52. Among employed women ages 25–54 with at least a BA, the percentage in STEM professions increased from less than 1 percent in 1970 to 3.7 percent in 2015, a big proportional change. But the main point of the discussion in the text is to understand women’s changes in occupations along the People-Things dimension. Over the time period I’m using, STEM professions amounted to only about 3 percent of all occupations, involving a trivial proportion of the job choices that women (and men) made.
53. Current Population Survey, 1971–2015, author’s analysis.
54. Among employed persons ages 25–43, the proportion of People jobs rose from 21 percent to 28 percent of all jobs from 1971 to 2018, while the same figure for Things jobs fell from 44 percent to 37 percent. Author’s analysis, Current Population Survey.
55. Murray (2012): ch. 9, 16.
56. Eberstadt (2016).
57. Each student’s score in math, reading, and science was standardized to a z-score based on the scores of that nation. (A standardized score, or z-score, has a mean of zero and a standard deviation of 1.) The average of the three z-scores formed a general score, which was subtracted from the individual z-scores. The three differences (zMath–zGeneral, etc.) were themselves standardized within nations. The result could be used to express each student’s score in math, reading, and science relative to that student’s overall skills and relative to the mean and distribution of his nation’s students. The authors use the example of a U.S. student who had z-scores of zScience = −1.39, zMath = −0.69, and zReading = −1.61. The student’s zGeneral score was −1.23. The relative-strength differences produced by the algorithm were −0.71 for science, +2.23 for math, and −1.34 for reading. The authors’ explanation of the interpretation: “Note that although this student’s scores in all three subjects are below the standardized national mean (i.e., 0), his personal strength in mathematics deviates more than two standard deviations from the national mean of relative mathematics strengths. In other words, the gap between his mathematics score and his overall mean score is much larger (> 2 SDs) than is typical for U.S. students. Using these types of scores, we could calculate the intraindividual sex differences for science, mathematics, and reading for the United States (and similarly for all other nations and regions).” Stoet and Geary (2008): 4.
58. In Stoet and Geary (2018), effect sizes were determined by subtracting female means from male means. I have reversed the signs to be consistent with usage throughout the book (positive d indicates a higher female mean), which reverses the signs of the correlations as well.
59. Stoet and Geary (2018): Table S2. Lebanon, with an effect size of +0.09 favoring girls, was the lone exception.
60. This and subsequent effect sizes are calculated from Stoet and Geary (2018): Table S2.
61. Merton (1968) coined the term Matthew effect. The relevant verse (25:29) is: “For whoever has will be given more, and they will have an abundance. Whoever does not have, even what they have will be taken from them.”
62. Duff, Tomblin, and Catts (2015); Stanovich (1986). It so happens that the Global Gender Gap Index is highly correlated with economic wealth, which is also correlated with the quality of the educational systems. If you postulate that girls have a biologically grounded tendency to have better verbal skills than math and science skills, it may be expected that their relative strength will be accentuated as their educational opportunities increase. The same applies in reverse if boys’ inborn math and science skills tend to be better than their verbal skills. The better their educational opportunities, the larger the disparity between what they do best and their overall ability.
63. Stoet and Geary (2018): 5.
64. Stoet and Geary (2018): 10. In the main text I restrict the discussion to relative strengths based on the actual test scores. But the questions about students’ opinions of their own abilities in the 2015 PISA also allowed the authors to create a measure of self-efficacy for each field. For example, a boy’s positive self-efficacy score in mathematics meant that he was more confident than his performance warranted; a negative score means that he underestimated his math ability. The more gender-equal the country, the greater the sex difference favoring boys in science self-efficacy, interest in science, and joy in science (r = –.60, –.41, and –.43 respectively; p < .001, .003, and .001 respectively).
5: Sex Differences in the Brain
1. To get a sense of how much that was known about sexual differentiation through studies of rodents, birds, and other species has turned out to apply to humans, see de Vries and Södersten (2009).
