New York University Professor and Heterodox Academy founder Jonathan Haidt writes that many of the claims about sex differences in the Google Memo are scientifically-defensible.
Haidt and his co-author, Sean Stevens, cite several peer-reviewed studies to analyze the contents of the memo that led to the firing of James Damore. The memo focused on ideological diversity at Google and cognitive sex differences that could lead to a disparity in gender representation at tech firms like Google. Haidt and Stevens point out that while there is no difference in ability between men and women with regards to performance in math and science fields, there is significant scientific research that purports that there is a gap between men and women with regards to interest in math and science fields.
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).
After analyzing several studies on the cognitive differences between men and women, Haidt and Stevens come to the conclusion that “Damore seems to be correct that there are ‘population level differences in distributions’ of traits that are likely responsible for understanding gender gaps at Google and other tech firms.”
In conclusion, based on the meta-analyses we reviewed above, Damore seems to be 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 may be part of the explanation for 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 contribute to outcome disparities, nor does it imply that the differences in interest are biologically fixed and cannot be changed in future generations.
You can read the entirety of Haidt and Steven’s analysis here.
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