reasonable to conclude that sex differences in

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occupational preferences are not purely a product of culture or socialization. Biology

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plays a role as well.

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Evolutionary Rationale

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Although we have good reason to think that there is an innate contribution to

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men and women’s occupational and lifestyle preferences, we have much less idea why

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this might be the case. The most plausible evolutionary explanation is for sex

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differences in lifestyle preference. In most parental species, females invest more than

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males into offspring (Janicke, Häderer, Lajeunesse, & Anthes, 2016; Trivers, 1972).

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Among mammals, for instance, females gestate and nurse the young, and females

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usually provide the bulk of the direct parental care. In our species, sex differences in

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parental investment are comparatively modest: Both sexes tend to invest substantially

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in their young, rather than only the females. But there is still a difference: Men in all


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known cultures invest less into offspring than women, and this has probably been the

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case for most of our evolutionary history (Stewart-Williams & Thomas, 2013).

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Importantly, the minimum biological investment is also notably smaller for men than

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for women. Women’s minimum is a nine-month pregnancy and – until recently –

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several years of breastfeeding. Men’s minimum is the time and effort required to

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impregnate the woman.

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As a result of these sex differences in parental investment, ancestral men could

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potentially produce many more offspring than ancestral women, simply by mating

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with multiple partners (Clutton-Brock & Vincent, 1991). Consequently, human males

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evolved to be more interested than females in mating with multiple partners (Schmitt,

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2005; Schmitt & Project, 2003), less choosy about their low-commitment sexual

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partners (Buss & Schmitt, 1993; Kenrick, Groth, Trost, & Sadalla, 1993), and – of

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particular relevance to the present topic – more inclined to compete and take risks to

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obtain the status and resources that helped to make them attractive to women (Byrnes,

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Miller, & Schafer, 1999; Daly & Wilson, 2001; Deaner, 2013; M. Wilson & Daly,

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1985). In light of this theoretical framework – which is well-supported by research in

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other species (Andersson, 1994; Janicke et al., 2016) – it is little surprise that more

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men than women prioritize the pursuit of status over family, whereas more women

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than men prioritize family and work-life balance.

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Evolutionary explanations for sex differences in occupational preferences are

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somewhat more of a stretch. For most of human evolution, there were no scientists, no

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technologists, no engineers or mathematicians. As such, any innate contribution to sex

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differences in interest in these vocations must be a byproduct of traits selected for

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other reasons. One possibility is that the differences trace back to the sexual division

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of labor among our hunter-gatherer forebears, and specifically the fact that women


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specialized in caring for the young whereas men specialized in hunting and perhaps

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waging war with other groups (a division of labor found also among our close

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relatives, the chimpanzees; Muller, Wrangham, & Pilbeam, 2017). To fit them to

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these roles, women may have evolved a stronger attentiveness to the needs of the

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young, and by extension, to people in general, whereas men may have evolved a

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stronger interest in the tools used for hunting and warfare (Geary, 2010; Hrdy, 2009).

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Sex differences in interest in people-focused vs. things-focused jobs may just be an

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adaptively neutral side effect of these ancient, more primal differences. It is worth

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emphasizing that, although this hypothesis seems reasonable enough, it has yet to be

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rigorously tested. Regardless of the explanation, however, the evidence for an innate

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contribution to the relevant sex differences is strong.

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Sex Differences in Aptitudes Sex differences in occupational preferences are not the only reason we might

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expect uneven sex ratios in certain STEM fields, even if discrimination were entirely

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removed from the picture. A second, more controversial suggestion is that STEM

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gender gaps are due in part to average sex differences in a number of STEM-relevant

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cognitive capacities, which result in somewhat more men than women having a

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suitable profile of aptitudes for working in some STEM fields. This statement could

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easily be misconstrued, so it is important to be absolutely clear what is meant.

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First, the claim is not that men perform better than women in every cognitive

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domain. On the contrary, men perform better in some domains whereas women

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perform better in others. The best-known examples are that men score higher than

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women on most tests of spatial ability, whereas women score higher than men on

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most tests of language ability, including verbal comprehension, reading, and writing


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(Guiso, Monte, Sapienza, & Zingales, 2008; Halpern, 2012; Reynolds, Scheiber,

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Hajovsky, Schwartz, & Kaufman, 2015).

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Second, even in areas where men do perform better, the claim is not that all

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men – or even most – perform better than all or most women. As with occupational

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preferences, members of both sexes vary enormously in every cognitive aptitude, and

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the distribution for men overlaps almost entirely with that for women (Hyde, 2005).

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However, for some aptitudes, the distribution for one sex is shifted somewhat to the

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right of that for the other, such that the average score for the former is somewhat

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higher than that for the latter. In saying this, it is worth stressing that the average score

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does not describe all members of the group, or even the typical member, but merely

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represents the central tendency within a broad array of scores. Most people fall above

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or below the average.

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Third, the claim is not that these cognitive sex differences are especially large.

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On the contrary, at the center of the distribution, they tend to be rather small (Hyde,

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2005; Hyde, Lindberg, Linn, Ellis, & Williams, 2008; Stewart-Williams & Thomas,

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2013). The only reason they matter at all is that even small differences at the mean are

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associated with progressively larger differences the further from the mean one looks

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(see Figure 1). For jobs requiring normal-range abilities – including many STEM jobs

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– cognitive sex differences are likely to make little difference: The pool of potential

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female candidates is similar in size to the pool of males. However, for jobs requiring

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exceptional abilities, the sex ratio of potential candidates may be skewed in favor of

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one sex or the other – even when the relevant sex differences in the general population

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are small or even negligible (Baye & Monseur, 2016; Halpern, 2012; Halpern et al.,

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2007; Pinker, 2002).

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--------------------Insert Figure 1 about here--------------------


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Figure 1. For two groups, A and B, where the average score on a normally distributed

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variable is higher for the former than the latter, the ratio of A-to-B gets progressively

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larger the higher above the average one looks. For both groups, fewer and fewer

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people occupy each segment above the mean, with the percentage decline getting

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larger with each further step. For instance, the number of people falling between 1 and

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2 standard deviations above the mean is 40% of that falling between the mean and 1

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standard deviation, whereas the number of people falling between 2 and 3 standard


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deviations above the mean is only 15% of that falling between 1 and 2 standard

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deviations. This is the case for both groups. However, because the lower-scoring

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group (B) starts this accelerating decline before the higher-scoring group (A), the

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percentage decline for the lower-scoring group is always larger than that for the

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higher-scoring one. The net result is that the ratio of A-to-B gets progressively larger

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for scores above the mean. Meanwhile, the ratio of B-to-A gets progressively larger

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for scores below the mean. The sex difference depicted in Figure 1 is large – a full

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standard deviation between the average for each sex – and thus the skew quickly

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becomes large as well. However, even small differences at the mean may be

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associated with high levels of skew at sufficiently extreme levels of any given trait.

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Fourth, the claim is not that women lack the cognitive talents to make it in

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STEM. Most people lack the cognitive talents, and of those who do possess them,

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some are men and some are women. The claim is simply that, because of small

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average differences in a small subset of abilities, somewhat more men than women are

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suited to work in some areas of STEM – and for the same reason, somewhat more

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women than men are suited to work in others.

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The Relevant Differences

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With these important qualifications in mind, let us now consider some of the

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sex differences in cognitive aptitudes that may help to explain the uneven sex ratios

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found in some STEM areas. The first concerns spatial abilities. As mentioned, the

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average score on most spatial tests is somewhat higher for men than for women. This

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is a well-established finding (Voyer, Voyer, & Bryden, 1995), especially with respect

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to mental rotation (Maeda & Yoon, 2013), and it seems reasonable to think it might be

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part of the reason that somewhat more men than women gravitate to fields that require


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above-average spatial abilities – fields such as physics and engineering. Consistent

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with this suggestion, more than fifty years of research in educational psychology

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indicates that spatial ability is indeed an important predictor of STEM success (Wai,

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Lubinski, & Benbow, 2009).

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A second, less widely appreciated cognitive sex difference is that, on average,

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males as a group score higher than females as a group on tests of mechanical

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reasoning – that is, tests of the ability to solve problems involving mechanical

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principles and physical laws (Flores-Mendoza et al., 2013; Hedges & Nowell, 1995;

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Lemos, Abad, Almeida, & Colom, 2013). Unlike most cognitive sex differences, this

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one appears to be rather large, even at the mean, and is thus larger still at the right-

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hand tail of the distribution (Hedges & Friedman, 1993). As with the spatial sex

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difference, the difference in mechanical reasoning has clear implications for the

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gender composition of fields such engineering and physics.

