Google Memo and the Greater Male Variability Hypothesis

may help explain

why there are fewer women in the applicant pool, but the women who choose to enter the pool are

just as capable

as the larger number of men in the pool. This conclusion does not deny that various forms of bias, harassment, and discouragement exist and may contribute to outcome disparities,

nor does it imply that the differences in interest are biologically fixed and cannot be changed in future generations.

> Our Conclusions about the Greater Male Variability Hypothesis:

> On average, male variability is greater than female variability on a variety of measures of cognitive ability, personality traits, and interests. This means men are more likely to be found at both the low and high end of these distributions (see Halpern et al., 2007; Machin & Pekkarinen, 2008 and, especially, the supplementary materials; for an ungated summary click here). This finding is consistent across decades.

> The gender difference in variability has reduced substantially over time within the United States and is variable across cultures. It is clearly responsive to social and cultural factors (see Hyde & Mertz, 2009; Wai et al., 2010); Educational programs can be effective. It is also clear that there are cultural/societal influences, as the male:female variability ratios can vary considerably across cultures (e.g., Machin & Pekkarinen, 2008).

> While the gender difference in the male:female ratio for the upper tail of the distribution of math test scores (SAT, ACT) narrowed considerably in the United States in the 1980s, it appears to have remained steady since the early 1990s. This can be seen visually in Figure 1 from Wai et al. (2010)

> Therefore at the top end of any distribution of test scores where men have higher variability, we’d expect men to make up more than 50% of the upper end of the tail. Thus, any company drawing from the top 5% is likely to find a pool that contains more males. As one goes further out into the tail (i.e. becomes even more selective) the gender tilt becomes larger. Further compounding the gender tilt: the women in this elite group generally have much better verbal skills than the men in that elite group (see Reilly, 2012). This means that these women may be better employees than men who match them on quantitative skills, but because they have such superior verbal skills they have more choices available to them when selecting a profession.

> Our Revised Conclusions About the Damore Memo We maintain that the research findings are complicated. This is evident in both this post and our original one. There are many abstracts containing both red and green text, and some of the top researchers in psychology are represented on both sides of the debate. Furthermore, many of the experts have concluded that:

>> … early experience, biological factors, educational policy, and cultural context affect the number of women and men who pursue advanced study in science and math and that these effects add and interact in complex ways. There are no single or simple answers to the complex questions about sex differences in science and mathematics (Halpern et al., 2009).

> In light of of the research on the Greater Male Variability Hypothesis however, we have revised our original conclusions: Gender differences in math/science ability, achievement, and performance are small or nil. (See especially the studies by Hyde; see also this review paper by Spelke, 2005). There are two exceptions to this statement:

> Men (on average) score higher than women on most tests of spatial abilities, but the size of this advantage depends on the task and varies from small to large (e.g., Lindberg et al., 2010). There is at least one spatial task that favors females (spatial location memory; see e.g., Galea & Kimura, 1993; Kimura, 1996; Vandenberg & Kuse, 1978). Men also (on average) score higher on mechanical reasoning and tests of mathematical ability, although this latter advantage is small. Women get better grades at all levels of schooling and score higher on a few abilities that are relevant to success in any job (e.g., reading comprehension, writing, social skills). Thus, we assume that this one area of male superiority is not likely to outweigh areas of male inferiority to become a major source of differential outcomes.

> There is good evidence that men are more variable on a variety of traits, meaning that they are over-represented at both tails of the distribution (i.e., more men at the very bottom, and at the very top), even though there is no gender difference on average. Thus, the pool of potentially qualified applicants for a company like Google is likely to contain more males than females. To be clear, this does not mean that males are more “suited” for STEM jobs. Anyone located in the upper tail of the distributions valued in the hiring process possesses the requisite skills. Although there may be fewer women in that upper tail, the ones who are found there are likely to have several advantages over the men, particularly because they likely have better verbal skills.

> Gender differences in interest and enjoyment of math, coding, and highly “systemizing” activities are large. The difference on traits related to preferences for “people vs. things” is found consistently and is very large, with some effect sizes exceeding 1.0. (See especially the meta-analyses by Su and her colleagues, and also see this review paper by Ceci & Williams, 2015).

> Culture and context matter, in complicated ways. Some gender differences have decreased over time as women have achieved greater equality, showing that these differences are responsive to changes in culture and environment. But the cross-national findings sometimes show “paradoxical” effects: progress toward gender equality in rights and opportunities sometimes leads to larger gender differences in some traits and career choices. Nonetheless, it seems that actions taken today by parents, teachers, politicians, and designers of tech products may increase the likelihood that girls will grow up to pursue careers in tech, and this is true whether or not biology plays a role in producing any particular population difference. (See this review paper by Eagly and Wood, 2013).

> In conclusion, based on the meta-analyses we reviewed and the research on the Greater Male Variability Hypothesis, Damore is correct that there are “population level differences in distributions” of traits that are likely to be relevant for understanding gender gaps at Google and other tech firms. The differences are much larger and more consistent for traits related to interest and enjoyment, rather than ability. This distinction between interest and ability is important because it may address one of the main fears raised by Damore’s critics: that the memo itself will cause Google employees to assume that women are less qualified, or less “suited” for tech jobs, and will therefore lead to more bias against women in tech jobs. But the empirical evidence we have reviewed should have the opposite effect. Population differences in interest and population differences in variability of abilities may help explain why there are fewer women in the applicant pool, but the women who choose to enter the pool are just as capable as the larger number of men in the pool. This conclusion does not deny that various forms of bias, harassment, and discouragement exist and may contribute to outcome disparities, nor does it imply that the differences in interest are biologically fixed and cannot be changed in future generations.

> If our conclusions are correct then Damore was drawing attention to empirical findings that seem to have been previously unknown or ignored at Google, and which might be helpful to the company as it tries to improve its diversity policies and outcomes.