60. van Anders (2013), ibid.
61. van Anders, S. M., Tolman, R. M., & Volling, B. L. (2012). Baby cries and nurturance affect testosterone in men. Hormones and Behavior, 61(1), 31–36.
62. A specific instance of a point made by van Anders (2013), ibid.
63. van Anders, S. M., Steiger, J., & Goldey, K. L. (2015). Effects of gendered behavior on testosterone in women and men. Proceedings of the National Academy of Sciences, 112(45), 13805–13810. Each actor did this twice: once in a stereotypically masculine way (for example, a cold expression and dominant posture), and once in a stereotypically feminine way (such as hesitant cadence and avoiding eye contact). This wasn’t an important factor, indicating that T is linked with competitive behaviour per se, rather than masculinity.
64. van Anders et al. (2015), ibid. Quoted on p. 13808.
65. See van Anders & Watson (2006), ibid.
66. Oliveira, G. A., & Oliveira, R. F. (2014). Androgen responsiveness to competition in humans: The role of cognitive variables. Neuroscience and Neuroeconomics, 3, 19–32. Quoted on p. 21. Studies are summarized in Table 1, pp. 22–23.
67. One possible explanation for these inconsistencies is provided by the “dual-hormone hypothesis,” according to which T levels interact with cortisol levels, such that T’s positive effect on competitive behavior is blocked when cortisol levels are high. Mehta, P. H., & Josephs, R. A. (2010). Testosterone and cortisol jointly regulate dominance: Evidence for a dual-hormone hypothesis. Hormones and Behavior, 58(5), 898–906. For overview of the data, see Hamilton et al. (2015), ibid.
68. Oliveira & Oliveira (2014), ibid. Quoted on p. 23. For an empirical example, see Oliveira, G. A., Uceda, S., Oliveira, T., Fernandes, A., Garcia-Marques, T., & Oliveira, R. F. (2013). Threat perception and familiarity moderate the androgen response to competition in women. Frontiers in Psychology, 4, 389.
69. See the summary in Oliveira & Oliveira (2014), ibid.
70. Carré, J. M., Iselin, A.-M. R., Welker, K. M., Hariri, A. R., & Dodge, K. A. (2014). Testosterone reactivity to provocation mediates the effect of early intervention on aggressive behavior. Psychological Science, 25(5), 1140–1146. Quoted on p. 1140.
71. Carré et al. (2014), ibid. Quoted on p. 1144.
72. Cohen, D., Nisbett, R. E., Bowdle, B. F., & Schwarz, N. (1996). Insult, aggression, and the southern culture of honor: An “experimental ethnography.” Journal of Personality and Social Psychology, 70(5), 945–960.
73. Cohen et al. (1996), ibid. Quoted on p. 957.
74. Herbert (2015), ibid. Quoted on p. 194.
75. Wade, L. (2013). The new science of sex difference. Sociology Compass, 7(4), 278–293. Quoted on p. 284.
76. For example, Bleier, R. (1984). Science and gender: A critique of biology and its theories on women. New York: Pergamon Press; Fausto-Sterling, A. (2012). Sex/gender: Biology in a social world. New York: Routledge.
77. Fuentes, A. (2012). Race, monogamy, and other lies they told you: Busting myths about human nature. Berkeley: University of California Press. Quoted on p. 16.
78. For example, Ridgeway, C. L. (2011). Framed by gender: How gender inequality persists in the modern world. Oxford, UK: Oxford University Press.
79. Liben, L. (2015). Probability values and human values in evaluating single-sex education. Sex Roles, 72(9–10), 401–426. Quoted on p. 415.
CHAPTER 7: THE MYTH OF THE LEHMAN SISTERS
1. Herbert, J. (2015). Testosterone: Sex, power, and the will to win. Oxford, UK: Oxford University Press. Quoted on pp. 116–118, reference removed.
2. Sunderland, R. (January 18, 2009). The real victims of this credit crunch? Women. The Observer. Retrieved from http://www.theguardian.com/lifeandstyle/2009/jan/18/women-credit-crunch-ruth-sunderland on January 15, 2015.
3. Prügl, E. (2012). “If Lehman Brothers had been Lehman Sisters…”: Gender and myth in the aftermath of the financial crisis. International Political Sociology, 6(1), 21–35. Quoted on p. 21.
4. John Coates, interviewed in Adams, T. (June 18, 2011). Testosterone and high finance do not mix: So bring on the women. The Guardian. Retrieved from http://www.theguardian.com/world/2011/jun/19/neuroeconomics-women-city-financial-crash on February 20, 2014.
5. Kristof, N. (February 7, 2009). Mistresses of the universe. New York Times. Retrieved from http://www.nytimes.com/2009/02/08/opinion/08kristof.html?_r=0 on January 13, 2015.