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A third difference relates to mathematical ability; in this case, however, the

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findings are much less straightforward. Sex differences in average mathematical

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ability are generally small and highly variable: In some nations, boys do better on

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tests of mathematical aptitude; in others, girls do (Baye & Monseur, 2016; Else-Quest,

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Hyde, & Linn, 2010; Guiso, Monte, Sapienza, & Zingales, 2008; Hyde, Lindberg,

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Linn, Ellis, & Williams, 2008; Stoet, Bailey, Moore, & Geary, 2016). At the same

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time, though, there appear to be a number of math-related sex differences which may

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be relevant to STEM outcomes. First, whereas females tend to do better in tests of

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mathematical computation (Hyde, Fennema, & Lamon, 1990), males tend to do better

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– at least from adolescence – in tests of mathematical reasoning (that is, tests of the

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ability to solve problems using mathematical concepts; Benbow, 1988; Halpern, 2012;

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Hyde et al., 1990). This is a potentially important finding because, as Hyde (1990)


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notes, mathematical reasoning “is critical for success in many mathematics-related

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fields, such as engineering and physics” (p. 151). Second, despite small and cross-

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culturally variable differences in average scores, males consistently outnumber

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females at the highest levels of mathematical performance (Baye & Monseur, 2016;

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Makel, Wai, Peairs, & Putallaz, 2016; Reilly, Neumann, & Andrews, 2015; Wai,

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Cacchio, Putallaz, & Makel, 2010). The upshot is that somewhat more men than

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women are likely to have the mathematical aptitude to work in math-intensive STEM

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fields – even though, once again, most men do not.

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The gender gap in math-intensive fields may be amplified by the fact that,

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among the minority of people who possess exceptional mathematical abilities, the

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women are more likely than the men to possess exceptional language abilities as well

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(Wang, Eccles, & Kenny, 2013). This means that mathematically gifted women have

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more vocational options than their male counterparts, and consequently that fewer

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mathematically gifted women end up pursuing a STEM career. To the extent that this

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explains the gender gap in math-intensive fields, the gap results not from gifted

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women having fewer options, but rather from them having more.

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A final cognitive difference bearing on the question of STEM sex ratios relates

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to Baron-Cohen’s (2003) distinction between systemizing and empathizing.

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Systemizing refers to the desire and ability to understand or build “systems,”

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including mechanical systems like cars, physical systems like galaxies, and abstract

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systems like logic and mathematics. Empathizing, in contrast, refers to the desire and

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ability to understand people: their thoughts, their desires, their feelings. Virtually

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every human being possesses both abilities to a greater or lesser extent. On average,

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though, men score somewhat higher than women on tests of systemizing, whereas

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women score somewhat higher than men on tests of empathizing (Baron-Cohen,


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Wheelwright, Hill, Raste, & Plumb, 2001). As usual, these differences are not large

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among the majority in the normal range. However, among the minority of exceptional

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empathizers, women outnumber men, and among the minority of exceptional

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systemizers, men outnumber women (Baron-Cohen et al., 2014). It seems reasonable

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to suppose that sex differences in systemizing and empathizing are part of the reason

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that more men than women go into fields such as physics and engineering, whereas

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more women than men go into fields such as psychology and education. Consistent

410

with this suggestion, Ruzich et al. (2015) observe that STEM workers score

411

consistently above workers in non-STEM fields on measures of systemizing ability.

412

Most commentators agree that sex differences in cognitive aptitudes are too

413

small to explain STEM gender gaps in their entirety, and that sex differences in

414

occupational preferences are a much more important contributor (Ceci, Williams, &

415

Barnett, 2009; Johnson, Carothers, & Deary, 2008). Still, the evidence for the

416

cognitive differences is robust, and it is perfectly plausible that they help to shape men

417

and women’s career choices and trajectories. Indeed, given the clear relevance of the

418

aptitudes in question to STEM, it would be surprising if this were not the case.

419

The Nature and Nurture of Sex Differences in Aptitudes

420

Where do these cognitive sex differences come from? As with sex differences

421

in occupational preferences, scientists do not yet have a complete or definitive

422

answer. Nonetheless, a range of evidence suggests that the differences are not due

423

solely to socialization, but rather have a substantial innate component. First, many of

424

the differences appear early in the developmental process. The sex difference in

425

mental rotation, for instance, can be detected by five months of age (Moore &

426

Johnson, 2008), and the sex difference in language ability can be detected by seven

427

months (Bando, López Bóo, & Li, 2016). These findings do not rule out purely


428

environmental explanations, but they do render them less plausible. At the very least,

429

they eliminate the possibility that social influences appearing after the age of one year

430

could provide a complete explanation for the differences. Certainly, sex differences in

431

cognition may sometimes get larger with time, which one might argue shows that the

432

differences are environmentally determined (see, e.g., Kersey, Braham, Csumitta,

433

Libertus, & Cantlon, 2018; Spelke, 2005). However, there are several responses to

434

this. One is that changes in the magnitude of sex differences are not necessarily a

435

product of social causes alone; to some extent, they may just reflect the natural

436

maturational process – especially when the changes coincide with the onset of

437

puberty. Another response is that, even if the changes were entirely a product of social

438

causes, this would not imply that the initial sex differences were a product of social

439

causes too. The initial differences could well still be innate.

440

Second, many of the traits under discussion have been linked to sex hormones.

441

Spatial ability, for instance, has been linked to both prenatal and pubertal testosterone:

442

Individuals exposed to higher levels of testosterone in the womb, and higher levels at

443

puberty, typically exhibit stronger spatial skills (Berenbaum & Beltz, 2011).

444

Similarly, testosterone supplements appear to boost spatial ability in female-to-male

445

transsexuals (although note that the studies demonstrating this effect tend to have

446

small samples; Slabbekoorn, van Goozen, Megens, Gooren, & Cohen-Kettenis, 1999).

447

In addition to spatial abilities, prenatal testosterone has been linked to higher

448

systemizing, and to lower social skills, empathizing, and verbal ability (Auyeung et

449

al., 2006; Chapman et al., 2006; Lutchmaya, Baron-Cohen, & Raggatt, 2001, 2002;

450

summarized in Baron-Cohen, 2003).

451 452

Third, sex differences in cognitive abilities appear to transcend cultural boundaries. Across cultures, girls typically outperform boys on tests of linguistic


453

ability (Stoet & Geary, 2013), whereas boys typically outperform girls on most spatial

454

tasks (Cashdan, Marlowe, Crittenden, Porter, & Wood, 2012; Lippa, Collaer, &

455

Peters, 2010; although see Hoffman, Gneezy, & List, 2011). Certainly, there is

456

variation in the magnitude of these differences from nation to nation and from age to

457

age, suggesting a sizeable role for malleable social factors. Still, the direction of the

458

spatial/linguistic differences is essentially invariant. This is not what one would

459

expect based on the hypothesis of a purely environmental origin of these differences.

460

Fourth, efforts to eliminate the gaps seem to quickly reach the point of

461

diminishing returns. Wai et al. (2010) looked at the ratio of boys-to-girls in the top

462

.01% of US seventh graders taking a test of mathematical reasoning between 1981

463

and 2010. In the early 1980s, the ratio was 13 boys for every girl. By the early 1990s,

464

this had dropped to just four boys for every girl, perhaps as a result of increasing

465

access to a good math education for girls (see also Hyde et al., 1990). Since then,

466

however, the ratio of boys-to-girls among the top math performers has remained

467

largely the same, despite intensified efforts to eliminate the remaining gap.2

468

Meanwhile, the ratio of girls-to-boys among the top performers on tests of verbal and

469

reading ability has consistently favored girls (Makel et al., 2016; Wai et al., 2010).

470

Again, these findings are not what one would expect based on the assumption that the

471

gaps have a purely environmental origin.

472

Fifth, gay men tend to have spatial and linguistic abilities comparable to those

473

of straight women, whereas lesbians tend to have spatial abilities comparable to those

474

of straight men (but female-typical linguistic abilities; Xu, Norton, & Rahman, 2017).

475

This finding is difficult to explain on the assumption that social forces alone create the

2

Indeed, some research suggests that the ratio of males to females at the top levels of mathematical reasoning has increased in recent years (Lakin, 2013).


476

usual pattern of sex differences. Gay men were presumably subject to essentially the

477

same gender-specific social forces as straight men, and lesbians the same gender-

478

specific social forces as straight women. As such, the near-reversal of the usual spatial

479

vs. language pattern is hard to reconcile with the claim that this pattern is due

480

primarily to social forces. Other variables, such as prenatal hormones, plausibly play a

481

larger role.

482

Finally, some of the most popular sociocultural explanations for STEM gender

483

gaps are flawed in a number of ways. This includes the claim that the gaps are a

484

product of a widespread stereotype that “girls can’t do math,” which persuades girls

485

and women that math is not for them, or which causes them to underperform in math

486

as a result of their anxiety about confirming the denigrating stereotype (a putative

487

example of a phenomenon known as stereotype threat; Spencer, Steele, & Quinn,

488

1999). Although such theories are very popular, they face a number of critical

489

challenges. To begin with, it is unclear whether the theories can account for the data,

490

even in principle. How, for example, would a blanket stereotype about female

491

mathematical inferiority explain the fact that girls get better grades than boys in math

492

class, or that they do better at mathematical computation but worse at mathematical

493

reasoning? It is also debatable to what extent the social influences point in the

494

direction the theory presupposes. According to one study, by four years of age, girls

495

tend to assume that boys are academically inferior, and by seven, boys assume the

496

same thing (Hartley & Sutton, 2013). In a similar vein, teachers tend to view their

497

female students as superior at math (and reading), even when aptitude tests indicate

498

that the boys are doing better (Robinson & Lubienski, 2011). Popular culture often

499

mirrors these trends, with girls depicted as academically superior to boys (consider,


500

for instance, Bart and Lisa Simpson). In short, there is no simple story about the social

501

influences to which boys and girls are exposed.

