The research findings are complicated, as you can see from the many abstracts containing both red and green text, and from the presence on both sides of the debate of some of the top researchers in psychology. Nonetheless, we think that the situation can be greatly clarified by distinguishing
abilities from
interests. We think the following three statements are supported by the research reviewed above:
1. 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:
A) Men (on average) score higher than women on some tests of spatial abilities, such as the ability to rotate 3-dimensional objects in one’s mind. This ability may be relevant in some areas of engineering, but it’s not clear why it would matter for coding.
B) There is some 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. There is an ongoing debate about whether or not this is true across nations and decades; We are currently reviewing this literature, and will post our conclusions and links to studies next week.
2. 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).
3. 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 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.
If our three 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.