6. Adams (2011), ibid.
7. Kristof (2009), ibid.
8. (July 13, 1902). Excluding women from brokers’ offices; movement started in Wall Street to put an end to female speculating—reasons why brokers object to business of this kind—instances of woman’s lack of business knowledge—why they are “bad losers.” New York Times. Retrieved from http://query.nytimes.com/mem/archive-free/pdf?res=9502E0D9113BE733A25750C1A9619C946397D6CF on January 13, 2015.
9. Time cover on May 24, 2010. Cited in Nelson, J. (2013). Would women leaders have prevented the global financial crisis? Teaching critical thinking by questioning a question. International Journal of Pluralism and Economics Education, 4(2), 192–209.
10. The introduction of the term “big swinging dick” is credited to Lewis, M. (1989). Liar’s poker: Rising through the wreckage on Wall Street. New York: Norton.
11. Croson, R., & Gneezy, U. (2009). Gender differences in preferences. Journal of Economic Literature, 47(2), 448–474. Quoted on p. 467.
12. Nelson, J. (2014a). The power of stereotyping and confirmation bias to overwhelm accurate assessment: The case of economics, gender, and risk aversion. Journal of Economic Methodology, 21(3), 211–231.
13. Nelson (2014a), ibid. See Table 1 on p. 216. Two studies obtained results encompassing greater female financial risk taking, with d ranging from –0.34 to null to 0.74, and from –0.25 to null to 0.49. In four further studies, no statistically significant differences were found. In five studies, results ranged from null to a low of d = 0.37 and a high of d = 0.85. In the final seven studies, the range of results spread from a low of d = 0.06 to 0.17, to a high of d = 0.55 to 1.13.
14. Nelson (2014a), ibid. Quoted on p. 212.
15. Stanley, T. D., & Doucouliagos, H. (2010). Picture this: A simple graph that reveals much ado about research. Journal of Economic Surveys, 24(1), 170–191.
16. More accurately, the y-axis plots “precision”; the inverse of standard error, which generally decreases with sample size.
17. Nelson (2014a), ibid. Quoted on p. 221.
18. Note this exercise treats “financial risk taking” as a single construct that, as Nelson notes, is an unexamined assumption.
19. Respectively: Hartog, J., Ferrer-i-Carbonell, A., & Jonker, N. (2002). Linking measured risk aversion to individual characteristics. Kyklos, 55(1), 3–26; Sunden, A. E., & Surette, B. J. (1998). Gender differences in the allocation of assets in retirement savings plans. American Economic Review, 88(2), 207–211; Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. Quarterly Journal of Economics, 116(1), 261–292.
20. For instance, Hartog et al. (2002), ibid., found that risk aversion decreases with increased income and wealth.
21. Schubert, R., Brown, M., Gysler, M., & Brachinger, H. W. (1999). Financial decision-making: Are women really more risk-averse? American Economic Review, 89(2), 381–385. Interestingly, they also found that when abstract gambles were framed as losses (for example, Would you rather lose $30 for sure, or take a 50 per cent chance of losing $100?), women were significantly more risk taking than were men. But, again, this difference disappeared when the gambles were put into the less abstract context of insurance decisions. However, for contrasting findings, see Powell, M., & Ansic, D. (1997). Gender differences in risk behaviour in financial decision-making: An experimental analysis. Journal of Economic Psychology, 18(6), 605–628.
22. Vlaev, I., Kusev, P., Stewart, N., Aldrovandi, S., & Chater, N. (2010). Domain effects and financial risk attitudes. Risk Analysis, 30(9), 1374–1386. In
this study, the researchers determined that the decisions fell into three kinds of financial decisions: positive (abstract “gain” gambles, pensions, and salary questions), positive and complex (mortgage and investment decisions), and negative (abstract “loss” gambles and insurance). There were no sex differences overall, or within each of these three groupings.
23. Henrich, J., & McElreath, R. (2002). Are peasants risk-averse decision makers? Current Anthropology, 43(1), 172–181.
24. When controlling for all other variables measured. Cameron, L., Erkal, N., Gangadharan, L., & Meng, X. (2013). Little emperors: Behavioral impacts of China’s one-child policy. Science, 339(6122), 953–957.
25. Gneezy, U., Leonard, K. L., & List, J. A. (2009). Gender differences in competition: Evidence from a matrilineal and a patriarchal society. Econometrica, 77(5), 1637–1664. These studies used nontrivial stakes.
26. Gong, B., & Yang, C.-L. (2012). Gender differences in risk attitudes: Field experiments on the matrilineal Mosuo and the patriarchal Yi. Journal of Economic Behavior and Organization, 83(1), 59–65.