502

Alongside these theoretical concerns, recent research directly challenges the

503

notion that stereotype threat is a major contributor to STEM gender gaps (Stoet &

504

Geary, 2012). A meta-analysis of studies looking at the impact of stereotype threat on

505

girls’ performance in math, science, and spatial tests found a near zero effect, and

506

evidence of substantial publication bias (Flore & Wicherts, 2015). Soon afterwards, a

507

large, pre-registered study found no effect of stereotype threat on women’s math

508

performance (Finnigan & Corker, 2016). Finally, an analysis of thousands of real-life

509

chess games revealed that, in spite of stereotypes about male superiority at the

510

chessboard, women in fact played better when competing against men than when

511

competing against other women: the exact opposite of what stereotype threat would

512

predict (Stafford, 2017). At the very least, the effect of stereotype threat appears to be

513

more modest than originally assumed.

514


515

It seems reasonable to conclude that the cognitive sex differences considered

516

in this section are shaped to an important degree by unlearned biological factors. As

517

with sex differences in occupational preferences, however, it is not at all obvious why

518

this might be the case. Some evolutionary psychologists argue that sex differences in

519

spatial ability trace to the fact that Homo sapiens is an effectively polygynous species:

520

that is, a species in which males have somewhat greater reproductive variability than

521

females (Betzig, 2012; Labuda, Lefebvre, Nadeau, & Roy-Gagnon, 2010). In

522

polygynous species, the argument goes, males tend to have larger ranges than females,

523

and therefore tend to evolve stronger spatial and wayfaring skills (Gaulin, 1992). The

524

polygyny-related spatial sex difference may have been further amplified in our species


525

by the fact that human males are specialized for hunting and tracking animals, and

526

perhaps for engaging in coalitional warfare with neighboring groups (Silverman &

527

Phillips, 1998).

528

Many evolutionary psychologists are unpersuaded by these ideas, however. In

529

their view, cognitive sex differences were not specifically favored by natural

530

selection, but rather are byproducts of other differences that were. Clint, Sober,

531

Garland Jr, and Rhodes (2012) argue, for instance, that the male advantage in spatial

532

skills is simply an adaptively neutral side effect of hormonal sex differences that were

533

selected for other reasons. If this is right, then the spatial sex difference may have an

534

innate basis but not be a direct product of natural selection. At this stage, the ultimate

535

origins of human cognitive sex differences are uncertain. What does seem certain,

536

however, is that the differences are not solely a product of social forces. To some

537

extent – perhaps to an important extent – they are a part of human nature.

538 539 540

Sex Differences in Variability We see, then, that small mean differences in certain STEM-relevant aptitudes

541

result in somewhat more males than females occupying the right-hand tail of the

542

distribution (Reilly et al., 2015). However, even if there were no differences at the

543

mean, males could still outnumber females among the minority at the right-hand tail.

544

This is because males and females differ in another way as well. In a wide variety of

545

traits, males as a group are more variable than females: The male distribution is

546

slightly flatter, and stretches out somewhat further on both sides of the mean (see

547

Figure 2). This is the case for a range of physical traits, including birth weight, adult

548

weight, adult height, and running speed (Lehre, Lehre, Laake, & Danbolt, 2009),

549

average heart rate during exercise (Hossack & Bruce, 1982), daily energy expenditure


550

(Westerterp & Speakman, 2008; Yamauchi et al., 2007), basal metabolic rate

551

(Westerterp & Goran, 1997; Yamauchi et al., 2007), and various aspects of brain

552

structure (Ritchie et al., 2018). It also appears to be the case for a range of

553

psychological traits, including creativity (Karwowski et al., 2016), general knowledge

554

(Feingold, 1992), physical aggression (Archer & Mehdikhani, 2003), and at least four

555

of the Big 5 personality traits: extraversion, openness, agreeableness, and

556

conscientiousness (Borkenau, McCrae, & Terracciano, 2013). Of particular relevance

557

to the present topic, males appear to be more variable than females in a number of

558

cognitive traits relevant to STEM (Baye & Monseur, 2016; Feingold, 1992). In this

559

section, we discuss these differences and where they might have come from.

560


561 562

Figure 2. A fundamental sex difference in humans and many other animals: For a

563

wide range of traits, males are more variable than females. As such, although only a

564

small percentage of people occupy either extreme of the distribution, more males do

565

than females, even when the mean scores for both sexes are identical.

566 567

Variability in STEM-Relevant Cognitive Traits

568

To begin with, many studies have found greater variance among males in

569

specific cognitive capacities, including mathematical aptitude, spatial ability, and


570

science knowledge. In one classic paper, Hedges and Novell (1995) analyzed the

571

cognitive test scores of six large, nationally representative US samples, together

572

covering a 32-year period. They found that, for 35 out of the 37 tests examined in the

573

study, male variability was greater than female. Importantly, this included all the tests

574

of mathematics, spatial ability, mechanical reasoning, and science knowledge. In most

575

cases, sex differences in average scores were small. Nevertheless, because males were

576

more variable, they tended to outnumber females among the minority with especially

577

high scores. (An exception was reading comprehension, for which males outnumbered

578

females at the bottom, but females outnumbered males at the top.)

579

Similar results have been found in other nations and using other tests. For

580

example, in a large sample of UK students (N ≈ 320,000), Strand, Deary, and Smith

581

(2006) found that, although sex differences were small at the mean, males

582

outnumbered females at the top and the bottom of the distribution for both

583

quantitative and nonverbal reasoning; for verbal reasoning, in contrast, males

584

outnumbered females only at the bottom (see Lohman & Lakin, 2009, for a US

585

replication of this pattern). Likewise, analyses of OECD and IEA data indicate that, in

586

most countries for which data are available, males are more variable than females in

587

math, reading, and science (Baye & Monseur, 2016; Machin & Pekkarinen, 2008).

588

As well as greater male variability in specific cognitive aptitudes, males may

589

be more variable in general cognitive ability or IQ (Deary, Irwing, Der, & Bates,

590

2007; Feingold, 1992; Strand et al., 2006). The gold-standard study on this topic is

591

Johnson et al. (2008). Unlike earlier studies, which used potentially unrepresentative

592

samples, Johnson et al. utilized IQ data from two population-wide surveys of 11-year-

593

old school children in Scotland. As expected, IQ variability was greater among boys

594

than girls, such that there were more boys than girls at both extremes of the IQ


595

distribution: more at the top, but also more at the bottom (although see Iliescu, Ilie,

596

Ispas, Dobrean, and Clinciu, 2016, for a recent failure to replicate this pattern in a

597

large, nationally representative Romanian sample).

598

To the extent that greater male variability results in more males than females

599

occupying the upper echelons of ability, whether for specific aptitudes or general

600

cognitive ability, this may help to explain the preponderance of males in certain

601

STEM fields (Levy & Kimura, 2009; Steven Pinker, 2002). As Johnson et al. (2008)

602

observe, sex differences in cognitive variability are not large enough to explain the

603

entirety of the STEM gender gap. They may, however, be one more piece of the

604

puzzle. See Box 1 for further discussion.

605

--------------------Insert Box 1 about here--------------------

606 Box 1: Exploring the Implications of Greater Male Variability The claim that men are more variable than women in cognitive ability is controversial. Here are some questions to ask about this claim: 1. Is it sexist? Is it sexist even if it turns out to be true? 2. If it is sexist against women to say that there are somewhat more men than women at the highest levels of ability, is it sexist against men to say that there are also more men than women at the lowest levels? If not, how might we explain this asymmetry? 3. Assume for a moment that males really are more variable in cognitive ability. Should we suppress this information? Could we suppress it, even if we wanted to? 4. Might it be possible instead to emphasize the importance of avoiding exaggerating small average differences, of keeping sight of the variation among individuals within each sex, and of treating individuals as individuals, rather than as instantiations of the statistical properties of the groups to which they belong?

607


608

The Nature and Nurture of Sex Differences in Variability

609

What might explain sex differences in within-sex variability? Given that the

610

magnitude of the differences fluctuates somewhat across cultures and times, it is

611

unlikely that they are a product solely of biological factors (Feingold, 1992; Hyde,

612

Mertz, & Schekman, 2009). However, as with average differences in preferences and

613

aptitudes, several lines of evidence suggest that biological factors play a crucial role.

614

First, greater male variability is found not only in psychological traits, but in

615

traits that are largely impervious to socialization and cultural norms, such as height,

616

birth weight, and BMI (Lehre et al., 2009). The sex differences in psychological

617

variability thus appear to be part of a broader pattern. Considerations of both

618

parsimony and plausibility suggest that this pattern probably has a single, common

619

cause, rather than distinct causes for its physical and psychological components.

620

Second, sex differences in psychological variability emerge in early childhood.

621

The sex difference in IQ variability, for instance, appears before children begin school

622

(Arden & Plomin, 2006). This does not definitively rule out a Nurture Only

623

explanation for the difference. However, it does add some weight to the scales on the

624

biological side of the argument, and it reduces the range of non-biological factors that

625

any Nurture Only explanation can invoke. Whatever the ultimate causes of greater

626

male variability, those causes are in place by the age of three.

627

Third, sex differences in variability are not unique to humans, but are found in

628

many other species. Among red deer, for instance, males are not only larger than

629

females but are also more variable in size (Clutton-Brock, Guinness, & Albon, 1982);

630

among primates, males are more variable in lifespan (Colchero et al., 2016); among

631

guenons, males are more variable in skull size (Cardini & Elton, 2017); and among

632

barn swallows, males are more variable in tail-length (Møller, 1991; Safran &


633

McGraw, 2004). Examples of greater variability in females are notably less common.