2. Cahill (2017): 12.
3. Balaton and Brown (2016).
4. Migeon (2017).
5. Herrera, Wang, and Mather (2018).
6. Giedd, Raznahan, Alexander-Bloch et al. (2014).
7. Braitenberg (2001).
8. E.g., Riès, Dronkers, and Knight (2016); de Schotten, Dell’Acqua, Forkel et al. (2011).
9. Sahakian and Gottwald (2017); Satel and Lilienfeld (2015); Poldrack, Mumford, and Nichols (2011).
10. See Jahanshad and Thompson (2017) for a concise review of findings from neuroimaging as of 2017.
11. Joel, Berman, Tavor et al. (2015): 5.
12. Joel, Berman, Tavor et al. (2015): 5.
13. Joel, Berman, Tavor et al. (2015): 3.
14. Joel, Berman, Tavor et al. (2015): 3.
15. David Schmitt, “Statistical Abracadabra: Making Sex Differences Disappear,” Psychology Today, December 2, 2015.
16. Denworth (2017).
17. Del Giudice, Lippa, Puts et al. (2016); Rosenblatt (2016); Chekroud, Ward, Rosenberg et al. (2016); Glezerman (2016). The authors’ reply is Joel, Persico, Hänggi et al. (2016).
18. Del Giudice, Lippa, Puts et al. (2016).
19. Rosenblatt (2016).
20. Chekroud, Ward, Rosenberg et al. (2016).
21. Anderson, Harenski, Harenski et al. (2018).
22. Andrew Sullivan, “The He Hormone,” New York Times Magazine, April 2, 2000.
23. Sullivan, “The He Hormone.”
24. Bos, Hofman, Hermans et al. (2016).
25. Olsson, Kopsida, Kimmo et al. (2016).
26. Domes, Heinrichs, Michel et al. (2007). Oxytocin’s effects on behavior have been the subject of many articles, but as a meta-analysis by Bartz, Zaki, Bolger et al. (2011) made clear, the effects vary by context, and especially by sex. My citations are limited (Domes (2007) is an exception) to recent ones that have incorporated these complications into the design and analysis of their experiments.
27. Soutschek, Burke, Beharelle et al. (2017). The authors point out that the results could have been produced by socialization (since response to dopamine can be affected by reward systems) as well as by hardwired biological sex differences.
28. Nave, Nadler, Zava et al. (2017).
29. Sapienza, Zingales, and Maestripieri (2009). Risk aversion is one of the aspects of executive function. Grissom and Reyes (2019) found relatively few and minor sex differences in executive function in terms of the ability to process relevant information in making decisions, but acknowledged that observed gender differences in decision making as measured by the Iowa Gambling Task (the most frequently used decision-making task in such research) “are driven by [women] wishing to avoid frequent loss, not by a gender difference in the ability to detect loss magnitude.” The authors added that “observations that women avoid frequent losses in the IGT may be the other side of the coin wherein men are willing to make choices associated with a higher probability of loss, even when loss is highly probable.” (p. 2).
30. Goldstein, Jerram, Poldrack et al. (2005).
31. Stiles and Jernigan (2010).
32. The wording refers to the arguments in McCarthy and Arnold (2011) and Arnold (2017) for genetic sex differentiation that occurs during the embryonic phase. It is already known that such genetic differentiation occurs in other species prior to gonadal differentiation. Davies and Wilkinson (2006); Dewing, Shi, Horvath et al. (2003).
33
. Savic, Garcia-Falgueras, and Swaab (2010).
34. Phoenix, Goy, Gerall et al. (1959).
35. Wallen (2009): 561.
36. Hines (2010).
37. See Cohen-Bendahan, van de Beek, and Berenbaum (2005) for a discussion of the role of other hormones in the feminization process.
38. Savic, Garcia-Falgueras, and Swaab (2010).
39. McCarthy (2015).
40. For a review of the early literature and an example, see Berenbaum and Hines (1992).
41. E.g., Ehrhardt and Meyer-Bahlburg (1981); Dittmann, Kappes, Kappes et al. (1990).
42. Udry, Morris, and Kovenock (1995): 367. The full text: “A substantial part of the variance in women’s gendered behavior in a normal, non-clinical sample is explained by an empirical application of the two-stage behavioral endocrinological theory derived from vertebrate and non-human primate research. This supports other previous research on clinical samples and on normal samples confirming separate parts of the theoretical model for selected ranges of gendered behavior in females. It is concluded that gendered behavior is not entirely socially constructed, but partly built on a biological foundation.”
43. Geschwind and Galaburda (1985): 431. Source numbers in the text have been omitted from the quotation.
44. This summary of the linked hypotheses in Geschwind and Galaburda (1985) is taken from Baron-Cohen (2003): 98.
45. Fetal testosterone seeps into amniotic fluid. Amniocentesis, a procedure for women whose pregnancies carry higher than normal risks of birth defects, involves the collection of amniotic fluid. Addenbrooke’s Hospital in Cambridge near Baron-Cohen’s lab routinely kept the samples of amniotic fluid until the babies were born. Amniocentesis carries a small risk of causing a miscarriage, but using the amniotic fluid for research did not raise ethical issues because the pregnant women had chosen to accept the risk of amniocentesis independently of the research. With the mothers’ consent, it was thus possible to assemble a sample of children whose traits as infants and toddlers could be analyzed relative to their prenatal levels of testosterone.