27. Cárdenas, J.-C., Dreber, A., von Essen, E., & Ranehill, E. (2012). Gender differences in competitiveness and risk taking: Comparing children in Colombia and Sweden. Journal of Economic Behavior and Organization, 83(1), 11–23.
28. Booth, A., & Nolen, P. (2012). Gender differences in risk behaviour: Does nurture matter? Economic Journal, 122(558), F56–F78; Booth, A., Cardona-Sosa, L., & Nolen, P. (2014). Gender differences in risk aversion: Do single-sex environments affect their development? Journal of Economic Behavior and Organization, 99, 126–154.
29. Sometimes economists define “risk” tasks as situations in which probabilities of pay-offs are known, and use “uncertainty” to describe situations in which the probabilities are not known. However, this convention is not followed here.
30. Cross, C. P., Copping, L. T., & Campbell, A. (2011). Sex differences in impulsivity: A meta-analysis. Psychological Bulletin, 137(1), 97–130. The effect size was d = 0.36.
31. Cross et al. (2011), ibid. The effect size was d = –0.34. The authors suggest women are more likely to choose the high-risk packs because of greater punishment sensitivity. However, the high- and low-risk packs are equated overall for frequency of reward and punishment.
32. Holt, C. A., & Laury, S. K. (2002). Risk aversion and incentive effects. American Economic Review, 92(5), 1644–1655. See also Harbaugh, W., Krause, K., & Vesterlund, L. (2002). Risk attitudes of children and adults: Choices over small and large probability gains and losses. Experimental Economics, 5(1), 53–84. This study presented participants from ages 5 to 64 years with gambles involving “real and salient payoffs” (p. 55). The authors note “While many other researchers have found that men are less risk averse than women, with this protocol we find no evidence to support gender differences in risk behavior or in probability weighting, either in children or in adults.” Quoted on p. 66, footnote removed. See also note 25.
33. Henrich & McElreath (2002), ibid. Quoted on pp. 175 and 175–176.
34. Akerlof, G. A., & Kranton, R. E. (2000). Economics and identity. Quarterly Journal of Economics, 115(3), 715–753.
35. Akerlof, G. A., & Kranton, R. E. (2010). Identity economics: How our identities shape our work, wages, and well-being. Princeton, NJ: Princeton University Press. Quoted on p. 10.
36. Akerlof & Kranton (2010), ibid. Quoted on p. 6.
37. For example, Nguyen, H., & Ryan, A. (2008). Does stereotype threat affect test performance of minorities and women? A meta-analysis of experimental evidence. Journal of Applied Psychology, 93(6), 1314–1334. For a more sceptical conclusion with respect to the magnitude of the stereotype threat effect, see Stoet, G., & Geary, D. C. (2012). Can stereotype threat explain the gender gap in mathematics performance and achievement? Review of General Psychology, 16(1), 93–102.
38. Carr, P. B., & Steele, C. M. (2010). Stereotype threat affects financial decision making. Psychological Science, 21(10), 1411–1416.
39. Brooks, A. W., Huang, L. Kearney, S. W., & Murray, F. E. (2014). Investors prefer entrepreneurial ventures pitched by attractive men. Proceedings of the National Academy of Sciences, 111(12), 4427–4431.
40. Gupta, V. K., Goktan, A. B., & Gunay, G. (2014). Gender differences in evaluation of new business opportunity: A stereotype threat perspective Journal of Business Venturing, 29, 273–288.
41. Gupta, V. K., Turban, D. B., Wasti, S. A., & Sikdar, A. (2009). The role of gender stereotypes in perceptions of entrepreneurs and intentions to become an entrepreneur. Entrepreneurship Theory and Practice, 33(2), 397–417.
42. Lemaster, P., & Strough, J. (2014). Beyond Mars and Venus: Understanding gender differences in financial risk tolerance. Journal of Economic Psychology, 42, 148–160; Meier-Pesti, K., & Penz, E. (2008). Sex or gender? Expanding the sex-based view by introducing masculinity and femininity as predictors of financial risk taking. Journal of Economic Psychology, 29(2), 180–196.
43. Twenge, J. (1997). Changes in masculine and feminine traits over time: A meta-analysis. Sex Roles, 36(5–6), 305–325.
44. Meier-Pesti & Penz (2008), ibid. This study primed masculinity and femininity by showing participants a picture of either a man in a business suit or a woman with a baby (or, in a control condition, a gender-neutral picture), and asking them to write about the scene, followed by similarly themed sentence completions.
45. Reinhard, M.-A., Stahlberg, D., & Messner, M. (2008). Failure as an asset for high-status persons: Relative group performance and attributed occupational success. Journal of Experimental Social Psychology, 44(3), 501–518; Reinhard, M.-A., Stahlberg, D., & Messner, M. (2009). When failing feels good: Relative prototypicality for a high-status group can counteract ego-threat after individual failure. Journal of Experimental Social Psychology, 45(4), 788–795.