634

When we find this pattern in other species, the only realistic explanation is a

635

biological one. When we then find the same pattern in our own species, considerations

636

of parsimony and plausibility again suggest that a biological explanation is

637

appropriate for us too. Indeed, without a strong reason to think otherwise, the default

638

assumption should be that humans fit within the same explanatory framework that

639

applies to the rest of the animal kingdom, and thus that greater male variability in our

640

species has the same root cause as that in our nonhuman kin.

641


642

If sex differences in variability are due in large part to innate factors, how

643

might those factors have evolved? Once again, the answer is not yet certain, but

644

biologists have put forward a number of plausible suggestions. One popular

645

suggestion traces greater male variability in general to another, more fundamental sex

646

difference: greater male variability in reproductive success. As a result of sex

647

differences in parental investment, males in many species have greater variability than

648

females in the number of offspring they produce (Clutton-Brock & Vincent, 1991;

649

Trivers, 1972). At one extreme, some males have a very high number of offspring:

650

more than any female. At the other, because mating opportunities are finite, many

651

males have no offspring or very few. Most females, in contrast, fall somewhere in

652

between these extremes. In species where male reproductive variability is high,

653

selection favors any trait that increases a male’s chances of being among the few that

654

have many offspring, rather than the many that have few or none. One such trait

655

appears to be risk-proneness. In many species, selection has favored a greater

656

willingness among males to risk life and limb in the pursuit of status, resources, and

657

mating opportunities. Male risk-taking sometimes paid off and sometimes did not.


658

When it did pay off, however, it paid off so handsomely that, on average, risk-taking

659

males had more offspring than males who were more risk-averse. For females, in

660

contrast, risk-taking offered fewer reproductive advantages, because the ceiling

661

number of offspring for females is so much lower. Thus, males in many species

662

evolved a greater propensity to take risks than did females (Daly & Wilson, 2001).

663

According to one explanation of greater male variability, this calculus applies

664

not only to behaviour but to development: Male development is somewhat more “risk-

665

prone” than female development, such that males have a greater chance of developing

666

especially impressive traits but also a greater chance of developing less impressive

667

ones. The former males have a sufficiently high number of offspring that, on average,

668

males with the risky developmental program have more offspring than those with a

669

more conservative or risk-averse one. As such, the risky male developmental program

670

is selected – and with it, greater male variability (see, e.g., Pomiankowski & Møller,

671

1995; Rowe & Houle, 1996).

672

Might this apply to humans? Compared to most mammals, the human sex

673

difference in reproductive variability is rather modest (Stewart-Williams & Thomas,

674

2013). Nonetheless, genetic and anthropological data strongly suggest that there is

675

still a difference (Betzig, 2012; Labuda et al., 2010; summarized in M. L. Wilson,

676

Miller, & Crouse, 2017, Table 1). This plausibly resulted in the evolution of males

677

who were somewhat more risk-prone than their female counterparts, not just

678

behaviorally but developmentally. In other words, just as men evolved a riskier,

679

boom-or-bust behavioral strategy, so too they evolved a riskier, boom-or-bust

680

developmental strategy – one which sometimes pays off but sometimes backfires.

681

(For other evolutionary explanations of greater male variability, see, e.g., Archer &

682

Mehdikhani, 2003; Hill, 2018; Reinhold & Engqvist, 2013.)


683

Certainly, environmental forces may exert some influence on the size of the

684

variability gap (Hyde et al., 2009), perhaps enlarging it and perhaps sometimes

685

making it smaller. The basic pattern itself, however, is plausibly a part of our

686

evolutionary heritage – one that helps to shape the modern occupational landscape.

687 688 689

Discrimination We have now discussed three reasons why men and women might not be

690

equally represented in STEM, even if there were no discrimination. This does not

691

imply, of course, that there is no discrimination, or that discrimination is not one of

692

the factors shaping STEM gender gaps. Even if fewer women than men are interested

693

in working in math-intensive STEM fields, it could still be the case that those who are

694

interested face a hostile environment in the classroom and the workplace, are subject

695

to disparaging stereotypes and low expectations, and are less likely to be hired,

696

published, cited, or awarded grants than their male colleagues.

697

This is certainly a plausible suggestion; after all, no one denies that there was

698

considerable sexism in science prior to the second wave of the feminist movement,

699

and it might be unduly optimistic to think that this would evaporate entirely in little

700

more than half-a-century. At the same time, though, the discrimination hypothesis

701

represents a rather serious accusation against people working in STEM, and thus it is

702

only fair to look carefully at the evidence for and against the hypothesis. For the

703

reasons given already, gender disparities are not in themselves direct evidence of

704

discrimination; unless the sexes were psychologically identical, equality of

705

opportunity would almost certainly not translate into equality of outcomes (Steven


706

Pinker, 2002; Radcliffe Richards, 1980). Nonetheless, various lines of evidence do

707

bear on the question of how much discrimination remains in the world of STEM.3

708

Discrimination against Women in STEM

709

The most abundant source of objective evidence for sexism against women in

710

STEM comes from experimental studies looking at people’s reactions to hypothetical

711

applicants for STEM jobs. Otherwise identical job applications are given either a

712

female or a male name, and then evaluated by participants naïve to the purpose of the

713

study. In one widely cited paper in this genre, Moss-Racusin, Dovidio, Brescoll,

714

Graham, and Handelsman (2012) had science faculty from six major universities rate

715

applications for a laboratory manager position. They found that the raters – female

716

and male alike – gave higher ratings to supposedly male applicants than they did to

717

supposedly female ones. Specifically, participants rated the males as more competent

718

and hirable, and as deserving a higher salary. In another, earlier study, Steinpreis,

719

Anders, and Ritzke (1999) found that, for middling job applications, academic

720

psychologists – again, female and male alike – expressed greater willingness to hire a

721

male job candidate than an identical female one. For outstanding applications, in

722

contrast, there was no effect of gender.

723

Admittedly, both of these studies had a number of weaknesses, including the

724

fact that the samples included only 127 and 238 participants, respectively. However,

725

the findings are broadly consistent with a large body of research in this area. A meta-

726

analysis of studies looking at simulated employment decisions (N = 22,348) found a

727

weak pro-male bias in male-dominated fields (d = .13), although no gender bias in

3

Note that our discussion applies largely to the Western world, and especially the Anglophone world, as this is where most of the relevant research and commentary has focused. Note also that we limit the discussion to measures of bias other than self-report surveys (e.g., Funk & Parker, 2018), as the latter are vulnerable to complaints of subjectivity and expectation effects.


728

balanced or female-dominated fields (Koch, D'Mello, & Sackett, 2015). The meta-

729

analysis did not focus specifically on STEM-related decisions; however, given that

730

many STEM fields are male-dominated, the results nonetheless increase the

731

plausibility of the STEM-specific findings. Of course, even if those findings are valid,

732

it is not clear whether they generalize to real-world hiring decisions, where decision

733

makers have more experience and are more motivated to make the best choice.

734

Indeed, the Koch et al. meta-analysis showed that pro-male biases were attenuated in

735

just those circumstances, and fake-résumé experiments (in which fictitious job

736

applications are sent out in response to genuine job advertisements, and subsequent

737

call-backs counted) have yielded mixed results with respect to gender bias (Baert,

738

2018). Still, it is possible that bias plays a role in STEM hiring decisions, at least

739

sometimes, and this is something that every academic and STEM worker who cares

740

about gender equality and fairness should take extremely seriously.

741

Moreover, discrimination in hiring is not the only form of discrimination that

742

ought to concern us. Other studies have uncovered other possible examples of

743

discrimination, in STEM and in academia more generally. For example, professors are

744

less likely to respond to informal inquiries about a PhD program when the inquirer is

745

a woman or non-white (Milkman, Akinola, & Chugh, 2015); papers authored by

746

female economists need to be better written to be accepted into top-tier journals

747

(Hengel, 2017); male researchers are somewhat more likely to share their data and

748

published research with other males than with females (Massen, Bauer, Spurny,

749

Bugnyar, & Kret, 2017); and female academics less often give talks at prestigious US

750

universities, even controlling for the rank of the available speakers and even though

751

that women are no more likely to turn down an invitation (Nittrouer et al., 2017).

752

More egregious still, a Japanese medical school was recently accused of lowering


753

women’s scores on an entrance exam in order to reduce the number of women on their

754

program (Cyranoski, 2018). One might point to weaknesses in any particular study.

755

Nonetheless, the overall pattern of findings is disquieting, to say the least.

756

Challenges to the Discrimination Hypothesis

757

At the same time, a number of cautions and qualifications are necessary. First,

758

it is important to emphasize that discrimination is almost certainly not the entire story

759

when it comes to gender gaps in STEM. The gender bias demonstrated in

760

experimental hiring studies, for instance, tends to be relatively subtle, whereas the

761

gender gap in math-intensive STEM fields is relatively large. This implies that the

762

former is unlikely to be a complete explanation of the latter.

763

Furthermore, discrimination alone cannot readily explain why women are less

764

well represented in some fields than in others. Why would discrimination stop women

765

from going into math-intensive STEM fields such as physics and engineering, but not

766

into other prestigious, high-paying fields such as law, medicine, or veterinary science?

