对于工薪收入男性高于女性，我也提出一个假设来瞧瞧：简单化地举例，工种AB两个，对工人的智力水平要求相同，但是工种A只接受男工人，工种B男女工人皆可，哪个行业的平均收入比较低？
当然是工种B，supply and demand 而已。连我这种没学过经济学的人都知道这个规律。
在美国这个“世界先进水平”的国家，男女同酬，即同样工作应该有同样报酬，联邦法律是哪一年通过立法保护的？1963 年，我这还是看了 Mad Men 才知道的。（
http://www.eeoc.gov/facts/qanda.html）在此之前与之后的年代里，都有很多很多的行业是“女人与狗禁止入内”，可以入内的都是极少数的例外。1964 年通过 "Title VII of the Civil Rights Act of 1964 (Title VII), which prohibits employment discrimination based on race, color, religion, sex, or national origin"，至今半个世纪都不到，市面上的各个行业，特别是利润丰厚的行业，有没有提供男女公平竞争的平台？对男女申请者的门是否开得一样大？
女工人收入比男工人低，不是因为女工人入的行业的价值比男工人入的行业价值低，而是反过来，某个行业薪水低，因为这个行业让更多女工人进来。
如果女工人比男工人平均收入低是因为女人的智商没有男人高，那么显然北欧、瑞士、缅甸、肯尼亚要么就是那里的男人特别笨，要么就是那里的女人特别聪明，因为在这些国家里，女工人的收入跟男工人的比例都比美国高。(
http://unstats.un.org/unsd/demographic/ ... /tab5g.htm)
当然啦，人家 Larry Summers 是顶尖的经济学天才，我只上过一堂大学入门课 Economics 101，作业还大多是抄的。我的假设没法跟哈佛名学者相比。
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一篇直接驳斥“男孩子数学能力两头分布广，女孩子比较集中”和“高端数学能力中男生占多数”的说法，发表在 PNAS 今年六月一日:
Gender, culture, and mathematics performance 作者是两个 Janet。
Janet S. Hyde, Department of Psychology, University of Wisconsin, Madison, WI 53706
Janet E. Mertz, McArdle Laboratory for Cancer Research, University of Wisconsin School of Medicine and Public Health, Madison, WI 53706-1599
登于
http://www.pnas.org/content/106/22/8801.full
Full text 需要注册交钱，我就摘录几段。
Do Gender Differences Exist Among the Mathematically Talented?
The hypothesis that the variability of intellectual abilities is greater among males than females was originally proposed by Ellis in 1894 to explain a phenomenon that seemed obvious at the time: There were both an excess of males among the mentally defective and very few female geniuses (1). If this Greater Male Variability Hypothesis were valid, it could account for the existence of a preponderance of males at the highest levels of performance even when a mean gender difference does not exist, as shown schematically in Fig. 1. This is the hypothesis to which Lawrence Summers was referring[/b] when he stated at the National Bureau of Economic Research Conference held on January 14, 2005, “There are issues of intrinsic aptitude, and particularly of the variability of aptitude, and that those considerations are reinforced by what are in fact lesser factors involving socialization and continuing discrimination. It's talking about people who are 3 ½, 4 standard deviations above the mean in the one-in-5,000, one-in-10,000 class. Even small differences in the standard deviation will translate into very large differences in the available pool substantially out.”
....Theoretical models are, of course, just that. Actual distributions rarely conform exactly to normal ones. Thus, gender ratios in the upper tails of actual distributions were calculated using data from the Minnesota state assessments (13). Results were analyzed separately by ethnicity to ensure that the findings were not limited to the predominantly White samples that have been the mainstay of U.S. research. For students scoring above the 95th percentile, the M:F ratio was 1.45 for Whites, close to theoretical prediction. At the 99th percentile, the M:F ratio was 2.06, again close to theoretical prediction. However, the M:F ratio was only 0.91 for Asian-Americans, that is, more girls than boys scored above the 99th percentile. Analysis of data from 15-year-old students participating in the 2003 Program for International Student Assessment (PISA) likewise indicated that as many, if not more girls than boys scored above the 99th percentile in Iceland, Thailand, and the United Kingdom (18). The M:F ratios above the 95th percentile on this examination also fell between 0.9 and 1.1 for these above-named countries plus Indonesia, that is, were not significantly different from equal variances (19). These findings challenge the Greater Male Variability Hypothesis, which, if valid, should hold for all representative populations, regardless of ethnicity or nationality.