46. Reinhard, M.-A., Schindler, S., & Stahlberg, D. (2013). The risk of male success and failure: How performance outcomes along with a high-status identity affect gender identification, risk behavior, and self-esteem. Group Processes and Intergroup Relations, 17(2), 200–220.
47. Weaver, J. R., Vandello, J. A., & Bosson, J. K. (2013). Intrepid, imprudent, or impetuous? The effects of gender threats on men’s financial decisions. Psychology of Men and Masculinity, 14(2), 184–191. In the comparison condition, participants were asked to trial a power drill.
48. This second study used delay discounting as the dependent variable and found more impulsive behaviour in the masculinity threat condition.
49. Nelson, J. A. (2014b). Are women really more risk-averse than men? A re-analysis of the literature using expanded methods. Journal of Economic Surveys, 29(3), 566–585. Quoted on p. 576.
50. Beckmann, D., & Menkhoff, L. (2008). Will women be women? Analyzing the gender difference among financial experts. Kyklos, 61(3), 364–384.
51. Nelson (2014a), ibid. Quoted on p. 225.
52. Hönekopp, J., & Watson, S. (2010). Meta-analysis of digit ratio 2D:4D shows greater sex difference in the right hand. American Journal of Human Biology, 22, 619–630.
53. Voracek, M., Tran, U. S., & Dressler, S. G. (2010). Digit ratio (2D:4D) and sensation seeking: New data and meta-analysis. Personality and Individual Differences, 48(1), 72–77. Quoted on p. 76.
54. Herbert (2015), ibid. Quoted on p. 52.
55. Hönekopp, J., & Watson, S. (2011). Meta-analysis of the relationship between digit-ratio 2D:4D and aggression. Personality and Individual Differences, 51(4), 381–386. A small correlation was found for men only (r = –.08 for the left hand and r = –.07 for the right hand), but this reduced to a nonsignificant correlation for r = –.03 after correction for weak publication bias.
56. Voracek et al. (2010), ibid. The authors note the complexity of the biological system thought to underlie sensation seeking, as well as the many psychosocial factors known to influence it, and thus conclude that “Given these knowns, it appears unsurprising that rather simplistic approaches, such as studies only utilizing 2D:4D
(a putative, not yet sufficiently validated marker of prenatal testosterone), are prone to be barren of results.” Quoted on p. 76.
57. Vermeersch, H., T’Sjoen, G., Kaufman, J. M., & Vincke, J. (2008). 2D:4D, sex steroid hormones and human psychological sex differences. Hormones and Behavior, 54(2), 340–346.
58. Apicella, C., Carré, J., & Dreber, A. (2015). Testosterone and economic risk taking: A review. Adaptive Human Behavior and Physiology, 1(3), 358–385. Quoted on p. 369. Note that “risk taking” here was defined according to the economist’s definition, thus referring specifically to lottery/gambling tasks. However, their subsequent review of 2D:4D findings for “risk-related constructs” reveals further inconsistencies.
59. Respectively, Apicella, C. L., Dreber, A., Campbell, B., Gray, P. B., Hoffman, M., & Little, A. C. (2008). Testosterone and financial risk preferences. Evolution and Human Behavior, 29(6), 384–390; Stanton, S. J., Mullette-Gillman, O. D. A., McLaurin, R. E., Kuhn, C. M., LaBar, K. S., Platt, M. L., et al. (2011). Low- and high-testosterone individuals exhibit decreased aversion to economic risk. Psychological Science, 22(4), 447–453; Schipper, B. C. (2014). Sex hormones and choice under risk. Working Papers, University of California, Department of Economics, No. 12, 7; Sapienza, P., Zingales, L., & Maestripieri, D. (2009). Gender differences in financial risk aversion and career choices are affected by testosterone. Proceedings of the National Academy of Sciences of the United States of America, 106(36), 15268–15273; Doi, H., Nishitani, S., & Shinohara, K. (2015). Sex difference in the relationship between salivary testosterone and inter-temporal choice. Hormones and Behavior, 69, 50–58.
60. Stanton, S. J., Liening, S. H., & Schultheiss, O. C. (2011). Testosterone is positively associated with risk taking in the Iowa Gambling Task. Hormones and Behavior, 59(2), 252–256; Mehta, P. H., Welker, K. M., Zilioli, S., & Carré, J. M. (2015). Testosterone and cortisol jointly modulate risk-taking. Psycho-neuroendocrinology, 56, 88–99.
61. Cueva, C., Roberts, R. E., Spencer, T., Rani, N., Tempest, M., Tobler, P. N., et al. (2015). Cortisol and testosterone increase financial risk taking and may destabilize markets. Scientific Reports, 5, 11206.
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