767

One might argue that math-intensive fields are particularly inhospitable to women, as

768

a result of the supposedly widespread stereotype that women are intellectually inferior

769

in mathematics. The problem with this idea, however, is that, until recently, many

770

people assumed that women were intellectually inferior in every area, but this did not

771

stop women from approaching parity with men in virtually every other desirable

772

occupation. Why would discrimination only hold women back in math-intensive

773

STEM fields? And why would it hold them back in math-intensive fields everywhere

774

in the world, rather than, say, math-intensive fields in the United States, psychology in

775

South Africa, and law in Scandinavia? Bias and discrimination fail to explain why

776

women are consistently underrepresented in some fields but not in others. In contrast,


777

sex differences in interests and cognitive specializations provide a straightforward

778

explanation of the pattern.

779

Not only does discrimination fail to explain all the data, but the evidence for

780

discrimination in STEM is actually weaker and more mixed than is often assumed.

781

Certainly, as we saw in the previous section, some studies find evidence of anti-

782

female discrimination in the sciences. At the same time, however, other studies fail to

783

find such discrimination, or find discrimination in favor of women (see, e.g., Baert,

784

2018; Bhattacharya, Kanaya, & Stevens, 2017; Blank, 1991; Breda & Hillion, 2016;

785

Lloyd, 1990; Lutter & Schröder, 2014; Miller & Wai, 2015; Riegle-Crumb, King, &

786

Moore, 2016; Veldkamp, Hartgerink, van Assen, & Wicherts, 2017). This raises the

787

possibility that our picture of the level and nature of discrimination in STEM is at

788

least somewhat distorted.

789

The most important voices on this issue are Stephen Ceci and Wendy

790

Williams. In their view, the idea that women are routinely discriminated against in

791

STEM, while true in earlier generations, is no longer true. The culture of science has

792

changed a great deal over the last half century, but people’s beliefs about that culture

793

have not kept pace with the change. Consider hiring practices. Real-world data

794

suggest that, although fewer women apply for math-intensive STEM jobs, those who

795

do are no less likely to be interviewed and no less likely to be offered the job. On the

796

contrary, they are often more likely to be (Ceci & Williams, 2011). This is the

797

opposite of what we would expect if there were pervasive anti-female bias in STEM.

798

If anything, it looks like there may be a pro-female bias, at least in the West.4

4

The situation in non-Western nations is less clear. In some cases, overt anti-female discrimination may still be prevalent (see, e.g., Cyranoski, 2018).


799

Of course, an alternative explanation would be that the female candidates tend

800

to be better than the males, perhaps because those few females who manage to survive

801

and thrive in male-dominated fields tend to be especially gifted. To determine whether

802

there really is a pro-female bias in STEM hiring, Williams and Ceci (2015) conducted

803

a large-scale hiring-decision study: the largest such study to date. The pair sent

804

hypothetical job applications to tenure-track professors in biology, economics,

805

engineering, and psychology, and asked them to assess the applicants’ suitability for a

806

tenure-track position. The final sample included nearly 900 professors from 371 US

807

universities. Averaging across conditions, Williams and Ceci found a 2:1 bias in favor

808

of female applicants. The pro-female bias was found in all four fields and among both

809

male and female faculty. (The only exception was male economics professors, who

810

showed no significant bias in either direction.) Thus, rather than being biased against

811

women, this study suggested that, when it comes to employment decisions, STEM

812

faculty appear to be biased in their favor.5

813

As well as finding little evidence of anti-female bias in hiring, Ceci and

814

Williams find little evidence of bias in promotion, publication, citation, or the

815

awarding of grants (Ceci et al., 2014; Ceci & Williams, 2011; see also Hechtman et

816

al., 2018). Studies that claim to find such bias are often widely discussed and cited

817

(see, e.g., Budden et al., 2008, on gender bias in acceptance rates for papers first-

818

authored by females,6 and van der Lee & Ellemers, 2015; Wennerås & Wold, 1997,

819

on gender bias in grant success). However, according to Ceci and Williams (2011), a

5

A comparable pro-female preference has also been documented outside STEM, in the Australian Public Services (Hiscox et al., 2017). Such findings are consistent with the idea that concerns about anti-female discrimination might sometimes overshoot and inadvertently produce anti-male discrimination instead. 6 Note that a reanalysis of the data by Webb, O’Hara, and Freckleton (2008) found that there was in fact no evidence of gender bias in the review process.


820

systematic review of all the available evidence suggests that deviations from gender

821

fairness are rare, and that they just as often favor women as men. Again, such results

822

are not what we would expect if anti-female bias were endemic.

823

To be clear, Ceci and Williams do not deny that women still face unique

824

challenges and hurdles in STEM. This includes, for example, the poor mesh between

825

the demands of a STEM career and the demands of motherhood – a point we come

826

back to later. But discrimination is no longer a major barrier. As Ceci and Williams

827

(2011) sum up the situation, “the ongoing focus on sex discrimination in reviewing,

828

interviewing and hiring represents costly, misplaced effort. Society is engaged in the

829

present in solving problems of the past” (p. 3157).

830

A Mixed Picture

831

It seems fair to say that the evidence for pervasive discrimination in STEM is

832

equivocal, with some studies finding pro-male bias (Cyranoski, 2018; Moss-Racusin

833

et al., 2012), some finding the reverse (Williams & Ceci, 2015), and some finding a

834

mixture of both (Breda & Hillion, 2016). What should we conclude? In our view,

835

there are two main interpretations. The first is that the apparently mixed findings are

836

not in fact inconsistent. Rather than there being uniform bias against women, or

837

uniform bias against men, there are pockets of bias against both sexes (and quite

838

possibly no gender bias at all at some institutions and in some cases). The second

839

interpretation is that, at this stage, the findings are simply inconclusive: The jury is

840

still out. But this in itself suggests that sex-based discrimination could not be hugely

841

prevalent in STEM; if it were, it would be easier to detect a clear signal and the

842

research would paint a more consistent picture of the situation. This, in turn, suggests

843

that factors other than discrimination are probably the primary explanation for the

844

persistence of gender gaps in STEM.


845

A Hidden Barrier to the Progress of Women in STEM

846

Before shifting topics, we should briefly consider another potential barrier to

847

the progress of women in STEM – one that often is overlooked: stereotypes of the

848

sexist academy. In the quest to promote women in STEM, academics and activists

849

may sometimes inadvertently overstate the ubiquity of bias and discrimination against

850

women in this sector. An unintended consequence may be to scare away some women

851

who would otherwise be interested in a STEM career (Sesardic & De Clercq, 2014;

852

Williams & Ceci, 2015). If women are given the impression that the STEM workplace

853

is a hotbed of sexism, and an unwelcome place for women, many might quite

854

understandably decide to look for other fields in which to make their mark. Ironically,

855

the consequent dearth of women in STEM might then itself be taken as further

856

evidence that STEM is a hotbed of sexism, creating a self-reinforcing, vicious cycle.

857

Needless to say, if the STEM workplace really were a hotbed of sexism, this

858

would be something we would need to confront, even if doing so put off some

859

budding female scientists. However, given that we have reasonable evidence that, for

860

the most part, science is fair for women, and that discrimination is the exception

861

rather than the rule, conveying such a dark image of the STEM workplace might do

862

more harm than good. See Figure 3 for a summary of the many factors contributing to

863

the gender gaps in STEM.

864



865 866

Figure 3: Occupational outcomes are a product of many different factors; workplace

867

discrimination is one among many.

868 869 870

Policy Options Sex differences in STEM representation are not just an academic matter. The

871

question of what should be done about these differences – or indeed whether anything

872

should be done – is one of the most widely discussed political issues related to the

873

modern academy. In this section, we look at some of the most important policy

874

prescriptions that have been mooted or implemented over recent years. The discussion

875

is not intended to be exhaustive, but rather aims to highlight broad trends and

876

commonly overlooked consequences of the policies in question. Table 1 provides an

877

overview.

878 879

--------------------Insert Table 1 about here--------------------


880

Table 1

881

Potential Unintended Consequences of Policy Interventions Aimed at Reducing

882

Gender Gaps in STEM Policy Outreach

Potential Unintended Consequences If handled poorly, may inadvertently put undue pressure on girls to pursue a STEM career or may send a discouraging message to boys.

Incentives for women to

Costly.

go into STEM

May push women to make choices less in line with their preferences and long-term interests. Arguably discriminatory against men.

Blinded evaluation of job

May result in fewer women being hired in certain STEM

applications

fields.

Diversity training

Tacit accusation of bias. Dubious efficacy, as quite possibly not targeting the primary causes of current STEM gaps. May sometimes backfire (i.e., increase bias).

Affirmative action and

Abandons the moral principle that we should avoid

quotas

discriminating on the basis of sex. Casts doubt on women’s genuine achievements. May strengthen the stereotype that women cannot succeed in science.