Two recent studies directly address the question of whether greater male variability in mathematics is a ubiquitous phenomenon. Machin and Pekkarinen (19) reported that the M:F VR in mathematics was significantly >1.00 at the P < 0.05 level among 15-year-old students in 34 of 40 countries participating in the 2003 PISA and among 13-year-old students in 33 of 50 countries participating in the 2003 Trends in International Mathematics and Science Study (TIMSS). However, these data also indicated that the math VR was significantly less than or insignificantly different from 1.00 for some of the countries that participated in these assessments (e.g., Table 2), a finding inconsistent with the Greater Male Variability Hypothesis.
Current research provides abundant evidence for the impact of sociocultural and other environmental factors on the development and nurturing of mathematical skills and talent and the size, if any, of math gender gaps. The evidence comes from both cross-ethnic and cross-national studies and the above-cited changes observed within countries over time in the general, SMPY, and IMO-level populations.
Several researchers have investigated cross-national patterns of gender differences in math performance, studies that also provide clues as to the specific cultural factors that most affect outcomes. Baker and Jones (29) found that the magnitude of the mean gender difference in mathematics performance on the Second International Mathematics Study (SIMS) significantly correlated, across nations, with measures of gender inequality. For example, the size of the math gender gap correlated −0.55 with the percentage of women in the workforce in those nations. Likewise, Guiso and colleagues (18), using 2003 PISA data testing 15-year-olds from 40 countries, found that gender inequality as measured by the World Economic Forum's Gender Gap Index (GGI) (30) significantly correlated with the magnitude of the mean math gender gap. The GGI provides a measure of the gap between men and women in economic participation and opportunity, educational attainment, political empowerment, and health and survival; the closer it is to 1.00, the smaller the gender gap on these measures. In other words, Guiso et al. (18) concluded that the math gender gap varies across nations; nations with greater gender equality typically have a smaller math gender gap.
Regarding change over time, the likeliest explanation for the dramatic improvement in math performance by U.S. females lies in 2 recent cultural trends: (i) girls in general taking more mathematics and science courses during high school due, in part, to changes in requirements for graduation and admission to colleges, and (ii) the opening up to females shortly before or after enactment of Title IX in 1972 of STEM-intensive specialty high schools, colleges, and graduate schools along with career opportunities in STEM fields. These 2 trends are intimately connected.
The increase in women pursuing careers in STEM has been quite dramatic in some STEM fields. For example, only 14% of the U.S. doctoral degrees in the biological sciences went to women in 1970, whereas this figure had risen to 49% by 2006 (31). Entry into other STEM areas has been slower, yet substantial. For example, 5.5% of U.S. doctoral degrees in the physical sciences were awarded to women in 1970, compared with 30% in 2006; the percentages in mathematics and statistics were 8% in 1970 and 32% in 2006 (26, 32). Clearly, numerous women are willing and able to learn the mathematics needed for advanced degrees in these areas when provided with an appropriately nurturing sociocultural environment along with educational and career opportunities.
STEM = science, technology, engineering, mathematics.
另一篇研究也很有意思：
National differences in gender–science stereotypes predict national sex differences in science and math achievement
http://www.pnas.org/content/106/26/10593.abstract
About 70% of more than half a million Implicit Association Tests completed by citizens of 34 countries revealed expected implicit stereotypes associating science with males more than with females. We discovered that nation-level implicit stereotypes predicted nation-level sex differences in 8th-grade science and mathematics achievement. Self-reported stereotypes did not provide additional predictive validity of the achievement gap. We suggest that implicit stereotypes and sex differences in science participation and performance are mutually reinforcing, contributing to the persistent gender gap in science engagement.
World Economic Forum 收集数据计算出来的性别差距社会综合指数 (gender gap index)，根据2007 年数据，猜猜美国排第几？31，在古巴，哥斯达黎加，白俄罗斯，斯里兰卡这些国家之后。难怪女不如男是自然现象的理论在美国大有市场。
http://www.weforum.org/pdf/gendergap/rankings2007.pdf
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下次我还要提读书无用论，读书太多，脑子都读坏了。Larry Summers 也许在算经济模型上是天才，但是在常识上，哼哼。
第一次是高中时在林语堂的《吾国吾民》里。第二次是很多年前在一个卖便当的中餐馆里一个台湾太太严厉地拉我去教会并尖锐指出是因为普遍信教所以台湾经济比大陆好。我基本没忍住不耐烦之后，她坚定地长久地直着脊背严厉地注视着每一个进门的人。
... No. There is no proof for that.