46. Baron-Cohen (2003): 100. The technical accounts of the results are given in Lutchmaya, Baron-Cohen, and Raggatt (2001) and Lutchmaya, Baron-Cohen, and Raggatt (2002).
47. Auyeung, Knickmeyer, Ashwin et al. (2012).
48. Auyeung, Ahluwalia, Thomson et al. (2012); Auyeung, Baron-Cohen, Ashwin et al. (2009).
49. Knickmeyer, Baron-Cohen, Raggatt et al. (2006); Chapman, Baron-Cohen, Auyeung et al. (2006).
50. Auyeung, Baron-Cohen, Chapman et al. (2006).
51. Knickmeyer, Baron-Cohen, Raggatt et al. (2005).
52. Knickmeyer, Baron-Cohen, Raggatt et al. (2005).
53. Hines, Constantinescu, and Spencer (2015).
54. In the U.S. version, The Gendered Brain is titled Gender and Our Brains.
55. Here are links to reviews in prestigious mainstream outlets. The URLs were current as of May 2019.
Annie Murphy Paul, “Not from Venus, Not from Mars: What We Believe About Gender and Why It’s Often Wrong,” New York Times, February 23, 2017, nytimes.com/2017/02/23/books/review/testosterone-rex-myths-of-sex-science-and-society-cordelia-fine.html.
Antonia Macaro, “Testosterone Rex by Cordelia Fine—Men, Women and Myths,” Financial Times, February 17, 2017, ft.com/content/946956e6-f2df-11e6-95ee-f14e55513608.
Sarah Ditum, “Testosterone Rex by Cordelia Fine Review—The Question of Men’s and Women’s Brains,” Guardian, January 18, 2017, www.theguardian.com/books/2017/jan/18/testosterone-rex-review-cordelia-fine.
Sheri Berenbaum, “A Spirited Polemic Takes Aim at Biological Sex Differences but Misses Opportunities to Highlight Relevant Science, Science blog Books, Et Al., January 18, 2017, blogs.sciencemag.org/books/2017/01/18/723/.
Rachel Cooke, “The Gendered Brain by Gina Rippon Review—Demolition of a Sexist Myth,” Guardian, March 5, 2019, www.theguardian.com/books/2019/mar/05/the-gendered-brain-gina-rippon-review.
56. The panel of judges consisted of a paleontologist, a psychologist, a television journalist, a novelist, and one neuroscientist. The neuroscientist, Sam Gilbert, has posted a defense of Testosterone Rex at his web page, www.samgilbert.net.
57. For reviews in the technical literature of Brain Storm and Delusions of Gender, see Halpern (2010) and McCarthy and Ball (2011). For Delusions of Gender, also see Brown (2017). For Testosterone Rex, see Berenbaum (2017). Simon Baron-Cohen reviewed The Gendered Brain for the Sunday Times of London, March 8, 2019.
For online responses: Robert J. King is a psychologist specializing in biological psychology. He is currently a lecturer at the School of Applied Psychology, University College Cork. His review of Testosterone Rex (“Estrogen Promise”) was posted on the website of Psychology Today, April 11, 2017 (www.psychologytoday.com).
Stuart Ritchie is a psychologist specializing in psychometrics and the genetics of cognitive ability. He is currently a lecturer in the Social Genetic & Developmental Psychiatry Centre at King’s College London. His review of Testosterone Rex was posted on Quillette, March 21, 2017 (www.quillette.com). Jerry Coyne is an evolutionary biologist at the University of Chicago. He posted three long essays about Fine’s work at his blog, Why Evolution Is True, on January 20, 2017, March 9, 2017, and September 21, 2017 (whyevolutionistrue.wordpress.com). Gregory Cochran, coauthor of The 10,000 Year Explosion, posted his review of Testosterone Rex at his blog, West Hunter, on March 20, 2017 (westhunt.wordpress.com). Larry Cahill, a neurobiologist at UC Irvine, reviewed The Gendered Brain on Quillette, March 29, 2019 (www.quillette.com).