Family-Friendly Policies

If benefits offered to mothers only, may force some couples to avoid gender-atypical parenting arrangements,


even if they would prefer those arrangements. 883 884

Outreach

885

An initial, relatively uncontroversial intervention is outreach: educating

886

children and young people about science-related careers, and emphasizing that these

887

are careers that girls as well as boys should consider. Interventions of this kind would

888

not need to deny that there are average differences between the sexes in STEM-

889

relevant traits. On the contrary, average sex differences provide a strong argument in

890

favor of the interventions. After all, even if gender gaps in STEM representation are

891

primarily a result of sex differences in preferences, aptitudes, and variability, the mere

892

existence of those gaps could still help to sway the career choices of individuals

893

whose interests and talents buck the usual trend. In fields where the gender gap is

894

large, some girls and women who would otherwise pursue a science career might be

895

put off by the fact that many more men than women take that path (Dasgupta & Stout,

896

2014). That being the case, it may always be necessary to encourage and support these

897

individuals, and to encourage everyone else to accept atypical career choices and be

898

tolerant of individual differences. (Notice, incidentally, that the same argument would

899

presumably weigh just as strongly toward encouraging boys and men with atypical

900

career preferences to follow their interests as well.)

901

Of course, as with any intervention, there is a danger of creating harm as well

902

as good. One potential harm could come from outreach programs that focus on girls

903

alone: girls-only science workshops, for instance, or advertising campaigns that depict

904

girls but not boys engaged in science-related activities. Such interventions could

905

inadvertently convey the message to boys that they are no longer welcome in science,

906

and that if they choose to pursue a career in that area, they may face an uphill battle


907

due to institutional favoritism toward girls and women. Girls-only programs could

908

also risk losing the support of people who would otherwise be allies, but who worry

909

that the issue has been captured by a strain of gender politics more concerned about

910

eliminating sex differences than about opening the doors for all.

911

Another potential harm is that well-meaning efforts to encourage girls to

912

pursue careers in STEM could sometimes tip over into excessive pressure to take that

913

road. Susan Pinker (2008) interviewed women who had left successful STEM careers

914

to pursue careers in other areas. Many reported that, as girls and young adults, they

915

were so strongly encouraged to go into STEM that they ended up in jobs they did not

916

especially enjoy. In light of this potential pitfall, we suggest that the aim of outreach

917

efforts should not be to get women into science per se, but rather to give everyone

918

accurate information about STEM career options so that they can make an informed

919

choice about what would suit them best.

920

Incentives for Women to Go into STEM

921

A second type of intervention involves offering incentives to women to go into

922

male-dominated STEM fields. Examples include fee waivers, scholarships, and

923

monetary incentives for completing one’s training in a targeted area. As with

924

outreach, this is already a common practice, and it seems probable that the incentives

925

on offer would encourage more women to make gender-atypical choices (Navarra-

926

Madsen, Bales, & Hynds, 2010).

927

But although the incentives probably work, a number of arguments can be

928

leveled against this practice. One is that it discriminates on the basis of sex: It offers

929

advantages to some individuals but not others, purely on the basis of a fixed biological

930

attribute. Even leaving this aside, however, it is worth considering the wisdom of

931

devoting large amounts of resources to encouraging women to do something they


932

would not otherwise do. Although rarely described that way, this is clearly what the

933

practice amounts to – after all, if the targeted women did want to do it anyway, the

934

incentives would not be necessary. By interfering with women’s choices, it is possible

935

that immediate incentives could nudge some women away from options that might

936

suit them better in the longer term and which might ultimately make them happier.7

937

Certainly, people are not always right about what will make them happy. The

938

question, is though, whether those who call for incentives and other measures to help

939

shrink STEM gender gaps are more likely to be right – or whether women’s happiness

940

is the main motivation behind their efforts.

941

Blinded Evaluation

942

A third intervention is blinded evaluation of job applications, journal article

943

submissions, grant applications, and the like – in other words, removing any evidence

944

of the applicant or author’s sex before beginning the evaluation process. Where this

945

can be done, it is a relatively easy way to neutralize the potentially distorting effects

946

of demographic stereotypes. A possible criticism of the practice, at least as applied to

947

hiring decisions, is that it could only be implemented during the earliest stages of the

948

process: Gender can be concealed in a CV, but not in an interview or job talk.

949

Fortunately, however, a great deal of research suggests that stereotypes exert most of

950

their influence on person perception during those earliest stages, when perceivers

951

have little individuating information about the person being perceived (Koch et al.,

952

2015; Rubinstein, Jussim, & Stevens, 2018). As such, blinded evaluation could well

953

eliminate most of the biasing effects of demographic stereotypes. Another point in

7

To be clear, this is not because STEM does not suit women; clearly, it suits some but not others. The point is that the incentives may encourage some women who might be better suited to other areas to take the STEM option instead.


954

favor of the practice is that blinded evaluation automatically eliminates all forms of

955

bias, including not only anti-female bias but anti-male bias as well. Moreover, if there

956

is little bias in either direction, the policy would cost little and do little damage –

957

unlike some of the more heavy-handed interventions on offer.

958

Despite its many merits, however, blinded evaluation may prove to be a

959

politically unpopular option. If Ceci and Williams (2014; 2015) are right that women

960

are often favored rather than disfavored in science hiring, blinded evaluation of job

961

applications would presumably result in somewhat fewer women being hired than is

962

presently the case. Given the strong push toward increasing the numbers of women in

963

STEM, such an outcome is likely to rule against the policy. This is not just a

964

theoretical possibility; the Australian Public Services recently suspended a blinded

965

evaluation trial when they discovered that the practice slightly increased men’s

966

chances of getting hired, and slightly decreased women’s (Hiscox et al., 2017). Notice

967

that, in abandoning the trial, the policy makers effectively revealed that their goal is

968

equality of outcome rather than equality of opportunity.

969

Diversity Training

970

Another intervention aimed at tackling discrimination in STEM is diversity

971

training, also known as anti-bias training. Diversity training takes many forms, but the

972

common thread is the aim of increasing awareness and tolerance of diversity in the

973

workplace, and of helping people from different backgrounds to avoid bias and work

974

together harmoniously. The practice has become increasingly popular over the last

975

few decades, and is now a billion-dollar industry (Anand & Winters, 2008).

976

In spite of its popularity, though, a number of criticisms and concerns have

977

been raised regarding diversity training. To begin with, in its application to the STEM

978

gender gap, the enterprise is premised on the assumption that bias is the primary cause


979

– or at least a major cause – of the differential representation of men and women in

980

STEM. As we saw earlier, however, the evidence for endemic anti-female bias is

981

inconclusive, and the main cause of STEM gender gaps appears to be average sex

982

differences in people’s vocational preferences. This raises several issues with respect

983

to diversity training.

984

The first is ethical. To mandate diversity training is tacitly to accuse people of

985

bias – probabilistically if not in each individual case – which is a rather serious

986

accusation to level at people on the basis of conflicting and contested evidence. A

987

second issue is practical. If bias is no longer the main driver of the gender gaps in

988

STEM, then interventions targeting bias are unlikely to have a positive impact. And if

989

that is the case, then anti-bias training represents a considerable waste of resources –

990

resources that could otherwise be used more productively. Consistent with this

991

assessment, research on the efficacy of anti-bias training paints a mixed picture at

992

best. Several studies have concluded that the most popular anti-bias programs have

993

little impact on diversity outcomes (Dobbin, Kalev, & Kelly, 2007; Kalev, Dobbin, &

994

Kelly, 2006). Furthermore, in some cases, the programs may actually backfire,

995

increasing bias rather than reducing it (Duguid & Thomas-Hunt, 2015; Moss-Racusin

996

et al., 2014). Of course, it is possible that bias is still pervasive but that we have not

997

yet found effective interventions to tackle it. It is also possible, however, that the

998

programs are not targeting the real root causes of women’s lower representation in

999

politically progressive nations (Ceci & Williams, 2011).

1000

In discussing diversity training, it is worth paying special attention to the

1001

concept of implicit or unconscious bias. This concept has become central to diversity

1002

training over recent decades, based primarily on research using the Implicit

1003

Association Test or IAT, a putative measure of implicit bias. In the last few years,


1004

however, the IAT has come under increasing fire, and with it interventions aimed at

1005

thwarting implicit bias. The main criticisms are as follows: (1) The IAT produces

1006

inconsistent results for individuals. Its test-retest reliability is low (Gawronski,

1007

Morrison, Phills, & Galdi, 2017). (2) The IAT does a poor job of predicting

1008

discriminatory behavior (Oswald, Mitchell, Blanton, Jaccard, & Tetlock, 2015).

1009

Explicit bias is a better predictor. (3) Although it seems to be possible to change

1010

people’s implicit biases, the effects of such changes on behavior are trivially small or

1011

non-existent, even in the immediate wake of the intervention (Forscher et al., 2017).

1012

For all three reasons, it seems unlikely that diversity interventions targeting implicit

1013

bias represent a wise allocation of resources. Indeed, even one of the creators of the

1014

IAT, Anthony Greenwald, recommends against the use of interventions promising to

1015

eliminate implicit bias (cited in Lopez, 2017).

1016

Affirmative Action and Quotas

1017

Another, more heavy-handed strategy would be to implement a policy of

1018

affirmative action for women in science, or even a strict quota system. As discussed

1019

earlier, there is some reason to believe that this is already happening informally:

1020

Women appear to be preferentially hired in at least certain STEM fields (Ceci et al.,

1021

2014; Ceci & Williams, 2011). Some suggest, however, that this process should be

1022

formalized, and that more stringent rules about numbers of women ought to be

1023

established (see, e.g., Wallon, Bendiscioli, & Garfinkel, 2015).8 Among the most

1024

common arguments for affirmative action and quotas are that these policies would

1025

provide a counterweight to existing biases, compensate for past injustices, break the

1026

cultural “habit” of male-dominance in certain areas, increase the pool of same-sex role

8

Indeed, in some countries, this is happening already; the Max Plank institute in Germany, for example, offers jobs that only women can apply for (Max Plank Society, 2017).