58. The straw man problem is also an issue with a 2018 major technical article published in American Psychologist. Titled “The Future of Sex and Gender in Psychology: Five Challenges to the Gender Binary,” the first author was Janet Shibley Hyde, originator of the gender similarities hypothesis, and one of her coauthors was Daphna Joel, first author of the controversial article on the brain mosaic discussed in chapter 5. Here is the abstract of the article:
The view that humans comprise only two types of beings, women and men, a framework that is sometimes referred to as the “gender binary,” played a profound role in shaping the history of psychological science. In recent years, serious challenges to the gender binary have arisen from both academic research and social activism. This review describes 5 sets of empirical findings, spanning multiple disciplines, that fundamentally undermine the gender binary. These sources of evidence include neuroscience findings that refute sexual dimorphism of the human brain; behavioral neuroendocrinology findings that challenge the notion of genetically fixed, nonoverlapping, sexually dimorphic hormonal systems; psychological findings that highlight the similarities between men and women; psychological research on transgender and nonbinary individuals’ identities and experiences; and developmental research suggesting that the tendency to view gender/sex as a meaningful, binary category is culturally determined and malleable. Costs associated with reliance on the gender binary and recommendations for future research, as well as clinical practice, are outlined. (Hyde, Joel, Bigler et al. (2018): 171).
There is a distinction to be made between dimorphic and binary. Dimorphic means two forms but allows for substantial overlap. This is an empirically accurate description of humans’ biological sexuality, as discussed in Appendix 2. But when it comes to sex differences in the brain, hormonal systems, personality, cognitive functioning, or social behavior, the Hyde study is criticizing a scholarly school that doesn’t exist. No one who is cited in Human Diversity argues for the “gender binary.” Everyone accepts large degrees of overlap.
59. Cordelia Fine, Daphna Joel, and Gina Rippon, “Eight Things You Need to Know About Sex, Gender, Brains, and Behavior: A Guide for Academics, Journalists, Parents, Gender Diversity Advocates, Social Justice Warriors, Tweeters, Facebookers, and Everyone Else,” S&F Online, issue 15.2 (2019), sfonline.barnard.edu/neurogenderings/eight-things-you-need-to-know-about-sex-gender-brains-and-behavior-a-guide-for-academics-journalists-parent
s-gender-diversity-advocates-social-justice-warriors-tweeters-facebookers-and-ever/.
60. Marco Del Giudice, David A. Puts, David C. Geary et al., “Sex Differences in Brain and Behavior: Eight Counterpoints,” Psychology Today, April 8, 2019, www.psychologytoday.com/us/blog/sexual-personalities/201904/sex-differences-in-brain-and-behavior-eight-counterpoints.
61. Hines, Constantinescu, and Spencer (2015).
62. Berenbaum (2018).
63. Cohen-Bendahan, van de Beek, and Berenbaum (2005): 359.
64. Beltz, Swanson, and Berenbaum (2011): Table 3.
65. Hines, Pasterski, Spencer et al. (2016), summarizing the literature. Dessens, Slijper, Drop et al. (2005) found 5.2 percent incidence of sex dysphoria (not necessarily living as a male) among CAH females raised as females.
66. Hines (2010); Cohen-Bendahan, van de Beek, and Berenbaum (2005).
67. Imperato-McGinley, Pichardo, Gautier et al. (1991).
68. Savic, Frisen, Manzouri et al. (2017).
69. Savic, Frisen, Manzouri et al. (2017): 9.
70. SHBG stands for sex hormone binding globulin, a large protein molecule that binds testosterone. Increased levels of SHBG imply less androgenization of the fetal brain.
71. Udry (2000): 451.
72. The authors took three specific hypotheses into the research: one regarding the role of hormones, one regarding the role of childhood socialization, and one regarding a “cultural interactionist frame,” which draws from the social-roles theory discussed in the introduction to Part I—in the words of Davis and Risman, that “gender is not the property of individuals, but is instead the product of social interactions which reproduce and legitimate institutional arrangements based on sex categories.” Davis and Risman (2015): 112. Their analysis did not support the cultural interactionist hypothesis—which they acknowledged in a straightforward way that one wishes were more common in social science: “As sociologists whose work has been primarily within a gender structure approach, we expected that the cultural interactionist frame would be most strongly supported by the data, that normative pressure to ‘do gender’ in adult social roles and the current social context would be most influential in shaping gendered selves.… To foreshadow the findings, we found the results much more complicated than the cultural interactionist framework predicted. We were wrong.” Davis and Risman (2015): 113. I have reported their findings in accordance with their own conclusion that socialization played a stronger role than prenatal testosterone, but I will note another way of thinking about the situation: One independent variable, exposure to testosterone, was limited to the second trimester in the womb. The other, gender socialization, presumably acted upon them every day after birth for decades.
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