1027

models available to girls and women, and increase the diversity of available

1028

perspectives.

1029

Some of these goals seem self-evidently desirable; others are topics of debate.

1030

However, even if we were to accept the desirability of all the goals, there are reasons

1031

to be wary of affirmative action as a means of achieving them, especially if this

1032

involves quotas that depart markedly from existing sex ratios. To begin with, there is

1033

an ethical question. Pro-female favoritism represents an explicit rejection of the

1034

principle of equality of opportunity in favor of discrimination on the basis of sex:

1035

precisely what feminism originally set out to overcome. As the philosopher Janet

1036

Radcliffe-Richards (1980) observed, the moral foundation of the women’s liberation

1037

movement – and indeed of all liberation movements – is the idea that people should

1038

be treated fairly and equitably, and that unjust barriers should be dismantled. A policy

1039

that advantages one demographic group over another necessarily abandons those

1040

ideals. In doing so, it risks leaving the women’s movement without its moral

1041

foundation, thereby reducing the issue to a mere power struggle between competing

1042

groups, rather than a matter of principle.

1043

Certainly, throughout history, men were often advantaged over women in a

1044

similarly unprincipled way. It is not clear, however, why any individual woman today

1045

should be advantaged over any individual man, simply because other men were

1046

advantaged over other women in the past. Reversing historical injustices does not

1047

erase them; it merely adds to the total number of injustices in the world. The question

1048

we face today, therefore, is this: Is the appropriate response to injustice to eliminate it,

1049

or simply to turn it on its head?

1050 1051

Of course, one might argue that affirmative action for women in STEM would not in fact be unjust; instead, the policy would help to equalize men and women’s


1052

chances of advancing in STEM, which are presently unequal due to anti-female

1053

discrimination. As mentioned, however, the evidence for pervasive discrimination in

1054

STEM is equivocal, with some studies suggesting that, at least in some ways, women

1055

are favored over men. Given that advantaging one demographic group over another is

1056

not an ethically trivial act, we should insist on much stronger evidence before

1057

enacting such a policy.

1058

Moreover, it is not only men who might be harmed by affirmative action. In a

1059

number of ways, women themselves could be harmed as well. To begin with,

1060

affirmative action policies may cast a shadow of doubt over women’s genuine

1061

accomplishments. If such policies become widespread, then whenever women win

1062

jobs, grants, or awards, people may find themselves wondering – secretly and despite

1063

their best intentions – whether the women in question were judged by a lower

1064

standard, simply because of their sex (a rather sexist practice in itself, one might

1065

argue). This is not just a pitfall for onlookers; successful women themselves might

1066

end up harboring doubts about their own achievements. As the physicist Stephanie

1067

Meyer (2015) once put it: “How am I supposed to build confidence if I never know

1068

whether I earned my success? Women scientists already suffer from imposter

1069

syndrome; practices that make us think we may be given unearned advantage aren’t

1070

helpful.”

1071

As well as casting doubt on the success of individual women, affirmative

1072

action could harm the image of women in STEM more generally. One of the primary

1073

goals of the women-in-STEM movement has been to eliminate the pernicious and


1074

demonstrably false stereotype that women cannot succeed in science.9 Affirmative

1075

action policies are unlikely to contribute to that project. On the contrary, the policies

1076

may bolster the stereotype. Aside from the fact that they seem to imply that women in

1077

science need the extra help, strong preferences could lower the average level of

1078

performance of women working in STEM. This is not because any individual woman

1079

would perform any worse, but rather is a simple statistical consequence of the fact that

1080

the pool of female STEM candidates is smaller than that of the males. As Figure 4

1081

shows, if equal numbers of top performers are drawn from two samples, but one of

1082

those samples is smaller than the other, the mean level of ability of those from the

1083

smaller sample will necessarily be lower than that from the larger, even if the means

1084

and variances of the two samples are identical. In effect, equalizing the number of

1085

individuals taken from each group would mean lowering the minimum standard for

1086

the smaller group. For less stringent quotas, the minimum standard would not need to

1087

be lowered as much. Nonetheless, it would still need to be lowered.

1088


1089

1090

9

Note that we appear to be making slow but steady progress toward this goal (Miller, Nolla, Eagly, & Uttal, 2018).


1091

Figure 4. If equal or similar numbers of top performers are drawn from samples of

1092

different sizes, the average level of ability of those drawn from the smaller sample

1093

will be lower than that of those drawn from the larger. This is the case even if the

1094

means and standard deviations of the two groups are identical.

1095 1096

This could have damaging consequences for women. In the absence of

1097

affirmative action, a person’s sex tells you little about their probable STEM abilities:

1098

Any woman who has been accepted to a given university, or secured a job at a given

1099

institution, is likely to be just as talented as any man at the same university or in the

1100

same institution. However, if strong affirmative action policies are enacted, sex

1101

suddenly does tell you something about women’s probable STEM abilities: It tells

1102

you that they might not necessarily be as good (cf. Haidt & Jussim, 2016). Again, this

1103

is not because women cannot succeed in STEM – some can and some cannot, just like

1104

men. Instead, it is an inevitable consequence of the fact that enacting strong

1105

preferences for members of a smaller group necessarily means lowering the minimum

1106

standard by which members of that group are judged.

1107

Family-Friendly Policies

1108

A final proposal is that STEM career paths could be reconfigured in ways that

1109

would make them more family-friendly. This is a view that Ceci and Williams (2010,

1110

2011) have championed. In their estimation, one of the main remaining barriers to

1111

career success for women in STEM is the incompatibility of jobs in this area with the

1112

demands of motherhood. Not only do women alone get pregnant and nurse their

1113

young, but women are more likely than men to take time out from their careers to care

1114

for their young children. Nowhere is the clash between motherhood and STEM more


1115

apparent than with regard to the academic tenure system in the United States. As Ceci

1116

and Williams (2010) put it:

1117 1118

The tenure structure in academe demands that women having children make their greatest

1119

intellectual contributions contemporaneously with their greatest physical and emotional

1120

achievements, a feat not expected of men. When women opt out of full-time careers to have

1121

and rear children, this is a choice – constrained by biology – that men are not required to

1122

make. (p. 278)

1123 1124

It is worth pointing out that the family-friendliness of STEM jobs varies a

1125

great deal from nation to nation, and that the lack of family-friendly policies would

1126

not explain why women are less well represented in math-intensive fields than in

1127

others. Nonetheless, finding ways to make STEM occupations more compatible with

1128

parenthood could help to level the playing field in math-intensive and non-math-

1129

intensive fields alike. Suitable policies might include increasing the flexibility of the

1130

window in which academics are able to complete the requirements of tenure, and

1131

increasing the provision of subsidized childcare.

1132

Of course, some might take issue with the “assumption” that women are the

1133

primary caregivers for their young. But this is not an assumption in any normative

1134

sense; it is simply an observation about what tends to happen. And given that it is

1135

what tends to happen - and that it is not something we know how to change, even if it

1136

were ethically acceptable to do so – family-friendly policies might help to equalize

1137

men and women’s opportunities by removing a barrier that faces more women than

1138

men. Furthermore, if enacted in a gender neutral way, such that mothers or fathers

1139

could avail themselves of any parental benefits, the policies would not exert any

1140

special pressure on women to take the primary caregiver role. Either sex could take it,


1141

if they so desired (although see Antecol, Bedard, & Stearns, in press, on possible

1142

unintended consequences of such policies).

1143 1144 1145

Levelling the Playing Field vs. Equalizing Sex Ratios Having considered a range of possible policy interventions, we should now

1146

step back and ask another, more fundamental question: What should the ultimate goal

1147

of these interventions be? Should we strive for a 50:50 sex ratio in every profession

1148

where one sex currently dominates? Or should we strive instead simply to eliminate

1149

bias and equalize people’s opportunities, then let the cards fall where they may?

1150

If men and women were identical in their aptitudes and aspirations, these

1151

would quite possibly amount to the same thing: Levelling the playing field would

1152

automatically result in a 50:50 sex ratio. Given, however, that men and women are not

1153

identical in their aptitudes and aspirations, we have no reason to expect gender parity,

1154

even under conditions of perfect fairness. On the contrary, the natural expectation

1155

would be that men and women would not be at parity, but rather that men would be

1156

more common in some fields, and women in others, as a result of their freely made

1157

choices. To the extent that this is the case, it becomes much more difficult to justify

1158

pursuing a 50:50 sex ratio in every field. Most women do not want a career in science

1159

and nor do most men. Why should the small fraction of women who do want such a

1160

career be the same size as the small fraction of men? To put it another way, as long as

1161

everyone has the opportunity to pursue a STEM career, and as long as the selection

1162

process is fair, why would it be important to get as many women as men into jobs that

1163

fewer women want?


1164

The Pursuit of Happiness

1165

One way to start tackling this question is to observe that a 50:50 sex ratio in all

1166

STEM fields is not actually a good in itself, but is only a good to the extent that it

1167

increases aggregate happiness and social wellbeing. Importantly, though, to the degree

1168

that occupational disparities are a product of men and women acting on their own

1169

preferences and pursuing their own best interests, it is doubtful that forcing a 50:50

1170

sex ratio would actually achieve these ends.

1171

To begin with, men and women could have different life outcomes, but still be

1172

happy with their lives. One longitudinal study found that, among two cohorts of

1173

individuals identified as academically gifted as children, men and women had

1174

somewhat different aspirations and took somewhat different paths, but ended up

1175

similarly happy with their careers, their relationships, and their lives overall (Lubinski

1176

et al., 2014). Thus, even among those best positioned to achieve their life ambitions,

1177

occupational gender parity appears not to be necessary for happiness.

1178

Not only might it not be necessary, but policies that artificially engineer

1179

gender parity – financial incentives and quotas, for instance – could potentially lower

1180

aggregate happiness. To the extent that these policies work, they necessarily mean

1181

that some people will be funneled into occupations that are less in line with their

1182

tastes and talents. To get more women into university physics programs, for instance,

1183

would require persuading at least some women to choose that option when they

1184

otherwise would not have done so. (At the same time, unless enrolment numbers were

1185

increased, it would also mean turning away some men who otherwise would have.)

1186

The women in question presumably would not come from the ranks of housewives or

1187

secretaries; more than likely they would be women who would otherwise have gone

1188

into other, equally prestigious fields, such as law or medicine. Is there any reason to


1189

think that these women would be happier doing physics? Given that people tend to

1190

make the decisions they think will suit them best and be most satisfying for them, it

1191

seems plausible to think that, on average, they might be somewhat less happy. Again,

1192

people are not always right about what will make them happy. What reason, though,

1193

do we have to think that STEM policymakers are more likely to be right? What reason

1194

do we have to think that third-parties – academics, activists, and politicians – have a

1195

better idea than women themselves about what choices women should make?10

1196

Admittedly, this whole line of argument is premised on the assumption that

1197

our aim as a society should be to maximize happiness, and some might reject that

1198

assumption. Anyone who does, though, should be expected to make a strong case for

1199

their position. Why should we put a statistical, collective goal – i.e., more equal sex

1200

ratios in STEM – above the happiness and autonomy of the flesh-and-blood

1201

individuals who constitute those collectives? Why should policymakers’ preference

1202

for gender parity take precedence over individual men and women’s preferences

1203

regarding their own careers and lives?

1204

Sex Differences as a Sign of Social Health

1205

A recurring theme of discussions of occupational gender disparities is the

1206

often-unspoken assumption that sex differences are inherently problematic, or that

1207

they constitute direct evidence of sexism and the curbing of women’s opportunities.

1208

Some research, however, points to the opposite conclusion. A growing body of

1209

evidence suggests that, in nations with greater wealth and higher levels of gender

1210

equality, sex differences are often larger than they are in less wealthy, less equal

1211

nations. This is true for a wide range of variables, including attachment styles, the Big

10

One might also argue that, even if policymakers did have a better idea, they would still have no right in a free society to interfere with women’s choices (or with men’s).


1212

Five personality traits, choice of academic speciality and occupation, crying,

1213

depression, enjoyment of casual sex, intimate partner violence, mathematical ability,

1214

mental rotation, self-esteem, subjective wellbeing, and values (summarized in

1215

Schmitt, 2015). Importantly, it is also true of objectively measurable traits such as

1216

height, BMI, and blood pressure, which gives us some reason to think that the pattern

1217

is not simply a product of cross-cultural differences in the ways that people answer

1218

questionnaires or take tests.

1219

What, then, is the cause of the pattern? One possibility is that when people are

1220

free to develop relatively unrestrainedly, nascent differences between individuals –

1221

and average differences between the sexes – have more opportunity to emerge and

1222

grow. In the case of psychological traits, the suggestion would be that men and

1223

women in wealthier, more developed nations have greater freedom to pursue what

1224

interests them and to nurture their own individuality. This freedom may, in turn, result

1225

in larger psychological sex differences (Schmitt, Realo, Voracek, & Allik, 2008).

1226

Regardless of the explanation, though, if certain sex differences are larger in

1227

societies with better social indicators, then rather than being products of a sexist or

1228

oppressive society, these differences may be indicators of the opposite: a

1229

comparatively free and fair one (Sommers, 2013). If so, this casts society’s efforts to

1230

eradicate the sex differences in an entirely new light. Rather than furthering gender

1231

equality, such efforts may involve attacking a positive symptom of gender equality.

1232

By mistaking the fruits of our freedom for evidence of oppression, we may institute

1233

policies that, at best, burn up time and resources in a futile effort to cure a

1234

misdiagnosed disease, and at worst actively limit people’s freedom to pursue their

1235

own interests and ambitions on a fair and level playing field.


1236

The Sexist Assumption Underlying the Demand for Parity

1237

Finally, the strong emphasis on increasing the numbers of women in male-

1238

dominated fields is arguably somewhat sexist. As Susan Pinker (2008) points out, it

1239

tacitly assumes that women do not know what they want, or that they want the wrong

1240

things and thus that wiser third-parties need to “fix” their existing preferences.

1241

Furthermore, it tacitly assumes that the areas in which men dominate are superior. The

1242

psychologist Denise Cummins (2015) put the point well when she observed that, “The

1243

hidden assumption underlying the push to eliminate gender gaps in traditionally male-

1244

dominated fields is that such fields are intrinsically more important and more valuable

1245

to society than fields that traditionally attract more women.” Given that traditionally

1246

female-dominated fields include education, healthcare, and social care, this

1247

assumption is not only sexist; it is also clearly false. As Judith Kleinfeld observed:

1248 1249

We should not be sending [gifted] women the message that they are less worthy human

1250

beings, less valuable to our civilization, lazy or low in status, if they choose to be teachers

1251

rather than mathematicians, journalists rather than physicists, lawyers rather than engineers.

1252

(cited in Steven Pinker, 2002, p. 359)

1253 1254

Certainly, many female-dominated fields pay less, on average, than male-

1255

dominated STEM fields.11 There is a great deal of debate about the reasons for this,

1256

and the extent to which it is a product of sexism vs. the fact that, on average, women

1257

view pay as a less important consideration in choosing a career than do men, and see

1258

things such as job security and flexible work hours as more important (see, e.g.,

11

Note that this is not always the case. Various non-STEM professions, including law, medicine, and pharmacy, pay considerably more than STEM, and now attract more women than men (Susan Pinker, 2010).


1259

Lubinski et al., 2014; Redmond & McGuinness, 2017). Such matters are beyond the

1260

scope of this article. We would point out, however, that even if sexism were the entire

1261

explanation for current pay disparities, the most appropriate solution would

1262

presumably be to strive for fair pay in female-dominated fields, not to try to get more

1263

women into fields that pay more but which, on average, they find less appealing. And

1264

to the extent that the explanation is that women place less weight on a high income in

1265

choosing a career, efforts to get women to prioritize income tacitly assume, once

1266

again, that women’s existing priorities are misguided, and that they ought to adopt

1267

more male-typical priorities instead.

1268

To be clear, we entirely agree that we should try to root out sexism wherever it

1269

still lurks, and tear down any lingering barriers to the progress of women in STEM (as

1270

well as any barriers to the progress of men). These are eminently good goals.

1271

However, for the reasons discussed, a 50:50 sex ratio is not a good goal.

1272 1273

Conclusion: Many Factors at Play

1274

In summary, any exhaustive discussion of the relative dearth of women in

1275

certain STEM fields must take into account the burgeoning science of human sex

1276

differences. If we assume that men and women are psychologically indistinguishable,

1277

then any disparities between the sexes in STEM will be seen as evidence of

1278

discrimination, leading to the perception that STEM is highly discriminatory. Such a

1279

perception, however, is almost certainly false. A large body of research points to the

1280

following three conclusions:

1281 1282

(1) that men and women differ, on average, in their occupational preferences, aptitudes, and

1283

levels of within-sex variability;

Men, Women, and Science 57 1284

(2) that these differences are not due solely to sociocultural causes but have a substantial

1285

innate component; and

1286

(3) that these differences, coupled with the demands of bearing and rearing children, are the

1287

main source of the gender disparities we find in STEM today. Discrimination appears to play a

1288

smaller role, and in some cases may favor women, rather than disfavoring them.

1289 1290

These conclusions have important implications for the way we handle STEM

1291

sex differences. Based on the foregoing discussion, we suggest that the approach that

1292

would be most conducive to maximizing individual happiness and autonomy would

1293

be to strive for equality of opportunity, but then to respect men and women’s

1294

decisions regarding their own lives and careers, even if this does not result in gender

1295

parity across all fields. Approaches that focus instead on equality of outcomes –

1296

including quotas and affirmative action – may exact a toll in terms of individual

1297

happiness. To the extent that these policies override people’s preferences, they

1298

effectively place the goal of equalizing the statistical properties of groups above the

1299

happiness and autonomy of the individuals within those groups. Moreover, policies

1300

aimed at achieving equality of outcomes necessarily involve abandoning one of the

1301

principles that made the women’s movement possible in the first place: the principle

1302

that we should treat all people fairly and avoid discriminating on the basis of sex.

1303

Some might derive different conclusions from the emerging understanding of human

1304

sex differences. Either way, though, it seems hard to deny that this understanding

1305

should be factored into the discussion.

1306


1307

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